approx_positioning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1ac4248-3e68-43b6-a8bd-f66823842ef8",
   "metadata": {},
   "source": [
    "# Approximation of the Minions location base on the Logfile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9677479a-2c37-4c43-9115-0d9dfa140714",
   "metadata": {},
   "source": [
    "## If first time you run this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dbcbf75-60e6-48e8-8c8d-70b5675032fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install -c conda-forge ipyleaflet\n",
    "!pip install utm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6f08e8e-4661-4171-a3f2-965cfaac5bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-leaflet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0c632e-40fb-4d12-a9ae-031aebbde5c7",
   "metadata": {},
   "source": [
    "*Then restart jupyterlab or notebook*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7851a4be-843a-4279-8ea7-e04a31d515d9",
   "metadata": {},
   "source": [
    "## Importing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "451b4907-e150-4302-9f81-7c92db8d9df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as  mpl\n",
    "from ipyleaflet import Map, WMSLayer, basemaps, Marker, MarkerCluster\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6de390-6828-4e0b-b86c-e6e1858e52d2",
   "metadata": {},
   "source": [
    "## Defining local module to the path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88911c2f-78af-4108-bd8b-be00fe2519f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "src = os.path.abspath('./PyMod/')\n",
    "if src not in sys.path:\n",
    "    sys.path.append(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab132dfe-82f3-4519-8e82-d7208fc271f0",
   "metadata": {},
   "source": [
    "## Importing local modules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576917c2-fa87-4e12-9503-0c57e39ae3fb",
   "metadata": {},
   "source": [
    "**Python module to extract temperature and location data :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3904e491-5cf5-46fe-b1f6-7e585e9842d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from LogExtract import nodes_gnss_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9f67f9-b6cb-4efb-b1df-7a9daf78a056",
   "metadata": {},
   "source": [
    "## Extracting data from LogFile from 1 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60162d81-4e5e-4235-9f80-d121d5f4f017",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"E:/PhD/Supervision/Martin/DATA/usquare_logfile/\"\n",
    "stt = '2023-11-13T09:40:00'\n",
    "end = '2023-11-13T10:13:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac8daed1-6161-46d0-94cf-85241618778e",
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'E:/PhD/Supervision/Martin/DATA/usquare_logfile'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m TEMP, TEMPUTC, GNSSUTC, LAT, LONG, ALTI, COMPASS, TILT \u001b[38;5;241m=\u001b[39m \u001b[43mnodes_gnss_temp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstartdate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menddate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mE:\\GitHub\\ULB-SeismoLab\\Minions-Toolbox\\PyMod\\LogExtract.py:48\u001b[0m, in \u001b[0;36mnodes_gnss_temp\u001b[1;34m(root, nodenbr, startdate, enddate, utm_bool)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nodenbr \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m :\n\u001b[0;32m     46\u001b[0m     path \u001b[38;5;241m=\u001b[39m root\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDigiSolo_\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39mnodenbr\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.LOG\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m---> 48\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file :\n\u001b[0;32m     49\u001b[0m     readcsv \u001b[38;5;241m=\u001b[39m csv\u001b[38;5;241m.\u001b[39mreader(file, delimiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     50\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'E:/PhD/Supervision/Martin/DATA/usquare_logfile'"
     ]
    }
   ],
   "source": [
    "TEMP, TEMPUTC, GNSSUTC, LAT, LONG, ALTI, COMPASS, TILT = nodes_gnss_temp(path, startdate=stt, enddate=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6799f1b-bf1c-4247-af86-703f48480157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Latitude (m)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(LONG, LAT)\n",
    "ax.set_xlabel('Longitude (m)')\n",
    "ax.set_ylabel('Latitude (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e42e2678-9439-4c49-bc65-68d5116bb08c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Median position => Approximate the location of the geophones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec7aee27-a0f9-44de-a4fc-18cbe9ca0b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "approxPos = [np.median(LONG), np.median(LAT)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c88a749-c771-496b-9d00-90010ce47a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Latitude (m)')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(LONG, LAT)\n",
    "ax.scatter(approxPos[0],approxPos[1], c='r', marker='+')\n",
    "ax.set_xlabel('Longitude (m)')\n",
    "ax.set_ylabel('Latitude (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8186c434-9f2e-44eb-a43d-2a39f0e4f549",
   "metadata": {},
   "source": [
    "### Showing on a map"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d44f643f-a6fb-46ee-aaf6-990fd4663e18",
   "metadata": {},
   "source": [
    "wms = WMSLayer(\n",
    "    url='https://cartoweb.wmts.ngi.be/1.0.0/WMTSCapabilities.xml',\n",
    "    layers='TOPO',\n",
    "    format='image/png',\n",
    "    transparent=True,\n",
    "    attribution='IGN', \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b8d9c68-1230-410b-9a41-0851889c0838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14150090246748bca21f2c171624811f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[50.685637333, 4.646578], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = Map(basemap=basemaps.CartoDB.Positron, center=(approxPos[1],approxPos[0]), zoom=15)\n",
    "\n",
    "marker1 = Marker(location=(approxPos[1],approxPos[0]), draggable=False)\n",
    "marker_cluster = MarkerCluster(markers=(marker1,))\n",
    "\n",
    "m.add_layer(marker_cluster)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5839fa17-d489-46b1-85ce-fe8ce2a3e427",
   "metadata": {},
   "source": [
    "## For multiple geophones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "13051531-7949-41a5-addf-1472568b179f",
   "metadata": {},
   "outputs": [],
   "source": [
    "base = \"E:/PhD/Supervision/Martin/DATA/usquare_logfile/\"\n",
    "stt = '2023-11-13T10:21:00'\n",
    "end = '2023-11-13T11:01:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5e902410-5884-4b4a-ad08-1fdc5258c9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['453001250',\n",
       " '453003484',\n",
       " '453003625',\n",
       " '453004444',\n",
       " '453004465',\n",
       " '453004512',\n",
       " '453004639',\n",
       " '453004744',\n",
       " '453004823',\n",
       " '453013956',\n",
       " '453019524',\n",
       " '453020066',\n",
       " '590001596']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dirLog = os.listdir(base)\n",
    "dirLog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb640702-8bf9-4903-9671-7388b3b651c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\julie\\anaconda3\\envs\\bas2\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "C:\\Users\\julie\\anaconda3\\envs\\bas2\\lib\\site-packages\\numpy\\core\\_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "PosNodes = []\n",
    "\n",
    "for nodes in dirLog :\n",
    "    path = '%s/%s' %(base,nodes)\n",
    "    for folder in os.listdir(path) :\n",
    "        if folder.startswith('20231113') :\n",
    "            path = '%s/%s/%s/DigiSolo.LOG' %(base,nodes,folder)\n",
    "            TEMP, TEMPUTC, GNSSUTC, LAT, LONG, ALTI, COMPASS, TILT = nodes_gnss_temp(path, startdate=stt, enddate=end)\n",
