Copy_of_BPML2_2.ipynb

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    "colab": {
      "name": "Copy of BPML2-2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNBcFALIYlFiNFSSpsW4Jmv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/arya1406/airline-twitter-statement-classification/blob/master/Copy_of_BPML2_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dE2TsAmyWhV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd1e3f02-c8b1-4d15-b223-bd3009793613"
      },
      "source": [
        "# untuk menginstall library loss plot \n",
        "!pip install livelossplot"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: livelossplot in /usr/local/lib/python3.7/dist-packages (0.5.4)\n",
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            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->livelossplot) (54.2.0)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejzEld3Sc-Fy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0087c07-b83b-4abb-e626-02120f9a06eb"
      },
      "source": [
        "# import library yang dibutuhkan\n",
        "\n",
        "import pandas as pd        # manipulasi dan analisis data\n",
        "from nltk.corpus import stopwords\n",
        "import seaborn as sns\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwO79wRY-5X2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "outputId": "4f0af61c-f090-4bd6-f813-b7dc7a79d854"
      },
      "source": [
        "# inisialisasi dataset\n",
        "\n",
        "df = pd.read_csv('Tweets.csv')\n",
        "\n",
        "df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>airline_sentiment</th>\n",
              "      <th>airline_sentiment_confidence</th>\n",
              "      <th>negativereason</th>\n",
              "      <th>negativereason_confidence</th>\n",
              "      <th>airline</th>\n",
              "      <th>airline_sentiment_gold</th>\n",
              "      <th>name</th>\n",
              "      <th>negativereason_gold</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>text</th>\n",
              "      <th>tweet_coord</th>\n",
              "      <th>tweet_created</th>\n",
              "      <th>tweet_location</th>\n",
              "      <th>user_timezone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>570306133677760513</td>\n",
              "      <td>neutral</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>cairdin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:35:52 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>570301130888122368</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.3486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:59 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>570301083672813571</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.6837</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yvonnalynn</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:48 -0800</td>\n",
              "      <td>Lets Play</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>570301031407624196</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Bad Flight</td>\n",
              "      <td>0.7033</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:36 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>570300817074462722</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:14:45 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14635</th>\n",
              "      <td>569587686496825344</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.3487</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>American</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KristenReenders</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@AmericanAir thank you we got on a different f...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-22 12:01:01 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14636</th>\n",
              "      <td>569587371693355008</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Customer Service Issue</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>American</td>\n",
              "      <td>NaN</td>\n",
              "      <td>itsropes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@AmericanAir leaving over 20 minutes Late Flig...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-22 11:59:46 -0800</td>\n",
              "      <td>Texas</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14637</th>\n",
              "      <td>569587242672398336</td>\n",
              "      <td>neutral</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>American</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sanyabun</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-22 11:59:15 -0800</td>\n",
              "      <td>Nigeria,lagos</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14638</th>\n",
              "      <td>569587188687634433</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Customer Service Issue</td>\n",
              "      <td>0.6659</td>\n",
              "      <td>American</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SraJackson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@AmericanAir you have my money, you change my ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-22 11:59:02 -0800</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14639</th>\n",
              "      <td>569587140490866689</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.6771</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>American</td>\n",
              "      <td>NaN</td>\n",
              "      <td>daviddtwu</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@AmericanAir we have 8 ppl so we need 2 know h...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-22 11:58:51 -0800</td>\n",
              "      <td>dallas, TX</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14640 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 tweet_id  ...               user_timezone\n",
              "0      570306133677760513  ...  Eastern Time (US & Canada)\n",
              "1      570301130888122368  ...  Pacific Time (US & Canada)\n",
              "2      570301083672813571  ...  Central Time (US & Canada)\n",
              "3      570301031407624196  ...  Pacific Time (US & Canada)\n",
              "4      570300817074462722  ...  Pacific Time (US & Canada)\n",
              "...                   ...  ...                         ...\n",
              "14635  569587686496825344  ...                         NaN\n",
              "14636  569587371693355008  ...                         NaN\n",
              "14637  569587242672398336  ...                         NaN\n",
              "14638  569587188687634433  ...  Eastern Time (US & Canada)\n",
              "14639  569587140490866689  ...                         NaN\n",
              "\n",
              "[14640 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyhcP88CkUXm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "6449202d-9678-43c5-ead1-555e0a6fb4ee"
      },
      "source": [
        "# men-drop kolom yang tidak digunakan\n",
        "\n",
        "df = df.drop(columns=['tweet_id',\n",
        "                      'airline_sentiment_confidence',\n",
        "                      'negativereason',\n",
        "                      'negativereason_confidence',\n",
        "                      'airline_sentiment_gold',\n",
        "                      'negativereason_gold',\n",
        "                      'retweet_count',\n",
        "                      'tweet_coord',\n",
        "                      'tweet_created',\n",
        "                      'tweet_location',\n",
        "                      'user_timezone',\n",
        "                      'name',\n",
        "                      'airline'])\n",
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>airline_sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>14635</th>\n",
              "      <td>positive</td>\n",
              "      <td>@AmericanAir thank you we got on a different f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14636</th>\n",
              "      <td>negative</td>\n",
              "      <td>@AmericanAir leaving over 20 minutes Late Flig...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14637</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14638</th>\n",
              "      <td>negative</td>\n",
              "      <td>@AmericanAir you have my money, you change my ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14639</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@AmericanAir we have 8 ppl so we need 2 know h...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14640 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      airline_sentiment                                               text\n",
              "0               neutral                @VirginAmerica What @dhepburn said.\n",
              "1              positive  @VirginAmerica plus you've added commercials t...\n",
              "2               neutral  @VirginAmerica I didn't today... Must mean I n...\n",
              "3              negative  @VirginAmerica it's really aggressive to blast...\n",
