Playing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from shapely.geometry import Point\n",
    "import copy\n",
    "import time\n",
    "import bisect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO DO\n",
    "# Alter packing metric: Probably need to define something else which will penalise \"holes\"\n",
    "# Weight neighbour metric by (normalised) border length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "geodata = gpd.read_file('./Data/LADs.geojson').to_crs('epsg:27700')\n",
    "# geodata = geodata[['E09' in c for c in geodata['LAD20CD']]][['LAD20CD', 'LAD20NM', 'geometry']]\n",
    "geodata = geodata[['W' in c for c in geodata['LAD20CD']]][['LAD20CD', 'LAD20NM', 'geometry']]\n",
    "geodata['dummy'] = geodata['geometry'].apply(lambda g: 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "geodata['centroid'] = geodata.centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>LAD20CD</th>\n",
       "      <th>LAD20NM</th>\n",
       "      <th>geometry</th>\n",
       "      <th>dummy</th>\n",
       "      <th>centroid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>Isle of Anglesey</td>\n",
       "      <td>POLYGON ((240000.004 394577.908, 240445.514 39...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (241835.228 379233.676)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>W06000002</td>\n",
       "      <td>Gwynedd</td>\n",
       "      <td>MULTIPOLYGON (((267978.708 372366.304, 270868....</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (264136.399 334667.737)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>W06000003</td>\n",
       "      <td>Conwy</td>\n",
       "      <td>POLYGON ((299724.870 380889.607, 300371.113 37...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (284072.324 361552.108)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>W06000004</td>\n",
       "      <td>Denbighshire</td>\n",
       "      <td>POLYGON ((324414.214 353387.707, 323145.711 35...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (309507.880 355562.623)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>W06000005</td>\n",
       "      <td>Flintshire</td>\n",
       "      <td>MULTIPOLYGON (((313293.515 382786.601, 315287....</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (322547.437 368615.133)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>W06000006</td>\n",
       "      <td>Wrexham</td>\n",
       "      <td>POLYGON ((351298.712 343127.004, 350716.010 34...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (331695.185 344175.768)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>W06000008</td>\n",
       "      <td>Ceredigion</td>\n",
       "      <td>POLYGON ((279749.210 249226.602, 277768.013 24...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (258559.569 263733.729)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>W06000009</td>\n",
       "      <td>Pembrokeshire</td>\n",
       "      <td>MULTIPOLYGON (((216364.016 243983.404, 217396....</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (201337.424 221180.436)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>W06000010</td>\n",
       "      <td>Carmarthenshire</td>\n",
       "      <td>POLYGON ((279749.210 249226.602, 282307.116 24...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (251556.904 225139.754)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>W06000011</td>\n",
       "      <td>Swansea</td>\n",
       "      <td>MULTIPOLYGON (((262123.115 210278.302, 264479....</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (258170.595 195828.191)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>W06000012</td>\n",
       "      <td>Neath Port Talbot</td>\n",
       "      <td>POLYGON ((275483.512 211465.301, 277901.616 20...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (279710.728 199475.964)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>W06000013</td>\n",
       "      <td>Bridgend</td>\n",
       "      <td>POLYGON ((296179.514 180365.102, 293926.115 17...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (289261.086 185303.662)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>W06000014</td>\n",
       "      <td>Vale of Glamorgan</td>\n",
       "      <td>MULTIPOLYGON (((302165.914 179592.505, 302783....</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (302837.916 172825.539)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>W06000015</td>\n",
       "      <td>Cardiff</td>\n",
       "      <td>POLYGON ((322508.409 183565.008, 325932.914 18...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (316429.793 179758.895)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>W06000016</td>\n",
       "      <td>Rhondda Cynon Taf</td>\n",
       "      <td>POLYGON ((300546.815 214081.303, 300743.609 21...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (300727.655 195600.211)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>W06000018</td>\n",
       "      <td>Caerphilly</td>\n",
       "      <td>POLYGON ((322947.410 199706.208, 323910.210 19...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (316772.372 195606.935)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>W06000019</td>\n",
       "      <td>Blaenau Gwent</td>\n",
       "      <td>POLYGON ((320318.110 213680.006, 321092.609 21...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (317687.889 208263.319)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>W06000020</td>\n",
       "      <td>Torfaen</td>\n",
       "      <td>POLYGON ((333723.012 192653.907, 331617.809 19...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (327271.207 200527.491)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>W06000021</td>\n",
       "      <td>Monmouthshire</td>\n",
       "      <td>POLYGON ((355272.716 214366.801, 354547.712 21...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (339908.627 207883.316)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>W06000022</td>\n",
       "      <td>Newport</td>\n",
       "      <td>POLYGON ((343091.766 184213.492, 340000.004 18...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (333160.397 187189.889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>W06000023</td>\n",
       "      <td>Powys</td>\n",
