FinalProject_group085.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project was to look at the top songs of 2017 and see what attributes play a significant role in the placement of those songs on Spotify Charts. We looked at 14 attributes that were determined and calculated by Spotify. Our focus was to compare both the differences between each attribute and the number of streams per song. This was to see if we would be able to determine the chances of a song charting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- James Zeng\n",
    "- Eunice Chan\n",
    "- Raju Ivaturi\n",
    "- Gen Barcenas\n",
    "- Isabel Li\n",
    "- Sarah Cho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What factors make a song chart the Top 10 Spots on the Spotify Top chart in 2017? Factors included danceability, speechiness, valence, and other musical attributes that may influence a song’s ability to gain popularity through streams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spotify is a company that provides music streaming services for their subscribers. They gather a large collection of songs containing millions of tracks. Spotify works both as a radio and personal music collection. Spotify offers both free and premium versions of its services. The free version includes ads every thirty minutes, has a limited amount of skips, and subscribers are unable to listen to songs offline. The paid version is ad free, unlimited amounts of skips, and playlists can be saved to listen to offline. Spotify gains access to music through contracts with artists; however, because it is dependent upon if an artist agrees with the contract, some music is not available on Spotify, like Jay Z. Also, Spotify works mostly with more established artists, so it doesn’t always have music that is published independently. \n",
    "\n",
    "Spotify’s Top 200 Chart is made through counting the number of streams of each song. A stream is counted when a song has been played for over 30 seconds. The chart is updated daily, but they also do keep track of the numbers for a week as well. \n",
    "\n",
    "This question was of interest to our group because we are young college students that enjoy music and utilize Spotify. We were influenced to use this approach because of a past team member’s recollection of a data visualization project involving Kanye West and his lyrics. We also wanted to use data that was relevant to our daily lives and we thought exploring pop culture and trends would be the best way to do so. We also all marked “music” on the course survey. \n",
    "\n",
    "A prior study we found looked at was one on Billboard Top 200 songs and they looked at the chords, structure, instrumentation, and timing of popular songs. From that they had they analyzed the parts of the music to understand what made it a popular charting song. \n",
    "\n",
    "Another prior study we looked at was one on Kaggle: This link is a composition of data that analyzes top Spotify songs of 2017. The dataset contains the daily ranking of the 200 most listened to songs from 53 countries from 2017 to 2018. The data directly correlates to our research project because our team is also analyzing different factors that contribute to a songs popularity. It contains more than 2 million rows, which comprises 6629 artists, 18598 songs for a total count of one hundred five billion streams count. Variables included with the dataset are song name, artists, danceability, energy, key, loudness, speechiness, etc. These all take into account different factors of a song, similar to the second link, and illustrate the common factors that top songs share.\n",
    "\n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.spotify.com/us/about-us/contact/\n",
    "- 2) http://ddmal.music.mcgill.ca/research/billboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on our background knowledge of currently trending artists in America, we hypothesize a strong positive correlation between the ranking of a song on Spotify’s Top 200 chart and danceability, valence, speechiness. We chose these three attributes because they each are unique attributes and seemed unlikely to have any correlation with one another. We hypothesize a positive correlation because based off of our own knowledge and experience on music trends, happy-sounding, rap-like steady songs tend to chart well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found a dataset from Kaggle which consists of the top 100 songs from Spotify in 2017. We wanted this dataset because it contained exactly what we were looking for: attributes of popular songs. For each song it contains its: id, name, artists, danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, duration_ms, and time_signature. We are hoping to see some factor in these audio features that contributed to the song’s success.\n",
    "\n",
    "- Dataset Name: Top Tracks of 2017\n",
    "- Link to the dataset: https://www.kaggle.com/nadintamer/top-tracks-of-2017\n",
    "- Number of observations: 100\n",
    "\n",
    "This dataset consists of the track name and artists of the top 100 songs of 2017 played on Spotify. It also contains a number of audio features associated with each song.\n",
    "\n",
    "- Name is the title of the song.\n",
    "- Artists are the main artist of that song. This doesn’t always include the artists that were featured because it is dependent on what an artist decides to put on Spotify.\n",
    "- Danceability is how suitable a song is for dancing. This includes a combination of the regularity of the beat, the song tempo, and rhythm / beat pattern of the song.\n",
    "- Energy is the intensity of the song which is measured by the wide range of sound volumes. High energy songs are characterized as “fast, loud, noisy” while low energy songs are characterized as “slow, quiet”.\n",
    "- Key is the key signature that the song was written in. There are 11 different keys and  0 = C, 1 = C♯/D♭, 2 = D, etc.\n",
    "- Loudness is the overall loudness of a track in decibels and is averaged across the track.\n",
    "- Mode indicates if the song is written in a minor or major key.\n",
    "- Speechiness is the presence or absence of verbal speech or communication. For example, talk show-like speech is closer to 1.0 on the scale of 0 to 1.\n",
    "- Acousticness is a measure of the amount of electricity used in a song. If a song is less electrically amplified it scores higher on a scale from 0 to 1.\n",
    "- Instrumentalness measures the lack of vocals; a score closer to 1.0 means that there is no vocal content. Liveness is measuring the amount of presence there is of an audience in the recording. If a song was performed live, the liveliness value would be higher than a song recorded in a studio.\n",
    "- Valence is a measure of the positivity of the sounds. The higher the rating the more positive / happy it sounds.\n",
    "- Tempo is the number of beats per minute or the pace of the song.\n",
    "- ID is the unique Spotify URL of the song.\n",
    "- Duration_ms is the length of the song in milliseconds.\n",
    "- Time_signature is a number specifying how many beats are in each bar.\n",
    "\n",
    "After finding this dataset, we felt that this dataset was inherently limited because we focus only on the most popular songs in 2017. This means that there may not much variance between them. All these songs may have somewhat of the same factors, which led to them being popular in the first place. Therefore, we decided to add all the songs that were in the top 200 at any point in 2017 to try to include more variance between the songs we analyze.\n",
    "\n",
    "We went to spotifycharts.com and pulled the top 200 United States songs for each week in 2017, for more songs to include in our dataset.\n",
    "\n",
    "- Dataset Name: Spotify Charts Top 200 Songs for each Week in 2017\n",
    "- Link to the dataset: https://spotifycharts.com/regional/us/weekly/ (weeks in 2017 dropdown menu)\n",
    "- Number of observations: 200 (each)\n",
    "\n",
    "This dataset consists of the the position, track name, artists, and # of streams of the top 200 songs of the week specified. We felt that this dataset represents trending songs during particular weeks in 2017.\n",
    "\n",
    "- Position is the song's popularity rank during that week.\n",
    "- Track name is the name of the song.\n",
    "- Artists are the main artist of that song. This doesn’t always include the artists that were featured because it is dependent on what an artist decides to put on Spotify.\n",
    "- Streams is the number of plays that the song had that week.\n",
    "\n",
    "We combined the datasets by pulling chart data from spotifycharts.com for each week in 2017 that the song was on the top 200 and added it to the Kaggle dataset. We first included a 'streams' column because we wanted to keep track of how popular a song was. We added it by first checking if the song already exists in the dataset. If it did, we would just add the stream count to that song entry. If it didn't, we would append the track entry onto the Kaggle dataset, with its stream count. We kept doing this until there were no more songs to analyze in 2017 from spotifycharts.com. Because spotify does not include the song features, we also went to Spotify’s API and pulled the song features and filled each song in our dataset with its corresponding features. We essentially created a dataset featuring all the songs that were trending at any point in 2017."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for dataframes, and easy tables\n",
    "import pandas as pd\n",
    "# for its variety of data types (for dataframe columns)\n",
    "import numpy as np\n",
    "# to ping requests from spotifycharts and get the chart content\n",
    "import requests\n",
    "# in order to read strings as file streams\n",
    "# (so we can use pd.read_csv easily)\n",
    "import io\n",
    "# python wrapper for the Spotify API\n",
    "import spotipy\n",
    "# so spotify knows who is pulling from their API\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "# data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "# more data visualization\n",
    "import seaborn as sns\n",
    "# OLS regression\n",
    "import statsmodels.api as sm\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first dropped columns we felt were completely irrelevant to our hypothesis. The attributes that we ended up dropping were ID, mode, key, duration_ms, instrumentalness, liveness, and acousticness. The Kaggle dataset described ID as being the Spotify ID of the track. A track’s ID is important because we can find all the music features corresponding to the track using Spotify’s API. However, we found that the ID’s were broken and some did not refer to the song that they were supposed to. Mode, key, duration_ms, instrumentalness, liveness, and acoustic-ness were all attributes that we felt had no correlation with how popular a song is. A track that is in a different key, has different duration, was acoustic, or contained a possible audience should also not have any correlation with how popular a song is.\n",
