source code.ipynb

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          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.11.17)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.3)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n",
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /content/kaggle.json'\n",
            "Downloading internet-firewall-data-set.zip to /content\n",
            "  0% 0.00/754k [00:00<?, ?B/s]\n",
            "100% 754k/754k [00:00<00:00, 95.7MB/s]\n"
          ]
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      ],
      "source": [
        "!pip install kaggle\n",
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR']= \"/content\"\n",
        "!kaggle datasets download -d tunguz/internet-firewall-data-set"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip internet-firewall-data-set.zip -d firewall-data\n"
      ],
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          "text": [
            "Archive:  internet-firewall-data-set.zip\n",
            "  inflating: firewall-data/log2.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scipy\n"
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      "execution_count": 3,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.11.4)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy) (1.23.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/firewall-data/log2.csv')\n",
        "df"
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2b022b42-5677-498e-9f28-f889065d59e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c3d022a7-2699-4db8-8831-4a02b5c423ec\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c3d022a7-2699-4db8-8831-4a02b5c423ec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYD0LclQTYGm",
        "outputId": "e69ee317-cd37-44a5-8bb8-76e1d16530d2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 65532 entries, 0 to 65531\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   Source Port           65532 non-null  int64 \n",
            " 1   Destination Port      65532 non-null  int64 \n",
            " 2   NAT Source Port       65532 non-null  int64 \n",
            " 3   NAT Destination Port  65532 non-null  int64 \n",
            " 4   Action                65532 non-null  object\n",
            " 5   Bytes                 65532 non-null  int64 \n",
            " 6   Bytes Sent            65532 non-null  int64 \n",
            " 7   Bytes Received        65532 non-null  int64 \n",
            " 8   Packets               65532 non-null  int64 \n",
            " 9   Elapsed Time (sec)    65532 non-null  int64 \n",
            " 10  pkts_sent             65532 non-null  int64 \n",
            " 11  pkts_received         65532 non-null  int64 \n",
            "dtypes: int64(11), object(1)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    unique_values = df[column].unique()\n",
        "    print(f\"Unique values for '{column}': {unique_values.size}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TrpnNgxTbCy",
        "outputId": "cb6e4ae3-d8a3-47d9-848b-b71c0787ef3d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values for 'Source Port': 22724\n",
            "Unique values for 'Destination Port': 3273\n",
            "Unique values for 'NAT Source Port': 29152\n",
            "Unique values for 'NAT Destination Port': 2533\n",
            "Unique values for 'Action': 4\n",
            "Unique values for 'Bytes': 10724\n",
            "Unique values for 'Bytes Sent': 6683\n",
            "Unique values for 'Bytes Received': 8814\n",
            "Unique values for 'Packets': 1116\n",
            "Unique values for 'Elapsed Time (sec)': 915\n",
            "Unique values for 'pkts_sent': 749\n",
            "Unique values for 'pkts_received': 922\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "\n",
        "random.seed(42)  # For reproducibility\n",
        "random_subset = random.sample(range(len(df)), 1000)\n",
        "df2 = df.iloc[random_subset].copy()\n",
        "\n",
        "# Display the first few rows of 'df2' for verification\n",
        "df2\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "ccpEoXzPn9R3",
        "outputId": "41081efd-355a-4beb-ff25-6a545ac4e295"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Source Port  Destination Port  NAT Source Port  NAT Destination Port  \\\n",
              "41905        59511             21247                0                     0   \n",
              "7296         58638              5900                0                     0   \n",
              "1639         46822              3389            46822                  3389   \n",
              "48598        40103               443             5740                   443   \n",
              "18024        45837             25174                0                     0   \n",
              "...            ...               ...              ...                   ...   \n",
              "59436        52071               445                0                     0   \n",
              "25822        63964             60705             4477                 60705   \n",
              "38790        49752                53            61999                    53   \n",
              "36987        42968             56205                0                     0   \n",
              "43450        62894               993            44411                   993   \n",
              "\n",
              "      Action  Bytes  Bytes Sent  Bytes Received  Packets  Elapsed Time (sec)  \\\n",
              "41905   deny     66          66               0        1                   0   \n",
              "7296    deny     62          62               0        1                   0   \n",
              "1639   allow   3457        1568            1889       17                  21   \n",
              "48598  allow    416         210             206        7                  34   \n",
              "18024   deny    146         146               0        1                   0   \n",
              "...      ...    ...         ...             ...      ...                 ...   \n",
              "59436   drop     66          66               0        1                   0   \n",
              "25822  allow    312         132             180        6                   7   \n",
              "38790  allow    182          94              88        2                  30   \n",
              "36987   deny     62          62               0        1                   0   \n",
              "43450  allow   8840        4151            4689       80                 190   \n",
              "\n",
              "       pkts_sent  pkts_received  \n",
              "41905          1              0  \n",
              "7296           1              0  \n",
              "1639          10              7  \n",
              "48598          4              3  \n",
              "18024          1              0  \n",
              "...          ...            ...  \n",
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              "25822          3              3  \n",
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              "36987          1              0  \n",
              "43450         41             39  \n",
              "\n",
              "[1000 rows x 12 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cc4d30b-99b6-4cf2-aede-19be2879ec18')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3cc4d30b-99b6-4cf2-aede-19be2879ec18 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-3cc4d30b-99b6-4cf2-aede-19be2879ec18');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f3d4b51-0dfe-49cf-92d6-8fd990b2b19e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f3d4b51-0dfe-49cf-92d6-8fd990b2b19e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f3d4b51-0dfe-49cf-92d6-8fd990b2b19e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_eca14358-d961-4d03-9f58-4a0355522952\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df2')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_eca14358-d961-4d03-9f58-4a0355522952 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a directed graph\n",
        "G = nx.DiGraph()\n",
        "\n",
        "# Add nodes\n",
        "nodes = set(df2['Source Port']) | set(df2['NAT Source Port']) | set(df2['Destination Port']) | set(df2['NAT Destination Port'])\n",
        "\n",
        "print(\"number of nodes : \",len(nodes))\n",
        "G.add_nodes_from(nodes)\n",
        "\n",
        "# Add edges\n",
        "for _, row in df2.iterrows():\n",
        "    src_port, dst_port, nat_src_port, nat_dst_port = row['Source Port'], row['Destination Port'], row['NAT Source Port'], row['NAT Destination Port']\n",
        "    packets = row['Packets']\n",
        "\n",
        "    # Adding edges between source and destination ports\n",
        "    G.add_edge(src_port, dst_port, packets=packets)\n",
        "\n",
        "    # Adding edges between NAT source and NAT destination ports\n",
        "    G.add_edge(nat_src_port, nat_dst_port, packets=packets)\n",
        "\n",
        "# Extract a subgraph with 100 nodes\n",
        "subgraph_nodes = list(nodes)[:50]\n",
        "subgraph = G.subgraph(subgraph_nodes)\n",
        "\n",
        "# Use kamada_kawai_layout for a more evenly spaced layout\n",
        "pos = nx.kamada_kawai_layout(subgraph)\n",
        "\n",
        "# Draw the subgraph\n",
        "nx.draw(subgraph, pos, with_labels=True, node_size=200, node_color='skyblue', font_size=7, font_color='black', font_weight='bold', arrowsize=10)\n",
        "\n",
        "# Draw edge labels (packets)\n",
        "edge_labels = nx.get_edge_attributes(subgraph, 'packets')\n",
        "nx.draw_networkx_edge_labels(subgraph, pos, edge_labels=edge_labels)\n",
        "\n",
        "plt.title('Directed Network Communication Subgraph (50 Nodes)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "rqP5WxGboYZ5",
        "outputId": "481e26f6-c9c7-463a-f5ed-c2cd3c8a779d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of nodes :  1624\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RInFeM5VKJYoWLSoaNGgQ77NSsGBBUa9ePfUyU1NT0bdv36+eT6xixYolmGhLmiWbvDMZY2PjJI32dnV1xd7eXv1vIQQ7duygadOmCCHw9fVV/zVo0IDAwEB108KOHTvIlSuXekDQ575VTsXf35+TJ0/Spk0bgoKC1Mfw8/OjQYMGPHr0SN3Me/DgQSpXroyzs7N6e0tLy6828SamRo0a1KpVi+nTpyc6oGLbtm2YmppSr169OOfv5OSEsbExf/311zeP4+Liwrt373j48CEAZ8+epUaNGri4uHD27FkAzp07hxACFxcXAK5evYqPjw99+vSJ05eqSZMmlChRIsHmpN69eycaw6ZNm2jbti29evViyZIlcUaPJuTjx49ATNNXUhw8eBBra+s4Awl0dXXp378/wcHBnD59Os76rVu3xtTUVP3vSpUqAdCxY8c4fRErVarEp0+f4jXz29vbU6VKlXjb165dm/z588db/vTp0ySdR0Latm2Lubm5+t+xr9HX9rl7925UKhVjxoyJ91x//nn4vP9u7HvfxcWF0NBQHjx4kOxY37x5w82bN+nSpQs5c+ZULy9Tpgz16tXj4MGD8bb5cpSwi4sLfn5+6vdAYsaPH8/GjRspV64cR44cYdSoUTg5OVG+fPl4zevJoaOjQ69evdT/1tPTo1evXvj4+KgHiRw+fJi8efPi5uamXs/AwIAePXokuE99fX26du0ab3lynv+kxBWra9eu6Onpqf+dlPcMgJ+fHzo6Ogn289u7dy/Dhw+nWbNmdOvWjdOnT9OgQQNmzpyJt7e3er2wsDD09fXjbR97HUnO4LEBAwZgbm7O+PHjE13n4MGDODs7U716dfUyY2NjevbsyfPnz7l37556vTx58qj7dQIYGRnRs2fPOPu7efMmjx49okOHDvj5+amvuSEhIdSpU4czZ86ouziZmZlx+fJlXr9+/c1zMTc3x9fXN8nnLqUPmVBmMsHBwUlKDAoWLBjn3+/fv+fDhw8sXboUS0vLOH+xF+fYzvdPnjyhePHi3zUw4fHjxwghGD16dLzjjB07Ns5xvLy8KFq0aLx9FC9ePNnHhZh+ZG/fvk20L9SjR48IDAzEysoqXmzBwcFJGnwQ+2Vy9uxZQkJCuHHjBi4uLtSoUUOdUJ49exYTExPKli2rPs/EzqtEiRLqx2Pp6OiQL1++BI//7NkzOnbsSKtWrZg3b16S6iWamJgAJLnsVOzr8mXyVLJkSfXjn/s86QPUyaWtrW2Cyz/vr5oa2yfHl8eKTS6/ts8nT56gpaUV5wdaQu7evUuLFi0wNTXFxMQES0tL9aCLwMDAZMf6tfdNyZIl1V/Mn/ue84vVvn17zp49S0BAAEePHqVDhw7cuHGDpk2bEh4enuz4AWxsbOINKCtWrBiAuh+ml5cXhQsXjvdeLlKkSIL7zJs3b5wEL1Zynv+kxBUrJc9pUikUCgYNGkRUVFScPqOGhoYJ9kuMfT2SMwjR1NSUgQMHsnfv3nh9kWN5eXkl+n6LfTz2v0WKFIn3mn25bexI9M6dO8e75i5fvpyIiAj1azN9+nTu3LmDra0tzs7OjBs3LtGkXQiR5eovZwVylHcm4u3tTWBgYKIX2s99eaGJ/RXYsWNHOnfunOA2ZcqUSXGMsccZOnQoDRo0SHCdpMT/PWrUqEHNmjWZPn16gvXcVCoVVlZWbNiwIcHtYwcafY2NjQ0FCxbkzJkzFChQACEEVapUwdLSkgEDBuDl5cXZs2epWrXqN+8cJkZfXz/RbfPkyUOePHk4ePAgV69ejVMNIDElSpQA4J9//vmueL5FW1s7WcvFZ4OXUrq9QqGItz8g3kCM5MaUXB8+fMDV1RUTExMmTJhA4cKFMTAw4Pr164wYMSLdilqnxvmZmJhQr1496tWrh66uLmvWrOHy5cu4urom+iWe2POdFhJKotLy+f/e59TCwoKoqCiCgoKSdBMg9gfU5wPX8uTJw5s3b+KtG7vMxsbmm/v93IABA5g1axbjx49Pl1qosc/7jBkzcHR0THCd2Du4bdq0wcXFhV27dnH06FFmzJiBp6cnO3fupFGjRnG2CQgISPBmhKRZMqHMRNatWweQaKL2NZaWluTIkYPo6OivjjoEKFy4MJcvXyYyMjLBEcGQeNN3oUKFgJgm0m8dx87OLsFaarHNyd9j3Lhx1KxZkyVLlsR7rHDhwhw/fpxq1ap985f91379uri4cObMGQoWLIijoyM5cuSgbNmymJqacvjwYa5fvx6nWcnOzg6IOa/atWvH2dfDhw/VjyeFgYEB+/fvp3bt2jRs2JDTp0/j4ODw1W2KFStG8eLF2bNnD3PmzPlmqQ07Oztu376NSqWKk9jGNhsmJ960Zm5unuBdjC/voqZE4cKFUalU3Lt3L9EvxVOnTuHn58fOnTupUaOGevmzZ8/irZvUOyufv2++9ODBA3LlypXiclLfUqFCBdasWaNOYGLvzn348CHOeok9369fv45X9urff/8FUI9ut7Oz4969e/HuOiVlJHWs5Dz/SY0rpWJ/yD179ixJP9Zj38ef/7B1dHTk7Nmz8T6Lly9fxsjISH1XNali71KOGzcuwRsLdnZ2ib7fYh+P/e+dO3fivWZfblu4cGEg5kfKt74PICaB7tOnD3369MHHx4fy5cszefLkOAllVFQUL1++jNNFQsoYZJN3JnHy5EkmTpxIwYIFv6uPoba2Nq1atWLHjh3cuXMn3uPv379X/3+rVq3w9fVl/vz58daL/VUeW/ftyy8WKysrdUKX0C/rz4/TuHFjLl26xN9//x3n8cTuICaFq6srNWvWxNPTM14zXZs2bYiOjmbixInxtouKiopzLkqlMt65xXJxceH58+ds2bJF3QSupaVF1apVmTlzJpGRkerlEPOlbGVlxeLFi+M0Xx06dIj79+/TpEmTZJ2jqakpR44cUZfVePLkyTe3GT9+PH5+fnTv3j3B8kJHjx5l//79QMzr8vbtW7Zs2aJ+PCoqinnz5mFsbIyrq2uy4k1LhQsX5sGDB3HeV7du3YpXkiQlmjdvjpaWFhMmTIh3pyv28xB7F+vzu1afPn1i4cKF8fanVCqT1ASeJ08eHB0dWbNmTZz34p07dzh69CiNGzf+ntOJJzQ0lIsXLyb42KFDh4D/N2XGJghnzpxRrxMdHc3SpUsT3D4qKirOj7tPnz6xZMkSLC0tcXJyAmJ+IL969SpOWbHw8HCWLVuW5HNIzvOf1LhSKrZf8NWrV+Ms9/f3j3dHNzIykmnTpqGnp0etWrXUy93d3Xn37l2cmax8fX3Ztm0bTZs2TbB/5bcMHDgQMzMzJkyYEO+xxo0b8/fff8d5P4SEhLB06VIKFCig7vbRuHFjXr9+HWdaytDQ0HjvAycnJwoXLswff/xBcHBwvOPFfm6jo6PjfSasrKywsbGJ1+R/7949wsPDqVq1ajLPXEpr8g5lBnTo0CEePHhAVFQU79694+TJkxw7dgw7Ozv27t373cWPp02bxl9//UWlSpXo0aMH9vb2+Pv7c/36dY4fP65uaunUqRNr165l8ODB/P3337i4uBASEsLx48fp06cPzZo1w9DQEHt7e7Zs2UKxYsXImTMnpUqVolSpUixYsIDq1atTunRpevToQaFChXj37h0XL17E29tbPW3W8OHDWbduHQ0bNmTAgAEolUqWLl2qvkP2vWJnhPmSq6srvXr1YurUqdy8eZP69eujq6vLo0eP2LZtG3PmzFF3MndycmLRokVMmjSJIkWKYGVlpb67GJssPnz4kClTpqj3X6NGDQ4dOqSubxhLV1cXT09PunbtiqurK+3bt+fdu3fMmTOHAgUKMGjQoGSfY65cuTh27BjVq1enbt26nDt3jrx58ya6ftu2bfnnn3+YPHkyN27coH379tjZ2eHn58fhw4c5ceKEukhyz549WbJkCV26dOHatWsUKFCA7du3c/78eWbPnp3kwT3poVu3bsycOZMGDRrw888/4+Pjw+LFi3FwcPjmQJSkKlKkCKNGjWLixIm4uLjQsmVL9PX1uXLlCjY2NkydOpWqVatibm5O586d6d+/PwqFgnXr1iXYLOrk5MSWLVsYPHgwFStWxNjYOMGp8SCmqbBRo0ZUqVKFn3/+mbCwMObNm4epqWmcuq8pERoaStWqValcuTINGzbE1taWDx8+sHv3bs6ePUvz5s0pV64cAA4ODlSuXBkPDw/8/f3JmTMnmzdvTrQGqo2NDZ6enjx//pxixYqxZcsWbt68ydKlS9WtH7169WL+/Pm0b9+eAQMGkCdPHjZs2KC+ziXljm5ynv+kxpVShQoVolSpUhw/fjzODDh79+5l0qRJuLu7U7BgQfz9/dm4cSN37txhypQpcerXuru7U7lyZbp27cq9e/fIlSsXCxcuJDo6+quDa77G1NSUAQMGJLj9yJEj2bRpE40aNaJ///7kzJmTNWvW8OzZM3bs2KG+S9qjRw/mz59Pp06duHbtGnny5GHdunXxCsxraWmxfPlyGjVqhIODA127diVv3ry8evWKv/76CxMTE/bt20dQUBD58uXD3d2dsmXLYmxszPHjx7ly5Uq8WXGOHTuGkZER9erV+67zl9JQ+g4ql74mtiRH7J+enp6wtrYW9erVE3PmzFGXa/lcYmWDEiu/8O7dO9G3b19ha2srdHV1hbW1tahTp45YunRpnPVCQ0PFqFGjRMGCBdXrubu7iydPnqjXuXDhgnBychJ6enrxSgg9efJEdOrUSVhbWwtdXV2RN29e8cMPP4jt27fHOc7t27eFq6urMDAwEHnz5hUTJ04UK1asSHbZoC/Flgb5sg6lEEIsXbpUODk5CUNDQ5EjRw5RunRpMXz4cPH69Wv1Om/fvhVNmjQROXLkEEC80jRWVlYCEO/evVMvO3funACEi4tLgvFu2bJFlCtXTujr64ucOXOKH3/8UXh7e8dZp3PnzkKpVCa4/Zd1KIUQ4vHjxyJPnjyiZMmSCT4PXzpx4oRo1qyZsLKyEjo6OsLS0lI0bdpU7NmzJ8567969E127dhW5cuUSenp6onTp0nHK2QiRcNkRIf5fbmXbtm1xlidUFiuhWqFCJPweTux469evF4UKFRJ6enrC0dFRHDlyJNGyQV9uG3usz9+7CX2mhIgppxX7+pmbmwtXV1dx7Ngx9ePnz58XlStXFoaGhsLGxkYMHz5cXQLp89I3wcHBokOHDsLMzExdg/DzGL98no8fPy6qVasmDA0NhYmJiWjatKm4d+9enHUS+ywkVubnc5GRkWLZsmWiefPmws7OTujr6wsjIyNRrlw5MWPGjDglc4SI+WzXrVtX6Ovri9y5c4vffvtNHDt2LMESPw4ODuLq1auiSpUqwsDAQNjZ2Yn58+fHi+Hp06eiSZMmwtDQUFhaWoohQ4aIHTt2CEBcunQp3j4TktTnP6lxJfY+Tux1SsjMmTOFsbFxnJJGV69eFU2bNhV58+YVenp6wtjYWFSvXj1OCZ7P+fv7i59//llYWFgIIyMj4erq+tXScp9L7PkKCAhQl2f78jPx5MkT4e7uLszMzISBgYFwdnYW+/fvj7cPLy8v4ebmJoyMjESuXLnEgAED1CXYPn++hRDixo0bomXLlsLCwkLo6+sLOzs70aZNG3HixAkhREzZpmHDhomyZcuKHDlyCKVSKcqWLSsWLlwY77iVKlUSHTt2TNL5S+lLIUQKe6NLkiRJUiqbPXs2gwYNwtvb+6t335OrZs2a+Pr6Jtj1J7UFBgZSqFAhpk+fzs8//5zmx8vqbt68Sfny5bl+/Xqi/ZklzZEJpSRJkqRRYWFhcQbKhYeHU65cOaKjo9WDZVJLeiaUAJ6enqxatYp79+59d+UHKUa7du1QqVRs3bpV06FICZAJpSRJkqRRjRo1In/+/Dg6OhIYGMj69eu5e/cuGzZsoEOHDql6rPROKCUpu5CDciRJkiSNatCgAcuXL2fDhg1ER0djb2/P5s2badu2raZDkyQpieQdSkmSJEmSJClFZIcOSZIkSZIkKUVkQilJkiRJkiSliEwoJUmSJEmSpBSRCaUkSZIkSZKUIjKhlCRJkiRJklJEJpSSJEmSJElSisiEUpIkSZIkSUoRmVBKkiRJkiRJKSITSkmSJEmSJClFZEIpSZIkSZIkpYhMKCVJkiRJkqQUkQmlJEmSJEmSlCIyoZQkSZIkSZJSRCaUkiRJkiRJUorIhFKSJOkrhBDZ8tiSJEnJoaPpACRJkjKSt6FR3PYLxzs4Et+IaFQCtBSQS1+bfMa6lLEwwNoobS6dmjy2JElSSiiE/AksSZJEQEQ0B72CeBkShRagSmCd2OW2Sh0a2+XAXF870x9bkiQpNciEUpKkbO+ufzgHXwSjEpCUC6KCmDuHTfLnwD6nfqY99tcIIVAoFGm2f0mSshaZUEqSlK3d9Q9nn1fwd2/f1M4Yh5wGme7YX5LN7ZIkpYRMKCVJyrb8w6NZ8SCA6BRcBbUV0L2kebKboDV57M/J5nZJklKDTCglScq2Nvz7Ae+QKATg++IJuyYN4c2je0RHfqJg+Sq0+H0mr+7f4sSS6fh6PUFX35DS9dz4YdgUtHVi7tYpgHxKHX4sZpbqxw58+4qto/vw4e0r9JXGFKtahxaj/kDP0ChFx46VUZvbJUnKfGTZIEmSsqW3oVG8/C+hA/jo8xYhBPV+GUG5xq15eO44+2f8xpt/75K7cEmaDJmEdVF7Lm1bxeXtq9X7EcDLkCjehkal+rG1dHRwcmtPy9EzKeBYiZsHt3Fl9/oUHTtWbHN7dBKTydjjRQvY6xXEXf/wZB9TkqSsS3aIkSQpW7rtFx6niTd/2Yr0XLZH/fiNA1t59+QhbacsQUdXD4A8xUux8Kf6+Dx9GGdfWv/tz9rIOFWPnc/eEesiJQn7+AF/by/unToUb1/JPTbENLcffPH9fTcBDr4IxkapK5u/JUkC5B1KSZKSKKv1jvEOjozTXzA2aQR4fvMyn8JCKVCuUpzljy/+BYCdY6U4+1IB3iGRqX5siEkup9QvxYmlMyhSyZWKzTsm+dh+fn60a9eOHDlyYGpqSteuXXnx4gWVq1bFwzkvHuUteXr1fLztdk4chEd5SxZ2avj/46hUHF0whcn17BldxZbZ7Wuz68E7QkNDcXJyQqlUkiNHDmrVqsXDhw/j7TM5stp7TZKyA3mHUpKkBGX1Ub++EdEJLvd5+i8bh3fDskBRGvYfo17+z7E9HF86g9L1muHYqFX8/YUnvL+UHrt49Xp0nruRO8f3cX3fZu6ePEC5Jq2TdOxu3bpx4MABhgwZQuHChXn8+DHeH0JQ5rHDTls3wWTS69bf3Dq8M97yCxuX8teKWZSq25RiVWvjfecGL4M+8TYkkjp16tCvXz8ePHiAp6cnHh4e7NwZfx+JyervNUnKDuQnVJKkOL426lclwCc8Gt/waK77hmfaUb9CCFQJ3AR79+QBy3u1RM9Iyc+LtmNkag7ArSO72Pp7b4pVrUPbSYsSrM+oEkmr3ZjcY5tYWmNiaY2tQ3mu7d3ErSO74iWUCR37yZMn7N27l59++okJEyagq6uLlpYWR18G037SQg7OnRAvoYyOimL35GHU6j6YI/MmxXns3IZFmNvkp+3kxQBUbN4RLeBxuDbTpk3Dz88Pa2trPD09v3r+n8sO7zVJyi5kQilJktrno34h4RIyny/3Doli+f2ATDfqV6FQoKUgTmL34e0rlvVsQdjHAFx+6sPzm5fRMzBCoaVg6++9MTLNSel6btw5uR9zm/zkL+0UZ59aCpJUCDw5x3555xooFFjkK8D9M0cAsCpYLN4+Ezr2/fv3Abh06RJKpRIDAwOmTZuGUZ0fE31dz29cghAqXDr2iZNQRoQGE/juNSaW1kyqU4LI8DDKNmyJ+7h5eIdE8vTpU4oWLQpA/vz5mT59+jefh+zyXpOk7EImlJIkAd9XZPvzUb8CkWpFttNDLn1tfD5rKvb3fk5IgC8Ah+aMB8Asjy1OTduhio4m2P8928b8CkD5pm3jJZS5DJJ+5yypx67Wvidn1y8kxN8XpbkFzi07UfeXYfH3l8Cxw8PD1f/dtm0b48ePZ+DAgQzf74JJ7rzx1g8LCuTEkuk0HT6VgDcvAYiKjCDw3Wt09GISuCA/H1r+PpMnV85xY/9WilWpjU7jVuQtnpcjR45w8+ZNfvvtNzw9PVm2bFmi55/d3muSlB3IhFKSpGw56jefsS6+4dHqO2CFKlRj6vX3Ca5b95fhX92XFpBPqZsmx67e8ZfvOnaBAgUAqFatGi1atODvv//m1q1b+L/xTjChDA8K5FNYKDvGD1Ave/PwDusGd+LXDcfRMzRCX2lMheY/YmKVh5uHthPwyguVAAMDA+rXr0/9+vVZu3YtmzZtSjShzI7vNUnKDmRCKUkSh14EJdivLzlUAg56BX13ke30VsbCgOu+qVNLUfXf/jLSsZ2cnChRogQnT55k2bJl7Nq1C1NTU/IUKcmVXet48+geAA/OHiPYz4cSNRrQYfoK9fYbh/+MRf5CNBowFgDHRu5c2bWO06vn4XXzMhAz2v3O8b30WXqFihUr8ujRI+7du4eTk1O8eGJlx/eaJGUHMqGUpGwutsh2rMRmbfF78ZRlPZvH2bbLvM0Ur1YHiFtkOzOMyLU20sFWqaOereZ7xc5Wk5xzTo9jKxQKNm3aRPfu3enXrx/Fixdnx44dXIsIYufEwer1zq5bgFkeW8o0aEHpum5x9mFkYk5hZxcAGvYfTXCAL8eXTEdpbsEPQydRqEI1/P+5yN4Vf7Fq1SqUSiWNGjVi1qxZ8fp0jhgxgmfer9m6YV2c5QWdqtJz2R4ubl3JX8tnEvrBH+NcVlRq1ZlaPw8CYkoWHV80jSu71xMeHIRVwaL0WrGft/mM0Q0LpG/fvhw4cAAtLS1atmzJqlWrUvCsSpL0PTL+VV+SpDT1ZZHtz2dteffkAZe3r2b/jN+o0rY7ALV7DMGqUHEAbIqXirOv7ymyrUmN7XKw/H7K5tPWUsTsJyMe29HRkatXr8ZZFv0yGM/r7xMdBBPryyZ4QxMzfvpzTdzjA3Vr1WJ6pwcJ7qNVq1a4u7sDYG9vz6nnfmiXckEA3ndvcG79IvIUK6Xev2vX/ugbGXNu/SKOLphCCZf65CnmkGDJIoVKxW2/cBb8Gr80kiRJ6U8mlJKUzX1ZZDuxWVti2TlWolCFanGKccdKboFvTTPX16ZJ/hzs9Qr67n00yf99pWw0dez0bOq3t7fHzc0NI6OYucev6gZQxrY0APdPHwbAuVUnABwbtiQiNJiwwA/cObGfd0/+n6QmVLII4Pr9fxMsjSRJUvqTnzxJyoRScyaRL4tsf23WFoDVv7ZlTBVbVvR2JyTAL/7+klHgOyOwz6lPUztjtBUxTchJoQC0FeBml7ISNpo4dmxze1KP97U4bL/R1D9p0iSUSiUlS5bkypUr6vdayAd/7p48QAHHSuT+7243wO7Jw/BsUo6H545Rs9tA8hRzUJcsio78xKQ6JRhbzY6tY/qiUql4+CAm6YwtjWRiYsL8+fNTeGaSJH0PmVBKUibwNjSKoy+DWXk/gOk3ffG86cf0m76svB/A0ZfBvA2N+vZOEpBYkW2IP2uLcU5LGg0cx0+z1uPcshOPL5/mxNIZ8baLLbKdmTjkNKB7SXPyKWOSo8QujLHL8xnr0L2kearUQ9TEsRvb5UArhRnlt5rbPTw82LVrF7NmzeLx48d069ZN/V67sX8rUZ8iqNiyU5xtanYbQMc/VmNXtiIXtyzH5+m/REVEADEli34YPJEy9ZpzY/9Wbh/ZxaeIuKWRihQpwsCBA3n58mXKTk6SpGSTTd6SlIGl9UwiCRXZhoRnbTEyNceqUExR7UIVqnJ5+2p8nj2Kt8+kFvjOaMz1tfmxmNn/pwEMiYwp7RM7DaCBNvmUaTMNYHofOz2a26dMmaL+//Xr13P79m0UCAQKruxej6GJGaXrxR0ElLtwCXIXLoFKFc3G4T/z4NwxanTqm2jJouJVawPxSyO9ePECW1vb7z43SZKSTyaUkpRBpddMIl8W2U5s1ha/l88IePOCPMVK8ejSKQDyOTjG398XRbaTMh1hRmJtpBNnUFF6xp+ex7bPqY9AqN9jSbmnrCAmwf3We+zcuXPMnz+f2rVr8+rVK27cuIGzszOWBjr8ffE8Pk8fUrV9D3T1/9//csOwruQvUxGDHCZc3LwcAKuCMbPvJFayqGz58gmWRipVqlT8oCRJSlMyoZSkDCg9ZxL5ssh2YrO2NPPw5Pr+zVzZtR59I2OcW3aidvchcfalBeTU1+boy2C8gyPxjfjsLpu+NvmM0+YOX1rSZDKc1sd2yGmAjVI30bvgsWKX5zPWoXESBgLZ2Njg4+PD8OHDEULQoEED5s2bxxM9XRbsiikb5PxFc7e2nj5/rZxNZFgoprltaDRwHCVc6gMJlywqUqEatsZ6CZZGMjU1TfmTI0lSsihEZuvsJElZnH94NCsepKycjLYCupc0T1Lz99vQKFY//PD9B0vAtxKT72mel9JWejS3p/Z7rUtxs0z140SSsjL5SZQkDUmo8PPbt29ZsyZurb9vFX4OeP2C6T/En5nkw9p9jGpVGxcXFx48eICWlhYVKlRg8eLFFC/+/5G1qVVk+3Np1TwvpZ30aG7XZDF5SZLSlvw0SpIGfVn42TsgiIgS1YCkF362sC1Au6lL1fs8PGcCQb7vUNgU5m1IJHXq1KFfv348ePAAT09PPDw82LlzZ5w4UqPIdnJ8b/O8lH7Sqrldk8XkJUlKOzKhlCQN+rLw89uXwZQzKY6KpBd+1jNUUrZBCwDeez3hw1tvStdrhklOSx6HazNt2jT8/PywtrbG09MzwThSY9Tv9zr4Ihgbpa5s/s4mNFlMXpKktCPrUEqSBn1Z+Dl21prkFH7+3JWd/x/wEDtrzdOnT7GysqJRo0bkz5+f6dOnJxhLcops+718xugqtniUt+Th+RN8eOPN4q6N+b1SXjzKW/L06nn1uieX/8kfzZwZXcUWzyblubz9/036H96+YknPluQ2UVK8eHFOnjyZrOdPypw0WUxekqS0IRNKSdKQhAo/x84kkpzCz7GiIj9xff8WLGwLUtjZBYiZtSZv3rwcOXIET09PXr16lehdSkh6ke2900agUPz/0ajICMzzFsCubMV463rfvYlDrSY0HToZgD1Th/H20T0Adk8Zxss712gwYAxaOrq0bt2akJCQbzxzUlagyWLykiSlPplQSpKGTJkyhWbNmjFw4EDKli3Lw4cPif6v6OTXCj871G5CtR9/ISIkmAfnjqkfu/fXQUICfKnYoqO6/5tKgIGBAfXr12f48OGUKFGCTZs2fTWu2CLbXYqb4ZjLACtDbfWsKloK8Dq1l/eP79KgXWf1NrnyF6btpIXYliofb38dpq+g0cCxOLfqRJU23RBC4PP0ISEf/Pn3/HFK1GiAS/ueNO7aB39/f/bt2/ddz6eU+XzrvWZlqI1jLgO6FDfjx6JmsplbkjIw2YdSklJJckbFJlb4WVtLwdPrl5Jd+Bng753r0NbRxcmtvXrZneN76bP0ChUrVuTRo0fcu3cPJ6f4I8ITktCo36CgIEo2Gc382bPYcv5Wkvbz+dzgjy6fRltHl3ylyvPhzUuEEJhYWqMChHluAJ49e5ak/UpZhyaLyWtCVj8/KXuSCaUkfSd13b7vKOCdWOHn05+02fwdhZ/9vZ/z9MpZHOr8gHFOy/8fJ7cle1f8xapVq1AqlTRq1IhZs2Z91/kqFAqmT5+OlZUVzs7OLNj3FwBBvu/4FBaKnqHRV7ffN2MUjy+dwm2kJznz2vHq4wfg//N+B0ZEf2VrKTvJaslWSq4VkpRZyHewJCVTasyvXahQoQQHoDx5GUy7CQtoM2FBvMfaTV6caEw58xVgyjWfOMu0gLq1ajG904PknN5XeXt7c/PmTYoW/f+d0R3jB6A0z0XJGvUT3W6v50gubllBg36/U6VNNyBm9h2FQkHQ+7cABL57A0DBggVTLV5J0qTUuFZIUmYh+1BKUjLc9Q9n+f0AvEOigKQX8L7nH5Gk/ZexMEh0n8ml+m9/qenXX39l27ZtbNu2Td2/s9bPg7ApXooru9bx5r/BNg/OHuP2kV0AHJ47gYtbVmBXtiLmNvm5dWQX/q+8UJrlpFi1Ojw4e5Tzm5ZyfuMSzM3Nadq0aarGLEmakNbXCknKaOTUi5KURN8zv/bnmtoZJ6mA94Z/P6TaTCI/FjNLwV6+rlnfEexdOJ0u8zZjVbBovNl6zPLYMuLAdZb2aMazaxfiPOY+bi5Obu358MabbWN/xevWFXLZ2LJu2SLq1auXZjFLUnpIr2uFJGUkMqGUpCRIz/m1AyKiUzyTSHLm8v5eR18Gc9M3PFXuqGoBjrkMqG9r/M11JSkjS89rhSRlJLLJW5KA0NBQnJycUCqV5MiRg1q1avHw4UMAbt68SZVq1fitUj4m17Pn0rZVAPi/8mJFb3fGVi/AuBqFWNWvHR//6w/47ulDlvzclDFV7ZjZsipPrpxDJeCPrYdQKBTx/p4/f66OJXYmkZRIj5lEMnrzvCRpwqEXQagSSSY3DOvK+BqF8ShvyfHFCU8wADH9Kw9qYNYqSUoJmVBKEjEjjevUqcOCBQvo27cvp06dwsPDg9DQUBo2asyrp49p0O93anTupy7ofXThVB5fPk2lVl1wbNiKf8+f4PTquQBsGtGdd08e0GTwBKKjItk0ojsRYaEobAqzaPV6Nm3axMaNG1EqlVhaWmJjYxMnnswwk4i1kQ62Sp0kx5cYBWCr1JGjXKVM721oFC+/0l1FW0+fkq4Nv7kfAbwMieJtaFSqxidJaUkmlJIEKJVKpk2bRpMmTahZs6Z6+aZNm3j39g2N+o+mUqvOuHTsTSX3mILeQhVzf66gU1XsyjoDYGhiTsgHf949eUAhp2pUcu9M6XpuhHzw498LJzDJaUmh2s1o164dFhYWhISE0LVrV/T09OLFlBlmEmlsl0NdiPp7aSli9iNJ8P8yUpnRbb/wr36ptpu8mLINWyZpX1r/7U+SMgt5S0CS/vP06VN1OZzYOa8XL44p1XN67Xy2TxiIqXVe2k5aRMHyVWjYbzSv7t9i7cAfAShYvgq1ug0EQEdPn3dPH+Dv/RzvuzcBCHj9Uj2/NsCyZctQKBT06NEj0ZhiZxJR17ELicQ3/LM6dgba5FNqro5dbPP83hQ0z6VH87yUcWWlGo3ewZGp2g0k9lohSZlB5viUSlI6iJ3z+ubNm/z22294enqirx9zt8/UOh91fxnBzomD2DFhIEN3X+b6ga34vXhK0+FTCPZ7z18rZnFhy3JcOvam/q+jODRrLDPcKqKvjBlooqMXsy/f8Gh8fHzYs2cPtWrVokiRIt+MLSPPJGKfUx+B4OCLYFSCJI1OVxCTNDTJnz7N81LGkxVrNPqmcnF+33BZ7F/KPGSTtyT9x9DQMN6c13Z2dgCUqv0DZeo3x6pQCT68jpky8PbhnRgYm1C1XQ9cOvUF4PHl0wC4dOzN8AM36L3mMNU79gHAqmAxIObLctWqVURGRtKzZ8/vijWjJJOxMkPzvJRxZMUajUKIRAfjfC+VyNxdAKTsRd6hlCRg+/btnDhxIt6c123btmXkb79x48AWVKpo3j66S/6yFVEoFJjntcPn2b8cW+RJyAc/ACztYu42Xty6Em1tHcKCPnBu/ULyFC9FoYrVAVAgWLFiBZaWlrRo0UJj55zavtY8HxX5iVz62hTKqcxUTZhS6vueGo0CiBaw1ysIgciQNRoVCgVaCr6aVN4+sosX/1wD4PWD21zZtY4yDVqgb5RwuSwtRcb78ShJiZFXdUkCcuXKxV9/xZ/zOn/+/HSfuoitf47n8JzxFHCsRPNRfwDww9BJRH2K4PzGxSi0tClVtym1ewwBIMTfl/MblxAV+YmC5avQ/LcZ6i8Gv9sXefToEUOHDk1wME5m92Xz/MuX3uTPb8vevXupX1bOgpOd+YdHc/DF9xf8Bjj4IhgbpW6GbP7Opa+Nz1eaqQ/NnciHNy8BuH/mCPfPHKFIJddEE8pcBhnvHCUpMbKwuZTlpbS/oSzgnTJCCMzMzBg5ciQeHh6aDkfSoMwyC9T3ymjXiozU11rK+uQdSinLSe1Ro2UsDLjumzrlO7JjAW+FQoGDgwN3797VdChSOvkyiRkxYgTPvF+zdcO6OMsLOlWl57I9XNy6kr+WzyT0gz/Guayo1KoztX4eBIBKpeL4omlc2b2e8OAgrAoWpdeK/TzNqUXrBrV48OABWlpaVKhQgcWLF1O8ePF0O88vafpakZVGzEuZj3xnSVlGWo0ajS3gnVp3VrLjBd3BwYGrV69qOgwpHbVq1Qp3d3cA7O3tOfXcD+1SLgjA++4Nzq1fRJ5ipQAwNDHDtWt/9I2MObd+EUcXTKGES33yFHPgwsal/LViFqXqNqVY1dp437mBQqXijl8YderUoV+/fjx48ABPT088PDzYuXOnxs5ZU9eKrDhiXsp8st83m5Ql3fUPV5etgaSPGk1q2ZrGdjlSPL92di7g7eDgwPr164mOjkZbW36RZQf29va4ublhZGQEwFXdAMrYlgbg/unDADi36gSAY8OWRIQGExb4gTsn9vPuyQP1fs5tWIS5TX7aTo6pCVuxeUcAfIU206ZNw8/PD2trazw9PdPt3L4mva8VaX3tk6SkkmWDpEwvdtRodBJrIELcUaN3/b/dRJVZ5tfOqEqVKkV4eDhPnz7VdChSOpk0aRJKpZKSJUty5coVdY3GkA/+3D15gAKOlchd6P/N07snD8OzSTkenjtGzW4DyVPMgYjQYALfvSY68hOT6pRgbDU7to7pi0qlwjc8mqdPn2JlZUWjRo3UkxFoWnpeK9Lj2idJSSUTSilTS61RowFJKEicGebXzqgcHBwAZD/KbMLDw4Ndu3Yxa9YsHj9+TLdu3dR30G7s30rUpwgqtuwUZ5ua3QbQ8Y/V2JWtyMUty/F5+i9RETF1J4P8fPhh8ETK1GvOjf1buX1kFyoBNjY2HDlyBE9PT169epVh7lKmx7UiPa99kpQUMqGUMrVDL4JSXExYJeBgEqcOlAW8v4+1tTXm5ubcuXNH06FI6WDKlCk0a9aMgQMHUrZsWR4+fIjiv3toV3avx9DEjNL13OJsk7twCRxqN6Haj78QERLMg3PHUJpboGdohHHOXFRo/iPlmrQGIOCVF1oKMDIyijcZQUaR1teK9L72SdK3yD6UUqYza9YsZs+ezbt37zDKZY1r535YF7Xn4Kyx+Dx9CAoFxavVoflvf2BgnIMru9ZxbsMSAl6/wMDYBOdWnanbaxgA754+ZPfkoby6f5txdvlZumghtWrV+urxM/r82hmRHOmdfZw7d4758+dTu3ZtXr16xY0bN3B2dsbSQIe/L57H5+lDqrbvga7+/0cwbxjWlfxlKmKQw4SLm5cDYFWwKACOjdy5smsdp1fPw+vmZQDsHCvx/PR+ei+5HG8ygowkpdeKR48e0bNnT27fvs2nT5+oUaMGS5cuRds8N1tWLOLUytlEhARTul4zmv82Qz29K8DOiYO4sms9tqWc6LM2ps/qo0unODhrHL4vnqBvZEzpem40HTaFnYdP0KpR3XjHf/bsGQUKFEiz50fKWuS3nZSpPHr0iMGDB1OiRAm6/z6FjYvmsGfqMFqNm4vS3IKG/cfw5MoZbh3eiYmlNY0Hjcf73i0KOVXF5sdeXNyyghNLppO3ZFlK1qjPphHd+fj+LT8MnsClDQtp06YNXl5e6oEEX5OR59fOiBwcHLhw4YKmw5DSmI2NDT4+PgwfPhwhBA0aNGDevHk80dNlwa6YskHOXzR3a+vp89fK2USGhWKa24ZGA8dRwqU+AA37jyY4wJfjS6ajNLfgh6GTKFKhGopHV1ixKP5kBBnR914rXr16hRCCCRMmcOfOHRYvXsyAAQOo2aU/+6b/RknXhlgXKclfK2aRy64wNbsOAMDr1t/cOhx/tPv2sf0ICwqkYf/R3D91mEtbV1LUuQZFatdQ390VQtCjRw+MjIywsbFJpWdAyg5kQillKipVzFhFW1tbbMpXx9hiE6GB/jjUbIxT03YAONRpwj/H9qpHijYdPgUd3ZgZaQyMTdg4ojs+Tx+Qv0wF3j15gEOtJji7dybivTcHl83m0KFDtGrVKtmxyWTy6xwcHFi1ahVRUVHo6MhLT1ZVqFAhTp48GW+5MjSK1hMW0HrCgniPtftvBHdCDE3M+OnPNXGWqYAuP9RjWJsHCW+UwSX1WlG1alVOnTql/ve6deu4e/cuH7ZsAKDRgLFYFijC9QNbub5vMzW7DiA6Kordk4dRq/tgjsybFGd/QqjQM1RSpJIrvl5PeHLlLAYmZgQbmtGtXcz18+jRo4SEhNC3b98sOZOXlHZkH0opUylevDiTJk3i2LFjjGpSidcPbtN20mIMcpio13l08RQABRwrAaiTSYhp8gGwK+uMgTIHOnr6vHv6AH/v5/x76zoAz58/T5dzyW5KlSrFp0+fePz4saZDkTQgtkZjSn92KQDbbFLP9fOE7vz584SEhFC9enVev/ACwMTSGgBTKxsCXr9ECMH5jUsQQoVLxz7x9uc+fj6R4aHMdq/Opa0rce3an0IVquH72XSRy5YtQ6FQ0KNHjzQ+OymrkQmllKn4+PiwYMECqlSpQsc/12CWJx+7Jg/hU1gIAM+uX2T3lKEUcKyES6e+cbY9t34xV3atp0anXylQrjLaurrU/3UUfi+eMsOtIi/vxiSUBgbZayab9BI70lsOzMm+GtvlQCuFGWV2rOd6//593N3dKVGiBNOmTVMvj505Ofa/4UGBnFgyneodexPw35zhUZERBL57DcCZNfPR1Tekg+dySro25Oy6hby8cx2ViNmHj48Pe/bsoVatWhQpUiSdz1LK7GRCKWUqp06d4s2bN7Rp04bStRtTvFpdgnzf8e7JQ55eO8/qfu2wKlScznM2xumgfmbtAg7MHE0l9y40GjhWvdylY2+GH7hB7zWHcfkp5hd9yZIl0/28sgMrKyty5colB+ZkY7Kea/LdvXuXmjVrolQqOXbsGBYWFpjb2ALw8f3b//77BnMbW8KDP/IpLJQd4wfwZ/OYFpo3D++wbnAngv3f8+TvM9g5OlO6XjOc3Nqjiori6dXzaClimuFXr15NZGQkPXv21Nj5SplX1m8zkLKUQoUKAbBq1SrKBSm4+9cBtHX1EEKwul8HFAqo4NaehxdOYGyei8LOLlzevppDs8eRy64wBcpX4daRXeQuXALrIiW5uHUl2to6hAV94Ny6hTg6On5zlLf0/eRIb8k+pz4Cwb5ngQgUKLS+fV9DQcydyew2u8vLly+pVasW/v7+DB06lHPnzqFUKqndoh0XNi/n8NwJWBcpSeDbV9Tv+xtK81x0mL5Cvf3G4T9jkb8QjQaMxcg0J/rGOXh69TyXtq3in2N7AbAsUIRcBtoIIVi+fDmWlpa0aNFCU6csZWIyoZQylQoVKjB9+nTmz5/PxskjMLXOR+sJ83n/7F8iw0MB2D0lpiRQQaeqFHZ24cU/MXNI+3o9YctvvQCo03MY1kVKEuLvy/mNS4iK/ETZytXZtmqpHFyThhwcHOIMMpCyJ5OQ98xv34Be8zaga2Ubb/7pWLHL8xnr0Dib3ZkEePLkCe/fvwdg+PDhANjZ2bH07B1+GDKRU6vm8vjyGco3bYvLT33Q0dOndN249T2NTMwp7OwCQPupSzk8dxIHZo7BMIcprl37U6pmI/IpdTl16hSPHj1i6NChcjCO9F0UIrbzhSRlMm9Do1j98EOq7a9LcbNs0dFfkxYuXMiAAQMICQmRX1rZWLt27Th16hT//vsvoTpGsp5rMslrn5QRyXeQlGnFjhr1DolK8jy2CVEA+bLJqFFNK1WqFFFRUTx69Eg9SEfKXk6fPs2WLVtYvXo1JiYmmICs55pM8tonZURyUI6UqclRo5mLHOmdvUVFRdGvXz8qV67MTz/9lOA6MplMGnntkzIamVBKmZocNZq5WFhYkDt3bjkwJ5tavHgxd+7cYf78+WglYTCOlDh57ZMyGnmfW8r0YkeNHnwRHFNPLQnbZNdRoxmBHOmdPb1//57Ro0fTvXv3DDfndmYlr31SRiITSilLcMhpgI1Sl4NeQbwMiZKjRjMwBwcHjh49qukwpHQ2atQoACZPnqzhSLIWee2TMgo5ylvKct6GRslRoxnYkiVL6Nu3LyEhIejryzsk2cHVq1dxdnZmzpw59OvXT9PhZFny2idpkkwopXShyZGbctRoxnL+/HmqV6/OrVu3KFOmjKbDkdKYSqWiWrVqhISEcP36dXR0ZEKTXuS1T0pP8pMtpQn1L+XgSHwjPvulrK9NPuP0/aUsL6gZy+cjvWVCmfWtW7eOS5cucerUKZlMpjN57ZPSk/x0S6kqICI60b48KgE+4dH4hkdz3TccW6UOje1kX57sxszMDBsbGzkwJxsIDAxkxIgRtG3bFldXV02HI0lSGpJ1G6RUc9c/nOX3A/AOiQIS7hj++XLvkCiW3w/gnn9EusQnZRxypHf2MHHiRIKCgvjjjz80HYokSWlMJpRSqrjrH84+r2Cik1i6AmLWixaw1yuIu/7haRmelMHIhDLru3//PnPmzGHUqFHky5dP0+FIkpTGZEIppZh/eDQHXwSnaB8HXwQTEBGdShFJGV2pUqV48uQJYWFhmg5FSgNCCAYMGICdnR1DhgzRdDiSJKUDmVBKSaZQKOL8jRw5kkuXLmFfohgeznmZWLs4W37vw6ewUABOLv+TP5o5M7qKLZ5NynN5+xr1vh5dOsWctjUZXcWWSXVKsmvqCPY//QBAeHg4vXv3JmfOnBgbG1O3bl1NnK6UhhwcHBBC8ODBA02HIqWB3bt3c+zYMWbPni1LQ0lSNiEH5UjJ0qpVK9zd3QGwt7fn9cdQyv7QDtPcNtw9eYCbB7eRz8GRau174n33Jg61mmBhW5C/Vs5mz9Rh2JWtiHVRe7aP7UdYUCAN+4/m/qnDXNy6ksLONahn9yNTPUawePFievXqRYUKFbh69aqGz1pKbfb29kDMSO9y5cppOBopNYWFhTF48GAaNWpEkyZNNB2OJEnpRCaUUrLY29vj5uaGkZERAG9fBuP6ky0hHz/g7+3FvVOH1Ot2mL4CHV09AMKDP3Joznh8nj7Euqg9QqjQM1RSpJIrvl5PeHLlLEYmZvzt7c+SJUtwcXFh3rx5KBQKunfvrpFzldKOiYkJtra2sh9lFjR9+nRevXrFkSNHZNkaScpGZJO3lCyTJk1CqVRSsmRJrly5gndwJNcObGVK/VKcWDqDIpVcqdi8I4A6mQR4dPk02jq65CtVHgD38fOJDA9ltnt1Lm1diWvX/hSsUI2bDx4RERHBy5cvyZEjB0qlEg8PD42cq5S25MCcrOf58+dMmzaNwYMHU6xYMU2HI0lSOpIJpZRkHh4e7Nq1i1mzZvH48WO6deuGb0Q0xavXo/PcjTi5tefJ32e4e/JAnO32zRjF40unaDJ0Ejnz2gFwZs18dPUN6eC5nJKuDTm7biEv71zHNyhmkIafnx9r167F1dWVadOmceHChXQ/XyltyYQy6xkyZAg5c+bk999/13QokiSlM5lQSkk2ZcoUmjVrxsCBAylbtiwPHz4kWiUwsbSmRPV6NBowFiEEt47sUm+z13MkFzYtpUG/36nSphsAwf7vefL3GewcnSldrxlObu1RRUXx9Op5TPLElBdxcHCgTZs2tGrVCoBnz56l/wlLaapUqVI8e/aMkJAQTYcipYLjx4+zc+dOZsyYgbGxsabDkSQpnck+lFKSnDt3jvnz51O7dm1evXrFjRs3cHZ25viiqQgUWOQrwP0zRwCwKhjT1HV47gQublmBXdmKmNvk59aRXdiWKo+ZdT70jXPw9Op5Lm1bxT/H9gJgWaAIJjlzUb9+fc6ePcvChQtZv349Ojo6ODs7a+zcpbQROwXjvXv3qFixooajkVIiMjKS/v374+LiQvv27TUdjiRJGiDvUEpJYmNjg4+PD8OHD2f27Nk0aNCA9evXY2lhwbW9m9g1aQgv/7mGc8tO1P1lGAAv/rkGgNetK2z26Mlmj548u3YBLW1t2k9dilkeWw7MHMP7549w7dof+5qNyGWgzdKlS6lcuTKDBw/G19eX9evXU7RoUU2evpQGSpYsCSCbvbOA+fPn8/DhQ/VAOkmSsh+FECKpE5tIUjxHXwZz0zc80WkWk0MLcMxlQH1b2VyWXRQsWBB3d3dmzJih6VCk7/Tu3TuKFStGx44dWbBggabDyRaEEDJxlzIc2eQtJSipF6wyFgZc902daRNV/+1Pyj7kwJzMb+TIkejq6jJx4kRNh5JlvQ2N4rZfON7BkfhGRKMSoKWAXPra5DPWpYyFAdZGmfvrXCbJmV/mfgdKqeZ7L1jWRjrYKnXwDolK8hzeCVEA+ZQ6mf6iKCWPg4MDmzdv1nQY0ne6dOkSq1evZvHixeTMmVPT4WQ5ARHRHPQK4mVIFFoQpyVIJcAnPBrf8Giu+4Zjq9ShsV0OzPW1NRVusmSHJDm7kU3e2dzXLlixYpcndsEKiIhm+f0Aor/znSRUKhCCX0rnyjQXQyl1rFu3jk6dOhEYGIiJiYmmw5GSQaVSUalSJaKjo7ly5Qra2vKzm5ru+odz8EUwKkGSfqwriEnImuTPgX3OjDvdZWp850gZkxyUk43d9Q9n+f0AvEOigIQ/2J8v9w6JYvn9AO75R8R53Fxfmyb5c3x/IAoFW37vzewpE5C/b7KXz0d6S5nLypUruXr1KvPmzZPJZCq76x/OPq9gopOYTELMetEC9noFcdc/dbohpbbU+s6RMiaZUGZTqX3Bss+pT1M7Y7QVMb+Uk0IBaCugWQET2teqxIQJExgxYoRMKrOREiVKoFAoZD/KTCYgIAAPDw86duxItWrVNB1OluIfHs3BF8Ep2sfBF8EERESnUkSpI6smydL/yYQyG0qrC5ZDTgO6lzQnnzKm30tib67Y5fmMdehe0hz7nPqMGDGCOXPmMGPGDPr3749KlRrjxqWMzsjIiEKFCsmEMpMZO3Ys4eHhTJ8+XdOhZEoKhSLO38iRI7l06RJFixYlt4kR42oVZ8vvffgUFgrAyeV/8kczZ0ZXscWzSXkub1+j3tfSHs3wKG+p/lvYqSEqAQe9gnB3d8fMzAyFQsG4ceM0dLZZN0mW4pIJZRaV2IXk5s2bVKlWjd8q5WNyPXsubVsVZ7udEwepL0qx1g/pwtQGpRldxZZ5HWrz7PpFVAJWnr5JrVq1sLCwIEeOHDRp0oRQ37f8WMyMLsXNcMxlgJWhNlr/3bLUUoCVoTaOuQzoUtyMH4uaxekb079/f5YsWcKCBQvo1asX0dHy4pEdyJHemcs///zDwoULGTNmDHny5NF0OJlWq1at2LRpE5s2baJDhw7o6Ojg3rEzLUbPpIBjJW4e3MaV3esB8L57E4daTWg6dDIAe6YO4+2j/3cTsSpYjHZTl9Ju6lLq9/VAAC9DolBp6+Lm5qaJ04vj0IsgVClseIpNkqWMSw6hyqL09fVxc3Nj3bp16mWhoaE0bNSY4PBPNOj3+39lGv7/m8Lr1t/cOrwz3r7ePLpL1fY9UGhpc2zhVDYM68Zvx+7yxMubiGgVEyZM4M6dOyxevJgBAwawfft2rI10sDb6fz3JpJaE6NmzJwYGBnTt2pWwsDBWr16Njo58m2ZlDg4OrF27VtNhSEkghKB///4UKVKEAQMGaDqcTM3e3h43NzeMjIzUy96aFUTn2Vv8vb24d+qQenmH6SvQ0dUDIDz4I4fmjMfn6UOsi9oDoMyZixIu9dD/7JqrBfzyxzJUd8/F+R5IS19e40eMGIFLwx8Y0KkTH96+Ql9pTLGqdWgx6g/0DI04ufxPru/bQqDPG4xzWlKz6wAquXdWbx8dGcnc9rXwefoQ1y79aNh/DG9Do9i0ZB6zZ8/m3bt32NjYMGLECHr16pUu5yglTt6hzKI2bNhAhw4d4izbtGkT796+oVH/0VRq1RmXjr3VH97oqCh2Tx5Gre6D4+1r0I7zuHbpT41OfSns7EJIgC8hAb4UKFuRcRsO0LdvXxYtWoRSqUz0TlNy6ot16tSJTZs2sWXLFtq3b8+nT5+SceZSZlOqVClevXrFhw8fNB2K9A3btm3j1KlTzJkzBz09PU2Hk6lNmjQJpVJJyZIluXLlCgDbN61nUv1SnFg6gyKVXKnYvCOAOpkEeHT5NNo6uuQrVV697Pn1i4yrXpCJtYtzbe8mIGZgi3dIZPqd0H++vPP6LFhFBbf2tPyOO69n1y0g4PVL9b+1gENX7zB48GCMjIz4888/EULQu3dv/P390/U8pfhkQpmN3L9/H4DTa+czpmp+pjV25Nn1iwCc37gEIVS4dOwTb7vYi1loYACv7t3CIn8hjHNaoqWrp75gnT9/npCQEKpXr54qsbZp04bt27ezd+9e3N3dCQ+XHbKzqtiR3rLZO2MLCQlhyJAhNGvWjAYNGmg6nEzNw8ODXbt2MWvWLB4/fky3bt0AyFepDp3nbsTJrT1P/j7D3ZMH4my3b8YoHl86RZOhk8iZ1w6AUnV+oO2UJbSdsgRdfQN2TRrCx/dvAfANT/9uQ7F3Xtu1a0eZMmUwKlya6j/1oWjlmuQpVirOuh2mr6DRwLE4t+pElTbdEELg8/QhAAGvX/DXilnU7j5Ivb4KeBMSc4PB1taWunXrYm1tjbGxMbq6uul2jlLCZEKZjcQmZabW+Wg/bRnhQYHsmDCQsKBATiyZTvWOvQl4E/NrMCoygsB3r9XbRoQEs3ZQRz6FhdB20iL1HUff8Gju37+Pu7s7JUqUwNPTM9XibdasGXv27OHYsWO4ubkRGhqaavuWMo7ixYujpaUlE8oMburUqbx//56ZM2dqOpRMb8qUKTRr1oyBAwdStmxZHj58iEqlwtjSmhLV69FowFiEENw6sku9zV7PkVzYtJQG/X6nSptu6uVV2/WgbIMWODZsSZn6zYmOisTf+zkQ0+8wvatmfHnn1TcimhsHtjIlmXde93p6ULZhK/KXqRhn/zp5CjFp0iSOHTtGiRIluH79Ohs2bCBHjhSUrpNShUwosxE7u/9+0db+gTL1m2NVqAQfXr8k7OMHPoWFsmP8AP5sXgmANw/vsG5wJwDCg4NY2bcNrx/8Q6dZ67H9rKnlzeMH1KxZE6VSybFjx1J9toyGDRty8OBBLly4QOPGjQkKkp2ysxoDAwOKFCkiE8oM7MmTJ8yYMYNhw4ZRqFAhTYeTqZ07d4527dqxdOlSxo4dy40bN3BycmLMmDEcWziVa3s3sWvyECBmsA3A4bkTuLhlBXZlK2Juk59bR3bh/8oLgMVdG3N+4xIubF7O9f1b0TM0wvK/7f45uoujR48CcP36dZYvX05wcMpGW39NQndeVQKKV6+XrDuvjy+f5unVc1Rs0ZFAnzcAhAV9JOSDPx/93rNgwQKqVKnCrl27sLOzo1evXoSEhKTZeUlJI0c7ZFFbtmzh0qVLwP8vJG3btmXkb79x48AWVKpo3j66S/6yFTHOaUmH6SvU224c/jMW+QvRaMBYAFb2ac3LO9eo2KIjwQG+3Dqyi5I16hMa+IFlPVsQ/jGAoUOHcu7cOZRKJU2bNk3Vc6lVqxZHjhyhcePG1K9fn0OHDmFmZpaqx5A0S470ztgGDRpE7ty58fDw0HQomZ6NjQ0+Pj4MHz4cIQQNGjRg3rx57N27lxvLZ/DRzxeluQXOLTtR95dhALz45xoAXreu4HUrpr+l+7i55Mxrh4lVHk6tmkNESDCWBYrQsP8YlGYxP+yPzJ2I3399EPft28e+ffuoW7cuxsbGCUSWclOmTFH///r167l9+zYKBCaW1phYWmPrUJ5rezdx68guyjVpDcTceb24ZUWcO6+B717zKSyUhZ3+37Xi7x1r0DdSYluqHG/evGH48OE0b95c3af37t27ODs7p8l5SUkjp17MogoUKICXl1ecZc+ePWPq9uNs/XM8oYH+2JV1pvmoP9R9cWJ5lLfEtpQTfdYeVv/7S8P3XyPg9UuW9WweZ7mdnR3Pnz9P1XOJdfXqVerXr0/BggU5evQoFhYWaXIcKf2NGTOGZcuW8ebNG02HIn3h0KFDNG7cmK1bt9K6dWtNh5OlHX0ZzE3f8ERnkEkOLcAxlwH1bdMmefzSuXPnmD9/PrVr1+bVq1dMmjQJZ2dnLMpWIzRaYJGvAPfPHOHuyQO4/NSXxoPGcXjuBE6vnodd2YpUadcDANtS5VEotPC+dwOAd08ecmLJdOxrNabeLyMw0YpiYus6lClThn79+jFhwgTevXuHt7c3lpbxv6uk9CMTymwmM1+wAG7dukW9evWwtrbm2LFj5M6dO92OLaWdLVu20K5dO3x9feUPhQwkIiKC0qVLky9fPk6cOJGsag1S8r0NjWL1ww+ptr8uxc2wNkqfhsinT5/SvXt3rl+/jhCCatWqMW/ePGas3camxXMJ8Y+581rCpT5NhkxAz1DJ0h7NeHbtQpz9uI+bi5Nb+//v9+p5lvVsjmuXfjTuPwbHXAbc2ryI+fPn8+7dO+zs7Bg/fjzt2rVLl/OUEicTymwmM1+wYt27d4+6detiYmLCiRMnyJs3b7oeX0p9d+7coXTp0pw+fZoaNWpoOhzpP56enowaNYpbt26pR+NLaUelUjHr3L9E5bBAJHkS2/gUQD6lDj8WM0u12L5XVvjOkZJGDsrJZqyNdLBV6qTgUhVDqKLJZ6StkQ+2vb09Z86cITQ0lBo1asRr2pcyn2LFiqGjoyP7UWYgr169YuLEifz6668ymUwHAQEBNGvWjKkdG0MK7/NoKaCxXcYY9Zxa3zkKwFapI5PJDEwmlNlQY7sc6ukQv4sQREdFsW5kT16/fv3t9dNAkSJFOHPmDAAuLi48fvxYI3FIqUNPT4+iRYvKhDIDGTFiBEZGRhqdAzq7uHnzJhUqVODcuXOsXTyfpgVMU7S/JvlzxJnWVtNS/J1DxkqSpYTJhDIbMtfXpkn+FHwwFQpK4c+dvy9Qrlw5Tp06lWqxJUeBAgU4c+YMRkZG1KhRQ124Xcqc5EjvjOPcuXNs2LCBadOmyYoKaWzt2rVUqVIFExMTrl27RuPGjbHPqU9TO2O0FST5zp4C0FaAm10O7HPqp2XIyZbi7xwyXpIsxScTymwqpRes5pVKc+PGDUqVKkWdOnXw9PRM9wK6AHnz5uX06dPkypULV1dXbt26le4xSKmjVKlSMqHMAKKjo+nXrx/Ozs506dJF0+FkWREREfTu3ZvOnTvTrl07Lly4EKfGp0NOA7qXNCefMqaJN7Ev69jl+Yx16F7SPMMlk7GyWpIsxScH5WRzARHRHPQK4mVIFFqQ4Ojv2OW2xjo0/uJXYnR0NGPHjmXy5Mk0a9aM1atXa+SOhp+fH/Xr1+fZs2ccPXqUChUqpHsMUsps376d1q1b8+7dO6ysrDQdTra1aNEi+vTpw+XLl2VdvzTy4sULWrduzc2bN5k3bx49evT46gj6t6FR3PYLxzskEt/waFQipgk4l4E2+ZS6lLEwyDR9C1P6nSNlXDKhzIaEEPEuXim9YO3fv5+ffvoJCwsLtm/fjqOjYxqfRXwfPnygUaNG3Lt3j0OHDlG1atV0j0H6fvfv38fe3p6TJ09Sq1YtTYeTLfn5+VGsWDGaNWvGypUrNR1OlnT8+HHatWuHUqlk+/btVKxY8dsbfSGha3hmkxZJclZ4XjIzmVBmA+oPbnAkvhGffXD1tclnnPAH93s+mE+fPsXd3Z379++zaNEijTSXBQUF8cMPP3Dt2jX2799PzZo10z0G6ftERkaiVCqZOXMmv/76q6bDyZZ69+7Nxo0b+ffff2WN11SmUqmYNm0ao0ePpm7dumzYsIFcuXJpOqwM43u+c77nu01KOzKhzMKS1bSg1KGxXcqbFsLDw+nXrx/Lly+ne/fuzJs3DwMDgxTtM7lCQ0Np3rw5Z8+eZffu3TRo0ODbG0kZQunSpalevTqLFi3SdCjZTuyc0jNnzmTgwIGaDidL+fDhA507d2bv3r38/vvvjBs3Dm1t2Yz7vTTx3SZ9m0wos6i7/uEcfBGMSkBSXmAFMb/smuRPnc7Pq1atok+fPpQsWZLt27fH6WyeHsLDw2ndujVHjx5l27ZtuLm5pevxpe/Trl07Xr9+rS4JJaUPIQQuLi4EBARw8+ZNdHV1NR1SlnH79m1atmyJr68v69ato2nTppoOKVPT9HeblDg5yjsLuusfzj6vYKKT+IGDmPWiBez1CuKuf3iKY+jatSsXL17k48ePODk5sX///hTvMzkMDAzYsWMHTZs2pVWrVmzbti1djy99n9iR3vJ3bvrasGED58+fZ968eTKZTEXr1q2jcuXKGBsbc+3aNZlMplBG+G6TEicTyizGPzyagy+CU7SPgy+CCYiITnEsjo6OXL16lRo1atC0aVNGjRpFdHTK95tUenp6bN68mbZt29KuXTvWrVuXbseWvo+DgwP+/v68fftW06FkG0FBQQwfPhx3d3dq166t6XCyhE+fPtG3b186depEmzZtuHDhAoULF9Z0WJlaRvpukxImE8os5tCLIFQpvLmjEnDQKyhV4jEzM2PXrl1MmzaNadOmUb9+fXx8fFJl30mho6PDmjVr6Nq1K507d2bZsmXpdmwp+WKn+JP1KNPPpEmT+PDhA3/++aemQ8kSvL29qVGjBsuWLWPx4sWsWrUKIyMjTYeV6WW07zYpPplQZiFvQ6N4GRKlbgr4FBbKvA51GFPVjrHVC7CsZ3PeP3/MtrG/4lHeMs7f0h7N1PsRwMuQKN6GRqVKXFpaWowYMYITJ05w9+5dypcvz4ULF1Jl30mhra3N0qVL6dOnDz179mTevHnpdmwpeQoXLoy+vr5MKNPJw4cPmTVrFh4eHuTPn1/T4WR6J0+epHz58rx69YqzZ8/Sq1cvWcYmGfz8/GjXrh05cuTA1NSUrl278uLFC5yrVKVzqdyMLG/J06vn4223c+IgPMpbsrBTwzjLoyMjmeVeHY/ylhyeOwEBPPH9SJly5VEqleTIkYNatWrx8OHDdDrDrE0mlFnIbb/wL15QQRHnGjQbOY0qbbrx9Op5jsybRCX3rrSbupR2U5dSvWNvAPIUKxVnS63/9peaatasyfXr1ylYsCCurq7MmTMn3frKaWlpMW/ePIYOHUr//v2ZPn16uhxXSh5tbW1KlCghE8p0IIRg4MCB5MuXj2HDhmk6nExNCMG0adOoV68eZcuW5fr161SqVEnTYWU63bp1Y/v27fTp04cZM2ZgaWlJREQEJnnssCubcL1Or1t/c+vwzgQfO7tuAQGvX8ZZpoWgRKUaLFiwgL59+3Lq1Ck8PDxS/VyyI5lQZiHewZFxyifoGSpp0H80xV3qUdCpmnp5/tJOlG3QgrINWhDk+w4A51ad4uxLBXiHRKZ6jDY2Npw8eZL+/fszcOBA2rVrR1BQ+jRBKBQKpk+fzpgxYxgxYgTjx4+Xgz8yIDmnd/rYt28fhw8fZubMmele2isrCQwMpGXLlnh4eDBy5EgOHz6MpaWlpsPKdJ48ecLevXvp0KEDEyZMoHv37kyfPp2iRYvSYfJCbEuVj7dNdFQUuycPo1b3wfEeC3j9gr9WzKJ290FxlusYKqnffzRNmjSRdYpTmUwosxDfBDob+3s/Z3Kdkqzu1w4z63w0HDBG/VjIB3/unjxAAcdK5C5UPP7+wtOm87Kuri5//vkn27Zt49ChQ1SsWJF79+6lybG+pFAoGD9+PFOmTGHcuHF4eHjIpDKDkSO90154eDiDBg2ifv36NGvW7NsbSAn6559/qFixIidPnmTPnj1MnjxZ1pf8Tvfv3wfg0qVLKJVKTExMmD9/PpDwdxvA+Y1LEEKFS8c+8R7b6+lB2YatyF8m/p3Nfx8/wcrKikaNGpE/f37ZYpVKZEKZRQghEuywbGqVh64LttKw/xg+vn/DmTX/7z94Y/9Woj5FULFlp/gbEtOBOS2/1N3d3bl69Sq6uro4OzuzadOmNDvWlzw8PJg1axaenp4MHDhQJi8ZiIODA4GBgbx69UrToWRZf/zxBy9evGDOnDmyj9932rBhA5UrV8bAwIBr167JWrcpFB4erv7vtm3bKFKkCAMHDuTFixcJfreFBQVyYsl0qnfsTcCbmGbtqMgIAt+95vHl0zy9eo6KLToS6PPmv/U/EvLBH4Aclnk4fPgwnp6evHr1Ck9Pz/Q5ySxOJpRZhEKhQCuB7wVdA0OKVamFa5d+WBYoGqevyZXd6zE0MaN0vYQvhFoK0vzLplixYly6dIkWLVrQoUMH+vXrx6dPn9L0mLEGDhzIokWLmDt3Lr/88gsqVULzLUjpTY70TlsvXrxgypQpDBw4kBIlSmg6nEzn06dP9OvXj44dO9KyZUsuXbpEkSJFNB1WplegQAEAqlWrRosWLWjUqBHR0dG8fPkywe+28KBAPoWFsmP8AP5sHtNf9c3DO6wb3InAd6/5FBbKwk4N2Pp7zDiBv3es4fSqOQDoGxrSoEEDhg8fTokSJdL1ZkZWJie5zEJy6Wvj81kz9T/H9/Lk77Pks3fE9+VTfJ4+xKZkWQCe37iEz9OHVG3fA139hPtP5TJIn6YbpVLJ2rVrqVq1KgMHDuTKlSts27YNW1vbND/2L7/8gqGhId26dSMsLIyVK1eioyM/FppUsGBBDA0NuXv3rpw2Mw0MHToUU1NTRo8erelQMp1Xr17RunVrrl69yoIFC+jdu7e8w5tKnJycKFGiBCdPnmTZsmXs2rULU1NTSpUqxbK5q3nzKKZb1IOzxwj286FEjQZ0mL5Cvf3G4T9jkb8QjQaMJWe+AurH3j15yIkl07Gv1ZjyP7Tln+N7eX3tHLnqV+fRo0fcu3cPJycnjZxzViO/ObOQfMa6+IZHqwfmKM0seHr1HNf2bkLX0Ihi1erQZMgkAP7eFVPk2zmR5m4tIJ8y/WbMUCgU9O7dmwoVKuDu7k65cuXYtGkT9erVS/Njd+7cGQMDA3788UfCw8PZsGGDnC1Eg7S0tChZsqS8Q5kGTp48ybZt21i7di0mJiaaDidT+euvv2jXrh26urqcOXOGypUrazqkLEWhULBp0ya6d+9Ov379KF68ODt27CAgIIA1Ywaq1zu7bgFmeWwp06AFpevGbV0zMjGnsLMLAOY2MTcklGbnOQFY2hXGuqg9YYEBPL56jj67N6JUKmnUqBGzZs1Kr9PM0uRc3lnI29AoVj/8kGr761LcDGuj9P/N4efnR8eOHTly5Ajjx49n1KhRaGmlfe+M3bt306ZNGxo3bsyWLVvQ15fzvmpK586defjwIZcuXdJ0KFlGVFQUjo6OmJqacu7cOXlnLYmEEMyYMQMPDw9q1qzJpk2bsLKy0nRY2UpW+W7L6mQfyizE2kgHW6UOKf2aUAC2Sh2NfeAsLCw4cOAA48aNY+zYsfzwww/4+fml+XGbN2/Onj17OHLkCM2aNSM0NDTNjyklLLZ0kPy9m3oWLlzIvXv3mDdvnkwmk+jjx4+4u7szYsQIhg8fzpEjR2QyqQFZ5bstq5MJZRbT2C5Hgh2Yk0NLEbMfTdLS0mLMmDEcOnSIy5cv4+TkxNWrV9P8uI0aNeLAgQOcPXuWJk2aEBycsrljpe/j4OBAcHAwL1680HQoWYKPjw9jxoyhZ8+elC8fv56fFN/du3epWLEix48fZ9euXUydOlX2r9agrPLdlpXJhDKLMdfXpkn+lH1gmuTPgbl+xqil1qBBA27cuEHu3LmpVq0aS5YsSfO7VrVr1+bIkSNcu3aNBg0aEBgYmKbHk+KTI71T12+//YaWlhaTJk3SdCiZwubNm3F2dkZPT4+rV6/SvHlzTYeU7WW177asSCaUWZB9Tn2a2hmjrSDJTQQKQFsBbnY5sM+ZsfoO5s+fnzNnztC9e3d++eUXunTpkubN0dWrV+f48ePcu3ePunXr4u/vn6bHk+LKnz8/SqVSJpSp4MqVK6xcuZKJEyeSK1cuTYeToX369IkBAwbQvn17mjdvzqVLlyhatKimw5L+k9W+27IaOSgnCwuIiOagVxAvQ6LQAhKqshi73NZYh8aZ4Nfbhg0b6NmzJ4ULF2bHjh1pfrG/efMm9erVw8bGhmPHjsn+U+no7Nmz2NvbY2FhoelQMi2VSkWVKlUIDw/n2rVrssn2K16/fk2bNm24fPkys2bNom/fvrKvaQaVFb/bsgKZUGYDb0OjuO0XjndIZExZIRHTlySXgTb5lLqUsTDIVJ2U79y5Q6tWrXj79i2rVq2iZcuWaXq8e/fuUadOHczNzTl+/Dg2NjZpejwphkqlSpfR/VnZqlWr6NatG6dPn6ZGjRqaDifDOn36NG3btkVbW5vt27dTpUoVTYckJUFW+27L7GRCmQ0JITL9L++PHz/y888/s337doYOHZrmHeYfPXpE7dq10dfX5+TJk+TPnz/NjiVJqSEwMJBixYpRp04dNm7cqOlwMiQhBDNnzmTEiBG4uLiwefNmcufOremwpO+UFb7bMjP58z8bygofOBMTE7Zu3cqsWbOYPXs2derU4c2bN2l2vKJFi3LmzBlUKhU1atTgyZMnaXYsSUoN48aNIyQkhBkzZmg6lAwpKCiINm3aMHToUIYMGcKxY8dkMpnJfc93m7ynlnrkHUop0zt37hxt2rRBpVKxZcsWXF1d0+xY3t7e1KlTh+DgYE6cOCHnQpYypHv37lGmTBkmTZrEyJEjNR1OhnP//n1atmzJq1evWL16dZp3m5EyDnUzeXAkvhGfNZPra5PPWDaTp4RMKKUs4d27d7Rv354zZ84wdepUhg4dmmZ3Yt++fUvdunV5//49x48fp3Tp0mlyHClx0dHRHD58GBMTE1xcXDQdToYihKBevXq8ePGCf/75R8749IWtW7fSrVs3ChQowI4dOyhevLimQ5LSQbIG8ih1aGwnB/Ikl2zylrKE3Llzc/ToUYYNG8bw4cNp2bJlmtWPtLa25tSpU+TNm5eaNWty/fr1NDlOdhcYGKieIUmlinv519bW5tixY4wePZqQkBBNhJdh7dy5kxMnTjB79myZTH4mMjKSQYMG0bZtW9zc3Lh06ZJMJrOJu/7hLL8fgHdIFJBwMvn5cu+QKJbfD+Cef0S6xJdVyDuUUpazd+9eOnXqhKWlJTt27KBMmTJpcpyAgAAaNWrEgwcPOHTokBwZmsq0tLQwMzNj9uzZdOrUSb08tuP9mzdvqFq1KuvXr6datWoajDTjCA0NpWTJkpQpU4Z9+/ZpOpwM482bN7Rp04ZLly7x559/0q9fvyzRl1z6trv+4ezz+v4Zz5raGeOQ0yAVI8q65B1KKctxc3Pj2rVrKJVKKleuzJo1a9LkOObm5hw9epQyZcpQv359Tp8+nSbHya5q1KiBnZ0dM2fOpFOnTvj4+AAxHe+joqLIkycPRYoU4caNGxqONOPw9PTk7du3zJo1S9OhZBhnz56lfPnyPHnyhFOnTtG/f3+ZTGYT/uHRHHyRsulzD74IJiAiOpUiytpkQillSYULF+bixYu0b9+eLl260KtXL8LDw1P9OCYmJhw6dIjKlSvTqFEjjh49murHyK5y585N3bp12bJlC2/evKFOnTocOHAAAB0dHV69ekVwcLAcpfmf9+/f4+npyZAhQyhSpIimw9E4IQSzZs2iVq1aFC9enOvXr8s72dnMoRdBqFJ4eVAJOOgVlDoBZXVCkrK45cuXC319fVG+fHnx9OnTNDlGWFiYaNKkidDT0xP79u1Lk2NkNzNnzhSVKlUSQggREBAgpk2bJszNzYW7u7uYO3euqFmzpihdurR4+/athiPNOK5duyZCQkI0HYbGffz4UbRp00YAYujQoSIyMlLTIUlppFWrVsLU1FQAYuzYserlxy5cEXaOzkJHT18YW1iKZh7TxdTr74XbSE+RI1duoa2jK0yt84r6fX8TU6+/F1OvvxfdFm4T1kUdhI6+gVCa5xKV23QTk6+8FT2W7hZAvL9nz55p7LwzInmHUsryfv75Zy5evEhAQABOTk4cPHgw1Y9hYGDAzp07+eGHH2jRogXbt29P9WNkN87Ozty+fRsAMzMzRowYwZkzZwgJCWHhwoWYmJgwd+5cWTvwM46OjhgZGWk6DI26f/8+lSpV4uDBg2zbto0ZM2bIKSezMH19fdzc3OIsCw0NpW3zpvg+f0KDfr9To3M/FIqYdMfQxAzXrv1pPuoPDJQ5OLpgCm/+vQvA9rH98Hv5lIb9R2NdpCSXtq7k/unDWBcqhsf8VWzatImNGzeiVCqxtLSUs6Z9QSaUUrZQrlw5rl27RvXq1WnSpAmjR48mOjp1+8Xo6emxZcsWWrduTdu2bdmwYUOq7j+7cXR0pEiRIgQH/78PVKlSpTh48CD3799n27Zt1KxZU3MBZkDZfarK7du34+zsDMCVK1dwd3fXcERSWtuwYQMdOnSIs2zTpk34+7ylYf/RVGrVGZeOvank3hkAx4YtqdCsA0Wca2CWxzbOdkKo0DNUUqSSK5YFiwIxCahRTkuK1GlGu3btsLCwICQkhK5du6Knp5c+J5lJZO+rj5StmJubs3v3bqZMmcKUKVNo2LAh79+/T9Vj6OjosG7dOjp37sxPP/3EihUrUnX/2YlSqWTdunXxLtrivz6T2fViLmSf0XiioqIYOnQorVu3pnHjxvz9999y0oFs7P79+wCcWTufMVXzM62xI8+uX1Q/vnvyMDyblOPhuWPU7DaQPMUcAHAfP5/I8FBmu1fn0taVuHbtT6EKMf1ufcNjbkAsW7YMhUJBjx490vmsMj6ZUErZipaWFh4eHhw7doxbt25Rvnx5Ll68+O0Nk0FbW5vly5fzyy+/0L17dxYsWJCq+89OypYtGy9xzM4jdP39/bP1+Sfk7du31KlThzlz5jBr1iw2b96MsbGxpsOSNCgsLAwAU+t8tJ+2jPCgQHZMGKh+vGa3AXT8YzV2ZStycctyfJ7+C8CZNfPR1Tekg+dySro25Oy6hby8E1NnWCViJtDYs2cPtWrVkgPfEiATSilbql27Njdu3CB//vzUqFGDefPmpeqdHy0tLRYsWMDgwYP59ddf+eOPP1Jt31L28+zZM3777TfKli1LxYoV2b9/v6ZDyhDOnTtH+fLl+ffffzl58iQDBw6UCbdEwYIFAShV+wfK1G+OVaESfHj9Un2Nz124BA61m1Dtx1+ICAnmwbljBPu/58nfZ7BzdKZ0vWY4ubVHFRXF06vngZjpGdesWUNkZCQ9e/bU2LllZLKnspRt5c2bl1OnTjF8+HD69+/PhQsXWLZsWard3VAoFPzxxx8YGRkxbNgwwsLC+P333+UXnpRsXbp0wcjIiEGDBnHnzh1atWrFtm3b4g1GyC6EEMydO5ehQ4dSpUoVtmzZQp48eTQdlqQBW7Zs4dKlSwBcv36d5cuX06ZNG0Z6eHDjwBZUqmjePrpL/rIVUSgUbBjWlfxlKmKQw4SLm5cDYFWwKEamOdE3zsHTq+e5tG0V/xzbC4BlgZg7kRb6WnguX46lpSUtWrTQzMlmdBodYy5JGcSWLVuEsbGxKFmypLh3716q73/SpEkCEB4eHkKlUqX6/rMylUolPn36pOkwNGbNmjXC1tZWBAYGqpe1b99edOvWTQghst37KSgoSLRr104AYtCgQdn6vSEJYWdnl2A5n5HzVgoz63xCz9BIFK1cUwzbd1VMvf5elG3UShiamgsdPX1hYVtQNBo4Tl02qMu8TeqyQTly5RauXfuLqdffC8/r74Xn5gPqMlRSwuTUi5L0nwcPHtCqVSu8vLxYvnw57dq1S9X9z5o1i8GDBzNw4EBmzpwp71Qmw8aNGwkICKBv376aDiXdtWzZkooVK+Lh4UF0dDTa2trs3buXPn368OjRIwwNDQHYv38/Dg4O6ua+rOjhw4e0bNkSLy8vVq5cSZs2bTQdkpRBvQ2NYvXDD6m2vy7FzbA2ko26XyP7UErSf0qUKMHly5dxc3Ojffv2DBgwgE+fPqXa/gcNGsTChQuZPXs2vXv3RqVSpdq+s7rz58+zaNEiTYeR7j59+kSePHnUgwy0tbVRqVRUr14dHR0d/vrrLyCmL2GzZs1QKpWaDDdN7dixg4oVK6JSqbhy5YpMJqWvsjbSwVapQ0p/tisAW6WOTCaTQCaUkvQZY2NjNmzYwPz581m0aBE1a9bE29s71fbfu3dvVq5cydKlS/n5559TvRZmVuXg4MDDhw9TNcHPDPT09Jg/fz5DhgwBYvoOamlpkTNnTsqVK8eVK1cA6N+/P71798bKykqT4aaJqKgohg8fjru7Ow0aNODvv/+mZMmSmg5LygQa2+VAK4UZpZYiZj/St8mEUpK+oFAo6Nu3L2fPnsXb25ty5cpx/PjxVNt/165d2bBhA+vWrePHH38kMjIy1fadVTk4OBAVFcWjR480HUq6UygUmJqaqv8/tpdSxYoVuXXrFuvXr+fp06fMnz9fk2GmiXfv3lGvXj1mzpzJn3/+ydatW8mRQ365S0ljrq9Nk/wpe780yZ8Dc33tVIooa5MJpSQlolKlSly/fp3y5ctTv359Jk+enGrN1O3bt2fr1q3s3LmTNm3aEBERkSr7zaocHGIKD9+9e1fDkWhebN/bunXrcurUKTp16pQlk8kLFy5Qvnx57t+/z8mTJxk8eLDsdywlm31OfZraGaOtIMnN3wpAWwFudjmwz6mfluFlKTKhlKSvyJUrFwcPHmT06NH8/vvvuLm54e/vnyr7btmyJbt37+bQoUO0aNFC3U9Oii9XrlxYWVnJhPIz9vb2aGtr4+zsTMeOHTUdTqoRQjBv3jxcXV0pWLAg169fp0aNGpoOS8rEHHIa0L2kOfmUMf0gE0t8YpfnM9ahe0lzmUwmkxzlLUlJdOjQITp27IiJiQnbt2/HyckpVfZ7/Phx3NzcqFGjBgcOHEBbWzavJKROnTqYm5uzfft2TYeS7oQQCd6di4yM5OPHj1hYWGggqtQXEhJCz5492bhxIwMHDmT69Ono6upqOiwpC3kbGsVtv3C8QyLxDY9GJWL6SeYy0CafUpcyFgZyAM53kgmlJCXD8+fPad26Nf/88w/z5s2je/fuqdIMd+7cOUJCQqhXrx5aWrLhICH9+/fn2LFj6nl6s5OXL19ia2ubaGKZFfz777+0atWKZ8+epUnZLklKSFb+TKU3+c0lSclQoEABzp07R9euXenZsyddu3YlNDQ0xfutXr069evX/2oy+enTJ65cucLr16/x8fFJ8TEzGwcHBx49epTt+ps+fvyYokWLsnPnziz7xbdr1y4qVqxIZGQkly9flsmklG6y6mdKE2RCmUHJG8cZl76+PosWLWLt2rVs3bqVKlWq8Pjx4xTv92sXtrVr19K0aVPq16+vLp9y+vTpFB8zM3FwcCA6OpqHDx9qOpR0NXDgQHLnzk3Dhg01HUqqi4qKYuTIkbRs2ZK6devy999/qwdgSZKUuciEMoN4GxrF0ZfBrLwfwPSbvnje9GP6TV9W3g/g6Mtg3oZGaTpE6Qs//fQTly9fJiwsDCcnJ3bv3p0mx1m6dCm//vorFSpU4MqVK+zZs4cWLVrQuHFjTp48mSbHzIiy40jvAwcOcODAAWbOnImRkZGmw0lVPj4+1K9fnxkzZjBjxgy2b9+OiYmJpsOSJOk7yT6UGhYQEc1BryBehkShBSRUlCZ2ua1Sh8Z2siZWRhMYGEi3bt3YuXMnw4cPZ/LkyejopE6n7nXr1tG5c2f09fWpWbMm5ubmzJs3DwsLC37//Xdev37NvHnzsvQMKZ+zsbGhW7duTJo0SdOhpLmIiAhKlSqFnZ0dx44dy1JNc5cuXcLd3Z3IyEi2bNlCzZo1NR2SJEkpJBNKDbrrH87BF8GoRMyM9t+iIGY0WpP8sjZWRiOEYObMmYwYMYLq1auzefNmrK2tU7TPyMhI3N3dMTMzo1+/fgBMnz4dPz8/Dh48SGBgIH5+fhQvXjzbDOSpX78+SqWSXbt2aTqUNDd16lTGjBnDrVu3sLe313Q4qUIIwcKFCxk0aBAVK1Zk69at5M2bV9NhSZKUCmRCqSF3/cPZ5xX83ds3tTPGIadBKkYkpYazZ8+q5xjeunUrLi4u370vlUqFEILAwEBy5swJwI0bN+jatStbt26lWLFiREdHZ6syQ4MGDeLAgQP8+++/mg4lTXl7e1O8eHF69erFzJkzNR1OqggJCeGXX35h/fr19O/fnxkzZqCnp6fpsCRJSiXZ47ZGBuMfHs3BF9+fTAIcfBFMQIScBzqjcXFx4caNGxQrVoxatWrx559/fnOAVeydxi9paWmhra2tTiYBXrx4wevXr9XTNWanZBJi+lE+efIkyxeBHz58ODly5GDs2LGaDiVVBAYGUqVKFXbu3MnGjRuZM2eOTCYlKYuRCaUGHHoRhCqF94VVAg56BaVOQFKqsra25sSJEwwZMoSVK1cSGRmZaFIZGhpKs2bNaNWqFWvXrv3qfkNDQzl48CBFihSJk2RmJw4ODqhUKh48eKDpUNLMmTNn2LRpE9OmTVPP4Z3ZmZqa0qhRIy5fvkz79u01HY4kSWlANnmnsSdPnlCqVCnCw8M5dOgQ9pVr0O6X/tw+shuVKppKrTpT/9dRKBQKHp4/zqHZ4/F5+hAhBFOvv1fv5/nNy+yb/hs+z/4ld+EStBozmzzFHOhS3Iw8yrgzSYwYMYJp06al96lmWSkpfBsSEoK+vv5XB+ns2bMHb29vxo0bR/v27Zk7d26i644dO5YKFSrQtGnT74onswsMDMTMzIx169ZlqekGY0VFReHk5IShoSEXLlzI8H1jb968yevXrxFC0KRJk0TXU6lUKBSKLDWwSJKkuOT8Qmmsb9++cb4UJv4xi4tbVlC1fU/Cgj5watUcbEqUpnS9ZkSGh1HQqSoRoSF8ePNSvc2nsFDWD+mCnpGSH4ZM4ujCKWwZ9QuDtp7htl84AK1atcLd3R0gy3Tg1xT11FzBkfhGfDY1l742+YyTNzXX10ZfxyaqzZo1A6BGjRq0a9eOI0eO0KBBgwS3GTJkSLYurWJqaoqtrW2WLR20ZMkS/vnnHy5fvpzhk8nx48ezdOlScufOza1bt2jTpg1LlixJ8P2Z0c9FkqSUk5/yNLR161Zu3rxJjx491MuunD8LQL3eI6nVbRAA1/dvBaBUnaY0G+lJDgurOPt5/+wRIQG+2NdsRCX3zhSp5Mq7Jw/wvn8b75CYvnT29va4ubnRrl07ypQpkx6nl+UERESz4d8PrH74gZu+4fj8N88rxHQx8AmP5qZvOKsffmDDvx9S3IdVoVCom8IjIyMpVaoU+vr6XLx4MdFtsnMyGcvBwSFLJpS+vr6MHj2abt26UbFiRU2H81VDhw5lzZo1bNmyhcOHD3P9+nW2bdvG/v37ATkxgyRlRzKhTCMfP35k0KBBzJw5EzMzM/VyXdOYvm9Prpzl6dVzAAR8djcyIUpzCwBe37+Fv/dz3j669992L/ANj0lqJk2ahFKppGTJkly5ciW1TyfLu+sfzvL7AXiHxBSQT6ge6OfLvUOiWH4/gHv+KZsGMLYJUFdXF19fXx48eJBoGZXEvqSfPn2aohgym6yaUI4aNQqVSsWUKVM0HcpXvXjxgosXL7JgwQKqV69Ozpw5KVu2LC1atODYsWOaDk+SJA2RCWUamT59OlZWVjg7O+Pv7w/A69evqf5TX4wtLFk/pDMHZo5BS1sbHb2v15Q0y5OPqu178Oz6RWa4VSTw3SsAdPT0UQkYOXIku3btYtasWTx+/Jhu3bql+fllJbElnKKTWA8UYtaLFrDXK4i7/uFJPlZsUvjhwwceP37MmTNnWL16NT///DOOjo64uLiom8C/9GX/MyEEBw8epESJEuzcuTPJMWR2Dg4OPHv2jJCQEE2HkmquXbvGsmXLmDBhAlZWVt/eQIPy58/Pjz/+iLOzM4C6f3B0dLT6DrrsKylJ2Y/sQ5lGvL29uXnzJkWLFlUv+/nnn+kyZwND9/zNu8cP0NHTY16HOlgVLPbN/TUdNoUqbX8mPDiIv3es4cqu9VgWKIqWIqYAcqz169dz+/btFA0kyU5Sq4STjVI3STMYDR48mEuXLhEaGoqvry9RUVEUKlSIwoULM2DAAIYOHZrk/mYKhYJ69erRqlUr2rRpw9q1a+nQoUOKziUzcHBwQAjB/fv3qVChgqbDSTGVSkW/fv1wcHCgT58+mg4nSWLjFEIQHR2Njo4O0dHRFCpUSL1cXn8kKXuRCWUa+fXXX/nhhx+AmL6U27ZtY9SoUURbmHNh41KMc1nx9851KBQKqnXoCYDviyc8u3aBkA8xNQmv7FpHrvyFKehUlVMrZ6PMmYuAVy+4vm8L9jUbYWFbkIC7V2g3bQ21a9fm1atX3LhxA2dnZ3kx/0K80fb29rRv356/r1wlKvITPZbuplCFagBERX5i77QR3DqyCz0DI1y79qf6j78A8OHtK7aP7YfXrb8xy5OP5h4zKOLswkGvIHb91p3jx48TGBjI2LFjGTduXJwYAgICWL16NYGBgRw/fpwyZcqgUCiwsLCIs15yvox1dXVZv349BgYGdOzYkYiICLp27ZryJywDix10dvfu3SyRUK5fv56LFy/y119/pdqUnenl85HbL1++VM83rlAoOHnyJLa2tnF+VEuSlHXJJu80UqFCBdzd3XF3d1d/AVavXp18ZsZc2bWePVOGExYYQNspS8hbsiwAXjf/ZufEwfh7Pwdg58TBXN27EQA/7+fsnzGK8xuXYF+rES3HzEYLKGGXDx8fH4YPH87s2bNp0KAB69ev18QpZ2hfjraPiIggT/4C5C8bf/DDhU3LuLJrPVXadMPO0ZkDf47mxe2rAOyeMoyXd67RcMAYtHX12DDiZyLCQngZEoVKWxc3N7dEYzA3N8fPz4+GDRvSpUsX7t+/r04mhRDq5vDk/hjQ1tZmxYoV9OrVi27durFw4cJkbZ/ZGBsbU6BAgSzRj/Ljx48MHz6cNm3aZNr5rLW1tQkLC8PHx4eyZWOuZe3bt//qZ0GSpKwnc/0czqTGjRunvlv1NjSKoAPXE1zPya09Tm4JF/1tNWY2rcbMjrNMBTQoX5LOJ0+mYrRZz+ej7efMmQNA0aJF6Tl9CVPH/MbTq+fjrH/jwDZy5LKiYf8x+Hs/5+7JA1zbtxmL/IX49/xxStdvTrX2PdE3VLJjwkDunzlKuQYt+OWPZajunmPdunWJxqKlpcXBgwcZM2YMNWvWZMqUKYwYMSLFd5S1tLRYuHAhBgYG9O3bl/DwcAYPHpyifWZkWWVgzsSJE/n48SN//PGHpkNJkTdv3pA7d24iIyNxdHQkOjqax48fp3g+e0kC2YUis5AJZTqzNtLBVqmDd0hUkgeAJEQB5FPqJLkeYnb1+Wj7L+d/9g6OTPA1CHjthYVtTF8wk9w2McteefHhzUuEEJhYWsd7TAV4h0Rik8S4JkyYQKVKlZg2bRqNGzemdOnS8dZJ7kVUoVAwc+ZMjIyMGDJkCGFhYYwaNSrJ22cmDg4ObNmyRdNhpMiDBw+YPXs2Y8eOxdbWVtPhJEli78kPHz5w48YNXFxcaNu2LZs2bdJAdFJWkZq1gKX0I18RDWhsl4Pl9wOITkFGqaWI2Y/0dZ+Ptr906RIQczclNDQU36/WkfzvxUmgVI+6fM8Xj/mGRyc5oQRo0qQJ9erVS7AcUHR0NIGBgRgbGydrzmOFQsHkyZMxNDTk999/JywsjIkTJ2a5X/cODg54eXkRFBREjhyZ73MghGDAgAHkz5+foUOHajqcbxJCsGzZMry9vZkwYUK8x0uWLImenh4rV67MFgPDpLQREBHNQa8gXoZEoUXc8m2xtYB9w6O57huOrVKHxnY5kjQYUkofsg+lBpjra9Mkf8q+BJvklx+kpPh8tP28efMA6NatGydOnEh0PnXzPPn5+P4tAB993sQsy2uHWR5bFAoFQQk8BjEXvOQWdNbT00NfP37ZKJVKRZMmTbCwsGDr1q3J2ifA77//zowZM5g8eTJDhw7NcoWmHRwcALh3756GI/k+e/bs4ejRo8yaNQsDAwNNh/NVYWFhdOvWjV69euHv7090dPwfYoaGhgQGBspkUvpumqoFnBReXl506dKF06dPp/mxMjOZUGqIfU59mtoZo62Iab5OCgWgrQA3uxzY5/x67Uopxq+//sq2bdvYtm0brVu3BmIKSJcrV46ru9bx5r8i8Q/OHuP2kV0AlGvSmmC/9xyeO4GDs8cBUP6HNijNclKsWh0enD3K+U1LObdxCYYmZpSsUR+AGwe3qb9QL126xLJlywgO/r6SRLq6uhw7dowffviBtm3bMmjQICIjI5O1j6FDhzJ//nxmzpzJr7/+ikqV2CU68ylZsiQKhSJT9qMMCwtj0KBBNGzYMMPPyf706VOqVq3Kli1bWLt2LfPnz0dbO+Efsgn9MJKkpEjPWsDf49KlS6xZs4aaNWvi6uoqE8tEyCZvDXLIaYCNUjfRW/yxYpfnM9ahsbwzmSwVKlRQl5a5c+cOEDPaPioqih0T/z9o5ey6BZjlsaVMgxZUbd+D988fcXHLCnQNjGg8aDx2ZWOKODf3mMG2sb9yaPZ4zPLko/20ZegZxszXfWz+ZD58+ADAkSNHOHLkCPXq1UOpVH5Xk7OxsTEbN26katWqDBkyhL///putW7cmOpNOQvr27YuBgQE9evQgLCyMZcuWJZoQZCZGRkYUKlQoUyaUM2bM4NWrVxw5ciRDd0U4cOAAHTt2xMLCgosXL6pHcEtSakrvWsApdf78eWrWrEmNGjWYMGECrq6uaX7MzEIhslpbWCal7oQcEolv+GedkA20yaeUnZDTwtGXwdz0DU+0aSU5tADHXAbUtzUGYpqsnz59SmBgII6OjgDfTOS+Ngjn4sWLtG7dmk+fPrFp0ybq1KmTrPg2bNhA586dadOmDWvWrEFXVzdZ22dEzZo1IyIigsOHD2s6lCTz8vKiRIkS9O/fH09PT02Hk6Do6GgmTJjAhAkTaNq0KWvXro0zfawkpaYN/35ItUGqPxYzS6Wo4tq8eTPt2ydcgcXNzY1r164RFhaGkZERJUqUoFKlSuq/jD7zVWqSCWUGJcskpL23oVGsfvgh1fbXpbjZV5P+r72mkZGRPHnyhL59+2Jvb0+5cuUoV64cDg4O6kE579+/p0OHDpw8eZJJkyYxYsSIJM+qA7Bjxw7atWtH06ZN2bx5c7IG+2REv/32G+vWrePly5eaDiXJ3N3duXDhAg8fPsyQg4n8/Pz48ccfOXr0KJMmTWLkyJHJeo9JUmK+vPaNGDGCZ96v2bohbpm1gk5V6blsDxe3ruSv5TMJ/eCPcS4r/sfeecfXdP5x/H2ztyQiJBJbRBKR2MQekdg1as+q1qhSo2hpjWrRmqUo2tqipEXFqB2b3CSEkBohdpbsec/vj/vLrdsMkT2e9+vlhTOe85ybm/N8znc27zeSDh9MVR2XnprK6sEdeHn/Du1GfYLH5HmqZ/B/G1l4eHjkac4KhYLff/+dadOmERYWpnYvkiRRt25dNmzYQGhoKM+fPycuLo7AwEAuX77My5cvAejYsSOzZ8+mU6dOZX5NFyavEkpZ/+KVBIq6hFN2P9MMoenr60vlypX5+++/Wbt2LZIkoa2tjZOTE40aNcLV1ZV58+bRqFEj5syZw4ULF9i6dStmZma5mme/fv3w9vamX79+9O3bl99//73EJ4TkhKOjI2FhYbx+/ZoKFSoU93TeyokTJ9i3bx/bt28vkWLy2rVr9O/fn7i4OFW4hkBQkPTr14/+/fsDyo5Xpx9GoOnUBgkIC5Lju/0nrOycANA3MaXd6MnoGhjhu/0njq1djH0bd6zslAl557atJerpvy+TGkBgRBJVDIwyNbLICwcOHGDOnDkEBQXh7OxMWFgYmpqapKen06lTJxYsWEDLli2zPFeSJEJDQzl79iyrVq2iS5cuNG7cmK+++qrEx03nB/HqKSjXdKtujEY+tXt+SzjJZDK0tLQYO3YsO3fu5Pbt28TExHD+/HlWrFiBq6srfn5+TJ06lbZt27Js2TJsbGw4evQoderUYf369URGRubqWj169ODQoUOcPHmSnj17Eh8fn+d5FzcZmd6lIY4yNTWVyZMn4+bmViIzoTdt2oSbmxuWlpb4+fkJMSkoFBwcHOjVqxeDBg3C2dkZo7ouOHd9j4Zd3yM2/AUAzfqNAMDFoy9Neg+hTrO2mFqp12mNevqIU5tX0HHsvxbLjFrAbzayyAsKhYLZs2fTu3dvrK2tuXDhAkuXLgWgQ4cOXLhwgePHj2crJkH5TK9RowYjRozg2rVrHDt2DBMTE3r16sWnn376zgmWpQVhoSwBCPd28ZFRwulAaGyexyiMEk5GRka0atWKVq1aqbalpKQQFBSEXC7Hz8+PS5cuIZfLGT9+POPHj6d69eq4urqqrJmurq5YW1tn+m516dIFHx8fevTogaenJ4cOHcLExKRA518U2Nvbo6GhQVBQkNrnVBJZu3Ytt2/f5vr16yXqdz0xMZFJkyaxZcsWPvroI1atWiWytQWFxqJFi1i4cCH29vZs3bqVcO2aAMRHRxJ08i9quDSncq16quP/+GYG/j6/A9B+zBSVdfLAktk09OhHNWf11rlh4dFsyKaRRW6Ii4tj+PDh/Pnnn3z//fd89tlnyGQyFAoFT548wdpavdJwbtZumUxGly5d6Ny5M+vWrWPKlClcv36dvXv3YmVl9c5zLMkIQVkMiC4AJQsHc10kJA4/ilPWkszFOTKUP7Pu1YquhJOOjo5KKI4ZMwaA+Ph4xowZg5eXF8bGxrx+/ZqVK1eqLJaWlpaqczKEZq1atWjXrh3Hjx/Hw8MDd3d3fHx8cu06Lyno6elRp06dEm+hfPHiBV999RUfffQRrq6uxT0dFQ8ePKB///7cunWLX375hVGjRhX3lARlmNmzZ9O8eXMePHjAjBkzGDNmDEO3ngJAfsiLtJRkmvYdoXZO+zGf4tSpB+e2reXink24dhtAzKtn3L/my9gN3oQ/ug9AYmwM8dGRnN+xPttGFgYGBjnOLz09nX79+nHhwgX+/PNPNde0hoYG1tbW+Vq7ZTIZEydOxNXVlQEDBtC6dWsuX76MhYVFnj/TkoZIyilCcuoCkEHGdtEFoOh5p59PCSvh9Ntvv/Hxxx9jb2/P3r170dHRwc/PT2XNlMvlPHnyBABjY2OVyDQzM2P58uXUrFmTv//+u9Q93Pr27UtsbCzHjx8v7qlky5gxY/jzzz+5e/cuFStWLO7pAODj48PQoUMxNTVl//79qkoEAkFR0KRJEwIDA1l46QmSTMaK/q2JDX/B7KM30NbNHNd94+8D7Jz5AZ5TvsbQ1Jzfv56c6Zg2wyeQEB3B9YOZW7IePHiQHj165Dinr776ioULF3Ls2DE6d+6stq+g1+4HDx7QrFkzHB0dOXbsWKlPkMxACMoiIigyqcRbwARKSmsJp8DAQPr168erV6/47bff6N27t9r+ly9fIpfLVX/8/Pz4559/VPv19fXp168fbm5uNGrUiAYNGqCvr1/Ut/FOzJ07l82bN/P06dPinkqWXL58mRYtWrBu3TrGjx9f3NNBoVCwcOFC5s+fT7du3di2bVups0wLSh++vr78+OOPuLi4cPXqVby9vdHX12fslr9Ijo9jwwc9aTX4Q3rOWKw6Z8eM0VRzboqesQkXd2/i2d0gRq7aQeXa9Qm7JQfgxb07nNiwFIcO3ejy8eeYaKThjNI74+Xlxd69e/niiy+YNGkSVapUyXZ+R44coVu3bixYsIAvv/xSbV9hrd2+vr507NiR0aNHs2HDhlyMXPIRgrIIyOgCkFd6VjfC0bz0ZuOWdkpTjOvr168ZNWoUf/zxB59//jmLFi1CSyt78RsTE4O/vz9Hjhxh5cqVpKenq/5oampib2+vcpU3atQIFxeXEpVRnVEfLjExscRlrCsUClq0aEFqairXrl0r9oLykZGRDBs2jCNHjrBgwQLmzJkjSgIJCo309HRu3LiBr68vR48e5fjx4yQnK9skGhgY0LVrV9qM+5zta5fjd8iLKXvPUbm2ver83V98zN0LJ0lNTKBCZWua9RtJ2xET1a5x/9p5fh7Xh3ajPqHb5HlqtYC//vpr5s+f/9ayQSkpKdjZ2VGvXj18fHzUficKe+3etGkTH374IcePH89kFS2NCEFZyEQmpbM5OIr0fHzKmjIYW9+sxLhXBSUbSZL44YcfmDVrFm3atGH37t1Urlz5refdu3dPVTB99erVPH/+XGXNDAgIIClJ2d6sVq1aaok/jRo1ytX4hcGNGzdwdnYmOTm5xLmNNm/ezNixYzl37hytW7cu1rn4+fnRr18/YmJi2LlzJ127di3W+QjKHrGxsVy+fJnz589z/vx5Ll26RGxsLNra2jRq1Ag3NzfVn4znRVHXAs6Kn3/+mY8++ojAwECcnJxU24ti7ZYkCTc3N5KSkrh27Vqpf8ETgrKA+G8h1Y4dOzJt2jR+27GLtPR0mvcbifukL5DJZNw5/zc+K+fz8v4dJEniW79XqnEe+l/m4NI5vHxwl8q17ek3byXWdo7cP+rFxtkTM11X/PgE2XHmzBkGDhyIhoYGXl5euRI1jx49olOnTiQlJXHy5Enq1q0LQFpaGnfu3FGLyZTL5bx+/RoAKysrtcSfRo0aUb169UK37KakpFCnTh0ePXpUqNd5V6Kjo7Gzs8Pd3Z3t27cX61y2bNnChAkTcHJy4vfff6dGjRrFOh9B2eDx48cq8Xj+/HkCAgJQKBSYmZnRqlUrlXhs2rRpjqEzxdkpJyUlJVNVg88//5w+ffrQZ9BQwp+GoWtohF2rTrz3xffo6BtwctMP+B3cw+uXzzAyr0T70Z/SvP9I1fn/Lbj+4ewFDLUzZePGjXz77be8ePECDw8PtmzZgqmpKb6+vrRp04YdO3aUyJJi74IQlAWEh4cH586dIyEhAR8fH27evMmMGTNoNXgcibHRyA95MWTJJhp06c3NEwe5d9WX4HPHiX72WCUoUxITWNqjMToGhrQb+QnH1i3GuKIln3qdJerpI2pFBGOqq0lcXBzjxo3D2dkZf3//4r1xQYnm2bNnDBw4kAsXLrBs2TKmTJnyVpH39OlTOnXqRHR0NCdOnMDBwSHL4yRJ4sGDB2oi08/PjxcvlPXkzMzMcHFxUbNm1qtXr8Bdv+PHj+enn34q0DHzy6effsrmzZu5c+fOO/VeL0iSkpKYPHkyP//8Mx9++CGrV68ucWEBgtJBeno6gYGBagIyo0NVnTp11KyPGeW8cktUcjqbbhePF2/Pnj0MGjSILl26qCpnODg48DQmgRW7D1KhsjVBJ//i1mkfesz4BrfB49g6dTiVqtehom1NTm1ZyevnYUzefZoqdZXPydNbVnJy0wpSkxJUHXxc4kLo1s4Nd3d32rVrx5w5c5gwYQJr164FlO0b7927V+IrVryNkpdZUAp5s5DqqlWrAKV1CKDr+Fm8Dn+B/JAXfoe8aNClN06deuLUqSdPbgUQ/ezfSv+vHoQQHxWOS7f+NO8/kvvXzxN41JuntwOxdWhIrYb1cLc1YuPGjUiSxEcffVQs9ysoPVhZWXHixAnmzJnDZ599xoULF9i8eXOOdSetra05c+YMXbp0UZUXyioLWCaTUatWLWrVqkW/fv1U2589e6aW+LN//35++OEHQBk75ezsrGbNdHJyylftw3bt2pGWlpZjrGhRcvPmTdauXcs333xTbGIyNDSUfv36cfPmTbZs2cLo0aOLZR6C0klsbCyXLl1Sc1/HxcWhra1N48aNef/993Fzc6NVq1b5DncpzlrABw4cAKBFixb06tVLVVro+eM42g23JT4mmsiwUG6d9lGdM2TpZrS0leE1SXEx+KxSehur1HVQK7h+9MdvAGX29/5jp5AkiQkTJtCrVy9Wr17N1q1bVYJy3Lhx9OzZk6CgIFXDhtJIyXgCl2JiYmKYmkUh1UqVKgEQcvUccRHKnp5Rz3LuOWxopiwp8vR2AJFhD3kecuv/5z2iqkNDwuKV1fV//vlnDA0NGTp0aIHfj6Dsoa2tzbJly2jZsiWjRo2iadOm7Nu3Ty1e6L9YWlpy6tQpunbtSocOHTh69CjNmjXL1fWsrKywsrKiW7duqm1RUVH4+/urROaZM2fYsGEDCoUCLS0tHB0d1URmw4YNc92esGHDhm+1uiYnJ7N06VK8vb1ZsGBBphIiBZl49euvv1KrVi2mTJlSIOO9K0ePHmXIkCGYmJhw4cIFGjVqVCzzEJQeHj16pGZ9DAwMVHNfz5kzJ1fu67xSHLWA09LS8PFRCsX/FlwPM6rD9b+82L/wMwDqNG9H0z7DAFRiEiDk8hk0tbSxcVL+jmVVcF0BKAyVlRR8fX2pWrUqERERpKamEhERQcWKFenSpQsmJibs3btXCMryzNKlS7MspDp9+nT2HjjE9mkj0dbTR0NTEy2dnL/0plY2tBr8IRd2/cyyXk3RNVRmq2WcF56Ujr+/P9euXWPMmDGlsruJoPjo27cvTk5O9O/fn+bNm7Nx48YcX0rMzc35+++/6datG507d+bw4cN5Ti4xMzOjQ4cOdOjQQbUtISGBwMBANWvmzp07SUlJQSaTUbdu3UxF2bOqk1mjRo0s3egKhQINDQ0iIyPZsmUL33//PRYWFqoXv/+KyD///JPatWvnKLTfhkKhwNDQsFg6zigUCr755hu++uorPDw82L59O+bm5kU6B0HJJy0tjRs3bqjEo6+vL2FhYcC/7usJEybkyX2dHxzN9bA21M51vUebfNYCvnDhAlFRUYwaNYo+ffqoFVwfvu0U9Vp3YeTqndz8+yB+B3cTdPIvXLsPUJ1/cNkX/HPpNL1mLcG8anX+uXwm24LrNTv0pHnz31i2bBnLli3DyMiI1NRUVQiKrq4uvXv3Zu/evXz99dd5up+SgBCU+SQsLAx/f39V8gIoCxkfOHCA6X9e4cU/wWjp6LBmSCcsa9q9dbyeMxbTcuAHJMXFcmXfb1z13k6lGsqxFRJs2KisVzVu3LjCuSFBmcbOzo5Lly4xfvx4hg0bpuoXnp34qVChAkePHqVXr1507dqVgwcP0rFjxwKZi4GBAS1atKBFixaqbampqdy+fVstJvOvv/4iLk5ZusPGxkYt8adx48ZZupUlSVKJyalTp2JqasrcuXM5fPgw9eopW7ulp6ejpaXF69ev8fLy4qOPPsLS0pLTp09jb68sX5KYmMjTp0+xsrJ6a6eNjDFbtGiBp6dnQXxEuSYqKorhw4dz+PBhvvrqK+bOnVvqM0YFBcPb3NcDBw4sMPd1fjHT1WSonWmR1AL29/dHV1eXzZs3q35Xtm/fTmBgIOkKCZNKVTCpVAVbx0ZcP7CLgKPeKkF5YMksLu7ZTNdPvqTl+8rYy9cvnpKSmMC6Ef9WULiy7zd0DQzpNnU+vr6+3LhxAwMDA/r06UN8fDyGhoaqYzt37sy2bduIjY3NtXempCEEZT6ZNGmSyn32ZiFVc3NzLu7aiGFFS67s34ZMJsNtiFIEhj+6x4PrF4iPjgDgqvc2LKrVpmbjVpzeshJDcwuinjzC7+AeHNp7UtFW2e80LSmBnTt34uzsTPPmzYvnhgWlHgMDA3799Vfc3Nz45JNPuHbtGnv37qV69epZHm9kZMRff/3Fe++9R7du3fD29i40waStrY2zszPOzs6qVoAKhYJ79+6pZZevXbuW8PBwOnTowMmTJzONI5PJSEhIYPjw4VhZWbF48WJ+/PFHJElS3WdGzOWMGTMwMTGhdevWGBkZqVn1fH19WbBgAaGhoaSnp/Pdd98xfPjwHO+hqH83/f396devH1FRURw+fDjHmnuCsk927mtzc3NatWrFF198gZubG02aNCmxjQuqGGhRxcBI9f/CqAV8//59KleuzJAhQ+jYsSNPnjxBLpfTrFkz/v7pWyRkVLSpwe2zRwFUBqEjqxdwcc9mqjdsipl1NQKOemPr1IhaTVozZOlmQL3geqMeA1GkpTBjxgwaNmzI8ePHCQ4OZsWKFWrzqV+/PgB37tyhSZMmBXqvRYUQlPmkSZMmqh/+zZs3AWjdujV6enpc895O9MvnmFrZMHDxBqrWbwhAqP8VVWwGwP6Fn9Go50BqNm5FRNhDTm1WftEcOnjSe/Yy1XH3Tx0gJiZGWCcF+UYmkzFu3DgaNWpE//79adSoETt27MhWjOjr6/Pnn38ycOBAevfuzZ49e3jvvfeKZK4aGhrUrVuXunXrMnDgQEC5wDx58oTXr1+rXNtvkpSUxNChQ3F1dWXRokUAXL9+HUtLSzW3ef/+/dHR0eHHH3+ke/fuWFtbqxVuNzIyYty4cTx79ow1a9aoanFmFH7/L9ra2kXqZv71118ZP348Dg4O/P3339SsWbPIri0oftLS0jJlX2e4r+vWrYubmxsTJ07Ezc2NevXqlVqrdWGUH7t//z61atXi5cuXzJw5E0mS6Nq1K2vWrOGLn3fj88ta4iPDMTSrSLO+I+j88QwAHt24DkBowFVCA64C0P/r1TTuNRgza1sADE3PcwKoVL02Veo6YK6lYPeJE/z000+YmJgwZ84cJk9Wbx+Z4RUJDg4utYJSlA0qRI49jsM/PCnLOJB3RQPUugAIBAXFmx1U3uYuTU1NZdiwYezbt4/t27czaNCgIp5t7sgoVvztt9/SunVrGjRoQJ8+fWjfvj3z5s0jISGBSZMmERkZybp167C2tsbExITFixczceLETAvYrl27WLhwIdu2baNx48bF3j0pOTmZyZMns3HjRj744AN+/PFHURKoHJDhvvb19eX8+fNcvnxZzX3t5uZG69atadWqFZaWlsU93RJNw4YNcXNzY926dZn2FdfabW1tzYcffsj8+fML4MpFj7BQFiLOFfXwC08qkLEU/x9PIChozM3NOXTokCqh4+LFi2zfvj3LBBhtbW127NiBnp4eQ4cOJTk5mZEjR2YxavHSokULpkyZwh9//MGyZcuIjo5GU1OT0NBQ9PT0iIyM5Pr16wQEBKjOiYuLw9nZWU0oZlg/Hz16hIaGhir+sjjF5KNHj+jfvz+BgYH8/PPPjB07ttjmIihcyoL7uqQiSVK2pcaKa+02MDAgJSWlQK5bHAhBWYhUMdDC1lCrwLoA5DcIWSDIDg0NDebOnUuLFi0YPHgwjRs3Zu/evVmWCtLS0uKXX35BT0+PUaNGkZiYyMcff1wMs86eBg0asHz5cpVres2aNaxatQo7OzsePnyInp4eN27coEqVKtSqVQsrKyv09PRUQjFDSGpoaBAfH09oaCiVKlXCyCh7K0NRWC2PHz/O4MGDMTIy4vz58zRu3LhQrycoOsqL+7qkoK2tTWpqapb7xNqdN8rHXRYj3aob57sLgIZMOY5AUNh06dIFuVzOgAEDaN26NStXrmT8+PGZhJKGhgbr169HT0+P8ePHk5SUVGx1F3NDTEwMNWrUYOnSpTg5OREXF8f48eMJCgri3r17rF+/nrS0NHr06MHSpUv56KOPVGI0PDycx48fY2enDMrPLn6yMMWkQqHg22+/Ze7cuXTt2pXt27dTsWLFQrueoPCJiYlRy75+033dpEkTBg0apMq+Fu7rgicnQQli7c4LQlAWMsXZBUAgyAu2tracPXuW6dOnM3HiRC5cuMCGDRvUSlyAUkCtXLkSfX19pk6dSmJiIrNnzy6mWWdNhhXn1q1byGQylRvfyMiIunXrUqdOHWQyGUeOHKF58+bMnz9f1ZQgg2fPnvHixQu6du2aafz/8vjxY6ysrAq0a090dDQjRozg4MGDqhjXgm5fKShcJEnK5L6+ceMGCoWCihUr0qpVK7788kuV+1rEwxY+RkZGREVFZbu/ONbuuLi4Uv2zF4KyCCiOLgACQX7Q0dFh9erVtGzZkrFjx+Lv78++fftUMYQZyGQyvv32WwwMDJgzZw6JiYnMnz+/WGMM3yRjHr/++isvXrzIts7eiRMnWLRoETVr1lSJ0AzR9vTpU2JjY1XtJ3NyNXbp0oXQ0FBVe8mMepkNGjTI00IREBBAv379iIiI4K+//lLrPiQouaSlpREQEKAmIJ88eQL8677+5JNPVO7rkvL7Up5wcHDg+PHjOR9ThGt3dHQ0L168UHlCSiNCUBYRRd0FQCAoCAYPHkzDhg3p168fTZs2ZcuWLfTv31/tGJlMxrx589DT0+Pzzz8nMTGRpUuXlqhFUltbGxsbm0zbZTIZaWlpfPTRR7Rt21ZNLC5cuBA9PT1CQkJQKBTUqFFDdU5WSJLEhg0bVLUyz58/z6ZNm1Qu8vr166uKsru6uuLi4qJWoui/bN26lY8//ph69epx7NgxatWqlb8PQVBo/Nd9fenSJeLj49HR0aFx48YMHjxYuK9LGE5OTqxbt46kpKQcX/aKau0ODg4G/i0fVBoRZYOKgaLoAiAQFCSxsbGMHTsWLy8vpk6dypIlS9DW1s503Jo1a5g8eTITJ05k9erVpTZxIDk5mYULF+Lt7c3jx4+Ji4ujatWquLq6snPnzhyTc94kMTGRmzdvqhVlDwwMVNWzrF27tlprSVdXV0xNTZk6dSo//fQTo0ePZu3atSKDtwSRG/e1m5ubcF+XcC5evEirVq24fv16rvvdF+ba/csvvzBmzBji4+Nz1ZWrJCIEZQmguGvaCQS5QZIk1qxZw7Rp02jevDleXl5YW1tnOi6jBuSYMWPYsGFDmYj3e/bsGSdOnODx48fMnDkzX/eUlpZGcHCwmsiUy+XExMQASmtqWloa3bp1Y8yYMTRq1Ijq1auLZ0QxkZP72s7OTiUehfu6dJGYmIilpSWff/45X375ZZ7GKMi1e9CgQdy5cwe5XF4g4xUHQlAKBIJ34sKFC7z//vukpqayZ88e2rdvn+mYbdu2MWrUKAYPHsyvv/5aoEkqZRGFQsGOHTtURdUdHR25d+8eL1++BMDMzEwtJtPV1RU7O7syIdbzSnBwMEeOHOHevXt07tyZTp06YWRklO9F/vXr15myrzPc102aNFGJx1atWmVK4BKULoYNG4a/v7+qy11xkZiYSKVKlZgzZw5z5swp1rnkByEoBQLBO/Py5UsGDx7M6dOn+eabb5g5c2Ym9/bevXsZMmQIvXv3ZufOnejo6BT6vNLT05HJZKXK1a5QKFiyZAlffvklnTt3ZseOHVhYWCBJEs+ePVNZMDMsmg8fPgSURZAbNmyoJjQdHR3R1S3bSXzx8fFMnz6d48ePY21tTY0aNTh//jz16tXj8OHD7zSWJEmEhoZmcl9LkqTmvm7dujWNGzcW7usyxsGDB+nVqxc3btzAycmp2Oaxf/9++vXrx927d6lbt26xzSO/CEFZjAhXt6A0k56ezldffcU333xDr169+O233zA1NVU75sCBAwwYMAB3d3f27t1b6Avy8uXLadmyJS1btizU6xQU0dHRjBw5kgMHDvDll1/y9ddfv9XqGBkZib+/v5rIvHPnDgqFAi0tLRwdHdViMhs2bIixcdmphZeSkkKnTp1UbTUBfH19adu2LefOncPNzS3bc//rvvb19eXp06eAcF+XR5KTk6latSr9+vVjw4YNeR4nv2t5ly5diIiIwM/PL89jlASEoPw/RSHuVAG9camEJ78R0KuriY2RSMYRlE4OHTrE8OHDMTc3Z9++faryOhkcPXqUPn360KZNG/74449CDTgfMWIEISEhXLx4sdCuUVDcuHGDvn378urVK7Zv306PHj3yPFZ8fDyBgYFq1sybN2+SkpKCTCajbt26mZJ/smqtWVqIiYnBxMQEUIpELS0tTE1N2bp1K7169crynDFjxuDl5SXc1wI1li9fzsyZM7lx4wb169fP1TkFuZYfP34cd3d3vL296dOnTz7upPgpt4KyKMVdVHJ6rksO2Bpq0a26KBckKF08ePCA/v37c+vWLdatW8fo0aPV9p8+fZoePXrQuHFjDh06VGgWsyVLlrB48WKio6NLtHVpx44dfPjhh9jZ2bFv3z5q165d4NdISUnh9u3bask//v7+xMXFAcoC9v8VmTY2NiX6c/svGWLy8OHDjB8/nj179tCiRYtMx0mSxNq1a0lISMDNzU24rwUqkpOTqV+/Pk5OThw4cCDHYwt6LVcoFKo+7L6+vqXqdy8ryp2gLGpxFxSZJAqaC8oFSUlJTJ48mZ9//pkPPviANWvWqJW7uXDhAp6enjg4OODj45PJPV4QHDp0iJ49e/Lo0SNsbW0LZExJklAoFAWSAJOSksJnn33G2rVrGTlyJOvWrSvSEiEKhYJ//vlHTWT6+fkREREBgIWFRabknzp16pT4mNTu3btToUIFdu7cqerD/iYZHigRZiTIit27dzN48GB2797NwIEDszymMNby5cuXM23aNHx9fXMM1SgtlCtBWdTiLigyiYOhce98XgY9qxvhaC7eogWli19++YUJEyZQv359fv/9d7WC3NeuXcPd3Z2aNWty7NixAu9H/fDhQ2rWrImPjw8eHh4FMubx48dJSkrC09MzX9nqYWFhDBgwAD8/P1avXs24ceNKhLiRJImwsDC1mEw/Pz/CwsIAZYs6FxcXNaHp4OCQZR3S4mDr1q3MnTuXK1euYGlpqfaZijAjQW6QJIlhw4axf/9+zp49S9OmTdX2F8Za7uPjQ48ePZg2bRpLly7N89gliXIjKIta3EUmpbM5OH+N5TVlMLa+mXB/C0od/v7+9O/fn4iICLZu3UrPnj1V+wIDA+ncuTOVK1fm77//zrYdYl5QKBSYmJjw9ddfM3369HyPd/fuXZycnPjiiy/46quv8jzOqVOnGDhwIHp6evz+++80a9Ys33MrbMLDw9VEplwu5+7du4CyNaeTk5Oau9zZ2TlTv/e88mb2dffu3bO1Zj9//pwOHTowceJEJk2apNouwowE70pSUhIdOnQgNDSUy5cvqzwchbGW37p1i5YtW9K2bVv++OOPMlP+q1wIyuIQdzvuRhMWn5YrS2h2yAAbQy2G2pnmYxSBoHiIjo5m1KhR/Pnnn8yZM4cFCxaoHpy3b9+mU6dOmJiYcOLECapWrVpg123WrBmOjo788ssv+RpHkiS6d+/OrVu3uH37dp661UiSxLJly5g9ezYdO3Zk586dpTr5IzY2loCAADWhGRQURFpaGhoaGtSrV09NZLq6umJmZvbWcVNTUzMVD8/Ivj5w4ADdunXLctGdMWMGISEh/PHHH0iShFwuJyROQahxDSREmJHg3Xjx4gXNmzcnNTWVvXv30qpVqwJfy318fBg6dChVq1bl/PnzquSyskCZEpT37t3DycmJpKQkfHx8cHBwYPDgwVy5eo201BQ+3PgHtZoo4xTSUlM48N3nBBz1RkfPgHajJ9N66McARD9/wu9ffUJowBVMrWzoM3sZdZq14f5RLzbOnpjpunp6eqprduzYkfGfTmXP7j0oFOk07zcS90lfIJPJuHP+b3xWzufl/TtIksS3fq9UYzz0v8zBpXN4+eAulWvb02/eSqzsHAHQPL+XdT8s5cWLF3h4eLBly5ZCiT8TCAqaNwVV+/bt2bVrl6qX8T///EOnTp3Q0tLi5MmTVK9evUCuOXr0aIKCgrhy5Uq+xsmoUbdv3z769u37zue/fv2a0aNH4+3tnUlQlyWSk5O5efOmmsgMCAggMTERgBo1aqjFZDZq1AgDAwMuXryoVjw8ISEBHR0dmjZtqpZ9nV02enBwMA4ODjRp0gQDAwOuXbtG3bYevL/oJ5Re73cPJxBhRoJnz57x/vvvc+nSJb7f+CuJLl1zPP7+tfP8sXgGUU8fYdugMQPm/4iZddbx26lnvfhq6iS6devGtm3bcvWyVZooU4LSw8ODc+fOkZCQgI+PD7Vr12b2vK+R/xPK/Wvn1QTl2a1r8Vn5Ne1GfUL4o/sEnfyL8b/6UM25Cb9OHsKD6+dxn/QFV723E/PqObMOy4mLDKdWRDCmuprExcUxbtw4jIyMSE9PV13z5s2bzJgxA7fB40iIjUZ+yIshSzbRoEtvbp44yL2rvgSfO070s8cqQZmSmMDSHo3RMTCk3chPOLZuMcYVLfnU6yxPb/mzdkRX3N3dadeuHXPmzGHChAmsXbu2OD9qgeCdOH36NIMGDUJTUxMvLy9VAHpoaCgdO3YkNTWVkydPUqdOnXxf6/vvv+frr78mJiYmz8kkSUlJODo6UqtWLY4dO/bOsY43b96kb9++vHz5MsdSNmWV9PR0VRs5Pz8/Ll68SEBAAAkJCWrH6evr4+TkRMeOHenZsydNmjTJdWH2R48eUbduXQYMGICrqyttPXpyOtVchBkJ8k1qaiozZ87knk5l3AZ9gJTNy0lqUiJLurliaFaR5gNGcWztYqo5N2XMWq9Mx8qQuLB7M40NU/jyyy9LfKJbXigzd+Tl5YW/vz8ffvihalvdunUZt3QD1ZwyN36X/7UXYwtLPCbPo9uUrwG4fnA38dGR3D3/N/Ztu+I2eByth3xE4usobp89hkXV6tTq2JtBgwahUCjI0OJvXvPMmTMAdB4/iw5jpgLgd0j55XLq1JPes5ZgXNFSbS6vHoQQHxWOQ3tPmvcfSZ3m7XhxL5intwO573cRSZKYMGECs2fPpkqVKmzdurXgPjiBoAho3749fn5+1KpVi/bt27Nq1SokSaJ69eqcPXsWQ0ND2rZty61bt/J9LUdHR+Lj43n06FGex/jhhx949OgRq1evfmcxuWvXLpo3b46enh7Xrl0rd2IyNTUVuVzO8ePH+fPPP9m9ezcXL14kISGBWrVq0alTJ3r06EH79u0xNTXl6tWrLFmyhO7du9O1a1emTp3Ktm3buHnzJmlpaQBkZfeoVq0aycnJbN++nWnTpnFX2xJFPs0jCgkOh8bmbxBBqUdbW5sVK1bQqd+QbMUkwN0LJ4mPjqDV4HG0GvQhDu27EXLxFDGvnmc6VkJG5wHDmDdvXpkUkwBlIr0tJiaGqVOnsnz5clXQeAZhcalZxj5EPQ2loq0y+9SksrVy25NQop89RpIkTCpVybRPAYTFpwKwfv16ZDIZy5cvV2VDAqr4qHtXzxEXoezDG/XscY7zNzRTZro+vR1AZNhDnofc+v95j1T7fH19qVq1KhEREaSmphIREVHgGbICQWFibW3NyZMnmT17NlOmTOHChQts2rSJqlWrcvr0abp06UL79u05fvw4DRs2zPN1MlqoBQUFUaNGjXc+//HjxyxevJjJkyfnutAxKEsCTZ8+nTVr1jBs2DA2bNhQpCWBiovXr19n6b7W1dWlSZMmDBs2LEf39fPnz1VJP3K5nAMHDrBy5UpAGU7k7OzMhg0baNCgQbYhA88T0ngcn6a2zXf7es7v3EBc5CuMK1Wh3chPaN5/JIHH/sBn1QLiIl9h17ID/b9ejb6JKaCMuXwcn8bzhDSR/S0gWccgx0DcyKfKl9YMvVDB0gqAqKePVdveJFGrbIdTlAmZvHTpUiwtLWnWrBmRkZGAMg4iISGB8OT0HM78/zcli7df1Rvxf/aFJ6Zx6dIl5HI5ZmZmtG/fXu2a06ZNw6hiJbZPG8lfy+ehoamJlk7OLhxTKxtaDf6QB34XWdarKa9fPAFAS0eXBl16Y+vUmGXLlqm5g0RRXkFpRFtbm++//57ff/8dHx8fmjZtSlBQEJUrV+bUqVPY2trSoUMHrl69mudr2NjYYGJiws2bN/N0/owZMzA2Nn6nrO4nT57QoUMH1q9fz9q1a9m6dWuZFJOSJPHgwQO2b9/O+PHjcXZ2xszMDE9PT9avX6/KsD9//jyvX7/G19eXJUuW0KtXr2xjIatUqYKnpydz5sxh79693Lt3j+joaE6fPs3ixYupV68ex48fz9JKmUFgRJLaYhb+6B5/LZ+Ltp4+3abOB0niz29nEPPqOV5zJ1KhshVdPv6c4HPHOLJmkdpYGv8fT1C+kSQp1xZv6f9aQnpL6o5CytraXlYoE69gYWFh+Pv7qzVVHzNmDBYWFihssu7pa2ZVTWWWjnn5TLmtanVMrWyRyWTEZrEPQIFM1Sc4MjIy0zVHjBjBdO/LvLh/By0dHdYM6YRlTbu33kPPGYtpOfADkuJiubLvN656b6dSjbpo6+ox4de/6KrxFAMDA/r06UN8fHyBlecQCIqDfv360aBBA/r160ezZs34+eefGTJkCCdOnKBbt2507tyZw4cP56nYr0wmw8HBgaCgoHc+9/Tp0+zZs4dff/0119mXp0+fZuDAgejo6HD27NksO7WUVlJTU/H391fLvn72TPlMtLe3x83NjalTp+Lm5kbdunULrK5mhQoVaNeuHe3atQOUBaBzqgEaFpeqVhpI+r8SqFClKnWat0N++HcSXkdy9Y8dpKem0HHsNOxadeT22aPI//Ki9+ylKjfkm54oQflFJpOhISNHUWlmpUy++VcvKP/OLilHQ0aJqD1bWJQJQTlp0iRVH1wvLy/27t3LF198gaurK79t3Maz/7uQg88dJy7iJc5d38O1+wB8Vs3nyOoFhD+6D0CjHu9jaGqOnVsngs8d4/yujVz13o6+iSn127oDkJoQh76+PoaGhrRp04aYmBhu377N06dPMTc3x9vbG8ckbaztnbiyfxsymQy3IeMA5Vvzg+sXiI9WdqW46r0Ni2q1qdm4Fae3rMTQ3IKoJ4/wO7gHh/aeVLStSVpqCmfXLqRKh+YcP36c4OBgVqxYUdQfsUBQ4NjZ2XHp0iU+/vhjhg4dyoULF/jhhx84evQoPXv2pGvXrhw8eJAOHTq889iOjo7I5XJAWZtSJpO99UGelpbG5MmTadGiBcOHD3/rNSRJ4ocffmDWrFm0a9dOLYO9tJLhvvb19eX8+fNcuXJF5b5u2rQpI0aMwM3NjZYtWxZpL3AvLy8mTJiQrWfmv56oSjXq4D5hNsfWfcvyvi3R1NZh6NIthFw+DbzhoqxszUP5JeIiXqq5KMOTcvJsCcoLFrqavMzhu1DPrRMGpuZc2PUz6Wlp3Dp9mDot2mfp7gaw0CvbyV5lQlA2adKEJk2aAKjcXK1btyYtLY19Cz9THXdu21pMrWxx7voerQZ/yKuHIVzcsxltPQO6TZ1P9YbKYsN9Zi9j71eT8Fk5H1MrGwZ/9zM6+kqL4IMzh0hMTGTZsmVMnKgsIfT1118zf/58duzYQaVKlejWpy9X92/F1MqGgYs3ULW+Mh4s1P8K+9+Yz/6Fn9Go50BqNm5FRNhDTm1WCkWHDp70nr0MACk9nevHD3Fi+0Z0dHRo2bIlqampbN26lUqVKlGpUiUsLCyoVKmSsFoKSh2GhoZs3boVNzc3Pv30U65du8bevXs5fPgw7733Ht26dcPb2zvXXW9evnyJn58fr169IiAgAFdXV4KDg+nTpw+7du3K8dyffvqJmzdvcvXq1bcGzcfExDBmzBj27dvHrFmzWLhwYb666BQHkiTx8OFDNevjzZs3kSSJSpUq4ebmxvz583Fzc6NRo0a5zr4uDAYNGsTVq1dp3bp1pheDrFyTcZGvuOi1hWrOTWk7chI+q+bj/c007Nt0UZ3z5t//JcM1WZatSYK3Y2OkTXhSepaF8QG09fQZ8t0m/vxuJodXfEW1Bo3p++UPWR6rAdgYlozuUoVFmSoblBXHHsfhH56U7RfiXdAAXCz0cLc1yvG45wlp/HonugCuqOTBzhWE3fInPDycV69e8erVK+LiMnf90dfXV4nLN4VmVv+3sLDAzMyszGabCUofV69epX///sTHx7Nz507atm3L+++/z9GjR/Hy8qJ37945ni9JEubm5kRHR6v6NmcwadIk1qxZo3b83bt3GTlyJFOmTKFDhw7Uq1ePAQMGsHHjxhyvExQURL9+/Xj27Bm//fYbffr0yfM9FyW5cV9n/ClI93VB8Pz5c16+fEmDBg2ynNdS/3A1URl47A92zfqQ7tMW0nroxxxc9gUXdm3EY/I8jqxewKg1u6nn1okNH/Tkye0AvvZ9qPYs1JDBTJeis8AKSiYFvZaPqmdappO9yu6d/R/ninr4hRdMgLXi/+O9jSoGWtgaahVYdf1Zy77JtC8pKUklMN8Umm/+/9GjRyprTURERKa3cU1NTSpWrJil2Mzu3zo6Ovm4o+JDoVDwzTffcPLkSSIiIqhZsyaDBw9m0KBBxT01wf9p2rQpfn5+DBs2DA8PD77++mu8vLwYPnw4/fv3Z8eOHbz//vvZni+TyRg1apSqJNGbZFW658qVK1y6dIlBgwZRuXJl0tPT+eabzL9rb7Jnzx4++OADatasqSyk/UYMdUkjOjpaLfs6O/d1q1atSnzFiI8++ogDBw5gbGzMd999R9u2balcuTKSJGFmZpbJNWluUwOA6wd2o2tgSNCpv9DU1sHZvQ/H133Lqc3LefHPbR4FXqVxryGZXqzLumtSkDsKei0vy2ISyoGFEoqnDWJUcjqbbpecXt7p6elERkaqic/shGjGn5SUlEzjmJiYvFV4vvl/IyOjEmHpSExMxMPDg4EDB2JsbExAQABHjhxh8uTJjBs3rrinJ3gDhULBokWL+Prrr/Hw8OCXX35h+vTp7Ny5k19++YURI0YAsHnzZmrWrEnHjh1V5yYnJ9OsWTOCgoJIT1cKDENDQyIjIzO9DK1Zs4YpU6agUCj9FzKZjAkTJrBw4cJMHSwyCh2vXLmSIUOGsHHjxhIVYpLhvs6IfTx//jxBQUFq7uuMP8Xtvs4LGWFFGWhoaKh+bg0bNmTpQd9Mnqizv/3IxT2biYt8hamVDZ0//pyGXd8j4Kg3R/5fNqhui/b0n78Ggwr//rxz64kSlA9K2lpekikXgrK4vhC3IpM5kI8iub2qF19vWUmSiIuLe6vwfPPfMTExmcbR1dV9q+v9zX+bm5sXWnu6hIQE9PT0VIvRpEmTOH36dIEU0xYUPEePHmXo0KEYGhqyZ88eNm/ezKZNm1i7di2XL19m69at1K9fP9PPLyQkhIYNG5KYmIhMJmPAgAHs2bMn0/jz58/nm2++ITVVPaO3cePGeHl5UauWsk7tm63YVqxYwcSJE4v9JSkn93X9+vXVBGSdOnWKfb75ITY2lvXr1zNz5ky17TKZDGNjYw4fPkxt1+bCNSkoNErzWl6UlIvfGDNdTbpXM87XF6J7NeN3frtwMNdFQuLwozhlkHcuzpGhjN/pXq14v4AZD2tjY2PVwvo2kpOTiYiIyFF4PnnyBH9/ZTxoeHi4ysqQgYaGBubm5rl2w1eqVCnX1paMuoDp6eloamri6OjI8ePHiYmJyXWJGEHR0bVrV/z8/BgwYADt2rVj5cqVTJw4UZUMB3D79m1u3LhBgwYNVNvq1q3Lhg0bGDFiBJIkZRt7GRkZqfb909DQwNraGn9/f9q1a0dAQAA3b97k/fffR1NTk7Nnz6pKhhU1ObmvmzVrxsiRI1XZ1yXdff02JEnixo0bHDlyBB8fH86fP09qaqqaVVJTU5PatWtz4sQJbGxsAIRrUlBolOa1vCgpFxbKDIIik4rlCxGVnM7h0Fgex6ehAVkmCGVstzXSolsexGtpRKFQEBUVlWsL6KtXr0hKyhwPa2RkpBKX3bt3Z968edleMy0tDS0tLf755x8GDRpE48aN2bBhQ2HepiCfJCcnM23aNNauXUutWrW4f/++ap+GhgYzZ87k22+/zXRe8+bNuXLlCpGRkZlc2AADBw7Ey8tLZb377LPPSE5O5qeffgKgTp06hISE0LZtW3bv3k3lypUL6Q7VySge/qb1MSv3devWrWnUqFGpjWt+k6ioKP7++2+OHDnCkSNHePpUWXe3Y8eOeHh44OHhwZdffomXl7KNbdOmTTl8+DDm5ub/jiFck4JCRqzlOVOuBCUU7xfieUIagRFJhMWnKksRSErBaqGniY2hNs4V9cSb8VuIj4/PUXg2bNiQTz75JMtzMyyTDx48YNiwYZiamrJu3TqqV6+eZYmQkJAQTExMqFixYqkrCVMWGTFiBNu2bcu03dramsePH2dKrEhKSuLy5cu0a9cuy59vnTp1uHfvHnXr1mXHjh3UrFkTW1tbtZcWNzc3Tp8+Xag//4ze128KyOfPlQWSy5r7OgOFQoGfn5/KCnnp0iUUCgWOjo4qAdmmTRs178PWrVsZOXIknp6e/P7771l2IhKuSUFRINbyrCl3gjKDkvCFEHXOio6Mz/rKlSuMGTOGhg0bsmTJEmxsbLL9OZiZmalK0JiZmb1TLGhZbLtXnMTExGBnZ8eLFy+y3H/27FnatGmj+r/q9zsulfDkN36/dTWxMVL+fi+e9RlRUVFs3rwZHR0d5s2bxzfffKPmBpfJZBw+fDjXdTBzw3/d15cvXyYxMRE9PT2aNm2qEo9lwX39Jq9eveLYsWMcOXKEo0eP8urVK0xMTOjcubNKRNraZt1hBJTC+88//6R3795oa2dfz6+4PFGC8otYy5WUW0H5X8QXouzj7e3NxIkTGTRoEF999RUVKlRAoVBkWYtTkiQuXryYKzd8QkJCpvMNDAxyJTwz/m1qaiq+fznwzTff8OWXX6rF0b1JmzZtOHv27Lt5IAy16FZd6YGIiYmhcuXKatZJTU1N0tPTcXR0zHNf8Jzc15aWlpmyr8uC+zqDtLQ0rly5onJjX7t2DUmScHV1VQnIli1b5igO84pwTQoERY8QlIJywe7du5kyZQpz585VS+ooCBISEt5aE/TNf0dGRmYaQ0tLS1UTNDclmSwsLAplIS6pvHjxgu3bt3P9+nUuX77MgwcPVLUm9fT0WLRoER6jJ+bZMrVq9mS1gubVq1enefPmNG3aFAcHB2rVqoW9vf1bx8zJfe3g4KAmIGvXrl3mXiKePn3K0aNHOXLkCMePHycqKgpzc3Pc3d3x8PCga9euVKmSdVu6wqAkeKIEgvKCEJSCcoGrqysBAQFUqFABU1NTzM3NMTY2ZsaMGXh6ehZpx6C0tDQiIyNzlYiU8e//lrYBMDU1fafOSIaGhmVGwMTGxhIQEIC3tze6uroMnT6Pg6GZu0flljoJYRzevJqhQ4fSuHFjTE1NAdi7dy/Dhw+nTp06WVopo6KiMmVfZ7ivmzVrpua+fjOBpKyQkpLChQsXVLGQgYGByGQymjVrprJCNm3atNBKgb2NyMhIbG1tsbS0RE9Pj+jXr/GXy4sswUpQthGeTXWEoBSUC1JTU4mOjiYsLIynT5/y9OlTHj9+TKdOnWjXrl1xTy9HJEkiJiYmV8Izp9acenp6uRKeGf8uLa05I5PS2RxcsNm9CoWC+fPns2DBAkAZSxkVFUVERESm4uFAmXdfv0loaKjKjX3ixAliY2OxtLRUCcguXbpgYVH8bQsVCgUeHh4cP35cbfv48eNZt25dMc1KUJrJTWx2ebZ4C0EpEJRBctOa881/Z9WaU0NDI1du+Ix/W1hYFHgHlpcvX+Ls7MygQYNYsGBBlvVC/9sJa3ajSmr72436BIf23fCaO4Ho50/QNTTCrlUn3vvie3T0lclTb3bCio+PZ/jw4Xh7e6uNY2ZmRlRUFFA+3NcZJCUlcfbsWZUVMjg4GE1NTVq1aqUSkS4uLiXu5eO7775j9uzZmbZraWlx7949qlWrVgyzEpRG8hqbXd4QglIgEJCenk5UVFSu64Hm1JoztwXpLSwsMDY2zlGInT9/ntatWyOTybCwsGDVqlUMGjRIdc7zhLRMHVJmN6qEU6ceOHVW9u+2rGlHeloqIRdPUaGyNUEn/+LWaR96zPgGt8HqbTe7GMXh6daMV69eZZqLm5sbs2fPLrPu6zcJCQlRCcjTp0+TmJhI1apV8fT0xMPDg86dO1OhQoXinma2nD59mo4dO2Z6SQJlstWQIUPYunVrMcxMUNoQVQNyT/m1zQoEAhWampqqZJ/69eu/9fjsWnP+V3gGBwdz7ty5bFtz6ujo5Cg8Hzx4oLpeeHg4Q4YMYf369axfv5769esTGJGUpcXAslY96rftqrJAAlSpU5/EmGgiw0K5ddon01w0gNMhz1Ri8r8Z5VpaWnTv3v3tH2YpJD4+nlOnTqlc2ffu3UNbW5u2bduyYMECPDw8cHR0LBVW2GfPntG/f/9s96enp7N9+3Y+++wzXFxcim5iglJHUGTSO8dmS0C6BAdCY5GQcDTXK5zJlUCEhVIgEBQJKSkp7+SGz6o155ssWrQIq74TeJmUrrZ9dqNKyGQyZWeZGnUZsHAtto6uXPXexv6FnwFQp3k7hi/fqiY4ASz1NRloq8OlS5c4e/Ysp06d4vLly6SkpGBmZpZlhn5pRJIkbt26pRKQZ8+eJSUlhZo1a6qskB06dMDIyKi4p/rOzJo1iyVLlqi+A1khk8no0KEDJ06cKOLZCUoLhRGbXdYRglIgEJRIFAoFEydOZMOGDSphkGE1rFy5Mhs2bOBOdTcU/3mCHV2zCNsGjYl8EorPqvlUqlGXKV5niXn1nKd3bnDz74P4HdzNgAVrce0+QO1cDRnMdFFPKElOTub69eukpKTQvn37wrzlQuX169ecOHFCJSIfP36Mnp4eHTp0UMVC1q1bt1RYIXMiMjKSP/74g7Vr1+Ln55ftcTY2Njx+/LgIZyYoTfw3NjsvvBmbXR4QglIgEJRYGjZsSGBgIKC0Krm6ujJ58mSGDh2KpqYmS/wjcjz/x6GdeR5yi4WXn6iEUnxUBIs62VOvdRdGrd6Z6ZzPXSqWelEFSkEeEBCgEpAXLlwgLS0Ne3t7lYBs27Yt+vr6xT3VQuHq1as0a9aMLVu2MGbMGMaOHcumTZtYsGABM2fOREdHp0z8nAX547/fgc8//5w2Hj0YOWJElkl8F722cGrTchKiIzGysKR5v5F0+GAqABs/7M2D6xdUY9k6NWbC1iO8b6vDgK4dCA4ORkNDgyZNmrB+/Xrq1atXpPda2IgYSoFAUGJxcHBQCUpJkvD392fUqFGMHTuWkSNHYjfpOzUL5UP5JS7u2Uztpm14/fIZT+/cwMaxEcfXfQsyGRVtanD77FFAmazzXzRkmReY0kRERATHjx9XicgXL15gZGREp06dWLNmDV27dqVmzZrFPc0iQS6Xo6GhQZ06dQD46KOP2LRpE9WrVy/wagSC0k2/fv1UcbcODg6cfRRNk16DMfl/Ep//4b3YOLrgNngc+iamtBs9GV0DI3y3/8SxtYuxb+OOlZ0joHyudBw3HQBDU3M0gJsRiXTq1IlPPvmE4OBglixZwuzZs9m/f39x3XKhIASlQCAoUaSlpREcHIxcLic9XT0+MiOmMj09nSpVqmChq6kWQ2lSqQpxkeH4rJqPhETdlh3oNfNbbp85yrnt64iPDMfQrCLN+o6g88czMl3bQq90xTqlp6dz7do1VUb2lStXkCQJZ2dnRo4ciYeHB25ubmW2JmZOyOVy7O3tVTVZK1eujKamZpatUgXlGwcHB3r16oWBgTKm+pp2FK2H184yic/Foy/JCXEkvo7m5olDvLgXrDaWobkF9m26oGugjD9WAOGSDt999x0RERFUqVKFJUuWFNm9FSVCUAoEgmIjPj6ewMBA5HI5/v7+yOVybty4QXJyMgA1atRQO15TU5PKlSvj7e1Ns2bNOPY4TtlS7//7zW1q8OFG9fqRAK2HfUzrYR/nOBcNwMaw5LezfPHihaq94bFjx4iIiMDU1JQuXbowbtw4unbtStWqVYt7msWOXC7H1dWV8PBwACwsLNDX1ycxMbGYZyYoaSxatIiFCxdib2/P1q1bCdeuifwvL7UkvqZ9hqmO/+ObGfj7/A5A+zFTVNZJgId+F/m6dU0MTM3pNuVrGvcaTHhSOvfv36du3boAVKtWjaVLlxbhHRYNIoZSIBAUCS9fvlQTjv7+/ty9exdJktDS0sLR0RFXV1dcXFxwdXWlYcOGVKhQAVtbW8LCwgDw9PRk+/btqjqQWdWhzA+j6pmWuE4XqampXLp0SWWFlMvlADRp0kQVC9m8eXO0tErWvIuT9PR0jI2NWbRoERoaGnz55ZfExcVRuXJlJk+ezBdffFHcUxSUEObMmUPz5s158OABM2bMwN7enqFbT+WYxPfiXjDhofc4t20tz/+5zYTfjmJZy44Lu3/G0MwCSZI4smo+cRGvmPmXHyaVqjC5ngG+vr74+/szZ84cRo8ezc8//1zMd1+wiCeQQCAoUBQKBQ8ePMgkHp8+fQqAsbExDRs2xN3dnZkzZ+Lq6oqDg0O2cW0tWrRg3759LF68mJkzZ6p1ZKlioIWtoVaBZWOWFDEZFhamioM8fvw4MTExWFhY0LVrVz777DPc3d2xtLQs7mmWWO7cuUNiYiKurq6cOHFC1QrSwMBAuLwFaixevFj17+3btyv70SNhUqkKJpWqYOvYiOsHdhFw1FslKCvXtqdybXsUinR2zvyAYN/jWNayo9WgD1VjPb0dwLlt64gMe4ipZRUMDAxwd3fH3d2drVu3smvXLiEoBQKBIIOUlBSCgoLUhKO/vz+xsbEAWFlZ4erqyqhRo1TWx1q1ar1Tm74VK1Ywb948GjRokOX+btWN2XQ7f/XiNGTKcYqL5ORkfH19VSLy5s2baGho0KJFC6ZPn46HhweNGzcuce0NSyoZVlwXFxf27NmjEpTC5S14E19fX3788Uc6duzIkydPkMvlNGvWjHMblpCQLmWZxLdjxmiqOTdFz9iEi7s3/X+f0pW9fnQ3GnTpjUxDE79DXujoG1Cpph0Pzxxi/IbLNG3alJCQEG7dukXjxo2L56YLESEoBQJBrnj9+jUBAQFq4jEoKIjU1FRkMhl169bF1dWVbt26qcRj5cqV831dGxsbbGxsst1vpqtJ92rGHAiNzfM1ulcr+t679+/fVwnIkydPEh8fj5WVFR4eHsydO5cuXbpgZmZWpHMqK/j5+VGjRg3MzMwIDw8XFkpBllhbW/Py5UtmzpyJJEl07dqVNWvWsGzrXnatX51lEp+mji6ntqwkNTGBCpWt8ZzyNfZt3AEwsbTi9C+rSI6Po1KNOnhMnoexqTkmVpXZ/NMpfvnlFwwNDfH09GTFihXFeeuFgoihFAgEakiSxNOnT9WEo1wu5/79+wDo6urSoEEDVayji4sLzs7Oxd5VpaT33E1ISODMmTOqWMiQkBC0tLRo3bq1KhbS2dm5VJctKil07NgRU1NT9u/fT/v27alatSo7duygTZs21KpVi99++624pygowZSH2OzCoOzfoUAgyJb09HRCQkIyiceMftampqa4urrSu3dvlXi0t7dHW7vkZUM7muthZaDF+nO30LCwzrLHN6DabmOkRbdsLJNpaWloaWkhSVKeBZ4kSdy5c0dlhTxz5gxJSUlUq1YNT09Pli5dSseOHTExMcnT+IKskSQJuVzOZ58pM3TDw8Np2LAhICyUgtxRVmOzC5vycZcCgYDExERu3rypJhwDAwNVC6ytrS2urq5MmDBBZX2sVq1aqbGYJSYmMrhPH44dO4ZTq/Y0e28obj36KcsKSUprpIWeJjaG2jhX1FN7yEuSxLFjx9i6dSuhoaG0atWKjz/+mFq1ar3THGJjYzl58qRKRD58+BBdXV3atWvH4sWL8fDwwN7evtR8pqWR0NBQoqOjcXV1BZTF3t90eYsYSkFuKAux2UWNEJQCQRkkMjIyU5Z1cHAw6enpaGhoUL9+fVxcXOjfvz8uLi64uLhQsWLF4p52nnn+/Dk9e/bk2rVrANy8cJqQ6xfZNO0DZDLZWy2NP/zwA7/88gtubm6MGjWKpUuX4u/vz5o1a3JsjyZJEjdu3FC5sc+fP09qaip169alZ8+eeHh40L59e1XBZEHhk5GQ06hRIyRJUouh1NfX5/Xr18U5PUEpobTGZhcnQlAKBKUYSZJ49OhRJvH46NEjQGmRcXZ2pk2bNkyePBlXV1ecnJzKVP/mwMBAPD09efHihdr25ORknj9/jpWVVY5i8smTJ2zcuJExY8Ywa9YsACpWrMiCBQsICwvLUlDGx8czefJkjhw5wtOnTzEwMKBjx46sWLECDw8PateuXbA3Kcg1crkcS0tLrKysiImJIS0tTSTlCPKEg7kuElKJjs0uSQhBKRCUElJTUwkODs5UoicqKgpQdgJxdXVl0KBBKpd13bp10dQsu2/IJ06coGfPnqSkpGRq0whKcWFlZZXjGBoaGpiammJqaqralpCQgKmpKc2bN8/yHENDQ54+fcrgwYPx8PCgTZs2oj90CSGjQ45MJlN1ycmwvouyQYJ3xdFcD2tDbQ6HxvI4Pi1fsdllHSEoBYISSFxcHIGBgWri8c2WhLVq1cLFxYXPPvtMlSxjbW1d7mLzDh06RGJiYpb1GTU1NfH396dbt245jlG5cmUaNWrEjz/+yN27d7lw4QJXrlzh448/JjQ0FAcHh0yfqyRJ+Pj4ZDOioDjx8/NjxIgRAGptF0FYKAV5w0xXk6F2pjxPSCMwIomw+NRcxWaXN8rvnQsEJYQXL15kyrIOCQnJ1JJw2LBhai0JBbB8+XJ69+5Nv379iIyMVNuXke37NjQ0NFi/fj3r16/nhx9+4P3332fevHmsWrWK4cOHM3fuXN577z21c8qbcC8tvHz5kqdPn6oScv4rKIWFUpAfqhhoUcXg3/Jo+akCURYRglIgKCIUCgX379/PJB6fPXsG/NuSsGvXrnz++edvbUkoUAq79u3b4+joSHh4OAqFgjt37gDKzzswMDBX4yQnJ7Nv3z4GDx7MggULAGjevDlTpkxh8+bNmQSloGSS8QLxX0GZ4fIWFkpBQSLEpDpCUArKHREREVy4cAF/f38qVqzIsGHDCrwWYHJyMrdu3VITjgEBAZlaEo4ePTrPLQkFSvz9/Tl37hx79+7lxIkTpKamcvbsWU6ePImhoaHquJSUFK5cuUKrVq0yfc4ZP6/vv/9etU1fX5/o6GiRoV2KkMvlGBsbq5KiIiIiMDY2Vr2UibJBAkHhIQRlCUWY0guHjRs3snHjRmJiYnB2dubhw4f89NNPHDp0iOrVq+dpzIyWhG+Kx1u3bmVqSdi9e/cCbUkoULJmzRpsbW3p3bs306dPp2fPnlStWpXhw4cTGhrKhg0bOHLkCCdOnCA2Npbr16+rkjYyMDExQUtLi40bNzJt2jSqV6/OyZMnuXXrFj/88EMx3p3gXZDL5bi4uKheGN4sGQTKl4SkpCQUCoV4eRO8E2JNfjtCUJYQVMG+camEJ78R7KuriY2RCPYtKB49esTQoUP54IMPMDEx4cGDB3Tq1In169ezePHiHB8YGS0J/1ui578tCZs2bcq4ceNKTEvCskx4eDg7duxg/vz5/PPPP4SGhlK5cmU+++wzfHx8CA4ORlNTk1atWjFr1iw8PDxwcXHJ8ue8Zs0aPv/8c65du8azZ89ITExk4sSJuLu7F8OdCfKCXC7H09NT9f/w8HC1+qoZ1ubExEQ167VA8F/EmvzuiE+jmIlKTs+2HIFCgpdJ6YQnpeMXnoStoRbdqpfPcgQFxdy5c1Xur9TUVGrWrEmrVq0ICQnJUmQoFApOnTrFkiVL8Pf3L5UtCcsyP//8M6AU+3379gWUP+OqVavi6enJokWL6Ny5c66SmHr16oWTkxPnz5+nWrVqtGvXrlDnLihYYmNjCQkJYc6cOaptWVkoQQhKQfaINTnvCEFZjARFJqkKpkLWta3e3B4Wn8am21HlsmBqQZEhJhUKBdra2kRFRXHnzh06dOgAZHZrZPxfX1+f8ePH4+rqWupaEpY14uPjOXXqFH/99Rc///wz6enpzJs3D2NjY+zs7Ni3bx+Ojo55+vnUqlXrndstCkoGAQEBwL8JOaAUlG+GsmRYKEVijiArxJqcP4SgLCaCIpM4GBr3TudIQLoEB0JjkZBwNNcrnMmVAzLip7Zu3crLly/58ssvs4yR0dTUpGPHjnTs2LE4pilAKepv3bql6o999uxZUlJSqFSpEunp6axatYr333+f6tWrM2/ePJycnIp7yoJiwM/PDx0dHRwcHFTbwsPDady4ser/b1ooBYI3EWty/hFRycVAZFI6hx+92xf3vxx+FEdUcubOIAIlkiSRmJiIJGXfLCsoKIgffviB5cuXY2JiIiyOJYjXr1+zf/9+xo0bR/Xq1XFycuLLL79EW1ubZcuWcefOHezs7Gjfvj2TJ0/m6tWrpKSkqMXPCcoXcrkcJycntbCTiIgINZe3sFAKskKsyQWDEJSFSEREBIMGDcLY2JgKFSowevRoHj16RItWrZjdrCqzG1Xi/rXzmc7bv3AqsxtVYt0ID9W2kEunWTWwPXNb2rKoU328v/2cQ/ejAfjzzz+pX78++vr6dOzYkcePHxfVLZYIUlNTuXHjBlu3buWzzz6jQ4cOmJubs2rVKlJTUzMdL0kSkiQxffp02rVrR69evYph1oI3USgUyOVyvv32W9q1a4eFhQX9+vXj3Llz9OvXjyNHjhAZGcnhw4eZPHkysbGxnD9/nsmTJwPg4+NDrVq1qFu3bjHfiaC4yGi5mIFCoRCCUqBCJpOp/Zk1axajRo1CJpNRUV+Lma6VmN2oEhs/7K12Xlbr8e9ffcL8dnWY29KWle+35faZoygk8A5+QePGjTE0NMTY2JgOHTqo6uKWB4TLuxAZM2YMf/31F9OmTaN27dr8888/hEXHY2hVneqa2lmKydCAKwQc2Z9p++9ffUJi7Gs8Js/l9ukjXPTaQu1mbXHUasegQYNwdHRk2bJlTJ8+nY8//pi//vqrKG6xyMloSSiXy1VZ1jdv3szUknDatGm4u7ujo6OTaQyZTMbq1auJiorit99+Q1tbm6SkJF69eoWFhYXKLSYoXCIiIjh+/LjKlf3ixQuMjIzo1KkTa9asoWvXrtSsWTPLc9esWUP16tXp2bOnqg1i9+7dhZW5nJKcnExQUBDjxo1TbXv9+jXp6elqWd7C5V2+6devH/379wfAwcGBxMREWnTowplnCYQFyfHd/hNWdv+GzGS3HltUr023KV+RmpzEsbWL2fvVJOadDiEsPpUWbTvwySefEBwczJIlS5g9ezb792ceoywiBGUhce/ePQ4cOMDw4cNZsGAB2traaGhocOxxHIMXrePw6gWZBGV6Whp/fDODDmM/4+iaRWr7JEmBjr4hdZq3Izz0HveunsPAxBTvk+dJSkpixIgRTJo0iR07dnD48OFM2Y2lkRcvXmQq0ZNVS8Lhw4e/U0vCZ8+eMXv2bOrXr8/s2bM5d+4c//zzD02bNmXr1q3Uq1evCO6u/JGens61a9c4cuQIPj4+XLlyBUmScHZ2ZuTIkXh4eODm5pblS8CbvHz5kl27drFo0SK0tLQIDg7m4cOHwt1djgkKCiItLS1TQg4gLJQCFQ4ODvTq1UutWcFra0diwpO4feYIAM36KfvA57Qetx8zhcSYaGJePUfPuAIpicrvk56+IT2nzKOxQTJVqlRhyZIlRXRnJQMhKAuJ27dvA3Dp0iUMDQ3R09Pju+++w6DT0Gwzx87v3IAkKWgzbEKmL3D/+T+yY/ooVvZvDUC70ZOp2cSNqJuXVdfp2rUr9+7dAyA0NLTUCMqMloRvWh3lcjnPnz8H1FsSzpo1CxcXl3y1JNTR0SExMRFLS0u0tbVZuHAhLVq0yHNhc0H2vHjxgqNHj3LkyBGOHTtGREQEpqamdOnShXHjxtG1a1eqVq36TmNu3LgRLS0txo4dCyjd3bq6uqpMfUH5Qy6XI5PJcHZ2Vm3LSlAKC2X5ZtGiRSxcuBB7e3u2bt1K06ZNCYtLJTY6kqCTf1HDpTmVaykNCjmtxwAr329LzMtnaOnqMfg7ZfkyBSAPDsHTowkA1apVY+nSpUV2f8WNEJSFRFJSkurvvXv3Mn/+fKZMmcLMQ20wqZx5AU2Mfc2JDUvpOfNbop4pYyDTUpN5/eIpFSpbc/a3H9HW1af/16uRH/6dc9vW4dihO9WcmtGnTx927drFrl27MDY2BkBPr2Rmm2W4pt60OmbVknDMmDGF1pKwYsWKKBTZyXpBfkhNTeXSpUsqK2RGb+UmTZowfvx4PDw8aN68OVpaeXv0pKamsm7dOoYPH46ZmRmgFJTt2rUTLRLLMXK5nHr16qnVlsxJUAoLZflj9uzZNG/enAcPHjBjxgzGjBnDjRs3CE9OR37Ii7SUZJr2VVon37YeAwz+diMRYQ85umYRx378BrtWHdHS1kFRwZKjR4/i7+/PnDlzWLJkiapebllHCMpCokaNGgC4ubnx3nvvceXKFQICAoh8FpaloEyKfU1KYgL75n+q2vbszk22fTaCUWt2ce/KWRzae9KgS280tLS5feYI96+dx9apEfv37ycoKEiVaHLmzJlsY8+KktevX6sJx4yWhGlpaWotCXv06IGLi4toSVhKCQsLU8VBHj9+nJiYGCwsLOjatSufffYZ7u7uWFpaFsi1fv/9d549e8Ynn3wCKGtSnjlzpty5lgTq/DchB5QxuoBaDKWWlpbKQyEoXyxevFj17+3btxMYGIhCoUAhwdU/tqNvYkqDLsoEzZzW40k7/gaghmsLari2ICxIziWvLTwPuYWNgwuauvp06dIFd3d3tm7dyq5du4SgFOSPxo0bY29vz8mTJ/n555/x9vamQoUKWNWpz1XvbTwLuQVA8LnjxEW8xL5tV4Ys3aw6f+fMD6hYrRaen36FQQVzdI2MuX/tPJf2/sKN4wcAqFSjDhoy5ZtXvXr1kMvlHDt2jE8//bRIrTWSJPHkyZNM4vHBgwfAvy0JmzVrxkcffSRaEpZykpOT8fX1VYnImzdvoqGhQYsWLZg+fToeHh40bty4UHolr169mk6dOuHo6AjAqVOnRLmgck56ejr+/v706dNHbXt4eDgVKlTI1L1KX19fWCjLGb6+vvz444907NiRJ0+eIJfLadasGRoaGoT6X+Ll/Tu0Gvwh2rpKz56hmUW263H0szAOLpuDXauOpCYnEXDUGy1dPcysq3Hj7wPcv3IOS882hISEcOvWLbU6qGUdISgLCZlMxq5duxg7diyffPIJ9erVY9++fVxPjmX/ws9Ux53bthZTK1ucu75Hg87q5WsMTMyo3awNoDSvH1m9iL+Wz0PfuALtRk/Gob0nFnqanJDLWbVqFXp6eowbN45vv/220O4rPT2du3fvZkqWyXAvZbQkfO+993BxccHV1ZV69eqJloSlnPv376sE5MmTJ4mPj8fKygoPDw/mzp1Lly5dVC7owuLKlStcunSJP//8U7Uto1yQnZ1doV5bUHIJCQkhISEhk4Xyv328MzAwMBCCspxhbW3Ny5cvmTlzJpIk0bVrV9asWQNA4J87AGj2f3c3gI6+QbbrcXxUBDHhLzi84isALGvVo/PHMzE0NcfQtCIPr59nwsFdGBoa4unpyYoVK4roLosfmZRT5WdBgXPscRz+4UnZJua8CxqAi4Ue7raFY+lLSEjg5s2bauIxMDBQ5S6qVq2aSjRm/C1aEpYNEhISOHPmjCoWMiQkBC0tLVq3bo2Hhweenp40aNCgSH/Ww4cP5/z584SEhKCpqYkkSdSuXZtu3brx448/Ftk8BCWLXbt2MWTIkEwCcuzYsdy4cYPLly+rHV+7dm3ef//9Qn3xFpQeStOaXNIRFsoixrmiHn7hSQUyluL/4xUEERERmayOwcHBKBQKNDU1sbe3x9XVlQEDBqhK9GT19i8onUiSxJ07d1RWyDNnzpCUlES1atXw9PRk6dKldOzYERMTk2KZ3/Pnz9mzZw/fffcdmpqaANy9e5cHDx4Id3c5Ry6XU61atUzPo+xKpwkLpeBNSuqaXBoRgrKIqWKgha2hFmHxaeTHNCwDbAy1qGLwbj9CSZIIDQ3NJB4zuusYGBjg7OxMu3btmDJlCi4uLjg5OYli32WQ2NhYTp48qRKRDx8+RFdXl3bt2rF48WI8PDywt7cvERbnDRs2oK2tzZgxY1TbRLkgAWSdkANKQVm7du1M2/X19UVSjkBFca/JZYnye+e5QJKkQllMu1U3ZtPtKNLz8e3VkCnHyYnU1FRu376tJhz9/f2Jjo4GoFKlSri6ujJ48GCV27pu3boqC5CgbCFJEjdu3FC5sc+fP09qaip169alZ8+eeHh40L59+xJXficlJYWffvqJkSNHYmpqqtouygUJJElCLpersv7fJCIigubNm2faLiyUgv9SVGtyWUcIyjd4npBGYEQSYXGphCeno5CUXxILXU1sjLRxrqhXIG8fZrqadK9mzIHQ2DyP0b2aMWa6/wq/uLg4AgIC1MTjf1sSurq6Mn36dFW8o5WVVYmwPgkKj6ioKP7++2+VFfLp06cYGBjQsWNHVqxYgYeHR5ZWnJLE3r17efHihZpoyIjx/O6774pxZoLiJiwsjIiIiGwtlNkl5QgLpeBNCmNNLo8IQQlEJadzODSWx/FpaIBacK5CgpdJ6YQnpeMXnoStoRbdquf/i+NgrouExOFHcSgkcmVql6EUuG4V0nl05RR/vuG2/ueff5AkCW1tbRwdHXFxcWHEiBG4uLjkuiWhoPSjUCjw8/NTWSEvXbqEQqHA0dGRwYMH4+HhQZs2bfLcZaiokSSJVatW4e7uTv369VXbT506RXJysoifLOdkFM7/r6BMT08nMjIyyxhKUTZIkBX5WZO7VzPGwbx0PFMLk3IvKIMik1RfICDbTK+M7WHxaWy6HVUgXyBHcz2sDbWzFbMqJAXINIi6f5s/vpnGTPlVQNmS0MXFBU9PT5XV0cHB4a29kAUFR2GFRbwLr1694tixYxw5coSjR4/y6tUrTExM6Ny5M+vXr8fDwwNbW9tinWNeuXz5MlevXuXQoUNq2w8fPkzNmjVFuaByjp+fHxUrVsTGxkZte3R0NAqFItuknIwyZwLBm+R2Tc7YbmOkRTdhmVRRrgVlUGQSB0Pj3ukcCUiX4EBoLBISjub5y+gy09VkqJ0pj6ITOHf/BU8T00nTNUamqUl6agov7t3hof9lHl08TlVjPfp3c8dlzkxcXV2pWbNmoRSPFmRPUYVF5ERaWhpXrlxRubGvXbuGJEm4uroyduxYPDw8aNmyZZmo/bl69Wpq166tZomUJAkfHx88PT2LXcwLihe5XE6jRo0yfQ+yaruYgUjKEeRExpqsetbHpxKe9MazXk8TG8OiedaXNsrtpxGZlM7hR+8mJv/L4UdxWBtqv/PbSXR0NAEBAdm2JLSzs8PVtREuLg3p4eqKy7QxWFrOytdcBfmjOMIi3uTp06ccPXpU1d4wKioKc3Nz3N3dmThxIl27dqVKlSoFdr2SwNOnT9m7dy/ff/+92ouTKBckyEAulzN48OBM23MSlCIpR5AbqhhoUcXg33qSJcEbVdIp84Lyv1+Azz//nOfPn/Pbb7+pba/ZuBXjfv6Ti15bOLVpOQnRkRhZWNK830g6fDAVgIf+lzm4dA4vH9ylcm17+s9byWGDhgy1M83yOt9++62qzdObyTL/bUnYvHlzPvroI1xdXXF2dsbQ0LAQPxHBu1IcYREpKSlcuHBBZYUMCAhAJpPRrFkzJk+ejIeHB02bNi3TGfnr169HT0+PUaNGqW338fFBR0dHlAsq50RERPD48eMsE3Iy+ngLC6WgoBBi8u2UeUEJ0K9fP/r37w+Ag4MDYVGxJNu7ARAWJMd3+09Y2TkBoG9iSrvRk9E1MMJ3+08cW7sY+zbuVLStyfZpo9AxMKTHtEUcW7eY3V98TGWvszxPSAPA3d0dOzs7Hj58yOnTp7G0tFS9KZuZmalaEmaU6LG3t0dLq1z8CEotRRkWERoaqhKQJ06cIDY2FktLSzw8PPj8889xd3cvN8Xkk5OTWb9+PaNGjcqUUJZRLki8eJVvskvIgX8tlObm5pn2CQul4E2E5bHgKBdqxsHBgV69eqnq1T1/HIerST0UwO0zRwBo1k/Zx9PFoy/JCXEkvo7m5olDvLgXDMCrByHER4Xj0q0/zfuP5P718wQe9ebJLTnzTj8B4NixYxw7doxq1arh6uqKh4cHrq6uuLq6YmtrK760pYzCDotISkri7Nmzqozs4OBgNDU1adWqFbNmzcLDwwMXF5dyGSe7Z88eXr16xaRJk9S2Z5QLEm3zBHK5HENDQ+rWrZtpX3h4OKamplm+sAtBWb4pCXHwZZVy8aktWrSIhQsXYm9vz9atWwkzqoMCiI+OJOjkX9RwaU7lWvVUx//xzQz8fX4HoP2YKVjZORL9LAyAp7cDiAx7yPOQWwBEP39C7QbKN2SZTIYkSRgYGPDFF1/QtGnTor1RQYFw7949nJycSEpKYvSa3VjWqsfuOeMIuxVAemoKH278g1pN3NTO2b9wKle9t2Pr1JgJW4+otqempNLAyYkn/wTz+eef88EHH6iskKdOnSIxMREbGxs8PDxYtGgRnTt3LvclniRJYvXq1Xh6elKvXj21faJckCADuVxOw4YNs3zhyq7tIgiXd3mluOPgywNl3vQxe/ZsvL29WbFiBf/88w9jxowhPDkdAPkhL9JSkmnad4TaOe3HfMqw73+lesOmXNyziZf372JqZUOrwR/ywO8iy3o15fULpVVSS0cXw8o2WV5HUDqZOHEisv8vUhKQlpqMWdUaVG+Y9QtCaMAVAo7sz3Lf2W1reRn2CFC2D7Szs2PatGkkJyezYMECbt68yaNHj/j555/p169fuReTABcvXuT69etMnjw50z4fHx9q1KiRSWgKyh9+fn5ZurshZ0FpYGBAamoqaWlphTk9QQkiKDKJTbejCItX/sxzGwd/KzK5SOZXVijzgnLx4sX07t2bKVOm0LBhQ+7cuUP6/7Mrrv6xHX0TUxp06aV2TuXa9jh27I7b0I9Jjo8j2Pc4AD1nLGbaH5eYuP04zu59AKhUoy4KCb755ptM15Gk/HQGFRQHXl5e+Pv703XQKNU2i2q1GbhoHbZOjTIdn56Wxh/fzKDD2M8y7Yt6+ohTm1fQYcynANSsWZMDBw4QGRnJ33//zfTp03F0dBShEP9h1apV2NnZ4e7urrZdlAsSZBAXF8fdu3dp1Cjz7yS83UIJCCtlOSEjDj49l8XKQT0OPigyqTCnV6Yo0y5vX19ffvzxRzp27KjKtm7WrBmaGjLu+13i5f07tBr8Idq6/yZN7JgxmmrOTdEzNuHi7k0AWNZUxuic3rISQ3MLop48wu/gHhzae1LRtiah/pcYvGR7puuIRa90ERMTw9SpU1m+fDl7zgfk6pzzOzcgSQraDJvA0TWL1PYdWDKbhh79qO6i7Cfs7u5Oz549C3zeZYmwsDD27dvHypUrM7kyQ0JCuH//vnB3CwgMDFTVXs2KiIiIbIveZ8TSJyQkYGxcvnsvl3WKszxgeaRMC0pra2tevnzJzJkzkSSJrl27smbNGs6kaLLbexsAzf7j7tbU0eXUlpWkJiZQobI1nlO+xr6N0lISEfaQU5tXAODQwZPes5cBUMOmKneyuI6gdLF06VIsLS1p1qwZaw+eAiA2/AUpiQno6BtkOj4x9jUnNiyl58xviXr2GFC6x1+/eMqrhyHcv+bL2A3ehD+6Dyjrj0ZERJSbTO288NNPP2FgYMDIkSMz7csoF9SxY8dimJmgJCGXy1VtZrMiPDycli1bZrkvQ1AKC2XZx+dRrKrcG4Dv9vWc37mBuMhXGFeqQruRn9Cox/v89ulQHt24TmpSAv2/Xk3jXv/WNlVIcDg0lqF2pkV/A6WMMi0oa9WqxcmTJzNtv/c4jkEL1vL+grWZ9g36Zn224/Wbt5J+81aqbdMAXOzr8nkW1xGULsLCwvD391fLGt03/1MMzSyo39Y90/FJsa9JSUxg3/xPVdue3bnJts9G0HLgB6QkJrBuRFfVvg0bNmBkZMT3339fuDdSSklMTGTDhg2MGTMmS8uRj48Pbdu2FeWCBMjlchwdHbNtM5sbl7fI9C7bPE9I43H8v3Gy4Y/u8dfyuVSqUZduU+dzbts6/vx2BvXcOmFgakbdFu24ddon0zgS8Dg+jecJaSL7+y2U+RjKrHCuqJdtUO67ovj/eILSz6RJk9i7dy979+5VxdV2+GAq1vWcuOq9jWf/z+wPPnecwKPeGJpZMGTpZtUfgIrVauH56VfUatJatb3TRzMB6NOnT5aWN4GS3bt3ExkZmalUECgX/9OnTwt3twBQCsrs3N1paWlERUXlmJQDQlCWdQIjktQEjvR/U2WFKlWp07wdRhUt0TEwRN/ElCFLNmPXKnvPh8b/xxPkTLmU21UMtLA11CIsPi3XQbpZIQNsDLXEW0sZoUmTJjRp0gSAbaeucuP4Aaq7NEeRnsb+hf8m3ZzbthZTK1ucu75Hg87qCV0GJmbUbtYGADNrWwAMTc9zAqhXrx4NGjQompspZWSUCurWrRt16tTJtP/06dOiXJAAgNTUVG7evJmpg1IGUVFRSJIkknLKOWFxqWqGo0o16uA+YTbH1n3L8r4t0dTWYejSLegaGmU7RgYKICw+tdDmWlYot0qoW3VjNt2OIj0filJDphxHUPaYOGsubmNnqB5I3/q9eus52R1Tp4kbRx/F4m779gdXecXX1xd/f3+WLl2a5X4fHx+qV6+Ovb19Ec9MUNK4desWKSkpOZYMgqzbLoKwUJYXMsoDZhAX+YqLXluo5tyUtiMn4bNqPt7fTKN2s9bo6L89jCY8Kf2tx5R3yqXLG8BMV5Pu1fInBrtXE4VPyyoiLKJoWb16NfXr16dz585Z7hflggQZ+Pn5IZPJaNiwYZb73yYohYWy7CNJkloyDsD9a+eJDX9Bgy69cOzQjXpunYkNf8GLe3dyNaZCQpQCfAvl1kIJ4GCui4TE4Udxyi9LLs6RobRMdq9mjIO5bmFPUVBMiLCIouPRo0d4e3uzZs2aLAVjSEgI9+7dY/ny5cUwO0FJQy6XU7du3WxL/kRERABkW01BWCjLPjKZDA0ZaqLS3KYGANcP7EbXwJCgU3+hqa2DWdVqXPXexkP/KwA88LuIIj2Npu8NVxtTQ4Z4oX0L5dZCmYGjuR5j65thY6hc8LP7QDK22xhpMba+mRCT5YBu1Y3RyOfzQ4RFvJ1169ZhZGTE8OHDs9wvygUJ3iSnhBxQWihlMhlmZmZZ7tfTU3oLhKAs21j8x3to4+CC56dfkRT7mgNLZqOtq8eABT9iZGbB/oWf4X94LwDXD+xSi5lXjacnvJFvQ5hNULq/h9qZ/ts0Pj6V8KQ3msbraWJjKJrGlzcywiIOhMbmeQwRFpEzCQkJ/Pzzz3zwwQcYGWUdY5pRLii7/YLyg0KhwN/fnx49emR7THh4OGZmZmhpZf2s1tDQQE9PT7i8yzg2RtrKdfyNbW1HTqLtyMxVJN4WI68B2BhqF+wEyyBCHb1BFQMtqhj8u2hJkiRM3OUcERZRuOzcuZOoqCgmTpyY5f7ExEROnz7NN998U8QzE5RE7t27R1xc3FstlNnFT2ZgYGAgLJRlHOeKeviFF0ypHxEHnzuEoMwBISYFoAyLsDbU5nBoLI/j09CALBN2MrbbGGnRTVgm30pGqaCePXtSq1atLI85ffo0SUlJolyQAFC6u4F8C0p9fX1hoSzjiDj4okd8QgJBLhBhEQXPmTNnuHHjBitWrMj2GFEuSPAmcrmcqlWrUqlSpWyPCQ8Pf2t7U2GhLB+I8oBFi1j5BIJ3QIRFFByrV6/G0dExx2QbUS5I8CZyuZxGjRrleExERAT169fP8RgDAwNhoSwHiDj4oqXcZ3kLBPlBCJ288fDhQ/78808mT56c7WcYEhLCP//8I9zdAkD58ubn55ejuxty7/IWFsrygYO5Lj2rG6EpU7qvc4MM0JRBr+oiDv5dEBZKgUBQ5Kxdu5YKFSowdOjQbI8R5YIEb/L06VNevXpVIIJSuLzLFyIOvmgQglIgEBQp8fHxbNq0iQ8//BBDw+xbnvn4+NCmTRtRLkgA5C4hJzU1lejoaJGUI8iEiIMvfMSnJhAIipTt27cTExPDhAkTsj0mo1zQokWLinBmgpKMXC7HzMyMatWqZXtMZGQkkH3bxQwMDAyIjo4uyOkJSgkiDr7wEDGUAoGgyMgoFdS7d29q1KiR7XGiXJDgv2R0yMlp8c/o4/22LG9hoRRkIMRkwSEEpUAgKDJOnjzJrVu3mDx5co7H+fj4UK1atbdm6wrKD29ruQj/9vEWMZQCQdEjBKVAICgyVq9ejbOzM+3atcvxOFEuSPAmUVFRPHz48K0lgzIslLkRlMJCKRAULEJQCgSCIuH+/fscPHgwx1JBAP/8848oFyRQw9DQkGPHjuXYwxuUwlNDQwNTU9McjxNlgwSCgkcmSVJ+uhIJBAJBrpg2bRq//vorYWFh6OvrZ3vcmjVrmDZtGhERERgbiw4VAiW5TZ5ISkpCTy/nvstxcXGkp6dToUKFgpqeQFDuEVneAoGg0ImLi2Pz5s2MHz8+RzEJ/5YLEmJS8Ca5DX94m5gERCkqgaAQEC5vgUBQ6GzdupW4uDjGjx+f43GJiYmcOnVKuLsFAoGglCEEpUAgKFQUCgVr1qzhvffey7GGIMCZM2dEuSCBQCAohQiXt0AgKFT+/vtvgoOD2bhx41uP9fHxwdbWFgcHhyKYmUAgEAgKCpGUIxAICpUePXrw5MkT/Pz83hoHZ2dnR4cOHdiwYUMRzU4gyExKSgoymQxtbe3inopAUGoQLm+BQFBohISE8Ndff721VBDAvXv3CAkJEe7ucsydO3d49eoVycnJFKat48iRI2qtFyVJQpIkFAoFAL/88gs//fRToV1fICiLCEEpEAgKjbVr12JhYcHgwYPfeqyPjw/a2tp06tSpCGYmKGkEBATQvn17Ro8ezSeffMK6devw9/fP9vj/Cs53EaDdunXj1q1bqv/LZDJkMhkaGsol0dDQkF9++UUUPxcI3gERQykQCAqF2NhYtmzZwuTJk3NVysXHx4fWrVuLckHllPv37/PixQtiY2P5559/2L17N23btuXQoUNZHv+mxTu3NSozMDAw4OnTpzx58oS4uDgSEhKIi4sjPj6ehIQE7ty5Q0BAAPHx8W8tcyUQCJQIQSkQCAqFX3/9lYSEhLeWCgJlMepTp04xf/78IpiZoKQhSRK9e/fmp59+4t69eyxevBgtrZyXp5kzZzJz5kwsLCyQyWRcvXqVpk2b5up65ubmvP/++yrLpKamJlpaWmhra6OtrU2FChVwdHQUrT8FgndAJOUIBIICR6FQYG9vT6NGjdi9e/dbjz969CgeHh7cuHEDJyenIpihoCQSHR3N9OnTSUpKYtmyZVhZWZGeno6mpmamY/X09Hjw4AFWVlbs37+fXbt2sXfv3lxdx9ramg8++IBevXqhqampEpJaWlpoaWmhqamJQqHAxsYmy2sLBILMCAulQCAocI4ePUpISAi//vprro7PKBfk6OhYuBMTlGhMTU3ZtGkTffv2ZeLEiezfvz9LQRkTE0NKSgpGRkbEx8ezZMkShg4dmuvr6OjoULt27VxbNAUCwdsRSTkCgaDAWb16NY0bN6Zly5a5Ot7HxwdPT0/hYiznZDjM9u/fT4UKFdi3b1+W34lXr15haGiIsbExkiTx/PnzXCV+ZTBq1Cjq1KmT4xwEAsG7ISyUAoGgQLlz5w5Hjhzht99+y5VAvHfvHnfv3mXJkiVFMDtBSebN78uaNWt48OBBli7nqKgo4uPjWb16NY8ePQLeTQh+/fXXOc4hIiKCxMREqlSp8tZYToFAoERYKAUCQYHy448/YmlpycCBA3N1vCgXJMgKIyMjHBwcVKV83kRTUxNHR0cOHTrEqVOnqF27Nk+ePMn12Pv37+fixYuZtmfUoTx//jzff/89Dx8+zPP8BYLyhnj1EggEBcbr16/59ddfmTp1Krq6urk6R5QLEiQnJ/P1119z9epVHBwcGD9+PPXr1882IcbJyYlDhw4RHx9PbGwsqampVK5cOdfXW716Na6urplCMjLEq62tLRcuXKBTp07ZusYFAoE6QlAKBIICY8+ePSQlJeWqVBCIckECJZMmTeLUqVN06NCB8+fPc+DAAY4ePUq9evWyPF5bW5vq1avn+XoVK1bkwYMHXL58mdjYWOLi4khMTCQuLo6kpCRevXrFtWvXVO50gUDwdoSgFAgEBYIkSYwePRo7OzusrKxydc6ZM2dITEwU7RbLMQ8ePMDHxwcvLy9atWpFWloaw4cPZ8CAAQQEBBRKolbVqlX58ccf+fvvvwFUZYN0dXXR1dXF2NiYhg0bUrt27QK/tiBr3rU4vaDkIQSlQCAoEGQyGdra2rRt2zbX5/j4+GBjYyPKBZVjLl26hLm5uUpMamlpsXHjRgYMGJCtwHjw4AGxsbGYmpqiq6uLjo4O2traGBoa5kqUpKSk0KlTJ7Zt24ampqaqFmXGvzU0NJAkSSTkFCLPE9IIjEgiLC6V8OR0FBJoyMBCVxMbI22cK+pRxUB8/qUJ8dMSCAQFSlZJFNkhk8no16+fsEyUY549e4adnR0xMTGYmJiQmpqKsbFxjmEQX375Jd7e3pibm2NgYICpqSkVK1bkjz/+yFXsrqGhIQYGBlSpUqUgb0WQC6KS0zkcGsvj+DQ0AMUb+xQSvExKJzwpHb/wJGwNtehW3RgzXVFcvjQgBKVAICgWJElixYoVou5fOadevXrs3r2be/fu4erqira2Nunp6TRv3jzbc+7evct7772Hp6cnr1694smTJzx//pzk5ORcCcr33nuPZ8+eFeRtCHJBUGQShx/Fofj/r7wim+MytofFp7HpdhTdqxnjYJ67JD9B8SFaLwoEAoGgWElNTVW5mnPDgAEDqFmzJkuXLi3kmQkKiqDIJA6GxuX5/J7VjXA01yvAGQkKGlGHUiAQCATFira29juFSowcOZLatWuTmJiIJEmkpaWRlpYmrN0llMikdA4/yruYBDj8KI6o5PQCmpGgMBAWSoFAkGvS0tK4fPkyFhYW1KxZEx0dHZGdKShyJElCoVBkW6dSULLYcTeasPg08iM2ZICNoRZD7UwLaFaCgkZYKAUCQa64evUqw4cPp0OHDjg5OTFmzBji4+OFmBQUOTKZTIjJEsi9e/fQ19dHJpNx5MgRTp8+jUwmY1g9M2Y1qsTsRpW4c/4EAHfO/83KAW2Y09iS2Y0qqY2zfdoovu3agLktbVkzpCMP/C4iAY/j01j240/UrFkTAwMD+vbtS3R0dNHfqCBLhKAUCAS5Yvr06WhqanLmzBkuXLjAlStXcuyJnB3CKSLIieTk5OKegiCPTJw4McvQhU4fTmPQtxsZ9O1GrOs5AZCalEjNxq2oUMUm0/HPQoJoNfhDuoyfxcv7d9kxYwwKhYInQXI+nzyRevXqMXfuXLy9vfniiy8K/b4EuUMISoFA8FYCAgIIDAxk5cqVtGzZkqZNmzJ37lx+++037t27l+O5cXFx7N27l/379/Py5Uth0RTkyE8//YS1tTXp6YUbL5eWlkZKSkqhXqM84eXlhb+/Px9++GGmfdVcmuPYsTsNu76HsYWyRaZTp570nrUE44qWmY6fuu887UZNpu2IidRu1ob4qHDio8K573cRSZKYMGECs2fPpkqVKmzdurXQ702QO4SgFAgEb+Wvv/7C3d1drd/28OHDqV+/Pt7e3kDWlsfg4GDGjRvHyJEjGTx4MA0bNmTv3r2ZjktNTS28yQtKFXK5HBsbm3dyaa9evZopU6a803X8/f1Zv379O85OkBUxMTFMnTqV5cuXY2pqmmn/r5MGMq+lLZvH9yc+KuKt42lp6wCQ8DqKJ7cCqFitFkbmlTA0qwiAr68v169fJyIigri4OCIi3j6moPARglIgELwVW1tbXr58qXJHZgjA/v37c/DgQYAsLY8rVqzg9evX+Pr6kpyczPvvv8+SJUt4+vQpCoWy2tyVK1fYsmVLEd2JoKQjl8txdXV9p3MuX76Mv7//O51z4cIFZs2a9U7nCLJm6dKlWFpa0qxZMyIjIwFlwXoLCws8p3zN8BXbadZ3BP9cPsOJjctyNWZyfBxbpw4jJTGegYt+QiaT0aBLb2ydGrNs2TKaNGmiqjmqpyfKCZUEhKAUCARvZfjw4ezfvx8TExMkSUJbWxtQ1gOsUaNGtucdPHiQTz/9lEaNGgGwcOFCNDU1Wbt2rSrWauHChVy4cKHQ70FQ8klMTOTWrVuq70tuCQ8Px8LC4p3O0dfXV5UdEuSPsLAw/P39qVu3LmvWrAFgzJgxPHz4kPYjJ1K/rTueU74C4OWDkLeOlxQXy5aJ7/M0+AYjVmzH1kn5fdDW1WPCr3/h5+dHcHAwNjY22NraYmhoWHg3J8g1olOOQCDIFWZmZsC/lkhJkqhSpQpDhgwhNTVVJTIzePz4MSYmJlhbW6u2mZiYsGjRIgYNGsTw4cOxt7fnypUr7Ny5s+huRFBiuXnzJunp6e9soQwPD6dmzZrvdI6BgQEASUlJ6Ovrv9O5AnUmTZpEjx49AGUs5d69e/niiy+4ceMGJ/YcxLS2IyGXTgNg4+gCQPijezy4foH4aKW7+qr3Niyq1aZm41ZsmTCAxzev0/S9YcRFhRNw1Jv6bd3R0NLm7NqFVOnQCKQP/QAAOu9JREFUnOPHjxMcHMyKFSuK45YFWSAEpUAgyBMZwtLJySlLK098fDyVK1fmypUrqmNkMhldunTB09OT6dOn88MPPxAZGUmnTp2KevqCEohcLkdTU5MGDRq803l5sVBmCMqEhAQhKPNJkyZNaNKkCaB8KQBo3bo1CoWCTdt38fj3begaGNGs7wg6jp0GQKj/FfYv/Ew1xv6Fn9Go50BqNm7F45vXAbjqvZ2r3tsBmHnoOqaVrPjnyjk+3vMLJiYmzJkzh8mTJxflrQpyQBQ2FwgE+Sa74uavX78mKSmJypUrq2ImNTQ08Pf3Z+zYsWhqaqKvr8/p06eLeMaCksj48eM5d+6cSpTkFkNDQxYtWsTUqVNzfc6RI0fw9PTk8ePH2NhkLl0jKBieJ6Tx653oAhtvVD1TqhgIW1hJRMRQCgSCdyItLS1TSZfsSgFVqFCBypWVZUI0NDTQ0NAgPT0dFxcX3N3duXr1KqNGjSrsKQtKCXlJyElMTCQhISFfFkpB4VHFQAtbQy3yWyxMBtgaagkxWYIRglIgEOSawMBArK2tuXXrVp7HyCgHM2/ePHbt2kXv3r0LanqCUkx6ejqBgYHvLCgzSsbkJSkHlIJUULh0q26MRj4VpYZMOY6g5CIEpUAgyDWrV69GV1cXe3v7fI+lp6fHwIEDVck+gvLNnTt3SExMzFNCDry7oBQWyqLDTFeT7tXyJwa7VzPGTFe02yzJCEEpEAhyRXh4ODt27GDChAmZMrpzg0KhUMVRCgT/RS6XA+Di4vJO5+VVUAoLZdHiYK5Lz+pGaMrItftbBmjKoFd1YxzMdQtzeoICQAhKQYlA5IaVfDZt2oQkSVm2VnsbycnJzJkzh8ePHxfCzARlAT8/P2rWrPnOFmthoSwe8vLMdjTXY2x9M2wMlXGQ2QmQjO02RlqMrW8mxGQpQUS3CoqF5wlpBEYkERaXSnhyOgpJGSNjoauJjZE2zhX1RPB1CSItLY21a9cydOjQd164Ac6ePcuSJUsYMmRIIcxOUBbIS0IOKAWltrY2RkZG73SeEJTvRkE9s810NRlqZ/rvePGphCe9MZ6eJjaGYg0ojYiflqBIiUpO53BoLI/j09AA3nSAKiR4mZROeFI6fuFJ2Bpq0a26iJspCfzxxx+EhYXlueabj48PVatWfef6goLygSRJyOVypk2b9s7nZtSgzK7SQHYIl3fuKKxndhUDLaoY/PsSkF3pMUHpQbi8BUVGUGQSm25HERafBqg/mN4kY3tYfBqbbkdxKzK5SOYnyJ7Vq1fTrl07GjZsmKfzfXx88PDwEAuGIEtCQ0OJjo7Ok4UyIiIiT1ZzbW1ttLS0hIUyB4rymS2eDaUfISgFRUJQZBIHQ+NIlyC3kTcSkC7BgdBYgiKTCnN6ghyQy+WcO3cuz9bJhw8fEhwcjKenZwHPTFBWyEjIyavLOy+CEv7t5y3IjHhmC94VISgFhU5kUjqHH8Xla4zDj+KISk5/+4GCAmf16tVUq1aNXr165el8Hx8ftLS06Ny5cwHPTFBWkMvlWFpaYmVl9c7n5kdQGhgYCAtlFohntiAvCEEpKDBkMpnan1mzZnHp0iUc7O2Y3awqCzvWY8+XE0hJVD7AL3ptYbG7E182s+a7bi6c2rxCNVb08yds+qgvc1vY8MN7Lbh7+RyHQ2NV++/du4e+vj4ymYwjR44U+b2WF16+fMnOnTuZOHEiWlp5C7n28fGhVatWVKhQoYBnJygrZCTk5MXtmV9BWZ4tlAX5zN4+bRTfdm3A3Ja2rBnSkXvXL3I4NJaQkBA6dOhAxYoVMTY2pnv37jx58qS4bllQiAhBKShQ+vXrx65du9i1axdDhgwhOg0a9hhE37nLqeHSHP/De7n6x3YA9E1MaTd6Mn2++B49Q2OOrV3Ms7tBAPyxeAaPb17H49N5aGrrsOPzD7gX/prnCcpYnokTJ6KhIb6+hc3PP/+MpqYmY8eOzdP5ycnJnDx5Uri7BTni5+eXJ3c3KAVlxYoV83Suvr5+ubdQFtQz+1lIEK0Gf0iX8bN4ef8u22eMITQ2hRv3HiFJEgsWLGDYsGEcPnyYTz/9tDhvWVBIiCxvQYHi4OBAr169VCU5nj+Oo91wW+JjookMC+XWaR/VsS4efUlOiCPxdTQ3Txzixb1gAOKjI7l7/m8auPfBbfA4dPUN2bdgCsFnjxFoO5izhw7j7+/Phx9+yKpVq4rlPssDqamprFu3jmHDhmFubp6nMc6dO0d8fLwQlIJsefnyJU+fPqVRo0Z5Ol+4vPNHQTyzAabuO4+Wtg4A96/5csf3bxKjwtGt68rp06dVx23bto2goKCiuTlBkSJMPIICZdGiRRgaGlK/fn2uXr1KWFwq1//yYrG7Eyc2LqNO83Y07TNMdfwf38xgSXdX7vgep/2YKVjZORL97DGSJGFSqQoAJpWtAYh8EkrIiwimTp3K8uXLMTU1LY5bLDfs37+fp0+f8sknn+R5DB8fH6ytrXF2di7AmQnKEvlJyElISCApKUkk5eSDgnhmAyoxmfA6iie3AqhYrRYG5pV4kfpvGMP58+eJj4+ndevWRXuTgiJBCEpBgTF79my8vb1ZsWIF//zzD2PGjCE8OZ16rbswcvVOGvcazL0rZwk6+ZfqnPZjPmXY979SvWFTLu7ZxMv7d1X7VJ0Y3ujIsH/DKiwtLWnWrBmRkZEAPHv2rNxbGQqD1atX07Fjx3zVjhTlggRvQy6XY2xsTK1atd753Lx2ycmgvFsoC/qZnRwfx9apw0hJjGfgop+QyWSEJykTc27fvk3//v2xt7dnyZIlRX6vgsJHuLwFBcbixYtV/96+fTuBgYGkK5SWRpNKVbB1bMT1A7sIOOqNa/cBAFSubU/l2vYoFOnsnPkBwb7HadxrMDKZjNhXzwGIefkMALOq1bl74QT+/v7UrVtXda0xY8ZQqVIlevToUYR3W7a5du0aFy5c4I8//sjzGKGhody+fZsFCxYU3MQEZQ65XI6Li0ueYqLzKyjLu4WyoJ7ZlrXsSIqL5ZdJA3l29yYjV+7A1kkZwqCQ4ObNm3Tq1AljY2OOHz+e5xAaQclGCEpBgeDr68uPP/5Ix44defLkCXK5nGbNmvH3T98iIaOiTQ1unz0KgGVNOwB2zBhNNeem6BmbcHH3pv/vq4uhqTl2bp0IPneM87s2ctV7O/omptRv604l25rMHtEfAC8vL/bu3csXX3xBkyZNiufGyyhr1qyhRo0a+RLpPj4+aGpqinJBghyRy+V5jrEtCAtldHR0ns4t7RTkMxtgy4QBPL55nabvDSMuKpyAo97Ub+tOUkw0HUd0ITIykunTp+Pr64uhoSE9e/YsnhsXFBpCUAoKBGtra16+fMnMmTORJImuXbuyZs0avvh5Nz6/rCU+MhxDs4o06zuCzh/PAEBTR5dTW1aSmphAhcrWeE75Gvs27gD0mb2MvV9NwmflfEytbBj83c/o6Bvi0rgx/e3NAOVbL0Dr1q2pUqVK8dx4GeTFixfs3r2bxYsXo6mZ97aXGeWCRKyrIDtiYmIICQlhzpw5eTo/Q1DmNcu7PJcNKuhn9uOb1wG46r2dq97KrPCZh64jRTzh1atXyv/PnAlA9erVhaAsg8gkScptEXyB4J059jgO//CkbFt2vQsagIuFHu62Rm89VpB3FixYwJIlSwgLC8PMzCxPYyQnJ1OxYkW++OILZs+eXcAzFJQVzp07R9u2bfH3989TW8/Vq1czc+ZMEhMT8xSnO3nyZE6fPk1gYOA7n1tWEc9sQV4RSTmCQsW5ol6BPJhA2S/WuaJeAY0myIqUlBR++uknRowYkWcxCUp3migXJHgbcrkcHR0dHBwc8nR+Rh/vvCZ9lfeknKwQz2xBXhGCUlCoVDHQwtZQi/zm+MoAW0MtqhiIKI3C5Pfff+f58+f5KhUESne3lZVVnqxOgvKDXC6nQYMGaGtr5+n8/NSgBJGUkxXimS3IK0JQCgqdbtWN0cjn00lDphwnL4iojtyzevVqOnfunGeLUQY+Pj54enqKckGCHMlouZhX8isoy7qFMq/PvuJ+ZgtKJ+LVQVDomOlq0r2aMQfe6MX9rnSvZoyZbu4SRJ4npBEYkURYXCrhyekoJOXDzUJXExsjbZwr6om35iy4fPkyly9f5uDBg/kaJzQ0lFu3bjF//vwCmpmgLJKcnExQUBDjxo3L8xjh4eFUqlQpz+eXNQtlQT37ivqZLSgbiFVVUCQ4mOsiIXH4URwKCXLz3ixD+TDsXs0YB3Pdtx4flZzO4dBYHsenoQFqcUAKCV4mpROelI5feBK2hlp0qy4eeG+yZs0aateuTbdu3fI1jigXJMgNQUFBpKWl5dtCaW9vn+fzDQwMSE5OJj09PV8VDYqbwnj2FcUzW1C2EIJSUGQ4muthbaid7YMvg4ztNkZadMvlW25QZJLqwUc24765PSw+jU23o8SD7/88e/YMLy8vli5dmqcC028iygUJcoNcLkcmk+WrLWdBuLwBkpKSMDQ0zPM4xUlhPvsK85ktKHsIQSkoUsx0NRlqZ/qvayY+lfCkN1wzeprYGL6bWzooMomDoXHvNA8JSJfgQGgsEhKO5uU7E3H9+vXo6OgwevTofI2TnJzMiRMn+OKLLwpoZoKyilwup169enkWcpIkqbK884q+vj6g7AleGgVlUTz7CuOZLSibiJ++oFioYqBFFYN/a5NJkpSnBI7IpHQOP3q3B+p/OfwoDmtD7XL7Vp2cnMz69esZNWoUFSpUyNdYolyQILf4+fnly90dHx9PcnJygVgoS2NiTlE/+wrqmS0ou4gsb0GJ4G0PJplMpvZn1qxZXLp0CQd7O2Y3q8rCjvXY8+UEUhKVC8NFry0sdnfiy2bWfNfNhVObV6jGin7+hE0f9WVuCxt+eK8Fdy+f4/D/g883btxIzZo1MTAwoG/fvuWiLZuXlxcvX75k0qRJ+R5LlAsS5Ib09HQCAgLyHT8JeW+7CP9aKEtyYs69e/fQ19dHJpNx5MgRUlJS+OSTT6hhXZmv2tXl6JpFqmzuk5t+4PvezZjb0pYl3Rtx+fffVONsnzaKb7s2YG5LW9YM6cgDv4soJDgcGpvl8/VtCDEp+C/CQikoNfTr14/+/ZV9vB0cHHgak0DDHoOoUNmaoJN/4X94LzaOLrgNHoe+iSntRk9G18AI3+0/cWztYuzbuGNl58gfi2fw+OZ1PD6dx1Xv7ez4/ANsD8s58uI2H3/8Me7u7owbN445c+ZgZWXF2rVri/nOCw9Jkli1ahVdu3bNV3JDBj4+Pnh4eIjFRpAjISEhJCQk0KhRozyPURCCsjRYKCdOnKgW17x69Wp+/PFHWg0eR2JsNKd/WYW1fQMadOlNWJA/jh26U9G2Jqe2rOTPb2dQvWFTqtR14FlIEK0Gf4hMQ5Pj675lx4wxzDkexOP4NCDz81UgeFeEoBSUGhwcHOjVq5dqEXj+OI52w22Jj4kmMiyUW6d9VMe6ePQlOSGOxNfR3DxxiBf3ggGIj47k7vm/aeDeB7fB49DVN2TfgikEnz1GWPwrJEliwoQJ9OrVi9WrV7N169YyLSgvXbrE9evXOXz4cL7HevToEbdu3eLrr7/O/8QEZRq5XA5QIBbKvPbxhpJvofTy8sLf358PP/yQVatWAXDmzBkAuo6fxevwF8gPeeF3yIsGXXozZOlmtLR1AEiKi8Fn1Xxe3r9DlboOTN13XrXv/jVf7vj+TXxUOBUqWgKZn68CwbsiXN6CUsOiRYswNDSkfv36XL16lbC4VK7/5cVidydObFxGnebt+F97dx4f0/k9cPwz2ZMRlUSIyGItsYut9q2W1tIqaqmWaqutWqqIhn5LUYpfLUGLWoooRUUpodFWK5ZKZYKE2EWEkF1W2eb3x8hISEjMDUmc9+vl9ZJZnnvnhpuT53nOOS1eH6Z//c6vJzOvV1PO+fvRaeSnVHmxPvE3w9FqtZS3dwCgfGVHAGIjwshW61oN+vv7c+LECWJiYkhKSiImJubpf9inxMvLi9q1a9OjRw+Dx8opF9StWzcFzkyUZRqNBhcXF2xtbZ94jLI+Q3nnzh0mTJjAwoUL81RMyKm7eSHgEJf/8wcg7mY4gD5gBLjw798Ym5ji1MA9