DiabetesPrediction_KNN.ipynb

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              "   gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
              "0  Female  80.0             0              1           never  25.19   \n",
              "1  Female  54.0             0              0         No Info  27.32   \n",
              "2    Male  28.0             0              0           never  27.32   \n",
              "3  Female  36.0             0              0         current  23.45   \n",
              "4    Male  76.0             1              1         current  20.14   \n",
              "\n",
              "   HbA1c_level  blood_glucose_level  diabetes  \n",
              "0          6.6                  140         0  \n",
              "1          6.6                   80         0  \n",
              "2          5.7                  158         0  \n",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import math\n",
        "\n",
        "df = pd.DataFrame(data=pd.read_csv('diabetes_prediction_dataset.csv'))\n",
        "# df = df.sample(1500)\n",
        "display(df.head())\n"
      ]
    },
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      "source": [
        "#  750 rows sample for class 1 (diabetes == \"1\")\n",
        "df1 = df.loc[df['diabetes'] == 1]\n",
        "df1 = df1.sample(750)\n",
        "\n",
        "#  750 rows sample for class 0 (diabetes == \"0\")\n",
        "df0 = df.loc[df['diabetes'] == 0]\n",
        "df0 = df0.sample(750)\n",
        "\n",
        "\n",
        "dfData=df1.append(df0)\n",
        "display(dfData)\n",
        "\n",
        "# check data size\n",
        "diabetes_counts_grouped = dfData.groupby('diabetes').size()\n",
        "print(\"Count of diabetes 1 (grouped):\", diabetes_counts_grouped.get(1, 0))\n",
        "print(\"Count of diabetes 0 (grouped):\", diabetes_counts_grouped.get(0, 0))"
      ],
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              "       gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
              "85505    Male  65.0             0              0         current  41.39   \n",
              "75611    Male  47.0             1              0         current  33.64   \n",
              "89504  Female  42.0             0              0         No Info  36.09   \n",
              "46058  Female  57.0             1              0           never  39.28   \n",
              "2250     Male  54.0             0              0         No Info  34.15   \n",
              "...       ...   ...           ...            ...             ...    ...   \n",
              "22796  Female  19.0             0              0         current  29.99   \n",
              "97662    Male  24.0             0              0           never  31.87   \n",
              "65555  Female  64.0             0              0           never  51.00   \n",
              "3423     Male  56.0             0              0           never  27.32   \n",
              "59907    Male   6.0             0              0         No Info  19.58   \n",
              "\n",
              "       HbA1c_level  blood_glucose_level  diabetes  \n",
              "85505          7.0                  240         1  \n",
              "75611          7.0                  140         1  \n",
              "89504          5.7                  260         1  \n",
              "46058          5.8                  240         1  \n",
              "2250           6.0                  240         1  \n",
              "...            ...                  ...       ...  \n",
              "22796          5.7                  100         0  \n",
              "97662          5.7                   80         0  \n",
              "65555          6.2                  126         0  \n",
              "3423           5.8                  130         0  \n",
              "59907          5.0                  155         0  \n",
              "\n",
              "[1500 rows x 9 columns]"
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              "      <td>never</td>\n",
              "      <td>31.87</td>\n",
              "      <td>5.7</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65555</th>\n",
              "      <td>Female</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>never</td>\n",
              "      <td>51.00</td>\n",
              "      <td>6.2</td>\n",
              "      <td>126</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3423</th>\n",
              "      <td>Male</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>never</td>\n",
              "      <td>27.32</td>\n",
              "      <td>5.8</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59907</th>\n",
              "      <td>Male</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Info</td>\n",
              "      <td>19.58</td>\n",
              "      <td>5.0</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1500 rows × 9 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-fec1f136-708a-45e6-916c-aeb66dc02e44')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </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",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-fec1f136-708a-45e6-916c-aeb66dc02e44 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-fec1f136-708a-45e6-916c-aeb66dc02e44');\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-785996f4-b31c-4ea0-8456-4e563bddf808\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-785996f4-b31c-4ea0-8456-4e563bddf808')\"\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",
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              "    }\n",
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              "    }\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-785996f4-b31c-4ea0-8456-4e563bddf808 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count of diabetes 1 (grouped): 750\n",
            "Count of diabetes 0 (grouped): 750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# # Select 15% of data with diabetes\n",
        "# diabetes_data = df[df['diabetes'] == 1].sample(frac=0.015)\n",
        "\n",
        "# # Select 15% of data without diabetes\n",
        "# non_diabetes_data = df[df['diabetes'] == 0].sample(frac=0.015)\n",
        "\n",
        "# # Concatenate the two subsets into a new DataFrame\n",
        "# dfData = pd.concat([diabetes_data, non_diabetes_data])\n",
        "\n",
        "# # If you want to shuffle the selected data\n",
        "# dfData = dfData.sample(frac=1).reset_index(drop=True)\n",
        "# dfData\n",
        "\n",
        "# count diabetes data from shorter data\n",
        "# diabetes_counts_grouped = dfData.groupby('diabetes').size()\n",
        "\n",
        "# # Or, if you used groupby and size\n",
        "# print(\"Count of diabetes 1 (grouped):\", diabetes_counts_grouped.get(1, 0))\n",
        "# print(\"Count of diabetes 0 (grouped):\", diabetes_counts_grouped.get(0, 0))"
      ],
      "metadata": {
        "id": "MyMiAeLbXGN9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df.info()"
      ],
      "metadata": {
        "id": "-Mf0Gb_7XNkO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mengubah data numerik dan kategorikal\n",
        "cat_cols = [col for col in df.columns if df[col].dtype == 'object' ]\n",
        "num_cols = [col for col in df.columns if df[col].dtype != 'object' ]"
      ],
      "metadata": {
        "id": "cT6wSBGgaRm4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# preprocessing"
      ],
      "metadata": {
        "id": "NdiejpNlmz2Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "menghapus outlier"
      ],
      "metadata": {
        "id": "3yGHpRb0m2g3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  fig, ax = plt.subplots(figsize=(5, 4))\n",
        "  ax.scatter(dfData[col],dfData['age'])\n",
        "  ax.set_xlabel(col)\n",
        "  ax.set_ylabel('age')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Qm2GyB6xmzh-",
        "outputId": "11f3f921-ae7f-4c99-e278-bb25e5abe24a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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x4zF8+HB8++23apRKRC4kd/r0zliTQpWQt1M9GP38/BAWdvV/4VmtVixZsgSZmZkYOHAgAGDp0qXo1KkTdu/ejT59+jR2qUTkIrxHkbRE9Ytvjh07hoiICLRv3x4jR45EcXExAKCgoACXL19GUlKS49iYmBhERkYiNzdXrXKJSCFbC82IStsoKxT/mD4lUpKqHWN8fDyWLVuGjh074ty5c5g1axb69++Pw4cPw2w2IyAgACEhIU5jTCYTzOZrPyfRbrfDbrc7frbZbK4qn4gk4lWnpGWqBuPgwYMd/xwbG4v4+Hi0a9cOq1evRlBQkKRzZmRkYNasWUqVSEQKYyiS1qk+lVpbSEgIbr31Vhw/fhxhYWGoqqpCWVmZ0zEWi6XONck/pKenw2q1Oj4lJSUurpqI6kvuWzE+ebQHQ5FcTlPBWF5ejh9//BHh4eHo2bMn/P39kZ2d7dhfVFSE4uJiJCQkXPMcer0eBoPB6UNE6vti1ymMWVUgefwnj/ZAUo9wBSsiqpuqU6l/+ctfMHToULRr1w5nz57FzJkz4evri8cffxxGoxHPPPMMpkyZghYtWsBgMGDChAlISEjgFalEbkaJ6VOGIjUWVYPx9OnTePzxx/Hzzz+jVatWSExMxO7du9GqVSsAwLvvvgsfHx+kpKTAbrcjOTkZCxYsULNkImogrimSu9EJIYTaRbiSzWaD0WiE1WrltCpRI9lxuBRPrciXfR5On5JSGpIFqt/gT0SehR0iuTtNXXxDRO6NoUiegB0jESlix+FSWeP5VgzSCnaMRCTbziPnZa0pvvPAbQxF0gx2jEQkixLTp8P7RskvhEgh7BiJSDKuKZInYsdIRA2y/aAFozP3yj7POw/cxk6RNInBSET1xg6RvAGnUomoXhiK5C3YMRLRDW0/aJE1fvmTt2NAl9YKVUPkWgxGIrqu4+ZyWWuKK57qjcTOrRSsiMi1GIxEdE3RaRsh92HKDEVyN1xjJKI6KRGKXFMkd8SOkYic7D76Mx77dLesc3D6lNwZg5GIHOReecoOkTwBp1KJCABDkegP7BiJCLuP/ix57LIneuHOWJOC1RCpS1YwHj9+HD/++CMGDBiAoKAgCCGg0+mUqo2IXGzLvnP48+p90sdPugO3hAUrWBGR+iQF488//4wRI0Zg69at0Ol0OHbsGNq3b49nnnkGzZs3x9/+9jel6yQihcmdOtUBDEXySJLWGCdPngw/Pz8UFxejSZMmju0jRozApk2bFCuOiFxDiVA8yTVF8lCSOsZ///vfyMrKQps2bZy2d+jQAT/99JMihRGR8pS4FYPTp+TpJAVjRUWFU6f4h4sXL0Kv18suioiUx4eAE9WPpKnU/v37Y/ny5Y6fdTodampqMG/ePNx1112KFUdEymAoEtWfpI5x3rx5GDRoEPbu3YuqqipMnToV3333HS5evIhvv/1W6RqJSAY5t2IAwKoxfdDn1lCFqiHSPknB2KVLFxw9ehQffvghmjVrhvLycgwfPhypqakIDw9XukYikmDXDxfwxLI8yeM/ebQHknrw95m8j+Qn3xiNRrzyyitYvXo1vvrqK8yePVtWKM6dOxc6nQ6TJk1ybKusrERqaipCQ0MRHByMlJQUWCzy3gtH5A2i0jbKCkUADEXyWpI6xoMHD9a5XafTITAwEJGRkQ26CCc/Px9///vfERsb67R98uTJ2LhxI9asWQOj0Yjx48dj+PDhnK4lug6uJxLJIykY4+LiHE+4EeL3F9PUfuKNv78/RowYgb///e8IDAy87rnKy8sxcuRIfPzxx5g9e7Zju9VqxZIlS5CZmYmBAwcCAJYuXYpOnTph9+7d6NOnj5TSiTzWziPn8eTyPbLOwelTIolTqWvXrkWHDh2wePFiHDhwAAcOHMDixYvRsWNHZGZmYsmSJdi6dSumTZt2w3OlpqZiyJAhSEpKctpeUFCAy5cvO22PiYlBZGQkcnNzr3k+u90Om83m9CHydFFpG2WH4qm5QxiKRJDYMc6ZMwfvvfcekpOTHdu6du2KNm3aYPr06dizZw+aNm2KF198EW+//fY1z7Nq1Srs27cP+fn5V+0zm80ICAhASEiI03aTyQSz2XzNc2ZkZGDWrFkN/0sRuSlOnRIpS1LHeOjQIbRr1+6q7e3atcOhQ4cA/D7deu7cuWueo6SkBBMnTsTKlStvON3aEOnp6bBarY5PSUmJYucm0pqdR87LGr9qTB+GItEVJHWMMTExmDt3LhYvXoyAgAAAwOXLlzF37lzExMQAAM6cOQOT6dqvoikoKEBpaSl69Ojh2FZdXY0dO3bgww8/RFZWFqqqqlBWVubUNVosFoSFhV3zvHq9nk/fIY8ndz0xc3Q8+sa0VLAiIs8hKRg/+ugjPPDAA2jTpo3jStJDhw6huroaGzZsAACcOHECzz///DXPMWjQIEd3+Yenn34aMTExeOmll9C2bVv4+/sjOzsbKSkpAICioiIUFxcjISFBStlEHkGJqVOGItG16cQfl5U20KVLl7By5UocPXoUANCxY0c88cQTaNasmeRi7rzzTsTFxWH+/PkAgHHjxuGrr77CsmXLYDAYMGHCBADArl276n1Om80Go9EIq9UKg8EguTYiLeB6IpE0DckCyS8qbtasGQYMGICoqChUVVUBALZt2wYAeOCBB6Se1sm7774LHx8fpKSkwG63Izk5GQsWLFDk3ETupPBUGYYtknf/LqdPiepHUsd44sQJPPTQQzh06BB0Oh2EEE73MVZXVytapBzsGMndsUskkq8hWSDpqtSJEyciOjoapaWlaNKkCQ4fPoycnBz06tUL27dvl3JKIqoDQ5Go8UmaSs3NzcXWrVvRsmVL+Pj4wNfXF4mJicjIyMALL7yA/fv3K10nkdcpPFUma/yKp3ojsXMrZYoh8iKSgrG6utpxkU3Lli1x9uxZdOzYEe3atUNRUZGiBRJ5m5OlFbj3vRzYqyVdF8dAJJJJ8munDhw4gOjoaMTHx2PevHkICAjA4sWL0b59e6VrJPIa7dM3okZaHjowFInkkRSM06ZNQ0VFBQDgtddew/3334/+/fsjNDQU//jHPxQtkMhbKBGKXE8kkk9SMNZ+Ruott9yCH374ARcvXkTz5s2drk4lohsrr/wNY5fnyQpFTp8SKUfyfYxXatGihVKnIvIaD3z4DQ6elvcGGHaJRMqSdLsGEcnHUCTSJsU6RiKqv/LK32SF4rrn+iEuKkS5gojIgcFI1MhOllZg4DvbJY3dNuVORLduqmxBROSEwUjUiORceeqjA0ORqBFwjZGokcgNxRMZXE8kagzsGIkawcnSCkmhGOADZE3i9ClRY2IwErnQcXM5Br+fg8s1DR8b28aAL8f3V74oIrouBiORi0SnbYTUe/YZikTqYTASuYDUUNQBOPRqMoID+atJpBZefEOkIHNZJbrN/Epyp7h1yp0MRSKV8TeQSCGdpn+NX6UsJv4/3o5BpA3sGIkUoEQo8nYMIm1gx0gkk7msUnIo6n112DTxDnaKRBrCYCSSqODEf5CyeJfk8Vsm3YFbwoIVrIiIlMBgJJIgKm2jrPE6gKFIpFFcYyRqICVC8SRfF0WkWewYierpyGkb7vvwG8njfQD8m9OnRJqnase4cOFCxMbGwmAwwGAwICEhAV9//bVjf2VlJVJTUxEaGorg4GCkpKTAYrGoWDF5q6i0jbJCMcjfByfmDmEoErkBVYOxTZs2mDt3LgoKCrB3714MHDgQDz74IL777jsAwOTJk7F+/XqsWbMGOTk5OHv2LIYPH65myeSF5E6dBvn74PvXBytUDRG5mk4IIfUhHS7RokULvPXWW3j44YfRqlUrZGZm4uGHHwYA/PDDD+jUqRNyc3PRp0+fep3PZrPBaDTCarXCYDC4snTyQHKmT1s29ceGCQMQFhKocFVE1FANyQLNrDFWV1djzZo1qKioQEJCAgoKCnD58mUkJSU5jomJiUFkZOR1g9Fut8Nutzt+ttlsLq+dPM+hYiseWLBT8qPdPh/bFz3bN1e0JiJqHKoH46FDh5CQkIDKykoEBwdj7dq16Ny5MwoLCxEQEICQkBCn400mE8xm8zXPl5GRgVmzZrm4avJkcqdOATAUidyY6rdrdOzYEYWFhcjLy8O4ceMwatQoHDlyRPL50tPTYbVaHZ+SkhIFqyVPp0QonuKtGERuTfWOMSAgALfccgsAoGfPnsjPz8d7772HESNGoKqqCmVlZU5do8ViQVhY2DXPp9frodfrXV02eZjj5nLcMz9H1jk4fUrkGVQPxivV1NTAbrejZ8+e8Pf3R3Z2NlJSUgAARUVFKC4uRkJCgspVkieR80LhP7BLJPIcqgZjeno6Bg8ejMjISFy6dAmZmZnYvn07srKyYDQa8cwzz2DKlClo0aIFDAYDJkyYgISEhHpfkUp0IwxFIrqSqsFYWlqKp556CufOnYPRaERsbCyysrJw9913AwDeffdd+Pj4ICUlBXa7HcnJyViwYIGaJZMHOW4ulxyKPgA2jO+Pzm14CxCRp9HcfYxK432MVJczF39Fv3lbJY1d/3wiukYaFa6IiFzJLe9jJGost77yFaqqpf/3IEORyLOpfrsGUWOSG4pcTyTyfOwYyeMVnirDsEXfyjoHp0+JvAeDkTwa351IRA3FqVTyWAxFIpKCHSN5pMJTZbLGb+ELhYm8FoORPM5xc7msNcVvpw7ETS2CFKyIiNwJg5E8itwn2QT46hiKRF6Oa4zkMZQIxaNz7lOsHiJyT+wYya0dN5dj8Ps5uFwj/RyGQF98/cId7BSJCACDkdwYHwBORK7AqVRySwxFInIVdozkduS8FQMA1j3XD3FRIUqVQ0QehsFIbuXMxV+RND9H8njen0hEN8JgJLch9wHgOoChSEQ3xDVGcgtKhCIf70ZE9cGOkTSr4MR/kLJ4l6xz+PsAX7/A6VMiqj8GI2kSHwBORGrhVCppDkORiNTEjpE0peDEf2SN51WnRCQXg5E0o+jsJVlrinwrBhEpgcFImiB3+pRvxSAipXCNkVSnRCjyrRhEpBR2jKSKPccv4tFPcmWdg2/FICJXULVjzMjIwO23345mzZqhdevWGDZsGIqKipyOqaysRGpqKkJDQxEcHIyUlBRYLBaVKiYlRKVtlB2Kp+YOwcFX72UoEpHiVA3GnJwcpKamYvfu3di8eTMuX76Me+65BxUVFY5jJk+ejPXr12PNmjXIycnB2bNnMXz4cBWrJjnkTpsCfCsGEbmWTggh9+09ijl//jxat26NnJwcDBgwAFarFa1atUJmZiYefvhhAMAPP/yATp06ITc3F3369LnhOW02G4xGI6xWKwwGg6v/CnQdcqdPPx/bFz3bN1ewIiLyFg3JAk1dfGO1WgEALVq0AAAUFBTg8uXLSEpKchwTExODyMhI5ObW/S9Yu90Om83m9CH1nbfZZYVi1gsDGIpE1Cg0c/FNTU0NJk2ahH79+qFLly4AALPZjICAAISEhDgdazKZYDab6zxPRkYGZs2a5epyqQFiX82CrfI3WefoGNFMoWqIiK5PMx1jamoqDh8+jFWrVsk6T3p6OqxWq+NTUlKiUIUkhRKhyDVFImpMmugYx48fjw0bNmDHjh1o06aNY3tYWBiqqqpQVlbm1DVaLBaEhYXVeS69Xg+9Xu/qkukGjpy24b4Pv5E83lcHfDVhADtFImp0qgajEAITJkzA2rVrsX37dkRHRzvt79mzJ/z9/ZGdnY2UlBQAQFFREYqLi5GQkKBGyVQPcq88ZYdIRGpSNRhTU1ORmZmJf/3rX2jWrJlj3dBoNCIoKAhGoxHPPPMMpkyZghYtWsBgMGDChAlISEio1xWp1PgYikTk7lQNxoULFwIA7rzzTqftS5cuxejRowEA7777Lnx8fJCSkgK73Y7k5GQsWLCgkSul+jhyWvoVwKv/nIDet7RQsBoiImk0dR+jK/A+RtcrvvAL7n0vB79crpE0Pv/lJLQycF2YiFynIVmgiYtvyH3d8vJG/CYtDwEAhkA/hiIRaYpmbtcg96NEKB58NVm5goiIFMCOkSQpvvCL5FBs1dQXX028i50iEWkSg5Ea7LzNjgFvb5M8Pn/6vQpWQ0SkLAYjNYjcJ9nwdgwi0joGI9Wb1FD0AbBhfH90bsOrgolI+xiMdF3mskrc/8EO/FxxGVLu69nxl7sQ2bKJ4nUREbkKg5GuqdP0r/GrxHsTAcDPBwxFInI7vF2D6qREKB5/g+uJROR+2DHSVcxllbJCkdOnROTOGIzk5GRpBe56Z7vk8Xy8GxG5OwYjObRP34gaGU/O5ePdiMgTcI2RACgTiny8GxF5AnaMXuy8zY6HFuzE+UuVkkIxyF+H0KZ6rH0+kZ0iEXkMBqOXkvsEmyB/H3z/+mAFKyIi0gZOpXohhiIR0bWxY/Qy5212WaG4O20QwkICFayIiEhb2DF6kfLK35D4Zrbk8dum3MlQJCKPx47RSzzw4Tc4eNomebyPDohu3VTBioiItIkdoxdQIhRPZPDxbkTkHdgxerDyyt8wIbNAcijqfXXYNPEOdopE5FUYjB5KTpfIm/WJyJtxKtUDMRSJiKRjx+hhyit/kxSKel8ddr40iE+wISKvp2rHuGPHDgwdOhQRERHQ6XRYt26d034hBGbMmIHw8HAEBQUhKSkJx44dU6dYjTtvsyNxbjZiX82SNL5g+j0MRSIiqByMFRUV6NatGz766KM698+bNw/vv/8+Fi1ahLy8PDRt2hTJycmorKxs5Eq1LfbVLNz+xhacLquElLcoxrYxIDiQkwdERIDKU6mDBw/G4MF1P1pMCIH58+dj2rRpePDBBwEAy5cvh8lkwrp16/DYY481ZqmaJffxbrFtDPhyfH8FKyIicm+avfjm5MmTMJvNSEpKcmwzGo2Ij49Hbm6uipVpg7msEt1f/VpyKN51aygOv5rMUCQiuoJm58/MZjMAwGQyOW03mUyOfXWx2+2w2+2On2026Te2a1Wn6V/j18tSJk1/F9vGgKVj+ihYERGR59BsxyhVRkYGjEaj49O2bVu1S1KUEqHILpGI6No0G4xhYWEAAIvF4rTdYrE49tUlPT0dVqvV8SkpKXFpnY3JXFYpKRR9dcDdnVpz6pSIqB40O5UaHR2NsLAwZGdnIy4uDsDv06J5eXkYN27cNcfp9Xro9Z5128HF8io8tngXjpZWSBq/Oz2Jt2IQEdWTqsFYXl6O48ePO34+efIkCgsL0aJFC0RGRmLSpEmYPXs2OnTogOjoaEyfPh0REREYNmyYekU3sttnb8b58irJ4w2BfgxFIqIGUDUY9+7di7vuusvx85QpUwAAo0aNwrJlyzB16lRUVFRg7NixKCsrQ2JiIjZt2oTAQO94J6ASocjHuxERNYxOCCHULsKVbDYbjEYjrFYrDAaD2uXUS3nlbxi/sgDbj12QND6sqT/WT7yDnSIR0f9rSBZodo3RW8l9d2KQvw92T79HwYqIiLyLZq9K9UZKhOL3r9f9JCEiIqofdowaIfWtGADQsqk/NkwYgLAQ71h7JSJyJQajyoov/IJ738vBLxJv2t837W60CA5QuCoiIu/FYFTRLS9vxG/SH2KDVsEBDEUiIoVxjVElSoRi/rS7lSuIiIgAsGNsdOdtdgx5f7vkUGwfGoR/jktkp0hE5CIMxkbEdycSEWkfp1IbCUORiMg9sGNsBOdtdsmheHen1nh3RHcEB/J/KiKixsB/27qQuawS93+wAxcqLksav+MvdyGyZROFqyIiouthMLqI3BcK+/mAoUhEpAKuMbqAEqF4/I0hClZERET1xY5RQcUXfsHd72yDXWIm6n2BzZM5fUpEpCYGo0Lk3rDPdycSEWkDp1IVwFAkIvIc7BhlKr7wi+RQbBMSiLXPJ/KFwkREGsJglOG8zY4Bb2+TNHZ32iC+JoqISIMYjBKUV/6GbrOyUC2kjQ/y92EoEhFpFIOxge5/7xscPifthcLA76H4/euDFayIiIiUxGCsp/LK39D11SxIbBJh1OuQNXkgO0UiIo1jMNbD0A++waEz0rtEPx/gwKz7FKyIiIhchcF4A7fP3ozz5VWSx/MpNkRE7oXBeB0zvzwkKxT5EHAiIvfDYLyGqt9q8NmuYsnj819O4v2JRERuyC2efPPRRx8hKioKgYGBiI+Px549e1z+Z36266TksYZAP4YiEZGb0nww/uMf/8CUKVMwc+ZM7Nu3D926dUNycjJKS0td+ufmn/qPpHF8vBsRkXvTfDC+8847ePbZZ/H000+jc+fOWLRoEZo0aYJPP/3UpX9u0wDfBo/JfzmJoUhE5OY0HYxVVVUoKChAUlKSY5uPjw+SkpKQm5tb5xi73Q6bzeb0kWJ4jzb1PrZFkB9OzR3C6VMiIg+g6WC8cOECqqurYTKZnLabTCaYzeY6x2RkZMBoNDo+bdu2lfRn972lZb26xjtvbYl9M9klEhF5Ck0HoxTp6emwWq2OT0lJiaTz+Pro8LdHu133mGcSo7BsTLyk8xMRkTZpOhhbtmwJX19fWCwWp+0WiwVhYWF1jtHr9TAYDE4fqe7tEo5FT/ZAmMH5MW4tmvpjwRPdMf3+2ySfm4iItEnT9zEGBASgZ8+eyM7OxrBhwwAANTU1yM7Oxvjx4xulhnu7hOPuzmHYc/IiSi9VonWzQPSObgFfH12j/PlERNS4NB2MADBlyhSMGjUKvXr1Qu/evTF//nxUVFTg6aefbrQafH10SLg5tNH+PCIiUo/mg3HEiBE4f/48ZsyYAbPZjLi4OGzatOmqC3KIiIiUoBNCSH2Tkluw2WwwGo2wWq2y1huJiMh9NSQLNH3xDRERUWNjMBIREdXCYCQiIqqFwUhERFSL5q9KleuPa4ukPjOViIjc3x8ZUJ/rTT0+GC9dugQAkp+ZSkREnuPSpUswGo3XPcbjb9eoqanB2bNn0axZM+h0DXtajc1mQ9u2bVFSUuIWt3qwXtdzt5rdrV7A/Wpmva6nRM1CCFy6dAkRERHw8bn+KqLHd4w+Pj5o06b+r5Cqi9xnrjY21ut67lazu9ULuF/NrNf15NZ8o07xD7z4hoiIqBYGIxERUS0MxuvQ6/WYOXMm9Hq92qXUC+t1PXer2d3qBdyvZtbreo1ds8dffENERNQQ7BiJiIhqYTASERHVwmAkIiKqhcFIRERUC4PxGj766CNERUUhMDAQ8fHx2LNnj9olOezYsQNDhw5FREQEdDod1q1b57RfCIEZM2YgPDwcQUFBSEpKwrFjx9QpFkBGRgZuv/12NGvWDK1bt8awYcNQVFTkdExlZSVSU1MRGhqK4OBgpKSkwGKxqFLvwoULERsb67iZOCEhAV9//bUma63L3LlzodPpMGnSJMc2rdX86quvQqfTOX1iYmI0Wy8AnDlzBk8++SRCQ0MRFBSErl27Yu/evY79Wvu9i4qKuuo71ul0SE1NBaC977i6uhrTp09HdHQ0goKCcPPNN+P11193erZpo33Hgq6yatUqERAQID799FPx3XffiWeffVaEhIQIi8WidmlCCCG++uor8corr4gvvvhCABBr16512j937lxhNBrFunXrxIEDB8QDDzwgoqOjxa+//qpKvcnJyWLp0qXi8OHDorCwUNx3330iMjJSlJeXO4557rnnRNu2bUV2drbYu3ev6NOnj+jbt68q9X755Zdi48aN4ujRo6KoqEi8/PLLwt/fXxw+fFhztV5pz549IioqSsTGxoqJEyc6tmut5pkzZ4rbbrtNnDt3zvE5f/68Zuu9ePGiaNeunRg9erTIy8sTJ06cEFlZWeL48eOOY7T2e1daWur0/W7evFkAENu2bRNCaO87njNnjggNDRUbNmwQJ0+eFGvWrBHBwcHivffecxzTWN8xg7EOvXv3FqmpqY6fq6urRUREhMjIyFCxqrpdGYw1NTUiLCxMvPXWW45tZWVlQq/Xi//93/9VocKrlZaWCgAiJydHCPF7ff7+/mLNmjWOY77//nsBQOTm5qpVppPmzZuLTz75RNO1Xrp0SXTo0EFs3rxZ3HHHHY5g1GLNM2fOFN26datznxbrfemll0RiYuI197vD793EiRPFzTffLGpqajT5HQ8ZMkSMGTPGadvw4cPFyJEjhRCN+x1zKvUKVVVVKCgoQFJSkmObj48PkpKSkJubq2Jl9XPy5EmYzWan+o1GI+Lj4zVTv9VqBQC0aNECAFBQUIDLly871RwTE4PIyEjVa66ursaqVatQUVGBhIQETdeampqKIUOGONUGaPf7PXbsGCIiItC+fXuMHDkSxcXFALRZ75dffolevXrhkUceQevWrdG9e3d8/PHHjv1a/72rqqrCihUrMGbMGOh0Ok1+x3379kV2djaOHj0KADhw4AB27tyJwYMHA2jc79jjHyLeUBcuXEB1dTVMJpPTdpPJhB9++EGlqurPbDYDQJ31/7FPTTU1NZg0aRL69euHLl26APi95oCAAISEhDgdq2bNhw4dQkJCAiorKxEcHIy1a9eic+fOKCws1FytALBq1Srs27cP+fn5V+3T4vcbHx+PZcuWoWPHjjh37hxmzZqF/v374/Dhw5qs98SJE1i4cCGmTJmCl19+Gfn5+XjhhRcQEBCAUaNGaf73bt26dSgrK8Po0aMBaPP/E2lpabDZbIiJiYGvry+qq6sxZ84cjBw5EkDj/ruNwUiNKjU1FYcPH8bOnTvVLuW6OnbsiMLCQlitVvzzn//EqFGjkJOTo3ZZdSopKcHEiROxefNmBAYGql1OvfzRBQBAbGws4uPj0a5dO6xevRpBQUEqVla3mpoa9OrVC2+88QYAoHv37jh8+DAWLVqEUaNGqVzdjS1ZsgSDBw9GRESE2qVc0+rVq7Fy5UpkZmbitttuQ2FhISZNmoSIiIhG/445lXqFli1bwtfX96qrsywWC8LCwlSqqv7+qFGL9Y8fPx4bNmzAtm3bnF4FFhYWhqqqKpSVlTkdr2bNAQEBuOWWW9CzZ09kZGSgW7dueO+99zRZa0FBAUpLS9GjRw/4+fnBz88POTk5eP/99+Hn5weTyaS5mq8UEhKCW2+9FcePH9fkdxweHo7OnTs7bevUqZNj+lfLv3c//fQTtmzZgj//+c+ObVr8jv/6178iLS0Njz32GLp27Yo//elPmDx5MjIyMgA07nfMYLxCQEAAevbsiezsbMe2mpoaZGdnIyEhQcXK6ic6OhphYWFO9dtsNuTl5alWvxAC48ePx9q1a7F161ZER0c77e/Zsyf8/f2dai4qKkJxcbFmvvOamhrY7XZN1jpo0CAcOnQIhYWFjk+vXr0wcuRIxz9rreYrlZeX48cff0R4eLgmv+N+/fpddYvR0aNH0a5dOwDa/L37w9KlS9G6dWsMGTLEsU2L3/Evv/xy1QuEfX19UVNTA6CRv2NFL+XxEKtWrRJ6vV4sW7ZMHDlyRIwdO1aEhIQIs9msdmlCiN+vPty/f7/Yv3+/ACDeeecdsX//fvHTTz8JIX6/pDkkJET861//EgcPHhQPPvigqpeNjxs3ThiNRrF9+3any8d/+eUXxzHPPfeciIyMFFu3bhV79+4VCQkJIiEhQZV609LSRE5Ojjh58qQ4ePCgSEtLEzqdTvz73//WXK3XUvuqVCG0V/OLL74otm/fLk6ePCm+/fZbkZSUJFq2bClKS0s1We+ePXuEn5+fmDNnjjh27JhYuXKlaNKkiVixYoXjGK393gnx+xX1kZGR4qWXXrpqn9a+41GjRombbrrJcbvGF198IVq2bCmmTp3qOKaxvmMG4zV88MEHIjIyUgQEBIjevXuL3bt3q12Sw7Zt2wSAqz6jRo0SQvx+WfP06dOFyWQSer1eDBo0SBQVFalWb121AhBLly51HPPrr7+K559/XjRv3lw0adJEPPTQQ+LcuXOq1DtmzBjRrl07ERAQIFq1aiUGDRrkCEWt1XotVwaj1moeMWKECA8PFwEBAeKmm24SI0aMcLonUGv1CiHE+vXrRZcuXYRerxcxMTFi8eLFTvu19nsnhBBZWVkCQJ11aO07ttlsYuLEiSIyMlIEBgaK9u3bi1deeUXY7XbHMY31HfO1U0RERLVwjZGIiKgWBiMREVEtDEYiIqJaGIxERES1MBiJiIhqYTASERHVwmAkIiKqhcFIRERUC4ORiIioFgYjERFRLQxGIje2adMmJCYmIiQkBKGhobj//vvx448/Ovbv2rULcXFxCAwMRK9evbBu3TrodDoUFhY6jjl8+DAGDx6M4OBgmEwm/OlPf8KFCxdU+NsQaQODkciNVVRUYMqUKdi7dy+ys7Ph4+ODhx56CDU1NbDZbBg6dCi6du2Kffv24fXXX8dLL73kNL6srAwDBw5E9+7dsXfvXmzatAkWiwWPPvqoSn8jIvXxIeJEHuTChQto1aoVDh06hJ07d2LatGk4ffo0AgMDAQCffPIJnn32Wezfvx9xcXGYPXs2vvnmG2RlZTnOcfr0abRt2xZFRUW49dZb1fqrEKmGHSORGzt27Bgef/xxtG/fHgaDAVFRUQCA4uJiFBUVITY21hGKANC7d2+n8QcOHMC2bdsQHBzs+MTExACA05QskTfxU7sAIpJu6NChaNeuHT7++GNERESgpqYGXbp0QVVVVb3Gl5eXY+jQoXjzzTev2hceHq50uURugcFI5KZ+/vlnFBUV4eOPP0b//v0BADt37nTs79ixI1asWAG73Q69Xg8AyM/PdzpHjx498PnnnyMqKgp+fvzXARHAqVQit9W8eXOEhoZi8eLFOH78OLZu3YopU6Y49j/xxBOoqanB2LFj8f333yMrKwtvv/02AECn0wEAUlNTcfHiRTz++OPIz8/Hjz/+iKysLDz99NOorq5W5e9FpDYGI5Gb8vHxwapVq1BQUIAuXbpg8uTJeOuttxz7DQYD1q9fj8LCQsTFxeGVV17BjBkzAMCx7hgREYFvv/0W1dXVuOeee9C1a1dMmjQJISEh8PHhvx7IO/GqVCIvsnLlSjz99NOwWq0ICgpSuxwiTeKiApEHW758Odq3b4+bbroJBw4cwEsvvYRHH32UoUh0HQxGIg9mNpsxY8YMmM1mhIeH45FHHsGcOXPULotI0ziVSkREVAtX14mIiGphMBIREdXCYCQiIqqFwUhERFQLg5GIiKgWBiMREVEtDEYiIqJaGIxERES1MBiJiIhq+T/bzvw/4aOUdgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mendata value fitur outlier\n",
        "conditions = [\n",
        "    [\"bmi\", \">\", 60],\n",
        "]\n",
        "# menampung outlier\n",
