Playlist_analysis_ .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Music Taste Analysis Using Spotify API and Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Audio Features and building a Machine Learning Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Few days back I was reading a really interesting article written by a friend of mine (Luz) who analysed his spotify playlist using it's API (Application Programming Interface). After reading it I opened the app, looked at my playlist and realised that I listen to really distinct styles of music throughout the week or even throughout the day. I could be listening to Samba one minute and after a while swipe to heavy metal. Thinking of that I decided to **analyse not only mine but also my Fiancée's taste in music** to figure out what the data has to say about this.\n",
    "\n",
    "\n",
    "So,in order to do that I started by scraping the audio features of 10 artists that we listen to the most on spotify using it's API so that I was able to perform the music taste analysis. In addition to that, I trained a machine learning model to predict each list a song would be more likely to belong to.\n",
    "\n",
    "Another noteworthy fact is that I had as a reference some materials from the Data Science do zero which is one well-known data science community in Brazil. \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check all the code I develop on my github page\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tools \n",
    "* Spotipy Library to get access to Spotify platform music data \n",
    "* Seaborn and matplotlib for data visualization\n",
    "* Pandas and numpy for data analysis\n",
    "* sklearn to build the Machine Learning model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spotify Audio Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we know Spotify is one of the most popular audio streaming platforms around the globe. Likewise Twitter,Slack and Facebook they have an API for developers to explore their music database and get insights into our listening habits.\n",
    "\n",
    "Based on that, I chose ten artists that I often listen to in my daily routine and asked my Fiancée to give me one list with the ones she likes the most. After having the artists lists, I used the *Rare Loot* post as a reference to obtain the audio features data for my analysis.\n",
    "\n",
    "The first data set acquired contains 16 columns with 1433 songs from my list and 691 from Emily's lit. So, In order to balance the number of songs I built a function to ramdonly remove some rows from my list. After the removal process, we came up with a total of 1394 songs (703 from my list and 691 from Emily's list).\n",
    "\n",
    "It's important to mention that I did not use all of the 16 columns for the analysis. Instead, I selected only those columns related to the audio features. You can find below the explanation of each feature ( past/copy from Spotify website).\n",
    "\n",
    "* Acousticness : A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.\n",
    "\n",
    "* Danceability : Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.\n",
    "\n",
    "* Energy : Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. \n",
    "\n",
    "* Instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. \n",
    "\n",
    "* Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.\n",
    "\n",
    "* Loudness: he overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.\n",
    "\n",
    "* Speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. \n",
    "\n",
    "* Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). \n",
    "\n",
    "* Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn import metrics\n",
    "from sklearn import svm\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1433, 16)"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataframe shape\n",
    "df_jhon = pd.read_excel('playlist_jhon_onews.xlsx')\n",
    "df_jhon.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load all sheets into a dictionary of dataframes and then concatenate all the values in the dictionary into one dataframe. \n",
    "dict_emy = pd.read_excel('playlist_emy.xlsx', sheetname=None, ignore_index=True)\n",
    "#Concatenate all dataframes\n",
    "df_emy = pd.concat(dict_emy.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Check all rows\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>album</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>track_number</th>\n",
       "      <th>uri</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.09360</td>\n",
       "      <td>Red Pill Blues + (Deluxe)</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0EBEgnRLPTEAmofirXu4hQ</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>-3.499</td>\n",
       "      <td>Wake Up Call - Mark Ronson Remix</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>89.237</td>\n",
       "      <td>18</td>\n",
       "      <td>spotify:track:0EBEgnRLPTEAmofirXu4hQ</td>\n",
       "      <td>0.921</td>\n",
       "      <td>Maroon 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00334</td>\n",
       "      <td>Red Pill Blues + (Deluxe)</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.795</td>\n",
       "      <td>5uZr1d2CPHznf39x0c2Zc1</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-3.047</td>\n",
       "      <td>If I Never See Your Face Again</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0366</td>\n",
       "      <td>106.005</td>\n",
       "      <td>19</td>\n",
       "      <td>spotify:track:5uZr1d2CPHznf39x0c2Zc1</td>\n",
       "      <td>0.922</td>\n",
       "      <td>Maroon 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.01170</td>\n",
       "      <td>Red Pill Blues + (Deluxe)</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.764</td>\n",
       "      <td>5Ku5Qg4TJxlfxu1gDaKll8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3070</td>\n",
       "      <td>-4.514</td>\n",
       "      <td>Moves Like Jagger - Studio Recording From The ...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>128.042</td>\n",
       "      <td>20</td>\n",
       "      <td>spotify:track:5Ku5Qg4TJxlfxu1gDaKll8</td>\n",
       "      <td>0.609</td>\n",
       "      <td>Maroon 5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  acousticness                      album  danceability  energy  \\\n",
       "0      0       0.09360  Red Pill Blues + (Deluxe)         0.621   0.903   \n",
       "1      1       0.00334  Red Pill Blues + (Deluxe)         0.768   0.795   \n",
       "2      2       0.01170  Red Pill Blues + (Deluxe)         0.719   0.764   \n",
       "\n",
       "                       id  instrumentalness  liveness  loudness  \\\n",
       "0  0EBEgnRLPTEAmofirXu4hQ          0.000000    0.0786    -3.499   \n",
       "1  5uZr1d2CPHznf39x0c2Zc1          0.000001    0.1100    -3.047   \n",
       "2  5Ku5Qg4TJxlfxu1gDaKll8          0.000000    0.3070    -4.514   \n",
       "\n",
       "                                                name  popularity  speechiness  \\\n",
       "0                   Wake Up Call - Mark Ronson Remix           2       0.1120   \n",
       "1                     If I Never See Your Face Again           2       0.0366   \n",
       "2  Moves Like Jagger - Studio Recording From The ...           4       0.0445   \n",
       "\n",
       "     tempo  track_number                                   uri  valence  \\\n",
       "0   89.237            18  spotify:track:0EBEgnRLPTEAmofirXu4hQ    0.921   \n",
       "1  106.005            19  spotify:track:5uZr1d2CPHznf39x0c2Zc1    0.922   \n",
       "2  128.042            20  spotify:track:5Ku5Qg4TJxlfxu1gDaKll8    0.609   \n",
       "\n",
       "     artist  \n",
       "0  Maroon 5  \n",
       "1  Maroon 5  \n",
       "2  Maroon 5  "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Her dataframe\n",
    "df_emy.reset_index(inplace=True)\n",
    "df_emy.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Drop index column\n",
    "df_emy = df_emy.drop('index', axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['acousticness', 'album', 'danceability', 'energy', 'id',\n",
       "       'instrumentalness', 'liveness', 'loudness', 'name', 'popularity',\n",
       "       'speechiness', 'tempo', 'track_number', 'uri', 'valence', 'artist'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns\n",
    "df_emy.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>album</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>track_number</th>\n",
       "      <th>uri</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.192</td>\n",
       "      <td>Love Ritual Rare &amp; Unreleased 1968-76 Remaster</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.911</td>\n",
       "      <td>241KGZFakmf213AP73fbzg</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>-7.921</td>\n",
       "      <td>Love Ritual - Bwana Mix</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>98.329</td>\n",
       "      <td>1</td>\n",
       "      <td>spotify:track:241KGZFakmf213AP73fbzg</td>\n",
       "      <td>0.929</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.454</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.488</td>\n",
       "      <td>2urwWshyKW7dlNCsf8H005</td>\n",
       "      <td>0.001450</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>-10.160</td>\n",
       "      <td>Let's Stay Together</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0591</td>\n",
       "      <td>101.882</td>\n",
       "      <td>1</td>\n",
       "      <td>spotify:track:2urwWshyKW7dlNCsf8H005</td>\n",
       "      <td>0.570</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.610</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.536</td>\n",
       "      <td>41CbErP6D25qGVIux11KxI</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>-8.222</td>\n",
       "      <td>Call Me (Come Back Home)</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>99.533</td>\n",
       "      <td>2</td>\n",
       "      <td>spotify:track:41CbErP6D25qGVIux11KxI</td>\n",
       "      <td>0.684</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness                                           album  danceability  \\\n",
       "0         0.192  Love Ritual Rare & Unreleased 1968-76 Remaster         0.738   \n",
       "1         0.454    Free Soul. the treasure of Al Green Remaster         0.726   \n",
       "2         0.610    Free Soul. the treasure of Al Green Remaster         0.634   \n",
       "\n",
       "   energy                      id  instrumentalness  liveness  loudness  \\\n",
       "0   0.911  241KGZFakmf213AP73fbzg          0.000544    0.1020    -7.921   \n",
       "1   0.488  2urwWshyKW7dlNCsf8H005          0.001450    0.0437   -10.160   \n",
       "2   0.536  41CbErP6D25qGVIux11KxI          0.000071    0.3930    -8.222   \n",
       "\n",
       "                       name  popularity  speechiness    tempo  track_number  \\\n",
       "0   Love Ritual - Bwana Mix           1       0.0402   98.329             1   \n",
       "1       Let's Stay Together           1       0.0591  101.882             1   \n",
       "2  Call Me (Come Back Home)           1       0.0268   99.533             2   \n",
       "\n",
       "                                    uri  valence    artist  \n",
       "0  spotify:track:241KGZFakmf213AP73fbzg    0.929  Al Green  \n",
       "1  spotify:track:2urwWshyKW7dlNCsf8H005    0.570  Al Green  \n",
       "2  spotify:track:41CbErP6D25qGVIux11KxI    0.684  Al Green  "
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_emy.set_index('index', inplace=True)\n",
    "df_jhon.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Set Balancing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see below, we have way more songs in my list.So, in order to have a fair comparison between the data sets, we have to balance both of them. To do that, let's create a for loop that goes through my dataframe and randomly drops some rows of the most frequently occurring artists. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1433"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total of rows - Jhon\n",
    "df_jhon.artist.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "691"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total of rows - Emy\n",
    "df_emy.artist.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Balance the numbers of songs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2Pac': 216,\n",
       " 'ACDC': 206,\n",
       " 'Al Green': 178,\n",
       " 'Bryan Adam': 133,\n",
       " 'Iron Maiden': 143,\n",
       " 'Mumuzinho': 63,\n",
       " 'NF': 42,\n",
       " 'Rob Bailey & The Hustle Standar': 39,\n",
       " 'Tiee': 27,\n",
       " 'motorhead': 252,\n",
       " 'the offspring': 134}"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary in which the keys are the artists of my list and the values are the total of songs for each singer or group.\n",
    "my_dict = dict(df_jhon.artist.value_counts())\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a temporary subset\n",
    "temp_subset = df_jhon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for k,v in my_dict.items(): \n",
    "    if v > 200 :\n",
    "        drop_rows = 150\n",
    "        drop_indexes_array = df_jhon[df_jhon.artist == k].sample(n = drop_rows).index\n",
    "        temp_subset = temp_subset.drop(drop_indexes_array)\n",
    "    elif v == 143 or v == 133 or v == 134:\n",
    "        drop_rows = 60\n",
    "        drop_indexes_array = df_jhon[df_jhon.artist == k].sample( n = drop_rows).index\n",
    "        temp_subset = temp_subset.drop(drop_indexes_array)\n",
    "    elif v ==178:\n",
    "        drop_rows = 100\n",
    "        drop_indexes_array = df_jhon[df_jhon.artist == k].sample(n = drop_rows).index\n",
    "        temp_subset = temp_subset.drop(drop_indexes_array)\n",
    "\n",
    "df_jhon = temp_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "703"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Balanced dataframe\n",
    "df_jhon.artist.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setting new column to identify the users\n",
    "df_jhon['who'] = 1\n",
    "df_emy['who'] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>album</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>track_number</th>\n",
       "      <th>uri</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
       "      <th>who</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.536</td>\n",
       "      <td>41CbErP6D25qGVIux11KxI</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>-8.222</td>\n",
       "      <td>Call Me (Come Back Home)</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>99.533</td>\n",
       "      <td>2</td>\n",
       "      <td>spotify:track:41CbErP6D25qGVIux11KxI</td>\n",
       "      <td>0.684</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.581</td>\n",
       "      <td>06BA6xre08wcIwVW5HUNEh</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.0852</td>\n",
       "      <td>-6.975</td>\n",
       "      <td>I'm Still In Love With You</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>97.630</td>\n",
       "      <td>3</td>\n",
       "      <td>spotify:track:06BA6xre08wcIwVW5HUNEh</td>\n",
       "      <td>0.824</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.422</td>\n",
       "      <td>21GJBVgndtsseyPRLA7l1Z</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>-8.685</td>\n",
       "      <td>How Can You Mend A Broken Heart?</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>146.917</td>\n",
       "      <td>4</td>\n",
       "      <td>spotify:track:21GJBVgndtsseyPRLA7l1Z</td>\n",
       "      <td>0.442</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness                                         album  danceability  \\\n",
       "0         0.610  Free Soul. the treasure of Al Green Remaster         0.634   \n",
       "1         0.125  Free Soul. the treasure of Al Green Remaster         0.643   \n",
       "2         0.428  Free Soul. the treasure of Al Green Remaster         0.427   \n",
       "\n",
       "   energy                      id  instrumentalness  liveness  loudness  \\\n",
       "0   0.536  41CbErP6D25qGVIux11KxI          0.000071    0.3930    -8.222   \n",
       "1   0.581  06BA6xre08wcIwVW5HUNEh          0.000434    0.0852    -6.975   \n",
       "2   0.422  21GJBVgndtsseyPRLA7l1Z          0.000024    0.0874    -8.685   \n",
       "\n",
       "                               name  popularity  speechiness    tempo  \\\n",
       "0          Call Me (Come Back Home)           1       0.0268   99.533   \n",
       "1        I'm Still In Love With You           1       0.0325   97.630   \n",
       "2  How Can You Mend A Broken Heart?           1       0.0411  146.917   \n",
       "\n",
       "   track_number                                   uri  valence    artist  who  \n",
       "0             2  spotify:track:41CbErP6D25qGVIux11KxI    0.684  Al Green    1  \n",
       "1             3  spotify:track:06BA6xre08wcIwVW5HUNEh    0.824  Al Green    1  \n",
       "2             4  spotify:track:21GJBVgndtsseyPRLA7l1Z    0.442  Al Green    1  "
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine both data frames \n",
    "df_couple = df_jhon.append(df_emy)\n",
    "df_couple.reset_index(inplace= True)\n",
    "df_couple.drop('index', axis = 1, inplace= True)\n",
    "df_couple.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Mean values of The Audio Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create data frame with features\n",
    "def features(df,who):\n",
    "    if who == 'both':\n",
