notebooks/Confident Data Chapter 08.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "<img src=\"../images/CONFIDENTDATASCIENCE.png\" alt=\"An image that reads Confident Data Science: The Essential Skills of Data Science, Companion Jupyter Notebook + Coding Examples. Also shows book image.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Chapter 8 *One Weekend Crash Course*\n",
    "\n",
    "To give citation to these coding examples give reference to an appropriate chapter and page of:\n",
    "\n",
    "*Confident Data Science: Discover the essential skills of data science* <br>\n",
    "(Confident Series, 15) 1st Edition by Adam Ross Nelson (Author) <br>\n",
    "Publisher : Kogan Page (September 26, 2023).\n",
    "\n",
    "## What Is Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
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    {
     "name": "stderr",
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     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/adamrossnelson/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word 1</th>\n",
       "      <th>Sentiment 1</th>\n",
       "      <th>Word 2</th>\n",
       "      <th>Sentiment 2</th>\n",
       "      <th>Word 3</th>\n",
       "      <th>Sentiment 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adoringly</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Intelligent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Rebellious</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brave</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Joyful</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Strong</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Creative</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Kind</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Trick</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daring</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Lost</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>Trustworthy</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doomed</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>Loyal</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Unkind</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Energetic</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Magnificent</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Verdict</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Friendly</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Noble</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Wreck</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Generous</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Worry</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Grin</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Playful</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Youthful</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Honest</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Questionable</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>Zealot</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word 1  Sentiment 1        Word 2  Sentiment 2       Word 3  Sentiment 3\n",
       "0  Adoringly          2.4   Intelligent          2.0   Rebellious         -1.2\n",
       "1      Brave          2.4        Joyful          2.9       Strong          2.3\n",
       "2   Creative          1.9          Kind          2.4        Trick         -0.2\n",
       "3     Daring          1.5          Lost         -1.3  Trustworthy          2.6\n",
       "4     Doomed         -3.2         Loyal          2.1       Unkind         -1.6\n",
       "5  Energetic          1.9   Magnificent          2.9      Verdict          0.6\n",
       "6   Friendly          2.2         Noble          2.0        Wreck         -1.9\n",
       "7   Generous          2.3    Optimistic          1.3        Worry         -1.9\n",
       "8       Grin          2.1       Playful          1.9     Youthful          1.3\n",
       "9     Honest          2.3  Questionable         -1.2       Zealot         -1.9"
      ]
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   "source": [
    "# Import the Pandas and the NLTK libraries\n",
    "import pandas as pd\n",
    "import nltk\n",
    "\n",
    "# Necessary for the first run only (download the dictionary)\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "# Import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "# Load VADER lexicon\n",
    "vader_lexicon = SentimentIntensityAnalyzer().lexicon\n",
    "\n",
    "# Specify a list of adjectives\n",
    "adjectives = [\n",
    "    'Adoringly', 'Brave', 'Creative', 'Daring', \n",
    "    'Doomed', 'Energetic', 'Friendly', \n",
    "    'Generous', 'Grin', 'Honest', 'Intelligent', \n",
    "    'Joyful', 'Kind', 'Lost', 'Loyal', \n",
    "    'Magnificent', 'Noble', 'Optimistic', \n",
    "    'Playful', 'Questionable', 'Rebellious', \n",
    "    'Strong', 'Trick', 'Trustworthy', 'Unkind', \n",
    "    'Verdict', 'Wreck', 'Worry', \n",
    "    'Youthful', 'Zealot']\n",
    "\n",
    "# Declare empty lists to be populated in the for loop\n",
    "word_col = []\n",
    "sent_col = []\n",
    "\n",
    "# Use for loop to populate list\n",
    "for word in adjectives:\n",
    "    word_col.append(word)\n",
    "    sent_col.append(vader_lexicon[word.lower()])\n",
    "\n",
    "# Convert lists to Pandas DataFrame for display\n",
    "pd.DataFrame({'Word 1':word_col[:10], \n",
    "              'Sentiment 1':sent_col[:10],\n",
    "              'Word 2':word_col[10:20], \n",
    "              'Sentiment 2':sent_col[10:20],\n",
    "              'Word 3':word_col[20:], \n",
    "              'Sentiment 3':sent_col[20:]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Words</th>\n",
       "      <th>Sents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>ilu</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>ily</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>aml</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4541</th>\n",
       "      <td>magnificently</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6539</th>\n",
       "      <td>sweetheart</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633</th>\n",
       "      <td>euphoria</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>lya</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>ecstacy</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>cheerfullest</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>best</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Words  Sents\n",
       "267             ilu    3.4\n",
       "269             ily    3.4\n",
       "205             aml    3.4\n",
       "4541  magnificently    3.4\n",
       "6539     sweetheart    3.3\n",
       "2633       euphoria    3.3\n",
       "305             lya    3.3\n",
       "2470        ecstacy    3.3\n",
       "1366   cheerfullest    3.2\n",
       "1054           best    3.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the most positive lexicon words\n",
    "pd.DataFrame(\n",
    "    {'Words':vader_lexicon.keys(),\n",
    "     'Sents':vader_lexicon.values()}).sort_values(\n",
    "    'Sents', ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Pandas NumPy and Seaborn libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set environment variable to cope with Release 4.21.0 of protobuf\n",
    "# https://github.com/protocolbuffers/protobuf/issues/10051\n",
    "import os\n",
    "os.environ[\"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION\"] = \"python\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Google Cloud's NLP API\n",
    "\n",
    "```Python\n",
    "# To install the Google Cloud language API\n",
    "!pip install google-cloud-language\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additionally import Google cloud libraries\n",
    "from google.cloud import language_v1\n",
    "from google.protobuf.json_format import MessageToDict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A One Sentence Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify a services.json location\n",
    "sl = '../../../services/services.json'\n",
    "\n",
    "# Check if the services file exists\n",
    "import os\n",
    "if not os.path.isfile(sl):\n",
    "    raise FileNotFoundError(f\"\"\"NoFileError - {sl} does not exist.\n",
    "        Read more at: \n",
    "        https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account\"\"\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a google Cloud client\n",
    "client = language_v1.LanguageServiceClient.from_service_account_json(sl)\n",
    "\n",
    "# Save text to a variable\n",
    "my_text = 'I am happy to announce a promotion at work!'\n",
    "\n",
    "# Create a Google Cloud document from the text\n",
    "document = language_v1.Document(\n",
    "    content=my_text, \n",
    "    type_=language_v1.Document.Type.PLAIN_TEXT)\n",
    "\n",
    "# Send the document to Google's API\n",
    "a_sentiment = client.analyze_sentiment(\n",
    "    document=document)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of the document is <class 'google.cloud.language_v1.types.language_service.Document'>\n",
      "The type of the a_sentiment is <class 'google.cloud.language_v1.types.language_service.AnalyzeSentimentResponse'>\n"
     ]
    }
   ],
   "source": [
    "# Further inspect above code with the type() function\n",
    "print(f'The type of the document is {type(document)}')\n",
    "\n",
    "print(\n",
    "    f'The type of the a_sentiment is {type(a_sentiment)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'documentSentiment': {'magnitude': 0.9, 'score': 0.9},\n",
       " 'language': 'en',\n",
       " 'sentences': [{'text': {'content': 'I am happy to announce a promotion at work!',\n",
       "    'beginOffset': -1},\n",
       "   'sentiment': {'magnitude': 0.9, 'score': 0.9}}]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save and then display the results of the API call\n",
    "result_dict = MessageToDict(\n",
    "    a_sentiment.__class__.pb(a_sentiment))\n",
    "\n",
    "# Evaluate the results for display\n",
    "result_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doc Sentiment : 0.9\n",
      "Doc Magnitude : 0.9\n"
     ]
    }
   ],
   "source": [
    "# Code to print the document's overall sentiment score.\n",
    "print(\n",
    "    'Doc Sentiment : {}'.format(\n",
    "        result_dict['documentSentiment']['score']))\n",
    "\n",