z3MpCXFEnDmJnUsNytna67O2H7y/ClFUElCKUsHT0xMfHx8WLVrExYsXGTlyJNF3s6jTrhvDvX6iWd8hXDr+DyF/7tG/p9PI8Qz7vx9xbdyCoz+v5vbl8/rn9B0kcnWSqN65D61atWLBggU0b94cc3NdSQ0Li7KZAR4REcH27dsZO3aswaWCQBdQtm7dWsoFiccytEMO6Pp4W1paGjSjVpIDyvnz51OpUiVatmxJbGwsoCvvNWnSJMrZ2eM9cTh7Fn6JkbExJmZ5y//sXjCNi8cO0mvSbGyruuofv5ucxIYJw0hPTWbQ7O/1W1M6jxz/0P1ViKKSJW9RKsyZM0f/d29vb06dOkVWtm6msby9A8713TmxazMn9/vQtNdAACrXrEvlmnXJzs7iJ4/3CPX3o1nfIahUKhKjIgG4c/smADZVXTE2s8Df35/Tp09jZWXF66+/TnJycqksJ1IYK1aswNLSkuHDhxs8Vnp6On/88Qeenp4KnJkoy7RaLRqNxuB+8YbWoISSveR9/fp1goKCqF27tv6xkSNHsmvXLib9epxbF0MxMTNj6dCuVKr+ov41u+Z9ztGf19Bj7Be0fvN+YJiWlMi6MYO4eT6Y4Ys34dzg/v7V7mO+oG8TO1Qqlf7+KlncoqgkoBQlnr+/P8uWLaNLly5ERESg0Who2bIlB76fixYVdk7VOPvPfgD9jXXT5HdxadQCC+vyHN2y+t5ztVFXsOXFtl0JPfQ7hzevIsDHG8vyFXDr0J3szHQmT55M48aN8fPzIzQ0lEWLFhV4XqVZWloaK1eu5N1336V8+fIGj+fv709SUpKUCxKPFR4eTkxMjMEzlEoGlCVxhnLMmDH07t0b0O2l3LZtG9OmTcPW1pajm1ehtqvE8R0bUalUtB2qa1+5z2smR39eg2vjFtg4unByvw/ODdyxrerK2tEDCQ8+QYt+w0iKi+bkfh/cOnTnRuhpjv68Btv+r+S5v0owKYpKAkpR4jk6OnL79m08PDzQarX06NGDpUuXMu2HLfiuW05ybDRqGztavvEOL380GQBjM3P+WruYjNQUXqjsyCufzqBu++4AvO65gG3Tx+C7+CsqVHFiyDc/YGapxtYkmy1//MH3339P+fLlmTp1KuPGjXuWH73YbNmyhaioKEVKBYFuudvBwYEmTZooMp4ou5RIyAFlAkpjY2PMzMxK5Axl8+bNad68OQDBwcEAtGvXDgsLC/7z8Sb+diQVqjgxaM5KqrrpynRdO30CgLCTAYSd1O2DHDDDC9uqroQH654L8PEmwMcbAI/fTlDe3oH0hJiH7q9CFJVKq9UWpkWnECXO7+FJBEWnFdhqrCiMgCYVLejuXO6xry3ttFotzZo1o0qVKuzZs+fxbyiEBg0a0KJFC9atW6fIeKLsmjFjBsuWLSMqKsqgWbDOnTvj4ODA5s2bDTofGxsbpk6dyuTJkw0a52mSe58oiSQpR5RajewsFLmhgq4PbSO7spl886DDhw+j0WgUm30NDw8nJCRElrtFoWg0Gtzd3Q1eUlVihhJ0y94lcYbyUeTeJ0oiCShFqeVgZYKz2gRDd/qoAGe1yXPTh9bLy4u6devSvXt3Rcbz9fXFyMhIygWJQlEiwxswuI93DisrqxK5h/JR5N4nSiIJKEWp9qqrNUYG3lWNVLpxngfh4eHs2LGDsWPHKrbpPqdckI2NjSLjibIrJiaG8PBwgwNKrVar2AxlaQwoQe59ouSRgFKUajbmxvRyMeyG2MvFGhtzY4XOqGT7/vvvUavVvPPOO4qMl56ezoEDB2S5WxSKUgk5iYmJZGRkPLdL3iD3PlHyyDy3KPXq2ZqjRcvea0lka6EwWWYqdL+d93Kxpp6t+WNfXxakpqayatUq3nvvPcqVU2YD/uHDh6VckCi0wMBA1Gp1ntqKT0KJLjk5SusMJci9T5QsElCKMqG+rQWOalP2hiUSnpyJEeS7aT3ncadyJrz6nP12/tNPPxEbG8snn3yi2JhSLkgUhUajoXHjxgZ3ZlKij3eO0jpDmUPufaKkkIBSlBk25sa89WIFIlMyORWTxvXkDKLTssjW6n4jr2hhjJPalEZ2Fs/dJnStVouXlxe9e/emZs2aio27d+9eevbsqUjrRlH2aTQaXn75ZYPHUXqGMikpyeBxniW594mSQP5liTLHwcoEB6v7S7rSQgz++ecfTp06xbfffqvYmDnlgr788kvFxhRlV1JSEufPn1ek3mNMTAyg3AxlVFSUweOUBHLvE8+STCuIMk9uqLpSQfXq1aNr166KjSnlgkRR5PSHdnd3f/yLHyM6Ohq1Wq1vnWiI0ryH8nHk3ieeJpmhFKKMCwsLY+fOnXz33XeK/oCRckGiKDQaDaamptSvX9/gsZQqGQRlO6AU4mmSGUohyrjvvvuO8uXLM2zYMMXGlHJBoqg0Gg3169fHzMzM4LGUDChLe1KOECWFBJRClGEpKSn88MMPvP/++6jVasXGlXJBoqiU6pADuoBSif2TIDOUQihFAkohyrBNmzaRkJCgaKkg0C13V65cWcoFiUJJT0/n9OnTigaUMkMpRMkiAaUQZZRWq2XJkiX07duXatWqKTq2r6+vlAsShXbmzBkyMjIUCyiV6uMNMkMphFLkp4EQZdRff/1FSEgI48aNU3Tc8PBwgoODZblbFJpGo0GlUtG4cWNFxlM6KSczM5OMjAxFxhPieSUBpRBllJeXFw0aNKBTp06Kjrtv3z4pFySKRKPRUKtWLaytDes9DbqZd6WXvAFZ9hbCQBJQClEGXblyhV27djFu3DjFa9H5+vry0ksvYWtrq+i4ouzSaDSK1J8ESEhIICsrS9EZSkCWvYUwkASUQpRBy5cvx8bGhrfeekvRcaVckCiq7OxsgoKCFE3IAWW65IDMUAqhFAkohShjkpKSWL16NR988IF+9kUpR44cITExUQJKUWiXLl0iKSlJ8YBSZiiFKFkkoBSijPH29iYxMZHRo0crPravry+VKlVSLDgQZV9gYCCAohneoFxAKTOUQihDAkohyhCtVouXlxf9+vXDxcVF8fGlXJAoKo1GQ9WqVbG3t1dkPKWXvGWGUghlyE8FIcqQAwcOcPbsWcVLBQGEh1/n9OnTstwtikTJDjmgCyitra0xNzdXZLycgFJmKIUwjMmzPgEhhHK8vLxo3Lgx7du3N3isyJRMTsWkcT0pg+i7WWRrLZj1bwR3rMz4PTyJRnYWOFjJLUQUTKvVotFo+OijjxQbU8mSQXB/yVtmKIUwjPw0EKKMuHjxInv27GH16tUGlQqKu5vF3rBEwpMzMQKycz1nYmpGbAbER6cRGJ2Gs9qEV12tsTE3Nvj8Rdlz48YNoqKiFJ+hVGq5G2TJWwilyJK3EGXE8uXLsbOzY+jQoU88RkhsGqvPxnE9ORPIG0zmlvP49eRMVp+N40zs3Sc+pii7NBoNgGI1KEH5GUoLCwtAlryFMJQElEKUAYmJiaxdu5ZRo0bpf0AWVUhsGrvDksjSgraQ79ECWVrYFZZISGzaEx1XlF0ajQYbGxtFE8SU7OMNoFKpsLS0lBlKIQwkAaUQZcCGDRtITk7m448/fqL3x6ZlsfdakkHnsPdaEnF3swwaQ5QtgYGBNG3aVNFuTUrPUIJuH6XMUAphGAkohSjlsrOzWbp0Kf3798fJyanA1124cIHOnTtjZ2eHtbU1vXr1IiIiAoAJX3/LrG71md7Wle0zxpGZrlvCPrBiPp7u9nn+ZNy9PxOZlZHBogHt8HS3Z8+SmewNSwRgwIABVKhQAZVKxYwZMwz+jFptYedMhVKUuOZKZ3hD8QSUVlZWMkMphIEkKUeIUs7Pz49z586xZs2aR74uIiICrVbLzJkzCQ4OZsWKFYwfP573P53EhtlTcOvYE4dabvy1ZhEVXWvS6d3x+vcOnrtK/3djUzP93w9tXE7cjXD91+HJmUSmZGJubk7fvn3ZuHHjE32mhzPMwUgFFc2NcSpnKhnmxUDpax4bG0tYWJiiAWV2drbiS96gCyhlhlIIw8gdWYhSbsmSJbi7u9OmTZtHvq5NmzYcPHhQ//XGjRsJCQlh5doNALwyfjr21WoRuGcrgbu35Ako3Tr0wMTcIk9B87gb1/hrzSK6vD+B/cu+BnRLHqdi0ti0aRP79u0rckD5qAzzbC3cTssiOi1LMswVVFzXPCgoCFCuQw5AfHw82dnZimZ5A7KHUggFyJK3EKXY+fPn8fX1Zdy4cY/dp2Zmdn9m8fDhwyQnJ9OuXTuuXL0CQHl7BwBeqORI3I3wPEueM9pVY3obF3bN+1z/+K55njTu2R+XRi30r8sGridnPNFnkQzzp684r7lGo8HS0pI6deooc7Io38c7hyx5C2E4CSiFKMWWLVuGvb09gwYNKvR7zp49y4ABA6hbty7z5s0jPUsXIOYEirkDyapujXnjfwsZtnADzg3cOfrzGkL+/I2L//7N5f/8adFvGAm3bwKQmniH5PhYotOKnphTkjPMn+X+zeI8dnFfc41GQ6NGjTA2Vm4GWek+3jkkKUcIw8mStxCl1J07d1i3bh0TJkwodKmgkJAQunTpgrW1NX5+ftjY2FDB0Vk3XlQkFuWsuRN1ExtHZ1QqFW4de+jfq1IZcSXwKLevnOeFSo6kp6bw3Tv3nz/+y3rMrdS8OuGrIgVCSmWYO6pNFVn+fpb7N5/WsYv7mud0yOnYsaNBx3iQzFAKUXJJQClEKfXjjz+SlpZW6LZ24eHhdO7cmdjYWCZNmoS/vz9qtZpmvd/k6JbV7POaiUMtNxIiI+j+yVQAtk8fi01VV16oVIVj29YB4FSvKfbVajN0vi4J6Nalc/yxcj71Or+Ke+9BGKlg69atHDt2DNCVjlm9ejWDBw+mXLlyD52X77VEsg2ciMvWwt6wRN56scITj/Es928+7WMrfc3zC4TfWv8Ham2Gom06cwLK4thDGR8fr+iYQjxvJKAUohTKKRU0cOBAHB0dC/WeS5cuERUVBYCHhwcArq6ufOmrodfEWfy9zouL//6De59BtH97NAD21Wvz7y8bSIyKxNregV4TZ/Fimy4A2Nyb2VRXOMwfgL1rTRxq16OihTFTpkwhLCwMgN27d7N7927WrFnD+fPnSU9Pp0OHDqxatQpjm8r8vOZ7Dq5dzN3kJBp2e43Xpy7AxMycAyvm88eqBXk+w8yj4Zia62ZjszIy8BrSmduXz9FxxFh6jvuSyJRMqqhN87xnypQpfPPNN4+8NiGxaey9lqQPsgq7l7CXizX1bM0fOfbjFPexL126RIMGDUhLS8PX15d6L3Vg+ZeTOLV/J9nZWbTqP5zuY6ahUqn4c/W3BO7+mYTbNylna0+nd8fTasBwALwnjiA8+AQpd+KpVL02vSd9jda9NetC4zh66B92zplM3I1rODdsxsCvlmHj6EwqJgQp2KYzKjqaF154AVNT08e/uAisrKy4cfOmomMK8bxRaaXAmxClzt69e+nVqxdHjx7lpZdeMmis38OTCIpOKzCQKQojoElFC7o7552JPHjwIDNmzGDgwIH6kkX9+/en04hxjO3TMU/Joh5jv6DTu+P1AWXukkUNu72mzzQ/uHYxf65eREZaCh1HjOXVcV/SpKIFPVys6d+/PwMGDACgXr16NGrUqMBzztlL+KT6uJajvq1h3YmK89g9e/bk0KFDpKSk4Ovryy/+J1j99Re0GTKK1MR4NL9tZei81TTs9hobJryNvWst7Jyr89faxSREXmfcloM41K7Hgr4taPnG26iMjPH7bi7mamum+oWQlX6Xea82RW1jR6uBI/h9+RxcGrVg5PKtec5DhW75vihBeH4zn9rsLCpbmRm8BSD32LdT0sHIWEpTCWEA+d8iRCnk5eVFixYtaNWqlcFjNbKzIDBamaSW7HvjPaigkkXxP28ClClZlDvDvF69evTt2xcrK6tHnu+z3L/5NI69detWgoKC+OCDD1iyZAkAAYcPAdDt489JjL6F5retBP62lYbdXmPo/DWY3KszmpZ0B98lX3H78jkcatdjwi+H9c9d/s+fc/4HSI6L5trJAJLjY+g22pNWA4ZzPSQIzZ6t3ImK1FcOgLwJPVq0jwyEH7UFQGVkbNAWgHzHNtK9V0pTCfHkJMtbiFImNDSU/fv3F6pUUGE4WJngrDbB0JFUgLPaJN9ZnYJKFt24plsWV6JkEaDPMJ89ezZqtRo3NzcCAgK4dOkSlpaWqFQq9u3bR3p6OmPHjqWaY2Wmd6zN/qWz9WP/ufpb/u+1lvyvtTPzernz7/b1+vEPrl3MN680xtPdnlUfvAbc30u4aNEiXF1dsbCwoEaNGqxcuVJ3XVSqPH8+//xzRowYgZ2lCR5N73cgyhkvx45ZE/B0t+e7d3oWePycY4NuFtjNzQ0LCws6depEcHAwEyZMYOHChVSoUEE/hukLtgBcCjjE5f/8AYi7qStOb5KraP2Ff//G2MQUpwbueZ5LSYgj4sxJ7FxqUM7Wntgb1x74HlbRjZmr4P2DHtWmszhLGUlpKiGKjwSUQpQyS5cupXLlygwcOFCxMV91tcbIwIjSSKUb51FylyzKva9RqZJF2Vr4/PPP8fHxYdGiRVy8eJGRI0fyySef5Jnh9PLyYtmyZTTs2Z+6HbpzcN0Sgg/sAuB6SBD1O/eizyTdzOevcycTeeEMAFmZmTTu2T/PZ9ICmrPn+Oyzz7CysuLbb79Fq9Xy8ccfExsbC0D//v3ZvHkzmzdvZujQobz57gcMnruKwXNX0W6Yrv96lRcb6McMO3mck/t2PHT9Hjy+Fl13oisxiQwcOBCVSsWCBQsIDAzktddeo1KlSrRs2VJ/Hjdu3KDd259Qzs4e74nD2bPwS4yMjTExy7sEvXvBNC4eO4gWLQv6NOfc4T+Iv3md797pyawudUiKjaLdWx/l+YXmyM+rmd6uGoc3r8ozlvfEEczt0ZD/tXZm6dAuXAk8qg+EV61aRfXq1bGysuKNN97g6JXIYitlVJJLUwlRFsiStxClSHx8POvXr2fSpEmYmxuWDJKbjbkxvVys2XVvtutJ9HJ59NLggyWL7Ozs9Ik9SpUs6v3ZV8ydO1f/uLe3N0FBQURFReVZ9v37778B6PHx5yQUYdm366hJZNxN45/1S/N8NtW9jBpnZ2defvllvL29iYmJ0SePPLgEHxmeRNPydcgGzv69D4CW/d8BdEHjzq8n0/n9z9i/dHae4+R3fCPgxx27iY6OZvbs2Xz44YcEBATouxTVrl1b/9r33nuPEUs2MenX49y6GIqJmRlLh3alUvUX9a/ZNe9zjv68BjuXGty5HUl2pm42Lzk+lpjrV1GpVGi1Wv17bKrovocXjx2k44ixBO3bQUJkBCkJuiD25oUQ2gz5QL/3ctPkkUz1C+HI8QC+/+gjunfvzqhRo5g6dSqRphXo+/n8/P8BFUJBWwBKWmkqIcoiCSiFKEXWrVtHenp6oUsFFUU9W3O0aNl7LYnMrGxURo9fwChsokVBJYu69BvMEQVLFsWFBDD4m/V06dKFiIgIAgMDMTExYeHChZw/f15/Pvb29gBcCDhEUsxtoHDLvgWxq1aLfuOn4bPka+rWrYuZmRnbt2/H2lo3Yzt79mxmzZpF3bp12bBhA9fL1SIbXZAW8uceqjVpReUauo4yh39aiVabTfthox8KKPOTDYReugygz/h3cnLSH7dOnTps3bqVbdu2MW3aNLLsbDjy0yrKVazE8R0bUalUtB06CoB9XjM5+vMaKrrWJCkmiururTl/5A8Afp3rQUp8DA616xN5IYSLAYdwqt+EOm27ojIyRqWC8pUcSU2IAyD0kB9uHXrku/fyekggK0f2QavV8tJLL9G6dWsAjm5dx9Gtuu/1iKVbqNO2K+cOH8B3sS6o12q1zA2M0n/2BzPP+0z6mnXRTkzr04a0tDQ6dOjAiRMnSEtPR6UywtTC8omz2vtM+pq9Vu3p52RG+/btCQ0NxcjIiObNm7NixQpFOwIJURpJQClECafValGpVGRlZbFs2TIGDRqEg4PD49/4BOrbWuCoNmWWz0Eq1Wn8UEJEjpzHncqZ8OpjZiah4JJFqw4F03viLA4qULLIsXY9Kiff5N/bt/Hw8ECr1VKjRg3MzMxo2bKlvi7mzZs3mTRpEtt2/Yb3xOGYWlg+ctm37+fzsK3q+sjPlxQbxYFNq2ndujUeHh54eHjw4Ycf0qVLFzw9PWnVqhVXrlxh8uTJjBw5krc3/gWA5retZKbfpcUbutnJ1MQE/lg5nz4ec/UBbmbGXRJu3eCFygWXh0rOyL/bUZcuXWjdujXBwcEAtGvXjktZalb6eHMnKpIKVZwYNGclVd0aA3Dt9AkAosMuAeiDSYDwYN1zkRdCAPjrh29p8dpQbBxdMDEzQ5udzd5F03Gu35SrQf8SF6HbH5vf3ssDK+brfmHJ0s1cW9lV0h+n3xcLMVeXw7GObgtARloq1Zu14W5KMvE38+7LfHD203vySBzrNtT/MuTv789Lbdtz5NDfVHVrTKWadTi4bgmOdRvSsNtr+u0NOVntv86djGvjFjjUrpfv2C5+IUQmq+jatStjx44lNDSUefPm4enpyY4dD29REOJ5IgGlECVMQd1SrFVZNBg4incHvFKsx0+NucWiIS+z7pddOLbozPXkDKLTcnVtsTDGSV20siqdOnXKt3tOZEombd/6iLZvPTzj2nHEODqOGPfIcWs0b6ufscoGeri7MfzPP/XPjxgxgvXr1+dZ9h05ciS7du0q1LJvj7Ff0PrNkY/9fJf/O0xi9C0GTp3C66+/zsGDB1myZAkhISHMmTNH/zpvb29OnTpFVrbul4SAnd5Ylq9Aw259AUhLTCA9NYVfvrqf5X7zXDAbP3uHMZsOFHj8CveWnW/cuAFAREQEANWqVQNgxowZzJgxA9Bd88Q9gfmOM+qHX/l9+RxC/f0YOm8NR7b8wNEtq0mMvsVXh8Mws7Rin9dM/v5xKR+s2omNowsARsbG2FWrxdif/iQz/S7/e8kpz7h3k5PYMGEY6anJtBnyAUe2/ECLfm9zbOsatm/fzvbt2/WvbfByb6zK29z/umsfGnTtQ8SZkw8FlPnNft4IPUW7V/vht30TnTp1IlGr+zf6/kofg7PaU+OiuVjJhW+++YaYmBgcHByYN29egd8XIZ4nElAKUUI8rltKgtaYNoPe4zhw83x8sZUz2bdvHyqVit4dWlOx4v16kjkzpUrKyTC/npxZ6ESJ/KgAp3wyzMeMGUPv3r0B8iz72tracnTzKtR2BS/7ujZugY2jCyf3++DcwB3bqq5cOXGE21d0S+eJ0bcI8NlI9WZtsHWqBui6F1lbW7Njxw7MzMy4desWgwcP1i/BazQaWrZsibGRisuBx7h9+RxthnygL9autqmoX84H+MnjPexcavDK+OkABR7frV1X7Ozs8PLyIjMzk507d9KtWzeqVKlS5GuecPsGN88F8+3r90tS/fLVeNQ2FXHr0D3f629TxYU7UZEA3LmXKGVzb1Y3LSmRdWMGcfN8MEPm/sDOOZPo9dks/Szo0qVLCUoyZo2nbmZ6dpe61GzRnsFzVqK2eXRHnNyzn9dDNKiMjek1cTZXj+iC79DQUH3B8j2LvsTp3kzsk2a1W9nacz05g8uXL+t/SXFxcWH+/Cff9ylEWSFZ3kKUAIUtZ5ITABRnORNfX19atWr1UL9kpYPJHMWZYd68eXMGDBjAgAEDqFevHqBb9rWwsOA/H29+neNBakJcvsu+YScD2OI5ii2eo7hy4ggA/+36iZ1zJgO6ZeEdsz4jLOg4TvWaMHDSDOLj4xkzZgyWlpasX7+e+vXrc/veEvzixYvp0aMH3t7eVDQ35riPLmmm5b3lbgAzSysavtxX/wfAqrwNNVu2f+Txq1Qox9atW8nOzmbixIk0bdqUVavyZlvn9qhr3nrQ+wydv4ah89foZ047vzcBxzoNCPDZyM17Ge+hh/w4td8HgKa9BpIUE8U+r5nsXTwDAPfebwKwdvRArp0KoMkr/flv108YmZhSuVZdkuNjANBoNPjv0Y3T/PVhtHzjHS7++/dDXZIKkjP7mZqYgF3Vajg3cCchIR6A1NRU+kyajZGxCf/5ePPbt/975PaGXpNm59nekHtmddDs71GpVESnZVG1alX279/PvHnziIiIkFlKIZBOOUI8c8+yU8uDMjIysLe357PPPuPLL79UZMzCOBN716AM876uRW+B+DQ6BJXEY+cozDXP6VY0YukWKlWvzfzezfI8X6GKM1P2BJKZfpdf53pw6vedmFpY0XHEWP1eWE93+8eei7m6PK0HjaTbaE8y0lKY0a46NVt24P0Vv+hf8907PQkPPpEnKSf37Ge1Ji9x4dhfD439zuJNXP7vEP7eK+g/fQm/fDWepr3e5M1Zy4G82xtyF9TPPfbwxZv0QT3AlCZ2+l+wGjRowNWrV0lKMiyLXIjSTpa8hXiGSko5k5zl7KNHj5KQkMArrxTvPs0H5c4wzy5kncAnaeWX29PoEFQSj52jMNf85Y88ePkjD/3XuYO53EzMzOk/fQn9py956Lnc77l+Joi4e4XQT/v9ymm/XUybNo0TCXAn5jaBu7dw4dhBAJzqNwEg+tolrpw4op/RDPDZSEWXmlRv1oa1owcSHnyCFv2GYePoQqUaL1K1XmPO/r2P0367sLKyIizoGIG/bcXE3IJjW9cWentD7rGT4qI5ud8Htw7dOX/4D0avCqBFixZcuHCBM2fO0KxZ3kBbiOeRBJRCFLMHl4qnTJlCZGQk69evz/N49WZtGPXDr/qvd8yaQICPN84NmjF6g65WYXxkBNunjyXs5HEqVHHidc8F1GrZnr1hifS0y+KTTz5hz549GBkZ8cYbb7Bu3bp8z6mgxJ+MLHvenL5Yv/z7NOVkmBe0jzRHUTPMC1Lc+zdL6rFzK+o1r2xpzK3U/DvcFIZTvSY41WsCwK2LoYBuC0LS1TtsXTSTAB9vzK3K0fKNd+jy/kQAwoKOs2PWZ/oxdsz6DPc+g6jerI0+8zzAx1v/vMdvJ0i7eZXTfrtwdHTE33uF7hprtaQl3Slwe0PYyQAABszwwraqa56xc8b3+O0EjpXt2bXmL9atW4dareaVV15h0aJFT3xNhCgrZMlbiGKmUqno378/AwYMAHRFrlNTU9GEXuTvmylcD9Hg7/09bYaMos9kXXeWsJPHWTt6IOmpKXkCyh/HDeXKicN0HzONgHulXz7fq8HMUs3B/43kwL69TJw4kZo1a3Lx4sWHkgUelfijp80GldEz7WOsD3gVyDB/lLi7Waw+G0eWAXdBYxW872ZT5Ov0LI+dn8Je803n4xULhN96sQJQvFsASsL2AiGeBzJDKcRT8GCnFIAEx/rciU4rdKeU5PhYzh8+QMPur9N2yCjMLdX8MvNTzv7zOy71mrB/z27efvttZs6ciampaZ5Wg6Dbq5mzvAkFJ/6g0r0vJ/HnSZeUDeFgZYKDVfFmmMPT6RBUEo+dn8Je81ddrQ0OhB9MoirOLQAlYXuBEM8DyfIW4imYPXs2arUaNzc3AgJ0S2vXkzJIfEynlNzib4aj1Wopb68ral7+XqHruIgwIq9cAODYsWOo1WrKly/PsmXL9O8t7X2MiyvDHHR7Cfu4lsNYpZs5K9T5oJsdfJJkoJJy7Mcep4BrnhMIG+LBQDhnC4Ch32UV4PzAFoDiHFsIcZ8ElEIUM09PT3x8fFi0aBEXL15k5Ehdoezou1kFdkppN+zjhzql5NDvUsm1WyUzXRfwpaWlsW3bNmrVqsWnn35KeHi4Yok/cXeffO9cSVff1oL33WxwUuuChYJujDmPO5Uz4X03G0UCumd57CdVHIFwcZaPKs6xhRA68quWEMUsv04p2dnZZGspUqeUd5dvRaVSkZhPAemcjiVt27alX79+HD9+nJMnT3Lt2jWuplrrl7mfVLYW9oYl6ve8lUU25sa89WKFp7Z/s6Qc+0kpnURVnFsAStr2AiHKopJxZxKijPL392fZsmUPdUoxMjIiLKhonVLUFWx5sW1XQg/9zuHNqwjw0QWjbh26Y2phhX312vz++++YmZmRkZGBlZUVGWblmPZmd66fOUlWRjofrNpJjeZtAcjMSGfXN1M4ud8HMwsrOr47jnb3WiB6TxxBePAJUu7EU6l6bXpP+hqte2uOnDrLtPGjOXXqFOnp6XTo0IFVq1ZRtWrVp39xi8nT2r9Z0o79JJQOhIuzfNSzKE0lxPNEAkohipGjo6O+U4pWq6VHjx4sXboUgFO/bgLy75SSW+5OKa97LmDb9DH4Lv6KClWcGPLND5hZqgEY/e1qFo3oQ0ZGBgBffPEFV1K12FathrGpGZf/O5xn3CObfyDAx5uOI8YSfe0ye779Hy4Nm+PSqDk3L4TQZsgHqIyM8ftuLpsmj+QLvxAOX7iKVqtl5syZBAcHs2LFCsaPH5+nF3NZ8ywDupIcTOamZCBcnOWjnnZpKiGeJ1I2SIhnROlyJglH97JmpgeDBw9myZIl+Pr6csO1FbfTstjnNZO/f1yaZ4ZyyaBOJMdFMfX3EGKvX2VB3xa07D+cftP+j8yMdH0v4x/HDeGc/wGm+oXgWsmOUY0q649brlw5nJ2dOXv2rAKfQoi8inMLQGnaXiBEaSD/W4R4RpQsZ5KSlMja2Z4sXLiQ8+fP6x+PfkQiTdyNMOycawB5M8YBfTCZkhBHxJmT2LnUoJytPfHZ92edDh8+THJyMu3atVPkMwjxoOLcAlDathcIUdJJlrcQz4iS5UyCflqOQ+VKtGzZktjYWABu3LhBWkrKY979cMZ4jrvJSWyYMIz01GQGzf4elUql23um1XL27FkGDBhA3bp1mTdvnoGfQIjCKc6AT4JJIQwjAaUQz5BS5UyskqIICgqidu3a+j2a7733Hlf+8y/wfTZVXLiTT8Y4QFpSIms/eZMboad5Z5E3zg3c9cc6c+YMnTp1Qq1W4+fnh62trWEfQAghRKknAaUQz5BSRaI/Gz+Wbdu2sW3bNgYOHAjAtGnTqN+wEQE+G7l54QwAoYf8OLXfB4CmvQaSFBPFPq+Z7F08AwD33m8CsHb0QK6dCqDJK/1Jiovm5H4f0lOTMYq7SefOnYmJieHDDz/E39+f3bt3G3T+QgghSj9JyhGiBMjdFtHQciYzZszgq6++wtfXl9vlnRnetkGe5ytUcWbKnkAy0+/y61wPTv2+E1MLKzqOGEv7t3XdeTzd7R865pTfTmCXfAuPQa/medzV1ZWrV68W5eMKIYQoYySgFKKEiLubVehyJs6FLGcSmZLJj+fiFTvHEXUqSOarEEKIh8hPBiFKiOLolpKT+HM9ObPQPbzzowKcpI+xEEKIAsgMpRAlnKHlTOLuZrH6bBxZBvxPN1bB+242UuBZCCFEviQpR4gSztByJkol/kgwKYQQoiCyfiXEc0D6GAshhChOsuQtxHOkOBJ/hBBCCAkohXgOSR9jIYQQSpKAUgghfYyFEEIYRJJyhBASTAohhDCIBJRCCCGEEMIgElAKIYQQQgiDSEAphBBCCCEMIgGlEEIIIYQwiASUQgghhBDCIBJQCiGEEEIIg0hAKYQQQgghDCIBpRBCCCGEMIgElEIIIYQQwiASUAohhBBCCINIQCmEEEIIIQwiAaUQQgghhDCIBJRCCCGEEMIgElAKIYQQQgiDSEAphBBCCCEMIgGlEEIIIYQwiASUQgghhBDCIBJQCiGEEEIIg0hAKYQQQgghDCIBpRBCCCGEMIgElEIIIYQQwiASUAohhBBCCINIQCmEEEIIIQwiAaUQQgghhDCIBJRCCCGEEMIgElAKIYQQQgiDSEAphBBCCCEMIgGlEEIIIYQwiASUQgghhBDCIBJQCiGEEEIIg0hAKYQQQgghDCIBpRBCCCGEMIgElEIIIYQQwiASUAohhBBCCINIQCmEEEIIIQwiAaUQQgghhDCIBJRCCCGEEMIgElAKIYQQQgiDSEAphBBCCCEM8v8ETVAGSbKaWgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Check if there is a directed edge from node 53306 to node 53309\n",
        "if G.has_edge(53309,53304):\n",
        "    print(\"Nodes 53306 and 53309 are connected.\")\n",
        "else:\n",
        "    print(\"Nodes 53306 and 53309 are not connected.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_BOwqBIKLW_",
        "outputId": "8196e59b-f5f9-4c16-9d3a-bed680087a0b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nodes 53306 and 53309 are not connected.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Extract a subgraph with 1000 nodes\n",
        "subgraph_nodes = list(nodes)[:1000]\n",
        "subgraph = G.subgraph(subgraph_nodes)\n",
        "\n",
        "# Use kamada_kawai_layout for a more evenly spaced layout\n",
        "pos = nx.kamada_kawai_layout(subgraph)\n",
        "\n",
        "# Draw the subgraph\n",
        "nx.draw(subgraph, pos, with_labels=False, node_size=10, node_color='blue', alpha=0.6, edge_color='gray', arrowsize=5)\n",
        "\n",
        "\n",
        "plt.title('Directed Network Communication Subgraph (1000 Nodes)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "CK5Nds40A2yi",
        "outputId": "f7eced75-92ee-4ee2-d061-31157f632596"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Analysis**"
      ],
      "metadata": {
        "id": "QhWxN_lOOtep"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Average degree**"
      ],
      "metadata": {
        "id": "DK4aFWoCR0Xj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Average Degree\n",
        "avg_degree = sum(dict(G.degree()).values()) / len(G)\n",
        "print(f\"Average Degree: {avg_degree}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZR72TI9AcNE",
        "outputId": "db2d9416-3d76-4b1e-9964-4d9803ad6083"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Degree: 1.8472906403940887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Degree distribution**"
      ],
      "metadata": {
        "id": "EZtVGj1mR_JU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Calculate network density\n",
        "num_edges = G.number_of_edges()\n",
        "num_nodes = G.number_of_nodes()\n",
        "\n",
        "network_density = (2 * num_edges) / (num_nodes * (num_nodes - 1))\n",
        "\n",
        "print(f\"Network Density: {network_density}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oK70OUTfOimO",
        "outputId": "25f315c5-5ec9-43db-c272-0c6c941448ea"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Network Density: 0.0011381950957449715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Calculate degree centralization\n",
        "degree_centralization = nx.degree_centrality(G)\n",
        "print(\"degree centralization\",degree_centralization)\n",
        "\n",
        "\n",
        "# Identify nodes with high degree centrality\n",
        "high_degree_nodes = [node for node, centrality in degree_centralization.items() if centrality > 0.1]\n",
        "print(\"high degree nodes\",high_degree_nodes)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyMcq0e1NgJv",
        "outputId": "dfe2554f-e0a6-4bda-892f-eb2e2315b886"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "degree centralization {0: 0.0012322858903265558, 53249: 0.0006161429451632779, 57344: 0.0006161429451632779, 16387: 0.0018484288354898336, 61443: 0.0006161429451632779, 53248: 0.0006161429451632779, 53251: 0.0006161429451632779, 20487: 0.0006161429451632779, 1: 0.0012322858903265558, 57363: 0.0006161429451632779, 57366: 0.0006161429451632779, 22: 0.0012322858903265558, 4120: 0.0006161429451632779, 61463: 0.0006161429451632779, 23: 0.0030807147258163897, 25: 0.0006161429451632779, 57373: 0.0006161429451632779, 36897: 0.0006161429451632779, 8229: 0.0006161429451632779, 53286: 0.0006161429451632779, 16422: 0.0006161429451632779, 37: 0.0006161429451632779, 41001: 0.0006161429451632779, 49195: 0.0006161429451632779, 49197: 0.0006161429451632779, 49199: 0.0006161429451632779, 41007: 0.0006161429451632779, 61489: 0.0006161429451632779, 4144: 0.0006161429451632779, 57395: 0.0006161429451632779, 53: 0.2914356130622304, 53304: 0.0006161429451632779, 53309: 0.0006161429451632779, 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            "high degree nodes [53, 443, 445]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate the degree of each node\n",
        "degrees = dict(G.degree())\n",
        "print(len(degrees.keys()))\n",
        "print(\"nodes and their degrees\",degrees)\n",
        "# Plot the degree distribution\n",
        "plt.hist(list(degrees.values()), bins=100, alpha=0.5,log=True)\n",
        "plt.title(\"Degree Distribution\")\n",
        "plt.xlabel(\"Degree\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "I61ExoUCAelT",
        "outputId": "1ec9337c-3168-4695-c090-c374a01927f1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1624\n",
            "nodes and their degrees {0: 2, 53249: 1, 57344: 1, 16387: 3, 61443: 1, 53248: 1, 53251: 1, 20487: 1, 1: 2, 57363: 1, 57366: 1, 22: 2, 4120: 1, 61463: 1, 23: 5, 25: 1, 57373: 1, 36897: 1, 8229: 1, 53286: 1, 16422: 1, 37: 1, 41001: 1, 49195: 1, 49197: 1, 49199: 1, 41007: 1, 61489: 1, 4144: 1, 57395: 1, 53: 473, 53304: 1, 53309: 1, 57406: 1, 41022: 1, 20541: 1, 20545: 2, 49218: 1, 57410: 1, 49220: 1, 41029: 1, 45126: 1, 49223: 1, 49225: 1, 53322: 1, 45129: 1, 80: 114, 81: 1, 57433: 1, 53353: 1, 53356: 1, 57452: 1, 49262: 1, 49263: 1, 49266: 1, 49269: 1, 123: 5, 49277: 1, 57470: 9, 49278: 1, 45183: 1, 20611: 1, 49286: 1, 49288: 1, 53385: 1, 32905: 1, 61580: 1, 49294: 2, 61583: 1, 37008: 1, 53392: 1, 37011: 1, 49301: 1, 49304: 1, 53404: 1, 57504: 1, 61600: 1, 49315: 1, 53412: 1, 4269: 1, 49326: 1, 49328: 1, 16562: 1, 49332: 1, 16564: 1, 49334: 1, 4278: 1, 49342: 1, 12479: 1, 53442: 1, 49349: 1, 32971: 1, 61643: 1, 41170: 1, 16597: 1, 49367: 1, 53467: 1, 53470: 1, 61667: 1, 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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RCLC6gLoKCwvT+vXrFRISovLycvXo0UNjx45Vu3btrC4NAABYrMmf0fH391dISIgkqbKyUsYYGWMsrgoAADQGlged9evXa9SoUYqNjZXD4dBbb71Vq4/T6VRCQoKCg4PVt29fbdq0yWP9qVOnlJSUpLi4ON1///2KjIxsoOoBAEBjZnnQKS8vV1JSkpxO51nXL126VNnZ2Zo5c6a2bNmipKQkZWRk6OjRo+4+rVu31vbt27Vv3z69/vrrKioqaqjyAQBAI2Z50Bk+fLgeffRRjRkz5qzr58yZo8mTJ2vixIlKTEzUggULFBISokWLFtXqGx0draSkJH300Ufn3F9lZaVKSko8PgAAwJ4sDzrnU1VVpfz8fKWnp7vb/Pz8lJ6ero0bN0qSioqKVFpaKkkqLi7W+vXr1b1793Nuc9asWYqIiHB/OnbsWL8HAQAALNOog87x48dVU1Oj6Ohoj/bo6GgVFhZKkg4cOKD+/fsrKSlJ/fv315133qmePXuec5vTp09XcXGx+3Po0KF6PQYAAGCdJn97eZ8+fbRt27YL7h8UFKSgoKD6KwgAADQajfqMTmRkpPz9/WtNLi4qKlJMTIxFVQEAgKaiUQedwMBApaSkKDc3193mcrmUm5urtLQ0CysDAABNgeWXrsrKyrR371738r59+7Rt2za1bdtW8fHxys7OVmZmplJTU9WnTx/NnTtX5eXlmjhxYp3263Q65XQ6VVNTU9dDAAAAjZTDWPwY4bVr12rQoEG12jMzM7VkyRJJ0rPPPqvZs2ersLBQycnJmjdvnvr27euT/ZeUlCgiIkLFxcUKDw/3yTbP+EvO7lpt9wy9zKf7AACgObrQ72/Lg47VCDoAADQ9F/r93ajn6AAAANQFQQcAANgWQQcAANhWsw06TqdTiYmJ6t27t9WlAACAetJsg05WVpZ27typvLw8q0sBAAD1pNkGHQAAYH8EHQAAYFsEHQAAYFsEHQAAYFvNNuhw1xUAAPbXbIMOd10BAGB/zTboAAAA+yPoAAAA2yLoAAAA2yLoAAAA2yLoAAAA22q2QYfbywEAsL9mG3S4vRwAAPtrtkEHAADYH0EHAADYFkEHAADYFkEHAADYFkEHAADYFkEHAADYVrMNOjxHBwAA+2u2QYfn6AAAYH/NNugAAAD7I+gAAADbIugAAADbIugAAADbIugAAADbIugAAADbIugAAADbIugAAADbarZBhycjAwBgf8026PBkZAAA7K/ZBh0AAGB/BB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbzTbo8FJPAADsr9kGHV7qCQCA/TXboAMAAOyPoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyr2QYdp9OpxMRE9e7d2+pSAABAPWm2QScrK0s7d+5UXl6e1aUAAIB60myDDgAAsD+CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CDgAAsK0mH3QOHTqkgQMHKjExUVdeeaWWLVtmdUkAAKCRCLC6gLoKCAjQ3LlzlZycrMLCQqWkpGjEiBFq1aqV1aUBAACLNfmg0759e7Vv316SFBMTo8jISJ08eZKgAwAArL90tX79eo0aNUqxsbFyOBx66623avVxOp1KSEhQcHCw+vbtq02bNp11W/n5+aqpqVHHjh3ruWoAANAUWB50ysvLlZSUJKfTedb1S5cuVXZ2tmbOnKktW7YoKSlJGRkZOnr0qEe/kydPavz48XruuecaomwAANAEWH7pavjw4Ro+fPg518+ZM0eTJ0/WxIkTJUkLFizQe++9p0WLFmnatGmSpMrKSl1//fWaNm2arrrqqvPur7KyUpWVle7lkpISHxwFAABojCw/o3M+VVVVys/PV3p6urvNz89P6enp2rhxoyTJGKMJEyZo8ODB+tWvfvWj25w1a5YiIiLcHy5zAQBgX4066Bw/flw1NTWKjo72aI+OjlZhYaEkacOGDVq6dKneeustJScnKzk5WZ988sk5tzl9+nQVFxe7P4cOHarXYwAAANax/NJVXfXr108ul+uC+wcFBSkoKKgeKwIAAI1Foz6jExkZKX9/fxUVFXm0FxUVKSYmxqKqAABAU9Gog05gYKBSUlKUm5vrbnO5XMrNzVVaWpqFlQEAgKbA8ktXZWVl2rt3r3t537592rZtm9q2bav4+HhlZ2crMzNTqamp6tOnj+bOnavy8nL3XVjecjqdcjqdqqmpqeshAACARsphjDFWFrB27VoNGjSoVntmZqaWLFkiSXr22Wc1e/ZsFRYWKjk5WfPmzVPfvn19sv+SkhJFRESouLhY4eHhPtnmGX/J2V2r7Z6hl/l0HwAANEcX+v1tedCxGkEHAICm50K/vxv1HB0AAIC68CrofPXVV76uAwAAwOe8Cjpdu3bVoEGD9Oqrr6qiosLXNTUIp9OpxMRE9e7d2+pSAABAPfEq6GzZskVXXnmlsrOzFRMToylTppzzjeKNVVZWlnbu3Km8vDyrSwEAAPXEq6CTnJysp59+WkeOHNGiRYtUUFCgfv36qUePHpozZ46OHTvm6zoBAAAuWp0mIwcEBGjs2LFatmyZnnjiCe3du1f33XefOnbsqPHjx6ugoMBXdQIAAFy0OgWdzZs36/bbb1f79u01Z84c3Xffffryyy+Vk5OjI0eOaPTo0b6qEwAA4KJ59WTkOXPmaPHixdq1a5dGjBihl19+WSNGjJCf3/e5qXPnzlqyZIkSEhJ8WSsAAMBF8SrozJ8/X7/+9a81YcIEtW/f/qx9oqKi9OKLL9apOAAAgLrwKujs2bPnR/sEBgYqMzPTm803CN51BQCA/Xk1R2fx4sVatmxZrfZly5bppZdeqnNRDYHbywEAsD+vgs6sWbMUGRlZqz0qKkp/+tOf6lwUAACAL3gVdA4ePKjOnTvXau/UqZMOHjxY56IAAAB8waugExUVpR07dtRq3759u9q1a1fnogAAAHzBq6Bzyy236K677tKaNWtUU1OjmpoarV69WlOnTtX//d//+bpGAAAAr3h119Ujjzyi/fv3a8iQIQoI+H4TLpdL48ePZ44OAABoNLwKOoGBgVq6dKkeeeQRbd++XS1btlTPnj3VqVMnX9dXb7i9HAAA+3MYY4zVRVippKREERERKi4uVnh4uE+3/Zec3bXa7hl6mU/3AQBAc3Sh399endGpqanRkiVLlJubq6NHj8rlcnmsX716tTebBQAA8Cmvgs7UqVO1ZMkSXXvtterRo4ccDoev6wIAAKgzr4LOG2+8ob///e8aMWKEr+sBAADwGa9uLw8MDFTXrl19XQsAAIBPeRV07r33Xj399NNq5vOYAQBAI+fVpat///vfWrNmjVasWKErrrhCLVq08Fj/5ptv+qQ4AACAuvAq6LRu3VpjxozxdS0NiufoAABgfzxHh+foAADQ5Fzo97dXc3Qkqbq6Wh9++KEWLlyo0tJSSdKRI0dUVlbm7SYBAAB8yqtLVwcOHNCwYcN08OBBVVZWaujQoQoLC9MTTzyhyspKLViwwNd1AgAAXDSvzuhMnTpVqamp+uabb9SyZUt3+5gxY5Sbm+uz4gAAAOrCqzM6H330kf7zn/8oMDDQoz0hIUGHDx/2SWEAAAB15dUZHZfLdda7lb7++muFhYXVuSgAAABf8CroXHPNNZo7d6572eFwqKysTDNnzuS1EAAAoNHw6tLVU089pYyMDCUmJqqiokK/+MUvtGfPHkVGRupvf/ubr2sEAADwildBJy4uTtu3b9cbb7yhHTt2qKysTJMmTdK4ceM8JicDAABYyaugI0kBAQH65S9/6ctaGhRPRgYAwP68Cjovv/zyedePHz/eq2IaUlZWlrKystxPVgQAAPbjVdCZOnWqx/Lp06f17bffKjAwUCEhIU0i6AAAAPvz6q6rb775xuNTVlamXbt2qV+/fkxGBgAAjYbX77r6oW7duunxxx+vdbYHAADAKj4LOtL3E5SPHDniy00CAAB4zas5Ou+8847HsjFGBQUFevbZZ3X11Vf7pDAAAIC68iroXH/99R7LDodDl1xyiQYPHqynnnrKF3UBAADUmVdBx+Vy+boOAAAAn/PpHB0AAIDGxKszOtnZ2Rfcd86cOd7sAgAAoM68Cjpbt27V1q1bdfr0aXXv3l2StHv3bvn7+6tXr17ufg6HwzdVAgAAeMGroDNq1CiFhYXppZdeUps2bSR9/xDBiRMnqn///rr33nt9WiQAAIA3vJqj89RTT2nWrFnukCNJbdq00aOPPtpk7rpyOp1KTExU7969rS4FAADUE6+CTklJiY4dO1ar/dixYyotLa1zUQ0hKytLO3fuVF5entWlAACAeuJV0BkzZowmTpyoN998U19//bW+/vpr/eMf/9CkSZM0duxYX9cIAADgFa/m6CxYsED33XeffvGLX+j06dPfbyggQJMmTdLs2bN9WiAAAIC3vAo6ISEh+utf/6rZs2fryy+/lCR16dJFrVq18mlxAAAAdVGnBwYWFBSooKBA3bp1U6tWrWSM8VVdAAAAdeZV0Dlx4oSGDBmiyy67TCNGjFBBQYEkadKkSdxaDgAAGg2vgs4999yjFi1a6ODBgwoJCXG333zzzVq5cqXPigMAAKgLr+bofPDBB1q1apXi4uI