6G_Analysing music preference in Spotify with ANOVA.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "celltoolbar": "Slideshow"
  },
  "nbformat_minor": 5,
  "nbformat": 4,
  "cells": [
    {
      "id": "b7e6cecc-b6f5-4a84-94d6-cdd3d1848b3c",
      "cell_type": "markdown",
      "source": "# Analysing music preference in Spotify with ANOVA\n\nΕμπνευσμένο από το άρθρο του Economist [Data from Spotify suggest that listeners are gloomiest in February](https://www.economist.com/graphic-detail/2020/02/08/data-from-spotify-suggest-that-listeners-are-gloomiest-in-february).",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "4b7fb870",
      "cell_type": "markdown",
      "source": "* We want to explore the most popular music tracks in the world.\n\n* These are provided by [Spotify Charts](https://spotifycharts.com/).\n\n* Unfortunately, although the data is available in CSV format, we can only download data for each individual week.\n\n* Fortunately, data for some countries and for the entire world, for the period 2017-2019, is available at Zenodo https://doi.org/10.5281/zenodo.4778562:\n\n * Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "21d7e048",
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport scipy.stats as stats\nimport statsmodels.api as sm\nimport statsmodels.formula.api as smf\n\nimport glob\nimport re\nfrom datetime import datetime\n\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n%matplotlib inline",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "id": "b522c8a3",
      "cell_type": "markdown",
      "source": "* So from Zenodo we can download the file with the popular tracks (`charts.zip`).\n\n* If we unzip the file, we will see that it places the files for each region in a separate directory.\n\n* In each directory, data is stored in weekly files.\n\n* We will read them all, recording the week and region.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "c408b7bc",
      "cell_type": "code",
      "source": "header = 0\ndfs = []\nfor file in glob.glob('Charts/*/201?/*.csv'):\n    region = file.split('/')[1]\n    dates = re.findall('\\d{4}-\\d{2}-\\d{2}', file.split('/')[-1])\n    weekly_chart = pd.read_csv(file, header=header, sep='\\t')\n    weekly_chart['week_start'] = datetime.strptime(dates[0], '%Y-%m-%d')\n    weekly_chart['week_end'] = datetime.strptime(dates[1], '%Y-%m-%d')\n    weekly_chart['region'] = region\n    dfs.append(weekly_chart)\n\nall_charts = pd.concat(dfs)",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "id": "healthy-warren",
      "cell_type": "code",
      "source": "all_charts",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>position</th>\n",
              "      <th>song_id</th>\n",
              "      <th>song_name</th>\n",
              "      <th>artist</th>\n",
              "      <th>streams</th>\n",
              "      <th>last_week_position</th>\n",
              "      <th>weeks_on_chart</th>\n",
              "      <th>peak_position</th>\n",
              "      <th>position_status</th>\n",
              "      <th>week_start</th>\n",
              "      <th>week_end</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>rockstar</td>\n",
              "      <td>Post Malone</td>\n",
              "      <td>17532665</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>I Fall Apart</td>\n",
              "      <td>Post Malone</td>\n",
              "      <td>8350785</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>Bank Account</td>\n",
              "      <td>21 Savage</td>\n",
              "      <td>7589124</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>Gucci Gang</td>\n",
              "      <td>Lil Pump</td>\n",
              "      <td>7584237</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>1-800-273-8255</td>\n",
              "      <td>Logic</td>\n",
              "      <td>7527770</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>-2</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>6kex4EBAj0WHXDKZMEJaaF</td>\n",
              "      <td>Swalla (feat. Nicki Minaj &amp; Ty Dolla $ign)</td>\n",
              "      <td>Jason Derulo</td>\n",
              "      <td>3747830</td>\n",
              "      <td>191.0</td>\n",
              "      <td>52</td>\n",
              "      <td>8</td>\n",
              "      <td>-5</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>0CokSRCu5hZgPxcZBaEzVE</td>\n",
              "      <td>Glorious (feat. Skylar Grey)</td>\n",
              "      <td>Macklemore</td>\n",
              "      <td>3725286</td>\n",
              "      <td>189.0</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>-8</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>7oK9VyNzrYvRFo7nQEYkWN</td>\n",
              "      <td>Mr. Brightside</td>\n",
              "      <td>The Killers</td>\n",
              "      <td>3717326</td>\n",
              "      <td>195.0</td>\n",
              "      <td>17</td>\n",
              "      <td>148</td>\n",
              "      <td>-3</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>7EUfNvyCVxQV3oN5ScA2Lb</td>\n",
              "      <td>Next To Me</td>\n",
              "      <td>Imagine Dragons</td>\n",
              "      <td>3681739</td>\n",
              "      <td>122.0</td>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>-77</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>6u0EAxf1OJTLS7CvInuNd7</td>\n",
              "      <td>Vai malandra (feat. Tropkillaz &amp; DJ Yuri Martins)</td>\n",
              "      <td>Anitta</td>\n",
              "      <td>3676542</td>\n",
              "      <td>177.0</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>-23</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273600 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     position                 song_id  \\\n",
              "0           1  7wGoVu4Dady5GV0Sv4UIsx   \n",
              "1           2  75ZvA4QfFiZvzhj2xkaWAh   \n",
              "2           3  2fQrGHiQOvpL9UgPvtYy6G   \n",
              "3           4  43ZyHQITOjhciSUUNPVRHc   \n",
              "4           5  5tz69p7tJuGPeMGwNTxYuV   \n",
              "..        ...                     ...   \n",
              "195       196  6kex4EBAj0WHXDKZMEJaaF   \n",
              "196       197  0CokSRCu5hZgPxcZBaEzVE   \n",
              "197       198  7oK9VyNzrYvRFo7nQEYkWN   \n",
              "198       199  7EUfNvyCVxQV3oN5ScA2Lb   \n",
              "199       200  6u0EAxf1OJTLS7CvInuNd7   \n",
              "\n",
              "                                             song_name           artist  \\\n",
              "0                                             rockstar      Post Malone   \n",
              "1                                         I Fall Apart      Post Malone   \n",
              "2                                         Bank Account        21 Savage   \n",
              "3                                           Gucci Gang         Lil Pump   \n",
              "4                                       1-800-273-8255            Logic   \n",
              "..                                                 ...              ...   \n",
              "195         Swalla (feat. Nicki Minaj & Ty Dolla $ign)     Jason Derulo   \n",
              "196                       Glorious (feat. Skylar Grey)       Macklemore   \n",
              "197                                     Mr. Brightside      The Killers   \n",
              "198                                         Next To Me  Imagine Dragons   \n",
              "199  Vai malandra (feat. Tropkillaz & DJ Yuri Martins)           Anitta   \n",
              "\n",
              "      streams  last_week_position  weeks_on_chart  peak_position  \\\n",
              "0    17532665                 1.0               3              1   \n",
              "1     8350785                 2.0               6              2   \n",
              "2     7589124                 4.0               5              3   \n",
              "3     7584237                 5.0               3              4   \n",
              "4     7527770                 3.0              26              1   \n",
              "..        ...                 ...             ...            ...   \n",
              "195   3747830               191.0              52              8   \n",
              "196   3725286               189.0              24             14   \n",
              "197   3717326               195.0              17            148   \n",
              "198   3681739               122.0               2            122   \n",
              "199   3676542               177.0              12             30   \n",
              "\n",
              "    position_status week_start   week_end  region  \n",
              "0                 0 2017-10-20 2017-10-27      us  \n",
              "1                 0 2017-10-20 2017-10-27      us  \n",
              "2                 1 2017-10-20 2017-10-27      us  \n",
              "3                 1 2017-10-20 2017-10-27      us  \n",
              "4                -2 2017-10-20 2017-10-27      us  \n",
              "..              ...        ...        ...     ...  \n",
              "195              -5 2018-03-02 2018-03-09  global  \n",
              "196              -8 2018-03-02 2018-03-09  global  \n",
              "197              -3 2018-03-02 2018-03-09  global  \n",
              "198             -77 2018-03-02 2018-03-09  global  \n",
              "199             -23 2018-03-02 2018-03-09  global  \n",
              "\n",
              "[273600 rows x 12 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 3
    },
    {
      "id": "528348da",
      "cell_type": "markdown",
      "source": "* There are tons of tracks, but not all of them are unique:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "a0f2559f",
      "cell_type": "code",
      "source": "len(all_charts['song_id'].unique())",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "13880"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 4
    },
    {
      "id": "d12c4665",
      "cell_type": "markdown",
      "source": "* Θα φτιάξουμε ένα αρχείο`spotify_config.py` με τα ακόλουθα περιεχόμενα (όπου `XXX` θα βάλουμε τα δικά μας στοιχεία):\n\n  ```\n  config = {\n      'client_id' : 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',\n      'client_secret' :'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\n  }\n  ```",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "e19fa008",
      "cell_type": "markdown",
      "source": "* Now we can start using the Spotify API through the [Spotipy](https://spotipy.readthedocs.io) library.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "42da4d12",
      "cell_type": "code",
      "source": "from spotify_config import config\n\nclient_credentials_manager = SpotifyClientCredentials(config['client_id'],\n                                                      config['client_secret'])\nsp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "id": "a1522a81",
      "cell_type": "markdown",
      "source": "* For each of the tracks we have we will request its audio features, or [audio features](https://developer.spotify.com/documentation/web-api/reference/#endpoint-get-several-audio-features ).\n\n* To do this, we will make a dictionary where keys will be `song_id` and values ​​will be the audio characteristics of the corresponding track.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "c78bd9a2",
      "cell_type": "code",
      "source": "features = {}",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "id": "5bbd3d91",
      "cell_type": "markdown",
      "source": "* The call that gives us audio features can request up to 100 at a time, so we should request them in batches of 100.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "47b1f304",
      "cell_type": "code",
      "source": "all_track_ids = list(all_charts['song_id'].unique())\nstart = 0\nnum_tracks = 100\nwhile start < len(all_track_ids):\n    print(f'getting from {start} to {start+num_tracks}')\n    tracks_batch = all_track_ids[start:start+num_tracks]\n    features_batch = sp.audio_features(tracks_batch)\n    features.update({ track_id : track_features \n                     for track_id, track_features in zip(tracks_batch, features_batch) })\n    start += num_tracks",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "getting from 0 to 100\n",
            "getting from 100 to 200\n",
            "getting from 200 to 300\n",
            "getting from 300 to 400\n",
            "getting from 400 to 500\n",
            "getting from 500 to 600\n",
            "getting from 600 to 700\n",
            "getting from 700 to 800\n",
            "getting from 800 to 900\n",
            "getting from 900 to 1000\n",
            "getting from 1000 to 1100\n",
            "getting from 1100 to 1200\n",
            "getting from 1200 to 1300\n",
            "getting from 1300 to 1400\n",
            "getting from 1400 to 1500\n",
            "getting from 1500 to 1600\n",
            "getting from 1600 to 1700\n",
            "getting from 1700 to 1800\n",
            "getting from 1800 to 1900\n",
            "getting from 1900 to 2000\n",
            "getting from 2000 to 2100\n",
            "getting from 2100 to 2200\n",
            "getting from 2200 to 2300\n",
            "getting from 2300 to 2400\n",
            "getting from 2400 to 2500\n",
            "getting from 2500 to 2600\n",
            "getting from 2600 to 2700\n",
            "getting from 2700 to 2800\n",
            "getting from 2800 to 2900\n",
            "getting from 2900 to 3000\n",
            "getting from 3000 to 3100\n",
            "getting from 3100 to 3200\n",
            "getting from 3200 to 3300\n",
            "getting from 3300 to 3400\n",
            "getting from 3400 to 3500\n",
            "getting from 3500 to 3600\n",
            "getting from 3600 to 3700\n",
            "getting from 3700 to 3800\n",
            "getting from 3800 to 3900\n",
            "getting from 3900 to 4000\n",
            "getting from 4000 to 4100\n",
            "getting from 4100 to 4200\n",
            "getting from 4200 to 4300\n",
            "getting from 4300 to 4400\n",
            "getting from 4400 to 4500\n",
            "getting from 4500 to 4600\n",
            "getting from 4600 to 4700\n",
            "getting from 4700 to 4800\n",
            "getting from 4800 to 4900\n",
            "getting from 4900 to 5000\n",
            "getting from 5000 to 5100\n",
            "getting from 5100 to 5200\n",
            "getting from 5200 to 5300\n",
            "getting from 5300 to 5400\n",
            "getting from 5400 to 5500\n",
            "getting from 5500 to 5600\n",
            "getting from 5600 to 5700\n",
            "getting from 5700 to 5800\n",
            "getting from 5800 to 5900\n",
            "getting from 5900 to 6000\n",
            "getting from 6000 to 6100\n",
            "getting from 6100 to 6200\n",
            "getting from 6200 to 6300\n",
            "getting from 6300 to 6400\n",
            "getting from 6400 to 6500\n",
            "getting from 6500 to 6600\n",
            "getting from 6600 to 6700\n",
            "getting from 6700 to 6800\n",
            "getting from 6800 to 6900\n",
            "getting from 6900 to 7000\n",
            "getting from 7000 to 7100\n",
            "getting from 7100 to 7200\n",
            "getting from 7200 to 7300\n",
            "getting from 7300 to 7400\n",
            "getting from 7400 to 7500\n",
            "getting from 7500 to 7600\n",
            "getting from 7600 to 7700\n",
            "getting from 7700 to 7800\n",
            "getting from 7800 to 7900\n",
            "getting from 7900 to 8000\n",
            "getting from 8000 to 8100\n",
            "getting from 8100 to 8200\n",
            "getting from 8200 to 8300\n",
            "getting from 8300 to 8400\n",
            "getting from 8400 to 8500\n",
            "getting from 8500 to 8600\n",
            "getting from 8600 to 8700\n",
            "getting from 8700 to 8800\n",
            "getting from 8800 to 8900\n",
            "getting from 8900 to 9000\n",
            "getting from 9000 to 9100\n",
            "getting from 9100 to 9200\n",
            "getting from 9200 to 9300\n",
            "getting from 9300 to 9400\n",
            "getting from 9400 to 9500\n",
            "getting from 9500 to 9600\n",
            "getting from 9600 to 9700\n",
            "getting from 9700 to 9800\n",
            "getting from 9800 to 9900\n",
            "getting from 9900 to 10000\n",
            "getting from 10000 to 10100\n",
            "getting from 10100 to 10200\n",
            "getting from 10200 to 10300\n",
            "getting from 10300 to 10400\n",
            "getting from 10400 to 10500\n",
            "getting from 10500 to 10600\n",
            "getting from 10600 to 10700\n",
            "getting from 10700 to 10800\n",
            "getting from 10800 to 10900\n",
            "getting from 10900 to 11000\n",
            "getting from 11000 to 11100\n",
            "getting from 11100 to 11200\n",
            "getting from 11200 to 11300\n",
            "getting from 11300 to 11400\n",
            "getting from 11400 to 11500\n",
            "getting from 11500 to 11600\n",
            "getting from 11600 to 11700\n",
            "getting from 11700 to 11800\n",
            "getting from 11800 to 11900\n",
            "getting from 11900 to 12000\n",
            "getting from 12000 to 12100\n",
            "getting from 12100 to 12200\n",
            "getting from 12200 to 12300\n",
            "getting from 12300 to 12400\n",
            "getting from 12400 to 12500\n",
            "getting from 12500 to 12600\n",
            "getting from 12600 to 12700\n",
            "getting from 12700 to 12800\n",
            "getting from 12800 to 12900\n",
            "getting from 12900 to 13000\n",
            "getting from 13000 to 13100\n",
            "getting from 13100 to 13200\n",
            "getting from 13200 to 13300\n",
            "getting from 13300 to 13400\n",
            "getting from 13400 to 13500\n",
            "getting from 13500 to 13600\n",
            "getting from 13600 to 13700\n",
            "getting from 13700 to 13800\n",
            "getting from 13800 to 13900\n"
          ]
        }
      ],
      "execution_count": 7
    },
    {
      "id": "47dd908f",
      "cell_type": "markdown",
      "source": "* Let's confirm we got everything:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "f0cca615",
      "cell_type": "code",
      "source": "len(features)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "13880"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 8
    },
    {
      "id": "b7a5f899",
      "cell_type": "markdown",
      "source": "* Here's what the audio specs look like.\n\n* We will deal with *valence*, which corresponds to the positive mood that a piece exudes.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "0c05bb47",
      "cell_type": "code",
      "source": "features['1mqlc0vEP9mU1kZgTi6LIQ'] # September by Earth, Wind & Fire",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'danceability': 0.693,\n",
              " 'energy': 0.911,\n",
              " 'key': 9,\n",
              " 'loudness': -3.691,\n",
              " 'mode': 1,\n",
              " 'speechiness': 0.0305,\n",
              " 'acousticness': 0.183,\n",
              " 'instrumentalness': 0.000107,\n",
              " 'liveness': 0.3,\n",
              " 'valence': 0.982,\n",
              " 'tempo': 125.895,\n",
              " 'type': 'audio_features',\n",
              " 'id': '1mqlc0vEP9mU1kZgTi6LIQ',\n",
              " 'uri': 'spotify:track:1mqlc0vEP9mU1kZgTi6LIQ',\n",
              " 'track_href': 'https://api.spotify.com/v1/tracks/1mqlc0vEP9mU1kZgTi6LIQ',\n",
              " 'analysis_url': 'https://api.spotify.com/v1/audio-analysis/1mqlc0vEP9mU1kZgTi6LIQ',\n",
              " 'duration_ms': 216520,\n",
              " 'time_signature': 4}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 9
    },
    {
      "id": "4e62cbdb",
      "cell_type": "markdown",
      "source": "* We will now convert the dictionary to a `DataFrame`.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "04ec1332",
      "cell_type": "code",
      "source": "tracks = pd.DataFrame.from_dict(features, orient='index')\ntracks",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>type</th>\n",
              "      <th>id</th>\n",
              "      <th>uri</th>\n",
              "      <th>track_href</th>\n",
              "      <th>analysis_url</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7wGoVu4Dady5GV0Sv4UIsx</th>\n",
              "      <td>0.577</td>\n",