       "      <td>POLYGON ((322891.616 333139.303, 323688.509 33...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (303988.076 270511.052)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>Merthyr Tydfil</td>\n",
       "      <td>POLYGON ((308057.316 211036.204, 308367.116 20...</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (306057.506 205651.722)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LAD20CD            LAD20NM  \\\n",
       "357  W06000001   Isle of Anglesey   \n",
       "358  W06000002            Gwynedd   \n",
       "359  W06000003              Conwy   \n",
       "360  W06000004       Denbighshire   \n",
       "361  W06000005         Flintshire   \n",
       "362  W06000006            Wrexham   \n",
       "363  W06000008         Ceredigion   \n",
       "364  W06000009      Pembrokeshire   \n",
       "365  W06000010    Carmarthenshire   \n",
       "366  W06000011            Swansea   \n",
       "367  W06000012  Neath Port Talbot   \n",
       "368  W06000013           Bridgend   \n",
       "369  W06000014  Vale of Glamorgan   \n",
       "370  W06000015            Cardiff   \n",
       "371  W06000016  Rhondda Cynon Taf   \n",
       "372  W06000018         Caerphilly   \n",
       "373  W06000019      Blaenau Gwent   \n",
       "374  W06000020            Torfaen   \n",
       "375  W06000021      Monmouthshire   \n",
       "376  W06000022            Newport   \n",
       "377  W06000023              Powys   \n",
       "378  W06000024     Merthyr Tydfil   \n",
       "\n",
       "                                              geometry  dummy  \\\n",
       "357  POLYGON ((240000.004 394577.908, 240445.514 39...      1   \n",
       "358  MULTIPOLYGON (((267978.708 372366.304, 270868....      1   \n",
       "359  POLYGON ((299724.870 380889.607, 300371.113 37...      1   \n",
       "360  POLYGON ((324414.214 353387.707, 323145.711 35...      1   \n",
       "361  MULTIPOLYGON (((313293.515 382786.601, 315287....      1   \n",
       "362  POLYGON ((351298.712 343127.004, 350716.010 34...      1   \n",
       "363  POLYGON ((279749.210 249226.602, 277768.013 24...      1   \n",
       "364  MULTIPOLYGON (((216364.016 243983.404, 217396....      1   \n",
       "365  POLYGON ((279749.210 249226.602, 282307.116 24...      1   \n",
       "366  MULTIPOLYGON (((262123.115 210278.302, 264479....      1   \n",
       "367  POLYGON ((275483.512 211465.301, 277901.616 20...      1   \n",
       "368  POLYGON ((296179.514 180365.102, 293926.115 17...      1   \n",
       "369  MULTIPOLYGON (((302165.914 179592.505, 302783....      1   \n",
       "370  POLYGON ((322508.409 183565.008, 325932.914 18...      1   \n",
       "371  POLYGON ((300546.815 214081.303, 300743.609 21...      1   \n",
       "372  POLYGON ((322947.410 199706.208, 323910.210 19...      1   \n",
       "373  POLYGON ((320318.110 213680.006, 321092.609 21...      1   \n",
       "374  POLYGON ((333723.012 192653.907, 331617.809 19...      1   \n",
       "375  POLYGON ((355272.716 214366.801, 354547.712 21...      1   \n",
       "376  POLYGON ((343091.766 184213.492, 340000.004 18...      1   \n",
       "377  POLYGON ((322891.616 333139.303, 323688.509 33...      1   \n",
       "378  POLYGON ((308057.316 211036.204, 308367.116 20...      1   \n",
       "\n",
       "                          centroid  \n",
       "357  POINT (241835.228 379233.676)  \n",
       "358  POINT (264136.399 334667.737)  \n",
       "359  POINT (284072.324 361552.108)  \n",
       "360  POINT (309507.880 355562.623)  \n",
       "361  POINT (322547.437 368615.133)  \n",
       "362  POINT (331695.185 344175.768)  \n",
       "363  POINT (258559.569 263733.729)  \n",
       "364  POINT (201337.424 221180.436)  \n",
       "365  POINT (251556.904 225139.754)  \n",
       "366  POINT (258170.595 195828.191)  \n",
       "367  POINT (279710.728 199475.964)  \n",
       "368  POINT (289261.086 185303.662)  \n",
       "369  POINT (302837.916 172825.539)  \n",
       "370  POINT (316429.793 179758.895)  \n",
       "371  POINT (300727.655 195600.211)  \n",
       "372  POINT (316772.372 195606.935)  \n",
       "373  POINT (317687.889 208263.319)  \n",
       "374  POINT (327271.207 200527.491)  \n",
       "375  POINT (339908.627 207883.316)  \n",
       "376  POINT (333160.397 187189.889)  \n",
       "377  POINT (303988.076 270511.052)  \n",
       "378  POINT (306057.506 205651.722)  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geodata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "geodata.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tpeterken/anaconda3/envs/AutoCartogram/lib/python3.10/site-packages/pandas/core/dtypes/inference.py:383: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  iter(obj)  # Can iterate over it.\n",
      "/home/tpeterken/anaconda3/envs/AutoCartogram/lib/python3.10/site-packages/pandas/core/dtypes/inference.py:384: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  len(obj)  # Has a length associated with it.\n",
      "/home/tpeterken/anaconda3/envs/AutoCartogram/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1981: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  result[:] = values\n",
      "/home/tpeterken/anaconda3/envs/AutoCartogram/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:122: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n",
      "  arr = construct_1d_object_array_from_listlike(values)\n"
     ]
    }
   ],
   "source": [
    "g_copygeo = copy.copy(geodata)\n",
    "g_copygeo['geocopy'] = g_copygeo.geometry\n",
    "neighbours = g_copygeo.sjoin(g_copygeo, how='inner', predicate='touches')\n",
    "neighbours = neighbours.loc[neighbours['LAD20CD_left']!=neighbours['LAD20CD_right']]\n",
    "neighbours['intersection'] = neighbours.apply(lambda row: row['geocopy_left'].intersection(row['geocopy_right']), axis=1)\n",
    "neighbours['weight'] = neighbours.apply(lambda row: row['intersection'].length/row['geocopy_left'].length, axis=1)\n",
    "\n",
    "neighbours = neighbours[['LAD20CD_left', 'LAD20CD_right', 'weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>LAD20CD_left</th>\n",
       "      <th>LAD20CD_right</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>W06000002</td>\n",
