{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f31d0d5"
      },
      "source": [
        "# Example: Hierarchical clustering on a CITE-seq dataset\n",
        "\n",
        "This notebook presents a structured GraphHDBSCAN workflow on a CITE-seq dataset.\n",
        "\n",
        "Sections:\n",
        "1. installation\n",
        "2. imports and setup\n",
        "3. data loading / preparation\n",
        "4. model construction and fitting\n",
        "5. condensed tree visualization\n",
        "6. optional interactive exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e62cb70e"
      },
      "source": [
        "## Installation\n",
        "\n",
        "These commands are only required when running the notebook in a fresh environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d6208d6"
      },
      "source": [
        "Install required package(s):\n",
        "\n",
        "```bash\n",
        "!pip install git+https://github.com/Campello-Lab/GraphHDBSCAN.git\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4e2f914f"
      },
      "source": [
        "## Build and fit the model\n",
        "\n",
        "Configure GraphHDBSCAN, fit the model, and inspect the resulting hierarchical clustering state."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzoye5UBSRAc",
        "outputId": "f2aec48c-54b8-4a11-db2b-10a0ac6259f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/hdbscan/robust_single_linkage_.py:175: SyntaxWarning: invalid escape sequence '\\{'\n",
            "  $max \\{ core_k(a), core_k(b), 1/\\alpha d(a,b) \\}$.\n"
          ]
        }
      ],
      "source": [
        "from coresg_graphhdbscan import GraphCoreSGHDBSCAN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6182ebb"
      },
      "source": [
        "## Load and prepare the dataset\n",
        "\n",
        "Load the CITE-seq data and prepare the representation used by the clustering pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "M1FwLvVWSw-P"
      },
      "outputs": [],
      "source": [
        "import scanpy as sc\n",
        "adata =  sc.read_h5ad(\"/content/T_cells_CiteSeq_GEX_processed.h5ad\") #file path\n",
        "count_matrix = adata.X #cells as rows and genes as columns\n",
        "true_labels = adata.obs['cell_labels']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "1x2y_IyFTN12"
      },
      "outputs": [],
      "source": [
        "g = GraphCoreSGHDBSCAN(\n",
        "    min_samples=range(2,6),\n",
        "    sim_graph_method=\"sc_umap\",\n",
        "    n_neighbors=7,\n",
        "    no_noise=True,\n",
        "    metric=\"euclidean\",\n",
        "    min_cluster_size=55,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXK_0VcyVxlS",
        "outputId": "845d6f87-d662-41aa-b313-063ddf0447f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CORE-SG] (precomputed) CORE-SG graph has 20841 edges\n",
            "[CORE-SG] m= 2: MST+tree+labels in 0.1340s\n",
            "[CORE-SG] m= 3: MST+tree+labels in 0.1288s\n",
            "[CORE-SG] m= 4: MST+tree+labels in 0.1228s\n",
            "[CORE-SG] m= 5: MST+tree+labels in 0.1331s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GraphCoreSGHDBSCAN(min_samples_list=[2, 3, 4, 5], metric='euclidean', eps=1e-12, min_cluster_size=55, X_=None, N_=None, D_=None, core_={}, kmax_=None, edges_ut_=None, idx_with_self_=None, dst_with_self_=None, idx_no_self_=None, dst_no_self_=None, A_knn_=None, msts_={}, mst_times_={}, models_={}, times_={})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "g.fit(adata.X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d6c4376"