    "         features = df.loc[: ,['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'speechiness', 'valence', 'who']]         \n",
    "    elif who == 0 or who == 1:   \n",
    "          features = df.loc[df.who == who,['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'speechiness', 'valence']]           \n",
    "    else:\n",
    "        return 'Error'\n",
    "    return features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>0.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.0852</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>0.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.403</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.148</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  energy  instrumentalness  liveness  \\\n",
       "0         0.610         0.634   0.536          0.000071    0.3930   \n",
       "1         0.125         0.643   0.581          0.000434    0.0852   \n",
       "2         0.428         0.427   0.422          0.000024    0.0874   \n",
       "3         0.403         0.536   0.717          0.000000    0.1200   \n",
       "4         0.148         0.755   0.577          0.000573    0.2150   \n",
       "\n",
       "   speechiness  valence  \n",
       "0       0.0268    0.684  \n",
       "1       0.0325    0.824  \n",
       "2       0.0411    0.442  \n",
       "3       0.0374    0.878  \n",
       "4       0.0384    0.944  "
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_jhon = features(df_couple, 1)\n",
    "features_emy = features(df_couple,0)\n",
    "features_jhon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alicia Keys         119\n",
       "Amy winehouse       102\n",
       "Ed Sheeran           88\n",
       "Rihanna              83\n",
       "Ludmilla             83\n",
       "Maroon 5             58\n",
       "Projota              57\n",
       "Marilia Mendonça     57\n",
       "Bruno Mars           36\n",
       "Carol Biazin          8\n",
       "Name: artist, dtype: int64"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emy.artist.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "motorhead                          102\n",
       "Iron Maiden                         83\n",
       "Al Green                            78\n",
       "the offspring                       74\n",
       "Bryan Adam                          73\n",
       "2Pac                                66\n",
       "Mumuzinho                           63\n",
       "ACDC                                56\n",
       "NF                                  42\n",
       "Rob Bailey & The Hustle Standar     39\n",
       "Tiee                                27\n",
       "Name: artist, dtype: int64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jhon.artist.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of songs:\n",
      "Jonathan: 703\n",
      "Emily:691\n"
     ]
    }
   ],
   "source": [
    "jhon_total_songs = df_jhon.artist.count()\n",
    "emy_total_songs = df_emy.artist.count()\n",
    "print(f'Total of songs:\\nJonathan: {df_jhon.artist.count()}\\nEmily:{df_emy.artist.count()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've never really thought about my music taste until I decided to do this article. Actually, after thinking about this I realized that I don't have a specific genre which I listen to every single day. In fact, that depends on my mood...some days I wake up and listen to pagode (Brazilian style music from Rio de Janeiro), an other days, after getting up, I just want to listen to a really energetic song such as Iron Maiden, and so on. So, I am curious to investigate not only mine but also Emily's Spotify audio features. That being said, let's jump right into the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will commence by plotting a Bar chart and a Radar Chart showing the means of our features in order to compare both of them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots show that the predominant feature in my list of artists is **energy** - probably because of rock music such as ACDC, Iron Maiden and Motorhead. On the other hand, looking at Emily's features, we can notice that **danceability** and **energy** are the prevalent audio attributes of her list. That really makes sense since she loves listening to a song by artists such as Ludmila ( a brazilian funk singer) and Rihanna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F37LwFKiZmS03l1xyZblLaJBHgGZmlksOQDMzyyUHoJmZ5ZID0MzMcskBaGZmueQANDOz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\n8r7qEcLIbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9aa9f5eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of features\n",
    "N = len(features_jhon.mean())\n",
    "# Array with the number of features\n",
    "ind = np.arange(N) \n",
    "\n",
    "width = 0.35  \n",
    "#Bar plot with my list\n",
    "plt.barh(ind, features_jhon.mean() , width, label='Jonathan', color = 'lightslategray')\n",
    "#Bar plot with her list\n",
    "plt.barh(ind + width, features_emy.mean(), width, label='Emily', color = 'mediumvioletred')\n",
    "\n",
    "#X- label\n",
    "plt.xlabel('Mean', fontsize = 12)\n",
    "# Title\n",
    "plt.title('Mean values of the audio features')\n",
    "#Vertical ticks\n",
    "plt.yticks(ind + width / 2, (list(features_emy)[:]), fontsize = 12)\n",
    "#legend\n",
    "plt.legend(loc='best')\n",
    "# Figure size\n",
    "plt.rcParams['figure.figsize'] =(8,8)\n",
    "# Set style\n",
    "style.use(\"ggplot\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d9a9e1ae80>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HuPDCC1m0aBEnn3wy999/P5qmsXjxYvbv38/8+fO5//772bFjh/GYr776Kvv2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IOu67AdO/ES+IJUwKKziJnazIaETSfmYVEUQQhh6j4QIUkSkykd1s71OA3VnJE/K4QQ5t58Fs24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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9a9f379b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "labels= list(features_jhon)[:]\n",
    "stats= features_jhon.mean().tolist()\n",
    "stats2 = features_emy.mean().tolist()\n",
    "\n",
    "angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)\n",
    "\n",
    "# close the plot\n",
    "stats=np.concatenate((stats,[stats[0]]))\n",
    "stats2 =np.concatenate((stats2,[stats2[0]])) \n",
    "angles=np.concatenate((angles,[angles[0]]))\n",
    "\n",
    "#Size of the figure\n",
    "fig=sns.plt.figure(figsize = (18,18))\n",
    "\n",
    "ax = fig.add_subplot(221, polar=True)\n",
    "ax.plot(angles, stats, 'o-', linewidth=2, label = \"Jonathan\", color= 'gray')\n",
    "ax.fill(angles, stats, alpha=0.25, facecolor='gray')\n",
    "ax.set_thetagrids(angles * 180/np.pi, labels , fontsize = 13)\n",
    "\n",
    "\n",
    "ax.set_rlabel_position(250)\n",
    "plt.yticks([0.2 , 0.4 , 0.6 , 0.8  ], [\"0.2\",'0.4', \"0.6\", \"0.8\"], color=\"grey\", size=12)\n",
    "plt.ylim(0,1)\n",
    "\n",
    "ax.plot(angles, stats2, 'o-', linewidth=2, label = \"Emily\", color = 'm')\n",
    "ax.fill(angles, stats2, alpha=0.25, facecolor='m' )\n",
    "ax.set_title('Mean Values of the audio features')\n",
    "ax.grid(True)\n",
    "\n",
    "plt.legend(loc='best', bbox_to_anchor=(0.1, 0.1))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tempo is an important feature in terms of music analysis. It can be as significant as melody, harmony or rhythm due to the fact that it represents the speed of a song and the mood it evokes. For instance, the higher the BPM of a song, the faster the song is and consequently more inspiring and joyful it tends to be. On the other hand, a low BPM means that the song is slower, which can indicate sadness, romance or drama. \n",
    "\n",
    "\n",
    "Looking at the graph below we can see that our lists have tempo close to 120 BPM on average which indicates a moderate/fast tempo. Based on that I would say that both types of track could be of interest for those who enjoy fast walking and running since a song with 120 BPM can usually be synchronized with the one's stride pattern and their pace in order to make the run/walk harder and faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TTdXZbwxDRFTXqhxF1a5dO7Rr1w6jRo1CRkYGzp07hx9++AGZmZno0KEDvL29\n4e7uDkNDw+fauJmZGQwMnpRgbm4OlUoFOzs7JCYmwtXVFXFxcWjfvv1ztU1EuuXPP/98rvWqc44o\nKirC6tWrkZ+fDwMDA0yZMgVNmzZ9ru0SUf1WZcD5O2tra7z11lt46623kJubi5iYGISFhUEQBERG\nRj7XxgcNGoS1a9di/vz5UCqVGDlyJOzt7bFu3ToolUrY2Niga9euz9U2EemWRYsWVXud6j57648/\n/oC9vT2GDh2Ko0ePYs+ePRg3bly1t0tE9Z/WAQcAMjMzERsbi/Pnz+Pq1ato06YNOnXq9NwbNzY2\nxqefflpu+sKFC5+7TSLSXevXr0ejRo20WjY3Nxf/+c9/qtX+wIEDNSNDs7Oztb4ETkS6p8qAIwgC\nrl27hvPnzyM2NhaZmZlwcXFBly5dMGXKFDRr1qyu6iQiPTd8+HCYmJhovbyJiQmGDx9e6fzo6GhE\nRUWVmTZp0iQ4ODhg4cKFSE9PR2Bg4HPXS0T1W6UBJzw8HHFxcVAqlfD09MTQoUPh5eUFU1PTuqyP\niBqIIUOGlPlcUFCA48eP486dOzAwMICNjQ169uypuefPyMio3Dp/5+vrC19f3wrnLViwAHfu3EFI\nSAjCwsKeWRtvktZdHKWpm2rimKs04MTExEAmk8HOzg45OTk4ePAgDh48WOGyvKRERDXpzz//RGho\nKCwtLWFnZwdBEHDmzBn8+OOPmDNnDuzs7J6r3V27duFf//oXevXqBWNjY61Hg3Kkn+7ivtNNNTFK\ns9KAM3To0OpXRERUAyIiIvDvf/8bY8aM0TwXS61WY+PGjVi3bh2Cg4Ofq10fHx+Eh4cjOjoaarUa\nkyZNqsmyiageqTTgDBs2rC7rICLSyMzMxIwZM8o8XFQqleL111/HrFmznrvdxo0b86XARA1EpQFH\nrVZj165dOHv2LAwMDNC5c2cMGjRI89waIqLa4uXlhRMnTsDPz6/M9NOnT8PV1VWkqohIl1SaVrZu\n3YrDhw+jR48ekMlk2LNnDzIzM6v9Qk0iIm2sWbNG87NarcbPP/+MuLg4tG3bFhKJBOnp6bh+/Tp6\n9+4tYpVEpCsqDTgnT57Exx9/jA4dOgAAunbtiqVLl2LChAmQyWR1ViARNQx/v+HX3NxcE2RKSkoA\nAFZWVrCystI8x4aIqCqVBpzc3Fy8/PLLms9OTk5QqVTIzc3Fv/71rzopjogajsmTJ4tdAhHpkSrv\nwfn7X1TNBNcqAAAaUUlEQVRSqRRyubzMyzCJiGrKTz/9pPWyHOVJRM/CO4aJqF5ISEjQ/CwIAq5f\nv47GjRvjpZdegkwmQ3p6OnJycuDh4SFilUSkK6oMOHv27IGRkZHms1KpRFRUVLn3t1T1uHQiIm18\n/vnnmp83bdoEW1tb+Pv7a+75U6vV2Lx5M/Ly8sQqkYh0SKUBx9nZGampqWWmOTo64tatW7VeFBE1\nbNHR0QgJCSkzoEEqlaJ///6YPXu2iJURka6oNOAEBQXVYRlERP+nSZMmiI+PL/cI9lOnTqFFixYi\nVUVEuqTa9+CoVCokJCRArVbD1dUVxsbGtVEXETVg7777Lr744gucO3cOL730EgAgOTkZqamp7MEh\nIq1UGXBOnDiBkydPAgB69eoFT09PBAYGIj39ybtXLS0tERgYiDZt2tR+pUTUYHh7eyM0NBRHjx7F\n7du3ATx5VMWUKVPQsmVLkasjIl1QacDZvXs3du3ahR49esDIyAibNm1Cs2bN0KhRI6xbtw5qtRrf\nffcdduzYgZkzZ9ZlzUSkh3766Se88cYbmoENrVu3xujRoytdvqioCPv27eOQcSKqkLSyGYcPH8bU\nqVMxYcIEvPfee5g3bx6SkpLwzjvvoHHjxmjatClGjBiBy5cv12W9RKSndu7cicLCQqjVaq3+Kygo\nwM6dO8Uum4jqqUp7cO7fv1/mScatW7eGgYEBmjRpoplmaWmJgoKC2q2QiBqM//znP2KXQER6oson\nGf/zzeEymQwSiaTWiyKihmfBggVil0BEeqTKm4wFQSjzYjuJRFJmGl96R0Q1xcXFRewSiEiPVBlw\nKuou/vjjj2utGCIiIqKaUGnAYXcxERER6apKA87ly5fLDNl8Fg7ZJCIiovqi0mHiHLJJREREuqra\n9+AQEdW2rKwsbNu2DcnJyVCpVBAEocz8r7/+WqTKiEhX8B4cIqp3wsLCkJeXh9dffx0mJiZil0NE\nOqjSgMMhm0QklpSUFCxbtgytW7cWuxQi0lGV3oNDRCQWa2trPHz4UOwyiEiHVXkPDhGRGN544w2s\nW7cOAwcOhJWVVbmnqrdv316kyohIVzDgEFG9Ex4eDgDYuHFjhfN37NhRl+UQkQ4SPeDs2rULsbGx\nUCqV6N+/P1xcXBAeHg6JRAJbW1v4+/tDKuWVNKKGpLYDzJ07dzB37lx8++23MDQ0rNVtEZE4tAo4\ntTVkMzExEdeuXcOiRYtQUlKCX3/9FZs3b8aIESPg6uqK9evXIzY2Ft7e3s/VPhHpLqVSiQcPHmje\neScIApRKJVJTU9GzZ8/nbregoACRkZGQy+U1VSoR1UNaBZzaGrJ58eJFtGnTBitWrEBhYSFGjx6N\nP/74QzOCy8vLCxcvXmTAIWpgzpw5g/Xr1yMvL6/cvKZNmz53wBEEAevXr8fIkSMRGhr6omUSUT2m\nVcCprSGbjx49QnZ2NgICApCVlYVly5ZBEARIJBIAgImJCQoKCmp0m0RU/23fvh1dunTBoEGDEBgY\niICAADx+/BgREREYMmSIVm1ER0cjKiqqzLRmzZqhe/fusLOzq1Y91tbW1Vqe6g9t9l16HdRB1VMT\nx5xWAefpkM2aDjgWFhawsbGBgYEBrK2tYWhoiPv372vmFxYWwszMTKu2eALSXTwB6abaPOaysrIQ\nEBCAli1bwt7eHrm5uejcuTNkMhkiIyPh4+PzzDZ8fX3h6+tbZtrUqVMRHR2N6Oho5ObmYsmSJVi4\ncOEz28rIyHju70Li4r7TTdXZb5Wdi7QKOLU1ZNPJyQm//fYbBg0ahAcPHqCoqAjt27dHYmIiXF1d\nERcXp3Xb/Eesu7jvdFNNnIAqY2ZmhpKSEs26qamp6Ny5M6ytrZGVlVWttv4uLCxM8/OUKVPw2Wef\nPXdbRFS/aRVwamvIZseOHXHlyhXMnTsXarUa/v7+aNGiBdatWwelUgkbGxt07dr1udomIt3VsWNH\nfPfdd5gwYQJcXV2xZcsWdOjQAadPn0bTpk3FLo+IdIBWAac2h2yOHj263DRtuoyJSH+NHTsWmzZt\nQkpKCnr16oXTp0/js88+g7GxMaZOnVoj23j6hxsR6Setn4NTW0M2iYj+ydjYGBMnTtR8njZtGiZM\nmAC5XF7uEjkRUUW0OlPU1pBNIqLK3Lx5E/v370dmZiamTZuGs2fPomXLlvD09BS7NCLSAVo9Ivjp\nkM0vvvgC5ubmWLx4MWbPno0WLVrAz8+vtmskogYmPj4e8+bNg1qtRlJSEkpLS/Ho0SMsW7YMJ06c\nELs8ItIBWgWcrKwsvPnmm7C2ttYM2ezQoQPGjx+Pffv21XaNRNTA/PDDD3j//fcxefJkyGQyAICf\nnx/GjRuHn3/+WeTqiEgXaBVwKhqy+fTnFxmySURUkTt37sDd3b3cdHd3d55ziEgrWgWcp0M2b926\nBVdXVxw/fhw3btzA77//ziGbRFTjWrRogevXr5ebfv78eVhZWYlQERHpGq1uMq6LIZtERE8NHz4c\n4eHhuHHjBlQqFY4cOYKsrCycOnUK06ZNE7s8ItIBWgUcDtkkorrk7e2NFi1aYN++fWjTpg0uXLgA\na2trfP7552jXrp3Y5RGRDtA6nXDIJhHVJTs7O3z00Udil0FEOkqrgBMfH4+VK1filVdeKTNkc/Pm\nzZgyZQp69OhR23USkZ6rzhPThw8fXouVEJE+0CrgPB2y2adPH5w+fRrAkyGbjRs3xs8//8yAQ0Qv\n7JdffoFEIsHLL78MY2NjscshIh2nVcCpasjm5s2ba7woImp4/P39ERsbi2vXrsHZ2Rne3t7o1KkT\nLC0txS6NiHSQVgHn6ZDNFi1alJnOIZtEVFP69euHfv36obCwEHFxcTh37hy2bt2K1q1bw9vbG97e\n3mjevLnYZRKRjtAq4HDIJhHVFRMTE3Tr1g3dunWDSqXCpUuXEBsbiwULFsDCwgKdO3fG0KFDxS6T\niOo5rQIOh2wSkRhkMhnc3d1haGgIQ0ND/PHHH9izZw8DDhE9k9bDxDlkk4jqytPLVOfPn0d8fDyk\nUik6duyIqVOnVng/IBHRP1UacDhkk4jqUlZWFs6fP4/Y2FhcvXoVzZs3R6dOnTBz5kw4OjpCIpGI\nXSIR6ZBKAw6HbBJRXZo6dSoMDAzg7OyMMWPGoHXr1gAApVKJxMTEMsu2b99ejBKJSIdUGnA4ZJOI\n6ppSqcSlS5dw6dKlKperTg8zETVMlQYcDtkkorrE0EJENemZNxlzyCYRERHpGml1Fn46ZLNbt27o\n2rUrMjMzsWfPntqqjYiIiOi5aDVMnEM2iYiISJdUGnA4ZJOIiIh0VaUBh0M2iYiISFdVeYmKQzaJ\niIhIF9XIk4yJiHSBIAiYOHEiWrVqBQBQKBQYNWqUyFURUW3Q+l1URES67u7du3j55ZcREBAgdilE\nVMsYcIiowUhJScGDBw+wcOFCGBoa4v3334e1tbXYZRFRLWDAISK9FB0djaioqDLT/P39MXjwYLzy\nyiu4evUqwsLCEBwc/My2GIJ0lzb7Lr0O6qDqqYljjgGHiPSSr68vfH19y0wrLi6GTCYDADg5OSEn\nJweCIDzzsRcZGRm1VifVLu473VSd/VZZGKrWk4yJiHTZzp07Nb06aWlpaNasGZ/pRaSn6kUPzsOH\nDxEQEIB58+ZBJpMhPDwcEokEtra28Pf3h1TKHEZEL27w4MEICwvDhQsXIJPJMHnyZLFLIqJaInrA\nUSqVWL9+PQwNDQEAmzdvxogRI+Dq6or169cjNjYW3t7eIldJRPrA3Nwcc+bMEbsMIqoDoneNbNmy\nBX379kWTJk0APBnl4OLiAgDw8vJCQkKCmOURERGRDhK1B+fo0aOwtLSEp6cndu/erZn+9Jq4iYkJ\nCgoKtGqLoxx0F0c56CYec0RUn4kacI4cOQIAuHTpEtLS0rBmzRo8fPhQM7+wsBBmZmZatcU75XUX\n951uqolRDkREtUXUgLNw4ULNz0FBQZgwYQK2bNmCxMREuLq6Ii4uji/yJCIiomoT/Sbjf3rvvfew\nbt06KJVK2NjYoGvXrmKXRERERDqm3gScoKAgzc9/79khIiIiqi7RR1ERERER1TQGHCIiItI7DDhE\nRESkdxhwiIiISO8w4BAREZHeYcAhIiIivcOAQ0RERHqHAYeIiIj0DgMOERER6R0GHCIiItI7DDhE\nRESkdxhwiIiISO8w4BAREZHeYcAhIiIivcOAQ0RERHqHAYeIiIj0DgMOERER6R0GHCIiItI7DDhE\nRESkdxhwiIiISO8YiF0AEVFdUavV2Lx5M1JSUlBaWophw4ahY8eOYpdFRLWAAYeIGoyYmBioVCos\nWrQIOTk5OHXqlNglEVEtYcAhogYjPj4ebdq0QXBwMABg3LhxIldERLWFAYeI9FJ0dDSioqLKTLO0\ntERmZiYCAgJw5coVfP3111i4cOEz27K2tq6tMqmWabPv0uugDqqemjjmGHCISC/5+vrC19e3zLQv\nv/wSHTp0gEQigYuLCzIyMrRqS9vlqP7hvtNN1dlvlYUhjqIiogbDyckJcXFxAIC0tDQ0a9ZM5IqI\nqLawB4eIGox///vf+Pbbb/HZZ59BEARMmDBB7JKIqJYw4BBRgyGXyzF58mSxyyCiOsBLVERERKR3\nGHCIiIhI7zDgEBERkd5hwCEiIiK9I+pNxkqlEl9//TXu3buH0tJSvPPOO2jdujXCw8MhkUhga2sL\nf39/SKXMYURERKQ9UQPO8ePHYWFhgalTpyIvLw8zZ86EnZ0dRowYAVdXV6xfvx6xsbHw9vYWs0wi\nIiLSMaJ2jbzyyisYPnw4AEAQBMhkMqSkpMDFxQUA4OXlhYSEBDFLJCIiIh0kag+OsbExAKCwsBCr\nVq3CiBEjsGXLFkgkEgCAiYkJCgoKtGqL74rRXXxXjG7iMUdE9ZnoD/rLzs7GihUr0K9fP/To0QPf\nf/+9Zl5hYSHMzMy0aofvG9Fd3He6qSbeFUNEVFtEvUSVm5uLJUuW4N1339W8FM/Ozg6JiYkAgLi4\nODg7O4tZIhEREekgUXtwdu3ahby8PPz888/4+eefAQBjx45FREQElEolbGxs0LVrVzFLJCIiIh0k\nasAZN24cxo0bV276woULRaiGiIiI9AUfMENERER6hwGHiIiI9A4DDhEREekdBhwiohe0ess+rN6y\nT5RtH/9tBx7n3n/hdgryHuGv9OQabZNITAw4RESEe3+l4/7dO2KXQVRjRH/QHxERvZjSkmKcPLQL\nypJiCIIaLh16oLl1G/yxazOatWyNhw+yIQHQtc9gGBjIEXfyEArzH6OoIB+t2rSFs1c3XE84C5VS\niX9ZPXko49W4UygqKoCqtBSdXx0IU3PLcuu5dOyB8zEHIJXJUJD3EEUF+ejY8zU0bmYl7i+ECOzB\nISLSeUl/xqKF9UvoNXAEvH3ewIUTv0MQBChLS9Da3gm9Xh8OY1Nz3L2dioL8x2javBW69x+KV994\nF6lXL0IilULh7o3W9k5o1cYBAGBla4+eA/xg1fpl3Em7XuF6T5maW6J7/6Gwd/FC6jW+P5DqB/bg\nEBHpGGVpCaQyGaRSGQBAVVqKZi1tAAAmZhYwkBuhuOjJe/wa/6uFZrpKpYShkTEeZN/Fvb+iIDc0\nglqlqnAbT3thjE3NUFSQX+V6jZo+2YapmQVy7vLVK1Q/sAeHiEjHnI85gPt370AQBBQXFcDI1Az3\nM5/cP1OY/xilJUUwNDL5/0tLyqybnpQIuaEROr86EA7tO0KpUkIQBEggASBoliu7VuXrAYDknwsT\n1QPswSEi0jEO7Tsi4fQRAIC1nQIOrh1w4fjvuJN2HSqVEl7d+0Iqrfjv1+bWbXDu6G/IycqAVCaD\nuWVjFBXkwbJpM1y7eAaN/1Xx/TOVrUdUX0mEpxFcx2n7ZmOxhnJSxUJmf6jVvkv33FT7xVC1tIkf\nq/Wyuv42cZ5fdBPPL7qrJs4vvERFREREeocBh4iIiPQOAw4RERHpHd5kTEQNxu7duxEfHw8AyM/P\nR25uLr799luRqyKi2sCAQ0QNxuDBgzF48GAAQEhICEaPHi1yRURUW3iJioganDNnzsDMzAweHh5i\nl0JEtYQ9OESkl6KjoxEVFVVm2qRJk+Dg4IDdu3fj448/1rqtZw1zP91iKQBgcPXLrFDXrLnPXObM\nmTOYPn06HBwcNNOaNGmC1atXV7leTEwM/vrrL3Tv3h2ffvopfvzxxxeutz7T5hEF6XVQB1VPTTxa\nggGHiPSSr68vfH19y02/ffs2TE1N0bJlS63b0vY5ODVFm+3dv38fHh4emD9/frXWdXBwgIODAzIz\nM1FSUlLn360uWVtb6/X302fV2W+VhSEGHCJqUBISEuDl5SV2GbVm+vTpaNu2LVJTU2FiYgJ3d3ec\nO3cOeXl5CA0Nxf/+9z+kp6fjzTffBADcunULS5cuxddffw0AWLhwIfz8/ODs7Czm1yB6YQw4RNSg\nZGRkwN3dXewyakRcXBymT5+u+dy1a1cAgJOTE6ZOnYpZs2bByMgIK1asQHBwMC5evFiuDVtbWxga\nGiItLQ1NmzZFZmYmww3pBQYcImpQxo8fL3YJNcbLy6vcJarTp09DoVAAAMzNzWFnZwcAsLCwQElJ\nSYXtDBo0CAcOHICVlRX69OlTqzUT1RWOoiIi0jOSar7eu3fv3oiNjcXx48fRt2/fWqqKqG6xB4eI\nSEf98xIVABQXF1e7HUNDQ7i7u+Phw4ewtLSsqfJ0RnVe7Ei6gwGHiOgFifE/SE9PT+zatavKZf5+\n+eqjjz4qN3/t2rWan9VqNQYOHFhzBRKJjJeoiIgauJkzZ+Lx48fo0KGD2KUQ1Rj24BARNXChoaFi\nl0BU49iDQ0RERHqHAYeIiIj0DgMOERER6R0GHCIiItI79fImY7Vaje+++w43b96EXC7HxIkTq/Vi\nPCIiImrY6mUPzrlz51BaWoolS5Zg1KhRiIyMFLskIiIi0iH1MuBcvXoVnp6eAACFQoHk5GSRKyIi\nIiJdUi8DTmFhIUxNTTWfpVIpVCqViBURERGRLqmX9+CYmJigsLBQ81kQBMhksirXsba21qrtkNkf\nvlBtVPO02XfWWXProBKiiml7fqH6h/uu4aqXPTiOjo6Ii4sDAFy/fh1t2rQRuSIiIiLSJRJBEASx\ni/inp6Oo0tPTIQgCJk+eDBsbG7HLIiIiIh1RLwMOERER0Yuol5eoiIiIiF4EAw4RERHpHQYcIiIi\n0jsMOPTCFi9ejEePHoldBhHpoMTERMyYMaNG2jp9+jSCgoJqpC3SfQw49MISEhLELoGIiKiMevmg\nP9Ida9euBQAsXLgQAQEB2LRpE7Kzs6FSqdCtWzcMGTIEWVlZ+Pzzz9G+fXtcv34dKpUKY8aMwaFD\nh5CRkQF7e3t8/PHHyM7ORlBQEFxcXHDz5k0AwAcffABnZ2colUpERkbi0qVLkEqlaNeuHd5//32Y\nmJiI+fWJqIYUFBRoXrIMAF5eXhg5ciRkMhn8/Pzw3XffwdLSEgDKfN6xYwdOnDgBc3NztGrVStNe\neHg4TExMcOvWLWRnZ8PGxgbTp0+HsbExbt++jU2bNuHx48dQq9UYMGAAfH19UVRUhLVr1+Kvv/6C\nVCrFyy+/jA8//BAlJSUVTpdK2UdQn3Hv0AuZPHkyAGDBggVYu3YtfHx8sGzZMixduhSXLl3CyZMn\nAQBZWVno1KkTVq1ahfbt2yMiIgIff/wxVq5ciatXryIpKQkAkJ2dDU9PT4SGhmLUqFH48ssvoVQq\n8csvvyAnJwehoaEIDQ2FWq3Gli1bRPveRFSzNm7cCAsLC6xYsQIhISG4efMm9u7dW+U6586dw5kz\nZ7B8+XIsXrwYBQUFZeanpqZi7ty5+OKLL/DgwQOcOnUKKpUKq1atwqhRo7Bs2TIsXLgQe/fuxfXr\n13H27FkUFhYiNDQUwcHBAJ6cuyqbTvUbe3CoRhQXF+Py5cvIy8vDjh07AABFRUVIS0uDg4MDZDIZ\nOnbsCACwsrKCo6Oj5n1jTZo0QV5eHpo0aQIzMzP06NEDwJO/4KRSKdLT0xEXF4eRI0fCwODJP9kB\nAwYgNDRUhG9KRLUhPj4eixYtgkQigVwuR9++fREVFYXBgwdXus6lS5fg7e2t6cn18fHB/v37NfM9\nPDwgl8sBALa2tsjLy8Nff/2Fu3fv4uuvv9YsV1JSgrS0NHh6emL79u0ICgqCu7s7Bg4ciJYtW0Iq\nlVY4neo3BhyqERKJBMCTG46NjIwAAI8ePYKhoSEePXoEAwMDzTIAKn232D+nq9VqSKVS/PN5lIIg\n8AWsRHrkn8e4Wq2u8BhXKpWVtvHP84ehoaHmZ4lEAkEQoFarYWpqWuYPpNzcXJiamsLQ0BCrV69G\nYmIi/vzzTyxatAgffPABunbtWul0qr94iYpemFQqhUwmQ7t27bBv3z4AQH5+PgIDA3Hu3LlqtfXo\n0SPEx8cDAGJjY2FgYIA2bdrAw8MDBw8ehFKphFqtxoEDB+Dm5lbj34WIxOHh4YHff/8dgiCgtLQU\nf/zxB9zd3QEAlpaWSE5OBgCcOXNGs46npydOnTqF/Px8qNVqxMTEPHM71tbWMDQ01CybnZ2NGTNm\nICUlBQcPHsTatWvh4eGB0aNHw8PDA+np6ZVOp/qNPTj0wry9vTF//nzMnDkT27Ztw4wZM6BUKtG9\ne3f07NmzWteq5XI5YmJisHXrVhgaGmLmzJmQSqV45513EBkZiVmzZkGlUsHBwQEffPBBLX4rIqpL\n48aNw8aNG/Hf//4XSqUSHh4eGDJkiGbehg0bYGZmBjc3NzRp0gQA0KFDB6SnpyMgIADm5uZ46aWX\nnvnICgMDA8ycORObNm3Cr7/+CpVKheHDh8PJyQl2dnZITEzEp59+CiMjIzRr1gyvv/46DAwMKpxO\n9RvfRUX1RlZWFmbMmMGbh4mI6IXxEhURERHpHfbgEBERkd5hDw4RERHpHQYcIiIi0jsMOERERKR3\nGHCIiIhI7zDgEBERkd5hwCEiIiK98/8Af9C7tUtiJgIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9a9d4b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempo_emy = df_couple.loc[ df_couple.who == 0, ['tempo']]\n",