        "df_filtered = pd.DataFrame()\n",
        "display (df_filtered)\n",
        "for col in conditions:\n",
        "  attribute, operator, threshold = col\n",
        "  if operator == \">\":\n",
        "        df_temp = dfData.loc[dfData[attribute] > threshold]\n",
        "  elif operator == \"<\":\n",
        "        df_temp = dfData.loc[dfData[attribute] < threshold]\n",
        "  df_filtered = df_filtered.append(df_temp, ignore_index=True)\n",
        "# hasil outliernya dicocokan dan dihapus\n",
        "dfData = pd.merge(dfData,df_filtered, indicator=True, how='outer').query('_merge==\"left_only\"').drop('_merge', axis=1)\n",
        "\n",
        "display(dfData)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "mcV7qnuvno8S",
        "outputId": "4b657bfd-d2a2-4028-e883-c43d87571571"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cd471f45-d840-44e1-a44f-a90e0908af9b');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
              "0       Male  65.0             0              0         current  41.39   \n",
              "1       Male  47.0             1              0         current  33.64   \n",
              "2     Female  42.0             0              0         No Info  36.09   \n",
              "3     Female  57.0             1              0           never  39.28   \n",
              "4       Male  54.0             0              0         No Info  34.15   \n",
              "...      ...   ...           ...            ...             ...    ...   \n",
              "1495  Female  19.0             0              0         current  29.99   \n",
              "1496    Male  24.0             0              0           never  31.87   \n",
              "1497  Female  64.0             0              0           never  51.00   \n",
              "1498    Male  56.0             0              0           never  27.32   \n",
              "1499    Male   6.0             0              0         No Info  19.58   \n",
              "\n",
              "      HbA1c_level  blood_glucose_level  diabetes  \n",
              "0             7.0                  240         1  \n",
              "1             7.0                  140         1  \n",
              "2             5.7                  260         1  \n",
              "3             5.8                  240         1  \n",
              "4             6.0                  240         1  \n",
              "...           ...                  ...       ...  \n",
              "1495          5.7                  100         0  \n",
              "1496          5.7                   80         0  \n",
              "1497          6.2                  126         0  \n",
              "1498          5.8                  130         0  \n",
              "1499          5.0                  155         0  \n",
              "\n",
              "[1494 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d32942a7-6262-4a1f-99f6-84ecb89a04df\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>smoking_history</th>\n",
              "      <th>bmi</th>\n",
              "      <th>HbA1c_level</th>\n",
              "      <th>blood_glucose_level</th>\n",
              "      <th>diabetes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>current</td>\n",
              "      <td>41.39</td>\n",
              "      <td>7.0</td>\n",
              "      <td>240</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>current</td>\n",
              "      <td>33.64</td>\n",
              "      <td>7.0</td>\n",
              "      <td>140</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Info</td>\n",
              "      <td>36.09</td>\n",
              "      <td>5.7</td>\n",
              "      <td>260</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>57.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>never</td>\n",
              "      <td>39.28</td>\n",
              "      <td>5.8</td>\n",
              "      <td>240</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Info</td>\n",
              "      <td>34.15</td>\n",
              "      <td>6.0</td>\n",
              "      <td>240</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1495</th>\n",
              "      <td>Female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>current</td>\n",
              "      <td>29.99</td>\n",
              "      <td>5.7</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1496</th>\n",
              "      <td>Male</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>never</td>\n",
              "      <td>31.87</td>\n",
              "      <td>5.7</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1497</th>\n",
              "      <td>Female</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>never</td>\n",
              "      <td>51.00</td>\n",
              "      <td>6.2</td>\n",
              "      <td>126</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1498</th>\n",
              "      <td>Male</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>never</td>\n",
              "      <td>27.32</td>\n",
              "      <td>5.8</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1499</th>\n",
              "      <td>Male</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Info</td>\n",
              "      <td>19.58</td>\n",
              "      <td>5.0</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1494 rows × 9 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-d32942a7-6262-4a1f-99f6-84ecb89a04df')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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",
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              "\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-d32942a7-6262-4a1f-99f6-84ecb89a04df 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-d32942a7-6262-4a1f-99f6-84ecb89a04df');\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-2e7eee69-8159-4298-adf8-1260daec85e3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e7eee69-8159-4298-adf8-1260daec85e3')\"\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-2e7eee69-8159-4298-adf8-1260daec85e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  fig, ax = plt.subplots(figsize=(5, 4))\n",
        "  ax.scatter(dfData[col],dfData['age'])\n",
        "  ax.set_xlabel(col)\n",
        "  ax.set_ylabel('age')\n",
        "plt.show()\n",
        "\n",
        "# data yang sudah tidatk ada outlier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YC83pwbtoAVg",
        "outputId": "3ac60a33-96ef-4309-a7a4-f656875965ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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Lvn37rK9NnToVn3/+OTZt2oS9e/eisLAQgwcPduJoiYjI0UyCt3oTzSnl5ZCtKhmAlxdCQkLuWF9cXIxVq1Zh/fr16N27NwAgPT0d0dHROHDgALp3717XQyUiojrQPLCBqjmlnL7HeObMGYSFhaFly5YYPnw48vPzAQA5OTmoqKhAfHy8NRsVFYWIiAhkZWU5fFy8JRwRkXMM6xahak4ppxZjbGwsVq9ejYyMDCxbtgx5eXno1asXSkpKYDQa4ePjg4CAAJv3GAwGGI1G2W1aLBaYzWabxR68JRwRkXN8eihf1ZxSTj2U2qdPH+s/d+rUCbGxsWjevDk2btwIPz8/u7aZmpqKt99+W60hEhFRHbtw5bqqOaWcfii1uoCAALRp0wZnz55FSEgIysvLUVRUZJMxmUw1fid5W0pKCoqLi61LQQH36IiI3Emov1bVnFIuVYzXrl3DuXPnEBoaipiYGHh7eyMzM9P6em5uLvLz8xEXFye7Da1WC51OZ7MQEZH7OJx/VdWcUk49lPraa69hwIABaN68OQoLCzFr1ix4enrid7/7HfR6PcaOHYvk5GQEBgZCp9Nh0qRJiIuL4xmpRET12E9FYne0Ec0p5dRi/Omnn/C73/0Ov/zyC5o2bYqePXviwIEDaNq0KQDg/fffh4eHBxITE2GxWJCQkIClS5c6c8hERORgEY39kGus/VmLEY3tOxelNhpJkiSHbNlFmM1m6PV6FBcXKzqsOn/bESzdd7HW3Ms9QzGtf5d7GSIREVVz5Vo5usz5qtbckRlPIbCR2G3hlHSBS33H6EpESlFJjoiIxGw5+pOqOaVYjERE5FJ4uQYREVE1wf5ih0dFc0qxGImIyKXsOnVJ1ZxSLEYiInIpF80WVXNKsRiJiMilhOl9Vc0pxWKUMadPG1VzREQkZuXIbqrmlGIxypjxxWlVc0REJCbXVPvF/UpySrEYiYjIpVwqEbvVm2hOKRYjERG5FJ2P2N1KRXNKsRiJiMilrBN8ALFoTikWIxERuZSCqzdUzSnFYiQiIpci+tQMRz1dg8UoY+ngB1XNERGRmPd+20nVnFIsRhnTd3yvao6IiMQs3iV2GZxoTikWo4xSS6WqOSIiEvPjL2JPzRDNKcVilCF6ErBjThYmIrp/hQt+dyiaU+qeivHs2bPYuXMnbty4dWaQJEmqDMoVWAR/FdEcERGJiQhsoGpOKbuK8ZdffkF8fDzatGmDvn374uLFW0+xHzt2LF599VVVB0hERPeXwmKxO9qI5pSyqxinTp0KLy8v5Ofno0GD/zb2sGHDkJGRodrgiIjo/tNccE9QNKeUXcX45ZdfYt68eWjWrJnN+tatW+PChQuqDMzZZj/TWtUcERGJ+e1DzWoPKcgpZVcxlpaW2uwp3nblyhVotdp7HpQreDPjjKo5IiIS8/o/jquaU8quYuzVqxfWrl1r/Vmj0aCqqgrz58/HE088odrgiIjo/pMveKs30ZxSdl1tMH/+fDz55JM4fPgwysvLMW3aNPzwww+4cuUK9u/fr/YYiYjoPhLe2A+5xtqftehSl2t06NABp0+fRs+ePTFw4ECUlpZi8ODBOHr0KFq1aqX2GImI6D4yvFuEqjml7L6OUa/X44033sDGjRuxY8cOzJkzB6GhoXYPZO7cudBoNJgyZYp1XVlZGZKSkhAUFIRGjRohMTERJpPJ7s8gIiLXZy6/qWpOKbsOpX733Xc1rtdoNPD19UVERISik3AOHTqEDz/8EJ062d4QdurUqdi+fTs2bdoEvV6PiRMnYvDgwTxcS0RUjwX7+6qaU8quYuzcuTM0Gg2A/97t5vbPAODt7Y1hw4bhww8/hK/v3Qd+7do1DB8+HCtXrsScOXOs64uLi7Fq1SqsX78evXv3BgCkp6cjOjoaBw4cQPfu3e0ZurA5fdpgxhe136B2Tp82Dh0HEdH9JqZ5Y3hogKq73FnMQ3Mr5wh2HUrdsmULWrdujRUrVuD48eM4fvw4VqxYgbZt22L9+vVYtWoVdu3ahRkzZtS6raSkJPTr1w/x8fE263NyclBRUWGzPioqChEREcjKypLdnsVigdlstlnsIVKKSnJERCQm58LVu5YicKs0cy5cdcjn27XH+O677+KDDz5AQkKCdV3Hjh3RrFkzzJw5EwcPHkTDhg3x6quvYsGCBbLb2bBhA44cOYJDhw7d8ZrRaISPjw8CAgJs1hsMBhiNRtltpqam4u2331b+SxERkUu4VCJ2qzfRnFJ27TGeOHECzZs3v2N98+bNceLECQC3DrfevodqTQoKCjB58mSsW7eu1sOtSqSkpKC4uNi6FBQUqLZtIiJyvMAGPqrmlLKrGKOiojB37lyUl5db11VUVGDu3LmIiooCAPz8888wGAyy28jJycGlS5fQpUsXeHl5wcvLC3v37sXixYvh5eUFg8GA8vJyFBUV2bzPZDIhJCREdrtarRY6nc5mISIi9/Fvo9hXYKI5pew6lJqWloZnn30WzZo1s55JeuLECVRWVmLbtm0AgPPnz+Pll1+W3caTTz5p3bu87aWXXkJUVBRef/11hIeHw9vbG5mZmUhMTAQA5ObmIj8/H3FxcfYMm4iI3ECB4B1tRHNK2VWMPXr0QF5eHtatW4fTp2+dfPLcc8/hhRdegL+/PwBgxIgRd92Gv78/OnToYLOuYcOGCAoKsq4fO3YskpOTERgYCJ1Oh0mTJiEuLs7hZ6QSEZHzOPvpGnY/gN7f3x+PPvooWrRoYT2kunv3bgDAs88+q8rg3n//fXh4eCAxMREWiwUJCQlYunSpKtuuzXt92+LPO3KFckREpJ5h3SLwzvZTQjlH0Ei3L0RU4Pz58/jtb3+LEydOQKPRQJIkm+sYKysrVR3kvTCbzdDr9SguLlb0fWOL6duFsz/O7WfP0IiIqAar/nVeqBhn9ovG2F4thbappAvsOvlm8uTJiIyMxKVLl9CgQQN8//332Lt3L7p27Yo9e/bYs0kiIiIAwIUr11XNKWXXodSsrCzs2rULTZo0gYeHBzw9PdGzZ0+kpqbilVdewdGjR9UeJxER3SceCBB7aoZoTim79hgrKyutJ9k0adIEhYWFAG5dx5ibW/v3ckRERHI0tUcU5ZSya4+xQ4cOOH78OCIjIxEbG4v58+fDx8cHK1asQMuWYsd7iYiIavJTkdhlGKI5pewqxhkzZqC0tBQAMHv2bPTv3x+9evVCUFAQPv30U1UH6CweAKoEc0REpB5nX65h11mpNbly5QoaN25sc3aqK7D3rNQ935kwev3hWnOrX+iKxzvJ3+GHiIiUuVZ2Ex3e2llr7vu3EtDIV2z/zuFnpdYkMDDQ5UrxXryyWewEItEcERGJ+fRQvqo5pXgkUEapRexaTNEcERGJcfblGixGGQ28xaZGNEdERGKc/R0j/6rLeDBcr2qOiIjEjIhrAY9avpnz0NzKOQKLUcZ/Sm+qmiMiIjE+Xh4Y3yvyrpnxvSLh4+WYCmMxyohoLHZHBdEcERGJS+nbDr9/NPKOPUcPDfD7RyOR0redwz7b7qdr1HevPNEGX526JJQjIiL1pfRth1efjsLHWT/iwpXraB7YACPiWjhsT/E21a5jdFX2XsfY9o0dsFTWPjVaTw1y3+17L0MkIiIHc8p1jPWNSCkqyRERkXtgMcrQeordrEA0R0RE7oHFKOPDYTGq5oiIyD2wGGWM31D7fVKV5IiIyD2wGGVUiDxaQ0GOiIjcA4tRhujEcAKJiOoX/l2X4a8VO6lGNEdERO6BxSjjhuCd3kRzRETkHliMMnSCD78UzRERkXtgMcqY26+jqjkiIlKuskpC1rlf8M9jPyPr3C+orHL8TVWcWozLli1Dp06doNPpoNPpEBcXhy+++ML6ellZGZKSkhAUFIRGjRohMTERJpOpTsY2buMRVXNERKRMxvcX0XPeLvxu5QFM3nAMv1t5AD3n7ULG9xcd+rlOLcZmzZph7ty5yMnJweHDh9G7d28MHDgQP/zwAwBg6tSp+Pzzz7Fp0ybs3bsXhYWFGDx4sDOHTEREdSDj+4v449+P4GJxmc16Y3EZ/vj3Iw4tR5e7iXhgYCD+53/+B0OGDEHTpk2xfv16DBkyBADw73//G9HR0cjKykL37t2FtmfvTcRbTN8unP1xbj/hLBER3V1llYSe83bdUYq3aQCE6H2x7/Xe8Kzticb/zy1vIl5ZWYkNGzagtLQUcXFxyMnJQUVFBeLj462ZqKgoREREICsrS3Y7FosFZrPZZiEiIvdxMO+KbCkCgATgYnEZDuZdccjnO70YT5w4gUaNGkGr1eIPf/gDtmzZgnbt2sFoNMLHxwcBAQE2eYPBAKPRKLu91NRU6PV66xIeHu7g34CIiNR0qUS+FO3JKeX0Ymzbti2OHTuG7Oxs/PGPf8SoUaNw8uRJu7eXkpKC4uJi61JQUGDXdkQfmsGHaxARqSvY31fVnFJOL0YfHx/85je/QUxMDFJTU/Hggw/igw8+QEhICMrLy1FUVGSTN5lMCAkJkd2eVqu1nuV6e7HHkt8+qGqOiIjEPBwZiFC9L+T2OzQAQvW+eDgy0CGf7/Ri/LWqqipYLBbExMTA29sbmZmZ1tdyc3ORn5+PuLg4h48jafNxVXNERCTG00ODWQPaAcAd5Xj751kD2gmfeKOUU2/bkpKSgj59+iAiIgIlJSVYv3499uzZg507d0Kv12Ps2LFITk5GYGAgdDodJk2ahLi4OOEzUu+F6Km6LnVKLxFRPfFMh1Ase7EL3v78pM2JOCF6X8wa0A7PdAh12Gc7tRgvXbqEkSNH4uLFi9Dr9ejUqRN27tyJp556CgDw/vvvw8PDA4mJibBYLEhISMDSpUudOWQiIqojz3QIxVPtQnAw7woulZQh2P/W4VNH7Sne5nLXMarN3usYW07fDpFHLXoAOM/rGImIXJpbXsfoakSfP8znFBMR1S8sRhnegjMjmiMiIvfAP+sy0l98WNUcERG5BxajjJc35qiaIyIi98BilFFqqVQ1R0RE7oHFKMNb8Gxg0RwREbkHFqOMMsHTTUVzRETkHliMRERE1bAYiYiIqmExylg6WOypGaI5IiJyDyxGGZO3ij01QzRHRETugcUoo0LwpBrRHBERuQcWowzRqzB4tQYRUf3CYpTBYiQiuj+xGGXw6RpERPcnFqMMPl2DiOj+xD/rMvh0DSKi+xOLUQafrkFEdH9iMcrg0zWIiO5PLEYZ/I6RiOj+xD/rMsoEdwRFc0RE5B5YjERERNWwGImIiKphMcpYPKijqjkiInIPTi3G1NRUdOvWDf7+/ggODsagQYOQm5trkykrK0NSUhKCgoLQqFEjJCYmwmQyOXxsU/95QtUcERG5B6cW4969e5GUlIQDBw7gq6++QkVFBZ5++mmUlpZaM1OnTsXnn3+OTZs2Ye/evSgsLMTgwYMdPrZKSd0cERG5By9nfnhGRobNz6tXr0ZwcDBycnLw6KOPori4GKtWrcL69evRu3dvAEB6ejqio6Nx4MABdO/e3RnDJiKiesylvmMsLi4GAAQGBgIAcnJyUFFRgfj4eGsmKioKERERyMrKqnEbFosFZrPZZiEiIhLlMsVYVVWFKVOm4JFHHkGHDh0AAEajET4+PggICLDJGgwGGI3GGreTmpoKvV5vXcLDwx09dCIiqkdcphiTkpLw/fffY8OGDfe0nZSUFBQXF1uXgoIClUZIRET3A5coxokTJ2Lbtm3YvXs3mjVrZl0fEhKC8vJyFBUV2eRNJhNCQkJq3JZWq4VOp7NZ7PHR0C6q5oiIyD04tRglScLEiROxZcsW7Nq1C5GRkTavx8TEwNvbG5mZmdZ1ubm5yM/PR1xcnEPH9taXJ1XNERGRe3DqWalJSUlYv349/vnPf8Lf39/6vaFer4efnx/0ej3Gjh2L5ORkBAYGQqfTYdKkSYiLi3P4GalXSitUzRERkXtwajEuW7YMAPD444/brE9PT8fo0aMBAO+//z48PDyQmJgIi8WChIQELF261OFja+gDXBfovIY+Dh8KERHVIacWoyTVfnW8r68v0tLSkJaWVgcj+q//lIo9NkM0R0RE7sElTr4hIiJyFSxGGaITwwkkIqpf+HddxkfPx6iaIyIi98BilDHxH0dVzRERkXtgMcq4UVGlao6IiNwDi1GGn7fY1IjmiIjIPfCvuoyPXxK7gYBojoiI3AOLUcbkjUdUzRERkXtgMcrgLeGIiO5PLEYZOj9PVXNEROQeWIwybpSVq5ojIiL3wGKUcaNSo2qOiIjcA4tRhs5X7P7qojkiInIPLEYZfx0mdqs30RwREbkHFqOMUWuyVc0REZF7YDHKsFTW/qxIJTkiInIPLEYZohdh8GINIqL6hcUoo0kjb1VzRETkHliMMkosYk/NEM0REZF7YDHKCGwoticomiMiIvfAYpTx8RjBp2sI5oiIyD2wGGW898VJVXNEROQeWIwy8q/eUDVHRETugcUoI0zno2qOiIjcg1OL8ZtvvsGAAQMQFhYGjUaDrVu32rwuSRLefPNNhIaGws/PD/Hx8Thz5kwdjU705uC8iTgRUX3i1GIsLS3Fgw8+iLS0tBpfnz9/PhYvXozly5cjOzsbDRs2REJCAsrKyhw+tkKzRdUcERG5B6c+GqJPnz7o06dPja9JkoRFixZhxowZGDhwIABg7dq1MBgM2Lp1K55//nmHji2isR9yjSVCOSIiqj9c9jvGvLw8GI1GxMfHW9fp9XrExsYiKyvL4Z//5z7tVM0REZF7cNliNBqNAACDwWCz3mAwWF+ricVigdlstlnsMeJvB1TNERGRe3DZYrRXamoq9Hq9dQkPD7drO1dKK1TNERGRe3DZYgwJCQEAmEwmm/Umk8n6Wk1SUlJQXFxsXQoKCuz6fJ2P2NSI5oiIyD247F/1yMhIhISEIDMz07rObDYjOzsbcXFxsu/TarXQ6XQ2iz0slZWq5oiIyD049azUa9eu4ezZs9af8/LycOzYMQQGBiIiIgJTpkzBnDlz0Lp1a0RGRmLmzJkICwvDoEGDHD620gqxBxCL5oiIyD04tRgPHz6MJ554wvpzcnIyAGDUqFFYvXo1pk2bhtLSUkyYMAFFRUXo2bMnMjIy4Ovr6/Cx6Xy9cFng+0Odr1OnkIiIVKaRJKle7/KYzWbo9XoUFxcrOqx68icz+v71X7XmdkzshXbN7DtcS0REdUNJF7jsd4zONmXjEVVzRETkHliMMi6VlKuaIyIi98BilBHYQOy7Q9EcERG5BxajjIjAhqrmiIjIPbAYZVwsEXtqhmiOiIjcA4tRhuhTM/h0DSKi+oXFKGPqk21VzRERkXtgMcoY8uF+VXNEROQeWIwyblRUqZojIiL3wGKU4eOpbo6IiNwDi1GG3k/s+kTRHBERuQcWo4xrFrFbyIrmiIjIPbAYZQQ29FY1R0RE7oHFKGPZC11VzRERkXtgMcp4aU22qjkiInIPLEYZ5rKbquaIiMg9sBhliJ5sypNSiYjqFxajjLIKsbNNRXNEROQeWIwyygVvaCOaIyIi98BilOHnLTY1ojkiInIP/Ksu4+OXuquaIyIi98BilDFxwyFVc0RE5B5YjDKM5gpVc0RE5B5YjDJEzzXlOalERPULi5GIiKgatyjGtLQ0tGjRAr6+voiNjcXBgwedPSQiIqqnXL4YP/30UyQnJ2PWrFk4cuQIHnzwQSQkJODSpUsO/VzRqzB4tQYRUf3i8n/WFy5ciPHjx+Oll15Cu3btsHz5cjRo0AB/+9vfHPq5XcL1quaIiMg9uHQxlpeXIycnB/Hx8dZ1Hh4eiI+PR1ZWVo3vsVgsMJvNNos9Xn6ijao5IiJyDy5djJcvX0ZlZSUMBoPNeoPBAKPRWON7UlNTodfrrUt4eLhdn92zTVNove4+PVovD/Rs09Su7RMRkWty6WK0R0pKCoqLi61LQUGBXdvx9NDgg+c73zXzwfOd4emhsWv7RETkmly6GJs0aQJPT0+YTCab9SaTCSEhITW+R6vVQqfT2Sz2eqZDKJa/2AUGf63N+hCdFstf7IJnOoTavW0iInJNLv00QR8fH8TExCAzMxODBg0CAFRVVSEzMxMTJ06skzE80yEUT7ULwcG8K7hUUoZgf188HBnIPUUionrKpYsRAJKTkzFq1Ch07doVDz/8MBYtWoTS0lK89NJLdTYGTw8N4loF1dnnERGR87h8MQ4bNgz/+c9/8Oabb8JoNKJz587IyMi444QcIiIiNWgkSarXt/s0m83Q6/UoLi6+p+8biYjIfSnpApc++YaIiKiusRiJiIiqYTESERFVw2IkIiKqxuXPSr1Xt88tsveeqURE5P5ud4DI+ab1vhhLSkoAwO57phIRUf1RUlICvf7uT0Wq95drVFVVobCwEP7+/tBo7L9bjdlsRnh4OAoKCnjZRzWcl5pxXuRxbmrGeZGnxtxIkoSSkhKEhYXBw+Pu3yLW+z1GDw8PNGvWTLXt3ev9V+srzkvNOC/yODc147zIu9e5qW1P8TaefENERFQNi5GIiKgaFqMgrVaLWbNmQavV1h6+j3BeasZ5kce5qRnnRV5dz029P/mGiIhICe4xEhERVcNiJCIiqobFSEREVA2LkYiIqBoW4/9LS0tDixYt4Ovri9jYWBw8ePCu+U2bNiEqKgq+vr7o2LEjduzYUUcjrXtK5mblypXo1asXGjdujMaNGyM+Pr7WuXRXSv+duW3Dhg3QaDQYNGiQYwfoRErnpqioCElJSQgNDYVWq0WbNm3q5X9TSudl0aJFaNu2Lfz8/BAeHo6pU6eirKysjkZbN7755hsMGDAAYWFh0Gg02Lp1a63v2bNnD7p06QKtVovf/OY3WL16tbqDkkjasGGD5OPjI/3tb3+TfvjhB2n8+PFSQECAZDKZaszv379f8vT0lObPny+dPHlSmjFjhuTt7S2dOHGijkfueErn5oUXXpDS0tKko0ePSqdOnZJGjx4t6fV66aeffqrjkTuW0nm5LS8vT3rggQekXr16SQMHDqybwdYxpXNjsVikrl27Sn379pX27dsn5eXlSXv27JGOHTtWxyN3LKXzsm7dOkmr1Urr1q2T8vLypJ07d0qhoaHS1KlT63jkjrVjxw7pjTfekDZv3iwBkLZs2XLX/Pnz56UGDRpIycnJ0smTJ6UlS5ZInp6eUkZGhmpjYjFKkvTwww9LSUlJ1p8rKyulsLAwKTU1tcb80KFDpX79+tmsi42NlX7/+987dJzOoHRufu3mzZuSv7+/tGbNGkcN0SnsmZebN29KPXr0kD766CNp1KhR9bYYlc7NsmXLpJYtW0rl5eV1NUSnUDovSUlJUu/evW3WJScnS4888ohDx+lMIsU4bdo0qX379jbrhg0bJiUkJKg2jvv+UGp5eTlycnIQHx9vXefh4YH4+HhkZWXV+J6srCybPAAkJCTI5t2VPXPza9evX0dFRQUCAwMdNcw6Z++8zJ49G8HBwRg7dmxdDNMp7Jmbzz77DHFxcUhKSoLBYECHDh3w3nvvobKysq6G7XD2zEuPHj2Qk5NjPdx6/vx57NixA3379q2TMbuquvj7W+9vIl6by5cvo7KyEgaDwWa9wWDAv//97xrfYzQaa8wbjUaHjdMZ7JmbX3v99dcRFhZ2x7/I7syeedm3bx9WrVqFY8eO1cEInceeuTl//jx27dqF4cOHY8eOHTh79ixefvllVFRUYNasWXUxbIezZ15eeOEFXL58GT179oQkSbh58yb+8Ic/4M9//nNdDNllyf39NZvNuHHjBvz8/O75M+77PUZynLlz52LDhg3YsmULfH19nT0cpykpKcGIESOwcuVKNGnSxNnDcTlVVVUIDg7GihUrEBMTg2HDhuGNN97A8uXLnT00p9qzZw/ee+89LF26FEeOHMHmzZuxfft2vPPOO84eWr133+8xNmnSBJ6enjCZTDbrTSYTQkJCanxPSEiIory7smdubluwYAHmzp2Lr7/+Gp06dXLkMOuc0nk5d+4cfvzxRwwYMMC6rqqqCgDg5eWF3NxctGrVyrGDriP2/DsTGhoKb29veHp6WtdFR0fDaDSivLwcPj4+Dh1zXbBnXmbOnIkRI0Zg3LhxAICOHTuitLQUEyZMwBtvvFHrMwXrK7m/vzqdTpW9RYB7jPDx8UFMTAwyMzOt66qqqpCZmYm4uLga3xMXF2eTB4CvvvpKNu+u7JkbAJg/fz7eeecdZGRkoGvXrnUx1DqldF6ioqJw4sQJHDt2zLo8++yzeOKJJ3Ds2DGEh4fX5fAdyp5/Zx555BGcPXvW+n8WAOD06dMIDQ2tF6UI2Dcv169fv6P8bv+fB+k+vsV1nfz9Ve00Hje2YcMGSavVSqtXr5ZOnjwpTZgwQQoICJCMRqMkSZI0YsQIafr06db8/v37JS8vL2nBggXSqVOnpFmzZtXryzWUzM3cuXMlHx8f6X//93+lixcvWpeSkhJn/QoOoXRefq0+n5WqdG7y8/Mlf39/aeLEiVJubq60bds2KTg4WJozZ46zfgWHUDovs2bNkvz9/aVPPvlEOn/+vPTll19KrVq1koYOHeqsX8EhSkpKpKNHj0pHjx6VAEgLFy6Ujh49Kl24cEGSJEmaPn26NGLECGv+9uUaf/rTn6RTp05JaWlpvFzDUZYsWSJFRERIPj4+0sMPPywdOHDA+tpjjz0mjRo1yia/ceNGqU2bNpKPj4/Uvn17afv27XU84rqjZG6aN28uAbhjmTVrVt0P3MGU/jtTXX0uRklSPjfffvutFBsbK2m1Wqlly5bSu+++K928ebOOR+14SualoqJCeuutt6RWrVpJvr6+Unh4uPTyyy9LV69erfuBO9Du3btr/Jtxey5GjRolPfbYY3e8p3PnzpKPj4/UsmVLKT09XdUx8bFTRERE1dz33zESERFVx2IkIiKqhsVIRERUDYuRiIioGhYjERFRNSxGIiKialiMRERE1bAYiezw+OOPY8qUKc4ehkvhnFB9cd/fRJyovhk9ejSKioqwdevWOv3czZs3w9vbu04/k8gRWIxE9URlZSU0Go3TPr8+PYya7m88lEpkp6qqKkybNg2BgYEICQnBW2+9BQAYM2YM+vfvb5OtqKhAcHAwVq1aBeDWYceJEydi4sSJ0Ov1aNKkCWbOnGnz1ASLxYLXXnsNDzzwABo2bIjY2Fjs2bPH+vrq1asREBCAzz77DO3atYNWq8WYMWOwZs0a/POf/4RGo4FGo7G+p6CgAEOHDkVAQAACAwMxcOBA/Pjjj9btjR49GoMGDcKCBQsQGhqKoKAgJCUloaKiwppZunQpWrduDV9fXxgMBgwZMsT62q8PpV69ehUjR45E48aN0aBBA/Tp0wdnzpy5Y/w7d+5EdHQ0GjVqhGeeeQYXL160938SIlWwGInstGbNGjRs2BDZ2dmYP38+Zs+eja+++grjxo1DRkaGzR/4bdu24fr16xg2bJjN+728vHDw4EF88MEHWLhwIT766CPr6xMnTkRWVhY2bNiA7777Ds899xyeeeYZm3K5fv065s2bh48++gg//PADFi9ejKFDh1oL5uLFi+jRowcqKiqQkJAAf39//Otf/8L+/futRVReXm7d3u7du3Hu3Dns3r0ba9aswerVq7F69WoAwOHDh/HKK69g9uzZyM3NRUZGBh599FHZ+Rk9ejQOHz6Mzz77DFlZWZAkCX379rUp2uvXr2PBggX4+OOP8c033yA/Px+vvfbaPf3vQnTPVL0lOdF94rHHHpN69uxps65bt27S66+/LkmSJLVr106aN2+e9bUBAwZIo0ePtnl/dHS0VFVVZV33+uuvS9HR0ZIkSdKFCxckT09P6eeff7b5jCeffFJKSUmRJEmS0tPTJQDSsWPHbDI1Pbnj448/ltq2bWvzeRaLRfLz85N27txpfV/z5s1tnmrx3HPPScOGDZMkSZL+8Y9/SDqdTjKbzbJzMnnyZEmSJOn06dMSAGn//v3W1y9fviz5+flJGzdutBn/2bNnrZm0tDTJYDDUuH2iusI9RiI7derUyebn0NBQXLp0CQAwbtw4pKenA7j1dPEvvvgCY8aMscl3797d5jvBuLg4nDlzBpWVlThx4gQqKyvRpk0bNGrUyLrs3bsX586ds77Hx8fnjnHU5Pjx4zh79iz8/f2t2woMDERZWZnN9tq3b299GO6vf6ennnoKzZs3R8uWLTFixAisW7cO169fr/HzTp06BS8vL8TGxlrXBQUFoW3btjh16pR1XYMGDdCqVasaP4/IWXjyDZGdfn0GpkajsT6FfuTIkZg+fTqysrLw7bffIjIyEr169RLe9rVr1+Dp6YmcnBybogKARo0aWf/Zz89P6ISba9euISYmBuvWrbvjtaZNmwr9Tv7+/jhy5Aj27NmDL7/8Em+++SbeeustHDp0CAEBAcK/W3U1fZ7EJ+GRk7EYiRwgKCgIgwYNQnp6OrKysvDSSy/dkcnOzrb5+cCBA2jdujU8PT3x0EMPobKyEpcuXVJUqMCtvcjKykqbdV26dMGnn36K4OBg6HQ65b/Q//Py8kJ8fDzi4+Mxa9YsBAQEYNeuXRg8eLBNLjo6Gjdv3kR2djZ69OgBAPjll1+Qm5uLdu3a2f35RHWBh1KJHGTcuHFYs2YNTp06hVGjRt3xen5+PpKTk5Gbm4tPPvkES5YsweTJkwEAbdq0wfDhwzFy5Ehs3rwZeXl5OHjwIFJTU7F9+/a7fm6LFi3w3XffITc3F5cvX0ZFRQWGDx+OJk2aYODAgfjXv/6FvLw87NmzB6+88gp++uknod9n27ZtWLx4MY4dO4YLFy5g7dq1qKqqQtu2be/Itm7dGgMHDsT48eOxb98+HD9+HC+++CIeeOABDBw4UOjziJyFxUjkIPHx8QgNDUVCQgLCwsLueH3kyJG4ceMGHn74YSQlJWHy5MmYMGGC9fX09HSMHDkSr776Ktq2bYtBgwbh0KFDiIiIuOvnjh8/Hm3btkXXrl3RtGlT7N+/Hw0aNMA333yDiIgIDB48GNHR0Rg7dizKysqE9yADAgKwefNm9O7dG9HR0Vi+fDk++eQTtG/fvsZ8eno6YmJi0L9/f8TFxUGSJOzYsYM3ASCXp5F4QJ/IIa5du4YHHngA6enpdxxqfPzxx9G5c2csWrTIOYMjIln8jpFIZVVVVbh8+TL+8pe/ICAgAM8++6yzh0RECrAYiVSWn5+PyMhINGvWDKtXr4aXF/8zI3InPJRKRERUDU++ISIiqobFSEREVA2LkYiIqBoWIxERUTUsRiIiompYjERERNWwGImIiKphMRIREVXDYiQiIqrm/wBfEGbefLIbJQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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3P7kxqkBt46NWi4hs3zGXQP2bpJrrNc8xlOi9Du6hFMrqFCI5XcpWNUNvuIgyXONEMZv6VilHHXpgtULZAH1FEjV45pL6/ic3RpNCrUoU3T6P6zWlu7ZW9F6Hw2cuup3PGHUXq6u+yHANnjAC6tAD6uoePFDPmRo8c0l9/5PhGipQp6Sidlc3IlxkSKc4DEyNxc9HilB4vhzRzWqP9zZ/PMdQoDe8wWFjcYQBLHqoJ25+bYPAHtYyskcSk5ze8XRuFeHSJksYgbJvLOEvWjL5aOWFoalYkpXHJGcWqOdMjYkDOjDN5cQBHZx/K9ePN1jltCKfGFWg1nFTu6sbpUoODLAhrV0LDOvaCmntWjAdz3OMv9Eb3gC42lg+2HRQZ488s2Kb7xsUoH88yqorrG0q5airW1BX9+CBes7UmPP9fs1yIqv78CA3RhWoddzU7Uu0IeI6KG0sRtmuWEMn9I5H2X+ecA0z0LZlE12fS2rhsTFS23jlxqgCT0Z4kVDr2KnDRaxGZKj+8AplRQqjbFeeql54Qu+6UrbD2qZSjjVbTmVVDV8HfUDdPg9m7XNSFNv1V8pR20vlxqgGj4+5QKjDFYxSJVuhUgYP63JP6z5HkuJLbpTtirXqha/15wvlWFjbVMrxZMsRCXX7PJi1z4NSYzXLDewYw3QMq5xWpPONCjwZ4UWipyKFCIxQ5VqlUgYPItSA+Yp5YaluwoOnqhee8LX+fKEcC2ubSjlq9St1+zyYtc/FZWyhR0q54nLGYxjltCKfGFWgVmVSI3r8VqqUwYMI1ac/3Oi1rFdfaby8oRwLz1qiVqVRt8+DWftsxcxXcmNUgVqVSW3jEzl+vemdrKB+nTyko+5z/L57ovNvljRaWtC6XvW07x4SwLOWxqS1gS9liJGhB9Tt88BaNcPf1TUcoT/ecA894zlGJHJjVIE68wp1uIjI8esZy5q9p9B79gaMWpCFp5fvwqgFWeg9e4PpnjD3nCzRfY7Xv9vn/Ft0qjGtqnc97buHBPCE/lCHHlC3z4NZq2vwhJ5Rh6uZ56qaEMrMK2YI1xA1ft6xWEn9aoVwDS2qUdHt78zzfgPz9Hn60FQ81je53pNDgA14rG8y0g0OrvdV2FlL4Wd/YIZ7hp72zFRdRzrf+IAq84pZMuWLGD+PvcBqhYrDG+n/KsVH1oX+tGnRGJsP6D6lC+7ZdbzBGmLhCfeiwzyFih2kD03Fs4NS/F6o9kJ5FX454T3ZtpbCz/7ALPcM3vaUcpFhbOFPrHJakU+MDFBkXtlXwFhdgFFOD3rHz2NjolYla+WT7WzZXbwRoPg6GhWuwaoiTdLhoOEeEmDWMAJvsBZ0ZpXzB2atrsFzL/sup4DpGFY5rZBujG3atIHNZqv3mjBhAgCgvLwcEyZMQIsWLdC0aVOMGDECp0/rjxezAtTVBUTCY6+kVqVoRUTFAmV1CZaUZDywqkjzdZTQAvQXKnaQsToHKVO+dangnjLlW2QYXO6JtaAzq5w/MGt1DZ57WV4R4/wzymmFdGPctm0bTp065XytW7cOAHDvvfcCACZOnIhVq1bhs88+w8aNG5Gfn4977rmHsst+w6yu17xotVdSu2trJUmH6tGBe8jHiOsTdJ/TVxtq6F1XIiplZKzOwbxNR+o9bdbYgXmbjhi6ObJeTxHXXRRmvWfw9Iu6ugnpxnjVVVchNjbW+fr3v/+Ndu3aoV+/figpKcHChQvx5ptvon///ujevTsWLVqEn376CVlZWX7tJ0W4gBXdxX0xpFMctkzuj0/H98Lb93XFp+N7Ycvk/h6deKjDZbTy+r1ddZ9DGfLhK1yHl78MSmGSU4aO8HB753jN51LKUac3mz3iOqFy/oBnnv0Bz72MNfxJRJiUx/4YclYOKisrsXTpUjzyyCOw2WzIzs7G5cuXMWDAAKdMSkoKkpKSkJmZqXqeiooKlJaWurz0QBUuYEV3cRZY7ZXU4TJayTml326jDPnwZWPl5V/Zx5nklKEjPIz9R92PV9ZzKeWo7ZJf7mTz4GWV8wc88+wPeO5lrOFPIsKkPGGau+pXX32F4uJiPPTQQwCAgoICBAcHIzIy0kUuJiYGBQXqBteMjAxEREQ4X4mJ/L+OqMMFqN3VqbFSoWIRtk4ed3WtsNr79IZrFJ6vS+/GUymBOr0Zdfs8UFekEAm1j4E5/IwBLFy4ELfddhvi4+N9C3shPT0dkyZNcv5fWlrKtTmaJVyAyl3dLFilULGI6hpKe6mI83mCNQwjIVKf7SyycV1ICE91DWp7WSvG8bPK+YPWUY2xmVHOn/CE61CHnpji7nrs2DF8//33+OMf/+h8LzY2FpWVlSguLnaRPX36NGJj1bO1h4SEIDw83OXFg9XCBRoyVihUrKe6hid7qYhqHZ5grXQBuz47enlltfPvpOZsN2KlHLWN3cY4fFY5f0BdkUINHrU4deiJKZ4YFy1ahOjoaNx+++3O97p3745GjRph/fr1GDFiBABg//79yMvLQ1pamuF9on6Ud5CxOgcLNrt65s1cnYvxfRq+KtVK6FGpeUqJZpSKjrXSxQmd9s3S8jqnmPxStnMp5Rx2qXmb1J80jLSxnyhhCwNglfMH1BUp1OBRS1OHnpA/MdbU1GDRokV48MEHERRUt09HRERg3LhxmDRpEn744QdkZ2fj4YcfRlpaGnr16mV4v8wQLkDpri7Rhl63cfeUaEa5obOuV73tRzWpUwXzqkUpbezUqlwezHDP8gTPXFLPP/nG+P333yMvLw+PPPJIvc/mzJmDO+64AyNGjEDfvn0RGxuLL774wi/9os7uTu2ubhRWqJTBA2sYhBru1/Kp/u31dskFreEtetv/8s+9nX+P7JHEdIwnufShqdg3/TZMub0jxqa1xpTbO2Lf9NsM15aYtVKFN8wa4sQTRqJnzYiAfGMcNGgQ7HY72rev/0UMDQ3F3LlzUVRUhIsXL+KLL77wal8UCXV2d2p3dSOwSqUMHljDINRwv5YvfPmLzh65orW6hp72w0ODcFV4Xd7XFdvY0uWpyQUHBWBcn7Z4ZVgnjOvT1i+OZ2atVOENs4Y48YSR6F0zeiHfGM0KtY3Riu7i3qAOfTEaEddBeQ4jUo1pqa6hp/1RN7o+IVhxLVN//3kxY4iTFcN1TOF8Y0ao9fXUOnaRmCX0xUhiw/Wvg4SIOtf/2GaNsF9wfmQt1TWSmodhP2eCenfX+0RGr1RWOX/QsmmIbyENcv7EbCFOSVGM6fUUctRrRj4xqkCtr6d2V1ei1y54JYS+ZB/VX5vPrrjeJwzytmOtrjFnZDfuNtzVwikxzZiOU5MjsUuzNmFSE3l1jR05+SXIPnYOOfklpLb8Qals5i+lnN41oxf5xKiCQ1//+NIdHj/XarPRCrW7uoM1e09h2qocl40tLiIUU+9MNbxQsZU4UVKh+xwnFdU1zl6s0n0+T7CqtZqGBqFLQrjPmoRqKFVcRWVsISKe5ESsPx7OXGS7nqxy/sRsIV7FZYxhJAo5PWtGBPKJ0cRQp4QTZRekVkv7AxFVFpRq8ehmxmT00BKG8edbruZux3UsfNef0i5t1TVrxhAvnrmknn+5Margq7qBwy5mtIqCyl3dl10QYB9/51YRTG2yypmR6cM66z6H0l190UM9dZ/PExMHdGCS01vdQ+lGzxP6JHL98UAdrsWDWUO8eL7/1PcMuTGqYCa7GIW7usjxz16Ty9Qmq5wZmfL1Ht3nULqrf7DpoO7zeWLO9/uZ5PRW91C60fOEPlF//6jDtXgwa4gXz/ef+p4hN0YVrgS7mDdEjr8hZf1XQ0R4hXL8Rs0F63n1rmuljZFnLVF//6jb54E6xEENnu8/9T1DbowqUOu4qRE5fupq3P5AhI1ROX6j5oL1vHrXtV4bI/X3j7p9Hswa4sXz/ae+Z8iNUQXqcA1qRI6fuhq3P3j93q66z6FMK6c3xZwaLzDaprsmRupqR5kqjWctsbavt59qWPH7T51GTQ3W9IJKOep7htwYVTBreiV/4Ri/mslCS7gKdTVuf5BzSn/5G2VaOb0p5jwxMDWaKbgfAJZtPaarLWWqNJ61xNq+3n6qYcXvP3UaNTVY0wsq5ajvGXJj9IIZ0ytZESvaa7Qiou9K248RdqAR1ydw9YUHkTZKEXI8WO37b4Y58wSr/V0pR33PkAH+PjBbeiV/wRquwpLGrWUTxvRajHJmREQlcaXtR3SqK61p9xJ12kyV88GzlqhTgjmw0vc/rhnb94dVThSs6QWVdnrqlHzyiZEB6gryFCmxhLrLs06XipwVSlXt48wr6sA9vZ/oVFdawxtSYsN1taecD561lBLLmBKMUU4PZZXV+MeWw3jvx0P4x5bDKKusNrxNHrbnsV1bVjlRsKYXdJEjTsknnxhNDlVKLJGqjDMXGNNreZCjGr9Wjp/Tp55yT+9nVKor1utadElf+8r54FlLrO3r7acv7np3s0tavP0F59Hp5bXokhCOlU/0MbRtrZwoZvuescqJgiW9YJeEcDQNrduOqFPyySdGE9NQUmJZMSWYVvS6wB8+c9Hlf6uHIVg9XAOovykq+eVEKe56d7NhbfPAGjIkIrRIK77SC7p/Tn395cbIAIUqjzollkh3dZ5zUY9fK3dd10rX8Y6SUA6MCkNgTaHl65r5Qpnejie9F3W4xIXyKp8J1H85UYoL5cYke+eBNWRIRGiRFnjSa1Kn5JMbow+oqs5Tp8QS6a7O465PPX6tPL50u+5zKEtCGRWGwJpCy9c184UyvR1Pei+R4UI8TFyxU6icP2ANGRIRWqQFnu8ydUo+uTF6gVKVR+2uDNC6q5th/FrI15FX1AFPBXM9bRiJiPReO/O83/R8fa4HnhADasz6nbFiSkDpfKMCddV5ah27AxHu6jzu+tTu2lqJjwjVlXQbAFpF1l1LveESaiQ0Z1sveqtrxEXUXZc2LRpj8wHfxyjTe7FWinh2UIohSfUTm4cyhRgkMs6nPzBrWBTPd5n6+y+fGFWgVuVR21iU6K0GzjWXFqug/o+HbtR9DpviausNl1CjdVQTJjm91TXOnK/zFmVNQ6eUo64UMfrG1r6FNMj5BZ1hUYbB812W4RrmhPpR3mFjeXzpDo+fG21jcSCiGjjPXFK7a2slonEjtG4RhmNn+VVrJ4rrjjUqDIFV5at3XZ9UtBMWHIiBqdFYl1OoKu+ero46i0tpBZtTDaucP9ATFmUkPN9l6u+/fGJUwSyqTEpEVQO3qru+ViLCGuk6XqlKNGpcrGElett3Dwlo29L7k6r759SVIqy4/szaZyt+/+XGqAK1KpPHxVkkIquB87heU7tra4XFvd8X43u3c/7NMn4eWCsr6K2MPuvuLs6/edaSsjqHN1jltEJdQZ4Hs/aZ515K/f0n3xhPnjyJBx54AC1atEBYWBg6d+6M7dvrXN/tdjteeuklxMXFISwsDAMGDMCBAwyWfJ1Qu4tT2zhF2nh4XK+p3bW1IsJt//4PM51/s4yfB9bKCnoro7+z4Vfn3zxrSVmdwxusclqhriDPg1n7zHMvpf7+k26M586dw80334xGjRrh22+/RU5ODt544w00b173K+C1117DO++8gw8++ABbt25FkyZNMHjwYJSXG+9yTOkuTm3jFGnjsaK7tlZEuO0XXbzs/Jv6uuoN6+AJPVHKUV9/6gryPJi5z5/vOKHpc+rrT7oxzp49G4mJiVi0aBFuvPFGJCcnY9CgQWjXrlalZLfb8dZbb+HFF1/EsGHD0KVLFyxevBj5+fn46quvDO2bSFUiD9Q6dpE2HivaGLTSKkK/23jzxnW+cJGh+uyVarSKZAsDiY/UN6/KMAaetUQdekBdQZ4Hs/a5rLLaq+MVUD/zE+v6N+p7Qroxrly5EjfccAPuvfdeREdHo1u3bliwYIHz8yNHjqCgoAADBgxwvhcREYGePXsiMzPT0ylRUVGB0tJSlxcP1O7i1DbOMWltmHT8yooQavCMhXr8WokRsEHf26Mujdq63ALd5/MEq+L/jM6YzKTmdc40XGuJOPSAJ8SEGrP2eRajk55SjnX9G/U9Id0YDx8+jPfffx/XXHMN1q5diz/96U946qmn8PHHHwMACgpqBx0TE+NyXExMjPMzdzIyMhAREeF8JSYmepTzBbW7OHUF8eCgAIzvk+xVxr0ihBo8NgZqG69WTgjIfKNUpR4rMiajijIkxKtcqT43+PzSuvngWUvUoQeOEBNvuIeYUGPWPvOoeFnXv1HfE9KNsaamBtdffz1mzZqFbt264dFHH8X48ePxwQcfcJ8zPT0dJSUlztfx48e5zkPtLg7QVxBPH5qKx/om1/u1H2ADHuvLHscIaLcxWA0R6inlWjJK3cW6XvVWYHBvp1uSd+9B98/NoEofcX2Crs8pMGOfeVS81Gph0o0xLi4OqamuN9eOHTsiL6/Wcy42NhYAcPr0aReZ06dPOz9zJyQkBOHh4S4vHqjdxR0M6RSHLZP749PxvfD2fV3x6fhe2DK5v99qEaYPTcV/pw3BmF5J6HNNS4zplYT/ThuiaVPksTFQh6toRYR6ShlKMXlIR93n84Sy6oU3lOEWetvhuZas1UWMqkJitfUHmLfPfxmUolmOWi1MujHefPPN2L9/v8t7v/76K1q3rt1skpOTERsbi/Xr1zs/Ly0txdatW5GWlmZo36jdxZUEBtiQ1q4FhnVthbR2LfyqPlyz9xT6v/EjlmTlYfOBM1iSlYf+b/yoKYE6j42BOlxFKyxqLF8oQyn2nCzR2yWPKKteeEMZbqG3HZ5ryVpdxKgqJFZbf4B5+/yvbDatnVKOWi1MujFOnDgRWVlZmDVrFg4ePIhly5Zh/vz5mDBhAgDAZrPhmWeewYwZM7By5Urs2bMHY8eORXx8PIYPH25o36jdhc2AqOoiPDYGK86/r+wuvuAJV9CKv1z69V5Lahu/FdefWfvMey0p1cKkG2OPHj3w5Zdf4tNPP0WnTp0wffp0vPXWWxg9erRT5rnnnsOTTz6JRx99FD169MCFCxewZs0ahIYa66ZPnd2dGpGFgnnstWawMWmBJbzHF4nN68YfHmJMGuPWUYxV3hnDOtRQhmvwhF4kMLbPKqeVqLBgoXL+gDrEQY2ECMZrqZCjVguTZ7654447sGfPHpSXlyM3Nxfjx493+dxms+GVV15BQUEBysvL8f3336N9+/bGd8xE1R2qa+zIPHQWX+86icxDZ/1iIxCplhmYEuNTxl3OauEaLOE9vkiJbeb8+5OfjVERDuzo2TbvToJOpwZluAZP6AX112/fad8lp7TI+QPqEAc17IzXXylHrRaW1TVUoM7u7mDN3lOYtirHZZHERYRi6p2plikUXFxx2aeMu5xZqouwIkKlp6yocfycMequ4nK2a6G3tqQyXIMn9OIkY1gJq5xWjp9ju56scv6AOsRBDZ5rSa0WJn9iNCtmUOWJsvHxIHL8ZphLoxERtqMcv95wCZY2vKF3PHrV4tThUtTt80Ad4qCGFU0pcmNUgVqVJ9LGx4PI8fNkyqe2MWiFNQzCG8rQg9fv7ar7fO5oqUagdzzKMCaetUQdLsVahYRVzh/whEX4A55rSR2uIzdGFagzr1Dr2EWOnydTPvX4tcIaBuENZehBzil9Jaw8oaUagd7xKMOYeLI4UYdLsVYhYZXzBzxhEf6A51pSh+vIjdELlNlaqHXsIrkSqmuIqFjgj3AN1vPqHY97O1qzOFFff+pwER7M2mcrhutI5xsVtGRrMSLIlFrHzqrKHJga6/OpkWcsVguXSRRgE4xtVjcWo8YVGcbmqp/QXN+68hSiMaRTHAamxuLnI0UoPF+O6Ga16lNP64e6uoYVbYyslVNY5UTBcy1Zv08ivneekE+MKvBkaxEJtY1TpCqTayzU/voaSWqu/waZrazvadC4vsthc9VvHaUvWYHuqhfE1TVEVpfxF8RTpr9BhVxKLFsqT1Y5rciNUQXqop/U1TVEqrJ47JVmCZdhRRmewMuJYkWIg0HjymN01c/XGa7hKURjzd5T6D17A0YtyMLTy3dh1IIs9J69waN3NXV1DZHVZfwFc+UUg0Jc1OC5lsrQJW+wymnFPFfVZJjB9Zmyuga1Kpe6fa2IUKkpQzSMGhfretU7Hvf+aw09str1NwNmVf9asVC53BhVoM7u7oCquoZId2me0AtqVbJWRLjtK0M0WEJceGB11ecdj6frwhN6xBPiIxKWFH8LNh9BZVWNIe3zQB3iogbPtaT+/suNUQXq7O5KKKpriHSX5rFXUquStSLCbV8ZosES4sIDq6s+73g8qcV5rj9PiI9IWFL81dhr5cwCdYiLGjzXkjpcTm6MXlgwtofq5jgwNRoLxvbwc4/8h0h3aV57JXWhZi2IcBvnCVfRij/c4N3DmKwYrkMdLsAD9Zzpbc9d7r0fD3qV9/W5HmS4hg8WjO2BsspqzFqdg6NnL6FNi8Z4YWiqX54UKRFpr9ATeqHFxZ8SEW7jSsVRVGNjqjYkMnrPxkXwh0G4hzFZ0cYUF8F2XlY5f0A9Z2qwrmWl3IXyKvxywnuSi19OlOJCeRWahorfxuQTIwPBQQEY2jkev++egKGd4/3uiUZRXUOou7rFQi94EOE2/sqaXOff+wqMqdqgrODhjRM6E00rw5h47EXUNqYTjN7mrHL+gHrO1GBdy0q5iSt2Mh3DKqcV+cToA6rqFtTtO9zV521Sd0BgdVfXE3pBPf+siHAbP3exrvKFUVUbWPuZd07fxqgMY+KplEJdXSWPMaSBVc4fUM+ZGjyVSljXn951qoZ8YvQCZXULM7SfPjQVj/VNrvfkGGADHuubjHRGj1xeFQ/1+LUgQj0V1aQuK41RLvWs/dQbhuTvCg6iMUO4VkOBxyzDWl3GqCo0cmNUgbq6BXX7DtKHpmLnlEHonhSJuIhQdE+KxM4pg5g3RQDo3CpCs5xZxs+KLzUWC/96/Gbn30a41GsJb9AbhvRU/7pi4jzhOtTVVSYP6ShUzh9Qz5kaPGEks+7uwnQMq5xW5MaoAnV1B+r2HYxfvA3XvfIdsvOKcaqkHNl5xbjule8wfvE25nPMVtjOWOXMMn5WfLmXs3Dk7EXn30a41GsJb2AJV/LGC1/+4vyb51pSX/89J0uEyvkD6jlTgyeM5J0NvzIdwyqnFbkxqkDt+kzdPlC7KaolUl+XU8i8OfKk1zPD+LUy5au9uo73R7iGlvOOuD6Bux2l7ceK4RrU7fNg1j7z9Is6JafcGFWgru5AXV1AS3URXyQwZvNXylGPXytFFyrx2wV9DjjKtWRUuAarjdGXWs4XiZH6Qi+or79ZQx+8YdY+81xLahuv3BjVoA4xIE6VL7S6iJ1xkpRypi0V4Jn75v+k/ySK4e8rEFuoWKurvi+1nC/u79nG+TdXGAHx9Tdr6IM3TNtnjmtJnZJTbowqUFd3oK4uIFKVcYLxBquUox6/VgrP6w/XUK6l44Ld0LW66utVt52vqAs94aquQnz9rZaSEDBvn3muJXVKTrkxqkCtlqBuX6Qqg+dc1OPXSnQz/apP5ViMCNfQkkJL77z663gjr7+VUhI6MGOfea9l25bea4L6+lwPMsBfBUdGeG+ezUZm96duf+KADliS5TuR9MQBHXzKTB7SkelcStd3h1qooKTc45OGDbVfdrOoshY/0gu9Xl3Pfbz7tRyT1gYzV+cKTSSuJYUWa4gNy/GsYQQDU2OdTzPU69/BkE5x6J8SgyWZR3Gs6BJaRzXGmLQ2pqrD6I7Z0ijyXEvW6ibPDkox5FqQXt2XX34ZNpvN5ZWSUlcWp7y8HBMmTECLFi3QtGlTjBgxAqdPn/ZL36iz+1O3P+f7/cLkeFzfzaoWUuObPfm6jne/liyFcnlgTaHFGmLDcrwVq2s4WLP3FPr93w+Y/k0uFmcew/RvctHv/34wVXIJT1BU5FGD51pSVzch/9lz7bXX4tSpU87Xli1bnJ9NnDgRq1atwmeffYaNGzciPz8f99xzj1/6Re36TN2+SBujrK7Bhvv4D5+5qCLJD2sKLb1u8HpDb6jXP2CtzEtmhudaUlc3IVelBgUFITY2tt77JSUlWLhwIZYtW4b+/fsDABYtWoSOHTsiKysLvXr1MrRf1OEa1DaWpCjGlEwMcnrGYja1kBrxAqoshAfXfR1ZwmV4SIhk66feaiFx4XXfCyuGa/jKvORJ/SvxDM+1ZK0CwyqnFfInxgMHDiA+Ph5t27bF6NGjkZdXa4vKzs7G5cuXMWDAAKdsSkoKkpKSkJmZqXq+iooKlJaWury4IA7XoHa9HpRa/8cKrxz1WPxBnoBfrp9sqyv6zBouoxVWm1ySTuefA6frKiVYMVzDrFlkWKmsqsHCzYfx0td7sXDzYVRW1dB1huNaslaBYZXTCukTY8+ePfHRRx+hQ4cOOHXqFKZNm4Y+ffpg7969KCgoQHBwMCIjI12OiYmJQUFBgeo5MzIyMG3aNN19ow7XoM6UX1x22bcQo5yesViluoaI8Irj57Rn/tBKQSnbes3XEcMIAAWK8BXH9f/T0h2wwfW3pJq9mDpcwwyqXF4yVudgweYjLja6matzMb4Pe+J/kfBcS9YqMCKq2niC9Inxtttuw7333osuXbpg8ODBWL16NYqLi/HPf/6T+5zp6ekoKSlxvo4fP851HmpVJlC/ErrWz/Ugevw787w7SXj63Eo2HhEZOJSVAozK6CGyALU33FXLWu3F1N8/6vZ5yVidg3mbjtRzXKmxA/M2HUGGQZoIb1ixUDW5KlVJZGQk2rdvj4MHDyI2NhaVlZUoLi52kTl9+rRHm6SDkJAQhIeHu7x4oFb/iUzJxoPDxdobrO7yrK7XSnWP1apriKiyMHvEdULP5w5zYWkAv++eqKutufd3r/fekE5x2DK5Pz4d3wtv39cVn47vhS2T+3t88qf+/lG3zwPP98wf8NxLRN5/eDDVxnjhwgUcOnQIcXFx6N69Oxo1aoT16+tiw/bv34+8vDykpaUZ3heebB0iEZqSjQOR7vI8rtdWs/GIqLLw5c46DYARVRtYC0sDwOvf7dPV1twfD3h8nzWMgDpch/r7zwN1iIMaPPcS6nAd0o3xL3/5CzZu3IijR4/ip59+wt13343AwECMGjUKERERGDduHCZNmoQffvgB2dnZePjhh5GWlma4R6oDHvWfKKizy4u0sfC4XlvNxiOiHzzj14KW8A+R4Rq8WClcxwxQhzioYcVwHVLnmxMnTmDUqFE4e/YsrrrqKvTu3RtZWVm46qqrAABz5sxBQEAARowYgYqKCgwePBjvvfeeX/pGnXmhFaNbPaucVkS6y8eHs/VRKUcdLqMVEf2IU2wATQPF54B0qN5Z8ksmNNe3rkSltKMK1+HJ1kONv+zHWuGxF7JWlzGqCg3pE+Py5cuRn5+PiooKnDhxAsuXL0e7du2cn4eGhmLu3LkoKirCxYsX8cUXX3i1L4qEWi1hY/RxZpXj6IAwubxzbL9QXeSoq5toRUA/TiiesuZuPqT/hB5gVb23jtKXh3JgxxhdxyuprrEjJ78E2cfOISe/xC92Zaup8oFa+zGLXY7VziwKHnsta3UZ0VVoHJAH+JsVarXEiWI2939WOa2IdJdnDWVQylGHy2hFRD/yFNdST8knb7CqOPWGaxSXs4X7+IIq9IBalceDI43gvE3qmi4tdmZR8ITr8NwzRGIq5xszQa2WoC7UKdJd+sqorqG/H8rxi8ik46sNb+hd1yLmgzL0wGrrz0G3JO9emr4+Nwqt9mLq+6/cGFW4v2droXJaYXXXN8KtHxDrLs0zlq6JkUzHsMoZjd5qFADwl0F1CfQXjO2h+3yeYC3sOrJHEtf5RYUxUIceUIcL8MBqF6UKcdISrkN9/5Ubowq7jhcLldMKT0UKkYh0l+YZy7Ktx7xI1sEqZzR6q1EAwL+y65JR7FekVBOFlsKuK7b5LhOmhrcwhrLKakz5ag/GLNyKKV/tUY3DpbbxU4cL8GAFuyhruA71/VfaGFWgtjE0pPatmF1fKyLCE4wM1+iSEK7pKZR3XjsnhKuGUYxfvM0lacXmA8CSrDwMTI2u1zfq60/9/ePBin1Wg3os8olRBWobQ0Nqn+dc1DYGrYiw9SrHIvq67jlRqimFHu+8/nKiFOMXb6v3vvumqGRdTmG9Y6ivP/X3jwcr9lkN6rHIjVEF6pRQ1DY2kePnOZdZXc/VEGHrveu6Vs6/WWxcWtFiX9KTEs49VSFPekNqG5MVbYzU9ywWqmvsyDx0Fl/vOonMQ2dV1yP1WOTGqAJ1SihqG5vI8fOk92KpYE/heq6GCFvv40u3O/9msXFpQat9SW9KOGW8JE96Q2obkxVtjNRp9HyxZu8p9J69AaMWZOHp5bswakEWes/e4FGTQX3/NcddRVIPahuLaHjSe6UPTcVjfZPr/XIPsAGP9aUpoaOGCFuHMnaQ2nYsMiUcT3pDahsTdfu8mDWNnpUq5QDS+UYV6pRQ1BWsjRg/T3qv9KGpeHZQCpZkHsWxoktoHdUYY9LamOZJ0YGISvKxzerSWxmV6or1vHER+tpPVJTQah3VGJsZjlHaC0WmJOSB2salB6o0emr4qpTj6V5Cff81193FRFC7PlNXsDZq/Kzu2pZDwDBu7ViX7tCoVFf7CtjCQM6c15e5JkmxyQ1MYUsP5yInMCUhD9Q2Lr2Y6XvGcy+hvv/KJ0YVqFUp1BWsqcfvwGzVyNUQUUm+UHEOo1JdHWfMW3tSZ0o4pVq4uIJtk1XKiUxJyANPGjOJZ6xYXUM+MapArUq50tsHzFmNXA0R86BUJRoVhsB63iSFKlRvO1as4A6Y115nNax4/eUTowoOd21vnmlGumtTt+9QJRWUlHu0DdhQe4PQqkqqrrEz2T6oy35pxdd8saAMPbi7WwKmf6M/m463NrwxZ2Q3dHp5rZB2eNaSUetPK2az11kRK15/+juKSaF216Zu3wh3aS3u2tQpwbTizVWeFWXoweTPd+vvlI82vNE0NAhdEsKFtMMTRkDtrq/ETPY6K2LF6y83RhWoddzU7QPA5ztO6PpciVZ3bSuGq6ip3iIbN2I6Xnkt8wyyMWpZL3++5Wph7fCoJXfmef/R5+tziXmwmlpaqlJVoNZxU1ew1pKtxFdiah537URGGxernL/wpHorr6jCw4u3+zw2MrRuA01qHob9jB6kWmBdL77c5X3hKYxCi1rSaqp0iW+0XH9Lh2scPHgQa9euRVlZ7a9bu90s5dT1Q+2uzepWzyqnFZ5sJWrwuF6nxLKp8Vjl/ElZZTX+seUw3vvxEP6x5TC+zWELXl6XW+D8e87Ibob0jXW9+LpmPtF5rzKTKp01jZlEHJYM1zh79ixGjhyJDRs2wGaz4cCBA2jbti3GjRuH5s2b44033hDdT79D7a7N6lbPKqcVnmwlavCohanDVXi5693N+OVEXQyilqe+Y0V16lOHjU95LhGwrhe9KnpPYRRr9p7CtFU5Lje8uIhQTL0ztZ4qzSyqdC19lnhHy1xSm5K4nhgnTpyIoKAg5OXloXHjOrfskSNHYs2aNcI6Rw2lXpy6ugBrtQgWOSu6a/PgvilqxX0uY8LFj411veidV/fjtdqYqdc/YL00ZmZG61xSf/+5NsbvvvsOs2fPRkJCgsv711xzDY4dM0fhWFEM6RSHDc/egjG9ktDnmpYY0ysJG569xfBfi6wV1HkrrftCWU1erxxPpQKrVTe4UF6l++nuBUXCAhYbLw+s4RqpcXwqak8mBl82ZqB+5Q/q6ho8fTYTlVU1WLj5MF76ei8Wbj6Myqoasr7wzCX1959rY7x48aLLk6KDoqIihIQYk7uQiozVObh26hosycrD5gNnsCQrD9dOXWN4cDlrBXU9lda9oawmr1eOJ/SEOlxFKxNX7NR1/MDUaBcnJlYbr1ZYwzX+8tkuzedWMzHw2Iuoq2tQ27j0kLE6BylTvsX0b3KxOPMYpn+Ti5Qp35IlxOCZS+rvP9fG2KdPHyxevNj5v81mQ01NDV577TX87ne/E9Y5aigzr1DbWES2b8WUUFrRG14x4npX7Yve6hZqsM4Xz3jUTAxWvP7U7fNixmxRVrz+XM43r732Gm699VZs374dlZWVeO655/Df//4XRUVF+M9//iO6jyRQu4tTV9cQaePhCT2JDGOL/WOVM5rE5qHc4RWeXM9bRRqjeWGtRsEaLtI9KRJjb2rj1fXeijZm6vZ5oL5nqdGyKWOlFIUc9fxzzU6nTp3w66+/onfv3hg2bBguXryIe+65Bzt37kS7du1E95EEanfxlBjG6hqMcloZk9aGScc/Jq2Nz3PxhJ58l1PgRbIOVjmjGd2D39blSZVkMyr3BqMTNWu4yBMMSQB4Qp+ow6Wo2+eB+p6lCqsZViFHPf/c376IiAj87W9/wz//+U+sXr0aM2bMQFwcv0PKq6++CpvNhmeeecb5Xnl5OSZMmIAWLVqgadOmGDFiBE6fPs3dhhaoVZlFZYzhCoxyWgkOCsD4PsleZcb3SWb65ckTepJXxKbKY5UzmtLKKt3nUKqFThQbMy7WahQsKeGCAmx4ePF2Q6qxU6cE40ljRg31PUuNMxcZK6Uo5Kjnn2tj/OWXXzy+9uzZgwMHDqCiQlspmG3btmHevHno0qWLy/sTJ07EqlWr8Nlnn2Hjxo3Iz8/HPffcw9NlzVC7i1OrEgDg8JmLuj53wDOXIsNF/IGI66A8h1Hj0tLPtLYtvH5e5fZ40tDCGKyWxoz6nqUG772Mcv65bIxdu3aFzVa7Uzuy3Tj+B4BGjRph5MiRmDdvHkJDvU/KhQsXMHr0aCxYsAAzZsxwvl9SUoKFCxdi2bJl6N+/PwBg0aJF6NixI7KystCrVy+erjNzf8/WTNUNjHIXp84uLzIlHGuliLu71TmgvDA0FUuyfHvcvmCSmox6q2u4u55PHtKRafxa6ZoYySTHYq9yR2Q1duqUYA6sVF1jZI8kpu+ZUSFeauipFEQ1/1xPjF9++SWuueYazJ8/H7t378bu3bsxf/58dOjQAcuWLcPChQuxYcMGvPjiiz7PNWHCBNx+++0YMGCAy/vZ2dm4fPmyy/spKSlISkpCZmam6vkqKipQWlrq8uKB2l2cWpUkMiUca6UIpVxYcCAGpkZ7lXcPcaDE1/Xyhbvr+Z6TJWI65sayrWxxxiz2Kk+IqsZupnAJq1TXoA7xUkNv6AXF/HM9Mc6cORNvv/02Bg8e7Hyvc+fOSEhIwJQpU/Dzzz+jSZMmePbZZ/H666+rnmf58uXYsWMHtm3bVu+zgoICBAcHIzIy0uX9mJgYFBSoO1xkZGRg2rRp2gflBrW7MMBWXcAodYLIlHCsrv/ucgvG9sD4xds8PrkOTI3GgrE9mM7rL9778aCu43nc1bXiLzuUXtd7M3z/rIZZbYxWvJZcG+OePXvQunV9FWLr1q2xZ88eALXq1lOn1G0Nx48fx9NPP41169b5VLdqIT09HZMmTXL+X1paisTERM3nYXVrZ5XTCrXrdWJztmvCIpcYyRbKkBhZ/1wLxvZAWWU1Zq3OwdGzl9CmRWO8MDTVNE+KDkRkvokMqQs9iQpjC3G5/8ZEBAUGIP/cJXy/7zef8qzVSPTaocKD624tVgzXsCJmtTHyhGtQw3VHTUlJwauvvorKyjqPyMuXL+PVV19FSkptirCTJ08iJiZG9RzZ2dkoLCzE9ddfj6CgIAQFBWHjxo145513EBQUhJiYGFRWVqK4uNjluNOnTyM2Nlb1vCEhIQgPD3d5ccH6tG7QUz2163VS8ybC5O7v2YbpXGpywUEBGNo5Hr/vnoChneNNWWZIb+YbAFi3r87jet9p9hCXI2cuMrvEs1YjYQnX8cYn2+pUtlYM17AiIkOshMIRrkEN1xPj3LlzcddddyEhIcHpSbpnzx5UV1fj3//+NwDg8OHD+POf/6x6jltvvdX5dOng4YcfRkpKCiZPnozExEQ0atQI69evx4gRIwAA+/fvR15eHtLS0ni6rQlWt3ZWOa1Qq0XyS9nUGixy5ysuM53Lk5xVqhuIKCysvJasIS478oo1tcFajcQRrjNvkzYHHAfHz9VdL55KNY5jHl+6w+P5jbaxWxGWa8YaYiUSnnANarg2xptuuglHjhzBJ598gl9//RUAcO+99+L+++9Hs2a1Aedjxozxeo5mzZqhU6dOLu81adIELVq0cL4/btw4TJo0CVFRUQgPD8eTTz6JtLQ0wz1SAXpVDrVaRGT7vHPpyMjv/kPSERZgJpd5EYWFlSEa1GFAANAtqTkAvo0xyU1l63C9d/+RE2vCHzlWJv1/XtoLNrumhQuw1W6K6QRe3NT3Uh64NkagdmPr27cv2rRp41Sp/vDDDwCAu+66S0jn5syZg4CAAIwYMQIVFRUYPHgw3nvvPSHn9oUeF2MRjElrg5mrc322b5RaRKTrN2uIgFLOV0Z+f7nrszJ9WGesy12v6xzK0BPWcCEtaFmvvsIlfDF7xHX13hvSKQ79U2KwJPMojhVdQuuoxhiT1sbjE4xZwjUcfbFCuIaD9KGpeHZQCtM8+wO9oWcU88+1MR4+fBh333039uzZA5vNBrvd7hLHWF1dzdWZH3/80eX/0NBQzJ07F3PnzuU6nx60uBintfMeCM0DtVpEi+v3uD5tvcqwhggs23rMeS4t7vpGzL9Wpny9x7eQF9xDT4wIA9KyXn3Nvy++3Hmi3rrwpBb/cMsRj0+MZrn+VlHluxMcFODze+kv9BR9p5p/rrvq008/jeTkZBQWFqJx48bYu3cvNm7ciBtuuKHe5mZVzOBinD40FY/1Ta5nUA+wAY/1NVYtItLGyXMuM8y/FvTaGNu2dHVisnrVCPdrrrVQrRmuvyxULA6eLDaU88/1xJiZmYkNGzagZcuWCAgIQGBgIHr37o2MjAw89dRT2LlTv4ceNazu8qxyvFCpRUTaGFtFsoUIKOWsZpdI1GljdA+9oa4aoQwd4UFZ9YVHLU59/a2myrcCWrLYUM8/1921urra6WTTsmVL5OfnA6iNY9y/f7+43hHC7C7PKGc1RLp+2xjdsJVyVnPXH60zzZZ76I2v8WtF63wpQ0d4SImtq/rCk8WG+vqbKfNOQ4I1iw31/HOXndq9uzZ9V8+ePfHaa6/hP//5D1555RW0bWsOvbZeeCpCGAFVNW6R1TVOlLCpGZVy1Nn1tSKiuoZS/eht/DxoDW/QGwakDAvhUYtSX38zqHKvZKjnn2tjfPHFF1FTUwMAeOWVV3DkyBH06dMHq1evxjvvvCO0g1RQh0sA9NW4Rdk4eefSStUNRKj0WMfPi68Ug0r0VvcQkcWG8vpTq3KvdKjn32Z3lMfQSVFREZo3b+7inWoGSktLERERgZKSEk1ZcCqrapAy5Vuf4RL7pt9miL2Pun0lelOy6R2LFdzlyyqr0fGlNdzHs44/PDgIDy/eLrwNdy6UV6HTy2uFtFNdY0fv2Rt8uutvmdxf1d7k7+uvt88Sz7BeSyPuf1r2AmF31KioKNNtinoQqUrkgTolnIM1e0+h/xs/YklWHjYfOIMlWXno/8aPmjzC9M6lFaobsIakqME6/sNn2WpgekLLetFT3cO9UoJetSjF9adW5TZE1uw9hd6zN2DUgiyfxa31VuTQi/mSTpqI2swf/J/rgTolHCDWXZoy9MQf8F4HrePXe71Zj9dru3E/3kpqcQdW7LNZsVq4Dnfmm4YOdeYNahunEe7SZsvIIRJleII3nh/cAY2CArjHr/d6sx6v13bj6XgrFf11YMU+mw0rhuvIjVEF6swb1CnhjBp/YIANqfERaNksBNHNQpluMJVVNabfTFNimvkWAnBNy6b48dBvOHr2Eux2O6oZqgEr7TLtY5rVyx7Cipb14iuNlxq+0ns51KJWwop9NhM89xK9aeT0IjdGFagf5alTwhkxfp70Thmrc+olRJ65OpcsIbIaRWVsVSvGfZLt/HvzAWBJVp7Xosue5qxJcCAuVmpPu6hlvXhL4+UNWfVC4o6ecB2eNHIiMNfPbhNB/SgP0NrlRI+fx15JHa6iBT3rYF1OIcYv3lbvfbU5u/S/TdHorUfNxta8sb6sOJIrCyuG68gnRhWoH+UdpA9NxZP922Piip3IO1eGpOZhmDOyG5qGGnvpRFYX4bExVFbVYMFm7yWP3NOoUcKrenSwLqcQZZXVzjAYljm7qmkjJEQ1xqmSCsSFB2Pn8VKvbfPM15BOcejXPtoZrtM6qjG++6+605Uv27MVQm8kYtFzL6Wy8dLfUUyKWdy1M1bnoMu0tViXW4j9BeexLrcQXaatNfxpSaS7NE96J7OEq7DiWC96goJnKa4py5wVXriMHXklOFVSjh0+NkWAb74yVufg2qlrnOE6S7fmofCCeuFpb6m6tLjrSxoOVgzXkRujF6jdtSlViSJtjDznMkO4ir85elZ7dRGtaJkvtfXHgnv/ZaWKKxvqe6lWpCrVB1SP8tSqRJE2Rp5zUYeraEVvYV/AdSwtm4To7ZLPNrzBsv68oew/daUEiTmwUuiLfGJkgOJRnlqVKLK6Ac+5RFb38Ad6C/sCwMCUmLp/DFhiWuaLZf15RdF/6koJEvNghSxWgNwYTQu1KlGkjdWX/c2Tiz91Sj6tiFB9FlfU2e7OXKjQfT53tMyX3nWl7D916JNEohVz3FUk9TCDKpHaLmClNHIiwnZ4KlKwwDNfeteViOoaEgkV0sbIAIWLOXXmGwfu7vo81TX0pNer3Ryvxn3zf0Lh+UpENwvG8kdvQlTTYN4hGQJLeIsantzV9YZ/PDeoPQrOV3BnChrZIwnTv8nlaLmWromRzr/1hv7IEI8rG4rrLzdGH/BkaxEBdeYbB+6ZZzYfAD7Zmqcp84ye9HJ3vbsZv5wodf5fXHYZ189Yhy4J4Vj5RB/N4zEKlvAWNTypkh3q58eX7uA6Z7fWUbrSmK3Ylsd9LFBbbWRcn9qi5VpCf9z7TPX9k5gDqusvValeoHYxp6zuAYgLF+G1Mblvikp+OVGKu97dzHRef2A2+5je/ois4sF7/am/fxJaKK+/3BhV8OViDtSq/1iSQOtpXw2H+tGo9lnDRSqranyei8fGdKG8SnVTdPDLiVJcKK9iOrfRRDXmV+16upZ6wz/02utYq4WoHx/m/Js19ERLiAdg7PqX0EJ9/eXGqAK1izl1+yLDRXjCNSau2MnUT1Y5o9lXcJ77WE/Xkjf8wzGXNTV2fL3rJDIPneW6ebBWC1E9PlZRIZ3VHCRDPIRRXWNH5qGzutYAJdTXn3RjfP/999GlSxeEh4cjPDwcaWlp+Pbbb52fl5eXY8KECWjRogWaNm2KESNG4PTp037pG7WLOXX7IsNFeMI18s6VMbXPKmc0x8/pD5tRXkve62oHUHa5GqMXbtWVdo21Wojq8ZfqjmcNPZEhHmJoCKn3qK8/6caYkJCAV199FdnZ2di+fTv69++PYcOG4b///S8AYOLEiVi1ahU+++wzbNy4Efn5+bjnnnv80jdqF3Pq9qnDRZIUqjgRckYjYh5EhWsUX3LNZcpjkxFZqJhnLVOvf6vSUOyy1NefdGO88847MXToUFxzzTVo3749Zs6ciaZNmyIrKwslJSVYuHAh3nzzTfTv3x/du3fHokWL8NNPPyErK8vwvjlczL3BWl3Ciu3f37O1MDkee+nr93Zlap9VzmhG9kjSdbz7tWS5/qzw2GT0tO8+Fh5VusjMS1cK1HY5kVBff9PYGKurq7F8+XJcvHgRaWlpyM7OxuXLlzFgwACnTEpKCpKSkpCZmal6noqKCpSWlrq8eBBZXcKK7e86XixMjsdekHOK7bqxyhmN3vAG92upJ/zDE1ptMnradx8LTxYls1S3sRLUdjmRUF9/8o1xz549aNq0KUJCQvD444/jyy+/RGpqKgoKChAcHIzIyEgX+ZiYGBQUFKieLyMjAxEREc5XYmIiV7+oddwNqX2ec1GPXysiUvOJsDFqaUOEHOvxPFmUqDMvWQ2rfWd8cUUXKu7QoQN27dqFkpIS/Otf/8KDDz6IjRs3cp8vPT0dkyZNcv5fWlrKtTlS67gbUvtXgo3JTDZG1jZEyGk5nqe6gpUqMlBjte8MC1TXn3xjDA4OxtVXXw0A6N69O7Zt24a3334bI0eORGVlJYqLi12eGk+fPo3Y2FjV84WEhCAkRH/JHpEV7HnQU/VaBKlx4b6FGOV45pJ6/FphSeHnDffxK1OqiULLetWTks5bO47qClrgOeZKxArfGZ70bhTXn1yV6k5NTQ0qKirQvXt3NGrUCOvXr3d+tn//fuTl5SEtLc3wflDb+HhCHETyl892CZPjmUtqG4NWWKqBeMN9/Mu2HhPRLa9teMPX+hPVjkQcZv/OWCmMhHRjTE9Px6ZNm3D06FHs2bMH6enp+PHHHzF69GhERERg3LhxmDRpEn744QdkZ2fj4YcfRlpaGnr16mV438ygr9+Z5/3m4utzPYiMI+SdS6vZmPSm6FOO36hyYlrWq571ZRU7VkPDrN8Zq4WRkKpSCwsLMXbsWJw6dQoRERHo0qUL1q5di4EDBwIA5syZg4CAAIwYMQIVFRUYPHgw3nvvPb/0jSeNlUhYU7I9OyjFkETiSc3DsJ8hmwtLHKEe24dVbEx6U7gBruPXm5JNDdbUdSzrz2s7YeaqfnIlYbbvjK8wEm/Vdagg3RgXLlzo9fPQ0FDMnTsXc+fO9VOPFHCksRKJlpRsjioGIpkzshs6vbyWSc4Xem0fVrAx8aZwAzyPX29KNjX2FZSiT/urfMqxrD+v7Zw+jz4dfLcjMQYzfWf0VNehwnQ2RrPAk8ZKJCJTsvHQNDQIXRK8O9Z0SQhH01Dfv63MbvsQgR7VoSd7sd6UbGocZ1SR611XIlLkSRoGZjBLaUVujCpQuz5Tp2QDgD/fcrWuz5WY1fYhCtHrwOrrysh1KbEW1PdSHsjDNcwKdbgGi/t/gK1WzghY07hpsQvw2j6sUMFdT3iDp7nUcz41tKyX33dPxPRvcg1vR9LwsUIYiTvyiVEF6nANFvf/8X2SDXG8AYxLL+WwfQzr2gpp7Vr43OCs4uKtJ7zB01zqOZ8aWtbL69/t80s7koYPdegZD3L1qmAGvXj60FQ81je5XjLnABvwWN9kpA9NNaxtM4zfai7eejHaxnL4zEVm2aNntdsI/bEuJdaEMvSMB6lKVaFlU8ZwDUY5XtKHpuLZQSlYknkUx4ouoXVUY4xJa2P4L3LqcBWruXiLCNeIDGsk9HzurMspRFllNcKCA33KJjKW87o+MQKdEiL9ti4l1oM69IwHc/TCjLDqsPxQwSUwwIbU+Ah0b90cqfER/tkIiMNVrFYpQE+4hoPvcuqS44s4nydmrWbbbJMY4yg7xIb7d11KLIeW0DNPVNfYkXnoLL7edRKZh876pWyWfGJU4cxFxnANRjle1uw9hWmrclxuknERoZh6Z6qhnpzU4SpmUOVqQUQ/8orqQimMGherijS/lK39T7cdx6fbjgPwz7qUWA89oWdU9z/5xKiCGVyMKW1s1OOnbl8rIvrRpkXdU5pR41K24Q2ecIuGavuV6IM3RIjy/ic3RhVYKpgbGa5BXY2btbqD1ioQlVU1WLj5MF76ei8Wbj6Myqoaj3LU868VEdUwXlA4rbCMn4fJQzoyyd3fs7Xmc1utSrzEP4xJa8P0XVaG+FDf/+TGqAJ1uAa1jY21uoOWKhAZq3OQMuVbTP8mF4szj2H6N7lImfItMjzYvajnXyt6q2EMTI12cYphGT8Pe06WMMntOl7MdX6z2X4l9PCEnlHf/6SNUQVqGxd1+6JT0mWszsG8TfU902rscL6vdPOnHr9W9KZQa9uyicv/Vl9XZrkuEnPg+G4v2HzE5QdfgK12U3QP8aH+/suNUQXqcA1qG5vI1GE87trU49dKfIS+friPn3Vd/W1oR0SHh+DM+QqmTDX+mtfI0Ea+hSRXFFpCz6i//1KVqgZxuIYjjZKaat6GWu8so9Io8dgF1OBx16Yev1bydD4x1nNXZ15XtYIpseGIDRc3X77m3xfrcgt8CzFC4a4vMYbgoACM69MWrwzrhHF92qrGLVJ//+XGqAJ1uAZ1GiWRKel41LLU49cKa9UKbyjHz7quZq7eh6eX78LohVtRXlUtbL70pqQ7VqR/PgDrpASUiIW6Io/cGFWgfpQ3A6JS0vGqZa2URoo1DMIbyvHzrKviS5d190EUIubjSksJKHGFsiKPzW63N2i9RGlpKSIiIlBSUoLwcO/1BZVUVtUgZcq3Pqtb7Jt+myFpjKpr7Og9e4OqZ5YjI/2Wyf0Nf2q6UF6FiSt2Iu9cGZKah2HOyG5MdRgd8Mwl9fxrpayyGh1fWsN9PM/4taB1vfhaf77IfWUIU+o53vb9uf4ltIiqrqNlL6C/o5gU6nABandlBxmrc9Bl2lqsyy3E/oLzWJdbiC7T1noMsVCDRy2rN42UvwkLDsTA1Gju493HLzpcQ+t60ZOSzj30hAezrH8JPVor8ohAeqWqQO0uTN0+oD3Ewhta3bVFh4v4A/eQCy24V76w8rrSMw9a25dhIRIjkBujCtQ2Rur2jciIr8Vd21+V5kXBMl/ecK98EdU4WFTXXFBW8PCGnnUlolIC9fqXXNlIVaoK1O7C1O1TqzJFhov4A5b58oWy8sW+gvM6e+QZZQUPb+gJ1xCxLqjXv+TKRm6MKlC7C1O3b4QqU0tKOJHhIv5AhEpXWfni+DljVMR5jGEU3tYfC3rng3r9S65szHFXMSmU7sLU7YtWZTrsle5PVQ57pafNUVS4iD8QodJVhjgYpSLWEkahtv5YENF/6u+f5MpFhmswIMpdmJfKqhomu5xIWMMPWNzy9YZe6A0X8Qciwit2vzQIEY1rbYAXyqvQ6eW1gnpXx44XByKqqTb7pXL9RTcNwevrfvV5jHIseqH+/lkROWf10bIXkN5dMjIy8MUXX2Dfvn0ICwvDTTfdhNmzZ6NDhw5OmfLycjz77LNYvnw5KioqMHjwYLz33nuIiYnxWz8d7sIUeCrU+eGWI4YX6mStrrDreLHPudFirxzXp63L+xmrc1w8WfcXnEeXaWs9erJS4lD9evLiZeVf2ced41+xLU9U11yY8/1+TB/emVne0/pj4fXv9mlqxxuU3z8rQlXctyFBqkrduHEjJkyYgKysLKxbtw6XL1/GoEGDcPFinev6xIkTsWrVKnz22WfYuHEj8vPzcc899xD22n9QZv4Q6S7Pa6/kUb9aGeX4jQpDUdoxfaG2/kS3IxGHzBYkBtInxjVrXFV1H330EaKjo5GdnY2+ffuipKQECxcuxLJly9C/f38AwKJFi9CxY0dkZWWhV69eFN32C74KddpQW6hzYGqsISqS8GC2pcEix2OvNCJcxEj0hmsAQGLzuvEnRIbp7ZJHkpqzndfb+mOhdZQx/ZeoQ33PaEjQ31EUlJTUFlGNiqp1wc7Ozsbly5cxYMAAp0xKSgqSkpKQmZnp8RwVFRUoLS11eemFIrs/deaPT7axFd5lkeMJvaAOF9GKiHCNlNhmzr+NWmEJjBuWnsw3ADCwYyz3sRI+qO8ZDQnTeDDU1NTgmWeewc0334xOnToBAAoKChAcHIzIyEgX2ZiYGBQUeI7HysjIwLRp04T1i0pfT5354/g5tvOyyLHY39xDL6yW+UZEP4ouVTr/PlkspjqFO6dK2Kp26F1XxeXmSWh+pUB9z2hImOaJccKECdi7dy+WL1+u6zzp6ekoKSlxvo4fP859Lkp9PXXmD1aVG6tct6Tmmj63WuYbEf1QXkujxsV6Xr3rSmak8T/U94yGhCk2xieeeAL//ve/8cMPPyAhIcH5fmxsLCorK1FcXOwif/r0acTGelbVhISEIDw83OXFgy99PVCrrzdKrUqd+WPOyG7C5BxzqYbD9qGcS6tlvvl990TuYz1dS5bx83B/z9ZMcnoy3wTYgO6tvf8QkoiH+p7RkCDdGO12O5544gl8+eWX2LBhA5KTXTOddO/eHY0aNcL69eud7+3fvx95eXlIS0sztG/U+nrqQr1NQ4PQJcH7j4ouCeFM8YQ8c2m1zDevf7eP+1hP15Jl/DywhuHoKVRsZNUZiTrU94yGBOldZcKECVi6dCmWLVuGZs2aoaCgAAUFBSgrq7WvREREYNy4cZg0aRJ++OEHZGdn4+GHH0ZaWprhHqlSXw+sfKKP6ubYJSEcK5/ow3SeK2EuKcITGnOUdvLXHFv5WloZKxX3NjOkG+P777+PkpIS3HLLLYiLi3O+VqxY4ZSZM2cO7rjjDowYMQJ9+/ZFbGwsvvjiC8P71rJpiFA5rfCoH41g5RN9sPflwRjYMRodYpthYMdo7H15MPOmCPDNJWu4RmVVDXM/jERPxXpP15Jl/OWXq7Hk4Rvx9n1d8fzgDl5lHUSGsmWj8bX+fCHtWP7Hat8ZM0OuSvX0euihh5wyoaGhmDt3LoqKinDx4kV88cUXqvZFsZ0TLKcRalWukrDgQDzSuy3+fEs7PNK7rfYitBxzabVwjRd0ZOHxdC1Zx/9r4XkM69oKJ4rZnljX5bJV1+AN15B2LDqs9p0xM6YJ1zAbZy6yubWzymnFLOpHEeEqPHNptXCNsOBADEyNxrqcQu5zKK+l1vEfY6yawSrHu66kHYsOq31nzIw5PBdMCLXrM3X7gLhwFZ6xWC1cA9BfuV7P+FlVuaxyUhVqPaz4nTErcmNUgdr1mbp9keEqXRMjmdpUyo3skcR0DKuc0YhICaccv9ZwFVZVLqscb7iGv2zfkvpYLcTJzMiNUQVq12fqQq0ibZzLtrKll1PKsVaXMKoKhVZEpIRTjl9ruIpDleuNganRzPZh3nANmXaMDquFOJkZOUMmhrJQK3V1DavZS0T0w/0cWrMFLRjbQ3VzHJgajQVje+jroAZkuAYNVirubWak840KrOESRmeqH9IpDgNTY/1edFRkuAqP7cNq9pJWAqphKMfCu/4WjO2BsspqzFqdg6NnL6FNi8Z4YWiqZk9iveEaLZsYE8Yk8U360FQ8OyjF78XNGxJyY1RBiyrR6CKq1TV25OSXOBd599bNjff6ExiuMiatDWauzvWqanS3ffAcQ4lNpxrVfSys6+/llXths9lcbn6BATa0adHE+T7PWtFbXYMrl5xEGMFBAfWKfkvYkRujCmYJl3CvYA8AM1fnGl7BXmS4Ck91DZ5jKDlRoq8ahvtYWNfVkqw6G+vM1bno1Coce0+W6l4vetf1mQvGhDFJJP7AHHcVE2KGcAnKCvaix6/VXgZYy17Cq9JVGwvPuqqxA7+cKBWyXmR1DcmVjHxiVKF76+YIsMGnKs+oKgLUFewd7voFJeUetaU21DoBsYSL6LHXWsVewqr63TllEF7/bp9P+5+v+edBy3rhbV/LupBIzIq57i4mIvvYOab0SkZVEaBO7yQyXEVv6IfDXvLKsE4Y16et6TZFgM1V/taO0Rjy9iYsycrD5gNnsCQrD/3f+NFjogQ91S3U0LJevIULeUNmvpE0BMx3hzEJ1DZGM4QriMrUTz2X/sKXunhdTqGmLEJGVELQsl7UwoVCfPww+XzHCa6+SSRmQapSVaC2MSY2Z3P/Z5XTikhVLvVc+gPe8AY7PKuSRWTS8YRWW6h7uFBkWCM8uGib12PW5RSirLJae7J5icQkyCdGFahTsqXEei8SrFVOKyJVuXrnsrrGjsxDZ/H1rpPIPHTWlOnG9IQ38FbX0IqI8JalWWxZjGYZ6BgmkRiNfGJUwWFj+dPSHbDBNVzPHynZii5VCpXTikhVrp65FFHdwx+IUAPzVNfQAk94i6f5Z4GicLNEIgr5xOgFypRs1OpH0ZlneOZSVHUPfyDiOvBU19CCLxuoO2rzz4Kews0SCTXyidEHVCnZRIZL8HB/z9aY/k0ukxwrWubSV3UPf6XkY0VveIV76A/r/LOidb68zT8Lk4d05DxSIqFHPjEyEBhgQ1q7FhjWtRXS2rXwy42YurrGruPFQuW0IrK6hz/gDW9w4B76I3petc6X3pRwe06WcB8rkVAjnxhNjEP96G7jifWDjc2IEAst9kIrhnioXa/IsEYoLrvs83jlWIwal7/m1UzXRSLRitwYTQ6VKle0jdNhr3JXzTnshe52RmobKy+erleN3Y7RH271eaxyLKzjmnJ7R7RsFoIz5yuYVK/+mlezXReJRAtyY5R4RGRKPB57IXVKPj04VO8OKqtqNI+F1cb80M3JCAywobrGjg+3HBFmk5Yp4SRXMtLGaHLW7D2F3rM3YNSCLDy9fBdGLchC79kbDPfIFJkSj8deSJ2STyQ8Y9Gakk9kCj/l+QCZEk5y5SE3RhNDGa4g0hbFcy4r2hjVsOpY1EJsmjduRNQjicQ/yI3RpPhSPwK16kejssCItEXxnMuqNkZP8IyFtSKJ4/prlWdlSKc4bJncH5+O74W37+uKT/7Y02uuVN52JBIzQboxbtq0CXfeeSfi4+Nhs9nw1VdfuXxut9vx0ksvIS4uDmFhYRgwYAAOHDjg935WVtVg4ebDeOnrvVi4+TAqq2oMb5M6XEFkSjyec1Gn5NODewq77q2bax6L1uvvr/Wy71QpCkrVixCbLYxGIuGB1Pnm4sWLuO666/DII4/gnnvuqff5a6+9hnfeeQcff/wxkpOTMWXKFAwePBg5OTkIDfXPk0LG6hws2HxEd0V0rVCr3xw2pseX7vD4uRZbEk9KOJHt+xO1kJS7rovD/E1HmMev9fobtV54U8KZTS0skWiB9Inxtttuw4wZM3D33XfX+8xut+Ott97Ciy++iGHDhqFLly5YvHgx8vPz6z1ZGkXG6hzM23RESEV0rTQkVSJAm17PX3izCc/fdASP9k1mHr/W62/EetGTEs4q61Ii8YRpwzWOHDmCgoICDBgwwPleREQEevbsiczMTNx3332Gti+y7BIP1CnhWG1WWlKy8aSEE9m+kbCEpKzcfQob//o7ZB8753P8LOEqNgCrdp/Eez8eRFJUGGLDQ3C6tELIeuFNCSfDNSQNAdNujAUFBQCAmJgYl/djYmKcn3mioqICFRV1NpDS0lKu9rWUXRrXpy1XG96gru6hxWaljNnzhXuMn7/bNwrW/mYfO8fUX5YQDzuAZT8fZ+qfVtUzb0o4s6q4JRItNDiv1IyMDERERDhfiYmJXOcRWXaJF0r1I7WNk7p9rYjuL/W4qNuXSCgx7RNjbGwsAOD06dOIi6vbAE6fPo2uXbuqHpeeno5JkyY5/y8tLeXaHEWXXeKFKiVcyyYhQuU0t9+UsX1GOaMRPV+ibXRaVc+87ZtNxS2R8GDaJ8bk5GTExsZi/fr1zvdKS0uxdetWpKWlqR4XEhKC8PBwlxcPY9LawNf3WkRFdBYoqnswpzsxqiusxi2zhMsJni9f4Spa0RpGwdu+DNeQNARIN8YLFy5g165d2LVrF4Bah5tdu3YhLy8PNpsNzzzzDGbMmIGVK1diz549GDt2LOLj4zF8+HDD+xYcFIDxfZK9yvBURLcKZy6ox6rxyGlu/yJj+4xyRiN6vvSWsVKDVUXqK8WcqHYkEjNCqkrdvn07fve73zn/d6hAH3zwQXz00Ud47rnncPHiRTz66KMoLi5G7969sWbNGr/FMF7JUIeLULevFSP6q1bGSg/+mi+zXBeJhAfSjfGWW26B3a7+m9Rms+GVV17BK6+84sde1UIdrkENdbgIdftaMaq/7jbmyNBGePCjbVx91FKNxFe4jC+6JkZyH2s2qmvsfrfxS2hpeHd0QWgJ12iIeFPl+SNchLp9rRjZX6WNuV9KNAamRnP1UUs1Et5wDQfLth7jPtZMUFW3kdAiN0YVzBCuQQ11thrq9rXir/4uGNuDe3P0V7hIQ/heUFa3kdBi2nANahKbhwmVsypU4SJmaV8r/urvgrE9UFZZjVmrc3D07CWEBNrw/b7ffB7nrzAco8OYjIanuLak4SA3RhVSYtnCPFjl9FBZVYMlmUdxrOgSWkc1xpi0Nn61a7Jmq2GBx15TWVWD1XvycfTsJbRp0RhdEyMRFhwopD9WQG3OgoMCMLRzPArPl6OwtIJpY/RHGI6/wpiMxGqZlyRikRujCkWXKoXK8UJV3cMI1CpPTL0zVVXNOH7xNqzLKXT+v/kAsCQrDwNTo7FgbA/D+6wVnjHynO+u6+KwcvcpzXZAf4ThNIQwJqtkKJIYg7VXr4GYIVyAsrqHaHjsNe6bopJ1OYUYv5jPO9MoRNuk1M53qqQc8zYdMbTqBc+6DrABj/W13g82T5jh+y+hQz4xqsBS3UCL+7tWGlK4CI+9pqyyWnVTdLAupxBlldWmUKuKtknxVrdQQ2u4CGv4ySM3t8Hxc2UkKn4jsVq4kEQsDWMVGwBLdQMt7u9aaUjhIjzV5WcxPg2zyhkNzxj1nE8rWqte+Mp84zjf+L7t8MqwThjXp22D2RQB64ULScTScFayYKhtDA0pXIRnLo+eZRsXq5zRNLTqGhLrhQtJxCFVqSpQ2xgSm7O5u7PKUcITItCmRWNsPuD7mDYtzDF+0eslKixYT3fqwavKFXU+q2K1cCGJGOTGqAK1jSElpplQOT3oTonFESLwwtBULMnK83nICyZx9GBdL10TI7Fw82GfoTf7Tp8X2j+t4QUyXKEOkeFKEmsgN0YVHDaGPy3dARtcqxv5w8ZQVMYYLsIox4uI8AOeEIGw4EAMTI326oAzMDXaFI43ANt66dQqHNdOXcMUenP8nDEqYqnylUh8I22MXqC0MVCrcgFx4Qe8YxlxfYJXeV+f+xtv62XA/zZ51tAbozLHiF5XMlxB0hCRT4w+GNIpDv3aRztTb7Vp0RgvDE01/EmFOlxEZPgBz1isauMa0ikO/VNiXDIVjeyRhC7T1no9zj30ZkxaG8xcnevTM5kVrap/1mtWVV2Dr3edlLY3SYNCbow+cM88s/kA8MnWPMMzz2gJFzHC/iHSxsQzFqvauDypnt9a/ytz6M24Pm0B1BXKnrfJeywrCzyqf9ZrNuYfPzv/15PhRyIxE1KV6gXKzDPUNh6R7fOci3r8PKipns+XVzMd7x560y1JjDaAR/XPM6+y6oSkoSCfGFWgzjzjryoI/mifx15FPX6tiMhU0yqyrlKL3kLBU27viJbNQrhVnDy2Q1l1QtJQkE+MKpBnnvFDFQR/te8IZVATtaFWDedi/6Iev0ZEZKqxKdYb7/lsAGLDQ5ASp6/qi69rpobWDD8SiRmRG6MK1Jln/FEFwV/t86TXoh6/VkSodE+UlOk+nx1AeVUNRn+41Vlx/uZXtVec93bNWDCTilsi0YrcGFVgdZe3ils9dftDOsXh0b7JsLndZW024NG+yfXsX9Tj14qIfijXkp7zFV+67PJ/QWk5Huew/TnCT2LCtffFLNdFIuFBbowqjOyRJFROKw53eW8YGa7hUKV5o5760wtr9p7y6sjkftO+MTkKkY0beT1nZONGpqluwKt6dOBe3Jfl+msl/Ys9qOaI/7DbazTJG7kuJRJ/IDdGFVZs852OTIucVqirewQG2HDXdd69GO+6Lo457+bzX+zxKsNz0zaJeREAm7rYG+7FfVmuv1bOXbqMrMNnmeXX7D2Fx5fuwOnz2rIrGbkuJRJ/IDdGFahtjNThCtU1dqzc7V31tnL3KabNLOvw2XrqPXfcb9o/HyliOsZMTh7eMt988MD1GJga7fG4ganR9WJijbqu/zl4hkmO5ceMN6SNUWJlZLiGCle6jZHFK5I1wP4nxpvxTwfP4OarWwKg/2HAi1o1hnU5BfheJe/r9zmFWLP3lIud1agwlPziMt9CALIO+f4x4w2zhNFIJDzIJ0YVxqS1YbLxKe1CIuEKcRBIQQnbDZRF7iTjzVgpR/3DQA+OagzDurZy/mjwFeM4bVWO69O3QXpiZaykNzIPs/2YUaPGLlgPLJH4EUtsjHPnzkWbNm0QGhqKnj174ueff/Z9kE4cKbm84W4XEgl1BfEzF9jsSixy8Yw3Y6Uc9Q8DkWhJb+fAqDCUm/73RO4bfetqq4lU3BKJVky/Ma5YsQKTJk3C1KlTsWPHDlx33XUYPHgwCgvVyxGJIn1oKh7rm1zvyTHABjzW19hcqQBtdY9ixnJWLHI3M96MlXLUPwxEwqMWNuJJuHnjRujVli2vrP78s/KJUWJdTG9jfPPNNzF+/Hg8/PDDAIAPPvgA33zzDf7xj3/g+eefN7z99KGpeHZQiku1BLXiskZAVUFcZOKZXm1bILJxI682K083bccPA/ek3LEWS1bNoxb2VfiYh4x7OjOvG5Zr5o20tqxPphKJ+TD1xlhZWYns7Gykp6c73wsICMCAAQOQmZnp8ZiKigpUVNSpoUpLS3X3IzgowFn1gAKKCuJpbVvi3R8OMcn5IjDAhlfv6YzHl+5QlVG7aVP9MBCJr03OU0kob4WPfeG+ofFUvWC5Zt7a72WiiicSiVZMrUo9c+YMqqurERMT4/J+TEwMCgoKPB6TkZGBiIgI5ysxMdEfXW1w9GrXginAnvUGOKRTHD544HrEumVRiftfKIO3m7a7M4uVNkWAXy2spkqPiwjFY32TVecy+8WB+HR8L7x9X1d8Or4Xtkzuz/V0rXbNmvtYF69qeDKVSMyIzW43r/tYfn4+WrVqhZ9++glpaWnO95977jls3LgRW7durXeMpyfGxMRElJSUIDxcX2LlKw1HgLcavjY0T1TX2C399KcHT7UaWZ7m1ObMX3PpqZ11OQV4eeV/UVBa912LDQ/By3ddaxkVt+TKorS0FBEREUx7gak3xsrKSjRu3Bj/+te/MHz4cOf7Dz74IIqLi/H111/7PIeWyZDUZ83eU/IGKJCG9MOgIY1F0vDRsheY2sYYHByM7t27Y/369c6NsaamBuvXr8cTTzxB27krhIZg4zMTFPZio2hIY5FIlJh6YwSASZMm4cEHH8QNN9yAG2+8EW+99RYuXrzo9FKVGI+8AUokkisJ02+MI0eOxG+//YaXXnoJBQUF6Nq1K9asWVPPIUcikUgkEhGY2sYoAmljlEgkEomWvcDU4RoSiUQikfgbuTFKJBKJRKJAbowSiUQikSiQG6NEIpFIJApM75WqF4dvkYicqRKJRCKxJo49gMXftMFvjOfPnwcAmTNVIpFIJDh//jwiIiK8yjT4cI2amhrk5+ejWbNmsNmsm63FkfP1+PHjV2TYiRy/HL8cvxy/nvHb7XacP38e8fHxCAjwbkVs8E+MAQEBSEhIoO6GMMLDw6/IL4YDOX45fjl+OX5efD0pOpDONxKJRCKRKJAbo0QikUgkCuTGaBFCQkIwdepUhISEUHeFBDl+OX45fjl+f42/wTvfSCQSiUSiBfnEKJFIJBKJArkxSiQSiUSiQG6MEolEIpEokBujRCKRSCQK5MZoIV599VXYbDY888wz1F3xCy+//DJsNpvLKyUlhbpbfuXkyZN44IEH0KJFC4SFhaFz587Yvn07dbf8Qps2bepdf5vNhgkTJlB3zS9UV1djypQpSE5ORlhYGNq1a4fp06cz5fpsKJw/fx7PPPMMWrdujbCwMNx0003Ytm2b4e02+Mw3DYVt27Zh3rx56NKlC3VX/Mq1116L77//3vl/UNCVs2TPnTuHm2++Gb/73e/w7bff4qqrrsKBAwfQvHlz6q75hW3btqG6utr5/969ezFw4EDce++9hL3yH7Nnz8b777+Pjz/+GNdeey22b9+Ohx9+GBEREXjqqaeou+cX/vjHP2Lv3r1YsmQJ4uPjsXTpUgwYMAA5OTlo1aqVYe1eOXcZC3PhwgWMHj0aCxYswIwZM6i741eCgoIQGxtL3Q0SZs+ejcTERCxatMj5XnJyMmGP/MtVV13l8v+rr76Kdu3aoV+/fkQ98i8//fQThg0bhttvvx1A7RP0p59+ip9//pm4Z/6hrKwMn3/+Ob7++mv07dsXQK0WadWqVXj//fcNvRdKVaoFmDBhAm6//XYMGDCAuit+58CBA4iPj0fbtm0xevRo5OXlUXfJb6xcuRI33HAD7r33XkRHR6Nbt25YsGABdbdIqKysxNKlS/HII49YuhiAFm666SasX78ev/76KwBg9+7d2LJlC2677TbinvmHqqoqVFdXIzQ01OX9sLAwbNmyxdjG7RJT8+mnn9o7depkLysrs9vtdnu/fv3sTz/9NG2n/MTq1avt//znP+27d++2r1mzxp6WlmZPSkqyl5aWUnfNL4SEhNhDQkLs6enp9h07dtjnzZtnDw0NtX/00UfUXfM7K1assAcGBtpPnjxJ3RW/UV1dbZ88ebLdZrPZg4KC7DabzT5r1izqbvmVtLQ0e79+/ewnT560V1VV2ZcsWWIPCAiwt2/f3tB25cZoYvLy8uzR0dH23bt3O9+7kjZGd86dO2cPDw+3f/jhh9Rd8QuNGjWyp6Wlubz35JNP2nv16kXUIzoGDRpkv+OOO6i74Vc+/fRTe0JCgv3TTz+1//LLL/bFixfbo6KirqgfRgcPHrT37dvXDsAeGBho79Gjh3306NH2lJQUQ9uVNkYTk52djcLCQlx//fXO96qrq7Fp0ya8++67qKioQGBgIGEP/UtkZCTat2+PgwcPUnfFL8TFxSE1NdXlvY4dO+Lzzz8n6hENx44dw/fff48vvviCuit+5a9//Suef/553HfffQCAzp0749ixY8jIyMCDDz5I3Dv/0K5dO2zcuBEXL15EaWkp4uLiMHLkSLRt29bQdqWN0cTceuut2LNnD3bt2uV83XDDDRg9ejR27dp1RW2KQK0T0qFDhxAXF0fdFb9w8803Y//+/S7v/frrr2jdujVRj2hYtGgRoqOjnU4oVwqXLl2qV1A3MDAQNTU1RD2io0mTJoiLi8O5c+ewdu1aDBs2zND25BOjiWnWrBk6derk8l6TJk3QokWLeu83RP7yl7/gzjvvROvWrZGfn4+pU6ciMDAQo0aNou6aX5g4cSJuuukmzJo1C3/4wx/w888/Y/78+Zg/fz511/xGTU0NFi1ahAcffPCKCtUBgDvvvBMzZ85EUlISrr32WuzcuRNvvvkmHnnkEequ+Y21a9fCbrejQ4cOOHjwIP76178iJSUFDz/8sLENG6qolQjnSrIxjhw50h4XF2cPDg62t2rVyj5y5Ej7wYMHqbvlV1atWmXv1KmTPSQkxJ6SkmKfP38+dZf8ytq1a+0A7Pv376fuit8pLS21P/300/akpCR7aGiovW3btva//e1v9oqKCuqu+Y0VK1bY27Ztaw8ODrbHxsbaJ0yYYC8uLja8XVl2SiKRSCQSBdLGKJFIJBKJArkxSiQSiUSiQG6MEolEIpEokBujRCKRSCQK5MYokUgkEokCuTFKJBKJRKJAbowSiUQikSiQG6NE4gc++ugjREZGUnfDJy+//DK6du3q1zatMjeSKwe5MUokOnnooYcwfPjweu//+OOPsNlsKC4u1nS+srIyREVFoWXLlqioqKj3+fz583HLLbcgPDyc6/wSicQ7cmOUSEzG559/jmuvvRYpKSn46quv6n1+6dIlDBkyBC+88IL/OyeRXAHIjVEi8SNfffUVrrnmGoSGhmLw4ME4fvx4PZmFCxfigQcewAMPPICFCxfW+/yZZ57B888/j169eqm2c+LECYwaNQpRUVFo0qQJbrjhBmzdupWrzx9++CE6duyI0NBQpKSk4L333nN+dtNNN2Hy5Mku8r/99hsaNWqETZs2AQAqKirwl7/8Ba1atUKTJk3Qs2dP/Pjjj1x9kUj8gdwYJRI/cenSJcycOROLFy/Gf/7zHxQXFztr7Tk4dOgQMjMz8Yc//AF/+MMfsHnzZhw7dkxTOxcuXEC/fv1w8uRJrFy5Ert378Zzzz3HVa7ok08+wUsvvYSZM2ciNzcXs2bNwpQpU/Dxxx8DAEaPHo3ly5dDmXJ5xYoViI+PR58+fQAATzzxBDIzM7F8+XL88ssvuPfeezFkyBAcOHBAc38kEr9geJpyiaSB8+CDD9oDAwPtTZo0cXmFhobaAdjPnTtnX7RokR2APSsry3lcbm6uHYB969atzvdeeOEF+/Dhw53/Dxs2zD516lSP7f7www/O8yuZN2+evVmzZvazZ89qHsvUqVPt1113nfP/du3a2ZctW+YiM336dHtaWprdbrfbCwsL7UFBQfZNmzY5P09LS7NPnjzZbrfb7ceOHbMHBgbaT5486XKOW2+91Z6enm632+32RYsW2SMiIjT3VSIxCvnEKJEI4He/+51LQeldu3bhww8/dJEJCgpCjx49nP+npKQgMjISubm5AIDq6mp8/PHHeOCBB5wyDzzwAD766CNNT3u7du1Ct27dEBUVpWtMFy9exKFDhzBu3Dg0bdrU+ZoxYwYOHToEALjqqqswaNAgfPLJJwCAI0eOIDMzE6NHjwYA7NmzB9XV1Wjfvr3LOTZu3Og8h0RiNq6syp8SiUE0adIEV199tct7J06c0HSOtWvX4uTJkxg5cqTL+9XV1Vi/fj0GDhzIdJ6wsDBN7apx4cIFAMCCBQvQs2dPl88CAwOdf48ePRpPPfUU/v73v2PZsmXo3LkzOnfu7DxHYGAgsrOzXY4BgKZNmwrpp0QiGvnEKJH4iaqqKmzfvt35//79+1FcXIyOHTsCqHW6ue++++o9ed53330enXDU6NKlC3bt2oWioiJd/Y2JiUF8fDwOHz6Mq6++2uWVnJzslBs2bBjKy8uxZs0aLFu2zPm0CADdunVDdXU1CgsL650jNjZWV/8kEqOQT4wSiZ9o1KgRnnzySbzzzjsICgrCE088gV69euHGG2/Eb7/9hlWrVmHlypXo1KmTy3Fjx47F3XffjaKiIkRFRaGgoAAFBQU4ePAggFp1ZbNmzZCUlISoqCiMGjUKs2bNwvDhw5GRkYG4uDjs3LkT8fHxSEtL09TnadOm4amnnkJERASGDBmCiooKbN++HefOncOkSZMA1D4tDx8+HFOmTEFubi5GjRrlPL59+/YYPXo0xo4dizfeeAPdunXDb7/9hvXr16NLly64/fbbdc6qRCIe+cQokfiJxo0bY/Lkybj//vtx8803o2nTplixYgUAYPHixWjSpAluvfXWesfdeuutCAsLw9KlSwEAH3zwAbp164bx48cDAPr27Ytu3bph5cqVAIDg4GB89913iI6OxtChQ9G5c2e8+uqr9VSZLPzxj3/Ehx9+iEWLFqFz587o168fPvroI5cnRqBWnbp792706dMHSUlJLp8tWrQIY8eOxbPPPosOHTpg+PDh2LZtWz05icQs2Ox2hZ+1RCKRSCRXOPKJUSKRSCQSBXJjlEiuIK699lqXsAnlyxFyIZFc6UhVqkRyBXHs2DFcvnzZ42cxMTFo1qyZn3skkZgPuTFKJBKJRKJAqlIlEolEIlEgN0aJRCKRSBTIjVEikUgkEgVyY5RIJBKJRIHcGCUSiUQiUSA3RolEIpFIFMiNUSKRSCQSBXJjlEgkEolEwf8DxHWG/zxRVwoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = dfData.corr()\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "rPBfTWtK659n",
        "outputId": "a530362a-0c52-4e9d-8c8f-c9e76f38f1dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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yZI4fP868efP4+uuveeKJJ67pcSXjLoQQQggh7IKirx055z179tC7d2/L339d1DpmzBgWLVpEYmKiZRIPEBERwapVq3jiiSd45513qFevHp988gn9+/e/pseVibsQQgghhBDXoFevXvzdTyHZ+lXUXr16sX///n/1uDJxF0IIIYQQdsFe17hXFZm4CyGEEEIIu1Cd20Hag9qxUEgIIYQQQgjxtyTjLoQQQggh7EJdXyojGXchhBBCCCHsgGTchRBCCCGEXZA17kIIIYQQQohaTzLuQgghhBDCLsgadyGEEEIIIUStJxl3IYQQQghhFxRt3c64y8RdCCGEEELYBU0dn7jLUhkhhBBCCCHsgGTchRBCCCGEXVA0knEXQgghhBBC1HKScRdCCCGEEHZB0dbtnHPd7r0QQgghhBB2QjLuQgghhBDCLsiuMkIIIYQQQohaTzLuQgghhBDCLtT1XWVk4i6EEEIIIexCXV8qIxN38Z/i3a0dkU/eh0eb5jgG+7Pn1kdIXrnh74/p0YGmc5/BtWkjis4ncnrO/5Hw2fdWMWEPjyJy0n0YAv3IOXScoxNfJHv34ersSrXo1FRLz5Y6XJ0gMUNl5fZSElJVm7Hto7S0aawl0Mv8JpmQZmLt7jKr+BE99bRtrLU67sR5IwvXlFZfJ6pJu0YKnaM1uDpBcias2WvkYobt2NYNFFqGa/DzNP+dmKGy6aDJKl6vgxtiNETVU3BygKx82HXSxL7Ttse7NusYraFbcy2uTpCUofLTTiMX0mz3o10jDa0aagjwND9vLqarrNt3ZbyfB9zYVkdEoIJGgZRslS83lZGdX+3dqVJ/7FjC4S0LKMxLwzswms5DnsMvtKXN2LNH1nHw1/nkpMdjMpbh7htG825jadR6qCVGVVX2rX+PE3u+oaQwl4Cw1nQZOh0P3/Dr1KOqc3T7Eg7+9imFuWl4B0XTdejz+FcwNnFH1rF/40eWsfHwDaNFj3E0bmM9Nnt/eY9ju76hpDCHwPA2dBtun2Oz6scVrPjuazIzMwiPaMADD/+PxlHRNmM3/LKGd9963apMr9fz7Q9rACgrK2PJZwvYu3sXSUmJOLu4ENOqDaPHjcfHx7fa+yKuL5m4i/8UrYszOYdOcH7Rd7T79oOrxjuF16P9yo+In7+MA6OfwqdPZ1p89BJFiamk/bIVgKARA2ny+lSOPDqdrF0HiXh8DB1XfcrmZgMoSa1gZlcLtYzUMLiTju+3lnE+xUTX5lruG+jA3K+LyS+6Mj4yWMPB00ZWJpsoM0LPGHP8W98Wk1NQHnfivJFvfi2fqBuN16EzVaxpfYV+rTWs3m3iQrpKxygNo3prmfeTkYLiK+PD/BWOnDORsFelzAhdmmq4q7eWD1cbyS00x9zYWkN4gMKKHSay8lUiAxVuaqchr9DEyQv2M3lvHq5hYHstK3cYOZ9qoktTLWP76Xj7+1Kbz5uIQIVDZ0zEp6qUGVV6NNcy9kYd764oJffP5423G9w/UM/eUyY2HjBSXKri76lQZmfPnTOHVrNz9at0HTYDv3otObr9M9YsvJ/bJq3GydXniniDsycxvR7E0y8SjVZP/PHNbPnuOZxcfKjXuBsAh377hD92fEGP2+bg5lWPvevfZe3C+7ll4k/o9Ibr3cV/LPbganb89Ardh8/Av34Mh7cuZvWn47n9qZ9tj42TB637PISnXyRanZ5zxzbz6zfP4uTiTWhUdwAO/voJR7Z9Tq+Rr+DmXY89695h9afjGTFplV2NzZZfN7Hg4w95+LGJNI6O5scVy5nxwhTmzV+Ep6eXzWOcnV2YN3+R5W/lkqRzcXERsadPMfLOuwmPbEB+Xi4ff/gBL898gTff/b9q7s31p9TxjLtcnFoHrVmzhm7duuHp6YmPjw+DBw8mNjbWcv/27dtp1aoVjo6OtGvXjhUrVqAoCgcOHLDEHDlyhIEDB+Lq6kpAQAD33HMPaWlpNdAba6lrf+Pk9LdJ/mF9peLDHriDwrgEjk1+lbzjZzg3bwlJ360lYsJYS0zExHGc//RrEhYvJ+9YLIcfmY6xoIjQsbdWUy+qR7cWOnYdN7L3pJGULJUVW8soKYN2UVqb8V9tKuX3Y0YSM1RSs1W+21KGokDDEOu3jTIj5BWW3wpLrkdvqlanKA37Y1UOxqmk5cCq3SZKy6BVpO0PiBU7TOw9rZKcBem58NMuE4oCEQHl8fV8FQ7FmTiXopKdD/tjzfHB3vb1odO1mYY9J03sO20iNRtW7jBSWgZtG9n++Phmi5FdJ0wkZaikZcP3240oQIOg8vi+bbScvGBi7V7z8ysjF46fV21+EajNjmxdTFT7ETRuewteAQ3pOnQGOgdHTu5dbjM+KLID4c364enfAHef+jTvOhrvwMYkn9sLmDPKR7d/RqveDxHW9Aa8g6LoOeIVCnJTOPdH5d7TaotDWxYR3WEEUe1vxSugId2Hz0Snd+TE7u9sxgc36EhE8354BZjHpkW30XgHRpF0dh9gHpvDWz+jdZ+HCG92Az5BUfQe+SoFOSmcPWpfY/PD999y44Cb6HvjAOrXD+fhxyZiMBhYv25NhccoCnh5e1tunl7elvtcXFyZNft1uvXoRb16oURFN+XBR/5H7OmTpKYkX48uietIJu51UH5+PpMmTWLPnj1s2LABjUbD8OHDMZlM5OTkMGTIEFq0aMG+fft48cUXmTJlitXxWVlZ9OnTh9atW7Nnzx7WrFlDcnIyI0eOrKEe/XOenVqRtnGHVVnqL1vx6tQKAEWvx6NNM9I2bC8PUFXSNm7Hs1Pr69jSf0ergRBfhdMXTJYyFTh9wUSYf+XeBvQ6cz2XZ6AjgzQ8f7eBJ0c4MKyrDmf7SXwBoNFAkDfEJVlnweOSVer5Vm6SrdeCRrH+0pKQptI4RIObk/nvMH8Fbzc4k2SyXUktpNVAsI9CbKL18yY20USoXyWfN1pzPYXF5vFVgKh6GtKyVcb00/HM7XoeHKSjSX37+kJjLCsh7eJRght2tpQpGg3BDTqTEn/gqserqsrF0zvITj1LYHg7AHIzEyjMTSO4QXmdDo5u+NVrSUr8wSrvQ3UxlpWQduEo9Rp1sZQpGg0hDTuTXMmxuXB6B9mpcQRF/Dk2GQkU5qYSckmdDk5u+Ie2rNR41xalpaXEnj5JTKs2ljKNRkNMqzacOP5HhccVFhYyfsyd3Dv6Dl6e9QLx587+7ePk5+ejKAourq5V1fRaQ9Foqu1mD2SpTB10663WmeIFCxbg5+fHH3/8wdatW1EUhY8//hhHR0eaNm3KhQsXuP/++y3x77//Pq1bt2b27NlWdYSGhnLy5EkaN258xWMWFxdTXGw94ytVTeiVmn2hGAJ8KU62PlNQnJyG3sMNjaMBvZcHGp2O4pT0y2LScYmKvJ5N/VecHUGrUcgrtJ6c5hWq+HlW7v9gYAcdOQWq1eT/xHkjR+KMZOSq+Lgr9G+vY9wAB+atLEG1k9UgzgbQaBTyiqwbnF8Evm6Vm0ze0EpDbiGcuWTyv2aviUEdNEwcpsNoUlFVWLXLRHxqlTa/Wjkb/nreWJfnFYKvR+Xq6N9OS24BxCaax8bFCQx6hR4ttKzfb2TtXpXGIQp39taxYE0ZZ5Pt44lTVJCFajJesezDydWH7NS4Co8rKcrly1d6YSwrQaPR0OXmaYQ06gpAYW6apQ7rOn0pzLOfJ05RQabtsXHzJevvxqYwly9m97SMTddh06nX2Dw2Bbnm/jvbGJuC3Jo/21tZOTnZmEwmPL2sl8R4enqRcP68zWNC6oXyvyeeJjw8koKCfFZ89zVTnnyc9z78FF9fvyviS0pK+Gzhx3Tv2QdnZ5dq6YeoOTJxr4NOnTrFtGnT2LlzJ2lpaZhM5olYfHw8J06coGXLljg6OlriO3ToYHX8wYMH2bRpE642vsnHxsbanLjPmTOHmTNnWpXdqXhzl1YunLEHPWO0xERqmb+qxGod8qEz5ZP45EyVpIxSJt9hIDJIQ+xF+8ks/xtdmig0q6/w2UYjxku63L6xQj0fhWW/GskuUKnvpzCgnYbcQhNxdjI5/bd6tNDQIkLDp2vKLM+bv74KHTtvYvsf5gFLylAJ9dPQIUrD2WQ7W+h+jfQOLgz/33JKiwu4GPs7O1e/ipt3KEGRHa5+8H+c3uDCrRO+p7SkgIund/D7T6/g7l2P4AYda7ppNSq6STOimzSz+vvRB8exdvVP3DV6nFVsWVkZr82ZhaqqPPzYhOvd1OtCtoMUdc6QIUMICwvj448/Jjg4GJPJRPPmzSkpqdzi5Ly8PIYMGcKrr756xX1BQUE2j5k6dSqTJk2yKtvo3fbaG1/FipPTMARYf3kwBPhSmp2LqaiYkrRMTGVlGPx9LovxoTjJfrI8BUVgNKm4OimYFzuYuTop5BX8/SSyewstvWJ0fLK6hKSMv4/NyFXJKzRn32MvVkXLq19BMZhMKq6O1mPj4sgVWfjLdYpW6NpUwxebjKRklZfrtNCnpYavt5o4fdFcR0qWSqCXSqcmit1M3AuK/3reWJe7OnFFFv5yXZtp6N5Cy8K1ZSRnlvf3rzpTs6zHIDVbJczffj6QHZ09UTRaCvOsz8YV5qXj5FZxQkLRaHD3CQPAJ7gJWamxHPx1PkGRHSzHFeal4+zuf0mdaXgHNamGXlQPR2cv22OTm4bzVcbGw9c8Nr7BTchMOcOBTfMJbtARZzdzZrnAxtj4BNvP2Li7e6DRaMjKzLQqz8rKxMvbu4KjrOl0OiIbNCQx8YJV+V+T9tSUZF6cM1ey7f9R9rGgR1SZ9PR0Tpw4wfPPP88NN9xAkyZNyLzkDSQqKorDhw9bLWvZvXu3VR1t2rTh6NGjhIeH07BhQ6ubi4vtNwqDwYC7u7vVraaXyQBk/X4Anz6drMp8b+hC5u8HAFBLS8nedxTfPuVrTlEUfHp3Juv3/dexpf+O0QQX0lSrC0sVoGGwhnMpFWfGe7TUckMbHQvWlFS4/d+l3F3My3Jyr/JloDYxmSAxA8IDrSeNEQEKCX/T585NFLo307B0s5HEyzYX0iig1SpXLBcyqeUZZ3tgNJm3c4wMsn7eRAZpOJ9a8fOmW3MNvWO0LP6ljIvp1oPw13PR18N6JHzdFbLy7ed5o9U54BvcjMTTv1vKVJOJi7G/41+/VaXrUVUVY5k5aeLmVQ8nN18uxpbXWVKUR2rCIfzrx1RZ26ubVueAb0gzLpwuv35INZm4ePp3Aq5pbEwYjX+OjXc9nNz8uHhJnSVFeaScP3RN413T9Ho9DRo25tDB8s8Pk8nEoQP7iYpuWqk6jEYj587G4XXJBap/TdoTL15g1uzXcXev5Fo2O6TRKtV2swc1P3MS15WXlxc+Pj7Mnz+f06dPs3HjRqtM+KhRozCZTDzwwAMcO3aMtWvXMnfuXACUP/efevTRR8nIyODOO+9k9+7dxMbGsnbtWsaNG4exhvcC1Lo44x4TjXuMeT9c54h6uMdE4xhqPhMQ9dIkYhaWnyk4N38ZzhGhRM95GpeoSMIeGkXQiIHEvbPIEhP39kJC7xtJyD3DcI2OpPkHM9C5OHF+se2dI2qrrYfLzHuzN9Lg56kwrJsOBz3sPWn+PxvZS0//9uUn4XrGaLmxnY5vfy0lM9ecdXV1Aoc/Qxx05nXvof4KXq4KDYI1jO7nQHqOyskE+1om8/sJE20aKLSMUPB1h5vaa9Dr4GCceSI5tJOGPjHlb5ddmij0aqHhx50msvLN2XkXR/MFvAAlZXA2WaVvKw1h/gqeLtAyQqFluMKJBPuZnAJsO2qiXWMNrRto8POAmztrcdDB3lPm/+Nbu2np16Z8Z6LuzTX0ba1l+bYysvKufN4AbDlipHm4hnaNNHi7mfeJjwpV2HXcvp43zbuN4cSebzi1bwVZKbFs+2EmZSWFNG4zHIBfv5nC7rVvWuIPbp7PhVPbyMk4T1ZKLIe3LOT0/pU0bDUEML/HNusymgObPuTcsY1kJJ3k12+ewdnNn7CmfWukj/9Uy+5jOb7rG07u/Z7M5Fi2fD+D0tJCGre7BYBNX01h189vWOL3b/qIhJPbyEk/T2ZyLId+W8CpfStp1PpmwDw2LbqNZt/GDzn7x0YyEk+w6aspOLv7E97MvsZm6PDbWLdmFRvXr+V8/Dk+/OBtioqL6NuvPwBvzX2FzxZ+YolftvQz9u/bQ1LiRWJPn+StuXNITUmm34CbAPOk/dXZMzl96iSTnn4Wk9FEZkYGmRkZlJba329qXI2iUartZg9kqUwdo9FoWLZsGY8//jjNmzcnKiqKd999l169egHg7u7Ojz/+yMMPP0yrVq1o0aIF06ZNY9SoUZZ178HBwWzbto0pU6Zw4403UlxcTFhYGAMGDEBTw1dle7RtTucNn1v+bjr3WQDOf7acQ/dNxRDkh1No+XKewrMJ7L75QZq+MZXw/42mKCGJww8+b9nDHSDxm59x8POm8fTHzT/AdPAYuwaPp+SyC1Zru0NnTLg4ltGvrR43Z3MmdcHPJZYlD54u1hniTk106LQKd/dzsKpn/d4y1u8rw6RCkI9C28YOODpAbgGcTDDyy94yq7Xe9uCPeBVng4meLTS4Opp/gGnpZqNle0J3ZwX1ksFp21CDTqsworv1Vpq/Hjbx2xFz55dvN9InRsOwzhqcHCC7ADYdMm8jaU+OnDXh4gg3tNbi6qQlMUNl8S9llrHxdFVQL1li1CFai06rMKq33qqejQeMbDxg/pJ4LF5l5Q4jPVpqGdRRS1qO+ceXzqXY19hEtryJovxM9q5/l8LcNHyCmtB/3HzLkpe8rESUS84slpYUsH3lLPKzk9HqHfH0i6DXyFeJbHmTJaZlj/GUlRSy7fvplBTlEBDWhv7j5tvVPuUADWJuojA/gz3r3qMgNxWf4CbcdO/HlqUyeVkXLckggLKSQraumEV+dhK6P8emzx2v0SCmfGxieprHZst30ygpyiEwvC0D7/3Y7same8/e5ORks/TzRWRmZhIR2YDps16xbPGYlpqC5pJJZF5eHh+88waZmZm4urnSoGFjXn3jXerXDwcgPT2NXb+bdz6b+NgDVo/10itv0KJlq+vSL3F9KKpqL3s/iJqyZMkSxo0bR3Z2Nk5OTlc/oJJW6aOqrK7/ki3z7Gfbt+vNxVVyDRUpLrazb0vXkbubPG8qorWT5QE1YVCLC1cPqqOiG9Srscc+dFOvaqu75erN1VZ3VZF3M3GFzz77jMjISEJCQjh48CBTpkxh5MiRVTppF0IIIYQQ10Ym7uIKSUlJTJs2jaSkJIKCghgxYgQvv/xyTTdLCCGEEHWcvaxFry4ycRdXmDx5MpMnT67pZgghhBBCiEvIxF0IIYQQQtgFe9m2sbrIdpBCCCGEEELYAcm4CyGEEEIIu1DX17hLxl0IIYQQQgg7IBl3IYQQQghhF5Qa/qHHmiYTdyGEEEIIYRdkqYwQQgghhBCi1pOMuxBCCCGEsAuScRdCCCGEEELUepJxF0IIIYQQdkEy7kIIIYQQQohaTzLuQgghhBDCLtT17SDrdu+FEEIIIYSwE5JxF0IIIYQQdkGjrdtr3GXiLoQQQggh7IJcnCqEEEIIIYSo9STjLoQQQggh7IJcnCqEEEIIIYSo9STjLoQQQggh7IKscRdCCCGEEELUepJxF0IIIYQQdkEy7kIIIYQQQohaTzLuQgghhBDCLtT1XWVk4i6EEEIIIexCXV8qIxN3UWO2zDtY002olbo/ElPTTai13rljWU03odZy9/ao6SbUWqUlpTXdhForJz2rpptQayX2blHTTai15j5c0y2ou2TiLoQQQggh7EJdXypTt3svhBBCCCGEnZCMuxBCCCGEsA9K3V7jLhl3IYQQQggh7IBk3IUQQgghhF2o67vKSMZdCCGEEEIIOyAZdyGEEEIIYRfq+q4yMnEXQgghhBB2QZbKCCGEEEIIIWo9ybgLIYQQQgi7UNeXytTt3gshhBBCCGEnJOMuhBBCCCHsgqxxF0IIIYQQQtR6knEXQgghhBB2QTLuQgghhBBCiFpPMu5CCCGEEMI+1PFdZWTiLoQQQggh7IKiyFIZIYQQQgghRC0nGXchhBBCCGEX5AeYhBBCCCGEELWeZNyFEEIIIYRdkO0ghRBCCCGEELWeZNyFEEIIIYR9kDXuQgghhBBCiNpOMu5CCCGEEMIu1PU17jJxF0IIIYQQdkFR6vZikTrV+169ejFx4sSabkatImMihBBCCGEfJONeS4wdO5asrCxWrFhxXR93+fLl6PX66/qY10Onplp6ttTh6gSJGSort5eSkKrajG0fpaVNYy2BXubTbwlpJtbuLrOKH9FTT9vGWqvjTpw3snBNafV1oop5d2tH5JP34dGmOY7B/uy59RGSV274+2N6dKDp3GdwbdqIovOJnJ7zfyR89r1VTNjDo4icdB+GQD9yDh3n6MQXyd59uDq7Ui2G9PVlxE3+eHvoOXO+kA8+S+DEmQKbsWEhjoy+NYhG4U4E+hn4vy8S+H5tqlVMiygXRgwKoFG4Mz5eema8fYbte7OvR1eq3IBu7tzcxwNPdy3nLpTw6XfpnI4vthlbL1DPHTd5E1nPAX8fPQuXp7Hq1xyrmJEDvBg50Muq7EJyCRNmJ1RbH6rLTT09GdbPGy93LWcTipn/VQqnzhXZjA0NcmDUEF8a1HckwEfPJ9+k8OPGTKuYAT08GdjdE38f88dzfGIJX61OZ9/R/GrvS1Ub1j+A24cE4e2pJ/ZcAe8uOMvx2Ir70bOTN/feXo9APwMJSUXMXxLPzv3lrxkvDx0P3FWfdi09cHXRcuhYLu8uOMuFJNvPxdqsSzMdvVrpcHNWSEw38f3WUs6nmGzGdmyipW2UjkBvc641IdXEzzsrjr+1h57OzfT8sK2ELYfKqq0PNaaOL5WpUxn32shoNGIy