    "<br><br>\n",
    "The Kaggle dataset and the Spotify Charts datasets had small differences in the column names which made merging slightly harder. In order to remedy this, we adjusted the track name and artists column to match the Kaggle datasets column names.\n",
    "<br><br>\n",
    "After building our dataset, we noticed that spotifycharts.com contained a couple NaN values in their charts on 2017-07-14 and 2017-11-03. This would skew our chart, because we have no idea what these songs are and which entry to add its corresponding number of streams to. So, there would be weird NaN entries in our data. We felt that it was best to just omit adding the data from these two charts, because we were concerned about the credibility of a dataset with NaN values. Because this is just a small amount of data, our resulting dataset should not be greatly affected by this.\n",
    "<br><br>\n",
    "There were also some songs that did not have the exact same name in the Kaggle dataset when looking at the spotifycharts.com datasets. Songs such as “I’m the One” and “Wild Thoughts” were listed as such in the Kaggle dataset, but in Spotify Charts, the listing of the song were “\"I'm the One (feat. Justin Bieber, Quavo, Chance the Rapper & Lil Wayne)\" and \"Wild Thoughts (feat. Rihanna & Bryson Tiller)\". We think the problem is that the Kaggle dataset decided to make some simplifications to make the chart more readable, but it ultimately caused some problems when trying to automate the counting of streams over 2017. It created multiple entries of the same songs, with varying stream counts. As a result, we changed all the names of the Kaggle dataset in the beginning to match with the names that will show up in Spotify Charts.\n",
    "<br><br>\n",
    "One song in particular also caused a few problems. Along with the problem listed above, “I’m the One” had a different name for one of the datasets pulled from Spotify Charts. For that one chart from the website, it had decided to change to a name without all featuring artists. Although it doesn’t make a large difference, we located the duplicate song entry and combined the stream counts.\n",
    "<br><br>\n",
    "All that remains in our dataset is the track name, artists, danceability, energy, loudness, speechiness, valence, tempo, and streams.\n",
    "<br><br>\n",
    "The following snippets of code is shown to demonstrate these data cleaning steps taken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the commented line will rename each returned DataFrame\n",
    "#   from spotifycharts to match the columns on Kaggle.\n",
    "# we do this to make such no duplicate columns with the\n",
    "#   same values appear\n",
    "def get_chart(start):\n",
    "    end = start + pd.DateOffset(weeks=1)\n",
    "    date = f'{start.date()}--{end.date()}'\n",
    "    url = f'https://spotifycharts.com/regional/us/weekly/{date}/download'\n",
    "    res = requests.get(url)\n",
    "    if res.status_code == 404:\n",
    "        return []\n",
    "    csv_file = io.StringIO(res.content.decode('utf-8'))\n",
    "    df = pd.read_csv(csv_file)\n",
    "    df.columns = df.iloc[0]\n",
    "    df = df[1:]\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    df.Position = df.Position.astype(np.int64)\n",
    "    df.Streams = df.Streams.astype(np.int64)\n",
    "    df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')\n",
    "    # the above line renames first to easy accessing variables\n",
    "    df.rename(columns={'track_name': 'name', 'artist': 'artists'}, inplace=True)\n",
    "    # the above line renames columns\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming the songs to what Spotify has them listed as\n",
    "main = pd.read_csv('featuresdf.csv')\n",
    "main = main.drop(columns=['id', 'time_signature', 'mode', 'key', 'duration_ms', 'instrumentalness', 'liveness', 'acousticness'])\n",
    "# drop the initial columns from Kaggle\n",
    "main.at[4, 'name'] = \"I'm the One (feat. Justin Bieber, Quavo, Chance the Rapper & Lil Wayne)\"\n",
    "main.at[32, 'name'] = \"Wild Thoughts (feat. Rihanna & Bryson Tiller)\"\n",
    "main.at[33, 'name'] = \"Slide (feat. Frank Ocean & Migos)\"\n",
    "main.at[42, 'name'] = \"Feels (feat. Pharrell Williams, Katy Perry & Big Sean)\"\n",
    "main.at[47, 'name'] = \"Chantaje (feat. Maluma)\"\n",
    "main.at[65, 'name'] = \"Don't Wanna Know\"\n",
    "main.at[74, 'name'] = \"Cold\"\n",
    "main['streams'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# will not work independently\n",
    "\n",
    "# if statement: data cleaning\n",
    "# len(new) != 0 ensures that get_chart returns something\n",
    "#   that actually contains something\n",
    "# get_chart will return [] if the url returns 404\n",
    "# ~new.isna().any().any() ensures that when chart data comes\n",
    "#   and NaN values are found at all, we discard the chart\n",
    "# isna() checks every value in table if it's null\n",
    "# .any() will see if there exists any True values in the large table.\n",
    "# .any() on the returned list will return one single boolean variable.\n",
    "while start != end:\n",
    "    print (start)\n",
    "    new = get_chart(start)\n",
    "    if (len(new) != 0) & (~new.isna().any().any()):\n",
    "        # gap in late may / early june, and NaN (broken)\n",
    "        new = new.drop(columns=['position'])\n",
    "        # unnecessary column (cleaned)\n",
    "        add_plays(big_data, new)\n",
    "        new = new[~(new['name'].isin(big_data['name']))]\n",
    "        big_data = big_data.append(new, sort=False, ignore_index=True)\n",
    "    start = start + pd.DateOffset(weeks=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is data cleaning in a sense because when we combined\n",
    "#   the two datasets, we will have a lot of NaN values.\n",
    "# No audio features from spotifycharts, so we have to fill in\n",
    "#   the audio features of each track ourselves\n",
    "def get_features(url):\n",
    "    features = sp.audio_features(url)\n",
    "    d = features[0]['danceability']\n",
    "    e = features[0]['energy']\n",
    "    l = features[0]['loudness']\n",
    "    s = features[0]['speechiness']\n",
    "    v = features[0]['valence']\n",
    "    t = features[0]['tempo']\n",
    "    return [d,e,l,s,v,t]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were able to graph scatter plots of every musical attribute against streams, and this provided us useful visualizations of the correlations between each relevant relationship.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('song.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Danceability vs. Energy')\n",
    "plt.xlabel('Danceability')\n",
    "plt.ylabel('Energy')\n",
    "plt.scatter(df.danceability, df.energy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher danceability can generally be correlated with high energy, where danceability is defined as how suitable a song is for dancing and energy is described as the intensity of a song’s sound volumes. While higher danceability is typically associated with a steady beat, this scatterplot demonstrates the positive correlation between danceability and energy. The fluctuation and magnitude of a song’s sound can also determine that same song’s danceability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117194128>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([0, 1.0, 0, 350000000])\n",
    "plt.title('Danceability vs. Streams')\n",
    "plt.xlabel('Danceability')\n",
    "plt.ylabel('Streams')\n",
    "plt.scatter(df.danceability, df.streams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Danceability is the suitability of a song for dancing. This scatterplot demonstrates that the higher the danceability of a song is the more streams it gets.\n",
    "<br>\n",
    "From this, we saw that danceability and energy had the clearest positive correlation with streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117b98e48>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Danceability vs. Tempo')\n",
    "plt.xlabel('Danceability')\n",
    "plt.ylabel('Tempo')\n",
    "plt.scatter(df.danceability, df.tempo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher danceability can be correlated with a certain tempo as many popular songs share similar tempos, with danceability being the suitability of a song for dancing and tempo being the beat pattern of the song. As the scatter plot demonstrates, there is a positive correlation between higher danceability and tempos between 80 and 160 beats per minute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117c89780>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Valence vs. Tempo')\n",
    "plt.xlabel('Valence')\n",
    "plt.ylabel('Tempo')\n",
    "plt.scatter(df.valence, df.tempo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valence measures the positiveness of a song while tempo looks at the rhythm of a song. The scatterplot shows that valence and tempo doesn’t have a correlation. Because valence includes both sad and happy songs, it is understandable while they generally don’t have a correlation because sad songs usually don’t have the same rhythm as happy songs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117e207f0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r1tZm6eJ5dBY6xhzrLHSwdPG8JvXIcdoLFxQ1wFesrc2Shd1cf+58urs6EdDd1cn15853taDjZMRtFDVg6eJ5Y2wU4CvWVmPJwm4XDI5TIS4oakBxAsq715N7djnO5MQFRY3I+4rVPbscZ/LiNgonE+7Z5TiTFxcUTibcs8txJi+uenIycVhXJ/0xQqHVPbvcruI41eM7CicT7RiLULSr9A8MYozaVXr7+pvdNcdpK1xQOJlox1gEt6s4Tm1w1ZOTmXbz7HK7iuPUhlwLilrrp13f3V60q13FcVqN3Kqeaq2fdn13+9GOdhXHaUVyKyhqrZ/Oi767t6+fk5ev5ahl93Dy8rW5FnTtaFdxnFYkt6qnWuuns9yv1VVTkzG6ut3sKo7TiuR2R5Gkh+6aXqjp/YrH20E1lZddkeM4jSW3gmLp4nkUOjTu+OtvDFc0eZfTd7fDJOxeQI7jVEJuBcWShd3MmDZesza01yqavMvpu9thEva6GY7jVELdbBSSjgBuAd4CGLDSzG4saSPgRuBMYBdwsZn9ohbP7+3rZ2BwKPZcpZN3mr67HVwxvW6G4ziVUM8dxTBwhZkdCywC/lDSsSVtPggcHf5cCvxdLR5ctBckUY/Jux1cMd0LyHGcSsi0o5D0OeAfgNeArwELgWVmdm/SNWb2PPB8+Pk1SY8C3cAjkWbnALeYmQHrJHVJOjS8tmLi7AVF6jV5t0vxIvcCchxnomRVPX3SzG6UtBiYBXwc+BaQKCiiSJpLIFweLDnVDTwT+f5seKwqQZGmWqrnCtonYcdx8khW1VPRfehM4FtmtjVyLP1C6QDgTuAyM3t14l0ESZdKWi9p/fbt28u2T1ItdXd1+kTuOI4zQbIKig2S7iUQFGskHQjsLXeRpAKBkPiOmd0V06QfOCLy/fDw2BjMbKWZ9ZhZz+zZs8t2th3sBY7jOO1CVkFxCbAMeJeZ7QKmAb+fdkHo0fR14FEz+6uEZquBTyhgEbCjWvsExBttzzuxmxVrHpsUqSscx3FqSSYbhZntlTQMvFdS9JrNKZedTGDL2CJpY3jsj4E54T2/AvyAYJfyOIF7bKrwmQhRe8FkTF3hOI5TK7J6PX0DeAewlVGVkwFx6qTgpNlPKGPHCL2d/jBTT6sgLWraBYXjOE46Wb2eFoXxEG1JO0RNO47jtCpZbRQ/iwmWaxs8dYXjOE7lZN1R3EIgLP4T2E2gUjIze0fdelZD2i11RaunK68nk/ndHadVySoovk5omCaDW2yr0S5R0zC5De+T+d0dp5XJKii2m9nquvakzrRL1PRkNrxP5nd3nFYmq6Dok/Rd4G4C1RMACUF0ThVMZsP7ZH53x2llsgqKTgIBcXrkWKp7rFMZ7ZCuvF5M5nd3nFYma8BdzQLhnHTazfBeSybzuztOK5M14G5/gjQexwH7F4+b2Sfr1K+m0kzPm3YyvNeayfzujtPKKAiOLtNIuh3YBvw34DrgYwQ5nD5X3+6Np6enx9avX1+3+5d63kCwqvUCP+2Hu9o6ziiSNphZTyXXZg24e7uZ/W9gp5l9EzgLOKmSB7Y6aZ43TvtQFPj9A4MYo662ngzScSZOVkFRLD49IOl4YCbw5vp0qbm4500+cIHvOLUjq6BYKWkW8L8JUoM/Avx53XrVRDzdRz5wge84tSOr19PXwo//Cry1ft2pDUXddP/AIB0SI2Z0Z9RRu+dNPnBXW8epHZl2FJLeIunrkn4Yfj9W0iX17