              "      <td>0.522</td>\n",
              "      <td>5</td>\n",
              "      <td>-6.594</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0984</td>\n",
              "      <td>0.1300</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>0.1420</td>\n",
              "      <td>0.119</td>\n",
              "      <td>159.772</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>spotify:track:7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/7wGoVu4Dady5...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/7wGo...</td>\n",
              "      <td>218320</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75ZvA4QfFiZvzhj2xkaWAh</th>\n",
              "      <td>0.556</td>\n",
              "      <td>0.538</td>\n",
              "      <td>8</td>\n",
              "      <td>-5.408</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0382</td>\n",
              "      <td>0.0689</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1960</td>\n",
              "      <td>0.291</td>\n",
              "      <td>143.950</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>spotify:track:75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/75ZvA4QfFiZv...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/75Zv...</td>\n",
              "      <td>223347</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2fQrGHiQOvpL9UgPvtYy6G</th>\n",
              "      <td>0.884</td>\n",
              "      <td>0.346</td>\n",
              "      <td>8</td>\n",
              "      <td>-8.228</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3510</td>\n",
              "      <td>0.0151</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.0871</td>\n",
              "      <td>0.376</td>\n",
              "      <td>75.016</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>spotify:track:2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/2fQrGHiQOvpL...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/2fQr...</td>\n",
              "      <td>220307</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43ZyHQITOjhciSUUNPVRHc</th>\n",
              "      <td>0.936</td>\n",
              "      <td>0.523</td>\n",
              "      <td>5</td>\n",
              "      <td>-6.710</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0597</td>\n",
              "      <td>0.2390</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1170</td>\n",
              "      <td>0.699</td>\n",
              "      <td>119.889</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>spotify:track:43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/43ZyHQITOjhc...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/43Zy...</td>\n",
              "      <td>124056</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5tz69p7tJuGPeMGwNTxYuV</th>\n",
              "      <td>0.620</td>\n",
              "      <td>0.574</td>\n",
              "      <td>5</td>\n",
              "      <td>-7.788</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0479</td>\n",
              "      <td>0.5690</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1900</td>\n",
              "      <td>0.357</td>\n",
              "      <td>100.023</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>spotify:track:5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/5tz69p7tJuGP...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/5tz6...</td>\n",
              "      <td>250173</td>\n",
              "      <td>4</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3js3wKPw8VxBWtcXtwyUnA</th>\n",
              "      <td>0.798</td>\n",
              "      <td>0.627</td>\n",
              "      <td>9</td>\n",
              "      <td>-6.234</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1110</td>\n",
              "      <td>0.2630</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2110</td>\n",
              "      <td>0.762</td>\n",
              "      <td>149.989</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>3js3wKPw8VxBWtcXtwyUnA</td>\n",
              "      <td>spotify:track:3js3wKPw8VxBWtcXtwyUnA</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/3js3wKPw8VxB...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/3js3...</td>\n",
              "      <td>195947</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4VVG3HBGaqSNZqIpmewIA6</th>\n",
              "      <td>0.777</td>\n",
              "      <td>0.721</td>\n",
              "      <td>6</td>\n",
              "      <td>-6.097</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0719</td>\n",
              "      <td>0.0774</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0801</td>\n",
              "      <td>0.665</td>\n",
              "      <td>161.976</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>4VVG3HBGaqSNZqIpmewIA6</td>\n",
              "      <td>spotify:track:4VVG3HBGaqSNZqIpmewIA6</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/4VVG3HBGaqSN...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/4VVG...</td>\n",
              "      <td>205013</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6csZYoffpZ7iuSw83x2zVy</th>\n",
              "      <td>0.913</td>\n",
              "      <td>0.788</td>\n",
              "      <td>10</td>\n",
              "      <td>-2.889</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2630</td>\n",
              "      <td>0.0546</td>\n",
              "      <td>0.000640</td>\n",
              "      <td>0.1680</td>\n",
              "      <td>0.544</td>\n",
              "      <td>120.934</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>6csZYoffpZ7iuSw83x2zVy</td>\n",
              "      <td>spotify:track:6csZYoffpZ7iuSw83x2zVy</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/6csZYoffpZ7i...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/6csZ...</td>\n",
              "      <td>143314</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0kHTkvGavvk2MjBTSUtOZx</th>\n",
              "      <td>0.599</td>\n",
              "      <td>0.734</td>\n",
              "      <td>3</td>\n",
              "      <td>-7.568</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4130</td>\n",
              "      <td>0.1960</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1870</td>\n",
              "      <td>0.133</td>\n",
              "      <td>211.842</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>0kHTkvGavvk2MjBTSUtOZx</td>\n",
              "      <td>spotify:track:0kHTkvGavvk2MjBTSUtOZx</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/0kHTkvGavvk2...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/0kHT...</td>\n",
              "      <td>223787</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49VSioK272TASR4r9vRTuP</th>\n",
              "      <td>0.740</td>\n",
              "      <td>0.595</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.082</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>0.4860</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1630</td>\n",
              "      <td>0.316</td>\n",
              "      <td>131.960</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>49VSioK272TASR4r9vRTuP</td>\n",
              "      <td>spotify:track:49VSioK272TASR4r9vRTuP</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/49VSioK272TA...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/49VS...</td>\n",
              "      <td>187273</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13880 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                        danceability  energy  key  loudness  mode  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx         0.577   0.522    5    -6.594     0   \n",
              "75ZvA4QfFiZvzhj2xkaWAh         0.556   0.538    8    -5.408     0   \n",
              "2fQrGHiQOvpL9UgPvtYy6G         0.884   0.346    8    -8.228     0   \n",
              "43ZyHQITOjhciSUUNPVRHc         0.936   0.523    5    -6.710     1   \n",
              "5tz69p7tJuGPeMGwNTxYuV         0.620   0.574    5    -7.788     0   \n",
              "...                              ...     ...  ...       ...   ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA         0.798   0.627    9    -6.234     1   \n",
              "4VVG3HBGaqSNZqIpmewIA6         0.777   0.721    6    -6.097     1   \n",
              "6csZYoffpZ7iuSw83x2zVy         0.913   0.788   10    -2.889     0   \n",
              "0kHTkvGavvk2MjBTSUtOZx         0.599   0.734    3    -7.568     0   \n",
              "49VSioK272TASR4r9vRTuP         0.740   0.595    1    -9.082     1   \n",
              "\n",
              "                        speechiness  acousticness  instrumentalness  liveness  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx       0.0984        0.1300          0.000090    0.1420   \n",
              "75ZvA4QfFiZvzhj2xkaWAh       0.0382        0.0689          0.000000    0.1960   \n",
              "2fQrGHiQOvpL9UgPvtYy6G       0.3510        0.0151          0.000007    0.0871   \n",
              "43ZyHQITOjhciSUUNPVRHc       0.0597        0.2390          0.000000    0.1170   \n",
              "5tz69p7tJuGPeMGwNTxYuV       0.0479        0.5690          0.000000    0.1900   \n",
              "...                             ...           ...               ...       ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA       0.1110        0.2630          0.000000    0.2110   \n",
              "4VVG3HBGaqSNZqIpmewIA6       0.0719        0.0774          0.000000    0.0801   \n",
              "6csZYoffpZ7iuSw83x2zVy       0.2630        0.0546          0.000640    0.1680   \n",
              "0kHTkvGavvk2MjBTSUtOZx       0.4130        0.1960          0.000000    0.1870   \n",
              "49VSioK272TASR4r9vRTuP       0.0623        0.4860          0.000000    0.1630   \n",
              "\n",
              "                        valence    tempo            type  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx    0.119  159.772  audio_features   \n",
              "75ZvA4QfFiZvzhj2xkaWAh    0.291  143.950  audio_features   \n",
              "2fQrGHiQOvpL9UgPvtYy6G    0.376   75.016  audio_features   \n",
              "43ZyHQITOjhciSUUNPVRHc    0.699  119.889  audio_features   \n",
              "5tz69p7tJuGPeMGwNTxYuV    0.357  100.023  audio_features   \n",
              "...                         ...      ...             ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA    0.762  149.989  audio_features   \n",
              "4VVG3HBGaqSNZqIpmewIA6    0.665  161.976  audio_features   \n",
              "6csZYoffpZ7iuSw83x2zVy    0.544  120.934  audio_features   \n",
              "0kHTkvGavvk2MjBTSUtOZx    0.133  211.842  audio_features   \n",
              "49VSioK272TASR4r9vRTuP    0.316  131.960  audio_features   \n",
              "\n",
              "                                            id  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx  7wGoVu4Dady5GV0Sv4UIsx   \n",
              "75ZvA4QfFiZvzhj2xkaWAh  75ZvA4QfFiZvzhj2xkaWAh   \n",
              "2fQrGHiQOvpL9UgPvtYy6G  2fQrGHiQOvpL9UgPvtYy6G   \n",
              "43ZyHQITOjhciSUUNPVRHc  43ZyHQITOjhciSUUNPVRHc   \n",
              "5tz69p7tJuGPeMGwNTxYuV  5tz69p7tJuGPeMGwNTxYuV   \n",
              "...                                        ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA  3js3wKPw8VxBWtcXtwyUnA   \n",
              "4VVG3HBGaqSNZqIpmewIA6  4VVG3HBGaqSNZqIpmewIA6   \n",
              "6csZYoffpZ7iuSw83x2zVy  6csZYoffpZ7iuSw83x2zVy   \n",
              "0kHTkvGavvk2MjBTSUtOZx  0kHTkvGavvk2MjBTSUtOZx   \n",
              "49VSioK272TASR4r9vRTuP  49VSioK272TASR4r9vRTuP   \n",
              "\n",
              "                                                         uri  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx  spotify:track:7wGoVu4Dady5GV0Sv4UIsx   \n",
              "75ZvA4QfFiZvzhj2xkaWAh  spotify:track:75ZvA4QfFiZvzhj2xkaWAh   \n",
              "2fQrGHiQOvpL9UgPvtYy6G  spotify:track:2fQrGHiQOvpL9UgPvtYy6G   \n",
              "43ZyHQITOjhciSUUNPVRHc  spotify:track:43ZyHQITOjhciSUUNPVRHc   \n",
              "5tz69p7tJuGPeMGwNTxYuV  spotify:track:5tz69p7tJuGPeMGwNTxYuV   \n",
              "...                                                      ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA  spotify:track:3js3wKPw8VxBWtcXtwyUnA   \n",
              "4VVG3HBGaqSNZqIpmewIA6  spotify:track:4VVG3HBGaqSNZqIpmewIA6   \n",
              "6csZYoffpZ7iuSw83x2zVy  spotify:track:6csZYoffpZ7iuSw83x2zVy   \n",
              "0kHTkvGavvk2MjBTSUtOZx  spotify:track:0kHTkvGavvk2MjBTSUtOZx   \n",
              "49VSioK272TASR4r9vRTuP  spotify:track:49VSioK272TASR4r9vRTuP   \n",
              "\n",
              "                                                               track_href  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx  https://api.spotify.com/v1/tracks/7wGoVu4Dady5...   \n",
              "75ZvA4QfFiZvzhj2xkaWAh  https://api.spotify.com/v1/tracks/75ZvA4QfFiZv...   \n",
              "2fQrGHiQOvpL9UgPvtYy6G  https://api.spotify.com/v1/tracks/2fQrGHiQOvpL...   \n",
              "43ZyHQITOjhciSUUNPVRHc  https://api.spotify.com/v1/tracks/43ZyHQITOjhc...   \n",
              "5tz69p7tJuGPeMGwNTxYuV  https://api.spotify.com/v1/tracks/5tz69p7tJuGP...   \n",
              "...                                                                   ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA  https://api.spotify.com/v1/tracks/3js3wKPw8VxB...   \n",
              "4VVG3HBGaqSNZqIpmewIA6  https://api.spotify.com/v1/tracks/4VVG3HBGaqSN...   \n",
              "6csZYoffpZ7iuSw83x2zVy  https://api.spotify.com/v1/tracks/6csZYoffpZ7i...   \n",
              "0kHTkvGavvk2MjBTSUtOZx  https://api.spotify.com/v1/tracks/0kHTkvGavvk2...   \n",
              "49VSioK272TASR4r9vRTuP  https://api.spotify.com/v1/tracks/49VSioK272TA...   \n",
              "\n",
              "                                                             analysis_url  \\\n",
              "7wGoVu4Dady5GV0Sv4UIsx  https://api.spotify.com/v1/audio-analysis/7wGo...   \n",
              "75ZvA4QfFiZvzhj2xkaWAh  https://api.spotify.com/v1/audio-analysis/75Zv...   \n",
              "2fQrGHiQOvpL9UgPvtYy6G  https://api.spotify.com/v1/audio-analysis/2fQr...   \n",
              "43ZyHQITOjhciSUUNPVRHc  https://api.spotify.com/v1/audio-analysis/43Zy...   \n",
              "5tz69p7tJuGPeMGwNTxYuV  https://api.spotify.com/v1/audio-analysis/5tz6...   \n",
              "...                                                                   ...   \n",
              "3js3wKPw8VxBWtcXtwyUnA  https://api.spotify.com/v1/audio-analysis/3js3...   \n",
              "4VVG3HBGaqSNZqIpmewIA6  https://api.spotify.com/v1/audio-analysis/4VVG...   \n",
              "6csZYoffpZ7iuSw83x2zVy  https://api.spotify.com/v1/audio-analysis/6csZ...   \n",
              "0kHTkvGavvk2MjBTSUtOZx  https://api.spotify.com/v1/audio-analysis/0kHT...   \n",
              "49VSioK272TASR4r9vRTuP  https://api.spotify.com/v1/audio-analysis/49VS...   \n",
              "\n",
              "                        duration_ms  time_signature  \n",
              "7wGoVu4Dady5GV0Sv4UIsx       218320               4  \n",
              "75ZvA4QfFiZvzhj2xkaWAh       223347               4  \n",
              "2fQrGHiQOvpL9UgPvtYy6G       220307               4  \n",
              "43ZyHQITOjhciSUUNPVRHc       124056               4  \n",
              "5tz69p7tJuGPeMGwNTxYuV       250173               4  \n",
              "...                             ...             ...  \n",
              "3js3wKPw8VxBWtcXtwyUnA       195947               4  \n",
              "4VVG3HBGaqSNZqIpmewIA6       205013               4  \n",
              "6csZYoffpZ7iuSw83x2zVy       143314               4  \n",
              "0kHTkvGavvk2MjBTSUtOZx       223787               4  \n",
              "49VSioK272TASR4r9vRTuP       187273               4  \n",
              "\n",
              "[13880 rows x 18 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 10
    },
    {
      "id": "5114bf72",
      "cell_type": "markdown",
      "source": "* The index contains the code of each track, as does the `id` column, so we'll keep one of the two.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "e1499fde",
      "cell_type": "code",
      "source": "tracks = tracks.reset_index(drop=True).rename(columns={'id' : 'song_id'})\ntracks",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>type</th>\n",
              "      <th>song_id</th>\n",
              "      <th>uri</th>\n",
              "      <th>track_href</th>\n",
              "      <th>analysis_url</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.577</td>\n",
              "      <td>0.522</td>\n",
              "      <td>5</td>\n",
              "      <td>-6.594</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0984</td>\n",
              "      <td>0.1300</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>0.1420</td>\n",
              "      <td>0.119</td>\n",
              "      <td>159.772</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>spotify:track:7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/7wGoVu4Dady5...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/7wGo...</td>\n",
              "      <td>218320</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.556</td>\n",
              "      <td>0.538</td>\n",
              "      <td>8</td>\n",
              "      <td>-5.408</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0382</td>\n",
              "      <td>0.0689</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1960</td>\n",
              "      <td>0.291</td>\n",
              "      <td>143.950</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>spotify:track:75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/75ZvA4QfFiZv...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/75Zv...</td>\n",
              "      <td>223347</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.884</td>\n",
              "      <td>0.346</td>\n",
              "      <td>8</td>\n",
              "      <td>-8.228</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3510</td>\n",
              "      <td>0.0151</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.0871</td>\n",
              "      <td>0.376</td>\n",
              "      <td>75.016</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>spotify:track:2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/2fQrGHiQOvpL...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/2fQr...</td>\n",
              "      <td>220307</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.936</td>\n",
              "      <td>0.523</td>\n",
              "      <td>5</td>\n",
              "      <td>-6.710</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0597</td>\n",
              "      <td>0.2390</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1170</td>\n",
              "      <td>0.699</td>\n",
              "      <td>119.889</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>spotify:track:43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/43ZyHQITOjhc...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/43Zy...</td>\n",
              "      <td>124056</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.620</td>\n",
              "      <td>0.574</td>\n",
              "      <td>5</td>\n",
              "      <td>-7.788</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0479</td>\n",
              "      <td>0.5690</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1900</td>\n",
              "      <td>0.357</td>\n",
              "      <td>100.023</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>spotify:track:5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/5tz69p7tJuGP...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/5tz6...</td>\n",
              "      <td>250173</td>\n",
              "      <td>4</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13875</th>\n",
              "      <td>0.798</td>\n",
              "      <td>0.627</td>\n",
              "      <td>9</td>\n",
              "      <td>-6.234</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1110</td>\n",
              "      <td>0.2630</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2110</td>\n",
              "      <td>0.762</td>\n",
              "      <td>149.989</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>3js3wKPw8VxBWtcXtwyUnA</td>\n",
              "      <td>spotify:track:3js3wKPw8VxBWtcXtwyUnA</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/3js3wKPw8VxB...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/3js3...</td>\n",
              "      <td>195947</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13876</th>\n",
              "      <td>0.777</td>\n",