2X7xXQ/e3t64ubnV2ONXh5aRGgZ30rF+XxnvfV9CYrqJ+wY64OJoOz4yWMPB00bm/1TCvB9KyM5TuW+gA+7O1nEnzht56Ysiy23ZRvuZtANoXZzJOXSCI4/PrFS8U3g92q/8iPTNO9nabihx7y2mxUcv4duvmyUmaMRAmrw+lVMvfcDWDsPJPXScjqs+xcHPu7q6US16dvTkwVEhfPF9Eo+8cIIz8YXMntwAT3fbuQ2Dg4aklGIWfH2R9CzbzwNHg5Yz8YW8v/h8dTa92nVp7cKY4T58szaTya9f4OzFEp5/OBB3V9sfHwYHDclppSz5MYPM7IonDfGJJYx//pzl9vw7F6urC9WmW1s37r3Vj69WpTFp9jniEoqZ8Xg9PNy0NuP/GpvPV6SSUcHYpGeW8tmKVCbNOceTr5zj8IkCnn0ohNAgh+rsSpXr3dmbh0fXZ/G3CTww5Qix5wp47bnoCl9TzRq78sKEhqzemMr9Uw6zdXcmLz7dmPBQJ0vMi083JsjfwPOvn+SByUdITi1m7gtNcDTY11QmpoGWm7vq+WVPKW9/W8TFdJX7BxtwdbId3yBYy4FTRj78oYj3lheRnafywGAD7i5XTmCbR2ipH6AlO6/m5hWietnXs70KmEwmJk+ejLe3N4GBgcyYMQOAe++9l8GDB1vFlpaW4u/vz6effgqYl5U89thjPPbYY3h4eODr68sLL7yAqpZnZouLi3nqqacICQnBxcWFjh07snnzZsv9ixYtwtPTk5UrV9K0aVMMBgP33nsvixcv5ocffkBRFBRFsRxz/vx5Ro4ciaenJ97e3gwdOpSzZ89a6hs7dizDhg1j7ty5BAUF4ePjw6OPPkppaflkYt68eTRq1AhHR0cCAgK47bbbLPddvlQmMzOT0aNH4+XlhbOzMwMHDuTUqVNXtH/t2rU0adIEV1dXBgwYQGJi4j/9L6ly3Vro2HXcyN6TRlKyVFZsLaOkDNpF2f4w/WpTKb8fM5KYoZKarfLdljIUBRqGWL88yoyQV1h+Kyy5Hr2pOqlrf+Pk9LdJ/mF9peLDHriDwrgEjk1+lbzjZzg3bwlJ360lYsJYS0zExHGc//RrEhYvJ+9YLIcfmY6xoIjQsbdWUy+qx60D/fl5czrrtmQQf7GIdxaep7jYRP8ePjbjT8YV8PGyi2z+PYvSUtsfkLsP5bDo20S22WmW/S9DenmwfnsOm3bmkZBcyvyv0yguUenTyfYX/tj4Yj5fmcG2/fmUltk+ywVgNKpk5Rott9x8+5toDL3Bi3XbstmwI4fzSSX835fJFJeY6NvZw2b86XNFLFqeypY9uRWOze7D+ew9mk9iaikXU0r5YmUaRcUmoiIqmNXVUiMGB7FqQwprNqdx7kIhb34cR1GJiYG9/WzG33pTILsOZPHVj4nEXyhi4VcJnDpTwPABAQDUC3KkWWM33v7kLCdi8zmfWMRbn5zF4KChT1fbr9PaqmeMjp1/lLH7hJHkTJXvfi2htFSlfbTtLzVLN5Sw/WgZF9NVUrNUvt5cgqJAo8s+o9xdFIZ107N0fTFG+3s5VZqi0VTbzR7YRyur0OLFi3FxcWHnzp289tprzJo1i19++YXx48ezZs0aqwnoTz/9REFBAbfffrvV8Tqdjl27dvHOO+/w5ptv8sknn1juf+yxx9ixYwfLli3j0KFDjBgxggEDBlhNfgsKCnj11Vf55JNPOHr0KO+++y4jR460TIATExPp0qULpaWl9O/fHzc3N7Zs2cK2bdssE+WSkvJZ46ZNm4iNjWXTpk0sXryYRYsWsWjRIgD27NnD448/zqxZszhx4gRr1qyhR48eFY7P2LFj2bNnDytXrmTHjh2oqspNN91k9UWgoKCAuXPn8vnnn/Pbb78RHx/PU0899a/+X6qKVgMhvgqnL5S/a6nA6Qsmwvwr93TX68z1FFx29jUySMPzdxt4coQDw7rqcDZUYcNrIc9OrUjbuMOqLPWXrXh1agWAotfj0aYZaRu2lweoKmkbt+PZqfV1bOm/o9MqNAp3Zv/RXEuZqsL+o7k0aej8N0f+9+m0EBlq4NDJQkuZqsLhk4VEhVdwCquSgvz0zJ9Vnw9eCGXCPX74etn+Yl1b6bTQoL4jB4+XL6dSVTh4vICoyH83Nn/RKNC9nRuODgonzhRe/YBaQqdVaBzpwt7D5UukVBX2Hc6mWWPbX/iaNna1igfYfTCLZo1cAdDrzNnlkku+KKsqlJaaaBFtP2eNtRoI8dNwMsH6M+rUBRNhAZX7jHKwfEaVf/lTgFE3OLD5QBnJmRV/YRZV64MPPiA8PBxHR0c6duzIrl27/jb+7bffJioqCicnJ0JDQ3niiScoKrK9tK4idW6Ne8uWLZk+fToAjRo14v3332fDhg288sorREVF8fnnnzN58mQAFi5cyIgRI3B1dbUcHxoayltvvYWiKERFRXH48GHeeust7r//fuLj41m4cCHx8fEEBwcD8NRTT7FmzRoWLlzI7NmzAXMmf968ecTExFjqdXJyori4mMDAQEvZF198gclk4pNPPkFRFEubPD092bx5MzfeeCMAXl5evP/++2i1WqKjoxk0aBAbNmywtMnFxYXBgwfj5uZGWFgYrVvbnlSdOnWKlStXsm3bNrp06QLAkiVLCA0NZcWKFYwYMcLS/g8//JAGDRoA5i8rs2bN+ttxLy4uprjYeiZcVqqi01ft7NfZEbQahbxC6zeuvEIVP8/KvSkO7KAjp0C1mvyfOG/kSJyRjFwVH3eF/u11jBvgwLyVJaj/0fdIQ4AvxclpVmXFyWnoPdzQOBrQe3mg0ekoTkm/LCYdl6jI69nUf8XdTYtWq5CZbb3kJTOnjNDgqpmA2Ss3F/PYZOcarcqzco2E+P/za2NOnSvig6UlXEwpxdNdy8gBXrz4eDBPvJJAUbF9vKDcXc1jk5VjveQlK8dIvYB/t6wlLNiBV58Ow0GvUFhsYs5HFzmfZD+n+DzcdebX1GXLyDKzSqkfbPvMgben/srXYHYpXp7msYy/WERSajH3jwrljflxFBWZuG1wIP6+Bnw87ec6LRdHxeZnVG6Bin8lP6MGddKTna9y6pLJf+/WOowm2Hr4P7im/TK1ZTvIr776ikmTJvHhhx/SsWNH3n77bfr378+JEyfw9/e/In7p0qU888wzLFiwgC5dunDy5EnGjh2Loii8+eablX7cOpdxb9mypdXfQUFBpKSkADB+/HgWLlwIQHJyMj///DP33nuvVXynTp0sk2iAzp07c+rUKYxGI4cPH8ZoNNK4cWNcXV0tt19//ZXY2FjLMQ4ODle0w5aDBw9y+vRp3NzcLHV5e3tTVFRkVV+zZs3QasuzVZf2qV+/foSFhREZGck999zDkiVLKCiwfcHdsWPH0Ol0dOzY0VLm4+NDVFQUx44ds5Q5OztbJu2XP15F5syZg4eHh9Xt959fv+oYXG89Y7TERGr5/JdSyi6Zqxw6Y+JYvInkTJU/zplYvLaUUH8NkUF17iUkxL+2/1ghOw7kc+5iCQePF/LyR0k4O2np0tr16gfXAReSS5g4+yxPv3aONb9lMWFMIKGB9rXGvaoZjSrT556kXpAjPy5sx5ov2tO6mTu/78vCZB/f9apE79Y6WjXUsXhNseUzKsRXoVtLPV9ttJ8vd7VVcXExOTk5VrfLk45/efPNN7n//vsZN24cTZs25cMPP8TZ2ZkFCxbYjN++fTtdu3Zl1KhRhIeHc+ONN3LnnXdeNUt/uTqXcb98BxVFUSwXh44ePZpnnnmGHTt2sH37diIiIujevXul687Ly0Or1bJ3716riTRglbV3cnKymvz/XX1t27ZlyZIlV9zn51e+TvDv+uTm5sa+ffvYvHkz69atY9q0acyYMYPdu3fj6elZ6b5dytbjqVdJO0+dOpVJkyZZlc36ourfbQuKwGhScXVSMJ+ANHN1Usgr+PvH695CS68YHZ+sLiEp4+9jM3JV8grN2fdY+7umrlKKk9MwBPhalRkCfCnNzsVUVExJWiamsjIM/j6XxfhQnGSdqa/NcnKNGI0qXh7Wz2svdx0ZFVx4Wlfk5pvH5vKLLT3dtGRdloX/NwoKTSSmlhDoaz8fSTl55rG5/GJLT3ctmTn/LutZZoSkVPNzLza+mEbhjgzu48X/LU3+V/VeL9k5ZebX1GWZcC9PfYWvqYys0itfgx56MrPKJ6Mn4wq4f/IRXJy06HQK2bllzHu5GSfO2M+OO/lF6iWfUeXcnBVyrvIZ1TNGR5/Wej76sZjESz6jIoO1uDrBc/eUnyHUahSGdNbTvYWO2UuubSlGrVeN+7jPmTOHmTOtN3CYPn265XrIv5SUlLB3716mTp1qKdNoNPTt25cdO6yXmP6lS5cufPHFF+zatYsOHTpw5swZVq9ezT333HNNbbSfd8nrwMfHh2HDhrFw4UJ27NjBuHHjrojZuXOn1d+///47jRo1QqvV0rp1a4xGIykpKdc04QdzFt5otP4gbNOmDV999RX+/v64u7tfe4f+pNPp6Nu3L3379mX69Ol4enqyceNGbrnlFqu4Jk2aUFZWxs6dOy1LZdLT0zlx4gRNmzb9x48PYDAYMBisl8Xo9FX/ZmI0wYU0lYYhGv44Z/7yogANgzVs/6PiD9MeLbX0aa3j059LuJB29S8U7i7mZTm5V3mjtWdZvx/Ab6D19RC+N3Qh8/cDAKilpWTvO4pvn87l20oqCj69O3Nu3hfXubX/XJlR5dTZAlo1dbNs16go0KqZGyt/sZ8vINWhzAhnzhfTorETuw+bz9QpCrRo7MTPW6ruoltHB4UAHz1ZOXlVVmd1KzNCbHwRLaOc2XnQ3G5FgZZRzqzenFWlj6Uo5Wu87UGZUeXkmXzaNHdn227zdpeKAm2ae/D9miSbx/xxMo82Ldz5bnX5/W1benD01JXPifxC82dlSKCBxg1cWPCV/WwjajTBhVQTjeppOHrW3A8F82YI245U/BnVq5WOG9ro+XhVMQmp1lee7j1RxqkE6/nD/YMM7D1pZPeJ/97SmepcKmMryXj53AUgLS0No9FIQECAVXlAQADHjx+3WfeoUaNIS0ujW7duqKpKWVkZDz30EM8+++w1tVHO819m/PjxLF68mGPHjjFmzJgr7o+Pj2fSpEmcOHGCL7/8kvfee48JEyYA0LhxY+666y5Gjx7N8uXLiYuLY9euXcyZM4dVq1b97eOGh4dz6NAhTpw4QVpaGqWlpdx11134+voydOhQtmzZQlxcHJs3b+bxxx8nIaFyb1Q//fQT7777LgcOHODcuXN89tlnmEwmoqKiroht1KgRQ4cO5f7772fr1q0cPHiQu+++m5CQEIYOHVqpx6sNth4uM+/N3kiDn6fCsG46HPSw96T5jW1kLz3925d/Z+0Zo+XGdjq+/bWUzFwVVydwdTJfAATmfwd20BHqr+DlqtAgWMPofg6k56hWFxjVdloXZ9xjonGPiQbAOaIe7jHROIYGARD10iRiFr5qiT83fxnOEaFEz3kal6hIwh4aRdCIgcS9s8gSE/f2QkLvG0nIPcNwjY6k+Qcz0Lk4cX7x8uvat3/ru59TuKmXD/26eRMabODxsaE4GjSs/c28fv/pB8O4d2SQJV6nVYis70RkfSf0Og2+Xnoi6zsR7F++nMHRoLHEAAT6ORBZ3wk/H/tZjwvw4+Zs+nZ2o2d7V0IC9Nw/wheDg8KmneYJ1f/u8mPU4PI92XVaCA9xIDzEAZ1OwdtDR3iIg1U2ffRQb5o2cMTPW0dUuIGnxwdgUmHrXvuZuAP8sCGTG7t50LuTO/UCHXjozgAcDRrW7zB/qZk4JpB7hpaftdJpIaKegYh6BvRaBR9PHRH1DAT6lT8n7hnqS9OGTvh76wgLduCeob40b+TMr7tyrnj82uybnxIZfIM//Xv6Uj/EkSfGh+No0LBms/n3DqY+Gsn4O0Mt8d+tTqJDjAcjBgcSGuzImBEhRDVw4fs15WcZenbyJqapG0H+Brq282Lu803YtjuTPYfsa+emXw+W0bGJjnZRWvw9FW7pocdBr7D7uHmSfUcfBwZ2LH9O9G6lY0AHPV9vLiEzx4SbE7hd8hlVUAxJGarVzWiC3ELzLjSi8gwGA+7u7lY3WxP3f2Lz5s3Mnj2befPmsW/fPpYvX86qVat48cUXr6keybhfpm/fvgQFBdGsWTPLBaaXGj16NIWFhXTo0AGtVsuECRN44IEHLPcvXLiQl156iSeffJILFy7g6+tLp06drthq8nL3338/mzdvpl27duTl5bFp0yZ69erFb7/9xpQpU7jlllvIzc0lJCSEG264odIZeE9PT5YvX86MGTMoKiqiUaNGfPnllzRr1sxm/MKFC5kwYQKDBw+mpKSEHj16sHr1arv6kaZDZ0y4OJbRr60eN2e4mK6y4OcS8v7clMHTRbG6oLRTEx06rcLd/azXkK7fW8b6fWWYVAjyUWjb2AFHB8gtgJMJRn7ZW2ZXW255tG1O5w2fW/5uOtf8Lf/8Z8s5dN9UDEF+OIWWT04Lzyaw++YHafrGVML/N5qihCQOP/g8ab9stcQkfvMzDn7eNJ7+uPkHmA4eY9fg8ZRcdsFqbffrziw83HSMvjUILw8dZ+ILee71WMuFh/4+eqvlYD5eej58Odry94hBAYwYFMDBY7k8Pfs0AI0jnJn7XCNLzEN31QNg3ZZ05s6Pvx7dqhLb9+fj7qrljpu88HTXcTahmJc/TLJcsOrrpbNaY+zloWPu5HqWv4fe4MnQGzw5eqqQ6e+bd+3y8dQxcYw/bi5acvKMHD9TxLNvXiDHzraE3Lo3F3dXLaMG++LlriUuoZiZ7yWUj4233mpsvD10vP1cuOXv4f28Gd7Pm8MnC3j+LfN+/x5uWiaODcLbXUt+kYlzF4qZ8V6C1e419mDTjgw83PWMHVnP/ANMZwuYMvu4ZW9/f1+D1dgcPZnHS+/Gcu8d9Rh/ZygXEot44fWTnD1fvpuOj5eeR0bXx8tTT3pmKet+S+Pzby9c7679awdjjbg6ldK/vR43Z4WLaSY++anY8hnl5Wr9GdW5mfkzakx/6wnkut2lrNtTB5fz1YJtG319fdFqtSQnWy9fS05Ottpk5FIvvPAC99xzD+PHjwegRYsW5Ofn88ADD/Dcc8+hqWS/FPVqi5PrmLy8PEJCQli4cOEVS0l69epFq1atePvtt2umcf8xz3z8H1t3V0W6PxJz9aA66p07ltV0E2otd2/be4cLKC2pg5ObSspJz6rpJtRabXu3qOkm1FpzH665rXJz3p509aB/yH1i5Xd36dixIx06dOC9994DzL8TVL9+fR577DGeeeaZK+Lbtm1L3759efXV8jPbX375Jffddx+5ublXXBtZEcm4/8lkMpGWlsYbb7yBp6cnN998c003SQghhBBCXKIym3tcD5MmTWLMmDG0a9eODh068Pbbb5Ofn2+5PnL06NGEhIQwZ84cAIYMGcKbb75J69at6dixI6dPn+aFF15gyJAhlZ60g0zcLeLj44mIiKBevXosWrQInU6GRgghhBBCXOn2228nNTWVadOmkZSURKtWrVizZo3lgtX4+Hir5S/PP/88iqLw/PPPc+HCBfz8/BgyZAgvv/zyNT2uLJURNUaWytgmS2UqJktlKiZLZSomS2UqJktlKiZLZSpWk0tlct97utrqdvtf7ft9mcvV/Ap/IYQQQgghxFXJehAhhBBCCGEXqnMfd3sgE3chhBBCCGEfqvGXU+1B3e69EEIIIYQQdkIy7kIIIYQQwj7U8aUyknEXQgghhBDCDkjGXQghhBBC2AVF1rgLIYQQQgghajvJuAshhBBCCPsga9yFEEIIIYQQtZ1k3IUQQgghhF1QNHU751y3ey+EEEIIIYSdkIy7EEIIIYSwD0rdXuMuE3chhBBCCGEfZKmMEEIIIYQQoraTjLsQQgghhLAPdXypjGTchRBCCCGEsAOScRdCCCGEEHZBtoMUQgghhBBC1HqScRdCCCGEEPZBqds557rdeyGEEEIIIeyEZNyFEEIIIYR90NTtXWVk4i6EEEIIIeyCIktlhBBCCCGEELWdZNxFjXFxlaefLe/csaymm1BrTVh2R003odaaM2B+TTeh1vIM9KvpJtRaLh5uNd2EWisvp7imm1CLOdfcQ9fxpTKScRdCCCGEEMIOSMpTCCGEEELYB1njLoQQQgghhKjtJOMuhBBCCCHsgyJr3IUQQgghhBC1nGTchRBCCCGEfdDU7ZyzTNyFEEIIIYR9kItThRBCCCGEELWdZNyFEEIIIYR9kB9gEkIIIYQQQtR2knEXQgghhBD2Qda4CyGEEEIIIWo7ybgLIYQQQgj7ID/AJIQQQgghhKjtJOMuhBBCCCHsg/wAkxBCCCGEEHZAlsoIIYQQQgghajvJuAshhBBCCPsg20EKIYQQQgghajvJuAshhBBCCPtQxy9Ordu9F0IIIYQQwk5Ixl0IIYQQQtgH2VVGCCGEEEIIUdtJxl0IIYQQQtiHOr6rjEzchRBCCCGEfZClMkIIIYQQQoja7j85ce/VqxcTJ06s6Wb8Y2PHjmXYsGGWv+29P0IIIYQQVUKjqb6bHZClMlXo7NmzREREsH//flq1alVl9S5fvhy9Xl9l9dUF7RopdI7W4OoEyZmwZq+Rixm2Y1s3UGgZrsHP0/x3YobKpoMmq3i9Dm6I0RBVT8HJAbLyYddJE/tOq9Xel6o0pK8vI27yx9tDz5nzhXzwWQInzhTYjA0LcWT0rUE0Cnci0M/A/32RwPdrU61iWkS5MGJQAI3CnfHx0jPj7TNs35t9PbpSpby7tSPyyfvwaNMcx2B/9tz6CMkrN/z9MT060HTuM7g2bUTR+UROz/k/Ej773iom7OFRRE66D0OgHzmHjnN04otk7z5cnV2pFrfcFMydt4Ti7eVAbFweb310mmOnciuM793Vl/F3RxDo70jCxQL+b1Ecv+8tf0Ft/bGnzeM+WBDLl98nVHn7q9OgXl7c0t8HLw8dceeL+ejLRE6eLbIZWz/YwF03+9EwzJEAXwfmL0ti5QbrN6aBPb24qZcXAT7m9/z4i8V8+VMae4/kVXtfqpqMTcV6tjZwY0cD7i4aElKMfLW+gLOJRpux3WIc6NjMgWA/LQDxSUZ++K3QKr5VYz09WhmoH6jF1UnDSwtzSEixXZ+wb/bx9cIOlJSUVFvd3t7euLm5VVv9/zVN6yv0a63htyMmPl5jJDlLZVRvLc4G2/Fh/gpHzpn4fIORheuM5BTAXb21uDmVx9zYWkODIIUVO0z832ojO0+YGNhWQ+MQ+1lr17OjJw+OCuGL75N45IUTnIkvZPbkBni62/7+bnDQkJRSzIKvL5KeVWozxtGg5Ux8Ie8vPl+dTa92Whdncg6d4MjjMysV7xRej/YrPyJ98062thtK3HuLafHRS/j262aJCRoxkCavT+XUSx+wtcNwcg8dp+OqT3Hw866ublSLPt38eGx8AxZ+eZb7Ju7ldFweb85qgaeH7WRC82h3pj/dlJ/WJXLvhL1s+T2dOc81I6K+syXm5nu2W91mv30ck0nl1+1p16tbVaJ7O3fGjwzgyx9TmfDiGeISipg1MQwPN63NeIODQlJaCYuXp5BRwWsqPbOUxd+lMPGlOCa+HMfB4/k8/2go9YMreAOrpWRsKtY2Ws9tfZz4aVsRsxeZJ9j/G+mKm7Ptz5PGoTr2HCvlrS/zeO3zXDJzTTw+0hVP1/J4g17hdEIZ328uvF7dqDGqolTbzR78ZyfuJpOJyZMn4+3tTWBgIDNmzLDcl5WVxfjx4/Hz88Pd3Z0+ffpw8OBBy/2xsbEMHTqUgIAAXF1dad++PevXr7eqPzw8nBdffJHRo0fj7u7OAw88QEREBACtW7dGURR69ep11XYajUYmTZqEp6cnPj4+TJ48GVW1zuJevlRm3rx5NGrUCEdHRwICArjtttus+j1nzhwiIiJwcnIiJiaGb7/91urx7rvvPsv9UVFRvPPOO1aPt3nzZjp06ICLiwuenp507dqVc+fOWe7/4YcfaNOmDY6OjkRGRjJz5kzKysqu2tfrpVOUhv2xKgfjVNJyYNVuE6Vl0CrS9otyxQ4Te0+rJGdBei78tMuEokBEQHl8PV+FQ3EmzqWoZOfD/lhzfLC3fbzQAW4d6M/Pm9NZtyWD+ItFvLPwPMXFJvr38LEZfzKugI+XXWTz71mUlppsxuw+lMOibxPZZodZ9kulrv2Nk9PfJvmH9VcPBsIeuIPCuASOTX6VvONnODdvCUnfrSViwlhLTMTEcZz/9GsSFi8n71gshx+ZjrGgiNCxt1ZTL6rHHcPq8ePaRFZvSObs+QJen3eKomITg/sF2owfcXMIO/dl8OX3CZxLKOCTJWc5GZvHrYNDLDEZWaVWt26dfNl3OIuLybazsbXVsH4+rN2Sxfrt2ZxPLOGDLxIpLjHRr6unzfhTZ4tY+G0Kv+3OobTM9tm6XYfy2HMkj4spJVxMLuHzFakUFZuIinSyGV9bydhUrG97R7YdLGbH4RIS000sXVtAaSl0aeFgM37BTwX8ur+YhBQjyRkmPv+5AEVRiAor//K882gJq7cXcfxs7fksFtXjPztxX7x4MS4uLuzcuZPXXnuNWbNm8csvvwAwYsQIUlJS+Pnnn9m7dy9t2rThhhtuICPDfFouLy+Pm266iQ0bNrB//34GDBjAkCFDiI+Pt3qMuXPnEhMTw/79+3nhhRfYtWsXAOvXrycxMZHly5dftZ1vvPEGixYtYsGCBWzdupWMjAy+//77CuP37NnD448/zqxZszhx4gRr1qyhR48elvvnzJnDZ599xocffsjRo0d54oknuPvuu/n1118B88S+Xr16fPPNN/zxxx9MmzaNZ599lq+//hqAsrIyhg0bRs+ePTl06BA7duzggQceQPnzm+iWLVsYPXo0EyZM4I8//uCjjz5i0aJFvPzyy5X9r6lWGg0EeUNckvUbf1yySj3fyk2y9VrQKFB4yUmUhDSVxiEaSxY+zF/B2w3OJNme0NY2Oq1Co3Bn9h8tX96gqrD/aC5NGjr/zZHCFs9OrUjbuMOqLPWXrXh1agWAotfj0aYZaRu2lweoKmkbt+PZqfV1bOm/o9MpNG7oxp6DmZYyVYU9BzJpFuVu85jm0e7sOZBpVbZzfwbNo23He3nq6dLOm1W/JFVdw68DnRYahjly4Fi+pUxV4cCxfKIbVM1rSqNAj/buODooHI+1vaStNpKxqZhWA/UDtRw7Vz7BVoFjZ0uJDKnc6mUHvbmegiL7WqpZZRRN9d3swH92jXvLli2ZPn06AI0aNeL9999nw4YNODk5sWvXLlJSUjAYzKfX5s6dy4oVK/j222954IEHiImJISYmxlLXiy++yPfff8/KlSt57LHHLOV9+vThySeftPyt1ZpPAfr4+BAYaDsbdbm3336bqVOncssttwDw4Ycfsnbt2grj4+PjcXFxYfDgwbi5uREWFkbr1uaJQHFxMbNnz2b9+vV07twZgMjISLZu3cpHH31Ez5490ev1zJxZvhwgIiKCHTt28PXXXzNy5EhycnLIzs5m8ODBNGjQAIAmTZpY4mfOnMkzzzzDmDFjLPW/+OKLTJ482TLethQXF1NcXGxVVlaqRaev2lOczgbQaBTyLntDyy8CX7fKTdxvaKUhtxDOXDL5X7PXxKAOGiYO02E0qagqrNplIj71byqqRdzdtGi1CpnZ1qegM3PKCA12rKFW2S9DgC/FydbLOoqT09B7uKFxNKD38kCj01Gckn5ZTDouUZHXs6n/ioe7Hp1WISPT+nmTkVVKWD3bEzBvTwcys6yXDmZmleLtaTubOLBPIAWFRn7dbicvpj+5u+rQahWycqwznFk5ZdQL/Hfva2EhBuY+E4GDXqGw2MTL8xI4n1h9yzGrmoxNxVydFbQahZx866RPboFKoI/tZUSXu6WnE9l5Jo6dtb2kSPy3/acn7pcKCgoiJSWFgwcPkpeXh4+P9fKAwsJCYmNjAXPGfcaMGaxatYrExETKysooLCy8IuPerl27f9XG7OxsEhMT6dixo6VMp9PRrl27K5bL/KVfv36EhYURGRnJgAEDGDBgAMOHD8fZ2ZnTp09TUFBAv379rI4pKSmxTO4BPvjgAxYsWEB8fDyFhYWUlJRYLqb19vZm7Nix9O/fn379+tG3b19GjhxJUFAQAAcPHmTbtm1WGXaj0UhRUREFBQU4O9v+MJ8zZ47VFwaAXre8QJ/bplV+wK6DLk0UmtVX+GyjEeMl76vtGyvU81FY9quR7AKV+n4KA9ppyC00EZdcR7MeQlSBQf0CWbc5hZJSeR395UJSMY/PisXZSUu3tu48cW8wz7x+1q4mqNWlro9N/44G2jVx4M0v8yirq9ee2klmvLr8Zyful+/CoigKJpOJvLw8goKC2Lx58xXHeHp6AvDUU0/xyy+/MHfuXBo2bIiTkxO33XbbFReguri4VFfzK+Tm5sa+ffvYvHkz69atY9q0acyYMYPdu3eTl2e+sn7VqlWEhIRYHffX2YVly5bx1FNP8cYbb9C5c2fc3Nx4/fXX2blzpyV24cKFPP7446xZs4avvvqK559/nl9++YVOnTqRl5fHzJkzLWcILuXoWHHmdurUqUyaNMmq7I0VlcsuXIuCYjCZVFwdFcwnIM1cHLkiC3+5TtEKXZtq+GKTkZSs8nKdFvq01PD1VhOnL5rrSMlSCfRS6dREsYuJe06uEaNRxeuyCwq93HUVXggmKlacnIYhwNeqzBDgS2l2LqaiYkrSMjGVlWHw97ksxofiJPu5ADM7p5Qyo4q3l/XzxttTT3qm7YlSRlYJXpdl17089WRkXRnfsqkHYfWcmf7qH1XX6OskJ68Mo1G94uJuT3cdmTn/bp1xmRESU0uBUmLji2gU7sjNN/jwwReJ/6re60XGpmJ5BSpGk4q7iwYon3m7OV+Zhb9cvw4G+ndy5O2v8riQWldn7djNRaTVpc59bWnTpg1JSUnodDoaNmxodfP1NX8Qb9u2jbFjxzJ8+HBatGhBYGAgZ8+evWrdDg7mDyujsXIvKA8PD4KCgqwmzWVlZezdu/dvj9PpdPTt25fXXnuNQ4cOcfbsWTZu3EjTpk0xGAzEx8df0bfQ0FBL37p06cIjjzxC69atadiwoeVMw6Vat27N1KlT2b59O82bN2fp0qWW8Ttx4sQV9Tds2BDN3+yBajAYcHd3t7pV9TIZAJMJEjMgPND6hR0RoJCQVvEEu3MThe7NNCzdbCTxsm0jNQpotQqXnwQxqWAvbx9lRpVTZwto1bR8dyJFgVbN3Dh22n7Wh9YWWb8fwKdPJ6sy3xu6kPn7AQDU0lKy9x3Ft0/n8gBFwad3Z7J+338dW/rvlJWpnDydS9uWXpYyRYG2MV4cPZFj85gjx3NoF+NlVda+lRdHjl8ZP/jGQI6fyuX02fwr7qvtyoxw+lwRMU3KEziKAjFNXKp8zbWiUdDr7eXdRsbm7xhN5u0co8PKv9QoQHS4njMXKv5Sc2MHAzd1ceK9b/KIT6q7k3bxH864V6Rv37507tyZYcOG8dprr9G4cWMuXrzIqlWrGD58OO3ataNRo0YsX76cIUOGoCgKL7zwAibT1S9C9Pf3x8nJiTVr1lCvXj0cHR3x8PD422MmTJjAK6+8QqNGjYiOjubNN98kKyurwviffvqJM2fO0KNHD7y8vFi9ejUmk4moqCjc3Nx46qmneOKJJzCZTHTr1o3s7Gy2bduGu7s7Y8aMoVGjRnz22WesXbuWiIgIPv/8c3bv3m3ZEScuLo758+dz8803ExwczIkTJzh16hSjR48GYNq0aQwePJj69etz2223odFoOHjwIEeOHOGll16q/H9ENfr9hImhnTQkZihcTFfpEKVBr4ODceaZ99BO5jXsGw+a/0+7NFHo2ULD99tNZOWbs/MAJWVQWmb+92yySt9WGsqMJrLzVer7K7QMV/hlv31cnArw3c8pPP1AGKfiCjh+Jp9b+vvjaNCw9jfzOuynHwwjPbOEBV+bM1c6rUL9EPNg6HUafL30RNZ3oqjIyMUUc/bU0aAhOKD8C1ignwOR9Z3IzS8jNd1+MvlaF2dcGta3/O0cUQ/3mGhKMrIpOp9I1EuTcAwJ4OC4KQCcm7+MsEfuInrO05xf9B2+vTsRNGIgu29+0FJH3NsLiVnwKll7j5C9+xDhj49B5+LE+cVXv2i9Nlm2IoHnnojm+Olcjp3MZeTQEJwcNaxab76Y9PknokhNL+Gjz+IA+GblBd6fE8Mdw+qxfU86fbv7E93QjdfeP2lVr7OTlt5d/Xj/0ysTB/ZixS/pPHFvMKfOFnIyrpChfX1wdNCwflsWAJPuDSY9s4zF36cA5rN3oX9uXajTKfh46YgINVBUZPoziwxjhvuz50geqRmlODlq6NXBgxaNnZn2drzNNtRWMjYVW7+7iLGDXDiXZORsYhl92jnioIfth83vq2MHOZOVa2LFb+Zdlm7saGBINycW/JhPerYJdxfzF5XiEpXiP99mnR0VvN01li0iA7zNibScfBM5+bX/rPA1kaUydYuiKKxevZrnnnuOcePGkZqaSmBgID169CAgIACAN998k3vvvZcuXbrg6+vLlClTyMmxnV26lE6n491332XWrFlMmzaN7t2721ySc6knn3ySxMRExowZg0aj4d5772X48OFkZ9veXs/T05Ply5czY8YMioqKaNSoEV9++SXNmjUDzBfS+vn5MWfOHM6cOYOnpydt2rTh2WefBeDBBx9k//793H777SiKwp133skjjzzCzz//DICzszPHjx9n8eLFpKenExQUxKOPPsqDD5onJP379+enn35i1qxZvPrqq+j1eqKjoxk/fnylxv96+CNexdlgomcLDa6O5h9gWrrZSP6fO825OytW1xC0bahBp1UY0d166c6vh038dsQ8MV++3UifGA3DOmtwcoDsAth0yLyNpL34dWcWHm46Rt8ahJeHjjPxhTz3eqzlAjJ/H73VuPh46fnw5WjL3yMGBTBiUAAHj+Xy9OzTADSOcGbuc40sMQ/dVQ+AdVvSmTvffj5MPdo2p/OGzy1/N51rfr2c/2w5h+6biiHID6fQIMv9hWcT2H3zgzR9Yyrh/xtNUUIShx98nrRftlpiEr/5GQc/bxpPf9z8A0wHj7Fr8HhKLrtgtbbbuDUVTw894+8Kx9vLgdNn8nhy+mEy/1xiFeDniOmSl8GR4znMnHuM+++O4IHRESRcLGTqy0eJi7fOtPbt4Y+iwPrfUq5nd6rUlj05eLhpuXuoH17uOs6cL2baO/Fk5Zozon7eequx8fbU8960Bpa/b+3vy639fTl8Ip+pc81b7nq4a5l0bzDeHjryC02cTShi2tvxVju02AMZm4rtPV6Km3MhQ7o5Wn6A6b2v88gtMA+It7vG6gxvz9YG9DqFB4e7WtXz09ZCftpm/mCLaahnzKDyMxz3D3W9Ikb8NyhqRVdBClHNXvxS9pu1Zctq+/tlzetlwrI7aroJtdacAfNrugm1lmegX003Qdiheg2Drh5UR304xevqQdWkYMs31Va3c/cR1VZ3Vanb5xuEEEIIIYSwE3Vuqcz15urqWuF9P//8M927d7+OrRFCCCGEsGN/sxFGXSAT92p24MCBCu+7fMtGIYQQQgghKiIT92rWsGHDmm6CEEIIIcR/Ql3fx10m7kIIIYQQwj7U8e0g63bvhRBCCCGEsBOScRdCCCGEEHZBlYy7EEIIIYQQoraTjLsQQgghhLAPdfziVMm4CyGEEEIIYQck4y6EEEIIIeyCrHEXQgghhBBC1HqScRdCCCGEEPahjq9xl4m7EEIIIYSwD7JURgghhBBCCFHbScZdCCGEEELYBbWOL5WRjLsQQgghhBB2QDLuQgghhBDCPsgadyGEEEIIIURtJxl3IYQQQghhF1RkjbsQQgghhBCilpOMuxBCCCGEsAuqrHEXQgghhBBC1HaScRdCCCGEEPahjmfcZeIuhBBCCCHsgvwAkxBCCCGEEOKafPDBB4SHh+Po6EjHjh3ZtWvX38ZnZWXx6KOPEhQUhMFgoHHjxqxevfqaHlMy7kIIIYQQwi7UlotTv/rqKyZNmsSHH35Ix44defvtt+nfvz8nTpzA39//iviSkhL69euHv78/3377LSEhIZw7dw5PT89relxFVVW1ivogxDV5flFJTTehVjq+P6Gmm1BrJZ05X9NNqLWmrnmgpptQa618cUdNN6HWUur4soO/06qVV003odZ6qH/NPXbGoS3VVrd3y+6Vju3YsSPt27fn/fffB8BkMhEaGsr//vc/nnnmmSviP/zwQ15//XWOHz+OXq//x22sHV9bhBBCCCGEuBpFqbZbcXExOTk5Vrfi4uIrmlBSUsLevXvp27evpUyj0dC3b1927LCdKFi5ciWdO3fm0UcfJSAggObNmzN79myMRuM1dV8m7kIIIYQQos6bM2cOHh4eVrc5c+ZcEZeWlobRaCQgIMCqPCAggKSkJJt1nzlzhm+//Raj0cjq1at54YUXeOONN3jppZeuqY2yxl0IIYQQQtiF6lzjPnXqVCZNmmRVZjAYqqRuk8mEv78/8+fPR6vV0rZtWy5cuMDrr7/O9OnTK12PTNyFEEIIIUSdZzAYKjVR9/X1RavVkpycbFWenJxMYGCgzWOCgoLQ6/VotVpLWZMmTUhKSqKkpAQHB4dKtVGWygghhBBCCLugolTbrbIcHBxo27YtGzZssJSZTCY2bNhA586dbR7TtWtXTp8+jclkspSdPHmSoKCgSk/aQSbuQgghhBDCTqiKptpu12LSpEl8/PHHLF68mGPHjvHwww+Tn5/PuHHjABg9ejRTp061xD/88MNkZGQwYcIETp48yapVq5g9ezaPPvroNT2uLJURQgghhBDiGtx+++2kpqYybdo0kpKSaNWqFWvWrLFcsBofH49GU/5lIDQ0lLVr1/LEE0/QsmVLQkJCmDBhAlOmTLmmx5WJuxBCCCGEsA+16LcHHnvsMR577DGb923evPmKss6dO/P777//q8eUpTJCCCGEEELYAcm4CyGEEEIIu6DW8Zxz3e69EEIIIYQQdkIy7kIIIYQQwi6otWiNe02QjLsQQgghhBB2QDLuQgghhBDCLlzrfuv/NTJxF0IIIYQQduFafuH0v6huf20RQgghhBDCTkjGXQghhBBC2IW6vlSmbvdeCCGEEEIIOyEZdyGEEEIIYRdkO0ghhBBCCCFErScZdyGEEEIIYRdkVxkhhBBCCCFErScZdyGEEEIIYRfq+q4yMnEXQgghhBB2QZbKCCGEEEIIIWo9ybgLIYQQQgi7UNeXytTt3tdBvXr1YuLEiVVa56JFi/D09KzSOoUQQgghhDXJuIt/7fbbb+emm26q6WZY6RitoVtzLa5OkJSh8tNOIxfSVJux7RppaNVQQ4Cned3cxXSVdfuujPfzgBvb6ogIVNAokJKt8uWmMrLzq707VWZAN3du7uOBp7uWcxdK+PS7dE7HF9uMrReo546bvIms54C/j56Fy9NY9WuOVczIAV6MHOhlVXYhuYQJsxOqrQ/V5ZabgrnzllC8vRyIjcvjrY9Oc+xUboXxvbv6Mv7uCAL9HUm4WMD/LYrj970Zlvu3/tjT5nEfLIjly+/tZ3y8u7Uj8sn78GjTHMdgf/bc+gjJKzf8/TE9OtB07jO4Nm1E0flETs/5PxI++94qJuzhUUROug9DoB85h45zdOKLZO8+XJ1dqRa92hjo19ERDxcNCSlGlv2Sz9lEo83YbjEGOjV3INhPC0B8kpEVvxZcET+kuxPdYww4GRRiL5SxdG0+KZmmau9LVevZ2sCNHQ24/zk2X62/sq9/6RbjQMdm1mPzw2+FV45NN0e6XTI2X64rsMuxOfDbEvZu/JT8nFT8QqLpfdsLBIa1tBl76uA6dq37kOy0eIzGMrz8wmjTexxNOwyzxOxY/R4n9q0iNysJrVaPf2gzug5+gqDwmOvUo+tH1rgL8S85OTnh7+9f082waB6uYWB7LZsOGJm3spSkDJWx/XS4ONqOjwhUOHTGxKdry/hodSnZ+Spjb9Th5lwe4+0G9w/Uk5at8umaMt5fWcrmg0bKbH8G1UpdWrswZrgP36zNZPLrFzh7sYTnHw7E3dX224DBQUNyWilLfswgM7uswnrjE0sY//w5y+35dy5WVxeqTZ9ufjw2vgELvzzLfRP3cjoujzdntcDTQ28zvnm0O9OfbspP6xK5d8JetvyezpznmhFRv/xJc/M9261us98+jsmk8uv2tOvVrSqhdXEm59AJjjw+s1LxTuH1aL/yI9I372Rru6HEvbeYFh+9hG+/bpaYoBEDafL6VE699AFbOwwn99BxOq76FAc/7+rqRrVoF+3AbX2cWbW1kJcXZpOQUsbjt7vh5mx7YtG4vo7df5Tw5tJcXv0sh8wcExNud8PTtTy+f0dH+rQ1sGRtPq98lkNxqcrjt7uh016vXlWNttF6buvjxE/bipi9KIeEFCP/G+la8diE6thzrJS3vszjtc9zycw18fhIV6uxubGjgd5tDSxdW8Crn+dSUqryv5Gudjc2J/at5rfv59BpwKPc9fT3+IZEs3zefRTkptuMd3T2oOOND3P7E19xz5SVNO14C+uWPsvZY1ssMV7+4fQeMY17nvmRkROX4uEdwvJ591KQm2GzTmG/ZOJeB5WVlfHYY4/h4eGBr68vL7zwAqpqzi6Hh4fz0ksvMXr0aFxdXQkLC2PlypWkpqYydOhQXF1dadmyJXv27LHUV9uWynRtpmHPSRP7TptIzYaVO4yUlkHbRraf7t9sMbLrhImkDJW0bPh+uxEFaBBUHt+3jZaTF0ys3WskMUMlIxeOn1fJL7pOnaoCQ3p5sH57Dpt25pGQXMr8r9MoLlHp08nNZnxsfDGfr8xg2/58Sstsn60AMBpVsnKNlltuvv1lv+4YVo8f1yayekMyZ88X8Pq8UxQVmxjcL9Bm/IibQ9i5L4Mvv0/gXEIBnyw5y8nYPG4dHGKJycgqtbp16+TLvsNZXEy2oycNkLr2N05Of5vkH9ZXKj7sgTsojEvg2ORXyTt+hnPzlpD03VoiJoy1xERMHMf5T78mYfFy8o7FcviR6RgLiggde2s19aJ69O3gyNaDxWw/XEJiuoklawooKYUuLQ024xf8mM+v+4tJSDGSnGHis5/zURSF6PDyL4g3tHdk9fYiDp4q5UKqkYU/5ePpqqFVY4fr1a0q0be9I9sOFrPjz7FZuraA0lLo0sJ2Pxb8VGA1Np//XICiKESFXTI27Rz5eUcRB09fPja2v2DXVvs2LaR5l5E063QrPkEN6TtyJjoHR478/p3N+NBGHWkY0w+fwAZ4+tWnTa8x+AVHcfHMXktMdLshhEV1wdM3FN+gRvQYPpWSojzSLp64Xt26blRFU203e2AfrRRVavHixeh0Onbt2sU777zDm2++ySeffGK5/6233qJr167s37+fQYMGcc899zB69Gjuvvtu9u3bR4MGDRg9erRlsl+baDUQ7KMQm1g+eVSB2EQToX6Ve7rrteZ6CovN/VOAqHoa0rJVxvTT8czteh4cpKNJffs5XafTQmSogUMnCy1lqgqHTxYSFV7BqYhKCvLTM39WfT54IZQJ9/jh62Vf6S+dTqFxQzf2HMy0lKkq7DmQSbMod5vHNI92Z8+BTKuynfszaB5tO97LU0+Xdt6s+iWp6hpeS3l2akXaxh1WZam/bMWrUysAFL0ejzbNSNuwvTxAVUnbuB3PTq2vY0v/Ha0G6gdqOXa21FKmAsfPlhIZUrlVqA56cz35heb3Gl8PDR6uGqs6i4pV4i6WVbrO2sAyNufKz9SpwLF/MDYFRZePTXmdRSWYxybYfsbGWFZC8vmj1I/qYilTNBrqR3UhMW7/VY9XVZX4EzvISIkjpEH7Ch/j8PavMDi54RcSVWVtF7WD/TzbRZUJDQ3lrbfeMmczoqI4fPgwb731Fvfffz8AN910Ew8++CAA06ZN4//+7/9o3749I0aMAGDKlCl07tyZ5ORkAgNtZyQvV1xcTHGx9VrqslIFnd52ZuqfcjaAVqOQV2hdnlcIvh6Vq6N/Oy25BRCbaP7AcHECg16hRwst6/cbWbtXpXGIwp29dSxYU8bZ5Nr3BeZybi5atFqF7FzrtT1ZuUZC/P95turUuSI+WFrCxZRSPN21jBzgxYuPB/PEKwkUFdf+cQHwcNej0ypkZJZalWdklRJWz9nmMd6eDmRmlViVZWaV4u1pO5s4sE8gBYVGft2eWjWNrsUMAb4UJ1svBypOTkPv4YbG0YDeywONTkdxSvplMem4REVez6b+K67OClqNQm6+9fM8J99EoE/lXlO39HImO89kmaj/tWwt57KzVjn5Jjxc7CdR8NfYXN6P3AKVQJ/KfbG/pafTZWNj7r+tOt1d7CcHWZifiWoy4uzmY1Xu7OZDZvKZCo8rLszl4xd6YCwrQdFo6DNiOmHRXa1izhzZxOpFkygtLcTF3Y9bHlmAk6t9LT+rDFnjLuqcTp06oSjlT/zOnTtz6tQpjEbzpK5ly/ILZAICAgBo0aLFFWUpKSmVfsw5c+bg4eFhddu+6rV/1Y/q0KOFhhYRGpZsKrOsX/9rpI6dN7H9D/OSmt8OmzhxXqVDVN1+Ce0/VsiOA/mcu1jCweOFvPxREs5OWrq0dq3pptUqg/oFsm5zCiWl9vFlRlS//p0cad/Egf9bnmdX18pcD/07GmjXxIEPv8+XsfmTg8GFu6es4M6nvqXr4Cf4bcUrnD+10yomtFFH7p6ygjsmLiO8SXdWLZxY4bp5e6YqSrXd7EHdnnUIm/T68mzRXxN8W2UmU+XXMk+dOpXs7GyrW5dBk6uoxeUKisFoUnF1si53deKKLPzlujbT0L2FlkXrykjOLJ9g/VVnapb1pCs1W7WbLFhuvhGjUcXDzTrb5emmJSu36j4ZCwpNJKaWEOhrPyfzsnNKKTOqeHtZZ0m9PfWkZ5bYPCYjqwSvy7LrXp56MrKujG/Z1IOwes78tC6x6hpdixUnp2EI8LUqMwT4Upqdi6momJK0TExlZRj8fS6L8aE4yX4u3M0rUDGaVNwuew9wd9GQfZXrPPp1cGRAJ0fe+SqXC6nlr7+cPJOljivrtJ8vfX+NzeX9cHO+Mgt/uX4dDPTv5Mg7X+ddNjbm/v+TOmsTJxcvFI32igl1QW46zm6+FRxlXk7j6ReGf70mtO1zL41i+rP7l/lWMXqDM55+YQRFtOLGUbPRaHUc2fFttfRD1ByZuNdBO3daf0v//fffadSoEVpt9a1NNhgMuLu7W92qepkMgNFk3s4x8pILSxUgMkjD+dSK39y7NdfQO0bL4l/KuJhu/QFpNMGFNBVfD+sPaF93hSw7+TAtM8KZ88W0aFz+jUZRoEVjJ06crbqLJR0dFAJ89GTl2E+arKxM5eTpXNq2LN/WUlGgbYwXR0/k2DzmyPEc2sVYb4PZvpUXR45fGT/4xkCOn8rl9Fk72jf0X8j6/QA+fTpZlfne0IXM3w8AoJaWkr3vKL59OpcHKAo+vTuT9fvV1/jWFkaTecvCJpdcWKoA0WF6zlyoeBemGzs6MqiLI+9+ncu5JOvXSVq2iew8k9XFqo4OEBGs+9s6a5u/xiY6rPwLvAJEh19lbDoYuKmLE+99k0d8RWNzSZ2WsbloP2Oj1TkQENqM8yfLrwNRTSbOn9hBUETlr/FQVRPGMtuJhUvrvVqMPVJVpdpu9kAm7nVQfHw8kyZN4sSJE3z55Ze89957TJgwoaabVWW2HTXRrrGG1g00+HnAzZ21OOhg7ynzxP3Wblr6tSn/ktK9uYa+rbUs31ZGVp45W+/qBA6XJI23HDHSPFxDu0YavN3M+8RHhSrsOm4/mZ4fN2fTt7MbPdu7EhKg5/4RvhgcFDbtzAPgf3f5MWpw+WRUp4XwEAfCQxzQ6RS8PXSEhzhYZdNHD/WmaQNH/Lx1RIUbeHp8ACYVtu7Nu+79+zeWrUhgSP8gBvQJIKyeM0890ggnRw2r1psvJn3+iSgeHB1hif9m5QU6tvHijmH1qF/PiXvvDCO6oRvf/XTBql5nJy29u/rxox1n27UuzrjHROMeEw2Ac0Q93GOicQwNAiDqpUnELHzVEn9u/jKcI0KJnvM0LlGRhD00iqARA4l7Z5ElJu7thYTeN5KQe4bhGh1J8w9moHNx4vzi5de1b//W+l1Flr3ZA300jOrvjIMDbD9kvp5n7GAXhvUs/7Lcv6MjN3d3YvHP+aRnm3B3UXB3UTBccrJnw+4iburiSMuGeoL9tIwb7EpWnokDJ+1rArZ+t/XY3NnfGQc9bD9s7sfYQc4M61F+YfyNHQ0M6e7EZ6v/Zmz2FDHwr7Hx1TB2kMufY1N6+cPXam16j+Pw9q85uvN70pNi2fD1DEpLCmnW8RYA1nw+ma0r37DE71r3EeeObyMr7TzpSbHs3biAY7tXEt3uZgBKiwvY+uObJMYdICfjAsnxR1i3ZCp52ck0aj2gRvooqo/9nM8WVWb06NEUFhbSoUMHtFotEyZM4IEHHqjpZlWZI2dNuDjCDa21uDppScxQWfxLmWXrRk9XBZXyTHmHaC06rcKo3tZLJTYeMLLxgDnrcyxeZeUOIz1aahnUUUtajvnHl86l2EfGHWD7/nzcXbXccZMXnu46ziYU8/KHSZYLVn29dJgu6Y6Xh465k+tZ/h56gydDb/Dk6KlCpr9vnoj6eOqYOMYfNxctOXlGjp8p4tk3L9jVqWuAjVtT8fTQM/6ucLy9HDh9Jo8npx8mM8s8IQjwc7QamyPHc5g59xj33x3BA6MjSLhYyNSXjxIXX2BVb98e/igKrP+t8teD1DYebZvTecPnlr+bzn0WgPOfLefQfVMxBPnh9OckHqDwbAK7b36Qpm9MJfx/oylKSOLwg8+T9stWS0ziNz/j4OdN4+mPm3+A6eAxdg0eT0mKfa3H3XO8BFdnhZu7O1l+ZOjdr3LJLTA/WbzdNVy6+VaPNgb0OoWHhltvwfrj1kJ+2mpey7d2ZxEODgp3D3DB2VHhdEIZ736Va3drvfceL8XNuZAh3RwtY/Pe13kVjk3P1uaxeXC49fUxP20t5Kdt5jfvdTuLMegV7urvbBmb9762v2sEotrcRGFeBjtWv0tBTip+9Zow/OFPcHE3L5XJzUxEuWRrwtKSAjZ+M5PcrCR0eke8/SMZMPp1otqYf/hQ0WjJTD7Dj7u+pygvE0cXTwLqt2DkhCX4BjWqkT5WJ7WO55wVtTbu6SfqhOcX2VcG6Xo5vt9+flXzeks6c76mm1BrTV3z3/nyXdVWvrjj6kF1lGInF+TVhFatvK4eVEc91L/mHvtU7Llqq7tRg7Bqq7uqSMZdCCGEEELYBdkOUgghhBBCCFHrScZdCCGEEELYhbqecZeJuxBCCCGEsAt1feIuS2WEEEIIIYSwA5JxF0IIIYQQdkEy7kIIIYQQQohaTzLuQgghhBDCLqiqZNyFEEIIIYQQtZxk3IUQQgghhF2QNe5CCCGEEEKIWk8y7kIIIYQQwi7U9Yy7TNyFEEIIIYRdqOsTd1kqI4QQQgghhB2QjLsQQgghhLALsh2kEEIIIYQQotaTjLsQQgghhLALJlnjLoQQQgghhKjtJOMuhBBCCCHsguwqI4QQQgghhKj1JOMuhBBCCCHsQl3fVUYm7kIIIYQQwi7IUhkhhBBCCCFErScZdyGEEEIIYRfq+lIZybgLIYQQQghhByTjLoQQQggh7IKscRdCCCGEEELUepJxFzXG3U2efraUlpTWdBNqLc9Av5puQq218sUdNd2EWuvmFzrXdBNqrYwNJ2q6CbXWTd7ba7oJtViXGntkWeMuhBBCCCGEqPUk5SmEEEIIIeyCqaYbUMNk4i6EEEIIIeyCLJURQgghhBBC1HqScRdCCCGEEHZBtoMUQgghhBBC1HqScRdCCCGEEHZB1rgLIYQQQgghaj3JuAshhBBCCLsga9yFEEIIIYQQtZ5k3IUQQgghhF0wqTXdgpolGXchhBBCCCHsgGTchRBCCCGEXajra9xl4i6EEEIIIeyCbAcphBBCCCGEqPUk4y6EEEIIIeyCKhenCiGEEEIIIWo7ybgLIYQQQgi7YKrjF6dKxl0IIYQQQgg7IBl3IYQQQghhF2RXGSGEEEIIIUStJxl3IYQQQghhF+r6rjIycRdCCCGEEHahrv9yqiyVEUIIIYQQwg5Ixl0IIYQQQtgFUx1fKiMZdyGEEEIIIeyATNwradGiRXh6etZ0M65qxowZtGrV6ro+pr2MjRBCCCHsm6oq1XazB7JUBhg7dixZWVmsWLHCqnzz5s307t2bzMzMa6qvsLCQkJAQNBoNFy5cwGAwWN0/f/58li5dyr59+8jNzSUzM1MmvlXsjx1LOLxlAYV5aXgHRtN5yHP4hba0GXv2yDoO/jqfnPR4TMYy3H3DaN5tLI1aD7XEqKrKvvXvcWLPN5QU5hIQ1pouQ6fj4Rt+nXpUNW7q6cmwft54uWs5m1DM/K9SOHWuyGZsaJADo4b40qC+IwE+ej75JoUfN1q/Fgb08GRgd0/8fcxvJfGJJXy1Op19R/OrvS9VbVAvL27p74OXh46488V89GUiJ8/aHpv6wQbuutmPhmGOBPg6MH9ZEis3ZFjFDOzpxU29vAjw0QMQf7GYL39KY++RvGrvS1Xr1cZAv46OeLhoSEgxsuyXfM4mGm3Gdosx0Km5A8F+WgDik4ys+LXgivgh3Z3oHmPAyaAQe6GMpWvzSck0VXtfqpJ3t3ZEPnkfHm2a4xjsz55bHyF55Ya/P6ZHB5rOfQbXpo0oOp/I6Tn/R8Jn31vFhD08ishJ92EI9CPn0HGOTnyR7N2Hq7Mr1WLPpiXsWPspedmpBIRG0//OFwiJsP0+fHzfOrat/pCMFPP7sLd/GB1vHEfLzsOs4tISY9nw3evEn9yNyWjEN6gBtz38Hh4+wdehR1Xnu583sOSHn8nIyqZheH0m3XcXTRtFVhifm1/AR0u/49ff95KTl0+gnw8Txt1Jl7Yx/7hOYZ8k414NvvvuO5o1a0Z0dPQVXwYACgoKGDBgAM8+++z1b1wdcObQanaufpXWNzzK0Ee/wzsoijUL76cwL91mvMHZk5heDzLkoS8Z/vgKGrUZzpbvniPh5FZLzKHfPuGPHV/QdegMbn74K3QOzqxdeD9lpcXXq1v/Wre2btx7qx9frUpj0uxzxCUUM+Pxeni4aW3GGxw0JKeV8vmKVDKyy2zGpGeW8tmKVCbNOceTr5zj8IkCnn0ohNAgh+rsSpXr3s6d8SMD+PLHVCa8eIa4hCJmTQz7m7FRSEorYfHyFDKySm3GpGeWsvi7FCa+FMfEl+M4eDyf5x8NpX6wwWZ8bdUu2oHb+jizamshLy/MJiGljMdvd8PN2XZ2qnF9Hbv/KOHNpbm8+lkOmTkmJtzuhqdreXz/jo70aWtgydp8Xvksh+JSlcdvd0Nne7hrLa2LMzmHTnDk8ZmVincKr0f7lR+RvnknW9sNJe69xbT46CV8+3WzxASNGEiT16dy6qUP2NphOLmHjtNx1ac4+HlXVzeqxdHdq/nl6zl0H/Io41/4noB60Xz59n3k59h+H3Z08aDrTQ8zbupX3D99JTFdb+HHRc8Se2SLJSYjJZ7Fr47CNzCSe576nPunr6T74EfQ6e3rNbV+207eXbSMe0cOZeHrM2gYFsoTL75BRnaOzfjS0jImzHydxJQ0Xn76UZa9N4dnHh6Ln4/XP67Tnqlq9d3sgUzcr9GKFSto1KgRjo6O9O/fn/Pnz18R8+mnn3L33Xdz99138+mnn15x/8SJE3nmmWfo1KlThY+TkJDAnXfeibe3Ny4uLrRr146dO3f+ozZ/8sknNGnSBEdHR6Kjo5k3b57lvi5dujBlyhSr+NTUVPR6Pb/99hsAxcXFPPXUU4SEhODi4kLHjh3ZvHnzP2rL9XBk62Ki2o+gcdtb8ApoSNehM9A5OHJy73Kb8UGRHQhv1g9P/wa4+9SnedfReAc2JvncXsCcbT+6/TNa9X6IsKY34B0URc8Rr1CQm8K5P9Zfz679K0Nv8GLdtmw27MjhfFIJ//dlMsUlJvp29rAZf/pcEYuWp7JlTy6lZbbf0XYfzmfv0XwSU0u5mFLKFyvTKCo2ERXhVJ1dqXLD+vmwdksW67dncz6xhA++SKS4xES/rp4240+dLWLhtyn8tjunwrHZdSiPPUfyuJhSwsXkEj5fkWoem0j7Gpu+HRzZerCY7YdLSEw3sWRNASWl0KWl7cnSgh/z+XV/MQkpRpIzTHz2cz6KohAdrrfE3NDekdXbizh4qpQLqUYW/pSPp6uGVo3t6wtf6trfODn9bZJ/qNz7QNgDd1AYl8Cxya+Sd/wM5+YtIem7tURMGGuJiZg4jvOffk3C4uXkHYvl8CPTMRYUETr21mrqRfXY+ctCWncfSauut+IX3JCb7p6J3sGRA9u+sxkfHtWR6Db98A1qgLd/fTr0HUNAvSjOn95ridm84i0atOjBDbdNJrB+U7z969O41Q24uPtcr25ViWU/ruPmvj0Y3Kc7EaEhTH5wNAaDAz9t2GIz/qeNW8jJy+fVKf+jZXQjgvx9ad0smkbh9f9xncJ+ycT9GhQUFPDyyy/z2WefsW3bNrKysrjjjjusYmJjY9mxYwcjR45k5MiRbNmyhXPnzl3T4+Tl5dGzZ08uXLjAypUrOXjwIJMnT8ZkuvbTyEuWLGHatGm8/PLLHDt2jNmzZ/PCCy+wePFiAO666y6WLVuGeslXza+++org4GC6d+8OwGOPPcaOHTtYtmwZhw4dYsSIEQwYMIBTp05dc3uqm7GshLSLRwlu2NlSpmg0BDfoTEr8gaser6oqF0/vIDv1LIHh7QDIzUygMDeN4AbldTo4uuFXryUp8QervA/VQaeFBvUdOXi8wFKmqnDweAFRkY5V8hgaBbq3c8PRQeHEmcIqqfN60GmhYZgjB46VL+9RVThwLJ/oBs5V8hgaBXq0d8fRQeF4bMHVD6gltBqoH6jl2NnyswoqcPxsKZEhlVtp6aA315NfaH6P8fXQ4OGqsaqzqFgl7mJZpeu0V56dWpG2cYdVWeovW/Hq1AoARa/Ho00z0jZsLw9QVdI2bsezU+vr2NJ/x1hWQuK5o0Q06WIpUzQawpt04ULs/qser6oqccd2kJ4UR/3G7c1lJhOnD23GJyCcpW/dx5uTOrNg9ghO7Lef5AmYs+cnYs/SrmUzS5lGo6F9y6YcOXna5jFbd++neVQD5n78BYPuncBdE59n8Xc/YTSa/nGd9syEUm03e/Dffpe8Bj/99BOurq5WZUaj9ZrM0tJS3n//fTp27AjA4sWLadKkCbt27aJDhw4ALFiwgIEDB+LlZT6F1b9/fxYuXMiMGTMq3ZalS5eSmprK7t278fY2nx5t2LDhP+rX9OnTeeONN7jlllsAiIiI4I8//uCjjz5izJgxjBw5kokTJ7J161bLRH3p0qXceeedKIpCfHw8CxcuJD4+nuBg8xrCp556ijVr1rBw4UJmz55dqXYUFxdTXGy9rKSsVF/lpziLCrJQTUacXK0zME6uPmSnxlV4XElRLl++0gtjWQkajYYuN08jpFFXAApz0yx1WNfpS2FeapW2v7q4u2rRahWycqyXvGTlGKkX8O+ynGHBDrz6dBgOeoXCYhNzPrrI+aSSf1Xn9eTuqqtgbMqoF/jvnp9hIQbmPhNhGZuX5yVwPtF+xsbVWUGrUcjNtz6rkJNvItBHX8FR1m7p5Ux2nskyUXd31VjquLxODxf7+OD8pwwBvhQnp1mVFSenofdwQ+NoQO/lgUanozgl/bKYdFyi7GetckFeJqrJeEUm3NXdh/SkMxUeV1SQyzuTe2AsK0FRNAy8azqRTc3vw/m56ZQUF7D954/pNWwifW59itijW/jm/x7jnic/IyyqQ7X2qapk5eZiNJnw9nS3Kvf28ODchSSbx1xITiXpyDFu7N6ZN557goSkZObO/5wyYxn3jRz2j+q0Z7VpScsHH3zA66+/TlJSEjExMbz33nuW+eDfWbZsGXfeeSdDhw61uaT670jG/U+9e/fmwIEDVrdPPvnEKkan09G+fXvL39HR0Xh6enLs2DHAPNFfvHgxd999tyXm7rvvZtGiRdeULT9w4ACtW7e2TNr/qfz8fGJjY7nvvvtwdXW13F566SViY2MB8PPz48Ybb2TJkiUAxMXFsWPHDu666y4ADh8+jNFopHHjxlZ1/Prrr5Y6KmPOnDl4eHhY3TYtf+Vf9a8q6R1cGP6/5Qx95Gva9pvIztWvknhmV003yy5cSC5h4uyzPP3aOdb8lsWEMYGEBtrXkofqciGpmMdnxTJpdhw/b87kiXuD7W79/7/Rv5Mj7Zs48H/L8yizfS2rEBYGRxfun7aCe5/9lt7Dn+CXr1/h7AnzElFVNX+GNm51Ax37jSWwfhO6DnyARi17sffXZTXZ7GqnqipeHu5MeWgs0Q3C6du1I2NuHcKKtZtruml12ldffcWkSZOYPn06+/btIyYmhv79+5OSkvK3x509e5annnrKkiy9VpJx/5OLi8sVWe2EhIRrqmPt2rVcuHCB22+/3arcaDSyYcMG+vXrV6l6nJyqZg1sXp5594qPP/7YcpbgL1pt+VVgd911F48//jjvvfceS5cupUWLFrRo0cJSh1arZe/evVbHAFecofg7U6dOZdKkSVZl76+uXMbuWjg6e6JotFdciFqYl46Tm2+FxykaDe4+YQD4BDchKzWWg7/OJyiyg+W4wrx0nN39L6kzDe+gJlXeh+qQk2fEaFTxdLd+yXu6a8nMsX3haWWVGSEp1ZxNjY0vplG4I4P7ePF/S5P/Vb3XS05eWQVjo6uSsUlMLQVKiY0volG4Izff4MMHXyT+q3qvl7wCFaNJxe2yTLi7i4bs/L9PRvTr4MiATo68vSyXC6nls/acPJOljpz88nJ3Fw3nU/7bs/vi5DQMAdbvQ4YAX0qzczEVFVOSlomprAyDv89lMT4UJ1ln6mszZ1cvFI32igtR83LScXX/+/dhb3/z+3Bg/SakJcayffV8wqM64uzqhUarwzeogdUxvoENrNbB13aebm5oNRoysqwvGs3Izr4iY/4XHy9PdFotWm15rjW8XhDpWdmUlpb9ozrtWW3ZtvHNN9/k/vvvZ9y4cQB8+OGHrFq1igULFvDMM8/YPMZoNHLXXXcxc+ZMtmzZQlZW1jU/rmTcr0FZWRl79uyx/H3ixAmysrJo0sQ8efv000+54447rsjc33HHHTYvUq1Iy5YtOXDgABkZGVcP/hsBAQEEBwdz5swZGjZsaHWLiIiwxA0dOpSioiLWrFnD0qVLLdl2gNatW2M0GklJSbmijsDAwEq3xWAw4O7ubnWrjp0AtDoHfIObkXj6d0uZajJxMfZ3/Ou3qnQ9qqpiLDMvaXDzqoeTmy8XY8vrLCnKIzXhEP71YyqqolYpM0JsfBEto8rXbCsKtIxy5sQZ21se/lOKAnpd7XhjrYwyo/lC3JgmLpYyRYGYJi5Vvh5d0Sjo9fYzNkaTeTvHJpdcWKoA0WF6zlyo+EvNjR0dGdTFkXe/zuVckvVkPC3bRHaeyepiVUcHiAjW/W2d/wVZvx/Ap4/1pgS+N3Qh8/cDAKilpWTvO4pvn/LraVAUfHp3Juv3q68Nry20OgeCwpoRd6x8Pb9qMnH22A5CGlR+rb6qmij7831Yq3MgOLwF6cnWSx4zks/i4RNSNQ2/DvR6HVENwtl7+A9LmclkYs+hYzRvbHtJbMvohiQkJVuduY+/mISvlyd6ve4f1SlsKy4uJicnx+p2+TJfgJKSEvbu3Uvfvn0tZRqNhr59+7Jjx44r4v8ya9Ys/P39ue+++/5xG2Xifg30ej3/+9//2LlzJ3v37mXs2LF06tSJDh06kJqayo8//siYMWNo3ry51W306NGsWLHCMhFPSkriwIEDnD5tvmjk8OHDVhP1O++8k8DAQIYNG8a2bds4c+YM33333d8+GSoyc+ZM5syZw7vvvsvJkyc5fPgwCxcu5M0337TEuLi4MGzYMF544QWOHTvGnXfeabmvcePG3HXXXYwePZrly5cTFxfHrl27mDNnDqtWrfo3w1ltmncbw4k933Bq3wqyUmLZ9sNMykoKadxmOAC/fjOF3WvL+39w83wunNpGTsZ5slJiObxlIaf3r6RhqyEAKIpCsy6jObDpQ84d20hG0kl+/eYZnN38CWva12YbaqMfNmRyYzcPendyp16gAw/dGYCjQcP6HdkATBwTyD1Dy7NhOi1E1DMQUc+AXqvg46kjop6BQL/yCdc9Q31p2tAJf28dYcEO3DPUl+aNnPl1l31tQbbil3T6d/ekT2cP6gU68MhdQTg6aFi/LQuASfcGM2Z4+dkWnRYiQg1EhBrQ6RR8vHREhBoIumRsxgz3p1kjZ/x99ISFGBgz3J8WjZ3Z/Hv29e7ev7J+V5Flb/ZAHw2j+jvj4ADbD5k/zMYOdmFYz/KzhP07OnJzdycW/5xPerYJdxcFdxcFwyUn2DbsLuKmLo60bKgn2E/LuMGuZOWZOHDSftb/g3k7SPeYaNxjogFwjqiHe0w0jqFBAES9NImYha9a4s/NX4ZzRCjRc57GJSqSsIdGETRiIHHvLLLExL29kND7RhJyzzD+v737joriasMA/uzC0rsgIFIFAQEL9hoL1sQeu0KILTFWjBpj7MbejbH3nhh77DWKvWEXRRBEmtI77O73B5+LK5CQRJjd7PM7Z88Js3eGZya63r3z3jtGHi7wXjkN2ob6iNpS/KpYqqp+60DcufgLQi7vx5uYMBzdMQ15uVmo0bhgvtXBDeNxdt8iRfvgo2vw4lEwkhKi8CYmDFdPbsT9q4fg06CTok2DNgPx6MYx3P7jFyTGv8SNs9sReu8cajfvU+T3q7LeHdvg0OkLOHruEiJevcaCtVuRnZODz1oWLAs6Y/k6rNr+q6J917YtkJqegaUbdyLydSyCb4Vg677f0a1dy1If879EJi+7V3FlvXPmzCmS4c2bN5BKpbC2tlbabm1tjdjY4ucVXLp0CRs2bMC6dev+1fmzVOZvMDAwwIQJE9C3b19ER0ejadOmipH0rVu3wtDQEK1atSqyX6tWraCvr4/t27dj5MiRWL16NaZPL1z3t1mzZgCATZs24YsvvoCOjg5OnjyJsWPHokOHDsjPz0e1atWwcuXKv5150KBBMDAwwIIFCzBu3DgYGhrCx8cHo0ePVmrXr18/dOjQAc2aNYODg4PSe5s2bcKsWbMwduxYREdHw9LSEg0aNMBnn332t/OUB5fqHZCdkYRbp5cjK+0NKth6om3gWkXJS3pyDESiwu+sebmZuHxoBjJS4qAl0YOZlTOa95wHl+odFG2qNxuE/NwsBO+fitzsVFg7+qJt4Fq1Wj/40q00mBhpoe9nljA30UL4qxxMX/EKKWkFI6KWFhLI3pv0Y2GqjaWTnBQ/d21tga6tLXA/NBM/LClYBtXUWAujv7CFhYkWMrJleBmdg2krXimtXqMOLt5MhamxFvp3toK5iTZeROVgyrJIJP//2lh9eG3MJFgxpfCWffe2luje1hL3n2Zg4sKCVaRMTbQQ9GUlWJhqIyNLhohX2ZiyNFJp9Rp1cPNJLowMROjUVB8m/38A0/I9aUjLLLggFiZipclizXx1IdEW4auuxkrHOXwpC0cuFaw2dOJaNnR0ROjfzhAGeiI8f5WP5XvS1K4O3rS2Nxqe2ab4udrCgmdzRG3dh3sDJ0LX1gr6/+/EA0BWxCvc6DQU1RZNhNMIf2S/isX9oT/gzanCZ0bE/HoMOlYWqDp1ZMEDmEIe4/png5D7wYRVVedVtwMy0xJx4eByZKQmwNreE31GrVeUyqQkKn8O5+Zk4tiO6UhLioW2RA+Wti7oPHABvOoWfg57+LZGh/7TEHxsLU7unoUK1s74/OvlcHCrU+7n92/4Na6P5JQ0rNt9AInJKXBzdsDiH4JgYVawNG/cm7cQiwrvzFlbVsCSyWOxfNMu+AdNhqWFOXp+2hr9u3Qo9TGpdIor6/3wIZr/RFpaGgYMGIB169bB0rLkcrHSEMnlqjQ/lzTJ/N/U6ymJ5SX4tOots6kqpOrWsytHdlVKX7qmaTpNbvjXjTRU4pmnQkdQWR0sLv91Iw1VwbvRXzcqI/uvl92/A13rle4pcLm5uTAwMMDevXvRpUsXxfaAgAAkJyfj4MGDSu3fLTry/lzBd6VPYrEYT58+RZUqyvM3SsJSGSIiIiKiUtLR0UHt2rVx5swZxTaZTIYzZ86gYcOiAwUeHh6Ksuh3r06dOilWNLS3ty/172apjJrx8vIq8YFOa9asUZpYSkRERPRfIleRByUFBQUhICAAderUQb169bB06VJkZGQoVpnx9/eHnZ0d5syZAz09PXh7eyvtb2ZmBgBFtv8VdtzVzNGjR5GXl1fsex9OkiAiIiL6L5GpSIF3r169kJCQgClTpiA2NhY1a9bE8ePHFX2xyMhIiMUfv7CFHXc14+joKHQEIiIiIo03fPhwDB8+vNj3zp8//6f7bt68+R/9TnbciYiIiEgtaPqSKpycSkRERESkBjjiTkRERERqgSPuRERERESk8jjiTkRERERqQSZXjeUghcIRdyIiIiIiNcARdyIiIiJSC5pe486OOxERERGpBU3vuLNUhoiIiIhIDXDEnYiIiIjUgowj7kREREREpOo44k5EREREakHO5SCJiIiIiEjVccSdiIiIiNQCV5UhIiIiIiKVxxF3IiIiIlILmr6qDDvuRERERKQWWCpDREREREQqjyPuRERERKQWOOJOREREREQqjyPuRERERKQWNH1yKkfciYiIiIjUAEfciYiIiEgtaHqNOzvuJBgtLZHQEVRS6ttkoSOoLENTY6EjqCyRiH+fSpJ45qnQEVSWRSt3oSOorOyHe4WOQFQEO+5EREREpBZkMqETCIsddyIiIiJSC5peKsPJqUREREREaoAj7kRERESkFjjiTkREREREKo8j7kRERESkFvgAJiIiIiIiUnkccSciIiIitSAv0yJ31X8eBkfciYiIiIjUAEfciYiIiEgtaPqqMuy4ExEREZFa0PQnp7JUhoiIiIhIDXDEnYiIiIjUgqaXynDEnYiIiIhIDXDEnYiIiIjUAh/AREREREREKo8j7kRERESkFljjTkREREREKo8j7kRERESkFuRlWuQuKsNjfxwccSciIiIiUgMccSciIiIitaDpq8qw405EREREaoGTU4mIiIiISOVxxJ2IiIiI1IJMw2tlOOJORERERKQGOOJORERERGqBNe5/Q/PmzTF69OgS33dycsLSpUv/ZaS/JhKJcODAgY9yrPPnz0MkEiE5OfmjHE9o5fX/4H1/9eeCiIiIiP49jrjTf9LDyzsQ8scGZKW9gYWtBxp3/gEV7asX2zb8wUncObsGqW8jIZPmw9TSET7NAlHVt7OijVwux61TK/D4+q/IzUqFjZMvmnSdClNLp3I6o4+jS1tr9OpoCwszCcJeZmL5xgg8Ccsosf0nDSzwZa/KsLHSxavYbKzdEYlrd1IU75ubamNIPwfUqW4KI0Mt3HuchuUbIxAdm1Mep/NRfdrcHN3aVoC5qTbCo3KwZlcMQiOyi23rUEkX/TpZwdVRD9aWOli7OxaHziQqtWn/iTk6NDeHdQUJACDydQ52HXmDWw/Sy/xcPrZPaumiTX1dmBiK8Speij2nMxERIy22bZMaOqjvpYNKVloAgMhYKQ7+kVWkfccmemhSQxf6uiKERedj18lMxCfJyvxcPrab53bgyokNSE9JgLW9B9r2mQw75+I/a57cPongo6uRGF/wWWNR0RH12wSiesMuSu3exIThzG8LEBl6AzKpFJa2VfD51ytgWqFSOZzRv2fRpA5cxg6Eqa839CpVxM3uwxB36Myf79OsHqot/A5G1dyQHRWD53NW4dXW/UptHL/uC5eggdC1sULqvSd4OHomUm7cL8tTKTMHfj+GPfsOITEpGVWcHTFi6EB4VnUrtu3x0+cwf9lKpW0SiQQn9u1S2vYy6hXWbt6Oew8eQSqVwtG+MqZN/BbWFa3K7DyEwBF3ov+YsJCjuHJkLmq3+gbdRu5DBVt3HN0wCFnpb4ttr6tvilotv0LnYbvx+ZiDqFqnGy78+j2inl5UtAm5sB4Pgrehaddp6DL8F2jr6OPohkHIz1OfDmqLhhb42t8BW/a+wpAJDxD2MhPzJ3nAzKT47+9eVY0weZQrjp5NwOAJ93HpRhJmjqsKJ3t9RZuZ46rCtqIuflgQiiHjHyAuIQcLJ3tCT1e9Plqa1jHBoJ7W2HU4AaNmvkD4q2zMGO0IU2OtYtvr6ogQ+yYXW/bFIzE5r9g2b5PysOW3eIyeFY7RP4Yj5EkGfvjGHg6VdMvyVD662h4SfN5SH0eCszF7cypexUsxoqcRjA2Kf8JgVXtt3HychyW70jF/WxqS0mQY2dMIZkaF7dvU10WL2rrYeSIT87alITdPjhE9jaBd/OVWWQ9vHMWpX+agacdvMGjyflhX9sCupQORkVr8Z42eoSkad/gagRP3YPDUQ6jRuBsOb/4eYQ8KP2sS4yOxZV5fWNq4YMC32zB46iE0/WwYtCXq8+dGy9AAqfee4sHI6aVqr+9UGXUPrcHb89dwqU5nhK/YAp81s2DZuomijW2P9vBcMBHPZq3EpXpdkXbvCer/vgE6VhZldRpl5tzFYKxavwX+fXpgzdL5qOLshAlTZiEpOaXEfQwNDLB36zrFa9eGVUrvR8fEYtSEH+BQ2Q6LZ0/DuhWL0L/359DR0Snr06Fy9rf/dc3Pz8fw4cNhamoKS0tLTJ48GfISvv5ERkaic+fOMDIygomJCXr27Im4uDilNqtWrUKVKlWgo6MDd3d3bNu2Ten9Z8+eoVmzZtDT00O1atVw6tSpv5X38uXLqFmzJvT09FCnTh0cOHAAIpEId+/eLbb9tGnTULNmTaVtS5cuhZOTk9K2jRs3wsvLC7q6urC1tcXw4cNLfd4hISFo0aIFjI2NYWJigtq1a+PmzZuK9y9duoSmTZtCX18f9vb2GDlyJDIySh4V/TPJyckYNGgQrKysYGJigpYtWyIkJAQAEBoaCpFIhCdPnijts2TJElSpUkXx84MHD9C+fXsYGRnB2toaAwYMwJs3b/5RnvJw7+JmeNTrAfe63WFu7YqmXadDW6KHpzd+K7Z9pSr14ezdGubWVWBSwQE+TfxhYeOO2IjbAApG2+9f2opaLb+Ck1crVLB1R4ue85CZGo+Ih6fL89T+lR6f2eL3M/E4fv4NXkZnYfG6cGTnytC+RfGjMd072OD63WTsORyDyOhsbNrzCs9eZKJrO2sAQGVbPXhVNcbS9RF4GpaBqJhsLFkfAV0dMVo2rlCep/avdWldAScuJuP05RRExeRi5fYY5OTK0LqxWbHtn0VkY9PeePxxIxV5+cV//l2/l46bD9LxOj4Xr+Nyse1AArJzZHB30S+2varyq6uH4JAcXLmfi5i3Muw8kYm8PKCRT/Edgo1HMnHhTg5exUsRlyjDtmOZEIlEcHeUKNq0qqOHY1eyEfI8D9EJUmw6kgEzIzFqVpUUe0xVde3UJtRq2hM1G3eHVSVXdOg/HRIdPdwNLv6zxsm9Pjx8W8PStgosKjqgnl8ArCu7I+r5LUWb8weWoIpPM7T6fDxsHKrBoqIDqtZsBUMT9fk7lXDiD4ROXYq4g6X7fHQc0htZ4a/wePw8pD95gZc/70DsbyfgPOoLRRvn0YGI2vALXm3Zh/THYbg/bCqkmdmw/6J7GZ1F2fn1wGF0aOuH9n4t4eRgjzHDhkBXVxfHTp0teScRYGFu/t7LTOntjdt2ol5tXwwNHAC3Ki6ws7VB4/p1YW5mWrYnIwCZXF5mL3XwtzvuW7Zsgba2Nq5fv45ly5Zh8eLFWL9+fZF2MpkMnTt3RmJiIi5cuIBTp07hxYsX6NWrl6LN/v37MWrUKIwdOxYPHjzA0KFDERgYiHPnzimO0a1bN+jo6ODatWtYvXo1JkyYUOqsqamp6NixI3x8fHD79m3MnDnzb+1fklWrVuGbb77BkCFDcP/+fRw6dAiurq6lPu9+/fqhcuXKuHHjBm7duoXvvvsOEknBP1hhYWFo164dunfvjnv37mHPnj24dOmS0heDv6NHjx6Ij4/HsWPHcOvWLfj6+qJVq1ZITExE1apVUadOHezYsUNpnx07dqBv374ACjr+LVu2RK1atXDz5k0cP34ccXFx6Nmz5z/KU9ak+bl4E/0Qld0aKbaJxGLYuTZEXOTdv9xfLpcj+vkVpCSEw9a5DgAgLfEVstISYPfeMXX0jVHRvjriS3FMVaCtJUJVF0Pcup+q2CaXA7fvp8CrqnGx+1SraqTUHgBuhCTDy80IACDRLhhBzc0rLG+Qy4G8PBl8PIo/pirS1gJcHfVw93Hhl2O5HLj7OAMeVQw+yu8Qi4BmdU2gpyPCk7DMj3LM8qAlBhxstPD4Zb5imxzA44g8uNiVrtJSR1JwnMzsgn8ULU3FMDUS43FE4TGzc4Hw1/lwqaQ+1ZvS/FzEvHwIZ0/lzxonz0aIDrvzl/vL5XKEP76Ct7HhcKhat2CbTIbn986jgrUTdi4ZiMVBDbFxdg88vaM+AwT/hFmDmnhz9orStoRTl2DeoCYAQCSRwNTXC2/OXC5sIJfjzdnLMGtQqxyT/nt5eXkIff4CtWsUllOJxWLUrumDR0+flrhfVlY2en/5FXoFDsUPs+Yi/GWU4j2ZTIarN2/D3s4W46fMRLf+X2LY2O9w6cr1Mj0XochlZfdSB3/7U9Le3h5LliwpGEFxd8f9+/exZMkSDB48WKndmTNncP/+fYSHh8Pe3h4AsHXrVnh5eeHGjRuoW7cuFi5ciC+++ALDhg0DAAQFBeHq1atYuHAhWrRogdOnT+PJkyc4ceIEKlUqqO2bPXs22rdvX6qsO3fuhEgkwrp16xQj9tHR0UWy/l2zZs3C2LFjMWrUKMW2unXrlvq8IyMjMW7cOHh4eAAA3NwK69rmzJmDfv36KSZ7urm5Yfny5fjkk0+watUq6OnplTrnpUuXcP36dcTHx0NXt+A268KFC3HgwAHs3bsXQ4YMQb9+/fDTTz9h5syZAApG4W/duoXt27cDAH766SfUqlULs2fPVhx348aNsLe3R2hoKKpWrVqqLDk5OcjJUS4ryc/T+ei3f7MzkyCXSaFvpDw6pW9sieSE8BL3y81Kw/bZn0CanwuxWIzGXaaictXGAIDMtAQAgMGHxzSyRGaa6t55eJ+piTa0tERI+qCsIyk5Dw6Vih8BtjCTICnlg/YpeTA3KxhpjXydjdiEHAzua49Fa8ORnS3D55/ZoKKlLiqYqc/IqYlRwbVJTs1X2p6cmo/KNv/uz6ejnS4WfucMHYkIWTky/PjzK0TF5P6rY5YnIwMRtMQipGYo/4uWlimHTYXS1bV0+0QfKekyPI4o+LNk8v+SmeKOaWKoPiVWmekFnzUfjoQbmVTA29gXJe6XnZmGZeObQZqfC5FIjPb9psKlWsFnTUbaW+TmZOLysXVo3mU0Wnb/FmEPL+LXVcMxYOxWOLrXK9NzEoqutSVy4pQ/S3Pi3kBiagyxni4k5qYQa2sjJ/7tB23ewtDdpTyj/mspqWmQyWQwN1ceCTc3M0Pkq+hi97GvXAnjRw2Di5MjMjIysWf/IYwcPwkbVy6BlWUFJKekICsrG7v2HkBg/94Y8kV/XL91F1PnLMDiH6ehho9XeZwalZO//SnZoEEDiESFtYoNGzbEs2fPIJUqTzx6/Pgx7O3tFZ1XAKhWrRrMzMzw+PFjRZvGjRsr7de4cWOl9+3t7RWd9ne/r7SePn2K6tWrK3V269X7dx988fHxeP36NVq1alXs+6U576CgIAwaNAh+fn6YO3cuwsLCFG1DQkKwefNmGBkZKV5t27aFTCZDeHjJHc/ihISEID09HRUqVFA6Xnh4uOJ39u7dGxEREbh69SqAgtF2X19fxZeKkJAQnDt3Tmn/d++9n/uvzJkzB6ampkqvM7/N+VvnU5YkuoboPmo/uo74FXXbjsbVI3PxOuya0LFUmlQqx9SFoahsq4fDm+rg+Pa6qOVlgqu3k6Hhz8dQiI7NwcgZYQiaHY5j55Mw5stKsLfVnJrTtvV1UcdTB6v3ZyC/+LmsGkdXzxCDpxzAl9/vRYuuY3Dql7mIeFrwWSP//5Bf1ZqtUL/1F7Bx8ETj9kPgVr05bl3YLWRsEpCXhzvatGwOVxdn1PDxwozvx8HU1ASHj58EUPhAokb166JHl45wdXFG3x5d0aBubRz6f5v/ErlcXmYvdaA+9yXLiVgsLvI/Ly+vcNRRX//f16dOmzYNffv2xe+//45jx45h6tSp2L17N7p27Yr09HQMHToUI0eOLLKfg4PD3/o96enpsLW1xfnz54u8Z2ZmBgCwsbFBy5YtsXPnTjRo0AA7d+7E119/rXSMjh07Yt68eUWOYWtrW+osEydORFBQkNK2VSc+fgdGz8AcIrFWkYmoWWlvYGBsWeJ+IrEYppaOAADLSp5Iin+Bu+fWolKV+jAwLqgBz0x/CwOTioXHTH+DCpU8P/o5lIWU1HxIpXKYfzASbm4mKXFyZWJyHsxNP2hvKkFScuGIcWh4JgaPfwBDfS1oa4uQkpaPn3/0wtMX/2xOhhBS0wuuzYeTdM1MtJH0wSj835UvBWIS8gDkISwyG25OeujUqgJWbo/5V8ctL+mZckhl70bCC3vexgZFR+E/1LqeLto20MPSPemITijcNzW94PPVxFCM1AzlY76KV5/evYFRwWfNhxNR01Pfwsjkzz9rLCoWfNbYOHjiTUwYLh9dCyf3+jAwModYSxuWtlWU9rG0qaJUB/9fkxP3BrrWytdM19oSeSlpkGXnIPdNEmT5+dCtWOGDNhWQE6sedz3fMTUxhlgsRlKS8kTUpOTkInXrJdHW1oarixOiY2IVx9TS0oKjQ2Wldo72drj/6ElxhyA19rdH3K9dUx6FvHr1Ktzc3KClpXzb1NPTE1FRUYiKKqzDevToEZKTk1GtWjVFm+DgYKX9goODld6PiopCTEzhP3LvRoZL410pz/slGjdu3PjTfaysrBAbG6vUeX9/IquxsTGcnJxw5kzxS1uV5rwBoGrVqhgzZgxOnjyJbt26YdOmTQAAX19fPHr0CK6urkVef3d2uK+vL2JjYwv+kn9wLEvLwg/Jfv36Yc+ePbhy5QpevHiB3r17Kx3j4cOHcHJyKnIMQ0PDUmfR1dWFiYmJ0qssVknQ0taBpZ0Xop8X1kvKZTK8fn4V1g41S30cuVwGqbSgg2psURn6xlZ4/d4xc7PTER91DxX/xjGFlC+VI/RFBny9TRTbRCLA19sUD0PTit3nUWg6fH1MlLbVrm6Kh8+KLmeYkSVFSlo+7Gx0UbWKIYJvJH3cEyhD+VLg+cts1PAs/PMsEgE1PA0/ej26SCyCRFL8aiyqSCorWM7Rw7HwS40IgIeTBC+iS/5S06aeLjo00seKX9MRGavcGX+TIkNKukzpmHo6gHMlbbx4/e++KJUnLW0d2Dp6Ifyx8mdNxOMrsKtS+rpruVyG/PxcxTErOfngbZzy3dXEuAiYVrD7OMFVUPLVu6jQsoHSNstWjZB09S4AQJ6Xh5TbD2HZ8r077iIRKrRoiOSrfz2fQJVIJBJUdXXB7XuFy1jKZDLcDrmPau7upTqGVCpFeEQkKpibK47p7lYFUa9eK7WLio6BtdV/aylIAJDJyu6lDv52xz0yMhJBQUF4+vQpdu3ahRUrVijVer/j5+cHHx8f9OvXD7dv38b169fh7++PTz75BHXqFEz6GzduHDZv3oxVq1bh2bNnWLx4Mfbt24dvv/1WcYyqVasiICAAISEhuHjxIiZNmlTqrH379oVMJsOQIUPw+PFjnDhxAgsXLgQApXKf9zVv3hwJCQmYP38+wsLCsHLlShw7dkypzbRp07Bo0SIsX74cz549w+3bt7FixYpSnXdWVhaGDx+O8+fP4+XLlwgODsaNGzfg6VkwcjthwgRcvnwZw4cPx927d/Hs2TMcPHjwH01O9fPzQ8OGDdGlSxecPHkSERERuHz5MiZNmqS0ik23bt2QlpaGr7/+Gi1atFAqTfrmm2+QmJiIPn364MaNGwgLC8OJEycQGBhYpDxKVVRv+gWeXP8Vobf2IykuDBf3T0NeXhaq1ukGADi3ZwKuH1ukaH/n3Bq8Cg1G6tsoJMWF4d4fG/Hs9iG41eoEoODPik8Tf9w+uxoRj84iMeYpzu2ZAAOTinDy8hPkHP+JX4/E4LNWFdH2E0s42OlhzCAn6OmKcfx8QQ3/xG9cMKhPYYnXb0djUa+GKXp8ZgP7SnoI6GEH9yqG2H+8cIWkTxpYoEY1Y9hW1EXjOuZY+IMngm8k4ea9kpc1U0UHTr1F26ZmaNnQFJVtdDCsny30dMQ4HZwMAAj6shICuhbebdHWApztdeFsrwttbREqmGvD2V4XtlaFdygCulaEl5sBKlaQwNFOFwFdK8KnqgHOX1Wva3P6Rjaa1NBFA28d2FQQo09bA+hIgMv3CzqbX3xqgC7NCssR29TXRcem+th6NANvU2QwMRTBxFAE3fdu3py5mY32jfRQ3VWCSpZifPGpIZLTZbgbWvzdH1VVv3Ug7lz8BSGX9+NNTBiO7piGvNws1Ghc8FlzcMN4nN1X+FkTfHQNXjwKRlJCFN7EhOHqyY24f/UQfBp0UrRp0GYgHt04htt//ILE+Je4cXY7Qu+dQ+3mfcr9/P4pLUMDmNTwgEmNgrJKA+fKMKnhAT37gru07rOCUGNT4V3cl2t3w8DZHh5zxsHQ3QWOX/WFbY/2CF+2WdEmfOkm2A/sCbsBXWDk4QLvldOgbaiPqC37yvXcPoYeXTri9xOnceLMebyMeoWlP69DdnYO2vm1AADMWbwc67YULhqxddevuHH7Ll7HxiH0+QvMXrwccQlv0KFNYclur26dcf7SZRw5cQrRr2Ow/8gxXLl+E507tC3386Oy9bdLZfz9/ZGVlYV69epBS0sLo0aNwpAhQ4q0E4lEOHjwIEaMGIFmzZpBLBajXbt2ig4uAHTp0gXLli3DwoULMWrUKDg7O2PTpk1o3rw5gIKylf3792PgwIGoV68enJycsHz5crRr165UWU1MTHD48GF8/fXXqFmzJnx8fDBlyhT07du3xEmenp6e+PnnnzF79mzMnDkT3bt3x7fffou1a9cq2gQEBCA7OxtLlizBt99+C0tLS3z++eelOm8tLS28ffsW/v7+iIuLg6WlJbp164bp0wvWu61evTouXLiASZMmoWnTppDL5ahSpYrSqjSlJRKJcPToUUyaNAmBgYFISEiAjY0NmjVrBmtra0U7Y2NjdOzYEb/88gs2btyodIxKlSohODgYEyZMQJs2bZCTkwNHR0e0a9cOYrFqTiSrUqMDsjIScfPkCmSmJaBCJU90+HKdolQmPfm10he3/NwsXDowAxkpsdCW6MHMyhkte89HlRodFG1qfDII+blZuPjbFORmp8LGqTbaf7lOrdZWPnclEaYmEnzRs3LBA5giMjFh9hMkpRSMcla01FWqTX8Ymo5Zy8PwZe/KGNTHHtEx2Zi8IBQRUVmKNhXMJRjm7wBzMwneJuXh5B9vsG1v8ROsVNnFm6kwNdZC/85WMDfRxouoHExZFonktIIvp1YWEqVrY2EmwYopheUM3dtaontbS9x/moGJC18CAExNtBD0ZSVYmGojI0uGiFfZmLI0Umn1GnVw60kejA2y0LGJnuIBTCt+SUdaZsEFsTARKz0Q5ZNaupBoizC0q5HScY5cysKR4IIHWp28lgNdiQj92hrAQE+E56/yseKXdLWrg/eq2wGZaYm4cHA5MlITYG3viT6j1itKZVISYyASFX5O5uZk4tiO6UhLKvissbR1QeeBC+BVt/CzxsO3NTr0n4bgY2txcvcsVLB2xudfL4eDW51yP79/yrS2NxqeKVzaudrC7wEAUVv34d7AidC1tYK+fWGpZVbEK9zoNBTVFk2E0wh/ZL+Kxf2hP+DNqUuKNjG/HoOOlQWqTh1Z8ACmkMe4/tkg5H4wYVUdtGjaGMkpqdi0YzeSkpJRxcUJ86ZPUpTKxCe8gfi9Pzdp6elY9NNqJCUlw8jICFVdXbBi/iw4ORQOtDRtWB9jhg3Gzl/346e1m2BvVwnTJ34LHy/1KOf8O9SlFr2siOQadgV27NiBwMBApKSkfJR6dfrnFh3QqD96pXZk539zCa+PwdBUfZaZLG+VXUs/50TTNG5oLnQElWXRqnTlGZqo5sO9QkdQWXZVfQT73VO2lN3KXDMCVH/xgP/85NStW7fCxcUFdnZ2CAkJwYQJE9CzZ0922omIiIjUjKavWqaatQ6lNHv2bKVlCt9/vVvrPTY2Fv3794enpyfGjBmDHj16KJW9qJOLFy+WeL5GRkZ/fQAiIiIiNSaXycvspQ7UesT9q6++KvEJnu9G1MePH4/x48eXZ6wyU6dOHaUVboiIiIhIc6h1x93CwgIWFhZCxyg3+vr6cHV1FToGERERkSA0a2ZmUWpdKkNEREREpCnUesSdiIiIiDSHTE1q0csKR9yJiIiIiNQAR9yJiIiISC1o2OOHiuCIOxERERGRGuCIOxERERGpBblM6ATCYsediIiIiNSCjKUyRERERESk6jjiTkRERERqgZNTiYiIiIhI5XHEnYiIiIjUAh/AREREREREKo8j7kRERESkFjS8xJ0j7kRERERE6oAj7kRERESkFuQaXuPOjjsRERERqQU+gImIiIiIiFQeR9yJiIiISC1oeqkMR9yJiIiIiNQAR9yJiIiISC1wxJ2IiIiIiFQeR9yJiIiISC1o+IA7R9yJiIiIiNQBR9xJMJ/6RAsdQSXFtPAROoLKSk/NETqCyqpZ01zoCCqrg8VloSOorOyHe4WOoLLuen0udASVZZf3VLDfrUo17itXrsSCBQsQGxuLGjVqYMWKFahXr16xbdetW4etW7fiwYMHAIDatWtj9uzZJbYvCUfciYiIiEgtyOXyMnv9HXv27EFQUBCmTp2K27dvo0aNGmjbti3i4+OLbX/+/Hn06dMH586dw5UrV2Bvb482bdogOvrvDWKy405EREREGi8nJwepqalKr5yc4u/0Ll68GIMHD0ZgYCCqVauG1atXw8DAABs3biy2/Y4dOzBs2DDUrFkTHh4eWL9+PWQyGc6cOfO3MrLjTkRERERqQSaTl9lrzpw5MDU1VXrNmTOnSIbc3FzcunULfn5+im1isRh+fn64cuVKqc4jMzMTeXl5sLCw+Fvnzxp3IiIiItJ4EydORFBQkNI2XV3dIu3evHkDqVQKa2trpe3W1tZ48uRJqX7XhAkTUKlSJaXOf2mw405EREREauHv1qL/Hbq6usV21D+2uXPnYvfu3Th//jz09PT+1r7suBMRERERlZKlpSW0tLQQFxentD0uLg42NjZ/uu/ChQsxd+5cnD59GtWrV//bv5s17kRERESkFuQyeZm9SktHRwe1a9dWmlj6bqJpw4YNS9xv/vz5mDlzJo4fP446der8o/PniDsRERER0d8QFBSEgIAA1KlTB/Xq1cPSpUuRkZGBwMBAAIC/vz/s7OwUk1vnzZuHKVOmYOfOnXByckJsbCwAwMjICEZGRqX+vey4ExEREZFaUJUHMPXq1QsJCQmYMmUKYmNjUbNmTRw/flwxYTUyMhJicWFhy6pVq5Cbm4vPP1d+sNfUqVMxbdq0Uv9edtyJiIiISC3IynBy6t81fPhwDB8+vNj3zp8/r/RzRETER/mdrHEnIiIiIlIDHHEnIiIiIrWgKqUyQuGIOxERERGRGuCIOxERERGphbJ8AJM64Ig7EREREZEa4Ig7EREREakFGWvciYiIiIhI1XHEnYiIiIjUgqavKsOOOxERERGpBU5OJSIiIiIilccRdyIiIiJSC3KZTOgIguKIOxERERGRGuCIOxERERGpBS4HSUREREREKo8j7kRERESkFriqDBERERERqTx23AXWvHlzjB49GgDg5OSEpUuXlnrfzZs3w8zMrExyEREREakauUxeZi91wFIZFXLjxg0YGhqW++8ViUTYv38/unTpUu6/u6z8fvgADvz2C5KSEuHkXAVDvh6Bqu4exbY9c+o4li9ZoLRNIpFg78HjAID8/Hzs2LoRt25cR2xsDAwMDVGjpi/8AwehQgXLMj+Xj6mRlzaa19SGsYEIMW9l2H8pD1HxxS+tVd9TC7XdtWFjUfD9/lWCDMeuldy+ezMJGnpJcDA4Fxfv5ZfZOZSVT2rpok19XZgYivEqXoo9pzMRESMttm2TGjqo76WDSlZaAIDIWCkO/pGl1L5mVQma1dSFg40WjPTFmLUpFa/iiz+eqrv7xw7cOrsBGakJsLLzQIvPJ8PGsXqxbZ+FnMT1k6uR8iYSUmk+zK0c4dsiENXqdVG0uXJ0BZ7e/h1pybHQ0pKgor0XGn82BrZONcrpjD6e346dwY6Dx5CYnAJXJwcEDeyHam4uJbZPy8jEmp2/4cLVW0hNz4CNVQWMCuyDRrVr/ONjqqoDvx/Dnn2HkJiUjCrOjhgxdCA8q7oV2/b46XOYv2yl0jaJRIIT+3YpbXsZ9QprN2/HvQePIJVK4WhfGdMmfgvrilZldh4fk0WTOnAZOxCmvt7Qq1QRN7sPQ9yhM3++T7N6qLbwOxhVc0N2VAyez1mFV1v3K7Vx/LovXIIGQtfGCqn3nuDh6JlIuXG/LE+FBMKOuwqxslKPDx5Vd/HCOWxctxpfDx+Nqh4eOHxgH6ZNnoCf126GmZl5sfsYGBji57WbFT+LRIXv5eRkI+z5M/Ts0x9OLlWQkZ6GdatX4sfpk7F4+aoyPpuPp0YVLXRqLMFvF3IRGS9D0+oSDP5MF/N3ZSE9q2j7KpW0cPeZFBGxuciTAi1rSTDkM10s2JON1AzlkQlvZy04WGshJV0919et7SHB5y31sfNkJiJe56NlHT2M6GmEaetSkZZZdBSmqr02bj7OQ9jpLOTly9G2gR5G9jTCjA2pSE4vaK8rEeH5q3zcepKLAe3L/wv5x/L09lH8sX8OWvWaDhvHGrh9YQv2/TwQX/xwHAbGFYq01zMwRf02X8Pc2gVaWhK8eHgOJ3d+DwPjCnDybAoAMK/ohBY9psC0gj3y87Jx59xm7Pv5SwROPgUDY4vyPsV/7HTwNSzfvBvjhvrDy80Fe46cwpiZi7BrxRxYmJoUaZ+Xl49R0xfA3NQEP477BlYW5ohNeAMjQ4N/fExVde5iMFat34LR3wyBZ1U3/Hbod0yYMgtbVi+HuZlpsfsYGhhgy+pl720RKb0fHROLURN+QPvWrfBF354wMDBARGQUdHR0yvBMPi4tQwOk3nuKqM2/oc7elX/ZXt+pMuoeWoPItbtx1/9bVGjZED5rZiE7JgFvTl0CANj2aA/PBRPx4JupSL4eAueRAaj/+wac92qH3ITEsj6lcqcuI+NlhaUy5SgjIwP+/v4wMjKCra0tFi1apPT+h6Uyixcvho+PDwwNDWFvb49hw4YhPT29yHEPHDgANzc36OnpoW3btoiKilJ6/+DBg/D19YWenh5cXFwwffp05OfnK34nAHTt2hUikUjx81/tJ5fLMW3aNDg4OEBXVxeVKlXCyJEjP8JV+vcO7t+LNu06wK9NOzg4OOHr4aOhq6uL0yePl7iPSASYW1goXmbmhZ0HQ0MjzJi9AE2aNUflyvZw96iGocNGIOx5KBLi48rjlD6KT2po49qjfNx4KkVckhy/XchFXp4cdT2K//6+80wuLj/Mx+u3ciQky/HL+VyIRICbnfLHhomhCF2aSLDzdA6k6tlvh19dPQSH5ODK/VzEvJVh54lM5OUBjXyK7xBsPJKJC3dy8CpeirhEGbYdy4RIJIK7o0TR5trDXBy9nI0nEep39+F9t89tgnejnvBq0B0VbF3h13M6tHX08ODqb8W2t3erD9carVHBpgrMrBzg2zwAVpXc8frFLUUbjzod4ejeCGaW9rC0dUOzrhORm52ON6+fltdpfRS7D59EJ79m+KxlUzjb22H8UH/o6urgyJmLxbY/cvYiUtMzMG/CCFT3cINtRUvU8vKAm5PDPz6mqvr1wGF0aOuH9n4t4eRgjzHDhkBXVxfHTp0teScRYGFu/t7LTOntjdt2ol5tXwwNHAC3Ki6ws7VB4/p1S/wioIoSTvyB0KlLEXfwdKnaOw7pjazwV3g8fh7Sn7zAy593IPa3E3Ae9YWijfPoQERt+AWvtuxD+uMw3B82FdLMbNh/0b2MzkJYMrmszF7qgB33cjRu3DhcuHABBw8exMmTJ3H+/Hncvn27xPZisRjLly/Hw4cPsWXLFpw9exbjx49XapOZmYkff/wRW7duRXBwMJKTk9G7d2/F+xcvXoS/vz9GjRqFR48eYc2aNdi8eTN+/PFHAAXlOQCwadMmxMTEKH7+q/1+++03LFmyBGvWrMGzZ89w4MAB+Pj4fNTr9U/k5eUh7HkoatT0VWwTi8WoUdMXT588KnG/rKwsDArogy/9e+PHGZMR+TLiT39PRkYGRCIRDI2MPlb0MqUlBuysxAh9VfjBJAfwLFoGR+vSfQzoaBccJzOncLRDBKBvKx2cv5uPuCT1HAXREgMONlp4/LKwgy0H8DgiDy52pbspqSP5/7XJVs9rUBJpfi7ioh7Cwb2RYptILIaDeyPEhN/5y/3lcjkin15BYnw47KrULfF33L+8B7r6xrCyc/9o2ctaXl4+noZFoE51L8U2sViMutWr4UHo82L3uXTjDrzdq2Dhuu349MtR6Df6B2z57Qik///G+0+OqYry8vIQ+vwFatcoLKcSi8WoXdMHj56W/OUsKysbvb/8Cr0Ch+KHWXMR/rJwEEomk+Hqzduwt7PF+Ckz0a3/lxg29jtcunK9TM9FaGYNauLN2StK2xJOXYJ5g5oAAJFEAlNfL7w5c7mwgVyON2cvw6xBrXJMSuWFpTLlJD09HRs2bMD27dvRqlUrAMCWLVtQuXLlEvd5N2kVKBgZnzVrFr766iv8/PPPiu15eXn46aefUL9+fcUxPT09cf36ddSrVw/Tp0/Hd999h4CAAACAi4sLZs6cifHjx2Pq1KmK8hwzMzPY2NgojvtX+0VGRsLGxgZ+fn6QSCRwcHBAvXr1SjyXnJwc5OTkKG3LzcmBjq5uaS5fqaWmpkAmk8HMXLkkxszMHK8+uBPxjl1le4wYMw5OTi7IzMzAgd9+wYSxI7Fi9QZYWhYtX8rNzcXWTevQ9JOWMDBQjxIIQz0RtMQipGcpdyzTMuWoaFa6jvunDSRIyZDj2Xud/xa1tCGVAZfuq++ospFBwbVJzVAebUnLlMOmglapjtHtE32kpMvwOCKvLCIKJisjCXKZtEhJjIFxBSTFvShxv5ysNKyb3AzS/FyIxGK07DEVjh6Nldq8eHAORzcHIS8vC4YmVug2bCP0jdSnTCY5LQ1SmQwWZsrlKxampngZHVvsPtFxCYh98BhtmjbEoklj8Co2DgvXbkO+NB8De3b5R8dURSmpaZDJZDA3Vx4JNzczQ+Sr6GL3sa9cCeNHDYOLkyMyMjKxZ/8hjBw/CRtXLoGVZQUkp6QgKysbu/YeQGD/3hjyRX9cv3UXU+cswOIfp6GGj1exx1V3utaWyIl7o7QtJ+4NJKbGEOvpQmJuCrG2NnLi337Q5i0M3dVvXkRpsFSGykVYWBhyc3MVHWwAsLCwgLt7ySNMp0+fRqtWrWBnZwdjY2MMGDAAb9++RWZmpqKNtrY26tYtHMny8PCAmZkZHj9+DAAICQnBjBkzYGRkpHgNHjwYMTExSsf50F/t16NHD2RlZcHFxQWDBw/G/v37FWU0xZkzZw5MTU2VXmtX/3V9X3nw8PRCy1Zt4FLFFd4+NfDdD9NhYmqKE0ePFGmbn5+P+XNmQC6X4+vhowRIK4wWtbRR01UbW47nIP//8yvtLEVoUl2CPWdzhQ0nsLb1dVHHUwer92coro2m09E1RP8JB9Dn271o/NkY/HFgLqKeXVNqY+9WH/0nHEDv0bvh5NkUv28ajcy0tyUc8b9BLpfD3NQEE776Ah5VnODXuD4CunfEgRPnhY4mOC8Pd7Rp2RyuLs6o4eOFGd+Pg6mpCQ4fPwmg8GmZjerXRY8uHeHq4oy+PbqiQd3aOPT/NkSagCPuKioiIgKfffYZvv76a/z444+wsLDApUuXMHDgQOTm5sLAwOCvD4KCkf7p06ejW7duRd7T09P7x/vZ29vj6dOnOH36NE6dOoVhw4ZhwYIFuHDhAiQSSZF9Jk6ciKCgIOVzfJVQqnP4O0xMTCEWi5GclKS0PTk5CeYWpRvN09bWhksVV8TEKI8Mveu0J8THYeachWoz2g4AGdlySGVyGOkrT/YyNhAhtZjJl+/7pIY2WtaSYM3hHMQkFrZ1qaQFI31g0oDCP0daYhE6NpSgqY82Zu/I/rgnUUbSMwuujYmhGEBhz9vYoOgo/Ida19NF2wZ6WLonHdEJ/71eu76hOURirSId6sy0tzAwLnlFJZFYDDMrRwBAxcqeSIwNw41Ta2HvVjhwIdE1gJmVI8ysHGHrXBObZrbBgyt7Ua/N0LI5mY/MzNgYWmIxEpNTlbYnpqQUGTF/p4K5GbS1tKClVThm5lTZFm+TU5CXl/+PjqmKTE2MIRaLkZSUorQ9KTm5SN16SbS1teHq4oTomFjFMbW0tODooHyX2tHeDvcfPfkouVVRTtwb6For/13TtbZEXkoaZNk5yH2TBFl+PnQrVvigTQXkxCqP1P9XcMSdykWVKlUgkUhw7VrhqFNSUhJCQ0OLbX/r1i3IZDIsWrQIDRo0QNWqVfH69esi7fLz83Hz5k3Fz0+fPkVycjI8PT0BAL6+vnj69ClcXV2LvMTigv/9EokEUqlyp6M0++nr66Njx45Yvnw5zp8/jytXruD+/eKXn9LV1YWJiYnS62OXybw7lyquVXEvpLD+ViaT4d7dO3D3qFaqY0ilUryMCIf5exNU33XaY15HY8bsBTAxUZ/JUAAglQHRCTK4VS78Ky8C4Gonxsu4kjunzWtqw6+2BOt+z8GrBOV2t57mY/Ev2Vjya+ErJV2G83fzse73nBKOqHqksoLlHD0cC8cxRAA8nCR4EV3yXaQ29XTRoZE+VvyajsjY/16nHQC0tHVgbe+FqNDCGlu5TIaop1dg61z6+lm5XAZp/p/fmZHL/rqNKpFItOFexQm37hfOnZHJZLh57zG8q7oWu091D1e8io2DTFb4dynydSwszc0gkWj/o2OqIolEgqquLrh9r/DfA5lMhtsh91HtT+4yv08qlSI8IhIV/l/2KJFI4O5WBVGvlP8djIqOgfV/eEW25Kt3UaFlA6Vtlq0aIenqXQCAPC8PKbcfwrJlw8IGIhEqtGiI5Kt/PQ+F1A9H3MuJkZERBg4ciHHjxqFChQqoWLEiJk2apOgEf8jV1RV5eXlYsWIFOnbsiODgYKxevbpIO4lEghEjRmD58uXQ1tbG8OHD0aBBA0W9+ZQpU/DZZ5/BwcEBn3/+OcRiMUJCQvDgwQPMmjULQEH9/JkzZ9C4cWPo6urC3Nz8L/fbvHkzpFIp6tevDwMDA2zfvh36+vpwdHQsu4tYSp27fo5li+fB1a0q3Kp64PDB35Cdkw2/1m0BAEsWzkWFCpbwDxwEANi9cyvcParB1rYSMjLSsf+3X5AQH4fW7ToAKOi0z5s9HWHPn2HytB8hk8qQlFiwxJaRsXGxdxhU0YWQfPRuqYNXCTJExsnQtLo2dCQi3HhS0Dnt3VIHKRlyHLtWUKfdoqY22taTYMfpXCSlymCsX3CcnDwgNx/IzFGeqAoUdILTsgpWoVEnp29k44tPDfEyVoqImILlIHUkwOX7BR3JLz41QHKaDAf+KLiL0Ka+Ljo20cfGwxl4myKDiWHBnYycXDly/l/mbqAngoWJGGZGBe9Z/389/NQMWZHlNFWZb4tAnNg+ARXtvWHjWB13zm9BXm4WvOoX3I07vm08jEyt0aTTWADA9ZNrYO3gDVNLB0jzcxHx6AIe3ziElj2nAQDycjJx7eRqVPFuCUNTK2SlJyHk4g6kp8TBrVY7oU7zH+ndsQ1mrVgPjypOqObmgj1HTiI7JweftWwCAJixfB2sLMzwdf8eAICubVtg77EzWLpxJz7v4IeomDhs3fc7enTwK/Ux1UWPLh0xd8lPcHetAo+qrvjt4O/Izs5BO78WAIA5i5fDskIFDA7oBwDYuutXeLq7wa6SLdLTM7Bn/0HEJbxBhzatFMfs1a0zZs5fgurenqjl443rt+/iyvWbWDJ7uiDn+E9oGRrA0LVwFSED58owqeGB3MQUZEfFwH1WEPTsrBESOAEA8HLtbjgO6wePOeMQtfk3WLZoANse7XGjU+GdqfClm1Bj4zwk33qAlBv34DQyANqG+ojasq/cz688yOXq8/lZFthxL0cLFixAeno6OnbsCGNjY4wdOxYpKSnFtq1RowYWL16MefPmYeLEiWjWrBnmzJkDf39/pXYGBgaYMGEC+vbti+joaDRt2hQbNmxQvN+2bVscOXIEM2bMwLx58yCRSODh4YFBgwYp2ixatAhBQUFYt24d7OzsEBER8Zf7mZmZYe7cuQgKCoJUKoWPjw8OHz6MChWKrutc3pp+0gKpqSnYuW0zkpKS4OxSBVNnzFUs8fgmIR5icWHJSHp6OlYuW4SkpCQYGRuhimtVzFu0HA4OTgCAt2/f4PrVghn7o4cPUfpds+Yugk/1muVyXv9WSJgURvp5aFtXAmMDEV6/kWH9kRzFGu7mRiK8/3nY0Esb2loiBLRVvjNy8kYeTt78b03CvPUkD8YGWejYRE/xAKYVv6Qr1nC3MBErXZtPaulCoi3C0K7KqwoduZSFI8EFnfsarhIEfFpYTjW4s1GRNurA3bcDstITceXocmSmJsCqsie6fr0ehiYFt+/TkmIgEhUOQOTlZuLsr9ORlhwLbYkeLCq6oJ3/Arj7FnwRFom1kBT3Aoev70d2ehL0DM1g7eCDnqN2wNK2+IfzqCq/xvWRnJKGdbsPIDE5BW7ODlj8QxAs/r88YdybtxC/91AIa8sKWDJ5LJZv2gX/oMmwtDBHz09bo3+XDqU+prpo0bQxklNSsWnHbiQlJaOKixPmTZ+kKJWJT3gD8Xt/btLS07Hop9VISkqGkZERqrq6YMX8WXBysFe0adqwPsYMG4ydv+7HT2s3wd6uEqZP/BY+Xp7lfXr/mGltbzQ8s03xc7WF3wMAorbuw72BE6FrawV9e1vF+1kRr3Cj01BUWzQRTiP8kf0qFveH/qBYwx0AYn49Bh0rC1SdOrLgAUwhj3H9s0HIjf9vzxnRVCK5pn91IcE8CXsldASVtP6k+qysUd7SU9WnBKe81axZ/MPFCOhhd/mvG2mobB1joSOorLtenwsdQWV9mifcMxc6Dn1cZsc+vEb1vwRyxJ2IiIiI1AInpxIRERERkcrjiDsRERERqQW5/M+X6f2v44g7EREREZEa4Ig7EREREakF1rgTEREREZHK44g7EREREakFjrgTEREREZHK44g7EREREakFmYavKsOOOxERERGpBZbKEBERERGRyuOIOxERERGpBblMs0tlOOJORERERKQGOOJORERERGqBNe5ERERERKTyOOJORERERGpBruHLQXLEnYiIiIhIDXDEnYiIiIjUgkzDa9zZcSciIiIitcDlIImIiIiISOVxxJ2IiIiI1AKXgyQiIiIiIpXHEXciIiIiUgtcDpKIiIiIiFQeR9yJiIiISC2wxp2IiIiIiFQeR9yJiIiISC1o+jruIrlcrtn3HEjj5eTkYM6cOZg4cSJ0dXWFjqNSeG1KxmtTMl6b4vG6lIzXpmS8NvQ+dtxJ46WmpsLU1BQpKSkwMTEROo5K4bUpGa9NyXhtisfrUjJem5Lx2tD7WONORERERKQG2HEnIiIiIlID7LgTEREREakBdtxJ4+nq6mLq1Kmc9FMMXpuS8dqUjNemeLwuJeO1KRmvDb2Pk1OJiIiIiNQAR9yJiIiIiNQAO+5ERERERGqAHXciIiIiIjXAjjsRERERkRpgx52IiIiISA2w405EREREpAbYcSeN9vz5c5w4cQJZWVkAAK6OSkREqkYqleLu3btISkoSOgoJjB130khv376Fn58fqlatig4dOiAmJgYAMHDgQIwdO1bgdKQOsrOzhY5ARP9Ro0ePxoYNGwAUdNo/+eQT+Pr6wt7eHufPnxc2HAlKW+gAREIYM2YMtLW1ERkZCU9PT8X2Xr16ISgoCIsWLRIwnbDi4uLw7bff4syZM4iPjy9yF0IqlQqUTHgymQw//vgjVq9ejbi4OISGhsLFxQWTJ0+Gk5MTBg4cKHTEcmVhYYHQ0FBYWlrC3NwcIpGoxLaJiYnlmEx4f3U93qdp1yYoKKjUbRcvXlyGSVTX3r170b9/fwDA4cOHER4ejidPnmDbtm2YNGkSgoODBU5IQmHHnTTSyZMnceLECVSuXFlpu5ubG16+fClQKtXwxRdfIDIyEpMnT4atrW2pOx+aYNasWdiyZQvmz5+PwYMHK7Z7e3tj6dKlGtdxX7JkCYyNjQEAS5cuFTaMiuH1KNmdO3dK1U6TP3vevHkDGxsbAMDRo0fRo0cPVK1aFV9++SWWLVsmcDoSEjvupJEyMjJgYGBQZHtiYiJ0dXUFSKQ6Ll26hIsXL6JmzZpCR1E5W7duxdq1a9GqVSt89dVXiu01atTAkydPBEwmjICAgGL/m3g9/sy5c+eEjqDyrK2t8ejRI9ja2uL48eNYtWoVACAzMxNaWloCpyMhscadNFLTpk2xdetWxc8ikQgymQzz589HixYtBEwmPHt7e07SLUF0dDRcXV2LbJfJZMjLyxMgkeqJj4/HgwcPcO/ePaWXpgsLC8MPP/yAPn36ID4+HgBw7NgxPHz4UOBkqoELBSgLDAxEz5494e3tDZFIBD8/PwDAtWvX4OHhIXA6EhJH3EkjzZ8/H61atcLNmzeRm5uL8ePH4+HDh0hMTNT42sGlS5fiu+++w5o1a+Dk5CR0HJVSrVo1XLx4EY6Ojkrb9+7di1q1agmUSjXcunULAQEBePz4cZFOl0gk0ui5ERcuXED79u3RuHFj/PHHH/jxxx9RsWJFhISEYMOGDdi7d6/QEQXz9u1b9OzZE+fOnYNIJMKzZ8/g4uKCgQMHwtzcXGPnG02bNg3e3t6IiopCjx49FHeCtbS08N133wmcjoQkkmv611rSWCkpKfjpp58QEhKC9PR0+Pr64ptvvoGtra3Q0QRlbm6OzMxM5Ofnw8DAABKJROl9TZtI976DBw8iICAAEydOxIwZMzB9+nQ8ffoUW7duxZEjR9C6dWuhIwqmRo0aqFKlCiZMmABra+si9ckfftnRJA0bNkSPHj0QFBQEY2NjhISEwMXFBdevX0e3bt3w6tUroSMKxt/fH/Hx8Vi/fj08PT0V1+bEiRMICgriHQkUrGClp6cndAxSEey4E5GSLVu2/On7ml67e/HiRcyYMUPpC9+UKVPQpk0boaMJytjYGHfu3Cm2lEjTGRkZ4f79+3B2dlbquEdERMDDw0Ojlxa1sbHBiRMnUKNGDaVr8+LFC1SvXh3p6elCRxSEVCrF7NmzuYIVFcFSGdJIJdXcikQi6OnpwcHBQWMnqWp6x/yvNG3aFKdOnRI6hspp1aoVQkJC2HEvhpmZGWJiYuDs7Ky0/c6dO7CzsxMolWrgQgHF+/HHH7mCFRWLHXfSSDVr1lTcyn930+n9W/sSiQS9evXCmjVrNPIWpVQqxYEDB/D48WMAgJeXFzp16qTxqxlERUVBJBIplhG9fv06du7ciWrVqmHIkCECpxPW+vXrERAQgAcPHsDb27tIiVWnTp0ESia83r17Y8KECfj1118VE+GDg4Px7bffwt/fX+h4gnq3UMDMmTMBcKGAd7iCFZVITqSBDhw4IHd3d5evX79efu/ePfm9e/fk69evl3t6esp3794t3759u7xy5crysWPHCh213D179kzu5uYmNzAwkNeqVUteq1YtuYGBgdzd3V3+/PlzoeMJqkmTJvKtW7fK5XK5PCYmRm5sbCxv2LCh3NLSUj59+nSB0wnr0KFDclNTU7lIJCryEovFQscTVE5OjnzQoEFybW1tuUgkkkskErlYLJb3799fnp+fL3Q8Qd2/f19esWJFebt27eQ6Ojryzz//XO7p6Sm3trbW6M8bPT09eUREhFwul8uNjIzkYWFhcrlcLn/48KHc0NBQyGgkMNa4k0aqV68eZs6cibZt2yptP3HiBCZPnozr16/jwIEDGDt2LMLCwgRKKYwOHTpALpdjx44dsLCwAFCw8kP//v0hFovx+++/C5xQOObm5rh69Src3d2xfPly7NmzB8HBwTh58iS++uorvHjxQuiIgnFycsJnn32GyZMnw9raWug4KikyMhIPHjxAeno6atWqBTc3N6EjqQQuFFBU7dq1MWbMGPTv31+p9n/GjBk4deoULl68KHREEghLZUgj3b9/v9hVLhwdHXH//n0ABeU0MTEx5R1NcBcuXMDVq1cVnXYAqFChAubOnYvGjRsLmEx4eXl5irrb06dPK8o/PDw8NPLPyvvevn2LMWPGsNNejEuXLqFJkyZwcHCAg4OD0HFUjqmpKSZNmiR0DJUyZcoUBAQEIDo6GjKZDPv27VNawYo0Fx/ARBrJw8MDc+fORW5urmJbXl4e5s6dq3i4RXR0tEZ2QnR1dZGWllZke3p6OnR0dARIpDq8vLywevVqXLx4EadOnUK7du0AAK9fv0aFChUETiesbt268YmYJWjZsiWcnZ3x/fff49GjR0LHUSmurq6YNm0anj17JnQUldK5c2ccPnwYp0+fhqGhIaZMmYLHjx/j8OHDGr3sLHE5SNJQly9fRqdOnSAWi1G9enUABaPwUqkUR44cQYMGDbBt2zbExsZi3LhxAqctX/7+/rh9+zY2bNiAevXqASh4Wt/gwYNRu3ZtbN68WdiAAjp//jy6du2K1NRUBAQEYOPGjQCA77//Hk+ePMG+ffsETiicH3/8EUuXLsWnn34KHx+fIpNTR44cKVAy4b158wa7d+/Grl27cOXKFVSvXh39+vVDnz59FBOdNdWSJUuwc+dO3Lp1C7Vr10b//v3Rq1cv2NjYCB2NSCWx404aKy0tDTt27EBoaCgAwN3dHX379oWxsbHAyYSVnJyMgIAAHD58WNH5ys/PR6dOnbB582aYmpoKnFBYUqkUqampMDc3V2yLiIiAgYEBKlasKGAyYX241OH7RCKRRtf/vy88PBw7d+7Erl278OTJEzRr1gxnz54VOpbgQkNDsWPHDuzatQvh4eFo0aIF+vfvr7Gr7ri4uODGjRtF7uQlJyfD19eXf580GDvupNEePXqEyMhIpZIZQLOXrnvn2bNnimXHPD09uT43lZq8mCVWqZBUKsWxY8cwefJk3Lt3D1KpVOhIKuXq1av4+uuvNfraiMVixMbGFhkMiIuLg4ODA3JycgRKRkLj5FTSSC9evEDXrl1x//59iEQiyOVypU6Gpv5j8T43NzeuelGMvXv34pdffin2C9/t27cFSqUaNmzYgCVLlijqld3c3DB69GgMGjRI4GSqITg4GDt27MDevXuRnZ2Nzp07Y86cOULHUhnvnouwZ88epKamokePHkJHKneHDh1S/PeJEyeU7nBKpVKcOXMGTk5OAiQjVcGOO2mkUaNGwdnZGWfOnIGzszOuXbuGxMREjB07FgsXLhQ6XrkLCgrCzJkzYWhoiKCgoD9tu3jx4nJKpXqWL1+OSZMm4YsvvsDBgwcRGBiIsLAw3LhxA998843Q8QQ1ZcoULF68GCNGjEDDhg0BAFeuXMGYMWMQGRmJGTNmCJxQOBMnTsTu3bvx+vVrtG7dGsuWLUPnzp2LfWKopvmwRKZly5aYN28eunXrBiMjI6HjlbsuXboAKLhb9eFTrCUSCZycnLBo0SIBkpGqYKkMaSRLS0ucPXsW1atXh6mpKa5fvw53d3ecPXsWY8eOxZ07d4SOWK5atGiB/fv3w8zM7E+fVigSiTS6HtfDwwNTp05Fnz59lNZWnjJlChITE/HTTz8JHVEwVlZWWL58Ofr06aO0fdeuXRgxYgTevHkjUDLhNW7cGP369UPPnj1haWkpdByVIhaLUbduXfTt2xe9e/fWyJW8iuPs7IwbN27wzwsVwRF30khSqVQxCdXS0hKvX7+Gu7s7HB0d8fTpU4HTlb/3l/Hjkn4li4yMRKNGjQAA+vr6imUzBwwYgAYNGmh0xz0vLw916tQpsr127drIz88XIJHqCA4OFjqCynr69ClL8ooRHh6u+O/s7Gzo6ekJmIZUCddxJ43k7e2NkJAQAED9+vUxf/58BAcHY8aMGXBxcRE4nWpJTU3FgQMHFBNVNZmNjQ0SExMBAA4ODrh69SqAgn9kNf3m5YABA7Bq1aoi29euXYt+/foJkEi1bNu2DY0bN0alSpXw8uVLAMDSpUtx8OBBgZMJy83NDcnJyVi/fj0mTpyo+Pt1+/ZtREdHC5xOODKZDDNnzoSdnR2MjIwUq8hMnjwZGzZsEDgdCYkdd9JIP/zwA2QyGQBgxowZCA8PR9OmTXH06FEsX75c4HTC6tmzp2LkOCsrC3Xq1EHPnj3h4+OD3377TeB0wmrZsqVi8lhgYCDGjBmD1q1bo1evXujatavA6cpfUFCQ4iUSibB+/Xp4e3tj0KBBGDRoEHx8fLBu3TqIxZr9T82qVasQFBSEDh06IDk5WTH53czMDEuXLhU2nMDu3bsHNzc3zJs3DwsXLkRycjIAYN++fZg4caKw4QQ0a9YsbN68GfPnz1d68J23tzfWr18vYDISGmvcif4vMTER5ubmGr+EnY2NDU6cOIEaNWpg586dmDp1KkJCQrBlyxasXbtW4+r/3yeTySCTyaCtXVBluHv3bly+fBlubm4YOnSoxj1Z9s/mQ7xP0+dGVKtWDbNnz0aXLl2U5kY8ePAAzZs31+j6/1atWqF27dqYP3++0rW5fPky+vbti4iICKEjCsLV1RVr1qxBq1atlK7LkydP0LBhQyQlJQkdkQTCGnei/7OwsBA6gkpISUlRXIvjx4+je/fuMDAwwKeffqpxT5H9kFgsVho97t27N3r37i1gImFxPkTphIeHo1atWkW26+rqIiMjQ4BEquPmzZtYu3Ztke12dnaIjY0VIJFqiI6OLvbZGTKZDHl5eQIkIlWh2fcviagIe3t7XLlyBRkZGTh+/DjatGkDAEhKSuIEKQAXL15E//790bBhQ0UN7rZt23Dp0iWBk5GqcnZ2xt27d4tsP378ODw9Pcs/kArR1dVFampqke2hoaGwsrISIJFqqFatGi5evFhk+969e4v9EkiagyPuRKRk9OjR6NevH4yMjODo6IjmzZsDAP744w/4+PgIG05gv/32GwYMGIB+/frhzp07iqcXpqSkYPbs2Th69KjACUkVBQUF4ZtvvkF2djbkcjmuX7+OXbt2Yc6cORpfr9ypUyfMmDEDv/zyC4CCsqrIyEhMmDAB3bt3FzidcKZMmYKAgABER0dDJpNh3759ePr0KbZu3YojR44IHY8ExBp3Iiri5s2biIqKQuvWrRUPQfn9999hZmaGxo0bC5xOOLVq1cKYMWPg7++vVHd6584dtG/fXqNv7dOf27FjB6ZNm4awsDAAQKVKlTB9+nQMHDhQ4GTCSklJweeff46bN28iLS0NlSpVQmxsLBo2bIijR4/C0NBQ6IiCuXjxImbMmIGQkBCkp6fD19cXU6ZMUdwFJc3EjjsRUSkZGBjg0aNHcHJyUuq4v3jxAtWqVUN2drbQEUnFZWZmIj09HRUrVhQ6ikq5dOkS7t27p+ig+vn5CR2JSCWxVIaIlEilUmzevBlnzpxBfHy8YtnMdzR5dRAbGxs8f/4cTk5OStsvXbrE9f+pVAwMDGBgYCB0DJXTpEkTNGnSROgYKufmzZt4/PgxgIK699q1awuciITGjjsRKRk1ahQ2b96MTz/9FN7e3hq/POb7Bg8ejFGjRmHjxo0QiUR4/fo1rly5gm+//RaTJ08WOh6pkFq1apX6787t27fLOI1q+TvPyhg5cmQZJlFdr169Qp8+fRAcHAwzMzMAQHJyMho1aoTdu3ejcuXKwgYkwbBUhoiUWFpaYuvWrejQoYPQUVSOXC7H7NmzMWfOHGRmZgIoWBXj22+/xcyZMwVOR6pk+vTppW47derUMkyiepydnUvVTiQSKZ4YqmnatWuH5ORkbNmyBe7u7gCAp0+fIjAwECYmJjh+/LjACUko7LgTkZJKlSrh/PnzqFq1qtBRVFZubi6eP3+O9PR0VKtWTTGBl+jf2LVrFzp16qTREzKpgL6+Pi5fvlxk6cdbt26hadOmioED0jxcx52IlIwdOxbLli0Dv9OXTEdHB9WqVYOHhwdOnz6tqEEl+jeGDh2KuLg4oWOoJBMTE40afbe3ty/2QUtSqRSVKlUSIBGpCta4E5GSS5cu4dy5czh27Bi8vLwgkUiU3t+3b59AyYTXs2dPNGvWDMOHD0dWVhbq1q2L8PBwyOVy7N69W6PXnaZ/j1+WS6Zp12bBggUYMWIEVq5ciTp16gAomKg6atQoLFy4UOB0JCR23IlIiZmZGbp27Sp0DJX0xx9/YNKkSQCA/fv3QyaTKepQZ82axY47Ef1j5ubmShOaMzIyUL9+fWhrF3TV8vPzoa2tjS+//BJdunQRKCUJjR13IlKyadMmoSOorJSUFFhYWAAoeFx99+7dYWBggE8//RTjxo0TOB0RqbOlS5cKHYHUADvuRFREfn4+zp8/j7CwMPTt2xfGxsZ4/fo1TExMNHoipr29Pa5cuQILCwscP34cu3fvBgAkJSVBT09P4HREpM4CAgKEjkBqgB13IlLy8uVLtGvXDpGRkcjJyUHr1q1hbGyMefPmIScnB6tXrxY6omBGjx6Nfv36wcjICI6OjmjevDmAghIaHx8fYcMR/Ydp8vMksrOzkZubq7TNxMREoDQkNK4qQ0RKRo0ahTp16iApKQn6+vqK7V27dsWZM2cETCa8YcOG4cqVK9i4cSMuXboEsbjgI9TFxQWzZs0SOB2pO0dHxyKTwamApk1OzcjIwPDhw1GxYkUYGhrC3Nxc6UWaiyPuRKTk4sWLuHz5MnR0dJS2Ozk5ITo6WqBUqqNOnTqKVR7e+fTTTwVKQ+rgxo0bkMlkqF+/vtL2a9euQUtLS/Hn6cGDB0LEUwm5ubkIDw9HlSpVFJMx33fs2DHY2dkJkEwY48ePx7lz57Bq1SoMGDAAK1euRHR0NNasWYO5c+cKHY8ExI47ESmRyWSQSqVFtr969QrGxsYCJBJWUFAQZs6cCUNDQwQFBf1p28WLF5dTKlIn33zzDcaPH1+k4x4dHY158+bh2rVrAiUTXmZmJkaMGIEtW7YAAEJDQ+Hi4oIRI0bAzs4O3333HQCgSZMmQsYsd4cPH8bWrVvRvHlzBAYGomnTpnB1dYWjoyN27NiBfv36CR2RBMJSGSJS0qZNG6XVDUQiEdLT0zF16lR06NBBuGACuXPnjuJBKHfu3CnxdffuXWGDksp69OgRfH19i2yvVasWHj16JEAi1TFx4kSEhITg/PnzShO8/fz8sGfPHgGTCSsxMREuLi4ACurZExMTARR8gfnjjz+EjEYC44g7ESlZtGgR2rZti2rVqiE7Oxt9+/bFs2fPYGlpiV27dgkdr9ydO3eu2P8mKi1dXV3ExcUpOmLvxMTEFFsWokkOHDiAPXv2oEGDBkoTUL28vBAWFiZgMmG5uLggPDwcDg4O8PDwwC+//IJ69erh8OHDMDMzEzoeCUizPzGIqIjKlSsjJCQEe/bsQUhICNLT0zFw4ED069dPabIqEZVOmzZtMHHiRBw8eBCmpqYAgOTkZHz//fdo3bq1wOmElZCQgIoVKxbZnpGRodEryQQGBiIkJASffPIJvvvuO3Ts2BE//fQT8vLyWJKn4URyTZuqTUR/6o8//kCjRo2KjATm5+fj8uXLaNasmUDJhNGtW7dSt923b18ZJiF1FR0djWbNmuHt27eoVasWAODu3buwtrbGqVOnYG9vL3BC4TRr1gw9evTAiBEjYGxsjHv37sHZ2RkjRozAs2fPcPz4caEjqoSXL1/i1q1bcHV1RfXq1YWOQwLiiDsRKWnRogViYmKKjIKlpKSgRYsWxU5c/S97N0IKFCxJt3//fpiamipWArl16xaSk5P/VgefNIudnR3u3buHHTt2ICQkBPr6+ggMDESfPn00fvnH2bNno3379nj06BHy8/OxbNkyPHr0CJcvX8aFCxeEjqcyHB0d4ejoKHQMUgEccSciJWKxGHFxcbCyslLaHhoaijp16iA1NVWgZMKbMGECEhMTsXr1amhpaQEApFIphg0bBhMTEyxYsEDghETqJywsDHPnzlWU5vn6+mLChAka91Cz5cuXY8iQIdDT08Py5cv/tO3IkSPLKRWpGnbciQhAYUnIwYMH0a5dO+jq6irek0qluHfvHtzd3TX61rWVlRUuXboEd3d3pe1Pnz5Fo0aN8PbtW4GSkTpKSkrC4cOH4e/vL3QUUgHOzs64efMmKlSoAGdn5xLbiUQivHjxohyTkSphqQwRASgsCZHL5TA2NlaaiKqjo4MGDRpg8ODBQsVTCfn5+Xjy5EmRjvuTJ08gk8kESkXqKjIyEoGBgRrdcb99+zYkEolidP3gwYPYtGkTqlWrhmnTphV5ENx/WXh4eLH/TfQ+dtyJCACwadMmxWPFV6xYASMjI4ETqZ7AwEAMHDgQYWFhqFevHoCCp1/OnTsXgYGBAqcjVfNXZWVpaWnllER1DR06FN999x18fHzw4sUL9OrVC926dcOvv/6KzMxMpWdK/Nf91QPe3hGJRFi0aFEZpyFVxVIZIlKQyWTQ09PDw4cP4ebmJnQclSOTybBw4UIsW7YMMTExAABbW1uMGjUKY8eOVdS9EwEF80X+bElDuVwOkUikcRO+32dqaorbt2+jSpUqmDdvHs6ePYsTJ04gODgYvXv3RlRUlNARy02LFi2Ufr59+zby8/MVd/hCQ0OhpaWF2rVr4+zZs0JEJBXAEXciUhCLxXBzc8Pbt2/ZcS+GWCzG+PHjMX78eMVoqomJSZF2wcHBqFOnjtI8AdI8xsbGmDRpEurXr1/s+8+ePcPQoUPLOZVqkcvlijKz06dP47PPPgMA2Nvb482bN0JGK3fvP+Bt8eLFMDY2xpYtW2Bubg6gYE5EYGAgmjZtKlREUgEccSciJYcPH8b8+fOxatUqeHt7Cx1HLZmYmODu3btFnpRJmqVFixZo3749xo8fX+z7ISEhqFWrlkbPj2jZsiXs7e3h5+eHgQMH4tGjR3B1dcWFCxcQEBCAiIgIoSMKws7ODidPnoSXl5fS9gcPHqBNmzZ4/fq1QMlIaBxxJyIl/v7+yMzMRI0aNaCjo1PkaamJiYkCJVMfHA8hAOjbty+ysrJKfN/GxgZTp04tx0SqZ+nSpejXrx8OHDiASZMmwdXVFQCwd+9eNGrUSOB0wklNTUVCQkKR7QkJCZwboeE44k5ESrZs2fKn7wcEBJRTEvVlbGyMkJAQjrgT/UPZ2dnQ0tLS2AdU+fv74+LFi1i0aJHSRPhx48ahadOmf/k5Tf9d7LgTEX1k7LhTaSQnJ2P79u0YPny40FEEd+vWLTx+/BgAUK1aNfj6+gqcSFiZmZn49ttvsXHjRuTl5QEAtLW1MXDgQCxYsACGhoYCJyShsONOREWEhYVh06ZNCAsLw7Jly1CxYkUcO3YMDg4ORWouqSh23OnPnDlzBhs2bMD+/fthYGCg0Q/uio+PR69evXDhwgWYmZkBKPhC06JFC+zevbvIE5w1TUZGBsLCwgAAVapUYYedIBY6ABGplgsXLsDHxwfXrl3Dvn37kJ6eDqBgIp2m1+OW1p8tAUiaKSoqCjNmzICzszPatGkDkUiE/fv3IzY2VuhoghoxYgTS09Px8OFDJCYmIjExEQ8ePEBqaipGjhwpdDzBGRoaonr16qhevTo77QSAHXci+sB3332HWbNm4dSpU0pPLWzZsiWuXr0qYDL1wRuZBAB5eXn49ddf0bZtW7i7u+Pu3btYsGABxGIxJk2ahHbt2mlsDfc7x48fx88//wxPT0/FtmrVqmHlypU4duyYgMmIVBM77kSk5P79++jatWuR7RUrVtS4dZU/1LJlSyQnJxfZnpqaipYtWyp+TktLY5kMwc7ODitWrED37t0RHR2Nffv24fPPPxc6lkqRyWTFfnmRSCQavUwmUUnYcSciJWZmZoqngr7vzp07sLOzEyCR6jh//jxyc3OLbM/OzsbFixcFSESqLD8/HyKRCCKRiE/VLUHLli0xatQopXXJo6OjMWbMGLRq1UrAZESqieu4E5GS3r17Y8KECfj1118hEokgk8kQHByMb7/9Fv7+/kLHE8S9e/cU//3o0SOlumSpVIrjx49r/JcaKur169f47bffsGHDBowaNQrt27dH//79OQfiPT/99BM6deoEJycn2NvbAyiYD+Dt7Y3t27cLnI5I9XBVGSJSkpubi2+++QabN2+GVCqFtrY2pFIp+vbti82bN2vkyKFYLFZ0tor7yNTX18eKFSvw5Zdflnc0UhPvVmrasmULoqOj0adPH3zxxRdo2bKlRv6dep9cLsfp06fx5MkTAICnpyf8/PwETkWkmthxJ6JiRUZG4sGDB0hPT0etWrXg5uYmdCTBvHz5EnK5HC4uLrh+/brSEnU6OjqoWLGixne+qHRkMhmOHz+OjRs34vDhwzA2Ntb4uSNEVHrsuBNRid59PPDWfsEKIUOGDMGUKVPg7OwsdBz6D0hISMC2bdsQFBQkdBTBjBw5Eq6urkWWfvzpp5/w/PlzLF26VJhgRCqKHXciKmLDhg1YsmQJnj17BgBwc3PD6NGjMWjQIIGTCcvMzAx37txhx53+kZs3byo9HbR27doCJxKenZ0dDh06VORa3L59G506dcKrV68ESkakmjg5lYiUTJkyBYsXL8aIESPQsGFDAMCVK1cwZswYREZGYsaMGQInFE7nzp1x4MABjBkzRugopEZevXqFPn36IDg4WOnpoI0aNcLu3btRuXJlYQMK6O3btzA1NS2y3cTEhCVERMVgx52IlKxatQrr1q1Dnz59FNs6deqE6tWrY8SIERrdcXdzc8OMGTMQHByM2rVrF3mSIZ/0SMUZNGgQ8vLy8PjxY7i7uwMAnj59isDAQAwaNAjHjx8XOKFwXF1dcfz4cQwfPlxp+7Fjx/gsBKJisFSGiJSYmZnhxo0bRSajhoaGol69esU+gEhT/FmJjEgkwosXL8oxDakLfX19XL58GbVq1VLafuvWLTRt2hSZmZkCJRPexo0bMXz4cIwbN07xELMzZ85g0aJFWLp0KQYPHixwQiLVwhF3IlIyYMAArFq1CosXL1bavnbtWvTr10+gVKohPDxc6Aikhuzt7ZGXl1dku1QqRaVKlQRIpDq+/PJL5OTk4Mcff8TMmTMBAE5OTli1apXGPjeC6M9wxJ2IlIwYMQJbt26Fvb09GjRoAAC4du0aIiMj4e/vr/R48g8790RU1MGDBzF79mysXLkSderUAVAwUXXEiBGYMGECunTpImxAFZGQkAB9fX0YGRkJHYVIZbHjTkRKWrRoUap2IpEIZ8+eLeM0qufVq1c4dOgQIiMjkZubq/Qev8jQO+bm5krLqGZkZCA/Px/a2gU3ut/9t6GhIRITE4WKSURqhqUyRKTk3LlzQkdQWWfOnEGnTp3g4uKCJ0+ewNvbGxEREZDL5fD19RU6HqkQrj9eOs7Ozn/6nAjOGyFSxhF3IlKyadMm9O7dG/r6+kJHUTn16tVD+/btMX36dBgbGyMkJAQVK1ZEv3790K5dO3z99ddCRyQVkpqaWqp2JiYmZZxEdS1btkzp57y8PNy5cwfHjx/HuHHj8N133wmUjEg1seNOREqsra2RlZWFHj16YODAgWjUqJHQkVSGsbEx7t69iypVqsDc3ByXLl2Cl5cXQkJC0LlzZ0RERAgdkVSIWCwu1VOHpVJpOaRRLytXrsTNmzexadMmoaMQqRSWyhCRkujoaBw+fBibN29G8+bN4eLigsDAQAQEBMDGxkboeIIyNDRU1LXb2toiLCwMXl5eAMCHxVAR75edyeVydOjQAevXr4ednZ2AqdRD+/btMXHiRHbciT7AjjsRKdHW1kbXrl3RtWtXxMXFYfv27diyZQsmT56Mdu3aYeDAgejYsSPEYrHQUctdgwYNcOnSJXh6eqJDhw4YO3Ys7t+/j3379ilW4CF655NPPlH6WUtLCw0aNOCDhUph7969sLCwEDoGkcphx52ISmRtbY0mTZogNDQUoaGhuH//PgICAmBubo5NmzahefPmQkcsV4sXL0Z6ejoAYPr06UhPT8eePXvg5ubGFWWI/oFatWoplRPJ5XLExsYiISEBP//8s4DJiFQTO+5EVERcXBy2bduGTZs24cWLF+jSpQuOHDkCPz8/ZGRkYMaMGQgICMDLly+Fjlqu3h8pNTQ0xOrVqwVMQ6T+PlzDXiwWw8rKCs2bN4eHh4cwoYhUGCenEpGSjh074sSJE6hatSoGDRoEf3//Ires4+PjYWNjA5lMJlBK4SQnJ2Pv3r0ICwvDuHHjYGFhgdu3b8Pa2pq1y/SnjI2Nce/ePTg7OwsdhYjUFEfciUhJxYoVceHCBTRs2LDENlZWVggPDy/HVKrh3r178PPzg6mpKSIiIjB48GBYWFhg3759iIyMxNatW4WOSCqkW7duSj9nZ2fjq6++gqGhodL2ffv2lWcswZV2mUxAs5fKJCoOR9yJqIgzZ87gzJkziI+PLzKqvnHjRoFSCc/Pzw++vr6YP3++Yh13FxcXXL58GX379uVykKQkMDCwVO00beWU0iyTKZfLIRKJuFQm0Qc44k5ESmbMmIHp06ejTp06sLW1LdU61Jrixo0bWLNmTZHtdnZ2iI2NFSARqTJN65CXFp/OTPTPseNOREpWrVqFzZs3Y8CAAUJHUTm6urrF3uYPDQ2FlZWVAImI1M+Hy2QSUemx405ESnJzc/m01BJ06tQJM2bMwC+//AIAEIlEiIyMxIQJE9C9e3eB0xGpn3v37hW7XSQSQU9PDw4ODtDV1S3nVESqizXuRKRkwoQJMDIywuTJk4WOonJSUlLw+eef4+bNm0hLS0OlSpUQGxuLBg0a4NixY0UmHRLRn/ureneJRIJevXphzZo10NPTK8dkRKqJHXciQlBQkOK/ZTIZtmzZgurVq6N69eqQSCRKbfmgISA4OBghISFIT0+Hr68v/Pz8hI5EpJYOHjyICRMmYNy4cahXrx4A4Pr161i0aBGmTp2K/Px8fPfdd+jVqxcWLlwocFoi4bHjTkRo0aJFqdqJRCKcPXu2jNOoNq64Q/Tx1KtXDzNnzkTbtm2Vtp84cQKTJ0/G9evXceDAAYwdOxZhYWECpSRSHaxxJyKu8lBK06dPx4wZM7jiDtFHcv/+fTg6OhbZ7ujoiPv37wMAatasiZiYmPKORqSS2HEnIiql1atXc8Udoo/Iw8MDc+fOxdq1a6GjowMAyMvLw9y5c+Hh4QEAiI6OhrW1tZAxiVQGO+5ERKXEFXeIPq6VK1eiU6dOqFy5MqpXrw6gYBReKpXiyJEjAIAXL15g2LBhQsYkUhmscSciKiWuuEP08aWlpWHHjh0IDQ0FALi7u6Nv374wNjYWOBmR6mHHnYjoT3DFHSLhffrpp1i/fj1sbW2FjkIkKJbKEBH9iTt37ij9XLNmTQDAgwcPlLZzoipR2fnjjz+QlZUldAwiwbHjTkT0J7jiDhERqQqx0AGIiIiIiOivseNORERERKQG2HEnIiIiIlID7LgTEREREakBdtyJiIhIpX3//fewsLAQOgaR4LiOOxEREZWbQ4cOlbptp06dyjAJkfphx52IiIjKjVisfLNfJBLh/a7I+89EkEql5ZaLSB2wVIaIiIjKjUwmU7xOnjyJmjVr4tixY0hOTkZycjKOHj0KX19fHD9+XOioRCqHI+5EREQkCG9vb6xevRpNmjRR2n7x4kUMGTIEjx8/FigZkWriiDsREREJIiwsDGZmZkW2m5qaIiIiotzzEKk6jrgTERGRIJo1awY9PT1s27YN1tbWAIC4uDj4+/sjOzsbFy5cEDghkWphx52IiIgE8fz5c3Tt2hWhoaGwt7cHAERFRcHNzQ0HDhyAq6urwAmJVAs77kRERCQYuVyOU6dO4cmTJwAAT09P+Pn5Ka0uQ0QF2HEnIiIiIlIDnJxKREREgrlw4QI6duwIV1dXuLq6olOnTrh48aLQsYhUEjvuREREJIjt27fDz88PBgYGGDlyJEaOHAk9PT20atUKO3fuFDoekcphqQwREREJwtPTE0OGDMGYMWOUti9evBjr1q3jOu5EH2DHnYiIiAShq6uLhw8fFlk95vnz5/D29kZ2drZAyYhUE0tliIiISBD29vY4c+ZMke2nT59WLA9JRIW0hQ5AREREmmns2LEYOXIk7t69i0aNGgEAgoODsXnzZixbtkzgdESqh6UyREREJJj9+/dj0aJFinp2T09PjBs3Dp07dxY4GZHqYcediIiIiEgNsFSGiIiIBHXr1i3FiLuXlxdq1aolcCIi1cSOOxEREQkiPj4evXv3xvnz52FmZgYASE5ORosWLbB7925YWVkJG5BIxXBVGSIiIhLEiBEjkJaWhocPHyIxMRGJiYl48OABUlNTMXLkSKHjEakc1rgTERGRIExNTXH69GnUrVtXafv169fRpk0bJCcnCxOMSEVxxJ2IiIgEIZPJIJFIimyXSCSQyWQCJCJSbey4ExERkSBatmyJUaNG4fXr14pt0dHRGDNmDFq1aiVgMiLVxFIZIiIiEkRUVBQ6deqEhw8fKp6UGhUVBW9vbxw6dAiVK1cWOCGRamHHnYiIiAQjl8tx+vRpPHnyBEDBA5j8/PwETkWkmthxJyIiIiJSA1zHnYiIiMrN8uXLS92WS0ISKeOIOxEREZUbZ2fnUrUTiUR48eJFGachUi/suBMREZHg3nVHRCKRwEmIVBeXgyQiIiLBbNiwAd7e3tDT04Oenh68vb2xfv16oWMRqSTWuBMREZEgpkyZgsWLF2PEiBFo2LAhAODKlSsYM2YMIiMjMWPGDIETEqkWlsoQERGRIKysrLB8+XL06dNHafuuXbswYsQIvHnzRqBkRKqJpTJEREQkiLy8PNSpU6fI9tq1ayM/P1+ARESqjR13IiIiEsSAAQOwatWqItvXrl2Lfv36CZCISLWxxp2IiIjKTVBQkOK/RSIR1q9fj5MnT6JBgwYAgGvXriEyMhL+/v5CRSRSWaxxJyIionLTokWLUrUTiUQ4e/ZsGachUi/suBMRERERqQHWuBMRERERqQF23ImIiIiI1AA77kREREREaoAddyIiIiIiNcCOOxERERGRGmDHnYiIiIhIDbDjTkRERESkBv4HNfcCWhN6H2QAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# normalisasi"