       "      <td>W06000004</td>\n",
       "      <td>0.042755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>W06000003</td>\n",
       "      <td>W06000004</td>\n",
       "      <td>0.300156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>W06000005</td>\n",
       "      <td>W06000004</td>\n",
       "      <td>0.395938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>W06000006</td>\n",
       "      <td>W06000004</td>\n",
       "      <td>0.268325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>W06000023</td>\n",
       "      <td>W06000004</td>\n",
       "      <td>0.017204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>W06000023</td>\n",
       "      <td>W06000019</td>\n",
       "      <td>0.026017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>W06000019</td>\n",
       "      <td>W06000021</td>\n",
       "      <td>0.074281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>W06000020</td>\n",
       "      <td>W06000021</td>\n",
       "      <td>0.468389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>W06000022</td>\n",
       "      <td>W06000021</td>\n",
       "      <td>0.314966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>W06000023</td>\n",
       "      <td>W06000021</td>\n",
       "      <td>0.053515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LAD20CD_left LAD20CD_right    weight\n",
       "358    W06000002     W06000004  0.042755\n",
       "359    W06000003     W06000004  0.300156\n",
       "361    W06000005     W06000004  0.395938\n",
       "362    W06000006     W06000004  0.268325\n",
       "377    W06000023     W06000004  0.017204\n",
       "..           ...           ...       ...\n",
       "377    W06000023     W06000019  0.026017\n",
       "373    W06000019     W06000021  0.074281\n",
       "374    W06000020     W06000021  0.468389\n",
       "376    W06000022     W06000021  0.314966\n",
       "377    W06000023     W06000021  0.053515\n",
       "\n",
       "[88 rows x 3 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def angle_between(loca, locb):\n",
    "    xa, ya = loca.xy\n",
    "    xb, yb = locb.xy    \n",
    "    return np.arctan2(yb[0]-ya[0], xb[0]-xa[0])\n",
    "\n",
    "crossjoin = geodata.merge(geodata, how='inner', on='dummy', suffixes=('_left', '_right'))\n",
    "crossjoin = crossjoin.loc[crossjoin['LAD20CD_left']!=crossjoin['LAD20CD_right']]\n",
    "crossjoin['distance'] = crossjoin.apply(lambda row: row['geometry_left'].distance(row['geometry_right']), axis=1)\n",
    "crossjoin['target_distance_rank'] = crossjoin.groupby('LAD20CD_left')['distance'].rank()\n",
    "crossjoin['target_angle'] = crossjoin.apply(lambda row: angle_between(row['centroid_left'], row['centroid_right']), axis=1)\n",
    "distance_ranking = crossjoin[['LAD20CD_left', 'LAD20CD_right', 'target_distance_rank', 'target_angle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>LAD20CD_left</th>\n",
       "      <th>LAD20CD_right</th>\n",
       "      <th>target_distance_rank</th>\n",
       "      <th>target_angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>W06000002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.106822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>W06000003</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.396460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>W06000004</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.336486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>W06000005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.130809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>W06000006</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.371977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>W06000019</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.220886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>W06000020</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.237013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>W06000021</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.065829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>W06000022</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.597980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>W06000023</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.602692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LAD20CD_left LAD20CD_right  target_distance_rank  target_angle\n",
       "1      W06000001     W06000002                   1.0     -1.106822\n",
       "2      W06000001     W06000003                   2.0     -0.396460\n",
       "3      W06000001     W06000004                   3.0     -0.336486\n",
       "4      W06000001     W06000005                   4.0     -0.130809\n",
       "5      W06000001     W06000006                   6.0     -0.371977\n",
       "..           ...           ...                   ...           ...\n",
       "478    W06000024     W06000019                   4.0      0.220886\n",
       "479    W06000024     W06000020                   7.0     -0.237013\n",
       "480    W06000024     W06000021                   9.0      0.065829\n",
       "481    W06000024     W06000022                  10.0     -0.597980\n",
       "482    W06000024     W06000023                   2.0      1.602692\n",
       "\n",
       "[462 rows x 4 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# orientation_ranking = geodata.join(geodata.bounds)\n",
    "\n",
    "# ix_list = orientation_ranking.index\n",
    "# minxlist = orientation_ranking['minx']\n",
    "# maxxlist = orientation_ranking['maxx']\n",
    "# minylist = orientation_ranking['miny']\n",
    "# maxylist = orientation_ranking['maxy']\n",
    "\n",
    "# def get_orient_position(index, loc, loclist):\n",
    "#     ixlist_to_use = [i for i in ix_list if i != index]\n",
    "#     loclist_to_use = np.sort(loclist[ixlist_to_use])\n",
    "#     return bisect.bisect(loclist_to_use, loc)    \n",
    "\n",
    "# orientation_ranking['x'] = orientation_ranking['centroid'].apply(lambda c: c.xy[0][0])\n",
    "# orientation_ranking['y'] = orientation_ranking['centroid'].apply(lambda c: c.xy[1][0])\n",
    "\n",
    "# orientation_ranking['EW_lo'] = orientation_ranking.apply(lambda row: get_orient_position(row.name, row['minx'], minxlist), axis=1)\n",
