GoT_Dataset_Generation.ipynb

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        "\n",
        "# Relationship-based Character Selection\n",
        "---\n",
        "\n",
        "In this module, we aim to select characters based on the available relation information. By doing so, the story generator can integrate more contextual knowledge about the universe and make the story more coherent. \n"
      ],
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        "main_characters = {\"Jon_Snow\": \"Q3183235\",\n",
        "                   \"Daenerys_Targaryen\": \"Q2708078\",\n",
        "                   \"Arya_Stark\": \"Q3624677\",\n",
        "                   \"Bran_Stark\": \"Q3643599\",\n",
        "                   \"Cersei_Lannister\": \"Q3665163\", \n",
        "                   \"Tyrion_Lannister\" : \"Q2076759\",\n",
        "                   \"Margaery_Tyrell\": \"Q12900933\",\n",
        "                   \"Petyr_Baelish\": \"Q4360302\",\n",
        "                   \"Robert_Baratheon\" : \"Q13634885\"}"
      ],
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        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "## Using the \"Network of Thrones\" data\n",
        "\n",
        "The Network of Thrones data uses the Game of Thrones scripts to create a weighted relationship graph. The interaction types between Character A and Character B are defined as following:\n",
        "\n",
        "1. Character A speaks directly after Character B\n",
        "2. Character A speaks about Character B\n",
        "3. Character C speaks about Character A and Character B\n",
        "4. Character A and Character B are mentioned in the same stage direction\n",
        "5. Character A and Character B appear in a scene together\n",
        "\n",
        "The weight of the edge between two characters is determined by the number of times they interact in the script.\n"
      ],
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          "text": [
            "Cloning into 'gameofthrones'...\n",
            "remote: Enumerating objects: 62, done.\u001b[K\n",
            "remote: Counting objects: 100% (7/7), done.\u001b[K\n",
            "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
            "remote: Total 62 (delta 0), reused 2 (delta 0), pack-reused 55\u001b[K\n",
            "Unpacking objects: 100% (62/62), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/mathbeveridge/gameofthrones"
      ]
    },
    {
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      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "f = \"gameofthrones/data/\"\n",
        "edges = []\n",
        "nodes = []\n",
        "\n",
        "for d in sorted(os.listdir(f)):\n",
        "  if \"edge\" in d:\n",
        "    edges.append(pd.read_csv(f+d))\n",
        "  elif \"node\" in d:\n",
        "    nodes.append(pd.read_csv(f+d))"
      ],
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          "ename": "FileNotFoundError",
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-9cef6c14ac4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mnodes\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\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[0m\u001b[1;32m     10\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;34m\"edge\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0medges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0md\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Network_of_Thrones/'"
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        "## Given a character and desired season, return a character selected \n",
        "## randomly from the top N relations with respect to their weights.\n",
        "\n",
        "def find_related_character(season, character_id, n):\n",
        "  so = edges[season-1][edges[season-1].Source == character_id][[\"Target\", \"Weight\"]].rename({'Target': 'Relation'}, axis=1)\n",
        "  ta = edges[season-1][edges[season-1].Target == character_id][[\"Source\", \"Weight\"]].rename({'Source': 'Relation'}, axis=1)\n",
        "\n",
        "  relations = so.append(ta).sort_values(by = \"Weight\", ascending=False)[:n]\n",
        "  related_character = random.choices(list(relations[\"Relation\"]), weights=list(relations[\"Weight\"]))\n",
        "  return related_character\n",
        "\n",
        "find_related_character(1, \"CERSEI\", 1)"
      ],
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              "                                   Id                             Label\n",
              "0                      Addam_Marbrand                    Addam Marbrand\n",
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              "4                   Aegon_V_Targaryen                 Aegon V Targaryen\n",
              "..                                ...                               ...\n",
              "791                       Yorko_Terys                       Yorko Terys\n",
              "792                            Ysilla                            Ysilla\n",
              "793                 Yurkhaz_zo_Yunzak                 Yurkhaz zo Yunzak\n",
              "794                               Zei                               Zei\n",
              "795                             Zollo                             Zollo\n",
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        "edges = pd.read_csv(\"asoiaf-all-edges.csv\")\n",
        "edges_subset = []\n",
        "for line in edges.iterrows():\n",
        "  for char in main_characters:\n",
        "    if(line[1]['Source'] == char or line[1]['Target'] == char):\n",
        "      edges_subset.append(line[1])\n",
        "\n",
        "nodes = pd.read_csv(\"asoiaf-all-nodes.csv\")\n",
        "nodes_subset = []\n",
        "for line in nodes.iterrows():\n",
        "  for char in main_characters:\n",
        "    if(line[1]['Id'] == char):\n",
        "      nodes_subset.append(line[1])\n",
        "\n",
        "pd.DataFrame(edges_subset).to_csv(\"edges_subset.csv\", index = False)\n",
        "pd.DataFrame(nodes_subset).to_csv(\"nodes_subset.csv\", index = False)"
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              "                               Source              Target        Type    id  \\\n",
              "0                      Addam_Marbrand    Cersei_Lannister  Undirected     1   \n",
              "1                      Addam_Marbrand    Tyrion_Lannister  Undirected     9   \n",
              "2                   Aegon_I_Targaryen  Daenerys_Targaryen  Undirected    16   \n",
              "3                   Aegon_I_Targaryen    Tyrion_Lannister  Undirected    23   \n",
              "4    Aegon_Targaryen_(son_of_Rhaegar)  Daenerys_Targaryen  Undirected    25   \n",
              "..                                ...                 ...         ...   ...   \n",
              "681                  Tyrion_Lannister         Willis_Wode  Undirected  2805   \n",
              "682                  Tyrion_Lannister              Yandry  Undirected  2806   \n",
              "683                  Tyrion_Lannister    Yezzan_zo_Qaggaz  Undirected  2807   \n",
              "684                  Tyrion_Lannister               Yoren  Undirected  2808   \n",
              "685                  Tyrion_Lannister              Ysilla  Undirected  2809   \n",
              "\n",
              "     weight  \n",
              "0         3  \n",
              "1         7  \n",
              "2        13  \n",
              "3         3  \n",
              "4        11  \n",
              "..      ...  \n",
              "681       4  \n",
              "682      15  \n",
              "683      17  \n",
              "684      10  \n",
              "685      11  \n",
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              "      <td>Aegon_I_Targaryen</td>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aegon_Targaryen_(son_of_Rhaegar)</td>\n",
              "      <td>Daenerys_Targaryen</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>Willis_Wode</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>2805</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>Yandry</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>2806</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>Yezzan_zo_Qaggaz</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>2807</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>Yoren</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>2808</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>685</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>Ysilla</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>2809</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>686 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-390e1133-ea77-4686-9623-23ea8b688b87')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-390e1133-ea77-4686-9623-23ea8b688b87');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def read_network_data():\n",
        "    edges = pd.read_csv(\"edges_subset.csv\")\n",
        "    nodes = pd.read_csv(\"nodes_subset.csv\")\n",
        "    return nodes, edges\n",
        "\n",
        "def relation_based_pick(ist_class, related_to_char, n):\n",
        "    so = edges[edges.Source == related_to_char][[\"Target\", \"weight\"]].rename({'Target': 'Relation'}, axis=1)\n",
        "    ta = edges[edges.Target == related_to_char][[\"Source\", \"weight\"]].rename({'Source': 'Relation'}, axis=1)\n",
        "\n",
        "    relations = so.append(ta).sort_values(by=\"weight\", ascending=False)[:n]\n",
        "    related_character = random.choices(list(relations[\"Relation\"]), weights=list(relations[\"weight\"]))\n",
        "    return related_character\n",
        "\n",
        "nodes, edges = read_network_data()\n",
        "relation_based_pick(\"text\", 'Cersei_Lannister', 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JXi4dQX7eUt",
        "outputId": "835da969-7804-4902-e80d-1f8548f8a498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Tyrion_Lannister']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "relations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KZDshBzw8DY7",
        "outputId": "5c3887d9-8b2e-41a6-cbfd-5a6fc427e742"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Relation  weight\n",
              "304   Tyrion_Lannister     209\n",
              "303   Tyrion_Lannister     209\n",
              "250  Joffrey_Baratheon     173\n",
              "289   Robert_Baratheon     134\n",
              "290   Robert_Baratheon     134"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-28d01ed3-bb6c-47a6-b8f8-eaddf4d91037\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Relation</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>Tyrion_Lannister</td>\n",
              "      <td>209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>Joffrey_Baratheon</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>Robert_Baratheon</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>Robert_Baratheon</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-28d01ed3-bb6c-47a6-b8f8-eaddf4d91037 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-28d01ed3-bb6c-47a6-b8f8-eaddf4d91037');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Even though this approach represents the dynamics of the series, it does not provide information on the nature of the relation. For example, Ned Stark is the top candidate to be Cersei Lannister's \"mentor\", even though they are enemies. Therefore, we need a different approach to identify positive/negative relations."
      ],
      "metadata": {
        "id": "ydgKuulnCa3T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "## Using Game of Thrones Fandom Wiki\n",
        "\n",
        "Fandom Wiki contains more detailed information about the dynamics of the series, with information such as family relations and love interests. Using the info bar, we can scrape such information to generate a graph. However, the information stored there is unstructured and inconsistent, and requires a lot of manual processing. "
      ],
      "metadata": {
        "id": "_d04yWz7CaCf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup"
      ],
      "metadata": {
        "id": "pk1lZ0fzJKt7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wikiurl=\"https://gameofthrones.fandom.com/wiki/Jon_Snow\"\n",
        "table_class=\"wikitable sortable jquery-tablesorter\"\n",
        "response=requests.get(wikiurl)\n",
        "\n",
        "soup = BeautifulSoup(response.text, 'html.parser')\n",
        "table_text =soup.find('aside',{'class':\"portable-infobox\"}).text\n",
        "print(table_text)"
      ],
      "metadata": {
        "id": "yQy-S-VBJWkF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d654ee3-d51b-41fe-9132-eddfd469e9a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Jon Snow\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Biographical information\n",
            "\n",
            "Status\n",
            "Alive\n",
            "\n",
            "\n",
            "Born\n",
            "Aegon Targaryen280 AC (age 25) at the Tower of Joy\n",
            "\n",
            "\n",
            "Died\n",
            "302 AC at Castle BlackStabbed to death by his own men in a mutiny (later resurrected)\n",
            "\n",
            "\n",
            "\n",
            "Political information\n",
            "\n",
            "Allegiance\n",
            "House Targaryen (by birth)House Stark (raised)Night's Watch\n",
            "\n",
            "\n",
            "Title(s)\n",
            "998th Lord Commander of the Night's Watch (formerly)King in the North (formerly)Warden of the North (formerly)\n",
            "\n",
            "\n",
            "Predecessor(s)\n",
            "{Jeor Mormont} (Lord Commander){Robb Stark} (King in the North){Ramsay Bolton} (Warden of the North)\n",
            "\n",
            "\n",
            "Successor(s)\n",
            "{Eddison Tollett} (acting Lord Commander)Sansa Stark (Queen in the North)\n",
            "\n",
            "\n",
            "\n",
            "Personal information\n",
            "\n",
            "Also known as\n",
            "Lord SnowThe Bastard of WinterfellKing CrowThe Prince That Was PromisedThe White Wolf\n",
            "\n",
            "\n",
            "Culture\n",
            "Northman\n",
            "\n",
            "\n",
            "Religion\n",
            "Old Gods of the Forest\n",
            "\n",
            "\n",
            "Father\n",
            "{Rhaegar Targaryen} (biological){Eddard Stark} (adoptive)\n",
            "\n",
            "\n",
            "Mother\n",
            "{Lyanna Stark}\n",
            "\n",
            "\n",
            "Lover(s)\n",
            "{Ygritte}{Daenerys I Targaryen}\n",
            "\n",
            "\n",
            "Sibling(s)\n",
            "Paternal half-siblings:{Rhaenys Targaryen}{Aegon Targaryen}Maternal cousins raised as half-siblings:{Robb Stark}Sansa StarkArya StarkBran I the Broken{Rickon Stark}\n",
            "\n",
            "\n",
            "\n",
            "Behind the scenes information\n",
            "\n",
            "Series\n",
            "Game of Thrones\n",
            "\n",
            "\n",
            "Season(s)\n",
            "1, 2, 3, 4, 5, 6, 7, 8\n",
            "\n",
            "\n",
            "Appeared in\n",
            "63 episodes (see below)\n",
            "\n",
            "\n",
            "First seen\n",
            "\"Winter Is Coming\"\n",
            "\n",
            "\n",
            "Last seen\n",
            "\"The Iron Throne\"\n",
            "\n",
            "\n",
            "Death shown in\n",
            "\"Mother's Mercy\"\n",
            "\n",
            "\n",
            "Mentioned in\n",
            "\"Greyjoy Rebellion\"\"Robert's Rebellion\"\"The North\"\"The Hand of the King\"\n",
            "\n",
            "\n",
            "Portrayed by\n",
            "Kit Harington\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "important_info = [\"Allegiance\",\"Title\",\"Predecessor\",\"Successor\",\"Also known as\", \"Culture\", \"Religion\", \"Father\",\"Mother\", \"Lover\", \"Spouse\",\"Child(ren)\",\"Sibling\"]\n",
        "\n",
        "info_title = \"\"\n",
        "character_info = {}\n",
        "for line in table_text.split(\"\\n\\n\\n\"):\n",
        "  #print(repr(line))\n",
        "  line = line.replace(')', ')\\t')\n",
        "  line = line.replace('}', '\\t').replace('{', '')\n",
        "  for point in line.split(\"\\n\"):\n",
        "    #print(repr(point.strip(\"\\t\")))\n",
        "    if info_title:\n",
        "      ###detail = re.findall(r'\\([^)]*\\)', str(point))\n",
        "      point = re.sub(r'\\([^)]*\\)', '', point)\n",
        "      info = list(filter(None, point.split(\"\\t\")))\n",
        "      \n",
        "      if len(info) == 1:\n",
        "        character_info[info_title] = info[0]\n",
        "      else:\n",
        "        info = [(i.strip() if i != \" \" else \"\") for i in info]\n",
        "        info = list(filter(None, info))\n",
        "        character_info[info_title] = info\n",
        "      info_title = \"\"\n",
        "\n",
        "    if point.strip(\"\\t\").strip(\"(s)\") in important_info:\n",
        "      info_title = point.strip(\"\\t\").strip(\"(s)\")\n",
        "\n",
        "character_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJFeNYoDZOmO",
        "outputId": "80a322b9-87cf-4efd-98a0-781cf7d52f9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Allegiance': ['House Targaryen', 'House Stark', \"Night's Watch\"],\n",
              " 'Culture': 'Northman',\n",
              " 'Father': ['Rhaegar Targaryen', 'Eddard Stark'],\n",
              " 'Lover': ['Ygritte', 'Daenerys I Targaryen'],\n",
              " 'Mother': 'Lyanna Stark',\n",
              " 'Predecessor': ['Jeor Mormont', 'Robb Stark', 'Ramsay Bolton'],\n",
              " 'Religion': 'Old Gods of the Forest',\n",
              " 'Sibling': ['Paternal half-siblings:Rhaenys Targaryen',\n",
              "  'Aegon Targaryen',\n",
              "  'Maternal cousins raised as half-siblings:Robb Stark',\n",
              "  'Sansa StarkArya StarkBran I the BrokenRickon Stark'],\n",
              " 'Successor': ['Eddison Tollett', 'Sansa Stark'],\n",
              " 'Title': [\"998th Lord Commander of the Night's Watch\",\n",
              "  'King in the North',\n",
              "  'Warden of the North']}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_wiki_page(character_id, character_label):\n",
        "  wikiurl=\"https://gameofthrones.fandom.com/wiki/\"+character_id\n",
        "  table_class=\"wikitable sortable jquery-tablesorter\"\n",
        "  response=requests.get(wikiurl)\n",
        "  #print(response)\n",
        "  soup = BeautifulSoup(response.text, 'html.parser')\n",
        "  try:\n",
        "    table_text =soup.find('aside',{'class':\"portable-infobox\"}).text\n",
        "    #print(repr(table_text))\n",
        "\n",
        "  except:\n",
        "    wikiurl=\"https://gameofthrones.fandom.com/wiki/\"+character_label.replace(\" \", \"_\")\n",
        "    table_class=\"wikitable sortable jquery-tablesorter\"\n",
        "    response=requests.get(wikiurl)\n",
        "    #print(response)\n",
        "    soup = BeautifulSoup(response.text, 'html.parser')\n",
        "    try:\n",
        "      table_text =soup.find('aside',{'class':\"portable-infobox\"}).text\n",
        "    except:\n",
        "      print(character_id)"
      ],
      "metadata": {
        "id": "i9R-hbrAJraK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Wikidata information on Game of Thrones characters\n",
        "\n",
        "Wikidata provides well-structured information about the characters. However, information is more surface-level (e.g. family or formal partners) and does not contain certain details (e.g. enemies or allies).\n",
        "\n",
        "We manually investigate the interesting properties to include, and note their IDs to use for querying. We also create a list of main characters and their IDs for more detailed extraction, whereas side characters that are related to main characters will not contain any additional info."
      ],
      "metadata": {
        "id": "sM1MwHaxKUPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wikidata\n",
        "\n",
        "from wikidata.client import Client\n",
        "client = Client()  # doctest: +SKIP\n",
        "entity = client.get('Q3183235', load=True)\n",
        "entity\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYMHrb-zKWrC",
        "outputId": "6ba6b385-3b8c-4214-e3fc-417642d5b68a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wikidata\n",
            "  Downloading Wikidata-0.7.0-py3-none-any.whl (29 kB)\n",
            "Installing collected packages: wikidata\n",
            "Successfully installed wikidata-0.7.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<wikidata.entity.Entity Q3183235 'Jon Snow'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Promising part"
      ],
      "metadata": {
        "id": "-5ux2t83yAet"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from string import Template\n",
        "\n",
        "query_template = Template(\"\"\"SELECT ?o ?oLabel WHERE {wd:$char wdt:$p ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}\"\"\")\n",
        "\n",
        "query_template_q = Template(\"\"\"SELECT ?o ?oLabel ?q ?qLabel WHERE {wd:$char p:$p [ps:$p ?o; pq:$pq ?q; ]. SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}\"\"\")\n",
        " \n",
        "# Substitute value of x in above template\n",
        "char_id = 'Q2708078'\n",
        "p = 'P3373'\n",
        "pq = 'P1039'\n",
        "\n",
        "print(query_template.substitute({'char': char_id, 'p': p}))\n",
        "print(query_template_q.substitute({'char': char_id, 'p': p, 'pq':pq}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ihu17cr6XgMo",
        "outputId": "b8e0f53c-ab51-4d4d-a555-edef1a47d9d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P3373 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}\n",
            "SELECT ?o ?oLabel ?q ?qLabel WHERE {wd:Q2708078 p:P3373 [ps:P3373 ?o; pq:P1039 ?q; ]. SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install SPARQLWrapper\n",
        "import sys\n",
        "import re\n",
        "from SPARQLWrapper import SPARQLWrapper, JSON\n",
        "\n",
        "endpoint_url = \"https://query.wikidata.org/sparql\"\n",
        "\n",
        "def get_results(endpoint_url, query):\n",
        "    user_agent = \"WDQS-example Python/%s.%s\" % (sys.version_info[0], sys.version_info[1])\n",
        "    # TODO adjust user agent; see https://w.wiki/CX6\n",
        "    sparql = SPARQLWrapper(endpoint_url, agent=user_agent)\n",
        "    sparql.setQuery(query)\n",
        "    sparql.setReturnFormat(JSON)\n",
        "    return sparql.query().convert()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZO2prV9DXgJN",
        "outputId": "53244d11-fb03-401c-9a42-78fd08ac0936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting SPARQLWrapper\n",
            "  Downloading SPARQLWrapper-2.0.0-py3-none-any.whl (28 kB)\n",
            "Collecting rdflib>=6.1.1\n",
            "  Downloading rdflib-6.1.1-py3-none-any.whl (482 kB)\n",
            "\u001b[K     |████████████████████████████████| 482 kB 10.7 MB/s \n",
            "\u001b[?25hCollecting isodate\n",
            "  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 729 kB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from rdflib>=6.1.1->SPARQLWrapper) (3.0.7)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from rdflib>=6.1.1->SPARQLWrapper) (57.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from rdflib>=6.1.1->SPARQLWrapper) (4.11.3)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->rdflib>=6.1.1->SPARQLWrapper) (3.10.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->rdflib>=6.1.1->SPARQLWrapper) (3.7.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from isodate->rdflib>=6.1.1->SPARQLWrapper) (1.15.0)\n",
            "Installing collected packages: isodate, rdflib, SPARQLWrapper\n",
            "Successfully installed SPARQLWrapper-2.0.0 isodate-0.6.1 rdflib-6.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_lst =  {'father' : {'p': 'P22', 'range':'Character'},\n",
        "             'mother' : {'p' : 'P25', 'range':'Character'},\n",
        "             'sibling' : {'p' : 'P3373', 'range':'Character'} ,\n",
        "             'child' : {'p' : 'P40', 'range':'Character'} ,\n",
        "             'partner' : {'p': 'P451', 'range':'Character'},\n",
        "             'spouse' : {'p' : 'P26', 'range':'Character'} ,\n",
        "             'relative': {'p': 'P1038', 'range':'Character'},\n",
        "##             'relative': {'p': 'P1038', 'pq': 'P1039'},\n",
        "             'family' : {'p': 'P53', 'range':'House'},\n",
        "             'occupation': {'p':'P106', 'range':'Occupation'},\n",
        "             'title' : {'p' : 'P97', 'range': 'Title'},\n",
        "             'place_of_death' : {'p': 'P20', 'range':'Place'},\n",
        "             'cause_of_death': {'p': 'P509', 'range':'Cause_of_death'},\n",
        "             'battles_participated' : {'p': 'P607', 'range':'Battle'}}\n",
        "\n",
        "main_characters = {\"Jon_Snow\": \"Q3183235\",\n",
        "                   \"Daenerys_Targaryen\": \"Q2708078\",\n",
        "                   \"Arya_Stark\": \"Q3624677\",\n",
        "                   \"Bran_Stark\": \"Q3643599\",\n",
        "                   \"Cersei_Lannister\": \"Q3665163\", \n",
        "                   \"Tyrion_Lannister\" : \"Q2076759\",\n",
        "                   \"Margaery_Tyrell\": \"Q12900933\",\n",
        "                   \"Petyr_Baelish\": \"Q4360302\",\n",
        "                   \"Robert_Baratheon\" : \"Q13634885\"}\n",
        "\n",
        "\n",
        "def generate_query(char_id):\n",
        "  char_query = {}\n",
        "  for label in info_lst.keys():\n",
        "    query_dict = {'char':char_id, 'p': info_lst[label]['p']}\n",
        "    try:\n",
        "      query_dict['pq'] = info_lst[label]['pq']\n",
        "      char_query[label] = query_template_q.substitute(query_dict)\n",
        "    except:\n",
        "      char_query[label] = query_template.substitute(query_dict)\n",
        "\n",
        "  return char_query\n",
        "\n",
        "\n",
        "def query_wikidata(char_query):\n",
        "  results = {}\n",
        "  for label in char_query.keys():\n",
        "    if \"pq\" in char_query[label]:\n",
        "      results[label] = [[result['oLabel']['value'], result['qLabel']['value']] for result in get_results(endpoint_url, char_query[label])['results']['bindings']]\n",
        "    else:\n",
        "      results[label] = [result['oLabel']['value'] for result in get_results(endpoint_url, char_query[label])['results']['bindings']]\n",
        "    \n",
        "  return results"
      ],
      "metadata": {
        "id": "8H60JPneXgLM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(generate_query(\"Q2708078\"))\n",
        "print(query_wikidata(generate_query(\"Q2708078\")))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-mTUMmLJGKJ",
        "outputId": "20a37d8f-f9ef-418f-d268-f8767e6adc93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'father': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P22 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'mother': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P25 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'sibling': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P3373 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'child': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P40 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'partner': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P451 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'spouse': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P26 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'relative': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P1038 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'family': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P53 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'occupation': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P106 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'title': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P97 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'place_of_death': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P20 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'cause_of_death': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P509 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}', 'battles_participated': 'SELECT ?o ?oLabel WHERE {wd:Q2708078 wdt:P607 ?o . SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }}'}\n",
            "{'father': ['Aerys II Targaryen'], 'mother': ['Rhaella Targaryen'], 'sibling': ['Viserys Targaryen', 'Rhaegar Targaryen'], 'child': [], 'partner': ['Jon Snow', 'Daario Naharis'], 'spouse': ['Khal Drogo', 'Hizdahr zo Loraq'], 'relative': [], 'family': ['House Targaryen'], 'occupation': ['ruler', 'revolutionary'], 'title': ['fictional princess'], 'place_of_death': [], 'cause_of_death': [], 'battles_participated': []}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_side_character_rdf(prop, line):\n",
        "  if not line in main_characters: \n",
        "    return \"HERO:\"+line+\" a HERO:\"+info_lst[prop]['range']+\" .\\n\"\n",
        "\n",
        "def generate_main_char_rdf(character, output, general_instances):\n",
        "  rdf = \":\"+character.replace(\" \", \"_\") + \" a HERO:Main_Character;\\n\"\n",
        "  for prop in output.keys():\n",
        "    for line in output[prop]:\n",
        "      if len(output[prop][0])==2:\n",
        "        rdf += \"\\tHERO:\"+prop+\" HERO:\"+line[0].replace(\" \", \"_\") +\" ;\\n\"\n",
        "        rdf += \"\\tHERO:\"+line[1].replace(\" \", \"_\")+\" HERO:\"+line[0].replace(\" \", \"_\") +\" ;\\n\"\n",
        "        general_instances.add(line[0].replace(\" \", \"_\"))\n",
        "      else:\n",
        "        rdf += \"\\tHERO:\"+prop+\" HERO:\"+line.replace(\" \", \"_\") +\" ;\\n\"\n",
        "        general_instances.add(generate_side_character_rdf(prop, line.replace(\" \", \"_\")))\n",
        "  rdf += \".\"\n",
        "  \n",
        "  return rdf\n",
        "\n",
        "general_instances = set()\n",
        "print(generate_main_char_rdf(\"Jon_Snow\", query_wikidata(generate_query(\"Q2708078\")), general_instances))\n",
        "general_instances"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtZh6S5y1Ihx",
        "outputId": "6f00417d-838b-42fd-8ad8-5a0c00497667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ":Jon_Snow a HERO:Main_Character;\n",
            "\tHERO:father HERO:Aerys_II_Targaryen ;\n",
            "\tHERO:mother HERO:Rhaella_Targaryen ;\n",
            "\tHERO:sibling HERO:Viserys_Targaryen ;\n",
            "\tHERO:sibling HERO:Rhaegar_Targaryen ;\n",
            "\tHERO:partner HERO:Jon_Snow ;\n",
            "\tHERO:partner HERO:Daario_Naharis ;\n",
            "\tHERO:spouse HERO:Khal_Drogo ;\n",
            "\tHERO:spouse HERO:Hizdahr_zo_Loraq ;\n",
            "\tHERO:family HERO:House_Targaryen ;\n",
            "\tHERO:occupation HERO:ruler ;\n",
            "\tHERO:occupation HERO:revolutionary ;\n",
            "\tHERO:title HERO:fictional_princess ;\n",
            ".\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'HERO:Aerys_II_Targaryen a HERO:Character .\\n',\n",
              " 'HERO:Daario_Naharis a HERO:Character .\\n',\n",
              " 'HERO:Hizdahr_zo_Loraq a HERO:Character .\\n',\n",
              " 'HERO:House_Targaryen a HERO:House .\\n',\n",
              " 'HERO:Khal_Drogo a HERO:Character .\\n',\n",
              " 'HERO:Rhaegar_Targaryen a HERO:Character .\\n',\n",
              " 'HERO:Rhaella_Targaryen a HERO:Character .\\n',\n",
              " 'HERO:Viserys_Targaryen a HERO:Character .\\n',\n",
              " 'HERO:fictional_princess a HERO:Title .\\n',\n",
              " 'HERO:revolutionary a HERO:Occupation .\\n',\n",
              " 'HERO:ruler a HERO:Occupation .\\n',\n",
              " None}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def just_please_work(main_characters):\n",
        "  rdf = \"\"\" @base <http://hero_ontology/> .\n",
        "            @prefix HERO: <http://hero_ontology/> .\n",
        "            @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
        "            @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
        "            @prefix owl: <http://www.w3.org/2002/07/owl#> .\n",
        "            @prefix sem: <http://semanticweb.cs.vu.nl/2009/11/sem/> .\n",
        "        \"\"\"\n",
        "\n",
        "  general = set()\n",
        "  for character in main_characters.keys():\n",
        "    print(character)\n",
        "    char_id = main_characters[character]\n",
        "    output = query_wikidata(generate_query(char_id))\n",
        "    rdf += \"#### \" + character + \" ####\\n\" + generate_main_char_rdf(character, output, general) + \"\\n\\n\"\n",
        "\n",
        "  print(\"Main characters added.\")\n",
        "\n",
        "  for line in general:\n",
        "    if line:\n",
        "     rdf += line\n",
        "\n",
        "  return re.sub(r\"[()\\'\\,]\", \"\", rdf)\n",
        "\n",
        "instance_rdf = just_please_work(main_characters)\n",
        "text_file = open(\"got_instances.ttl\", \"w\")\n",
        "text_file.write(instance_rdf)\n",
        "text_file.close()\n",
        "\n",
        "print(instance_rdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tg066RZJCa3V",
        "outputId": "789f3457-0302-405e-e175-ccb78341a391"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jon_Snow\n",
            "Daenerys_Targaryen\n",
            "Arya_Stark\n",
            "Bran_Stark\n",
            "Cersei_Lannister\n",
            "Tyrion_Lannister\n",
            "Margaery_Tyrell\n",
            "Petyr_Baelish\n",
            "Robert_Baratheon\n",
            "Main characters added.\n",
            " @base <http://hero_ontology/> .\n",
            "            @prefix HERO: <http://hero_ontology/> .\n",
            "            @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
            "            @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
            "            @prefix owl: <http://www.w3.org/2002/07/owl#> .\n",
            "            @prefix sem: <http://semanticweb.cs.vu.nl/2009/11/sem/> .\n",
            "        #### Jon_Snow ####\n",
            ":Jon_Snow a HERO:Main_Character;\n",
            "\tHERO:father HERO:Rhaegar_Targaryen ;\n",
            "\tHERO:mother HERO:Lyanna_Stark ;\n",
            "\tHERO:sibling HERO:Rhaenys_Targaryen_daughter_of_Rhaegar ;\n",
            "\tHERO:sibling HERO:Aegon_Targaryen ;\n",
            "\tHERO:partner HERO:Daenerys_Targaryen ;\n",
            "\tHERO:partner HERO:Ygritte ;\n",
            "\tHERO:relative HERO:Eddard_Stark ;\n",
            "\tHERO:relative HERO:Daenerys_Targaryen ;\n",
            "\tHERO:relative HERO:Viserys_Targaryen ;\n",
            "\tHERO:relative HERO:Aerys_II_Targaryen ;\n",
            "\tHERO:relative HERO:Rhaella_Targaryen ;\n",
            "\tHERO:relative HERO:Rickard_Stark ;\n",
            "\tHERO:relative HERO:Lyarra_Stark ;\n",
            "\tHERO:relative HERO:Benjen_Stark ;\n",
            "\tHERO:family HERO:House_Targaryen ;\n",
            "\tHERO:family HERO:House_Stark ;\n",
            "\tHERO:occupation HERO:squire ;\n",
            "\tHERO:occupation HERO:ruler ;\n",
            "\tHERO:occupation HERO:soldier ;\n",
            "\tHERO:occupation HERO:spy ;\n",
            "\tHERO:title HERO:Lord ;\n",
            "\tHERO:title HERO:fictional_king ;\n",
            "\tHERO:place_of_death HERO:Castle_Black ;\n",
            "\tHERO:cause_of_death HERO:stabbing ;\n",
            "\tHERO:battles_participated HERO:Battle_of_Castle_Black ;\n",
            "\tHERO:battles_participated HERO:Battle_of_the_Bastards ;\n",
            ".\n",
            "\n",
            "#### Daenerys_Targaryen ####\n",
            ":Daenerys_Targaryen a HERO:Main_Character;\n",
            "\tHERO:father HERO:Aerys_II_Targaryen ;\n",
            "\tHERO:mother HERO:Rhaella_Targaryen ;\n",
            "\tHERO:sibling HERO:Viserys_Targaryen ;\n",
            "\tHERO:sibling HERO:Rhaegar_Targaryen ;\n",
            "\tHERO:partner HERO:Jon_Snow ;\n",
            "\tHERO:partner HERO:Daario_Naharis ;\n",
            "\tHERO:spouse HERO:Khal_Drogo ;\n",
            "\tHERO:spouse HERO:Hizdahr_zo_Loraq ;\n",
            "\tHERO:family HERO:House_Targaryen ;\n",
            "\tHERO:occupation HERO:ruler ;\n",
            "\tHERO:occupation HERO:revolutionary ;\n",
            "\tHERO:title HERO:fictional_princess ;\n",
            ".\n",
            "\n",
            "#### Arya_Stark ####\n",
            ":Arya_Stark a HERO:Main_Character;\n",
            "\tHERO:father HERO:Eddard_Stark ;\n",
            "\tHERO:mother HERO:Catelyn_Stark ;\n",
            "\tHERO:sibling HERO:Sansa_Stark ;\n",
            "\tHERO:sibling HERO:Bran_Stark ;\n",
            "\tHERO:sibling HERO:Rickon_Stark ;\n",
            "\tHERO:sibling HERO:Robb_Stark ;\n",
            "\tHERO:family HERO:House_Stark ;\n",
            "\tHERO:occupation HERO:warrior ;\n",
            "\tHERO:occupation HERO:mass_murderer ;\n",
            "\tHERO:occupation HERO:fictional_vigilante ;\n",
            "\tHERO:occupation HERO:assassin ;\n",
            ".\n",
            "\n",
            "#### Bran_Stark ####\n",
            ":Bran_Stark a HERO:Main_Character;\n",
            "\tHERO:father HERO:Eddard_Stark ;\n",
            "\tHERO:mother HERO:Catelyn_Stark ;\n",
            "\tHERO:sibling HERO:Sansa_Stark ;\n",
            "\tHERO:sibling HERO:Arya_Stark ;\n",
            "\tHERO:sibling HERO:Rickon_Stark ;\n",
            "\tHERO:sibling HERO:Robb_Stark ;\n",
            "\tHERO:family HERO:House_Stark ;\n",
            "\tHERO:occupation HERO:prophet ;\n",
            ".\n",
            "\n",
            "#### Cersei_Lannister ####\n",
            ":Cersei_Lannister a HERO:Main_Character;\n",
            "\tHERO:father HERO:Tywin_Lannister ;\n",
            "\tHERO:mother HERO:Joanna_Lannister ;\n",
            "\tHERO:sibling HERO:Tyrion_Lannister ;\n",
            "\tHERO:sibling HERO:Jaime_Lannister ;\n",
            "\tHERO:child HERO:Joffrey_Baratheon ;\n",
            "\tHERO:child HERO:Tommen_Baratheon ;\n",
            "\tHERO:child HERO:Myrcella_Baratheon ;\n",
            "\tHERO:partner HERO:Jaime_Lannister ;\n",
            "\tHERO:partner HERO:Lancel_Lannister ;\n",
            "\tHERO:partner HERO:Euron_Greyjoy ;\n",
            "\tHERO:spouse HERO:Robert_Baratheon ;\n",
            "\tHERO:family HERO:House_Baratheon ;\n",
            "\tHERO:family HERO:House_Lannister ;\n",
            "\tHERO:occupation HERO:regent ;\n",
            "\tHERO:occupation HERO:advisor ;\n",
            "\tHERO:occupation HERO:mass_murderer ;\n",
            "\tHERO:title HERO:queen_regnant ;\n",
            "\tHERO:place_of_death HERO:Red_Keep ;\n",
            "\tHERO:cause_of_death HERO:injury ;\n",
            ".\n",
            "\n",
            "#### Tyrion_Lannister ####\n",
            ":Tyrion_Lannister a HERO:Main_Character;\n",
            "\tHERO:father HERO:Tywin_Lannister ;\n",
            "\tHERO:mother HERO:Joanna_Lannister ;\n",
            "\tHERO:sibling HERO:Cersei_Lannister ;\n",
            "\tHERO:sibling HERO:Jaime_Lannister ;\n",
            "\tHERO:partner HERO:Shae ;\n",
            "\tHERO:spouse HERO:Sansa_Stark ;\n",
            "\tHERO:family HERO:House_Lannister ;\n",
            "\tHERO:occupation HERO:bureaucrat ;\n",
            "\tHERO:occupation HERO:official ;\n",
            "\tHERO:occupation HERO:murderer ;\n",
            "\tHERO:occupation HERO:advisor ;\n",
            "\tHERO:occupation HERO:revolutionary ;\n",
            "\tHERO:title HERO:Lord ;\n",
            "\tHERO:battles_participated HERO:Battle_of_the_Blackwater ;\n",
            ".\n",
            "\n",
            "#### Margaery_Tyrell ####\n",
            ":Margaery_Tyrell a HERO:Main_Character;\n",
            "\tHERO:father HERO:Mace_Tyrell ;\n",
            "\tHERO:mother HERO:Alerie_Tyrell ;\n",
            "\tHERO:sibling HERO:Loras_Tyrell ;\n",
            "\tHERO:sibling HERO:Willas_Tyrell ;\n",
            "\tHERO:sibling HERO:Garlan_Tyrell ;\n",
            "\tHERO:spouse HERO:Joffrey_Baratheon ;\n",
            "\tHERO:spouse HERO:Renly_Baratheon ;\n",
            "\tHERO:spouse HERO:Tommen_Baratheon ;\n",
            "\tHERO:family HERO:House_Tyrell ;\n",
            "\tHERO:title HERO:queen_consort ;\n",
            "\tHERO:title HERO:fictional_queen ;\n",
            "\tHERO:place_of_death HERO:Great_Sept_of_Baelor ;\n",
            "\tHERO:cause_of_death HERO:explosion ;\n",
            ".\n",
            "\n",
            "#### Petyr_Baelish ####\n",
            ":Petyr_Baelish a HERO:Main_Character;\n",
            "\tHERO:spouse HERO:Lysa_Arryn ;\n",
            "\tHERO:relative HERO:Robert_Arryn ;\n",
            "\tHERO:occupation HERO:bureaucrat ;\n",
            "\tHERO:occupation HERO:murderer ;\n",
            "\tHERO:occupation HERO:advisor ;\n",
            "\tHERO:occupation HERO:procurer ;\n",
            "\tHERO:title HERO:Lord ;\n",
            "\tHERO:place_of_death HERO:Winterfell ;\n",
            "\tHERO:cause_of_death HERO:cut ;\n",
            ".\n",
            "\n",
            "#### Robert_Baratheon ####\n",
            ":Robert_Baratheon a HERO:Main_Character;\n",
            "\tHERO:father HERO:Steffon_Baratheon ;\n",
            "\tHERO:mother HERO:Cassana_Baratheon ;\n",
            "\tHERO:sibling HERO:Stannis_Baratheon ;\n",
            "\tHERO:sibling HERO:Renly_Baratheon ;\n",
            "\tHERO:child HERO:Edric_Storm ;\n",
            "\tHERO:child HERO:Gendry ;\n",
            "\tHERO:spouse HERO:Cersei_Lannister ;\n",
            "\tHERO:family HERO:House_Baratheon ;\n",
            "\tHERO:occupation HERO:ruler ;\n",
            "\tHERO:occupation HERO:rebel ;\n",
            "\tHERO:occupation HERO:military_leader ;\n",
            "\tHERO:occupation HERO:hunter ;\n",
            "\tHERO:occupation HERO:revolutionary ;\n",
            "\tHERO:title HERO:Lord ;\n",
            "\tHERO:title HERO:King_of_the_Andals_the_Rhoynar_and_the_First_Men ;\n",
            "\tHERO:place_of_death HERO:Kings_Landing ;\n",
            "\tHERO:cause_of_death HERO:injury ;\n",
            "\tHERO:cause_of_death HERO:hunting_accident ;\n",
            "\tHERO:battles_participated HERO:War_of_the_Usurper ;\n",
            "\tHERO:battles_participated HERO:Greyjoy_Rebellion ;\n",
            ".\n",
            "\n",
            "HERO:Aerys_II_Targaryen a HERO:Character .\n",
            "HERO:Myrcella_Baratheon a HERO:Character .\n",
            "HERO:squire a HERO:Occupation .\n",
            "HERO:hunter a HERO:Occupation .\n",
            "HERO:revolutionary a HERO:Occupation .\n",
            "HERO:Lord a HERO:Title .\n",
            "HERO:warrior a HERO:Occupation .\n",
            "HERO:Lancel_Lannister a HERO:Character .\n",
            "HERO:hunting_accident a HERO:Cause_of_death .\n",
            "HERO:Red_Keep a HERO:Place .\n",
            "HERO:Euron_Greyjoy a HERO:Character .\n",
            "HERO:Gendry a HERO:Character .\n",
            "HERO:official a HERO:Occupation .\n",
            "HERO:rebel a HERO:Occupation .\n",
            "HERO:Daario_Naharis a HERO:Character .\n",
            "HERO:Willas_Tyrell a HERO:Character .\n",
            "HERO:advisor a HERO:Occupation .\n",
            "HERO:Rhaenys_Targaryen_daughter_of_Rhaegar a HERO:Character .\n",
            "HERO:Catelyn_Stark a HERO:Character .\n",
            "HERO:Robert_Arryn a HERO:Character .\n",
            "HERO:Aegon_Targaryen a HERO:Character .\n",
            "HERO:King_of_the_Andals_the_Rhoynar_and_the_First_Men a HERO:Title .\n",
            "HERO:Robb_Stark a HERO:Character .\n",
            "HERO:stabbing a HERO:Cause_of_death .\n",
            "HERO:fictional_vigilante a HERO:Occupation .\n",
            "HERO:regent a HERO:Occupation .\n",
            "HERO:fictional_king a HERO:Title .\n",
            "HERO:House_Lannister a HERO:House .\n",
            "HERO:Renly_Baratheon a HERO:Character .\n",
            "HERO:Kings_Landing a HERO:Place .\n",
            "HERO:Winterfell a HERO:Place .\n",
            "HERO:Joanna_Lannister a HERO:Character .\n",
            "HERO:soldier a HERO:Occupation .\n",
            "HERO:Cassana_Baratheon a HERO:Character .\n",
            "HERO:Jaime_Lannister a HERO:Character .\n",
            "HERO:Rickon_Stark a HERO:Character .\n",
            "HERO:Battle_of_Castle_Black a HERO:Battle .\n",
            "HERO:Battle_of_the_Bastards a HERO:Battle .\n",
            "HERO:Castle_Black a HERO:Place .\n",
            "HERO:fictional_queen a HERO:Title .\n",
            "HERO:murderer a HERO:Occupation .\n",
            "HERO:Khal_Drogo a HERO:Character .\n",
            "HERO:queen_regnant a HERO:Title .\n",
            "HERO:prophet a HERO:Occupation .\n",
            "HERO:ruler a HERO:Occupation .\n",
            "HERO:Loras_Tyrell a HERO:Character .\n",
            "HERO:assassin a HERO:Occupation .\n",
            "HERO:bureaucrat a HERO:Occupation .\n",
            "HERO:spy a HERO:Occupation .\n",
            "HERO:Lyanna_Stark a HERO:Character .\n",
            "HERO:Rickard_Stark a HERO:Character .\n",
            "HERO:Ygritte a HERO:Character .\n",
            "HERO:Tywin_Lannister a HERO:Character .\n",
            "HERO:Benjen_Stark a HERO:Character .\n",
            "HERO:Lyarra_Stark a HERO:Character .\n",
            "HERO:Battle_of_the_Blackwater a HERO:Battle .\n",
            "HERO:House_Targaryen a HERO:House .\n",
            "HERO:Great_Sept_of_Baelor a HERO:Place .\n",
            "HERO:cut a HERO:Cause_of_death .\n",
            "HERO:Mace_Tyrell a HERO:Character .\n",
            "HERO:Rhaegar_Targaryen a HERO:Character .\n",
            "HERO:queen_consort a HERO:Title .\n",
            "HERO:injury a HERO:Cause_of_death .\n",
            "HERO:House_Tyrell a HERO:House .\n",
            "HERO:Edric_Storm a HERO:Character .\n",
            "HERO:Viserys_Targaryen a HERO:Character .\n",
            "HERO:War_of_the_Usurper a HERO:Battle .\n",
            "HERO:Stannis_Baratheon a HERO:Character .\n",
            "HERO:military_leader a HERO:Occupation .\n",
            "HERO:House_Baratheon a HERO:House .\n",
            "HERO:Tommen_Baratheon a HERO:Character .\n",
            "HERO:mass_murderer a HERO:Occupation .\n",
            "HERO:Greyjoy_Rebellion a HERO:Battle .\n",
            "HERO:Shae a HERO:Character .\n",
            "HERO:Joffrey_Baratheon a HERO:Character .\n",
            "HERO:procurer a HERO:Occupation .\n",
            "HERO:Garlan_Tyrell a HERO:Character .\n",
            "HERO:fictional_princess a HERO:Title .\n",
            "HERO:Sansa_Stark a HERO:Character .\n",
            "HERO:explosion a HERO:Cause_of_death .\n",
            "HERO:Alerie_Tyrell a HERO:Character .\n",
            "HERO:Lysa_Arryn a HERO:Character .\n",
            "HERO:Rhaella_Targaryen a HERO:Character .\n",
            "HERO:House_Stark a HERO:House .\n",
            "HERO:Steffon_Baratheon a HERO:Character .\n",
            "HERO:Eddard_Stark a HERO:Character .\n",
            "HERO:Hizdahr_zo_Loraq a HERO:Character .\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analyzing the relations"
      ],
      "metadata": {
        "id": "ESnNdQTPGGrz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdflib\n",
        "\n",
        "from rdflib import Graph, ConjunctiveGraph, Literal, BNode, Namespace, RDF, URIRef, RDFS\n",
        "from rdflib.namespace import DC, FOAF\n",
        "\n",
        "import pprint"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oowvk493Dl2",
        "outputId": "a24e20fe-83d0-4e04-e013-898c3af0f2e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rdflib in /usr/local/lib/python3.7/dist-packages (6.1.1)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from rdflib) (3.0.7)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from rdflib) (4.11.3)\n",
            "Requirement already satisfied: isodate in /usr/local/lib/python3.7/dist-packages (from rdflib) (0.6.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from rdflib) (57.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->rdflib) (3.10.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->rdflib) (3.7.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from isodate->rdflib) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "got = Graph()\n",
        "got_instance = Graph()\n",
        "\n",
        "results = got.parse(\"got_prop_class.ttl\") + got_instance.parse(\"got_instances.ttl\")\n",
        "print(results.serialize(format = \"nt\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "sKxscRZEJwXV",
        "outputId": "a898ebc2-3b90-407f-ac84-ea9ed00bfa50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-e6b8c178a20e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mgot_instance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGraph\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"got_prop_class.ttl\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgot_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"got_instances.ttl\"\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      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nt\"\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/usr/local/lib/python3.7/dist-packages/rdflib/graph.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, source, publicID, format, location, file, data, **args)\u001b[0m\n\u001b[1;32m   1238\u001b[0m             \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1239\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1240\u001b[0;31m             \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformat\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   1241\u001b[0m         )\n\u001b[1;32m   1242\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/rdflib/parser.py\u001b[0m in \u001b[0;36mcreate_input_source\u001b[0;34m(source, publicID, location, file, data, format)\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m             \u001b[0minput_source\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_source\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m             \u001b[0mlocation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocation\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    331\u001b[0m         )\n\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/rdflib/parser.py\u001b[0m in \u001b[0;36m_create_input_source_from_location\u001b[0;34m(file, format, input_source, location)\u001b[0m\n\u001b[1;32m    371\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mabsolute_location\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file:///\"\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    372\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murl2pathname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsolute_location\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file:///\"\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m         \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\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    374\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0minput_source\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mURLInputSource\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsolute_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/got_prop_class.ttl'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Community Detection with Networkx"
      ],
      "metadata": {
        "id": "rKf0kPWB9pRd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using SPARQL, we query the generated RDF network and extract the total number of relations between each character, using the query below. Then using the output (downloaded as .csv), we create a NetworkX graph with weights. \n",
        "\n",
        "The NetworkX package provides methods for community detection. Using these we can identify the communities within our graph and return related characters from within a community. The visualization of the communities are also below."