VplRHXTACOhsT6rjjqu6JEbstsPr3LoOLUjq9fTD4F/AP7EzE4Iixf1mdn8enewlHJeTycvXxu7kizS3dXJA8tOq0fXnAqop2eSez05zijVeD1ljcw+xMxuk3QlgJkNSxopd1EzKKeDdh1161DvJIDtkt/LcVqdrMbsnZLeRJC2g2J967r1qgrK6aBdR906uGeS47QHWQXF5wm8nd4m6QGC+hSfrVuvqiBON13EddSthXsmOU57UFb1JGkKQdqO3wbmERQteszMhlIvbBLRNBCVeD055amV7t89kxynPSgrKMxsr6Qvm9lCYGsD+lQ1jdBNT1ZDaS3tCu6K7DjtQVbV079IOk+S6tqbNmEyp4eopV3BXZEdpz3I6vX0KQI7xbCkNxitmX1Q0gWSvgF8CHjBzI6POX8K8E/Ak+Ghu8zsugn0vSZUsjPIayW2LGNRa7uCeyY5TuuTNTL7wArufTNwE4HhO4l/M7MPVXDvmlCpGqVVjbDVqMOyjoXbFRxn8pE1MvtfshyLYmb3Ay9X2K+GkFWN0tvXz8nL13LUsns4eflaZnYWYu/XzMmyWnVY1rHwiGfHmXykCgpJ+0s6GDhE0ixJB4c/c4Fa6AveI2mTpB9KOq4G98tMb19/YgR3dGcQNwHv3DNMYcpYc02zJ8tqbQdZd0luV3CcyUc51dOngMuAw4ANkeOvEaiVquEXwJFm9rqkM4Fe4Oi4hpIuBS4FmDNnTtkbR1UwMzsLSDCwa4jDujo59ZjZ3LP5eV7ZlezdG90ZXHv31nET8NCIMWt6genTpraM11O16rCJqJTcruA4k4tyguKnwG3A+Wb2N5J+DzgPeAr4bjUPNrNXI59/IOlvJR1iZi/GtF0JrIQg11PafUt17QODowKhf2CQb697OrVf0Z1Bb19/okAZ2DVE3xdOT71XI6nWdpDmqjpZXYEdxwkoZ6P4KrA7FBLvBa4HvkmQvmNlNQ+W9GtFd1tJ7w778lI194R4FcxEiKpR0tQ2rWa8rdZ2kKRSAiatK7DjOAHldhQdZlY0SF8ArDSzO4E7JW1Mu1DSrcApBPaNZ4GrgQKAmX0FOB/4tKRhYBD4qGVJZVuGajyPurs6x6yU0+7VasbbqHCrdOUfp1I6efnaXLoCO46TnbKCQtJUMxsG3kdoJ8hyrZldWOb8TVRv5xhHkgqmHIUpGjf5J92rq7PQkpNkPWwHreoK7DhO4yineroV+FdJ/0Sw6v83AElvp0Wzx6YlBUwlJuY8SZ1zzdkNddBqKl4a1nGcVEFhZn8KXEEQPPebEdXQFFo0e2xR194ViXUoerJ2d3Vy0aI5cTKBoREbZ5NwV1CPm3AcJ1tSwHUxx/69Pt2pHbuH9+77vNfGTm5Jnk9x6pTJ7gpaC9uH4zjtTdZcT21FpcFnrk6JZ7ILS8eZ7ORSUFRqgN25e5ijlt3jq2bHcZwIuRQUacFnO3cPjwnCi1I8Xuvaza2EB885jjNRstajaCuSDLCnHjObnXuGM90ji6qqNFlgqwehTeY6Go7jVE4uBUWSt9J927YzNJI9pi9NVdWOk24tiw45jjN5yKXqCeINsJevSg0mH0eacbsdixd58JzjOJWQyx1FKUUV0UTyg5SLFWjHSdeD5xzHqYTc7ShKjbWnHjObOzf0TyhRoIDzTkx3CW3HSm9pGWKdieOOAc5kIVeCIq6c53fWPT2hnQSAAfdt257aph0n3WqC53xSHEulZXQdpx3JlaCIsxtUmo62nAqpXSOWKwme80lxPO1oo3KcSsmVoKilfSCLCilt0s3TCtwnxfG0o43KcSolV8bsWtkHqlUhtaPrbBo+KY7HHQOcyUSuBEVSoN2MaelpxwsdoquzULMMsXmLV/BJcTyeVdeZTORK9ZRkNwDGGZ5FYL/oroNaKG8r8HY03NebdrVROU4l5EpQwPg/4BVrHmPp4nlcf+78hv1Rt6rrbDV2k/2mTtknKGZNL3D1h4+b9JOiZ9V1Jgu5Uj1Bsn0A4IFlp3HDBQuAIEq7XvmZTj1m9rjiSM1egVdqNyleF02k+MbQ3pQrHMfJG7kTFGn2gUYYmXv7+rlzQ/8Yt9wsAXz1plK7Sd7sLY7jTJzcqZ7S7AONcPNMiuUoBvA1y222UrtJ3uwtjuNMnNwJijT7QCMmvbRnNDNwrVK7SavaWyBfsSqO08rkTvWU5rbYCDfPtGc0U41TqTtnq7qB5i1WxXFamboJCknfkPSCpIcTzkvSX0t6XNJmSe+sxXOTalEsWdg9oUmv0qJEac9ophonbVzqcV29cduJ4zSOeqqebgZuAm5JOP9B4Ojw5yTg78J/qybORbb0eP/AIB3SmMmleL4aFVGaf33xuaU0So1TqTtnK7qBuu3EcRpH3QSFmd0vaW5Kk3OAW8zMgHWSuiQdambPV/vs3r5+lt6xaV81u/6BQZbesQkYncjTBEG1Ru+kidUD12pHK9tOHCdvNNOY3Q08E/n+bHhsnKCQdClwKcCcOXNibxY1bCKwkrSxQyPGtXdvzSQIklalcRPTRMgSzesG2my40HWcxtEWXk9mthJYCdDT0zMuc3ipqigpt/gru4a4qndL4oRfFBBJq1WFz6pm4k5T41zVu2VM/QxP552Mp9BwnMbRTEHRDxwR+X54eGzCxO0Qkvj2uqcTzxXVFksXz+PyVRvHyRsDrrhtE5ev2ljziam3rz+2yNJkT+edRivaThwnjzRTUKwGPiPpewRG7B2V2idqYcDsLHRw6jGzOXn5Wp4LXS7jGLFRu0ctV/sr1jyW+Ew30DqO00zqJigk3QqcAhwi6VngaqAAYGZfAX4AnAk8DuwCfr/SZyWpiibCeSd2s+qhZxjam70mXpbVflabQ5owcAOt4zjNpJ5eTxeWOW/AH9biWXGGzYlSSW1tSJ/gJ+Jmm2YXqZWB1g3ljuNUQi4is+OCwi5aNIdCR2kO12TShISAKQm36ppeSLwuybvqspjMtXGBegI+tmhOTSZzj2R2HKdS2sLrKQtxhs2eIw/m2ru38squoYSrsvHk8rNYcO29Y1JtFyl1w42SttvoHxhk6e2buPburQzsGmJmZ2GMMOrqLHDN2bWr+dDMutd528nk7X0cpxy52FEksWRhN31fOJ3uKnT8s8Idw44YIZF0vJj+o5wqa2iv8cquIQwYGBxi557RiXz3cG1rPjQrkjlvO5m8vY/jZCHXggKCP+xKDd2FDnH1h48DsteNjk4k1VDrvEXNqnudt5xMeXsfx8lCrgVFcdKuhO6uTlacf8I+lULWhIITiekoRy1X+83KApu3nEx5ex/HyUJubBRxVDJpdxY6YrOjZo0EruWEUcvVfq0imSeqn2/HnExp79iO7+M41ZJrQZF10u4OixoVJwVgX+BddKLIEglci5gOqM9qv7T/RVtK1km/kqy6leZkapbBuNw7eo4pZzIiS3PbaUF6enps/fr1mdqevHxt2Um7Q+LCk47gvm3beW5gkK7pBV5/Y3hM4F3SLiNuMgMmFNPRIbHXjJmdBSQY2DXUkIlxXH4skt+zSNJ4dnd18sCy01KfNZFJP6lv553Yve/3VK8xyvKO7vXktCOSNphZT0XX5llQxE04lVI6GaZNtDBWxTP3TZ088MuXY+8rAvfbWlNuMqtk0j9q2T2xnly1foekvomx8S7lBFslE3qj3tFxGk01giLXxuwlC7s578RuOhQEKCj8qYRSNVa5uIRi6dXnBgZ56qVBZkwba0guUg/ddhYXzkqMso3ynErqQ1LCxDgqdWNtlneY47QyuRYUvX393Lmhf18iPyM9AjuNw7o6x5RHTUtVHjdJ7RneOy5SvBLddpYSrVlcOJMiytMizRvlOTWRSTlJqFTqxtqqNcIdp5nk2phdK1fVYmbZLGqsw7o6Y587tNfo6iwwY7+pFeu2sxqT03YLRXVMUrR6UROZprapt35+6eJ5YyoUppEkVCp1Y/U6F44znlwLimpcVYv68O5wosgidIorz8tXbYw9v2NwiI1Xnz6hfkQn7CnSvt1Rkbg0HEmeVzM7Cyy9fVNqhtwdg0NlBVJDJs0MW7+0lX41bqxe58JxxpJr1VPapNBZmDImgeCsEpWLMToRpZVHBfbd57wTu1PrSkxUz12qwioVEkVK+5akPtkzPFI2jXrSjqiR0ccr1jxWtp9dnYVUQ7arkBynduRWUPT29bNz93Di+cGhvcEKe/E8vrhkfkKb0ckxaZLv7urkyeVnsXTxPO7ckJwuRMDcN3WWtS9Eyao6K+1bXDbd68+dz66h9PxRxYm02dHHWZ4zY7+pqav+pDHwnYLjTJzcqZ6u6t3Cdx98miz1hwYGh7jyri2s/9XLiTr74qRVLtCq3KRuMMZFNkuwWpYJM2mVHKc+uSxBJQbwoyiDAAAUs0lEQVSjKrYlC4NdUS2jj2sVzR0ly9i4CslxakOudhRX9W7h2+uyCYkig0Mjmepol1uhVrLaLqfOSZqYO6SKVsml6rXo8QeWnTbhvFZZqMRNNe75pcSNTRaPMMdxJk6uBMWtDz5T83suXTxv3wR0+aqN7NozzP6FKfQPDHLZqo0svO5eevv6K15tpwmYpAn7L3/3BJ5cftaYyT0LV3/4uHEuutEMuUVqqbapxN4RfT6Mj32JE1qe/ttx6keuVE9Jxt5KCeP0xqicSlVUr+waYukdm7jgXUdw54b+CbvjpgmYWrtqTuR+tVLbVOOmWnx+FtVVMwszOU7eyZWg6IhxH60GM7j8to2pVewAhkaM+7Zt57wTu7n1wWcYMQuq1RmkmY+zqHNqrWfPer9a5TOqRbbVLH1utgHecfJMrlRPF550RM3vmVXu9A8MjokC32vQ0SE6C6NDPGNaB12dharUOY3Qw9dSjdPsaG5PveE41ZMrQdFz5MFNe3aHND4ae8R4I+KSunPPCLuH93LDBQsmbF+AxunhaxlH0Sg31TiBVOgQO3cPu3HbcaqkrqonSWcANwIdwNfMbHnJ+YuBFUDxL/gmM/taJc/q7etn6R2bqujtKLOmFxLdZeModCgx3URSIrtaG4bL3S+qSuqaXsAsiMKOqpWKbdLyWKXdN0lFlaY6qpWKq9T+UkwXPxDWNM/ijuw4Tjx1ExSSOoAvAx8AngV+Lmm1mT1S0nSVmX2m2uetWPNYptxAaRTTVqflQoKx6a6ndahsFHEplerNK9XDl6bkiL5bcQJd/6uXyxrjk+qDT6SQUS2vLyUqkE5evnbc79CN245TGfVUPb0beNzMnjCzPcD3gHPq9bBqjZZRlUjavQpTxA0XLOCp5Wdx0aI57BmxzHaMIpXqzZOuM0hVrZQLBhwcGuHWB59JbSOCiTz6nKQdzmWrNlYceV6rVCFu3Hac2lFPQdENRAMbng2PlXKepM2S7pAUa42WdKmk9ZLWb9++PfZh1RotoyVQk+Z9ASs+csK+FWmlcRu79gzXzDBcJM1ekWVyTPMWi+6gos9Ju28W+0k9J3M3bjtO7Wi2MftuYK6ZvQP4EfDNuEZmttLMesysZ/bs2bE3qtaL5prVW/cZiuMQ8LFFc8aoLdIm16QoaAhUP9FJNIsn01W9W7jitk1ldwZxq/Esk2OxuFPc8SQ7S7n7Vhp5XovJ3JMCOk7tqKeg6AeiO4TDGTVaA2BmL5nZ7vDr14ATK33YkoXdXLRoTqWXMzA4VDZX03fWPc3cyGSeOrmWUUcVJ9GiET7qybT0jk1jhEUxNUmWGJG41Xi5lBidhQ4uPOmI2Ik1LWNtllQblUSeVzuZFw3kg0Mj+35HnhTQcSqnnoLi58DRko6SNA34KLA62kDSoZGvZwOPVvPALy6Zz0WL5iRO4NVSqn5Z9NZZse0uPOkIdgyW95p6bmCQa+/eOs4IPzRiXHv31n3fJ6LiiluNl7qozppeGBPfsX9hCj1HHhzrxtqdsuovTbWRtT9J/arFZB51IYZg1xdNFx/XfuF19zJ32T3MXXYPC669191oHaeEunk9mdmwpM8AawjcY79hZlslXQesN7PVwB9JOhsYBl4GLq7mmaWlT+vJ4NAIT700yEWL5uyLxu6QuPCkI/jikvnct2172QyoaVlSox47Wd8nbTVemhKj6F1UfNaVd23h+nPn88Cy08Zdm5Y1t3jfUg+mcv2J61ctmIgLcXE3FxXUA4NDLL19076+OY4DsgZMqrWkp6fH1q9fH3vu5OVry07Oteap5WfFHo+bOKMUbR5pmWuL95677J7ENt1dnROKQejt6+eK2zYlCp/urk5OPWY2923bvu++pd/TVufNLiF61LJ7Yp0RBDxZ8rtK+//S3dUZKzQdp12RtMHMeiq5Nle5nhrt+iiCyTEpqR6MBoBNn9bBzj2jQsOAOzf0M70wJbagUFdnYAwvpwaZyGRcFF5pO5T+gcExwquYmiSLSqiZ9R+KQmoi1QXT/r+4G63jjJIrQTGzs7AvErcRGOzz6olbSZcGgO3cM3byGRwaoauzwNCIjQnaK0wR15x93DgVURzFaPQsE3TWinml1CNQrZa7j6t6t/CddU8nCokkFVia6s/daB1nlFwJijrZsFPpHxjk8lUbxxm61//qZe7Z/HzZVCA7Boe44YIFsZPmycvXlp3Yi4bvLJNsNavkrNdmEQC1jMju7etPFRLdKUJo6eJ542wUEAhqd6N1nFFyJSgGJpCfqZbExRmk2R5Kr12x5rHYySzr5Jw1L1WWEqNp15YjqwCoJGdVkgBKUzcJUu0MxWdde/fWfWPY1VngmrOPG9OPcsKvFWwzjlNPciUoqpkIm0nShFrr94mr+12YIhCpebKyxjZkFQBJAjDpXdMEUJowzSLcytlVygm/uPOXr9rIZas2pu5mHKedaHZkdk059Zj4qO12IC6KOUtAG4wavssRF7ew4iMnsOL8E8Ycu2jRnIpiG7Km5EiawIvOAaUkCaBr796aeq9aqI/K5aOKOx+X7sRx2plc7Sju2xafB6qVKLqzxq3fSyfU4uR8zeqtqUb6ouG7mhKntVj1Zqlm19vXz649w7HXF9VwWVVwr+wa4qx3HDou621cupVKKSf8yqkHPWOtkwdytaNoB7VT/8AgUxKs7kmr493DyQVVizmlyhU0akRlvHIpOYpqmjSbStzEm6ZCum/b9nG7pBsuWMAXl8yv7CUyPrt4PIt6y11tnXYnVzuKWtfMjiOtSFFW4vqYZAdIc2ntLHRw1jsOjQ2gi65ke/v6WXr7pn0uuP0Dg1yWokeP7k5mdhaQAkeBcoba0tiR0vZZ3HPjJt6li+dx2aqNse2fGxisa2GkJLvOrj1B5byu6QUKU9JrkrirrdPu5EpQ1FtITBFc8K4jyno0zZjWQaFjyr4KcgO79owJtivSIbHXLHUCS1uNnndid2rKkuK1V961OXEiK2ecjaq8igIm6g5c6iVUKiyKuvwlC7vL7viShOWShd2J6re0SbgWbril7zOzs8DOPcP7dkWv7Bqi0CG6whieaEr2tHdynHYiV4Ki3uy1QNWRViq1s9DBn/7OqPG3t68/cTU8YlZ2RT8lYZfUIfH9Tc+XzXi78Lp7GYyJ/I4SNc6mpfeI3rfIwOAQn78teL8kL6BiXEnpJBql3DjErdzLTcJZvLAmWsr15OVrxwmsoRFjxn5T2Xj16S3nKttq/XHak9zkekqbkGtNOVVDcdJLqz9der8D9p/KwK6hfSvWatVblVCNWk2CG353QeI7J6kFBdxwwYKyQXnF/s2YNnVcre8kyuV9SkpkmOblNZFcUs2mkvdz8ks1uZ5yY8yuRfnMrJSrkV1cRWc1rg/tNV7ZNYQRrNCbISRUJpaiHGakvnPSLsWIVwPF7QaK/Tss9Bwr1vNIopwhupJSrLUqttQI54J6lpptNI0YLyeZ3KieWsnjSaKinErNorPQUZP+FgsFTcRW1N3VGWs8T1LtDQwO7VP9pNkcevv62bl7vBtuVF2VNe6jtH+lO6+J2iFqmcIkjbzUDW/UeDnJ5GZHUa9iRZXQZto8zjsx/Y+tMIH/JcVCQVnoLHRw6jGzx7j2DgwOZU5JAvEr5Kt6t3D5qo3jbAmzphfGqF2y7A56+/r5/KqNY/o3NGLMml6ouNhS1pV+tavovNQNz9POqF3JjaBoRLGivHLftu2p0d0H7F/gSxcsSK1kF2VKRpl9/blBgadqdzPRFXJaksDp06aOmdCzlGK98q7NxLkCvDE0wpPLz+KBZadNeFWbZaUfrdSXFBtTjrzUDc/LzqidyY2gaKUdRbvx3MAgHzrh0MTzr+waYsnCbh5YdhpPLT+LL12wYF+gXxxxrsBxLFnYXZM/9ugKOS1JYFzke7lSrEkeY+U8ybL2N+l4LVbR9Sg12wzysjNqZ3Jjo/AdReVMkbhn8/OpbaIFmoruoguvu3dCaqIoxR1MtYkPS1fIWZIENttlNC6IL+t7TFSwNrOYVK3IMl5OfcmNoHAqZ8Ss7IQfV0c6S1r3pAp+u4dHMkc2JxEXe5EkeIpJArNkg42mHU/j5OVrxzx/Ivm2IDmCPe09JuMqOst4OfUlN3EUaXWlndpQrCNdnBCz7ASmAO9528H89JcvJ6qEohSmQDmtTlosQFqt8u6uTnbuHo6N8C4KnbhCRln6AtQ0ZsFjIJxaU00cRS4ERSOD7ZzmUswMG5f0LyrAKsn71V2hGqxo5I+7tihcK6HZKjKndajF/4VJLyiq0ZU7TlbSUpCknXuqRSK2XfBUTzPGsFa7y5aNzJZ0hqTHJD0uaVnM+f0krQrPPyhpbiXPcSHhNIK0IkkzE9yLk4oxNZpauNtOdpo1hq0QR1I3QSGpA/gy8EHgWOBCSceWNLsEeMXM3g7cAPxZvfrjONUwa3qBpYvnEeeEbQTR+EnnWiEwrBUmm3anWWPYCnEk9dxRvBt43MyeMLM9wPeAc0ranAN8M/x8B/A+aeIBEVlLgTpOpVz94SCVepJ6aSDM1RVHKwSGtcJk0+40awxbIY6kbjYKSecDZ5jZH4TfPw6cZGafibR5OGzzbPj9l2GbF0vudSlwafj1eODh6PkpnQcdPHXmm4+qy4u0MCO7dtAxfWazu9ES1HMsbO/e4aEXntgEUJg9d746pk4b12ZkeA9A0rmh7U9tqUvn4jkEGPM3lNbvBvet0Ywbi0pp1hhO6Tzo4KkHzT4SaXRhb7Z3+NXtv9o7+OrLE7jVPDM7sJI+tEUchZmtBFYCSFpfqUEmb0haP7zjBR8LfCyi+N/IKD4Wo0gaH1eQkXqqnvqBIyLfDw+PxbaRNBWYCbxUxz45juM4E6SeguLnwNGSjpI0DfgosLqkzWrg98LP5wNrrd38dR3HcXJO3VRPZjYs6TPAGqAD+IaZbZV0HbDezFYDXwe+Jelx4GUCYVKOlfXqcxviYzGKj8UoPhaj+FiMUvFYtF3AneM4jtNYcpNm3HEcx6kPLigcx3GcVFpWUDQq/Uc7kGEsPi/pEUmbJf2LpCOb0c9GUG4sIu3Ok2SScusamWUsJP1u+H9jq6TvNrqPjSLD38gcSfdJ6gv/Ts5sRj/rjaRvSHohjFGLOy9Jfx2O02ZJ78x0YzNruR8C4/cvgbcC04BNwLElbf4n8JXw80eBVc3udxPH4lRgevj505N5LMJ2BwL3A+uAnmb3u4n/L44G+oBZ4fc3N7vfTRyLlcCnw8/HAk81u991Gov3Au8EHk44fybwQ4KMM4uAB7Pct1V3FA1L/9EGlB0LM7vPzHaFX9cRxKzkkSz/LwD+D0HesDca2bkGk2Us/jvwZTN7BcDMXmhwHxtFlrEw4KDw80zguQb2r2GY2f0EHqRJnAPcYgHrgC5JyXWQQ1pVUHQDz0S+Pxsei21jZsPADuBNDeldY8kyFlEuIVgx5JGyYxFupY8ws7xXssry/+LXgV+X9ICkdZLOaFjvGkuWsbgGuEjSs8APgM82pmstx0TnE6BNUng42ZB0EdAD/Haz+9IMFOTC+Svg4iZ3pVWYSqB+OoVgl3m/pPlmNtDUXjWHC4GbzewvJb2HIH7reDMrU0/RgdbdUXj6j1GyjAWS3g/8CXC2me1uUN8aTbmxOJAgaeSPJT1FoINdnVODdpb/F88Cq81syMyeBP6dQHDkjSxjcQlwG4CZ/QzYnyBh4GQj03xSSqsKCk//MUrZsZC0EPgqgZDIqx4ayoyFme0ws0PMbK6ZzSWw15xtZhUnQ2thsvyN9BLsJpB0CIEq6olGdrJBZBmLp4H3AUj6DQJBsb2hvWwNVgOfCL2fFgE7zOz5che1pOrJ6pf+o+3IOBYrgAOA20N7/tNmdnbTOl0nMo7FpCDjWKwBTpf0CDACLDWz3O26M47FFcDfS7qcwLB9cR4XlpJuJVgcHBLaY64GCgBm9hUC+8yZwOPALuD3M903h2PlOI7j1JBWVT05juM4LYILCsdxHCcVFxSO4zhOKi4oHMdxnFRcUDiO4zipuKBwcomkPwkzpm6WtFHSSQ145lNhvELp8bPTMt06TqvTknEUjlMNYYqGDwHvNLPd4eQ9rVn9Cf34J02Mh5M/fEfh5JFDgReLqUzM7EUzey5c8f+5pC2SHpL0dgBJsyXdKenn4c/J4fEZYX7/h8I6BueExzsk/YWkh8MdSzTB3Gcl/SJ8xjFh+4sl3RR+vjmsB/BTSU9IOr94oaSl4fM3S7o20od7JG0Kn3dBeHy5RmuQ/EX9h9SZzPiOwskj9wJfkPTvwD8T1Of41/DcDjObL+kTwJcIdh43AjeY2U8kzSGI8P0NgtxZa83sk5K6gIck/TPwCWAusCCMCj448uwXzeydkv4n8L+AP4jp36HAbwLHEOw07pB0OkEepncT1ApYLem9wGzgOTM7C0DSTElvAn4HOMbMLOyb49QN31E4ucPMXgdOBC4lyOezStLF4elbI/++J/z8fuAmSRsJJu6DJB0AnA4sC4//mCA/0Jyw/VfD9PaYWTT//13hvxsIhEkcvWa218weAd4SHjs9/OkDfkEgRI4GtgAfkPRnkn7LzHYQpNR/A/i6pHMJUjE4Tt3wHYWTS8xshGBy/7GkLYwmkIzmrCl+ngIsMrMxhY4UJM46z8weKzme9uhi5t4Rkv++otl9Ffn3ejP7amljBTU2zgS+KOlfzOw6Se8mSHJ3PvAZ4LS0TjlONfiOwskdkuZJiqbTXgD8Kvx8QeTfn4Wf7yVSyEbSgvDjGgKbg8LjC8PjPwI+pSC9PSWqp0pZA3wy3MkgqVvSmyUdBuwys28TJH98Z9hmppn9ALgcOKEGz3ecRHxH4eSRA4C/CXX3wwSZMi8lsEfMkrSZYFV/Ydj+j4Avh8enEtTb/h8EJVW/BGxWUBTpyfAeXyNI2b1Z0hDw98BN1XTYzO4N01//LJRLrwMXAW8HVkjaCwwR1EQ/EPgnSfsT7EQ+X82zHaccnj3WmTQoKGbUY2YvNrsvjtNOuOrJcRzHScV3FI7jOE4qvqNwHMdxUnFB4TiO46TigsJxHMdJxQWF4ziOk4oLCsdxHCeV/wtnq121fpR1/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([0, 1.0, 0, 350000000])\n",