    "# orientation_ranking['EW_hi'] = orientation_ranking.apply(lambda row: get_orient_position(row.name, row['maxx'], maxxlist), axis=1)\n",
    "# orientation_ranking['NS_lo'] = orientation_ranking.apply(lambda row: get_orient_position(row.name, row['miny'], minylist), axis=1)\n",
    "# orientation_ranking['NS_hi'] = orientation_ranking.apply(lambda row: get_orient_position(row.name, row['maxy'], maxylist), axis=1)\n",
    "\n",
    "# orientation_ranking['EW'] = orientation_ranking.apply(lambda row: np.sort([row['EW_lo'], row['EW_hi']]), axis=1)\n",
    "# orientation_ranking['NS'] = orientation_ranking.apply(lambda row: np.sort([row['NS_lo'], row['NS_hi']]), axis=1)\n",
    "\n",
    "# orientation_ranking = orientation_ranking[['LAD20CD', 'EW', 'NS']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "orientation_ranking = copy.copy(geodata)[['LAD20CD', 'centroid']]\n",
    "orientation_ranking['x'] = orientation_ranking['centroid'].apply(lambda c: c.xy[0][0])\n",
    "orientation_ranking['y'] = orientation_ranking['centroid'].apply(lambda c: c.xy[1][0])\n",
    "orientation_ranking['EW'] = orientation_ranking['x'].rank()\n",
    "orientation_ranking['NS'] = orientation_ranking['y'].rank()\n",
    "orientation_ranking = orientation_ranking[['LAD20CD', 'EW', 'NS']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>LAD20CD</th>\n",
       "      <th>EW</th>\n",
       "      <th>NS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>W06000001</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>W06000002</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>W06000003</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>W06000004</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>W06000005</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>W06000006</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>W06000008</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>W06000009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>W06000010</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>W06000011</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>W06000012</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>W06000013</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>W06000014</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>W06000015</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>W06000016</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>W06000018</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>W06000019</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>W06000020</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>W06000021</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>W06000022</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>W06000023</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>W06000024</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LAD20CD    EW    NS\n",
       "357  W06000001   2.0  22.0\n",
       "358  W06000002   6.0  17.0\n",
       "359  W06000003   8.0  20.0\n",
       "360  W06000004  14.0  19.0\n",
       "361  W06000005  18.0  21.0\n",
       "362  W06000006  20.0  18.0\n",
       "363  W06000008   5.0  15.0\n",
       "364  W06000009   1.0  13.0\n",
       "365  W06000010   3.0  14.0\n",
       "366  W06000011   4.0   7.0\n",
       "367  W06000012   7.0   8.0\n",
       "368  W06000013   9.0   3.0\n",
       "369  W06000014  11.0   1.0\n",
       "370  W06000015  15.0   2.0\n",
       "371  W06000016  10.0   5.0\n",
       "372  W06000018  16.0   6.0\n",
       "373  W06000019  17.0  12.0\n",
       "374  W06000020  19.0   9.0\n",
       "375  W06000021  22.0  11.0\n",
       "376  W06000022  21.0   4.0\n",
       "377  W06000023  12.0  16.0\n",
       "378  W06000024  13.0  10.0"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientation_ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an initial grid\n",
    "\n",
    "scale_factor = 0\n",
    "sfs = [scale_factor]\n",
    "# scale_factor_interval = len(geodata)/200\n",
    "scale_factor_interval = 0.1\n",
    "\n",
    "initial_grid_size = -1\n",
    "gss = [initial_grid_size]\n",
    "full_bounds = geodata.total_bounds\n",
    "axis_ratio = (full_bounds[3]-full_bounds[1])/(full_bounds[2]-full_bounds[0])\n",
    "\n",
    "while initial_grid_size < len(geodata):\n",
    "\n",
    "    scale_factor += scale_factor_interval\n",
    "    \n",
    "    initial_y_length = int(np.round(scale_factor*np.sqrt(len(geodata)/axis_ratio), decimals=0))\n",
    "    initial_x_length = int(np.round(axis_ratio*initial_y_length))\n",
    "\n",
    "    initial_grid = np.meshgrid(range(initial_x_length), range(initial_x_length))\n",
    "    initial_grid_size = initial_y_length*initial_x_length\n",
    "    \n",
    "    sfs.append(scale_factor)\n",
    "    gss.append(initial_grid_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f47e774faf0>]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sfs, gss, 'r.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbour_distance_factor = 8\n",
    "# neighbour_angle_factor = 0\n",
    "\n",
    "proximity_distance_factor = 0\n",
    "# proximity_angle_factor = 0\n",
    "\n",
    "packing_factor = 1\n",
    "\n",
    "orientation_factor = 70\n",
    "\n",
    "max_move_jump = 1\n",
    "\n",
    "\n",
    "def generate_geodataframe(codes, coords):\n",
    "    \n",
    "    grid_pdf = pd.DataFrame({\n",
    "        'code': codes,\n",
    "        'y': coords[0],\n",
    "        'x': coords[1]\n",
    "    })\n",
    "    grid_gdf = gpd.GeoDataFrame(grid_pdf, geometry=gpd.points_from_xy(grid_pdf['x'], grid_pdf['y']))\n",
    "    grid_gdf.crs = 'epsg:27700'\n",
    "    \n",
    "    return grid_gdf[['code', 'geometry']]\n",
    "\n",
    "\n",
    "def angle_difference(angle1, angle2):\n",
    "    d = angle2-angle1\n",
    "    d = d+(2*np.pi) if d < -np.pi else d\n",
    "    d = d-(2*np.pi) if d > np.pi else d\n",
    "    return d\n",
    "\n",
    "\n",
    "def neighbour_metric(it_gdf):\n",
    "    \n",
    "     # Find the distances and angles to all the neighbours\n",
    "    it_neighbours = it_gdf.merge(neighbours, left_on='code', right_on='LAD20CD_left')\n",