      ],
      "metadata": {
        "id": "Te-HKWEJGNbO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The SPARQL query below returns the number of edges between two characters.\n",
        "# The output is used to generate a weighted NetworkX graph, and to detect communities.\n",
        "\n",
        "# The query is run on GraphDB and the output downloaded as .csv is used here.\n",
        "\n",
        "\"\"\"\n",
        "PREFIX : <http://visualdataweb.org/GoT/>\n",
        "PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
        "SELECT ?char1 ?char2 (COUNT(?char1) as ?valueSum)\n",
        "WHERE\n",
        "{\n",
        "  ?char1 ?p ?char2 .\n",
        "  ?char1 a :Character .\n",
        "  ?char2 a :Character .\n",
        "}\n",
        "GROUP BY ?char1 ?char2\n",
        "\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "dr-m7AqGaYLI",
        "outputId": "52de5280-4843-400a-f836-75b2be38907d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nPREFIX : <http://visualdataweb.org/GoT/>\\nPREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\\nSELECT ?char1 ?char2 (COUNT(?char1) as ?valueSum)\\nWHERE\\n{\\n  ?char1 ?p ?char2 .\\n  ?char1 a :Character .\\n  ?char2 a :Character .\\n}\\nGROUP BY ?char1 ?char2\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"query-result.csv\", sep = \",\")\n",
        "df['char1'] = [i[-1] for i in df['char1'].str.split(\"/\")]\n",
        "df['char2'] = [i[-1] for i in df['char2'].str.split(\"/\")]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HlAZROSV_jT1",
        "outputId": "70b578c3-e4dd-48bc-f034-481a946cacf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              char1                                  char2  valueSum\n",
              "0          Jon_Snow                      Rhaegar_Targaryen         1\n",
              "1          Jon_Snow                           Lyanna_Stark         1\n",
              "2          Jon_Snow  Rhaenys_Targaryen_daughter_of_Rhaegar         1\n",
              "3          Jon_Snow                        Aegon_Targaryen         1\n",
              "4          Jon_Snow                     Daenerys_Targaryen         2\n",
              "..              ...                                    ...       ...\n",
              "59  Margaery_Tyrell                          Willas_Tyrell         1\n",
              "60  Margaery_Tyrell                          Garlan_Tyrell         1\n",
              "61  Margaery_Tyrell                      Joffrey_Baratheon         1\n",
              "62  Margaery_Tyrell                       Tommen_Baratheon         1\n",
              "63  Margaery_Tyrell                        Renly_Baratheon         1\n",
              "\n",
              "[64 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-147d47d9-8510-449d-adea-f0e4c7eed377\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>char1</th>\n",
              "      <th>char2</th>\n",
              "      <th>valueSum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jon_Snow</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>Jon_Snow</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jon_Snow</td>\n",
              "      <td>Rhaenys_Targaryen_daughter_of_Rhaegar</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Jon_Snow</td>\n",
              "      <td>Aegon_Targaryen</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jon_Snow</td>\n",
              "      <td>Daenerys_Targaryen</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Margaery_Tyrell</td>\n",
              "      <td>Willas_Tyrell</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Margaery_Tyrell</td>\n",
              "      <td>Garlan_Tyrell</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Margaery_Tyrell</td>\n",
              "      <td>Joffrey_Baratheon</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Margaery_Tyrell</td>\n",
              "      <td>Tommen_Baratheon</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Margaery_Tyrell</td>\n",
              "      <td>Renly_Baratheon</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-147d47d9-8510-449d-adea-f0e4c7eed377')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-147d47d9-8510-449d-adea-f0e4c7eed377');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "\n",
        "tuples_lst = [(x[1]['char1'], x[1]['char2'],x[1]['valueSum']) for x in df.iterrows()]\n",
        "\n",
        "G = nx.Graph()\n",
        "G.add_weighted_edges_from(tuples_lst)\n",
        "G"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdtL3IfN9ozr",
        "outputId": "2cc2c151-0191-42e7-fdae-e9d2478cfbef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<networkx.classes.graph.Graph at 0x7ff5e5486250>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "\n",
        "tuples_lst = [(x[1]['char1'].split(\"_\")[0], x[1]['char2'].split(\"_\")[0], {'weight': x[1]['valueSum']}) for x in df.iterrows()]\n",
        "\n",
        "G = nx.Graph(tuples_lst)\n",
        "#G.add_weighted_edges_from()\n",
        "G"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsWh_EIdBSyk",
        "outputId": "b29d3c43-5084-4e56-d2af-bdf1973835c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<networkx.classes.graph.Graph at 0x7ff5e5485d10>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
        "\n",
        "\n",
        "pos=nx.spring_layout(G) # pos = nx.nx_agraph.graphviz_layout(G)\n",
        "nx.draw_networkx(G,pos)\n",
        "labels = nx.get_edge_attributes(G,'weight')\n",
        "nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NMPj7tZ9A3Ju",
        "outputId": "d0b99cac-a917-4131-ddac-994daea2a50e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('Arya', 'Bran'): Text(0.42309577864649606, 0.012972062420625267, '1'),\n",
              " ('Arya', 'Catelyn'): Text(0.4952498014516012, 0.0257693699761425, '1'),\n",
              " ('Arya', 'Rickon'): Text(0.48421617290735747, 0.062483906794596214, '1'),\n",
              " ('Arya', 'Robb'): Text(0.44854234967675155, 0.06732337950929589, '1'),\n",
              " ('Arya', 'Sansa'): Text(0.3425721875163339, 0.09650554155750708, '1'),\n",
              " ('Bran', 'Rickon'): Text(0.4678861505584393, 0.06662360238989032, '1'),\n",
              " ('Bran', 'Robb'): Text(0.4322123273278333, 0.07146307510458999, '1'),\n",
              " ('Catelyn', 'Bran'): Text(0.478919779102683, 0.0299090655714366, '1'),\n",
              " ('Cersei', 'Euron'): Text(-0.27356947789161634, 0.36350773503789546, '1'),\n",
              " ('Cersei', 'Jaime'): Text(-0.23411221990222855, 0.43035086803528705, '2'),\n",
              " ('Cersei', 'Joanna'): Text(-0.20750319786576285, 0.47741698390327436, '1'),\n",
              " ('Cersei', 'Joffrey'): Text(-0.42135920312034786, 0.4280834513310884, '1'),\n",
              " ('Cersei', 'Lancel'): Text(-0.29529290444798245, 0.3379928557032712, '1'),\n",
              " ('Cersei', 'Myrcella'): Text(-0.250586649684659, 0.39186098461767804, '1'),\n",
              " ('Cersei', 'Robert'): Text(-0.3646258705897645, 0.5861883535898236, '1'),\n",
              " ('Cersei', 'Tommen'): Text(-0.4169924932812585, 0.3983994641962089, '1'),\n",
              " ('Cersei', 'Tyrion'): Text(-0.16008717875131623, 0.41882492852707953, '1'),\n",
              " ('Cersei', 'Tywin'): Text(-0.23327404056363799, 0.459224917544726, '1'),\n",
              " ('Daenerys', 'Aerys'): Text(0.5962348552742593, -0.7870077880683433, '1'),\n",
              " ('Daenerys', 'Daario'): Text(0.6713754330812579, -0.9073415947671952, '1'),\n",
              " ('Daenerys', 'Hizdahr'): Text(0.7299847057536908, -0.8298722587113089, '1'),\n",
              " ('Daenerys', 'Khal'): Text(0.7130302113036442, -0.8794478939761039, '1'),\n",
              " ('Daenerys', 'Rhaella'): Text(0.6094764841285489, -0.7424953679128088, '1'),\n",
              " ('Daenerys', 'Viserys'): Text(0.5829668004024768, -0.8208337583788246, '1'),\n",
              " ('Eddard', 'Arya'): Text(0.4353284176815068, -0.12664587988970868, '1'),\n",
              " ('Eddard', 'Bran'): Text(0.41899839533258865, -0.1225061842944146, '1'),\n",
              " ('Joanna', 'Tyrion'): Text(-0.07046286560851871, 0.45619854099330404, '1'),\n",
              " ('Joffrey', 'Margaery'): Text(-0.6538720849649156, 0.3895431560932546, '1'),\n",
              " ('Jon', 'Aegon'): Text(0.36277783009375136, -0.6791920727528596, '1'),\n",
              " ('Jon', 'Aerys'): Text(0.49050986798465723, -0.7095389299344567, '1'),\n",
              " ('Jon', 'Benjen'): Text(0.3541507980010077, -0.6333055860595846, '1'),\n",
              " ('Jon', 'Daenerys'): Text(0.5348346522559146, -0.7372143314005037, '1'),\n",
              " ('Jon', 'Eddard'): Text(0.43017034966695594, -0.46093479993568287, '1'),\n",
              " ('Jon', 'Lyanna'): Text(0.4064744288946065, -0.7219577510540238, '1'),\n",
              " ('Jon', 'Lyarra'): Text(0.3719850797209394, -0.7495457655238884, '1'),\n",
              " ('Jon', 'Rhaegar'): Text(0.5254975336411047, -0.682581316584687, '1'),\n",
              " ('Jon', 'Rhaella'): Text(0.5037514968389467, -0.6650265097789223, '1'),\n",
              " ('Jon', 'Rhaenys'): Text(0.40438905251148305, -0.5944149594631364, '1'),\n",
              " ('Jon', 'Rickard'): Text(0.42226965312572406, -0.7635594530943883, '1'),\n",
              " ('Jon', 'Viserys'): Text(0.4772418131128747, -0.743364900244938, '1'),\n",
              " ('Jon', 'Ygritte'): Text(0.34068712266159057, -0.7118671837265453, '1'),\n",
              " ('Margaery', 'Alerie'): Text(-0.835181207341664, 0.41725983113347, '1'),\n",
              " ('Margaery', 'Garlan'): Text(-0.8345738466937411, 0.2999440995076706, '1'),\n",
              " ('Margaery', 'Loras'): Text(-0.7938660569027058, 0.2840163024885414, '1'),\n",
              " ('Margaery', 'Mace'): Text(-0.8516780400767232, 0.3364842592907502, '1'),\n",
              " ('Margaery', 'Willas'): Text(-0.8567243448640413, 0.37798314483650197, '1'),\n",
              " ('Renly', 'Margaery'): Text(-0.6890274018838116, 0.46173877089184856, '1'),\n",
              " ('Rhaegar', 'Daenerys'): Text(0.631222520930707, -0.7600501747185735, '1'),\n",
              " ('Robert', 'Cassana'): Text(-0.4363328276323224, 0.8267188939864047, '1'),\n",
              " ('Robert', 'Edric'): Text(-0.4772681309892725, 0.8182794187486359, '1'),\n",
              " ('Robert', 'Gendry'): Text(-0.35669298614572453, 0.7792699647848429, '1'),\n",
              " ('Robert', 'Renly'): Text(-0.524012879620448, 0.6464240482824561, '1'),\n",
              " ('Robert', 'Stannis'): Text(-0.39572620390376634, 0.8161714398420583, '1'),\n",
              " ('Robert', 'Steffon'): Text(-0.506000062592344, 0.7856173330023921, '1'),\n",
              " ('Sansa', 'Bran'): Text(0.3262421651674157, 0.10064523715280119, '1'),\n",
              " ('Sansa', 'Tyrion'): Text(0.11133586377159073, 0.2908926009533961, '1'),\n",
              " ('Tommen', 'Margaery'): Text(-0.6495053751258262, 0.3598591689583751, '1'),\n",
              " ('Tyrion', 'Jaime'): Text(-0.09707188764498441, 0.4091324251253168, '1'),\n",
              " ('Tyrion', 'Shae'): Text(0.0377260924277629, 0.46318286153520916, '1'),\n",
              " ('Tywin', 'Tyrion'): Text(-0.09623370830639383, 0.4380064746347557, '1')}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ],
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WO/2Tp6enduzYIYPBoN69e8vHx0dt27bVxYsXM22fVqGmVLqKDAajnMtWlVvNx5R4+lCGNiUfeUFGJxc5l7tXzmWrKvnS7+n3XH1ryuxXX8cu3VC3bt20f//+TMdxcnLS8ePHFR0dLXd391yfTAUAAAA4KoIaAMWOf3lPuZiNcvNvrOvha2WNjVZqYpxid3+Z7T5czUa1fLaLpkyZooiICNlsNh0/flynTp0qwMr/5u3uoj6P3afpnR6UtoXprccrqs9j98nb/fZ7yORUQECAFi9erKioKB06dEjnzp3TwIEDM2174vAvurDsLZ2Z+YJOT++o6/u+VlpCbIY2JvdS6T8bzC5KS0m85V5sYopKlCihxMREWa3WW8ZZuHChjh07Jn9/fzVo0EDr1q3Lj1cFAAAAHAabCQMoFtq0aSOT6ebJSTZJBt868m43QilXz+r8v/rL4FJCJR9+RomnDmSrP5ukyYNfVb1yTnrhhRd09uxZ+fn56dNPPy2wfWpux8PDQ9evXy/QMfz9/dWjRw99/PHHeuqpp265v/3jd1Tigafk0XGsDGZnXf1+3i1BTXZ4uma9GfP999+v5cuXKy0tTV999ZU6dOigK1euyM3NLcdjAQAAAI6IoAZAkXfy5Mlbrr36abi+O3JRpZv3VunmvdOve9Rvnf5z+RffveW58i++K4NBeryGj7zdXfTaa6/ptddeK5C6s8vd3T3fg5rIyEitX79enTp1kq+vr86cOaPly5crKChI5cqVU1RUlJKTk+Xs7CxJSk2+IVc3TxnMzko6d1Txv26VpepDORrT1WyUfwWPLNssXbpULVu2lI+PT/pGxkYjk0MBAABQdPC3WwDF0htNq8nVbMrVs65mk15vWi2fK8o9Dw8PxcXF5Xufe/bsUcOGDeXm5qagoCA98MADmjp1qpo1a6ZatWqpfPnyKlPm5mbLM2fN1pWtn+r0tOd1befncvN/NMdj2iR1qOebZZtvvvlGtWrVkru7u0JCQvT555/LYrHk5hUBAAAAh2Sw2Wy3vRkYGGj75xGpAFCULN19UhM3HFFCSlq2n7E4GfN07HVBaNOmjXr37q22bdvatY6/Zill8cfKbRkMUsua5RTWNTD/CwMAAAAcjMFgiLDZbJn+5ZcZNQCKra5BfgptFSCLkymrw51usqXJpFSHC2mkwtmjJjuK0iwlAAAAwF4IagAUa12D/LTi1SC1rFlOLmajXM0ZvxZdzUa5mI1qfK+XYr8ap8YV7pToFL6C2KMmN+pW8lJoK39ZnHL2R8vNWUr+eT5SHAAAACgK2EwYQLFXx9dLYV0DdSUuSSv3Riny/HXFJqbI09VJ/hU81KGer7zdXTTuQlsNHTpUX3zxhb1LzqAg9qjJrb9mG03YcESJSVYpi41+DYabM2lCW/k73CwlAAAAwF4IagDgv7zdXdTnsftue3/o0KEKCAjQli1b9PjjjxdiZVlzlKVPf+ka5KdTv+zQsn3RspatIYOkROvf+wC5mo2y6ebJWa83rcZMGgAAAOAfCGoAIJssFoumTp2qAQMGaN++fTKbHeMr1N3dXefOnbN3GelSU1P1ybRxmjdvnmoHBmc5SwkAAABARo7xrwwAuEs8++yzmjt3rsLCwtSvXz97lyPJsZY+SdKXX36psmXLqkmTJjIYDFnOUgIAAACQEZsJA0AOGAwGzZw5U+PGjVN0dLS9y5HkWEuf0tLSNHHiRIWGhspwx6O0AAAAAPwvghoAyKEHHnhAnTt31qhRo+xdiiTHOfVJktauXStnZ2c99dRT9i4FAAAAuCsR1ABALowdO1ZfffWV9u3bZ+9SHGZGjc1m04QJEzRy5Ehm0wAAAAC5RFADALlQqlQpjR8/XgMGDJDNZrNrLY6yR83GjRuVkJCgdu3a2bsUAAAA4K5FUAMAudSzZ0/Fx8drxYoVdq3DUWbU/LU3jdHIHy0AAABAbvG3aQDIJZPJpFmzZmno0KGKj4+3Wx2OsEfNtm3bdP78eXXs2NGudQAAAAB3O4IaAMiDRx55RI899pgmT55stxocYenThAkT9NZbb8lkMtm1DgAAAOBuR1ADAHn0/vvvKywsTCdOnLDL+G5uboqPj1daWppdxt+zZ4+OHj2qrl272mV8AAAAoCgx27sAAMhMdFySVkZEKfJCrGITrfJ0Ncu/vKeer+8rb3cXe5eXQcWKFTV48GANHjxYq1atKvTxTSaTLBaL4uPj5eHhUejjT5w4UcOHD5ezs3Ohjw0AAAAUNQQ1ABzK/jMxmvPDcW09dlmSlGT9e5aIq/mCpn9/TE1r+Oj1JtVUt5KXvcq8xaBBg/TAAw9o48aNevLJJwt9/L+WPxV2ULN//36Fh4friy++KNRxAQAAgKKKpU8AHMbS3SfVef5ufXfkopKsaRlCGklK/O+1jb9eVOf5u7V090n7FJoJV1dXTZs2TSEhIUpJSSn08e118tOkSZM0ePBgubq6FvrYAAAAQFFEUAPAISzdfVITNxxRQkqqbLa/r1/fu0FnPuyq01M7KDUhVolRvyoqrLeOvvuMRkxb6FBhTZs2bVSlShXNnj270Me2R1ATGRmpLVu2qE+fPoU6LgAAAFCUEdQAyHc7duxQo0aNVLJkSZUuXVqPPPKIfv75Zy1evFiPPvroLe33n4nRxA2RSkjJOIPGlmrVn5sXqFyncao8eKVMFk9d275UHvVaq/LglTLfF6SJGyJ1ICqmsF4tSwaDQTNmzNCkSZN08eLFQh3bHkd0T548WSEhIXJ3dy/UcQEAAICijKAGQL6KjY1V69at1b9/f129elVnz57V6NGj5eJy+w2A5/xwXInW1Fuup8bHyGZNlpNPlfRr1muXM/w+0ZqquT8cz9+XyAN/f391795doaGhhTpuYR/R/ccff2j9+vXq169foY0JAAAAFAcG2z/XGPyPwMBAW3h4eCGWA+BuFx4erubNmysmJuMslyNHjuihhx5SSkqKLBaLzGazYmJidPZKrGq3flmxv26XzZqiEtWDVOqJ3kq9Hq3ziwbIlpIkg5OrnCtUV2rsJVljLspgdpIMRlUKWa7UhFjFbJwrp+hj8i5dWsOHD1fv3r0lSWPGjNGvv/4qV1dX/ec//1HlypX1ySefKDAwsEA/g2vXrsnf319r1qxRgwYNCnSsv3Tu3Fnt2rVTly5dCmW81157TWXKlNGECRMKZbyCdDedMAYAAICiwWAwRNhstkz/YcKpTwDyVfXq1WUymdS9e3d17txZQUFBKlWqlAICAhQWFqYFCxZox44d6e1fei1EyVfOqsLLH8pgNCl6zQe6tnO5SjXtoXt6ztXZsJ6qNGiFDEaTJClq7ivybjVAFr8HJUnRq9+XpZyfxs/5lx71SVaLFi103333qVmzZpKkNWvW6KuvvtKiRYs0cuRI9evXT7t37y7Qz6BkyZKaNGmSBgwYoJ07d8poLPjJi4W59CkqKkpffPGFjh07VijjFZS79YQxAAAAFG0sfQKQrzw9PbVjxw4ZDAb17t1bPj4+atu2baZ7tthsNm1b+7lKNuslk8VDRpcSKtmoo+KPbM/WWNbYy0o6e0Qej3XXiavJevDBB9WrVy8tWbIkvc2jjz6qVq1ayWQyqVu3btq/f3++vWtWunfvrtTUVC1durRQxuvZs6eaNGlSKGNNmTJFr7zyisqUKVMo4xWEu/mEMQAAABRtzKgBkO8CAgK0ePFiSTdPBuratasGDhyoli1bZmh3+fJlWZMSdX7xwH9ctUlpGf/RfDupcVdldHWX0aWEYhNvHoldpUoV/XPJZvny5dN/LlGihBITE2W1WmU2F+zXn9Fo1KxZs/Tss8/qmWeekYeHR4GOFxwcXKD9/+XSpUtasmSJDh8+XCjjFYS/Txi78/9nNpuUkJKqiRuOaNv6lfp1y6oMM8IAAACA/MaMGgAFyt/fXz169NChQ4dkMBgy3CtTpozMzq66p9ccVR604r+/vlDlwSuz1bfJvbTSEuOUlnRDnq5OkqTTp0+rYsWK+f4eudGwYUO1aNGiSOzj8pfp06erS5cuqlChgr1LSff555+rYcOGcnNzU9myZdWwYUPNnTtXme3BdrsTxu4kISVNaw+cV3yyNb/KBgAAADJFUAMgX0VGRmrq1KmKioqSJJ05c0bLly9XUFCQypUrp6ioKCUnJ0u6OeukcZtOurZ5gVLjb24+bL0erYTfI7I1ltnTRy4V/XV9+xLdV9pZBw4c0MKFC9W1a9eCeblcmDx5shYuXHjX7+ciSVevXtW8efM0bNgwe5eSburUqQoJCdHQoUN14cIFXbx4UWFhYdq5c2f6/2f/dLsTxrIjJTVNF64lZnrPaiXAAQAAQP5g6ROAfOXh4aE9e/Zo2rRpiomJkZeXl1q3bq0PPvhArq6uqlWrlsqXLy+j0ajo6GgtCZupuu1e1YUlg5WaECuTh7c8Hmoly731szVembbDFLNxjkZ1elSlS5XS2LFj1bx58wJ+y+yrUKGCRowYoUGDBmn9+vX2LidPZs2apfbt26tKlSp3blwIrl27pnfeeUdLlizRc889l379oYce0meffSZJSkpKUmhoqL744gslJiYpuVJ9eT7eS0YnFyWeOqDodVPlGdhO1/b8WwaDUV5NXpJ7nRaSpNSEWF1ZP0OJpw/KydtXlqr1FH8jRVfikuTt7iKDwaDZs2drxowZslqtatWqlVxdXTV16tT0Wtq2bavHH39cgwYNKtwPBwAAAHctjucGYHevfhqu745cVBZfR7dlkNSyVjmFdS3YI7fzIjk5WREREWrQoEGB7Y1js9luWVomSQkJCbp27VqGvXpyIyEhQffff7+2bNmi+++/P0995ZdvvvlGrVu3VmJi4m0/10GDBunEiRNavHixluw5o5Ehr8rkXVmlmvZQ4qkDuvj5SJUM7qiSj3RW4sl9uvyfd1Wx3ycyubrr8ur3JJtN3q0Gynrtgi6teEfOXuU1/bM16vPYfTIYDGrevLlWrFghi8WigwcPqn379oqKikoPIitXrqw//vhD5cqVK+RPBwAAAI4sq+O5WfoEwO7eaFpNrmZTrp5NS0lSpetHMt2PxFE4OzurYcOGWYY0f/75Z57GePbZZzP9DE6ePKk2bdooJSUlT/2bTCZFRkY6TEgjSdHR0Tf3OfrH59qoUSN5eXnJYrFo69atmjdvnqZPn67SpUvrZGyaPIKez3CqmMFkVslHu8hgMstyXwMZnF1lvRIlW1qqbhz9UV6NX5TR2VXOPn5yq/2E0iRFnv/7GPS33npLpUuXlsVi0cMPP6ySJUtq06ZNkm7undO0aVNCGgAAAOQIS58A2F3dSl4KbeWf7ZN4/mJxMqp7/XJa+W6IfvhqiebNm6eqVasWYKW5ZzTePhf//fffFRISovbt26tnz5656j8iIkLfffedXFxcdO3aNcXFxen69etKSEjQL7/8ovj4eHl5eeW2fDk7O8vZ2TnXzxcEb29vRUdHZzjF68cff5Qk+fr66uLFi7px44bq17+5jC4hOVUpaWkZThUzWjxkMP4dEhrMLkpLSVTajVgpLVUmD5/0e2bPskrS4fQTxiSpUqVKGWrq3r27li5dqhYtWmjp0qUKCQnJ/xcHAABAkUZQA8AhdA3ykyRN3BCpRGtqlsugDAbJ1WxSaCt/dQ3y05D2uzV9+nQ9/PDDCg0NVf/+/WUy5W6Gjj3ce++9atq0qUaNGqXu3bvnanlUWlqa3nzzTfn4+MjJyUkWi0Vubm7y8PDQ22+/LRcXlwKo3L6Cg4Pl4uKi1atXZ9ij5i9lypSRxWLR4cOHVbFiRQ1csU+rfjmXrb6NJTwlo0mp1y/L6H0zjLHGXpak9BPGJN2y3Kxr16564IEHtH//fh05ckTt27fP7esBAACgmCKoAeAwugb5qY6vl+b+cFxbjl6WQVKi9e/ZD65mo2ySHq/ho9ebVlMd35szRMxms4YOHar27durd+/e+vzzz7Vw4ULVqlXLPi+SDWlpaTIajYqKitLUqVO1detWhYSE5HoPm7Jly2r9+vUOdWx2QfPy8tLo0aP1+uuvy2azqWXLlnJzc9OBAwcUHx8vo9Go3r17a9CgQZo9e7b8y3vKeOOQ4i/8ccfNqg1Gk0pUb6SYHY+dMioAACAASURBVMvk3SpE1muXFH9ok5xLlpN/BY/bPufr66sGDRqoW7dueu6552SxWPL7tQEAAFDEEdQAcCh1fL0U1jVQV+KStHJvlCLPX1dsYoo8XZ3kX8FDHer5yts989kh999/vzZv3qz58+eradOm6t+/v0aMGOEwS3bS0tI0ZcoU9evXTyVKlNCsWbO0ePFi1a1bV+vWrdM999yT677HjBmTHvKkpt56/HROZxilpqbKaDRmukGxIxk2bJgqVqyo999/Xy+99JLc3Nx077336r333lOjRo0UFBSkcePGKSgoSJejo5XkXFLu2TxVrPSTr+nK+hmKmtVNTt6+cq/dXEmnD6pDPd8sn+vevbu6deummTNn5tdrAgAAoBjh1CcARVJUVJT69u2rkydPauHChXr44YftXZIk6c0339SePXtUtmzZ9OOlmzZtKulmkGMwGOwajqxevVqtWrWSk9PN5T13S2BzJwkJCZo+fbrmHkiR2a+eZMj5XvoGg9Sy5p1PGNu2bZu6du2qU6dO3fWfGwAAAAoGpz4BKHZ8fX21Zs0ahYaGqm3btho8eLDi4+PtXZbee+89HT9+XJUrV9bmzZszhDT2DkSWLl2q5557TkFBQZo4caLi4+NlMpnSa4qNjbVbbbmVlpampUuXyt/fXxEREQoLeVYWZ6c7P5gJV7NJrzetlmWblJQUzZw5U7169SKkAQAAQK4Q1AAosgwGgzp37qxDhw7p4sWLqlOnjjZv3mzXmpycnDRu3DglJydL+nuZUlanQmXX0aNH0/vNjbVr12rWrFlasGCBDh06pEaNGik0NDS9zylTpuT5mO/CtG3bNjVs2FAffvihli5dqn//+99qHVxboa38ZXHK2edtcTIqtJV/+r5ImTly5Ii8vLx0/vx5DRw4MK/lAwAAoJhi6ROAYmPDhg3q27evWrRooSlTpuTpuOq8ioiIUJ06ddKXGOWH++67T99++62qVct61sftREdH6+rVq6pevbokadeuXQoLC1NkZKQuXryoxx9/XIsWLcq3egvKb7/9pmHDhmnv3r2aPHmyOnfufEsQtnT3yVydMAYAAADkB5Y+AYCkVq1a6eDBg3JxcVGtWrW0atUqu9VSv3799JAmLS3tDq2zx8PDQ9evX8/182XKlEkPaaSbx19/8sknmjJlii5duqRJkyblR5kF5sqVKwoJCVFwcLAaNmyoyMhIvfDCC5nOVuoa5KcVrwapZc1ycjEb5WrO2MbVbJSL2aiWNctpxatBhDQAAAAoNJz6BKBY8fT01Jw5c9S5c2f16tVLy5Yt06xZs1SuXDm71JOSkqJff/1VderUyfOeJu7u7nkKam5n79696ty5s8Me/Z2UlKQ5c+Zo8uTJev755/Xrr7+qbNmyd3wuLyeMAQAAAAWFoAZAsdS4cWPt379f48aNU506ddKPdy7sDWANBoNeeOEFTZw4Ue3bt89TXx4eHoqLi8tTHzabTUlJSXJycko/0rtXr16ZHvltbzabTf/+9781fPhwBQQEaNu2bQoICMhxP97uLurz2H0FUCEAAACQcyx9AlBsubq6atKkSfrmm280c+ZMPfXUUzp58mSh1mA2mzVz5ky9+eabSkxMzFNfeV36JEmbNm3SE088kWG5kJubmzw9PfPUb37bs2ePGjdurAkTJmjevHlat25drkIaAAAAwNEQ1AAo9h566CHt2bNHzZo1U4MGDTRr1qxCnUHSvHlzPfjgg5o6dWqe+smPoGbChAnq27evwx4tffLkSXXu3FnPPvusevbsqYiICD3xxBP2LgsAAADINwQ1AKCbx2YPHz5cO3bs0JdffqnGjRvryJEjhTb+1KlTNW3aNJ05cybXfeR1j5odO3bozJkz6ty5c677KCjXrl3T8OHDVb9+fQUEBOjYsWN6+eWX05dnAQAAAEUFQQ0A/EONGjX0ww8/qFu3bnrsscc0YcIEJScn37Z9ampqviyXqlq1qt544w0NGzYs133kdY+aiRMnasSIETKbHWf7spSUFM2ZM0c1atRQdHS0Dh48qNGjR8vNzc3epQEAAAAFgqAGAP6H0WhU3759tXfvXu3atUufffbZbZdCff/992rVqpWGDh2a53FHjBihnTt3avv27bl6Pi9Ln8LDw3Xo0CG99NJLuXo+v9lsNq1du1a1a9fWqlWr9O2332rhwoW655577F0aAAAAUKAIagDgNipVqqR169apW7dut11i07RpU40ePVqLFi3ShQsX8jReiRIl9MEHH6h///652iMnL0ufJk6cqGHDhsnFxf7HUe/bt09PPPGEhg8frmnTpmnjxo2qW7euvcsCAAAACgVBDQBkwWAwZLkUyGq1avny5Ro+fLjKly+vtLS0PI3XsWNHlSxZUvPnz8/xs7ld+nTw4EHt3r1bvXr1yvGzOWGz2bK8f/bsWfXo0UNPP/20OnbsqAMHDqhVq1YOu7ExAAAAUBAIagAgD8LCwhQfH5++9CmvoYLBYNCHH36o0aNH6+rVqzl6NrdLnyZPnqxBgwbJYrHk+NmcuHHjhiTdMlvIZrPps88+U506dVShQgUdO3ZMr732mkPtlQMAAAAUFoIaAMilvXv36pNPPtF7770n6WYAkR+zP+rWrasOHTpo9OjROXouN0HNsWPH9N1336lv3745ei4nPv/8c/n6+qaP8b/LyAwGg55++mnt3btXkydPlqenZ4HVAgAAADg6ghoAyAGbzaYNGzZIkqZPn642bdqoXr16stls+XpU9Lhx47RixQodPHgw28/kZo+ad999V/3795eHh0dOS8wWq9WqNWvWaOTIkfrtt9+0Y8cOSbfOqildurSqVKlSIDUAAAAAdxPmlQNADsTFxWn27Nnq1auXSpUqpU8//VTSzQDHYDCk/zevvL29NWbMGA0YMECbN2/OVp853aPm1KlTWr16tY4fP56XUrNkNps1Y8YMlS1bVomJiZo4caK+/vrrfA21AAAAgKKEGTUAkAMeHh7asGGDQkNDdebMGa1atUo2m01G482v06tXr+rMmTP5Mtarr76qq1evauXKldmuLSczat5//3316dNHpUqVym2J2VK2bFlJ0iuvvKL4+HitWLFC0q2zagAAAAAQ1ABArrzxxhs6cuSI3N3d06/ZbDZ9/fXXqlevnubMmZPnE6DMZrM+/PBDDRkyJH0j3qzkZOnTuXPntHz5cg0aNChPNeaEp6en+vbtq+nTp0u6uVdNSkpKoY0PAAAA3A0MWR2XGhgYaAsPDy/EcgDg7hcZGamePXvKaDRq/vz58vf3z1N/nTp1UkBAgMaMGZNlu5SUFFksFqWkpNxxqdTgwYOVlpaWHprkN5vNlmGmkSSlpaXJaDSqa9eu8vb2louLi9q1a6dHHnmkQGoAAAAAHJXBYIiw2WyBmd1jRg0A5DN/f39t375dnTp10qOPPqpJkyblaebIBx98oFmzZunkyZNZtnNycpLZbFZiYmKW7S5fvqxFixZpyJAhua4pK1u3blX79u2VnJyc4brRaFRSUpIuXbqkxYsXy93dnZAGAAAA+B8ENQBQAIxGo/r166eIiAht375dDRo00N69e3PVV+XKlTVw4MBsBSvZWf40Y8YMderUSRUrVsxVPbdz7NgxPfPMM+revbs6d+4sFxeXW9rMmzdP1atXV1RUlN555518HR8AAAAoCghqAKAAValSRRs2bNDgwYP19NNPa/jw4UpISMhxP0OGDFFERIQ2b96cZbs7bSgcExOjjz/+WMOGDctxDbdz5coVhYSEqFGjRgoKClJkZKS6dOmS6fKrN954Q7Nnzy6w48ABAACAux1BDQAUMIPBoG7duunAgQM6deqU6tatq61bt+aoD4vFomnTpmnAgAGyWq23bXenI7pnz56t1q1bq2rVqjkaPzNJSUmaOnWq/P39ZbVadeTIEQ0fPlyurq63feafe9YAAAAAuBV/YwaAQlKuXDl9/vnn+uCDD/Tiiy+qb9++io2Nzfbz7du3V/ny5fXRRx/dtk1WM2ri4uL04Ycf6q233spx7f9ks9n05ZdfqmbNmvrhhx+0bds2zZkzRz4+PnnqFwAAAABBDQAUunbt2unQoUNKTU3VAw88oHXr1mXrOYPBoJkzZ2rcuHG6fPlypm2y2qMmLCxMzZo1U40aNXJd++7du9M3SJ4/f77Wrl2rgICAXPcHAAAAICOCGgCwAy8vL82bN0+LFy9WSEiIXnjhhduGL/9Uq1Ytvfjiixo5cmSm92+39CkhIUHTpk3T22+/nat6//jjD3Xu3FkdOnRQ7969FR4ermbNmuWqLwAAAAC3R1ADAHbUrFkzHTx4UBUrVlTt2rW1bNky2Wy2LJ8ZM2aMVq9erX379t1y73ZLn/71r3+pQYMGqlOnTo7qi4mJ0bBhwxQYGKiaNWvq6NGj6tGjh0wmU476AQAAAJA9BDUAYGclSpTQBx98oLVr1+rdd99VmzZtdObMmdu29/Ly0vjx49W/f/9bQp3Mlj4lJyfrvffeU2hoaLZrSklJ0ezZs1WjRg1dvXpVhw4d0jvvvCM3N7ecvRwAAACAHCGoAQAH0aBBA4WHh6thw4aqV6+ePvroI6WlpWXa9pVXXlFCQoKWL1+e4XpmS58+/fRTBQQE6OGHH75jDTabTWvWrFHt2rW1Zs0afffdd1qwYIEqVKiQ+xcDAAAAkG2GrKbYBwYG2sLDwwuxHACAJP3666/q2bOnnJyctGDBAlWvXv2WNjt37lSnTp0UGRmpRDlpZUSUVn6/SzdS0vTwQ7XlX95TzzxYQY/Ur6NFixapcePGWY65b98+DR48WBcvXtSUKVP01FNPyWAwFNQrAgAAAMWWwWCIsNlsgZneI6gBAMeUmpqqOXPmaNy4cRo6dKgGDx4ss9mcoU37V0IUXa6+LptvHo2dZP17Bo6r2Shraqqcon/T56NeVt1KXpmOc/bsWYWGhurbb7/VmDFj1LNnz1vGAQAAAJB/sgpqWPoEAA7KZDJpwIABCg8P16ZNm/Twww9n2EB46e6TOlKxpc6klVKSNS1DSCNJidY0WW0GJXpXV+f5u7V098kM9+Pi4vTOO++oTp06uueee3T06FH16dOHkAYAAACwI4IaAHBwfn5++vbbbxUSEqKWLVvq7bff1qIdxzVxwxElWW06G9ZLCSd/ue3zNkkJKamauOGIlu4+qdTU1PTlVL///rv27dunSZMmydPTs/BeCgAAAECmCGoAwEH4+fnJYrHI3d1d5cuXV48ePdI3BjYYDOrevbsOHDigfaevauzqg0pIyXyj4dtJSEnTuLWHVadpay1ZskSrV6/W0qVLVbly5Vvq+P777/PtvQAAAABkH0ENADiQtWvXKi4uTr/88ov27dunyZMnZ7hfvnx5VWrZSwazU676T7am6b42r2vr1q1q0KBBhntWqzXXdQMAAADIHwQ1AOCAypcvr5YtW+qXX24uaVqzZo1q1aqlkl5eWjryFSVHR2Von3z+mM7N76sz0zspev0M2azJ6fduHP9J5/7VX6end9KFz4bpwPEzuhp/876fn5/ee+891alTR25uburSpYtOnz6tNm3ayN3dXe+//37hvTQAAAAAghoAcERRUVH6+uuvVa1aNR07dkxdunTRjBkzNOnfu+VWLVCXVo6TLTUlvX384R9UttM43fPaAlmvnlXMjyskSckXTujKhpnyfqqfKoUsk/uDT+vsF2O1fPeJ9GeXL1+u9evXKyYmRsuXL1flypXTZ/YMGzas0N8dAAAAKM4IagDAgbRv314eHh6qVKmSypYtq7Fjx2rFihX6v//7P7Vo0UK/RSeoRINnZLMmKSnqSPpzHvVby+zpI5PFQyUbddSNX7dKkq7v/0YeDz4ll3tqyGA0yb32E5LJSVu3/5j+7IABA1SpUiVZLJZCf18AAAAAGXEGKwA4kFWrVql58+baunWrXnjhBUVHR+vcuXOqUqWKJCk20SqDwSiTp4+scVfSnzN5+Pz9s2dZpcZdlSRZr11S/MHNio1Y9/cgqVZdvnQh/beVKlUq4LcCAAAAkF0ENQDggJo0aaIePXpoyJAhql+/vg4ePChJ8nQ1y2azKTX2sszu3untU69f/vvn2MsyuZeWJJk9feTaqKNKNuqUof+HHqyY/rPBYMhw739/DwAAAKDwsPQJABzUwIED9d1336lt27Zav369Nm3apPvLWHQjfJUMJie5+Aakt72+d72ssdFKTbiua7u+UImAxpIk97otdX3f10o6d1Q2m01pyYmy/hEuv5K3D2PKlSun33//vcDfDwAAAMCtmFEDAA7Kx8dHL730ksaNG6elS5eqf//+ijp7VlavyvLp8I4Mpr+P6Har2USXVoySNe6qStzfMH0GjUuF++X9dH9d3RimlD/PyWh2lqVyLbWr+8Ztx33rrbfUv39/DRs2TCNHjtSQIUMK/F0BAAAA3GSw2Wy3vRkYGGgLDw8vxHIAAHfy6qfh+u7IRWXx9X1bBoPUsmY5hXUNzP/CAAAAAGSLwWCIsNlsmf6lnKVPAHCXea1xVRnTrLl61tVs0utNq+VzRQAAAADyC0ENANxFrl27prdfe1Hep7fJ1Zyzr3CLk1GhrfxVx9ergKoDAAAAkFcENQBwlzhx4oSCg4N1//3368cl72nk/wXI4mTSHQ9psqXJ4mRSaKsAdQ3yK4xSAQAAAOQSQQ0A3AW2bdumRx55RP369dOsWbNkNpvVNchPK14NUsua5eRiNt4yw8bVbJSL2SjjuUMaWFuENAAAAMBdgFOfAMDBLVq0SMOHD9dnn32mFi1aZLhXx9dLYV0DdSUuSSv3Riny/HXFJqbI09VJ/hU81KGerzZ/Haf33x+lVzu0lOGO028AAAAA2BOnPgGAg0pNTdVbb72lr776SuvWrZO/v3+u+klLS9NDDz2kcePGqV27dvlcJQAAAICc4tQnALjLxMXF6dlnn9VPP/2kPXv25DqkkSSj0ajx48frnXfeUVpaWj5WCQAAACC/EdQAgIM5ffq0Hn30Ufn4+Gjjxo3y9vbOc59t2rSRi4uLVq5cmQ8VAgAAACgoBDUA4ED27Nmj4OBgdevWTfPnz5ezs3O+9GswGDR+/HiNHj1aqamp+dInAAAAgPxHUAMADuLzzz9X69atFRYWpsGDB+f7xr9PPvmkypQpo2XLluVrvwAAAADyD0ENANiZzWbT6NGjNWLECG3atElt2rQpkHEMBoMmTJigMWPGKCUlpUDGAAAAAJA3BDUAYEcJCQnq3LmzNm7cqD179qhOnToFOl6TJk1UtWpVLV68uEDHAQAAAJA7BDUAYCfnz59XkyZNZDKZtGXLFpUrV65Qxh0/frzGjx+vpKSkQhkPAAAAQPYR1ACAHezbt08NGzZUmzZt9Nlnn8nV1bXQxg4ODladOnU0f/78QhsTAAAAQPYQ1ABAIVu1apWefPJJTZkyRaNGjcr3TYOzY9y4cZo0aZJu3LhR6GMDAAAAuD2CGgAoJDabTe+995769eunDRs2qGPHjnarpV69egoODtbcuXPtVgMAAACAW5ntXQAAFAdJSUnq06ePDhw4oN27d8vX19feJWns2LF64okn1KdPH3l4eNi7HAAAAABiRg0AFLjLly+refPmio2N1fbt2x0ipJGkBx54QE888YQ+/PBDe5cCAAAA4L8IagCgAB0+fFgNGzZU48aNtXLlSrm5udm7pAzGjBmj6dOnKyYmxt6lAAAAABBBDQAUmG+++UaPP/64xowZo0mTJslodLyv3OrVq6tt27aaNm2avUsBAAAAIIIaAMh3NptNH374oV5++WV99dVXeumll+xdUpZGjRqlOXPmKDo62t6lAAAAAMUeQQ0A5KOUlBS9/vrrmjdvnn788Uc9+uij9i7pjqpWraqOHTvq/ffft3cpAAAAQLFHUAMA+eTPP//U008/rVOnTunHH39U1apV7V1StoWGhmrhwoW6cOGCvUsBAAAAijWCGgDIB7/99puCgoJUu3ZtrVmzRp6envYuKUd8fX3VrVs3TZ482d6lAAAAAMUaQQ0A5NEPP/ygxo0b680339T06dNlNpvtXVKujBgxQp9++qnOnDlj71IAAACAYougBgDyYMGCBerUqZOWLVumPn362LucPClfvrxeffVVTZw40d6lAAAAAMWWwWaz3fZmYGCgLTw8vBDLAYC7Q2pqqoYNG6a1a9dq3bp1ql69ur1LyhdXrlxR9erV9fPPP+vee++1dzkAAABAkWQwGCJsNltgZveYUQMAOXT9+nW1b99ev/zyi3bv3l1kQhpJ8vb2Vr9+/TRu3Dh7lwIAAAAUSwQ1AJADp06d0iOPPKJ77rlH33zzjUqXLm3vkvLdoEGDtH79eh09etTepQAAAADFDkENAGTTjz/+qODgYL3yyisKCwuTk5OTvUsqEF5eXho0aJDGjBlj71IAAACAYufuPJoEAArZxo0b1bVrVy1evFitWrWydzkFbsCAAbrvvvt08OBB1a5d297lAAAAAMUGM2oA4B9SU1MzvR4cHKzt27cXi5BGktzd3TV8+HCNHj3a3qUAAAAAxQpBDYBi7+rVq5oyZYokyWQyKS0t7ZY27u7uqlGjRmGXZld9+/bVnj17FBERYe9SAAAAgGKDoAZAsXb8+HEFBwfr/fff18CBAyVJRqNRNpstQzuDwWCP8uzKYrHo7bff1qhRo+xdCgAAAFBsENQAKNacnZ318ssv69ChQ4qIiNCMGTMkFc9gJjO9evXS4cOH9eOPP9q7FAAAAKBYIKgBUKxVrlxZ/fv3V9myZfXBBx9o8eLFWr16tb3LchguLi565513mFUDAAAAFBKCGgDFnpubmyQpKChII0eO1MiRI3Xu3DlJ0oEDB+xZmkN46aWXdOrUKW3ZssXepQAAAABFHkENAPxDhw4dNHjwYLVo0UL+/v7as2ePvUuyOycnJ40ZM0ajRo26Ze8eAAAAAPmLoAZAsZbZCU+VK1fW77//rl69eql37952qMrxdOnSRVevXtW3335r71IAAACAIo2gBkCxdfDgQe3cuVMpKSnp11JSUjRr1iwtX75cQ4YMsWN1jsVkMmns2LHMqgEAAAAKGEENgGJp/fr1atasmc6dOycnJ6f0605OTvriiy/Uvn17O1bnmJ577jmlpKRozZo19i4FAAAAKLIIagAUKzabTdOnT1fv3r21Zs0aderU6ZY2/wxu8Dej0ajx48dr1KhRmS4ZAwAAAJB3BDUAio3k5GT16dNHixYt0q5duxQcHGzvku46rVu3lsVi0ZdffmnvUgAAAIAiiaAGQLFw9epVtWzZUufPn9fOnTtVpUoVe5d0VzIYDBo/frxGjx4tq9Vq73IAAACAIoegBkCRd/ToUTVs2FD169fXqlWr5OHhYe+S7motWrRQ2bJltWzZMnuXAgAAABQ5BDUAirRNmzbpscce04gRIzRlyhSZTCZ7l3TX+2tWzdixYzOcmAUAAAAg7whqABRZH3/8sV588UWtWLFCPXv2tHc5RUqTJk107733avHixfYuBQAAAChSzPYuAADym9Vq1ZAhQ/TNN99ox44dqlatmr1LKpLGjx+vjh07qlu3bnJ1dbV3OQAAAECRwIwaAEXKtWvX1LZtWx0+fFi7dv0/e/cdXvP9/nH8eXIyZUhEImIFsTVGkdhqb1JBqkFUzSraqtL87E3RUprWiBFqtlE7YkcJWtRIFG1CkIgQ2eucz+8PlcpXaEKSk3E/rutcl3M+6/U5yLjP/X6/T0uRJg+5uLjg5OTEqlWrdB1FCCGEEEKIIkMKNUKIIuPvv/+mWbNmVK5cmX379mFlZaXrSEXezJkzmTdvHomJibqOIoQQQgghRJEghRohRJEQGBhIs2bNGDVqFCtWrMDAwEDXkYqFhg0b0qxZM1auXKnrKEIIIYQQQhQJUqgRQhR6GzZs4N1332XdunWMGTNG13GKnRkzZrBo0SLi4uJ0HUUIIYQQQohCTwo1QohCS6vVMnnyZGbMmMGxY8fo1KmTriMVS3Xq1KF9+/Z88803uo4ihBBCCCFEoSeFGiFEoZSQkICbmxunTp0iKCiI2rVr6zpSsTZt2jS++eYbHj9+rOsoQgghhBBCFGpSqBFCFDrh4eG0aNGCkiVLcujQIUqXLq3rSMVe9erV6dGjB0uWLNF1FCGEEEIIIQo1KdQIIQqVc+fO4eLiwoABA1i7di1GRka6jiT+MXXqVFauXMnDhw91HUUIIYQQQohCSwo1QohCY9u2bXTt2pUVK1bw+eefo1KpdB1JPMfBwYH+/fuzcOFCXUcRQgghhBCi0JJCjRCiwFMUhZkzZzJhwgQOHTpEr169dB1JvISXlxdRUVFoNBpdRxFCCCGEEKJQ0td1ACGEeJWkpCSGDh3KrVu3CAoKomzZsrqOJF6hXLlyeHt7o1ardR1FCCGEEEKIQkk6aoQQBVZERATvvPMOWq2WY8eOSZGmkJB5g4QQQgghhHh9UqgRQhRIly5dwtnZmc6dO/Pjjz9iYmKi60giF6SkpLB8+XIOHTqk6yhCCCGEEEIUSFKoEUIUOL/88gvt27dnwYIFTJ8+XSYNLkL09fXR19dn3Lhxuo4ihBBCCCFEgSSFGiFEgaEoCosWLWLUqFHs2bMHd3d3XUcSuUBRlIw/nzlzhrNnz5KcnExYWJgOUwkhhBBCCFEwSaFGCFEgpKamMnToUDZt2sSZM2dwdnbWdSTxBhRFYf369Wg0GlQqFUFBQQwePJjRo0dTtWpVrl27RqVKlXQdUwghhBBCiAJHVn0SQujcw4cP6dOnD1ZWVgQGBmJmZqbrSOINqVQqjh8/jr+/P3Z2dvj7+9OrVy+WL1+OhYUFABqNRlaHEkIIIYQQ4n9IoUYIoVPBwcH06NGDPn36MG/ePPT0pNGvqJgxYwYODg6MGjWKc+fOYWxsDDwt0Ojp6UmRRgghhBBCiCxIZ+cr3AAAIABJREFUoUYIoTP+/v54eHiwcOFCPD09dR1H5LIKFSowcOBAqlWrhrGxMenp6ajVainQCCGEEEII8QpSqBFC5JqH8Sns+C2ckIhYYpPTsTDWp6adBX3fLo+1mVGmfVesWMGsWbPYuXMnLVu21FFikdeWLVvG2bNn0Wg06OvLtxwhhBBCCCH+i+r51Tj+V6NGjZTz58/nYxwhRGF06U4MK47d5PifUQCkpGszthnr66EAbWrYMLq1I3XKmjF+/HiOHDnCnj17qFKlio5Si/yWnp4uxRohhBBCCCEAlUr1m6IojbLaJj8xCyHeiO+ZUObsCyE5XUNWdd/kf4o2/tciOX49CotbAZR6dJPTp09TsmTJfE4rdEVRFA4cOICdnR2NGmX5/UgIIYQQQgiBLM8thHgDT4s0wSSlZV2keZ6iQPzD+5z7cQn9Ji+TIk0xo1KpiIyMZOLEibqOIoQQQgghRIEmhRohRJYcHBwwMTHBzMwMOzs7PD09iY+Pz9h+6U4Mc/aFkJSmfcVZsrbw0A3+CI/JzbiiEBg0aBB37tzhyJEjuo4ihBBCCCFEgSWFGiHES+3evZv4+HguXrzIhQsXmDdvXsa2Fcdukpyuea3zJqdrWHnsZm7FFIWEgYEB06dPZ8qUKbxqfjQhhBBCCCGKMynUCCH+k52dHZ06deLixYsAHDhygo1fDiJsSX/urRlDctgfGftGbJpEzImNRGz8nNtL+hK5ZQqaxCeZzqco8MvPP1GvQcNMry9ZsoRevXrl/Q0JnXF3dycmJoYDBw7oOooQQgghhBAFkhRqhBD/KTw8nP379+Po6Mjdu3dxc+1FqRbuVBj/I1ZthxL187xMxZiEa8ex7jae8h/7omjSiD378wvnNKvuws1bfxEcHJzx2saNGxk0aFC+3JPQDbVazYwZM6SrRgghhBBCiJeQQo0Q4qV69+6Nubk5FSpUwNbWlhkzZuDr60sFp2YYOLyNSqWHSeUGGJZ1JOnW+YzjTN9qj0GpcugZGGFaqyWpkX+9cO5U1FR17oCvry8AV69eJTQ0lO7du+fb/QndePfdd9FoNOzatUvXUYQQQgghhChwpFAjhHgpPz8/4uLiOHbsGCEhITx8+JCwsDBunAng9tL+GY+U8Gto4h9lHKc2s8r4s0rfCCUtOcvzV3LpwubNm1EUhY0bN9KvXz+MjIzy/L6Ebunp6TFz5kymTJmCVpvzyaiFEEIIIYQoyvR1HUAIUfC1bt0aT09PJkyYgLOzMzVbdSO+ydA3Pq9j3Yb8aWjIyZMn2bx5M5s3b86FtKIw6N69O7Nnz2b79u30799f13GEEEIIIYQoMKSjRgiRLePHj+fQoUM0a9aMOxdOkh52AUWrQUlPJTnsD9JjH+bofMb6etQsa86gQYMYM2YMBgYGtGjRIo/Si4JGpVIxe/Zspk2bRnp6uq7jCCGEEEIIUWBIoUYIkS02NjYMGjSIZcuWsW3nTzw6tZXwZe8TvsKT2LM/gZKzISwK4NawPAMHDuTKlSt4eHjkTXBRYLVv3x5bW1s2bdqk6yhCCCGEEEIUGKpXrbrRqFEj5fz58y/dLoQovoZvPM+h4EheZ+EelQo61S6Dt0cjkpKSsLW15ffff6datWq5H1QUaCdOnMDT05Pr169jYGCg6zhCCCGEEELkC5VK9ZuiKI2y2iZz1AghXstHbRw5cSOK5LScTwZrrK9mdBtHAL777jsaN24sRZpiqlWrVjg6OuLj48Pw4cN1HUcI8Y+H8Sns+C2ckIhYYpPTsTDWp6adBX3fLo+1mUz6LoQQQuQlKdQIIV6LU/mSODz6neumdVD0st8JYWKgh1fXmjiVt8TBwQFFUfDz88vDpKKgmzVrFm5ubgwaNAhjY2NdxxGiWLt0J4YVx25y/M8oAFLS/y3GG+tHsDTgT9rUsGF0a0fqVbDUVUwhhBCiSJM5aoQQr2X58uXcO7EVr661MTFQo1K9en+VCkwM1Hh1rYWHiwMAoaGhhIWF0aBBg7wPLAosZ2dn6tevz6pVq3QdRYhizfdMKO6rznAoOJKUdG2mIg1A8j+v+V+LxH3VGXzPhOomqBBCCFHEyRw1QogcO3ToEIMGDeL06dM4ODjwR3gMK4/d5Oj1KFQ8/WH+GWN9PRTgnRo2jG7jiFN5+QRWvOjChQt069aNmzdvUqJECV3HEaLY8T0Typx9wSTlYDjr0w7Jf4vvQgghhMi+V81RI4UaIUSO/Pnnn7Rs2ZLt27fTqlWrTNui41PY8Xs4IffjiE1Ow8LYgJplzXFrKHMaiP/m5uaGs7Mzn3/+ua6jCFGsXLoTQ+cxs3j8+wHsPBbm6FgTAzVbh7tIEV4IIYTIIZlMWAiRK2JiYujZsyezZ89+oUgDYG1mxIhWVXWQTBQFM2bMoG3btowcORJzc3NdxxGiSAoMDGTixIlcvXoVtVpNrVq1sO04nDRNzieGB0hO17Dy2E28PbL8OVMIIYQQr0HmqBFCZItGo+G9996jY8eODBs2TNdxRBFUp04dOnTowDfffKPrKEIUSbGxsXTv3p2PP/6YR48ecffuXcZPnMyFuwnw8gbrV1IUOHo9iuj4lNwNK4QQQhRj0lEjhMiWiRMnkp6ezpIlS3QdRRRh06ZNo2nTpnz00UdYWVnpOo54TbK0c8H0559/AvDee+8BYGJiQrRlLYzKqIm/ewOAx0fWEH/JH5WxGdYdR2FS9WmnTPwfh3gStBNNXDRqEwssXNwwb9AFABUw9dsNnNqygtDQUGrXro23tzdOTk75f5NCCCFEESCFGiHEf1q3bh2//PILQUFB6OvLlw2Rd6pVq0avXr1YvHgxs2fP1nUckUOytHPBVr16ddRqNYMHD8bd3R0XFxdCImIz/p5S7l3HtG5byo/bTPzFg0TvX0a5j9ajUqnQK2GJrds09C3tSLlzhQfbpmNYthpGdo7Eht/A56fpHPXfT6NGjfD19aVnz55cv34dIyMpzAkhhBA5JUOfhBCv9OuvvzJx4kR2795NqVKldB1HFANTpkzhu+++IyoqStdRRA7I0s4Fn4WFBYGBgahUKoYNG4aNjQ0/zR+HJuExAPoWtpjX74xKT43pW23RxD9CmxADQAnHxhhYlUWlUmFc8S2MKzcg5c5VAOIuHaB6a1ecnZ0zCkFGRkacOXNGZ/cqhBBCFGby0bgQ4qVu376Nm5sb69evp2bNmrqOI4oJBwcH+vfvz8KFC1m0aJGu44hsyMnSzooCSWka5uwLBpClnfNZrVq1WLduHQAhISG07vYu8QGrMKncELXZv8MN9QyMAdCmJaHGiqRb54k59SPpj+6iKApKWgqGNpUASH/ygKtXj2JpuTPj+NTUVO7du5d/NyaEEEIUIdJRI4TIUkJCAr169eKzzz6jS5cuuo4jihkvLy/WrFnD/fv3dR2lWGrTpg2rV6/O1r6X7sQwZ19Itoo0z0tK0zJnXwh/hMe8TkSRC2rWrMk73fuS/jDslfsp6WlE/TwPiyaulP/Yl4qfbMWkaqOM+YeNStrSddBoYmJiMh6JiYkZc+EIIYQQImekUCOEeIFWq8XT05P69evz6aef6jqOKIbKlSuHp6cn8+bN03UU8R9WHLtJcrrmtY59trSzyB8hISEsXryY8PBwAO7cucNfQf4Yl3t1x6SiSUPRpKEuURL01CTdOk/y3xcytpds0Inz+7cSFBSEoigkJCSwd+9e4uLi8vR+hBBCiKJKCjVCiBfMmjWLu3fv4u3tjUql0nUcUUxNmjQJX19fbt++resoxdqqVatwdHSkVKlS9OzZM9NwluGjx+DzURfCFvflvs84ku9cydgWc3ITUX7zebh7MbeX9OXe6tGk3L+RsT185QfEnPkJn8/6YlGyJP379yc5ORmAx48f0717d2xsbLCysqJ79+4ZxQXx+szNzQkKCsLZ2RlTU1NcXFxoUM8J11GTni7d9BJ6RiUo1X44UX4LuPO1OwnXjmNSrQkAKhV0eac5a1avZsyYMVhZWeHo6JgxvEoIIYQQOadSFOWlGxs1aqScP38+H+MIIXRt586dfPrppwQFBWFnZ6frOKKYmzx5MtHR0fzwww+6jlKstGnTBg8PD6pUqUK/fv3w9/enTp06TJgwgUuXLnHixAkAhngt5mRyedL0TYg7t4snZ3+i/Ki1qPQNiTm5iSdndmD77pcYV25IzImNJN++TNlBi4GnhRq1aUkq9JvK6Pa1WPW5B+PGjWPkyJFER0dz7NgxunTpgkaj4YMPPiAtLQ0/Pz9dvi1F1qU7MfT7/ldSNC//mfBlTAzUbB3uglN5WcVLCCGEyAmVSvWboiiNstomHTVCiAwXLlxg1KhR+Pn5SZFGFAiff/45P/30E7du3dJ1lGJp06ZNfPDBBzRs2BAjIyPmzZvH6dOnCQ0NBaCkU1vSDc1Q6amxcH4X0tNIi/6388W4fG1MqjZ+uopQ3bakPfg70/nN3+5JuokVdxLU9OjRg4sXLwJgbW1Nnz59KFGiBObm5nh5eXH8+PF8u+/iRhP1F/En12OgylmhxsRAD6+uNaVII4QQQuQyKdQIIQCIjIykd+/erFy5kgYNGug6jhAAlCpVijFjxjBz5kxdRymW7t27R6VKlTKem5mZYW1tzd27dwE49fM67q4aye2l/bi9tD/alEQ0SbEZ+2daRUjfCCU9FUWreWF7bHIaJUqUID4+HoDExERGjBhBpUqVsLCwoFWrVsTExKDRvN5cOOLlgoKC6NKlC9+Od2daz7qYGKj5rxGvKtXTThqvrrVk1S4hhBAiD8jy3EIIUlJScHV1ZciQIbi5uek6jhCZfPLJJzg6OhISEiLLxOcze3t7wsL+XREoISGB6OhoypUrx8mTJ7m6fyM2brMwsKmISqXHnaX9X+s6FsYGmZ4vXryY69evZwzBvHjxIg0aNOBVw7VFzp08eZI+ffrg4+NDt27dAHAqb8nKYzc5ej0KFZCc/u9qXsb6eijAOzVsGN3GUTpphBBCiDwihRohijlFURg5ciT29vZMnTpV13GEeEHJkiX57LPPmD59Olu2bNF1nGLlvffe47333mPAgAHUqlWLL7/8EmdnZxwcHLh06RIqQG1sCloNMae3ok1NyvE1jPX1qFnWnPvB/74WFxeHiYkJlpaWPHr0iBkzZuTeTQkADh8+jLu7O5s3b6ZDhw4ZrzuVt8TboxHR8Sns+D2ckPtxxCanYWFsQM2y5rg1LI+1mZEOkwshhBBFnxRqhCjmli5dysWLFwkMDERPT0ZDioJpzJgxODo68scff+Dk5KTrOMWCSqWiffv2zJo1iz59+vD48WOaNWuGl5cXY8eOZfPmzZiZluDu6tHoGRhj3rgXavPSOb6OArg1LM/yI/++Nn78eAYMGEDp0qWxt7fns88+k4mEc9H+/fsZNGgQO3bsoHXr1lnuY21mxIhWVfM5mRBCCCFAVn0Soljbv38/Q4cO5cyZM1SsWFHXcYR4paVLl3L8+HH5hT0fNGzYkKlTp9K7d2/g6RxWmzZtYt26dcTFxTF48GAGDRpElSpVGL7xPIeCI3mdUUkqFXSqXQZvjywXPBB5YNeuXQwbNgw/Pz+aNWum6zhCCCFEsSWrPgkhXhASEsLgwYPZvn27FGlEoTBy5EjOnz+PfICQt65evUpwcDC1a9dm586d9OjRgxo1anD58mWWLVvGrVu3mD59OlWqVAHgozaOGOurX+taxvpqRrdxzM344hW2b9/O8OHD2bdvnxRphBBCiAJMCjVCFEOPHz+mZ8+eLFiwgObNm+s6jhDZYmJiwpdffsmUKVN0HaXImjhxIu+88w6NGzemefPmfPvtt7i5uREeHo6Pjw9t2rR5YYhkvQqWeHWtiYlBzn6kUGnTmNSpukxIm098fX0ZO3Ys/v7+NGokHUxCCCFEQSaFGiGKmfT0dPr160f37t0ZMmSIruMIkSNDhw4lODiYU6dO6TpKkRIREcHixYvZv38/ZmZmtGvXjrNnz3L06FEGDx6MmZnZK4/3cHHAq2utbC/tbGygh82dE/y6YYGs5JQP1qxZwxdffMHhw4epV6+eruMIIYQQ4j/IHDVCFDPjxo3j+vXr7NmzB319mU9cFD5r167F19eXI0eO/PfOIsPD+BR2/BZOSEQsscnpmBrooX10h1uHfAk6cRhXV1c8PT1p0aLFa08s/kd4TLaXdq5cUs0777xD9+7dmT59eq7co3jRypUrmT9/PgEBAVSvXl3XcYQQQgjxj1fNUSOFGiGKkdWrV/PVV19x5swZLC1luIEonNLT06lVqxbff/89bdu21XWcAu/SnRhWHLvJ8T+jAEh5rnii0qSh1tendXUbxrarQb0KufN1IbtLOz948IDmzZvz2WefMXLkyFy5tvjX0qVLWbZsGUeOHKFy5cq6jiOEEEKI50ihRgjByZMncXNz4+TJk/Kpqij0Nm3axIoVKzh16hSq/xprU4z5ngllzr4QktM0vGqAkUr1dGJfr6418XBxyK94ANy6dYuWLVuyYsUKXF1d8/XaRdm8efNYu3Ythw8flgnjhRBCiAJICjUCeLHt3cJYn5p2FvR9O/MnnKLoCQ0NpWnTpqxfv56OHTvqOo4Qb0yj0eDk5MRXX31Fly5ddB2nQPIJvMncfSGkKdkvZJkY6NFGfZ1bp/bh7++fh+ky+/333+ncuTM7d+6kZcuW+XbdokhRFKZPn862bds4fPgw9vb2uo4khBBCiCxIoaaYe1Xb+7M5A9rUsGF0a8dca3sXBUd8fDzNmjVj6NChjBs3TtdxhMg1O3fuZO7cuZw/f77YddU8P7lvYmIiRkZGqNVPl8j+4osvCHmQRKBRI1QGOS/Cmxio2TrcJd9XYwoICOD999/n8OHD1K1bN1+vXVQoisKkSZPYv38/AQEB2Nra6jqSEEIIIV7iVYUaWfWpiPM9E4r7qjMcCo4kJV2bqUgDTyd6TEnX4n8tEvdVZ/A9E6qboCJPaLVaPDw8aNKkCWPHjtV1HCFylaurK1qtFj8/P11HyTNPnjzh7NmzwNMuomfi4+MzHhUrVsTHx4epU6dSsWJF1q9fT3jJOui9RpEGIDldw8pjN3Mlf060b9+er7/+mi5dunD79u18v35hpygK48ePJyAggKNHj0qRRgghhCjEpFBTBHl6evJ///d//8xNEExSmob/Wv1UUSApTcOcfcGUr1KdY8eO5UtWkbemTp1KdHQ0K1euLHYdB6Lo09PTY9asWUydOhWtVvvfBxQiiqLwf//3f1SpUoVevXqRkpKS0THz/D5Xr17lwYMHfPjhhwQHBxMeHs4a3y1EqEqhAJqEGG5/1QdN4hMiNk0iIeTpsubJ4dcIm9+dxJvnAEgKvci9tR8DEHcpgPWTBxMdnwKASqXC29ubatWqYWlpyUcffZRnS2q/9957fPrpp3Tu3JlHjx7lyTWKIq1Wy6hRowgKCuLw4cNYW1vrOpIQQggh3oAUagq5Nm3aYGVlRUpKSqbXI2OTmbMvhKS0nP3ykpSmxdxjGaUc6+dmTKEDW7ZswdfXl507d2JoaKjrOELkiW7dumFqasq2bdt0HSVXqVQqmjRpwvHjx2nZsiVbt24FMnfVqFQq7O3tMTU1ZcuWLfj4+ODh4cHcZasy9km4dhzjSk6oS5TEuEJdUm5fBiDl9hX0Le1IuXMl47lxhX+HG6mAHb+HZzzfs2cP586d448//mDbtm0cPHgwz+79k08+oXv37nTv3p3ExMQ8u05RodFoGDp0KNeuXePQoUOyop8QQghRBEihphALDQ3l5MmTqFQqfvnll0zbzoc9Jjld85Ijs6Zon+6vq7Z3kXvOnz/Pxx9/zK5du6T9XRRpKpWKWbNmMW3aNNLT03UdJ1d17dqVunXr0r59+4xClJ5e5m/bVlZWmJqaYmxsDMDgwYP51f8XktOefj1PuHoU07pPlzA3qliX5H8KM8l3rmDRtG+m58YV38o4rxYIuR+X8XzSpElYWlpSsWJF3nnnHS5evJg3N/2P+fPn4+joSP/+/Yvc32tuSktLY+DAgdy+fZv9+/djbm6u60hCCCGEyAVSqCnENmzYgIuLC56enqxfvz7j9eQ0DbejEzKGOyXePMu9tR9ze2l/IjZOIPXB3xn7hq/8gCdndnBvzRhuL+6DotVwZ8UH7NnvT3R8Clqtlvnz51O1alWsra3p16+ftKMXcPfv38fV1ZUffviBevXq6TqOEHmuffv22NnZsWnTJl1HyVX6+voAdO/enTt37hAWFoZKpXrlsCNnZ2fUBkak3L5MWvQd0h7fp0Q1ZwCMytUk7dFdNAmPSX3wF2Z126KJjUKT+ITU+39iVKFOpnPFJqdl/NnOzi7jzyVKlCA+Pj43b/UFenp6rFmzhrS0NEaOHJlnQ60Ks9TUVNzd3YmJiWHPnj2YmprqOpIQQgghcokUagqxDRs28P777/P+++9z8OBBIiMjAQiL/rdVPDXiFtH7vsG68xgqjNuMWf0uPNgxCyX93x/AE64dx7bvNCqM34pK7+kcCM/a3pcvX46fnx/Hjx/n3r17WFlZ8dFHH+XrfYrsS05Opnfv3owYMQJXV1ddxxEiXzzrqpkxYwapqam6jvNGsipI2Nvb8/bbb7Nu3TqAV843pSgKjk07En/1KPFXjmJaozkq/adDH/UMjDGycyT2/C8Ylq6ESm2AUblaxJ7zQ9/SDnWJkpnOZWFskHs39hoMDAzYsWMHf/zxB1OnTtVploImJSWFd999F41Gw88//4yJiYmuIwkhhBAiF0mhppAKDAwkLCyMfv368fbbb1O1alU2b94MwJOkNNL/+Vk/7tIBzOt3xsi+Bio9NWZvtUOlb0DKvZCMc1m83QN9C5tMK4SkahVC7sfh7e3NnDlzKF++PEZGRkyfPp0dO3ZIK3oBpCgKN2/epFatWnh5eek6jhD5qlWrVjg6OuLj46PrKK/lwYMHLFiwgLi4uCy3jxw5kuPHj5OQkMBvv/32QkHn2rVrDB06lEqVKnEvOoakP09nGvb0jFHFusT9tgejik/nozGu+NY/z9/KtJ8eULOs7ofRmJmZsXfvXrZu3crKlSt1HadAUBSFlJQUKleuzPbt2zEyer3VvYQQQghRcEmhppBav349HTt2pHTp0gAMGDAgY/hTmubfCYTTnzwg9qwft5f2z3hoYh+iif93+JLawibLa8QmpxEWFoarqyuWlpZYWlpSq1Yt1Gp1RveOKDhUKhXVqlVj7dq1ssKTKJZmzZrF7NmzSU5O1nWUHElLS8PHx4e//vqL+Pj4LLtqQkJCOHbsGPb29vz666+kpaW9sE/9+vXx9/fn0p4NGNs5AqoXhjMZV6iLkpqUMXGwUcVnzzPvp0WhfVXdF2oAbGxsOHjwIHPmzGHHjh26jpOvnp88+hmVSoWpqSnLli3DwEC3XU9CCCGEyBv6ug4gci4pKYlt27ah0Wgy5g1ISUkhJiaGS5cuYaDWg38aXvQtbDBu1o+Szfq//IQv+aXewtiAChUqsHbtWpo3b57btyHegKIoWRZj5JNVUZw5OzvToEEDfvjhB8aOHavrOC+4cuUKGo2GevXqodFoMpbbNjAw4PPPP39houBnLly4wJYtW1i/fj0DBgx4YZnu0NDQF44pX6ECkfY1Xvg6YVLlbSpN2pPx3NDGIdNzAHOn9jhUr0kTp1qMHj2a6OhoSpUqlbH92RCs/FS5cmX27NlDp06dsLGxoXXr1vmeIT9dv36dcuXKYWZmlunfyjP/+1wIIYQQRYt01BRCfn5+qNVqrl27xsWLF7l48SLBwcG0bNmSDRs2UNLEAP1/fjY3q9eJuAv7Sbl3HUVR0KYmk3jzHNqUVy95aqinomZZc0aOHImXlxdhYWEAREVFsWvXrry+RfEfVCpVlp+0ClHczZw5k3nz5hWoZZ2Tk5Pp2rUrzZs3Z9SoUcCLqze9rEgD0KBBA/bt28fAgQOz9Qt6aGgo9y4dx7ph59fKa2ygZu1n/Tl79iz37t2jWrVqeHl5ER0d/Vrnyy0NGjTIWP1Kq9X+x96FV0BAAA0bNqRr164kJCSgVqvl670QQghRzEihphBav349Q4YMoWLFitjZ2WU8xowZw6ZNmyhvaZixr1HZalh3+ZhH/t7c+dqde98PI+FywH9eQwHcGpZn3Lhx9OzZk44dO2Jubo6LiwtBQUF5eHfiVRYtWsTkyZMB5Id3IbJQv359WrRowYoVK3QdJYOxsTGjRo3iypUrpKSk8Pjx4xc6XRRF4ezZs4wbN46ZM2eSkJDwWteaMmUKdevWZdLEicx4vw0mBjn7Nm9ioIdX15o4lbekatWqrFmzhvPnzxMVFUX16tWZPHkyDx8+fK1suaFNmzY0b978pYUtRVFYt24dv/32Wz4nyx3p6en4+PiwZMkSmjdvTp8+fUhJSZGv90IIIUQxo3rVkpeNGjVSzp8/n49xRG4ZvvE8h4Ijea0VTRUtratasn5Yy1zPJV7fnj17GDZsGLVr18bKyor169djamqKVqt95afxQhQ3165do02bNty8eRMLC4t8vbaiKAQHB+Pg4ECJEiUyXn/2/7RLly4MGzaMd999N9P/XY1Gw759+7hy5QqdO3emfv36uTLXlO+ZUObsCyE5XfPK7wcqFRjrq/HqWhMPF4cs9wkLC2PevHls27aNYcOGMWHCBGxssp7jTFcePnyIr68v8+bNIygoCAcHB11HyrH4+HhSU1MpUaIE48aNIzw8HD8/P5mPRgghhChiVCrVb4qiNMpqm/x2V0R91MYRY/3XG8OuVikcWPIpa9euzXJSS6EbpUqVYv78+Rw8eJDSpUvTqVMngoODM37Rk5W4hHiqdu3adOzYkW+++Sbfrnnnzh3mzp1LjRo18PDwwNDQMNP2Z/9PmzVrhr+/P5B5KW61Wk2PHj2YPHkyDRo0yLUJwT1cHNg63IVOtctgpK+HsX7mb/tusAvXAAAgAElEQVTG+noY6evRqXYZtg53eWmRBqBSpUp4e3tz8eJF4uLiqFGjBhMmTChQk8uXLl2a2rVrA3Dr1i0dp3k9ZmZmlCpVCmNjY5YsWYKtrS19+/YF4MaNG1y9elXHCYUQQgiR16Sjpgh7+klqMElp2R/L/7TtvRZOJWLx9PSkTJkyrFq1ivLly+dhUpFdKSkpGBkZodVqWbx4MRs3buTrr7+mbdu2+Pj40KdPn3zvIBCiILpx4wZNmzblxo0baAxKsOO3cEIiYolNTsfCWJ+adhb0fbs81mavPwF3YmIiP//8M+vWreP333+nX79+eHp60qRJk5cWWo4dO8bnn3/OuXPnXvu6rys6PoUdv4cTcj+O2OQ0LIwNqFnWHLeGr/c+hIeHM3/+fDZv3oynpycTJ07MmOBeV7y9vVmxYgXz58+nW7duOs2SW2JjY5k8eTJHjx4lMTGRAwcOULNmTV3HEkIIIcQbelVHjRRqirg3aXtPS0tj/vz5LF++nAULFuDp6SnLPhcwu3btYvbs2YSFheHu7s6yZct0HUmIAqPvyAlEWDnxQP/p8JyU9H+L1sb6eihAmxo2jG7tSL0Kltk6p6Io/Prrr6xbt46dO3fi4uKCp6cnPXv2RKVScfr0aQ4dOkT58uX54IMPXliJTaPR0KpVK/z9/TE1NSUtLa3QD2m5e/cuCxcuZOPGjQwaNIgvvviCsmXL5su1k5KSMDExIT09nYULF7J9+3a8vb1xdnYGXr5CXkGW1XDW77//ni+//JI9e/bQtGlTHSUTQgghRG6SoU/F2Ju0vRsYGDBlyhQCAgJYtmwZ3bp1Izw8PJ/vQLxKr169qFixIvXr15cijRDP8T0Tyh82bbmttSIlXZupSAOQ/M9r/tcicV91Bt8zoa883+3bt5kzZw7Vq1dn2LBhVKtWjcuXL7Nw4ULu3r3Lu+++i42NDRMnTgSgSZMmLwx/gqdLdN++fZty5crx/vvvc+/evVy7Z10pV64c33zzDVevXkWlUlGnTh3Gjh3L3bt38/S6qampuLm5sXPnThYuXMju3bvZunUrzs7OGatCFbYizZ9//snff/+daeLg2NhYdu7cycGDB6VII4QQQhQT0lFTjLxJ23taWhrz5s3j22+/le4aHUlISECr1WJqaprxaev169cZOXIku3fvxszMTMcJhSgY3mTY5/PF6sTERH766SfWrVvHxYsXqV+/Po8ePWL8+PEEBAQQEBCAsbExHTp0oEOHDrRt25ZSpUq99BoREREMGDCAOnXq0KNHD9q2bYu+vv6b3GqBFBERwaJFi/Dx8WHAgAFMmjQpz4bPHj58mJ49e+Lg4JAxd0thnWD9ypUrdOzYkSVLluDu7p5p27Nhr0IIIYQoOmTok8g1ly5dwtPTE3t7e3744QfKlSun60jFgkajoXfv3jg5OTF79uxMRbLExMRMq8sIUVRt3ryZJUuWEBISgrm5OfXr18fLy4sWLVpk7HPpTgzuq86QlJbzpYxNDNRsGeZMXNjVjKFNzs7OvP3228TFxXH06FHCw8Np27ZtRnGmatWquXmLRUpkZCRfffUVa9aswd3dnUmTJlGxYsVcv862bduYMGECN27cKLTFjAsXLtClSxe+/vrrF4o0QgghhCiaZOiTyDX16tXj7NmzNGnShAYNGrBu3TpZGSofeHl5ERcXx/Tp01/oZJIijSgOlixZwvjx4/nyyy+JjIzk9u3bjB49ml27dmXab8WxmySnv7xIo2hfvi0pLZ3eX65k0KBB3L17l9q1a3Pq1ClOnjyJjY0Nq1atIioqip07dzJy5Egp0vyHMmXKsGjRokyFtREjRhAWFvbSYxISEhgyZAjbtm3L9nX69evH6tWrCQ4Oznjt+aFDBV1QUBCdO3fmu+++kyKNEEIIIQDpqBFv4OLFi3h6elKuXDnprslDvr6+TJ06lbNnz1K6dGldxxEi3z158oRy5crh4+OTsUzx87RaLQsXLuT7H37gdsRDjCvVo1Snj1CbmJMeE8ld76GU6jKWJ6d+RN/CljLus4jev4ykW7+hKFoMrOyx7TsVtakVmoTHxPmOJy0lCWNjYz744APmz5+PWq1m3bp1rF69msDAQB28C4Xfw4cPWbJkCd9//z0LFy5kyJAhLwxRCg0NZc+ePYwfPx4fHx8GDhyY4+ukp6fTu3dvZs+eTf369XMrfp4IDAzk3XffxcfHp8isUiWEEEKI7JGOGpEn6tevz9mzZ2ncuDENGjRg/fr10l2Ty4KCgvjkk0/45ZdfpEgjiq3Tp0+TnJyMq6trltuXL1+On58fwxdsoMo4X/SMzXjk/12mfVJuX8b+w++w7T+T+MuH0SYnUO4jHyqM20ypzh+h0n86ZObxwZU4tejAgwcPCA4O5vjx46xevTrP77E4KF26NHPnzuXPP//Ezc0ty3lkHBwc6Nq1K3Xr1qVz584AOf6+oq+vj6enJ926dePvv//Olex54ciRI7i6uuLr6ytFGiGEEEJkIoUa8UYMDQ2ZPn06/v7+LF26lB49ehSJVUwKgrt379KnTx/WrFlD3bp1dR1HCJ2Jjo6mdOnSL51419vbmzlz5hCpNSUNNSVbDCDx+qlMw5xKtnwfPUNj9AyMUOmp0SbHkf74Pio9NUZ2jugZlUCT8JiEW+eo13ccpqam2Nra8sknn7Bly5b8utViwdrampIlS750+/z58+nQoQM2NjZotdrXmrjezc0NLy8vOnXqRFRU1JvEzRMHDx7E3d2dHTt20LFjR13HEUIIIUQBU/SWmxA68ay7Zu7cudSvX59FixYxaNAgWRnqNSUlJdG7d2/GjBlDz549dR1HFHJPnjwhJiYGe3t7DAwMdB0nx6ysrHj48CFXrlzh0aNHREZG8uDBg4zHjRs36NSpE1o9A9BTPz1IpYcm4XHGOfTN/+1IM63blvS4hzzctRBtSgKmddpg2WoQ6U8egEaD9/B2rP9IjZ6eHlqtlgoVKuTKfSQnJzNt2jQeP36Mg4MD/fr1w9HRMVfO/V8exqew47dwQiJiiU1Ox8JYn5p2FvR9+79X/ctPly5d4vTp0xw+fBh4s+W1R48ezb179+jWrRtHjhwpMCvj7d69m6FDh+Ln50ezZs10HUcIIYQQBZDMUSNy3YULF/D09KRixYp8//332Nvb6zpSoaIoCgMGDECtVrNx40Ypdok3snPnTiZOnMjt27cJDAzE2dkZRVF0/u8qKSkpU7Hlfx/PF2OioqJIT0+nbNmyVK1aFVtbW8qUKYOtrS22trbMnj0bLy8vgvRqcuJ2cqbrPJujpuLEXaieFXH+Z/uD7dMxb+KKSdVG3PP+EJsuY4nasxhFUTAxMcHa2pry5cujp6fH/fv3+fHHH2nYsGGOi16RkZHs27ePtLQ0QkJCiIqKwtvbG1NT0zd6L1/l0p0YVhy7yfE/n3aVpKT/u2S5sb4eCtCmhg2jWztSr4JlnuXIrhEjRmBqasqSJUtyZZltRVEYNmwY4eHh7N69W+eFyh07djBmzBj27NlDo0ZZDkkXQgghRDHxqjlqpKNG5LoGDRpw7tw55syZQ/369fnqq68YOHCgzn8xLCzmzZvHrVu3OH78uLxnb6iwdBHkpcaNG3Pq1CnGjRtHeHh4nhVqNBoNjx49yrLQktUjJSUlU7Hl2aNs2bLUq1cv0zYbGxuWL1/OwoUL+eyzz+jYsSMGBgYEBASwd+9eWrVqxbRp01CqNseizRDSkxNJCQ+mRHWXLLMmh/2BnokFBqUroDIqAXpqVCoV+malMK3SkDqGUfjFxBAVFcVPP/3EmTNnePLkCZcvXyY6OppmzZqh1WoxNDTEysoKe3t7HB0dcXJyomnTpri4uGRZfLG1tWXIkCEZz52cnAgPD6dGjRq5+nfxjO+ZUObsCyE5XUNWn8kk/1O08b8WyYk/H+LVtSYeLg55kuVVEhMTWbt2Lc7Ozly8eBF/f/9cO7dKpcLb2xtXV1eGDh3KunXr3rj487o2bdrEhAkTOHDgQIGf5FgIIYQQuiUdNSJPSXdNzuzatYsxY8YQFBQk79UbKGxdBPlhyJAhNG7cmNGjR2e7UyE+Pj7bXS+PHj2iZMmSGcWVrIowzz8sLCxyXCzatGkTS5cu5dq1a+jr66Ovr09KSgpdunTByMiI02eCCA2/h7pESUrUaolV68FZdtQkXDtOTOBmNHEPURmYYFqrJVbtPkSlp0Y/PYmWj/05dGAfcXFxVKlShS+++AJ3d/dMqz49ePCAwMBAzp49y9WrV/nrr7+IiIggNjYWPT09EhISspxT51mR7Pr164wZM4ZVq1bh4OCQo/chO54WaYIJ3TQF01qtMHur3X8eY2Kgh1fXWvlerImNjWXs2LFs3bqVsmXL8tdff2XanpqayunTpyldujR16tR5rWskJibSvn17WrZsyYIFC3Ijdo6sXbuWKVOm4O/v/9r3IIQQQoii5VUdNVKoEXkuNTWV2bNn4+3tzeLFi/Hw8JBOkSxcvnyZdu3asXfvXho3bqzrOIXWf3URPKNSgbG+WmddBPlt4sSJGBsbM3PmTDQaDWr1v8OA/vrrL+bMmfNCIUZRlFcWXJ7f9qrJft+UoihcuHABPz8//Pz8iIyMpGfPnvTu3Zt27dphbGycse/wjec5FBz5yr/7l1GpoFPtMnh7vNmQlISEBIyMjF54P56973///TczZszAzMyMb7/9Fnj6dTImJgYbG5tsfX1s06YNHh4efPjhhy9su3QnBvdVZ0hK02Rx5KuZGKjZOtwFp/L5X8AMDQ1l0qRJVKtWjc8//xwLCwvg6d+/t7c306dPp3Xr1kyZMoW33norx+ePjo6mZcuWDB8+nPHjx+d2/Jfy9vZm7ty5BAQEUL169Xy7rhBCCCEKNhn6JHTK0NCQmTNn0rt3bzw9Pdm+fTvff/89ZcuWfekx9+7dK1YdJVFRUfTs2ZOvv/5aijSv6dSpU/Rx9+BBZASle3yGUbmaRP08n9QHf2FWrxOl2mX+hVZRIClNw5x9wQCFslijKAqxsbGZiisNGjTIskOjdOnS3Lp1K+O451lYWNC0adMXCjG6nHw1LS2NEydO4Ofnx65duzAyMsLV1ZXvvvsOFxeXTIWm533UxpGTNx6+VpHCWF/N6DZvPrlvVsOeFEVBrVYTGBjIokWLcHJyYurUqRnbk5KS6NGjB+fOnaNEiRKULl2aihUrUqNGDRo0aEDLli2pU6dOtjqhVhy7SXJ6zu8fICk1jW8OhbBmSNbDxvKSg4MDW7ZsISkpCRMTk4zXVSoVo0aNYuDAgXz33Xd06NCB5s2bM3XqVOrVq5ft81tbW3PgwAFatGhBmTJleO+99/LiNjL5+uuv+eabbzh27BhVqlTJ8+sJIYQQomiQjhqRr5511/zwww8EBwdjaWn5wqfHv/32Gx9++CHdunVj9uzZOkqaf1JTU2nfvj0tWrRg7ty5uo5T4Lyqc+B5zs1bE2pWC5OGPQCIOfUjqZF/YeP65X92KOiyi+B/paamEhUVle25XgwNDTMVV8aNG8c777zzwnk3bdrE3r172bx5sw7uKnvi4+M5ePAgfn5+7N27F0dHR3r37k3v3r2pVatWtjvxng37SUrT/vfOz2hScUq/id9Xn+XZHCa7d+9mx44d9O3bl+7du7+wXVEUgoODCQwM5LfffuP69euEhYXx8OFDEhISUBQFY2NjSpUqRVxcHLVr16ZLly4cPHiQGzdukJ6eTmPnptys8R7aEqUAiNg0CdO672BerxPxfwQQf+kghmWrE385ALWxGdY9JpD+6C4xJ31RNGlYvfMBpep34PSktpgZgJeXF9u2bSMlJQVXV1eWLl2aqYiiCwkJCXz//fcsWrQIFxcXpk6dSoMGDbJ9/JUrV2jXrh2bNm2iffv2eZZz/vz5rFmzhsOHD1OxYsU8u44QQgghCicZ+iQKnLt372JlZUWJEiVe2BYXF8fx48fp168f586dK9Lj+RVFYcSIEURGRvLzzz/rbJLLgiy7hZqSZSpg0nYkxg5PJ+mM3rcMPTMrrFoNzHJ/RavJmLMkt4a8ZHkdReHx48fZmuflwYMHxMfHY2Njk625XmxsbLL8P/S/0tPTWb9+PUuXLmX16tXY2toWmE/3IyMj2b17N35+fpw4cYKmTZvSq1cvevbsSfny5V/7vDkdAvfJO5XYOHU4lStXZs2aNbm+OlBERARvvfUWBgYGDB06lCpVqtCmTRsqV66creMVReHOnTsEBgZy/vx51q1bh6GhIUlJScTFxaEoytN5e6zsUZW0w9btabfO/xZqovcvw7rLx5jWbUtM4GYSrhzBxLEJpdp+SPKdy0T9PA/H8b581q0eIT9/y61bt1i3bh0GBgYMGDCAunXrMm/evFx9b15XYmIiP/zwAwsXLqRx48ZMnTqVt99+O1vHnjhxAjc3Nw4cOEDDhg1zNZeiKMyYMYOtW7dy+PDhYtUdKoQQQojsk0KNKFQ0Gg0jR47E1NSUr7/+Wtdx8tTy5cv54Ycf+PXXXzE3N9d1nALp+ULNqlWrWLBgAY8ePaJFixZ4e3tjb29P5SpVCP07FJW+Aaj0MHFsQuL1U4AKlVofmz7/R8qdq6Q9DEOlb0jijSCs2n2IaY0WPDqymuRb50FPj3EjP2ThvDloNBrs7Ow4fvx4xlwYDx48wMHBgbCwMMzNzV9ZbHn+ERUVhamp6Ssn1n2+GGNpaZmrBTutVkuPHj24fv06ZmZmVKhQgUmTJtG8efNcu0ZO3bhxg127duHn58eVK1fo1KkTvXv3pkuXLlha5l5X0x/hMaw8dpOj16NQ8e8qRwCkp2JgaEi7WmUY3cYRp/KWJCYm4ubmhoGBAVu3bs00982bUhSFkJAQwsPDuXz5MteuXaNXr1706NHjtc73vwXMR48ecerUKWb8fIELmxdS4ZOtwIuFmient1JuxCoAUh+Ecn/tGMp/vBG1qRUAd75+jzLvzaFvx+asHtqSP/74g6pVqwJw+vRpBgwYwN9///2mb0euSkpKyvja0LBhQ6ZOnZqtIaQ///wzH330ESdPnsy4xzelKApffvkle/fuJSAgAFtb21w5rxBCCCGKHpmjRhQqGzZs4MqVK5w8eRIg2yvUFDYBAQHMmTOH06dPS5EmG44cOcLkyZMzVk2ZMGEC7u7unDhxgi98DvFxr2aU6jIWk386ah7uWYraonRGR03Knask3gjCpvckrLt/ipKexsNfFqE2tcR+xCoMlBS2/DQbJT0VZ2dn6tSpw4gRI3jrrbeIjIzk0qVLqFQqqlatSkpKSpaFlmdLS/9v14uRke6WAtfT02Pv3r06uz48/eX1/PnzGZMBR0dH06tXL7y8vGjbtm2evT9O5S3x9mhEdHwKO34P58d9x9EaGPN23drcCz6HWdRVvD3+LQaXKFECPz8/PD096dKlC7t27cqY0PZNqVQqatWqRa1atejQoUOunBOedpV88sknHDhwgMePH5OcpkWbkpCpY+x5zwoyACoDwyxf06Ym8eDBAxITEzN1qCiKgkbzenPf5CUTExPGjh3L8OHDWb16Na6urjg5OTFt2jScnZ1fepyrqyuRkZF06tSJU6dOUaZMmTfKoSgKn376KSdOnODo0aNYW1u/0fmEEEIIUXxJoUYUKNevX2f58uUsWLAAfX39F1anKSpu3LjB+++/z7Zt27I99KG427RpEx988EHGMIV58+ZhZWVFaGgoIRGx2Vrlx8i+JiWqNwVAm5pI0l/nqTB+K3oGRmgwJt2uDmvXriU8PJwyZcoQEBDAwIEDsbOzY/LkycydOxdPT8/XWlq6uElNTeX48eMZkwGbmpri6urKmjVraNKkSb4WX63NjBjRqirhh54O4ZnafwB//WWBi4sX6d98lWl1JkNDQ3x9fRkzZgxt27Zl//792NjY5FvWnFq8eDHXr18nKCgIOzs73l+whc2T3nySXFsbG0xMTLh69SoajYZWrVplTG783Xff0aJFi2xPbpxdPj4+bNiwgTp16jB37twcF8mMjY0ZM2YMw4YNY+3atfTt25fatWszbdo0mjZtmuUxI0eO5P79+3Tr1o2jR4++dtFcq9UyZswYfv/9dw4fPpyrnWFCCCGEKH6kUCN0TlEUAgIC6NChA4sXL6ZFixYZnzgXxSLNkydP6NmzJzNnzqR169a6jlNo3Lt3L9NcEmZmZlhbW3P37l1ikw2zdQ61RemMP6c/eQAaDeHfDsp4TV8F1apUYtu2bQDUrFmTmjVrUrZsWSIiIhgxYkSuDocpauLi4jhw4AB+fn7s27ePGjVq0Lt3bwICAqhZs6au42FgYEBaWhoAVapUoXLlyhw9evSFDhc9PT1WrFjBlClTaNWqFf7+/lSoUEEXkf9TXFwcJiYmWFpa8ujRI67uWfvG5zRU61G7nCXDhg3jk08+4f/+7//o3Lkzly5dYvfu3WzZsiXT5MbW1taUL1+e6tWrU79+fZo3b07Dhg1zPM9PmTJlcHd3Z+rUqcyfP/+18xsZGTFq1Cg++OAD1q1bh7u7OzVq1GDatGlZDvmbPn06ERER9OnThz179mBo+O/Xk4fxKez4LZyQiFhik9OxMNanpp0Ffd8uj7XZ004wjUbDsGHDuHHjBv7+/rnWhSWEEEKI4ksKNULn4uPjWbx4McOGDcPU1JSrV68CRXPIk0ajwd3dnXbt2jFixAhdxylU7O3tCQsLy3iekJBAdHQ0Dx484N7fydk6x/NdMGoLG1T6BlQYtzljiIhr/XIs7V8/Y5/Bgwfj6+uLnZ0dbm5uUqTJQkREBL/88gt+fn4EBgbSvHlzevfuzaJFiwrcJKoGBgYkJiZmPO/fvz9btmzJciiSSqVi9uzZlCpVipYtW+Lv70/16tXzM+5/UqlUjB8/ngEDBlC6dGns7e0ZMWYslwIPvdF5tYqWXk52DG6ygJkzZ9K7d28ePnxIuXLl+PLLLxk7diyKohAWFpYxufG1a9c4c+YMu3btIj4+Hq1Wi6GhIVZWVtjb21OtWjWcnJxwcXGhVatWWRZxunbtys2bN1m1alWuLA1vZGTEiBEjGDJkCOvXr8fDw4OqVasybdo0WrZsmbGfSqVixYoVuLm5MWTIEDZu3Mjlu7GsOHaT439GAZDy3PxGxvoRLA34kzY1bBjRojKLvcYSERHBgQMHslyaXQghhBAip6RQUwBl5xO8osTc3JwDBw6wcOFCZsyYweHDh2nXrl1GkSY1NRWNRqPzJWFzwxdffEFqaipLly7VdZRCx93dHXd3d0qXLk14eDjbt29Hq9Uyc+ZMbFsPIKcjkfTNSmHs0IDHh1dj2WogJiVKYK19xPHjxzM6nTw8PKhXrx7m5uZs3LgxD+6qcLp+/XrGZMDBwcF07tyZwYMHs2XLlgLdTfB8Rw1Av379qFevHitXrnzpPDmffvoplpaWtGnThr179+ZoGei8FBsbi7W1Nfb29hw7dizTtutWLhwKjkRRwO79fztTzJzaY+b073LUBlb2VJq0J9Ox5T9ax1uWWkqbGaGvr8/cuXOZO3fuC9dXqVQ4ODjg4OCAh4fHC9sfPHhAYGAg586d4/Lly1y5coWAgABiY2PZsWMHvXr1yvK+goODKVmyJJB7xXpDQ0OGDRuGp6cnGzZsYPDgwTg4ODBt2rSM/+v6+vr8+OOPdOjQgT5ffE2ISe2Xrhj2bFJq/2uRHLp8F1tVWY7uWV0kvkcJIYQQomCQQk0BculOTLY+wRvd2pF6FYre+PeJEyfSq1cvbty4kekH9PT0dOrVq8fMmTNxd3cvtHODrF+/Hj8/P86ePZvrS/8WRUlJSfw/e/cdFdW5NXD4N0Mbqgj20IwYsWJBARVLgr2hYomixhKNJblibqI3JrF8GuM11kRFI7H3EisaJRYMggTFGkVBVOwiAiJ9mO8PrxOJiILAgO5nLZdy6j7DOHD22e9+T548SWxsLIsXL+bOnTtkZGQwZcoUNBoNzs7OHD58mBo1ahCXnE7lFT/k+xzlOo/j4ZEV3Fo2Ek1GKptqVMfxPxO0621tbWnYsCFRUVE5nsC/bbKzs/nzzz+1zYATExPp1q0bkyZNolWrVjptlpwfhoaGZGRkaL+2sbGhdu3a7N+/P8/Zl4YMGYKlpSXt2rVj69atOn8vnD9/ngsXLrwwaTS6lSNHL8eRmpn/xr/GBvpM7++Wo29PQVSoUIEePXrQo0eP59bl1ZA4JiZGO8zs2Vkp4+LiyMzMpHLlygWO6enU6AMHDmTt2rUMHToUGxsb7fvY2NiYAVOXMmNvJOTy2qXFnuPB3h95Z/iS/8UHGqU+Ce9+wNbTd/FxcyhwbEIIIYQQz5JETQmxJvQq0wMuvtITvKBLcUzs6PRG/lJYo0YNatSokWOZiYkJ69evZ9CgQWzatAk/P7/Xnp2juB07dowvvviCw4cPY2VlpetwShyNRsPVq1cJCQkhNDSUkJAQ/vrrL2rVqsWjR4/o1q0bY8aMoWrVqrkm6sqZGTH4xz3aKgKAcp19c2xj6dH/uf2UKlOs241G0X407WpVxM/n+dnx7OzscHNzK7UJwmcdO3aMmjVrUrZsWTQaTZ7XlJGRwaFDh7TNgC0tLfHy8mLlypW4uLiUymGJ/6yogSeVWhs3bnzpNNk9evTAwsKCnj17snz5cjp16lSUob7Q+PHjWbNmDTNnzsTe3j7XbZxtLZnY0YnpARdIzczOdZvcGBsomdjRiXo2RfsgIK/eY1FRUTg4ODy3fPfu3QwePBilUompqSnly5fH3t6eWrVq4eLiQvPmzalWrVqu7+lnh1GlpKRgZGSkjcHT05MRI0ZQqVIlBoz9mrmn1KCfe88rlW0dbZLmWWmZ2UwPuEg9G8sif+2EEEII8XZQaPKYKsXFxUUTHh5ejOG8ndaEXmWi32Zu7Zqv/SXwxqIhWHd8MtVwwtG1ZCXcplyXf2v3efILdc03MqPuWCAAACAASURBVFnzIunp6UyZMgV/f3/mzZtXaqprYmNjcXV1ZdmyZXTs2FHX4ZQIjx8/5s8//yQ0NFSbmNHT08Pd3R13d3fc3Nxo1KgRV65cwcXFhYsXL77wpvSp07EJ9P05tIBVBHpsHO723E3W1atXqV+/PhEREaV6dq6zZ8/Sr18/Lly4wHfffceXX36Z64xqqamp2iFN+/bto3bt2nTr1o1u3bo9l0AtjZYtW0ZISAj+/v7aZXfv3qVGjRrcunULExOTlx7j+PHjdOvWjblz5/Lhh68/u1JRetkDgKcUClDp65WIBwCDBg1i0KBBvP/++8+tU6vVnD59muDgYCIiIoiMjCQ2NpYHDx6QkpKCQqHA2NiYcuXKaWeoatiwIc2aNaNu3boolUocHBxYtmwZnp5/DwHLyspiw4YNfHvgBtmVa4Pi+STki6Y7f0qh4IXJXiGEEEKI3CgUihMajSbXXx6koqYIzZgxg6CgIPbu3atdVr16dRwdHbXLTscmMKRTM8o075/rk7oXSX0Ln+AZGRnx3Xff0b17dz766CM2b97M4sWLS3R1zePHj+nWrRu+vr5vbZJGo9EQFRWVo1rm0qVL2sai/fr1Y8GCBdja2uZIvL1K5cCzCruK4JtvvmHu3Ln85z//KdVJGgAHBwfmzp1LWloaCxYs4Msvv8y1IubSpUusXr0aLy8v5s2bR6VKlXQQbdHJraKmYsWKNG7cmICAALy9vV96DFdXV37//Xfat2+Po6MjjRs3znU7jUbDlStXqFatWqHEXhA+bg7Us7Fk0eEoDkXeR8Hf1ZkAKn0lGqB1jfKMauWo058lfn5++Pn5cebMGeLj47GwsMDZ2TnHMFE9PT0aNmyYY/a3pzQaDZcuXeKPP/7gxIkTXLhwgYMHD7Jx48YcM1RlZmYyZswYGjRowI4dO/Dz8+PDDz+kvVcvJp3cxvXZvXhn1C9k3r9G3O7ZmDfqQtKf2zF2aIBZvTbE7Z6NzeiVAGTGxfLgt4Vk3ItB39yaXe9/xHSvulibGfHRRx9hamrK1atXCQoKolatWqxbt06n7wchhBBClB5SUVOEgoOD6dixI/Hx8ejp6XH79m3c3d1JS0vj5s2b6Onp4fPjPtZ+1oF3Rq9E39xau+/LKmrg7X6Cl5aWxpQpU1i+fDnz5s2jT58+Ja66Jjs7mz59+mBsbMzKlStLXHxFJSkpibCwMG1SJjQ0FDMzM9zc3LTVMg0aNCiyvialsYqgOGRnZ3Pnzh3at2/P/v37c03CvGw4VGm3bt06du3axfr163Ms9/f3Z+/evWzZsuWVjxUXF4epqekLG8gGBgYybtw4/v3vfzNw4MBctylOD5LT2XLyBhdvPyIpLRMLlQFOlc3xblgymtSnp6dz6dIlrly5wokTJ6hXrx5eXl6v3SvnqdjYWI4ePcrIkSN57733SEpK4urVq9qeRWWb9kZjZEb69bNU6DWJtGtnuLvhayyadMfSwweNJpuMW5HaRI1GncWtn0diVq8NFq7dSYv9i/vbpjHJfweTfNrw0UcfsWvXLvbu3UvDhg0ZNGgQarWaDRs2FMr1CCGEEKL0y6uipvQ1GShFGjduTGZmJqdOnQLg6NGjtG7dmho1anDq1CniktM5ePgI+paVyYq/yY2Fg17puPd/nUHsjz5cm9ObVV99RPCfEdp1AQEB1KpVC3Nzc9555x1++CH/DVZLA5VKxYwZM9i5cydTp07F29ubu3fv6jqsHP7v//6PGzdusHTp0hJ983v37l2OHj1aoH2zs7P566+/+OWXXxg+fDh169alSpUqTJkyhaSkJIYOHcrZs2e5du0aGzduZOzYsbi5uRVp81kfNwc2DnejXa2KGOkrUenn/JhT6Ssx0lfSrlZFNg53eyuSNABKpZIqVarg5OTErl27gOebupbk92lhyK2iBqB79+4cOHCAR48evfKxypUrl+csP56envTp04fx48dz9+5d8nooUhyszYwY0aIac/vUx39QY+b2qc+IFtVKRJIGnlRM1q1bl27dumk/0wsrSQNPGoP369ePsmXLMmPGDCIjIwkKCsLW1pb79+/zfncfUi4EYVrnmSFXCiWWHv1R6BugNMj5OqXfukh2ZioW7t4o9AwwdnDGuFpj9m7fqt2me/fuNGnSBH19ffr376/9XUAIIYQQ4mVk6FMRMjQ0xNXVlaCgIBo1akRQUBAeHh5UqVKFoKAgjJMtSbl+DiPb2vk6rvG7jbDu+C8UegY8ClpB3379ib38FwBDhw5l06ZNeHh48PDhQ2JiYori0kqMJk2acPLkSaZMmYKzszPz58+nd+/eOr/h3Lp1K/7+/oSFhaFSqXQaS17mzZvH3LlzuXv3LqdOncLJySnPqoqHDx9y/PhxbbVMWFgYVlZW2kqZ4cOHPzdcQRfq2Vji5+NS4qsIilJGRgYKheK578UHH3zA7t27+fjjj/Ns6vomelGixsrKihYtWrBz507693++6XRBBAUFsWPHDmbPnl2ih2e+zVxdXTExMeHs2bNkZ6aR+fA2JtVdtev1TCxQvKCxsDo5Hn3z8iie6WejX6YCD+/f0X79bNWaiYkJycnJRXAVQgghhHgTSUVNEWvZsiVBQUHAk4oaDw8PPDw8CAoK4uKdJFKunUNlVzdfxzRzbovSyASFvgFmTT/kRtQFEhMTgSc3In/99RdJSUmULVs217H8b5pnq2umTJmCt7c39+7d01k8p06d4pNPPmH79u0lvsdHx44dCQ0NpX///gQHBwM5p8RVq9UcPHiQwYMHU7NmTezs7Jg5cyYZGRmMHj2ayMhIoqOjWbNmDWPGjMHFxUXnSZpnlfQqgsKWkJDAunXr6N27N1WrVs21isPb25s7d57cTMbHx5OVlVXcYerMixI1AH369Cm0YSkRERGMGzeOIUOG0K9fPwCdV9SI3A0aNIg1a9ZwI2wfpjWa5UzM5JHw1zOzIuvRfTSav3v+ZCXdp2z5kv2ZL4QQQojSQRI1RaxFixb88ccfxMfHc//+fapXr07Tpk05duwY9+MekBl3PV8VNZpsNQ8Pr+Cm3zCuz+nFjcVDAbh8+TIajYatW7cSEBCAvb09LVu2JCQkpKgurcR5Wl1TvXp16tWrx8aNG4v95uju3bt069aNhQsXlook2XvvvUflypWpXr06oaGhQM4bSoVCgVKpxNXVlfXr1/Pw4UMOHTrEd999R9euXalQoYKuQhf/c+PGDRYtWkTbtm2xs7Nj/fr1tGvXjpMnT2Jo+Hw1wJkzZzh//jympqZ8/fXXJCQk6CBq3TA0NNT2JPmnrl27EhQUxMOHDwt07KfJr0uXLjF69Gh69+7NJ598Arz5vX9KMx8fH3799VeiQ/Zh6fzBK+9nVKUGSn0jkkK3olFnkXbtDKlRYXTo1qMIoxVCCCHE20KGPhUxd3d3EhMT+fnnn2nWrBkAFhYWVKlShWvBu9Azs8LAshLqxFerAHn81xFSLodSoe809MtURJP+mNh5fWndujX6+vraWaVGjx5NTEwMXl5enD9/Hmtr67fiRkGlUvH999/nmBlq0aJFxZJQSE9Pp0ePHgwaNIjevXsX+fkKU5MmTbR9S56dDUipVNKqVStatWqlo8jEP2k0Gv766y+2b9/O9u3buXLlCp06dWLEiBFs27YNMzOzF+4bExPD9OnTGT9+PL17934jptzOj7wqaiwsLGjTpg2//vorQ4YMyddxU1NT6d+/P40bN+bcuXN4enry5ZdfAk/6OOU2w5YoGWxtbWnYsCGXLl9GZVeHDPWrJfcVegaU9/6W+P2LSQzdjL6ZNZW6fs6Y7i2LOGIhhBBCvA0kUVPEjI2NcXFxYc6cOUycOFG7vHnz5qxcuxaTfA570mSkotAzQM/YAk1mOo+Orgbg5MmTmJqa8vPPP1OpUiVu3bpFVFQUCQkJVK9eHQBHR0dtIufZv4syibN27Vp++eUXatasybRp07C0LJ7pX11dXYmIiGDSpEnUq1ePBQsWFGnyRKPRMHLkSCpWrMjkyZOL7DwFdefOHUJDQ6ldu7b2/fAsJycnkpKSePDgAdbW1rkcQeiSWq0mJCSEHTt2sH37dtLT0/Hy8mLmzJl4eHi88nCzqlWrEhgYWMTRllx5JWoA+vbty9KlS/OdqDE2Nsbf35927dqRlZXF7t27AUnSlERXr159bpmdnR1ubm7cq1GBAxfuotGAyr6edhrup/65zLC8PZX6fw+AAmhXu6J2WOWKFSty7NuqVStu3LhRqNcihBBCiDeXJGqKwdMhSM2bN9cu8/Dw4KeffqKCW518Hcu0zvukXjnJjYWDUKrMKN9qIJzYg0KhoFy5coSEhHD8+HHUajU1atTg999/p1mzZsTHx3P58mWioqK4fPky+/bt4/Lly9ohU7klcBwdHSlXrtxrJXGsra3p06cPX3/9NdOmTSvwcQpCpVIxc+ZMevTooa2uWbhwYZFU18ybN48TJ04QHBys8xuzjIwMTp06pZ0aOzQ0lMTERNzc3Bg3bhzvvvvuc01kq1Spgr29PXfu3MHa2hq1Wv3WNZotadLS0ggMDGT79u3s2rWLSpUq0a1bNzZu3EiDBg3eigq5wvayRE3Hjh0ZNmwY9+7dy/fnhIODA8eOHaNz587s2bOHTp066fyzQLzc1atX2bZtGxERESTpl+Xo5ThSM9Uv3/EfsjPTsbwVRnZ2Q/m+CyGEEOK1KfLq4eHi4qIJDw8vxnDePsNXh2uf4OWXQgHtalXEzyfXqddf2YMHD7QJnH/+nZ2d/cJKnFdN4ly7do0ePXpw4sSJ14rzdaSlpTFp0iRWrlzJjz/+SK9evQrt2L/99hsfffQRoaGh2NvbF9pxX9WNGzdyJGVOnTpF9erVcXNzw83NDXd3d6pXr57nzcO1a9fo3bs3N27coG3btnzxxRfUqlWrGK9CwJNZtfbs2cP27ds5cOAA9evXx8vLi27duvHuu+/qOrxSLyIigsGDB+c5TXL//v1p1qwZo0aNKtA5srKyuHXrFnZ2dgCkpKQQHx+PjY1NgY4nis4333zD3Llz+c9//qOteF0TepXpARdIzcx+yd5/MzZQMqJJBTZ99ynm5uasWLGCypUrF1XYQgghhHhDKBSKExqNJtebeUnU6Njp2AT6/hxaoCd4xgZ6bBzuRj2bohtO9M9KnKd/P5vEcXR0pE2bNgwYMCDX5qUBAQHMmjWLQ4cO6XwoQGhoKIMHD6ZOnTosWrSI8uXL57l9XHI6W07c4OKdJJLSsrBQ6eNUyYJejZ5M7xwZGYmHhwfbtm3LUTFVVNLS0jh58qQ2MRMSEkJ6erp2emx3d3dcXFwwNzd/5WMmJibi5eVFuXLlaNmyJa1bt6Z27fxNGS8K7vr16+zYsYMdO3YQFhZG69at8fLyonPnzi99f4r8OXfuHH369OH8+fMv3GbXrl3MmjVLO1vf6zp//jzvv/8+27Zt0/YpEyXbk2TNRdKy1Hk+RFEoQKWvx8SOTvi4OZCVlcW0adPw8/Nj6dKldO3atfiCFkIIIUSpI4maEq6gT/AmdqyJj5tD0QX2Es8mcczNzWnbti0qleq57RYuXEhoaCirV68uEUNqUlNTmTRpEqtWrXphdc3p2AQWHo7iyKX7AKRn/f29Uekr0QBNq1pydMnX/Gd4P4YOHVrocWo0Gq5du5ajWubcuXPUrFlTm5Rxc3Pj3XfflWEwpYhGo+HcuXPaZsDXrl2jc+fOeHl50aZNG0xNTXUd4hsrMjKSLl26cOnSpRduk56eTpUqVTh9+nShVcHs378fHx8fVq1aRfv27QvlmKJonbmRwKLDURyKvI8CSMvlZ0DrGuUZ1crxuYclwcHB+Pj40L59e2bPno2JiUnxBi+EEEKIUkESNaVAQZ/glQa+vr6Ym5szderUHIkatVrN5MmTtVU51atXp3z58sWWdAgJCWHw4ME4Ozvz008/aasXXvV7gSYbPTRM8apXKN+Lx48fc+LEiRzVMgqFAnd3d21SplGjRvJLfymkVqsJDg5m+/bt7NixA7VajZeXF15eXjRv3hx9fWkXVhyuXLnCBx98QExMTJ7bbdmyhQ8++ICyZcsW2rlDQkLw8vJiwYIF9OnTp9COK4rWg+R0tpy8wcXbj0hKy8RCZYBTZXO8G9poGwfnJjExkdGjR3PixAnWrVtHgwYNijFqIYQQQpQGeSVq5O6ghPBxc6CejWWBn+CVZAkJCXTq1AnIOfVzeno6+vr6BAYGsnjxYqKiosjIyHhhY+MKFSoUahLH3d2diIgIvv32W+rVq8ePP/5Imo3Lq1c3KZSogekBFwDylazRaDRER0fnSMpERkZSt25d3Nzc6Nu3L/PmzcPOzk6qZQrZy4azFZbU1FQOHDigbQZsY2ODl5cXW7duxdnZWb6vOmBoaEhGRsZLt+vevXuhV/65u7tz4MABOnToQEJCAiNGjCjU44uiYW1mxIgW1fK9X5kyZVizZg1r166lbdu2jB8/nnHjxkmjYSGEEEK8EqmoKYEK+gSvpFmyZAl+fn6cPn2aLl26aBMieU0l/PDhwxc2Ns7IyMiRuFmyZAmJiYmcPn2amjVram98GzRowKlTp4iJicHBweGVYg0JCWGQ77dktRxDtjL/+cuX9Qt69OgRYWFh2qRMaGgoJiYmOYYwNWjQINehY6JwvMpwtlY1yjOqpSPOtgVLhD548EDbDPj333+nYcOG2mbAr/peFEXn7t271K1bl3v37ukshujoaNq0acPw4cOZMGGCzuIQxScmJgYfHx9MTExYuXIlVapU0XVIQgghhCgBZOiT0In09HQiIyOJjo4mPDwcZ2dnevToUeBhHk+TOE8TN//973+1Q6mUSiWOjo6UL1+e8PBw4uPj2bRpEx4eHlSsWPGVqheG+Adz8HI8KPL/xPPZGbiys7OJjIzU9pUJCQkhOjqaBg0aaJMyLi4uOpkh6m1VlEMLr169yo4dO9i+fTsnTpzA09MTLy8vOnXqhLW1deFcgCgU8fHxVKtWjYcPHxZof41Gw6VLlwgLC2PAgAEFjuPWrVu0bduWjh07MnPmTKmuegtkZWXx3XffsWjRIvz8/PDy8tJ1SEIIIYTQMUnUiDeSg4MDw4YNY8eOHezfv5/o6GgmT55MWloav//+Ow0aNCA6Oprk5GQAjIyMaNSoEUOGDMHR0RGVSkWTJk1YtmwZkyZPJl5RhvIffsfDQ8t5fO4gCkNjLJp48fDAEuy+3IFCqUfymQMkHt+K+tED9IwtsHDzxrxBBwD0yMY8cDpnT/4JgJWVFUOHDqVnz544Ozvz3nvvMXLkSNauXUtkZCTTpk0jNDSUrVu3aq/ps88+Q6FQMH/+/GJ/PZcvX87UqVPJysrijz/+eGMSSYXdrFuj0XDmzBltM+AbN27QpUsXvLy88PT0lB5CJdijR4+oXLmy9jOhIMLCwujVqxfR0dGv1VsoPj6ejh07UqdOHZYsWaLzJuuieISEhNC/f3/atGnDnDlzpHm4EEII8RbLK1Ejg6VFqebm5kZSUhJ37tzRDnny9/cHYNu2bezYsYOgoCBCQkKYNGkSERERrFq1ivHjx9OuXTsAvvjiC2w/GEjF3pNIPv0baVdOUHnwAioPnk/qpdAc51OaWFLBexK2vpuw7jSWh78vI/1OFADpd6K5dPEiu3fvJiMjgx9++IENGzZQr1497bTl69evZ8+ePSQkJODj48O+fftISEgAnjxx3bBhAwMHDiyuly+HRo0asWfPHsqXL09mZqZOYnjKzMxM+0epVGJsbKz9eu3ata98nNOxCUwPuJivJA1AamY20wMucubG39+bw4cPM3bsWKpWrUr37t1JTExk/vz53L59m19++YWuXbtKkqaEMzAweOX39sWLF3N8nZaWRnBwMPv37+f27duEhIS8VixWVlYEBgZy7do1+vbtS3p6+msdT5QO7u7unDp1itTUVBo1asTJkyd1HZIQQgghSiBpJixKvQEDBrBq1SpatmxJzZo1eeedd7TrWrVqpf13kyZNuH37NgqFgrlz53L16lWqVq3KL7/8wopIuJ1gwOMLf2Du0hV9i3IAWLj3Iu3aae0xTBwba/+tsquLqmoD0mPPY1TJkcTT+6natANmZmaEh4dTr149FAoFW7ZswcPDA7VazdChQ7GyssLAwIBKlSrRokULNm/ezMcff8y+ffsoV64cjRo1KvoXLRf16tUjJSUFpVKpTSzpyrMVDw4ODixbtgxPT898H2fh4SjSstQFiiEtU83X645i9ddWdu/ejb29PV5eXuzatYs6derIcJVSKD+Jmn/961+MGjWKOnXqsHz5co4cOUJKSgoeHh4cOXIEd3d3NBrNa70PzMzM2L17N/369aNLly5s27YNMzOzAh9PlA4WFhasWrWK9evX0759e/7973/z73//WxoNCyGEEEJLEjWi1BswYAAtWrQgJibmuWqU48ePM2HCBM6dO0dGRgbp6en06tUrxzadOnViZ+IpSLiHOjlem6QB0Dcvl2Pb1OhwEoLXkxV/E41GgyYzHcPyT4YIZSXe48LZQFr/vhl4MkRGo9Hw+eefY2hoyK1bt/jqq6+YMGECGRkZ2v46Bw4c4KuvviI5ORmVSoWTkxOGhoYYGhpiZGRUJP+2s7PDwcHhuRsDtVqNWq3Os+GzLqWnpzN+/Hg2bdoEQO/evZk5cyZGRkYcPnwYHx8fPvvsM3744QcUSj30WwwjCz3iA38mOzUJiyY9KNO0NwAJR9eSGXcdhb4BKZePo1+mAuW7f0VKZDBJf+5AoWdAavvR+DZtzNSpUylTpgzjxo2jbdu2KJVKBg8ezJQpU9DT02PFihUsW7YMNzc3/P39sbS0ZNGiRXTo0EGXL5f4h6fDi57+38tLp06d6NGjB66urtSpU4epU6fSunXrHNsURrLOyMiIjRs3MmLECNq0acOePXuwsrLKsU1xzVQmiteHH35I06ZN8fHx4bfffmPlypXY2NjoOiwhhBBClACSqBGlnr29PVWrViUgIEA77Ompfv36MWbMGPbu3YtKpWLs2LHExcXl2Ob69evcu3ENMEbPrCxZjx5o12U9+ntbTVYm93+dgXVnX0yqu6HQ0+fe1mk87fKkb1Gehj2HE7zhp1zj/GdlSHZ2NklJSTg4OLB161Y6dOjArl27KFeuHBkZGdrEUn7+nZSU9Erbe3t7M2LEiFwTNRqNJteKmpSUFGrWrFnkSaR//jszM5Nbt24RGxvL3LlzCQ4O5tixYxgZGdGzZ0+mTZvG//3f/wFw584d0tLSuHnzJoO/+i8bFv0XI4cGVP5oHllJ97mz0heTWi0wsKz05JqiwqjQ82usO/nyYM887m78FnPnttiMXkny2UDi9/thOSYYOzs7unfvToUKFYiKiuLx48d07twZW1tb7TTLx48fZ9CgQcTFxbF06VKGDh3KzZs3pfKmhHlaVfOyRE2/fv2YMGECx44dy7FcrVajUCgKtfpBX1+fZcuW8cUXX9CyZUv2799P5cqVXzJT2R3mBl567ZnKhG7Z29tz+PBhZsyYQaNGjVi8eDE9evTQdVhCCCGE0DFJ1Ig3gr+/Pw8fPsTU1JSsrCzt8kePHmFlZYVKpSIsLIx169bRsmVLtm7dyrZt24AnPQNq9vgMfStnTJ08eBS+E+NqLigNVCSFbtEeS6PORKPORM+kDCj1SI0OJy0mAoP/VdRYN2zPhV3fc/z4AJo0aUJKSgqHDx+mRYsWmJubPxezUqnE0tKSXr16MWbMGFxdXWnevHkRv1J5y8rKIjs7O9ebWJVKRVBQUJ6JoJclk9LS0khMTMxX8unevXuMHz8efX19bt26hUqlolatWqSnp6PRaAgJCWHBggUoFArUajXLly9n/fr1ZDh7o05JwsKlK0ojEwzL22NgbUvmvRhtokZlWxvjd58MNTNxak7KpRAs3LxRKPUwrdmC+H0/cSrqFndrmBEQEEBCQgLGxsaYmpri6+vL0qVLtYkae3t7Pv74YwAGDRrEqFGjuHv3LpUqVSqm7554FYaGhmRkZKBSqfLcrly5cowbN46oqCgcHR3Jzs5GqVQWWdNfhULBrFmzsLa2pnnz5nw6fxNLwu6/cKaytP8lbfb/dZegS3H5mqlMlCx6enp8/fXXtGnThv79+xMQEMC8efNkGJwQQgjxFpNEjXgjVKtWLdflixYtYty4cYwcOZIqVaoAsH37dlJSUmjYsCEAN2/eJDE9m2YzD2JWvx2Z8Te57f8pCiMTLBp1Ie36WVAoURqZYOU5nPvbZ6JRZ2Li2ATj6k205zKq8h5+S5YyZswYLl++jLGxMc2bN6dFixZ5xj5o0CCWLVvGL7/8UkivRsE9nerc0vL5p/NKpVInM0E9W4lkbGxMWFgYtWvXBuD8+fM0aNCAa9eucejQIUaNGkVgYCAZGRlM/C2WKxtBz+Tva1EYGJGdkab9Ws805zqlsQUKpZ72a4CHiYlcu3aNzMxMKleurN0+OzsbW1tb7dfPJmSeNhV+ndmFRNHIT5+aCRMmYGxsDFAs/UMUCgX/+c9/iNG3Ye6Rayj0Xz6sSaOB1Ew10wMuAORI1jz7f2fy5MlERUWxZs2aogpfvCZXV1ciIiL49NNPadiwIevWrcPFJdeJIIQQQgjxhpNEjSi1rl69mutyhUJBeHg4GzduJDAwkIcPH9KgQQM8PT3x9PTE1dVVO7Tn6ZCZcgbQoJIRodezsPL8GCvPJ5URqdHh6JlZaYevmDfqjHmjzrmcE1rXKE/v7i707t4lX/Ha2dlhbGxMz54983P5he7jjz8mICCA27dvY2dnx7fffsuwYcN0GtM/ValShWvXrmkTNTdv3qRKlSpYWlpStmxZ9PT0tEk7m4jCSZKYGRlga2uLkZERcXFxrzUls9A9Y2PjV07UFKSiwcHBgVu3bnHr1i3Klfu7x9XTWeliYmJwcHB44f6naBMh+gAAIABJREFUYxM4+qg8Cv38NcF+OlNZPRtL6tnIMKjSytzcnBUrVrBx40Y6duzIuHHj+OKLL2T6diGEEOItI1MMiFJPo9EQFRWFn58f3t7eVKhQgYEDB3Lr1i0+++wzbt68SXBwMFOmTMHDwyPX/ivr16/n0E8T0MvOIDX6TzTZarIexZEQvB6T99xfGoNKX49RrRzzHXt2djZz5syhb9++WFhY5Hv/wvTzzz9z7do1UlJSOHPmDD4+PjqNJzcffvgh06ZN4/79+8TFxTF16tQXxlmjYuG8no4VTKlcuTJt27bl888/JykpiezsbKKjozly5EihnEMUn9jYWCpWrJivfdLS0sjIyHjl7atWrcr69eu1X589e5aUlJRX2vefM5Vpsl89YZOWpWbR4ahX3l6UXH369CE8PJy9e/fi6elJbGysrkMSQgghRDGSRI0ole7du8eGDRsYNmwYVatWpWXLloSEhNCtWzfOnj3L+fPnmT9/Pl26dMkzAZKRkcG//vUvvvnmGw5sXMZXnWqR9Mc6Yuf15fbyf2FgbYulR94JC2MDJRM7OuX7Kfbjx4+xsLDgwIEDTJkyJV/7FhV9fX1UKhWWlpYv7eGhC19//TUuLi7Uq1ePunXr0rBhQ77++utct+3ZqHBmT+lU98lwp1WrVpGRkUGtWrUoW7Ys3t7e3L59u1DOIYqPUqnMd4Pne/fu4eTkRHZ29ss35slMdKtWrdJ+vXLlSu2MdPfv36dixYqo1X8nYLZt24azszNxyels/Xku97Z9R9yuH7g+pxfJZwNRpz4ibs88bvw0kNi5fbi3dZp235SoMG798inX5/bh9qp/sy8ojAfJ6S+NsVevXlSqVIkyZcrQokULzp8//6ovhygmdnZ2HDx4kDZt2uDi4sKWLVtevpMQQggh3ggKTW5dCv/HxcVFEx4eXozhCJG7x48fc/ToUQIDAwkMDOTq1au0bNlSO5zJyckp3zdft27donfv3pQtW5bVq1dr+7KsCb3KlJ3nyMzWgOLFuUyF4kkljTTxLLmGrw7nwIW7uTZjfRmFAtrVqoifj/SIEODs7MyPP/740p5TT/vCjB49mu3bt/Pee+9hb29PcHAwDg4OxMTE0LFjR2bPnq2dvr179+40b94cUxcvJn7zLfHBGynvNQHj6q5osjKJ2z4DhYExVu1HozRQkX7zAiq7umTciebupm+p4P0thpUceXz+MIl/rGXulkOM8ayVZ4+aX375hV69emFoaMj48eM5fPgwp06dKvLXURRMWFgY/fv3x8PDgwULFkijYSGEEOINoFAoTmg0mlxvNqSiRhSL8+fPc/LkSYAcT5JfJCsri9DQUKZNm0arVq2oWLEiM2bMoEyZMixevJi4uDh27NjBp59+Ss2aNfOdpAkKCqJx48a0b9+eHTt25Gie29/VHpPQpdSzBiN9JSr9nP9NVPpKjPSVtKtVkY3D3SRJU4KNbuWISr9gvR0KOpxNvJn69u3Lxo0bX3n7p1U1Bw4coGbNmrzzzjvadYMGDdImTOLj4/ntt9/o168fF+8koc7WYFTFCZP33FEolGSnPyY1+gRW7UejpzJDoaePyq4uAI9O78O8fnuMqtRAodTDrO4HoGfAkaPHco3pWUOGDMHc3BwjIyMmT57M6dOnSUxMzOerIopLkyZNiIiIQKFQ0KBBA8LCwnQdkhBCCCGKkHTFFEUqIyMDb29vgoKCcHR0JDw8PNfZUzQaDZGRkdqKmSNHjmBnZ4enpyfjx4/Hw8OjUJ4gajQa5s2bx/fff8+qVato167dc9scPXqU1BsX2f55Jx6mZLLl5A0u3n5EUlomFioDnCqb493QBmuzl8/IInTL2daSiR2dmB5wgdTMVxu2AgUfziZKL41G88Kp6eFJzxB3d3fmz5//Sg2lBwwYQIsWLYiJidEOe3rKx8eHmjVr8vjxYzZt2oSHhweVK1cmKe0GAHoWfzchVifdR2lsjp7q+c+/rMR7PD57kKQTu/9eqM7i/r07ecamVquZOHEimzdv5v79+9rP5Li4OMqUKfPSaxO6YWZmhr+/P5s3b6ZLly7861//Yvz48dJoWAghhHgDSaJGFClDQ0OGDx/O4sWL8fLy4t69e1SoUCHHNqmpqXzxxRfs3LmTNm3a0KdPH5YsWZLvhp8vk5yczNChQ4mKiuL48eMvnHll9uzZ+Pr6olQqsTYzYkSL3Kf+FqXD04qn6QEXSctS5zkMSoazvfl69+7Npk2bnlseFhbGtGnT2LlzZ64Veu+++y729vYcOnSINm3avPQ89vb2VK1alYCAAPz9/XOse+edd3B3d2fbtm2sXr2akSNHAmChevIj+dnz61mUJzv1EdlpySj/kazRtyiPqmlvyjTtk2N5g/rvkJd169axY8cOAgMDcXBwIDExkbJly5LXUGhRcvTq1Qs3NzcGDhzIb7/9xurVq7Gzs9N1WEIIIYQoRDL0SRS5jh078s4771CpUiUOHjwIkKMpp7GxMTNmzODatWv4+/vz4YcfFnqSJjIykiZNmmBubq7tFZGbS5cuERISwqBBgwr1/EK3fNwc2DjcjXa1KspwtrdccHAwZ86c4fz585w6dYrw8HBCQkJ49OgRQUFBZGVlvXDfvn37smHDhlc+l7+/PwcPHsTU1PS5dQMHDuS///0vZ8+epUePHgA4VbJAT5kzSaRvZoVxtUY82L8YdVoyGnUWadfPAWDm3I5HEXtJvxX5pCIoI42smHAcyuQ9FPTRo0cYGRlhbW1NSkoKX3311StfkygZbG1tCQwMpEOHDri4uOSafBRCCCFE6SUVNaLIPS2rb9asGYGBgfTt2/e5J7fm5uZFdv5t27YxYsQIZsyYwbBhw/Lcdt68eYwYMQITE5Mii0foRj0bS/x8XHiQnC7D2d5ijx49ol+/fhgZPfle6+npoVQq0dfXx83NLc99e/fujbOzM4sXL8bQ0PCl56pW7cXVeN27d2fkyJF0795d+3nj3ciGiblsa935cx7+/jO3ln4C6kyM7OuhsquDUeXqWHf4lPj9fmQ+vIVS3xBju9p0cx6dZ1xPKzHeeecdrKys+L//+z8WL1780usRJYuenh4TJkzA09OTfv36ERAQwI8//likP0+FEEIIUTxk1idRaDQaDefPn6dMmTLY2to+t/6PP/5g7NixFNd7Kisri4kTJ7Jx40Y2b95M48aN89z+wYMHODo6cuHCBSpVqlQsMQohile1atW4cOHCKyVactOiRQu+/PJLOnfuXCixLFmyBE9PT+0ymalMFERycjK+vr4cOnSItWvX4urqquuQhBBCCPESMuuTKDKxsbGsWLECHx8fKleuTLdu3QgODs51+ECzZs1QqVQkJycDkJmZWWRx3bt3j7Zt2xIREUF4ePhLkzQAfn5+dO/eXZI0QrzBpk6dqh16qdFocvx5dkjmi/Tp0ydfw59eZOvWrSgUCt5///0cy2WmMlEQZmZm/Pzzz8ycOZOuXbsybdq0V5phUQghhBAlkyRqRL4kJCSwfft2xowZg5OTEw0aNGDv3r20bNmSY8eOER0dTd++fXOdFeXs2bNcv34dW1tb+vfvz82bN4skxtDQUFxcXGjatCl79+6lXLlyL90nPT2dhQsX4uvrWyQxCSFKhv79+6NSqYAnTXuf/ZPbjHT/5O3tze7du0lJSSlwDK1atWLkyJEsXLjwuXM+nanM2CB/P55lpjIB0LNnT06cOMGhQ4do1aoV165d03VIQgghhCgA6VFTSsQlp7PlxA0u3kkiKS0LC5U+TpUs6NWoaPtqpKenc+zYMX7//XcCAwM5f/48TZs2xdPTk/Xr1+Ps7PxKNzd3795l7NixdOnShc6dO/P+++9re0QUFo1Gg5+fH5MmTWLZsmV07dr1lfddv349devWpW7duoUakxDizVKxYkUaN25MQEAA3t7eBTrG4cOH81wvM5WJ12FjY8OBAweYPXs2jRs3ZsGCBfTt21fXYQkhhBAiH6RHTQl3OjaBhYejOHLpPgDpWX+X5qv0lWiAVjXKM6qlI862r/8kNTs7m9OnTxMYGEhgYCDHjh2jVq1aeHp64unpibu7u/ZpdEmSkpLCyJEjiYiIYOvWrVSvXv2V99VoNDg7OzNr1izatWtXhFEKIXQtNTUVPT29AveogSezOe3bt4/NmzcXYmTPO3MjgUWHozgUeR8FkJbL53/rGuUZ1cpRKmlErk6cOEG/fv1wdXXlp59+wsLCQtchCSGEEOJ/8upRI4maEmxN6NVieaIaExOjTcwcPHgQKysrbWKmVatWlC1btuAXUQyio6Pp2bMntWvXZunSpblOhZuXAwcO4Ovry9mzZ1Eo8p7WVghRuvn5+VGzZk1atmxZ4GPEx8dTtWpVbty4USwz7MhMZeJ1PH78mHHjxnHgwAHWrl2Lu7u7rkMSQgghBHknamToUwn1JElzgdTMlze31GggNVPN9IALAC9N1jx48ICDBw9qkzPJycl4enrSvn17Zs2ahZ2dXWFcQrHYs2cPQ4YM4ZtvvmH06NEFSrTMmTOHcePGSZJGiLdAeHg4enp6r5WosbKywsPDg507d9K/f/9CjC531mZGjGjx4qm+hciLqakpS5Ys4ddff8XLy4vRo0fz1Vdf5dpLTgghhBAlgzQT1iEHBwcMDQ2Ji4vLsdypTj0GuFfl0f3b+TpeamY20wMucuZGQs7lqakcOHCA8ePH06hRI6pWrcqKFStwcnJi+/bt3Llzh7Vr1zJ48OBSk6RRq9VMmjSJESNG8OuvvzJmzJgCJVrOnz/PqVOniuVmSwihewYGBoUy41zfvn0LZfYnIYpL9+7diYiIICgoiFatWnH16lVdhySEEEKIF5BEjY5VrVqV9evXa78+e/Ysdx4kFvh4aVlqFh6K4s8//2TGjBl88MEHlC9fnsmTJ6NSqZg3bx5xcXHs2bMHX19f6tatW+oqSeLj4+ncuTOHDx8mPDycpk2bFvhYc+bMYdSoUYXe2FgIUTIVVqKma9euBAUF8fDhw0KISojiUaVKFfbv30/37t1p3Lgxa9eu1XVIQgghhMiF1L3q2IABA1i1ahWffvopAH4//4K+Uyu4swqAlKg/SQhaTVbCbZRGppjVa4Olx9/VH2mx50k4vJyMuFiUhsZYeviwL7MFR36YjoleNrGxsZiYmFC3bl0mTJiAsbGxLi6z0Jw8eZKePXvSs2dPZsyYgYGBQYGPdffuXbZt28bly5cLMUIhRElWWIkaCwsLPD09+fXXXxkyZEghRCZE8VAqlXz++ee8//779OvXj71797Jw4ULKlCmj69CEEEII8T9SUaNjbm5uJCUlceHCBdRqNevWr8eiTmvteqWBEeU6j8PWdyMVek3iUcReUi6FAJCVeI97mydj3qgLtp+tpfKQBRhWfBeVkRHv1KiLjY0NkZGRREdHc/PmTaZOnaqryywUy5cvp127dvz3v//lhx9+eK0kDcDChQvp06cP5cqVK6QIhRAlXWElakCGP4nSrUGDBpw4cQJzc3Pq169PcHCwrkMSQgghxP9IoqYEeFpVc+DAAcwrO6A2sdKuU9nXw7CCAwqFEsMKVTGt1YK06+cAePzXYVT2zpjWaolCTx89YwsMK75LWlY2QTs3MHfuXKysrDA3N+err74qtTcUWVlZjBgxgpkzZ3LkyBF69er12sdMTU3Fz88PX1/fQohQCFFaFGaiplOnToSFhXHv3r1COZ4Qxc3ExITFixczb948evbsyeTJk8nKytJ1WEIIIcRbT4Y+lQADBgygRYsWxMTE4ODWgWvPrEu/FcnDwyvIvH8NTXYWmqxMTJ2aA5CVFIdB2crPHS87JZGs9DQaNWqkXabRaFCr1UV9Ka8lOzsbpfL53GFWVhYVK1YkLCwMCwuLQjnXqlWrcHV1pUaNGoVyPCFE6VCYiRoTExM6derE1q1bGTlyZKEcUwhd6NatG02aNGHQoEG0aNGCNWvW8O677+o6LCGEEOKtJRU1JYC9vT1Vq1YlICCAus3a5FgXt3MWJo6u2IxegZ3vJswbdAA0AOhblCPz4fMzQylNLNA3VHH+/HkSEhJISEggMTGR5OTk4ricfMnMzGTs2LGcO3cOpVKZazJJpVIxZcqUQkvSZGdnM3fuXD7//PNCOZ4QovQozEQNQJ8+fUpttaIQz6pcuTL79u2jV69euLq6snr1ajQaja7DEkIIId5KkqgpIfz9/Tl48CB1HSpipP/3tyU7IxWlsTkKfUPSb0Xy+K8j2nWmtVqRdu00jy8cRZOtRp2aRMbdKxgb6OPRpQ++vr7akvybN2/y22+/Fft15SU+Pp7WrVuzd+9ePvnkE1JTU9HT08s1WVOYM1MFBARgampKy5YtC+2YQojSwdDQkIyMjEI7Xrt27Th3+Rozd55k7MYIhqz8k7EbI/A7Es2D5PRCO48QxUGpVOLr68uBAweYMWMG/fv3JyEhQddhCSGEEG8dSdSUENWqVcPFxQXvRjY5llu1HUnC0TVcn9OLxOAN2mFPAPplKlCh1ySSwn4ldl5fbv/yGRn3YtAAq/3m4+joiJubm3Z2ksjIyGK+qrxZWlrSr18/zpw5g7OzM8OGDQNAT0+P7OzsIjvvnDlzGDduXKmbllwI8foKs6LmdGwCn246S5mBC1gacpPtp25x8OI9tp+6xbzASzSdeZARa8I5HSs3uqJ0qV+/PuHh4VhZWVG/fn3++OOPPLd//PhxMUUmhBBCvB0UeZW1uri4aMLDw4sxHAEwfHU4By7cpSAVxwoFtKtVET8fl8IPrAio1Wr09PS4f/8+H374Ia1bt2bixIlFdr6IiAi6du3KlStXXnvWKCFE6bN48WJOnz6Nn5/fax1nTehVpgdcJC1LnedntUIBKn09JnZ0wsfN4bXOKYQu7N69m48//pigoCAcHR2fe8iRkJDAhx9+iKenpwwpFkIIIfJBoVCc0Gg0ud64S0VNCTS6lSMqfb0C7avS12NUK8dCjqjo6Ok9uc7y5cvzww8/sHnzZo4ceTK86+LFi4V+viNHjvDZZ59JkkaIt1RhVNQ8SdJcIDUz7yQNgEYDqZlqpgdcYE3o1dc6rxC60LlzZy5evIiNjU2ulagZGRmMHTuW8ePHs23bNh1EKIQQQrx5JFFTAjnbWjKxoxPGBvn79hgbKJnY0Yl6NpZFFFnRql+/PrNmzWL06NG4ubmxbt26Qp+patiwYfLET4i32MsSNQ4ODhgbG2NmZqb9M2bMGO3607EJTA+4SGpm/oZnpmZmMz3gImduyDAoUfqUKVMGY2PjXNdVqFCB69ev07x5c3r06FHMkQkhhBBvJknUlFA+bg5M7FgTYwM9XtZKRaEAYwM9JnasWapK63MbdmdtbU1MTAxubm5MnTpVW3FTWMzMzHKdAlwI8XZ4lYqaXbt2kZycrP3z008/adctPBxFWlbeCWSNRoNG83wiJy1LzaLDUQULXIgSKiQkhAULFjBnzhyAQn/AIoQQQryN9HUdgHgxHzcH6tlYsuhwFIci76MA0rL+/uVfpa9EA7SuUZ5RrRxLVSVNUFAQJ0+eZNSoURgaGgJPfrn74YcfWLx4MQMHDtRxhEKIN1FBhz5NnjyZ8xcjOVN9IBoNZCXc5abfUOy+3IFCqcedtRMwsqlF+vWzZNyNpvLQn1A/TuRh4FIyH97EoOw7lPUcziE9JQ+S0+nZuR0eHh4cPHiQM2fO4O7uzrp16yhXrlwRXLUQReP+/ft88803DBkyhIYNG5KdnV3oD1iEEEKIt5Ekakq4ejaW+Pm48CA5nS0nb3Dx9iOS0jKxUBngVNkc74Y2WJsZ6TrMV6bRaJg3bx7ff/89K1eu1CZp4Em/mlWrVqGvL29LIUTReJ0eNdcfpED1F69/fP4QFXpNxsDahuzUJO6s8KVsmxGY1mpJysU/uL95Muaj/dly8gYA69atY+/evdja2tKhQwd++OEHvv/++wLFJkRx+uOPP2jevDnz58+nTJky+Pr6AshsikIIIUQhkTviUsLazIgRLarpOozXkpyczLBhw7h8+TKhoaFUrVr1uW0KI0mj0WhQKBRoNBp5uieEyMHQ0JCMjIw8t/Hy8srxWTRr1iwAEtMySc96cW8aszofYFjeHoDUmAj0rapgVud9AExrtSQpfCcPL4ZysUkNAAYPHsx7770HQO/evdm5c2fBL0yIYpKcnMyUKVO4c+cOGo2Gc+fOAX/P4iiEEEKI1yfNOkSxiIyMxNXVFVNTU4KDg3NN0hQWhUJBTEwMCoVC+0ujjJkXQsCrVdRs376dhIQE7Z+PP/4YgEx13lM86Vn8PWxJnRyPvkWFHOv1y1RAnfyApLQn569UqZJ2nYmJCcnJyfm6FiF0wczMjAMHDtC+fXuuXLnCpUuXAHL8vM2tB50QQgghXp0kakSR27ZtG82bN2fs2LH4+/ujUqmK9HyLFi2iTp069OjRg/379wPkeMr36NGjIj2/EKLkKujQJ1NTUxRZ6dqv1Y8fPr/RM8M+9MysyEq6l2O1Ouk+embWWKgM8n1+IUqaWbNm8euvvxIeHk5WVpZ2eVpaGiNHjiQ+Pl6H0QkhhBClmyRqRJHJyspiwoQJ+Pr6EhAQoH0qXdQ2bNjAnDlz6NChA1OmTKFly5b4+/sD8PDhwxwzuAgh3i4FTdTUr1+fO5GnUD6OIzvtMYkhm/Pc3riaC5nxt3h8/jCabDWPLwSREReLpZMrTpXNCxq+ECVKu3bt6NevX46hggYGBqhUKurXr8/hw4d1F5wQQghRikmPGlEk7t27R9++fdHX1+fEiRPFNpOJRqNh+vTpWFlZUbt2bfr27cuePXtYuXIly5cvJzo6mhEjRhRLLEKIkudVEjVdunTJUYXXpk0bfv31V3r36c3KpaNRGltg4daT1KjjLzyGnrEFFby/5WHgUh7sX4SBZWUqeH+LnkkZvBvasL7QrkiIksXQ0JB58+bRvn17+vXrx8CBA5k6dWqOyQOEEEIIkTdFXuOIXVxcNOHh4cUYjngTHD9+nF69ejFgwACmTp1aYpoLbt68mWHDhnH79m1MTEx0HY4QQgciIiIYPHgwp06dKtD+w1eHc+DCXQrSgkOhgHa1KuLn41KgcwtR2ty7d4+hQ4dy+/Zt1q1bp22eLYQQQghQKBQnNBpNrr8YytAnUWg0Gg1+fn506dKFH3/8kenTp5eYJA1AdHQ0Pj4+kqQR4i32OtNzA4xu5YhKv2Cfayp9PUa1cizwuYUobSpUqMDOnTsZMmQIzZo1Y9myZdJoWAghhHgFMvRJFIqUlBRGjhzJyZMnCQ4Opnr16jqNJzMzE7VajZGREYr/NfgcOXKk9t9CiLfT6yZqnG0tmdjRiekBF0jNfPFU3f9kbKBkYkcn6tlYFvjcQpRGCoWCUaNG0bJlS/r168fevXtZunQp1tbWug5NCCGEKLGkoka8titXrtC0aVOysrIIDQ3VeZIGYP78+YwfPz5HYqZMmTJYWFjoMCohhK4ZGhqSkZHxWsfwcXNgYseaGBvo8bLcr0IBxgZ6TOxYEx83h9c6rxClWe3atQkLC8Pe3p769etz8OBBXYckhBBClFiSqBGvJSAgAHd3d4YOHcqaNWswNTXVdUhkZmayYMECBg4cqOtQhBAlzOtW1Dzl4+bAxuFutKtVESN9JSr9nD9OVfpKjPSVtKtVkY3D3SRJIwRgZGTEnDlz8Pf3Z8CAAXz55ZevnTgVQggh3kQy9EkUiFqtZurUqfj7+7Nt2zaaNWum65C0tmzZwrvvvkujRo10HYoQooQprEQNQD0bS/x8XHiQnM6Wkze4ePsRSWmZWKgMcKpsjndDG6zNjArlXEK8Sdq2bcupU6cYNmwY7u7urF27FicnJ12HJYQQQpQYkqgR+RYfH0///v1JSUkhPDycSpUq6TokLY1Gw+zZs5k0aZKuQxFClECFmah5ytrMiBEtqhXqMYV405UvX57t27ezZMkSPDw8mD59Oh9//LH0khNCCCGQoU8inyIiInBxcaFWrVoEBgaWqCQNwNGjR3n06BGdOnXSdShCiBKoKBI1QoiCUSgUfPLJJwQFBbF48WJ69OhBXFycrsMSQgghdE4SNeKVLV++nLZt2/L9998ze/ZsDAwMdB3Sc2bPno2vry9Kpby1hRDPk0SNECVPzZo1CQ0NpVq1atSvX5/AwEBdhySEEELolEKj0bxwpYuLiyY8PLwYwxElUXp6Op999hlHjhxh27Zt1KpVS9ch5erSpUs0b96cq1evYmJioutwhBAlkFqtxsDAgOzsV59aWwhRfAIDA/noo4/o27cv06dPx8hI+jwJIYR4MykUihMajcYlt3XSo+YtE5eczpYTN7h4J4mktCwsVPo4VbKgV6Pcm15ev34db29vbG1tCQsLK9HTW8+bN48RI0ZIkkYIkaunn39WnccxeEUYZYwN8vz8E0IUP09PT22jYTc3N9atW0fNmjV1Hdb/s3fnYVWV6xvHv3tgFBBFnNLEIUVzKnEuxdmDmqmZ5VB2zCGH0rLBIdP60ZxaJlGammlqqZXzkIlDziUmKZqWAygoCALCBvZm//7wRBGgoox6f67L65L1vmutZ2lnH7l532eJiIgUKq2ouUMcOhvP7JATbDt+EYBU698/TXY2G7ED/nW8Gdm2Fo2qegJXf6o1cOBAXnjhBcaPH1+sG/zFxsZSq1Ytjh49Wuz65ohI0bqZzz8RKVp2u505c+YwadIk3njjDYYPH16s/x0iIiKSV9daUaOg5g6waM8pAteFY7HauMZfNwYDOJtNTPyPL5HblvLhhx+yePFi2rdvX3jF3qTAwEBOnjzJvHnziroUESlG8vr5NynAl4EtfAqtPhG5tvDwcAYMGECVKlWYO3cu3t7eRV2SiIhIvrhWUKOOq7e5q9+kHCUlPfdvUqzx0Zx+uzsZNhsp6TamfBfK4n1n2L9/f4kIaVIS2XzLAAAgAElEQVRTU5k9ezbjxo0r6lJEpBi5kc+/v9jtkJJuI3DdURbtOXXNuSEhIVSpUiX/ChWRXPn6+rJ79258fX1p3LgxmzZtKuqSRERECpyCmhJq6dKlNG/enFKlSlG+fHmaN29OUFAQ/1whdehsPIHrwklJz1vTTLvRgfT6PbiEW36XXSCWLFlCgwYNaNCgQVGXIiIFyMfH54bfBnOzn38p6RkErgvn14j4mylRRAqAo6Mj77zzDl9++SVDhgzh+eefJzU1tajLEhERKTAKakqgDz74gOeee44XX3yRqKgooqOjCQ4O5qeffiItLS1z3uyQE1istmtey56R83iqNYOgkBP5WndBsNvtTJ8+neeff76oSxGRYuRGPv9yY7HaSsTnn8idpn379oSGhnLq1CmaNWvGkSNHirokERGRAqGgpoS5fPkyU6ZMISgoiEceeQR3d3cMBgP33XcfixcvxsnJibVr19KgUWM+f7oNZz8eTPyOxZnn/7XNKfHQJiKCniL6q4nZ7pH062YiPhvBZ0Pa4FO9Op9++mnm2F9L/j/44APKly9PpUqVmD9/fqE8e05++OEH7HY7nTt3LrIaRKToxMXF0b17d7y9vSlTpgzdu3fn12Mn2Xb8InY7RC1+hfjtXxL15Yucmd6X6KWvYku+nHm+5exvRH05njMz+hExezBJv/6A3Q5bfjvHqGfHcffdd1OhQgVGjBhBSkpKET6piAB4eXmxYsUKxowZQ9u2bbOtJhYREbkdKKgpYXbv3k1qaio9e/bMdU6pUqV4eOyb1HzxG8r3fY3Eg+tJPr47y5zUM4ep/PQnlO/3erbzja6elH/kNe55cTl9xgUybtw4fvnll8zxqKgoLl++TGRkJJ9//jmjRo0iLi4u/x4yD/5aTaM3QYjcmTIyMnjqqac4ffo0Z86cwcXFhSeGjMgy58qRbXh1G0uVMYuw29JJ2PctANbLF7jwzVTcm/Sg6rOLqfTfj3CsUAOA2B/ns/PnXwkNDeXEiRNERkby+uvZPy9FpPAZDAaefvppfvrpJ+bNm8dDDz3EhQsXirosERGRfKOgpoSJiYmhXLlymM3mzGOtWrXC09MTFxcXtm/fjr+/P4mulUizgWP56pSq1wbLmbAs1yn94ACMjs4YHZyy3cO1VlMcylQi1WYno0I9OnfuzI4dOzLHHRwcmDJlCg4ODgQEBODm5saxY8cK7qFz8dtvvxEaGkr//v0L/d4iUjx4eXnRp08fXF1dcXd3Z9KkSYSH7s3yCu5SDTriUPYujA5OlKr7IGnRfwBw5UgIztUaUapeWwwmMyYXDxwr1MButxN/cD33PzqWsmXL4u7uzsSJE1m6dGlRPaaI5KB27drs2rWL+vXr07hxYzZs2FDUJYmIiOQL8/WnSHHi5eVFTEwMVqs1M6zZtWsXAFWqVCEjI4O9e/eybOooLpz+HXuGFbs1nVK+D2S5jtm9XK73SDl5gPiflmC9FEmQAezW1CyNer28vLIERa6uriQlJeXnY96QGTNmMGrUKJycsodNInJnSE5OZty4cWzYsCFzZV/qlUTsGTYMRhMAJrcymfMNZifs6RYArAkxOJSplO2aGcmXsaen8tWE/nz72tVr2O12bLab63kjIgXH0dGRt956i86dO/Pkk0/Su3dv3n77bZydnYu6NBERkZumFTUlTMuWLXFycuL777/PdU7//v25p2lbqoxawN3jvsb9vv8A/9q/nctWIbs1nYvfvoVHs15UGbOIkfN3EBAQUOz2f0dHR7Ny5UpGjBhx/ckictv64IMPOHbsGHv37iUhIYHt27ff8Llmj3Kkx53Pdtzo6oHB7MQTH6wgPj6e+Ph4Ll++XCSBtIjcmHbt2hEaGkpkZCTNmjUjLCzs+ieJiIgUUwpqShhPT09ee+01Ro4cyfLly0lMTCQjI4PQ0FCuXLkCQGJiItXvqoizszOp545x5ci2G76+3ZaO3ZaOybU0zo4OWE//wqZNmwrqcW5aUFAQ/fr1o1y53FcGicjtJz09HYvFkvkrLi4OFxcXPD09uXTpEtOmTQPAyXz9/3srVc8fy+lDXDm6A3uGDVtKAmnRf2AwGCl9Xxd++XpmZt+LyMhINm7cWKDPJiK3pmzZsnz99deMGzeOdu3a8fHHHxe7HzSJiIjcCAU1JdBLL73E9OnTeffdd6lQoQIVKlRg+PDhvPPOO7Rq1YqgoCB+XDyL39/tw+Wflmbb9nQtRidXynYcxsXv3uHE+48SffAHHnrooQJ8mrxLSUkhODiYsWPHFnUpIlLIAgICcHFxyfwVHx9PSkoK5cqVo0WLFnTt2vWGr2UuXZ7yfV8jYd+3nJ35GOfnPUvahT8BKNfhv7RpUp8WLVrg4eFBx44di6QXl4jkjcFg4KmnnmLXrl0sXLiQ7t27Ex0dXdRliYiI5InhWj9p8PPzsx84cKAQy5H8NOzLA2w+Gs3N/DDJYIAu9SoQPNAv/wu7RZ999hlr1qxh1apVRV2KiBRTt+vnn4jcuPT0dKZOncr8+fOZO3cuAQEBRV2SiIhIJoPB8LPdbs/xH5xaUXMbG+VfC2ez6abOdTabGOlfK58runUZGRmZr+QWEcnN7fj5JyJ54+DgQGBgIEuWLOGZZ57h2WefJSUlpajLEhERuS4FNbexRlU9mRTgi4tD3v6aXRyMTArwpWEVzwKq7MbEJKUSvO0kY5cd5L9f7GfssoM8P2c9Lp7etG3btkhrE5HiraR//olI/mnbti2hoaFER0fTrFkzDh8+XNQliYiIXJO2Pt0BFu05ReC6cCxW2zW3ARgMV3+SPCnAl4EtfAqtvn87dDae2SEn2Hb8IgCp1ozMMYMtHZPZTId6FRnZthaNquqbKRHJXUn7/BORgmO321m4cCHjx49n8uTJPPvssxhyeQumiIhIQbvW1icFNXeIXyPiCQo5wdZjFzEAln+EH85mI3agXR1vRvrXKtKfJOubKhHJbyXl809ECseJEycYMGAAZcuWZf78+VSsWLGoSxIRkTuQghrJFJuUyvJfIgg/n0iCJR0PZwd8K7nzyP1V8HJzKtLaroY0R0lJz7j+5P+5uk2hrsIaEbmu4vz5JyKFKz09nTfeeIM5c+YwZ84cunfvXtQliYjIHUZBjRQ5Hx8f5s6dS8eOHXMcP3Q2nsfm7CEl3Zbna7s4mFg2rIV+Ei4iIiJ5smPHDgYNGkT37t157733cHFxKeqSRETkDqG3PkmxNzvkBBZr7iGNPSP3MYvVRlDIiYIoS0RERG5jDz74IKGhoVy6dAk/Pz8OHTpU1CWJiIgoqJGik5qaytixY6lYqTLzRnYhdvNn2K3pAFhO/0rE7Ce5vGc5Z2cNJHbtTGyWJC58M42zH/bn7Ix+XPhmGtaEGOx22HrsIh8Hz6FGjRq4u7tTvXp1Fi9eXMRPKCIiIsWdp6cnixcvZsKECXTs2JEZM2aQkXHj27BFRETym4IaKTKBgYHs2bOH5z/5jmpDZ5N2/jiXdy3NHLclxZGRkkiVZ+ZTtutosGdQqkFH7ho5j7tGzsdgduTS5mAA7GkWXnxhHOvXrycxMZFdu3bRuHHjono0ERERKUEMBgMDBw5k7969fPPNN/znP//h/PnzRV2WiIjcoRTUSJFZvHgxU6ZM4VyqAzYnd0q3fpyksK1/TzAY8XxwAAazA0YHJ0wuHpTybY3RwRmjkyulW/Uj9cxh4OpbXDIwEBYWRkpKCpUqVeLee+8toicTERGRkqhGjRps376dli1bct9997Fq1aqiLklERO5ACmqkyJw7d45q1aqRYLECYC5dHlvSpcxxk6sHBrNj5tcZ6RZiN3xMRNBTnJnel6jFL5ORegV7hg2jozP+zwQSHBxMpUqV6NatG+Hh4YX+TCIiIlKymc1mpk6dyooVK3juued45plnSE5OLuqyRETkDqKgRopM5cqVOX36NB7OZgCsCRcxuZX9e4LBkGV+wr5vSY+NoNIT07n7+W+oOOCdLOP1mrZh8+bNnD9/Hl9fX4YOHVrgzyAiIiK3p9atWxMaGkpiYiJ+fn6EhoZec35MUirB204ydtlB/vvFfsYuO0jwtpPEJqUWUsUiInK7MBd1AXLnSE9Px2KxZH79+OOP83//93/0enkm5rRELv+0lFL12+V6vj0tBYODE0bnUthSEonf+VXmmNlyGcvJs1y5cg8uLi64ublhNCqHFBERkZtXunRpFi1axOLFi+ncuTMvv/wy48aNy/JvjENn45kdcoJtxy8CkGr9uxGxszmKGT8cx7+ONyPb1qJRVc9CfwYRESl59J2sFJqAgABcXFwyf1ksFvz8/Hh/eE9OfTYSxwo18WzVL9fz3f16Yk9P5eyH/Yla+AIuNZpkjtntdg6tW0zlypUpW7Ys27Zt45NPPimMxxIREZHb3IABA9i7dy8rV66kS5cunDt3DoBFe07x2Jw9bD4aTao1I0tIA1d76KVaM9h0JJrH5uxh0Z5TRVC9iIiUNAa73Z7roJ+fn/3AgQOFWI7cqYZ9eYDNR6O5xn+OuTIYoEu9CgQP9Mv/wkRERET+x2q18uabbxIUFMTAaZ+yNtKRlPQbf5W3i4ORSQF1GdjCp+CKFBGREsFgMPxst9tz/CZWK2qkWBjlXwtns+mmznU2mxjpXyufKxIREZE70c6dO2nVqhWlS5embNmytG7dmv379wNXGw1PmTKF9+d9w/ITtjyFNAAp6RkErgvn14j4gihdRERuEwpqpFhoVNWTSQG+uDjk7T/Jqz+Z8qVhFe35FhERkVuTkJBA9+7dGTNmDJcuXSIyMpLXXnsNJyenLPO2x7pgMDvlcpVrs1htBIWcyI9yRUTkNqWtT1KsLNpzisB14VistmtugzIYrq6kmRTgq+XDIiIiki8OHDhAx44diY/PvuLl5MmTDB06lNDQQ1y2WHGufh9enZ/B6OwGQETQf3Fv0p0rYT9iTbiAS/UmlOs+DoPZEVvyZWLXzsQScQSDwYCTdzVOh+3H28OFt99+mzlz5nDhwgWqVq1KYGAgvXr1KuxHFxGRQqatT1JiDGzhw7JhLehSrwJOZiPO5qz/iTqbjTiZjXSpV4Flw1oopBEREZF8U7t2bUwmE08++STr168nLi4uc8xutzNhwgSmLduBz4hPsSXEZHkDJUBy+A7KPzqNu0Z8TtrFP0k6/AMACfu+xeTuRdVnF1NlzCLKtXuSFQcjAahZsyY7duzg8uXLvPbaawwcOJDz588X3kOLiEixo6BGip2GVTwJHujHrpfbM65TbXo1vosOvuXp1fguxnWqza6X2xM80E/bnURERCRfeXh4sHPnTgwGA0OHDsXb25uHHnqI6OhoatWqRadOnTh5KRWbkzsezR7GciYsy/nuTR7C7O6FycUd11rNSIv+AwCD0YwtKQ5rwgUMJjPGyvU4FpUEQN++falcuTJGo5F+/fpxzz33sG/fvkJ/dhERKT7MRV2ASG683JwY3qZmUZchIiIid5C6deuyYMECAMLDwxk4cCBjx45l5syZPPfcc6ze+CMpyUlgt2due/qLya1M5u8NZifsSZcA8Gjem/idX3Fh6asAuDXuSoLvKAAWLlzI9OnTOXXqFABJSUnExMQU8FOKiEhxpqBGRERERCQHvr6+DB48mE8//ZSJEydiMBgY9P7XbDhxheTju7m0OfiGrmN0cqVsh6ehw9OkXTxF9JJJxD/QktOnyzN06FC2bNlCy5YtMZlMNG7cmGv1kBQRkduftj6JiIiIiHB1Bc0HH3xAREQEAGfPnmXJkiW0aNGCxMRE3NzcaFi9MsbkSyTsXXnD100+sY/0uHPY7XaMTqUwGI1UK1eKK1euYDAY8Pb2BmD+/PmEhYVd52oiInK704oaERERERHA3d2dvXv3Mn36dOLj4/H09KR79+689957nD17lieeeIKvltyPza0CrvX8STjw/Q1d13rpHJc2BZORchmjsxueTbox6em+eLk58cILL9CyZUuMRiNPPPEErVu3LuCnFBGR4k6v5xYRERERyYNhXx5g89FobmaHksEAXepVIHhgjm9kFRGRO4Rezy0iIiIikk9G+dfC2Wy6qXONGTaGP1g9nysSEZHbiYIaEREREZE8aFTVk0kBvrg45O2f0gZbGpbdX/H84EeIjY0toOpERKSkU1AjIiIiIpJHA1v4MCmgLi4OJgyGa881GMDFwcSrPepzv8cVzpw5g5+fH4cPHy6cYkVEpERRUCMiIiIichMGtvBh2bAWdKlXASezEWdz1n9aO5uNOJmNdKlXgWXDWvDfB+9h5cqVtGrVCicnJ/z9/Vm58sbfHiUiIncGNRMWEREREblFsUmpLP8lgvDziSRY0vFwdsC3kjuP3F8FLzenLHMzMjJ47rnn2LRpE0lJSQwdOpQpU6ZgNOpnqCIid4prNRNWUCMiIiIiUsjsdjtTp05l8eLFeHp6UrVqVRYuXIi7u3tRlyYiIoVAb30SERERESlGDAYD06ZNY/To0Vy4cAGz2UzLli05efJkUZcmIiJFTEGNiIiIiEgRGTt2LK+//jo7d+6ke/futGrVih9++KGoyxIRkSKkoEZEREREpAgNHjyYTz75hHnz5jFx4kQGDRrEzJkzuVaLAhERuX2Zi7oAEREREZE73cMPP4y7uzuPP/44b7/9Nh999BGhoaEEBwfj7Oxc1OWJiEgh0ooaEREREZFioEOHDqxevZoJEybw3HPPkZycTNu2bTl37lxRlyYiIoVIQY2IiIiISDHRvHlztmzZwquvvkrbtm3p2bMnzZo1Y8+ePUVdmoiIFBIFNSIiIiIixUj9+vXZsWMHM2bMwG63ExQUxEMPPcSCBQuKujQRESkE6lEjIiIiIlLMVK9enR07dtClSxfi4uIICQnh4YcfJjQ0lPfffx+zWf+Mzw8xSaks/zmC8KgEEixWPJzN+Fb0oG+TKni5ORV1eSJyh9InvIiIiIhIMVSpUiVCQkLo1q0bcXFx/PTTTwwaNIiuXbuybNkyvLy8irrEEuvQ2Xhmh5xg2/GLAKRaMzLHnM1RzPjhOP51vBnZthaNqnoWVZkicofS1icRERERkWKqbNmy/PDDD5w9e5YRI0awcuVK7r//fpo1a0ZYWFhRl1ciLdpzisfm7GHz0WhSrRlZQhoAy/+ObToSzWNz9rBoz6miKVRE7lgKakREREREirFSpUqxevVqDAYDPXv2ZMqUKUybNo127drx7bffFnV5JcqiPacIXHeUlHQbdnvWsYig/5JyKjTza7sdUtJtBK47yqSgpVSpUqWQqxWRO5W2PomIiIiIFHNOTk4sXbqU4cOH06lTJ9auXUudOnXo3bs3v/76K6+++ipGo34GC+Dj40N0dDQmkwk3Nze6du3Kxx9/zMk4K4HrwklJz7j+Rf4hJT2DL/ecId1mv/7kfKC+OSKioEZEREREpAQwm83MnTuXF198kbZt27Jp0yb2799P7969OXToEAsXLsTNza2oyywWVq9eTceOHYmKiqJLly689dZbXPTthcVqu6nrpdlsXEm15nOVWalvjoj8RbG7iIiIiEgJYTAYeO+993j88cd58MEHSU5OZuvWrZQtW5aWLVvyxx9/FHWJxUrFihXp0qUL+w78wrbjF7lyfC/n5o7kzIx+RC1+hfSYs1nmp50/zrk5z3B2Rj9i1s7Ebk27OmCHVKuNya+9Trly5fDx8WHx4sX5Vqf65ojIPymoEREREREpQQwGAxMnTuSFF16gTZs2/P7778yZM4fhw4fTqlUrtmzZUtQlFhsRERGsX78em3sF0mIjiFn1LmU6DKXqs4txqenHheWvY7elZ86/8lsI5fu9TuURc7FeiiR+17LMMWtSHHuOniIyMpIvvviCYcOGcezYsVuu8Vp9c/7tn31zFNaI3L4U1IiIiIiIlEDPPPMM7777Lh06dGDfvn2MHj2aJUuWMGDAAD766CPs1/uu/zb28MMP4+7uTtWqVSlfvjx1Av5LXNg2XGo2xaX6fRhMZjya98ZuTSU14mjmee5NumP28Mbk4k7pVo+SfGRbluv6dhuKk5MTbdu2pVu3bnz99dd5qmvEiBG88cYbmV8fOhufY9+cqMWvkHhoY67XSUnPIHBdOL9GxOfp/iJSMiioEREREREpofr378+8efPo0aMHW7ZsoV27duzevZvPP/+cIUOGkJqaWtQlFonvvvuOxMREQkJCCA8P52JMDLakS5g9ymfOMRiMmDy8sSbFZh4zuXv//XuP8tiSLmV+bXR2I+UfLT6rVavGuXPnst3bx8cHFxcX3NzcqFixIoMHDyYpKQmA4OBgXn311cy5s0NO3HTfHIvVRlDIiZs6V0SKNwU1IiIiIiIlWLdu3Vi+fDmPP/443377LdWrV+enn34iISEBf39/zp8/X9QlFpm2bdsyePBgDn4zC5NbWawJFzLH7HY7toSLmN28Mo/ZEi/+/fuEi5jcymZ+nWFJwoW/GwqfOXOGypUr53jf1atXk5SURGhoKAcPHuStt97KNicmKZVtxy9ed7tTbux22HrsIrFJd2YYJ3I7U1AjIiIiIlLCtWnThg0bNjBy5EgWLFiAm5sb33zzDd26daNp06bs27evqEssMmPHjiUibC+evi1JObmflFOh2G1WEvZ9i8HkgFOVuplzE39ZizUhBltKIpd3f41r3QezXOvY+s9JS0tjx44drFmzhr59+17z3n81Mw4NDQVg8ODBTJ48GYDlP0eQdGw35+aN4cz0vkQGP03KHz9nu4Y16RLnPh/N5b0rAEj+/e+GyGcXvsyslSGZc318fHj//fdp2LAhpUuXpl+/flgslpv6cxORoqPXc4uIiIiI3Abuv/9+QkJC6Ny5M/Hx8YwdO5bJkyfToEEDunXrxgcffMATTzxR1GUWOm9vb/oPGMjyHV9RrvsLxG3+FGtSLI7la+D9yBQMJofMuaXqteXCslexJl3C9Z7mlG7VL3PM7FaGZnWqUrlyZVxdXQkODsbX1/ea9/6rmXH79u2zjYXs3MX5VR/g/fAEnH0aYUu6hD0tJcuc9PgoLiybgkfz3rg37kr6pUhiVr2Ld+/JON/dgIT93zHzpaFMfOwEjo6OAHz99dds2LABZ2dnWrduzYIFCxgxYsSt/BGKSCFTUCMiIiIicpuoU6cOO3bsoFOnTly6dIlp06bRs2dPatWqxYgRI+jVqxdubm4YDIaiLrXAnDp1Ktux+XM/w+HLA2w+Go1rnVY5nldl5DwASrd8NNuYi09DhgRtIHCgH4HTXrtuDQ8//DAGg4GkpCTat2/PtGnTss05+MO3uDXohEv1+wAwu5fLMp4ec4bLPy2jjP+TlKrXFoArR7dnNkQG8Gjem5jQtezatQt/f38Ann322cwtWT169MhczSMiJYe2PomIiIiI3EbuvvvuzK05zz77LBkZGdx7772EhITg4uJyW4c01zLKvxbOZtNNnetsNjHSv9YNz/93M+OYmJhsc1LjL2AuUzHXa1z5bRtmdy9c67TOPJZTQ2SPchWJjIzMPFax4t/XdHV1zWxkLCIlh4IaEREREZHbTPny5dm6dSuHDh1i+PDh2O12TCYTZnPWBfV2u50//vijiKosXI2qejIpwBcXh7x9C+TiYGRSgC8Nq3jm+Z5/NTMeP358trGqVativxyd67meD/TH6OJBzKr3sGdcfTPUvxsiO5kMpMRf4K677spzbSJSfCmoERERERG5DZUuXZoNGzYwfvx40tPTc5yzdetWevXqxcKFCwu5uqIxsIUPkwLqYrRbud7rlgwGcHEwMSmgLgNb+Nz0PceOHcvmzZs5dOhQluOvPj+KhEObrzY3tmdgTYwhPfbs3xNMJrx7vUJGuoWYNdOx2zMo5ftglobIl/asxNPNhVatct7OJSIlk4IaEREREZHblKurK7Vr185sNPtv7du359FHH+Wll14iOjoa+82+K7oEaeltI+m7N2hXuyyOJgP29Kyvt3Y2G3EyG+lSrwLLhrW4pZAGrjYzfuKJJ3j99dezHO/s/wDth79G3Ja5nJ3Rj+jFE7BevpBljsHkQPnek8i4Ek/s2g8xl62c2RD57Ef9cYj4hbVr1uT69ysiJZPhWh/Gfn5+9gMHDhRiOSIiIiIiUlh27tzJCy+8wJgxYxg4cGCOczZu3IjFYqFnz56FXF3BGDRoEPfccw9Tpkxh9eYQJs1ZRYc+T5BgScfD2QHfSu48cn8VvNycCrSO+Ph4+o+ZwJGKncGc96DFxcHEsmEtbmpLlogUPYPB8LPdbvfLaUxvfRIRERERuQOFhoYybtw4nnrqqcyQxm63Z2k2nJKSwoULF3jhhRc4fvw4L774YlGVmy/CwsLYtGkTQUFBAJw5/hsty1xhRr/GhVrHrl276N+/Pw899BANfR1ZcjQVg8ONB0O30jdHRIo/bX0SEREREblDREVFAfD7778zcuRI+vTpw8iRI4HsIQ2Ai4sLjzzyCLVr1yYjI6PQ681vU6ZM4aWXXsLd3R2Aw4cP06BBg0K7v81mIzAwkN69ezNr1iwGDBjA568M5slG7rg4mLjeC7nyq2+OiBRvWlEjIiIiInIHSElJYcCAATRv3pzDhw/ToUMHXnnlFQAyMjIwGrP/DDc9PZ0RI0ZQs2ZNXn75ZSDnQKck2L9/P/v27WPx4sWZx8LCwnjssccK5f6RkZGZK5d+/vln4uPj6dChA/Pnz6d79048EhFPUMgJth67iAGwWP8OxpzNRuxAuzrejPSvpZU0Irc59agREREREblD/Pnnn3Tp0gWbzcbJkyeBq6s8TCZTjvNff/11tm7dyg8//IDJZMo10CkJunTpQq9evRgxYgRwNXAqU6YMJ06coFy5cgV671WrVjFs2DDGjBnDK6+8wpkzZ2jTpg1vv/02AwYMyDI3NimV5b9EEH4+sdD75ohI4VGPGhERERERoXr16uzatYvu3buzdu1aujguvekAACAASURBVHXrli2k+f3337nnnntYvnw5K1asYOXKlZhMpmsGOsXdtm3bOHHiBP/9738zj0VERODi4lKgIY3FYuHFF19kzZo1rFy5klatWhEVFUWnTp14+eWXs4U0AF5uTgxvU7PAahKR4k9BjYiIiIjIHaRcuXLs2LGD8+fPZxtLTk5m2LBheHh4EBERQVBQEDVr1iQjI6PEhjR2u51JkyYxderULK+xDgsLo379+gV236NHj/LYY49Rp04dDh48iKenJ3FxcXTp0oUnn3yS0aNHF9i9RaRkK5nrFkVERERE5KY5ODhw9913Zzvu6urKunXrsFqtnDlzhtatWwOU2O1OAOvXrycuLo7+/ftnOV5QjYTtdjtz586lTZs2jBkzhmXLluHp6cmVK1fo1q0bHTp0YPLkyfl+XxG5fZTcT1wREREREcl3Li4urF27lqeeeorPP/8cq9UKXF1ts3r16syvS4KMjAwmT57MG2+8kW1FUEGsqImPj6dfv37MmjWL7du38/TTT2MwGEhNTaV3797UqVOH999/v0Q2YxaRwqOgRkREREREsnn33Xfp168fZvPVbgkmk4mPPvqIgIAA4uLiiri6G7NixQqMRiO9evXKNpbfK2p27dpF48aNqVChAnv37qVu3brA1WbNgwYNwtXVlTlz5pTo1UkiUjj0KSEiIiIiIjlyc3PL/L2TkxPr16+nfv36NGvWjCNHjhRhZddnt9u59957WbBgQbYVLFarlWPHjlGvXr1bvo/NZiMwMJDevXvz0UcfMWvWLJydnTNreOaZZ4iNjWXJkiWZoZeIyLXok0JERERERHJlt9szgw6z2cz06dNp1KgRbdu25fPPP+ehhx4q4gpzZjAYMle1/NulS5cYMGAApUqVuqV7JCYm8uijj2KxWPj555+56667soxPmDCB0NBQtmzZkhneiIhcj8Fut+c66OfnZz9w4EAhliMiIiIiIsWFxWJhwoQJ/N///V+2UGPv3r306dOHESNGMGnSpBLVdyUjI4OUlJRbDmpSUlJITU3F3d09Ww+cd955h4ULF7J9+3a8vLxu6T4icvsxGAw/2+12v5zGtPVJRERERERyZDabiYuLo3PnzsTHx2cZa968Ofv27WPNmjU8+uijJCUlFVGVV4OXvDAajbcc0sDVxsuenp7ZQpo5c+YQHBzMpk2bFNKISJ4pqBERERERkRyZzWbmzZtH06ZN8ff3Jzo6Ost45cqVCQkJwc3NjdatW/Pnn38WSZ1/NejNa2BzPTdzva+//pqpU6eyefPmbFuhRERuhIIaERERERHJldFoZMaMGfTp04cHHniAU6dOZRl3dnZm3rx5DBkyhJYtW7J169ZCre+dd95h586dmbXa7fZbCmzGjBnD+++/D5Dn7VxWq5XFixezbt06atWqddM1iMidTT1qRERERETkhsyaNYv33nuPDRs25PjGpC1btjBgwAAmT57MqFGjCrxvzZo1a+jbty8NGzbk7rvvZsKECdx///2Z44mJibi7u9/QtdLT0xk0aBCnTp0iOTmZmTNn0r59e2w2W7atTdeS1/kicmdSjxoREREREbllY8aM4c0336R9+/bs378/23iHDh3YtWsXn376KUOHDiU1NbVA69m1axdvvvkmP/74Iw0bNmTIkCEMHDiQ33//HYB3332XS5cu3dC1HBwcGDVqFBs2bGDChAm88sorJCYmYjKZsNls2ebn9gNvhTQicqu0okZERERERPJk1apVPP3003z99df4+/tnG09KSuKJJ54gKiqKlStXUrFixQKpIyMjg9jYWLy9vQE4e/YsQUFBbN26ldjYWHx8fNi8eXOWc/7aGmUwGDJ//ZvdbmfkyJEkJSXx5Zdf5nr/f766XEQkL7SiRkRERERE8s1DDz3EsmXLePTRR1m1alW2cTc3N5YvX07Xrl1p2rRpjqtv8oPRaMwMaQCqVq3KW2+9xRdffEFERAQzZ87Mdo7BYMBkMmE0GomPjyc9PT3HOa+//jqnT59m7ty5wNUQKKd5+d3AWEREQY2IiIiIiORZu3btWLduHcOGDctx1YnRaGTKlCl89NFHBAQEsGjRokKrLSQkhN69e3PvvfdmOW6xWOjbty+DBw9m/vz5PPfcc+S2g8Db25vZs2cza9YsunXrRmBgIImJidnmGY1GhTUikq/MRV2AiIiIiIiUTH5+fmzdupUuXboQHx/PmDFjss3p1asXtWrV4uGHH+bQoUO8/fbb+d7HJTU1FQcHh8zXdA8ePJjHH388yxy73Y7FYuHpp58mLCyMI0eOsHTpUtq2bUvLli1zvK6Liwtnz56ldu3azJw5E2dn5yzj6enpWe5bFGKSUln+cwThUQkkWKx4OJvxrehB3yZV8HJzKrK6ROTmqUeNiIiIiIjcktOnT9OpUycGDhzIq6++mmPfltjYWPr164fJZGLp0qWUKVMmX+79xx9/MGLECNauXYuDg0Ou83LqJ9OqVSvee+89WrduneP8oUOHct999zFq1Khs4xkZGVy4cIFPPvmEadOmsXbtWho0aMDdd9996w91Aw6djWd2yAm2Hb8IQKr171U9zmYjdsC/jjcj29aiUVXPQqlJRG7ctXrUaEWNiIiIiIjckmrVqrFjxw66dOnCpUuXmD59erZVJl5eXmzYsIEXX3yRZs2a8f333+f4iu+8mjp1Kq1bt75mSAPkGB6NHj2aJk2a5Do/ODgYsznnb5mMRiPJycn8/PPP1KxZk/Lly7Njx468P8BNWLTnFIHrwrFYbeT0c3fL/0KbTUei2X48hkkBvgxs4VMotYnIrVOPGhERERERuWUVKlQgJCSEAwcOMGTIEKxWa7Y5ZrOZGTNmMGnSJNq2bcvq1atv6Z6//fYbGzduZNy4cTc0PyYmhrS0NOx2O1euXKFPnz7ZtjP9u95rqVGjBg0bNsRkMrFmzZrrzs8