    "plt.title('Speechiness vs. Streams')\n",
    "plt.xlabel('Speechiness')\n",
    "plt.ylabel('Streams')\n",
    "plt.scatter(df.speechiness, df.streams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Speechiness is the presence or absence of verbal speech or communication. This scatterplot shows that there are more streams for songs with less speechiness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1175555f8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([0, 1.0, 0, 350000000])\n",
    "plt.title('Valence vs. Streams')\n",
    "plt.xlabel('Valence')\n",
    "plt.ylabel('Streams')\n",
    "plt.scatter(df.valence, df.streams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valence is the positivity of a song. This scatterplot shows that valence does not really impact the number of streams a song gets as the points are spread out across the chart, demonstrating that both positive and negative valence songs can achieve a high number of streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1176e0588>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([0, 1.0, 0, 350000000])\n",
    "plt.title('Energy vs. Streams')\n",
    "plt.xlabel('Energy')\n",
    "plt.ylabel('Streams')\n",
    "plt.scatter(df.energy, df.streams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Energy is the intensity of a song’s sound volume.This scatterplot demonstrates that if a song has more energy it will have more streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117735da0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([-35, 0, 0, 350000000])\n",
    "plt.title('Loudness vs. Streams')\n",
    "plt.xlabel('Loudness')\n",
    "plt.ylabel('Streams')\n",
    "plt.scatter(df.loudness, df.streams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loudness is the overall loudness of a song based on decibels. This scatterplot shows that there a preference for a certain range of loudness of between -10 decibels and -3 decibels. This range has the most streams. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1177ffef0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([40, 225, 0, 350000000])\n",
    "plt.title('Tempo vs. Streams')\n",
    "plt.xlabel('Tempo')\n",
    "plt.ylabel('Streams')\n",
    "plt.scatter(df.tempo, df.streams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tempo is the rhythm of a song. This scatterplot shows that the optimal tempo for a song is between 60 to 180 beats per minute. Anything outside this range won’t garner as many streams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used linear regression to further understand the potential relationships between the music attributes as the explanatory variables and stream counts as the dependent. We began with single linear regression models, which demonstrate a single attribute’s performance as the explanatory x along with number of streams as the dependent y, in an equation in the form of y = mx + B. When viewing the outputs of our regression functions, we were able to learn the slopes/coefficients of the explanatory variables and the p-values, allowing us to understand correlation and perform hypothesis tests on the data. Danceability and valence had high coefficients and low p-values, demonstrating high correlation and statistically significant effects on the total count of streams for a given song, respectively. Speechiness, however, had a very high p value and a relatively small coefficient comparatively, demonstrating its ambiguous relationship with number of streams of songs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2223: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>streams</td>     <th>  R-squared:         </th> <td>   0.022</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.021</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   24.41</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 12 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>9.02e-07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:27:38</td>     <th>  Log-Likelihood:    </th> <td> -20257.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1070</td>      <th>  AIC:               </th> <td>4.052e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1068</td>      <th>  BIC:               </th> <td>4.053e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>-5.618e+06</td> <td> 6.02e+06</td> <td>   -0.933</td> <td> 0.351</td> <td>-1.74e+07</td> <td>  6.2e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>danceability</th> <td> 4.291e+07</td> <td> 8.68e+06</td> <td>    4.941</td> <td> 0.000</td> <td> 2.59e+07</td> <td> 5.99e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>745.702</td> <th>  Durbin-Watson:     </th> <td>   0.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9011.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.169</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>15.727</td>  <th>  Cond. No.          </th> <td>    10.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                streams   R-squared:                       0.022\n",
       "Model:                            OLS   Adj. R-squared:                  0.021\n",
       "Method:                 Least Squares   F-statistic:                     24.41\n",
       "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           9.02e-07\n",
       "Time:                        22:27:38   Log-Likelihood:                -20257.\n",
       "No. Observations:                1070   AIC:                         4.052e+04\n",
       "Df Residuals:                    1068   BIC:                         4.053e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const        -5.618e+06   6.02e+06     -0.933      0.351   -1.74e+07     6.2e+06\n",
       "danceability  4.291e+07   8.68e+06      4.941      0.000    2.59e+07    5.99e+07\n",
       "==============================================================================\n",
       "Omnibus:                      745.702   Durbin-Watson:                   0.824\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9011.724\n",
       "Skew:                           3.169   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.727   Cond. No.                         10.3\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.danceability\n",
    "x = sm.add_constant(x)\n",
    "y = df.streams\n",
    "model = sm.OLS(y, x).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>streams</td>     <th>  R-squared:         </th> <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>0.007058</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 12 Jun 2019</td> <th>  Prob (F-statistic):</th>  <td> 0.933</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:27:41</td>     <th>  Log-Likelihood:    </th> <td> -20269.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1070</td>      <th>  AIC:               </th> <td>4.054e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1068</td>      <th>  BIC:               </th> <td>4.055e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>  2.34e+07</td> <td> 1.88e+06</td> <td>   12.465</td> <td> 0.000</td> <td> 1.97e+07</td> <td> 2.71e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>speechiness</th> <td>  8.18e+05</td> <td> 9.74e+06</td> <td>    0.084</td> <td> 0.933</td> <td>-1.83e+07</td> <td> 1.99e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>761.240</td> <th>  Durbin-Watson:     </th> <td>   0.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9480.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.251</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>16.053</td>  <th>  Cond. No.          </th> <td>    7.96</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                streams   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
       "Method:                 Least Squares   F-statistic:                  0.007058\n",
       "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.933\n",
       "Time:                        22:27:41   Log-Likelihood:                -20269.\n",
       "No. Observations:                1070   AIC:                         4.054e+04\n",
       "Df Residuals:                    1068   BIC:                         4.055e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const         2.34e+07   1.88e+06     12.465      0.000    1.97e+07    2.71e+07\n",
       "speechiness   8.18e+05   9.74e+06      0.084      0.933   -1.83e+07    1.99e+07\n",
       "==============================================================================\n",
       "Omnibus:                      761.240   Durbin-Watson:                   0.820\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9480.693\n",
       "Skew:                           3.251   Prob(JB):                         0.00\n",
       "Kurtosis:                      16.053   Cond. No.                         7.96\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = df.speechiness\n",
    "x1 = sm.add_constant(x1)\n",
    "y = df.streams\n",
    "model1 = sm.OLS(y, x1).fit()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>streams</td>     <th>  R-squared:         </th> <td>   0.006</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.054</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 12 Jun 2019</td> <th>  Prob (F-statistic):</th>  <td>0.0140</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:29:44</td>     <th>  Log-Likelihood:    </th> <td> -20266.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1070</td>      <th>  AIC:               </th> <td>4.054e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1068</td>      <th>  BIC:               </th> <td>4.055e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td> 1.737e+07</td> <td> 2.79e+06</td> <td>    6.223</td> <td> 0.000</td> <td> 1.19e+07</td> <td> 2.28e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>valence</th> <td>  1.42e+07</td> <td> 5.77e+06</td> <td>    2.460</td> <td> 0.014</td> <td> 2.88e+06</td> <td> 2.55e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>756.521</td> <th>  Durbin-Watson:     </th> <td>   0.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9329.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.226</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>15.947</td>  <th>  Cond. No.          </th> <td>    5.54</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                streams   R-squared:                       0.006\n",
       "Model:                            OLS   Adj. R-squared:                  0.005\n",
       "Method:                 Least Squares   F-statistic:                     6.054\n",
       "Date:                Wed, 12 Jun 2019   Prob (F-statistic):             0.0140\n",
       "Time:                        21:29:44   Log-Likelihood:                -20266.\n",