    "    it_neighbours = it_neighbours.merge(it_gdf, left_on='LAD20CD_right', right_on='code', suffixes=('', '_r'))\n",
    "    it_neighbours = it_neighbours[['code', 'geometry', 'code_r', 'geometry_r', 'weight']]\n",
    "\n",
    "    it_neighbours['distance'] = it_neighbours.apply(lambda row: row['geometry'].distance(row['geometry_r']), axis=1)\n",
    "#     it_neighbours['distance'] = it_neighbours['distance'].apply(lambda d: 0 if d==1 else (0.5 if d < 1.5 else d))\n",
    "    it_neighbours['distance'] = it_neighbours['distance'].apply(lambda d: 0 if d<1.5 else d)\n",
    "#     it_neighbours['angle'] = it_neighbours.apply(lambda row: angle_between(row['geometry'], row['geometry_r']), axis=1)\n",
    "#     it_neighbours['angle_diff'] = it_neighbours.apply(lambda row: angle_difference(row['angle'], row['target_angle']), axis=1)\n",
    "\n",
    "    # Define the neighbour metric\n",
    "#     it_neighbours['neighbour_metric'] = it_neighbours.apply(lambda row: (neighbour_distance_factor*row['distance']**2)+(neighbour_angle_factor*row['angle_diff']**2), axis=1)\n",
    "    it_neighbours['neighbour_metric'] = it_neighbours.apply(lambda row: neighbour_distance_factor*(row['weight']*row['distance'])**2, axis=1)\n",
    "    neighbour_metric = it_neighbours.groupby('code')['neighbour_metric'].sum().reset_index()\n",
    "#     it_gdf = it_gdf.merge(neighbour_metric, on='code', how='left').fillna(0)\n",
    "    \n",
    "    return neighbour_metric\n",
    "\n",
    "\n",
    "def proximity_metric(it_gdf):\n",
    "    \n",
    "    # Define the proximity metric\n",
    "    it_prox = it_gdf.merge(distance_ranking, left_on='code', right_on='LAD20CD_left')\n",
    "    it_prox = it_prox.merge(it_gdf, left_on='LAD20CD_right', right_on='code', suffixes=('', '_r'))\n",
    "    it_prox = it_prox[['code', 'geometry', 'code_r', 'geometry_r', 'target_distance_rank', 'target_angle']]\n",
    "    \n",
    "    it_prox['distance'] = it_prox.apply(lambda row: row['geometry'].distance(row['geometry_r']), axis=1)\n",
    "    it_prox['distance_rank'] = it_prox.groupby('code')['distance'].rank()\n",
    "    it_prox['distance_rank_difference'] = it_prox['distance_rank']-it_prox['target_distance_rank']\n",
    "#     it_prox['angle'] = it_prox.apply(lambda row: angle_between(row['geometry'], row['geometry_r']), axis=1)\n",
    "#     it_prox['angle_diff'] = it_prox.apply(lambda row: angle_difference(row['angle'], row['target_angle']), axis=1)\n",
    "    \n",
    "#     it_prox['proximity_metric'] = it_prox.apply(lambda row: (proximity_distance_factor*row['distance_rank_difference']**2)+(proximity_angle_factor*row['angle_diff']**2), axis=1)\n",
    "    it_prox['proximity_metric'] = it_prox.apply(lambda row: proximity_distance_factor*row['distance_rank_difference']**2, axis=1)\n",
    "    proximity_metric = it_prox.groupby('code')['proximity_metric'].sum().reset_index()\n",
    "#     it_gdf = it_gdf.merge(proximity_metric, on='code', how='left').fillna(0)\n",
    "    \n",
    "    return proximity_metric\n",
    "\n",
    "\n",
    "def packing_metric(it_gdf):\n",
    "    \n",
    "    # Define the packing metric\n",
    "    it_pack = copy.copy(it_gdf)\n",
    "    it_pack['dummy'] = [1]*len(it_pack)\n",
    "    it_pack = it_pack.merge(it_pack, how='inner', on='dummy', suffixes=('', '_r'))\n",
    "    it_pack['distance'] = it_pack.apply(lambda row: row['geometry'].distance(row['geometry_r']), axis=1)\n",
    "    it_pack['packing_metric'] = it_pack.apply(lambda row: packing_factor*row['distance']**2, axis=1)\n",
    "    packing_metric = it_pack.groupby('code')['packing_metric'].sum().reset_index()\n",
    "#     it_gdf = it_gdf.merge(packing_metric, on='code', how='left').fillna(0)\n",
    "    \n",
    "    return packing_metric\n",
    "    \n",
    "# def _orient_func(row, comp):\n",
    "#     loc = row[comp+'_current']\n",
    "#     ref = row[comp]\n",
    "#     if loc > ref[1]:\n",
    "#         return loc-ref[1]\n",
    "#     elif loc < ref[0]:\n",
    "#         return loc-ref[0]\n",
    "#     else:\n",
    "#         return 0\n",
    "    \n",
    "# def orientation_metric(it_gdf):\n",
    "#     it_orient = copy.copy(it_gdf)\n",
    "#     it_orient['x'] = it_orient['geometry'].apply(lambda g: g.xy[0][0])\n",
    "#     it_orient['y'] = it_orient['geometry'].apply(lambda g: g.xy[1][0])\n",
    "#     it_orient['EW_current'] = it_orient['x'].rank()\n",
    "#     it_orient['NS_current'] = it_orient['y'].rank()\n",
    "    \n",
    "#     it_orient = it_orient.merge(orientation_ranking, left_on='code', right_on='LAD20CD', how='left')\n",
    "#     it_orient['orientation_metric'] = it_orient.apply(lambda row: orientation_factor*(_orient_func(row, \"EW\")**2 + _orient_func(row, \"NS\")**2), axis=1)\n",
    "    \n",
    "#     return it_orient[['code', 'orientation_metric']]\n",
    "\n",
    "def orientation_metric(it_gdf):\n",
    "    # Define the orientation metric\n",
    "    it_orient = copy.copy(it_gdf)\n",
    "    it_orient['x'] = it_orient['geometry'].apply(lambda g: g.xy[0][0])\n",
    "    it_orient['y'] = it_orient['geometry'].apply(lambda g: g.xy[1][0])\n",
    "    it_orient['EW_current'] = it_orient['x'].rank()\n",
    "    it_orient['NS_current'] = it_orient['y'].rank()\n",
    "    it_orient = it_orient.merge(orientation_ranking, left_on='code', right_on='LAD20CD', how='left')\n",
    "    it_orient['orientation_metric'] = it_orient.apply(lambda row: orientation_factor*((row['EW_current']-row['EW'])**2 + (row['NS_current']-row['NS'])**2), axis=1)\n",
    "#     it_gdf = it_gdf.merge(it_orient[['code', 'orientation_metric']], on='code', how='left')    \n",
    "    \n",
    "    return it_orient[['code', 'orientation_metric']]\n",
    "    \n",
    "\n",
    "def get_metric(it_gdf):\n",
    "\n",
    "    it_gdf = (\n",
    "        it_gdf\n",
    "        .merge(neighbour_metric(it_gdf), on='code', how='left')\n",
    "        .merge(proximity_metric(it_gdf), on='code', how='left')\n",
    "        .merge(packing_metric(it_gdf), on='code', how='left')\n",
    "        .merge(orientation_metric(it_gdf), on='code', how='left')\n",
    "        .fillna(0)\n",
    "    )\n",
    "    \n",
