CERN Rucio GSoC Task

cern-rucio-gsoc-task-final.ipynb

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  "metadata": {
    "colab": {
      "name": "CERN Rucio GSoC Task",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/arghyadeep99/1825b89415d85daa48fb6a54280fc9ff/cern-rucio-gsoc-task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tK7eZmmE5pyB",
        "colab_type": "text"
      },
      "source": [
        "#Few Downloads, Installations and Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAwukPX3IaPz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpFxMnXw2TOD",
        "colab_type": "code",
        "outputId": "0e6eed7a-a159-47cd-b041-05792b04f772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import requests\n",
        "import json, csv\n",
        "import nltk, string\n",
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from pprint import pprint\n",
        "\n",
        "open_issues_url = \"https://api.github.com/repos/rucio/rucio/issues?state=open\"\n",
        "closed_issues_url = \"https://api.github.com/repos/rucio/rucio/issues?state=closed\"\n",
        "headers = {'Content-Type': 'application/json'}\n",
        "FILE = '/gdrive/My Drive/GSoC-2020/corpus.csv'\n",
        "CLOSED = '/gdrive/My Drive/GSoC-2020/closed.csv'\n",
        "nltk.download('punkt') # if necessary...\n",
        "\n",
        "stemmer = nltk.stem.porter.PorterStemmer()\n",
        "remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEeQCVsr5wxO",
        "colab_type": "text"
      },
      "source": [
        "#Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0Ai1R8WiTmg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Defining some important functions to call later\n",
        "\n",
        "def stem_tokens(tokens):\n",
        "    return [stemmer.stem(item) for item in tokens]\n",
        "\n",
        "#remove punctuation, lowercase, stem\n",
        "def normalize(text):\n",
        "    return stem_tokens(nltk.word_tokenize(text.lower().translate(remove_punctuation_map)))\n",
        "\n",
        "vectorizer = TfidfVectorizer(tokenizer=normalize, stop_words='english')\n",
        "\n",
        "# cosine distance, perhaps the best for a text\n",
        "def cosine_sim(text1, text2):\n",
        "    tfidf = vectorizer.fit_transform([text1, text2])\n",
        "    return ((tfidf * tfidf.T).A)[0,1]\n",
        "\n",
        "# method to get a dict with an api\n",
        "def request(url):\n",
        "    r = requests.get(url, headers=headers)\n",
        "    r_dict = json.loads(r.text) \n",
        "    return r_dict"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FITRN-pDvqi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#For making GitHub API calls\n",
        "def caller(url, limit):\n",
        "    pages = range(1,limit)\n",
        "    issues = {}\n",
        "    row = 0\n",
        "    k = 0\n",
        "    data = pd.DataFrame(columns = ['issue_no', 'issue_title', 'issue_body', 'comments', 'status'])\n",
        "    lists = [[0 for _ in range(int(20000))],[0 for _ in range(int(20000))],[0 for _ in range(int(20000))],[0 for _ in range(int(20000))],[0 for _ in range(int(20000))]]\n",
        "\n",
        "    for page in pages:\n",
        "        for issue in request(url+'&page=%i' % page):\n",
        "            # if issue with api, it is a string\n",
        "            if type(issue) == str:\n",
        "                print('Issue is: ', issue)\n",
        "                continue\n",
        "            if '/pull/' in issue['html_url']:\n",
        "                continue\n",
        "\n",
        "            # getting comments under issues\n",
        "            for comment in request(issue['comments_url']):\n",
        "                if type(comment) == dict:\n",
        "                    print(comment['body'])\n",
        "                    print('Type of body is: ', type(comment['body']))\n",
        "                    lists[0][row] = issue['number']\n",
        "                    lists[1][row] = issue['title']\n",
        "                    lists[2][row] = issue['body'].strip('/n/r').strip('----------').strip('Motivation').strip('Modification')\n",
        "                    lists[3][row] = comment['body']\n",
        "                    lists[4][row] = issue['state']\n",
        "                    row += 1\n",
        "\n",
        "                else:\n",
        "                    try:\n",
        "                        print('Comment is: ', comment)\n",
        "                    except:\n",
        "                        print('Type of comment: ',type(comment))\n",
        "\n",
        "    for col in data.columns:\n",
        "        print(lists[k])\n",
        "        data[col] = lists[k]\n",
        "        k += 1\n",
        "    return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Xv2lmdjETqC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "open_data = caller(open_issues_url, 10)\n",
        "open_data = open_data[open_data['issue_no']!=0]\n",
        "closed_data = caller(closed_issues_url, 40)\n",
        "closed_data = closed_data[closed_data['issue_no']!=0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lc22n9Gkxwhi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "closed_data.head(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gn2vIT970E-t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1b21915b-8eba-4d0c-d10b-ac31c7c476ed"
      },
      "source": [
        "from nltk import sent_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "import re\n",
        "nltk.download('stopwords')\n",
        "stop_words = stopwords.words('english')\n",
        "table = str.maketrans('', '', string.punctuation)\n",
        "\n",
        "#Self-defined Cleaning Function\n",
        "def cleaner_x(x, colname):\n",
        "    modified = [0 for _ in range(int(1000))]\n",
        "    col = colname\n",
        "    row = 0\n",
        "    regex = []\n",
        "\n",
        "    for n,i in enumerate(x):\n",
        "        sentence_i = sent_tokenize(i)\n",
        "        l = []\n",
        "        for j in sentence_i:\n",
        "            j = j.strip().strip('----------').strip('Modification')\n",
        "            j = [w.lower() for w in j.split()]\n",
        "            res = [w.translate(table) for w in j]\n",
        "            res = [w for w in res if w.isalpha()]\n",
        "            res = [w for w in res if w not in stop_words]\n",
        "            res = ' '.join(res)\n",
        "            regex.append(re.findall(\"lib\\w+\", res))\n",
        "            regex.append(re.findall(\"http\\w+\", res))\n",
        "            l.append(res)\n",
        "            #print(res)\n",
        "        modified[n] = l\n",
        "    regex = [r for r in regex if r]\n",
        "    regex = [r for sublist in regex for r in sublist]\n",
        "    stop_words.extend(regex)\n",
        "    modified = [m for m in modified if m != 0]\n",
        "    modified = [' '.join(m) for m in modified]\n",
        "    closed_data[col] = modified\n",
        "\n",
        "#cleaner_x(closed_data.issue_title, 'cleaned_issue_title')\n",
        "#cleaner_x(closed_data.issue_body, 'cleaned_issue_body')\n",
        "#cleaner_x(closed_data.comments, 'cleaned_comments')"
      ],
      "execution_count": 135,
      "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": "scZiVqzXyS-m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "open_data.to_csv(FILE, index = False)\n",
        "closed_data.to_csv(CLOSED, index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Cvit0jSylgA",
        "colab_type": "code",
        "outputId": "526c38c8-35c4-471b-a58d-d956baf57d6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "c,d = pd.read_csv(FILE),pd.read_csv(CLOSED)\n",
        "\n",
        "#Combining the two CSVs\n",
        "data = pd.concat([c,d])\n",
        "DATASET = '/gdrive/My Drive/GSoC-2020/dataset.csv'\n",
        "data.to_csv(DATASET, index = True)\n",
        "data = pd.read_csv(DATASET)\n",
        "data.columns = ['index','issue_no', 'issue_title', 'issue_body', 'comments', 'status', 'cleaned_issue_title', 'cleaned_issue_body', 'cleaned_comments']\n",
        "data.describe()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>issue_no</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>21.602273</td>\n",
              "      <td>3211.920455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.947764</td>\n",
              "      <td>147.254820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2703.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10.750000</td>\n",
              "      <td>3129.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.500000</td>\n",
              "      <td>3261.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32.250000</td>\n",
              "      <td>3312.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>3410.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           index     issue_no\n",
              "count  88.000000    88.000000\n",
              "mean   21.602273  3211.920455\n",
              "std    12.947764   147.254820\n",
              "min     0.000000  2703.000000\n",
              "25%    10.750000  3129.250000\n",
              "50%    21.500000  3261.000000\n",
              "75%    32.250000  3312.000000\n",
              "max    46.000000  3410.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nf90fUObyqMg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "92a87577-2cc9-4cff-e687-b3452433a005"
      },
      "source": [
        "data.cleaned_comments[2]"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'said client numpy version compile python installation needed install newer numpy version set venv'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwV2OMkG65yj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "876abc45-6951-46f4-e9cf-57d958e9fc52"
      },
      "source": [
        "#Remove stop words and normalize sentences by stemming \n",
        "data['norm_comments'] = data['cleaned_comments'].apply(normalize)\n",
        "data['norm_issue_body'] = data['cleaned_issue_body'].apply(normalize) \n",
        "data.head(10)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>issue_no</th>\n",
              "      <th>issue_title</th>\n",
              "      <th>issue_body</th>\n",
              "      <th>comments</th>\n",
              "      <th>status</th>\n",
              "      <th>cleaned_issue_title</th>\n",
              "      <th>cleaned_issue_body</th>\n",
              "      <th>cleaned_comments</th>\n",
              "      <th>norm_comments</th>\n",
              "      <th>norm_issue_body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3410</td>\n",
              "      <td>Fixes for multi IdP support for OAuth2/OIDC</td>\n",
              "      <td>\\r\\n----------\\r\\nFrom first local tests, ther...</td>\n",
              "      <td>- the minimal scope and audience claims expect...</td>\n",
              "      <td>open</td>\n",
              "      <td>fixes multi idp support</td>\n",
              "      <td>first local tests fixes necessary make clientc...</td>\n",
              "      <td>minimal scope audience claims expected token r...</td>\n",
              "      <td>[minim, scope, audienc, claim, expect, token, ...</td>\n",
              "      <td>[first, local, test, fix, necessari, make, cli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3402</td>\n",
              "      <td>Documentation shows wrong Python versions for ...</td>\n",
              "      <td>\\r\\n----------\\r\\nDocumentation shows wrong Py...</td>\n",
              "      <td>You are right. We do not support 2.6 anymore a...</td>\n",
              "      <td>open</td>\n",
              "      <td>documentation shows wrong python versions serv...</td>\n",
              "      <td>documentation shows wrong python versions prer...</td>\n",
              "      <td>right support anymore say clients server right...</td>\n",
              "      <td>[right, support, anymor, say, client, server, ...</td>\n",
              "      <td>[document, show, wrong, python, version, prere...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3402</td>\n",
              "      <td>Documentation shows wrong Python versions for ...</td>\n",
              "      <td>\\r\\n----------\\r\\nDocumentation shows wrong Py...</td>\n",
              "      <td>Why I said 3.8 for the client only: Numpy in v...</td>\n",
              "      <td>open</td>\n",
              "      <td>documentation shows wrong python versions serv...</td>\n",
              "      <td>documentation shows wrong python versions prer...</td>\n",
              "      <td>said client numpy version compile python insta...</td>\n",
              "      <td>[said, client, numpi, version, compil, python,...</td>\n",
              "      <td>[document, show, wrong, python, version, prere...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3402</td>\n",
              "      <td>Documentation shows wrong Python versions for ...</td>\n",
              "      <td>\\r\\n----------\\r\\nDocumentation shows wrong Py...</td>\n",
              "      <td>Ah good to know. `numpy` is not a very strict ...</td>\n",
              "      <td>open</td>\n",
              "      <td>documentation shows wrong python versions serv...</td>\n",
              "      <td>documentation shows wrong python versions prer...</td>\n",
              "      <td>ah good know numpy strict dependency though al...</td>\n",
              "      <td>[ah, good, know, numpi, strict, depend, though...</td>\n",
              "      <td>[document, show, wrong, python, version, prere...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>3398</td>\n",
              "      <td>Rucio / FTS make two requests for multihop fun...</td>\n",
              "      <td>\\r\\n----------\\r\\nHi, I understand that the in...</td>\n",
              "      <td>That's strange, it shouldn't be the case. I wi...</td>\n",
              "      <td>open</td>\n",
              "      <td>rucio fts make two requests multihop functiona...</td>\n",
              "      <td>hi understand intended behaviour multihop crea...</td>\n",
              "      <td>thats strange shouldnt case try reproduce</td>\n",
              "      <td>[that, strang, shouldnt, case, tri, reproduc]</td>\n",
              "      <td>[hi, understand, intend, behaviour, multihop, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>3398</td>\n",
              "      <td>Rucio / FTS make two requests for multihop fun...</td>\n",
              "      <td>\\r\\n----------\\r\\nHi, I understand that the in...</td>\n",
              "      <td>Yes, I would be surprised if you hadn't notice...</td>\n",
              "      <td>open</td>\n",
              "      <td>rucio fts make two requests multihop functiona...</td>\n",
              "      <td>hi understand intended behaviour multihop crea...</td>\n",
              "      <td>yes would surprised hadnt noticed testing atla...</td>\n",
              "      <td>[ye, would, surpris, hadnt, notic, test, atla,...</td>\n",
              "      <td>[hi, understand, intend, behaviour, multihop, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>3398</td>\n",
              "      <td>Rucio / FTS make two requests for multihop fun...</td>\n",
              "      <td>\\r\\n----------\\r\\nHi, I understand that the in...</td>\n",
              "      <td>SiteX-&gt;EOS-&gt;CTA is still working as far as i k...</td>\n",
              "      <td>open</td>\n",
              "      <td>rucio fts make two requests multihop functiona...</td>\n",
              "      <td>hi understand intended behaviour multihop crea...</td>\n",
              "      <td>sitexeoscta still working far know focusing re...</td>\n",
              "      <td>[sitexeoscta, still, work, far, know, focus, r...</td>\n",
              "      <td>[hi, understand, intend, behaviour, multihop, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>3398</td>\n",
              "      <td>Rucio / FTS make two requests for multihop fun...</td>\n",
              "      <td>\\r\\n----------\\r\\nHi, I understand that the in...</td>\n",
              "      <td>In addition I also tried using fts3.cern.ch an...</td>\n",
              "      <td>open</td>\n",
              "      <td>rucio fts make two requests multihop functiona...</td>\n",
              "      <td>hi understand intended behaviour multihop crea...</td>\n",
              "      <td>addition also tried using different destinatio...</td>\n",
              "      <td>[addit, also, tri, use, differ, destin, site, ...</td>\n",
              "      <td>[hi, understand, intend, behaviour, multihop, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>3383</td>\n",
              "      <td>Selection page for authentication method if no...</td>\n",
              "      <td>\\r\\n----------\\r\\nCurrently Rucio WebUI does n...</td>\n",
              "      <td>+ new login method selection page was introduc...</td>\n",
              "      <td>open</td>\n",
              "      <td>selection page authentication method specified...</td>\n",
              "      <td>currently rucio webui offer user possibility c...</td>\n",
              "      <td>new login method selection page introduced cas...</td>\n",
              "      <td>[new, login, method, select, page, introduc, c...</td>\n",