       "No. Observations:                1070   AIC:                         4.054e+04\n",
       "Df Residuals:                    1068   BIC:                         4.055e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.737e+07   2.79e+06      6.223      0.000    1.19e+07    2.28e+07\n",
       "valence      1.42e+07   5.77e+06      2.460      0.014    2.88e+06    2.55e+07\n",
       "==============================================================================\n",
       "Omnibus:                      756.521   Durbin-Watson:                   0.846\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9329.460\n",
       "Skew:                           3.226   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.947   Cond. No.                         5.54\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2 = df.valence\n",
    "x2 = sm.add_constant(x2)\n",
    "y = df.streams\n",
    "model1 = sm.OLS(y, x2).fit()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still using a constant, we performed a multiple linear regression as well, comparing the effects of all 3 of our hypothesized effective music attributes as a collective, in the form of y = β0 + β1x1 + β2x2 + ··· βkxk… . For this, we saw that danceability had a P-value far below 0.01, demonstrating highly statistically significant effect on streams. Valence had a p-value slightly greater than 0.01, demonstrating significant contribution to stream counts as well, while speechiness had a p-value of 0.416. Danceability and valence also sported large positive coefficients in the millions while speechiness had a negative coefficient in the millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>streams</td>     <th>  R-squared:         </th> <td>   0.267</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.265</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   129.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 12 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>1.29e-71</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:30:21</td>     <th>  Log-Likelihood:    </th> <td> -20256.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1070</td>      <th>  AIC:               </th> <td>4.052e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1067</td>      <th>  BIC:               </th> <td>4.053e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>valence</th>      <td> 7.501e+06</td> <td> 5.66e+06</td> <td>    1.325</td> <td> 0.186</td> <td>-3.61e+06</td> <td> 1.86e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>speechiness</th>  <td>-7.957e+06</td> <td> 9.77e+06</td> <td>   -0.814</td> <td> 0.416</td> <td>-2.71e+07</td> <td> 1.12e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>danceability</th> <td> 3.198e+07</td> <td>  4.5e+06</td> <td>    7.100</td> <td> 0.000</td> <td> 2.31e+07</td> <td> 4.08e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>752.424</td> <th>  Durbin-Watson:     </th> <td>   0.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9239.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.203</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>15.892</td>  <th>  Cond. No.          </th> <td>    6.85</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                streams   R-squared:                       0.267\n",
       "Model:                            OLS   Adj. R-squared:                  0.265\n",
       "Method:                 Least Squares   F-statistic:                     129.7\n",
       "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           1.29e-71\n",
       "Time:                        21:30:21   Log-Likelihood:                -20256.\n",
       "No. Observations:                1070   AIC:                         4.052e+04\n",
       "Df Residuals:                    1067   BIC:                         4.053e+04\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "valence       7.501e+06   5.66e+06      1.325      0.186   -3.61e+06    1.86e+07\n",
       "speechiness  -7.957e+06   9.77e+06     -0.814      0.416   -2.71e+07    1.12e+07\n",
       "danceability  3.198e+07    4.5e+06      7.100      0.000    2.31e+07    4.08e+07\n",
       "==============================================================================\n",
       "Omnibus:                      752.424   Durbin-Watson:                   0.838\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9239.019\n",
       "Skew:                           3.203   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.892   Cond. No.                         6.85\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult = df[['valence', 'speechiness', 'danceability']].copy()\n",
    "lm = linear_model.LinearRegression()\n",
    "model2 = sm.OLS(y, df_mult).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We feel these results demonstrate that speechiness performs oppositely when compared alongside categories that do strongly positively correlated to stream amounts, and that high amounts of danceability and valence result in high streaming, ‘hit’ songs. This is supported by the calculated p-values and coefficients. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also chose to do bar graphs to see the relationship in these attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data to plot\n",
    "n_groups = 10\n",
    "\n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    "rects1 = plt.bar(index, df['danceability'].head(10), bar_width,\n",
    "alpha=opacity,\n",
    "label='danceability')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, df['energy'].head(10), bar_width,\n",
    "alpha=opacity,\n",
    "label='energy')\n",
    "\n",
    "plt.xlabel('Top songs')\n",
    "plt.ylabel('Trait level')\n",
    "plt.title('Top songs and their musical attributes')\n",
    "plt.xticks(index + bar_width, df['name'].head(10))\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the relationship of the musical attribute danceability and energy with the Top 10 songs on the Spotify charts. As illustrated in the graph, the level of danceability and energy is high for most of the top songs on the Spotify charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data to plot\n",
    "n_groups = 10\n",
    "\n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, df['speechiness'].head(10), bar_width,\n",
    "alpha=opacity,\n",
    "label='speechiness')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, df['valence'].head(10), bar_width,\n",
    "alpha=opacity,\n",
    "label='valence')\n",
    "\n",
    "plt.xlabel('Top songs')\n",
    "plt.ylabel('Trait level')\n",
    "plt.title('Top songs and their musical attributes')\n",
    "plt.xticks(index + bar_width, df['name'].head(10))\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the relationship of the musical attribute valence and speechiness with the Top 10 songs on the Spotify charts. As illustrated in the graph, the level of valence is high, however, the level of speechiness for the songs in the top charts remains low. This means that songs that are more instrumental with high valence tend to chart more than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df['name'].head(10), df['valence'].head(10))\n",
    "plt.xlabel('Top Songs', fontsize=9, horizontalalignment='center')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('valence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows that relationship of the musical attribute valence with the Top 10 songs on the Spotify charts. As shown in the graph, songs that chart high on Spotify have varying valence so it's difficult to have a definitive conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dz6+u1yIiomOy3Xcb5pG0PfBs26+rz18JPNH2W4Y+Z3dg9/p0PeCSDpu4MvDXDuMldmIndmK3YXXbcxb2SUt20ZJFcA3wiKHnq9Zr89g+FDi0y0YNSJpre5PETuzETuzpEnsyozbE9EtgHUlrSloa2BH4Ts9tioiYkUaqB2H7LklvAX4MzAIOt/3rnpsVETEjjVSCALD9A+AHfbdjAr0MbSV2Yid2YvdhpCapIyJidIzaHERERIyIJIiIiGg0cnMQo0TSfzZdt/2hDmI/3fZPx1zb1faRbcceireM7Tu6ihfRJUnLAe8EVrP9eknrAOvZ/l6LMZ9SH95p+8y24iwu6UFM7u6hj6WAFwHrdBT7A5IOlrS8pIdI+i7wgjYDqthB0rcl/QW4QtINki6Q9GFJa7YZv7ZhT0kr1rYcJulcSc/sIO6qkt5Vv/dfSjpV0uckPU/StP49kbSspO0lHSTpOElfkvQfkh7bUXxJ2kXSB+rz1SRt1kHoI4A7gCfV59cA/9VyzN2AVwMvbznOYpFJ6kUgaVngR7a37iCWKHc3e9RLH7B9dMsxTwFOBr4N/Mr23fX6g4GnATsBx9n+Sott+JXtDSU9i/K9vx84yvbGLcY8glLS5XvAXOA6YFlgXcr3/QRgb9unttWGSdq2se1zW/z7Pwg8n/L/fg7jv/dlgXfavqDFNhwM3ANsY/vRklYCTrC9aVsxa9y5tjeRdJ7tx9drv7K9YYsxd60Pb7d9XFtxFpcMMS2aZSi7u7uwEqV44WU15uqS5HYz+jObhpRsXwccAxxTNzC2SfXP51ISw69rsmzTgbYvarh+EfCN+j2v1nIbJvJG4PUt/v1n2953gtc+UW8O2v7en2h7Y0nnAdi+qYOfM4A7Jd0PMICktSk9ijatUf+8reU4i0USxCTqD+zgDXkWsArQ+vxDdSbwEduH1x/ijwKnA1u0FXA4OUjaHFjX9pckPQhY3vYfa5XdNp0j6QRgTWAfSbMpd5etGU4O9d96NduXDL1+J3Bpm22YpG1tJgdsfx9A0pOBMwa9xnpt0Hu5rs02AP+qpf4Hb9RzaPn/vNoX+BHwCElfAbakDP+0xvYH2/z7F7cMMU2i3lEM3AX8uatJW0mr2f7jmGtP6WKYQ9L7KL8sa9teV9LDgWNsb9VB7CWAjYDLbd9ck9PD2xziGIq9HfBxYGnba0raCNjf9nYtx93F9pfr4y1tnz702ltsf6bN+DXO7ZRSNy+rPUYkndvm0N5Q7J0pY/IbA0cC2wPv62IIpv58bU7puZ5pu/WCeXWo+vnAk4GHAf+g9Fa/P2qVI5IgFqJO1D25Pj3V9sUtx3uU7d9KavzFbHM8eqgN5wOPB84dGpu9wPYGLcYche/7HGAb4OSh7/tC249rOe68N+Kxb8odvkmfR5nv+TjwWttnDI/NdxD/UcC2lDfqE23/poOY+9v+wNDzJSjDmju3GPODlMUmJ9HTnM+iyBDTJFTqQr0J+Fa9dJykz9r+XIth96KUMz+w4TVT3sDadodtSxp0+ZfrIOYofN//sn3LmCmPLu6gNMHjpudtse3vSbqEMtd0OC1/75IeOPT0OuDo4dds39hmfMrQ0j62PyxpGeBY4LyWY47CnM+UpQcxCUkXAFvY/lt9vgJlnLa1O+lRIOk9lB/SZ1OW/b0W+LrtT/XasJZJOgw4EdgbeCnwNmAp229oOe5I9CCGek3LU5aAvsR2azeRkv5ASUJNSdC212ordo0v4CvAhZS79x/a/mSbMYdir2n7D2OubWr7l13En6okiElIuhB4wmBitt5lzG17yKHGmgU8j7LqYd4vqe1PtB27xn8O8EzKL++Pbf+wi7g19haM/76/1EHc5YD3MvR9AwfY/mfLcW+nTIILWJv5E+IC1rK9fJvxJ2nXuHmw6WDMMOZSwCGUBSCHQWfDmecCL7B9TX3+VOAzXby3LIokiElI+g/K2v/j66UXA0fb/p8OYv8A+Cfl7mbeio6uVkFIWhVYx/ZJdVJtlu2/dxD3KMqb5PmUDYpQ7ibf1nbsvkhafbLXbV/ZQRvWBQ4GHmJ7fUkbANvZbnvj2CD+S4CtKD2Kn9v+1kK+5N7EOmmSl2279eFMSZsCn6PMR2wMfBh4vu2rJv3CjiVBLETd0TlYvfPzrrqAbU8KLyT2a4C3APe3vXZ98/ic7ad3EPs3wGNa3u8xUex1gXcxvvfSxfzHRG063faWHcQ5BXg3cMjQUNNFttfvIPbngEcyfw7i5cBltt/cduw+SXoSpffyT+B5tq/vuUnjZJK6Qb17f5PtK2yfDZzdQzN+KOmZtk/oIfbbKJv0zgKw/bs6edaFi4CHAtd2FG/YccDngS8yv/fSt64mLJezffaYCfq7Ooq9DfDowU2BpCOB1pZ7DpYVS9qr6fU2h3FVSuYM3/wsB9wCHCaJtpdUL6okiGZHACfUH9SP2f5XD204E/hmXXr3L8p4tG2v2EHsf9q+c/BmUedDWl1NM/SLMxu4WNLZDO1q7egX5y7bB3cQZ1F01ZP6a933M3iT3p7ukvSllEQ4GEp7BO1uTBzM6cxueK3tf+/Wh6cXpwwxTaCuWHo/ZSXPUSw4D9D6RHFd4fFC4MKuh1skHQj8hVJY7E3Am4Hf296nxZhPnex126e0FXuoDftRllt+kwWTU6vLLev4e+NLwOdtz2kzfm3DWpRTzbYAbgL+AOxi+4oWYw5uCu4PbErpqRt4ImU56NZtxZ6kTW/vYrWeSuHLawcLIOoO/oe0+e/970iCmIBKLZi9gVdQ6hB1OlEs6VRga9tdlBwYG3sWZU/C8Gqez7eZqCSdYLv1qq0LacMfGi53sdzyiMlet71bm/HHtGV5YAnbrdcKGoWbgrEk/dF268N6kuZSltAPVkguDZzulgsULqoMMTWQ9GzgE8B3gI1t395DMy4HTpb0Qxa8m+1imevj6lDLvOGWuuy1zaWuK7f4d0+J7dbLmU8Qt7MEMBFJDwBeRZ2gHwwvtrl6rI8EMAVdbUxc0kN1zeqQbhcFChdJEkSz91Jq0vRZF+UP9WPp+tGlw+tE3sUAkl4G/AftJogHTDLUgu1vtBgbmFdq4zDgq7ZvbjvemNhPBW6yfYGkHYCnUCr5fs7d1P/6AWXea4Fl1V1QKQz5aeDRlJ/1WcDfO5pvG6urIZXrJW1n+zsAkl4ItF4HalFliGnESVqu6x6MpEdSyg7sSCna9zrKGu2bWox5A+Uciol21b6mrdhDbXgkZd7l5ZRzIY6gnEvQdsmJzwIbUOrwXAKsQKkyuiVluKe12kBDbehkx/YEsedSftaOAzah9GTWbWvOS9JtNCcCAfdrc/f4UBvWpuzifliNexXwKtu9VA2eSBLEiKprpA8DVrC9mqQNgT1sv6mj+I8CvkE5ZeuFbSepPt+gxqorx55PGWK7m5IoDmprslrSxbYfUzckXgM82PbdtRTEBR3t3H8H8DfKoUmdTdDX2IODe+bt/VGHhQL7VBfD4FrOZ9RkiKmBRuMs5k8Bz6LMg2D7V5p/nm0rtOD5FwAPqH+eVtdot/kG3tXY76TqDuLdKAcWHU+5y9sK+BmlDHkb/glg+5+SrnQ9k8G2JXW1xPpOSiXX9zL/Z8BAqxP01e11/P18SR+jLK+d1se8Akh6HvBYYNmhOZ/9e23UGEkQzX4BbCzpKNuv7KsRtq8as3Gp7c1b27f890+mt3/ngToHcTOl57b30E3CWZLa3M384LppS0OPqc9bX+JavRN4pDs4D6HBKykJ4S3AOyj7ICacj5oOJH2esknuaZSNmdvTz4bcSSVBNFta0iuALZomTruYMAWuqkXrLGkpYE+g1Rr5ti8bPK7DG3Po6GfEzUd+du1lti9vesF2m29YX2D+pq3hx1DePLpwKdDHaj2AF9k+iNKT+iCApD2Bg3pqTxe2sL1BHVb7YN171FlBzKnKHEQDSVsBOwM7UId4hnQ1Yboy5Rfk6ZQ7yROAPW3f0EHsNwH7Azcwf0WLbT+m7dh9G+72D66NWre/DZK+Sfm+T2LBOYjWiyQ2zT9N9zkISWfb3kzSmZTe0g3Ar20/suemLSA9iAa2T6OMu8+1fVhPbfgrJUnNUzcxdWEvSm2cXoqHqeFc6I7i9tLtl/S/k73exZs05VCs1iqoNpG0E2Uj6pqShm/EZgOtTY4vZBVTV+Vsvlv3nnwcOLe25wsdxF0k6UFMok6cvYGyJh3gFMqO4lYnDlXOgF6FsoLlzloo7+3Aq20/rM3YNf7JwLYeOsC+K5JeQKlX0+m50DX2BUPd/g3qCpMf2n7yQr/43sW9k1Kk8FjgT4yZsLd9ZJvxh9qxNOXoS4BLOvg5Xx1Yk1Lqeu+hl26j/Ox3VSywU3WV3Oa2z6jPlwGWtX1Lvy0bLz2IyX2OcqDI4IjRV1KWPr6urYCS3k5ZSXIpsIxKKeSPAl8CntBW3DEuBX4maeySx0nvdBeT/SiVZE+uMc+vdWu68I/65+2SHkbp9q/SQdxVgJdR9l/cRSnt8vUuN+tJ2ho4EriCkqAeIWlX26e2FdPlnIsrJZ06dle1pI8C72krdp9s31P3vjy+Pr+Dod+zUZIEMblNbW849Pxnkn7VcszdgfVs3yhpNeB3wJa2z2k57rBr60cfO1n7Ohca4Ht9dPvrvNLngc+rHNS0I6Wi7XtsH9V2/OpA4JmDYT2VszGOppubkmcwPhk8p+HadHKipJcC32h7I+a9kQQxubslrT1Y3aNS8bLtYZd/DjYn2f6jpEs6Tg7Yfn+X8cb4dV1BNkvSOpSzKc7oIrDtA+rD42vvqdNuv8pRmDtR3jB/CHT5/77U8JyPyxkgS7UZUNIbKdWC11Y5/31gNuUI0OlsD8pc312S/km38x9TljmISUjalrKL9nLKf+DqwG62Jzuy8N7GvA742tClHYefd7SqZGXKuvixq3lar7aqBc+FhrJ6q9VzoWuv8PT6cYbHHCbfNkn7U84f/w3l//pHXY+/SzqcsmLty/XSzpRjZltbsSfp/sBKNMxBdLSD+6O237OwazNZEsRC1Amk9erTS9reYS1p18le72LCUtKPKGcivINyFsSuwJ9t/0cHsdcc+wYtaVO3eNSrpPUp5yAMPpanbJYcJIyz2opd499DKcw42Icw+KUc3FW2fvRs/Tl/M0PH69JdocDB0vJ1bB9Rb1Bmt52oJ1he28lRv5JOtL3twq71LQkixpF0ju0nDK3mEXCW7c06iH0u8ALb19TnTwE+20U9oqE2rEzpub0dWNP2rJbjrT7Z63Uyd9qStC+lSN96ttetCwSOc0tncQ8Nba1FqZg7MJtyJsMubcStsZelLKU+Cdia+SvWVqT0HB/VVux/R+YgoslgeeOfJT2LsvTyQR3F3gP4Vl3uujFl+OG5bQZUOSDp8ZTew5bA2pSieV+k9CRa1WcCUCkzvartz9bnZzG/vMd7bB/XQTNeTPn3PxfA9p8kNR0Hurh8lTLH08fQ1h6UG4+HUb/f6lbgMy3HXmTpQcQ4kraj7PlYHfgs5e7mgx2VGBlUsj2EUnrheW1v2JN0O3Ax5Xs9uYc5iLEbt0w5G+Akypt0a7vnJZ0O7Gj7qvr8fGBbyjDbEV0MeQztKj7X9sZ1Q+gvuhjqqfEfzIJzbX/sIOZbbX+67Tj3VhLEJOrQys7AWrb3r8tOH2p75IpqLQ6SNrd9Zk+xB+cTDzyGstT2JoA2N8rVHb1PoizpvBv4JaXn8IvBUFfXJK0EvJpSs+dlLcb5pYeOuZT0GdtvqY/PtL15W7GHYr4LWIeyeuvDwGsohza1+gZae6mfoNzNX0e5IfqN7ce2GHPSml5d3YRNVRLEJCQdTFnZsY3tR9df2hPc8bmxTZNpbcWhvDm+p8tNWjX2SJxPXFdRbUYZbtqNsqN70jmCltvT6v+9pEs9Qf0fSZfZXrut2GNiPYOhM9Bt/6SDmL8CtgF+avvxkp4G7GL7tS3GHJw/PocynPmz+vxplAURz28r9r8jcxCTe2Lt8p4HYPsm9XNubFdnJWxC2XdwtqQDOtyk1fv5xHVY44nMn4fYlHLKV2/r8es+hLZ/R8+S9HrbC2wIlLQHHZafrgmh9aQwxr9s3yBpCUlL2D5J0qfaDOh6/riknwCPsX1tfb5LOcS2AAAgAElEQVQK8H9txv53JEFM7l91AtMAkubQ8Xm91fe7CGL7HuBTkk4AflHLfJgONvFIOs32Vg3j8V3EPo9yBsFcyqa8A4Ez3dEpXxMMO6xEKb3x9ZbDv4OyKOAVzJ80fQKwDPCilmMDExbPu4Xy//FOT1CCfTG4WaXe1qnAV+oepL+3FGusVQfJofoLsFpHsacsQ0yTkLQz5Zf0CZTsvj3wvo5WdvRC0mspKzsOoiwvnfY/ICqnyF3Y1/c6NOwwYEodqJNtd3JzIGkbysZIKGWnfzbZ5y/m2AcAV1NWF4myxHhtSsJ6o+2tW4q7PGUhxGCu8f7AV9pcFDAU+zOUeZej66WXA5fafmvbsRdFEsRCqJzNPFjJ8TPbrR7a0ydJZ1CKte1l+889N2ceSX+0PXJ3V7F4SPqVF6x5hqTzbW/U9Np0UXuOg0rBp9r+Zp/taZIhpoVbDhgMM92v57a07QO2f9p3IxqMxHnVbdFonAfRp9sl7cD84bTtqed002KhxvoG/VHgwZSfsU7rIdUVSyO1ammsaX8w+L0h6QOUEsgPBFYGjpD0vg7jLy1p/frRauE0gBFNDtBdNde+nDP0sd2Y550WauzJzpRS+tdRxuJfCeyicnDUW1qM+zFgO9v3t72i7dldJQdJL5H0e0m3SLpV0m2Sbu0i9qLIENMkJF0CbOhaKK7+wJ5ve73Jv3KxxN6aMfX5gV3dYn3+Pknaa6KXgPfafmCLsUdmbbqm+VGbY9VFIG+z/ckeYp/eVjmPKcS+lFJSZqSHrDPENLk/UXZYDrq7y1BKMHSht/r8kma5+9PkJiut0Pbh9S+Y5DXT7TBAXxPlmwOfBh4NLE0ZVv1723fUtu+uGxU7SxBDNwRzJR1DOWp1+GCsLv6//zLqyQHSg5iUpG9R1sP/hPKL+wzK2vCrod2xYTVUlWy61lLsy4HjKaUWLm47XszX1abIhrhzKauHjqPsh3kVsK7tfTqI/UnKyY3HMLTM1Pa5E37RvYs3dtXYMLvFEudDbTgIeCj9JKcpS4KYhHosva0e6vMPxZ5NebPYjTJPdTjwNdsjN0a6OEjaxfaXJxrmsv2JluMP7wNYjvllvzubNJU01/YmwzchXQ13SWo6X8W2t2kp3kq2b2rj716ENjQlqU6S06JIghhRaq7P/1nbd3bcjqdS1qc/gLLK5ADbl3bZhrZJ2sP2ISplp8ey7f07b1THJJ0KPJ1SwfbPlDpYr56OS0zrhri/Us/7oJT4/l2/rRpNSRCTUDny8sOUwnHD1R7X6iD2nrYPWti1lmLPopxwthuwBnAU8BXKmu0P2V63xdhNBwaNu9YVSW+33Wr5hVGgcibFXyjzD++gbBr7XBc3Ayony+0LPKVeOgXY3y0e91rn9IYPiZoDnElJFh9rK+5Q/CNomG9KD+I+RNJplB/cT1ImMncDlrD9gQ5iN5121VWX/3JKqenDbJ8x5rX/bXnupen7Psd265PzE7Qnm/RaJul44CLKqj0oy1w3tD3p6rLFGH9typkjewIPt936fidJLx16uizlTIw/jdqelySISWj+yWoXup5o1vabVV3R8QrK0NLPh16aDdzjburzr9BVHaKhmI+ilHr4GPDuoZdWBN7tFkswL6RdV9l+RB+xuyRpS2A/SsnreasbO+otn297o4VdW4zxBr2GJ1GWj19O6T2cCZzb9TBubdMSwGm2t+g69mSyzHVyd9T/uN9LegtliesKLcc8gzL+uzJlqevAbcAFbQaW9GnmFyYc93rLdzfrAc+nzHUMLzu9DXh9i3EXZqbcQR1GGVo6h3ImRpf+IWkr26fBvGT1jxbjnUap8/RJ4Ju2b1/I53dhHcqO7pGSHsQkJG0K/IbypnUAZVz2Y+7pUJ22Da3a2pIy73JMff4y4GLbb+igDU+y3foxn2NiNlUThbKK6H62p/2NlKSzbD+xp9gbAl+i/H5BOSRqV9ut3BBJeijz5x42o9won8v8Q6Laqh473IbBz5zqn38G9rF9fNuxF0USxIhRj2Wvh9pwJrCV7bvq86WAn7ub08U+BvwX5Q7yR8AGwDtsf3nSL4x7RdJHKJvjvsGC6/Jb2YtQY+5p+yBJW9o+XdKKNWany6lVDol6DeWs6DVtz+oy/ihLgphEXenwbsaPy7ayPntU1BIjT3I9wF3lJL0zOyoxMqji+WLKkNNelEqX02655Sjpei9CjTn4v+50c2BdNfUk5vciHg/8ntKDON1222dwDNqxHfNXbp1s+3tdxF0U077rfC8dB3we+AIdj8vW0ge/tn1bfT6bcgLVWR2E/whwXn3TEOWHeL8O4kLZUQtlme1xtm9pmg+Jxcv203oI+xtJvwceJml4OGnQW26rasCl1OEkYH/gl7bbnPMYp/bYNqUsHwfYU9IWtv+zy3YsTHoQk+h5eeV5wMau/0F1snxuV3dadZx2MCZ9ljs6H6L+4ryIMsS0GWX+53t9jY/PFJIeAnwIeJjt50h6DKUXeVjLcR8K/JhSxXYBtq9sM3afakLcyOUUx8Heo/O6KKWzKJIgGkgaVA59G6UE8TdZcFz2xg7a0LT0r9VaTJImTT5tjkePaccDgVtcCrktB6zYVYKq8WcP9dweOd12jjeR9EPgCErl3A0lLUl5w3pcz02blmqC2HpoGPeBlGGmkUoQGWJqdg7zVxjAguvyDbS+Nhy4XNLbgIPr8zdR1mu3abCsdllKwbZfUf4NNqCcD/ykluMj6VVDj4df+lLbsYecJukPlBIjH6YcfzndrWz7WEn7ANi+S1Inw6oNezAGQ0xd/J715cOMH8bdu98mjZcE0cD2mn23AXgD8L/A+yhJ6URg9zYDDsahJX2DMrx1YX2+Pt3NQWw69HhZynGv59Jigqi9lDsHq7bqHfQbKeXVd2wr7oj5u6QHMX8fzOZAa6UuxuhzD0YvbB8t6WTKz7uB93TZS54y2/kY80H5T3vo0PNXAd+mvGE/sO/2dfD9/3oq1zpqywOAH7Uc48wx/98vpmxKfDrw/b7/Pzr6d96YUrzulvrn74ANOop9Vk/f88coO/WXotyAXQ/s0nLM1YH7Dz1/GuW8k72Apfv+ORj7kTmIBpLOBZ5u+0ZJTwG+BrwV2Ah4tO3tO2jDssBrKeUnhgsFdlHu+2hKXf7hUuMr2N6p7dgNbVkKuMgtLrGV9CvXZbSSdqfs3H6u7etVy2C3FXuU1HmH9ShDHpfY/ldHcTvfg1Hjdr6kWtJZwItt/0nSRsBPKcNNGwD/sv26tmL/OzLE1GyW509Evxw41GWH4/GSzu+oDUcBvwWeRVmKtzNlV3cXdgPeSCleBnAq8+dCWiXpu8zfILgEZUf3sS2HvUGl1PcjKL2HR9q+SdIqlOqm05YmPm51XUm4mwNsBivUBisGB7uL295vNHj/63JJ9f1s/6k+3gU43PaBdZViV+8tU5YE0WyWpCVdxqS3ZcGx/67+zR5p+2WSXmj7SElfZcHifa1xOYP7k3R4DOSQ/xl6fBdwpe2rW475MkpC/B3l//oESRdSuv/vbTl233o7blXzD2gabBAzZZjnNHdT3v17kn5LWVL9RklzmH+8cFuGM9A2wGBRwD2juN8nCaLZ0cApkv5K+eH5OZQlj3Q3cTfo3t9cJ4n/TEfFvNTjORi2T2k7RkPMGyjlPQCQ9AtKPaqPup4JPl3Z3q3H8E3nkK8OvFfSfra/1mZw23vX0i6DJdW3Ay9sMybwM0nHUgpyrgT8DKD2VjuvIrswmYOYQF3FsQpwgu2/12vrUsbiW98PIOl1lHOhN6CsT18BeL/tQzqI3fk5GJMUzIMyLn0ZZY3+iW21YSbra6PcBG15IPBTt7QptM4rQlm51mnhTZVuwssp7y3H2r6mXn888GDbP+6yPQuTBBHjqIdzMBbSnlnA+sBXbK/fRxumu1HbKKcWD8fS/POgb7b9jjZiTBcZYhpRdU36fpShDlOGuQ6owyFt6+McjAnZvhv4lcp5FdGO3jbKjSXpaZSS360YDKtJGrffST0ebzuKkiBG19coq4cGRxPuTDmf4ekdxN4TWI5SauQAymTarpN+RQfaHF6rvZSfup+idaOg841ydSHA2CGMBwJ/ouw9atvxlP0fw77O/NVUM14SxOhaxfYBQ8//S9LLuwhs+5f14d8o8w/TXp2kvEfS/W13tRBhlOwFfAdYW9LpwByg7f0+zx/z3MANgzm/tmj+8bb3H7PMd0WGFmW0TdL9gNVGeSFEEsToOkHSjszfA7A9peplq1ROlduTsmEKyt6L/7XdZS2kvvwNuFDSTygbBYHWj1odCbbPlfRUOtwo5/6qtfZ+vK2kF1CWdC8NrFk3ze1ve1xV2z5lknpE1VU9y1Nq04iyaWzwpmW3cLJcTQ5vp9xNnlvjbgx8HPiU7aMWd8yGNjStZrqFUizwnW7xOEjNP3J1AbaPbCtm31SO1b3KtQ5QLZb4UuBKYD93ULm4L+rheNuh2OdQhm5PHkzGDy8KGRVJEDGPylGjO9q+Ysz1NYCvuZsjRw8ArqZUUhWlWN7alIT1Rttbtxx/5Lv9i9MolJXpS8/lbM60vfnwai21XM7/37FE3w2IZpK2lLR8fbyLpE9IWq3lsCuOTQ4A9VrrZ2FX29k+xPZttm+1fSjwLNvHUDYWtaZ2+8+nnIWNpI0kfafNmCOgsayM7fcDj+yxXV04CngopZzNKcCqlGGmLvxa0isoVRvWqSv0zugo9pQlQYyug4HbJW0IvJOyUaztIZ7Jjl3s6kjG2yXtIGmJ+rED88sftN3d3Y9yit3NALbPp5uzP/o0q+55gFJW5mdDr033OcpH1kT49zqM+Dzm14Vq21spPZc7KL3lWyjDuyNluv8A3JfdZduSXgh8xvZhkl7bcsxHa8GzgQdEd2+UO1PKH3+OkhDOBHapQz9vaTn2vxoKtt3Tcsy+jUJZmb70Us6mLqne3/a7GPFaX0kQo+u2umnplcCT68a1pVqO+eiW//6FqpPQExWQO63l8At0+yn7QEau27842f5vSScyv6zMcCXdt/bXsk4cKmkl4P2UJb4rAK2VkxmoS6q3ajvO4pBJ6hGlcpj7K4Bf2v55nX/YerovN60VNV8PrMHQDUxHE4fLUe7onknpNf2Ysnu97QqfMcNIOhh4OHAcCy6p7qK8+pQlQYwwSasD69j+aX3zmmW7q0m0Xkg6gzLMscDxk/U8jq7asGIJOb3/rWc6SctQlvSuwYI3I/t3EPuIhsvu4kZoUSRBjChJr6ecTfBA22vXIY/P296256a1SvWUr55ibwoczvwy1LcAr7F9Th/tiXZJ+hHl/3jszciBvTVqxGQOYnS9mbKi5iwA27+X1Ml5ED37nqTn2v5BD7EPA95kezBRuxWlwulIrU2PxWZV28/uI3DtQYy7Ox+1HkQSxOi6w/adgxU1dSliJ929CYqoDXYz/1fLFWX3BP5T0h2UVSaipZ3jDe4eJAdK0NMk3dVB3N7VmkQfpaziEd3+u/flDEmPs31hD7G/N/R4WcpRt3+a4HN7kyGmEaVy0tXNlKqWbwXeBFxsu/VlcTX23ZT12VB2My9HWQa4le3Jjqm8z5L0KeB+lKWfpmwc+yfwZSj1ivprXbskXQq8wHZX5573RtJFlOXLSwLrAJdT9iMMkmLnPca6SvE021t0HXsySRAjqv7AvJYFV9R80R38h0k6d+xpXoNrbdWLkfQo27+V1HiKWBdvzpJOmuRl296m7Tb0RdLptrfsux1dkHQTpZRIoz6KCEpaD/i+7ZHavZ4hphHlcoj5t4Bv2b6+4/CzJG1m+2yYN3k7q77W1pDLXpRJ+aYJQlMKm7VqJp4FMVTueq6kY4BvUe6mgdFbdrmY/KHHSrJAY1HKPwPv6ak5E0oPYsSoTDrsS9k1PCiFcjfw6S6W39U2DFbzrEDpvdwKvA74NfA828dO8uX3WfXAnH2BrSi/vKdRdrx2cYpfLyZYbjkwcssuFwdJVwOfmOh12xO+NtOkBzF63kE5ZnRT16MPJa0FHCzpHbY/2XYD6oFBj5N0//p8uORC68lB0haMX5vexQbBPk/x64XnH7+5pe3Th1+TNF2HnGYx/+anF5JOHLtkvela39KDGDGSzgOeYfuvY67PoZRCaOUg9zGx+txAdBSlvPf5zF+bbndwaI+ki2yvP+bayNXob8Nk8059taktfX5ftcT4csBJwNbMT1IrAj+y/ag+2jWR9CBGz1JjkwOA7esltV2LaeDbzN9AdMdCPndx2wR4TBeT8Q16OcWvT5KeBGwBzJG019BLKzJ/3mm66a3nAOxBqdr6MMrv16AttwKf6atRE0mCGD13/puvLU69bSACLqLU6L+2h9ivp/zyfrk+XwL4u6Q9mL57ApamDLcsyfwd5FDesKbrYUG9DePYPgg4SNJbbX+6r3ZMVRLE6NlQ0q0N10V3B6p3voFI0ncpE8OzgYslnc2Cq2laP6vX9uyFf9b0YvsUSrnv/+t7ZU9XPBrHqN4j6QG2bwaoVWV3sv25ntu1gMxBxDiSLqacJvYHOtpAJOmpk71e38haV39R12HBIyhP7SJ2HyR9yvbbhxL0ArpIzDNRU80xDR0/OirSg4gmz+k64CABSPqo7QXWg0v6KOVIyFZJeh2l1MeqlEnyzYFf0MEejB4NTin8n15bMfPMkqTBXFs9RGjpnts0TnoQMY+kFW3fKumBTa930TWfYDVNJ4e51xpUmwJn2t5I0qOAD9l+yUK+9D5P0rbAGba7Olp2RpP0cWB14JB6aQ/gKtvv7K9V46UHEcO+CjyfsrrCLLjaw7R47KikN1LqTa015tjT2XR3qts/bf9TEpKWqaU/1usodt9eRdlrcyPlPI5TKbWBbuq3WdPWeyhJ4Y31+U+AL/bXnGbpQcQC6k7uR9j+Y8dx7w+sBHwY2Hvopdu6mlSU9E1gN8pKpm2AmyjLjp/bRfxRIOlhlNVL7wIeZjs3kTNYEkSM0/fmsHoOwzq2j5C0MjB7sKu8wzY8Fbg/8EPb/1rY59/XSdoFeDLwOOCvlDIjP7f9i14bNs1IOtb2DhOU1KePSrKTSYKIcSQdCXymltzoOva+lM1y69let97RHtdHpVFJDwDebPu/u47dNUl/BS4DPg+cZPuKfls0PUlaxfa19TjhcUZtqfESC/+UmIGeCPxC0mWSLpB04Zh5gTa9GNiOepC77T+x4AauxU7SIyQdKul7kl4naXlJBwK/oxygM+3ZXhl4DWV5739LOruWPYnFqCaHFwEvAx5l+8rhj77bN1bGF6PJs3qMfadtSxos/1u+g5hfoiyjPR54NuXkvPOBDWz/uYP4vZO0IrAaZWXNGpThtXv6bNN0JOlzwGMpCy8OqGX1D+i5WRPKEFOMI2m1putdTFxLehdlo9ozKBPWrwG+2mZZAkm/sr3h0POrgdVsz5g3yNpDPK1+nGr76p6bNC3V0+w2tH23pOUo8zxP6LtdE0kPIpp8n/nLXJcF1gQuodz5tMr2/0h6BqUW0HrAB2z/pO24dQf1YFnvDcD964quUSnN0KpRmxydxu60fTeA7dsHP2OjKj2IWKh6DOibbL+uw5grsmCp8dbepCVdQRlOafplte3W9n/0TdIXgP9tqrtVh/deDtxh+yudN24aknQ7cOngKaW0/aX0eB72ZJIgYkq6WvpaK6d+EPgn89+0p/WbdJ8kbQT8J2V560XA9ZRe4zqUkt+HA5+33XXZ92lpotVLA6M2UZ0EEeOMORdgCWBj4EG2W5+8lvR74ElNZ2JEeyStQFlevArwD+A3ti/pt1XRt8xBRJPhZaV3UeYkju8o9mXA7R3Fisr234CT+25HjJYkiGhyse3jhi9Iehlw3ASfvzjtQzmP4iwWPA+i9SNHI2JBGWKKcfo8n7geFHQacCFD6/BtH9l27Bp/uMzHHGCFrst8RIyK9CBiHknPAZ4LPFzS/w69tCJlqKkLS9nea+GftvgNl/kAjgCWohw/2nmZj75IWs52hvhaJmlLYD/KxsQlGdHFGEkQMexPlF3E21FKfg/cBryjozb8UNLuwHdZcIipi70ILwYeD5xbY/5J0ow4hlTSFpRy0ysAq0naENjD9pv6bdm0dRjld+oc4O6e2zKhDDHFOJKW6quCqaSm4ZxO7qwknW17s8FwWt0H8ItRW5vehjrnsz3wncGxl5Iusr1+vy2bniSdZfuJfbdjYdKDiCZrSPow8BgWPJu59Tdp22u2HWMSx0o6BHiApNdTynx8ocf2dMr2VWM29o7sne00cFI9Ve4bLNhTPre/Jo2XBBFNjgD2BT4JPI1yiE4nlX8lLUU5Zesp9dLJwCFd9Gj6KvMxIq6qw0yu/wd7Ar/puU3T2aD3sMnQNTNi559niCnGkXSO7ScM754eXOsg9hcpk8ODVUuvBO7usszHTFQPZjoIeDplwvQEYE/bN/TasOhVehDR5A5JSwC/l/QW4BrK5GUXNh2urAr8TNKvuggs6SXARylnQIj5K0tW7CJ+n+rO9Z37bsdMUY/Y3Zf5PeVTgP1t39Jfq8ZLgogmewLLAW8DDqAMM+3aUey7Ja1t+zIASWvR3Vj4x4AX2J4xQytjljOPkw2KrTmcUvtqh/r8lZSh3Zf01qIGGWKKCfWxJl7StpRflMspd/CrA7vZPqmD2Kf3cbRpnyTdSXmjOpayzHmBWequNijONJLOt73Rwq71LT2IGEfSkyjrtDtfE2/7REnrUCaJAS5pu5JoHVoCmCvpGOBbLLiy5Bttxu/ZKpTjL19O2Qx5DPB12zf32qrp7x+StrJ9GszbOPePnts0TnoQMU4fa+IlbQpcNTjiU9KrgJcCVwL7tXwexBGTvGzbr2kr9iiRtCqwI7AX8B7bOZO6JbXM+pGUo10F3Ai82nYn821TlR5ENOphTfwhlBU0SHoK8BHgrcBGwKGUhNUK27vVuFvaPn34tXpnN+3VQ6F2ohz1+kMW3Ekfi5nt84EN68FY2L615yY1SoKIJn2siZ811Et4OXCo7eOB4yWd33LsgU9Tzr5Y2LVpQ9L+wPMo/79fA/ax3VXdrRlH0i62vzzmzBUGN2O2P9FLwyaQBBFN3kBZE/9wyhLXE4A3txxzlqQl65vTtsDuQ6+1+nNa51y2AOaM+cVdEZjVZuwR8D7gD8CG9eND9c1qJI/AnAaWr3821fgaufH+JIgYp6c18UcDp0j6K2Wy7ucAkh4JtL02fGnKhPySLPiLeystDm2NiD5Lm8w4tg+pD396XxjOzCR1zCPpBNvPrI/3sf3hjuNvTllVc4Ltv9dr61LOZGi9Ro2k1UftTOCYnvo8c2VRpAcRw+YMPX4Z0GmCsH1mw7XfdRg/ySFadV8bzkyCiGHpTka06z41nJkhpphH0s3AqZQJyifXx/PY3q6PdkX7JO1p+6CFXYvFYzCcKWkFANt/67tNTZIgYh5JT53sddundNWWPtT5joOBh9heX9IGwHa2/6vnprVugjHx8wYbJWPxkrQ+cBTwwHrpr8Cuti/qr1XjJUFEVJJOAd5NOX9iRpyqJmkn4BXAVtSVY9Vs4B7b2/bSsGlO0hnAewc1xiRtDXzI9ha9NmyMzEHEPJK+S9m1/KOxB/TUqqqvBq6wfXgPzevCcrbPHrODfLpvGjsDuBZYGThw6PptwAW9tGhmWH64AKXtk+sRtyMlCSKGvZ5Sh+dTkm4ErqccOboGcBnwGdvf7q95rfurpLWpk/WStqe8eU5bdeXWlcCT+m7LDHO5pPdThpkAdqFUMB4pGWKKRpLWoOxJ+Afwu67Lfveh9pIOpSxDvImyw3gX21f02a42SfoDJSFeb/uJC/v8WDwkrQR8kDK0B2V4bz/bN/XXqvGSICLGqF39JWzf1ndbIvqUIaaIStKHgI8NzkKod3nvtP2+flvWDUmzgIcw9L5g+4/9tWj6kfSdyV4ftaXk6UFEVE3LOkex/EEbJL2VckbyX4B76uUU61vMJF0PXEWpPXYW40/wG6ml5OlBRCNJSwPr1qeXjF3VNE3NkrTM4AQ7SfcDlum5TV3ZE1jP9g19N2SaeyjlzI3B8uLvA0fb/nWvrZpAEkSMU9dkHwlcQbnDeYSkXW2fOtnXTQNfAU4cOmFuN8q/w0xwFe1XzZ3xbN8N/Aj4kaRlKIniZEkftP2Zfls3XoaYYhxJ5wCvsH1Jfb4u5S7nCf22rH2SnkM5jwLgJ7Z/3Gd7uiLpMMo54N9nwfO4R+oAm+mgJobnUZLDGsB3gMNtX9Nnu5qkBxFNlhokBygVVevJctOe7R9Sjtycaf5YP5auH9ECSV8C1gd+AHxw1EprjJUeRIwj6XDKROWX66WdKUeCvqa/VrVP0kuAjwIPpgytDU5VW7HXhsW0Ieke4O/16fCb70j+rCVBxDi1C/xmFtzE81nbd/bXqvZJuhR4ge22z98eGZI+ZfvttczKuDeDUVt2Gd1KgohxZmrpZ0mn2x65Yx/bJOkJts+ZqJLvqC27jG4lQcQ4M7X0s6SDKMsQv8WCE7Xf6K1RPZH0CGBH2x/vuy3Rn0xSxzxDpZ/XHLPjczZwYz+t6tSKwO3AM4euGZgRCULSHMpRszsBDwO+2W+Lom9JEDFsRpd+tr1b323omqTZwEsoNwbrUpLhmrZX7bVhMRIyxBRRSVoWeC3wWEqZcwCm8+otSf8AzgbeB5xm25Iut71Wz02bliTdRvPZ7yO5immJvhsQo0PSafXP2yTdOvRxm6Rb+25fB46izEE8CzgFWJXSe5rO9qGUE/kcsE89DyNaYnu27RUbPmaPWnKA9CAi5hlMxEu6wPYGdXPgz21v3nfb2lbPwtiRMv+wDqVw3zdt/67Xhk1zkh7Mgr3Vkaqemx5EjCNp8zo2PXg+W9JMOExmUJDw5nqo/P0pm+amPduX2/6Q7ccBm1Am7H/Qc7OmLUnbSfo95VCqUyh1z0ZuB396EDGOpPOAjV1/OCQtAcyd7mWvJQXbC0QAACAASURBVL0OOB7YADgCWAH4gO3P99qwmHYk/QrYBvhp7bU+jXJ64Wt7btoCsoopmshDdw6275E07X9WbH+xPjwFyCRttOlftm+QtISkJWyfJOlTfTdqrGn/Sx//lsslvQ04uD5/EyN4oPriVkuMvJRSYXP4VLX9+2pTTFs3S1oBOBX4iqTrmF+jaWRkDiKavAHYArgGuBp4IrB7ry3qxreBFwJ3UX5ZBx/TnqQ9p3ItFpsXAv8A3kE5H+Iy4AW9tqhB5iAiKkkX2V6/73b0YaaWV4nJZYgpxpmJG8aqMyQ9zvaFfTekKymv0o/7Smn5JIhochTwW8qGsf0p50FM2xLYki6k7G5dEthN0uWUYn2DX9oN+mxfy2Z0eZUefYz7QGn5DDHFODNtw5ik1Sd73faVXbUlZob7Smn59CCiydgNY39mem8YezCwcj1udJ56PvV1wLRNEPe12kDTyFxJxzDipeWTIKLJoZJWAt5POVB9hfp4uvoo0FTJ9WLKhrltum1Od2zPXvhnRQvuE6XlM8QUM56kX9redILXLpjmcxARE0oPIsaR9CBgP2BLyl3Nz4EDbN/QZ7tatNIkry3XWSti2pP0H7Y/JunTNJ8B/rYemjWhJIho8jXKDs+X1uc7A8cAT++tRe36qaT/Bt43VH9KwAeBn/XasphuBquW5vbaiinKEFOM07RhTNKFtdLntCNpeeCLwGbA+fXyhpRf4tfZ/ltfbYvoU3oQ0eQESTsCx9bn2wM/7rE9rbL9d2CneibCY+vlX9ue9vWnoh+Svsv4IaZbKDclh9j+Z/etGi89iBinLn1cHribstxxCebXJMrSx4h7SdJBwBzg6Hrp5cCtlKSxou1X9tW2YUkQEREda1o5N7gm6de2HzvR13Yp1VxjHElb1nF5JO0i6ROSVuu7XRHTyArDv1P18Qr16Z39NGm8JIhocjBwu6QNgXdSShEf1W+TIqaVdwKnSTpJ0smUpeTvqjdmR/basiEZYopxBqWfJX0AuMb2YU3loKc7SYMliZ+1/ZleGxPTTj2g6lH16SWjMjE9LKuYosltkvYBXgk8uZ5JvVTPbeqc7UfXTYPTskhhdE/SNrZ/Vst9D1tbUmoxxX3CyylnBLzG9p/r+OjHe25TJyQ9BBhMHp5t+zrg+z02KaaXp1I2XzadHpdaTHHfUEtgr2P7p5KWA2bZvq3vdrVJ0v+3d+dhdlV1use/bwIYpjCjgMgQCQrKPIPYgGArSNuIcgUEBblOzeB4G9tusLVbpb1iq40KeGkF7SsIyCTIYABlEEiYR5VJBGSGyJCE5O0/9i7qpM6uQTvsderU+3me89TZa1fFF59UfmcNe633UBXCS6mW974J+LTtn5TMFf2l7pHvbfvUUb+5sBSI6CLpEKozqFe0PU3SesB3bO9SONrLStKNwK51rwFJqwAX2964bLLoN5Kus71F6RyjySqmaPIxqo36ngGw/Rv6+zyIAZMGikPtcfI7Ei+PiyV9StKaklYceJUONVTmIKLJHNtzq/3qQNJiNB8q028ukPRzFn669WcF80T/2qf++rGONgPrFsgyrAwxRRdJxwBPAQcAhwIfBW6z/Q9Fg7WgXl2yQ335S9tnlswTUVIKRHSpJ9EOpjrtSlQb9Z3oPv/LIukTwI9t/6F0luhfklal6jm8tDEk1bM2jwz/U2WkQESjeoIW24+WztIWSUcB7wGeoDr/4jTbfyybKvqJpO2BHwH/CcysmzcHDgT2s31FoWiNUiDiJfUhOUcBf8fg5Ox84Ju2/7lYsJZJ2ohqjPhdwAO2+/WgpGiZpKuBj9i+fkj7JlTbfG9dJlmzrNCITh+nWr20pe0Vba8IbA1sL+njZaO16hHgYapVTBNh9Va0Z+rQ4gBg+wZg2QJ5RpQCEZ3eB7zX9j0DDfWhOftTTVj3NUkfrTdOuwRYCTjE9kZlU0WfkaSuM9DrJa499+9xlrlGp8VtPza00fajkibCXkxrAkfUn+YiXg7HUp3Y+ClgVt22OfCV+l5PSYGITiPtQ98ze9S/XGwfCS+tMpnS0X5/sVDRV2wfL+lB4AtUq5gM3AZ80fY5RcM1yCR1vETSfAaPFl3oFjDFdl/3IiS9A/gasDrVPMRawO29crpXRNt6bswryrE92fbUhtey/V4cal+k2tr7LtvrALsAV5eNFP1E0udG2lJD0s6S9mgz00gyxBQxaJ7txyVNkjTJ9gxJXy8dKvrKzcA5kl6gmoN4lGo4cz1gE+Bi4F/LxVtYhpgiapIuBt4JfAlYmWqYaUvb2xUNFn2n3iF5e2A14HngduBy288XDTZECkRErT4P+AWqOZf9gOWAH9p+vGiwiEJSICIiolHmICJqkmYzuK35ElTncD9re2q5VBHlZBVTRK1erTW1LghLUu3FdFzhWNFnJE0eL1vXZIgpYgSSrre9aekc0V8kXWN7q9I5RpMhpohafVjQgEnAFlST1hGL2hWSvkW1rfxLD6fanjX8j7QvPYiImqSTOi5fBO4FTujFg1xifJM0o6HZtnduPcwIUiAiIqJRJqkjAEl/I+kKSU/Urwsl7VDfW650vugvkl4p6XuSzq+vN5B0cOlcQ6VAxIQn6SPAP9avtevXl4FjJO0DXF4sXPSr/6Q66331+vou4IhiaYaRIaaY8CTdDmxv+4kh7SsBDwAft/2dIuGiL0m61vaWnavkJN1ge5PS2TqlBxEBDC0OddvjwH0pDvEyeLb+AGIASdsAT5eN1C3LXCPgGUkb276xs1HSxvTgL230hU8AZwPTJF0BrALsXTZStwwxxYRXT0b/EDgJmFk3bwEcCOxv+1elskV/kiRgMrA+1eaQdwKTbM8pGmyIDDHFhFcXgK2ofh/eX78mAdukOMTL5Hu2X7R9q+1bqPb++lnpUEOlBxER0TJJXwBWsv1RSSsA51E9lHnSKD/aqhSIiIgCJB0DTAU2B75s+/TCkbqkQEREtGTIfl+ievbmGuACANtnlMg1nBSIiIiWDNnvayjbPqi1MGOQAhExDEn/SrXM9cQcOxqLiqTJwGG2jy2dZTRZxRQxvGuodnXt+V/kGD9szwfeWzrHWKQHEcH4+lQX45+kY6mOtM15EBHjwXg55SvGv5wHETHOjJdPdRFtSYGIqI2XT3XRHyTtDmwITBlos/3P5RJ1y2Z9ETXbO5XOEBODpO8ASwE7ASdSbdR3TdFQDbKKKaI2Xk75ir6wne0DgCdtfx7YFpheOFOXFIiIQf/JODjlK/rC8/XX5yStDswDViuYp1EKRMSglW2fCiwAsP0iML9spOhT50paHvg3YBZwL/CjookaZA4iYtC4OOUrxj/bX6jfni7pXGCK7Z77u5YCETFoXJzyFeNb/SFkX+B1ddPt9GDvAbLMNWIhkhaj45Qv2/MKR4o+Iun1wC+o5rqup/p7timwK7Cz7TsKxuuSAhFRkzQF+CiwA9Uw0y+B79h+oWiw6BuSfgKcWs91dba/C9jX9rvKJGuWAhFRk3QqMBs4pW7aF1je9rvLpYp+IulO2+v/ufdKyRxExKA32N6g43qGpNuKpYl+9OxfeK+IFIiIQbMkbWP7agBJWwPXFc4U/WVVSZ9oaBfVooiekgIRE56km6nmHBYHrpR0f329FtBTk4Yx7p0ALDvMvRPbDDIWmYOICU/SWiPdt31fW1kiekmepI4Jz/Z9Ay9gTarlhvdRjQnndyQmrPQgImqSjgK2ANa3Pb3eI+c029sXjhZRRD4dRQz6W2BP6tUkth9k+PHiiL6XAhExaK6rLvXAXkxLF84TfUrS4ZKmqvI9SbMk7VY611ApEBGDTpX0XWB5SYcAF1OtOolY1A6y/QywG7AC8D7gy2Ujdcsy14ia7a9K2hV4hmo/pn+yfVHhWNGfVH99O3Cy7VslaaQfKCGT1BERLZN0ErAGsA6wMTAZuNT25kWDDZECEROepF/Z3kHSbOr5h4FbgG1PLRQt+pSkScAmwN22n6q3AF/D9k2Foy0kcxAx4dneof66rO2pHa9lUxziZWJgA+Cw+nppYEq5OM1SICKGkLSUpC0krVw6S/St44BtgffW17OB/ygXp1kKREx4kvaUdG+91PDtwK3At4BbJB1YOF70p61tfwx4AcD2k8ASZSN1yyqmCPgC1XLD5YAZwEa275a0KnAJ8P2S4aIvzZM0mcFnblYBFpSN1C0FIgIW2L4LQNI9tu8GsP2IpBfLRos+9Q3gTKrtv/+F6uzzz5WN1C0FIgImSVqBash1Qf1+YE16hmFjkbP9Q0kzgV2o/q690/bthWN1yTLXmPAk3UvVvW96UMm21203UfQrSSuOdN/2E21lGYsUiIiIlki6h2reQcBrgCfr98sD99tep2C8Luk+R0S0xPY6dY/0YuAdtle2vRKwB3Bh2XTd0oOIiGiZpJttv3G0ttIySR0R0b4HJX0OOKW+3g94sGCeRulBRHSo16a/ko4PT7bvL5co+lE9WX0UsGPddDnw+UxSR/QoSYdS/dL+kcGHlmx7o3KpIspJgYioSfot1RYIj5fOEv2tfnL6M8CGdGzSZ3vnYqEaZBVTxKDfA0+XDhETwg+BO6jOg/g8cC9wbclATdKDiKhJ+h7VSXLnAXMG2m1/rVio6EuSZtreXNJNA0OYkq61vWXpbJ2yiili0P31awl6cGfN6Cvz6q8PSdqdagXTiE9Zl5AeREREyyTtAfwSWBP4JjCVahXT2UWDDZECEROepK/bPkLSOSx85CgAtvcsECuiuAwxRcDJ9devFk0RE0a9iukQYG0WfubmoFKZmqRAxIRne2b9dhPb/955T9LhwGXtp4o+dxbVENPFwPzCWYaVIaaImqRZtjcb0na97U1LZYr+JOkG25uUzjGa9CBiwpP0XmBfYB1JnZOEU4Ge2vog+sa5kt5u+2elg4wkPYiY8CStRfXA0peAv++4NRu4yXaOHY1FQtJsBs+DWJrqeZuBv1+2PbVUtiYpEBE1SUsDz9teIGk68DrgfNvzRvnRiL6UAhFRq88IfhOwAnAF1dYHc23vVzRY9I26t/qU7afr652Ad1JttfEftucWjNclezFFDJLt54C9gONsv5tqM7WIReVUqqElJG0CnEb19P4mwHEFczXKJHXEIEnalurwloPrtskF80T/WdL2wMFA+wP/z/b/lTQJuKFgrkbpQUQMOgI4EjjT9q2S1gVmFM4U/UUd73cGLgGwvaD528vKHEREREsk/TuwGvAQsCcw3fY8SasB59jeomjAIVIgImqSZtC8F1NPHeIS45ckAftQFYlTbf+hbt8UWNX2z0vmGyoFIqImafOOyynAu4AXbX+mUKSIolIgIkYg6RrbW5XOEVFCVjFF1CR1HtgyCdgcWK5QnIjiUiAiBs1kcBuEF4F7GFzuGrHISHoHcF6vrl4akCGmiIiWSToF2BY4nepZiDsKR2qUAhETnqS9Rrg9B/hdr/4Cx/glaSrwXuADVD3Xk4D/sj27aLAOKRAx4Uk6aYTbiwGvB660fVhLkWKCkLQS8D6qhzRvB14LfMP2N4sGq6VARIyi3gbhZtvZlykWCUl/A7yfqiD8APi+7UckLQXcZnvtgvFekknqiFHU23+/pXSO6Ct7Acfavryz0fZzknpmYUT2YooYA9sPlc4QfeXhocVB0lcAbF9SJlK3FIiIiPbt2tD2ttZTjCJDTBEjkPQq2w+XzhH9QdJHgI8C0yTd1HFrWapDqnpKJqkjRiDpPNu7l84R/aHelO9JGs4/t/1EmVTDS4GIiGiJpJm2N5d0ie1dSucZTYaYImqSTrb9vtHaIv4HJkn6LDBd0ieG3rT9tQKZhpVJ6ohBCz3nIGky1YZ9EYvK/wLmU304X7bh1VMyxBQTnqQjgc8CSwLPDTQDc4HjbR9ZKlv0J0lvs31+6RyjSYGIqEn6UopBvJwk7W/7FEmfpPn0wp4aYsocRETN9pGSVgDWozpRbqD98uF/KuLPsnT9dZmiKcYoPYiImqQPAocDrwZuALYBrsqZ1DFRZZI6YtDhwJbAfbZ3AjYFniobKfqJpAs73vf8cGYKRMSgF2y/ACDpFfUZEOsXzhT9ZZWO9+8ulmKMMgcRMegBScsDPwUukvQkcF/hTNFfxtWYfuYgIhpIejOwHHCB7bml80R/kPQUcDnVMuo31e9fYnvPErmGkwIR0UHSDsB6tk+StAqwjO17SueK/lB/8BiW7cvayjIWKRARNUlHAVsA69ueLml14DTb2xeOFlFE5iAiBv0t1cqlWQC2H5TUc9sfxPglaQbVPMQTtvcunWc0KRARg+batiQDSFp6tB+I+DO9n6pAzC+cY0xSICIGnSrpu8Dykg4BDgJOKJwp+sulVAXiUWDrslFGlzmImPDqZx7m1O93BXajWmXyc9sXFQ0XUVAKREx4kmbZ3ixnP0QsLENMEbCEpH2B7STtNfSm7TMKZIooLgUiAj4M7AcsD7xjyD0DKRAxIWWIKaIm6WDb3yudIyYOSVPp+KBu+4mCcbqkQMSEJ2kH278a4f5U4DW2b2kxVvQxSR8CPg+8wOD+TLa9brlU3VIgYsKTdCzVksMLgJlUSxCnAK8FdgLWAj5p+9piIaOvSPoNsK3tx0pnGUkKRAQgaUXgXcD2wGrA88DtwHkj9S4i/hKSLgD2sv3cqN9cUApERETLJG0KnAT8Gpgz0G77sGKhGmQVU0RE+74L/AK4GVhQOMuw0oOIiGiZpOttb1o6x2hSICIiWibpX4F7gXNYeIgpy1wjepGkpYBPUi1pPUTSelRnQ5xbOFr0GUlNh1BlmWtEr5L0Y6plrgfYfkNdMK60vUnhaBFFZJI6YtA02/tIei+A7eckqXSo6D+SFgc+AuxYN10KfNf2vGKhGqRARAyaK2lJ6idbJU2jY3w4YhH6NrA4cFx9/b667YPFEjVIgYgYdBTV09RrSvoh1UNz7y+aKPrVlrY37rj+haQbi6UZRgpERM32RZJmAdtQHRh0eK9vhRDj1nxJ02z/DkDSuvTgMaQpEBELmwI8SfW7sYEkbF9eOFP0n08DMyTdTfVhZC3gA2UjdUuBiKhJ+gqwD3Arg0+3GkiBiEVG0iSqvb7WA9avm+8cOPa2l2SZa0RN0p3ARr34ixr9Zbw8ST2pdICIHnI31cqSiJfbJZLe1evLqNODiKhJOh3YGLiEHt5hM8Y/SbOBpYEXqQ4NEtWT1FOLBhsicxARg86uXxEvK9vLls4wFulBRNQkDZwiB/Bb2y+UzBP9R9Lf2f5W/X5D27eWzjSSzEHEhCdpMUnHAA8A3wd+APxe0jH1lggRi8pBHe9PLpZijFIgIuDfgBWBdWxvbnszYBqwPPDVosmin/X0BDVkiCli4AD56R7yyyBpMnCH7fXKJIt+Uz8Y90mqD+fHUD0w9xLbZ5TINZwUiJjwJN1le/qfey/izyXppBFu2/ZBI9xvXVYxRcBtkg6w/YPORkn7A3cUyhR9yHbPbacxkvQgYsKTtAZwBtX2BzPr5i2AJYG/tf2HUtkiSkqBiKhJ2hnYsL68zfYlJfNElJYCERERjbLMNSKiIEnHl84wnBSIiIiytigdYDgpEBEdJK0l6S31+yUljYs9c2Jce6R0gOGkQETUJB0C/AT4bt30auCn5RLFRGD7r0tnGE4KRMSgjwHbA88A2P4NsGrRRBEFpUBEDJpje+7AhaTFqI4cjZiQUiAiBl0m6bPAkpJ2BU4DzimcKaKYPAcRUasPkz8Y2I1qp82fAycO3cQv4n+iPndkD+BNwOpUT/DfApzXa+dDpEBERLRE0uepisNlwHVUK5imANOBner3n7R9U7GQHVIgImqStgeOBtai2shy4JzgdUvmiv4haXfb541wf1XgNbavazHWsFIgImqS7gA+TrVh3/yBdtuPFwsVfac+Z+Qrtj9VOstost13xKCnbZ9fOkT0N9vzJe1QOsdYpAcRE56kzeq37wEmU239PWfgvu1ZJXJF/5L0bWANqpVyzw6050S5iB4jacYIt21759bCxIQwzMlyPXeiXApERE3SurbvHq0tYqLIg3IRg37S0HZa6ymi70maLukSSbfU1xtJ+lzpXENlkjomPEmvozpJbjlJe3Xcmkq1Lj1iUTsB+DT1xpC2b5L0I+CLRVMNkQIRAetTPby0PPCOjvbZwCFFEkW/W8r2NZI6214sFWY4KRAx4dk+CzhL0ra2ryqdJyaExyRNo94MUtLewENlI3XLJHVERMskrQscD2wHPAncA+xv+96SuYZKgYiIaJmkV9ieI2lpYJLt2ZJWtP1E6WydsoopIqJ9Z0hazPazdXF4FXBR6VBDZQ4ioiZpeeAAYG06fjdsH1YqU/StnwKn1XMPawJnAz23N1MKRMSgnwFXAzcDCwpniT5m+wRJS1AVirWBD9m+smyqbpmDiKhJmmV7s9G/M+IvI+kTnZdUPdabgOsBbH+tRK7hpAcRMehkSYcA57LwZn09NXEY49qyQ67PGKa9J6QHEVGT9DHgX4CnqNenkwODYhEbT+dBpEBE1CTdDWxl+7HSWaK/SbrK9ralc4wmQ0wRg34LPFc6REwIN0g6mx4/DyIFImLQs1S/uDNYeA4iy1xjUZsCPA50njViBuckekKGmCJqkg5sarf9/bazRPSCFIiIiJZJmgIcTLXN/EtbyvfaiXLZaiMmPEkzJP1CUtOBQREvh5OBVwFvBS4DXk21vXxPSQ8iJjxJa9Vv59t+oGiYmBAkXW97U0k32d5I0uLAL21vUzpbp0xSx4Rn+77SGWLCmVd/fUrSG4CHgVUL5mmUAhETnqTZDD4Yt9AtqgflprYcKfrf8ZJWAP6RaqO+Zer3PSVDTBER0Sg9iIiIFklaH/jfwOvqptuB423fVS5Vs6xiiohoiaRtgUuBP1EdOXoC1QOal0rqqQlqyBBTRERrJJ1PtVHfpUPa3wz8ve23FQk2jBSIiIiWSLrL9vRh7t1pe/22M40kQ0wREe0Z6WG4Z0e4V0QmqSMi2rOmpG80tAtYo+0wo0mBiIhoz6dHuHddaynGKHMQERHRKHMQERHRKAUiIiIapUBERESjFIiIiJZJOkbSVEmLS7pE0qOS9i+da6gUiIiI9u1m+xlgD+Be4LWMvMKpiBSIiIj2DTxisDtwmu2nS4YZTp6DiIho37mS7gCeBz4iaRXghcKZuuQ5iIiIAiStCDxte76kpYFlbT9cOlenDDFFRLRM0lLAR4Fv102rA1uUS9QsBSIion0nAXOB7errPwBfLBenWQpERET7ptk+BpgHYPs5qg37ekoKRERE++ZKWhIwgKRpwJyykbplFVNERPuOAi6g2v77h8D2wPuLJmqQVUwREQVIWgnYhmpo6WrbjxWO1CUFIiKiJZI2G+m+7VltZRmLFIiIiJZImlG/nUK1rPVGqh7ERsB1trctla1JJqkjIlpieyfbOwEPAZvZ3sL25sCmVEtde0oKRERE+9a3ffPAhe1bgNcXzNMoq5giItp3k6QTgVPq6/2AmwrmaZQ5iIiIlkmaAnwE2LFuuhz4tu2e2rAvBSIiIhpliCkiomWS1gO+BGxAtaIJANvrFgvVIJPUERHtO4lqJ9cXgZ2AHzA4H9EzMsQUEdEySTNtby7pZttv7Gwrna1ThpgiIto3R9Ik4DeS/o7qGYhlCmfqkh5ERETLJG0J3A4sD3wBWA44xvbVRYMNkQIRERGNMsQUEdEySdOBTwNr0fHvsO2di4VqkB5ERETLJN0IfAeYCcwfaLc9s1ioBikQEREt68UVS01SICIiWiJpxfrtYcAjwJl0HDVq+4kSuYaTAhER0RJJ91CdQ62G2+61J6lTICIiolG22oiIaImkLSW9quP6AElnSfpGx/BTz0iBiIhoz3eBuQCSdgS+TLUP09PA8QVzNcpzEBER7ZncMRG9D3C87dOB0yXdUDBXo/QgIiLaM1nSwAfzXYBfdNzruQ/sPRcoIqKP/RdwmaTHgOeBXwJIei3VMFNPySqmiIgWSdoGWA240Pazddt0YBnbs4qGGyIFIiIiGmUOIiIiGqVAREREoxSIiIiWSTpU0gqlc4wmBSIion2vBK6VdKqkv5bUtDdTcZmkjogooC4KuwEfALYATgW+Z/t3RYN1SA8iIqIAV5/OH65fLwIrAD+RdEzRYB3Sg4iIaJmkw4EDgMeAE4Gf2p4naRLwG9vTigas5UnqiIj2rQjsZfu+zkbbCyTtUShTlwwxRUS0b92hxUHSyQC2by8TqVsKRERE+zbsvJA0Gei5M6pTICIiWiLpSEmzgY0kPVO/ZlOdT31W4XhdMkkdEdESSdvbvkLSl23/fek8o0kPIiKiPd+ov+5WNMUYZRVTRER75kk6HlhD0jeG3rR9WIFMw0qBiIhozx7AW4C3AjMLZxlV5iAiIlomaWPbN5bOMZr0ICIiWiLpM7aPAT4oqevTeYaYIiImroGH4K4rmmKMMsQUEdESSYvZfrF0jrHKMteIiPZcM/BG0jdLBhmLFIiIiPZ0Hgy0fbEUY5QCERHRnnE1pp85iIiIlkh6DvgtVU9iWv2e+tq2NyqVrUlWMUVEtOf1pQP8OdKDiIhoiSR5lH90x/I9bckcREREe2ZIOlTSazobJS0haWdJ3wcOLJStS3oQEREtkTQFOAjYD1gHeAqYAkwGLgSOs319uYQLS4GIiChA0uLAysDztp8qnadJCkRERDTKHERERDRKgYiIiEYpEBERLZI0WdKM0jnGIgUiIqJFtucDCyQtVzrLaPIkdURE+/4E3CzpIuDZgcYcGBQREWfUr56WZa4REQVIWhJ4je07S2cZTuYgIiJaJukdwA3ABfX1JpLOLpuqWwpERET7jga2otpqA9s3AOuWDNQkBSIion3zbD89pG1BkSQjyCR1RET7bpW0LzBZ0nrAYcCVhTN1SQ8iIqJ9hwIbAnOAHwFPA0cUTdQgq5giIlomaTPbs0rnGE0KRERE3g4EmAAAChlJREFUy+qtNl4F/AT4se1bCkdqlAIREVGApFcB7wH2AaZSFYovlk21sBSIiIiCJL0R+Aywj+0lSufplEnqiIiWSXq9pKMl3QJ8k2oF06sLx+qSHkRERMskXQX8f+A02w+WzjOcFIiIiAKyF1NERHTJXkwRETGco+nei2mdkoGapEBERLSvaS+mnhvvz15MERHty15MERHRKHsxRUTEwiRNBr5i+1Ols4wmPYiIiBbZng/sUDrHWGQOIiKifdfXy1pPA54daLR9RrlI3VIgIiLaNwV4HNi5o81ATxWIzEFERESjzEFERLRM0qslnSnpkfp1uqSe26wvBSIion0nAWcDq9evc+q2npIhpoiIlkm6wfYmo7WVlh5ERET7Hpe0v6TJ9Wt/qknrnpIeREREyyStRXVQ0LZUq5euBA6zfX/RYEOkQERERKMMMUVEtETShR3vjyyZZSxSICIi2rNKx/t3F0sxRikQERHtGVdj+pmDiIhoiaSngMsBAW+q37/E9p4lcg0nBSIioiWS3jzSfduXtZVlLFIgIiKiUeYgIiKiUQpEREQ0SoGIiChE0lKlM4wkBSIiomWStpN0G3BHfb2xpOMKx+qSAhER0b5jgbdSb9Bn+0Zgx6KJGqRAREQUYPv3Q5rmFwkygpxJHRHRvt9L2g6wpMWBw4HbC2fqkucgIiJaJmll4N+Bt1A9VX0hcLjtnjoTIgUiIqJlklax/WjpHKPJHERERPuukHShpIMlLV86zHBSICIiWmZ7OvA5YENglqRz62NHe0qGmCIiCqrnI74G7Gd7cuk8ndKDiIhomaSpkg6UdD7VedQPAVsVjtUlPYiIiJZJugf4KXCq7atK5xlOCkRERMskyePgH988KBcR0RJJX7d9BHC2pK4C0WsnyqVARES05+T661eLphijDDFFRPQAST+2vU/pHJ2yiikiojdsWzrAUCkQERHRKHMQEREtkbTZcLeAxdvMMhaZg4iIaImkGSPdt71TW1nGIgUiIiIaZQ4iIiIapUBERESjFIiIiGiUVUwREQVIWgFYD5gy0Gb78nKJuqVARES0TNIHgcOBVwM3ANsAVwE7l8w1VIaYIiLadziwJXBfvbR1U+CpspG6pUBERLTvBdsvAEh6he07gPULZ+qSIaaIiPY9IGl5qkODLpL0JHBf4Uxd8qBcRERBkt4MLAdcYHtu6TydUiAiIgqQNBl4JR0jObbvL5eoW4aYIiJaJulQ4Cjgj8CCutnARsVCNUgPIiKiZZJ+C2xt+/HSWUaSVUwREe37PfB06RCjyRBTRET77gYulXQeMGeg0fbXykXqlgIREdG+++vXEvWrJ2UOIiIiGqUHERHRMknTgU8Ba7PwMtee2ospPYiIiJZJuhH4DjATmD/QbntmsVANUiAiIlomaabtzUvnGE0KREREyyQdDTwCnMnCq5ieKJWpSQpERETLJN3T0Gzb67YeZgQpEBER0SirmCIiCpD0BmADFj5y9AflEnVLDyIiomWSjgL+iqpA/Ax4G/Ar23uXzDVU9mKKiGjf3sAuwMO2PwBsTHUmRE9JgYiIaN/zthcAL0qaSrWiac3CmbpkDiIion3X1UeOnkD1sNyfgKvKRuqWOYiIiIIkrQ1MtX1T4ShdUiAiIloi6Szgivp1ba+dQT1UCkREREsk7QFsV782Bm4HrqQqGFfa/mPBeF1SICIiCpA0GdiUarnrh4F1bE8uGmqITFJHRLRI0soM9iK2oXpQ7mIySR0RMXFJ+g3VWdSnA1dTzUP8qWyq4aVARES0RNKRVL2GNYC7qHoNVwHX254/0s+WkAIREVFAfarcdsC2wA7AY7bfXDbVwvIkdUREyyStC2wFbE3Vo1gVmF00VIP0ICIiWiLpTKqi8AzV8tYrgSts31402DBSICIiWiJpT6rnHR4rnWUsUiAiIqJR5iAiIqJRCkRERDRKgYiIaJmkS8bSVlq22oiIaImkKcBSwMqSVgBU35pK9fBcT0mBiIhoz4eAI4DVqQ4KGigQzwDfKhVqOFnFFBHRMkmH2v5m6RyjSYGIiGiJpH2As20/XzrLWGSSOiKiPbsD50l6ZekgY5EeRERENMokdUREAZLWANai499h25eXS9QtBSIiomWSvgLsA9wGDJwDYaCnCkSGmCIiWibpTmAj23NKZxlJJqkjItp3N7B46RCjyRBTRET7ngNuqLfXeKkXYfuwcpG6pUBERLTv7PrV0zIHERFRgKQlgOn15Z2255XM0yQFIiKiZZL+Cvg+cC/VfkxrAgf22jLXFIiIiJZJmgnsa/vO+no68F+2Ny+bbGFZxRQR0b7FB4oDgO276MFVTZmkjoho33WSTgROqa/3A64rmKdRhpgiIlom6RXAx4Ad6qZfAsf12oNzKRAREQVIWgXA9qOlswwncxARES1R5WhJjwF3AndKelTSP5XO1iQFIiKiPR8Htge2tL2i7RWBrYHtJX28bLRuGWKKiGiJpOuBXW0/NqR9FeBC25uWSdYsPYiIiPYsPrQ4wEvzED23zDUFIiKiPXP/wntFZIgpIqIlkuYDzzbdAqbY7qleRApEREQ0yhBTREQ0SoGIiIhGKRAREdEoBSIiIhplN9eIiJZImg00rQwSYNtTW440oqxiioiIRhliioiIRikQERHRKAUiIiIapUDEhCVpOUmX1q+nJF1Vv9/7L/zzdpR0haTLJF0t6U2LOnNEmzJJHQFIuhTY3/YD/4M/4zpgT9sPSloMWNr204sqY0Tb0oOIGELSOyX9uu5RfLZue4ukSySdKekGSXs1/OhzwC6SlrH94kBxkPTh+s/7taT3120flHRG/efdImm7un2X+s8/W9LpkvaXtIykn3f0dqa19H9FTHB5DiKiQ/3J/9+ALYDZwCWSzqpvrwy8FVgGuEbSmV64C34A8A/A0ZJ+BxwMzAM+RHVqmIDrJJ1df/8C23tL2hE4DLgS+CqwO/AgcHH9fRsAf7R9QJ0xH+yiFfmLFrGwVwJ/sP207QXAr4H163uz6p7BU8ATwIqdP2j7XtuH2J4G/Bg4GpgG3Gh7ru05wK3A2vWPzKy/3g+sVL9f2vYf6sJzbd12LXCzpFMkfR1YdtH+J0c0S4GIWNgfgTXqCexJVJ/876zvbSJpsqTlqIrDE50/KOl1HZcPUf1+3V3/3OKSXgFsCNxbf09n70P11+ckrSZJVL0YgFcAX7W9P/AUsO8i+O+MGFWGmCI62H5R0v8BLgIWAOfavlXSasDDwOlUPYDPunuFxxGSNqWai5gPfMj2Q5JOAK6ov+dY209U//43+jRwPvBA/efMBd4IHCtpHlXROWDR/NdGjCyrmCLGQNJbgL1tf/hl/t9Z3Pa8ugdxMfBJ2ze8nP+bEcPJEFNEb3m7pMuo5h2uT3GIktKDiIiIRulBREREoxSIiIholAIRERGNUiAiIqJRCkRERDRKgYiIiEb/DeQTbHjY9HTOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df['name'].head(10), df['tempo'].head(10))\n",
    "plt.xlabel('Top Songs', fontsize=9, horizontalalignment='center')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('tempo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the relationship of the musical attribute tempo with the Top 10 songs on the Spotify charts. As shown in the graph, songs that chart on Spotify have high tempo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df['name'].head(10), df['loudness'].head(10))\n",
    "plt.xlabel('Top Songs', fontsize=9, horizontalalignment='center')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('loudness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows that relationship of the musical attribute loudness with the Top 10 songs on the Spotify charts. As shown in the graph, songs that chart on Spotify have high levels of loudness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have permission to use Spotify Top 200 Chart data because it is accessible to the public. We didn’t violate the terms of use in collecting the data because the terms of use are associated with how Spotify collects their information. The potential biases to our data is that it involves music and artists that are being streamed and/or associated with Spotify. Our data is only extracted from users who stream on Spotify as well. Therefore, this would exclude artists that promote exclusively on other platforms such as Tidal,  Apple Music, or SoundCloud such as Jay Z. We chose to use these constraints because our dataset would be too big and therefore, unfeasible to manipulate for our project.\n",
    "<br><br>\n",
    "Additionally, since we constrained our data to only contain US streaming counts, we will only understand the relationship between musical factors in a song and it’s amount of streams in the US. This prevents us from examining what factors make a song popular worldwide. \n",
    "<br><br>\n",
    "We also don’t need to encrypt our data due to the fact that there is no personal information such as (email, users’ names, etc.) tied to the Spotify Chart data. The data displays how often a song is streamed and doesn’t include information that would reveal which users’ streamed that particular song. The only information the chart data reveals is the song title, artist’s name, and the number of streams per song. If an artist doesn’t want their name to be associated with our dataset, we can remove all of the songs for that particular artist so that there is no reference to our name dataset.\n",
    "<br><br>\n",
    "After this project concludes, we will upload it to GitHub. There is no way to guarantee that the project can be completely removed and unavailable to the public once it has been posted online. Although there is no personal information attached to the Spotify Chart data, if an artist decides that they no longer want their song and name information associated with our dataset, we can only remove the references for future copies of the project.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the data set we analyzed through graph analysis and linear regression, our results confirms two of the three attributes in our hypthesis. As stated above, we hypothesize that songs with high valence, high speechiness, and high danceability songs will place in the top 10 charts of Spotify. This is true for the songs with high valence and high danceability; however, songs that place in the top 10 charts of Spotify have low speechiness. As a result, songs that do not contain a lot of lyrics tend to chart as long as their measure of valence and danceability remain high. Therefore, we conclude that songs that place in the top 10 charts have attributes that primarily express happy emotions and are danceable, however, our data set has its limitations.\n",
    "<br><br>\n",
    "One of the limitations of our project is the possible lack of variability in song attributes of popular songs.  Since our data only includes the top 100 songs in the U.S. Spotify charts, the traits that make a song popular may not significantly differ across songs.  This is why including an outside factor-- such as social media activity-- can potentially narrow down what defining features influence whether or not songs gain popularity. Additionally, “one-hit wonders” act as outliers in the dataset and demonstrate a high potential of confounding variables notably affecting the ranking of a song on Spotify charts. Another key factor that could influence streaming charts could be the ongoing current events, such as the passing of an artist which could cause a song to be streamed more to celebrate the artists' work and legacy.\n",
    "<br><br>\n",
    "The key impact of this work on society is providing music producers and artists with a clearer idea of what technical characteristics of a song are linked to a greater likelihood of song popularity. Along with artists understanding what it would take to create a charting song, this project also benefits producers and others in the music industry that create songs. By breaking down the songs to their individual attributes, it is easy to determine a “recipe” that creates a hit, which producers can use for future projects. This project will also help Spotify users understand the general preferences of other users across all demographics within the United States.\n"
   ]
  }
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