      },
      "source": [
        "## Visualize the hierarchy\n",
        "\n",
        "A static condensed tree is included first for reliable rendering in the documentation. The interactive widget is shown afterwards for live notebook use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "_K6MiT0imWau",
        "outputId": "2561ffa5-f153-4f5b-a2db-50735d5cc814"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Condensed Tree for m = 3'}, ylabel='$\\\\lambda$ value'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "g.plot_condensed_tree(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2e90695"
      },
      "source": [
        "> The widget below is most useful in a live Jupyter environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427,
          "referenced_widgets": [
            "f0c21bbf2aa848448baabc077f1723a0",
            "d6a88d45a0774d958ee209b655571936",
            "5de4f858cbd84fa4a3dffa144efc30d4",
            "a9b6ce5ce9bc4b42a953c1ef712499c5",
            "0f9ee36c8da64a919b0e9f223dd77e6b",
            "a135cddd9e1541f3a49df0a4bc91dfb9",
            "6aa3ddf7ce4b4f8193a1addffba667a7"
          ]
        },
        "id": "113d6029",
        "outputId": "8eaa59e8-714b-455a-bd0b-451dab07a178"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(SelectionSlider(continuous_update=False, description='m', layout=Layout(width='500px'), options…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0c21bbf2aa848448baabc077f1723a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "SelectionSlider(continuous_update=False, description='m', layout=Layout(width='500px'), options=(2, 3, 4, 5), …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6a88d45a0774d958ee209b655571936"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "g.interactive_condensed_tree()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "f0c21bbf2aa848448baabc077f1723a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d6a88d45a0774d958ee209b655571936",
              "IPY_MODEL_5de4f858cbd84fa4a3dffa144efc30d4"
            ],
            "layout": "IPY_MODEL_a9b6ce5ce9bc4b42a953c1ef712499c5"
          }
        },
        "d6a88d45a0774d958ee209b655571936": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SelectionSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SelectionSliderModel",
            "_options_labels": [
              "2",
              "3",
              "4",
              "5"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "SelectionSliderView",
            "continuous_update": false,
            "description": "m",
            "description_tooltip": null,
            "disabled": false,
            "index": 0,
            "layout": "IPY_MODEL_0f9ee36c8da64a919b0e9f223dd77e6b",
            "orientation": "horizontal",
            "readout": true,
            "style": "IPY_MODEL_a135cddd9e1541f3a49df0a4bc91dfb9"
          }
        },
        "5de4f858cbd84fa4a3dffa144efc30d4": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_6aa3ddf7ce4b4f8193a1addffba667a7",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 2 Axes>",