              "4              negative  @VirginAmerica and it's a really big bad thing...\n",
              "...                 ...                                                ...\n",
              "14635          positive  @AmericanAir thank you we got on a different f...\n",
              "14636          negative  @AmericanAir leaving over 20 minutes Late Flig...\n",
              "14637           neutral  @AmericanAir Please bring American Airlines to...\n",
              "14638          negative  @AmericanAir you have my money, you change my ...\n",
              "14639           neutral  @AmericanAir we have 8 ppl so we need 2 know h...\n",
              "\n",
              "[14640 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-95Q9KUSSSnQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7383ce98-e283-496a-d226-dd78414377b6"
      },
      "source": [
        "# menghitung tiap kelas\n",
        "count_neg, count_neu, count_pos = df.airline_sentiment.value_counts()\n",
        "\n",
        "#  kelas menjadi dataframe baru\n",
        "df_class_neg = df[df['airline_sentiment'] == 'negative']\n",
        "df_class_neu = df[df['airline_sentiment'] == 'neutral']\n",
        "df_class_pos = df[df['airline_sentiment'] == 'positive']\n",
        "\n",
        "print (\"jumlah negatif statement = {}\".format(count_neg))\n",
        "print (\"jumlah netral statement = {}\".format(count_neu))\n",
        "print (\"jumlah positif statement = {}\".format(count_pos))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "jumlah negatif statement = 9178\n",
            "jumlah netral statement = 3099\n",
            "jumlah positif statement = 2363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6-chV4xO1l_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "6f6bf094-f1db-4950-a9bc-957ddaf154dd"
      },
      "source": [
        "# dikarenakan sample tidak seimbang, maka dilakukan pemerataan dengan resample\n",
        "\n",
        "df_class_pos_over = df_class_pos.sample(count_neg, replace=True)\n",
        "df_class_neu_over = df_class_neu.sample(count_neg, replace=True)\n",
        "df_test_over = pd.concat([df_class_neg, df_class_pos_over, df_class_neu_over], axis=0)\n",
        "\n",
        "print('Random over-sampling:')\n",
        "print(df_test_over.airline_sentiment.value_counts())\n",
        "\n",
        "df_test_over.airline_sentiment.value_counts().plot(kind='bar', title='Count (airline_sentiment)');"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random over-sampling:\n",
            "neutral     9178\n",
            "positive    9178\n",
            "negative    9178\n",
            "Name: airline_sentiment, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhKSdGyEkXEz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "6103e588-d88d-42b4-f2ae-fef560a9712c"
      },
      "source": [
        "# one hot encode (untuk mengubah label statement menjadi angka)\n",
        "\n",
        "category = pd.get_dummies(df_test_over.airline_sentiment)\n",
        "df_fix = pd.concat([df_test_over, category], axis=1)\n",
        "df_fix = df_fix.drop(columns='airline_sentiment')\n",
        "df_fix"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>text</th>\n",
              "      <th>negative</th>\n",
              "      <th>neutral</th>\n",
              "      <th>positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>@VirginAmerica SFO-PDX schedule is still MIA.</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>@VirginAmerica  I flew from NYC to SFO last we...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11643</th>\n",
              "      <td>@USAirways I just hope that pilot had a good d...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9765</th>\n",
              "      <td>@USAirways what happens to frequent flyer mile...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6639</th>\n",
              "      <td>@SouthwestAir followed.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>@VirginAmerica you got cheese pLate Flights too.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10468</th>\n",
              "      <td>@USAirways Hello we are doing a world record a...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27534 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text  ...  positive\n",
              "3      @VirginAmerica it's really aggressive to blast...  ...         0\n",
              "4      @VirginAmerica and it's a really big bad thing...  ...         0\n",
              "5      @VirginAmerica seriously would pay $30 a fligh...  ...         0\n",
              "15         @VirginAmerica SFO-PDX schedule is still MIA.  ...         0\n",
              "17     @VirginAmerica  I flew from NYC to SFO last we...  ...         0\n",
              "...                                                  ...  ...       ...\n",
              "11643  @USAirways I just hope that pilot had a good d...  ...         0\n",
              "9765   @USAirways what happens to frequent flyer mile...  ...         0\n",
              "6639                             @SouthwestAir followed.  ...         0\n",
              "473     @VirginAmerica you got cheese pLate Flights too.  ...         0\n",
              "10468  @USAirways Hello we are doing a world record a...  ...         0\n",
              "\n",
              "[27534 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bPInpVccz1J"
      },
      "source": [
        "# fungsi untuk membersihkan text\n",
        "def clean_text(text):\n",
        "    # menghapus backslash-apostrophe \n",
        "    text = re.sub(\"\\'\", \"\", text) \n",
        "    # menghapus semua karakter kecuali alfabet \n",
        "    text = re.sub(\"[^a-zA-Z]\",\" \",text) \n",
        "    # menghapus whitespaces \n",
        "    text = ' '.join(text.split()) \n",
        "    \n",
        "    return text"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAaIdA9ac_Bl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "be2f0589-4051-4a18-b2c0-bee4f980901a"
      },
      "source": [
        "# mengaplikasikan pada dataset\n",
        "df_fix['text'] = df_fix['text'].apply(lambda x: clean_text(x))\n",
        "\n",
        "df_fix\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>text</th>\n",
              "      <th>negative</th>\n",
              "      <th>neutral</th>\n",
              "      <th>positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VirginAmerica its really aggressive to blast o...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VirginAmerica and its a really big bad thing a...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>VirginAmerica seriously would pay a flight for...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>VirginAmerica SFO PDX schedule is still MIA</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>VirginAmerica I flew from NYC to SFO last week...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11643</th>\n",
              "      <td>USAirways I just hope that pilot had a good da...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9765</th>\n",
              "      <td>USAirways what happens to frequent flyer miles...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6639</th>\n",
              "      <td>SouthwestAir followed</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>VirginAmerica you got cheese pLate Flights too</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10468</th>\n",
              "      <td>USAirways Hello we are doing a world record at...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27534 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text  ...  positive\n",
              "3      VirginAmerica its really aggressive to blast o...  ...         0\n",
              "4      VirginAmerica and its a really big bad thing a...  ...         0\n",
              "5      VirginAmerica seriously would pay a flight for...  ...         0\n",
              "15           VirginAmerica SFO PDX schedule is still MIA  ...         0\n",
              "17     VirginAmerica I flew from NYC to SFO last week...  ...         0\n",
              "...                                                  ...  ...       ...\n",
              "11643  USAirways I just hope that pilot had a good da...  ...         0\n",
              "9765   USAirways what happens to frequent flyer miles...  ...         0\n",
              "6639                               SouthwestAir followed  ...         0\n",
              "473       VirginAmerica you got cheese pLate Flights too  ...         0\n",
              "10468  USAirways Hello we are doing a world record at...  ...         0\n",
              "\n",
              "[27534 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-4Y_dnHKXCT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "8634ef10-6107-4c83-861d-86a028f9bdc3"
      },
      "source": [
        "# fungsi untuk menghitung jumlah kata terbanyak di dalam dataset\n",
        "\n",
        "def freq_words(x, terms = 30): \n",
        "  all_words = ' '.join([text for text in x]) \n",
        "  all_words = all_words.split() \n",
        "  \n",
        "  fdist = nltk.FreqDist(all_words) \n",