    "    it_gdf['metric'] = it_gdf['neighbour_metric']+it_gdf['proximity_metric']+it_gdf['packing_metric']+it_gdf['orientation_metric']\n",
    "    return it_gdf\n",
    "\n",
    "\n",
    "def find_it_gdf(codes_list, coords):\n",
    "    it_gdf = generate_geodataframe(codes_list, coords)\n",
    "    return get_metric(it_gdf)\n",
    "\n",
    "def single_swap(gdf, from_loc, to_loc):\n",
    "    new_df = copy.copy(gdf)\n",
    "    new_df.loc[(gdf['geometry']==to_loc), 'geometry'] = from_loc\n",
    "    new_df.loc[(gdf['geometry']==from_loc), 'geometry'] = to_loc\n",
    "\n",
    "    metric_columns = [c for c in new_df.columns if 'metric' in c]\n",
    "    new_df = get_metric(new_df.drop(columns=metric_columns))\n",
    "    return new_df\n",
    "    \n",
    "\n",
    "def do_swaps(gdf, max_metric):\n",
    "    \n",
    "    original_metric = gdf['metric'].sum()\n",
    "    original_loc = max_metric['geometry'].values[0]\n",
    "    original_x, original_y = original_loc.xy\n",
    "    \n",
    "    directions = []\n",
    "    metrics = []\n",
    "    new_dfs = []\n",
    "    \n",
    "    for x_shift in range(-max_move_jump, max_move_jump+1):\n",
    "        for y_shift in range(-max_move_jump, max_move_jump+1):\n",
    "            \n",
    "            if (x_shift,y_shift)==(0,0):\n",
    "                new_df = copy.copy(gdf)\n",
    "            else:\n",
    "                new_loc = Point((original_x[0]+x_shift, original_y[0]+y_shift))\n",
    "                new_df = single_swap(gdf, original_loc, new_loc)\n",
    "                            \n",
    "            directions.append((x_shift, y_shift))\n",
    "            new_dfs.append(new_df)\n",
    "            metrics.append(new_df['metric'].sum())\n",
    "\n",
    "    dir_to_use = directions[np.argmin(metrics)] if np.min(metrics) < original_metric else (0,0)\n",
    "    output_df = new_dfs[np.argmin(metrics)] if np.min(metrics) < original_metric else gdf\n",
    "    return dir_to_use, new_dfs[np.argmin(metrics)]\n",
    "\n",
    "\n",
    "def do_random_move(gdf_in):\n",
    "    \n",
    "    bounds = gdf_in.total_bounds\n",
    "    \n",
    "    P1 = np.random.choice(gdf_in['geometry'])\n",
    "    \n",
    "    x2 = np.random.randint(bounds[0]-max_move_jump, bounds[2]+max_move_jump)\n",
    "    y2 = np.random.randint(bounds[1]-max_move_jump, bounds[3]+max_move_jump)    \n",
    "    P2 = Point(x2, y2)\n",
    "    \n",
    "    gdf_out = single_swap(gdf_in, P1, P2)\n",
    "    return gdf_out\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "def do_iteration(gdf_in):\n",
    "        \n",
    "    gdf = copy.copy(gdf_in)\n",
    "    gdf['is_tested'] = [False]*len(gdf)\n",
    "    \n",
    "    all_tested = False    \n",
    "    iterate = True\n",
    "    \n",
    "    old_gdf = copy.copy(gdf)\n",
    "    while iterate:    \n",
    "        max_metric = old_gdf.loc[(old_gdf['is_tested']==False), 'metric'].max()\n",
    "        row_to_test = old_gdf.loc[(old_gdf['metric']==max_metric) & (old_gdf['is_tested']==False)]\n",
    "        d, new_gdf = do_swaps(old_gdf, row_to_test)        \n",
    "        \n",
    "        if d != (0,0):\n",
    "            iterate = False\n",
    "            \n",
    "        else:\n",
    "            new_gdf.loc[(new_gdf['metric']==max_metric) & (new_gdf['is_tested']==False), 'is_tested'] = True\n",
    "            num_tested = new_gdf['is_tested'].sum()\n",
    "            all_tested = True if num_tested == len(new_gdf) else False\n",
    "            iterate = True if num_tested < len(new_gdf) else False\n",
    "            old_gdf = copy.copy(new_gdf)\n",
    "            \n",
    "    \n",
    "    \n",
    "    return all_tested, new_gdf.drop(columns=['is_tested'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly allocate codes to grid locations\n",
    "codes_list = geodata['LAD20CD'].values\n",
    "\n",
    "# num_initialisations = len(geodata)\n",
    "num_initialisations = 20\n",
    "initial_dfs = {}\n",
    "initial_metrics = []\n",
    "for random_seed in range(num_initialisations):\n",
    "    initial_coords = np.unravel_index(random.sample(range(initial_grid_size), len(codes_list)), (initial_x_length, initial_y_length))\n",
    "    initial_dfs[random_seed] = find_it_gdf(codes_list, initial_coords)\n",
    "    initial_metrics.append(initial_dfs[random_seed]['metric'].sum())\n",
    "    \n",
    "initial_df = initial_dfs[np.argmin(initial_metrics)]\n",
    "\n",
    "# initial_layout = np.zeros((initial_x_length, initial_y_length), dtype=object)\n",
    "\n",
    "# for code,x,y in zip(codes_list,initial_coords[0],initial_coords[1]):\n",
    "#     initial_layout[x,y] = code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rand 0: -13043.515517016902\n",
      "rand 1: -1189.943896251556\n",
      "rand 2: 15543.866950211232\n",
      "rand 3: -9679.838270815526\n",
      "rand 4: 11342.491159403144\n",
      "rand 5: 3905.2938381761196\n",
      "rand 6: 21820.767780505877\n",
      "rand 7: -5268.056987033153\n",
      "rand 8: 24272.860294822938\n",
      "rand 9: 28563.858734572146\n",
      "rand 10: 58463.4592962686\n",
      "rand 11: 858.4792053863493\n",
      "rand 12: -11890.011920550955\n",
      "rand 13: 3137.2525212801556\n",
      "rand 14: 30776.70362998928\n",
      "rand 15: -1449.4768691038626\n",
      "rand 16: 14822.49004605021\n",
      "rand 17: 27034.99999999997\n",
      "rand 18: -13247.525905721166\n",
      "rand 19: 591.0242718759546\n",
      "rand 20: 19892.849499259057\n",
      "rand 21: 10392.227318548234\n",
      "rand 22: 12021.699853475118\n",
      "rand 23: 20179.050504212064\n",
      "rand 24: 5402.332708029717\n",
      "rand 25: 20363.714626278816\n",
      "rand 26: 1978.27610591962\n",
      "rand 27: 22150.117026981592\n",
      "rand 28: 12189.942223457008\n",
      "rand 29: 4432.432709136599\n",
      "rand 30: 12189.942223457008\n",
      "rand 31: 335.77731179799594\n",
      "rand 32: 6305.787064643431\n",
      "rand 33: 6817.2012377998035\n",
      "rand 34: 14386.070278552157\n",
      "rand 35: -256.8941403544013\n",
      "rand 36: 40336.66835626529\n",
      "rand 37: 14838.38438880704\n",
      "rand 38: 7234.448212089439\n",
      "rand 39: -8051.46980810308\n",