                  "image/png": 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p0sV46aWXXI4G/umnn4xBgwYZdrvdCAsLMwYNGmRs377dY8cvl/aZii1YsMCIiYkxgoODjdDQUKNdu3bG+PHjjSNHjrjMc+f7K01Zxy8bhmFs27bNiIuLM2rXrm2EhIQYN9xwg7Fx40aXOcXHL3/11VdufAO/Hr+8ZMkSt+IU/90eP37crfieUHzEeGnD7PHNAIBf2QzDxK5aAAAAALAQe2QAAAAA+BwKGQAAAAA+h0IGAAAAgM+pNIXM3Llz1bRpUwUFBalTp04uR62WZsmSJYqOjlZQUJDatWunzz77rIIyBQAAAGC1SlHIvPfee0pMTNTTTz+tbdu2qX379oqLi9OxY8dKnb9x40YNGDBAw4YN0/bt29W/f3/179+/3HcfBwAAAOCbKsWpZZ06ddIf//hH5z0KHA6HIiMj9cgjj2jChAkl5t97773Ky8vTsmXLnNc6d+6sDh06aP78+RWWNwAAAABrWH5DzIKCAm3dulUTJ050XvPz81OvXr2UlpZW6mvS0tKUmJjoci0uLk5Lly4tdX5+fr7zLtySZBiGCgoKVL9+fdlstkv/EAAAAKhWzp49q4KCAq/EDggIUFBQkFdiVyWWFzI//vijioqKFB4e7nI9PDxcGRkZpb4mKyur1PlZWVmlzk9KStIzzzxT4npOTo7sdrvJzAGgenBk/cHqFKoVv4jvrU4BwEWcPXtWUU1qK+tYkVfiR0RE6MCBAxQzF2F5IVMRJk6c6NLByc3NVWRkpIUZAQAAwFcVFBQo61iRDmxtInuoZ7ec5/7iUFTMIRUUFFDIXITlhUz9+vVVo0YNZWdnu1zPzs5WREREqa+JiIgo1/zAwEAFBgZ6JmEAAABAkj3Uz+OFDNxn+TcfEBCgmJgYpaamOq85HA6lpqYqNja21NfExsa6zJeklJSUMucDAAAAnlZkOLwy4B7LOzKSlJiYqPj4eF177bW67rrrNGvWLOXl5Wno0KGSpMGDB+uKK65QUlKSJGn06NG6/vrrNX36dPXt21fvvvuuvv76ay1YsMDKjwEAAACgglSKQubee+/V8ePHNWnSJGVlZalDhw5auXKlc0N/Zmam/Px+bR516dJFixcv1pNPPqm//e1vuuqqq7R06VK1bdvWqo8AAACAasYhQw559k4mno5XlVWK+8hUtNzcXIWFhXFqGQC4gVPLKhanlgGVX/HPklm7r/TKZv+Ilpn8nOqGStGRAQAAAHyNQw55ekeL5yNWXZZv9gcAAACA8qIjAwAAAJhQZBgq8vAuDU/Hq8ooZAAAAAAT2OxvLZaWAQAAAPA5dGQAAAAAExwyVERHxjJ0ZAAAAAD4HDoyAAAAgAnskbEWHRkAAAAAPoeODAAAAGACxy9bi44MAAAAAJ9DRwYAAAAwwfG/4emYcA+FDAAAAGBCkReOX/Z0vKqMpWUAAAAAfA4dGQAAAMCEIuP88HRMuIeODAAAAACfQ0cGAAAAMIHN/taiIwMAAADA59CRAQAAAExwyKYi2TweE+6hIwMAAADA59CRAQAAAExwGOeHp2PCPRQyAAAAgAlFXlha5ul4VRlLywAAAAD4HDoyAAAAgAl0ZKxFRwYAAACAz6EjAwAAAJjgMGxyGB4+ftnD8aoyOjIAAAAAfA4dGQAAAMAE9shYi44MAAAA4KMmT54sm83mMqKjo53Pnz17VgkJCbrssstUu3Zt3XnnncrOznaJkZmZqb59+yokJEQNGzbUuHHjVFhY6DJn3bp16tixowIDA9WiRQslJydXxMe7IAoZAAAAwIQi+XlllFebNm109OhR59iwYYPzubFjx+rTTz/VkiVLtH79eh05ckR33HHHr5+hqEh9+/ZVQUGBNm7cqEWLFik5OVmTJk1yzjlw4ID69u2rG264Qenp6RozZoyGDx+uVatWXdoXeIlYWgYAAACYYHhhs79hIl7NmjUVERFR4npOTo7eeOMNLV68WDfeeKMk6c0331SrVq20adMmde7cWatXr9Z3332nzz//XOHh4erQoYOmTp2qJ554QpMnT1ZAQIDmz5+vqKgoTZ8+XZLUqlUrbdiwQTNnzlRcXNylfeBLQEcGAAAAqGRyc3NdRn5+fplz9+zZo8svv1zNmjXTwIEDlZmZKUnaunWrzp07p169ejnnRkdH68orr1RaWpokKS0tTe3atVN4eLhzTlxcnHJzc7Vz507nnN/GKJ5THMMqFDIAAACACcWb/T09JCkyMlJhYWHOkZSUVGoOnTp1UnJyslauXKl58+bpwIED6t69u3755RdlZWUpICBAderUcXlNeHi4srKyJElZWVkuRUzx88XPXWhObm6uzpw5c8nfo1ksLQMAAAAqmcOHD8tutzsfBwYGljqvT58+zj9fffXV6tSpk5o0aaL3339fwcHBXs/TSnRkAAAAABOKDD+vDEmy2+0uo6xC5vfq1KmjP/zhD9q7d68iIiJUUFCgkydPuszJzs527qmJiIgocYpZ8eOLzbHb7ZYWSxQyAAAAQBVx6tQp7du3T40aNVJMTIz8/f2VmprqfH737t3KzMxUbGysJCk2NlY7duzQsWPHnHNSUlJkt9vVunVr55zfxiieUxzDKhQyAAAAgAkO2eSQn4dH+U4te/zxx7V+/XodPHhQGzdu1J///GfVqFFDAwYMUFhYmIYNG6bExEStXbtWW7du1dChQxUbG6vOnTtLknr37q3WrVtr0KBB+s9//qNVq1bpySefVEJCgrMLNHLkSO3fv1/jx49XRkaGXn31Vb3//vsaO3asx7/T8mCPDAAAAOCj/vvf/2rAgAH66aef1KBBA3Xr1k2bNm1SgwYNJEkzZ86Un5+f7rzzTuXn5ysuLk6vvvqq8/U1atTQsmXL9NBDDyk2Nla1atVSfHy8pkyZ4pwTFRWl5cuXa+zYsZo9e7YaN26s119/3dKjlyXJZhiGYWkGFsjNzVVYWJhycnJcNlEBAEpyZP3B6hSqFb+I761OAcBFFP8s+X/fNFet0BoejZ33S5H+dPU+fk51Ax0ZAAAAwITfbs73XMxq12MwjT0yAAAAAHwOHRkAAADAhPOb/cu3Od+dmHAPHRkAAAAAPoeODAAAAGCCQ34q8nBfwCH2yLiLjgwAAAAAn0NHBgAAADCBU8usRUcGAAAAgM+hIwMAAACY4JCfHOyRsQyFDAAAAGBCkWFTkeHZ45I9Ha8qY2kZAAAAAJ9DRwYAAAAwocgLxy8XsbTMbXRkAAAAAPgcOjIAAACACQ7DTw4PH7/s4Phlt9GRAQAAAOBz6MgAAAAAJrBHxlp0ZAAAAAD4HDoyAAAAgAkOef6+Lw6PRqvaKGQAAAAAExzyk8PDC5w8Ha8q45sCAAAA4HPoyAAAAAAmFBl+KvLw8cuejleV8U0BAAAA8Dl0ZAAAAAATHLLJIU9v9vdsvKqMjgwAAAAAn0NHBgAAADCBPTLW4psCAAAA4HPoyAAAAAAmFMlPRR7uC3g6XlVGIQMAAACY4DBschge3uzv4XhVGSUfAAAAAJ9DRwYAAAAwweGFpWUO+gxu45sCAAAA4HMqTSEzd+5cNW3aVEFBQerUqZO2bNlS5tzk5GTZbDaXERQUVIHZAgAAoLpzGH5eGXBPpfim3nvvPSUmJurpp5/Wtm3b1L59e8XFxenYsWNlvsZut+vo0aPOcejQoQrMGAAAAICVKkUhM2PGDI0YMUJDhw5V69atNX/+fIWEhGjhwoVlvsZmsykiIsI5wsPDy5ybn5+v3NxclwEAAABciiLZvDLgHssLmYKCAm3dulW9evVyXvPz81OvXr2UlpZW5utOnTqlJk2aKDIyUrfffrt27txZ5tykpCSFhYU5R2RkpEc/AwAAAICKZXkh8+OPP6qoqKhERyU8PFxZWVmlvqZly5ZauHChPvnkE/3rX/+Sw+FQly5d9N///rfU+RMnTlROTo5zHD582OOfAwAAANULe2Ss5ZPHL8fGxio2Ntb5uEuXLmrVqpVee+01TZ06tcT8wMBABQYGVmSKAAAAALzI8kKmfv36qlGjhrKzs12uZ2dnKyIiwq0Y/v7+uuaaa7R3715vpAgAAACUUCR5fE9LkUejVW2W964CAgIUExOj1NRU5zWHw6HU1FSXrsuFFBUVaceOHWrUqJG30gQAAABcsLTMWpZ3ZCQpMTFR8fHxuvbaa3Xddddp1qxZysvL09ChQyVJgwcP1hVXXKGkpCRJ0pQpU9S5c2e1aNFCJ0+e1IsvvqhDhw5p+PDhVn4MAAAAABWkUhQy9957r44fP65JkyYpKytLHTp00MqVK50HAGRmZsrP79fq9Oeff9aIESOUlZWlunXrKiYmRhs3blTr1q2t+ggAAACoZooMPxV5uIPi6XhVmc0wDMPqJCpabm6uwsLClJOTI7vdbnU6AFCpObL+YHUK1YpfxPdWpwDgIop/lpyYdouCavt7NPbZU+eUFLuSn1PdUCk6MgAAAICvMWSTw8Ob/Q1uiOk2elcAAAAAfA4dGQAAAMAE9shYi28KAAAAgM+hIwMAAACY4DBschie3dPi6XhVGYUMAAAAYEKR/FTk4QVOno5XlfFNAQAAAPA5dGQAAAAAE1haZi06MgAAAAB8Dh0ZAAAAwASH/OTwcF/A0/GqMr4pAAAAAD6HjgwAAABgQpFhU5GH97R4Ol5VRkcGAAAAgM+hIwMAAACYwKll1qKQAQAAAEwwDD85DM8ucDI8HK8q45sCAAAA4HPoyAAAAAAmFMmmInl4s7+H41VldGQAAAAA+Bw6MgAAAIAJDsPzm/MdhkfDVWl0ZAAAAAD4HDoyAAAAgAkOL5xa5ul4VRnfFAAAAACfQ0cGAAAAMMEhmxwePmXM0/GqMgoZAAAAwIQiw6YiD2/293S8qoylZQAAAAB8Dh0ZAAAAwAQ2+1uLbwoAAACAz6EjAwAAAJjgkM3zN8Rks7/b6MgAAAAA8Dl0ZAAAAAATDC8cv2zQkXEbHRkAAAAAPoeODAAAAGCCw/DCHhnuI+M2OjIAAACACcXHL3t6XIrnn39eNptNY8aMcV47e/asEhISdNlll6l27dq68847lZ2d7fK6zMxM9e3bVyEhIWrYsKHGjRunwsJClznr1q1Tx44dFRgYqBYtWig5OfmScr1UFDIAAABAFfDVV1/ptdde09VXX+1yfezYsfr000+1ZMkSrV+/XkeOHNEdd9zhfL6oqEh9+/ZVQUGBNm7cqEWLFik5OVmTJk1yzjlw4ID69u2rG264Qenp6RozZoyGDx+uVatWVdjn+z0KGQAAAMCE4qVlnh5mnDp1SgMHDtQ//vEP1a1b13k9JydHb7zxhmbMmKEbb7xRMTExevPNN7Vx40Zt2rRJkrR69Wp99913+te//qUOHTqoT58+mjp1qubOnauCggJJ0vz58xUVFaXp06erVatWGjVqlO666y7NnDnz0r9IkyhkAAAAgEomNzfXZeTn519wfkJCgvr27atevXq5XN+6davOnTvncj06OlpXXnml0tLSJElpaWlq166dwsPDnXPi4uKUm5urnTt3Ouf8PnZcXJwzhhUoZAAAAAATHP87ftnTQ5IiIyMVFhbmHElJSWXm8e6772rbtm2lzsnKylJAQIDq1Knjcj08PFxZWVnOOb8tYoqfL37uQnNyc3N15syZ8n1xHsKpZQAAAEAlc/jwYdntdufjwMDAMueNHj1aKSkpCgoKqqj0KgU6MgAAAIAJ3twjY7fbXUZZhczWrVt17NgxdezYUTVr1lTNmjW1fv16zZkzRzVr1lR4eLgKCgp08uRJl9dlZ2crIiJCkhQREVHiFLPixxebY7fbFRwcfMnfpRkUMgAAAICPuummm7Rjxw6lp6c7x7XXXquBAwc6/+zv76/U1FTna3bv3q3MzEzFxsZKkmJjY7Vjxw4dO3bMOSclJUV2u12tW7d2zvltjOI5xTGswNIyAAAAwITKcEPM0NBQtW3b1uVarVq1dNlllzmvDxs2TImJiapXr57sdrseeeQRxcbGqnPnzpKk3r17q3Xr1ho0aJCmTZumrKwsPfnkk0pISHB2gkaOHKlXXnlF48eP1/333681a9bo/fff1/Llyz3wqc2hkAEAAABMqAyFjDtmzpwpPz8/3XnnncrPz1dcXJxeffVV5/M1atTQsmXL9NBDDyk2Nla1atVSfHy8pkyZ4pwTFRWl5cuXa+zYsZo9e7YaN26s119/XXFxcR7P1102wzAMy97dIrm5uQoLC1NOTo7LJioAQEmOrD9YnUK14hfxvdUpALiI4p8l41Y8IP9aAR6NfS6vQKv6LODnVDfQkQEAAABM8JWOTFXFZn8AAAAAPoeODAAAAGCCITlvYOnJmHAPHRkAAAAAPoeODAAAAGACe2SsRUcGAAAAgM+hIwMAAACYQEfGWhQyAAAAgAkUMtZiaRkAAAAAn0NHBgAAADCBjoy16MgAAAAA8Dl0ZAAAAAATDMMmw8MdFE/Hq8royAAAAADwOXRkAAAAABMcsskhD++R8XC8qoyODAAAAACfQ0cGAAAAMIFTy6xFIQMAAACYwGZ/a7G0DAAAAIDPoSMDAAAAmMDSMmvRkQEAAADgc+jIAAAAACawR8ZadGQAAAAA+Bw6MgAAAIAJhhf2yNCRcR8dGQAAAAA+h