              "      <td>0.721</td>\n",
              "      <td>6</td>\n",
              "      <td>-6.097</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0719</td>\n",
              "      <td>0.0774</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0801</td>\n",
              "      <td>0.665</td>\n",
              "      <td>161.976</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>4VVG3HBGaqSNZqIpmewIA6</td>\n",
              "      <td>spotify:track:4VVG3HBGaqSNZqIpmewIA6</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/4VVG3HBGaqSN...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/4VVG...</td>\n",
              "      <td>205013</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13877</th>\n",
              "      <td>0.913</td>\n",
              "      <td>0.788</td>\n",
              "      <td>10</td>\n",
              "      <td>-2.889</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2630</td>\n",
              "      <td>0.0546</td>\n",
              "      <td>0.000640</td>\n",
              "      <td>0.1680</td>\n",
              "      <td>0.544</td>\n",
              "      <td>120.934</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>6csZYoffpZ7iuSw83x2zVy</td>\n",
              "      <td>spotify:track:6csZYoffpZ7iuSw83x2zVy</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/6csZYoffpZ7i...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/6csZ...</td>\n",
              "      <td>143314</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13878</th>\n",
              "      <td>0.599</td>\n",
              "      <td>0.734</td>\n",
              "      <td>3</td>\n",
              "      <td>-7.568</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4130</td>\n",
              "      <td>0.1960</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1870</td>\n",
              "      <td>0.133</td>\n",
              "      <td>211.842</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>0kHTkvGavvk2MjBTSUtOZx</td>\n",
              "      <td>spotify:track:0kHTkvGavvk2MjBTSUtOZx</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/0kHTkvGavvk2...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/0kHT...</td>\n",
              "      <td>223787</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13879</th>\n",
              "      <td>0.740</td>\n",
              "      <td>0.595</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.082</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>0.4860</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1630</td>\n",
              "      <td>0.316</td>\n",
              "      <td>131.960</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>49VSioK272TASR4r9vRTuP</td>\n",
              "      <td>spotify:track:49VSioK272TASR4r9vRTuP</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/49VSioK272TA...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/49VS...</td>\n",
              "      <td>187273</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13880 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       danceability  energy  key  loudness  mode  speechiness  acousticness  \\\n",
              "0             0.577   0.522    5    -6.594     0       0.0984        0.1300   \n",
              "1             0.556   0.538    8    -5.408     0       0.0382        0.0689   \n",
              "2             0.884   0.346    8    -8.228     0       0.3510        0.0151   \n",
              "3             0.936   0.523    5    -6.710     1       0.0597        0.2390   \n",
              "4             0.620   0.574    5    -7.788     0       0.0479        0.5690   \n",
              "...             ...     ...  ...       ...   ...          ...           ...   \n",
              "13875         0.798   0.627    9    -6.234     1       0.1110        0.2630   \n",
              "13876         0.777   0.721    6    -6.097     1       0.0719        0.0774   \n",
              "13877         0.913   0.788   10    -2.889     0       0.2630        0.0546   \n",
              "13878         0.599   0.734    3    -7.568     0       0.4130        0.1960   \n",
              "13879         0.740   0.595    1    -9.082     1       0.0623        0.4860   \n",
              "\n",
              "       instrumentalness  liveness  valence    tempo            type  \\\n",
              "0              0.000090    0.1420    0.119  159.772  audio_features   \n",
              "1              0.000000    0.1960    0.291  143.950  audio_features   \n",
              "2              0.000007    0.0871    0.376   75.016  audio_features   \n",
              "3              0.000000    0.1170    0.699  119.889  audio_features   \n",
              "4              0.000000    0.1900    0.357  100.023  audio_features   \n",
              "...                 ...       ...      ...      ...             ...   \n",
              "13875          0.000000    0.2110    0.762  149.989  audio_features   \n",
              "13876          0.000000    0.0801    0.665  161.976  audio_features   \n",
              "13877          0.000640    0.1680    0.544  120.934  audio_features   \n",
              "13878          0.000000    0.1870    0.133  211.842  audio_features   \n",
              "13879          0.000000    0.1630    0.316  131.960  audio_features   \n",
              "\n",
              "                      song_id                                   uri  \\\n",
              "0      7wGoVu4Dady5GV0Sv4UIsx  spotify:track:7wGoVu4Dady5GV0Sv4UIsx   \n",
              "1      75ZvA4QfFiZvzhj2xkaWAh  spotify:track:75ZvA4QfFiZvzhj2xkaWAh   \n",
              "2      2fQrGHiQOvpL9UgPvtYy6G  spotify:track:2fQrGHiQOvpL9UgPvtYy6G   \n",
              "3      43ZyHQITOjhciSUUNPVRHc  spotify:track:43ZyHQITOjhciSUUNPVRHc   \n",
              "4      5tz69p7tJuGPeMGwNTxYuV  spotify:track:5tz69p7tJuGPeMGwNTxYuV   \n",
              "...                       ...                                   ...   \n",
              "13875  3js3wKPw8VxBWtcXtwyUnA  spotify:track:3js3wKPw8VxBWtcXtwyUnA   \n",
              "13876  4VVG3HBGaqSNZqIpmewIA6  spotify:track:4VVG3HBGaqSNZqIpmewIA6   \n",
              "13877  6csZYoffpZ7iuSw83x2zVy  spotify:track:6csZYoffpZ7iuSw83x2zVy   \n",
              "13878  0kHTkvGavvk2MjBTSUtOZx  spotify:track:0kHTkvGavvk2MjBTSUtOZx   \n",
              "13879  49VSioK272TASR4r9vRTuP  spotify:track:49VSioK272TASR4r9vRTuP   \n",
              "\n",
              "                                              track_href  \\\n",
              "0      https://api.spotify.com/v1/tracks/7wGoVu4Dady5...   \n",
              "1      https://api.spotify.com/v1/tracks/75ZvA4QfFiZv...   \n",
              "2      https://api.spotify.com/v1/tracks/2fQrGHiQOvpL...   \n",
              "3      https://api.spotify.com/v1/tracks/43ZyHQITOjhc...   \n",
              "4      https://api.spotify.com/v1/tracks/5tz69p7tJuGP...   \n",
              "...                                                  ...   \n",
              "13875  https://api.spotify.com/v1/tracks/3js3wKPw8VxB...   \n",
              "13876  https://api.spotify.com/v1/tracks/4VVG3HBGaqSN...   \n",
              "13877  https://api.spotify.com/v1/tracks/6csZYoffpZ7i...   \n",
              "13878  https://api.spotify.com/v1/tracks/0kHTkvGavvk2...   \n",
              "13879  https://api.spotify.com/v1/tracks/49VSioK272TA...   \n",
              "\n",
              "                                            analysis_url  duration_ms  \\\n",
              "0      https://api.spotify.com/v1/audio-analysis/7wGo...       218320   \n",
              "1      https://api.spotify.com/v1/audio-analysis/75Zv...       223347   \n",
              "2      https://api.spotify.com/v1/audio-analysis/2fQr...       220307   \n",
              "3      https://api.spotify.com/v1/audio-analysis/43Zy...       124056   \n",
              "4      https://api.spotify.com/v1/audio-analysis/5tz6...       250173   \n",
              "...                                                  ...          ...   \n",
              "13875  https://api.spotify.com/v1/audio-analysis/3js3...       195947   \n",
              "13876  https://api.spotify.com/v1/audio-analysis/4VVG...       205013   \n",
              "13877  https://api.spotify.com/v1/audio-analysis/6csZ...       143314   \n",
              "13878  https://api.spotify.com/v1/audio-analysis/0kHT...       223787   \n",
              "13879  https://api.spotify.com/v1/audio-analysis/49VS...       187273   \n",
              "\n",
              "       time_signature  \n",
              "0                   4  \n",
              "1                   4  \n",
              "2                   4  \n",
              "3                   4  \n",
              "4                   4  \n",
              "...               ...  \n",
              "13875               4  \n",
              "13876               4  \n",
              "13877               4  \n",
              "13878               4  \n",
              "13879               4  \n",
              "\n",
              "[13880 rows x 18 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 11
    },
    {
      "id": "42ff361e",
      "cell_type": "markdown",
      "source": "* We can now merge the valences of each piece with the data from Zenodo.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "1ead8846",
      "cell_type": "code",
      "source": "all_charts = pd.merge(all_charts, tracks[['song_id', 'valence']], on='song_id', how='left')\nall_charts",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>position</th>\n",
              "      <th>song_id</th>\n",
              "      <th>song_name</th>\n",
              "      <th>artist</th>\n",
              "      <th>streams</th>\n",
              "      <th>last_week_position</th>\n",
              "      <th>weeks_on_chart</th>\n",
              "      <th>peak_position</th>\n",
              "      <th>position_status</th>\n",
              "      <th>week_start</th>\n",
              "      <th>week_end</th>\n",
              "      <th>region</th>\n",
              "      <th>valence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>rockstar</td>\n",
              "      <td>Post Malone</td>\n",
              "      <td>17532665</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>I Fall Apart</td>\n",
              "      <td>Post Malone</td>\n",
              "      <td>8350785</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>Bank Account</td>\n",
              "      <td>21 Savage</td>\n",
              "      <td>7589124</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>Gucci Gang</td>\n",
              "      <td>Lil Pump</td>\n",
              "      <td>7584237</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>1-800-273-8255</td>\n",
              "      <td>Logic</td>\n",
              "      <td>7527770</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>-2</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.357</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273595</th>\n",
              "      <td>196</td>\n",
              "      <td>6kex4EBAj0WHXDKZMEJaaF</td>\n",
              "      <td>Swalla (feat. Nicki Minaj &amp; Ty Dolla $ign)</td>\n",
              "      <td>Jason Derulo</td>\n",
              "      <td>3747830</td>\n",
              "      <td>191.0</td>\n",
              "      <td>52</td>\n",
              "      <td>8</td>\n",
              "      <td>-5</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273596</th>\n",
              "      <td>197</td>\n",
              "      <td>0CokSRCu5hZgPxcZBaEzVE</td>\n",
              "      <td>Glorious (feat. Skylar Grey)</td>\n",
              "      <td>Macklemore</td>\n",
              "      <td>3725286</td>\n",
              "      <td>189.0</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>-8</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273597</th>\n",
              "      <td>198</td>\n",
              "      <td>7oK9VyNzrYvRFo7nQEYkWN</td>\n",
              "      <td>Mr. Brightside</td>\n",
              "      <td>The Killers</td>\n",
              "      <td>3717326</td>\n",
              "      <td>195.0</td>\n",
              "      <td>17</td>\n",
              "      <td>148</td>\n",
              "      <td>-3</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273598</th>\n",
              "      <td>199</td>\n",
              "      <td>7EUfNvyCVxQV3oN5ScA2Lb</td>\n",
              "      <td>Next To Me</td>\n",
              "      <td>Imagine Dragons</td>\n",
              "      <td>3681739</td>\n",
              "      <td>122.0</td>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>-77</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273599</th>\n",
              "      <td>200</td>\n",
              "      <td>6u0EAxf1OJTLS7CvInuNd7</td>\n",
              "      <td>Vai malandra (feat. Tropkillaz &amp; DJ Yuri Martins)</td>\n",
              "      <td>Anitta</td>\n",
              "      <td>3676542</td>\n",
              "      <td>177.0</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>-23</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273600 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        position                 song_id  \\\n",
              "0              1  7wGoVu4Dady5GV0Sv4UIsx   \n",
              "1              2  75ZvA4QfFiZvzhj2xkaWAh   \n",
              "2              3  2fQrGHiQOvpL9UgPvtYy6G   \n",
              "3              4  43ZyHQITOjhciSUUNPVRHc   \n",
              "4              5  5tz69p7tJuGPeMGwNTxYuV   \n",
              "...          ...                     ...   \n",
              "273595       196  6kex4EBAj0WHXDKZMEJaaF   \n",
              "273596       197  0CokSRCu5hZgPxcZBaEzVE   \n",
              "273597       198  7oK9VyNzrYvRFo7nQEYkWN   \n",
              "273598       199  7EUfNvyCVxQV3oN5ScA2Lb   \n",
              "273599       200  6u0EAxf1OJTLS7CvInuNd7   \n",
              "\n",
              "                                                song_name           artist  \\\n",
              "0                                                rockstar      Post Malone   \n",
              "1                                            I Fall Apart      Post Malone   \n",
              "2                                            Bank Account        21 Savage   \n",
              "3                                              Gucci Gang         Lil Pump   \n",
              "4                                          1-800-273-8255            Logic   \n",
              "...                                                   ...              ...   \n",
              "273595         Swalla (feat. Nicki Minaj & Ty Dolla $ign)     Jason Derulo   \n",
              "273596                       Glorious (feat. Skylar Grey)       Macklemore   \n",
              "273597                                     Mr. Brightside      The Killers   \n",
              "273598                                         Next To Me  Imagine Dragons   \n",
              "273599  Vai malandra (feat. Tropkillaz & DJ Yuri Martins)           Anitta   \n",
              "\n",
              "         streams  last_week_position  weeks_on_chart  peak_position  \\\n",
              "0       17532665                 1.0               3              1   \n",
              "1        8350785                 2.0               6              2   \n",
              "2        7589124                 4.0               5              3   \n",
              "3        7584237                 5.0               3              4   \n",
              "4        7527770                 3.0              26              1   \n",
              "...          ...                 ...             ...            ...   \n",
              "273595   3747830               191.0              52              8   \n",
              "273596   3725286               189.0              24             14   \n",
              "273597   3717326               195.0              17            148   \n",
              "273598   3681739               122.0               2            122   \n",
              "273599   3676542               177.0              12             30   \n",
              "\n",
              "       position_status week_start   week_end  region  valence  \n",
              "0                    0 2017-10-20 2017-10-27      us    0.119  \n",
              "1                    0 2017-10-20 2017-10-27      us    0.291  \n",
              "2                    1 2017-10-20 2017-10-27      us    0.376  \n",
              "3                    1 2017-10-20 2017-10-27      us    0.699  \n",
              "4                   -2 2017-10-20 2017-10-27      us    0.357  \n",
              "...                ...        ...        ...     ...      ...  \n",
              "273595              -5 2018-03-02 2018-03-09  global    0.782  \n",
              "273596              -8 2018-03-02 2018-03-09  global    0.356  \n",
              "273597              -3 2018-03-02 2018-03-09  global    0.232  \n",
              "273598             -77 2018-03-02 2018-03-09  global    0.351  \n",
              "273599             -23 2018-03-02 2018-03-09  global    0.557  \n",
              "\n",
              "[273600 rows x 13 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 12
    },
    {
      "id": "8be9fa74",
      "cell_type": "markdown",
      "source": "* As we said, we are interested in valence, in the `valence' column.\n\n* Let's see the strength by week:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "25395fff",
      "cell_type": "code",
      "source": "weekly_valence = all_charts.groupby('week_start').mean()['valence']\nweekly_valence",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "week_start\n",
              "2016-12-30    0.502642\n",
              "2017-01-06    0.493741\n",
              "2017-01-13    0.490104\n",
              "2017-01-20    0.488434\n",
              "2017-01-27    0.490052\n",
              "                ...   \n",
              "2019-11-22    0.526818\n",
              "2019-11-29    0.531499\n",
              "2019-12-06    0.534468\n",
              "2019-12-13    0.536318\n",
              "2019-12-20    0.544564\n",
              "Name: valence, Length: 156, dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 13
    },
    {
      "id": "cd76a3a3",
      "cell_type": "markdown",
      "source": "* During the year:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "913a8bfc",
      "cell_type": "code",
      "source": "sns.set_theme()\nplt.figure(figsize=(20,5))\n_ = sns.lineplot(x=weekly_valence.index, y=weekly_valence)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 14
    },
    {
      "id": "8cb3fa19",
      "cell_type": "markdown",
      "source": "* How important Christmas is!\n\n* And what a melancholy month February seems to be...",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "ce6a71cd",
      "cell_type": "code",
      "source": "weekly_valence_2017 = weekly_valence[weekly_valence.index.year == 2017]\n_ = sns.lineplot(x=weekly_valence_2017.index, y=weekly_valence_2017)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 15
    },
    {
      "id": "8dedb6c1",
      "cell_type": "markdown",
      "source": "* We see the same next year!",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "95f082ef",
      "cell_type": "code",
      "source": "weekly_valence_2018 = weekly_valence[weekly_valence.index.year == 2018]\n_ = sns.lineplot(x=weekly_valence_2018.index, y=weekly_valence_2018)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 16
    },
    {
      "id": "50cd28ab",
      "cell_type": "markdown",
      "source": "* But we don't see it in 2019.\n\n* People seem to be having fun in general, apparently they had no idea what was coming in 2020...",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "629c00a0",
      "cell_type": "code",
      "source": "weekly_valence_2019 = weekly_valence[weekly_valence.index.year == 2019]\n_ = sns.lineplot(x=weekly_valence_2019.index, y=weekly_valence_2019)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 17
    },
    {
      "id": "d4d8de9b",
      "cell_type": "markdown",
      "source": "* More generally, however, the strength of the pieces seems to be towards the middle.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "c5812155",
      "cell_type": "code",
      "source": "sns.histplot(x='valence', data=all_charts, kde=True)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='valence', ylabel='Count'>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 18
    },
    {
      "id": "linear-problem",
      "cell_type": "markdown",
      "source": "* We can see the image in a specific region, such as in the US:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "boolean-miracle",