        "  words_df = pd.DataFrame({'word':list(fdist.keys()), 'count':list(fdist.values())}) \n",
        "  \n",
        "  #  top 20 most frequent words \n",
        "  d = words_df.nlargest(columns=\"count\", n = terms) \n",
        "  \n",
        "  # visualize words and frequencies\n",
        "  plt.figure(figsize=(12,15)) \n",
        "  ax = sns.barplot(data=d, x= \"count\", y = \"word\") \n",
        "  ax.set(ylabel = 'Word') \n",
        "  plt.show()\n",
        "  \n",
        "# print 100 most frequent words \n",
        "freq_words(df_fix['text'], 100)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYVwqJF5KoT9"
      },
      "source": [
        "# menghapus stopword pada dataset\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# function to remove stopwords\n",
        "def remove_stopwords(text):\n",
        "    no_stopword_text = [w for w in text.split() if not w in stop_words]\n",
        "    return ' '.join(no_stopword_text)\n",
        "\n",
        "df_fix['text'] = df_fix['text'].apply(lambda x: remove_stopwords(x))\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2cZjc7XLEsn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "47a1b374-ec3e-496e-8700-83bab3930134"
      },
      "source": [
        "freq_words(df_fix['text'], 100)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3UD0t2Au4C3iWarnVaWX/v1ClIY+mKkrWrDn3x8AxwPHAD8rYfgp8HHgN+B9J+7X9tiMiIiIiel6vLg5sLwWebvjDugSnHQDcK+lAlQQyqrTiZcBiqkLgn2oyCLYEPiSpxX/L2/4TsCEw0vbvqVZDOo2qaAAYCDxdVj46liq0rcEU4NTSz0NlrFsBv7f9beBGYMeOfg8RERERET1hVVit6DiqbIJvls/n2V4k6cvARZJeBd4GJlAtaXoANXkHtl+RdC/wsTZc6wFW/NE/DfgqVZEAcBlwXZmofCs1Tx1sPyPpYeAXNX19AjhW0lvAX4FW1+TrN3SzLN0XEREREXXTq3MOVhXlqcR8YCfbL3W0n9FbbOT/OWN81w0soo/Z7DPfqfcQIiIier2Wcg569WtFqwJJ+wMPA9/pTGEQEREREVFvKQ46yfZvbA8DXih5BnMl/aTkItxR2m6XtEW9xxoRERER0ZIUB11A0vbA54H9bI8G/hn4DvAj2zsCV7EiFyEiIiIioldKcdA19qPKTGhYYvUFqvC0n5b9P6FaUvUdVg5BazIKISIiIiKiR6Q4qLOVQ9DWqfdwIiIiIqIPS3HQNe4AjighbA15DL8Fjiz7J7AiTC0iIiIioldaFXIOej3bC0vuwt2SlgGzgVOAH0o6nSpt+fjW+um30RZZijEiIiIi6iY5B73IjlsM8a9OP6Dew4joVTY/5ap6DyEiIuJdJTkHnVCWJF3QRPtESZvWfD61hKFFRERERKySUhx03ERg05rPpwIpDiIiIiJilZU5B22zuqQrgN2Bv1AtTToWuErSa8APqQqFOyU9Z3tfSUuBK4APA38FjrT9bH2GHxERERHRujw5aJuRwKW2twcWAwZmAhNsj7F9MfAUsK/tfcs56wEzyzl3A+c01XFtzsELS1/v9huJiIiIiGhOioO2ecL2nLI9CxjehnOWA1eX7StpJgStNudgcP+1Oz3QiIiIiIiOSnHQNm/UbC+jY69jZVmoiIiIiOjVMueg45YAA5r4/Fz5vBpwOPBz4Gjg3tY6XHOjLbNsY0RERETUTZ4cdNwU4HJJcyStA0wGbpV0Z9n/CrBLWQZ1P+D8+gwzIiIiIqJtEoLWTSQttd1f0iHAY7Yfau2cHbYY5BtP36f7Bxd90lan/KLeQ4iIiIheICFo9XUIsF29BxERERER0ZrMOegASV8AjgGeBf5EtYLRDcClwFDgVWCspN2BjwMflPR54DDbi+oz6oiIiIiIlqU4aCdJ44DDgNFAP+BBquJgMnCS7ccl7QpcZns/STcBN9u+tpn+JgGTADbdYJ2euIWIiIiIiCalOGi/PYAbbb8OvC7pl8DaVOnJ10hqOG6ttnRmezJVYcEOWwzKBJCIiIiIqJsUB11jNWCx7TH1HkhEREREREelOGi/+4DvSfoq1fd3ENV//p+QdITta1Q9PtjR9lzemYfQrLU22jorykRERERE3WS1onayPQO4CZgH3ALMB14CJgAnSpoLLAQOLqf8HDhd0mxJI+ow5IiIiIiINukzOQeSBgFH275M0j7AabYP6mBf/W0vlbQuVSEw3fb4zo5x1BaDfO0ZH+xsN1En23zmxnoPISIiIqJVyTmoDAI+3UV9TZY0h2qloplUS5pGRERERKzS+lJxcAEwovxRfyHQX9K1kh6RdFWZJ4CkL0qaIWmBpMk17XdJ+pqk6cBY4BTb2wC/ariApAMl3S9pQ0lHlD7mSrqn5283IiIiIqJ9+lJxcCawqKwodDrwfuBUqvTiraiWKAW4xPY426OAdagmHDdYw/Yu5bxzajuXdGi5xkdtPwd8EfgH26OpgtCaJGmSpJmSZr649M2uuM+IiIiIiA7pS8VBY9Nt/9n2cmAOMLy07yvpAUnzgf2A7WvOub78nlVzPOW4M4ADbb9Y2u4Dpkj6JLB6c4OwPdn2WNtjN+i/ZmfvKSIiIiKiw/pycfBGzfYyYA1JawOXAYfb3gG4girgrPE5y1h5GdhFVMuVvrehwfZJwOeBzYFZkoZ0+R1ERERERHShvpRz0Ja8gYZC4DlJ/YHDgWvb0PcfqF5Vur5kHSyUNML2A8ADkj5CVSQ83+LFN9o6K95ERERERN30meLA9vOS7pO0AHgNeKaJYxZLugJYAPwVmNGO/h+RNAG4RtLHgAsljQQE3A7M7Yr7iIiIiIjoLu/6nIPyOs/t5ePfUb0S9CzVnIGnbG/XBdfYh07kJjTYftgg//zMvTs7nOhBO5x8U72HEBEREdEufTrnwPbztseUVYouBy4q22OA5fUdXURERERE7/GuLw5asbqkKyQtlDRV0joAkj5Zsg7mSrquJCEjaYqkb0v6raTfSzq8cYeSxkmaLWmEpA9KmlN+Zktqbc5DRERERETd9PXiYCRwqe3tgcXAYaX9+pJ1MBp4GDix5pxNgD2p8g8uqO1M0u5UTycOtr0IOA34THlSsRfVXAcanZOcg4iIiIjoFfp6cfCE7Tlluza7YJSkaSXrYAIrZx38wvZy2w8BG9e0bwtMBj5m+4+l7T7gm5I+Bwyy/XbjASTnICIiIiJ6i75eHLwj66BsTwE+W7IOzqPprAOoViJq8DTwOlXyMgC2LwD+iSpp+T5J23TZyCMiIiIiulifWcq0nQYAT0vqR/Xk4C9tOGcx1etHv5b0iu27StbBfGC+pHHANsAjzXWwztCts/pNRERERNRNioOmfQF4gGrJ0wdoPTwNANvPSDoIuEXSCcAxkvalWhVpIXBLN403IiIiIqLT3vU5B91B0vnAPbZ/05X9bjdskK/89726ssvoQjud9Mt6DyEiIiKi01rKOciTg2ZIWqOpCcQAtr/Y0+OJiIiIiOhu7/oJyZLWk/SrklmwQNJ4STtLulvSLEm3SdqkHHuXpG9JmgmcLekPklar6edPkvqVvIPDS/u4knswV9J0SQMkrS7pwpKVME/Sp+r4FUREREREtElfeHJwAPCU7QMBJA2kevf/YNvPShoPfBk4oRy/ZsNjFkk7AR8E7qTKNbjN9ltStUiRpDWBq4HxtmdIWp8qy+BE4CXb4yStRbVS0VTbTzQenKRJwCSAvxu8Tvd8AxERERERbdAXioP5wH9K+hpwM/AiMIpqVSGA1amWIW1wdaPt8VTFwZHAZY36fh/wtO0ZALZfBpD0YWDHmgTlgVSBa+8oDmxPpspHYLthgzIBJCIiIiLq5l1fHNh+rDwB+CjwJeAOYKHt3Zo55ZWa7ZuAr0gaDOxczm0LAafYvq2Dw46IiIiI6HF9Yc7BpsCrtq8ELgR2BYZK2q3s7ydp+6bOtb0UmAFcDNxse1mjQx4FNikZBpT5BmsAtwEnl5wEJL1X0nrdcHsREREREV3mXf/kANgBuFDScuAt4GTgbeDbZf7BGsC3qHIImnI1cA2wT+Mdtt8scxa+I2kdqvkG+wPfB4YDD6p6d+lZ4JDWBrru0K2zXGZERERE1M0ql3Mg6e+o/pgfR5VK/Axwqu3Huvm6S233lzSc6inCqHacO6Wcc21Lx207bJCnnLVnp8YZnbPrp26u9xAiIiIiutW7Jueg/Bf+BuBHto8sbaOBjYFuLQ4iIiIiIt7tVrU5B/sCb9m+vKHB9lxgtqTbJT0oab6kgwEkDZf0sKQrJC2UNLW8/oOkrSX9puQTPChpRGk/vSaf4LyWBtNcnoEql0h6VNJvgI266fuIiIiIiOgyq1pxMAqY1UT768ChtneiKiD+Uw1hBNUSopfa3p7qNaTDSvtVpX00sDvwdFmCdCSwCzAG2FnS3i2M5295BlSvOX1S0pbAoVTLnG4HHFf6b5KkSZJmSpq5eOmbrX8DERERERHdZJV6ragFolpydG9gObAZ1atGAE/YnlO2ZwHDJQ0ANrN9A4Dt1+Fv+QQfBmaX4/tTFQv3NHPd5vIM9gZ+VlY3ekpSs0ug1uYcbJucg4iIiIioo1WtOFgIHN5E+wRgKLBzSTB+Eli77Huj5rhlQEsxxAK+avt7bRxPk3kGkj7axvMjIiIiInqNVe21ojuAtSRNamiQtCMwDPjfUhjsWz43y/YS4M+SDil9rCVpXap8ghMk9S/tm0lqab5Ac3kG9wDjy5yETahedYqIiIiI6NVWqScHti3pUOBbks6gmmvwJHAuVW7BfGAm8EgbujsW+J6k86nyD46wPVXStsD9ZcrCUuAY4H+b6aO5PIMbgP2Ah4A/Ave35f7WG7p1ltKMiIiIiLpZ5XIOelJ7Mw0knQsstf2Njlxvm2GD/F9nJ+egJ+wxKUVYRERE9E0t5Rysaq8VRUREREREN0lx0LrVG+ckSBoh6VZJsyRNk7RN45Mk3SXpYklzJC2QtEs9Bh8RERER0VYpDlrXVE7CZKpVinYGTgMua+bcdW2PAT4N/KAnBhsRERER0VGr1ITkOnlHTgJVqNk1K3LWWKuZc38GYPseSetLGmR7ce0BZeWlSQAbD25pldWIiIiIiO6V4qB1jXMSNgYWlycCrWk82/sds79rQ9C2SQhaRERERNRRXitqv5eBJyQdAaDK6GaOHV+O2RN4yfZLPTTGiIiIiIh2y5ODjpkAfFfS54F+wM+BuU0c97qk2eWYE1rrtP/QrbPEZkRERETUTYqDFth+EhhV87k2v+CAJo4/t1HTlbZP7ZbBRURERER0sRQHTZA0CDja9mWSNgW+bfvw7r7ukuce564rDuzuy/RZ+3zyV/UeQkRERESvljkHTRtEtfwotp/qSGFgex/bM7t8ZBERERER3SRPDpp2ATBC0hzgcWBb26MkTQQOAdajyj/4BrAmcCzVqkYftf2CpBHApcBQ4FXgk7Yf6fnbiIiIiIhouzw5aNqZwKKyXOnpjfaNAv4PMA74MvCq7fcD9wPHlWPaGpKGpEmSZkqa+dKSN7v4NiIiIiIi2i5PDtrvTttLgCWSXgJ+WdrnAztK6k/bQ9JWyjl43/CByTmIiIiIiLpJcdB+taFoy2s+L6f6Plej7SFpERERERG9RoqDpi0BBnTkRNsvS3pC0hG2r1H1+GBH203lIKxkwIYjs6JORERERNRN5hw0wfbzwH2SFgAXdqCLCcCJkuYCC4GDu3J8ERERERHdQXbXv+Yu6U7gAtu31bSdCvwz8D3bF7SjrzbnDEgaA8wGPmL71vaPvGvH017vHT7Ql31+j67uNoD9/+l/6j2EiIiIiF5B0izbY5va111PDn4GHNmo7UjgH5sqDCQ1+3pTO3MGjgLuLb+7nKQ1Opp7EBERERHR23VXcXAtcKCkNQEkDQc2pcoOuKS0TZF0uaQHgK9LGiHpd5LmS/qSpKUN55bXe5A0UdL1km6V9LikrzdcsLzbfwQwEfiQpLVrzn+kXO8xSVdJ2l/SfaWPXcpx60n6gaTpkmZLOrjmmjdJugO4vdF4Vpf0DUkLJM2TdEpp/6KkGaV9smqWLYqIiIiI6K26pTiw/QIwHfhIaToS+G+g8TtM7wF2t/2vwMXAxbZ3AP7cQvdjgPHADsB4SZuX9t2BJ2wvAu4CDqw5Z2vgP4Ftys/RwJ5UGQRnlWPOBu6wvQuwL3ChpPXKvp2Aw21/sNFYJgHDgTG2dwSuKu2X2B5nexSwDnBQczeTnIOIiIiI6C26c0Jy7atFR5bPjV1je1nZ3g24pmz/tIV+b7f9ku3XgYeAYaX9KODnZfvnrPxq0RO259teTjVB+HZXky3mU/1xD/Bh4MySinwXsDawRdn361LwNLY/1RyKt+FvRRHAvpIekDQf2A/YvrmbsT3Z9ljbYwcOWLOF246IiIiI6F7duZTpjcBFknYC1rU9S9IOjY55pQP91uYMLAPWkLQ6cBhwsKSzAQFDJA1o4pymsgko5xxm+9Hai0natT3jLK8zXQaMtf0nSedSFRoREREREb1atxUHtpeWVYt+QNNPDRr7HdUf+FfzzsnMrfl7YJ7tf2hokPQj4FDgnjb2cRtwiqRTbFvS+23PbuWcXwOfknSn7bclDaYqOACeK2nJh1PNwWjV+huOzKo6EREREVE33Z1z8DNgNG0rDk4F/lXSPKo5Ai+14zpHATc0aruO9q1a9B9AP2CepIXlc2u+D/yxnDMXONr2YuAKYAFVwTGjHWOIiIiIiKibbsk56AhJ6wKvlf/aHwkcZTV4hgMAACAASURBVLvXh4dJWmq7f1f09d7hA/3tLyTnoLMOODFPXyIiIiKa01LOQXfOOWivnYFLyrKfi4ET6jyeiIiIiIg+pdcUB7anUb2CtEqStA9wHlVhswPV0q3zqVKh1wEOKcusRkRERET0St0956CvGQ2cBGwLHAu8t+QmfB84pakTknMQEREREb1FioOuNcP207bfABYBU0t7bZ7CSpJzEBERERG9RYqDrtWWPIWIiIiIiF4pf7D2IutvODIr7URERERE3eTJQUREREREAHlygKRlVHMCGhxCNT/gNNsHSfo4sJ3tC5o633b/slLRabYPqmnfp/R/KjC5dl9zXnrucW7+wUc6eit93kEn3FLvIURERESs0vp8cUAVvDamtkHS8IZt2zcBN3Wi/1OBK4FXO9FHRERERES3y2tFrZA0UdIlZXuEpN9Jmi/pS5KW1hzaX9K1kh6RdJUqnwM2Be6UdGddbiAiIiIioo1SHMA6kuaUnxtaOfZi4GLbOwB/brTv/VRPCbYDtgL2sP1t4ClgX9v7NtXhSjkHS5NzEBERERH1k+KgvFZUfg5t5djdgGvK9k8b7Ztu+8+2lwNzaCbXoLGVcg76J+cgIiIiIuonxUHXqc04WEbmc0RERETEKibFQfv8DjisbB/ZxnOWAAO6ZzgREREREV0n/91un1OBKyWdDdwKvNSGcyYDt0p6qrl5Bw0Gbjgyy3FGRERERN3Idr3HUBdlJaGTgfWBG2x/VtJJwKu2f9zMOesCnwDGAvcCR9k+uGb/Wba/0tExbT18oL/xxd07enqfdEiKqYiIiIh2kTTL9tim9vXlJwefBvYvP2MBbF/eyjk7A+cD6wI7Aic02n8W0OHiICIiIiKinvrknANJl1MtN3oLsEFN+7mSTivb4yTNK0ucXihpge1pwBeBe6hCzW6R9PVy/AWsWBb1KknrSfqVpLmSFkga39P3GRERERHRHn2yOLB9EiV/AHixmcN+CHyqpCcva7RvDDAe2AEYL2lz22eyYlnUCcABwFO2R9seRTVH4R1qcw5eTs5BRERERNRRnywOWiNpEDDA9v2lqXGmwe22X7L9OvAQMKyJbuYDH5L0NUl72W5y8nJtzsH6yTmIiIiIiDpKcdAxrWYa2H4M2ImqSPiSpC/20NgiIiIiIjokxUETbC8GlkjatTS1NdPgLUn9ACRtSrXy0ZXAhVSFQkREREREr9WXVytqzYnAFZKWA3fT9kyDeZIeBH4MXFjOf4tq2dQWDdpwZJbmjIiIiIi66bM5B62R1N/20rJ9JrCJ7X/uzmuOGD7QXztnt+68xLvO4cc3Oc87IiIiIpqRnIOOOVDSv1N9R38AJtZ3OBERERER3StzDpph+2rgSarJx8OBQwEkLS25Bwsl/UbSLpLukvR7SR8vx0yUdGNpf1zSOfW6j4iIiIiItkpx0LITbO9MlaD8OUlDgPWAO2xvDywBvgR8iKp4OL/m3F2Aw6iSlI+Q1OSjm4iIiIiI3iKvFbXsc5IOLdubAyOBN1kRaDYfeMP2W5LmUz1haPBr288DSLoe2BOY2fgCkiYBkwA2HLJ2d9xDRERERESb5MlBMyTtA+wP7GZ7NDAbWBt4yytmcS+nZB7YXs7KxVbjmd5NzvxOCFpERERE9BYpDpo3EHjR9quStgE+0M7zPyRpsKR1gEOA+7p8hBERERERXSivFTXvVuAkSQ8DjwK/a+f504HrgPcAV9p+xytFjW2w4cgszRkRERERdZPioBm23wA+0sSu/jXHnNvonP41H/9s+5D2XPOF5x7n5z88oD2n9