82rt166YDBw74pDAAAIC68uqMTnl5uceZnDNOnjypoKCgOhcFAADgC14Fnf79++vll192LzscDrlcLj355JMaNGiQz4oDAACoC68uXT355JMaMmSINm/erKqqKj3wwAP67LPPdPLkSW3YsMHXNQIAAHjFqzM6PXr00O7du9WvXz+NHj1a5eXlGjt2rLZu3aouXbr4ukYAAACvXPQZndOnT2vYsGFasGCB/vCHP9RHTQAAAD5x0Wd0WrRooR07dtRHLQAAAD7l1aWrX/7yl3rxxRd9XQsAAIBPeTUZubq6WosWLdKHH36olJSUWu+4mjNnjk+KAwAAqIuLCjpfffWVEhIS9Omnn6pXr16SpN27d3v0cTgcvqsOAACgDi4q6HTr1k0FBQVas2aNpO9f+TBv3jxFR0fXS3EAAAB1cVFzdH74dvIVK1aovLzcpwU1FKfTqcTERPXu3dvqUgAAQD3xajLyGT8MPk1JVlaWdu7cqby8PKtLAQAA9eSigo7D4ag1B4c5OQAAoLG6qDk6xhhNmDDB/eLOiooK/eY3v6l119Wbb77puwoBAAC8dFFBJzMz02P5l7/8pU+LAQAA8KWLCjqLFy+urzoAAAB8rk6TkQEAABozgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtgg4AALAtWwSdMWPGqE2bNrrhhhusLgUAADQitgg6U6dO1csvv2x1GQAAoJGxRdAZOHCgwsLCrC4DAAA0MpYHnfXr12vUqFGKjY2Vw+HQW2+9VauP0+lUQkKCgoOD1bdvX23atKnhCwUAAE2O5UGnvLxcSUlJcjqdZ12/dOlSZWdna+bMmdqyZYuSkpKUkZGho0ePNnClAACgqQmwuoDhw4dr+PDh51w/Z84cTZ48WRMnTpQkLViwQO+9954WLVqkadOmXfT+KisrVVlZ6V4uKSm5+KIBAECTYPkZnfOpqqpSfn6+0tPT3W1+fn5KT0/Xxo0bvdrmrFmzFBER4f507NjRV+UCAIBGplEHnePHj6umpkbR0dEe7dHR0SosLHQvp6en68Ybb9T777+vuLi484ag6dOnq7i42P05dOhQvdUPAACsZfmlK1/48MMPL7hvUFCQgoKC6rEaAADQWDTqMzqRkZHy9/dXUVGRR3tRUZFiYmIsqgoAADQVjTroBAYGKiUlRbm5ue42l8ul3NxcpaWlWVgZAABoCiy/dFVWVqa9e/e6l/ft26dt27apbdu2io+PV3Z2tjIzM5Wamqo+ffpo7ty5Ki8vd9+F5S2n0ymn06mampq6HgIAAGikHMYYY2UBa9eu1aBBg2q1Z2ZmasmSJZKkZ599VrNnz1ZhYaGSk5M1b9489e3b1yf7LykpUUREhIqLixUeHu6TbZ7xl5zdtdruGXqZT/cBAEBzdKHf35YHHasRdAAAaHou9Pu7Uc/RAQAAqAuCDgAAsK1mG3ScTqcSExPVu3dvq0sBAAD1pNkGnaysLO3cuVN5eXlWlwIAAOpJsw06AADA/gg6AADAtgg6AADAtgg6AADAtix/BYRVrHoFxA8fIsgDBAEAqD/N9owOd10BAGB/zTboAAAA+yPoAAAA2yLoAAAA2yLoAAAA2yLoAAAA22q2QYeXegIAYH/NNuhwezkAAPbXbIMOAACwP4IOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwrQCrC7CK0+mU0+lUTU2NpXX8JWd3rbZ7hl5mQSUAANhPsz2jwwMDAQCwv2YbdAAAgP0RdAAAgG0RdAAAgG0RdAAAgG0RdAAAgG0RdAAAgG0RdAAAgG0RdAAAgG0126DjdDqVmJio3r17W10KAACoJ8026PBkZAAA7K/ZBh0AAGB/BB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbBB0AAGBbzTbo8FJPAADsr9kGHV7qCQCA/TXboAMAAOyPoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGyLoAMAAGwrwOoCrOJ0OuV0OlVTU2N1KbX8JWe3x/I9Qy+zqBIAAJq2ZntGJysrSzt37lReXp7VpQAAgHrSbIMOAACwP4IOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLYIOAACwLVsEnXfffVfdu3dXt27d9MILL1hdDgAAaCQCrC6grqqrq5Wdna01a9YoIiJCKSkpGjNmjNq1a2d1aQAAwGJN/ozOpk2bdMUVV6hDhw4KDQ3V8OHD9cEHH1hdFgAAaAQsDzrr16/XqFGjFBsbK4fDobfeeqtWH6fTqYSEBAUHB6tv377atGmTe92RI0fUoUMH93KHDh10+PDhhigdAAA0cpYHnfLyciUlJcnpdJ51/dKlS5Wdna2ZM2dqy5YtSkpKUkZGho4ePdrAlQIAgKbG8qAzfPhwPfrooxozZsxZ18+ZM0eTJ0/WxIkTlZiYqAULFigkJESLFi2SJMXGxnqcwTl8+LBiY2PPub/KykqVlJR4fAAAgD016snIVVVVys/P1/Tp091tfn5+Sk9P18aNGyVJffr00aeffqrDhw8rIiJCK1as0IwZM865zVmzZunhhx+u99rR+P0lZ7fH8j1DL7Ookqbrh2MoMY5NHb9T1FVj+2+r5Wd0zuf48eOqqalRdHS0R3t0dLQKCwslSQEBAXrqqac0aNAgJScn69577z3vHVfTp09XcXGx+3Po0KF6PQYAAGCdRn1G50Jdd911uu666y6ob1BQkIKCguq5IgAA0Bg06jM6kZGR8vf3V1FRkUd7UVGRYmJiLKoKAAA0FY066AQGBiolJUW5ubnuNpfLpdzcXKWlpVlYGQAAaAosv3RVVlamvXv3upf37dunbdu2qW3btoqPj1d2drYyMzOVmpqqPn36aO7cuSovL9fEiRPrtF+n0ymn06mampq6HgIAAGikLA86mzdv1qBBg9zL2dnZkqTMzEwtWbJEN998s44dO6YHH3xQhYWFSk5O1sqVK2tNUL5YWVlZysrKUklJiSIiIuq0LQAA0DhZHnQGDhwoY8x5+9xxxx264447GqgiAABgF416jg4AAEBdEHQAAIBtNdug43Q6lZiYqN69e1tdCgAAqCfNNuhkZWVp586dysvLs7oUAABQT5pt0AEAAPZH0AEAALZF0AEAALZF0AEAALZl+QMDrXLmFRDV1dWSpJKSEp/vo6K8zCfbqY/aUPv3wzhfvLP9jTOOTRu/U9RVQ/239cx2f+yhww7zYz1s7uuvv1bHjh2tLgMAAHjh0KFDiouLO+f6Zh90XC6Xjhw5orCwMDkcDp9ss6SkRB07dtShQ4cUHh7uk23iwjH+1mHsrcX4W4exb3jGGJWWlio2NlZ+fueeidNsL12d4efnd94kWBfh4eH8wVuI8bcOY28txt86jH3DupCXcjMZGQAA2BZBBwAA2BZBpx4EBQVp5syZCgoKsrqUZonxtw5jby3G3zqMfePV7CcjAwAA++KMDgAAsC2CDgAAsC2CDgAAsC2CDgAAsC2CTj1wOp1KSEhQcHCw+vbtq02bNlldUpO3fv16jRo1SrGxsXI4HHrrrbc81htj9OCDD6p9+/Zq2bKl0tPTtWfPHo8+J0+e1Lhx4xQeHq7WrVtr0qRJKivzzfvI7GzWrFnq3bu3wsLCFBUVpeuvv167du3y6FNRUaGsrCy1a9dOoaGh+vnPf66ioiKPPgcPHtS1116rkJAQRUVF6f7773e/aw7nNn/+fF155ZXuB9GlpaVpxYoV7vWMfcN5/PHH5XA4dPfdd7vbGP/Gj6DjY0uXLlV2drZmzpypLVu2KCkpSRkZGTp69KjVpTVp5eXlSkpKktPpPOv6J598UvPmzdOCBQv08ccfq1WrVsrIyFBFRYW7z7hx4/TZZ58pJydH7777rtavX6/bbrutoQ6hyVq3bp2ysrL03//+Vzk5OTp9+rSuueYalZeXu/vcc889+te//qVly5Zp3bp1OnLkiMaOHeteX1NTo2uvvVZVVVX6z3/+o5deeklLlizRgw8+aMUhNSlxcXF6/PHHlZ+fr82bN2vw4MEaPXq0PvvsM0mMfUPJy8vTwoULdeWVV3q0M/5NgIFP9enTx2RlZbmXa2pqTGxsrJk1a5aFVdmLJPPPf/7TvexyuUxMTIyZPXu2u+3UqVMmKCjI/O1vfzPGGLNz504jyeTl5bn7rFixwjgcDnP48OEGq90Ojh49aiSZdevWGWO+H+sWLVqYZcuWuft8/vnnRpLZuHGjMcaY999/3/j5+ZnCwkJ3n/nz55vw8HBTWVnZsAdgA23atDEvvPACY99ASktLTbdu3UxOTo4ZMGCAmTp1qjGGv/2mgjM6PlRVVaX8/Hylp6e72/z8/JSenq6NGzdaWJm97du3T4WFhR7jHhERob59+7rHfePGjWrdurVSU1PdfdLT0+Xn56ePP/64wWtuyoqLiyVJbdu2lSTl5+fr9OnTHuP/k5/8RPHx8R7j37NnT0VHR7v7ZGRkqKSkxH1mAj+upqZGb7zxhsrLy5WWlsbYN5CsrCxde+21HuMs8bffVDT7l3r60vHjx1VTU+PxBy1J0dHR+uKLLyyqyv4KCwsl6azjfmZdYWGhoqKiPNYHBASobdu27j74cS6XS3fffbeuvvpq9ejRQ9L3YxsYGKjWrVt79P3h+J/t93NmHc7vk08+UVpamioqKhQaGqp//vOfSkxM1LZt2xj7evbGG29oy5YtysvLq7WOv/2mgaAD4IJlZWXp008/1b///W+rS2lWunfvrm3btqm4uFjLly9XZmam1q1bZ3VZtnfo0CFNnTpVOTk5Cg4OtroceIlLVz4UGRkpf3//WjPui4qKFBMTY1FV9ndmbM837jExMbUmhFdXV+vkyZP8bi7QHXfcoXfffVdr1qxRXFycuz0mJkZVVVU6deqUR/8fjv/Zfj9n1uH8AgMD1bVrV6WkpGjWrFlKSkrS008/zdjXs/z8fB09elS9evVSQECAAgICtG7dOs2bN08BAQGKjo5m/JsAgo4PBQYGKiUlRbm5ue42l8ul3NxcpaWlWViZvXXu3FkxMTEe415SUqKPP/7YPe5paWk6deqU8vPz3X1Wr14tl8ulvn37NnjNTYkxRnfccYf++c9/avXq1ercubPH+pSUFLVo0cJj/Hft2qWDBw96jP8nn3ziETZzcnIUHh6uxMTEhjkQG3G5XKqsrGTs69mQIUP0ySefaNu2be5Pamqqxo0b5/53xr8JsHo2tN288cYbJigoyCxZssTs3LnT3HbbbaZ169YeM+5x8UpLS83WrVvN1q1bjSQzZ84cs3XrVnPgwAFjjDGPP/64ad26tXn77bfNjh07zOjRo03nzp3Nd999597GsGHDzE9/+lPz8ccfm3//+9+mW7du5pZbbrHqkJqM3/72tyYiIsKsXbvWFBQUuD/ffvutu89vfvMbEx8fb1avXm02b95s0tLSTFpamnt9dXW16dGjh7nmmmvMtm3bzMqVK80ll1xipk+fbsUhNSnTpk0z69atM/v27TM7duww06ZNMw6Hw3zwwQfGGMa+of3vXVfGMP5NAUGnHjzzzDMmPj7eBAYGmj59+pj//ve/VpfU5K1Zs8ZIqvXJzMw0xnx/i/mMGTNMdHS0CQoKMkOGDDG7du3y2MaJEyfMLbfcYkJDQ014eLiZOHGiKS0tteBompazjbsks3jxYnef7777ztx+++2mTZs2JiQkxIwZM8YUFBR4bGf//v1m+PDhpmXLliYyMtLce++95vTp0w18NE3Pr3/9a9OpUycTGBhoLrnkEjNkyBB3yDGGsW9oPww6jH/j5zDGGGvOJQEAANQv5ugAAADbIugAAADbIugAAADbIugAAADbIugAAADbIugAAADbIugAAADbIugAAADbIugAsNSECRPkcDjkcDjUokULRUdHa+jQoVq0aJFcLpfV5QFo4gg6ACw3bNgwFRQUaP/+/VqxYoUGDRqkqVOnauTIkaqurq63/VZVVdXbtgE0DgQdAJYLCgpSTEyMOnTooF69eun3v/+93n77ba1YsUJLliyRJJ06dUq33nqrLrnkEoWHh2vw4MHavn27x3YeffRRRUVFKSwsTLfeequmTZum5ORk9/oJEybo+uuv12OPPabY2Fh1795dknTo0CHddNNNat26tdq2bavRo0dr//79Htt+4YUXdPnllys4OFg/+clP9Ne//rU+hwSAjxB0ADRKgwcPVlJSkt58801J0o033qijR49qxYoVys/PV69evTRkyBCdPHlSkvTaa6/pscce0xNPPKH8/HzFx8dr/vz5tbabm5urXbt2KScnR++++65Onz6tjIwMhYWF6aOPPtKGDRsUGhqqYcOGuc/4vPbaa3rwwQf12GOP6fPPP9ef/vQnzZgxQy+99FLDDQgA71j9VlEAzVtmZqYZPXr0WdfdfPPN5vLLLzcfffSRCQ8PNxUVFR7ru3TpYhYuXGiMMaZv374mKyvLY/3VV19tkpKSPPYVHR1tKisr3W2vvPKK6d69u3G5XO62yspK07JlS7Nq1Sr3fl5//XWPbT/yyCMmLS3too8XQMMKsDpoAcC5GGPkcDi0fft2lZWVqV27dh7rv/vuO3355ZeSpF27dun222/3WN+nTx+tXr3ao61nz54KDAx0L2/fvl179+5VWFiYR7+Kigp9+eWXKi8v15dffqlJkyZp8uTJ7vXV1dWKiIjwyXECqD8EHQCN1ueff67OnTurrKxM7du319q1a2v1ad269UVts1WrVh7LZWVlSklJ0WuvvVar7yWXXKKysjJJ0vPPP6++fft6rPf397+ofQNoeAQdAI3S6tWr9cknn+iee+5RXFycCgsLFRAQoISEhLP27969u/Ly8jR+/Hh3W15e3o/up1evXlq6dKmioqIUHh5ea31ERIRiY2P11Vdfady4cV4fDwBrEHQAWK6yslKFhYWqqalRUVGRVq5cqVmzZmnkyJEaP368/Pz8lJaWpuuvv15PPvmkLrvsMh05ckTvvfeexowZo9TUVN15552aPHmyUlNTddVVV2np0qXasWOHLr300vPue9y4cZo9e7ZGjx6tP/7xj4qLi9OBAwf05ptv6oEHHlBcXJwefvhh3XXXXYqIiNCwYcNUWVmpzZs365tvvlF2dnYDjRIAbxB0AFhu5cqVat++vQICAtSmTRslJSVp3rx5yszMlJ/f9zeHvv/++/rDH/6giRMn6tixY4qJidHPfvYzRUdHS/o+sHz11Ve67777VFFRoZtuukkTJkzQpk2bzrvvkJAQrV+/Xr/73e80duxYlZaWqkOHDhoyZIj7DM+tt96qkJAQzZ49W/fff79atWqlnj176u67767XcQFQdw5jjLG6CACoD0OHDlVMTIxeeeUVq0sBYBHO6ACwhW+//VYLFixQRkaG/P399be//U0ffvihcnJyrC4NgIU4owPAFr777juNGjVKW7duVUVFhbp3767/9//+n8aOHWt1aQAsRNABAAC2xSsgAACAbRF0AACAbRF0AACAbRF0AACAbRF0AACAbRF0AACAbRF0AACAbRF0AACAbRF0AACAbf1/l2fjxfqH0R0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort nodes based on their degree in descending order\n",
        "sorted_degrees = sorted(degrees.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# Extract the sorted nodes and their degrees\n",
        "print(sorted_degrees)\n",
        "print(\"highest degree node\",sorted_degrees[0][0],\" its degree :\",sorted_degrees[0][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kjt5PBjXUSS",
        "outputId": "1bf2416f-8355-4346-adf9-a4b571b85143"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(53, 473), (443, 318), (445, 197), (80, 114), (25174, 18), (64147, 16), (50584, 14), (44847, 12), (37965, 11), (22114, 11), (26467, 11), (6881, 10), (57470, 9), (27005, 9), (51505, 8), (5222, 6), (5228, 6), (27015, 6), (23, 5), (123, 5), (48817, 5), (49418, 4), (50265, 4), (62582, 4), (27017, 4), (35440, 4), (2710, 4), (56205, 4), (16387, 3), (21247, 3), (37807, 3), (993, 3), (17572, 3), (30188, 3), (51221, 3), (3389, 3), (0, 2), (1, 2), (22, 2), (20545, 2), (49294, 2), (16654, 2), (33038, 2), (33058, 2), (12637, 2), (57750, 2), (29104, 2), (25067, 2), (57866, 2), (57868, 2), (49845, 2), (17089, 2), (29384, 2), (8999, 2), (49991, 2), (62348, 2), (45964, 2), (29587, 2), (46081, 2), (42053, 2), (1202, 2), (9407, 2), (50393, 2), (25826, 2), (1305, 2), (42298, 2), (9587, 2), (1433, 2), (54741, 2), (54777, 2), (1564, 2), (26258, 2), (55060, 2), (47013, 2), (38904, 2), (55298, 2), (18468, 2), (55415, 2), (51322, 2), (2297, 2), (31000, 2), (27011, 2), (27014, 2), (27016, 2), (27019, 2), (27020, 2), (27037, 2), (10657, 2), (35253, 2), (55840, 2), (43555, 2), (27212, 2), (43618, 2), (51814, 2), (64128, 2), (60043, 2), (47758, 2), (31381, 2), (15000, 2), (51876, 2), (47784, 2), (51902, 2), (19165, 2), (6889, 2), (64241, 2), (27400, 2), (64277, 2), (35624, 2), (27480, 2), (47993, 2), (64449, 2), (27613, 2), (3054, 2), (7205, 2), (40010, 2), (56395, 2), (40014, 2), (40036, 2), (60517, 2), (11392, 2), (11528, 2), (48407, 2), (52508, 2), (60705, 2), (44348, 2), (36237, 2), (20101, 2), (40642, 2), (20259, 2), (61282, 2), (20413, 2), (24535, 2), (53249, 1), (57344, 1), (61443, 1), (53248, 1), (53251, 1), (20487, 1), (57363, 1), (57366, 1), (4120, 1), (61463, 1), (25, 1), (57373, 1), (36897, 1), (8229, 1), (53286, 1), (16422, 1), (37, 1), (41001, 1), (49195, 1), (49197, 1), (49199, 1), (41007, 1), (61489, 1), (4144, 1), (57395, 1), (53304, 1), (53309, 1), (57406, 1), (41022, 1), (20541, 1), (49218, 1), (57410, 1), (49220, 1), (41029, 1), (45126, 1), (49223, 1), 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1), (7905, 1), (32480, 1), (3812, 1), (52968, 1), (28396, 1), (61170, 1), (65275, 1), (61184, 1), (65280, 1), (7937, 1), (57091, 1), (65284, 1), (32520, 1), (65289, 1), (32526, 1), (3855, 1), (57107, 1), (57109, 1), (57111, 1), (53016, 1), (48927, 1), (20256, 1), (12074, 1), (57131, 1), (65324, 1), (53037, 1), (7981, 1), (36656, 1), (61234, 1), (65332, 1), (65337, 1), (44867, 1), (16195, 1), (57155, 1), (32579, 1), (53063, 1), (57163, 1), (65357, 1), (57168, 1), (65363, 1), (44884, 1), (57173, 1), (48987, 1), (57179, 1), (57183, 1), (40801, 1), (20321, 1), (8038, 1), (53096, 1), (57195, 1), (12140, 1), (28526, 1), (57207, 1), (8055, 1), (44933, 1), (65413, 1), (28549, 1), (3975, 1), (53129, 1), (65418, 1), (57224, 1), (57228, 1), (53132, 1), (57231, 1), (65425, 1), (53137, 1), (61332, 1), (16279, 1), (57240, 1), (53146, 1), (20378, 1), (24477, 1), (44960, 1), (36772, 1), (20389, 1), (24489, 1), (65453, 1), (57262, 1), (12209, 1), (53170, 1), (20403, 1), (53173, 1), (65461, 1), (49077, 1), (53176, 1), (20405, 1), (65466, 1), (32702, 1), (61377, 1), (61389, 1), (12242, 1), (65495, 1), (45015, 1), (57305, 1), (65497, 1), (49113, 1), (61405, 1), (4063, 1), (20449, 1), (65505, 1), (61411, 1), (57317, 1), (36839, 1), (49128, 1), (65514, 1), (65516, 1), (57325, 1), (57326, 1), (53228, 1), (20462, 1), (8175, 1), (65524, 1), (12286, 1)]\n",
            "highest degree node 53  its degree : 473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Path length**"
      ],
      "metadata": {
        "id": "DL9lhs04SEKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Shortest Path Length\n",
        "shortest_path_length = nx.shortest_path_length(G)\n",
        "\n",
        "# Flatten the lengths into a list of tuples (source, target, length)\n",
        "all_shortest_paths = [(source, target, length) for source, lengths in shortest_path_length for target, length in lengths.items()]\n",
        "\n",
        "path_lengths = [length for _, _, length in all_shortest_paths]\n",
        "\n",
        "# Sort the list of lengths in ascending order\n",
        "sorted_lengths = sorted(path_lengths)\n",
        "\n",
        "# Print the shortest path length\n",
        "shortest_path_length_set = set(sorted_lengths)\n",
        "shortest_path_length=list(shortest_path_length_set)[1]\n",
        "print(f\"Shortest Path Length: {shortest_path_length}\")\n",
        "\n",
        "# Print all nodes that have the shortest path length\n",
        "nodes_with_shortest_path = [(source, target) for source, target, length in all_shortest_paths if length == shortest_path_length]\n",
        "print(f\"Nodes with Shortest Path Length ({shortest_path_length}):\")\n",
        "for source, target in nodes_with_shortest_path:\n",
        "    print(f\"Shortest path from Node {source} to Node {target}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GIdC8JRwAhJf",
        "outputId": "ef40411a-f143-495d-e83e-a3d089ab038b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shortest Path Length: 1\n",
            "Nodes with Shortest Path Length (1):\n",
            "Shortest path from Node 53249 to Node 443\n",
            "Shortest path from Node 57344 to Node 53\n",
            "Shortest path from Node 16387 to Node 3655\n",
            "Shortest path from Node 16387 to Node 16862\n",
            "Shortest path from Node 16387 to Node 44427\n",
            "Shortest path from Node 61443 to Node 445\n",
            "Shortest path from Node 53248 to Node 443\n",
            "Shortest path from Node 53251 to Node 53\n",
            "Shortest path from Node 20487 to Node 50584\n",
            "Shortest path from Node 57363 to Node 53\n",
            "Shortest path from Node 57366 to Node 445\n",
            "Shortest path from Node 4120 to Node 53\n",
            "Shortest path from Node 57373 to Node 445\n",
            "Shortest path from Node 36897 to Node 443\n",
            "Shortest path from Node 8229 to Node 22114\n",
            "Shortest path from Node 53286 to Node 53\n",
            "Shortest path from Node 16422 to Node 53\n",
            "Shortest path from Node 41001 to Node 80\n",
            "Shortest path from Node 49195 to Node 445\n",
            "Shortest path from Node 49197 to Node 2710\n",
            "Shortest path from Node 49199 to Node 445\n",
            "Shortest path from Node 41007 to Node 53\n",
            "Shortest path from Node 61489 to Node 443\n",
            "Shortest path from Node 4144 to Node 80\n",
            "Shortest path from Node 57395 to Node 53\n",
            "Shortest path from Node 53304 to Node 445\n",
            "Shortest path from Node 53309 to Node 445\n",
            "Shortest path from Node 57406 to Node 53\n",
            "Shortest path from Node 41022 to Node 53\n",
            "Shortest path from Node 49218 to Node 443\n",
            "Shortest path from Node 57410 to Node 443\n",
            "Shortest path from Node 49220 to Node 2710\n",
            "Shortest path from Node 41029 to Node 80\n",
            "Shortest path from Node 45126 to Node 80\n",
            "Shortest path from Node 49223 to Node 80\n",
            "Shortest path from Node 49225 to Node 443\n",
            "Shortest path from Node 53322 to Node 445\n",
            "Shortest path from Node 57433 to Node 21247\n",
            "Shortest path from Node 53353 to Node 26467\n",
            "Shortest path from Node 53356 to Node 445\n",
            "Shortest path from Node 57452 to Node 57470\n",
            "Shortest path from Node 49262 to Node 53\n",
            "Shortest path from Node 49263 to Node 53\n",
            "Shortest path from Node 49266 to Node 53\n",
            "Shortest path from Node 49269 to Node 53\n",
            "Shortest path from Node 49277 to Node 53\n",
            "Shortest path from Node 57470 to Node 49294\n",
            "Shortest path from Node 57470 to Node 31381\n",
            "Shortest path from Node 49278 to Node 80\n",
            "Shortest path from Node 45183 to Node 443\n",
            "Shortest path from Node 20611 to Node 53\n",
            "Shortest path from Node 49286 to Node 53\n",
            "Shortest path from Node 49288 to Node 80\n",
            "Shortest path from Node 53385 to Node 445\n",
            "Shortest path from Node 32905 to Node 53\n",
            "Shortest path from Node 61580 to Node 53\n",
            "Shortest path from Node 61583 to Node 80\n",
            "Shortest path from Node 37008 to Node 42707\n",
            "Shortest path from Node 37011 to Node 25174\n",
            "Shortest path from Node 49301 to Node 53\n",
            "Shortest path from Node 49304 to Node 445\n",
            "Shortest path from Node 53404 to Node 445\n",
            "Shortest path from Node 57504 to Node 44847\n",
            "Shortest path from Node 61600 to Node 53\n",
            "Shortest path from Node 49315 to Node 53\n",
            "Shortest path from Node 53412 to Node 443\n",
            "Shortest path from Node 4269 to Node 26672\n",
            "Shortest path from Node 49326 to Node 445\n",
            "Shortest path from Node 49328 to Node 445\n",
            "Shortest path from Node 16562 to Node 443\n",
            "Shortest path from Node 49332 to Node 53\n",
            "Shortest path from Node 16564 to Node 27037\n",
            "Shortest path from Node 49334 to Node 53\n",
            "Shortest path from Node 4278 to Node 443\n",
            "Shortest path from Node 49342 to Node 443\n",
            "Shortest path from Node 12479 to Node 27480\n",
            "Shortest path from Node 53442 to Node 443\n",
            "Shortest path from Node 49349 to Node 80\n",
            "Shortest path from Node 32971 to Node 443\n",
            "Shortest path from Node 61643 to Node 53\n",
            "Shortest path from Node 41170 to Node 443\n",
            "Shortest path from Node 16597 to Node 53\n",
            "Shortest path from Node 49367 to Node 53\n",
            "Shortest path from Node 53467 to Node 445\n",
            "Shortest path from Node 53470 to Node 445\n",
            "Shortest path from Node 61667 to Node 80\n",
            "Shortest path from Node 12522 to Node 27017\n",
            "Shortest path from Node 37106 to Node 53\n",
            "Shortest path from Node 57588 to Node 445\n",
            "Shortest path from Node 28923 to Node 53\n",
            "Shortest path from Node 49408 to Node 445\n",
            "Shortest path from Node 12545 to Node 25174\n",
            "Shortest path from Node 53504 to Node 445\n",
            "Shortest path from Node 33028 to Node 443\n",
            "Shortest path from Node 16648 to Node 53\n",
            "Shortest path from Node 49418 to Node 27400\n",
            "Shortest path from Node 49418 to Node 27015\n",
            "Shortest path from Node 49418 to Node 31000\n",
            "Shortest path from Node 49418 to Node 27016\n",
            "Shortest path from Node 57613 to Node 445\n",
            "Shortest path from Node 16654 to Node 50265\n",
            "Shortest path from Node 16654 to Node 80\n",
            "Shortest path from Node 57614 to Node 443\n",
            "Shortest path from Node 61712 to Node 443\n",
            "Shortest path from Node 49424 to Node 53\n",
            "Shortest path from Node 49426 to Node 57470\n",
            "Shortest path from Node 37134 to Node 80\n",
            "Shortest path from Node 49428 to Node 53\n",
            "Shortest path from Node 20768 to Node 53\n",
            "Shortest path from Node 53537 to Node 80\n",
            "Shortest path from Node 57634 to Node 80\n",
            "Shortest path from Node 57636 to Node 445\n",
            "Shortest path from Node 49445 to Node 53\n",
            "Shortest path from Node 45352 to Node 443\n",
            "Shortest path from Node 24873 to Node 443\n",
            "Shortest path from Node 33069 to Node 80\n",
            "Shortest path from Node 53550 to Node 445\n",
            "Shortest path from Node 49456 to Node 53\n",
            "Shortest path from Node 53553 to Node 2297\n",
            "Shortest path from Node 24881 to Node 53\n",
            "Shortest path from Node 37179 to Node 443\n",
            "Shortest path from Node 53563 to Node 445\n",
            "Shortest path from Node 45374 to Node 80\n",
            "Shortest path from Node 28991 to Node 53\n",
            "Shortest path from Node 45377 to Node 80\n",
            "Shortest path from Node 24898 to Node 443\n",
            "Shortest path from Node 49479 to Node 56205\n",
            "Shortest path from Node 29003 to Node 443\n",
            "Shortest path from Node 16721 to Node 22114\n",
            "Shortest path from Node 49489 to Node 443\n",
            "Shortest path from Node 49491 to Node 445\n",
            "Shortest path from Node 41303 to Node 53\n",
            "Shortest path from Node 24919 to Node 53\n",
            "Shortest path from Node 20829 to Node 443\n",
            "Shortest path from Node 24927 to Node 443\n",
            "Shortest path from Node 49505 to Node 445\n",
            "Shortest path from Node 8549 to Node 53\n",
            "Shortest path from Node 20844 to Node 64147\n",
            "Shortest path from Node 49516 to Node 53\n",
            "Shortest path from Node 61804 to Node 51505\n",
            "Shortest path from Node 53620 to Node 445\n",
            "Shortest path from Node 16764 to Node 443\n",
            "Shortest path from Node 45437 to Node 53\n",
            "Shortest path from Node 49534 to Node 53\n",
            "Shortest path from Node 53631 to Node 443\n",
            "Shortest path from Node 20864 to Node 51221\n",
            "Shortest path from Node 4477 to Node 60705\n",
            "Shortest path from Node 41346 to Node 30188\n",
            "Shortest path from Node 45441 to Node 443\n",
            "Shortest path from Node 24961 to Node 27017\n",
            "Shortest path from Node 61833 to Node 443\n",
            "Shortest path from Node 61837 to Node 443\n",
            "Shortest path from Node 24974 to Node 53\n",
            "Shortest path from Node 57745 to Node 445\n",
            "Shortest path from Node 53651 to Node 47758\n",
            "Shortest path from Node 57750 to Node 445\n",
            "Shortest path from Node 57750 to Node 15000\n",
            "Shortest path from Node 20894 to Node 51505\n",
            "Shortest path from Node 49575 to Node 53\n",
            "Shortest path from Node 61864 to Node 80\n",
            "Shortest path from Node 49577 to Node 53\n",
            "Shortest path from Node 53677 to Node 10657\n",
            "Shortest path from Node 41389 to Node 80\n",
            "Shortest path from Node 12719 to Node 53\n",
            "Shortest path from Node 25010 to Node 30188\n",
            "Shortest path from Node 33203 to Node 443\n",
            "Shortest path from Node 8628 to Node 443\n",
            "Shortest path from Node 61880 to Node 445\n",
            "Shortest path from Node 25017 to Node 27011\n",
            "Shortest path from Node 443 to Node 39711\n",
            "Shortest path from Node 443 to Node 15564\n",
            "Shortest path from Node 443 to Node 11817\n",
            "Shortest path from Node 443 to Node 56117\n",
            "Shortest path from Node 53691 to Node 11528\n",
            "Shortest path from Node 37316 to Node 22114\n",
            "Shortest path from Node 8644 to Node 2710\n",
            "Shortest path from Node 12742 to Node 1\n",
            "Shortest path from Node 16843 to Node 53\n",
            "Shortest path from Node 53709 to Node 445\n",
            "Shortest path from Node 49614 to Node 53\n",
            "Shortest path from Node 20946 to Node 53\n",
            "Shortest path from Node 41427 to Node 443\n",
            "Shortest path from Node 45525 to Node 53\n",
            "Shortest path from Node 49631 to Node 443\n",
            "Shortest path from Node 45543 to Node 15000\n",
            "Shortest path from Node 53736 to Node 443\n",
            "Shortest path from Node 49642 to Node 80\n",
            "Shortest path from Node 33259 to Node 443\n",
            "Shortest path from Node 57840 to Node 53\n",
            "Shortest path from Node 16883 to Node 22\n",
            "Shortest path from Node 61945 to Node 445\n",
            "Shortest path from Node 49658 to Node 53\n",
            "Shortest path from Node 45564 to Node 443\n",