      "cell_type": "code",
      "source": "sns.histplot(x='valence', data=all_charts[all_charts['region'] == 'us'], kde=True)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='valence', ylabel='Count'>"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 19
    },
    {
      "id": "endless-gauge",
      "cell_type": "markdown",
      "source": "* Or in Brazil:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "equivalent-input",
      "cell_type": "code",
      "source": "sns.histplot(x='valence', data=all_charts[all_charts['region'] == 'br'], kde=True)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='valence', ylabel='Count'>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 20
    },
    {
      "id": "hired-vatican",
      "cell_type": "markdown",
      "source": "* Or in all regions together:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "labeled-motor",
      "cell_type": "code",
      "source": "plt.figure(figsize=(16, 10))\n_ = sns.displot(x=\"valence\", data=all_charts, hue=\"region\", aspect=2.5, kind=\"kde\")",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 969.225x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 21
    },
    {
      "id": "valued-purpose",
      "cell_type": "markdown",
      "source": "* Or in the US, Brazil, and worldwide:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "negative-geology",
      "cell_type": "code",
      "source": "g = sns.displot(x=\"valence\", data=all_charts[all_charts['region'].isin(['br', 'us', 'global'])], \n                hue=\"region\", aspect=2.5, kind=\"kde\", fill=True)\ng.ax.annotate('0.0789 Bridge Over Troubled Water (Artists for Grenfell)', \n              xy=(0.0789, 0.0), \n              xytext=(0.0789, -0.15), \n              arrowprops=dict(facecolor='black', headwidth=8, width=2),\n)\n_ = g.ax.annotate('0.982 September (Earth, Wind & Fire)', xy=(0.982, 0.0), \n                  xytext=(0.982, -0.15),\n                  arrowprops=dict(facecolor='black', headwidth=8, width=2))",
      "metadata": {
        "scrolled": true,
        "slideshow": {
          "slide_type": "fragment"
        },
        "tags": [
          "remove_input"
        ]
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 969.225x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 22
    },
    {
      "id": "sustainable-chain",
      "cell_type": "markdown",
      "source": "* The thecograms can show us the size of the difference by region:",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "precious-qualification",
      "cell_type": "code",
      "source": "plt.figure(figsize=(10, 8))\nsns.boxplot(x='region', y='valence', data=all_charts)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='region', ylabel='valence'>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 23
    },
    {
      "id": "excited-rebound",
      "cell_type": "markdown",
      "source": "* One idea is to put all the histograms in one chart, but in a better way.\n\n* We'll start by taking the average valence in ascending order.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "composite-promotion",
      "cell_type": "code",
      "source": "valence_by_region = all_charts[['region', 'valence']].groupby('region').mean().sort_values(by='valence')\nvalence_by_region.reset_index(inplace=True)\nvalence_by_region['region_valence_pos'] = valence_by_region.index\nvalence_by_region",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>region</th>\n",
              "      <th>valence</th>\n",
              "      <th>region_valence_pos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>us</td>\n",
              "      <td>0.450253</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ca</td>\n",
              "      <td>0.459242</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>au</td>\n",
              "      <td>0.469923</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>global</td>\n",
              "      <td>0.495207</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>de</td>\n",
              "      <td>0.499084</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>gb</td>\n",
              "      <td>0.501666</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fr</td>\n",
              "      <td>0.510750</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>jp</td>\n",
              "      <td>0.529292</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>br</td>\n",
              "      <td>0.606860</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   region   valence  region_valence_pos\n",
              "0      us  0.450253                   0\n",
              "1      ca  0.459242                   1\n",
              "2      au  0.469923                   2\n",
              "3  global  0.495207                   3\n",
              "4      de  0.499084                   4\n",
              "5      gb  0.501666                   5\n",
              "6      fr  0.510750                   6\n",
              "7      jp  0.529292                   7\n",
              "8      br  0.606860                   8"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 24
    },
    {
      "id": "alpha-updating",
      "cell_type": "markdown",
      "source": "* We will add the position of each region, in terms of valence, to the `DataFrame` `all_charts`.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "civil-portland",
      "cell_type": "code",
      "source": "all_charts = pd.merge(all_charts, valence_by_region[['region', 'region_valence_pos']], on='region')\nall_charts",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>position</th>\n",
              "      <th>song_id</th>\n",
              "      <th>song_name</th>\n",
              "      <th>artist</th>\n",
              "      <th>streams</th>\n",
              "      <th>last_week_position</th>\n",
              "      <th>weeks_on_chart</th>\n",
              "      <th>peak_position</th>\n",
              "      <th>position_status</th>\n",
              "      <th>week_start</th>\n",
              "      <th>week_end</th>\n",
              "      <th>region</th>\n",
              "      <th>valence</th>\n",
              "      <th>region_valence_pos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>7wGoVu4Dady5GV0Sv4UIsx</td>\n",
              "      <td>rockstar</td>\n",
              "      <td>Post Malone</td>\n",
              "      <td>17532665</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.119</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>75ZvA4QfFiZvzhj2xkaWAh</td>\n",
              "      <td>I Fall Apart</td>\n",
              "      <td>Post Malone</td>\n",
              "      <td>8350785</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.291</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6G</td>\n",
              "      <td>Bank Account</td>\n",
              "      <td>21 Savage</td>\n",
              "      <td>7589124</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.376</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>43ZyHQITOjhciSUUNPVRHc</td>\n",
              "      <td>Gucci Gang</td>\n",
              "      <td>Lil Pump</td>\n",
              "      <td>7584237</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.699</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5tz69p7tJuGPeMGwNTxYuV</td>\n",
              "      <td>1-800-273-8255</td>\n",
              "      <td>Logic</td>\n",
              "      <td>7527770</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>-2</td>\n",
              "      <td>2017-10-20</td>\n",
              "      <td>2017-10-27</td>\n",
              "      <td>us</td>\n",
              "      <td>0.357</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273595</th>\n",
              "      <td>196</td>\n",
              "      <td>6kex4EBAj0WHXDKZMEJaaF</td>\n",
              "      <td>Swalla (feat. Nicki Minaj &amp; Ty Dolla $ign)</td>\n",
              "      <td>Jason Derulo</td>\n",
              "      <td>3747830</td>\n",
              "      <td>191.0</td>\n",
              "      <td>52</td>\n",
              "      <td>8</td>\n",
              "      <td>-5</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.782</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273596</th>\n",
              "      <td>197</td>\n",
              "      <td>0CokSRCu5hZgPxcZBaEzVE</td>\n",
              "      <td>Glorious (feat. Skylar Grey)</td>\n",
              "      <td>Macklemore</td>\n",
              "      <td>3725286</td>\n",
              "      <td>189.0</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>-8</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.356</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273597</th>\n",
              "      <td>198</td>\n",
              "      <td>7oK9VyNzrYvRFo7nQEYkWN</td>\n",
              "      <td>Mr. Brightside</td>\n",
              "      <td>The Killers</td>\n",
              "      <td>3717326</td>\n",
              "      <td>195.0</td>\n",
              "      <td>17</td>\n",
              "      <td>148</td>\n",
              "      <td>-3</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.232</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273598</th>\n",
              "      <td>199</td>\n",
              "      <td>7EUfNvyCVxQV3oN5ScA2Lb</td>\n",
              "      <td>Next To Me</td>\n",
              "      <td>Imagine Dragons</td>\n",
              "      <td>3681739</td>\n",
              "      <td>122.0</td>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>-77</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.351</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273599</th>\n",
              "      <td>200</td>\n",
              "      <td>6u0EAxf1OJTLS7CvInuNd7</td>\n",
              "      <td>Vai malandra (feat. Tropkillaz &amp; DJ Yuri Martins)</td>\n",
              "      <td>Anitta</td>\n",
              "      <td>3676542</td>\n",
              "      <td>177.0</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>-23</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>2018-03-09</td>\n",
              "      <td>global</td>\n",
              "      <td>0.557</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273600 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        position                 song_id  \\\n",
              "0              1  7wGoVu4Dady5GV0Sv4UIsx   \n",
              "1              2  75ZvA4QfFiZvzhj2xkaWAh   \n",
              "2              3  2fQrGHiQOvpL9UgPvtYy6G   \n",
              "3              4  43ZyHQITOjhciSUUNPVRHc   \n",
              "4              5  5tz69p7tJuGPeMGwNTxYuV   \n",
              "...          ...                     ...   \n",
              "273595       196  6kex4EBAj0WHXDKZMEJaaF   \n",
              "273596       197  0CokSRCu5hZgPxcZBaEzVE   \n",
              "273597       198  7oK9VyNzrYvRFo7nQEYkWN   \n",
              "273598       199  7EUfNvyCVxQV3oN5ScA2Lb   \n",
              "273599       200  6u0EAxf1OJTLS7CvInuNd7   \n",
              "\n",
              "                                                song_name           artist  \\\n",
              "0                                                rockstar      Post Malone   \n",
              "1                                            I Fall Apart      Post Malone   \n",
              "2                                            Bank Account        21 Savage   \n",
              "3                                              Gucci Gang         Lil Pump   \n",
              "4                                          1-800-273-8255            Logic   \n",
              "...                                                   ...              ...   \n",
              "273595         Swalla (feat. Nicki Minaj & Ty Dolla $ign)     Jason Derulo   \n",
              "273596                       Glorious (feat. Skylar Grey)       Macklemore   \n",
              "273597                                     Mr. Brightside      The Killers   \n",
              "273598                                         Next To Me  Imagine Dragons   \n",
              "273599  Vai malandra (feat. Tropkillaz & DJ Yuri Martins)           Anitta   \n",
              "\n",
              "         streams  last_week_position  weeks_on_chart  peak_position  \\\n",
              "0       17532665                 1.0               3              1   \n",
              "1        8350785                 2.0               6              2   \n",
              "2        7589124                 4.0               5              3   \n",
              "3        7584237                 5.0               3              4   \n",
              "4        7527770                 3.0              26              1   \n",
              "...          ...                 ...             ...            ...   \n",
              "273595   3747830               191.0              52              8   \n",
              "273596   3725286               189.0              24             14   \n",
              "273597   3717326               195.0              17            148   \n",
              "273598   3681739               122.0               2            122   \n",
              "273599   3676542               177.0              12             30   \n",
              "\n",
              "       position_status week_start   week_end  region  valence  \\\n",
              "0                    0 2017-10-20 2017-10-27      us    0.119   \n",
              "1                    0 2017-10-20 2017-10-27      us    0.291   \n",
              "2                    1 2017-10-20 2017-10-27      us    0.376   \n",
              "3                    1 2017-10-20 2017-10-27      us    0.699   \n",
              "4                   -2 2017-10-20 2017-10-27      us    0.357   \n",
              "...                ...        ...        ...     ...      ...   \n",
              "273595              -5 2018-03-02 2018-03-09  global    0.782   \n",
              "273596              -8 2018-03-02 2018-03-09  global    0.356   \n",
              "273597              -3 2018-03-02 2018-03-09  global    0.232   \n",
              "273598             -77 2018-03-02 2018-03-09  global    0.351   \n",
              "273599             -23 2018-03-02 2018-03-09  global    0.557   \n",
              "\n",
              "        region_valence_pos  \n",
              "0                        0  \n",
              "1                        0  \n",
              "2                        0  \n",
              "3                        0  \n",
              "4                        0  \n",
              "...                    ...  \n",
              "273595                   3  \n",
              "273596                   3  \n",
              "273597                   3  \n",
              "273598                   3  \n",
              "273599                   3  \n",
              "\n",
              "[273600 rows x 14 columns]"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 25
    },
    {
      "id": "surgical-water",
      "cell_type": "code",
      "source": "sns.set_theme(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)}, font_scale=1.5)\npal = sns.cubehelix_palette(len(valence_by_region), rot=.45, light=.7, reverse=True)\ng = sns.FacetGrid(all_charts, \n                  row='region', \n                  hue='region_valence_pos',\n                  row_order=valence_by_region['region'],\n                  height=0.8, aspect=8,\n                  palette=pal)\ng.map(sns.kdeplot, \"valence\",\n      bw_adjust=.5, clip_on=False,\n      fill=True, alpha=1, linewidth=1.5)\ng.map(sns.kdeplot, \"valence\", clip_on=False, color=\"w\", lw=2, bw_adjust=.5)\ng.refline(y=0, linewidth=2, linestyle=\"-\", color=None, clip_on=False)\n\ng.figure.subplots_adjust(hspace=-.5)\n\n# Remove axes details that don't play well with overlap\ng.set_titles(\"\")\ng.set(yticks=[], ylabel=\"\")\ng.despine(bottom=True, left=True)\nfor i, region in enumerate(reversed(valence_by_region['region'])):\n    plt.text(0.05, 0.5 + 1.2*i, region,\n             horizontalalignment='center',\n             fontsize=14)",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        },
        "tags": [
          "remove_input"
        ]
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 460.8x518.4 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 26
    },
    {
      "id": "nearby-nicaragua",
      "cell_type": "markdown",
      "source": "### It is obvious that the strength varies from region to region.\n\n### But are these differences statistically significant?\n\n* To clarify this we will use Analysis of Variance (ANOVA).\n\n* In ANOVA we control the within-groups variation against the between-groups variation of our data.\n### If the between-group variation is large relative to the within-group variation, then it is unlikely that the differences in mean values ​​are simply a product of chance.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "efficient-certification",
      "cell_type": "markdown",
      "source": "* To use ANOVA some conditions must be met.\n\n* A condition is that the data in each group must follow a normal distribution.\n\n* To check this we first construct histograms for each group, i.e. region.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "sorted-toolbox",
      "cell_type": "code",
      "source": "sns.set_theme()\n_ = sns.displot(all_charts, kde=True,\n    x=\"valence\", col=\"region\", col_wrap=3, height=2.5, facet_kws=dict(margin_titles=True),\n)",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAIQCAYAAADQAFeJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAADSUklEQVR4nOzdd3hTV5r48e9VtWW5Wy7YYIoNxqFDKIFASAECIZkAO2mTMi2ZspnZySQ/mASGnbRJsplk2ibTsrM7TEmBVEIoKfRuOhjT3Huvsur9/WFkXGRbkiVLss/nefIESVf3Hl+9OnrvOeeeI8myLCMIgiAIguAGhb8LIAiCIAhC8BEJhCAIgiAIbhMJhCAIgiAIbhMJhCAIgiAIbhMJhCAIgiAIbhMJhCAIgiAIbhMJRJD57ne/y6VLl/xdDEEQsSgElECLxzVr1vDWW2/5uxg+JYl5IARBEATBu9asWUN6ejrf/va3/V0Un1H5uwBDxaFDh3jhhRfQ6XS0tLSwceNG9u7dy5tvvonFYiEkJITVq1czdepUjEYj69ev5+TJk4SHh5OWlgbASy+9xM0338xvfvMbJk6cyDvvvMOGDRtQKBTExcWxbt06Ro0axZo1a9Dr9eTk5FBWVsbo0aN57bXXCAsL61Sm559/niNHjnR6TqPR8N5773Ur+3PPPcfmzZu7Pb58+TLPPPMMZrMZWZZZtWoVDzzwgA/PpNBfwRyLABs3buSvf/0rCoWC6OhoXn75ZRISEnjxxRc5efIkzc3NyLLM888/z/Tp0313IgWvCPZ4/NOf/sTGjRsJCwtjxowZfPHFF3z55ZcAZGVlsW3bNpqampg7dy6rV69GpRpEP7uyMCAOHjwoZ2RkyEVFRbIsy3Jubq58xx13yDU1NbIsy/KFCxfkuXPnys3NzfKrr74qP/HEE7LNZpMbGxvl5cuXy6tXr5ZlWZYXLlwonzp1St6/f7986623ytXV1bIsy/KmTZvk22+/Xbbb7fLq1avle+65RzaZTLLZbJa/9rWvyRs3buxX2ZctW+b08c9+9jP5j3/8oyzLslxRUSH/x3/8h2yz2Tw+luB7wRyL2dnZ8qxZs+SSkhJZlmX5r3/9q7xu3Tr52LFj8uOPP94ee3/84x/lxx57zOPjCAMnmONx9+7d8uLFi+X6+nrZbrfLP/vZz+SFCxfKsizLq1evlu+++265ublZNplM8je+8Q35H//4R39OVcAZRKlQ4EtKSiI5ORmAffv2UVFRwSOPPNL+uiRJFBQUsGvXLn72s5+hUCjQ6/Xcfffd5OTkdNrXnj17WLp0KTExMQCsWLGCF154gaKiIgBuvPFGNBoNAGPHjqW+vr5bedzJsnty2223sXr1ak6dOsWcOXNYu3YtCoUYWhPogjUWDxw4wLx580hKSgLoVObIyEjefvttCgsLOXToULerSiFwBWs87tq1iyVLlhAREQHAAw88wMGDB9tfv+uuu9DpdADceeed7Nq1i/vvv9/t8xOoRAIxgByBBGC325kzZw6//vWv258rLS0lPj4elUqF3GFoirMfZNnJ0BVZlrFarQCEhIS0Py9JktPt165d61K5u77fYrG0/3vhwoVs27aN/fv3c+DAAf77v/+bt99+mxEjRri0b8E/gjUWlUolkiS1P25tbaW4uJjCwkJeeOEFvvnNb3LLLbcwevRoPv74Y5f2KfhfsMZj1/IolcpOr3d9PKi6LxB3YfjN7Nmz2bdvH5cvXwbaMtk777wTk8nEggUL2LRpE3a7HaPRyObNmztVmgDz5s1jy5Yt1NTUALBp0yaioqJITU31elljYmIoKSmhuroaWZb5/PPP21/76U9/ypYtW1i2bBnr169Hr9dTWlrq9TIIvhNMsThr1iwOHDhARUUFAG+//Tb/9V//xb59+1i4cCH3338/EydO5PPPP8dms3n9+ILvBVM8LliwgO3bt9PY2Ai0jc/p6NNPP8VsNmMymXj//feZP3++18vgT4MrHQoi6enpPPvsszzxxBPIsoxKpeLNN99Ep9Px2GOP8eyzz7J8+XLCw8OJjY3tlDUDzJ07l0ceeYSHH34Yu91OTEwMf/zjH33SfZCWlsa9997LypUrMRgM3HTTTe2v/eAHP+CZZ57hnXfeQalUcuuttzJz5kyvl0HwnWCKxXHjxvHUU0/xne98BwCDwcCLL75IU1MTTz75JMuXL0epVDJjxgy2b9+O3W4XXWpBJpjicc6cOXz961/nnnvuISQkhPT0dEJDQ9tfT0lJ4b777qOlpYXbbruNu+++2+tl8CdxG2cA+vTTT9Hr9SxYsAC73c7jjz/O3LlzB1XfmRAcRCwKgSTQ4vH06dMcP36chx56CIC//vWvnDx5slP3y2AmEogAdOHCBX7+859jNBqxWCzMmjWLp59+GrVa7e+iCUOMiEUhkARaPDY1NfH0009z5coVJEkiKSmJ5557joSEBL+UZ6CJBEIQBEEQBLeJzkFBEARBENwmEghBEARBENwmEghBEARBENw2aG7jrK5uIjIylNraFn8XJSBER+vEueig6/kwGMJ9ejwRj52JeOxsoOPRarWJ89+BiMdr+hOLg6oFQqVS9r3RECHORWf+OB/iM7hGnIvOBvp8iPPfmTgf1/TnXAyqBEIQBEEQhIEhEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENzm0wTio48+YtmyZSxbtoyXX34ZgOzsbFauXMnixYt55plnsFqtAJSUlPDAAw+wZMkSvv/979Pc3OzLogmCIAiC0A8+SyCMRiMvvPACGzZs4KOPPuLo0aPs37+fp556inXr1rFt2zZkWebdd98F4Be/+AX3338/W7duZcKECbzxxhu+KpogCIIgCP3kswTCZrNht9sxGo1YrVasVisqlYrW1lamTJkCwIoVK9i6dSsWi4UjR46wePHiTs8LgiAIghCYVL7asV6v58c//jG33347ISEhzJw5E7VajcFgaN/GYDBQXl5ObW0ter0elUrV6Xl3xMbqr7433Ht/RJAT56KzgTwfIh67E+eis4E+H+L8dybOxzWengufJRDnz59n06ZNfPXVV4SHh/Pkk0+yb9++bttJkoQsy06fd0d1dROxsXoqKxs9LvNgYjCEi3PRQdfz4evKQ8RjZyIeOxvoeATE+e9AxOM1/YlFn3Vh7N27lzlz5hAbG4tGo2HFihUcOnSIqqqq9m0qKyuJj48nJiaGpqYmbDZbp+cFQQh+bl4LCIIQJHyWQGRkZLB//35aWlqQZZkvv/ySmTNnotVqycrKAuDDDz9k/vz5qNVqZsyYwZYtWzo9LwhCcJMk2HGkkA93XRKJhCAMMj7rwpg3bx7nzp1jxYoVqNVqJk6cyKOPPsptt93G2rVraW5uJjMzk4ceegiA9evXs2bNGt58802SkpJ47bXXfFU0QRAGkNlqQ2W1+7sYgiB4mc8SCIBHH32URx99tNNzGRkZbNy4sdu2ycnJbNiwwZfFEQRBEATBS8RMlIIgCIIguE0kEEFIksTANEEQBMG/RAIRZByD0nYcKRRJhCAIguA3Ph0DIfiG2WrzdxEEQRCEIU60QAxComVCEARB8DXRAjHIOLo4QnUa5l2XgJNJPgVBEASh30QCMQiJ++4FQRAEXxNdGIIgCIIguE0kEIIgCIIguE0kEH7kjcGOYsCkIAiC4A8igfATSYJdJ0r6lQB4Yx+C4AkxmZkgCCKB8CNvzOcg5oQQBpqYzEwQBBB3YQQVUVkL/uKIPcdtwSJxFQRBtEAEsI4Jg+iuEPylrxYHEZOCMDSJBCJAOUsYxFWfMFC6jnEwW21O408ktoIwdIkEIgA5Km+RMAj+4O4Yh97iVCQWwmAi4rkzMQYiwDgqb7VKIYJVGDC+GOMgSbDvdJmIY2FQcLS2LZgyTCwRcJVIIPxMkugWjGarDRkZhULUvILvOZJWgNuuH+7VfYtWNGEwEfHcmejC8CO1SiH6j4WA0NMYB3eJWBZ8QcRVYPJpAvHll1+yYsUKlixZwvPPPw/A/v37Wb58OYsWLeL1119v3zY7O5uVK1eyePFinnnmGaxWqy+LFjA6Vtq9fUlOX65m3+ky/vzJOWobTQNQMmEoEhObCYGmv3El4tF3fJZAFBYWsn79et544w0++eQTzp07x65du3j66ad544032LJlC2fOnGHXrl0APPXUU6xbt45t27YhyzLvvvuur4oWkHr6ksiyzLtfXeK1d09SXtPC8YtVfHmsmJKqZv8UVBi0vNEiJpp4BV/wNK5EUutbPksgduzYwdKlS0lMTEStVvP6668TGhpKamoqw4cPR6VSsXz5crZu3UpxcTGtra1MmTIFgBUrVrB161ZfFS3g9HTXhSzL/GPHRbYeKuDmacncdeMo/uv7c4jSazl0roLKOqPTfQmCp0QCIAw2IqZ9x2eDKPPz81Gr1Xz729+msrKShQsXkp6ejsFgaN8mPj6e8vJyKioqOj1vMBgoLy/3VdECilqlYMeRQlTKzndd2GWZrOxKLhU3sHjmcO69JY0dR4sIC1Uz57oEth8p5H8+zWZKelz7e8Sod8EbRPwIQ4GzAeyCe3yWQNhsNo4ePcqGDRvQ6XT84Ac/IDQ0tNt2kiQhO/kUJTdrsdhYPQAGQ7hnBfaD0BAtAHYsqFQK1EjExoajDdGw/1QJuSUNfP3WsehDVRw6X4UuVEtsbDhxMWHMGJ/AwTNlxEWHERd37W9WqqoIDW3bb8fnhYGNjWCLR51Oi1qlQK1UcvRCDTpd5xjq6XFsbDi6UO215zv8G9piXJYUnd4rtBno2AiWWHSma1z1970f7roEwNcWpA1YOQKZp7HhswQiLi6OOXPmEBMTA8Att9zC1q1bUSqV7dtUVFQQHx9PQkICVVVV7c9XVlYSHx/v1vGqq5uIjdVTWdnonT/AxyQJjK2mtv8bLViUChQKiaKSWnYcyqei1sjUsXEsnZnC5v15qFVtr1dXN9LSYiI5NpS0lEgOny0jr7CGsBB1+z6NrRZCQ7VUVTWKDPsqgyG8U2z4ujL1Vzx6clUlSdDSYkKtUqDVKGk1tcWkLENVVVv5W1raBu52fVxd3UiL0UR19dXnjab2uBPx2LOBjkcgaOrGriSpc1y5sr3jtuRFM4d3e68kgcVqp6Wl+/O9zfPgbjmCRX9i0WdjIBYuXMjevXtpaGjAZrOxZ88elixZQm5uLvn5+dhsNjZv3sz8+fNJTk5Gq9WSlZUFwIcffsj8+fN9VbSA1dJq4cUNx6isNTIrM57MkdE9bitJEg8vGYfZamPTrssDWEohULk6YMwbXRSyLFPXaOLkpSosVjs7jhTy1bFi0f0hBARPb0sW4yXc47MWiMmTJ/Od73yH+++/H4vFwty5c7nvvvsYPXo0jz/+OCaTiQULFrBkyRIAXn31VdauXUtzczOZmZk89NBDvipaQDKZbew4UozVZmfupESGx+t73V6tUlBY0cS4EVHsOl7C3IlJpCVHDlBphUDVVwXojdn0LhTW8dXxEmoaTHye1ZY0jEoKZ3JaLLoQtWc7FYQOOrakiaQ0cPl0JspVq1axatWqTs/NmTOHjz/+uNu2GRkZbNy40ZfF8Zuu0wR3Jcsy+8+U0dJq5ekHp3G+oNal/ZqtNiaNiaGsuoX//ew86x6e4aUSC4OZp1dZJrON//3sPLtOlBCqUTI1PZals1PZtPsKOQV1FFU2M3F0DKMSIyioaOJCYR2XiuqJjdQyLSPRy3+FMFh1THKh94HhfXXZOd7XVx0seMalLoynn36623OPP/641wszGLmyMFFBeRNlNUZmZBhIS+m9FaHrPtQqJd9aNp6Symb+tjXH6YBUQXCX3S5TUN7Ivz6/yH9/cJqdx0vYfCCfPSdLuX3WCBbPGs6Y5EjSh0cxKzOBRdenEKXXkpVTxfN/y+JvW3PIzqtFoZDIKahn57Ei7HYRm4JrOia5PSW8fXXZqVUK9p0uQ6NWuLU4nOC6Xlsg1q9fT3l5OVlZWdTU1LQ/b7VauXLlis8LN1j0dsVnt8uczaslSq8hLSWi1/04vhBdvwQTR8dy57xRfLQ3F7ssMzw+zBvFFoYok8XGl8dKqG00oVEriI0IQZZl0pIjeeT2DIbFhfHpgbxO74kI03DL9GQaW8yMHhZJYkwYiTGh7DpZwpkr1WTlVLHzeDELp6X4548SBqW+WtMcr5utNtH64AO9JhCrVq3i4sWL5OTksHjx4vbnlUolU6dO9XnhhoIrJQ00G63Mn5zk0q2rPX1hls8dyeXieg5nV6APHdZ+K6cguGv/6TLqmkzMyozn0TuvQyFJ7QnDsLjek9NIvZap6Yb2uzCgbXxEUWULH+/LY96kJNQqZa/7EARvkmUZs8WGSun5PQOOyf6EznpNICZOnMjEiRO54YYbSEwUfZi+cKWkgbAQFcPidE5fdzVoFZLEd+/M5Gd/OMjBsxXcGdv7IExBcKasuoX8siYmjIpmeLwehRdqTUmSmDbOwGcH8jmcXcHciUleKKkg9K2ooomDZytoMlrQh6rJGBFNWkqUW/twdEOrVQqvJxHBPpmVSylZQUEBDz74IHfeeSfLly9v/0/on1azjdLqFlLiw5y2PvTUZdGTCJ2GeZMSaTJaOHul2sulFYaC01dqCNUqGTvcu3f0JMWFEa5Ts+90qVf3Kwg9yStr5POjxahVEhNHt81H9PI/j1NQ7v58GN5arRautWYMhnU6XLoL49lnn2XlypVkZma6PUOk0LPC8rYJSXq7ZdPdoE2KC2N4fBhnLldTVWckNrL77J+C4IzRZKWkspmJY2JQKhVevTKSJInRwyI4eamaijoj8VGuxWWwX6EJ/lFd38qhs+UkxIRy2/UpmMw2xiRHsO1wIW9tzma+G2NxvPmT13WSq2Cfd8KlFgi1Ws03v/lNZs2axcyZM9v/E/onr6yRcJ2aKL3Gq/udNCYWGdh8IN+r+xUGt7yyRmRgTJf5RHqrQN2pXEcNC0cC9l9thXBlwqtgv0ITfK9rfNQ2mjhwtpzQEBULpyW3j30I1aqYlZlAQUUT5/NqnOzJ+b69vb6QN1sz/M2lBCI9PZ2cnBxfl2VIaWqxUFrdwsjEcK+36uhCVKSlRLLvdCk1Da1e3bcweOWVNmKICiGyQ0LbtQLtGKrudrGFhajJSI3mcHYFILuUHAyWilbwja5L0FusNn6/6TRWm535k5MI0XQesJuaqCfZEMapy20zqDr0NkhSxGDPXEogCgsLWblyJYsWLRJjILzk2MVKZBlGJvlmDvwJo2Ox22H71eYyQehNU4uFuiYzo4Z1v5XYUYF2raw7vuaqWZnxlNW0UFjRJCpmwSsccSTLMn/blsOV0gauz4gnSt/9TjRJkpiaHofZYudycf3V5xBTsXvIpTEQP/nJT3xdjqDlaR/t4ewKwnVqYiK0tLRavV4uvU7D9ePj2X2yhLvmjSJU69NJR4UgV1rTAtDnHCL9/dGfMS6eDdsucOhcOVHh4lZjwXu2HMxn3+ky7pw7Eo2652vjuKgQkg1hXCiqp9VsJUSjapsnAhmFQmQQ7nCpBWLs2LFO/xvqPO2jbTJayM6rJdUH3RcdLbp+OK1mG3tPiZHvQu9Kq1uICFMTrvPueJyO1CoFWRcqyRwZw6Hsih5nTRX33Avu2na4gE27rjA7M4G7bhzV5/aT0tpaIb46XuLVcgy1uHXpsnT27NlIkoQsy+0/eAaDgd27d/u0cMHAkyuyYxcqscsyIxN9u4Tv6GERpCVHsuNoIbdMTxHZteCU0WSlss7IuBFRPj+W2WpjVmY8f9mcTXV99/E5XUepC4ODr+6msVjtHMmu4GJRPTPGGfjWsvEuzV1iiNYRHx3K1kMF3Dwt2Stl8cZCdcHGpQTi/Pnz7f+2WCxs376903OCe4u1HDlfQXxUKDERvm/CXXT9cN748AwnLlUxbazB58cTAlNvderZvBpkGZL7mGXSW6amG1Apc8gvb3L6urfHRoiFlPyrvz+shRVNZOfVcuBMOcPj9YxKiiAyTENxVTOfHcqn2WhlyawRrFowxq2LpPGpUew6UcqXx4rdL1QPhtq4Hrfn9lSr1Sxbtox9+/b5ojxByZUFsxwaW8xk59UyY3z8gMypMXVsHLERIWwXC8kMWX3F58lL1ahVCuIiQ7x+XGd0ISomjYmloKyx2wJbvpjpTyyk5H+e/LC2JR7FfJlVTGOLBY1awZHsCv73s/P8ZuMpNu68jE6r4tYZKdxzc5rbLayGqFAmjIrh0/15ne7IEFznUgtEXV1d+79lWebMmTM0NDT4qkwBz1lznKtfkMPZFdhlmVnj47lS6vtzqFQouGV6Cu9+dYn3d11hxYLR4kpsCOopPu12mVOXqkiIDvVqF1df98/Pzkzg2IVKTl+pZtKYuG7v8WaMDrWrwkDjSeKmVinYuPMyWw7kk2wI48bJSSy+fgR2u0xFrZEWk5VIvYbTV6qx2jr/+LtzvJU3jeEXfz1Cdn4t08c5b6E1mqycuFhFbaOZqHANU9MNxIR7N9kOVi61QMyePZs5c+a0/3/NmjU88cQTvi5bQHJn4GTXbWRZZteJYlITwxmR4NvxDx0tmJKEVq3kdG51nwPUxAC24OPs83L1c7xUXE9Di8Wt7gtX46O3H+6pY+MI1SjZcbTI5fcIwUWSQKHwfCKmYxcqUSoVzJuUiPJqcitJEgkxOkYlRRAb0f1H3N2Jn0YmhjNvUhIXC+udjskprmzi0/35XCpuQJIgt6SR//yfIxRVOu9+G2rcHgMhuFbJORKNm6YOa3/uSkkDRZXNPLRknC+L14njCzwqKZwLhXV8vDeXEI2K264f3ukqz/GFcwxg6/q6EJg69i93fG774WufY2+OXahEpZRIjHW+mFtX7k4e1ROVUkH68ChOXa6mpKq5z1U+heDSaQEqDxbBrGs0UVLVwsTRMYRqVX12MfRnbpL7bkkj63wFB86Ws3jWte/Lmdxq3vzwLCBzy/RkDFGhNBkt7DlZyuvvnuSZB6cT00MSA0NjzI1LH63dbufPf/4zDz74IPfddx+///3vsVq9P3fBYNM1kHccKUSrVjJrfIJPjte1Undk4xabjbSUCGQZzuTWdCtXx1aVwTTN6lDh7PNy5XOUZZljV2+rVKucVwXOEgVvxUdaSgQatYJNuy57ZX9CYOlPXXKpuB5JgrSUvhd1U6sU/RrnogtRMzMznmajhe2HC9l7qpR3vrzI6++cJDZCy9I5qURfnbMkIkzDE/dModVs5TcbT9Fq7vw72N+yBBuXEohf/epXHDx4kIcffphvfvObHD9+nFdeecXXZRsUHEFU09DKoewKbp2R4pNJnXpqunN8gcN1GsYkR3CpuMHpxFX9/VEYCl+WwSavrJGq+tYe787xVmtDT8J1GiaNjuX4xSpOXKxy+/0i5gYnu13mSkkjiTG6TlNR9zXddH/qMENUKDdNHYbVJvPWp9lsP1zI3IlJrH1oBvpQdadth8fr+f7XJlBU2cSfPzmHvUtTw1C6CHMpgdizZw9/+MMfuPXWW1m0aBFvvvnmkJsDorfg7el5RwVsttg4nF1JdLiW22el+qyMzloWOpoyNg5kmTO5ri0k4ypn40JE5R74vjpejFatZOb4+B638XVFmD48kmRDGG99es6tZZbFQluDV3Z+LUaTlZGJ11Yp7npl74uxWgkxOu6aN5LnvzOT1x+fx7eWjSdEq+y2nSRBWXUL09INHL9YxaadQ7cFzaUEQpZl1OprWZhGo+n0uDcvv/wya9asASA7O5uVK1eyePFinnnmmfZukJKSEh544AGWLFnC97//fZqbm939O3yqt1vB+rpKq2sysftkKS0mK9+76zp0IQMzpbSzculD1YwdHkVBeRPnXFyNzlUdf2hE5R74mo0WDp8rZ1ZmwoBPc96x8lcoJP5j1SS0GiUv//M4l4vre5yhsquhcpU31Ow/U4ZapSCpy7gcx5W9L9euUCgkkg16IsI0vQ7INFttjEzSs3BqMp8dKuC9nZdcjtvBxKUEIiMjgxdffJGCggIKCgp48cUXXZrK+sCBA3zwwQftj5966inWrVvHtm3bkGWZd999F4Bf/OIX3H///WzdupUJEybwxhtvePjn+E5vzVI9PV9c2czHe/NobLEw57oExg6P6vM43l42tqvMkVGEhaj429YcWs1Wn911ISr3wLb7ZAlmq52FU70zC5+rHFeSHSt/Q3Qoq++fRlKMjr2nyth7qswn68MIga/VbOVoTgWjksJRKnv+eRqoboLejiFJEg8uHsvY4VF8drCA379/GpNlaNV7LiUQ69evp6GhgXvvvZevf/3r1NbWsm7dul7fU1dXx+uvv873vvc9AIqLi2ltbWXKlCkArFixgq1bt2KxWDhy5AiLFy/u9Hwwq2sy8aePz/L50SJCNEpunZHcLZt2xtM+Z3eSAKVSwezrEqioNbJhew7bDxeIVeiGmFazlU8P5HPdqBhSfTydujMdK39HQnHyUhXPPDSd6zMMVNa3sv1IIScu9T0uQsRtcOrpczt2oRKzxc5oJ6vCBiKFQmL6uDjuvSWNk5eq+WRfntPbQT0V6PHda9ul2Wxm3bp13Hbbbbz00ksAPProoyiVSvR6fW9v5ec//zk/+clPKC1tW8ipoqICg+HaYC2DwUB5eTm1tbXo9XpUKlWn590VG6u/+n7fVIg6nfbqccLRhWqJi2s7TmiIFpvdTlOdiZr6Vv66NYeDp0uxyzBtXDzTxsW3V5aO9ziEhrTt044CtUqBWtnW3xYaqu30uOvrHR+HaGHPmXLUSkWn/QGEaLvv346CkeGhTGuycOBMOVZ728IyaqS2v+3q39m1rH2enw7nxNnjQOCr2HDG1/HYUcdzrQvVOv0cHY9jYvQcPV+FyWLn+ysnt5dPp3M95np7LCF1+m44XgPn8dj22AKAwRDB9PFJDE+KZGdWEb/deJqbpqb0GEcR+lAOna9i6dzeF0/yNKZ9bSDj0R/H6xhTKpW90/nfsi/X6ed2NOc0CTE6UpOiMJqt3WIMaI/vnl4LDdEiS73Ha4vJgk6n7fT96Bq/0L2OlrrUk7Gx4YTpQrhrwRiunzCM//zzAb46XsKE0bHcf7sOtar7GIqefkuc6ek8eZunsdFrAvHb3/6WpqYmpk6d2v7cc889xy9+8Qt+97vf9bjM93vvvUdSUhJz5szh/fffB3DaP+RYoMvZ8+6qrm4iNlZPZaXrA7FcJUnQ0mK6epxGWowmqqoaKa1uZvuhPPLLGrFdnZI3LETF3IlJLJo5nPMFtVitFoxGC7IMVVWN7fcGSxIYW01t/zdasCgVaK+OOHb3cbPRgqrL6+rwUFpNpm7bO/49bkQEsmznyLlydBoFKfH6tr/t6t/Zsaxdz4VDx7/FcU5kufvjQGAwhHeKDV9Xpr6Kx47zizjOreNcO/5dU9P5c4Rr8fs/H52moLyR+25JR6eSqKxsbI9vtcrzGHQ8Viik9mN2jO+e4tHxWJbbvlvGVhNK7Nw4KZHs/Fq+OlbIpNHR7bNVdjwPxlYTVpu9U9x1jbeO392hHI+AT+rGnnSNKYu18+dU32js9nnUNpo4ebGS5XNH0moyY2ztOU6cxWvHGHL23o6PQUFLi4nq6kb2nS7DaDR1il9HObvW0QqF1Kme7Ph7EKNTcccNqRw4U8bpy9X86NWvuP/WsWSkRnf63jp7b091rbPz5G39icVeE4idO3eyceNGQkKuTZaRkJDAK6+8wj333NNjArFlyxYqKyu56667qK+vp6WlBUmSqKq61iRZWVlJfHw8MTExNDU1YbPZUCqV7c8HMlmW+fxoEe98eQmQSUuJICZcS5Rey78tTAMkJAnOF9T6u6g9kiSJx+68jtV/OMC+02UurXzYcXIYhUJi/uShs+pcIJGR2XGkEFnuvmKlSimx91Rptx9Tu13m9JUaLhbVMyopnFuvTxngUrtHrVLwxD2T+dkfD/HfH5zhp/dM6XUMkbMJtURsBgdHrB44W4YMzJmQ6HHd6UmTf3/GUnQ9nkat5Prx8QyLC+NcXi2v/Os4Y1MiuX12KibL4BvX02sCoVarOyUPDnq9Ho1G0+P7/vrXv7b/+/333+fw4cP88pe/5I477iArK4vp06fz4YcfMn/+fNRqNTNmzGDLli0sX768/flA4Ww66hMXq8nOr2XymFjGpUahC1HRfDUDbmtV8U9Z3aXVKJk7MZEvjxWz83gJi/qYtRDavmwyslga3M/MVlunOCusaOKD3Vc4faUaaLvjJi4qhNiIEOqbzew4WkRji6VtRr3oUBQBHqdqlYJjF6q4efow9p0q5zcbT/LUfVMZmdi9b9zxHXWM0Hd1Fk7B/xyJ3/zJSew+WcLY4VEkxug8SiB8PW+JO8cbFhfGw0sy2HWihM8OFfCbjafQh6pJjHFtxtdg0WsCoVAoaGpq6jbeoampyaOZKF999VXWrl1Lc3MzmZmZPPTQQ0DbIM01a9bw5ptvkpSUxGuvveb2vn3BEdwLp7Vd1ciyzN+3XyQ7v5abpyXzwKKx7D5Z4udS9k+oVsX8ycP4/GgRv3nvFJPTYlH1MvpZCDxXShp476vLaNVKxiRHtjXD1hm5UtLIpaJzAETpNdwwIYEHF49j+9Xpyn3FWxW42WojRKPiyfum8MsNWfzy78eYP2kYcVEhGE1WzuXVIAO5JQ2MGRbZKZEQgofZaiOnsI6KWiN3zh3Z730NpN6Op9UouXXGcG6amszxi5X8c8dFXn/3FONTo7huVMwAltJ3ek0g7rjjDtauXcuLL76ITteWObW0tLB27VoWLVrk0gFWrFjBihUrgLbbQTdu3Nhtm+TkZDZs2OBu2QeEjNyeZZ7Pr+dMbg0ZqVF8Y9FYj8ZqBKKYCC3zJiWy+0QpZqud2ZmB3YUkXPNFVhEHz5aTOTKax+66jmMXKpGktnExVpvMtLEGQjRKDp1zf2CyJxx3VaiUnq2B4ExsRAhrH5rBO19dYtfJYqw2GQkI0aoAmcvFDVworGdBh3VnhOCy81gxoVoV08cNjrpHrVJcbVkZhkqpYOb4BGoaWympbGHPqVK06u6DK4NRrwnEww8/zPr165k7dy7p6enY7XYuX77M8uXL+eEPfzhQZfQ7s9VGUWUzZ3JrmJ2ZwKhh4YMmeXBIidfz9VvSeOeLS5zJreWOG3w/8lfon6LKJg6erSA5LoyffH1yt5YjpUIixdD73VK+YLbasMtyhwFr/RcVriVjRDTfWjoei9WORq1g7+lSQCY7r5asnCp2nyhhycwRXjumMDAams0cPl/B4pkjgvaH1ZU1Y5QKBd9alsGl4npOXq6muDKwJkz0RJ9dGM899xyPPfYY586dQ6FQMHHiRBISfLMYlL84G73dUUOzmcPZFcREaPnWHRnsPB7c3RY9WTJzOIfPlZNTUMfeU6XMnZjUbRuTxcbFonoaWyxoVEpumJAoujz8oKHZzNHzlYxJjmBGhmFIfAZmqw21StHWunG1wpYkidTE8Kv90eX8Y8cF4iK7j9sSAtfpKzWoVQqWzGpL/oLt2szVVje1SsGBs+Vcn2Fg66FC/u+z80zPcL4OTbBwaQ7blJQUUlICe9S2pzqO3naWRNjtMntOlqKQJGZnJqBxcl/vYCFJElPT42hutfK/n50nLjKEcSOi218/dqGSbYcLMVvsaDVK/rrlPEfPV/CjVZP8WOqhx2K1s+90OUqFgn9fMZEj5yv8XaQB01OyPyIhnJoGEzuPtw3Ii48O9XhfwsDJKaglv6yRpbNHEKHrffroQNZbq1vHif7MVhtajZKJo2PIulBFQpAPqhzYSfADVG8DYXIK6qisa2VWZrzX1rHw1fTR3qBQSMzOjOfI+Up+s/EU/3bTGBJjw/jyWBFZOZVE6TXcPC2BhJhQzuXVciS7ko07LxMrrvoGzMlLVTQZLcyfnNS+zPBQ0LFf2ZlJabHUNJjIyqlkUR+3qfZ14SB4h90uc6monsq6VpAlJoyKIfHqrLyl1c28+eFZ9KFqlncYPOm4m8afPFk8sadttx9uu/W9Y+vEyKRwiqtaOH2lGrssIxGgPwh9EAlEL+qbzRy/WMWwOB3D48O8ss+Ocyl4a5CZtzi+GBq1kifvm8LL/zjOhu0XgLbKe9rYOEYlhaNRK5EkietGxVDbYGL7kUJuny36ngdCcWUT5/JqGZkU7tJVtiv8XVm7o7dkX6VU8PCScbzyrxNcKm7o174EzzniyWK1s/N0GVX1rSgVEjkFdQCMSNBjtdl576vLhGqULJiSRIhGFTCJXE8/+uDZraKOW9+1nZYml7hz3kj+9PE5jl2oZPrY4Bw8KhKIXrz31SVsNjuzMhNcnt/BlWZRZwHlbx2/GLLcNvL9lunJjB0eTWOzmdHDIjiSU0FLq6XT+zJHRVNQ0UTW+Ur+7aY0P5V+aJBlmQ3bLqBRKZg02ju3gQVjk3FvZc0cFUNSrI7s/Foams1EhPU8X43gfe3zOkxJ4uC5cqobWrlxchLD48OYmmbgyPlKTl2uoqymhbkTE1l+w0iyLlT6u9jd9FZHu5N49harszMT+NfnF/l0fz7T0g0oFIE9N4szAXYN7H+OD/xCYR37Tpdx3agYIvWuVUKOJlaFIriu6hy6fjEkSWJkYjgTRscSFup8+XatWsl1o2IorzVysah+IIo5ZB08V05OYR3Txhr6nXx27ZcNFs5W8+xq0pgYbHaZD/ZcGdjCDTKe1mFmq43dJ0oorzEyfZyBMckRSJJEXFQoS2aNYPUD01g6J5WHl2QQE+Hfrk9f1tN9tVYoFBLjU6PJK2skO7+WXSdK+ixPoP2uiASiA0f2LMsy/9xxgehwLRPHxLq1D8c0w0Nphcu05Ag0agVbDub3ut1QOR++YDRZ2LAth7jIEMaOiPR4P5J07Ud4x5HCgPpMXC1LX0s5h+s0jBkWwa4TJRRVNnmpdEOLoy70aGpoi41NO68QFxlCWrL3V9X0VswOxMyVfSXno4aFE6XXsPlAXp/b9ucz8RWRQHRhttrYe7qUgoom7rk5DbXK/VM0UGvVBwqVUsG44VGcuFhFSZXze5sDMfgDXcdWgvd359JqtjF9XJzHc5B0rDADLUa9XZmPHxlNqFbFe19d7vR8IA9gDjSexsepy9U0tVqYkh7ba6x68ll4O078/R1QKhQsnjmC8/l1VLmwDLi/y9uVSCC6sFhtbNp1hbTkSGaO9+7AlmCqvNwt59gRUWhUCrYeKuhxm0AL/kDmGGy740gh+eWNfHmsiDHDIvrd5BvIn4E3yuaIW61aydLZIzh1ubq9Yu54ToPlexhsCiuauFhUz8KpyUTpe75DyJWuqJ74OoYHOjYWTBlGWIiKc7k1A3tgLxAJRBdnc9sGX913a7pXs2e1SsH+M2XsPxM4A9Z6KkfHLN/VvzNEo2TepCQOnC2jttHk3YIOEV3Ptdlqw2SxsmFbDuGhaq4bFd3zm4VuV6e3Th9ORJiGM1dqkK+OTgu0lpdg1jVeZVnmn59fQK1SsGLB6D7f3/WzCIQLrIFekAva1iO6ZXoKRZXNXCnp++6hQCISiA4q64ycz6/jhgmJjErque/O0+w5kCqvvr4ojln/3LliWzxzBHZZZsfRa4s1eVIp+LsS8YeOV8eKDt/KvNJGrpQ0cM/NaWg8mOY3ECrlgdTx+6XVKFl+w0iq6lsprzX6sVSDj7N4zcqp5Hx+HZPGxKLvYdB1X/sLhLFjA11HSxLcPnsEIRol//z8QnuyGwxEAtHBu19dQpJg1U1j+tw2kJIBT7lSfsff6cqXOj46lBnj4tl5vBjL1fe422Q8lMdKOG4dc/z9ZouN01dqGDs8kjkTEt3eXyC2eg20BVOGodOqOJtbG1QVczDoGK9mq413vrxEiiGMtGTPBvkOhjrVXY6LtINny5mSHsvl4ga+Ol7s72K5TCQQV+WVNXL0fCWZI6OH1Ox+rnCnWW/JrBEYTTYuFbU1xXlSKQy1SqQrx99/JrcWi83O+NRoFArPMoChWCl3pFYpyBwZTW2jiWMXqvxdnEHJbLXx3leXqW5o5RuLxnocq0OV4zs6elgEE0fH8vYXF73SlTEQFw0igbjq47256EJUjBsRNWSv1nrjyo+QJMGopAjGp0aTnV+LyTx0f7j6K6+sresiPSUSvc695mB3DIVYH5GgJ1yn5v1dV0QrhA+UVDXzRVYRt85I6bR2jjuGQhz2RZIkHl2eSZRey6/eOU52fm0/9jUwLbkigQDqGk0cv1jFouuHExaqHrJN6P3hmERLkuDu+aNpNdvYfqSw7zcK3ciyzD+2X0Crblt0pzf9iVN/DBjzB4VCIjM1muKq5kGxhHIgaWwxs+9UGSmGMFYt6Lvr15mhEoeu0OvUrHlgGlF6Lf/1z+McPV+B0WTttI2rg9sHouVRJBC0zTqpVim4ZXrbAjy9nXgR5D1znLf0lEiS48LYcrAAs0W0QrirpKqFi0X1XDcquteBk96oeIOhe8PTgaAd35MSH0ZSrI5zeb2PhRhqg077w2K1s/tkGUjwo1WTXB7k6+z8BkMcDpSYiBDWPjSDhdOSuVBYzzN/PsTJS23db44xEzuPF7P7pP8vdId8AmE0Wckra2R2ZgLhfTQVi0zZdZPTYmk1WTl/dQGd3ojzeY0sy5y+XE1cZAgjE8P73H6wV7ye3vHUsUUM2pqH75o3koYWS4+tEM4G/YqEwjmTxca+02U0tpi5cVLb8umunKtgXHvFH0K1Kh5aMo5F16eg06r4zcZT/HVLNna73D5mIhC++0M6gZAk+Pv2C1htMmEhKpcqqUD40IJBVLiWeZOSuFhUT32TucfthvJdF86U1xqpbjCxfO5IMRjtKk8ry67vmTm+7SLhXH4t9h5aIToeS0w85ZzFauN3m05TVd/KnOsSGJ6gZ/+ZMj4/6lqiJ+rQvjkSLUN0KP/5retZOjuV3SdL+epYMXZ74Izj8WkC8fvf/55ly5axbNkyXnnlFQD279/P8uXLWbRoEa+//nr7ttnZ2axcuZLFixfzzDPPYLVae9qtV10sqiM6XEN4mFoEtpd9/eYxqJUKjl2o7LHCBlGhdHShoJ4QjRKFuPL1OsfiRQ3NFrLOu7YCZKBc6fmbIxabjBb+6+0TnM2tYfq4OFKvtpIF0lXxYOE4lyqlglU3jeHBRWMpqmzm+MWqgBkM7LMEYv/+/ezdu5cPPviADz/8kLNnz7J582aefvpp3njjDbZs2cKZM2fYtWsXAE899RTr1q1j27ZtyLLMu+++66uitatuaKWyrpWRTiaNEk2X/Reu0zBpTCzVDSZ2nyjxd3ECXrPRQnFVM2OHR2ILkApisBkeH0Z4qJqP9uZ6vRIerPWFo5Wwsq6FF/52lLzSRr7/tet6nWxP8L6bp6cwcXQMV0oayS1t9HdxAB8mEAaDgTVr1qDRaFCr1YwZM4a8vDxSU1MZPnw4KpWK5cuXs3XrVoqLi2ltbWXKlCkArFixgq1bt/qqaO2Onq8AYFRS577m/szTLnSWmqjHEBXC219coqnF4u/iBLTc0kYkYNyIKH8XZdCSJInxI6MoqmymsMJ7K3UO9q640upmnv9bFk1GC0/dN4WZ4xP8XaQhaerYOAxRIWTlVNJk9H996rMEIj09vT0hyMvLY8uWLUiShMFgaN8mPj6e8vJyKioqOj1vMBgoLy/3VdHaHc6uICZCS0SYpttrojnOOyRJ4vqMeCRJ4sDZsoDqvwsksixTWNFEQkwoYU6mAXZlcNpg/fHqD2fnZHi8nsQYHWeu1PTateauwVpfXCis44usYkI0StY+NIOxw6P8XaQhq60+bfutPHyuwmkr2kDWBSpfH+DixYs89thjrF69GpVKRW5ubqfXJUnq4SS4dwZiY/UAGAx9j1wHKK9p4UpJA7MyEwnRarGjQK1SoFa23YrUn8fe3JenjwFCtNoBLZtKZScuru3860K1xMaGo9Np0em0/GDlJH71z2N8fryE+xdndPosdKHa9vc5e+wtrsaGN7gbj+fza2hutTI9I8Hp53b0Qg06ndbp5xiihT1nylErFYSGdH/vQD92Jx59/dhx3to+k/D2fz+wJINf/fMY5wrquWn68PbPwfF6exx3edwbd+N2IOPRk+PpQrVYUPDbTafRh6q55foR5Fc2U1DVwtK5ozrFI1w77xJS+7l2Vjd4I0b7GyMtJktb+QYo/p19H2JjwwkN0SJL185bx/gJDdFitdm7PRcXo+D6zAQOnC7jTEE9CzvEL8CHuy6hVircikdPY9GnCURWVhY/+tGPePrpp1m2bBmHDx+mquradLIVFRXEx8eTkJDQ6fnKykri491bSru6uonYWD2Vla71DW2/uux0siGUVpMJo9GCRalAq2n7kPvz2Jv78vSxOrzt7xrIslmsdqqq2s5/i9FEdXUjLS1tZZgwOZGRSeH8a3sO8RFaJqfFAW2ZcovRRFVVI7Lc/bG3GAzhnWLD15W3u/G4bV8uCkkiPkrr9HNrNfX+OTYbLaj8HHO9xVhP8ejrx47zJst0isfrJiUSE6HlrY/PMDpBT6hW1RZ7V19vj+MOj3uLR3fjdqDjEXA5FqHt72lsNvLsXw6ALHPz9GHYrFbqG+0oFFL7uVSrup93Z693rBuMrSYkyb91LChoaTENWPx3feyIR2OrCWPrtfPWsR40tpqw2uzdnjO2WkiND+NiZAh/+uA0I+J0ROg07Z9bfYMRtarz/nrTn1j0WRdGaWkpP/zhD3n11VdZtmwZAJMnTyY3N5f8/HxsNhubN29m/vz5JCcno9VqycrKAuDDDz9k/vz5vioaAIezyxmVFE64rnv3heB9kiQxd2IiMeFa3vjgDKXVYkZAB5vdzuHscpLidKhVQ/rO6gGjUEjMyIinrsnM+7uu+Ls4ASknv46iyma+c0emV+pJ0c3WN2fnx9l5kySJWdfFYzRZeeeLiwNTOCd8Vlu99dZbmEwmXnrpJe666y7uuusu3n//fV566SUef/xxli5dyujRo1myZAkAr776Kr/85S+5/fbbMRqNPPTQQ74qGpV1RvLKGomJCBEBPYBUSgVzJiSgUEi88s/jIom4Kju/loYWCyPi9f4uypASFxnCouuH88WxIk5c7HuhraFUV9Q2mjiTW8OU9DimpMf1e39iYHrfuk5+5niup7lIovRals1J5cDZcs5cqR7Ywl7lsy6MtWvXsnbtWqevffzxx92ey8jIYOPGjb4qTidHrt59kRynG5DjCdfoQlQsmJLEwbPlPP+3o3xj0biAuad5IEkS7U2LB8+WE6pVkRgT6t9CDUGrbhrD+YJa/vjJWX56z+Qet1Mo4KtjJSyYMsyrXWuBauuhAmx2mXtvTvPaPs1WG3ZZ7tCNIHTlbCCu2Wpr78Lo6o4bRnLkfAVvbcnm5w9fT0zEwK4kPSTbS4+cryAmXOt0tLvgexFhGtY9PINhcWH8+ZNzfLo/n0/25VFW0+LWfoK1SbTjLX9mi41jFyoZHq9HJbovBpxapeDHqyYTpdfy6tsnyCmo67Z+i2MKe4ttcN5l0ZEkQUurhd0nS0hNCCchxvlFVjB+74JZT8soaNQKvn/XBFrNNn678RTNfdza6e060+d3YQQKx0krq2khv6yRSWN6X+VQ8K24qFDWPDCNrJxKNu68zPu7r/D+7iukJUeSlhLR6QrdGcc0wwC3XT886K4KHVcaJy9X02q2MTJRdF/4S3S4ltX3T+X/tp7n5KVqTl+p4cj5SgxRobS0WoiLDCF9eKS/i+lzju/U+YJaWs02MkdFO/2xEWsC+UfX1glHl8f8ycP4/l3X8fv3T/PLfxxj8phYIvXdx6z4os4cEglExxPnWBo1xSAqbH9TKhTMykygocXM9LEGjmRXsOVgPpdL6jGZbTy0JKPXIB8M990fPFtGlF5DQqyOVtPATN8udBel1/If/zaZv209T1lNC7oQNZV1RkqqmrlU3EBOYT3zJiX6u5g+Z7JYySmsIzFWR3x0KDuOFKJSKpC6NI4Nhu9eoHInMXN8DpPGxPHjf5vMf79/mi+yipg7MZGkuLAet/eWIdNmarbaMFms7D9TRsaIKHQhQyJ3ChoxESEsmjmCFx+djSEqlJ0nSjh9pXpQX+U0Gy2cvlLNrMwEFIP5Dw0isZEhXDcqhn9fMZFffGsmd80byY2TEmlptbL9cBHlbnazBZvKulaajVbSU9paXMSEegOrP4NNJ4yK4eePzECjVvLV8RKy83tfut4bhkwCAVDTYKK81sgNEwf/lUQg8OQ3MSxUza0zUojSa/nv98/w0d7cQZtEHM2pxGqT2+cgEHyrr1vknL8ukRKvZ+mcEQD86p0TATGFsLc5/va8skbUKgWpokvN53r6znuStDnGVQ2LC+Pmackkx4VxLKeKDdsu+DSJGFIJREF5E2qVghnj3JukSnCfK/2kPQ3oUasULJiahNVm58CZskF7l8aBM2UkxuiICBODeX3NWTx2vEVOoaDb6x3/HanXcNPUJGobTfxl8zlkBk9MOn58jCYrRZXNpCboUSn799MgEuLe+WIciSPpUKsUzJuUSObIaL46XszGnZe9d5AuhkwCYbe3rTUwbaxBdF8MkN6yaMe4lJ6a6qL0WiaNiaG81sje06U+LKV/NBst5BTWMWdCgtvTtgue6ekWOcfzHV+XpO4JRVJcGFPT4zh1uZr/fv/0oPqRNFttHDpXjt0uM3pY/1bZFIMsXePLriFJkpiSHsvCqcl8dqiAg+fKfHKcIZNAlFQ1Y7bamTsxUQS3Hzg7312b6rq2SIxJjiAuMoR/fX6JuibTAJRy4OSVtU0dO+c60Z3mD658/51V8KmJelIT9By/UMW5vBoflMx/9pwqJSJM7ZW5BMS4Cf+TJIn7b0snPSWS//3svE/G7wyZBOJScQO6EBUTRsWI4PYhZ90SrnZndG2RkCSJ6eMMWG12Xn/3JAySZmNZlsktbSA9JRJDlJg8aqD15wpZkiSuHx9PeJiGP350lur6Vu8X0A/qmkxcKWlgZGK4aBEbRFRKBd+7awJKhYK/fnbe693BQyKBKChvpKq+lbTkCBQK8eXwld5GELuStDkbPBQToWXa2DgKK5o4nF3hzeL6TW5pIw3NFuZMEK0P/tKfiwi1SsH8yYlYbDIv//MYFXVGL5bMPy4XN6BUSKQmDOwKoYJ3Ocv9osO13HNzGjkFdeSWur6gmisGfQIhSfBFVhFKhcTIRPHl8DVXRxC7c5EzLjWK2AgtG7bluD1bZSDaebwYpUJi1vgEfxdF8FCkXstT903BaLLyi78eYX8QD/a1WO3kljYwbaxBTDMdxHprWbtxUhLjU6M4dbmaVpPVa134gzqBkCT4MquIfafLSE3Uo1GLL0cgcBbovQW0QpKYOykRhULi1++epCqIr/iaWy0cOte2EqwYzBvcRiVF8PNHrmdYnI4/f3KOL7KKKaxo6rRNMEy3fuxCJWaLnQVTkvxdFMEDHWOsp4s3hUJiwuhYbHaZ7Py6bot2eWpQJxAAJy5XYbPL7ROjCIGhY6C70icdrtPw41WTaDRaePb/jnLoXHmnK75Ar6Qd9p8uw2y1kz48yt9FETrwbDwEnLxUxfUZ8Ty8ZBx1TSbW/89h/rnjAlabvX1cj7OVFAPJ7pMlhIWoyBwlpvcPNq5MPOVIMHQhKtJTIrlc0uC1ltxBnUA0Gy2cz6tjZGI4EWH9X89e8B1Xuj3GJEfy84dnEBOu5Q8fneXzo8XklTZitdk7ZdSBWlnLssxXx4sxRIUM+Kp5Qs/6M6jSbLVhtdlZOC2Z5XNHctOUZD7PKmLtnw/RZDQH/EyO5TUtnMurZfSwCDEbapDqLcYUis6D08enRqFRK8jKqfRKl9ugTiC2HynEYrMzOS3W30URvCQhRsfPH7me7y7PREbmaE4lP/3v/ZzLq8Euy51Wugw0py5XU1bTwrgRUf4uitCFN37ktWolDy0Zxw++NoGqhlZe/deJbit7BppthwtQKRWkiRbaQafjKrKO+NaolUwaE0tFrZFjF6r6fYxB2wlrsdrYcbSIEQl6oiO0fS5zKgQPpVLihgmJ1DQYqag1UlHbyuHsChpbLHzva9cF5BWfLMts3p9HbGQIo5IisAfpgLuhoj8J6PXj4zmXX8Oek6U0t1qZPzkwxxbUN5vZe7qMuRMTCdUO2p+CIc1ZXThmWATFlc00t/b/N3HQtkBIksSMcQamjzP4uyiCFzlaGBSKts84IUbHmm9MZeb4eC4W1fP8/x0NyLUKLhXXc7mkgaWzR4hbiQOcJ10aXbdNTQxnwZRhVNW3cuxCVUDeofHpgTxsdjuLZw73d1GEAaRQSNw8PZn5k4f1f19eKE9AUikVfGvZ+G5jHwKxaVtwj4zcqYKXJInxI6O5ZXoy9c1mPj9SRFl1YN3uGa7TsHBqMjdOunY1Ggwj9Icqd1qxeko4RiTqyRwZRV5ZI1sPFXi5hP1TWt3MV8eKmT95GMOcLPssCK4YtAmEM2KO9sHDWQUfEabm5mnJ2OwyL/3jGKXVzX4omXOJMToeWjKu/Vbi/izbKwSGjp9bTwlH5shoUgxhvPfVZY5fqBygkvVt14kS1CoFMeFaEYOCx4ZUAgFijvZg5soVe3S4ltuuT0YGXn37BHZ7YDQdO5uqO9BH6As9c/ViRJIkrs8wMCopgi+OFQ1M4Vyw6PrhPP3gdJRKScSg4LGAGjnzySef8Oabb2KxWHjkkUd44IEH/F0kIUA4rthVSgVSl7S3ayUeqdey5oGpXhll7E1mqw0ZWcz2N0i4+sOrVCp4+qFptJrsPi6R62IiQohTwKnLgfUdEYJLwCQQ5eXlvP7667z//vtoNBruvfdeZs2aRVpamr+LJgQIs9WGXe78A9zTleCwuDASY0TfrjCwemqRUCkV6EICp8HX2XLlohtDcFfAJBD79+9n9uzZREVFAbB48WK2bt3Kv//7v7v0fsfI9o4j3CWp7WpUAjQqJUqFAo267Uvsy8cDeayeHofpNNhtyoAsmy/KEqpVdXp8/FI109Lj6Dj4fSDvfugaj5IE0eEhqJSBd+68/dideAyEsnnz8ZHzFSiVCqL02k6v+zseux5PkiA0RIUkhbSX/9SVWmIiQoIi5vq7LzsSVo0yIMs2EOdNoWiLB0c8ehqLAZNAVFRUYDBcu+UyPj6eU6dOufz+6Oi2q83YWH2n52+brXe2uTAEdY0NX3IWj3felD5gxxcC30DGo7PjibpRcPA0FgOmTc3ZfdJiXXpBEARBCEwBk0AkJCRQVXVtQE9FRQXx8fF+LJEgCIIgCD0JmATihhtu4MCBA9TU1GA0Gtm+fTvz58/3d7EEQRAEQXAiYMZAJCQk8JOf/ISHHnoIi8XCqlWrmDRpkr+LJQiCIAiCE5IciJO0C4IgCIIQ0AKmC0MQBEEQhOAhEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEghBEARBENwmEogg8t3vfpdLly75uxiCAARGPG7dupUHH3zQr2UQAoc/YvLQoUPccccdA3rMQKHydwEE1/35z3/2dxEEoZ2IRyHQiJgcWCKBGACHDh3ihRdeQKfT0dLSwsaNG9m7dy9vvvkmFouFkJAQVq9ezdSpUzEajaxfv56TJ08SHh5OWloaAC+99BI333wzv/nNb5g4cSLvvPMOGzZsQKFQEBcXx7p16xg1ahRr1qxBr9eTk5NDWVkZo0eP5rXXXiMsLKxTmZ5//nmOHDnS6TmNRsN7773X6Tm73c6LL77IyZMnaW5uRpZlnn/+eaZPn86aNWtIT0/n29/+NkC3x0JgCuZ4BPjNb37DJ598QlRUFKmpqe3Pm81mXn31VY4cOYLNZiMzM5O1a9ei1+t9cBYFbwr2mGxpaeFHP/oR+fn5RERE8Oyzz7Yfq66ujsLCQm666Saeeuop351Ef5AFnzt48KCckZEhFxUVybIsy7m5ufIdd9wh19TUyLIsyxcuXJDnzp0rNzc3y6+++qr8xBNPyDabTW5sbJSXL18ur169WpZlWV64cKF86tQpef/+/fKtt94qV1dXy7Isy5s2bZJvv/122W63y6tXr5bvuece2WQyyWazWf7a174mb9y40eOyHzt2TH788cdlm80my7Is//GPf5Qfe+wxWZZlefXq1fJf/vKX9m27PhYCUzDH444dO+SlS5fKjY2NssVikR999FH5G9/4hizLsvy73/1Ofumll2S73S7Lsiz/6le/ktevX+/xsYSBE8wx6Sh