Blockchain.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "How can this project be improved:\n",
        "1. Try to set up clusters so there are two of them and they already have 100% purity\n",
        "2. Figure out how to get more hot-tokens (tokens that appear 4 times more across all users of one subreddit than the other, and there is at least 10 of them for one subreddit)\n",
        "    - Better attempt of pickinig two distinct and specific subreddits\n",
        "    - Eliminate overpopulated tokens\n",
        "    - Try dfferent size of small tokenizer (currently most common 500)\n",
        "3. Lemmatization process probably has erros in full implementation across the project\n",
        "4. Play with the simulation itself. Try different hyperparameters, dfferent strategiies for voting, fiix SLACK mechanic. Try odd number of clusters\n"
      ],
      "metadata": {
        "id": "eRrhxFZN_BLY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uw_vHitmsBzs"
      },
      "source": [
        "###### Important set up\n",
        "- Sets up google drive and a lemmatizer function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5hXskjnIHOv",
        "outputId": "f451552d-9d86-47a0-f896-ac8827655c9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Mount your drive so you can read/write files permanently\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h35SDM0WIGjV",
        "outputId": "73cf1b25-6a66-4910-ce67-6342b297fc6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/p2p tokenizer\n"
          ]
        }
      ],
      "source": [
        "# Path to your folder on drive\n",
        "%cd /content/drive/MyDrive/Colab Notebooks/p2p tokenizer/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpZc6xR2GxkG",
        "outputId": "fff42da4-5abb-4736-b7f3-dcec442054ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "# Lemmatizing helper funcion\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download(\"wordnet\")\n",
        "nltk.download(\"omw-1.4\")\n",
        "\n",
        "wnl = WordNetLemmatizer()\n",
        "lem = lambda word: wnl.lemmatize(wnl.lemmatize(word, pos=\"n\"), pos=\"v\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WN01UidMg_5J"
      },
      "source": [
        "#### Token preparation\n",
        "- you do not need to run this every time. Run it once to store all tokens in a file and then you have a checkpoint to work with\n",
        "- Stores 8502 unk tokens in unk_tokens.txt from the two datasets (NBA and Politics\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uf9-WhJ2PCAV"
      },
      "source": [
        "##### General tokenizer (with full vocab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sjn4gM2ZMHZx"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "from tokenization import FullTokenizer\n",
        "from collections import Counter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ypS7-QHfcVqJ"
      },
      "outputs": [],
      "source": [
        "# Tokenizer with full vocab\n",
        "tokenizer = FullTokenizer(\"vocab.txt\", True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "49CsU9JzgORP"
      },
      "outputs": [],
      "source": [
        "with open(\"nba.txt\") as n, open(\"politics.txt\") as p:\n",
        "  nba_comments = \" \".join(json.loads(row)[\"comment\"] for row in n)\n",
        "  politics_comments = \" \".join(json.loads(row)[\"comment\"] for row in p)\n",
        "\n",
        "all_comments = nba_comments + ' ' + politics_comments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOD8iEQHPIy-"
      },
      "source": [
        "##### Small tokenizer (with 500 most common tokens in vocab)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xqOJGQw4Ix6"
      },
      "source": [
        "###### Small Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gCvgMthOjecf"
      },
      "outputs": [],
      "source": [
        "# et most common 500 tokens and save them to a file\n",
        "tokenized = tokenizer.tokenize(all_comments)\n",
        "tokenized_counter = Counter(tokenized).most_common(500)\n",
        "small_vocab = \"\\n\".join(key for key, value in tokenized_counter)\n",
        "\n",
        "with open('small_vocab.txt', 'w') as f:\n",
        "  f.write(small_vocab)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHY9k--cPVDS"
      },
      "source": [
        "###### Get UNK tokens from the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-HJVVXR1Dm6"
      },
      "outputs": [],
      "source": [
        "small_tokenizer = FullTokenizer(\"small_vocab.txt\", True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yq2zlwM72QBN"
      },
      "outputs": [],
      "source": [
        "all_tokens = list(lem(token) for token in Counter(tokenized).keys())\n",
        "small_vocab_lemmmed = list(lem(key) for key, value in Counter(tokenized).most_common(500))\n",
        "\n",
        "# Get all tokens not in most common 500\n",
        "set_diff = set(all_tokens) - set(small_vocab_lemmmed)\n",
        "\n",
        "clean_token_diff = [i for i in set_diff if \"#\" not in i]\n",
        "unclean_small_vocab_lemmmed = [token for token in list(small_vocab_lemmmed) if \"#\" in token]\n",
        "\n",
        "small_vocab_lemmmed_list = list(small_vocab_lemmmed) # 500 most common tokens\n",
        "clean_token_diff_list = list(clean_token_diff) # Not first 500 tokens without wildcard (all other tokens without wildcards)\n",
        "unclean_small_vocab_lemmmed_list = list(unclean_small_vocab_lemmmed) # 500 most common tokens with wildcard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VZSnGzv0m-vv"
      },
      "outputs": [],
      "source": [
        "# From all tokens that are classified as unique tokens (dont have a wildcard after tokenization),\n",
        "# find those that do not get matched with wildcard tokens if we choose to use tokenizer with only 500 most common tokens.\n",
        "# Those are UNK tokens since the wildcard tokens (500 most common) cannot catch them\n",
        "\n",
        "import re\n",
        "\n",
        "to_rmv = [\".\", \"^\", \"|\"]\n",
        "\n",
        "for i in to_rmv:\n",
        "  try:\n",
        "    unclean_small_vocab_lemmmed_list.remove(i)\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "i = 0\n",
        "not_found_tokens = []\n",
        "count = 0\n",
        "for clean in clean_token_diff_list: # clean uncommon\n",
        "    for reg_token in unclean_small_vocab_lemmmed_list: # wildcard common\n",
        "        i += 1\n",
        "\n",
        "        try:\n",
        "            # Match with regex\n",
        "            if re.match(reg_token.replace(\"#\", \".\") + r\"\\b\", clean) is not None:\n",
        "                break\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "    else:\n",
        "        count += 1\n",
        "        not_found_tokens.append(clean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-n8c3ksDxT9a"
      },
      "outputs": [],
      "source": [
        "# Check which of the potential UNK token is an actual UNK token\n",
        "actual_unks = []\n",
        "for i in not_found_tokens:\n",
        "  if \"[UNK]\" in small_tokenizer.tokenize(i):\n",
        "    actual_unks.append([i, small_tokenizer.tokenize(i)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9wpSVqdz2WTz"
      },
      "outputs": [],
      "source": [
        "_unks = list([i[0] for i in sorted(actual_unks) if i[0].isalnum()])\n",
        "_word_tokens = [token for token in _unks if not any(char.isdigit() for char in token)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FCCaA4ff0bJH"
      },
      "outputs": [],
      "source": [
        "# Store unk tokens in a file\n",
        "with open('unk_tokens.txt', 'w') as f:\n",
        "  f.write(\"\\n\".join(_word_tokens))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9fMkZ884x3T"
      },
      "source": [
        "#### Simulation preparation\n",
        "- uses binary representation of integer to represent user tokens for faster simulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2f3e-0CC9vuM"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "from tokenization import FullTokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YYSu8_V1klT"
      },
      "source": [
        "##### Get all comments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fOd4S_fB0Dvf"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ulbYhXGx0Dvh"
      },
      "outputs": [],
      "source": [
        "with open(\"nba.txt\") as n, open(\"politics.txt\") as p:\n",
        "    nba_comments = defaultdict(list)\n",
        "    politics_comments = defaultdict(list)\n",
        "\n",
        "    for row in n:\n",
        "        nba_comments[json.loads(row)['author']].append(json.loads(row)['comment'])\n",
        "\n",
        "    for row in p:\n",
        "        politics_comments[json.loads(row)['author']].append(json.loads(row)['comment'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nw8erwzhYzma"
      },
      "outputs": [],
      "source": [
        "with open('unk_tokens.txt', 'r') as n:\n",
        "  unk_tokens = n.read().split()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A3D0pbOW0Dvi"
      },
      "outputs": [],
      "source": [
        "all_comments = nba_comments.copy()\n",
        "all_comments.update(politics_comments)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtYjHvW31xIb"
      },
      "source": [
        "##### Get all users' UNK tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WKjopRSS1we7"
      },
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "\n",
        "\n",
        "all_unk_tokens = unk_tokens\n",
        "\n",
        "\n",
        "all_tokens_count = len(all_unk_tokens)\n",
        "all_tokens_user_count = [0] * all_tokens_count\n",
        "\n",
        "# Uncomment this to remove tokens that have more then 50 occurences\n",
        "\n",
        "\"\"\"\n",
        "for user in _users:\n",
        "    user_bin = \"0\" * (all_tokens_count - len(bin(user)[2:])) + bin(user)[2:]\n",
        "    for i in range(len(user_bin)):\n",
        "        all_tokens_user_count[i] += int(user_bin[i])\n",
        "all_unk_tokens = list(map(lambda x: x[1], filter(lambda token_key_value: all_tokens_user_count[token_key_value[0]] <= 50, enumerate(all_unk_tokens))))\n",
        "\"\"\"\n",
        "\n",
        "def get_user_unk_tokens(user):\n",
        "    \"\"\"\n",
        "    Get a user's UNK tokens\n",
        "    :param user: user index.\n",
        "    :return: A list of integers representing a count for each token (array of 8502 integers).\n",
        "    \"\"\"\n",
        "    joined_comments = \" \".join(all_comments[user]).lower()\n",
        "    return [joined_comments.count(n) for n in all_unk_tokens]\n",
        "\n",
        "def get_user_unk_tokens_optimized(user):\n",
        "    \"\"\"\n",
        "    Get a user's UNK tokens using Counter\n",
        "    :param user: user index.\n",
        "    :return: A list of integers representing a count for each token (array of 8502 integers).\n",
        "    \"\"\"\n",
        "    joined_comments = \" \".join(all_comments[user]).lower()\n",
        "\n",
        "    word_counts = Counter(joined_comments.lower().split())\n",
        "    return [word_counts[unk] for unk in all_unk_tokens]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X8u6F93DfLcO"
      },
      "outputs": [],
      "source": [
        "\"\"\" Get all users and their tokens\n",
        "{\n",
        "    user1: [3, 0, 0, 1, 4...],\n",
        "    user2: [0, 4, 2, 1, 1...],\n",
        "    user3: [7, 0, 1, 8, 2...],\n",
        "}\n",
        "\"\"\"\n",
        "\n",
        "rels = { user: get_user_unk_tokens_optimized(user) for user in [i for i in all_comments] }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mteel81OxMBB"
      },
      "outputs": [],
      "source": [
        "# Converts array of booleans to an integer that has a binaru representation of that array\n",
        "# [1, 0, 0] => bin 100 => int 4\n",
        "bool_arr_to_int = lambda arr: int(\"\".join(map(lambda x: str(int(x)), arr)), 2)\n",
        "\n",
        "# All 200 users represented as array of 8502 bit integer representing existance of tokens\n",
        "# User with binary representation of 101100... has first token, third token, fourth...\n",
        "_users = list(bool_arr_to_int(list(map(bool, i))) for i in rels.values())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTIx3Dv2A3S1"
      },
      "source": [
        "##### Optimized token search with binary digits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GewuRjJfK5d"
      },
      "outputs": [],
      "source": [
        "def matching(source_user: int, target_user: int):\n",
        "    \"\"\"\n",
        "    Get a matching score for two users. Matching score is calculated by seeing how many\n",
        "    tokens both users have in common divided by the amount of tokens a source_user has\n",
        "    This method uses bitwise and to quickly determine the amount of same tokens\n",
        "    :param source_user: 8502 bit integer representing first user.\n",
        "    :param target_user: 8502 bit integer representing secoond user.\n",
        "    :return: Matching score between two users (float)\n",
        "    \"\"\"\n",
        "\n",
        "    if source_user == target_user: return 0 # This case would return too high value for the matching matrix\n",
        "    source_user_shift = 2 ** (len(bin(source_user)) - 2) + source_user\n",
        "    return bin(source_user_shift & target_user).count(\"1\") / bin(source_user).count(\"1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "imDmfP4RhnO6",
        "outputId": "b07c9a01-dad8-40a2-d436-e1da0ff72c77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n     u1, u2, u3, ..., un\\nu1    1 0.2 xxx\\nu2  0.2   1 yyy      ...\\nu3  xxx yyy   1\\n...\\nun      ...            1\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "# focus = _users[80:120]\n",
        "focus = _users\n",
        "matrix = list(list(matching(user_j, user_i) for user_i in focus) for user_j in focus)\n",
        "\"\"\"\n",
        "_user1 = {token1, token2, token3, token4}\n",
        "_user2 = {token4, token5, token6, token7, token12}\n",
        "...\n",
        "_usern = {token3, token6, token7}\n",
        "\n",
        "_users = [user1, user2, ..., usern]\n",
        "\n",
        "matrix = [\n",
        "    [matching(user1, user1), matching(user1, user2), ..., matching(user1, usern)],\n",
        "    [matching(user2, user1), matching(user2, user2), ..., matching(user2, usern)],\n",
        "    ...\n",
        "    [matching(usern, user1), matching(usern, user2), ..., matching(usern, usern)],\n",
        "]\n",
        "\"\"\"\n",
        "\n",
        "# User x with all others\n",
        "\n",
        "# User x with user y\n",
        "\n",
        "# Should look like this\n",
        "\"\"\"\n",
        "     u1, u2, u3, ..., un\n",
        "u1    1 0.2 xxx\n",
        "u2  0.2   1 yyy      ...\n",
        "u3  xxx yyy   1\n",
        "...\n",
        "un      ...            1\n",
        "\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "W06_xByCyR05",
        "outputId": "3aedfd8e-c5cf-4561-c3ec-0e98e4e93cc7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "ax = sns.heatmap(matrix)\n",
        "\n",
        "# Plot the matching matrix (matrix of matching scores for all users)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EKzy_ySHcw0B",
        "outputId": "8413e28a-d035-49a0-c077-7bbc8ddf82ae"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IaFCuxF4OBEEQQaSSB4GmA14YQ8FOAFHnhYN30REEQVQilay6JzPBDuQo2AkgqYDLiQRBEJVIpoI9O0EvjKFgJ4BUsuOfIAgiqKQq2bMT8BQdBTsBJBVwOZEgCKISqeRBoGIglw3ga6NgJ4CQZ4cgCMJ/qEvPK0t1V2+yg/faKNgJIEGXEwmCICqRSt6IpsizQ5QadV44eBE2QRBEJSLemyutLUg64PYJCnYCSCU7/gmCIIJKJXt20gFXrSjYCSA09ZwgCMJ/jBnPTgBVKwp2AggpOwRBEP6jku/NKs+OHLzXRsFOAFFddAGMsAmCICqRiu6zE/CMAgU7AaSSdw8EQRBBpZI9O6mArzsU7AQQdel5ZeWFCYIggko64AGBGWplJ3jrDgU7ASTo/Q4IgiAqkQwZlH0LBTsBRIyqg3jREQRBVCLpCvZTBn2TTcFOAAn6RUcQBFGJVHJ3ezIoEyWnkqVSgiCIoFLJ4yJUTQWp9JwoBdRUkCAIwn+IAUGqwjaiQS+rp2AngFDpOUEQhP8QgwBZBrIVdH8O+iabgp0AoipvDGCETRAEUYlobQWVtBmlQaBEyVGVAFaYVEoQBBFUtIpHEBUQI1IqP1Lw1h0KdgJIJZvgCIIggopW8agk3w4pO0TJURnFAnjREQRBVCIFyk4F2QzEdSeIXiQKdgJIJc9fIQiCCCopTXBTSZvRFCk7RKkhZYcgCMJ/aL0slbQZTQe8mS0FOwGEPDsEQRD+o8Czk6kcz05K0x1aDlhjQQp2AohYgVVJHyaCIIggU8nVWNo2J0F7aRTsBBCajUUQBOE/tAFBJdkMtBvroLU9oWAngFAHZYIgCP9R2FQwWAGBGVrzddA22hTsBJCgt+0mCIKoNLJZmad2YuHc0lpJHe61gVvQ1p6yBjtXXXUVJElSfc2ePZs/PjIygiVLlqClpQV1dXU4+eSTsX37dtXv2LhxIxYtWoSamhq0trbikksuQTqdLvVLKSlag3LQjGIEQRCVhjgJPB7JLa1BCwjM0AZuQXttkXIfwD777INnnnmG/zsSUQ7pwgsvxN///nc89NBDaGxsxLnnnouTTjoJL7/8MgAgk8lg0aJFaG9vxyuvvIJt27bh9NNPRzQaxc9//vOSv5ZSoScnRsJSmY6GIAiCEIOBeDSM/kS6omwGyQLPTrBeW9mDnUgkgvb29oLv9/b24q677sL999+Po446CgBw9913Y86cOVi5ciUOOeQQPPXUU3j//ffxzDPPoK2tDQcccAB++tOf4gc/+AGuuuoqxGKxUr+ckqDt5ZDOyoiEy3QwBEEQhCrNUxVlaazK8exoX0vQlJ2ye3Y+/vhjTJo0CTNnzsTixYuxceNGAMDq1auRSqWwYMEC/tzZs2dj2rRpWLFiBQBgxYoVmDdvHtra2vhzFi5ciL6+Prz33nuGfzORSKCvr0/1FSS0EXXQImyCIIhKQ1z8KzKNFfB1p6zBzvz583HPPffgiSeewG233Yb169fjC1/4Avr7+9HZ2YlYLIampibVz7S1taGzsxMA0NnZqQp02OPsMSOuu+46NDY28q+pU6e6+8I8RlsCWEnzVwiCIIKIuPjH8lJ70AICMwrsEwFbd8qaxjruuOP4/++3336YP38+pk+fjj//+c+orq727O9edtlluOiii/i/+/r6AhXwaHcLlVTeSBAEEUSYZycSkhDNeygrStnRbrIDVhhT9jSWSFNTE/bee2988sknaG9vRzKZRE9Pj+o527dv5x6f9vb2guos9m89HxAjHo+joaFB9RUkgt7vgCAIotJgm85IWEI4lAt2KqnDfUFGIWCbbF8FOwMDA/j0008xceJEHHTQQYhGo1i+fDl/fO3atdi4cSM6OjoAAB0dHVizZg26urr4c55++mk0NDRg7ty5JT/+UlGo7FCwQxAEUU7YfTkSCiESqjxlJ+gT3cuaxvr+97+PE044AdOnT8fWrVvxk5/8BOFwGKeccgoaGxtx5pln4qKLLkJzczMaGhpw3nnnoaOjA4cccggA4JhjjsHcuXNx2mmn4YYbbkBnZyeuuOIKLFmyBPF4vJwvzVMKI+xgXXQEQRCVBlv8wyFF2QlaQGBGQXdo8uzYZ/PmzTjllFOwa9cuTJgwAYcffjhWrlyJCRMmAABuuukmhEIhnHzyyUgkEli4cCF+85vf8J8Ph8NYtmwZzjnnHHR0dKC2thZnnHEGrrnmmnK9pJJAyg5BEIS/UHt2KrAaK+D2ibIGOw888IDp41VVVbj11ltx6623Gj5n+vTpeOyxx9w+NF9TUAJYQXlhgiCIIFLpnp2gNxX0lWeHsEfh9NlgXXQEQRCVRqV7dpiyE9RKMwp2Aoj2IgvaRUcQBFFpjBXPTlW+h1DQ1h0KdgJI0F3xBEEQlYbo2YlUmGdHlmW+7sSjFOwQJULb3yBo/Q4IgiAqDdGzE6kwz44Y2PC5XwFbdyjYCSBsB8Gl0oCVABIEQVQaGZ7GCvF7c9DUDyPEbEI1KTtEqWBpq6oKHDZHEAQRRNJZofQ8FFJ9L+ikVBPdgzn3i4KdAMKNYvmLLhWwi44gCKLSyAiKezhcWaq7+DqCOtGdgp2AoTKK8YsuWLlTgiCISoNtQqOCZ6dS7s2sl1tIQmAbJlKwEzDE64vLiRWyeyAIgggqlVx6zhoKRsMhRKjPDlEKRHd/LKByIkEQRKUhNhUMqvphhNJQMBTYQI6CnYChLgEMplGMIAii0hCrZJVxEZVxb9Yrqw9aio6CnYAhpqxYv4NK2T0QBEEEFR4QhIIbEBiRyiiqVUgiZYcoAWIJYDzftrtSGlcRBEEEFV56Hq48z444F4s8O0RJyIi9HAJ60REEQVQalezZEQ3KYdZDKGApOgp2AgZTcSrR8U8QBBFUKtqzkyn07GTlYL02CnYCBtspRMMhREKVtXsgCIIIKpXs2WEb6miIqrGIEpHS2T0E7aIjCIKoNCrZs5PSUXaCtsmmYCdgKMpO5e0eCO9JprM4857X8LsXPy33oRBERaGMiwghUmGenZRen52Apego2AkY5NkZHet3DuK/n/wQ3YPJch9KWXh/Wx+Wf9iFu1/+Z7kPhSAqCnEQaKRCPTtRQbUK2iabgp2AkRYc/2z3ELQIu5zc+Y91uPW5T/HIm1vKfShlIZnO3aAS6WDdqAjC7zDPjrgRDVpAYERKWHeCusmmYCdgZHSGzQXtoisn/SNpAMBA/r9jDaYMJinYIQhXSetYDCrl3qxXjZWhaizCS/QMypWyeygFfLHPZMp8JOWB9ctIpMfm6ycIr9Dz7FSK6q6ejZX3IwXstVGwEzDUpeeVtXsoBUzRqJRculNSwuvP0nVDEK6h59mpFINyMhP8jAIFOwFDZVBmHZTH6MJdDMkxnsZJCqNFkjRmhCBcQ8+zk64Q1V1JY4WEjEKw1h0KdgIGb0lOyk5RKGmsyrgJOUWco5ZIjc1zQBBeILYFqbRRPkpTweBWAVOwEzCU6bOSkjsN2EVXTpiiM1aVnVRauVYSY9S3RBBekBY8O+zeXCnpcr7uqJSdYN1DKdgJGGJL8miFSaWlgH1ox+qk+CQpOwThCZkK9uykhEGgQc0oULATMESDMvPsVIrjvxSMeWWHPDsE4Qls8a9kz47YVDBoBQ4U7AQMsfS80nYPpYAt9mNV2SHPDkF4A1fdK9CzIzYVJGWHKAliU0GWFw7aRVdOlD4zY3OhFxUtUnYIwj3SGSUgqDTPjkrZCejcLwp2AgYpO6ND6bMzNhf6pHDzTaTIoEwQblHZnh1lojspO0RJEEvPKy0vXArG+rgE8uwQhDfoe3aCFRAYIRqUw1IwAzkKdgIGu+gqcfdQCqiDMnl2CMIL9D07lfEZU4+LCGYgR8FOwNCbej5WF+5iYOeKlB1SdgjCTfQ8O5VSKZsSWp5EwlSNRZQAsUsnKTvOkGWZL/Dk2aFhoAThJplsoZ8yaOqHEWmdpoJBs09QsBMwVLOxAnrRlYuUaqEfm+dMVY01Rs8BQXiBOAg0qPOjjGDrTizAm2wKdgKG3tTzoF105UJUc8aqsqPqs0PBDkG4Bh8EGlZSPZWyEVWPiwhmyxMKdgKGWHoeVKNYuaAeM9RUkCC8gpt4QyFE8gFBVg6et0UPcUxR3ioauE02BTsBIyM4/iMV1qXTa1TKzhhVNcaiQXnjriHeFI0gvCKjU3oOVMZmVF2NFUzzNQU7AUOceh4J6EVXLpJjcKHXMtaaCr7yyU588b+fw8/+/kG5D4UwYUvPMG544kNs7xsp96EUjdIDTSk9F78fZHjLE/LsEKVCkRPFGSVjb+FOZ7J44aMd6BtJ2f4ZMY2VysgVIS87JSlUYCXGQMD36c5BAMAH2/rKfCSEGfe8vB6/ef5T/OnVjeU+lKJh5dlaZSdVAfdnVVNBFuzIwbp/UrATMMTS87Hs2Xn83U6c8ftX8Ysn19r+GW0/okq4CTlFVZE2Bjw7LMDtGbIfFBOlZ0d/AgDQP5Iu85EUT0bHsyN+P8ikK6DlCQU7AUMxKIfGtGdna88wAKCz177srS21Houl12PNs8Pe491DyTIfCWFGXz7ICXLvJ3FchCDsVMRmNKVqmBjMjAIFOwFDreyMXc/OcN5