      "rand 40: -208.97572812400176\n",
      "rand 41: 15391.992081660428\n",
      "rand 42: 10067.501382246031\n",
      "rand 43: 3401.1810632155975\n",
      "rand 44: 13413.75553620902\n",
      "rand 45: 5240.726490631845\n",
      "rand 46: 2507.2727945547667\n",
      "rand 47: 17396.09513786387\n",
      "rand 48: 23363.661099495162\n",
      "rand 49: 841.6300897327019\n",
      "rand 50: 11702.227318548219\n",
      "rand 51: 40590.97956529999\n",
      "rand 52: 33804.70028669163\n",
      "rand 53: -5631.257230845746\n",
      "rand 54: -729.7903120055853\n",
      "rand 55: 16083.651954316912\n",
      "rand 56: -10863.61858660182\n",
      "rand 57: -2691.09844289365\n",
      "rand 58: 6206.702712639206\n",
      "rand 59: 16894.266215736017\n",
      "rand 60: 1052.1103829591157\n",
      "rand 61: 5402.517505093128\n",
      "rand 62: 14645.70389295509\n",
      "rand 63: 20456.37183586537\n",
      "rand 64: 3733.3338297651644\n",
      "rand 65: 1706.3746295152232\n",
      "rand 66: 13409.601894469743\n",
      "rand 67: 19927.317541280616\n",
      "rand 68: -392.86292576033156\n",
      "rand 69: -5805.251318925279\n",
      "rand 70: 2259.1022584497987\n",
      "rand 71: 13136.63494752266\n",
      "rand 72: 14851.98177252346\n",
      "rand 73: 17305.252880380023\n",
      "rand 74: 19568.687096300244\n",
      "rand 75: -2549.4381907280185\n",
      "rand 76: 18767.85311088532\n",
      "rand 77: 25893.88490854745\n",
      "rand 78: -697.3664019838616\n",
      "rand 79: 10026.259521778207\n",
      "rand 80: 7717.060080023148\n",
      "rand 81: 19633.366557564077\n",
      "rand 82: 5465.16418478862\n",
      "rand 83: 5379.0841986643645\n",
      "rand 84: 6835.391134703983\n",
      "rand 85: 54990.652738601595\n",
      "rand 86: 5050.993735682787\n",
      "rand 87: 9622.246725776247\n",
      "rand 88: 2219.576392510673\n",
      "rand 89: 5476.299799606233\n",
      "rand 90: 8292.775849558035\n",
      "rand 91: 37646.551699509175\n",
      "rand 92: 16730.643840945268\n",
      "rand 93: 2248.6657639818877\n",
      "rand 94: 18171.14645911142\n",
      "rand 95: 3922.0336825192935\n",
      "rand 96: 18204.31990366707\n",
      "rand 97: 16309.685596091076\n",
      "rand 98: 30340.565801899415\n",
      "rand 99: 22724.824834492436\n",
      "rand 100: 6543.970619541244\n",
      "rand 101: -850.0\n",
      "rand 102: 22948.998992643188\n",
      "rand 103: 36059.851233679045\n",
      "rand 104: 11929.340111991813\n",
      "rand 105: 17987.76267867154\n",
      "rand 106: 27696.286722651697\n",
      "rand 107: 8402.071091066668\n",
      "rand 108: 80228.43215557747\n",
      "rand 109: 30181.999999999985\n",
      "rand 110: -221.28915629581024\n",
      "rand 111: -1893.1277088593633\n",
      "rand 112: 4838.509193625658\n",
      "rand 113: 15594.528459194204\n",
      "rand 114: 3025.711243682672\n",
      "rand 115: 45798.601861619085\n",
      "rand 116: 30844.53631681307\n",
      "rand 117: -2175.6140391654553\n",
      "rand 118: 8720.531630074336\n",
      "rand 119: 410.13494830532727\n",
      "rand 120: 16290.054867445964\n",
      "rand 121: 12346.83941927598\n",
      "rand 122: 8064.936016541549\n",
      "rand 123: 35316.30648907505\n",
      "rand 124: 21832.295671620952\n",
      "rand 125: 33789.36215597696\n",
      "rand 126: 14772.686455431896\n",
      "rand 127: 20352.755749727636\n",
      "rand 128: 18123.83024343085\n",
      "rand 129: 3625.182073382719\n",
      "rand 130: 6396.5946857653325\n",
      "rand 131: 12355.244550008552\n",
      "rand 132: 4690.1247333249485\n",
      "rand 133: 25331.38855845019\n",
      "rand 134: 15220.000000000007\n",
      "rand 135: 14915.984401196743\n",
      "rand 136: -10933.01970566302\n",
      "rand 137: 37442.48569881159\n",
      "rand 138: 20657.03894202158\n",
      "rand 139: 3341.886998762544\n",
      "rand 140: 6054.2215578541945\n",
      "rand 141: -2741.560885062856\n",
      "rand 142: 18769.72941005515\n",
      "rand 143: 22393.49945706645\n",
      "rand 144: 778.9400817544665\n",
      "rand 145: 31989.904494452807\n",
      "rand 146: -1841.0452257468569\n",
      "rand 147: 5040.645846122956\n",
      "rand 148: 500.02085915697535\n",
      "rand 149: 10530.038701337828\n",
      "rand 150: 7366.415240934453\n",
      "rand 151: 23041.84108187601\n",
      "rand 152: 17871.925173438358\n",
      "rand 153: 33080.41947013078\n",
      "rand 154: 7704.407428021856\n",
      "rand 155: 8160.368766857871\n",
      "rand 156: 22820.583215791943\n",
      "rand 157: 47744.246193854044\n",
      "rand 158: 4501.037876442606\n",
      "rand 159: 46168.00000000001\n",
      "rand 160: 40932.438134089745\n",
      "rand 161: 4988.79204416852\n",
      "rand 162: 35598.250935975586\n",
      "rand 163: 22427.884007715336\n",
      "rand 164: 6403.265227015021\n",
      "rand 165: 36244.05771041534\n",
      "rand 166: 34762.79679898827\n",
      "rand 167: 2952.1496490585414\n",
      "rand 168: 10264.039143268732\n",
      "rand 169: 5061.5240059977\n",
      "rand 170: 39153.97229336389\n",
      "rand 171: 19538.474684821704\n",
      "rand 172: 11662.0\n",
      "rand 173: 18576.247291618136\n",
      "rand 174: -418.0216787869722\n",
      "rand 175: 41953.53603798354\n",
      "rand 176: 5223.600958493691\n",
      "rand 177: 3949.6958154017993\n",
      "rand 178: 18617.35922128852\n",
      "rand 179: 20015.647516089863\n",
      "rand 180: -864.8851030020087\n",
      "rand 181: 40804.506594467675\n",
      "rand 182: -271.3468445241888\n",
      "rand 183: 30873.276716123204\n",
      "rand 184: 938.0923762003731\n",
      "rand 185: -1809.8246009842333\n",
      "rand 186: 21854.218422215985\n",
      "rand 187: 2526.702988312565\n",
      "rand 188: 24465.59877318175\n",
      "rand 189: 4443.649023229082\n",
      "rand 190: 6443.074300331151\n",
      "rand 191: 9033.052039200287\n",
      "rand 192: -336.8446586427017\n",
      "rand 193: 13645.21910510071\n",
      "rand 194: 12347.251946654142\n",
      "rand 195: 22738.518843889156\n",
      "rand 196: 18398.75615899996\n",
      "rand 197: 2890.571756102785\n",
      "rand 198: 12292.299729182043\n",
      "rand 199: 13840.695724540965\n",
      "rand 200: 27601.067327416582\n",