    "            MdLat = np.median(LAT)\n",
    "            MdLong = np.median(LONG)\n",
    "            MdNorth = np.median(COMPASS)\n",
    "            PosNodes.append([nodes,MdLat, MdLong, MdNorth])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e51b987-452f-4727-9417-aa2fddd6f0d4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'approxPos' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mipyleaflet\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Map, WMSLayer, basemaps, Marker, MarkerCluster\n\u001b[1;32m----> 3\u001b[0m m \u001b[38;5;241m=\u001b[39m Map(basemap\u001b[38;5;241m=\u001b[39mbasemaps\u001b[38;5;241m.\u001b[39mCartoDB\u001b[38;5;241m.\u001b[39mPositron, center\u001b[38;5;241m=\u001b[39m(\u001b[43mapproxPos\u001b[49m[\u001b[38;5;241m1\u001b[39m],approxPos[\u001b[38;5;241m0\u001b[39m]), zoom\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m)\n\u001b[0;32m      5\u001b[0m markers \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m pos \u001b[38;5;129;01min\u001b[39;00m PosNodes :\n",
      "\u001b[1;31mNameError\u001b[0m: name 'approxPos' is not defined"
     ]
    }
   ],
   "source": [
    "from ipyleaflet import Map, WMSLayer, basemaps, Marker, MarkerCluster\n",
    "\n",
    "m = Map(basemap=basemaps.CartoDB.Positron, center=(approxPos[1],approxPos[0]), zoom=15)\n",
    "\n",
    "markers = []\n",
    "\n",
    "for pos in PosNodes :\n",
    "    markers.append(Marker(location=(pos[1],pos[2]), draggable=False))\n",
    "\n",
    "marker_cluster = MarkerCluster(markers=markers)\n",
    "\n",
    "m.add_layer(marker_cluster)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6547772-d555-4dff-83b3-0c9d7218acca",
   "metadata": {},
   "source": [
    "### Visualize the serial numbers and compass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "735e0f3d-d48b-424e-b98c-6d759b1d6c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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6OubNmwcjIyO89957AIAbN24gMjISMpkMxsbGuHPnDlasWAE9PT24u7sDKPvZHTBgAPz9/cVfVkycOBGDBw9WeCz22rVrKCgoQFZWFv766y+kpaUBAOzt7aGtrY0HDx5g165d6N27N/7++2+EhYXhxx9/RFxcnDhGUFAQrK2t0b59exQXF2PHjh3YvXu3wi9svvrqKyxcuBA//PADrK2txfdMy99Rbdy4MRwcHBRS9mxeiIiIiIgqU2eFZ3FxMZKTkzF37lyFdplMVuGqnomJiZDJZAptbm5u2LJlC+RyObS0tJCYmIiZM2cqxZQXq1U9b2lpKXx9ffHJJ5+gffv2FV5HeHg4duzYAVNTUwwcOBBffPEFGjduXGF8UVGRwiI0+fn5AAC5XA65XF7hcZUpP+5FjycAn34KaGoCgwYB/foBz+RSEASUlpZCLpejQYMG+OOPP7Bjxw48evQI5ubm6NWrF3bs2AFdXV3I5XJoaGjg6NGjCAoKQm5uLkxNTfH2228jLi4OhoaG4p/Ttm3bMHPmTPFnevDgwVi3bp3Cn+P48eMVFgFy/F9BfOXKFVhbW0Mul2Pbtm2YM2cOBEGAi4sLfvvtNzg6Oorj/PXXX5gzZw7u3LkDPT092Nvb4+eff8bAgQPFmODgYBQXF2P48OEKaVmwYAE+//xzlSl7Ni81iT/PtYN5Vj/muHYwz7WDeVY/5rh21Pc819d51QSJIAhCXZz47t27aN68OY4fP67waOGyZcvw3Xff4fLly0rHtGnTBn5+fpg3b57YlpCQgO7du+Pu3bswNzeHtrY2tm3bBm9vbzHmhx9+wAcffICioqIqn3f58uU4fPgwDh48CIlEAmtra8yYMQMzZswQjwkNDYWNjQ3MzMxw7tw5BAQEoFWrVoiNja3wugMDA7Fo0SKl9h9++AENGzasWvKIiIiIiOiV8+TJE3h7eyMvLw8GBgZ1PZ0aVeer2v5zBc/nreqpKv6f7VUZs7KY5ORkrFu3DikpKZXOxd/fX/zewcEBrVu3hrOzM1JSUtClSxeVxwQEBGDWrFni5/z8fFhaWkImk73wD5dcLkdsbCz69+8vrrZKVVRcDLi6AteuAdOnA4sXVxjKPNcO5rl2MM/qxxzXDua5djDP6scc1476nufypyFfRXVWeBoZGUFDQ0Npz8Ls7OwKV/U0MzNTGa+pqYlmzZpVGlM+ZlXOGx8fj+zsbLRo0ULsLykpwezZsxEUFIT09HSV8+vSpQu0tLRw9erVCgtPHR0d6OjoKLVraWn96x/+mhjjtZOTAxgbA9nZQEAAUIX8Mc+1g3muHcyz+jHHtYN5rh3Ms/oxx7Wjvua5Ps6pptTZqrba2tpwcnJSeiw1Nja2wlU9XV1dleJjYmLg7Ows/iFVFFM+ZlXO6+vriz/++ENhBU8LCwt88sknOHjwYIXXdP78ecjl8ueubkr1iJkZ8NtvQFIS8Io9zkBEREREVF/U6aO2s2bNgq+vL5ydneHq6oqQkBDcunULkydPBlD2WOqdO3ewfft2AMDkyZOxYcMGzJo1C/7+/khMTMSWLVvE1WoBYPr06ejZsydWrFgBDw8P/Pzzz/jtt99w7NixKp+3WbNm4h3UclpaWjAzMxNXHL1+/TrCw8Ph7u4OIyMjXLhwAbNnz4ajoyO6d++u1rxRDZNIAEvLup4FEREREdErq04LTy8vL+Tk5GDx4sXIzMyEg4MDoqKiYGVlBQDIzMxU2FvTxsYGUVFRmDlzJr755htYWFhg/fr14lYqANCtWzdERERgwYIFWLhwIVq2bInIyEh07dq1yuetCm1tbfz+++9Yt24dCgoKYGlpiUGDBuGLL77gnoZERERERETPqPPFhaZMmYIpU6ao7Nu2bZtSW69evZCSklLpmMOHD1faFqI651Xln+91WlpaKuyVSERERERERKrV2TueRERERERE9Hpg4UlERERERERqxcKTiIiIiIiI1IqFJxEREREREakVC08iIiIiIiJSKxaeREREREREpFYsPImIiIiIiEitWHgSERERERGRWrHwJCIiIiIiIrVi4UlERERERERqxcKTiIiIiIiI1IqFJxEREREREakVC08iIiIiIiJSKxaeREREREREpFYsPImIiIiIiEitWHgSERERERGRWrHwJCIiIiIiIrVi4UlERERERERqxcKTiIiIiIiI1IqFJxEREREREakVC08iIiIiIiJSKxaeREREREREpFYsPImIiIiIiEitWHgSERERERGRWrHwJCIiIiIiIrVi4UlERERERERqxcKTiIiIiIiI1IqFJxEREREREakVC08iIiIiIiJSKxaeREREREREpFYsPImIiIiIiEitWHgSERERERGRWrHwJCIiIiIiIrVi4UlERERERERqxcKTiIiIiIiI1IqFJxEREREREakVC08iIiIiIiJSKxaeREREREREpFYsPImIiIiIiEitWHgSERERERGRWrHwJCIiIiIiIrVi4UlERERERERqxcKTiIiIiIiI1IqFJxEREREREakVC08iIiIiIiJSKxaeREREREREpFYsPImIiIiIiEitWHgSERERERGRWrHwJCIiIiIiIrVi4UlERERERERqxcKTiIiIiIiI1IqFJxER1Znly5dDIpFgxowZYpufnx8kEonCl4uLi8JxkyZNQsuWLaGnpwdjY2N4eHjg0qVLCjG5ubnw9fWFVCqFVCqFr68vHj16pBBz69YtDBkyBPr6+jAyMsK0adNQXFyscq7Xrl1D48aN0aRJkwqv5/jx49DU1ETnzp2V+h49eoSpU6fC3Nwcurq6aNeuHaKiosT+wMBApes2MzOr8FxEREQvE826ngAREb2ekpKSEBISgo4dOyr1DRgwAGFhYeJnbW1thX4nJyf4+PigRYsWePjwIQIDAyGTyXDz5k1oaGgAALy9vXH79m1ER0cDACZOnAhfX1/88ssvAICSkhIMGjQIxsbGOHbsGHJycjB27FgIgoCvv/5a4XxyuRyjRo1Cjx49kJCQoPJ68vLyMGbMGPTt2xf37t1T6CsuLkb//v1hYmKCXbt24Y033kBGRgYaN26sENe+fXv89ttv4ufyayEiInrZ1fkdz+DgYNjY2EBXVxdOTk6Ij4+vND4uLg5OTk7Q1dWFra0tNm3apBSze/du2NvbQ0dHB/b29ti7d++/Ou+kSZMgkUgQFBSk0F5UVISPP/4YRkZG0NfXx9ChQ3H79u2qXTgR0WusoKAAPj4+CA0NhaGhoVK/jo4OzMzMxK+mTZsq9E+cOBE9e/aEtbU1unTpgiVLliAjIwPp6ekAgIsXLyI6OhrffvstXF1d4erqitDQUOzfvx+XL18GAMTExODChQvYsWMHHB0d0a9fP6xevRqhoaHIz89XON+CBQvQtm1beHp6VnhNkyZNgre3N1xdXZX6tm7diocPH+Knn35C9+7dYWVlhbfffhudOnVSiNPU1FS4bmNj4yrlk4iIqL6r08IzMjISM2bMwPz585GamooePXpg4MCBuHXrlsr4mzdvwt3dHT169EBqairmzZuHadOmYffu3WJMYmIivLy84OvrizNnzsDX1xeenp44efLkC533p59+wsmTJ2FhYaHUN2PGDOzduxcRERE4duwYCgoKMHjwYJSUlNRAdoiIXl1Tp07FoEGD0K9fP5X9R44cgYmJCdq0aQN/f39kZ2dXOFZhYSHCwsJgY2MDS0tLAGX/LZBKpejatasY5+LiAqlUKt6xTExMhIODg8Lf725ubigqKkJycrLYdujQIfz444/45ptvKpxDWFgYrl+/ji+++EJl/759++Dq6oqpU6fC1NQUDg4OWLZsmdJ/L65evQoLCwvY2Nhg5MiRuHHjRoXnJCIiepnUaeG5Zs0ajB8/HhMmTEC7du0QFBQES0tLbNy4UWX8pk2b0KJFCwQFBaFdu3aYMGECxo0bh1WrVokxQUFB6N+/PwICAtC2bVsEBASgb9++Cncrq3reO3fu4KOPPkJ4eDi0tLQU+vLy8rBlyxasXr0a/fr1g6OjI3bs2IGzZ88qPCZFRESKIiIikJKSguXLl6vsHzhwIMLDw3Ho0CGsXr0aSUlJeOedd1BUVKQQFxwcjEaNGqFRo0aIjo5GbGys+EhuVlYWTExMlMY2MTFBVlaWGGNqaqrQb2hoCG1tbTEmJycHfn5+2LZtGwwMDFTO9+rVq5g7dy7Cw8Ohqan6DZYbN25g165dKCkpQVRUFBYsWIDVq1dj6dKlYkzXrl2xfft2HDx4EKGhocjKykK3bt2Qk5OjckwiIqKXSZ2941lcXIzk5GTMnTtXoV0mk1X4/kxiYiJkMplCm5ubG7Zs2QK5XA4tLS0kJiZi5syZSjHlhWdVz1taWgpfX1988sknaN++vdJckpOTIZfLFeZjYWEBBwcHJCQkwM3NTeU1FBUVKfzjqfxxLrlcDrlcrvKY5yk/7kWPp6phnmsH81w76irPGfHxmD59Og4cOAANDQ3I5XIIgoDS0lJxLsOGDRPj7ezs0KlTJ7Rq1Qo///wz3nvvPbHP09MTvXv3RlZWFtasWYMRI0YgLi4Ourq64p3Ef17fs+cqLS2tMKakpARyuRzjx4+Hl5cXXF1dIZfLlcYtKSnBqFGj8Pnnn8PGxkaMEQRBIcclJSUwMTHBN998Aw0NDXTs2BEZGRlYs2YNAgICAEDh7m/btm3h7OyMtm3bYuvWrQqLL5Ei/p1RO5hn9WOOa0d9z3N9nVdNqLPC88GDBygpKVH6bbOpqan4m+Z/UvXbaVNTUzx9+hQPHjyAubl5hTHlY1b1vCtWrICmpiamTZtW4Vy0tbWV3k2qbP5A2QqOixYtUmqPiYlBw4YNKzyuKmJjY//V8VQ1zHPtYJ5rR23n+cSJE8jOzlZ4BLa0tBTx8fH45ptv8OOPP6pcUMfIyAgHDhyAjo6OynH9/PwwevRoBAYGomfPnsjOzsadO3cUVo0FgLt37+LevXuIiorC48ePcfXqVYWYgoICyOVypKenIyoqCrGxsfjll1+wZs0ahfnq6upiypQpcHFxQXJyMlJTU8X/XgiCAEEQ0LhxYwQGBgIoe2e1YcOGOHjwoDjO48ePkZWVhZ9//lnpqZpyZmZmOHToENq0afOczBL/zqgdzLP6Mce1o77m+cmTJ3U9BbWp81VtJRKJwmdBEJTanhf/z/aqjFlZTHJyMtatW4eUlJRK56LK8+YfEBCAWbNmiZ/z8/NhaWkJmUxW4WNczyOXyxEbG4v+/ftX+I8X+veY59rBPNeOOsnzlCnosWMHPLt3B55ZNdbf3x92dnaYM2cOHBwclA7LycnBw4cP0atXL7i7u6scuri4GA0aNIC9vT3c3d1hY2ODDRs2wNjYGG+++SYA4NSpU3jy5AkmTpwIOzs7NGjQALt27YKjoyPMzc0BAP/973+ho6ODDz/8EAYGBkhMTFR4D/OXX37BqlWrEBcXh+bNm0MqlcLe3l5hLps3b8bhw4exY8cO3LhxA/3790dCQgIiIyMxYMAANGhQ9pbL9evXYW5uDg8PD5XXVFRUhKlTp8LDw6PC6yb+nVFbmGf1Y45rR33P8z8Xt3uV1FnhaWRkBA0NDaW7g9nZ2Up3I8uZmZmpjNfU1ESzZs0qjSkfsyrnjY+PR3Z2Nlq0aCH2l5SUYPbs2QgKCkJ6ejrMzMxQXFyM3Nxchbue2dnZ6NatW4XXraOjo/I39lpaWv/6h78mxqDnY55rB/NcO2otzxcuAFu3omlpKZquWQM4OopdjRo1grGxMRwdHVFQUIDAwEC8//77MDc3R3p6OubNmwcjIyOMGDECWlpauHHjBiIjIyGTyWBsbIw7d+5gxYoV0NPTw5AhQ6ClpYWOHTtiwIAB+PDDD7F582YAwIcffojBgweLxa27uzvs7e0xbtw4rFy5Eg8fPsTcuXPh7+8v/jfln1u9nDlzBg0aNIDjM/N/9nug7L9Denp66Ny5M+7evQstLS189NFHCA4Oxpw5c/Dxxx/j6tWrWLFiBaZNmybmf86cORgyZAhatGiB7OxsLFmyBPn5+Rg3