v4mTB1j53LHaeTo6xcRGisiMHTHYfS/QN55S3IF+TomdHksSREcG/7tIqz05+6nnA7p8U7AQMsalgUOVEN+DBjgN1Rttbh5Sdyn/9TClIZrIYSgZXNah0evPBzkiAP5PivVn8byX09FKaCobAMnRBU6wo2AkY+lPPg3XRucFI0nmwo31uJdyEnJIaY9VY4ntOqSz/wgoNgnxNctU9Hw1wBaQC7s9iU8Ggvi4KdgKGWHpeaZN1nVBMGksb3IzFDsJjrbGi+BrJpOxfmLIT5M8k9+yE1cpOJWxG1U0FqRqLKAHqi24se3Zy54GUHWeMtQ7KpOz4n0Q6g5H8tRhkgzJX3fPBQGV5dnTGFAVs3aFgJ2DozcaqhJ2DU4aLSWORZ2fMNVZUBzuk7PiRvmGl3HwkoAG4LMuqaizxv5WwqRKbCgZVsaJgJ2DoTT2vhJ2DU0byaSwnsndBn50KuAk5RT0INLi7aLskVGksUnb8iNgYNKhpLPEezDahQfW26MHunbFwcFueULATMDI6zZ2C1u/ADYoqPac+O2O2qSAA7B4kZcePsLJzILgBuKhyRPKTMoOqgOiRFmYyhqVgrju+CXauv/56SJKECy64gH9vZGQES5YsQUtLC+rq6nDyySdj+/btqp/buHEjFi1ahJqaGrS2tuKSSy5BOh3cLpxWiE0F2YepUibrOqGYNFZB6fkYU3YyWVm1GxsLr588O/6nVwx2AhqA6yo7AVVAtMiyrNtUMGjrji+Cnddeew2//e1vsd9++6m+f+GFF+LRRx/FQw89hBdeeAFbt27FSSedxB/PZDJYtGgRkskkXnnlFdx777245557cOWVV5b6JZQMsXGVqiV5wJzxo2WkiD47Y13ZKahGC+jC4gRRKaBgx5/0CSMiKkHZCfM0Vl4BCfimQgzWogFed8oe7AwMDGDx4sW44447MG7cOP793t5e3HXXXbjxxhtx1FFH4aCDDsLdd9+NV155BStXrgQAPPXUU3j//fdx33334YADDsBxxx2Hn/70p7j11luRTFbmjY0bxUIS3zkAY68iizooO6fAoB3wm7AdyKDsfypB2REDmgg3KOerZQOkfugh3icj4RAvrQeCpVqVPdhZsmQJFi1ahAULFqi+v3r1aqRSKdX3Z8+ejWnTpmHFihUAgBUrVmDevHloa2vjz1m4cCH6+vrw3nvvGf7NRCKBvr4+1VdQSGcL5cTc94N5kygWFuxoUzNmFKaxvN9F3vjUWvxs2fue/x07pNJaZSeYu2gnJMmg7HvUnp1g3scyQiWWJKmVnSAFBHqIA5NFrygQrNcWKecff+CBB/DGG2/gtddeK3iss7MTsVgMTU1Nqu+3tbWhs7OTP0cMdNjj7DEjrrvuOlx99dWjPPryoGdQFr8/VhgRFupkOovqWNjyZwr67KS9PWepTBa3PPcJZBk458g90VIX9/TvWR+P+vWOPWWHgh0/IgY7yUwW2ayMkDhJMwBoy84BxbMTdGVHzBpEQyFV6ipIr61sys6mTZtw/vnnY+nSpaiqqirp377sssvQ29vLvzZt2lTSvz8a9ErPgWBddKMlm5VV/Tjsem8KPCseL/aJdBbsviD6EsqF9vWnMnKgDIbFIF4bPVSN5UvE0nMgmOqOtqGg+P9B9+yw4w9JQCikVGMBwdpkly3YWb16Nbq6uvDZz34WkUgEkUgEL7zwAm6++WZEIhG0tbUhmUyip6dH9XPbt29He3s7AKC9vb2gOov9mz1Hj3g8joaGBtVXUBCbCkrS2Oy1o70ZJmymowqVHW9vQuLfG/BBsJMU/F7a71Uq4nvQn0iPyd5Kfkf07ADBNClrh4CK/x/0jWhKmMcI5AIeFu8EyT5RtmDn6KOPxpo1a/DWW2/xr4MPPhiLFy/m/x+NRrF8+XL+M2vXrsXGjRvR0dEBAOjo6MCaNWvQ1dXFn/P000+joaEBc+fOLflrKgVK6XnlzV+xy7DGa2JX2UmWOI2jWmhHyq8qsOOpq1Ky10E1hNpF+x7TfCz/IXZQBoKt7ETDypLK/j/oG1G2KYwJry2IfqSyeXbq6+ux7777qr5XW1uLlpYW/v0zzzwTF110EZqbm9HQ0IDzzjsPHR0dOOSQQwAAxxxzDObOnYvTTjsNN9xwAzo7O3HFFVdgyZIliMfL64/wCjb0k81diYQkJBG8OSWjodhgp8CgXEJlx09prOpoGL1SCrLMVLFoeQ/MQ7QLZ89QEhPqK/PeEFQK0lgBDMD1PDuVMi5CbCjICIckpDL2i0P8QFkNylbcdNNNCIVCOPnkk5FIJLBw4UL85je/4Y+Hw2EsW7YM55xzDjo6OlBbW4szzjgD11xzTRmP2lvSQlNBQImwUwGSE0cLayjIsKvQlHoQqFjtNZDwT7ATi4QQj4QwksoGcmFxAgt2YpEQkukslZ/7EG0aaySAaSwzz06QAgI9xIaCjNz/ZwP12nwV7Dz//POqf1dVVeHWW2/Frbfeavgz06dPx2OPPebxkfmHVFbtu4hUiFTqhJFRKjvV0TCGUxnPlZ2E79JYynybWDgX7FSyZ0eWZf4etzXEsal7mCqyfEif1rMTwAC8kj07ysTz4l/b/67ejKws45i57WisKY+SXPY+O4QztLlhftFRGssS9rzaeK5MvZSeHT8YlNkNORoOIR7NnYMgLix2EUvt2+pzFZ/Ua8dfyLLMU7xV0dw9LYgGZT3PTqVsRHlhQ7h41eqaZe/jkr+8gx0DCfcP0CYU7AQIcUaJtiV50D9QTihIY9k2KLNgJ+Lo54pFWwlUbniwEwlxs2EQFxa7iMFsW0Mu2KE0lr8YTGb4vYu9R0E0KOv22akUz46wSWKEHG6y2QaVBbTlgIKdACHGM0xSVOTEYH+gnKBVduz2y2E3ndpYRPVvrxAX234fKDtJXlUhIZ6/6VTyfDDxtbU25EzJlMbyF8yvEwuH0FidS29o09RBQM+zUyltQVggFw0VV42VzSrp5KqodfNXr6BgJ0CIi/NYVnaK9ezw0utyKDt+8OyIaaxIPo01BoKdSEhCS20MALB7kIIdP8H8Og3VEVQF+JrU8+xEKsSzk9JJYznZZIvvZzUFO4Qd1NNnNZ6dgH+gnFBsGoulAJlnx+tBoGJw6o9qLMVXEIuMHWUnFgmhqSYf7FAay1cwZaehKsrVxiCmVjOaxnvi/wd9I8pSVRGdPjtZG1PPxc0pKTuELcT8KLvYWNBDBmVrFINyTtnxegeprsbyQ7CjLj0HgrmLtgsr/Y9FQhiXD3bIoOwvFGUnqlyTATTNp01Kz4M+LoLfN/SUHRvrDrtfR8PqEUelhoKdACH20insoBzsD5QTCoIdh54dlsby3LPj02qsWFgJdkox+b1c8B474RDG5ctdSdnxF6wSq6E6yisEg+zZqcTScz4uoqDPjj3VaoSbk8un6gA+67NDmCOa4CRpDHt2glKNpTIol3+RZa83GpYCvYu2S0InjUXKjr9gaazG6igvugii2mim7AT93pzW8eyEHARybGgzBTuEbSq5cZUTRjQ3w2LTWKVUdvyQxhINytyzE3CJ3QzRszOuNqfs9AylIMsy3ywQ5YWnsaoivNo0kMGOnkGZWQwCfm9OC14/hpNAzg9l5wClsQKFbuMqB3JipVDsuAgljVWGpoLJNLJlfo9S+Q7K0YhQjVXByk5SlcbKKTvprOyLnkdEDlHZUXxkwUtjpXXvzZXh2eFNBYvcZCdYsBMpr7JDwU6A0DYUFP8/6LsHJxT02XGo7NTESl96LsvAYLK8i6zo2WFNBceCshOPhlEVDfOdZc9g+VOKRA42BLShOsrTHCMBDMAr2bPDmwpGilN22Kyz6hgFO4RN0pqJ54CSR82MQYMy+8A5LT2vr8oHOyVsKgiUv/xcVY3FynwDaAa1Czv/8XxgN46Xn5Nvxy/0VZiyI6of0UopPedNBYtrmMg9O6TsEHZJ606fZS3Jg/2BcgIzKDfkO67aCXZkWVYMyqXqoKw5rnL7dhTPjiSMi6jcIFn07AAU7PiRvuF8NZbYZyeAyo6eZ6dSlJ2UXp+dsP0qYGY7iAfNs/PEE0/gpZde4v++9dZbccABB+Bb3/oWdu/e7erBEWr05q+Ex6JnJ69GsPbydsqnxWCwVNVY2kCi7MEOr8YSlJ1RnIO/vrkF59y3GoM+9cAUBDuCSZnwB0oaKxLort76fsrK8OykM4UZBSfrDktjlbsay3Gwc8kll6Cvrw8AsGbNGlx88cU4/vjjsX79elx00UWuHyChkNFLY1XI7sEJLNhxouyIKk7JxkVktMFOeRdZcep5LDz6heVXyz/G4+924tX13a4cn9skBI8SAKGLMik7fkE0KDNPVRD77OhvRCvj3izeNxhsCXJSel7OURFAEaXn69evx9y5cwEA//u//4svf/nL+PnPf4433ngDxx9/vOsHSCjoGpSZZyfguwcnMFm00UGwIz6npkTjIrTHVX7PTu71xsIhhEO5/y824EtlstjYPQQAGEr6c3FifiQljUWNBf1GnzguogKUnYhO6XnQVXe9poKOlJ2glp7HYjEMDeVucs888wyOOeYYAEBzczNXfAhvMJVKA/6BcsKIJo1lJ2hhu5OQpOwwSlmNBfggjSXI0aM1g27qHuLXo7Y6zi8kM5o0Vg0NA/UTqUwWg8LGJcgGZbNBoEH3U+qlsZysO4HtoHz44YfjoosuwmGHHYZXX30VDz74IADgo48+wpQpU1w/QELBrKlg0HcPTlA8O/ZnXIkLX1Qou/aywVxhsFPmNBb3sIQhY3TKzvqdg/z/fRvsaDw7lMbyF2LwX18V4YthkJUdVaonVBmVsopBuTCjYKd3mF+CHcfKzq9//WtEIhH85S9/wW233YbJkycDAB5//HEce+yxrh8goWA2fXYsKTsFaSwbKTzRnBsT+kV4uevipc/5v2c0H2vpqg049c5Vnqe5UiplZ3QLy7odSrCjHd/hF8SmgoCSxiKDsj9gfp26eAQRYV5bEPvs6Hl2KuXezO4belXAFT0uYtq0aVi2bFnB92+66SZXDogwRq/fQWQMTj1nHx7Fs2O/GiseURrq5b6fVQU/bsIW2/F1cWzpGeZDD7X8/qX1+HTHIFat24Wj57R5ciyAWt1ialaxys46Qdnxq6FUaSpIped+RBwVASDQaaxK9uywtUW8TzpRrfzi2bEV7PT19aGhoYH/vxnseYT7sJ4GeruHoEuldklnsnzRLsagrFV2kuksauMeHKjwN5trY9jSM2yo3OzKe0i8Vhz4uIhwiF9DxS4s63cO8P/3bRpL01SwiZQdX8GUHVZVyaaeB7HPjplnJ+gb0VRWZ1yEZF/ZGfbJuAhbwc64ceOwbds2tLa2oqmpSdfjwLwPGRs9T4jiMMsLB10qtYs4BNRRGksonwyHJIQkICt721iQlT631OUUBT3PTjqT5Ytvz7C3i7B4Dlj+vdgu0kH07JCy4y/EURGAsvMPsmcnontvDt7rEdG1T/Aq4ApLYz377LNobm7m/08Tg8uDXum5kxkllYA4BLS+ynmfHbbwxSIhjKSynt5YRWUH0C897xYW3h6PF+GUyqSdV3aK2EUPJtLY3pfg//Z7Gksb7AwlM0ikM9y3RJQHsXsyAGE4rT+vJzP0xkVUyr1Zb0yRo0GgfDZWANJYRxxxBP//I4880qtjISzQayrI+h2MGWUnfyOsjoZ5jt9pGov9dySV9VTZYV6i8XW5PJle6Xn3oBjseJzG0ik9L0bZEVUdwL+G0oTGoFxfFeGKXs9QCm0NFOyUE7GhICB6dvx5PZmR0duIhivj3pxMF2YUIsX02QnabKyrrroKWR1Zrre3F6eccoorB0XoQ8qOEOzEwnzHXoyyM5rF3i7sd7cwZUcn2Nk1IAQ7HqexxKaCyi569MHOsE+rsRJCqT0AhEISlZ/7CHFUBKD+TNopafYTer6WSvHspPU8O2zdkR14doJWen7XXXfh8MMPx7p16/j3nn/+ecybNw+ffvqpqwdHqGHNnSoxL2wXxeymGI2dlJ7H8qoY26Uw064XaNNYetVYuwZLl8bSM2kXY1BmwQ5TTHzr2dEEuIBiUt49SCblcqNVdsTF0MtNiBdkdErPK+XezII1vWa2TqaeB24Q6DvvvIMpU6bggAMOwB133IFLLrkExxxzDE477TS88sorXhwjkUev9JyltIK+e7ALUxGqYmFHk7tFcy4AIVDybqEWS88BfYNy94DiffE6jSWeAycpQC0s2Nm7vQ6Aj4OdtHpcBKD4drwOLAlrxFERgKLsAP71gRmRNulu70fVfc3mXpx65yq8u6XX8rm8z46eZ8eWQVmxHpQTx312xo0bhz//+c/40Y9+hP/8z/9EJBLB448/jqOPPtqL4yMElMZVhTNKgp4Xtsuw8MFxlsZS94rgXZRLqOwk0lkk0+q+PqKy43VqRUnlSYKy4zzYWbcjV3Y+d2ID3t3S59uFiffZUQU7NB/LL2hLzyP5SslMVg6cbydonp2/rN6Elz7Zib++uQX7Tm40fa5eU8Hi+uwELI0FALfccgt+9atf4ZRTTsHMmTPxve99D2+//bbbx0ZoUErPybOjCnbyYx/M0BqUmSrkpVzOAiwW7ACFFVlisNPreZ8dlsoLc89OOis7unZkWeYNBedMzPXU8m2wo5vGKr9nZySVwSuf7PTUHB8EWFqXpbEAwaTsU9O7EXq+Fj97dlg15ZCNzy4L1mKR4qqx/FJ67jjYOfbYY3H11Vfj3nvvxdKlS/Hmm2/ii1/8Ig455BDccMMNXhwjkcdsNpYfdw9eMCwYlOPh3IdHlq1fv17pOaAEAG4jyzJfbKtjYdTEcseqNSnvEtJY/Ym0pwsgC76iEUmldjhJZe0aTKJ/JA1JAj7TXg/Az2ksdVNBQAk8y5nG+t2L6/CtO1fhgdc2le0Y/EC/poMyoCyIIwHroqw3LsLP9+au/hEA9ka98NlYOuMinFRjlTuN5TjYyWQyeOedd/Cv//qvAIDq6mrcdttt+Mtf/kIjIzxGd+p52L6cWAkMJ5VdgrYTshnaOUleKzvi741FQqiL527ofRrfTrdmAnevRxVZYvCl10XaLsyvM6mxmvtf2HviN7R9dgDFoNxdRoPy5t1DAIAtu4fLdgx+gBuUa4Kv7Ojdm6M+vjdzZcdGsJPW9ezYKz1PZbI82Cv3uAjHf/3pp5/GpEmTCr6/aNEirFmzxpWDIvTRKz13YhSrBPQ8O4CNYEfoMQMIyo5XwY5wPLFwCPX53WtBGmtAHex4ZVIWB55GwyFE8l2kAWcVWevzA0BnTqjlOzXfprF0gh0/GJSH8wt5EGdAuYUsy0rpeZVOsBOwc5PWvTf7c26hLMvY0Z8LduyosqMxX4v3hsClscwYP368m7+O0KAXYY9lz044JPGbi5VCo01j8Q7CHqWxtMFOXf6Grm0syDw7rCl577A3i7AY1MXCuUGgxZiUmV9n5vhaJeXg02AnoRvsMINyGYOd/G7ar80YS8FwKsMDcLVnJ9//KWgGZZMOyn5LY/UMpfj90k6wo/U7AvZTdOI1LqbOy0FRaaxf/OIX+PznP4/29nY0NzervgjvUErPCztZpnz2gfIKtlBU5z0wPB1lM41VUHruVbAjKEmhkMR9CQMJRblJZbJcyp8yrhqAd/1fxGCHBXrFLCxsAOiM8Yqyk87KvjTbalOXgGJQLucwUBYcBk29cBM2KiIckrifDVBSHX4NoI1I6QxpDvt0I9rV72zUi2lTQZvKTlU0VPYxU46Dnauvvho33ngjvvGNb6C3txcXXXQRTjrpJIRCIVx11VUeHCLB0J167uO8sBdou3HaVScKlZ3SpLHYQss8O6Kyszuv6oQkYI+WWgDedVFmwVdIUkpiiwn4mGdnxoQ6VAmzbvxoUk7oVGP5YRjoUDJ3DQRNvdBjJJXRnflmRa9gThYXwaArO3p+Sr81FdzeN8L/355nx2wAtflrYwF9uVNYQBHBztKlS3HHHXfg4osvRiQSwSmnnII777wTV155JVauXOnFMRJ59ErPx7JnB7C/YBcYlL1WdjQpFObZEYMdlsJqro15XiWU0rlhOfVHZLIy/rkrZ66dOb4WsXCI+37sVHWUElmWDTw7uZRJ73CqbCMJuGfHhwGiU06+7RV88YbneABnF+bXEVNYgNJlN2iql55nh6nuWRm+Gn8hKjt2Rr3oNRW0a5/gBSU+GLrrONjp7OzEvHnzAAB1dXXo7c11YPzyl7+Mv//97+4eHaFCMSgHo0unF4zwNJazqqqkZrGPeazsJDRps3odzw4zJzfXxtCUv+l7lV7RS+k4Dfi29gzzpoiTmqohSRIPOv2m7IiGbHG6OUtjZeXCyrhSoaSx/LXjd4osy3h/Wx+6B5MF89KsYD2lGrTBTsCrsfRSPYC/fDus7Bywl8biGyWdpoKWnp20ksYqN46PYMqUKdi2bRsAYM8998RTTz0FAHjttdcQj8fdPTpChb6y498unV6gVXbsjj0w6rPjtWeH/R0ljaUssLsGczus5toYGpmXxGODcjQiKjvOUgbMnLxHSw2/2SkmZX8tTmLwKxojY5EQavMekXJ1UVYMyt4EiF5d01rSWRmsl6eYGrGDsbLjb9O7EXqeHTHw8dNmtKtPUHYceHaiET37hF3PTgCVna997WtYvnw5AOC8887Dj3/8Y8yaNQunn346/uM//sP1AyQU9JoKjjVlx8iz49SgzMdFeJT+M0pjif4Gpuy01MV5esVrZUcMlJ1WY63foZiTGVU+VXa01XAi5e6iPOyhsvPwG5ux70+exFPvdbr+u7WI57izN2HyzEK0c7EY8SIqBP0AV3Z0PDuAv3w7orIznMqYdp+XZVm3qaDdPjt+6Z4MFDEb6/rrr+f//41vfAPTpk3DihUrMGvWLJxwwgmuHhyhhuWF9aae+7Eaxgu0OwW7Az1Lruxo0kZ6nh3WULClNsab3XnXZ6ewfNTpMFBuTh5fx7/HquLs5P5LCXtNkVCuGk5kXG0UW3qGuUG81PBgxwM1bOW6XUhmsnjpk504Zp9213+/iBiQdDpUdnrz1ViFaaxgGpT5vVnHsyM+7ge2C8qOLOfOtVEwIgYzemOKrDIKwyn/pLEcBztaOjo60NHR4caxEBboTT23KydWCszcyQ3KDkvPY/nzVapqrDhXdnI39QGVQTl302mpjaOp2us0lnoQqnhsds2gYo8dhl8bCyZ0Jp4zmmtz6XZt9+pSkMkqxmkvRiKwKqetPc6Cj2IQP3NdRaaxGqrVS1BQmwqy+6+ouosxtp9sBqKyA+Q2KkbBjnjc4iY7JI2BNJZIQ0MD1q1b59axEBbolp7blBMrhRFtnx2bsndSo2w4VTWcYuTZEU2x3KBcVzplJ+aGsjNBTGP5sy+KXiUWoyVf+VaOYEc8T14oO6x/zdYe70dRiAGJc2VH37PjVw+YFbwXjaB+SJLkO5uBLMsqZQcwT0EndfpzAfaVnYTGY1lObAc7W7duLfie1aRpwl30ejn4edicFxTr2THqoOx5nx0zz05+sR1fG/O82Z1eF1QnKYORVAZb8gtoEDw7iXRhcMdoLmOwI54nL5QdFkxv7fU+2FF7dhwqO5aeHX9dT1aks4W+FsB/vXb6htP8fWMbFbNeO2L6TVWNZbO/m588O7aDnX322Qf333+/l8dCWEAGZZ0+O3ZLz7UdlPP/TZSoqaCZZ0csPR/waPK5djYY4My3tLF7CLKcawLHlBEAvi091yprIizY2VWOYCfpsbKTD3Z6hlIYLKLZnxPEINlpNRZvKmjUZydgyk5Gx7OT+7e/5mOxFFZjdZSnzs1U2bTQjDSku+6Y/z0/eXZsH8G1116L//zP/8TXv/51dHd3AwBOPfVUNDQ0eHZwhBrTpoI+2Tl4jVEay1rZUXtWYnlVI+VRGkvbvZd7dhJprojuGsh7dupiaKiOCvOx3Fd3zAzKdnbR61gl1oQ6VcdbvxuU9ebx+CaNlTavhCkGlsYCgG0eqztisLN7KOUoldmXD/oLSs+DalDW8eyI//aL8s5SWG0NceWza/K+pXSqzABxXISVspP73fEgNRX8r//6L7zzzjvYtWsX5s6di0cffRS33XYbDf8sIbpNBcecQdmdDsosYLRShIpFSWPljpN5djJZGcOpDJLpLL/ht9TGEQ5JXNL3oouyNo0n/r8dZYdNSZ7YUKX6vl8NytrzL1JOZUdMGWRldfPD0ZLNyqo+Tl6blLXXTVef/fLzPmFchIhfPWBW6Hl2AOU+45f7M1N2WuurlBS0yUaFbQajRoqVzdLz6lj5gx1H1VgzZszAs88+i1//+tc46aSTMGfOHEQi6l/xxhtvuHqAhILeByrCS8/98WHyklQmyz9cxTYV1A4CLdVsrJpYbkp7JiurUlnhkMR3t001UfQOpzzx7aTSsup4AGc9TRKaPD/Dr4ZSU4NyHVN2nPWGcQPtLjqRzugeYzEMJNMQ1x6vTcrajUJn3wimtdTY+tk+A4Ny5So7/ng9TNlprY/zER+mnh3eUFCr7OT+azkugqWxfKDsOC4937BhAx5++GGMGzcOX/3qVwuCHcI70jptu8dSNZa4UFQ5Hheh6bNjs2S9WLSLrSRJqItH0DucQv9ImqeOxtXEeC68qSaGDbuGPAl2tNVo4rE5CXa0crRfDcrs9cZ1DMpsGGj3QHkNykAuSKyvMniyQ/o06U+vgx3tbC+7FVmZrIz+hFGfneAZlLNCJ+mo1qDsU89Oa0MVn5FlpqLpNRQEhM79Fq8r4SPPjqNIhQ0AXbBgAd577z1MmDDBq+MidNDbPfht5+AlzK8TkpwP9NR2EFaaEXrUQTnDctXKh1wJdlK8Kks0+zKTshedffnrL3JcRMJAKfGrQdmsz05Lvs/OYDKDkZRxjxEv0A5MdXNRF/06ALDF6zRWRpvGsvf3xFSbthqLvRdBUnbEVE447G/PTpeg7NjZqCgTz7VpLJt9dnw09dx2sHPsscfi1Vdfxa9//WucfvrpXh4TYYCeQXkseXZEvw4zydpVJ1i6Ks5Lz0ur7ADq8nNlVIQQ7AgTud0mNcpqLCPDLxvI6rep52ZprIbqCCIhCemsjO7BJCY1VZfsuArTWO5df9rBpp6nsTTHbrf8nAVl1dFwwfvDri+/pUXNEDeaBdVYPrs/M2WnraGK+2jM0lgpAy+S7UGgmiaw5cR2sJPJZPDOO+9gypQpXh4PYYJe6bnfdg5ewoMdwewWC+f+33HpOV/ovR3GKHpkxPLzXULZOWOch712zJoK2lEX2HPiUX1lx4ueMaNB7/wzJEnCuNoYdvQnyh7suGnEZWmskJQzP5eyGguwn8bqT+SOs66qcPkJYhpLpewUGHnz92eL+5Msy7j27x9g5oQ6fGv+NPcPMg9LXbU2xFFtwwyuGJS16bnc68paVBMy87P2vlEObB/B008/7Xqgc9ttt2G//fZDQ0MDGhoa0NHRgccff5w/PjIygiVLlqClpQV1dXU4+eSTsX37dtXv2LhxIxYtWoSamhq0trbikksuQTrtbX+JcqHXVJDlUmU5lzuuZNgHR5REnZaeaweBemXsTuqMZxBHRrCy8/F1cf54o5dpLJ3jKcazw4JLRtxGRUc5MEq7MVrKVJGlPU/uKju5+x5r+ri1d8TTewL7zLEF3m6vHXYOanQqdNj1FKQ+OxmDxnuA4G2xeB/W7RzEnS+tx/974kP3DzBPrnsyq8aKoyaWCzbNPrtpnTUHEDbZFkGcn9JYZQ23pkyZguuvvx6rV6/G66+/jqOOOgpf/epX8d577wEALrzwQjz66KN46KGH8MILL2Dr1q046aST+M9nMhksWrQIyWQSr7zyCu69917cc889uPLKK8v1kjxFKT0vVHaAyld3tGXngL1gR5blAoOy5+MidBZbcWREt66yky89L1mfnbDqWM3gaSwDZcdvnh2zpoKA2EW5tBVZ2l20F8rO3m31kKTce+ZlMMfUl0lNOYe1XWVH73PMYEbWIHp2JE3jPcC+t4VVaIp9uNymP5HmaSVV6bmpQVk/jWW3MCaQHZS94IQTTsDxxx+PWbNmYe+998a1116Luro6rFy5Er29vbjrrrtw44034qijjsJBBx2Eu+++G6+88gpWrlwJAHjqqafw/vvv47777sMBBxyA4447Dj/96U9x6623Ipksz0RjL+FlgDql5+LjlcqIXhrLRtAiqjeFyo5HwY5O2kjl2dEJdpSRER4alItOYxl4dvgN01/XnllTQUDotVPiiiwvPTvM69VcG0NbvsTLS98OO8fTm3NK0va+hK2FeihZ+DlmcNO8z4JnM9iCr/XrAOK4CPPzMpQvWMhkZc/UZmYgr6+KoDoW5p9dO+MitE0FmYBVkbOxvCaTyeCBBx7A4OAgOjo6sHr1aqRSKSxYsIA/Z/bs2Zg2bRpWrFgBAFixYgXmzZuHtrY2/pyFCxeir6+Pq0N6JBIJ9PX1qb6CQEanDFCMuCte2UnmdwlC+XPcRum5GNCwxc+JObcYkjrVQHWiZ4ensZRgp9HDYaCKZ6c4gzK7aRVUY+UXrCAZlIHydVFm1zDDzXSNMkk8ytWWUgQ7U8ZV83/vtnHtmqaxHKRW/YKel5Jh17MjzszzKiXcxbsn564NXlxgQ9kxaipou89OkDw7XrFmzRrU1dUhHo/j7LPPxiOPPIK5c+eis7MTsVgMTU1Nque3tbWhs7MTANDZ2akKdNjj7DEjrrvuOjQ2NvKvqVOnuvuiPCJlMvUcUOeOKxH2oaxyqOyIj0VL3kFZeX9YmW2/Ko2leHZKYVDWV3ZspLF4NZu2z07+hukzQ6lVsMPOe8mDnZTaT+hF6XlDVRQT86brrQ4HdDqBXTd1cWVemp2KLLM0Fk8vZ7KB8SByL2Wo8FqzW0AiqitepYS39yt+HQCotuHZSVl5dizTWOTZ4XzmM5/BW2+9hVWrVuGcc87BGWecgffff9/Tv3nZZZeht7eXf23atMnTv+cWegZlMeCueGWH3ySdjTxgC31IUj6kMeGm6kWOPJkpTKNYlp5XezcuIpnvoBzVMSjbU3b000J2Ws6XA55206nGAoDmOp8YlD1Qdhqro5jMgh0PlR3RBM7UAragmqGksQqrscRF0auNiNvw/mdhPWXHngIiKjuss7HbFCg7tvrsGHl2lH+bBaWBLD33ilgshr322gsAcNBBB+G1117Dr371K3zjG99AMplET0+PSt3Zvn072tvbAQDt7e149dVXVb+PVWux5+gRj8cRj8cNH/cjsizrGpQlSeI9Q/zSy8ErRvQMyjaml+uZVdnPyXLuRqT9MI8WvdJnZlDeOZDkHWRVTQXzaazBZG52lltjBACj0nMnTQUN0lh+NSj7NY2lNSi7quywNFYEkxpLkMbKiMFOHO9vA7bbUXbyi3mNibIDoOQNH4vFFc+OEOB49VniZedM2bGxUUlnDJQdjX0ipvPaZVnm13egSs9LRTabRSKRwEEHHYRoNIrly5fzx9auXYuNGzeio6MDANDR0YE1a9agq6uLP+fpp59GQ0MD5s6dW/Jj9xLxs6LtZjlWuigP6xgbnaSx9EYlAN7sIPWbCuaCmY27hgDkbo5iB9mGKu8mn/M0lqqDsoOmgjpKFSB4dvwW7Nisxtpd8mDHS8+OksaaVAplJ6WkNtsb7Vdk6fXLYkTCIX4/C4pvxw3PzmBCSGN5pJKysvMJPI2V+2yYTz3PKzsGVWaAsWqVSGf5GA0/BK1lVXYuu+wyHHfccZg2bRr6+/tx//334/nnn8eTTz6JxsZGnHnmmbjooovQ3NyMhoYGnHfeeejo6MAhhxwCADjmmGMwd+5cnHbaabjhhhvQ2dmJK664AkuWLAmccmOFaLLVa1yVgH+6dHrFsE7+V+mEbD3fJa6j7AD5IZmxgh8bFXp9XpiywxaEcbUxValqKD8UtGcohZ6hJL8puYGiNCl/z1E1lrCwibDdYSojI53JFlRtlAuzpoJA+frsMCN3Q1UEfcKMNDdQlJ0oD+a8HBmhVnbyaSwbwY5ZNRaQuy6HkpnA9NpRlJ3iPTuDidIpO0oaK2L591I6G0UACEmispMFUPheiu9fIAeBuklXVxdOP/10bNu2DY2Njdhvv/3w5JNP4l/+5V8AADfddBNCoRBOPvlkJBIJLFy4EL/5zW/4z4fDYSxbtgznnHMOOjo6UFtbizPOOAPXXHNNuV6SZ4gfFiOzWKVPPjfts2OWxtL5wIZDEiQpl8ZKZDIAogY/XRxmpecMMYXFaGLBjsvKjtkgUFueHYO0kBh4jqSzqPNJsKMcr/5NlgUDvcMppDLZgs+UV7BreFxtDH0jaVfHIvBgpyrCze47BxJIpDMFQaobsA1GPBJCbX4BtWVQZtVYBrv9qmg4F+z4zPRuBLs366XC7Xp2BgU1x6wUfDR09WkNyjbSWDojigB7yg5LYYVDUsHPl4OyBjt33XWX6eNVVVW49dZbceuttxo+Z/r06XjsscfcPjTfIVZaaSVFdqOudGVHz7NjJxWjt9BLkoRYOIREOutJkGg2G4shmpMZTTUxwIPJ52ZNBW1VYxn0rRH/PZzMcPWq3FilsZpqYjzY3T2URKtbo8ctYMFOU3UUG+BeNZZ2knhTTRRV0RBGUll09o5gekutK39HRAyAmQrZ2WfdpNEsjQUEbz4Wu+/qprFsenZEZcerlHChsmOnqaB+nx07zWx5JVYkxGcZlhN/bMMIS1JZ4zQWeXbsVWNpF75Y2L6y4RQzzw5DLDtnMJOy2yMjtOMyxGOzY25P8Lbv6nMoSZIyH8tHvh1RddAjHJK4+lFKkzK7hlkDSbcW9IERZbHMeb8k7tvZ4pFvRwyA211OYwHBmY/FK5Z0gh27YxVEg7IXys5AIs1/b2tD7r5TY0fZ0RkgDOQ+9+y1Gd079GwH5YSCnYAgOv61UbLdluRaBhJp3LdyA5YsfQNvbtztzoF6iN6Hx07AopfGApTF3osuynqGXq3qYZTGAoBej5SduI5BGbAO+IxmYwGCHO6rYMdc2QGEkREl7KLMAkIW1Lq1oLOyc3GSuFJ+7o1vR+yqzdSC7sGk5WsyayqY+332FUc/kDbx7ERse3a8NSizILQ+HuEzscRxEUbtN/i4CBM/kmEay0ejIgAflJ4T9jBz/IdtSqWMNZt7cf+rG/C3t7byaD+RzuLOMw526Wi9YVinZ4Mdz46RshMtgbKjVVLikRC/iesGO6yx4LC7C7BVRVoinTHcaYs/r1dCWsXTDj4KdizSWIAwMqKUyg7z7OTfZ7cW9F6h7JwxqdHbiiwxoBxXE0UsEkIynUVXXwJTm2sMf86sqSAgzsfyz/VkRsbMs2PTYjDocek567EzoUFRk8XPeyKd1Q1KjJoKArlALgmzYMc/3ZMBCnYCg15DQYZdE5wsy/juH17HMx8opfrj62LYOZDEhl2DLh6tN4yYpLFSGRnZrFwwiA/Qr0QSf7ZUpedAzreT4A0FzdJYXhmU1XPVQlKurYFZwJfOZHkgrZcWqrIhh5caXj1mYjwuda8dWZYVz07+fXYrQOSjIoRUKUtjbev1ONgJhyFJEtoa4tjUPYztfSOmwY5ZU0FAUXaC4tlJm3l2iqnG8uBz1KXpngyog82hpH5PI6M0FmBdaean7skApbECg15DQYaSF7bu0skCnRP2n4QHzjoE/3vOoQCADd1Dvm/PblaNBRgHLXoG5dy/8yMjXFZ2slmZ3wC0pc+ib6e5DGkssc+OJEm2UgbiedVTSvzYWNCPyo7Yd4S9z24pO2LZOWNifj6WV+XnCU3DOObbseq1wxZBwzRWwJQddzw73o6L0HZPZsfGPh9Gf1MxKBu/toyBV9RvwQ4pOwFBb+I5I2LToMw+UJIE3PzNAyBJUq43SkhCMp1FZ98I3w36ETPPDpBb4HSl2PwHtsCgHGE9YtwNdsyCA7Eia7xONda4Wm/SWKm0fvAVi4QwnMqYBzvCY3p9a/xpULYOdhRlx7qCyA3EHbtiUHZJ2eFzsZTry+uREdpeRm02y8+ZGdcojcUNyhWk7DgbF+GdZ6dV07urOhpGMp01VJPSvKmgcz+S4tnxh6bij6MgLEnrTDxn2C1vFPtbMJNzJBzikvM/fZ7KYscvfnhUwY7Bgm1oUPZI2REDB+1iK5qU9ZSdxvzOfPeg96XngL3KF/Z6IiFJt2mg0kXZP4uTI4NyiZQdFqzHwiGuarim7IwUKjtiF2Uv57+xc2y3IsuyGisaLIOyqcUgrFQ8GiHLsirA8WLToC07Z1htVNgmyTSNZZBR0GsVUk4o2AkIZruHMPPsWKSxjHLl01tywc6G/BgDvzKi058jJDSsMgpaDEvPHTTVc4KZEiIqOy26pedKsztXj2kU54DtsI0CB5YK81Uay6KDMgA05z1Tu0pUjTUsGDb5gu5SgMjSWI1iGis/wmEomXH9egIKh8MqIyPMlTLLNJYPDe9m2PLsmKjHiXRWpfx4MQiUeXa0XdnZvdRITUpl9TdJgLLxzhoE0ux6j1OwQzghYyuNZaHspPID+DQ3mT3yDcf8ruwY7RSsys+NFj5ejeVRGisWLmymVRfPLUaRkKSqnGGMq/Fm8nnKwGioKDtmnh3znjV2OrGWGjYY1uxGW2qDstgnilewuVZ6rszFYlRFwzxV6kX5eUITQLcyZcckjZXKKE08jdNYwVJ2bHl2TO7NojkZKJyf5gZ6nh3A2m+XNmgqCNgxKOfTWD4YFQFQsBMY7BiUrfLCQwb9Lbiys9O/yo5YyVIQ7FhUVelVIql+ziNlR08JYcpOc21Mt6toU3VucWKTz10/pgLPTlj1uB4jBnOxGNVRc5NjqZFl2ZayU+qmgmKw7pWyow2gvRoIqjrH2jRWv3GwIyoIFdNUcJSeHa2qMuyJsqOeeM6w2qjY8YoGpfTcH0dBWJLW6YDLcGpQ1t5k3FJ2Xvp4J178aMeofocRyUyWT36vihkEOw7TWOxcuj0uwk6wo1d2zh5n90y3TMqZrMzPnbFnx1xmB4zTWKUyKD/69las2dxr+Tzx/TQ1KOdVj91DyZJUIooGe7d7yfA+O5ou3SyVtdXl8nNxY8GC4HbBoGzkEWLXSDgkGQaiVS4Hgm7y+JpteEPTgFXps1OcZ2egQNlx93M0mEjzv9Hq0LOTTBt7RUNWnp00eXaIImCBzGiUHaPOpaJnp1gjYzKdxXf+8Bq+c+/rBR9eNxhJKjc+I2XHaMG26qBsNjG9GMxUBR7s6JiTAWXyOeBe+blYbRYtwrNjNBeLUeUw2Nk9mOQeArt8umMA5/3pTZz/4JuWzxUDCKNjBhRlJyvD9cGreohpLJ6qcUvZ0TEoA/BsZIR4vcR5GisXwCfSWUOPEN9wCUUSWuIup/jcYvPuIZyz9A0sWfqG6vtKB+XiPDtaj47b1Vjb8mnF2li4oIs7++wa/c1RKTtJJbj3AxTsBASz3GnEIsJmKDca9QU/ZVwNQlJuR7Gjv7gy3L6RFEZSWSQzWWzzoNSV7XYiIUmnqsqmsmPwc64rO3mPi56qMHdiIwBg3pRGw59nJmW3GguKu3Bjz45ZNZbx6wHUbeetkGUZJ932Co775T8cmWaZD2SHjUGTVqXy/LFIiAefpSg/HxaMuXYUNScopefqYMerkRF657gqGuaeM6NeO7zs3KRbN++z4zNlZ8vu3H2ts29EpQRmbGxEzT076s/NiMvBzrodAwCAGRMKh8HWWIx6McsoWM1kpNJzoijYhyWqO1nXWioFlBuNVtmJRUKYPC53U/xnkRVZosluq0WfjWIwazHPfSdGnh2DNEzMK4My62mjExwcPms8Xv3R0bh04WcMf54pO26ZlFMmi7+dqfEJC2VHyftbn8e+kTTW7xzErsEkVny6y/L5ys/lAqPBZNpSfUwKhlG9jtoiTGErRUWW6NlhAWIyk3U8004Pdn4aDZQdtzcgibSy4xfPsVWvHatKLEAxtPrNoMyaT8oy+IR5QGi8V6Rnh907WVftIZfTWJ/kg509J9QVPGZZes4+S8UoO2lSdsY0PUNJy26aepilsZSLzvz3mg3gG61vp1+YutzpQXt63mNH59itUjFJg92JZwZlAyWJ0dpQZSjhA0JFlkupFdGgrf27tjoo82DHyKBsP421a0BRUF7+ZKfl8xlMucjK1gqSnR47jFL22hkSZH0nQ1jtUGqDslGqts2i146YxjLCrx2UxU7bYop5tJ6dwfw5GZ/38bld1fhpV+6evpdesGNpUDb27FgPAqVgZ8yypWcYn792Oc6+b7XjnzVrXGVHKgWUHYOehKz4dooLdkSfjhdlrmbKTtxuGstoEKjryo79xVYPPgzUNWXH+NqJ2UpjuWdQFoMKR8HOiLK4WHnCnAU7+V47JQh2xGtYDHZGa+xOZ7J8wdSmsSY1KSMcitlkGcHVPs3nUTEp66cFrRoKAv6djSUG6mIK1syzY2dcBFN2WJsAbd8dMxLpDO54cR0+2t5v+Bym7OzVWhjsWHl2jFpWANYzGYd9NvWcgp0S8lFnP5KZLF7+ZJfj6g+z0nO7LcntKTvFpbEGVMqO+8GOWTdOZRhocQbllNvKziiDHSWN5bayU3g8dtJYVgbluIPSczGoWLdz0LZxtk+4vrQeBy0JA9VBj1L22hEH2UbCIf65HW26RlRVxaaVADC+No5YOISsbD2zygmGyk6jefm5nTSWX0vPxVSnWClp5tmx0wONTTwfL1Ro2g2An/twB6597AP85G/v6T4uyzI+7cqnsXSCHSvPTsqWZ4dKzwkNbHc6nMo4ro4wnz7r1LNT2Mxuej7Y2VhssKPy7PgrjaUYlDV9dljnZdebCpo34bNinMsGZaNREYDd0nPzTqhOBoFqvTF21Z0+YSetbcKmhb2fcRs32ea60gU7WnXSrUWdqQy1+SBKJBSSeGfjbS5uQoxM+O0WjQXtpLF46bnvPDsGyo6ZZydsrn4AwFA+eBfHx9ityGLHtGZLr+4Guqs/gYFEGuGQxNV7EStVNm3i2bFKYyVYsENNBcce4u704y5j2VGP0bYkB8xvNHu0KPOxiik/Fw17Xig7ykKhk4rJ31ASTg3KFopQsdhpaGcGMyr2utRnh70+veDL1rgIi9fjpIOyturJdrAjpLFEJUMPJ+e/pYSTz7WDbJWS/dFdf0Zl5wyWynLTt6MdFcFg1674fokYjawR8esg0J2isjOkk8Yy7YFm3WenLh5x3LOKBf4DiTQ2dhduVD/JqzrTmmt0PXdVNj07xSk7uffPLGVZSijYKSH9wg3g4+0Djn5WibD1THAOB4HqXHxTm2sgSbmFpBhFQdxtu7mDZJhXY1kZlPWVDfZvt3eQo/fsuJvGMsu7OzIoGygl1Q524mzB+Oy0JgC5YMdOcM0MyoC1sqN4jKxvss0lnHzOqtXYzd8tZceo7JwxyYPyc+2oCAZLoxkFpDyNZWZQ9mmfHSPPTmaUnh2muNfGI/zebFfZGRBSuu9t7St4/FOTSixA+ewaVYCxz755pZn+axumNNbYRbxhf9zlMNgxKz13OC5CL9KuioYxMS9BF1ORJXp2BhJpVWDnBmbO/mI7KCvKjrt9dqwMvVa43WcnYeBZAqwbMuYeM0/L8T47tpSdXLCzYG4bqqNh7BxIYq2JuZIhKgWDFu30i6nGKnXpOeC+sqMtO2dM9qAiy+gc1+cDLqNgx16fHX92UFZVY6kMynYqZa2rsWpjYUc9qwB14P/e1sLu4p9wv05hjx1A2fga9fZJmwwCVdJY+sfGrnejKs5SQ8FOCVErO8WmsfQuOpueHW4O1JeQmW+nmIosbYWM2+rOsEkKzlLZMVjseTWW2x2ULUrPrWCl526pDWYmQ1tNBS2mnju5QTOPwcTGKnx+RjOA3JgRK0TPjt1qrLitNFbOFKr17CTSGTy3tsvVFKdWnbRTCWcHo7JzxsRG94Mdo95LrEOv0WbHTjWWMkrDP8FOKpNVKa1i6bkdz46dQaA1KmXHXhd6cZNppuzolZ0D9geB6o4pCpsrOyMm1b/lgIKdEqL27Aw48sYoped6Hyi71Vj6TQUZe4zP+3aKGAiq3cm5HuwwZUfPoMxLyM0rCgqHYHrUQXmUyg5bnLr6E64stqzaTDsqArA5LoJ7fgwMyhYVHSJMQWmujePwvcYDAF6y4dvpH7GfxjLrYK2lWZiPJX4er3n0ffz73a/hvpUbLH+HXbQme7cUDO7ZMUxj5RRbN0dGJA1ShQ35NNZAQr/5o1lVJUNJrfonjbVbEwyL1VhmG1Fbg0ATTNmJ8M+SXc/OQNI82GHKjl7ZOWC9UTFrKmi1yR5JU+n5mEXc7QwlnVVksYvOtCW53XERBsHO6JQd9U7O7Y6tpn12bCo7hh2UfebZaamNIRYOQZaNm7M5wagaDbDn2TEyozLYe5K00R+EKSgttTEcPisX7Kxa1235Hqj77LjXVJAZlFMZmZvse4dS+N83NgMAXl3fbfk77DKs8atUueRN4UNALdJYrlZjGSk7+WAnK+v7ToZMfIMMPxqUd2rSnHqeHf1K2dz3zDYtTKmsjYcVD41Nz44Y+O8cSKBLuF/0jaSwPT9eRa/sHLDfVDCql1HIv1y9z3wmK/NrpKrI+6Db+OMoxgh9mo64Tnw7dqaej6aDMiBWZDlXdgb57iT3u91WdtiNr5g0llHpNf85nzUVDIUkTOQVNKM/j0kDz5L4PfNqLHPPjviemO3GZVlWgp26GD7TVo/xdTEMpzJ4UzNJWiSblVWpK0tlx0E1VlU0zD8P3fkF7S9vbOY+mve3Fe6Wi0Ur67um7HCDskEaKx/s9A6nXBvSazQvrToa5gu8nm/HXlNB5XPpxjT6dCaLdzb3jGosxy5NSlmvGqtYz45oUHZS2QgUfhZEdWfdjtymtbU+bqj6WffZKU7ZEe8DpOyMQdiHn5kiP3FQkWX2geK7B7sGZYOLbzTKDtsV79VWD8D98vNhk5uk1Ywro8U+6pWyw9I+RXp2AGCSiz6L0Xp2kial69rvm92k+4bT/Dpuro0hFJJw6J45dcesBL0/kYaYEbFbjWWnzw47FiBnQJVlGUuF1NWGXUOuBQjiuAjAPWXHqvS8Lh7h5mW3FFcjX5QkSdy3o1V7AXtpLHFxdGMjctdL6/GVX7+M79z7WtHdqln6tT7/2vpUfXZMKpYcjIuoiYUtgw8tTOVkCqVoUubmZAO/DiB4dnQ+t7Ism947zAI50XRPwc4YhN2UWNmtWYtvLWbNndiFmDFJY2WzspIKMkxj5ZSd3UMplQHPDgP51zYrL5e63VhQ26NExKqiSDEoa5oKejUba5TKDqCUC7vhszAyaAM2lR2extK/bkIhiQc8ZjfpnfndcX08wn+XHd+OVhG1NCg7NIiLXZRf+XQX1u0cRF08wtv3f+iSulPQVNA1ZcfcswPkDOGAe74ds4CSlZ/3mSg7dtJYgDuprA/y799za3fg9N+/WlSl6M582fnM/P2tR2dchJlnx8xiMCj02XFS2Sj+7Of2yJn9RWXnU5MxEQzRs6P1WIlBjG6KLmz82ti1HguHdDfo5YCCnRLClJ3PTh8HwGEayyx3aqNxlbh7NKrGqolF0Fqfq07Z0O1M3WELEAt2XFd2RtFnJ2Ww+LEPsGdNBUcR7Ex2sRGc0esH7HZQtn49doyVLIXFTMEAcFjet/P25l7DRnTa71sqOxbVY1rEXjt/XJFTdU7+7GTsN6UJgHuprBGNOmnn3NuBBRVGyg4glp+787k0SxVyZcc0jWXcVDAiLJBu9Nrp6ldSUK+u78a37ljluGM2Kzvfc3xO/R5KZvg5UAaBmvgpDe7N2awsBIDF9NnJnWNW2SgGO4qyo192Dqg3vtoWCOIxmzVMzJgY0e2qq6XAP0dS4aQyWX4BHzQtF+x84qAia7RTz8UPj5mEXOyMLHZj2zufxnLbs6P4HZyrE0bBR9wjz05ilKXngOKzcOM8mjUVtNNnx2o2FiC2nTf+PawpW4vQFn9yUzVmjK9FJitj1Tp9M7DYnwqwno1l5lHSgw0DfX9rH57+YDsA4NRDpmPOxNy1/IFHyg4rsR7tIFCr0nNAUQq3uaS4mp3jBpNeO3bSWIC7JmUW7Fx+/Bw018awZksvvvHbFY42ZOza3WN8LaT8x4iZlM0GgdptvAcU10FZG+xs7B7im4NPeSVWveHPi++DVpUV74tmDRP1XpvfJp4DFOyUDHGXs9+UJkRCEgYSaduLmR3Hv5myw2TReMRcVuTTz3faV3ayWZnnnZlk6nZjQdM+OxaeHaO8cyyc+13eDQIt/oM+ycVGcOaDQJVKKiOU2VjWwY5ZGovtjllwwWCpLCPfjlbZcXPqOZAzSwPAA69tQiYrY/6MZsxqq8fciY0AckHQaEllsvzzqczGcmcGlFXpOeBuWhRQ5h7ppTbrqow9O0MW7S8Yynys0Ss7rKLxS7Nb8ef/7EB7QxU+7hrAKXestP37mRI0QTD7snEuaZNKWSvPDlMpJSkX/DLFy46yk8pk+bU+ZVw1JuVTle9v7UMyncWG/PgIszRWOCTxz4n2syump8w8O3qvbcSkoKRcULBTItgNqToaRnUsjD3ycqjdVJYy9bzIXg42cuUA+HE5UXbEjrYT6uOKGdJFdceOZ0dvwZZl2digHPFoEKiBR8gJk13sjZJK5/sM6Sz+tpoK8pSFdddbM68Bq3YaL6SxAGD/qU0AFI+BFqZcMHOo3Q7Kdg3iLI3FXudpHdMBAHMnNQAAPuzst5w7Z4W4kFTl1Um3lB2mMBh1UAbcn49lpuyYjYywU40FuJfiG05m+HG0NsSxV2sdHjq7Aw1VEazfOYgPttnzTbLS85baGD/PhcqOc8+O0j05AkmSHA3VFdO5tfEI5k5SgvMNuwaRycqoi0fQ1hA3+hUAjE3K7JoPSfqBXIitOzqvzW8TzwEKdkpGP8+r524EzNtit5PyaKee8x4fJrlyQFB2HFRksZ12NJwzqk70YMpysX12Uia7E7/22QGUxoL9I6NXyOxMPTdtKmijuokNaLWn7KiDHRb8GI1sYJ4UVo5vu/TcoWcHyAXrx8xtBwBMb65BTSyMRDpb1AgVEebXCUnKdeeGspNIZ/gu2o6y45Znx8zHZRbsOE1jjTYQ7OrPvd6qaIgHy1Oba7Dv5FxgYLdIhJWet9TFC2bX2fHsGG1EB4UeO4BQCm5D2WH33VgkhGg4hH3ywfl7W/uEmVi1kCTzTZdRsJPir0v/c2Su7FAaa8zCd6f5G9KsvLfF7kDQ0U49tzOTBijOs8NSdHXx3O6knQU7LjYW1Jo7Rcz65Yjm40KDskd9dixKte1QK5YLjzJoNNuF22oqaMezY8OgvIv32FHvNI1GNjBYsNeeDwBtV2PZTWMJwc4pn5vKfy4UkjC7Pfc51etO6wQxWGeLjxuDQMWAos6gzw6g9uy40bvG7Jqoi+t7dlKZLN98WCnMdgPBkVSGG3H1YH6dtoYq1aLPNptmPyuyS1AljZUdk3uzgWeHBzv5TagzZSf3HBbEKcFOr62yc4ZRuTvfYBvYHtgm26z0vMonc7EACnZKBq+YqNIoO112lR3jKNvO1HOrhoKMaXllZ+dAwnZ/EdZjh91smSpRKmWHpVf01Anxe0YGZb+Ni2C45bMwS6s5aSpoWo1lw1ipZ1AGlOqs7sGkrmGfGZSZJ2EklTUN7J00FQSU4CscknDK/Gmqx1gqa7QVWUrbByUgqXKh9FzZREVMvXht9XGEpNy1vnNg9DPXzK5xRdlRK5KqIglLz469QPDSv7yDBTe+YNiUkvl1WJUpQ9lsWt9/h5JpfuwtdUqaXlF2rD07WRm6QSb7vbX5gKWaV2NZ33uVzsv5YCevVn3SNcCvV6POySJGIyOUhoLmyo5ZNZbeeJ9yQcFOiWCeHUXZYWksexVZZlPPraRSwLqhIKOhKsoXo+X5yhQrFGUn99qUNJZ7yk6xnp2USd6Z9yfKyqPqrqolacPjYge3ys/tpLHSJudA2cWP0rNjkMZi11syk+WBswj77LAgGjCvyFJ6wNg7//MmN+LrB03Bj46fo/obADBnYi7YsevtMEJpiqm8B24oO8omyjiFBeQWrPaG/PXkwiYkaXJN1AvzsUTYAiim8oxgv9dqIjyrlFuzpXDiNwB05ccltOZfO4NtNj+yoawzVScWCaE2Fi5UdjLGnh3xnqO3GWXniG1CFWXHOgDWBjuTGqvQVBNFOivjuQ93ALCn7Bh1bTZrKAgI645Jnx2/jIoAKNgpGf2aXhgzxtciHJLQn0jz+SVmmJee2/Ds2FR2AODoOa0AgAsefAu3LP/YUvZWmmLlfrfbnp1sVlZkUYdNBe2MSgDc7bXjNI1ihFsVWWbBjniMRuqOrTSWjZs0N3lqDMp6IxtEmHoxvj7G1akBk52v06aC4ZCE//76/jjz8BkFj83NBzujrcjSUyarovYWdDP6LOZiibhZ4Wem9hl5dsR+MlY+krhNZYepVFt2678mlsbSKjusRcaWnmFLDxhLv46vjUGSJO7ZKUhj6Q1pFu7XepsJcVQEIKSDbXh2tPddSZJ4Kotdb2aVWAwljaU+D8qIIv33ijw7hC6i3Azkdi7MDGwnlaWUnhvvHuzMX7EyKAPAz06ch9M7pkOWgf95+iP819I3TFNa/UIHUMD9NNaQIK8yI5+IWem5Wfdg8XujrfrQ+5vuBTujO49sh6YXrKi61eosLLIs23o9Vl6DbFbG7iFW0VJYHcICoF06vh2xtJotCmYLFCuLHu35B4DZ7Q0ISfkhi/3Fvw96rRPcUHb4EFATvw5jopvtDEybCuY9O5r3yGzkixY7fXZSmSx251NJRqleNhizTaPsjKuNcWO8URUgg6df8+lOrbKTMfPshEVlp/C18JmC2jRWynkaC1CCc3Y8bI0xQ+narD6+VJalsYw8O8Z9dtj9lKqxxiD9OnLz3vlmT3akVLOp52YRNmMoZf9GE4uEcM1X98X1J81DNCzhifc6cdJvXjas0OJprPxrY1UzbnVRZrv9qmhIN1gzT2Ply651gx1JeJ4Hys4omgoCikI22sXJrM9OJBwCu6R0PU/CebFjUE4YBDt9Iym+KGjTWLnvGZuU+aDL6ig3cpoF324pa0Dudc3It2MYTSpLLw2rqBejUHYs5mKJTHKxnYGdcRFazw5TDuz0XuGjNEzOjVi9ZxjsGCg7gKJ6WN1/d2kUyabq3H978sG7HdUd0N+MKgZlTRoraX1NDOoEO/vky8+BXGWtUQpKxGijwvqP6XXtB8z7u9mtuislFOyUCMWzo1yYzLfziQ1lx0xSZDNKzDooO0ljMb75+Wl44KwOtNbH8dH2AVz6l3d0nzdQoOxU8e8bjQBwAi/71FEEAPPyaTNVQpIkT8rP3VJ2eIv/UXqfzNQtwLzyRfyemWfHyOTIYCms+qqI7nlhvp1dOuZZRdmJ8GvMTNlx6/wz5riQytJTNaq4L2UUnp3hwk2UEZM9UHb0ehkZjYuw2+sLsFd6LhqtjdJY2w2UHUBJZVkp6zs1958GB54dMf7RK4QY1BiUldJza2WHp7FiYrCjKDt2UliAcXFB2iSbAJj3d2PXO6WxxiD9OjswuzsLYPTD5uw289Jy0PRx+N3pBwMwLtNkwQ4L5GpiStm0G+oO2+2Pq9W/oZuVnpupGuLPuqrs2PC42IGlsTp7R0ZloDYbFwGYe57ENIJZk0QmVxsZlNl7qK3EYoiTx7WIbRtYGtNOsDOaqfMiblRk6e103VV2rNNYk1xMLxczLsJZGsta2dkhBDtd/QnddKCZsqP0OrOn7LC0F++zY8OzI0mSaVBgqOzoDObUwiaeiy0HZk6o459FO+ZkwLgCTKnGMi8911V2eNd1CnbGHMoOTLkw9xbKH60u7LRJ/tSOQZnvqqLWN0UtTP7ePZTUNSuzm1qtsMNwKwUDiFU8+spOzKSqKmWR0mALuKvKjktplNb6OMIhadTlwlbnwMw7IvYMMjOVWnl2tL4HLeLkcZFsVubBdEN1hO+AB0yqsdxMYwGiSVm/4scOegbluCvKjnX3ZIarBuWUdTXWcCqjahFg1j5Cix0/045+9Wdim8bbNpLKcPWltb5Q2ZllU9lRrt3cNcrOdZ8Nzw4gtgbR8ewwL6XGs5OVrYNgvTRWOCRh33wqa7bg3zFDqcZS/z2zdieA8nr11gSloMQ/IYZ/jqTC6U8Uzq+ZMb4WISlXPqr94GrhaSw9ZSdsbVAetjmTRo9xNbkPeVZWpFuRQU2fHUAJdtxUdoxUAbOKIqsUjpkqVAxiwDVaz45YLjwanwUbF2F1DvSVHWWmmhlWfXaMuiczWur0g53BZBrssm6oipYljcWCnfU7B211ttWDLSRi35EqV5Qd+2kstmnZOZAcdWdis4BSvA+I3ionaSw7PYi0GwBtEMfuqfFISFf5YsrO5t3Dpn1teDPM/GZL7KAsy7LpbCxA2IzqKO9DWoOyEAhavUcDmmosxk9P3Bc/OHY2jt+33fTnGUYbFRacGTcVJM8OoQNTdkTPTlU0jOn5jsVWqSyzDsrKRWd8Yyg2jQXkFkl23N1DhWkGnsYSdhjtLkrmPI1V4zzY4aqGgRQbddmzY9bEsBh4Gf8oKrKsDNNmnidlLID5daN0UNY/j+w91M7FYjDVTpvGYot5LBJCVTSs+EFKGOxMqI9jfF0MWRlYa3O8gBYzZceNpoJ2DMqN1VEeaIy6K7fJOY6GQzyQE1NZShrLWl3mnh0TZWdnv/pa2awJdnhDwYa4rirZUhdHS20Msgx82mU8DkTbMoEpO+msjKFkxnQ2FmAeFDBlh6WxIuEQ/5xaDQPVq8YCch6zc47c01CR0WK0UWF/32hMjNkmm0rPxzB6nh1A8e1YmZTNpp5HTJo7MZTZWMVdfExV2a3jqVCqsZQP3SQXGwtyZcdgoYyEJLB7WSKj/sBaLXx2Ogg7we1gx43UA1e3DNNYxv4Iu/4jK4MySwUYKjsGBmW+mOeVCzul52YN74pBkqRRm5RHdD5/bBEZSVv7M4xwUnouSZJrqSyWXjK6LvRGRigBn/XngvuZbCg77LOvNSnzURE6KSzGXjY62XfnDcrj8ynY6miYByQ9wyllI2rRj8bUsxMXN8F5/5uFsjOoKQwpFiPPDrtGtI02GSHJeJNNaawxiizLfIdar7kpTR2X64NgtdMyKz032zkwnEjIeowzMZBq++wAUOZjuajsGC2UZlVVlgblMDMou9NBmQVbkmScw3eCGyMj7BqUzZQd28GOwW5UmwrQ0mzg2VGUi9y1ZSeNZTaksljm8k7KxQU7etUpLBiT5eKvPyel54A7wXM2KystHQzOcYNO+bmTXl9VdgzK+WBmr7wRV/sZ6RKUHSN4J3uD4gtZlgtKzyVJUiqyhpSWCkbpHlPPDktjCeeEnR+rlKl2rlaxGDUEZeeTVfFpMQviuLJDs7HGFsOpDL8gtLn19sbcB9EqKEibtO6OmAxkYww5kJD1aK4xUXbyfiSVstPkXhrLyu8BGC/YVjdlxbMzOg8DQ2y2ZtUl1g5ujIxI2Uxj6ZlB7czFAmx4dgy6JzPEaixR5dB6UqwMyrIsu9bnSGS0FVn6HZTNGzpqGUqm8cG2PvX5cVB6DojX0+jTooCJsqMzMoL5lmxVY9nooMyUnf2nNgEo/Ixs55VYxsrO3hYzsvqG03wTKd5/lIqsJL/vFuPZUQzKyjnhhmGbnh1tGsspRl2bN+eVssnj9IMds0027ytFs7HGFuyGFA5JBcoK87Z09lkEOzY8O2bl06MxKAPCzlvHs8N2J3rKjpsGZbNgJ25gNLY0KHNFyB1lx22/iBvdqK1m3JgpOzwlZJF7twp2rN5DFgQl01neewQo9KTUWZSei++/V8pOMVPD9TxzYjBmNTIim5Vx6p2rcNyv/oH//ONqrmowZaexxl6ww66n0QTPCRupWr2REY6aCtqYjaUNdgqVHTYXy1jZUdJY+soO67FTXxVRpUWZb0dsbFiMZ4ddF+K9k50fK88On3puI4VphpFBmaUFpxgEO0yx0q/GImVnTNIvNBTU7vbtVi1xZ7xOKiJqoxrL7iBQI3iwozO7SBkEWliN5UZjwd12lB2DNJaVqsENyi5VY4ml2m7gimfHsvTcuqmgVc8aNuDS0LNj0RiyJhbh16Z4jYkNBQHBs2NQPSO+/269B0CucjISkjCUzGB7EWMj9KpTJEmyPTLi4Te34I2NPQCAp97fjmNuegF/Wb2Zv147nh1AuJ5G4aVT+dIMrgt2L+gvshrL6ryIoyIOmNIEIGfiFxdeNt7DzLPDlJ2N3UO6gbrSY0d93TbxYEfxmBXj2dEOAgWMB3Nq0fP7FIOeZ0eWZR48TmnSHzlh2meHPDtjE73uyQxWWtzZN2JqUszYmKzr1iBQPcYZKDuJdIYvpmIay63Ggol0ht8wjUrPAWN1wq5BOeWyQdmtFArLl+8aLL5c2GwQKGBVjWVekaH8DmPpPTcXK/cZMEpjAUowy3bTgKIM1BeksayDHTfTWJFwCE08les8eNcbFyH+20zBGEykccMTHwIATj1kGuZMbMDuoRS+/9DbAHJdeu36NtwYGSGmNo1StfW8saByrpw0FeSl5wafSxaEhEMSPtNej5CUC+pVjQZtKDsttTGMq4lClvWbpvIeO5p7D7u3iR4zp312Upksv17FjaLRYE4RWZb5MFy9eYFOqNa5BncOJJFIZyFJikqvxSyIY9dIMdW/XkHBTgkw64XBPojJtLJT0SNlY/6KkbIjyzKfjWXHHKiHkWdHbAmvveG60ViQLSzhkGTqSzAMdizMua4rOy6nsRqqI7wstdjzaBWAmTYVtGlQFkvPtbJ277Bi4jRqHwAIvXZEZcfAoKwdRcCPl3V9DUkIuWAQFxF9Gk4xWujtKDu/ef4TdPUnMK25Bj/+8lz8bclhOP/oWXyxqa+K2n6tLHje1mO+uTLDTodqvfepqKaCBkHgTiEIiUX0+1ExBc7MsyNJEmblZxTqBTs7DVRlljbcaSPYCRt4dsQ0lXhfNhrMqf1Z9va5VY0lblTYeWxvqDK8lynVWCbjIiiNleO6667D5z73OdTX16O1tRUnnngi1q5dq3rOyMgIlixZgpaWFtTV1eHkk0/G9u3bVc/ZuHEjFi1ahJqaGrS2tuKSSy5BOm09W6RUaCeei8QjYd57xKxM287U83RW1r2BJTNZ/vPFRtpG1TIDQrtzbSDmRmNBpceO+Q2dN8bLFO6exMe1sJuqW+Mi3A521OXCxZ1HruxE9M8fL4HWWVjsVjaJC5h2N85SWA0Gc7EYeteYOPEcsC49d/v8i7DURY/JpsQIoyZrVgrGpu4h3PGP9QCAHx0/B/FIGLFICBf+y97465LDcOieLTijY7rt42C79OFUpqjXAdjrUN2g49kpajaWQRDIFByWXmImWuYzSaSV19dmouwASkXWRzomZaPO3416aSxDg7K+AsKu4WhYUp3LGoNScL2fDUmjb9xXrVNJuXn3EADjSizAos8On3pOwQ4A4IUXXsCSJUuwcuVKPP3000ilUjjmmGMwOKg0eLrwwgvx6KOP4qGHHsILL7yArVu34qSTTuKPZzIZLFq0CMlkEq+88gruvfde3HPPPbjyyivL8ZJ00Zt4LsKG1G03MCnLslLqaTb1HAD0xJ1h1Q7C3TQWHxWhs7tg5uutLgQ7Zn4dwNizY2VQdntcRMLGQuCUiaPwWYjXjtE5YIqcXgBhNhZARLypadNtSiWW+aKjNx9LnHgOKAZlqzSWm34dBktjFRMkGKkaVgMvr3/8QyTTWXTMbMHCfdpUj+07uRH3f/cQXHTMZ2wfR25zlXsfik1lKdeE8TlmaSx1NZaDpoIWHZSZQXt8fubVZE2LBvZ4LBKyHKUxy8SkrJ2LxWjSGJTDIckwpaeksbTKjv6908rsD4ibzEIfqFNEZYepslssKrEA42a2KWFz7SfPzuj0r1HyxBNPqP59zz33oLW1FatXr8YXv/hF9Pb24q677sL999+Po446CgBw9913Y86cOVi5ciUOOeQQPPXUU3j//ffxzDPPoK2tDQcccAB++tOf4gc/+AGuuuoqxGLmi2QpUDw7+h+6iY1VeG9rn2HFjRg5m009B3IXXjikvqGyHVU0LBkueFY01+r7FfRGRTB4Y8FRpLGYKmAZ7BiWnpuncNweF+G2ZwcYXfm52L/F6L3XKxNm2DVch0O53WkyncVwKoNxwmO7LMZ9MNgi3C14dowNyrlGfNobvRc9dhijSmOxYCemPi4zZefV9d34+5ptCEnAlSfMdaWVAZC7nnYOJLC1Zxj7Tm50/PN2lJ06nT47TtJYVqM0WBprgoGys535der1uyeLMJOyrmdn0MCzU6P27Jj11FKUHfVrGdDpsQMo14RZNdagZszEaNCqstWxsGWPHcC4ma0YpJGyY0Bvb27QXnNzMwBg9erVSKVSWLBgAX/O7NmzMW3aNKxYsQIAsGLFCsybNw9tbcquZ+HChejr68N7771XwqM3his7BpOJmbJjlO4RdwRWyo7u/JVRVmIBSrAxkEir/AV6oyIYblR+2FZ28sqDU4OyV+Mi3FxsJ42iXDhlpyeKiemXzcay83qqIvoVWXb6JImP79KrxqpWp7EyWdm0esxvaSy9poKA6E1Rn7NsVsY1y3L3r298bhrv4OwGo63ws3ON65WeDzlof6FUCOov+GxUxPj63DWjfU07uF/HXE0EgL3yaawNuwYL1JSdBqpkU3XeTJ8PusyCHaU1iEbZ0anEEv9t1mdnwGST6RTxmmR/czMvO9evxAKU15XRWCfY75AkbxTWYvHNkWSzWVxwwQU47LDDsO+++wIAOjs7EYvF0NTUpHpuW1sbOjs7+XPEQIc9zh7TI5FIoK+vT/XlJYpnx1jZAewFO2ZNBbXPZTipgjCioSrCL25R3TH70LH+DJt3Fx/s2Ck7B4Q0lrbPjs0eM+57dtzb0YzGsyO+LkNlx8QHY7eDMmBcMms18ZxhmsZinh1hF6yrRHmgrDFYKrdHp9eUGVkhMNMWCMQNFIxnP+zCu1v6UB+P4OJj9i72kHVRNiHFpZeVURHG1zgvPdeZjWVnt2/XoMyVHU0aiyk7bCNpxoS6OBqro8jKwLod6hlZ2onnDBZ8s+ITI78OYFxAwvpJGaWxzErP3WooCCiqLKAEKnbSWEavS0xzuqVGuoFvgp0lS5bg3XffxQMPPOD537ruuuvQ2NjIv6ZOnerp31M8O/oXplVjQVEm1NtBiN/TM4s5adNuhCRJvJJGNJD26/TYYbAPytae4aIasQGiKmC+UBqVT1sZlI28PsXiRffeiSyNVYRCxl5XSDK+IdfqLEwMJdixXqCqo/q7caup9YwWE4NyY14VDYck/nf0gjMlxeK+fN5YpLIjmmwLDMoGw0DX78wtukfObi3o8TJaWLCzcddQUT9vT9nJl54nCtNYTgzKyUxhdR8geHby52aKJo3V5UDZkSQJe/OxEWqTMrv/FPTZ0TRxNBu6aeTZUfrkqM+HnQ7Kylwsd65zriYl06oeO2ZpLPaSta/Lj0NAAZ8EO+eeey6WLVuG5557DlOmTOHfb29vRzKZRE9Pj+r527dvR3t7O3+OtjqL/Zs9R8tll12G3t5e/rVp0yYXX00h2ooSLaxs0sizkxJyvXoLVkgYhKk3f8VJrtyM5trc8e8WdrZmO4z2hiqEQxJSGZkP5XOK3YXSqs+OtbLjbgdlN+XbyYJE77Rc2Go2GGDu2XGSFjIqmeWeHZMeO0BhNZYsywV9dgDzXjueVmPVFBfsiDt07XUhDgMVYb2GJrgc6ADAAVNzPp1nP+wqqlLSjtrH0lis9DyVyfLPmJ1gR1wo9fx0XNnJBzMsgOtPpNE7nFI8OzaUHQDYq5WNjVB8O6lMlr/XRn12GMV4dpSJ5xplh1dj2TMou0G18NntG07z328e7OSVnQLPTlb1O/1CWYMdWZZx7rnn4pFHHsGzzz6LGTNmqB4/6KCDEI1GsXz5cv69tWvXYuPGjejo6AAAdHR0YM2aNejq6uLPefrpp9HQ0IC5c+fq/t14PI6GhgbVl5dYeXZYOeh2C4NyxMzxb9LgabQNBRl6aYZBE89OJBziKTpWyugUXnpeZBpLMSib99kxGzjoBC8WW3Z9jKTMezHpwWeDmQQ79aZpLPPp1iJGk8+tJp4z2O5550Ai1xsqqT9Tji2kgzrzsez0gCkWpmw6NSgP8fRNqKB9gpGyYzVLbDQcNL0Zn5/RjGQmi9tf+NTxz9sJgEXPjizLqmvCSRoL0E9l7dSUntfEIhiXD0a39gzzzZUdZQcAV3ZWb9jNv8dS6CFJqcRjOAl2jD07+mmsGt6zyo6y43Kwk8pgU/5ePb4uZmp9iIT0FSujBprlpqzBzpIlS3Dffffh/vvvR319PTo7O9HZ2Ynh4ZyE1tjYiDPPPBMXXXQRnnvuOaxevRr//u//jo6ODhxyyCEAgGOOOQZz587FaaedhrfffhtPPvkkrrjiCixZsgTxuPu7omKw8uywxaw/kdbdrZpNPGfwMkAzg7JLwc5uvTSWQYputL4dp8qONmix3UHZ5XERRk0MiyEeCfMdrFNTqdJjx/ijbkcpseXZMQh2lPfQnmcnkc5iKJnhimg0LKlKWGtN5mOxga5eKDtFp7FMlFWlx5F+gKgteXaLC46eBQC4/9WNhi0vjLDji2KLcDrvV2IbrpBN02okHOL3NK3qJY6KEM+PWJGlTDy3p+wcsfcEREISVqzbhafey3k9RWO99t4bDYd4s0/AeFQEYOxt0RsVAdibjeXWqAiGuFGxk8ICBIOyQRrLT+ZkoMzBzm233Ybe3l4ceeSRmDhxIv968MEH+XNuuukmfPnLX8bJJ5+ML37xi2hvb8fDDz/MHw+Hw1i2bBnC4TA6Ojpw6qmn4vTTT8c111xTjpeki1Wfnbp4hO+u9WRls4nnjIjJnJIhB7lyM/Q8O9ygHNd/bczNX2xPD/vVWFYdlEvj2fGqGmiSxoBpFycLk5lnx44Hxmh6sl2VoiYW5jfI7sGkypwsKppMui9nGstJOtEsjWw0l2yXzQCxWDr2bMHn9hiHZDqL2553pu7wNJbJzj3X/yX3/30jKaGhoP2+MOx+o03vi71txI7cokmZKTtWDQUZMyfU4btfnAkAuOr/3sNgIq1ctwbvgajuGA0BBaz77GjVGdaHyNygnB8g6kI1FqD27NgxJwNCNkEmz44lsizrfn3729/mz6mqqsKtt96K7u5uDA4O4uGHHy7w4kyfPh2PPfYYhoaGsGPHDvziF79AJFLWFkIqzGZjMcymhJtNPGeEDfLCgDjxfHTnhKkrKs+OhbLDbkDFpLFyM5VGF+xYGZSjLis7StrM3Q8661m0xaFCZtU9GVCuy0Q6W3AeilF2xJ14RngPrdQ5SZL4c3YNJgvKzhluVY85hS2syUzW1DyqhVch6Ww2lGosjWenX78KyC0kScL5R+eqvP706kauhNjBTgAdCkmqkRFOKrEYcybmfDQfblNXy4qjIsS04OT8wMr1Owf5JslsVISW7x01C1Obq7G1dwS/fOYjpceOwXvQKARadtJYac1niwUs2vuykUIq4noaKzY6ZUcM/ln3ZPLsjDHSmSzf1Whv2iIs2NEbGWE28ZwRNdg9AO6lscbpeHYGLKoCRpPG6h1O8Y7QZjOVANGzo75BWBmU4wHoswOA91lZuW5XUcdjpgqKUrg2gLA7CBQQDcrKe9AzlFTeQ4tgBwCa2XyswYQyF0sTSJfLoFwTC/PPmZNUlh1lRxzVIcsyn7nkdiWWyGF7teCg6eOQSGdx+wvrbP+c3XNcLyiGbKilE3V5dnsu2PlAE+xoR0Uw2JDTtzf3AMjdE8fVGN9ztVTHwrjmq7m2J79/+Z/4x8c7ARi3TGgUPJjmpedWHZQN+uzYMii7E1CIxQVsY2rWYwdQq1liKmtE8Kj5CX8dTQUipgZMlR2TkRFpk4nnDDPPDjcoj7oaS8ezYzONVUyww4KqeouZSoCZsmNu0GWKh18HgTKOmt0KAHjpk52Opp/bMShHwyGuhGgDiISNXTyD3dzEHSnbYTdWR21172Ypg50DxsqOMh+r8Dw4OV6nSJKkTD530GvHzLNTpaPsDCTS/DryStkBmLqT8+4sXbWBl2tbYde0Lo6McDIXi8EC/A861eXgOzWjIhhsY/Xellxw1Fpf5bjPy5c+04pF8yYik5Xxl9WbARgrkqyxIKCkqvQIG/XZMTAoKx2UjWdjudlnB1CrSXaVHXE5EgM5puxSGmuMwYKd6mjY9GY/sdG4/NxOGsts8nkxNxo99Dw7VnKq2P/Caa8du+ZkwLjPjqVBOazfeblYvJrNtM+kBrQ1xDGUzGDV+m7bP5eyUXoOGHdRTtrwZzD05He7oyIYYq8dbUNB5VjzBmWdxcBLZQdQuij3FqPs6KWxdJQd5hWpiYVHnXq24guzxuPAaU1IpLP4nU11x+41Lo6MKCaNNbs9H+xs61OlSXYYmLdZGottXFpt+nW0XHnCXFV1qZFJXPTshE02okaq+6CBQVmpxjK+J7mdxirOs2Og7JBnZ2xix68DAG0mnp2MjTRW2EAqBcQ01ug+GHpTqQd4HxT93z2xMddrJ5nJ8ly7XeyWnQPGM66sFns+CNTlaiy3lQVJknDU7Fxn8OUfbLd4duHxWFWH1Wn6ojCK6aCc0Fm47SoUzapgR/+zY5rG8mAQq4gyH8tBsJM09jDwTsFpMUD01q8jIqo7963aYOszavcci+XnThoKMvZqrUMkJKF/JK3q9sxGRUzQKDvaxdlu2bmWtoYqfH+hMlzVKI0lNhYsxrNjaFDOXyfJTLbgZxhuKzssMNk5kOSVblbBjrj5Ftcddr1TsDPGMJLitfCRETppLLOJ5wzTPjtF5Mv1aBYMymynNWCxw4iEQzxFt8lhKsuJsmM49dyqg7Jn4yLc/2gdnU9lLf+gy3Y1kJVBm2Gk7LBF2FlTQTGNZa/HDoN5dnYNJHmK1MigrA3MgBIoO0WkscyUHb1BoDstqoDc5oi9J+AzbfUYSWXxmg3V0M7Uc0Bd5VdMr69YJIS98hPJP9iq+Ha0oyIY42qiKp+IE3OyllMPmY4DpzUBAD6T9w5paai2F+wY3ZsHhQo1EfE6MTIpDxpMTC8W9jc/3ZFrqthQFTGsHmYYde5X0lj+Ci/8dTQVCJPirZSd9ob8yIgiS8+Ndg+A+312UhkZA4k0slnZ1kC6yeOKq8hyslBa9dkxUjaCUnoOAIftNR7xSAhbeobxkdDp1Qw7BmXAWC1xkpbTaypoNEjRCCWNZW1Q1u2z42FTQaC4YaCmfXYihX12dnrcY0eLJEmY2pxLAdlRrOwrO/mRESNpobGis3sQ8+182KkEO9pREQxJklQ+E7tl53qEQxKWfmc+lp13OD47bZzuc1TKjg3PjlEaS7tRzM2Uyv2/YbCT9/tYrSt2Ydcmm/w+2cKcDKg794vBjlVfuXJBwY7H9FuMimCwaqxdg8mCMlRWjWWrqaCHnp2qaJj/ju7BpMozYZY7LrYiy+5cLMBG6XmpxkV4lMYCcsHqYXuNBwA8YzOVZfX6GfUGakkxs7HYDTqRzuDlT3IVLeNte3Zy77VZ6Xm5qrEAZYHrdZTGMl7o9ZQdlvrzshJLi5OGiUmb1wQfGZFIFZXGAsSKLMWkrO2eLCIu0qNRdoCc4rLv5EbDx516dgoNynnFXVONJUkSLyYxqshyO43F3hfmGbUyJzP0VCsninwpoWDHY/osPC2McTVRfoPu6lPnzXk1lkVFDeDtuIjccSqeCra7iIQk051/sY0FlYnn1jsEw6nnFotf1PXSc+86+AJKVdazH3ZZPDN/PDZUQcB4PpbdlAUAVMcUlSKdyeKCB97C6xt2oyYWxgn7T7J1vGIaqyiDcsZ+cFYMPI01aD+NZaasKsqOGOyUzrPDcBLE2U1t1uumsZwtzkpFlk4aS8eTIy7SxRqU7aKqxrI1LkLdXoBPPdc5J2bDQJPpLL9f1blkYNcG4lMs/DqMkMQ22cprc+K1LCUU7HhMv03PjiRJhhVZTK2JFq3ssIqw0X8wRN/OQH6icV2VeVfUKU3lVHbMF3sjY3OxeK0ssGDnjY27VUZxI1IsjWVxPIZprIyDYEfYjf7okTV4/N1OxMIh/O60gzGrTd/3oEVVjcU/O5o0Vsys9NzbYLMog7KNNJao5u70uHuyHkyl6LUx98tueT83KCeKT2PNzjcW/OfOQQwnM4ajIhiTmxQ1Z7TKjhWNo/DsJNJZ/m9tnx1ALD8vvMbF9K3ezxaD9tq0G+yQskNw7Hp2gFwVAFDYWNBOGstosi7grrKjVMuklLlYFlLqlKI9Ow4MyjrBjizLlv4C15Udj6uBJjVVY+7EBsgy8JwNdcdpGku8kaYzyg3ZjlLCbtCrN+7Gn1/fjJAE3HzKATh81njLn2Ww62s4leE9p7TKTlnTWPndvJPSc/M+O3oVbP5WdpR2BFZqoeLZKbZIorW+CuPrYsjKwEfb+w1HRTDECqLReHbsYNezwxR5cSMqBjF6ahcvP9cJdth1H4+ETNV+J2hVR7tpLL1NNjPvWzWCLTUU7HiMXc8OoFRkaRsLOjIoezgbCxCDnYRlJRaDp7F2DzuaKbTbgRwa11FoRB+OYQdlj6qxvDLIAsDRc+ynspRqLPPSc70AQvSROKnGYm/x9Sfvh2P3nWj5cyJ18QgPzLbn07nFjIvwwjMFgHfkdVSN5XBchN89O06VnQGhz04x9yCx3w5LYTVrRkUwWK+diEEw5CYNNj07euoHu3aro2HdTazZMNBBg5L10VAQ7NhVdjT2idyIn9w1VMpg3Q4U7HgMl+JtKDvtFmksU2XHwAQHuFeNBYienZQyF8viQ9feWIWQlLtJ7rDZa0eWZUcN6fSaA4oBjGEH5YCMixBhqawXP9phedx2q7H0hoEmHQY74nVwxaI5+LeDp1r+jBZJkgqq77SqKPMXDSUzBY0qvT7/jS6nsaoihcrOzjIoO0oay76y48SzU2waCxBmZHX28/uHtuxcfO74ujg69mzRDYbcpD4eAfsTTj07gwajIhhmnh23J54Demks62osoHDyed9Iiv9/k4NRHaXAP9MyKxQ+8dzCswMYj4xI22gMx3YW2qqiTFbmNyc3urEys/DuQaUPitXk3Vgk12tna+8ItuwetpVLH0pm+A7SSem5uEDbWazZ99NZGdmsPOobZCmCnf2nNKGlNoZdg0m89s9uXqGlezwODcqDOspONCyZBtqMuRMb8O+H7YFZrfX41vxpls83oqUupuo3VWhQFmZ5JdOqElfvmwoqaSxZlm2NIzD17OSVHdabJK3ypPhV2WHjIqyqsZRxEcVWYwGKsvP+tj7sMyn3/9pREeLffPmHX0LURGlxi1BIQkN1FD1DKceenUGDIaCMapNqLD7x3KNgpzoatj1TTPva+IifeMSzIoFiIWXHY+x2UAaMR0bYGxeh79kR56u4k8ZSSoOdtCyf7LD8nPl14pGQrePWC3bYTiokGZ87MYB0w6Ts9WIL5G6yXxIaDJoxmnER3OxrMyUUCkn4yQn7jCrQAdTBbTgkFbz/8UiIv59ak7LXwSZbBJLCgF8rRkwWerYgpDIyMlkZ3fn0mCSV1vPAgrg+F/vs1IkdlEeRxuK9drb1GY6KEIlHwp6rOgzWd8mpZ8dKnWFBkJmy42awI74vk8dV254pprVPOLEelBoKdjyGKzs2PDvMoKxtLMiUHTMzmtFkXXaTkSR35jVxZWcoaTkqQsTpQNBuXokVs/XB400FhYDFzk1ZfEzbkLAYvPaMMBbkfTvLP9xu6oNSDMoW4yJM0lh25mK5iZi2bNCp9JMkiU97NmyC6NH5r46G+XtrN5Vl3mdHOc5kOsv9Os01MVtqmlswZac/kTYcUcCw22iyXmhnwN6nYtJYbGxE30gaazb3AjBOY5Uadt7s9EDLqAzK5lPLzaqx2H3XrUos8e8B9s3JgPjactfELuG+7Tco2PEYJ90kJzbmLrKu/oTqg/Fhfuqvme/HyLPDGwpGw44nAOsxTugzYtegDDivyOp2+KHhM67SWb742/GrxCNKo0QnvVOMKEUaCwAOnzUBsXAIG3YN4eMu427KtsdFsDRWsjCN5XXgpkVsNWCU/jUyKXutrEmSpPh2bJqU7QwCBXIKkNNZYm4h3lv6dMZwiNidlybeF3by4abO1QhxbARrUqnXY6ccNObvhxEbBmXxWh0wmHjOqDHx7LjdUBBQX5t2y84BsXO/WtmhYGeMIcuy4NmxvjAn1McRDknIZGVuUuzqG8HDb2wBAJz02SmGP8tbkmf0g53RDgFlsJvwLsGzY+dDx6ef22ws6HSHEA8rH1ambjH/ktVizQYGdvU7G1SqxU6pu1vUxSM4bK8WAMAT73YaPi+ZtunZ0emgzL0ZJZ5xIy70Roqo0cgIpQmid2rUuBr7/hbAvPQ8HJJ4oJ5IZ5UhoCXssQPkVGNmKLYK4uwG9FWCCsZeV7GpdJbKYoFYKf1MZjBlx8yzwzxHq9Z3Y/WG3OyxIbsGZZ3GmV6kscRr024lFmDs2aFgZ4wxnMrwhdeOshMOSVyeZb6d37/8TyQzWRw0fRw+t0ez4c8aDZtzawgogyk7vcMp3mvElmenyVkay+kOQbzxspux3Ztyq4Ex3CnprMzLrsXgyyuOy5d1P24W7Dj07IgeGCcTz91ElcYy2CRYNUH0MthkvXbsBjt8w2GQwmGB2UgqwxUQIwOulzTa7LVjd1wEoCiG7HNR7CTs2ZphnH4JdphnJ2ySJp47qQHfyFcm/ujhd5HKZO0blPWUHZeHgIp/D3CaxlL7kUjZGaMwVSckGedmtbDy887eEfSNpLB05QYAwNlH7Gn6c0Z9dtyai8VorI7y4W+b8yqNPc+Oksay02vH6Q5BL9h5+dOdto7PLWXHaan2aPmXuW0IhyR8sK0PG3YN6j7HaQflZCbLFR0vh5qa0Vxrrezw4Cyp79nx8piV8nPrNJYsy3zBqoqZ93pKpLNK2XkZFgtekWUS7GSyMr/H2DnH2s/eaJUdxvh6fyymX95vIuZObMAxc9tMn/fD42ajuTaGtdv7cddL6y3VGabsmHVQdjPYCQkjf5yksfgmO39Pd2o/KCUU7HiI6Nex65eZyIOdYdy/aiP6E2nMaq3D0fnqGyO4QVljLhx2sccOkJO72U1xU3fOf1MXt+FHaqqCJOVmAO2y4Y3pHnR20w+HlPLoZCaLj7f348anPwIA/MdhM0x/lhnDu0ap7JQ62BlXG8MhM3Nqn5G649SgDCiprFKkhPSwl8ZiBuXSVmMBztJYiXSWqxpGyo4yDDTDuyeXauK5COuLYlaR5fQadyvYYWMjGH4xKM+f2YLHzv8CDppurLoDuc/qj46fAwD45TMfYe32nA/T6HzwDsq61Vj5iecuBjsAsP/UXEuLvW2OdgEEg3LeMsCqCZt91j0ZoGDHU/oc+HUYbOHd0D2E37+0HgDwn0fsaVlKaaTsjKa/hRFKF+XchW3VZwfILZht+f46dlJZ3Q7mYjGYP2AomcHFD72NZDqLIz8zAd/4nHlzO9eUnYwy1qNUlTTHWqSy7KaxxBJvdjN1MhfLTcT33EiVq9XxGPUOpfgxu+ln0MLKtO0YlMXFyiiFIw4DVQzKZUhj2ei1IwY7dq4L7ftQbBqLjY0AjEdF+J2TPzsZh8xsxkgqi6ff3w7AWJ0xrcbyQNkBgPu/Mx8v/eAoW5YLhnbdIWVnjMJ77NhQPhhM2Xno9c3o6k9gYmMVvmJjYrTR1HMrv0AxaKP2OpslkNyk7CjYsX/u2E7z5uUf453NvWioiuD/nbyfparGlZ1+d5SdUlYvLdynDZIEvL2pB1t1zN92q7EA5ebZnx/wmkh5O1TTCFUay0E11hubdgMAZoyvVQ1pdBsnDfjYZiMaloxHlgjKzs4yDlFsrFb8eEawFKckmZtyGeLCGRpl+wtm9DUaFeF3JEnCz06cp+rtZWRv4NVYJSo9B3KqvdMMgLas3q8TzwEKdjzFSSUWg3l2WPR+5uEzbC02Vp4dt6qxgMIL2U4aCxAbC1qXnxel7OTP0yNv5qrXrvnqvjyQMYMpO2wWU7GUw+PSWl+Fg6ePA6BflWU19V2kXqOWlMug3FAV4QuCUbsFPYPymxtywc6B05o8PT7efsFOsGNjTIJa2WGjIvyp7IjtCOyk5sVUS/Uo21+wsRF+SWEVw16tdSr/ZY2RZ8fEoOzFbKxiUfq75a4Lv048ByjY8RQnPXYY7cLi3FgdxSmft9eN1qiDMitdrHFR2dFeyHbSWIBoUrZWdoopYRQVlYX7tOGrB1grYgDQmp+OPFrPjhMVxU1YKksb7GSzMk+12FGbajWmXydVN24izsdyouys3pgLdg7KB39eoUwIt05j8ZSDyWZDMSgrfXbKsaDbmXzuNAAW05Cj3XDtP7UJADCt2d7cJr+y5Et7YY+W3GuY1KhvBq42U3Y8SmMVg6jsjKQyfHNNys4Yw0n3ZMZE4eI/vWO67QvaWtlxb8EqVHbsBjus/Nxc2UllsvzcOdkhsBtwc20M135tnu1dJCs97xtJ6xoC7VKONBYAHLtvOwDgtQ3dqlTcr5Z/jE93DCIaljCrrc7y92i7KJerGgsAn59m5M1g8j8LzDJZGW9t7AFQgmDHQRprR94HZtYEj6k+uweTfCdfjonRyjBQ4yBOMYDbu5+IG6HR+gaP23cibvy3/XHlCXNH9XvKTVU0jAfO6sDNpxzIe2VpMZ167kGfnWIRW54wVScSkmwNvi41FOx4iJO5WIz2xpwRr6EqgjMO3cP2zxn12XG79BzQ8+w4U3asGguyXg0hCY68F9Pzu6VrT9zXUR+O+niEt+3vGkUqq1QNBbVMbqrG/lMaIcvgxsdl72zFr5Z/DAC49sR5tqYYK8NAWek5G/hY+tvE9xd+Bmd0TMehBouBksbKHePazn4MJjOoj0cwq9V+NUkxNDlIY7HUaFuD8fXIzi9r5VAVtTcPzm2abEw+d2paF1Xt0foGwyEJJ312CiY56APjV9rzXkyjDVm1jWosfyg7ildU9Ou40a3fbcp/tiqY/nywY2fiOSMWCeFv5x4OWZYdLdhGU89HM4DPCFHZqYmFbVcesWZVm3cPm06MZimscTXOjIi//OaB6OwdwWfanS12kiShraEKG3YNYXv/CKa1FCeTl0vZAXKprLc39+KJdzux/5QmfP+htwEA3zl8Bv7NohqNoQwDzV235UpjAcARe0/AEXtPMHy8XjOlnaWwDpjW5HklnJjGspp8zhpVtpp4x5iys7Un99yW2nhZFgtbnp2UswBY3Ai5qS5XOjXR3HnTKjvZrOxTz47sa78OQMqOp/QNszSWs4tyclO1rZ24iDIbSzP1POW+QVm8mJ3sLtiObCiZMd0VF1u+2FgddRzoMHj5+WiUnTKmfVgq65VPd+E7976OkVQWR+w9AZfle3vYQTsyopxpLCu04yLeyJuTPzvN2xQWoAQ7qYxsOfmcpRVbTdJYLHDYkk/vlqPHDmCvg7JT9bLexTTWWII1oBxOZVRNWIdSGd63yQ/BjujZ2T2kbFL9iP/uYhUEV3YceHaKxXjqubvjIgC1suOksVVVNMxv+hu7jX075ShfbHWh/LycwcGM8bWY3V6PTFZGZ98I9pxQi1u+daAjlaOuSp0aKmcaywptNdbqDaUxJwP5yef5c7LbotdOF09jGSs7bPYYV3bKVG1kp4Oy3YnnjAYX01hjCXGMxEhK2cCy4D4kgafey4noFWXm+uYyBetWlP9sVTCsqaATz06xaPsdMLz27NitxGLsMynXK+PFj3YYPqcccqhV+bnY3dYIvustQxoLUGZlNVZHcdcZn3McZBumsXxwU9UiVmPt6E9gY/cQJCmXxvIaSZJsm5S354NnM89OVT5NyJ5bLmWHeZGS6ayhUd9pQF9XRWmsYhADQ7H8XKzE8oMvRqwC3u3j7skABTueUoxnp1iMlB1PmgoKN2OnUurx83IL8rJ3tho+pxyTc1n1j5Gyc9Gf38ah1z+LtZ39hr+jnGksAPiPw/fAd78wA/edOR97jK91/PN1GrUkUUYPkhVKGiuDN/J+nc+01ZdERQXslWkDSvDMri89WDDJ0hPlUnZqBf+dURDn9BqnNFZxhEMSP8dDwvw3P1ViARplx8fdkwEKdjylpTaOtoa4p91cGWHWQdnQoOzeh6M2FuYLoNMP3TFz2xENS/ho+wA+2q4fOOwqwzDEtgZjz44sy/jHRzuQSGfx8BubDX9HuYOd+qooLl80F/OmNBb189oKJz4by4fph7qYMrh05bpdAIDPliCFxVAqsozTWOmMMtjTLI1VpTGAl8vgKSpWRkGcU9O6yqDsw+vIz7DzNWKg7PgB5hXNZmVfTzwHKNjxlD+ddQhW/WgB9p1c3OLjBENlJ5X7cLgpIUuShHH5MQ5Og53Gmii+OCtXZbPsnW0Fj6cyWTzzQa58ei8HA+lGi5mys2swyVOSj7/baTi1PZkuz3gFt1AMyvk0VplmY9lBbJX/j49z0+1LYU5m2Elj7RpMQpZzu1+zAEabJnRShek2SkWWfhDHfFx21T5V6bmLG66xgDIyQvTs5M4/KTvO8d9djCiKsKZtN8OL0nNAcdw79ewAwJf3V1JZ2sDhsTXbsL0vgQn1cSzcp230B2oTpuzoeXbW7Rjk/7+xewjvb+vT/R08OPBh2scO9T7qs2NFJBzix/VJ1wCA0piTGXbSWKzsfEJd3LSFglYlKUdDQYZVRZZjz06c0ljFojQW9HEaSxKqsSjYIUpBKZsKAsoNuZgP3YI5bYhFQli3YxAfbFNSWbIs80nvpx0yvaT9XZiy0zucKjBnrtsxoPq33gwqoPxprNGirXDiaSyfvh7x2muujfEW/KWAz8caNE5j2WkoCBRW1fhC2bEIduxeE+GQxLtdUxrLGXxkhHA/6udpLH+cS9bfTeyzQ8EO4SmRvJqQFjw7sizzD4rblRBsQKeTuV+M+qoojsw3jPv7GsWo/MbGHry9uRexSAjfmm9vJphbNFRH+A2ctfhnrNuZU3ZYxValBjvKuAhtGssfN1Ytom/hs9PGlbQ6hSkgZmXadhoKAv5Sdlh6rs/Cs+PkGmfqL1VjOYMPAxV6OQ361LOTSgvVWBTsEF6ip+yMpLK8wsNNgzIALJ4/DUfsPQHH5ZvZOeXL++eGdC57ZxtPZTFV58QDJpV8dytJkjIQVOPbYcrOGYfugWhYwsddAzx1IpIoc+n5aOFprGSukRlTdvwavKmCnelNJf3bTdW5G7qZZ4cNljVrKAgUKjvlLN216qJczEgUtiGiNJYz9JQd36Wx8utO91ASbOmhpoKEp+h5dsRcr9sS8iEzW3Dvf3y+qBJnADh6diuqoiFs2DWEd7f0YfPuITz+bs6w/B+Hz3DzUG3DTcoa3w7z7Ow/pQmH7jkeAPDEu4Xm6qArOyx4yE0wzvraswMAdYKUf1AJzckAMK7G3MgLAF391pVYgFrZGVcT5SptOWjML1SGnp2Uc7WPBW/FqMBjGb1hoL6rxsqvO0wNr49HfHv/8+dREY7RU3bYhyQeCXk+L8gptfEIjprdCiBnVP7jig3IysBhe7VgdntDWY5JMSkryk4qk+XdnmdOqOVK1uM6qaygBzs10TBYJqg/kSrrbCw7sBt+JCRhvylNJf3bTtJYVp4dMZgsV48dhpVnJ5lxXnF48TF74zuHz8AXZo0f/QGOIWp0hoH6VdlhwY5fuycDFOxUDOyiEweBMvnTr/Lxl/fLpbIefXsr/vTqRgDAfxxWHlUHEMvPFWVnU/cQ0lkZ1dEw2huq8C9z2xCSgPe29mHjLmXkRfdgEi/ku0I3laCvkheEQhJqY0pFlp9nYwFKsLPPpIaS+0HspLF4Q0ErZUdQXcs9RNF+nx3718T8mS244stz+cBTwh7smh5SeXb8VXquVXb8msICKNipGCJ5V7yesuO2X8ctvvSZVtTEwtjaO4K+kTT2aKnBlz7TWrbjadUpP2cprBnjaxEKSWipi2P+jBYAwBPv5VJZyXQW59y3Gpt3D2Nacw1OPHByiY/cPcRhoE4rb0oN65Z8YIlTWAB4n6meoaRh3yU7Q0AB9fkdb/Fcr2HKTq9hnx1/XxOVBPssPr+2i6ev/JbGYi0Vdvl84jlAwU7FwJSdoVSa33yZZ8evVRDVsTAWzFF66fz7YTNM+5F4jV5jwXU7c0bkGRMUb9Jx85RUlizL+Mn/vYtV67tRF4/grjMO5t11g4gyDDTt69lYQM4kf/y8dnz70D1K/reZspPOyhjUmXyeymT5AmDl2REVj/HlVnYs+uwEPVUbJE767BTUxsJ4Y2MPTr1zFXqGkjzYqfNJ6XlEc78u5fBmp9AVWyHs1VqHWCSETd3DeDHfUdarhoJu8uX9cg0G66si+NeDppT1WJi3Qiw9Z8rOnoIRe+E+uWDnzY09uOHJtfjTq5sgScAtpxyIWSXs+uwFbMfYN5Iq+2BTK/ad3IjfLD6oaJP8aKiKhviCr2dS3jmQgCznFgOr6qpAeXZI2SkZcyY24P7vHoKmmije2tSDb/x2JTp7cxsxvyg7rM8Og5QdwnMm1Mdx+iHTAQA3PPEhslnZkyGgbrNgThuuWDQHvz31oLJ/gJmyIxqUWbAzc0Id/15bQxXv1nvb858CAH503Bx8aXb5UnBuUZ9/D8RmeX6cjVVuJEkSKrIKAwNlAKh592RAreyUs8cOoBiv+4ZTyGYL03N+93FVGvtPbcKDZ3WgtT6Otdv7saVnGIB/gh1Sdoiy8F9f2gt18Qje29qHv6/Z5nuDMpDL+X7nCzNx6F7lr9Rg3ordQyleds3SWDMnqNUDsb/Qvx40Bd/5QvmM1W7CfAK7xGCHFjZdzEzKfFSERQoLUKcJW2r9oexkZaVbrwhPY4X9e0+pND7TXo+Hzu7AlHHV/Ht+MShrq3z92lAQoGCnomiujeGsL84EAPzPU2t5F1S/GpT9RlNNlKdsdvQn0Ducws6B3KI/Q5Mq+cr+k9BcG8Phe43HtV/bt6Tde72E7Rh35V+3JBXu3ogcSvl5YRqL99ixYThWGZTLrOzEI2He5FCvi7Lfey9VKtNbavGXsw/FPpMaMLmpGtOaSzcaxQztvaGcDTGtoFWwwjjz8Bm495V/4p+7hnD/qlw5t18Nyn5DkiRMqI9jS88wuvoT3LvTWh8vaIjW2lCF1y5fgJCEigl0AKWLcvdg7rXHI6GKen1uwtJYu3WUnS7eY8da2YmFQ5AkQJbL79kBcopVZ2oEPUMpTG1WP0ZprPLR3liFZecdjkxWLmvjSRFtipb67BAlozYewXlH7QVAmenk5zSW32Am5a6+hODX0TfAhkNSxQUCbMAgS2P51ZzsB1gaS69M225DQSAXLP/bQVNxxN4TfLFjbzTptVPMuAjCPSRJ8k2gA5CyQ5SZb82fjjtfWo/Nu3NmNlJ27COWn7MFSzQnVzp18dxCxyYYkznZmCZbBmVrZQcA/t+/7ufegY0Ss/ScMi7CPwsuUT4KPDuk7BClJBYJ4eJj9ub/9nM1lt/QVXbKUNpcLup4Gisf7NCiZgjrp6SbxmIpUBvKjt8gZYewS0QoPY+GJV7N6Ufoiq1QvrL/ZMxuz/V88XMLb7/BWvtv7xtReuyMKWUnn8bKG5RpUTOGeXbEJpQMJ54dv9FkMvnc7/PSiNIiKjvjamK+TuuX9U724osv4oQTTsCkSZMgSRL++te/qh6XZRlXXnklJk6ciOrqaixYsAAff/yx6jnd3d1YvHgxGhoa0NTUhDPPPBMDAwMlfBX+JByS8JvFn8XZR+yJEw8I7viCUjMhXz3T2TeC9bvMPTuVCEtjsR08LWrG7Du5EUCuuWQ6f76AXEBgt3uyH2HKDlVjEVaInh0/l50DZQ52BgcHsf/+++PWW2/VffyGG27AzTffjNtvvx2rVq1CbW0tFi5ciJERZSe1ePFivPfee3j66aexbNkyvPjiizjrrLNK9RJ8zcwJdfjhcbN5Dp6whi1Ob2/qQTKdRTQsYXJTtcVPVQ7a/h20qBkzZ2ID6qsiGEik8f62Pv79HQO5FFY0rDQeDBJmXiQaF0GIhMPBCXbKmmA77rjjcNxxx+k+JssyfvnLX+KKK67AV7/6VQDAH/7wB7S1teGvf/0rvvnNb+KDDz7AE088gddeew0HH3wwAOCWW27B8ccfj1/84heYNGlSyV4LURmwxoJ9I7mGatNban1V/eA12mCHFjVjwiEJ82c045kPurBy3S7sN6UJgFKJ1Vpf5WtZ3wgjz046kwVrqkxBMAEAYeH69nP3ZMDHnp3169ejs7MTCxYs4N9rbGzE/PnzsWLFCgDAihUr0NTUxAMdAFiwYAFCoRBWrVpV8mMmgo827TCWzMmAYlBm0KJmzvwZLQCAVeu6+fe6+oJrTgaAxrzHT1uNxXrsABQEEznENJaf52IBPi497+zsBAC0tbWpvt/W1sYf6+zsRGureh5RJBJBc3Mzf44eiUQCiYQy7LGvr8/wucTYYlxNFNGwhFQmt4UdS2XngNJnh0GeHXMOmZkLdl5d341MVkY4JHHDcpvNsnO/oSg76nERSTHYGUNqJ2GM1qDsZ8bkFXvdddehsbGRf02dOrXch0T4BEmSVL1RxpI5GQDq42qPCSk75syd1ID6eAT9iTQ+yPt2eBoroMoOq8bSNktkyk445K/GdkT5iAienXIPsbXCt1dse3tu0OL27dtV39++fTt/rL29HV1dXarH0+k0uru7+XP0uOyyy9Db28u/Nm3a5PLRE0FmgjDPaM8xFuxURUOq3RoFO+aEQxI+NyM3U2Hlul0AlIaCQazEAow9O8oQULomiBxhoc8OKTtFMmPGDLS3t2P58uX8e319fVi1ahU6OjoAAB0dHejp6cHq1av5c5599llks1nMnz/f8HfH43E0NDSovgiC0SoEOzPHj600liRJqBU6bosTuQl9DpnJgp2cb6dLmKkWRFg11mAyg5RYUp/JlZ2TX4dgkGfHJgMDA/jkk0/4v9evX4+33noLzc3NmDZtGi644AL87Gc/w6xZszBjxgz8+Mc/xqRJk3DiiScCAObMmYNjjz0W3/3ud3H77bcjlUrh3HPPxTe/+U2qxCKKhu3Ix9VEfV9h4AX1VVFejUa7eGuYSfnV9buQycqBbigIQDX0tnc4hfH54aQjNCqC0BAKUDVWWYOd119/HV/60pf4vy+66CIAwBlnnIF77rkHl156KQYHB3HWWWehp6cHhx9+OJ544glUVSk3kaVLl+Lcc8/F0UcfjVAohJNPPhk333xzyV8LUTmwHflYMyczRJMyzcayZp9JDaiLR9A3kvPtBN2zEw5JaKjKvZ6eISXYoVERhBaVZ4eCHWOOPPJIyLJs+LgkSbjmmmtwzTXXGD6nubkZ999/vxeHR4xRDtpjHEIS8MVZE8p9KGVB7LVDu3hrIuEQDt5jHJ5fuwP/+Hgnn5UV1GosIDcMtG8krfLtKKMi6Jogcoj+viafe3Z8W3pOEOXi0D3H4+2fHKOS88cSdcLrpjSWPQ6Z2YLn1+7Ao29vBZA7b00B7J7MaKqOYROG0Sv02knw7smk9hE5mmtiiIQkTGyq8r3iR8EOQegwVgMdQBkGCpBB2S6s3w4bG9HaEA9k92SGXkUWjYogtIyrjeHh/zoUTdX+VnUACnYIgtCgTmPRLt4O+05qQG0sjMFkrmIpqJVYjEad+ViUxiL0YGNS/A5dtQRBqKgTGgvSLt4eOd9OM/93UCuxGHrKDk08J4IMXbUEQahQpbFoYbPN/JmVE+ywLsp6yg75uIggQlctQRAqxGGglMayD/PtAMEtO2cwZadP9OzkS8/Jx0UEEbpqCYJQUSt4diiNZZ95kxtRk+8+3RrgsnNA6aLcI6axUqTsEMGFrlqCIFRQn53iiIZD+Mr+kxALh3DQ9HHlPpxRoVuNRU0FiQBD1VgEQaior6Jgp1h+duK+uPKEuaiJBfvW2pgvJe7qH0HvcAqN1VEkUsygTKlNIngE+xNJEITr1MYojVUskXAIkQpI84yvywU7m7qHceA1T2H/qU18KChdE0QQoauWIAgVZFAm9mqtw/eO2gszx9ciKwNvbuzBu1tyDRNJ7SOCCCk7BEGoqBf67FDlzdhEkiRcdMxncNExn8GWnmG8/PFO/OOTndiyewgn7D+p3IdHEI6hYIcgCBXi1HOqvCEmN1Xj3z43Ff/2uanlPhSCKBq6kxEEoaKODMoEQVQYpOwQBKEiHgnjy/tNRO9wChMCPuOJIAgCoGCHIAgdfv2tz5b7EAiCIFyDNGqCIAiCICoaCnYIgiAIgqhoKNghCIIgCKKioWCHIAiCIIiKhoIdgiAIgiAqGgp2CIIgCIKoaCjYIQiCIAiioqFghyAIgiCIioaCHYIgCIIgKhoKdgiCIAiCqGgo2CEIgiAIoqKhYIcgCIIgiIqGgh2CIAiCICoaCnYIgiAIgqhoIuU+AD8gyzIAoK+vr8xHQhAEQRCEXdi6zdZxIyjYAdDf3w8AmDp1apmPhCAIgiAIp/T396OxsdHwcUm2CofGANlsFlu3bkV9fT0kSXLt9/b19WHq1KnYtGkTGhoaXPu9hD50vksLne/SQue7tND5Li3Fnm9ZltHf349JkyYhFDJ25pCyAyAUCmHKlCme/f6Ghgb6sJQQOt+lhc53aaHzXVrofJeWYs63maLDIIMyQRAEQRAVDQU7BEEQBEFUNBTseEg8HsdPfvITxOPxch/KmIDOd2mh811a6HyXFjrfpcXr800GZYIgCIIgKhpSdgiCIAiCqGgo2CEIgiAIoqKhYIcgCIIgiIqGgh2CIAiCICoaCnY85NZbb8Uee+yBqqoqzJ8/H6+++mq5DynwXHfddfjc5z6H+vp6tLa24sQTT8TatWtVzxkZGcGSJUvQ0tKCuro6nHzyydi+fXuZjriyuP766yFJEi644AL+PTrf7rJlyxaceuqpaGlpQXV1NebNm4fXX3+dPy7LMq688kpMnDgR1dXVWLBgAT7++OMyHnFwyWQy+PGPf4wZM2aguroae+65J37605+q5izR+R4dL774Ik444QRMmjQJkiThr3/9q+pxO+e3u7sbixcvRkNDA5qamnDmmWdiYGDA2YHIhCc88MADciwWk3//+9/L7733nvzd735Xbmpqkrdv317uQws0CxculO+++2753Xffld966y35+OOPl6dNmyYPDAzw55x99tny1KlT5eXLl8uvv/66fMghh8iHHnpoGY+6Mnj11VflPfbYQ95vv/3k888/n3+fzrd7dHd3y9OnT5e//e1vy6tWrZLXrVsnP/nkk/Inn3zCn3P99dfLjY2N8l//+lf57bfflr/yla/IM2bMkIeHh8t45MHk2muvlVtaWuRly5bJ69evlx966CG5rq5O/tWvfsWfQ+d7dDz22GPy5ZdfLj/88MMyAPmRRx5RPW7n/B577LHy/vvvL69cuVL+xz/+Ie+1117yKaec4ug4KNjxiM9//vPykiVL+L8zmYw8adIk+brrrivjUVUeXV1dMgD5hRdekGVZlnt6euRoNCo/9NBD/DkffPCBDEBesWJFuQ4z8PT398uzZs2Sn376afmII47gwQ6db3f5wQ9+IB9++OGGj2ezWbm9vV3+7//+b/69np4eOR6Py3/6059KcYgVxaJFi+T/+I//UH3vpJNOkhcvXizLMp1vt9EGO3bO7/vvvy8DkF977TX+nMcff1yWJEnesmWL7b9NaSwPSCaTWL16NRYsWMC/FwqFsGDBAqxYsaKMR1Z59Pb2AgCam5sBAKtXr0YqlVKd+9mzZ2PatGl07kfBkiVLsGjRItV5Beh8u83//d//4eCDD8bXv/51tLa24sADD8Qdd9zBH1+/fj06OztV57uxsRHz58+n810Ehx56KJYvX46PPvoIAPD222/jpZdewnHHHQeAzrfX2Dm/K1asQFNTEw4++GD+nAULFiAUCmHVqlW2/xYNAvWAnTt3IpPJoK2tTfX9trY2fPjhh2U6qsojm83iggsuwGGHHYZ9990XANDZ2YlYLIampibVc9va2tDZ2VmGoww+DzzwAN544w289tprBY/R+XaXdevW4bbbbsNFF12EH/3oR3jttdfwve99D7FYDGeccQY/p3r3FjrfzvnhD3+Ivr4+zJ49G+FwGJlMBtdeey0WL14MAHS+PcbO+e3s7ERra6vq8UgkgubmZkfvAQU7RGBZsmQJ3n33Xbz00kvlPpSKZdOmTTj//PPx9NNPo6qqqtyHU/Fks1kcfPDB+PnPfw4AOPDAA/Huu+/i9ttvxxlnnFHmo6s8/vznP2Pp0qW4//77sc8+++Ctt97CBRdcgEmTJtH5rjAojeUB48ePRzgcLqhI2b59O9rb28t0VJXFueeei2XLluG5557DlClT+Pfb29uRTCbR09Ojej6d++JYvXo1urq68NnPfhaRSASRSAQvvPACbr75ZkQiEbS1tdH5dpGJEydi7ty5qu/NmTMHGzduBAB+Tune4g6XXHIJfvjDH+Kb3/wm5s2bh9NOOw0XXnghrrvuOgB0vr3Gzvltb29HV1eX6vF0Oo3u7m5H7wEFOx4Qi8Vw0EEHYfny5fx72WwWy5cvR0dHRxmPLPjIsoxzzz0XjzzyCJ599lnMmDFD9fhBBx2EaDSqOvdr167Fxo0b6dwXwdFHH401a9bgrbfe4l8HH3wwFi9ezP+fzrd7HHbYYQWtFD766CNMnz4dADBjxgy0t7erzndfXx9WrVpF57sIhoaGEAqpl8FwOIxsNguAzrfX2Dm/HR0d6OnpwerVq/lznn32WWSzWcyfP9/+Hxu1vZrQ5YEHHpDj8bh8zz33yO+//7581llnyU1NTXJnZ2e5Dy3QnHPOOXJjY6P8/PPPy9u2beNfQ0ND/Dlnn322PG3aNPnZZ5+VX3/9dbmjo0Pu6Ogo41FXFmI1lizT+XaTV199VY5EIvK1114rf/zxx/LSpUvlmpoa+b777uPPuf766+Wmpib5b3/7m/zOO+/IX/3qV6kUukjOOOMMefLkybz0/OGHH5bHjx8vX3rppfw5dL5HR39/v/zmm2/Kb775pgxAvvHGG+U333xT3rBhgyzL9s7vscceKx944IHyqlWr5JdeekmeNWsWlZ77iVtuuUWeNm2aHIvF5M9//vPyypUry31IgQeA7tfdd9/NnzM8PCz/13/9lzxu3Di5pqZG/trXviZv27atfAddYWiDHTrf7vLoo4/K++67rxyPx+XZs2fLv/vd71SPZ7NZ+cc//rHc1tYmx+Nx+eijj5bXrl1bpqMNNn19ffL5558vT5s2Ta6qqpJnzpwpX3755XIikeDPofM9Op577jnde/YZZ5why7K987tr1y75lFNOkevq6uSGhgb53//93+X+/n5HxyHJstAqkiAIgiAIosIgzw5BEARBEBUNBTsEQRAEQVQ0FOwQBEEQBFHRULBDEARBEERFQ8EOQRAEQRAVDQU7BEEQBEFUNBTsEARBEARR0VCwQxAEAeD555+HJEkFc74Iggg+FOwQBOErMpkMDj30UJx00kmq7/f29mLq1Km4/PLLPfm7hx56KLZt24bGxkZPfj9BEOWDOigTBOE7PvroIxxwwAG44447sHjxYgDA6aefjrfffhuvvfYaYrFYmY+QIIggQcoOQRC+Y++998b111+P8847D9u2bcPf/vY3PPDAA/jDH/5gGOj84Ac/wN57742amhrMnDkTP/7xj5FKpQAAsixjwYIFWLhwIdj+rru7G1OmTMGVV14JoDCNtWHDBpxwwgkYN24camtrsc8+++Cxxx7z/sUTBOE6kXIfAEEQhB7nnXceHnnkEZx22mlYs2YNrrzySuy///6Gz6+vr8c999yDSZMmYc2aNfjud7+L+vp6XHrppZAkCffeey/mzZuHm2++Geeffz7OPvtsTJ48mQc7WpYsWYJkMokXX3wRtbW1eP/991FXV+fVyyUIwkMojUUQhG/58MMPMWfOHMybNw9vvPEGIhH7+7Nf/OIXeOCBB/D666/z7z300EM4/fTTccEFF+CWW27Bm2++iVmzZgHIKTtf+tKXsHv3bjQ1NWG//fbDySefjJ/85Ceuvy6CIEoLpbEIgvAtv//971FTU4P169dj8+bNAICzzz4bdXV1/Ivx4IMP4rDDDkN7ezvq6upwxRVXYOPGjarf9/Wvfx1f+9rXcP311+MXv/gFD3T0+N73voef/exnOOyww/CTn/wE77zzjjcvkiAIz6FghyAIX/LKK6/gpptuwrJly/D5z38eZ555JmRZxjXXXIO33nqLfwHAihUrsHjxYhx//PFYtmwZ3nzzTVx++eVIJpOq3zk0NITVq1cjHA7j448/Nv373/nOd7Bu3TqeRjv44INxyy23ePVyCYLwEAp2CILwHUNDQ/j2t7+Nc845B1/60pdw11134dVXX8Xtt9+O1tZW7LXXXvwLyAVG06dPx+WXX46DDz4Ys2bNwoYNGwp+78UXX4xQKITHH38cN998M5599lnT45g6dSrOPvtsPPzww7j44otxxx13ePJ6CYLwFgp2CILwHZdddhlkWcb1118PANhjjz3wi1/8Apdeein++c9/Fjx/1qxZ2LhxIx544AF8+umnuPnmm/HII4+onvP3v/8dv//977F06VL8y7/8Cy655BKcccYZ2L17t+4xXHDBBXjyySexfv16vPHGG3juuecwZ84c118rQRDeQwZlgiB8xQsvvICjjz4azz//PA4//HDVYwsXLkQ6ncYzzzwDSZJUj1166aX4/e9/j0QigUWLFuGQQw7BVVddhZ6eHuzYsQPz5s3D+eefj8suuwwAkEql0NHRgT333BMPPvhggUH5vPPOw+OPP47NmzejoaEBxx57LG666Sa0tLSU7FwQBOEOFOwQBEEQBFHRUBqLIAiCIIiKhoIdgiAIgiAqGgp2CIIgCIKoaCjYIQiCIAiioqFghyAIgiCIioaCHYIgCIIgKhoKdgiCIAiCqGgo2CEIgiAIoqKhYIcgCIIgiIqGgh2CIAiCICoaCnYIgiAIgqhoKNghCIIgCKKi+f++ee1yewMFsAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "224.15\n",
            "480.61\n",
            "203\n",
            "413\n"
          ]
        }
      ],
      "source": [
        "# Get number of tokens for each user in both subreddits\n",
        "# Also get meian and mean for each subreddit\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import statistics\n",
        "\n",
        "nba_users_unk_counts = [bin(user).count(\"1\") for user in _users[1:100]]\n",
        "politics_users_unk_counts = [bin(user).count(\"1\") for user in _users[101:200]]\n",
        "\n",
        "indices = range(len(nba_users_unk_counts))\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(indices, nba_users_unk_counts)\n",
        "\n",
        "ax.set_xlabel('X-axis')\n",
        "ax.set_ylabel('Y-axis')\n",
        "ax.set_title('NBA Plot')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "indices = range(len(politics_users_unk_counts))\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(indices, politics_users_unk_counts)\n",
        "\n",
        "ax.set_xlabel('X-axis')\n",
        "ax.set_ylabel('Y-axis')\n",
        "ax.set_title('Politics Plot')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(sum(nba_users_unk_counts) / 100)\n",
        "print(sum(politics_users_unk_counts) / 100)\n",
        "\n",
        "# Calculate middle values\n",
        "print(statistics.median(nba_users_unk_counts))\n",
        "print(statistics.median(politics_users_unk_counts))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWYcGGMwK5hQ"
      },
      "source": [
        "##### Simulation setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EptdoTAAbh8P"
      },
      "outputs": [],
      "source": [
        "from numpy.random import randint\n",
        "\n",
        "def get_random_cluster():\n",
        "    \"\"\"\n",
        "    Get 10 random clusters using randint to\n",
        "    :param source_user: 8502 bit integer representing first user.\n",
        "    :param target_user: 8502 bit integer representing secoond user.\n",
        "    :return: Matching score between two users (float)\n",
        "    \"\"\"\n",
        "\n",
        "    token_cluster_indices = [randint(0, 10) for unk_token in all_unk_tokens]\n",
        "\n",
        "    clusters_count = 10\n",
        "    cluster_str_len = len(all_unk_tokens)\n",
        "    clusters_arr = [list('0' * cluster_str_len) for i in range(clusters_count)]\n",
        "\n",
        "    for index, token_cluster_index in enumerate(token_cluster_indices):\n",
        "        clusters_arr[token_cluster_index][index] = \"1\"\n",
        "\n",
        "    return list(map(lambda cluster_arr: int(''.join(cluster_arr), 2), clusters_arr))\n",
        "\n",
        "clusters = get_random_cluster()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4U2zne5ckvQ",
        "outputId": "d0f2f23d-edc7-4bfe-be0a-18f4218b4c6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Matching score of user x in regards to a cluster\n",
        "def get_cluster_index_with_max_matching_score(_user):\n",
        "    \"\"\"\n",
        "    Get index of cluster for which a user has most tokens in\n",
        "    :param _user: 8502 bit integer representing user.\n",
        "    :return: index of cluster with max score\n",
        "    \"\"\"\n",
        "\n",
        "  matching_scores = [matching(_user, cluser) for cluser in clusters]\n",
        "  return np.argmax(matching_scores)\n",
        "\n",
        "# Matching score of user x in regards to a cluster\n",
        "def get_max_matching_score(_user, _clusters):\n",
        "    \"\"\"\n",
        "    Get max matching score for a user and all clusters\n",
        "    :param _user: 8502 bit integer representing user.\n",
        "    :param _clusters: array of 10 elements\n",
        "    :return: index of cluster with max score\n",
        "    \"\"\"\n",
        "\n",
        "  matching_scores = [matching(_user, cluser) for cluser in _clusters]\n",
        "  return max(matching_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5PPk8HSFwNI",
        "outputId": "98573885-43ae-4f7c-9d85-f7f0ee5b42b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[18, 21, 19, 18, 33, 18, 11, 8, 43, 18]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "def get_better_distributed_clusters(min_count=0, max_count=9999):\n",
        "    \"\"\"\n",
        "    Attempts to find clusters which will contain at least min_count users for each cluster\n",
        "    and at most max_count users in all clusters. It attempts with \"while True: find()\"\n",
        "    :param min_count: min number of users in final clusters.\n",
        "    :param max_count: max number of users in final clusters.\n",
        "    :return: Array of 10 clusters represented as 8502 bit integer.\n",
        "    Adding up all tokens should result in 2**2503 - 1 (all ones in binary representation)\n",
        "    \"\"\"\n",
        "\n",
        "    global clusters\n",
        "    while True:\n",
        "        cluster_user = [list() for i in range(10)]\n",
        "        clusters = get_random_cluster()\n",
        "\n",
        "        for index, user in enumerate(_users):\n",
        "            cluster = get_cluster_index_with_max_matching_score(user)\n",
        "            cluster_user[cluster].append(index)\n",
        "\n",
        "        if min(list(map(len, cluster_user))) >= min_count and max(list(map(len, cluster_user))) <= max_count:\n",
        "            return clusters, cluster_user\n",
        "\n",
        "clusters, cluster_user = get_better_distributed_clusters(5, 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6UNv9ekL3-5O"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "all_tokens_count = len(all_unk_tokens)\n",
        "\n",
        "# Get an array of all tokens with user count\n",
        "all_tokens_user_count = [0] * all_tokens_count\n",
        "for user in _users:\n",
        "    user_bin = \"0\" * (all_tokens_count - len(bin(user)[2:])) + bin(user)[2:]\n",
        "    for i in range(len(user_bin)):\n",
        "        all_tokens_user_count[i] += int(user_bin[i])\n",
        "\n",
        "def get_x_tokens_not_in_cluster(_user, _cluster, x=100):\n",
        "    \"\"\"\n",
        "    Get x (count) amount of user tokens that are not already in cluster\n",
        "    :param _user: 8502 bit integer representation of user.\n",
        "    :param _cluster: 8502 bit integer representation of user.\n",
        "    :padam x: number of tokens\n",
        "    :return: Array of x indices of tokens that are not in cluster.\n",
        "    \"\"\"\n",
        "\n",
        "    tokens_int = _user & ~_cluster # Exists on user and not in cluster\n",
        "    tokens_cut = bin(tokens_int)[2:]\n",
        "    missing_leading_zeroes = all_tokens_count - len(tokens_cut)\n",
        "    tokens = \"0\" * missing_leading_zeroes + tokens_cut\n",
        "\n",
        "    one_indices = [i for i, val in enumerate(tokens) if val == \"1\" and all_tokens_user_count[i] <= 10]\n",
        "\n",
        "    return random.sample(one_indices, min(x, len(one_indices)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RapiVgbI-51H"
      },
      "outputs": [],
      "source": [
        "import bisect\n",
        "\n",
        "def binary_search(arr, target):\n",
        "    index = bisect.bisect_left(arr, target)\n",
        "    if index < len(arr) and arr[index] == target:\n",
        "        return index\n",
        "    else:\n",
        "        return -1  # Target not found"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zSEqzMpXAp27"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "\n",
        "# Map to fetch cluster by token\n",
        "def get_unk_tokens_cluster_map():\n",
        "    \"\"\"\n",
        "    returns a dictionary of key = token index; value = cluster index where token is present\n",