7VlaWLMuy/Pbbb8urVq2SZbmtTnz44Yc93negEy0QAyQpKYnk5GQA9u3bR0VFBY888kj765IkUVBQwK5du/jZz36GQqFAr9dz9913k5OT02lfe/bsYenSpcTExACwYsUKXnjhBYqKigC48cYb0Wg0AIwdO5b6+vpu5XE1u546dSqRkZG8/fbbFBYWcujQoW6ZuhB8gjUeDxw4wG233dbeqrBy5Uo2bNgAwM6dO2lsbGT//v0AWCwWYmNjPTo/wsAL1pgEGDduHNOmTQPg7rvv5j//8z9pbGwEYPr06Z6cjqAgEogBotPp2v9tt9uZM2cOv/71r9ufKy0tJT4+HpVKhSzL7c8rFN3HuXZ8veNzVqsVgJCQkPbnJUlyuv3atWtdKvfOnTt54YUX+OY3v8ktt9zC6NGj+fjjj53u22KxuLRPwf+CNR67vl+pVHb6O55++mkWLFgAQHNzMyaTyaX9Cv4XrDHprAySJKFStf28dvy7BhtxF4YfzJ49m3379nH58mUAdu3axZ133onJZGLBggVs2rQJu92O0Whk8+bNSJLU6f3z5s1jy5Yt1NTUALBp06Zu/cHesm/fPhYuXMj999/PxIkT+fzzz7HZbABER0dz5swZAGpqajh69KjXjy/4XjDF44033sjWrVtpaGjAbrfz0UcfdSrHP/7xD8xmM3a7nXXr1vHaa695vQyC7wVTTALk5OSQnZ0NwDvvvMP06dMJDQ31ybECiWiB8IP09HSeffZZnnjiCWRZRqVS8eabb6LT6Xjsscd49tlnWb58OeHh4cTGxnbKlgHmzp3LI488wsMPP4zdbicmJoY//vGPTjPx/rr33nt58sknWb58OUqlkhkzZrB9+3bsdjsPPvggTz75JIsXLyYlJYWZM2d6/fiC7wVTPC5YsICcnBxWrlxJREQEGRkZ1NbWAvCDH/yAl19+mbvvvhubzcb48eNZs2aN18sg+F4wxSTA6NGj+f3vf09hYSGxsbG89NJLPjlOoJFkZ203gt98+umn6PV6FixYgN1u5/HHH2fu3Lncf//9/i6aMASJeBQCjYjJwCESiABz4cIFfv7zn2M0GrFYLMyaNYunn34atVrt76IJQ5CIRyHQiJgMHCKBEARBEATBbWIQpSAIgiAIbhMJhCAIgiAIbhs0d2FUVzcRGRlKbW2Lv4sSEKKjdeJcdND1fBgM4T49nojHzkQ8djbQ8Wi12sT570DE4zX9icVB1QKhUin73miIEOeiM3+cD/EZXCPORWcDfT7E+e9MnI9r+nMuBlUCIQiCIAjCwBAJhCAIgiAIbhMJhCAIgiAIbhMJhCAIgiAIbhMJhCAIgiB4qMs6XkOKSCAEQRAEwQOSBLtOlAzZJEIkEIIgCILgIbPV5u8i+I1IIARBEARBcJtIIARBEARBcJtIIARBEARBcJtIIARBEARBcJtIIARBEARBcJtIIARBEARBcJtIIALIUL2XWBAEQQg+Pk0gPvroI5YtW8ayZct4+eWXAcjOzmblypUsXryYZ555BqvVCkBJSQkPPPAAS5Ys4fvf/z7Nzc2+LFrAGeoTkgiCIAjBxWcJhNFo5IUXXmDDhg189NFHHD16lP379/PUU0+xbt06tm3bhizLvPvuuwD84he/4P7772fr1q1MmDCBN954w1dFC1hDeUISQRAEIbj4LIGw2WzY7XaMRiNWqxWr1YpKpaK1tZUpU6YAsGLFCrZu3YrFYuHIkSMsXry40/OCIAiCIAQmla92rNfr+fGPf8ztt99OSEgIM2fORK1WYzAY2rcxGAyUl5dTW1uLXq9HpVJ1el4QBEEQhMDkswTi/PnzbNq0ia+++orw8HCefPJJ9u3b1207SZKQZdnp8+6IjdUDYDCEe1bgAKAL1RIX573yB/O58IWBPB+DIR69TZyLzgb6fIjz35m3zoe3621/8PRc+CyB2Lt3L3PmzCE2NhZo65Z46623qKqqat+msrKS+Ph4YmJiaGpqwmazoVQq2593R3V1E7GxeiorG736dwwUSYIWo4mqqkac5FNuMxjCg/Zc+ELX8+HryjTY49HbRDx2NtDxCIjz34G34tGVettxLeyNet0X+hOLPhsDkZGRwf79+2lpaUGWZb788ktmzpyJVqslKysLgA8//JD58+ejVquZMWMGW7Zs6fS8IAiCIAQrSYIdRwrZcaRwUN5h57MWiHnz5nHu3DlWrFiBWq1m4sSJPProo9x2222sXbuW5uZmMjMzeeihhwBYv349a9as4c033yQpKYnXXnvNV0UTBEEQhAExmO+u81kCAfDoo4/y6KOPdnouIyODjRs3dts2OTmZDRs2+LI4giAIgiB4iZiJchCQJDGLpRB8RMwKQnATCUSQG+x9bMLgJGZeFYTg59MuDGFgDOY+NmHwEnErCNdIUuDeqdET0QIhCIIgCF7iSZdysLbIiQRCEASfCbYKURD6oz9dysHYIicSCEEQfCJYr6oEoT/MVltQJgOeEAmEIAg+M1QqUkEYikQCIQiCIAiC20QCEeRE87AgCILgDyKBCGKSBPtOl4kkQhAEQRhwIoEIcqKPWRAEQfAHkUAIgiAIguA2kUAIgiAIgpcNha5lkUAMQkMhcAVBEAKVWqUYEnOgiLUwvMQRKL6cy9yVudIdM6GF6jTMuy4h6OZWFwRBCBSerE/R0Gymqr6V2MgQ3xQqgIgEwgscP9oAt10/3Cc/2o5Z/RZMGdbn/s1WGyqr3fuFEARBGCLcqXMdjp6vYMfRovbtI3SafrVCuJvADPSCXKILw0v6M32pq4uviDsuhIHiyYJAruzTG9s4e89gbyoW/MOdOreizshfNmcTHa7ljhtGMDIxnE27rlBRa/To2O5OBe+PqeNFAuFnjtaLr44Vi0pQCAj9WRCoN31Vbp5UgL4qqyC46/1dl5GRmZ2ZQLhOw6zr4jFEhXDqcjWyh80C7l40DvRFpkggAkAgLL4iKl+hI1/EpCv78+SYgfD9EYa26oZWjpyv4NbpKehC2kYGqJQK7rhhJHVNZirqWv1cQt8QCcQA8GUTa1OLhTNXasjOq6WxxexxE/BQGDEsCILgC/tOlSLLsGBKcqfn50xIQKtWcrGwzj8F8zGfJhBffvklK1asYMmSJTz//PMA7N+/n+XLl7No0SJef/319m2zs7NZuXIlixcv5plnnsFqtfqyaAOmP02sPW3vSEjO5dXw0d5csvPrOJtXy7P/e5TPjxZ5lAiIKzhBEAT3ybLMnlOljE+NJj46tNNrGpWSMckRlNUYPR4LEch8lkAUFhayfv163njjDT755BPOnTvHrl27ePrpp3njjTfYsmULZ86cYdeuXQA89dRTrFu3jm3btiHLMu+++66vijbgPGli7alVwJGQbNp5md9tOo0+VM2yOSNYOHUYdU1m9pwq6bStIAiC4DtV9a1U1bcyb1KS09dHJoYDsP9M6UAWa0D4LIHYsWMHS5cuJTExEbVazeuvv05oaCipqakMHz4clUrF8uXL2bp1K8XFxbS2tjJlyhQAVqxYwdatW31VtKDRU9Jhslg5lF2OXZa57fq2PrfYyBDuuCGV/LImahtNQ2YiE0EQBH8qrW5BkmDSmFinr+tCVMRHh7LvdBn2QTYxj8/mgcjPz0etVvPtb3+byspKFi5cSHp6OgaDoX2b+Ph4ysvLqaio6PS8wWCgvLzcrePFxuqvvjfcO3+Am3Q6LQBxcd2P39trjtfVKgUSUqdtdKHabo9jY8MprjZSWdfKTVNTiI3S02KyALD0hlFs3p9PXlkTqcOiUKpUPR6zWxm6HGswGsjY8Hc89ldfMevyfjrElbN47rp/T+LQW2UdaAMdG8Eai77iyvlwJR4raltJHx7FyOExbe/pUJ/Hxoaj02kZOyKavSdLqGgwMzEtzuVjuft98LQe9zQ2fJZA2Gw2jh49yoYNG9DpdPzgBz8gNDS023aSJDm9xUVy89K5urqJ2Fg9lZWNHpfZU5IELS0mAKqqGjtN5NHbax1fV6sUKBRS+zaSBC1GU7fHpeV1nLhQSVxkCKmJOlpNJoxGC7IMrS2tjEoK50pJA3Mn27FarE6P6bT8HY41GBkM4Z1iw9eVqT/jsb/6illn2zubSK1jXMXFhTuN54779yQO3S1roBjoeASCMhZ9pev5d8aVeDSarFTWtTBzvIHKysZu9XlNTSMtLSbiIjSEapV8uucyiZFal47l7vfB03q8P7Hosy6MuLg45syZQ0xMDCEhIdxyyy3s27ePqqqq9m0qKiqIj48nISGh0/OVlZXEx8f7qmhBbe/JUlrNNiaOjnGaZI1MCsdmlympbPZD6YShStxKKQxF5wtqkWWYMCqmW3exWqVg3+kyJKntls6Z4xM4mlNJq3lw3CAAPkwgFi5cyN69e2loaMBms7Fnzx6WLFlCbm4u+fn52Gw2Nm/ezPz580lOTkar1ZKVlQXAhx9+yPz5831VtIDh7vgEm13m0wP5xERoSYjp3poDkBCjQ6WUyC9r8EIJBUEQhK4cd8Kdza1BpZTIK2tgx5FCFF1+UTsm1TdOSsJksXH0fKXTfQUjn3VhTJ48me985zvcf//9WCwW5s6dy3333cfo0aN5/PHHMZlMLFiwgCVLlgDw6quvsnbtWpqbm8nMzOShhx7yVdECguMui4XThrn8ntySBqobTMybmNhjF49SIZEUq6OgrJHp45z3tQmCqxxz6/dnsbgrJQ3sO1dBWXXL1e7KIK0tBYHOXXbn8mpJiNFhs8tISrm9xaErtUpBUWUTiTE69p0ubb9jw7EvtUoRlEmESwnE008/zYsvvtjpuccff5zf/e53vb5v1apVrFq1qtNzc+bM4eOPP+62bUZGBhs3bnSlOIOGTM8B15XVZudsXg2jksJ7bH1wGBYXRmFFM9X1g3P2M2FgOO7kWTBlGNsPu79YnCzLHMup5J87LrY/V99s5tHlmWjUSl8UWRD61Fd968qCVGarjZZWK2U1LczIMHR6vicWm525ExPZtOsK5TUtJMTo2t8jI6NQBF8G0WsCsX79esrLy8nKyqKmpqb9eavVypUrV3xeuKHA1X7jg2fLaTZaueuOURRVNvW6bcLVyUzKawbfxCXCwHLEpyfjG7YeLuR8QR03T0vmoTuu49f/yiIrp5K/aXL4zh2Z3i6qIPTJ0fK78tZxvb6+YErfLcPltS1A2wWbq26cnMQn+/L4ZH/eoPgO9JpArFq1iosXL5KTk8PixYvbn1cqlUydOtXnhRPaWG12Pt6XS3S4lslpsX0mEFqNkpgILeU1LZ2eH+ilXoWhK7e0gfe+vMSIBD0PLBpLbGQomSNjGG4I55P9eUxNb7tqC/TlioXBp69k2NVkubzGSJReQ5Reg9HU98BItUrByUvVLJyWzPYjhSybk0qywfXkIxD1mkBMnDiRiRMncsMNN5CYmDhQZRpyahtNHDlficlsw2aTuW3G8E5jHL46Xnx13odhvd7e2vGlxNgwcvJrsdrsKBUKj9a2FwRP2GWZv2/PISJMw6zMeBQdAvPOeSM5dbmaDdtyWHZDKrtOlDB/smsxKWJY8Kb+JKOyLFNRZ2RGRrxbUw6YrTaWzkll54kS/rYth3mTkoJy7IODS3dhFBQU8OCDD3LnnXeyfPny9v+E/jNZbOw9VYbZYiMyTMPbX1ziHzsuYLe3RXZDs5lP9uWROTKapFhdj/vpeMsQQFJcGDa7TG7ptbsxXMmsg3lEsOA5b37ue0+VkFvayD23pKFWdR7roFIqeGDRWOqbzWTn1wb8csXC4KNWKfhw16V+LQFf12TGbLGj8WDwY4ROwzduG0tOQR1Z5ys8K0CAcGkQ5bPPPsvKlSvJzMx0e4InoXdnLtfQaraxeGYKsZEh1DSY2XqogIZmM3FRIfz+/dO0mm3cd2s62fm1ve6rY+WaeDXZyM6vJS05yqWySBIeDZYTglvXiaD6o8lo5p+fXyQ+KpQ51yWw42hRt23SkiOZkh7HmSs1jEmO6NfxBMETFqu9X8lo+dWFsbounuWqGyYkci6vhgNnywkNUbWvlxFsXEog1Go13/zmN31dlkGnr1zLarOTU1BHiiGMmIgQJEninpvTiAzT8O6Xl5Bpuy3zu8szSTHo+0wgOgrRqIgO15BTUMfyG1wvs7jCG5o8WewNujcBv/vVZSwWO5PTY51ebDiajVfMH82Ji1Vk59WybPZID0vtfN+C4GsVNUYiwzToQjybCUGSJB65PYNLxfUcPldBeKgaw9VkJJiu0V3qwkhPTycnJ8fXZRlUelpNs6PiymbMVjujh3XOPhfPHMELj85i9nUJvPjobGaOT/CoDPHROi4V1WOx2j16vyB05ejqcLZEfXlNC3tOljJ2eCSRYZpu7+24wNvweD0jk8LJKaijocXsURl62rcg+JLJYqOqvpXEWM9aHxw0aiULpyWj1SjZfbKUllZLj9+tQOVSAlFYWMjKlStZtGiRGAPhhr6u6vLKGtFqlBiiugdiUmwYo4dFeNxEBpAQE4rZaudKSb3H+xAGB3fHOFisdkqqmqmqa21fq6bjj3TXqauNJitHcypJjNGROTK6/Zhdma229rJMTovFZpPZerDA5XKpVYoeK1jReib0pb8/ypIEZ67UYJdlt27f7EmoVsWNkxKxWO0czq5EluWgmhbepfaXn/zkJ74ux5Bjttgoqmhi9LAItyYQcecLkBAdiiS1jYPISI32oJTCYNDTYlc9ySmoZeuhAkyWtpark5eqSTGEMSY5wmnF1mS0sP9MOWaLne9/7TpOX6nuNqjXwZEAqJQKosO1pCaF80VWEYtmjiBK373Vwlm3hNlqE10VgtskifaY9CR8HK3KOYW1aFQK4p1c+HkiIkzDxDExnLhYzbELVX2/IYC4lECMHTvW1+UYck5dqcZqkxmZ5PrgmZ4q5Z5o1EpSE8LJzq/lbg/LKQwOrl7RVNUb+c3GU2jUSuZMSMRktlFa3cLRnEpyCuuYMDqGlta2e95tdpnD2eW8++Ul6ppMzLkugdTEcE5fqe71mGarDbsso9UomTg6hoKyJjbvz+MbizrXM+K2TcHbHC1gnjKarZy4WE1SnM7jmSOdHX/c8CjySht596tLzJ2Q6JVZKQdiTJBLCcTs2bPbl912DIwyGAzs3r3bp4UbzI5mV6BVK0mM0bk0CYmDu01bmSOj2Xa4EJPZt01iYgBb8OpYof1jx0VkGeZNTCQqXItCIfHonZn86eNznM2r5cCZcg6cKUetUrSPrUmIDmXh1GHERIS4feyIMA0Lpgzjy2NFzM7sPtYnWJpyhaGhvKYFo8lKsofdF44WOFmGRTOv3fGkUEhMSotl94lSCiuaSO3hrgxX69mBSr5dSiDOnz/f/m+LxcL27ds7PTfUuZvRmi02TlyqZkSi3ufzn2eOjGHLwQIuFNb57BjiSjF4dWzWra5v5cTFKlYuGN0ppiVJItkQRmqinppGE/oQNScvVaNRK1gyK5UJo2L47FC+x2X4t4VjOHW5mj9/co65k5xPWCfLMjmFdew+UUJtowldiIoUg57JaWLBOKH/XK3DLxTWE6FTt69j4YmeuuBSDGGMSNCTnV/H8Hi90zK6U88ORPLt9nLearWaZcuWsW/fPl+UJ+h0rIBddfpKDSaLzaV7f/s76CctJRKVUuLc1VtAfTVRlLhSDB49DT7MLW1Eq1ai16mdxogkSRiiQrlz3iimjo3julExTBoT2+8kOFSr4rE7r6Ou2cQXR4sorLg2VbvZYuPLY0X8/K3DvPyP4xRXNROiUVLbaOLX753inzsutA/yFARPuHrnw5WSBkqqmlk4LRmlDy78JEnirnmjaDJaOn0HOgq0etalFoi6urr2f8uyzJkzZ2hoaOj5DUOMux/qkfPl6EPVJDrJYjs2UTlGvc+f7PqS311p1UrGDIskO7+Wm6YOa28+82SiKNFNEfwkCXYe734VY7XZKaxoYvZ1nt0y3HH/nkhLieSn90zhV++c4OdvHSYhOhSVSkFJVTOyDDHhWr69LINWiw2zxYZCkjh1pYYdR4uYMCqGiWNi+lVuYWjrqw5vbrXwP59mE6JRsmTWCL7I6j5BmjdMGxtHlF5Ddn4dNnvg337v9hgIgNjYWJ555hmfFmywam61cOxCFQumDOt25eYsYfBGxjl+ZDQf7cml1WzFbpc9SgI87aboacIhwX+cxVRRZTNWm8z8ycMormr2aL/uDvLtatyIKO64YSRatZLz+bVtiUOElvioEKLDQ7hx8jC+Ol7clkAoJKamxxKp03DwXDkJMaHts686iIRX6I+SqmaulDTwr88vcuR8BQ3NZhZMGUao1rPJo1whSRLjU6M5cLacQ+cCf5prt8dACP1zOLsCq83OvElJ5JV1b8XxRRPV5DFxfLgnl8Lypn6t/ubJbIXu3D4o+E9eaSP6UDWVdcZ+dXF5Gr+OrsBQrZJbpw+/uqAc7DxRTEurxWnsSJLEN5dmcOpyNUfOV7B0Tmqn/YlxOUJv7HYZi9XWab2WJqOFC4V17DtdRn5ZIwAalYIxyZH84GsTyHVSZ3vbsDgdkWEaPt6bx03TPG99HgguJRB2u5233nqL3bt3Y7VamTt3Lt/73vdQqXyXiQUyT69sZFlm76kSUgxhpCbonSYQvjAiQY8hKoS8ssZeEwgxNmJoamg2U1nXyqQxMVhs/ms29SRWtBolU9Jj2X+mnAsFdSy+fkS3/YlWMKGjttuPy7hYUMf7u3PRh6rRqhXYZXh/Vy52WW5bhv62dJpaLUTptdw0JRlgQBKItlaIKA6eq2hPYgKVS4Mof/WrX3Hw4EEefvhhvvnNb3L8+HFeeeUVX5ctILkyRXVPsvNryS1tZMGU5AFdlEySJGZkxFNa3YLZ4ryS7m/zsxC8Lhe3VYqjkjxb2KqvmPF1TA2LCyMxJpSzubXdbonubYCciPWhad+pMnLyaxmRoGfF/NFMGxuHJEmoVQpunz2CJbOG84tvzeTWGcOJ0mux+iGpTr56R8bJS9WYeqizA4FLCcSePXv4wx/+wK233sqiRYt48803h/QcEJ5cKdnsMv/6/CLR4VrmT07yQal6d31GPLLc1tfdE9FaMPTY7TKXi+tJjAn1aGGgvu5Cctz3/uneKz79wZ44JhaTxeZ0cJuzqYH7cyEgDCxvfkYF5Y1cKWlgcnocMzIMLJ87km8uHc9NU4cxf3IS/7ZwjEfzmXibJEk8cFs6La1WPnNjqveB5lICIcsyarW6/bFGo+n0uDcvv/wya9asASA7O5uVK1eyePFinnnmGazWtquFkpISHnjgAZYsWcL3v/99mps9G8QVqCxWO4fPlVNU2cyDi8Z16nMbKCMTw4kO13CpuEHc9ia0O5dXQ3Or1a0ZUbvqK/E0W21e6Rrp7YckLjKE5LgwPjtUQEurxaX9iYQ58Hkz0ZNlmZOXqonQqZk4JvDnDxk3IpoRCXo+O5hPdX2rv4vjlEsJREZGBi+++CIFBQUUFBTw4osvujS99YEDB/jggw/aHz/11FOsW7eObdu2Icsy7777LgC/+MUvuP/++9m6dSsTJkzgjTfe8PDPGXgdA7ul1cqBs2V8vDeXf+64wP4zZbz2zkm2HCyguKqF+25JZ0q6fwK3rV8thvomMyVVLX4pgxB4dp0sQatWeGVhIF9ypYttUlrbNNvbrw7cFQYHbyV6Z3JrqGkwMdELc5cMlKlXfy/+ti0nIC/8XEog1q9fT0NDA/feey9f//rXqa2tZd26db2+p66ujtdff53vfe97ABQXF9Pa2sqUKVMAWLFiBVu3bsVisXDkyBEWL17c6flg4Gi+lWWZYxeq2HKwgD99fI4P9+ay93QplXVGGprNJMXquHnasE5Tl/rDmOQIwnVqTlyqor65bQnlhhYze0+VcvpyNTUNgZnlCr5R12Ti+IUq0lIifTIxjrf19UMSExHCtLFxfH60qH29DkFw2Ha4EJ1Wxejka2N9Ar0LKyxUzb8tHMPpK9XsPV3q7+J002unp9lsZt26ddx222289NJLADz66KMolUr0+u5TbXb085//nJ/85CeUlrb90RUVFRgMhvbXDQYD5eXl1NbWotfr2+/ocDwfLEwWK3tPlXGlpIExyRE8vCSDxBhdW9/v0UIWXT+czfvz/F1MoG2+9VmZ8Xx1rIR1fzlEfFQoV0ob2kenH7tQxdT0WMYkR/q3oMKA2H2yBJtdZuzwKI/3EWgV8PK5ozh24QifHy0k1IMxHcLgVNPQyrncGialxaJUSNi5Nu/OgimBfavkzdNTOHq+kn99ftHvF6Fd9foN++1vf0tTUxNTp05tf+65557jF7/4Bb/73e96XOb7vffeIykpiTlz5vD+++8DOG1+6Tg5Vdfn3RUb25bQGAye9+W6SheqJS6u7TjnCxq4UtLAlLEGJqXFMTUzqdN2sbHh6HRagPb3OISGtD1vR4FapUCtVKJS2du3Cw1pGwHc8XHX7Xt67Ni+4zFDQ7QMi1ewZI6G6noj9U1mvn7rWGZPSOJETiW7jhVx/GI1sVG6bmXt+ne7+lpPf7s/DERsOAxkPLrC8TnExoajC9WiDw9l54kSpo2LJyE2nBaTxWlMSUjtMezs8dELNYSGaJGlazHYUzyGaN2Ld8dzHffdV9lmTBjGzMxEdhwtYsVNab1+/3qL54Ew0LERKLHoLm98Tl+eLEEGrhttIESrocVkQafTdoohuPb96CkmO9bpPdXBrtbPsbHhneK7p7LEGyJ48sEZPP7qVxw9X8m37pzUYxdMx7K7c948jY1eE4idO3eyceNGQkKujUpNSEjglVde4Z577ukxgdiyZQuVlZXcdddd1NfX09LSgiRJVFVdW+u8srKS+Ph4YmJiaGpqwmazoVQq2593V3V1E7GxeiorfXvfrCRBi9FEVVUjpdXNHM0uJzVRT9owPS0tbc/L8rXtqqsbaWkxAbS/5tiPsdXU9n+jBYtSgVajxGK1U1XV9jcYW01YbXaqq6897ro9OH+sDg/F2Nq5PMZWE8ZWC6FqiceWZ3YqC7KNeZMTqG4wsv9UKavm16FRK53+3V1zvp5ekySc/u3+YDCEd4oNX1emAxWPXTmbo6Tj51Bd3UiL0cQ7289T12hiyczhFFc1YWx1HlMKhdQew2pV2+uf7LqI0WjColRguzoQumMM9hSPrSZTt22dxbsjVhSKazHrStkUComqqkZun5nC4XNlnLxYwc1Tk3r8/vUUzwNhoOMRGPBY9AZPPqeuE9jZ7TLbD+aTMSIKtVK+GocKWlpMnWIIrn0/nMUkdK7THd8H6Dv+uz6WZaipaewU372VRSnD129O429bc1j/p31kpsZ0m5yv47lylNWV89afWOx1DIRare6UPDjo9Xo0Gk2P7/vrX//K5s2b+eijj/jRj37EzTffzC9/+Uu0Wi1ZWVkAfPjhh8yfPx+1Ws2MGTPYsmVLp+eDwd+3X0CllJh1XYLP5nVw3Ab31bFinzQXd1xcS6VUMGOcgRaTlQ/2XPH+wQSfcnXEeqvZxmcH85k0Jpb0FPe7qwbi7gVPFqlzvO9iUT0phjCy87vPCyEMDR1v3b1YVE9FrZG5Ewf+9vmeuDPvjqOOvmnKMFITwzl5qZqS6sC4U7HXBEKhUNDU1H1VsKampvZbMN3x6quv8stf/pLbb78do9HIQw89BLQN0nz33XdZunQpR48e5T/+4z/c3revdV3F8mxuDefyapmcFovOh3Ojg/P72L3Bkal3TE7iokIYlRTOjiNFFFU6XxHO1X0LA6+vOJEkyDpfQavZxqqbxgxQqdzj+K55GvNmq43MkdGYLXafLXokBI/9Z0rRapRcn+F6y7avVi3uyJX4dlxA7jhSiEIhccOEBHQhKg6eLaei1v930/X6y3fHHXewdu1aXnzxRXS6toVqWlpaWLt2LYsWLXLpACtWrGDFihVA2+2gGzdu7LZNcnIyGzZscLfsA6Zjk9iimcORZZlNuy4TE6Fl3Igo/xaun8xWGzJye/MawITRMVTWtfK3bTmsvn8qSsW1PNOVaYEdV8I3TQ3swUmDmbPPSa1S8PdtF8gvb+Lu+aNIMfQ+ENofHBWmSqlAcukeMediI0NIitWx9VAht05PRqXqx86EoGWy2DicXcGMcYZOdVxvvBWDnnCWtJittvbvsUatZO7ERHYeK+GVf53g8RUTGZHgepeDt6d17/X0PPzww4SHhzN37ly+/vWvs2rVKubOnUtERAQ//OEPvVOCINGxFaCospnc0kbumjcKpXLwVUxatZL7b03nUlE9m3Ze68romA33lZ2LSXr8p6fpm01mG3tOlRITrmXp7NSed+Bn3mpxmzQmhiajhcslrq9fMBBXnsLAOZZTSavZxjw3uy981erbG1e7NSLDNMyblITFaue5/zvKX7dkc+pyVZ+3Lvc2rbunem2BUCgUPPfcczz22GOcO3cOhULBxIkTSUhI8M7Rg5DdLnPqUjWJMTrmTkxkz6nAuze3I08X/pozIZFLxfVsPVyAQiGxbE7bIkUds2EhcDmr/I5dqMJssXHz9GRUSoVbg9ICVa+zU0aFMmFUDBcK6xiT3Pc6H2L12MFn3+lS4iJDSO/HrcoDydWkJSZCy/PfmcnGnZc5lF3BnlOlqJQSI5MimD9pWI+tLd5OilzqvE9JSSElJcWrBw5WB86WUd9s5oHbxnZq2g9Ejvuc50/2rCvh3lvSsVjtbDmYz9ZDBUSFa9CHqgnXqUmO0zNuRBQhGnGvfTAorW4hr6yRyWmxRIdrXX5fIC+y5krZ7rpxFC/8LYsrLrZCiJaz4OBKU3xLq5WzebXcPX8UikAM4H6KCNPwyO3juf/WsVwurudoTiU7jxfz640nefLeKQNSBlH7u8ARe3a7zId7cokO1zI9w9D7mwKE2WrzuPJXqxRXF5pJ5sTFKrIuVFBZZ6SgvImzubWolBLpKVFcPz4ei9XeZ2uHp60hQv8YTVaOXagkMkzDpLRY7Hb3PoRA/lHtq2zpKZHER4eSU1CPyWJD44d1aATvcrWlKKewDqVCYuHU5AEs3cDoeHGoUSsZPzKGzFExGM1WDp4t57ODBS6P+egPkUD0oePtZIUVTVTWtTJ/SlLQZLSeDgiSJNh5vG2WtlFJEYweFkFMpJaW1rZ7mBtbLCgkiROXqvjb1hy0aiXlNS18c+n4PvcnkgjPeDoA6t2vLmE02bhxUlLbLHxuJhDBbnxqFLtOlLLreAm3XR9YM/kJnukrcWw1WcktbWT0sHDCdZp+d9cFYnXvOAcdyzZ6WARmi51PD+SzdI7vxzkFdht8gGjr95c5X1DH8Hg9yQG+8FBXng4I6uk9apWCuKgQvn5zGs9/ZxbPPDidcJ2a3SdL+WD3lR4XfQnkK9lA5+kAqHN5New8XsLY4ZHERvp/mWJ/MESFYogKYcvBfCz9aJETgoMkwbn8OmRZZnxqdPtzfSUHPXWJ+Xounv5wVi+smD+KVrONy8X1Pj++SCBcVFzVQmOLhWVzUn02aZTDQIwEd3X/fZVFkiTSUiK5fc4IxiRH8PG+PLEaoo+4mwiaLTb+sjmbxBgdmSOjfViywJc5Mpr6ZjN/+vicV0ehC4FFkuDTA/nkljaQlhxJRJjG6Xw3Dl2Thp6+X/64K8NVXcuWEq9nfGo0l4rqfb6Cp0ggXCDLMufz69CHqpg5vu/JSDr+6HYMWFcqrYHIdh39Z33t353bNhVS20Jd08YaeO+rywG7fv1g5OyzsdtlDmdXUN9k5rvLM1H1cLvxUPkhNUSFMm5EFGfzajCaLP4ujtAHT+NSlmX2nCpFqZC4btS1pLm3BCBQEwNX9NRaEhOupbnVSl6Zb6cvFwmEC6rqWqltNJGeEtnnOvIds12N+lp22/FHu6+r+oHIdl3dvztlkSSJby3NICpcy4EzZVis9v4UUXCBs+mrLVY7R3MqKasx8o3FYxk9zPktjJ5OFx2s7po3klazjVwfV6pC/ziLaVdjdOeJEsqqW5icFkuoj2cI9reeulzMVhsJsaFts87mVPq0DCKBcMH5gjpUSsnlGb86/uh2/PE1W20oFD03pwUyV8saFqrmocXjaGixsO1wgc+PJ3SOsZqGVl7ckEVBeRPXjYrucwR6MF99uWt8ajSxkSHkFNSJ5NaH+vvd7TqNuastpgXljfzr84skxeoYlRScK4+6q6fvr1atJCE6lKycSp92Y4gEog8NzWbyShtJTQxH3c/pcB0Zo8UWuP1pzvSU6fb0hZ40JpYUQxgf78vzaDEjVxeFErMGXlPd0MqfPznH1kOFVNW3Mvu6+PYBZENZ56tYicyR0RhNNj475HlyK/TM1e9ub+/v6aq6N7WNJn7//mnCQlTcODnJ5+PU/MHdP2l4gp6ymhZKqn23ZoZIIPqw51QJdlkmzYWZ7FwRTIlDR13L3ddVwdSxcdhsbWNHvHG8rnwxLWswsljtnM2t4ek/HeRwdgUTRsewcsFohsdfW+ci0M+PrxJBZ4lvQnQoKYYwPtqTS02DGKfjC653efa89gOA1Wbny6with0uZNuhQt7afI6zuTXYu1xRV9YZefXt4zQaLTy+ctKg7LrwZEK3YVfvFszOq/FRqcQ8EL2y22W+Ol5CYoyOiLC2e4lduSoeKnqrKMJ1GuZPHsbOE8WkD3d/yej+Hn8oqKgz8uaHZ8gva2TaWAP33ZLGmbwa6NJ3HMjjHHy9cFHHGHGcg6lj42g6Wcqek6UsmTmCiLDuM3OKSc98q6/JoJpaLDz7v0cprGgiOlyLRqXg+MUq9p4uIzYyhNgILRFhGj7YfYVtRwpRSPDE1yczelgEBRWDc4yLu/WdPlSNISqE7Pxabp3hm/lPRALRi5OXq6iub21fVbKvLDCQp/31hzvnjWT3yRLO5db6uyiDTlFFE6/86zhmq435U5L45u3OJ/CCwE+0zFYbdln26cx5Hb+bWrWSH//bJJ7/vyxee/ckT3x9ChFhmk7bOmb5E0mE7/QUl/VNJrYfKUIhwb+vmEhDiwmFQqLVZKWwvJmGFjNnrtTQ9tFUMnlMLCOTwskvawz61ZG9bfzIaI5kV2K3yyiV3v9hEl0YvfjqWDFRek2n5uC+KuNAr6wHUnS4ljHJERRUNFFY0eTv4gwaVfVGXvnXcdQqBUvnpHaKT6FnHb+bo5IiuHFyEmXVLbz49ywq64w9bisMnILyRrYeKkSWZVY/MI3p4wzt4xlsdpmkOB0/vXcKd904kiUzh/O7H9/If3x9MmqVYsA+s2C5QFSrFEhIGE1W8st90yojEogelNW0cCa3hjHJkT7J3AaLvvqvM0ZEoVYq2LTrSs8bOdmn4JxCIfHyP45js8v8v/umEtnhyllwz7C4MJ66byrNRgsvbsiiqFIkuf6UU1DLS/84jlIhcdv1Kb0mxiqlAr1OjV6nHsASBl8rc2xkW/fc+XzftAKLBKIHbf2ykkvLAA9Vrkx6pVErGTsikhMXq7pd5TkT6H32/nYut4aq+la+vWw8ibE6fxcn6KWlRLLmG9ORJHjp78fEwEo/OXahkl+9c5IovYYls0d06lIKNMHUOhWqVZEYo+NikW+mtRYJhBNNRgv7Tpcy57rEQTmi18EbP9JdJ5py1iKRnhxJhE7NyUvVLt2THExf0IHUbLSQnV9HaqKe6eOCYzXYYJAcF8aab0xHF6Ji76kymlqcz1Qpklrf2HWihP/+4DQjEvQ8/Y3p6EM9b1UQn1F3ackRXCr2zbTWQz6BcDa3wa4TxZitdhbNHLwr9/miKa6nFgmVSsHyuSOpqDVyLk8MqPTUmdy227Gmjo3zc0kGn/ioUH56zxRkYNfJUqy2zhNN9Xd+A8G58/l1/O9n57luVAz/776phIe5lzx0/DyCrXthoKSlRNJktFBe23cLsLuGdALRtVKQJPgiq4gvsoq4blQMKYbBPTjNF1f6PU19fdPUZHQhKjbtuuzzBV4Go9LqZgormhk7IpKwkIHt9x0qEmJ0zM6Mp6HZzMmL1d1eFy1j3tF2i6zMubxazuTWMCszgR+vmsTukyVuzdDrbC4a8Rl1l5bcdhv9JR90Y/g0gfj973/PsmXLWLZsGa+88goA+/fvZ/ny5SxatIjXX3+9fdvs7GxWrlzJ4sWLeeaZZ7Ba3Z/B0BNdA+5cfg11TWZunzViQI4/VKhVCiaOjiG3tJHjF6sAMZOkO7YcyEepkMi4epuaOHfe0fU8JsToSE+J5HxBHReL6vxWrsFIkkChaLto+/RAPufyaklN0PPo1cXeXF13RyQM7kmKCyNUq+KSD5b39lkCsX//fvbu3csHH3zAhx9+yNmzZ9m8eTNPP/00b7zxBlu2bOHMmTPs2rULgKeeeop169axbds2ZFnm3Xff9VXRetRqtnL2Si3jU6PJHBkz4Mcf7EYlRZAYo+P93VeQZVnMJOmiqnojB86WMyopnBCNakBWbB0Kelpt1rEQ0z92XOw266HgmY6LDF4sqmPTriuMSNAzI8PQ5wKFHXnSTTHUvyMKqe1mgKBKIAwGA2vWrEGj0aBWqxkzZgx5eXmkpqYyfPhwVCoVy5cvZ+vWrRQXF9Pa2sqUKVMAWLFiBVu3bvVV0Xr0yf58TBYbY5IjhnzQ+YJCIbFi/mhKqprZd6Z0QFYdHQy2HS7ELsudJslxZfBqbwZrC4a7f5OzGFSrFEwdG0t+WSP7TpX67NiDWU9TVJdUN3PgbDljh0e2z/HQ03nra6prV4hxEW3SkiMpqWzGbPFufeuzWwzS09Pb/52Xl8eWLVt48MEHMRiujR6Pj4+nvLycioqKTs8bDAbKy8vdOl5srP7qe91bhU0XqiUuLpz8sga2HSpg3IhoEuPCiYtr209oSNt9tHYUqFUK1EolKpW919c7/rvrawP1GCBE61nZ2s5nOKEhWmTJs7JISMTGhqPTadv3pwvVsnjuaL44Xsz7u3JZMicVjVrZfi4dQkO0WG32bs93++yu7ruv7RzcjY3+8DQeu6ptbGXPyRLGjYgmNjrM6ecYooU9Z8pRKxWd4rGtHN0/x56291YMuhOP3n589EINOp2217J1PS9wLYYc7x0/UktNg5kP9uSyZN5odCHq9rqiJ1v25bJ07iiXP9uBjMeBPF6EPpRD56u6nQu7pGDPyVLCQlT8/Dtz2H4oH7VK0b5tx8+trxh1Vs/0FiOhoZ1fbzFZ2t7jgxj0Vv3tTh3c0/aO36oZmUl8uCeXZrOdqEhdtzj2NDZ8fo/ixYsXeeyxx1i9ejUqlYrc3NxOr0uS5HRQnburqVVXNxEbq6ey0vUZtyQJWowmKisb+O3bxwjRKpmSFoOx1URVVdt+jK0mJAmMRgsWpQKtRonFaqe6uufXO/4b8MtjdXgorSZTt9ddea8sQ3V1I8ZWE8ZWz8qiUEhUVzfS0mK6+vk00mI0UVPTxL0L03j2/45w5FwZU9PjqK5uxG6/9pkYW01YbXaqqhp7nEpYkmjfd2/bORgM4Z1iw9eVqSfx6My7X13CYrOTkRqF0Wjq8XNsNlpQufE5OtveWzHoTjx6+3Grqe+ydT0vjsf7Tpe1n2OFQuLfbhrDc/93lL9+fIZ7bk6jxWjqMdYkCeobjS7FIgx8PAL9jkVX9PT9bTVb+fxwPja7zK1ThmEzm2lpMaFWKbBplHyy62Kn+IbeY7Tjv529t6+YAQUtLaYBi393H7tbB/e0vcXa9jlE61RIEpRUNBIdpu702fQnFn06iDIrK4tHHnmEn/70p9x9990kJCRQVVXV/npFRQXx8fHdnq+srCQ+Pt6XRetk3+lSLhTWM2l0LKEhfedUog+6f1ITw1k4NZnLxQ00NJvF7XE9aGwx89WxYmaNTxAzTvqYJHVvGh89LIK5ExPZcaSQ0upmP5Us+LW0WvjV2ydobLYwd2IicVEh3boV+tOVKbpB+xaqVZFi0FPdYPLqfn2WQJSWlvLDH/6QV199lWXLlgEwefJkcnNzyc/Px2azsXnzZubPn09ycjJarZasrCwAPvzwQ+bPn++ronXSarby9peXiI0MITXR9ds2Rf99/6xcMBqdtm3inoZm7wb1YLHjaCFmi4075o70d1EGtd76yVfdlIZGreAfOy6K2489kFfWwPN/yyKvrJHZ1yWQdHX2VFF3Dry05EhqGlq9Gsc+68J46623MJlMvPTSS+3P3Xvvvbz00ks8/vjjmEwmFixYwJIlSwB49dVXWbt2Lc3NzWRmZvLQQw/5qmjAtQE6xy5U0WqyMXdCotvdJoL7HKdYF6JmzoQEdh4v4ctjJVyfkUB8tM7ptu7Ge7Auxdzx720xWdh6qJCUeD0phjBxS6GP9fSDFhmm4WvzRvOvLy4SE9F92e+hrLfvZ5PRwl82n2P/mTKi9FqevHcKl0t8M53yYOXNnyNJArssY7XJNDQ7n2nVEz5LINauXcvatWudvvbxxx93ey4jI4ONGzf6qjidgt1xS1FlnZG80kbunDsSjVrhdPuhyhd/v6PrR5Zh0czhRIdrmTcpkX2ny1nzx4OMHhZBakI4RrOVkUnh7dvedv1wlxMCx+RgC6YE11LMjpiEtnPz+02nsdnt7fM+CAPP8R24ZUYyu0+VkJVTyQO3jUWj8t2y48GiY7x2/H62tFo5dK6cCwV1IEmMGx7FD1dMJFynFgmEG3xxu2r01QS42ovrvQzehR466Brs0NZ1cfBcOeE6NXfMTWX74cL27R0/dCqlAmkIztXpy1ufzFZbpx/2YXFhLJ+bCsDpyzUcyi6npdXKsQuVzMyI92jBqGBrHnWcZ0e5LxbVc76gjrHDI4nUi7EP/qBWKdh/pozmqwPUHlo8ll/+/Tgf7snl6wvT/F28gND1e5Zb2sAbH56hpr6V9OGRpKdEEqpVEa5Ti1spPeDt21UjdGo0aoVXF4wbEgkEOAv2RhpbLCyYmuT0isJstWGX5Q6jdocWX/8Idwx0XYiKW6cPZ/kNowCZD/ZcYf/pMvadKWPBlGE+LYe/OVpMbpra9nfa7TL/99l5wkJUXDcy2s+lG9oc45xkGcaNiCYtOYJthwqYNDqWjFTx2XR0+ko1//3BacJ1batpJsSEtidfEHxJfTDq6xxLkkRsRIhXB1IOwevrttaHc3m1GKJCSI4L83dxhpzesmVJkoiJCGHRrOHoQlQcPFtOQ7N54As5gDp+8S8U1VNU2czMzARUqiH59RwQ7l4NSxJMG2cgPjqUv3x6jpZW7/UjB7vzBbX8btNpEmN0rHtoOvHRof4uktCD2AgtjS0Wmozeid8hWUNtP1yIyWJjSlqsGDg5QLqe5r6yZa1ayQ0TEjBb7PxtW86QGAHfbLSQnVfLtLFxjEi4dkeQCFHvcreLzrG9WqXg0Tuvo77JzB8+OovNbu/7zYNcY4uZ1945SXxUCE/eO5VIvRhoGshiI0MAyC1p8Mr+hlwC0Wq2su1IIUmxOuKiRKY8EDwdUxGl1zJxTAxZOZUczanwTeEChCzLHL9YhSTBA4vGtj/f3/EoIvlwzt0mdcf2Y5IjeHDxWM7k1vB/W3OG1FoZXaeWNplt7DnZllj99N4p6EPFKrGBLia8LcHz1roYQy6B2H2ilJZWa6fR7aKS9T1P+0AzRkQRG6Flw7YLNLQM3q6MI+crKasxct3IGGIjQjq95um5E+sAeJdjcLXZYufOuSPZe6qUv2w+h9VmH/Tn2DEQ3bHwmF2WOXK+glazlQVTkojpErNCYFKpFESGabgsEgj32e0y244UMG54VHtTjqhkA5tCITF7QgKtZisbBukVn9li4587LhCl1zAmOcK7+xaD17zKMbByxYLRTBtr4ODZcl579wRfZg3+WWk7Tp732cECymqMTBtrEMlDkImN1HKltAG7vf916ZBKIAormqhpMDEyKVysKT8AvFWhRum1rFgwhqwLlfxzx4VBkUR0PDenr9RQ32Rm2tg4t5Y2FvwrIzWK7y7P5GJhPR/tzaWpZWgMrLxYVMf7u66QYggjLaUt4R3sydNgEhcZitFko7Ciqd/7GjIJhCzLXCisJzkujIQYMfbB17zdsrNk5nAWzxzOl8eKeeFvWXx1vJj6ZnNQDq503LopSVBa3cyFwjrGjbjWKiYq4+Bxw4REnrxvCkazla2HCr3WNByobHaZX793CkNUSPty3GqVQqxnE8C6jl2Ju1rP5BTW9XvfQyaBKK8xUt9s5vbZI8SdFwPEmy07kiTx9YVpPHJ7BkaTlb9tzWHHkSI+3JPHixuyOHq+IqiSCce5efuLS6iUCianxwKiSy0YZYyIZunsVNQqBS//8zhnrlT7u0g+IcsyB86W02q28YMVE1B3uM1YtOIGJmcLP+pCVKQlR4ouDHfkFNYRqlEy+7oEfxdF8JAkScyfPIwXvjuLFx+dxfUZBkYlhdNktPDGh2d444MzWIKoIjt1uZpTl6uZnBZLqPbanG6iMg4ejqu7SL2GxbNSSIzR8eZHZwbl6p3nC+ooq25hxjgDqQmuL/kskmH/crbw49MPTmPJrBH93veQSCCulDRQWdc2vapKOST+5EHJUVlLkkRSbBipieFMSY/j+e/M4t8Wto2R+M17p7ySWfua3S7zr88vkhAdKmY1DFJdr+5CNCp+vGoSapWSV/55fFAlgicvVXE2t5bUBH37uAdXiBa1wOStVvgh8Wv62cF81CoFo5K8O8JdGDhdbyPrSKGQWDo7lVmZ8ZzNq+X4xUr/FNINFwrrKKtp4d5b0lGKgZNBq+vVXWxkCI/dmUl9s5n3vrrsx5J5T0lVM3/8+CxReg0zM+Pd/vEZTImU0NmgTyDKa1rIyqlkzLAI1CqFyISDmLOmOAdJguHxesaNiCKnoJ6snMBNIhpazJy+UsOEUTFMTov1d3EED/RWj2SOjGHciCi+yCoK2vEQjr+vss7I6++eQK1UcMOExPYWXFGPCjAEEoithwtQKhWkpUSI5rQA13W0cF/bdvy343OdkhZLdLiWv20977X53r1t487LWG127rs1XQzoDUKu1CPXZxiI0mv4ny