              "      <td>[current, rucio, webui, offer, user, possibl, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>3377</td>\n",
              "      <td>Specify client configuration file via env var ...</td>\n",
              "      <td>\\r\\n----------\\r\\nThe current ways of specifyi...</td>\n",
              "      <td>I think that's a good idea. Not the highest pr...</td>\n",
              "      <td>open</td>\n",
              "      <td>specify client configuration file via env var ...</td>\n",
              "      <td>current ways specifying client config file fle...</td>\n",
              "      <td>think thats good idea highest priority though</td>\n",
              "      <td>[think, that, good, idea, highest, prioriti, t...</td>\n",
              "      <td>[current, way, specifi, client, config, file, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  ...                                    norm_issue_body\n",
              "0      0  ...  [first, local, test, fix, necessari, make, cli...\n",
              "1      1  ...  [document, show, wrong, python, version, prere...\n",
              "2      2  ...  [document, show, wrong, python, version, prere...\n",
              "3      3  ...  [document, show, wrong, python, version, prere...\n",
              "4      4  ...  [hi, understand, intend, behaviour, multihop, ...\n",
              "5      5  ...  [hi, understand, intend, behaviour, multihop, ...\n",
              "6      6  ...  [hi, understand, intend, behaviour, multihop, ...\n",
              "7      7  ...  [hi, understand, intend, behaviour, multihop, ...\n",
              "8      8  ...  [current, rucio, webui, offer, user, possibl, ...\n",
              "9      9  ...  [current, way, specifi, client, config, file, ...\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYmFPVULARKS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "810e86f7-82e8-47b5-91e6-ba220df607cd"
      },
      "source": [
        "data['norm_comments'][44]"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['implement',\n",
              " 'procedur',\n",
              " 'style',\n",
              " 'think',\n",
              " 'someth',\n",
              " 'nicer',\n",
              " 'buildblock',\n",
              " 'dont',\n",
              " 'want',\n",
              " 'call',\n",
              " 'protocol',\n",
              " 'wan',\n",
              " 'pfn',\n",
              " 'alreadi',\n",
              " 'work',\n",
              " 'check',\n",
              " 'lan',\n",
              " 'pfn',\n",
              " 'registr',\n",
              " 'still',\n",
              " 'done',\n",
              " 'wanpfn',\n",
              " 'rais',\n",
              " 'except',\n",
              " 'nore',\n",
              " 'wan',\n",
              " 'neither',\n",
              " 'lan',\n",
              " 'pfn',\n",
              " 'fit']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4FGdFJe2fkV",
        "colab_type": "text"
      },
      "source": [
        "#Sentiment Analysis using TextBlob (alternative to nltk)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qd-3Q79fIVW2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "e9d5c318-4f18-4a08-ffcf-bba03ce926d9"
      },
      "source": [
        "#Sentiment Score using TextBlob\n",
        "\n",
        "from textblob import TextBlob\n",
        "\n",
        "def senti_textblob(x):\n",
        "    return TextBlob(x).sentiment.polarity\n",
        "def subjectivity_textblob(x):\n",
        "    return TextBlob(x).sentiment.subjectivity\n",
        "\n",
        "data['comments_sentiment_tb'] = data['comments'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop_words)).apply(senti_textblob)\n",
        "data['comments_subjectivity_tb'] = data['comments'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop_words)).apply(subjectivity_textblob)\n",
        "\n",
        "data.comments_sentiment_tb.head()\n"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.114497\n",
              "1    0.285714\n",
              "2    0.000000\n",
              "3    0.700000\n",
              "4   -0.050000\n",
              "Name: comments_sentiment_tb, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSNFRncyKMg3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_all_words(cleaned_tokens_list):\n",
        "    for tokens in cleaned_tokens_list:\n",
        "        for token in tokens:\n",
        "            yield token\n",
        "\n",
        "all_pos_words = get_all_words(data['norm_comments'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yraUs49fRAr9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "93647486-cb2d-49f9-f1c3-9b2a2a60ae17"
      },
      "source": [
        "from nltk import FreqDist\n",
        "\n",
        "freq_dist_pos = FreqDist(all_pos_words)\n",
        "print(freq_dist_pos.most_common(20))"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('file', 45), ('line', 24), ('use', 23), ('rucio', 21), ('work', 20), ('think', 19), ('would', 17), ('debug', 17), ('also', 16), ('one', 15), ('still', 15), ('info', 15), ('upload', 15), ('server', 13), ('client', 13), ('tri', 13), ('request', 12), ('chang', 12), ('error', 12), ('method', 12)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_N4QGqVhGKh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dffilter = data.loc[(data.loc[:, data.dtypes != object] != 0).any(1)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaeDr-TEj7tF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "cb810aa6-8bea-4725-d876-f91736a15676"
      },
      "source": [
        "dffilter = dffilter.drop(['index', 'issue_no'], axis = 1)\n",
        "dffilter.describe()"
      ],
      "execution_count": 164,
      "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>comments_sentiment_tb</th>\n",
              "      <th>comments_subjectivity_tb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.103844</td>\n",
              "      <td>0.348757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.216223</td>\n",
              "      <td>0.255276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.162500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.042857</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.232143</td>\n",
              "      <td>0.542347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       comments_sentiment_tb  comments_subjectivity_tb\n",
              "count              88.000000                 88.000000\n",
              "mean                0.103844                  0.348757\n",
              "std                 0.216223                  0.255276\n",
              "min                -0.500000                  0.000000\n",
              "25%                 0.000000                  0.162500\n",
              "50%                 0.042857                  0.333333\n",
              "75%                 0.232143                  0.542347\n",
              "max                 0.700000                  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUeKqaXlj_H9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "outputId": "ea1028fc-4d73-45bf-c86f-5938e8886cd9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "boxplot = dffilter.boxplot(column=['comments_subjectivity_tb','comments_sentiment_tb'], \n",
        "                     fontsize = 15,grid = True, vert=True,figsize=(10,10,))\n",
        "plt.ylabel('Range')"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Range')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5d5KdSc6dvM1JVV2T5PLW2tNaa19OsjA5yMSFEu9trb1j9mUDAGysuQp1rbU9VfXIJC9O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\nqkYAgHkzV6Guqn4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h+3inDOdc/p/W2nnrGGvxA+HPV9VfJ/lca22/eybX8By/tap+L8N7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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sExvbj27kKlN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "863a6d6b-8425-49f5-f086-c78d60588840"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.lmplot(x='comments_subjectivity_tb',y='comments_sentiment_tb',data=dffilter,fit_reg=True,scatter=True, height=10,palette=\"mute\") "
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0ad4b70908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tGyvFmFaEJGOAR/8jIAMAsIvpybyWVsvKj9z/sVmq+To6mY9xVUgSBngMFkosAADYxakT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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ek1OdRSs39ax",
        "colab_type": "text"
      },
      "source": [
        "The plot shows us that you can't really say anything about this dataset, as there are mostly neutral statements and you can't comment on relation of sentiment and subjectivity. What can instead be focussed on is 'tone' of the sentence, focussing on important technical words. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pLSzLVuzZvi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "outputId": "4abf6bcf-134f-41ad-f9cf-fce1c2d30a6e"
      },
      "source": [
        "sns.distplot(dffilter['comments_sentiment_tb'], hist=True, kde=True, \n",
        "             bins=int(30), color = 'darkred',\n",
        "             hist_kws={'edgecolor':'black'},axlabel ='Polarity')\n",
        "plt.title('Sentiment Distribution')\n",
        "\n",
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 10,15"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AQAUxBQBQQUwBAFQQUwAAFcQUAEAFMQUAUEFMAQBUEFMAABXEFABABTEFAFBBTAEAVBBT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\npYp2hPrmVdMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYA1CMQD0OIL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "f16ad106-5fd8-47c3-c39d-750e3828075b"
      },
      "source": [
        "from PIL import Image\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
        "import random\n",
        "\n",
        "rslt_wordcloud = pd.DataFrame(freq_dist_pos.most_common(100),\n",
        "                    columns=['Word', 'Frequency'])\n",
        "\n",
        "wordcloud = WordCloud(max_font_size=60, max_words=100, width=480, height=380,colormap=\"brg\",\n",
        "                      background_color=\"white\").generate(' '.join(rslt_wordcloud['Word']))\n",
        "                      \n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis(\"off\")\n",
        "plt.figure(figsize=[10,10])\n",
        "plt.show()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XjWkMqk3EyU8msLTLwcqIxTTsa5Xqvx6CICB5HMgeZ6kLy7IpxDIY2fyKBI/s\ndeJuq0H2rDDFfYmIDoXQfetxNVdRiKZmayhpE3EmXzxG80/vBeH6NZ70RIbUuVFS50tvbP/6Jnw9\njYs6Nqamk70YQY9nSmsmBdy4mpZfF0gQBGxRQAl6ERW5RExZeYP8ZBLXCoWKsy6Isza4rIzC9xum\nWSCRHObU6e8wMrKv+GJKj2GaheusZ2KaeQqFUheyorjJ5eOsXfvRGznsm4au5zjf+yxaPsFiBY/H\nXYOsrE4hzWsxFT1P/8AL9PU9z/jEEVKpsYW/aINhmBiGRj6fJJkaITJ1mkjkNFNTZ+jseJSNGz6D\nqnqXJFDzhST9Ay8STwxh20VLss9XT3vbwyiKm/6BFzhx6hsMD79DNjs1+525XOqbZhgamhYjkRhi\nMnKSaPQcicQwmzZ9jpqaDSjy9Y+nrucIT57g7NnvkUyNkstFyeWmyeamyeWm0bTYgvV2CoU0Fwdf\nW/R+A9RUbyRUdfWyFVcjMZLm7I8vcvKfe5GdMudfuMiWT3Zj7bDIpwuMH4uQi+dnBY+/swZ/x0xS\njyCgp7Ri0cFb5dKaGDMZHymeyIoVtIdwruK71b++mbqndpA8O0ryxOVgYStXIHF8kNT5MaJvnqHy\nru7ZZpyejlpcTVWr0hj0ErmRKHo8W7rQsotp3//0ZrHK7zKJvn0OIzs/JsnMaFet4LxYZJeKq/7G\nx35ciShLuBoqqNjVRXZwinw4DhRTt+OHL5AbieJsqLiu9SPdN0Hy9AhGovTYh+5fj7utZlHuPitX\nIDMQntc6Qk9miR3oo//LP17i3s3deNFqdWWmgW1a6IkM9hIrWl+JGvKvSPC+X8jnU0Qipzh95rsc\nP/lPpNMTC76UloIgSKiKF7e7inLTzBtHJHKGk6e/xekz3yUSOTVjYSlFQEQQi88Kyyp9JlqWQTwx\nSDoTJho9j2nqrFv7MTye6iVZVK4kk5kkHD6GYRZ498CXGBnZt4DIKFqcbNtaUICYZoHI1BlS6Qls\nbLZt+TlqazdfV4wZZp6J8FEOHf6fZHOrk/G52kyeizHw+ij+Ri8bPtzJxbcvi1TFJaNrBhfeHKGQ\nyKL4nOjpfEnYRi6SYuL181Rtb1lWavqKBc/d96kEgiu/sUM1q2f6Vat8hB7cQGE6zcW/e4l0f7ho\nZZl5j1iaTvr8OOnz44x8622C29qp2rOWyt3dxU7kdUHUaj+CtLJmaoXp9Gxfo0vYpsXkSyeYfOnE\nSnbxqpjZwpLbCFyJoMjIvptr3ZlL7eNbib51blbw2IaJFo4z+cpJGj++G/Ea6dK2bRM/fKGk+SuA\n5HFQde9aXI2LE3JWwUBboCnt0tkAACAASURBVNFmbjjK0FdfY+irS5sRLQbbsjCSuWX3fLuE7HEs\nKqvOtkxsQ0eQZBBXHjd0J6HrOSYnT3LoyN9y7PhX5r14hJmGii5XJU5HAFl2IEoqAgK2bWFaOrqe\npVBIk88nKBQy2LZJwN9ES/MeHOp7I45HklQaGnbOuPYKmJaOZelYpjH7/9lsdMVCcbHYtkU2O8Xx\nE//I8RNfJ564OPuZIIg4nRV4vbU4nUFUxYssF2N09EKGvJ4ml42SSk+g61nAxjA0wpMnee31/4Ki\nuOjufgqPu3rZ90IiMcy53mdJJocZGnpzdlxudzUedzVOZwBF8aAobizLQNczaFqcdCZMNjtVYlnU\ntBinTn0Tv68Bn78Rj/va5UsEioLb6apkYbFtoxsahpErEYiCIOFQfYiidJX15uN0BZGVpb8j4sMp\nstM5Hv1Pu2neVY/ivvycUr0qqkchMZwkemSYmrs7iLx7oWjVUYvCVYukmTo0OC82c7GsWPB88gsu\n4MabL5eKp7Wa1p9/CHd7Db1/9kPSvWPoscy8vlpWwWD63V6m3+1FqfBQsaOT+g/toObRzTjrKpB9\nLgR5ecLHzGhX7eN1o7AME9taWaacIIuLrtlyIwjtXYu7JTTTiqF4/Ix0nvHv7af2sS3IXudVz4eV\nK5A4dpF075xAcVHEv74ZX09Dsbr2IrBNa6YdxYp3Z/HMFJJcaVkH0SFft9eMbdtY6RSFoV6UxjYk\nfwWL7vR7h2NZJtOxPk6e+gZHjv7dvM9l2YXHU0Ooqofmpnuor9+G11OHy1WBKCqYZoFcbpp4cpCp\nqXOMTxwhGj1PLhelunoDba3334K9ujG4XJV88Mm/QstNk8vHyWsJtHyi5L/HTnyNfH55tbKWgm3b\n6HqW8+ef4djxr5JIXp7USJKK11tHe/vD9HQ9RX3DTnzeemTZgW3bGGae6Wgvg0Ovc+bsvzA+cRRN\ni2HbFrZdDFR/653/jtdbT1vrAyjLdMlNRc8Riw+Qy03PjMuB399Id+cTdHY+Rl3dNnzeeiSp+KLX\ntAThyeOcO/9Devuen7E2Xbbcp9LjDFx4ierqDXR3PXnN95AoKlQE2+nueoJCYX4Wlm1bRCKnmIyc\nLsnSUlUvra3343JVLtql5/c3UhFcemV4I29gFkzclfPFkqVbmAULM28wuW+Aqu0tXPj2QfRMHnd9\nMfFBT2lkJ5LLtoK/p59wSsBNw4d3UvPwRga/8jqDT79C8hqNFPVYhsmfHCfy2ilcDRU0/8wDtP/C\nI8Ug0mU0KzMyeSz99ulRsiRucED3tVCCHip2dJA8MUjmQrFAlpnVCL94jMJUqtgo9SpurcSJQdJ9\n45hzShCIikj9h3eihhY/67ZMq9gN/RbWlFo2gnB9t6xlkTt5gLE/+CXqfuvP8N77KILnvWGVuB75\nfJJz537AkWP/MO8zSXLQ0f4wO3f8Mp0djyJJl4T//OPZxG6g+CJOpUYZGd2PLDuoqdl0A0d/cxFF\nmVDV3Aq/pfeDDfQNvHBTBI9l6SQSw7z2+h+RSs2pei7K1NVs5uGH/oj29ocXtFTIkoOamo3U1Gxg\n7dqP8c6+/8HRY0+TnVOPJhI5zfETX8PtrqKxYdeyxjj3OIiiTF3dVh77wJ/QUL9jRkSVjsvh8NPS\nvJfGhrtoad7LG2/9V0ZG9pV8Z3TsICMj++juemLe+nNRFDftbQ/R3vbggs8ty9J5d/9fkd7/JRJz\nBI/PW8/9e3+Purqti3bnFYXX0t8RTp+K4pKZPBenqrPU2h4bTDLVF6fprkY2/84TIED9w2sJrqun\nclMx0SEzEuPUX75865qH3s4UA0SLQbgtn7+P2kc3M73vPOPPHmLqtdPFDuALYOsmudFpLnz5x4Sf\nP0rb//YQ9R/aueSeTrZhLqhEJa+zWPH5BrgQZI9zVZpQ3koEQaBq7zqib52dFTzYxcaYkVdO4qj2\n46xbuEfX1OtnyFwoLaoluVTqnti6YLmCq2LZC5pNBUVCcjtuSPaaUuFFdCg3xbVkxqfIXziLra3c\nhXan0T/wAn39P6ZQSJcsd6g+dmz/JTZt+hyhqjVI0vVKMlzqcA5ebz1dnY8jCMIdn5k1l/n7L1zj\nrxtLMjXGvnf/nFRmAsu+PJFsarybPff+Ni0texHF+Q2US/8W8Hpq2LH9F7Etg/0H/hpzTnxP/8AL\ntLU9QG3NJmR5+W59WXJSXb2ep578EtWhtSiKa8Hr4tLYJEmlteU+CoUUU1Nn0bT47Hc0LUY8fpFk\ncpRAoPmqv3l5P4UFXy22bRb7QC3wmSCICII4IxZvHHWbQoSOTPLCH7xD5Pw0WiLPhbfGiJyPMbR/\nAiNvcP9vbJ9NImr+4GYkhzRb88xV42fdL99/67K0bneEmdmuWuFB8btQq7wEt7WT+VyxA3n0jTPE\njlyYFzthGxaF6TR6WqPvL54lOxih+af3LqnBo+RU5xVBFBSZ1p97kOoHN6AEV6dX0VzcLSFci+wR\ndTsT3NqGt6eRqTfPFDvEA1g24ReOEXpwwzzBc6nR6vS7veRGLwfsSR4Hlbu7cbWEltRfSZDE4k11\nxZPDv66Jps/upWJn5/J37ipIDgVv99LbnywHY2qCfN/JBYM938toWpzhkXcIh4/P2/fNmz7Phg2f\noqZ6/ZJfdsW053JW3I3CMPLEYxc41/sMhqHNLq8IttPZ8SitLfehqot7noqiTDDYRmvrA4yM7md4\n5O3Zz7LZKUZG99NQv5P6uq3LHq/XW8fuu36N6tA6FMV93UmMIAg4nQFqazbT1vYQ5859f/b6tG2L\ndCZMLD5wTcFzJ1DVEWTrp9egumX6XhnGBs48dwFPyEX1mgp6HmuhrsfP4d//wVW2YCNIEhv/9w8g\nepcuzlb8ZD11TOfcyaLa7tkos2HL7XvTC5KII+THUeXDu6Ye/+ZWQnvWkjo3RvzoBWIH+skMTFx+\nwQJ2wSgGN3/7HQRZKtb56ahd1O/JXue8l6wgCribqgjtWYvzFmRC3SkoFR4Cm1rwdNSRPHk50y52\noI/sxUl83fWlqf22TfzoBTIDYcw52WtK0EPdUzuQva4llR4QZBE54Eag1Igve5341zZS+4HNK9i7\nW0NhuJ/cmSMUhvvJD5whd/RtrEya2L/8Hel3X0KQL1+rjs51VHzyl0qqSZvpJJkDr5LvO4Vn14O4\nNu9GO3OY3KlDGBMj2HoeweVBbWrHe+9jSJXVCKKEPjlG5t2XKQz14dqwA9+DH543Nn1yjOzhN8md\nOohn14O4d+xF8vhnP7ctEyudInvsbfIDZzCnp7AtE8kXQG3pxrVpF0pD23VfLGPjh5icPDmTZl1E\nQKSmZiM9PT9FTfWGFc3sy9wY0ukJhkfemXFlXb4j6+q20d7+EC7X0p6lsuSguno9LS17SwSPbVtM\nTp5gKnp22YJHUTxUV6+nq+vJGcvO4p47xeDmEC1N93LuXOkLX8snSCVHlzWe2wmHV6F+UwhnwEHd\nxhAbPtyJkTdRPQpVHQFq11XicAr4u4oB2pH9F1G8DjxNFQiKhDaVJjcev3UxPAff1nnmOxoI8NTH\nnbe14JlFEJCcKt6OWrwdtYTuX0/yzAixA/3EDvQSOzhA8vRwSTpc9sIkE88ext1aTfsvPrKotgKy\n34XomN80MT+VXHaU+fsFQRAIbGkluLWtRPDkJxMkjg0S2NKGZ07TVduyibx0gnwkebmnpCwWe489\numX+ebgOoiyhBj3zLDymVqAQvzPKsl+JMTWBdvowWt9J9LFBjKkJbEMnf+EsRmQM5l7TtjXP1WVp\nWbJH3iL10vcQBBG7kCfx3D+RHzyPlYxh5bLYpoGjbQ3u7fchzaxvxqdIv/1jMgdewy5oCwoeMx4l\n8+7LJJ79GoLqwLV+O8wIHtvQ0SeGSb36DOk3f4Q+MVxsVGpbxUKOlbW4d95H4MmfRm1dg6hevXru\n8Mg+YvGLJcsEUWLNmg9RU7MBVV15LbAyq08yOczg0OvMFTuy7KSmZgM11RuXtU2ft56amo0IglSS\nZRaPXSAev4htW8tyT3o9NTQ17cbnrbv+l69AVT1Uh9YWwzHm3H6GniU3x811J6O4FGp6KqleU4Ft\nz3jY5tRXs02Llo8UxWZ6cJrAmlpq7ulEVCSS/REufPvgsn97xYLnYp/B2ZM6Xr9IIn5nmscll0rF\n9g6CW9uoeXQz4R8fZfTb7xB951yxps3MhZc+P8bkC8do+sTdqFXXD/B01gSQvaWzRduyyQ5OYWrX\nLmxWBnxrGghsbkH2OmcrLwNE3zlH6L51s4LHtmzMXIGp109TiF2Oy1D8bvwbm/GuWXobCNGh4Gqq\nnBe8rSdzq9ag9WYjBSpxbdiJ2tpFfuAMqdeexQiP4Nv7BI6ujQiOy0JBqW26eiZcNk3uzGG03uMY\n8SiOrg3IlTXYegEjPIIgK0jBqmK8wCpgRMOkXn2G6N//KSgqro27UJs7QBTRx4fQzhwl9o0vY6VT\nhH7+txBqGudVMbdtG9s2mZo6UxKoCgKq6qWr83HcrsW3Lilz87jk0glPlpby8HpqCQZal2zducSl\nlg4Oh68kZiabnSKdGqegZ3EsQwB7vHXLDnqWJAceby1XPrBMszCTSn9nY9s2etYgPpwiOZ6hkNVp\n3FqDr9ZNQdPRkgVkVcITcs1+38wb6GkNQRIxsnkKieyy4w5XLHhi0zbplE17t0jPhjs7JEgQRbwd\ntbh+/iEqd3Vx6F9/mdTpkdnUcjNXIDsYIXlqmND966+7PXd7bTGjSBJnC9jZlkXq9DBGKodt2Te8\nvcWdjBL04FvbiG9tI7GD/bPLYwf7yQyEqdrTg6jI2LpBbjhK8vRwSXaWq6mKmkc2LesYS24H3rVN\nSA6lpIaTPp0m0zdRPJ+icEfVrnF0rMPRsQ6A9L6X0E4fxoxP4bnrYbx7HkNcZJaWGZ8ie+QtHG1r\nqPvdP0dt75m1qlh5DTuXRfL6r7OVxWGbBrlTB4l97++xCnlCn/93BD/688gVIRAEjHiU1Ks/JPzf\nf5vpb34Zzz0fwOMLLvj7Wj5BMjlCPn+5OrIkqQQDrYRCa1GUcrHG2xHD0MhkJknPycwCCAba8HiW\n1kZkLoIgoCguPJ7aEsFj2SY5LUY2M7ksweNyVlBZ2b2sMYmitOB1aFoGhqktsMadhaGZTJyKcvDp\nU/S9OsJUb4yf/vvH2fBTncSHUgwdmED1KGz55BoAXLUBpo8Nk+wNI6oS+VgWT1Plsho3wyp0S9dy\nNoUCuD3CqhYPvJVIThXf2kZ6fvdj8yw5eio3r2XB1VD8LtzNodJtWDap82NoE3GswsoqIr8f8HTW\nU7V3XcmyQiRJ6swIudFirQsjU0xZv7LIY1HwLC/WRlQkHCEf3q66kjisQixDum+CQuzOdGutBrZe\nQJAVqn/190vEDoDocCIFVy9o3oxH0c4eo3DhHI7OdVR+/t8hzYgdADlYhXvTXXj3Pg6mQfrN5zGi\n4YVGzVTkDFo+yXy3yEakFVTXLXNjSafDJBJD2FekxLvdIRyqH8u2sGxzgX/WAv9KvyMIIuoCAsMw\ntAVr2VwfAYfDT8C//OBiYcHXsr0K2ZS3fnIWPh1l/9+fpPelIbZ8shvVc/nZKogC8aEUx751fnZZ\nzy/upeeX7ie