{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5556432f",
      "metadata": {},
      "source": [
        "# Example 1: Shape Import"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8899e69a",
      "metadata": {},
      "source": [
        "This Tutorial demonstrates how to create a connected topological network of nodes and pipes in SIR 3S via the SIR 3S Toolkit based on shape data. The data used comes from 'SIR 3S\\Modelle\\Beispiele\\Wasser\\Manual\\Projektdaten\\01 Shape-Dateien'."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5571a633",
      "metadata": {},
      "source": [
        "## SIR 3S Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "94054444",
      "metadata": {},
      "outputs": [],
      "source": [
        "SIR3S_SIRGRAF_DIR = r\"C:\\3S Consult\\Sir3S-90 Entwicklerversionen\\SirGraf-90-15-00-12_Quebec_x64\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17583a76",
      "metadata": {},
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f0406d8",
      "metadata": {},
      "source": [
        "Note: The SIR 3S Toolkit requires the Sir3S_Toolkit.dll included in SIR 3S installations (version Quebec and higher)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "03bf0973",
      "metadata": {},
      "outputs": [],
      "source": [
        "import sir3stoolkit"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64c11922",
      "metadata": {},
      "source": [
        "The core of sir3stoolkit is a Python wrapper around basic functionality of SIR 3S, offering a low-level access to the creation, modification and simulation of SIR 3S models. In the future pure python subpackages may be added."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "039a00ff",
      "metadata": {},
      "outputs": [],
      "source": [
        "from sir3stoolkit.core import wrapper"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "e54c9f45",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<module 'sir3stoolkit' from 'C:\\\\Users\\\\aUsername.3S.000\\\\AppData\\\\Local\\\\anaconda3\\\\Lib\\\\site-packages\\\\sir3stoolkit\\\\__init__.py'>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sir3stoolkit"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2fae5d6a",
      "metadata": {},
      "source": [
        "The wrapper package has to be initialized with reference to a SIR 3S (SirGraf) installation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "4bcc7659",
      "metadata": {},
      "outputs": [],
      "source": [
        "wrapper.Initialize_Toolkit(SIR3S_SIRGRAF_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6380a433",
      "metadata": {},
      "source": [
        "#### Additional imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a9c9474e",
      "metadata": {},
      "outputs": [],
      "source": [
        "from datetime import datetime\n",
        "import geopandas as gpd\n",
        "import os\n",
        "from shapely.geometry import LineString\n",
        "import shutil"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4551acd2",
      "metadata": {},
      "source": [
        "## Initialization"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c75cc1f5",
      "metadata": {},
      "source": [
        "The SIR 3S Toolkit contains two classes: SIR3S_Model (model and data) and SIR3S_View (depiction in SIR Graf). All SIR 3S Toolkit functionality is accessed via the methods of these classes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "39042e6f",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initialization complete\n"
          ]
        }
      ],
      "source": [
        "s3s = wrapper.SIR3S_Model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "00254572",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initialization complete\n"
          ]
        }
      ],
      "source": [
        "s3s_view = wrapper.SIR3S_View()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "efdd4bb3-7b18-45f6-affb-b4c4a5b5cf9b",
      "metadata": {},
      "source": [
        "Disable additional output comments of SIR 3S Toolkit methods:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "00c82d38-7e74-424f-bf70-a1c41a167793",
      "metadata": {},
      "outputs": [],
      "source": [
        "s3s.EnableOrDisableOutputComments(outputComments=False)\n",
        "s3s_view.EnableOrDisableOutputComments(outputComments=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2d0f6dc9",
      "metadata": {},
      "source": [
        "## Open empty model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8dd834b5",
      "metadata": {},
      "source": [
        "### Create new empty model file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "989eb4c4",
      "metadata": {},
      "outputs": [],
      "source": [
        "src = r\"C:\\DevOps\\Github_Direct_sir3stoolkit\\Test\\Examples\\EmptyModel.XML\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "2013640a",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Generate a unique filename with timestamp\n",
        "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "dst_folder = r\"C:\\DevOps\\Github_Direct_sir3stoolkit\\Test\\Examples\"\n",
        "dst_filename = f\"EmptyModelCopy_{timestamp}.XML\"\n",
        "dst = os.path.join(dst_folder, dst_filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "e677e778",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'C:\\\\DevOps\\\\Github_Direct_sir3stoolkit\\\\Test\\\\Examples\\\\EmptyModelCopy_20250728_141218.XML'"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Copy the file\n",
        "shutil.copy2(src, dst)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84f5f3f6",
      "metadata": {},
      "source": [
        "### Open new file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "49adf19c",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Open the copied file\n",
        "s3s.OpenModelXml(dst, True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b21a7ab7",
      "metadata": {},
      "source": [
        "### Python Shape Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "78adc87b",
      "metadata": {},
      "outputs": [],
      "source": [
        "fp = r\"C:\\3S\\Modelle\\Beispiele\\Wasser\\Manual\\Projektdaten\\01 Shape-Dateien\\Wassernetz_Leitungen.shp\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "5e211607",
      "metadata": {},
      "outputs": [],
      "source": [
        "gdf = gpd.read_file(fp) # Read file using gpd.read_file()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "718062a1",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
            "RangeIndex: 619 entries, 0 to 618\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   3SPK        619 non-null    object  \n",
            " 1   TYPE        619 non-null    object  \n",
            " 2   _FROM       619 non-null    object  \n",
            " 3   _TO         619 non-null    object  \n",
            " 4   IDREFERENZ  619 non-null    object  \n",
            " 5   DN          619 non-null    object  \n",
            " 6   L           619 non-null    float64 \n",
            " 7   MAT         619 non-null    object  \n",
            " 8   geometry    619 non-null    geometry\n",
            "dtypes: float64(1), geometry(1), object(7)\n",
            "memory usage: 43.7+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(gdf.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "b6277e24",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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QwTUR3sS9MoVRYW5Nuk990N2dWIjB2PF7CguCcGUQQVcwmzssEIA/43LN2b+dgYVSwSe3DcLB2qLJ9xng74yDtYrSah1xnWxmLwhC5yWCrmA2OLAbXk5WWColvtlzZdcnVikVjAwxzYr/vcQsyzKlVbXtMSxBEK5wIugKZpIk8db1/ajQGvhwSyJHM0rbe0itasx59nULK7R8vTuFyDe2csOne1m+P43iSlGnWRCEliGCrtDAuJ4ezOjnjQw8/ctxNLqO3eD+cowJN810j2aWUV6jA2DTiVz+tyEenUEmJqOMl349SeQb27jz20OsP5pNVSdadhcEoeMRQVc4xyvX9sHN3oqkgkoWb0ts7+G0Gr9utnR3t8NglNmXZDo69MfxXAAemxjKi9f0op+fEwajzI4zhTz+cyxDXt/KwlWxZJWKnryCIDSfCLrCOZxtLXn9ur4AfLU7hWV7U1m8LRFZvvKyfEeH1u3rJhaSr9ZwKM1Uq/nmoQHcPbo7vz08im1PjuXRiWEEutpSozOwNiabie/v5IPNZ8hXa9pz+IIgdDKSfCW+kl4itVqNk5MT5eXlODo6tvdw2pXRaGTC+zvJKKmm/kTNsZen4GTT9AzhjkqrM7ArsYidCQX8ejQbrUFmbLg7I7q78OofpxkU4MzaB0eecz9ZNi05v7MpnoOpJQ0+99L03tw42O+K+P4IgtB8TY0fogykcF5HMspIK67GQikxOsQNH2ebTt8KL7e8hh8OZPDjoQysLRRkl/0zS91yKp8C9T8FNs5HkiQGB3bjp3uHselEHp/vSuF4VhmyDP/3xymGBHWjn59zW3wpgiB0UiLoCue17qipyfvMAb68N6t/O4/m0hmNMofSSli+P42/TuY3KITh182G/10fwec7ktmfUkxm3T5tPz+nCz6mJElMi/BmWoQ3JVW17E0qwlKlEAFXEISLEkG3i5JlGUk6f31ird7An3UJRTcM9G3LYbWYCo2OHw5msGJ/Ot1sLTiRowZgWHcXJvb05PsDadw+LIix4e6cyC5nf0qxucNSfSZzU7jYWTKjEzaIEAShfYig20Ut2Z7E0j2pOFirsLFQYmOpxNpCiY2FErVGR3mNDi9Ha6K6d65+s7nlNSzbl8bBlBJiM8sAyC6rwd5Kxar7htHbxzSLvXt0sPlNx4i6nrpavZHxPdwxijQHQRBaiQi6XVS+WkN5ja7RWZ1/NxtuGuKP8gLdejqSE9nlfL07hT+O56I3ylipFNhaKnnxml6si8nm2gE+hHo4mK8/e5Yf4euEg5WKCq2ev88UYpTh692p3D4sUMxiBUFoUSLodlHPTevJnSOCqNTqqdEZ0OgM1NQaKanS8spvp8gsrWFqH8/2HmaTvLnxNF/s/KdsZYSvI3HZaoJdbbllaABzogIveH+VUkFUdxe2ni5ApZDYWVehKl+tEUFXEIQWJYJuF+VgbXHeBgErD2ZgkGV6ejnQ06vjH5vKV2s4Vbdfe21/b+4ZHUJfX0cOp5UyJLDbBfvqnm1EiBtbTxegVEh4OlqTXVZDWnE1eeUavJxEk3tBEFqGKI4hNLDuaBYA13eSBCprCyWHUkuI8HXitZkRRPg5IUkSkcEuTQ64ADcN8WNsuDtavRG1RkdfX9MbjoOpxa01dEEQuiARdAWzzJJqDqeVIkmmo0KdgZONBbufHc/vj4zCyfbSC1M4WluwdN4QbC2VVGj09PA0Bd0/jueKesuCILQYEXQFs1XRmQAMDerWokuqsiyz8mAGvx/LwdgKDeM9HFpmrCqlggH+zgDYWSoBU9GMv07mtcjjC4IgiKArAKA3GDmcVoKtpZKjGWW8uD6uxQLk+thsXlgXxyM/HuW6T/cSnVZy8Tu1k8GB3QAoranF1c4SgDN5Fe05JEEQriAikUoAYGdCIQdSSlAqJAxGmZwyTbP2RC+kpOqfY0nHs8qZ9cV+ls2PZGxdP9uOpD7oxmWVs3XhWHQGIx6OIpFKEISWIYKuAMBPh01Ly3cOD2RyHy/srVruV2PBqGCm9vFEkiRe/e0km0/l88GWBMaEuTVaFau9DAwwBd204moMsiwCriAILUosLwsUqDVsjy8AYHZkAMO6u9LX98L1h5vLr5stvs42vHFDBFYqBccyy9if3PEyg51sLAj3tAfgSHppO49GEIQrjQi6Ar/EZGEwygwO7EaYp8PF73AZ3OytuGWoP2DqzFNfEWtnQiFf704hp6ymVZ+/KQYHugAQUxd0DUaZlMJKPvk7ifHv7WDg/20mt7z9xykIQucjlpcF/jphys69eYhfqz6PLMukFlXh5WiNBMTnVTD1w53YWqpIKaoCwN/FFh9nm1Ydx8UMDuzGj4cyWHs0m33JxSQWVKDRmZoh2Foq+eqOIXg7te8YBUHonETQ7eJ0BiOn67Jzo4JbtrmB0SiTUFDBodQSDqaWcCi1hMIKrfnzzjYq8tRawHSbs60FoR72LTqGCzEYZY6kl1JQoWFMuDuOdRW6htQlUxVWaM3jtVKZjhPdOMjXfKxIEAShuUTQvUKVVdeSVlx90QCRXFhJrd6IvZWKABfby3pOvcHI6dwKDqYWczC1hMNpJZRVN2yoYFkXvPr4OPLt3jQAvpo7hB5eDng5WWOpat0dD4NR5nBaCRvictl4Is8cVJ1sVHx1xxAig10JdLVFpZDQG2Um9fJgcm9PTuVUsCuxEEcbC+xaMMlMEISuRbx6XIES8iuY8uEuHKxUxL485YKdgk5km+oW9/ZxbPSIkCzLGIwyemPdfw0yeqMRvdG0XByXXc7epCKi00qp/Ff1JhsLJYMDuxEV7EJksAv9/Z2xtjAVnthxppDUoiqUSghwvbyAfzHHMstYFZ3JXyfzKar8Z7btaK1CozNSXqNnzlcHOfifibjaWeHlZE1WaQ3b4wvYerrAfP2OM4Vc1de7VccqCMKVSwTdK1B3NztTOUOtnnUxWbjaW1Gp1VNdq6dSa6C8RkdRpZaiCq255+zJ7HKG/m9rXVA1moNsfaC9kCGB3YiuSzpysFYxNMgUYKOCXejr64SF8vyz16FB3UgtquJQaikTerZeR6Okggqu/3Qv9V+Gk40FU3p7cnU/b0aGuDHr830czypHb5R55beTLLl1ENMivFh7JJviqlq8HK0Z18OdcT08GBnaufoLC4LQsYigewWp1Rv5+XAGuxKL0OgMWKsUPPXL8Yvez0qloKrWQFWtoVnPp5DA09Gafn5OXB3hTWSwC728HZvcg3dokAurorM43MoVqjQ6ozngfjt/KCND3BosY//68CjuWR7NllP5bD6Zj9Fo5Ko+Xny1KxVnGxX7nhuPQiES/QVBuHwi6F5BXlwfx6roLPPHOoOMtYWCUA977CxV2FupsLNS4WCtws3eCjd7S/anFLMhLo/IoG7cPjwQd3trPBytUCkklAoJC6UCpUI652OlJF12xarIYNPRnONZZaY3CXXLzi2tp5cDD48PZWCAM6ND3VCdZ+b94jW92HIqH63eyKroLK4f5IulUkFZjZ6MkhqC3OxaZWyCIHQtIuh2Ihvicvl6d0qD/dX6f0WVWtQaPZIET0wKZ1wPd/r6OF00MMZll+NgpeJQWimH0kqZOyyQ167r2yZfT4CLLR4OVhRUaDmeVW4OwmBKyjpfcLwUKqWCp6b2uOA1ga52BLjYklFSzdd7UpkdGUCEnxNH0ks5kl4qgq4gCC1CBN1OpLhSS0xG2QWvmT8imEcnhjX5Me2tLKjQ6nG0VmFloaTbZbTHay5Jkujn58TW0wVEpxVTWKFlT1Ihe5KKuHmwP4804+toCdcN9GHxtiRSC6vMxUKOpJcSnV7KjYNb9wyzIAhdgwi6nci4Hh58OdfatLyrkFApFOb/VyrAKMOgutrBTWVnZVrSHd/Tg0WzB7bGsC+ol7cjW08X8M5fCQ1u35dc3OZBd05kAIu3JWGQZfYmFZmbH8SIcpCCILQQEXQ7EX8XW/wv8yzt2apr9ayKzsTaQtHk5KeWFuHrRKCLLekl1fTwdGBkqBujw9waLDW3FS8nGxysVFRoTd+Xl2f0ASChoILyGh1ONm23CiAIwpVJBN0uSpZlXlgbR77adGb130Us2kqIhz3pJdVYqxRseGx0uwX/s8cTm1nGscwy3B2sCHK1Ja24mqMZpYzr4dGuYxMEofMT5yC6qI0n8lgfm0N9Zz1ri/b5VQhytcPaQoFGbyS9uKpdxnC2+ob14XWNHwZdZIm5ulaPLF/4HLMgCEI9EXS7qKMZpiAyuG4PWKJ9ZphKhUSPugAXXxfw2kOlVk90Wom5EEh9tnL9vm70eYJuda2eB3+I4bpP95mLjAiCIFyIWF7uourLNTpa1/0KtOOqbi9vR45llXM6V83VEW1TYrGgQkN0WimH00w1ok/lqM0FNBQSLN2TysqDGdhbmxLNYjJK0RtMpS9PZJcTk1HKd/vSyS6rQSGBhbJ9l8UFQegcmjXTfeWVV5AkqcE/Ly+vRq+/8847z7lekiT69OljvmbZsmXnvUaj0Ziv2bVrFzNmzMDHxwdJkli/fv05zyXLMq+88go+Pj7Y2Ngwbtw4Tp482Zwvr0tJrWulZ1FXmUkhtV/Q6OllmumezlW3+nPFZpZxyxf7ifzfNh78IYZv96ZxItsUcL2drHG1tzSXeqzRGTAYTJHYxkLJ1I920fflv7jp8/28sSGe7LIafJ1tWHrnUPr4OLX62AVB6PyaPdPt06cPW7duNX+sVDZeRWjRokW89dZb5o/1ej39+/dn1qxZDa5zdHTkzJkzDW6ztrY2/39VVRX9+/dn/vz53Hjjjed9rnfeeYcPPviAZcuWER4ezuuvv87kyZM5c+YMDg6t25i9s9mTWMSBlBJUCgkvR9P32TzjbQManan+c/2/Kq2p/OTp3NZbXs4sqebtTfH8cTwXAEmCnl6ORAZ1Y0iQC0ODXPByMn0vZFlGXaOnqEpLcaWWxdsSOZmtJrnQ9EbF3cGKgf7ORAa7MCcqAFtLsWAkCELTNPvVQqVSXXB2ezYnJyecnP6ZAaxfv57S0lLmz5/f4LqLzZinTZvGtGnTGv28LMt89NFH/Oc//+GGG24A4LvvvsPT05OVK1dy3333NWm8XYHBKPP6n6cAuH1YoPn21joOU16t45k1x0gurDIH2Vq98bzXOttakFNag0+3lmsQX1Zdy8fbk1i+P51agxFJghsG+vHklHB8nM//PJIk4WRrgZOtBSHu9owJL2NPUjG9vR34at5QfJyskdpxZUAQhM6r2YlUiYmJ+Pj4EBwczOzZs0lJSWnyfZcuXcqkSZMIDAxscHtlZSWBgYH4+fkxffp0jh492qwxpaamkpeXx5QpU8y3WVlZMXbsWPbt29fo/bRaLWq1usG/K93PhzOJz6vAycaCxyeFUV5jOiq0J6mI9/46Y/64pcRll5NWVE1SQSWFFVpzwFVIpiAb5GpLfz8nHK1VnMxRsy42u0WeV6s38PXuFMa+u4Ov96RSazAyKtSNPx4Zxfs392804J7P2HDTUaHkwipc7SxFwBUE4ZI1a6YbFRXF8uXLCQ8PJz8/n9dff50RI0Zw8uRJXF0v3PIsNzeXjRs3snLlyga39+zZk2XLlhEREYFarWbRokWMHDmSY8eOERbWtIpEeXl5AHh6NmwP5+npSXp6eqP3e/PNN3n11Veb9BxXgiqtng+2mJbxH5sYhrOtJSDjbGvB8axyjmeVc8+Y7i36nKPC3Pj14ZHEpJfiaGOBk41pBmlvqWpQF/qXI1k8tfoYPx/O5IGxIRetGV2h0fHx30kk5VfyxdzBDeo0J+RXcN+KI+Z96x6eDjx/dU/GhrtfUsAM97TH28ma3HINB1KKxXldQRAuWbOC7tlLvBEREQwfPpyQkBC+++47Fi5ceMH7Llu2DGdnZ6677roGtw8bNoxhw4aZPx45ciSDBg1iyZIlLF68uDnDO+cFVZblC77IPv/88w3GrVar8ff3b9Zzdia2lkreuD6Cnw5nMne4abXhVE4FZdU6bhrki721Ravs7VpbKBkR6nbBa66O8OLV306SUVLNgZTii15vY6FkzZEsiipr2RZfwNQ+pu2JbafzeeynWCq1ejwcrHhySjg3Dfa/rKIbkiQxroc7Px7KZFV0Jlq9sa4fcS1FlVq8na15cFzoJT++IAhdx2W9wtrZ2REREUFiYuIFr5NlmW+++Ya5c+diaWl5wWsVCgVDhw696GOerX4/OC8vD2/vf46cFBQUnDP7PZuVlRVWVlZNfp7OTpIkpvTxYkqff/bPc8trALh/XAihHu2XcGZrqeLaAT78cDCDnw5nXjToqpQKbhzkxxe7UvjpUAZTenvy+c4U3vkrHlmGYd1d+PS2wbjYXfj3ral6ezsCsCEujw1xeQ0+19/fWQRdQRCa5LKKY2i1Wk6fPt0g0J3Pzp07SUpKYsGCBRd9TFmWiY2Nvehjni04OBgvLy+2bNlivq22tpadO3cyYsSIJj9OV1Ol1aPWmM7rejm1XPLSpZo9NACATSfyKK2qvej1tww1rUrsTCjkvhVHeHuTKeDeFhXAigVRLRZwAcaEu5v/v5e3A5N6eXJrpD+PTAhl3vDAC9xTEAThH82a6T711FPMmDGDgIAACgoKeP3111Gr1cybNw8wLddmZ2ezfPnyBvdbunQpUVFR9O17bp/WV199lWHDhhEWFoZarWbx4sXExsbyySefmK+prKwkKSnJ/HFqaiqxsbG4uLgQEBCAJEk8/vjjvPHGG4SFhREWFsYbb7yBra0tc+bMadY3pCvJLTedhXawMjW4b299fR3p7e3IqVw162OzmT8y+ILXd3e3Z1CAMzEZZWw+lY9SIfHKtX2YO6zlg2Cgqx1Dg7pxOK2UucOCmBMV0OLPIQjCla9ZM92srCxuvfVWevTowQ033IClpSUHDhwwZyPn5uaSkZHR4D7l5eWsWbOm0VluWVkZ9957L7169WLKlClkZ2eza9cuIiMjzddER0czcOBABg40tZ5buHAhAwcO5KWXXjJf88wzz/D444/z4IMPMmTIELKzs9m8ebM4o3sBeXVBt/58anuTJInZkabZ60+HMi9a0/h0rpqUwipc7CzxcLBkxYLIVgm49UaHmWa7uxMLW+05BEG4skmyqNZuplarcXJyory8HEdHx/YeTqv7dm8qb2w4jUqp4O5RwUzt40UfH8d2PRJTXq0j8o2taPVG1j80kgH+zue9LqO4mhs+20dRpZYwD3u+umMwQW72LT6eokotsgz2VipO56q54bN9OFqriPnv5AYZ04IgdG1NjR/tv6YotJs9iUXoDTI6g4El25NYsj2Jh8aH8PTUnu02JidbC66O8Gbd0Wx+Ppxx3qBbWKFl7jcHKarU0svbkZ/uHdZqxT1u//rgOY0Y1Bo98bkV9PUTpR8FQWge8Va9i8or1/D3mQJk4IWrezK5tyfPXtUTd/v2z+auT5D6LTaHqrrGDPUqNDru/PYQ6cXV+LvY8N38oa3aXL6H17nbEw7WSg6mlbTacwqCcOUSM90uak1MFkYZIoNcuHdMCPeOae8R/SMq2MXcPP7P47ncXBeEtXoD9604wskcNa52lqy4KwoPx9bdj/7olgF8ePMAanQGqmr15pKS8W3QnEEQhCuPmOl2QUajzM+HMwHMAa0jkSSJW+qOD/102JSYZzDKPPFzLPuSi7GzVLJsfqS5521rj0WhkLCzUuHhYM2w7qbKa2fy26/3ryAInZcIul3QgdRiMkqqcbBScXVE05pXtLUbB/uiVEjEZJSRkF/Bh1vOsCEuDwulxJd3DCGinfZT69sQJuRXcCyzjKMZpUSnlVChadma1YIgXJnE8nIXVD/LnTHAp8O2pfNwsGZiTw82n8rn3U3xxKSXIQE6g8ynO5Iora5lcm9PrFSNt5ZsDd1sLVEqQKMzMvOTvebb+/s78+tDI8kr13SYI1iCIHQ8YqbbxZRX69h4wlTGcHYHXFo+W/2Z3Z0JhYR72eHXzQZJgr1JxTy88igT3ttJQhsu82p0BhZ8d5ggVzsUEoS6m8akVEgcyyxj2qLdDHtzG5kl1W02JkEQOhcRdLuY9bHZ1OqN9PRyIMK3fZZodQYjW07lk1tec8ECGGPC3Onv74S9tQWWShU7nx7HrqfH88iEUNzsrSiq1DapXGRLic0sIyajjOTCKkaFurH+4VHseXYCE3uaug6dzlWjkCAmo7TNxiQIQufSMdcWhVYhyzI/1S0tjwm7cEOB1pRUUMk9y6MBcLWzpK+vE319Henr40RfX6e6Ga1EQn4lJ7LK6e3jxNNX9UChUODvYsuTU3qwYFQwp3MriOp+4ZaSLSlfbargNcDfmeULosy3PzutJ3ZWKgb4O3N1hDfuDu1/7EoQhI5JBN0uJC6rnFqdAYAvd6fy+/FcJvXy5I7hgYR5tl25zAqNnp5eDiQWVFJcVcvOhEJ2JvxTWtHJxoK+vo7kl2swyBDkZkfff83KnW0tGR7SMgH3u31pLN2TyrS+XtwxIgjfRhrcF1ZoAfB3sW1we4i7PR/eMqBFxiIIwpVNBN0uZNWRTJKLqvByskZdoyO3XMPGE7nsSixk59Pj22wckcEubHp8DBqdgfi8CuKyyzmZXc6JnHLO5FVQXqNjb1IxEuDrbM39Y7u36niSCyvJKKnmi10pfL0nlav6enHXyGAGB3ZrcF190O0IBUQEQeicRNDtIqq1enYnFAHw/qz+DA7sxr7kIpxsLPHr1j5t/awtlAzwd25Q6rFWbyQhv4IPtiSwO6EQOysVfXxad+/51Wv70NPLkd+P5bA/pZg/j+fy5/FcRoe58drMvubzwFmlpt7DPs4iO1kQhEsjEqm6iN+P55Cn1jCxpwfDu7tibaFkQk9PBgd2w7OVqzo1h6VKgaejNbsTC9EZZZILqziS3rqJSZIkMScqgB/vHcbGx0Yza7AflkoFuxOLmPLRLpZsS0RdU0tsZhlAmy7FC4JwZRFBt4v4+XAmWr2RocEuKBTt10WoKb7dm4rOIONkY4HBKPPA90fMSUytrZe3I+/O6s/mJ8YwOsyNWr2R97ck0O/VLWSX1WBtoSAyyKVNxiIIwpVHBN0uIKmggpiMMpQKiRsG+bb3cC5IrdGxYn86AK/N7EOYhz0FFVoe+P4IWr2hzcYR5GbH8rsiWTR7AEGupsQpBysltwz1x8aybQtyCIJw5RBBtwv4bp8piE3o6YGHQ8dZSj6flQczqNDqCfWwZ3o/H766YwiO1ioySqrbvOiEJEnMHODL5ifGEhXsQoXWwK4zRby/+QxHM0oxGhs/Y3w4rYSvd6e04WgFQegMRCLVFS67rMZc9vGukcHtPBq4b0U0p3MrmDXYj/vGhmCp+ud9n0ZnYOmeVADuHxuCQiER5GbH1/OG4u9ig7dT+yR8bTyRy8FUUyu/1OIqc+9hDwcrJvbyZHo/b0aEuCJJpmX71dGZvLAuDmsLJbcPC8TaQsyMBUEwETPdK9wnfydRazAyvLtri51rvRyvXdeXbrYWvL8lgf+si2tQkWptTDaFFVp8nKy5tr+P+fbIYJd2C7gA3x8wrRTcNTKId27qxzX9vLG3UlFQoeXHQxnc9vVBdiYUYjTKvL0pnqd/OY7OIDM6zA39BWbDgiB0PWKmewXLLKlmVd0s94nJ4e08GhMnGwtujQwgLjuO1UeyqNYZuGtkMP18HfliVzIAd4/u3mAG3J6SCys5nFaKSiFx39gQPB2tuXmIP1q9gQMpJXy+I5n9KcX8ciSLnw5lsumkqa71IxNCeWJSeIdPWhMEoW2JoHsF+3h7EnqjacYVGdwxMm6tVEpmRwZQazDy0q8nzWdifZ1tyC6rwdnWwtzooCPYciofgBGhbg2OVlmplAS72mGlUuBgrWJ7fAHVtQYslQreujGCGwb5nffxZFnmcFopq6MzeW5aT1xFoQ1B6FJE0L1CpRdX8UtMFgCPT+oYs9yz3TE8iP5+zizfn87vx7LJLqvBt5s1Nw3y61DtBrfWBd1JvTzMtxWoNSzenshPhzLNy8fWKgXeTlYsvnUQQ89zpKigQsOaI9msjs4kpagKAB9nmw6zAiEIQtvoOK9uQotasj0Jg1FmbLj7OeUMO4r+/s687+/MpF4ePPBDDCWVOuaNaP9kr3olVbXmjkET6joJFVRomPzhLsprTE3rx4S709/PiSXbk6jVy/T3czbfX2cw8nd8Aauis/j7TAGGugBta6lkej9vJp4VyAVB6BpE0L0CpRRWsrZultsZZlIrD2UAcMtQf1zsLNt5NP/Yeiofowy9vR3x62Y6qyshUV2rB2D5XZGMCXdHozPw8+FMCiq0rInJIjLYhVXRmebEsHqDApy5Zag/1/Tzwd5K/OkJQlck/vKvQIu2JWKUTbOzs+saX8zWU/kczyrjnjHdcbC2aL0BnuXP4znsTixCqZBYMKrjzHIBNpzIBWBaXy/zbe4OVux6ZnyDbGprCyX3jw3h//44xSu/nUSrN5o/52ZvyQ2D/Lh5iB+hHqJ8pCB0dSLoXmHO5FXw27EcABY2Y5b7y5Esnv7lGMGudjw8Iay1hteALMu88vtJAK6J8D6nZV57KqrUsjvR1CDi6n7eDT53vuNLt0YG8PPhDLJKa9ACE3t6MGuIPxN7eWCh7BiZ2IIgtD/xanCF+XBLArIMV0d4ndODtjHrj2bzzC/HkGUYEtStzY7rnM5VU1hRi4e9Jfe1cvu+5vrzeC4Go0w/PydC3O0ver2NpZJ5wwPrGjZYsfjWgVzV10sEXEEQGhCvCFeQuKxyNp3MQ5LgiSZkLBfW1TR+/OdYjDLcOMiPt27o1wYjNVkVbdp37uHt2Ort+5pr3dFsAGYOaHqt6usH+WNrqSJfreWdTfGtNTRBEDoxEXSvIB9sOQPAdQN8L9h+zmCUWXEgnckf7mTjiTxUColHJ4Ty1o0RbVbMobSq1lye8r4xIW3ynE2VWlRFbKapQcTZlbEuxsZSyds3mt60rDiQzqkcdWsNURCETkoE3SvEkfQS/j5TiFIh8djExvdkD6YUM33JHv67/gRl1Tp6ezvy68MjWTilR5suha6PzQZZxtFa1eGawq+vm+WOCnXD3aF5xStGhblxVV8vjDJ8tDWhQZlLQRAEkUh1BajVG/lyl6mjTS9vB6pq9RRUaHCysaBCoycuu5yT2eUcSitlV0IhYCrHuHByOLdFBaBqxWBrMMoo/zV71ugMLNmWiEZvpEZvZMqHu5gd6c+jE8PavQuSLMv8GmsKutcNbPost151rZ4CtQYJ+Du+gC2n8pnSx+ui9xMEoWsQQfcKsGR7In+dNFVOOpGt5prFexq9ViHBnKgAFk7u0epnYk/nqrnz20N8OXcI/c86uvTLkSxKqnV4OFjR29uRHQmFfH8ggzVHsnl3Vj+m92t+sGspsZllpBVXY2upZGozg2WVVs9dyw4Tk1GGhVJCZ5B5a2M843uKDGZBEEzEK0En98fxHJZsTwLgtqgAxoS742itoq7LHJIEIe52zBzgw3+u7sVfj4/h9esiqNUb2ZdUhN5gvMCjX54Hvj9CvlrLjjOF5tsMRpmv6vrM3j82hGV3RfLTvcMY4O9MrcFIb2/HVhtPU9QvLU/p7dmscpRVWj3zvz3MwdQSHKxUfDt/KK52lqQUVbF4W2JrDVcQhE5GzHQ7seSCSt7aaMqSndzLk/+b2de8lGs0ylRo9VgopXOCh0ZnYNYX+8gsqeHPR0e1Wuawh4M1acXVlFbXotUbsFIp2Xwyj/Ti6gaNDYZ1d2XdgyM4laumexOO57QWncHIH8dNBTFmDmx61jLAmfwKTuaU42CtYsWCKAb4O/PSjN489lMsS7Yn0cfHiav6XnjmrNbo+PTvZB6fFCZ68ArCFUoE3U6su7sdQa62ZJXWsOV0PuPe+5vbogK5ZYg/3ewscbI5t6qUwSjz3l9nyCypAaCXV+vNLB2sTb9ey/al8cfxHGYPDWB7vGkZ/I5hgQ3eDEiS1O7Hhv6OL6C4qhY3e0tGh7o1676DArqx6v7h1OqN5ipgMwf4EptZxrd703hyVSwh7iMbzSqXZZk7vzlETEYZJVVa3r6xH5Ik2gIKwpVGkkV6pZlarcbJyYny8nIcHdt3mbOpUouqWLE/jV+OZKHWmGoCW6kUjAp1o5e3Iz29Hejl7YirnSW/Hcvh+wPpJORXArBo9oBmnUNtrvIaHd8fSGfF/nTy1Brz7ZZKiX3PT8StFdra7UksIiG/gvkjg5odtG798gD7U4q5b2x3np/Wq0XGozMYmbv0IAdSSgh2s2P9QyPP+2YIYM2RLJ5cfQyArQvHiLKRgtCJNDV+iKB7ls4YdOvV1Br4/VgO3+1P4+RFzoc6WKt49qqe3BYV0CazKZ3ByOaT+fxnXRxavYG7R3fnySk9WuW5bvv6AHuTinnzhghujQxo8v2OZ5Vx7cd7USokdj8zHh/nc0s9XqriSi3TFu2muFLL7MgAXrm2zzmJVeXVOm78fB9JBZX09HLg14dHYqUSS8yC0Fk0NX6I5eUrhI2lkpuH+jNriB9x2eUczSgjPk/NqdwKEvIqqNEZ6OHpwK2R/lw/0A8n27ZpaABgoVTQw8uBsrp2eI01eL9cueU17E0qBsDLqXlHj5bvSwegv59Ti8/Au9la4myroqBCyw8HM8guq+HT2waZl9e1egP3rIgmqaASL0drvrlzCNtOF5BVWs29HaxwiCAIl0cE3SuMJEn083Om31l9XQ1GmdLqWlztLNttn3DpnlQAJvf2JNjNrlWeo5utJWEe9iQWVPLIyqPcPMSfWyP9L1idC0zlM3+JyUIhQUxGGWPf/Zt7RndndqR/szKYz6e6Vs9/1p0gIb8Ka5UCGdhxppBbvzrIt3cOxdnGgqdWH+dQXdbzu7P68fJvp9hyKh+VQmJsuAc9vMQysyBcKcTy8lk68/JyR1ZUqWXEW9up1RtZdd9wIoNdWu25SqtquWd5NNHppebbLFUKrFQKrFRKrFQKevs48tL03uauRnOXHmR3YhF9fRwpqNBSUNcDt7+fE78+POqSx5KYX8GDP8SQWFCJUiHx7k39CHS1Y8F3hymr1tHd3Y5hwa6sPJSBUoI7RgTxS3QWFVo9KoXEg+NCeHB8qMhkFoROQCwvCx3Giv3p1OqN9PdzYmhQt1Z9rm52lvx833B2JRTy46EMtsUXUKs3Uqs3UoEp0Sy7rIb9ycX8d3ov/LvZsjuxCAulxKe3DcbD0YrVR7I4lllGWXXtJY2hoELDYz/GcjSzFI3OiIeDFUtuHUhUd1cAfrl/OHcsPURKYRUphVUABLja8e3eNAAG+Dvz1o0R9PxXZvnuxEJqag1M7u0pMpsFoZMSQVdoVRqdgRUHTPul94zp3ibBQqmQGNbdlSPppTjZWjBrkB9uDlZo9UYKKrQs2ZZIdHopz66Jw0JpGs+tkQEEuNbNfIcFMndY4CU//6d/J7M/xbS3PCrUjY9mD2iwTxzq4cC6h0by8A8xJBdVUVpVS2pRFbaWSp6e2oM7hgedUzoTILOkmu/2pROfp+bRiU3vlSwIQschKlIJrWpNTBYlVbX4OttwVRvWIJYk2JdcxOroLG5feojPdyYDMDbcnZ/vG85z03qiVJhKNQ7v7sIjExpvEtFcvbwdiPB14qkp4Xx3V+R5E7M8Ha359q5IhgZ1w83eVI5zeoQ3tw8LPG/ABdiXXMyZ/AoySqpbbKyCILQtEXSFZtmdWEhGcTVaveGi1xqNMkt3mxKo7hoV3KqNFf7N2kLJF3OHMCSwG7UGI6uis5i2aDe3fnmAbafzmdjTA1VdcJvR37fZ3YQu5JahAfz+yCgenhDWaAAFsLdS8cmcQeZs7tVHsnj2l+PklWvOudZglCmvy/7OKTv384IgdA5ieVlolg+2JBCbWYaFQsEjE0LxcrLGy8kaT0drPB2scbRRmZeQt8cXkFJUhYO1iluG+rf5WN0drFh9/3BiMkr5Zm8am07ksT+lmP0pxXg6mpabR4e5cWtk24xNbzCikKQGPYtVSgUPTwjlx0PpqDUGtscXsDOhkCVzBjIixFQVy2CUefTHo+xOLEKSYP7I4EafY19SEX18nNr0SJjQccVmltHfz0nkAHQgIugKzfL0lB48+tNRiipreX9Lwjmft7ZQmANwQr6pSMecyADsrdrnV02SJAYHujA40IWcshqW70/jmz1p6A2mpP1BAa2b2FVPZzBy0+f7SS2s5OEJocwbEWQufvHhlkTUGtPKQVWtHp1B5vavD/L01J7cP7Y7vx3L5s+4XCyVCj64pT+Te3ue9zmqtHoWfBeNwSiz6fHRLVbHurxGh1IhtdvPULg0m07k8uAPMdw4yI83b4ho05UmoXHir0holhGhbmx+YiwrD6aTXlxNfoWW/HIN+RUayqp1aHRG0ourSS+uRqmQGOjvzJ0jg9p72AD4ONvgYG1BrcFIcZUpM3nRtkQyS6p544aIVj2aE5tZxrHMMgDe2BDPigPpPHdVL4LdbFm2L9V83cdzBvLXyXzWxmTz9qZ4YtJLSS40le18bFLYBdse/nUyjxqdgSBX2xY7C12p1fPpjiR2nilk0+NjWuQxhbZRpTW9kTuYWsITPx/j3Vn9xPGzDkAEXaHZXOwsefg8iUcanYECtZb8Cg3vbornUFopRlnGy7F9G9PX++lQBu/+dQaAV2b0xijD/zacZu3RbM7kV/DjvcNwtG6dZdmhQS7seGocH2xJ4EBKMZklNTy0MgY7SyXGupPyk3p5MrWPN1N6ezEk0IVXfjvJltOmBhE2Kom5wy+cUb2uri3hdQN9G11O/OtkHtW1erQ6I1f3877o1/vjwQy+2JlCmEf7dX8SLs2Ng/2wt1bx6u8n+f14DvkVGlYsiBTlRduZWG/ogv4+U0BxpbbFH9faQkmAq625KpRKIXEsq5yrPtrNllP5Lf58Z/tmTyqTPtjJ17tTzpvk9efxXJ5fFwfAA+NCuHNkMHeNCmbFgkgsVQpO56o5mFLSqmMMcrNj8a0D2fH0OB6bGIaFQkKrN/UztlQqeGl6b8C0JD4nKoBfHhiOr5M1Pk5WKJUKXv/jFPuTizEaz61nk6/WsDepCIDrLtDEYkNcLk/8fIzn1sYx9PWtPP7TUT7cknDeM8lavYGv95h6H989uvF9ZKHjmtrHi6v6eANwKLWEoxll7TsgQcx0u5rCCi2PrDyKg7WKH+6OapX+teuOZlNaraObrQVanYEz+RWUXmKhiaaq1OpJKqjk9T9P8+3eNB6eEMq1/X2ws1Lx95kCHv/5KLJsOo/7zNR/mi2MCHEj7pUpxGaUMSDAuVXHWM/WUsUjE0JZfzSb9LrjP/eP7W4+J1yvn58zV/X1YuneNKxUClZFZ7EqOgsfJ2uuHeDL9QN9zSUif4vNwSjDoABngi6wtPx/1/YlzMOe9bE5JBVUsj42BwBnW4tzErTWH80mX63Fy9Ga65rZX1hof0ajzAdbEvhmr2n74poIb4YEtk0Og9A4EXS7mAqNDg9HK1IKq7h3xRF+fWgkdi2YICPLMj8dygTgicnhzBzgyy9Hspg5oPG9yJbw0PhQyqp1bIjLJbushufXxvH6H6eI8HPiSFopOqPM9H7evH5d33OWXq1USnO1qLbyc3SmOeB6O1rzwLjQc67JV2v4br+psMiYMDccbCzYciqfnHINn+9M5vOdyfTyduS6AT78ciQLgOsv0kzCydaChyeE8dD4UNbEZPNUXSvB0WHuDa4zGGU+3/nPLFcsSXYuNbV6nlx9jA1xeQA8PD6UhZPDG2TOC+1DBN0upru7PT/fO5zpS3aTVFDJq7+f5J2b+rfY48dklHEmvwJrCwUzB/jiZGPBglGtvzSpVEi8NKM3z1zVg+8PpPP9gXTSiquJSS/DwVpFVHdXPrh5wAXPzbaVCo2O9+r2lgFemtEbG8tzg9qK/enojTJDg7rx1byhgGnf/O/4AtYdzebvMwWczlVzOteUJS4BBoORCo0Oh4vs1UqSREJ+BQARvk6E/mvP9q+TeaQWVeFkY8HsZrRIFNqfLMvcsfQQSYUVONta8OI1vblpcOt09hKaT+zpdkHuDlYsnj0QSYJV0VkcqCtZ2Bz1fTJKq2p57Y9TvPtXPGuOZPHJ34kAXBPh02iz9paWkF/B9njTnrG1hZK7R3fn76fGmQtjDA12YcmtA7FUdYxf9y92plBabSp0MTzEhav6nr9S159xuQDcMTzIfJu1hZJpEd58eccQDv9nEv+7vi9Dg7oxOMAZGXjl91MMeX0rD6+MYdvpfHQGY6PjOFLXFCIuu5x7lkfz9e4USqtqkWWZz3aYKnjNGxEkjgp1EHFZ5bzy20n0F/iZyrLM4m2JHE4vxdPRmldm9BIBt4MRf01dVFR3V2YN9mNVdBabTuQxrJnLq8+uOU5xZS35FRpOZKvP+XxbFZwor9Zxz/JoMkqqeWpKDx4cF4IkSUiShIejqcrU8O6uHeaMYk5ZDV/sMgU0hWTaYz1fpnF6cRWpRVWoFBLjerif83kAZ1tLbosK5LaoQDJLqvk1Npu1R7NJKazij+O5/HE8Fxc7S6b38+a6gb4M9Hdu8Fwvz+jNi+tPcDyrnC2n8tlyKp///XmaYDdbUoqqsbZQcOeIoFb5PgjN99uxbJbtS2PzyTz2PjfhnN8bjc7AC2vjWHs0G2sLBVq9zIz+IuB2NB3jlUhoF1HBpkB7KufcoHkhNbUGfj+Wy7b4Ak5kq+lma8GcqABC3E0JPEMCuzG4FRI2zteF0sZSychQN2QZ3v3rDA+tjKFKq0ejM5BcUImvszXd7CxbfCyX6p1NZ9DVFeaYPyKo0V6/uxJNmciDA7tddKkYwN/FlocnhLFt4Vh+f3gUd40Mxs3eipKqWpbvT+eGT/cx/r0dfLUrmSqtqdtSPz9nfnt4FJseH42dlWl5WwZSikx7zbOHBuDSgb53Xd2tkQFYKCVyyjVsPV3Q4HMZxdXc8uUB1h7NRiGZ9uRTi6pYvC2xnUYrNEYE3S6sj6+pddypXPV5j6E0xsZSye+PjOSBcSGMDnNj+V1RvHF9hPkFemKv1mk99+5fZ/hg8xnWxmSZb7NUKXjj+gjeuD4CC6XEhrg8rv9kL3d/F82Z/ErsrFQXLCjRlk5kl7M+1nSW1slGxWOTG+8UtD/ZFHRHhbo16zkkSSLCz4mXZvTmwPMT+O6uSK4f6IuNhZK04mo+/juZse/uoOisI2NhHg7U1JqOWc2qW4pUKSTuGdO9Wc8tXD69wciGuFyOZpSe87nu7vbcPdr0M3n195NodAZkWWbNkSyuXrybY5llONlYsGJBFG/f2A+AmIzSZv1tC61PLC93YaHu9jhYqajQ6tmVWMi4Hh5Nv6+HA89e1dP8cVpRFYfTSlFIcMOg1jtesnh7EgDR6aXcHhVIiIcdViolc6IC6OFlz/3fx1BcVUtCQSWWSgWvXNunQyRPGY1Gnq7LFAb4zzW9L1iYon6/9XKyqlVKBWPD3Rkb7s7r1+mZ980house96oPd+HpZM3MAT5odEZzgY7iKlMwnjnAF19nm0t+buHS/N8fp1i+P50Fo4IZeJ4SpfVHzbJKa/hwSwLZZTX8cdy09x8Z5MIHt/THr5vp6JnOYGTmAF+RsdzBiKDbhamUCq4b6MuKA+k8+EMMNw32Y2ofLyKDXbBo5h7omrrZ55hwdzxboQJVda2eecODMBplPt+VwsqDGaw8mIGtpZJRoW48NbUHgwK6MS7cndVHsujv68QD40PMTQPa26+xOeYjQtYqxXmLUZytl7cj3d2MBP7r7O6lsrNS8fW8IXy1O4Wlu1Mpqa6lqKqWtKIqqupmuZZKie3xhYDp3LDQ9mzqyjQ2FiZtLVW8eE1vHloZwxe7TEe6lAqJJyaF8cC40AZvMG8ZKrLOOyIRdLu456b15EROOUczyli+P53l+9NxsrFgYk8PpvTxZEy4O7aWF/41MRpNS1xAi2dK6g1GHloZw18n/6loFe5pT3mNjooaHdW1BjafyudASjFT+3iy+ohp+XbeyCCu6uvdomO5HOtjs6muC24+zjZUai/cGnHZ/MgWH4OzrSUWSgUavRFnGwsqNDqqag3YWirxdrQmuagKZxsLanQG9iUXE+BqK87ntrH6IjKN5SHoDEZO5ZbjYmuBQiFha6Fk8ZxBDPB3bsNRCpdDBN0uzs5Kxar7hrMroZDNJ/PZejqf4qpa1h41ZcKODnXj5qH+zOjf+L7o/pRicso1OFqrmNTr/B1wLpVao8fB2gJLpYLauqMSCfmmBgAjQlyZNdifFQfSiMkoMwfc12b2Mfeo7SiOZZYDMLm3J2/dEIHreRrbt7aUwkrzUaCXZ/TmiVWm5e5dz4zndI6a3UmF7DhTSEJ+JS//dpLPdybz0PhQbh7i32GOW3UV9V2wzpZaVMXjPx3lWFa5+bZP5gwUAbeTEUFXwEKpYGIvTyb28sRglDmSXsqKA2n8fiyXfclF7E4qIre8hntGdz9vgtTqaFMFqmsH+LR4FxMXO0vem9Wf/13fF4UkUVChZenuVL4/kM6+5GL2JRfzxKQwjmaUEeBiy6whfsw961xrR1Ch0VFW14D+psF+7RJwl+1N5ZXfTwGmI1QjQk17xZIELraWjA53Z3S4O09NMbIqOpOPtyeRW67hxfUn+GxHMo9MCOXGwX7N3nYQmie8Lpt9y+k8Hp0Yav57++1YDs+tOU51rQFHaxVDg13YdrqA97ckMKWPl/i5dCLiJyU0oFRIRAa7MLGnJ0oJczLNGxviefqX4+c0E1BrdGw6aSo1d9Pg1juba6VSYqFU4Otsw0szerPtybEMrKuV/MnfycjAgABnHhp/bjnF9nB2UYo1dTNwCZjYs+nJai2pfpVgYIAzb9/Yj0qtgWA3Owb4OzdItLFUKbh9WCA7nh7HKzN64+5gRXZZDc+tjWPi+zv55UjWBYszCJfnhkF+2FgoOZGtZnt8AXqDkTf+PMWjPx6lutbAsO4ubHp8DB/eMgBXO0tSCqv4/kB6ew9baAZJPt/hxy5KrVbj5OREeXk5jo6O7T2cdnckvZRwT3tWR2fx+p+nMMqmc6Of3z4YdwfTbO2nQxk8tzaOEHc7ti4c2ypHhRoTn6fmqo92AxDsZsufj46+6P5zW6jQ6Ljqo93093eisELLiWw1NToDPk7W7Ht+YruMqbxaR63BaP65NZVGZ+D7A+l8vjOZosp/kr/evKEvt0ZeuNWgcGne2hjP5zuT6eFlj7ONJeU1OuLzKrhvTHeeuaqnOVlq5cEMXlgXh5ONBTueGtehzqN3RU2NH2KmKzSqvjDDXaOCWTY/EkdrFUfSS7n24z2cyDbtKx1KLcHRWkVuuYbF25IorGj5loGNsa7bZ+zpac/bN/brEAEXTN15sstq2BCXx+G0Ump0ptWBCb3aZ5YLpkYHzQ248E9ZzV3PjOfxSf/0UH5+7Qke/OEI+WrNJY0np6yGr3enmAt1CP+4t+589Jm8Sg6mlhCfV4GPkzUjQlz/lZ3sT08vB8prdHy2M7m9his0kwi6QpOMCXdn/UMj6e5uR265hps+38c3e1JZezQbtUZPda2BD7cmMPKt7SxcFXvRIzEt4avdppZlnk42RAa3bZegC7l9WCDf3DmE56b15KNb/mkmcWsnbhxgY6E0N0iwt1QiARvi8pj4/k6W7U3F0MwCDF/tTuH1P0/z+M+xLT/YTkyjM/Da76dQ1QXX7u523DLEj5xyDc+uiaO8rmY31DX5mN6bu0YG89B5ulQJHZMIukKTdXe3Z92DIxkb7o5GZ+T//jAl5owNd2fR7AEM8Hem1mBkV0JRi7YLPJ+CCg2r644pPTAupFWfq7kkSWJCT0/uHxvCdQP96O/nBMDJZpbb7EiKq2o5nlWOSiGx8t5h/PnoaAb4O1Op1fPK76e4/tO95tWPiymprGVVXfLd3GFiibpeTEYpVy/azbrYbPRGmQfHhbDh0dG8fG0fgt3syFNreGF9XINyqCNC3XhpRm+cbNumuYhw+TrGepzQaTjZWPDNnUN5Y8Nplu5JJdjNjlmD/Zje34eZA3w5mlFKTpmm1bMpv9mTRq3eyKAAZ6KCXVr1uS7XiFA3VEpFm3Vdag1u9lZsfGw0B1NK6OfnDMDaB0aw8lAGb2+K53hWOdd+vIf5I4NZODn8gm+6lu1LxdFaxcSenowO6xjFS9qTRmdaJfpqVwpGGTwdrXjrxn6MP6tC3Ee3DOCGz/bx5/FcJvXy4PqBHetInNB0IpHqLCKRqumOZ5Xx7JrjFFZq2P30BGxaaD9VlmUWfHeYESFu3DTYD2fbc5NDqmv1RP5vG5VaPV/dMYTJvVv2bLDQPAUVGl774zS/H8sBwNvJmlev7cOUPue2LKzU6hn51nbKa3R8etsgro7oOAVM2kNcVjkLV8WSWGA6e37DQF9entHnvDPXJdsSeX9LAg5WKjY8Nhp/l5apVia0jFZJpHrllVfMbdPq/3l5nb8XKMCdd955zvWSJNGnTx/zNcuWLTvvNRpNwwSNTz/9lODgYKytrRk8eDC7d+9u8PnKykoefvhh/Pz8sLGxoVevXnz22WfN+fKEZth6Kp/04mpqdTKDXtvKpA92Mu+bQ7z2+yk+3ZHEr7HZHM0obfbe7r7kYrbHF5JeXG3uOftvB1NKqNTq8XW2abcjOMI/PBysWXLrQL67KxJ/FxtyyzXcu+II9yyPJqespsG1Px3KoLxGR3c3O6aeJyh3FBqdgSdXHUOtOf/v4OWq1Rv4YPMZrvt0L4kFlbjZW/Ll3MF8cMuARpeKHxgXwqAAZyq0ep755XirjEtofc2envTp04etW7eaP1YqGy+GsGjRIt566y3zx3q9nv79+zNr1qwG1zk6OnLmzJkGt1lb/1O/9+eff+bxxx/n008/ZeTIkXzxxRdMmzaNU6dOERBgSk554okn+Pvvv/n+++8JCgpi8+bNPPjgg/j4+DBz5szmfpnCBdTqjWyIy6ObrQWVWj01OgNJBZUkFVQSm1lGeU3DFypnWwsCXe3wdbbGSqXEUqnAUmX6V1ypJbWoCi8na/LUWjLr6hNvPJHLiRzTHqGjtQVhHvb09XXiqr5e7K5rezcm3E0Uc+9Axoa7s/nxsSzZnsiXu1LYciqfvUlFLJwczp0jgqjQ6Hn9z9MA3D82pEM0omjM3/EFbD2dz4JRwfT2adltgTN5FTy1+hg1tXoMRplr+nnz2sy+F22jqFIq+ODm/jzy41Em9zYVsunI30Ph/JoddFUq1QVnt2dzcnLCycnJ/PH69espLS1l/vz5Da672Iz5gw8+YMGCBdx9990AfPTRR/z111989tlnvPnmmwDs37+fefPmMW7cOADuvfdevvjiC6Kjo0XQbWG7EwtJKqzE3cGK/c9PpECtJau0hsXbEziUWkp3Nzvc7K1IL6kiX62lrFpHWXUZxzIbf8zTeRXU6k1FF7wcrchTaxucC92ZYCrE7/anFaaurzCymW3vhNZnY6nkmat6MnOAL/9ZF0d0eimv/3madUezzcVMAGYO7BjtFhsTn1dBeY2Ob/em8u6s/he/QxMYjDLf7Enl3b/OUGsw0sfHkY/nDGxW68kgN3u+uTOSlMJKEXA7qWYH3cTERHx8fLCysiIqKoo33niD7t2b1pFk6dKlTJo0icDAhhmLlZWVBAYGYjAYGDBgAK+99hoDBw4EoLa2liNHjvDcc881uM+UKVPYt2+f+eNRo0bx22+/cdddd+Hj48OOHTtISEhg0aJFjY5Hq9Wi1f5zrlSt7rzZpW1pfaxp725GPx9sLVUEuakIdLXlydWmWerL1/ZhbLg7YNp/TS+uJr24irxyDbUGI7V60z+twYijtQWbT+ZxLKuc6wb4cE0/H5QS1JeelWWZ4qpazuRVsOVUPtl1y5VKyVTOUOiYeng5sOq+4fwcncmbG05zMkdtzt5+YlJYh2+kUH88qoeXQ4s8XmZJNU+uPsah1BIAJvT04K0bIvC4hI5c7g5Wl3TmWugYmhV0o6KiWL58OeHh4eTn5/P6668zYsQITp48iavrhV8Ac3Nz2bhxIytXrmxwe8+ePVm2bBkRERGo1WoWLVrEyJEjOXbsGGFhYRQVFWEwGPD0bJgs4+npSV5envnjxYsXc8899+Dn54dKpUKhUPD1118zatSoRsf05ptv8uqrrzbnW9DlVWn1bDll+r7PHNDwHfoPdw9je3w+w7r/k01sa6mil7cjvbwbTyz4bl8aAHOHBzE48NweovX+c00vlmxPYvG2RNwcrNqlhrHQdAqFxK2RAYwKdWPGx3uoqNGhVJjO907v70OIu317D7FR9UH3853J+LvYMjbc/ZLqisuyzC9Hsnj191NUavXYWir57/TezB7q36bV24SOo1lBd9q0aeb/j4iIYPjw4YSEhPDdd9+xcOHCC9532bJlODs7c9111zW4fdiwYQwbNsz88ciRIxk0aBBLlixh8eLF5tv//Qsqy3KD2xYvXsyBAwf47bffCAwMZNeuXTz44IN4e3szadKk847p+eefbzButVqNv3/r1Q++Emw+lYdGZyTYzY5+fv9sHUiSRKiHPaEezXshNRplSqpMy8gXa5puoVTQz9f0nK3Rs1doHd/sTaWsWoe9lQqVQuJMfgUzluzh/2b25cZBvh0y+PT0diS5sIqiylruW3EEW0slE3p6cE2EN+N6eGBjefEAfDSjlA+2JJhzEIYGdeP9WQMIaKEeyULndFnnPOzs7IiIiCAxMfGC18myzDfffMPcuXOxtLxwsoBCoWDo0KHmx3Rzc0OpVDaY1QIUFBSYZ781NTW88MILrFu3jmuuuQaAfv36ERsby3vvvddo0LWyssLKSsyWzlarN6JSSI0mKP1at7R8bX+fFnmxVGt06OuqGV0skQSgsNK0HeDeirPcv88U8NHWRFbfN1y0tLtMhRVaVh7MAGDJnIH09nbkiZ9j2ZdczFOrj7E3qYjXruuLfSsXU2muJbMHctfIYDbE5bIxLpeccg1/HM/lj+O5OFipuHaADzcP8aefn9N5/w6yy2q49asDaHSmv6eFU8K5b0zHTh4T2sZlvaJotVpOnz6Nt/eFz9rt3LmTpKQkFixYcNHHlGWZ2NhY82NaWloyePBgtmzZ0uC6LVu2MGLECAB0Oh06nQ6FouGXo1QqMRpFR5TmiMkoZfqSPQ3KzdUrqtSa37X/e2n5UlVoTLV37x3TvUkBrqiutnNr7ml9syeV1MJK0ourWu05uopv9qai1RsZ4O/MuHB3PB2tWbEgiqemhKNUSKw7ms3tXx9scjWrtqJQSAwO7MZ/p/dm73MTWP/QSO4d0x1fZxsqtHp+OJjBzE/2Mm3Rbr7Zk2peral3ILkYjc5IqIc9258cx4PjQkXAFYBmznSfeuopZsyYQUBAAAUFBbz++uuo1WrmzZsHmJZrs7OzWb58eYP7LV26lKioKPr27XvOY7766qsMGzaMsLAw1Go1ixcvJjY2lk8++cR8zcKFC5k7dy5Dhgxh+PDhfPnll2RkZHD//fcDpiNHY8eO5emnn8bGxobAwEB27tzJ8uXL+eCDD5r9TenKMoqrOZWrZvz7Oxge4mrqvRriSrCbHRvicjEYZfr5OdG9hfbjfJ1tuDUyoMnlAOtnum6tNNOVZZkT2eWoNXo0OvGG7XKU1+hYsd/Udu6h8f/0hlUqJB6eEEaohz33fx9DalEV13+6lxeu7sWdI4I63HKzJEkM8HdmgL8zz13Vk/0pxfx8OJNNJ/OIz6vg//44xVsb45nc25NZQ/wYHebO8awywHSMSiwnC2drVtDNysri1ltvpaioCHd3d4YNG8aBAwfM2ci5ublkZGQ0uE95eTlr1qxpNIu4rKyMe++9l7y8PJycnBg4cCC7du0iMjLSfM0tt9xCcXEx//d//0dubi59+/Zlw4YNDbKgf/rpJ55//nluu+02SkpKCAwM5H//+585MAtN09vHEVc7S4qravnzeC5/Hs8FTKXpdHUpxdf2b7njHgqFxJs3RDT5+sJWnunmlGsordahUkiEe3XcRJ+ObFV0Jm72luYiJj08Hc4pYmI0yny3zxSQZVlGZ5B59fdT5Ku1PHtVjw4XeOspFBIjQ90YGepGebWOX49lsyo6kxPZav6My+XPuFy8nawx1hX6OzvvQRBAlIFsQJSBNKnVG4nNLGN/cjH7kos4mlFGrcGIBPTydmTZ/KGXdNShJdz02T6i00tbrYTgXyfzuG/FEXp5O7LxsdFNuk9hhRYbS2WH25dsD3qDkd4v/2U+cw2waPYAZg7wbXDdFzuTeXNjPDYWSv54ZCRbTxfw5sZ4AOaPDOKl6b07bOA9n5M55ayOzmLd0WxzcRgnGwvWPzSSYDe7dh6d0BaaGj/Eq4RwDkuVgshgFyKDXXhsUhganYEX1sax9mg2bg5W7RZwwbSvDK23vFx/lrSPz8XfdJ3ILufj7UlsOmlK8rthkC8f3DygVcbV0urfa7d0YKvSGpjS25OE/ApSi6ro4+PENf96c3Qiu5z3Npsq0L1ybW9CPBwI8XDAzkrFi+tP8O1eUzOL12b27TQVx/r4ONHnWieem9aT7/al8ebGeMprdHTrxE0uhNYhgq5wUVYqBdHppYCpIHt7qm+ssPFELpGt0F0oqcB0PrPnRYoivLMpnk93NGwcHu7ZMoUUWts9y6M5mFLMynuG0de3ZZc/nWwt+HjOIMBUgUkhNQzsNbUGXvvjFDqDzFV9vLh5yD9H9G4fFoilSsGza47zw8EMavVG3rqxX6dKQLK2UDJvRBArD2WQXlzNC+vj+GTOoE41axdalwi6wkUdSS8lo6QaW0slU/q0b0efZ67qwfxvD/Pt3jQm9/ZkREjLloJMKTRlLF+scEN9WcrxPdx54epe+HazwbaFOi21NgulhFqj59fY7BYPumc7X7A0yDKBrrbYWCp584aIc4LRzUP8sVIpWLjqGKuPZFFrMPL+rP6oWrlV5OXQGYysO5pNYn4FWr0Rrc6Ifzdb0our2RCXx5qYLG4a3Drn/4sqtayOzqK/vxPDu7uK4N4JdI5XCaFdrT2aDcCUPp7tHljG9/Dg1sgAfjyUwdOrj7Pp8dE4WLfMEp4smwrI9/FxRG+4cOayt5M1J3PUTOzlSVgnmeHWmznAlw1xefx2LIfnpvVq05mkvZWKd27qT2GFlm6NnMueOcAXWYYnfo4lLrucx36O5cObB3TYM9MZJdU8vzYOg/Hc9BhnGwv+s+4E4Z4O5j7ELUFvMPL9gXTe35JgPnYX4evEPWO6c3Vfrw79JqWrEz8Z4YJq9UZyympQKSR2ninko60J5Ks1F79jK/rPNb3wd7Ehu6yG/9V1rWkJkiRhZ6niZI6aJ1cf41hmWaPX1lfP+nfrus5gXA93nGwsyFdrWROT1S5juFD2eZVWz4+HMpCB1MIq/jyey4M/HEGrN1zy85VW1fLbsRyM5wmMlyvE3Z4Hx4Vw54ggHp0QytNTe/DS9N4smT2QcC8HtHoj9604Ys5HuFyHUkuYvmQPr/x+igqNnu7udlhbKIjLLufRH48y9t0dfLMnlSqtvkWeT2hZInv5LCJ7+Vw7zhRw57eHkajv7WNaNpw5wKddk4YOphQz+6sDyDJ8c+cQJvRsmWVvtUbH/G8PcyS9FAcrFcvuGsrgwHP3juuzb2cO8GHR7IEt8txtqX783Wwt2PbkuCZVA2sLlVo98789xOE00/f/sUlhvPvXGbR6I2PC3fly7uBLqoH85sbTfLEzhesG+PBRG/681Bod1328l5SiKoZ1d+H7BVGXPAstUGt4Y8Npc8MRZ1sLnp7ag9lDA8xnopfvT6O4rlCHo7Wp2b1fN3FOuC20ShN7oevZEGc6p3trZACLZg8gMsgFg1HGtgm1Z1vLqRw1G0/k4Vi3rPzdvnTz+d3L5WhtwXd3RRIZ7EKFVs/cpYc4mFJ8znX1S6PqmpZtcl5YoeXbvakt+pjnc9eoYHp6OVBareP1P0+1+vM1hVqjY+7SgxxOK8XRWsWKu6O4e3R3vr1zKDYWSnYlFDL/28NU1zZvBpev1pibavz76FJrc7S24Iu5g7GzVHIgpYS36o5FNYfOYOTr3SlMeH8n62NzkCSYExXA30+O47aoQJQKCRc7Sx6bFMbe5ybwxvURdHezI8jN7qL1zIW2J4Ku0CidwcjmU/kAzOjvw8wBvqy6fzibnxjDfWNC2nw8JVW1LFwVywvr4li2L43yGh0SpqSmhatiW2zp0N5KxbL5QxkZ6kp1rYF53x5iT135y3rJhZUA2LXw2dwvdyXzxobTxF5gabslWCgVdYlMsDYmm31JRRe/UysyGGXu/OYQRzPKcLKxYOU9wxjg7wzAiFA3li+IxN5Kxf6UYuZ9c4gKTdPf7Hy8PQmNzsiQwG6M6+HeSl9B48I8HXivrifv13tS+TU22/w5jc7A0YzSRu+7L7mIqxft5vU/T1Op1TPA35lfHxrJG9dHnHdP3NpCyZyoALYuHMvXdwwRiVUdkAi6QqP2JRdTVq3Dzd6ywfGccE8H/F3abslKlmXWHc1i0gc7WRuTTWxmGTcO8uXz2wez9sERWFso2J1YxOe7ki/+YE1ka6li6byhjA13R6Mzcteyw2yuO49bqzey5ohpL3RGC1bnAtMMWmeQeb/uHGtrGhjQzVx+8z/rT6DRXfqe6eVSKiRuHxaIm70lK++JOieremiQCysWROJgreJwWim3Lz103vrg/1ZaVctPh01V8p6e2n6VrqZFePPAONMb1WfXHOd0rprYzDKmL9nD7V8fJKu02nxthUbH6uhMZn+5nzlfHSSxoBIXO0veubEfax8Y0aSELIVCatfz9ELjRPay0KgNdSUgp/bxapezkon5FWw9nc+ibYnmOsg9vRx484YIBgb803f3lRl9eG5tHB9uSWBYsCuDLtCTtzmsLZR8ecdgZn68l+TCSu7//gg/3DOM8upaiiprcXewYsK/yhtervq+w3nlbZOs9tTUHmw6kUdqURWf7khm4eTwNnne87lhkB+Te3s2mo0+MKAbP94zjNuXHuRYZhlzvj7AirsicblAoZRNJ/PQGWR6ezsS1f3CPb9b21NTenAiu5zdiUXc8sV+KrV6jLKp0EtqYRXrYkwlJfPVGmrrSq4qJJg7LJCFk3vgZCsKbVwJxExXOC+dwchfdTO7f1cUaiuH00r4/VguGp0RS5WCp6f24PdHRjUIuCmFleblOlsLFQ+vjKGs+p+OL8WVWnQXOf5zIVYqJd5O1owMdcUow21fHeC/v54E4KbBfli08NGM3nVBN6Woqk1mno7WFrxybR8APtuRRFJBZas/54Vc7PhXX18nfrp3GNYWCkqqann4x6MX3OP9/VhdK8oW6op1OZQKicWzB+Jso6K6Vo+lSsGw7i642llwx7eHeH9LApmlNdQaZLrVJUntfnYCr87sKwLuFUQEXeG8dsQXYJBlJGDVkUz+OJ5jrinbVgb4O2NjqWRKb0/+enwMD40PNQc5jc7Ah1sSuOqj3exPKcFSKSEpTA0LnvnlOLIsU16t4+Yv9nPXssMNagE319J5Q3lkfBgRvo4YZVDXBfU9iUVsOpF33vOZl8rDwYputhYYjDKJ+W0TAKf19WJCTw90BpkX1sa1yrGalhTqbk+wqy255Rr2pxRz34oj531jVaA2fR7a743jv9XoDPg62+BgpcJolDmQUsKZ/EpkGSRM578B7KyUPDguRCRCXYFE0BXO6++EQio0emRg/dEcHl55lEGvbeHmL/a32fm/3j5OrHlgBF/eMaRB0fjyGh23fHmARdsSqTUYGRvuztaF4/h+QRSWSgWbT+Xz7d5Upny0k+TCKnYnFlFcdenZzQqFxOAgF35/ZDR/PjqK+SODsVBKxGWXc//3R5jw/g6+25fWIt8XSZLMS8zxeerLfrymPuf/zeyDjYWSQ2klfNMG2dOXQ6VUsP7hUUzt4wky7E8u5qdDGedctyEuF1mGgQHOjeYgHEotaZM3k3qDkbc3xTPu3R2czK2gtEZvXkJ2sFbx6MRQTrw6lW1PjsXWUomDtSVFlbUXeVShMxJBVziHwSjz1wnT0vLz03py75juhHnYYzDKFFVoWzxjtznUGh13fHOIY5lldLO14JM5g1g2fygBrrb09XXihat7AvD6n6fJV5sC7aLZA/B2apkZQx8fJ567uhd7np3AQ+NDcLKxIL24mpd/O8nwN7fx1sb4y96Pre9WVN9KsS34dbPlxem9AHjnrzPtvsx8MVYqJZ/eNpjp/b3RG2X+++tJPth8hrPLDvxel5Mwo9/5l5b3JhVx29cHGPPO3636Bqe6Vs/938fw2Y5kag1GooJdGBrojKXSlCex99kJLJzcAzsrFbaWKv5+ahwbHxvdau0rhfYlEqmEcxxKLaG4qhYnGwvuGhWMhVLBC1f3IrOkmrx2rEZVqdUzry7gOtuajpXUzwrrzRsRxIGUEg6mFlNarePlGb1b5Wymp6M1T0/tyUPjQ1lzJIule1JJK67m853JfL07hRn9fVgwKviSahvX1O3l2lhe+nviuKxy/LrZNFpq8XzmRAaw6UQeuxOL+OVIFs9N63nJz98W6vdIQ90d+HBrAou3J1Fdq+e5ab3Ir9ByJL0USYJr+p27tHwqR123LC0zOsyNcI/WKeWZXVbDPd9FcypXjZVKwfs39+eaCG/2Jxcx5+tDWFsozklS9BRZx1c0MdMVzrHphGmGMKW3Z4NEIX8XW4YGtXxnn6ZafzSboxllAPxwd9Q5ARdMS6Vv3xiBa11G68GUEppadO1SygzaWqqYOzyIbU+O48u5g4kMdkFvlFl3NJvpS/Yw+8v9bDud36x90voEKptLqLwE8MpvJ5nx8R6W7mneMrEkSVxf10VqX3L7ntttKkmSeGxSGPeO6Q7A0j1pPPD9EdbX1QuPCnY5J4hll9Uwf9khKrV6ooJdeP/m/q3SQvBwWgnXLtnDqVw1bvaW/HB3FNP7+SBJEsO6uxHsZodGZ2qWIHQdIugKDRiNMhvrlpan9fVq59E0NDbcnfvGdif2pcn08Wl8Bulka8n7s/pjoZTYdDKP5fvTL/i4eoORT3ckMfi1rbz2xykqL2FvVqmQmNLHi1X3Dee3h0cyc4APSoXEgZQSFnwXzaQPdvL9gXRqai8e2OtnupdS7hBMgQbgp8MZzc7cHhlq6toUl11+Sd+H9vLC1b24Y3gACoXEltMFfPJ3EgDT/7W0XF6t485vDpGv1hLuac+XdwzBStXy1dV+PJTBnK8OUFxVS29vR359eBRDznrDqqg7lwzw/YH0Jr8xFDo/EXSFBmIySvF0NM0SP9+Z3KEyWf1dbHl+Wi+cbS++ZNrf35nnppn2KP/vj1M8t+Y4mSXV51wnyzLPr43jnU1nqNTqSSyoxPIyjwH183Nm0eyB7H5mPPeN6Y6DtYqUoipeXH+CEW9t4/3NZyioaHyZvj4wX+pMd1JvT9zsrSiqrOXv+IJm3bc+GcxCocBC2bmqGf3fzAh+uDsKe0slNbUGJGDwWWe2tXoD966IJrGgEk9HK5bNj8SphZvMa3QGnv3lOM+vjUNnkLkmwptfHhh+3izkmwb5YW2hID6vgsNpjVelEq4sIugKDWyIyyMu25RUciitlNTiqnYe0aW7a2QQswb7YTDK/HQ4k/Hv7eD5tccbVP/5Zm8aq49koZDgnRv78d38oS3WQs7H2Ybnr+7F/ucn8vKM3vi72FBarWPJ9iRGvfU3T68+xpm8inPuV18IxOYS61tbKBXcMMi0TLz6SPO6CNUvyw4J6tYqM8DWNqy7K6sfGMHIUFdk4IMtZ8gsqcZolHly1TEOppbUlfmMxKcZx3E2xuXy1a6UC2aoZ5ZUc9Pn+/g5OhOFZKqA9fGcgY22w3SytWBmf9PP6efDmc36OoXOSwRdwUyWZfN+bv0RnRPZ5e05pMsiSRLvzurPmgeGMzrMDb1R5sdD9cE3jrUxWfyvrtj/i9f05uah/q1SJtDeSsX8kcHseGo8n942iEEBztQajKw+ksXUj3ZxxzeH2J1YaF5irGnGnm5pVe15e//OGuwHwN/xBRRfoKVcSVWtuY5xrd7IykOmF/85UQHN+yI7kF7ejjw2MRxLlYKtpwu4felB/vvrCf44nouFUuKLuYPPmw9wISsPZfC/Dafp8/Jf7E4sPOfzf8cXMH3JHk5kq3Gxs2T5XVE8ND70or9PNw81NbffEJeLuqaW2Mwylu5JEcvNVzARdAWzY1nl5JRrsLVUMjzEVDKvMwfdeoMDXVixIIpf7h/OqFA3dAaZHw9lsHDVMYwyTO/nzfyRQa0+DqVC4uoIb9Y+OJI1D4zg6ggvFBLsSihk7tJDTFu0m1+OZFFdN5u62J7u1A93MfC1LSScp4hGmKcDvb0d0Rtl/j5zbpAAePeveIa9uY3f6qo2bTyRS1GlFg8HK6b26Vj7+c01NNiFtQ+MwNXOkvTian44aDrH++5N/c371s3x0PhQfOoKV9z57WFW1j1erd7I+5vPMH/ZYcprdAzwd+aPR0YxKqxpzzEowJlQD3tqdAZGv7OD6z7Zy6+xOcRcoAmC0LmJI0OCWX3JvAk9PRjg58zKgxmcyG6bAg1tYUiQC9/fHcXhtBI+2ppAlUZPbFY5W07l89yaOB6dGIpvG/UeHRzYjcGBg8koruabvamsis4kPq+Cp1YfM19jddYy99ZT+fx0OANJknCwUmFvrUJdN0NdeTCdJ6f0OOd40KReHpzKVbPtdD431c18z7Y7sYhavdE8o65f4pwTFdDi5S3bQ19fJ9Y9OJIFyw9TUK7FydaCqO4u7EsqwihDuJc9Hg5NO54zrLsr258axwtr41h7NJsX1sWx80w+SYVVJBeatmDmDgvkxem9mrUsL0kSNw/246s9KRRW1GJrqaS7m91l5xUIHZdoYn+WrtzEXpZlBr+2BZVSwf+uj8DX2YarF+/GwVrF8ZenXJEtwg6lFPPOX2eITjfNKiwUEo9ODOOuUcFtXgCkvFrHD4fS6ypbGajU6rl5iB/PT+vFa3+cYm0jx0o8Ha3IV2uxVCmYHuHNbcMCGRTgjCRJbDmVzz3Lo+nl7cjGx0Y3uF+lVk//VzdjMMrsfW4CKoXEsDe3Icuw+5nxbdpFqrWdzi3nps/2U1VrwMFKSYX2n+X7T24byISenk1+LFmWeWtjPF/sSsHRWolaY8DVzpKXr+3Dtc3sOJVTVsOS7Yn8GpuDk60FeWUafrg7ihGXMBMX2l9T44eY6QoAnMxRU1Ktw1qlYFSoGyqlhLWFggqNnt+O5bR58++2EOhm12DvTGeUeX9LAt/tT+Oh8aHMiQpos2QiJ1sLHhwXyt2juvNnXA5PrjrGqugs/jieS3WtAYUEd44IJtTDnkqtjkqNngqtHqUksSepiPi8CtYezWbt0Wx6eTtyW1QARXV7uUGu5wbQI+mlGIwyvs42+Drb8PXuFGTZNAO/kgIuQC9vJxbfOpC3N50mvdiURGelUlCjM3DP8iO8fWO/864E/Jssy6w+ksXP0Zk4Waso1+gJdLHh14dHNSmj/t9UColV0VkYjDJGo4wMnMpVi6B7hRNBVwBMiRwAE3p5mLNm7xsTwqJtibz060migl3xcroyKuXsTChk5cF0DqQUU16jx8FKxTuz+lGrN/LBlgTSi6t59fdTfLEzhTtGBDInMuCcF9Ws0mp0BrlBTejGFFZoqdLqCWrCtZYqBdcP9CNfreXtjfHYWanwcrTm3Vn9Gxx/OZssy8RmlvHDwQx+P5bD6Vw1L64/Yf58cmEl645mMTrMHTdz0RBTI4Co7qazo7/GmrYWrusA3Xhaw8Renvg627A3qZDX/oxHpZCYGOHFhrg8nlp9jEqNjjtHBjd6/4T8Cr7alWLOBre1MC3/ppfUkFZczYBLCLoejta8cHUvens7cjq3nP/74zSbTuRx9+jul/ZFCp2CCLoCsiybg+60vv+UzHt4Qijb4wuIyy7n6V+O8d38yFap3NOWjmeVsWDZYfRGmRB3O7ydbPjs9sHm4Hl1hDero7NYtC2BPLWGdzadYcm2JG4c7Mv8EUGEeDhgNMpM+mAnGp2Rl2f0Zv4FXqw1OgMzP95DWY2Ogy9MvGjrOjDV6l1zJAsZ6O5mx3d3RV4wqUqSJAYGdGNgQDdevKYXa2Ky+eVIFvG5akLc7UnIr+SJn48hSRDh68S4cHd21WXgRga5kFxYSVx2uTnR60rV09uRkzmmHIWqWgNT+3jh7WTD0j2pvPL7KfRG+ZyAV1NrYPH2RL7alYJRlrFUSYwJc2dMuDsv1bV4/L/fT7LmgRGXtAWzYJTpdyfYzY7X/jxNdHopmSXVV9xqg/APEXTbmcFoyqQ9mlHGs1f1wKMd6q6ezq0grbgaK5WC8Wc1ZbdQKvjwlv5cs3gPuxOL+GZvaqd+F16rMzDnq4PojTI9vRxYOm8ILnZWDc7DWigVzIkK4MbBvvx+LJele1I5navm+wMZnMpR42xrST8/J/NZ2sjgC5fFXBuTTU5dAwT7Ju4Tv/zrSRILKnF3sOLjOYOaVZnK2daSBaOCWTAqGI3OQEx6KXuSithxppBTuWqOZ5VzPOufjPQ/43LZVldAY0yYm7l85qXacaaA7fEFvHptnw6ZBxDu6YBCMr25nNrHi2v7+2BrqWTJ9iRe//M0ZdU6HpkYipVKyZZT+bzy20myy2oA6OvryIlsNfuTi3n9