GlHppCKiIiI6FJ98rUiSUMkzSk/f5X0l7K9WNJDXXSZzSTd3EV9RURERER0uz5ZHNh+3vYY22OAy4GLyvYYqknGne1/CnBxZ/uJiIiIiOhJfbI4aMXqkq4oIWdTy4RiJH1S0gxJcyVdJ2nd0j5F0rcl/bYEoR3euENJ4yTNljSip28mIiIiIqKtUhy800jg0hJytpgqyAzgetvjyrKmDwMn1pyzCVWOwUHABbWdSdqd6unEwbYXNb6YpEmSZkqauWTpm11/NxERERERbZTi4J2esD2nbM9iRbDZKEnTStjZBGD7mnN+YXu57YeAjWvatwUmAx+z/cemLlabczAgOQcRERERUUcpDt7pjZrtZaxY0WkK8FnbOwDnUQWiNXWOarafBl4H3t/1w4yIiIiI6FpZyrTtBgBPS+pH9eTgL204ZzHV60e/lvSK7btaOnjwhiOzPGdERERE1E2eHLTdF4AHqJKOH2nrSbafoZqLcKmkXbtpbBERERERnSbb9R5DXUg6y/ZX6j2OWlttOdDnn/OBeg9jlXDMxNvqPYSIiIiIVZKkWbbHNrWvLz85OKsnLyYpr3BFRERERK+2yhYHko6TNK/kDvyk5A0cXrN/afm9iaR7SgLyAkl7SboAWKe0XVWO+9eyf4GkU0vbcEmPlL4fk3SVpP0l3SfpcUm7lOPWk/QDSdNLnsHBpX2ipJsk3QHc3tPfUUREREREe6yS/82WtD3weWB3289JGgx8s5nDjwZus/1lSasD69qeJumzJRUZSTsDxwO7Uq029ICku4EXga2BI4ATgBmlvz2Bj1M9fTgEOBu4w/YJkgYB0yX9plx/J2BH2y80cy+TgEkAQ4as3dQhERERERE9YlV9crAfcI3t5wCa+8O7mAEcL+lcYAfbS5o4Zk/gBtuv2F4KXA/sVfY9YXu+7eXAQuB2VxM15rMiA+HDwJmS5gB3US1zukXZ9+uWxlebc7D+gOQcRERERET9rKrFQVPeptyPpNWANQFs3wPsTbX06BRJx7Wz39oMg+U1n5ez4smLgMNsjyk/W9h+uOx7pd13EhERERFRB6vka0XAHcANkr5p+/nyWtGTwM7Af1O98tMPQNIw4M+2r5C0FtVrPj8G3pLUz/ZbwDSqwuECqj/0DwWObcd4bgNOkXSKbUt6v+3Z7b2pwUNGZhWeiIiIiKibVbI4sL1Q0peBuyUtA2YDZwA3SpoL3MqK/9jvA5wu6S1gKdDw5GAyME/Sg7YnSJoCTC/7vm97tqThbRzSfwDfKv2tBjxBlW0QEREREbHK6LM5B12tzGlYavsbjdqHAzfbHtVaH1tuub7POTc5Bw0m/uPUeg8hIiIi4l0nOQcREREREdGqPlscSDpd0ufK9kUliwBJ+5U8g6MkzS+5B1+rOW9pzfbh5XWkxn3vXPIX5gKf6f67iYiIiIjovD5bHFBNQm5YrnQs0F9Sv9L2GPA1qiVTxwDjJB3Sjr5/CJxie3RrB0qaJGmmpJlLlrzVrhuIiIiIiOhKfbk4mAXsLGl9quVJ76cqEvYCFgN32X7W9tvAVVTLobaqhKANKkuoAvykpeNrcw4GDOjXwVuJiIiIiOi8PlsclCVMnwAmAr+lepKwL1Ui8pMtnVqznUjjiIiIiHjXWCWXMu1C04DTgBOoEo+/SfVEYTrwbUkbAi8CRwHfKec8I2lb4FGqPISVEpdtL5a0WNKetu8FJrR1MEOGvDcr9ERERERE3fTZJwfFNGAT4H7bzwCvA9NsPw2cCdwJzAVm2b6xnHMmcDPV04anm+n3eOBSSXOoQtUiIiIiInq9Pp1zUOYHHG37Mkn7AKfZ7nR4maSJwFjbn23PecO3HOgvnJecA4ATj0tSdERERER3SM5B8wYBn673ICIiIiIieoO+XhxcAIwor/9cSLWc6bWSHilZBwKQ9EVJM0rmweSa9rskfU3SdEmPSdqr8QUkHSjp/jJ/ISIiIiKi1+rrxcGZwCLbY4DTgfcDpwLbAVsBe5TjLrE9zvYoYB2g9tWjNWzvUs47p7ZzSYeWa3zU9nNNDWDlnIM3u/DWIiIiIiLap68XB41Nt/1n28uBOcDw0r6vpAckzacKRtu+5pzry+9ZNcdTjjsDOND2i81dcOWcgzW76DYiIiIiItovxcHK3qjZXgasIWlt4DLgcNs7AFewcr7BG7XH17QvAgYA7+2+4UZEREREdJ2+nnOwhOoP+JY0FALPSeoPHA5c24a+/0D1qtL1ko6wvbC1EzYcMjKr9ERERERE3fTp4sD285Luk7QAeA14poljFku6AlgA/BWY0Y7+H5E0AbhG0sdsL+qqsUdEREREdLU+nXPQHEnDgZvLBOSO9rEP7cxNGLblQJ91ft/NOfjUsXlqEhEREdHdknMQERERERGtSnHQvDVK1sHDJftg3RbyDraW9BtJcyU9KGlEbUeSxkma3bg9IiIiIqI3SXHQvPcBl9neFniZKkm5ubyDq4BLbY8GdgeebuhE0u7A5cDBTc05qM05WJqcg4iIiIiooxQHzfuT7fvK9pXAnjSRdyBpALCZ7RsAbL9u+9Vy3rbAZOBjtv/Y1EVqcw76J+cgIiIiIuooxUHzGs/UNi3nHTTlaeB1quTliIiIiIheLcVB87aQtFvZPhq4t2zX5h1gewnwZ0mHAEhaS9K65djFwIHAV8vqRRERERERvVafzjloxaPAZyT9AHgI+C6wAU3nHRwLfE/S+cBbwBENO2w/I+kg4BZJJ9h+oLkLDh0yMst5RkRERETdJOegF9liq4E+o4/mHHzmmBRFERERET0hOQcREREREdGqvFbUBRonKks6DegPvACcBLwNPGT7yHqNMSIiIiKiNSkOuteZwJa235A0qKkDJE0CJgFsMKS1xY8iIiIiIrpPXivqXvOAqyQdQ/X04B1WyjlYPzkHEREREVE/KQ66xtus/F02PAI4ELgU2AmYISlPaiIiIiKi10px0DWeATaSNETSWsBBVN/t5rbvBM4ABlLNQ4iIiIiI6JXyn+wuYPutknEwHfgL8AiwOnClpIGAgG/bXtxSPxsNHpklPSMiIiKibpJz0AJJdwGn2Z7ZE9fbfKuB/rcv9Z2cg1OPTiEUERER0dOScxAREREREa1KcUCVUyDpEUlXSXpY0rWS1m10zHclzZS0UNJ5Ne1PSjpP0oOS5kvaprSvJ+kHkqZLmi3p4J6+r4iIiIiI9khxsML7gMtsbwu8DHy60f6zy+OXHYEPStqxZt9ztncCvguc1nA8cIftXYB9gQslrdetdxARERER0QkpDlb4k+37yvaVwJ6N9n9C0oPAbGB7YLuafdeX37OA4WX7w8CZkuYAd1Etb7pF44tKmlSeSMx8ZcmbXXEfEREREREdktWKVmg8M/tvnyVtSfVEYJztFyVNYUWWAcAb5fcyVnynAg6z/WiLF7UnA5OhmpDc4dFHRERERHRSnhyssIWk3cr20cC9NfvWB14BXpK0MfCRNvR3G3CKJAFIen9XDjYiIiIioqvlycEKjwKfkfQD4CGq+QMfA7A9V9JsqvyCPwH3NdvLCv8BfAuYJ2k14AmqcLRmbTx4ZJb3jIiIiIi6SXGwwtu2j2nUtk/Dhu2JTZ1ke3jN9syGc2y/Bnyqi8cYEREREdFtUhx0M0mHAI/Zfqi1Y5954XG+8bN/6IFR1d9pR+UJSURERERvkzkHgO0nbY9q7ThJHSmmDmHllY0iIiIiInqlPDmoIekLwDHAs1RzC2ZRzROYQ7W06c8k3QV8E+gPPAdMtP20pE8Ck4A1gf8HHAuMAT5OlYvwearVixb16E1FRERERLRRioNC0jjgMGA00A94kKo4AFjT9lhJ/YC7gYNtPytpPPBl4ATgettXlL6+BJxo+zuSbgJutn1tM9edRFVUMGjDtZs6JCIiIiKiR6Q4WGEP4EbbrwOvS/plzb6ry+/3AaOAX5cVSlcHni77RpWiYBDVU4U2vVSfnIOIiIiI6C1SHLTNK+W3gIW2d2vimCnAIWXZ04nUrHQUEREREbEqSHGwwn3A9yR9lep7OYjyH/0ajwJDJe1m+/7ymtF7bS8EBgBPl7YJwF/KOUvKvlZtPHhkVvGJiIiIiLrJakWF7RnATcA84BZgPvBSo2PeBA4HviZpLtVE5d3L7i8AD1AVGY/UnPZz4HRJsyWN6NabiIiIiIjoBNl5zb2BpP62l0paF7gHmGT7wU72ORGYavup1o7dbKuB/sxXPtCZy60yzjoyT0giIiIi6kHSLNtjm9qXJwcrmyxpDtVKRdd1tjAoJgKbdkE/ERERERHdKnMOatg+ui3HNZOH8BvgcmBdYBHV8qZ/D4wFrpL0GrCb7de6YegREREREZ2WJwft1CgP4SNUf/wD/Bg4w/aOVPMVzinZBjOBCbbHNFUYSJokaaakma8sebNnbiIiIiIiogkpDtrvb3kItpcAvwTWAwbZvrsc8yNg77Z0Znuy7bG2x643YM3uGXFERERERBukOIiIiIiICCBzDjqiuTyEFyXtZXsacCzQ8BShzTkHmwwemVV8IiIiIqJuUhy0k+0ZkhryEJ5hRR7CPwKXl2VQfw8cX06ZUtozITkiIiIierXkHHRAd+QhAGw6YqAnffXdnXNw7ifyZCQiIiKinlrKOciTg46ZLGk7YG3gR12UhxARERERUVeZkNwOkv5V0gJgR6rXhQ4AjpN0haSFkqZKWqccO0LSrZJmSZomaZs6Dj0iIiIiolUpDtpI0s5U8wh2BT4AfBLYABgJXGp7e2AxVQYCVJOUT7G9M3AacFkz/f4t5+DVl5NzEBERERH1k9eK2m5P4AbbrwBIuh7YC3jC9pxyzCxguKT+wO7ANZIazl+rqU5tT6YqJNh0xMBMAImIiIiIuklx0Hlv1GwvA9aheiKz2PaY+gwpIiIiIqL9Uhy03TRgiqQLAAGHUuUZTGp8oO2XJT0h6Qjb16h6fLCj7bktXWDTDUZmNZ+IiIiIqJvMOWijsiLRFGA68ADwfeDFFk6ZAJwoaS6wEDi4u8cYEREREdEZfT7nQNIg4GjbTU4YbuacKcDNtq/tyrFsMmKgJ16wW1d22Wt89Yhb6z2EiIiIiKDlnIM8OYBBwKfrPYiIiIiIiHpLcQAXACMkzZF0YflZIGm+pPEAqlwi6VFJvwE2ajhZ0hclzSjnTC7HjpD0YM0xI2s/R0RERET0RikO4ExgUVlZ6HfAGGA0sD9woaRNqCYfvw/YDjiOapnSBpfYHmd7FNVKRQfZXgS8JKlhtaLjgR82dfHkHEREREREb5HiYGV7Aj+zvcz2M8DdwDhg75r2p4A7as7ZV9IDkuYD+wHbl/bvA8dLWh0YD/y0qQvanmx7rO2x666/ZjfdVkRERERE61IcdIKktamSjw+3vQNwBbB22X0d8BHgIGCW7efrM8qIiIiIiLZJcQBLgAFlexowXtLqkoZSPTGYDtxT074JsG85vqEQeK6kIh/e0Knt14HbgO/SzCtFERERERG9SZ8PQbP9vKT7JC0AbgHmAXMBA//X9l8l3UD1ytBDwB+B+8u5iyVdASwA/grMaNT9VVTzFaa2ZSybbTAyS35GRERERN30+ZyD7iTpNGAgVZLyWNvPtXT8xiMG+qivvztzDr51WIqeiIiIiN6gpZyDPv/koLuUpw0jqJ44HFvn4UREREREtCpzDmpIGi7pYUlXSFooaaqkdUpuwa2SZkmaJmmbcvxQSdeVnIMZkvYo7UOA9YDVqXIUVL+7ioiIiIhomxQH7zQSuNT29sBi4DBgMnCK7Z2B06hWKAK4GLjI9rhy3PdL+znAvaWPG4AtmrtYbc7Ba8k5iIiIiIg6ymtF7/SE7TllexYwnCr07Brpbw8A1iq/9we2q2lf//+zd+fhelXl3ce/PxFkCBkQanGMJCgyRglWRoGX+ipiAYkVQQXUploVbYtKX7Fi1SqDWuuE0SqgqMikCKJYkCkMEoYEUMQyWBUnNGEQmcL9/rH3kZPjGZ4zPic53891nevsZ+21177389e5z9pr3e2uRbsBLweoqnOTLB/oZlW1iCb54ElzZrgARJIkSV1jcvDnHux1vBJ4ErCiraDc1+OAF7Tblv5Jr2RBkiRJWm34WtHQ7gFuT/IKgDS2a8+dD7y1p2OSngTiEuCgtu0lwKyJC1eSJEkaGWcOOnMw8JkkRwFrA1+jqYVwOPCpJMtovstLgDcC7wO+muQm4HKa2ghDetqszd3yU5IkSV1jnYNhSHJfVU1L8mTgP6tqQZJDaWoYvGW0428yd0btd9yaV+fgc/ub8EiSJE0W1jkYY1V1J7Cg23FIkiRJY8k1ByPQ1kO4sZ/2lya5IsnGSV7UHl+b5LR2FyNJkiRp0jI5GCNJ9geOBPZum44C9qqq5wFLgH/qVmySJElSJ3ytaGzsCcwHXlRV9yTZB9gSWNxua7oOcEV/FyZZCCwEmLbJuhMTrSRJktQPk4OxcSuwGfAsmlmCAN+rqlcNdWHvImibzLUImiRJkrrH14rGxk+BA4CTk2wFXAnsnGQuQJINkjyrmwFKkiRJQ3HmYIxU1c1JDgZOA14GHEpT6+AJbZejgFsGG+MZMzd3209JkiR1jXUOJpGN5s6ovT6yZtU5+Pq+JjuSJEmTyWB1DnytSJIkSRJgciBJkiSpZXLQjyTvSfLjJJcl+WqSI5JclGR+e37jJHe0x5ckmdfr2suSbJfkhUmub3+uS7Jhlx5HkiRJ6ojJQR9JdqDZeWg74CU09QsG8180i49pdyRat6qWAkcAb66qecCuwB8HuN/CJEuSLHnwnofG5iEkSZKkETA5+HM7A9+sqgeq6l7gW0P0Pw3YJ8nawOuAE9v2xcBHkxwOzKyqR/q7uKoWVdX8qpr/hOnrjM0TSJIkSSNgctC5R3js+/pTKeOquh/4HrAv8LfAKW37h4E3AOvRVEreYkKjlSRJkobJOgd/bjHw2SQfovl+9qGpYHwHsD3wA2BBn2s+TzPDcGlVLQdIMqeqbgBuaF9V2gK4ebAbbzZzc7f+lCRJUtc4c9BHVV0NnA0sA84DbgDuBo4H3pTkOmDjPtdcA9wDfLFX89uT3JhkGfBwO5YkSZI0aU35ImhJZgPnVNXWvdqmVdV9SdYHLgEWVtW1g4zxZOAiYIuqenSkscyYO6t2+sieI7180jlv3zO6HYIkSZL6sAja8C1Kcj1wLXDGEInBa4GrgHf3JAZJfF1LkiRJqx2Tg8ZaST6X5KYk5wOvp9medAXwqiRnJZkF0LfeAfBvVfU0YIMkZye5ELggyaZtDYTr29eLdu3Oo0mSJEmdMTlobA58qqq2okkIDgBOBt5VVdvSrDt4bwfjPA9YUFUvBA4CvtvWOdgOuL6/C3rXOXjongfH4FEkSZKkkTE5aNxeVT1/vF8DzKGpTXBx23YSsFsH43yvqn7fHl8NHJbkaGCbtmbCn+ld52Cd6U8Y+RNIkiRJo2Ry0Oj9L/uVwMxB+vZb76D1h56DqrqEJqH4BXBiuzZBkiRJmrRcONu/u4HlSXatqkuB1wA9swh3MHC9gz9J8gzg51X1uSRPoHnl6OTBbrr5zDnu8CNJkqSuMTkY2CHACe12prcBh7XtxwNfT7IQOHeQ63cH3pHkYeA+wJkDSZIkTWpTvs7BWEmyH3BLVf1wpGPMmLtx7fSRfccwqu45b9//6nYIkiRJ6od1DibGfsCW3Q5CkiRJGilfKxpEkvcArwZ+C/yMZieju4GFwDrA/9CsR5gH/A3wwiRH0WyFCvApYBPgfuDvqurmCX0ASZIkaRicORhAkh1o/sjfDngJ0DP1cmZV7VBV2wE/Al5fVZcDZwPvqKp5VXUrsAh4a1VtDxwBfHqA+/Sqc/DAOD+VJEmSNDBnDga2M/DNqnoAeCDJt9r2rZN8gGa702nAd/temGQasBNwWpKe5n6LGFTVIppEghlzN3YBiCRJkrrG5GD4TgT2q6qlSQ6l2ZWor8cBK9rqyJIkSdJqweRgYIuBzyb5EM33tA/Nf/g3BH6ZZG3gYJoiZwD3tueoqnuS3J7kFVV1Wprpg22raulgN9x85mx3+ZEkSVLXuOZgAFV1Nc06gmXAecANNIuR3wNcRZM89F5g/DWaugbXJZlDkzi8PslS4CZgzdijVJIkSWss6xwMIsm0qrqvLYR2CbCwqq7t0+dw4E3AdOCsqnrLSO83Y+6TaqfjDxxVzJPBeft9vNshSJIkaQCD1TnwtaLBLUqyJbAucFLfxKD1D8Be7U+/X7IkSZK0OjA5GERVHTTY+SQnAJvRvHb0hbZtQ5pXkZ5VVQ8nmQ4s7fk8ziFLkiRJI+aag1GoqjcCdwJ7AMvbtnuBi4CXtt0OpKmN0G9isGqdgz+Of9CSJEnSAEwOxsfngcPa48OALw7UsaoWVdX8qpq/zvT1JiQ4SZIkqT8mB+OgqhYDs5PsDqxVVTd2OSRJkiRpSK45GD8nA18B3t/pBZvPfJo7/UiSJKlrnDkYP6cAs4CvdjsQSZIkqRPOHAwiyWzgnKraeqA+VTW7PTwxye5JFlTV6cAuwOlVtaJ9veiIqtpnsPv9ZMXP2fsb7xqT2Lvp2/sd0+0QJEmSNAImB+MgySeAlwB7dzsWSZIkqVMmB0NbK8nngJ2AXwD7As8GTgDWB24FXldVy3suqKq3JnkxcHaS+4HLJj5sSZIkaXhcczC0zYFPVdVWwArgAJrFxu+qqm2BG4D39r4gybrA54CXAdsDfznQ4NY5kCRJ0mRhcjC026vq+vb4GmAOMLOqLm7bTgJ263PNFu11P6mqAr480ODWOZAkSdJkYXIwtAd7Ha8EZnYrEEmSJGk8mRwM393A8iS7tp9fA1zcp8/NNEXQ5rSfXzVRwUmSJEkj5YLkkTkEOCHJ+sBtwGG9T1bVA0kWAue2C5IvBTYcatDNZz7VbUAlSZLUNWleiVcnNQ3G24y5T66dj3tDt24/at/e/9+6HYIkSZKGkOSaqprf3zlfK5IkSZIEmBz0K8lmSa5L8o4kZyb5TpKfJDm2V59XJbkhyY1JjmnbXpHko+3x25Lc1mu8xd15GkmSJKkzrjnoI8mzga8BhwLPBea1vx8EftxWP14JHENTw2A5cH6S/WjWFryzHWpX4HdJntIeXzLA/RYCCwHW3WTG+DyUJEmS1AFnhm/mnQAAIABJREFUDla1CfBN4OCqWtq2XVBVd1fVA8APgWcAOwAXVdVvq+oR4BRgt6r6FTAtyYbA04Cv0NRA2JUmcfgzq9Y5WH9cH06SJEkajMnBqu4G/hfYpVdb3zoHQ822XE6ze9GPaRKCXYEdAV8rkiRJ0qRmcrCqh4D9gdcmOWiQfj8AXphk4yRr0dQx6Kl1cClwBM1rRNcBewAPVtXd4xe2JEmSNHquOeijqv6QZB/ge8CXBujzyyRHAt8HApxbVd9sT19K80rRJVW1MsnPaIqiDWnzmU92O1BJkiR1jXUOWkkuAo6oqiXdimHG3KfUzse9qVu3H7Vv739Ut0OQJEnSEKxzIEmSJGlIUy45SDI7yc1JTknyoySnJ1m/T58XJbkiybVJTksyrW3/1yRXt7UNFiVJ2354kh8mWZbka23bBkm+kOQHbc2EfSf+aSVJkqTOTbnkoPVs4NNV9RzgHuAfek4k2Rg4Ctirqp4HLAH+qT39yaraoaq2BtYD9mnbjwSeW1XbAm9s294NXFhVz6dZlHxckg3G+bkkSZKkEZuqycHPqqpna9Evs+rWpS8AtgQWJ7keOISmtgHAHkmuSnIDsCewVdu+DDglyauBR9q2FwFHtmNcBKwLPL1vIEkWJlmSZMlD9/xhzB5QkiRJGq6pultR31XYvT8H+F5Vvap3hyTrAp8G5lfVz5IcTfMHP8BLaYqdvQx4d5Jt2nEOqKofDxpI1SJgETQLkkf2OJIkSdLoTdWZg6cn2bE9Pgi4rNe5K4Gdk8yFP60deBaPJQJ3tWsQFrTnHwc8raq+D7wLmAFMA74LvLXXuoTnjvMzSZIkSaMyVWcOfgy8OckXgB8Cn6H5rz9V9dskhwJfTfKEtv9RVXVLks8BNwK/Aq5uz60FfDnJDJrZgv+sqhVJ3g/8B7CsTSBu57E1Cv3afOambgcqSZKkrplydQ6SzAbOaRcVTyrz58+vJUu6VmZBkiRJU8BgdQ6m6szBmEjydmBRVd3ffv42cFA7c3BfVU0bTjLykxW/Yu+zPjyuMY+Xb+9/ZLdDkCRJ0ihNuTUHVXXHGM4avB34U42Eqtq7qlaM0diSJEnShHLmoENtjYKvA0+lWWdwGvBk4PtJ7qqqPZLcQbOb0V3di1SSJEkaGZODzr0YuLOqXgrQLkA+DNhjNMlAkoXAQoB1N5k5FnFKkiRJIzLlXisahRuAv05yTJJdq+rusRi0qhZV1fyqmr/OdAsoS5IkqXucOehQu5Xp84C9gQ8kuaDbMUmSJEljyeSgQ0meDPy+qr6cZAXwBuBeYENgTNYYbD7zL931R5IkSV1jctC5bYDjkjwKPAy8CdgR+E6SO6tqj65GJ0mSJI3SlCuCNpnNmPu02uXYt3c7jGE79+X/3O0QJEmS1KHBiqC5IFmSJEkSYHIwpCTvSHJ4e/yxJBe2x3smOSXJZ5IsSXJTkvf1OveNXmP8dZKzuvMEkiRJUmdMDoZ2KbBrezwfmJZk7bbtEuDd7bTMtsALk2wLfB/YIskm7XWHAV/ob/AkC9vkYslDd/9hPJ9DkiRJGpTJwdCuAbZPMh14ELiCJknYlSZx+Nsk1wLXAVsBW1azkONLwKuTzKRZuHxef4OvUudghnUOJEmS1D3uVjSEqno4ye3AocDlwDJgD2Au8EfgCGCHqlqe5ERg3fbSLwLfAh4ATquqRyY4dEmSJGlYTA46cylNEvA6mkrJH6WZUZgO/AG4O8mTgJcAFwFU1Z1J7gSOAvbq5Cabz3ySO/9IkiSpa3ytqDOXApsCV1TVr2lmAy6tqqU0rxPdDHwFWNznulOAn1XVjyYyWEmSJGkknDnoQFVdAKzd6/Ozeh0fOsilewL/0+l9frLiN7z0zI+PJMSuOvflb+t2CJIkSRoDJgfjJMk1wCPAtG7HIkmSJHXC14oGkeS1SZYlWZrkS0leluSqJNcl+e92nQFJjk7yhSQXJbktyeFVtT1wO7BZkuuTHNfdp5EkSZIG58zBAJJsRbOYeKequivJRkABL6iqSvIG4J1AzwriLWh2MdoQ+HGSzwBHAltX1bxB7rMQWAiw7sazxu15JEmSpKGYHAxsT5otSO8CqKrfJ9kGODXJpsA6NDMDPc6tqgeBB5P8BnhSJzepqkXAIoAZc59eY/kAkiRJ0nD4WtHwfAL4ZFVtA/w9j9U0gKZAWo+VmHhJkiRpNeMfsAO7EDgryUer6nfta0UzgF+05w/pYIx7aV4z6sjmM//CnX8kSZLUNc4cDKCqbgI+CFycZClN4bOjgdPanYju6mCM3wGLk9zogmRJkiRNdqla/V9zT7KSpnJxj/2A2cARVbVPkr8BtqyqDw8yxu49/fs593ZgUVXd336+A5jfsx6hg/gObfu/ZbB+M+Y+vXY59p2dDDmpnPvyQR9LkiRJk0iSa6pqfn/n1pTXiv7Yd0egJLN7jqvqbODsUYz/duDLwP2jGEOSJEma1KbEa0VJDk3yyfZ4TpIrk9yQ5ANJ7uvVdVqS05PcnOSUNA4Hngx8P8n3+xn71Ul+0NYy+GyStdr2w5LckuQHwM4T8ZySJEnSaKwpycF67R/n1yc5a4i+Hwc+3u449PM+555LM0uwJbAZsHNV/SdwJ7BHVe3Ru3OS5wCvbPvNo9ml6OB2q9P30SQFu7Tj9SvJwiRLkix56O77BuomSZIkjbs19rWiQexIsyYB4CvA8b3O/aCqfg6Q5HqadQuXDTLW/wG2B65OArAe8Bvgr4CLquq37VinAs/qbwDrHEiSJGmyWFOSg7Ey3FoFAU6qqn9ZpTHZb4D+kiRJ0qS1prxWNBxXAge0xwd2eM1A9QouABYk+QuAJBsleQZwFfDCJE9MsjbwilHGLEmSJI27qThz8Hbgy0neDXwHuLuDaxYB30lyZ+91B1X1wyRHAecneRzwMPDmqroyydHAFcAK4PpOAmuKoLktqCRJkrpjjahzMBxJ1qdZo1BJDgReVVX7jsN9DqWtbdAmCvdV1fGDXTNj7jNql2OPHOtQxtW5L39Tt0OQJEnSMEyFOgfDsT3wyTQriFcAr+tyPJIkSdKkMOWSg6q6FNhupNcneS1wBFDAMuDrwFHAOsDvgIOr6tdjEKokSZI0oaZccjAaSbaiSQR2qqq7kmxEkyS8oH1N6Q3AO4F/HsaYC4GFAOtuvNE4RC1JkiR1xuRgePYETququwCq6vdJtgFObQufrQPcPpwBV61z8IyptQBEkiRJk8pU3Mp0rH0C+GRbcfnvgXW7HI8kSZI0IiYHw3Mh8IokT4SmrgEwA/hFe/6QbgUmSZIkjZavFQ1DVd2U5IPAxUlWAtcBRwOnJVlOkzw8c6Tjbz5zE7cGlSRJUtdMuToHg0nybeCgqlrRz7k7aOoW3JXk8qraaazvP2POM2qXY9891sOOq3MPWNjtECRJkjQM1jnoUFXt3betrYeQPv3GPDGQJEmSum3KrjlI8o0k1yS5qd1OlCR3JNk4yewkP05yMnAj8LQ+197X/t49yUVJTk9yc5JT2mSCJNsnubi9x3fb3YwkSZKkSWsqzxy8rt2KdD3g6iRn9Dm/OXBIVV0J0P7N35/nAlsBdwKLgZ2TXEWzi9G+VfXbJK8EPojVmCVJkjSJTeXk4PAk+7fHT6NJBnr7aU9iMIQfVNXPAZJcD8wGVgBbA99rk4q1gF/2d7FF0CRJkjRZTMnkIMnuwF7AjlV1f5KL+PP6BH/ocLgHex2vpPlOA9xUVTsOdfEqRdDmWARNkiRJ3TNV1xzMAJa3icEWwAvGePwfA5sk2REgydpJthrje0iSJEljakrOHADfAd6Y5Ec0f8h38vpQx6rqoSQLgP9MMoPme/4P4KbBrtt81iZuDSpJkqSusc7BJDJjzuza5dj3dDuMjp17wOu7HYIkSZKGabA6B1P1taJha7c3vbHbcUiSJEnjxeRAkiRJEmByMFyPbwud/agtfLZ+kn9NcnWSG5Ms6lUEbYcky5Jcn+Q4Zx0kSZI02ZkcDM+zgU9X1XOAe4B/AD5ZVTtU1dbAesA+bd8vAn9fVfNotjjtV5KFSZYkWfLQPfeOc/iSJEnSwEwOhudnVbW4Pf4ysAuwR5KrktwA7AlslWQmsGFVXdH2/cpAA1bVoqqaX1Xz15m+4bgGL0mSJA1mqm5lOlJ9t3Yq4NPA/Kr6WZKj+fNiapIkSdJqweRgeJ6eZMd2RuAg4DJgJ+CuJNOABcDpVbUiyb1J/qqqrgIO7GTwzWdt7PagkiRJ6hqTg+H5MfDmJF8Afgh8BpgF3Aj8Cri6V9/XA59L8ihwMXD3BMcqSZIkDYvJQed+CtwFnFJVrwZI8gqaV4rm9NP/pqratu13JLBkqBv8z/Lfsc8ZJ45dxOPsnAMO7XYIkiRJGkMmBx2qqkryRuC0JN+n+e7+HXjxAJe8NMm/tP1+Chw6IYFKkiRJI2RyMAxVdWOSbwHvAjag2bHoI0k2A+4HFlbVsnZh8n3tNqa0NQ42AH7bncglSZKkoZkcDN/7gGuBh2gWJF9XVfsl2RM4GZg3nMGSLAQWAqy38RPHOFRJkiSpcyYHw1RVf0hyKnAf8CrggLb9wiRPTDJ9mOMtAhYBzJzzzL5bpUqSJEkTxiJoI/No+zOQR1j1u7X2gSRJkia9QWcO2vfrB/xvdlX9zZhHtHq5FDgYeH+S3YG7quqeJHcA+wAkeR7wzE4Gmzvrie4AJEmSpK4Z6rWi49vfLwf+kmYBLjSv0/x6vIJajRwNfCHJMpoFyYe07WcAr01yE3AVcEt3wpMkSZI6l6qhX3NPsqSq5g/VtrpKcjjwJmA6cFZVvWWQvv8GXFJV/z3Wccyc88za5Zh/G+thx8U5C17T7RAkSZI0AkmuGejv+E4XJG+QZLOquq0d8Jk0W3OuKf4B2Kv9GTThqap/nZCIJEmSpAnW6YLktwMXJbkoycXA94G3jV9YEyfJCcBmwHnArLZtwyS3J1m7/Ty953OSE5MsaNvvSPK+JNcmuSHJFm37Jkm+l+SmJJ9P8tMkG3fpESVJkqSODJkcJHkcMAPYnCYhOBx4dlWdP86xTYiqeiNwJ7AHsLxtuxe4CHhp2+1A4MyqerifIe6qqucBnwGOaNveC1xYVVsBpwNPH+j+SRYmWZJkyUP33DsGTyRJkiSNzJDJQVU9Cryzqh6sqqXtz4MTEFu3fR44rD0+DPjiAP3ObH9fA8xuj3cBvgZQVd+hTTr6U1WLqmp+Vc1fZ/qGo41ZkiRJGrFOXyv67yRHJHlako16fsY1si6rqsXA7HaL0rWq6sYBuvYkSiuxqJwkSZJWY53+MfvK9vebe7UVzbv6a7KTga8A7x/mdYuBvwWOSfIi2rUMQ5k764nuAiRJkqSu6Sg5qKqOinitgU4BPgB8dZjXvQ/4apLXAFcAvwJcUCBJkqRJrdM6B2vT1AHYrW26CPjsAAt0J5UkRwP3VdXxwz3f7kq0b1UN69/5SZ4ArKyqR5LsCFxWVWsNdd3MOZvVLscMd5Ji4p2z4OBuhyBJkqQRGos6B58B1gY+3X5+Tdv2htGHNzkl+QTwEmDvEVz+dODr7U5PD/HYugRJkiRp0up0QfIOVXVIVV3Y/hwG7DCegY1GkncnuSXJZcCz27Y5Sb6T5Jokl/bUJOhz3d8luTrJUuDJwLbALwepeTDQmI8AD9B8v98FHp2Ax5YkSZJGpdPkYGWSOT0fkmxGszvPpJNke5q6BPNo/uvfk8QsAt5aVdvT1CP4dD+Xn1lVO1TVdsCPgNcPUfNgoDE/DnymqrYBfjlEvL3qHNwzomeWJEmSxsKgrxUleTtwOXAkcGGS29tTs4HXjW9oI7YrcFZV3Q+Q5GxgXWAn4LQkPf2e0M+1Wyf5ADATmEbzX39oah68E/gGTc2Dv0sybZAxdwYOaI+/BBwzULBVtYgmyWDmnM2GXgAiSZIkjZOh1hw8FfgP4DnAT4DfA98HzqiqO8c5trH0OGBFVc0bot+JwH5VtTTJocDu0NQ8SLJKzYMk04cY0z/0JUmStFoZNDmoqiMAkqwDzKf5T/nuwL8kWVFVW457hMN3CXBikg/RPN/LgM8Ctyd5RVWdluZf/dtW1dI+125Is8ZgbeBg4Be9zq1S86Cq7mnXHvQ35mKa14++3I7TkbmzNnInIEmSJHVNp2sO1gOmAzPanzuBq8YrqNGoqmuBU4GlwHnA1e2pg4HXt4uNbwL27efy99A812Lg5j7nTqEpZta75sFAY74NeHOSG4CnjPaZJEmSpIkwaJ2DJIuArWgKeF0FXAlcWVXLJya8yWMUNQ9mA+dU1dZD9W3qHPz7yAKcQOcsOLDbIUiSJGmERlPn4Ok0i2x/QvOKzc+BFWMb3uQ3ypoHkiRJ0mphqDUHL27fpd+KZr3BP9Ps6PN74Iqqeu8ExDihkrwHeDXwW+BnwDXAf9FsiXp6kluB11XV8iTzgBOA9YHe7dsDX2iHPH+in0GSJEkaiSHXHFTjRuDbNO/wLwbm0LxXv0ZJsgPNFqTb0cwU9Ey3nAy8q6q2BW4A3jtE+xdp6h9s18E9e9U5uHfsHkaSJEkapkGTgySHJ/lakv8FLgb2oVmo+3JgowmIb6LtDHyzqh5oi599C9gAmFlVF7d9TgJ2SzJjgPaZbfslbfuXBrthVS2qqvlVNX+d6RuO+QNJkiRJnRpqzcFs4DTgH6tq0Eq/kiRJklZvg84cVNU/VdUZUygxWAy8LMm6bQXkfYA/AMuT7Nr2eQ1wcVXdPUD7CmBFkl3adgsXSJIkabUw1MzBlFJVVyc5G1gG/JpmHcHdwCHACUnWB24DDmsvGaj9MOALSYphLEhuiqC5TagkSZK6Y9A6B1NRkmlVdV/7B/8lwELgb4D7qur4YY41D3hyVX27k/4z58ypXY750LBjnmjnLPjbbocgSZKkERqszkGnFZKnkkVJrgeuBc5oKy6P1DysjSBJkqTVhMlBH1V1UFXNo9ll6LAklwHPhmYmIMmVSZYlOSvJrLb9oiTHJPlBkluS7JpkHeDfgFcmuT7JK7v2UJIkSVIHTA760RYxO5DH/vO/Q3tqoLoGAI+vqucDbwfeW1UPAf8KnFpV86rq1AHu1avOwT3j9ESSJEnS0EwO+rcrcFZV3V9V9wBnM0C9g17XnNn+voZmC9iOrFrnYProI5ckSZJGyORg7DzY/l6Ju0BJkiRpNWRy0L9LgP2SrJdkQ+BlDFDvYIhx7gUseyxJkqTVgv/h7kdVXZvkVGAp8Bvg6vbUQHUNBvJ94Mh296MPDbTuoMfcWbPcJlSSJElds9rVOUgyEzioqj7d7VjG2sw5c2rXY47tdhiD+taCA7odgiRJkkZhTatzMBP4h4m6WZLHD/ZZkiRJWlOsjn/ofhiY076q87227SVAAR+oqlOT7A68D1gBbAN8nWbr0bcB6wH7VdWtSWYDXwA2Bn4LHFZV/5vkROAB4LnA4iQb9fl8MnACsD5wK/A6YG3gvKraPsl2wPXAM9rxbgW2qar7x+9rkSRJkkZndZw5OBK4tS1UdiVNLYLtgL2A45Js2vbbDngj8ByaxcPPausQfB54a9vnE8BJbd2CU4D/7HWfpwI7VdU/9fP5z+odVNVvgHWTTKfZCnUJsGuSZwC/MTGQJEnSZLc6Jge97QJ8tapWVtWvaXYP6ilYdnVV/bKqHqT57/75bfsNPFaHYEfgK+3xl9rxepxWVSv7fk4yg4HrHVwO7Nx+/vf2967ApQM9gEXQJEmSNFms7snBYB7sdfxor8+P0tnrVH8Y4nN/LqFJBp4BfJNm9mIXBkkOLIImSZKkyWJ1TA561w64FHhlkrWSbELzn/ofDGOsy4ED2+ODGeSP+B5VdTcD1zu4FHg18JOqehT4PbA3cNkwYpIkSZK6YrVbkFxVv0uyOMmNwHnAMpp6BAW8s6p+lWSLDod7K/DFJO+gXZDc4XX91juoqjuShGYGAZqk4KlVtbyTQefOmuVWoZIkSeqa1a7OwZps/vz5tWTJkm6HIUmSpDXYYHUOVruZg9VFux3qOVV1eqfX/M/yFbzs9G+MX1Cj9K0F+3U7BEmSJI2j1XHNgSRJkqRxYHIAJJmd5OYkJya5JckpSfZq1zb8JMnzkxyd5Ihe19zYFlEjyWuTLEuyNMmXeg29W5LLk9yWZMEEP5YkSZI0LL5W9Ji5wCtoqh1fDRxEsw3p3wD/j6bi8Z9JshVwFE2BtLvaaso9Nm3H2AI4G/izV4ySLAQWAqy38SZj9SySJEnSsDlz8Jjbq+qGdgvSm4ALqlmt3btoWn/2pCmQdhdAVf2+17lvVNWjVfVD4En9XWydA0mSJE0WJgePGapo2iOs+n2tO8wxM6roJEmSpHHma0WduwPYByDJ84Bntu0XAmcl+Whbg2GjPrMHHZs7a6Y7AkmSJKlrnDno3BnARkluAt4C3AJQVTcBHwQuTrIU+Gj3QpQkSZJGziJovSS5A5jfs35gos2cs3ntdszkzS3OXvCybocgSZKkURqsCJozB5IkSZKAKZoc9KprcEqSHyU5Pcn67em3Jrk2yQ1Jtmj7b5TkG20tgyuTbNu2H53kC0kuamsZHN7rHq9O8oMk1yf5bJK1uvCokiRJUsemZHLQejbw6ap6DnAP8A9t+11V9TzgM0BP0bP3AddV1bY0NQ9O7jXOFsD/BZ4PvDfJ2kmeA7wS2Lmq5gErgYP7CyLJwiRLkix56J67x/YJJUmSpGGYysnBz6pqcXv8ZZpiZQBntr+v4bH6BrsAXwKoqguBJybpKUpwblU92K5T+A1NPYP/A2wPXJ3k+vbzZv0FsWqdgxlj9nCSJEnScE3lrUz7rsTu+dxTm2AlnX0/vWsZ9FwT4KSq+pdRRShJkiRNoKmcHDw9yY5VdQVwEHAZ8NwB+l5K81rQ+5PsTvPq0T3JgHXNLgC+meRjVfWbJBsBG1bVTwcLaO6sGe4IJEmSpK6Zyq8V/Rh4c5IfAbNo1hgM5Ghg+yTLgA8Dhww2cFX9EDgKOL+95nvApmMRtCRJkjRepmSdgySzgXOqausO+t5XVdPGPSiaOgcvPObjE3GrEfnmgr27HYIkSZJGyToHkiRJkoY0JZODqrqjk1mD3tI4LsmNbQ2EV7btX0vy0l79TkyyIMlabf+r2/oIfz/WzyFJkiSNpSmZHIzQy4F5wHbAXsBxSTYFTgX+FiDJOjTblp4LvB64u6p2AHYA/i7JM/sOap0DSZIkTRYmB53bBfhqVa2sql8DF9P80X8esEeSJwAvAS6pqj8CLwJe29Y5uAp4IrB530GtcyBJkqTJYipvZTomquqBJBfRVEl+JfC19lSAt1bVd7sVmyRJkjQcJgeduxT4+yQnARsBuwHvaM+dCrwBmA8c2rZ9F3hTkgur6uEkzwJ+UVV/GOgGc2fNcEcgSZIkdY3JQefOAnYEltJUU35nVf2qPXc+8CXgm1X1UNv2eWA2cG2aamm/Bfab0IglSZKkYZhydQ6SnEhT4+D0bsfS18w5z6oXHvOJbocxoG8u+L/dDkGSJEmjZJ0DSZIkSUNa45ODJK9t6wwsTfKltnm3JJcnuS3JgrbftCQXJLm2rWOwb9s+O8mPknwuyU1Jzk+yXntuh3bs63tqILTt1jiQJEnSameNTg6SbAUcBexZVdsBb2tPbUqzNek+wIfbtgeA/avqecAewEfatQLQbEH6qaraClgBHNC2fxH4+6qaB6zsdeuOahy0MVrnQJIkSZPCGp0cAHsCp1XVXQBV9fu2/RtV9WhV/RB4UtsW4N+TLAP+G3hKr3O3V9X17fE1wOwkM4ENq+qKtv0rve7bUY2DNibrHEiSJGlSmKq7FT3Y67hnduBgYBNg+3br0TuAdfvpvxJYb4jxrXEgSZKk1c6aPnNwIfCKJE8ESLLRIH1nAL9pE4M9gGcMNnBVrQDuTfJXbdOBvU731DhYu73vs5JsMNKHkCRJkibCGj1zUFU3JfkgcHGSlcB1g3Q/BfhWkhuAJcDNHdzi9cDnkjwKXAz0LBoYUY2DubOmu12oJEmSumbK1TnoVJLZNPUQth6kz7Squq89PhLYtKreNlD/ocyc86za/ZhPj/TycfWNBXt1OwRJkiSNgcHqHKzRMwcT4KVJ/oXme/wpcOhgnZM8vqoemYjAJEmSpOFa09ccjNZafesbJJmX5Mp2V6MDgT3a2YUNaNcpJNm4XdBMkkOTnJ3kQuCCbj2IJEmSNBSTg8H1V9/gZOBdVbUtcAPw3g7GeR6woKpe2PeEdQ4kSZI0WZgcDK5vfYM5wMyqurhtOwnYrYNxvterxsIqrHMgSZKkycLkYHB96xvMHKTvIzz2fa7b59wfxjIoSZIkaTyYHAzP3cDyJLu2n19Ds4UpwB3A9u3xggmOS5IkSRo1dysavkOAE5KsD9wGHNa2Hw98PclC4NyRDDx31nS3DJUkSVLXWOdgmNpiajcAa9O8SnQy8LGqejTJ7sD3gb+rqs+3/efRFF97R1UdP9jYs+Y8u3Y/9rPjGf6InHXA7t0OQZIkSWNksDoHvlY0fH+sqnntDkZ/DbyEVXcsuhH4216fXwUsncD4JEmSpBExORiFqvoNsBB4S5K0zT8F1k3ypLbtxcB53YpRkiRJ6pRrDkapqm5LshbwF72aTwdeQfM60bWsuuuRJEmSNCk5czA+vk6THLwK+OpgHXsXQXvQImiSJEnqIpODUUqyGU0NhN/0tFXVr4CHadYkXDDY9b2LoD3BImiSJEnqIl8rGoUkmwAnAJ+sqnps2QEA/wr8RVWt7NMuSZIkTUomB8O3XpLreWwr0y8BH+3bqaouH+7Ac2Zt6LahkiRJ6hrrHEwis+ZsUbsf+/luh7GKsw7YpdshSJIkaQxZ52AcJTk0ySe7HYckSZI0WiYHkiRJkgCTAwCSzE5yc5ITk9yS5JQkeyVZnOQnSZ7f/lyR5Loklyd5dj/jvLTts3GSF7XH1yY5Lcm0bjybJEmS1ClCYqXQAAAgAElEQVSTg8fMBT4CbNH+HATsAhwB/D/gZmDXqnouzU5E/9774iT7A0cCe7dNRwF7VdXzgCXAP/V301XrHKwY84eSJEmSOuVuRY+5vapuAEhyE3BBuz3pDcBsYAZwUpLNgaLZrajHnsB84EVVdU+SfYAtgcXtNqbrAFf0d9OqWgQsgmZB8ng8mCRJktQJk4PHPNjr+NFenx+l+Z7eD3y/qvZPMhu4qFf/W4HNgGfRzBIE+F5VvWp8Q5YkSZLGjslB52YAv2iPD+1z7qfAO4Azk7wCuBL4VJK5VfU/STYAnlJVtwx2gzmzprl1qCRJkrrGNQedOxb4UJLr6CepqqqbgYOB04DpNAnEV5Mso3mlaIuJC1WSJEkaPougDSLJfVXV0S5DSXYHHhpJZeQes+Y8p/Y49osjvXxcnHnAC7odgiRJksaQRdAmxu7ATt0OQpIkSRopk4NhSvKyJFe19Q7+O8mT2gXKbwT+Mcn1SXZNskmSM5Jc3f7s3N3IJUmSpMG5IHn4LgNe0G5z+gbgnVX1z0lOAO6rquMBknwF+FhVXZbk6cB3gef0HSzJQmAhwHob/+WEPYQkSZLUl8nB8D0VODXJpjT1C24foN9ewJZtnQOA6UmmVdV9vTutWufgOS4AkSRJUteYHAzfJ4CPVtXZ7SLkowfo9ziaGYYHJiowSZIkaTRMDoavd72DQ3q130uzhWmP84G3AscBJJlXVdcPNvCcWRu4O5AkSZK6xgXJg1s/yc97/fwTzUzBaUmuAe7q1fdbwP49C5KBw4H5SZYl+SHNgmVJkiRp0rLOwQglOZpeC5DHwqw5W9aex548VsONiTMO6HcLXEmSJK2mrHMgSZIkaUgmB8OQ5N1JbklyGfDstu3v2joGS9u6Busn2TDJ7UnWbvtM7/1ZkiRJmoxMDjqUZHvgQGAesDewQ3vqzKraoaq2A34EvL6q7gUuAl7a9jmw7fdwP+MuTLIkyZIH71k+3o8hSZIkDcjkoHO7AmdV1f1VdQ9wdtu+dZJLk9wAHAxs1bZ/HjisPT4M+GJ/g1bVoqqaX1XznzB91jiGL0mSJA3O5GD0TgTeUlXbAO8D1gWoqsXA7LYWwlpVdWPXIpQkSZI6YJ2Dzl0CnJjkQzTf28uAzwIbAr9s1xMczGM1EABOBr4CvL+TG8yZtb67A0mSJKlrnDnoUFVdC5wKLAXOA65uT70HuApYDNzc57JTgFnAVycoTEmSJGnEnDkAknwbOKiqVgzWr6o+CHywn1OfGeCSXWgShycDg44NcOvyP7LgjKVDdZswpx+wXbdDkCRJ0gRy5gCoqr37JgZpjPj7SfIJ4MPA74EtRxmiJEmSNO6mXHKQ5BtJrklyU5KFbdsdSTZOMjvJj5OcDNwIPC3JfUk+1va/IMkm7TXzklyZZFmSs5LMatsvSvIfwI40OxTtDhyX5Pokc7ry0JIkSVIHplxyALyuqrYH5gOHJ3lin/ObA5+uqq2q6qfABsCSqtoKuBh4b9vvZOBdVbUtcEOvdoB12u1JP0iz5ek7qmpeVd3aNxjrHEiSJGmymIrJweFJlgJXAk+jSQZ6+2lVXdnr86M0C5EBvgzskmQGMLOqLm7bTwJ263XNqXTIOgeSJEmaLKbUguS25sBewI5VdX+Si2jrEvTyhyGGqQ5uNdQYkiRJ0qQzpZIDYAawvE0MtgBe0ME1jwMWAF8DDgIuq6q7kyxPsmtVXQq8huaVo/7cS1MLYUhzZq3nDkGSJEnqmqn2WtF3gMcn+RHNTkJXDtEfmlmA5ye5EdgT+Le2/RCahcbLgHm92vv6GvCOJNe5IFmSJEmTWao6eUtmzZVkNnBOVW09wPn7qmraRMSy0Zyta69jvz4Rt+rI1w9wB1ZJkqQ1TZJrqmp+f+em2syBJEmSpAGYHDTWSvK5tpbB+UnW66ljANzWTx2Dj7Xbj/4oyQ5JzkzykyQf6BkwyauT/KCtb/DZJGt17ekkSZKkDpgcNDYHPtXWMlgBHMDgdQweaqdiTgC+CbwZ2Bo4NMkTkzwHeCWwc1XNA1YCB/d341XrHPx+nB5PkiRJGtpU261oILdX1fXt8TXAHP68jsFpvfqf3f6+Abipqn4JkOQ2mtoJuwDbA1cnAVgP+E1/N66qRcAiaNYcjNUDSZIkScNlctB4sNfxSmBmh/0f7XPtozTfaYCTqupfxixCSZIkaZz5WlH/7gaWJ9m1/TxYHYP+XAAsSPIXAEk2SvKMMY5RkiRJGlPOHAzsEOCEJOsDtwGHdXphVf0wyVHA+UkeBzxMsy7hp4Ndt9msdd0+VJIkSV0zpeoc9FfTIMnRwH1VdfwA18wHXltVhyfZnWYx8uXDvO8dwPyqumuwfhvN3ab++tizhjP0uDr15XO7HYIkSZLG2GB1Dpw5GEJVLQGWtB93B+4DhpUcSJIkSasD1xy02voFx7S1CW7pWW+QZPck57SzDm8E/rGtXbBrkk2SnJHk6vZn5/aaJ7b1Em5K8nmaBcqSJEnSpGZysKrHV9Xzgbezal0DquoOmroGH6uqeVV1KfDx9vMONLURPt92fy9wWVs34Szg6QPdcJU6B3db50CSJEndM9VeKxpogUVP+5nt72uA2R2MtxewZVvLAGB6kmnAbsDLAarq3CTLBwyod52DudtMnQUgkiRJmnSmWnLwO2BWn7aNgNvb456aBSvp7Lt5HPCCqnqgd2OvZEGSJElabUyp14qq6j7gl0n2hKb+APBi4LIOh7gX2LDX5/OBt/Z8SDKvPbwEOKhtewl/npBIkiRJk85UmzkAeC3wqSQfbT+/r6pu7fC//d8CTk+yL01ScHg71jKa7/ISmkXL7wO+muQmmp2N/reTwTeb+QS3D5UkSVLXTKk6B5PdxnO3rZcde063w/iTL758wHXUkiRJWk0NVudgSr1WJEmSJGlgJgfDkGSDJOcmWZrkxiSvTLJ9kouTXJPku0k2bfvOSfKdtv3SJFt0O35JkiRpMFNxzcFovBi4s6peCpBkBnAesG9V/TbJK4EPAq+j2Z70jVX1kyR/BXwa2LNLcUuSJElDMjkYnhuAjyQ5BjgHWA5sDXyvXdC8Fs1uSNOAnYDTei10fkJ/AyZZCCwE2GDjp4xr8JIkSdJgTA6GoapuSfI8YG/gA8CFwE1VtWPvfkmmAyuqal4/w/Qd809F0Daeu62rwyVJktQ1rjkYhiRPBu6vqi8DxwF/BWySZMf2/NpJtqqqe4Dbk7yibU+S7boWuCRJktQBZw6GZxvguCSPAg8DbwIeAf6zXX/weOA/gJuAg4HPJDkKWBv4GrB0sMFnz1zH7UMlSZLUNSYHw1BV3wW+28+p3frpezvNAmZJkiRptWByMIn8fMVD/PNZP+92GH/ykf2f2u0QJEmSNIFccyBJkiQJcOZgTCR5D/Bq4LfAz4BrgP8GTgDWB24FXldVy7sWpCRJkjQEZw5GKckOwAHAdsBLgPntqZOBd1XVtjT1Ed47wPULkyxJsuT+e34/ESFLkiRJ/TI5GL2dgW9W1QNVdS/wLWADYGZVXdz2OYl+Fi1DU+egquZX1fz1p280MRFLkiRJ/TA5kCRJkgS45mAsLAY+m+RDNN/nPjQVj5cn2bWqLgVeA1w8yBgAPHXmOu4QJEmSpK4xORilqro6ydnAMuDXNOsL7gYOAU5Isj5wG3BY96KUJEmShmZy0EeSE4Fzqur0YVx2fFUd3SYCl9DsVvT2tr3jce5c8TBHn3XnsOIdL0fv/+RuhyBJkqQJZnIwNhYl2RJYFzipqq5N0u2YJEmSpGGZ8guSk7w2ybIkS5N8qW3eLcnlSW5LsqBX33ckubrt/75ew3yH5rt8ENiyn3u8P8mJSdYa14eRJEmSRmFKzxwk2Qo4Ctipqu5KshHwUWBTYBdgC+Bs4PQkLwI2B54PBDg7yW7A7/oZo/c9jgM2BA6rquonhoXAQoAZmzxlfB5UkiRJ6sCUTg6APYHTququ/9/enUfZVZV5H//+hChDgDAGEDAK2AhIAgkoKApo004toigitiJqtFVwwpZW7G5nbVqgUUGjr+KAouCAUzPIGAMICYQwq21wbmbCIGAIz/vHOaU3Rc2pqltJvp+1at1z99lnn31q3XVTT/bwAFTVne10oO9X1SPA9UmmtnX3b3+uat9PpgkWpvduo6P9DwA/r6rZ/XWgqubQ7G7ElttNf1TwIEmSJI2X1T046M9DHcfpeP14VX2+s2KSIwZo5wpgZpKNegUNkiRJ0oSzugcH5wPfS3JcVd3Re0pQL2cDH05yalXdl+TxwNK+2ugIBM5qr/txkv3bDMr92nLKJHcJkiRJUtes1sFBVV2X5KPARUmW8bcpQ33VPSfJU4BL26lH9wGv7qeNwzquOz3JejRrFF5QVQ+M4SNJkiRJI5Y+1siqleQw4Jyq+mP7/mZgVs/6gtG29XbT6+3Hnj0WTQ/LUQdu3u0uSJIkaYwkWVBVs/o6t9pvZTqIw4BRmeeTZLUepZEkSdLEt0oFB20egiPb4+OTnN8e75fk1CT7J7k0yZVJTk8yuT3/b23+gmuTzEnjIGAWcGqShUnWbm9zRHv9NUl2aK9fN8mXklye5KokB7TlhyX5QduP88b79yFJkiQNxyoVHABzgb3b41nA5CST2rJFNPkInltVuwHzgXe1dT9TVbtX1c7A2sCLquqMts6hVTWjY63A7e31JwNHtWXvB86vqj2AfYFjk6zbntsNOKiqnt1Xh5PMTjI/yfz77rljVH4JkiRJ0kisasHBApqtQ9en2Y70UpogYW/gAZrsxfOSLAReCzyhvW7fJD9Pcg1N7oOdBrjHdzvuNa093h84um33QmAtYJv23LkDbWNaVXOqalZVzZq8/sbDeVZJkiRpVK1S8+CrammSxTRrBS6hGS3YF9gOWEzzh/ohndckWQs4iWah8e+S/AfNH/f96cmBsIy//f4CvKyqburV9tOA+1fkmSRJkqTxskoFB625NNN9DgeuAY6j+V/+y4DPJtmuqn7VTvt5PHBre93t7RqEg4Az2rJ7gfWGcM+zadYiHFFVlWTXqup3W9T+TJ0yyZ2CJEmS1DWr2rQiaIKDLYBLq+oW4EFgblXdRjOi8M0ki2imHO1QVXcDXwCupfkj/4qOtk4BPtdrQXJfPgxMAhYlua59L0mSJK1UzHMwREk2B04AdgfuBm4B3lFVvxite2yz3fR677HnjFZzI/LWA6d29f6SJEkaWwPlOVgVpxWNujQpkb8HfKWqXtmWTQemAqMWHEiSJEndtCpOKxoL+wJLq+pzPQVVdTVwVZLzOvIe9OQ3mJbkhiRfSHJdknMGmZYkSZIkdZ3BwdDsTLOoubcHgQPbvAf7Ap9qRxkAtgc+W1U70UxDellfDS+f56DfHU8lSZKkMWdwsGICfKxd4PxTmt2PeibtL66qhe1xZ06E5Syf52Cjse6vJEmS1C+Dg6G5DpjZR/mhwKbAzKqaQbNIuSdHwkMd9TpzIkiSJEkTksHB0JwPPC7J7J6CJLvQZFi+tU2+ti9/y7gsSZIkrXT83+whaBObHQickOS9NGsNbgb+AzgxyTXAfODGFbnPZlMmuZWoJEmSusbgYAiSHAn8M7A58Mmq+kTH6T37uWznJNOAvarqv4Zyn9vvfpj/991bB684Rl7/0s26dm9JkiR1n8HB0LwFeG5V/b6vk0nWrKqH+zg1DXgV8I0x7JskSZI0KgwOBpHkc8CTgP9J8iVg26p6W5JTaKYX7QrMS3Im8N/tZQU8C/gE8JQkC2kSqB0/7g8gSZIkDZHBwSCq6s1JnkeTx+BFvU5vRTNtaFmSHwJvrap5SSbTBA5HA0dVVe/r/qpd5DwbYKNNthqTZ5AkSZKGwt2KVszpVbWsPZ4HHNeuT5jSzzSjR+nMc7DeBhuPWUclSZKkwRgcrJj7ew7aRcpvANammWa0Q9d6JUmSJI2A04pGSZJtq+oa4JokuwM7AL8D1utuzyRJkqShMTgYPe9oE6E9QpNR+X/a42VJrgZOGWxB8iZT1nQ7UUmSJHWNwcEQVNU0gCQ/BQ5M8kuaKVlLkvygqv5SVUf0c/l+Q73PnXc9zKnfuW2F+ztSh75s067dW5IkSd3nmoMhShLgu8D3q2p74MnAZOCjw2hjjTHqniRJkrTCDA6Gbj/gwar6MkC7S9E7gcOTvCXJZ3oqJvlRkn3a4/uSfKqdWtRfNmVJkiSp6wwOhm4nYEFnQVXdA/yWgadnrQv8vKqmV9XPxrB/kiRJ0goxOBh7y4Dv9Hcyyewk85PMv+eeO8axW5IkSdLyDA6G7npgZmdBkvWBbYC7Wf53uVbH8YMdidIepTMJ2vrrmwRNkiRJ3WNwMHTnAeskeQ38dXHxp4BTgF8DM5I8JsnWwB5d66UkSZI0Qm5lOkRVVUkOBE5K8gGawOonwPuAvwCLaUYXbgCuHMk9NtpwTbcTlSRJUtcYHAxDVf0O+Md+Th/azzWTh9r+XXc9zBnfuX0kXRsVB71sk67dW5IkSd3ntKIxkOSLSXZsj9/X7f5IkiRJQ2FwMAaq6g1VdX371uBAkiRJKwWDgwEkeU+SI9vj45Oc3x7vl+TUJCe325Bel+SDHdddmGRWkk8AaydZmOTULj2GJEmSNCQGBwObC+zdHs8CJieZ1JZdDLy/qmYBuwDPTrJL58VVdTTwQFXNqKo+1ySY50CSJEkThcHBwBYAM9t8Bg8Bl9IECXvTBA6vSHIlcBVNBuUdh3sD8xxIkiRponC3ogFU1dIki4HDgEuARcC+wHbAA8BRwO5VdVeSU1g++ZkkSZK0UjE4GNxcmiDgcOAa4DiaEYX1gfuBJUmmAs8HLuzj+qVJJlXV0sFutOGGa7qdqCRJkrrGaUWDmwtsAVxaVbcADwJzq+pqmulENwLfAOb1c/0cYJELkiVJkjTROXIwiKo6D5iU5D5gclU9uePcYf1cs0/H2z8BM6vqz4Pd6+67HuYHp3cvCdqLX+6ohSRJ0urMkYOx9w5gnW53QpIkSRqMIwfDlGQycCawITAJOKaqzkyyLvBtYCtgDeDDwFRgS+CCJLdX1b5d6rYkSZI0KIOD4XsQOLCq7kmyCXBZkh8AzwP+WFUvBEiyQVUtSfIuYN+q6nO+UJLZwGyATTfZanyeQJIkSeqD04qGL8DHkiwCfgo8nmaE4Brg75N8MsneVbVkKI2Z50CSJEkThcHB8B0KbEqzyHgGcAuwVlX9AtiNJkj4SJJ/62IfJUmSpGFzWtHwbQDc2iZI2xd4AkCSLYE7q+rrSe4G3tDWvxdYDxh0G6IpG67pjkGSJEnqGoOD4TsV+GGSa4D5NHkOAJ4KHJvkEWAp8M9t+RzgrCR/dEGyJEmSJrJUVbf70DVJpgCvqqqTxvg+NwOz+luU3GP7bWfUiR/76Vh2ZUDPP9hRC0mSpFVdkgVVNauvc6v7moMpwFt6FyZxREWSJEmrndU9OPgEsG2ShUmuSDK33Zb0eoAk30+yIMl17ZajJHlzkmN7GkhyWJLPtMevTnJ5297nk6zRjYeSJEmSRmJ1Dw6OBv633XXoPTS7Db29qp7cnj+8qmYCs4Ajk2wMfAc4sKONg4HTkjylPX5G294ymp2NBpRkdpL5Sebfc88do/ZgkiRJ0nCt7sFBb5dX1eKO90cmuRq4DNga2L6qbgN+neTpbbCwAzAPeA4wE7giycL2/ZMGu6F5DiRJkjRROLd+eff3HCTZB3gusGdV/TnJhcBa7enTgFfQ7FT0vaqqJAG+UlX/Or5dliRJkkbH6h4c9OQg6MsGwF1tYLAD8PSOc98D3g/sCry3LTsPODPJ8VV1a5KNgPWq6jdD7cwGG67pjkGSJEnqmtU6OKiqO5LMS3It8ABNtuMeZwFvTnIDcBPN1KKe6+5qy3esqsvbsuuTHAOck+QxNLkO3goMOTiQJEmSumm1znMwGpK8hGYk4SlVdWNH+QzgKuD5VXXWUNp68pNm1EkfOXdsOjqI575q067cV5IkSePLPAdj6xDgZ+3rUMolSZKkCcngYAUkmQw8E3g98MqO8gAvBw4D/j7JWn02IEmSJE0gBgcr5gDgrKr6BXBHkplt+V7A4qr6X+BC4IX9NdCZ52DJveY5kCRJUvcYHKyYQ2i2NaV9PWSQ8kfpzHOwwXrmOZAkSVL3rNa7Fa2IdqvS/YCnJilgDaCSvBd4GXBAkvcDATZOsl5V3du9HkuSJEkDMzgYuYOAr1XVm3oKklxEk/9gUVX9Q0f5V4ADga8O1OD6G63prkGSJEnqGqcVjdwhNFuYdvoO8MR+yt21SJIkSROaIwcDSDIN+FFV7dz7XFXt20fZiX20sQ9we1U9f7D73Xvnw1z09dtG1NcV9exXO2IhSZK0unPkYOztQ7N7kSRJkjShGRwMbs0kpya5IckZSdZJMjPJRUkWJDk7yRYASY5Mcn2SRUlOa0ce3gy8M8nCJHt380EkSZKkgTitaHB/B7y+quYl+RLwVprFxQdU1W1JDgY+ChwOHA08saoeSjKlqu5O8jngvqr6r74aTzIbmA0wdeOtxuN5JEmSpD45cjC431XVvPb468A/ADsD5yZZCBwD9PxVvwg4NcmrgYeH0vhyeQ7WN8+BJEmSuseRg8FVr/f3AtdV1Z591H0h8CzgH4H3J3nqWHdOkiRJGi2OHAxumyQ9gcCrgMuATXvKkkxKslOSxwBbV9UFwHuBDYDJNMHEel3otyRJkjQsjhwM7ibgre16g+uBTwNnAycm2YDmd3gC8Avg621ZgBPbNQc/BM5IcgBwRFXN7e9G6220pluKSpIkqWtS1XvWjLplhyfOqC998Nyu3Huv1xiUSJIkrQ6SLKiqWX2dc1qRJEmSJMDgYMwkmZbk2o73RyX5jy52SZIkSRqQwUGXJZmdZH6S+Xffe0e3uyNJkqTVmMFBl3XmOZiynnkOJEmS1D0GB2PnYZb//a7VrY5IkiRJQ2FwMHZuATZLsnGSxwEv6naHJEmSpIGY52CMVNXSJB8CLgf+ANw42DWTN17TLUUlSZLUNQYHY6iqTgROHGr9+29/mMu/fOsY9qh/e7xus67cV5IkSROH04okSZIkAQYHg2rzFdyQ5AtJrktyTpK1k2yb5KwkC5LMTbJDkjWSLE5jSpJlSZ7VtnNxku27/TySJElSfwwOhmZ74LNVtRNwN/AyYA5wRFXNBI4CTqqqZcBNwI7AM4Ergb3bBclbV9Uvu9J7SZIkaQhcczA0i6tqYXu8AJgG7AWcnqSnzuPa17nAs4AnAh8H3ghcBFzRV8NJZgOzATbfeKsx6LokSZI0NI4cDM1DHcfLgI2Au6tqRsfPU9rzFwN7A3sAPwGmAPvQBA2PslwStMkmQZMkSVL3GByMzD3A4iQvB2jXGExvz11OM6rwSFU9CCwE3kQTNEiSJEkTltOKRu5Q4OQkxwCTgNOAq6vqoSS/Ay5r680FDgGuGazBdTdZ0y1FJUmS1DWpqm73Qa1Zs2bV/Pnzu90NSZIkrcKSLKiqWX2dc+RgmNqsxxdX1U9Hu+0/3/4wV32xO0nQdn2DIxaSJEmrO4ODYaqqf+t2HyRJkqSx4IJkIMkHktyU5GdJvpnkqCQzklyWZFGS7yXZsK17SpKD2uObk3wwyZVJrkmyQ1u+aZJz26RpX0zymySbdPMZJUmSpMGs9sFBkt1pkppNB54P9My/+irw3qrahWYx8b/308TtVbUbcDJNMjTauue3SdPOALYZ4P6zk8xPMv+ue+9Y4eeRJEmSRmq1Dw6AZwBnVtWDVXUv8ENgXWBKVV3U1vkKTWKzvny3fe1JjgZNduTTAKrqLOCu/m7emedgw/XMcyBJkqTuMThYcT0J0pbhGg5JkiStxPxjFuYBn0/ycZrfx4uAOcBdSfauqrnAPwEXDdBGX22+Avhkkv2BDYdy0TqbrOmuQZIkSeqa1T44qKorkvwAWATcQrO+YAnwWuBzSdYBfg28bhjNfhD4ZpJ/Ai4F/g+4d1Q7LkmSJI0yk6ABSSZX1X1tIHAxMLuqrhzCdQUcV1Xvbt8fBUwGPg4sq6qHk+wJnFxVMwZrb6cnTK9vve+cFXqWkdr5TVO7cl9JkiSNL5OgDW5Okh2BtYCvDCUwaD0EvDTJx6vq9o7ybYBvJ3kM8BfgjaPbXUmSJGn0uSAZqKpXVdWMqtqhqj4+jEsfplmf8M5e7f0SOBC4A3gc8Ikk/W5nKkmSJE0EBgcr7rPAoUk26FX+aZpRiF2AU4ET+7p4uTwH9905xl2VJEmS+mdwsIKq6h6ahGlH9jq1J/CN9vhrNLkP+rr+b3kOJm80dh2VJEmSBmFwMDpOAF5PkzxNkiRJWim5IHkUVNWdSb5NEyB8qS2+BHglzajBocDcwdpZe9NJ7hokSZKkrnHkYPR8Ctik4/0RwOuSLKJJovb2rvRKkiRJGiJHDvqRZArwqqo6aYBqOye5tqp2rqpbgHV6TlTVb4D9hnPPB29dyk2fvWVkHV5Bf/dWRywkSZJWd44c9G8K8JZud0KSJEkaLwYH/fsEsG2ShUmOT3JekiuTXJPkgN6VkzwpyVVJdk+ybZKzkixIMjfJDl3ovyRJkjQsTivq39HAzlU1I8mawDpVdU+STYDLkvygp2KSvwNOAw6rqquTnAe8uap+meRpwEn0M8UoyWxgNsCWG241xo8kSZIk9c/gYGgCfCzJs4BHgMcDPZP0NwXOBF5aVdcnmQzsBZyepOf6x/XXcFXNocmyzM7bTK+x6b4kSZI0OIODoTmUJgiYWVVLk9wMrNWeWwL8libJ2fU0U7XurqoZ3eioJEmSNFIGB/27F1ivPd4AuLUNDPYFntBR7y/AgcDZSe6rqm8kWZzk5VV1eprhg12q6urBbrjWZpPcNUiSJEldY3DQj6q6I8m8JNcCVwA7JLkGmA/c2Kvu/UleBJyb5D6akYaTkxwDTKJZjzBocCBJkiR1k8HBAKrqVUOotsh3Rs8AAB5TSURBVHNb925g947y5yV5CfA94NtDud9Dty7l1yf+37D7uaKedOTm435PSZIkTTxuZTq2DgF+1r5KkiRJE5rBwRhpdy16JvB64JVd7o4kSZI0KIODsXMAcFZV/QK4I8nMviolmZ1kfpL5d953x/j2UJIkSepgcDB2DqFZiEz72ufUoqqaU1WzqmrWRpM3HrfOSZIkSb25IHkMJNmIJiPyU5MUsAZQSd5TVSY6kyRJ0oTkyMHYOAj4WlU9oaqmVdXWwGJg7y73S5IkSeqXIwdj4xDgk73KvtOWX9zfRY/bbJLbikqSJKlrDA7GQFXt20fZiYNd95dblvK7T41/noOt321AIkmSJKcVDVsSAypJkiStkgwO+pDkNUkWJbk6ydeSnJLkc0l+Dvxnkm2TnJVkQZK5SXZIskaSxWlMSbIsybPa9i5Osn2XH0uSJEkakP8L3kuSnYBjgL2q6vZ256HjgK3asmVJzgPeXFW/TPI04KSq2i/JTcCOwBOBK4G924Bi66r6ZT/3mw3MBnj8ho8f8+eTJEmS+mNw8Gj7AadX1e0AVXVnEtqyZW3m472A09tygMe1r3OBZ9EEBx8H3ghcBFzR382qag4wB2CXrae7zakkSZK6xmlFQ3d/+/oY4O6qmtHx85T23MU025XuAfwEmALsQxM0SJIkSROawcGjnQ+8PMnG8NeEZn9VVfcAi5O8vD2fJNPb05fTjCo8UlUPAguBNzHA9qWSJEnSROG0ol6q6rokHwUuSrIMuKqPaocCJyc5BpgEnAZcXVUPJfkdcFlbby5NboNrhnLvx06d5LaikiRJ6ppUOc19OJJcCBxVVfNHu+3pW02vs478yWg3O6gt/sWF0JIkSauLJAuqalZf55xWNI7MkSBJkqSJbJUNDpJMS3JDki8kuS7JOUnWTnJhklltnU2S3NweH5bk+0nOTXJzkrcleVeSq5Jc1mvtwT8lWZjk2iR7tNevm+RLSS5vrzmgo90fJDkfOG+cfw2SJEnSkK2ywUFre+CzVbUTcDfwskHq7wy8FNgd+Cjw56raFbgUeE1HvXWqagbwFuBLbdn7gfOrag9gX+DYJOu253YDDqqqZ4/CM0mSJEljYlWf5rK4qha2xwuAaYPUv6Cq7gXuTbIE+GFbfg2wS0e9bwJU1cVJ1k8yBdgfeHGSo9o6awHbtMfnVtWdfd1wuSRoU5z7L0mSpO5Z1YODhzqOlwFrAw/ztxGTtQao/0jH+0dY/nfVexV3AQFeVlU3dZ5oMyjfTz86k6BN38okaJIkSeqeVX1aUV9uBma2xweNsI2DAZI8E1hSVUuAs4Ej0qZNTrLrCvZTkiRJGler+shBX/4L+HY7nefHI2zjwSRX0eQ4OLwt+zBwArAoyWOAxcCLhtPopM0nua2oJEmSusY8BxPI9K12qbPf/oNxvefm75k2rveTJElSd5nnYJiSfDHJju3x+3qdu6Q7vZIkSZLGlsFBH6rqDVV1ffv2fb3O7dWFLkmSJEljbpUODpK8J8mR7fHxbSIykuyX5NQkJyeZ3yZJ+2DHdRcmmZXkE8DabcKzU9tz97Wv+7T1zkhyY9tez2LkF7RlC5KcmORH4/7wkiRJ0jCt0sEBMBfYuz2eBUxOMqktuxh4fzvfahfg2Uk6cxlQVUcDD1TVjKo6tI/2dwXeAewIPAl4RpK1gM8Dz6+qmcCmA3Uwyew2QJl/x/13jPhBJUmSpBW1qgcHC4CZSdanyVlwKU2QsDdN4PCKJFcCVwE70fyRPxyXV9Xvq+oRYCFNkrUdgF9X1eK2zjcHaqCq5lTVrKqatfG6Gw/z9pIkSdLoWaW3Mq2qpUkWA4cBlwCLgH2B7YAHgKOA3avqriSn8OikaIPpnWRtlf59SpIkadW2OvwxO5cmCDgcuAY4jmZEYX2azMVLkkwFng9c2Mf1S5NMqqqlQ7zfTcCTkkyrqptpE6YNxaTNH+vWopIkSeqaVX1aETTBwRbApVV1C/AgMLeqrqaZTnQj8A1gXj/Xz6FJbHbqUG5WVQ8AbwHOSrIAuBdYsmKPIEmSJI09k6CNgSSTgWtp1jd8CPhlVR0/2HXTt35qnf2O745195az+bu3H9f7SZIkqbtMgjb+ZgNb0oxGbECze5EkSZI0oRkc9DKE3AiHJLkmybVJPtlx3X1JPpXkauBy4I/AM4A3AN9J8sYuPI4kSZI0ZAYHjzZQboRfAJ8E9gNmALsneUlbd13g51U1vap+1pZNBn4IfLOqvtDXzZbPc3Dn2DyRJEmSNAQGB482UG6Eu4ELq+q2qnoYOBV4VnvdMuA7vdo6E/hyVX21v5stn+dgo1F+FEmSJGnoDA56abcs7cyNMJe/5Ua4eYBLH6yqZb3K5gHPS5LR76kkSZI0ulaHPAcj0V9uhMuBE5NsAtwFHAJ8eoB2/q39+SzN9qYDmjT1ce4eJEmSpK5x5KBv/eVG+BNwNHABcDWwoKrOHKSttwNrJ/nPseywJEmStKLMczBCSe6rqsmj2eb0rXeus9/57dFsclCbv2vHcb2fJEmSuss8B5IkSZIGZXCwgpKcluSFHe9PSXJQkjWSHJvkiiSLkrypm/2UJEmSBmNwsOK+BbwCIMljgecAPwZeDyypqt2B3YE3Jnli74vNcyBJkqSJwuBgxf0PsG+SxwHPBy6uqgeA/YHXJFkI/BzYGHjUVkTmOZAkSdJE4VamK6iqHkxyIfAPwMHAae2pAEdU1dnd6pskSZI0HAYHo+NbwBtoMikf1padDfxzkvOrammSJwN/qKr7+2tk0tS13D1IkiRJXWNwMDrOAb4GnFlVf2nLvghMA65sMyTfBrykO92TJEmSBmdwMAJJjgeO6Sj6EfDdqnpDe/5TNKME7wPeN9R2l97yALccv2hU+zqYqe/cZVzvJ0mSpInLBckjMw/YCyDJY4BNgJ06zu8FXNKFfkmSJEkjZnAwMpcAe7bHOwHXAvcm2bDdtegpQCW5KMmCJGcn2aJbnZUkSZKGwuBgBKrqj8DDSbahGSW4lGa70j1pFiXfABwPHFRVM4EvAR/tq63OPAd33n/XuPRfkiRJ6otrDkbuEprAYC/gOODx7fES4A80eQ7ObdYiswbwp74aqao5wByA6VvvVGPea0mSJKkfBgcj17Pu4Kk004p+B7wbuAe4EHh8Ve3Z79WSJEnSBGNwMHKXAEcBv66qZcCdSabQrEF4E3BEkj2r6tIkk4AnV9V1AzU4aera7h4kSZKkrnHNwchdQ7NL0WW9ypZU1a3AQcAnk1wNLKTd3UiSJEmaqBw5GKF2tGD9XmWHdRwvBJ41nDaX3vJnbjlhwaj0b6imvmPmuN5PkiRJE9cqM3KQxirzPJIkSdJ4W6n/mE4yLclNSb5Ksyj4A0muSLIoyQfbOusm+XGSq5Ncm+TgtvzmJP+Z5JoklyfZrqPN89s2zmu3KyXJKUlOTHJJkl8nOagt3yLJxUkWtu3v3Zbvn+TSJFcmOT3J5G78jiRJkqShWqmDg9b2wEnAO2m2E90DmAHMTPIs4HnAH6tqelXtDJzVce2Sqnoq8BnghLbs08BXqmoX4FTgxI76WwDPBF4EfKItexVwdlXNAKYDC5NsAhwDPLeqdgPmA+/qq/PmOZAkSdJEsSoEB7+pqsto8grsD1wFXAnsQBM4XAP8fZJPJtm7qpZ0XPvNjteebUf3BL7RHn+NJhjo8f2qeqSqrgemtmVXAK9L8h/AU6vqXuDpwI7AvCQLgdcCT+ir81U1p6pmVdWsjdbdcGS/AUmSJGkUrAoLku9vXwN8vKo+37tCkt2AFwAfSXJeVX2oPdWZdGwoCcge6mwWoKoubkcoXgickuQ44C7g3Ko6ZHiPIkmSJHXPqjBy0ONs4PCeuf1JHp9ksyRbAn+uqq8DxwK7dVxzcMfrpe3xJcAr2+NDgbkD3TTJE4BbquoLwBfb9i8DntGxjmHdJE9e0QeUJEmSxtKqMHIAQFWdk+QpwKVJAO4DXg1sBxyb5BFgKfDPHZdtmGQRzYhAz//yHwF8Ocl7gNuA1w1y632A9yRZ2t7zNVV1W5LDgG8meVxb7xjgFwM1NGnqOm4tKkmSpK5J1VBm06x6kvwW+HRVHTuMa7YETqyqg8aiT9O3eUqd8+5TxqLpfk19+9PG9X6SJEnqriQLqmpWX+dWpWlFw/UY4PDehUn6HU2pqj+OVWAgSZIkddsqM61oBH4GHNDuJrQUeJBmIfEOSU4D7qyqEwCSfBS4FTgT+FFV7dxOG3oxsA6wLfC9qvqXtv7rgfcCdwNXAw9V1dvG8+EkSZKk4Vqdg4OjgZ2rakaSfYAft+8XJ5kGfBc4oc26/Eqa/Anr9WpjBrArzZqFm5J8GlgGfIBmYfK9wPk0AUKfkswGZgNsteHmo/VskiRJ0rCtztOKeru8qhYDVNXNwB1JdqXNnVBVd/RxzXlVtaSqHgSup8llsAdwUVXdWVVLgdMHuulyeQ4mTxnN55EkSZKGZXUeOejt/l7vvwgcBmwOfKmfazrzHizD36ckSZJWYqvzyMG9PHqaUKfvAc8DdqfJoTBUVwDPTrJhu7j5ZSPvoiRJkjR+Vtv/6a6qO5LMS3It8ABwS6/zf0lyAXB3VS0bRrt/SPIx4HLgTuBGYMlQrp202bpuLSpJkqSuWW2DA4CqelXvsiRH0iRKWx94LLBXR/2bgZ3b41OAUzrOvaizGZrRhnfQjEB8fyj9WXrrfdzy3/OG9xAraOrbnzGu95MkSdLEtTpPK+rPW9qfScDvq+qXI2jjJcDBwLXAYoYYHEiSJEndZHDQIcnngCcBJwIfB+a15Zsm+U6SK9qfZ7Tl6yb5UpLLk1yV5IC2qW8B36qqHWhGEC5Jssn4P5EkSZI0dAYHHarqzcAfgX1pEqL1+G/g+KranWaB8Rfb8vcD51fVHu01xyZZt+eiJAfS5FN4QVXdPg6PIEmSJI3Yar3mYBieC+yYpOf9+kkm0+RAeHGSo9rytYBt2uP9gFnA/lV1T38NL58EbeoYdF2SJEkaGoODoXkM8PQ22dlfpYkWXlZVN/UqfxrwvzRTlJ4MzO+v4aqaA8wBmL7NDjXK/ZYkSZKGzGlFQ3MOcETPmyQz2sOzgSPaIIE2o3KP39BMQfpqkp3Gq6OSJEnSSDlyMDRHAp9Nsojmd3Yx8Gbgw8AJwKIkj6HZmeivW5pW1Y1JDgVOT/KPVfW/A91k0maT3VpUkiRJXZMqZ7JMFLNmzar58/udgSRJkiStsCQLqmpWX+dW+5GDJFOAV1XVSUm2BE6sqoMGqH8zMGsouw+104+2rKqfDKUvS2+9l1tOvGiIPR8dU4989rjeT5IkSROXaw5gCk3SM6rqjwMFBiMwA3jBKLYnSZIkjRmDA/gEsG2ShUlOT3ItQJI1kvxXkmuTLEpyROdFSdZO8j9J3thXMrQkjwU+BBzctn1wF55NkiRJGrLVfloRTZKynatqRpJpwI/a8tnANGBGVT2cZKOOayYDpwFfraqvJvkYTTK0w9tpSpcDPwX+jWYK0tv6u7l5DiRJkjRROHLQv+cCn6+qhwGq6s6Oc2cCX66qr7bv9weOTrIQuJDlk6ENqKrmVNWsqpq10eQNRq3zkiRJ0nAZHIzMPOB5PfkNgJ5kaDPan22q6oYu9k+SJEkaNqcVwb3Aen2Unwu8KckFPdOKOkYP/q39+SzNYuaeZGhHVFUl2bWqrhqg7T5N2mw9dw+SJElS16z2IwdVdQcwr12IfGzHqS8Cv6VJcHY18Kpel74dWDvJf9IkQ5vU1r2ufQ9wAbCjC5IlSZK0MjAJ2gQyY5u/q3Pec/K43W+zI/Ybt3tJkiRpYhgoCdpqP3IgSZIkqWFwMMqSvCfJke3x8UnOb4/3S3Jqd3snSZIk9c/gYPTNBfZuj2cBk5NMassu7l05yewk85PMv+O+u8exm5IkSdLyDA5G3wJgZpL1gYeAS2mChL1pAofldOY52HjylPHtqSRJktTBrUxHWVUtTbIYOAy4BFgE7AtsB5j