0q53A2no6P7ub7b//EWTv8pq+rvgu9/oFXK5bfyBXG9GpUHXPmnk9r2zDKnGvXA9v\nTwOejpqSFHjbsIi8fAJvZx2+tY3XWLuMu62ayru7EZ1KSd+p5KlhUmdH8bTVYKQ0wi8cw5zzubOh\nksCWNhy1gYU2uyhEVSZ03zoyFyYpXGrXYdtoEzEmnj1Ey8/cD9J779q/LpKEVFGFa9MuBOXGFqjU\nxwbRh/sRvX6ca7cVq0JfgeDyoDR1AFAY6sXKpuZ9ByCTm5rXI0uWHYRCPStqJ1DmxqLlE2S16XnL\ne/ufZ3jk7RUFmRtmnmx2av5yIz/TMHVpSJKKqnrm1G8qM5dIb5xUOMsTf3gvXQ+3cODp07OfuSoc\nuIIO4iOXwxLip8cY/tFJYidGsAomgiyi+l3s/OOPowaWXhxyxXd5c5tEqFYknbIJj9+ZMTxXZQF3\nhSCLyO7Fq8vKu7qJbjvH9P4+mBNZPv7MISp2deFuCSEtYXvvNySniqethsq71zD16qnZ5cnTRcFT\n88hmtEiS2Lu9JVWtvWvqqdzdjbiCejmSU6XpU/cw8dxhCtOpWcOANhZj+J/epPbxLThCVy+C+F5F\nUJ3IlTWIjhtfYd1MxjES01jJOKlXvk++/xRXzlTtgoY+WbS6Wsk4tj7fcmrbNnktMa+nkijKuF0h\nBOH9dQ7vJAxDQy/Mj1/R9UxJxeDVxLKM6zaQXQhJUpGvW7/p/YuWyKNndOo2hnBVOEvCDURJRJAE\njDmFgYd+eAzZrdLxmV2zz1lREpGcy0uOWrEPasfdCt1rJcJjJkf36xiGfWur09qrYfortpyIH7lQ\nYjUAkJzKkqwGruYqKnZ04O2qL1meuTDJ6D+/WwyMXmE1Ztu2sa1bfNxvEIIo4GyooPbRUteUNhEn\nMxAmMzBB8vhgUZDMCEpBkfCtbSS4femlz0t+W5bwb2qlYlcXasVlX76RyRM/PMDA//ciWjg+r8Ho\nUikG1N45506QZETnKse72HYx6+rKxaaObehFjTPTDsPKJEv+2XoBuSKEa/Nu1I51iK6F67HkC6kF\ne2apDl/5BXUbYxr5mx6wa2Mvq0aVKMqIZevOVZFUEVEWyMbmN75OR7IkxzP4ai4/W/RUHk9zFbX3\ndlF7bye193ZSvbvj1lVa3rBF4YFHHUQmLN59s8DrLxbY85CK6rBvyUMkdniAfDiBo8ZfDBq+VF13\nCWMxtQLp3nGGv/Z6sZ/SJUQBpcKLr2fxbijJqVKxq4vax7eQG56ajTOxdZOpV08hOVWsgkHlXd2L\nyvy6RLHQXh5tPFZ0ucQy1D2+9T3ZJVut8lG1Zy1KpRc9nilWQc4bZIemmN7Xy/S+8yXFclxNVfjX\nN624zpEgCsheJ42fuJvMhTDT+/uKpQpsm0IszeDTryI5FGqf3Ia3qx7ZszhLnW3bYBeby2aHImhj\nMZQKD6E9a1c03pvKkjOwhGtORmxTx9Jy89dSVATVgejy4Nq4C9/DH71mwKLoCyKHFk4HNvX8vD5z\nAuJMg8my4LldMS19QWuLKMrFysorn7fPQ5Ycy6o6LAgi4nuo0vZqU9Hiw1fr4di3zhfDQzSD2GCS\ngTdGGT08SeTcNK27agi/3Q8C6GmNVP8kEa8D2aMCAoIkUrWt+dZ0S6+oEnn4gw6yWZvnv5/nb/8y\nSy5r09wuEQgIqE4BceFq1iVIMlRVr9ysHHnlJJGXTuCoC1KxqxtvVx1qlQ+1woPsdSJ5nUgOpbT9\ngm1j6QZGOk9hOkWmP8zkSycYf+ZQSfq4WunFv74JT+fS6it4exqo//AuEieGmHrt1OzLOR9JMv7D\nA2jhOI2fuJvA5lYcIR+y310c40yVZtuwsE0TM6djZPMYqRx6PENuLEbq9AjxIwMYKY3KXV3vScEj\nux14O+uo2NlJ9I0zs6JRG5tm6o0zxA/1l3w/uK2dwKbWFbmz5lLzgc0kTg6Rn0zONiW1DYvshUn6\nvvQjcqPTVD+4AU9nXfE687mKMxBJBNPCNi2sgoGRK2BmiuevMJ0mMxAmfmSAzMUIlbu7b67gEYRi\n3R17pmv6jfwtcaY0vGli5ReOf7OyaYypiXnLpWAIubIGRAkpUEXg8U+BvLz2G4IoLjDxsbEtk5vb\nJbbMUhBYOBuysqKTqqo1OJ0Lt5lZCXW1W3Bfpzt5maVTs7aKtj0NHP3WeQ7+o46eM+h9eZjhQ2G0\nRJ6qjiBrHmki8m4vUJx0pi5GKSRzOIJuEAREVaZyYwPcCsETHjexLNiyQ2Fi1OJrf5vlyP4Cd9+n\n0rVWpjIkohaF2TUJVoh84mdWHhNg5XWSZ0bIPn+UoX98HUfIT3BHZ7Fqb2ct7pZqHNV+RKdS9B/a\nxVRxPZ4hczFC/NAAU6+fJnaov+QZKCgSwa1t1D6xbcnmNMmhULGrk65f/yDZi5PkRqaxjWKhKz2e\nZfKFY0TfPkdwWweVuzrx9jTgCPlmY3vMmRelFk6QG4mSGQiTPDNCbmgKK6/PuHCall198k5A9jpp\n/Phu4ocvzBE8MUxNL+mMLigSwW3tqxoMXuzFdj96LMPQV18jP5WatVRoo9P0f+lHjP/wIMGdnQS3\ntOHpqEHxu4uB1gUDM1soCtTxGLmhKdL9RTecnshimxZqtZ/A5tZVG+9iEGSlGINjW5jJGFg3rhCm\n6PIgOF1Y+RzG1DhWLoPgnCm3b9tYWhZ9bJDCxXPz1lXqmnG0riFpGuTOHEYPjyDXNpWIHtu2wbKw\ntCyiwwnS/H5KUKy7cuWs3bIt8oX0HeVSfL8hy44FU7Xb2h5i145/Q13dllswqjLLIdDoZdPHuvHV\neTj89bPUrKskPZnFtm26Hmxm4091Eur0E7GLLi9/dy2iKs2227FNCyNbWHa/yBULnj/7gzQ/+JZG\nOmVjzrjHC/mitYfvz/fTXY3utdKqCJ652IaFNhFn4tlDTDx76PIHMypRcqsw04PJ0o2rT/KEYo+q\nuie3U/fktmWNRQ16qHl4E5v/7Oc4/ptfITcSnRU9AGZaI/rGaaJvnL7GVt6/SF4nDR/Zxfk//QGF\naFFw5EanZ1PTL+Furca/ofmqzUWXi6e9hvZ//QEESWDg/30RI1XqfskORsgORhj77r6rbOH2QvL4\nkEN12HmN7MHX8N335Ezsy0wzDUFYMCNqOcihWuSqWiwti3buOLlj+3BtvRdkuXgej+0j9fL30cMj\n8+Jv5IoQzvXbcXZvQjtzhKn/+V+p+oXfQalvhhnxYlsWVjpB9uAbuLbeXbQIyVe0dBEEVIdvXjaW\nbZvktcS8lOcytw+S7FxQ8BQKaQxjvhu0zO2Nu9LJ+qc6WP9UMbPStuySSssADQ/2AMWgZW9LJcG1\nxRhYLZJi8HtHbl1rifi0TTJhLxRvePti21h5/bLIuc7szr+hhc5fe5LGT+xeUSdy2eek9rGt7Hra\nx5k//C7Rd85jZu78YlI3A0ESUUN+qvb0oMfTFKLpBb9X/cB6PO3LL0Z2LTydtXT86hO4W6s5939/\nj9xI9Por3aYoje24dz5A4rl/IvnK9zEiEygtHQiyiq1lUTvWUf0Lv7sqvyWoTlwbduHZcT+Zg68x\n+h9/HteW3YjeAMbUBIWhPgRZxrPrIXIn989b37VpF1U/+0Um/+I/knj+m2SPvYPa0oVUEcI2dczI\nRLFVRiJKy1/8C5K/sqQv2Ox2HBVIYuly08gzPd2Hba0scaDMjUNVPDjU+fGNOW2afGHh50CZO4hr\neH+mjw2jeB2zgkfP5Insv0D7p5dXyXrFgufjn3eyddfK+2dVVK1OoFflPT1kBiYJv3iM3PB1XkjX\nUImCJOKoC9L4sd3UPbWd4LYO1ErvigKxBVFE8jgI7uhk4598galXTzH+zCFi+3uXVNvnEqIi4Wyo\npGrvWuo+uANHaPFBz3cagiCALFL3xFZiB/oXFDyCJBK6bz3u1hvjexcVGVdjJY2fuAdvdwMTPz7K\n+Pf3kxuOlqTELxbJ7cC/sZmaRzZR98EdN2DEV0d0e3Fv30vNr/9fJH70DQrjg+SHemfbQsh1q9eV\nWRAE3FvuBvvXkStryB59i8zB1xFUFTkYwrPzAVxbdmPGowsKHtHtw7Pjfur+w1+QevWH5E4eROs9\nXiyAKMmIbi9SbSPeB55CrmtaUOyAQGVVN8oVHbUNQ2Ny6jSmWS4Cervi8dbiX6BLeCIxTCY7eQtG\nVGa55FMF8hkdV8CB4irKj3xaZ+LEFLl4nsr2YgPRSxiZPGbBwLbtou3ZtMgncrfOwrN7r8qWHSs3\nBy/4jFoGwS1tKD4X1Q9tJN0/QfbiJNnhKIVoCj2WwUjlMLWidcc2LARRQHTISC4HSoUHZ20AV3MI\n35oGfOsa8W9swdNeg+xZnQ7KgiAge5wEt7ThrPET2NRC8vQIqfNjZPomyA5OoidyGNk8Vq6AbVoI\nqozkUJADbhxVPpx1AVzN1Xg6avC01eBuq8HTWTuvb9f1EBUJV3MVm/7kCxhzO4wH3PjWNa3K/q42\nofs3sOEPFfLR+cXlBFGg+sENKP4bVx9GVGQctQFCD6zH1VxF1e5uUmdHSfeOkxkIo43H0P9/9t47\nSo7rPPP+VeqqztPd05MDMJgZ5EgABCOYSVEUKVESFWjZkvXZ67Rr7dq7/s7aXq99dr3HYfWtrbUl\ny5IVTMlKJhUo5iSChEgCRCCINAAGk2PnXPn7owYDIs8MCBKU8eAAg6muunUr9L3PfcPzlmrYZd2r\nwyZ67lM5qKHUBVGTEbSmOoJdjQQWNxBclCSwuBF/6/zViZWIn6a71p+R3RfoSF4whkmQJJRkC5E7\nPoK6aCl2IeNlSYkiYiCEr3XRGcdIoQiR2+5H7VqO0ji/90OKxglsuB450Uj41vtwykWviG8oitK6\nGKWxFbuYR6qrR+tZjfiW0hCCKCJG4wQ23ICcbCF0/V3Y+QyuYYAkIWoBpEgMpbENOdEE0tlj7MKh\nZoLBRmRZw7K8BYZlG6TTfRRLY6haFPlKSvFlB9UXJhxuxe9PUK2eXMQW8sMUC6OYVg3lSoX79wQm\nDqQZeGmU5rVJem7twLEcdn3rIP0vjlLN6SR76lj/iaV0bmkBINLbRGrHAIUjU15piVSJ+KpWROVd\niuF5uywzbxd8iTC+RJjo2kUYuTLV4RTVsSxmtoyZL2OXdWzdxDVtTz/lRDyPpqBEA/iSEbSWOMFF\nDWjNdZcstV4QBfytCbTmOLHNPVRH0lQGp6iOpLGKNY/w6Cau7SIqEqKqIIc1fLGQN2m2xvG3xFFi\nwQX3UZBE1PqIpxj8HoAgCDMWli2XpP1MbYy+3GuMlA6jiCoN/k42N95zhhtEEAQkVSGyvI1wbwv6\ndJ7KUIrKYAp9Ko9VVuuf/QAAIABJREFUrmFXDS+gXBQRFC9eTIkG8MVDqA1RAu31aE11XvD8Ap+f\n5FepW7+YuvUL0xsSFAWloQWloWVO+4tagMCaqwmsuXoBJxOQIjH8qzfjX735rLvIiUbURb3nOFxA\nUDW07pVo3SsXcHqvblI8toQxf5xi0RMqdF2bajXN0NBLhEPNyBdRm+kKLg0kSSEcbCJZv4yh4Zdn\nt+t6nlT6MNlsPw3JC9c2vAJmUubPkqnoXrqkhbcifSxP/7ZREt2eZMjUoSyHHh9ACcjUL4lSnKyw\n/yf9s4Sn8bpuUjsHqE4VvASPeJDkpsVI6sIsJL+weuqiT0ZriKI1RIm9s96CeUEQBZSIH2VFG5EV\nl6dV5d8KpqvDPD/yENsn/pWAHGFN/a1saLgLiXN/uQRJRGuKoTXFiG9eWMHAK3jn0NqyiaHhl2YJ\nD3iqugcPPUJL81WoanRGl+cKLidEIq20t1/H0PB2TmSXuLiMT+xmaPhl4rElV57bHCDLfoTTdIIc\n15mpG3bpA/f1koFRtajvrgMX+p4aANdlzf09tG5oYN/DRzj85ODs/mo8QOudK3EMG8e0UcIatm4u\nuOj25WWeuYIruIIruITo7LieZGL5KbWOXNfh+MDz9B9/jkJh5Aw15it49xGJtNO1+BYC/jhvtVBM\nTe3nWP9TZDJHcFz7ouUF3muq5/OBIAhoWt0ZmYq2rZMrDOG8E1Yex4vFkRQRo2LR9+wQjSsTNCyL\nE4hraFGNSvZkPOv0jgH0dJlAc5RQRxzXdRl//jD2AmIm4W0gPK7j4rxNf6/gCq7gCi4lIpE2Wlo2\nEot1nbLddR1e3fEF+o48SqWSwnHseaWqnyhF4NVguhIA/XZDUfzE4930dN99Clm1bZ3BwRd55bUv\nUK1mcV1nXoTlBMFxHBvbNrCs6i8w4RUIh1vOsIQZeonh4ZexLf2Skz017EMJyIzvTzOya5KpQ1la\n1iaJL45gGw6WbiHJIq7jYhsW6dcHKQ2lsXULW7fQp4uMPLYXR1/YM7pol1Yq5VItvz1Byy1tVwr4\nXcEVXMGlRU/3XaQzfWQzx7DfUky0XJ7i5e1/RTrdx7p1n6alZeO82i0Uxxgb24muF1i39pff7m7/\nm0ck3MqWLb/L8Mh2cvnBWWJSqaQ4eOhhKuVpbrzhv5JMrkRR5p64YFpVpqf3c/Tok6QzfaxZ/SDd\nS+68VJfxrkEQRBobV6P6IszqbQE1PceRI4+xetUnaGu9Gp8vdN52LgYNy+KEGwM8/FvPIEgiXde1\n0LgigS+gkB0qUp6uEExoFI+n6P/eDsaePUj+0Djjzx3EtV2MfBWzuHBidtGE50t/Xebl5+dfVfZ0\ntC+S+Mfvv/0S4VdwBVdwBW9FJNLO0t57KRRHOXjw4dntrutQLE2w/8APGJ/YTWPjWpqbN5CsX4Zf\ni6OqYQRBwrYNDLNEuTxNqTROJnuMVLqPfH6QaiVNY+OaORMex7EolSYpV6YxzQqmVcEyKhhWFcus\neNvMCtVqhqnp/Wccn8sNsn//d0mn+1CUALLix6cEkGU/ihJAUfwosh/FFyQYSBIIJJGkCwd81mp5\nisUxDKOIaVYxrMpMf6qzffL6W6VaOVX+wzBLjI7u4PkX/vtsH2TFjyLP9EcJeH1SAqhqlFCoCVW9\nsKSGJPlIxHu5aeuf8PwLf0I2NwAzlrVqNUP/wHNkcv20tW6muWkD9fVLCYWa8ClBREnBsmpYZpVK\nNUOpNE6+MEwmc4x0+jDlSopqJY2m1bG09945Pbv3IlRfhObmDWSzxyiVJwHvvS9Xpnn6mT9g9eoH\nWdJ1G/HYklmxxxOWTsMoo+t5qtUMuBAKNREMzk8CJLk0zpZfX0PTynos3abntg7ql9R52dKSQH13\njGRvnEBzlPa7VlEZzhDqTBBd3owAiKqMvzGKMs+M5BO4aMIzcMxi786LN+FWy+8l5cIruIIreK9C\nllVamjewdvUvodfy9B9/dvYz17UpV6ao1rKkM0cZHtlOwF+PoviRJBVBEGbdH6ZZxjBKVGtZKpU0\nhlECXMLhuZc10fUC+w98j6P9T+HYFrZj4tjm7E/H8f5vWwa6caYUQ7kyxcDQNsYn9yKJCqIkez9P\n+7+i+Fm06CbWrHqQUKjxgv0an9jFzp1folrLeX2Y7Yt1Sv9sxzyjX5ZVI53pY88b30QS5Zm+KGf0\nTxIVItF2rlr/WdraLpx5KQgiPl+Q7u67KBbH2fvGN0mlDs3E7jjoep7Jyb0Ui2MMj7xCwJ/A5wsh\nST4EQcRxLBzHwrQqGEYJvVagWstQrWZmrUWS5FtQlfT3AgRBQBAkenvuZnLqDUrlKU5YeRzHYmJi\nD6ZZpb//GYLBJIoSRBQlHNvCsmtYtoFt1TDNKolEDytXPDBvwqOGFJpWJog0h3Ash3BTAHGmZmSk\nKUjX1jZkn4TkV4gua6LjA2vRGiME2+IIglc6SAlrCw5afluqpZcK5zcvuS7YtldyopBzGBtxqJRd\nFB/0LJdZv0lhydJf2ISxK7iCK7jM4PfH6ei4Htd1UH1h+o8/NzNxn5gATCqVaSqV6Xm1e3oGzIVg\nWTUmJvZw9OgT8zruBGzbmFM/JcmHqkZZvuxDc2q3UBjlaP/T1GrZeffJdR0MozRDAM+PeGwJS3vu\nmXPbgiASDCRZtfIBZFnlwIEfMDaxC9OszO6zkOf2bwmtLZvp6X4f5fI0qdTB2e2OazOdOsB06gCC\nICFJygxRtGeq1Z+c52v6dXR13bag88uqTKT5zPlei6po0ZPxRbLfR3RpE7VUifyh8VmxQUESSV69\n+N2pln7bPSprN56/GdcBy4JqxSU17XD0oMX+NyzGhm2a2yTuuE9j620LF/zyige6mOPTmBNp7EwB\np1LzalVJIqKmIieiKK1J5EQdojb3c9WODqP3DWEXy6iLW/Gv60X0KV6gW7GCMTqFNZnBLpZxDcvT\n9dEUxHAQub4OX0sSMXJurRxzOot+ZAhjeBJBUYjcthkx5EcQRZyajjWdwxid8q6ppntljhQZMehH\njoWRmxIozfUAF9RzcWoG1lQGczyFlS3iVHWwLK+9gIYUj6C0JFGSMQRl7q+GMTqF3jeEOZVBjkUI\n3bgBQfX0ZexyFWty5py5Iq5ueNegKkihgHfO5nrk+kuneXQFV3A2BANJerrfRyjYhD9Qz8TEHrLZ\nfmp6bkGrfEEQ0dQ6wqHmS9DbK3gr6uoWsWb1gwQC9Rzu+wkTk3vJZQew7IWU6hGQJB/hcAvNzRvm\nZAF7LyMQSLBs6X3YlsGBgz8gnTkyK8R5Aq5rY1nvjDbP+VA4NkWxP0V1Io9Z0lHCGo5pkVjX/u4Q\nnjUbFDiPTsnZYJkuzz6u840vVjj4hslTP66xfrNCon7+E55r29jFCsbxMYrP7aC0fS+1gwOY4ymc\nas2Tr6+P4l/dTfimjQSvWY3W24EUi8xpgi089SpTf/Nt9L4hEp++h9a//F2EWBhzKktlxwEKz7xK\n+bUDGINjOMUKiCJSLIK6qJnAxuXEH3wfgfXLzlkvpHZwgKkvfJfcD55BioZYuu0rqL2d2JUStUMD\nlLbtofjC69QO9GOlc7i2ixQOoLQk8a9YTOSOLcR/6W44l4lvJrjLnM5S6xuitG0P5e17qR0awJzO\n4pSrSKEASmMcbdUSwjduIHT9OnyLW5FjcytVUd5xgKm/+Q6lF3YS2LCMJT/+PEpzPWYqR3XfUYov\nvE7ppT3oR4awswVcB6S6EL72RgJreojet5Xo3dfBe5Dw2K5NujZK2cxiOxaiIKLJYRr9ixAFafYd\nMx2DopEmUxsDAZJaB0GlDkmQMZwqZTNP1SpgOjq2ayEgIAkKPslPQI4QVKLI4vwWBY5rY9g1SmaW\nml3Ccgxs1/baFmV8ooZfjhBSYkjCmRXGTwQGDpcOYDg1ZMFHndpAnXrmhGDaNbLGFAXdW1kLgkiD\nv4OAHEU6LQ3WcW2mKgNUrCIuDjG1mZja9K4QXkUJ0N5+LQ2Nqzl67AkOH/4JU1P7qNZymGYZ0/Sy\ndlzXxpmpKH/CNSCKMpLkQ5Y1FMWPX4tRX7+M7u65B7yKokJd3SKamzdcqksEQBJ9xOoWIUlz06oJ\n+OM0Na5FNwqXtF/RSDuaFl3QsYFAPatWfZy2ti0cOfI4hw7/kEJhBN0oYpoVLEuffXau6yAIIoIg\nnnxukoas+FF9YYKhBjo7bmRJ1200JFfN6fyy7KehYRX+QP2scJ+m1hGJXIyemoAsa7Q0bzgloD4S\nbiP0NhLpZP1yNqz/LJFIK2/s+xa53AC6UcI0K9i2MXPP3Le86xKiqCDLKrKsEQm34lMuXXDzCYw8\nuZ9gax1KxI9RrBFsj5F5Y+TdC1pe0EkVgTvv1chlXP7pC2Ve3Wbw7E91HviV+ZUEcB0HO1uk8OwO\nxv/7P6D3j+IaJogiiAKCIODWdIzBcYzjY+R/so3g1auo//UPEfvYHUiB+QU+ORUdO5PHNUymv/Bd\npr/8CHa24JGNE4O14+KUKpjDE+j9owS3rCZ41fK5XY/rYqVyKG0NFB57makv/oDy9jcA17smbyes\nag1rKoPePwKCQOJXzm0Sdl0Xt6qT+eZPmf7HH6IfHQbH8e6P5E3IdqmCXShROzxI/ifbCGxYRvJ3\nPkbsw7fMWmrmCte0sKazSHVhsv/yBNNfepjaoQHvwxOkzHWxxmtY4yn0Y6PIzfXU3XPDnM9xOcD7\nwrnkjSm+0/dnvDLxQ4pmhrASY3X9LfzOmn8gKEcR8DIPc/oEz418k4cO/zEiEr++6m+5tul+/EqE\n4/k97Jj6KW+mX2CycpyimUESFaJKkvbwCtbW38qmhvfTGFh8VmJyRt/w0mxLZpaB4j5enfwxh7Ov\nkKoNUzayiKJM1NdAa2gpqxM3cW3T/cS1ZhRRm1FiPQnHtfmr3Z9kqHiAuNrMfV3/kfsWf+6MPkxV\nh/hh///miaEvA+ATNX5rzZfY3HAPYV/ilH1rdpmvHvh99qafw3J0PtH7J3x4yR8gnK+K4CWEIAho\naoRVKx5gWe+9ZHMDjI3tZGxsJ9OpA1SrGWrVHLpRxHUdJFnF5wsRDNQTibQRj/fQ1LiG5qYNRKMd\ncwoKPoFgMMnNN/0pN9/0p5fwCuePnp676em5+93uxgUhChLx2BI2b/ot1q//DJMTbzA8+grj47vI\n5QepVjPotTymWZkhpgGCgaT33GJdJBtW0tJ8FfX1ywBhXmNdPNbFJz/+47f3ekSJWGwxv/bZV97W\nds+GSKSV9es+w7KlH2Rw8GcMDb/MxNQ+isVRarUcjmUiygqqGsHnCxMONRGPdZNI9NLYuIaG5PwV\nz+cLJawRX9uOazvIIZXG63uYfvX4gjUS39XAmZXrZJavVXjqRzVeenb+hMeczJB75HnG/tuXsHMl\nsG0EVUFd3ILa04EUCWGXKtT292MMTeAaJuWdB7wJPpWj8T/PL3XUqdao7DlCZddBpr/wXZyagaD6\nPJdMss5zq824jLBs/Ot6UZrnEdTluJjTWSoPPU7moceo7DrkWYzqQqgdTQh+FSuVx5xI4ZQqKM1J\n/OuXnrdJK51n/M++QvYHz2BNZ8FxEAMaSnsj/mWLEMNB7FyR6v5+zLFpXN2gvPMA1p98CSuVpf6z\n9yGFAnO+BNe0MIYnKTyzg+kvfh9jcBxkCWXGpSgoMuZUDnN8GrdmoHa3ofV2zv0eXSZwcalYeb52\n4L+we/pJSmaGiC/B5sYP8GDvnxGU6845gbu4TFUHyOhj7Br+Gi+MfovJ6nEsx8BxHVwcTMdAtyqk\na6McyGxj2+h3eKDnD9nQcCeKcOFV+qHsdp4a+io7px+jZpWxXXO2bRwB3SqTqg5zIL2Nxwb+jvsW\nf47rmj9Kwn9qwK0giDQHupmqDJKfteC4nG6ynK4Oka69Rb3YdRgsvMmaxM2EOZXwOK7DWLkP065S\n728n6mt418jO6ZAklUS8m1jdIpYv++CMhcCdcXGdGGW9BY6AgCBKiDPWHlGU5x3DcwVvFwQUOUBL\ny0aamtZ6AcquDbNCgjPvrAAColef7S3P7bwlu3+hIeD3x+jpeT9Lltwx8747p92zmXddEBFFada6\nKYqXXkZGVCQEUcSqGGTfGKY8mKZ4fNorC7UAvKuEJ5YQicUFymWXoYH5XYBdrFB6cRfTf/sd7EwB\nXJfQ1g3EP34ngY3LkSIhBEnEdRycco3i8ztJf/0nVN88hn5kiOwjz6Ot7iZy+9UI5yg2eDqM42Nk\nv/cU5Vf3I4YC1P/WA4Rv2oCvJYng81Z1jm5gZ4tUduzHt6QNdfHczZCuaVJ4YjvlHQewpnNE33ct\n0Xu3EljdjRjUQBRxTQu7WEE/PICrmwQ2nruGjDmZJvfIC2S/+xRWJg8uhO/YQvyjtxHYuBwx6EeQ\nJFzLcwuWt+8l+72nKb28F2Nogqm//Q7q4lbCWzcgRedmvrSyBXI/fIHicztxKjXin3wfkbuuQevp\nQPSrIAjeNeRLVN84ghjyE7gAabvcYLsW6eoo3+r7b+xJPU3RyFCnNnJd80e5u/M3iWlNMwPEuQZR\nl9FSH4/0f57jhT2MlY+giCrtoeXEtVYkUSanTzFePkrJzGBZBgPFfXyn70+p0xpZFF6DKp17cbB9\n/GGeHfk6BzPbKZlZBARCSh2toaWElBi2Y5HRx5isDFC1i+jVCo/0f56sMcXNrQ/SEfZWboIggOvS\nGuylL/cqlVqBopmmYKSJqqcS+enqIJna6OzvDg5Dpf3U7PKp986xKBkZcsYUlmtRr7VT52u4bOK3\nPBO+fIYa7RVc3jjx/kiSMi8L2zsBjyyf33pULIxy7NjTFPLD3Lj1j7yN78B3wuuTMCNGePmV5uj5\n5Wu9chI1EyWoUh7J0HLbcuTgwvr6rn6rbcvFtsEyoVScH+GpvnmU/KPbqB0bAVEgsHYpyd/8COGb\nrkKujyFIp6605GQdrmmSeegxqvuOUTs0SPqfHyN8w3oIaHMacPWBcaxMATHkp+nPfoPgNavxdTYj\n+tXZ413XxTUttN52BE1Fiszdz+maFoWnXsXRDeIfv5PY/TejrVqCnIgiiCevx7Vs/Es7cXQTKRI8\ne1uWjX50mNTXf+JZdoDIXdeS+PQHCN+8ESVZd8oXynVclOZ6xJAfRJHSi7swBsdJfflh1CWtaOcJ\nvH4r7GyRwlOv4FRqNPzuJ4nedY1nbYuGTkkl9O5RBwDiOa7hcoTtmIyW+3hs4IvsnHqMkpEhpjVz\nY8vHuan1l2gJ9iAJFwjix6Uv9yq2ayMisi55Oxsb3kdzoBu/HEIQRHS7wmipjx1Tj/JG6nkMp8pQ\n6SDbRr9DvKuZpL/jrG0fzr7KS2Pf5UD6JcpWnrjazJam+1gV30pUTaKIGi4OVavIeOUYe6afYcfU\no6Rqw2wb/Q5BOUpAjlDvb59pUaA1tBRNCgEuRTNDRh8/g/BMzVh4QkqMmNrESOkQw8UD1KwyLu6s\nBcd0dCYrxzFtHXBJ+tvPaOsKruAXCTte+b+0d15PU/O6c1oALUunVBwjnz3+Dvfu8kZtuoAgCmiN\nERIbOol0N+CLBxGVhVmX3lXCMznmMDnmIIrg882dzTo1g8quQ5Re3A2WjeBXiX/yLsI3bURpjJ/1\nGF9rA3UfuJHageNUDxzHLpQov7KP2pFhtGWLEOaQueWUKkh1Ieruv4XYR29DDGqnEBGYWSH6FHwd\nCwgwc1zM0Smi924l9pFbCW5ZjXiWqrCCLCEnY+dtyprOUt55kOruwwBIsQh1H7yJ8M1XoTSceawg\nCvha6oncsQUrnaey6xBOqULxxd1Udh9GaU4ixyMXvATXMLFSOWIfvY3YR29F624/a8aXoMiz2WWX\nJ4Qz3Cy2YzJY3M8Low/x8vj3KRgpEmorN7Z8nK2tD9IZXnVGgO65kNUnkAUfVzfdyy1tv8KK+HWE\nlFOfy5LoBvxymKpVZH9mG6ajs3PqMW5u+2XqtbZTBk/XdbFdk5fGv8/B7HbKVo6k1sGWpg9ye8ev\n0hlaNRO0eZKYl60czYEl2K7JvvTPmKwe57XJn9Dg7+T6lgdm43laQ734ZY+4l4ws6dooiyNrZs9d\nsQqka6OUzCwtwR5WJbYyXDpItjZO3pjCtHV8khcvZzhVRst9OHhBnidcWldwBfPF8MES/XvypIZr\n2JZDXYPK1k+24vOffM8dx2VqoMqeZ6ZZf3uSRJuGrLwzbkfXdTGMEocP/ZBQuJmm5nXn3Ffzx+jo\nvIFkw6r3ZPLGxcJ1HKxqEauSR5AUtLg3f6Z2DZFY206gpQ5f1I8vOr+wl9PxrhAe23YZH3H4+YsG\nh9800QICTa1zfwnNqQy1Q4MYw5Mgicj1dUTff/0FJ2S1ux21twMpEsTOFrEzBcqvvYmvs2luqeqi\niNrVSvzjdyCF5x7XMh8Iikzs/pvxr+o6K9mZK4yhCSqv7feCuAH/yi4CG5ahXIAoKS1JghtXoPa0\nU919GKdYpvzqmwTWL50T4UEQkCJBEp/+AL6Opnmlt19OEAQBWTx5/23XYqh0kBfH/oUXRr9NwUhR\npzZxfctHubX9M7SFll7QsnM6moPdXNN0P2vrbz2riyqkxFiV2MpEpZ8DmZdwcRiv9JM3prAcA0U6\nGXTvuDZj5SO8kXqOrD6BIqp0123k1vZPsziy9qzXd6J9EBgrH2Gi3E9/YQ/7My+yOrGVmOYNOk2B\nLvxyBAGBopkh/RbXFUCqOkzemMLBoSHQycr4DTw++CVM12CyMkAlmj9JeOwqY+U+HNdBRKJeayPi\nu5yJ7xVcjihmDF754QRHdua8rE9ZINakcsPHWk4JMXMdl8njFZ788hBtS0PUNarIl9jj5boOtWqO\n4eHtFAsjZDPHGDj+PJatIwgCgUCSriW3AzM6TGOvUyiO4brOGSnxpeIE5fIUup7H0Eu4rkNL60am\npw9Qq+WJxRYTi3fj8wWxLJ1s5ijZbD+mWUWR/UTrOqiLdc1JyfrdhGPWyB/ZSfrNF/BFG1j8gX8P\ngFmoYeSr2LqJdBHz4Qlc9Gw0PGBTyM3NHeXiJQhVyy6vbDP4yfdrHDlk09YpsX7T3C/G6B/FGJ4A\n10XUVLTeDuSmxAUnV0GRURriKA1x7GwR17So7jvqZQidnwcAIPpVfJ3NBFZ3z7mv84IgICdj+Nf0\nIMcXlqp5AuZkhuqJ7CgguGUVciJ6wdWDIAjIDTGCm1bMWoeq+45hTmXxzyEoX1BklKZ6glctm3cW\n3OUEEQlVCiLgkYmJcj/Pj/wzPxv9Nnljmjqtiasb7+O+xZ8j4W9DFOZvYl1bfyuLIuePx0loLbSF\nlqKIKoZTw3EtcvokNbtyCuGxXINd00+S0ydxXJuE1kZP9Cq6oudeVQL4RD/rk7fTEVpJTp+kYhUY\nLh3kWGE3G2cIT52vgaivHkXUKJkZ0rWRU9oYLfdRMFL4pRAN/kW0hpYSkCNUrDxj5SOzMU4AhlNj\ntOxVtg7IEerURvzy/AdjPV3ESBURVQV/SwxxAZocMFP82LAQ1Qtnv13B5YOhAyX2PjNNU3eQOz7b\nQaTeh1mzUYPSaSq8AoGITOfqMKGYwjsQZ4vrulQq0xzY9x3S6T4qlRTH+59hYmIPgiDQ0Liarq5b\nQRAxzTL9x56mv/9ZisVRksmVLFp882xb01P7OXb0SQqFYUyzSqk4zvqr/h9GhrczPX2QxYtvZtWa\nB4kneshl+9m762tMTR/ANMrIskZj0xq6e99PR+f1l3VcmlUtkdrzNMd+8BeEOlbMEh7Jr1A8Po3o\nk/DVeUYGQRSJ9jYuyK110Xfg7/+qzAtP6nPa13WhVnVJpxysGYkBnwpLeiXuvG/uk+MJsT8AQVFQ\nmuuxc0WYg1CSa9sImhfw5DoO1kTaEyicA+R4BF9H08kU8bcZgiSiLe1ADGgXbda0swXMoYnZ39Wu\nNsTg3MyBUjiI2nUyW0fvH/ECw+cA0e8RUGT5PW2aFQURTQwAAgUjzaMD/5eXx79PTp8ipnpk5zMr\n/hJNDCw4M6c7ehX12vnLEIiCR7yCSgxDHwe8tG7bPbWci+WY9GVfmw0Sbgl00xFedcHsJ0EQEJBY\nHr+WgeIbVKwCmdo4Q8X9bGy4ezaoscHfSUiJUTKzZGpjsxodAGPlIxSMNBFfPUl/B5oUpCXUS39+\nN+PlI5RML4bMdd0ZC49HeBqDiwkqCxOcTL10mMGHXiTQXs/S37sHf+vZXdnng+u62DWD8sA04Z5m\nhAXGBbwX4ImzOiCKvxDEbuJoGUkRWbIhSu/mc9dglGSBnk11fO5r5yf+bydEUSJRv5R7P/R1JsZ3\n86OHf4Xrt/5XVq355BljRSBQz9Zb/pSVqz/O7te/Qip1+Iz2Mukj+ANxVq/9Jd7Y8888+/Qf8IEP\n/hP1yRVk0n1MjO/G5wty8MC/Mjj4Irfe8RfU1y9jYnw3e3d/g9d3fJFkwwqCwcvXdewYNRzzTB5R\nmy4yue0IVs30MrYAKeBj6zc/ixqbf+znxVdLn7IZOj53RUbXndXCQ5Lhmq0+PvvvAyxZOvfBxilW\nsMtVAOx8kez3niH/k21zO79p4egzxU4dFytfmnOKm6D5LpkrCwBRQKqLIMgXP/A6NR27cDJDRopH\nEOa4ChZUBSl20n3l5EueQvJcIEtIsfB7flAVBQmfHMDG4vtH/5yfTzxCTp8kobVyfcsDfLznj9HE\nhQdby4KPhNaCX76wm1AUxFPda46Fc5oSsONaDJcOYtje9yKqNhDXWubcn+Zgz6ylJW9MM1HpP+Xz\nhsAiwr442eI4WX0Sw6nNWqbGSkcoGilagr3U+9vwSRqdoRUMFvYxVj5CyfQWJ16wdImpygAuDs2B\nboLywgoGN79vHUaqgJErX3jnc8DRLcr9Uwx9+yWW/cF9iNFL+N1+t+E6GFMjKIkmBN/ll40zX5QL\nFoLoabr9okPzx4jFltDQuJqW1o1MjO+isXE1Pl/IEwzUC+TzQxw/9gwbN/82Tc0bUNUwi5fcTip1\niL7DjzI2uoMY7EBjAAAgAElEQVSe3ve/25dyTtiWjmOdOces/v07WfW5209ZYAHvXpZWZ5fMmqvm\nRngEQJQFQiGBtk6JdZsV1m1S6OySkKT5BC3ruPrMCtf1AmVtYyEFTN0Z687cVIwEUUSQL6VZ0At4\nvljLiOu4uKaNa568J6LmOyNz7Zy9kCQvhXwGTrWGa1pzO3YmaPtygWtbGNkpxh/5O5re/1m0lq45\nHScKErguDx/7K16bfJScPoGLS1toGVsaP0hghqgslNgFlSiKqM1Df+bCYoNFI4Xtes/JL4dm+zgX\nRJQEsuDFsRl2lYqZP+XzhsAiQkocF5eqVSRVHaYl2IPuVJmqDlK2CkR8Ceq1Nnyin47wSiRBZro6\nTMFIYzkmhl0hXRvBcr2BrTm4hKDiuW7zbw4x9uPXsYpVHN2k+b5NxNYtIr9/hNRLBzHSJUSfTOen\nbiS4KInokxGU090XMPjQNkrHJrBKNSIr22m6cy1K2M/UC/tJvXQI13IIdTcRWdmGXTEYfeRVcnuH\nwHWIb+4hvqUHf/Mc/NvvIbiWiZmZIvfU94jf/SBicu5E+GyoFi369+R5+QcTDL5ZwKg6+MMyy66p\nY/M9jXRv9Eis47jseHSSnT+dYuxIGUGEzlURNn+gkbW3nozbeuyLAxRSBskOP8f3FDi6K48kiyxa\nHebGT7TQtT6K4hOZOF7hX//iGGN9JSb6K5SyJsMHSzz294OE4grrb09y7+cWzwYlP/6lQV741ghG\nzSEUU/jYH/bQs6kONXDqgrKcN3n5++PseWaazLiOUXVwHRdJFmjsCvCxP+qhpSf4jgU7n44TSt5e\nEdggmlaHKCnIkg9cB9vW0fUC42O7yOeHee2Vv5m1JJXLU2haHaXSxAXO8u7iXBae1OuDBNvihBd5\nWl56rsLEzw7TducqJG3+88xFz94f+ZTGzXfNXfJeEAUUBcJhgfpGiWhMQJknSxdEEWGGIHkZUU34\nOpthjhP6bDuKjH9FF6J/ju40Qbj0bpq3o3nBc48hSbNuPtdy5q5O6bqnuPkERZ77vRW4rFxZnkyA\nQW3sOI4x9zo7FavA/sw2csYU6erILJEYrxxjd+opFkXWzItQnA6fFDil9MTFwHVdHNdGd6qesCCe\nBUkR574KUuXgbIaZ5RroduWUzxv9noUHoGYVmaoO0BLsZrx8lKKZwXEtomoDSX8nPsk/m7FWNYuk\nayMUzTS2YzJVGZxtsznQTVCpA9elOpKhcHCUzgevRwr4CHY1YmRLlI6OI2k+mu5aS3U0y+BDL9L9\nO3ehNZwa4+YYFuWBKUpHJ4iu6UDSFMoD00w8uZf665Yy8oNXaPvIFpRoAF9dEF8ihJGrEFnRjpmv\n0HDbGoKLkiiRi8sCme1PtUzu2X/FLhVwTR21azm+5k7sQg67lCdy7V045QL5lx9H9GkIkoQ+NuCJ\nrBVzxG79MK5pUjt+ADM7DY4NokTdLfejxBspvPIUxtgArqGjNLYS3nQLYjBC+uEvI4XrcA0D0R/E\n17oYQZLJb3uU8u5tuKZBYMVVaN2r8TXMvar7CegVmz3PpnjhoREcG9bcXI8WkqgWLWJNGorqjRO1\nksX+bRme+IdB6tv9rL2tHteBqcEKj31xEHBZcV0cRZNIj9bY9eQ09a0abctDXPOhJip5i73PpZB9\nIooq0rUuSjimsOWDjZQycXY+PsXAG0V6NkZZeUMcnybSsDiA+BYCvPy6GMGYwrHX8zz7jWHKORPH\nOXUQLGYM9v0sw9NfHeKaDzcTb1IZO1pm15MpHNvluo80E2tUT2n3nYaXXSnNDK0ioqh4C6UZnSzX\ndXAcC0lWWbHqAeKJbqS3lKFRteiMkvTlC8fUz0l4BFGYJTxWscbEtiM037zs3SE8y1a986t5MaDN\nxuGIAQ3/hmXEP3HnvIqCegeLyLHwObVs3qsQBMFzSwX92HmvYrFTKM3ZSuMaJk7hZKVjMRS8KKtN\nZfAQlaFDOEYNQRQJL9+Mr76FysAB9IlB7FoFORglvGIzciRBdfAQ1aFD3vZIDH9rD75EM7ndz+Ho\nNVzbIrBoOUq0HiM1Rm38OIgSdrlAsGcd/rYezMwE5WP7cG0TxzTOai49H3SrwmBxHzW7Qmd4FTW7\nTE6fJFUd4ZWJH5HQ2rit/dPI+BZEWt4usnMSAgIiHuN0Z//MFSfVVZlVVX0rElobEaUeUZCo2WWm\nK0O4uDNaOyV8op86tYk6tQFZUGgJ9qJKAcpmjunqMDl9EkmQmawOAB4hawh04pc8N5qajBDqaaIy\nnEKJhQgvbaU8kqbUN45V0fHFQxjpIrWJnEfeT4NjWhQOjlI6Oo4SDaDUBdCnC4g+hdpkHqtYI76p\nG7XhZA09UfMR6mmifHyS+FVdKHVv0zjgujh6ldzzjxDedAtKsgU5Wo9rWRjjg1iZKbj2Lhy9RrVv\nL1IghKComOkJgmuuwa6WKe54DjlaT/XYfgRJRu3oxpweI//iTwiuuppa/wEkfxApnsTKpSm+8jTR\nrfdSePlxIte9D6WxDSlUhxSM4LoOouZH1ILI9U1I0QTiAt1aE/1l9j2fJjuuc8/vLGLZNTG0kEyl\n4I0toZg3TpRyJk99dQhBFNhwVwPdG6K4jsvB7Vme/9Yoz3xt2LPcaJ61pVayUAMS13ywicbFAaol\nm9RIldG+EuNHK3StixKIyJ5lyIXcpE4hZbB0Sx1bP+kRN0EUeOtru2h1hPblIfwhiWe/MXzW6yll\nTI68lqNWttlwZ5LW3hCpkSqlrMXwwSJd6yKEEwssbC14BAVhZuHlugtaCwqz/8z8flojwkz9rWAw\nSX39UpYu/yA+36n6b/ONMzTLeazKpa2j9lbUUsOY5dyZ26cKmCXdu3eAbViUR7KzldPni8s3bPs8\nkBJR5LqZzA5BQAr5idyy6ReOuFwMpHAQpTE+S3iM0WmvOvoc4FRqGGOp2d+VpsS8ykucAtel1Pc6\nhX0v42/rQdQCuKaBlU9TPPAaViGN6NPQp0ew9Qrxa95P9tXHvTouqlc13iplsWtlcrueR2tejGvU\nsCsFj/BkpygefJXIymupTQ5iVQq4toU+OUTp4GuoTZ1YlaK3Qp5Pt3ERBZkNDXeyKfl+SmaG16ef\n4EhuJ2PlIzw19I9eCnbsejT50hfROx8EQUAURPxymLKVw3FtTEefjeeZC6pWcdaKpYgqmnTqd8nL\nqGogIEc8wlMbwnVdRsuHqdllomqSuNqMKgVwXXfm9xYKRopUzSM8fjnCdHUIAZE6rZGIr94zyyMQ\n6mmi6a51pF86RLFvHCXs9woD10ysQhWrWEXy+2i8fQ1y6CyTtetZMe2KgZkvI2kKwc4kWlMd2I7n\n/hJONaB6E4mAazkLLc1zbogSSn0LUjCKHEuixBtwz3gHT55VVHyoLYuJbr0XY+w4k1//CwLLrkJU\nNbTOpYSvvYvq4d1MPfT/zYQGKATXXYfa3k3xtWfJv/goka334lRKaItXEFx7DaLqWavsSonAys1Y\nU+NErr4NX/PCS7mM9pWZGqzQtizENfc3Ifu8IOhI/UlS4NgupYzJ3mfTfOT/XULvpjoSrZ4V3bZd\nhg4UeeornsUlGPUIUjSp0n1VlN6rPXditAEWrYmw68lpSllvsSKIAvKMpUWUBETB+yn7zjeZn1/h\n2LZdjKqNoor4NBFREvD5JRRVxLHBNi/mzRBQtQiiqFApT1GtpFG1KK7roChvjyXRO41IMNhAsmEV\nx/ufo6l5A3WxxZ6AqZ7HdRxC4aZ5kZ780Z2k9z7/9vXxAqimhiiPHT1ju78hQvHYNGPCQURZpDKe\nI9AcPcOVPVe8JwmP2tmM0uKpszo1ndrB4zjlKmLIf4YQ4L9VyA0x1N4Oan1DgKdMbedL0NZwXpeT\n67pY2SK1AycVP7XeDi+lfYFwHQc5kiCy6lrkaD1qQzulvtfRJ4fwt/UQWLySysB+8rueJ7x8E6W+\nXTR/6LeJrNwCgoCeGiO38xmUWJLGOz+F6zpM/OQfqY0PoDUvxhdvpuneX6M21s/4j/6B0qEdCKKM\nv3M5yVs+RvHwTspH9syrz7Ko0Bzo4sHeP2NReBWmo+OXwxSNDKPlPgaL+3n42F8RWhq7YGr5OwEB\nkbjWQk6fxHCrlK38bLDwXJA1JjAcz+WnScEzCn4KgkBca6FObSSvTzNdHcbFYax8BN0u0xFeORsk\n7REwmY7wCsYrx0jVRsjpU4BAqjaMKEi0h5bjE/0ICLi2g2PYyEGVhltWMfHEHsrHp4isaCXc24xt\nWCRvWoEgiijRAKIiUx3LoE/lMTJlKiNpAgKEe5sJ9TaT2NJLoLMeSVWQQxp6qoBrORT7xtFTRaSA\nii8eQlAkZL8Ps1Ch1DdOoKMeJRa8eL0PQUAKhKj/6G9S2PYolUO7CK7ajK99iUewHAvwrEAniLgg\nKwiKD0EUPauMXgPHRlRUb7ssI2oBnGoZu1xECkVmtvsQJBmnVvFcHooPORqfJTtv7ZM7U1vqYlDK\nmjiWS7xFQ1HPnlxhWy7lvEW1aJFo1fBpJ8dkLSgRa9aoFEyqBQvH9voTiinUNZ1KZH1+CddxL5J0\nnB/BqEz78hA7H59i3wtp0qM600MVCtM67StCxJoXLq0hCCLBUBPxRDfT0wc4euQxwuFWVC1Ca9vV\nABTyw1QqKVLThygWRqnVsgwPbUcUJaLRTmxnbpbpSLSd3mUf4LWf/w1HDj9Kon4poiRTKU+jqlF6\nl94zr7T0zJsvcvR7/3NB1/12ouGaJYw+fYDhR/ciKhKuC213rkRSF0ZdLprwVKsulumiKAKqJszL\nZGfbLpbpZUvKMihzVFtWu1pRu1oQVAW3qlM70E/14HGC4YBXH+oyiiF5t6C0NuBfu5T8Yy+D41Le\n/gbm6BTa0s7z6hW5hoUxNEH5tf3eBlHAv7YH+RwK1heEIFC3/mZwHcZ+9EXkcJzWj/wHjNQY1dEj\nVAb2U9j/c0Sfhr+1GzM7jVyXRNROWpRcQ8cqZlCTbSDJyFrAW/1XS4haADXZCgiIWhAcG6uYRalL\nIodjCLKCL940byKsiCrJwCI6w6vwSX58kp/rmj9Kza7wyLG/pmzleCP1HE8FvsI9i36HjtDKOass\nXwpIoszi8BpGS4cwnCrZ2jip6vApZR3OhhNurNHSYaqWZ8KO+OppCpwZ3B3XWoipTUxVBknVhnFc\nh/HyUXS7QtLfSeItKfaiINIRXsne1HNkqqPk9ElUKUCmNjZDeFagSt4ztnWTzI6jDH/nZSTNh13R\n6fp3t1G3bjEAk0+/Qd/nf4qkKSSu6SVx7VKmnnuTzGvHsCs6CFB/7VKa7l5PqKeZiSf2YJV1Au0J\nkjetoG7tIlo/tImBr7+A60J4aTMNt64mtm4R/tY4jm7R/+VnaLhlFcmbVuBvOfe77loWVMpQrpz5\nYTiEEAx5dhtBQGloIfHhf0dpx3MYk552kaioOMU8djGHMXIcp1pGCkWxKyVcU8fKpagdP4iSbEXU\nAujpScT0JFZ6EmNyBLWjB7Wzl9rAIcz0JKLPj10u4ms+UWrkzGctiCKiT8PWq1ilPFK1jOhTEaQz\n31fXMKBaBV1nRtEPAgHQPFV5QfRiRxzbPekCPW28dR0Xx3YRRM9C8laO5TjethOWlBNGB0kRL2Cp\nuTSoa1C56u4Gdj+d4rv/4wiyTyKa9LHm5gQ3fLyFQOTUe+SWy97zP13KRNMgGETwnbR0CYKAqoZZ\nv/HX2bfnm2x/6S8RRYVFi2+aJTyHD/2YvkM/Jpc9jmlVAZcfP/xpFDXINdf9PoocQNWiKEoQQRBR\nlAB+fwJBkJAkH6oWxecLEgw20Lv0XlzHYf++f2Hvnq+DIFJX10lv7z0XWdT2HYhdxT0rGU+s7yC6\nrJlaqohVNlDjQfyN4Tkn4JyOix6hn/xRjb79Fqs3KNxxr8Yc63ACkJ52OPiGxeS4Tc9yhfWb57ay\nEqMh/OuWEdiwnPLP38Au15j486/R/n9+D23F4ssqaPbdgtrZTOj6tcjxKFYqhzEyRf7x7ShtjfhX\nnjtTqbb/GIUntmNNe9YBOR4ldMN6fG0L13Dw1beQvO2TRNfeSGb7T0m/+AiBxSsJdCwjtHQjsU23\nM+PoxirnMFKjONWT6caiFsAXb6Y88KaXcZJLIUgycmjG6nQamZFCdbiui5mbxrUM9NToWdwJ80dC\na+Ha5vspWzkeOfbXADw3/A3iagv+thBNwSUXfY6FQhZ8rIhfz87pxymaGcbKfQwU93E9H7vgsS4O\nB9IvUdA9N2ZMa6E9fGZR2rjaQkxtxnBqZPUJylaedG0U09Fp8HeQeEsavIhIZ3gVmhwkVRsmq4+j\nyUEKRgpVCtAeXo5vxip2wlXVcPNKTsQgiYoMokD9dcuIX91zsnCzKCJIIp2fuoGOT1w3czJhdgBc\n9Ms3ev59dyamQxJBFGh/4FraPrwFOHV7sKuBTV//LcDbdsGB9Ngx3K99Fffv/+6Mj4Q//m8I/+n3\nvRTw8UHG/u6PEGQFp1YhvOU2guuvx5weJ/3oNxj9/O8h1dVjF3MoDa04epXKgR3ow8ewslM0fPq/\n4Oo61aP7KLz8GKXXX0CQFRo+9Xuobd0Yo8fJPvpNnFoVtaOHxL2fOWeXBZ+G2rIIO5di8p/+nNDG\nmwhffTta+1kEVPfswf32t3CffgpKRViyBOHTv4pw330QrSOa9CFKXiyPpTvI6pn3S9FEYk0qobiP\n0cMlqjcnZl1elbzFxPEKsSaNQFRGnEd27qWCqTsMvlnkU/9zGatvShCO+xAlAUk+s2/uP38T9yv/\nCH2n6eXcex/i5/4jbNx0xjFdXbeyaNHWmTFIQHiL+uGGjb/Gug2/OlNg9CQEQfCCkwWB7t67ZyuV\nr1z9cZav/AiyrBIMNdLYdLI+l6pGWLXmk6xY9cBseyeOE8UFWi0FEVkLIioXrw13PriWga1Xzoi1\nLBydQk0ECTRFyR0aZ+SJfTTduJTw4sSC5FsumvD87CmDpx/V+dAnXG57vzqv9PJcxuX5J3VefNrg\n5rt8cyY8giAQ3LKKug/dTPXNY175g1f2MfIHXyD+4F1EbtmI0nSmXL1jmJijU1T2HqF28DiCItP4\nu594z5Y/OC9kCW3ZIup/48NM/I+vguuS+ZcnwHFIfPoeAuuWnkIUXMeh/Np+0v/0Y3KPPO9NGIpM\n8jc+jNrVxryY7FtgV8tkXvkphX3bvWDOWpmG93+GQMcyjPQ4hX0vkd/zM+RwHeFlm4muv4m6TXeQ\n2vYw0y98DzXZRqj3KsLLNlJ482VG/vl/4RhVgj3rCPWux8hMnnFOrakT17HJvvoktfHjiJoXD3Kx\nKXCCINIU6OKG5gdIVYZ5afz7WK7JM8P/hF8OcUvbL88qCr/TkEUf65N38MTQP5CtjVM0M/TldrAv\n9QJr6m8+53E1u8yrkz9itNyH4VRRpSAdoRX0RDeesW9ixsLDTGr6gcw2dLuKJCgktDai6klSLAgS\nHaGVs7FAk9VBLNeciY2S6AitmHUDCoLgDV5nG8AkAeksJEQQZTjLcHGu77KgSHA2YUFBQJpPsoPr\ngGFA5SwWnhPSGJKM0thOy3/4X57LznWRgmGkUBS5rp62//x/vElPksG2MCaGqR55g8i1dxG98QO4\ntoWcaKJ6aBdKUzuBVZsJrb3Om3wSDQiKSvzuB3H0Kq7jIPpUpGgCQZJp/8Mvopyedi4ISNE4rf/p\n8173/EHE4FkyDEdGcL/4d7iPPQaFvGeOyWRw+/uhvh6uv4GudRG61kZ46ftjfPtP+7jhY60E62TK\nWZNK0SLWpNLSEyIUV7j9M23sfGyKaKPK6q0JHNtl91PTHHo5w52/3oEWvPTq1q7rWZtwPVfb6cFa\nhu6QGasxPVQh2qASrFPOSFs/BaYJ1cqZz1/Xvft1FoiifE53kiT5Lji0vvVYSRJnq8ELgidyeAKC\nILzt1eK1RAud7/9tGq66C+ESVqHPH9vF6PMPkdr91Cnbhx7dS8OWLlzbZfzFPmS/wv6/eZqNf34/\nvgVkVF70TF/IO6SnHUrF+eSEeFB83vszdNym78D8VuBKMkbkzi2Yo1NMf/lhnHKV0ku7MUcnyX7v\nGZSWeuSYJ7bnGiZ2oYyVymNl8tjpPK5lEdi0Etdx35ZM8MsNgiCgNCWIffhWagePU3ji51hTWbKP\nPE/t0ADa8sVepfeAhlOuog+MUTt4nNqB41jpPGI4QOS2q4k9cDtyMrbggUlQfISWbvTcUYKIIIj4\nO5ci+UPUbbiFYNdqHNNAVHwosQYEWSF+9V0YPetwTRPRH8BX14AcjtF492dwTRPXsfHVtyAF/n/2\nzju6juu89r8zM7dX9F4JkgDBTopFElVIipQoq1qyZMtNdmw/yS1O7GQ5L36Jk5c4Lml+Tlzi2HJk\n2ZZkdYmyukRJlNh7AQiCBEGC6PVe3DYz5/1xABIgwAKAFIu518LCWjNzp925Z/b5yt4BrFgEOSDb\nbfjDZN/4KZzpuSBtXJmFAGguN2nzb8CZNfY23BPh0FwU+atYVfoA7fFG9vdspiPexJojv8NjBLi+\n4BO4jQ++eF4gCDgzWJJ/D73JdmUP0bOZFw6qSER1+pJhnWFSSnqTbWxue5lnDvwrPclWJJLKtEXM\nzLx+1Jb7oDOLNFcuhnASMyNsb3+dlB0n011I2JWNQ7iGnU/YlUPYlYNDc9Paf4BoqhuBhtsIkO0p\nHVPb/MUEpUXlwpVfNnKd4cBVMDzCakV6EIZDRTKHFBQLoaE5XBjhTJz5pcM+Y6SN7jDvKhwZZRRC\ngG7gOmEfJ0Ju2YLcvRs6jjcsEIvB4cOw9l2oqiJcUMqCW3NAwL6NPRzYtkfZ0TgFpTODzF+VjRDg\nDztYfn8RUsLetV1se7UNhMDt1Zm/KpslH8nD6Tl3yta73u5g80vtHKmJ0HEkTrTX5Mnv7+fNR46Q\nlufimnvzqV6Sga4L3H6DYKaT33+nDm/IQDc03H6doio/827MpmzW+OUnLnZoTg/+okr8xdPQjHF2\nq50BzHgEZzBj5PJIgmRPjFRfHKQke9EkOrc1wsXYpeX2CNxuSCQkne1npnY8COEwcFcUkfHJmxEO\ng+5n3iTZ0Exs2z5iu+rRfR40vxdhaEjTwu6PY0dix1qzjew0NKfj7OjeXKDQ3C7cU0vI/sq96AEv\nva+sI9XUjtncQXTzXhxZaaoOKpEk1dqllJltiaMwm+CyBWR8+hZcU4rRXON/0DXDgSe/HM8ogn+u\n7CJc2UUjl2cVKoJ0AvyT54xY5gger7fQXR78FceNMp0Z43CsPwO4DB8VofncWvanPF73HRojuzkU\n2cU7TY8TcGSwOO8ONM522/mpoSwiBPOzb6al/wBxq5+2WAPbO94gacfZ1bGGTE8RHiOALS0iqU6a\novuo7V5Pfc9WJDZF/mkszLmNKeGFo+b8HbqLoCuTkDOLnlQ72zveJGUnyPNVEHRkDrteIQQO3UWO\npxSfEaQ11oARP4pDc5HtKcZjBCZYV3DpwJGRi2/6AhXxGbo8vwSfy40eHL2myN6+A0wLUTEJETzu\nRyZ7elTNjWtshFIealBprNHWNTQgIlEMp0bRtAAev0FhZYCu5ji2KXF6dHIneY91YxlOjfzJPq7/\nRCEN23vpblXGmWm5LkpmBMguOV6jN2dFFhXzwxRVDu92nHJFGF/IIK9i5ASi6qo00vNcFEwdvUMy\nkOGkqMqPL2Rg25KFt6nIq6Ypb61AhhNpS1obYmx8voWpC9PILHTjcCtph/6eFPVbe+luSXL/9yrR\n9FN3e12q0HQDwxc+52UiutON5hj5vBo+J81v78OV5iNzbgm+gjDSGntw5dj+JnaaZwMC24J4fOyX\noHndeGZUkJUZxpGfSXTdThL1R0g1d2B192G2dioBPV1DcznR04MY6UGMnAzcU4oJLJ1/yXd1aR4X\ngWvmorkcOItyiK7fTeJAE2ZbF4n6I0jTRDgcaH4P7ooinKX5+BZUE7xxMf7FM8/36V+QEAhcupeF\nubfSFmvg1cZfcjhaS13PRt44/DAZ7nwq0648L+eW4y3l6jyVw1/f+gLN/fvZ1LqaXZ1vk+0pxmsE\nsaVFb7KdzsRRElY/ujAoDszgmvx7mZu1knT36ERRIAg6lF9WR+LIgCeWSb5vMkHnyNkZKMsKvzOd\nI5FaAHyOMIX+KnShj0Fl+tKGkZ6NkT6yRs6ZXYgzeyTxP4ZEEixLpdkYKECPx7G3bkcrL4OiU3x2\nNMiRKZ/R4PLoFEz1n5RsDEIIQUl1gJLqU5vDTr9m9GenbFbwpNGVinlhKuad3JakeFqA4mmnPm48\nanJgWy/vPdXMXd+sYOrCMB6/gZTQfKCft3/XxJaX20hEJ+MOGBd8aaiZitHVtIu0vGp0h3sYQZO2\njZmK0d/bjC+Uj+E8s3SQ0AwcvvA5/61qDreqEzoBmfNK6dzWiK8wjcwrShFCkLWwbFzGoXCeCU9P\nl01Pl40Q4y4RQTgMXMW55HztPmJ7D9K/eS/xHXUkDzVj9USw4wmEw4EeVLo0zrJ8PNWTcE+fhDN/\n9LDwUDgLsvAtnI4jP0spOhed3RoNPezHM70cs33eEOXnsx/q9y2cgXvaJPq31hB9fwfxvQcx27qx\nY3E0nwdHVhruylJ8i2bgmTl5TJ5hjsww3jlTAIkRDuCeXDxm1euLDUIIHMLF8qL76Um2EW96lNbY\nIfZ0rWV1w4/JcBeS7s4f5oH1QWFK2gI8hp+gK4uNLS/QFj9MNNXFkWgtlm2qFIRw4tK9ZLgLyHQX\ncU3+vVyR86GTkp1BBJ2ZZHmL2dv9HvaAbk++bzIB58iaucF1ypJCvZRduodif9W43OWHQjYfhUhE\nhbbdbkR2Nrhcl1zDgmxpRXZ1qa4gQ0fk54HfD+3t6np9XnC5VI1aJIq9ew/2cy8gF1yBZtuItDA4\nXcgjRyCeANtCZGVBetqwjiIAUViI9I9CYoRAlJdD4PzqTZ1tJOM2PW1Jor0mZbOCBDOdGA6NVNLG\nMaDwbCFDVNoAACAASURBVDgF5ii1PxciEtFOdrz5/1h42z/iDeYN+y3Ytkm8r436Lb+nYt69BDJO\nrcXk8KfhySnDk12Mw58G5zgaqzlcxyI8Vtykc7vqanRnBchfVgVAvLUPadlkLSw/94RHSolpcszl\nfBCDzS+WKYn3S6wzEPOVUpJKwvZNJrV7TJxuQWb2xG+op7IUT2XphPcDihHTHyW0aCqhlQsR7vHr\nMZwK3pmT8VSVkvfVu1UxZGbW8KiTlKpVtLsLQmFwOscdldIDXgJL5hBYMjI1NBQyHke2toDbA37/\naY/nv3o2/qvPrRuxtCyIRlRNQThtzOH6M4GhOY/5QXmMAEFnxmlnNgFnBitLvkDKTrCu+VksmeJg\n73Y2tq7m2oKPYWiqk0wTOh4jSKa7EBCku/IwtDNLFTo1N+nuPKWjgvLJOh1hKApMI8dbzsKcW9nQ\nupq9XWtpjx2mP9WDrhkEnJnkeyuoSr+KK3I+RNCZgS7UcGCZkkTMJhGXhDPVssGxM+TKpthfPXAd\ng8eqwu8Y3X8q31dBnncS7TGlB5XtLaUkOGNihMc0kU8/hdy8GVIpRPkk+MQnoaTk0ooZSYn1ymvY\na94FJCIYRL/vXkR1Ffa6jZi//i2iYhLGFz6LyM5CHjiI9cjvsLdsRzQ1I1vb0K9cBOEQ5s9+AZEo\nRCJo11+Ltuw6xAkRIDFvPmLSJGRtrSrMlVIVWmakI665FpF14bptjwcen05GgQuXR+etR44wZ0UW\nbp9Ob0eSnW91UrOui8nzwwTHq7T8ASMZ66V2w2+YveIv8QRzBpTXByFJJvqoXf9r8idfd1rCE566\niPJbNZzhbJyhrA8kpaW7vAjDSbI7wfbv/wEAM5pE6ALNoSJstmWjGTqLf/Qx9DM0wx6KM/5EKgmH\nDlrU7hrOaFpb1Myt6YjNmy8nORNvzWhEsnu7yRt/SLBvj0lOnsa0WReO4SQA0QjWo7/B/OXPcfzj\n99GvPXmny0Qh99ViPfxLrPXv43pqNQSHhHEtC7llE8mvfwXH3/0T2sJF4Du3My37/Xcx//Hv0Vbe\niPH5L0Lg1KHhDwQ9PVg//wnmi8/h+M4/oy86+ymjsuAsvjD9R3xe2qoFGu0YCTgVcj2l3F/1fT5d\n+V21QCiCo3H8pZ7uyufmkge5qfgLx7bRxZk989MzruU7i988NsvUNAON05PewQLrAt9UZNlXldXE\nQHv3gIGEan9lOPnoaDF567ke3lndy789M7z2Ks9bzt0V3+TDk/7i2DJdM04YXI8j3zeZr8z67+Nt\ntwJ0cfLtzwjNzcinn4Y3XlcTgisWIFatgpLxKwhfqJAHDiLyczG+9ACEgioULgTaqpXoLa3IxIB6\nusuFqK7C+OTHsDLS0VfegFgwHzq7sNdvRADGP34bOjowH/4N8v31I1NehYWIL30FgiHky3+A/n5E\n9XT4sz+DBQuUHs8lBMOlMXVRGnf95SRW/7iBP/zXIRJRE0/AoLDSzxU353DdfRMzWr2QILFJxHqw\n7dNHJcJTFhCePF8RnQ+g1k5zefFkFxMsm4U7s4SqT94JQN2v38ed6SdjTjG6x0FvXRvtGw+inWsd\nnmhEsuaVBD/6bnTY8q4ONZBtfj/F/preMyKCtg2JuCQaUd4ipRU6t959biIoE4JlqajLSdoNzxqk\nBNNULWujxU4tS63TdT6QKmtbquOdKK51XiFVlCeZOlazcLahCQ1tHD9uITSM03xOCIEuDPRxZJE1\noY8rIqJy+AJ9zNckkfboorxCaOhCO+PrOJN7M1bIrVtUJ5E18Hye69/neYR+843Yb6wh9e1/QJSW\nYHzufhV11XUlKzEw4AohkNqAYfDAf2EYyGgUWVuL/d46Uv/n79SXqmkQGkU5XQiYOwdRXo74+jfU\nfXW7ICNTkZ1LLF0ohCCQ7mTBrTlUXZ2OmVQu6ZomMFwa3oCBN3SBTcRPgGUliXQ20tdxkEinMmVt\nPfA+0e7DiIExQyJJ9HfSuPsl3N50jFFqZU6Eiup/cGUJhttH/pJ7yJqzUnUyhtXzaacsfEXphKcX\nqMYMXefQc9vOvZeWxyuYNtNg5a1u9u0x2bfHpKPdPpbCivVLYv1jO4m0DI2FSxzceZ+Hispz16J4\nUUPTEFXTcHz/39Gqp6u8/WVcxjlEKN3gqpsCVM3zXJDvOLllM3SeuW3GRQshEFOnoIVCiD012Htq\nsF56Ff3mG08ebdENSJkDulMoBeDsbERRIfrdd6rUvMuJyM0d/ZAeL3gurUjOqaAbAl/IcczT62KD\ntCx62/ezf8sTdDbtRNoW217/V3TjRKFAFdqduvATeEMXXtRKaDoOf5qqFxoCzanT+t5+Ys09aE6d\n6OFumIBz/RkTHqcLqmY4CIU1mptsmpssGg9aPPtonP21FhWVOouvdZ4oejsqdEPg9wty8jWqZjio\nnGHg9V3aRa6nxIl2uEOhaYj0DPRrrvsAT+jiR6K1h56t9XRv2o/Z048R8hKeX0FoTjmuzPOnq5FK\n2hypT/Ly493ccFeY4inHxTp7u0zeWd2LlLB4RYD0bAdNB5PsXB+lfnecVFKSmetg/nV+Cie58Pg0\n4v02DbUJ1r/ex4p70tj4Rh+NdQksCypne7jyxgBur/Kg2fJ2hJqtMTpbU4AgLctg6iw38671IzRB\nf8Ri3asRdm/sx0xJ0nMMZiwc3hIsbUl7s8mWdyLs3xUnGZdk5TuYfZWPsioXHp9O+9EUT/28g0Ur\nAhzYm6DpQALbhoJyF7Ov9FIyZZzRXKnk5+XWrdDZNcFv4sKHjMWxN2xC1h9UBdqdHYjpVRBPYK3f\niL1pMySS2F4vzJ2NmD4NkZuDjMexX38TolFEaQlaVSX2tp3I2n1IXUfk5yPSx2kVcxkXFDTdIJBR\nRlHVCnyhPNobt1A0bSVuX+Yw2QdNd+D2pZNXsQS37+L57vOXTaN3XwupvjhmwsSdFSBjbgm6e3wE\n9YwJj6YJQmmCUJpG1UC3cke7Td0ek8YGi6nVBp/9sheH4/TsSzfAHxAEghpO18SmkDIRRzYeQjY0\nKIO89jZEKITIy0f29CDr6xD5BWgLr0QEg8h4DHnkCHJfDbK9HRJxcHsQRcVolVWI7FN0YVkWsvko\n9qYNoOtoS65F+AOgacjuLuT+Oux9taq41utDlJahTZ6KyMhQbFtKpG0hd+5Q2/X1qfoY0xzV+sDe\nsgl7yyaV7nK60K65DlFYNKy7Qto2dHdhPfk4+k0fQra1Ytfvh54e8PvRplYhZsw8LjjXdAR7Xy3y\nSCPE4uBwINLSEDNmIfLyhxQDS0gmsN9dg+zshGRS3ddJkxFV0xCO4w+cbGnGrq1BNjaoVtlAAK28\nAjG1EjEQCpfJJLS2YO/ZjWxrgXhcFWDn5aNNqUQUFR+/cCmx6/Yha/ci29rUdoHgQArj9FFEMxqn\n4909HPrFq3Stqx0gPD7SFk2h7H/dSMY11Rj+85NCVdlLyYu/6SKvxElGjkEw3UBKSVebxZM/62DW\nlT7mLvHT1pRiwxt9bH03iq4rn7qWxiRtTSmW3xVmyiwPibhN/Z44j/5HO26vRltTit5Oi3jMJjvf\ncUxh9nB9glce6yIeV753tg3xmE04Qz9e1jOwbVebyf5dceIxm0/82fBC1bajJutf6+Od1b14fBoI\naDmcpLM1xVWxILOu8tHdbvL7n7bT1JDE5dawLEmkx+JgTYK2phSf+JoTp3vsuibStlV3Un298jO6\n5DGY6k6Cx42YVoU2ZzY4HGBZiMkV6jfhcYNtq/uZmYG28Aro6FSfdbsRWZno11yFbG1VKWHLnLCJ\n6GVcGNB0B+HsyYSyKsgomEntul9TeeVnCWaUnaDCrA2ztLhYEK7KJdERoa++DSthEpqaS9YVJeen\nLT0jU6OgRCcY0ggENcomGzjP0AD0rKG/H3vjBqxH/gdRXII8dBBcbrRJFWBaWDu2qhbi7/0bTKuG\nWAy5awfWM08qwpNMKAXgrCy442605SsQo7VmWhay6QjWKy9hPf0EYvoMtLnzwedHRiPYmzZgrX4e\nWb9fdRJ5PYicXFh1C9qVSxAZGUol+MhhzF/+HFlbowaejExFxLpGzljlvlqsF55DNh9FHtiP40c/\nQ8/MhKHtpLaFPNpE6s+/ArZEdnVg79oJ7W2Qng4rb0afoRiqjPRhvfU69uuvItvbVJuqrkMwiOH1\nKWI2SHgSSUU6mpqQrc1KSl3TELPnYnzqs4gpU9U+e7qx3ngNe82byCOHVe2Pz4edl4/xsU/A9Jnq\nfiYS2PX7sR7/rSIxCeXMLdLSkctuQP/wPYiBugLZ2or11O+x33tXCaEFQ4icHGQsNlDndGrED3fQ\n+tIWWl7YeIwfJdt6aHluA75JuXjLcwlUTlx1eTxwODVKprgpLHdxYHecyjkegukGybikrSnFgZoE\nn/7LHIJpOute7WPb2iiZeQ7u+GwGvoDGxjcj/Pd3WsjMc5BXonyNLFPS3W6yd3M/N388nZKpLuL9\nEk0Dj18nFrXZtb6f3ZtifOTBTK5cGUBK6G5X+ehB3uEN6Cy/K0zVPC+rH+ni9ae6h527lJK9W2Ks\neaGXYFjnU9/Ixh/S2fRWhOcf7gQEpVWKSFoWbH8vyme+mcO8a/x0tZs8/3AXbzzdw6r70sgtdDDm\nsqRkErZuUR2L1oVUX3ZuIDwe9OVLYfnSEev0ZdejLxulkcIwMO6+c+T2t6w6F6d4GRcIhBA43UHK\nZt2OyxtG1x0XJcE5EV07jtC9u4lYWx/SkvTWt9Gytp78pVPRXWOP8kxYhyevQCct4zwn+i0Te9tm\nHNcvgwWLsJ5/BmvNGxh3fxTHh24l+fGPYO/ZhV5crHJzWdloV16NNnc+Ii0De+8uzB/8E9bLq1XB\n3swhLdaapiIz7e1YL76A9cKzaLPm4Pjrv1XEQwjsmr1Yzz6FPNSA8eBX0SomYx9pxPyX72E99ltw\nudBX3AQ93dhPP4H14vM4PvcA2nVLkdEo1nNPYb/4AqKkdNhl6R/5KPqdd2OteZPkx+8+7W0wf/0Q\n+s23YHzhi4i0dEVSvMcFpuSBeqznnwUpMb7+TURBIXR1Ym/dglZSinAP2TbSh3zjVYwHvozxmc+B\npmH+/lHk2newMrPQpnwDQBG9Jx5F5Obh+MY3ISsbe18N5re/hSklxucfRMyYqSJJ6RmImbMxFiyC\njEzkoQasn/0n1urnEFXV6IuVGaT96kvH7rNx1z3g82GteRPrB9+B9NFFyoYisu8o/QdaRg0GRWqb\niDW0njfCIwRoOixc5mfH+n6aD6WomO6mu8OkZmuMYJrO1NluPH6N7e9FifbaLF7hxrYlfT0WU+d4\ncHs0DtbEaWlMklfqRNrKEHPpnWEq53rw+vURx9QNgcMp6O+z6OuxSMtyUDzFjWMMExQzKTmwJ07H\n0RQf/XImRRWKHC+9I8SOdf0cqU+wb1uM9GwDXYeFywPMudpPbrGTnCInB2sSrHu1j6YDSbLzHYx5\nPE4kkO+thf7YGD94GZdx6cMbzOHKD//gfJ/GuCClRJpJ7FQSNGVYCtD4wnbSZhYy5TNXozkNOrY1\nsuuHr5G9uPw8EZ5CjbSM811/IxCOgZRPWTn2zu1gmWi33IZIz0Tk50N3FzIWQ4TT0BZdCUPamrWS\nErTXX0N2tiOPHoWhhMflgmQS65FfYb3+KtqSazG+9g0VVh6AveZNZGcn+t33oi1fofZZWoZevx/r\nd7/B3r4NfcVNyK4urKefUGTrtjsRFZOPle/I3buQkcjol3eGujvazFnot92JmFo16nqZSIBlIvwB\nRE6uIlglpeiz547c2O1GmzkH43MPqC4NQO/qxGppQdbsPbaZ9eTjiIws9DvvRly1RG1XWIjcvRPr\nyd8jV30IZswcaJudjlE96IgNonwScsc27LXvQsNBGCA81urnEPkF6LfejlhyLQBGbp66z40Np70P\nZiSGFY2Pus7qi2H1J067j3MJTYOFNwR47akejjYkMVOSjmaTfdtjXDVQc2Nb0Nlm8srj3bzy+ImR\nFkjPNoj2He9O0nUoneLC5R75rDjdgmtuCbLl7Qi//F4rzzzUxbIPh1h6R4jSqWee2uvttujpMHG4\nBAVlx6OMQoOMXIO2phTtR1OkZxtomqCw3IXLc5xQGYbAMASxqD2+jEoiAWvfUxoxl3EZl3HJQFop\n+hr30Fe/FcMXJnfx7QBoDh13ph9HSE3GvbkhdKcx7l7lCROea1e4mDnPgdMphnKADxZCKO0aw1BC\nWYYBThciGFLrnAMupZaFSKWw9+zGevF57M0bobUFLAu78RDarDkjlBWFbmD+/MfI9nb0hYsx7v+c\nql8ZKtvd2oL9xqvY76zB/Kf/e3x5by/09aLNm3+sxV0ebkRfvgLh9R6rYRCBIKKoGLln9+jXdqa3\nYWolhMInrY3Qqqejr7wZ67HfkvzEPWhz5qLdcCP69cvUvRpCrITbjZhWrSIzg/tzucHpGFY/IQ8d\nwt68Aeu1l1SHB6iIWHcX9EVUsaVlIaRUEZ3nnsZe/z6ypVnN2FuaIb8AbUiqSh45jJg+U0WEBmuP\nnE5ExWRkc9Np74MR8KD7Rn+RG2EfRmDsLrtnGyVTXOQVO2k5nOLQviTtzSnqd8f54v/Nw+VWDttS\nwtWrAtz+mQyKJw/vzvMFNcKZBsmEYg4C5V802kggBLi9Gl/4m1xu+EiYre9E2fhGhPdf7uP2z6Zz\n40fT0M6g82HoYyXlcN8sFU0b4nEjwOUWx/Y74W6vREK5eW/bouq/LkQIRl5oMonctg25YR3s3AWH\nGpBtrSpKZehKUysUUtHWqirEzFmIRYvA4znjic4fDWwbGY3Ce2uRGzdCbY1SkO7qVLVJDgMCQUQo\nDJPKoboaMWceYto01a12iUPaFqlEhNr1j9DeuIVYpA3bGlkCsPiO75KeP/08nOHJYfb30bZxNQee\n/SH+wqnHCI/hd9H8Vi2RQ53oLoPIwQ5caZ5xd2pNmPAEghr+wHEtiPMGwxgQSRr40zSOqSAeG5DB\nWvu2ijx0d6GvugWRkwMOJ9YvfjYwZg+fesrWZlWsC9j1+7E3b0C/8ebjG9g2xGKIvHz0pSsQM2dx\nIrRp1cdSY6RSKq02dDAzjLPygxS+AOIUyo/C7UG/cZWKrOzaib1rB9ZP/wP7+WfQH/gy2oxZx4uW\ndR0RDg8/z8F7a8vjHTPRCFpVNdq1SxFlIw1CxRULQdOwt2/FeuRh7Lpa9JtuRuTmg8eN9ezTyIMH\nht/3VEq9DIb6jWjagBbI6V8C/oo8fGXZtAmGpbWErhGcXoy3/Ozag4wVQgicLkH1Ai+H9iXY+m4E\nMyVxezWmzfdgDKSZQukGiZiN26tRPu2E52Pgq0gmrCH7Hb3XTwhV8Jye48Dj18kvdVI2zc27L/by\nwsNdrLwn7YwkN3xBnWCajmVKWg8nySlUMxzbknR3mNg2pGUef/7GLv8zoCre0gyNjcjDh6GxEQ43\nqhfb0aPQ3T1Sd6e2BvtrX4XgKNoyp4EoLkH7/g8mTjB0XXVjDDYn9Efh9deRzz+H3LsXWlqgtwei\nUUXeLEttaxjgcCA9XnjzDWR6GhSXoN2wAm68SUWnx+u7MwjbVk0VDz5w8ijyaaD91f+GWbMRvpFG\nnhOB3LUT+ZtHkNu2HVsmfD7ED/4FkZen7o9tIxsbka+8jHzpRWhoUEXZfX0q2jeolTYw5kunEzZt\nhFAQmZ2DmDYNccNKuOkm1fBxIWotmCZy00bk736L3LdvxGqRkwPXL0W7597j77oTEO/vYvc7P6Vh\n52q8wVzc/sxhhcuD0I0LT9rESsZI9nWQ7G4hOcQ1vfSOuXTvbiJ6pBsrliI4OZuS22ZjeMenfj1h\nwqPegRfAA3TiKYw22wLsHduRNXvQlt6AftPNMBBFsJ54DDo7Ru5X09CXLodAEHvtO1hPPIbIyUWb\nM2/gOEJFaNIzEOXl6LfdMXIfjoEfmaFDWjqyrQWZTB4/5Xh81KLlMUPTOKUwoRCqKyojE6ZWos2c\nhb1zB+Yvfop4921Edg6ieFCtVgy4N59mfwM1Ndqs2WjXLxu5jVvpQcgD9Vjr1qItvgpt5Sp1LMPA\n3rAOuf+EH3haGkQiajY3CNOEttYzKlZ1F2aQtWIOscMddK3fh9nTjyMjSNb108m+YTbuvNFtED5o\nzFrsY//OOBvfjFBY7qJyrodA+PhPcto8Dx0tKda91kfBJBfZ+Q4SMZujDUky8xykZ5/Zz9dMSRpq\n4xgOQSjdIJxhkJXnwOHUiPSeefGv0ykomepi9yaDN57pIbvQgdevsXdzjMa6BLlFikhFe8ZeUCz3\n7EY+/5yK4nR1KZ2drq6Bv07VdZhMjv7hnh5Yu3bMxwSQ06Ypgu2ZYNTP6VTRT9NENjQgf/848pWX\nYPt2dX6jiSNKqa4pmVREqL0NDgjYuRN7/35EbS3c+WHE7NkTP794AvnqK4owjgPys3+CMM/AN2is\n6OxCrl+vVLMHj+X3Iz7/BWRaGsLhQK5bh3zmaeRbb8Ke3YowjgbLUn+JhCJDzUehrk49WzU1iNoa\nuOdeRSLPWzpiFJgm8r33kA//Sv0GWluHr88vgFWrlJ/ZKUh5Kt5H/danyCldQMHUpfhC+cfEB4fC\nEzy/E77RYKcS2KmR36vZn8Rfmom/LAtp27jSffjyT24aezqcVfNQKcGyJJ3tNl0dSojQsiTpGRqF\nJfqYCiTPGeKq00eEQiqcHIlgbduMPFA/epTF7UHMmIU2ey643VjPPoX561/hyMyGvDyEYSCmT4ea\nPdhbNqEtugpRVKRmeb29qp07PR2yssHnR7tiAfa2rcid25EOJ9IysXftQNbtY1RfDimPD5bSnpCJ\nnTzapM7J5VIkrWoaWjAEP/+xGmzHUhsxQCa1xVdhv/Ea9rYtiMlTlYnjwIwSy1aDS8ChZu6xGCIY\nUumzeBx77241GPUPP642Zx721s3IbVuQhUXgdKp29u3bVVfdaWD43GRcXYXhddG1oQ6ztx9HRoDM\na6oJVBehnwNz1vGgtNKFL6ixe2M/LrfG0juHRyhmXeWjo9Vk98Z+nv1lJ4GwjrQlsX6bJTcHj/lc\nnQ5mSnJgT4KDNXFlHOoQ9HSaJOM213wodGxeULcjxsGaBA21CXaui9LVZvL4j9sxnILK2R6KJ7uY\nMstDW1OK9W9EeernHThdGkcPJQlnGsy71k92gYMD4yA81NYif/YzONRwcXZgud2g68i6fchHH0U+\n/Cs4dGjs7d8Djuds24o82gS9vfCJTyIWL554pOdigWUh9+xBTJuG3LsX+T//oyI7JxKBM9wX7e3w\n9hrVQZtKwUc/higtvTBShqaJXPe+IjsvPD/yGgsLER+6BfGx+xALF53yGbCtJJGOg8y/8X+TP+V6\nXN7xE4MPGnYqrgqWT0D7xoOkzSwkc+7ZsY05a4QnHpO0tVjU1Vjs221y6KBFb7dNKglXXOXkzvvc\nhIcQnkRc0txk0dMlCQQFxeX6MQG2cwM18Ghlk5DZOdjr31dmnLaN3L4VmUoiMkd3fAYQaWno1y2D\naFQVMD/+W/R7P47MzkZbsBh5qAF7zVtYv/s1VExWH+rpQQRDaFcsRGRlq4Lp2z+M/d1/UO3m++vA\nMLDrahG6PozLyKNN2I2HoL0Ne/tWSKWQmzdhCQ2RkYk2qQJRWDSmO2AfPIDcsgkZ60eElQOubGlW\ndT9TK1Vn10nu28mg37gK2diAvXMHWDYUFiqC1tWFyM1Du26pIle5eWiTpyC3bMJ64RlAKJ2dzg6V\nOhsC7cabsev2Yb+zRtUAhUJq8D9JKHc0uLLDZN84l+wbRynIvkDgD+rMXOwj2muRU+hk2vzhCrcF\nZS6uuy2EL6CzbW2U+t0xPF6NgkkunC6hIvgOQXahg0U3BHC6R89paRoE0nR6uyxaDqewTEkwzWDa\nfC/X3ho6lnpqOZxi5/p+mg4mEAKmzPSw7rU+3B6NQFgnp8hBfqmTJTeHkFKw7d0I8bgkr8TJklVB\nqhd40XWBN6CzYKmf3CLnsElOZp7BnCU+0rONEekuGYsNt4u42OByQ1sb8rnnkL/4OTQNqTUTQqVj\nA0HVNelwAELp60QiitSMVpfU2op88glFpvLyEJMmjf/8dB3y81UTRio1/O9Cu+eWDbt3QXk58rHH\nkC88pyJ9gxBCWWP4/eq+G7oiiokk9PWq+3liNMqy4NAh5E9+DNnZcNvtanJ2vjAwkZVbtyJ//l/I\nP/xBRbCHIi8PcdvtiE9+CjFn7mkJmtB0XP5MLCt53LvuIsHJIjzxzijJ7n6spDkus9ATcVYITyIu\n2V9jsvrJOL97KEZT4/Cbremw6g4XDMkkRPokLzyR4N03klTNNHjgz31kZI2D8Og6IhhEFJeqh1/T\nEJlZKhKg6arWobgE0tJVNGbJtchEHOux32H+2/cRoTD6bXdiFBYhTVP9iEAVIITDiJJSJZ4HiLw8\n9JU3QV8v1pOPo121BBEOo5WWwb0fR+TkYj33DPK1l8EwELl56MtWHJOBF4EA+vKV0NKM9cJzmFs3\nI4qL0a5YhFY9A+uNV4891Pb2bViP/xZ764AjdH4B1vPPwIsvIHLzMD7/IHphEao61IWYPEVFrU4x\nAxA+P3ZHO/bad5HdnSollZuHce996MtXHhNdFF6vEgJMTx/+I/MMaAv5j6eatGnTMb7wJVUE/uZr\nyNXPKqHAgkL02+86VhOkzZgJ930S8xf/hfnv/4IIBtGWr0BfdQsyHh9mmKovvgo62rGeeQrzmSfV\ntgsXY3zt65iP/25Y+/zFjhvuCnPDXSefiRVNclE0ycWdnxu9Hd8X0Fm4LMDCZSc3eHW6tdNuA3DV\nTUGuuun0KtR5JU7u+WIm93xx9AlCfqmTb/9y5IxsztV+5lx9EuNbnw+Ki1UqYjQMpn+am0euczqV\nXIFrHHn9/IKzNtOXa96CaP9xsiOEqitKT4fJkxGVlVBQcKxBQPb0qJTLrp1Qv1+pRydOID5dnSoV\nI/KT8wAAIABJREFUVVyM+Oqfjh4FPh0GCJe4+yMqgtDTi+zrVTVFvb0qnWZZijC0t53/onDLRG7f\nrmq4tm87TnYcDkV0MjKU1MWkCsjJRnh9ykqjowP27kXu2A5NR9S1najbdbQJ+dvfqCLxlSvPT9RM\nSmQqpQrYf/A95GuvDid0A15n4q67Ef/rAaV5dgYTPcPpo2DydTTVvY0vlE8wqwJNGzlJdHlCaPqp\nU3rSMrGScaxEP0LTMDxBhOEYVr5iWyZW4uwIgKb6OrHiI/fl8Lvob+qhe1cTzrSB96gm8BakjctA\nVMhTh1xPG4+VUrJzi8kv/yPK734ZR8rj93dw13fe5+Zb3w2QW6AP+Rx891t9/OJH/RSV6Pz53/hZ\ndeelX0l/GZdxGSMhu7vg8OGTRxuSSWRtLfKz94+cvVdPR/vmX0HV6HIMp4TbjaiYPMyI85TnuXcP\n8mc/Rf7w30+9oRAqMnP//Wif/iyiuvrkPnjt7cjnn8P+6U9gw/qR6zUNVqxE+/kvTuqBNW4MGhd3\ndyN378L+1l/D2ndHXs7jTyCWLjsmDnrWDv/229h/97fDanhGha5DWRnis3+C+PRnVJnAychKYyP2\nT3+MfOwxRSRH2Zf41t8gvvglRNrY6vns//dD5E/+E2pqhq+44060b/wFYsHCU+9ADpggNx1Bfv5z\nSlcqesKLPhRC/MnnEH/+9VMr/5+ARH83+7c8zqYX/h63P4P0/Bl4w/kjipSrFn+GQMapU0SJ7ha6\ndq+lY+dbGN4ABdfdhzenDM1xfF+JrmbaNr98xud3KkQO76V9y8v01G3CXzyNa/9zFwAb/+pJmt7Y\nixVLojkMEGB4nSx74kHcGSeZPJ2i8HTCEZ6uTsmzj8d54ckEmg5ZORqr7nBTVKbz6C9j7N158kK3\n0kk6JeU6HW022zamLhOey7iMP1YEgjBl6slrXpIJRDKJHI2UeNxQWgqV4yA8gx2dZxO6DkXFiH/+\nF8T8K+BEdfQTkZYGd34YLT8f+5++A2veGr7etlU04MXViPs/c3bPFVTUKD1dRbsmWhx9LuBywfLl\niG/8JWL6DGXHc6rvLC8P8bU/h+wc5H//F+zaNXy9ZSE3bYTNmxDLlp/bcz8RlgU7dyL/9v8MiGie\nUDdZUIi4/zOIL35JfSdjQDLWzd61v8Th8mNbKTqbdtLdvGcEkS+fdcdpCU/r+uepf/pfibc3InQH\nXbvfZcaXfoovf/Kxbfqb69nz318f0zmeDLaVxEqMFBSd8fWVVH1pqeoMHrgMoQlc4fEZ3E6Y8Gxc\nm2Tr+hSJuGTuQgdf/Ss/5VN0vF7Bmy8l2Ltz9M8JAbn5Ojn5Og31FvX7LrA88mVcxmV8YBC6fur0\ngpRI4yRheKGpdIfTeWF0jJZPQnzzm4hrr1W6MKdLm+g6IhBAXnEF2gNfxN68Sc36h5K/9nbYsAHO\nNuEZvF+6riQtLoT7dyKW34B48IuIuXMR3jNoizcMFQG69TZVB1Vfr+x+hqJuH+zbBx8k4UmlkO+/\nj/zPHw2kP0+I7EytRNz3ccTHPw6ZmWN+lj2B7DNSWg5mjpQPORHJ3nZirQ1Y8QgIQd/BHSMIiW2m\nSPa2jekcxwo7adLybh1d2w9jmxaBsiwKV8047v83RkyY8OzcYtJQb1FYrLPiQy6uXurE6VKt6q7T\nGIOGMzTS0gSJmKT16GXCcxmXcRkXOXJyEUuXIm66+ZQioCMgBCIURs6fD1ddraI8Q1/SPb3Imj0D\nGlUXKDE5F5haiVh5I2LR4jMjO4MQAlFYCAsXIqdPV2RxKI4cgcONKuJyNup4TpcSTSaR77yNfOgh\nVbPT2zt8/axZiHvuRXz4riHSIGOD4fSSW37cQUDa9oBe19gjmN7ccoJlM+mp24RmuEiffi2G5+Q1\ngA5/Gob39LVBJ4OV7CcV7R5Rx9P44g5SfXGCk3MQusCMJKh7+D2qv7wMzT/2btsJE56mwxad7TZz\nFjiYu8iBy33mP0SPV+D2CkwTIpFz794rpSTVFSHW2EG8qYNkRx9mXww7qdyDha6huZ0YfjeOsB9X\ndgh3fhrOjACa8wLSbTgPkFJi9sVINHeTaO4i0dGH2atsGmTKRFq2GmQMHc2ho3tdGAE3RtCHMyOA\nKzuEI90/Lv+TSwXSsjGjcWKH2ogf7SLZGcHsjWHHk0hTCdFpTgMj4MGZ7seVl4anMBNnRgAxjgK9\nyzgPmDwZccutyox4rNAGGiWWL0duWD+c8CQTquC4qwsyMv5oWtTFkiWIK68aX+2QYUBJKWLuPOSJ\nhKevD9ra1f/wWWjfdjpP/p0kk8h33kH+6iHVXt/ZeXydEDB7NuLejyFuv2NCnXhSSpA2HUe209W8\nh1hfG2l5VeRVLAEEve31aJqBP60Iw3nq1GVo8nxKPvRF+g7uRDOcpFdfjTN08i7mtMrFhCsX4Qyc\n3utwNESP1tGx/U1667cMW95b10b6zELyl1ehGTpdu5vY9e+vYpvjC5BMmPD0RySJuMTnF2Rmj21Q\nFhwPS411vmJGE8QOtRKpHW41EKwuxlOchXZCC1uirYfo/mZ6dzTQs6WeyJ5GYo0dJNt7MWNKqVM4\nDAy/G2dGAHdeGt6yXPyVBQSmFhCaOwlP4em/TDtlkWjtoXf7AUWkhsAR8pJxzfQB1j3+GZqUkr7d\njUTrjo5Y55+cj6ckC+Mk1gpjPY5MWcSOdBBrbKd/fzN9e48QrWsidqidRFsvZk8UKzbkhe0y0FwO\nHOFBohPGU5yJtywXb1k2nrw0nAPLNKcxpvsgpQRb0rWulkRbz7iuKVBZiKckC909PqXO8cBOmiTa\neuivbyGyr4merQeUiWlju3r++mLIpAmaQPc4cWYE8RRl4J9SQHBGCYHpxXjLcvAUZSJ07cJI21zG\nSHi8iMpKxKLF49+Hy6Ve0CcWOA+0XcvWFkhLO32a7FJAMIRYsBAxefLptz0ZsjKPy4QMhZQQjSC7\nu0bIYowHwuMZ2UE3UBAuN2xQreevvjyS7FRXIz51P+LWW8cd2RmEbSXpbaunZt3DdDbtpOPIdspm\n30Fm8Vw0odN2aBP9vUeZPP+j+J2nljTx5pThzSmDa8/s2GnVSyi47j48WWOTShlE1973SHQ0jSA8\nhteJFU/Rf6QLhCDZGUXTNXrrWnH4XXjzwjhDZ153NmHCo+njr/mL9NlEIhLDAYHQ2AbxRGs3hx9Z\nQ+0/PD5s+bTvfYri+5fhygwee0EmWntofWkzjb9+i+71+zD7RndblmaSZCxJsq2XyN4j8IYqQPJN\nyafy7z5G4T1Xn/a8rFiCzrV72PbAT0h1DG+xDc4u47oNP4CJztZtm8aHXqfuB0+PWDX5m3dR/Jll\n+Cvyxr9/KbEtm1R3lP76Fpqf20DL6o1E9hzGip1E7XYAlmlhRROkOiP017cMW6f73QSnF5O1bBZl\nX1qFK2fsA400Tfb+n9/Q9tr2MX8WoPLvPkbJn9yAnvfBEB6zL0a0vpm2V7dz9Mn36FpfizRPopFh\ngZmKYfbG6D/QQsea3QingX9KPnl3LCL/w1fiLcvG8LkvR3wuRBQUwNTKCUUMhMOBLCuH0eqVbEul\nQkZTbb4UUVkJpWVKsmCcEIEg5OWN2m4sk0nEiUXD44X7BMIz2HpeU4P8/neRb69RituDMAwlKvjV\nP0Ws+pCyjpggkrEeatb9ipaD71Mw+TrM5JBrExqa4eTAtmconLocf9r4iMnJYHiDaCersTsDaA43\nwjlykq45dVrf20/37iMIQyfW3IttWhx+cSeaQ6Po5lmkzyg48/Mc9xkOIBQWeP2CSJ+k5ajNlGln\n9jkpJYcbLI4csvB6BQVFZ2fGkuqKYvbFcGYEwJYkO/uo+fvHOPLoO6Q6T6LxcRq4skIY/guwe+Ec\nYJAkJlt7aPz1m9T/8HkSzd0qZTVBWJE43ZvrAUHx/csu2UjFoNSDtGzaXt1K/Y9W0/H2bmRqHJYL\nSZO+nYeI1DbR9Ni7VH3nE2ReNx1H2HfJ3r+LFaK4GFFRMbGdaJrSpBptFmnbf1SER8yYoSI0E4HT\nCSer/UkmlG7S2YDHc5ykSqk03RobkV/5kuoIG0qsdB1yc5Vf2NJliODpta/OBMl4L/XbnmL+jX9N\ncfVNxKPHrZJ0w0kws5z+niYs8+zrLBneIMIY/yRSc7qGtbwPInvxJIKTstU9HVgmAGEojT1neGzv\n5QkTnopKB7kFKQ7Wmbz9WpIly86skKi9VfL+mhS7tppk52rMmn92ajtS3RGsiIrgROub2fvtR2l9\nYSOp3vE/2MEZJWeUzroUIE2b7o111P3z07S9uh0rEjsrZGcQusdJ/l2LcYTG11Z40UBC/b8+S+PD\nbxKpbRoX2Rm2u6RJdH8z2x/8CZP+7DYKPnIV3pLzqBR7GSORlQUT1cnRtGP+cyMwaD78x4KSknEZ\nwg6Drp9ckNKywTpL/mAej3JrB6VrtHMn8i++rsjO0FosXVdprL/5NmLpsglFr06EbZn09zTjC+Wj\nO04kAgIhBLZtcRrtvXHBMcEIj+5wo49CeAyPk+49TXTvacZOqe9Kdzu44rt34Qx60JxjC5RMmPAs\nvNrB+ncMXnw6zuurExSX6Xz4Pg+uUUpIBu/zzq0pHn0oxmurEyQTkqJSnaU3nR1/o1R3FLMvTmTv\nERr++1VaX9xEqic6TEJRcxo40vw4M4MYAZUesPqTJNp6SLb3YseHDyrBmX8chEeaFu2vbePAj/9A\n+1u7MHtOraKp+1w4M4M4Qj50n0uZsPYnSHZHSTR1qgd06G9LEzjS/OSsmocRHGfETNMIXzEZ27Qw\n+2KYfXHMSAyzTxVQY5/74vfTwezp59BDr3HoodeJ7j+KnRg5qApd4KvIx1uahTMrhO5xYadMzO4o\n/YfaiNQ2YUWGz8SkaZFo6ebgf76INC0K7rkaX/lZFqK7jPFj0DNvorgQPJ4uAIiCwuPK9+PeiTh1\n99TZGi68AxEey0Ju2YL84b8h168bHtlxOGDxlUrw8LrrwO8/q1FaTTfwBrKJdB8mPTV8gm8morTs\nfw9fKA/DcXb07jzZJVTc+y0A/EXT0CawX83hHjXCc/gPO3EEPFR8YhGaQ5EboWu40n3jspqYMOEp\nrdC55gYnB+pManebPPQf/WzbkKK4XKehXg30hw5YPPNYHE2Dpkab/TUmO7akaG+xmTLN4Mbb3RSV\nnr2UVqTmCKnefo4+sZZUZwQAzWUQnFVGeO4k/FPzceWmYfjcqrhZE8iUiRlNkOzoI9bYTqTmCL3b\nDiItG29ZDkbwEo9IAB3v7qHxkbfoWDM62RG6hjs/nfAVFQSmFeEtzcER8qK5nQNMW91HK5Yk1dtP\n/GiXKtStPUK07ih2IkXagsl4irMQjvE9ekLXKPjoErKWz8ROmNiJFHZS/bcSJlZ/gkRbDx1v7KBz\n7d4J3pGxI9XTT+d7e2n4r5eJ7GtGpoaQHQFG0EvmtdVkLKnGW56DMyOganIcOtKyseMpVTvV2E73\n+zV0vLOb6P7m4wOzhP6DrRz+zRoMv4eCe5fgyjo7IfHTIdHdQufe97ASUQqv+di4W6PVDFMpaVxS\naTmvDzHRF/RlHEcodMya5oKHxwuGobyxfv0/yg5kqM6OYcB11ytRweuXIkJn39jT4Q5SNusO9m99\ngmS8l972A+gON427/kCk6zAHtj1NYeUNuP3j6CAcBa60HAqXfQoAd3o+Qh8/ndBdXry55YSnLlTF\n0gMw+5OkVeeTvWgSwjg+ERgkP2PFhAmP16dx1fVOon2SJx6Js2NLito9Jjl5Gp3tKhWyv8bk0Ydi\nJBOqzicakWgCplYb3Havm2WrlBni2UC8qZOWFzeTaOmm/2AraAJvSRbZK2aTfnU1oVmleEqycJyE\nwFjxFMm2HqL1zfTtOYydSOGfWjDuG3yxoL+hleZn19P+2nZS3SPJjis3jfD8SWRcU03aFZPxVeTh\nyg2P2jWk2iPlMfLYf6CVaF0TqZ5+QnPK0VyOcb3oxMBsLTSzdMQ6dUyw40n6D7WR6uj7wAmPnTKJ\n7Gvi0EOv07f78LB1QtfwlGSTd9sCclbNIzy/AiPgOWnxsRVLkjZvEv6qQlpe2Ejnu3uHpRb7djdy\n9Kn3ceenkXf7og+kiDnV30PP/s2kIl2K8IwTtpmgac3vyJ53E85Q9qVDelxOlY66jLMDr3fAaPUi\ngMcDdfuQL65GPv+8Eoo8AaKwEFFdrUQRzwGc7iAVV9zLnrW/oOXAOiJdjdh2ipp1DyOERjCznLKZ\nt+LxT7AuagC604MvbwKGtkP35fKSPu3qY3o+x5cb9O5vRXc7MHxOJX2ia2TOKzk/ER6A4jKDVXcK\nfAHB6qc0GvZbdLQpp3SArg5JV4eJpoHPLygpV5YSK25xs+wmJ8VlZ49MxBrbiR3pwIrE0ZwG3rIc\nCj66hOJPXo+7KBPNOPWxdLcDT1EmnqJMMq+djp1IqQKpSxSDRcqtr2yj7fUdxI92jdjGXZBOzk3z\nKLh3CWmLp2J4Tz3rGiQmrqwQrqwQ4bmTsBIprGgcMfDAnm2oY4LudeEIedG9H1zb+SASzd10vLmT\nlhc2jljnKc4k/67FlH/5ZjyFpx9wdI+T8PwKPCXZuPPSsfqTdG+sO76BLeneWEfzsxsIzSn/QFJb\nhjtAoLhaGQaON7pj26QiXdQ98T0CJTNwBrMuHRE9w3FqC4nLGBtcrotHb6izA/nM08gXV8OhhpHr\nTRO5dy9s2gRFRcpA9izDcLjJLJzD9Gse5PDeV3C4AyRj3Wi6k3D2FIqrbyQ9fwb6BIqLzxU0h5NA\n6QwCpTOGLXdn+emtayXVF8cZ9irpE4dOxqxCOF+EByC/SOeuj3u4eqmT11Yn2bZJFTL39khsJdGC\nyw2FJTqzr3Cw9CYXZRUGbs/ZHexSXSqFha7hLcmi6FPXU/7VW9A945Od1/4IhPKSHb20PLuOSM2R\nEet0r4u82xZS+oWVBGeUjPvlpLscl7TooLRtenc00PzseqxoYtg63ecia/lsSj+/8ozIzlC4soJk\n3zgHmTLZ8bVfYPb1H0tvmX0xujfW0bJ6E2UP3qRmP0O+HysZI9nXiRntRtoWmsOFM5ipnI91Ayse\nJd55BMPtxxnOQdMNJRff144Zi+BOz0d3eZGWSbzjMGasj0BhFYZvZApNSokVj5Ds68CMRcC2ELqB\n4QniDGagOT2Y/b3E2g4RPbqPWHsjkcbdiAGFWqc/HXdm0cUd7TmdPcZljA2G46KpZ5KvvwZ1dXDw\n4Mk3Wr8O6XYrr69rr1PRq3PwvAczy6lcfD/StpFSRYUHf1dmMooQGtoE0k8fJNKmFxKYlIPuPn6+\nQtfGHYQ4q1ftcAqKSg0+/aDarW1DPGaTSKhxwOfXztSUeOLnEvKSsWQaFX92K8I5vhTKHwVsSetL\nW4jUNmGfqLGjaYTnV1DyhZUEJkB2/hhgRRP07W6ka13tiHXhuZPIXjEb36TxRWFcWSEyrptO9g2z\naH5uwzBBy+j+o7Q8u56S+5ehDYm8Sdsi0riHxtceomXjasxYH968coqX3U/2vFW403LpPbCV3Q/9\nJeFJ86i4+5u40nJJdDVz8A8/oXPvWqo++R3Spiwk2dvOnv/5Kzp2vIGVjJMzbxVzv/GbYecobYuu\nmvc59Mov6Kp5DzPWh8OfTtbsGyhZ+XmCJdPp3PMuNY98i/7meqx4lO0/fgChqYGr4JqP8v/Ze+/4\nuM7zzvf7njJ9BhgMeicJsPciNlWq07aKJctFcVwS23GSjVOcTTbZJDd3797N3dSN187Gjjd2LMt2\n1rYsq/cuUWJvYicIorcBps+ZU977xwEJggBFNIqkhJ8+EIGZM+e8p8w5v/d5fs/vWfYb3wQxSxhm\ncRXi+edH/60o7o91jobPtuGtN5F//d+Rc+dBY+NZwj8TkNLBNg3iXQfJDLVjGmm3vcR5qFt8O4HI\n9H1/3g8YgxmKmisoXjQNX7lzcElpnhDg9wt8/pG/3y9EljZQ97ktiA95S4iLQUpJzxM7MXrGOher\nfg/z//QTBOdeHV+Oy4nE3hYGtx8b466NgNIblxK7foIGVReAt6KYxt+8k97n947ahmNYZE/30/fS\nfspvX3VWDJ5sPUDnGz8lP9jFit/5Lt6icnp3PU3Hqz/GKRjU3foFQnWLmXvX19j3v36L2IotxJZe\nT/fbj5JuO0TV+rspnrfa3XZxOcu/+i0SLXtpf/H7OAVjzPiSLXvo2f44iuZh7R/9H1R/mHx/G45t\nupb0ikrp8i2E65cweGQbe7/x66z5wx9TNG81CIHq8btNQGcxi6sd4TA0NbkOz4/+AgrnTCTzedi5\nE/n130d8+1/cyr4Zigpmkz1sf/wvaDv0LJruQ9X9ZycU56KkZtlVQ3gSh7vdzuhXC+FBiCl1NZ0O\n9KIAkaX1FK9tmo3svAekZVPoT5LY0zLGfVoNeinZvJDwkjrUgHf2OF4E6SOdpA61j3k90FhBcH41\nesmFG+9NBKrfQ3hJPcGmKlIHTuMYI9YJhcE0fc/vo2zL8rNeIEPHt5MbaKPymrsoblqLonmouf5T\nDB56k1T7ITLdJ4nULSG6YCPV136C9he/T6r1AOmOwwSr5lG18T6UM7l+oaKHonjCbmpqPMJj57NY\nuRRCVfGV1uKJlOKP1SAdG9UTQAiB6vHhCcfQQ1E3jRWO4S2ucGe5s5jF1Q5VhRUr3Qagt94KmoZj\nmvDiC6ObhaaSyDffhP/6/8DX/xBRUzMj0QDLSNN+6FmWXv9VyhrW4fFFxr1vF5dN0xzzfYSn2O8W\nEiVyk2ohcSFcHYm8ScJXW0p4cR16+MPhjjxV2IZJYu8pzMH0GHNBLRKg8qPr0IuCsw+ki0DaDrnT\nfeROj63MiCxvIFBfdlGx/MUgFAUt7KdkwwJyrX0UziE8VjJLfNsRHNNGGW4gmO9vZ/DdNygM9dK3\n94WzyyZO7iLSsIzCUC+icTne4goabv8Kh7//n+h682cUz7+G8jV34otVT2p8gcq5hOsW07fnOY48\n/BcUN60jtuQ6/OWNKPqVJ5KcxSxmFGXliI98FLF1K6xZg6irR9o2yu/+Hk46DW9vcxuVgqv1GIwj\nf/4zqK+DTzyAqKuf9hBUj5+KORtwbAuPL0woWucKlM+LnI41JbxyYQxk6N/ZSsdz77pVWoDq0Vj8\nOzejhyZfETltwnPssEXLMddgbs58jeaFl59D+etiU9ZLfJjg5E0Su0+OTcMAWshP7MalKN7pn8+O\n7HGydpIafxMBbUTw2pLeT3e+FZCUeetoCq8EIGMl8CoBNOXqSEcWBlLkuwddQfF5CDVX45lCz7Dx\nIFSFolVz6HliB4X+kRmjY1jkTvdR6EugBrwgHBwjB0Lgi1bhjY58F6o3f4Jg9Xz8pbXuOjWNUM18\nEIJsTwuxJdfhi9WOGwp/L/iilZStvBWhaKTaDhI/+BrJk7spXXUbJYs24S2adYWexQcYTU1uA9Bb\nbnXL6RkWCm/chPKFL+IYedi+3U1pgUt6OjuQDz0EJTG4c+u0+2lpmp/S2lW0H36eod6j+IIxFHWs\nMHrJtV8hUjrnAmuZOmwjSyHRRyHZj21kcaypuYKrviDRBesBiK1pwBMN4Ji2K1bG7a811Un4tJ9m\n77xe4ImfuSdx672+K4LweMqK8NV88J2RpwvHMEkf6cA5r+2B0FT0khDBeZUzUpKftVOkzEEs3+gv\nwN6hV+g3Oij31eNXQ0jpUHAMjqd20xBcTLHn6nhI5tr7KfQlx3V59tWV4imeGft4oSiEmqtQ/OdF\nTKTEyRXItPTgrYyi+jUU3UewupmaGx8ktnR0y2MhlLOzPsc0GDryNnYhS6hmAWYmQartIIGKxpGU\n1kTGpmpE5qwgUDWPbNcJBg6+Svfbv8RI9uGJxEYRHjGc5pYzZnM7i1lcZlRWuj+Bc/zdhABVRXzs\nLsRAPzKdgX17XfHyGezbi/zxjxCRCPKWW6fVV8u2Cwz1HsUyc+SSPRRyiXFTWpY5fvPsqUAOR5RT\nrQdJtx4g3X6Y/EAHZi6FY45NfU8E/rK6s4SnYlMTwZpirIyBvyKCv7KYbNfQlCfi02YnJ49YHNht\n4Q8KerqvjKZ2WtiPHp11PL0YHNMme6oXaY8mPGrAi78mNsZvR0pJ3s4wZPYikeSsFD41RIm3Elta\npM1BbGkjhKBYL8OnBslaKYJqhLAWxaeGkFJiOnkS5gAn0/uoCyxkY+xj+NQAGTtJW/Yor/c9QsEx\nmBdaga54saRJzkohhMCSJsV6GQEtgiouP7kGMHqGMBPj92rzVRbPmEu3UAT+hvJxy/ul7ZBt6aV4\nzTy0kA9faS1q635SrQcobr4G1RdEOja2kUXRPKheP45lku9v5+Qv/wf+0jpiS28kfugN+nY/S7hu\nMaGaBRMem2VkkZaJUDVC9YsJVjejh6Kc+MXfkh8YsTsQioLi8YMQWJkh7EJuOOUlrppS2VnMYlII\nBFA+8UmcdAY5NAgtLaPff/EFnKIISkkJbNo8ZS8ny0jTfvh5Fm78AnNW3kuoePxIre6duWejtC0y\nnUc59dg36N3+BMZg17TWJxSVyJwVZ/8eereDrpePkO0YIra6nto7l9H2xD7mf34zSnDyLtzTvsMM\n9EtSSYeGeTqLll0ZNyzVq6OePwuexRhI26bQM7YTuhrw4K+Jjl0eycn0Ph7v+jYKKnuHXmZBeB33\n1/8eQ4Ve3ux/DMsxUITGHdVfpDm0kkPJbTzX/RBBLcID9V+n3FtHr9HOK73/ztHUTjpzJ8nbGZpC\nKwnrJTze+c+0Zt4lXuhhafFmKrz19Bkd7B96jZAepd/o4JaKB1kZvYliz8xYpE8XdsbAKYwN3wpF\nuCaIM3UtKq6ZoxjH9Vs6DkbP0NloXXTBetLth+na9gj+8kYijcuxjQyp0wcJVMylaM4KCulBenc/\nw+CRt1jzH/+d4ua1CFWj551f0v7yD5n/yf+MUHUcM49jmVjDszbHKmCmh4arq3wITSfTcYQERLYM\nAAAgAElEQVRcfzt6sBhfSRW2aZA8tQ9POIbmH5m1KpoHf0kNeqCI+KE30MMxPJFSVI8PX8l76IaE\nAOVCwk450qhvFrO4ElFWhnjgk5DLIv/ub0e3nXAceOIJpMcL1TWI5mb39UkKmTVPkJr5N+INFKPq\nXoTqtvu5VJDSwUwNcOR7f0L/vhex88MeeIqKUFQ3kgzDkR7pvqaoIARSOu5E+4xPkKq7PmHhEkL1\nIxWtrb/YTXRJDb7SMEY8i5Ux6Nt2kqYHN0xpzNNmKPm8xCxAMCgoLb8yxK2KR5sR7ckHHdJ23DYS\n56ViFF1FDY4VhOXtNENmHyGtmF9p+M881vFPlHirKNbLOJbchU8J8EDjnxHUivEoPlShsTK6haQ5\nQK/hVjApQqPKP5f76n6PwUIvc0JLubniQVShYkmTO6q/yLb+x7mx/AHmBJfRZ7TRb3QS81bxuTl/\nSWv2Xbb1P0Gxp5yVnhvfj8N0UdhZY1TV1Bkofg9MUgtzMSh+z/hpRikxE5mz5DVUt5jamz6L0HSO\n/OgvKAz2ovqDBKvn03D7lwnVLSJ5cjenn/kOc+/5A8L1i1E8firWbCUf76Rv1zP07X6W8rVbaXn8\nf9LzzmNku09iZgZBwqu/uwotWETzA39K6fKbsYwsvbueZmDvi1i5JIrmJVA1j4bbv0Jx87pzdkDF\nG62g6YE/pf3lh2h/6Qdo/jA113+a5gf+9D12XAHPBWZ0tgPG1MLns5jF+4a6OsR998PQEPIb/zj6\nvXwe+dyzSJ8P9W//3i1tnySkdCjkU+x+7q/Z88Lf4fGGUXXfGOJ044Pfoaxu1XT2BAArmyR+6M1h\nsuMSONXjJ1DVRGTuCjxFZQhVo+3Z72Km4kTmriTcuBxFUSmkBkie3EOuvw1pW8RW3ETtLZ8ntuT6\nURMk1avjq4hgpvIUEtNPxU2bFQRDAq/ftfW/Yop5LtYhdxYupMTOm8PNHEcgNBU1MJbweNUAQS1C\notDHox3fojN7nKXF11Gkl7GkeBOmLPDTtn+gxFPB7dVfJKJF0RUPmuJFGTaUE0KgoqKqATSh4xE+\nfKqb8pEOeBQfmvDgVQJ4VT+KUPEoPqKeCvxqiBrfPLJ2iqydHDO+ywU7a4wr/Fb9nhlto3EmH694\ndYSmIK2RyJwc7iPGsNGYomqEahcw5yO/Rc31n8IxCwhFRfX68RZXonmDRBduZOXv/RveaCVasBgh\nBHooSt2Wz1F5zcfwRqsAQc0Nn6Z89R04Zn7YuVUghDJcgl6HFghT1LgcX7SKhlt/HelYIBRUrx9f\ntArVHx61D4ruo2rTfZQs3oxTyCMUFU/kIg7Uqup2zh7vJlMwoK9vuod3FrO4pBCqimxqRnz6M8iO\nDvjlo6ONCQcH3fTWX/2/iD//v9zGqZN4juneIHWLb6Ni7gaQDJsajv2++MMzo400U3H69zyPU8gD\nklDdImpu+ixlq29HDxUjhgXTPW8/hpkepGj+Oupu+QKeonKkbWHn0/Ttfpa2Z79LqmUfiaPbic5f\njxYbuV94y8L0vnmCTFuc/EAaM5kj1Fg65fvqtAlPTb1KrFQhm5b0914ZGp5ZTBDSjfKM0Y4KZdy0\niSLUYQLiZ2X0RlYU30BjaAleNUCNvwmvEuB09jCn0vvZO/gya6K3ENInXqEkABUNSxaQjFxLljTJ\n2m5JZ8ZOoQsPurhyUpaOZY9JCwLDrR5mfnsu8Rm7YqGMfl31+PGX1uEvrRt3PXooSlFodOpSqCq+\nkip8JSNGX/5YLf5Y7XuOSfHraP6JzUqFouCNlOK9GMkZ9SHhNueMRl1yc67uLJlEHjyIuPueia9v\nFrO4DBA+H3LxEpTf/C2c7m7YvQtyw5EL24bOTuQjj0BDAzzwKUR0rLTgQtC9IeqXbD3790hbidHk\nwB+cmYIeK5ci1bIXKR0UzUPVtQ9Qdd0nCFTMGaUdUr1+hFBQPQG8xRX4yxuGxyfRwzGkY9P58sP0\n7XwGb3EljXd/DVV3o7m1ty0hcbQHPeLDyhQomu+6Lqu+qVXwTnv6uWqdzrz5Kr3dDvt2mtiWHBMx\nmMXMQtoSOU5F0FQgVDH22ek4yHEiFpZTwHDymI5Bn9HOQKGLo8kd9BvtnM4c5mhqB4lCHylrCCEE\nBWlwJLmdY6mdtGYOsn/odU6m9+HI8YmxIlRKPJXk7Qy7B1/kwNDrDBV6MR2D7lwLr/X9nNf6fk6F\nr4Fy3/R9K2YKQlWGycZoSNOesfPkrtDVqjimNcYyXgjhGkReUOdylUMI0D2wYMFYUWc8jnzjdejt\nQVpjr9tZzOJKgggG4Zr1KL/127BoMfjOiaabJpxqQX7n2/DyS8h4fOLrVTRCxTVkE120HXqGo2//\ngHjHPnyhUvzhMozsIKaRuuD9