    "# Code to print the document's sentiment magnitude score.\n",
    "print(\n",
    "    'Doc Magnitude : {}'.format(\n",
    "        result_dict['documentSentiment']['magnitude']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentence-Level Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Name/define the function google_api_sentence_scores()\n",
    "def google_api_sentence_scores(series: pd.Series) -> pd.DataFrame:\n",
    "    \"\"\"Returns sentiment scores of all sentences of \n",
    "    a given Pandas Series that contains text using \n",
    "    the Google Cloud API.\n",
    "\n",
    "    Takes the text data contained within a provided \n",
    "    Pandas Series and returns a DataFrame reporting \n",
    "    sentiment scores for each sentence in the \n",
    "    original Series. The DataFrame will have an \n",
    "    index that matches the Series index.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    series : pd.Series\n",
    "        Pandas Series containing text data to be \n",
    "        analyzed by the Google Cloud API.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    scores_df : pd.DataFrame\n",
    "        DataFrame reporting each sentence's \n",
    "        resulting sentiment scores.\n",
    "    \"\"\"\n",
    "\n",
    "    # Instantiate Google Cloud API's client\n",
    "    client = language_v1.LanguageServiceClient.from_service_account_json(sl)\n",
    "\n",
    "    # Define lists to contain sentence sentiments.\n",
    "    index = []\n",
    "    sentences = []\n",
    "    sentiments = []\n",
    "    magnitudes = []\n",
    "\n",
    "    # Loop through each Series item; have Google \n",
    "    # Cloud API analyze sentiment scores\n",
    "    for i in series.index:\n",
    "    \n",
    "        # Take the next item in the Pandas Series\n",
    "        my_text = series.loc[i]\n",
    "        # Create a Google Cloud document from the text\n",
    "        document = language_v1.Document(\n",
    "            content=my_text, \n",
    "            type_=language_v1.Document.Type.PLAIN_TEXT)\n",
    "        # Send the document to Google's API\n",
    "        a_sentiment = client.analyze_sentiment(\n",
    "            document=document)\n",
    "        # Save API results as a Python dictionary\n",
    "        a_sentiment_dict = MessageToDict(\n",
    "            a_sentiment.__class__.pb(a_sentiment))\n",
    "\n",
    "        # Extract each sentence score; append lists\n",
    "        try:\n",
    "            # Iterate through each sentence\n",
    "            for s in a_sentiment_dict['sentences']:\n",
    "\n",
    "                    # Append the sentiments list\n",
    "                    sentiments.append(\n",
    "                        s['sentiment']['score'])\n",
    "                    # Append the magnitude list\n",
    "                    magnitudes.append(\n",
    "                        s['sentiment']['magnitude'])\n",
    "                    # Append the original sentence\n",
    "                    sentences.append(\n",
    "                        s['text']['content'])\n",
    "                    # Append the index list\n",
    "                    index.append(i)\n",
    "        \n",
    "        # When there is an error, skip that entry.\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    # Compile results of entire analysis in df\n",
    "    scores_df = pd.DataFrame(\n",
    "        {'index':index, \n",
    "         'Sentence':sentences,\n",
    "         'Sentiment':sentiments,\n",
    "         'Magnitude':magnitudes}).set_index('index')\n",
    "\n",
    "    # Return the resulting DataFrame\n",
    "    return scores_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was not a fan of The Hobbit.</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This is not a fav.</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wha were you thinking?</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>That is a bad idea.</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Sentence  Sentiment  Magnitude\n",
       "index                                                      \n",
       "0      I was not a fan of The Hobbit.       -0.8        0.8\n",
       "0                  This is not a fav.       -0.8        0.8\n",
       "1              Wha were you thinking?       -0.3        0.3\n",
       "1                 That is a bad idea.       -0.9        0.9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store sentiment analysis results in new df\n",
    "txt = ['I was not a fan of The Hobbit. This is not a fav.',\n",
    "       'Wha were you thinking? That is a bad idea.']\n",
    "\n",
    "# Put results of sentiment analysis in new df\n",
    "df = google_api_sentence_scores(pd.Series(txt))\n",
    "\n",
    "# Display the result\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Document-Level Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wc/nb27fl9d0q3c1d_95gm_slx80000gn/T/ipykernel_8217/740127283.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df.groupby(by='index').mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sentiment  Magnitude\n",
       "index                      \n",
       "0           -0.8        0.8\n",
       "1           -0.6        0.6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate and group the scores by the index for document level results\n",
    "df.groupby(by='index').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Name/define the function google_api_document_scores()\n",
    "def google_api_document_scores(series: pd.Series) -> pd.DataFrame:\n",
    "    \"\"\"Returns sentiment scores of a given text using \n",
    "    the Google Cloud API.\n",
    "\n",
    "    Takes the text data contained within the provided \n",
    "    Pandas Series and returns a DataFrame reporting \n",
    "    sentiment scores for each item in the original \n",
    "    Series. The DataFrame will have an index that \n",
    "    matches the Series index.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    series : pd.Series\n",
    "        Pandas Series containing text data to be \n",
    "        analyzed by the Google Cloud API.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    doc_scores_df : pd.DataFrame\n",
    "        DataFrame reporting each document's resulting \n",
    "        sentiment scores.\n",
    "    \"\"\"\n",
    "\n",
    "    # Instantiate Google Cloud API's client\n",
    "    client = language_v1.LanguageServiceClient.from_service_account_json(sl)\n",
    "\n",
    "    # Define lists to contain sentence sentiments.\n",
    "    index = []\n",
    "    documents = []\n",
    "    doc_sentiment_scores = []\n",
    "    doc_magnitude_scores = []\n",
    "\n",
    "    # Loop through each Series item; have Google \n",
    "    # Cloud API analyze sentiment scores\n",
    "    for i in series.index:\n",
    "\n",
    "        # Take the next item in the Pandas Series\n",
    "        my_text = series.loc[i]  \n",
    "        # Create a Google Cloud document from the text\n",
    "        document = language_v1.Document(\n",
    "            content=my_text, \n",
    "            type_=language_v1.Document.Type.PLAIN_TEXT)\n",
    "        # Send the document to Google's API\n",
    "        a_doc_sentiment = client.analyze_sentiment(\n",
    "            request={'document':document}).document_sentiment\n",
    "\n",
    "        # Extract sentment & magnitude scores for each doc\n",
    "        doc_magnitude = a_doc_sentiment.magnitude\n",
    "        doc_score = a_doc_sentiment.score\n",
    "        \n",
    "        # Append original text scores to each list\n",
    "        documents.append(my_text)\n",
    "        # Append the sentiment scores\n",
    "        doc_sentiment_scores.append(doc_score)\n",
    "        # Append the magnitude scores\n",
    "        doc_magnitude_scores.append(doc_magnitude)\n",
    "        # Append the index list\n",
    "        index.append(i)\n",
    "\n",
    "    # Compile results entire analysis in df\n",
    "    doc_scores_df = pd.DataFrame(\n",
    "        {'index':index,\n",
    "         'Documents':documents,\n",
    "         'Sentiment':doc_sentiment_scores,\n",
    "         'Magnitude':doc_magnitude_scores}).set_index('index')\n",
    "\n",
    "    # Return the resulting DataFrame\n",
    "    return doc_scores_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Documents</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was not a fan of The Hobbit. This is not a fav.</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wha were you thinking? That is a bad idea.</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Documents  Sentiment  Magnitude\n",
       "index                                                                         \n",
       "0      I was not a fan of The Hobbit. This is not a fav.       -0.8        1.7\n",
       "1             Wha were you thinking? That is a bad idea.       -0.6        1.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store sentiment analysis results in new df\n",
    "txt = ['I was not a fan of The Hobbit. This is not a fav.',\n",
    "       'Wha were you thinking? That is a bad idea.']\n",
    "\n",
    "# Put results of sentiment analysis in new df\n",
    "df = google_api_document_scores(pd.Series(txt))\n",
    "\n",
    "# Display the result\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Natural Lanauge Tool Kit (NLTK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "# Import NLTK's sentence tokenizer\n",
    "from nltk.tokenize import sent_tokenize\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have not yet worked with the `nltk.sentiment.vader` and its `SentimentIntensityAnalyzer` you will also need to add to your computing environment the `vader_lexicon` with the following one-time code executions.\n",
    "\n",
    "```Python\n",
    "# Import the Natrual Language Toolkit Library\n",
    "import nltk\n",
    "\n",
    "# Necessary for the first run only (downloading the dictionary)\n",
    "nltk.download('vader_lexicon')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.0, 'neu': 0.6, 'pos': 0.4, 'compound': 0.6114}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the sentiment analyzer\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Execute sentiment analysis with the sentiment analyzer\n",
    "sid.polarity_scores(\n",
    "    'I am happy to announce a promotion at work!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.214, 'neu': 0.583, 'pos': 0.203, 'compound': 0.2751}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute sentiment analysis with the sentiment analyzer\n",