bhz/v1AF/DujdjDP6scc1476muf6OKeaUmeLC2lra8PJyUnpNndsbGyFhZurq6tSfExMDJydncU/pIpiysesynl9fX3xxx9/IC0tTfyysLDAJ598Ij4m5eTkBC0tLYVxMjMzce7cuUoLTyKi19KCBUBpKTBsGPDWWxWGaWho4OzZs/Dw8ECbNm0wduxYtGnTBomJieKel7q6uoiPj4e7uztatWoFT09P6OvrIyEhQWFBofDwcHTo0AEymQwymQwdO3bE999/r3CuAwcOQFdXF927d4enpyfeffddhQXraoqlpSViYmKQlJSEjh07Ytq0aZg+fbrCegO3b9/GqFGjYGdnh2HDhkFbWxsnTpyAlZVVjc+HiIio1gl1KCIiQtDS0hK2bNkiXLhwQZgxY4agr68vpKenC4IgCHPnzhV8fX3F+Bs3bggNGzYUZs6cKVy4cEHYsmWLoKWlJezatUuMOX78uKChoSH85z//ES5evCj85z//ETQ1NYUTJ05U+byqWFlZCWvXrlVomzx5svDGG28Iv/32m5CSkiK88847QqdOnYSnT59WOQd5eXkCACEvL6/Kx/xTcXGx8NNPPwnFxcUvPAY9H/NcO5jn2lGreT5xQhAAQWjQQBAuXFD/+eoJ/izXDua5djDP6scc1476nueaqA3qqzp9x9PLyws5OTlYvHgxMjMz4eDggKioKPG3u5mZmQp7a9rY2CAqKgozZ87EN998AwsLC6xfvx7vv/++GNOtWzdERERgwYIFWLhwIVq2bInIyEiFhSyed96qWrt2LTQ1NeHp6Ym//voLffv2xbZt21QujEFE9FoSBOB/q7Zi7FigXbu6nQ8RERHViTpfXGjKlCmYMmWKyr5t27YptfXq1QspKSmVjjl8+HAMHz78hc+rSnp6ulKbrq4uvv76a3z9zCIZRET0jIKCsuJTWxv43wqvRERE9Pqp88KTiIheYY0bA4cOAVevAs8s2EZERESvlzpbXIiIiF4TEgnAfSiJiIheayw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBK9ZpYvXw6JRIIZM2aIbX5+fpBIJApfLi4uCsdNmjQJLVu2hJ6eHoyNjeHh4YFLly4pxOTm5sLX1xdSqRRSqRS+vr549OiRQsytW7cwZMgQ6Ovrw8jICNOmTUNxcbHY//fff2PdunVwdHSEpqYm3n33XaVr2LNnD/r37w9jY2MYGBjA1dUVBw8eVIjZtm2b0jVJJBL8/fffCnHBwcGwsbGBrq4unJycEB8fX41sEhEREVFVsPAkeo0kJSUhJCQEHTt2VOobMGAAMjMzxa+oqCiFficnJ4SFheHixYs4ePAgBEGATCZDSUmJGOPt7Y20tDRER0cjOjoaaWlp8PX1FftLSkowaNAgFBYW4tixY4iIiMDu3bsxe/ZshRgdHR189NFH6Nevn8rrOHr0KPr374+oqCgkJyejT58+GDJkCFJTUxXiDAwMFK4pMzMTurq6Yn9kZCRmzJiB+fPnIzU1FT169MDAgQNx69at6iWWiIiIiCqlWdcTIKLaUVBQAB8fH4SGhmLJkiVK/To6OjAzM6vw+IkTJ4rfW1tbY8mSJejUqRPS09PRsmVLXLx4EdHR0Thx4gS6du0KAAgNDYWrqysuX74MOzs7xMTE4MKFC8jIyICFhQUAYPXq1fDz88PSpUthYGAAfX19TJ48Ge7u7jhx4oTSHVMACAoKUvi8bNky/Pzzz/jll1/g6Ogotkskkkqvac2aNRg/fjwmTJggjnvw4EFs3LgRy5cvr/A4IiIiIqoe3vEkek1MnToVgwYNqvAu4pEjR2BiYoI2bdrA398f2dnZFY5VWFiIsLAw2NjYwNLSEgCQmJgIqVQqFp0A4OLiAqlUioSEBDHGwcFBLDoBwM3NDUVFRUhOTn7haystLcXjx4/RtGlThfaCggJYWVnhjTfewODBgxXuiBYXFyM5ORkymUzhGJlMJs6XiIiIiGoGC0+i10BERARSUlIqvIs3cOBAhIeH49ChQ1i9ejWSkpLwzjvvoKioSCEuODgYjRo1QqNGjRAdHY3Y2Fhoa2sDALKysmBiYqI0tomJCbKyssQYU1NThX5DQ0Noa2uLMS9i9erVKCwshKenp9jWtm1bbNu2Dfv27cPOnTuhq6uL7t274+rVqwCABw8eoKSkRGk+pqam/2ouRERERKSMj9oSveIyMjIwffp0xMTEKLzf+CwvLy/xewcHBzg7O8PKygoHDhzAsGHDxD4fHx/0798fmZmZWLVqFTw9PXH8+HFxXIlEojS2IAgK7VWJqY6dO3ciMDAQP//8s0Lh6+LiorBAUvfu3dGlSxd8/fXXWL9+fYXz+TdzISIiIiLVeMeT6BWXfPw4srOz4eTkBE1NTWhqaiIuLg7r16+HpqamwuJA5czNzWFlZSXeHSwnlUrRunVr9OzZE7t27cKlS5ewd+9eAICZmRnu3bunNNb9+/fFu4pmZmZKdxNzc3Mhl8uV7jxWRWRkJMaPH4///ve/FT5CXK5BgwZ48803xWsyMjKChoaG0nyys7NfaC5EREREVDEWnkSvMkFA3507cfatt5D2v1Vm09LS4OzsDB8fH6SlpUFDQ0PpsJycHGRkZMDc3Pw5wwvi47iurq7Iy8vDqVOnxP6TJ08iLy8P3bp1E2POnTuHzMxMMSYmJgY6OjpwcnKq1qXt3LkTfn5++OGHHzBo0KDnxguCgLS0NPGatLW14eTkhNjYWIW42NhYcb5EREREVDP4qC3Rq2z7djTetw8OmppA06aAgwMAQF9fH82aNYODgwMKCgoQGBiI999/H+bm5khPT8e8efNgZGSE9957DwBw48YNREZGQiaTwdjYGHfu3MGKFSugp6cHd3d3AEC7du0wYMAA+Pv7Y/PmzQDKVsIdPHgw7OzsAJQt3GNvbw9fX1+sXLkSDx8+xJw5c+Dv7w8DAwNx2hkZGUhLS8PDhw/x+PFjpKWlAQA6d+4MoKzoHDNmDNatWwcXFxfxrqWenh6kUikAYNGiRXBxcUHr1q2Rn5+P9evXIy0tDd988414nlmzZsHX1xfOzs5wdXVFSEgIbt26hcmTJ6vpD4SIiIjo9cTCk+hVdfMm8PHHZd8vXgx06aIyTENDA2fPnsX27dvx6NEjmJubo0+fPoiMjETjxo0BALq6uoiPj0dQUBByc3NhamqKnj17IiEhQeG9yvDwcEybNk1cKXbo0KHYsGGDwrkOHDiAKVOmoHv37tDT04O3tzdWrVqlMKfFixfj/v374ufyLVIEQQAAbN68GU+fPsXUqVMxdepUMW7s2LHYtm0bAODRo0eYOHEisrKyIJVK4ejoiKNHj+Ktt94S4728vJCTk4PFixcjMzMTDg4OiIqKgpWVVbVSTURERESVY+FJ9CoqKQF8fYHHj4G33wY+/VSh+8iRI+L3enp6OHjwYKXDWVhYICoq6rmnbdq0KXbs2FFpTIsWLbB///5KY0JDQ+Hu7g4tLS2V/c/OvyJr167F2rVrnxs3ZcoUTJky5blxRERERPTi+I4n0atoxQrg+HGgcWPg++8BFe9xEhERERHVFhaeRK+a5GTgiy/Kvt+wAbC2rtPpEBERERHxUVuiV42pKdCzJ9CsWdnjtkREREREdYyFJ9Gr5o03gNhY4K+/AImkrmdDRERERMRHbYleSQ0aAPr6dT0LIiIiIiIALDyJiIiIiIhIzVh4EhERERERkVqx8CQiIiIiIiK1YuFJREREREREasXCk4iIiIiIiNSKhScRERERERGpFQtPIiIiIiIiUisWnkRERERERKRWLDyJiIiIiIhIrVh4EhERERERkVqx8CQiIiIiIiK1YuFJRK+t5cuXQyKRYMaMGWKbn58fJBKJwpeLi4vCcZMmTULLli2hp6cHY2NjeHh44NKlSwoxubm58PX1hVQqhVQqha+vLx49eqQQM336dDg5OUFHRwedO3dWmt/ly5fRp08fmJqaQldXF7a2tliwYAHkcrlC3DfffIN27dpBT08PdnZ22L59u0L/tm3blK5JW1sbxcXFCnF37tzB6NGj0axZMzRs2BCdO3dGcnJyFbNJREREVDHNup4AEVFdSEpKQkhICDp27KjUN2DAAISFhYmftbW1FfqdnJzg4+ODFi1a4OHDhwgMDIRMJsPNmzehoaEBAPD29sbt27cRHR0NAJg4cSJ8fX3xyy+/iOMIgoBx48bh5MmT+OOPP5TmoaWlhTFjxqBLly5o0qQJzpw5A39/f5SWlmLZsmUAgI0bNyIgIAChoaF48803cerUKfj7+8PQ0BBDhgwRxzIwMMDly5fFz3K5HCkpKeLn3NxcdO/eHX369MGvv/4KExMTXL9+HU2aNKlOWomIiIhUYuFJRK+dgoIC+Pj4IDQ0FEuWLFHq19HRgZmZWYXHT5w4Ufze2toaS5YsQadOnZCeno6WLVvi4sWLiI6OxokTJ9C1a1cAQGhoKFxdXXH58mXY2dkBANavXw8AuH//vsrC09bWFra2tuJnKysrHDlyBPHx8WLb999/j0mTJsHLy0s85sSJE1ixYoVC4SmRSBSu6Z93TVesWAFLS0uFgtva2rrCHBARERFVBx+1JaLXztSpUzFo0CD069dPZf+RI0dgYmKCNm3awN/fH9nZ2RWOVVhYiLCwMNjY2MDS0hIAkJiYCKlUKhadAODi4gKpVIqEhIQXnve1a9cQHR2NXr16iW1FRUXQ1dVViNPT08OpU6cUisuCggJYWVnhjTfewODBg5GamqpwzL59++Ds7IwRI0bAxMQEjo6OCA0NfeG5EhERET2LhScRvVYiIiKQkpKC5cuXq+wfOHAgwsPDcejQIaxevRpJSUl45513UFRUpBAXHByMRo0aoVGjRoiOjkZsbKz4SG5WVhZMTEyUxjYxMUFWVla159ytWzfo6uqidevW6NGjBxYvXiz2ubm54dtvv0VycjIEQcDp06exdetWyOVyPHjwAADQtm1bbNu2Dfv27cPOnTuhq6uL3r174+7du+I4N27cwMaNG9G6dWscPHgQkydPxrRp05TeFyUiIiJ6EXzUloheGxkZGZg+fTpiYmKU7hKWK39kFQAcHBzg7OwMKysrHDhwAMOGDRP7fHx80L9/f2RmZmLVqlXw9PTE8ePHxXElEonS2IIgqGx/nsjISDx+/BhnzpzBJ598glWrVuHTTz8FACxcuBBZWVlwcXGBIAgwNTWFn58fvvrqK/F9UxcXF4UFkrp37w5HR0ccOHAAEyZMAACUlpbC2dlZfHfU0dER58+fx8aNGzFmzJhqz5mIiIjoWbzjSUSvjeTvv0d2djacnJygqakJTU1NxMXFYf369dDU1ERJSYnSMebm5rCyssLVq1cV2qVSKVq3bo2ePXti165duHTpEvbu3QsAMDMzw71795TGun//PkxNTas9b0tLS9jb22PUqFH4z3/+g8DAQHGuenp62Lp1K548eYL09HTcunUL1tbWaNy4MYyMjFSO16BBAzg7OyMzM1PhOu3t7RXi2rVrh1u3blV7vkRERET/xMKTiF4bfSMjcRZA2n/+g7S0NKSlpcHZ2Rk+Pj5IS0sT7xA+KycnBxkZGTA3N690bEEQxMdxXV1dkZeXh1OnTon9J0+eRF5eHrp16/avrkEQBMjlcgiCoNCupaWFN954AxoaGoiIiMDgwYPRoIHqv+IFQcCZM2dgaGgotnXv3l1h1VsAuHLlCqysrP7VfImIiIgAPmpLRK+LnBw0PnsWDgAwejTwvxVe9fX10axZMzg4OKCgoACBgYF4//33YW5ujvT0dMybNw9GRkZ47733AJS9CxkZGQmZTAZjY2PcuXMHK1asgJ6eHtzd3QGU3SkcMGAA/P39sXnzZgBlK+EOHjxYXNEWKFssqKCgAFlZWfjrr7+QlpYGALC3t4e2tjbCw8OhpaWFDh06QEdHB8nJyQgICICXlxc0Ncv++r5y5QpOnTqFrl27Ijc3F2vWrMG5c+fw3XffiedZtGgRXFxc0Lp1a+Tn52P9+vU4c+aM+FgtAMycORPdunXDsmXL4OnpiVOnTiEkJAQhISHq+hMhIiKi1wgLTyJ6PcTFAYIA2NuLRec/aWho4OzZs9i+fTsePXoEc3Nz9OnTB5GRkWjcuDEAQFdXF/Hx8QgKCkJubi5MTU3Rs2dPJCQkKCwoFB4ejmnTpkEmkwEAhg4dig0bNiicb8KECYiLixM/Ozo6AgBu3rwJa2traGpqYsWKFbhy5QoEQYCVlRWmTp2KmTNniseUlJRg9erVuHz5MrS0tNCnTx8kJCQobIXy6NEjTJw4EVlZWZBKpXB0dMShQ4fw8OFDMebNN9/E3r17ERAQgMWLF8PGxgZBQUHw8fF5wYQTERERPUOgOpWXlycAEPLy8l54jOLiYuGnn34SiouLa3Bm/2/ZsmUCAGH69Oli29ixYwUACl9du3ZVOG7ixImCra2toKurKxgZGQlDhw4VLl68qBDz8OFDYfTo0YKBgYFgYGAgjB49WsjNzVWI+fPPP4XBgwcLDRs2FJo1ayZ8/PHHQlFRkdh/6dIloXfv3oKJiYmgo6Mj2NjYCPPnz1fKx99//y3MmzdPaNGihaCtrS3Y2toKW7ZsEftDQkKEt99+W2jSpInQpEkToW/fvsLJkyfF/uLiYsHLy0vpuk1NTV80taSC2n6ep04VBEAQPvqoZsd9San77w1ijmsL81w7mGf1Y45rR33Pc03UBvUV73hSpZKSkhASEoKOHTsq9Q0YMEBhs/nyrSTKOTk5wcfHBy1atMDDhw8RGBgImUyGmzdviu/SeXt74/bt24iOjgZQ9jiir68vfvnlFwBld3MGDRoEY2NjHDt2DDk5ORg7diwEQcDXX38NoOzdtjFjxqBLly5o0qQJzpw5A39/f5SWlio8Sujp6Yl79+5hy5YtaNWqFbKzs/H06VOx/8iRIxg1apS4dcVXX30FmUyG8+fPo3nz5mKcvb09fv/9d/GzqvcCqR46dKjsf995p27nQURERPQaqnbhWVRUhFOnTiE9PR1PnjyBsbExHB0dYWNjo475UR0qKCiAj48PQkNDsWTJEqV+HR0dmFXwyCJQVkSWs7a2xpIlS9CpUyekp6ejZcuWuHjxIqKjo3HixAl07doVABAaGgpXV1dcvnwZdnZ2iImJwYULF5CRkQELCwsAwOrVq+Hn54elS5fCwMAAtra2sLW1Fc9lZWWFI0eOID4+XmyLjo5GXFwcbty4gaZNm4pzelZ4eLjC59DQUOzatQu///67wnYSmpqalV431UOZmcDFi4BEAvTqVdezISIiInrtVHlV24SEBIwaNQpNmjRB7969MWPGDHz55ZcYPXo0WrVqhdatW2PlypV4/PixOudLtWjq1KkYNGgQ+vXrp7L/yJEjMDExQZs2beDv74/s7OwKxyosLERYWBhsbGxgaWkJAEhMTIRUKhWLTqBsv0GpVIqEhAQxxsHBQSw6AcDNzQ1FRUVITk5Wea5r164hOjoavZ4pMPbt2wdnZ2d89dVXaN68Odq0aYM5c+bgr7/+qnDOT548gVwuFwvVZ8e3sLCAjY0NRo4ciRs3blQ4BtUTR4+W/a+jI/CPP08iIiIiUr8qFZ4eHh4YPnw4mjdvjoMHD+Lx48fIycnB7du38eTJE1y9ehULFizA77//jjZt2iA2Nlbd8yY1i4iIQEpKCpYvX66yf+DAgQgPD8ehQ4ewevVqJCUl4Z133hG3kygXHByMRo0aoVGjRoiOjkZsbKz4SG5WVpbCYizlTExMkJWVJcb8c99DQ0NDaGtrizHlyh+Rbd26NXr06IHFixeLfTdu3MCxY8dw7tw57N27F0FBQdi1axemTp1aYQ7mzp2L5s2bKxTebdq0wdatW3Hw4EGEhoYiKysL3bp1Q05OToXjUD3g6QmcOwesW1fXMyEiIiJ6LVWp8JTJZEhPT8eqVavQs2dPNGzYUKHf1tYWY8eORXR0NH777bdqTSA4OBg2NjbQ1dWFk5OTwuORqsTFxcHJyQm6urqwtbXFpk2blGJ2794