    "tempo_jhon = df_couple.loc[ df_couple.who == 1, ['tempo']]\n",
    "\n",
    "N = len(tempo_emy.mean())\n",
    "\n",
    "ind = np.arange(N) \n",
    "\n",
    "plt.subplot(221)\n",
    "width = 0.35      \n",
    "plt.bar(ind, tempo_jhon.mean() , width, label='Jonathan', color = 'lightslategray')\n",
    "plt.bar(ind + 1.1*width, tempo_emy.mean(), width, label='Emily', color = 'mediumvioletred')\n",
    "\n",
    "plt.ylabel('Mean [BPM]', fontsize = 14)\n",
    "plt.title('Tempo Means')\n",
    "\n",
    "plt.xticks(ind + width / 2, (list(tempo_emy)[:]), fontsize = 12)\n",
    "plt.legend(loc='best')\n",
    "style.use(\"ggplot\")\n",
    "\n",
    "\n",
    "plt.subplot(222)\n",
    "\n",
    "loud_emy = df_couple.loc[ df_couple.who == 0, ['loudness']]\n",
    "loud_jhon = df_couple.loc[ df_couple.who == 1, ['loudness']]\n",
    "\n",
    "N = len(loud_emy.mean())\n",
    "\n",
    "ind = np.arange(N) \n",
    "\n",
    "width = 0.35      \n",
    "plt.bar(ind, loud_jhon.mean() , width, label='Jonathan', color = 'lightslategray')\n",
    "plt.bar(ind + 1.1*width, loud_emy.mean(), width, label='Emily', color = 'mediumvioletred')\n",
    "\n",
    "plt.ylabel('Mean [db]', fontsize = 14)\n",
    "plt.title('Loudness Means')\n",
    "\n",
    "\n",
    "\n",
    "plt.xticks(ind + width / 2, (list(loud_emy)[:]), fontsize = 12)\n",
    "plt.legend(loc='lower left')\n",
    "style.use(\"ggplot\")\n",
    "plt.rcParams['figure.figsize'] =(12,13)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tempo    119.632323\n",
       "dtype: float64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempo_emy.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tempo    122.29203\n",
       "dtype: float64"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempo_jhon.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purpose of comparing the audio features, it is important to look at the bar chart below. It shows the difference between the mean of each attribute of** mine and Emily's song lists. **\n",
    "\n",
    "**In line with that**, we can see clearly that my list is more **energetic, livier and instrumental** than hers. Besides, we can also notice a slight difference in our **speechiness** variables and see that **valence, danceability** and **accousticness** take the top spot on her side.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9+px//vnZvHlzPvKRj2TYsGEVWT8AAHRURcO7paUlDQ0N7R9XV1dn8+bNqamp2WpbfX19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\nCCHawf8DL0ezG8GicIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9aa6c0320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['lightslategray','lightslategray','lightslategray',\n",
    "          'mediumvioletred','mediumvioletred','mediumvioletred','mediumvioletred']\n",
    "dif_features = features_jhon.mean() - features_emy.mean()\n",
    "\n",
    "index = dif_features.sort_values(ascending = False).index\n",
    "\n",
    "#Plot\n",
    "fig, ax = plt.subplots()\n",
    "x = np.arange(dif_features.count())\n",
    "y = dif_features.sort_values(ascending = False)\n",
    "plt.bar(x,y, color = colors)\n",
    "plt.xticks(x, (features_jhon.columns))\n",
    "ax.set_xticklabels(index, rotation=-45)\n",
    "\n",
    "\n",
    "\n",
    "#Create Legend\n",
    "import matplotlib.patches as mpatches\n",
    "gray_patch = mpatches.Patch(color ='lightslategray', label = 'Jonathan')\n",
    "violet_patch = mpatches.Patch(color = 'mediumvioletred', label = 'Emily')\n",
    "plt.legend(handles = [gray_patch,violet_patch], loc = 'upper right')\n",
    "\n",
    "#labels\n",
    "plt.ylabel('Mean', fontsize = 14)\n",
    "plt.xlabel('Features', fontsize = 14)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## How diversified are the lists ?!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The variety of the lists can be investigated by checking the difference in genres of our songs. In case most of the songs belong to the same genre we would say that they are low varied.\n",
    "\n",
    "The question is: How would we analyze this ? well, the answer is simple : Let's check the standard deviation of each audio variable and examine them. \n",
    "\n",
    "Although the standard deviation of the audio features themselves do not give us much information ( as we can see in the plots below), we can sum them up and calculate the mean of the standard deviation of the lists. By doing that we get the values represented in the plot \"Variety of Audio Features\" with values of **0.22 in **my list and **0.20 in** Emily's list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How would we interpret that?! Well, let's say we can have songs with a high value of a specific feature such as energy and other songs with a really low value for the same attribute. In short, those numbers suggest that my songs are a bit more diversified than Emily's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(features_jhon.std().mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(features_emy.std().mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22191480992097065"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_jhon.std().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19850736896114182"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_emy.std().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgAKCGgAACghqAAAoIKgBAKCAoAYAgAKCGgAACghqAAAoIKgBAKCAoAYAgAKCGgAACghq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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9aa2bc048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [features_jhon.std().mean(), features_emy.std().mean()]\n",
    "names = ('Jonathan','Emily')\n",
    "pos = np.arange(len(names))\n",
    "colors = ['lightslategray', 'mediumvioletred']\n",
    "\n",
    "plt.bar(pos, data, align = 'center', alpha = 0.5, color = colors)\n",
    "plt.xticks(pos, names)\n",
    "plt.ylabel('Variety')\n",
    "plt.title('Variety of Audio Features')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QnVVZg20ymZSQkFBsWmhoqOP7+Ph4xcfHlxh35RgAAACgOuODZgAAAAAD0WAD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\niIiIqA79P9KpXVo3fEnOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9aa0c3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "\n",
    "features_jhon.std().sort_values(ascending= False).plot(kind = 'bar', color = 'lightslategray')\n",
    "\n",
    "plt.xlabel('Features', fontsize = 14)\n",
    "plt.ylabel('Standard Deviation', fontsize = 14)\n",
    "plt.title(\"Standard Deviation of My Audio Features\")\n",
    "\n",
    "plt.subplot(222)\n",
    "features_emy.std().sort_values(ascending= False).plot(kind = 'bar', color = 'mediumvioletred')\n",
    "\n",
    "plt.xlabel('Features', fontsize = 14)\n",
    "plt.ylabel('Standard Deviation', fontsize = 14)\n",
    "plt.title(\"Standard Deviation of Her Audio Features\")\n",
    "plt.rcParams['figure.figsize'] =(15,15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>valence</th>\n",
       "      <th>who</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>0.684</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.0852</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.403</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.878</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.148</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  energy  instrumentalness  liveness  \\\n",
       "0         0.610         0.634   0.536          0.000071    0.3930   \n",
       "1         0.125         0.643   0.581          0.000434    0.0852   \n",
       "2         0.428         0.427   0.422          0.000024    0.0874   \n",
       "3         0.403         0.536   0.717          0.000000    0.1200   \n",
       "4         0.148         0.755   0.577          0.000573    0.2150   \n",
       "\n",
       "   speechiness  valence  who  \n",
       "0       0.0268    0.684    1  \n",
       "1       0.0325    0.824    1  \n",
       "2       0.0411    0.442    1  \n",
       "3       0.0374    0.878    1  \n",
       "4       0.0384    0.944    1  "
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_couple = features(df_couple, who = 'both')\n",
    "features_couple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Between Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also build correlation plots , such as scatter plots, to show the relationship between variables. In our case, we will correlate the feature valence which describes the musical positiveness with danceability and energy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to interpret the plots below, we have to keep in mind that numbers one ( green dots) and zero (gray dots) represent mine and Emily's songs, respectively. That said, let's check the scatter plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valence and Energy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The correlation between valence and energy shows us that there is a conglomeration of songs with high energy and a low level of valence. This means that many of my energetic songs sound more negative with feelings of sadness, anger and depression ( NF takes special place here haha). whereas when we look at the grays dots we can see that as the level of valence - positive feelings increase, the energy of the songs also increases. Although her data is split , we can identify this pattern which indicates a kind of 'linear' correlation between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X+ST8CSkjhUtxYcNGjpz1vl0Z/s9dj9ZT8DPep/WRzCXHdRxd2S4rGALQ0OjK\ndlmjvoqijDiX716wyLOIh0of4nrqOnbFzubgZuv3Yjgum4vHyh8b8X0hZiOv3csTFU/M9GHcN3Jm\nbkga4u3sbQ6HD4OZr3ZbzHsGISZDAqJ71OAmqcM1TZ2sBe4FrPKtoinRZKV+3dJuWX1mPk98TtJI\nss6/bkqGwC/EL/Bp+FPrpjaiR9hTtmfYz56JnaFP7wPy80YuJS7REGgYdR5LzsyhoLAlsIWIHqFH\n68GrerEpNmK5GG7Vbc0h6meaJieiJ+jIdFh9ewY3bE0baTRTw6XcvTrR4EqCU3Edi+Fs/KxV4EFH\n5/PE52wKbLrrdT8XP2cFQ5AvMLAqs8oqcBDVo/yy+5fWnK9rqWu8WPXihCo7nY+ft7aTMTP4VT+a\nqZExM8xzzmNzYPgALmgPFrwO2APjnv9U6ajErbqt1DIVlVLH1P5D35RooinRhKIorPOvG3b0q5iW\neZcVpbKhEGJusCk2fDZfwZzeW9lbtGXaALievs5TlU8N+RssxEyQgOgeVeWsKqgyN1KPoMlQFIWH\nSh+iIdBAzswRtAf5u5t/Z72fI8fRyFHOxc+xzr8Ony0/orTYs7goAdL19PWCJ/wdmQ5M0xw25Wpw\nBbyRKrlBPqj5qO8jWtOtqKis8a/huarniGkxvHYvdsWOq9RFvCc+5Mb4fPw8n8U+s17Hc3FWeVdx\nNXXV2l+Fo2LMvXeqndVW76H+dQeL63HaM+2U28updBX/Oo/FkEao5vBV1boyXfTpfSx0LcRn92GY\nQ69BzrxzTU9FTxX8YxnPxTkfPz9kJG4ix+i2uXmh4gXSuTQhR2jEn8mNgY2YbpOrvVetIHek0aSR\nBB1Btoe2cz5+npyZY7FnMW7VTVOiiUXuRUUv3duZ7uRw+LBVKKRX6+UZ+zOjjqIKIcR02126m8Zw\nI2kjjUNxFMzX7U/DH61qqRDTRQKie9S20DYcioM+vY+ALcDW0FZM0+R8/DwRPUKls5IVvhVF2dfA\n1KH+6mz9DAziuTiNkUbrhnSxezH7y/dPOigafFM6uOjAQKt8q2hLtVlznhZ7F49Yye1i4mJBSeCT\n0ZPUuesod5Zby6q8VejhoXNl+ivJ9evT+ljkWYSiKHRkOnCpLh4IPjDmeTJbQ1uxq3Z6tB78Nj8P\nhAp72XSkOzjUe4i4Ec/nXge3FpTmvptkLsmZ2BlM02S5b/moxQJGs8K3gs5MpxW8LPEsGXJ9T0ZP\ncjJ6Eh3JFsjSAAAgAElEQVSdoC3I3vK9rPLlg8X+0btaV21BhcBUrrBSX/8xT8Qy7zJ6sj1oaNix\nU++px2PzYGJyNHJ0xHOgKipPL36aDmfHCFsemwXuBfRqvZimSZ/Wx8nYSUxMyh3lHCg/YDVjLYbO\nTGdB1cSUkaIj0yEBkRBiVqlyVlnp/K2pVt7peafg/bFWpBViqklAdI9SFIUtocJqX4f7DnMukZ/L\noiZUUrkUG4Mbh1t9wjYFNnEkcmRIA9iBT+db0i1cS12bdKW7Bn8DYS1Mr96LV/WyKTDyU6RKZyVP\nVjzJtfQ1vKqXlb6VIwYlg2+4dXTCerggIBpJ0DZ8etUa/5oJlUtWFGXUxqXn4uesIE8zNS4kLow5\nININnTdvvUkkl8/hvpS8xLOVz1LmLBv3cda56zhQcYD2VDt+u39I6pRhGlxMXLRKk0dzUc7GzrKv\nfB9fqPgCTckmbIqNNf41BYHUSt9KWjOtBdvq1Xu5lb1FlXP4IiExPUZjpJFULkWls5IHQw+iKvmR\nvoA9wO3MbSqcFdR56tANnbe737YaGV9LX+NA+YEJnYPRDN7PQD1aD2fiZ4paMbDcWV4wR8qpOEc8\nX3dzMX6R5mQzNtXGev/6ovVrms1yRg7DNKTalRDTqM5dxyrfKi4nLmNgsMSzhJW+lTN9WEIAEhDd\nVwY2MTXI95YpdkC0zLeMhZ6FNCea+STyyYjNWu9WiSyVS9GWbiNoDxY01+x3I32DI+EjJHIJKuwV\nPFL2yF1vYsfatHSBewEXEhes+R4he4haV+1d1wPYGNxI3IhzM3MTp+pkc3DzuNOrxmO4VLWR0gYH\nu5y4bAVDAFkzy8noSfZV7Bv283E9zo3MDSocFcMGh5XOylFTMwen0fW/9tq9I6ZELPYuZqu2lXOx\nc6TJX4+b2Zt80PsBL1S9gF0dem4P9h4sqFpkV+xW1aLh+loNDFLiuThXU1eLHhAN3s9gI6UYTtQi\nzyI2BzfTnGxGURRW+1ZPaPTvejrfcFj7p1rDfdk+nq16dtrKDeuGTkuqBZfNxQLXgimtQAj56/Bp\n+FNabrZgGiarfKuGPFgSQkwNRVF4uPRhNvk3YWIStAen/HdeiLGSgOg+MvjmcbibyWJwqS7WBNZw\nOnaamBEb8v4857xRR4f6sn38uvfXhPUwNmxsDGwcUobzaOSo1eg1o2c4HTvNo+WPFuX457nm8Ujp\nI1xOXsam2Njg30BHpoPOTCcBW2DUERhVUXmo9KFRt38zfZNLyUv0ar1UOatY61874VSm5d7l3Mzc\nJGNmUFBY4lky5n9ABo/iwdB+Mv06050c7DtIPJdPzXsg+ADrAuvGfJyqorLEs8QaoezvtzQWm0Ob\nSRkpzifOW8v69D4iemRIYDZc1aJerXfEbXtt3oL+UTC2xrKj6cn20JRsQkVlY2Ajbpt72P30C9qC\nozZunaiGYMOoo4tj0ZXpsoIhgLgRpyvbNS0BUTqX5u3ut605BSu9K3m49OEpvUG6krxi/YwCnIqd\nYr57/phaH2iGxqnoKTRTY6ln6awrjy/EvSLokCIKYvaRgOg+0hBo4HDfYeJGnBJ7yYhVtYql1FFK\nLHMnICqxl7DSu5K1/rWjjpqciZ8hrIeBfGGGi4mLbApuKlhnuEaQxVTnqbNKIl+IX+BI+IiV7hXO\nhflS7ZcmtN2b6Zv8uvfX1lyb29pt2tJtE66ks9S7FI/NQ2e6k6AjOK4qX8u8yzgVO1VQmGKkIOVs\n/Czx3J3UvPOJ8+MKiAB2lOygwllBXI+zwL1gXH2xBp8bv+onYB/azHO4qkWj3bxXu6pZ4883GM6R\nY5F7Eev9Y5+DNVhfto93e961Ku51ZDp4pvKZ/H58a7iYyO+nzl1HrasWzdBY6l06a+f2DNdwuNxx\n99TRYjgbO1swwbop2cRa/9oJz3Mbi/7r1i9Hjj6t764BkWEavNPzDh2Z/Dyz5lQz+8v2U+Mefw+5\nYjFNk+PR43RluvJzF0MP5HusocpT9zmgq6uLQ4cOkclkWLx4MQ899JBcdyEmQQKi+8hiz2JqXDXE\n9Bghe2jST8LvZlfpLsw+k2guSsgW4qHSh8Y0cXzwU3QTM1+NbMDf8gpHRcHNy0TnR4zF9fR1KxiC\nfLreRFOcmlPNBTfrMPlKOuNt3muYBtdT1wHYVbKLs/Gz5Mwc9d56lvuWD7vOZFLz+imKMuFCHuv8\n64jqUToyHdgVOw3BhhGb+/ZXLUoaSSod+TlEo9lZspP1vvVopkapo3RSNw3NqeaCn8v+gHeJdwk7\nS3ey3l+c/UyXZd5lhLVwQcPh6QreDGXo34GBFQinQp27rqA8e9AWpM5dd9f1wlrYCoYgX8Tiaurq\njAZEZ2JnOBk7ab3uyHSgKioO1cEm/yZW+Ys/Kilmh1wuxxtvvMHNm/nU4ZaWFjweDw888MBd1hRC\njEQCovuMS3Xhck6sxK9hGpyNnSWRSzDfNZ/F3sWjfj5gD/Bk5ZPj3s9K70pupG9YKV2L3IuG3Pw+\nWvYovqiPZC5JhaOCjYHizoUaaDzV7Ma7LWubUxyc9jNMg3d73uV6Oh8Q1bnreLH6xbtOHp9Mal4x\nKIrCrtKxdYUfWLVorAKOoaNNEzH4OqqoeG3eou9nOm0NbR2xc7xhGpyJnSGZS1Lrri1qr6OV3pW0\npFqsFMiF7oVT0j5goJSRotpZTVJJEjADbAhuuGt6YFu6jauJq0NSIqdy7uBYDK54mTEzYOa/47Ho\nMRZ7F0sFr/tULBbj9u3b1mvTNOns7JzBIxLi3icBkbB82Peh1XT1UuISu8xdRSvdPdB893wOVByg\nNdWK1+Yddn6FXbWzs2Rn0fc9nM2BzQXV7CYTfDUEGujKdhX0iFriXsJq3+pJHeOF+AWupa5hV+xs\nDGwcthAF5Jt19gdDkB/9ako03fVp8VLvUjyqh45MByFHaMYacA7sEYUJ9Z56dpTumDWjLev86+jM\ndHI9fR0bNlb5VlHtqp7pw5oyh/oOcSV5Bcj/TdhdunvEUcbxKnGU8GTFk1xJXMGu2qeswXO/a8lr\nfND3gVWu3Of23TUAG7yOgoKJSY2zhobA5OZvTZbPNvJofMpIkdSTuJ1jD4hOnjzJ+fPnsdls7Nix\ng8WLFxfhKMVU8Pl8hEIhenvvzJ8sLZ3aRtBC3O8kIBJA/knwjfSdKnUaGi3plikJiAAqnBVTOldg\nPMqcZTxf9bzV02lwM9bxcNqcPF35NH1aH5lcBrfNPen0qWvJawVznPq0Pl6ofgGXWjgSGNNjdGaH\nPiXUTG3IsuHUuGtmNAUI4LPoZwU9os4lz+Gz+4peLXGibIqNJ8qfoE/rw67Y7+vJwYZp0JG+kybW\n/zehWAER5OeOjbcJr6ZpNDU14fV6Wbx48Zh/t66lrhX0burIdJA1siOmZg63jh07T1Y8SZWrasZL\ndm8NbSVpJLmVvQVmvoJi/3zBCkcFIUdozNu6cuUKb7/9Ntls/rt2dXXxe7/3e/j901NtcDLSuTTt\n6XZC9tCMNa6ebg6HgyeffNKaQ7Rw4UJ27RrbCLsQYngSEAkg/+TTrtoZOL2nmCkhcT3OuXi+utNa\n/9phJ8zPJLtqL1q6jqqoY+ppNFZd2a6COU7RXJTubDe17julwluSLXwc/pikkbSeYgNUOipZ4Z2a\noHYq3NZuD1nWnR25nPVMUBSFbq2b7mw3pY5SVvlWzZoRrGJSUIb8DZjpNLFUKsXf/M3f0N7enu/h\n1dDA008/PabzP9x3sSm2ca3jVJ1UuipnPBiCfHC+p3QPZ2NnSeaSmJgkjSR2xc7m4Oa7freB2tra\nrGAIIBKJ0Nraytq1a0ddrzfba1Vc3BDYMKmHSRMxsGKpHTsbgxvZEpwbZdSXLVvGsmUzM5IvxP1I\nAiIB5G/yNvo3cjR6lLSRpsxeNuYqdQk9wZHIEdJGmkpHJQ+EHii4QUnmkrzd/Ta9en54vy3dxhcq\nvoDX7h1pk2KAkL3wSa9H9VBiL5z4fjZ+1pqTZWLiU335ZrG+NbhsE5tTNhMCtqGB8nT1xBmrk9GT\nfBb9DAMDBYW4HueBkvtvMrOiKGwMbORY9BhpI025o3zG08Q+/fRT2tvbgXx65alTp3jwwQeprr57\n2uLm4OZ8IKt147F52OjfeNegYeA6TsXJxsDd15lOH/R9wOXkZSA/93F3ycRSGisqKlBVFcPIPxHz\ner3Mmzd6WfFwNsw7Pe8UVFx8uurpaQ2aB1Ys1dG5GL/IxsDGGQ/chRD3HvmrISyr/KtY5FlENBel\n3F4+5j5GB3sP0pHNp9bcyNzAptgKmh02J5utYAjyPWaaU82j9vsRd6zyrSKshbmeuZ6fQ+TfOKSa\n3+AqcR6bZ9I9agYKa2F6tB6qndVTGqBsL9lOn9ZHV7YLBYUaZ82IE/6nim7qtKXacKtu5rnmDRl9\nuJ6+bk2uNzFpy7TxAPdfQASw2r+axZ7FxHNxSh2lM36j2X/DPvD1wJGN0fjtfp6tepY+rY+l85cS\n6x7aQ220dXw2X0EBjZmWM3O0p9ut15qp0ZpunVBAtH79em7dusXnn3+OzWZj27ZtlJePPsp9JXWl\noOLiLe0W7an2uxbjKSbDHPTzgJGvEDqBAdtkMsm1a9eorKykqmrqqpoKIWYnCYhEAY/Ng8fmGfPn\nDdOgT+8rWDa4+tHguS4DlzUnm7mVvUXIFmK1f/WsSj3STd1qxFjvqR9XX51iUhSFHaU72MGOET+z\n1LOU7mw3Ojo2bCz1LC3a/i8lLnEkfISMmcGv+nm47GEWuBcUbfsD2RX7uCvITUZfto9LyUuEciGW\nkj9nb/e8bQVkK335ZqEDDR4hmOkgYaqN92/CVInrcZRlCt7zXpLh/GhofX09tbW1d1nzDruST40N\nOAPEuHtANHCd2UZFxaE4SHGnzP9kRq/27dvHvn37xvx5h1JYcVFBmfaUueW+5bRn2q0y6ovciyZU\n0bOzs5PXX3+d3t5enE4ne/bsYefO6SnqMx5NiSZ6tB4rVVcIUTz397/kYsqpijqkUebg6kfLvctp\nS7fRnGoG8jfvy73LORs7y9HIUWsicESPsKN05Jv+6WSYBu92v0t7Jv8EtjnZzP7y/SNWdxtOVI9y\nLJJPN5rnmMem4CZs6tSk26wLrCNgD3A7e5syRxlLPEvG3UdoJOfi5/IlfYG4Eeds/OyUBUTTKazl\nU36iuSjE4ZLjEjWuGrqyXUB+9OdS4hJrfWsL5oQ1BBqI63GiuSh+1c+mwMT6Sw1kmAZHIkeskYgd\noR2jpjoOTFOtclaxNbgVE3NWzG2ZCslckn/s/kf67H349vvwtnhZG1rLQzseQlVn7jsbpjFj57w/\npfFo5CgZM0O5fXpTGtcF1tGR7aAt3YaKyirfqnH9fSyGBe4FPFH+BNfT1/HavBOq5mmaJocPH7Yq\ntmWzWY4fP86OHbOjuqVh5lNzT0VP8VmsMFV3ukfPhbifSUAkJm1XyS6OhI+QMlJUOCvYFtpW8L6i\nKOwt20uD1oCJSZmjDEVRuJ66bgVDAG2ZtlFHQaZTT7rHCoYAkkaS5mTzmP/BN02T93vfz1eAIp9K\neCZxhnpvPQ+VTE1H8UWeRSzyLOJ45DiNkUYgH4xO9h/NIWkpg9KWdFOnPd2OW3VT7ayeFTcRY3El\neSUfDP2TW9qtISMhJia6qRcsq3XX8sXqL9Kn9VHiKBl2BHS8Pg1/yvnEeet11sjyeMXjI35+cJpq\nU6IJ3dTx2X08UfbEPdkPaTRXkleskWh7yA4boTxUjs02M/N5WlOt1sOOKmcVe8v3jnmk0DRN2tvb\nSSaT1NfXY7fffb1IJEJ7ezu1tbWUlNyZP7jav5pFnkXE9BjljrGnORdDf8XFHq0Hh+KY0oa+/emB\nDsVBjaum4G9MlatqwqP3lxKXOBU7RVuirXB/uVzRHihNlGEaHOo7RGe6E4fqwDCNglTd6+nrEhAJ\nUUQSEIlJq3ZV81z1c6N+RlEUypxlBcsG/+M9m1KPXDYXDhxo3ClZPZ50lIyZIayFC5Zppsbnic8p\ntZdO2fyp66nrnIqdsv7hPBU7RaWzclINNRd7FhOJRTAwcOBgqfdOOl4mlylIMVvlW8VDpQ9N+ntM\nh/5gdaClnqX0aX1WoLTAtWDYdCmn6ixq/6Ferbfg9eA01IFyZm7I+wkjAUBGy/DGrTd4df6rs2ry\n/3glc0nOxc5hKiarfatHTbudbv2jef0NZVvSLRyLHGNHydge5vzqV7/ixIkTmKbJwoULefXVV3G7\nR041u3TpEr/61a+IxWL4/X4OHDhQUP3Na/PO2NwmVVELfj9yZo4zsTNkjAyL3YuZ5578iJFu6Pxj\n9z9aLQVWeFewp3TPpIOVtJHmaOQoKSOFY6kDpUPBzOTnYq5YsWJGRx4BTsdOWz3AMPIpkgPNpn8v\nhbgfyG+UmDEbAxsJ62EiemTSDVGLLegMsi6wjjOxM+TIUe2sHlc6ilNx4rP5yOpDJ3wncoliHmqB\nsB62giHITzLuv3Eb7HLiMleSV1AVlQ3+DdS4a4Z9KvpA6AFKHaWEtTDVrmoWuhda752JnylIMfs8\n8TlrfGuKWna8mHqyPZyInkAzNLq0roL3QvYQy33LqXJVcTlxGYfiYF1gapuF9hucZjpa002bYsNr\n8xakqQ6UMlNcjF9kXWBdUY9xumRyGd7ufptuLV9uvTXVypPlT7LUs5SrqavAnbTb4Uz1k/2smSWV\nKzz3g18PdC52jrZ0G3bFTl26jpMnT+Yn/pMvd/3pp5/y6KOPjrj+kSNHiMXy853i8TiNjY13LYc9\nE0zT5L2e9/JNlYHLycvsLdtb0B5gIs7Gzxb0V2tKNrHCu4L57vmT2m5CT1i/Q57FHmxuG8FbQdbN\nW0dDw8xWU4T8Q4GBDAy8qpekkSxaqu50GZgS7Lf52R7afk9VPxVzgwREYsbMc83jhaoX6Mn2UOIo\nwWPz0Kv1EtWizHfPH7Vh4nR4IPQAK70rrVTA8TxxVxWVnSU7aYw00qv1WkGKQ3FM+h/y0Sx0L+Rs\n/KwVdPlsPha6Fg75XHu6nY/DH1tNW3uyPfhtfqK5KF6bl52hnQVPd5d5h+93MTD4guFTzGYL3dQ5\n2HuwoOLhQFWOfNpNyB6a9lSU7SXbyRpZ+vT8DcPO0OgTuneFdtEYaSRlpEgb6YLmoTD2ZryzUUu6\nxQqGIB/kN6ea2Ve2j01a/iaw3FE+JOhJp9P89Kc/paurC5/Px2OPPcaSJUuKfnwuxUW5o9y6SVdQ\nRhwtvJy4TGOk0UoN7uzuHJJymsvlhlt1xPfDmfCMp3MNJ2kkC6repYwUzanmSQdEOXPo+SnGz3fI\nEaLcUW4VAfLO87J7ze6CEfCZVOOq4VLiktWDrtxRzoGKA0S1KKXOUtzq9BawmIwhKcFmlsfKH5vB\nIxJiKAmIxIxyqk5q3DVAvr/LyehJdHTK7GXsL98/pXnpYxF0BAkSnNC6te5avuj+Ir3ZXk7GTpIj\nxxL3EurcdUU+yjtKHaXsKd3DxfhFID/HoNRZOuRzNzM3C24q4kacuBEH8jc2RyJHeN79/F33t9Kz\nkmvJa1aKWa2rtiCFxjANzsbOkjJS1LnrpjQYvJuIFhkxGPLZfSzzzVyTQ6/Ny4HKA2P+/Dz3PJ5z\n59NUz0XPcTh62HrPr/hZ6V1Z9GOcLsOlwjlUB4qiUOGsGHG99957j8uX8z15otEo77zzDr//+79f\n9MBBURT2le3jaPQoGSNDjauGtf7hR2y6sl0F8yRTZSkWLllI27X8nJWysjI2bRr5SX9bug3nYid0\nAjqggrnQ5Hz8/KwbAbQrdhyqg5xx5/vamHza5grfCq6mrlr9hmqcNUUp6mJX7Owr28eJ2Al0Q6fO\nU1e0YKgn28OV5BVsio1NgU0Tmtu11LuUrJmlNdWKQ3WwObAZn8036ujxbDU4JXjwayFmAwmIxLQZ\n7almzsxxPn7eehrWq/dyOnaaPWV7pvMQp0SZs4x95WMvZztZC9wL7nrDELQHUVCs/kUD/x/yQdFY\nnkKXOEs4UHGAK8kr2BX7kBSzg70HreqClxKXeKTskWHnM03HE2+/3Y9P9VnzbQAWuRZR7iynYUED\ntti9OefmUupSweugIzjlTY8vJy7TnGpGJZ9uOdpckfFe20XuRazwrqAp2WS9Hkv1sEQiMeS1YRhT\nUnjBa/fySNkjd/3c4EbDfruf57/0PKeOnULTNNavXz9iv58rySt81PcR2gqNUlcp2e4sjjIHniUe\n4rl4Mb5GUblUF2v9azkdPY2OTqWjsihV74L2IAcqDtCUaMKm2FjvX1+0+XEljhL2lu0tyrb6dWe7\nebf7XesBU2emky9UfmFCqberfKtmbXntgamgW4JbhswRHmjwHLd7MagT9z8JiMSUu5m+yaeRT0nm\nkpQ5yni07NEh/SpyZm5I+tXg6maieJZ7l9Or9dKabkVFxW/z05a5U2lpuJSkkZQ4SoakmN3O3iaq\nR2lL3dlmxsxwNXm1ICC6kb6RT/3KpSh3lvNo6aNTllvuUl1sD23nVCzfW2q+e75V8W9+YD4dsY4p\n2e9UK9bviWZodGY68dl8o84Ba0+380n4EytNr0fr4bnK54YEYZcTlzkZO4lmaNS6a3m49OEx3RQq\nisKe0j2s86/DwKDSUTmmn8Xa2lo+//xz63VVVdWMVaHrtyGwgUguQke6A4fiYFNwEz6nj127dt11\n3WvJa9YornuRG/ei/N/MqU67nYwtwS0s8ywjmUtS6aos2sT/oD14z1RUu5K8YgVDAJ3ZTm5nb1tp\nlalUiuvXr1NeXk5FxcgjnrPZ4FTQiB7h+ernR7zeO0I7yBpZwnoYn83HZudmLl26dE+fA3H/kYBI\nDNGUaKJP66PCWUG9t37S2zsSOcJt7TYAiUy+f8rgp6tO1clC90IuJ/MpL27VPaMpTLNZTI/xeSJ/\n47fev35CzRAVRWF7yXa2sx3IP8U/ET1Bt9aNR/UMKZ0+HkfCRzgXP2f1yxho4A2xaZociRyxcvgT\n6QTHosfYXbp7wvu+m3pfPfW+yf9MzyaL3IsIx8OYmEOqAI5VXI/zTs879Gg92LGzIbBhxBvQzkxn\nwZylWC7G5eRlNgbvFEVJG2kaI40kjfzE8KZkEyX2EjYFxzYR/G7pccPZtWuXVdLa5/Oxf//+ca0/\nFRRFGdLYd6yGNADGznzXfOq99VOadjtZIUeIkCM004cxYwYHBSqqNR+2q6uLn/zkJ/T09OByuXj0\n0UfZtm3if2tnyuBU0F69l4gWGfFBitfu5cnKJ/PrdnXxk7+698+BuP9IQCQKHIsc41TsFCYmNmwk\n9AQbghsmtc3+m6J+I1VkeqT0ESodlaSMFAvdC6e9yd+9IKEn+Mfuf7Ty6dvSbTxd8TRO2+QKUCiK\nwpbQlskfXy7BhfiFgn4ZKioGBuWOchqCd1JoDIwhlZQG/6xMhemckD4d+3qw5EFMTFrSLTgVJx7V\nc/eVBjkdO20Fpjo65+PnWR9YP+x8nsHplgBnYmdY6F5opc0k9MSQazmW6or91dcmcs4UReGhh+6N\nku9j0RBooEfroU/vw6k4eSD0wIhzlaaKpmm8++67hMNhysvLeeyxx2Z81G222xjYSGemk85sJyoq\na/xrKHXk53F+8skn9PTkf88ymQyNjY08+OCDs65Axt34bf6C1z7Vh9/uH+HThT7++OP74hyI+48E\nRKLA9dR160YnR47WdOukA6JyR3lBzvtIT34VRZl1E4Vnm6ZkkxUMAXRr3VxNX501eeaaoRU8OQRY\n5lmWL2ftrMKhOqzlNsVGmaOMG5kb1rJKx9CeP8WS0BN80PeBVfp1V+mucY9CjNWVxJV8upipMd81\nf8zpYhMR1aNcSV6x5kZ92PchXpt3XH2SBl8z3dSHre4F+T4wbak2rqavWstSZorLyctsc+af9A6u\n4GXHTo2rZtRj+M1vfsP58+fzwfmWLezcOXqlvfvF7du3iUQiLFq0CIfjzu9HqbOU56qe43b2NkF7\nkIB9+pvtvvnmm5w9exaAK1euoOs6Tz/99LQfx73EoTp4qvIpbmVu4VbdBUVtBlcM1HV9VlYMvJsN\ngQ1E9AgdmXwqaEOwYcx9we6XcyDuPxIQiQI2tfDpXzEmrz5S+oiVPlPhqGBLcPIjEXPVcKXIXcrs\n6edgw0bAFrCqzvlUH2sDawsqz11PX6cz3UnQHmRv6V6ORvPNEasd1VPaW+NI5AjtmXxZ4ISR4NPw\npzxT9UzR95MxMhyJHJlwuth4tafbCwpFZMwMNzI3xhUQLfcspzXVavVlqXPXjTjSpCgKm4ObaUm3\nFMz7G/i3or+C12fRz9BNnTr36BW8zp8/z+HDh62S1IcOHaKuro4FC/LFQUwz3+MqqkepcdfM6pSx\n8Th06BCffPIJuq5TU1PDK6+8QiBwJ/Bxqs5Jl62ejFu3CpsXX+m4wo30jRk9pnuBTbFZ1VMHWrt2\nLVevXiWdTgOwfPnyGW8AOxGqok644NG6deu4du2adQ5mQxNcIUAColkpqSdpjDSSNtNUO6ppCDag\nKAo5M0djpJGIFiFoD7KtZFvRu1Vv9G/kk/An+eZvNn9RmqW6bC4eLptYHr0otNq3mrZ0G9fT14F8\nf6DFnsVF2/6lxCWupa5hw0ZDsGFcIygJPcHbPW9bwZBH9bC/bH9BMNSUaOKTvk/QyE8W7/X1DvsP\n61Q8MZyu9Ly4Hh93uphhGhyLHKNX68VvzzcuHDiaNppyRzl27FaFRhWVEvv4ytXXuGt4ouIJWlIt\nuFU36/zrRj3/Zc4yVvtWczFxEQODGmfNkL8VJY6SMVdX7OnpKejPo2kaN2/etAKiw+HDVh+TC/EL\n7CrdxQrfinF9x9kmlUpx9OhRdD1/3To7O/noo4946qmnZvjI7ggEAnR13WlgnHamea/nPR4qfWjW\n9OuB6UuDnex+1qxZg8fj4erVq4RCIbZsmZ0PB6fyfN4r50DMPRIQzULv975PRzZf8aot3YaqqGwK\nbsyu0R8AACAASURBVOKT8CfWZHoy+eZ0Yyn9Oh5LvEuoclbRq/dS6aic0IR9MXVUReXx8sfpynRh\nU21jrsA1Fq2pVg73HbaClT69j+ernh9zg9zLycv06X3W65SRIpwLU82dkYqrqavW9iE/WrTT3Gl9\nB83QeL/3fXq0Hqu4Q7EqapU7yws63pfZRy4TOxkhR4hyezk9ej5dzIbtrqM1RyL5QhQAZPKjTPvL\nx1YUoNpVzdbQVi4mLmKaJvXe+gndrFY5q6hyVo3587tKd7HKt4qMkaHaVT2p0eSlS5fS2NhIMpkP\nJEOhEMuW5YuqmKZpPQAA0NC4mrp6zwdEuq6jaYUNRu/WpHW6PfHEE2iaxo2eG6hBleC2YL5aZOrq\nrAiIzsfPcz5+npyZY4lnCdtC26bkRv7YsWMcPXqUXC7HqlWreOyxxya8nyVLlkxJw+Bi6C9005Jq\nwabYWOtfOyXz1mbzORBzlwREs4xu6kOaR/ZXaOvJ9hQsn6rmZj67D59d+gTMVqqiDpuOMVmdmc6C\nYCWsh7mdvT3m9JjhRiudSmEwNbhRo02xFdxYNEYaaU23AhDPxfk08ikvul8c83cYzfbQdmzY6NV7\n8dvyozBTwa7Y2Vu2lxOxE2imRp27jmXe0SsmDv7d7p97M1YbAhvYENgw7bn4o5XnHo8FCxbwzDPP\ncPr0aQC2bdtGSUl+lEtRlCHBVjEafs40v9/PsmXLrFLhfr+fDRsmN1+z2CoqKvhn/+yf8bedf1tQ\nSnqq5sONR6/Wy7HIMavi4dn4WcocZUUPlG/fvs1vfvMbMpkMAEeOHKG6upqNGyefPTHbNCWaOBs/\na70+HjlOrat2xhukCzEdJCCaZWzY8Kpe0kbaWuZV8/09vDYvA+5XJ1RNSoiRDG4i6Vbd40q9WuNf\nQ3um/U46n2doOt/G4MZ8iVY9gktxsd6/vuD9walmyVySnJkrylw2VVHZVjI95V1LnaXjasY7uHFh\n/+/8eN3LE5NXrVrFqlXDFwfZ4N9AY6SRjJkhZA9N2XyssehId1ijeSu8K1jsXTyh7SiKwm/91m/R\n2NhIKpVi1apV1NbOvrk5iqKwIbCB49HjZM1sfj6cf2rOfyKR4Ne//jWpVIra2loeeuihEX+me7O9\nBeXfTUyierTox9TV1WUFQ5AfRentnZqHkTOtP925X8bM0Kv1SkAk5gQJiGYZRVHYUbIjP4fISFPh\nqODB0IMAbC/ZTrYvS1SLErQH2VGyY4aPVtxP1vjX0Kv30p5ux6bY2ODfMK6RwoHpfKry/9k7z+84\nrjPN/6q7OmfkxARmEMwgKYmZlEiaVLIkeyTb8shzzmj8af+bPWfOftjdM2eCdyx7xh6RooYjM2eR\nCCQBggABkMixG51TVe2HHhTZiA2gG4n10xdVs6r6dqPRuM+9z/u8OoqMRRMmM0XGIj4u+pj+eD8e\n0TMhOavQUEhHpEM99oieacWQN+ElIkXmbdlabN5yvUVMjqUS8ET7ovxuD8QHuO29TUgOpRoo57BJ\n7mzZYt9Cubkcb9JLsbE440SrMYaGhvj222/x+Xzk5eXxt3/7t3Mahz/p58rIFXW3pC/eh1Vvpcg0\n0Wp4f/Q+z8PPEQSBrbat7HC82v0Zjg8Tl+MUmYqWRZpetaOaVeZV+CU/xcbijG20s+WPf/wjbW2p\n9MJnz56h0+k4dGjyvmSlplIcegcBKQCkdqNnSjKcC2vWrMHj8eD1puzAZrOZtWvXZv15lgKlplKe\nBJ+oQtOpd1JqzP57qqGxFNEE0RKk3FzOJ+ZPJthfXKKLDwo/0CIqM+R5+DnDiWEKDYWss2p+5ZkQ\nBIHDnsPz+nxlYucz6UxTpoTtcuxCVmQG44OYdKZpG8Te9d3lcfAxEhJFxiLO5J9ZtjVvNtHG2cKz\ni/q7fct7i4FEKlUsKAW5M3pnzklSucAhOuYcPf3999/z4kXKijk6Osrvf/97Pvzww1nfpyvalWYd\ni8pRumPdEwRRW7iN+kC9msL34+iPFBuLKTYVc9N7My2M4kzBmYwDNBaTXDdcTSaTaQEOAN3d3VOc\nnfqdOZ53nEeBR8jIrLesz0n6ncPh4Kc//Sm3b99GkiSqq6tXbP1LhbmCg+6DtEXa0KFjh2MHFlFz\nomi8GWiCaAkz1cRIE0Mz89D/kIf+h8jI6NGzT9qXtkKrMTWL+fnKtEFsSAqliqn/u3/OQHyA+kD9\nglnicsVivfeKokxqV1wpBAKBtOPR0dE53SffkI8Bg1prp0OHS5woEnwJX1okeZIkw4lhjIJRFUMA\nvfFe6gP11Lhq5jSelYRer8dutxMKvUpktNunb/ZZYipZkAbeq1atYtWqVTl/nqXARttGNto2LvYw\nNDQWnMWvjNTQyAEdkVc9UiQk2iPtWbv3QHyAP/X/iX/p/RcuDl0kJsVmvmgFEpNiXBy6yL/0/gt/\n6v8TA/GBmS/KEnE5rsZMjzFVI9FsoSgKQ/Eh+uP9KIoy7bkxOUZ3tJtwcnmICkEQ8Bg8aY/lG+Yf\nmOBL+OiN9ub8ZzMTRUXpOzhlZXNLLiw2FbPHtQen3olD72CHfcekaWvl5vK0/mA2nY0KcwVROZom\nlCD3n9vlgiAInD59mtLSUpxOJ5s2beK9995b7GEtCeLxOB0dHXMW8hoaGjOj7RBprEhymUr1urUo\nIAWWnLVoobgzekcNUAhIAW55b/Fx8ccL8twu0cUq0yo6Y51AasI5U5LbfFAUhSveK7SEWwBYY17D\ne/nvTZq21Rft44r3Cn7Jj0Vn4R3XO6y3rc/Z2LLFcc9x7ozeISJFyDfkz3vX4t7oPR4HHpMkSbGx\nmDP5ZzDpTSiKQnOoGb/kp8xURoW5IkuvYGo++OADTCaTWkP0+eefMzg4OKd77XTsZIc9tds81Y5e\nsamYw57DPAs/Q0Cg2l6NU3Ri19spM5apbRXsejsbrdpq/Bjr1q3j66+/1mzhrzE4OMg333zDwMAA\nFouF48ePs2/fvsUelobGikMTRBorku327fh9fiJyBJvOxnbH9pkvypCVbC2aDZM1Ol2oiYxO0PFe\nwXs0BBpIyAniSpzaQC3mkJkDrgPzriUajA/SEGhQaxN0gk4VQwAvoi9oCjVN2qOjLlinpjVF5Aj1\nwfplIYjMenPW+pqFpJAqhgD64/3UBmp5y/0Wd3x3eBRKRfs2Bhs56D6Yc4uOwWDg3LlzacfzIZPP\neKW1csLukU7QcbrgNA2BBpIk2WjZSJ4xN/2wljOaGHrF9evXGRhILcBFIhFu375NTU2N9h5paGQZ\nTRCtYCJSBF/CR74xP2epQEuVSmslRcYiBuODFBmLstpXySN6CEqvCqvnYy16MPqA1kgrOnRssW3J\nqnDLNfmGfHWHBlLvy0L+kRYFkT3OPdT566j316uPB6Ug5wrPTXPl9ESSEX4Y/kEVNT3RHjbbNk84\nLyEnJjwGIMnpFqhsWqL8CT8hKUShqXDSvk9LhZgUm2BplBUZRVHoiHaoj8WVOM8jz3MuiCRJore3\nF7PZTEFBQcbXRaUoI4kR8o35s062mwqDzpBRndx8kRWZa95r9MX6MOqM1DhrWG2ZPMxkIUjICa6M\nXGEoMYRFZ+Et11uUmCev/1EUhRu+G3RHuzHoDOx27F4SjWAXg2QyOeFY20HT0Mg+S/cvqsa8aA21\ncnv0NhE5gkt0cdxzfNJo2JWMXbRjF6cvyp0Lx/NS1qKwFJ6Xtag93E5toDYtiarIWESxqTibw80Z\nNa4aFBSGE8NYdVbecuem0elMjNkXxxhJjMyrd1FPvCetH0dMiYECBYYChhJDALhF95QWvTWWNfTF\n+9TAh1Xm7BRjP/Q/pM5fR5IkhYZCTuefxipO369IVmSeBJ8Qk2OstaylwJi5GJgJSZF4FHhEUkmy\nwbohrVeJ2+CmwlRBV6wLSFka11vXL0qT1UQiwT//8z/T0dGBKIrs27ePr776asbrXkRecMN7g5Ac\nwqF3cMRzJCcpZrmi1l/Ls/Cz1IEEt3y3KDOXLZqQvjt6l/ZoqpYzIAW4NXqLT8yfTHpuQ6CBplBT\n6kCCO747qbqsLInS5cTWrVtpa2tTeyGtX78enU4r/9bQyDaaIFqh1AfqicgRAEaTo9QGajltOr3I\no1oZZMta5E160wqsEyQYTgwvG0G0kI1Op2N8g2KrzopuHnkxeYY8DIKBhPJqB6g71k2BsYByUzmC\nILDFtmVKsV3tqMaqt9If78ctutlim7zZ6GyIyTEeB19Z0AYTg9QF6njHM3UPG0VR+MvIX2iLpPq6\nNIeaOZl/MiupXLIi8/3Q96rgaQ23cqbgjCqKdIKOUwWnaPA3kFASrLOuo8iYWpCptldzf/Q+MSWG\nU+/MefrjzZs36ejoAFKr6/fv3+f06Zm/C+sCdYTkVOJZQApQG6hdVoJorD/PGCEpRFSK5mSRKBPG\nW2xDUmjKhYvxYw/KQYLJICbjwgmi7mg3TcGUKNtq37rgP/uRkRGuXLlCIpFg9+7dCIKAy+Vi//79\nCzoODY03BU0QrVAWOoFLY/aUmcowC2aiShRIraKXm5bPhGupsN+5n5AUYig+hEWfsuLMx07iMXio\ncdbwJPiEsBRORSYnhxlODlNpqeTd/HdnvMdk9SPzIakkJ1j0xv+OjyciR3gZeakeh+QQz8LPsiKI\nBuODqhiCVIf7R4FHbLJtosBYgF7QpyyNrj0Trq2yV1FmKmMkMUKpsTTnfU4msxyFQiGs1ul318Z/\nZy6379AiYxEt4RYUUomIHoMHq37615xLCgwFaXZJj2HqpsvFxmKehp6qC0Z5Yl5OeyCNxxv3csV7\nhZCUEsT98X7OFp6dkMSYK5LJJN988w29vb1Aqubt448/pqqqakGeX0PjTUQTRCuUClMFo8lURKeI\nyBrLmkUekcZ4SkwlHPIcojncrCZRLeQf/YUimAzSHGpGJ+jYbt+OqMvu145Jb+JMwRlkRZ409W0u\nbHdsp9pezbeD39Ib71UfH04MZ+X+s8Wqs1JuLldT/cw6M5WW6QWXTtChF/QklVeCIFv2NFEQERDU\nyTZAc7iZpnATRYYiTuWfmtbOZ0qa6Pqxiy662LdvHxZL7kRRdXU1jx8/ViOL16xZw9q1a9VC9alY\nZVrFcGIYBQUduimbCS9VquxVxOU4PbEetYYoW78fc2G3czeSIjGQGMCis6hNl7uj3fTGenGKTjZa\nNyIIAhttG4nKUTqjnRgEA3ucexbU6tcZ61TFEKQWEzqjnQsmiIaGhlQxBCnb54sXLzRBpKGRQzRB\ntEJ5x/0ObtFNQApQZCx6YwtSlzqiTsSAAQQwCEu/W/1sCSQDfDf0Hb6kD4CuaBdnC8/Oub5nOrIx\n2Qsmg/zo/5G4EqfCVIFFP9GOtxgIgsB7+e9RH6hP1QOZ11JqLp32GrPOzFb7Vh4FHiEhkW/IZ6dj\nZ1bGk2/MZ7NtM82hZhQUBAR1NX8gMUBtsJaD7oOTXhuNRvnHf/xHenpS0dPNzc18+eWXmM3zSwac\nipKSEj7//HMePXqEKIocPHgQUZz5T98+9z6cBifehJd8Q/6ybFa5y7mLXexa7GEAqc/wPnd6XHRL\nqIWbvpvElTgCAiOJEbUWcbtje8YhM7Isc/XqVQYGBnC5XJw8eXJeSYIu0YUOnfqZ1qHDLbpnuCp7\nOJ1O7HY7weCr8B6Hw7Fgz6+h8SaiCaIViiAIbHNMjATWyD5Pg095EnyChMRa81r2uzPzeA/GB7k2\nck2N8R6IDfC2+23KzeUZpQIGEgGiSpR8Q/6irvxOx7PQM1UMAfTGe+mMdrLWsnbxBjUFsiLzX8P/\npYY0dEY62efcR1SO4k14sevtC1ozdc93j45oB3r0bLNvY4t9C3ucEy1o07HftZ915nUEpADlpnJM\n+ok1GKFkiKAcJN+QP6tV+MPuw2y0bmQ0McoN34203aLp7GX19fWqGALo6emhtr6Wyt2V2HX2rCZC\njlFSUkJJyeytgpOlC74ptLS0cOXKFWKxGKtXr+b999/PejH/88hz4kocAAWFjkjHnMJZfvjhB27d\nuqUeh0IhPv300zmPa41lDTsdO9Wo/Y3WjXNK6PMlfCSVJHmGvFl9R1utVk6ePMmNGzdIJBKsWbOG\nd96Zul5QQ0Nj/miCSENjHvgSPu6O3k0lkQH1wXo8Rk9GzRa7ol1pPY2CcpBLI5dwiS6Oeo4i+ASa\nmpqw2Wzs3bs3bTLy4+iPai+TUmMpp/JPTTrZXWwmmwQYhaUZAR+WwmmWOAkJb9LL+4XvZ9WOlwkt\noRbqg/WqyLg7epcSU0laklumFJoKKaRw0n97FHjEA/8D4kqcAkMBp/JPZVx0LwgCpaZSSowlvIi+\n4EX0BTCznW+ylfsnkSc8GniEUTCy17l3WcXPr0QSiQQXL15kZGQEgOHhYdxuN0eOHJn7PeUEj4OP\nURSFLbYtWMWJ4Sdz/R17XWAD9PX1zXmcY+xz7aPGmUoQnUtN4g3vDbUOarV5Ne/lvzernfFdu3ax\nc+dOFEXRUuU0NBYATRBpaMyD4cSwKoYgtcrpS/imueIVTtE5oQ4DUqmA11uu03WpC78/Ff/84sUL\nPv30UwRBICyFeRR8pBbV98Z7qQvUzXn3IiEnuDt6l7AcplAsZJdzV9Z6XGy3b6cz2klfPDVB2WDd\nQKlpeqvXfJnr6zHrzFj0lrQeU2MWuYXegfMlfWmfi5gSYzgxPCdBNBWSItEQaFBX6IcSQzz0P+RI\n3uwmvYIg8G7+u9T764kpMdaY11BmLks7JxQK8cMPPxCJRFizZg2bNm3i2bNUJLR9tR2pUkJAIK7E\naQg0UGWvyomtUiMzQqGQWnM1hs+X2ffaZCSVJBeGLtAf7wegLdrGuYJz7LDvYDgxTEAKYBSMkzY6\nzoTxARk2W3Z2Gef6Pdgb66Up1KT+Dr+MvuRJ8MmsExUFQdD6DWloLBCaINKYE53RztTKshyn2FjM\nYc/hJWvbyiWlxlLsers6iTZgyDjFq9JSyaB9kOfh50TkSFoE90DTgCqGAJ49e0YwGMThcBCTYmmR\n0EBa4fxsuTxyWU1/6qADGTlrjSNFnci5wnO8jLzEoDOosdW5ZK6vR9SJ7Hfu52HgITE5RomxZNKU\ntIWgxFSCIWAgQern7NA7KDG++lx1dXVx6dIlQqEQJSUlfPTRR7OumZAUacLnZq6fI72gn/K9UhSF\nb775Ro2+fvbsGadPn2bv3r08CjxiqHAIQffqM5FUkvPqI6UxfxwOB0VFRWphvyAIlJbOfSHjefi5\nKoYg1SusOdTMLucuPiz8kN5YL3nGPPIMeXO6/3vvvUc4HGZoaAin08m7786cBJlLIlJkwkJXXI4v\n0mg0NDQyQRNEGrMmqSS55bulptj5kj5MggmT3oSAwDb7Ngy6lRcQMBlW0coxzzEagg3IikyltTLj\nRpyCIPCW+y32u/Zzx3uHx+HHQCoJzCyaGWJIPVev16uF4C6Di3JTOd2xbiDVh2e9Zf2cxq8oyoTk\ntIH49Olbs0Uv6FlnXZfVe06Foihq89QxZvN6Ntg2sN66PpUstogCf5V5FW973qYt3IZO0LHDviOt\ntua7775TbULDw8PYbDZ+8pOfzOo5jDojZaYytVmmSTCxzpL9n1M0Gk2zMMmyTFdXF/v37+dR3yOE\nZLpALjOXZVRDp5E9FEXhaegpYSnMGssaCowFfPzxx2oN0bp169i3b9/MN5qCyWrTxgSvTbSxQZy8\nyXGmuN1u/vqv/xpJktDrF19IrzavptBQyGBiEACn3jllI2cNDY2lgSaINGZNVIoSSKY3zmsON6vW\nsRfRF5wrPLdoHdEXmjJz2QSL0GzQCTre9ryNy+BiNDlKkbGIguMF/K7/d/T396PX69m7d68aTawT\ndJzOP01toJakkmSdZd2ce8sIgoBZZ05rhGjW5SbtayEQBAGLLt32NtvXIwgCAotvU9li2zJpU1dJ\nkibYmV7fTZwNJ/NPUh+oJypFWWVZRYW5Yk73mQ6TyYTVaiUajaqPjVmczHozr7dTculdnMw7mfUx\naEzPNe81msPNADSFmjied5zyonJ+/vOfZ+X+lZZKnpufqzu35aZyquzZj5BeCmIIUrvNPyn4CfWB\nemRkNls3Z9XuutBIisT90fsEpABuvZu9rr1vpCNEY2XzZsxY3zACiQAJJYHb4M7Jl5ZVb8Vj8KTt\nLLxeR9Mf7+d5+PkbndA0WyZLBfzqq69oaWnB7XazalX6rpOoE9nnmvuK7RhxOc5m62aSoSRROYpH\n9Kj9QZYidXV13L17F0mS2LJlCydOnJhwzgHXAW6P3iYshZf865kLrdFWFJcCr9qkUFxcPKd76QQd\nu527szSyKZ5Dp+Pdd9/l8uXLRCIRysrK1J/bAdcBbnhvEJACOEUnR91HtYlWlolLca76rjKSGMGm\nt/G2623yjfmv/l2O0xHpUI/Dcphn4WeUm7PXJHosNr4z2omkSKyxrFnxP2ez3rygqZS55Ib3hiqY\nAeJKnIOeyWP1NTSWK5ogWmHc9t2mMdiIhMQq8ypO5Z/KuhdfJ+g47jnOj/4fScgJdOjojHemnfOm\n7A7lErPZzPbtuUvb6on2cN13ndHkKA69gyOeIxk18O2MdjIQG6DAWLCgDX+Hh4e5dOkS4XBYPS4s\nLJzwHpWZy/jE9Akycs7qUBbrPfAlfNzx3cF2yIZ8X0aOymxYtYGjR48u2BjmwtatW9myZQuyLKet\n4hcYC/i4+GOtZiiH3Bm9Q3skZYscTY5y03eTD4s+VP9dJ+hS4uS1kpfx6W/ZQBCEOUVXa0zOixcv\naG9vp6CggOrq6jnfp729nRcvXlBcXMzWrVsnPWcwPph2PN6WrKGxEtBmrSuIwfggT4JP1OL8zmgn\njwKP2OXMfmO+PGMepwpOAant9ItDF9WalrXmtdPG7mosDWoDtWodWEAKUB+on3Fy3xhs5K7vLgkS\niIjsde5lpzM7zT5nore3VxVDkKpFedL9hOrq6glBDYIgoCc3E+wnwSfc891blPdgLNVQdIh4TngA\nWOtYuyySqARBmNLSpImh3BGSQmnHr9tJIbV4tdW2lfpAPRISbtHNDvvs0tA0FpYnT55w/vx5IpEI\nOp2Ovr6+OQVJ1NbW8v333xOLxdDr9Rw+fHjSxRWL3pJmbTXpll6LhzeZo90/W+whrAg0QbSCiMrR\ntKQyQI3UzSV6Qc+ZgjO8iLxAL+hZbV69LCZobzpzSRhrDbeqyWdJkjyPPJ+TGAgkA9zw3sAv+XGJ\nLo64j2AVrdNes3r1alwu16v6GQMMugd5FHjEDufCTeCeh5+r70FCSdDY18iq5Co8Hk/OP/fFxmLs\nOjtBefaphhpzR1EURpOj6NDhNDgXezizwm1w0xl7tYPvFifWstS4alhtXs1ocpQKc0VqArzEkGWZ\n8+fP8/LlS8xmM8ePH6eyMjsLb9FolL//+78nEAhgMBg4c+YMu3bNbyExFovh9XrJy8vDaJxbSEgg\nECASiVBQUKD2IvL7/dy7d49IJAKk3pfGxkZOnjzJyMgIOp0Oj8eT0f0fPXpELJayu0uSRGNj46SC\n6IDrANe91wlIAVyii7ecs2+eq6Gx1NEE0Qqi1FRKkaGIgUQqVcuusy/YTo1e0FNp1XaFlhOrTKsY\niA+o8bDlpplrBsaHDcy1DuCm96Y6SRtNjnJr9Bbv5k+/wul0Onn/w/f545U/IkkS5nVmTBUm+hJ9\n7GDhBNHYe6AoCr5rPvra+nguPKe6upqPPvoop6LILto5kneER8FHqVRDS+aphhpzQ1Zk/jLyF9oj\n7ejQsdG2kcPuw8tm0eeA6wAKCiPxESx6C2+73p70vCJTEUWmIvW4s7OTtrY2CgoK2LZtbv2BssmN\nGzd4+PChenzhwgV++9vfqumb8+H//t//qy60xGIxLly4MC9B1Nrayvnz5/H5fOTn5/PBBx+wZs3s\nrLVXr17l1q1bxONx1q5dyxdffMG1a9e4d+8eiUR62wWdTsc333zD06dP0el07Ny5k3Pnzs34GR3f\n8HWqBrAFxgJ+WvxTkkpSs8NrrFi0T/YKQhREzhScSVkfFIlNtk0UGAsWe1gaS5Q9rj1YRSvD8WF0\ngo5QMsTl4ctstW+dcteh2l6Nz+cjIkcw68xss81tojTBxpMMTnFmOuvXrWeddR3epFd9zCwsbCre\nNvs2fD4fw8+GibamktMkJOrr66msrGTHjtyKswpzRU7S4JJyknv+e4SlMIWGQnY4diybSX8ueRp6\nSlukDUj9nJ+GnrLGvGZBa8fmg07Q8Y77nVld09jYyLfffptmyTp5MrP0v7ZwG+2RdvSCnl2OXVlL\nV/N6vWnHPp+PUCiEy+Wa971DofTvo0QiQTwen/POzo0bN9RGtsPDw1y7do0vv/wy4+sDgYAqhgA6\nOjq4ePEijx49IplM38k3mUwUFxfT2NgIpHaMHjx4gNfr5cMPP5z2/Tlw4AADAwMEAgHMZvOM0eqa\nGNJYyWif7hXGSkq2WW5EIhH+/Oc/q80Bz549S35+/swXLiJbbFvwGX1cGLyg2rB6Y72cLTw76URm\nnXUdeYY8euO9FBuL8Rgys2aMx2VwMZwcTjvOBEEQOOA6wN3Ru0TkCPmGfPa79s9pDHOl0lpJviGf\nK61XqKMu7d9er3Fablweuaz2JGqLtCEp0qI1pp0tvoSPW75bBKUgboObo+6jmPQz1zk0BZtoDDWi\nKAobrBsmrbeMybEJj0XkSFbGPRcUReHe6D06o53oBT07HTuzvjtfX18/qSVrJrqiXVz3XldTR4fi\nQ3xU9FFaX7pwOEwwGCQ/P39WMdmlpaXU19ejKKkd7cLCQux2+2xe1pTk5eURDL5alDGZTHMWQ4Aq\nZMYY29GJy3GCySBO0Ymom3r6FYlEJtwjGo1OEEMbNmzg5MmTtLS0qIJojLa2Nn7/+9/z1VdfTbmL\ntnHjRn7zm9/Q0dFBWVnZnNMqNTRWApog0tDIEhcvXuTp06cADA0NceHChVmtCi4WL6MvVTEE+26f\neAAAIABJREFUEJSDvIy+nHJl12VwZSxgpuKw+zCiIBJIpuKWD7ozj3BdbVnNKvMqJEWadlKRS1wG\nFyf3nKT7cTeDg6kEpsLCwnmlPS0miqKoTSTH6I/3L9JoZs9N30011MWX9GHAwPH842nnvIy8ZCg+\nRLGpmDLKGIwNcnf0rlpn+dD/kDxD3oQktPXW9TSHmvFLqV5P+YZ81pkXptHwZDwNPaU+WK8e3/Ld\notRUmtW6n0ytVOPpjHamtWAYSY4wGB9U+7Tdv3+fq1evEgqFqKio4Oc//zkOhyOje+/bt49IJEJH\nRwcmk4njx49nre/Ql19+yf/+3/+bkZERjEYjn3322bzut27dOnp7e4HUe7d+/Xo6Ih2qaPeIHo7l\nHaPQWDjp9QUFBaxdu5aOjg4AbDYbNTU1BINBOjtTVmOn08nx48cpKSnBaDRSV1fHyMhI2n26u7sZ\nGRmhqKho/FOoeDyejGuONDRWMpog0tDIEuObY861WeZCISsytf5aumJdaY/r0OEUc1s4btKbOJZ3\nbM7XC4Kw6PYNu93O559/zr179xAEgX379s24Yp1UktwfvU9ICs3LlvbgwQPa29uxWCycOHFCbdo7\nV8Ya9M6noe1iMj45bbwlsyHQwI+jP5IkiSFgABuMJkbTQmeSJBlODE8QRE7Ryen80zSFmhAEgR32\nHRntPk1Ga2srDQ0N6PV63nnnHQoLJ58QT4cv6Us7DsthvAlvVgXRgQMH6O3tZXR0FJPJxN69ezO6\nzqazpR0bBSMOMSV4JEnixo0bqj2tq6uLq1ev8v7772d0b0EQOHr0aE4i5kVR5Ouvv87a/d59913c\nbjeDg4OUlJSwZ88e/q3/39TPqTfp5aH/IacLTk96vU6n44svvuD69evE43GqqqpYs2YNpaWl3Lx5\nk0QiwY4dOygrSwnNvLw8Pv/8c/70pz/R3d2t3sdms2VtF01DY6WjCSINjSxRUFCgruhB6o/UUubO\n6B0eBx+rx3r0GHVGNlk3sdaydsbr43KcUDKE0+BcVrHJo8lRbnpvEpJCuAwujrrnPsHKy8vjzJkz\nGZ9/eTjdliYrMrtds2uM+vDhQ7777jskSQJSNQpffvmlKqyScpJAMoBdtKdZlWbigOsAt3y3iMgR\nPKJnRitiY2Mjt27dIpFIsGnTJk6cOLFoNUcu0aVGyMNEC2ZruJUkSeKDcYbuD/FP8X9idelqzPvN\nRIVUHZhRMFJinLx2zmP08I5xdnU44+ns7OTf/u3fVFtld3c3v/nNb6YVs36/H0mScLvd6ntbbCym\nkVSvOQCn3pnWaDUbrF27lq+++oq2tjZKSkrUifdMVDuqGUoO0RXtwiAYqLZXq4JorC7ndeLxOJIi\n4U/4sYk2jDojiqIQSAbQCTrsYnYm84qiEEgEgFQwSa4/p2MLJK8zPvE1KU+f6mk0GifYFC0Wy5Tx\n2oWFhfzmN7/hj3/8o7qLdujQIazW6dM7IfUd8t133+H3+ykoKODDDz/EbM7+gsjAwAB+v5+1a9dm\nJQxDQyObaJ9IDY0scfr0aRRFYWhoCIfDMauJcjZJyAkaQyk/eZWtaspJ8UB8IO3YJbr4afFPMxI3\nz0PPuTt6l6AcJN+QzwnPCTzG5WG7uOm9qe6KeZNejBhZtyr3FqjJbGl98b5Z36ejo0MVQ5DqzxSL\nxTCbzfRF+7jqu8pochSn3slhz2HKzTOnBwKUm8v5rPgzkkpyRiEVDAa5ePEigUBqkjk4OIjH42HP\nnsWpOTriOcId3x1CUgi3wT0hRGBsAuy/5ScxlCBBgubhZqpMVeQfyEdRFDbZNlFqLs3ZGFtaWtJq\nzAYHB2lra5s0wU1RFC5cuEBdXR2KorBlyxY+/fRTBEGg0lpJUAryMvISvU7PTvvOnPSFcbvds/55\n6gQdJ/JOkFSS6NClpVCazWbWrFlDc3MzkKrTqVhfwb8P/DvDiWHsOjv7nfvpiHXQEelAQGCzbTMH\n3QfnJWAUReHf2/+dx0OpxZ8N1g0c8RxZcPFebipXRbsOXU6CUfR6PT/72c9IJBLo9fqMrY7nz5+n\nvT21UDM4OIjRaOTjjz/O6tj+4R/+QX0O0Szyi69/wTrP4llPNTTGowkiDY0sIYpixvaPXJGQE1wY\nuqDWf3REOjhbcHbSCe74dDaL3pLxTk9toFatOxpODPMg8GDG2OylQkAKTHucK7JlSxu/o2C1WtUC\n8IeBh+qkyy/5eeB/kLEgGhujQZh5V2ksmWqMsYWAxcKqt3Ii/8SU/77Nto1b0VtIISntcTkkc67w\nXK6HBzChVsZgMEwZutLS0sKDBw/UAIEnT56wbt061bq2w7GDHY6l2zx1KjvrZ599xo0bNwiHw2zY\nsIHO/E6GI6lwlaAc5I7/DmH5lWhsCjWxxryGcnM5df46vEkvLtHFHueejCP/W8Ot1Htf1Vw1h5sp\nN5WzwbZhHq9w9hx0H8Spd+KX/BQZi9hk25Sz5zIYMt8Zhon2brXXW5bo7e1VxRBAMprkd7/7HZ/9\n+jM22jZm9bk0NOaKJog0NFYQzeHmtGL4/ng/zeFmqu0Ti/33u/YT9UYZTY7iEB3sc6UsHgk5QTAZ\nxGFwTDqxURSFhJLeB2P88VLGLbrT7FXZigXOhP2u/dz23SYshfEYZralJZUkgUS6/e3EiRMMDw/T\n09Oj1hCNrQSP/zlk0mx3LpSWluLxeNQoZFEUKS/PXHgtNJtsmygwFPAvef/CUPcr4TaXGp65UlNT\nQ09PD8+ePUOv17Nv3z5KSia36AUCAVUMjTGW+racEUWRY8eOqcfPB5+n/fv4z6uCQkgOTbD3RuQI\nhz2HM3rOyRIBXxddC4UgCAvaQHo25OXlMTz8KvWzoCC77TrGR6YDyEmZlnCLJog0lgyaINLQWEHo\nmbjDM9ljAHnGPD4q+oiEksAgGBAEge5oNzd8NxhNjuIW3RxxH6HEnD5pEwSBUlMpgXBqh0CHjlWm\npdUcNB6P8+DBAwD27t2bFqF72HMY0Seq9qq33ZM3qswFFeYKPiv+jISSwKibPta3L9rHNd81fEkf\nLtHFIfchys3lmEwmfvWrXxGPxzEYDGnWnzJTWZogLjNlVvsxWywWCx9++CE3btwgmUyyefPmJdG8\nczryjHl88ckXXLp0iWQySUFBQdrkPNcIgsBHH31EIpFAp9NNm5BWVVXFvXv3GBhI2Vo9Hg9VVVUL\nNdQFY5V5Fd2xbmRkIPV5HUmMqIl+eWIea81reRp6mnbdYGxwwr2mYr1lPS2xFoajqQm/W3Sz3rI+\nS69gZfDRRx/x3XffEQgEKCgo4PTpycMe5sqmTZswmUzEYq8SCC0bLRMafWtoLCaaINLQWEFssm2i\nPdKu1shUmCqmtWYIgoBReDUxf+h/ZbnyJX08DDzkrPnshOuOeI7gEl2EpBDFxuIFXeV7GnpKT7QH\no87IPue+CYlf8Xicf/zHf1TjaZuamvjVr36liiKr3srJ/MyaTOaC8e/5VDwMPFQTxUaTozz0P6Tc\nXM7Q0BA3b95ElmV27NjB+vWvJnc1zhpsehvDiWE8oodt9tyJlLVr17J27dqc3T8X5OXl8Vd/9VeU\nlZXR09Oz4M/fFe2iJdSCTtCx07Fzyt1Ji8XCF198wZ07d1AUhT179kwZ0qIoCvWBekYSI9j1dva6\n9i5qyIksy1y9epWRkREKCws5fPjwlPU61Y5qTDoT/fF+bHobOx078SV8NIWa0Ak6ttu3Y9abMQnp\nv+OzSfmziTa+2PgFl9suA6nGyjbRNsNVbxY2m23eUePTIYoiv/3tb/mH3/0DgXgAy0YLRTuLqLKv\nPJGvsXzRBJGGxgpCL+g5U3CG5+GUFWW9dX3GXnuYaLkan4w0hk7Qsds5u3S0bPA09JSb3ptqwtZo\ncpSzBWfTJlx1dXWqGIJUulddXR379y9sA9f5MsGWKCeIRCL867/+q9r76Pnz53z++edUVKQKtAVB\n0CYZS5T+WD+XRy6rFq7+eD8fFX405eTe7XZnFMzyo/9HagO16nFIDnE87/g0V+SWCxcuqLuzkGoo\neurUqSnP32jbmLagkmfM46AxvS/ZAdeBSe29mVJgKeCgJ/NeZ8sVRVG4dOkS7e3tGI1GDh8+zIYN\nC1srNRVut5v/8dv/wWBskMHEIGWmsgW1K2tozIQmiJYJ/qSf9nA7Zr2ZTdZNixZvq7H00Qm6Oe/Y\nlJnKGEq8qrEoNy2tupCeaI8qhgAG44PElXjaCvJkyUqZpi0tJSbY38xltLW1qWIIIBQK0dLSogoi\njaVLZ7QzrZ7Fl/TRHeum0lo5r/v2x9Ib6A7GM7eT5YLX++BAqt/QfMkz5vFx0cdp9l6Nidy7d4/b\nt2+rxxcuXODv/u7vMJmyn0I4VwpNhRSaFq52T0MjUzRBtAwYjg9zafiS6qvuifVwzHNM+6OgkXUO\nuA5g09vwJX3kGfKosi2t3Ybx8cJmnXlCKtru3btpamqira0NgMrKSnbvXvjdrPlS46zBqrcykhjB\nLbqptlfTF+nDYDCQSLzaPZpL40VZkflx9EeCUio2fS4NYv1JP3X+OiRFotJayRrLmlmP403Cokul\nAyYDSYL1QQRJYGT3CJVVcxNEvoSP+kB9WkAIMMFettCMT0HMVj+bTK2mbzLjkx69Xi8+n4/i4uJF\nGpGGxvJBE0TLgKZQkyqGANrCbex37cem13zQGtlFEAS2O7Yv9jCmpMZZgy/pYzA+iFlnpsZZM8ES\nqNfr+cUvfsHjx6lUqurq6mkL2JcqgiBMqAEqLS3l0KFD3L9/H0mS2LRpEzU1Neq/R6RIKhQjkbIW\nHXIfmrRe4qr3Ki3hFgBaI60klAQ1rlf3URSFkBRCFETM+okT2qSc5NLwJYYTqUL1l9GXvJf/HmXm\n7Ic4JJNJAoEAdrt91nHCS4kqexV94T7u/nCXxHBK0P5X53/x47UfEQSBNWvWcPr06YyEaUyKcWn4\nEt5kKr1LQECPfk52smxz4sQJzp8/j9frJT8/f0JzUY3cUVJSgiAIakJhfn4+Hs/y6A+nobHYaIJo\nGTA+iUX47/80Vj4vIi/wJryUm8spNGo2A5PexNmCs8SVOAbBMGV9lF6vZ+fOnTkdiz/ppy3chlVv\nZaN144Lt2B45coR33nkHWZbT0vMAbvpu0h5J9fsYSY6AD04XTEyMGt+Uty/2qkGspEhcGr5EV7QL\nURDZbt/OXtfeCdePiSGAmBLjZfRl1gVRV1cXf/7znxkaGiIvL4/3338/oyAHWZapra0lFouxY8eO\nOe2iZRtBEFgfWc+N4RvqY7FYjP7+lOWtr68Ph8PBwYMz17p0xbpUMQSpeOoqexUHXAcW3TlQUVHB\n119/TTQaxWw2L/p4ck04HKa+vh6DwcDu3bsXdfFl7969BINB2traMBgMHDlyZMJ3hIaGxuRogmgZ\nUO2opjvWjS/pQ0Bgk20TVr11sYelkWMejD6gNlCLjIw5aOaI+whrrWsXe1iLjiAIi24LGooPcWn4\nktrUtTfWy9G8owv2/KI4+Vf3601fAYLJ4KTnmQUzo7yyWr1uRWwINPAy+hJIhWrUBerYYN2Ay+BS\nz7HpbRgEQ1rww5glLJtcuXJFrZkaHh7m6tWrMwoiRVH4/e9/z9Onqajm+vp6fvGLX+Byuaa9biFw\nOp0T4odf5/VeMNPh0rsQEUnyqm+PU3QuGfEhCMIE69xKJBgM8k//9E/09aUWFJqbm/niiy8WtWbx\n6NGjHD26cN9FGhorheVXafwG4hJdvF/wPofchziVf4qD7pWflqORsjKN9eeIylGaw82LPCKNMZqC\nTaoYgpSNNSzlptmjL+Hju8Hv+EPfH7g8fHnaZqtOvTP92OCc9Lx9rn14RA8GwUChoTDNZhWT0yfr\nEtKE1+YyuNjj3INVZ8UoGKm0VObEahmPp6ccTiUkXqe7u1sVQwADAwPcv38/62ObC3l5eRw+fBiH\nw4HJZJpQ7F5UVJTRfQpMBex07MSis2ASTGyybmKrbWsuhryiuHz5Mv/rf/0v/s//+T88e/Zs2nMT\niQQ+nw9JkqY858GDB6oYAmhtbVVrFzU0NJYX2g7RMsEqWrU4XY0lR29vL21tbRQWFrJp09T9jlY6\ngiCgy9H60nXvdXrjvQAMJ4cx+Awc8hya9NxD7kMICPglP3a9fcrFkzJzGZ8Wf0pcjmPSmdJ2FtZa\n1tISblET0QoNk6dC7XTsZJt9G7Iiz9hkdq6sXbs2LUJdKVKQFGnaPjuTrc4vlZ0TgIMHD3LgwAGS\nySTDw8NcuXKFeDzO6tWrOXDgQMb32evay07nzpy+/yuJuro6bty4gSynFpl8Ph8VFRVYrRPdFk1N\nTfznf/4nfr+fkpISPv300yn7QI1nOSZaLgUG44N0R7vxGDxaQIvGoqAJIg2NJcpm62Ye+h+SJIlN\nb8tpk8258OzZM/785z8TCoUQRZFDhw69MVaNans1PfEeRpOjCAhstG6cNHxgviiKkrYTBRBIBqY4\nG4x6I8fzM+tBoxN0k465xFTCybyTPI88Ry/o2eXYhShM/qdCFERyWc6459AeGhINxEfiiC4Rtqcs\nfdP1wCotLaW6uloN1SgpKZmV0FgIRFFEFEXKy8v55S9/Off75Pj9X0n09/erYgjA7/czMDAwqQXz\n6tWr+Hyppsg9PT1cvnyZTz/9dMJ5+/fvp6WlRY0a37JlC+vWrZv3WDs7O6mtTfWWevvttyksXNn1\noy8iL7jmvUZEjqAn9Z0zvm5RQyPXaIJIQ2OJssu5iyJTESOJESpMFUuuid3Dhw8JhUJAKgmsoaGB\nI0eOLKnV+FzhMXp4v+B9OqId2PQ21pjTVzQVReGHH36gra0No9HIkSNHqKycfbyyIAg4RWdabdDr\ntTy5osxclhaQ0NbWxrVr14jH41RWVnLy5Mk5/ZxfRF5QH6gnqSRZY14z46QnLIexbrNi5dUqflye\nvFnwGIIg8Mknn7B582ai0Sjbtm17I+pZNKantLQUvV6vWuBcLtekcdSKohCNRtMem8qqabFY+PWv\nf01DQwMmk4lt27bN+/tvYGCAb775Br8/lSz78uVLvvrqqyURDJIJSSVJVIpi1VszbgreFGpSd6Ql\nJFojrZog0lhwNEGkofHfhKUwreFWjDojJUrJYg8HSDXnLDNlP8oYUtG9z8LPEAWRzbbNGf/xmorF\nFELeuJeXsZc4RSfrLPNfoc0Emzj1rt2PP/7IrVu31Pjb8+fP8/XXX8+pQeJRz1Fu+24TlsPkGfI4\n4FrY3Y5YLMa3336L15tKNevr68PlcrFv3+zinaNSlBveG4TklIgeSYzgEB1ssk1ttSwyFVFoKGQw\nkQpWsOgsGdlpBEGgurp6VuMbTzAY5NGjR5jNZqq2V/Es8gy9oGezbfO0lj2NpcmOHTsYHR2lpaUF\nURQ5ePDgpEJ5LAK9oaEBSCVWTreYYTQa06Lv58vTp09VMQSpoI1nz56xZ8+erD1HrmiPtHPHd4eQ\nFCLfkM+J/BO4xJkXcLTUXI2lgCaINDSAUDLEhaELapTtUOsQB60HV+xuR1SKcn7ovBqd/CL6glP5\np2Ylimpqaujp6SEQCGAwGNi1a9eivF890R4uj1wmJIfQoWOHfQf73fsXfByvMzg4qIohgJGREbxe\nLyUlsxfaDtHBqYJT2RzerPB6vaoYgtQK+ljy22wYTgyrYghARk6L7p4MURA5nX+aukAdSZJssGyg\nxJT7xYrR0VH++Z//mYGBVDz5lcdXsB6zIggC7ZF2flLwk3kvIKwknjx5QmtrKyaTiWPHjmWtGet0\nyIpMnb8Ov+THI3oyai58+PBhDh8+POO9P/zwQzweD4FAgIqKiqw3du7o6KChoQGdTsc777yTVp80\n2Xu3FBISM+HB6APV4juYGOTB6ANO5J9Q/11WZB76H6oNoavt1QiCQJW9isH4IGE5jIjIJuubW4+q\nsXhogkhDA3gSepLW16PR28hGw0aKjSuzw/eT4JO0yejL6Eu6ol2stqzO+B4bNmzgyy+/pK2tjaKi\noqx45+dCU6hJnWjLyLSEW6hxTWzYOoaiKETkCKIg5qwYvbi4OK1BYl5eXsZF2TMhKzK3fLcYiA9g\n0pnY79qf0x5VY2MfGRkBUivok1mNZiLfkI9dZycop+x/OnQUGApmvM4qWnnH886sn28+3L9/XxVD\nAP42P4ZtBoxFRrpj3TwPP2ejbeOCjilXSJLEhQsX6O3txWaz8d5772WcdgfQ2NjIn//8ZzURsL+/\nn1//+tc5Xxy55btFY6hRPY7KUQ64s7N7qtfrOXbsWFbuNZ7u7m7+8Ic/EAymfg+6urr46quvVCE0\nPlkRUr2OljqKohBXxqVCKulWw2veazwLv0r3i8tx9rr2ssq8inOF5+iKdpFnyKPcXL4gY9bQeB1N\nEGloTMFK3saf9LXN4eUWFhYuq4LfpJLk0vAleqI9GHVGdjp2ssOxI+vPs2fPHvx+f04aJNb6a9Mm\ngte81/ik6JOcTUCNRiMffPAB165dI5FIUFlZOSf7jllv5nDeYer99SRJ1RAtVVEx6Xv5mr5eSd8N\nf/nLX3j48KF6HIvF+Ju/+ZuMr29tbU2bxHd1dREIBHA6J498zxb9sf60495YLw0NDYRCIaqrq3E4\nHDl9/rnS3NysiiFICcj29na2bk3Fpi/1lMSpEASBEmMJrZFWILXgUWGqSDunPz7xZzaGx+DBY/Dk\nfqAaGlOgCSINDWCbbRsvoy8ZSaRWwbflbaPQsHwm+rOlyl5FR7SDocQQAGvMayb88VouVNmq6Iv1\nqZa5TbZNU+4O1fpr6YymYpwjcoSH/odstGZ/Ui4IAsePH+f48cwS32bDaHI07TiQDBBX4jltVrt2\n7doZG6JmQp6Yh9vgRlKknO5qzZex9LD+/tQEzr3BjSHfAECFuYJK6+wDMpYqYzt/rx9LkoRen1md\n1HiLl8ViyZllrrGxkZaWFsxmM7oqnbqIoygKXZe7qG+uB1KBL1988UXWdmWzic1mSzsWRRG3+1Vg\nTk1NDU1NTXR1dQGpnfgxsbTUOZZ3DKffSUgKUWwqZottS9q/v94AGtDi4jWWFJog0nhjUBSFUCiE\n0WicsFpvE228X/A+rZFWTIKJw+sP09fbN8Wdlj9mvZlzBedoibRgEAxstG5ctjURpeZSzhWe42X0\nJW7RPa3tb3xCWVyJE5EiuR5iVnGL6WmDTtGJUZjbxEJRFMJyGJPONGW0drZIKkm+H/5eFeEvoi84\nlXeKEvPiBpj09PTwl7/8hUgkQkVFBadPn8bhcPDXf/3XaqjC1m1baYm0oBN0bLJOLbiXI/n5+WnH\neXl5GYshgGPHjtHf309XVxdmszmru6Gv09jYyJ/+9Cd1N6qstwzPaQ9+yY8lYuF563P13KGhIe7f\nv8/p06ezPo75sm/fPrq6unj27BmiKFJTU0Npaan670ajkV//+tfU1dUhiiI7duyY1c9jMdEJOmpc\nUwdM7HPu46bvJoFkAI/Bw37n4tZ6ami8jiaINN4Ikskk//qv/0pHRwcmk4mDBw/y1ltvpZ1j1pup\ntqeSqVbShGcqTHqT+nqXO26DO6NY8tWW1bSGW1Vve7GxeMnFmc/ELucuInIkrYZIQeFp8CkSElus\nWxB1M3+1h5Ihfhj5gcH4IFa9lf3O/ay3rZ/1eLoiXerOKgJUWiqxixMjggdiA6oYglTNR0e0Y1EF\nkaIofPvtt/T2pqw7PT09WK1Wjh49isViYf/+VxO2ldoY+/jx40SjUfr6+rBYLJw6NbsAD6PRyK9+\n9StCoRAmkwmDwZD270PxIXpiPRQYC+aVmDnemjfQPcDPrD/DaDcS9oX5e/5+zvdeSHQ6HZ988gmR\nSAS9Xj9p8qTBYJh1iuNyoNxczmfFnxGVo5h15jfi76zG8kETRBpvBNevX6elpQWARCLBtWvX2L59\n+wT7gsbKZpV5FcfzjtMeaUfUiex17F2UP8oxKcbDwEPicpzV5tWss2YeSKETdBz0HFSPZUXm4tBF\numIpi83z8HPOFpzFoDNMdQsA7vvv0xdP7YIGpAAPAg+otFbOql6h1l9Lrb+WJEn1saZQE+/lvUee\nMd2uZNPbEBHTzjXrcp9GNh2RSGSCZWx4ePrku5WGXq/n/fffz/h8WZa5fv06Pp+P0tJS9u3bhyAI\nk/bJaQu3ccN3g6gcxYCBGlcN2x3b5zTO8cLBbDZjtVgx6o1Y8ixs27ZNjcouKChY0oJCEASsVuvM\nJ65AdIIOq/7NfO0aSxtNEGm8EYxvtBeJRAiFQgsmiEaTo9zx3SEspXrJHPIcylkvk6ehpzwLpZJ8\nNtk2TfBxL0W6o93UBmpJyknKTeXUuGpyVki82rI6ozS9uBxHVmRMOlNWx6IoSirYId5DpCPC7cbb\nOAUnu7bt4p13Zp+m1hZuU8UQpAqXG0ON7HTsnPa6hJJIO47JMWRk9GT2uVQUhWfhZ2kCB1Kf9SfB\nJxzOS483dhlc7HLu4nHwMZIsUW4uz0mgxWywWCx4PB76+l7ZY8dbyDTS+fbbb6mtrQWgvr6eaDTK\nkSNHJj33aegpUTn13ZsgQXOoec6C6Pjx4wwMDNDZ2YnFYlGteSPxEe6O3kV5W6GqpIoKoYLt1dsz\namQ6MjKCXq9f8FjraDTK+fPn8Xq9uFwuzp49qy3OaWgsMpog0ngj2LRpEw0NDaowWrVq1YJOfK57\nr9MT6wFS/RlEnchB98EZrpo9/bF+7vruqpawkcQIbtG9IL1b5kpCTnDDd0MNCxhIDGATbYtqUbo/\nep+mUBOyIrPavJrjecezJorCcpj+eD/JQBL/LT9yRGaQQa4MXiEvL48tW2YnYBWUmU+ahHJTOS8i\nL5CRgZR9MNcNR/c491Btr0ZSJMw686KnZwmCwAcffJBWQ5RJn5o3mc7OTvX/FUWho6NjSkGUTaay\n5l33Xqc/8d/pZatgtX31jGJIlmX+5//8n+ruYFlZGX/7t3+b0/G/zvnz53n8+DGQiuGrT17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fpUdiTNrTj/xRdfJCsri29avsGf7seV5eLq8FUOcGDKa2nMsHM4Mky2KZtsUzamJBOS73H9pz15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es1Wu2t/gMZGiwN5OvzxfwkIapEQiTMaL29vdy7dy/oa9Eoufjwww+5du0a4B+c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\nEAEAAACwrf8B4CNcPAEykEUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9a8964160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "style.use('seaborn')\n",
    "df_couple.plot(kind='scatter',x='valence', y='energy',ax = ax ,c='who', colormap = 'Accent_r' ,title=\"Valence x Energy\")\n",
    "ax.set_xlabel(\"Valence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Valence and Danceability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, looking at the relationship between valence and danceability we can see that Emily's songs have high values of danceability split in the first and second quadrant of the plot. On the other hand, my songs are mostly either in the third or in the first quadrant, showing a kind of variety in terms of these two features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F1NRxpKb6rxmuWLGGAwc+xWIxExs7lMceW+bz+bBhwwdd8ffu8FYTmkBkpEHM\n2LR06Qo0Gg0NDfWEhoaxaNHSPl0zISERmUzmM8IrLi5mQQ9+aTX2KlF0ASxuM4WWm0RqosX2b2pq\n4q23/pfq6koCAgJ57LHHSU/PACA9fRrp6dPE86uqKikrK2HYsGEMGzaiT7bfidzca6LodlBeXkpN\nTbXP/zo+fgQvvPBXXLp0DrPZREpKqs8UO3iTjFitvr/pq1cv0dJi5Omnv4ZOd/t3PW/eQlpajJSV\nlaDT6ViwYImPiHdGp9PzzW9+12dfgNLXG1wtUxOk7N+CDBJ9RxLeh4Dk5DHk5FwV1w+HDYv3y6Qz\nGOkanjIYR373wvDhI/xecI8KcrmchQuX3PP5Ho/gN63a1tbz2r5eGYgCBW7c4j6N3Pd7c+TI51RX\nVwIdjlZHmDx5mp/HdVbWZfbt+wSr1YJWq2XJkhVMnTr9np+lM92FWimVym5Hn9HRMSxbtrrHawUG\nGhg+PN6vaElZWTHHjh1i6dKV4j6FQvGlEoOMD5pMQ3s9FbYylDIFE4LTCVIN3hC3rwqS8D4EeEdd\nm7hxIw+1WsOCBY89FHmS581bxN69OzEam4mMjCYzc/FAmyRxn1GpVGi1Wh8nszs5loWrIxgfNJkc\nUxYuwcVwXQITgn1THzqdzi7bDjFGuDMXL54Tp7XtdjsXL57vs/Da3XZyTdnIZTLGGSagvLUWOnt2\nJqWlJeKSj0KhYPbs+YSF9d1vQS6X8+STz7F//24uX77g85nD4ezhrHtDLpOzOGoZTo8ThUyBXDb4\n3xtfBSThfUhITU0jNTVtoM3oFWazmT17PqalpYUhQ4ayceMWYmKGPDIj3oHGbrdx9OgXOBx2UlPT\nxILshYU3yM3NRq3Wkpm5cECWIpRKJbNnZ3Ls2Bc4nU4iIiJFj++emBk+lwnBk3F6nASrQvxGsikp\nqRQU5ItezAkJSX6i66WrY1PPlQc8godiSyEe3CQFJKOQKbA5beyu3UGDw+s8V2ItYqZyHuWlpcTF\nDee5516koaEeu91GWFj4l0qOotPpWLzicW7U52Kp9HYWFFolY1K7DxfqoLm5idOnTyAIHqZMmU5s\n7JBe3c/tcXG4+TPanK0EqYKZF7YQrXJwL1U9ykjCK9Hv7N69g9zcHACqqkChUPLEE08NsFWPBh6P\nh3fe+ZO4Xp6Tk83GjU8ik8n44IN3xRFfVVUFX/vaSwMyM5KZuYiUlDE0NjaQlJTcY9apzgQoA3v8\nbPLkKWjfaB1WAAAgAElEQVQ0GkpKCgkMNDBnzvwejptKXV0NNpsNlUpFUtJI3G63n8OSR/BwoG43\nJTZvG+aaclgRvZbLjddE0QUoLigk9/R1bGYrWq2OJUuW99vUNUCuORvDQgNkgeAU0MZr0Q7tWQwt\nFjPbtr1Ffb13TfzmzRs8//w3elXr+FjzIQot3tF6vaMWAVgaNfgSu3xVkIRXot9pbvZNcn8vSe8l\nusdobKKsrETctlot5OXlin93UFZWgtHYRHh4/xQ/z8m5Sn5+Lmq1mgULHiMw0HDH42NjhxIbO7Tb\nz0qtxRSab6CQKUgPmd6rNcexY73hTj0hCAIkCCQvH0Pl2XJaaowcP36EiopynnnmBZ812kLLDYpN\nhbRdbkOwCpgjzYyYnUCgwTecx3LdgsPsHWXb7TbOnz/dK+FtamoiPz+HkJCwO9osk8mQKWUETbn9\n/Ap67ijl5uaIogve78L77/+ZhISRLFq09I4zSm3O1i7b/olCJB4ckvBK9DuhoWHU1lZ32n54woj6\nC4vLQo29iihNNEGq/nOE02r1aDRabLbbHrbdJVTRaLRotXcfafaGvLwcdu78kPZ277ptTU01L774\n7V4XBOhMpa2CQw37xRCXekcd62I3o5KraG5uoqamimHDRhAU1Ldp3DPNJ7jSdgGX3EVTTSMdvlol\nJUUcP36YRYtuZ45yC25ajrVgL721Dl0E+/P2EBcTh3a4HvtwbzIKweg7Ve12e+5qR0VFOR98sA2j\nsRm5XM706bNZtmyV+PmlS+fJyrqEXK5g8rSpRIXEUH/Lq3tUwGhitd13VgCCgoJ8ajpDR1hTNU1N\njX5FHDpjUAVT32k0H6Qc/M6ZjzKS8Er0OytXrkMmk9HS0kxYWKRYQq0zDkc77777Z+rr6wgMDGTp\n0hUPTdHuqqpybt68QUhIKBMnpvutSZZbSznS+DlmtwmdXMfssPkkG8b0y70DAgKYO3cBJ04cob3d\nTnx8IpmZi5DJoLq6ktLSYjQaLXPmZHbrcXsvFBTcEEUXoLKyHKOxiYgIb1KMP/3pTYqLCwCvx/eL\nL34bk8nEwYP7sdutJCQkMWPGHADKrMWi6FoLrNwsz+c9w9skDEnixImjWK0WgoNDWLv2Cb8cx0eO\nHCQr6xIgIz19KnPn3o5Rqm33dvQ87R46OUgD/g5LI/UpuBt9D7K0WrjRegN9RQDzYhdgrGum1lrr\nc0xvqnCdP39anOHxeDxcvXpZHI0WFxewf/9u0fGsrq6W57/+DYxBTahkapICRt0xN3Zy8himTp3O\nlSuX/LJ0VVWV43K5KCi4ydWrF5HJFMycOYdhw4Zjd9uYZJiC4BFoc7cSrAxhXrh/Ni6JB4ckvBL9\nTlBQEFu2PNfj54Ig8Ic//FbMatXW1sLevZ/w3e/+bb/WD74f3LyZz8cfb8dsNiGTyaiqqvDrWGS1\nXsTs9sbF2jw2rrZd7jfhBZgzJ5P09Gm0t9sJDg4R13Gfe+4btLa2oNFo77iuWlJSRH19HSkpqX4J\nGrqj67X0+gD0eu+a7OnTJygsvCF+VlpazJ/+9AZ2u43KygoA8vNzkcsV5OZmU1pWjFvmRh2rxlHj\nACfcJJ/ivEJcLhfgLTJw6tQxH+HNz8+9ldvZe8yRIweJixsudtY6QpBUYarb18b7XUxL862tq1Ko\nCAuMoM5cQ1esVgvll8pobm70+6ynjqHH4+H69Wwcjnaf0WhXyspKfby9zeY2KkrLer1uLJPJWLly\nHZmZi9i/fzfXrmWJn+l0eqqrK9m5c3undf5yhq+Mp15Zi4BAuCryK5+1yuPx8JOf/IQbN7yZyX72\ns58RHx8vfr57927eeust5HI569evZ8uWLffFDkl4HwIEQeCG+TrNzmai1bEkBQ6+FIR94fr1bJ9U\nkuBdB+4uRGSwceXKRbEsoiAIXL+ezdKlK33sduP78nULrn63Q6PR0NjYgNPpJCoqGvCGqdytlu0X\nX3zOyZNHcDqdnDx5lCeeeJrIyDt70s6bt4ja2tpbo2k1c+cuEsW4sPCm3/Fd93k8Hk6fPi7W5AVw\nlPvmWe4Q1J62GxrqfPY5nU7q6mpEMcwInY3VbaXF2czIZcnob+hRe7SkpU1g2LB4n2sJgkBMdCzN\nTY243W4fsZTL5eTmZvvFIkdFRZOU5F9lyOPx8MEH28jJ8RYoiIyMJjg4hNbWFuRyORMmTBbXXmNj\nh6BSqcTwKJ1OT1xc3xOtGAxBLFu2mrY2E7W1VQQEBLJo0VIKC2/6rPM3NzfhLnahT/b+r5qcDZxu\nPs68iK/uaPfQoUO3CkhsJysri3/7t3/j9ddfFz//xS9+waeffoper2f58uUsX76c4OD+n5a/b2+5\nwdKzeBQ4ZzzF5dbzCAgoUWJ2z2VC8KS7n4j3JZOVdQmLxUxq6njCwh58kfGudOQ/7kxgYOCgEF1v\n1ZxDBAYaWLlynZ9NXUfkcrncz3N4pH4U9fZaXDiRIydR378dJYfDwbZtWykuLkSpVDJt2ow7Jmzo\nwO12c/nyOfHFbzQ2c+bMCSZPvrPwqlQqnn76a6K3sG+b9G6GomvxiK4olUo8Hg8ejwe1Ws24cd5R\nqtPpZOfODykrK/FZ3wwKChbDqAAiNJFsGLKFdo8djVyLLMHXrpqaanbseBe7vZ3AwACqq6tEcQ0J\nCUWj1WJxm7A5bXhabguxWq1mXNpEps6Yhkbnv5ZeXFwoii54OwgzZswhJCSUsLAwn2pJo0ePJTNz\nMdnZWcjlMqZNm9nrcKCuBAYa+PrXv4XNZkOj0VBeXsrVq5d9jlEoFShDfb+/Jpf/b++rxKVLl5gz\nx7vsMXHiRHJycnw+T0lJwWQyoVQqEQThvs3A3bc33WDpWTwKlFiLEG7FJLpwUWIt6LXw7tz5gRik\nf/78GbZseY6YmNs/9kpbBcWWAtRyNekhGQ9kGmrs2PGcP39GLDagVmvYuHHgw40KCvL5y1+2ii/3\n4uICvv/9V32EddasOVRWltPc3IRKpWLq1Ol+wpsWPIlApYHa9hrC1BGkBI7xucfJk8dwudykpo5j\n1qy5fbbz5MmjYtYjl8vFuXOnUSrVtLfbiYmJZcqUjB5fGF1Hcl2370TnVIYdaDQ9f1/UajVOpxMB\nAafTdwQrk8mISxxGY10jSpWS9FkZRGgjqK+vo6mpgWvXrnDzZh5arZZr1650up+WhIQkMjJm+hVe\nkMlkaBX+NlqtVv74x/8RCy60tvpm0rLb7Sx8djGnLSdwHXHhbrm9/hscFoJrWjv7bJ9gqApibvhC\nnzzHHk+XBeVb7dTT/3XevAW9quvcG2QyGXq9Ho/Hw6ef7vKZUdBotKTOGEdtZJXPOSmBqf1y74cV\ns9lMYODt0DWFQoHL5RI7k6NGjWL9+vXeOOvFi/vs5Ndb7pvwDpaexaOAUubrPaqQ9c6b1GRqEwtj\ng3fq6ezZ06xZ462oUmEt42DDXmweb97Y+vZaVsSsu+/ZbYKCgnjqqec5c+YkABkZM8Xp0oHks8/2\n+Uw7Go1GSkuLfdb2hg4dzje+8R1u3swnKirabxqzg4SAkSQE+K4Jms0mdu3aIVYuqq6uICQkhLFj\nx/fJzq6ZnNxuN8ePfwF4R+Btba3dpn1UKBSMGzeBs2dP4fF4MBiCSE+f2qd7dyU+PpHr17P91jYN\nBgNCoCCutTocdjQaDYIgIJfLiR0/FHualWBPEMigQJaLxjABnUnH9evZ4nU0Gt8QH5lMxqZNT/eY\nr7g7SkoKRdHtjqCgIDy3Lhc4KRBnsxN3m5sAQyBh6eE0OOoBMDqbOWc85SO8SUnJjBqVQkGBd53b\n2/Hpv1jf3mC32/06E8nJo1m/aDPnm05x3XwNkDEuaAKjAlO6v8hXhMDAQJ8CFh6PRxTd/Px8jh49\nyhdffIFer+eHP/wh+/fv5/HH762W8p24b8Lb3z2L0FB9tzVE74XIyDvHIA425shmc6D8ABaXhRB1\nCPPj5+GymPnwww9pbW1l6NChPP30035xfBqNf4emoCCPgABvO1YLpaLoAlTZK1AFuQnT3v+Zh8hI\nA2PGJFJjqeFC/QWKzXlMi55GtP7BC3DH90Gl8v9+hYTo/b4vkZEGEhN7DvvoiZqaElF0wSugzc11\nff4+ZmbOJjf3Gk1NTYB3ZNmR1cnj8VBeXtzjNZ999inGjEmmqamJsWPHMnz4cPGZ7oWVK5cSEKCm\noKCAgIAA4uPjMZvNuKJdfP7p5z7HagO1/OK1X9Bka+L3Ob8HAWRy7/fTg4dcczZRRt9RbEdN2Q6c\nbgfBwZpeeWxfu3aNy1cu42h3+H2m1+sJDAxEp9exZs0a4kbEUZibR8HlAtytbpDBuDFjYSS0mW//\nz9xyp19b/c3fvMzx48dxOBzMnDmzV6Mkm83Gtm3bqK2tJTQ0lE2bNhEZGUlrayvvvvsujY2NREZG\nsmXLFgpNhZTZy9ApdWQOzUSr9O2MCEIgQ4YMobi4WNw3alQikZEGlkcuZTl9K3QBD987srdMnjyZ\nI0eOsGzZMrKyskhOvr1ubzAY0Gq1aDQaFAoFYWFhYgWp/ua+CW9/9yyMRmuPn/WFzpVYHhZiGMGG\nmKdocjYQpY5B167nzbf+R0ykUFlZCShZubJr2I6MsWPH++SDbW1t5aOPdvHMM1tw2X2nGVVyNeYW\nJ27Fg2mfVmcLu2t30ObyBvcXGotYHbMRwwNM4t75+7B48XLeeut/xelXjUZDQED4PX9fqqoqOHLk\nEE6ng1GjRpOWNpHAQIPonKVQKDAY+n59jSaYzZuf49q1yygUSoqLC31K+clkijteMyFhDAkJ3r8b\nGkxf+jeRljaVtDTfkfPFlnOoY9Q4qm6LXnhsJA0NJhraW3AL/lO0bsGNRtelmo5Og818+7fvdrp5\nd+921szfeEebCgryeXf7n3HavfdX6VQ4bV4R1+q1PP/1b3DRc44GRx2f2w4yvXoOI5tHkl1ya7Qt\nwPlz55kSnQGdEkNFKmO6bauO529vh+LqKozOZqI1MWgU/jV2AT766H2uXLkIeH+/druD5577Bu+8\n82fy8nLE/Tm5ObhxowhWEDI3hMrWalbFbPDrUK9atZGDB/djs1kZPjyeSZNm3PP/tLffh4dRnBcv\nXsypU6fYvHkzgiDw2muvsWfPHqxWK5s2bWLTpk1s2bIFlUrF8OHDWbvWPxSyP7hvwjtYehaPCoEq\nA4Eq7xddEARaWnynlnrKDjVv3kKuXLnos5bXMTqaEjKdekcd1fZK1DI1k4KnoFP0T9KFzjQ2NpCb\nm01goIFJk6aIL40Sa6EougCtrhaKrYVMCJ7c7zb0htbWFp92am9vZ9++3axde+eXfHc4HA4++uh9\n0Xu7tLQYvT6AFSvWcOrUcVwuF6NHpzJx4r09a2zsENExp6DgBnv2fERzczMREZFkZvbstSoIAtnZ\nWRiNRpKTR4vXEASBzz/fS15eLgqFNwa0c7k98E5pX7hwFqfTQVxcPBUVpRgMQYwfPwmz2YROpxMz\nRI0JHEfBlDzKVWW4Gl0EhwTz1PKvARCmCsegCKKhvB5XowtlhBLtUC0jdIksWbACl81NeXmp9x0R\npeXG+VwfOzwK77T2oUP7ycnJRqFQkJExk2nTZojHZOddE0UXwGlzErYqDIVegUav4az7FDXt3vVP\nu8fO2eYTJLf7OsEJgsAQhhIfOoJGRyPBqmDSQzLu+H/JNWVzpvkENoeVAJeBpfEriNX7z4709Pvt\nut/Z7u0seOo9tF1oo3ZRNRa3mUClr+hFRETy5JPP3tE28HZurG4LekVAr5esHiXkcjk//elPffYl\nJSWJfz/55JM8+eST992O+ya8g6Vn8Sgik8kID4/wmbaMjOw+NWBoaBiJiSMpKvImONDrA8RiCxqF\nltUxGzE6m9HKdQQo+yfhQmeqqyt5770/iy+W0tJiscxZgMI3P68MGYGKnnP23m927vzAb19DQ203\nR96d5uYmn5Apt9tNZWU5q1atEz12+4tRo1L4znf+lubmZsLCwrvNZNXB/v17OHv2JB6Ph7NnT7Jh\nw5NERk4mK+sSp04dF9dqP/vsU0aMSBTzAHfkiL55M8/vmrt2fQh4PW0XL17KxIlTCFAGsCp2A9cD\ns5HL5EwImoxKrsLhcPD5pU+pKCrHVmgDF8hUMtLmTmBJ5koUMgWrVq0Tr21qb+N/6n6NucwMQMCw\nABZmLCE7O4sTJ47idntHzp9/vpf4+BFER8cCIOuSPVGmkaEMVKLQKTAXm2nMy8HtcaNL0hGQGoDd\nY2PCpAmcPHFKdFKKjo4lNXV8nxKRXG29hLG4mbbzbXgstbwd+SbffOqv/aoYhYdH+NSn7mjn8PAI\namp8HaI6ENoFNAotarkGj8fT5zzc1fZKjjUeotXZQrAqlPkRjxGjje3TNST6h/smvIOlZ/GosmbN\nExw4sBuz2Ux0dAyLFy/r9ji5XM5TT32No0cP0d7eTmrqWJ94RLlMTrj67knW75Xz58/6jMZzcq7y\n2GPLCAw0MDIghSpbBQWWfJDJSAkYQ2JA/4beCILAddNVLC4zw/UJd0zJ113yg6CgeysaHhISIsZz\ndhAe3vcScr1Fo9HeNTTF7XaTk5MlPqfJ1MbFi+eYPn0yjY0NPs9vtVqpra0WBaG4uLBb0e24Lnhn\nDA4fPsj48ZORy+UEKAOZFnp7FGqzWXn77T9SWVnuc77gFKi/UYdivv8IzKAJ4qVnX+bw1c8QBFgw\n4TGCNSE0NNSL9wWvg1FVVaUovEvnr+BGeR7mahNylZyAtAAUOgVOo5O2M2147N5ndRqdKIIVDEuK\nZ2jMUJ566mtcuHAGmUzOzJlz+5z9yyW4MF8x4zF5r2+uM3Po0AGxSEhdXQ01NTUsXLgUj8dDY2M9\nBkMIy5d7w8FWrVqHXC6npaUZk8nk89vRR+uJbxvBH//we0ymNqKjY9iwYYuPL013FFsKqW+vpcRS\nSLPL6xfQ7GzkgvE0K2PX9+n5JPqHgQ+clLgnwsLC2LLl+V4dq1areeyx7oX5fnOnuFeZTEZm5GKm\nh80G6DYU5MtypPFz8szeNbPrpmssjFxKvD7R77gTJ4747ZPJZL2Kj+0OrVbHsmWrOXbsCxwOB0lJ\nI8W0iQOL7//DIXi9fePjE9BoNKL3b2hoGMOHexeC25xtXDNdoTfY7XZcLqdPUYJ2dzs3zLkcefcg\nDVX13Z6nlvec4D9UE8b6ab6d9BEjEn3q/gYFhYje5x6Ph5v5+UwdPR3zHBMag4pAXRAF5hs01NWL\noguAE8JaI1hyq1JPVFQ0y5ev6dWzdsbtdnP48Oe01rbiNvuuX3e06bvv/kms2qVSqZi4IZ3E2SOJ\n1ycQFOj1a9DrA0SRdrlcfPHFZ7S0NBMaGUHm3AX835t/oLq6EvB2nD77bC/r12/q0a6rrZc523wC\nF/5JXNo9PXt6S9xfJOGVuK/MnDmH0tIiGhrqkcvlTJo0Fb3edxTRG8G9fPkCNTXVREdHk57ec5xq\nZ1weJ6XW256eNo+Nm+Y8UXirqir44IMjmEwWnxhI8Iru3//9j79UzdW7VdR50CgUCiJTI2m70Apu\nkAfK8SR7RSI5eTTLlq0iJ+caCoWCWbMyMRgMOD1O9td/QkNwHdoELfaSOyfCGDYs3kd07S4be+o+\npjS3mNaq1m7P0ep1TJs2q0/Pkpg4kmXLVnPw4AEsFhMORztXrlwkM3MRH330vphMIjo6hmeeeZGQ\nkBAmBU+lkAL2XtmJ1ep12FKp1MwaNReNQku+MZ+8xpsEKA1MDPbPwX0ndu78kKysi377FQoFI0eO\nwm63k5d3XdzvdDq5vO8CURuiKLYWIEPGCH0SlbYySq4VYW22ERs7hCVLllNQkM/p0yf4y9tb/VJZ\n1rfdeSmkyHKzW9EFiNXG9fr5JPoXSXgl+oTNZSPXfA2FTME4wwSUd0m4ERkZxQsv/BV5eTkEB4eQ\nktL3nMVHjhzk6NFDuN3uW9NwRp9qMz0hk8mRd3Eg6dh2Op189NF2nzJrnYmPT8BgePQSukRNicEY\n2oyr1YVmmAZPkIBH8I4A09MzSE/PwO1289lnezl27Av04XoaxtQhU8gImR+CfYQdRaESY1UzHo8H\nlUpNSEgI4eERREZG+xW9zzFdpd5Ri8vo//KPiIgiI2MmI0Yk9FhC8E64XC5MJq+Y2+02vvjiM86e\nPYXFYhaPqaur5ezZEyxdupIAZSAT4ichLPNw7txpPB4P48dPIjl5DPmm6xxv/gKn51ZWL2cTCyL9\nY6E7cLvdHD/zBTXmapLGJVNRWerzuTxQjmaIhqFxw5gxYw7Xrl3xT17i9m47BScl1iKutl3m5ul8\nLFctIHhFu66ulpycq+KSRdfOgCmojWMNX6BX6BkRkEikxjccr2tMfqDCQJwunjBVOBOD0+/WxBL3\nCUl47wNZWZcoLS3GYAgmM3PhPZVPG4zYXDZ21+2g8VZCgRJrEati1qOQ3flrZDAYfDxO+8rNm/ni\nep7H46Gg4EavhFchU5AWNJGLLWdxCU5CVeFMDJoCeL1Iu4puRx5drVZ7x6QW9fW1HDjwKRaLmSFD\n4lixYu2X+h+bTCby868TGhaGEO3Bg8DIgGQunj9HVtYlZDIZU6fOYNKknl+UZrNJLCIwfvxkEhL8\np9MBwtThaIZo0AzxjkpDVSHiy7msrJSsrEvk5WVjNt8Sr0LQNwcQPDcImUyGLlHHjPS5jJQn43K5\nCA0Nu+PIsCPjmipS5X3b3NJfrVbL1772TYKD720NHRBFtzOdRVe0oUtyrokT05k40bcti62FougC\nVNjKekzs4/F4+NO2Nygp8DpH5Z2/Dm7f49QRakLmhOCSO2l32Th27Au/68g1clwmF0qDkhankXpH\nLe2V7dxqsltr8td8/AQEQUAZrkQRqEAVrkI/UUeO2VssIdt0hUWRyxmuv53UZUJwOi1OIxa3GZ1C\nz8ywuYwKHO1ni8SDRRLefubcudPs379bTOje1NQgrtk87OSas0XRBa+XZKGlwCcl4v2ga5YipbL3\nWYvSQ6YRr0ugxWkkTjsMrdI7rR0cHExoaJiP80pi4kiKi4uw2+0cPerN19y1sg14pxUrKsoAqKqq\nRKvVsWTJ8nt5NOrqanjvvT/T2NiATCFDP05P0JQgzuWdouzzEnF9sLGxgZiYmG5Hhk6nk23b3hKd\nlvLyrvPUU893m1UrI3QWLsFJQ3s9OoWOWWHzAK/3+fbtf6GtzV/MrIUW3GY3Hqcbd6ubA6pPSU4e\nzdq1T/gJU1FRAUVFBQQHhzBt2gzGGSZSYi2iYUQdbosbKmRE6WKYM3velxJdgJSUVC5cOCfGRXdH\nVFQ0GRl3n8buGlqjlKnIyrpMdnYWCoWcmTPniZ2ZqqoKUXQBBIeAqJaAKlyFYYpBvI7L6aG11b9d\nXU0uWg62MG7deCI10dQ7apErfEeodrvN77zgCcGoE/zXxG0eG/nmHB/hTdAnEREbSV17DVGamH6t\nDS1x70jC288UFNzwqaJSVlZyT67/X5ZKWzn5puvIZHImBqUTrvnynsvdpZJUcP9H8/PmLcBobMZo\nbCYkJNSnDqtH8GD32NDJ9aIINDTUc+NGLqGh4Ywdm0aEJpIIjW+4lUajZfnytZw+fQSz2UJCQhJV\nVRU4nd7YT4vFzPnzp0lLm8D1tmvkmK4iCB4StaMwGpt8rtXU1HDPz3b69HEaG73nC24B2w0bhokG\naqqqfNIcWq0W/vznrSxc+BhTpvjGklZUlPl4CpvNJnJzc7oVXrlMzpxw/1zB16/ndCu6XsOgveb2\n2q7VaSEr6xJKpZLY2KG43W7S06dx82Yen3yyQ3R4qq2tZvXqDayK2cAN83WUc5WMMaT1W0rSuLjh\nbNy4hUuXzpOXlyOm0lQoFEycmE5UVDRpaZMICgqioaGevXt30draQkREFGvXbvTxNZgSPJ02j5F6\nWz1auY7Y1qF88skOMXNWdXUlL730PYKCglAolF4ftW7SXMtkMhKXJNGma0Ut0zApOB2lXNnj799p\ndJLYkkxkdCSXcy7itrlBDnggODgUEHxqIYeEhLJoyuNcb7tKk9O/dGHnUXsHBlXQA01KI3F3JOHt\nZ7rGUGo0mgcuug3tdRxs2IfV7c0cVmevYm3sZnTKL5ccI80wgVJrEdV2r1dlom4kiV3yEfeFvLwc\niooKMRgMzJ6d2eN0bVJSMt/+9vepr68jKioK3a3sRpW2ck40HcbkaiNUFcbCyMdpq27lww/fFcuy\nzZw5h6VLV3Z73dGjxzBnzjQxS8/rr/+Xz+cej4fG9gbONB+n/Zb3b4vTiFqvAfPtKc3wcP8Y6suX\nL1JdXUFERCSTJ0+jsPAmen0AI0YkdLmHf9ECwSOgjFCiVKtwdSribjK18tlne4mLG05MzO34S4Mh\nyKfcHHi9qu9EfX0dBw/uw2q1kpIyCp2u+wxLdyI7O4uLF8/d+vsKOl2ATxWiGzfy8Hg8aBVaJtxa\nT+xLUYbekJQ0imvXrojPrlKpyMiYxdKlK3yO+/TTnWIse0NDPRqNhg0bbntKh2sieDH1RW5UFxOk\nCuHQ3gM+6SpbW1spKspn0qRpxMYOYdz4CeRcvUpXgoND2JC0hVahhUCFN+nN7t0f+ZTr64xCoUBj\nUHGs5hDNpxvxWG97XA8dGofL5fSZalapVDRcrGfD3Kc42LSPYmuBz/U6pvbtdjsnTx5Fp9MzY8bs\nB/4OkrgzkvD2MwsXLqGxsYGamiqCgoLJzFz8wG0otRaLogtgdBkps5Uy2vDlKpMo5SpWxayn0HIT\nBQoSA0bd8+glK+sSu3d/jMPhFbS6ulq/KXmP4OGc8RTNjiaCVEHMGDYXpfz2V/ac8RTNTu/os95R\nx9nmk5jPW8QXlcfj4erVKyxa9HivSg6mpU2gtrYat9uNRqNhwoRJ1LfXiqIL0JrTiqXZ27YKhZKU\nlNEsWuSbC/fYscMcPvzZrXVpGYcOH8ButYMM4tKG8dLGl8XReXr6NIqKCsTRpnaEFrlazvCEEQxZ\nHC3zV7EAACAASURBVMepE8d9RqI2m5XKynJReK9cuUheXg6hoeG0trbgdrsYNiyejIye19QFQeDj\nj7eLo+SyshIWLlxCauo4bt7MB8DlctPtkK4TnUfkFRXlPp0B8Jb663jOnJxrHD16ELvdTnx8AuvW\nbeoX3weTqY3s7NsF4Z1OJ5WmMkoshT5FKjqLF0BxQyGH6g8wI2w2AUpvHKxaoSbmVpx3aWkxXcnL\ny2PSpGneQg0bniZ1bBrnsk5Tlnv7WJlMhkqhJkZ5O6a6x5kEvGFRrWGt1Fyt9hFdgNzcbGbOnINC\noaSuroaWlhYaGuo5fPhzqqur2LD5ScqtJT5eyyq5CovFzG9+829iJ+js2VN8//v/IInvIEIS3n4m\nLCycb37zu7S2thAQEHjHLEL3i64ZoRQoCVF9ufU08VoyZb+UFsvPzxVFF7wJGjoX0QA41XyMa223\naozawO62szjqdjxyu9s3tMUhtNPVF6a3ESEOh4OCghtiTvHU1HFMmzaTVmcLAfIALB4LgiBgy7Ph\ncXlfkG63C4VC6ScgBQX5nZI7CF7R9f5JZU4FJ6cdZc6I+YD3xfvssy+Sn3+dxqYGympLsO9tZ0h6\nHLNmzCMmagjvvfd2p3jVIBISRmI2m9m27f+orKwQ7xsUFIxMpqGkpIg33vgda9duYtgw/0Lrdrvd\nb3q8vr6O2Ng4mpoaUSpVjBqVTJOxmeyrvYvfBe8aucvlorGxAa1Wy8yZc5DJZDgc7Rw4sEdMh9jS\nYiQ8PEL0gDabzeTmZmMwBDF6dOqXrlRW76hjT97HxJqGMnt0JrGxQ4mIiBSn9AGcBgc3LNdpc7Ww\nNnaT3z0NhiCfzGPgrSbVgUwmY3zqRK5d9q2BazQ209raIiYeAYiMjCY/3zftZQdtbW3UN9RgutD9\nOnVFRTkvvfTXfPTR+zQ33w5XKiop4FLpOdQlWiz6JjRDNKj/P3vvHRzHmaZ5/jLLe3hvCUfQgwC9\nN6IX1WqpZVpqtbpbPTM9vTc7cxdzd3ERuzdxERe7sRF7E303tzOxO7Mz2+qZbqrlRVL03oMESRAE\nCO99FUyhvMm8PwrMQqIAilJT5jbw8B9WoTIr88usfL/vfZ/3eQQ9i8xlvPfeP6syD+PjLm7cuMrG\njd+FPvKnw7aUT/+ArZ/sL/1dwELg/RoQ05/++lSKvgiVtmUMBQfo9LUhoGGpbTlZxq9muP11YTZB\nSq/XJ8zIR4PqB58zpA4WWcYcxj1xclS2IZfsdbl0d3cyMTGORqOhvHwJH3zwW7xeL6FQCJ1Oj93u\n4MCB57FY4hOUS5fOKqnISCRCY2MDzz03icOexPb0PdRP1DH0YBBmcV1mqifNPJd5IcGUX70CysrK\nJhwOc+XKRYVMc+7MKbIycikpKWP//sPU199FFEXWr99ENBrhV7/6FX6/2jhk5spqZGSY8+dP89Zb\nP0s4BIPBgMORjN8fP5lIJMzFi2cUfsLk5AT5pXNbHs4FU7qJbTt2sWPHHrq7O0lLSyctLZaC93g8\nCau+x6/Hxsb4zW/+gZGRYQRBoKZmHS+88PJTf6/NZmflytXcvn0TWZbRODSIFpHRz0cZCY7Qer2F\nAwcOc+jQiwwM9OH2uEED+vTYNXKFRmMcgVka5S+88DJ//df/XpUaF8XEx6XHk8iinhnEQ1KIys1L\nGXe7aGpoJBpVt1U5nSOMf+Ii6k68jyBOLJz9e4kQ5sTvjyJ7ZUStSN6yApJNKXTpOxQt9pmYL9W9\ngG8HC4H3O4h2TyvtvhY0goaapPU4vuRqVRAEdqbvJSTt4NL5c1yqu8BVzSX27XueJUuWfU1H/eWw\nY8dzjIwMMjDQj8ViYevWHQmB1zRLWGP2w3F72nNYNFbckUnS9RmsdFQjpAj85Cd/QnPzQ1JSUrl8\n+YLi4jQTgYCXH/3onRmv1So+wWAQn8+L3e6gwFjEneu36LnbrT4ek5kVK1YB4PV6EUUBk8nMtm27\nGBtzqVZYj2HMM1KWm8gC7+vrVjFYg8Eg/f09lJSUUl29VmVY8Jvf/GNC0IXYdVebYcytTCSKIocP\nv6TUeEtLFyGKepXAg8czRdD79MpGklWiJfSIKkcNixerMyIORxJZWTmK4pJGo6GgoAiA69cvKytL\nWZa5d+8O27fvnpfxPDDQR3t7K6mp6cq9fPjwS5SUlHO17wKBPD8T5yaQg7Fx8Pm83Lp1neHhobgR\niwRTdVOYFpkwGc3oxcSsVEpKKlVV1dTVxVeZc0ly5uTkqYhtOp0erV6DJzJFIBrg9OhxxsJO/Hp/\nQtB9fM4R99wCFykpqWzfvhuI/V4GB/vp6+tB0AtoLBoiY7HtpIhEX30PPVLX9HZpiKKoSIAajUY2\nb94+53cs4NvBQuD9jqHH18l550mlrugMjvL9nNfQfYFQxVy4W3uHC+fPKK+PHHmXP/uzv1Slwb4t\npKam8vOf/ytGRoZJSkpSrT4fY1PKNgLRABPhcWw6O5unW18eQxRE1qXEW0UkSeLSpfNMTk5QWFhM\ncXEJH3xwZM7vHx1VSxcuW7acBw/uKX2gRUWLSE/PxO/38S//8t9Ugvax40/je997haKiYj744Hc0\nNj5AEESqq9eyf//z/OIXf84//dN/VtqOAAx2I4dfe4kye6IZeUFBMRqNRrWCns9xaq7Vi6gTKcov\npqMjdpyiKFJSMr/udUFBIT/72S+AmL3bhQvXVAQtURRxjYyi0WmIhmPH5MhJYveGfTidTm7dvorf\nG58oyCEZWU7UuoZYoH3llTc4d+4kwWCI0tIyVq+O2egliErI8pya2RAja3300Xt4PFNoNBo2b97G\nc88dQBAEli1bganIxKWxs0zIEwn7nGlRCiAFJOxRBxtTts3r0vPCCz/AYrEyMjKM3e5g//5Ekt7m\nzdvp7u5keHgQjUZDxepKPh3/AG/Eg1bUEpBiKV/Z+OR6uWAUkKdtOguXFLF/62HS0jIwGmOkN7vd\nzjvv/CnXOy9zN1yL+7pbCbyg1hkfG3Oyan0N7T3NoIENBzd/KyWvBcyPhcD7HUOPv1tF5nGGR3CG\nRp4o7j8f7t+/o3odjUa5dev6nA+QbwM6nY7c3Pll6+y6JF7MeRVJlp6KxPXxx79XvIfv3q1l795D\nOByOOVeHs80PXC7ntLOPkaKiIvbvfwGNRsPZs6cSgi5AVlYOxcWLuHPnluKrCnDjxhXKyhbj8bjR\naEQMBiPhcIi0tHQOH36JoqS5hS2ys3PQ6XSqwDtXf2ooFCQ5OUXlvytaRVIPp7I0awUVTUtwuUbJ\nzs5jzZr1Cdt3d3fS1taCw5FEdfVaJS26dOlyxsf3ce/ebYaHh5AkSSEkZWVlU1JSzvbtuzGZYlkI\np3OYhw8fKPtNykrCVefi3f7/isViZs+eQyrx/rS0dF555U3GxlzcvXuHy5fPs2HDFtasWUdLyyNF\nCnHp0uUkJSXPOUZ37txSxiQajVJff4912zfR7GlEFDSssFfxfcNrnF91mvqLdwmFQhgMBlatqsZg\nMPDgQZz9XJBbxJvlP3si4Uij0bB376F5/w4xzfR33vkFzc1NJCUlcVN3jclQbNzC0TCRqQjIYCw1\n4n8UIDSamEXQaLXkbcgHjUyWPYeDq74353FptVo2lGxhdGiIztIOgoNBJVjPhtM2jHV/bPwfhO/h\ncCd/a3abC0jEQuD9jsE8K52qFw3YNF+tBy/WB9ilem8uss23gUgkwqeffkB/fx9Wq5W9ew+SkzN3\nEH6aoCvLsrLagxi7tbX1EQcPfo9Tp47j9XoIh8PodLrpGm/c/KCx8QFHj36i9PBqtVo000IGM3so\nHyMlJU3pJZ6aUvtIR6NRmpsbuXu3ViG4JCen8JOf/Ak22/zG4aIo4nAkEwgMKu+ZZpnC9/b28OGH\nv2N0dAS9Xo+ULGHINGCrtiGIAg+n6rEUWSmqXMSalMSg29TUwEcf/R6fz4sgCAwM9HH4cNydZvPm\nbXi9HoaGBlXb+Xw+HI4kxsac5ObmA/Dyyz/EZj9G/1gP9swkMnQZnD8bz6643ZO8/fYfq/bjdI7y\n7rv/oOhit7e38qMf/Yy33/4jGhruY7FYVH7NszH7fRmZz4Y+YCISI231+Ds5nPUS39v2A5bnraKv\nr4e8vEJKSmLs5mg0SltbCwaDgV279j0zlq/JZFaUsC71nosdmywzcWmCQEfsHjAWG1n/ykb6T/Qp\nXIKUlDRycnIpK6tI8D6eiQF/H6OhYXKNBaQZ0tli2EXTvcZY0NWCeZGZFCmNvrYeBEGgctlSxvKd\nCDMMMVyhxJ7fBXx7WAi83zGsctTgDI3S6+9GJ+pY5ajBqpv/gf0kvPTSqwwM9OJyOREEgZKSsqf2\ngm1sbODWrWvIMqxevYaVK6u+0jHMhzNnPldWp8PD8OmnH/Anf/Kvv/L+BEFIIDXpdAaKi0v44z/+\nH564bWdnhxJ0ISYJOTw8RH5+IWVlFTx8WK8QVrKycvnZz/5YCYpLlizn9u0bTEzEVjnp6RlIkjSL\nVTrGwEDfF+pU79lzkJMnP2NqaoqsrOwER6kLF04rKfJQKIRZNmNfE5+U+SUf/pAPV8iJQTSSrE8m\nQ5+l9G/fv1+npKllWebRo4cqJ56RkWG83sRV9tSUm+PHP8FstnDgwAusWrWazs52xlxO9FEDFWmV\nCazdoaGhBJZ6XV2tyoyira2Fnp4uiotL2Lp1xxPHBmDduo309fUwOTkRm0BV2JWgCzEltW5fJ4ss\nZZSUlCWk2mfXyr8OZBtycYcnmbg8QaAtfg8E2gOkDWaw+8f76e3tQqvVkZOT94UM7vuTddwcv0pY\nDmESzWxL3c39i3X4R6fT/BHQjGh558//lP7+PjQaDUKKwMfD6hJLl6eNo1Evqx015Jjyn/l5L+DL\nYSHwfscgCiJ7Mg4SlsJoBM0fpPKj1Wr5i7/4X/H5fGi12iezbWdgeHiITz/9QEnrDQz0kZycrBBi\nZqKrq52urk6ysnISSDVPwuz65fj4uOpB/ejRQ86di/V9FhUVK0zX27dvEgwGWL68iuTkZG7fvkFr\nawsGg5G1azdw6dI53O5JsrNz2Lnz6Xqok5LUaWeLxaqw0lesqEIQBJqbm5TU48yVaEZGJq+++hZ3\n7tycFuzYSkeHWtTAZDKTnp7xhcdRUbGYsrJywuEQBkOioMVMgQwAs2xlua2Kycg4Pf4u5X0ZiRvj\nl4kSxa51sCttLzmmfERRXcvUaOJ9tmfPnuTKlQuEw2G0Wq3Cbp5J2PL5vNy+fYPJyXHOnz+tfKa/\nvy9BGMRmsyW0Wc1eYYqiqHIy+iIsWlTKT3/6J7S2PiI1PZ0L2lMwqxysE748F+Kr4N69Ozx61IhO\np2PHjj2kpKQAsDN9L13nOwi0JmZKfD4foiiSkpLOmTOfEwgEKCkpe6KOedNUA2E5NunzSz4ap+pV\nk0QAKSwhyzJDtn7avM0ERhNlJv346fZ3MB4e45WcNzBovrxgygKeHRYC7zeIUChEY+MDjEYjFRVP\n7lf8KmSq+WA2fznFqo6OVlV90e/30dXVkRB47969zfHjn+D3+6cfQM+p5ByfhLQ0dSBKTU1Tgm4o\nFOLYsU8VacaxMScORzLDwwOKn2ldXS01Nes4e/akEpCcziJ++cv/EY9nipSU1ASN5/mwYcMWRkaG\naW9vRafTsWXLDhXZS6vV0t3dids9yeDgAK+88obSKgOx9P3MFH5qahrDw0M0Nzeh1WrZuHHrU7eX\niaI4Z9AFKC2toLu7U6kDl5aUsTVtJ4Gon/f6f8NUNJb2lmUZT48HwiAXydRN3ibHlM/mzdvo749l\nQPR6PWvXbkQURbxeLzduXFXGMRKJsGzZCtat28Tnn3+msJEh1nd69uxJFZknEPCTnh5zxRkcHMBi\nsbJv38GE+3vTpm10dLTR09OFIAhUVa0hJ+fLcRdSU9NITd2MN+Il2qtuwbFpHOSZElugAtEAXb52\nLBor+eanb5GaDw8fPuCjj36vsJTb+1v55R/9BRajFUEWcPW4ErZJT8+gsnIZH354hIaG+0oGpbm5\nEUmS6OnpxOVykZSUzPPPf39GfVxdw5VkiYqKJTQ3P1JY68XFJfSEOqmbrEVi7rakx3BHJhgJDZM/\nxzgt4JvDQuD9huD3+/n1r/9eYbmuXLmal19+/Q8WC/BFvFwbu4Rf8pGhz2Jt8sY/eJ+5ufkYDEal\nvqnT6cnMzFaUpJyhEcwaC511nUovaDgcpr7+7lMH3l279hIOh+jv78NkMqsk/rxeD263mpk6NDTA\no0fxdhenc5S6utuqVeDAQB+RSJjMzKwvdb6iKPLii6/M60Zz7twpZYU+MNDHmTMneO21H827P0EQ\neP7573PokKy8fhbYunUHVquN/v5eUlNT2bAhJohg1JjYnraHe5O1hKUwLScf4e2IpZR1j3TkHo6l\nFnNy8vj5z/8VH3/8Hn19vdy4cZVQKMS+fbtU8ogARqOZ4uISqqvX4nKNEgwGMZlMWK3WBE9YjUZD\nXl4+e/cenHcMAUwmEz/+8TvcunUdm83BypVVX3lszBozmcZsRb5URGRt0oaE/bnDbo4Pf4Qr7ERE\nZJltJVvSnu4enQuyLHPmzOeq1iD3yCQft77Pq0vf5PToMQKCesWZmprGG2/8hOvXLynllceIRCKc\nv3wa72SMTd/f34skS7zxw7cBKLcuoXb8OhHCaNDiDI8ynjLG0n3L0A8bsdlsbNmygzr3FwddALNo\nIUX37WkMLCCGhcD7DeH69cuq1pL6+rusXbuBwsLiJ2z1xTg9+jl9gdh+e/xdiILImuQnW/BJksT7\nH7/P3fa7iA6RRWvL2Jm+B7M2Jho/OjpCWlo6Y2MuzGYL1dVrqKio5PrYZeombyn78UbUbGHhS6TF\nRVFUEZxmwm53kJmZray0RFEkKyuH5uZGVfvJ7NS51WpLICR9GQiCQFdXB/39fRQUFComA7MJVvP1\nx861v7kQDoeJRiNfqKc8F1avrmH16pqE9wvMhRSYC2lra+FuR5zNHh4OM3p5BLkgFhB7erpobW1W\nVs2XL59j+fLFlJUtprExxlK2Wm2sXBnrT16xooqUlBSGhgYpKCiisbFBzagWRTZt2YacL/PAfY/F\n1qXzpnvD4TC//e2vaWtrQavVMTTUP6+ONsRa6a6OXcAX9ZKiSyPLmE1jUwMRd5TnVu1jX/phbk5c\nISSFKDAVsdiWqFh0b7JWMROQkGjyPGR10josWkvCZ2eio6ONs2dP4vf7ycnJQafTEw6HycrKTmhF\nQ4DOm218MPg7RkuHsCy34K51IwdkTDYzlZVLkWVZVd+eCX9Y/TvqH4krZK1OWkOaPp1OXytNU40E\npFhQ96V72VW5nwpbjDeQa8zDIBoJTrcvmQULiyyluEJOhkODSEiIiFRYKxWJzAV8e1gIvN8QZvcm\nyrKcsMr40vuUpQS24kzbvvlw7Ngn3Lx5VXnt8XvQ7dSxN+MQd+7c4ujRj5SVZE5OLkU1i+j1dzM6\na9/WSgvSuITHM4XRaGTt2kQm7VeBRqPh5Zdfn+77DLJoUSmbN29nYmKMe/fuIMsySUkpbNu2m7q6\nW/T0dGEymdix47mnrmPPhVu3rnPy5DGCwQAmk5kDBw5TVVVDYWExY2NjyrEtWjR/f+wX4eLFs9y4\ncZVIJExZ2WJefvl1/JKP++7YeS2xrSBZn6J83ukcpbb2Oj6fD41Gi8lkZNOm7apWnZmY6Yz1GD2P\nujl27GMOHXoRl2tU1bIUiUQYHh7mwIHD6PU6tFo91dVryMnJ469/9e9wOV2IgsjqmrVs2rRN5Sur\n0Wg5cPB5BvMHuOiKMZpbPE0cznp5zlLJlSsXaGtrmf7eMDduXGXVqpoEjWdlrFxnGAoOADAWdlF3\noRbvQy9I0Hm3nR+/9g7bC59cx5cTUrVRonKUfn8PEVki31SQwKOIRqN89tmHSoCd6ds8p+a3DIGu\nAA+67mGZsGBfY0efq8dd6ybQ6efKlYvU199LYO2LooijMAk/PgKd8cmdwaGuexeYi/BEpmigXnVe\ntydukGHIJFmfQo4pj62pu2jxNCIgsNy+mgJzIZ8Ovo80XQiXkBicHs8FfLtYCLzfEGpq1tPY+EBR\n6SktraCoqOQP2qcoiFi1VvyhWFoxGogy/GiYy9YLrFu3YV7iSm9vl+p1aDiEJxyrD3Z0tKnSt129\nHXzS83tEg4hVo2ZXZ5Zm84PKN+noaCMvr4Dc3Dy8Xi/BYICkpOQ/qF0jIyOT1157C4itlFyuUQ4d\nepHi4kWcO3eaiYkxjhz5NevWbeLVV9+MsTmfMm05MTHB2bMnCAaDVFWtoLIyxtiuq6tVVrd+v487\nd25RVVXD9773CsnJqbjdE+TlFVBTM/cEQ5ZlGqce4I/6KDaXJlgxjo6OcPHiWaW+V19/l6ycbAaL\n+5UJU6evg8NZL2PX2RkfH+M3v/mvCQpYHR1t/Oxnv0CvN9DS8oj+/l5yc/MoL6+krKyCkpIypWXl\nMWJmEfsoK6vg6tVLSg3farViNpv5h3/4T4yPj2M2WygoKOTsxZO4RmO1SkmWuH3rBllZWYqJAsS0\nqrtcHXQNdRAaDiEaRKJrojRa6hU3opmYTQqKRCJ4vV68Xg/BYFB1z8iyzFQkzjOQozL+Nr9CpgpN\nBbl589oXZoyW2JbT7e9iKhKTqDRrLFxynqE7EFMzKzAVcSDze4qIRiDi53jXJzjH5rZ6jEQiJCUl\nKSz2xxZ+jyE4Y8evMWuQhiXkafcpt3uSvLwCqqpqGB4exGq1U71jHRciJ9EFY4/hiDuCxqphx/7E\nyUSBuQj7pAN3JC69OREZ46zzBC9lx0pW5dbFlM8yuY/IkSe+XsC3g4XA+w0hKSmJH//4j7h7t1Yh\ntjwLd5bNqdu55rqE2z/J8OlhRkZGaKOFpqYG3n77j+ZcAc5Ox4p6Ecf0Kish/WkAQRcLaJ7oFKm6\ndEJyEIvGyqaU7aQbMxTG7oULZ7hy5SLhcIhFi0r54Q/fnpfgFJWjtHlbkGWZMmvFvOpBPT3dfPzx\ne4yOjpCSkkp2drYith+JRKitvc6mTVux25/O4DsajXLkyLtK2r+19RGHD0epqqqZVzFJo9Eogv7z\nIRwNc8F1mhZvEwAPp+rZnb6fbGMuAgI3x6/ysLU+QUe3b6KX8RlZi8nIOK3eJqqT1lFff29O2cn+\n/j6am5vweKY4depzwuEQOp2O557bz8aNW3nzzZ/y93//t/T39yRsm5WVQ25uHs3NTdPjIXH+/HnG\nx2Nj6vN5uX79Mp5IogZxz0Ci9KZzYJSprimFAxToDHA2/RShNbG07JkzJwgE/OTnF1JVVUN9/V0l\naBUUFNHe3sJvf/tPhMNhSkvLef31HyuuRsm6ZLzR+U3uQSAYDHL//l1MJhNLly5HFEWFmS0IAumG\nTJZaV3Bz4goyMlNRt0JCg1h5psF9T5koXB2/yIDQhyZJQ8Q1d5BavnwVdnsSgaCfhof1DA/G+55z\n7fksS1lOVIpyXnOaSeKBcnCwnz//8/9F+d0fG/oo1g5k0JC8MyYakmvIZ0VWYuueVWvjufQDnBz5\nDE80fm0mwuPcm7xDu7cZEFhqX0GlLS4LW2AqZjgYSzULCAukqu8IFgLvNwiHw6Forz4r5BjzeDn3\nh1y5cpGOkbiARE9PF/fv1yWoF0WjUQRBVLRcdRYdK7euYlvqLgB2796H0zlCX18vWqMW7SoN4bFp\nr9NUHZW2pXOuZiYnJ7h06bxS/2xtbebSpXPs2rU34bNROcrx4Y+VFphmz0MOZb2IRki8Hc+fP61k\nCVwuZ0LgikQic4rCzwe3e1LF0g2Hw9y9e5vPPvtQtR+TyfxUPZ/eiJfTo8dwhkaV+hrA2KiLfzz1\nnxGjAhmFWYQqAkTTomiTtUTGYw90s9lCcWkJE7hUKdGIN0rneDs63dw/T1EUsVisXL9+WVlFxsht\n99i4cSs6nY6N+zfx8XtDhN0hEKBy2VKMRhORSIT+/vj5+/0+XC41CzcSiZCSm8bU6AxxEAE2bNpK\nyBNWenZzcvKwGxwMyvH0pRyWGRtwcerUMUwmM2NjsX339/dhNltYvHgJvb29ZGRksnbtRv7hH/5W\nKbk0Nzdx5coF5TeyM30vV10X8UW9JGmTkSvqGa4fBgksditJSQ7+3b/735X0+qJFpdPZESfJySm8\n8MLL5OTk4o5MJKScZyI8YxXoi3oRRAHHVgeeOx6kkIRZb0aeiNX2CwsXsXPnXmVCWVxUwqeffojL\nNYrJZKastJwqR0wOc6JqggsX4qIi4+NjnDlzgr17DwIQlNRcARGRbWm75j3OLGMO5ZZK6txxgpZB\nMFA7cY2wHBvDibFxUvVpZBhiBMOa5Fg92xkcJVmfwlLbinn3v4BvDguB978TzLV6nqsedeXKRdra\nmpXXOlnPrsL9Sk3OZDLx9tt/hN/vQ9JI/O1vf4XrYuzh6ShxUP7m3CIQHo8ngXQ0U0RiJlo8Taq+\n075ADw/dD1jhSJzpzyY2GQx6NJp4qq+8fPGXcoKyWCzYbHZl1QyxScrM2qhOp+Ott36mkKuehGuu\ni7Q1thD1RDEWGNEmaZEjMpMXJxUt3a7+DuyCHUu5heTdyXjqPWTrctm8ejtlZRX4nT6aPDFiU6Q5\nyqk7xwn6A2RkZFJSUkZHR5tCKtNoNFRXr6W4uGTaMzeOmSv2Vn0zSfscBLoDaCwaXMWjdE128ODS\nfZUZA0DQGEQb0BIJRxBFkcrKZWzftZtfTf4H3P2TCKLA0i3LyE8v5PXXf8y9e3cIhUKsWlXNyZPH\n5hwXv99PMKieEF26eY6INzYmU1OTitG76lhmXG+b1s6+zDj5asdLe6ivvMvUuJvS0nL+/u//k+q6\ndXS0Kf/3+bycPHmUn/zkjxPMNUREpe6Zokul0honZaXpM+jxd6FP0ZPyXCwLZNc6eD37bcLhEEaj\nSVXSSMlNw1HoYGR0GI9niuPHP2ViYoJ9+w6xffturl+/rPItvn+/juTkFNau3UCaPoPBYL/yfaHm\nbgAAIABJREFUt3xjIcn6J9/La1M24ZN8jASHMYpGUvSpNEzdj4+fFKB7vBN7UhIGg4GA5KfEUq5a\nBS/g28dC4P0CPKk94ruENWvW8+jRQ6W2t3jxElasSAxks/V/fT4vExMTWK3x+q0gCJjNFurqapls\ni7f1TLZPcvbkKQ4d+h6zkZmZRWFhseIEZLFYWbp07h97VE5se5BnKyFMo6SkjN7ebiXwlJdXUlW1\nhoaGesXz9XFdsKGhnps3ryLLMqtWrZ6zFqvXG9iz5wDnz8fEOcrLy7h//77qM7IMeXlPJ6356FIT\nE/UTIIO30UvyjmQEg6ASsEeC8GgYykFr15K0OYlSWxnihEhT00PkJGn6e2XG6l1E/bHxGRkZJisr\nh1/+8i8QhOkMhU5HWlo6siwrfc6PMTMIBaJ+tDYt1mUxElaIIL9771083errr7FrsG20gR+KvaUU\nZhVTVRVjTf9PP/3fcIVGMYhG7LpYKv9x4Ac4ceIoDx/WIwgCoiiqSFtGowmzOb7iBZSgC7F+4OHh\nYfLyChR3H6vVzpIly+cda0EQWLk0pjc8NeX+wkzHY5WuNckbmAyPMxAYQC/qqXJU4416kWSJpfaV\nKpbvuuRN9Pg6cYbjKX6HLgmtVqtMZCfDEwwHBzEIRi6PnaOzpT120xCb/Fy7dpm8vHyWLVtJRkaW\nqpvB7Z7ks6MfEoj62bR+G6IgMB4ew6KxsiX1i9ucNIKGXen7AJgIjXHOeRIQADlWF7/s4fOeo5zW\nnCClMhVNlYBG0LLcvpI1yRu/cP8L+GawEHjngS/i4+zo57jCzukfxY7vnKftTGi1Wt566x0aGx8o\nq5a5yE0lJWUqElF2du68fa9zrVhv3bpOMDmAscSARWtlY/JW9BoDWq2WN9/8CRcunGVkZAiz2aKa\n6c/EYusSmj2NCmM1U59NpW3uB+7OnXuwWGwMDw+QlpbOxo1bEQQhwaJtZGSYzz77UHEXGhwcICUl\njUWLSpXPPG7pKCgo5s/+7C+RJImsrCT+zb/5tyrTc41G5D/+x/8Tvd7I9u27Feu/2WhsbGD0wbBS\n35S8EnIrVO9ew1nrKaKeeCDSWuM/NVmWeXiygVNNnwOgz9KTsjcFRJAj6pRoNBolKyvxvnM6R1R+\nuoBy7gDp+kwmI/FJkyzJ+EbUDj3aFC229Ta0Ni1imkhpSjnLHHFJUVEQSTdkznnu7e2tXL9+WQm2\n0WiUVatqlB7f6uq1So13ZGqYUFIQf4cfZsRKvV7Hm2/+lMuXzxEOR1ixYhXj4+O0tDyisLCY0tLy\nOb8bYhO77Oxc+vvjrTcmk0k1Jo9ZxBpBw97M54nKUUTEJ06kRUHkpZzXueg6y1jIhUVjZXPqduXv\nbZ4WLo2dxR/1oUVLhEjCU1SSopw+fZzCwkW8+OIP+O2n7zLaHb9PZEnmdssNtm7YyVJxFR8dP0KT\ns5FH1ib27TukumefhHPOUyqWcrQlirc1dg+ECOK76yUlKwVDloG6iVoWWcpI1afPt7sFfIPQ/NVf\n/dVffdsH8TTw+Z6+jvckWCyGp9rXRecZOvythOUQ3qiHsbCLidA418Yv0eJpwqyxkKSb20Xl24Io\nimRmZpGRkTnvwyUtLZ2kpGTMZgOZmTkcOvT9eVtTkpNTaW19pHqgy7LMhDRGMC/AaGiYifAYZdNM\nSp1Ox/DwIHfv3mZgoJ+mpgYMBkNCylYUNJRZFmPUGMk3FbI5ZQcGzdwMbEEQyMvLZ/HiJRQUFM17\nXg0N9TQ0xFeu0WiE1NQ0iopibkCSJHHkyLscP/4JN29ew+udYvHiJVgsBioqVijpXKPRiM/nJRAI\n4PV66Ovrobp6bULafmCgnyNHfpOQXi/Pr+TAuheQTTKjrmEkrYSh0ICtxqYcu9Vpo/tqnKgU9UQR\ndAKGbAPhsbCyWjYYYj6qc7Xb+P1+bty4qnrP4YjZK3788e8ZfDCIz+vFkB0fV99DH3I4HtjlqIz/\nkR9/mx+D1cjW0h0YNU/XW9zR0UZTU4PqvWXLVvDSS69RXb2Wzs42Ll48RzQaYXHVUsJLg0RCEcLO\nMMiQnZPL4cMvY7VaKSuroKKikjt3bnHixGfT+36I1WqdV9lKEAQWL17K1JQbWY6pN73zzp/Gxtdq\npaJiCfv3P6+afIrC3EE3EokQjUax2Uz4fCFEQcMiSylL7Ssosy5WyStecJ5mPBxrLXucrha1IoEe\n9STV7/fT3t5Cdc06OrNbmWybVDyCITbZ2rhsKx999B4tLY8IBoNMTbm5d+8O0Wj0iZaOAFE5ws3x\nq0TkeKpeP2TA3R8ncyGDPluPLlWHhESBqVjVqjYfnvYZabF8fVaDcveVr7ytULTl2R3I14SFFe88\n8EnqpvaJ8DjDwTh78ZLrDK8Y38Iwh4n2dx0rV1axe/dWRkefxBiNPcDefPOn/M3f/F+q2pugiT+8\nnCE167ah4b7SjhQOh3nw4J6isDQTjw0gnhUKCgoxmcyKBaBer1c9tGtrb8ywsYvZI1ZWLiM9fTV6\nvV4xUvjkk/eprb2hbOd2T+LxuBVf1Mdob29RTUggRsjauGEzADur97Czeg8nhj+l3Rdv7ckyZFNh\nXUob6naf0FgI10kXSJBekUF52mJKSsopL1e3hzxGamoaK1dWcf/+XSCW2j148AXef/+38eMaifm8\nSl6JkDOkpLAhJnYih6b7O30S0QdRknYmPpSvXr1ES8sjdDot27fvVlLwixcvIT09Q+l1dTiSFGP6\nlpYmzpw5odwHI85hNvxgM6wRsCyyoA8aOLT8e6oJX29vNzduXFVW0MFggAcP7j2R4Gaz2XjllTdU\n7z333P55Pz8XTpw4ys2b14hGo6SlpfKjH/2c5OT5J9TSHJ7DplITYXcY7z2vSuFxaGiQO3W3kIsk\nbDU2pm5PIQUldKk6UovTGRjoSyj9yLLM1auXWLWq+on63hpBi01rxx+KP6fyy4qYbJrE7Y4FX12S\nDkN+7PmUqksnd8Ec4TuDhcA7D9L1GfTOIADpBT1B4sHHHXHjDk/Mm4r77wXJySns33+I06dP4PV6\nMKdbsFTFySpmjVoBaLYQ/+zXzxoRKRKTy8zKYt++Q9y6dR1ZllixYjXl5XEi2GxPXkmSuHLlAp9/\n/gkgsHHjNlavjoll3L17W6mXZmfnkpSUGJBSU9NVxvWiKHLw4Ivk5qprw1tTd+Gb8tP3qBerycKe\nLc9jyjBRXFyi+PzqbXpCAyHFW3XCOM7SbSsUbWyPx8OZMycIBv2UlJRRU7MeQRB4+eUfsnjxMrxe\nD8uWraS7u0M9GZDAc9eD7E9k9M42rZfDsTHxSz40ghajxsj9+3c5ffq4MhY9w13kvViA0WBkTdIG\ndu3ax+nTx5EkiY0bt5KREStZDA4OqHrBQ4EQTZ0P0VSIaFI0RIlQO3WDPFssE9LZ2c7vjrybcI1E\nUSQihXk4VY+MTKV1+byZka+C5uZGrl69qPAHRkZGePfdv+fP/uwvVZ/zRKa4NR5jDtu0dkZCQwks\naftqOwjgrVOn8/VaA0XmEnzFPoyFRuSgjPeKj45jbfyd8H+Tmqru9YaYuIjX65kz8Pb393Dt2hVk\nWaZkRRl6hx5/1EeGIZNtRc+x9NXl3K67hSRHKV9bidMwggaR1Unr0ItfXVxmAc8WC4F3HqxP3oyI\nyGhoBIvGQooujavjFxUSkEObjOM7lmr+ulBTs57KyuX0uDq5FrlIQIhNQEREVtvXqD67fv0mXC4n\nXq8Hq9XG+vWbv7bj6vV1c8l1lqmImxRdKjuX7SM/v4CrVy9hMKgf0MuWreTOnVqFkGSz2WltjbO7\nP//8UxYtKmHVqmqCwSCtrc3o9Xp27twzJzt8yZJlbN68jfr6u4BAdfVaVq1Sk9m6u7u4desqLS2P\n8Pv9jAC/bf9vChGprGwx+fkF3Ju8zdiduFtTOBCmszNmSiFJEr/73a/p6uoAYgL9oVCYjRu3IAgC\ny5fHa7L5+YXY7Q5lxYPInEF3LhQvWsSp0WN0+zvRChqW2lbi7B1VEbZ84z5crlH0aXrO9Zxk4OMB\ngtM8gHPnTlNYWEROTh75+QVodBqi4elJiVFEmBVDAtOtV2EpzGc3P8TrUWcPbDY7a9dv5LOhDxiY\nZv62eVo4nPUSeo2BwcAAk+FxCk3Fiu3hXJDkWP/qXCnm+vp7KglSSHTJispRTox8ynAwpl6lQ0eq\nLh1nOFEhzrbKRnQsSqArdm5FxcWsXbsBrVZLhiEbb3SKsXoXV3ouArHVrdM5SnFxCd3dnQorPT+/\nUEXuC0T8dPk7EfwCx458rBDWOjvbeeutn5GdF8/s2LLtRNeGGAu5eCAH2GLZQaH5D5OlXcCzx0Lg\nnQeCILAuZZPqvZAcpNffhVbQUfMdm0G63W5OnTqGz+ejoKCQbdt2PVM2tsViwRPyEJiIr/olpISU\n/IoVVeTk5PPgwV0ePnzAsWMfc+PGFb7//Ve/VNvP0+DWxFXFj3U0PMLxO5/QdqIVSYpy584t7t+v\nU+p+aWnp/PCHP+bOnVuIokBLyyOmZmT5/H4fg8P9dNAKZTIHq174wnrYc88dYNeufQhC4oO9r6+H\nI0fejQfBaczUOdZqtVRXr8FoNUI9ML1IFDQCWVmx1aPP52VgIN5yIkkSZ8+epKKiMmG1ZLc7eOGF\nl7l45SwjviFwQKBl7pauxzCbzezduxfNIj1XJy4BEJHD1E3WUmBVP7BFs4jWFntkDFzuJxiI17f9\nfi/NzU3k5OSxaFEZRZsX0dMUcyEyV5oxp5qVYAsomaI7EzdwB9VjJIoib/zobfoMPQy44+c+HBqk\n0fOAYDTI3cnbRImQrE1hb8bzCSphkixx1nmCAX8vWkHH6qR1VM7Qch4c7FdERGYiKSlZNdGaCk8y\nEowT78KESdIlE5HDyr1XYi6n29dJRAiTtDOJYF8Qk2RmuWkVFy+eZdGiEoY6BxgY6MPtdid8p8lk\nQpLiE4Ds7NwZDOpxjg9/yljYia/Jz+RYnDA3NeWmubmJ7Ox44L05flWRdh1zO7noOsMPl/1kbqnL\nBXxrWLgaXwJrkjd8oQHBt4Xf//6fldRla+sjNBoNW7Z8sbn4l4Fda0dAUNJsWrRz9h2mpaXR2dnO\n0FCMcTk15ebEic/44bTjyrNCMKomNnVd6USS4nXMrq5OAoGAUp/Nzs5RWqFmrnYh9rC/Ld5ivN+J\nv9PPTdN13tj8dsIDfTZmM8dlWeb+5B1u1l5LCLqz8VjMwrzYjK3ahr/ZDzJkVWRTURHzNn7cljOT\nxBUMBvi7v/sV+fmFPP/890lOjk8QKioqkXNiQSdGhIsbshcWFtHX16tq+9m6dQf79u3jaMvn6vNA\noregk8KVRXj7fUjaCCwB0RA7X21USxD1+I+MDBMOh9HpdBzY/AJnyo8zERnHKJrYkLwVX9SDK+zE\nKtqYuu3mb9r+Iz78RMRZClECXPadYywYb0WSIzK+Vh9tllYm88eJCrFtxiNj3HPfYVe6Wqjl7mQt\nLZ54YL0xdoli8yKFPNbd3ZnQz2yz2XjxxVdU75m0FkyiGZ8UTyEn6ZLZmLKVNl8zRtHERHicyPSs\nSRAETPkmku6lcPTsx8iyzJUrFxL6xB+n4vPyCpiammJmcXhm+9G9yTuMTZs8aBwiaOCxCZEgCAll\nkLAUI0VN3Z/CW+9lJDTCf7n5//LWm+9gsTzZGGIB3xwWAu//D+GNeLgydgFfxEuqPp01tg0MD8eF\n3GVZVqkTPSuUWysZDg7S7m1FEASW2JaTY5ybder1ep/4+lkg25jLuCeWohUQ0DC7niyr7NtmIicn\nT+Uyk5SZzJhvlPHT40QmIkwxxXvDv+FPXv/X82YOmpoe0tnZjsPhYMOGWD/x0aEP6Ql0McXcxLWZ\nD12NRkNWVg7jGifWpVasS2Nko0XmuIa3Vqtl27ZdfPLJ+6r9+P1+Wloe8cmn75O7Pw9naBS71s6e\njENkGXMwiSb8kp+krUlIS2XWOjYw1uZicHBQ6QeuqKikpiY2kSw2l9I49RDfDDlCSZAI1QR57dCb\nZJlyqJu4RV+gB52gJ1oicaNfzTx98OAeLpeTrVt3Ulpaxg9y32AkOEyyNgWLLk6kqq29ztWrl5Q0\nr6BVj6+ExKh/BI01dj3liMzYyTFCQyHucQdjtpGkvUkKyW92vRpiv5GZ8Ek+PBGPEnjT0jIU9bbY\nOOt45513SE1V388G0cD6lM3cnrhJSAqSY8yjJnk9GkGjKFSdHT2p2kaQRfraepTzm21cIQgCW7bs\nQKPRsGnTVo4c+Y3q7zNlXl39LtxNbgS9gHWFFfsKO3J7rCVp+fKVrFypLm/kGvPpmezC2+BFDsW+\nv7+3lxPnjvLS868mjNMCvh0sBN5vAR6Ph9OnjxMI+CkqWjQn6/dJODd6gp5pK8CBYB8CAjabXREM\niH3HkxnLXwWCILA1bRebUrcBwrz6yhBbXQ4PD6peP2tsS9uNVWvHHZkkXZ/BxPpxTp06rvxdp9PP\na7136NCLRKNRJifHsFrt2Nbbabh1n8hE/CE50NzP+PjYnCnyurrbHD36kbISHR4epnrfGnoCXQBY\nl1sJDYcI9cVWIFqtlpSUNNasWU9Dw30ikTCLFy9l5coqzB4z18Yu4olOkabLoGZWVqWmZh1dXR3c\nv1+XcBz9rj683ti1Hgs7ea//N7yR/xO2pO6k3l2HJEuUlJVjGjRz/ManSqAJhUI8eHCf4eEhfvnL\nPyXDkMWe9ANcdp1TbPQe4/r4FV40vcLqpLWsJsYylnZLyEGJR48amZyMpz8HBvr43e9+TXJaCm++\n/hPyMhOFSEZGRlS1VTkiozVpifhjY28sMCJa4pmEQEuA0FC8vSUwGMDX7MOyxIJZY1VUmdzhCW5N\nXCciRTBrzGjRKSvRNF2Gqv2vqalBpfSl1+soLy/H5UqcIFballFhXYIkR9HOcl2KRqN0n+tkpGsE\nQSdgXW2lsKyYgHb+FH80GmXPngPKhG7Hjj1MTk4yOjpMamoaO3fGTBLa21tpOvYQvy9WzgmNhFj/\n4iZ2vhBb3c+VPl6dtBa/x8+J8FHV+yO+oYTPLuDbw0LgfYZ4mgZ9WZY5cuRdJS386FEjGo2WtWuf\nPoU9HhlXvZ6IjHHgwGH++Z//SQkE3d2d3Lhx5WshN82lqTwbL7zwMiaTmfHxMdLTM9i9e98zP46Y\n93BcnepB8j3V38PhELdv32TdukTFHpPJxGuv/Yj0dBujo1PcGr9OA2oFK5H53ZUePWpQpX/b21vI\n8ccnF4JGIGVPCu4TbnwDPiKRCCMjQ7S1NfPzn/9Sta8yawWF5iK8ES92nR2NoFXSwY9dl1566TUK\nC4uprb3O4GBcNEGwq++1icgY7vAkZdbFSn81wLXGS3OaQIyMDHPixAkOHnyJXFM+u9L38f7Avyg9\nqgBByZ+wnSiKPP/898nOzuXjj3+f8Pdx5xiXL5/n5Zd/mPC32Sles9nCjpd3c7HhLKJBxLzYrPoN\nJetSmGBCtU14IMyKdVUsti0l3ZBJVI5wcuQoI6FYPVaHjgprJX7Jh1bQUpO0Aa2oRZZl2r0tDE3O\nDkTCE5WwREFMsA8EuHjxHB0P4lKVgdogu9buo3l9I6dPn5jTu9lkUk8GCwuL+MUv/jWTkxM4HA7F\nVayh4b4SdAEiQxHWGjaqAu5goJ+xkItCczFWbaxXvDClGH22Xpn0CQYBbYGWC87TLDKXUWAumvc8\nF/DNYCHwPgNE5ShnRo4zEOhDL+qpSdpAhW3JnJ/1+/0MDsYJI9FolO7uzi8VeG0aO1OROEkjPBCi\nZbhJlXKTZZmenq6vlVX8JOh0Og4enNvo/uvCXEpbjx98kiRx48ZVPJ4pKiqWUFhYpPpcddIaequ6\neNB3n8hkbOW1fPkqVf10JmavNnQ6HSmz6t2iIGKMGPERf3i6pxLJNQB60aA8cE+ePMb9+3UIgsCq\nVdU899x+RFFk7doNVFXVcPTohzidThwOB2PLXURRpzLDUqLP8+LFy7h+/Qrj42MJf5tZ8003ZJJt\nyKU/GFeEsmntcx4zwKpV1dy/X6dMJFXHMYc3MJBgCG80GsjPK6TAVMh4JHZ8esFAvqmQVH0aizct\n4z9c/j9Uq+SoHMWuS1JIWhOhCSXoQowEFZJCVCetJVWfgUbQIMsyxwc+oaWviYAlABoBorF95uTk\nYTQamZpKHDtJlnCGRtAJeoVwNxmaICj7mZhUT4LDnhBCUGD12rX0OLpp+qyByFR8HARBoKZmQ8Lk\nXK/XE4mE+eCD3xEOh6moWIJWq15dG/UmldXn7fEb3Jm4RYQw9kkHe9IPkmnM5oHnHim7UvDc9yCF\nJQwFBkJZAR5O1dPubWVvxkHyFlyKvlUsBN5ngNvjN2jzxQy+fZKPq2MXKLaUoJ9DXMNgMGCxWFVB\n4suSHrak7pyu8XqQemUazzcS8CeuSjTGuS35vm50dXXQ19dDfn5RQoCrq7tFe3sbRqOJ3bv3Jcz+\n/xAsX76K27dvKjKCmZlZiu7w++//drr1B+7cucXLL79OaWk5H3zwAffu1aPV6ti8eStb3tnBo4dN\n2Cw2Vq2qnjd7sWzZKhobG5T6XV5eAeW2SibC47R5W2Jyi+NZXJ24qNrOY52ix9c176qjqamBa9cu\nKcHw8pXzhFPDbFy2hSR9MjqdjhdfjNXqfFEvv+75L6rtbaJ9TjZ2SkoKr732JjduXKO7u1NpSTGb\nLdTUqIVMnss4yAXnKSbDE9h0Dramxh1zJEni008/pKenE6PRxK5de3jrrXf46MoRGusbiIzGxkM0\niBRWzn2OswlpE74JPuj4LbnJ+VRYYhPWcusSCsyx4CDLMvZUB5PO+Ko3PBTm3K9PEVwXZO3aDVi0\nFsyiWcWyb/M10+p7RK4xn4OZL9I82sjt928SHg0j6AUM+XrSjRlIehlxNfxj0z9SY91EljGuFBaR\nIhwf+UTp6deLBgyCAW/Ug4SEwW5A1GiQpq9XZmY2druDK+PncZqGcWx34K51gxfSrBls376TpUvj\nLWCPEQoF+f3v/0WRLu3sbGfNmg04HMlMTo5jMBjYuHELZnOsdUqSJRqn6pVUujsyyX13HXuMBwlJ\nQQStgK3alvA9AclPh7d9IfB+y1iQjHwGaPM2Mzpjth2RI1g1NjKNiRrIoihitzsYHh5CFEWKi0s5\nfPj7aDTzz4FkWaarq4Ph4UGSklKw6m0sti1luaOKxqsNKps7hJhakT5Lj2m9kSxzDlZt4g/w6xgH\niBFnPvzwPZqbG2lqeoDFEpf9u3PnFp999iGDg/309/cyMND3xOD2GLdv36SurpaJiXFycvLm/bxW\nq2XJkuXodHqKioo5cOAF7HY7fr+fY8c+UoJkOBxCFEV8Ph/Hjx/F6/UwNeWmu7uLDeu20N/XS0PD\nAx4+rCclJY2RkUGuX78yPZkoRBRFzp8/pUr5RiIR1q3bRJ65gOWOVSyzr+TCsdMqP13BJJC8Lxmv\n7GHxjNaWmWhubqK1NW42L8syE7YxBqy9ZBqyVdfSFXLy0FOv2r7CUkmRpYS5YLc7WLJkGevWbcJi\nsZCVlcO2bbuprl6puhf0op5yayXLHVVUWCtVohUXLpzh6tWLeL1eJicn6OvrZe3aDSxftAqhBFyi\nE22qFusKK74ML6KgRS/qGRty0tvbjcPhwGg00tbWqhDf5IhMeCiMVBzFrk9ib+YhHLqk+LgJAukp\n6XT0tRGKhmOs3ij4vT66uzspKSknxZGKUWNiLORCllBlAaYibgQEHl6qZ6R9+ncaBTkoU3CoCF+O\nh5AYZDI0SZevnRJLhXLOdZO1NE7FxzgqRwnJQYXZH02OotfpcegcFOWVcOjQi1itNpo9DxkLu9BY\nNJjLzaQsT+Wnu35BVubcXIfBwUEuXjyrvJYkif7+XgIBPxqdhuL1JWzcvBWLNjZJl5G5576t2AFC\nLCVfaq3AG/HQF0j0Yn6MfFMheab5TUAWJCO/fiyseJ8Bcoz5PJxSPwA7fW0sd8wtrr906XKWLFlG\nNBr9wv46WZb56KP3uHv3NrIsU1S0iLfe+hlO5+g0sUWd6tJataR+PxVRK+LFQ8PU/W/U3KGu7rYi\nL+n3+6mrq1Vk/zo62lQMz/7+PgIBPybT3AIIsizzd3/3/6gM3cfHx9i799C832+xWNi1S21ar9Fo\nEiY2Go0Gp1NN8vF4prh8+Tw3b15T6qHDw4MEgyGlLjkw0M8bb7ydwFQNh8MJTlazLe80Zg2CViAi\nzZ2ChZjN4bVrl5RWJNEiYsw3MhV10+BWX8tUfTqpunRc0046OkFPvqVo3n0/hiiK85Yg/FEfwWgA\nuy5pzprmbEck1/go/9j4t4S6w5i0ZgyLDJiNsevplTxcHjuL746PqYYppIiEJc3C0kMrKCovpvlB\nvN0n7AwTngjTqW2jduwaeo2R/kBvjFWcvJny8kr+57J/y7lzpzh//rSyXSAQ4PLlc7z22ltU2pax\n2LqUwUA/Hw0dUR1nRI5gFmZlliKxtihPg4fQSAjRIBJdE+Xo0Ad8L/tVzFozkTnS9rOhX6pDs1TL\nmvR1pFtjSiGZ+hzava1KgE7TZyjWm3MhJSUFm8023VoUw+N7MBqO0tXaweeVn3Iw43ukGtIQBZES\ncwUPpu4iI2PWmBWSWXXyOh55Hip9xjOhFbTYNIkT8QV8s1gIvM8A5dbFXHVdUPX6zfXQmglBEJ6q\nqb23t0sJuhBL4x49+hEtLS14PLF6ocFgJBgMxNLYS62I2vh3C3z9loayLBOVI9OMz/mVkmYzjM1m\nM/39fTido5SXV5KSok6R1tbeUAVdiK0InxR4H1sOzmRc6/V61q/fxKVL5wiFQmRmZrNly04GB/vQ\n6/UKqUar1TI0NKQiIU1OqntxOzvb8ft9VFYuo62tVakhl5SUJ6RQy8oW09fXq+xPFmSGjwzj1k7R\neODBnBZ4GRmZ/OAHr3Pj5jW6fO0YFhvQJk3fJ7MupU7UsTfjELcnbxKVIiyylFBsnnu7T71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89LcpL5Q2eoTf1blmX6+/uwWKyquXxJSemU0pljCKWCtIXHg2QwFeBO8CYbs7dROUF6Mu37XmtQ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spX2uyzwaqi01mDCRpfA3YJec1FgeXtxlmYdDWXhnwf00F1VV+UPfb+kbyXQdyg5wOnKCp6tn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\noLvJVo4FD+u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mMAvmCYt6xtKX6aUn1UWluZLGaSS+lPLjHc5GSmxZIJ1LcSPRgkk083zN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\nFUIIIRJIglcIIYRIoP8PGbb7X303bcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9a9df3128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "df_couple.plot(kind = 'scatter', x = 'valence', y = 'danceability', c = 'who',ax = ax, colormap = 'Accent_r', title = 'Valence x Danceability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Machine Learning Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we've got some insights from the data. In order to keep this article short, let's jump right into the best part (at least to me) which is the machine learning (ML) algorithm.\n",
    "\n",
    "It's important to bear in mind that I'm not 100% focused on the accuracy of the model. Besides, we will be using a Support Vector Machine (svm)  due to the fact that it is the one I'm currently studying. In addition, I won't explain how svm works. Instead, let's say that it is a supervised ML approach which is used for both classification and regression problems. Also, it's main task (classification) is to separate all the classes by defining an optimal hyperplane that discriminates them. That being said, let's build an algorithm to predict if a song is more likely to belong to my list or Emily's.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to preprocess our data set in order to have a dataframe with numerical values in all of the columns. So let's start off dropping all features which are not relevant to our model such as id, album, name, uri, popularity and track_number and separate the target (who column) from our dataframe. We can easily do that by building the function features_removal which receives a list with the features we want to drop as a parameter.\n",
    "\n",
    "Notice that after its removal, we still have a categorical feature (**artist**). So, we'll have to deal with that in the second step. Also, important to mention that we have two slightly balanced classes (column who) which indicate whose list the song belongs to. In short, there are 703 songs in my list and 691 in her list and, that balance, in terms of quantity is important to our model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>album</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>track_number</th>\n",
       "      <th>uri</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
       "      <th>who</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.536</td>\n",
       "      <td>41CbErP6D25qGVIux11KxI</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>-8.222</td>\n",
       "      <td>Call Me (Come Back Home)</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>99.533</td>\n",
       "      <td>2</td>\n",
       "      <td>spotify:track:41CbErP6D25qGVIux11KxI</td>\n",
       "      <td>0.684</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.581</td>\n",
       "      <td>06BA6xre08wcIwVW5HUNEh</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.0852</td>\n",
       "      <td>-6.975</td>\n",
       "      <td>I'm Still In Love With You</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>97.630</td>\n",
       "      <td>3</td>\n",
       "      <td>spotify:track:06BA6xre08wcIwVW5HUNEh</td>\n",
       "      <td>0.824</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428</td>\n",
       "      <td>Free Soul. the treasure of Al Green Remaster</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.422</td>\n",
       "      <td>21GJBVgndtsseyPRLA7l1Z</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>-8.685</td>\n",
       "      <td>How Can You Mend A Broken Heart?</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>146.917</td>\n",
       "      <td>4</td>\n",
       "      <td>spotify:track:21GJBVgndtsseyPRLA7l1Z</td>\n",
       "      <td>0.442</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness                                         album  danceability  \\\n",
       "0         0.610  Free Soul. the treasure of Al Green Remaster         0.634   \n",
       "1         0.125  Free Soul. the treasure of Al Green Remaster         0.643   \n",
       "2         0.428  Free Soul. the treasure of Al Green Remaster         0.427   \n",
       "\n",
       "   energy                      id  instrumentalness  liveness  loudness  \\\n",
       "0   0.536  41CbErP6D25qGVIux11KxI          0.000071    0.3930    -8.222   \n",
       "1   0.581  06BA6xre08wcIwVW5HUNEh          0.000434    0.0852    -6.975   \n",
       "2   0.422  21GJBVgndtsseyPRLA7l1Z          0.000024    0.0874    -8.685   \n",
       "\n",
       "                               name  popularity  speechiness    tempo  \\\n",
       "0          Call Me (Come Back Home)           1       0.0268   99.533   \n",
       "1        I'm Still In Love With You           1       0.0325   97.630   \n",
       "2  How Can You Mend A Broken Heart?           1       0.0411  146.917   \n",
       "\n",
       "   track_number                                   uri  valence    artist  who  \n",
       "0             2  spotify:track:41CbErP6D25qGVIux11KxI    0.684  Al Green    1  \n",
       "1             3  spotify:track:06BA6xre08wcIwVW5HUNEh    0.824  Al Green    1  \n",
       "2             4  spotify:track:21GJBVgndtsseyPRLA7l1Z    0.442  Al Green    1  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_couple.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove columns\n",
    "def features_removal(features_list):\n",
    "    for i in features_list:\n",
    "        df_couple.drop(i, axis = 1, inplace = True)\n",
    "    return ';)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
       "      <th>who</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>-8.222</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>99.533</td>\n",
       "      <td>0.684</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.581</td>\n",
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       "      <td>0.0852</td>\n",
       "      <td>-6.975</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>97.630</td>\n",
       "      <td>0.824</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>-8.685</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>146.917</td>\n",
       "      <td>0.442</td>\n",
       "      <td>Al Green</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.610         0.634   0.536          0.000071    0.3930    -8.222   \n",
       "1         0.125         0.643   0.581          0.000434    0.0852    -6.975   \n",
       "2         0.428         0.427   0.422          0.000024    0.0874    -8.685   \n",
       "\n",
       "   speechiness    tempo  valence    artist  who  \n",
       "0       0.0268   99.533    0.684  Al Green    1  \n",
       "1       0.0325   97.630    0.824  Al Green    1  \n",
       "2       0.0411  146.917    0.442  Al Green    1  "
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply function\n",
    "features_removal(['id', 'album', 'name', 'popularity', 'track_number', 'uri'])\n",
    "df_couple.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1394 entries, 0 to 1393\n",
      "Data columns (total 11 columns):\n",
      "acousticness        1394 non-null float64\n",
      "danceability        1394 non-null float64\n",