        "    Used for fast accessing cluster based on token\n",
        "    :return: A dictionary of key = token index; value = cluster index where token is present.\n",
        "    \"\"\"\n",
        "    all_tokens_count = len(all_unk_tokens)\n",
        "    unk_tokens_cluster_map = [-1] * len(all_unk_tokens)\n",
        "    for i, c in enumerate(clusters):\n",
        "        one_indices = [j + all_tokens_count - len(bin(c)[2:]) for j, val in enumerate(bin(c)[2:]) if val == \"1\"]\n",
        "        for index in one_indices:\n",
        "            unk_tokens_cluster_map[index] = i\n",
        "\n",
        "    return unk_tokens_cluster_map\n",
        "\n",
        "def get_new_clusters_after_transfering_token(cluster_from_index, cluster_to_index, token_to_transfer):\n",
        "    \"\"\"\n",
        "    Transfer token from cluster to cluster and return new clusters\n",
        "    :param _cluster_from_index: 8502 bit integer representation of cluster where token exists.\n",
        "    :param _cluster_to_index: 8502 bit integer representation of cluster where the token will go.\n",
        "    :param token_to_transfer: token index to transfer\n",
        "    :return: Array of 10 clusters represented as 8502 bit integer\n",
        "    \"\"\"\n",
        "\n",
        "    # token transition from cluster to cluster\n",
        "    temp_clusters = [i for i in clusters]\n",
        "    temp_clusters[cluster_from_index] = temp_clusters[cluster_from_index] - (2 ** (all_tokens_count - token_to_transfer - 1))\n",
        "    temp_clusters[cluster_to_index] = temp_clusters[cluster_to_index] + (2 ** (all_tokens_count - token_to_transfer - 1))\n",
        "    return temp_clusters\n",
        "\n",
        "\n",
        "def get_new_clusters_after_transfering_token_multiple(clusters_from_indices, cluster_to_index, tokens_to_transfer):\n",
        "    \"\"\"\n",
        "    Transfer tokens from cluster to cluster and return new clusters\n",
        "    :param _cluster_from_index: 8502 bit integer representation of cluster where token exists.\n",
        "    :param _cluster_to_index: 8502 bit integer representation of cluster where the token will go.\n",
        "    :param tokens_to_transfer: array of token indices to transfer\n",
        "    :return: Array of 10 clusters represented as 8502 bit integer\n",
        "    \"\"\"\n",
        "    temp_clusters = [i for i in clusters]\n",
        "\n",
        "    for i in range(len(tokens_to_transfer)):\n",
        "        temp_clusters[clusters_from_indices[i]] = temp_clusters[clusters_from_indices[i]] - (2 ** (all_tokens_count - tokens_to_transfer[i] - 1))\n",
        "        temp_clusters[cluster_to_index] = temp_clusters[cluster_to_index] + (2 ** (all_tokens_count - tokens_to_transfer[i] - 1))\n",
        "\n",
        "    return temp_clusters\n",
        "\n",
        "\n",
        "def user_propose_tokens(user, batch_size):\n",
        "    \"\"\"\n",
        "    Propose batch_size of tokens to transfer to the user cluster\n",
        "    :param user: 8502 bit integer representation of user.\n",
        "    :param batch_size: number of tokens to transfer.\n",
        "    :return: An array of proposed token indices\n",
        "    \"\"\"\n",
        "    cluster_index = get_cluster_index_with_max_matching_score(user)\n",
        "    cluster = clusters[cluster_index] # User cluster\n",
        "\n",
        "    return get_x_tokens_not_in_cluster(user, cluster, batch_size) # Tokens to transfer\n",
        "\n",
        "\n",
        "def user_vote(user, token_index, unk_tokens_cluster_map, min_increase=0):\n",
        "    \"\"\"\n",
        "    Simulates a user voting for a single token.\n",
        "    If user has bigger matchinig score after transfer, vote +1, if smaller vote -1, otherwise 0 (with min_increase)\n",
        "    :param user: 8502 bit integer representation of user.\n",
        "    :param token_index: index of token that user votes for.\n",
        "    :param unk_tokens_cluster_map: special map (see get_unk_tokens_cluster_map)\n",
        "    :param min_increase: minimum required matching score change\n",
        "    :return: +1 for succesfull vote, -1 for ddeclined vote, 0 for neutral vote\n",
        "    \"\"\"\n",
        "\n",
        "    matching_score = get_max_matching_score(user, clusters)\n",
        "    cluster_index = get_cluster_index_with_max_matching_score(user)\n",
        "\n",
        "    new_clusters = get_new_clusters_after_transfering_token(unk_tokens_cluster_map[token_index], cluster_index, token_index)\n",
        "    new_matching_score = get_max_matching_score(user, new_clusters)\n",
        "\n",
        "    if new_matching_score > matching_score + min_increase:\n",
        "        return 1\n",
        "\n",
        "    if new_matching_score < matching_score - min_increase:\n",
        "        return -1\n",
        "\n",
        "    return 0\n",
        "\n",
        "def user_vote_multiple(user, token_indices, unk_tokens_cluster_map, slack=0, min_increase=0):\n",
        "    \"\"\"\n",
        "    Simulates a user voting for a multiple tokens.\n",
        "    If user has bigger matchinig score after transfer, vote +1, if smaller vote -1, otherwise 0 (with min_increase)\n",
        "    :param user: 8502 bit integer representation of user.\n",
        "    :param token_indices: array of indices of token that user votes for.\n",
        "    :param slack: Attempt of implementation for decreasing matching score\n",
        "    :param unk_tokens_cluster_map: special map (see get_unk_tokens_cluster_map)\n",
        "    :param min_increase: minimum required matching score change\n",
        "    :return: +1 for succesfull vote, -1 for ddeclined vote, 0 for neutral vote\n",
        "    \"\"\"\n",
        "\n",
        "    matching_score = get_max_matching_score(user, clusters)\n",
        "    cluster_index = get_cluster_index_with_max_matching_score(user)\n",
        "\n",
        "    cluster_indices = list(unk_tokens_cluster_map[token_index] for token_index in token_indices)\n",
        "\n",
        "    new_clusters = get_new_clusters_after_transfering_token_multiple(cluster_indices, cluster_index, token_indices)\n",
        "    new_matching_score = get_max_matching_score(user, new_clusters)\n",
        "\n",
        "    if new_matching_score > matching_score + min_increase:\n",
        "        return 1\n",
        "\n",
        "    if new_matching_score < matching_score - (min_increase * (1 - slack)):\n",
        "        return -1\n",
        "\n",
        "    return 0\n",
        "\n",
        "\n",
        "def do_vote(user, unk_tokens_cluster_map, batch_size, min_inc):\n",
        "    \"\"\"\n",
        "    Propose tokens and make all users vote. Token by token\n",
        "    :param user: 8502 bit integer representation of user who proposes tokens.\n",
        "    :param unk_tokens_cluster_map: special map (see get_unk_tokens_cluster_map)\n",
        "    :param batch_size: how many tokens will single user vote for\n",
        "    :param min_inc: minimum required matching score change\n",
        "    :return: array of indicies of proposed tokens and batch_size number of votes for each token\n",
        "    \"\"\"\n",
        "    proposed_tokens = user_propose_tokens(user, batch_size)\n",
        "\n",
        "    votes = [0] * len(proposed_tokens)\n",
        "    for _user in _users:\n",
        "        for i, proposed_token in enumerate(proposed_tokens):\n",
        "            votes[i] += user_vote(_user, proposed_token, unk_tokens_cluster_map, min_inc)\n",
        "\n",
        "    return proposed_tokens, votes\n",
        "\n",
        "def do_vote_multiple(user, unk_tokens_cluster_map, batch_size, min_inc, slack):\n",
        "    \"\"\"\n",
        "    Propose tokens and make all users vote. All at onc\n",
        "    :param user: 8502 bit integer representation of user who proposes tokens.\n",
        "    :param unk_tokens_cluster_map: special map (see get_unk_tokens_cluster_map)\n",
        "    :param batch_size: how many tokens will single user vote for\n",
        "    :param min_inc: minimum required matching score change\n",
        "    :param slack: Attempt of implementation for decreasing matching score\n",
        "    :return: array of indicies of proposed tokens and batch_size number of votes for each token\n",
        "    \"\"\"\n",
        "    proposed_tokens = user_propose_tokens(user, batch_size)\n",
        "\n",
        "    votes = 0\n",
        "    for _user in _users:\n",
        "        votes += user_vote_multiple(_user, proposed_tokens, unk_tokens_cluster_map, min_inc, slack)\n",
        "\n",
        "    return proposed_tokens, votes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQn2LtfJJWs2"
      },
      "source": [
        "#### The simulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNRM53uhJpC8"
      },
      "outputs": [],
      "source": [
        "# Save clusters to another variable\n",
        "def save_clusters():\n",
        "    global clusters_copy\n",
        "    clusters_copy = [i for i in clusters]\n",
        "save_clusters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a69uiGgnJsJw"
      },
      "outputs": [],
      "source": [
        "# Load clusters from variable\n",
        "def load_clusters():\n",
        "    global clusters\n",
        "    clusters = [i for i in clusters_copy]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hLRsE8Lcs9Tf"
      },
      "outputs": [],
      "source": [
        "# Get number of users for eeach cluster\n",
        "def cluster_user_status():\n",
        "    cluster_user = [list() for i in range(10)]\n",
        "\n",
        "    for index, user in enumerate(_users):\n",
        "        cluster = get_cluster_index_with_max_matching_score(user)\n",
        "        cluster_user[cluster].append(index)\n",
        "\n",
        "    return list(map(len, cluster_user))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpI8M9fcPPG5",
        "outputId": "aabe82b0-3f2b-4c9f-cf09-31f6756d11cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[23, False],\n",
              " [0, False],\n",
              " [0, False],\n",
              " [53, False],\n",
              " [14, True],\n",
              " [17, True],\n",
              " [15, True],\n",
              " [0, False],\n",
              " [4, False],\n",
              " [25, True]]"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ],
      "source": [
        "# Get a list of purity scores for each cluster\n",
        "def get_purities():\n",
        "    purity_cluster_user = [list() for i in range(10)]\n",
        "\n",
        "    for index, user in enumerate(_users):\n",
        "        user_cluster = get_cluster_index_with_max_matching_score(user)\n",
        "        purity_cluster_user[user_cluster].append(index)\n",
        "\n",
        "    try:\n",
        "        def calculate_cluster_score(cluster):\n",
        "            users_in_cluster = len(cluster) if len(cluster) > 0 else 1\n",
        "            score = 50 - sum(100 * list(map(lambda user: user <= (len(_users) / 2), cluster))) / users_in_cluster\n",
        "            return [round(2 * abs(score)), score > 0]\n",
        "\n",
        "        return list(map(calculate_cluster_score, purity_cluster_user))\n",
        "    except:\n",
        "        return []\n",
        "\n",
        "\n",
        "get_purities()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hWtvURa5oq-c"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "\n",
        "def simulation_loop(vote_batch_size, min_inc):\n",
        "    \"\"\"\n",
        "    Do a single simulation loop. Voting token by token\n",
        "    :param vote_batch_size: amount of tokens proposed pr simulation\n",
        "    :param min_inc: minimal matching score change needed for succesful vote\n",
        "    \"\"\"\n",
        "\n",
        "    global clusters\n",
        "    user = random.choice(_users)\n",
        "    unk_tokens_cluster_map = get_unk_tokens_cluster_map()\n",
        "    tokens, votes = do_vote(user, unk_tokens_cluster_map, vote_batch_size, min_inc)\n",
        "\n",
        "    user_cluster_index = get_cluster_index_with_max_matching_score(user)\n",
        "    for i in range(len(tokens)):\n",
        "        if votes[i] > 0:\n",
        "            unk_tokens_cluster_map = get_unk_tokens_cluster_map()\n",
        "            clusters = get_new_clusters_after_transfering_token(unk_tokens_cluster_map[tokens[i]], user_cluster_index, tokens[i])\n",
        "\n",
        "\n",
        "def simulation_loop_batch(vote_batch_size, min_inc, slack):\n",
        "    \"\"\"\n",
        "    Do a single simulation loop. Voting all at once\n",
        "    :param vote_batch_size: amount of tokens proposed pr simulation\n",
        "    :param min_inc: minimal matching score change needed for succesful vote\n",
        "    \"\"\"\n",
        "\n",
        "    global clusters\n",
        "    user = random.choice(_users)\n",
        "    unk_tokens_cluster_map = get_unk_tokens_cluster_map()\n",
        "    tokens, votes = do_vote_multiple(user, unk_tokens_cluster_map, vote_batch_size, min_inc, slack)\n",
        "\n",
        "    user_cluster_index = get_cluster_index_with_max_matching_score(user)\n",
        "    if votes >= 0:\n",
        "        for i in range(len(tokens)):\n",
        "            unk_tokens_cluster_map = get_unk_tokens_cluster_map()\n",
        "            clusters = get_new_clusters_after_transfering_token(unk_tokens_cluster_map[tokens[i]], user_cluster_index, tokens[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vbukIxTztLjC",
        "outputId": "ff50e50c-2794-4555-b52d-89668346e2fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial array [13, 18, 10, 17, 7, 12, 26, 28, 23, 16]\n",
            "0   13          17          10          16           6          12          26          28          27          15 Clusters over 25 users: 3\n",
            "                                                                         P  15       N   0       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "1   14          17           9          16           6          12          27          27          27          15 Clusters over 25 users: 3\n",
            "                                                                         P  19       P   4       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "2   14          17          10          17           5          12          26          26          28          15 Clusters over 25 users: 3\n",
            "                                                                         P  15       P   8       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "3   15          17          10          17           4          13          26          26          28          14 Clusters over 25 users: 3\n",
            "                                                                         P  15       P   8       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "4   15          17          10          17           4          12          26          26          27          16 Clusters over 25 users: 3\n",
            "                                                                         P  15       P   8       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "5   15          17          10          17           4          14          25          25          27          16 Clusters over 25 users: 1\n",
            "                                                                                                 P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "6   15          16          10          17           4          16          24          25          27          16 Clusters over 25 users: 1\n",
            "                                                                                                 P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "7   14          17          10          16           5          16          22          27          27          16 Clusters over 25 users: 2\n",
            "                                                                                     P   4       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "8   13          18          12          16           5          16          20          27          27          16 Clusters over 25 users: 2\n",
            "                                                                                     P   4       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "9   16          16          11          16           5          16          21          27          27          15 Clusters over 25 users: 2\n",
            "                                                                                     P   4       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "10   15          16          11          16           5          15          23          26          28          15 Clusters over 25 users: 2\n",
            "                                                                                      N   0       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "11   15          16          11          16           5          15          23          26          28          15 Clusters over 25 users: 2\n",
            "                                                                                      N   0       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "12   18          16          11          16           4          15          22          26          27          15 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "13   19          16          11          16           4          15          22          26          26          15 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "14   18          18          11          16           4          16          21          26          26          14 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "15   19          18          11          16           4          16          21          26          26          13 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "16   19          18          11          16           4          16          18          30          26          12 Clusters over 25 users: 2\n",
            "                                                                                      P  13       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "17   19          18          13          16           4          16          18          29          26          11 Clusters over 25 users: 2\n",
            "                                                                                      P  17       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "18   18          18          12          16           4          19          17          29          26          11 Clusters over 25 users: 2\n",
            "                                                                                      P  17       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "19   19          18          12          16           4          19          16          30          26          10 Clusters over 25 users: 2\n",
            "                                                                                      P  13       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "20   19          17          12          16           4          19          16          32          26           9 Clusters over 25 users: 2\n",
            "                                                                                      P  12       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "21   19          16          11          16           4          19          16          34          26           9 Clusters over 25 users: 2\n",
            "                                                                                      P  12       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "22   19          17          11          16           4          17          19          32          25          10 Clusters over 25 users: 1\n",