    "sid.polarity_scores('''I am happy to announce a promotion at work!\n",
    "                    This makes be very happy because I hated my \n",
    "                    former tasks. The past few years were tough and\n",
    "                    I was sad or depressed all the time1 I do not\n",
    "                    like to complain much becuase I am happy to\n",
    "                    have any job. I know what it is like to be\n",
    "                    unemployed and it is also not fun. Next year\n",
    "                    I anticiate growing and loving my new work!''')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.0, 'neu': 0.619, 'pos': 0.381, 'compound': 0.5719}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute sentiment analysis with the sentiment analyzer\n",
    "sid.polarity_scores(\n",
    "    'I am happy to announce a promotion at work.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentence-Level Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Name/define the function sentence_scores()\n",
    "def sentence_scores(series: pd.Series) -> pd.DataFrame:\n",
    "    '''Returns sentiment scores of a given text using \n",
    "    NLTK’s VADER sentiment analysis tools.\n",
    "\n",
    "    Takes a Pandas Series that contains text data. \n",
    "    Returns a Pandas DataFrame that reports sentiment \n",
    "    scores for each sentence in the original Series. The\n",
    "    DataFrame will have an index that matches the Series \n",
    "    index.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    Series : pd.Series\n",
    "        Pandas Series containing text data to be \n",
    "        analyzed using NLTK’s VADER sentiment analysis \n",
    "        tools.\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    doc_scores_df : pd.DataFrame\n",
    "        DataFrame reporting each document's resulting \n",
    "        sentiment scores.\n",
    "    '''\n",
    "\n",
    "    # Define empty lists to contain results.\n",
    "    index = []\n",
    "    sentences = []\n",
    "    neg_scores = []\n",
    "    neu_scores = []\n",
    "    pos_scores = []\n",
    "    compounds = []\n",
    "    \n",
    "    # Instantiate the VADER analyzer.\n",
    "    sid = SentimentIntensityAnalyzer()\n",
    "    \n",
    "    # Loop through each item in the Series.\n",
    "    for i in series.index:\n",
    "        \n",
    "        # Loop through each sentence in the Series item.\n",
    "        for s in sent_tokenize(series.loc[i]):\n",
    "\n",
    "            # Extract scores for each sentence; append to lists.\n",
    "            try:\n",
    "                \n",
    "                # Get sentiment scores from NLTK\n",
    "                scores = sid.polarity_scores(s)\n",
    "                # Append the index list\n",
    "                index.append(i)\n",
    "                # Append the original text\n",
    "                sentences.append(s)\n",
    "                # Append the negative scores list\n",
    "                neg_scores.append(scores['neg'])\n",
    "                # Append the neutral scores list\n",
    "                neu_scores.append(scores['neu'])\n",
    "                # Append the positive scores list\n",
    "                pos_scores.append(scores['pos'])\n",
    "                # Append the positive scores list\n",
    "                compounds.append(scores['compound'])\n",
    "\n",
    "            # When there is an error, skip that entry.\n",
    "            except:\n",
    "                pass\n",
    "    \n",
    "    # Compile & return Pandas DataFrame\n",
    "    return(pd.DataFrame({'index':index,\n",
    "                         'Sentence':sentences,\n",
    "                         'Negative':neg_scores,\n",
    "                         'Neutral':neu_scores,\n",
    "                         'Positive':pos_scores,\n",
    "                         'Compound':compounds}).set_index('index'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Compound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was not a fan of The Hobbit.</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This is not a fav.</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wha were you thinking?</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>That is a bad idea.</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Sentence  Negative  Neutral  Positive  Compound\n",
       "index                                                                       \n",
       "0      I was not a fan of The Hobbit.     0.282    0.718       0.0   -0.2411\n",
       "0                  This is not a fav.     0.453    0.547       0.0   -0.3570\n",
       "1              Wha were you thinking?     0.000    1.000       0.0    0.0000\n",
       "1                 That is a bad idea.     0.538    0.462       0.0   -0.5423"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store sentiment analysis results in new df\n",
    "txt = ['I was not a fan of The Hobbit. This is not a fav.',\n",
    "       'Wha were you thinking? That is a bad idea.']\n",
    "\n",
    "# Put results of sentiment analysis in new df\n",
    "df = sentence_scores(pd.Series(txt))\n",
    "\n",
    "# Display the result\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Level Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wc/nb27fl9d0q3c1d_95gm_slx80000gn/T/ipykernel_8217/3551377323.py:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df.groupby(by='index').mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Compound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3675</td>\n",
       "      <td>0.6325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.29905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2690</td>\n",
       "      <td>0.7310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.27115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Negative  Neutral  Positive  Compound\n",
       "index                                       \n",
       "0        0.3675   0.6325       0.0  -0.29905\n",
       "1        0.2690   0.7310       0.0  -0.27115"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate and group the scores by the index \n",
    "# for document level results\n",
    "df.groupby(by='index').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Name/define the function post_scores()\n",
    "def post_scores(series: pd.Series) -> pd.DataFrame:\n",
    "    '''Returns sentiment scores of a given text using \n",
    "    NLTK’s VADER sentiment analysis tools.\n",
    "\n",
    "    Takes a Pandas Series that contains text data. \n",
    "    Returns a Pandas DataFrame that reports sentiment \n",
    "    scores for item within the Pandas Seires. The \n",
    "    DataFrame will havean index that matches the \n",
    "    Series index.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    Series : pd.Series\n",
    "        Pandas Series containing text data to be \n",
    "        analyzed using NLTK’s VADER sentiment analysis \n",
    "        tools.\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    doc_scores_df : pd.DataFrame\n",
    "        DataFrame reporting each document's resulting \n",
    "        sentiment scores.\n",
    "    '''\n",
    "\n",
    "    # Define empty lists that will contain each result.\n",
    "    index = []\n",
    "    posts = []\n",
    "    neg_scores = []\n",
    "    neu_scores = []\n",
    "    pos_scores = []\n",
    "    compounds = []\n",
    "    \n",
    "    # Instantiate the analyzer.\n",
    "    sid = SentimentIntensityAnalyzer()\n",
    "    \n",
    "    # Loop through each item in the Series.\n",
    "    for i in series.index:\n",
    "\n",
    "        # Extract sentence scores; append lists.\n",
    "        try:\n",
    "            # Get sentiment scores from NLTK\n",
    "            scores = sid.polarity_scores(\n",
    "                series.loc[i])\n",
    "            # Append the index list\n",
    "            index.append(i)\n",
    "            # Append the original text\n",
    "            posts.append(\n",
    "                series.loc[i])\n",
    "            # Append the negative scores list\n",
    "            neg_scores.append(\n",
    "                scores['neg'])\n",
    "            # Append the neutral scores list\n",
    "            neu_scores.append(\n",
    "                scores['neu'])\n",
    "            # Append the positive scores list\n",
    "            pos_scores.append(\n",
    "                scores['pos'])\n",
    "            # Append the positive scores list\n",
    "            compounds.append(\n",
    "                scores['compound'])\n",
    "        \n",
    "        # When there is an error, skip that entry.\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    # Compile & return Pandas DataFrame\n",
    "    return(pd.DataFrame(\n",
    "        {'index':index,\n",
    "         'Sentence':posts,\n",
    "         'Negative':neg_scores,\n",
    "         'Neutral':neu_scores,\n",
    "         'Positive':pos_scores,\n",
    "         'Compound':compounds}).set_index('index'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>Negative</th>\n",
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       "      <th>Compound</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was not a fan of The Hobbit. This is not a fav.</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wha were you thinking? That is a bad idea.</td>\n",
       "      <td>0.333</td>\n",
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      ],
      "text/plain": [
       "                                                Sentence  Negative  Neutral  \\\n",
       "index                                                                         \n",
       "0      I was not a fan of The Hobbit. This is not a fav.     0.357    0.643   \n",
       "1             Wha were you thinking? That is a bad idea.     0.333    0.667   \n",
       "\n",
       "       Positive  Compound  \n",
       "index                      \n",
       "0           0.0   -0.5334  \n",
       "1           0.0   -0.5423  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store sentiment analysis results in new df\n",
    "txt = ['I was not a fan of The Hobbit. This is not a fav.',\n",
    "       'Wha were you thinking? That is a bad idea.']\n",
    "\n",
    "# Put results of sentiment analysis in new df\n",
    "df = post_scores(pd.Series(txt))\n",
