Copy_of_CMPE188_M4_AssignmentP2v0-1.ipynb

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      "cell_type": "markdown",
      "source": [
        "### **Please submit the different parts of Module 4 in separate notebooks.**"
      ],
      "metadata": {
        "id": "4CIeFulNC-pu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Module 4, Part 2\n",
        "\n",
        "Please complete this assignment in a Colab.  \n",
        "\n",
        "Please submit all parts of the Module 4 assignment as separate notebooks in the Canvas assignment for Module 4 (\"Module 4 Assignment\")."
      ],
      "metadata": {
        "id": "r6D5BNPJji5e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Please submit the different parts of Module 4 in separate notebooks.**"
      ],
      "metadata": {
        "id": "9_T4x0yehR92"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Section 0: Setup & Initialization "
      ],
      "metadata": {
        "id": "pC0hSe-6hR93"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obAx6_JSBjqq"
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      "source": [
        "# importing required libraries\n",
        "from sklearn.datasets import make_blobs, make_moons\n",
        "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
        "from sklearn.metrics import silhouette_score, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating features for k means clustering; default number of features is 2\n",
        "# data belonging to each cluster is labeled with 0, 1...k where k is the number of clusters\n",
        "features, true_labels = make_blobs(n_samples = 450, centers = 3, cluster_std = 1.5, random_state = 100)"
      ],
      "metadata": {
        "id": "lD7MfXQ7DEw6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the features to dataframe format and printing them\n",
        "features = pd.DataFrame(features)\n",
        "features.head(5)"
      ],
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          "height": 206
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      "source": [
        "# printing the true labels (this is the target variable/class assignment ground truth)\n",
        "true_labels"
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1: Kmeans Clustering"
      ],
      "metadata": {
        "id": "K1I2C7VMiLQZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1\\. Apply standard scaling to the data & convert to data frame format"
      ],
      "metadata": {
        "id": "C3onzTvtiSrT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "CCEW2Y7CfBSA"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(features)"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "pkLpEM5Ef5ZE",
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            "text/plain": [
              "StandardScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_features = scaler.transform(features)\n",
        "scaled_features = pd.DataFrame(scaled_features)"
      ],
      "metadata": {
        "id": "iiBIJRhGEPm4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_features"
      ],
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-882e972b-63e5-4b9a-a018-42f42176829f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2\\. Create a scatter plot of the data, where each cluster is plotted in a different color"
      ],
      "metadata": {
        "id": "6J1kULuAiqLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "j6UTraCzh69K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = scaled_features[0], y = scaled_features[1], hue = true_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "XkhfRCgWgZ1m",
        "outputId": "f0ccab8c-1e11-461f-ccfd-738642bc1f24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f926dbf8810>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3\\.  Create an elbow curve for the data with k ranging from 1 to 11\n",
        "\n",
        "Hint: use intertia_ computed by Kmeans for the y-axis"
      ],
      "metadata": {
        "id": "x76k7Nydi3hI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inertias = []\n",
        "for i in range(1, 12):\n",
        "  # use to_numpy so we can reshape it\n",
        "  kmeans = KMeans(n_clusters = i).fit(scaled_features)\n",
        "  inertias.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "jQCuk9imkLAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xs = [1,2,3,4,5,6,7,8,9,10,11]"
      ],
      "metadata": {
        "id": "JtlOys2FjiQb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(xs, inertias)\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('distortion')\n",
        "plt.title('The Elbow Method showing the optimal k')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "4jAJfsvlk_pP",
        "outputId": "4433da92-6195-409b-c957-51046983c607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4\\. Run the kmeans algorithm on the data using k = 3"
      ],
      "metadata": {
        "id": "JUDzo3MBjgjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 3).fit(scaled_features)"
      ],
      "metadata": {
        "id": "GtaCQl4xj1Mj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = kmeans.predict(scaled_features)"
      ],
      "metadata": {
        "id": "Y6HiMGRyy2Iy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5\\. Create a scatter plot of the predictions, where the data points belonging to each predicted cluster are plotted in a different color"
      ],
      "metadata": {
        "id": "jIxFQheMj3_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = scaled_features[0], y = scaled_features[1], hue = pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ThDwVtPey4_H",
        "outputId": "a36141ae-c192-4a8b-90d0-51938f0acd67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92656dd390>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 2: Density-Based Clustering"
      ],
      "metadata": {
        "id": "LZYjT2XcjsNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# creating features for dbscan clustering\n",
        "features, true_labels = make_moons(n_samples = 450, noise = 0.05, random_state = 100)\n",
        "features = pd.DataFrame(features)\n",
        "features.head(3)"
      ],
      "metadata": {
        "id": "QF0y72OGMt-F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "1a3f675a-52cf-4ebf-ac1b-f54988c51175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1\n",
              "0 -0.941722  0.287927\n",
              "1  1.837622  0.002209\n",
              "2  0.771527 -0.496647"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ac176ae-f4f1-4ce5-8bbf-52e12db67247\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.941722</td>\n",