            "                                                                                      P   6                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "23   19          16          11          16           4          17          18          34          25          10 Clusters over 25 users: 1\n",
            "                                                                                      P  12                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "24   18          15          11          16           6          17          18          34          25          10 Clusters over 25 users: 1\n",
            "                                                                                      P  12                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "25   15          15          12          16           6          16          22          33          25          10 Clusters over 25 users: 1\n",
            "                                                                                      P   9                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "26   15          15          12          16           6          16          22          32          25          11 Clusters over 25 users: 1\n",
            "                                                                                      P   6                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "27   14          16          13          16           6          16          21          32          26          10 Clusters over 25 users: 2\n",
            "                                                                                      P   6       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "28   14          16          12          15           6          15          23          31          27          11 Clusters over 25 users: 2\n",
            "                                                                                      P   3       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "29   14          17          12          15           6          15          23          30          27          11 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "30   14          17          12          15           6          15          23          29          27          12 Clusters over 25 users: 2\n",
            "                                                                                      N   3       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "31   14          17          12          15           6          16          21          30          27          12 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "32   12          15          11          16           6          19          22          29          28          12 Clusters over 25 users: 2\n",
            "                                                                                      N   3       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "33   13          14          11          16           6          19          22          30          27          12 Clusters over 25 users: 2\n",
            "                                                                                      N   7       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "34   13          14          11          17           6          19          22          30          27          11 Clusters over 25 users: 2\n",
            "                                                                                      N   7       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "35   13          14          11          17           6          18          23          30          27          11 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "36   13          15          11          17           5          18          23          30          27          11 Clusters over 25 users: 2\n",
            "                                                                                      N   0       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "37   13          15          11          17           4          18          22          32          27          11 Clusters over 25 users: 2\n",
            "                                                                                      P   6       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "38   13          15          11          17           4          22          21          31          25          11 Clusters over 25 users: 1\n",
            "                                                                                      P   3                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "39   13          15          11          17           4          23          21          31          25          10 Clusters over 25 users: 1\n",
            "                                                                                      P   3                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "40   13          15          10          17           4          23          21          33          25           9 Clusters over 25 users: 1\n",
            "                                                                                      P   9                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "41   15          15           9          16           4          22          20          35          25           9 Clusters over 25 users: 1\n",
            "                                                                                      P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "42   15          15           9          16           4          23          20          34          25           9 Clusters over 25 users: 1\n",
            "                                                                                      P  18                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "43   15          16          10          15           4          24          20          34          23           9 Clusters over 25 users: 1\n",
            "                                                                                      P  18                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "44   15          16          10          15           4          24          19          38          22           7 Clusters over 25 users: 1\n",
            "                                                                                      P  26                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "45   15          17          10          15           4          23          18          40          21           7 Clusters over 25 users: 1\n",
            "                                                                                      P  30                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "46   15          17          10          15           4          23          18          40          21           7 Clusters over 25 users: 1\n",
            "                                                                                      P  30                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "47   15          17          10          15           4          24          18          39          21           7 Clusters over 25 users: 1\n",
            "                                                                                      P  28                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "48   15          15          10          15           4          26          18          39          22           6 Clusters over 25 users: 2\n",
            "                                                              P  38                   P  28                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "49   15          15          10          15           4          28          18          37          22           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P  30                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "50   14          15          10          15           4          31          18          36          21           6 Clusters over 25 users: 2\n",
            "                                                              P  35                   P  28                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "51   14          15          10          15           4          32          17          36          21           6 Clusters over 25 users: 2\n",
            "                                                              P  38                   P  22                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "52   14          15          10          15           4          31          17          36          22           6 Clusters over 25 users: 2\n",
            "                                                              P  35                   P  22                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "53   14          17          10          15           4          30          16          36          22           6 Clusters over 25 users: 2\n",
            "                                                              P  33                   P  22                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "54   13          17          10          15           4          32          15          36          22           6 Clusters over 25 users: 2\n",
            "                                                              P  25                   P  22                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "55   13          17          10          15           4          32          15          36          22           6 Clusters over 25 users: 2\n",
            "                                                              P  25                   P  22                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "56   13          17          10          15           4          32          15          36          22           6 Clusters over 25 users: 2\n",
            "                                                              P  25                   P  22                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "57   13          17          10          15           4          31          17          35          22           6 Clusters over 25 users: 2\n",
            "                                                              P  23                   P  20                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "58   12          17          10          15           5          31          17          35          22           6 Clusters over 25 users: 2\n",
            "                                                              P  23                   P  20                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "59   16          17          10          15           5          28          17          35          21           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P  20                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "60   15          17          10          15           5          29          17          35          21           6 Clusters over 25 users: 2\n",
            "                                                              P  31                   P  20                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "61   15          17          10          15           5          28          18          35          21           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P  20                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "62   15          17          10          15           4          28          16          38          21           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P  11                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "63   15          18          10          15           4          28          16          37          21           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P   8                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "64   14          18          10          15           4          28          17          37          21           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P   8                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "65   14          18          10          15           4          28          17          37          21           6 Clusters over 25 users: 2\n",
            "                                                              P  36                   P   8                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "66   14          18          10          15           4          27          18          37          21           6 Clusters over 25 users: 2\n",
            "                                                              P  33                   P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "67   14          18           9          15           4          27          18          37          22           6 Clusters over 25 users: 2\n",
            "                                                              P  33                   P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "68   13          17           9          15           5          28          18          37          22           6 Clusters over 25 users: 2\n",
            "                                                              P  29                   P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "69   13          17           8          15           4          28          20          37          22           6 Clusters over 25 users: 2\n",
            "                                                              P  29                   P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "70   13          17           9          14           4          29          19          37          22           6 Clusters over 25 users: 2\n",
            "                                                              P  31                   P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "71   12          17           9          15           4          28          18          37          24           6 Clusters over 25 users: 2\n",
            "                                                              P  29                   P  14                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "72   12          17           9          13           4          28          19          36          26           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P  11       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "73   12          19           8          12           3          28          19          36          27           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P  11       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "74   12          19           8          12           3          27          20          36          27           6 Clusters over 25 users: 3\n",
            "                                                              P  26                   P  11       P   4            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "75   12          17           8          12           3          28          20          36          28           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P  11       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "76   12          16           8          12           3          28          20          37          28           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P   8       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "77   12          16           8          12           3          28          20          37          28           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P   8       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "78   12          16           8          13           3          28          20          36          28           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P  11       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "79   12          15           8          13           4          28          19          36          29           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P  11       P  10            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "80   10          14           9          13           3          28          23          36          28           6 Clusters over 25 users: 3\n",
            "                                                              P  29                   P  11       P  14            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "81   10          14           9          13           3          26          22          39          28           6 Clusters over 25 users: 3\n",
            "                                                              P  23                   P  18       P  14            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "82   10          14           9          13           3          26          21          40          28           6 Clusters over 25 users: 3\n",
            "                                                              P  23                   P  20       P  14            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "83   10          14           9          13           3          26          21          41          27           6 Clusters over 25 users: 3\n",
            "                                                              P  23                   P  22       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "84   10          14           9          12           3          25          21          41          29           6 Clusters over 25 users: 2\n",
            "                                                                                      P  22       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "85   10          14           9          12           3          25          21          41          29           6 Clusters over 25 users: 2\n",
            "                                                                                      P  22       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "86   10          14           9          12           3          25          21          41          29           6 Clusters over 25 users: 2\n",
            "                                                                                      P  22       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "87   10          14           9          12           3          25          21          41          29           6 Clusters over 25 users: 2\n",
            "                                                                                      P  22       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "88   10          14           9          12           3          25          21          41          29           6 Clusters over 25 users: 2\n",
            "                                                                                      P  22       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "89   10          14           9          12           3          24          21          41          30           6 Clusters over 25 users: 2\n",
            "                                                                                      P  22       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "90    8          14           9          11           3          23          20          47          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  11       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "91    8          14           9          11           3          21          19          51          29           5 Clusters over 25 users: 2\n",