44MAAAAYIIhyTA8HxPuoSMDAAAAwOdYXsh88cUX6tevny6//HLZbDYtXbr0oq9Zt26dOnbsqMDAQLVo0ULJyclezxMAAAD4LYdsXhlwj+WFTF5entq3b6+5c+e6Nf/AgQPq27evbrjhBqWnp2vMmDEaPny4Vq1a5eVMAQAAgF8VH7/s6QH3WL5Hpk+fPurTp4/b8+fPn6+oqChNnz5dktSqVStt2LBBM2fOVFxcnLfSBAAAAFCJWF7IlFdaWpp69erlci0uLk5jxowp8zX5+fnKz893Ps7NzfVWegAAAKgmHIZNNg93UDx9nHNVZvnSsvLKyspSeHi4y7Xw8HDl5ubqzJkzpb4mKSlJYWFhzhEZGVkRqQIAAADwEp8rZMyYOHGicnJynOPw4cNWpwQAAAAfZxjeGXCPzy0ti4iIUHZ2tsu17Oxs2e12BQcHl/qawMBABQYGVkR6AAAAACqAzxUysbGx+uyzz1yupaSkKDY21qKMAAAAUB1545QxTi1zn+VLy06dOqX09HSlp6dLOn+8cnp6ujIzMyWdXxY2ePBg5/yRI0dq//79Gj9+vDIyMvTqq6/q/fff19ixY61IHwAAAIAFLO/IfP3117rhhhucjxMTEyVJ8fHxSk5O1tGjR51FjSRFRUVp+fLlGjt2rGbPnq3GjRvr9ddf5+hlAAAAVCg6MtayvJDp2bOnjAvsakpOTi71Ndu3b/diVgAAAMCFcfyytSxfWgYAAAAA5WV5RwYAAADwRd44Lpnjl91HRwYAAACAz6EjAwAAAJhwviPj6c3+Hg1XpdGRAQAAAOBzKGQAAAAAE4qPX/b0qIoWLVqk5cuXOx+PHz9ederUUZcuXXTo0CFTMSlkAAAAAHjVc889p+DgYElSWlqa5s6dq2nTpql+/fqmb2zPHhkAAADABON/w9Mxq6LDhw+rRYsWkqSlS5fqzjvv1AMPPKCuXbuqZ8+epmLSkQEAAABMYGmZ+2rXrq2ffvpJkrR69WrdfPPNkqSgoCCdOXPGVEw6MgAAAAC86uabb9bw4cN1zTXX6Pvvv9ett94qSdq5c6eaNm1qKiYdGQAAAMAMw0ujCpo7d65iY2N1/Phxffjhh7rsssskSVu3btWAAQNMxaQjAwAAAMCrcnNzNWfOHPn5ufZRJk+erMOHD5uKSUcGAAAAMMMb+2Oq6B6ZqKgo/fjjjyWunzhxQlFRUaZiUsgAAAAA8CrDKH3N3KlTpxQUFGQqJkvLAAAAABMM4/zwdMyqJDExUZJks9k0adIkhYSEOJ8rKirS5s2b1aFDB1OxKWQAAAAAeMX27dslne/I7NixQwEBAc7nAgIC1L59ez3++OOmYlPIAAAAACZ4474vVe0+MmvXrpUkDR06VLNnz5bdbvdYbAoZAAAAwAxvbM6vYoVMsTfffNPjMSlkAAAAAHhVXl6enn/+eaWmpurYsWNyOBwuz+/fv7/cMSlkAAAAABPY7O++4cOHa/369Ro0aJAaNWokm+3SO08UMgAAAAC8asWKFVq+fLm6du3qsZgUMgAAAIAZxv+Gp2NWQXXr1lW9evU8GpMbYgIAAADwqqlTp2rSpEk6ffq0x2LSkQEAAABM4Phl902fPl379u1TeHi4mjZtKn9/f5fnt23bVu6YFDIASjh9+rQyMjKsTgOVhOPHs1anUG5nzzp08HChmkb6KyjIt34o8DtS/v8zR+URHR3tcudyAOf179/f4zEpZACUkJGRoZiYGKvTAKop/rfny7Zu3aqOHTtanQYqUhXd0+JpTz/9tMdjUsgAKCE6Olo9Xr/H6jQqjZnXPGF1CiinM2fO6ODBg2ratKmCg4OtTgeVQPzf3/L6eyz6f4MUHR3t9fdB5cHSMmtRyAAoISQkRHVaNrA6jUqD3676Jk8e8QnfV6veWq+/B/+tAFzVq1dP33//verXr6+6dete8N4xJ06cKHd8ChkAAADADI5fvqCZM2cqNDRUkjRr1iyPx6eQAQAAAOBx8fHxpf7ZUyhkAAAAAFNs/xuejlk1FRUVaenSpdq1a5ckqU2bNvrTn/6kGjVqmIpHIQMAAADAq/bu3atbb71VP/zwg1q2bClJSkpKUmRkpJYvX67mzZuXO6afp5MEAAAAqgXDS6MKevTRR9W8eXMdPnxY27Zt07Zt25SZmamoqCg9+uijpmLSkQEAAADgVevXr9emTZtUr14957XLLrtMzz//vOlTJilkAAAAADM4tcxtgYGB+uWXX0pcP3XqlAICAkzFZGkZAAAAYIZh886ogm677TY98MAD2rx5swzDkGEY2rRpk0aOHKk//elPpmJSyAAAAAA+at68ebr66qtlt9tlt9sVGxurFStWOJ8/e/asEhISdNlll6l27dq68847lZ2d7RIjMzNTffv2VUhIiBo2bKhx48apsLDQZc66devUsWNHBQYGqkWLFkpOTi5XnnPmzFHz5s0VGxuroKAgBQUFqWvXrmrRooVmz55t6rOztAwAAAAwwTDOD0/HLI/GjRvr+eef11VXXSXDMLRo0SLdfvvt2r59u9q0aaOxY8dq+fLlWrJkicLCwjRq1Cjdcccd+vLLLyWdPxK5b9++ioiI0MaNG3X06FENHjxY/v7+eu655yRJBw4cUN++fTVy5Ei9/fbbSk1N1fDhw9WoUSPFxcW5lWedOnX0ySefaM+ePdq1a5dsNptatWqlFi1alO8D/4bNMDz99Vd+ubm5CgsLU05Ojux2u9XpAJXS7RtGWZ1CpfFJt1esTgHAJeo8cLrX32PT2495/T1QORT/LNn4lWfkFxzk0diOM2f131FPX9LPqfXq1dOLL76ou+66Sw0aNNDixYt11113SZIyMjLUqlUrpaWlqXPnzlqxYoVuu+02HTlyROHh4ZKk+fPn64knntDx48cVEBCgJ554QsuXL9e3337rfI/77rtPJ0+e1MqVK8udX3H5YbNd2jI6lpYBAAAAZnjx+OXc3FyXkZ+ff9F0ioqK9O677yovL0+xsbHaunWrzp07p169ejnnREdH68orr1RaWpokKS0tTe3atXMWMZIUFxen3Nxc7dy50znntzGK5xTHcNcbb7yhtm3bOpeWtW3bVq+//nq5YvyW6ULm3//+t/76178qNjZWP/zwgyTprbfe0oYNG0wnAwAAAECKjIxUWFiYcyQlJZU5d8eOHapdu7YCAwM1cuRIffzxx2rdurWysrIUEBCgOnXquMwPDw9XVlaWJCkrK8uliCl+vvi5C83Jzc3VmTNn3Po8kyZN0ujRo9WvXz8tWbJES5YsUb9+/TR27FhNmjTJrRi/Z2qPzIcffqhBgwZp4MCB2r59u7NCzMnJ0XPPPafPPvvMVDIAAACAz/