              "      <td>0.287927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.837622</td>\n",
              "      <td>0.002209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.771527</td>\n",
              "      <td>-0.496647</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ac176ae-f4f1-4ce5-8bbf-52e12db67247')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7ac176ae-f4f1-4ce5-8bbf-52e12db67247 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-7ac176ae-f4f1-4ce5-8bbf-52e12db67247');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cluster labels\n",
        "true_labels"
      ],
      "metadata": {
        "id": "RnJ2aN6PdtNo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f09a420-f048-457c-9727-0aae87b87939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
              "       1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
              "       0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
              "       0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
              "       0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
              "       0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
              "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1\\. Use standard scaling on the data and convert to a data frame"
      ],
      "metadata": {
        "id": "wVc50SU6knAr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(features)\n",
        "scaled_features = scaler.transform(features)\n",
        "scaled_features = pd.DataFrame(scaled_features)"
      ],
      "metadata": {
        "id": "jarvMmGbdaeE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2\\. Create a scatter plot of the data, using a different color for the data points that belong to each cluster"
      ],
      "metadata": {
        "id": "coJ_KisFkclT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = scaled_features[0], y = scaled_features[1], hue = true_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "sBHqQyEbzwAH",
        "outputId": "cff3c7d5-3e74-4e3e-b643-13f726df1b04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92655ff990>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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+RAfqWTo6kk+P9hUJ6tVKUsN82VnaIlWvO2FNz3Vig/QsHhnBSzv75jwxPhCtWsljnxa4U4a/LGzE4XSRFuFLc4eNTquDyyfF8uHBWnRqBfctzmBSgue2k1IQ3MFzgGumxlPd1s0tb+wHYHZaKH+5ZDQJId7bZTIyw52hzp5aIYpilCiKalEUY0VRfEkUxf/0CAaiKK4RRXGUKIrjRFGcJorirqGc79lMW6eNpg4rWpXSLRi9fJBTw+p5aVw9JZ6oAC33L87AoFGiVSm4ako8h6vaOVDVTlKogQeXZnq89/yMcHJrjHQNYDDYYLKSGubLnz7O4+uiJjqtDmxOFw6XSHVbF6nh3sHjaUkh/PLNHA5VGzFbHXxyuI4j1Uamp4QAEBWgI2aAlFe9RskDSzJZOSORYIOGaUnBvHnTlJ56Em/nXK1awbKxUVw4NoqUMAOXZ8UyIzWUooYOrxqTHSXNTOwRhnV7qzhU3c7rqyaz+VdzuG56Iv56NbXt3Xx4sIYnNhdwuLqdexelA2DQKPHTqfmqqK9T2o7iZjYd8a6Al5E5E9x4442Eh4czerRX6dsZYVhvT8mcOcL8tMQG6RmoaZcgQF6dide+rSDEV4u/VsWKqfFolQr89Gq6bU72V7Rh0Cq5bFIsKWG+7C1vRSEIPemndYyNDXRvZ/WSFuHLFwVNrN1bydq9lfz76onMSgtlRkoIu0pb0CgVjOy34siM9GNUtD/PflniMb/txU3ccX4qjSYLDyzJ5F9fljAzNZTJScEE+fQV5SWGGnj4ghHcfl4KBq0KQ0+F98T4IPx1Kkz90nDvXZBOVUsXxQ0dpEf4saeslcrWLv7+i7FeP59AHzWBejU3zkzko0O1FDV0oFcriQ7U09Jhpaypk8IGE+uzq9wV6w9fkMkrK7PIrTVx+DhTRIUAGqXAKzuPkVtr4rz0MKYlhxDa4+ElI3M6rFy5kjvvvJPrrrvuR7m+LBrnGIer2nlnfzU17d1cnhXLzJQQ/PUawvx0PHXleHYUN3v5SP1iYiyf5zXQZXPyl435/L+fjebFHce4d2E6L3xd5g4c/3OLknW3TGNGaig17d3c/+5h9zU2H63lb5eO4c+f5NFlcxIXJPlP/W1TgXvMP7cWMTM1lEcvGc0buyv46GANN8xK5L7QdPQaJT4aJduLvHIc8NOpmJ8ZjsXu5K51B7A6XLz2bQUPLc3kljnJHu0rVUoF4f6eTrVdNie3nZdCTVs37d12xsQEkBDig8MpUtRoprDB7B4bZFAzPTmYb8v6yofuXZBOfr2ZLpuTVbOSSA3zJTbIh6IGM6vXHaCg3oxCgMuz4ojw17Etv5G/bipg092zOT8zgtd2lfN5Xl/s5qqpCazLrqSkUaoLeXd/Nbedl8y9CzPkzoE/NQ6/Ddv+DMZqCIiF+X+AsVec1iXnzJlDeXn5mZnfAMiicQ6RV2fiyud3u20Jviho5InLxnJ5lmSTnJUYTEygnpkpIXxd3ERurZnxcQGUNnV69MSobe8mJcxAS6fNLRggOXC+svMYT14xnsWjIvDTTWL93kpigvRcnhXHuNgApieHYLTYqTN2s3rdQbcTLoDN4cIpiiSF+fK7C0Zyx/mp+OpUaFVSDcjh6naq2roYHxfIwaq+gPtvFmditjh4cccx+vP0tmIuGBtFbJB3F8BeXC6Rl745xsYjdYT5afHTqth4pI5fzk3hngXpvHBtFn/bnI9WqWBKUgjpEX784aJRHK0x0ml10Glzsj67irw6E9OTQ7DYXQgCvPDNMVZMjqOxZ6vPJcL/squ4f3EG2/IbcYm4K+CnJAUzOTGI7PI2QDKAfLOx02OeL+44xuWT4r6z3kPmHOTw2/DxarD3pJQbq6RjOG3h+DGRReMc4lBVu5fV8tPbipk/IhylQmBnSQtr91QQFaDn+ukJrJ6fxj3rD7HxqKeHU0q4gf+XNor12dVen3GsuROH04W/XsOS0ZEsGR3p8XpCv1oI13E5C7efl+LeTlIqBEJ8PbdjUkJ9sTtFRkb5My8znG6bk/gQHxaNDGfvsTavudidru81E3SJkj07QJPZ6o7ntHZa0agUjIn157a5KewobiapZ+4jovwJ9dVw8Zqd1Bn7KuG/LWvh3oXpdFid7D3WypFqI7fMSebpbVLWlkapwGyxo1UpGBMbQHyID4eq2ln1WjYzUkL51YJQwvy0KAfYI3S4RJxyjsdPi21/7hOMXuzd0nlZNGSGCun+JPB5boPHdtJHh2p57YYpXDEllu3FTXRYpf3+MTEBTE0KIS7Yhy67i48O1Xpc76op8WjV39/saExMAOtunsYrO8tpMFm4bnoCc3rqL74Lg07Fg0syyC5v5WCVkXGxAUxOCibEV0dGpB/+epW7ERTAtdMS3BXfnVYHx3rMDpNCDBh0PY68SgXXTU9kd5mnW82FY6Ox2p08s62Et/ZI2WIfHqxlXGwAL10/mS6r00MwerE7Xe7MqG67ExERvVrJrXOTsTlc+GiUPPbzsYyPC0BA4JGPcmnusHn8HP919UTCfLU0dXhuEcafYMUkcw5i9H4oO+H5YYIsGucQ42ID8NEoPSqW7zo/DaUAa44LLlsdLr4qauS9/dXcODORmCA9Ef46MiP93d3rpiYG8/gvxvCPz4uwOpzcPjeVBSMiTmougiCQlRjMhPggnKILjfLkuupFBui5aFwMF43z9OBJDvPlrVXTeP3bcvLqTPx8YizLRkeiViqoaevi0U357oykRaMi+MOFI93bVrNSQ3l6+Xj+/VUpGpWCO+elkpUQRHlLJ2v3Vnp8zqFqI3vLW4kL1LNgRDhb8xv7fScI99Oxv6Jv1aNVKbntvBRe2XmMth6bdpVC4M2bpiKKcKDKs64FoLjBzI2zEimoM1Pa3MHPxsWwbEzkSYmxzDlEQKy0JTXQ+WGMLBrnEoLAPQvSya830dphIysxGB+tEgEG3BIREDBZHDzzRQkGjZKNq2d7tDv106u5cnI852eE4xJPrRWqUiGg5MzcDMfEBvDYL8Zi66lU7+XLwiaPFNbPcxuYkRzCypl9leg/Gx/DvMxwFILgzqpyukQG2hEqrDdz+1s5PLt8PC5R5IuCJsL9tDywJJO8OiMbDkrGi1OTglk6KoLNeQ1uwQBpq+m5L0u4YUYi42K9W8oqFQKPby5kXkYYr98whWBfOWvqJ8n8P3jGNADUeun8abBixQq++uormpubiY2N5U9/+hOrVq06zcn2IYvGOURORRuPbsonMcSHAL2ap7YWEe6nJf2GyTxy8Uge3pBLZY8rq49GiUGrwtoTrO20OWnrspGIZ8GZKIq0dtooaezAoFUyIipgSPtoKxUCeoWnCG3tl5nUy2e59W7R6MXvODPGhBADi0dG8Fm/98cG6WnvKR68a/1BNtw2g99fOAqDRomPVkVUoI6YIB8i/XWMjwskOlCPcZ/3dkK9ycK7OdVcMiGGBpOVepMFhSBt7+0ua0GpELh+ZpIsGD9leuMWZzh7at26dWdgct+NLBrnCE6X6O5f0euFpFII/HxiDLuPtdLeZee+xemUNnbQYXUS4a/lua/6rC7C/bREDSAG2eWtXPvSXre4jI8L5F9XTSBmGO2/z0gN8SieA5iVduL4CUh9QB6+cCTj44PYdKSOjEg/EkJ8eLqfHUlVezfj+1V8z0gJZUaKZBtitTt5b381ob7eDZyumhLPk1uL+KKgicuzYgnQq0kM8cFPp2KCJYgHlmQyMsr/VL+yzLnC2CuGddB7IGTROEc4WmPE3G33KGK7e0Ea7+VUU94sicj4uEB+uzSTrMRgvixsRNGzZRUTqOfp5eOJDPCstO6w2Hn800K3YAAcrGrnULVxWInGwpERfHK4zl1ENyrKn6XHZXV9F3HBPtx2XgrLJ8fx4PuH+L/PPVcNCSHf/T3z60z8+p1DjI7x58ElmbybU02X1cHNs5M5LyOMl3Yeo6q1m9e/rQCkIsFP7pp1whRhGZnhjiwa5wgtHVZignQ8cvEoCurNCIg0mCxuwQDphn+gqp0pySHMHxHBJ3fNorXTRrif1qsgDqQtq9LmDq/zjcfZkAw1SaG+vLxyMqWNHYhASpgvYT+wujrIoOHu+emUt3RTWG/GT6vizz8bRcYJ2rv2ruiO1pgobexk/ohw9Gol80aEkxBi4OWVk/nDhqN8W9bKuLgAHrlolJdgNJosNJgtBPtoiQmSOwKeC4ii6FFwOtw4Xfs+WTTOATqtDvLrTTzxWREgWYb8ekEab+yp9Brbv/o5OlB/wib2ob5aLp0Qwyv9TP0ARkQOvz7Zob5aQk8zPjAyOoD1N0+j1tiNv05NXPCJVwRh/T6v2+7kk8N1XDohmiM1Rv74YS4xQVL7zgh/HX46tZezbXZ5K6vXHaDOaCHIR80Tl42TgvWK4XvDkTkxOp2OlpYWQkJChqVwiKJIS0sLOt2pxyVl0TgLqWvvJrfWiLHbgUohEB2o57/by9yvN5mtGC0OshKCvHpoz0gOOenPUSoEVs5IpK1TqjPw16v53bIRjIk9d3tCBBk0BBm8YxQDMSLaj8uzYnmnJxAe7qdlQnwQd6494B7zXk417982w2s7r669m9vfyqHJbEUhQFuXnTvW5rBx9SxSw4efKMucHLGxsVRXV9PU1PT9g4cInU5HbOypp/XKonGWUdJo5o63cihskLaNFo2M4Oqp8VjsfXEHf52K5g4beo2SKUnB7D3WiiDARWOjmZkaSnuXDVO3nSCDxiuj6HgSQgw8ftlY7l6Qjk6lIOoEK5OfGsEGLQ8vG8nlk2IxdtuJD/bhptf3eYyx2F0crGr3ar7UYLJw3bQE9Bol7V12fDRKPjpUS3Vbt1s0hvs2h4w3arWapKSk7x94FiOLxlmExe7glZ3lbsEA+DyvgbkZYayen8qRGhPZ5a346VQ0mCx8dKiWhSMjWD0/FQGBmrZuWjqt/Oa1I+TVmchKCOKRi0cxOubEKwetSum22JDxJMBHzZQkafXW1mlDYIB6mAFu/E6XyKdH68ir69sufGBJBmG+WvYea+HN3RV0WJ1cOy2BqcnB+Gjkf6oywwPZUvMsobSpgzd3V7Cj2NMFNtigwVej4mBVO2VNHVyRFctVU+KZPyIcgC15DTyzrYSntxWTHuHLHWtz3Fbk+yrauO2t/TSZve0yZH44QQYNv1rg2cLeR6NkfFygx7mCOhO7Sls8BAPgtV0VuESRFS/s4aNDdXxR0MgNr2bzbancrFJm+CCLxllAp9XBnz7K4+191Yw7Lp6wckYiD75/hK35jRQ3dvCf7WW0d9mYmx7G75aNwKBRolYK3DgzkXFxgVS0eBqkVbV2U9V6nGmazCmzcEQEL1w3iSWjI7lpVhLrb5nGiOPqMRrNVsL9tdyzII1FI/tsWZo7rBxr7vQyYXz+6zI6BuhWKCMzFMhr3rOA3FojXxdLgbVb5iSTV2eitEky59OqFF7Otq/sKmd2ehidFrtkjuenJT3Cj/wB2quqlQJ+OvnX4Ezhp1ezcGQkC0cOXCeSX2fiofcOU9NjhjgrNZQrsuJ4e18VF42LpqK1y+s9AvBOTjUT4wMZFxfk9bqMzGAi3y2GOVWtXWwvasJPq8JsdVBrtJCVGMyFY6NRCAyYnqlXK/kst4HXenp2T0oI5L/XZJES5stNs5J48Zu+vhT3Lc4gUY5XDAo2h5Pnt5e6BQPgm5JmfrUgjeWTY4nw16FVSSvD/i1nfz4plo2Ha2nttKFWKryC6jIyg4ksGsMYl0ukucNKfLAPDy3LpM5oQSVIzX56uXVOMtEBOmr73YiumZbAun7urfsr2ilsMDMzNZQ756Uyb0Q49UYLsUE+jIz2l7vFDRIdVgd7y737gnTbnPholDz7RQkR/jr+cfk49h5rxdhtZ1JCEK/ulJx9txc1Y7Y4+N0FfvLfmcyQMaSiIQjCy8CFQKMoil5d0AUp7eRpYBnQBawURTFncGc5NFS0dJJd3sob31a4XVINGiXPXjWBAL0KY09fiZd3HuPhC0bgEqGmrZusxCBe21VBc4fN43r2ng56gT4at3eSzODir1MzLzOMN3Z7Fl3GB/vQZLZy+/mp6FRKGkzdrJyRSFlTJ798a7+HE+9beyq4bnqC3OFPZsgY6r+/J1wAACAASURBVJXGq8Aa4PXveH0pkNbz31Tg3z3/P6ex2p08tbWIhBCDh612p83JizuO8czyCfz36zJya00sGRXJrLQwUnpuIp1WB+/u9/RPCvPTkhou32SGGpVSwfUzkjhSY+JgVTuCANdMTeBQdTtv9xQIapQK/n7ZGLptDkAkJlBPdVtfosLIKH80Ss8tSVO3HZ1agUYl9+OQ+fEZUtEQRfFrQRASTzDkZ8DromSWslsQhEBBEKJEUaw7wXvOemrau9mS1+Du7d2fogYzBfUmfr0ondhAH4INGtSqvq0Kg1bF7y8cychofzYericrMYiVMxJlk7xhQmq4L6/eMJmKli60KgWHa4z8pl9HxUAfNTaHyF825VPZ0s2CkREoFQJfFjRy3fQEsstbeXhDLtfNSCQt3MDGw/W8s7+KtHA/bp2bzIR4OVAu8+My1CuN7yMG6N/aqrrnnJdoCIJwC3ALQHx8/KBM7sdCo1IgIAzopTQ7LYx39tfwfk4ta2+e6iEYvSSEGLh3YQY3z05Gr1aikve/hxWBPhoCe3qlv5vjuSq8dnoCD284iq1nO/HN3RUsnxzH3fNTuf/dw/Rm435V1MTjvxjD/20pxO4UKW3q5OviJjbcMZP0E5gsysicLufM3UQUxedFUcwSRTErLOz7eykMFQ1GC0eqjdS0fXdtRGyQDw8szeSbkmZumZOMj0badjgvPYyYID0ljR0UNpipGiA9sz9+OrUsGMOc847rm253utyC0cvushaO1po4rnyDd/dXMzkx2H3cZXNSVO9ZMCgjc6YZ7iuNGqD/Hk1sz7mzkj1lLaxef4AGk5VAHzWPXjKaED8NcYEGL1vsS8bHEB/sQ1mTmaeXj6fD4uDdnGrWfCH1+lYqBHy0w/2vT+b7mBAfxDMrxvP3zYV02ZxEDmBRb9AqvQr+QPodcB1nc60ZYOUpI3MmGe53nY+AOwVBWI8UADeerfGM2vZu7lib485qau+yc+/bh7h1bjLr9h7klZWTPTygCupNPPNFMbVt3Vw5OY6Sxg52lvTZSdwyO5mkELm+4mzHoFVx8bgYZqaE4nSJ2JwuUsN8KWnq8xdbNCoKP613/cZlk2J54L0j7uP4YD1xwbKhpMyPy1Cn3K4DzgNCBUGoBv4IqAFEUfwPsAkp3bYEKeX2hqGZ6anR2mmluq0bg0ZFl83hlQZrdbhwiZKV+T+3FLHmqonoNUpKGs1c+9Jed6X3P7cWs3peKo9cNJJ6kwU/rZr5I8JQKAQ6LHYMWpXshnqWE9IvfvXSyiz2V7RR09aNSinweW4D9SYL/7xiPFvyG7A6XEyMD0IAfrM4g5LGDkJ8NSgEgWe2FvOPK8djkA0OZX4khjp7asX3vC4CdwzSdM4ohfUm7l5/kIJ6MxqlgnsXpTM7NYQd/VYLCgF3kda+ijZMFjt6jZKCOjPddiehvhrOzwzHanfx4jfHuGl2Ei/sOMZvl2YCAo98dJTdZa0sHhXJZZNi5cruc4SEEAMJIQaMXTaueWkPR2ok+5fCBjO5NSaUCoHNR+tZPT+VZ7aVEOmvw2Sx02WTHjLubu0iM1LuPy7z4yA/jvwIdNkcPP5pAQU9QUmb08Vjnxbw3NUT2VXWitMlIgiwalYynx2tB2BuRhiBPn29LZaNiSQ2yIePD9Xio1Gyel4qU5KCmZseTpivhqte3OPO3y9uLOFgVTvPXT3RqzuczNlLgI+Gx34xlgfePczRWhP7yluZmxHGSz02MMqe1WW9qc8NQK0UOM1unjIyJ0QWjR+Btk4b24+zMAfJRmLjXbMob+nC6nDy4o4y8upMjIjy487zU9H2FGfp1QoyI/15ckuR+72PbS7k9RsnMyc9nG+KmzwKvkDyMKps7fre3hgyZw+17d18eqSO9Ahflo6JZFR0ADVtXTy4NIOaNgtjYgIYHxfAwaq+AtDLJsVi7Lad4KoyMqeHLBo/An46NaOj/T2quQEi/XVkRvmTGeWP1e4kM9IPi91JW5edgjoTFpuTzCg/Qv10fNpPMHr5pqSFOenhA/oOCQKolHJc41zigwPVrPmy1OPcg0syeeLzImIC9SwbHcHs1DBmpoZRZ+wmIcRAfp2JvcdamZYsW8XI/DjIonGGMXbbyD7WysqZSfzxw6OYLJJH1BVZsYyK6dtn1qqVhPvp+PMnuXxwoBaQbvxrVkxgTmooYb5a8vHMuQ/3k4KlqeG+TE8J8WjOc83UeDmb6hyipcPK2j1VXufrTBYMWiUXjo3i1V0VdNmdZJe3Euyj4cODtThdIheMGdiWvRdjlw2z1UGorxadWrYekflhyKJxhvnoUC2/35CLv17FNdMS0KoUZCUEMzYuwKsfd0G9yS0YAKIID284ysbVs7lzXiq7Sltw9OTnB/momZ0mPT2G+Gp54rKxfFvWwtEaI1MSg5mcFIxWvgGcM+g1SmKC9NS0e25D+mpV2Owu1EoFn+U18PsLR3C42uh2OZ6WFMzE+OCBLglItUJ//CiX4sYOFmSGc9+SDNLC5QpymZNHFo0zSIPJwpOfS9tKpm4Hz30lbS08u2KCl2CA1MHteNq67HRaHWQlBPP+7TM4WNmOTqNgQlwQaf3sIWKDfLh8kg+XT/L2p5I5+/HRqLh3YTrXvrTHXZsRG6RHFEWcoojD5WLhyAhaO2zctyidDquDhBAfzBYH+ytasTgC3CaWvRTWm7j+lb1Y7FLF+Wd5DbR22Xnp+iw5gULmpJFF4wzicok4nN6pK64BqnkrWjpp6bCiUgju1QRAVkIgbV02qtq6GBsbyNjYQK/3yvw0mJIYzPu3z6So3oxOrSA+2IdDNe3cPS+NMXEB7Chu5uuiJqYkBXP9jETuXn+ATquUdhti0LD25qlk9KTeulwih6ra3YLRS3Z5K7Xt3bJoyJw0smicQSIDdNx2fgp/31zoPmfQKBkR7Z0zf6CynZd3lvObJZm8svMYdUYLkxODWDAiguXP7yZAr+b1VVMZI2dD/WRRKATGxAR4/A6MiQ2kvLmTi9d8446X7ShuptlsZX5mBB8dkrY7WzptfFHQ5BaN8pZOGkzeK1tfrUq2o5H5QchGNWcQQRC4YlIcT1w2lkkJQSyfHMe6W6YN6DpaZ+ymuq2bZ7cVc/mkGF64bhK+WjWPby7AJUrbVGu+KMZ6XP9vGZnylk63YPSSX28mIcTT/r66rc/QsrXTRl6diTlpnllV9y1KJz5Yts2XOXnkR4wzTKiflsuz4rhkQgwqhfCd9h6JPZlOeo0SH62a7UXNfFnY6DHmUJWRDqtDDnDLeOA7wMpAo1R4ueAuGBHh/nOEv45dpS3MTQ/j3oXp2JwufDUq5qQNX0domeGJvNL4kVArFSf0gxJFkV8vSmf55Die+7JkQHfTZWMi3X0XZGR6SYvw4+cTYjzO3bMwjdggHYE+aiL8pey6ifGBFNab2FXajMMl8t9rJ7KrtJkntxTx5u4K0iJ8SQqT07RlfhjySmOIiA3y4fcf5vLg0kxMFgc5lW1cOy2B9dmV2J0iM1JCuHZ6AkqFXLAn40mAXs1vLxjBxeOjqTdaSAjxYUxMAL46NfMyw1EIAgF6Ne/ur+YPH+bicIn4aJQ8d/VEPrlrFg0mKyG+Grmb49lI6zEw14JPGISkgmLwn/sF8Rw0qsnKyhL37ds31NM4IU6XyJ5jLewsbuaroiZya02khfuyaFQkKoXA4lERBPpoiArQyQ62Mj+Y3FojFz37jXvLKjZIj79OxQvXTfbo3dLcYUWnVuCrlbOnhj0l2+CdlWA1gUoHFz0Do38OyjPzdycIwn5RFLO+b5y80jjD1Bu7aem0EearJXyALadelAqBGSmhjIsNZMnoSB7dmM/uY61Y7DX8elEGt72ZQ3OHldXz07g8K5Zgg3frVxmZ76LVbOX281PRKBWkhPlS1GCipt1CaVMHkQE6Gs0W3s+p5o1vK4nw13L/4gymJYfInR6HK22V8N4qSTAAHBb48HaIHAMRIwd1KrJonCFEUeSbkmbuffsQTWYrUQE6nlo+nqlJISd8n0GrYkxsIC9en0WT2UpFaxe3v5Xjtrn+26cFxATquXBc9GB8DZmznGNNHZS3dGK1u3hpxzF3T5arpsRT1drFDa9ms/7maewsbeaprcWA5JJ7/SvZvHfbdMbHBQ3l9GW+i85G6G7zPOdygKlm0EVDfqw4Q5Q3d3LrG/tp6qnyrjNauO3NHHcvcEuPR9BbuyvYdKSOmjbP/t6+OjUJIQb+s73ULRi9fHK4FhmZ7+NIdTtrvixhd2krj3yc5xYMgLV7K5mVJnUHXJ9dxc4STxdmp0skt9Y02FOW6Y/LCXZP2xgcNrCawRAG+uMEXaEEv6jBm1/vxw76J56j1LZ3e93sWztt1LR3UdbUwadH6/i2tIX12VVsyWvg06P1fF3URJO5rxeCQiEM2DwnI1L2BpI5MZ1WB//4vJD3cmpQqxUePTZ6sTqkavAum4MQg2dW3uVZsWiUCl7cUcaOoibZXn2wqT0EH62GlxfD7v+AuQ6qsuGdG+DF+VI84+I1oO5JXlCq4cKnITR90Kcqb0+dIYJ9tSgEPHLltSoFDpfIhc9+Q5fNyczUEO5dmMYz20r44EANACOi/FizYiIp4ZJP0OVZsXxwoAZjtx2Q7CCWjhn8pwmZs4vqti62F0mrh9r2bpJDDZQ1d3qM0aoUTE4M4sZZiTicIlvyG3G6RK6YFEun1cH97x52j71/cTq3zkmRYxyDQXMxvH4xWNql47pD4BMCH9/Vt/LYeA/M+yPc+rUkKIYwCEkD5eDfwuXfiDNEcqiB310wwn0sCPCHC0fyj88K6LI5mZcZTkyAnl2lrRyoanePy68z8+GhGvfxqOgA3r99BmuumsBzV0/k3dtmyK07QUo1zH4ZNtwOh98GU/1Qz2hYISCJAsDGw3VcOz2B6AApEUOnVvCbJRnEB/sQbNBwzYt7eeKzQl69YTIvr5xETJAeX52ah5Zmktbz8PL01hIqW7u+6+NkziSNeX2C0UtLifdW1Y6/g8YXkuZA+IghEQwY4pWGIAhLgKcBJfCiKIqPHff6SuAJoPeuukYUxRcHdZIniVat5Oqp8UxODKbeaCE6UI9GKfC7DUcBmBAfyOaj9UQMkFH1TXEzd89LQ9nzVJcS5uvlUPqTpqMR3r8ZqrOl44NvwaQbYcnfQP3dGWo/JZwukeVT4nltVzlWh4u/by7k9vNTGB3tT2KIAX+9ihtf3eduDJZT2c5tb+awalYiT28rAaQHnd8uG8HjnxZgc7qwyBY2g4PiuNuwoIDgRJhzP9i74Mi7Ulzj/N/C7n9BQx6MvRJSzgff8EGf7pCJhiAISuBfwEKgGsgWBOEjURTzjhv6P1EU7xz0CZ4COrWqx5kWWjut1Bkt3DQrkTd2V2J1uChv7mROWhhfFHjahSwaGekWDJkBaCrsE4xecl6BKTdBxKihmdMwQ0SgoqWT+xZlUG/sJsRXS6PJim+SmqQwX3JrjF6dJDusDo/tVFGET4/UMTM1lG6bkzi5+G9wiBwDgQnQXiEdz/8j7H8NKr8FXSBMvx00frDjH9DV03itdBuc/ztJWAa5jmsoVxpTgBJRFMsABEFYD/wMOF40hjUOp4v8OhMWu4uEEB/C/XXsK2/lgfcOU9rUSWq4gYcvGIHF7qLL7sRksTMvM9wtHHPTw1j6PZ3WfvK4HN7nRBFcLu/zP1GSQg2E+mr5x+eFBPqo6bA4yEoMcsfK9BolWpXCHQzvRXWc40Cn1cmFY6OYmxGOn2yXPjgExsM170HJFilbqvwbSTBA2rb68q9w5Zt9gtHLN/+Eccul9w8iQykaMUD/fpbVwNQBxv1CEIQ5QBFwjyiK3j0wAUEQbgFuAYiPH5wfYpPJwvrsKp77qpRuu5M5aaHcszCdm1/fR1uXFMguaezkn1uL+cdlY3loaSYul7SV8NtlmYyKDmBsrHdHP5l+tJaB6IIFf4Kc16RjgPSl0hJeBpBE4b5F6cxMCeHbshYmxAVxXkYYwT1ZUgkhBu5blMGjm/Ld71k2JtIrzfam2UlcMj4GtUpe+Q4qoWnSf63lsPWP3q+bG77jjYPvFjHcs6c+BtaJomgVBOFW4DVg3kADRVF8HngeJBuRwZjc/so2/m9Lkfv46+JmogP1hPpq3aIBUuptTlU7a76Q9o4fvXQ0+bUmzksPkwXjeERRyibpXarvfBbKt0sphgv/DJV7IWqsZJ+glVOR+xMZoOfSibFcOC6a3aUtPLmlCFGEhaMiGBcXwIop8YyJ9edYcxcR/jpGRvlR0tRJh81Be5edm2YlMS8zXBaMoUTrB8HJfQ9HvfhHgiEUOvvV18z+NQTEDu78GFrRqAH69yqNpS/gDYAoiv3XYy8Cfx+EeZ00BfVmr3Nb8xtYNDKC4sYO9zmlQkDZb9/x6a3FLBoZIXtKDUTpl/C/q/oyR7JuBNEOFbvgs9/Byo2QMGNo5zjM2V3awvWv7HXHKz44WMOTV4xj2ZgopiWHMi25b2xkgJ7JCUE4XCIGuRnT0GMIgQv/CW9dDs6eWpmRl0DsFLjuIyko3nAUxlwByecNejwDhlY0soE0QRCSkMRiOXBV/wGCIESJoljXc3gxkM8wIjpQ73UuNdyXSQnBrN3bt4t2/fQEPsvtSxFt6bQxMT6I+BA50OiBuQ4+usMz1XDfyzDv95JoiC6oOwwxk0Elr9C+i3XZlR4BbqdLZEteA5MSggZ0ttWqlcjOZsOIpLlwy3Yp7VYfCOGjJDHxDR8WiR9DJhqiKDoEQbgT+Awp5fZlURRzBUH4M7BPFMWPgNWCIFwMOIBWYOVQzXcgJsYHMik+kP2VUo61QaPkzvNTmRAfREKoD3XtFgL0atZ8UeyxKrl4XBQzUkPRquTmSh50tYFpAMsUR4+ICArJqM3SNiSphmcLNod3goDdKX7n7repp5BU7hM+TBAEyU9qkD2lTpYhXY+KorgJ2HTcuT/0+/NDwEODPa+TJTXcj6dXTOBojZFOq5O0CF/GxgYCkJUQDAnSU55CgEc35VPZ0sXPJ8Zw0+xkIgPk+gIvFEqpyrWl2PO8SifFNGb+Cow10JAPJVshIA6ixoGup/ixu10a+xOv3Vg+JZ6t+Z5p3YtHRXjVCHVYHHxZ2MgzXxQjinDXvFTmZ4bjK8fZZE6A3E9jkDB22eiyOQnz08rWDANhroe1y2HclbDrWcm9U62HBY9IRm6WdijbAbNWw9vX9+33Tv0lTL0VCjbC/lcgMFHKXY+fNiT7vUONzeHiSE07RQ0dbDpci0qp4IrJcUyICyAywHNralt+A6te6/t3EmLQ8OSV45ibLq/iforI/TSGCU1mC0eqjdQaLSSG+GDQKvHXyy1cvWgqgLoD0FwI41aAT5BUh5E8DyxGaUsqKBk2P9QnGADFn4NKDzv/KR23lELFN7BqG0SNGZrvMoQcqGxj+Qu7UQgCE+ICcdlc+GlVXoIB8M7+agDC/LSsmpVEdVs3XxY04adVMy4uUO4aOVg05kF9rmQLEjkWQlI8X3e5pPiGqQb8Ino8p4ZuNSiLxhnA2GVnV1kzH+TUkBDiw8/GxzA6JgBjt51HN+az4WDfPv19i9K5dW4Kanm14UnvitfeBfte6js/5jKImyyl4VbtgbZyz/dlLIXsFzzPOaxShslPUDQ+OFCDKIJTFNlXIfVfeP3bCmamhnpl60X4SeHvm2cn83+fF2J1uNCqFDSaLNwyJ5nx8XJvjTNCexU05ILLDmEjIDS177WaHHjtQrD1mEv6RsJ1H0J4Zt+Ygk/g/Zuk32uFSnK3HXflkAmHfOc6A2w6Wsttb+bweV4DL+w4xornd1NYb6a40ewhGABPbyum/Dj3URkgLAOCUz3PjbkcgpLAWA3tlRCSDjPu8hxj6xy4XuMnGtcYKI1boxTotjuxHGfdf+nEWNLCfSmoN2F1uEgONXD/4gyOtXRy65v7+deXxTQYu72uJ/MDaC6BNy6FdVfC/66RbM5rD0mvuZyw5799ggHQUQ/Fn/Udt5TBhtskwQDJHeGTu6G5rz5ssJFF4zRpNlvdHdB6MVsdHK0x0mX1NnyzO0WP5jgyPfhHw4q1ffGIpY/D/D9A2zF4eYkUs2gvlzx4fv4CRI2XxCJpLiz8k+e1egPkP0EunRBD/12lyYlBzBsRwVUv7Obal/awLb8Bi12yZYkL0vPnS0bhdIlcPC6aexam89dN+eTXmWkwWXnisyLey6n5jk+SOSlKv/BM7LC0w97/gtMBTru0HXs8Lf0K+zqbwNbh+brLAR3fVSH+4yNvT50mIiID5RKolAIhBg33LEgDQeDLgkYOVrUzKtqfuGC5PmNAwjJg3sPSHq5CIf3D2vFrSJkHtTlQe6Bv7MVrJMEIigdrp7SkL/8G/GMgcZZUVfsTZGJ8IOtvmc47+6twuUTmpoexev1B9+vZr+3jjVVTCPHR8PsPj5JT1c6zyyewPruKIzVGj/oOgFd3lXN5VhxhfnIlxykxkCjUHQKnFTQGmLjS8/caIGk2lHwBwUngFymZFva3TldqpIesIUIWjdMkzE/H3fPT3BboAME+aoJ8NKx4YTcmiwNBgBWT45iTFsqF46IJ8pED4SdE0bMAtnVCxU5pmyrnNc8xW/8Iv/xG+rPWIFXHJp83eHMcpqiUCqYkBTMlKRiXS+TGV7O9xrydXcX4+CB3fVFNeze7y1qYmBDoNTbUT+vu0yFzCqTMg+zjujmMWyEJBkgxuY562Pk0qLRSWrnVDOU7JOfbtIVw2Uvw7ipJODQG+NlzUjB8iJBF4zRwukQqW7uYkBDIsysm8O7+KhJDDFyRFcfq9QcwWaRtAFGEtXurWH/LNNIjZL+kE2KslgLeTUUQMRrSloDa4P201d0Gdu+WpjJ9KBQC/nrvf+JatZKqVmkfXaUQaO+y43CJOF0QFaCjzij9XAUBfrM4Qy76Ox3ip0tp49sfl7L+Jt4Aoy7pe90vAuY+ABOugfZq+OYfULyl7/XORmmbtn/HvuDkIU0nl0XjFDF22XlzTwXPbCvG6nCxcEQ4f/v5WKID9ZQ1dVDa5B3sbhygb7NMPzqb4MM7oexL6ThhBmStkradJlwjxTC++adUFT7qUgiIGdr5ngVcOz2RjUfqcfbsO2lVCjIj/DBbpQcah0vEVyfdBl74uoybZiehVAgIwOy0MMbFea8+ZH4APsEw424YeSmIDghI8LbAEQTJeLDsK0/BAGmVMv5qqedGUMKgTftEyKJxihyoauOJz/r2K7fkNxIXXMYlE2Kw2J2MiPInv87TdlqvUWLsshPgIz+5DUhDfp9g6IOkmMV7q/pe9w2Xlu+mGph1r7SclzkhE+ODeOeX09le2IRSIRAbpOdvnxYwMyWUn42P5uNDtWwvauLu+Wk8/3UZz31Vyty0UP548SiS5e6RZwaF4uRs/JUD/D477d7nhhhZNE6RDouDXy2Q9hU7rA5e31XBxiN1mC0ONhys4eELRvLvr0qpN1lQKwVumJkkHU+wcO30xKGd/HDF3q8n9YiLveMYHY3Scn7UpRDy0wx0/1CUCoGJ8UFM7Km5OFpjxOpwsuFgDRPiArlvUQap4b58nlvPddMT0GuUzMsIkwXjx8Jhl7IARVFaObRVQOlWKTU3aY7kaJu3QRprCIWxK6Si1mGELBqnQGljB//4vJDyFukmd9HYKB7/xRjqjBb8dCoK6s089mkB/7h8rNuocNORekqbOihv6WLRqMgBe4X/5AlLl1YY3W1SnUV/t1ulGmbdA12tsP9VSF0AcVP6fKdkTorRMQF8cPtMCuvNKAUBf52KxzYXUNtuoalDqgX4qrCRN26cKnfuO9OYG2DXM7DnP4AIFz0LX/0NjD2O2PtegkWPSs62/tFSbUbFt5D9PIz42bB5UJJF4xTYktfgFozJiUHo1EruefuQ+/Xbz0vB2G2nqKGDZ3saL/XicLkGTNGVQQrwXfMBfPVXqNwDE6+TskoApt0OB9dKgXKAPf+Gi56GSSuHbLpnKylhvqSE+dLaaWVzbj2hflrGxgYS6qflX1+UkF9nxmx1yKJxpin7Er5d03dsrJQEQ1BI9iH2Lvj673DTNvjkV1IsD6ByFxR9DsvfkmIkXW1Qf0gy7wzsNe0MGLSvIYvGKXCoui+LZ3ZaGE9u8azOfOmbY6yalUSEvxZfrYoOa1+P6zvPS5Udbk9EzAS44nXpH4atQyrU2/+qZK/QKxi9bH1EMiiMHCP1G5A5aURR5H/ZVTy+uS8uF+6n5cZZidS0dxPiK6WFt3RYcbhEeWV8Jsj/2PNYFKXVcsYFUmq51g9iVkp1R72C0UvlLqkKXKmFw+t7Vis9zH1AivENkguCLBqnwMKREeTVmVgyKpLkMAMjo/zJ6xf0tjpcBPqomZwYxNqbp7J+bxUlTWauzIrnvIywIZz5WYJaDwE9Da7C0qW89sJN3uPs3VD2BTTlw7TbBneOZzk17d1eq+BGs5Ugg4bLJsVhd4psOlLNE5sL6bY7uXVuCr+YGEOYnywep0xMluQj1YvGAJkXwpY/9J3L/xiufm/g93c1Q/1RqaK8P18/IcUAI0ef+TkPgCwap8Ck+CCWjY7ilV3HcDhFfjY+misnx9LUYZM8p+rNLBkdSXywVMAzNjYQp0uUXUNPFa2vVLOh9vEMlo+9UrIX6W6VAoj+UUM3x7MMUcSdhtufmAA9qeG+fF3UyD3/69tyfezTAvy0Kq6eNjzSPs9KMi+AnNehrUyq9A5KgR1PeI5x2qB6r5TskftB3/nEOVD4mZQIcvz+tuiSCgIHCVk0fiDdNic17V18fLgWi13qkPZeTg0R/jrW7q0kKcTAE5ePdQtGL7JgnCYRI+H6j6U6jdZSSJkvWaa3lEhZJj/B3hmnQ3SgnpvnJLOm32oj0EdNQogPe8ta+DzP29vorT2VXDoxBh+NfNs4Jf5/e+cdHlWV/vHPSZ303kNIyZLatgAAIABJREFUQgm9ht4UUBAV7KIgqKjs/tziruuq6+qqu+6ybtFd18ba14oFRUW6gKh06TVAKAlJSO/9/v54Z5iZzAQCJpmU83meeTJz58ydMzcz973nLd83oifc/jnk7BcpkIaV4mdRMPJeSBwnBsEUBL4R8M510jvGP1IyCS0ExkFIIlQWS/Dcv2W9Gfq/fwFsP17As6sOk5ZTwiUpkfh4ufPqhmMAfHM4l0Hxwaw9dIaHF+/mg3tGEeav6wialfhUmPwkrFsAO9+D8jzZPuEhuXLTNBl3N8WckV2JD/bhw22n6BsbyBX9ornrra1U1tRz64gEh9d0CfXB001LivwoguLldmy99IKZ8CCctmqD4e4l2lMnvpfn07+RQPnAmTDxMVmZTHwUdi2S/jNdRsIVT0srgNVPyqp7xHzof7OsSloAbTSayKHsEm59ZePZ1cW7m09wZf8Y7hqThJ/Jg5ggE1uO5QOQllNGZmEFgSZPMosq8HJ3IybYx5XT7ziEJEi3vh6Xy9VWSAJ0HevqWbVLIgNNzByewA1D46moruWmhRvJLq7ilmFdGJIQTLi/F7ml1UT4ezNndFfC/bz5+mAOfWIDiQ/Rops/Cr8IMOpEBn3y4xL49g6AoXdCYBfIf1cMBshqY8e7MP056DkFVj0Bg26BKX+GyD7SwOzdG61uqxWPgnKHUfe2yNRdajSUUlOBfwHuwCuGYSxo8Lw38BYwFMgDbjYMI7215wliNCwGw8KyvVk8emVvHv98HwBT+0Vz49B4Pt+ViVKKx5bsZdHWk/h5u/Pw1N5cPSgWf29tpy+a8nw48rV05vPwhqAEKXzy1c2Cfgwe7m4UlNey/3QJt49OpLy6lre+P87M4Ql4uSm6Rfrz60U7qaqV73+PSH9emZtK1zC/8+xZ0yhe/jDlL7DsQTi1BWKHwrC7RaG5qghObnR8zclNord2y3uSZusbat6+0THOseklGDCzRbIKXbbWVEq5A88DVwB9gFuUUn0aDJsHFBiG0R14Bvhr687Sio+nu8M2Py93ThZYC9CW7ckiOsjE41f3ZeORXN7bfIK6eoPiiloeXrybHScKHfahuQAyf5AgoikIfMPlx3JyU6sGATsqof6ejOkWRoDJgx9OFBLu58V/1qSx5XgBb3yXftZgABzOKWVreoELZ9vOKTwBH8ySE/v4B2DMr2DiI1Kw6uYG3oHidmpIUBe5cApNthoMEDHPhvhFgEfLqGm70kE5HEgzDOOoYRjVwPvAjAZjZgAWLYmPgEnKWWuyVqB3TCC9o+2rj+8cm8TnO+0782UUVDC5dySvfZvusI8fTugf2kVTekYyT9b8SYLha/4IPqFSy1He4LhWl8uPS9Nk/L09eezqPuSXVVNYUcOlvaLw8XQnMsCbjALH7n2ZuqPfxZO5Q3pq5KXB2gXwzd/hqwetFz9u7jD8HgiMt74mfoQIeN74uqNwYcIo8LeJXyg3uPQR5x0tmwFX+krigJM2j08BIxobYxhGrVKqCAgDcltlhjbEBvvw8m1D2Xo8n4zCCgZ3CeZQdgk5JVV241ITQ/Dz9qR7pD+ZRfaqtrEhOq5x0eQfsWryWPjhLektYLnSqq+X1cfav8jV3NA7YMBNLm1Y055IiQ5kQs8IiitrOJhVzF+u60d+mdRuvPLNMbuxQ3T/8Iun0onHoegU1JYD5uMa1QfmLZeCPjdP6Rnu10hWVERPuP0LOLlZMqjiUyF2cItNv8M42JVS9wD3ACQkOGZ+XAz19QZZxZW4uymiAk0khPmSEOZLbV09Hu5uRAWa6BZx4qwM+oikUMb1CMfHy51fTu7J5vT8s3GQXtH+pHbVP7SLptpJX/X6OlnK+5hXgNm74a3pVmXQVX+Qq7dLH7E2dtKcpaSyhiNnyqiqrSM53I+IABP944PILq7kje+Os2JfNtMHxRLu58WMQbF8ues0ASYPfjetN4O0ZPrFE9FLUsRt4xCDZonqgS2WTKumEN5Tbq2AK41GBtDF5nG8eZuzMaeUUh5AEBIQd8AwjIXAQoDU1NQfre6UXVzJO5tO8N/1RzF5uvHglF4MSgjmy12n2ZCWy5R+0UzrH8N7d48kLacUD3dF90h/Qv0kzXZo1xA+u3csh7NLMHm60yc2kFidQXXxBHeVK62yM9ZtEb3AzeYrnL1XZBZ8Qqx57JtegtQ7de+NBmQVVfKnL/fxxa7TAHSP9OfFWUM4mlvGo5/tPTvuHysO8dDUXtTU1rHkZ2MI9fMiOkh/j38UMYPgprdh2UNyUTN1gSR2HF4hRazBXc6/DxfiSqOxBeihlEpCjMNM4NYGY5YAc4HvgRuANYbROnJ/y/Zk8e/V0hC+oqaOXRlFvPrtMQ7nSJP3H04WsvlYPv+eOYjR3cOd7iMlOoCUaN2pr1kI7w4znodvn4XTu8S/mzQefG2yQ/yjxEBUlUix056PpZajhQKC7Zkt6XlnDQZAWk4pb31/nNr6urPbLk2JZFCXIHy83HlgSi+StFx68+DhBb2vgoQRkH8M3rvZGoMLSYJZi1pt1XAxXLTRUErdYRjG6xf7enOM4mfAciTl9jXDMPYqpZ4EthqGsQR4FfifUioNyEcMS4tTXl3L+1tO2G2LCPA+azAsrDmQQ3peOf3iWk9hslMTP0y6oGXvFWMQMxhiB8lzZw7B4vn2K5HJT4jxaMwX3InZdarIYdupgnJuSu1CmL+J+BAfDmeX8MwquXDqGurDwjmppERrKfpmwycMdjxln7RRcEy693VEowE8AVy00QAwDGMpsLTBtsds7lcCN/6Y97gYvNzd6Bbhz/7T1lTOxnK23LR8RevhGwopU6Vi1s3D2rmvplyu2CoLocdlkq5YWyEBxNghrp1zG6VhG9cuoT6MTA7jp+9sP7tt9ogEBnUJZsfJQo7nV/DqhmM8dW1/PN11fKhZqKu2rwa3kL3XcVsb4pxGQym1q7GngJapUW8DeLi7MW9sEmsO5FBeLcv1gvJqhiQEs92m1uLawXEkhuvK2BalqgTOHISyXFk1hPcUdVDLc2mrpbGNm6dUyJZkSzru2dcXwdj7wVNLutgyLDGEG4bE8dF2CSPeNrIr/1hhL/H/9qYT/Oqynuw4Kd/5747kUVherZVumwtPkxTgZf5gv73HFNfMp4mcb6URBUwBGhYYKOC7FplRG2FwQgiL/280B7JK8HR3o29MIChYe/AMW47lMz4lgrHdw7V4W0tSWQzf/EPiGCAri5nvShEUwNF18OFc6/iTG+GyP9rvY/3fpOtZdN/WmXM7ISrQh8dn9GPumCSqa+uorTPsCvgs1NTVM6FnBAPig4gONJFRUKGNRnPS+2pZJW99RbpTjv6l1F20Yc53xvsC8DcMw2ENpZRa2yIzakOkRAc6+HDnjvZj7uhE10yos5Gz12owAFCw4jGI6iuqn5sXOr4mY5usRnLNV81GPVQVO47T4O/tQX9zPC67uJLoQBNZxdbaIh9Pd3pHB7ItvYDn1qTh7+3BPeOTSAjzPZslqPmRBMVJUDwgSqRFDi6Vyu8BN7XZBI5zOicNw5hnGMaGRp5rmOmk0TQvJWZ5bjcP6Uw26l7pdJa5U+IYzuQTvPyg1qaoMihe3FqacxIVaOLF2UPoFiGuv5ggEy/OHsyKfVl8f1Sy3EuravnnysNn+95rmoGC46KnVlEAu96TdNuSTOtFTxtE+1Ya4eiZUr45nMt3abmMT4kgtWsIASZPogNNVNfV46bAy8OdwvJqdpwsZP/pEhLCfBmSEEyMzmNvHkK6SgbCiJ/A3k+gIF22b3sdrnlRjMjBL6TID8R9NeBmyE2T3ssJY2Dqn3VzpiYyOCGERfNHkVtaTYivJ5mFFaxy0lcjPbeM0d2cp5lrLpCqEilKPbxCHmdsl7TbNqzcrI2GE47klLJg2QFW7stmVHIYp4squeW/m6ioruO2UQmYPN3ZfqKQ+eOSOZJbwuNL9p997aU9I/jHzYMI9WubS8t2RUQfuOYlkViwGAwLKx+F616VtNq8I+IP7nWVSEXf9AbU1YFPMJh0ncyFEObvfbYPjKeHKNw2TM/VfWKakYp8q8GwUHBMGoy1UXTunJn6eoOsogryS6vZebKAlfuyUQrG9QjnP2vSyC+rpqKmjoXrj1FZU8/ejCLmvL6Zmlr7/Xx96AyHs/XyvVnw9BZ3lMlJHUxlMRz9Glb8XvzA1eVwdC28PA5enwYZm3U3vx9JiK83j17ZB5On9TQxrGsIR8+UsuuUVmxuFmornX9P3RxVtdsKeqUBZBSU8/am47yz6QSRAd78erIU1sQF+zgU9AGsP3SGYYmhrNiXzYGsYuJDfDhlowRqSdPV/EiKM+D9WdD3GvAw2ccqUu+EgBi45CEoLwSTP6z/u/X5j+6E2Z9A90mtP+92RnVdHZ5ubjgTkB6WFMqH80fxbVoeVXX1pOeW8ddlBwnyOcqSn43RPTXOR12N1F2cOSjuU68AiEyxakq5+0Dv6bDvM+trogdAaJJr5tsEOr3RqK83eHvTcV5cexSALiG+FFfVcknPCLYdLyDcyVI8JsiH3FJRt/U3edilKob4ep4NJmp+JFl7IWcflOdKd7MDX4qratCt0oxm2YMybujtkLbJ8fUHv9JG4xycLqxg5f5sFm/PYGB8MDcP70LvGMeK77yyahYsO2C3raiihrScUm00zseBL+Djeda4W+o82P8ZXPo7aVEclgyh3WH8byB7H4R1k1hceA/rPsrzRUq9OBOCE8So+LhOhaLTG42c0kre3SQK7dP6R+Pn7cEji/dw++hEesUEEB1kIibQxGlzKqKPpzvjeoTz5Bf78PZwY2z3CGpqDVbuy2ZglyB+OaknCfqH1DxUm1d5pTmw/GFIHCeyz1H94P1brOOy98iqgwZFUloS/SyVNbWk55WDAV3D/PBwU7y47ghvfX8cEC21L3Zn8vFPRxPq58XhnFJKK2tJDPejqqYeL3c3quvs6zh0Zfh5KEiHJb+wGgyAra+K6nLWHjEaQXEwdA6c3CptXsO6WaVxAKrKpNZo4wvWbZf+HsbcBx6erfZRbOn0RsPHw52oQG9Kq2pJiQ7kmZWS6vbqhmOE+XnxwJSePHPzQDYey8fDTZRsNx7N47aRCVzaK5IHPtzF87OG8KvLehBg8sTkpMOf5iIJiBbp86pikZE+th76XgeHltmPy9gO0/8DR1ZDrbm/iU+I9BHXcLqogmdXHmLRtlMYBkwfFMs945J4Z5O9vlpuaTX7M4s5lFPKoq0nOVVQQbCvJy/NGsItIxJ487v0s2NHJYdqMc7zUVHovEbIO9C+p0ZIYuNp4bmH7A0GwLq/QK8rpeeGC+j0RiPI14uHp/XmsU/3kJ5r37Mhr6ya/36Tzmu3D6Wmtp7n1h+lrt4gKdyX+eO7sf5QLnll1ZwqKGdkcvP34u30ePlLzOLo15B/FJIvlW0WZVvfMBj5UzEUNeUwZwnkHpHAYvwQaQl7ZK2IGIYlQ2S/Tikn8s2hXD7Yeurs4yU7Mpk+IAZ3pajDKho9vkc4JVW17DxZyMjkMLpH+vPC12n8fcVB7pvck5ggb7KKqogM8GZcj3CiAnVl+DkJiBF3UqGNcXb3FAHNoCbKnzszOvV1Lm1x3OmNBsDY7uE8d+tgth0vZPEP1pYe43qEM61/NN8dyaN/fBAvzBrMgdMlhPt7U1VTx9sbZWmfHK4lo1uE0GTY9LJcsUUPlBhF8qUweI74eGMHwdd/FoMBUux38zuSwpj+DSSMhO1vyEoE4PpXoH+r61+6nFX7HWstvtiVyU8mJPPvNWmAxOKGdA3hgY+scnMB3h7cNT6Z59eksfFYHl1D/RiVHEZssI+WEmkKAVHSK2P578RV5RsqbqXv/i1SIcUnodfV5678Dk2Si6NymzZCwYkuLVjVRgPxzQ7qEkKYnxdLd2ey7XghvWMCSAr34+FP9pwdd/OwLtw5JpH/fZ/O25tOEuTjyeNX96FPrJaLbhG8/WHSo9LGMnsv9L1WUnADomHGf+CLX1sNBsiSf8+HYjSKM2Hnu1LHkb1XViNLHxAF3Dbe5Ka5SU0MYUWDIr2EMD/mjEqkZ3QAK/dlM6l3JH/+0j7YXVJVS119PcOSQth2vJDnvz7CZ/eO0QbjQqiukL4v/W+Q7pPf/gv8I6E0SwLkd6+1j2E0JDgBbv1Qkj4ytkLXcTDlKTFILkIbDRu6hPrx8uxUDmWXUG8YzH19i93zH2w5ycxhXXj4ij7MHZOEr6cHcbrvd8sSEA19psvNFncvKDzuOL7sjMQzijPl8d7Fsjo5tEykGly4rHcVl/WJZvH2DPab5T+Sw/2YPjCOcH9vrhoQy1UDYnl9w1EMHPub+Xi6c0nPSJ5aKgWs6XllDIjXrV6bTNwgWP24KBSAuE7H/grW/EnidPlHz200AOKHSvp4RSH4hoC3a2NJ2mg0wMvDjSFdQ9h/upi6escfUWlVLUWVNZzIK6emrp6qujrtnnIFxZmQcgUc/9Z+e9xQcWNZqK8RPzJIE6fAOJGi3vOJXO31vxESRsuqpoOSFO7Hm3cO53BOCfUG9IjyJzrQ/mIns6iS64fE88LaI2e3+Xq5Exfsw/0f7jy7zVkKeqelNBvqaiVLr7FC0rDuMOdTSN8gygU+IfDD/6w1R34RUHhSjId3gIhtOvsumgLl1gbQRsPMmZJKvtqTxVvfHyc60MT9l/eke6QfaTnW4HionxfH88rZf7qY9Ydy2ZCWS6CPB+/cNfKsWqimldj/OZz4XnzEu94XUcMx90kth2GTGtrvBvjm75Kue8XTUHQc3rhSXAUAuxbBDW9Av2td8jFai8hAE5HnCFxfNSCWF9em8avLerLpaB4R/t5cPzSeXy/aQU2dXDxd0S+aCG00oKpUvn+rH5fv0ch7pVaoMY2zsO5yO7wSFs2FGvN3b8jtYiD+e4n0iwHRWZvwW/s2xm0M1Uott1uV1NRUY+vWrRf0mpfXH+EvS60+XR9PN966cwQL1x9h/eFc+sYGcfXAGP6+/CBl1XX8YlJ33vzuOEUVNVw5IJpBXYIZFB/MwC4heHno/PUWpa4WXp8Kp7aIfzjlSqivBa9AqbY99JXIjPSeDt0uAeUp40wBsGkhfPWA/f4iesO8FW3mSs5V7MssZndGIaF+XnQJ8eXRT/cwtkc4tfUGnu5u7DhZyPVD4rhyQCevfznyNfzvGvttN78txiR7ryRgJIywthnOPyp1GUa9VIj7BIN3MITEw6I74OT39vu6bTF0m9g6n8UGpdQ2wzBSzzdOrzSQVcYr3xyz21ZRU8/B7BL+fcsQNh/L4/Xv0vnjF/uweKw+3pbB5X2i+HDbKQ5nl1JbZ/DnpQd4Z94IRnfXCqAtirsHdB0jRqM8TyrGI1IkwypmoFzVefmJVINfg/+F4UTipb7GfnXSSekTG3g2qeN0UQX7s0rYcty+/9r0gZ3cYIBonNky4GbY8KwEqgG+fw5G/wImPganf4B3brAKEPaZAcoNRvxULn4yNjvuvzjDcVsbQl8SI9lTASZH++nj6Y6PlztZxZWsPXgG2xBHvWGc1eoZ3S2c7ccLMQwpCqyt0yegFmfgTAjtBpc9CUUZoju17EEphkoYKcHFhgYD5Dn3BimO4+6Xqz/NWaIDTdwzPtluW/cIP7pH+HE8r4yazvwdt+hGWQhNthoMCxufh1ObZVVrq1i77zNRNNi0UOIbiRPEtTr6F3DJw+KaCoyH+rZ7fF2y0lBKhQIfAIlAOnCTYRgNW8qilKoDdpsfnjAMY3rDMc1BsK8Xv52Swk/e3n52W7i/F4O6yIlkQHwwPp7uVNRYr1KvHxLPB1tPcvWAGOrqDc6YtajKqmupd5KFomlmInvD7I9h0RzIMtcWFGeKvMg96xuvlo0ZBLd/KXIOxadFC6jbJa027fZAXmkV6XllDO4SzJ9m9OWDraeY0DMCb083bl64karaemaNSGD+hG7EBnfC7MGkCZJQca4VQX2drIBP73J8rrZCajQ8vODyJ+HQctj+ljUb0N0TZi+GpHEtM/8fiavcUw8Bqw3DWKCUesj8+EEn4yoMwzhPPlrzMCElgvfvHsm3abmEB3gzulsY3SIli6F3TCAfzB/JB1tOciK/nJnDupASHcD4nuH86cv9fL7r9Nn93DEmCS93LSXS4pQXiJBbWHc4s198xSB/8480bjSUklqPLsPlas5NL7ZtScsp4Zfv72BvplQiX94niqn9ovDycOepL619Y978/jixwT7Mn9DNVVN1HRE9Ye4XUmBaWwmRvWDra5JNZSFxHGTugK6jJXPKFg8fGHaXrHij+0vxqW36eF2NpOTO/rhNZvW5ymjMAC4x338TWItzo9Fq+Hh6MLJbGCO7Oc9aGBAfzID4YAwbt1R1bR0PTEnh5fVHqayu465xSYzR8YyW5+Rm+Pw+6SEe1Q8u+yOs+aM1I6qpmScWg1FdIc1wfELBqxNeOZupqzd4d9OJswYDYMW+bPrHB3HQSYvXj7ad4tYRCQSYXCOc51LCkuVmYfbHsPFFOLlRuu75hsG3z8h3s/QM5B4UIzH21xCeAomjra8tyXTcf9FJqKnQRsOGKMMwLJfnWUBj5Y0mpdRWoBZYYBjGp43tUCl1D3APQEJCQnPOteH7nL3v5eHOuB4RjEgOBUMea1qYguPw3s2yygBRuP32WRg0CzYvlDTGyAsQcsvaDWueEtmRxLEw8fdy9dcJKa2q4euDZxy2p+eWkRjuqNzcKzoAk/7OC9H94ep/SQbVltfh6ydke/Y+GHCTrEgMQ4pMu0+yXthUloApxHF/A2eCf0Trzf8CaDGjoZRaBUQ7eeoR2weGYRhKqcaCAF0Nw8hQSiUDa5RSuw3DOOJsoGEYC4GFICm3P2LqF4x2R7UihcetBsNCSZZkU/WeLisP26B25g7I2S9Ch7GD7CVESrKkyZPFNXBomfzI563olH3F/b09Gd8jnGMNhDu7R/oTaPIgKcyPY3ll5rEe3D0+GU+dXm7F3VMqtgdcD2XZsP11Sbz4/Bf241Y8CrM/kmI+o07Umyc9BtveENWCftdLz4w2SosZDcMwJjf2nFIqWykVYxjGaaVUDJDTyD4yzH+PKqXWAoMBp0ZD00kwBUtcwra+yM1dKmkbxjHSVsP7t1qrbyN6w7S/SUVuYJz8oG19yaYgKMuBgqOd0mi4uylmj+zK90fzOJQtvUyuGhDD9EGxRAWYuCQlin2ni6mprSclOuBszE/TgJCuog+VOg8OL3N8PmuXZFR5B8gFTq8rYdlDIpXjHSjf28FzWn/eTcRV7qklwFxggfnvZw0HKKVCgHLDMKqUUuHAGODpVp2lpu0R3hPGPwjrFli3TXwMwnrYj8tPh/VP27eIPbNfVh57P5Fg48RHrfscNEt8y56+oDpv+VKPqADeuWskx3JL8fJwp1uE39mYRVyIj9Zaayr11VBZ4Lz+p8fl1sI/gJRp0gp280LwjxY3V+zg1pvrBeKqX8cCYJFSah5wHLgJQCmVCvzEMIy7gN7Ay0qpeqSeZIFhGPtcNF9NW8HTBKPuheQJUp8RFA/R/Ry7mJVmi6ZPQyrywdMP6gpFDXf43RDcFVY+al29/PC2pOVGpLT852mDRAR4ExGg5UIuirw02Pm+pNEmT4CQJBj9c9j8X7mAiR0ikiMeNsfXN0RUcHtPl8I/97Z90eKS2RmGkQc4NG82DGMrcJf5/ndA54xIas6NKVBSGZ1RXiB9xN29oedUqcewJbyHtWvayU3Sq3nVE/burrIzcGJjpzUamoukLA8+mW8t9MvaJTG2bhPlQsfNHXLTpDmYM87VV6MN0bZNmkZzIWTthU9/Ij9WL3+45gXpM77nYzE0434DaWtkrClI8uV9w2T10RDbdpwaTVPIP+JYGZ69BwbeAiseETXcK/7msjatzYU2GpqOQVUJLH/YWh1eXSrV4nO/FG0g7wAI6iodzxLHSB68u7dU7o74qUiQWFAK4s6r26bR2OPWyOnU0wTTnxPZm8QxrTunFkAbDU3HoPQMHFvnuL04AwbebH1cEgsf32Xt+Lf+abjjK5jyZ/E7+wRLyuO2N6THc1iy4z41nRvDkMI7Tx/7Phph3aH31SKbbiFpPBxZI3GzDuLu1EZD0zEwBcqPNi/Nfrt/pP3j/UvsW8TW10ol74CZ0GWYrFhWPyHZVRG9YPz9LT93TfshLw12vAeHl0P3yZJ1F27O3DMFSk/wbpPg6Nfyfawskvqf1HltuvbiQtBGQ9Mx8AuHq56Bd260ptkOvs36Q60shtM7rG03bSnNgcztcGiFFGhZdKwOLYUxv7B2/tN0bsrzYfFPRb0WRE0gbbW0YvWPkGrwktMSuxhzn6x+a8pFZyqyt2vn3oxoo6HpOCSOg/nrJSBpChE5ER9zR8WDX8Gn82HyE7D7Q/vXjbhH4huDboHaKol7bH8Luk3WBkNjJe+I1WBYyNolTZaOrJFbYKwYiu1vwq2LIGWKa+bagmijoek4KCV+44a+4/ICKQY0DPE3X/WsxCzqamQlEZIEb0wT1xRIrvy0v4sEtkZjwc1GLihuCCRfCl4BojDww/+sz8UPkyrvz+8TSRpnfV3aMVo4RtMJMMRgePlB6p3w/fMiExLWDXZ9JCuTKhsVV6NemuU0bLaj6dyE9YA+18DYX0FQFxHKVMCOt+3Hndoi8Yz8I/YNmDoIeqWh6QQoGP1L+YEfXQd5h+VmIXmcyIfYBsgrCp23htV0XkwBcPmfYd1f5KICxJ1pONFHNQxIGO2YiNEB0CsNTcenOAOUAWkrIWun4/P5RyGggTr/qP+TlYlGY4u7u2RDgYheRqSIcbAlJAlQMO1pqQ/qYOiVhqbj4xMCp7ZK683uk0Uq3ZbEcdB7htRsVBZJv+bEcVL4V18r2VimING6ytolWTIRKSIRoTv/dUzyjsrFRmUR1FVLnUXeUVGtHXqHKAb4RcAnd8Oon8v34dQMWuF2AAAgAElEQVRmiB8uxaThPcGvic3A2hnaaGg6PkFxkhG14x0xIF1Hw/HvJHCeeqc0XwqIFqn04kwJan7zd9keOxj2fALjfi0aVZnmPvLunpJqmTTepR9N0wJkbIMtr0JplqTUgnTdm/w4HPxSCvsSx0ptT10NbHwBJjwkLYQri6Xor4MaDABlOPPHtXNSU1ONrVu3nn+gpvOQtQfevEqa3KRMk/qNxDFyZehpkjFlefDODVbDABLQHPcbuepc80f7fcYMgjlLrGm9mvZPdTl8MFsuIL5+yv650GToMgJ2viePb35bxk56TApEy8xdD/0jYfZiUV9uRyilthmGcV79HL3S0HR8svZA9m649mVRwDXqRFsqqr80XCrPk0yp0lx7gwFSAVyYDrXVjvstOAY1ZdpodCSqSqQINM5JP4v8o9DvOutjUxD0vxnyj1kNBkix6LbXITAeUqZ2qMI+0EZD09HJ2C4rjGpzC1OfULjtM/FB73wPlt4vPmvfULh2oQQubdNv/SIgtLsYh4YMmAn+jbW317RLfENFGsTDSbOppPESGwOIGyrFo1P/DJ/c5Tg27wic3glbX4M7ltq3GW7n6CiepmPzwztWgwEig77nQ8g9AF/8QgwGiETE57+QLoAW+t8AQ+bChmekQPCmt6Wzmpu7yF2PmG9f8KVpfxRlwLa3pFf8N/+Uro7uXpIBNfFRaS8MkiE1fD5UFUv84tqFUrTnFy6rjYbEp4osetEJOHOgdT9TC6NXGpqOTcFRx235x8xuqgbxvOJMiO4r1eBnDkka7umd4mLY9Z64I678m3T+W/c0HPhCAukdMK2yU1BdJskNuz+Qxwe+kIy4mIGw8XmY8xn0nALKXdyXpkBp1WppllRfJyuKoDi4/lX46kGR5B80S1xZNRUyTnWsa3NtNDQdmyFzRBPIloEzRSOoIf5RcoWZMBoKTsCHc6Tr2jf/sI5ZNFdk1DFE/DD3kLgqNO2P/KNWg2Ehew/0vkpO+EfXw4QHJMU6e49caATFmVOtPWHX+/DVb6XAzz8SbnhN4hnrFljVlsN6iFpyB0IbDU3HJukSuOpfsP6vcsU34WFIGicuiCv+Cst/J1eMXv5w7UtWY+IXJhLX+xbb78+olyyrwFjYtUhOPBP/ALEDW/2jaX4kjWWOWrbXlEkCxNZXYaWN23LSH0SX7PNfWreV5sjj2R/D+Adg/xfioup1pRiaDoRLjIZS6kbgcaA3MNzcG9zZuKnAvwB34BXDMBa02iQ1HQPfEEi9HXpfCQYiYW1h6J1SxFeWK1pCtg2XvP2lx/iR1U52asCh5SI7krZasrPuWt2hgp2dgtAk6HWVuKUshPcQA+DmAT2myP959RP2r1vzR7jWiS5ZwTEpBB04U24dFFetNPYA1wEvNzZAKeUOPA9cBpwCtiillhiGsa91pqjpUPhFOG7z8IKovo2/Jm4wjLtf3FSWq0+fEHmdrU5VabaI02mj0b7wDhBXY3yqSIPED5PYxcGvYNaHsOTn0PcaWYnaYtTL96AhgbHg03GL+iy4xGgYhrEfQNm2SnRkOJBmGMZR89j3gRmANhqalscwJMjp6QszP4CMrZKOGT0A3nNyFenl3/pz1Px4QrpKLxU3DzjwpcS06mokASLvsATL/cJlNdrjMqnvcXOH4ESY9DisecKsoOwP17wIgdGu/kQtTluOacQBtm3WTgEjGhuslLoHuAcgISGhZWem6fikb5Dq8LNdAOfA8LvBOwiG3Q0bbILjg2ZBeMfo/9wpcfOQ/zdIjAokSwpEUubSR6R47/ROWPsX2X5oBcz4D3SfJMWhwQkitd8JaDGjoZRaBTgzu48YhvFZc7+fYRgLgYUgMiLNvX9NJ6K8AJb+xmowAH54C/pfL26uiJ4w4wXwCQblIW4sUyNptxVFUo1enCmuj+j+OkW3rdF1tKwiy/PlsVIiH+MXIcZi7QJp35q2yvqaU5th90cw8RHXzNmFtJjRMAxj8o/cRQZg6ySON2/TaJqH+jpxP3gHyInCQlUJ5B50HF9TAa9MsubfByXAbZ803jOhpgK++7eIH1qY/ASMvBc8dBtZl2AYkHtY0qX9IkSNNrof3Pg/kc6vKRfj/u2/Yea7kLNPjIlPsCRNpH9j3dfh5SJk6emkerwD05bdU1uAHkqpJMRYzARude2UNB2GnP2w5RU4tk4EDAfPgfDu8pxfBKRcaZ9VkzBSWsRaDAZIte+xdZJxY0t1mchQnDlk78YCybzpOaXD6RG1G9JWwge3ySpSKbjsTzBsnvx/vXyk655yh6ufhc3/hW2vWV878qey8rBUeHefDB4m13wOF+KqlNtrgeeACOBLpdQOwzCmKKVikdTaaYZh1CqlfgYsR1JuXzMMY68r5qvpYBSfhvdukRRJgNx/wfGNMGuRXFF6+YhyaXkBnPgWvANhzK9g5aOO+8pLk1x+Dy8oOC4y6ns+EvXcHpc51gLU13bIFqDtgsKT8OlPJcut/01QVwW1FbLyiBkgRZqWQs1TW+0NBshFxpj7xGhEDxApmXMn83RIXJU9tRhY7GR7JjDN5vFSYGkrTk3TGchLsxoMC6c2SRA0bog8jkiBW9+H4lMiGxLSVV63ooEP2y9S+i/EDJJ8/j0fy/bsPeK28gkROXYLgbHS0EfTvJTmSL1MXQ2EJkoldsMGWeX5sjLof5MEtI162e7pJ9lPtZXSjc8nyLlhr6uBhFFwxzKJefg7SePuBLRl95RG0zK4NxJPcGuw3RQIpj7WxynTxHDsfE9awabeKcV/Rr2sUPZ+Yh3rYZIT2cx34fsX4cASiB0CV/4DAmOa/zN1Zkqy4PNfQfxQSZPN3ieFmYNmiQGxEBAFQ26HDf+0GoyovqIX9d9LpRtf/DC4+t/SO6OhwQ9PEV0qv/DW/HRtjo6lpKXRNIXwFGtKpYUhc8+fMhkQJZ3ZRsyXvgrb3xR57Kh+UJYD0WYpkVE/gxnPS4bNOzeCXyjcsw5u+9S6ktE0H6d3QnC8/D92vi8tedc/DcsetJe5D4iWk75tYWbf66TZUmWhPD61RaRl/CLg1g/FiLi5Q/fL4MbXOr3BAL3S0HRGfEPgyn9Ky9fTO6RNZ8Io8PI99+u8/GDIbHj7erlSvfxPoj+1eaGcWIbdLYq44SlSIZ4yVTSudrwnHeFm/Ef2U1cjqxAvf93AqTkoz5NVgbsnjP21uP88vSE3TeovEsaAT6CMjeorbsPSHHlcW+G4v6Nfy/NdhklL38pC8A2T/79GGw1NJyW4CwTfDAOd9EI4F4njxKede1hOLlm7ZHt9HWx6CWa