            "Shortest path from Node 49665 to Node 80\n",
            "Shortest path from Node 53762 to Node 9587\n",
            "Shortest path from Node 25093 to Node 53\n",
            "Shortest path from Node 49671 to Node 445\n",
            "Shortest path from Node 61961 to Node 11392\n",
            "Shortest path from Node 53774 to Node 51505\n",
            "Shortest path from Node 53775 to Node 8999\n",
            "Shortest path from Node 25105 to Node 53\n",
            "Shortest path from Node 25108 to Node 53\n",
            "Shortest path from Node 49686 to Node 44847\n",
            "Shortest path from Node 61976 to Node 53\n",
            "Shortest path from Node 33305 to Node 53\n",
            "Shortest path from Node 8733 to Node 53\n",
            "Shortest path from Node 49696 to Node 53\n",
            "Shortest path from Node 53792 to Node 1\n",
            "Shortest path from Node 4653 to Node 27212\n",
            "Shortest path from Node 33327 to Node 53\n",
            "Shortest path from Node 61999 to Node 53\n",
            "Shortest path from Node 16948 to Node 990\n",
            "Shortest path from Node 53812 to Node 443\n",
            "Shortest path from Node 16953 to Node 443\n",
            "Shortest path from Node 33342 to Node 443\n",
            "Shortest path from Node 57926 to Node 445\n",
            "Shortest path from Node 41543 to Node 443\n",
            "Shortest path from Node 45642 to Node 47784\n",
            "Shortest path from Node 29259 to Node 53\n",
            "Shortest path from Node 49742 to Node 80\n",
            "Shortest path from Node 62035 to Node 445\n",
            "Shortest path from Node 8788 to Node 443\n",
            "Shortest path from Node 49752 to Node 53\n",
            "Shortest path from Node 4702 to Node 53\n",
            "Shortest path from Node 49762 to Node 53\n",
            "Shortest path from Node 53866 to Node 443\n",
            "Shortest path from Node 49773 to Node 80\n",
            "Shortest path from Node 53870 to Node 24535\n",
            "Shortest path from Node 49775 to Node 53\n",
            "Shortest path from Node 53869 to Node 17089\n",
            "Shortest path from Node 49777 to Node 53\n",
            "Shortest path from Node 45682 to Node 37965\n",
            "Shortest path from Node 45679 to Node 53\n",
            "Shortest path from Node 49776 to Node 53\n",
            "Shortest path from Node 29294 to Node 47013\n",
            "Shortest path from Node 53878 to Node 443\n",
            "Shortest path from Node 62075 to Node 445\n",
            "Shortest path from Node 21116 to Node 443\n",
            "Shortest path from Node 53890 to Node 443\n",
            "Shortest path from Node 21133 to Node 27613\n",
            "Shortest path from Node 53902 to Node 55298\n",
            "Shortest path from Node 21140 to Node 53\n",
            "Shortest path from Node 53909 to Node 20545\n",
            "Shortest path from Node 25237 to Node 25174\n",
            "Shortest path from Node 12951 to Node 50584\n",
            "Shortest path from Node 21142 to Node 53\n",
            "Shortest path from Node 62111 to Node 35440\n",
            "Shortest path from Node 12965 to Node 37965\n",
            "Shortest path from Node 21160 to Node 53\n",
            "Shortest path from Node 37547 to Node 53\n",
            "Shortest path from Node 45742 to Node 443\n",
            "Shortest path from Node 33459 to Node 443\n",
            "Shortest path from Node 49845 to Node 445\n",
            "Shortest path from Node 49845 to Node 443\n",
            "Shortest path from Node 58038 to Node 51505\n",
            "Shortest path from Node 8889 to Node 29421\n",
            "Shortest path from Node 53948 to Node 445\n",
            "Shortest path from Node 62144 to Node 445\n",
            "Shortest path from Node 53954 to Node 57868\n",
            "Shortest path from Node 33477 to Node 53\n",
            "Shortest path from Node 41677 to Node 57470\n",
            "Shortest path from Node 53971 to Node 50265\n",
            "Shortest path from Node 37588 to Node 44348\n",
            "Shortest path from Node 53975 to Node 445\n",
            "Shortest path from Node 49882 to Node 443\n",
            "Shortest path from Node 58075 to Node 445\n",
            "Shortest path from Node 49891 to Node 445\n",
            "Shortest path from Node 58083 to Node 1305\n",
            "Shortest path from Node 8932 to Node 80\n",
            "Shortest path from Node 17125 to Node 40036\n",
            "Shortest path from Node 4838 to Node 53\n",
            "Shortest path from Node 13030 to Node 53\n",
            "Shortest path from Node 49898 to Node 53\n",
            "Shortest path from Node 62190 to Node 53\n",
            "Shortest path from Node 25328 to Node 80\n",
            "Shortest path from Node 49907 to Node 53\n",
            "Shortest path from Node 58104 to Node 53\n",
            "Shortest path from Node 37625 to Node 53\n",
            "Shortest path from Node 37627 to Node 443\n",
            "Shortest path from Node 49915 to Node 53\n",
            "Shortest path from Node 29440 to Node 443\n",
            "Shortest path from Node 49931 to Node 443\n",
            "Shortest path from Node 8971 to Node 443\n",
            "Shortest path from Node 45837 to Node 25174\n",
            "Shortest path from Node 17164 to Node 54777\n",
            "Shortest path from Node 8973 to Node 53\n",
            "Shortest path from Node 58129 to Node 26467\n",
            "Shortest path from Node 62227 to Node 443\n",
            "Shortest path from Node 58131 to Node 53\n",
            "Shortest path from Node 62231 to Node 445\n",
            "Shortest path from Node 33559 to Node 3054\n",
            "Shortest path from Node 62237 to Node 53\n",
            "Shortest path from Node 21278 to Node 53\n",
            "Shortest path from Node 37663 to Node 80\n",
            "Shortest path from Node 13088 to Node 53\n",
            "Shortest path from Node 45862 to Node 443\n",
            "Shortest path from Node 41768 to Node 27017\n",
            "Shortest path from Node 49961 to Node 443\n",
            "Shortest path from Node 37675 to Node 80\n",
            "Shortest path from Node 33582 to Node 44847\n",
            "Shortest path from Node 37682 to Node 5228\n",
            "Shortest path from Node 41779 to Node 443\n",
            "Shortest path from Node 58169 to Node 443\n",
            "Shortest path from Node 4925 to Node 443\n",
            "Shortest path from Node 54079 to Node 443\n",
            "Shortest path from Node 49989 to Node 53\n",
            "Shortest path from Node 9032 to Node 53\n",
            "Shortest path from Node 62285 to Node 445\n",
            "Shortest path from Node 29520 to Node 57868\n",
            "Shortest path from Node 41810 to Node 53\n",
            "Shortest path from Node 50005 to Node 53\n",
            "Shortest path from Node 50006 to Node 53\n",
            "Shortest path from Node 37723 to Node 443\n",
            "Shortest path from Node 9061 to Node 80\n",
            "Shortest path from Node 58214 to Node 445\n",
            "Shortest path from Node 62311 to Node 443\n",
            "Shortest path from Node 41832 to Node 5222\n",
            "Shortest path from Node 54128 to Node 445\n",
            "Shortest path from Node 50033 to Node 53\n",
            "Shortest path from Node 45941 to Node 23388\n",
            "Shortest path from Node 21366 to Node 53\n",
            "Shortest path from Node 62327 to Node 443\n",
            "Shortest path from Node 45944 to Node 44847\n",
            "Shortest path from Node 21370 to Node 53\n",
            "Shortest path from Node 62331 to Node 27019\n",
            "Shortest path from Node 50048 to Node 443\n",
            "Shortest path from Node 62342 to Node 445\n",
            "Shortest path from Node 58246 to Node 53\n",
            "Shortest path from Node 62347 to Node 25\n",
            "Shortest path from Node 62348 to Node 50584\n",
            "Shortest path from Node 62348 to Node 443\n",
            "Shortest path from Node 45964 to Node 2666\n",
            "Shortest path from Node 45964 to Node 2225\n",
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            "Shortest path from Node 56739 to Node 443\n",
            "Shortest path from Node 60835 to Node 53\n",
            "Shortest path from Node 56748 to Node 53\n",
            "Shortest path from Node 52653 to Node 80\n",
            "Shortest path from Node 60851 to Node 445\n",
            "Shortest path from Node 28083 to Node 443\n",
            "Shortest path from Node 56758 to Node 443\n",
            "Shortest path from Node 28087 to Node 53\n",
            "Shortest path from Node 48567 to Node 80\n",
            "Shortest path from Node 64956 to Node 53\n",
            "Shortest path from Node 56767 to Node 53\n",
            "Shortest path from Node 48575 to Node 123\n",
            "Shortest path from Node 64963 to Node 27037\n",
            "Shortest path from Node 11716 to Node 53\n",
            "Shortest path from Node 52679 to Node 27400\n",
            "Shortest path from Node 52685 to Node 445\n",
            "Shortest path from Node 56781 to Node 443\n",
            "Shortest path from Node 56783 to Node 445\n",
            "Shortest path from Node 56786 to Node 443\n",
            "Shortest path from Node 28114 to Node 443\n",
            "Shortest path from Node 56788 to Node 53\n",
            "Shortest path from Node 24021 to Node 6881\n",
            "Shortest path from Node 32214 to Node 53\n",
            "Shortest path from Node 56791 to Node 443\n",
            "Shortest path from Node 64983 to Node 443\n",
            "Shortest path from Node 56794 to Node 46824\n",
            "Shortest path from Node 52699 to Node 25174\n",
            "Shortest path from Node 64988 to Node 445\n",
            "Shortest path from Node 56801 to Node 445\n",
            "Shortest path from Node 52711 to Node 37965\n",
            "Shortest path from Node 65000 to Node 27019\n",
            "Shortest path from Node 52714 to Node 443\n",
            "Shortest path from Node 28138 to Node 19165\n",
            "Shortest path from Node 56816 to Node 80\n",
            "Shortest path from Node 52720 to Node 35894\n",
            "Shortest path from Node 65022 to Node 53\n",
            "Shortest path from Node 44546 to Node 53\n",
            "Shortest path from Node 52741 to Node 443\n",
            "Shortest path from Node 56840 to Node 20413\n",
            "Shortest path from Node 56843 to Node 64277\n",
            "Shortest path from Node 52750 to Node 445\n",
            "Shortest path from Node 3600 to Node 53\n",
            "Shortest path from Node 44560 to Node 53\n",
            "Shortest path from Node 60948 to Node 53\n",
            "Shortest path from Node 56860 to Node 49991\n",
            "Shortest path from Node 65056 to Node 53\n",
            "Shortest path from Node 15906 to Node 443\n",
            "Shortest path from Node 48676 to Node 53392\n",
            "Shortest path from Node 65060 to Node 53\n",
            "Shortest path from Node 65063 to Node 53\n",
            "Shortest path from Node 44583 to Node 443\n",
            "Shortest path from Node 3624 to Node 53\n",
            "Shortest path from Node 60970 to Node 20541\n",
            "Shortest path from Node 3627 to Node 53\n",
            "Shortest path from Node 40492 to Node 1488\n",
            "Shortest path from Node 60975 to Node 443\n",
            "Shortest path from Node 44592 to Node 5222\n",
            "Shortest path from Node 11826 to Node 53\n",
            "Shortest path from Node 40502 to Node 53\n",
            "Shortest path from Node 52791 to Node 80\n",
            "Shortest path from Node 7735 to Node 80\n",
            "Shortest path from Node 56892 to Node 53\n",
            "Shortest path from Node 40508 to Node 53\n",
            "Shortest path from Node 65089 to Node 53\n",
            "Shortest path from Node 48706 to Node 23393\n",
            "Shortest path from Node 44611 to Node 23\n",
            "Shortest path from Node 40514 to Node 25067\n",
            "Shortest path from Node 65093 to Node 53\n",
            "Shortest path from Node 65096 to Node 53\n",
            "Shortest path from Node 56909 to Node 50584\n",
            "Shortest path from Node 48718 to Node 443\n",
            "Shortest path from Node 56912 to Node 443\n",
            "Shortest path from Node 65109 to Node 53\n",
            "Shortest path from Node 56927 to Node 80\n",
            "Shortest path from Node 56928 to Node 53\n",
            "Shortest path from Node 36453 to Node 443\n",
            "Shortest path from Node 61030 to Node 21247\n",
            "Shortest path from Node 15973 to Node 33038\n",
            "Shortest path from Node 56938 to Node 80\n",
            "Shortest path from Node 65131 to Node 53\n",
            "Shortest path from Node 65133 to Node 53\n",
            "Shortest path from Node 15991 to Node 53\n",
            "Shortest path from Node 56952 to Node 443\n",
            "Shortest path from Node 65144 to Node 53\n",
            "Shortest path from Node 40567 to Node 443\n",
            "Shortest path from Node 48762 to Node 443\n",
            "Shortest path from Node 65153 to Node 53\n",
            "Shortest path from Node 52868 to Node 53\n",
            "Shortest path from Node 36486 to Node 80\n",
            "Shortest path from Node 32390 to Node 53\n",
            "Shortest path from Node 56968 to Node 53\n",
            "Shortest path from Node 61064 to Node 443\n",
            "Shortest path from Node 56970 to Node 443\n",
            "Shortest path from Node 65167 to Node 53\n",
            "Shortest path from Node 3730 to Node 17372\n",
            "Shortest path from Node 28310 to Node 53\n",
            "Shortest path from Node 56995 to Node 443\n",
            "Shortest path from Node 65189 to Node 27212\n",
            "Shortest path from Node 44715 to Node 53\n",
            "Shortest path from Node 48817 to Node 27294\n",
            "Shortest path from Node 48817 to Node 47005\n",
            "Shortest path from Node 48820 to Node 53\n",
            "Shortest path from Node 3765 to Node 64147\n",
            "Shortest path from Node 44725 to Node 53\n",
            "Shortest path from Node 36539 to Node 443\n",
            "Shortest path from Node 11964 to Node 53\n",
            "Shortest path from Node 52930 to Node 443\n",
            "Shortest path from Node 61127 to Node 445\n",
            "Shortest path from Node 11975 to Node 64241\n",
            "Shortest path from Node 61130 to Node 445\n",
            "Shortest path from Node 52946 to Node 445\n",
            "Shortest path from Node 11988 to Node 48817\n",
            "Shortest path from Node 52951 to Node 443\n",
            "Shortest path from Node 48857 to Node 53\n",
            "Shortest path from Node 40672 to Node 443\n",
            "Shortest path from Node 7905 to Node 22114\n",
            "Shortest path from Node 3812 to Node 443\n",
            "Shortest path from Node 52968 to Node 445\n",
            "Shortest path from Node 28396 to Node 53\n",
            "Shortest path from Node 61170 to Node 445\n",
            "Shortest path from Node 65275 to Node 53\n",
            "Shortest path from Node 61184 to Node 445\n",
            "Shortest path from Node 65280 to Node 53\n",
            "Shortest path from Node 7937 to Node 443\n",
            "Shortest path from Node 57091 to Node 445\n",
            "Shortest path from Node 65284 to Node 445\n",
            "Shortest path from Node 32520 to Node 47993\n",
            "Shortest path from Node 65289 to Node 25174\n",
            "Shortest path from Node 32526 to Node 80\n",
            "Shortest path from Node 57107 to Node 64147\n",
            "Shortest path from Node 57109 to Node 53\n",
            "Shortest path from Node 57111 to Node 443\n",
            "Shortest path from Node 53016 to Node 80\n",
            "Shortest path from Node 48927 to Node 53\n",
            "Shortest path from Node 20256 to Node 443\n",
            "Shortest path from Node 12074 to Node 10197\n",
            "Shortest path from Node 57131 to Node 35776\n",
            "Shortest path from Node 65324 to Node 445\n",
            "Shortest path from Node 53037 to Node 80\n",
            "Shortest path from Node 7981 to Node 33058\n",
            "Shortest path from Node 44847 to Node 20101\n",
            "Shortest path from Node 44847 to Node 6881\n",
            "Shortest path from Node 36656 to Node 64147\n",
            "Shortest path from Node 61234 to Node 443\n",
            "Shortest path from Node 65332 to Node 56205\n",
            "Shortest path from Node 65337 to Node 445\n",
            "Shortest path from Node 44867 to Node 53\n",
            "Shortest path from Node 16195 to Node 31381\n",
            "Shortest path from Node 57155 to Node 80\n",
            "Shortest path from Node 32579 to Node 80\n",
            "Shortest path from Node 53063 to Node 445\n",
            "Shortest path from Node 57163 to Node 443\n",
            "Shortest path from Node 65357 to Node 445\n",
            "Shortest path from Node 57168 to Node 445\n",
            "Shortest path from Node 65363 to Node 53\n",
            "Shortest path from Node 44884 to Node 443\n",
            "Shortest path from Node 57173 to Node 53\n",
            "Shortest path from Node 48987 to Node 49991\n",
            "Shortest path from Node 57179 to Node 443\n",
            "Shortest path from Node 57183 to Node 53\n",
            "Shortest path from Node 40801 to Node 443\n",
            "Shortest path from Node 20321 to Node 80\n",
            "Shortest path from Node 8038 to Node 53\n",
            "Shortest path from Node 53096 to Node 443\n",
            "Shortest path from Node 57195 to Node 53\n",
            "Shortest path from Node 12140 to Node 53\n",
            "Shortest path from Node 28526 to Node 35624\n",
            "Shortest path from Node 57207 to Node 53\n",
            "Shortest path from Node 44933 to Node 26467\n",
            "Shortest path from Node 65413 to Node 53\n",
            "Shortest path from Node 28549 to Node 80\n",
            "Shortest path from Node 3975 to Node 53\n",
            "Shortest path from Node 53129 to Node 80\n",
            "Shortest path from Node 65418 to Node 445\n",
            "Shortest path from Node 57224 to Node 40010\n",
            "Shortest path from Node 57228 to Node 53\n",
            "Shortest path from Node 53132 to Node 29384\n",
            "Shortest path from Node 57231 to Node 53\n",
            "Shortest path from Node 65425 to Node 53\n",
            "Shortest path from Node 53137 to Node 20101\n",
            "Shortest path from Node 61332 to Node 23\n",
            "Shortest path from Node 57240 to Node 445\n",
            "Shortest path from Node 53146 to Node 50393\n",
            "Shortest path from Node 20378 to Node 443\n",
            "Shortest path from Node 24477 to Node 443\n",
            "Shortest path from Node 44960 to Node 443\n",
            "Shortest path from Node 36772 to Node 40642\n",
            "Shortest path from Node 20389 to Node 64147\n",
            "Shortest path from Node 24489 to Node 123\n",
            "Shortest path from Node 65453 to Node 445\n",
            "Shortest path from Node 57262 to Node 53\n",
            "Shortest path from Node 12209 to Node 80\n",
            "Shortest path from Node 53170 to Node 20259\n",
            "Shortest path from Node 20403 to Node 53\n",
            "Shortest path from Node 53173 to Node 25067\n",
            "Shortest path from Node 65461 to Node 445\n",
            "Shortest path from Node 49077 to Node 443\n",
            "Shortest path from Node 53176 to Node 445\n",
            "Shortest path from Node 20405 to Node 443\n",
            "Shortest path from Node 65466 to Node 53\n",
            "Shortest path from Node 32702 to Node 53\n",
            "Shortest path from Node 61377 to Node 62582\n",
            "Shortest path from Node 61389 to Node 445\n",
            "Shortest path from Node 12242 to Node 51221\n",
            "Shortest path from Node 65495 to Node 11392\n",
            "Shortest path from Node 45015 to Node 443\n",
            "Shortest path from Node 57305 to Node 53\n",
            "Shortest path from Node 65497 to Node 53\n",
            "Shortest path from Node 49113 to Node 443\n",
            "Shortest path from Node 61405 to Node 53\n",
            "Shortest path from Node 4063 to Node 53\n",
            "Shortest path from Node 20449 to Node 53\n",
            "Shortest path from Node 65505 to Node 53\n",
            "Shortest path from Node 61411 to Node 80\n",
            "Shortest path from Node 57317 to Node 445\n",
            "Shortest path from Node 36839 to Node 80\n",
            "Shortest path from Node 49128 to Node 443\n",
            "Shortest path from Node 65514 to Node 53\n",
            "Shortest path from Node 65516 to Node 53\n",
            "Shortest path from Node 57325 to Node 60043\n",
            "Shortest path from Node 57326 to Node 53\n",
            "Shortest path from Node 53228 to Node 64277\n",
            "Shortest path from Node 20462 to Node 53\n",
            "Shortest path from Node 8175 to Node 443\n",
            "Shortest path from Node 65524 to Node 53\n",
            "Shortest path from Node 12286 to Node 29104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "filtered_paths = [(source, target, length) for source, target, length in all_shortest_paths if length > 0]\n",
        "\n",
        "if not filtered_paths:\n",
        "    print(\"No paths with length > 0 found.\")\n",
        "    exit()\n",
        "\n",
        "# Calculate average path length\n",
        "average_path_length = sum(length for _, _, length in filtered_paths) / len(filtered_paths)\n",
        "\n",
        "# Print the average path length\n",
        "print(f\"Average Path Length: {average_path_length}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAFBr9-k4RXD",
        "outputId": "cb3e4f16-607c-459e-a4b0-40ff5621eafc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Path Length: 1.486241727621038\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Diameter --- throws an error\n",
        "\n",
        "# diameter = nx.diameter(G)\n",
        "# print(f\"Diameter: {diameter}\")\n"
      ],
      "metadata": {
        "id": "mv7NtdDILjK2"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Clustering Coefficient**"
      ],
      "metadata": {
        "id": "2NzfWtWzZS_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Clustering Coefficient\n",
        "clustering_coefficient = nx.clustering(G)\n",
        "avg_clustering_coefficient = nx.average_clustering(G)\n",
        "print(f\"Clustering Coefficient: {clustering_coefficient}\")\n",
        "print(f\"Average Clustering Coefficient: {avg_clustering_coefficient}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcuoehpiAlJw",
        "outputId": "26fe435c-6b94-4ad0-ae29-9073d4feeb82"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clustering Coefficient: {0: 0, 53249: 0, 57344: 0, 16387: 0, 61443: 0, 53248: 0, 53251: 0, 20487: 0, 1: 0, 57363: 0, 57366: 0, 22: 0, 4120: 0, 61463: 0, 23: 0, 25: 0, 57373: 0, 36897: 0, 8229: 0, 53286: 0, 16422: 0, 37: 0, 41001: 0, 49195: 0, 49197: 0, 49199: 0, 41007: 0, 61489: 0, 4144: 0, 57395: 0, 53: 0, 53304: 0, 53309: 0, 57406: 0, 41022: 0, 20541: 0, 20545: 0, 49218: 0, 57410: 0, 49220: 0, 41029: 0, 45126: 0, 49223: 0, 49225: 0, 53322: 0, 45129: 0, 80: 0, 81: 0, 57433: 0, 53353: 0, 53356: 0, 57452: 0, 49262: 0, 49263: 0, 49266: 0, 49269: 0, 123: 0, 49277: 0, 57470: 0, 49278: 0, 45183: 0, 20611: 0, 49286: 0, 49288: 0, 53385: 0, 32905: 0, 61580: 0, 49294: 0, 61583: 0, 37008: 0, 53392: 0, 37011: 0, 49301: 0, 49304: 0, 53404: 0, 57504: 0, 61600: 0, 49315: 0, 53412: 0, 4269: 0, 49326: 0, 49328: 0, 16562: 0, 49332: 0, 16564: 0, 49334: 0, 4278: 0, 49342: 0, 12479: 0, 53442: 0, 49349: 0, 32971: 0, 61643: 0, 41170: 0, 16597: 0, 49367: 0, 53467: 0, 53470: 0, 61667: 0, 12522: 0, 37106: 0, 57588: 0, 28923: 0, 49408: 0, 12545: 0, 53504: 0, 33028: 0, 16648: 0, 49418: 0, 57613: 0, 16654: 0, 57614: 0, 61712: 0, 49424: 0, 49426: 0, 37134: 0, 49428: 0, 33038: 0, 20768: 0, 53537: 0, 57634: 0, 33058: 0, 57636: 0, 49445: 0, 45352: 0, 24873: 0, 33069: 0, 53550: 0, 49456: 0, 53553: 0, 24881: 0, 37179: 0, 53563: 0, 45374: 0, 28991: 0, 45377: 0, 24898: 0, 49479: 0, 29003: 0, 16721: 0, 49489: 0, 49491: 0, 8530: 0, 41303: 0, 24919: 0, 20829: 0, 12637: 0, 24927: 0, 49505: 0, 8549: 0, 20844: 0, 49516: 0, 61804: 0, 53620: 0, 16764: 0, 45437: 0, 49534: 0, 53631: 0, 20864: 0, 4477: 0, 41346: 0, 45441: 0, 24961: 0, 61833: 0, 61837: 0, 24974: 0, 57745: 0, 53651: 0, 57750: 0, 20894: 0, 49575: 0, 61864: 0, 49577: 0, 53677: 0, 41389: 0, 12719: 0, 29104: 0, 25010: 0, 33203: 0, 8628: 0, 61880: 0, 25017: 0, 443: 0, 53691: 0, 445: 0, 37316: 0, 8644: 0, 12742: 0, 16843: 0, 53709: 0, 49614: 0, 20946: 0, 41427: 0, 45525: 0, 16862: 0, 49631: 0, 45543: 0, 53736: 0, 49642: 0, 33259: 0, 25067: 0, 57840: 0, 16883: 0, 61945: 0, 49658: 0, 45564: 0, 49665: 0, 53762: 0, 25093: 0, 49671: 0, 61961: 0, 57866: 0, 57868: 0, 53774: 0, 53775: 0, 25105: 0, 25108: 0, 49686: 0, 61976: 0, 33305: 0, 8733: 0, 49696: 0, 53792: 0, 4653: 0, 33327: 0, 61999: 0, 16948: 0, 53812: 0, 16953: 0, 33342: 0, 57926: 0, 41543: 0, 45642: 0, 29259: 0, 49742: 0, 62035: 0, 8788: 0, 25174: 0, 49752: 0, 49756: 0, 4702: 0, 49762: 0, 53866: 0, 49773: 0, 53870: 0, 49775: 0, 53869: 0, 49777: 0, 45682: 0, 45679: 0, 49776: 0, 29294: 0, 53878: 0, 62075: 0, 21116: 0, 53890: 0, 21133: 0, 53902: 0, 41615: 0, 21140: 0, 53909: 0, 25237: 0, 12951: 0, 21142: 0, 62111: 0, 12965: 0, 21160: 0, 37547: 0, 45742: 0, 33459: 0, 49845: 0, 58038: 0, 8889: 0, 53948: 0, 62144: 0, 17089: 0, 53954: 0, 33477: 0, 29384: 0, 41677: 0, 53971: 0, 37588: 0, 53975: 0, 49882: 0, 58075: 0, 49891: 0, 58083: 0, 8932: 0, 17125: 0, 4838: 0, 13030: 0, 49898: 0, 29421: 0, 62190: 0, 25328: 0, 49907: 0, 58104: 0, 37625: 0, 37627: 0, 49915: 0, 21247: 0, 29440: 0, 49931: 0, 8971: 0, 45837: 0, 17164: 0, 8973: 0, 58129: 0, 62227: 0, 58131: 0, 62231: 0, 33559: 0, 62237: 0, 21278: 0, 37663: 0, 13088: 0, 45862: 0, 8999: 0, 41768: 0, 49961: 0, 37675: 0, 33582: 0, 37682: 0, 41779: 0, 58169: 0, 4925: 0, 54079: 0, 49989: 0, 49991: 0, 9032: 0, 62285: 0, 29520: 0, 41810: 0, 50005: 0, 50006: 0, 37723: 0, 9061: 0, 58214: 0, 62311: 0, 41832: 0, 54128: 0, 50033: 0, 45941: 0, 21366: 0, 62327: 0, 45944: 0, 21370: 0, 62331: 0, 50048: 0, 62342: 0, 58246: 0, 41864: 0, 62347: 0, 62348: 0, 45964: 0, 54155: 0, 29587: 0, 50074: 0, 50079: 0, 50082: 0, 33701: 0, 9133: 0, 62382: 0, 37807: 0, 54197: 0, 58294: 0, 58295: 0, 9142: 0, 46008: 0, 37819: 0, 17344: 0, 62413: 0, 54223: 0, 9176: 0, 50137: 0, 17372: 0, 54238: 0, 50143: 0, 990: 0, 993: 0, 995: 0, 13285: 0, 54246: 0, 50149: 0, 41966: 0, 21492: 0, 9206: 0, 58359: 0, 29688: 0, 50171: 0, 9212: 0, 41981: 0, 1024: 0, 1025: 0, 9216: 0, 46081: 0, 25604: 0, 5124: 0, 58374: 0, 1030: 0, 13321: 0, 1035: 0, 58382: 0, 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0, 49128: 0, 65514: 0, 65516: 0, 57325: 0, 57326: 0, 53228: 0, 20462: 0, 8175: 0, 65524: 0, 12286: 0}\n",
            "Average Clustering Coefficient: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. Strongly connected components**"
      ],
      "metadata": {
        "id": "e5esAQ6UafAg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of Strongly Connected Components (SCC)\n",
        "scc = list(nx.strongly_connected_components(G))\n",
        "num_scc = len(scc)\n",
        "print(f\"Number of Strongly Connected Components: {num_scc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKmZXz6SAnNs",
        "outputId": "ac559230-0ee8-4c15-dd0a-1300933aea5d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Strongly Connected Components: 1624\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7. Weakly connected components**"
      ],
      "metadata": {
        "id": "I3aqkYA0c9PO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of Weakly Connected Components (WCC)\n",
        "wcc = list(nx.weakly_connected_components(G))\n",
        "num_wcc = len(wcc)\n",
        "print(f\"Number of Weakly Connected Components: {num_wcc}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZTUu-HbD5Ua",
        "outputId": "5c9c6614-ddd1-4eae-9306-ed42f5d69801"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Weakly Connected Components: 134\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**8. Giant component coverage**"
      ],
      "metadata": {
        "id": "dFpMAVZBdCok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Giant Component\n",
        "giant_component = max(nx.weakly_connected_components(G), key=len)\n",
        "coverage = len(giant_component) / len(G)\n",
        "print(f\"Giant Component Coverage: {coverage}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2qtPGguDzZB",
        "outputId": "69a8e96f-c5c0-450c-ba8d-88e67d9fa4db"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Giant Component Coverage: 0.7038177339901478\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**9. Giant component properties- shortest path, average path length, diameter Avg CC**"
      ],
      "metadata": {
        "id": "5KzqRFCPQEmf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Giant Component Properties\n",
        "giant_component_graph = G.subgraph(giant_component)\n",
        "\n"
      ],
      "metadata": {
        "id": "T1IfKTz4D2Ir"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Shortest Path in Giant Component\n",
        "shortest_path_length_giant = nx.shortest_path_length(giant_component_graph)\n",
        "\n",
        "\n",
        "\n",
        "# Flatten the lengths into a list of tuples (source, target, length)\n",
        "all_shortest_paths = [(source, target, length) for source, lengths in shortest_path_length_giant for target, length in lengths.items()]\n",
        "\n",
        "path_lengths = [length for _, _, length in all_shortest_paths]\n",
        "\n",
        "# Sort the list of lengths in ascending order\n",
        "sorted_lengths = sorted(path_lengths)\n",
        "\n",
        "# Print the shortest path length\n",
        "shortest_path_length_set = set(sorted_lengths)\n",
        "shortest_path_length=list(shortest_path_length_set)[1]\n",
        "print(f\"Shortest Path Length in giant component : {shortest_path_length}\")\n",
        "\n",
        "# Print all nodes that have the shortest path length\n",
        "nodes_with_shortest_path = [(source, target) for source, target, length in all_shortest_paths if length == shortest_path_length]\n",
        "print(f\"Nodes with Shortest Path Length ({shortest_path_length}):\")\n",
        "for source, target in nodes_with_shortest_path:\n",
        "    print(f\"Shortest path from Node {source} to Node {target}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oh5G3E1w6lNs",
        "outputId": "3b9b2ed8-864a-4fc8-cc5f-0d9bc1724291"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shortest Path Length in giant component : 1\n",
            "Nodes with Shortest Path Length (1):\n",
            "Shortest path from Node 53249 to Node 443\n",
            "Shortest path from Node 57344 to Node 53\n",
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            "Shortest path from Node 60766 to Node 80\n",
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            "Shortest path from Node 56672 to Node 80\n",
            "Shortest path from Node 56671 to Node 443\n",
            "Shortest path from Node 56675 to Node 445\n",
            "Shortest path from Node 64871 to Node 445\n",
            "Shortest path from Node 64872 to Node 445\n",
            "Shortest path from Node 7528 to Node 53\n",
            "Shortest path from Node 60778 to Node 445\n",
            "Shortest path from Node 15727 to Node 443\n",
            "Shortest path from Node 56688 to Node 53\n",
            "Shortest path from Node 56695 to Node 443\n",
            "Shortest path from Node 19831 to Node 53\n",
            "Shortest path from Node 56704 to Node 53\n",
            "Shortest path from Node 60807 to Node 443\n",
            "Shortest path from Node 52616 to Node 80\n",
            "Shortest path from Node 15752 to Node 443\n",
            "Shortest path from Node 36236 to Node 443\n",
            "Shortest path from Node 56716 to Node 443\n",
            "Shortest path from Node 56720 to Node 53\n",
            "Shortest path from Node 40340 to Node 53\n",
            "Shortest path from Node 56726 to Node 53\n",
            "Shortest path from Node 60823 to Node 53\n",
            "Shortest path from Node 56728 to Node 443\n",
            "Shortest path from Node 44443 to Node 80\n",
            "Shortest path from Node 60830 to Node 50584\n",
            "Shortest path from Node 64930 to Node 53\n",
            "Shortest path from Node 56739 to Node 443\n",
            "Shortest path from Node 60835 to Node 53\n",
            "Shortest path from Node 56748 to Node 53\n",
            "Shortest path from Node 52653 to Node 80\n",
            "Shortest path from Node 60851 to Node 445\n",
            "Shortest path from Node 28083 to Node 443\n",
            "Shortest path from Node 56758 to Node 443\n",
            "Shortest path from Node 28087 to Node 53\n",
            "Shortest path from Node 48567 to Node 80\n",
            "Shortest path from Node 64956 to Node 53\n",
            "Shortest path from Node 56767 to Node 53\n",
            "Shortest path from Node 11716 to Node 53\n",
            "Shortest path from Node 52685 to Node 445\n",
            "Shortest path from Node 56781 to Node 443\n",
            "Shortest path from Node 56783 to Node 445\n",
            "Shortest path from Node 56786 to Node 443\n",
            "Shortest path from Node 28114 to Node 443\n",
            "Shortest path from Node 56788 to Node 53\n",
            "Shortest path from Node 24021 to Node 6881\n",
            "Shortest path from Node 32214 to Node 53\n",
            "Shortest path from Node 56791 to Node 443\n",
            "Shortest path from Node 64983 to Node 443\n",
            "Shortest path from Node 64988 to Node 445\n",
            "Shortest path from Node 56801 to Node 445\n",
            "Shortest path from Node 52714 to Node 443\n",
            "Shortest path from Node 56816 to Node 80\n",
            "Shortest path from Node 65022 to Node 53\n",
            "Shortest path from Node 44546 to Node 53\n",
            "Shortest path from Node 52741 to Node 443\n",
            "Shortest path from Node 52750 to Node 445\n",
            "Shortest path from Node 3600 to Node 53\n",
            "Shortest path from Node 44560 to Node 53\n",
            "Shortest path from Node 60948 to Node 53\n",
            "Shortest path from Node 65056 to Node 53\n",
            "Shortest path from Node 15906 to Node 443\n",
            "Shortest path from Node 65060 to Node 53\n",
            "Shortest path from Node 65063 to Node 53\n",
            "Shortest path from Node 44583 to Node 443\n",
            "Shortest path from Node 3624 to Node 53\n",
            "Shortest path from Node 3627 to Node 53\n",
            "Shortest path from Node 60975 to Node 443\n",
            "Shortest path from Node 11826 to Node 53\n",
            "Shortest path from Node 40502 to Node 53\n",
            "Shortest path from Node 52791 to Node 80\n",
            "Shortest path from Node 7735 to Node 80\n",
            "Shortest path from Node 56892 to Node 53\n",
            "Shortest path from Node 40508 to Node 53\n",
            "Shortest path from Node 65089 to Node 53\n",
            "Shortest path from Node 65093 to Node 53\n",
            "Shortest path from Node 65096 to Node 53\n",
            "Shortest path from Node 56909 to Node 50584\n",
            "Shortest path from Node 48718 to Node 443\n",
            "Shortest path from Node 56912 to Node 443\n",
            "Shortest path from Node 65109 to Node 53\n",
            "Shortest path from Node 56927 to Node 80\n",
            "Shortest path from Node 56928 to Node 53\n",
            "Shortest path from Node 36453 to Node 443\n",
            "Shortest path from Node 56938 to Node 80\n",
            "Shortest path from Node 65131 to Node 53\n",
            "Shortest path from Node 65133 to Node 53\n",
            "Shortest path from Node 15991 to Node 53\n",
            "Shortest path from Node 56952 to Node 443\n",
            "Shortest path from Node 65144 to Node 53\n",
            "Shortest path from Node 40567 to Node 443\n",
            "Shortest path from Node 48762 to Node 443\n",
            "Shortest path from Node 65153 to Node 53\n",
            "Shortest path from Node 52868 to Node 53\n",
            "Shortest path from Node 36486 to Node 80\n",
            "Shortest path from Node 32390 to Node 53\n",
            "Shortest path from Node 56968 to Node 53\n",
            "Shortest path from Node 61064 to Node 443\n",
            "Shortest path from Node 56970 to Node 443\n",
            "Shortest path from Node 65167 to Node 53\n",
            "Shortest path from Node 28310 to Node 53\n",
            "Shortest path from Node 56995 to Node 443\n",
            "Shortest path from Node 44715 to Node 53\n",
            "Shortest path from Node 48820 to Node 53\n",
            "Shortest path from Node 44725 to Node 53\n",
            "Shortest path from Node 36539 to Node 443\n",
            "Shortest path from Node 11964 to Node 53\n",
            "Shortest path from Node 52930 to Node 443\n",
            "Shortest path from Node 61127 to Node 445\n",
            "Shortest path from Node 61130 to Node 445\n",
            "Shortest path from Node 52946 to Node 445\n",
            "Shortest path from Node 52951 to Node 443\n",
            "Shortest path from Node 48857 to Node 53\n",
            "Shortest path from Node 40672 to Node 443\n",
            "Shortest path from Node 3812 to Node 443\n",
            "Shortest path from Node 52968 to Node 445\n",
            "Shortest path from Node 28396 to Node 53\n",
            "Shortest path from Node 61170 to Node 445\n",
            "Shortest path from Node 65275 to Node 53\n",
            "Shortest path from Node 61184 to Node 445\n",
            "Shortest path from Node 65280 to Node 53\n",
            "Shortest path from Node 7937 to Node 443\n",
            "Shortest path from Node 57091 to Node 445\n",
            "Shortest path from Node 65284 to Node 445\n",
            "Shortest path from Node 32526 to Node 80\n",
            "Shortest path from Node 57109 to Node 53\n",
            "Shortest path from Node 57111 to Node 443\n",
            "Shortest path from Node 53016 to Node 80\n",
            "Shortest path from Node 48927 to Node 53\n",
            "Shortest path from Node 20256 to Node 443\n",
            "Shortest path from Node 65324 to Node 445\n",
            "Shortest path from Node 53037 to Node 80\n",
            "Shortest path from Node 44847 to Node 20101\n",
            "Shortest path from Node 44847 to Node 6881\n",
            "Shortest path from Node 61234 to Node 443\n",
            "Shortest path from Node 65337 to Node 445\n",
            "Shortest path from Node 44867 to Node 53\n",
            "Shortest path from Node 57155 to Node 80\n",
            "Shortest path from Node 32579 to Node 80\n",
            "Shortest path from Node 53063 to Node 445\n",
            "Shortest path from Node 57163 to Node 443\n",
            "Shortest path from Node 65357 to Node 445\n",
            "Shortest path from Node 57168 to Node 445\n",
            "Shortest path from Node 65363 to Node 53\n",
            "Shortest path from Node 44884 to Node 443\n",
            "Shortest path from Node 57173 to Node 53\n",
            "Shortest path from Node 57179 to Node 443\n",
            "Shortest path from Node 57183 to Node 53\n",
            "Shortest path from Node 40801 to Node 443\n",
            "Shortest path from Node 20321 to Node 80\n",
            "Shortest path from Node 8038 to Node 53\n",
            "Shortest path from Node 53096 to Node 443\n",
            "Shortest path from Node 57195 to Node 53\n",
            "Shortest path from Node 12140 to Node 53\n",
            "Shortest path from Node 28526 to Node 35624\n",
            "Shortest path from Node 57207 to Node 53\n",
            "Shortest path from Node 65413 to Node 53\n",
            "Shortest path from Node 28549 to Node 80\n",
            "Shortest path from Node 3975 to Node 53\n",
            "Shortest path from Node 53129 to Node 80\n",
            "Shortest path from Node 65418 to Node 445\n",
            "Shortest path from Node 57228 to Node 53\n",
            "Shortest path from Node 57231 to Node 53\n",
            "Shortest path from Node 65425 to Node 53\n",
            "Shortest path from Node 53137 to Node 20101\n",
            "Shortest path from Node 57240 to Node 445\n",
            "Shortest path from Node 20378 to Node 443\n",
            "Shortest path from Node 24477 to Node 443\n",
            "Shortest path from Node 44960 to Node 443\n",
            "Shortest path from Node 65453 to Node 445\n",
            "Shortest path from Node 57262 to Node 53\n",
            "Shortest path from Node 12209 to Node 80\n",
            "Shortest path from Node 20403 to Node 53\n",
            "Shortest path from Node 65461 to Node 445\n",
            "Shortest path from Node 49077 to Node 443\n",
            "Shortest path from Node 53176 to Node 445\n",
            "Shortest path from Node 20405 to Node 443\n",
            "Shortest path from Node 65466 to Node 53\n",
            "Shortest path from Node 32702 to Node 53\n",
            "Shortest path from Node 61389 to Node 445\n",
            "Shortest path from Node 45015 to Node 443\n",
            "Shortest path from Node 57305 to Node 53\n",
            "Shortest path from Node 65497 to Node 53\n",
            "Shortest path from Node 49113 to Node 443\n",
            "Shortest path from Node 61405 to Node 53\n",
            "Shortest path from Node 4063 to Node 53\n",
            "Shortest path from Node 20449 to Node 53\n",
            "Shortest path from Node 65505 to Node 53\n",
            "Shortest path from Node 61411 to Node 80\n",
            "Shortest path from Node 57317 to Node 445\n",
            "Shortest path from Node 36839 to Node 80\n",
            "Shortest path from Node 49128 to Node 443\n",
            "Shortest path from Node 65514 to Node 53\n",
            "Shortest path from Node 65516 to Node 53\n",
            "Shortest path from Node 57326 to Node 53\n",
            "Shortest path from Node 20462 to Node 53\n",
            "Shortest path from Node 8175 to Node 443\n",
            "Shortest path from Node 65524 to Node 53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "filtered_paths = [(source, target, length) for source, target, length in all_shortest_paths if length > 0]\n",
        "\n",
        "if not filtered_paths:\n",
        "    print(\"No paths with length > 0 found.\")\n",
        "    exit()\n",
        "\n",
        "# Calculate average path length\n",
        "average_path_length = sum(length for _, _, length in filtered_paths) / len(filtered_paths)\n",
        "\n",
        "# Print the average path length\n",
        "print(f\"Average Path Length in giant component : {average_path_length}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DayBEd8A6uct",
        "outputId": "c4c69b68-3793-4bde-f7a6-7529a87bf967"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Path Length in giant component : 1.5457476823861347\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clustering Coefficient\n",
        "clustering_coefficient = nx.clustering(giant_component_graph)\n",
        "avg_clustering_coefficient = nx.average_clustering(giant_component_graph)\n",
        "print(f\"Clustering Coefficient in giant component graph: {clustering_coefficient}\")\n",
        "print(f\"Average Clustering Coefficient in giant component graph: {avg_clustering_coefficient}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INNMdqEC8Jcw",
        "outputId": "17028177-3641-416b-9114-4e1f14f4606e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clustering Coefficient in giant component graph: {53249: 0, 57344: 0, 61443: 0, 53248: 0, 53251: 0, 20487: 0, 57363: 0, 57366: 0, 4120: 0, 57373: 0, 36897: 0, 53286: 0, 16422: 0, 41001: 0, 49195: 0, 49199: 0, 41007: 0, 61489: 0, 4144: 0, 57395: 0, 53: 0, 53304: 0, 53309: 0, 57406: 0, 41022: 0, 49218: 0, 57410: 0, 41029: 0, 45126: 0, 49223: 0, 49225: 0, 53322: 0, 80: 0, 53356: 0, 49262: 0, 49263: 0, 49266: 0, 49269: 0, 49277: 0, 49278: 0, 45183: 0, 20611: 0, 49286: 0, 49288: 0, 53385: 0, 32905: 0, 61580: 0, 61583: 0, 49301: 0, 49304: 0, 53404: 0, 57504: 0, 61600: 0, 49315: 0, 53412: 0, 49326: 0, 49328: 0, 16562: 0, 49332: 0, 49334: 0, 4278: 0, 49342: 0, 53442: 0, 49349: 0, 32971: 0, 61643: 0, 41170: 0, 16597: 0, 49367: 0, 53467: 0, 53470: 0, 61667: 0, 37106: 0, 57588: 0, 28923: 0, 49408: 0, 53504: 0, 33028: 0, 16648: 0, 57613: 0, 16654: 0, 57614: 0, 61712: 0, 49424: 0, 37134: 0, 49428: 0, 20768: 0, 53537: 0, 57634: 0, 57636: 0, 49445: 0, 45352: 0, 24873: 0, 33069: 0, 53550: 0, 49456: 0, 24881: 0, 37179: 0, 53563: 0, 45374: 0, 28991: 0, 45377: 0, 24898: 0, 29003: 0, 49489: 0, 49491: 0, 41303: 0, 24919: 0, 20829: 0, 24927: 0, 49505: 0, 8549: 0, 49516: 0, 53620: 0, 16764: 0, 45437: 0, 49534: 0, 53631: 0, 41346: 0, 45441: 0, 61833: 0, 61837: 0, 24974: 0, 57745: 0, 57750: 0, 49575: 0, 61864: 0, 49577: 0, 41389: 0, 12719: 0, 25010: 0, 33203: 0, 8628: 0, 61880: 0, 443: 0, 445: 0, 16843: 0, 53709: 0, 49614: 0, 20946: 0, 41427: 0, 45525: 0, 49631: 0, 45543: 0, 53736: 0, 49642: 0, 33259: 0, 57840: 0, 61945: 0, 49658: 0, 45564: 0, 49665: 0, 25093: 0, 49671: 0, 25105: 0, 25108: 0, 49686: 0, 61976: 0, 33305: 0, 8733: 0, 49696: 0, 33327: 0, 61999: 0, 53812: 0, 16953: 0, 33342: 0, 57926: 0, 41543: 0, 29259: 0, 49742: 0, 62035: 0, 8788: 0, 49752: 0, 4702: 0, 49762: 0, 53866: 0, 49773: 0, 49775: 0, 49777: 0, 45679: 0, 49776: 0, 53878: 0, 62075: 0, 21116: 0, 53890: 0, 21140: 0, 12951: 0, 21142: 0, 21160: 0, 37547: 0, 45742: 0, 33459: 0, 49845: 0, 53948: 0, 62144: 0, 33477: 0, 53971: 0, 53975: 0, 49882: 0, 58075: 0, 49891: 0, 8932: 0, 4838: 0, 13030: 0, 49898: 0, 62190: 0, 25328: 0, 49907: 0, 58104: 0, 37625: 0, 37627: 0, 49915: 0, 29440: 0, 49931: 0, 8971: 0, 8973: 0, 62227: 0, 58131: 0, 62231: 0, 62237: 0, 21278: 0, 37663: 0, 13088: 0, 45862: 0, 49961: 0, 37675: 0, 33582: 0, 41779: 0, 58169: 0, 4925: 0, 54079: 0, 49989: 0, 9032: 0, 62285: 0, 41810: 0, 50005: 0, 50006: 0, 37723: 0, 9061: 0, 58214: 0, 62311: 0, 54128: 0, 50033: 0, 21366: 0, 62327: 0, 45944: 0, 21370: 0, 50048: 0, 62342: 0, 58246: 0, 62348: 0, 54155: 0, 50074: 0, 50079: 0, 50082: 0, 33701: 0, 9133: 0, 62382: 0, 54197: 0, 58294: 0, 58295: 0, 9142: 0, 46008: 0, 37819: 0, 54223: 0, 9176: 0, 50137: 0, 54238: 0, 54246: 0, 50149: 0, 41966: 0, 9206: 0, 29688: 0, 50171: 0, 41981: 0, 9216: 0, 25604: 0, 5124: 0, 58374: 0, 1030: 0, 13321: 0, 58382: 0, 13326: 0, 58387: 0, 9240: 0, 62492: 0, 50208: 0, 54304: 0, 1059: 0, 21541: 0, 33841: 0, 54325: 0, 58425: 0, 62524: 0, 54332: 0, 33858: 0, 9290: 0, 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0, 50685: 0, 26116: 0, 58885: 0, 34316: 0, 62995: 0, 50708: 0, 34326: 0, 62998: 0, 1561: 0, 1564: 0, 63007: 0, 54828: 0, 58934: 0, 1595: 0, 54844: 0, 54853: 0, 54856: 0, 63050: 0, 50767: 0, 9807: 0, 22097: 0, 63060: 0, 26196: 0, 30293: 0, 54877: 0, 58986: 0, 63083: 0, 5740: 0, 63085: 0, 1653: 0, 63095: 0, 46714: 0, 54910: 0, 1667: 0, 9860: 0, 9876: 0, 50839: 0, 9884: 0, 63136: 0, 42658: 0, 50854: 0, 1707: 0, 54956: 0, 50867: 0, 54963: 0, 59061: 0, 59062: 0, 46773: 0, 50873: 0, 50875: 0, 22207: 0, 30400: 0, 50881: 0, 18119: 0, 46792: 0, 54989: 0, 54990: 0, 54991: 0, 38606: 0, 9936: 0, 63193: 0, 63194: 0, 55002: 0, 42716: 0, 55004: 0, 55007: 0, 34531: 0, 38630: 0, 55021: 0, 55025: 0, 22265: 0, 55038: 0, 55039: 0, 38662: 0, 55047: 0, 22278: 0, 1807: 0, 55060: 0, 55061: 0, 55063: 0, 59161: 0, 50972: 0, 59165: 0, 38685: 0, 50977: 0, 10019: 0, 26406: 0, 46892: 0, 59181: 0, 46898: 0, 50995: 0, 30519: 0, 63289: 0, 5946: 0, 18236: 0, 63298: 0, 38725: 0, 63304: 0, 55123: 0, 22356: 0, 59223: 0, 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            "Average Clustering Coefficient in giant component graph: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**10. k-connectedness -- analysis w.r.t. the value of k**"
      ],
      "metadata": {
        "id": "RYGfxtZDQPFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "G_undirected = G.to_undirected()\n",
        "\n",
        "# k-connectedness (Analyze for different values of 'k')\n",
        "for k in range(1, 10):\n",
        "    is_k_connected = nx.is_k_edge_connected(G_undirected, k)\n",
        "    print(f\"Is {k}-Connected: {is_k_connected}\")"
      ],
      "metadata": {
        "id": "dBJldAaSAaYg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}