PV0Oao6Sk5xzS/JPdDr9/+CQTP/6KRXtOFXhtIpI/FNSIiIiIiEi+8PT0ZOPGjURFRdG3b18sFkuO8wYPHszq1at55plnCAwM5FrtGK5lypQpvPjii3h4eNzw/JdffpmWLVuyYMGCPPWWsdlsWK3WLLVGRUWxd+9eNm3ahJeXV55q/+qrr/Dz88PNzY1KlSrxn//8h507d17znENn4xnUsjoJFyLydK9Um53AdeH8GhGfp/NEpGgoqBERERERkXxTqlQpvv/+exwdHenWrVuOb0oCaNGiBfv27WPVqlX069ePK1eu5Ok+Bw4cYM+ePTn2j8mJzWZj3bp1fPHFF0yYMIFRo0Zdd7vUX9LS0jh8+DCJiYlZtlBVrFiRNWvW4OPjk6fap0+fztixY5k4cSLR0dGcOXOGkSNH8v3331/zvNkhJ/J0n3+yWG0E3cL5IlJ4FNSIiIiIiEi+cnR05KuvvqJWrVp06NCB2NjYHOdVrlyZbdu24erqSqtWrTh16tQN32Py5MlMnjwZFxeX686NjIykU6dOREZG8uOPP9KzZ88bvk96ejorVqygRo0aOTZAvpH7/9Ply5eZMmUKs2fPpnfv3pQqVQoHBwd69OjBe++9x759+2jZsiWenp5UqlSJ0aNHk5aWRkxSKosmDQbg/LwxnPngEa4c3Q5A8ol9nJs3hjMz+hH15XjSLvyZ7b7WxDg+e+oBfj99LvPYL7/8gre3N+np6SxYsIAHHniA8ePHU6ZMGapXr8769evz9Gwikj8U1IiIiIiISL4zmUwEBwfTvn172rRpQ2RkZI7znJ2dmT9/Pk899RQtWrQgJCTkutfevn07x48fZ8iQIdedu2bNGpo0aYKfnx9lypShcePGN/wMiYmJ2Gw2Hn300RveXnU9u3fvxmKx0KtXrxzHTSYTM2bMICYmht27d7NlyxaCgoJY/nMEVZ98D4BK/53F3S8sp1TdNqRFnSR23Yd4dR1N1ee+wq3xf7iw/A3s1vSs13Urg2u1Brz24dzMY19++SWPPfZY5sqivXv3UqdOHWJiYnjppZcYMmTITW9LE5Gbp6BGREREREQKhMFg4O233+aJJ57gwQcf5OTJk7nOGzt2LIsWLaJfv37Mnj0714DAbrczadIkpk6diqOjY673tlgsPPfcc4wePZoVK1bQoUMHGjRocMO1JyUl4erqirOzMyaT6YbPu57Y2FjKlSuXa+PhJk2a0KJFC8xmMz4+PgwfPpxt27YRHpWQ5RXcf0k8tAH3xl1xqlwHg9GEW4MOGMwOpJ4LzzbXpX57QtauBK5uBVuyZAmDBg3KHK9WrRpDhw7FZDLx5JNPcv78eaKjo/PpyUXkRun13CIiIiIiUqBefvllypQpQ5s2bVi/fj0NGzbMcV7Hjh3ZtWsXPXv2JDQ0lI8//hgnJ6csczZu3EhsbCwDBgzI9X7h4eE89thj3HPPPRw8eJAyZcrw/vvv33BQc+zYMapUqZKvAc1fvLy8iImJwWq15hjWHD9+nOeff54DBw6QnJxMeno6NWvWJO6344BbtvnWyxe4cvhHEn5e8/dBmxVb0qVsc13vaUHU5k/4888/OXbsGKVLl6ZZs2aZ4xUrVvx7rqsrcDWwEpHCpRU1IiIiIiJS4IYNG8b06dPp1KkTu3fvznVezZo12b17NzExMbRv3z7Lio6/VtO88cYbOYYodrudzz//nAcffJBRo0bx9ddfZ/aVCQsLo379+tet8+DBg3z22WfZAqK0tDR+++23zD46GRnZV7fciJYtW+Lk5MR3332XeSw1NZWwsDC++eYbunbtyokTJyhfvjw2mw0nJydOnTrFudM5NwI2e3hTutWj3D1u2d+/xq+gVL222eYazI7UbtmZRYsW8eWXX2ZZTSMixYeCGhERERERKRT9+vXjiy++oGfPnmzatCnXee7u7qxYsYJOnTrRtGlTDhw4AMDKlVe37fTu3TvbOfHx8Tz++ON8+OGHbNu2jaFDh2Z5Q9Phw4evu6Lm+PHjdOvWjV69emVZ7XL58mXatWtHs2bNmDhxIsBN9W65fPkyR48epUePHjz55JM0bdqUmjVr4uHhQdeuXZk4cSIWi4W77roLf39/6tevT0JCAkajkQZVvXAwgrGUJ9b4qMxrujXqQuLB9aSeO4bdbicjzULyif1kpCZnu7+z2UjXXo+yYMECVq1apaBGpJhSUCMiIiIiIoWma9eufPvttwwaNIjly5dnG49JSiV420me/+YQZ6p3w+/Zj+nx0kcEz1/Eq6++SmBgYJYABq426L3vvvsoV64c+/bto169elnGbTYbR48ezXb8nyIiIujcuTNvvPEGDzzwQJYxNzc3Zs6cycaNGzObIhuN2b+VioyM5Ndff8285z8lJCQwZMgQRo0ahdlsJiAggLi4OM6fP0/p0qUpV64cNWvWJDU1lR07dvDJJ5+QmJjI+PHjadiwIZ++8hRGoxHPB/oTu2YGZ2b048rRHThVugev/4zh0qZgzs58jHOfDuXK4R9yfEY78MqTPTEajdx///1Uq1Yt1z8PESk66lEjIiIiIiKFqnXr1mzatImAgAAuX77MkCFDOHQ2ntkhJ9h2/CLAPxrnmnBv1Y+3jqRhv+9xyvs2zbyOzWbjnXfe4cMPP+Szzz7L9bXbf/zxBxUqVMDd3T3H8ZiYGDp16sTo0aNzfJOUyWSiadOmnDx5kri4OIBsYRHApk2bWLBgAdu2bcs27u7uzjfffIPBYCAlJYUdO3awadMmNm/ezJkzZ6hZsyadO3cmKCiIGjVq5Fhn29rebLYF4H5fQJbjLjWa4FKjSY7nVBk573/1Qrs63ni5OVG1alX69++fZd7gwYMZPHhwlmN645NI0TBc6398fn5+9r+WGYqIiIiIiOSn33//nc6dO9N2yCT2plXBYrVx7WzAjrODiUn/qcuA5neTlpaGzWbDZDLh7Oyc61krV67M3O7zbwkJCXTo0IFOnTrx5ptvXrPexMRE/P39WblyZY6rUVJTU2nZsiXbtm3LMRRKTEykT58+7N69m0aNGtG5c2c6d+6Mn59frm+B+qdDZ+N5bM4eUtJt1537by4OJpYNa0Hq+d/p1KkTZ8+ezTW4EpGCZzAYfrbb7X45jWlFjYiIiIiIFIl77rmH5z/5lg+2/AnmGwkfDFjSM3hz/VEMBhjYwueG7pNbI2GLxULPnj1p0qQJzz//PDt37uTo0aMcPXqUfv360bx58yzz3dzc8PLy4rfffqNatWpkZGRk2QLl6OhIuXLlCAkJoUePHtnGnZ2dGT9+PM2bN6d06dI3VPs/NarqyaQAXwLXHSUl/cabGbs4GJkU4MsHk57ju+++48MPP1RII1KMKagREREREZEC5+PjQ3R0NCaTCQcHB1q1asWzU94haFcUmB3zdK2U9AwC14XTsIonDat4ZhuPSUpl+c8RhEclkGCxcijak6a17+JiYgrJly4QHh5OWFgYs2bNIjk5mbCwMJYsWULdunWpW7cuvr6+lC5dGrvdnmULk8FgoF69epw/fx7I3qfGYDDg7+/Pxo0b6dGjR7atQw4ODnTu3DlPz/pvf4VTgevCr7sCyWAAZ7OJSQG+DGzhw8AWX9zSvUWkcCioERERERGRQrF69Wo6duyIxWJh5MiRDB85GkOXF7PNs2fYMBizv377nyxWG0EhJwge+PfOgVz73HjUZENEGutf34A9MoyqSUeJPf4Lrq6ufPrppzRo0IBKlSrl2Hfmny5fvkxCQgLvvvsuFy5cICAggEaNGmWZ81evGyDHV4jnh4EtfGhYxZOgkBNsPXYRA2Cx/r3CxtlsxM7VnjQj/WvlGGaJSPGloEZERERERAqVs7Mznbr1ZNH3I6lsh5g1MzA4OGK9fIHUs2F493kVuzWd+O1fYo0/j9GpFG4NO+H54AAArPHRRAYPYWW351g6phMGoGO/IYSV75j7KhOzIwbA6HM/URmNcHcsy66F7+Lq6npDNVssFvr06UNycjIPPPAApUuX5q677so2r0mTJphMJlJSUnBxcSE9PR0HB4eb/8PKRcMqngQP9CM2KZXlv0QQfj6RBEs6Hs4O+FZy55H7q+Dl5pTv9xWRgqegRkREREREClVycjIfzvkC57t8M49dObKN8n2n4nTXa2CzkhoZTrnuz+PgfTfpF08TvfRVHCvUwLV2y8xzLBFHqdD6UZztKayY+yGVnqqFQ7mq17y33Q42gwlL3W6s/PXCNfvc/HPrk7OzMz/8kPNrr/8pMjKS06dPU6lSJbp3786bb77J3Xfffd3zbpaXmxPD29QssOuLSOEzXn+KiIiIiIjIrXv44Yfx9PSkdOnShO3bgVvT3pljrrWa41ylHgaDEYPZEedqDXEs74PBYMSxfHVK1WuD5UxYluslHd2B972tSG74CA7lq5N24Y8brsVivdrn5teI+MxjGRkZHDx4kHfffZeOHTsSHR2dp+ez2+28+eab9O7dm2+++YaFCxcWaEgjIrcnragREREREZFC8d1339GxY0dsNhudxn7AtrmvUPnpTwAweXhnmZt67hhxIQtIv3gae4YVuzWdUr4PAGBNiLk6yWDk5JFQXOt3xGB2IiPdkuUa1+t1Y7Ha+GBDGO0dTrJp0yY2b95MmTJl6Ny5M88++yxly5bN0/MZDAY++eSTPJ0jIvJvCmpERERERKRQmUwmGj7QiW2fB2KJ+O3qwX818o1Z9R7u93fH/dFpGMyOXPrhMzJSEgBIPr4LgFIN2pN4ZCcu9TsCkHRoM2lRJzJ73ZR+oD8Je7+lyugvMgOb5GO7iN/5FZWHfIwl8hhLF3zKVzF/4uriQs+ePZk7dy6Ojnl7C5WISH7S1icRERERESlUdrud1BN7ybAk4eCVc0+ZjLQUjC7uGMyOpJ47xpUj2zLHko9dDWo86vmTcuogtitxmWNXjmyjdKt+VH3+Gzya9MDk4o7lz4OZ40lhP+JWv/3VL4wmKnYZwYcbDnPo0CH2799PUFBQATyxiMiNU1AjIiIiIiKFokePHri5ueHh4UHI4llUfOgFHL2r5Ti3bOdniN+xiDPT+3L5p6WZ254sZ3/DlhQLgEPFmpg9K3Hlt79DnH/3uinVoANJv20FwJaSiOXPg7je6w+AU8VaGCrW5veLKfj4+DB8+HC2bduGiEhR0tYnEREREREpcKdOncp2bNiXB9h8NJpy3cdlGyvl+0BmOPNPses/wqXG/ZR/ZAr2lARK1WtLUtgWKv93FjFrZmBy98p6nXv9ubxrGRlpFpLDd+BUtR5mt6u9Z9IvRRK3ZS6fBp3k86HpWK1WmjRpkj8PLCJyk7SiRkREREREisQo/1o4m3Nv9vtvGempXAnfieVMGBEfP8H5z0eSuP870i/8SVr0/9749K9eN2b3cjjd5Uvy8V1cCdtKqXvbZ45d2jgbB68qPPXhKhISEnjzzTex2+358mwiIjdLQY2IiIiIiBSJRlU9mRTgi4vDjX1bkvL7HgwGIzWeCWbmsk28sXAjPs98ilOVe0kK+zHX80rVb0/C3hWkXTyFa52Wmccz0lJwcC5Fg+oVCA8P1xubRKRYUFAjIiIiIiJFZmALHyYF1MXFwfTvxTDZJB3eQunGnZn6eFtGd2vKkM73YXYri3uT7lw5EgJ2W47nudZuifXyBVxrt8To4Jx5vEy7IST8FsL4bo0ZOnQo/fr1y8cnExG5OYZrLe3z8/OzHzhwoBDLERERERGRO9GvEfEEhZxg67GLGACLNSNzzNlsxA60q+PNSP9aNKzimTn2V5+b6+1Yigx+mrJdR+Pi0zjzmMEAXepVIHigXz4/jYjItRkMhp/tdnuOHz5qJiwiIiIiIkWuYRVPggf6EZuUyvJfIgg/n0jUpcvs3raFcSOf5JH7q+Dl5pTtvFH+tdjxewwp6TmvpgG4Ev4TYMC5WsMsx53NJkb618rvRxERuSVaUSMiIiIiIsVSSkoKZcqUISUlBcM19kUt2nOKwHVHSUnPyDYWtfgV0mPPUq7787jU+PuNTi4ORiYF1GVgC5+CKF1E5Jq0okZEREREREocFxcXnJ2diYuLo2zZsrnO+ytsCVwXjsVqy7INquKAt7PMNRiurqSZFOCrkEZEiiUFNSIiIiIiUmxVqlSJ8+fPXzOogathTcMqnjfV50ZEpDhRUCMiIiIiIsXWX0HNvffee925/+5zs3r7z0THJfJAsyb4VnLPtc+NiEhxoqBGRERERESKrb+CmrzwcnNieJuapPyymj8v/smMfkMKqDoRkfxnLOoCREREREREcnMzQc1fLBYLLi4u+VyRiEjBUlAjIiIiIiLF1q0ENSkpKTg7O+dzRSIiBUtBjYiIiIiIFFu3GtRoRY2IlDQKakREREREpNi61a1PWlEjIiWNghoRERERESm2tKJGRO40CmpERERERKTYUlAjIncaBTUiIiIiIlJseXh4YLPZSEpKyvO52vokIiWRghoRERERESm2DAbDTa+q0YoaESmJFNSIiIiIiEixdrNBjcViUVAjIiWOghoRERERESnWbmVFjbY+iUhJo6BGRERERESKNW19EpE7iYIaEREREREp1m5l65NW1IhISaOgRkREREREijWtqBGRO4mCGhERERERKdYU1IjInURBjYiIiIiIFGva+iQidxIFNSIiIiIiUqxpRY2I3EkU1IiIiIiISLHm5eVFYmIiqampN3yOzWbDarXi6OhYgJWJiOQ/BTUiIiIiIlKsGY1GKlSoQFRU1A2f89e2J4PBUICViYjkPwU1IiIiIiJS7OV1+5O2PYlISaWgRkREREREir1KlSrd1IoaEZGSRkGNiIiIiIgUe1pRIyJ3CgU1IiIiIiJS7CmoEZE7hYIaEREREREp9vIa1Gjrk4iUVApqRERERESk2NOKGhG5UyioERERERGRYu9mVtQoqBGRkkhBjYiIiIiIFHv/z96dh0dV3X8c/9yZCZlANkIgCQRISxREIQIhLMqOoggKKktlkaq4IFZUtL8WLYqNolihFlRcUBRRLAqIooLIolZ2CCqExbIkhIQtK1ln5v7+QEfSJEAWCXN9v54nT2fuPffcMymeh/nwPedWpaKGpU8AfBFBDQAAAIALXkREhI4dOya3231O7Vn6BMBXEdQAAAAAuOA5HA7Vr19fR48ePaf2bCYMwFcR1AAAAADwCZVZ/kRFDQBfRVADAAAAwCcQ1AD4LSCoAQAAAOATKhPUsPQJgK8iqAEAAADgE6ioAfBbQFADAAAAwCdUtqKGoAaALyKoAQAAAOATIiMjK1VRw9InAL6IoAYAAACAT2DpE4DfAoIaAAAAAD6BzYQB/BYQ1AAAAADwCVFRUUpPT5dpmmdtS0UNAF9FUAMAAADAJwQEBMjpdCozM/OsbQlqAPgqghoAAAAAPuNclz+x9AmAryKoAQAAAOAzzjWooaIGgK8iqAEAAADgMypTUUNQA8AXEdQAAAAA8BmVqahh6RMAX0RQAwAAAMBnsPQJgNU5ansAAAAAAHCuBgwYoLy8vLO2YzNhAL6KoAYAAACAz2jZsuU5taOiBoCvYukTAAAAAEsxTVOfffaZ6tWrV9tDAYBKo6IGAAAAgKUYhqErr7yytocBAFVCRQ0AAAAAAMAFgqAGAAAAgE9xu93lHt+1a5dmzpx5nkcDADWLoAYAAACATxkwYIBKSkrKHK9fv76mTZumEydO1MKoAKBmsEcNAAAAAJ/y3//+V7Nnz1ZAQIAOHDigI0eO6MiRIyooKNCRI0eUlZWlsLCw2h4mAFSJYZpmhSfj4+PNTZs2ncfhAAAAAMCZde7cWbm5uerSpYtCQkLUpEmTUj/NmzeXzXZq8YBpmjIMo5ZHDAClGYax2TTN+PLOUVEDAAAAwKfExMRo7Nix6tOnz1nbzpkzR0OHDlVQUNB5GBkAVB971AAAAADwKRMnTlTTpk0lSS6XS263Wx6PRz+vFjBNU3l5eZKkKVOmKD8/v9bGCgCVRUUNAAAAAJ8SH//LE+Y6pQAAIABJREFUagGHo+xXmpMnT2ro0KFKT09XSkqKAgICzufwAKBaCGoAAAAA+LT8/Hz5+/t796Wx2+2aP3++7Ha7LrnkEtWpU6eWRwgA546lTwAAAAB8isvlksvl8r7fs2eP0tPTZRiGDMNQQECAQkNDFRQUpIEDB8rpdNbiaAGgcghqAAAAAPgUm82mbt26efekiYuLU5MmTcpt+9JLL53PoQFAtRHUAAAAAPApNptNqampSktLK/f8vn37tGvXrvM8KgCoGQQ1AAAAAHxOVFSUUlJSyj23cOFCvfrqq+d5RABQMwhqAAAAAPicqKgoHT58uNxzBQUFPOkJgM8iqAEAAADgcwhqAFgVQQ0AAAAAn3OmoKawsJAnPQHwWQQ1AAAAAHwOFTUArIqgBgAAAIDPoaIGgFUR1AAAAADwOVTUALAqghoAAAAAPoegBoBVEdQAAAAA8DkRERE6duyY3G53mXMsfQLgywhqAAAAAPgch8OhsLAwHTlypMw5KmoA+DKCGgAAAAA+qaLlTwQ1AHwZQQ0AAAAAn1RRUMPSJwC+jKAGAAAAgE+iogaAFRHUAAAAAPBJVNQAsCKCGgAAAAA+iYoaAFZEUAMAAADAJxHUALAighoAAAAAPqm8oMY0TRUVFbH0CYDPIqgBAAAA4JPKC2qKiork5+cnm42vOgB8E7MXAAAAAJ8UGRmp9PR0mabpPcayJwC+jqAGAAAAgE8KCAhQQECATpw44T3GE58A+DqCGgAAAAA+63+XP1FRA8DXEdQAAAAA8Fn/G9RQUQPA1xHUAAAAAPBZVNQAsBqCGgAAAAA+i6AGgNUQ1AAAAADwWSx9AmA1BDUAAAAAfBYVNQCshqAGAAAAgM8iqAFgNQQ1AAAAAHxWVFSU0tPTve9Z+gTA1xHUAAAAAPBZVNQAsBqCGgAAAAA+Kzg4WG63W3l5eZKoqAHg+whqAAAAAPgswzBKVdVQUQPA1xHUAAAAAPBpBDUArISgBgAAAIBPOz2oYekTAF9HUAMAAADAp1FRA8BKCGoAAAAA+DSCGgBWQlADAAAAwKex9AmAlRDUAAAAAPBpVNQAsBKCGgAAAAA+jYoaAFZCUAMAAADAp1FRA8BKCGoAAAAA+LQGDRooNzdXRUVFBDUAfB5BDQAAAACfZrPZFBERofT0dJY+AfB5BDUAAAAAfN7Py5+oqAHg6whqAAAAAPg8ghoAVkFQAwAAAMDn/RzUsPQJgK8jqAEAAADg86ioAWAVBDUAAAAAfB4VNQCsgqAGAAAAgM+LiopSWlqaSkpK5O/vX9vDAYAqc9T2AAAAAACgOo7lFWl9TrD2hHdVxJBOeuD9bWoVGawhHaLVIJDQBoBvMUzTrPBkfHy8uWnTpvM4HAAAAAA4N0kpWZq1eq/W7D4q0zRV7P7lu43TYZMpqWfLhhrXI1ZxTUNrb6AA8D8Mw9hsmmZ8eeeoqAEAAADgc+at26/EZckqdLlV3r89F7o8kqTlOzK0dvcxTerfSiM7x5zfQQJAFbBHDQAAAIAL3v79+2UYhlwu108hzU4VlPwS0mR99Y6OLX2uzHWmKRWUuJW4bKfmrdt/zvcbM2aMHn300RoaPQCcO4IaAAAAALUmJiZGAQEBCgwM9P6MHz++wvZJKVlKXJasghJPpe5TUOJR4rJkbU/Nqu6QAeBXRVADAAAAoFYtXbpUeXl53p+ZM2dW2PalNXtV6HJX6T6FLrdeXL23zHG3u2r9AcCvgT1qAAAAAFxw3G63/vznP+vNN99UcHCwHnroIUnS2j3HZJqGSrLSdfyTGSrO+FH+jVvKL6xJqeuPLnpahak/yHQVq07D3yms3zjVadhcq3Yd1R9GjFJocKAOHDigNWvWaMmSJWrQoIFuv/127dmzR/3795dhGLXxsQGAihoAAAAAF55XX31VH3/8sbZu3apNmzZp4cKFpc4f+2ia6kS2UNM/zVdI1+HK+/7LUucDft9BTe58RU3ve0d1Ilt4968xJO0/ka/58+dr0qRJys3NVUJCggYNGqRRo0bpxIkTGjJkiD744IPz9VEBoBSCGgAAAAC1atCgQQoNDfX+vPrqq3r//fc1YcIENW3aVGFhYfrLX/4iSSpyeeTKPqLiw3sU2m2UDIefnM0uU0BsQqk+A+Ouls2/rgyHn0KvvEUlR/bJU3hShS6PsvNLdMMNN+iKK66QzWbTtm3bVFJSogkTJsjPz08333yzOnbsWBu/CgBg6RMAAACA2rV48WL17du31LF//OMfatq0qfd98+bNva/deSdkcwbKVsfpPeYIbih37jFJkulxK2vt28pP/lru/GzJOPXv0+6CHNmc9VTi9pTqOy0tTU2aNCm13On0+wHA+URQAwAAAOCCExUVpZSUFO/7gwcPel/bA8PkKcyTp7jQG9a4co56g5aTO9Yof886NRr+dzlCImQWnVTKjOGSTj3L289uKxXKREVF6dChQzJN03v84MGDatGixa/9MQGgDJY+AQAAALjgDB06VC+88IJSU1OVmZmpqVOnSpL8HTY5QhqpTtRFyv76HZnuEhWm/KCCvRu815rFBTLsfrIHBMssKVLmmre855wOm0Lq+pW6V5cuXeRwOPTCCy+opKREH374oTZs2CAAqA0ENQAAAABq1cCBAxUYGOj9GTx4sMaOHat+/fopLi5O7du314033ljqmvDrH1ZR2i6lzPiDsr95V4GX9faeq3dZbzmCGyl11q1Ke+0e+Tdu6T1nSooJq1uqrzp16ujDDz/Um2++qbCwMC1YsKDM/QDgfDFM06zwZHx8vLlp06bzOBwAAAAAqNidb2/Sip0ZOsPXmAoZhtSvdYReHhlf8wMDgEowDGOzaZrlTkZU1AAAAADwGff2jJXTYa/StU6HXeN6xtbwiACgZhHUAAAAAPAZcU1DNal/KwX4Ve6rTICfTZP6t1Lb6NBfaWQAUDN46hMAAAAAnzKyc4wkKXFZsgpd7jMugzKMU5U0k/q38l4HABcyghoAAAAAPmdk5xi1jQ7Vi6v3atWuozIkFbo83vNOh02mpF4tG2pcz1gqaQD4DIIaAAAAAD6pbXSoXh4Zr+N5RVq4JVVLv9qi9OPZ6tYpXq2ignRz+2g1CPSv7WECQKUQ1AAAAADwaQ0C/XVX9xYykldq04FNmj7sjtoeEgBUGZsJAwAAALAEj8cjwzBqexgAUC0ENQAAAAAswTRN2Wx8xQHg25jFAAAAAFgCFTUArICgBgAAAIAlUFEDwAqYxQAAAABYAhU1AKyAoAYAAACAJVBRA8AKmMUAAAAAWAIVNQCsgKAGAAAAgCVQUQPACpjFAAAAAFgCFTUArICgBgAAAIAlUFEDwAqYxQAAAABYAhU1AKyAoAYAAACAJVBRA8AKmMUAAAAAWAIVNQCsgKAGAAAAgCVQUQPACpjFAAAAAFiCx+MhqAHg85jFAAAAAFgCS58AWAFBDQAAAABLYOkTACtgFgMAAABgCVTUALACghoAAAAAlkBFDQArYBYDAAAAYAlU1ACwAoIaAAAAAJZARQ0AK2AWAwAAAGAJVNQAsAKCGgAAAACWQEUNACtgFgMAAABgCVTUALACghoAAAAAlkBFDQArYBYDAAAAYAlU1ACwAoIaAAAAAJZARQ0AK2AWAwAAAGAJVNQAsAKCGgAAAACWQEUNACtgFgMAAABgCVTUALACghoAAAAAlkBFDQArYBYDAAAAYAlU1ACwAoIaAAAAAJZARQ0AK2AWAwAAAGAJVNQAsAKCGgAAAACWQEUNACtgFgMAAABgCVTUALACghoAAAAAlkBFDQArYBYDAAAAYAkej4egBoDPYxYDAAAAYAksfQJgBQQ1AAAAACyBpU8ArIBZDAAAAIAlUFEDwAoIagAAAABYAhU1AKyAWQwAAACAJVBRA8AKCGoAAAAAWAIVNQCsgFkMAAAAgCVQUQPACghqAAAAAFgCFTUArIBZDAAAAIAlUFEDwAoIagAAAABYAhU1AKyAWQwAAACAJVBRA8AKCGoAAAAAWAIVNQCsgFkMAAAAgCVQUQPACghqAAAAAFgCFTUArIBZDAAAAIAlUFEDwAoIagAAAABYAhU1AKyAWQwAAACAJVBRA8AKCGoAAAAAWAIVNQCsgFkMAAAAgCVQUQPACghqAAAAAFgCFTUArIBZDAAAAIAlUFEDwAoIagAAAABYgsfjoaIGgM9jFgMAAABgCSx9AmAFzGIAAAAALIGlTwCsgKAGAAAAgCVQUQPACpjFAAAAAFgCFTUArICgBgAAAIAlUFEDwAqYxQAAAABYAhU1AKyAoAYAAACAJVBRA8AKmMUAAAAAWAIVNQCsgKAGAAAAgCVQUQPACpjFAAAAAFgCFTUArICgBgAAAIAlUFEDwAqYxQAAAABYAhU1AKyAoAYAAACAJVBRA8AKmMUAAAAAWAIVNQCsgKAGAAAAgCVQUQPACpjFAAAAAFjCgAEDFBwcXNvDAIBqcdT2AAAAAACgJjz++OMKCAio7WEAQLUQ1AAAAACwhMDAwNoeAgBUG0ufAAAAAAAALhAENQAAAAAAABcIghoAAAAAAIALBEENAAAAAADABYKgBgAAAIDlzJkzp7aHAABVQlADAAAAwCeVlJR4f9xutzwejzwejyRp8uTJMk2zlkcIAJXH47kBAAAA+KQ2bdrIz8+vzHHDMJSeni7DMGphVABQPQQ1AAAAAHxSdna2li9frvDwcJmm6f2RpE6dOtXy6ACgaghqAAAAAPikP/zhD8rNzVWbNm3KnOvVq1ctjAgAqs8407rN+Ph4c9OmTedxOAAAAAAAANZmGMZm0zTjyzvHZsIAAAAALMHj8bCBMACfR1ADAAAAwBIiIiKUlZVV28MAgGohqAEAAABgCcXFxTzpCYDPI6gBAAAAYAmmacpm4ysOAN/GLAYAAADAEjweDxU1AHweQQ0AAAAAS6CiBoAVMIsBAAAAsAQqagBYAUENAAAAAEugogaAFTCLAQAAALAEKmoAWAFBDQAAAABLoKIGgBUwiwEAAACwBCpqAFgBQQ0AAAAAS6CiBoAVMIsBAAAAsATTNKmoAeDzCGoAAAAA+DzTNCWJoAaAzyOoAQAAAODz2J8GgFUQ1AAAAADweexPA8AqmMkAAAAA+DwqagBYhaO2BwAAAADAeo7lFWnh5lQlp+cop9ClYKdDrSKDNaRDtBoE+tf4/TweDxU1ACyBoAYAAABAjUlKydKs1Xu1ZvdRSVKRy+M953Ska/oXu9WzZUON6xGruKahNXZflj4BsAqCGgAAAAA1Yt66/UpclqxCl1s/PYSplMKfQpvlOzK0dvcxTerfSiM7x9TIvVn6BMAqiJwBAAAAlGvMmDF69NFHz6ntqZBmpwpKyg9pTmeaUkGJW4nLdmreuv3VH6ioqAFgHcxkAAAAwG9YTEyMAgICFBgYqMjISI0ZM0Z5eXmV6iMpJUuJy5JVUOI5e+PTFJR4lLgsWdtTsyp1XXmoqAFgFQQ1AAAAwG/c0qVLlZeXp23btmnr1q16+umnK3X9rNV7VehyV+nehS63Xly9t0rXno6KGgBWwUwGAAAAQJIUGRmpfv36adu2bd5jmZmZuu666xQUFKROnTrpxx9/9J67//771SQ6Wq/f0V1pc+5XYcr33nOm6VH2t//WoZfvUMqMP+jo4qlyF+RKklxZGTowdYDyvluplFl/1Kt39tGkyU9IktLT01W3bl0dP37c29eWLVvUsGFDlZSUaO/everRo4dCQkIUHh6uYcOGSaKiBoB1ENQAAAAAkCSlpqbq008/VWxsrPfYe++9p8mTJyszM1OxsbGaNGmS91zHjh31wIuL1WLi+6rXuoeOLp4q01UsScrdtFT5e9Yp4papih7/lmzOQJ1Y/lKp+xWl/qDGY19W05FP6dmnE7Vz505FRkaqZ8+eev/9973t3n77bQ0fPlx+fn567LHHdPXVVyszM1Opqam67777JFFRA8A6mMkAAACA37hBgwYpKChITZs2VaNGjfTEE094zw0ePFgJCQlyOBwaMWJEqWqbkSNHKrXArmKPoeBON0quEpUcT5Uk5W77VKHdR8kRHC7D4aeQK29R/q5vZHp+WSIVcsUtsvn5Sw1i1KDZxUpKSpIk3XrrrZo3b54kye12691339WoUaMkSX5+fjpw4IDS0tLkdDp15ZVXSqKiBoB1ENQAAAAAv3GLFy9Wbm6uVq9ereTkZB07dsx7LjIy0vu6bt26pTYafu655/TmAzfq4PShOjh9mDxF+XIX5EiS3NlHdfTDRB2cPkwHpw9T2qv3SIZN7pOZ3uvtgfW9rw2Hv7fvG264QTt27NC+ffu0YsUKhYSEKCEhQZL07LPPyjRNJSQk6NJLL9WcOXMkUVEDwDoctT0AAAAAABeGHj16aMyYMZo4caIWL158xrZfffWVnn32WQ14eKa+Oh4gw7ApZfow73l7cLga9L9fzujWZa51ZWWUOWa3/VIN43Q6NXToUM2bN0/JycneahrpVHD06quvSpK+/vpr9e3bV927d1dQUBAVNQAsgcgZAAAAgNeECRO0YsUK7zKkiuTm5srhcKhNi2byt5nK+vpdeYoLvOeDLr9WWWvekiv7iCTJnZ+t/N3ryu3L6bCprr+91LHRo0frzTff1EcffVQqqPn3v/+t1NRTy6vq168vwzBks9moqAFgGVTUAAAAAPBq2LChRo8erSlTpigoKKjCdv369dM111yjv4+5WgWmQ0HxN8geFO49H9TxekmmMhY8JnfeCdnrhqjuJd1U9+LOZfoyJTUM9C917IorrpDNZlP79u3VvHlz7/GNGzdqwoQJys7OVkREhP75z3/q97//vdLS0qioAWAJhmmaFZ6Mj483N23adB6HAwAAAMDX3Pn2Jq3YmaEzfLWokGFI/VpH6OWR8WXO9e7dW7fccovuuOOOs/Zz6NAhJSQk6NChQ5UfBACcZ4ZhbDZNs+zEJ5Y+AQAAAKime3vGyumwn71hOZwOu8b1jC1zfOPGjdqyZYuGDRtWzlVl8dQnAFZBUAMAAACgWuKahmpS/1YK8Kvc14sAP5sm9W+lttGhpY7feuut6tu3r2bMmHHG5VenY48aAFbBHjUAAAAAqm1k5xhJUuKyZBW63GdcBmUYpyppJvVv5b3udHPnzq30/amoAWAVBDUAAAAAasTIzjFqGx2qF1fv1apdR2VIKnR5vOedDpuKiovVOtTU06OuKFNJUx1U1ACwCoIaAAAAADWmbXSoXh4Zr+N5RVq4JVXJh3OVU1iiYKefWkUFKeBwkl751/Nq+5dBNXpfKmoAWAVBDQAAAIAa1yDQX3d1b1HmuMvVXJMm3q+tW7eqXbt2NXY/KmoAWAUzGQAAAIDzxuFw6J577tGsWbNqtF8qagBYBUENAAAAgPPqjjvu0AcffKATJ07UWJ9U1ACwCmYyAAAAAOdVo0aNNHDgQM2ZM6fG+qSiBoBVENQAAAAAOO/Gjx+vl156SW63u0b683g8VNQAsARmMgAAAADnXUJCgsLCwvTZZ5/VSH8sfQJgFcxkAAAAAGrF+PHjNXPmzBrpi6VPAKyCoAYAAABArRg2bJg2b96sPXv2VLsvKmoAWAUzGQAAAIBa4XQ6dfvtt+vFF1+sdl9U1ACwCoIaAAAAALXm7rvv1ltvvaW8vLxq9UNFDQCrYCYDAAAAUGuaN2+u7t2765133qlWP1TUALAKghoAAAAAternTYVN06xyH1TUALAKZjIAAAAAtap3795yuVz66quvqtwHFTUArIKgBgAAAECtMgxD9957b7Ue1U1FDQCrYCYDAAAAUOtGjx6tBg0aqKSkpErXU1EDwCoIagAAAADUuuDgYE2fPl1+fn5Vup6KGgBWwUwGAAAA4ILgdDqrfC0VNQCsgqAGAAAAwAXP5XIpOzu7wvNU1ACwCkdtDwAAAACAdCyvSAs3pyo5PUc5hS4FOx1qFRmsIR2i1SDQv7aHV6vy8/P1r3/9S1u3btV7771XbhsqagBYBUENAAAAUIuSUrI0a/Verdl9VJJU5PJ4zzkd6Zr+xW71bNlQ43rEKq5paG0Ns1bVrVtXffr00aOPPqqDBw+qWbNmZdpQUQPAKghqAAAAgFoyb91+JS5LVqHLLdMse77wp9Bm+Y4Mrd19TJP6t9LIzjHnd5DnkWmaWrt2rXr06CFJKioq0tdff6233npL3333nYYPH646deqUey0VNQCsgqAGAAAAqAWnQpqdKijxVNjm+GczZQ9qoNAr/qCCErcSl+2UJMuGNYZhaMaMGVq/fr1+//vf66WXXlJ2drZuuOEGTZs2TY0aNarwWipqAFgFQQ0AAABQTTExMcrIyJDdbpefn5+6du2ql19+WU2bNi23fVJKlhKXJZ8xpJGkBteML/W+oMSjxGXJahsdqrbR1lwGdc899+iaa67RoEGDdNddd2no0KHecx6PR6Zpym63l7mOihoAVkHkDAAAANSApUuXKi8vT4cPH1ZERITuu+++CtvOWr1XhS53le5T6HLrxdV7qzrMC97VV1+t5s2bKzEx0RvSuN1ub8VMeSGNREUNAOtgJgMAAABqkNPp1M0336wdO3ZIOrXPysSJE9WsWTNFRERozO1jteqHVJmmVHhgu1Jn3aqc9R8q5YURSv3XKOVtX+Ht69jH05W59m3v+/y9G3To9fv06h09ldCpi7Zv3+49FxMTo+eee05t27ZVSEiIhg0bpsLCwvP3wWvQrFmzdPjwYe97u91+1moZKmoAWAVBDQAAAFCD8vPztWDBAnXu3FmS9H//93/avXu3tm3bpr1792rLzh91/Kv53vbuvEx5ivIVfe9cNej/J51Y/rLchXll+i1O/1HHl/1TDa4Zr9iH3lPrPjfq+uuvV1FRkbfN+++/r88++0z79u3T9u3b9eabb/7qn/fX0L9/f/Xu3btS11BRA8AqmMkAAACAGjBo0CCFhoYqJCREK1as0MMPPyzTNPXKK69o+vTpCgsLU1BQkC65drRyfljrvc6wOxRy5R9k2B0KaNFRRh2nXMdTy/Sfm/SZgi6/Rv6NW6rIY6h+3FXy9/fXunXrvG3+9Kc/qXHjxgoLC9PAgQO1bdu28/LZfy2maaqwsFA5OTlnbUtFDQCrYDNhAAAAoAYsXrxYffv2ldvt1pIlS9SjRw9t27ZN+fn56tChg7ddfrFLntP2p7EFBMmw/bLviuHwl6ek7JIlV/YRnfzuS+Vs/liS9KLNkF1upaWledtERkZ6X9etW7fUOV9kGIaee+45HT16VP/85z/P2Nbj8VBRA8ASCGoAAACAGmS323XjjTfqrrvu0rp16xQQEKAffvhBTZo0kSRNWLBVi7dVPkBxBDeUs+tQhXQdJkkadHljzRjWrkbHfiEaPXq02rVrp8TERAUGBlbYjqVPAKyCmQwAAACoQaZpasmSJcrMzNSll16qsWPH6oEHHtCRI0ckSZH2fLkObK10v4Fx/ZS79VMVpe2SWVKsBS89q379+mnhwoWl9qmxmmbNmqlHjx6aN2/eGdux9AmAVRDUAAAAADVg4MCBCgwMVHBwsCZNmqS5c+fq0ksv1TPPPKPY2Fh17txZwcHBmjPpdhWXswfN2fhHXaQG196nE8tfVurMUSr5YaXS09P1j3/8QxERETp69KhWr16t3NzcX+HT1a7x48dr5syZMk2zwjZU1ACwCpY+AQAAANW0f//+Cs85nU499dRTeuqpp7zH7nx7k1bszJCzeVtF3zu3VPvocXN+eWN6ZNh++St7wO87qG6LDurXOkIvj4z3Hs/IyNBHH32kRYsWqUmTJurWrZsGDx6scePGVf/DXQB69eolj8ejNWvWqGfPnuW2oaIGgFUQOQMAAADn2b09Y+V02M/YxvS4VXLikByhEaWOOx12jesZW+pYRESExo4dq2XLlik1NVWjRo3SihUrdPHFF6t79+6aPn36GcOkC51hGN6qmopQUQPAKpjJAAAAgPMsrmmoJvVvpQC/iv86nvqvUbI566luy67eYwF+Nk3q30pto0MrvC44OFjDhw/XggULlJ6erkceeUTff/+9OnbsqHbt2unJJ5/U999/f8ZlRBUpLCzUF198USt74owaNUpffvmlUlJSyj1PRQ0AqyCoAQAAAGrByM4xmtT/EgX42VVevtD0/vmKGPakbH5OGYYU4GfXpP6XaGTnmHO+h9Pp1IABA/T666/r8OHDmjFjho4fP67rrrtOF198sR555BF9++238ng859Tfrl27lJiYqPr162vZsmXnPI6aEBQUpBEjRmj27NnlnqeiBoBVMJMBAAAAtWRk5xgtuLOz+rWOkL/DJqej9F/PnQ6b/B029WsdoQV3dq5USPO/HA6HevTooRkzZmj//v1asGCB/P39NXbsWEVHR+uee+7R8uXLVVxcXGEfv/vd7/Txxx+rZcuWatasmSRVqTKnqu6991699tpr5Vb0UFEDwCrYTBgAAACoRW2jQ/XyyHgdzyvSwi2pSj6cq5zCEgU7/dQqKkg3t49Wg0D/Gr2nYRhq37692rdvryeffFJ79uzRokWLNHnyZO3atUv333+//vrXv8rPz6/UdcHBwVqxYoX8/f112WWXefs6X1q1aqU2bdpo4cKFGjFiRKlzVNQAsAqCGgAAAOAC0CDQX3d1b1Er977ooov0yCOP6JFHHlFaWpqOHTtWYejx+eefq1u3bpIkt9stu/3MmyLXtPHjx2vq1KllghoqagBYBZEzAAAAAK/GjRurbdu2ZQIYj8ej3NxcffPNN7rpppsknd9qmp8NGDBAaWlp2rRpU6njVNQAsApmMgAAAABnZbPZlJaWppKSEnXu3Nl77Hyz2+0aN26cZs2aVeo4FTUArIKlTwAAAADOaMeOHZo+fboyMjLUoUOHMuddLpfWr1+vOnXqKD4+/lcPTG6//XZddNFFmjZtmsLDwyVRUQPAOpjJAAAAAJxrOWOkAAAgAElEQVRR06ZN1aZNG+Xk5Ojtt9/W888/r9zcXO95wzCUlJSkkSNHqlmzZrrvvvv05ZdfyuVy/SrjCQ8P16BBg/T66697j1FRA8AqjDM9Ti8+Pt7837WfAAAAAH67srOzdfDgQV166aVlKlhM09TOnTu1aNEiLVq0SPv379fAgQM1ePBgXXXVVQoICKixcWzatEk333yz1ift0KJth/Xx11uUfjxbV3bqoFaRwRrSoeaflgUANcUwjM2macaXe46gBgAAAMCv4eDBg1q8eLEWLVqkLVu26KqrrtLgwYN13XXXKTQ0tFp9J6VkaegTr8vdqKXsdruKXB7vOafDJlNSz5YNNa5HrOKaVu9eAFDTCGoAAAAA1Kpjx45p6dKlWrRokVavXq0uXbpo8ODBuuGGGxQVFVWpvuat26/EZckqLHHJVMXLnQxDcjrsmtS/lUZ2jqnmJwCAmkNQAwAAAOCCkZeXp88++0yLFi3SsmXLdMkll2jw4MEaPHiwYmNjz3jtqZBmpwpKPGdsd7oAP5sm9b+EsAbABYOgBgAAAMAFqbi4WF9++aUWLVqkJUuWqFGjRt7QJi4urtQGwfFdrtSh8Hj5X3ZVpe8T4GfXgjs7q200y6AA1D6CGgAAAAAXPLfbrXXr1nk3I/Z4PN7QpmvXrmp6WYKKf3eFAuP6Vbpvw5D6tY7QyyPL/V70m3Msr0gLN6cqOT1HOYUuBTsdbMIMnEcENQAAAAB8imma+u6777yhzeHMPOWYAap7WS8FxfVT7rbPlLPuA3kKc+Uf3Vph/e6VI6iBJOnA1AEK6zdOORsWyZ2fo3qX9lTYVXfL6WfXf/7cu1aDCNM0a/Ux4kkpWZq1eq/W7D4qSWzCDNSSMwU1tvIOAgAAAEBtMgxDbdu21eTJk7Vt2zbdP2O+91zB/iRlrXlL4YP+rOjxb8sR3EjHljxb6vqCvRsVdet0Nb7tX8rf+ZUK922RIWnhltTz+jlSUlLUqVMnRUdH65133pFhGPJ4zn1/nZo0b91+DX91nVbszFCRy1MqpJGkwp+OLd+RoeGvrtO8dftrZZzAbx1BDQAAAIAL3pFiP/28FuDkjtUKbNtX/pGxMhx+Cu15q4rSkuXKyvC2D+58s2zOQDlCGsnZvK2KM/6rQpdHyYdzz+u4AwICNHPmTHXt2lWHDx+WdKqqpqaNGTNGjz76aIXnf9mE2a2z3d40pYIStxKX7dT0RV/LMAy5XC5JUs+ePfXaa6/V5NAB/A9HbQ8AAAAAAM4mp9Dlfe3OOyH/iBbe97Y6AbIFBMmVd1yO0AhJkj2wvve84fCXp6RQkvTNxi36a9L7Cg0NVWhoqOrXr+99/fP7kJAQ+fn51ci4w8PDFR4errp16yosLOzUeKq49CkmJkYZGRmy2+0KDAzUNddco5kzZyowMPCM1yWlZClxWXKlnpQlSQUlHs1c9WOVxgqg6ghqAAAAAFzwgurYva/tgWFy5RzxvvcUF8pTkCtHYIOz9lPX71SVy9GjR7V7925lZWV5fzIzM5WVlaXs7Gw5nc4yQU55oc7p71u1aqWAgIBy75uZman69euXe64yli5dqr59+yo9PV39+vXT008/rcTExDNeM2v1XhW63FW6X1EVrwNQdQQ1AAAAAC44pmkqOTlZX3zxhVauXKlvswL185qdeq176NiSZ1WvdU/5NWiqrDVz5d+4pbeapiI2j0upOzbqmVVvq127dkpISNDVV1+thIQENW/e3FvpYpqm8vLyvMHN6SHOzz8HDx7U9u3bS51/5513dNlll5V775MnT3qDmtMrao4cOaKSkhI1btzYe2/DMM5adRMZGal+/fpp27Zt3mOZmZm67rrrtHbtWrVu3Vrz589XSES01uw+quPLZyt/97fyFJ2UX/3Gqt93rJxNL/vpnh7lrPtAeUmfy1N4Us6YOIX1u1f2gCD9vN7seF6RIkJLf3388ccfNXbsWCUlJckwDPXr10+zZs1SaCibEAPVQVADAAAA4IKQmpqqlStXesMZPz8/9e3bV0OHDtVTnbupQ6/+kgwFxFyu0O4jdXTRU/IU5sm/ySUKv/6Rs/bvV6eO/vPuDDncU7Vp0yZt2LBB8+fP1/333y+Px6OEhAQlJCSoU6dO6tixo5o1a6ZmzZpV+3MVFxcrPz9fQUFBZc45nU4FBwd7g5mf/7egoEAul0tOp1MZGRnaunWrsrKy9Morr2jjxo0yTVPvvfee2rVrp3Xr1ik7O1sfffSRPvroIy1ZskS33nqrJk2apJ73nKq2qRN1sUKu/INs/vWUu3GJji6equh75shw1FHupqXK37NOEbdMlb1uiE58MVsnlr+khjf88jv9YEuqxvVuWWrspmnqL3/5i7p3766cnBzddNNNevzxxzVjxoxq/86A3zKCGgAAAAC1IjMzU6tWrdLKlSu1cuVKHTt2TL1791afPn00efJktWjRQoZhaPPmzXrgnjvkyT0ue8Cp/ViC2vVXULv+5fbb/P8+LvU+fMADkkz1bNnwp0dz+6tPnz7q06ePpFOBw6FDh7R+/Xpt2LBBTz31lDZv3qzGjRt7g5uEhATFxcXJ37/yj/auU6eOJKl169aSSlfUBAcHl3vN6UuooqOjFRUVpdDQUC1ZskRLlixRcXGxGjVqpNzcXN1///1KTk5WcXGxevfuLbvdroCAAJ08eVJJAZerKLKNAi/r9cs9O92o7P8sUMnxVNWJ+L1yt32qsKvuliM4XJIUcuUtOvTiH2V6fln2tCu97CbMsbGxio2NlSQ1bNhQDz74oJ544olK/34AlEZQAwAAAKDSduzYoYKCAnXo0EFut1t2u/2s1xQUFOibb77xVs0kJyfriiuuUJ8+ffTuu+8qLi5ONtsvD6bdtWuXHnvsMX3zzTe67bbbtGbNWgU3uViuM9yjIp6SIm2am6j9V05XTExMqXOGYSg6OlrR0dG66aabJElut1s7duzQhg0btH79er322mvas2ePLrvsslLhzUUXXXTGZUp//vOftWDBAh08eFDt2rXTX//6V40YMeKcfl+n+7n9J598or59+2rNmjW65ZZb9Morryg2NlZjxoxRdHS0nnzySRUUFGjZsmUaP368WrVtp61HXMpe/6Hyti+XO++EJENmUb7cBTmnPmv2UR39MFEyTnsosGGT+2Sm921OYUmZMWVkZOj+++/XV199pdzcXHk8nhrZhwf4rSOoAQAAAHDOSkpKNGTIEK1Zs0YxMTHaunVrhaGD2+3W5s2bvUuZ1q9fr7Zt26pv37567rnn1Llz53IrVFJTU/XEE09o8eLFeuihhxQdHa05c+bo2WefUVhCD/39k50qdJ37E4wC/GzqE+nSmy+v0KWXXqpnnnlG48aNKxUK/S+73a42bdqoTZs2uv322yWd2mdmy5YtWr9+vT766CM9+uijysnJUceOHdW5c2c9/vjjZUKbp59+Wv/3f/+n3NxcHTt2TOHh4WcMaVwulx599FEFBQXp5ptvVsuWLctt16NHD40ZM0YTJ07U4sWLvccNw1DdunUVHh4uh8Oh5o0b6dvNy5Wz/gNFDE+UX8NmMgybUqYP++WzBoerQf/75YxuXXY8Pz3yPNhZ9ilYf/3rX2UYhr777juFhYVp8eLFGj9+fIWfDcC5IagBAAAAcM78/Px055136qWXXtL111+vtLQ070a4pysoKFCTJk3UpEkT9enTRw888IC6d+9e4VIfSTp+/LiefvppzZkzR3fddZd2797trdB4/vnnve3mzHlDB8Lay23YJVVczWJ6PPJ32DSp/yUa2TlGDwyI16BBg/S3v/1N8+fP11tvveVdunMu6tWrp27duqlbt27eY+np6dq4caM2b94sj8dTJoSx2WyqX7++6tevf0773RQUFKhBgwZ67bXXtG/fPr322mvyeDzlhkoTJkxQTEyMkpKSKuyvVWSw7O4iGTa77HVDJI9bWd8ukKe4wNsm6PJrlbXmLYUPeFCOkEZy52erKHWn6l7c2dumZWTZ/XVyc3MVEhKikJAQHTp0SNOmTTvr5wNwdhVHyAAAAABQjmuuuUZRUVFq3LixVq1aJUnyeEpXuDgcDu3evVvfffedZsyYoQEDBlQY0uTl5enJJ59Uy5YtlZ+frx9++EFPP/10ucto3nnnHR3+6n29e0cn1cnYKYch+TtKf61xOmzyd9h0eUObXJ9P041tG0mSWrRooc2bN+u2227Tzp071aFDB02fPl1ud9UfQR0ZGamBAwfq8ccfr/RypvIEBgbq4YcfVt++fdW+fXtJp/bQKU/Dhg01evRoTZkypcL+bu4QrXq/by/n79rr0Ct36tCLt8lw+MkeFO5tE9TxetW9qJMyFjymg88PUfpbD6no8K5S/dzUPrpM35MnT9aWLVsUEhKi6667TjfeeGNVPjKA/2FU9B+9JMXHx5ubNm06j8MBAAAA4CumTZumHTt26I033jjnfWpOV1RUpNmzZ+upp55Snz59NGXKFLVo0aLC9vv371fHjh21fPlyud1uDRkyRBu279CcL3do1vzFuqr/9Qp2+qlVVJBubh+tBoH+uuWWWxQdHa1nn322VF/Lly/XqFGj5O/vryZNmuiNN95Qq1atqvR7qKqfH8X9v4qKitSrVy/NnDlT7du3r7Ddubrz7U1asTNDZ/jqVyHDkPq1jtDLI+OrfH8AZRmGsdk0zXL/w6KiBgAAAPARx/KK9PKaHzVhwVbdNnejJizYqpfX/KjjeUW/2j0LCgr09ddfq6Sk7GayXbt21fbt2yWpUiGN2+3W3Llz1bJlSy1fvlyff/653nnnnTOGNG63W6NGjdIjjzyidu3aafbs2brzzjvVMChAVzezKWzXUr1+a0dNH3a57ure4qenO0kzZszQ3LlztWXLllL9XX311dq+fbsuueQSZWRkqEuXLpo6dapcrqpsVVx5pmlWWCmTlpam3NxctW3bVpKqFdJI0r09Y+V0VK3ax+mwa1zPc18eBqD62KMGAAAAuMAlpWRp1uq9WrP7qCSp6LSNdJ2OdE3/Yrd6tmyocT1iFdc0tFr3+nkD4J+fzPTzBsCff/65/PxKbyjbtWtXBQQEKCcnR8HBwSopKSnT5nSmaWrJkiWaNGmSwsLCNG/ePF155ZXnNK6pU6fKz89PDz30kLKzs7Vw4UIlJydLOrW3TVhYWLnXNWrUSNOmTdPtt9+uDRs2lBpfRESEPv30Uz3//POaOnWq5s+fr4ULF+qNN95QmzZtzmlcVWUYRoUBzPbt272bAZ++P43H45Hb7T7j77g8cU1DNal/KyUu26mCksptwjypfyu1ja7enykAlUNFDQAAAHABm7duv4a/uk4rdmaoyOUpFdJIUuFPx5bvyNDwV9dp3rr9lerfNE3t3LlTM2fO1ODBgxUeHq7bbrtNGRkZmjBhgtLS0vSf//xHQUFlN5P9/vvvlZKSopiYGI0YMUKHDh2q8D6rVq1Sly5dNHnyZE2bNk1r164955Bm48aN+uc//6m5c+fKZrNp3rx5uvrqqxURESFJOnHihBo0aFDh9aNGjVKjRo00ffr0MudsNpsmTpyoTz/9VPn5+apbt6569eqlKVOmqLi4+JzGV9MOHDigzp1PbeR7etXNvn37VL9+fV155ZV66KGHtGDBAu3fv7/CypzTjewco0n9L1GAn11nK9AxDCnAz+7dhBnA+cUeNQAAAMAFat66/VWsgjjzF+xDhw55K2ZWrlwpu92uvn37qk+fPurdu7eioqLOep+MjAwNHz5crVq10oABA9SnTx85nc4y7fbt26e77rpL//3vfzVlyhQNHz78jI/F/l95eXlq3769EhMTNWTIEJmmqbZt2+qFF15Qr169JEkvvfSSkpKS9PLLL1fYz759+9SxY0etW7euwic95ebmaty4cVq/fr0iIiKUk5OjN954w7up76/tk08+0YwZM7R161YNHTpUL774Ypk22dnZ2rx5s9avX68NGzZo/fr1crlcSkhIUKdOnZSQkKCEhIRyN2KWpO2pWXpx9V6t2nVUhlTqMedOh02FRUVq08CmxBHdq1RJcyyvSAs3pyo5PUc5hS4FOx1qFRmsIR2ivcvRAJx5jxqCGgAAAKAW9OzZU0lJSUpPT5e/f9kvsEkpWRr+6joVlFT+iUQBfnYtuLOz94t2ZmamVq9erZUrV2rlypU6cuSIevXq5Q1nYmNjq70PSnmKiop0ww036IYbbtDtt9+uOnXqVLqPsWPHqqSkRG+++aYk6T//+Y/++Mc/Kjk52Tvmv//97yooKFBiYuIZ+5o+fbqWLl2qlStXnvHzvv3223rwwQfVv39/LVu2THfeeaf+9re/lfv/U03Kz8/X2rVr9e233+qLL77QNddcowcffFD16tWr8BrTNHXo0KFSwc3mzZsVFRXlDW46deqkuLi4UuM/nlekhVtSlXw4VzmFJd5NmAOPfK9/PPWENm/eXKk/E2denmeTKdXY8jzACghqAAAAgAvI/v371aJFC4WEhGj27NkaMmRImTbVelKPpHYNbWp17Ct98cUX2rlzp7p27ao+ffqob9++uvzyyytV1VIVRUVF+vHHHxUTE6O6detWqY9FixZp4sSJ2rp1q/fR3qNHj9bll1+uBx980NvugQceUNOmTUsdK4/b7VaXLl10991367bbbjtj2z179mj48OFq2LCh7Ha79u3bpzfeeEOdOnWq0mc5n9xut3bs2OENbjZs2KA9e/bosssu81bcdOrUSbGxsWX+HHg8HsXFxenZZ5/Vtddee073O1X5laxCl/uMf14N49TmxJP6t2JJFX7zCGoAAACAC8iUKVP0+eefq1OnTtq9e7c+/vhjSaee9nPfffdpzZq1ynbZFdTxBgXHXy9J8pQU6cTns1SwZ71sgfUV2KavcjcvVfS9cyVJJcdSdPzzWSo+sk+OoAYK7TZCt7b213V9e+j1119XcHCw9u/fr7Vr16p169aaP3/+GZ+yVBVFRUVyu90yTVM2m00BAQFV7istLU3t27fXokWL1KVLF0mnNg1u0aKFfvzxx1J70owePVq9e/fWmDFjztpvUlKSrrrqKiUlJZ11iVdxcbH+8pe/6N///rf++Mc/avbs2Ro5cqSefPLJan222nDy5Elt2bLFG9xs2LBBOTk56tixY6llU40aNdK7776rCRMm6I477jhrldKvtTwPsDoezw0AAABcQN566y2NGDFCI0aM0Oeff66MjAx5PB4NHDhQcXFxevy9tYoe+ZRyNy5RwX83S5Kyv3lXruwjanzP64oY9ned/GG1tz/T7dKRhVMU8Lv2avqnearf9y4d+2SGAlt1Vc+ePWW32/Xee+9p8uTJyszMVGxsrCZNmlQjn8U0TRUXF2vVqlVatGiR8vLyVK9evWoFGR6PR2PGjNHdd9/tDWkkae7cuRo4cGCZjYPPtpnw6eLi4jR27Fj96U9/OmvbOnXq6B//+Idmz56tV155RSNHjlRKSori4uL09ddfV+5D1bJ69eqpW7dumjhxot5//33t379f9erV0+rVq/XMM8/opptuUnR0tJo3b64PP/zQuxdOfn5+hX0mpWQpcVlypUIaSSoo8ShxWbK2p2ZV92MBlkRQAwAAAJxHX3/9tQ4cOKChQ4eqQ4cOatGihebPn6+NGzfq6NGj+tvf/qa9xwtlBkUo8PJ+OrnzK0lS/s6vFNJliOzOQDmCwxUUP9DbZ1FasjwlBQrucrMMu58CYuIU0KKjPl38gbfN4MGDlZCQIIfDoREjRmjbtm3V+hwul0tr167VNddco9tuu03NmjXT8OHD1ahRo2r1K0kvvPCCcnNz9eijj3qPmaap2bNn6+677y7T/vjx4+cc1EjSY489pu3bt2vx4sXn1P7aa6/V1q1btX37dqWlpenhhx9WUlKSioqKym1f0fELjd1u1yeffKLi4mIdOHBAl1xyia699loNHDhQ9evX16pVqxQeHq527drprrvu0pw5c/T999/L7T61b9Ks1XtV6Kr8HkqSVOhy68XVe6s1fpfLVa3rgQuVo7YHAAAAAPyWzJ07V1dffbXCw8MlSbfccovmzp2rJk2aKC0tTaGhoSoodqvEY0qmR/7RrSVJrrwTsgc39PbjCAr3vnbnnZAjqKEM45d/h3WENFLm0XTv+8jISO/runXrKi8vr0rjd7vdevvtt/+fvfuOz+l8Hzj+eUb2DglixUpiryBVq0S1KEqjSlDUqCqKFkVban3VbBFbIxGzhPaHFi2ibazakpASxAgSiezkGb8/Ug9PMyQkEnq9X6+8XnLOue9zndNQz+W+r4uvvvqKOnXqMGfOHOrXr/9Uc+XkzJkzzJgxgyNHjqBWP/q4cuDAAUxNTWnevHm2MQVN1Jibm7NixQr69OnDa6+9hp2d3RPHlC1blj179jB37lz27NnDpk2bjOJ7KDU1FV9fX9q0acPHH3+c75iKW9myZenQoQPnz5+nX79+7Nu3j23btlG3bl3OnDnD/fv3uX37NrNmzSImJgYb+1LExCehS0/GxMEFB+/BmFesA4Ber+NB6A8knf4ZXVoy5q71cezwESqLrBbvSWf3Ex8SyMrMNBwiPmFDgD+rVq3C29ubo0ePMmrUKMLCwrCwsKBHjx7Mnz/fUIhaoVCwePFiFi5ciEaj4cqVK8X2zoQoKpKoEUIIIYQQ4jlJTU1l8+bNaLVaQ+IkPT2d+Ph4ypQpQ5UqVbh06RKjN50k+NRNo7Eqa0e0ifegdCUANIn3jM5pEu+i1+sMyRrNg7s41PQoUHw3b97ExcUFrVaLSqUyOqfX6wkODmby5Mk4OjoSGBhIixYtCvwO8pKWlkafPn345ptvqFq1qtG5ZcuWMWzYsBw7EcXGxuLo6Fige7Vu3ZpOnToxYcIE/Pz88jVGqVTy2WefodFockzSQFY78Q8++IAuXbrg4OCAr69vgeIqLtHR0ezevZu2bdsCWc+q1+sxMzMjISGB/v37o9VquXTpErGxsfT5dBYmtg3Rm9mQeGwHd4NnU+HDNSjUpiQe/5GUS6GU6T0blaUdcfuWE/eLH05dPyPj3jXifvHDuedU7Cp5cPzSTm7cuGGIQ6VSsWDBAjw9PYmOjubNN99k6dKljB492nBNcHAwR44ceeHqBAmRX7L1SQghhBBCiOckODgYlUrFhQsXOHXqFKdOnSIsLIyWLVsSHByMjY0N//vf/6jqYIqpUk/G3SjSb10EwMqjBQl/bkGbloQm8R6JJ34yzGvm4o5SbcaD0B/QazWkXT1DauRR3uzaPV9x/fTTT1SvXp233sraTvXvJA1kJUOmT5/ON998w6FDhwo9SQNZyYFff/2V/v37Gx2PiYnhl19+yTHpodPpSEhIwMHBocD3mzNnDj/++CMhISEFGpdbkgbAycmJ5ORkGjZsSNeuXQsc0/PWrVs3bGxsqFixIs7OzkydOtVwzsfHh/DwcCIiIoy2y5UqVQqPDn3Awh6FUoVts+6gySQzNhqAxFO7sW/VF7VtaRRqE+xa9CYl4nf0Oi0p4b9jUb0p5hVrk65X4d5xkFHyrXHjxnh5eaFWq3F1dWXo0KEcPHjQKOaJEyfi6OgoiRrx0pIVNUIIIYQQQjwn/v7+DBgwgEqVKhkdHzFiBCNHjuTo0aOMHz+e/fMXEPsgGbVjeexb9QXA7tX3iPt5CTf8BqGydsCqVhuSz+4DQKEywemdL4j7xY+E0C2orUtRtstYRrzdOl9xhYaG8tlnn7Fr1y5WrFjBkCFDsq2qsbOz49ixY0XW1lun02FqaoqTk1O2c99//z09evTIcYtSfHw8NjY2eSZPcmNnZ8fixYsZPHgwp06dwtzc/Klif9yZM2f4+uuvmT9/PjY2NjmuTipJgoOD8fb25uDBg/Tu3Zt79+5hb28PQIUKFRg1ahSzZs3igw8+MNou9/v277mxbxvapDhAgT49BW3qAwC0CXe5u20GPLYVD4USbfJ9tEmxqG0fbdtL1auNtq1dvHiRMWPGcPz4cVJSUtBoNDRu3Ngo5ooVKxbBmxCi5JBEjRBCCCGEEM/Jnj17cjzes2dPevbsCcCGDRsAGBJwnL1hMej1WdcoTc0p/dZYw5jEv3aheqxOjalTZcr2mQ2AQgEdapWhlLUZkJXoeFybNm2Ijo42fD9x4kSsrKwoV64cU6dOZfDgwahUKvR6vWG1g4mJyTM8ed4etvPO7dyQIUNITU3N8XxB69P8W7du3QgMDGTGjBl8/fXXTz0PZCWNJk6cSPfu3WnXrh16vb5EJ2ke17p1a95//33GjRtnVGR5+PDhVKtWjTfffNNwLCQkhPO7A3B652tMnCqhUCi5vuBdw3mVbWlKdRyF+T/1lR6nsnYkM+7RVidLpZbY2FjD9x9++CENGzZkw4YN2NjYsHDhQrZu3Wo0R07b34R4mcjWJyGEEEIIIUqgj9pUx1z96EO+JimOtOgL6PU6MmOjeXB0O5Zur+Q41lytYnib6vm+l5WVFQCdOnWicuXKzJs3D8DQ3acoPZ4MyolCocDBwQEXF5cczz9rogbgu+++Y/ny5Zw9e/apxoeGhhrmAfjyyy+NzusfZttKuNGjR7N3715Onz5tOGZnZ8eHH35oSCACJCYmYmpigoWtPei0xB/egC7jUSLNpsGbxB9chybhDgDalARSLma9I0v3V0mNPEpadBhmCi0Xd681ej+JiYnY2tpibW1NeHh4vusHCfEykUSNEEIIIYQQJVD9ivZM6uiBhck/f2XXaojbs5jr832I2fA5FjWaYdOoY7ZxFiZKJnX0oF4F+wLfU6lU8tFHHxEQEABk1WIp6hbImZmZzzT+aQoJ/1u5cuWYOXMmH3zwQYGTU0lJSUyePJlGjRqxefNmtm/fDmQluR4moO7du8f27dtLfMLGycmJfv36MW3aNKPjo0aN4uDBg4Z306FDBzq++QZRSwdzY+lAFGoTo9VdNmYb4E4AACAASURBVE26YFmjGTGbpnBtvg+3140l/VYEkLXyy9F7KPd2ziFyoS+NqpXF2dkZM7Os1V9z584lKCgIGxsbBg8ezLvvvosQ/zWKvP6w8PT01B8/fvw5hiOEEEIIIYR4XGBoFDN2hZOm0ZLX53yFImslzaSOHvh6ueZr7n+vZtHpdIZkjVKppFy5cnh5eRk6ARW21NTUZy4Iu2nTJn788UcCAwOfaR69Xk/btm3p2rWrUYeh/Bo5ciSrV68mIiKCChUqGI7rdDouXLiAj48PtWrVYsmSJUat0l8UY8aMQa/Xs2DBAsOxf2/PK4iH2/PmdvPA3t6eS5cuUaVKlUKMWIiSTaFQnNDr9Z45nZMVNUIIIYQQQpRgvl6ubBriRYdaZTBTKzFXG/8V3lytxEytpEOtMmwa4pWvJE16ejqrV68mLS3N6LhSqUSj0RATE8Py5cu5ceNGkXR3AsjIyMixLo1Wqy3QyhMXF5dn3voEWVusVqxYwfTp04mKiirw+G+//ZbAwEB+++03wyqkjIwMvvnmGyIjIzl58iQeHh7Ur1+fwMDAEr+65t/GjRuHv78/d+/eNRz79/a8/Eq5dARTfSbvNy3HuHHjqFu3Lq6uroUYrRAvNikmLIQQQgghRAlXr4I9y3w9iU1KZ+tf0YTfSuRBWia25iZ4lLPhnUYVDIWD86LRaAgICOCrr76iXr16dO3aNduKlu+//x4HBweuX79OmTJliuqROHr0KM2aNTN8/7A70sPiuzqdDuCJXabs7e0LJVEDUKNGDcaNG8ewYcPYvXt3gYvWvv3224ZVSQCmpqa0bt2a3r17s3fvXubOnUv37t0ZOHAgmzZtYtmyZZQvX75QYi9qLi4u9OzZk4ULFzJjxgzg0fa8GbvCSM3U5XuujL+PEP3zQjosVeDp6cnGjRulQLAQj5GtT0IIIYQQQrzk9Ho9wcHBTJo0iVKlSjFr1qxcV8o8nmgoKocOHaJu3bo4ODgYjn3zzTds2LCB/v378/777xu14tbpdKSmphqKHj9u3bp1JCYm8tFHHxVKbJmZmTRp0oRx48bh6+tbKHMmJCQwdOhQLly4wMaNG6levTqzZs1i8eLFzJ49m4EDB74QiYrLly/TpEkTLl++bPTfpyi35wnxspKtT0IIIYQQQvxH/frrr3h5eTF16lTmzp3LoUOH8tzOVNRJmvj4eMaOHWv0QR9gzZo1dOnShRMnTtCiRQsGDhxISEgIAKdOnSIoKCjH+X799ddnLib8OBMTE1atWsW4ceOMtvk8Czs7OzZs2MDo0aNp3bo133//PV988QX79+/Hz8+P119//am2Wz1vVatWpWPHjixZssTo+JO25yl0mgJvzxPiv0wSNUIIIYQQQryEjh07Rvv27RkyZAijR4/mr7/+omPHjsW+cmP48OE0a9bMKCGUmJhIt27dGDRoEOvWrWPz5s1UrFiRsWPH8sYbb+Dt7Y2lpWWO8924caPQtj495OnpSd++ffnkk08KbU6FQmFIPi1dupSePXtSsWJFQkNDadeuHZ6enixdutSw5Ssner2e69evF1pMT2PixIksWrSI5ORko+MPt+f9Mb4tn7R34+0G5Wnn4cxrrlYozv0ff4xvyzJfz6fqRibEf41sfRJCCCGEEOIlEh4ezuTJk/nzzz+ZMmUKgwYNwsTEpLjDAmD9+vXMmDGD48eP55h4eVin5iG9Xs/8+fOZO3cut27dynHORo0asXLlSho3blyosaakpFC3bl0WL17Mm2++Wahzp6WlMX78eHbs2EFQUBDNmzcnLCyMgQMH0qhRI7777rscVzaFhITw0Ucf0alTJ2bNmlWoMRVEjx496NGjB717937itRqNBjs7O27fvo2Njc1ziE6IF4NsfRJCCCGEEOIld+3aNQYNGkTLli1p0qQJly5dYtiwYSUmSRMVFcXo0aMJCgrKdXXMv5M0CoWChIQE+vbtm+u8sbGxhb6iBsDS0pLly5fj5+eX5yqXp2Fubs6iRYv47rvv6N69OzNmzMDNzY3Dhw8ze/bsXLefNW7cmGnTprFkyRIuX75cqDEVxPfff8/bb7+dr85VarWa2rVrc+bMmecQmRAvB0nUCCGEEEII8QK7e/cuY8aMoWHDhpQpU4aLFy8yfvz4XJMhxUGr1dK3b1/Gjx9PgwYNnngtYNiiNWLECCZPnpzr9bGxsYVao+Zx3t7ebN26tUjmBnjrrbc4ceIE+/btw9vbm5SUlDxXnajVarZs2cKoUaOoWrVqsbX4trGxwcLCIt/b6Bo0aMDJkyeLOCohXh6SqBFCCCGEEOIFlJiYyNSpU/Hw8CA9PZ1z584xc+ZMo05KJcXs2bMxNTVlzJgxT7x206ZNZGRkGL53dnbG1tY2x2vT09PJyMgo0i01pqamua5w0Wg07Ny585nmL1++PPv27aNv376o1eo8r121ahXXr1/n66+/fqZ7FiW9Xs+xY8c4ceKE4VjDhg0lUSNEAUiiRgghhBBCiBdIWloaCxcupEaNGly6dImjR4+yZMkSypUrV9yh5ejevXusXr0af3//J3aUunjxYoEK+MbFxeHo6FhsBZIjIiJYsGABM2fOfKZ5VCoVAwcOxNzcPNdrzp07x4oVKwz30mq1xV4YOifJyckcPnyYESNGGI61bNlSEjVCFIAkaoQQQgghhHgBaDQa1q5di7u7O/v37+eXX34hMDCQatWqFXdoebK2tub8+fNUqFDhideuWLGCAQMGYGpqmq+5i6o+TX5Vq1aNKlWqEBQUREpKyjPP9+/Ei16vZ8+ePQDMmzeP9u3b06JFC/R6vVE9n+L2+BasxMREtFotp0+f5uLFiwDUrFmT8PBwo5VSQojc5b22TgghhBBCCFGs9Ho927dvZ/LkyZQuXZqgoCBeffXV4g4r3/JaJfK4tLQ0/P39OXLkSL7nLs5Eze7du5k1axZOTk7s3LmzSGoCJSUlsWjRIgYPHoydnR3nzp0DHhVaBriXlM7WE9GE337AgzQNtuZqPMra4tO4AqWszQo9pof0ej3btm2je/fuKBQKoqKiWLt2LTt37qRGjRrs27cPNzc3IGvFUKtWrQgLC6N+/fpFFpMQLwtJ1AghhBBCCFFC7d+/n88//5z09HTmzZvHG2+8USK3uzz07/baBbF161YaN25M1apV8z2mKAsJP06r1TJlyhTGjh2Lubk5n332Gb///jujRo1iwIABhmsKe5WLjY0Nu3fvZsGCBUyZMoU9e/bwxhtvoFQqOX09nsW/XeLgpXsogHTNo85U5urbLNh3kTbuTgxvXZ36Fe0LNS7IWv2zbds2Dhw4gIuLCxs2bKBevXqsWbOGhg0bZru+V69enDx5UhI1QuSDJGqEEEIIIYQoRIWxwuHYsWNMnDiRqKgovv76a959990n1ncpTjt37qRLly6oVCp0Oh0KhaLACaVly5YxduzYAo2Ji4t7LitqVCoVpqamNG/enDJlylC1alX27t2Lk5MTUDRJmsd98skndOvWjYiICHQ6HUFHrzF9VxjpmVr0ZH/Paf8kbX65EMOhi/eY1NEDXy/XQo/r888/p27dunzwwQds27aN6tWrA1mrbfR6vdHPbPny5UlISCj0GIR4GUmiRgghhBBCiEJw+no8Sw5EcvDiXeDpVjiEh4czefJk/vzzT7744gsGDhyIiYnJc4n/ac2dO5cJEybQpk0bFi5cSJ06dYCCJS/OnTvHlStX6Ny5c4Hu/Ty3Pn311Vf88MMPuLu7s3LlSuDRMz6PejFVqlShSpUqBIZGMWNXGGmZOsghSfM4vR5SM7XM2BUGUOjJmtq1a+Pt7U379u2pXr26ocCxUqnMlqhr06aNofW6ECJvJTctL4QQQgghxAsiMDSKXitD2RsWQ7pGZ5SkgawVDukaHb9ciKHXylACQ6OMzl+7do1BgwbRsmVLmjZtyqVLlxg6dGiJT9IkJSXxyy+/sGvXLt5++23ee+89vvvuOwBD8kKj0TxxnuXLl/PBBx8U+HmHDh3Kp59+WvDAn5Kfnx8pKSlkZmYWaUHfNm3a4ODgQHp6utHx09fjmbErnNRMXS4jc5aaqWPGrnDORMcXZphA1kooW1tbw/vIbeWXiYlJvusVCfFfp3i8Qve/eXp66o8fP/4cwxFCCCGEEOLF8nCFQ04fnm+uGo5j+2GYV65ndNzCRMmkjjXpUM2KmTNn4u/vz7Bhw/j0009xcHB4XqEXiri4OLRaLU5OToSEhPDFF19QunRptmzZwoULFzhz5gy9evXKdXxycjKVKlXi1KlTVKxY8TlG/nT+/vtvXF1diyxJExUVRbVq1bCzs2P58uX4+PgYzg0JOM7esBjy+AgHgF6nRaE0jk+hgA61yrDM17MowhZCFJBCoTih1+tz/A0pK2qEEEIIIYT4h6+vr6E47EMHDx6kVKlS3Lp1K9v1T1rh4PLBUkOSJj5kPfd+nAtkrXD4cscZXCpX5fTp01y4cIGZM2e+cEkaAEdHR0OtlpYtW7Jp0yaqV69OnTp18PT0pHTp0nmO37hxI6+++uoLkaSBrJbcD5M0qamppKWlFer869atw8vLi/fffx9/f3/D8XOXrrBh5iiuLexNtN8gHhzfaTgXH7Keu9tncu/HuVyb70PCn1u4NrcH2tQHhmvSbkWyamg7bt9PIjIyktatW2NnZ0fp0qV59913nzluvV6fr9VTOYmPj+fYsWPPHIMQLwtJ1AghhBBCCPGPRYsWsXv3bvbu3QtktYwePHgw8+bNo1y5ctmuX3IgkjRN9robet2Ta3Fo9QocK1Snd+/elC1b9tmDLyGcnZ2ZNWsWer2eESNG4O3tnef1y5cvZ9iwYc8pusLl5+fH8OHDC3XOdevW0adPH/r06cPPP/9MTEwMOp2Ojp3fwsy5KhVG+FPmvRkkHttB6uUThnEpl45g6f4qFT/ZhG3TtzGrVIeUsMOG88nnfsOmVmt2nI1hypQpvP7669y/f5/o6Gg+/vjjZ45boVBw8+bNPK9JTk7O8fidO3fo168fDx48yPG8EP81kqgRQgghhBDiH6VKleK7775jyJAhJCcnM3XqVKpVq8b7779Pamoq/fv3x8HBgZo1a/Ll1zNZ+9Ebhm0o0UsHkhC6lZurR3BtXg/0Oi3RSweSGnWK1MsnSPhzC8lhIVyb9w43V4/g/qFA7kSeYcSIEVhbWzNixAggq6Bw+/btcXR0xN3dnc2bNxfjG8kfnc54RVFISAguLi7MmTMnz3EnTpzgzp07dOjQoSjDKzKDBw9m//797Nu3r1DmO3z4MFevXqVnz540btyYatWqERQUxLFjx7gfG4t1814oVCaY2JfFukEHksNCDGPNXDywdHsFhUKJ0sQM67rtSD7/G5CVOEwOO4h5rTaE30rExMSEq1evcvPmTczNzWnRokWhxF+2bNlsdXUe99Zbb3Hnzh0gq77R33//zU8//cTPP//MpUuXCAsLK5Q4hHjRSdcnIYQQQgghHuPj48PGjRt57733+P333zl16hQAU6dOJSoqisuXL5OcnIxX6+wrRZIvHMTZ50uUFrZGNUIsqjbG7hUfNPG3KP3WOMPxzBth9Hi3FwFzPs8an5xM+/btmTZtGrt37+bs2bO0b9+eOnXqUKtWrSJ+8qcTFxdHbGwsVapUQa3O+njRsmVLtm/f/sSxy5cvZ8iQIc+la1JRsLGxYenSpQwdOpSzZ89iaWn5TPP5+/vz+uuvG7aL9e7dG39/f8qXL0/y/bskL3hsi5Jeh1mFRz8TKlvjLWaWNbyI27OEzPjbaOJuoDSzwszFndjEFObMmcOUKVNo2rQpDg4OjB07loEDBz5T7ACmpqZ5bgUzMTFhwIABODg4EBERQXp6OqVKlcLDw4Nly5bh7u7+zDEI8TKQRI0QQgghhBD/snTpUqpVq8aMGTMMtVM2b96Mn58fDg4OODg44OHdk9sblhqNs238Fmpbp3zfR6fXczvh0Qfbn376CVdXV0OdnIYNG9KjRw+2bNnCl19+WQhPVrj0ej0DBgygdu3azJw50+ictbV1nmMfPHjAli1b8r2KQq/XG1buPGz9rFAoUCgUBWoFXtg6derE+vXr+eqrr564gigvqampbN68Ga1Wa9gKl56eTnx8PGXKlMHW2QX79/1yHf/vdtgKtSmWNVuSfP43MmOjsar9GgAHftnFUeemhhbjhw8fxtvbm1atWlG9evWnjv8hMzOzXM95enoSFBTEypUrKVu2rKGVuxDCmCRqhBBCCCGE+JcyZcpQunRpateubTh28+ZNo4K3KpvsRXJVBUjSPJSa+aiezdWrVzly5Aj29vaGYxqNhr59+xZ43udhxYoVXL9+nS1bthR47Pr16/H29s5XfR69Xo9Cocg1GbNp0yY8PT1xc3MrcByFYeHChdStW5devXrRqFGjp5ojODgYlUrF2bNnMTU1NRzv2bMnwcHB2NrYkHz0BywadkahUpMZex29JgOzcrk/s1WdtsT+NB9tSgIOrftjrlbStWNrhg7tx5o1a1i1ahUODg4oFIpc22oX1L8TRo9r0aIFycnJRnWLdDodDzsRK5XKPMcL8V8hiRohhBBCCCHyoVy5ckRHRxu2IGkT72W/KK8PmTmdUyiwMHmUfKhYsSKtW7c2FDMuySIiIpg8eTKHDh0ySiz8272kdLaeiCb89gMepGmwNVfjUdaGJavXseh/0/N1L4VCwbJly7hy5QpOTk6ULl2a0qVLY29vT7169Zg1axZLly4ttkSNs7Mzc+fOZdCgQRw9ehQTE5MCz+Hv78+AAQOoVKmS0fERI0YwcuRI9vwWQqt3BhO3bBBoMlGXqoB9q7wTeOYVaoFCgWmZaqjtnNHp4bN3WpFxuhcrV67E2dkZZ2dnFi1aRNWqVQscc0G1bdsWFxcXQ2KmMBNEQrxMJFEjhBBCCCFEPvTs2ZNZs2bRpEkTUlJSiNi/lYL827/Kyp60qFPo9ToUiqwPp6bWDqiT7xqu6dy5MxMmTCAgIIBevXoBcOrUKaytralZs2ZhPs4zycjIoE+fPkybNi3XuE5fj2fJgUgOXsx6vnTNo4LDpkrIaDueTTdtKX09nvoV7XOc43GzZs2ibdu2pKamcuLECRITE0lMTESn03H+/Pkcu3I9T76+vgQGBrJgwQI+++yzAo/fs2dPjsd79uxJz549Aeg1YS57w2IMBawfsm/ZJ9d5VTZOWNVuDXodaVf+4v+23WXevHksWLCA0NBQBgwYwC+//ELXrl0pU6ZMgeMuCDMzM+rXr1+k9xDiZSDpSyGEEEIIIfLhiy++oEKFClSpUgVvb298e/dEocr/yglLj6zOOtEL3+PW2lEA2DftysXQfTg4ODBy5EhsbGz45Zdf2LhxIy4uLpQtW5bx48fn2UmnOHz55ZeUK1cu17bagaFR9FoZyt6wGNI1OqMkDUCGDlCZsDfsDr1WhhIYGvXEe7q6ujJ48GC+/fZbNmzYwE8//cT+/fsJCQmhcuXK2NnZFcKTPb2Hq37mzJlDZGRkkdzjozbVMVfnvxZP+q2LZMT8jZVHSyxMTZjZpzVLly6lSZMmHDhwAC8vL06ePImbmxv16tUjMDDQsNpFCFF8FHn9RvT09NQfP378OYYjhBBCCCHEi8HPz49pi1Zi3v3rbCsc8kOhgA61yrDM17PwgytCBw8e5L333uPUqVM4OztnOx8YGsWMXWGkZupyGJ0zCxMlkzrWxNfLNddr4uLiUKvV2NraZjs3adIkpk2bViK6Ry1YsIAff/yR/fv3F0m9lfy+33s/zSflUiiO7Ybg1Ph1w/vV6/Vs2rSJCRMm0KBBA+bMmYObmxsnTpxgwIABVK5cmRUrVhTZCiW9Xs/Vq1cpX778U20RE+JloVAoTuj1+hz/ByAraoQQQgghhMiHW7du8fvvv6PT6YiIiGDevHn4vutToBUOjzNXqxje5tm77BQlV1dX9u3bZ/g+Pj6efv36sWrVqhyTNKevxzNjV3iBkjQAqZk6ZuwK50x0fK7XODo6GiVpHv8H5xkzZpSIJA3AyJEjSUpKYs2aNUUyv6+XK5M61sTCRJVnSaTSncdQecxmnBp3MEqCKRQKevXqRXh4OK+88grNmzdn1KhRuLq6cvz4cTw9Pfn555/RaDTZ5ty5cychISH5jjUjI4NTp06RkZFhOKZQKPDx8eHo0aP5nkeI/xpJ1AghhBBCCJEPGRkZDB06FBsbG9q2bUvXrl2ZMWkskzp6YGFSsL9WZ60g8aBehSfXZikp9Ho9H374IW+99RYdO3bM8ZolByJJ02hzPPckaRotSw/kf8vQoUOHKImr/1UqFStXrmTixIncunWrSO7h6+XKpiFedKhVBjO1EnO18c+fuVqJmVpJh1pl2DTEK8eVSubm5owfP54LFy6QmZmJh4cHS5YsYeLEifTr1w+12ricaXp6OhcuXODtt99m9OjR+doiZWpqypw5c1iwYIHR8YYNG3Lq1KmCP7gQ/xFSTFgIIYQQQoh8qFy5MufOnct2/OGH4Bm7wknTaPPcBqVQZK2kmdTRI89tPiWRv78/mzdvJjQ01HDszp07uLq6cvXqVRLStARNG07qjQjQaTGrUAvHDh+hts1qY357/QTMK9Ym7eoZMu5GYebiQeku41BZ2qGJj+HGskEEvzWGnya+TVpqKp988gmTJk0C4OjRo4waNYqwsDAsLCzo0aMHANWrV8fTs+RtHatfvz6DBw9m5MiRT9W6PD/qVbBnma8nsUnpbP0rmvBbiTxIy8TW3ASPcja806gCpazNnjiPs7MzS5cuZcSIEXz66ac0adKE5s2bZ7vOzMyMCRMm8Ndff1GqVCkUCoWhbXpeJk6cSPv27Rk5ciQWFhYANGjQgL/++uvpHlyI/wBJ1AghhBBCCPGMfL1cqVfBnqUHIvkt4i4KIO2xArrmaiV64DV3J4a3qf5CraQBuHLlCp9++ik9evRg69atNGnSBIANGzbQrl07nJyc8N95HNv67XHsOh50OmJ3LSJu7zKce0w2zJN84SDOPaeisinNnc1f8uDodhzavG84n3b9PF+v2UMrZw1Nmzale/fu1KxZE5VKxYIFC/D09CQ6Opo333wTKysrmjZt+rxfRb5NmTKF+vXrExwcTLdu3YrsPqWszRjaqtozz1OrVi3+7//+D61Wm2vL7ODgYK5cucKGDRvyPW/dunVp1qwZq1evZsSIEUDWiprVq1c/c8xCvKwkUSOEEEIIIUQhKKwVDiWNVqulb9++jB8/npYtW+Lj48Ps2bNRKBQEBAQYWlFHp6owrfFoJYZd83eJCZpoNJdVXW9MHMtn/bpmS1IuHTE6b938PS7HZfJx+wbUr1+f06dPU7NmTRo3bmy4xtXVlaFDhzJ79mxKlSpVVI/9zMzNzVmxYgV9+vThtddeK/auVPmVW62f5ORkFi9ezPDhw1GpVGi12nzXBZo0aRI+Pj4MGTIEU1NT6tWrR1hYGJmZmVJQWIgcSKJGCCGEEEKIQlRYKxxKio0bN2JmZsaYMWNQKpVYWlpy4MABypUrR2RkJF26dAEgLiGJ2D2LSb18Al1aEgD6jFT0Oi0KZdYHepW1g2FehdoMfWaa0b1U1g48SMsEwNLSkqSkrHkuXrzImDFjOH78OCkpKWg0GlQqVYlO1AC0bt2aTp06MWHCBPz8/Io7nGfy/fffo9PpGDBgAJB7QicnTZs2xc3NjcDAQAYOHIiVlRWVKlUiLCyMevXqFVXIQrywpJiwEEIIIYQQIkfp6ekEBwfj7+9v2A7Tv39/AgMDCQgI4J133sHc3ByAS/s2kBkbTbl+86k0Zgtl+/zvqe5pa559hcWHH36Ih4cHly5d4sGDB8ycORONRlPiEzUAc+bM4ccffyxQt6SSQqPRsG7dOmJjY9m8eTOff/45kLXK6qH8FBWGrFU1s2fPNoxt2LAhJ0+eLPyghXgJSKJGCCGEEEIIkU1SUhL37t1j+PDhlC5dmrS0NNLS0vD19WX79u0EBgbSr18/w/U2Kg0qUzOU5lZoUxOJPxxU4Huaq5V4lLPJdjwxMRFbW1usra0JDw/Hz8+PzMzMFyJRY2dnx+LFixk8eDBpaWlPHlCCpKSk8OOPP1KpUiXu3LmDt7c3YLya5vbt2zkW2f631q1bU7p0abZu3QpIokaIvEiiRgghhBBCCJHN6NGjMTc3Z+bMmVhYWBi+li9fTqNGjVAoFLRs2dJw/fxpE9FnZnB9UW9urxuLRdXGecyeMz3wTqMK2Y7PnTuXoKAgbGxsGDx4MD4+Pmi12hem7ku3bt2oU6cOM2bMKO5QCsTW1pYtW7awa9cu1Go1M2fOJCUlxXA+NTWVX3/9lddee42vv/6azMzMXOdSKBRMmjSJmTNnotfrpUW3EHlQ5LVUzdPTU3/8+PHnGI4QQgghhBCiuG3bto3PPvuMkydPYmOTfYXLwIEDcXFxYfr06UbHhwQcZ29YTJ4tynOjUECHWmVY5vvkdtv37t3D3d2d2NjYgt+omNy6dYv69euzf/9+6tatW9zhPJUrV65QpUoVIGvL061bt2jQoAGvv/46t27dIjY2lrVr19KwYcMcx+v1eho1asS0adPw8vKiRo0a3L9//4ktvoV4GSkUihN6vT7HP/BkRY0QQgghhBDC4MaNG3z44YcEBgbmmKSJiopi27ZtDBo0KNu5j9pUx1yd/yKzjzNXqxjepnq+ro2NjX0htj09rly5csycOZMPPvjAqMbLi+RhkgayVsi4uLgQHh6Os7Mzp0+fpkaNGrz++utMnjyZ9PT0bOMVCgWff/551soicxtsmnZn8JrDDPQ/xuhNJ1l28G9ik7KPE+K/RhI1QgghhBBCCAB0Oh39+/fno48+wsvLK9v5KVOmUKdOHT799FOjD+0P1a9oz6SOHliYFOxjhoWJkkkdPahXwT5f17+IiRqAQYMGYWlpyXfffVfcoRQafNHlQQAAIABJREFUR0dH5s+fT2hoKDqdDjMzM/bs2UPDhg05cuRItuurN23HHfduNJ+9H3XDruyLfMCv4XcIPnWThfsu0vx/vzI08Dinr8cXw9MIUTLI1ichhBBCCCEEAPPnz+eHH37g4MGDqNXqp54nMDSKGbvCSdNo89wGpVBkraSZ1NEDXy/XfM//448/snz5cn766aenjrG4XLp0iVdeeYXjx4/j6upa3OEUukOHDjF27Fju379PXFwcgwYNYtq0aVhYWBh+LlIzNUDu252e9udCiBeJbH0SQgghhBBC5On06dPMmjWLgICAZ0rSAPh6ubJpiBcdapXBTK3EXG38scNcrcRMraRDrTJsGuJV4A/jcXFxODo6PlOMxaVGjRqMGzeOYcOG5bu19YukVatWHDlyhK+++gpLS0s2btxIzZo1+SpwPzN2hZGaqSWvJA2AXg+pmVpm7AojMDTqucQtREkiiRohhBBCCCH+41JTU+nduzfz5s2jatWqhTJnvQr2LPP15I/xbfmkvRtvNyhPOw9n3m5Qnk/au/HH+LYs8/XM93anx72oW58eGjt2LDExMaxfv764Q8mVq6srFhYW2NjYYG9vT/PmzVm2bBk6ne6JY5VKJb6+vly8eJFhw4aRoLZn7akEUjOfPPZxqZk6ZuwK50y0bIMS/y3PlioXQgghhBBCvPDGjx9P3bp16du3b6HPXcrajKGtqhXqnC96osbExIRVq1bRqVMnOnTogJOTU3GHlKMff/wRb29vEhISOHjwIKNGjeLIkSOsXbs2X+MtLS2ZNGkSF8u04WBk3FPFkKbRsvRAZI7dwPR6PXq9HqVS1h+Il4v8RAshhBBCCPEftnv3bnbs2IGfn98L0yb5RU/UADRu3Ji+ffvyySefAHAvKZ1lB/9m9KaTJa4Lkp2dHV26dGHTpk34+/tz7tw5/u///o+GDRtia2tLxYoV+eqrr4zGhIaG0rx5c+zs7dk4qS9p184Zzt1eP4H4QwHcDviUa/N9iNk4BW1KguF8+o1wbgeM49qCd7mxagT/9/N+w3to06YNkyZN4tVXX8XS0pJ58+bRuHFjo3vPnz+frl27ArBr1y5q1aqFjY0N5cuXZ+7cuUX0loQoPLKiRgghhBBCiP+oO3fuMGjQIIKCgnBwcCjucPItNjb2ha1R87ipU6dSp1Unus79P8ITsv4NPV3zaHuQufo2C/ZdpI27E8NbV6d+xYJvE8uPuLg44uPjcXV1zXN1StOmTalQoQIhISHUrFmTdevWUbt2bc6dO0f79u1p0KAB3bp148aNG3Tq1ImAgACiLGowc9Vmbm2bhcuQZags7QBIvnAQ555TUdmU5s7mL3lwdDsObd5Hk3iPO1umUvqtMZhXbUxa1Glu/jCDtT6vMa5LUwACAgLYvXs37u7uaLVaZs2aRVhYGDVr1jScnzx5MpDVaWvz5s20bNmS+/fvc+XKlSJ5h0IUJllRI4QQQgghxH+QXq/ngw8+oF+/frRp06a4wymQuLi4F35FDcC2M3dQdxjH6bs60jU6oyQNQNo/x365EEOvlaFFUlh3+fLlNGrUiFq1anH58mWAPIscu7i4EBcXR5s2bahbty5KpZJ69erx3nvvcfDgQQACAwPp2LEjHTt25OKdJNSVGmBarjqpfz/qKGxV1xsTx/IoTcywqtmSjJiseyefP4BFNU8sqjVBoVBiUaUhpmWrs3fPHsPY999/n9q1a6NWqzEzM+Pdd98lMDAQgPPnzxMVFUXnzp2BrG1mFy5c4MGDBzg4ONCoUaPCfYFCFAFJ1AghhBBCCPEftGLFCm7cuMG0adOKO5QCe1G2PtWuXZsDBw7keC6rVXUYGTrgCTVWirILUrt27Th69Cjt2rUjOjr6idffuHEDR0dHjhw5QuvWrSlVqhTW1tYsXbqUP/74gy+//BJ/f3+CgoJQq9V827c51xa8S3r0BbRJj+rUqKwfreBSqM3QZ6YBoEm4Q3L4Ya4teNfwlR59gatX/iY8PBy9Xk/FihWNYurfvz9BQUHo9XoCAgLo2bMnZmZmAPzwww/s2rWLypUr07p1a/7888/CeG1CFCnZ+iSEEEIIIcR/THh4OJMmTeLw4cOYmpoWdzgFVlISNW+88QZNmzbNluzasWMHQ4cOJTo6OsdW56evxzNjV/hTd0GqV8H+qbpl5aR69epAVuHfmzdvAtlX1MTHxzN9+nTOnz9PdHQ0c+fOJSoqCoBy5cpRrVo17t27R1xcViLGzc2N8uXLM