+J2mZG1+Q7W7u0pdh44vSMtYwJA1390eysrn8Kek33QkDqs1GWHdJRugxRSTNa6skGyr5Uqnen/gojPmlGJWgOLjxTfj4Lvl/eDoxBMGJ1loaU6DcNGfRRkOjuRDcPSFhBIQkwion2VTVpVaDAWJMvv0XpN4hMZMVv4d9n8pzp3fC29fB3WshJEEMUU2FGJMAJ3WxNZVmeYs0ubKOHeRc4r09U18vjbK8/K29289H9ADJiLM97ic2QkCMFFOuXQC9rwYM8PCVYr6710gsxNNPsqF2mSXS3b3g6mc6bZC7KWijodFcKAXHJRMnLhUOr7B/rsaJu6OiQMbW1cL+BmIIddVy8tr4Aux4W7YFJ8It7zqKKR74Ej6+0/o4aQJc91+JtXQE8o/C1tclAy16oPSyOFcMKD8dynPFOBSdcnz+2Droc43cvvotHF0rjbSmLoC+11oN89QFEtMqzxeDbel/YRgS49j+JpSegaFzpeVrJ6/o10ZDo7kQ0lbDR3eKn/uKv0HmD1LwBRJcD+kqWkb1tdbXhCRB7GC5Hz9MagAs2TtKQUWu1WAAFKZLZ8BLHpYsHg8vkSFZ9lv7uRxbB1m726/RsIhCFhyTVVjBMTmpB8XDoaVSI3PX1xDeTWpmasqtJ/oDX8Jn90r7Vb8IKdJrSEQfcPOSE//RtbKtskhqNUKTxYUFIiGSOMbx9Zk/wBtXWlsCH14ON7wuSRCdGG00NJqmUnDcajAAVv1BMqniUkVKIriruK1ufENktSsK5OR0/SvSuGn/52JAUqbJCuX4d9BtshSdNeTEd7L6iBsKg26VFUx5nuO4qnZcKHhktVRn15RLg6xR98KxbyTGU1kIZw7Cmf3Ssnf578RwDp4DA26Ej+dZT+ZlZ6C+BrqOheNm4UFTEIz8CVQUwtZXHN87L81qNBojfYP1PSys/7tkUjWmNdYJ0EZDo2kqpVlWgwFystvwDNy2WNrCglw9R/aDm98Fo06CtBWF8PoU+4KxGS/Apb+Xq+T8I47vFZcqQoc73pF+DyHJ0Gu6vXvL3VO0k9ojRadg8Xxr+qtRD989J4qy65+Gsb+S1ZZRD+/eZF2ZbXpBXEi2J3OlZMUREAWX/k5WefV1UsE/6xMI6wlnGnRUcNZfpSHOkhPc3KGT5yzoOg2Npqn4Rjj2zXDzAH+boPWRNfDSaHjjCnhrOuxdLNXmtgajx2VQkA4fzoXXp0qAO3We9fmwbtBlBGRslxNgaY5Im0z+AwyYKcHaiF5SRxB5jiZSrqauVj6bM8rypEeFw2uqJCXZMCB+BHh4Ww2GhcpCWZmAnMSv+Ju4tEKTZEW2/S3p615RIP+fGf+R/VjoOVXqNc5H4jhHbanxv9ExDVdPQKNpN4QlS6OdT+6StE43D7jqWatgYVGG49Xzqj/AZX+07sPdU3Spvn7Kuu2j2+GWRRKcLTolrhZLT2oPk0hbgBiT6f+Gib8Xd1hpjrixghPkdrGUnpGTr29o42NqqySttalumVNbJc04/6hkjvW8wj4jyT9SMr+KM63blJs0RAqKl9qKIXMgc4f1eQ+TVOF7+koCwP7PJYvMqIfPfiarDS9/6dFdkC7H5ENz0easj6RWxtMkhrYp2VExA+H2L6UWpzRH3IRdRzft83dgtNHQaC6EXlfB/A2S4x8QJRpHFlmS8jxrUNxCXbWctDxMom0U1U8qxRuy8T+AkuC2fyRMeFBSda/8h7i4LHh4y8l96+uw6jFxw/iGilxJwqgL+yxlubIS+vZZWb1MfFRqHbz95LO4e0tB28nN4oYrOAZD75AWqM5Sgi1k7YE3r7Jmki3ZDpc9KbUSFgJj4LpXYNFt8l4eJhh7H2TvhZvftiYOoCRtNmu3GMvv/yPZZiCJByN+Cu/eYK38ri6Vxkn9brD29s7cLorF81aJDHpTUUpcg/GpTX9NJ0AbDY3mQnBzkyZMEU5iCf6Rsiootmn7ohQExMLcL2DLf+WKPSAGWGmzTw9IngTVJdJv/MwB+P55kR2J7OX4Ptn77IUTy/Ph03th3ooLk7k4vFKaTVn46A65ss7YJrLg/pEw7jdy8rXIgS97UCRTRv1cKuudkbXLMfV4wzMiFGiru5U4RuRVijNkheDlBz6h1ir5nP1yG/cbOWYbn7caDJBkgt7THd+r20TY/LL9tpoKiRFdiNHQOEUbDY2muQiIhuv/C4vmyFW8h0lWCuE9JG02bqgEx3P2i+hhZSH4R8G4+8UFkn9Erp5TpomoXl2V+P69/OwL3Yqd1CTkH5H3bKrRqKkUI2ZLYJykFG/4pzwuOgkfzBKZ+FWPW8dteQUSRkOPRvqsOROE9DCJC6whwV3k1pBTWyUmVF0mLVUHzxFj1pCCdMm2KjsjqxSQAkHbGIaFhoKUmotCGw2NpjnpOgbuXicndt8wCO1mleh2c4PTu+HE9zDjOQnUegdKHKS2Ssbs+kCMRo8pknL6/ixxaY1/QFRcwRrjsCUkSd6vKZTlijsnMN7+RNx9Mux8136sUS/ztLjXQIzYie+lB4XlM9iuIGIGifGyDXRPfLTxDocNqa2Cb/4pBqPf9fK+a/8i7rfdH9qPjewtKcvegdLfYsd7EjOKGyx1HBb8wuX4VBRbdag0F4U2GhpNcxMcL7eGZGyHN6ZZ3SluHnDFX60Gw8KhZRIYT1sBg2dLBtXh5SK/HtZNArmX/RFWPy4xDZ8QuOaFpgV3j38nNSR5aXDVM3DoK2v6al0NmELsXUAgUhv1NdbHQ+8Qw/XhHVIX4R8l++oxBdw9ZGV1+1eyoqosEomU2AtQ962pkPoMEP2ovMNyP7yHrNYytsmqZfh8yVbbbZYAOfA5XLsQMnbChPvhpv9JzMY/QlJs379FhCoHmRuA5h8Vg+MbJrEpTyerE40D2mhoNK3F3k/t/e/1tXKibogpSLKj8o6Kgi6AKVjUeMO6SaB6xE+g2yTRafKPkkr085F/TAT8LOm/6/4Kk/4gQXCQmEp0P4mXWDoOBsRILcglj4gLLLynSHdsXwUnN8mY0mz4YDbc9hlkbpMAekhXWP0HqCqVyvboJqS4WvAJFneUuwfs+0ySA6L6w8BbIaKvxCaol0LAXldKIkB5vsx5z8fynlXFcnyzdslqyFIYuf5vkHKFBNbfnyXjlJsE6lPnnV/pWKPrNDSaVqPcSV1CdZljzcC4+0Wy/cgq67bKQvj2ORkPEiPx9oOj6+DdG0W1NfOHc79/4XH7epGSLDEQMQMlPXX1k5JpdOU/pR/2uPth8CxYfI+sTLpPlpNzzylWg2HBqIf09RL7WP6wuNwG3yaxhi9/Lcq/TaGmUuo7+kyHHe9KT++6ajFGn/0fnN4O6xZI4d+J76UeY+AtthORFck3/xCDk3qHvaSLUlBRBIt/IgbDMvcVv4ecBgWAGqfolYZG01qk3gkHlzboBtcDrn4Ocvaa4wNBcpLMT3N8fdZOaQLl5ScrltVPWtvLFqTDsfVwz1pJBXb3dgw8m0LkpGnbt9zdU7KVAmIgKAFyDkBtjci+11ZaXVeePqLOm7FdAtd+EY7pxZYVC0jMpCRLXD/ledIcqc/0xo9NSbboSW1/U1KMB892PIlXFlmL+ixUFICHj/Vx0qVSsb38YfmcR9aInMi6p+X58Q9KWq5thpsF25oRTaO4xGgopW4EHgd6A8MNw9jayLh0oASoA2oNw9AJ05r2R366CPClrRF5jPo66acx7tdykvMJhFjzauPTe819H5zERJImiI8+5Qo5mdu2lwUpDtz9EexfIquH4Xfbr2IiespJc90CeZx6p8QaMndYe6JnbJPiuUNLra4zD5NUW299VR5//ZTIdXz1oLVau9eVIttuS1WJ7Iu8c3dFNAzY9gas/bM8Pr1Dxrt7OWo/NczM8gmR+Erv6dD/RllNrf2r1TBWFYNPmEijD5oliQq1laIkXJhuvy9nx1zjgKtWGnuA64CXzzcQuNQwDCfreo2mHVCeL26V49/K47SVIng3ezEEOlGn7XuN+OHTN0jcYutrcuKMHybxhuUPi1vmsiflZG6JkcQMhJoy+Nqs9pq1Swra7lplLQ709BFRwOQJYrhWPS77B5Etieov4n4BMaIBVVEo8YKIXtZMJD+zlMqml+D2pVByGnzDpQr+vQYNrSL7QNoqkR23cyE1oDhTGlPZsvcTqc+wGBKQwkr/aKsx8fKXwHfPy2HYnVB4QhRsq0vt9xWcACNtVIQJhGtfknTi8jwxRFMWSCaW5ry4xGgYhrEfQOluZZqOTt5hq8GwkLYSSk45NxpdR8tJrLIETu+CWz8Qw1NwTNJOQVYSo38OEx4SmRIQQ/Tts/b7qiiQDCbbinJToATODy2HDJsFfrdLJTAOYghWPS5X8bMXQ2WBnKQn/UHcOlXFEtgOioOuo6QuoixH+qJ/+y85mY/9lbjahs8X2ffcQxIQTxzr6DZTblJXYXuyzz0sq4KrnhVpFU+T1GF8+6xkjkX1kRVGaLL1NUFd5D1sCx9Du0lFucNxHiWFhUUnxTCGdpfAu+a8tPWjZAArlFIG8LJhGAtdPSGN5oKwjR80ZbuXHyRfAlEDYe9HIh1eXSqpphMflZO5h7fUewy8VU6c6evlJOruaR/0BUnrtaWuRtRkGxYBGvWOc6ookFhJdD+Y/CSs/L1VrmPXInEhVRbBltfEAA2eLf0mCtLFJRccB7vel8yvpPGSnusd4NhYKTAGJj4GX9jIjPhHy/bKAvjiPsdjlDTe8dgpJXMI7w5H1srfpEucFw9C44WFmnPSYkZDKbUKcCZQ84hhGJ852e6MsYZhZCilIoGVSqkDhmGsb+T97gHuAUhI+BHibRpNcxLWQ1w/ttlGSZfK9nORd8Be4iNjm5x8E8eK22fTC5A8Xlq+rnta9KEueVgC5bWVErR285LCQFsselNjfmkfFC88Kfu1DT4Hxokg4vY3RBnWYjAs5B6CJT+zFvF99VvoM0NWKic3ywrimpfEnbb+b2KYdrwnOlkxA+z31fc6MRIHl0n8pMdlYpR8QmHKn6Vla30NjLlPquYbwydY5tpz6rmPr+aiaTGjYRhGIxoDF7SPDPPfHKXUYmA44NRomFchCwFSU1MbuYzTaFoZvzA5ce5fIv79lKnim7foKzVGnpMeG8fWwg1vwg9vySrA01dcRDe8Iq6s4xtkBeDlL+J/3SY5Xkl7+4tx2PU+THpc6kBKzcV8M54XiZC0VWKMEsfBmj9KDCFxnON86msd5c33fy6puic3m6vJ82Hb69bni05KeuvMd+wlxn0CJZW35xT7/fmGSBymzzWyv6B4530uNK1Gm3VPKaX8ADfDMErM9y8HnnTxtDSaCycsWU7iY+87/1gLfk4kN8J7wr7FItR3/atiMACiB8C+JZLWChJzWP2kGI+QJHsD5R0gfTnevk5qGfpeK6KIplCJD4z/rawQzhyQkztIxXpUX8c2tv5OYjJuHvb9L2zrQiwc32BOL76AvhSWz6pxOS4p7lNKXauUOgWMAr5USi03b49VSi01D4sCNiildgKbgS8Nw1jmivlqNK1O7EC5urbg6SOB6B6Xw7yVslqxUJ4Puxc57iNnv8QXGmIKgVE/k/qFoDhZpXw6XwxFQLQUEJ7aYh3v7ikZSHOXQI+pEpOY9jcxCLZBdpC01gNfWh+HJDm+f5eREmTXtEtclT21GFjsZHsmMM18/yhwAdoDGk0HwqLnNPxu68k5IsX5WC8/CE+RlFNb/MJEVdeWulpZLWxeaF9kCJK+62mSvhVe/rDnQ2kzO/UvENFbBBdrKqWI8MCX8nfMfRJrKDolLqyI3pLuG5oMI++VPtyj7hWpdxDRwilPdfrud+2ZNuue0mg6Pb6hEvg+H54miSOc+M4qMxKRIkVtoTZFdRnbpZvemf0w9E4oy4YfzPULAdHW4HxYN5ESueRBMR4+wdZ9BCdIXKKiUNxQG/4pCrP+kWLYhs2DOZ+JcbL027j099JLo6oEQhJ1xlI7RxsNjaYj0HWUNHrK/EFiEMFdpD7BEs84c0D6U1gyoLJ2w/B7pC9G3FBImSIyJRV5EN5LFF+dVUiH9xADlb5BKrdBYijVJZIlBvariLoaee8zByT7q6EMiKbdoY2GRtNRiBviWANhIXuvY8rs9jdh/ndw7Gt482pJv1VKCuoGzW682C2qLxSfhslPSGqvl59Im+z5RIoTvfysY9NWwfu3WoPj8cPhxjd0YLsdo82+RtMZcNa1zsMEVUWw7CFrvYZhwNIHnAsm2pK1SyrI/SIABSsfk5Tg6nLrmLJc+Ooh+2yqU5sdNao07Qq90tBoOgPRA0Rmo+ikddvER0WvqmEVeV21tJk9V0+nuKGiN3V4hXXbwFvsK81rKpy3pq0svKiPoGkb6JWGRtMZCE2E2R/D1AUw7C645X1RhQ2Md8xkMgWf330UNxSmPy+ps27u0s1vyFz7wjv/aEehQuUmmV6adoteaWg0nYWIFMe0XZ9guPlt+OQe6cAXEAPXvixZTufC2x+GzBahw7oakRzxaOAC8/CUoLlyk97jgXFwxdOOEiKadoUyGhNOa8ekpqYaW7c6bdGh0WicUZwpTZX8IkUDqjmprRGpEk8fR6FETZtBKbWtKT2L9EpDo9FAYKzcWgIPTylWtA2Ia9ot2mhoNJqWo7ZatKY2/Esk3kf/HLpNlL4emnaJDoRrNJqW49QWEUc8tlZ6bnw4V/qPa9ot2mhoNJqW48CXjs2dvn9BNKw07RJtNDQaTcvh5e+4zTsAlLvjdk27QBsNjUbTcqRcIZXnFpSSuEbD9FxNu0EHwjUaTcsRNxjuXAaHVkgFecpUiB3q6llpfgTaaGg0mpYldrDcNB0C7Z7SaDQaTZPRRkOj0Wg0TUYbDY1Go9E0GW00NBqNRtNktNHQaDQaTZPRRkOj0Wg0TaZDSqMrpUqAg66eRxMIB3JdPYnzoOfYPOg5Ng96js1Hw3l2NQzjXP0agY5bp3GwKbrwrkYptbWtz1PPsXnQc2we9Bybj4udp3ZPaTQajabJaKOh0Wg0mibTUY3GQldPoIm0h3nqOTYPeo7Ng55j83FR8+yQgXCNRqPRtAwddaWh0Wg0mhZAGw2NRqPRNJkOYTSUUn9TSh1QSu1SSi1WSgU3Mm6qUuqgUipNKfVQK8/xRqXUXqVUvVKq0TQ3pVS6Umq3UmqHUmpra87R/P5Nnacrj2WoUmqlUuqw+W9II+PqzMdxh1JqSSvN7ZzHRSnlrZT6wPz8JqVUYmvM6wLneLtS6ozNsburlef3mlIqRym1p5HnlVLq3+b571JKDWnN+dnM43zzvEQpVWRzHB9r5fl1UUp9rZTaZ/5N/9LJmAs/loZhtPsbcDngYb7/V+CvTsa4A0eAZMAL2An0acU59gZSgLVA6jnGpQPhLjyW551nGziWTwMPme8/5Oz/bX6utJWP3XmPC/B/wEvm+zOBD9rgHG8H/uPC7+B4YAiwp5HnpwFfAQoYCWxqo/O8BPjChccxBhhivh8AHHLyv77gY9khVhqGYawwDKPW/HAjEO9k2HAgzTCMo4ZhVAPvAzNacY77DcNo81XqTZynS4+l+b3eNN9/E7imFd/7XDTluNjO/SNgklJKtbE5uhTDMNYD+ecYMgN4yxA2AsFKqZjWmZ2VJszTpRiGcdowjO3m+8x133oAAAPPSURBVCXAfiCuwbALPpYdwmg04E7EcjYkDjhp8/gUjgewLWAAK5RS25RS97h6Mo3g6mMZZRjGafP9LCCqkXEmpdRWpdRGpVRrGJamHJezY8wXOkVAWCvMzeH9zTT2v7ve7K74SCnVpXWm1mRc/f27EEYppXYqpb5SSvV11STMbtDBwKYGT13wsWw3MiJKqVVAtJOnHjEM4zPzmEeAWuCd1pybhabMsQmMNQwjQykVCaxUSh0wX9E0G800zxblXHO0fWAYhqGUaixvvKv5WCYDa5RSuw3DONLcc+2AfA68ZxhGlVJqPrIymujiObVHtiPfwVKl1DTgU6BHa09CKeUPfAzcZxhG8Y/dX7sxGoZhTD7X80qp24GrgEmG2VnXgAzA9oop3ryt2TjfHJu4jwzz3xyl1GLEndCsRqMZ5unSY6mUylZKxRiGcdq8lM5pZB+WY3lUKbUWudJqSaPRlONiGXNKKeUBBAF5LTinhpx3joZh2M7nFSSG1JZo8e9fc2B7gjYMY6lS6gWlVLhhGK0mZqiU8kQMxjuGYXziZMgFH8sO4Z5SSk0FfgtMNwyjvJFhW4AeSqkkpZQXEoRslYyapqKU8lNKBVjuIwF+p5kZLsbVx3IJMNd8fy7gsDpSSoUopbzN98OBMcC+Fp5XU46L7dxvANY0cpHjsjk28GlPR3zhbYklwBxz5s9IoMjGXdlmUEpFW+JVSqnhyPm21S4QzO/9KrDfMIx/NjLswo+lqyL7zZwlkIb45XaYb5bslFhgaYNMgUPI1eYjrTzHaxF/YRWQDSxvOEcko2Wn+ba3tefY1Hm2gWMZBqwGDgOrgFDz9lTgFfP90cBu87HcDcxrpbk5HBfgSeSCBsAEfGj+zm4Gkl3wPz7fHP9i/v7tBL4GerXy/N4DTgM15u/iPOAnwE/MzyvgefP8d3OObEQXz/NnNsdxIzC6lec3FomR7rI5N077scdSy4hoNBqNpsl0CPeURqPRaFoHbTQ0Go1G02S00dBoNBpNk9FGQ6PRaDRNRhsNjUaj0TQZbTQ0mlbgfMqyGk17QafcajQtjFLKHamLuAzJ598C3GIYRksXG2o0zY5eaWg0LU+bV5bVaJqKNhoaTcvTnlRZNZpzoo2GRqPRaJqMNhoaTcvTLlRZNZqmoI2GRtPyuFoVWKNpNtpNPw2Npr1iGEatUupnwHKkR/drhmHsdfG0NJqLQqfcajQajabJaPeURqPRaJqMNhoajUajaTLaaGg0Go2myWijodFoNJomo42GRqPRaJqMNhoajUajaTLaaGg0Go2myfw/SDOOTn+Gbs8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3\\. Apply Kmeans with k = 2 to the dataset"
      ],
      "metadata": {
        "id": "4WLK4TdOlCQG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 2).fit(scaled_features)"
      ],
      "metadata": {
        "id": "0T5sLmIclJP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4\\. Create a scatter plot of the predictions, where the data points belonging to each predicted cluster are plotted in a different color"
      ],
      "metadata": {
        "id": "H_hGqVc_lJpl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = kmeans.predict(scaled_features)"
      ],
      "metadata": {
        "id": "GryrNUMz2ao2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = scaled_features[0], y = scaled_features[1], hue = pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "F7dIW3O02d52",
        "outputId": "8e0d0ed7-5501-40a2-b035-58bdbe5fc45b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f926552a290>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5\\. Apply DBSCAN to the dataset with parameters eps = 0.25 and min_samples = 5"
      ],
      "metadata": {
        "id": "QAd01V49lcUj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_predict = DBSCAN(eps = 0.25, min_samples = 5).fit_predict(scaled_features)"
      ],
      "metadata": {
        "id": "1A1mvl00lliy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6\\. Create a scatter plot of the predictions, where the data points belonging to each predicted cluster are plotted in a different color"
      ],
      "metadata": {
        "id": "osqcnB8plX_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x = scaled_features[0], y = scaled_features[1], hue = cluster_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Tc8fDAb858OQ",
        "outputId": "ead7fad0-3b67-42e2-96a2-3c1214d00197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f92654a9ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7\\.  What are your observations about how the outputs of kmeans and dbscan differ?"
      ],
      "metadata": {
        "id": "uLB3WtuWlq_M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "dbscan seems to perform a better job at clustering"
      ],
      "metadata": {
        "id": "QAHldU0Plw6c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 3: Hierarchical Clustering"
      ],
      "metadata": {
        "id": "URysI-0rl12w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a dataset for hierarchical clustering\n",
        "X = np.array([[5,3],[10,15],[15,12],[24,10],[30,30],[85,70],[71,80],[60,78],[70,55],[80,91],])"
      ],
      "metadata": {
        "id": "itly_RnxqKv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the dataset\n",
        "labels = range(1, 11)\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X[:,0],X[:,1], label='True Position')\n",
        "\n",
        "for label, x, y in zip(labels, X[:, 0], X[:, 1]): plt.annotate(label, xy=(x, y), xytext=(-3, 3), textcoords='offset points', ha='right', va='bottom')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "KdWhWDfiqgXA",
        "outputId": "1dc84b51-f1ac-42fe-b6b2-89879110436d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1\\.  Plot the dendogram for the data using a linkage type of 'single' as shown below\n",
        "\n",
        "The libraries dendrogram and linkage were already imported during setup at the beginning of this notebook - please review their documentation online."
      ],
      "metadata": {
        "id": "VX6rfEFgl-A-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(X)"
      ],
      "metadata": {
        "id": "ltlSTVa2-i2B",
        "outputId": "26716c2e-c262-476c-e83f-b91a3e2a6680",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z = linkage(X, 'single')\n",
        "dn = dendrogram(Z)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "wyS_-jZvqgZ4",
        "outputId": "d8447d1f-8540-436e-c38c-2b73cd81cf89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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fWmnjSet7eF8BXtT/To6hG/P83JB1JDkmyel7Xock59KdfTHoWHySn+lreHCSl9OdmXPJkDXQHUB/q99XHgr8Dt2ZMYNK8vN0w1kTP/vk+6qqqRvwSuDSkdo+lm5s827ga8CWEWqYAf6F7oycO4Brgd8Y6XWoJbdXDlzDyX2736Yb4tpzO3fgOmaBBeA24Fa6YYMNI/xdfIpuSO924BPAM0b4u/jT/vdwF/BPwCNGqOEIulM7b6cbyvkz4MgR6vhL4J3TbMPJrCSpUS0MoUiSlmGAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUqP8DUbdTCfzE93kAAAAASUVORK5CYII=\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}