3ZQbduhuMuoSsl9bz0j2O0mm385J4p6ELaxun09Pe78/0VBodBnUBU1BnZe6qU+ZOTCNG0Bb9oTgtMCgXdRgv3tm3XCszxuSoUEnMmJNDcauWfOy74sMSeuVRcz56TpWSMiGaYWMgtaPVVjyiVCuZNTqK+2cyrb58Iqh9WWZY5eamKX/79GEqFxFP3TWVkYudBk13/fmej/YXBb1AnEB/uuYJSIZEYqxNBHcAcVzQWW+9dFF237Ul0uJa75o3iaE4FVlvgLHhks9v5+/YcosO1TBgd4+/iCD7Q8So8LjKE8alR5JU1cuxC4HapddRstPCbjac4frGKFEMYv/jW9YxICHep/uyti1EYnFR9bxKcjCYrB8+Ws3RO2/3ZwXR731DUW8XTtcnUlUpq+dyR3DQ1OaDuuvlkXx4F5U384O4J1DWZ/F0cwcscV+EqpQLpatiNT42mpKqF//vsPGOGRRIRpvFvIXtxubiO1989RavZxqzMeMYkR6ALaVsgS3T9Cs4ETu3qZSEaJU/eO4W7bxzl76IIXuDulY0kEVCrAxZWNPHJ/jxumJDI9Rnx/i6O4CNdr8IVConrM+JpMdn41xcX/ViyntllmS0H83jx78eQFHDb9cmMHxmNJEntLSqiZUFwJqBaID755BPefPNNLBYLjzzyCA888IDH+5IkicyRMSJrHoIcU+vOnzyMQJmcUq2SGJUYQXKcDsWgTdsFZyL1Gh5fOZGahlZ/F6Wbijoj/7slm/MFdYxMDGdK+rVJzZy1qAhCRwGTQJSXl/P666/z/vvvo9FouPfee5k1axZpaWn92q9IIIamQLtiSooNY9q4OFQdumNEbA4dk8bEBkwyC9BqtrLtcCGfHcpHqZD45tIMbHY7RpO103Zmqw27LKPVKP1UUiGQBUxeuX//fmbPnk1UVBQ6nY7FixezdevWfu2z4+19wuAVbJ+v2WoTo9aHmED7jD89kM9He3OZODqWF747C5VSIVaCFdwWMC0QFRUVGAyG9sfx8fGcOnXK5fc7gr/jl0CSIDREhQ4VGpUSpUKBRt2WM/ny8UAeq6fHYToNdpsyIMvm7bKculJLlF6LVn3tdavNjkIhdbrqG8gKsms8ShJEh4egUl4rv9Fk9fu5G6jPtad4DISyDcTj45eqmZYe57d47Hq8pXNSSYzTE6KWyCtvujrHQ+/1ZKDHmDuP7UhYNcqALJuvz5ssg1IpYe9wg5qnsRgwCYSzlRTdmWQnOrrtnvrYWH2n52+brXe2uTAEdY0NX3IWj3felD5gxxcC30DGY9fjxQIjksUaLEIbT2MxYLowEhISqKqqan9cUVFBfLwYrS4IgiAIgShgEogbbriBAwcOUFNTg9FoZPv27cyfP9/fxRIEQRAEwYmA6cJISEjgJz/5CQ899BAWi4VVq1YxadIkfxdLEARBEAQnJNnZ4ANBEARBEIReBEwXhiAIgiAIwUMkEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEIIgCIIguE0kEEHqu9/9LpcuXRrw465fv56bb76Z119/fcCPLQQmf8Ti6dOn+dGPfjSgxxT8x1/1XU/WrFnDW2+91es277//Po899phb+y0qKmLq1Kn9KdqAUvm7AIJn/vznP/vluO+88w47d+4kMTHRL8cXAo8/YnHixIn89re/HfDjCv7hr/pO6J1IIAbYoUOHeOGFF9DpdLS0tLBx40b27t3Lm2++icViISQkhNWrVzN16lSMRiPr16/n5MmThIeHk5aWBsBLL73EzTffzG9+8xsmTpzIO++8w4YNG1AoFMTFxbFu3TpGjRrFmjVr0Ov15OTkUFZWxujRo3nttdcICwvrVKbnn3+eI0eOdHpOo9Hw3nvvdXru/vvvR5Zlvvvd77J+/Xr+3//7f0yaNImcnByeeOIJbrvtNt+ePMGrgjkWDx06xHPPPcfmzZtZs2YNkiRx+fJlampqmDt3LmvXrkWtVvv2BAp9CuYYA/jTn/7Exo0bCQsLY8aMGXzxxRd8+eWXnbY5evQor7zyCkajEbVazX/8x38wf/58ACorK/n2t79NRUUFycnJPPfccxgMBk6cOMF//dd/YTabqays5IYbbuDFF1/05qkfGLIwoA4ePChnZGTIRUVFsizLcm5urnzHHXfINTU1sizL8oULF+S5c+fKzc3N8quvvio/8cQTss1mkxsbG+Xly5fLq1evlmVZlhcuXCifOnVK3r9/v3zrrbfK1dXVsizL8qZNm+Tbb79dttvt8urVq+V77rlHNplMstlslr/2ta/JGzdu7Ff5x44d236shQsXyr///e/7tT/Bf4I5Fg8ePCgvW7ZMlmVZXr16tfy1r31Nbmpqkk0mk/zAAw/IGzZs6M+pEbwkmGNs9+7d8uLFi+X6+nrZbrfLP/vZz+SFCxfKstwWc3/5y1/kmpoaec6cOfKJEyfa/56ZM2fKBQUF8qZNm+QpU6bIeXl5sizL8q9+9Sv5xz/+sSzLsvyTn/xEPnjwoCzLstzU1CTPmjVLPn36tFxYWChPmTLF4zIPNNEC4QdJSUkkJycDsG/fPioqKnjkkUfaX5ckiYKCAnbt2sXPfvYzFAoFer2eu+++m5ycnE772rNnD0uXLiUmJgaAFStW8MILL1BUVATAjTfeiEajAWDs2LHU19d3K487GXlXM2bMcP0PFwLOYInFu+++u/1K86677uKLL77gG9/4hhtnQvCVYI2xXbt2sWTJEiIiIgB44IEHOHjwYKdtTp06xYgRI5g8eTIA6enpTJs2jcOHDyNJEjfccAOpqakArFq1ilWrVgFtrSq7d+/mD3/4A1euXKG1tZWWlhaioqJcO6kBQiQQfqDT6dr/bbfbmTNnDr/+9a/bnystLSU+Ph6VSoUsy+3PKxTdx7x2fL3jc1arFYCQkJD25yVJcrr92rVrPfo7oPPfIgSfwRKLSqWy0zGdlU/wj2CNsa7l6RhjHf+ensqjVqu7xaVK1faT+8ADD5CRkcGNN97I7bffzsmTJ52WNdCJb5mfzZ49m3379nH58mWgLeu98847MZlMLFiwgE2bNmG32zEajWzevBlJkjq9f968eWzZsoWamhoANm3aRFRUVHvWKwiuCuZY/OyzzzCbzZhMJj744AMWLlzo82MK7gumGFuwYAHbt2+nsbERgI0bN3bbZvLkyeTm5nLq1CkALl68yJEjR5g5cybQNgakpKQEgH/961/Mnz+f+vp6zpw5w5NPPsmiRYsoLy+noKDAaTIS6EQLhJ+lp6fz7LPP8sQTT7RnqG+++SY6nY7HHnuMZ599luXLlxMeHk5sbGynDBtg7ty5PPLIIzz88MPY7XZiYmL44x//KK7ABLcFcyyGhIRw//3309DQwOLFi1m5cqXPjyn8//buO77N8lz8/+fRsLy3PGI7zh7OcgYhIZAQIAOSkJKEUXpKW+iBLtpyCt9CSOBAC20pLb+u9LSUck4pHSGQECAkhBVCpuMMx7Gz43gPecVTtqTn94exY9myLdmSJdnX+/XiRTQe+dajS7eu556u86cYmz9/PnfddRd33303gYGBjB8/nqCgILvnREdH85vf/Iaf/OQnNDc3oygKP/vZzxg9ejTHjh1jwoQJrF+/HpPJxJgxY3j22WeJiIjgwQcf5I477iAyMpKoqChmzZrF5cuXSUlJcfv78CRF9cd2k2HivffeIzQ0lEWLFmGz2Xj44YdZsGAB9957r7eLJoYZX4vFjz76iP/5n//hjTfe4PHHH2f8+PE88MADXimLcA9fi7GTJ09y7Ngx7rvvPgBeffVVTpw4Ydf9MtzJZaoPGz9+PH/84x9ZvXo1K1euJC4ujjvvvNPbxRLDkC/F4t///neeeuqpjgFpYmjwpRgDGD16NEeOHGHlypWsWrWKAwcO8MQTT3itPL5IWiCEEEII4TJpgRBCCCGEyySBEEIIIYTLJIEQQgghhMuGzDTOysp6IiKCqK5u9HZRfEJUVLCci066ng+jMcyjf0/i0Z7Eo73BjkeLxSrnvxOJx6sGEotDqgVCp+u+UthwJefCnjfOh3wGV8m5sDfY50POvz05H1cN5FwMqQRCCCGEEINDEgghhBBCuEwSCCGEEEK4TBIIIYQQQrhMEgghhBBCuEwSCCGEEEK4TBIIIYQQQrhMEgghhBBCuEwSCCGEEEK4TBIIIYQQQrhMEgghhBBCuEwSCCGEEEK4TBIIIYQQQrhMEgghhBBCuMyjCcTbb7/NihUrWLFiBb/4xS8AyM3NZe3atSxbtownn3wSi8UCQHFxMV/5yldYvnw53/72t2loaPBk0YQQQggxAB5LIJqamnjuued47bXXePvttzly5Aj79+/nscceY+PGjezatQtVVdm8eTMAzzzzDPfeey87d+5k6tSpbNq0yVNFE0IIIcQAeSyBsFqt2Gw2mpqasFgsWCwWdDodzc3NpKenA7BmzRp27txJa2srGRkZLFu2zO5+IYQQQvgmnadeODQ0lB/84AfceuutBAYGMnfuXPR6PUajseM5RqORsrIyqqurCQ0NRafT2d3vipiY0C+ODXPfm/Bzci7sDeb5kHjsTs6FvcE+H3L+7cn5uKq/58JjCcTp06d58803+eSTTwgLC+PRRx9l37593Z6nKAqqqjq83xWVlfXExIRSUVHX7zIPJUZjmJyLTrqeD09XHhKP9iQe7Q12PAJy/juReLxqILHosS6Mzz//nPnz5xMTE0NAQABr1qzh0KFDmEymjudUVFQQFxdHdHQ09fX1WK1Wu/uFEEII4Zs8lkBMmjSJ/fv309jYiKqqfPzxx8ydOxeDwUBmZiYA27ZtY+HChej1eubMmcOOHTvs7hdCCCGEb/JYF8b1119PTk4Oa9asQa/XM23aNB588EGWLFnChg0baGhoIC0tjfvuuw+Ap59+mscff5w//vGPJCYm8utf/9pTRRNCCCHEACmqowEIfkj6nO1JH589GQPhXRKP9mQMhHdJPF7lk2MghBBCCDF0SQIhhBBCCJdJAiGEEEIIl0kCIYQQQgiXSQIhhBBCCJdJAuHHXFysUwghhHAbSSD8lKLAnuPFkkQIIYTwCkkg/FiLxertIgghhBimJIEQQgghhMskgRBCCCGEyySBEEJ4hYzfEcK/SQLh56QSFv5IBgEL4f8kgfBjep2m10pYKmfhy2QQsBD+TRIIL1AU9/2491YJd00uJKEQQgjhLpJADDJFgd0ZBezOKPD4D3rn5EKajIUQQriTztsFGI681XQrTcZCCCHcRVogfJi0FgghhPBVkkD4KOlyEN4g8SaEcJYkED5MuhzEYJKkVQjhCo8mEB9//DFr1qxh+fLl/PSnPwVg//79rFq1iqVLl/LSSy91PDc3N5e1a9eybNkynnzySSwWiyeL5pekYheeJkmrEMJZHksgCgoKePrpp9m0aRPvvPMOOTk57Nmzh/Xr17Np0yZ27NhBdnY2e/bsAeCxxx5j48aN7Nq1C1VV2bx5s6eK5pfk6lAIIRxz59R44TyPJRC7d+/mtttuIyEhAb1ez0svvURQUBCpqamkpKSg0+lYtWoVO3fupKioiObmZtLT0wFYs2YNO3fu9FTR/JZcHQohhqPekgNHU+PbEwpJKjzLY9M4L1++jF6v54EHHqCiooLFixczfvx4jEZjx3Pi4uIoKyujvLzc7n6j0UhZWZmniiaEEMJPtLe+Lkofgao6fk7XNW92ZxSg12nQaBQWzuj5ODEwHksgrFYrR44c4bXXXiM4OJjvfOc7BAUFdXueoiioDj5dxcXUMSYmFACjMax/BR5EwcEGAGJjey9rcJDB7jldbwcFGrBYbT2+Tl/HDzeDGRv+FI+deTJGup4LicfBfe/+Foud6fS6XmOla52qC9Ch02lQUHo8zp/Ph7v191x4LIGIjY1l/vz5REdHA3DzzTezc+dOtFptx3PKy8uJi4sjPj4ek8nUcX9FRQVxcXEu/b3KynpiYkKpqKhzzxvwEEWBxkYzACZTXY+ZsaJAY5O54zmObjc1m7FYbQ5fx2gM6/P44ZSVG41hdrHh6crDX+Kxs64x4spxuzMKAFhyTYrDY7ue//7+raFisOMR8JtY7Nri0FesdK5TKyvr2HeylKYmM63athaInupHfzkfnjaQWPTYGIjFixfz+eefc+XKFaxWK3v37mX58uVcunSJy5cvY7Vaeffdd1m4cCFJSUkYDAYyMzMB2LZtGwsXLvRU0YY9GZAp3K3FYqXFYpWYEm4xkPFevjZWbCiPxfBYC8SMGTP45je/yb333ktraysLFizgy1/+MmPGjOHhhx/GbDazaNEili9fDsCLL77Ihg0baGhoIC0tjfvuu89TRRP43pdM+L/23WHd1ec83FrJxMD52g+1M61z/syje2GsW7eOdevW2d03f/58tm/f3u25kyZNYsuWLZ4sjhDCw9yVmDozcE4SDNGZXqdh38lSn0sihvLFmlNdGOvXr+9238MPP+z2wgjPG8rNaWJo6a3ilW444UhfP9ZS/7lXry0QTz/9NGVlZWRmZlJVVdVxv8Vi4eLFix4vnHAvvU7D7owCdFqNfImE32iP1a6tDUP5yk5c5a66SlFgf3YpFquNNTdPdM+LDnO9JhDr1q3j3LlznDlzhmXLlnXcr9VqmTlzpscLJ9yvxWLFpqpoNJJBCN+nKPDB4aHbhyx6pyi41C2hqirVdWbqG1sxRgZ1q+daLFYsVpvDv9N2/EBLPLz0mkBMmzaNadOmcd1115GQkDBYZRK9aGy2kHmmgosltVTUNJESF9rr86WfWPg7aWkY3pz9/C1WG5+fLKWgrB6AsGA9C6b1/bs11Ac6epJTgyjz8/N57LHHqK2ttVv06Z133vFYwYazrv10La1WTlww8dnxYjZ/fB6LVSUkUEdTi5VTl6qxWFXuWjwOndZ+SEv7qHhjZBAnzpu+SDhCuj1PDC+SVIqhKCO3gsKyemZNiEWv05B9sYrdGYVMSIkkfZyx12PdmaQOp9YMpxKIZ599lrVr15KWlubyCpHCNZ2XYa1rbOHVHblknC6nyWwlMEDL4plJzE2LZ+yIcD7MLOTY2Qo+PFLIifMmnvraNYQE6Tteq9ViY//JUvJK69BpNVisNnIvV3Pz7GQiQgO8+C6Ftzgzu0EIf1NS2UheaR3p42OYPi6GhqZWRsQG89nxEv7wVjbf+dJUZk3sPYlwRl/Jd9fWjKHOqQRCr9fzjW98w9NlEV8wt1rIulDDqbxq9DoN10yM47ppCRRU1LPsmpEdq7PpdRqunRJPeEgAGbkV/PRvR/j+uukkxoRw5nIV7+2/zJWGFu64YTS3zhvJ9n15fJBRwOdZJdw6b6S336bwkv5cbQ2nqyoxuAbaImZTVU5eqCQsWM+0sTEd9wcG6LhpdhKZZ0xs2pbNd9dMHXA5nUm+h1OXm1Nt2ePHj+fMmTOeLougrdXgwKkysi9Vk5oQxgvfvo4HVqaRNioajaI4HEw0Mj6UhemJNDRbeOqVwzzxpwM8+tu9WKw2bpqdxO3Xj0av0xITEciciUZq6lvIyat2a7mlYWrocrTboRDu0N/puJ27eY+cLudKYyvTxkSj7TJoMkCv5Ud3pzMyPow/vJXdMT6iv4ZTcuAMp1ogCgoKWLt2LSNGjMBgMHTcL2MgeudqZm2x2ti0LZtiUyOzJsQyKTWSiJCAjtfovNJfV7ERgfzkm3P563unCdBrWHJtKlaLpdso5CRjCClxIZy8WEVlbTPR4YEDeYuANIsPB+7uI5ZERLRzNbY6dxPcPCeZtz/PIzxYT0q84wHlwYE6fnT3DH717+N8eqyIG9ITO15H6quBcSqBeOSRRzxdjiHH1R9Vi9XG/7ydzfFzJmZNiGXiyEiH4016+7JFhRmYNjaaJXNSiI0NY8uHpx1OWZo5IZYiUz7b9l7k/hVp3coNrn+xJDMfPgZS8XYe4yNJhOiv9vomI7ecYlMD16bFoekloIID9Tx6TzpP/TWDvSdKCQvJIjxQ59Q4BUk0euZUF8aECRMc/id65+yPqs2m8vI7ORw9a2Lu5DjGJoV7tFwhgXompkSw72QpheVXm/QG2lQtPwhDnztWgGzfeEuIgbDaVN7cc4Gk2BCSjSF9Pj84UM+Sa5IJCdTxUUY+tQ3mPo+RFU9751QCMW/ePObPn9/x//nz53P77bd7umzDgk1VO2Za3H3TOCaPirJ73FOBO2VMNIEGHVv2XLC7v7+Ve3v3inzRhj758Re+4HxhLRU1zdxz8zinZwca9Fpump3UMdasawuto5eReO+ZUwnE6dOnyc3N5fTp02RlZfHiiy+yevVqT5dtyFNVldd3n2Vfdilfun40y6+1nxnhyR9lg17LyutSybpQyZl89wyolC+aEKKdJ8e61NSbycmrZvrYGKaOien7gE6iwgzMn5ZI1RUzOw5e7rhfLoJc5/KKQnq9nhUrVrBv3z5PlGdY+eRoEZ8cLWL5tSNZtWDUoGa/ep2GwAAtUWEG3vj0gt0CYY44KltvXzT5Evom+VzEYHDnzJ3Sykb2ZZXyh7dO8rddZ/jH7rPsOVaCXqfhG7dN6tdrjh4RQUpcCNs/zyO/rK7jfrkIco1TCURNTU3Hf9XV1ezdu5crV654umxDWnWdmX98eI7pY2NYd+NYNBpl0LeitakqX7phNBeLr5B5pqLHv+2oH7C3vkHpN/RNru4rIER/tMeXO8a6fHaimF2HCyipbKTY1MCR0+V8cqyImAgDi2eOIDLU0PeL9CB9fCwhQXr+8m4uNpuMkuwPp2ZhzJs3D0VROq5SY2JiePLJJz1asKFMVVWOnTMRoNfwn6vSOkYPt1isg165Xz8tkQ8OF7D5k/M0NLf2OOLYUUXQW+Ugmbxvks9FeFL7xcONM0d0u79zveLMzIbSqkZe23WGEbHB3DAjkeVzU784RrXreugvg17L15ZP5HdvniQ0SEf6+NgBv+Zw41QCcfr0aU+XY1i5VFJH1RUz102LJ/SLpae9dVWo0Sh87dZJ/Ozvmew5UcycibHA1cIMtB9TVjAUYnjpmqQqCnx67OqU9s5T3Hvzr4/OodNqWDAtEZ2287Rf91WWsyYYuW5qAgeyS0mJD8UYGdSv1xmurXpOJRA2m41XXnmFzz77DIvFwoIFC/jWt76FTufU4aKT9v0oYiMCGZUQhkYDuw59MS/eS3tcjU+OYOb4WI6eNREUoGXK6Ghg4HP2ZSvm4cHcYqXVYkOvk03ahGNdk4q+WsIqa5s5cb6S2RONhAS1/c7szihAVWHpXPfuMfGVJeM5draCg6fKuK0fS/x3XtjK3WXzdU5lAL/61a84ffo0X/va17DZbPz73//mhRdeYP369Z4u35CzP7uUJrOVa9PiCNBr2XeylFarFRUVQ4DWa+WaOiaausZWci/XoNdpWDG/7f4WS1vZuq5o6SxpMvdPzrQcXSiq5Z39eWRdqATAGBnINZPjPFIOMbzk5FUToNcwZkRYx30tFmu/LkLaY6inWAoO1DN7kpHPs0o5caGKZXNTXf4bw7WecyqB2Lt3L2+++SZ6fVtz+4033sjtt98uCYSLrDYb7x24TFRoAAnRwYBntpHt37EK86fF02i2kHWhit0ZBcNiNznRXdeWo67Kq5v45T+PkZNXTWiQnpXXpXKx+AoXi6/wweFCrpkYR2rCwBdD0+s0HYM+pfVq+CitbKSgvJ60UZHotANr1dLrNOzPLqWhqbWtG6SHl0uIDmZcUji5edWcOG9i+ljPjYcYSt26Tn06qqp2JA8AAQEBdrd784tf/ILHH38cgNzcXNauXcuyZct48sknsVgsABQXF/OVr3yF5cuX8+1vf5uGhgZX34dfyDhdTnl1E5NSo9y+LXrnyrYrZ8cxaBSFeWlxjIgN5h8fnuN9NwxUEv5HUbqPoLdYbBRW1PP8a5l8eKSQwvJ67rppHC98ez5rF40lbVQUy+amEKDX8Ns3T1Jb3/cqf85wR4Ite2/4l91HCtAoittW5G2P5b5iaeaEWKLDDPxx2ynOFdYA/Y+bVouNYlMDLa3dx4MMpY3pnEogJk2axPPPP09+fj75+fk8//zzTi1lfeDAAbZu3dpx+7HHHmPjxo3s2rULVVXZvHkzAM888wz33nsvO3fuZOrUqWzatKmfb8d32VSV9w5cZkRsCCNigz3yNxx9QfQ6DbszCvjkaJFzSYRGYV5aPHMnx7H5kwvkOrFr57nCGvaeKOHTY8XsO1nS55oSwnfpdRr2ZpWQX1bHoZxy/vvVDB79wz7e3pfHwVPlVF4xc83kOG6dNxKdRiHIcLURMzhQx6L0ETQ0t/Kn7ad8YmpcXxW2JBe+paG5lc9PljBmRBiBAZ4fY9f5s9dpNdw4awQRoQG88I9jbPn0PNv2XqKhuRVzS/dEwFHcXGlo4VBOGW9/nseTLx/i4d/sJftild13YSgt5e5UAvH0009z5coV7rnnHu666y6qq6vZuHFjr8fU1NTw0ksv8a1vfQuAoqIimpubSU9PB2DNmjXs3LmT1tZWMjIyWLZsmd39Q82JcyaKKhpYeV2q21sf+uJqwGo0Cg/dPoXrpiZwKq+arPOV3ZKCtmZlla2fXeT5145SVNFAbX0Lf3k3l/99/7QkEX7KYrXx/sF8DudWUFHTRHhwABNHRjExJZIbpifwy2/PJ21UFDZVdRhT0eEG7ls2kdP5NWzfd8nh32hptVJR00ST2dJrWVotNk5fruHYWRMXiq70+fye9BT/Q+1qcCj47HgxLa22bkv692Qgn5ujlSfDQwK4ZU4ysycaef9gPtv2XuLtvXl861d7+K/f7yPzTAUVNY0O46akspENfzlMUUUjE1IieHBVGjPHGzmdX8OhnHKstu4bG/q7XlO8lpYWNm7cyJIlS/j5z38OwIMPPohWqyU01PHWqe2eeuopHnnkEUpKSgAoLy/HaDR2PG40GikrK6O6uprQ0NCOGR3t97sqJib0i+PD+njm4AkOMhAbG4aqquz4eyaJMSHo9XqCg1X0Og16bdugSRuaHm/rdDZiY9veU1CgAYvV/nZfz+/6uDPPBYiLC+fHX5vLj377GafyqrGqsPbmSQTo28r4zmcXOHCqnLySK9xyzcgv+rxVahta2PLxOW5ITyImJozg4LbXbf873jSYseGL8QjdY6gzVVU5lFNOUUUD105JYMLISO66ZSIAb3x0FgCjMZygwErULzqT218nONjQNlsHhdWLxnKxtJ539ucxKy2B2ZPiAQgND+Lfu8/w7oF8bDYVBWhsUbl/1RS7lgyAS8W1vLMvj7rGtr5ri9XG069m8P++OodJqW2zhNq/X33pLQZ1X1zleiM+Bzs2BuvvBQcZ7L77MTFh3T6rzrfb/22x2vjkWBHTx8WSFBdBo7m1W73Y8XpfxNuh0yZuWzC647YzdSpAo7mV4GADCord67XXixu/OZ+qK828/v5pmlosjEoM53xhDYeyS3niT4cYlxzJ9PGxxMaGcaWhhRMXqjhXUMOoxHAWzUwiKjyQVTeOZ9WN43n2LwfJyC1jy2d5fHfdDJ+qE9v1NzZ6TSB++9vfUl9fz8yZMzvu+8lPfsIzzzzD7373ux63+X7jjTdITExk/vz5vPXWWwAOr0g7L07V9X5XVVbWExMTSkVFXd9PHgSKAo1NZkymOk6cN3GhsJZv3DaJ+sZmmppbadVqOmZdNDX1fvtfO3M6BgC1WmyYTG3vsanZjKLYP7/98djYMIePO3pu19dSVTrunzEmCr0WcvOqefjFT7hpVhJWm8rbey/R1GLhnpvHsfSaFPacKMZitXHbtcmcPF/BwewSzl6qoLGxrS/cZKpzOGjI1W3P+8toDLOLDU9Xpr4Wj9B2rpuazVisNoefx5HT5ZwvrGXmhFhGxgXT1NTSEQftn2NlZR1NzWaamu3jpLHRjF6nQaNRMJnquHPhGM7lV/Pcq4dZOT+V2OgQ3vjoLDX1LaTGhzIqMYzSqiZ2Hcjj6OkyHrp9CqMT2/q8M8+U85d3c9FqFJZfm0JokA5TjZnsS1U88Yd9/PDO6UwZHd3x/eotbhSFHmNQo+n5MU8b7HgEBiUW2+u9yso6u5jp/Fl1rhvh6r8PnCrFVNvMvUvGU9tgdlhPqiodr63XaWjWKHa3na1TQUNjoxmNg+M714uJMYFYrDZuSk/kpvREEqICqa1v4ZNjRZwtqGb/iWLKa5qwWGxMSIng0S+n88HhAhobr76/1PgQausj2HkgjzEJoXbnpfP6GN4ykFjsNYH49NNP2bJlC4GBgR33xcfH88ILL3D33Xf3mEDs2LGDiooKVq9eTW1tLY2NjSiKgslk6nhORUUFcXFxREdHU19fj9VqRavVdtw/VKiqyvZ9ecREBHLd1AQ+P1ni8mu0WKzYVO9M81QUhfRxscRHBXMmv4a/f9B2JRodbmBheiLL5l6dN63XafjoSCFTRkVxvrCWbXsvER/V98IsQ6U/0J+1tFr598fniAozMG1sNE3N/esuaGcI0PLoPem88l4uW/e2dWWMTgznu3dM40xBNXqdhuS4UL50w2hefieH51/L5ObZyTSaLXyeVcKYEeEdawA0NLUSGxnI01+/hp//4yi/e/Mk6786q9vfdGW2RudlvaW3zfssVhvbPrtEkjGEGWNj+Syr2NtFstN+TRtk0LH6+tEE6DRcLLlCaJCetC+6WyJCAwjQOa6jp46OptVi4//eP81Ns5I66nJ/r/t6TSD0er1d8tAuNDSUgICAHo979dVXO/791ltvcfjwYX72s5+xcuVKMjMzmT17Ntu2bWPhwoXo9XrmzJnDjh07WLVqVcf9Q8WRMxVcLL7C3ElxfrfQTueGoCRjCPctm4ipphmNRiHrogmrg0FyLRYrhgAt45Mj2JdVytJrkgkNdm7GjvCeXYfzqbxiZtnclI6l1cG1H+WuwoID+OGdM6iqayYiIhiN1YpGo3Cm4OrA3MmpUTxz/1xe23WGDzIK0GkVbpmTzF2Lx7Ivu9Tu9UKD9fzo7nSe/d8M/rjtFDfMSLQrp6stWf5eeQ8lnx4rorymiR/eOaPfa854SvtA9M6rYYYG65k+NoYV80cB8N6BPIfHtj/fEKBlxrhY3tmXR/alKmZPNDp8vr/p9RdNo9FQX1/f7f76+vqOKZiuePHFF/nZz37GrbfeSlNTE/fddx/QNkhz8+bN3HbbbRw5coQf/vCHLr+2L2q1WPnH7rNEhgaQHBfi7eI41NNoYkcbLymKQmxkEDERgX12M6WNjkKnVch101bhwnNq683sOJjP7IlGEmKuzhByx/bGigJHz7R1aXX9YWh//dAgPd9aPZXf//AG/vDIQu69ZQL6Hq7kIkMNPLhqCmVVjRw7Z7J7TBIC/1TX2MKbey6SNiqKaWOiXT5+oANgnTm+PzMnutahIUE6llyTwqWSOiprm/tVDl/TawvEypUr2bBhA88//zzBwW0VS2NjIxs2bGDp0qVO/YE1a9awZs0aoG066JYtW7o9Jykpiddee83VsvsER1do7T/KWReqqK1vYfGsET6XVUOXzNpBKjmQCjnIoOP66Yl8eqyoX5WCGDxb917CYrVx1+Kx5Fy2T/iciYHednFtX1NCZ3E8Ar3z6wcHOtdSNSk1ipvnJPPhkULyy+pIifOdwWjCNa0WK/uzy1AUhW/cOtnl8W+9rX8zGMdD7z/8Xb8/q68fxafHijh+zsRXl1/94RissWDu1msLxNe+9jXCwsJYsGABd911F+vWrWPBggWEh4fz3e9+d7DK6LN62uZ6d0YBWz65wNn8GhbPSiI6vHs3kK/w5JzkJdckY1PhYrHvDCQU9gor6tmbVcy4pAhy8qpdrkh72iJcUdqWbd+f7Vrl7MxzFQXuuGE0Br2Gf354zrUCC7fr74+vzabyp+05VNY2c/9tk4iJ6F89OdD6ayDHd43/vs5FkEHHtDHRVNe3cDin3G3l8JZeWyA0Gg0/+clPeOihh8jJyUGj0TBt2jTi4+MHq3w+z9GHbm61sP9UKYYALWtvHMMnR4u8UDL3c7U/PDEmhPjoIC4WX8FitaHV+NcYkKHOZlP5284zBBt0TBgZ0TF+pTeOEoWeKj5Xt6d3ZpfGzhu8TR0TQ+aZCs4W1DBxZKTzf0i4TX+vnK02G//z9ikyz1Qwa0IscybF+c2Vd9eYbo//rkuv9xT7I+NDOVtQy5t7LnDT7GQPl9aznKrRk5OTWbp0KbfccoskD064WNy2XfesibGEONks62u6Bn9/+8PHJ0XQ3GIlI7fcL/v4hrIPMgo4X1TLvUsmYND3PcOna3NvT82/A1nd0ZmrsPZWs3HJ4YQF63m3hwFsrpDY7D9Xr5xNtU18dKSIjNPl3H3TOCalOrdolC/oq8vDUTLRlaIoTB0TTUVNMxeKaj1YWs+TS0I3q603k32xivjoIEYleKZv1tPL7/YU/P1pYouPDiIsSM/uIwV8esy55bSF510urePNPReYNcHI/CnOXxT0tS2zq0unD4ROq2HpNSlkX6wir+RKv1/HHYNFhWM2m0p5dROllY3sOV7E33aeZv2fD1Hb0MJ3vjSV5de6vn22tzlbD/b2vIToICaNjCT7YhXNLQObMu1Nnl9sfJj510fnsdpszJlo9MiS1X0NfHQXd/XHKYrCuOQIjp0zMS55aG6S5m9aLW3Nx+EhAXz91kluj9PBXLfkplnJ7DiYz7sHLg+oG8Mf+599laqqnDhvYm9WCaaaZt767OqS5jqtwrwpCUSHG9y+9bs/URSFu24ax7P/e4QPDhdw+/WjvV2kfpEEwo1yL1dzMKeMyamRhIf0vE7GQHlzYan+SE0I5UxBDWfya7xdFAEcO2uirKqRR788k9CgvrvYfPnKPDhQx82zk3lvfx4J0Z7ZpE445qgltLrOzCdHiymtaiTYoGN0YhgL00dwubSO5deOJCo0EK1WYfeRgl5ft6+/OxSMGRFOsjGEnYcLuLmHsRC+vtCZdGG4idVm458fniU2IpBJMqDLjk6rYVH6CArK6x3OfxaDp6bOzPnCWm6Zk8xkJ/qe3THNzVn9/eFYMicZvV5DTl6V+wslHGofzNq5qyqv9Ar//dfDVNQ08R9LJ7D82hTSx8eycMYI4qODiY0I6nM6e0+zetoNZjwOhuljY2g2W9h5OL/bY45m+fkaSSDc5LMTxRRWNHD3TePQar17Wn0x4G75IsP+6Gihl0syvB07Z0Kr1bBqwSinjxmM5n1nfjgcVaaK0rbi5Y3pSeSV1lFR0+Txsoo2naeAXyq5wi/+cQy9TsPya1O4ZU5yR7Lgan3UV7wNpe6myDADcyfHsTuj0OFYCF9/r5JAuEFTSyubPz5PXFQQcyZ5d4nS9jES731+0euJROe/HxMRSLIxlD3Hi/160JA/K61qJL+snsmpkYQFe66Lrb+c+eHoHFOdk4rl145EQeHd/XmeLaTopsls4Rf/OEZ4sJ4n75tDRKih47Gh1mLgCatvGE2LxUpOnv+t2isJxADpdRr+8k4u5lYb08ZEe2TgpKtaLFZard7de95RxTF5VBSNzRY+PCKtEN6w72QJCjAuOcLbRekXRzHVnnREhRkYnxLB3hMlXCzu/4wM4RqrTeXTY8Woqsp/3Z1OVJih23N6Sgw7j6HwgWrTaxJjQlgwNYFzBbVU15m9XRyXSAIxQC2tVk5erCIpNtjhl2eocWUKaeeKQ1EgNiKQ9HGxvH8on/qmVg+VUHTW/lnZbCr7TpYywhhCcKDO41OBeyqLsytN9jXP3pHpY6OJCA3gf98/TUtr9+epqsrx8yZ+9a9jPPqHfXx8tEjicICyLlRSdcXM/CnxLg1i7TzdN0A/vKfRKkpbK4RNVf2uBU0SiAHKvVxNq8XG5FF9D0jz9y9If+f4d75yXLNoDM1mC29/fqnvA13g7+fWEzoPwsq9XE11nZlxSREdn+PujIJBO296ncappa0Hso6EXqfl67dOoqiinj9sze5IDqw2lYKyev771Qx+uyWL0qomxiVHUlrZyMdHi2kc4Nblw1VtfQtn8muYkBJBclyoy8d3HkPh63397uJogb7dGQVkX6xibFI4e44XY6r1n3E8kkAMQJPZQk5eNSlxoT22PvS1ap+/6e/eGe3HjIwPZfGsJD7OLOzW1OzqVXH78/1htLK3tJ/34+dNBOg0HbvCenIPlN7K4spKk/0xY1ws9y2fyKlLVTy6aR8b/nKI7Z/ncSi3nJZWGw+smMzPHprHt1ZP4dZ5I7FYbRw5Xd73Cws77a05eq2GWUNka2pP622BvhaLtaML/J19eV4pX3/IOhAD8OGRQlpabcwYH+Pw8a4BM1yy7J60Z9sx4QYiwwy88l4OT319DtCWBHxwuG1u+JJrUvqc+9w+jQxg6dyUYX9ue6OqKlkXTExOjULn5RlCg2FRehJjR0Tw6fEiqq6YCQrQkhATzDdum4zC1do7KsxAWmoUJy9WDXj2hq/P13e3I2cqqKhpZs5EI4EBWlp72G11qBvIDJOuxwYH6lk8awQfHSnitnmpJMQ47hJqP84X4m3o1yYe0mS2sOtwPsnGEGJ72UVOftjstVisqMA3V06mtKqR/3v/DOoX3wRXrzy9cRXtj8qqmqioaWb6WMeJ7lCUHBfKfyydyA/unE76+FgSooPROKjtxyWFo9dqOJvf/z0JhlsLmNVm481PLxAeomdsUri3i+M1A2lVdnSsXqchOjwQnU7h7X2Ou3g1Gga9+7E3kkD008dHC2lotjB9nH2l7Asfqj9IGxXNl64fzcGcMs4XOR41742BfkPRiQsmAJ9NINz9OXfu2up6f1c6nYbRI8LIL6ujtr7/I+Bd3XnUnx08VUZZdRNTRkX3uTBUZ/70fXa2rAO5gHF0rE6rcMvsZA6dKqOoor5bmfadLKXV6jsXTpJA9ENjcyvvH8xnxrgYjJFBHfcPlXEOg2XlglFMHRNN5ukKLpfW2T3W3kXhK5m2r3Lm3GRdqGREbAixnWLVV7h78632uPn0WBGfnbjaKtC+ZoRG0/2cjU+OwKbC3qySfv/d4bIhl8Vq4519eYyMD2VErOuzLvzh+zyYG8I5cuu1qRgCtLy552JH62w7X0kc2kkC0Q8fZBTQaLZwxw1juj3max+wp/X3qkJR4KMjhUwZFYUhQMMft53C0qUfVbooeudM03mrxcaZ/Bqmj/HN1gcY+OfcNQbbX6/ra6qoDn/EwkMCMEYGsu9kabcK2xVDPVYVBQ6cKqW8pok7bhjj8po3/vR99mZZQ4P1rJifyrFzJi6V1PX4PF/YM0QSCBfVN7XyQUYBsycaSfXQdt3+YqCZeovFikajcN20BMqqGjl+vtL9hRzi+qrkSqsasdpUpvlo98VAuRqDPf0wjBkRTmlVY68V9nCmKLDzUD6bPz7PqIQwZoxzfzz5UxeHp916bSqTRkaSkVvOhaLu43P6avEarHE5Hk0gfv/737NixQpWrFjBCy+8AMD+/ftZtWoVS5cu5aWXXup4bm5uLmvXrmXZsmU8+eSTWCy+OTd7y6fnMbda+ZKfbr/qbu7I1BOig1lxXSp5pXUUdun3EwNTbGogMEDLeD9dfdIZ7ojBsUnhaDUK+7N77sYYzj9uigLnCmtoaLZwx0LXWx/64u1uA1+j0Sg8tHoKQQYdL/7rONmXul9c+cKeIR5LIPbv38/nn3/O1q1b2bZtG6dOneLdd99l/fr1bNq0iR07dpCdnc2ePXsAeOyxx9i4cSO7du1CVVU2b97sqaK5pHMwn75czWcnSlg2dyRJRtcXThE9+9INo4kKDeDoGdOABrOJq1RVpdjUwJTR0b1O35QKGwL0WpLjQjicW47FwTLwGs3wmmkB9stMf3a8mNOXa4gOMzBtTLRH/p4/dXF4UntLTGSogVvmJBEdHsiv/32C13adcbjCqjd5LIEwGo08/vjjBAQEoNfrGTt2LHl5eaSmppKSkoJOp2PVqlXs3LmToqIimpubSU9PB2DNmjXs3LnTU0VzqKdR2+2VRmVtM3/afoqwYD1fukFaH9xNp9VwzeQ4LFaVV98/3a0vejhV3M5wprk3v6yeJrOV9HGxvb7OcBz46+j9jk4Mp76plZMXKrs9t330+3DRtQk853I1jWYLaaOjfGK/n6Gq62DT4EA9T319DjfPTuaTo0Vs/ewSl0vrBjRWx508lkCMHz++IyHIy8tjx44dKIqC0Xh11bK4uDjKysooLy+3u99oNFJWVuaponXTecS/pssZabFYMdU08evNx2mxWFk4IxGDXiv9dR4QHhLAtDHRnDhfaTcifrjNs+9Le7z21dx7/Hzf0zcVZegP/uuqp9lSiTHBhAfr2Z9d2u2YrudoOMRi+3u2WG2cvFhJdLiB+Ki2mTyO3r/Uie7RtSXGoNfylSUT2Pj1OYQG6TmcW8GhnHKsNu8nER5fifLcuXM89NBD/PjHP0an03Hpkv0CGYqiOMymXM1yY2LauhSMxv4NbNQF6NDrNBw6beK2BW0tDKqqUlrdzE/+lonVprLxgXlcLKwlNjaMbXvOo9dqCApsW8Lahga9ToNeq7X7d9fHBus2QKDBd8sWExNGcLABvU6DgtJxe/oEIzYU/vXROZbPH0VYcAAxMWHo9DpiY+0/2+Bgw9XXCjJ0e7yr/sZGfww0HvuiC9Ch02nQo/T4vk/lVRMXFcSY1KsJRNd4PXK2iqBAA6rivs/ZlXh05+3OceTodueyabRaArU6u9fS6WzcOCeFHfsuERhisNvyPCjQgMVq6zjXO/Zd6qgnHD3ujMGMx/78vfbv1EcZ+TQ0Wbg2LYGQkEBiY8P44OBlu/MaaIC92WXotRqCgwzdzjvYf/cHGnMDjZlGc2tb+TxQz7m1juxUrxmNYaytMXMgu4Ss8yZsKtyzLA1tD2txOFMntuv372a/jnJSZmYm3//+91m/fj0rVqzg8OHDmEymjsfLy8uJi4sjPj7e7v6Kigri4uJc+luVlfXExIRSUeH6KGpFgcZGM3qdBmuAlrc+OsP0sTH83/tnOH7eRGpCGNNGR3PmogmLzUZlZR21V5rQ6zQYAto+9KamVlq1bbc7/7vrY4N1Wx8WRLPZ7JNlU1WorKzrOOcajdJxG+C+ZePZ+JfD7D1WxKL0xLbHmsyYTHUdy7e2f2Ztn333x7syGsPsYsPTlfdA4rEvneNVo1Ecvu+aejPnCmqYPjam43FFgaZmc9v/v/hcrF8MVnbn5+xKPLrzduc46hpX7d/V3l6r1WJj1tgYtn92kfc/v8jimUkd57up2YzFasNkavs8a+uaup3X9sedaV0e7HgEXIpFRYHGJjPl5Vf41wdniAozEB2mp7HRTFVVHVfqm2hqtj+XDU2t6Bx8Do6++03N5m7HDzTGXLkNGhobzR6p59xaR3aq1xQFms0tTEwJR6eBo2dN/O5fRztahTpvAdD++TkTjwOJRY91YZSUlPDd736XF198kRUrVgAwY8YMLl26xOXLl7Farbz77rssXLiQpKQkDAYDmZmZAGzbto2FCxd6qmh9KiivZ+NfDpOTV8XMCbE89fXZBBq0Hc1K0kznWcbIIL6yZAKm2mbOFfZ/ieHh7OCpti7AkfGhwypeB/peR8aHkmwM5eOjhdh8pJ/ZmzLPVFBa1cj0sTEoiuKWxfKGUzx6ysSRkUxKjeSjzEIuFNd6rRvSYy0Qr7zyCmazmZ///Ocd991zzz38/Oc/5+GHH8ZsNrNo0SKWL18OwIsvvsiGDRtoaGggLS2N++67z1NF61VJZSOfHC0iLiqIh9fOIvtSFdpOAyNktUnPap/fvHBGIu8fvEz2xSryy2RuvitUVWXfyRLGJoUTExH4xfkc4e1ieZx7ftwUbps3kj+/k8OhnDLmT0lwXwH9jKqqvLM/j4ToYEYmhNJsbmutGsiPldSfzutrKfaZ42NptdjIPFNBdA+7QXuaxxKIDRs2sGHDBoePbd++vdt9kyZNYsuWLZ4qjlMamlv59GgRoUF6Hv/KLMJDAsi+VNXtecNt0Nlga9tXQGHWRCMfHinkN29ksTB96P8AukteaR1Fpga+tnwiMLzi1R2DHedOjmf3kUL++eE5JiRHEhvZ82Z5Q1lZVRMF5fXcv2KSywP2ejvvwyke+6trouVotpRGo/Ct1VNY/6dDHMwpZ82isXYXu4NBVqL8gqqqHMguw2pTWZieSHjI1QFUki17R2CAlgXTEqhrbOWzEyWYW6ULCXp//4oCuw7nY9BrmTs5zu7+4Xbe+nu1q9EofHPlZKw2G7/4x1GOnzP5zLS5wXQ6v4bwYD3z0uJdOk5aGdyja6LlKPGKiwpi9sRYquvMbPn0wmAVrYMkEF8oLG+goqaZuWlxhIcEdFS48mUYHD0110WFGXjw9jQqa5v5xetHqak3D+spnb2t26AosPnj82TklnPLnGSCA/XA8F7lr79Xu4kxITx6z0wUBX6zJYutn13i+DkTBeX1wyKZKKtupNjUwOJZSQTotX0f0IW0Mnhe+29TSnwoY5PC2XW4gIzT5YNaBo9P4/QHrRYb2ZeqiAwNYFxyBBpFYXdGATqtBkUjXwZP65qkdb6tqjBnUhwLZyRyMKeMx/90AGNEEHklV8gvq0Or1ZA2qm1lvPbjh3r93nl+/pUrrVwoqiVAr8VU28THR4uIjQxkxfzUbsfYVLXTKHTRl9GJ4Tz3n/M4lFPGjoOXyblUzVOvHGZEbAjR4QYmp0aRGB2CVju0sjJFgdd2nUWjgFajtCWecqnpk9rrghlfrPXy1/dyGRETTHLc4KyUPCwTiPYfpnafHC2iodnCjTNHoPniV0gq3MHVV3Ndclwoz85L5b2Dl8k6X8nRcybMLVZaLTae/d8jzJ5oRKHtc+08nWmoulRSR9b5SlosNnYdLui4PzRIzyN3zSAwYFh+td1Op9Vw/fRELDYbdY0thBj0HM4tJ/tiFdkXD6PVKMSEB6LVKiREBzN3Urzd+hH+mNQ2Nls4k1/