Ne3t76OjowN7eXtzUvTrnDQwMRNu2baGvrw9DQ0P069cPJ0+eVIjp3bs3JBKJwtfIkSOrdf31UUZGBqZPn44dO3ZAV1dXZYyXlxcGDRoEBwcHDBkyBL/++iuuXLmCAwcOKMT5+PggNTUVcXFxaN26NTw9PfH333+L/RKJRGlsQRAU2qsSAwCRkZFISUnBDz/8gAMHDmDVqlViX2lpKSQSCcLDw/HWW2/B3d0da9aswbZt21Te9fzqq6+wc+dO7NmzRyEHTk5OGDZsGDp06IB+/fqJ1/vs1hVUD0kkQPv2wNtv1/VMiIiIiF5LVSo8p06dqrRwTEXat2+P/v37Vyk2MjISM2bMwPz585GamooePXpg4MCBuHXrlsr4mzdvwt3dHT169EBqairmzZuHadOmYffu3WJMYmIivLy84OvrizNnzsDX1xeenp4KRWNVztumTRts2LABZ8+exbFjx2BtbQ2ZTIb79+8rzMnf3x+ZmZniV/mefS+z5IQEZGdnw8nJCZqamtDU1ERcXBzWr18PTU1NlJSUKB1jbm4OKysrXL16VaFdKpWidevW6NmzJ3bt2oVLly6JvwgwMzPDvXv3lMa6f/++eJfTzMxM6c5mbm4u5HK50p1QS0tL2NvbY9SoUfjPf/6DwMBAca7m5uZo3rw5pFKpGN+uXTsIgoDbt28rjLNq1SosW7YMMTExKhdVepa+vj46dOigdN1ERERERPT/qvyOpzqsWbMG48ePx4QJE9CuXTsEBQXB0tISGzduVBm/adMmtGjRAkFBQWjXrh0mTJiAcePGKdzZCgoKQv/+/REQEIC2bdsiICAAffv2RVBQULXO6+3tjX79+sHW1hbt27fHmjVrkJ+fjz/++ENhTg0bNoSZmZn49Wxh81LKyUHfNWtwFkBaly5IS01FWloanJ2d4ePjg7S0NJWruObk5CAjIwPm5uaVDi8Igvg4rqurK/Ly8nDq1Cmx/+TJk8jLy0O3bt3EmHPnziEzM1OMiYmJgY6ODpycnCo9j1wuhyAIAIDu3bvj7t27KCgoEGOuXLmCBg0a4I033hDbVq5ciS+//BLR0dFwdnau9FqAssW2Ll68+NzrJiIiIiJ6ndXYdipjx45FRkYGDpVvWfAcxcXFSE5Oxty5cxXaZTKZuLDMPyUmJoqbsZdzc3PDli1bIJfLoaWlhcTERIXN1ctjygvPFzlvcXExQkJCIJVK0alTJ4W+8PBw7NixA6amphg4cCC++OILcaN5VYqKihTeg8zPzwcAyOVyyOXyCo+rTPlxL3q86PZtYNgw6F6+DDtzc2D1aqBtWwBlBbahoSHs7OyQm5uLL7/8Eu+99x7MzMzw559/YuHChTAyMsLgwYMhl8tx48YN/Pjjj+jfvz+MjIxw9+5drFy5Enp6eujfvz/kcjlatWoFNzc3TJgwAcHBwQCADz/8EO7u7rC1tYVcLkefPn3Qrl07jB49GsuXL0dubi7mzJmD8ePHQ09PD3K5HD/88AO0tLTg4OAAHR0dpKSkICAgACNGjBAL0BEjRuDLL7/E2LFj8fnnnyMnJwdz5syBn58fNDU1IZfLsWrVKgQGBmL79u1o3rw5MjIyAEB8R1UulyMsLAy6urqwsbHB/fv3sWzZMuTn58Pb2/vf558A1ODPM1WKeVY/5rh2MM+1g3lWP+a4dtT3PNfXedWEGis8mzdvjgYNqn4D9cGDBygpKVF6XNLU1FTp0cpyqhaaMTU1xdOnT/HgwQOYm5tXGFM+ZnXOu3//fowcORJPnjyBubk5YmNjYWRkJPb7+PjAxsYGZmZmOHfuHAICAnDmzJlKF1davnw5Fi1apNQeExOj9O5sddXIok6Bgf///cOHQFQUgLI7mjdv3kRUVBSKiopw+PBhbN26FYWFhTA0NISDgwMCAwPFd2UfPnyIvXv3YtWqVSgsLIRUKkX79u3x5Zdf4vTp0+IpRo8ejW+//Vb8hcJbb70Fb29vRP3vvAAwY8YMbN68GT169ICOjg569OiBPn36iDHnzp3Dnj17cPfuXQCAsbEx+vTpg6FDhyqMM3fuXISGhuKtt95C48aN0b17d7i5uYkxa9euRXFxsdJ7ul5eXhg1apSYh1GjRuHx48cwMDBAmzZtsGzZMpw/fx7nz5//9/knERcpqx3Ms/oxx7WDea4dzLP6Mce1o77m+cmTJ3U9BbWpscJz2bJlL3TcPxeIUbVozPPi/9lelTGrEtOnTx+kpaXhwYMHCA0NFd8VLV+J1d/fX4x1cHBA69at4ezsjJSUFHTp0kXl/AMCAjBr1izxc35+PiwtLSGTyWBgYFDhdVdGLpcjNjYW/fv3h5aWVvUHSEwEvLyAvDzAzg7Yuxdo3lwhxN3dXeHze++999xhR48eXaXTe3l5PTdm7NixFfa5u7tX+efv2b1F/+nOnTuVHlv+G6gXzjNVyb/+eaYqYZ7VjzmuHcxz7WCe1Y85rh31Pc/lT0O+imqs8KwuIyMjaGhoKN1lzM7OVrobWU7VQjPZ2dnQ1NREs2bNKo0pH7M659XX10erVq3QqlUruLi4oHXr1tiyZQsCAgJUzq9Lly7Q0tLC1atXKyw8dXR0oKOjo9SupaX1r3/4X2gMQSi7y5mVBXTrBvzyC/c5fI6a+LOi52OeawfzrH7Mce1gnmsH86x+zHHtqK95ro9zqinVLjyfvVv3LIlEAl1dXbRq1QoeHh5o+pziRVtbG05OToiNjVW4exYbGwsPDw+Vx7i6uuKXX35RaIuJiYGzs7P4h+Tq6orY2FiF9zxjYmLExWpe5Lzlnl0YR5Xz589DLpe/XAvNSCTAjz8CixcDy5cD//JxXyIiIiIion+qduGZmpqKlJQUlJSUwM7ODoIg4OrVq9DQ0EDbtm0RHByM2bNn49ixY7C3t690rFmzZsHX1xfOzs5wdXVFSEgIbt26hcmTJwMoeyz1zp072L59OwBg8uTJ2LBhA2bNmgV/f38kJiZiy5Yt2Llzpzjm9OnT0bNnT6xYsQIeHh74+eef8dtvv+HYsWNVPm9hYSGWLl2KoUOHwtzcHDk5OQgODsbt27cxYsQIAMD169cRHh4Od3d3GBkZ4cKFC5g9ezYcHR3RvXv36qa1bjVrBqxbV9ezICIiIiKiV1S1C8/yu5lhYWHiO4n5+fkYP3483n77bfj7+8Pb2xszZ87EwYMHKx3Ly8sLOTk5WLx4MTIzM+Hg4ICoqChYWVkBADIzMxX21rSxsUFUVBRmzpyJb775BhYWFli/fj3ef/99MaZbt26IiIjAggULsHDhQrRs2RKRkZHo2rVrlc+roaGBS5cu4bvvvsODBw/QrFkzvPnmm4iPj0f79u0BlN05/f3337Fu3ToUFBTA0tISgwYNwhdffKFyuxEiIiIiIqLXVbULz5UrVyI2NlZhIRwDAwMEBgZCJpNh+vTp+Pzzz5W2PanIlClTMGXKFJV927ZtU2rr1asXUlJSKh1z+PDhGD58+AufV1dXF3v27Kn0eEtLS8TFxVUaQ0REREREREDV9z/5n7y8PGRnZyu1379/X1yFqUmTJiguLv73syMiIiIiIqKXXrULTw8PD4wbNw579+7F7du3cefOHezduxfjx4/Hu+++CwA4deoU2rRpU9NzJSIiIiIiopdQtR+13bx5M2bOnImRI0fi6dOnZYNoamLs2LFYu3YtAKBt27b49ttva3amRERERERE9FKq9h3PRo0aITQ0FDk5OeIKtzk5OQgJCYG+vj4AoHPnzujcuXNNz5WI6KW1fPlySCQSzJgxQ2zz8/ODRCJR+HJxcVE4btKkSWjZsiX09PRgbGwMDw8PXLp0SSEmNzcXvr6+kEqlkEql8PX1xaNHj8T+M2fOYNSoUbC0tISenh7atWuHr7/+WmmOZ8+eRa9evaCnp4fmzZtj8eLFEARBISYuLg5OTk7Q1dWFra0tNm3apDTOo0ePMHXqVJibm0NXVxft2rVDVFSUQsydO3cwevRoNGvWDA0bNkTnzp2RnJxc1XQSERHRS6badzzLZWVlITMzEz179oSenh4EQYBEIqnJuRERvRKSkpIQEhKCjh07KvUNGDAAYWFh4mdtbW2FficnJ/j4+KBFixZ4+PChuJDbzZs3xRW0vb29cfv2bURHRwMAJk6cCF9fX3Hf4+TkZBgbG2PHjh2wtLREQkICJk6ciNGjR8Pd3R1A2erk/fv3R58+fZCUlIQrV67Az88P+vr6mD17NgDg5s2bcHd3h7+/P3bs2IHjx49jypQpMDY2FlcXLy4uRv/+/WFiYoJdu3bhjTfeQEZGBho3bixeU25uLrp3744+ffrg119/hYmJCa5fv44mTZrUUMaJiIiovql24ZmTkwNPT08cPnwYEokEV69eha2tLSZMmIAmTZpg9erV6pgnEdFLqaCgAD4+PggNDcWSJUuU+nV0dGBmZlbh8RMnThS/t7a2xpIlS9CpUyekp6ejZcuWuHjxIqKjo3HixAlx26jQ0FC4urri8uXLsLOzw7hx4xTGtLW1xfHjx3H8+HGxLTw8HH///Te2bdsGHR0dODg44MqVK1izZg1mzZoFiUSCTZs2oUWLFggKCgIAtGvXDqdPn8aqVavEwnPr1q14+PAhEhISoKWlBQDiVlXlVqxYAUtLS4WC29raugrZJCIiopdVtR+1nTlzJrS0tHDr1i00bNhQbPfy8hJ/205ERGWmTp2KQYMGoV+/fir7jxw5AhMTE7Rp0wb+/v4qVw0vV1hYiLCwMNjY2MDS0hIAkJiYCKlUqrBXsYuLC6RSKRISEiocKy8vD40aNRI/JyYmolevXtDR0RHb3NzccPfuXaSnp4sx/9wqy83NDadPn4ZcLgcA7Nu3D66urpg6dSpMTU3h4OCAZcuWoaSkRDxm3759cHZ2xogRI2BiYgJHR0eEhoZWOFciIiJ6+VW78IyJicGKFSvwxhtvKLS3bt0af/75Z41NjIjoZRcREYGUlBQsX75cZf/AgQMRHh6OQ4cOYfXq1UhKSsI777yDoqIihbjg4GA0atQIjRo1QnR0NGJjY8VHcrOysmBiYqI0tomJCbKyslSeNzExEbt27YKbm5vYlpWVBVNTU4W48s/l41QU8/TpUzx48AAAcOPGDezatQslJSWIiorCggULsHr1aixdulQ85saNG9i4cSNat26NgwcPYvLkyZg2bRq2b9+ucr5ERET08qv2o7aFhYUKdzrLPXjwQOE35UREr7OMjAxMnz4dMTEx0NXVVRnj5eUlfu/g4ABnZ2dYWVnhwIEDGDZsmNjn4+OD/v37IzMzE6tWrYKnpyeOHz8ujqvq/fqK3rs/f/48PDw8MH/+fKVF4P4ZX76w0LPtz4spLS2FiYkJQkJCoKGhAScnJ9y9excrV67E559/LsY4Oztj2bJlAABHR0ecP38eGzduxJgxY1TmioiIiF5u1b7j2bNnT4XfSkskEpSWlmLlypXo06dPjU6OiOhllZyUhOzsbDg5OUFTUxOampqIi4vD+vXroampqfDoaTlzc3NYWVnh6tWrCu1SqRStW7dGz549sWvXLly6dAl79+4FAJiZmeHevXtKY92/f1/p7uSFCxfwzjvvwN/fH/PmzVPoMzMzU7pDWv7Yb/k4FcVoamqiWbNm4jW0adNGXPgIKHsXNCsrC8XFxWKMvb29wjjt2rXDrVu3lK6DiIiIXg3VLjxXrlyJzZs3Y+DAgSguLsann34KBwcHHD16FCtWrFDHHImIXi4FBegbFoazANI6dkRacjLS0tLg7OwMHx8fpKWlKRRm5XJycpCRkQFzc/NKhxcEQXwc19XVFXl5eTh16pTYf/LkSeTl5aFbt25i2/nz59GnTx+MHTtW4bHXcq6urjh69KhYHAJlr1ZYWFiIC/+4uroiNjZW4biYmBg4OzuLCwl1794d165dQ2lpqRhz5coVmJubi48Hd+/eHZcvX1YY58qVK0qLEBEREdGro9qFp729Pf744w+8+eab6N+/PwoLCzFs2DCkpqaiZcuW6pgjEdHL488/ge7d0Xj/fjhoa8Nh2jQ4dOoEBwcH6Ovro1mzZnBwcEBBQQHmzJmDxMREpKen48iRIxgyZAiMjIzw3nvvASh7F3L58uVITk7GrVu3kJiYCE9PT+jp6YnboLRr1w4DBgyAv78/Tpw4gRMnTsDf3x+DBw+GnZ0dgP8vOvv3749Zs2YhKysLWVlZyMvLE6ft7e0NHR0d+Pn54dy5c9i7dy+WLVsmrmgLAJMnT8aff/6JWbNm4eLFi9i6dSu2bNmCOXPmiON8+OGHyMnJwfTp03HlyhUcOHAAy5Ytw9SpU8WYmTNn4sSJE1i2bBmuXbuGH374ASEhIQoxRERE9Gp5oX08zczMsHjx4pqeCxHRy+34ceC994D79wETE2DvXuCZu47P0tDQwNmzZ7F9+3Y8evQI5ubm6NOnDyIjI8U9L3V1dREfH4+goCDk5ubC1NQUPXv2REJCgsKCQuHh4Zg2bZq44uzQoUOxYcMGsf/HH3/E/fv3ER4ejvDwcLHd2NgYo0aNAlD2OG9sbCymTp0KZ2dnGBoaYtasWZg1a5YYb2Njg6ioKMycORPffPMNLCwssH79enErFQCwtLRETEwMZs6ciY4dO6J58+aYPn06PvvsMzHmzTffxN69exEQEIDFixfDxsYGQUFB8PHx+TfZJyIionqs2oXn1atX8fPPPyM9PR0SiQS2trZ49913YWNjo475ERG9HLZuBSZPBuRyoFMnYN8+oEULhZAjR46I3+vp6eHgwYOVDmlhYYGoqKjnnrpp06bYsWNHhf2BgYEIDAxUaJPL5Upjd+jQAUePHq30XL169UJKSkqlMa6urjhx4kSlMYMHD8bgwYMrjSEiIqJXR7UKz+XLl+Pzzz8XVy0UBAH379/HZ599hmXLlik8bkVE9NooKQHCwsqKzvffB777DtDXr+tZEREREdUbVX7H8/Dhw1iwYAHmz5+PBw8eIDMzE1lZWbh//z7mzp2LuXPnPvc35UREryQNDWD3bmDVKuC//2XRSURERPQPVb7juWnTJkyYMEHpca2mTZti8eLFyMrKwsaNG9GzZ8+aniMRUf1nYgLMnl3XsyAiIiKql6p8x/PUqVPw9fWtsN/X1/e57/QQERERERHR66fKhee9e/fEvdxUsbGxUdpYnIiIiIiIiKjKhefff/8tbv6tipaWlsLG40RERERERERANVe1/fbbb9GoUSOVfY8fP66RCREREREREdGrpcqFZ4sWLRAaGvrcGCIiIiIiIqJnVbnwTE9PV+M0iIiIiIiI6FVV5Xc8iYiIiIiIiF5ElQrPiIiIKg+YkZGB48ePv/CEiIiIiIiI6NVSpcJz48aNaNu2LVasWIGLFy8q9efl5SEqKgre3t5wcnLCw4cPa3yiRERERERE9HKq0juecXFx2L9/P77++mvMmzcP+vr6MDU1ha6uLnJzc5GVlQVjY2N88MEHOHfuHExMTNQ9byIiIiIiInpJVHlxocGDB2Pw4MHIycnBsWPHkJ6ejr/++gtGRkZwdHSEo6MjGjTgK6NERERERESkqNqVYrNmzeDh4YHp06dj7ty5mDBhApycnFh0EhEREanZ8uXLIZFIMGPGDLHNz88PEolE4cvFxUXhuEmTJqFly5bQ09ODsbExPDw8cOnSJYWY3Nxc+Pr6QiqVQiqVwtfXF48ePVKIuXXrFoYMGQJ9fX0YGRlh2rRpKC4uVjnXa9euoXHjxmjSpIlSX3h4ODp16oSGDRvC3NwcH3zwAXJycsT+0NBQ9OjRA4aGhjA0NES/fv1w6tQphTE2btyIjh07wsDAAAYGBnB1dcWvv/5ahSwSUV1gtUhERET0EkhKSkJISAg6duyo1DdgwABkZmaKX1FRUQr9Tk5OCAsLw8WLF3Hw4EEIggCZTIaSkhIxxtvbG2lpaYiOjkZ0dDTS0tLg6+sr9peUlMDDwwOFhYU4duwYIiIisHv3bsyePVtpPnK5HKNGjUKPHj2U+o4dO4YxY8Zg/PjxOH/+PH788UckJSVhwoQJYsyRI0cwatQoHD58GImJiWjRogVkMhnu3Lkjxrzxxhv4z3/+g9OnT+P06dN455134OHhgfPnz1cvsURUK6r8qC0RERER1Y2CggL4+PggNDQUS5YsUerX0dGBmZlZhcdPnDhR/N7a2hpLlixBp06dkJ6ejpYtW+LixYuIjo7GiRMn0LVrVwBldx1dXV1x+fJl2NraIi0tDRcvXkRMTAwsLCwAAKtXr4afnx+WLl0KAwMD8RwLFixA27Zt0bdvXyQkJCjM5cSJE7C2tsa0adMAADY2Npg0aRK++uorMSY8PFzhmNDQUOzatQu///47xowZAwAYMmSIQszSpUuxceNGnDhxAu3bt684mURUJ3jHk4iIiKiemzp1KgYNGoR+/fqp7D9y5AhMTEzQpk0b+Pv7Izs7u8KxCgsLERYWBhsbG1haWgIAEhMTIZVKxaITAFxcXCCVSsXC8fLly2jfvr1YdAKAm5sbioqKkJycLLYdOnQIP/74I7755huV5+/WrRtu376NqKgoCIKAe/fuYdeuXRg0aFCFc37y5AnkcjmaNm2qsr+kpAQREREoLCyEq6trheMQUd3hHU8iIiKieiwiIgIpKSlISkpS2T9w4ECMGDECVlZWuHnzJhYuXIh33nkHycnJ0NHREeOCg4Px6aeforCwEG3btkVsbCy0tbUBAFlZWSp3JTAxMUFWVhaAsndATU1NFfoNDQ2hra0txuTk5MDPzw87duxQuAP6rG7duiE8PBxeXl74+++/8fTpUwwdOhRff/11hTmYO3cumjdvrlR4nz17Fq6urvj777/RqFEj7N27F/b29hWOQ0R154XveBYXF+Py5ct4+vRpTc6HiIiIiIqLgYgIZOzfj+nTp2PHjh3Q1dVVGerl5YVBgwbBwcEBQ4YMwa+//oorV67gwIEDCnE+Pj5ITU1FXFwcWrduDU9PT/z9999iv0QiURpbEASF9ufF+Pv7w9vbGz179qzw0i5cuIBp06bh888/R3JyMqKjo3Hz5k1MnjxZZfxXX32FnTt3Ys+ePUo5sLOzQ1paGk6cOIEPP/wQY8eOxYULFyo8NxHVnWoXnk+ePMH48ePRsGFDtG/fHrdu3QIATJs2Df/5z39qfIJEREREr530dGDUKCQPH47s7Gw4OTlBU1MTmpqaiIuLw/r166GpqamwOFA5c3NzWFlZ4erVqwrtUqkUrVu3Rs+ePbFr1y5cunQJe/fuBQCYmZnh3r17SmPdv39fvMtpaGgo3tksl5ubC7lcLsYcOnQIq1atEuc6fvx45OXlQVNTE1u3bgVQtjJv9+7d8cknn6Bjx45wc3NDcHAwtm7diszMTIXxV61ahWXLliEmJkblokra2tpo1aoVnJ2dsXz5cnTq1Anr1q2rYpKJqDZVu/AMCAjAmTNncOTIEYXfOvXr1w+RkZE1OjkiIiKqf+rTlh7m5uYIDQ19oS09yh0/fhyampro3LlzhTERERGQSCR49913K4xRlZcXdv06AKBvy5Y4e/Ys0tLSxC9nZ2f4+PggLS0NGhoaSofm5OQgIyMD5ubmlZ5CEAQUFRUBAFxdXZGXl6ewZcnJkyeRl5eHbt26ASi7u3j+/HmF4jAmJgY6OjpwcnICUPau6LNzXbx4MRo3boy0tDS89957AMpuYvxzG77y6xAEQWxbuXIlvvzyS0RHR8PZ2blKaXv2moiofqn2O54//fQTIiMj4eLiovC4hb29Pa7/7y9JIiIiejU9b0uPsLAw8XP5+4PlnJyc4OPjgxYtWuDhw4cIDAyETCbDzZs3xcLD29sbt2/fRnR0NICy1Vh9fX3xyy+/AChbRGbQoEEwNjbGsWPHcO/ePfj4+ODTTz9VWszm2S09/rmyarm8vDyMGTMGffv2VXnHDwD+/PNPzJkzR+XWIFXJywv537+pGrduDQcHB4UufX19NGvWDA4ODigoKEBgYCDef/99mJubIz09HfPmzYORkZFY6N24cQORkZGQyWQwNjbGnTt3sGLFCujp6cHd3R0A0K5dOwwYMAD+/v7YvHkzgLLcDx48GHZ2dpDL5ejcuTPatWsHX19frFy5Eg8fPsScOXPg7+8vvs/Zrl07hbmePn0aDRo0ULiGIUOGwN/fHxs3boSbmxsyMzMxY8YMvPXWW+LCRV999RUWLlyIH374AdbW1uKd1kaNGqFRo0YAgHnz5mHgwIGwtLTE48ePERERgSNHjog/O0RUv1T7juf9+/dVvnxeWFio8rl/IiIiejU8u6WHoaGhUn/5lh7lX/9cgXTixIno2bMnrK2t0aVLFyxZsgQZGRlIT08HAHFLj2+//Raurq5wdXVFaGgo9u/fj8uXLwMou8N24cIF7NixA46Ojujbty8++OADbNmyBfn5+QrnK9/Sw9PTs8JrmjRpEry9vStcCbWkpAQ+Pj5YtGgRbG1tXygvL+TGjbL/bdmy0jANDQ2cPXsWHh4eaNOmDcaOHYs2bdogMTERjRs3BgDo6uoiPj4e7u7uaNWqFTw9PaGvr4+EhASFf9OFh4ejQ4cOkMlkkMlk6NixI77//nuFc/3888/Q1dVF9+7d4enpiXfffRerVq2q1qX5+flhzZo12LBhAxwcHDBixAjY2dlhz549YkxwcDCKi4sxfPhwmJubi1/PnuvevXvw9fWFnZ0d+vbti5MnTyI6Ohr9+/ev1nyIqHZU+47nm2++iQMHDuDjjz8G8P8vmZfv9URERESvpme39FC1l2T5lh5NmjRBr169sHTpUpW/rAZebEsPOzs7JCYmwsHBQWFLD0dHR3FLjz59+gD4/y090tLSFAqaZ4WFheH69evYsWOHyusBgMWLF8PY2Bjjx49HfHz8C+XlhZQ/Raai2D1y5Ij4vZ6eHg4ePFjpUBYWFoiKinruKZs2bYodO3ZUGtOiRQvs37//uWOV8/Pzg5+fn1L7xx9/LP5bUpXyX0ZUZsuWLVWeBxHVvWoXnsuXL8eAAQNw4cIFPH36FOvWrcP58+eRmJiIuLg4dcyRiIiI6lh92dIjKytLaUuPRo0aVXtLj6tXr2Lu3LmIj4+Hpqbqfw4dP34cW7ZsQVpa2gvn5YVV8Y4nEdHLotqP2nbr1g3Hjx/HkydP0LJlS8TExMDU1BSJiYnii+VERET06sjIyHiltvQoKSmBt7c3Fi1ahDZt2qiMefz4MUaPHo3Q0FAYGRmpjKlKXl6IILDwJKJXTrXveAJAhw4d8N1339X0XIiIiKgeSk5OFrf0KFdSUoKjR49iw4YNKCoqUlpdtbItPcq39XBxcYGhoSH27t2LUaNGVWlLDzMzM5w8eVKhv6CgQGlLj3379onvAwqCgNLSUmhqaiIkJATDhg3D6dOnkZqaio8++ggAUFpaCkEQoKmpiZiYGDRt2hTp6ekYMmSIeJ7S0lIAgKamJi5fvoyzZ89WOy9VIghAXFzZ47ZWVtU/noioHqpS4fnPl/UrU9EjLURERPRy6tu3L87OmAHExwOrVgFGRvjggw/Qtm1bfPbZZ2rZ0uOtt94CoLylh6urK5YuXYrMzExx7LS0NKUtPZ7d3/Lnn3/GihUrkJCQgObNm8PAwABnz55VmEdwcDAOHTqEXbt2wcbGRly051kLFizA48ePsW7dOlhaWsLExEQp5nl5qZIGDQBn57IvIqJXRJUKzyZNmlR5xVpVGxkTERHRy6vxiRNwWL8eKC0F0tOB3r3rZEsPAJDJZLC3txe39MjOzkZYWBjGjx9frS09/rlFiYmJCXR1dSuNKd8LtLxdW1u70q1OiIjo/1Wp8Dx8+LD4fXp6OubOnQs/Pz9xFdvExER89913WL58uXpmSURERHXjzz+BUaPKis5x44CxY5VCyu8Obt++HY8ePYK5uTn69OmDyMhIpS09goKCkJubC1NTU/Ts2VPllh7Tpk2DTCYDAAwdOhQbNmxQONeBAwcwZcoUdO/eHXp6enBxccGKFSvUnAgiIvo3qlR49urVS/x+8eLFWLNmDUaNGiW2DR06FB06dEBISAjGqvgPEhEREb2E/voLGDYMyMkpe+zzm2+A/z0BVV+29JDL5YiKilJYOfefKtrS41mBgYEIDAysNGbbtm2V9gOKeSEiov9X7VVtExMT4azinQNnZ2ecOnWqRiZFREREdUwQgKlTgZQUoFkzYNcuoCZXbiUiotdKtQtPS0tLbNq0Sal98+bN4gbQRERE9JLbtg0ICytb6CYigqurEhHRv1Lt7VTWrl2L999/HwcPHoSLiwsA4MSJE7h+/Tp2795d4xMkIiKiOuDuDvTsCQwaBPTrV9ezISKil1y1C093d3dcvXoVGzduxMWLFyEIAjw8PDB58mTe8SQiInpVmJoCv/8OvOiWIERERM+oduF569YtWFpaYunSpSr7WrRoUSMTIyIiojqmWe1/JhAREalU7Xc8bWxscP/+faX2nJwc2NjY1MikiIiIiIiI6NVR7cJTEARI/reU+rMKCgqgy9XuiIiIiIiI6B+qXHjOmjULs2bNgkQiwcKFC8XPs2bNwvTp0+Hl5YXOnTtXewLBwcGwsbGBrq4unJycEB8fX2l8XFwcnJycoKurC1tbW5Ur7O7evRv29vbQ0dGBvb099u7dW+3zBgYGom3bttDX14ehoSH69euHkydPKsQUFRXh448/hpGREfT19TF06FDcvn272jkgIiIiIiJ6lVW58ExNTUVqaioEQcDZs2fFz6mpqbh06RI6depUpY2VnxUZGYkZM2Zg/vz5SE1NRY8ePTBw4EDcunVLZfzNmzfh7u6OHj16IDU1FfPmzcO0adMUVtNNTEyEl5cXfH19cebMGfj6+sLT01OhaKzKedu0aYMNGzbg7NmzOHbsGKytrSGTyRQeM54xYwb27t2LiIgIHDt2DAUFBRg8eDBKSkqqlQciIiIiIqJXmlBNfn5+Ql5eXnUPU+mtt94SJk+erNDWtm1bYe7cuSrjP/30U6Ft27YKbZMmTRJcXFzEz56ensKAAQMUYtzc3ISRI0e+8HkFQRDy8vIEAMJvv/0mCIIgPHr0SNDS0hIiIiLEmDt37ggNGjQQoqOjKxynonH/TU6Li4uFn376SSguLn7hMej5mOfawTzXDuZZ/Zjj2sE81w7mWf2Y49pR3/NcE7VBfVXt5erCwsJqpOAtLi5GcnIy5s6dq9Auk8mQkJCg8pjExETIZDKFNjc3N2zZsgVyuRxaWlpITEzEzJkzlWKCgoJe+LzFxcUICQmBVCpFp06dAADJycmQy+UK87GwsICDgwMSEhLg5uamcqyioiIUFRWJn/Pz8wEAcrkccrlc5THPU37cix5PVcM81w7muXYwz+rHHNcO5rl2MM/qxxzXjvqe5/o6r5pQ7cLznXfeqbT/0KFDVRrnwYMHKCkpgampqUK7qakpsrKyVB6TlZWlMv7p06d48OABzM3NK4wpH7M6592/fz9GjhyJJ0+ewNzcHLGxsTAyMhLnoq2tDUNDwyrPHwCWL1+ORYsWKbXHxMSgYcOGFR5XFbGxsf/qeKoa5rl2MM+1g3lWP+a4djDPtYN5Vj/muHbU1zw/efKkrqegNtUuPMvv+JWTy+VIS0vDuXPnMHbs2GpP4J8r5AoVrJpbWfw/26syZlVi+vTpg7S0NDx48AChoaHiu6ImJiYVzu958w8ICMCsWbPEz/n5+bC0tIRMJoOBgUGFx1VGLpcjNjYW/fv3h5aW1guNQc/HPNcO5rl2MM/qxxzXDua5djDP6scc1476nufypyFfRdUuPNeuXauyPTAwEAUFBVUex8jICBoaGkp3B7Ozs5XuRpYzMzNTGa+pqYlmzZpVGlM+ZnXOq6+vj1atWqFVq1ZwcXFB69atsWXLFgQEBMDMzAzFxcXIzc1VuOuZnZ2Nbt26VXjdOjo60NHRUWrX0tL61z/8NTEGPR/zXDuY59rBPKsfc1w7mOfawTyrH3NcO+prnuvjnGpKtffxrMjo0aOxdevWKsdra2vDyclJ6TZ3bGxshYWbq6urUnxMTAycnZ3FP6SKYsrHfJHzlhMEQXw/08nJCVpaWgrjZGZm4ty5c88dh4iIiIiI6HVS7TueFUlMTISurm61jpk1axZ8fX3h7OwMV1dXhISE4NatW5g8eTKAssdS79y5g+3btwMAJk+ejA0bNmDWrFnw9/dHYmIitmzZgp07d4pjTp8+HT179sSKFSvg4eGBn3/+Gb/99huOHTtW5fMWFhZi6dKlGDp0KMzNzZGTk4Pg4GDcvn0bI0aMAABIpVKMHz8es2fPRrNmzdC0aVPMmTMHHTp0QL9+/f5VLomIiIiIiF4l1S48hw0bpvBZEARkZmbi9OnTWLhwYbXG8vLyQk5ODhYvXozMzEw4ODggKioKVlZWAMruID67t6aNjQ2ioqIwc+ZMfPPNN7CwsMD69evx/vvvizHdunVDREQEFixYgIULF6Jly5aIjIxE165dq3xeDQ0NXLp0Cd999x0ePHiAZs2a4c0330R8fDzat28vjrN27VpoamrC09MTf/31F/r27Ytt27ZBQ0OjWnkgIiIiIiJ6lVW78DQwMFBYPKdBgwaws7PD4sWLlbY6qYopU6ZgypQpKvu2bdum1NarVy+kpKRUOubw4cMxfPjwFz6vrq4u9uzZU+nx5XFff/01vv766+fGEhERERERva6qXXiqKgaJiIiIiIiIKlLtxYVsbW2Rk5Oj1P7o0SPY2trWyKSIiIiIiIjo1VHtwjM9PR0lJSVK7UVFRbhz506NTIqIiIiIiIheHVV+1Hbfvn3i9wcPHoRUKhU/l5SU4Pfff4e1tXWNTo6IiIiIiIheflUuPN99910AgEQiwdixYxX6tLS0YG1tjdWrV9fo5IiIiIiIiOjlV+XCs7S0FEDZliZJSUkwMjJS26SIiIiIiIjo1VHtVW1v3rypjnkQERERERHRK6pKhef69esxceJE6OrqYv369ZXGTps2rUYmRkRERERERK+GKhWea9euhY+PD3R1dbF27doK4yQSCQtPIiIiIiIiUlClwvPZx2v5qC0RERERERFVR7X38Vy8eDGePHmi1P7XX39h8eLFNTIpIiIiIiIienVUu/BctGgRCgoKlNqfPHmCRYsW1cikiIiIiIiI6NVR7cJTEARIJBKl9jNnzqBp06Y1MikiIiIiIiJ6dVR5OxVDQ0NIJBJIJBK0adNGofgsKSlBQUEBJk+erJZJEhERERER0curyoVnUFAQBEHAuHHjsGjRIkilUrFPW1sb1tbWcHV1VcskiYiIiIiI6OVV5cJz7NixAAAbGxt069YNWlpaapsUERERERERvTqqXHiW69Wrl/j9X3/9BblcrtBvYGDw72dFREREREREr4xqLy705MkTfPTRRzAxMUGjRo1gaGio8EVERERERET0rGoXnp988gkOHTqE4OBg6Ojo4Ntvv8WiRYtgYWGB7du3q2OORERERERE9BKr9qO2v/zyC7Zv347evXtj3Lhx6NGjB1q1agUrKyuEh4fDx8dHHfMkIiIiIiKil1S173g+fPgQNjY2AMre53z48CEA4O2338bRo0drdnZERERERET00qt24Wlra4v09HQAgL29Pf773/8CKLsT2qRJk5qcGxEREREREb0Cql14fvDBBzhz5gwAICAgQHzXc+bMmfjkk09qfIJERERERET0cqv2O54zZ84Uv+/Tpw8uXbqE06dPo2XLlujUqVONTo6IiIiIiIheftW+4/lPLVq0wLBhw9C0aVOMGzeuJuZEREREREREr5B/XXiWe/jwIb777ruaGo6IiIiIiIheETVWeBIRERERERGpwsKTiIiIiIiI1IqFJxEREREREalVlVe1HTZsWKX9jx49+rdzISIiIiIioldQlQtPqVT63P4xY8b86wkRERERERHRq6XKhWdYWJg650FERERERESvKL7jSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkVnVeeAYHB8PGxga6urpwcnJCfHx8pfFxcXFwcnKCrq4ubG1tsWnTJqWY3bt3w97eHjo6OrC3t8fevXurdV65XI7PPvsMHTp0gL6+PiwsLDBmzBjcvXtXYYzevXtDIpEofI0cOfIFM0FERERERPRqqtPCMzIyEjNmzMD8+fORmpqKHj16YODAgbh165bK+Js3b8Ld3R09evRAamoq5s2bh2nTpmH37t1iTGJiIry8vODr64szZ87A19cXnp6eOHnyZJXP++TJE6SkpGDhwoVISUnBnj17cOXKFQwdOlRpTv7+/sjMzBS/Nm/eXMNZIiIiIiIiernVaeG5Zs0ajB8/HhMmTEC7du0QFBQES0tLbNy4UWX8pk2b0KJFCwQFBaFdu3aYMGECxo0bh1WrVokxQUFB6N+/PwICAtC2bVsEBASgb9++CAoKqvJ5pVIpYmNj4enpCTs7O7i4uODrr79GcnKyUlHcsGFDmJmZiV9SqbTmE0VERERERPQS06yrExcXFyM5ORlz585VaJfJZEhISFB5TGJiImQymUKbm5sbtmzZArlcDi0tLSQmJmLmzJlKMeWF54ucFwDy8vIgkUjQpEkThfbw8HDs2LEDpqamGDhwIL744gs0bty4wnGKiopQVFQkfs7PzwdQ9nivXC6v8LjKlB/3osdT1TDPtYN5rh3Ms/oxx7WDea4dzLP6Mce1o77nub7OqybUWeH54MEDlJSUwNTUVKHd1NQUWVlZKo/JyspSGf/06VM8ePAA5ubmFcaUj/ki5/37778xd+5ceHt7w8DAQGz38fGBjY0NzMzMcO7cOQQEBODMmTOIjY2t8LqXL1+ORYsWKbXHxMSgYcOGFR5XFZWdl2oO81w7mOfawTyrH3NcO5jn2sE8qx9zXDvqa56fPHlS11NQmzorPMtJJBKFz4IgKLU9L/6f7VUZs6rnlcvlGDlyJEpLSxEcHKzQ5+/vL37v4OCA1q1bw9nZGSkpKejSpYvK+QcEBGDWrFni5/z8fFhaWkImkykUtdUhl8sRGxuL/v37Q0tL64XGoOdjnmsH81w7mGf1Y45rB/NcO5hn9WOOa0d9z3P505CvojorPI2MjKChoaF0lzE7O1vpbmQ5MzMzlfGamppo1qxZpTHlY1bnvHK5HJ6enrh58yYOHTr03MKwS5cu0NLSwtWrVyssPHV0dKCjo6PUrqWl9a9/+GtiDHo+5rl2MM+1g3lWP+a4djDPtYN5Vj/muHbU1zzXxznVlDpbXEhbWxtOTk5Kt7ljY2PRrVs3lce4uroqxcfExMDZ2Vn8Q6oopnzMqp63vOi8evUqfvvtN7Gwrcz58+chl8thbm7+3FgiIiIien0sX74cEokEM2bMENv8/PzELfm0tbXx7rvv4u2331Y4btKkSWjZsiX09PRgbGwMDw8PXLp0SSEmNzcXvr6+kEqlkEql8PX1xaNHjxRibt26hSFDhkBfXx9GRkaYNm0aiouLxf7Lly+jT58+MDU1FbctXLBgQYXvHB4/fhyampro3LmzUl9QUBDs7Oygp6cHS0tLzJw5E3///XeV80Kvpjp91HbWrFnw9fWFs7MzXF1dERISglu3bmHy5MkAyh5LvXPnDrZv3w4AmDx5MjZs2IBZs2bB398fiYmJ2LJlC3bu3CmOOX36dPTs2RMrVqyAh4cHfv75Z/z22284duxYlc/79OlTDB8+HCkpKdi/fz9KSkrEO6RNmzaFtrY2rl+/jvDwcLi7u8PIyAgXLlzA7Nmz4ejoiO7du9dWComIiIionktKSkJISAg6duyo1DdgwACEhYVBLpfj999/x8CBAxX6nZyc4OPjgxYtWuDhw4cIDAyETCbDzZs3oaGhAQDw9vbG7du3ER0dDQCYOHEifH198csvvwAASkpKMGjQIBgbG+PYsWPIycnB2LFjIQgCvv76awBld9rGjBmDLl26oEmTJjhz5gz8/f1RWlqKZcuWKcwpLy8PY8aMQd++fXHv3j2FvvDwcMydOxdbt25Ft27dcOXKFfj5+QEA1q5dW+W80KunTgtPLy8v5OTkYPHixcjMzISDgwOioqJgZWUFAMjMzFTYvsTGxgZRUVGYOXMmvvnmG1hYWGD9+vV4//33xZhu3bohIiICCxYswMKFC9GyZUtERkaia9euVT7v7du3sW/fPgBQ+i3O4cOH0bt3b2hra+P333/HunXrUFBQAEtLSwwaNAhffPGF+JcAEREREb3eCgoK4OPjg9DQUCxZskSpX0dHB2ZmZpDL5TA0NETTpk0V+idOnCh+b21tjSVLlqBTp05IT09Hy5YtcfHiRURHR+PEiRPiv3dDQ0Ph6uqKy5cvw87ODjExMbhw4QIyMjJgYWEBAFi9ejX8/PywdOlSGBgYwNbWFra2tuK5rKyscOTIEcTHxyvNedKkSfD29oaGhgZ++uknhb7ExER0794d3t7e4pxHjRqFU6dOVSsv9Oqp88WFpkyZgilTpqjs27Ztm1Jbr169kJKSUumYw4cPx/Dhw1/4vNbW1uKiRRWxtLREXFxcpTFERERE9HqbOnUqBg0ahH79+qkssI4cOQITExNIpVLY2NjA2dkZzZs3VzlWYWEhwsLCYGNjA0tLSwBlhZ5UKlW4yeLi4gKpVIqEhATY2dkhMTERDg4OYtEJlG03WFRUhOTkZPTp00fpXNeuXUN0dDSGDRum0B4WFobr169jx44dKq/n7bffxo4dO3Dq1Cm89dZbuHHjBqKiojB27Nhq5YVePXVeeBIRERERvYoiIiKQkpKCpKQklf0DBw7EiBEjYGVlhatXr2LWrFmQyWRISUlRWIwyODgYn376KQoLC9G2bVvExsZCW1sbQNl2gyYmJkpjm5iYiK+Kqdpu0NDQENra2koLbnbr1g0pKSkoKirCxIkTsXjxYrHv6tWrmDt3LuLj46GpqbqMGDlyJO7fv4+3334bgiDg6dOn+PDDDzF37twq54VeTXW2uBARERER0asqIyMD06dPx44dO6Crq6syxsvLC4MGDYKDgwMGDx6Mzz//HFevXsWBAwcU4nx8fJCamoq4uDi0bt0anp6eCov1qNoS8J9bBVYlBgAiIyORkpKCH374AQcOHMCqVasAlL0n6u3tjUWLFqFNmzYVXveRI0ewdOlSBAcHIyUlBXv27MH+/fvx5ZdfVjkv9GriHU8iIiIiohqWfOoUsrOz4eTkJLaVlJTg6NGj2LBhA4qKipTWBWnatKl49/NZ5avVtm7dGi4uLjA0NMTevXsxatQomJmZKS3wAwD3798X73KamZnh5MmTCv25ubmQy+VKd0LLH+G1t7dHSUkJJk6ciNmzZ+Px48c4ffo0UlNT8dFHHwEASktLIQgCNDU1ERMTg3feeQcLFy6Er68vJkyYAADo0KEDCgsLMXHiRMyfPx/JycnVzgu9Glh4EhERERHVsL7x8Tjr7Ax89RVgbAwA+OCDD9C2bVt89tlnKour/Px8ZGRkPHdrPkEQUFRUBKBsK8G8vDzxnUoAOHnyJPLy8sStAl1dXbF06VJkZmaKY8fExEBHR0ehAFR1HrlcDkEQYGBggLNnzyr0BwcH49ChQ9i1axdsbGwAAE+ePEGDBooPVWpoaEAQBAiCgL59+yqN87y80KuBhScRERERUU3atw+N162DAwA8egT8b/EefX19NGvWDA4ODigoKEBgYCDef/99mJub49q1a1i6dCmMjIzw3nvvAQBu3LiByMhIyGQyGBsb486dO1ixYgX09PTg7u4OAGjXrh0GDBgAf39/bN68GUDZSriDBw+GnZ0dAEAmk8He3h6+vr5YuXIlHj58iDlz5sDf3x8GBgYAyrZB0dLSQocOHaCjo4Pk5GQEBATAy8tLfJ/TwcFB4TJNTEygq6ur0D5kyBCsWbMGjo6O6Nq1K65du4aFCxdi6NCh0NDQQOPGjZXGeTYv9Opi4UlEREREVFPS04HyFVynTwf+V0T+k4aGBs6ePYvt27fj0aNHMDc3R8uWLfHtt9+icePGAABdXV3Ex8cjKCgIubm5MDU1Rc+ePZGQkKCwoFB4eDimTZsGmUwGABg6dCg2bNigcK4DBw5gypQp6N69O/T09ODt7S2+vwkAmpqaWLFiBa5cuQJBEGBlZYWpU6di5syZ1br8BQsWQCKRYMGCBbhz5w6MjY0xZMgQLF26tFrj0KuHhScRERERUU0oLgY8Pcvucr71Vtljts84cuSI+L2enh4OHjwofpbL5YiKihLfsQQACwsLREVFPfe0TZs2xY4dOyqNadGiBfbv319hv5eXF7y8vJ57rmcFBgYiMDBQoU1TUxNffPEFvvjiiyqP82xe6NXFVW2JiIiIiGrCJ58ASUmAoSEQGQn8b8sTImLhSURERET07+3ZA6xfX/b99u2AtXWdToeovuGjtkRERERE/1a3bkDv3mWP2A4eXNezIap3WHgSEREREf1bZmZAbGxdz4Ko3mLhSURERERUEzT5T2uiivAdTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlIrFp5ERERERESkViw8iYiIiIiISK1YeBIREREREZFasfAkIiIiIiIitWLhSURERERERGrFwpOIiIiIiIjUioUnERERERERqRULTyIiIiIiIlKrOi88g4ODYWNjA11dXTg5OSE+Pr7S+Li4ODg5OUFXVxe2trbYtGmTUszu3bthb28PHR0d2NvbY+/evdU6r1wux2effYYOHTpAX18fFhYWGDNmDO7evaswRlFRET7++GMYGRlBX18fQ4cOxe3bt18wE0REREREFVu+fDkkEglmzJghtvn5+UEikSh8ubi4KBw3adIktGzZEnp6ejA2NoaHhwcuXbqkEJObmwtfX19IpVJIpVL4+vri0aNHCjG3bt3CkCFDoK+vDyMjI0ybNg3FxcUq53rt2jU0btwYTZo0UeorKirC/PnzYWVlBR0dHbRs2RJbt24V+/fs2QNnZ2c0adIE+vr66Ny5M77//nuFMR4/fowZM2bAysoKenp66NatG5KSkqqQRaordVp4RkZGYsaMGZg/fz5SU1PRo0cPDBw4ELdu3VIZf/PmTbi7u6NHjx5ITU3FvHnzMG3aNOzevVuMSUxMhJeXF3x9fXHmzBn4+vrC09MTJ0+erPJ5nzx5gpSUFCxcuBApKSnYs2cPrly5gqFDhyrMZ8aMGdi7dy8iIiJw7NgxFBQUYPDgwSgpKVFDtoiIiIjodZWUlISQkBB07NhRqW/AgAHIzMwUv6KiohT6nZycEBYWhosXL+LgwYMQBAEymUzh36ze3t5IS0tDdHQ0oqOjkZaWBl9fX7G/pKQEgwYNQmFhIY4dO4aIiAjs3r0bs2fPVpqPXC7HqFGj0KNHD5XX4unpid9//x1btmzB5cuXsXPnTrRt21bsb9q0KebPn4/ExET88ccf+OCDD/DBBx/g4MGDYsyECRMQGxuL77//HmfPnoVMJkO/fv1w586dqieVapdQh9566y1h8uTJCm1t27YV5s6dqzL+008/Fdq2bavQNmnSJMHFxUX87OnpKQwYMEAhxs3NTRg5cuQLn1cQBOHUqVMCAOHPP/8UBEEQHj16JGhpaQkRERFizJ07d4QGDRoI0dHRFY7zT3l5eQIAIS8vr8rH/FNxcbHw008/CcXFxS88Bj0f81w7mOfawTyrH3NcO5jn2sE8q19lOX78+LHQunVrITY2VujVq5cwffp0sW/s2LGCh4dHtc515swZAYBw7do1QRAE4cKFCwIA4cSJE2JMYmKiAEC4dOmSIAiCEBUVJTRo0EC4c+eOGLNz505BR0dH6d+xn376qTB69GghLCxMkEqlCn2//vqrIJVKhZycnGrN2dHRUViwYIEgCILw5MkTQUNDQ9i/f79CTKdOnYT58+dXOk59/1muidqgvtKsq4K3uLgYycnJmDt3rkK7TCZDQkKCymMSExMhk8kU2tzc3LBlyxbI5XJoaWkhMTERM2fOVIoJCgp64fMCQF5eHiQSifi4QHJyMuRyucJ8LCws4ODggISEBLi5uakcp6ioCEVFReLn/Px8AGW/GZLL5RWevzLlx73o8VQ1zHPtYJ5rB/Osfsxx7WCeawfzrH6V5fjDDz/EwIED0atXL3z55ZcoLS0V40pLS3HkyBGYmJhAKpWiZ8+eWLx4MUxMTFSep7CwEFu2bIGNjQ3MzMwgl8tx7NgxSKVSdOnSRRzXyckJUqkU8fHxsLW1xbFjx9C+fXsYGxuLMe+88w6Kiopw8uRJ9O7dGwBw+PBh/Pjjj0hKSsJPP/2kdE0//fQTnJycsHz5cvzwww9o2LAhhgwZgsDAQOjp6SnNVxAEHD58GJcvX8bSpUshl8vx119/oaSkBJqamgpj6+rqIj4+vtKf0/r+s1xf51UT6qzwfPDgAUpKSmBqaqrQbmpqiqysLJXHZGVlqYx/+vQpHjx4AHNz8wpjysd8kfP+/fffmDt3Lry9vWFgYCDORVtbG4aGhlUeByh7Nn/RokVK7TExMWjYsGGFx1VFbGzsvzqeqoZ5rh3Mc+1gntWPOa4dzHPtYJ7V7585jo+Px9GjR7Fq1SpERUUhJycHN2/eFB+nNTU1xccffwxjY2Pcu3cPP/zwA3777TesXr0aWlpa4jhRUVHYvn07/v77b7zxxhuYP38+fvvtNwBla6jo6+srPaKrr6+PuLg4GBsbIykpCQ0aNFCK0dTURHR0NJ48eYL8/HzMmjULM2fOxLFjx3DmzBnI5XKFY06dOoVz584hPz8fM2bMQH5+PjZv3ow//vgDH3/8sRhXWFiI8ePHQy6Xo0GDBpg0aRKKi4vFsezs7DB79mzMnj1bLJBPnToFc3NzpTlWJc/1xZMnT+p6CmpTZ4VnOYlEovBZEASltufF/7O9KmNW9bxyuRwjR45EaWkpgoODK7mSqs0/ICAAs2bNEj/n5+fD0tISMplMLGqrSy6XIzY2Fv3791f4C4ZqFvNcO5jn2sE8qx9zXDuY59rBPKufqhxnZGRg4sSJOHDgADp16gQAWLNmDWxsbODu7g4A4v+WmzRpElq1aoXS0lKFvu7du2P69OnIysrCmjVrEBISgri4OOjq6uKPP/7AqVOnlMZq2LAh2rZtC3d3d/zyyy8qzyeRSODo6Ah3d3eMGDECfn5++OSTTwCU3fDR0tJSOGbDhg3Q0NDAr7/+CqlUCgDo0KEDRo4ciT179oh3PUtLS+Ho6IiCggIcPnwYy5Ytg4eHB3r16gWgrPCcOHEixo0bBw0NDTg6OmLkyJFITU1VmuPz8lyflD8N+Sqqs8LTyMgIGhoaSncHs7Ozle5GljMzM1MZr6mpiWbNmlUaUz5mdc4rl8vh6emJmzdv4tChQwqFoZmZGYqLi5Gbm6tw1zM7OxvdunWr8Lp1dHSgo6Oj1K6lpfWvf/hrYgx6Pua5djDPtYN5Vj/muHYwz7WDeVY/LS0taKWlAbdu4Y+CAmRnZyusUltSUoL4+HgEBwejqKgIGhoaCse3aNECVlZWuHnzpsKflZGREYyMjGBvb4+3334bhoaG2L9/P0aNGoXmzZsjOztb6c/2/v37sLCwgJaWFiwsLJCUlKQQk5ubC7lcjubNm0NLSwtHjhzB/v37sXbtWgBlN2RKS0uhp6eHkJAQjBs3DhYWFmjevDmMjIzEcTp06ABBEHDv3j20bt1abG/Xrh0A4M0338SVK1ewcuVK9OvXDwDQtm1bHD16FIWFhcjPz4e5uTm8vLxga2tbpZ/R+vqzXB/nVFPqbFVbbW1tODk5Kd3mjo2NrbBwc3V1VYqPiYmBs7Oz+IdUUUz5mFU9b3nRefXqVfz2229iYVvOyckJWlpaCuNkZmbi3LlzlRaeRERERESVmjABGD4cfQ0McPbsWaSlpYlfzs7O8PHxQVpamlLR+X/t3XtcTfn+P/DX7rKrSUUq7S5ijEoiKhTGJakYZJjJ0DRlCA8HaTAuw2EMhjNmXMa4Tm7DmZwZ43JcIqOIcquJjORWSkqDdME3qc/vD6f1s1VktGtvXs/HYz8e7bXe6/P5rLePXe+91v5sALhz5w6ysrKgUCie24UQQlp3xNPTEwUFBTh16pS0/+TJkygoKJD+rvX09MT58+eRk5MjxRw8eBB6enpwc3MD8GQ9lqfHOnfuXBgZGSE5ORnvv/8+gCdXXm/evIni4mKpnUuXLkFLSws2NjY1Gu/TDA0NoVAokJ+fjwMHDsDf3/+55031p15vtf3ss88QFBQEd3d3eHp6Yu3atcjMzMSYMWMAPLktNTs7G5s3bwYAjBkzBitWrMBnn32G0NBQJCQkICIiAj///LPUZlhYGLp164ZFixbB398fu3btwqFDh3Ds2LEa9/v48WN88MEHSEpKwp49e1BWViZdITU1NYVcLoeJiQlGjBiBSZMmoXHjxjA1NcXkyZPRpk0b6Z0YIiIiIqKX9r+78IyKiuDs7Ky0y9DQEI0bN4azszOKi4sxZ84cDB48GAqFAhkZGZgxYwbMzMykQu/atWvYtm0bfHx8YG5ujuzsbCxatAgGBgbSLamtWrWCn58fQkNDsWbNGgDAqFGj0K9fPzg4OAB4shCnk5MTgoKC8M033+Du3buYPHkyQkNDpbsCK65QVjhz5gy0tLSUzmHYsGH46quvMHz4cHz55Ze4ffs2pkyZgk8//VS6zfbrr7+Gu7s7WrRoIX2uc/PmzVi1apXUTsXXwjg4OODKlSuYMmUKHBwcMHz48Fr7Z6DaVa+F55AhQ3Dnzh3MnTsXOTk5cHZ2xr59+2BnZwfgyRXEp7/Ts3nz5ti3bx/Cw8Pxww8/wMrKCsuXL8fgwYOlmM6dOyMyMhIzZ87ErFmz0KJFC2zbtg2dOnWqcb83btzA7t27AQDt2rVTGnNMTIy0ateSJUugo6ODgIAAPHz4EL169cLGjRurfPeJiIiIiKhGKj7+devWc8O0tbWRkpKCzZs34969e1AoFOjZsye2bdsGIyMjAP9/pdelS5ciPz8fTZo0Qbdu3RAfH6+08u3WrVsxYcIE6RsbBgwYgBUrVij1tXfvXowdOxZdunSBgYEBhg0bhsWLF7/UqTVo0ADR0dEYP3483N3d0bhxYwQEBGDevHlSzP379zF27FjcuHEDBgYGcHR0xJYtWzBkyBAppqCgANOnT8eNGzdgamqKwYMHY/78+a/1raqaTiYqVuehelFYWAgTExMUFBS80uJC+/btQ9++ffmfTYWY57rBPNcN5ln1mOO6wTzXDeZZ9ZRyPG0a8N13wKRJwEsWdvR86j6Xa6M2UFf19hlPIiIiIiKqQsUVz7y8+h0HUS1i4UlEREREpE5qeKstkSZh4UlEREREpE5YeNJrqF4XFyIiIiIiome4uQHbtwO2tvU9EqJaw8KTiIiIiEidmJsDgwbV9yiIahVvtSUiIiIiIiKVYuFJREREREREKsXCk4iIiIiIiFSKhScRERERERGpFAtPIiIiIiIiUikWnkRERERERKRSLDyJiIiIiIhIpVh4EhERERERkUqx8CQiIiIiInrK119/DZlMhokTJ0rbQkJCIJPJlB4eHh5Kx40ePRotWrSAgYEBzM3N4e/vj4sXLyrF5OfnIygoCCYmJjAxMUFQUBDu3bunFJOVlYX+/fvD0NAQZmZmmDBhAh49elTlWK9cuQIjIyM0bNhQaXtV45XJZGjdunWV7URGRkImk2HgwIEvlZeaYuFJRERERET0P6dPn8batWvRtm3bSvv8/PyQk5MjPfbt26e0383NDRs2bEBqaioOHDgAIQR8fHxQVlYmxQwbNgzJycmIiopCVFQUkpOTERQUpNROQEAA7t+/j2PHjiEyMhLbt2/HpEmTKo2ntLQUQ4cOxbvvvltp37Jly5TGmpWVBVNTU3z44YeVYq9fv47JkydX2U5N8lITLDyJiIiIiIgAFBcXIzAwEOvWrUOjRo0q7dfT04OlpaX0MDU1Vdo/atQodOvWDc2aNYOrqyvmzZuHrKwsZGRkAABSU1MRFRWFH3/8EZ6envD09MS6deuwZ88epKWlSe1cvHgRW7ZsQfv27eHt7Y1vv/0W69atQ2FhoVJ/M2fOhKOjIwICAiqN1cTERGmsZ86cQX5+PoYPH64UV1ZWhsDAQHz55Zd4++23/1ZeaoKFJxEREREREYB//OMfeO+99+Dt7V3l/tjYWFhYWMDe3h6hoaHIy8urtq379+9jw4YNaN68OWxtbQEACQkJMDExQadOnaQ4Dw8PmJiYID4+Xtrm5OQEKysr6bmvry9KSkqQmJgobTt8+DB++eUX/PDDDzU6t4iICHh7e8POzk5p+9y5c2Fubo4RI0ZUe+yL8lITOn/7SCIiIiIiotdEZGQkkpKScPr06Sr39+nTBx9++CHs7OyQnp6OWbNmwcvLC4mJidDT05PiVq5cic8//xz379+Ho6MjoqOjIZfLAQC5ubmwsLCo1LaFhQVyc3Ol5+bm5kr7GzVqBLlcLsXcuXMHISEh2LJlC4yNjV94bjk5Odi/fz/+/e9/K20/fvw4IiIikJycXO2xL8pLTbHwJCIiIiKiN1pWVhbCxo/Hweho6OvrVxkzZMgQ6WdnZ2e4u7vDzs4Oe/fuxaBBg6R9gYGB6N27N3JycrB48WIEBATg+PHjUrsymaxS20IIpe0vigkNDcWwYcPQrVu3Gp3fxo0b0bBhQ6WFg4qKivDxxx9j3bp1MDMzq/K4rKwshIWF4eDBg9XmpaZYeBIRERER0RstMT4eebdvw83VFdB68mnEsrIyHD16FCtWrEBJSQm0tbWVjlEoFLCzs8Ply5eVtlesVtuyZUt4eHigUaNG2LFjB4YOHQpLS0vcunWrUv9//fUXmjRpIj1/9hbe/Px8lJaWSjGHDx/G7t27sXjxYgBPitLy8nLo6Ohg7dq1+PTTT6VjhRBYv349goKCpCuvAHD16lVkZGSgf//+0rby8nIAgI6ODtLS0pCSkoK8vDy4ublJMS/KS3VYeBIRERER0RutV2IiUgDAwgLYvRt46y0MHz4cjo6OmDp1apXF1Z07d5CVlQWFQvHctoUQKCkpAQB4enqioKAAp06dQseOHQEAJ0+eREFBATp37iwdc+HCBeTk5EhtHzx4EHp6elIBmJCQoLRS7q5du7Bo0SLEx8fD2tpaqf8jR47gypUrlT7D6ejoiJSUFKVtM2fORFFREZYtWwZbW1tYWFhUinlRXqrDwpOIiIiIiN5cly/DaNkyOAPAypXA/wpCQ0NDNG7cGM7OziguLsacOXMwePBgKBQKZGRkYMaMGTAzM8P7778PALh27Rq2bdsGHx8fmJubIzs7G4sWLYKBgQH69u0LAGjVqhX8/PwQGhqKNWvWAHiyEm6/fv3g4OAgrVrr6OiIoKAgfPPNN7h79y4mT56M0NBQ6fOcrVq1UjqFM2fOQEtLC87OzpVOLyIiAp06daq0T19fv9K2iu8Crdgul8srxTydl5fBVW2JiIiIiOjNJAQwbhzw6BHg5wf8r4h8lra2NlJSUuDv7w97e3sEBwfD3t4eCQkJMDIyAvCkkIuLi0Pfvn3xzjvvICAgAIaGhoiPj1daUGjr1q1o06YNfHx84OPjg7Zt2+Knn35S6u8///kP9PX10aVLFwQEBGDgwIHSbbUvo6CgANu3b3/uirV1hVc8iYiIiIjozfTbb8DBg4BcDixfDjy1qE9sbKz0s4GBAQ4cOPDcpqysrLBv374XdmlqaootW7Y8N8bW1hZ79ux5YVsVQkJCEBISUmm7iYkJHjx4UON2Nm7c+MKYp/PyMnjFk4iIiIiI3jzFxcDEiU9+njoVaNmyXofzuuMVTyIiIiIievMUFAAODoCuLjB9en2P5rXHwpOIiIiIiN481tZAdDSQmwsYGNT3aF57vNWWiIiIiIjeTDIZ8IKvQ6HawcKTiIiIiIiIVIqFJxEREREREakUC08iIiIiIiJSKRaeREREREREpFIsPImIiIiIiEilWHgSERERERGRSrHwJCIiIiIiIpVi4UlEREREREQqxcKTiIiIiIiIVIqFJxEREREREakUC08iIiIiIiJSKZ36HsCbTggBACgsLPzbbZSWluLBgwcoLCyErq5ubQ2NnsE81w3muW4wz6rHHNcN5rluMM+qxxzXDXXPc0VNUFEjvE5YeNazoqIiAICtrW09j4SIiIiIiNRBUVERTExM6nsYtUomXsdyWoOUl5fj5s2bMDIygkwm+1ttFBYWwtbWFllZWTA2Nq7lEVIF5rluMM91g3lWPea4bjDPdYN5Vj3muG6oe56FECgqKoKVlRW0tF6vT0Xyimc909LSgo2NTa20ZWxsrJb/gV43zHPdYJ7rBvOsesxx3WCe6wbzrHrMcd1Q5zy/blc6K7xeZTQRERERERGpHRaeREREREREpFIsPF8Denp6mD17NvT09Op7KK815rluMM91g3lWPea4bjDPdYN5Vj3muG4wz/WHiwsRERERERGRSvGKJxEREREREakUC08iIiIiIiJSKRaeREREREREpFIsPImIiIiIiEilWHjWgZUrV6J58+bQ19eHm5sb4uLinht/5MgRuLm5QV9fH2+//TZWr15dKWb79u1wcnKCnp4enJycsGPHjlfqd/To0ZDJZFi6dKnS9h49ekAmkyk9Pvroo5qdeB1S1xyHhIRUyp+Hh4dSTElJCcaPHw8zMzMYGhpiwIABuHHjxt/Iguppcp41ZS4D6ptnAEhNTcWAAQNgYmICIyMjeHh4IDMzU9qvKfNZk3PMufzqeX42fxWPb775RorRlLkMaHaeOZ9fPc/FxcUYN24cbGxsYGBggFatWmHVqlVKMZoynzU5x5o0l+uNIJWKjIwUurq6Yt26deLChQsiLCxMGBoaiuvXr1cZf+3aNfHWW2+JsLAwceHCBbFu3Tqhq6srfv31VykmPj5eaGtriwULFojU1FSxYMECoaOjI06cOPG3+t2xY4dwcXERVlZWYsmSJUr7unfvLkJDQ0VOTo70uHfvXu0kp5aoc46Dg4OFn5+fUv7u3LmjNJ4xY8YIa2trER0dLZKSkkTPnj2Fi4uLePz4cS1n6tVoep41YS4Lod55vnLlijA1NRVTpkwRSUlJ4urVq2LPnj3i1q1bUowmzGdNzzHn8qvn+enc5eTkiPXr1wuZTCauXr0qxWjCXBZC8/PM+fzqeR45cqRo0aKFiImJEenp6WLNmjVCW1tb7Ny5U4rRhPms6TnWlLlcn1h4qljHjh3FmDFjlLY5OjqKadOmVRn/+eefC0dHR6Vto0ePFh4eHtLzgIAA4efnpxTj6+srPvroo5fu98aNG8La2lqcP39e2NnZVVl4hoWFPfcc65s65zg4OFj4+/tXO/Z79+4JXV1dERkZKW3Lzs4WWlpaIioqqtrj6oMm51kIzZjLQqh3nocMGSI+/vjjaseuKfNZk3MsBOdybf3+e5q/v7/w8vKSnmvKXBZCs/MsBOdzbeS5devWYu7cuUoxrq6uYubMmUIIzZnPmpxjITRnLtcn3mqrQo8ePUJiYiJ8fHyUtvv4+CA+Pr7KYxISEirF+/r64syZMygtLX1uTEWbNe23vLwcQUFBmDJlClq3bl3teWzduhVmZmZo3bo1Jk+ejKKioheced1R9xwDQGxsLCwsLGBvb4/Q0FDk5eVJ+xITE1FaWqrUjpWVFZydnasdf33Q9DxXUOe5DKh3nsvLy7F3717Y29vD19cXFhYW6NSpE3bu3CnFa8J81vQcV+BcVo75O68ZFW7duoW9e/dixIgR0jZNmMuA5ue5AuezcszL5rlr167YvXs3srOzIYRATEwMLl26BF9fXwCaMZ81PccV1H0u1zed+h7A6+z27dsoKytDkyZNlLY3adIEubm5VR6Tm5tbZfzjx49x+/ZtKBSKamMq2qxpv4sWLYKOjg4mTJhQ7TkEBgaiefPmsLS0xPnz5zF9+nScPXsW0dHRL05AHVD3HPfp0wcffvgh7OzskJ6ejlmzZsHLywuJiYnQ09NDbm4u5HI5GjVqVOPx1wdNzzOg/nMZUO885+Xlobi4GAsXLsS8efOwaNEiREVFYdCgQYiJiUH37t01Yj5reo4BzuXaeM142qZNm2BkZIRBgwYpjUXd5zKg+XkGOJ9rI8/Lly9HaGgobGxsoKOjAy0tLfz444/o2rWrNBZ1n8+anmNAM+ZyfWPhWQdkMpnScyFEpW0vin92e03afF5MYmIili1bhqSkpOeOJTQ0VPrZ2dkZLVu2hLu7O5KSkuDq6lrtcXVNHXMMAEOGDJF+dnZ2hru7O+zs7LB3795Kv3xfZvz1RZPzrClzGVDPPJeXlwMA/P39ER4eDgBo164d4uPjsXr1aqkoqoo6zmdNzjHn8qu/Zjxt/fr1CAwMhL6+frXjqun464sm55nz+dXzvHz5cpw4cQK7d++GnZ0djh49irFjx0KhUMDb27va8anjfNbkHGvSXK4vvNVWhczMzKCtrV3pnZq8vLxK76xUsLS0rDJeR0cHjRs3fm5MRZs16TcuLg55eXlo2rQpdHR0oKOjg+vXr2PSpElo1qxZtefk6uoKXV1dXL58+cUJqAPqnOOqKBQK2NnZSfmztLTEo0ePkJ+f/1Lt1DVNz3NV1G0uA+qdZzMzM+jo6MDJyUkpplWrVtKKq5ownzU9x1XhXP77rxlxcXFIS0vDyJEjK41F3ecyoPl5rgrn88vl+eHDh5gxYwa+++479O/fH23btsW4ceMwZMgQLF68WOpH3eezpue4Kuo4l+sbC08VksvlcHNzq3SJPTo6Gp07d67yGE9Pz0rxBw8ehLu7O3R1dZ8bU9FmTfoNCgrCuXPnkJycLD2srKwwZcoUHDhwoNpz+vPPP1FaWgqFQlGDDKieOue4Knfu3EFWVpaUPzc3N+jq6iq1k5OTg/Pnzz+3nbqm6XmuirrNZUC98yyXy9GhQwekpaUpxVy6dAl2dnYANGM+a3qOq8K5/PdfMyIiIuDm5gYXFxel7ZowlwHNz3NVOJ9fLs+lpaUoLS2Flpbyn/Ta2trSXRSaMJ81PcdVUce5XO9UtWoRPVGxRHNERIS4cOGCmDhxojA0NBQZGRlCCCGmTZsmgoKCpPiKpaHDw8PFhQsXRERERKWloY8fPy60tbXFwoULRWpqqli4cGG1S0NX129Vnl3V9sqVK+LLL78Up0+fFunp6WLv3r3C0dFRtG/fXi2X31a3HBcVFYlJkyaJ+Ph4kZ6eLmJiYoSnp6ewtrYWhYWFUjtjxowRNjY24tChQyIpKUl4eXmp3RLnQmh2njVlLguhvnkWQojffvtN6OrqirVr14rLly+L77//Xmhra4u4uDgpRhPmsybnmHO59n7/FRQUiLfeekusWrWqyvFrwlwWQrPzzPlcO3nu3r27aN26tYiJiRHXrl0TGzZsEPr6+mLlypVSjCbMZ03OsSbN5frEwrMO/PDDD8LOzk7I5XLh6uoqjhw5Iu0LDg4W3bt3V4qPjY0V7du3F3K5XDRr1qzKF+tffvlFODg4CF1dXeHo6Ci2b9/+Uv1W5dnCMzMzU3Tr1k2YmpoKuVwuWrRoISZMmFDp+xHVgTrm+MGDB8LHx0eYm5sLXV1d0bRpUxEcHCwyMzOV2nj48KEYN26cMDU1FQYGBqJfv36VYtSFpuZZk+ayEOqZ5woRERHinXfeEfr6+sLFxUXpO8yE0Jz5rKk55lyuvTyvWbNGGBgYVPs9e5oyl4XQ3DxzPtdOnnNyckRISIiwsrIS+vr6wsHBQXz77beivLxcitGU+aypOda0uVxfZEL871O4RERERERERCrAz3gSERERERGRSrHwJCIiIiIiIpVi4UlEREREREQqxcKTiIiIiIiIVIqFJxEREREREakUC08iIiIiIiJSKRaeREREREREpFIsPImIiGogIyMDMpkMycnJKmlfJpNh586dr9zOrFmzMGrUqFcfUC1JSUmBjY0N7t+/X99DISKqRAiBPn361Og1eNWqVWjbti2MjY1hbGwMT09P7N+/XymmuLgY48aNg42NDQwMDNCqVSusWrWqUlsJCQnw8vKCoaEhGjZsiB49euDhw4e1eWpqh4UnERGpvZCQEAwcOLBex2Bra4ucnBw4OzsDAGJjYyGTyXDv3r16HdfTbt26hWXLlmHGjBnStpCQEMhkMshkMujq6qJJkybo3bs31q9fj/LycpWPqU2bNujYsSOWLFmi8r6IiJ7Wo0cPbNy48bkxS5cuhUwmq1F7NjY2WLhwIc6cOYMzZ87Ay8sL/v7++PPPP6WY8PBwREVFYcuWLUhNTUV4eDjGjx+PXbt2STEJCQnw8/ODj48PTp06hdOnT2PcuHHQ0nq9S7PX++yIiIhqiba2NiwtLaGjo1PfQ6lWREQEPD090axZM6Xtfn5+yMnJQUZGBvbv34+ePXsiLCwM/fr1w+PHj1U+ruHDh2PVqlUoKytTeV9ERDV19uxZfPfdd1i/fn2N4vv374++ffvC3t4e9vb2mD9/Pho0aIATJ05IMQkJCQgODkaPHj3QrFkzjBo1Ci4uLjhz5owUEx4ejgkTJmDatGlo3bo1WrZsiQ8++AB6enq1fo7qhIUnERFpvCNHjqBjx47Q09ODQqHAtGnTlAqqHj16YMKECfj8889hamoKS0tLzJkzR6mNixcvomvXrtDX14eTkxMOHTqkdOvV07faZmRkoGfPngCARo0aQSaTISQkBADQrFkzLF26VKntdu3aKfV3+fJldOvWTeorOjq60jllZ2djyJAhaNSoERo3bgx/f39kZGQ8Nw+RkZEYMGBApe16enqwtLSEtbU1XF1dMWPGDOzatQv79+9XuhpQUFCAUaNGwcLCAsbGxvDy8sLZs2eV2po3bx4sLCxgZGSEkSNHYtq0aWjXrt1zx+Xr64s7d+7gyJEjz40jIqorDx48wNChQ7FixQpYWlq+9PFlZWWIjIzE/fv34enpKW3v2rUrdu/ejezsbAghEBMTg0uXLsHX1xcAkJeXh5MnT8LCwgKdO3dGkyZN0L17dxw7dqzWzk1dsfAkIiKNlp2djb59+6JDhw44e/YsVq1ahYiICMybN08pbtOmTTA0NMTJkyfxr3/9C3PnzpUKvvLycgwcOBBvvfUWTp48ibVr1+KLL76otk9bW1ts374dAJCWloacnBwsW7asRuMtLy/HoEGDoK2tjRMnTmD16tWYOnWqUsyDBw/Qs2dPNGjQAEePHsWxY8fQoEED+Pn54dGjR1W2m5+fj/Pnz8Pd3b1G4/Dy8oKLiwt+++03AE8+5/Tee+8hNzcX+/btQ2JiIlxdXdGrVy/cvXsXALB161bMnz8fixYtQmJiIpo2bVrlZ5eeJZfL4eLigri4uBqNjYhI1cLDw9G5c2f4+/u/1HEpKSlo0KAB9PT0MGbMGOzYsQNOTk7S/uXLl8PJyQk2NjaQy+Xw8/PDypUr0bVrVwDAtWvXAABz5sxBaGgooqKipNfay5cv194JqiH1vV+IiIioBlauXAlbW1usWLECMpkMjo6OuHnzJqZOnYp//vOf0mdm2rZti9mzZwMAWrZsiRUrVuD3339H7969cfDgQVy9ehWxsbHSO9/z589H7969q+xTW1sbpqamAAALCws0bNiwxuM9dOgQUlNTkZGRARsbGwDAggUL0KdPHykmMjISWlpa+PHHH6XPHm3YsAENGzZEbGwsfHx8KrV7/fp1CCFgZWVV47E4Ojri3LlzAICYmBikpKQgLy9Put1r8eLF2LlzJ3799VeMGjUK33//PUaMGIHhw4cDAP75z3/i4MGDKC4ufmFf1tbWL7xiS0T0KhYsWIAFCxZIzx8+fIgTJ05g3Lhx0rb9+/cjPz8fhw8fxh9//PHSfTg4OCA5ORn37t3D9u3bERwcjCNHjkjF5/Lly3HixAns3r0bdnZ2OHr0KMaOHQuFQgFvb2/ps/WjR4+WXkvbt2+P33//HevXr8fXX3/9KilQayw8iYhIo6WmpsLT01NpcYguXbqguLgYN27cQNOmTQE8KTyfplAokJeXB+DJVUtbW1ul2606duyosvE2bdpUKjoBKN2mBQCJiYm4cuUKjIyMlLb/3//9H65evVpluxWrIerr69d4LEIIKW+JiYkoLi5G48aNK7Vb0WdaWhrGjh2rtL9jx444fPjwC/syMDDAgwcPajw2IqKXNWbMGAQEBEjPAwMDMXjwYAwaNEjaZm1tjenTp+Pq1auV3jQcPHgw3n33XcTGxlbbh1wuxzvvvAMAcHd3x+nTp7Fs2TKsWbMGDx8+xIwZM7Bjxw689957AJ787klOTsbixYvh7e0NhUIBAEpXSQGgVatWyMzMfJXTV3ssPImISKM9XTw9vQ2A0nZdXV2lGJlMJr3zXFUbf5eWlpbUf4XS0tJKY3t2LE8rLy+Hm5sbtm7dWinW3Ny8yn7NzMwAPLnltrqYZ6WmpqJ58+ZSnwqFoso/uJ7+46y6XL/I3bt30aJFixrFEhH9HaamptLdKMCTN7wsLCykQrHCtGnTMHLkSKVtbdq0wZIlS9C/f/+X6lMIgZKSEgBPXutLS0srrU6rra0t/b5p1qwZrKyskJaWphRz6dIlpTtfXkcsPImISKM5OTlh+/btSsVjfHw8jIyMYG1tXaM2HB0dkZmZiVu3bqFJkyYAgNOnTz/3GLlcDgCVVmo1NzdHTk6O9LywsBDp6elK483MzMTNmzel22ITEhKU2nB1dcW2bdukRX5qokWLFjA2NsaFCxdgb2//wvjDhw8jJSUF4eHhUp+5ubnQ0dGptCpuBQcHB5w6dQpBQUHStqdXanye8+fP44MPPqhRLBGRKllaWla5oFDTpk2lN+MAoFevXnj//felW3VnzJiBPn36wNbWFkVFRYiMjERsbCyioqIAAMbGxujevTumTJkCAwMD2NnZ4ciRI9i8eTO+++47AE/evJsyZQpmz54NFxcXtGvXDps2bcLFixfx66+/1sHZ1x8uLkRERBqhoKAAycnJSo/MzEyMHTsWWVlZGD9+PC5evIhdu3Zh9uzZ+Oyzz2r8nWi9e/dGixYtEBwcjHPnzuH48ePS4kLVXQm1s7ODTCbDnj178Ndff0mfc/Ty8sJPP/2EuLg4nD9/HsHBwdDW1paO8/b2hoODAz755BOcPXsWcXFxlRYyCgwMhJmZGfz9/REXF4f09HQcOXIEYWFhuHHjRpXj0dLSgre3d5UrI5aUlCA3NxfZ2dlISkrCggUL4O/vj379+uGTTz6RxuXp6YmBAwfiwIEDyMjIQHx8PGbOnCkVl+PHj0dERAQ2bdqEy5cvY968eTh37pxSjnbs2AFHR0el/jMyMpCdnQ1vb+/n/jsQEamTq1ev4vbt29LzW7duISgoCA4ODujVqxdOnjyJqKgopfUAIiMj0aFDBwQGBsLJyQkLFy7E/PnzMWbMGClm4sSJmD59OsLDw+Hi4oLff/8d0dHRr/9dIYKIiEjNBQcHCwCVHsHBwUIIIWJjY0WHDh2EXC4XlpaWYurUqaK0tFQ6vnv37iIsLEypTX9/f+l4IYRITU0VXbp0EXK5XDg6Oor//ve/AoCIiooSQgiRnp4uAIg//vhDOmbu3LnC0tJSyGQyqa2CggIREBAgjI2Nha2trdi4caNwcXERs2fPlo5LS0sTXbt2FXK5XNjb24uoqCgBQOzYsUOKycnJEZ988okwMzMTenp64u233xahoaGioKCg2jxFRUUJa2trUVZWVmXudHR0hLm5ufD29hbr169XihNCiMLCQjF+/HhhZWUldHV1ha2trQgMDBSZmZlK52xmZiYaNGggPv30UzFhwgTh4eEh7d+wYYN49s+LBQsWCF9f32rHTURErz+ZEDX8cAYREdEb5Pjx4+jatSuuXLmiMe9CCyHg4eGBiRMnYujQoXXSZ+/evWFpaYmffvqpyv0lJSVo2bIlfv75Z3Tp0qVOxkREROqHn/EkIiLCk1tEGzRogJYtW+LKlSsICwtDly5dNKboBJ7cFrx27VrpK1Jq24MHD7B69Wr4+vpCW1sbP//8Mw4dOiR9H2pVrl+/ji+++IJFJxHRG45XPImIiABs3rwZX331FbKysmBmZgZvb298++23lb5e5E328OFD9O/fH0lJSSgpKYGDgwNmzpyp9FUFREREVWHhSURERERERCrFVW2JiIiIiIhIpVh4EhERERERkUqx8CQiIiIiIiKVYuFJREREREREKsXCk4iIiIiIiFSKhScRERERERGpFAtPIiIiIiIiUikWnkRERERERKRSLDyJiIiIiIhIpf4fK00wwL4SRHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "for node in PosNodes :\n",
    "    t = mpl.markers.MarkerStyle(marker='|')\n",
    "    t._transform = t.get_transform().rotate_deg(-node[3])\n",
    "    plt.scatter(node[2],node[1], c='r', marker=t, s=30)\n",
    "    plt.text(node[2],node[1], node[0])\n",
    "    \n",
    "plt.xlabel('Longitude (Deg.)')\n",
    "plt.ylabel('Latitutde (Deg.)')\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b30d0fda-83ab-4fc0-a8af-62a2a765b8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = pd.DataFrame(PosNodes, columns=['SN', 'Y', 'X', 'Northing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9299123b-af55-4bb2-97e1-2b4fc79ae266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SN</th>\n",
       "      <th>Y</th>\n",
       "      <th>X</th>\n",
       "      <th>Northing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>453001250</td>\n",
       "      <td>50.822379</td>\n",
       "      <td>4.386519</td>\n",
       "      <td>34.8915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453003484</td>\n",
       "      <td>50.822314</td>\n",
       "      <td>4.386881</td>\n",
       "      <td>37.3505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>453003625</td>\n",
       "      <td>50.822339</td>\n",
       "      <td>4.386443</td>\n",
       "      <td>48.0755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>453004444</td>\n",
       "      <td>50.822350</td>\n",
       "      <td>4.386738</td>\n",
       "      <td>41.2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>453004465</td>\n",
       "      <td>50.822404</td>\n",
       "      <td>4.386586</td>\n",
       "      <td>28.2990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>453004512</td>\n",
       "      <td>50.822486</td>\n",
       "      <td>4.386770</td>\n",
       "      <td>11.6570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>453004639</td>\n",
       "      <td>50.822193</td>\n",
       "      <td>4.386881</td>\n",
       "      <td>-4.7185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>453004744</td>\n",
       "      <td>50.822237</td>\n",
       "      <td>4.386919</td>\n",
       "      <td>30.0365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>453004823</td>\n",
       "      <td>50.822352</td>\n",
       "      <td>4.386793</td>\n",
       "      <td>3.9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>453013956</td>\n",
       "      <td>50.822517</td>\n",
       "      <td>4.386583</td>\n",
       "      <td>5.9625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>453019524</td>\n",
       "      <td>50.822390</td>\n",
       "      <td>4.386676</td>\n",
       "      <td>30.3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>453020066</td>\n",
       "      <td>50.822412</td>\n",
       "      <td>4.386690</td>\n",
       "      <td>49.5650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SN          Y         X  Northing\n",
       "0   453001250  50.822379  4.386519   34.8915\n",
       "1   453003484  50.822314  4.386881   37.3505\n",
       "2   453003625  50.822339  4.386443   48.0755\n",
       "3   453004444  50.822350  4.386738   41.2625\n",
       "4   453004465  50.822404  4.386586   28.2990\n",
       "5   453004512  50.822486  4.386770   11.6570\n",
       "6   453004639  50.822193  4.386881   -4.7185\n",
       "7   453004744  50.822237  4.386919   30.0365\n",
       "8   453004823  50.822352  4.386793    3.9995\n",
       "9   453013956  50.822517  4.386583    5.9625\n",
       "10  453019524  50.822390  4.386676   30.3280\n",
       "11  453020066  50.822412  4.386690   49.5650"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fe488c2d-c330-46ac-beb0-97a3a8c85551",
   "metadata": {},
   "outputs": [],
   "source": [
    "out.to_csv('./PosNodes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18bb75db-9d24-431e-9f0f-8ef57c971fa8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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