    "\n",
    "# Display the result\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing The Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data to cross-compare between Google and NLTK\n",
    "text_data = pd.Series([\n",
    "    'I am happy to announce a promotion at work!',\n",
    "    'I am happy to announce a promotion at work.',\n",
    "    'I was not a fan of The Hobbit. This is not a fav.',\n",
    "    'What were you thinking? That is a bad idea.',\n",
    "    'My gift for you: @>-->--',\n",
    "    'The Confident Series is a must read you will love it.',\n",
    "    'Video games can sometimes destroy productivity.',\n",
    "    'VADER sentiment analysis is extremely effect.ve',\n",
    "    'Google also has a sentiment analysis API.',\n",
    "    'Nothing ever works out for me.',\n",
    "    'I never get a break.',\n",
    "    'We always mess things up.',\n",
    "    'Nothing good ever comes my way.',\n",
    "    'You are so unlucky.',\n",
    "    'No one understands me.',\n",
    "    'Everything is going wrong today.',\n",
    "    'It just is not fair!',\n",
    "    'I can accomplish anything!',\n",
    "    'Data Science is your destiny',\n",
    "    'Nothing can stop me if I put my mind to it.',\n",
    "    'I am grateful for everything that comes my way.',\n",
    "    'I am killing it in the Data Science classes right now.'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve sentiment score results from NLTK\n",
    "results_nltk = post_scores(text_data)\n",
    "\n",
    "# Retrieve sentiment score results from Google API\n",
    "results_google = google_api_document_scores(text_data)\n",
    "\n",
    "# Append '_nltk' to the columns in the NLTK results\n",
    "results_nltk = results_nltk.add_suffix('_nltk')\n",
    "\n",
    "# Append '_ggl' to the columns in the Google results\n",
    "results_google = results_google.add_suffix('_ggl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I am happy to announce a promotion at work.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I was not a fan of The Hobbit. This is not a fav.</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>What were you thinking? That is a bad idea.</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>My gift for you: @&gt;--&gt;--</td>\n",
       "      <td>0.7184</td>\n",
       "      <td>0.3</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Confident Series is a must read you will l...</td>\n",
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       "      <td>0.9</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Video games can sometimes destroy productivity.</td>\n",
       "      <td>-0.5423</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VADER sentiment analysis is extremely effect.ve</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Google also has a sentiment analysis API.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nothing ever works out for me.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I never get a break.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>We always mess things up.</td>\n",
       "      <td>-0.3612</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nothing good ever comes my way.</td>\n",
       "      <td>-0.3412</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>You are so unlucky.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>No one understands me.</td>\n",
       "      <td>-0.2960</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Everything is going wrong today.</td>\n",
       "      <td>-0.4767</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>It just is not fair!</td>\n",
       "      <td>-0.3080</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>I can accomplish anything!</td>\n",
       "      <td>0.4753</td>\n",
       "      <td>0.9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Data Science is your destiny</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Nothing can stop me if I put my mind to it.</td>\n",
       "      <td>0.2235</td>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>I am grateful for everything that comes my way.</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                                           Sentence_nltk  Compound_nltk  \\\n",
       "index                                                                     \n",
       "0            I am happy to announce a promotion at work!         0.6114   \n",
       "1            I am happy to announce a promotion at work.         0.5719   \n",
       "2      I was not a fan of The Hobbit. This is not a fav.        -0.5334   \n",
       "3            What were you thinking? That is a bad idea.        -0.5423   \n",
       "4                               My gift for you: @>-->--         0.7184   \n",
       "5      The Confident Series is a must read you will l...         0.8126   \n",
       "6        Video games can sometimes destroy productivity.        -0.5423   \n",
       "7        VADER sentiment analysis is extremely effect.ve         0.0000   \n",
       "8              Google also has a sentiment analysis API.         0.0000   \n",
       "9                         Nothing ever works out for me.         0.0000   \n",
       "10                                  I never get a break.         0.0000   \n",
       "11                             We always mess things up.        -0.3612   \n",
       "12                       Nothing good ever comes my way.        -0.3412   \n",
       "13                                   You are so unlucky.         0.0000   \n",
       "14                                No one understands me.        -0.2960   \n",
       "15                      Everything is going wrong today.        -0.4767   \n",
       "16                                  It just is not fair!        -0.3080   \n",
       "17                            I can accomplish anything!         0.4753   \n",
       "18                          Data Science is your destiny         0.0000   \n",
       "19           Nothing can stop me if I put my mind to it.         0.2235   \n",
       "20       I am grateful for everything that comes my way.         0.4588   \n",
       "21     I am killing it in the Data Science classes ri...        -0.6597   \n",
       "\n",
       "       Sentiment_ggl  Agree  \n",
       "index                        \n",
       "0                0.9   True  \n",
       "1                0.9   True  \n",
       "2               -0.8   True  \n",
       "3               -0.7   True  \n",
       "4                0.3   True  \n",
       "5                0.9   True  \n",
       "6               -0.7   True  \n",
       "7                0.0   True  \n",
       "8                0.2   True  \n",
       "9               -0.6  False  \n",
       "10               0.6   True  \n",
       "11              -0.4   True  \n",
       "12              -0.6   True  \n",
       "13              -0.9  False  \n",
       "14              -0.7   True  \n",
       "15              -0.9   True  \n",
       "16              -0.8   True  \n",
       "17               0.9   True  \n",
       "18               0.0   True  \n",
       "19               0.8   True  \n",
       "20               0.9   True  \n",
       "21              -0.2   True  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate results from both NLTK and Google\n",
    "combined = pd.concat([\n",
    "    results_nltk, \n",
    "    results_google[['Sentiment_ggl', \n",
    "                    'Magnitude_ggl']]],\n",
    "    axis=1)\n",
    "\n",
    "# Create a column that flags if NLTK and Google agreed\n",
    "combined['Agree'] = (combined['Compound_nltk'] >= 0) == \\\n",
    "                    (combined['Sentiment_ggl'] >= 0)\n",
    "    \n",
    "# Display the results\n",
    "# Focusing on NLTK's compund, and Google's Sentiment\n",
    "combined[['Sentence_nltk', 'Compound_nltk', \n",
    "          'Sentiment_ggl','Agree']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wc/nb27fl9d0q3c1d_95gm_slx80000gn/T/ipykernel_8217/4290721114.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  combined.corr()\n"
     ]
    },
    {
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       "      <td>-0.816384</td>\n",
       "      <td>-0.734155</td>\n",
       "      <td>0.254446</td>\n",
       "      <td>0.260561</td>\n",
       "    </tr>\n",
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       "      <th>Neutral_nltk</th>\n",
       "      <td>-0.360246</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.615176</td>\n",
       "      <td>-0.176244</td>\n",
       "      <td>-0.115210</td>\n",
       "      <td>-0.344910</td>\n",
       "      <td>-0.453893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Positive_nltk</th>\n",
       "      <td>-0.513841</td>\n",
       "      <td>-0.615176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852034</td>\n",
       "      <td>0.726410</td>\n",
       "      <td>0.102150</td>\n",
       "      <td>0.197205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compound_nltk</th>\n",
       "      <td>-0.816384</td>\n",
       "      <td>-0.176244</td>\n",
       "      <td>0.852034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.829533</td>\n",
       "      <td>-0.062950</td>\n",
       "      <td>-0.006123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentiment_ggl</th>\n",
       "      <td>-0.734155</td>\n",
       "      <td>-0.115210</td>\n",
       "      <td>0.726410</td>\n",
       "      <td>0.829533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114057</td>\n",
       "      <td>0.325615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnitude_ggl</th>\n",
       "      <td>0.254446</td>\n",
       "      <td>-0.344910</td>\n",
       "      <td>0.102150</td>\n",
       "      <td>-0.062950</td>\n",
       "      <td>-0.114057</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agree</th>\n",
       "      <td>0.260561</td>\n",
       "      <td>-0.453893</td>\n",
       "      <td>0.197205</td>\n",
       "      <td>-0.006123</td>\n",
       "      <td>0.325615</td>\n",
       "      <td>-0.042942</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Negative_nltk  Neutral_nltk  Positive_nltk  Compound_nltk  \\\n",
       "Negative_nltk       1.000000     -0.360246      -0.513841      -0.816384   \n",
       "Neutral_nltk       -0.360246      1.000000      -0.615176      -0.176244   \n",
       "Positive_nltk      -0.513841     -0.615176       1.000000       0.852034   \n",