7QJIkSROWwcHYmAscBRwOXAMcByyoQbaGWnOz9dxBSJIkSV3jtKKxMRfYAri0qm4BHqSPKUWSJEnSROLIwRioqvNokqL1vH/yUK57+NZ7uPUz54xZv3rb7G37j9u9JEmSNPE5ciBJkiQJMDjoU5JXJ7k8ycIkn0/y1iTHdpw/LMln+qm7Rlt+X5KPJrk6yWVJpnbreSRJkqShMDjoJclTgIOBZ1TVDGAZcB9wYEe1g4HT+ql7aFtnXeCyqppOk9/gjf3cryPPwZIxeSZJkiRpKFxz8GjPAWYCVyQBWBu4Ffh1kqcDvwR2AOYBb+2nLsBfgB+1xwuAv+/rZlU1B5gDMGObJw+4m5EkSZI0lgwOHi3AV6rqX5crTA4HXgHcCHyvqipNRPCouq2lHVuXLsPftSRJkiY4/2B9tPOAM5McX1W3JtkIWA/4HvB+YFfgvQPVrarfjOTGa262vjsISZIkqWtcc9BLVV0PHAOck2QRcC6wRVXdRZPh+AlVdflAdbvTc0mSJGnFZJCkvVpBSV4C/KINJAY0Y5vt65x/+e9x6FVjs7e9YNzuJUmSpIkhyYKqmtXXOUcOxt5LgB273QlJkiRpMAYHA0iybpIft7kKrk1ycJKZSS5KsiDJ2Um2aOu+MckVbd3vJFknyV7Ai4Fj2zwI23b3iSRJkqT+GRwM7HnAH6tqelXtDJwFfBo4qKpmAl8CPtrW/W5V7d7mNbgBeH1VXQL8AHhPVc2oqv/tfQPzHEiSJGmicLeigV0DfCrJJ2lyFtwF7Ayc2+Y1WAP4U1t35yQfAaYAk4Gzh3KD5fMcbO8CEEmSJHWNwcEAquoXSXYDXgB8BDgfuK6q9uyj+inAS6rq6iSHAfuMVz8lSZKk0eC0ogEk2RL4c1V9HTgWeBqwaZI92/OTkuzUVl8P+FOSScChHc3c256TJEmSJjRHDgb2VJrFxI8AS4F/Bh4GTkyyAc3v7wTgOuADwM+B29rXnoDgNOALSY6kWavwqHUHPdbcbAO3F5UkSVLXmOdgCJJsTJMNGWBzYBlNEDCNZsHyqGxVOmOb7eqc9x43Gk0NyWZvffG43UuSJEkTg3kOVlBV3dHuNjQD+BxwfHs8A3iku72TJEmSRofBwYpbI8kXklyX5JwkawMk2TbJWW0+hLlJduh2RyVJkqSBGBysuO2Bz1bVTsDdwMva8jnAEW0+hKOAk/q6ePk8B/eMS4clSZKkvrggecUtrqqF7fECYFqSycBewOltPgSAx/V18fJ5DrZzAYgkSZK6xuBgxT3UcbwMWJtmRObudl2CJEmStFJwWtEYqKp7gMVJXg6QxvQud0uSJEkakCMHY+dQ4OQkxwCTaPIdXD3QBWtuNsXtRSVJktQ15jkYQ0n2Af5SVZcMpX6T5+A/x7ZTHTZ760vH7V6SJEmaGMxz0D370CxMliRJkiY8g4MOSd6T5Mj2+Pgk57fH+yU5NcnJ7baj1yX5YMd1Nyf5YJIrk1yTZIck04A3A+9MsjDJ3t14JkmSJGmoDA6WNxfo+SN+FjA5yaS27GLg/e0QzC7As5Ps0nHt7VW1G3AycFRV3UxHNuWqmjteDyFJkiSNhMHB8hYAM5OsT7NF6aU0QcLeNIHDK5JcCVwF7ATs2HHtdzvamDbUGy6fBG3Jij+BJEmSNEIGBx2qaimwGDgMuIQmINgX2A54gCbT8XOqahfgx8BaHZf35DtYxjB2gaqqOVU1q6pmbTx5gxV+BkmSJGmkDA4ebS5NEHBxe/xmmpGC9YH7gSVJpgLPH0Jb9wLrjVE/JUmSpFFlnoNHmwu8H7i0qu5P8iAwt6quTnIVcCPwO2DeENr6IXBGkgOAIwZbd9DkOXB7UUmSJHWHeQ4mkBlP2LbOee/Hxu1+m73l4HG7lyRJkiYG8xyMkiRTkrxlgPNDSnYmSZIkTUQGB8MzBXhUcJBkTYCqMuGZJEmSVlquORieTwDbJlkILAUeBO4CdgCenOS+qpqcZB/gQzQLkrcDLgDeUlWPdKfbkiRJ0uAMDobnaGDnqprRBgA/bt8v7qPuHjR5EH4DnAW8FDijd6Uks4HZAFtttMkYdVuSJEkanNOKVszl/QQGPed+XVXLgG8Cz+yr0vJ5DtYfs45KkiRJgzE4WDH3D3Cu9zZQbgslSZKkCc1pRcMznKRmeyR5Is20ooOBOYNdsOamG7q9qCRJkrrG4GAYquqOJPOSXAs8ANwyQPUrgM/wtwXJ3xus/QULFtyX5KZR6axWJ5sAt3e7E1rp+LnRSPnZ0Uj4uZlYntDfCYODYaqqVw1wbnLH23uq6kXDbP6m/hJSSP1JMt/PjYbLz41Gys+ORsLPzcrDNQeSJEmSAEcOxkRVXQhc2OVuSJIkScPiyMHEMuiiZakPfm40En5uNFJ+djQSfm5WEqlyh01JkiRJjhxIkiRJahkcSJIkSQIMDiaEJM9LclOSXyU5utv9UXcl2TrJBUmuT3Jdkre35RslOTfJL9vXDdvyJDmx/fwsSrJbR1uvbev/Mslru/VMGj9J1khyVZIfte+fmOTn7efjW0ke25Y/rn3/q/b8tI42/rUtvynJP3TnSTSekkxJckaSG5PckGRPv3M0mCTvbP+dujbJN5Os5XfOys/goMuSrAF8Fng+sCNwSJIdu9srddnDwLurakfg6cBb28/E0cB5VbU9cF77HprPzvbtz2zgZGiCCeDfgacBewD/3vOPu1Zpbwdu6Hj/SeD4qtoOuAt4fVv+euCutvz4th7tZ+2VwE7A84CT2u8prdr+GzirqnYAptN8hvzOUb+SPB44EphVVTsDa9B8d/ids5IzOOi+PYBfVdWvq+ovwGnAAV3uk7qoqv5UVVe2x/fS/CP9eJrPxVfaal8BXtIeHwB8tRqXAVOSbAH8A3BuVd1ZVXcB59J88WoVlWQr4IXAF9v3AfYDzmir9P7c9HyezgCe09Y/ADitqh6qqsXAr2i+p7SKSrIB8Czg/wFU1V+q6m78ztHg1gTWTrImsA7wJ/zOWekZHHTf44Hfdbz/fVsm0Q677gr8HJhaVX9qT/0fMLU97u8z5Gdr9XMC8C/AI+37jYG7q+rh9n3nZ+Cvn4/2/JK2vp+b1c8TgduAL7dT0r6YZF38ztEAquoPwH8Bv6UJCpYAC/A7Z6VncCBNUEkmA98B3lFV93Seq2YPYvch1l8leRFwa1Ut6HZftNJZE9gNOLmqdgXu529TiAC/c/Ro7ZSxA2iCyy2BdXGkaJVgcNB9fwC27ni/VVum1ViSSTSBwalV9d22+JZ26J729da2vL/PkJ+t1cszgBcnuZlmeuJ+NPPIp7RD/rD8Z+Cvn4/2/AbAHfi5WR39Hvh9Vf28fX8GTbDgd44G8lxgcVXdVlVLge/SfA/5nbOSMzjoviuA7dvV/Y+lWZTzgy73SV3UzsH8f8ANVXVcx6kfAD27f7wWOLOj/DXtDiJPB5a0UwHOBvZPsmH7Pzz7t2VaBVXVv1bVVlU1jeZ75PyqOhS4ADiordb7c9PzeTqorV9t+SvbnUWeSLPo9PJxegx1QVX9H/C7JH/XFj0HuB6/czSw3wJPT7JO++9Wz+fG75yV3JqDV9FYqqqHk7yN5gt0DeBLVXVdl7ul7noG8E/ANUkWtmXvAz4BfDvJ64HfAK9oz/0EeAHNIq4/A68DqKo7k3yYJgAF+FBV3Tk+j6AJ5L3AaUk+AlxFu+i0ff1akl8Bd9IEFFTVdUm+TfOP/MPAW6tq2fh3W+PsCODU9j+pfk3zPfIY/M5RP6rq50nOAK6k+a64CpgD/Bi/c1ZqaYI2SZIkSas7pxVJkiRJAgwOJEmSJLUMDiRJkiQBBgeSJEmSWgYHkiRJkgCDA0nSaiLJO5Ks0+1+SNJE5lamkqTVQps9elZV3d7tvkjSROXIgSRpwkjymiSLklyd5GtJpiU5vy07L8k2bb1TkhzUcd197es+SS5MckaSG5Oc2mbyPRLYErggyQXdeTpJmvjMkCxJmhCS7AQcA+xVVbcn2Qj4CvCVqvpKksOBE4GXDNLUrsBOwB+BecAzqurEJO8C9nXkQJL658iBJGmi2A84veeP96q6E9gT+EZ7/mvAM4fQzuVV9fuqegRYCEwbg75K0irJ4ECStDJ6mPbfsCSPAR7bce6hjuNlOEouSUNmcCBJmijOB16eZGOAdlrRJcAr2/OHAnPb45uBme3xi4FJQ2j/XmC90eqsJK2K/N8USdKEUFXXJfkocFGSZcBVwBHAl5O8B7gNeF1b/QvAmUmuBs4C7h/CLeYAZyX5Y1XtO/pPIEkrP7cylSRJkgQ4rUiSJElSy+BAkiRJEmBwIEmSJKllcCBJkiQJMDiQJEmS1DI4kCRJkgQYHEiSJElq/X+OBrlJ4P+eKgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_WLxGS9hF1a"
      },
      "source": [
        "# inisiasi variabel\n",
        "\n",
        "text = df_fix['text'].values\n",
        "label_sentiment = df_fix[['negative', \n",
        "                      'neutral',\n",
        "                      'positive']].values"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNighaQliO4a"
      },
      "source": [
        "# split data train dan data test\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "plot_train, plot_test, label_genre_train, label_genre_test = train_test_split(text, label_sentiment, test_size=0.2)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRoV22ayjFMn"
      },
      "source": [
        "# Tokenisasi (mengkonversi kata menjadi bilangan numerik)\n",
        "# A = 'ayah lupa bawa palu'          -> [1, 108, 77, 35]\n",
        "# B = 'Ibu lupa membeli palu ditoko' -> [2, 108, 71, 35, 11, 125]\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "tokenizer = Tokenizer(num_words=4000, oov_token='x')\n",
        "tokenizer.fit_on_texts(plot_train) \n",
        "tokenizer.fit_on_texts(plot_test)\n",
        " \n",
        "sequences_train = tokenizer.texts_to_sequences(plot_train)\n",
        "sequences_test = tokenizer.texts_to_sequences(plot_test)\n",
        "\n",
        "#Proses padding (mengubah setiap sequence agar mempunyai panjang yg sama)\n",
        "# A = [1, 108, 77, 35]          -> [1, 108, 77, 35, 0, 0]\n",
        "# B = [2, 108, 71, 35, 11, 125] -> [2, 108, 71, 35, 11, 125]\n",
        "padded_train = pad_sequences(sequences_train) \n",
        "padded_test = pad_sequences(sequences_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvEUk6yLmRif",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33d1090c-c3dc-4e62-ff4b-04367a4aa758"
      },
      "source": [
        "# membuat model \n",
        "\n",
        "import keras\n",
        "from keras.regularizers import l1,l2\n",
        "from tensorflow.keras import regularizers\n",
        "import tensorflow as tf\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim=4000, output_dim=128),\n",
        "    tf.keras.layers.LSTM(256),  #layer yg dapat memahami makna sebuah kalimat dari kalimat sebelumnya\n",
        "    tf.keras.layers.Dense(256, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),  # untuk mencegah overfitting\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 128)         512000    \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 256)               394240    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 3)                 387       \n",
            "=================================================================\n",
            "Total params: 1,005,315\n",
            "Trainable params: 1,005,315\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o0eTxkVvEiT"
      },
      "source": [
        "# membuat callback (mendapatkan data saat berjalannya training data)\n",
        "\n",
        "import livelossplot\n",
        "\n",
        "# inisiasi plot loss\n",
        "plot_losses = livelossplot.PlotLossesKeras()\n",
        "\n",
        "# ketika val_acc > 91%\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('val_accuracy')>0.90):\n",
        "      print(\"\\nAkurasi telah mencapai >90%!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks_end = myCallback()\n",
        "\n",
        "# learning rate scheduler\n",
        "def scheduler(epoch, lr):\n",
        "  if epoch < 1:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.9)\n",
        "callbacks_lr = tf.keras.callbacks.LearningRateScheduler(scheduler, verbose=1)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "231l8Dkdn3L3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "outputId": "3a5645fa-4dd7-43b6-954e-5be15ee293b5"
      },
      "source": [
        "# latih model\n",
        "\n",
        "history = model.fit(padded_train,\n",
        "                    label_genre_train,\n",
        "                    epochs=30,\n",
        "                    verbose=1,\n",
        "                    callbacks=[plot_losses, callbacks_end, callbacks_lr],\n",
        "                    validation_data=(padded_test, label_genre_test))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.970, max:    0.970, cur:    0.970)\n",
            "\tvalidation       \t (min:    0.906, max:    0.906, cur:    0.906)\n",
            "Loss\n",
            "\ttraining         \t (min:    0.112, max:    0.112, cur:    0.112)\n",
            "\tvalidation       \t (min:    0.364, max:    0.364, cur:    0.364)\n",
            "\n",
            "Akurasi telah mencapai >90%!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "po7hWSKD3dd7"
      },
      "source": [
        "\n",
        "yhat = model.predict(X, verbose=1)"
      ],
      "execution_count": 21,
      "outputs": []
    }
  ]
}