N6BHhaLUKLRar1IM+TqNR+O4d03jm1Qx+syWLDV+bMyh/d9jVMu2rGi5KH4GqQmNzK9v3XSI+KojEGOf3txeDLz46mPtvm8zuIwUsvSaFd/fn0WqxUVLZyOHccgIDNCTGhHi7mB6Xe7maI6criIsMZMqYaLQaDROSIzEEaCmurCcxJsSvfrB8VecBbABBBh03zU7mptnJbN93ichQA2VVTVTUNnG+sJZj50yczj/IQ7enMX1sbMeKoeBfSe3erGIsVpXJo6K8XRThgqgwA99fN51fvH6U376RxZzJxr4PGqBhl0CA/dXtewcu09hsYf4U1wYKiYFxpS++t+fqdRr+c1Ua5wprOXrWxLK5QQMvnJc4c7WaX1ZHRm45I2KDuWF6IkGBOlotNq75YsBkWXXjIJR06FMU2J9dSmNza0dXZmdBBh0LZ4zoWODn0+NFmGqbyL5Yze/ePMmPvzKLCSkRftf9abOpfJRZSFxUELERgTQ0tXq7SMIFoxPDefD2KfzhrZO0Wm0smzsSBc91sQ3rnq3K2mZ2Hylk/tQEIr00j3Y4cmVgqjPP1es0zJ0cR5PZSl6Jf64Z0Xk/lp7ea6vFxsvv5BAYoOWaSUY0PSxhK9yjfU8CZ5OAyFAD/+/edKLDDfxxWzYfZRb63aDVrAuVVNQ0MzlVWh/81eyJRu65ZRwF5fUen5kxrBOIrXsvArB20Rgvl2T4ceXKrK/nKgokxAQTE27gXGEtNh/YZKY/+vqx2r7vEoUVDcyfmtCvkfHCs/Q6DftPljJ7gpErDS0cyhm8DQHd5cPMAqLDDIyMH5xBeKJvrkzBbr8Q0Ws1jE+J4P2D+ew57rnZV8M2gcgvq+NAdilLr0nmdH6N310piDadWygmpETQ0Gzh2DlT3wf6mQvFtew4eJnrpyeSMkgjrIXrWixWQoP13DQ7iXMFtdTWt3i7SE5RFCg21ZOTV82oxLAhN7PEX7kyBbs90WixWGm12pgz0cjUMdG89sFZ3vz0gkd+44ZlAqGqKv/48BwhQXpWzE/1u35KYa/98xsRG0KwQccnRwu9XKKB6/xlb26x8Mq7uUSFGfjyzeO9VyjhtNXXj0av13DyYveVbH1N+8DyDzML0WgURiUM7i6honfOdKPpdRr2Z5eyP/tqd2/7gmhajcKBU6Ue2dtlWCYQRaYGzuTXMDk1ipAgvbeLI9xEo1FITQglJ68aU22Tt4vTb+0Vett0Y5X/23mGsupGHrhtMsGBzo17Ho4rT7pDX+fN2fMaGqQnbVQ0JZWN1NSZ3VdAD6lramH/yVJGxoXKlE0/5SjRiAw1MGNcDJVXzBzILnX73xx2CYS5xcqR0xVEhgYwMn7oT/kbbkYltl09fZ5V4uWSDEyLxYqqqry55yKHcsqYOT6WtNHRTh3b3g86HFeeHIj25uLdGQU42lLA1ebkyamR6LQKp/NrPFJedzqTX0OLxcb45AhvF0W4WWp8KFFhBt7cc5GWVqtb64Rhl0Bs3XuRxmYLM8fHokjtOuSEBOpJGx3FvpMlfjuYEsDcauXP7+Sw4+Blbpw5gokjI50+tr0fVLrmXNdisaKi9jjzx9nm5H0nSzEEaBmXFEFhRQMllQ0eKvHAtS0cVcPsCUYiQgP6PkD4jbYWM6Vjv4xNW0/2OtPLVcMqgThbUMMHhwuYOPLqDnuSQ/gPZ5qP9ToNsRFBVF4xk3u5enAK5kaqqnI4t5z39l8mI7ec1deP5r5lE1EUxen3L3u3DNxAk6/248enRKDVKOw4cNkdxfKIDzMLaLXYuP36Ud4uinCjznWBMTKI1IRQTuVVU9vgvi61YbOQVHOLhVfeyyE2MpDZE420WqxS2fqR9uZjnVbT5+eVGBtESKCOvVnFTHGy2d8XtLRayTxjosjUQHSYgR/8x3RS4sI6NnXrvD9Lr68jLQ8+IzBAy+jEMA6cKuP260cTG+FbC501Nlv44HABI2JDSE0I4+TFSm8XSbhR57pg9kQj+WVtM23cZVi0QKiqyt92nsFU08wDKyaj111921LZ+g9nm+W1Gg3zpyZw9GwFrRbbIJRs4EqrGvjwSBHFlQ3cuXgMS+emMDLefjS8dEv4pwkpkQDsPJTv3YI48M7+SzQ2W5g+1n8SbdE/4SEBjEuK4FJJHQXl9W55zSGfQCgKfJhZyMGcMtYsGsPEkbLC2nCw6rpRrFk4Fp0fzGcvKK/jmVePYLWpLJmTTFRYoN08fGkh813OdCsFB+pYNjeFKw2+tSZEaVUDH2QUMDYpgpiIQG8XRwyCtFGR6HUaNn983i3b0g/pLgxFgX9+eI6PMgtJig0hQNd387fwD319juEhASy/duTgFGYAzhfW8tIbJ9BqFG6YkUBUWGBHK0P7j5N0s/kmjQZ2HSpAr+u7W+nOxeN8alCv1WbjlXdz0Wk1TB0jF1XDRYBeS1pqFCcuVHL8vImZ4we24daQTiDOFdbwUWYhI2JDmDUhllarfzRni94NlbErOXlV/PbNLKLCDFw/PZHOW1u4MuZBDL72GGy1ts3acGbtBF+a9fXJ0SIuFF9h7uQ4ggxD+mdAdDE2KRwVaGiyDPi1hmzktFqs/H9vZBESqGNR+gjMrRa/2U5X9M3fxwJknqngT9tPER8dxI3pSej1SredD1ssVmyqcz9OYvD5cwxGhRm4Y+FotLIh27Cj0Sg8ctcMt/we+tS1zTvvvMNtt93GkiVLeP311wf0WhqNwuoFo1k6NwWdTr4kwjeYW6xs/vg8f9h6kpS4UB6/d5Zb4lNWnhSumDMpjtiIIIkZMSA+0wJRVlbGSy+9xFtvvUVAQAD33HMP1157LePGjevX62k1GpbOTeHT40V298sXRniD1WZjd0Yhu48UUF1n5saZI/jyzePdsqumdHeI/ujagiJ1o3CVzyQQ+/fvZ968eURGRgKwbNkydu7cyfe+9z2njtd80RSn0diPXo8INaAAATotWo2GrIvVRIYaMOjbbgfo22rc9sfdcdudr9Xf2yHBAdisWp8sm6fLotGAVqtg6zLkRTOIzbVd4/F8YR2fHi9i4shIxiZHEhsRSNAX+1p0jdH+nLsms8WnP9ee4tEXyubp26rq/Xjs+vfa68bO9WDWxWqiwwN96tx5qiw2FCwBWp8sm6fPm6N47G8s+kwCUV5ejtF4dURoXFwcWVlZTh8fFdW2r0VMjP1Wx0vmydbHok3X2PCkrvE4PyaU+TOTHT5XYnR4Gsx4dPT3JO5Eu/7Gos80eDqak+pLo5aFEEIIcZXPJBDx8fGYTKaO2+Xl5cTFxXmxREIIIYToic8kENdddx0HDhygqqqKpqYmPvjgAxYuXOjtYgkhhBDCAZ8ZAxEfH88jjzzCfffdR2trK+vWrWP69OneLpYQQgghHFBUdyyILYQQQohhxWe6MIQQQgjhPySBEEIIIYTLJIEQQgghhMskgRBCCCGEy/wygehr063c3FzWrl3LsmXLePLJJ7FYBr5tqa/q61z8/ve/Z/HixaxevZrVq1cPeJMyX1dfX8/KlSspLCzs9pgn4kJi0Z7Eoz2JR++SeLTn9nhU/Uxpaam6ePFitbq6Wm1oaFBXrVqlnjt3zu45K1asUI8dO6aqqqo+8cQT6uuvv+6FknqeM+fioYceUo8ePeqlEg6u48ePqytXrlSnTJmiFhQUdHvc3XEhsWhP4tGexKN3STza80Q8+l0LROdNt4KDgzs23WpXVFREc3Mz6enpAKxZs8bu8aGkr3MBkJ2dzcsvv8yqVat49tlnMZvNXiqt523evJmnn37a4QqmnogLiUV7Eo/2JB69S+LRnifi0e8SCEebbpWVlfX4uNFotHt8KOnrXDQ0NDB58mR+/OMfs3XrVq5cucKmTZu8UdRB8dxzzzFnzhyHj3kiLiQW7Uk82pN49C6JR3ueiEe/SyDUPjbd6uvxoaSv9xoSEsLLL79MamoqOp2O+++/nz179gxmEX2GJ+JCYtGexKPzJB49T+LRef2NDb9LIPradKvr4xUVFUN2U66+zkVxcTFbtmzpuK2qKjqdz6xePqg8ERcSi/YkHp0n8eh5Eo/O629s+F0C0demW0lJSRgMBjIzMwHYtm3bkN2Uq69zERgYyC9/+UsKCgpQVZXXX3+dJUuWeLHE3uOJuJBYtCfx6DyJR8+TeHRev2NjYOM6vWP79u3qihUr1KVLl6p//vOfVVVV1W9+85tqVlaWqqqqmpubq65du1Zdvny5+l//9V+q2Wz2ZnE9qq9zsXPnzo7HH3/88SF9LtotXry4Y5Sxp+NCYtGexGN3Eo/eI/HYnTvjUTbTEkIIIYTL/K4LQwghhBDeJwmEEEIIIVwmCYQQQgghXCYJhBBCCCFcJgmEEEIIIVwmCYQfe/zxx3nllVe8XQwhAIlH4TskFgeHJBBCCCGEcNnwXLfTR/3oRz8iLS2NBx54AIB//vOfHDhwgLi4OE6cOEFDQwOqqvLTn/6U2bNn2x174cIFnnvuOWpqarBarXz1q19l3bp1HDp0iJdeeomUlBTOnTtHS0sLTz31FPPmzaOhoYGf/vSnHD16FK1Wyy233MIjjzxCa2srL774IhkZGVitVtLS0tiwYQOhoaHeOC3CSyQeha+QWPRN0gLhQ+688062bdvWcfutt95i4sSJlJeX8+9//5sdO3Zwxx138PLLL9sdZ7FY+P73v8+PfvQj3nrrLf7+97/z17/+lePHjwOQlZXF/fffz7Zt21i3bh2///3vAfjtb3+L2Wxmx44dbNu2jaNHj3L48GH+/Oc/o9Vqeeutt9i+fTtxcXG8+OKLg3UahI+QeBS+QmLRN0kLhA+59tprMZvNnDx5kqCgIKqqqvjOd77DpUuX+Ne//kVBQQGHDh0iJCTE7ri8vDzy8/NZv359x33Nzc3k5OQwduxYRowYweTJkwFIS0tj69atAOzfv58nnngCrVaLVqvl73//OwC//OUvqaurY//+/QC0trYSExMzGKdA+BCJR+ErJBZ9kyQQPkRRFNatW8fbb7+NXq9n3bp17Nmzh+eee45vfOMb3HzzzYwZM4bt27fbHWe1WgkPD+ftt9/uuM9kMhEWFsbx48cJDAy0+xvtq5frdDq7LVtLSkoIDAzEZrOxfv16Fi1aBEBDQwNms9mTb134IIlH4SskFn2TdGH4mDvuuIOPP/6YXbt2sWbNGvbt28fixYu59957mTZtGh9++CFWq9XumNGjR2MwGDq+JCUlJaxcuZLs7Oxe/9b8+fPZunUrNpuNlpYWvv/975ORkcH111/P66+/TktLCzabjY0bN/LrX//aY+9Z+C6JR+ErJBZ9jyQQPsZoNJKWlsaECROIj4/nnnvuISMjg1WrVnH33XeTkpJCYWEhNput45iAgAA2bdrEli1bWLVqFffffz8/+MEPug0m6up73/seer2e1atX86UvfYlFixaxdOlSvvOd75CUlMQdd9zBbbfdhqqqPP74455+68IHSTwKXyGx6HtkN04hhBBCuExaIIQQQgjhMkkghBBCCOEySSCEEEII4TJJIIQQQgjhMkkghBBCCOEySSCEEEII4TJJIIQQQgjhMkkghBBCCOGy/x8DcRIoutDKBAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 540x540 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 27
    },
    {
      "id": "fifty-quarter",
      "cell_type": "markdown",
      "source": "* We proceed to construct QQ charts, one per region.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "flexible-budget",
      "cell_type": "code",
      "source": "fig = plt.figure(figsize=(10, 8))\nplt.rc('axes', edgecolor='gainsboro')\nplt.subplots_adjust(hspace=.5)\nfor i, region in enumerate(valence_by_region['region']):\n    ax = fig.add_subplot(3, 3, (i+1))\n    ax.set_facecolor('gainsboro')\n    ax.grid('on', color='w')\n    sm.graphics.qqplot(all_charts.loc[all_charts['region'] == region, 'valence'], \n                       line='s', ax=ax, linewidth=1, markersize=2)\n    if i < 6:\n        plt.xlabel(\"\")\n    if i % 3 != 0:\n        plt.ylabel(\"\")\n    plt.title(region)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "tags": [
          "remove_input"
        ]
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": 28
    },
    {
      "id": "mexican-insertion",
      "cell_type": "markdown",
      "source": "* Another condition that the data must meet is that the groups have the same variance.\n\n* We can check this directly.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "described-advance",
      "cell_type": "code",
      "source": "all_charts[['region', 'valence']].groupby('region').var()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>valence</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>au</th>\n",
              "      <td>0.043604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>br</th>\n",
              "      <td>0.044162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0.043102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>de</th>\n",
              "      <td>0.042368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr</th>\n",
              "      <td>0.044110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gb</th>\n",
              "      <td>0.047708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>global</th>\n",
              "      <td>0.046950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jp</th>\n",
              "      <td>0.041302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>us</th>\n",
              "      <td>0.042434</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         valence\n",
              "region          \n",
              "au      0.043604\n",
              "br      0.044162\n",
              "ca      0.043102\n",
              "de      0.042368\n",
              "fr      0.044110\n",
              "gb      0.047708\n",
              "global  0.046950\n",
              "jp      0.041302\n",
              "us      0.042434"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 29
    },
    {
      "id": "light-camcorder",
      "cell_type": "markdown",
      "source": "* Having established the above, we proceed to the ANOVA.\n\n* We can use the `f_oneway()` function of the SciPy library.\n\n* This function takes as parameters the groups we want to compare.\n\n* We will need to manage our data a bit first so that we have a table-line for each group.\n\n* We start by putting the valence of each piece in a separate column per area.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "adverse-provider",
      "cell_type": "code",
      "source": "region_valence = all_charts.pivot_table(values='valence', \n                                        index=all_charts.index, \n                                        columns='region', \n                                        aggfunc='first')\nregion_valence",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "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>region</th>\n",
              "      <th>au</th>\n",
              "      <th>br</th>\n",
              "      <th>ca</th>\n",
              "      <th>de</th>\n",
              "      <th>fr</th>\n",
              "      <th>gb</th>\n",
              "      <th>global</th>\n",
              "      <th>jp</th>\n",
              "      <th>us</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>NaN</td>\n",
              "      <td>0.119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>NaN</td>\n",
              "      <td>0.291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>NaN</td>\n",
              "      <td>0.376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>NaN</td>\n",
              "      <td>0.699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>NaN</td>\n",
              "      <td>0.357</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273595</th>\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>0.782</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273596</th>\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>0.356</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273597</th>\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>0.232</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273598</th>\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>0.351</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273599</th>\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>0.557</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273600 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "region  au  br  ca  de  fr  gb  global  jp     us\n",
              "0      NaN NaN NaN NaN NaN NaN     NaN NaN  0.119\n",
              "1      NaN NaN NaN NaN NaN NaN     NaN NaN  0.291\n",
              "2      NaN NaN NaN NaN NaN NaN     NaN NaN  0.376\n",
              "3      NaN NaN NaN NaN NaN NaN     NaN NaN  0.699\n",
              "4      NaN NaN NaN NaN NaN NaN     NaN NaN  0.357\n",
              "...     ..  ..  ..  ..  ..  ..     ...  ..    ...\n",
              "273595 NaN NaN NaN NaN NaN NaN   0.782 NaN    NaN\n",
              "273596 NaN NaN NaN NaN NaN NaN   0.356 NaN    NaN\n",
              "273597 NaN NaN NaN NaN NaN NaN   0.232 NaN    NaN\n",
              "273598 NaN NaN NaN NaN NaN NaN   0.351 NaN    NaN\n",
              "273599 NaN NaN NaN NaN NaN NaN   0.557 NaN    NaN\n",
              "\n",
              "[273600 rows x 9 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 30
    },
    {
      "id": "efficient-advocate",
      "cell_type": "markdown",
      "source": "* We break the `DataFrame` into individual columns to get one-dimensional arrays.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "accepted-training",
      "cell_type": "code",
      "source": "region_valence_groups = np.split(region_valence.values, region_valence.shape[1], axis=1)\nfor rvc in region_valence_groups:\n    print(rvc.shape)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n",
            "(273600, 1)\n"
          ]
        }
      ],
      "execution_count": 31
    },
    {
      "id": "three-package",
      "cell_type": "markdown",
      "source": "* We know there are `NaN`s, so we clean them up.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "pointed-bryan",
      "cell_type": "code",
      "source": "region_valence_groups = [ c[~np.isnan(c)] for c in region_valence_groups ]\nfor rvc in region_valence_groups:\n    print(rvc.shape, rvc)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(31200,) [0.813 0.632 0.811 ... 0.789 0.193 0.404]\n",
            "(31200,) [0.637 0.962 0.705 ... 0.583 0.506 0.781]\n",
            "(31200,) [0.931 0.219 0.175 ... 0.419 0.689 0.111]\n",
            "(31200,) [0.746 0.693 0.119 ... 0.62  0.344 0.552]\n",
            "(31200,) [0.371 0.535 0.319 ... 0.28  0.333 0.558]\n",
            "(31200,) [0.813 0.811 0.966 ... 0.268 0.51  0.863]\n",
            "(30800,) [0.931 0.471 0.535 ... 0.232 0.351 0.557]\n",
            "(24400,) [0.931 0.292 0.327 ... 0.571 0.894 0.41 ]\n",
            "(31200,) [0.119 0.291 0.376 ... 0.357 0.274 0.447]\n"
          ]
        }
      ],
      "execution_count": 32
    },
    {
      "id": "chicken-kitty",
      "cell_type": "markdown",
      "source": "* We can now pass the groups to the `f_oneway()` function.\n\n* We see that the value of the F metric is high, so the differences in mean valence values ​​are unlikely to be due to chance.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "finished-oliver",
      "cell_type": "code",
      "source": "stats.f_oneway(*region_valence_groups)",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "F_onewayResult(statistic=1527.2713324700505, pvalue=0.0)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 33
    },
    {
      "id": "expired-liechtenstein",
      "cell_type": "markdown",
      "source": "* ANOVA can also be used by creating a linear model with statsmodels (as when doing regression).\n\n* This is actually easier than `f_oneway()` as we don't need to adjust our data at all.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "periodic-tumor",
      "cell_type": "code",
      "source": "region_valence_mod = smf.ols('valence ~ C(region)', data=all_charts)\nregion_valence_res = region_valence_mod.fit()",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [],
      "execution_count": 34
    },
    {
      "id": "killing-criminal",
      "cell_type": "markdown",
      "source": "* From the linear model we can get the ANOVA matrix using the `anova_lm()` function.\n\n* This gives us a more complete picture of the results.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "healthy-pastor",
      "cell_type": "code",
      "source": "anova_table = sm.stats.anova_lm(region_valence_res)\nanova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 35
    },
    {
      "id": "controlling-elite",
      "cell_type": "markdown",
      "source": "* How is the table interpreted?\n\n* Remember that in ANOVA we want to compare the variance within groups relative to the variance between groups.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "severe-class",
      "cell_type": "markdown",
      "source": "* To estimate the variation between groups, we start by calculating the sum of the squares of the difference of the mean of each group $i$ from the overall mean:\n\n$$ (\\bar{x_i} - \\bar{x})^2 $$\n\n* Then we multiply each sum by the number of elements of each group:\n\n$$ n_i(\\bar{x_i} - \\bar{x})^2 $$\n\n* Then we add the sums for all our groups, $k$:\n\n$$ \\sum_{i=1}^{k} n_i(\\bar{x_i} - \\bar{x})^2 $$",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "brave-indian",
      "cell_type": "markdown",
      "source": "* To estimate the variation between groups, we start by calculating the sum of the squares of the difference of the mean of each group $i$ from the overall mean:\n\n$$ (\\bar{x_i} - \\bar{x})^2 $$\n\n* Then we multiply each sum by the number of elements of each group:\n\n$$ n_i(\\bar{x_i} - \\bar{x})^2 $$\n\n* Then we add the sums for all our groups, even $k$:\n\n$$ \\sum_{i=1}^{k} n_i(\\bar{x_i} - \\bar{x})^2 $$",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "indonesian-chile",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 36
    },
    {
      "id": "juvenile-committee",
      "cell_type": "markdown",
      "source": "* We divide the `sum_sq` we have found by the corresponding *degrees of freedom*.\n\n* Degrees of freedom measure how many of the entities we manipulate are free or independent of each other.\n\n* Here we work with group averages and overall average.\n\n* If we have $k$ groups and we know the averages of $k - 1$ groups and the general average, from them we can also find the other average that we don't know.\n\n* So for $k$ groups $k-1$ averages can vary freely. The other mean is derived from $k-1$ and the general term.\n\n* So for $k$ groups we have $k - 1$ degrees of freedom.\n\n* This displays the `df` column in the first row of the ANOVA table.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "finite-guess",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 37
    },
    {
      "id": "streaming-dominican",
      "cell_type": "markdown",
      "source": "* We divide `sum_sq` by `df` in the first line, so we get the mean sum of squares, `mean_sq`.\n\n* `mean_sq` in the first line is the estimate of the variance between groups.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "pleased-treatment",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 38
    },
    {
      "id": "ceramic-disposition",
      "cell_type": "markdown",
      "source": "* We proceed to variation within groups.\n\n* To estimate it, we start with the squares of the difference of each observation $j$ from each group $i$ , $x_{ij}$ , from the group mean.\n\n$$ (x_{ij} - \\bar{x_i})^2 $$\n\n* Then we add the squares:\n\n$$ \\sum_{i=1}^{k}\\sum_{j=1}^{n_i} (x_{ij} - \\bar{x_i})^2 $$\n\n\n* This appears in the `sum_sq' column in the second row of the ANOVA table and is called the *inside groups sum of squares*.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "nonprofit-specialist",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 39
    },
    {
      "id": "twelve-camera",
      "cell_type": "markdown",
      "source": "* We divide it within the `sum_sq` groups again by the corresponding degrees of freedom.\n\n* In each group, each observation can be derived from the remaining observations and the mean, so the degrees of freedom of each group are $n_i - 1$.\n\n* So if we have $k$ groups, the degrees of freedom within the groups are $n - k$, where $n$ is our total number of observations.\n\n* This displays the `df` column in the second row of the ANOVA table.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "cleared-naples",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 40
    },
    {
      "id": "polyphonic-extent",
      "cell_type": "markdown",
      "source": "* We divide `sum_sq` by `df` on the second line, so we get the mean sum of squares, `mean_sq`.\n\n* `mean_sq` in the second line is the estimate for the variance within groups.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "generous-console",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 41
    },
    {
      "id": "animal-reform",
      "cell_type": "markdown",
      "source": "* Η μετρική `F` στην τέταρτη στήλη είναι ο λόγος του `mean_sq` της πρώτης γραμμής με το `mean_sq` της δεύτερης γραμμής.",
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      }
    },
    {
      "id": "surrounded-matrix",
      "cell_type": "code",
      "source": "anova_table",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>df</th>\n",
              "      <th>sum_sq</th>\n",
              "      <th>mean_sq</th>\n",
              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(region)</th>\n",
              "      <td>8.0</td>\n",
              "      <td>538.003711</td>\n",
              "      <td>67.250464</td>\n",
              "      <td>1527.271332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>273591.0</td>\n",
              "      <td>12047.054952</td>\n",
              "      <td>0.044033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 df        sum_sq    mean_sq            F  PR(>F)\n",
              "C(region)       8.0    538.003711  67.250464  1527.271332     0.0\n",
              "Residual   273591.0  12047.054952   0.044033          NaN     NaN"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": 42
    }
  ]
}