       "Compound_nltk      -0.816384     -0.176244       0.852034       1.000000   \n",
       "Sentiment_ggl      -0.734155     -0.115210       0.726410       0.829533   \n",
       "Magnitude_ggl       0.254446     -0.344910       0.102150      -0.062950   \n",
       "Agree               0.260561     -0.453893       0.197205      -0.006123   \n",
       "\n",
       "               Sentiment_ggl  Magnitude_ggl     Agree  \n",
       "Negative_nltk      -0.734155       0.254446  0.260561  \n",
       "Neutral_nltk       -0.115210      -0.344910 -0.453893  \n",
       "Positive_nltk       0.726410       0.102150  0.197205  \n",
       "Compound_nltk       0.829533      -0.062950 -0.006123  \n",
       "Sentiment_ggl       1.000000      -0.114057  0.325615  \n",
       "Magnitude_ggl      -0.114057       1.000000 -0.042942  \n",
       "Agree               0.325615      -0.042942  1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Produce a correlation matrix to compare NLTK and Google results\n",
    "combined.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sentiment_ggl', ylabel='Compound_nltk'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the Seaborn context to talk for formatting\n",
    "sns.set_context('talk')\n",
    "\n",
    "# User Seaborn to produce a scatterplot\n",
    "sns.scatterplot(data=combined, \n",
    "                y='Compound_nltk',\n",
    "                x='Sentiment_ggl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f8078538520>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uIQYj3tuLMJ3GLfvIn4+Bo3PKTYcvY7GNc8rRKewrSf78eV7E230lkT+T3QS3s65cuYI777wTRUVF+OWXXxAWFubrLPmMo0bPUGiGqUggO78QmfmFiAoNRkRo8TPp+AslOcNkLoLRXITsfBOy8o2IDNUiIlQDrTrI41eflNx2dkEhIkKCvbbtyvDkQEQO+8TVPDibvnS6yNBgRIRoIAC7n1WkTsphH5bOh7djqzLksv9cZS8uS/aTN/MKEa33j35SLvXHE/XB5jpDNFCpgOwCE7Ly3Vfv5FCf5ZAHX/DFSbu797Vc4tBenjxdn7xdfuvtFSIiVINwnQbmIgGtJsilcUiVEA0KzUW4dsuAcJ1795G/xrSjmDSazDCZBbIKCpGZV4gofTAiQoIRrFZZXbktx5ggZXO2zhkKzTAXla3Dch/vcYKbyHny/JnKCTVq1MB///tfdO/eHa+++ipmzpzp6yzJTp7RhE1HLmPJ9rJXpo3qmoi05FjZXkFA8mCpQ4u2lf21e0yq5+uQ5v8HJnqtBjUjQzy2HX8ih33iah6cTe8oneWzPKMJ31eyTsphH/o6tipDDvvPXfz1OMgp356oD47WGarVIDbCffVODvVZDnkIFO7c13KKw5K8VZ98Uf6Kls3RclF6rVvzWN72/JVWo4ZWA+h1GtSKDLWZRq4xQcrlSp2zTGI7qsNE5N/8+mfU1NRUhISEYN26db7OiuzkGU2Y9dURPL36IH4/n40jF3Pw8+mbOHIxB7+fz8bTqw9i1ldHkWc0+TqrJFMl61DJAQMAHLrAOkTep5Q6qZRy+Dt/PQ7+mm8iJQn0OAz08lNZrBPkbaxzRFSaX09wq1QqBAUFISMjo/zEAcRkLsKmI5exco/j/bJyzxlsPnIFphIvdyMCWIdIfpRSJ5VSDn/nr8fBX/NNpCSBHoeBXn4qi3WCvI11johs8esJ7n379iEvLw96vd7XWZEVo7kIC7eedCrtwm0nYGSDT6WwDpHcKKVOKqUc/s5fj4O/5ptISQI9DgO9/FQW6wR5G+scEdnitxPce/fuxbBhw6BSqdC5c2dfZ0dWsvNNOHwxu/yEKL59Jzuft+2QNdYhkhul1EmllMPf+etx8Nd8EylJoMdhoJefymKdIG9jnSMiW2T3lofu3bs7/L6goABnz57FhQsXIISAVqvFlClTvJQ7/5CVb3QpfXZBoWJegELuwTpEcqOUOqmUcvg7fz0O/ppvIiUJ9DgM9PJTWawT5G2sc0Rki+wmuLdu3ep02vj4eCxatAjt2rXzXIb8UGSoa28DjwgJ9lBOyF+xDpHcKKVOKqUc/s5fj4O/5ptISQI9DgO9/FQW6wR5G+scEdkiuwnuqVOnOvxeo9EgOjoaLVq0QKdOnaBSqbyUM/8REapB07iIMm8TtqVpXAQiQmVXDcjHWIdIbpRSJ5VSDn/nr8fBX/NNpCSBHoeBXn4qi3WCvI11johskV2klzfBTeXTqoMwJjURT68+WG7asalJ0Kr99lHs5CGsQyQ3SqmTSimHv/PX4+Cv+SZSkkCPw0AvP5XFOkHexjpHRLbIboKbKk+jDkJaciyGdqyHlXsy7KYb2jEePZJrQMMGn0phHSK5UUqdVEo5/J2/Hgd/zTeRkgR6HAZ6+aks1gnyNta5wCOEQF5enk+3q9frvf4ECV9s05/JboK7e/fuqFq1KtauXetU+sGDB+PKlSvYvHmzh3PmX/RaDSb3SUaH+lWxcNsJq9t3msZFYGxqEnok14BeK7sqQDLhyTpkMhfBaC5Cdr4JWflGROm1iAjRQADSZ5GhWkSEaqBVB3FQojClj39kaDDCQ4KRW1AIqFSoEmL7uCulXVNKOfydvx4Hf803kTeU7V88M5YI9DgM9PJTWa7WCaPJjEKzQHZ+IW7mFSJaH4yI0GAEq1XQatS+Kgb5EbZDgSUvLw/h4eG+zobX3bp1C2FhYb7Oht+QXbRv3boVNWvWdDr9nj17kJFh/1e7QKbXatC7WU30SK6B7HwTsgsKERESzElDcpon6lCe0YRNRy5j0baTOHQhGwlV9Xj/0fbYcfwalu06VWZwMiY1EWnJsRycKETp42/RNC4Cj3RKQPv6MZjyye+4u2WczeOulHZNKeXwd0IA9avq8d6IdrhlMOFmnhHRei3CdBpcyymAEL7OoW2sP0RlOepfPDGW0Gs1SEuugZQG1XAzz4jrt4rbD11wEDYduYxtx64gtaFyJ1fYDlFpztaJXIMJ3x++jCU7ysbqqJRE9GwSizCdMuOG3IvtEBGV5Pc9R1FRES/Zd0Dz/w27XqtBzcgQX2eH/JA761Ce0YRZXx2RbiWLDA3GB492wMJtJ7Bqb9kfqg5dyMbTqw9iaMd4TO7TWLEniYGi9PEv6dCFbDy77jcM6VAPU/o1Qf+3d2HvqZs2j7tS2jWllMNf5RlMeGXjEazam4EgFdCoZhWE6zS4ZTDhz0s5KBLAkA71MLlPsixPtFl/iP5WXv/iibGEZZurfsqw2X4AUPz4he0QlVZenSjZ95Z26EI2xn10UNZ9L8kP26HAc8+cL6HRhXplWwU5N7HxxYEAgH6zv4AuVO/xbZoM+fjs2X4e344S+XWvYTabceXKFV6yT+QHTOYibDpy2erkc2jHePx06rrNQW5JK/ecQYf6MejdrCZ/ifdTto6/LR/+lIFW9aIwpEM9/G/rCR538gijyYzvDl+W2p4iARy5mFMm3Yc/ZaB9Qgx6N6/JW6aJZMrZ/sWdY4nS27TVfrh7m0T+rnTfaw/7XiJyRKML9doEt8aQ//f/a0O8tl2qGJ+PtLKzs5GRkSH9AcUT12fPnrX6vOTfmTNn8Ouvv2LixIkwGAxo3Lixj0tBROUxmouwcOtJ6d/qIBUe7lAPy3addmr5hdtOwGgu8lDuyNNKH39Hlu06jSEd4xGk4nEnzyg0Cyze7lx9XLzjJExmmT6rhIhc6l/c1af4YptE/o59LxEReZLPr+CeN28eZsyYYfXZtWvXkJCQ4NTyKpUKw4YN80DOiMidsvNNOHzx7+fsNYwNR4HRbPWZI4cuZCM736TY23yVrvTxd+TQhWwUGM1oVLMKjzt5RHZ+oUv1MaugEHreKk0kS672L+7oU3yxTSJ/x76XiIg8yedXcAOAEEL6U6lUVv929BcXF4cZM2bgySef9HURiKgcWflGq3+H6zS4kWe0k9q27IJCd2aJvKj08S/PzTwjwv//pIbHndztZp5rdSrTxfRE5D2u9i/u6FN8sU0if8e+l4iIPMnnP4mOGzcOjzzyCIDiie7ExERUr14de/futbtMUFAQIiIiEBkZ6aVcElFlRYZqrf59y2BCjF5rJ7VtESHB7swSeVHp41+eaL0WtwwmADzu5H7RetfqVJSL6YnIe1ztX9zRp/him0T+jn0vERF5ks8nuCMjI60mqrt27Ypq1aohPj7eh7kiIneLCNWgaVwEDl0ovjXx2OVbCNGqrT5zpGlcBCJCfd5kUQWVPv6ONI2LgC44CH9eyuFxJ4+ICA12re3h5BSRbLnav7ijT/HFNon8HfteIiLyJFk8oqSkrVu3Yt26db7OBhG5mVYdhDGpidK/zUUCq37KwCOdEpxafmxqErRq2TVZ5KTSx9+RkZ0TsOqnDBQJHnfyjGC1CqNSnKuPo1MSEaxWeThHRFRRrvQv7upTfLFNIn/HvpeIiDyJoy0i8gqNOghpybEY2rGe9NnKPWfQvn4MHm5fz8GSwNCO8eiRXAManiD6LVvH35YhHeqhXUIMPvwpg8edPEarUaNnk9hy254hHeohrUkstBq1l3JGRK5ytn9xZ5/ii20S+Tv2vURE5Ek+vV9u+/btAAC9Xo+2bdtafeaqrl27ui1fSmI0mVFoFsjOL8TNvEJE64MRERqMYLWKg4YAYzIXwWguQna+CVn5RkSGahERqoFWHeS1Ey+9VoPJfZLRoX5VLNx2AocuZGPEe3vx/qPt0SY+Gu/tOmV122LTuAiMTU1Cj+Qa0Gt5e6+/s3X8LZrGRWBk5wS0S4jBzC8PY+aAZpU+7nKo80qklH4lTKfBi32T0TGpKhbZqI9jUpPQo3ENhOnY9hDJXXn9i7vGEiX7lTyjCZPu8vw2XaWUNpqUydL3dqgfg8U7TpaJm9EpiUhrEiv1vazPysPxORF5ik/P2rp16waVSoXGjRvj0KFDVp+5QqVSwWQyeSKLfi3XYML3hy9jiY3Bw6iURPQsMXggZcszmrDpyGUs2la2LoxJTURacqzXTsD0Wg16N6uJHsk1kJ1vQnZBIUKC1ejTvCZ6N68pfRYREszBjgKVOf75hYgI1SA8JBi5//9SyfmDW1X6uMupziuJEvuV+lX1eG9EO9wymHAzz4hovRZhOg2u5hT4OmtE5AJb4wt3jiVs9SuRocEY37MBPnysAwoKzcguMPl0/KLENpqUJ0ynQe/mNdGzSSyyCgqRmVeIKH0wIkKsJ65Zn5WH43Mi8iSftx5CCBQVFZX5zNV1kLU8gwmvbDyCVXszynx36EI2xn10EEM61MPkPskcGChcntGEWV8dwco9tuvC06sPYmjHeEzu09hrAwrN/5/06bUa1IwMsfrO1mekLPaOf7ib2iI51nklUFq/UrI8QSqgUc0qCNdpcMtgwp+XclAk4FflISLH44vKsNevZOUXYtrnhzHji8OY0LMRHktJQEiwb9oLpbXRpGxajRpaDaDXaVArMrTM96zPysPxORF5mk8viywqKkJRURGOHDlS5jNX/+hvRpMZ3x2+bHNAUNKHP2Vg0+HLMJrMXsoZeZvJXIRNRy7bHEiUtHLPGWw+cgUmM2OJ/BvrvGcorV8xFJrxbYnyFAngyMUc/Hz6Jo5cLJ7cBorL8/3hyzAUyrs8ROQ5zvQrRQJ447s/8f1h3/QrSmujKbCxPisPx+dE5A2871+BCs0Ci7efdCrt4h0nYTLzCnilMpqLsHCrc3Vh4bYTMHIwQX6Odd4zlNavmIoEljhZniU7TsJcJO/yEJHn+EO/orQ2mgIb67Py+EM7SkT+jxPcCpSdX4jDF7PLT4ji24GyCgo9nCPylex8k0t1ITufz7In/8Y67xlK61eUVh4i8hx/6FfYppGSsD4rjz+0o0Tk/zjBrUA381zr5DNdTE/+Iyvf6FL6bA4Qyc+xznuG0voVpZWHiDzHH/oVtmmkJKzPyuMP7SgR+T9ZP71/9+7d+O2333Djxg0UFjpu5F566SUv5Ur+ovXBLqWPcjE9+Y/IUK1L6SNCWBfIv7HOe4bS+hWllYeIPMcf+hW2aaQkrM/K4w/tKBH5P1lOcH///fcYM2YMzpw54/QynOD+W0RoMJrGReDQhfJvA2oaF8EORMEiQjWu1YVQWTYJRE5jnfcMpfUrSisPEXmOP/QrbNNISViflccf2lEi8n+ye0TJ3r170a9fP5w+fRpCCCQkJKBTp07o2rWrw7+K2rJlC/r164fq1asjNDQUjRs3Rnp6OnJzc11e17Rp06BSqRz+LVy4sMJ5dVawWoVRKYlOpR2dkohgtcrDOSJf0aqDMCbVubowNjUJWrXsmgQil7DOe4bS+hVNkAqPOVmeUSmJ0ATJuzxE5Dn+0K8orY2mwMb6rDz+0I4Skf+T3U9jL7/8MgoLC9G4cWN8/PHHaNasmce29Z///AfPPPMMhBCoU6cO6tati8OHD2PmzJlYv349du7ciZiYGJfXW6NGDTRo0MDmd7Vq1apstsul1ajRs0ksHm5fD6v2ZthNN6RDPaQ1iYVWo/Z4nsg3NOogpCXHYmjHeli5x35dGNoxHj2Sa0DDwQT5OdZ5z1Bav6ILVuMfTpanZ5NY6ILlXR4i8hx/6FeU1kZTYGN9Vh5/aEeJyP/JboJ79+7dUKlUWLFihUcnt/ft24dx48YBABYtWoRRo0ZBpVLhwoUL6N+/P/bt24dRo0Zh/fr1Lq+7d+/eWL58uXsz7KIwnQYv9k1Gh/oxWLzjpNXtQE3jIjA6JRFpTWIRppNdFSA302s1mNwnGR3qV8XCbSfK1IWxqUnokVwDei3rAikD67xnKK1fsZSnff0YLLFRnlEpiejpR+UhIs/xh35FaW00BTbWZ+Xxh3aUiPyb7FqPvLw86PV6tGnTxqPbefnll1FUVIThw4dj9OjR0udxcXFYvXo1GjdujA0bNuC3337D7bff7tG8eEqYToPezWuiZ5NYZBUUIjOvEFH6YESEBCNYreKv3QFEr9Wgd7Oa6JFcA9n5JmQXFCIiJBgRoRpo1UH8lZwUh3XeM5TWr4TpiuvJP2yURxOk4pXbRCTxh35FaW00BTbWZ+Xxh3aUiPyX7Ca44+PjkZFh/7YVd7h16xa++eYbALCa3LZo0KABunfvjk2bNmHt2rV+O8ENFN/ipdUAep0GtSJDfZ0d8iHN/w8a9FoNakaG+Do7RB7HOu8ZSutXLJPYSikPEXmOP/QrSmujKbCxPiuPP7SjROSfZPcT2f3334+CggJs377dY9s4cOAADAYDdDod2rdvbzNNSkoKAGDPnj0ur//XX3/Fww8/jO7du+Oee+5Beno6Dh06VKk8ExEREREREREREZE12U1wT5o0CYmJiXjiiSdw/fp1j2zj2LFjAIB69eohODjYZpqkpCQAwJ9//uny+g8ePIjVq1djy5Yt+PzzzzFz5kw0b94c48ePh9lsrnjGiYiIiIiIiIiIiEgiu0eU7N+/Hy+//DKeeOIJNG3aFKNHj0aHDh1QpUoVh8t17drV6W3cuHEDABATE2M3jeW7mzdvOr3emjVr4rnnnsN9992HpKQkVKlSBceOHcP//vc/LFy4EG+99Ra0Wi1mz55d7roWLVqExYsXO7XdI0eOOJ1HIqoYxiSR/DAuieSFMUkkL4xJIvlhXBJ5huwmuLt16waVSiX9+5VXXil3GZVKBZPJ5PQ2CgoKAABardZuGp1OBwDIz893er1jx44t81nz5s3xzjvvoH79+nj++ecxd+5cPP7440hISHC4rosXL2L//v1Ob5uIPIsxSSQ/jEsieWFMEskLY5JIfhiXRJ4huwluABBCeDR9SEjxywyMRqPdNAaDAQAQGuqel1lMnDgR8+fPx4ULF/DFF1/gqaeecpi+Vq1aaN26tVPrPnLkiEsT8UTkOsYkkfwwLonkhTFJJC+MSSL5YVwSeYbsJriLioo8vo3o6GgAfz+qxBbLd5a0laVWq9GhQwd88skn0jPAHRkzZgzGjBnj1LrbtGnDXwCJPIwxSSQ/jEsieWFMEskLY5JIfhiXRJ4hu5dMekPDhg0BABkZGSgsLLSZ5sSJE1Zp3cHySBRXHqdCRERERERERERERLYF5AR369atodVqYTAYsHfvXptpduzYAQC444473LbdP/74AwBQp04dt62TiIiIiIiIiIiIKFAF5AR3eHg4evXqBQA23157/Phx/PDDDwCAgQMHumWbGzduxKFDhwAA//jHP9yyTiI5MZmLkGc04VJWAf68lI1LWQXIM5pgMnv+sUOkTKxT5A2sZ0RUHrYTRP6PcUxEpGyyewb3jBkzXEofEhKCqKgoNG3aFO3atZMeA1Ke9PR0fPnll1ixYgU6d+6MUaNGQaVS4eLFixg8eDCKioowYMAAtGjRwmq5Ll264Ny5cxg3bhzGjRsnfX7o0CEsWLAA//rXv6yWKSoqwkcffYSxY8cCAPr27Yt27dq5VEYiucszmrDpyGUs2nYShy5kS583jYvAmNREpCXHQq+VXXNDMsY6Rd7AekZE5WE7QeT/GMdERMonu1Z82rRpUKlUFVo2JiYG48ePx6RJkxAU5Pji9Hbt2mHu3LmYMGECxowZg5kzZ6JatWo4fPgwDAYDGjVqhCVLlpRZ7ty5czhz5gwyMzOtPi8sLMTixYuxePFixMTEID4+HhqNBn/99Rdu3rwJAEhJScHKlSsrVDYiucozmjDrqyNYuSejzHeHLmTj6dUHMbRjPCb3acyBIzmFdYq8gfWMiMrDdoLI/zGOiYgCg+weUdK1a1d07doVkZGREEJACIE6deqgQ4cOaN++PerUqSN9HhUVhZSUFDRv3hzBwcG4fv060tPTMXjwYKe2NW7cOHz//ffo3bs3cnNzcfjwYcTHx2Py5Mn45ZdfUK1aNafznZCQgJkzZ6Jv376IiorCX3/9hYMHD0Kr1aJ3795YsWIFtmzZgqioqAruGSL5MZmLsOnIZZsDxpJW7jmDzUeu8BZAKhfrFHkD6xkRlYftBJH/YxwTEQUO2f1EuXXrVkyePBnbtm3DiBEjkJ6ejsTERKs0p06dwsyZM7Fs2TKkpqZi+vTpyM3NxZtvvonp06dj3bp1+OSTT3DvvfeWu70ePXqgR48eTufv9OnTNj+PiorCiy++6PR6iJTAaC7Cwq0nnUq7cNsJ9EiuAY1adr+rkYywTpE3sJ4RUXnYThD5P8YxEVHgkN0E9/r16zF79mw888wzmDdvns009evXx9KlSxEZGYmZM2eiTZs26N+/P1566SXk5OTgzTffxPvvv+/UBDcRVVx2vgmHL2aXnxDFtwBm55t46x85xDpF3sB6RkTlYTtB5P8Yx6RkQgjk5eX5NA96vb7CjxgmcjfZ/Tz59ttvQ6VSIT09vdy0U6ZMAQAsWLBA+uypp54CAPzyyy+eySARSbLyjS6lzy4o9FBOSClYp8gbWM+IqDxsJ4j8H+OYlCwvLw/h4eE+/fP1BDtRSbKb4P7tt98QFRWFmJiYctPGxMQgKioKBw4ckD6rV68eqlSpgmvXrnkym0QEIDJU61L6iJBgD+WElIJ1iryB9YyIysN2gsj/MY6JiAKH7O6/MRgMMBqNyMvLg16vd5g2NzcX2dnZ0Ol0Zb4LDQ31VBaJ6P9FhGrQNC4Chy6Uf+tf07gIRITKrskhmWGdIm9gPSOi8rCdIPJ/jGMKFPfM+RIanXfmwEyGfHz2bD+vbIvIFbK7grtBgwYwm8145513yk27cOFCmM1mNGjQQPosKysLOTk5qF69uiezSUQAtOogjElNLD8hgLGpSdDypS1UDtYp8gbWMyIqD9sJIv/HOKZAodGFevWPSI5k14IPHz4cQghMmjQJr776KvLz88ukyc/Px2uvvYZJkyZBpVJh2LBh0nd79uwBADRr1sxreSYKVBp1ENKSYzG0Yz2H6YZ2jOdbyckprFPkDaxnRFQethNE/o9xTEQUOGR3D85TTz2FTz/9FDt27MCUKVMwa9YstG3bFnFxcVCpVLhw4QJ++eUX5ObmQgiBlJQUPP3009Lyy5cvBwD07NnTRyWQF5O5CEZzEbLzTcjKNyIyVIuIUA206iB24OQWeq0Gk/sko0P9qli47YTVLYBN4yIwNjUJPZJr8I3k/48xWT7WKfIG1jNSCvYrnsN2giwYZ/6LcWwf6zURKYnsWnGNRoOvv/4aEyZMwLvvvovc3Fxs27YNKpUKACCEAAAEBQXhsccew9y5c6HR/F2MJUuWYOHChQgPD/dJ/uUkz2jCpiOXsWjbyTId+ZjURKQlxwZkR07up9dq0LtZTfRIroHsfBOyCwoRERLMAVIpjEnnsU6RN7Cekb9jv+J5bCeIceb/GMdlsV4TkdLIssXS6/VYuHAhXnzxRaxfvx779+/H1atXIYRAjRo10Lp1a9x3332oV6/srUac2C6WZzRh1ldHsHJPRpnvDl3IxtOrD2Jox3hM7tOYHRe5heb/B4d6rQY1I0N8nR3ZYUy6jnWKvIH1jPwV+xXvYTsRuBhnysE4/hvrNREpkaxbq7p162LcuHG+zobfMZmLsOnIZZsdVkkr95xBh/ox6N2sZkD+ak3kLYxJIiJyJ/YrRJ7HOCMlYr0mIqViS6VARnMRFm496VTahdtOwGgu8nCOiAIbY5KIiNyJ/QqR5zHOSIlYr4lIqTjBrUDZ+SYcvphdfkIU34KUnW/ycI6IAhtjkoiI3In9CpHnMc5IiViviUipZP2Ikh9//BE7d+7EuXPnkJubK71gsjSVSoWlS5d6OXfylZVvdCl9dkFhwD+HjMiTGJNERORO7FeIPI9xRkrEek1ESiXLCe7jx4/j4Ycfxv79+60+F0JApVLZ/IwT3H+LDNW6lD4iJNhDOSEigDFJRETuxX6FyPMYZ6RErNdEpFSye0TJ9evX0b17d+zbtw81atTAAw88ACEEQkJCMHToUPTo0QPh4eEQQqBq1aoYMWIEhg8f7utsy0pEqAZN4yKcSts0LgIRobL8nYNIMRiTRETkTuxXiDyPcUZKxHpNREoluwnut956C+fPn0eHDh1w4sQJrFmzBgAQGRmJDz74AN999x0uXLiAZ599FteuXUNoaCiWLVvm41zLi1YdhDGpiU6lHZuaBC3fikzkUYxJIiJyJ/YrRJ7HOCMlYr0mIqWSXWu1ceNGqFQqzJo1C3q93maasLAwzJ49G8888wwWLVqEtWvXejmX8qZRByEtORZDO9YDAKiDVEiuVQXtEqKRXKsK1EHFj3kZ2jEePZJrQMNOi8ijSsekPYxJIiJyBvsVIs9jnJESsV4TkVLJ7n6TEydOQKVSISUlxepzo7HsyxAmTZqEBQsWYPHixXjggQe8lUW/oNdqMLlPMga3r4eqYTrkGky4kWdEjF6LMJ0G13MNqF8tDHqt7KoAeZDJXASjuQjZ+SZk5RsRGapFRKgGWnUQBy8eZonJDvWrYuG2Ezh04e+3lzeNi8DY1CT0SK7BmHQT1nXPUdq+NZrMKDQLZOcX4mZeIaL1wYgIDUawWgWtRu3r7BHZpeR+RWntjDdx37mXkuOMKkfOsVZe3liviUiJZNdiFRYWIjIyEhrN31nT6/XIyckpkzY2NhaRkZH47bffvJlFv3Liyi08t/23Mp3WmK6JqF8tzIc5I2/LM5qw6chlLNp2smx9SE1EWnIsBzEeptdq0LtZTfRIroHsfBOyCwoRERIsm8GwUrCue47S9m2uwYTvD1/Gkh1lyzMqJRE9m8QiTOc/5aHAo8R+RWntjDdx33mGEuOMKkfOseZs3liviUhpZDfCiYuLw+nTp2EymaRJ7tjYWJw+fRonT55EYuLfz4sqLCxEdna21WQ4FcszmjDrqyNYuSejzHeHLmTj6TUHMbRjPCb3acyBbgAotz6sZn3wFk2JKydqRob4OjuKw7ruOUrbt3kGE17ZeASr9touz7iPDmJIh3qY3CeZk9wka0rqV5TWzngT951nKSnOqHLkHGuu5o31moiURHY/y9WvXx9CCJw9e1b6rF27dgCAlStXWqVdvnw5ioqKULt2ba/mUe5M5iJsOnLZZsdW0so9Z7D5yBWYzEVeyhn5AusDBQrWdc9R2r41msz47vBlm5PbJX34UwY2Hb4Mo8nspZwRBS6ltTPexH1H5B1yjjU5542IyBtkN8H9j3/8AwDwzTffSJ8NGzYMQgjMnDkTTzzxBJYsWYInn3wSTz75JFQqFQYMGOCj3MqT0VyEhVtPOpV24bYTMLJzUzTWBwoUrOueo7R9W2gWWLzdufIs3nESJrPwcI6ISGntjDdx3xF5h5xjTc55IyLyBtlNcN9zzz2oW7cuduzYIX3Wt29fPPTQQzCZTFi4cCHGjh2Ld955B4WFhWjcuDFeeuklH+ZYfrLzTTh8Mbv8hCi+VSk73+ThHJEvsT5QoGBd9xyl7dvs/EKXypNVUOjhHBGR0toZb+K+I/IOOceanPNGROQNsnsAW8OGDXH69Okyn3/44Ye488478dFHH+Hs2bOIjIzEXXfdhYkTJyIyMtL7GZWxrHyjS+mzCwr5zC0FY32gQMG67jlK27c381ybsM7MK0StyFAP5YaIAOW1M97EfUfkHXKONTnnjYjIG2Q3wW2PSqXCqFGjMGrUKF9nRfYiQ7UupY8ICfZQTkgOWB8oULCue47S9m203rX8RbmYnohcp7R2xpu474i8Q86xJue8ERF5g+weUUKVFxGqQdO4CKfSNo2LQESo3/zOQRXA+kCBgnXdc5S2byNCg10rD08CiTxOae2MN3HfEXmHnGNNznkjIvIGn09wZ2RkuOWP/qZVB2FMaqJTacemJkGr9nk1IA9ifaBAwbruOUrbt8FqFUalOFee0SmJCFarPJwjIlJaO+NN3HdE3iHnWJNz3oiIvMHnP9slJCRAparciaNKpYLJxJckWGjUQUhLjsXQjvWwco/9yf+hHePRI7kGNOzcFI31gQIF67rnKG3fajVq9GwSi4fb18OqvfbLM6RDPaQ1iYVWo/Zi7ogCk9LaGW/iviPyDjnHmpzzRsoihJD+Pzc31yvb9NZ2yL/5fIIbsA4Qcg+9VoPJfZLRoX5VLNx2Aocu/P1G5aZxERibmoQeyTWg18qiCpCHsT5QoGBd9xyl7dswnQYv9k1Gh/oxWLzjZJnyjE5JRFqTWITp/KM8REqgtHbGm7jviLxDzrEm57yRcpiNBdL/x8bGen/75iJ5TGSS7MiiXqhUKiQkJOCRRx5B165dfZ0dxdBrNejdrCZ6JNdAdr4J2QWFiAgJRkSoBlp1EH+1DTCsDxQoWNc9R2n7NkynQe/mNdGzSSyyCgqRmVeIKH0wIkKCEaxW8cptIh9QWjvjTdx3RN4h51iTc96IiDzJ5xPcvXv3xnfffYdTp05h2rRpSExMxMiRIzFixAjUrl3b19nze5r/78T0Wg1qRob4OjvkY6wPFChY1z1HaftWq1FDqwH0Og1qRYb6OjtEBOW1M97EfUfkHXKONTnnjZSl3+wvoAvVe3w7BTk3sfHFgR7fDvk3n/98t3HjRmRkZGDWrFlo0KABTpw4gfT0dCQkJKB3795Yu3YtjEajr7NJREREREREREREADTaEGh0oZ7/0/KHGiqfzye4AaBWrVqYNGkSjh49ih07duCRRx5BaGgovv32Wzz00EOoVasWnn76aezfv9/XWSUiIiIiIiIiIiIimZDFBHdJnTt3xtKlS3Hp0iUsXboUnTt3xs2bN/H222+jXbt2aNGiBRYsWIDr16/7OqtERERERERERERE5EOym+C20Ov1GDlyJLZv347jx4/jhRdeQO3atfH7779j/PjxmD17tq+zSEREREREREREREQ+JNsJ7pKSkpIwcuRIPPzww9Bqtb7ODhERERERERERERHJgMbXGXAkLy8PH3/8Md577z3s2rULACCEQPPmzdGjRw8f546IiIiIiIiIiIiIfEmWE9w7d+7Ee++9h3Xr1iE3NxdCCERHR2Pw4MEYOXIk2rRp4+ssEhEREREREREREZGPyeYRJefPn8esWbPQsGFDpKamYvny5cjLy0PPnj2xevVqXLx4EW+//bbbJ7e3bNmCfv36oXr16ggNDUXjxo2Rnp6O3NxcWa2TiIiIiIiIiIiIiKz5/Arujz/+GMuWLcOmTZtQVFQEIQSSkpLwyCOPYMSIEahTp47Htv2f//wHzzzzDIQQqFOnDurWrYvDhw9j5syZWL9+PXbu3ImYmBifr5OIiIiIiIiIiIiIyvL5BPdDDz0ElUoFvV6PBx54ACNHjkRKSorHt7tv3z6MGzcOALBo0SKMGjUKKpUKFy5cQP/+/bFv3z6MGjUK69ev9+k6K8toMqPQLJCdX4ibeYWI1gcjIjQYwWoVtBq11/JBRETKoLR+RWnlIaLAxjaNyP8wbonIQggh/b83nwIhhEBeXh4AQK/XQ6VSeW3b7tqmzye4LfR6PbZu3YqtW7e6vKxKpcKJEydcWubll19GUVERhg8fjtGjR0ufx8XFYfXq1WjcuDE2bNiA3377DbfffrvP1lkZuQYTvj98GUt2nMShC9nS503jIjAqJRE9m8QiTCebKkBERDKntH5FaeUhosDGNo3I/zBuiagks7FA+v/Y2Fgf5sS7bt26hbCwsEqtQxYtpRACV69exdWrVyu0vKuz/Ldu3cI333wDAFYT0RYNGjRA9+7dsWnTJqxdu9apyWhPrLMy8gwmvLLxCFbtzSjz3aEL2Rj30UEM6VAPk/sks8MkIqJyKa1fUVp5iCiwsU0j8j+MWyIi9/F5Kzl16lSvb/PAgQMwGAzQ6XRo3769zTQpKSnYtGkT9uzZ47N1VpTRZMZ3hy/b7ChL+vCnDLRPiEHv5jV56xMREdmltH5FaeUhosDGNo3I/zBuiag8/WZ/AV2o3ivbKsi5iY0vDvTqdk2GfHz2bD+3rS8gJ7iPHTsGAKhXrx6Cg4NtpklKSgIA/Pnnnz5bZ0UVmgUWbz/pVNrFO06iZ5NYaH1eE4iISK6U1q8orTxEFNjYphH5H8YtEZVHow2BRhfqnW0Z8n2yXXcKyCbyxo0bAICYmBi7aSzf3bx50yfrXLRoERYvXuzUto8cOWL17+z8Qhy+mG0ntbVDF7KRVVAIPW95InKoMjFJ5O/k2q9UNC7lWh4if8e+0jfYppE9jEn5YtwGLsYlkWcEZAtZUFD80HatVms3jU6nAwDk5+fbTePJdV68eBH79+93atul3cwrdCl9Zl4hakX6368zRN5UmZgk8ndy7VcqGpdyLQ+Rv2Nf6Rts08gexqR8MW4DF+OSyDMCcoI7JCQEAGA0Gu2mMRgMAIDQUOc6EXevs1atWmjdurVT2z5y5IjVpHm03vYjUuyJcjE9USCqTEwS+Tu59isVjUu5lofI37Gv9A22aWQPY1K+GLeBi3FJ5BkBOcEdHR0N4O/Hithi+c6S1tvrHDNmDMaMGePUttu0aWP1C2BEaDCaxkXg0IXyb3lqGheBiBB2lkTlqUxMEvk7ufYrFY1LuZaHyN+xr/QNtmlkD2NSvhi3gYtxSeQZQb7OgC80bNgQAJCRkYHCQtu3Bp04ccIqrS/WWVHBahVGpSQ6lXZ0SiKC1SqP5oeIiPyb0voVpZWHiAIb2zQi/8O4JSJyr4Cc4G7dujW0Wi0MBgP27t1rM82OHTsAAHfccYfP1llRWo0aPZvE4uH29RymG9KhHtKaxEKrUXs0P0RE5N+U1q8orTxEFNjYphH5H8YtEZF7BeQEd3h4OHr16gUANt9ee/z4cfzwww8AgIEDB/psnZURptPgxb7JmD+oJZrGRVh91zQuAvMHtcTkPskI45uYiYjICUrrV5RWHiIKbGzTiPwP45aIyH0CtqVMT0/Hl19+iRUrVqBz584YNWoUVCoVLl68iMGDB6OoqAgDBgxAixYtrJbr0qULzp07h3HjxmHcuHFuWaenhOk06N28Jno2iUVWQSEy8woRpQ9GREgwgtUq/gpMREQuUVq/orTyEFFgY5tG5H8Yt0RE7hGwE9zt2rXD3LlzMWHCBIwZMwYzZ85EtWrVcPjwYRgMBjRq1AhLliwps9y5c+dw5swZZGZmum2dnqTVqKHVAHqdBrUiQ726bSIiUh6l9StKKw8RBTa2aUT+h3FLlWUy5HtvW8YCq/9Xqz3/YAhfbNNX2w2osrq53qqEEMKta/QzmzdvxptvvomffvoJubm5iI+Px8CBA/HCCy8gPDy8TPqEhAScOXMGU6dOxbRp09yyzsqKiYnBzZs3ERoaiuTkZLevn8ifNG7cGB9++KFP88CYJLLGuCSSF8YkkbwwJonkR+5xaTab8euvv/ooZ0Tu1aJFC6jVju9YKS8mA36CWwn0ej3y8733ix2RnLVu3Rr79u3zaR4Yk0TWGJdE8sKYJJIXxiSR/DAuieSlvJgM2EeUKEmNGjVw5coVhISEoH79+jbTHDlyBPn5+fxFXkZ4TDyjcePGvs6CUzHpb1hfqTRX6oQ/xCXrOJWm5DrhDzHpbko+nrawvP4lEGPS3fy9DgDKKAOgnHL4Q1wqZV8rFY+Pe5UXk7yCO0C0adMG+/fvl8WvkFSMx4T8Cesrlaa0OqG08lDlsU4oS6AdT5aXAo0S6oASygAopxz+gPta3nh8vMs7TysnIiIiIiIiIiIiInIzTnATERERERERERERkV/iBDcRERERERERERER+SVOcBMRERERERERERGRX+IENxERERERERERERH5JU5wExEREREREREREZFf4gQ3EREREREREREREfklTnATERERERERERERkV/iBDcRERERERERERER+SVOcBMRERERERERERGRX9L4OgPkHaNHj8bFixdRq1YtX2eF/h+PCfkT1lcqTWl1QmnlocpjnVCWQDueLC8FGiXUASWUAVBOOfwB97W88fh4l0oIIXydCSIiIiIiIiIiIiIiV/ERJURERERERERERETklzjBTURERERERERERER+iRPcREREREREREREROSXOMEtU1u2bEG/fv1QvXp1hIaGonHjxkhPT0dubq7X11lYWIg5c+agRYsWCAsLQ0xMDLp3744NGzZUOC/+yF3HRAiBH3/8EZMmTUKXLl1QtWpVBAcHo3r16vjHP/6BDz/8EI4ejZ+QkACVSuXwr6CgoLLFJQVzZ/sybdq0cuvjwoULPVAKcodLly5h5cqVeOaZZ9C5c2fo9XqoVCq0bdu20uv2RD/m6+37ukxUcWz3AsvJkyfx6KOPok6dOtDpdKhbty7++c9/4tSpUxVa39atW8s95g899JCbS/G3QGvPGK/kDH+IcyXELuNRvuTcjgcyT55fUTkEyc6CBQuESqUSAESdOnVEq1athE6nEwBEcnKyuH79utfWmZ+fL7p06SIACLVaLW6//XaRlJQkAAgA4vnnn69scf2CO4/Jpk2bpP0HQCQmJoo2bdqImJgY6bO+ffuKgoICm8vHx8cLAKJZs2aic+fONv8MBoO7ik4K4+72ZerUqQKAqFGjht36+Omnn3qoNFRZ8+bNs2qPLH9t2rSp1Ho90Y/5evu+LhNVHNu9wPLjjz+K8PBwAUBER0eLNm3aiKioKAFAVKlSRfz0008ur3PLli0CgNDpdHaP+dSpU91fGBF47RnjlZzhD3GuhNhlPMqXnNvxQOep8ysqHye4ZeaXX34RQUFBQqVSiUWLFomioiIhhBDnz58Xbdq0EQDEfffd57V1Pv300wKAqF+/vjh69Kj0+WeffSY1oJ9//nkFS+sf3H1Mvv/+e1G/fn0xf/58cfnyZavvPvjgA2m/2vvxwDLBvWXLlgqXiQKTJ9oXy0B1xIgRHsgxedrSpUtFWlqamDRpkli3bp2YNWtWpQdgnqhnvt6+r8tEFcd2L7Dk5uaKWrVqCQDi0UcfFfn5+UKI4gs2Ro4cKU0E5OXlubRey8RXfHy8B3JtX6C1Z4xXcoY/xLkSYpfxKF9ybsfJM+dX5BxOcMvMPffcIwCI4cOHl/nu2LFjIigoSAAQv/76q8fXeenSJaHVagUA8cMPP5RZNj09XQAQrVu3djov/sjdxyQrK0sYjUa737/yyisCgIiJiRFms7nM95zgporyRPvCgaqyLFu2rNIDME/UM19v39dloopjuxdYLFdN3XbbbWXuZjMYDNJdiAsWLHBpvb6a4A609ozxSs7whzhXQuwyHuVLzu04leWO8ytyDp/BLSO3bt3CN998AwAYPXp0me8bNGiA7t27AwDWrl3r8XV+/vnnMBqNuO2223DnnXeWWXbMmDEAgP379+PEiRNO5cffeOKYREREIDg42O73vXv3BgDcuHEDV69edTXLRDZ5oi4Tlebreia3fpR8i8cu8FiO4yOPPAKtVmv1nVarxciRIwEAH3/8sdfz5qpAa8/knDeSF7nHuRJil/EoXzw2RPZxgltGDhw4AIPBAJ1Oh/bt29tMk5KSAgDYs2ePx9dp+bfl+9Jq166N+vXru5Qff+OJY1Keki+IDA0NtZtu4cKF6NevH3r06IEhQ4Zg4cKFyMnJcUseSHk8XZd//fVXPPzww+jevTvuuecepKen49ChQ5XKM/kfX7SZnt6+r8tEFcd2L7CYzWb88ssvAOyPXS2f//zzzzCbzS5vIzs7G2PGjEFaWhr69OmDp556Ct9//33FM+1AoLVnjFdyhj/EuRJil/EoX3Jux4l8TePrDNDfjh07BgCoV6+e3St8k5KSAAB//vmnx9dpWfa2226zu/6kpCScOnXK6fz4G08ck/KsXr0aANCiRQtERETYTffRRx9Z/XvVqlVIT0/HqlWr0LNnT7fkhZTD03X54MGDOHjwoPTvzz//HK+88gqeeeYZvPHGG1Cr1a5nmvyOL9pMT2/f12WiimO7F1hOnz4No9EIwP7Y1XK8DQYDzpw5g8TERJe2cfPmTSxevNjqs7fffhs9evTAmjVrUK1atQrk3LZAa88Yr+QMf4hzJcQu41G+5NyOE/kar+CWkRs3bgAAYmJi7KaxfHfz5k2Pr9MT+fE33t4H+/fvx8KFCwEAkyZNspmmU6dOWLx4MY4cOYLc3FzcvHkTX3zxBVq1aoVr166hf//+2L9/f6XzQsriqbpcs2ZNPPfcc9izZw+uXr2KgoIC/Pbbbxg7diyEEHjrrbcwefLkymWe/Iav+w259aPkW2z3AovleAP2j3nJz1055qGhoRg+fDi+/fZbnD17FgaDAadPn8Ybb7yBsLAwbN68Gf3796/Q1aL2BFp7xnglZ/hDnCshdhmP8iXndpzI13gFt4xYHk1R+lliJel0OgBAfn6+x9fpifz4G2/ug8uXL+Pee+9FYWEh7r33Xjz00EM2061atcrq33q9XnpUSZcuXbB//348//zzHrtllvyTp+ry2LFjy3zWvHlzvPPOO6hfvz6ef/55zJ07F48//jgSEhJcyzT5HV/3G3LrR8m32O4FlpKPeLN3zC3HG3DtmHfo0AEdOnSw+iw+Ph4TJ05Ep06dkJKSgt27d2PNmjUYMmSIizm3LdDaM8YrOcMf4lwJsct4lC85t+NEvsYruGUkJCQEAKTbrmwxGAwAHD+b2V3r9ER+/I239kFWVhZ69+6NjIwMtGnTBsuXL3d5HaGhoXjllVcAAD/88AMyMzMrnB9SHl/E88SJExEXFweTyYQvvvjCLeskefN1vyG3fpR8i+2e/xg7dixUKpXLf926dZPWYTnegP1jbjnegPuO+R133IGBAwcCADZs2OCWdQKB154xXpUvUOJcCbHLeJQvObfjRL7GCW4ZiY6OBmB961Vplu8saT25Tk/kx994Yx/cunULd911Fw4cOICmTZvi22+/dfjsbUc6deoEACgqKsKJEycqtA5SJl/Es1qtlq6EsTwvjpTN1/2G3PpR8i22e/4jMjISsbGxLv+VvEW75DG0d8xLfu7OeLWMv9x5zAOtPWO8Kl+gxLkSYpfxKF9ybseJfI2PKJGRhg0bAgAyMjJQWFho86UBlklLS1pPrrNhw4bYtWsX/vrrL7vrdzU//sYTx6SkvLw89O3bF3v27EHDhg2xadMmVK1atcL5LXmrkslkqvB6SHk8XZftsdRJ1sfA4Kt65snt+7pMVHFs9/zH7NmzMXv27EqtIyEhAVqtFkajEX/99Rfi4uLKpLEcb51Oh/j4+EptryRPHPNAa88Yr8oXKHGuhNhlPMqXnNtxIl/jFdwy0rp1a2i1WhgMBuzdu9dmmh07dgAovk3K0+vs2LEjAGDnzp02lzt//jxOnTpllVZpPHFMLAoKCnDPPfdg+/btSEhIwObNm1GzZs1K5fePP/6Q/r927dqVWhcpiyfrsiOWOlmnTh23rZPky1f1zJPb93WZqOLY7gUWjUaDNm3aAPj7uJZm+bxdu3ZQq9Vu27YnjnmgtWeMV3KGP8S5EmKX8Shfcm7HiXxOkKzcfffdAoAYPnx4me+OHTsmgoKCBABx8OBBj6/z0qVLIjg4WAAQP/zwQ5ll09PTBQDRqlUrp/PijzxxTIxGo+jTp48AIOrUqSNOnjzplrw+8MADAoBo0qSJW9ZHyuKJuuzIl19+KQAIAGLv3r1uWSd51rJlywQA0aZNmwqvw9v1zBvb93WZqOLY7gWWN998UwAQt912mzAajVbfGQwGkZSUJACI+fPnu22b58+fF5GRkQKAeP311922XiECrz1jvJIz/CHOlRC7jEf5knM7TmW54/yKnMMJbpnZu3evUKlUQqVSiUWLFomioiIhhBAXLlwQbdq0EQDEgAEDyizXuXNnER8fL+bNm+e2dQohxJNPPikAiPr164ujR49Kn3/++edCp9MJAOLTTz91Q8nly93HxGQySRPRNWvWFMeOHXM6L3PmzBELFiwQ165ds/r82rVrYvTo0dKgYO3ata4XlBTP3XX5jz/+EKNHjy4zeDKbzWLVqlUiIiJCABB9+/b1WJnIvZwdgHmqz3EHufWj5Fts9wLLrVu3RM2aNQUA8eijj4r8/HwhhBD5+fli5MiRAoCIi4sTubm5ZZadOHGiiI+PF4MGDSrz3QMPPCC+/fZbUVhYaPX5Tz/9JJKTk6X1ZmVlubU8gdaeMV7JGf4Q50qIXcajfMm5HaeyOMHtPZzglqF58+YJlUolAIi6deuKVq1aSZPJjRo1ElevXi2zTHx8vAAgpk6d6rZ1CiFEXl6euOOOOwQAoVarRYsWLaRfxQGIiRMnurPosuXOY7Jq1Spp/yUkJIjOnTvb/du/f7/Vss8884wAIFQqlahfv75o3769aN68udBoNAKACAoKErNnz/bkriA/5866fODAAakux8TEiFatWol27dqJ6Oho6fOUlBRx8+ZN7xSOXJaRkSGqVq0q/YWHhwsAQqPRWH1eul3xVJ/jLnLqR8n32O4Flp07d4qwsDABQERHR4s2bdpIxyc8PFzs3r3b5nIjRowQAERqamqZ7yxXboaGhopmzZqJDh06iLp160rHvE6dOuLAgQMeKU+gtWeMV3KGP8S5EmKX8Shfcm7HA11Fz6+o8jjBLVObNm0SvXv3FjExMUKn04mGDRuKyZMni5ycHJvpy+sMK7JOC4PBIGbPni2aN28uQkNDRWRkpEhNTRXr1q2rTBH9jruOieUXPGf+tmzZYrXs7t27xdNPPy06duwo4uLihE6nE3q9XjRs2FCMGjXKYydXpCzuqss3b94UM2fOFH379hWJiYmiSpUqIjg4WMTGxorevXuLFStWCJPJ5IUSUUWdOnXKqbao9LH3ZJ/jLnLqR8n32O4Flr/++kuMGDFCxMXFieDgYBEXFyceeeQRceLECbvLOJr4euedd8TgwYNFcnKyiImJERqNRkRFRYlOnTqJ1157zeMTMoHWnjFeyRn+EOdKiF3Go3zJuR0PZBU9v6LKUwkhBIiIiIiIiIiIiIiI/EyQrzNARERERERERERERFQRnOAmIiIiIiIiIiIiIr/ECW4iIiIiIiIiIiIi8kuc4CYiIiIiIiIiIiIiv8QJbiIiIiIiIiIiIiLyS5zgJiIiIiIiIiIiIiK/xAluIiIiIiIiIiIiIvJLnOAmIiIiIiIiIiIiIr/ECW4iIiIiIiIiIiIi8kuc4CYiIiIiIiIiIiIiv8QJbqJyTJs2DSqVCt26dfN1VnwqISEBKpUKy5cvr9DyW7duhUqlgkqlcm/GiNzg9OnTUv08ffq0r7ODbt26QaVSYdq0aRVaXm7lId+z1IetW7dWaHm24cUeeeQRqFQqPPLIIxVeR2WPBZHSVLbPcyd3jPsrO2Ym33NHW0/ywrgMDP4wxlq+fDlUKhUSEhJ8nRWfc3f/zwnuAGYZwKlUKoSFheHChQt205acLJFzY+GKgwcPYtq0aXjrrbd8nRW/N23aNEybNo0TaV6khPj99NNPMW3aNHz66ae+zopfeeuttzBt2jQcPHjQ11khWMdiyb+QkBDUqVMH/fv3x8cffwwhhK+zalNmZqbUhmdmZvo6O35t+fLlmDZtmqzaWX9kNpvx8ccfY/jw4WjYsCGioqKg1WpRo0YNdOnSBS+88AL++OMPX2eTAhDHLdaEEFi7di3uvfdexMfHIzQ0FOHh4UhKSkKXLl0wYcIEfPLJJ8jOzvZ1ViVbt27FtGnTOMnpAM/r/EvpceiaNWvKXaZv375WywTCseZ4NzBwgpsAAHl5eZg+fbqvs+FVBw8exPTp08ud4K5WrRoaNWqEevXqeSdjfmj69OmYPn16QHSOcuSv8fvpp59i+vTpPFF00VtvvYXp06dzgluGYmNjpT+VSoXz58/jiy++wKBBg9C3b18YDAaf5a1Ro0Zo1KgR9Hq91eeZmZlSG+5owK/X66V1kG3Lly/H9OnTOcFdCXv27EGTJk0waNAgrFixAsePH0deXh6qVKmC69evY9euXXjttdfQvHlz3H///TAajb7OMgUQjlv+lpmZiTvvvBMPPvggPv30U2RkZMBkMkGn0yEjIwO7du3CvHnzcN9992HDhg2+zq5k69atmD59erkT3LVq1UKjRo1Qq1Yt72RMRnhe59+WLVvm8PsLFy7g22+/9VJuvK+y413yb5zgJsl7772HY8eO+TobsvPkk0/i6NGj+OCDD3ydFSK7GL9Evnfp0iXpLzc3F3/88Qd69uwJAPj6668xZcoUn+Xt6NGjOHr0KNq3b1+h5du3by+tg8gTvvjiC3Tr1g3Hjh1D1apV8eqrr+LYsWMwGo24fv06jEYjfv75Z0yaNAkRERHYsGED8vLyfJ1tooA0fPhwbNu2DWq1GhMnTsSxY8dgMBhw/fp15Ofn49dff8Xs2bPRokULX2e1Ql599VUcPXoUr776qq+zQuSUatWqISwsDJs2bcLZs2ftpvvggw9gNpsV+3iMyo53yb9xgptQt25d3H777TCZTJg8ebKvs0NELmD8EslTUFAQmjZtis8//xy33XYbAGDRokUwmUw+zhmR/Bw/fhxDhw6FwWBAkyZNcPDgQUyaNAkNGjSQ0qjVarRt2xavvvoqTp06hXvuuceHOSYKXMePH8cXX3wBAJg5cybeeOMNNGjQAEFBxVMLGo0Gt99+O5577jkcPHgQgwYN8mV2iQJCWFgYBg4ciKKiIrz//vt201mu8Obz5UmJOMFNCAoKkn6dXr9+Pfbu3Vuh9Rw4cACPPvookpKSoNfrER4ejhYtWmDKlCm4du2aw2W3b9+Ou+++G9WqVUNoaCgaNWqEF198Ebdu3XL4EP78/Hx8/vnnGDVqFFq2bInq1atDp9MhLi4OAwYMwNdff21zeyqVCiNHjgQAnDlzpsyzU0s+5N7Wy2YKCwtRvXp1qFQqLFiwwGHZli5dCpVKhYiICJtXGp04cQJPPfUUkpOTER4eDr1ej+TkZIwbNw4ZGRkO1+2Kki/WMBqNmDNnDlq0aIGwsDBERkaie/fu+Oabb1xap+UFLBZ33nmn1X505Zfh69ev44477oBKpUL9+vV5NbKTfBm/zryAx1b8Wl5WZxl8vf/++2VisOQt/iXr7q1bt/DSSy+hefPmqFKlitVz4woLC/H999/j6aefRtu2bVGrVi3pua29evXC6tWrvfYc5JIvzBBCYMmSJejQoQMiIiJQpUoV3HHHHVi5cqVL67S0RWfOnAEAjBw5ssx+c1Z+fj7uueceqFQqVKtWDXv27HEpL+S8kJAQPPDAAwCAnJwcqyugT5w4gccffxwNGjRAaGgoIiIi0Lp1a8yYMcPhM0vPnTuH8ePHo2nTpggLC5P6vTZt2mD8+PH4+eefyyxjK7a6deuG+vXrS/+uX7++VX0q2e/Ze8lk//79oVKpcN999zncDydOnJCW37lzZ5nvs7Ky8Morr6BDhw6Ijo6GTqdD3bp1MXjwYLfWz9Lt1rp169CtWzfExMRAr9ejZcuWmD9/PoqKipxep6Wd27ZtG4Di27tLx6azt3oXFRXh8ccfh0qlgl6vx2effeZqEf3SlClTkJ2djZCQEHzyySeoU6eOw/QxMTH49NNPERkZafX5pUuX8Oyzz6Jp06YIDw9HWFgYmjZtiueeew6XL1+2ua7SL+U9c+YMRo0ahXr16iEkJARJSUmYMmUKcnNzpWX++OMPDB06FHXr1kVISAgaNGiAmTNnorCw0OY2SvYJRqMRr732Gm6//XaEhYUhOjoaPXv2tDtmLWnDhg3o168fYmNjodVqERsbi379+uGTTz6xu4wzL3By9GJFd/RnZrMZb7/9Nlq3bo2wsDDExMSgW7duWLduXblldlXpsmzevBl9+/ZF9erVERISguTkZEyfPh0FBQVOr9PVcUt5Zs2aBZVKBbVajYULF7pSPFko+Yg0Z35oCg0Ntfl5Rc6BSo8r9+3bhwcffBC1atWCTqdDYmIiJkyYgJs3b1otZ4lzyyP9tm3bVuYYlnxsiaMxbsmYMJvNmDdvHlq1aoXw8HDUqFEDAwYMwK+//iqlz8vLw8yZM9GsWTOEhYWhatWqGDRoEE6cOOFwv5nNZixfvhy9evWSYr569ero1asX1qxZY3dMW9FzPnee19kjhMCyZctwxx13oEqVKoiMjESHDh2wePFiCCHKPbcoLCzEvHnz0LJlS5ttiTPtnZJZ5jeWL19us37s3LkTx44dQ2JiIrp27Wp3PX/++SfmzJmDtLQ0JCUlSWPUVq1aOTW3U5njVLJNzcnJwZQpU9C4cWOEhoaiatWq6NevH3766Se7267seNeZFw078+L1PXv2YMCAATbnt5zhrXGxxWeffYYePXogKipKmgd4/fXXUVhYWO4+qWxcu5WggDV16lQBQMTHxwshhEhNTRUAxJ133lkm7alTpwQAAUBs2bKlzPcvvfSSUKlUUhq9Xi+0Wq3071q1aon9+/fbzMeCBQuslo2MjJSWTU5OFvPmzbPKZ0nLli2TlgMgQkNDhV6vt/ps4sSJZZaLjY0VERERAoAICgoSsbGxVn9z5swps59SU1Ot1vHEE08IAKJt27b2d7IQolu3bgKAeOSRR8p8t3jxYhEcHCzlVafTidDQUOnfERER4rvvvnO4fmfFx8cLAOI///mP6NChgwAggoODRXh4uLQ9lUolli5d6nD5ZcuWSZ89/fTTIjY2Vlo+Ojraaj+W3DdbtmyR0pV2+vRp0bhxYwFAtGjRQly4cMEtZVYyOcTviBEjBAAxYsQIu/m0xGjJ+N21a5eIjY0VISEhAoAICQkpE4O7du2S0lvq3htvvCEaNmwoAAitViuioqIEAHHq1CkhhHUds8RTyfoNQDzwwAPCbDY73EeW9VWG5XhMmTJF3HPPPQKA0Gg0Urtj+XvppZccLj916lTpszlz5ojY2FgRFBQktQ+l95sz5blx44bo3LmzACDq1asnjhw5UunyBjJLLDoaUv33v/+V0ljq9kcffSR0Op30eZUqVaz+XbduXXH48OEy6zp48KCIjo6W0qnVahEdHW0Vw7Zi0lYbcO+994pq1apJ31WrVs2qPt17771SWntt+Nq1a6WYvH79ut19MG3aNAFA1K9fXxQVFVl9t2fPHqu+RK1WiypVqlj1TbNmzbK7bleUbLcs/XhQUJDUnlj+hg8fXu7yFmvWrBGxsbFSfx4WFlYmNjMyMqT09trj/Px8ce+990r96c6dO91SZrm7dOmS1K7985//rPB6tm7danUc9Xq9CAsLsxqj7Nixo8xyJdvL9evXS+uIiIgQarVa+i4lJUUYjUbx5ZdfSmPNyMhIq9gbNGiQzbxZ2vQXXnhBpKSkSH1C6XpXss0vyWAwiEGDBknpgoKCRHR0tLTfAIjBgwcLo9Fod9v21i2E/bFuyeUr2p8VFBSIXr16WeU9KipK2m/PP/+8U3l0VsmyvP7660KlUgmVSmW1Tct4yWQyObUvKjpuKTlmFkIIs9kstTshISFiw4YNlS6vL3z88cfSfqzoeUpFz4FKjis//PBDaR2RkZFW8dC0aVORk5MjLZeRkSFiY2OlNiE4OLjMMVyzZo2U3tEY11JfJ0+eLNLS0qQ+sGR7Ex4eLn7++Wdx7do10apVK+mYlyxjjRo1xJkzZ2zun0uXLknna5a/yMhIq3/3799fGAyGMstW9JzPlfO6ijCZTFbtmEqlsmrHBg8e7HC/37p1S3Tt2tXu+GfSpEkO2xJ7cenvSp4TFhUViaSkJAFAbNu2rUzaRx99VAAQM2bMsBrXlT5XsOwry3Eq3X7Wrl1bHD161GZ+KnucLMutWrVK3HbbbVLslJzjCQ4OFt98843N7Vd2vOuoP7RwNK8hhBBLly61ao9Kzm81btxYzJ07VzpmtnhzXCyEEBMnTrRqW6KiooRGoxEARNeuXcXkyZPt7pPKxrU7+38hhOAEdwArPUG2Z88eqWJ+/fXXVmkdTZBZJqCrVKkiXn31VXHx4kUhRHFl/+WXX0T37t0FAFGnTh2rgYYQxQNGS+Xv2bOn+PPPP4UQQhQWFoq1a9eKmJgY6UTeVgPwySefiNGjR4stW7aIa9euSZ9fuHBBTJ8+XRr0fPbZZ2WWtTXx5mg/lQ7on376Sdon9iaIzpw5IzXmP/zwQ5m8WxroSZMmidOnT4uioiJRVFQkjh49Kh544AFpgGdv8OMKS0cVHR0tateuLT799FPpROjo0aOiY8eO0oAsMzPT7vK2BgX26kZJ9jqC3377TcTFxUknG1lZWZUqZ6CQQ/xWdILbleWF+LvuhYeHi5o1a4oNGzZIdffs2bMiNzdX2gcPP/yw2Lhxo7h06ZI0iXb9+nUxf/586WR8/vz5ZbbhqQnu6OhoERkZKZYvXy7y8vKkPN99990CKD7RP3bsmN3lKzpAt1eejIwM0aRJEwFANG/eXJw/f76yRQ14zkxwP/vss1b9xb59+6T+qXPnzuLXX38VQhRPfnz++eeiVq1aAoBISkoqE3c9evQQAETr1q3F7t27pXpuMBjEsWPHxBtvvCFef/31Mnmw1wY4W/ftteEFBQVSP/3OO+/YXd5yklJ6EuzUqVPSJN/AgQPFvn37RGFhoRBCiMuXL4v09HRpkP3JJ5/YXb+zLO1OdHS00Gq1Yu7cuVK/c+3aNfHYY49J5dy8ebPd5SszSLd1LG7evClNfNapU0ccOnSoMsX0K6tXr5b2yZdfflmhdWRkZEj1qEmTJlY/Dmzfvl00atRIABAxMTHi3LlzVsuWjIGoqCjRo0cPaf/n5eWJBQsWSBPdU6ZMEZGRkWLQoEHi9OnTQgghcnJyxIsvviit4/vvvy+TP0vdiIyMFDqdTixcuFDk5+dLeR84cKC0vK0xq+XkU6VSifT0dHHz5k0hRPEPlpYTT6B4stjetis7wV3R/mz8+PFS3mfOnCnF2+XLl8Xjjz8u7Rd3neBayhIVFSWCgoLECy+8IK5evSqEECIrK0u89NJL0v6ydVGHo33h6rilZD9dUFAgHeeoqCibk0/+4tSpU9L5TfPmzaXzN2dV5hzIMq7U6/VCp9OJxx57TPoBMTc3V7z99ttS/5qenl5m285MXgnhXFsfFRUlqlatKtauXSuMRqMoKioSe/fuFYmJiQKA6NSpk7j33ntFQkKC+Pbbb4XZbBZms1ls2rRJVK9eXQAQQ4YMKbN+g8Eg2rVrJ/X1GzdulMa6t27dEu+//76oUaOGACDGjRtXZvnKnvM5c15XEa+++qq07gkTJkjn7llZWWLWrFnSxJi9/T5mzBiprZk9e7Y0Prp69ap4+umnpWNSmfGzPyp9Tvjyyy/b3Ie3bt0S4eHhIigoSGRkZDic4B40aJD4z3/+I/766y/pRxSDwSA2bdok2rdvL9VNWyp7nCx5io6OFk2aNBE//PCDMJvNUnxZ+vP4+HibFy1Vdrxb2Qnuffv2SePWbt26SfNERqNRrF69WkRFRUnlt3V+7O1xcckx2MMPPyyNkfLz88XixYtFSEiIFJe29kll45oT3OQ2pRtDIYR05VDLli2trrCyN0F29epVodfrhUqlEps2bbK5ncLCQtGmTRsBQMybN8/qO8uJepMmTURBQUGZZX/44Qdpu+VNRNsyZ84cAUD06NGjzHeVneAWQkgN7AsvvGBz2VmzZgmg+Eq8kvvTYDCI2rVr2x1cW/Tv318AEM8884zDPDrD0qnrdDqbE/JXrlyRrkxZuXKl3eXdOcG9detW6aTmgQcesFkHyDY5xK+3J7jVarXdO0GcYbnSNCkpqcx3nprgBsr+uCVE8Ymu5YedmTNn2l3enRPcv//+u6hTp44Ain+Nt0yQUOWUN8GdlZUlHeuYmBhhNpvFXXfdJQCI2267TTppLWn//v3S4LXkHUVCCOnqrx9//NGlfFZ2wO9oMG85mbnjjjtsLvvjjz9Kyx4/ftzqO8uEz7Bhw+xu23KlS4sWLeymcZal3XEUQ5Y277HHHrO7vDsnuM+ePSuaNWsmjYfOnj3rYqn825QpU6R9UtEf3caOHSudEFt+qC3p7Nmz0o+cTzzxhNV3JWOgadOmNsciw4YNk9L07NmzzF0IQgjpBwpbV6GX7BNsjfvMZrN0xVuTJk2svjt37pzUHtgbb06YMEEAxROGpe+Cc9cEd0X6s/Pnz0t5tzXZKIQQgwcPltbvzgluR+u77777BACRlpZmd3l3TnBnZmZK+7F27dri999/d7FU8jNq1ChpP6tUKtGqVSvxr3/9SyxdulT8/vvvNmNEiMqfA5W8e9fecbDEw2233VbmO3dOcAOweVfI5s2bpe9DQ0PL9HtCFF/hafm+9J0Xb7/9ttQeZWdn28zfL7/8IlQqldBqteLy5ctW31X2nM+Z8zpX5ebmSm2wvTt1SsZu6f1+5swZ6aK4l19+2ebyJfv3QJ7gzsjIEEFBQSIsLMzqIon33ntP6sOEEA4nuB3JycmRri4uXf/dcZws31WvXr1M3Rai+OI4Sxpbd7pVdrxb2Qnu3r17CwCiYcOG0o/BJX3zzTfSsrbOj705Li4qKhINGjRwOLYp2eaW3ieVjWsh3D/BzWdwk5VZs2ZBrVbj4MGDWL16dbnpP/zwQ+Tl5aFt27bo0aOHzTQajQaDBw8GAHz77bfS5zdu3MAPP/wAAHj22Weh0+nKLHvnnXciJSWlIkUBAPTt2xcAsHv3bpjN5gqvx55hw4YBKN4PQogy369YsQIAMHToUKtnNH399dc4f/48YmNjpWdl2TJ8+HAA1vutsgYOHIjGjRuX+bx69eq44447AAC//fab27Znz/r169GrVy9kZWXhySefxJo1a2zWAXKeN+PXF+666y60atWqwstb2oMTJ07g4sWL7sqWQ507d8add95Z5nOdTodevXoB8E687dy5EykpKTh37hzuu+8+fPfdd4iKivL4dgNZZmYmNm/ejO7du+PChQsAgGeeeQbZ2dlSLD377LPQ6/Vllm3VqpX0TOvSsWw5bt6qw86w9IW7d+/GX3/9VeZ7S194xx13SC/cBIrHARs2bAAATJo0ye76LX3hr7/+avc5yq6qW7eutN7S+vfvD8A7sXn48GF06tQJf/zxBzp37oydO3eW+/xppbl+/br0/zExMS4vL4TAxx9/DAAYO3YsatasWSZNnTp1MHbsWADAmjVr7K5r/PjxNscilvYaKK6rtp676UybXrduXZvjvqCgIEyZMgVAcZ34/fffpe/Wr18Pk8mEkJAQu3EyZcoU6HQ6FBYWeuS51kDF+rN169bBZDIhNDQU//73v22u11PPytXpdHa3aXlutDdi/MKFC0hJScG2bdvQuHFj/Pjjj2jWrJnHt+tp//vf/5Ceno6wsDAIIXDgwAH873//wz//+U80b94cNWvWxIQJE8q02e48B7LETGmW4/vXX3/ZfP+Ru3Tp0gVdunQp83lqaqrUjgwcONCq37OwxEx+fj6OHz9u9d27774LAPjXv/6FKlWq2Nx2mzZt0LRpUxiNRmzZssVmGrmc8wHFx9HybpEXX3zRZpqJEyfaHBMBxe1gUVER9Ho9xo8fbzNNenq6ezLr5+rWrYu0tDTk5uZKfSPw98slH3300UqtPzw8HKmpqQBQ5p0q7jxOo0ePRo0aNcp83rx5c+l52t6qv87KzMy0GuPbev9Ar169pPgrzdvj4oMHD0rtz+TJk22ObUaMGIF69erZXL6yce0JnOAmK40bN5YGG+np6XZflmNhadT++OMP1KxZ0+7fjBkzAEB6ORpQ/FI7y6SwpZG0xdED/gHg8uXLmDp1Ku644w5UrVoVGo1Geuh/kyZNABS/3KP0y0bcYdiwYVCpVMjIyJBeLmWxb98+HDlyBADKnERb9tvNmzdRq1Ytu/tt1KhRAKz3W2V16NDB7ndxcXEAihtXT/rvf/+LBx98EAaDAa+88gr+85//SG9ep4rzZvz6QufOnctNk5OTgzlz5iA1NRU1atSAVquV2oOSnev58+c9mVWJHOLtk08+Qc+ePZGZmYnHH38ca9eu5Y9JHlLyhTXR0dFIS0vDvn37ABT/0Pniiy9i//79Ut+XlpZmd109e/YEUDx4LxnL/fr1A1A84Jw4cSK2bdvm0RN4Z3Tu3BlJSUkAUOZlc0ajER999BGAsn3h7t27pZc5du/e3W4b1LRpU2kZd7VD7dq1s9vveCs2f/zxR6SkpODs2bO455578P333yM6Otqj25QjWxcIuOLUqVPSsXImpq5fv45Tp07