      ],
      "metadata": {
        "id": "X6CbiTXvoA0v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# normalisasi data menggunakan label encoder\n",
        "le = LabelEncoder()\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "\n",
        "for col in cat_cols:\n",
        "    dfData[col] = le.fit_transform(dfData[col])\n",
        "    model = scaler.fit(dfData[[col]])\n",
        "    dfData[col] = model.transform(dfData[[col]])\n",
        "\n",
        "\n",
        "for col in num_cols:\n",
        "  if col == 'diabetes' :\n",
        "    continue\n",
        "  else:\n",
        "    model = scaler.fit(dfData[[col]])\n",
        "    dfData[col] = model.transform(dfData[[col]])\n",
        "\n",
        "display(dfData)\n",
        "dfSample = dfData.sample(31)\n",
        "# dfSample.to_excel(\"samplus.xlsx\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "SNv8RdFUaWgV",
        "outputId": "594bb197-bcad-47fb-deba-8f4bcf86a526"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      gender       age  hypertension  heart_disease  smoking_history  \\\n",
              "0        1.0  0.810988           0.0            0.0              0.2   \n",
              "1        1.0  0.584173           1.0            0.0              0.2   \n",
              "2        0.0  0.521169           0.0            0.0              0.0   \n",
              "3        0.0  0.710181           1.0            0.0              0.8   \n",
              "4        1.0  0.672379           0.0            0.0              0.0   \n",
              "...      ...       ...           ...            ...              ...   \n",
              "1495     0.0  0.231351           0.0            0.0              0.2   \n",
              "1496     1.0  0.294355           0.0            0.0              0.8   \n",
              "1497     0.0  0.798387           0.0            0.0              0.8   \n",
              "1498     1.0  0.697581           0.0            0.0              0.8   \n",
              "1499     1.0  0.067540           0.0            0.0              0.0   \n",
              "\n",
              "           bmi  HbA1c_level  blood_glucose_level  diabetes  \n",
              "0     0.625157     0.636364             0.727273         1  \n",
              "1     0.463226     0.636364             0.272727         1  \n",
              "2     0.514417     0.400000             0.818182         1  \n",
              "3     0.581070     0.418182             0.727273         1  \n",
              "4     0.473882     0.454545             0.727273         1  \n",
              "...        ...          ...                  ...       ...  \n",
              "1495  0.386962     0.400000             0.090909         0  \n",
              "1496  0.426243     0.400000             0.000000         0  \n",
              "1497  0.825951     0.490909             0.209091         0  \n",
              "1498  0.331174     0.418182             0.227273         0  \n",
              "1499  0.169453     0.272727             0.340909         0  \n",
              "\n",
              "[1494 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ab9d0a9-b5c8-43fe-b381-dd4e99d0aaea\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>smoking_history</th>\n",
              "      <th>bmi</th>\n",
              "      <th>HbA1c_level</th>\n",
              "      <th>blood_glucose_level</th>\n",
              "      <th>diabetes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.810988</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.625157</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.584173</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.463226</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.521169</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.514417</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.710181</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.581070</td>\n",
              "      <td>0.418182</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.672379</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.473882</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1495</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.231351</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.386962</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1496</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.294355</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.426243</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1497</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.798387</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.825951</td>\n",
              "      <td>0.490909</td>\n",
              "      <td>0.209091</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1498</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.697581</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.331174</td>\n",
              "      <td>0.418182</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1499</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.067540</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.169453</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.340909</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    async function quickchart(key) {\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# membuat model"
      ],
      "metadata": {
        "id": "1I40c8BfcGCE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# pembuatan model dan spliting data untuk algoritma FKNN\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "ind_col = [col for col in dfData.columns if col != 'diabetes']\n",
        "dep_col = 'diabetes'\n",
        "\n",
        "X = dfData[ind_col]\n",
        "y = dfData[dep_col]\n",
        "\n",
        "# Split the data menjadi 20% uji dan 80% train\n",
        "dfData, dfTest, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "classData = y_train"
      ],
      "metadata": {
        "id": "ypZcauC5cFcI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "knn"
      ],
      "metadata": {
        "id": "QaPOeXihfuQw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "b4uZPqEhft1k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.spatial import distance\n",
        "from sklearn.metrics import pairwise\n",
        "\n",
        "\n",
        "# menghitung data euclidean\n",
        "dist = pairwise.euclidean_distances(dfTest, dfData)\n",
        "\n"
      ],
      "metadata": {
        "id": "0PSI2X6xfgiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "# Settings the warnings to be ignored\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# tranpos agar datanya mudah diolah\n",
        "euclidandf = pd.DataFrame(dist)\n",
        "euclidanTranspos = euclidandf.T\n",
        "euclidanTranspos = euclidanTranspos.set_index(classData.index)\n",
        "\n",
        "for col in euclidanTranspos:\n",
        "  euclidanTranspos.insert(euclidanTranspos.columns.get_loc(col) + 1, 'diabetes ' + str(col), classData)\n",
        "display(euclidanTranspos)\n",
        "\n",
        "# per 2 kolom merupakan hasil dari jarak euclidean dan class dari data uji ke data latih"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "-l3Ru3JYf5KS",
        "outputId": "2f3025b4-fe84-4127-d992-a727221b95fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "             0  diabetes 0         1  diabetes 1         2  diabetes 2  \\\n",
              "468   1.201724           1  0.898161           1  0.485144           1   \n",
              "1235  0.411583           0  1.085767           0  1.263819           0   \n",
              "1406  1.102979           0  0.631226           0  0.965812           0   \n",
              "1023  0.602117           0  1.089276           0  1.390809           0   \n",
              "428   1.175805           1  0.830405           1  0.245105           1   \n",
              "...        ...         ...       ...         ...       ...         ...   \n",
              "769   0.452438           0  1.095451           0  1.324037           0   \n",
              "841   1.171723           0  0.867006           0  0.354941           0   \n",
              "1222  1.042659           0  0.386836           0  0.892655           0   \n",
              "563   0.281899           1  1.108346           1  1.244867           1   \n",
              "689   1.537104           1  1.344985           1  1.380565           1   \n",
              "\n",
              "             3  diabetes 3         4  diabetes 4  ...       294  diabetes 294  \\\n",
              "468   0.813081           1  0.425045           1  ...  0.957311             1   \n",
              "1235  1.073785           0  1.228650           0  ...  1.174074             0   \n",
              "1406  0.373633           0  1.052710           0  ...  0.420047             0   \n",
              "1023  1.268754           0  1.346244           0  ...  1.383859             0   \n",
              "428   0.954883           1  0.255039           1  ...  1.060891             1   \n",
              "...        ...         ...       ...         ...  ...       ...           ...   \n",
              "769   1.224518           0  1.373274           0  ...  1.253231             0   \n",
              "841   0.857544           0  0.365229           0  ...  0.979895             0   \n",
              "1222  0.217221           0  0.906351           0  ...  0.464289             0   \n",
              "563   1.036988           1  1.259417           1  ...  1.101770             1   \n",
              "689   1.246140           1  1.487330           1  ...  1.277587             1   \n",
              "\n",
              "           295  diabetes 295       296  diabetes 296       297  diabetes 297  \\\n",
              "468   1.049912             1  1.017283             1  0.859423             1   \n",
              "1235  0.735243             0  0.741655             0  1.149362             0   \n",
              "1406  1.335953             0  1.344369             0  0.087603             0   \n",
              "1023  1.212755             0  1.183912             0  1.341039             0   \n",
              "428   1.206970             1  1.160404             1  0.970229             1   \n",
              "...        ...           ...       ...           ...       ...           ...   \n",
              "769   1.137272             0  1.131930             0  1.201608             0   \n",
              "841   1.102884             0  1.066096             0  0.862283             0   \n",
              "1222  1.327360             0  1.309590             0  0.251577             0   \n",
              "563   0.667029             1  0.644643             1  1.044617             1   \n",
              "689   1.714951             1  1.704915             1  1.089048             1   \n",
              "\n",
              "           298  diabetes 298  \n",
              "468   1.322275             1  \n",
              "1235  1.715445             0  \n",
              "1406  1.169195             0  \n",
              "1023  1.908963             0  \n",
              "428   1.383306             1  \n",
              "...        ...           ...  \n",
              "769   1.717378             0  \n",
              "841   1.292578             0  \n",
              "1222  1.277661             0  \n",
              "563   1.579605             1  \n",
              "689   1.441386             1  \n",
              "\n",
              "[1195 rows x 598 columns]"
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              "      <th></th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>841</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>563</th>\n",
              "      <td>0.281899</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.244867</td>\n",
              "      <td>1</td>\n",
              "      <td>1.036988</td>\n",
              "      <td>1</td>\n",
              "      <td>1.259417</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1.101770</td>\n",
              "      <td>1</td>\n",
              "      <td>0.667029</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.579605</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>689</th>\n",
              "      <td>1.537104</td>\n",
              "      <td>1</td>\n",
              "      <td>1.344985</td>\n",
              "      <td>1</td>\n",
              "      <td>1.380565</td>\n",
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              "      <td>1.487330</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "<p>1195 rows × 598 columns</p>\n",
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# membagi data menjadi 2 column\n",
        "num_cols = 2\n",
        "new_dfs = []\n",
        "k = 9\n",
        "# Iterate over the columns in steps of 2\n",
        "for i in range(0, len(euclidanTranspos.columns), num_cols):\n",
        "    # Slice the DataFrame to get the current 2 columns\n",
        "    new_df = euclidanTranspos.iloc[:, i:i+num_cols]\n",
        "    # Sort the new DataFrame by the values in the first column\n",
        "    new_df = new_df.sort_values(by=new_df.columns[0])\n",
        "    # get  5 data pertama\n",
        "    new_df = new_df.iloc[:k]\n",
        "    # Add the new DataFrame to the list\n",
        "    new_dfs.append(new_df)\n",
        "\n",
        "# Print DataFrames\n",
        "for i, new_df in enumerate(new_dfs):\n",
        "    # print(f\"New DataFrame {i}:\")\n",
        "    # new_df = new_df.sort_values(by=new_df.columns[0])\n",
        "    display(new_df)\n",
        "    # display(new_df['diabetes '+ str(i)].mode())\n",
        "    # print()\n",
        "    break\n",
        "\n",
        "# dari jarak perhitungan tersebut data dipisah tiap data uji menjadi 1 dataframe yang berisikan 5 jarak terkecil euclidean data uji\n",
        "# ke masing masing data train beserta kelasnya"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "y0Jq31vOgH-T",
        "outputId": "a496dfa7-2976-4350-9a36-f03167b4f363"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "             0  diabetes 0\n",
              "1378  0.085422           0\n",
              "1021  0.100925           0\n",
              "115   0.120386           1\n",
              "1375  0.165985           0\n",
              "1335  0.209180           0\n",
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              "139   0.231420           1\n",
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "hasilPred = []\n",
        "# new_dfs = pd.DataFrame(new_dfs)\n",
        "# membandingkan hasil nilai kelas untuk prediksi\n",
        "# jika hasil nilai kelas cenderung ke salah satu kelas maka itu adalah hasil prediksi\n",
        "\n",
        "for i, new_df in enumerate(new_dfs):\n",
        "  mode_value = new_df['diabetes ' + str(i)].mode().iloc[0]\n",
        "  # print(mode_value)\n",
        "    # print(new_df['diabetes ' + str(i)].mode().iloc[0])\n",
        "  if mode_value == 1:\n",
        "    hasilPred.append(1)\n",
        "  else:\n",
        "    hasilPred.append(0)\n",
        "\n",
        "\n",
        "    # print()\n",
        "    # break\n",
        "\n",
        "print(hasilPred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lcZEiLlkYcy",
        "outputId": "c4e71c9f-2d79-4e41-f95b-76d9183cff2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# menggabungkan data uji dengan hasil prediksi\n",
        "y_pred = pd.DataFrame({'diabetes': hasilPred})\n",
        "hasilPred = pd.DataFrame({'diabetes': hasilPred})\n",
        "dfTest.index = hasilPred.index\n",
        "dfHasil = pd.concat([dfTest, hasilPred], axis=1)\n",
        "\n",
        "display(dfHasil)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mSfSqT1Tuo60",
        "outputId": "f434a218-4b90-4401-dcc9-eaa6a88e9dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     gender       age  hypertension  heart_disease  smoking_history       bmi  \\\n",
              "0       1.0  0.332157           0.0            0.0              0.2  0.251776   \n",
              "1       0.0  0.243952           0.0            0.0              0.0  0.278103   \n",
              "2       0.0  0.155746           0.0            0.0              0.8  0.438571   \n",
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              "..      ...       ...           ...            ...              ...       ...   \n",
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              "298     0.0  0.609375           1.0            0.0              0.4  0.486210   \n",
              "\n",
              "     HbA1c_level  blood_glucose_level  diabetes  \n",
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              "297     0.472727             0.545455         0  \n",
              "298     0.454545             1.000000         1  \n",
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              "    <div>\n",
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              "    </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-46fc3841-db13-45b2-bde1-a00c0e8f2dd5 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-46fc3841-db13-45b2-bde1-a00c0e8f2dd5');\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-be7139bd-4b05-4441-ac2c-78ea50a44116\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be7139bd-4b05-4441-ac2c-78ea50a44116')\"\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-be7139bd-4b05-4441-ac2c-78ea50a44116 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " akurasi"
      ],
      "metadata": {
        "id": "DgkdTn8dPPMK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# library yang digunakan\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from scipy.stats import zscore\n",
        "\n",
        "# Accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Precision\n",
        "precision = precision_score(y_test, y_pred, average='weighted')\n",
        "\n",
        "# Recall\n",
        "recall = recall_score(y_test, y_pred, average='weighted')\n",
        "\n",
        "# F1 Score\n",
        "f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\",f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IeVeQ9-GzD7w",
        "outputId": "cbc12dc3-4e1f-4f67-f637-a0d54fc91cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8695652173913043\n",
            "Precision: 0.8695846707518723\n",
            "Recall: 0.8695652173913043\n",
            "F1 Score: 0.8695652173913043\n"
          ]
        }
      ]
    }
  ]
}