            "                                                                                      P  18       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "92   10          13           9          11           3          23          18          49          29           5 Clusters over 25 users: 2\n",
            "                                                                                      P  14       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "93    9          13           8          11           3          23          21          47          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  11       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "94    9          13           8          11           3          22          21          48          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  12       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "95    9          13           8          11           3          20          21          50          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  12       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "96    9          13           8          11           4          20          20          49          31           5 Clusters over 25 users: 2\n",
            "                                                                                      P  10       P  10            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "97    9          13           8          11           4          21          20          49          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  10       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "98    9          13           8          11           4          21          20          49          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  10       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "99    9          13           8          11           4          21          20          49          30           5 Clusters over 25 users: 2\n",
            "                                                                                      P  10       P   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "100    9          13           8          11           3          20          19          53          29           5 Clusters over 25 users: 2\n",
            "                                                                                       P  17       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "101    9          13           7          11           3          19          18          52          33           5 Clusters over 25 users: 2\n",
            "                                                                                       P  19       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "102   13          14           7          11           3          18          19          49          32           4 Clusters over 25 users: 2\n",
            "                                                                                       P  27       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "103   12          14           7          11           5          21          18          47          30           5 Clusters over 25 users: 2\n",
            "                                                                                       P  28       N   7            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "104   13          14           7          11           3          20          18          46          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  30       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "105   13          14           7          11           3          20          18          46          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  30       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "106   13          14           7          12           3          21          18          44          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  32       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "107   13          14           7          12           3          21          18          44          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  32       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "108   13          14           7          12           3          20          18          42          37           4 Clusters over 25 users: 2\n",
            "                                                                                       P  29       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "109   13          14           6          12           2          20          17          45          37           4 Clusters over 25 users: 2\n",
            "                                                                                       P  29       P   8            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "110   14          14           6          12           2          21          16          45          36           4 Clusters over 25 users: 2\n",
            "                                                                                       P  29       P   6            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "111   14          13           6          12           2          20          16          48          35           4 Clusters over 25 users: 2\n",
            "                                                                                       P  33       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "112   19          13           6          11           2          20          15          46          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  39       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "113   18          13           6          10           2          20          13          50          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  40       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "114   20          13           6           9           2          20          13          49          34           4 Clusters over 25 users: 2\n",
            "                                                                                       P  43       N   0            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "115   19          13           6          10           2          23          12          48          33           4 Clusters over 25 users: 2\n",
            "                                                                                       P  42       N   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "116   19          13           8          10           2          23          12          48          31           4 Clusters over 25 users: 2\n",
            "                                                                                       P  42       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "117   19          14           8          10           2          23          14          47          29           4 Clusters over 25 users: 2\n",
            "                                                                                       P  40       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "118   18          14           8          10           2          23          14          48          29           4 Clusters over 25 users: 2\n",
            "                                                                                       P  38       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "119   17          14           8           9           2          27          14          46          29           4 Clusters over 25 users: 3\n",
            "                                                               P  26                   P  35       P   3            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "120   15          14           8           9           2          27          19          45          27           4 Clusters over 25 users: 3\n",
            "                                                               P  26                   P  38       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "121   18          12           8           9           2          27          19          45          27           3 Clusters over 25 users: 3\n",
            "                                                               P  26                   P  38       P  11            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "122   18          12           8           9           2          27          24          41          26           3 Clusters over 25 users: 3\n",
            "                                                               P  26                   P  41       P  15            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "123   16          11           8          10           2          27          26          40          27           3 Clusters over 25 users: 4\n",
            "                                                               P  26       N  38       P  45       P  19            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "124   15          11           8          10           2          26          26          42          27           3 Clusters over 25 users: 4\n",
            "                                                               P  31       N  38       P  43       P  19            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "125   19          12           8           9           2          25          26          41          25           3 Clusters over 25 users: 2\n",
            "                                                                           N  38       P  41                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "126   17          12           8          12           1          26          24          41          26           3 Clusters over 25 users: 3\n",
            "                                                               P  31                   P  41       P  15            \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "127   16          11           8          12           1          24          23          49          23           3 Clusters over 25 users: 1\n",
            "                                                                                       P  35                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "128   20          11           8          11           1          24          23          48          21           3 Clusters over 25 users: 1\n",
            "                                                                                       P  38                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "129   21          11           8          12           1          24          21          46          24           2 Clusters over 25 users: 1\n",
            "                                                                                       P  35                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "130   21          11           8          12           1          20          19          53          23           2 Clusters over 25 users: 1\n",
            "                                                                                       P  36                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "131   21          11           7          11           1          19          19          58          21           2 Clusters over 25 users: 1\n",
            "                                                                                       P  34                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "132   21          11           7          11           1          19          19          58          21           2 Clusters over 25 users: 1\n",
            "                                                                                       P  34                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "133   21          11           7          11           1          19          19          58          21           2 Clusters over 25 users: 1\n",
            "                                                                                       P  34                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "134   21          11           7          11           1          19          19          58          21           2 Clusters over 25 users: 1\n",
            "                                                                                       P  34                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "135   20          11           7          11           1          19          18          62          19           2 Clusters over 25 users: 1\n",
            "                                                                                       P  35                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "136   20          11           7          11           1          19          18          62          19           2 Clusters over 25 users: 1\n",
            "                                                                                       P  35                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "137   23          10           7          10           1          19          18          61          19           2 Clusters over 25 users: 1\n",
            "                                                                                       P  38                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "138   23          11           8          11           1          19          18          58          19           2 Clusters over 25 users: 1\n",
            "                                                                                       P  45                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "139   25          11           9          10           1          19          18          55          20           2 Clusters over 25 users: 1\n",
            "                                                                                       P  49                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "140   19          12           8          10           1          19          18          61          20           2 Clusters over 25 users: 1\n",
            "                                                                                       P  38                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "141   19          12           8          10           1          19          22          58          19           2 Clusters over 25 users: 1\n",
            "                                                                                       P  41                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "142   20          12           8          10           1          20          21          56          20           2 Clusters over 25 users: 1\n",
            "                                                                                       P  43                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "143   19          12           8          10           1          18          23          55          21           3 Clusters over 25 users: 1\n",
            "                                                                                       P  42                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "144   17          12           8          10           1          18          23          56          22           3 Clusters over 25 users: 1\n",
            "                                                                                       P  43                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "145   17          12           8          10           1          18          23          56          22           3 Clusters over 25 users: 1\n",
            "                                                                                       P  43                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "146   17          12           8          12           1          18          23          54          22           3 Clusters over 25 users: 1\n",
            "                                                                                       P  48                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "147   17          12           8          12           1          18          23          54          22           3 Clusters over 25 users: 1\n",
            "                                                                                       P  48                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "148   16          11           7          10           1          18          22          60          22           3 Clusters over 25 users: 1\n",
            "                                                                                       P  33                        \n",
            "---------------------------------------------------------------------------------------------------------------------------------------------\n",
            "149"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-153-6ee53829b46a>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mcus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcluster_user_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mgp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_purities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m     \u001b[0mspread\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-150-ee88899ceb43>\u001b[0m in \u001b[0;36mget_purities\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_users\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_cluster_index_with_max_matching_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mpurity_cluster_user\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-140-919c3d143cec>\u001b[0m in \u001b[0;36mget_cluster_index_with_max_matching_score\u001b[0;34m(_user)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Matching score of user x in regards to a cluster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_cluster_index_with_max_matching_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mmatching_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmatching\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_user\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcluser\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcluser\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclusters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatching_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-140-919c3d143cec>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Matching score of user x in regards to a cluster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_cluster_index_with_max_matching_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mmatching_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmatching\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_user\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcluser\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcluser\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclusters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatching_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-131-c10bad1b6f56>\u001b[0m in \u001b[0;36mmatching\u001b[0;34m(source_user, target_user)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msource_user\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mtarget_user\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0msource_user_shift\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msource_user\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_user_shift\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mtarget_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "|----------------------|\n",
        "  MAIN SIMULATION LOOP\n",