ZTNO+fXubn8fGxkr/365dO4dpHI03LS/WsqVr167QaDQAgF9++UX63PL/7dq1Q0REhM1lo6Oj0bZt2zLLulNF+jNLXtq2bWs37w0bNkTt2rXdlMu/WV40aou3Yvzo0aPo1KkTfv/9d9xxxx3YtWuX3YkCf6PRaDBjxgycP38eK1aswGOPPYYWLVpAq9UCAK5cuYJ58+ahWbNmVi9Ad9c5UExMjM2JY+Dv42vZjqfYay/UajWqVasGoPz2ArDOY05OjjRpl56e7nB8/ueffwKwv4/kMAa12L9/PwCgXr16Vi/7K6lKlSpo06aNw+Xbtm2LsLAwm2mSkpJQt25dN+TW/1nOB9977z0AxT/27NixA1FRURgwYIBT6/jyyy8xaNAgJCYmIiwszGqMa5k4P3funNUy7jxOcqq/ztq/f7/VuNYee995e1xsOV7BwcHo1KmTzTQqlcruXF1l49oTNF7bEvmNadOm4cMPP8TJkyexcOFCPPXUU3bTWq5Ky8/PR35+frnrLnkSfvXqVen/Sw5ESnM06N29ezf69OmDzMxM6TPLW7hVKhXMZrP0lt/c3FxpsOEu9erVQ2pqKrZu3YoVK1ZYTcZbrlhr165dmV/PLfvNaDQ69aubM/vWWfauBAAgnViVNzFaWU8++SQA4IknnsDkyZM9uq1A46349QVbv+KXdOzYMfTo0cNqsKXX6xEVFSVNZFniLTc313MZLUEO8TZhwgQAxVew/+9///PotgJdyRNWnU6HatWqoVWrVhgyZIh05eOVK1ekNI76N8tVvCaTCTdu3JDW/frrr+Ovv/7Cli1bMHfuXMydOxdqtRotW7ZE3759MXr0aI9MFpVn2LBhmDZtGlasWGF1ReZXX32FGzduQKfTYdCgQVbLWNogAE5fgeKudkgOsWm52uX222/H+vXroVarPbo9uSo5Nrtx44bDMaEtrsaUZRlbJ2P26oWlTjiTxlG9cZQ/nU6HqlWr4vLly1Zlsvx/eXFtKV/JZd2pIjHjSt7d/cOzM/k1mUxu3WZps2fPBlDcN3z33Xd2J9z9WWRkJIYOHYqhQ4cCAAoKCrBz504sWLAAX3zxBa5du4b7778fx48fR0hIiNvOgZw5voBn23Fn8uBMm1Iyj5cuXZImuJydvLPXL8qhn7OwnPeX177baytcWf7s2bMVyKGy3HvvvYiOjsauXbtw7NgxvP/++wCAhx9+GCEhIQ6XLSoqwtChQ63uINRoNIiOjpZ+wMrKykJBQUGZ8yl3Hic51V9nVWQ8UpK3x8WW41W1alXp2Nrijrj0Fl4ySWXUrl1bmhSbOXMmbt26ZTet5bEfY8eOhSh+prvDv9OnT0vLlrxix97VLKXTlWQymTB48GBkZmaiZcuW+Oqrr5CdnY2cnBxcvnwZly5dwp49e8pdT2VZbs1et26dNAgzmUxSp2DrFmjLfrvrrruc2m+eyruvWAbBS5YswRdffOHj3CiLt+LXF8qb/Bk5ciTOnTuHhIQErF27FtevX0dubi6uXLmCS5cuWZ08Ky2mHLHE21dffYWFCxf6ODfKdunSJenvzJkz2LdvH959912bt/W7omQfGRUVhR9++AE7duzAc889h86dO0Oj0WDfvn2YMWMGGjRo4NQjitzN0heePHkSu3btkj63/Njbr1+/MlcnW9qg0NBQp/vC8u7q8idDhgyBSqXCb7/9FtC3Vpe8EunAgQOVWpej8WRF0rlbZbYr97I5Isc8ecMDDzwArVaLy5cv4/HHH/fI4xLlJiQkBGlpafj8888xYsQIAMVXeX7zzTcAeA5UnpJ1ZM+ePU7tH0895sedLMexvLbA3vGu7PKBRqfTSY+YXLp0KT744AMAcPhYIIulS5di9erVUKvVeOmll3D8+HEYDAbcuHFDGuMOHDgQQNn9zeNUOd4eFysxLjnBTTa98MILiI6OxpUrV/Dmm2/aTWd5zmHJZwU6q+TVmCV/rSrN3ne7d+/GmTNnoFar8eWXX6J3795lfum7dOmSy/ly1QMPPIDQ0FBkZ2fjs88+AwB89913uHLlCoKDg/HQQw+VWaYy+00J3n//fYwYMQJGoxH3338/Pv30U19nSVG8Eb/A37+eFxQU2E2TlZVVoXW76uzZs/jxxx8BFD+zeODAgWWe5eqN9kCOXn75ZaSnp0MIgX/961/473//6+ssBbSSfV/pWztLsnxnuWqmtC5dumD27NnYuXMnMjMz8dlnn6F58+bIz8/Ho48+6rZnVTsrMTFReoyQZVL75s2b2LhxI4C/J8BLsrRB+fn5Np/drXSPPfYYFi9eDJVKhVdffRXPPfecr7PkE3feead0l80nn3zi8vIlY8rRFWEl46169eoub8cdHMW8wWCQnkdeskyW/y/vajfLukuXzZd9tSXvjsoNeO+xYd7Wp08ffPLJJ9DpdFi5ciWGDRsWEJPcFqNHj5b+3/I4jUA/BypPyTvBlLSPLG2Bo3N+R99XdvlAZJnMfuutt3Du3Dk0a9ZMepSVI5b3VDz22GOYPn06brvttjKPdLN3TqWE41SZPrNk3+2oX7P3nbfHxZb8Xrt2DUaj0W46f4pLTnCTTVFRUdKD7d988027tztaTmb37Nnj8vN/WrVqJf3as3XrVrvp7H1nGehXr17d7m0PmzZtsrteS0Nd2V+UqlSpIj3LynJSb/lv7969bT4WxbLfzp8/X+blDP7Ichyd3ZdBQUF477338M9//hOFhYV48MEHsX79ek9mMaB4I34BSJNujk66f/rpJ7vfuSsGS+fB3osoHbUH/qQi+23GjBmYNm0ahBB48sknMX/+fE9lj8rRunVr6Rhu3rzZbjpLfW3RogWCg4MdrjMkJAT9+/eXXkxjuUXcGSVPWiobi5Y7lj7++GMYDAbpv9WqVUOfPn3KpO/UqZPUfzh68Z+/qEhsPvbYY1i6dCmCgoIwZ84c6ZFCgSQ2Nhb3338/AGDVqlU4duyY08sKIVC/fn3pB01nYqpq1ap2nxXpadu2bbNbP3bs2CE9MqPkJETJZ2vbO6nOzMy0elZ3SZXtqyujZN5zcnJspjl+/Hi5E+ByUZEY79OnDz777DOEhIRg9erVGDx4sMcfjSIXJR/JYnn3h6/Pgdw59vSE6Oho6R1SvuoXXT2vc0br1q0BFD8v2N4dobdu3ZLeW2Jv+V9++cXuYwZPnjzJx5OU0LZtWzRv3lyauHT25ZKWfWjvfOrWrVt2+ww5Hydnx7uV6TNLjvHtvfwVAH744Qebn3t7XGw5XoWFhdKFYqUJIbB9+3aHy1c0rj2BE9xk19NPP406deogJycHM2fOtJlm2LBhCA0NhdlsxhNPPOHwqoSioiKrZ2XHxMRIt22/+eabNn812r59O3bs2GFzfZGRkQCKn09k60q1c+fOYcGCBXbzY3nZTck8VZTlpP67777D8ePHpSu5bT2eBADuvvtu1KpVCwDwzDPPlPvsJLm9QKG0iuzLoKAgLFmyBKNHj0ZhYSEeeughqzc9U+V4On6B4ok3APj5559tDgKOHDkiTbjZ4s4YtLQHQPEbpUtztB/8TUX329SpU6V9MG7cOMydO9fdWSMnREVFoVevXgCAOXPm2Gz/f/31V+lHP8stpkDx468sz+a0peTb2p19nnPJF79VNhYffPBB6HQ63Lx5E19++aX0Y+9DDz1kc5K+Ro0auOeeewAU74vyJjaV2BcCxVdZLV++HEFBQZg3bx6eeeYZD+RO3mbOnInw8HDk5+fjvvvuK/eK3ps3b+L+++9HVlYWVCqV9Hz3RYsW2byy7MKFC1i0aBEA65jytoyMDOlZqCUVFRVh1qxZAIDk5GQ0b95c+u7++++HRqNBQUGB9Ezn0mbNmgWDwYDg4GDpxwILS1/97bff2pxw+OGHH7B79+4Kl8kRS97z8/Pt3lFmeZG1P6hojPfq1QtffPEFQkNDsXbtWjz00EOye3asK06dOuXUD1El67plIsTX50DuHHt6iuXK982bN5c7yeWJftET++gf//iHtF5LW1favHnz7NaH++67D0FBQcjNzbV7kcYrr7zinswqyOzZszFx4kRMnDhRemRheSznVLbOp4DiO0Pt/WAp5+Pk7HjX0mdeuHDB6nG3FleuXMGSJUtsLhsVFYV//OMfAIA33njD5lXgmzZtsjuZ7O1xccuWLaWX9b722ms2J/5Xrlxp90K4ysa1J3CCm+wKCQmRnull7znJNWvWxGuvvQYA2LhxI3r27Ildu3ZJE2VCCBw9ehRz585Fs2bN8OWXX1otP336dKhUKvzxxx/o378/jh8/DqD4JH7Dhg24//77bd6aDRTfnh0WFgYhBB588EGpATCbzfj2228dvqkeAJo1awYAyM7OrvTEas+ePVGzZk2YTCY8/PDDyM/PR3R0NPr162czfUhICP73v/9BpVJh//796Ny5M7799lurSf5Tp05h0aJFaN++vexfDGfZlx9++KFLDZhKpcLChQvxr3/9S9p3vnh2rBJ5I37vvvtuhIeHS1fhW24/LSwsxGeffYa0tDS7b9AG/q43O3bswNGjRytV3iZNmqBevXoAiq9QKPlL8e7du9GtWzert9T7M8t+W7dunctlevHFF6VjPnHiRLz++utuzx+V75VXXkFwcDD++usv9OrVS7oNuaioCF999RX69OkDk8mEpKQkjBkzRlru3JsCMDQAAA9ISURBVLlzaNCgAWbOnIkDBw5YXQX422+/SScvYWFh6Nq1q1N5iYqKku6CWrZsWaWuLIyKisLdd98NAHj11VelZ3HbejyJxZtvvomqVasiOzsbXbp0wXvvvWd1leq1a9ewYcMG3HfffT6dmHSGJTa/+uorlx+5MGzYMKxYsQJqtRoLFizAE088IdsrDD2hYcOGWLFiBbRaLQ4dOoSWLVti9uzZVrfoms1mHDhwAC+99BISExOtfkCdPHkyoqKicOPGDaSlpVmdPO7atQtpaWnIzMxETEyMdIeTL0RGRuLxxx/HkiVLpBPfs2fPYvDgwdLVXqVP/mvXri396PHaa69h6tSp0sl5ZmYm0tPTMWfOHADFLxW2TB5aPPjggwgKCsL169cxePBg6Wrp/Px8vP/++7j33nvLPNLLXWrXro1//etfAIonRV599VVpYuTq1at48sknsXLlSqsfqeWsMuOWtLQ0bNy4EXq9HuvXr8eDDz7o8JZwOTt06BCSk5PRt29ffPDBB1ZX7hUWFuLAgQMYOXKk9EN6+/bt0aVLFwC+PweyHMNDhw7ZnWTytbFjx6JDhw4AivuGKVOmWF1IkpeXh61bt+LJJ59EUlKS27df0fM6R8LCwvD8888DKH4H03PPPSdNzuXk5GD27NmYNm2a3fP++Ph4/POf/wQAvPTSS3jjjTek9wxdv34dEyZMwHvvvYeoqCi35FcpevfujTfeeANvvPGG04/muuuuuwAUH6fFixdLsXnp0iWMHz8er7/+OqpWrWpzWTkfJ2fHu506dUJ8fDwA4JFHHsEvv/wCIQSKioqwdetWdOvWzeHFJi+//DLUajWOHj2Kvn37SufHJpMJH3/8MR588EGH5ffmuFilUmH69OkAin8EHzFihPQ4kYKCAixduhRjxoyxG5eVjWuPEBSwpk6dKgCI+Ph4u2lMJpNo3LixACD9bdmypUy6119/XajVaimNVqsVVatWFcHBwVbLrly5ssyy8+bNs0oTFRUldDqdACCaNWsmfd+oUaMyy77zzjtWy4aHh4uQkBABQFSrVk18/vnn0nenTp0qs3yPHj2k76tUqSLi4+NFfHy8mDdvXpn9lJqa6nB/TpgwwSovY8aMcZheCCFWrlwp9Hq9tIxGoxFVq1aVym/5mzlzZrnrKk98fLwAIJYtW2Y3zYgRIwQAMWLECJeWX7FihZTX4OBgUbt2bREfHy86d+4spdmyZYuUxpann35aABBqtVp88MEHrhYv4Mglft99912rNFWqVBFarVYAEB07dhRvv/223XzeuHFDVK9eXVq2WrVqUgzu3r1bSudM3RVCiC+++EJoNBppfXq9XoovvV4vNm3aZHc/nDp1ymFb4arU1FQBQEydOtVuGkdti6Plt23bJlQqlRQvtWrVkvabs+V54403pO9feeUV1wtIEstxdHVItWbNGilWAIiIiAip/wIg6tatKw4fPmy1TMnjajn+MTExVuvRarVi7dq1ZbbnqA14+eWXpe91Op2oW7euiI+PF4MGDZLSlNeGW5Tsd+313aXt379fJCQkSMuoVCoRHR0twsPDrdaVlpZW7rrK46ifs1i2bJnddsvR8seOHZOOYVBQkIiNjZVi8+zZs1I6R8fio48+ktqxMWPGiKKiogqU0n/t3LlT3HbbbVbHXavVipiYGBEUFGRVRwYPHiyMRqO07NatW0VkZKSUJiwsTISFhVmNL7dv315mm860/87Uf0f1xtKmv/DCC6JLly7SeCk6OtqqrFOmTLG5boPBIB588EEpXVBQkIiOjrbaJ6X3R0np6elW24mMjJTq2YABA8SUKVMq1B9ZOOrP8vPzRVpamlW7FR0dLfVjzz//vFPbcJYz43ZHx9PR8u4Yt2zbtk1q2/r16ycMBkNFiulT33zzjVV9KhmnluNq+WvdurU4f/58mXVU9BzIUZxZOIrpwsJC0ahRI+n76Oho6RiW7DsdtfXO1Fdnxq6O+oKrV6+K7t27W+2LiIgIERUVZbWPNRpNhbbtqHzOnNdVRGFhoRg4cGCZdsxyDjJs2DAxfPhwqf8rLScnR2o/bbUlU6ZMEV27dhUAxKuvvlpmeWfPJ/yNM+eEtpRsB0vGyc2bN63OH4OCgqzq3ZgxYxzWn8oeJ0dxYeEoBis73hWiuI0reR6s1+ul8V2DBg3E6tWrHY4JFi1aZBWnkZGRUtvWuHFjMXfuXIfHzJvjYiGEGDduXJltWcrfvXt38cILLwgAolevXmWWrWxcu7P/F0IIXsFNDqnVaru3G5T07LPP4ujRoxg/fjxuv/12hISEIDMzE+Hh4WjXrh2ee+45/Pjjj3j44YfLLDtu3Dhs3boVffr0QXR0NAoKCpCQkIApU6ZIb48GYPOXrrFjx2Ljxo3o1q0bwsPDYTKZULt2bTz11FP49ddfrW7xtGXdunUYP348GjZsiMLCQpw5cwZnzpyp0C1ZpR9HYu/xJCUNGTIEf/31F6ZMmYK2bdsiPDwcmZmZCAkJQcuWLfHkk09i06ZN0i9jcjV06FCsWLECXbp0gV6vx8WLF3HmzBmXnqk4f/58TJgwAWazGY888giWL1/uuQwHCG/E7z//+U989dVX6N69OyIiImAymdCwYUO89tpr2LZtm8MruKOjo7F9+3Y89NBDqF27NrKysqQYdPRiD3v69euH7du3o2/fvoiKioLJZEK1atUwcuRI7N+/Hz169HB5nXLUtWtXbNy4EWlpaYiMjMTly5el/easiRMn4q233gJQfFW3P90irhSDBg3CoUOHMGbMGCQlJcFgMECj0aBly5aYPn06/vjjDyQnJ1stU7t2bXz++ecYP348OnbsiFq1auHWrVvQaDRo0qQJnnjiCfzxxx/S2+2dNXnyZMyfPx9t27ZFcHAwzp07hzNnzlToxay9e/e2ukrImb6wVatWOHz4MN5++22kpaWhWrVqyMnJQVFRERo0aICHH34Ya9ascfjIIzlo0KABtmzZgv79+6N69eq4fv26FJvOXhn/4IMPYs2aNQgODsaiRYswevTogLqSu3Pnzjh69ChWr16NIUOG4LbbbkNISAhycnIQExODLl264MUXX8SRI0ewatUqq0ffpKam4ujRo5g4cSKSk5NRVFQEIQSSk5Px73//G0eOHEFKSooPSwdotVps3rwZs2bNQqNGjWAwGBAZGYkePXpg48aNePnll+0u99FHH2H9+vXo3bs3qlatipycHFStWhW9e/fGhg0byuyPkmbMmIEVK1agY8eOCAsLg9lsRsuWLbFw4UJs2LDB6UcaVURISAi+/vprzJ8/Hy1btoRWq4UQAikpKfj444+lu4r8gTvGLV27dsXXX3+NKlWq4Msvv8SAAQNgMBg8nHP36tWrF44fP4758+fjgQceQHJyMnQ6HTIzM6HX69GgQQOpLfv5558RFxdXZh2+OgfSaDTYvHkzHnvsMSQkJCA3N1c6hpYrTeWgWrVq2LRpEz777DMMHDgQdevWhcFgQH5+PmrXro3evXvj7bfftvvc28pwx3mdLRqNBh9//DHeffddtG/fHqGhoTCZTGjbti3effddfPDBB9I5uK3z/vDwcGzevBlz5szB7bffLrUlqamp2LBhA15++WWHy5NzoqKi8OOPP2LcuHFISEiAWq2GRqNBt27dsHr1aixcuNDh8nI+Ts6Od3v16oUdO3agX79+iI6OhtlsRt26dTFp0iTs27dPehmkPaNHj8auXbtw9913IyYmBgaDAfHx8XjhhRewd+/ecq9o9va4eN68ediwYQO6deuGKlWqwGAwIDk5GXPmzLF6vJmt41XZuHY3lQikUTP5pSFDhmDVqlV49NFHsXTpUl9nh4iIiIjIad26dcO2bdswdepU6fFhRET0NyEE6tWrh3PnzuGDDz5w+HgzW27duoWqVavCaDRi+/btPv9Bk2zjcfI/nTt3xo8//ogZM2YgPT3dpWUrG9eu4hXcJGvHjh2TfpmyPA+KiIiIiIiIiJRhxYoVOHfuHDQaTYXuupw7dy6MRiNiYmLQrl07D+SQ3IHHyb9s27ZNel9BRebjKhvXruIEN/ncSy+9hLfffhsZGRnSA/tzc3Px0Ucf4c4770RBQQEaN26MAQMG+DajREREREREROSywYMHY926dbh27Zr02eXLl/Haa69h1KhRAIofbWbrsTY5OTl46KGH8M0331g9TvTMmTN49tlnpbtjxo0bh5CQEI+Wg+zjcfI/TzzxBJYvX45Lly5Jj8XLzMzEokWLcM899wAAunfvbvcHicrEtbvxESXkcwMGDMBnn30GAAgODkaVKlWQmZkpTXbXrl0b33zzjfRGZyIiIiIif8FHlBARFT+DNysrCwCg1+sRHBws/RsAUlJS8OWXXyIiIqLMspmZmVbPLq5SpQqA4glVi/vvvx9r1qyBRqPxVBGoHDxO/qdly5b49ddfAQA6nQ56vR6ZmZnSZHeTJk3w3XffoXbt2jaXr0xcuxtrFPnc+PHjERcXhx9//BEXL17EjRs3UKVKFTRs2BD9+vXDk08+iZiYGF9nUxbatWuHs2fPurTMzz//jLp163ooR0TKdd9990m3ZDlrw4YN6NSpk4dyREQA8Mwzz+Cjjz5yaZn58+dj0KBBHsoREbnTG2+8gTfeeMOlZf7973/j3//+t4dyRERA5cfGCxYswNdff40DBw7gypUruHXrFqpXr46WLVvioYcewrBhw+y+LDc8PBxvv/02vv/+e/zxxx+4evUq8vPzUatWLbRt2xbDhw/H/fffD5VKVelyUsXxOHlfZcfFM2bMwCeffIK9e/fi8uXLyMrKQnR0NJo2bYr77rsPo0ePhl6vt7uuysS1u3GCm3wuNTUVqampvs6GX7h69SouX77s0jJms9lDuSFSths3brgcb0aj0UO5ISKLrKwsl2MzPz/fQ7khKt/WrVt9nQW/cuvWLZdj/NatWx7KDRFZVHZsPHz4cAwfPrxC29ZoNHjiiSfwxBNPVGh58g4eJ++r7Li4f//+6N+/f4W3X5m4djc+ooSIiIiIiIiIiIiI/BJfMklEREREREREREREfokT3ERERERERERERETklzjBTURERERERERERER+iRPcREREREREREREROSXOMFNRERERERERERERH6JE9xERERERERERERE5Jc4wU1EREREREREREREfokT3ERERERERERERETklzjBTURERERERERERER+6f8AHBHtiyreW60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x1500 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See the pairplot for this combined data\n",
    "sns.pairplot(combined[['Sentence_nltk', 'Negative_nltk', \n",
    "                       'Neutral_nltk', 'Positive_nltk',\n",
    "                       'Compound_nltk', 'Sentiment_ggl',\n",
    "                       'Magnitude_ggl']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update + Change Log\n",
    "\n",
    "March, 2023 - Created notebook.<br>\n",
    "April, 2023 - Prepared for GitHub.<br>\n",
    "October, 2023 - Revised to match as published in book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}