ur78fDiTkzlqYjLK2JlQyLgeHhd59sZ5OVkz0N+ZmIwy/rMujuULolrqSxY6GJFIdZa2TqQ6mVPOC2vjOFb3Ivh/M/twx/CgVn/ef3t/8xmWbE9iSm9PvrxjyDmf//5AOi+uP4GlUsG6h0Y0KMHYmd6Vr47OZNXhTJQKiU9vH4xLE5oByLLMDwczeHH9CZQKqUHv3MEB3fh63pBGmwoYjTKTPtxJSmEV/53e2zyruZC1MVksXHUMhQTf3x3FiJCW298rUGvYmVDIjoRCTuWUk1rUsEKXXzcbZg7wYXwPDwb4O6NqZgbtoq2JfLg1AYAltw5kRjMTi9rKscwy+vk5NXhT8OGWBBZtSwTA3cEKGwslGXUVzHycrHl1Zl8m9vTghs/2EZtZxg0DfVkwOpjpi/cgA/7dbNj59PhLWgmSZZm3NsWzdHcqHo5WFFfWEv/aVR3yTYvQuKbGDxF0z9KSQVerNyAhnbe6UZVWz0dbE/hmb5r5RXx6P28+njPosp7zUsiyzMQPTIFh0ewB502YkmWZe1ccYcupfELc7fj9kVFISLy9KZ7vD6Sz6v7hDAo4/35jR2I0ymj1xmZVesoqreb6T/dRWKFlRIgLoR4O7E0qMh8TsVIpuH6gL3eODKKnV8Pfma2n8rl7eTQO1ir2Pz/xojPdpIJKrv14D9W1Bp6YFM5jk8Ka/0U2kU5v4FhWOTvOFHIwtZjotFLqXwhsLZUYjTLXDvDhpsH+DA3qdtEAoNUb6PHiJgBGhrqydN7QS64d3V5+jc3m9T9PU1hhSkBTKSTuGdOdRyaEmqtKHcssY+Yne/F0tGLzE2N5c8Npfqo7avXhLf25fuDFE7LO57k1x/npsKmSlVGG+SMCQZJQSBIKyVSrWSFJ6PRGrunnzcBO8PfW1Yjs5Xb28Mqj2FkqUSkVvHtTP/OL1rbT+bz06z9LVtf08+bl6b3bZVkZILGgkpTCKiyVCib0PP/ymKlzTz+OZ+0iubCKx3+KJanQdD8wNRHvDEFXoZAuGHANRhmFhPlndSClmP+si6OwQktPLwe+mDsEB2sLKjU6/ozLZcWBdE5kq/ntWA6rojOJDHbhzhFBTOrliUqp4KvdKYCpZd7FAq5GZ+DhlTFU1xoYEeLKwxNCW+4LPw8LlZIhQS7mIvwFFRp2JRSx40wBFRodOxOKWBWdxaroLEI97Jk3IoibBvk1+v2zUikZ1t2FAykleDpYd7qACzBzgC+TenlyPKsclVIiwMX2nA5F/f2d+fS2QYwJd8feSsXTU3uwNiabWoORnw5lXnLQfXJKD6b29eL3Yzmsjcnmt2O5FFedv4l9kJudCLqdmAi6rWTWED/uW34EGRga1I2x4R68+vtJcwcfv242vHZdX8Zfxj5QS6hPoBoT7nbBJB8XO0s+vHkAty09yJZT+ciAh4MV787qz9hw9zYabeuRZZlnfjlelz3sy+JtiexJMrW383Cw4ps7h5q/P/bWFtwyNICrI7wZ9+4OiqtqkSQ4kFLCgZQSfJ1tmNLbk4OpJagUEnc2odrVq7+fJD6vAjd7Kz6aPeCcHqutzcPBmpsG+3HTYD/0BiPR6aWsjTEdWUoqqOS/60/wweYzzB0WyB0jgsxZ0PVkWTZ3Jcopr2nTsbckOyuVuRpbY85ONnO1t+Kh8SF8uDWRnLIaavXGS6rd7e5gxfgeHtiolPwdX0B5jY67RgZhqVJilE1HigyyjKVS0ewSlkLHIoJuK5nS24unr+rBO5vO8OL6E6gUEjU6I0qFxD2ju/PYxLBLLmjfkjbG1bfxu3jW6ohQN56a3IOle1NQ1+hZflckPa+QF4C47HLWHs1Clk1t2QAslBI3D/Hn0Ylh520s/tHWRIqravF3sWH5/CjWxGSx8lAG2WU1fLsvDTAlyKQWVuHpYN3oft+vsdn8eCgTSYJFswc0qJIUk1GKlUpxwVaGLU2lVDCsuyvDurvy3+m9WXMki6V7U8ksqWHx9iS+3J3CLUP8uXt0d/N+/i9HsjiRrcZCKfHiNb3bbKwdwd2ju7PiQAaZpTX8fDiDuZeRlxEZ7IKdlYrSah0Rfk7NmjmnF1fx4ZYEenk7ct/YkAaf0xuMZJbWkFxQSVJhJW72Vtw4yFfsG7cDsad7lpbc09XoDHy7J5VPdyaj0xvR6I0M8HfmzRsiOsw71aSCSiZ9sBMLpUT0fybjZHvx4yw6g5GbPt/PscwyIoNc+PHeYW0+K2st2+PzeezHWKp1Bm4c5MsjE8IaTRI7kV3OtR/vwSjDsvlDzZmrGp2B347l8MOBdNJLqimrNpVq9Otmw9xhgUzt49XgvG5KYSUzluyhqtbAoxNCWTilh/lzK/an8d9fT6JSSCy9c2i7rigYjDKbTuTxxa5kc/azUiFxbX8frhvgy0MrY6jU6nl6ag8eGt+6S+Md0fL9abz060nc7K3Y+fS4y6potmRbIu9vSaC/vzPrH2z6UaTNJ/O4d8URwjzseXhCqDnAJhdUkVpURe2/GmN8fvtgrupgPbM7M5FIdQlaMuiuPJjBC+vi6O5mS3aZBq3eyO1RAbx2Xd8O8+6y/o97XA93ls2PbPL90ouruGbxHiq1+vMm/Kg1OhybcB61IyqpqkVnMJ53ZlvPYJS54dO9HMsqbzQBTpZljmeVsyo6k/Wx2VRrDVhZKNDoTG++bhjky+Tenty1LJrTuWqigl1Yec8/b2C+2ZPK//1xyvx4o0LdWLEgst1/d2RZZl9yMZ/tSDYvv9eL8HVk/UOjrpg3Yc1Rqzcy6YOdZJRU8+TkcB6ZeOlJcIUVWka9vR2t3sjKexrPYC+v1rE9Pp+NJ/I4lllGQYWWC72YW1so6O5mz6lc01nlWyMDePOGiEsep9CQSKRqZ7OH+nMwtZjhIa4oJYln1hzn+4MZBLracc+YjnHWdUPd/vLVTVhaPlugqx2vXdeHJ34+xqJtCQwPcSUy2IUqrZ4PtySw9mg2vz40stMcJTpbU44RLd2TwrGschysVLw0/fxLqZIk0d/fmf7+zrjYWbJkexKWSgU6g6l6VGxmGa/8dhKjDK52liy+daA5WG06kWcOuAtGBWNvpeK+sd3bPeCC6esaGerGyFA3jmeV8fnOZHYmFFKlNZBYUMl3+9KYNyKoywVeS5WCJ6eE89hPsXyxK4XbhgU26XfpfNwdrLh5iD8rDqTz2Y5kooJdySypJjazlP3JxZzIUZNZUo1ac/7evvZWKnp5OxDibk+ohz0hHvaEutvj62xj3uL4dm9qp+8k1VmJme5ZWvOc7te7U3j9z9MAfHrboHav/JNaVMX493agUkhEvzjpkmrHPrnqGGtisvB0sOK5aT15968z5kIQV+IyoynD+ChbT+cD8Mb1ERftO6s3GBn19t/kqTV8eEt/RoW689uxHNbEZHEqR42DtSlwzxpi6qtaoNYw9aNdlFbrmD8yiJem9+4QwfZCYjNK+d+G0xxOMzWO6OfnxBvXR9DXt+32oTsCo1Fmxsd7OJmjZsGoYP7byBuypsgsqWbcezswGGUCXWxIL2k8Oc3ZxoI+Po5M7OXJVX098XHufG92rwRNjR+if1QbWTAqmHnDAwF4/OdYjqSXtOt4NtY1qx8e4npJARdMdXslwCDLPLHqGDnlGvy62fDt/KFXXMAFeH5tnDngXjfAh1sj/S96n23xBeSpNbjYWTKtrzfuDlYsGBXMT/cOw95KSYVGz5LtSaQUVpoyqNccp7RaRy9vR56f1qvDB1yAAQHd+Pne4fzv+r6mrlRZpv3u9zef6VLN2BUKiWev6gnAiv3pZJVWX+QejfN3seXGQb44WKvILP0n4NpaKgnzsOfa/t58dMsAjr8yhdiXp/DDPcO4a1SwCLidgAi6bUSSJF6a0YdJvTyo1Ru5+7toUouqWuSxs8tqzL1Vm6r+qNClzLira/U8vfoYz/xyHBnTfhbAiBBXtjwxtt2PQbWWqyO8cbBWMcDfmTduiGhSQKzPhJ41xK/B2VVHawt+fXgU/i42ZJRUc+Nn+3hz42l2nCnEUqVg0ewBl3T0pL0oFBK3RQWybeFYBgc6A7BkexJj3/mbxdsSzb8jV7rRYW6MCHGl1mBqnnE57hoVjE5vxCjD/67vy7GXpnDq/65iy8KxLL51ENcN9O20uRNdWef5q74CKBUSi28dSD8/J9Py4beHKGnkAHxTfb4zmVmf7ePTv5ObfJ+M4mpOZKtRSDCld9N7iQKcyatgxpI9rD6ShUKCxyaG8fikcACi00pJLqxs1uN1JpN7exL94iTWPDDCXKHoYt65sR8LJ4dzW2TgOZ8Lcbdn7QMj6V/3+/DlrlQAnr2qJ+GeDi069rbi4WhNLy9H6qtlZpTW8MGWBK79eA+n6poNgOmN2te7U9AZrqxgLEn/zHbXHc0ms+TSZ7s9vRzNbf7issqbdLqgq4nPU7MvuQh9J/o9EkG3jdlaqvh63hB8nW1IK67mnuXRaHSGS3qsFfvTeGtjPDnlGiLO2j/7+XAG728+Yy5W8G/1S8vDurs2u8C9UZbJLK3B09GKH+4exhOTw5k/MojJvT2pNRh55MejjT7vlcBKpWxWkpCHozWPTgwj4Dw9bcGUNPPjvcOY2NOdHp4OSICzbefOb8wuq+GVa/vg383GfFt8XgXXf7qHDXG5/Hgog1mf7+P1P0/z2E9H23GkraO3jyO2lkpkGSoaSXZqqgfGmc7bronJIq8uX0Iw0egMzP7yAHO+OsjYd3ew4kB6ew+pSUTQbQceDtZ8d9dQHK1VHEkv5clVxzAam7f3telELi/9ZmottnByONf0My0TrzyYwbNr4liyPYmrF+0+795xfdbytEtYWu7l7cgXtw9m42NjzJV7JEni3Zv64WpnQWpRFYvrCscLTWNrqeLz24cQ4eeEDDy56jjL9qa297Au2aJbBzJveCC7nhnPwsnhKOuW4bV6mQd/iOH5tXGU1eiwUim4ecjF98U7m9jMMqprDXSztaCn1+WtWAwNcmFoUDd0Bpmle1JaaIRXBmsLJffUdT3LLqvhv+tPsDo6s51HdXEi6LaTUA8HPp87GAulxJ9xuby9Kb7J9z2UWsKjP8UiyzAnKoBH6ur07ksu4r+/mpqXO1ipyCipZtbn+/lgS4J5+SWrtJpjmWVIEkzt07yl5Xrje3o0OA5RXqPjrY3xFFfpUErw5a4U1tQ1+xaaxkKl4J0b+zG/rmTkmxtPm/eDOxtHawskSUKSJB6dGMYP90ThameJzVl72sFudux6etxltcPrqPYkms4vjwh1a5H+0w+OM/19f38gnbLqy9uOutI8ND6U+Neu4r6xpuD7319PEJ+nvsi92pcIuu1oRIgbr17bB4AfDqazaGvCRbM9j2eVseC7w9TqjUzp7clrM/8ptrEhLtfctWjPcxO4fqAvRhkWb0vktbpzn5vqZrmRQS4Nyg1eipKqWhZtTWTyBzvNnVbqj4n899cTpNTt75bX6Fi6J5WfD2fwweYzvPtX099gdCUKhcSU3p7YWChQKhQ8vzaOVZ3gnfvFDOvuyu+PjCTC959jFDvOFPLh1oROtRfXVPuS64LuRWo4N8WR9FI+qmuX6O1kY/47E/5hbaHk2ak9GRPujkZn5MEfYjr0FpcIuu1ElmXWxmSZMxz1RpkPtyby3Jq4RpNLjmeVcfvXB6nQ6Bka1K1BQQWA+8eGcP1AX07931ScbCz48JYBvHNTPwC+25/OjjMFl5W1/G+bTuTx4dYECiq0dHezY9V9w1n74EiGd3elutZ0pvVAShFXL9rNa3+c4tk1cSzensQnfycTm1l22c9/pTmcVsKC76Kp0RnxcDTttT+/No4tp/LbeWSXz8fZluULorhx0D+1hH86nMW9K6Kpqb20nIaOSsL0N2nbArXVs0qrOZZVjkohkVJUxWc7kpt9UqErUCgkPrplAF6O1qQUVvH82rgOe1xNFMc4S1s1sdfqDdyx9BAHU037rd3d7Rgd6saKA+kYZVPJv09vH9TgOMCJ7HLmfHUAtUbP4MBufHdX5EXbxdV76dcTLN+fTjdbC0rragEffGHiBUsdNoUsy7y5MR53eyvmDg80H4nJV2u4qq7AQz2/bja42ltRoNZwz+juzB8Z1CnOoLaVmIxS5n59kKpaA6PD3Phq7mBe/PUkvxzJwkqlYMWCKCKDXdp7mJdNlmW+2ZvG63+cMpcs7OfnxLL5kZdcwamjqX+TNLmZJwPOR5ZlPtuZzI2D/Ljt64MkFVTy+KR/TgwIDR1JL+GWLw6gN8q8dl1f5g4799RAaxG1ly9BWwVdgCd+jmXjiVwemRDGPaO7Y6lSsO10Po/8eJTqWgPhnvZ8c+dQ/LrZUlSp5dole8gp1zQ74IIpy++6T/aSVFCJ3igzKMCZtQ+ObMWvzvTC8+aG06QWVTE8xJUv5g6+YOvAri65sJI5Xx0gxN30c7e2UKI3GLlvxRG2xRfgYK1i1X3DO0yzjMu15VQ+b2+KJ6nAtAUR4GLLD3dHdcrSoW3lj+M5PLzyKA5WKnY/O/6Si9pc6b7alcL/NpzGUqlgzQMjiPBrm8pooiJVB/efa3qxdeFYHhofai6CMLGXJ6vuG46noxUJ+ZVc/+k+YtJLeOiHGHLKNXR3s+Pb+UObFXDBtOex5NaBeDpa42Zvid9ZRzlay+TenowIcUXG1M1IZxDv7S4kxN2eX+4fwdfzhphXDFRKBR/PGcSQwG5UaPTM++bQZZ377Egm9/bkjev7mpdgM0qqmfnJXk5kl5/3erGkaqqR3tPLgQqtni92iUzmxtw9Oth8hPGhlTEd7ndHBN124mZvhV+3c9/V9/V1Yv1DI+np5UBhhZZZX+znYGoJdpZKvrxj8CVXoHG1tyKnrIaiylpmR164XnBL+c81vQlxt6OgQsuza4532D2WjsLfxfacohs2lkqWzhtKD08HCiq03PHNIYoqte00wpYVGezKL/ePoFvdjK2kqpabv9hPXFY5ueU1fLs3ldlf7ifyf1vp98rmTn2MqiUoFBILJ5uWlZfuTiWpoKKdR9QxSZLEezf1x9fZVO3tv+tPnPe6vHINj/90tM3fyIqg2wF5O9nwywMjGBvujoOVKcheHeFNqMeln/nbHl+ADPT2dmy0VVhLs7FUsmj2QCyUEltO5fPU6mN8V9fcXWg6J1sLvrsrEl9nG1KLqliw7DBVHTg7szl6+ziy/qER+Dqb8guqaw1c/+lebvx0H6/+fooDKSUUVJjeZKw7mt1lykk2ZnJvT8b1cKfWYGT6kj38cTznvNdVafVsPpnXxqPrGArUGpZsT6S4yvR7s+NMYYM3/HqDqRraxPd3sD42x3yyo62IoNtB2VupWDpvCBN7mc4xxmWV89/1Jy75iEX9UaGWSO5ojr6+Tjwz1VQWb01MNq/9cdK8jyc0nZeTNcsXRNLN1oJjWeU88EPMFVNCMdDVjnUPjqRHXelLvVGmqFJLPz8nXri6J4MDuxHh68TSO4d2qnrUrUGSJGYP9UeSTGVl/7v+BPnqhpWqDEaZR388yr0rjvBVF1qGzi2v4eVfTzDqnb/5ek8qGp2Rfn5OfDF3sDlpMyajlBkf7+X1P09TVWtgUIAzT0xu26S0zl1v7gqnUiqorjtOEZ9fQXx+BWnFVXw8ZxBONk1fZi6prGVfUiFgagOmMxixUCqo1Rt5eGUMo8PcuHmoP1aqyz/i8G+55TVUaHRIgJ2VEmsLJZ6OzSs9KZjUJ1nN+eoguxIK+WhrAk9N6XFFZIF7OFrzywPD+d+fp9l4Io/yGh1anZFbhvx/e/cd3mTVPnD8m6Tp3nuXAoUCLQVaaMuUIUOWLAEFQUFEURzo++rPhRO3AoosAfFVFFmibJRN2S200EJpC6V70b3S5Pn9kRKpUGihTdf5XFcuSHKSnB5K7uc5zzn37cmsvm2QJKlZ/Jx1oaOLFQ7mhuQUlVNUpmbUN4dZ8XgQnd2tAfhg2wX+isnAyEBOYCubhu2sHlzLKea7A3FsOJVEeeWBaDdPa+YO9KFfOwdkMhl5xSo+2RXDuhOJSBJYmSh5bZgvE4M86iSBSW2I1cs30efq5ZoqVan5fNdFOrlZ8n+boihRqWnraM6qad2rzef7b2uOJPDxjhhKK6fmHC2MmNjdg1Z2Zsz77SwOFkYcf31gnf7y7b2Qzkc7oonP/KeSkpGBnLIKDeMD3fl8QkC1ry2rUJOWV4qXnVmd9ac52XshnRWH4jl5JYe3RnTkiV7eDd2lOnWtsupSRkEZgV42/G9GMCZ1sOe1OUnLK6WwtIJnfjpNbEYhRgZyPp8QwPXict7+XZse9ttHu+nSwzZX14vK6fvpPgoqL7cEe9syd6APPdvYIZPJkCSJzeHJfLgtmuzK4jLjurnzfw/51jrv/N2ILUP3oDEG3ZtFJecx84dTpOWXYmOqZNnUoBrt3Ry5+DCRyXn0bGPHxbQC3S/fDU/28ubtkfdecPt2YtMLGL74MBVqDf7u1szo7Y2DuSGPrTyORoLPxnfWFW6/WV6xilk/nuJqdjGb5/TExar+V1o3RV/vvcTXe2NxtTLmi0cCCNXTdXp9iUnL55GlYeSXVjDA15FlUwNRKlr21PLtFJSqmLsunH0XtTNZMkACXh3SvlnWtP63vBIVge/voUIjYW9uyJ/P98HZSrs+IC6zkDc3RxEWnw1AW0dzPnjYj5DW958p7HbElqFmyM/Nit+f64W/m7YU3GMrj901x/HFtAIik/NQKmR882g3wl4fyLePdtPt91TIZTzS3f2O73EvfJws+GVWCOFvD+b3Ob0YFeBKaBt7Xqrc1P/W71FcTKu6+vJaTjHjlh7leEIOhWUVXMspud1bt3hHL2ex7rg2L3Nafikv/hpx3yUiGxtfZ0tWTe+OkYGcv2My+O+Gc7UuCtISWBgrWTmtO0/18UYGWBgb4G5jwrRQ/SWFaEhWJkr6tXMAIKuwnJAFf1GqUvPlnksM+/oQYfHZGBnIeXVIe7bP7VNvAbc2xJnuTRr7me4NJeVqXl4fwY7KxVHPPtCGVwa3v+308Efbo1l+MJ7BHZ1Y/niQ7nGNRuJsUi725kZ6TUig0UhMW32CQ7FZtHEwY+tzvTEzMiAyKY8n1pwkq7AMZ0tjVj/Rvdkkgqgr5RUavth9keWH4pEk8LIzpUItkZxbwqAOjqx4PKjZXff8KzqdWT+eRq2RmNHbmzeHd2h2P2Nd2RqRzMvrI6jQgK+zBSseD2oRyUa2nk1h7rp/SkS2czLnUrp2sWa/dg68P9qvxpfi7oc4023GTAwVfPtoN+b019baXLI/jmd/OnNLDtsKtYbN4ckAjAusejYrl8vo6mmj9/+UN3KkOlkaEZdZxJtbovgrOo1HloWRVViGr7MFm+f0FAH3Xy5nFDBmyRGWHdQG3Mk9PNjxQh+WPx6IoULO3ugM1oY1jXqitTGwgxOfjtPmD//+cAJLD7Sc1bi1NaqLG7/MCsXe3IiYtAJGLD7MvosZDd2tOnGnc8MHOzhVyXN9Kb0QKxMl3zzalTVP1Hzti76IoNtEyeUyXh3iyxcTAlAqZOw8rw1cN28fOBSbRWZBGbZmhvRvRCXU7MyNWDy5Gwq5jM3hycz84TQlKm3O4d9mh4rruDeRJIn/HbvKiMWHOZ+SX3ktP5AFYztjamhAJ1crXn9IuyXrw+3RXEhp3GXN7sW4QHfeHN4BgE92xvDryaZZ8lAfglrZ8sfzvQhwtyKvRMWTa06ycG9sk52av5xRwNM/nmLa6pPVtjExVNCrrXZNw43JvjeHd2BEZ9e7zopk5JcSmXT7LGj1RQTdJm5coDs/PxWCrZkhkcl5jP7mn1R6G85or/eOCnBtdPsbe3jbMm9wO+zMtdmIBnd0YtX07iI/878s/CuWN7dEUarS0MfHnp0v9mVIJ+cqbab3bMUAX0fKKzQ8v+4MxeXNI3HGzWb2ac3sftqZndc3RbKrhSZ+qAkXKxPWzw7l0WBPJAm+2nuJGT+cJK+4caVDrInjCTnsOp9OXDV7+9VqDTsiUwmLy8bMUIFGAqVCRq/KhYVqjUTBv9JASpLEsfhs5vx8hp4f/82rG87