d7KwC3lyfadBSvzlDUQXbiBQ3jhGKK1oHlAUpG2OKlUXQuAvq6Ny\n831E5q0iP9BB355nSZ7cc3YZ6ThEmsqpu3MZDfesIramAXucZ9NEMW3Cs2y1xuabPASCgrdfK7Dj\nLRPTZJb0XEI4BQtp2Rdf8GJQBKp/rIuyY9lY2bGaiHihh5Q5QLmvnpQZZ7DQzbaBJ+nKtZC1k8SN\nbvJOhgpfPQvCa/EqPrJWirBWQoWvAVta5Ow0Z0JK88NrqPLPHRkOKlFPBc0hN7+cthJYjokqVDTF\nw5DZh5Q2CyPXUOFrmP7+zxBUnwdFHxsstXOFYWHezHwXJO65cUx7bAm8AC0cuCSd6K8YeD2I9RvB\nf17VWzoNe3Yjf/JjaDuNLBTG//wsRkNKNwVaKEA2i0ynR6dYzkU24wpt83l3mdn7+9QhBMLvR9xz\nL+JTn4Z580A75/5hmrBnD/Jf/zfs3IE8Y1h4ETiWwUDnAY6+8wNO7v4ZB1/7J1oPPoVlZrHNPAMd\n+2jZ8wj59Mykf6VlYqbigCRQMQc9XIIYp9JS0X0IRXULHsYpVQ9WNRNuWIYeKibbdZKBc4xSe986\ngZnOU7SgkuiSajS/TsfTB7DH0UxOBNNOaZWWq9x2t49MRvLsLw3+6W8zfOlrQapqFIJhga6LCbXI\nUVWIFH2Ab9YzCMcwZ4TwCFVBKwpi9CXgnNVJ0x7TagJgsNBDxkqwMLKOxuBSLFngic7voAiF5cXX\nszJ605jPrCrZwqqSLeNuf0vlp0ePRwg0ofORmi+ffa0zd4Lu/CmaQ6u5q+Y3prinlxZa2D/WGwdw\nCqZ7rmxnRvyMkGCl8uOfeyHQij7ohMeH2LIF+fOfwtDgWb0SAAMDyL/6b5DPIzZdi6yscGfOZ/oU\nOdJNGViW+2MWwLLB50MsmHj5/VUFy0ImEjAw4O67Y7v/2s7wvzZYJuTybvXQ6VboH+sWDsCuXYBA\nxmLg94PXO9IdXlFBVUZ+9/kgGkUUFb2vu3tVQQjwel1TwvgAMvMjaG0dRSTlU09CRSUiHEauXoO4\nSLm6kUtw9O1/I951kJr5N2EVRu7hQiiouo9TB56gfsmdhEumP2GU0sGx3MmF6g+NS3bANRIUioZt\nZLGNsfYdQgj85Q34SutItR4g2bL37HvJY70EqqNIKRGAnTMZfLcTx5palGrahCedcigtU7jjbh+F\nAnz3H7Nsf73Adbd4WLhUJ1au4J1AuXykSOGOeyZvFf1hhJ0vjCuSnSyEquKrKCLb0o08x3zQzhoY\nXfGzUbozEaBKXwPd+VO81vcIOwdfIGelWBXdMipKM9NQUPEoXkz1yr029FgYbZyqNiQUBtJYWQN9\nJrx4pOOel3HOvVAEnmhoZojVFQrh88GNN8HiJdDbO7o/kW1Dby/yT/4Tsnk+rFiBaJzjCp0FkDcg\nnUYmhiAed3VAySRi/nzEzx65XLt0SSETCVcE+/AP3ehMJg3pM/+mRyI2E4jWyP/xD6OlfprmmuwF\ng8M/obO/i3nzXD3Vrbddsn37wCAaRXz5K5DJIL//fUie08RZSuTDD0EkjCgthbnz3rMyyDRSnNr/\nGGu3/gX1i28nMzTif6VoHsKxRnLJbmxrZioahVBQdC+2bWEX8khn/Em4FoggNB0zPYSZGRp3GT1Q\nhBaI4BhZjPg5Xj5CYGUNComcK5NI5pD21KOL0yY8//3P0zz1C4PUkEMuJykYbtHEkz83ePpRY8IF\nU/MWaLOEZ4Io9Ccxk+Mb3U0Giq7ib6xAbD8OnNObKZMne6oPaZiIc0zuInopm0vvZkNsKwKBRKIK\n/ZIaAJb76ol5qxmrrL5y4KsquaDRZb4rjpXIzgjhkbYkc7IHOz82ZSNUldCCGpQpivmuGqgqyn/4\nHZyebnjnnfEf1ieOuzoIRRm5+ZxZ7kwaR0rwepE1k+sZdlUhm0UePgxPPcVZr6Jzj8N00lKW5RLO\nVGp0VawQyPZ2WLoUMUt4JobaOsTnvuCWpv/Ld0afF8NA/uhh8PkRf/gfofjCRSCObZFNdBEsqh6n\nX5ZACIHjWDMmNxGajh6MYuddkmIb45eNe4srUHUvxlAPxmD3uMtIaYPj4NgWVm4khVe+cS6tj+7l\nxI/eQfPpWFmD2tuXonouk9NyT6dDV5s9qmhCSjcNySTSbPnclftAmwyEECiaOr7dkyOxC9a0jeiM\n7iHMwczFF7wIFK9OeGENiq4yips7EiudI3monciSesTwxaUIBUV40Hn/KqTObPNKhr+2BE9ZxA3r\nn1etlTvdRyGewl83iYqkC0A6Duljndi50YRHqAqeWBh/QxmK5wNMeM48VNddg/jKV5GK6jZlPE/A\njeOMfW08nCE+H1RI6aaxptjTaMLbOP8YWubEjv8sALdqUS5Y4BoTxuPIn/109AIDA8hHf4ETDqP8\n/h+APrY/FrhWFMGialLxU5RULx31nmWk6Tr2GqHiWrQZah6qevwEKueQj3eS7WnBSg8iHXuMaDlQ\n3ojqC5HtPkGm4yiOVRjTtsZMDmCm48MGoyOfr7i2mVBjGbmeBNJy8MaChBpiqL7LRHjuuMfL3PnT\nD6PHyj4g2gNFuKZ941yQ0nGwUzm3NYA69Wqa7Om+Uc0jpwrF56F4bRPKOK0KrGSOvmd2E2qqQpki\nm/6wQA14CdSX4auKkm8fGPVecv9p8p1xilZMv1mftGzibx4eo69SA15CC2pcAfoHtUrrHIhQCG6/\n3TVne/QXyBdegK7Oyz2sWcxiyhB+P3L1asRnP4fs7oY33xghkrYNLSeRv3gEp6IC8fkvjDuh9viK\nmLv6Exzf+RPy6QESfcdRdT+n9v6SVLyV1n2PMWfF3TPmw6P5Q4QblzN46C2sbJJM13EKib5RzYoB\nwnOWoweLyfW0MHT0bfr3vUT5qtvOPiONRB+Jk7vJdJ1A1b2j7Co8ET9as4dQXRTpSFS/PuXoDswA\n4bnuZi9rNkx/Bq5/QCamQlHQIv5xbfCdgkWuI45eEpqyuFRaNulD7eQ6Bi6+8EWgejXCS+rwlBdR\niKdG6XjMZJbuJ3ZQ85nr3XLnD7IYdpoQikJwfjWh+TVjCE/qcDvZll7srOF2Mp8iHNPC6B4iufcU\ndmZ0Dl6L+CnZuAChfXjOkaiohC03Q3UNrF6DPLAfTp6Azi4YjEM261YfOY5bxu71gtfnanpiMdfq\nv7oaVk7ScbasDLH1I1A29qEhrr9h2oantuWQbk9xRN+EGWymZlM9laur8YS8EInAwoWjK3reC8VF\niC03X9ih+lKhpASxdu3Elm1sQHzx19xzeT4a6qdvICsENDUj/st/Hfte0zyoH9+j6oKr27jRvZYG\nzrv/LlgAtZNb15h1FxUjN25E/MHXYevWsZWYoRCi7sLb8PgjNK35JLaVp799D5mhDmzL4Gghg6r5\nKK1fRcPyu/CFZsaHRwsWU7xwA6ef+Q5YFoljO4ku2jSW8DQswVdWR+r0AZIn93L6yX8i19OCL1aL\nbWRJndpH/57nMdNxvNFKQnWLRn1e0VWUcXzhpjTm6a7gSmkncaVAaAre0gjKOOJRJ1cgeaCV8KIa\nGKeMeSLInuolfbgdc2BipYrvBaGpeEojFK+ZR74zPmqdTr5AYu8p+l8+QOVH1uKJTd7q/MOE8KJa\nIssbGHj1wCgHZHMgRWL3SdLXLqJoeeOU128ls/Q9v5d89+DoKi1VwVtRTOnNy2dUsOxIh4Qc5AXj\nKSxpIpEs1JbSrC0ipFwZ14IoKoL162HZMsTpVuShQ9B2Gvr76Uoc5lTmIJpUWRe6ya0s8vkhEsEp\nL+VEWZLeCsGSupspmcw2Y6Vwy62IW25FStcPy8yaqJqC4lFdd9tpwM5bdG7vYGdhJTmRZeXSdYQ/\ntw7f/MmnREWkCK67HnHd9dMa06WEqKtHfPozl3ADAjF3LuI//cmkPiYdiW3a2IaF5tdRNAUhBGLt\nOsTadRdfwRQhYjHEXXfDXXdP+rOq5qWkeimLN3+JzmOvECyqwsgnUVWdSOk8ahdsobhqMao6M9EF\nzR+iaO4qfLEaCkM9mOnBUfqbM/AWlRNduIH06YNkOo7St+sZUq0H8Vc0YOcyZLqOYaYHQQi3ifGy\nsdW+M4XZXMUMQ6gq3opi9OIARu/QqIeflc4Tf/UglR9di+LzjPG/eS9IKZGWQ89Tu8ic7Bnf2Xcq\n41UElVvXMLTjOGY8NaINlmBn8rR++xn8NTGiG+aPX4k0CwACDeUUrZyDr7qE3OnRpb0Drx8ivLSe\n4NxKtNDkj6FjmGSOd3P6+y/h5EfrMfRIgPDieorXzJvRKJzEYciJ80juhyTkEEetd3nQ/yW+EPjN\nK4bwnIEIBGDhIsTCkZnhUeNpfpr7AUERZn3RN0Ytb8sCr2f/mbcKL/O10KZJEZ5RcCTZ3jSdb7cT\nriuipCmGt2h63xHHcki2JzAzBay8SX4wh5ma7RP2fsPMFhg6EWfwRJyqtTWEqiMI7cpPFwshKK5Y\nQHGFa7XgCpTlGLflmYCiefDFaqhYfxe5nlMUL7gGX6xmvEFRvmYr2a4TFJL9mKk42a7jZLuOn7OM\ngi9WQ3TxtZQsvXQEfUaPgpQS23RIxw2cizyQnRl0C76SIBSB4tUJLawd04DTSmbpeXoXudY+1zxw\ngoJJKSXStMm29tH+8CtkW3pmcMCC8ttXEV5QMzbl4kjibxym/aGX3VRKvjAthb+U0jXOm8S+Xy1Q\nPBqR5Y2U3756TDozfbid3mf3MrTrBI45OTsBadlkT/fT8/QuBrcdGR3dEYLgvErKb1+FomuTItAX\ngyo05mjNPBR9ku8U/5TF2ooZW/eVgIAIElVieMXUCYptOrS9eornf/dxDv5wD6nO6UddNb9G3XVz\nKF1SQfmySqrX11I0Z+LtBWYxM0ieTrDve7t47muP07WjA7swA0avlwFCiLNkR0pJPhPHsWdOxK75\nQ8z7+NdZ+fUfMO/+PyJcv2Tc5cKNS6m58UEq1n0UTziG6guieHwoHj+aP4y/tJaqzfdRf+dX0EMT\nb0c06fHO5MpsU9JzIs3P/mIvD/7tamJ1wQsum+zN4wloBIo+IOKdc6EIitfNZ2jHCazESPm4tB2M\n3gRH/9vPWPhfPkNo/sRKYqXlkGvrZ+9vfIvk/tMz4sFzLtSwn6r7N5Fr62fwnWNj3m976GWM/iSN\nX7mD8ltXjCpVnwycvEmuYwArkSG8uA7V/z5rCy4xIovrqLpnPT1P7CDfOdoSvv/FfYBkyf/3OSJL\nJ94WI9c5SOf/eYMTf/OLMe9pkQDF1zRTedelC7EDqCiI8esOr0po6HzC/1k+7n8QnanffxzTpmNb\nG4XMzDk7q16NyjXVPPD4ryIBRVNQPkTarCsFqc4kA4d6L/cwZhSOZfDaT36b1Xf8CbHzqrimDEXF\nG6tmvN5+5yO6eDOB6iYqNn2cgX0vku/vQPH4CFU3E1uxhXDj8gn345sqZpTwnMlpW4bzntWe6cEC\ne5/upHZJMfOumRkB1ZUEoShU3b2OrkfeItfWN8pCRpo2vc/sQiiC+i/cTHTjggt6tEjHIXe6n96n\nd9H28Kskdp/EHqflw7TGOhwVKL99FamDp8l1xMmfJ4iWlkP/ywfInOima8MCKj+2jqI1TfhrSt4z\njWKlcmRaekkdaiO57xSJvacwOuNU3bsef13ZzBEeKc92C7ezhqtHGhrfp8gcTJPviqN4NFS/Z8Qi\nYAaiI8KjEVnRSNPX7+bgH35/VNrRMQrEX3uXvV/+JjWfuZ7qj2/EU140rtYL3GPX/9J+On7yOn0v\n7sfK5M/bmKD8thXUPXjDlNJkHw6Mf06FEGjo07752aZNx7bTmOmZIzxCCIQqUPyzJOdyIt2RpP/Q\nzLRguFIgpUN/2y7M/PQrfM/gQo2Mx11WUfEUlVO6fAvF89chLROGzQs1f2i4BcWlve5nhPAce6uP\nk9vj2JbjNg91JEjY9VgHPcdTFHI24VIvK+6sxrEddj7awaFXemjdM0TfqTT1y6OEy7y88dApHMsh\nn7Fo3ljKvHUxAsVXtgfLuBAQmFNB6fWLyXfGx1TuWMkcvc/vJdvaS2RJPcEFNfhrYqhBryuWyxoU\n+pJkT/WQOdZFpqWHbEvPWf1GaFEtWtiP0Rkn1z79ai0AvThIzQPXYiaytP/wFcx4etT7djpP5mgn\nhb4kib0teMuK8FWXoJeE0MJ+VK/u9nrKF7AzBkZfEnMojZnIYg5mMOMpCoPuOs2h7Jjml+8FO2+S\nO91LzxM7sHMF7LyJkysM/15wf88XkJaNtBzXOLFl/NlZ1y/fYWjXSdSg1xUiahqKT0f1eVB9Oorf\n4/bG8nvQwn6i65qILG+ckH5JCIG3rIiKj6wlfayL9odfHYnwSZfEDO06SSGepvepXYQW1RJoKMcT\nC6P4PUjbwUpkyHXESR9uJ3Osk2xLL4VxBOolGxdQdfd6IssaJnWTGHLi7DLfZoe5jU77NIbMExFF\nrPVs5gbPLZSrVRdfyXnIOzkeyn0Hn/CxQl/Hq8azvGvtByRrPZu42buVetUty087KXab7/CS8TQ9\nThc+4WO+tpiNnhtYqq9CGc6yDzh9vFV4hb3mTnqcLmxpElPKWefZzI2e2wgrkeHDKum1u3nJeJpt\n5qsUpEGj2oRHeLAZGwn9RvqvOGDtxpIW87T53Of/FRZo44fh3wuWYZFsSzB0chDrKk13zGJ85AZz\nJFqHyPSkCJSNbyh6JSAVP41VmJgfm5QSq5DFyA5e0BH5/YCiaiiBMFrg8ugAp014kr15Og4lySYK\nVC8sYqBt+AQIKK70AZJEd550vEDrnkGa1sfwRzT8EZ1otZ+SmgC+sIbmUahoCuHYks7DSXpOpCmq\n8FN/FRIeIQSqz0PVfZvItvbR+9QurPToGbo5kGIwniJ9qB1PRTGekrDrkislTt7ETOUo9CYwB9Oj\nIgWesgjV929C9XvofWrXjBEeIQShBTXUfuZ6pO3Q9dM3MXoTo5aRtkOhP3nWA0gN+lCDXlT/meaZ\nEqdg4xgmVjKLnTfHmJKp0xDtnvr2c26n8IKFc+7P8GsTQfZEN9kTo90+hUdzSx89mquF8WgoHg29\nKAhSEmyunrBgW/Fo+BvKafzy7TiGSc8TOzB6Ro6jY5ikj3SQPtbJ0K4TeGIRtxeXR3N9mrIGhXga\no3twjED5DIrXNVP3qzcSu2EJWnhyJmI2Nn1OD0lniFLFLa0ecgb5ae4H+ISf68QWipXJyXhtbPaa\nO+iwW+lzeihIg1q1nqzMoqCe1WulnCR7zO38IPfPxJRyKtUapHQ4aO6l1+lGIlmhu+XMFhZddgeG\nzFGuVGDjMOj0873sNykRMVbq6wgqIQbsPl4vvMDP8g9RpdRSqdRgYHDMPMRpu+Xs+s5gnrYABYXX\nCy/yZuEVbvLeMaF9lI6k/90e4scGyPZmSHeniB/tJz+YA0fS9topjKSBv2Ts+ShbWkHdtY0Uzx17\nXI1knpNPHaVrZ8eY9wDqr59DzaZ6/CUXduoeaolz8umjpDqSLP/iWjwhD13bO+g70I1j2hQ1RKm/\ncS7h6jBm3qJz22l69nZTSObxx4JUrqmmen0dyntEa3MDWfoOdBM/0k+mN42VtxCKgifipbgxSsWq\nKsI1RWgTMISz8hZDJwfo3d9Dqi2BkTSQUuIJ6ASrwsQWllGxogrVd2Fd2pGfH6TvQA9ISfX6Oube\nMf+C28sP5uh8p53WF08gpWTh/UspX1aJ5teRUmJmCnRuayPTmyHbmybZlqDt9VPYho2RyLH/33bT\n9vqpMalFT8hL9YY66m+Yg+Z9/+t/ju/4Ef0dey++IIAExzYp5JNXsGf9pce0z1K8I4uRsaheWMTy\nO6o4uX2AQ6/0YpsOqkchUOTBKjikBgz6WtKs+mg1jauiDHbmadpQyvzNZTiOJJc08QZUNI9CsFgn\n2WeQ6MkBl07AdKlRvKaJ6vs3u9VZbxzGOr8dhMSNgCSyXJSnK270oOreDVTfuwErnSd1oHVGx6t4\nNIpXzUXx6i6hemY36aOdFyQTdiaPfX6q5RJA2g5mIkP6cPulWX/Bwi5YY/xt1KAPo2do0o1aVa9O\n0co5NH7pNvc4PruH7KneUT5HOBKjewije/zeMmMgBGrANYqse/AGyu9cjb928qXKfhFkobaUOqWR\nWrUBj/DQbrfyx8nfZL+5k4XakkkTnjPodbrJOGnu8N1NnTqHrEwDgphSBkCn3cbLxjMM2gN8LvCb\nNKrzyMscT+Z/xtvm67xgPHmWoIREmOX6albq66hSa5DAQXMPf5T8KvvMHczRmggSosU+zquFF7Cw\neTDwJWrVenrsLn7o/AspKzFmjFt991KQBUxMnjeemPC+Scfh5NPHOP74YdLdKTI9aYyhkWu/e0cH\n3TvGJy3z711CUWN0XMJj5UzaXm/l4MN7sA0L27BHNUaUjqR0cfl7Ep5UR5J3H95L185OKtfUkOvP\ncvTRd+l6px27YBFtKiXVkWTVV67h9KstHPzhHtrfaMUYyhMoD1F/wxy8RT6iTbExD267YDNwuI/W\nF4/T9nor/Qd7SXclsXIWQhV4i/xEm0qo2VhP483zqFxdQ6BsfO2mdCSZnjTtb7TS+rK997UAACAA\nSURBVNIJund1kWgdxBjMuYQn5CVUE6F8WSUNW+bSsGUe4eoIqmds2rfl2WMceeQg0pGsMqz3JDxG\nyqD9jVO8/TevIR1JSXMpJfNL0fw6SJfM7fjHN0l3p0l3pcj1Z7Dy7j2vkCpw7NF3x11voDyEYznU\nbqyHy0B4Oo6+RD7dT1H5/IsWLMiJZ54+0Jj2WTLzDkIR6D4VVRX4QhoCSHTnOfpGH0bGAgGpPoPy\neW54UAjh6n2GZ35mzqb7aIq3ftxK1YIIA21ZhGBaTcKuBCi6SsXWNa5exOdh8J1jk3+ACoEa9BJo\nKKP0xmXM+e2tBOdVkmsfwFcz8/onxatTtGIO/uoSAnMq6PrF22RPdGP0Drn6oameEgGqz4O/ugRf\nVfQD3eTyDKIbFqCXhPBWRul9aheZ410Y/cmxhmLvBQFqwIevpoTIknoavnwbJZsWTrk3lw8vdWoj\nA6KPfqcXkwISSVCEGXTipJyp5/crlCrWejay1rNp3Pfb7Va2FV6lWVuEIXO0WK5AXhceDJlnt/kO\nDg4CgQ8/c9X5DDh99NhdWLheQBGliB6nm5zMnl1nq3WCaz1buEbfjBCCGrWeI9ZBWu0T445DDP83\nGUgJZm44pVwZJlAWJD+YY+BQH9KRhGsjhKoi6IGxIujSJeUXLFfX/DrV19Rg5UwKKQMjkSfZliB+\nrB/HnJz1hLQdOt5opWtnB0bCIFgRItufoWd3J+nOJLGFpez+9nZS7QkC5SE0v06qPcGRnx0g2lTC\nyi9dg1YxksKxDIuhEwPs/ue3OfST/ViGhT8WoKgxiqKrSMvBzJr07O6k8+12+vb3sOLX1zL3tmY8\n4bH6vExPmhNPHmHnN7fRs7sTfyxAoCxIuDqCUMDKWeT6sxz6yT5OvXCCdb+bYeF9S93tXULhtmNL\njKSBHtCJzishVB0m1Z4g3ZlC9apEm2L4SwJjXMx9UT9Fc6KXzZRVCJW5q+5n4aYvXLTsXEqJaaTp\nPv7GhK58Mz2IMdSDYxrooSi+WI27jRmsBL0cmDbhCZd6QEqGunIMdecZaMsiJXQcSuA4kuZNpfjC\nOkffGBGA6T4Vu+CQGSyQGSqQHSrQdSRJOOZl7T11nNw+QPex9y7xVDQVvTiIv75szHt6cfCKsdjX\nQj4qP7aO0IIa2n/0Kt2PbCPfm8AZ7nguLcfVs0hX94QQCFVB6CqKR0eP+AkvrqX2V26k+r5NKD7d\n1YqUFxFsrh6z/3px8IJC2IlCKAJvRTFzvnonlR9dS9fPt9HzxA7SRzuxMnl33KaNtG2XlJ5JW4nh\n8Z/ZB01FaG6q6AxpK7tlJRUfXTupVIxQBGrAN+65vpRQg1704tC0hHSh+TU0/cE9lN6wlI6fvE7v\ns7sx42lsw3SPoWUPn39Gjp/qOosqXg0t5Ce8qJbKezdQ88C1076243KA14znea3wAqfsEySdBBKH\nNvsU1WodNlPP70fVUqrU8Z1gJZKMTHPEPsgJ+wivFJ7l/CnnSn0tDg4qKn1OD88Zj/F24XXa7VbS\nMomJSbvVSs6TxcElA1mZJicz1KoNo8eixKYcqRoPqq6y8Y9vYMMfuh4hhZTB6ZdP8vjnf4qZNVnw\n8aWs+LU1lMwfe40KVVwwXeSN+Fj62dUs/exqAIykwfHHDvH87z9Btndy/fJs02b/Q3uo3VjH6t9Y\nj7fYx9FfHGLv/95BpjfNq3/+PI4tWfarq6ndXE/Prk52fnMbQy1xjj56iEUPLCdYHnQbgDqSdEeS\nff+6i93//A6qV6P6mjrmbZ1P9TV1eIt9mJkCg8cH2P9vu+ne2cHJZ44hFAiUBam7rnFU1MEyLFqe\nO8b2f3iD/nd78ZX4mfeRhczbuoDYglIUTSHVnuD0Ky0c+sk+EqcTvPVXr6B5NZY8uOKSaWmEIihu\njPLJp79w9rXB4/3s+MZb7Pn2dgKlQa79sy3Mub0Z7fzGvAIUVUFMo03QdBCM1hIqqScQrpjQ8pY3\nhDdUgpiA8eDQ8Z10vPQD8n3txJbdyNyPfx3VM5q0O6ZBIel6jqn+MKo3gKJe2dZ+0x5dRVOYyO5B\ndj/ewfFt/QRLPCAEtUuLefPhU5zcHidc5sXjU4jVu6HO0sYQVsHhte+3cHrfEEtuKqd8bogXv32M\neFcer1/BG3zvoflqY8z7vbuY+7WPjnlPKMq4rR0uGxRBaGENC/7sAeb9zkfpe3E/g+8cI7G3hVxr\nH+ZgGiudR1o2asCLrypKsLma4rVNlN64lOjaeSg+z6h9UgNeGr54C/Wf3zJqUzO67wL89WXM/Z2P\n0vDl28gc7SS+7QiJ3SdJHe4g3z6AOZjCzhaQtuM+pP0e1KAff3UUX20pwaZKIssbKVo1l+DcClfr\nM8nxaUUBKj+2loqtq2dmvyaBmTieik+nZPMiotc0Y/R9nL4X9hF/8zCpg6fJtvZjxlM4BQuhKWgh\nP76aGKGFNUTXNhG7bhHhJQ1uNdkMnNd/z32fZ43HmK8u4i/Df8cctRmv8PGlofsJiuk9VDS09yzz\ntjApEsV8PvBbbPXdO6YJrVf4UHHJ+rezf8/bhde41nMz/yH4R9SqjSTkIF8c+vj4zWvPm3mqQj27\nrpmCoiln75iqoaLoylnOJlSBoquo3hnY5lRn0RLy8Swrv7yehpvmonpUhCLofKeNnl2d9B3o4do/\nv5mFn1hKcWOUQGmQZFuCt//mNfoP9GCd05TWzJn07Oli57e2IW1J00cWsvGPb6B8ReUoIlOzvo55\nd87nyV9/hJPPHKNjWxvv/mgvddc2juKzPbu7OPHUUfoO9BAoC7L6q+tZ97ubRyJfAkoXl1O7uYE5\ntzfz6Kd/TLY3w8GH9xCqjrDogWVTOyYTgWDUeVM96ghBFcPn1aPNzLmdQWz6+F+jqBPXuApFo3HZ\nXfhDF0+FG/Eukid2k+k4ipkeZM7dXwNGE57kqX3s/XuXKNbd9kWqNt+Pv2zilhuXA9MmPEIIVtxR\nzYLNZWcZr1VwCJd6KGtcgm1KVE0gBKi6exFpHsGdv78Qy7DRfSregIYQ8NUfbELzDFt4KwJv8MIX\nmBACVHFV9Hg6e4PQVPRoiPI7VlF64xLs/PAs33bOzvKFIs5GRRS/By3gRfGPdWV+P/b/7DZVger3\nElpUh7+xnKp7NuAUzhn72ejU8GcUBUUfroDyDFdBeXWEZ2rmeEIIN/JxiUsWLxXc8QMeDW9llKp7\n1lN+2yocw8QZPoZI6R4/xY2MKV43DaoGvChefcYiloetA0RFCVu8W5mvLUbHrWbqs3sIiciMbONC\nCCtFlKmVdNqnKVMq8YvRablzU017zR3M1eZzg/dW5mjNqGgMOH102x1Y+oimLCjC+EWQ09bJUesa\ncPqIO/0UqTNn2nfxa1fMqPnjZCEUQXRejGB5ENWrIoTAXxKgZH4pPbs6QULl6mqCFSH3/lrkI1xX\nBLgRGCNVwLYcVF1l6ESclueOY+ctNL/Gyi+vI7awdJxIlcBXEmDO7c0MnRqk/0AP8aP9xI/2E1s4\nEu3qeKuVru3tqF6N2MJSVv3GNXgi5zW7FaAHPZSvqGLxp5Zz8Id76d3fQ9fODupvmkvwAtqgaR+3\nCZ6zy3lux4PuDTEZYY6i6qzY8rt4/BfXxboprV4U3Uegcq7bwfy8/beNHJlONy1dSPTjWDNnaHip\nMCPxJ39Exx8ZO7Pz+C+8+qLysTntssYrtwRwJnCGpOiRAExRg3G5IBSB6tNRzw/rjgcpIZdD/uVf\nwtGjYNmwdi3ic78KjY2jly0UkM8/D//6PcgPi0C//geI1avdbtgfIAgxTGaLgm4F2GWADz/dsoN+\np4e0TDHoxHk6/wt6nC4amDfuZ2xsJBIHB2eKIi6BoEGdx0bPDTyXf4wV+Z+z2rMevwiSdAZJyCHC\nIsIy3Y3i+UWAhDNI3OknKRN0Wm08bvyUrMwgGdG21KtzmKs183LhWTYUrqdRnUuP08U7xut02KeZ\nozaPGYsz/B9IHDkzLVquCAgIVYVHVTepPu2s4Fnz6wTKQ+jD92XVo+IJDUcIpKuhcYYJT6ojSffO\nDoSmULqonKLGqCvyHQeKqhCdV0IgFkA6knw8N4rwFNIG8aMDpNqT+EsDVF1TR7AiPO6zWigCT8jD\n3Dvmc+LJo2T7MgwdH2DwWP8lIzxXKybbLkIIMeFO6baRxc6nUXQfnkhsfLInJdJyo4LSsZi6wPP9\nw5WdcHsfYe54C/v0KaSRR4kUoS1bhcxlcXq60BavQARDWEcOIuP96JtuxDq8H/vIu2DbKBVVqPMX\nIyIRrEMHsI8ddnUYdQ1oC5ch00msd/chU0m3GuHam1AqqrHe3Yfdchxp5BGBAPqaDShVNZhvvoLT\n040sGChlFWjLV6OWV15w7AVZ4JBzgDec1+iVvQREgGViBdeoG4iJyVfyzAg0DVavhqIiePIpeOcd\nuPeescspCtTUwKZN0HIS/uc34dOfgmWXMIT9IcZ13pvJ5bM8bzzJQWsvOh48wsNCbSlFysjMr9fu\n5vXCCxyw9pB0hmi1T5CVaVIyQaVSQ7O2kHv9k2v6WK3Ucqf3HlJOglcLz/GW+SoqKgoKFUoVGzwj\nPXTu9N7Dy8azPJr/CW8WXh5OY0lW6uvwiRH9V6PWxBbvnXTbnXwv+y2KlChFogiP8NKgjhA4Rzq8\nZb7CzsI2+pwe9pjv0Gaf4nvZb/GC8SQlSikf9z9ItVo79YN7mSGEwFvsGyXwVTQFzauCAE/Yg+Y7\np8GpMlpb5Fj2sImnJDeQYfDEANJ2yPZl2PZXr4wrRD6DVEeCwZOuu7iVt8j0jvh4pbvTZPvS2IaF\nr9hH6aKy94xYKqpC+fJK9GEylu5KkTg1RO2mhgt+ZhacPXcDnfvoO72LzFA7lplH1bwEIpWU1a8m\nVrMcVfVcNG0qbRPHLKB6gyieyVlfXMmYJTwAUrokYzCOWt+IDIaQZgGntQXz8H6UmnoUXcc+egj7\n5FHUhUsxfvlTlMpqhM+HtG2wTOy2U5gvPYNSUYWUOtgOTnII+9ghrEMHUMorkYlBCi8/i/f2u7De\neQO7vRV1ThN4vGeFy4Xnn0L4fCgVVWDbY7xszoUlLXpkN9+x/hcvOc/TK3sIEGCdsoE8Oe7VPvE+\nHshhCAEeD+KTn4T+fmRnJ7ScGn9ZTUOsWAELFiB37YJv/dP7OtQPGzZ6bkBHZ5+5i6zMUKxEWevZ\nxGp9AwoK5YpLrCUSCwtTmniElzt8LlkVCEwKWOeImzWhc533ZmxpE1UuXDkYUsIs01fjF0G2FV6h\ny+nElhZBEWKO1kyNOpL/v8X7EXwiwFHrXQoYVChVLNFWsk7fjC48FAk3VRVVSlivX4fjd9hn7cSU\nJnVqI3O0ZlJO8qy4GcCWNiYmDjZL9VUs1Vedsz9uFdhVDQGaTx+V+hViOE0uBNp5vjbi7P+G4faZ\nxLEcCinD9RiSkGxLsPe7OyY8DMd2sHIjacdsX4bCsBu15tcJVlwkki9c4bPu10ERGIk8uYHJCbg/\njLDMPP3tezi2/WFS8VPYpsEZrYGi6vS2bmf+us9Q1rAWj+9i6WtXRiClg2N+cJrXzhKeM1BVlIoq\ntKWrUMrKUWJlOCfP6eY63HVWFgo4PV1YB/YQ/vLXUCuqQAicVBLnyLs4vd0E/+j/Hr7TCKwjB7Fb\nToAQaMtX43R3Unj6UfSNN7jbLKtAW7ICpbQcpaIalGHtS3Wd+3pFFUrswtVJBnmOyiP8yP4BedyU\nUJYsrzkvU2QX8zH1XlTUy5d/Hj4OE152FpcUpUo5d/juOUtgLoQKtYoH/J/jAf/nLrpOr/BOaDlw\nU1XL9FUs01ddZPvV3Od/8KLrEwjK1Aru8j/AXTxwweUUoXC99xau994yoXFerRCacsGvkVDEhCQf\njmljGfZZTWGgPEhRfTHKOH444yFcW0SwciQdbeets95CiqpcMDV2dpzDqV/Vo6IoArtgYxvT6B84\nTOQ+6DByg7z7xrcZ7HqX6qbrKaleiu4LYxoZhroPc/rdp7HNPMHi2osSHtXrR/UFccw8ub5W7EIe\nxeO7JF3X30/MEh4AIfB+5OMYLzxF/sf/ilo3B+9d9wNi2DNFgm25WhSzgEwnUSqqENo5hy+fg0IB\nUVo+6sEtU0mcjtNYJ47ixPsQQkGd24wIhvDc+hEKrzxH/iffR6moxvfgr6FUVeP95K9SePox8g9/\nF23xcrwfux9xgZRWliwt8gTmeTb6adJ0y07y5Agy/oxKFgrQ2QmlpQi/HywLmUi4+1FSAn4/GAZy\nYAAiEUQ4DKaJzGQgmQTLAlWFQAARCoHPN0taZjGLy4iZ+vadWY/qc7u3r//6tQTLJ6axVHR1VPrr\nTIQJXD8Y5yL+ameKIKTjpmhc+5dp7JmU2JP0NLoaYebTnD7wFGu3/hlzV91HIDLyzDCygxRVzGfH\nE39JPnNxd349WIy3qIxM5/H/n733jq/rru//n5+z7tbV1d6yJVue8Z6JsycEEhIgjCQQoKxC2gLf\n0vYL3/5o+dGWlpbxbaENo0CBQAghJJC9J/Ge8ZQla2/pDt11xuf7x5GHrCtbtuQZvfyQbZ1z7ud8\n7jrn9XmP14vYwa3ED+0kWDUXzRsAxV1Ay2Nc16Vj41jZqYkGCYGinRmHhWnCMwKltg7fRz+DvXcX\n2VefJ/PsY2hzL4FUEiwbZ3AAZ7APPF5EpACn6QDSPNrGKQIh8Hpw2lpGjSsKi1HrG1DrGvB+5NNj\nzuu96+PYl15F9oWnyDz+ML67Po46ay6+z87F2rIec9MbZF95Du/tp1YvMXJ2BMr4V8HmZrj8Cvju\nf8A110BLC3zrW7B7D3z1791tO3fC578An/okfPCDLkH6zW/gBz+EQ4eguBhuvAHuvtutw5kmPNOY\nxgUNzau5AooCd62XtQmW5xGqOr0uPiPkOdLS7Zg22fjJb4p21sZMWUhbonl1tMDp3wAd28FMZk9o\naH0xQEoHy0wRKZ+P7hnd8KHpPoqqFiOljZxAob63sBJ/aR3D7fvIDPWw/VsfofLqu8lvWIkRLkbR\nDFI9R5X+zVg/yY7GUffE04WiewlWz530OLlwXhKejc56ttib6JU91CgzuEG9iRIxMXGl04ETj5K+\n/7+xG/eDmUWE8/G+5y6Uqloyv3uA1Pe/jcgvQMaiKMUlqBXVGDffTvKbXwOhoNbNQl9zBWr9HLS5\nC4h//uPg9aHNXYB+6VWos+aQff4phv/3n4Gqoi1bjb5iDdnnnsDavQMsG+H14n3/PchkgtS/fx2n\nrxeZSqJWz0C76oZx5x4gyFwxHwOD1DFRnkKKmK004BMnKDjzeGDePJe4JJPIlhaIxUHXkXv2IK6+\nGvbvh7IyiESgpQX54IPwm4fgs5+F+jro64OHHkL+4AeITAauvXYq35ppTGMaZxtC4Mn3EaoMk+iK\n07u9EyttjtT8nPqCJq82H+9Ip1g2niXaNHjC46UjGWzsxxpRtvYVBwhWjCZbbupOYJs2VvbEN3Ar\nZRFvjXKx57V0T4DK2Vcy1L2X/NK56J6jXW1mNklP8wZKalfimUBbemjGJUTmX0bftmeQtkWqt4Xm\nR/+vm9ZS3CJ4J3uUuHa99hC9W55CTIHwYKCigTVfe2bS4+TCeUl4nrGf5A/2I/TKHlbJNaxUVp1R\nwiO8fozr3o5ckxj53YdaVQNeL76PfQZpWQjdAOmA14fIC+O55b04A30gJSIQRCkqRhhePLe9H2eg\n363PCeUhikvdmqDSCsikXT2Z/AKUgkKMq25AW7baPadhoFbPAF3H8873IDPuh0kJhlDKx+8c8eBh\nttLAvfrn+J31EB2yjYgo4Dr1Rt6v3nXEfTr3gz2wYAG0tLppqkMtbhqrYTbs3uNqAx1DeOTWrbB1\nG1x+OeK2d7lt4+k0sn8AnnkG+epriGnCM423AoQYUTR3CYDrgXVxuKYLIQiUBSleVEa8LUq8I0b3\n5g78RQG8kVPv2PGGva7QYYlrddG5oQ3Hsl019hwEyjEdWl5sIhN1axLzqsMUzB5dDG8EdBRdwezP\nkommsU0bVR9bYyQdSXowRfe2zgnzHaEqR7rIpJSYybEmyOcjNMNPWf1lHNj4Swa7dpNfMgfN8GNl\nh4n1HaRj/0uUzFhF18HX6O/YfuT18AQKqJl/46ixjHAxRUuuZbh9Hx0v/gJpW5iJgXHPbaXiWKkT\nuyNM+Hn4zpwcyXlFeKSUpEmzzdnCbmcXKVLMkDPJcmYFjYSuo9XlNp/T5uVuj1Yrq1Erx8roqzUz\nUWtmjtmu5I+VuVdn1OfUgtXmLDjxhI8dQ6gUyELuVD/MIrGEqBzCL/zMUhqYK+ad+MEeDyxcAM88\nA/E4tLe5beT19fDEE2CasG+/S4oiEdi4yW0vLytDfu2YronGRtizB0pK3K4y9fxSJJ3GNKYaiq7g\nLfAdSRcnOmKk+pInftAFhPy6AmZcXUfz0/uxMzY7f7YVf2mQilXVOf3CALfbNW3hmDaevKM6a4qm\nUL6ykvbXSzn0fBM9O7pofHwfM6+bNaaA2c5aRJsH2fvQLlL9SYLlIYoWlJI3IpB4GKHKMEbQQ9yK\nEWsdomdrJ+Urxy4Mo4eGaHmxiUTHxD3i9ICBPpJCszMW/Xt7Rxm6nq+wR7q0EkNtZNMxBjp2oWoG\ntpUlkxwkHe8l2rOfxGDrqCqHvOJZYwiPohkEa+ZT+7ZP4iupZWj/BlLdzZiJQRwzjbQtt25npGZH\nqDpCPT1h2eOhnsE2+POK8AAcks10yg5SpM71VC4Y6EJnlpjNLGWsyNqJIDwe5MKF8OOfQHs7DA9D\nSbErDphJQ1eXKxx43bWISASZSLjFypWVbpH2YVRWuD+rV0/tE5siSCkhm8XpbMPp7sTp70PGo8hU\nEmlZbpRO0xH+ACKc70bkyipRysqPFOid9TnbNk5fD05HG05vFzI6hBxOuNFGIUDXER4vIhhCKSxG\nKSlDqax25QDOQieFtCzkQB92RytObw8yOohMJiGbcQtNVRXh9SGCeYjCIpTyCpSScpTgxSEmqeoq\nwbIQgZIg0fQQPTu6aHmxCV+hj0BpCEVTcCwbM+kqFXvD3pN2Jx2GPDaacI4iC4GSINXraqm5so7m\nZw/Q8mIT3gIf8bYYhXOK8Bb4UHQVx3IwE1nSg0kSnQnMZJZwbYQZ144WsSxbVkHtVXX07+kl1hpl\n83ffwEyaFC8sxVfoGnNmYxkGD/TT9PQBOje0YZs2NVfVUbm2BiM4WgOodGkF/pIA/Xt76d/Ty47/\n2YJj2YSqwmheHSttEW+P0vJCE/sf3Y2iKdiZiUXgPGEvgdIgRp4HM5Hl0LONVK6upmhByZF52FkL\nx3Qw8jx4833nhXejonkoqLiEgopT0zALhMtzbtf9YcINq/CV1ZHfuJl0Twvm8CB2No20TJLdTXS+\n/CsAwrOXE65bih6cvLK5EZ6YOOLp4LwiPBKHLc4mhuTQuZ7KWwOGAbNnQyIBb77pigVWVkJRIRSX\nwObN0N3tprRCIdBUqKiAd74DcffdY8fzeM6r6I60LWQ8jt3ajHPoINbObVj73sRpaT5KIrIZl/B4\nvIj8CEpZhRt5m7sQ7ZKlqDUzUCtr3FTmJKwtZHIYu6MV+1ipA0BrmIdSVet2/EmJdByczjbspkas\nnVuxdm3DPrgPp6sDZ3BgJC2qjJCJIEphMWptHWp9A9qSFagzZ6PU1LpE4wwQNTk8jNPTid3ciLVn\n1xHxTKerHTk0iEwl3SifbiBCeShFJe5rOGcB2vxFqLPmoNbMRITzL1irEHCjFr6iADOum8Xeh3Yy\ndHCAPb/ZyXBPgsKGIlSPipW2yETTFC8opWJNzZgohZnMMnhggPRgCsdycGxX/6Z7S8eRm/PQwQEO\nPX+QgX19CE1BURUUTXEdvIv8OdM4UwHVUCmYU8yyz6whM5Sib3cPb/5yO91bOylbWjFCLDTsrE2q\nf5h4W4z+vb1oXo1571s0hvCEKsPMvGE2sfYY+367i6an9pPsHabqslrCtfkITSHZnaB7SweHXjiI\nlFC+opK5711I6eKxN+SSRWWUr6xicH8/sZYhdv9qO8meBCWLyjFCBtl4lt5d3fRs7cRKW1RdWkvT\nMwcmlNbSfTqF84opW15J64tNdLzRyubvvUHZikr8xQGQ7nsnhKDy0hoqVlWjTrBl/0zCFyxi2Y1/\ng5lJYJspHMfKSZi9wSJUbXwRyWOhaDregnK8BTeP2i5tm/4dz9P16oNIx6ZgweXU3PQJAuWzpuS5\nnCmcV4THQbLZ2Uh0mvCcHWgaFBZCJB927HTJTGUlhPPdf59/Afx+9xifDyoqoagIGg+6xYsB11kZ\n0xw/lXXYZ2siOHzcFKxqZTqN092JuXUDmd/8nOzLz0FyeNyxZXIYmRzG6WjD2rzejewUFOJ52614\nbns/2tyFEI4g9Imt0o+H3dNF+tc/I/XNr43a7v/SP+C751OQlw+ZNE57K+mHf0XmoV9gNx+Ecboe\npJlFxqM4ne1YO7e670MwhOf6m/G85070pashP4KYIgIqbRsZi2Lt2kb2yUfIPPYwTmvz+A/IpJGZ\nNHZfD/aenfDU7xHhCPry1Xju+BD66nUoxaVupOoC7ezT/RrLP7OGeNsQXZs6GGocoH93D9Ie8UVT\nFVRdZdFHlhGZXTiG8CQ6E7zxry/T8ccWzGGT7LCJmcziZI9GIvY9vJv9j+5B8+nofh0jYKD5ddb9\n7bXMuK7+iG3EmYA34qPuxtloHo31//Yy/Xt6iTYPMrDHTfFIx32eiqagGhqaT6NoXsm4cypbXjli\newH7H93D4IF+end0uWNJiaIqqB4NT9hLfl0hl37pKqovqz1qf3EMPGEv8963iEwsQ+Mf9pKJptjz\n4C52/2oHQhGuwrRfJ1JXwOx3zafq0lqan2t035sJoGx5JQvvXEy8Lcpwd4Kmpw/Q+PhewK1xUjRl\npBjbR9myyvOC8Di2STLWTX/HdlKxbqxsKmdHVt2S2wnkV0zqXEJVUTw+VH8eZovYwAAAIABJREFU\nVuLERejnE84bwiOlRCLZ4mwkRvRcT+etAyFgzlzYtAkqKxFVVZCXh6yogB/9yK3n8bvV/mLlCuT2\nbXD/L5EV5XD99aDr0NYGQ0OIGTNg3To3JO+MGGJms66GkW27uj/WSCeZOqLlcPg404TDVf+miTRN\n91hFObVIwIi8urV7B+n/uY/0r/8HMqehDeHYyL4e0v/zfbJPPorvU5/Dc+sdbjRmCm/QTmc7TncX\nqteHtXsniS//BdbWjeMSnXEhJcRjZB66H2vzenwfuxfPHXe7pGcS8z2cXpHxGOlf/JD0L36EvX/P\n6Y0VHST73BNkX3kez23vw//nf4M6YxZSPYfCmJOAoqmULCrjhv+4lT0P7KDx8b307+klE82g6Cre\nfC/5dRFKFpXlJAFWyiTaPMhg4+gbRi5DYCtlYaUsUv1uKjnVnzxSVyJGyJWiKQj1eHFB4Tq5a+7+\n47ushHKMAXGOt0D36dS/rYHKNVUceGwfTU/uo3trJ/H2GGbSRDVUgmVBChqKKV9VTe01dZQty30z\nFYqg5JIyrv76Tcy5fQFv/nI7HX9sJd4eQzoO/iI/RfNLmXHDbBZ8cDGesDeHWelRVKysIlgeompt\nDXt+s5OebZ2k+lNoXo286jDVV8xkzrsXUH1ZLYMHB1ENDce0J5R+CpQEmXfHIgrmFLPtBxvpeKOV\neGccaTlofp1gaZCyZRUUNhShaOfHZzcZ7+bVBz9H256n0T1BdE8QIcYSsbK6tZMmPODW+RjhIqzh\nCydAIeSJV9NnLYFsSpNW2cLbM9fQJluPyLxfrlzJvxjf4RJl0dmaylsK0rbh3/4N/us++NjHEJ/8\nBCiKa+j5oQ/Dx/8EPvc5xIwZLhFpbITf/x5+/weX6AjhRoCuvBLx7tth1Sq34+vb34FHHnHrgAYG\nXPISibhF0QsXwjf+BVFTg3zyKfjpT+GNN9waoq4uN4oUDLq1RB/6EOIj90z8CTkOmcd+S+rnP8T6\n48vuXCb7MVZURH4Ezy3vwXvnn6AvXn7KQ1jNjaTv/+8xER7j7e/Cd8+nQdMY/uevuNGl7CTFuzQN\ntbYO7wc+iu9Tn0MYp69hIqXE6Wwn+c2vkX3mDzjdXTAVrsiBIPrKS/Hf+1foa9a5XZAXKBzTJps0\nsZImjmmPRD7EkUiDHjTQffoojytgJB2UxDoNFWFfgR89oKOoClbaIj2Yws5Y6AEDT773SKrLHtG9\nycYyCE1xndQNd50rbQczZZIcKbYOlARd+4kchODwsVbKcutXLOmS7GMiWapHRfPqqIZ6QlIhHYmd\nscgOZ7EzI2Mhj4yj+TT0gDFKsHA8OJaDlTIxkyZ21hqJro1EeEb0hFSvhmM5JNpjSMBf6L52J1tI\nSUdiZ93Xz85YI4KJI+OrAtXQMEIGqmdqinUni6Huffzum1ey8h1/T1n9pRjecE4ZAd8ppLROhFjT\nNnZ+9zMM7nmdutu/cD6ltMZ9M86LCI9EMkyCl+0XGJbDF4SnjZSSIQbZ6mxmu7ONJnmQPtlDUg5j\nY+MRPiJEKBNl1IqZzFXmM1eZR4EY32voXEAoCrzvfchLL3XTWHkjehdXXgmPPgI1NYiyEcVOXUfM\nnIn8wAfgiitcggJu7U5REZSWHv39ve+By9dBJssowqGqbj1Q8YhdxtIlUFwEg0Nu2/+RiQk3jVZ1\namaOmcd+S/rnP8Ra/ypyODH2AEVBKSlHnVmPKClDCeWBbbvWIF0d2IcOIvt6Rqe+HBs50EfmsYfB\nshAf/jTa4mWnNK/x4HS0kX3yUZxEHGv7ptFkR1FQiktRZ9QjSsvduSoKMhHHbm91jWePnyuAZWG3\nNJN95g+otXV4bj09PzVp28ihQZLf+Duyzz2B093ppi6PgygqQZ1Z7xZ6B0OgachEAqe/B3vfbpz+\nXjeCdyyGE1gbXyf14++5xpbrrjmtOZ4PUHQVb1iFsPfkBx8D1VAJlk++iFvzauOOo+oqvgJ/zgiT\nUBWMoGdMQXAunMqxJx1LEWg+fcJF3CeCoikYIc8JjU3BfR3CM06toFYorv+Y5j0vbpMnhe4NUbvw\nZhKDrWSSg3h8+ajaWPX7qWpqUDQDT7j4ghKbPavvpESSIUOP00237Br56aRbdtMu29jpbCfB6F7+\n/XIfXze/ekpEYaao53L1KlYoK09pfjEZ5RX7RZ5wHhs11m3qe5ihuK3mDg4xGeV551lesp9nn7OX\nDtnOgOwnQQKTLA4OGjp+fIREHhEKKBGlLFAW8jHtk8wQdWhi4i99k3OQ/7C+RZaJpzkKKGSlspqb\ntVtOfKAQLqmpqRm9ubg4t4igx4OorHTJ0XhDappbDD375F1joqTEbWefJKTjYO99k/RD92NufB0Z\nP64N9bDg45p1aHMWoJSUIYJ54PW49iGZNDIew+7uwH5zB+YfX8bavnkUmZA9XWSffQLh9eErLkEp\nr5z0ys5ubnR1mzJpSI60Nasa2rJV6KvXoc1dgFJc5tp6eEYuXpkMMjqE3d6CtWU92Zefwzl0cPTA\nZhZrz04yTz2KfvUNiNCpFzHLWJT0z3+Qm+wYHtT6BoyrrkedM98lO6EwwuNxSVk2gxxO4PR0Y+3a\nhvnaC1hv7hjV3ScTccxXX0CtqEIpKUdrOImMwkWMzkMZtr2aoK/T5O13FxIu1FBV9/1q2Zempz3L\n7EV+woUXxs13GmcfqmYQLmmgefsj9LZuwvCGURRtTLxj+U1fIr90zqTPp4cKKFn1ToxIKflzVqP5\nT0+J+2zirH17EjLBHufNEUfvHnplD72ymx7ZQ5/sYYABLMaGdrtkJ7+1Hzylc61W1lIpKk+Z8KRk\nkk3ORn5o/deRbQvFItYolzKDmVjSpEW28Jj9KI/Yv2WLs4kkuV18TbJEyRKVUdpoRUhBh2znI9on\nTmlOAD2yix9bPzilVv0aUYuCws2chPBcBJCOg0zESP/6f7A2vIaMHpNTFgJ8fjw3vhPj+negr77M\nJSpa7tWlzGaxVx9CnbuQ7NO/J/vsEy4ZGYHT1U72mcdQZtThu+fTSN2YXI3M0CByaPC4ud6Ccf3b\n3cLe8qrRnm3HPjaVxFqyAqWimvQDP8VpaXJrpw7vjw5h7dqGtW0j+qVXnVIHnROPYW5+g/T9P8bp\n6R5FdkR+BG35GjzvfA/GZVe5r6cxzgpbSuwVa1BnzyX7+MOYLz2LTB79zsiBPrLPP4VSUY1SWY0S\nmJhf08UGRREkojbbX01w5a0R8iJwWKRLUUHTBRe4b+M0zjCsbIqeQ+vRDD9ef6GrtJzjQyOUqSmw\n1gP5FC+7kXDDCoxwMbo/fPIHnWOcNcITk1Fecp7n78wvXxApq8PolT1kSGNLm2bZzMP2g3zf+h5t\nsvWUxvHgoVxUUKPUouaUG5zGaSOVwtq0nszvHsDp7Rm1SwRD6Ouuxn/vX6HOmovwnjjtIAwDrW42\nalkFWsM8ZDLppscO36SlxO5oI/3AT9HXXYNWP8dN4U0BRCCIfsW1+O79ItpE5urzoy9cglJYBNkM\nye9/ZyTNKI/MVfb3kn3+KfQ1l0+Y8EjHwWlpIvPgz7Eb9x43xxDa0lX4PvoZPNffPM4Ixz5AuGm1\nohKUohLk8DDmy8+OOsQ+sAfzpWfQ116BsmTFhOZ4PuHAdjcyJ3GJS36RRrhQo/VAmnTSwbYkkRKd\nYJ7KcMxmqM9CUSCTkZTXGkSKNUqrDS5ZE6RxZ3rU2IM9JvEhG49XQdOPEuuBHpP+LpN00sHjU4gU\naxSU6hzcmcLMSixTkl+sUVpl4PFNM6W3Amwrw0D7TmavvpOZi28jmF+Zs2hZ80xNZ5+ie/AWVeIt\nGj/af77hrBGewyTHde4eS3gkEhNzTNpGRcWD58QWCcfBjx+NyeeHAQYZICVT9MhuHrUf5j+tf6dT\ndgDgwUsAPz7hR8dARUEgcHDIkiUt0yRJkiFNgShkkbIYL6eW5z98npmijmGGsbBxsLGlzeE/FiYJ\nctSrvAUgpcQZ6CP1s/twervBOSbtohuos+cS+MuvoM5ZcEot5cIfQFu8gsAX/g/xv/4s9t5dRzvM\nshmcgwfIPPBTlD/9XyjFpZMvWjw817/6e5eYTXSuQqCUVuC9+xNknnoU+8BetzNuBDIec1Nzto3U\n5ITmKYcTWLu2kfnDQ2POpc6Zh/f2D0yM7BwDJRBEX3WpO/a2jW7K8XC60HGwGveSffZxtEuWup15\nF1BdwG/+sxd/SMUfUvD4FOYu9TNzvo/Hfz6AmXFIDzvULfBRWeeh81CWrS/HKa816Gk3WXVtiBXX\n5FFSlbtou60xwxP3D5BKONzzN2VUz1JwbNj8Ypw9m5MkEw6FpTqXrAmQV6Dx8u+jxIdsBntMZi3y\nceWt+VTPOvVrzjQuPGi6l5IZK0FKMsMDKIqWs14nGKlCMd6aqdGz9qy9wssMUcc16nU592dkhibZ\nyEHZOCq1lU+ES5TF5ImJ5wcbxFzKRNmk5wyQJcsgAzxo/5KfWz+hU3YgEOjoLFQu4TLlCpYrK6gV\nMwiLfHQM4sRolk3stLez3nmdnc4OKkQly5VVpzWH2cocfmj8jAH6GZKDDDLEkBwkKgcZkkN0yU7+\n4DyCw/kvfz7lMF0F5ezTj0F69OpYqajCuOlWtIVLTmtoYRjoq9dhXHMjmcF+nI62I/tkcpj0/T/G\n85673AjLJE3zlMpqPG+/fVwrkxPOU1Vdb7Yb3km6p9stZD48z1QSu2k/MpN2004nIxJS4jQdwHzt\npdFq2gA+P/plV+O55fSKoJVwBH3JCvQrryf7xCOjWu+d1kNuuuujn0HkT16t9Wyiv9tkwaoAV9ya\nT15EIxG12LV+mOGYzV2fL0UIePbBQTa9EKey3kNBqc4nv1rJoT1pnrx/gPyS1LiE55K1QeJRm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aQaOQiHdqxIJPhGnCMwH4cK0jLgZYpiSTkZSUq7zvY4FR9QQVNRpllSqqJvAFBD6/oLXZIhhW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\nEnHJiXS8DAP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kWnzf+GO893yd/Ia15De8ibVrK/bRwyrqk06pLi+vH620DK2mHmPGHMyF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SyHxsinyaZGGOlppufYy/S1vE4+e5b/Zeo5It0HOX3gKfwlCyibf/2cZ5yK9pFJhDH0LFIa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I43wMvfjXTLzzNNZY/8xOBHqsinX/4s9Rw7FLYv5eKU+h5zhjb/6IqUOv4EyP4uan8Yp5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\n5jn1owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OuI0wbuM5kqO",
        "colab_type": "text"
      },
      "source": [
        "#Sentiment Analysis using NLTK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJjd327y5nI1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "033777e7-9fec-484a-aeba-aa0511376899"
      },
      "source": [
        "nltk.download('vader_lexicon')\n",
        "\n",
        "def nltk_sentiment(sentence):\n",
        "    from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "    \n",
        "    nltk_sentiment = SentimentIntensityAnalyzer()\n",
        "    score = nltk_sentiment.polarity_scores(sentence)\n",
        "    return score"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pw0xPWjx7sJ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "bc4b1c8c-54d7-40fb-cd19-7265ac6d98fa"
      },
      "source": [
        "nltk_results = [nltk_sentiment(row) for row in data['comments']]\n",
        "results_df = pd.DataFrame(nltk_results)\n",
        "data['sentiment_nltk'] = results_df['compound']\n",
        "results_df['compound'].head(20)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0.6562\n",
              "1    -0.5200\n",
              "2     0.0000\n",
              "3     0.4404\n",
              "4    -0.2023\n",
              "5     0.4558\n",
              "6    -0.9278\n",
              "7    -0.8871\n",
              "8     0.7269\n",
              "9     0.4404\n",
              "10    0.9100\n",
              "11    0.6705\n",
              "12    0.4404\n",
              "13    0.0000\n",
              "14    0.0000\n",
              "15    0.7964\n",
              "16    0.6868\n",
              "17    0.8860\n",
              "18    0.5267\n",
              "19   -0.2960\n",
              "Name: compound, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fLo4T6L77Tc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "65f609a0-ffd4-4765-c8a0-3dedcf98f4d1"
      },
      "source": [
        "data.comments_sentiment_tb.head(20)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    -0.114497\n",
              "1     0.285714\n",
              "2     0.000000\n",
              "3     0.700000\n",
              "4    -0.050000\n",
              "5     0.263889\n",
              "6     0.091667\n",
              "7    -0.079167\n",
              "8     0.011448\n",
              "9     0.700000\n",
              "10    0.457143\n",
              "11    0.266667\n",
              "12    0.250000\n",
              "13    0.000000\n",
              "14    0.200000\n",
              "15    0.566667\n",
              "16    0.216667\n",
              "17    0.000000\n",
              "18    0.000000\n",
              "19    0.000000\n",
              "Name: comments_sentiment_tb, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VeMo8N7U8nHQ",
        "colab_type": "text"
      },
      "source": [
        "##Important Observations:\n",
        "\n",
        "1. The nltk sentiment analysis using Vader lexicon doesn't seem to be the best because it is kind of obsolete. An improvement over it is StanfordCoreNLP as it was trained on movie reviews that can handle such data in a more apt fashion. \n",
        "\n",
        "2. The idea of analyzing GitHub issues for positive and negative sentiment requires hand curated corpus of essential words that are encountered in a typical repository. \n",
        "\n",
        "3. Even the original project needs to be open to other alternatives to NLTK, or have comparative analysis in order to determine what is best for our use case. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZzwNJy3_NXe",
        "colab_type": "text"
      },
      "source": [
        "#Task 1: Obtain sentiment distribution and extract outliers in positive and negative sentiments\n",
        "\n",
        "Things to observe:\n",
        "\n",
        "1. Most of the comments have been marked as neutral by NLTK package. Even with TextBlob, very few comments having sentiment rating>0.5 or rating<-0.5. [Assuming 0.5 and -0.5 as threshold limits on either side.\n",
        "\n",
        "2. The dataset is unlabelled and too small in order to apply NaiveBayesClassifier of NLTK. So, this is not simple supervised learning. The dataset relies on in-built methods which have seen other datasets and have generalized. Hence, we need to predict labels instead of verify if label is right or wrong."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diLA2Nvv-bKv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "outputId": "d40d6c0c-923b-4266-ee36-6999a0474f0f"
      },
      "source": [
        "sns.distplot(dffilter['comments_sentiment_tb'], hist=True, kde=True, \n",
        "             bins=int(30), color = 'darkred',\n",
        "             hist_kws={'edgecolor':'black'},axlabel ='Sentiment')\n",
        "plt.title('Sentiment Distribution for TextBlob')\n",
        "\n",
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 10,10"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AQAMxBQDQQEwBADQQUwAADcQUAEADMQUA0EBMAQA0EFMAAA3EFABAAzEFANBATAEANBBT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\nNMD/ASkA1R2KQ6aaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8N1rOaxDavI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "71769b2b-6df3-4ea3-f0d2-e329a5aa3ca7"
      },
      "source": [
        "sns.distplot(results_df['compound'], hist=True, kde=True, \n",
        "             bins=int(30), color = 'blue',\n",
        "             hist_kws={'edgecolor':'black'},axlabel ='Sentiment')\n",
        "plt.title('Sentiment Distribution for TextBlob')\n",
        "\n",
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 10,10"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9B6o+fXKgGjOm3hWpHpwBXR3ee+/l5VxGjz6QN9/MvVHnnw9HHAHL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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7Hr0bFR_5CG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "a4ba4db6-7228-4b9b-c6eb-c90ba13dbd38"
      },
      "source": [
        "# TextBlob\n",
        "pos_outlier = data[data['comments_sentiment_tb']>=0.5]\n",
        "neg_outlier = data[data['comments_sentiment_tb']<=-0.5]\n",
        "outliers = pd.concat([pos_outlier, neg_outlier])\n",
        "outliers[['comments', 'status','comments_sentiment_tb']]"
      ],
      "execution_count": 175,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comments</th>\n",
              "      <th>status</th>\n",
              "      <th>comments_sentiment_tb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ah good to know. `numpy` is not a very strict ...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>I think that's a good idea. Not the highest pr...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ok, sounds good to me :)</td>\n",
              "      <td>open</td>\n",
              "      <td>0.566667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>I think this is rather an list_replicas issue ...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>@TomasJavurek please have a look on this. I am...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Is this also the reason gridftp fails and thus...</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Still seeing this in Rucio-admin version 1.21....</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             comments  ... comments_sentiment_tb\n",
              "3   Ah good to know. `numpy` is not a very strict ...  ...              0.700000\n",
              "9   I think that's a good idea. Not the highest pr...  ...              0.700000\n",
              "15                           Ok, sounds good to me :)  ...              0.566667\n",
              "28  I think this is rather an list_replicas issue ...  ...              0.500000\n",
              "48  @TomasJavurek please have a look on this. I am...  ...              0.500000\n",
              "49  Is this also the reason gridftp fails and thus...  ...             -0.500000\n",
              "82  Still seeing this in Rucio-admin version 1.21....  ...             -0.500000\n",
              "\n",
              "[7 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wka0EfK7Aa1J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5b7f4fb7-1766-42dd-a7ea-6a00e66d2a06"
      },
      "source": [
        "# NLTK\n",
        "pos_outlier_nltk = data[results_df['compound']>=0.5]\n",
        "neg_outlier_nltk = data[results_df['compound']<=-0.5]\n",
        "outliers_nltk = pd.concat([pos_outlier_nltk, neg_outlier_nltk])\n",
        "outliers_nltk[['comments', 'status','sentiment_nltk']]"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comments</th>\n",
              "      <th>status</th>\n",
              "      <th>sentiment_nltk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>- the minimal scope and audience claims expect...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.6562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>+ new login method selection page was introduc...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.7269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>@mlassnig @TomasJavurek would you be okay with...</td>\n",
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              "      <td>0.9100</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>@bziemons sure. Thanks a lot. As far as I reme...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.6705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ok, sounds good to me :)</td>\n",
              "      <td>open</td>\n",