336dFadz2TvpQTyS21bGus6r1HqzZFGx83uR9C5c2cuX75MeHg4O3fuxMPDA3d3d9zd3VGpVBw6dIigoCCCgoIM45o0acKOHTvIzMxk8eLF9OzZk+vXrxf4PQnxPMmKGiGEEEIIIV4yeRWmzcjIoE+fPkyfPh0PD4/iDvWplJRETf/+/QkMDMyW2AgICKBPnz45JmkAlhyIJE2jLfD99Ho9qZmZLD0QWeCxmZmZ3Lhxg9u3b+d43snJKddETXp6OrGxsZw4cQJvb2+Cg4NxdHRk1apVREdHs3jxYpKSkmjWrBlTp07lu+++4+zZs8THx9PQ1RkTNKRdPYPmwb0nxmlV+zVSIo+SevkEep0WvSYDXfQ53vNuSmRkJC1atOCjjz5i4MCBlCpVit9//53x48dz8+ZN2rZty507d1i5ciUzZ85k48aNzJ49m5iYGExMTLC1tS20DlE///wzb775JiNHjuTOnTuFMqcQD8mKGiGEEEIIIV4Sp6/Hs+RAJAcv3gVyLkxbOvMODtUaMHTo0OIK85mkpqai1WqxtLQs7lDo1q0bw4YNIyQkhFatWgFw//59fvrpJ44cOYKrqyurVq3C29ubo0ePMnz4cCIuXiRVp8KyVmsc2w0Gsroc3f91FRn3rqO2dcLRewjmlesBWR2SzCrUIv3aWTJi/sauZR++/z6EGd3O4WhlSlJSEjNnziQkJISPP/6Y/fv3s23bNhISElCpVDg4OKDVarl//z5OTk6MHTuWjz/+ONtKKmdnZ27cuAE8StS89dZbqNVqlEoltWrV4ssvv2TYsGGoVCr8/PwYO3YsI0eOpHXr1vTs2ZP4+HgAKlasyI4dO/jss884c/YsielaTMu54fj68Ce+U7WtE849pnD/t7Xc2/kNKJRYlHfnzc+7A6BUKnFxceHtt982Gnft2jVcXV3p3bs3r776KhEREaxfv55ff/2ViRMnolAosLGx4fXXX8fPz8+wEsfFxeWpup1ZWFjQrVs3Pv/8c0aPHo2zs3OB5xAiN4q8CkV5enrqjx8/nut5IYQQQgghRMmQVfMknDSNljz+io9ep8PCVM3kTjXx9XJ9bvEVlhs3btCkSRPD6o/iNnjwYPR6PatWrQKyivP6+flx6tQpo0TNK6+8wvDhw0mu1Jx5u06TdOsKZuU90CTe49bqj426HN3bMcfQIen2+gloEmJw9vkKk1IVQKcjenFfyjbpQPyJ3QBoNBqqVq1KnTp12LVrF3369OGVV17BysoKrVbLa6+9hpOTEyqVKtfnWLNmDb///jurV68u9Hc0JOA4e8Ni8vy5zI0C6FC7DMt8PfO8LjU1FWdnZ/766y9q1KhhdC4zM5MrV64QERGR7SslJQU3NzdD4ubhl5ubG1ZWVrneT6fTERcXR/v27Tl+/Hie71aInCgUihN6vT7HH2xZUSOEEEIIIcQL7mFh2vzUPFEolaRpdMzYFQbwwiVrSsq2p4f69+9P586dWbx4Mebm5qxbt47+/ftnu87ExITIyEhup5UnU2mGWfmsbWePdzkCsroc/dMhybpuOwCs67TD1Kly1kRKFZY1W1LaxoLw27e5evUqLVu25NSpU5iZmVGpUiUaN25Mjx49sLW1zdczZGZmkpqaypEjRzhw4ADly5fPlux4Fh+1qU7IpXukZhZ8u5dOk04Lx5QnXufn50eTJk1yjNvExAQ3Nzfc3Nx46623jM7Fx8cbJW62bt1KREQEkZGRODk54e7uTkBAAGXLljUap1QqCQsLw8zMDJVKhV6vf6qVOc/TvaR0tp6IJvz2Ax6kabA1V+NR1hafxhUoZW1W3OGJx0iiRgghhBBCiBLo8dUYeSlJhWmfh5KWqGnRogWlS5cmODiYJk2acPToUbZt25btutWrV/PFF1+wff4i9NbO2LV4D8vqTQ1djlIijz66WKfBvFI9w7cq29JGc1nXacfl3fOwtrYmMDAwW5ej6dOnM2HCBOrVq8fs2bN55ZVXco1fp9PRtWtXIiMjcXBwYOnSpYwbN+4Z34qx+hXtmdTRI9/JxIcsTJR0qWbGmPffIWX6dIYMGZLjda6uruj1eoKDgwscm729Pc2aNaNZs2ZGx7VaLdeuXSMiIiLXn7fLly/j4uICZL3HkrqqJj9bItu4OzG8dXXqV3xx/ix4mUmiRgghhBBCiBdYQQvTZpU+0KNQKEnTaFl6IJLFvRrkWvi2pClpiRqAfv36sW7dOiIiIujQoQNlypTJdk2NGjXYsGEDozacIGjzVu5un0XFURty7XJk5F8rNczKe5BuYkpISMgzdzlSKpXs2rWr4A9dQA9XbuVne55CAeZqFZM6euDr5cqgVofp1q0bf/31F99++222+joPO1AVJpVKRZUqVahSpUqu14SHh1O1atVsxzMyMtixYwfXr183bKVydXUtlt9jT9oSmfZP0uaXCzEcunjP8M5F8ZKuT0IIIYQQQpRwGRkZODo6cvbsWcOxO3fuYGlpyf6TkWhSk7izZSrXF/Xm+oJ3ubNlqlGHndvrJ3D/4DpuB3zK9Xk90MTf5urszjw4/hNrRnal2j8tmkeNGkXFihWxtbWlcePGhISEPPdnfZK4uDgcHR2LOwwj/fr1Y9++faxcuTLHbU8AgYGB3L17l5ou9phZ2mQdVChy7HL0pA5J5molLTv1YMSIEZiYmNCiRQsg6+dk/fr1JCQkFHqXo8Lg6+XKpiFedKhVBjO1EnO1cWymKgV6TQbe7k5sGuJlSBi4ubkRGhpKTEwMr732Grdu3SqG6LOLi4szFJF+/D0rFAosLCyIiori22+/xdvbGxsbG2rVqsXbb7/NhAkTWLt2LX/88QexsbHPFEPt2rU5cOBAjucebYnMnqS5Orszmfcf1XnS6yE1U8uMXWEEhkYZjkdFRaFQKNBoNM8UpyiYFyNtLoQQQgghxH+YqakpvXr1IjAwkP/9738AbNiwgeoNvEi3siMjMR6rut6U7jYedDpidy0ibu8ynHtMNsyRfP63RwVp//nUlnIplMoDFzCyQ20gazXGF198gZ2dHYsWLcLHx4eoqCjMzc2f/0PnoiSuqHF1daV58+acPn2aLl265HjNnj17GDNmDMkpKWgsSuHU9TOUJmYoTbJ3OTJzybtDkh6YNmYYDWsvYMqUKUbnAgICGDFiBFqtFnd3d9avX1+Yj/rM6lWwZ5mvJ7FJ6Wz9K5rwW4k8SMvE1twEj3I2bJgxiteb9qdehaZG42xtbQ3bupo0acLWrVvx8vIqlmdYvXo1fn5+/PXXX8TGxlKtWjXc3d0NK2ZMTEzo3LkznTt3NoxJTU3l0qVLhlo4v/76K35+fkRERGBqapqtmLG7uztVq1bFzc0t2xbI77//nlWrVnH48GHOnz+fY4z/tS2RLxtJ1AghhBBCCPEC6N+/Pz4+PsyePRuFQkFAQAAVWvhwQaNDZWGLlcerhmvtmr9LTNBEo/FGBWn/YevlQ6aJFZfjMgHw9fU1nBs7dizTp08nIiKC+vXrF+GTFUxsbGyOW4uKW06rGh7fkhMYGGj49b+7IJm5uFO2z+wc5/33cYUCXnN3ws21AlZWVkb/zUxNTdmzZ8/TP8RzVMrajKGtqmU7bhHVE39/f3x8fLKdUyqVfPHFFzRo0IAuXbowa9YsBg0a9DzCNeLr60ujRo2IiIjgyJEjnDx5Ejc3tzzHWFhYUK9ePerVq2d0XK/XExMTY1TQ+ODBg0RERBAdHY1Wq2Xy5Mns2bPHkMBJSEh4YowF3RL5uIdbIp/UaSsnGo3mhdlGWZLJGxRCCCGEEOIF0KxZMywtLTlw4ADlypUjMjKSWkNfhcsJ6DLTuL9/FamXT6BLSwJAn5GKXqdFocwqcPrvgrQA6n+OPUjLStTMnTuX1atXc/PmTRQKBQ8ePODevdy34BSH2NhYatWqVdxhPJNn6YJkrlYxvE31PLscvci6d+/OyJEjiYmJyTUh16VLFw4dOmSoW7NgwYJsdWuKkpmZGQ0bNqRhw4b06tXrmeZSKBSULVuWsmXL0rp1a6Nz6enpVK1alTfeeANzc3P++OMP1q5dy5kzZ0hLS6NZs2acP38eHx8fOnXqxMCBA9HpdOiB1AwN+sx0yg9bjdq+DAlHfiDxaDAoFNi39DW6T0rkMeIPBaCJv4XSzArreu35TdWX2KR0wzXr169nypQppKSk8MknnzBp0iQAvvrqK86dO4e5uTk7d+5k/vz5fPDBB8/0ToQkaoQQQgghhHhh9O/fn8DAQMqWLcs777yDta0VkMCDo9vJjI2mXL/5qKwdyIi5zK21/ypOm1Pr4H+O2ZqbEBISwpw5c9i/fz+1a9dGqVTi4ODwT/HhkiM2NrbE1agpqGfpgjSpowddWjR46i5HJZ21tTVdu3YlKCiITz75JNfrPDw8OHLkCL6+vrRr146tW7eWyJVWz8LMzMxQg+jfW5/8/PyYP38+Xbt2JSUlhcDAQMqXL8+VK1co+5ov8fHxpEWHo7IpRerlEzw4sp0y701HbVeW2D3fGd1HaWJG6c5jMHGqRObdq8RsnIKVSzW2/uVGh0pZid7Dhw8TERHBxYsXadq0Kd27d6dmzZoA7Nixgy1btrBu3TrS09MRz67kVJYSQgghhBBCGMnMzCQtLc3w5evry/bt2wkMDKRfv354lLXFTK1En5GKwsQMpbkV2tRE4g8HPXnyf5irlXiUsyExMRG1Wo2TkxMajYZp06bx4MGDIny6pxMXF1fiatQ8DV8vVyZ1rImFiSrHHNrjFAqwMFExqWNNfL1ciYqK4urVqzRs2PD5BPuc9e/fH39//ydeZ2dnx44dO2jbti1NmjTh2LFjzyG6569bt27Y29sbvoYPH46JiQmvvvoq1tbWDB48mODgYMLCwkhKSqJKxQokXQjBqfvnKFRqksNCsK7njamTK0pTc+xb9Daa37xyPUydXVEolJg6V8GqVisSr5wl/Fai4Zovv/wSCwsL6tevT/369Tl9+rTh3CuvvEK3bt1QKpVYWFg8t/fyMpNEjRBCCCGEECVUx44dsbCwMHwtX76cRo0aoVAoaNmyJe80rgCAjWdX9JnpXF/Um9vrxmJRtXG+76EH3mlUgQ4dOvDGG2/g5uZG5cqVMTc3p2LFikX0ZE+vJBYTflpP6oJkrlZiplbSoVYZoy5IL7s2bdpw//59o2RAbpRKJVOnTuXbb7+lY8eOfP/990Uf4HMWHBxMfHy84Wvp0qW5Xnv27Fn+3LgI5+6TUFnaAaBNikNt82jro9rW2WhM+s0IbgdN5Pqi3lxb0JPEk7vRpT4wbIkEKFu2rOHXlpaWJCUlGb4viX9OvOhk65MQQgghhBAl0OOFaB83cOBAvLy8UCgUlLY2o7WbE3vDdNmKzto0fNPw65wK1Vae8JOhMG0pazMA1qxZw5o1awzXfPbZZ4XwJM/uXlI6W09EE377AYmNfPn2WAIN7v6NT+MKhthfVE/qgvROoxf/GQtKqVTSt29f/P39mT9/fr7GdOvWDTc3N0Pdmnnz5mFiYlLEkZYsd+7coVu3brQdOIELFo8KNausHdEkPqo1pXlwx2jcvZ3fYNOoMzY9p6JQmxK3bwW61AfYmufv/SmetCRMFJgkaoQQQgghhHhBREVFsW3bNk6ePGk4VhiFaUuq09fjWXIgkoMX7wKQrtGhrtSAPeFxHIiMZ8G+i7Rxd2J46+rUr/hitxLOrQvSf1W/fv1o1aoVc+bMyXcXoVq1anH06FH69OlD+/bt2bx5M87Ozk8e+BLQaDS88847+Pr6UvH1d/h730XSNVn1j6w8WnBv10Ks6rRFbedMwu8bjMbqMlJRWtigUJuSfjOC5AsHsaraCI9yNsXxKALZ+iSEEEIIIcQLYcqUKdSpU4dPP/2UKlWqGI4/LExrYVKwv9o/LExbr0LJTHAEhkbRa2Uoe8NiSNfoDB86H0r759gvF2LotTKUwNCo4glUFAk3NzeqVKnCzz//XKBx9vb27Ny5kxYtWtCkSRNOnDhRRBGWLNHR0YSEhLBw4ULGdqrPpf9159q8d9Ak3MGimie2nl2J2fA5N5cPwbyScYtwx9c/JD4kkGvzfUj4fSNWHi2ArC2Rongo8qri7unpqT9+/PhzDEcIIYQQQgjxNAJDo5ixK5w0jZa8GjUpFFkraSZ19CixNU+ynuVpOiLVLLHPJApu2bJl/Prrr//f3t1HR1Xf+x7/7MwkmZAHCBEDIYEoVCPLAtIIAREobUmll4cihEhDYqVi6lEPte29CIg8ZSHHc/GedRWi6A0BRYPghULDMaCCyhUIYPBWCRTKQwIYec4DmSHJ7PsH12lTIATMzOxM3q+/kvntvX/frVmuNR9/v+9Pq1evvqX7165dq+zsbC1evFiTJ09u4eqsberK3dq8v6LJ/xZcj2FIqb1ilZuR3PKFwcMwjD2maV7zHzIragAAAIAA0Noa02ZnZ2v+/PlXfb6v7IJyCks9Ic03b09X1b4br6qorXMrp7BUX5ZfaHYNc+bMUUZGRvOLhk9NnDhRRUVFOn/+/C3d//DDD+vjjz/W3Llz9bvf/U719fUtXKF1/cuwnnLYbbd0r9W3RLYF9KgBAAAAAoTVGtMmJiaqoqJCNptNERER+vnPf65XXnlFERERys3NveY9r249JGf9zffbkSRnfYOWbD3ESoAAER0drREjRqigoEDZ2dm39Ix7771XxcXFeuSRR5SamqqCggLddtttN76xlftuS+StrUyz7pbItoIVNQAAAECA+a4x7csT++rNrPv18sS+emJID7+cHrRhwwZVV1erpKREX3zxhRYuXHjda89Uu7Tt4Olb2q4hSaYpfXzgtM5Wu64aa0urKQJJVlaW8vPzv9czoqOj9ec//1n9+/fX/fff36gZdyDLSEnUzJH3KCzYphsdzGQYUliwje2DFkFQAwAAAMDrOnfurNTUVJWUlEiSHn30Uc2aNcszvn79et3X9z4d+reHdSL3N6r929VNYOurz+nkm0/p4s61kqRzm19T+auP6vjiCTqV969ylv1FhqQ1e8s1Z84czyk4UVFRWr58uY4cOaKhQ4cqMjJSP/vZz3TmzJmr5oC1pKam6siRIzp48OD3eo7NZtPChQu1aNEijRgxQu+8886NbwoArW1LJK5g6xMAAAAArysvL9emTZs0fPjwq8Z27dqlzMxMDX/qRe1tiFdD9TmZl2sbXVN34Rt9WzBbUQPGKbLvzyVJIV3uUvvBjygoNFxVxet1et2LCv3t/1LpqSq115Xw57333tOKFSvkcrk0fPhwDRw4UEVFRdq5c6d+8YtfaMyYMb54fdwiu92uSZMmacWKFVqwYMH3fl5aWpqSkpI0duxY7dmzRy+++GKzj/9uray2JRI3Fth/kQAAAAD8auzYsTIMQ9XV1Ro+fLjmzp171TVvvvmmHnvsMV28K1lG6beyRzbuIVJ35rgubi9Q9LAshfca6vk84t4fe36OGjBOF/9PgerOlqvSGa/2kgYOHKixY8dKkk6fPq3i4mJt2bJFoaGhGjJkiEaNGuWdl0aLysrK0ujRozVv3jwFBX3/TSG9e/f29K156KGH9O677yomJqYFKrW277ZEwvrY+gQAAADAa9atW6eqqipt3bpVpaWl19xuVFZWph49eijKce3/j1zz1TbZI2PU7u4HGn1+cef7OrEsW8dfTtPxlyfK7bqkhtpKRTmCJUkJCQmea0+ePKno6GiFh4d7PuvevXtLvCK8rE+fPoqOjtbWrVtb7JkxMTEqLCxU3759lZqa2mQPozVr1mjt2rUtNjdwIwQ1AAAAALxu6NChevTRR/WHP/zhqrGEhAQdPnxYSZ2jFGq/+itKh8GTFBQWpTN/ekmm+8qJUM6yv6hy51p1GjNdCdPeVbffFSgotJ1CggwldYmUJBn/0EG1S5cuOn/+vGpqajyfHT9+vKVfE17SEk2F/5ndbtdLL72kDz74QG73tU9Gqqmpkcvl0hNPPKGXXnqpRecHroegBgAAAIBPTJs2TZs3b9a+ffsafT5lyhTl5eXp9qqDMk236qvOqO5s2d8vsNnU6ZfT5a5z6szGxTJNt8zLtTKCbLK1ay+5G3Ths3fkvlwrU9L4fvFXzd29e3clJyfrhRde0OXLl/XZZ59pw4YNXn5jtJRJkyZp/fr1qq6ubvFnx8TEKCQk5Jpj4eHhGjVqlHr06NFoNRbgTQQ1AAAAAHyiU6dOyszM1Lx58xp93r9/f+Xl5WnOjP+mo/89TRVvP6f6i982usawBev2cTPlrrmgs3/+Dznu6CfHHf104vWpOrHkMRn2YNmiblPfhPbXbYy6atUq7dy5Ux07dtTcuXOVmZnptXdFy4qNjdXgwYP1/vvv+3Te2tpaPf744+rXr5+efPJJSZJ5q+fHA81kNPVHlpycbO7evduH5QAAAABoy/aVXVD6sh2qrWu46XvDgm0qmJqi3vEdvFAZ/O29995Tbm6uPvzwQ5/MZ5qmZs2apeLiYhUVFUmS3G53izQ0BgzD2GOaZvK1xvgLAwAAAGAZfRI6aObIJIUF39xXlbDgIM0cmURIE8BGjRqlkpISr/cW+uqrryRJ77zzjgoLC5WXlydJamhoIKSBT3A8NwAAAABLyUhJlCTlFJbKWd+gpnaaGIbksNs0c2SS5z4EJofDobS0NK1cuVIzZ870yhw1NTV6+umnFRYGMbcpAAASlUlEQVQWplOnTmnZsmXq2rWrGhoaZLPZvDIn8M+IAwEAAABYTkZKogqmpii1V6xC7UFy/NNpUA57kELtQUrtFauCqSmENG3Ed6c/eatPTHh4uAoLC2W323Xs2DElJ1/ZmUJIA1+iRw0AAAAASztb7dKaveUqPVWlSmedohzBSuoSqfH94q/bOBiByTRNJSUlKT8/XykpKV6d6/nnn1diYqKysrJkt9t1+fJlNTQ0KCwszKvzom1oqkcNQQ0AAAAAoNXIyclReXm5li5d6vW5ampqPMdyO51O3XfffcrJydG4ceO8PjcCG82EAQAAAAABYfLkyVq9erWcTqfX5/oupJGu9MhZsWKFpk2bpueff15ut9vr86NtIqgBAAAAALQa3bp1U0pKir788kufz33//feruLhY27Zt05gxY3Tx4kWf14DAR1ADAAAAAGhV1q9fr3vvvdcvc8fGxmrLli3q3r27+vfvr/379/ulDgQughoAAAAAQKtit9vVrl07v80fEhKiV155RdOnT9fQoUP1pz/9yW+1IPDY/V0AAAAAAAAtyTRNGYbh9Xl+/etfq1evXho/frz27t2r2bNnKyiI9RD4fvgLAgAAAAAEhIaGBm3atElz5szx2ZwDBgxQcXGxtmzZol/+8peqrKz02dwITAQ1AAAAAICAYLPZlJCQoCVLlqiqqspn83bu3FkfffSR4uLiNGDAAB04cMBncyPwENQAAAAAAFqdkpKSRr9XVFRo+fLlevHFF2UYhr7++muf1hMSEqKlS5fq2Wef1YMPPqiNGzf6dH4EDsM0zesOJicnm7t37/ZhOQAAAAAA3NjIkSM1adIk3XPPPXrttde0a9cuxcbGasKECUpPT1dERITfavv88881YcIEZWdna8aMGfStwVUMw9hjmmbytcZoJgwAAAAAaHUmTpyozMxMDR06VIMHD9aaNWvUs2dPz7jb7fZbQDJw4EDt2rXL02Q4Pz9fkZGRfqkFrQ8ragAAAAAArU5dXZ1iYmJUUVGhsLAwSVdOe3K73bLZbH6u7gqXy6Wnn35a27dv17p16/SDH/zA3yXBIppaUcP6KwAAAABAqxMcHKx58+aptLRU0pUVNIZhWCakkaTQ0FC9/vrreuaZZ/TAAw9o06ZN/i4JrQAragAAAAAArZLL5VJISIgMw/B3KTe0fft2paWl6amnntL06dNbRc3wHlbUAAAAAAACTmhoqCfwqK2t9XM1TXvggQe0a9curVu3TmlpaaqurvZ3SbAoghoAAAAAQKtmmqbmzZunQ4cO+buUJnXt2lXbtm1TVFSUBg4cqMOHD/u7JFgQQQ0AAAAAoFUzDEMul0vLly/3dyk35HA49MYbbyg7O1uDBg3SBx984O+SYDH0qAEAAAAAtHr79u3T6NGjdeTIEb8dy32zPvnkE6Wnp2vatGn64x//eN2+NWeqXVqzp1yl31Sq0lmvKIddSZ2jNOFH8YqJCPVx1WgJTfWoIagBAAAAAASEvn37avHixRo+fLi/S2m2srIyjRs3Tj169NCbb76p8PBwz9i+sgt6deshbTt4WpLkqnd7xhz2IJmSht3dSU8O7ak+CR18XTq+B5oJAwAAAAACXlZWlvLz8/1dxk1JSEjQJ598IofDoUGDBunIkSOSpLd2HFX6sh3avL9Crnp3o5BGkpz//7OiryuUvmyH3tpx1A/VwxsIagAAAAAAAWHSpElav359qztRKSwsTHl5eZoyZYoGDhyomcv/Uy+s2qrS+Q/J3dDQ5L2mKdXWNSincH+jsMYwDMs3V8a1EdQAAAAAAAJCbGysHnzwQb3//vv+LqXZEhMTtWXLFhmGoWeeeUZjMn6jhdnjVXP2m5t6Tm2dWzmFpfqy/IKXKoWvENQAAAAAAAJGZmZmq9v+9J38/Hzl576i2AlzZI+6/abvd9Y3aMlWVtG0dnZ/FwAAAAAAQEsZNWqUsrOzdfz4cXXr1s3f5TTba6+9phkzZip24lwZt/dU/YUKSVLNV1t14dO3ZNa5FHX/GLUfNFGS5Dp5QOe2vK66s+UKsoeo3d2DFP2T3+jjA6d1ttrlz1fB98SKGgAAAABAwHA4HEpLS9PKlSv9XUqzLV26VLNnz9Zv/225HHF3NRpzlX+luMdzFZu+QBe2v6O6M2VXBoJs6viTx5Xwr6vUefK/q/bYPlXtLZQhac3ect+/BFoMQQ0AAAAAIKB8d/qTaZr+LqVZNm/erJSUFFW163LV6U7tH5ikoOBQhcTeqZDb79Dlb/8mSQrt3FOhXZNkBNlk7xCryL4PyXn8/8pZ71bpqSp/vAZaCFufAAAAAAABZcCAATIMQzt37lRKSoq/y7mhpUuXasGCBdq/dK7MQVNlGIZnzBYR7fnZsIfKXeeUJNWdO6HzH74h1zd/lVnnktxuhXTuIUmqdNb59gXQolhRAwAAAAAIKIZhtKqmwrGxsfrwww/17V9LdK5oSbPuOffBqwqOiVfXqa+r27PvqcPQTM9YlCPYW6XCBwhqAAAAAAABZ/LkyVq9erWcTqe/S2mWuLg4/df/eFvOv+3VuS3Lbni9+3KtjJB2MkLCVHe2TFVfFEqSHPYgJXWJ9Ha58CKCGgAAAABAwOnWrZv69u2rDRs2+LuUZps6sr8SMhbq0oHtOr+t6dVA0T+eopqvt6ls8QSd3fQ/FZ70oCTJlDS+X7wPqoW3GE01V0pOTjZ3797tw3IAAAAAAGgZK1as0OrVq7Vx40Z/l9JsU1fu1ub9FbqVPsiGIaX2ilVuRnLLF4YWZRjGHtM0r/kvihU1AAAAAICANG7cOG3fvl0VFRX+LqXZ/mVYTznstlu612G36clhPVu4IvgaQQ0AAAAAICBFRERozJgxWrVqlb9LabY+CR00c2SSwoJv7ut6WHCQZo5MUu/4Dl6qDL5CUAMAAAAACFit6fSn72SkJGrmyHsUFmzTP5zUfU2GIYUF2zRz5D3KSEn0SX3wLoIaAAAAAEDAGjZsmM6fP699+/b5u5SbkpGSqIKpKUrtFatQe5Ac9sZf3x32IIXag5TaK1YFU1MIaQIIzYQBAAAAAAFt1qxZunTpkhYvXuzvUm7J2WqX1uwtV+mpKlU66xTlCFZSl0iN7xevmIhQf5eHW9BUM2GCGgAAAABAQDt48KCGDBmisrIyBQcH+7scgFOfAAAAAABt11133aU777xTRUVF/i4FuCGCGgAAAABAwGuNTYXRNhHUAAAAAAAC3sSJE1VUVKTz58/7uxSgSQQ1AAAAAICAFx0drREjRqigoMDfpQBNIqgBAAAAALQJWVlZbH+C5RHUAAAAAADahNTUVB05ckQHDx70dynAddn9XQAAAAAAAL5gt9v1q1/9Sq/nr1LPEZNV+k2lKp31inLYldQ5ShN+FK+YiFB/l4k2jqAGAAAAANAm7Cu7oPI7RmrH8So5thyUq97tGXPYv9HLWw5q2N2d9OTQnuqT0MGPlaItY+sTAAAAACDgvbXjqNKX7dCuk04ZtuBGIY0kOevdctW7VfR1hdKX7dBbO476p1C0eayoAQAAAAAEtLd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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx.algorithms.community as nxcom\n",
        "\n",
        "communities = sorted(nxcom.greedy_modularity_communities(G), key=len, reverse=True)\n",
        "print(communities)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgGNaR-CCOM3",
        "outputId": "aea77198-c23a-434c-f108-3def1d01dd0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[frozenset({'Aerys', 'Hizdahr', 'Daario', 'Benjen', 'Rickard', 'Lyarra', 'Rhaella', 'Ygritte', 'Daenerys', 'Rhaenys', 'Viserys', 'Khal', 'Aegon', 'Rhaegar', 'Jon', 'Lyanna'}), frozenset({'Lancel', 'Joanna', 'Shae', 'Tyrion', 'Euron', 'Cersei', 'Tywin', 'Myrcella', 'Jaime'}), frozenset({'Loras', 'Alerie', 'Mace', 'Joffrey', 'Garlan', 'Margaery', 'Willas', 'Tommen'}), frozenset({'Sansa', 'Catelyn', 'Rickon', 'Bran', 'Arya', 'Eddard', 'Robb'}), frozenset({'Renly', 'Robert', 'Cassana', 'Gendry', 'Steffon', 'Stannis', 'Edric'})]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "communities_dict = {}\n",
        "for com in communities:\n",
        "  for mc in main_characters.keys():\n",
        "    if mc in list(com):\n",
        "      com_copy = list(com)\n",
        "      com_copy.remove(mc)\n",
        "      communities_dict[mc] = com_copy\n",
        "\n",
        "communities_dict['Arya_Stark']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "AoK8Il1ULFDE",
        "outputId": "b2927fc9-7bc9-48df-b406-9407b32d9603"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-dbbcb9eb8b84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m       \u001b[0mcommunities_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom_copy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcommunities_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Arya_Stark'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m: 'Arya_Stark'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random \n",
        "def find_communities(weighted_input):\n",
        "  df = pd.read_csv(weighted_input, sep = \",\")\n",
        "  df['char1'] = [i[-1] for i in df['char1'].str.split(\"/\")]\n",
        "  df['char2'] = [i[-1] for i in df['char2'].str.split(\"/\")]\n",
        "\n",
        "  tuples_lst = [(x[1]['char1'], x[1]['char2'],x[1]['valueSum']) for x in df.iterrows()]\n",
        "  G = nx.Graph()\n",
        "  G.add_weighted_edges_from(tuples_lst)\n",
        "\n",
        "  communities = sorted(nxcom.greedy_modularity_communities(G), key=len, reverse=True)\n",
        "\n",
        "  communities_dict = {}\n",
        "  for com in communities:\n",
        "    for mc in main_characters.keys():\n",
        "      if mc in list(com):\n",
        "        com_copy = list(com)\n",
        "        com_copy.remove(mc)\n",
        "        communities_dict[mc] = com_copy\n",
        "\n",
        "  return communities_dict\n",
        "\n",
        "communities = find_communities(\"query-result.csv\")\n",
        "def hero_pick():\n",
        "  return random.choice(list(communities.keys()))\n",
        "\n",
        "hero_pick()\n",
        "\n",
        "def comm_based_pick(ist_class, hero):\n",
        "  category = ist_class.split(\"/\")[-1]\n",
        "  if category == \"Actor\":\n",
        "    return random.choice(list(communities[hero]))\n",
        "  return random_pick(ist_class) \n",
        "\n",
        "comm_based_pick(\"http://semanticweb.cs.vu.nl/2009/11/sem/Actor\", \"Jon_Snow\")\n",
        "\n",
        "\n",
        "# {'jon_snow: [\"bsd\",\"njd\",\"aknd\",\"daenerys_targ\"]}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "LtMh1Gs3NI_z",
        "outputId": "2037883c-e1a7-46a9-a0cf-ecfb1d5e8a2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Rhaenys_Targaryen_daughter_of_Rhaegar'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[{k: communities[k]} for k in communities.keys() if (not \"Jon_Snow\" in communities[k] and k != \"Jon_Snow\")]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CLkFT1Y9qre",
        "outputId": "dfc5db1b-5a8f-4fa6-b52e-5a6d01ed00f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'Cersei_Lannister': ['Jaime_Lannister',\n",
              "   'Tyrion_Lannister',\n",
              "   'Lancel_Lannister',\n",
              "   'Shae',\n",
              "   'Myrcella_Baratheon',\n",
              "   'Joanna_Lannister',\n",
              "   'Euron_Greyjoy',\n",
              "   'Tywin_Lannister']},\n",
              " {'Tyrion_Lannister': ['Jaime_Lannister',\n",
              "   'Cersei_Lannister',\n",
              "   'Lancel_Lannister',\n",
              "   'Shae',\n",
              "   'Myrcella_Baratheon',\n",
              "   'Joanna_Lannister',\n",
              "   'Euron_Greyjoy',\n",
              "   'Tywin_Lannister']},\n",
              " {'Margaery_Tyrell': ['Willas_Tyrell',\n",
              "   'Joffrey_Baratheon',\n",
              "   'Garlan_Tyrell',\n",
              "   'Alerie_Tyrell',\n",
              "   'Loras_Tyrell',\n",
              "   'Tommen_Baratheon',\n",
              "   'Mace_Tyrell']},\n",
              " {'Arya_Stark': ['Bran_Stark',\n",
              "   'Catelyn_Stark',\n",
              "   'Sansa_Stark',\n",
              "   'Eddard_Stark',\n",
              "   'Robb_Stark',\n",
              "   'Rickon_Stark']},\n",
              " {'Bran_Stark': ['Catelyn_Stark',\n",
              "   'Sansa_Stark',\n",
              "   'Eddard_Stark',\n",
              "   'Robb_Stark',\n",
              "   'Rickon_Stark',\n",
              "   'Arya_Stark']},\n",
              " {'Robert_Baratheon': ['Stannis_Baratheon',\n",
              "   'Steffon_Baratheon',\n",
              "   'Cassana_Baratheon',\n",
              "   'Renly_Baratheon',\n",
              "   'Gendry',\n",
              "   'Edric_Storm']}]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "communities = sorted(nxcom.greedy_modularity_communities(G), key=len, reverse=True)\n",
        "print(f\"The graph has {len(communities)} communities.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tA-6zwccX5r",
        "outputId": "2d927fda-3431-4823-bbc9-70c44846b34f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The graph has 5 communities.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def set_node_community(G, communities):\n",
        "    '''Add community to node attributes'''\n",
        "    for c, v_c in enumerate(communities):\n",
        "        for v in v_c:\n",
        "            # Add 1 to save 0 for external edges\n",
        "            G.nodes[v]['community'] = c + 1\n",
        "\n",
        "def set_edge_community(G):\n",
        "    '''Find internal edges and add their community to their attributes'''\n",
        "    for v, w, in G.edges:\n",
        "        if G.nodes[v]['community'] == G.nodes[w]['community']:\n",
        "            # Internal edge, mark with community\n",
        "            G.edges[v, w]['community'] = G.nodes[v]['community']\n",
        "        else:\n",
        "            # External edge, mark as 0\n",
        "            G.edges[v, w]['community'] = 0\n",
        "            \n",
        "def get_color(i, r_off=1, g_off=1, b_off=1):\n",
        "    '''Assign a color to a vertex.'''\n",
        "    r0, g0, b0 = 0, 0, 0\n",
        "    n = 16\n",
        "    low, high = 0.1, 0.9\n",
        "    span = high - low\n",
        "    r = low + span * (((i + r_off) * 3) % n) / (n - 1)\n",
        "    g = low + span * (((i + g_off) * 5) % n) / (n - 1)\n",
        "    b = low + span * (((i + b_off) * 7) % n) / (n - 1)\n",
        "    return (r, g, b)\n",
        "\n",
        "\n",
        "set_node_community(G, communities)\n",
        "set_edge_community(G)\n",
        "node_color = [get_color(G.nodes[v]['community']) for v in G.nodes]\n",
        "# Set community color for edges between members of the same community (internal) and intra-community edges (external)\n",
        "external = [(v, w) for v, w in G.edges if G.edges[v, w]['community'] == 0]\n",
        "internal = [(v, w) for v, w in G.edges if G.edges[v, w]['community'] > 0]\n",
        "internal_color = ['black' for e in internal]"
      ],
      "metadata": {
        "id": "GphkuHHp15FU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "G_pos = nx.spring_layout(G, k=2/math.sqrt(G.order()))\n",
        "plt.rcParams.update({'figure.figsize': (15, 10)})\n",
        "# Draw external edges\n",
        "nx.draw_networkx(\n",
        "    G,\n",
        "    pos=G_pos,\n",
        "    node_size=0,\n",
        "    edgelist=external,\n",
        "    edge_color=\"silver\")\n",
        "\n",
        "# Draw nodes and internal edges\n",
        "nx.draw_networkx(\n",
        "    G,\n",
        "    pos=G_pos,\n",
        "    node_color=node_color,\n",
        "    edgelist=internal,\n",
        "    edge_color=internal_color)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "w13r0Ex52WpR",
        "outputId": "2bc1eb13-8caf-4091-9057-669d4e492219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(list(communities[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MgbXXnx9Qlu",
        "outputId": "846b3156-6776-4131-9f3a-0d2d8cbf96c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    }
  ]
}