qNVte4/omFu5J91a2bHm2F20dzUnLL2XC0jA2nUnSVTsZH1j3C6Xqwuy+bWjvZI4EGMhlVIj8zLeY1N0TZ0tj3hzegR+e6HHbylAymYzPxnfG0UI7Za/vDDv68t+h7XkkyB2NBM+vC+dY5apU4VZGBgo+GuPPZ+M7Y2QgZ9/FTEZ8c6ja3NY1cSIhh0vpBXpLyiJJEj8d085qjOnqVuW5tLxSlh6IY8qq4/zf5kgKyypo62hOBxcLVGqJx1efYPiiQ3R8eyf+83ezNSKZyxkFrDwUz5CvDzJp+TG2nUulQiNhZmRAfon+DlTFQqqbNJWFVNXJL1Ux56czHIrN0j3W3smCnS/2aZSLT65kFTFhaRiZlbmEHwly59Px1e/fbalKVWpdEYQ7OXI5iynfH0eSmu8ezQq1hmd+OsOeC+lYGBmwblYIfm76qSLTVEUl5/HMT6e5llOCkYGcD8f41/pAXKORaPfmDio0EmGvD9DLJaC9F9KZufYUZoYKDv93AADbo1LZGpHCiSs5SJI2c5+hQsaE7h68NKgdiTnFjFh0WJck4walQlal6IqJUsHDXd2YEuJJJ9e6+f0RC6laIEtjJaund9fVkGxla0oXT6tGG3AnrzhGZmEZ7jbGyID1p5L47dS1hu5ao1OTgAvQq609z1ROwb626RxJ15tHRaKbGSjkLJ7clR7ethSUVTB99QmuZBXd/YUtmJ+bFX8815sH2jtQVqHhld/OMvTrg/xy4mqNp1XzSlRUVF4XtjOr/4xykiSx6O9YAELa2DHvt7N0/3Avb2yO4niCNuD6u1lRWFbB9WIVj4e2okIjcS2nmP6+DtibVy17qFJLKBUyere1Z/7Ijhx/YyALxvrXWcCtDXGme5OmfqZ7s1k/nmLP+XR6+9jx+YQu912wvq4di89m0vJjtHU0Z91TIaw7kciXey5hrJRXVllq2uPfUFRqDY8sCyM8MZdALxt+nRWCQRNKKnEjRend5JeqmLjsGNGp+XjamrJhdiiOjex3vLHRaCQW/hXLwr9isTYxILekgvZOFjzS3YOHu7jeMUPTpfQCBn91EGtTJRFvD67XfqbllfL94XhWHLq1qlQnV0tGBbgyIsAVN2sTJi8/pkt+IZfBzevFZDLwc9UuKEvMKeaZfm347zDfeuu3yEh1D5pL0D2fksfwRYd19wd1cGLF44GN7oz3+8MJjOzsgqOlMRqNxPQ1Jzl4KZPWlft3/103ODm3hPwSldhOdBfXcop5aOEhCsoqeH5AW+YNbt/QXaqR7w8nsCMyldVP1GxBXUZBKeO/CyMxp5gOLpb8MiukVjnJW6rpq46z/1IWCrkMdWWUUipkDPB1ZGAHJ/r42N8yfXzkchaPrTyOj6M5e17uVyf9KFWpuZZTTFxmEXGZhcSmF3AuOU+XOtbZ0oi0/DK87c0YGeDKqABX2jqaV3mPv2PSWXc8kT3R2q1Rnram9Paxp3dbe0Jb22FjZqjbx+tmbcKh//Svt1zLIujeg+YSdLMKy5iwNIyErCIM5FChgUWTuzIqwLWhu3ZH2YVlDF90mLT8UkYGuLJoUhfdgcKBS5m8+Es4ZkYG/Pl8b6xNDe/ybi3bH2dTeH5dODIZ/DwzhNA2DZ+J505OXclh/NIwoHa/q1ezixj3nXaPdxcPa36ZFVLj6fiWqEKtIejDveQWq1jxeBBpeSX8djqJc//aNhPoZUOAuzWt7E25kllEVmE5W8+lENrajp+fCq7RAbwkSeQWq7iSXURiTjFXsoq5mlPE1exirmYXUarSUPivEpVKhXZBpb+7FSM7uxLS2g4/N8s7ft6FlHxOJGQzwNfptntyS1Vqun+wl4KyCn6ZFdLgaSBF0L1Jcwm6ANGp+fx68hp5JeVsDk/B1syQPS/1rfMk33Xt1JUcJi4/hloj8cHDfjzaw5PFf1/m678uIUng52bJ8qlBuFqLvbx3858NZ1l/KgkXK2N2vdQXy0a6HetM4nWeWH2SvBIVPo7mbJjdEyvTmvf1fEoek5YdQ0KiRytblj8e1KSm1PUpLC6bySuOYWOq5OQbg3TjFJ2az/bIVHadTyc2vYCbg4JSDiqNNq+zrZmhLne7TKZ9TC6Taf8uk3EjNGokiQqNxJ2ii1Ihw0Aux8fJnDYO5mTkl3IkLptOrpZsm9unTn/u/244x6+nrjExyINPxneu0/e+QQTde9Ccgi5oC1kbyGUMX3yIyxlFTWZ18IqD8Xy4PRqlQoa/mxVnEnMBmNzDk3dGdhRnMjVUXF7BsIWHuJpdzOQeniwY69/QXbrFgUuZzP5Rmxylq6c1q6d3v6dZjOPx2cz56QxZReVMCHTn0/GdG93llMZg/tbzrDl6pdpKXzujUpn9vzN42ZrSy8eexOxilAo5EdeuY2QgJy2/rNafaWumpK2jBa3sTPGyM8PLzpTvDyUQfi2XT8b5M7G7JwA5ReVsOpPEqC6uOFrU7fX5G2tIzI0MOPXmoHr5Dqlp/BD1dJsxs8promO7ufP5rotsPZtC//aODPNv3FtJZvbx5sSVHPZcSOdMYi6GChkfje3caPcbN1amhgZ8PLYzk1ccY92JREYGuNCzEVUj+uNsCi+vj0CllujbzoGlU7phanhvX0nBre34aKw/s/93mt9OJ2FrZsjrD3Wo4x43bZIk6fbu/zvByg0JWdoV7108rfloTNWDtEV/XeLLPbG6+6umB+HnaoWE9sxWkrR/AhjI5Tz94ynOJuUxf5TfLZcLrmYX42hpVGWBp62ZITP7tL7vn/N2erSyxc3ahOTcEnZfSG/QS221moOZP3++dgrhppuz8+3/8QCmT59+S3uZTEanTp10bdasWXPbNqWlpVXea8mSJXh7e2NsbExgYCCHDh265fOio6MZNWoUVlZWWFhYEBISQmKiSBn3TL82BHhYU6rS8MaWKLIKa3+0qk8ymYzPxwcwqIM2dWVQK1vGdXO7y6uE2wltY8djwdozidc2RjaabFU/HrvK3F/CUaklRnR2YeXjQfcccG8Y3MmZjyvzNC87GM+yA3F10dVm43xKPsm5JZgoFfTxuf3B143tV61uU8t6Vt82LJ3STXdfqZDjaGmMk6UxLlYmuFqb4G5jiruNKc5WxrRzsgAgIfPWLV1z+rdl2dQgHtBTelq5XMbYyu+QTWfuXJmt3vtS2xd06tSJ1NRU3S0yMrLatgsXLqzS9tq1a9ja2jJhwoQq7SwtLau0S01Nxdj4nyOgX3/9lRdffJE33niD8PBw+vTpw7Bhw6oE1Li4OHr37o2vry/79+/n7NmzvPXWW1Xep6WSyWT8MisEX2cLcorK+b9NkXpNe3YvrEyVPD/AB6VCxtG4bFYfudLQXWqyXhvmi6uVMYk5xXyx+1KD9kWSJNYeTeCtLVFIEkwJ8WThpK51lqb0kSAPXqvcFrJgRwzrxb5vnd2VqTP7trOvdno1IVsbIL3tbw26xkoFQ/1c6OppDUDxvwqs/FtrB+1K4/is26dw1LcbWa0OxWaRUVB6l9b1p9a/6QYGBjg7O+tuDg4O1ba1srKq0vbUqVNcv36dJ554okq7G2fMN99u9uWXXzJjxgxmzpxJhw4d+Prrr/Hw8OC7777TtXnjjTd46KGH+PTTT+natSutW7dm+PDhODo2nkT/DcnIQMGXj3RBqZCx+0I6WyKSG7pLdxXgYc0blVOEC3ZEE3Ett2E71ERZGCt5rbIowroTiYQnXm+Qfqg1Em//fp73/ozGVCnnIX9n3h/th6KOt3DM7teGWX2105Svb4rUTam2dLvO33lqGWCYnzNjurrR0bX6a5JmlTMSd5s1ae2gDdzxtznTbQitHczp4mGNWiOxNSKlwfpR66AbGxuLq6sr3t7eTJo0ifj4mpfa+v777xk0aBBeXlULLBcWFuLl5YW7uzsjRowgPPyf2ojl5eWcPn2awYOrbsgePHgwR48eBUCj0bBt2zbatWvHkCFDcHR0JDg4mC1bttyxP2VlZeTn51e5NWcdXS15YaAPAG//fp7UvMZfJH5az1YM83NGpZaY89MZcotvX6y9Qq1p9GfvDWXNkQRe26idkdJIEq9uOEep6s5nKXUtv1TF7P+d5sdjV6nQSBSrNCTm1F/GrNeH+TI+0B21RmLOz2c43sLzNF/JKuJiegEKuXY/bnWe6OXNVxO76KaGb8eksoze3c50A71s+O6xbnw1sfEs3hynm2JuuJOOWgXd4OBg1q5dy65du1ixYgVpaWn07NmT7Oy7/0KnpqayY8cOZs6cWeVxX19f1qxZw9atW1m3bh3Gxsb06tWL2FjtBfusrCzUajVOTk5VXufk5ERamna6JCMjg8LCQj7++GOGDh3K7t27GTNmDGPHjuXAgQPV9mnBggVYWVnpbh4eHrUZjiZpdr82BLhbUVBawX83Nv5pZplMxifjO+Npa0pybgnz1p/lZEI2C/fGMnn5Mbp/uJf2b+6g7Rs7SMtvuCmjxszcWElxuZqOrpYYKhRczihk8d+xd39hHTmXlMuIRYfZcyEdQ4Wcj8f6Y6JUEJWcz4FLmfXymTKZjI/H+jOogxPlFRpm/nCK8yn6LeHWmOy+oP2uDGlte9973M1uBN2yOwdde3Mjhvm70Nax+gCubyM6u6JUyLiQms8rv51tkD7UKugOGzaMcePG4e/vz6BBg9i2bRsAP/zww11fu2bNGqytrXn44YerPB4SEsKUKVMICAigT58+rF+/nnbt2rF48eIq7f69/F+SJN1jGo02ufXo0aN56aWX6NKlC6+99hojRoxg6dKl1fbp9ddfJy8vT3e7dq35X/8xUMj54pEuGCrkHLyUyR9n7zzNUqHWcOBS5i3lsfTJ0ljJkse6oZTL2BeTwYRlx/hq7yXC4rPJLCijrEL77190ly+BlmpMVzdWT+/Otud78+l47YrUpQfi76viTE2oNRKrDicw7rujJOYU426jLTk3qYenbnHXt/su19vnGyjkfPPoP3map606ydXsxjHVqW+7K6eWB3esfmq5pkyNbkwvN73/bzZmhroz/Q2nk6rUH9eX+1ouaGZmhr+/v+6stDqSJLFq1SqmTp2KoeGdj7Lkcjndu3fXvae9vT0KhUJ3VntDRkaG7uzX3t4eAwMDOnbsWKVNhw4dOHz4MNUxMjLCyKhxJ4uoD3IZKORgiIz/2xxFSBu72+6Lu5ZTzAu/hHMmMZcAD2s2PdOzzq+/1ZRMpl1cVVBaQVmFhl5t7Hioswt+rlbYmRtiZmiApUgBeFsKuYz+lV80Q/1ceMjfme2Raby64Ry/z+mJoUHd71k8eSWHd/84T1Sy9pLNkE5OfDo+QJem8am+rVkbdpWTV65zPD6b4HrKEmSsVLByWpAuT/OU74+zcXbPFpWnObOgjNOV1/Ef7Oh0l9Z3d+pKDnD3a7qN1f891AFbM0M6ulg2yJ7/+1oyWFZWRnR0NC4ud973eeDAAS5fvsyMGTPu+p6SJBEREaF7T0NDQwIDA9mzZ0+Vdnv27KFnz566Nt27d+fixYtV2ly6dOmW68cCzP0lnBKVBplMRmFZBR/8GX1Lmz/OpvDQwkO6xBTJ10tIyW2Ya8DrTiQy5tujZBX+cz038XoxIwNcCfCwxt3GFBszwwY7IGhKVGqNLq9uhVrDd/vrdltNcm4Jz68LZ8LSMKKS87EwMuD90Z1YOiWwSl5kJ0tjxgdp911/U49nu6CdKfnhye542ZlyLaeEx1edIK+k6RV0v1d7o9ORJOjsblUnmdxulMjLbORbD6vjZWfGgrGdmRraqkFyddfqTPeVV15h5MiReHp6kpGRwQcffEB+fj7Tpk0DtNO1ycnJrF27tsrrvv/+e4KDg/Hz87vlPd99911CQkLw8fEhPz+fRYsWERERwbfffqtr8/LLLzN16lSCgoIIDQ1l+fLlJCYmMnv2bF2bV199lYkTJ9K3b1/69+/Pzp07+eOPP9i/f39tfsQWwcJIybujOuFtb8b01SfYejaFCUHu9PFxoLi8gvlbz7P+lHYvWzdPa6b1bMWDHZ3uex/lvShVqXlzSxRqjUTvtvZ8MMaPKSuPcy2nhDc3R7HwpvzMoD2qj0rJ43xyHoYGcmb1baP3PjdWV7KKePHXCN0q8NiMQhb/fZkBvk74u99fibOScjXLDsax9EAcpSoNMhlM6u7JvMHtsK8m9egz/drw68lrJOYUk1eiqtcvQEcLY358MphxS48Sk1bAzB9O8uOM4BaR3ezGVqHBdXCWC7Btbm8Sc4pvu5dXuLtafYsmJSUxefJksrKycHBwICQkhGPHjunOJlNTU29JRpGXl8fGjRtZuHDhbd8zNzeXWbNmkZaWhpWVFV27duXgwYP06NFD12bixIlkZ2fz3nvvkZqaip+fH9u3b69yFjtmzBiWLl3KggULmDt3Lu3bt2fjxo307t27Nj9ii3BzwvLHQ1ux5ugV3twSxZePBPDqhnPEZxYhk8Hz/dsyd6BPg+axNVYqeHGgD/19Henkqk18vnBSVx5ZFsbWsyn09bHH18WS3efT2HU+nYvpBVVe387JQm8b8BsrSZJYf+oa7/5xgeJyNRbGBnz4sB87z6exPTKNeb9FsPW53vcUgCRJ4s9zqSzYHk1Knvb6WI9Wtrw9suNdi8t72Jqy/ukQAtyt9fI75mlnytone/DIsjBOXrnOcz+fYemUwGadp7mgVMWRy9qFrnfaKlQbpoYGovTmfRC5l2/S3HIv10RBqYoHvzxIWn4pckADOFsa89XELo26Ms03f8fy+e5LVcqTgfbab2t7M/zcrPBztWJ0F9cWdf3udsITrzNmiXZ7XbC3LV9O7IKbtQk5ReUM/uoAWYXlPN2vNa8Pq13axPjMQl7fFMnxBO01PjdrE15/yJfh/i6NOu/xiYQcpn5/nLIKDeO6ufPZ+M71Vu6tof15LoXnfg7H296Mv+f1a9T/Lk2dyL0s1IiFsZIPHvbjvT8vcC2nmGBvW5ZOCcTGrHGXznvmgbZEpxZwMDaTwrIKHvR1Yqi/MwN8HUXZv3/p6mnD46FeuFiZMKtva921b1szQxaM7cxTa0+x7EA8D3ZwIqiV7V3fr0KtYcWhBL7ae4nyCg3GSjnP9GvLrL6tdXs4G7Me3rZ8+2g3nv7faTaeScLWTMn/PdShWQYk3arlTk7N8udriprvvIpQY4M6OtHeyRwJMDSQY27c+I/FFHIZb47ogFwmQ5LA28GMsd3cRcCtxnuj/XjmgTZVFptJkkR2YRkKuQw7M0Ne+e0sJXfZBnIhJZ+Hlxzhk50xlFdo6ONjz56X+vHCIJ8mEXBvGNTRiU8q8zSvOJTA0gM1T/LTlJy+ql213L+FX2JpTETQFQiLy+bQ5SwAjl7OYsWhpvEF5GJlwqfj/0lwfyi2fhItNEf5pSqeWxfOa5siUWskCkoruJJdzGe7Lt62falKzee7LjLqm8NEJedjZaLk8wkBrH2yBx62txYObwrGB7rr0ox+sjOGH8OuNGyH6phaI+kSxohFT42HCLoCnd2tsDMzorW9GWoJFu6N1VUbaeyGdHLWJVp4ef1ZspvoNgZ9Ck+8zvBFh9h2LhUDuYzXhvmydKq2eszqowmcqLxGe8PpqzkMX3SIb/ZdpkIjMczPmT0v92V8oHuTn7J8qm9r5vTXrnB/6/fz/NaMCiRkFpRVWe8gNA4i6AqYGRmwfnYoe17qS++29pRVaHhjS+NPEXnDm8M74uNoTmZBGa9uONdk+t0QNp1JYsLSMK7llOBuY8Jvs0OZ3a8NA3ydmNTdA0mCVzecpbi8gqIy7fax8UvDiMsswt7ciO8e68Z3UwLrvMh4Q3plcHue7OUNwH83nmPrXbK0NRXWpkqcLLXbtezNxWWXxkKsXr5JS1y9/G9Xs4sY8vVBSlUaPhvfmQlBTSMfdUxaPsMXHUaOxPtj/JnU3fOWNmqNREpuSZOdDq0LlzMKGLn4CAM6OPLRGP8qe2MLSlUM+eog14tVDPd3Jiw+h+TKhCgTAt15Y3iHZnvNXJIk3tgSxc/HE1HIZSx5rFudbbFpSKUqNddyivG5QwEDoW7UNH6IM12hCi87M14c1A6AD7dHN/qC96D9wvwrOgO1RkJCxlubo7iQUrViVGpeCY+uOMYjy8K4XnT7SkUtQVtHC7bN7c03k7vekozCwljJJ+M7Y2wgZ8OZZK4Xl+NmbcLaJ3vw2YSAZhtwQZvb/YPRfozt6oZaI/H8z+Hsv5jR0N26b8ZKhQi4jYwIusItZvT2poOLJbnFKt7/80JDd+eufjudpFsA5GZjgkojaVNdVq7E3XMhnWELD3E8IYf8EhXRqc27hOPdtHYwv+21WEmSOJeUx/USFYYKGYYKGVuf60XfdtXXzG5O5HIZn47vzHB/F8rVGp7+8TRhcS27JKBQ90TQFW6hVMj5ZJw/chn8HpHCvkZ+xD+6iys929jx7qhObH62F44WRlzOKOTVDRHM33qep9aeIrdYhZ+bJX/O7UPPtvYN3eVGp6BUxbM/ndEdvBgo5OSWVLD8YNNYyV5XDBRyvprYhUEdHCmr0DDjh5O6bTeCUBfENd2biGu6Vb3/5wW+P5yAm7UJu1/qi5lR492/q9ZIuj2oh2OzePbn0xSXqamoXL05o7c3/xnaHqN6qKjT1B2Lz+b1TZEkZBWhVMh4Z2QnnC2Nmbn2FHIZbHymJ109bRq6m3pVqlLz1NpTHIrNwshATv/2jjzY0YkBvo6NPnGM0DBqGj9E0L2JCLpVFZVVMPirgyTnljCjtzdvjeh49xc1El/uvsgf51JIyCrmk3Gdmdi9aSwI06f0/FI+3BatW63ramXMt4910wXYF38JZ0tECj6O5vw5t3eLO2ApKdcG3sOVe9hBWxYzqJUtgzs6MaiDE63sxf5XQUsE3Xsggu6t9l3M4InVJ5HLYMucXnR2t27oLtVIeYWGicvDCE/Mxd/Nig3PhLa4oFEdSZL44egVPtt1kaJyNTIZPBbsySuD21dZLHW9qJwHvzpIVmEZz/VvyytD2jdgrxuGJElEJeez50Iae6IzblkP4ONozqCOTjzY0Yku7tbNNoezcHci6N4DEXRvb+66cLaeTcHPzZItz/ZqMlVZknNLGL7oELnFKqaFevHu6FtLS7Y0hWUV/GfDWbZHasu9dfO05r3RftVWBNoZlcrs/51BIZfx+5xed60c1NwlXS9m74V09kSnczw+R3f5AsDe3IhBHRyZ2ac1bR3NG7CXQkMQQfceiKB7e5kFZQz8Yj/5pRW8PaIjT/b2vqVNYnYxnnaNb//rvpgMnlhzEoBvHu3KiM6ut7RJzy/ljc1RzB/VEXebxvcz1JXUvBKmrDxOXKb22u0bD3Xg8dBWdz07m/PzGbadS8XX2YKtz/XG0KBpHHTVt7wSFfsvZrA3OoP9MRkUlFUAYKiQ8/yAtjzdr40YqxZE7NMV6oytmSEdXbW/RLvOp3Etp7jK81vCkxn05QF2RqU2RPfuqL+vI888oE3z99pG7WKhmx2Lz2b4osPsjU7ntY2RDdFFvcguLGPuunDiMotwtjTm16dDmd7Lu0bToe+N6oStmSExaQUs2X/5tm1a4rG7lYmS0V3cWDy5K6ffepD/zQimXzsHytUavthziVHfHObstdyG7qbQyIigK9zVJztjOBavzcd7PCGH/27UplrUaCQW/RXLi79GUK7WsP9i4yw4MO/BdvRoZUthWQXP/nSGUpUaSZJYfjCOx1YeJ6uwDF9nCz54uPlOP1/JLubklevIZLD88UC61WI1sp25Ee+O6gTAN39frnJds6isgg+3XeCdrefrvM9NiaGBnN4+9qx5ojtfT+yCjamSmLQCxiw5woLt0ZRV3Ll6k9ByiOnlm4jp5duLSctn3JKjvDKkPZ/sjKFUpWFkgCux6QXEpBUAMKtva14b6ttoF5Kk55fy0MJDZBeVMz7QjcJSNTvPa69rjunqxkdj/JtUabp78fmui3TzsmaAr1OtXytJErP/d5pd59Pxc7Nk87O9OHAxk7d/jyIlT1vJZu/L/cS1zErZhWW8/+cFtkRoV4Z3cLFk4aQutBPZoZotcU33HoigW728YhVWpkpWHU7g48paqgAWRgb83/AOTO5xa67jO8kuLONcch59fRyq1HitT4djs5i66jg3fuOVChlvj+zElGDPJl8tRx8yCkoZ/NVBcotV+DiaE5tRCIC7jQnvP+wnarbexu7zaby2KZKconIMDeS8MKAtw/xdaO0gDk6aGxF074EIunen0UhMX32Cg7FZOFkYsfX5XjhZmtT6fV757SwbTifxZC9v3h6pv/2/C/deYtf5NKJTC1g4qQujurjp7bObg83hScxbfxZJ0qZNfKpPa14Y2LQK2OtbRkEpr/52jitZhRSVqckqKmfeg+14fqBPQ3dNqENiIZVQL+RyGR+N9cfC2ID0gjK+2hN7T4topoR4AbDqSIJeE8s/N8AHGzNDJGDx35cpLq+otm1BqYpPd8ZQqhLX4254uIsb4wPdkYC2Dua8OqS9CLh34WhhzJonuvP8AB+yKottHEsQOZ1bKhF0hVpztzFl8eSuyNCu/v35eGKt3yPA3YrelTmQ5+txEY5CLuPriV1xsDAiNqOQN7dE3fag4VxSLsMXHWbJ/jg+3hGjt/41djKZjMdDvVAqZFzOKOCXE7X/t2+JZDIZ44M8CG1tB4CTZfOpRyzUjgi6wj15oL0jbwz35Wp2MQt2RHPgUs3PVvOKVTz3c7guvd6N7Uj64mBhxOLJXZHLYNOZZH47laR7TpIkVh6KZ9x3R0nMKcbN2oSRAbfu7W2J0vJKmb/1POO+C0OllpDLZbz354VbtmEJ1XvIX1uj9/eIFA5eapyr/YX6JYKucM+e6OmNmZGC8gqJp9ae4lj83afMjsdnM2zhQbZFpmIgl/HqkPYsntxND72tKqS1HfMGa9MavvV7FNGp+eQUlTPzh1N8sC0alVpiaCdnts/tQ6BXy0r2/28puSW8/XsUfT/bx5qjVyir0NDFwwofRwvKKjR8tkvMBNTUlBAvxnbT1ux99qczxKS17DKTLZFYSHUTsZCqdo5ezuLRlcd1902UClZOC6LXbUrnqdQaFv0Vy7f7LqORwMvOlIWTutLFw1qPPa5Ko5F48oeT7L+YiYuVMWqNREZBGYYGct4a3oEpIV4telVzcm4JS/Zd5rdTSZSrtavVu7ey4YWB7ejV1o7o1AIeWnQIQ4Wck28OwspE2cA9bhrKKtQ8/v0Jjifk4GZtwpY5vXCwMGrobgn3SSykEurdzV8Und2tKFGpeWLNSfbF/DPVXKpS83tEMuO+O8riv7UBd3ygO9vm9mnQgAvaRWFfPtIFFytjMgvKyCgoo7WDGZuf7cnU0FYtNuBeyynm9U3neOCzffx0PJFytYaQ1rb8/FQw658OpbePPTKZjI6ulrR3sqBcrWF7ZOPLRtZYGRkoWDY1EG97M5JzS5j14ymxWK8FEWe6NxFnurUXlZzH46tOsGCsHxtOJ7PnQjpKhYz3RnXiQmoBv0ckk1+qXSFsYWzAgrH+t81/3JBOX81h7s/hJOeV8uaIDszs3bqhu9QgruUUs/jvWDadSdYl8u/V1o65A3wIrlwA9G/LDsSxYEcM9uZG7HyxD/bm4oytpuIzCxmz5Ch5JSpGBbiycFKXFnug1xyIfbr3QATde5OcW4KrlTEVGomXfo3gz3OpyIAbv1hu1iaMD3Tn0WDPRrtq843Nkfx0PJEe3ra8Nbwj/u4tq5pOcXkFi/66zNIDcQD08bHnhYE+BLWyvePrSlVqRn1zmEvphQz0dWTltCAROGrh6OUsHl91Qvt/Z1A7Xhj0z95dSZLEWDYhYnpZ0Bs3axNkMhlKhZyFk7oysbs7rR20xb0nBLpz4NUHeOnBdo0y4Go0Eov/imVd5daXEwk5zPn5DHklqgbumX4Vl6s5npDNAF9HNj7Tkx9nBN814AIYKxUsnNQVQwM5f8Vk8OOxq3robfPRs609H47R5vz+au8ldkSmotZIfLYrhvf+vNDAvRPqgzjTvYk4060bGo3ER9ujWXk4AYDJPTx4f7Rfo6zD+/bvUawN0waKUQGunLqaQ0puKQ92cOTbx7phKArf18iaIwnM/+MChgZy/niuN+2dRY7h2nj3j/OsPnIFY6UcfzcrTl65DsDvc3oR0MBrH4SaEWe6QoORy2W8OaIj80d2RCaDdSeuMeOHUxSWVZ/9qToajUROZRaf+jAlxAtbM0M+Hd+ZRZO78t1jgSgVMvZEZzB99cl6+9zmZlrPVvRv70B5hYa568LFwqBaeuOhDvTxsadUpeHklesYKmR8PbGLCLjNkAi6Qr2Z3subZVMCMVbKOXApk0eWhpGeX1rj1x+Pz2bI1weZ/ePpeutjOycLDv+3P48EeQAQ4GHNGw91wN/NiqNx2XpNUdmUyWQyPpsQgL25ERfTC0QWr1oyUMj5ZnI3hvppK0C1cTBnmL8zxeUVPPfzGQ7FikQazYUIukK9GtzJmV9mhWJvbsiF1Hwe/vZIjRIC7DqfxsTlx7SVbOp5LYmpoUGV+9N6tsLDVlvE4Y3NUeQW19+ZdnNib27E5xM6A7Dm6BX+jklv4B41LVamSv4zxBdLYwOi0wp48ZcIJiwN489zqbz0awQl5WL2oDkQQVeod108rNn8bC9aO5iRmlfK1O+PV3vkLkkS3+2PY/b/tGe3fXzsWfF4kD67i0wm4+NxnfGyMyU5t4R568+i0YilDzXxQHtHnuzlDcCrv50jo6DmMxsCtHYw59vHuqGQy9gRlcb5lHzszAxZOiVQFJZoJkTQFfTCw9aUxZO6opBBSbmG6atO6lYM35B0vZhpq0/yyc4YJAkeD/Vi9fTuDZLpyNJYybwH2yGTQWRyHssPxuu9D03Vf4e1p4OLJdlF5eKA5R708XHg7REd6OiiXYz2n6Hta7SSXGgaRNAV9EKtkfjvpnOoJTCQy1BLEq9viuSj7dGoNRIVag2z/3eag5cytck1RnfivQZa8ZyRX8r/bYrkhV8jkCTILCjj450x7D6fpve+NEVGBgoWTeqCkYGcQ7FZrDqS0NBdanIeD21FF09tzu/3/rggcjQ3IyLoCnqhkMt4foAPnrambJvbm5cGtQNg+cF4nlxzkmUH4olKzsfS2IBdL/bl8dBWDdbXsPhs4jILkSQYGeDK2K7aQvcv/hpBdOqtX36/RyQTn1mo7242Wiq1huMJOVRU5mv+bNdFrtfjCvTmSCaT8e6oTvRsY0dRuZoZa06RVVjW0N0S6oDYp3sTsU+3/qnUGpSVZ69bz6bwnw1nKVVpdBmsPhrjz6PBng3aR0mSePeP84wKcKWbly0Vag3TVp/gyOVs3KxN2PpcL+zMjShVqXn79yjWn0pCIZfx08xgQqpJl9hSZOSX8tzP4Zy4kgOAtYmS0V3deHdUpwbuWdOUW1zOmCVHScgqItDLhp9mBmOs1P+1XbVGori8AgtjUdSiOmKfrtAoKW+aLh4V4MqmZ3rhYWNCK3ttBquk60W3XaX59d5LfPN3rO7sqT7JZDLmj/Kjm5f2OpqBQs63j3ajVeXCqmf+d4bLGYWM++4o608lIZfByw+2I9i7ZV93OxafzUOLDnPiSg4WRga8PsyX5VODGODr2NBda7KsTQ35floQlsYGnL56ndc3RaLv86SNp5Po8eFePtt1Ua+f21yJoCs0qI6ulvzxfG+CKmvW7ohK46GFBzmTeF3X5kzidRb9Fcvnuy9xNO7uNXvrg7WpISunBWFhZMCJKzkMW3iQ8yn52JoZsvbJYOb0b9ti8+RKksSyA3E8tvI4WYVl+DpbsPX53jzdrw09WtvSr51DQ3exSWvtYM53UwJRyGVsDk9myf44vX6+jZmS7KJydp9PF4vi6oAIukKDszY15NWh7ZEBCVnFJGQXM/67o3y6M4ajl7N47qczaCR4uIsrfRvwC7ytowWLJnfF0kS7r9fN2oQ/n+9Nb59b6we3BEVlFVxKL+A/G86xYEcMao3E2K5ubH62F96VMxdC3ejV1l43Rf/ZrovsjLq1lGJ9ZQHr2cYeM0MFafmlRCbn1ctntCQGd28iCPXP3MiALx4J4Ny1PPJKVboj+iX74zA2kGOokDG7X5uG7ib9fR2Z80BbFuyIQSGD2IwCXK1NGrpbdU6SJLIKy0nOLSElt4Tk6yUk51beKv9+c1EIZ0sjAjys+eKRgBZ7xl/fpoR4cTmjkDVHr/DSr2dxtzHFz82KvGIVX+y5yG+nkjj+xkAs6/i6q7FSwQPtHdkWmcruC2kiNeV9EgupbiIWUjUeO6NS+XTnRUpVajRAWl4pFkYGvD2yI+MD3Rv0iz01t4SBXx6gVKXGzNCATc/2xMepaSf4zytR8eXui8RlFumCa3nF3a+fW5koMTNUkJJXSqCXDRuf6amH3rZcFWoNT/5wioOXMnG2NOaFQT4s/CsWM0MFcZlFrJoexABfpzr/3N8jknnhlwhcrIzp5mmDhIQkob0hoan8OzceR3vgpv0TNJVh5kb7mX1a079987rWL+rp3gMRdBun+MxCXvntLGcScwF4yN+ZBWM6Y2Va9Yh+9/k01p+6xrP929Ktco9jfTl5JYe3NkcRk16Ap60pW+b0wtbMsF4/sz6pNRIbTl/j892XyCzQbk2RycDJwhhXa2PcbExxszbBzcYEN2tj3KxNcbU2xsJYyeWMAgZ9eRATpYKTbw7C3EhMoNWn/FIVY5cc5XLGP9vUere159kH2tCzbd1f6ricUcDXe2PZdi4VSxMD8kpqX7jk3z4e68+kHg27S6GuiaB7D0TQbbzUGollB+P4cvclKjQSrlbGfDWxC8E3bdEZ991RTl+9zrMPtOE/Q33rvU85ReU8/O0REnOK6dHKlh9n9gAgNr0QPzerev/8ulah1rDvYiZmRgrcrU1xtjLG0ODuyz7UGonQBX+RUVCGn5slq6Z3x9Gi8dVObk6uZhfx2sZznEi4jgT88GR3+vjU3XoHSZKIuJbLikPx7IhK40aUmBjkjre9OaZGCm1KdJkMGSCXyZDJtGnStX9q71R5rvJxmUybGtbLrnld9xdB9x6IoNv4nUvKZe66cK5kF+Ntb8aIzi68MNCHiGu5jF8ahqFCzuHX+uvtSz82vYCHvz1CeYWacYEeRCbnkphTwva5ffCwNdVLHxqDc0m5PLH6JNlF5bjbmLD2yR60djBv6G41a1HJeby26RxRyfnYmCrZMqfXfQeyyxmFbIlIZmdkGpdvSvgypJMTc/q3pbO79X32uvkSQfceiKDbNOyMSmX2/86gkGnTSQ7p5ERxuZpDsVlM6u7Bx+M667U/z687wx9n/1lNamOqZOmUwCpn4S3B1ewiHl91gqvZxdiYKvl+evd6n+Zv6UrK1UxcHsa5pDy87c1YNS0I71oe7KTmlfDH2RR+j0jhfErVjGvD/Z2ZO7Ad7Z2b9poFfRDJMYRmqVSl5p2t5wEIamWDvbkhkUm5HIrNAmBidw+99ymrQJvi0N7ckDYOZmx/oU+LC7gAXnZmbHymJ53drbherOLRFcfYe0GU96tPJoYKVj4ehIuVMSZKOc+tC69RKcrE7GJWH0lg4rIwen78Nx9tj+F8Sj4Gchn92zvgYWOCqaGcrp42IuDWMXGmexNxpts0nE/JY9mBeD6b0Jn0/FLm/HSGojI18VlF2Jgqef2hDozv5o5cXv8rnFVqDZ3n76ZEpWbplG4M9XOp989s7IrKtIXX913MRAZ8+UgXxnRza+huNWs3zzL08bFn9fTuVYqFqDUSZxKv81d0Bn9Fp2vrVN+kh7cto7u4MszPBVszQxb/FcsXey7R2t6Mv+b1E9vAakBML98DEXSbpvIKDZvCk1j0Vywpudr6rf5uVgS4W/FYiBcdXOrv3zIyKY+R3xzG0tiAiLcH6yXQNwVZBaUM+vIgpRVqSlUa5g704aVBPuLLux5dSMln/NKjFJereaJXK+YO8OF4fDa7LqSz/2IG14v/2VetkMvo3sqGQR2ceMjfBVdrE/JKVGw7l8rm8CROXrmOhZEBDhaGfDyuMz28W97MTW2JoHsPRNBt2lRqDauPJPD13liKy9XIZaCRYHQXV14c1K5esiStPpLAu39c4IH2Dqx5okedv39TlFeiYsrK40Qm52GiVFCiUuNoYcToLq68MbxjQ3evWbux3uEGS2MD8ku1W3ysTJQ80N6BgR2c6OfjUGXL3cpD8Xy++yKlKu3ebJkMOrtZMamHB5O6e4qDpRqoafwQG+qEZkOpkDOrbxtGBrgy//dI9kZnAvB7RAp/nE3hwY5OPNnLmx7etnX2JXLqqjZH9I3c0S1JXomKS+kFdL+pwHpBqYrHV50gMjkPWzND1jzRnZd/jeByZhErDyUwMsBVtwL25opTQt0Y6ufCyw+248s9lwAoKNMG3M/Hd+bhrm7V1qf+7VSSLuC+NsyX0V1ccbFqfpnWGgMRdIVmx8XKBFcbM9RSJqGtbTExNODvmAx2nU9n1/l0OrlaMqO3NyM6u9ZoH+qdJF0vIdDLpkFzQjeE8goNjywN42J6Advm9sbZ0piMgjLe/j2Ks9dysTFV8trQ9rz4awTxmUWANkvRy+vPMqO3NwcvZRKXWcjul/o17A/SDM0d6MPgTk5Ymxgy5+cznL56HaWBHAOFnIz8Uq5kF1NYpqqSuWpsNzdOXrnOq0Pai4VT9UxML99ETC83H39Fp7Pm6BVm9mlNv3YOxKYXsProFTad+eeI3sHCiKkhXjwW7ImdudE9fU55hea+A3dTlFeiIuiDPajUt359WBgZ0LONHbsqVy7bmxvx6pD2fL77IpkFZbrayY8Ge/LRGH/9dryFeXNLJP87lkivtnbklahwtjRmb3QGFsYGRM4f0tDda1bENd17IIJu83e9qJyfTySyNuwK6fnadIeGBnJm923Ny4PbN3Dvmpb1J6/xn43ndPdtzQyxNDbAUCHjUob27HZyD09eG+qLlamSfTEZzPn5DGUVGvq1c2D51MBqpzuFuvHz8UQWbL9AQZm2AlFrezM0koSzlTFrnwxukQeM9UVc0xWE27AxM2RO/7bM6tua7ZGprDqcwNmkPCxN6rYyS0vwSHcPevvYI5dBZEoeC/fGEpWsTa5gaqjg47H+jOryz1ah/r6O+DiaczYpj15t7UTA1YPhfk4s2X8ZhbyCOf3bMi7QvUnnCG8ORNAVWiSlQs7oLm6MCnDlTGIuPk4iZeG9uJpdzBe7L+oWlJkZKhjTzY3/DvXF4jYl5oZ3duFsUh67z6czo3drfXe3xbEyM2LTsz1FLuxGRARdoUWTyWQEtsCVx/crIauId/84z/6L2hXiRgZypvVsxex+bao9k8orVrHpTDKg3Scq6IcIuI2LCLqCINSYJEks/CuWJfviKFdrUCpkTOruyXMD2uJkWf2Xe8S1XOb8dIbk3BLszY34bEKAHnstCI2HCLqCINTY+ZR8vt4bC0AfH3veHdXpjtWENBqJ7w8n8OmuGFRqCU9bU5ZOCcTNWuwBFVomEXQFQaixto7mvPxgOwb4OtLJ1fKOSUYy8kuZ99tZXTGKYX7OfDK+M5a3udYrCC2FCLqCINSYsVLB3IE+d22350Ia7/95gcScEoyVct4e0YnJPTxEOkGhxRNBVxCEOlNcXsH7f0az7kQi5kYGGMhh4+xQOrlZN3TXBKFREEFXEIQ6EXEtl5d+jSAhqwiZTLvoqkID2yLTRNAVhEoi6AqCcF8q1BqW7I9j4V+xqDUSLlbGfDEhgMKyCj7cHs35lHzKKtQYGSgauquC0OBE0BUE4Z5dzS7ipV8jOJOYC8CIzi58+LC/rmxcfqmKh/xdRMAVhEoi6ApCCxSTls+VrCKGdHK+p8VNkiTx2+kk3t16nqJyNRZGBrz/sB+ju7hWeb/xgR512W1BaPJqlfx0/vz5yGSyKjdnZ+dq20+fPv2W9jKZjE6dOunarFmz5rZtSktLq7zXkiVL8Pb2xtjYmMDAQA4dOlTt5z799NPIZDK+/vrr2vx4gtBiVKgl5vwczs8nEmv92utF5Tz70xn+s+EcReVqenjbsuPFPjzc1U2sThaEu6j1mW6nTp3Yu3ev7r5CUf200cKFC/n444919ysqKggICGDChAlV2llaWnLx4sUqjxkb/5Pd5tdff+XFF19kyZIl9OrVi2XLljFs2DAuXLiAp6dnlddt2bKF48eP4+rqWtsfTRBajMzCMgwVcj7cFk2ftg542pnW6HVHLmfy7E/h5JWoMJDLeHlwO57u20akdRSEGqp1mQ8DAwOcnZ11NweH6ot3W1lZVWl76tQprl+/zhNPPFGl3Y0z5ptvN/vyyy+ZMWMGM2fOpEOHDnz99dd4eHjw3XffVWmXnJzMc889x08//YRSKTbgC0J1+vk44O9uRXG5mlc3nEWjuXOFz1KVmnf/OM+UlScwVspp42DGljm9ePaBtiLgCkIt1DroxsbG4urqire3N5MmTSI+Pr7Gr/3+++8ZNGgQXl5eVR4vLCzEy8sLd3d3RowYQXh4uO658vJyTp8+zeDBg6u8ZvDgwRw9elR3X6PRMHXqVF599dUq09d3UlZWRn5+fpWbILQEcrmMz8cHYGqo4HhCDmuOXqm2bXRqPqO+OczqI1eQgIe7uPHn833wc7PSW38FobmoVdANDg5m7dq17Nq1ixUrVpCWlkbPnj3Jzs6+62tTU1PZsWMHM2fOrPK4r68va9asYevWraxbtw5jY2N69epFbKw2v2tWVhZqtRonJ6cqr3NyciItLU13/5NPPsHAwIC5c+fW+OdZsGABVlZWupuHh1j0IbQcnnamvP5QBwA+2RlDXGZhlec1GomVh+IZ/c0RLqUXYm9uyOrp3Xn9oQ6YGIrVyIJwL2oVdIcNG8a4cePw9/dn0KBBbNu2DYAffvjhrq9ds2YN1tbWPPzww1UeDwkJYcqUKQQEBNCnTx/Wr19Pu3btWLx4cZV2/16gIUmS7rHTp0+zcOFC3aKsmnr99dfJy8vT3a5du1bj1wpCczAl2JM+PvaUVWh45bezqCunmVPzSpjy/XE+2BZNuVrDoA6O7HyxL/19HRu4x4LQtNV6evlmZmZm+Pv7685KqyNJEqtWrWLq1KkYGt6+1qauQ3I53bt3172nvb09CoWiylktQEZGhu7s99ChQ2RkZODp6YmBgQEGBgZcvXqVefPm0apVq2o/y8jICEtLyyo3QWhJZDIZn4zrjIWRAeGJuSw/GM+2c6kM/foQR+OyMVEq+GiMPyseD8Le3KihuysITd59Bd2ysjKio6NxcXG5Y7sDBw5w+fJlZsyYcdf3lCSJiIgI3XsaGhoSGBjInj17qrTbs2cPPXv2BGDq1KmcO3eOiIgI3c3V1ZVXX32VXbt23eNPJwgtg6u1CW+N7AjAZ7timPPzGfJKVHR2t2Lb3N48GuwptgIJQh2p1ZahV155hZEjR+Lp6UlGRgYffPAB+fn5TJs2DdBO1yYnJ7N27doqr/v+++8JDg7Gz8/vlvd89913CQkJwcfHh/z8fBYtWkRERATffvutrs3LL7/M1KlTCQoKIjQ0lOXLl5OYmMjs2bMBsLOzw87Orsr7KpVKnJ2dad++fW1+REFocYrLK0jPK0Ehl2FloiS3uJxnH2jLC4N8UCru67hcEIR/qVXQTUpKYvLkyWRlZeHg4EBISAjHjh3TrUZOTU0lMbHqZvu8vDw2btzIwoULb/ueubm5zJo1i7S0NKysrOjatSsHDx6kR48eujYTJ04kOzub9957j9TUVPz8/Ni+ffstq6AFQai5CrWG9aeS+HrvJTIKygBwtzFhzfTudPawbtjOCUIzJZMk6c4b9FqQ/Px8rKysyMvLE9d3hWZLkiR2nU/n010xxGcWAdpg+8rg9owKcEUu9t0KQq3VNH6I3MuC0MTdvJL/bk5eyWHB9mhdgQIbUyXPD/DhsRBPUZRAEPRABF1BaML2X8zg4x0xfD4h4I7JKmLTC/hkZwx7ozMAMFbKmdm7NbP6tcbSWGRvEwR9EUFXEBoBSZLILVZhY3bnLXX/tiU8mZi0Aj7YdoF1T4XccsabmlfCV3suseF0EhoJFHIZjwR58OIgH5wsjat5V0EQ6osIuoLQCPzf5kgiruWxYXYoZkY1/2/56lBftkelcSw+h2PxOYS20a7izytR8d3+OFYfSaCsQgPAkE5OvDrEl7aO5vXyMwiCcHdiP4AgNAIX0wqITs3nk50xtXqdm7UJEwLdAVh9JIFSlZoVB+Pp++k+lh6Io6xCQ/dWNmx8pifLpgaJgCsIDUyc6QpCPcstLienqJzWDtUHvNFd3DiTmMuJhJxaLYwCeKJXK346nsjuC+n0+2wf6fna7T8+jub8d6gvAzs4iuQWgtBIiDNdQahns9aeZuXhBO60O+/hLm4YGciJSSsg4lpujd87Ji2fX05cQ6mQYayUk55fhrOlMZ+O68yOF/owqKOTCLiC0IiIM11BqGeRyXlEXMtlSrAXHV1vv3/PylTJ8M4ubDqTzDd/X+b76d2rfb+conK2RiSz4UwSUcn/lKMMcLdiYAdnnujVCmOl2P4jCI2RCLqCcBO1RqrzouzdvKw5cjmbsPjsaoMuwJz+bdkakcJfMRmExWXrFkUBqNQaDl7KZMPpJPZGp6NSa8+alQoZA32dGB/oTr/2DiJtoyA0ciLoCkKll9dHsDk8GWdLY15+sB0TguqmvvID7Rw5cjmb/RczmNHbu9p2bRzMmdzDkx+PXeXjnTFsebYnl9IL2XD6GpvDU8gqLNO19XOzZHw3d0Z1ccO2ltuMBEFoOCLoCgLaM8k/z6YiSfBYsCeGBnV3xuhhawpAYk7xXdvOHejDb6evcfZaLv0+21/lNfbmhjzcxY1xge50cBFpSgWhKRJBVxCAuMxCytUazI0MeCTIA8c6TBxxJvE6AN1b2Vbb5lpOMTuiUtkRlUapStuPxJxiDOQyBnZwZHygBw+I6WNBaPJE0BWaldNXr3P2Wi5P3mEa93YupRcQ5GWDl51ZnQZcgIrK66/GyqoBMy6zkJ1RacSkFvDHuRTd4zKZdvp4cEcnRndxw04UjxeEZkMEXaFZeWNzJCq1hhGdXaoNnkVlFZy4ksPu8+m4WBkT1MqG8Ku5nLp6/Y4Lne5VD29bVh1JICwum+jUfHZEpbEzKpVL6YUAWBhr/xv2aGXLyAAXhnRyrvPALwhC4yCCrtCslFdoiM8qYtKKY/RoZYtcLkMhk6GQy5DLZBgoZOy5kMbV7GIk4MbW2RvrlROyijgUm0mQly0mhve/7UaSJOzNtQud4jKLGLbwkO45pUJGaGs7YtLyKSgFf3crpoa2uu/PFASh8RJBV2hW2jiaE59VRHxmka5W7L9ZGCmwMTVkTDc30vPLOJmQQ1p+Ka3sTDkUm8Wh2CyUChldPKwJbW1HSBs7unna3HXvqyRJJF0vJjI5n3NJecRlFnIyIYfcEhUArexMSckrpa+PAw/5OzOwgxNWJkoOXsrk8VUn+DHsKrP7tcHBQkwnC0JzJYrY30QUsW/6SsrV7IhKJS2/FI1GQq0BtSRp/y5JaCQJG1PtKmBnK+0UrjZYlnAiIYejcdmExWWRklda5X0NDeQEetoQ2saO0DZ2BLhbk1lYRmRSLueS8ohMziM6NZ/isgrKKjSoJTBVKihWqTFUyOnbzoEx3dzo184B89sUNPhsVwwPdnSmi4e1PoZJEIQ6VtP4IYLuTUTQFUAbhBNzigmL0ya0CIvLJqOgrEobuQw0d/ifE+BuxdmkPIwM5IS/9SCmtagcJAhC01PT+CG+CQThX2QyGV52ZnjZmTGphyeSJBGXWURYfDbH4rI5Fp9NdlE5BnIZ7Z0t8HO15HJmEaevarcGWZko+ebRbnyw7QLtnSwQR7WCINwgznRvIs50hZrQaCSSc0twsDDCWKlg38UMnlh9ErkM3hvtx7hu7nWyCEsQhKZDnOkKQj2Ry2W6LFMAGfna6799fByYEuLVUN0SBKEJEOltBOE+Hb6cDdya/EIQBOHfxLeEINynKcGeWJsqmd6zdlmwBEFoecT0siDcp+DWdhz6T38sjJUN3RVBEBo5caYrCHVABFxBEGpCBF1BEARB0BMRdAVBEARBT0TQFQRBEAQ9EUFXEARBEPREBF1BEARB0BMRdAVBEARBT0TQFQRBEAQ9EUFXEARBEPREBF1BEARB0BMRdAVBEARBT0TQFQRBEAQ9EUFXEARBEPREBF1BEARB0BNR2u8mkiQBkJ+f38A9EQRBEJqSG3HjRhypjgi6NykoKADAw8OjgXsiCIIgNEUFBQVYWVlV+7xMultYbkE0Gg0pKSlYWFggk8kauju1kp+fj4eHB9euXcPS0rKhu9MkiDG7N2Lc7o0Yt3vTVMZNkiQKCgpwdXVFLq/+yq04072JXC7H3d29obtxXywtLRv1L2ZjJMbs3ohxuzdi3O5NUxi3O53h3iAWUgmCIAiCnoigKwiCIAh6IoJuM2FkZMQ777yDkZFRQ3elyRBjdm/EuN0bMW73prmNm1hIJQiCIAh6Is50BUEQBEFPRNAVBEEQBD0RQVcQBEEQ9EQEXUEQBEHQExF09WD+/PnIZLIqN2dnZ93z06dPv+X5kJCQKu+xfPlyHnjgASwtLZHJZOTm5lZ5/sqVK8yYMQNvb29MTExo06YN77zzDuXl5VXaJSYmMnLkSMzMzLC3t2fu3Lm3tImMjKRfv36YmJjg5ubGe++9d9d8ovVBH+N2s7KyMrp06YJMJiMiIqLKc01l3PQ5Ztu2bSM4OBgTExPs7e0ZO3ZsleebypiB/sbt0qVLjB49Gnt7eywtLenVqxf79u2r0qYljVtOTg7PP/887du3x9TUFE9PT+bOnUteXl6Vz7l+/TpTp07FysoKKysrpk6desv4NpVxExmp9KRTp07s3btXd1+hUFR5fujQoaxevVp339DQsMrzxcXFDB06lKFDh/L666/f8v4xMTFoNBqWLVtG27ZtiYqK4qmnnqKoqIjPP/8cALVazfDhw3FwcODw4cNkZ2czbdo0JEli8eLFgDbl2oMPPkj//v05efIkly5dYvr06ZiZmTFv3rw6G4+aqu9xu9l//vMfXF1dOXv2bJXHm9q46WPMNm7cyFNPPcVHH33EgAEDkCSJyMhI3fNNbcxAP+M2fPhw2rVrx99//42JiQlff/01I0aMIC4uDmdn5xY3bikpKaSkpPD555/TsWNHrl69yuzZs0lJSWHDhg26do8++ihJSUns3LkTgFmzZjF16lT++OMPoIn9vklCvXvnnXekgICAap+fNm2aNHr06Bq91759+yRAun79+l3bfvrpp5K3t7fu/vbt2yW5XC4lJyfrHlu3bp1kZGQk5eXlSZIkSUuWLJGsrKyk0tJSXZsFCxZIrq6ukkajqVEf64o+x2379u2Sr6+vdP78eQmQwsPDqzzXVMZNH2OmUqkkNzc3aeXKldW+timNmSTpZ9wyMzMlQDp48KDusfz8fAmQ9u7dK0lSyx63G9avXy8ZGhpKKpVKkiRJunDhggRIx44d07UJCwuTACkmJkaSpKY1bmJ6WU9iY2NxdXXF29ubSZMmER8fX+X5/fv34+joSLt27XjqqafIyMi478/My8vD1tZWdz8sLAw/Pz9cXV11jw0ZMoSysjJOnz6ta9OvX78qG9GHDBlCSkoKV65cue8+1ZY+xi09PZ2nnnqKH3/8EVNT01ueb2rjVt9jdubMGZKTk5HL5XTt2hUXFxeGDRvG+fPndW2a2phB/Y+bnZ0dHTp0YO3atRQVFVFRUcGyZctwcnIiMDAQEOMG2u8tS0tLDAy0E7FhYWFYWVkRHBysaxMSEoKVlRVHjx7VtWkq4yaCrh4EBwezdu1adu3axYoVK0hLS6Nnz55kZ2cDMGzYMH766Sf+/vtvvvjiC06ePMmAAQMoKyu758+Mi4tj8eLFzJ49W/dYWloaTk5OVdrZ2NhgaGhIWlpatW1u3L/RRl/0MW6SJDF9+nRmz55NUFDQbds0pXHTx5jd+FKdP38+b775Jn/++Sc2Njb069ePnJwcoGmNGehn3GQyGXv27CE8PBwLCwuMjY356quv2LlzJ9bW1oAYt+zsbN5//32efvpp3WNpaWk4Ojre0tbR0fGOY9JYx01c09WDYcOG6f7u7+9PaGgobdq04YcffuDll19m4sSJuuf9/PwICgrCy8uLbdu23bI4pSZSUlIYOnQoEyZMYObMmVWeu13JQkmSqjz+7zZS5UIDfZc71Me4LV68mPz8/Lte720q46aPMdNoNAC88cYbjBs3DoDVq1fj7u7Ob7/9pvvCbCpjBvoZN0mSePbZZ3F0dOTQoUOYmJiwcuVKRowYwcmTJ3FxcQFa7rjl5+czfPhwOnbsyDvvvFPluXsZk5q0aYhxE2e6DcDMzAx/f39iY2Nv+7yLiwteXl7VPn8nKSkp9O/fn9DQUJYvX17lOWdn51uO6K5fv45KpdId8d2uzY3poH8fJepbfYzb33//zbFjxzAyMsLAwIC2bdsCEBQUxLRp04CmPW71MWY3gkPHjh11jxkZGdG6dWsSExOBpj1mUH+/a3/++Se//PILvXr1olu3bixZsgQTExN++OEHoOWOW0FBAUOHDsXc3JzNmzejVCp1zzk7O5Oenn7Le2VmZt5xTBrruImg2wDKysqIjo7WfXn9W3Z2NteuXav2+eokJyfzwAMP0K1bN1avXn1LIeXQ0FCioqJITU3VPbZ7926MjIx015RCQ0M5ePBglaX2u3fvxtXVlVatWtWqP3WtPsZt0aJFnD17loiICCIiIti+fTsAv/76Kx9++CHQtMetPsYsMDAQIyMjLl68qHtMpVJx5coVvLy8gKY9ZlA/41ZcXAxwy/9LuVyumz1oieOWn5/P4MGDMTQ0ZOvWrRgbG1d5TWhoKHl5eZw4cUL32PHjx8nLy6Nnz566Nk1m3PS2ZKsFmzdvnrR//34pPj5eOnbsmDRixAjJwsJCunLlilRQUCDNmzdPOnr0qJSQkCDt27dPCg0Nldzc3KT8/Hzde6Smpkrh4eHSihUrdCsgw8PDpezsbEmSJCk5OVlq27atNGDAACkpKUlKTU3V3W6oqKiQ/Pz8pIEDB0pnzpyR9u7dK7m7u0vPPfecrk1ubq7k5OQkTZ48WYqMjJQ2bdokWVpaSp9//rn+BqySPsbt3xISEm5ZvdyUxk1fY/bCCy9Ibm5u0q5du6SYmBhpxowZkqOjo5STkyNJUtMaM0nSz7hlZmZKdnZ20tixY6WIiAjp4sWL0iuvvCIplUopIiJCkqSWN275+flScHCw5O/vL12+fLnK91ZFRYXuc4YOHSp17txZCgsLk8LCwiR/f39pxIgRuueb0riJoKsHEydOlFxcXCSlUim5urpKY8eOlc6fPy9JkiQVFxdLgwcPlhwcHCSlUil5enpK06ZNkxITE6u8xzvvvCMBt9xWr14tSZIkrV69+rbP//u46urVq9Lw4cMlExMTydbWVnruueeqLKGXJEk6d+6c1KdPH8nIyEhydnaW5s+fr/etCJKkn3H7t9sFXUlqOuOmrzErLy+X5s2bJzk6OkoWFhbSoEGDpKioqCrv01TGTJL0N24nT56UBg8eLNna2koWFhZSSEiItH379irv05LG7cb2qtvdEhISdO2ys7Olxx57TLKwsJAsLCykxx577JYtWU1l3ERpP0EQBEHQE3FNVxAEQRD0RARdQRAEQdATEXQFQRAEQU9E0BUEQRAEPRFBVxAEQRD0RARdQRAEQdATEXQFQRAEQU9E0BUEQRAEPRFBVxAEQRD0RARdQRAEQdATEXQFQRAEQU9E0BUEQRAEPfl/vRXttCDLBCQAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "gdf.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "78ff017e",
      "metadata": {},
      "source": [
        "## Add Topology to SIR 3S"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "506f0d4e",
      "metadata": {},
      "source": [
        "### Write node coords to dictionary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "68dfb392",
      "metadata": {},
      "outputs": [],
      "source": [
        "dictionaryNodes = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "581f3a78",
      "metadata": {},
      "outputs": [],
      "source": [
        "count = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "6b25d3d0",
      "metadata": {},
      "outputs": [],
      "source": [
        "for index, row in gdf.iterrows(): \n",
        "    geometry = row['geometry']\n",
        "    coords = [(coords) for coords in list(row['geometry'].coords)]\n",
        "    first_coord, last_coord = [ coords[i] for i in (0, -1) ]\n",
        "    key = first_coord\n",
        "    if not key in dictionaryNodes:\n",
        "        count = count +1 \n",
        "        dictionaryNodes[key] = count\n",
        "        \n",
        "    key = last_coord\n",
        "    if not key in dictionaryNodes:\n",
        "        count = count +1 \n",
        "        dictionaryNodes[key] = count"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "292920b4",
      "metadata": {},
      "source": [
        "### Insert nodes into SIR 3S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "559fccee",
      "metadata": {},
      "outputs": [],
      "source": [
        "dicNodes2 = {}  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "59480015",
      "metadata": {},
      "outputs": [],
      "source": [
        "s3s.StartTransaction(\"Add Nodes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "38968d26",
      "metadata": {},
      "outputs": [],
      "source": [
        "for key in dictionaryNodes:\n",
        "    Id = str(dictionaryNodes[key])\n",
        "    text = str(key)\n",
        "    coor = text.split(',')\n",
        "    x = float(coor[0].replace('(',''))\n",
        "    y = float(coor[1])\n",
        "    z = float(coor[2].replace(')',''))\n",
        "    Tk = s3s.AddNewNode(\"-1\", Id, \"QKON\", x, y,z,0, 1, Id, Id, 0) \n",
        "    dicNodes2[key] = Tk  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "9358ac43",
      "metadata": {},
      "outputs": [],
      "source": [
        "s3s.EndTransaction()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ae60e3f",
      "metadata": {},
      "source": [
        "### Insert Pipes into SIR 3S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "362a0eb0",
      "metadata": {},
      "outputs": [],
      "source": [
        "s3s.StartTransaction(\"Add Pipes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "28fc4127",
      "metadata": {},
      "outputs": [],
      "source": [
        "for index, row in gdf.iterrows(): \n",
        "    geometry = row['geometry']\n",
        "    coords = [(coords) for coords in list(row['geometry'].coords)]\n",
        "    first_coord, last_coord = [ coords[i] for i in (0, -1) ]\n",
        "    fromNode = toNode = \"-1\" \n",
        "    mat = str(row[\"MAT\"])\n",
        "    DN = str(row[\"DN\"])\n",
        "    IdRef = row[\"IDREFERENZ\"]\n",
        "    \n",
        "    if first_coord in dicNodes2:\n",
        "        fromNode = dicNodes2[first_coord]\n",
        "    if last_coord in dicNodes2:\n",
        "        toNode = dicNodes2[last_coord]  \n",
        "    \n",
        "    lines = [xy[:2] for xy in list(geometry.coords)]\n",
        "    new_p = LineString(lines)    \n",
        "    \n",
        "    s3s.AddNewPipe(\"-1\", fromNode, toNode, float(row[\"L\"]), str(new_p), mat, DN, 0.25, IdRef, row[\"TYPE\"], 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "233b54a2",
      "metadata": {},
      "outputs": [],
      "source": [
        "s3s.EndTransaction()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "956e61e0",
      "metadata": {},
      "source": [
        "## Save changes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "8d1019d4",
      "metadata": {},
      "outputs": [],
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