        "|----------------------|\n",
        "\"\"\"\n",
        "\n",
        "# Simulation hiperoparameters\n",
        "MIN_INC = 0.01\n",
        "BATCH_SIZE = 10\n",
        "SLACK = 0\n",
        "SIM_TIME = 2000\n",
        "\n",
        "load_clusters()\n",
        "MIN_INC *= BATCH_SIZE / 10\n",
        "print(\"Initial array\", cluster_user_status())\n",
        "for i in range(SIM_TIME):\n",
        "    # Dynamic hiperparameters\n",
        "    if i == 50:\n",
        "        MIN_INC *= 0.001\n",
        "        SLACK = 0.4\n",
        "        BATCH_SIZE = 10\n",
        "    if i == 100:\n",
        "        MIN_INC *= 0.001\n",
        "        SLACK = 0.6\n",
        "        BATCH_SIZE = 30\n",
        "    if i == 150:\n",
        "        MIN_INC = 0\n",
        "        SLACK = 0.6\n",
        "        BATCH_SIZE = 50\n",
        "\n",
        "    # Simulation and report\n",
        "    simulation_loop_batch(BATCH_SIZE, MIN_INC, SLACK)\n",
        "    # Prints the iteration number\n",
        "    print(i, end=\"\")\n",
        "    cus = cluster_user_status()\n",
        "    gp = get_purities()\n",
        "    spread = 7\n",
        "\n",
        "    # Prints usersfor each one of 10 clusters. At the end it prints the amount of clusters that have over 25 users\n",
        "    print((\" \" * spread).join(list(map(lambda x: \" \" * (5 - len(str(x))) + str(x), cus))), \"Clusters over 25 users:\", len(list(filter(lambda x: x > 25, cus))))\n",
        "\n",
        "    # Prints (P - politics) or (N - nba) and the purity score (1-100) if the corresponding cluster has over 25 users.\n",
        "    print(\" \" * len(str(i)) + (\" \" * spread).join(list(map(lambda x: f\"{'P' if x[1][1] else 'N'} {' ' * (3 - len(str(x[1][0])))}{str(x[1][0])}\" if cus[x[0]] > 25 else \"     \", enumerate(gp)))))\n",
        "    print(141 * \"-\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_user_status()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "231SBwccVhwv",
        "outputId": "96628c8c-6731-4409-9162-826d069cc51f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2, 12, 13, 16, 18, 21, 22, 23, 25, 27, 33, 34, 38, 40, 42, 43, 47, 49, 50, 51, 52, 54, 55, 57, 63, 64, 65, 66, 67, 69, 71, 73, 76, 79, 83, 84, 91, 92, 96, 97, 98, 103, 107, 111, 112, 114, 116, 123, 124, 125, 127, 132, 136, 140, 147, 150, 154, 158, 159, 160, 170, 171, 175, 176, 177, 178, 180, 189, 191, 192, 193, 195, 200, 202], [5, 9, 30, 32, 36, 39, 44, 59, 94, 100, 101, 139, 157, 167, 181, 184, 190, 201, 203], [35, 45, 58, 68, 81, 86, 115, 118, 122, 135, 151, 169, 183, 186, 205], [0, 6, 14, 15, 19, 48, 53, 61, 74, 77, 88, 89, 93, 119, 121, 185, 187], [1, 20, 24, 37, 41, 56, 60, 70, 72, 75, 78, 82, 85, 105, 106, 108, 110, 113, 120, 128, 131, 142, 144, 148, 149, 156, 161, 162, 165, 174, 182, 196], [8, 26, 28, 87, 99, 126, 134, 138, 152, 179, 197], [4, 29, 80], [17, 46, 102, 104, 109, 129, 130, 133, 137, 143, 146, 153, 155, 164, 168, 173, 188, 194, 198, 199], [7, 10, 62, 90, 95, 117, 141, 145, 166, 172, 204, 206], [3, 11, 31, 163]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[74, 19, 15, 17, 32, 11, 3, 20, 12, 4]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kat2zQQR3SI3",
        "outputId": "a6cba33a-5558-4753-8126-5308880ecaf5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">10 = 32.15823466092573\n",
            ">50 = 0.0\n",
            ">100 = 0.0\n"
          ]
        }
      ],
      "source": [
        "# Prints how the percentage of tokens that are found in more than 10 users, 50 users and 100 users\n",
        "print(\">10 =\", 100 * len([i for i in all_tokens_user_count if i > 10]) / len(all_tokens_user_count))\n",
        "print(\">50 =\", 100 * len([i for i in all_tokens_user_count if i > 50]) / len(all_tokens_user_count))\n",
        "print(\">100 =\", 100 * len([i for i in all_tokens_user_count if i > 100]) / len(all_tokens_user_count))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Token analysis"
      ],
      "metadata": {
        "id": "iO7zzKV6CXMd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fuB2Ka5Jjkbo"
      },
      "outputs": [],
      "source": [
        "all_unk_count = len(all_unk_tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2kSpMKbrynr"
      },
      "outputs": [],
      "source": [
        "# Copy of matching score function\n",
        "def matching(source_user: int, target_user: int):\n",
        "    source_user_shift = 2 ** (all_unk_count + 1) + source_user\n",
        "    return bin(source_user_shift & target_user).count(\"1\") / bin(source_user).count(\"1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uU_yRUuHitGl",
        "outputId": "46b9a187-cf5f-4a93-b07c-408e09542f19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0000000000000000000000000001000000001000000100001100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000001000000010000000000000000000000000000100000000000100000000000000000000010001000000000000000000000000000000000000000001000000000000000000000001000000000001000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000100000000000000000000000000000000000000000100000001001000000000000000000000001000010000010000000001000000000000000000000000000000010000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100010001000000000011000000000010001000000000000000000000000000010000100000000000000001010010100000010001000000000000000000000000000000000000000000000000000000000000000000000000001000100000000000000000000001000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000010000000000000000000010000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000001000001000000000000000000000000000000000000000000000000010000000000000000001000100000000000000000000000000000000100000000000000100000000000000000000000000000000000000000000010000000000000000000000000011001000110000010000000000000000000100000000000000010000000100000000000000000000001000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000100000000000000000000000000000000000011000010001100000000000000000000001000000000000001000001000000000000000100000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000100001000000000000000000000000000000000000000000000000000000000010000000001000000000000000000000000000000000000000000000010000000000000000000010000000000000000100000000000000000100000000000000000000000000000000000000000001000000001010000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000001000000000000000000000000000000000000000010001000000000000000000000000100000000000000000000000000000000000000000100000000000000000000000000001000000000000000000010110101000100000000001000000000000000000000000000000010000000000000010000100000000000000000000000000000000000000000000000000000000000000100000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000010011000000000000000100000000000000000000000000000000100000001000000000000000000000000000000000000000000000000000000001000000010000000011000000001000010000000000100000000100000000000000000000000000000000000001000000000000100000000000000001000000000000000000000000001000000000100000000000100000100000000000000000000000000000100000000000000000000000001000000000001000000000000000000000000000000000000010000000001100000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000010000000000000000000000000000000010000001001000000000000001000000000000000000100000000001000000000010000010000000000000000000000000000010000000000000000000000000000100000010000000000000000000000000010000000000000000000000000010000000000000000000000000000000000100000000000001000000000000000000000000000010000000110000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000001000000000000000100000000000000000001100001000000000011000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000100000000000000000000000000000010000000010001001000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000100000000000000001000000001000000000000000000010000000000100000000000010000001000001001000000000000000000000010000000010000100000000100000000000000100000001000000000000000000000000000000000000000000000000001000000010000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000010000000000000000010001000000100000000000000001000000000000000000100000000000000000000000000010000000000000000000000000000000000000000000000000100000100000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000001100000000100000001010000000000000000000000000011000000000000100000000000000000000011000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000001000010000000000000000000101000000000000000000000000000000000000000000000000000000000000010000000000000100001000000000000000000000000000000000011000000000000000000000000000000000000000000000000000001000000000000000000000100000001000000000000000000000000000000000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          ]
        }
      ],
      "source": [
        "# Helper function for int to bool str\n",
        "# 13 int -> 1101 string\n",
        "def user_tokens_as_binary_str(user):\n",
        "    bin_user = bin(user)[2:]\n",
        "    prefix_len = all_unk_count - len(bin_user)\n",
        "    return prefix_len * \"0\" + bin_user\n",
        "\n",
        "\n",
        "print(user_tokens_as_binary_str(_users[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLsr5j_Ulq03",
        "outputId": "0cd31789-7d76-4251-884b-d1975d51e634"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.013943756727664151\n",
            "0.03742868675995696\n"
          ]
        }
      ],
      "source": [
        "# Inspect average matching scrore for user 140 agains first subreddit and the second ones\n",
        "user_inspection = 140\n",
        "c1 = [matching2(_users[user_inspection], _users[i]) for i in range(100) if i != user_inspection]\n",
        "c2 = [matching2(_users[user_inspection], _users[i]) for i in range(100, 200) if i != user_inspection]\n",
        "\n",
        "print(sum(c1) / 100)\n",
        "print(sum(c2) / 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWybjBLApSD3",
        "outputId": "b94ab674-1c9a-4aaa-89c7-7a7e0474fd7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10355029585798817"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "matching(_users[0], _users[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37qEXsQPu-ZQ"
      },
      "outputs": [],
      "source": [
        "# For every token, count the number of users that has the token\n",
        "all_tokens_user_count = [0] * all_tokens_count\n",
        "for user in _users:\n",
        "    user_bin = \"0\" * (all_tokens_count - len(bin(user)[2:])) + bin(user)[2:]\n",
        "    for i in range(len(user_bin)):\n",
        "        all_tokens_user_count[i] += int(user_bin[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5sDsfCRZu_gV"
      },
      "outputs": [],
      "source": [
        "all_tokens_user_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkzwbjYqS6yb",
        "outputId": "ee9509b0-8367-4584-c8fb-4c560ee7e172"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User 137 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            "User 148 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            "User 163 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          ]
        }
      ],
      "source": [
        "# Find all nba tokens (as binary string) and politics tokens (as binary strings)\n",
        "_nba_tokens = 0\n",
        "_pol_tokens = 0\n",
        "for i in range(len(_users)):\n",
        "    if i < 100:\n",
        "        _nba_tokens |= _users[i]\n",
        "    else:\n",
        "        if _users[i] > 2**(all_unk_count - 2):\n",
        "            print(\"User\", i, user_tokens_as_binary_str(_users[i]))\n",
        "        _pol_tokens |= _users[i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7Y-iYe-TnJy",
        "outputId": "9cbccf04-67b5-47e0-92de-c385802d0412"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "584 2310 802 3736\n"
          ]
        }
      ],
      "source": [
        "# Analize the number of tokens that are in both subreddits, in only one, in only annother, or none\n",
        "ic = 0\n",
        "jc = 0\n",
        "nc = 0\n",
        "bc = 0\n",
        "for i, j in zip(user_tokens_as_binary_str(_nba_tokens), user_tokens_as_binary_str(_pol_tokens)):\n",
        "    if (i == \"1\" and j == \"0\"):\n",
        "        ic += 1\n",
        "    if (i == \"0\" and j == \"1\"):\n",
        "        jc += 1\n",
        "    if (i == \"0\" and j == \"0\"):\n",
        "        nc += 1\n",
        "    if (i == \"1\" and j == \"1\"):\n",
        "        bc += 1\n",
        "\n",
        "print(ic, jc, nc, bc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaPoYo9lTFRw",
        "outputId": "670a2f30-7e72-43b3-8091-5fecb6e2892e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            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          ]
        }
      ],
      "source": [
        "nba_tokens = [0 for _ in range(all_unk_count)]\n",
        "pol_tokens = [0 for _ in range(all_unk_count)]\n",
        "\n",
        "for i in range(len(_users)):\n",
        "    for j, val in enumerate(list(user_tokens_as_binary_str(_users[i]))):\n",
        "        if i < 100:\n",
        "            nba_tokens[j] += int(val)\n",
        "        else:\n",
        "            pol_tokens[j] += int(val)\n",
        "\n",
        "\n",
        "    if i == 137:\n",
        "        print(user_tokens_as_binary_str(_users[i]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxQ6wZ3_VIJm",
        "outputId": "3e550083-e045-4ddd-9c24-cd90dc286e42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 7 28 34 42 46 50 62 65 67 85 116 133 135 141 147 152 161 165 189 201 203 230 231 238 251 257 262 264 269 300 302 308 310 318 322 335 336 337 340 349 358 359 363 365 367 368 380 385 391 399 401 409 431 434 444 450 460 462 463 466 471 472 476 485 487 493 500 505 513 519 521 523 532 537 545 549 570 571 583 596 600 607 608 616 621 635 655 665 666 684 696 699 704 711 722 723 726 729 734 750 752 772 785 788 789 792 793 822 824 841 848 858 864 876 890 892 907 926 948 950 954 960 970 984 986 989 993 996 1003 1015 1016 1019 1022 1024 1027 1037 1042 1043 1045 1047 1078 1079 1103 1111 1141 1144 1154 1178 1179 1180 1194 1195 1209 1210 1216 1237 1241 1251 1252 1254 1291 1293 1296 1298 1304 1312 1314 1317 1347 1393 1415 1429 1446 1461 1478 1486 1490 1504 1542 1578 1587 1588 1590 1597 1603 1629 1641 1661 1664 1673 1682 1706 1723 1725 1727 1732 1760 1761 1777 1784 1790 1799 1801 1808 1830 1834 1842 1860 1885 1897 1900 1901 1902 1906 1910 1911 1918 1921 1922 1927 1937 1943 1949 1950 1964 1970 1987 2016 2037 2038 2059 2061 2080 2088 2099 2103 2147 2157 2190 2192 2195 2200 2205 2207 2224 2235 2247 2255 2261 2266 2279 2286 2287 2303 2304 2305 2320 2329 2331 2348 2359 2361 2366 2402 2403 2428 2442 2448 2455 2460 2474 2480 2483 2487 2489 2495 2497 2498 2507 2512 2514 2515 2519 2521 2543 2568 2602 2611 2613 2627 2638 2640 2642 2652 2665 2680 2681 2682 2692 2701 2706 2713 2736 2739 2752 2756 2761 2768 2773 2789 2793 2798 2803 2816 2830 2847 2869 2877 2892 2895 2921 2948 2952 2953 2960 2972 2978 2984 2987 2991 2992 3000 3003 3021 3022 3027 3048 3057 3065 3066 3067 3071 3073 3074 3089 3100 3101 3104 3105 3121 3122 3135 3146 3153 3185 3203 3215 3224 3230 3233 3248 3249 3251 3252 3262 3273 3279 3282 3292 3306 3315 3323 3343 3349 3350 3351 3366 3370 3377 3391 3395 3396 3405 3415 3418 3424 3426 3433 3442 3443 3451 3457 3466 3467 3475 3483 3532 3534 3543 3545 3559 3560 3561 3570 3603 3606 3656 3667 3682 3722 3724 3752 3762 3770 3786 3802 3821 3883 3890 3892 3900 3914 3924 3925 3939 3945 3947 3957 3958 3959 3963 3964 3970 3971 3972 3978 3979 3990 4005 4011 4016 4018 4040 4048 4057 4062 4068 4095 4107 4110 4117 4125 4140 4147 4154 4167 4177 4183 4205 4206 4216 4224 4236 4241 4245 4248 4256 4258 4261 4265 4267 4275 4284 4296 4299 4301 4303 4307 4319 4320 4325 4339 4357 4363 4366 4382 4387 4399 4401 4402 4403 4404 4414 4434 4479 4486 4497 4498 4514 4515 4516 4532 4541 4543 4547 4549 4552 4569 4574 4609 4619 4623 4625 4626 4632 4633 4656 4667 4673 4691 4694 4698 4706 4718 4723 4724 4748 4750 4754 4760 4784 4786 4787 4789 4804 4805 4817 4819 4827 4828 4829 4853 4878 4891 4895 4896 4906 4915 4919 4921 4958 4960 4966 4976 4980 5002 5006 5008 5019 5024 5029 5038 5039 5051 5062 5065 5073 5074 5090 5094 5095 5102 5106 5113 5116 5117 5129 5138 5140 5147 5156 5157 5163 5164 5183 5206 5219 5222 5230 5240 5244 5245 5249 5272 5280 5282 5283 5305 5326 5329 5342 5348 5357 5383 5385 5406 5411 5422 5425 5463 5466 5473 5486 5503 5508 5511 5514 5524 5532 5558 5565 5592 5611 5632 5668 5674 5684 5697 5702 5706 5719 5735 5763 5766 5781 5785 5789 5796 5801 5809 5817 5824 5836 5837 5844 5846 5847 5848 5860 5866 5892 5897 5903 5906 5909 5930 5936 5983 5985 5998 6028 6043 6055 6059 6071 6103 6109 6134 6137 6143 6145 6150 6155 6157 6168 6172 6173 6175 6180 6199 6203 6222 6233 6234 6267 6293 6306 6318 6329 6330 6341 6354 6357 6363 6373 6380 6381 6382 6388 6400 6416 6451 6461 6465 6475 6476 6508 6517 6526 6531 6562 6578 6593 6603 6622 6630 6643 6648 6664 6697 6707 6730 6745 6747 6750 6762 6773 6783 6812 6836 6842 6859 6873 6881 6906 6911 6915 6924 6925 6953 6954 6957 6962 6970 6973 6976 6988 6990 7020 7038 7072 7075 7103 7113 7128 7133 7137 7138 7143 7159 7165 7166 7191 7202 7203 7208 7210 7219 7224 7236 7239 7249 7270 7271 7279 7282 7287 7289 7300 7305 7309 7312 7330 7352 7359 7360 7365 7366 7368 7379 7386 7395 7399 7403 7404 7411 7413 7414 7416 7417 7430 "
          ]
        }
      ],
      "source": [
        "big_difference_1 = 0 # Any of reddits has x4 tokens then the other reddit (only if any of reddits >10 tokens)\n",
        "big_difference_2 = 0 # Any of reddits has x4 tokens then the other reddit (only if any of reddits >10 tokens)\n",
        "pure_tokens = 0 # Any of reddits has 0 tokens (only if any of reddits >10 tokens)\n",
        "non_tokens = 0 # Both reddits have 0 tokens\n",
        "small_tokens = 0 # No reddit has 5 or more tokens\n",
        "big_difference_any = 0 # Any of reddits has x4 tokens then the other reddit\n",
        "\n",
        "for index, (i, j) in enumerate(zip(nba_tokens, pol_tokens)):\n",
        "    if (i > 10) and (j * 4 < i):\n",
        "        big_difference_1 += 1\n",
        "\n",
        "    if (j > 10) and (i * 4 < j):\n",
        "        big_difference_2 += 1\n",
        "\n",
        "    if (i > 10 or j > 10) and (i == 0 or j == 0):\n",
        "        pure_tokens += 1\n",
        "\n",
        "    if i == 0 and j == 0:\n",
        "        non_tokens += 1\n",
        "        print(index, end=\" \")\n",
        "\n",
        "    if (i < 10 and j < 10):\n",
        "        small_tokens += 1\n",
        "\n",
        "    if (i != 0 and j != 0) and (i * 4 < j or j * 4 < i):\n",
        "        big_difference_any += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "1liKVsaVXuEV",
        "outputId": "30b8d501-fc53-4168-f0cc-d40f392c986a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "All tokens: 7432\n",
            "-----------------\n",
            "x4 >10:     143\n",
            "x4 >10:     908\n",
            "Pure:       270\n",
            "No tokens:  802\n",
            "<5:         5291\n",
            "x4:         1139\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nAll tokens: 8502\\n-----------------\\nx4 >10:     987\\nPure:       219\\nNo tokens:  2116\\n<5:         4678\\nx4:         1077\\n\\n\\nAfter bugfix\\nAll tokens: 8502\\n-----------------\\nx4 >10:     1284\\nPure:       283\\nNo tokens:  802\\n<5:         3976\\nx4:         1359\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "print(f\"\"\"\n",
        "All tokens: {all_unk_count}\n",
        "-----------------\n",
        "x4 >10:     {big_difference_1}\n",
        "x4 >10:     {big_difference_2}\n",
        "Pure:       {pure_tokens}\n",
        "No tokens:  {non_tokens}\n",
        "<5:         {small_tokens}\n",
        "x4:         {big_difference_any}\"\"\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "Uw_vHitmsBzs",
        "WN01UidMg_5J",
        "Uf9-WhJ2PCAV",
        "2xqOJGQw4Ix6",
        "GHY9k--cPVDS",
        "l9fMkZ884x3T",
        "1YYSu8_V1klT"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}