              "      <td>0.7964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>The ctrl+c is already implemented in the downl...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.6868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Yes the 12h default timeout is too short, give...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.8860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Since we are discussing this also via mail:\\r\\...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.5267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>In meanwhile, we discussed with Tobi whether c...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.5037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>You skipped the 'modifications' section in the...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.6361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>I think this is rather an list_replicas issue ...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.7506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Creating subdirectories is fine. This is in fa...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.6094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>For what it's worth, I would also find this ve...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.7955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>This would indeed be very useful and much appr...</td>\n",
              "      <td>open</td>\n",
              "      <td>0.7574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Oops, I should know better than that!\\r\\n\\r\\n&gt;...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.6343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Thanks David.\\r\\n\\r\\nI could've sworn we had t...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.8289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>yeah I know, but gfal stat is anyway calling p...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.9228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>@vokac i tried it like this in the beginning, ...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.5687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ok I asked around a bit but nobody knows anyon...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.6310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>there currently is no replica attribute that c...</td>\n",
              "      <td>closed</td>\n",
              "      <td>0.8374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>You are right. We do not support 2.6 anymore a...</td>\n",
              "      <td>open</td>\n",
              "      <td>-0.5200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SiteX-&gt;EOS-&gt;CTA is still working as far as i k...</td>\n",
              "      <td>open</td>\n",
              "      <td>-0.9278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>In addition I also tried using fts3.cern.ch an...</td>\n",
              "      <td>open</td>\n",
              "      <td>-0.8871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Ok in fact it was the server which rejected th...</td>\n",
              "      <td>open</td>\n",
              "      <td>-0.6702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Traceback :\\r\\n```\\r\\nrucio -v download test:/...</td>\n",
              "      <td>open</td>\n",
              "      <td>-0.9283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>I checked one job log and it looks like writes...</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.5719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>No this is somewhat hidden in the protocol def...</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.5994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>the md5 wasn't actually perferred, it was alre...</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.5106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>In case file doesn't exist davix-http still re...</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.7947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>This problem also applies to get-limits\\r\\n\\r\\...</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.7579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Yes, these methods are in the `accountclient`....</td>\n",
              "      <td>closed</td>\n",
              "      <td>-0.5789</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             comments  status  sentiment_nltk\n",
              "0   - the minimal scope and audience claims expect...    open          0.6562\n",
              "8   + new login method selection page was introduc...    open          0.7269\n",
              "10  @mlassnig @TomasJavurek would you be okay with...    open          0.9100\n",
              "11  @bziemons sure. Thanks a lot. As far as I reme...    open          0.6705\n",
              "15                           Ok, sounds good to me :)    open          0.7964\n",
              "16  The ctrl+c is already implemented in the downl...    open          0.6868\n",
              "17  Yes the 12h default timeout is too short, give...    open          0.8860\n",
              "18  Since we are discussing this also via mail:\\r\\...    open          0.5267\n",
              "22  In meanwhile, we discussed with Tobi whether c...    open          0.5037\n",
              "25  You skipped the 'modifications' section in the...    open          0.6361\n",
              "28  I think this is rather an list_replicas issue ...    open          0.7506\n",
              "29  Creating subdirectories is fine. This is in fa...    open          0.6094\n",
              "39  For what it's worth, I would also find this ve...    open          0.7955\n",
              "40  This would indeed be very useful and much appr...    open          0.7574\n",
              "42  Oops, I should know better than that!\\r\\n\\r\\n>...  closed          0.6343\n",
              "47  Thanks David.\\r\\n\\r\\nI could've sworn we had t...  closed          0.8289\n",
              "53  yeah I know, but gfal stat is anyway calling p...  closed          0.9228\n",
              "66  @vokac i tried it like this in the beginning, ...  closed          0.5687\n",
              "75  Ok I asked around a bit but nobody knows anyon...  closed          0.6310\n",
              "87  there currently is no replica attribute that c...  closed          0.8374\n",
              "1   You are right. We do not support 2.6 anymore a...    open         -0.5200\n",
              "6   SiteX->EOS->CTA is still working as far as i k...    open         -0.9278\n",
              "7   In addition I also tried using fts3.cern.ch an...    open         -0.8871\n",
              "31  Ok in fact it was the server which rejected th...    open         -0.6702\n",
              "34  Traceback :\\r\\n```\\r\\nrucio -v download test:/...    open         -0.9283\n",
              "46  I checked one job log and it looks like writes...  closed         -0.5719\n",
              "54  No this is somewhat hidden in the protocol def...  closed         -0.5994\n",
              "59  the md5 wasn't actually perferred, it was alre...  closed         -0.5106\n",
              "60  In case file doesn't exist davix-http still re...  closed         -0.7947\n",
              "78  This problem also applies to get-limits\\r\\n\\r\\...  closed         -0.7579\n",
              "84  Yes, these methods are in the `accountclient`....  closed         -0.5789"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEZveR_4HiTP",
        "colab_type": "text"
      },
      "source": [
        "#Task 2: Global frequency distribution of last comments of each closed issue"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98-v3N8RVkj2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7db4d090-54a9-44f4-fa45-c4ccdc7a16ea"
      },
      "source": [
        "import numpy as np\n",
        "glob_df = data[data['status'] == 'closed']\n",
        "glob_df = glob_df.drop_duplicates('issue_no', keep = 'last') #Extracting last comment of each issue \n",
        "glob_df.head()\n",
        "freq_dist = glob_df.comments.str.split(expand=True).stack().value_counts() #frequency count of comments instead of cleaned_comments\n",
        "freq_dist = freq_dist.to_frame()\n",
        "freq_dist['Word'] = freq_dist.index\n",
        "freq_dist.columns = ['Frequency','Word']\n",
        "freq_dist.reset_index(drop=True, inplace=True)\n",
        "freq_dist.head()"
      ],
      "execution_count": 182,
      "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>Frequency</th>\n",
              "      <th>Word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>36</td>\n",
              "      <td>the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19</td>\n",
              "      <td>is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18</td>\n",
              "      <td>in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13</td>\n",
              "      <td>to</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>not</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Frequency Word\n",
              "0         36  the\n",
              "1         19   is\n",
              "2         18   in\n",
              "3         13   to\n",
              "4         10  not"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjxeE_dvYEhe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "43ca6cbd-9e74-4259-d6b1-fd7fba0bc4ff"
      },
      "source": [
        "ax = freq_dist.iloc[:15].plot.bar(x='Word', y='Frequency', rot=90)"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d3Ul6O/c7vaouqqqLrrvuul0aFgBgESwroKpqv8zi6Y+6+0/ni79RVYfOrz80ybXbum93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\n9zKL8U7yySS/2t0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            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ckba2yrNHw-K",
        "colab_type": "text"
      },
      "source": [
        "#Task 3: Duplicate or similar tickets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrNc6YiHIWC-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "a32c2df3-2497-4fc9-a28c-3b47d10c688a"
      },
      "source": [
        "import graphviz\n",
        "sim = data[['issue_no','issue_body']].copy()\n",
        "sim = sim.drop_duplicates()\n",
        "gv = graphviz.Digraph()\n",
        "\n",
        "similarities = []\n",
        "for k,i in zip(sim['issue_no'],sim['issue_body']):\n",
        "    for l,j in zip(sim['issue_no'],sim['issue_body']): \n",
        "        similarity = k, l, cosine_sim(i, j)\n",
        "        similarities.append(similarity)\n",
        "print('Ticket Checking \\t Ticket1 \\t Ticket2 \\t Cosine Similarity')\n",
        "for s in similarities:\n",
        "    if s[0] == s[1]:\n",
        "        if s[2] < 0.99:\n",
        "            print('STRANGE:\\t %s \\t %s \\t\\t %f' % s)\n",
        "        continue\n",
        "    if s[2] > 0.15:\n",
        "        print('SIMILAR:\\t\\t %s \\t\\t %s \\t\\t %f' % s)\n",
        "        gv.edge(str(s[0]), str(s[1]))"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ticket Checking \t Ticket1 \t Ticket2 \t Cosine Similarity\n",
            "SIMILAR:\t\t 3410 \t\t 2881 \t\t 0.164261\n",
            "SIMILAR:\t\t 3398 \t\t 3164 \t\t 0.171759\n",
            "SIMILAR:\t\t 3398 \t\t 3023 \t\t 0.261481\n",
            "SIMILAR:\t\t 3398 \t\t 3153 \t\t 0.161027\n",
            "SIMILAR:\t\t 3377 \t\t 3209 \t\t 0.150536\n",
            "SIMILAR:\t\t 3164 \t\t 3398 \t\t 0.171759\n",
            "SIMILAR:\t\t 3164 \t\t 3023 \t\t 0.202557\n",
            "SIMILAR:\t\t 3164 \t\t 3153 \t\t 0.199585\n",
            "SIMILAR:\t\t 3031 \t\t 3204 \t\t 0.198318\n",
            "SIMILAR:\t\t 3023 \t\t 3398 \t\t 0.261481\n",
            "SIMILAR:\t\t 3023 \t\t 3164 \t\t 0.202557\n",
            "SIMILAR:\t\t 2881 \t\t 3410 \t\t 0.164261\n",
            "SIMILAR:\t\t 3209 \t\t 3377 \t\t 0.150536\n",
            "SIMILAR:\t\t 3204 \t\t 3031 \t\t 0.198318\n",
            "SIMILAR:\t\t 3153 \t\t 3398 \t\t 0.161027\n",
            "SIMILAR:\t\t 3153 \t\t 3164 \t\t 0.199585\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-up6eM_OoV4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "1d9755e2-5804-4d07-9ef2-4aebd0ba6380"
      },
      "source": [
        "#Plotting the similar issues as a graph. \n",
        "#Please note that the cosine similarity threshold has been kept very low, at 0.15\n",
        "gv"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f0ad6484048>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0NPGcKiH55z",
        "colab_type": "text"
      },
      "source": [
        "#Task 4: Identify questions in issue definition (title or body)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amPCHaZW-ypW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "710a1a68-6441-4da8-d11d-924df73454ad"
      },
      "source": [
        "nltk.download('nps_chat')\n",
        "posts = nltk.corpus.nps_chat.xml_posts()[:10000]\n",
        "\n",
        "\n",
        "def dialogue_act_features(post):\n",
        "    features = {}\n",
        "    for word in nltk.word_tokenize(post):\n",
        "        features['contains({})'.format(word.lower())] = True\n",
        "    return features\n",
        "\n",
        "featuresets = [(dialogue_act_features(post.text), post.get('class')) for post in posts]\n",
        "size = int(len(featuresets) * 0.1)\n",
        "train_set, test_set = featuresets[size:], featuresets[:size]\n",
        "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
        "print(nltk.classify.accuracy(classifier, test_set))"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package nps_chat to /root/nltk_data...\n",
            "[nltk_data]   Package nps_chat is already up-to-date!\n",
            "0.668\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRWCoPVIfBSE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "1669cf54-fbb1-4efd-bb3f-a962c43c9bda"
      },
      "source": [
        "from nltk.tokenize import PunktSentenceTokenizer\n",
        "tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()\n",
        "\n",
        "issues_df = data[['issue_no', 'issue_title', 'issue_body']].copy()\n",
        "issues_df = issues_df.drop_duplicates('issue_no', keep = 'first')\n",
        "\n",
        "for i,j,k in zip(issues_df['issue_title'], issues_df['issue_body'], issues_df['issue_no']):\n",
        "    title_question = classifier.classify(dialogue_act_features(i))\n",
        "    for sentence in tokenizer.tokenize(j):\n",
        "        body_question = classifier.classify(dialogue_act_features(sentence))\n",
        "        if title_question == 'ynQuestion' or title_question == 'whQuestion' or body_question == 'ynQuestion' or body_question == 'whQuestion':\n",
        "            print(f\"Issue number {k} is mostly a question.\")"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Issue number 3402 is mostly a question.\n",
            "Issue number 3084 is mostly a question.\n",
            "Issue number 3084 is mostly a question.\n",
            "Issue number 3084 is mostly a question.\n",
            "Issue number 2881 is mostly a question.\n",
            "Issue number 3286 is mostly a question.\n",
            "Issue number 3286 is mostly a question.\n",
            "Issue number 3204 is mostly a question.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKA5QO16IEuA",
        "colab_type": "text"
      },
      "source": [
        "#Task 5: Identify questions in comments "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPwiPm7iFB6f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "63e56706-7e06-4c10-84d2-3c423e6f4bc6"
      },
      "source": [
        "comments_df = data[['issue_no', 'comments']].copy()\n",
        "\n",
        "for i,j in zip(comments_df['comments'], comments_df['issue_no']):\n",
        "    for sentence in tokenizer.tokenize(i):\n",
        "        comment_question = classifier.classify(dialogue_act_features(sentence))\n",
        "        if comment_question == 'ynQuestion' or comment_question == 'whQuestion':\n",
        "            print(f\"Comment in issue number {j} probably has a question.\")"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Comment in issue number 3377 probably has a question.\n",
            "Comment in issue number 3260 probably has a question.\n",
            "Comment in issue number 3260 probably has a question.\n",
            "Comment in issue number 3031 probably has a question.\n",
            "Comment in issue number 3031 probably has a question.\n",
            "Comment in issue number 3031 probably has a question.\n",
            "Comment in issue number 3031 probably has a question.\n",
            "Comment in issue number 3031 probably has a question.\n",
            "Comment in issue number 3031 probably has a question.\n",
            "Comment in issue number 3312 probably has a question.\n",
            "Comment in issue number 3312 probably has a question.\n",
            "Comment in issue number 3312 probably has a question.\n",
            "Comment in issue number 3286 probably has a question.\n",
            "Comment in issue number 3286 probably has a question.\n",
            "Comment in issue number 3204 probably has a question.\n",
            "Comment in issue number 3160 probably has a question.\n",
            "Comment in issue number 3160 probably has a question.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}