DGKWP/i3f48GGXpWWBgYFlvqRly5ZKT09XTk6OPvjgA8XHx2v9+vWezesSzZs3T//4xz80YMAA57U//elPuvrqq/XII49oypQp5Y5pqiPz7LPPav78+frHP/4hf39/5/WuXbtq27ZtZkICAAAA+J/iU8iKx4UKmYCAALVo0UIxMTFKSkpS+/btNXv2bEVERKigoEAnT550mZ+dna2IiAhJUkRERIlTzIofX2yO3W5XcHCwW5/n3Llzuvbaa0tcj4mJKXFCmrtMFTK7d+9Wjx49SlwPCwsr8UUBAAAAVZHN8M64VA6HQ/n5+YqJiZG/v79SU1Odz+3evVuZmZmKjY2VJMXGxmrHjh06duyYc05KSorsdrtat27tnPPbGMVzimO4Y9CgQZo3b16J6wsWLNDAgQPL9fmKmVpaFhERob1796pp06Yu1zds2KBmzZqZSgQAAADwKb/ZnO/RmOUwceJE9enTR1deeaV++eUXLV68WOvWrdOqVasUFhamYcOGKTExUfXq1ZPdbtcjjzyi2NhYde7cWZLUu3dvtW7dWoMGDdK0adOUlZWlJ598UgkJCc4u0MiRI/XKK69o/Pjxuv/++7VmzRq9//77Wr58eblyfeONN7R69Wrne2/evFmZmZkaPHiwEhMTnfNmzJjhVjxThcyIESM0evRoLVy4UDabTUeOHFFaWpoef/xxPfXUU2ZCAgAAACinY8eOafDgwTp69KjCwsJ09dVXa9WqVbr55pslSTNnzpSfn5/uvPNO5efnKy4uTq+++qrz9TVq1NCyZcv00EMPKTY2VrVq1VJ8fLzLnpWoqCgtX75cY8eO1ezZs9W4cWO9/vrrbt9DRpK+/fZbdezYUZK0b98+SVL9+vVVv359l2Ody3Mks6lCZsKECXI4HLrpppt0+vRp9ejRQ4GBgXr88cf1yCOPmAkJAAAA+BYvbvZ31xtvvHHB54OCgjR37lzNnTu3zDlNmjS56GFdPXv21Pbt28uV22+tXbvW9GvLYqqQsdls+vvf/65x48Zp7969OnXqlFq3bq3atWt7Oj8AAAAAKMFUIVMsICDAuQkIAAAAqFYqwR6Z6sxUIXOxc57N3tQGAAAAANxhqpD5+OOPXR6fO3dOBw4cUM2aNdW8eXMKGQAAAFR9dGQsZaqQKW2jT25uroYMGaI///nPl5wUAAAAAN/WsWNHpaamqm7dupoyZYoef/xxhYSEeCy+qRtilsZut+uZZ57h+GUAAABUD4aXRhWxa9cu5eXlSZKeeeYZnTp1yqPxL2mz/+/l5OQoJyfHkyEBAACAyqkSHL9cmXXo0EFDhw5Vt27dZBiGXnrppTJPOTazNcVUITNnzhyXx4Zh6OjRo3rrrbfUp08fMyEBAAAAVCHJycl6+umntWzZMtlsNq1YsUI1a5YsP2w2W8UVMjNnznR57OfnpwYNGig+Pl4TJ040ExIAAADwKTbj/PB0zKqiZcuWevfddyWdrxdSU1PVsGFDj8U3VcgcOHDAYwkAAAAAqNocDofHY3p0jwwAAABQbXD8crns27dPs2bN0q5duyRJrVu31ujRo9W8eXNT8dwuZBITE90OOmPGDFPJAAAAAKh6Vq1apT/96U/q0KGDunbtKkn68ssv1aZNG3366ae6+eabyx3T7UKmtHvHlMZmqzonLQAAAAC4dBMmTNDYsWP1/PPPl7j+xBNPeLeQWbt2bbmDAwAAAMCuXbv0/vvvl7h+//33a9asWaZiXtIeme+++06ZmZkqKChwXrPZbOrXr9+lhAUAAAAqPZu8cGqZZ8NVGg0aNFB6erquuuoql+vp6emmTzIzVcjs379ff/7zn7Vjxw7ZbDYZxvm/weJlZUVFRaaSAQAAAFD1jBgxQg888ID279+vLl26SDq/R+aFF14o11783zJVyIwePVpRUVFKTU1VVFSUtmzZop9++kmPPfaYXnrpJVOJAAAAAD7FsJ0fno5ZBT311FMKDQ3V9OnTnfedvPzyyzV58mQ9+uijpmKaKmTS0tK0Zs0a1a9fX35+fvLz81O3bt2UlJSkRx991O2DAQAAAACfxfHLbrPZbBo7dqzGjh2rX375RZIUGhp6STH9zLyoqKjI+cb169fXkSNHJElNmjTR7t27LykhAAAAAFVXaGjoJRcxksmOTNu2bfWf//xHUVFR6tSpk6ZNm6aAgAAtWLBAzZo1u+SkAAAAgEqPjoylTBUyTz75pPLy8iRJU6ZM0W233abu3bvrsssu03vvvefRBAEAAADg90wVMnFxcc4/t2jRQhkZGTpx4oTq1q3LDTEBAECl4+kjcgHp/L8rjx+/zL9Vt5naIzN8+HCtW7fO5Vq9evUoYgAAAAC4OHfunG666Sbt2bPHo3FNFTLHjx/XLbfcosjISI0bN07p6emmE/jiiy/Ur18/XX755bLZbFq6dOkF569bt042m63EyMrKMp0DAAAAUG6Gl0YV4+/vr2+++cbjcU0VMp988omOHj2qp556Sl999ZViYmLUpk0bPffcczp48GC5YuXl5al9+/aaO3duuV63e/duHT161DnM3hEUAAAAgHf99a9/1RtvvOHRmKb2yEhS3bp19cADD+iBBx7Qf//7X73zzjtauHChJk2apMLCQrfj9OnTR3369Cn3+zds2FB16tQp9+sAAAAAj+DUMrcVFhZq4cKF+vzzzxUTE6NatWq5PD9jxoxyxzRdyBQ7d+6cvv76a23evFkHDx5UeHj4pYZ0S4cOHZSfn6+2bdtq8uTJ6tq1a5lz8/PzlZ+f73ycm5tbESkCAACgCmOzv/u+/fZbdezYUZL0/fffuzxndp+96UJm7dq1Wrx4sT788EM5HA7dcccdWrZsmW688UazId3SqFEjzZ8/X9dee63y8/P1+uuvq2fPntq8ebPzy/m9pKQkPfPMM17NCwAAVGJV9IdDwFesXbvW4zFNFTJXXHGFTpw4oVtuuUULFixQv379FBgY6OncStWyZUu1bNnS+bhLly7at2+fZs6cqbfeeqvU10ycOFGJiYnOx7m5uYqMjPR6rgAAAKjCDNv54emYVdjevXu1b98+9ejRQ8HBwTIMo2I7MpMnT9bdd99dafaoXHfdddqwYUOZzwcGBlZYoQUAAADA1U8//aR77rlHa9eulc1m0549e9SsWTMNGzZMdevW1fTp08sd09SpZSNGjKg0RYwkpaenq1GjRlanAQAAgOqE45fdNnbsWPn7+yszM1MhISHO6/fee69WrlxpKuYlb/a/VKdOndLevXudjw8cOKD09HTVq1dPV155pSZOnKgffvhB//znPyVJs2bNUlRUlNq0aaOzZ8/q9ddf15o1a7R69WqrPgIAAACAC1i9erVWrVqlxo0bu1y/6qqrdOjQIVMxLS9kvv76a91www3Ox8V7WeLj45WcnKyjR48qMzPT+XxBQYEee+wx/fDDDwoJCdHVV1+tzz//3CUGgEv3SbdXdPO6sVanYbmUnjOtTgEAUElxapn78vLyXDoxxU6cOGF6C4jlhUzPnj1lGGX/jSUnJ7s8Hj9+vMaPH+/lrAAAAAB4Svfu3fXPf/5TU6dOlXT+yGWHw6Fp06aZbkhYXsgAAAAAPokbYrpt2rRpuummm/T111+roKBA48eP186dO3XixAl9+eWXpmKa2uwPAAAAVHvGr8vLPDWqaiHTtm1bff/99+rWrZtuv/125eXl6Y477tD27dvVvHlzUzEvuSNTVFSkjIwMffvtt87x8ccfX2pYAAAAj7E5rM4AQFhYmP7+9797LF65Cpn9+/drx44dLkXLnj17dO7cOQUEBKhVq1Zq166dx5IDAAAAKi2WlpXLzz//rDfeeEO7du2SJLVu3VpDhw5VvXr1TMVzu5D561//qnfeeUc2m00hISHKy8tT3759NWnSJLVr105XXXWVatSoYSoJAAAAAFXXF198oX79+iksLEzXXnutJGnOnDmaMmWKPv30U/Xo0aPcMd3eI/PBBx9ozpw5OnXqlI4cOaJRo0Zp9erV+uqrr9SkSROKGAAAAFQv3BDTbQkJCbr33nt14MABffTRR/roo4+0f/9+3XfffUpISDAV0+1CZuzYsRo8eLCCgoJUu3ZtzZ49W19++aXWrl2rNm3amL4jJwAAgNd56wfOKv7DJ+Ape/fu1WOPPebS/KhRo4YSExO1d+9eUzHdLmSSkpIUGhrqci0mJkZbtmzR6NGjde+99+ovf/mLjh8/bioRAAAAwJd4+sQyb9xgs7Lo2LGjc2/Mb+3atUvt27c3FfOSTy2z2WwaPXq07rrrLj366KOKjo7WTz/9dKlhAQAAAPiwb775xvnnRx99VKNHj9bevXvVuXNnSdKmTZs0d+5cPf/886bie+yGmFdccYU+/PBDLV++3FMhAQAAAPioDh06yGazyTB+bTONHz++xLy//OUvuvfee8sd32OFTLG+fft6OiQAAABQ+XD88gUdOHDAq/E9XsgAqDpSes5Ut89L/uakutjQa5rVKQAA4LOaNGni1fgUMgAAAIAJ3ticX1U3+0vSkSNHtGHDBh07dkwOh8PluUcffbTc8ShkAABAlbfx/cfU9e7pXov/5ZLHvBYbqAqSk5P14IMPKiAgQJdddplsNpvzOZvNRiEDAAAAVKgq3EHxpKeeekqTJk3SxIkT5efn9h1gLsgzUQAAAACgDKdPn9Z9993nsSJGopABAADVhWF4b6B6Mrw0qqBhw4ZpyZIlHo3J0jIAAAAAXpWUlKTbbrtNK1euVLt27eTv7+/y/IwZM8odk0IGAAAAMIFTy9yXlJSkVatWqWXLlpJUYrO/GRQyAACgWqiqPyDCQtwQ023Tp0/XwoULNWTIEI/FpJABcEEbek3TdSv/ZnUaFW7LLc9ZnQIAAFVGYGCgunbt6tGYbPYHAADVg7c2ZlfR36Dj4oqXlnl6VEWjR4/Wyy+/7NGYdGQAAAAAeNWWLVu0Zs0aLVu2TG3atCmx2f+jjz4qd0wKGQAAAMAM9si4rU6dOrrjjjs8GpNCBgAAAIBXvfnmmx6PSSEDAAAAmEFHxlIUMgAAoFqoqpuoAV8QFRV1wfvF7N+/v9wxKWQAAAAAE7ghpvvGjBnj8vjcuXPavn27Vq5cqXHjxpmKSSED4KLyC2tYnUKF+s9tU61OAYA3GFX0J0RYh6Vlbhs9enSp1+fOnauvv/7aVEzuIwMAAADAEn369NGHH35o6rUUMgAAoFr499JxXrl54b+XmlsWgyqAG6xesg8++ED16tUz9VqWlgEAgOqjmv2QCFQW11xzjctmf8MwlJWVpePHj+vVV181FZNCBgAAVBtVdSM1rMFmf/f179/f5bGfn58aNGignj17Kjo62lRMChkAAAAAXvX00097PCaFDICL+s9tU9Xq42esTqNC7Pqz5/9DCwCooji1zFIUMgAAAAC8ws/P74I3wpQkm82mwsLCcsemkAEAAABMYI/MxX388cdlPpeWlqY5c+bI4XCYik0hAwAAqg9HFfspEdZiadlF3X777SWu7d69WxMmTNCnn36qgQMHasqUKaZicx8ZAAAAAF535MgRjRgxQu3atVNhYaHS09O1aNEiNWnSxFQ8OjIA3LLrz0/rqiXPWp2GV+25+0mrUwAA+BI6Mm7JycnRc889p5dfflkdOnRQamqqunfvfslxKWQAAKiiTp8+rYyMDKvTqFR+OfnDRec4is7p7JmfFRRSV35+/hedv23bNk+k5jOio6MVEhJidRr4n6SkJH300UfKyMhQcHCwunTpohdeeEEtW7Z0zjl79qwee+wxvfvuu8rPz1dcXJxeffVVhYeHO+dkZmbqoYce0tq1a1W7dm3Fx8crKSlJNWv+Wi6sW7dOiYmJ2rlzpyIjI/Xkk09qyJAhF8xv2rRpeuGFFxQREaF33nmn1KVmZlHIAABQRWVkZCgmJsbqNKq8mJg5VqdQobZu3aqOHTtanUalYPvf8HTM8li/fr0SEhL0xz/+UYWFhfrb3/6m3r1767vvvlOtWrUkSWPHjtXy5cu1ZMkShYWFadSoUbrjjjv05ZdfSpKKiorUt29fRUREaOPGjTp69KgGDx4sf39/Pffcc5KkAwcOqG/fvho5cqTefvttpaamavjw4WrUqJHi4uLKzG/ChAkKDg5WixYttGjRIi1atKjUeR999FE5P7lkMwyjCjawLiw3N1dhYWHKycmR3W63Oh3ApzRb/JzVKXjF/r/8zeoUAI87ffq0rr/hCY/Fe23eUI/FqszOnDmjgwcPqmnTpgoODrY6nUqHjsyvP0u2fvg51QgM8mjsovyz+u7Vv5n+OfX48eNq2LCh1q9frx49eignJ0cNGjTQ4sWLddddd0k6/0uOVq1aKS0tTZ07d9aKFSt022236ciRI84uzfz58/XEE0/o+PHjCggI0BNPPKHly5fr22+/db7Xfffdp5MnT2rlypVl5jNkyJCLHr8sSW+++Wa5PysdGQAAqqiQkBCF2q/wWLzq9Fv4rl27Wp0CfIEX98jk5ua6XA4MDFRgYOBFX56TkyNJqlevnqTzHbRz586pV69ezjnR0dG68sornYVMWlqa2rVr57LULC4uTg899JB27typa665RmlpaS4xiueMGTPmgvkkJydfNGezOLUMAAAAqGQiIyMVFhbmHElJSRd9jcPh0JgxY9S1a1e1bdtWkpSVlaWAgADVqVPHZW54eLiysrKcc35bxBQ/X/zchebk5ubqzJkzpj7jpaIjAwBAVWbuPnMlrEmd4JlAQBXizRtiHj582GVpmTvdmISEBH377bfasGGDZ5OqpOjIAAAAAGYYXhqS7Ha7y7hYITNq1CgtW7ZMa9euVePGjZ3XIyIiVFBQoJMnT7rMz87OVkREhHNOdnZ2ieeLn7vQHLvdbtleMgoZAACqMJtheGQAqJwMw9CoUaP08ccfa82aNYqKinJ5PiYmRv7+/kpNTXVe2717tzIzMxUbGytJio2N1Y4dO3Ts2DHnnJSUFNntdrVu3do557cxiucUx7ACS8sAlJujsIbVKXiUX80iq1MAvCZ1zUTddMPF19YDMMniOj8hIUGLFy/WJ598otDQUOeelrCwMAUHByssLEzDhg1TYmKi6tWrJ7vdrkceeUSxsbHq3LmzJKl3795q3bq1Bg0apGnTpikrK0tPPvmkEhISnJ2gkSNH6pVXXtH48eN1//33a82aNXr//fe1fPlyyz47HRkA5VIVjymuip8JAFA9zJs3Tzk5OerZs6caNWrkHO+9955zzsyZM3XbbbfpzjvvVI8ePRQREeFy35YaNWpo2bJlqlGjhmJjY/XXv/5VgwcP1pQpU5xzoqKitHz5cqWkpKh9+/aaPn26Xn/99QveQ8bb6MgAAFDVsTQM8ApvbvZ3lzu3hAwKCtLcuXM1d+7cMuc0adJEn3322QXj9OzZU9u3by9fgl5ERwYAAACAz6EjAwBAFefp3xgD+B8v3hATF0dHBgAAAIDPoSMDoNwODn5CTd6YZnUaQIU7ffq0MjIyrE6j3HJ/+aHM54oc53T27EkFB9aRXw3/Mudt27bNG6l5RHR0tEJCQqxOA9VQZdgjU51RyAAA4KaMjAzFxMRYnYYlYmJetTqFMm3dulUdO3a0Og1URywtsxSFDAAAboqOjlandiNNv/7V5BEezMYzzpw5o4MHD6pp06aW3Z3brIc7/U2vbn5O0dHRVqcCwAIUMgAAuCkkJET2Wpebfn1l7Rp07drV6hRMsdvqVdrvFNUDS8usRSEDwJRDw8ar6fyXrE7jkh0c+bjVKQAAABMoZAAAAAAz2CNjKY5fBgAAAOBz6MgAAACftLpgsdUpoLqjI2MpChkAAMqjyNxPGau3TPJwIgBQvVHIADDt4MjHFfXydKvTMO3AI49ZnQIAwIdxapm12CMDAAAAwOfQkQEAAADMYI+MpShkAFySA488pmYzZ1idRrntH5todQrwUTaHw+oUAFQSNsOQzfBs5eHpeFUZS8sAAAAA+Bw6MgAAAIAZLC2zFB0ZAAAAAD7H8kImKSlJf/zjHxUaGqqGDRuqf//+2r1790Vft2TJEkVHRysoKEjt2rXTZ599VgHZAiiN3zmbTw32xwAAPKH4+GVPD7jH8kJm/fr1SkhI0KZNm5SSkqJz586pd+/eysvLK/M1Gzdu1IABAzRs2DBt375d/fv3V//+/fXtt99WYOYAgOpo1deTz98Us7wDAOBRlu+RWblypcvj5ORkNWzYUFu3blWPHj1Kfc3s2bN1yy23aNy4cZKkqVOnKiUlRa+88ormz5/v9ZwBAAAA9shYy/KOzO/l5ORIkurVq1fmnLS0NPXq1cvlWlxcnNLS0kqdn5+fr9zcXJcBAIBZNoejXGPV9mesThkAqpxKVcg4HA6NGTNGXbt2Vdu2bcucl5WVpfDwcJdr4eHhysrKKnV+UlKSwsLCnCMyMtKjeQPV3d7xY+V3Tj4x9o4fa/XXBQCoItgjY61KVcgkJCTo22+/1bvvvuvRuBMnTlROTo5zHD582KPxAQAAUA0ZXhpwS6UpZEaNGqVly5Zp7dq1aty48QXnRkREKDs72+Vadna2IiIiSp0fGBgou93uMgAAMGvlf6ZKDof7AwDgcZYXMoZhaNSoUfr444+1Zs0aRUVFXfQ1sbGxSk1NdbmWkpKi2NhYb6UJ4CK+//tY+RWqUo/v/86yMgCA57C0zFqWn1qWkJCgxYsX65NPPlFoaKhzn0tYWJiCg4MlSYMHD9YVV1yhpKQkSdLo0aN1/fXXa/r06erbt6/effddff3111qwYIFlnwMAAABAxbG8IzNv3jzl5OSoZ8+eatSokXO89957zjmZmZk6evSo83GXLl20ePFiLViwQO3bt9cHH3ygpUuXXvCAAAAAAMCj2CNjKcs7MoZx8b+tdevWlbh299136+677/ZCRgAAXJzNzZtcrvjuOS9nAgDVk+WFDICqI+PpsWr95Eyr0yjVd8+yPwYA4HnsabGO5UvLAAAAAKC86MgAAAAAZhjG+eHpmHALhQwAAGYUcX8YoLrzxnHJLFVzH0vLAHjUd8+Ole2cKtVgfwwAAFUPHRkAAADADG8cl0xHxm0UMgAAmOVgeRkAWIWlZQA8bucLY+V3TpVi7HyBZWXwjhW7n7c6BQAWszm8M+AeChkAAAAAPoelZQAAAIAZ7JGxFB0ZAF6xY8ZY+RXK8gF4VZHjgmPFnmlWZwgAVRYdGQAAAMAE7iNjLQoZoAynT59WRkaG1Wn4tNPH/mv6tY7CcyrIPaFAez3ZavqbjrNt2zbTr60qoqOjFRISYnUaAFD1GMb54emYcAuFDFCGjIwMxcTEWJ0GLlHMezOsTsFyW7duVceOHa1OAwAAj6KQAcoQHR2trVu3auCzb1f4e7/95MAKf8/K5syZMzp48KCaNm2q4OBgq9PxadHR0VanAABVEkvLrEUhA5QhJCREHTt2VMhl6yv8vfnt+Xldu3a1OgXgwgo5UQIArEIhA1yEXxG/GgEAAKXg+GVLcfwycBFfLUz02p17SxtfLUy0+iMDAABUenRkAAAAABPYI2MtChm44Mjh0uX9WP5jhB1F55R/6oQCa9eTXw33jw+uDscFcxwwqooVmbPUp/GjVqcBANUShQxccOSw9WKWz7Q6Ba/jOGAAQJXAfWQsRSEDF8VHDg/521tef6/k5wZ5/T2sxPHBJT183QS9uuV5jgMGAFQJLC2zFoUMXBQfOVyr7lqvv1d1+I08xwe7stvqVYu/dwAA4H0UMiiVrci78Te+/5h33wCV0upz71qdAgAAnsPxy5aikEGpbA7+VwQAbikq/Tc/K47OreBEAKB64T4yKNWXHzzuXPfpjQEAAODr+DnJWnRkUCabw+oMAAAAgNJRyKDC/fvjx61OAQAA4NI5jPPD0zHhFgoZlIl9MgAAAKis2CODMn3xyTjZCg2PDwAAgCrB8NKAW+jIVIDTp08rIyPD6jRM+SXnh4vOcRSd09kzPysopK78/PwvOn/btm2eSM2joqOjFRISYnUaAADAh9jkhRtiejZclUY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