      "energy              1394 non-null float64\n",
      "instrumentalness    1394 non-null float64\n",
      "liveness            1394 non-null float64\n",
      "loudness            1394 non-null float64\n",
      "speechiness         1394 non-null float64\n",
      "tempo               1394 non-null float64\n",
      "valence             1394 non-null float64\n",
      "artist              1394 non-null object\n",
      "who                 1394 non-null int64\n",
      "dtypes: float64(9), int64(1), object(1)\n",
      "memory usage: 119.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_couple.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Remove who column from our data set\n",
    "target = df_couple['who']\n",
    "df_couple.drop('who', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: who, dtype: int64"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    703\n",
       "0    691\n",
       "Name: who, dtype: int64"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that we have a slightly balanced data\n",
    "target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.634</td>\n",
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       "      <td>0.3930</td>\n",
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       "      <td>0.0268</td>\n",
       "      <td>99.533</td>\n",
       "      <td>0.684</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.0852</td>\n",
       "      <td>-6.975</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>97.630</td>\n",
       "      <td>0.824</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>-8.685</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>146.917</td>\n",
       "      <td>0.442</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.403</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>-7.202</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>95.803</td>\n",
       "      <td>0.878</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.148</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>-6.954</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>119.651</td>\n",
       "      <td>0.944</td>\n",
       "      <td>Al Green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.610         0.634   0.536          0.000071    0.3930    -8.222   \n",
       "1         0.125         0.643   0.581          0.000434    0.0852    -6.975   \n",
       "2         0.428         0.427   0.422          0.000024    0.0874    -8.685   \n",
       "3         0.403         0.536   0.717          0.000000    0.1200    -7.202   \n",
       "4         0.148         0.755   0.577          0.000573    0.2150    -6.954   \n",
       "\n",
       "   speechiness    tempo  valence    artist  \n",
       "0       0.0268   99.533    0.684  Al Green  \n",
       "1       0.0325   97.630    0.824  Al Green  \n",
       "2       0.0411  146.917    0.442  Al Green  \n",
       "3       0.0374   95.803    0.878  Al Green  \n",
       "4       0.0384  119.651    0.944  Al Green  "
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_couple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second task is to transform all categocal data (artists names) into numeric data. Why do we have to do that? Well, the ML algorithm only accepts numerical data, hence, the reason why we have to use the class LabelEncoder to encode each artist name into a specific number. The encoding process is shown below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import LabelEncode\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LabelEncoder obj \n",
    "enc = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set Labels\n",
    "artist_label = enc.fit_transform(df_couple.artist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create column containin the labels\n",
    "df_couple['labels_artists'] = artist_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "';)'"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove colum artist (cathegorical data)\n",
    "features_removal(['artist'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.2510</td>\n",
       "      <td>170.031</td>\n",
       "      <td>0.7100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>0.003670</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>-0.996</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>137.122</td>\n",
       "      <td>0.8210</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.214000</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>-5.196</td>\n",
       "      <td>0.0913</td>\n",
       "      <td>131.983</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>0.084300</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7230</td>\n",
       "      <td>-5.070</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>110.027</td>\n",
       "      <td>0.1260</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>0.955000</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>-12.177</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>137.411</td>\n",
       "      <td>0.2230</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>0.446000</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.9350</td>\n",
       "      <td>-7.175</td>\n",
       "      <td>0.4210</td>\n",
       "      <td>93.813</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.006960</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.946000</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>-4.352</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>133.734</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      acousticness  danceability  energy  instrumentalness  liveness  \\\n",
       "691       0.028200         0.746   0.880          0.000000    0.3400   \n",
       "645       0.010600         0.591   0.721          0.000000    0.0622   \n",
       "158       0.004040         0.183   0.881          0.000745    0.0925   \n",
       "932       0.050800         0.535   0.737          0.000439    0.3190   \n",
       "570       0.003670         0.575   0.988          0.000000    0.3250   \n",
       "264       0.000001         0.387   0.893          0.214000    0.0696   \n",
       "709       0.084300         0.503   0.744          0.000000    0.7230   \n",
       "1118      0.955000         0.388   0.242          0.000047    0.0999   \n",
       "671       0.446000         0.623   0.873          0.000000    0.9350   \n",
       "238       0.006960         0.200   0.990          0.946000    0.1360   \n",
       "\n",
       "      loudness  speechiness    tempo  valence  labels_artists  \n",
       "691     -3.466       0.2680   91.688   0.7870               0  \n",
       "645     -5.022       0.3170  152.527   0.6820               0  \n",
       "158     -6.520       0.0640   76.914   0.1980               9  \n",
       "932     -6.533       0.2510  170.031   0.7100               3  \n",
       "570     -0.996       0.0905  137.122   0.8210              20  \n",
       "264     -5.196       0.0913  131.983   0.0541              19  \n",
       "709     -5.070       0.0374  110.027   0.1260              12  \n",
       "1118   -12.177       0.0481  137.411   0.2230               4  \n",
       "671     -7.175       0.4210   93.813   0.7500               0  \n",
       "238     -4.352       0.1590  133.734   0.0376              19  "
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame\n",
    "df_couple.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     119\n",
       "19    102\n",
       "4     102\n",
       "8      88\n",
       "10     83\n",
       "16     83\n",
       "9      83\n",
       "2      78\n",
       "20     74\n",
       "6      73\n",
       "0      66\n",
       "13     63\n",
       "12     58\n",
       "11     57\n",
       "15     57\n",
       "1      56\n",
       "14     42\n",
       "17     39\n",
       "5      36\n",
       "18     27\n",
       "7       8\n",
       "Name: labels_artists, dtype: int64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_couple.labels_artists.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data set almost ready we can build our model. You might be wondering : almost ready ?! Well, when we are dealing with some ML algorithms we have to keep in mind that some of them expect data in a specific range or structure. For instance, sometimes we have to transform our data into a higher dimension in order to find the boundary that separates the classes as can be seen in the figure below. To accomplish that, we use a technique called kernel trick which offers an efficient and less expensive way to do this mapping. In addition, it uses different functions ( kernel) for that and the most popular ones are polynomial, linear and radial basis function (RBF). The last one, for example, requires features centered around 0 and with variances in the same order and that is the reason why we have to preprocess our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"kernel.png\" width=\"700\" height=\"700\" align=\"center\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# radial basis function (RBF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"RBF.png\" width=\"500\" height=\"500\" align=\"center\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Cross-validation and accuracy of the model\n",
    "\n",
    "def accuracy(clf, data, target):\n",
    "    results = cross_val_predict(clf, data ,target ,cv = 10)\n",
    "    return metrics.accuracy_score(target,results)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pipelines \n",
    "pip_1 = Pipeline([('scaler',StandardScaler()),\n",
    "                  ('clf', svm.SVC())\n",
    "])\n",
    "\n",
    "pip_2 = Pipeline([('scaler',StandardScaler()),\n",
    "                  ('clf', svm.SVC(kernel = 'linear'))               \n",
    "])\n",
    "\n",
    "pip_3 = Pipeline([('scaler', StandardScaler()),\n",
    "                  ('clf', svm.SVC(kernel = 'poly'))                \n",
    " ])\n",
    "\n",
    "pip_4 = Pipeline([('min_max_scaler', MinMaxScaler()),\n",
    "                  ('clf', svm.SVC())               \n",
    "])\n",
    "\n",
    "pip_5 = Pipeline([('min_max_scaler', MinMaxScaler()),\n",
    "                  ('clf', svm.SVC(kernel= 'linear'))               \n",
    "])\n",
    "\n",
    "pip_6 = Pipeline([('min_max_scaler', MinMaxScaler()),\n",
    "                  ('clf', svm.SVC(kernel='poly'))               \n",
    "])\n",
    "\n",
    "pip_7 = Pipeline([('Normalize', Normalizer()),\n",
    "                  ('clf', svm.SVC() )    \n",
    "])\n",
    "\n",
    "pip_8 = Pipeline([('Normalize', Normalizer()),\n",
    "                  ('clf', svm.SVC(kernel='linear') )    \n",
    "])\n",
    "\n",
    "pip_9 = Pipeline([('Normalize', Normalizer()),\n",
    "                  ('clf', svm.SVC(kernel='poly') )    \n",
    "])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# list with pipelines\n",
    "pip_list= []\n",
    "pip_list.append(('Scaler_rbf', pip_1))\n",
    "pip_list.append(('Scaler_linear', pip_2))\n",
    "pip_list.append(('Scaler_poly', pip_3))\n",
    "\n",
    "pip_list.append(('Min_max_scaler', pip_4))\n",
    "pip_list.append(('Min_max_scaler_linear', pip_5))\n",
    "pip_list.append(('Min_max_scaler_poly', pip_6))\n",
    "\n",
    "pip_list.append(('Normalizer', pip_7))\n",
    "pip_list.append(('Normalizer_linear', pip_8))\n",
    "pip_list.append(('Normalizer_poly', pip_9))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to build models\n",
    "def model_accuracy(clf_models, data, target):\n",
    "    \n",
    "    results = []\n",
    "    names = []\n",
    "    \n",
    "    for name, pip in clf_models:\n",
    "        cross_val = cross_val_predict(pip, data, target, cv = 10)\n",
    "        accuracy = metrics.accuracy_score(target, cross_val)               \n",
    "        print(f'name: {name} - Accuracy : {accuracy:.4f}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: Scaler_rbf - Accuracy : 0.7059\n",
      "name: Scaler_linear - Accuracy : 0.6155\n",
      "name: Scaler_poly - Accuracy : 0.6851\n",
      "name: Min_max_scaler - Accuracy : 0.6255\n",
      "name: Min_max_scaler_linear - Accuracy : 0.6047\n",
      "name: Min_max_scaler_poly - Accuracy : 0.6162\n",
      "name: Normalizer - Accuracy : 0.5036\n",
      "name: Normalizer_linear - Accuracy : 0.4476\n",
      "name: Normalizer_poly - Accuracy : 0.5043\n"
     ]
    }
   ],
   "source": [
    "# Models\n",
    "model_accuracy(pip_list, df_couple,target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Tunning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model with RBF kernel and StandardScaler preprocessing gave us the highest accuracy but we can improve it by tunning  it's hyper-parameters. Let's use GridSearchCV to exhaustively combine different values for C and Gamma and test them in our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Values for parameter C\n",
    "\n",
    "C_list = [0.001 , 0.01, 0.1 , 1 , 10 , 100]\n",
    "\n",
    "#Values for parameter Gamma\n",
    "Gamma_list = [0.001, 0.01 , 0.1, 1, 10, 100]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
       " 'clf__gamma': [0.001, 0.01, 0.1, 1, 10, 100]}"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary with lists of parameters\n",
    "Grid_parameters = dict(clf__C = C_list, clf__gamma = Gamma_list)\n",
    "Grid_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Grid object with pip_1, Grid_parameters, numbers of folders (cv) and accuracy (scoring) as parameters \n",
    "grid = GridSearchCV(pip_1,Grid_parameters,cv = 10, scoring = 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))]),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'clf__C': [0.001, 0.01, 0.1, 1, 10, 100], 'clf__gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply gridsearch to our data and target \n",
    "grid.fit(df_couple,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.50430, std: 0.00142, params: {'clf__C': 0.001, 'clf__gamma': 0.001},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.001, 'clf__gamma': 0.01},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.001, 'clf__gamma': 0.1},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.001, 'clf__gamma': 1},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.001, 'clf__gamma': 10},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.001, 'clf__gamma': 100},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.01, 'clf__gamma': 0.001},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.01, 'clf__gamma': 0.01},\n",
       " mean: 0.61334, std: 0.11516, params: {'clf__C': 0.01, 'clf__gamma': 0.1},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.01, 'clf__gamma': 1},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.01, 'clf__gamma': 10},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.01, 'clf__gamma': 100},\n",
       " mean: 0.48924, std: 0.04736, params: {'clf__C': 0.1, 'clf__gamma': 0.001},\n",
       " mean: 0.63343, std: 0.15359, params: {'clf__C': 0.1, 'clf__gamma': 0.01},\n",
       " mean: 0.66069, std: 0.15371, params: {'clf__C': 0.1, 'clf__gamma': 0.1},\n",
       " mean: 0.60760, std: 0.07853, params: {'clf__C': 0.1, 'clf__gamma': 1},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.1, 'clf__gamma': 10},\n",
       " mean: 0.50430, std: 0.00142, params: {'clf__C': 0.1, 'clf__gamma': 100},\n",
       " mean: 0.63199, std: 0.15622, params: {'clf__C': 1, 'clf__gamma': 0.001},\n",
       " mean: 0.64204, std: 0.15490, params: {'clf__C': 1, 'clf__gamma': 0.01},\n",
       " mean: 0.70588, std: 0.16745, params: {'clf__C': 1, 'clf__gamma': 0.1},\n",
       " mean: 0.69369, std: 0.12259, params: {'clf__C': 1, 'clf__gamma': 1},\n",
       " mean: 0.53515, std: 0.03053, params: {'clf__C': 1, 'clf__gamma': 10},\n",
       " mean: 0.51578, std: 0.01481, params: {'clf__C': 1, 'clf__gamma': 100},\n",
       " mean: 0.61836, std: 0.14541, params: {'clf__C': 10, 'clf__gamma': 0.001},\n",
       " mean: 0.69440, std: 0.16946, params: {'clf__C': 10, 'clf__gamma': 0.01},\n",
       " mean: 0.76686, std: 0.12026, params: {'clf__C': 10, 'clf__gamma': 0.1},\n",
       " mean: 0.69154, std: 0.12325, params: {'clf__C': 10, 'clf__gamma': 1},\n",
       " mean: 0.53874, std: 0.03602, params: {'clf__C': 10, 'clf__gamma': 10},\n",
       " mean: 0.51793, std: 0.01442, params: {'clf__C': 10, 'clf__gamma': 100},\n",
       " mean: 0.64060, std: 0.14954, params: {'clf__C': 100, 'clf__gamma': 0.001},\n",
       " mean: 0.70947, std: 0.15401, params: {'clf__C': 100, 'clf__gamma': 0.01},\n",
       " mean: 0.75108, std: 0.10469, params: {'clf__C': 100, 'clf__gamma': 0.1},\n",
       " mean: 0.69154, std: 0.12325, params: {'clf__C': 100, 'clf__gamma': 1},\n",
       " mean: 0.53874, std: 0.03602, params: {'clf__C': 100, 'clf__gamma': 10},\n",
       " mean: 0.51793, std: 0.01442, params: {'clf__C': 100, 'clf__gamma': 100}]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__C': 10, 'clf__gamma': 0.1}"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76685796269727402"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In this Jupyter notebook I used the Spotify API to analyze the audio features of my Fiancée and my music list. Instead of checking our playlist, I used features of the 10th artists we listen the most in our daily routine. Based on that I concluded that my list is more varied than hers and surprisingly my songs are more energetics. Another insight (I expected that :] ) is the fact that Emily's songs are more danceable. To complement the analysis I also built a machine learning model (svm) to predict if a song is more likely to be listened to by her or me. The accuracy of the model was about 74% which is good(it can be improved though).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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