      "rand 201: 3017.433993834231\n",
      "rand 202: -1553.247527937987\n",
      "rand 203: 5865.0\n",
      "rand 204: 2238.666461809822\n",
      "rand 205: 49292.573823137434\n",
      "rand 206: 25194.190683669454\n",
      "rand 207: 20546.0\n",
      "rand 208: -902.652092299104\n",
      "rand 209: 1320.6418089118233\n",
      "rand 210: 2601.209592198189\n",
      "rand 211: 18673.903505025315\n",
      "rand 212: -808.8399728756485\n",
      "rand 213: 6655.769018514846\n",
      "rand 214: 24976.744508557298\n",
      "rand 215: 38148.578948872884\n",
      "rand 216: 7708.738726565694\n",
      "rand 217: 10154.987215162539\n",
      "rand 218: 6888.81986946526\n",
      "rand 219: 273.3920152626233\n",
      "rand 220: 19649.829465553303\n",
      "rand 221: 832.0342981836802\n",
      "rand 222: 0.0\n",
      "rand 223: 32231.09184000655\n",
      "rand 224: 24978.446612537693\n",
      "rand 225: 4500.070554440099\n",
      "rand 226: 42432.886311073955\n",
      "rand 227: 58997.09224958953\n",
      "rand 228: 133.75373415657668\n",
      "rand 229: 2978.1100115717127\n",
      "rand 230: 2983.9431764431793\n",
      "rand 231: 39422.46799446783\n",
      "rand 232: 8594.098133923122\n",
      "rand 233: 14453.44870659602\n",
      "rand 234: 14304.873760835668\n",
      "rand 235: 3132.699171719767\n",
      "rand 236: 9746.59920777096\n",
      "rand 237: 2415.0\n",
      "rand 238: 19069.796032733873\n",
      "rand 239: 13187.251946654142\n",
      "rand 240: 32812.69346889524\n",
      "rand 241: 26513.955851241495\n",
      "rand 242: 11831.092982549999\n"
     ]
    }
   ],
   "source": [
    "new_df = initial_df\n",
    "\n",
    "max_consecutive_bad_moves = 30\n",
    "consecutive_bad_moves = 0\n",
    "r = 0\n",
    "\n",
    "while consecutive_bad_moves < max_consecutive_bad_moves:\n",
    "    met_old = new_df['metric'].sum()\n",
    "    t_df = do_random_move(new_df)\n",
    "    met_new = t_df['metric'].sum()\n",
    "    \n",
    "    print('rand '+str(r)+': '+str(met_new-met_old))\n",
    "    \n",
    "    if met_new < met_old:\n",
    "        new_df = t_df\n",
    "        consecutive_bad_moves = 0\n",
    "    else:\n",
    "        consecutive_bad_moves += 1\n",
    "    r+=1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0:  1.8 -- -3311.124325370489\n",
      "it 1:  1.5 -- -3710.1314233190824\n",
      "it 2:  1.9 -- -2944.2328361858636\n",
      "it 3:  2.0 -- -2932.950404889136\n",
      "it 4:  2.0 -- -1962.8765543079498\n",
      "it 5:  3.0 -- -1477.0116602191774\n",
      "it 6:  3.2 -- -844.3825019187025\n",
      "it 7:  2.0 -- -1503.1661735827292\n",
      "it 8:  1.6 -- -1460.824255768881\n",
      "it 9:  1.8 -- -1189.628175979793\n",
      "it 10:  1.6 -- -782.805725129605\n",
      "it 11:  3.5 -- -348.26287093653445\n",
      "it 12:  5.8 -- -161.13562627825377\n",
      "it 13:  3.3 -- -35.979767426482795\n",
      "it 14:  7.6 -- -438.9931658306177\n",
      "it 15:  3.9 -- -420.58399809426373\n",
      "it 16:  6.6 -- -99.44001003778612\n",
      "it 17:  5.4 -- -248.28532267009905\n",
      "it 18:  6.5 -- -175.94422467266668\n",
      "it 19:  11.7 -- -523.8482021620584\n",
      "it 20:  1.4 -- -40.0\n",
      "it 21:  11.2 -- -80.45644771628213\n",
      "it 22:  3.7 -- -601.2991746630923\n",
      "it 23:  9.4 -- -151.8965650529608\n",
      "it 24:  8.4 -- -79.0\n",
      "it 25:  7.7 -- -274.1524893699443\n",
      "it 26:  1.4 -- -52.0\n",
      "it 27:  8.7 -- -368.8466612793545\n",
      "it 28:  5.5 -- -352.4680288606305\n",
      "it 29:  10.1 -- -72.76919711267328\n",
      "it 30:  26.1 -- -60.98152969671901\n",
      "it 31:  3.1 -- -170.25983191494925\n",
      "it 32:  36.2 -- 0.0\n",
      "TOTAL TIME: 3.5 minutes\n"
     ]
    }
   ],
   "source": [
    "c = 'y'\n",
    "# new_df = initial_df\n",
    "it = 0\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "while c == 'y':\n",
    "    \n",
    "    ti = time.time()\n",
    "    \n",
    "#     f, ax = plt.subplots(figsize=(5,5))\n",
    "#     ax.axis('off')\n",
    "    \n",
    "    old_df = new_df\n",
    "    met_old = old_df['metric'].sum()\n",
    "    \n",
    "    d, new_df = do_iteration(old_df)\n",
    "    \n",
    "#     new_df.plot(ax=ax, column='metric')\n",
    "#     for x, y, label in zip(new_df.geometry.x, new_df.geometry.y, new_df.code):\n",
    "#         ax.annotate(label[-2:], xy=(x, y), xytext=(0,0), textcoords=\"offset points\")\n",
    "#     plt.show()  \n",
    "    \n",
    "    met_new = new_df['metric'].sum()\n",
    "    \n",
    "    c = 'y' if d == False else 'n'\n",
    "    c = 'n' if met_new > met_old else c\n",
    "    \n",
    "    tf = time.time()\n",
    "    \n",
    "    print(\"it \"+str(it)+\":  \"+str(np.round(tf-ti, decimals=1))+' -- '+str(met_new-met_old))\n",
    "    it+=1\n",
    "    \n",
    "#     f.clf()\n",
    "    \n",
    "    \n",
    "total_time = time.time()-start_time\n",
    "print('TOTAL TIME: '+str(np.round(total_time/60, decimals=1))+' minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABFYAAAIuCAYAAACVYYzwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdeXxcVfnH8c9zJ3vSfUs3uqQNpQstO02grEFBBIpIUZRFwa3KzyJYtfkpP01FQCkqVRFcUBEqShVXiGwtDXspbYGSku5Lum/Zk5nz++NOStpm7yR3kn7fr1dezdx77jnPTGYG5plznmPOOUREREREREREpO28oAMQEREREREREemqlFgREREREREREWknJVZERERERERERNpJiRURERERERERkXZSYkVEREREREREpJ2UWBERERERERERaSclVkRERERERERE2kmJFRERERERERGRdlJiRURERERERESknZRYERERERERERFpJyVWRERERERERETaSYkVEREREREREZF2UmJFRERERERERKSdlFgREREREREREWknJVZERERERERERNpJiRURERERERERkXZSYkVEREREREREpJ2UWBERERERERERaSclVkRERERERERE2kmJFRERERERERGRdlJiRURERERERESknZRYERERERERERFpJyVWRERERERERETaSYkVEREREREREZF2UmJFRERERERERKSdlFgREREREREREWknJVZERERERERERNpJiRURERERERERkXZSYkVEREREREREpJ2UWBERERERERERaSclVkRERERE5JgRKc22SGl2YtBxiEj3ocSKiIiIiIh0e5HS7F6R0uwvAm8CyyOl2VMCDklEuglzzgUdg4iIiIiISMxESrOnA896mcX7ordvAOYDaQ2a1QCzgR8DCcAwYET051Uvs/jdzoxZRLouJVZERERERKTLipRmD/Yyi7c2uH01sADYBtwKPArcB9zSRBc7gb4cOpt/JXCql1lcHSnNNqC3l1m8pwPCF5FuQIkVERERERHpkiKl2R8CfuRlFk+M3j4JWAKkNmj2HFANfLiN3d8DLANuAzYAPwA+DfwFeMHLLA5Hx+yjpIvIsU2JFRERERER6XIipdmfA34G1AGFwDhgJP6ynlirjY5Tn7DZjj8T5kHgIeCjXmbxzg4YV0S6ACVWREREREQkLkVKs8cDX8dfqtMD6NngZ2CAoR3uFi+z+KdBByEiwVBiRURERERE4lKkNLsXfq2U5KBjacHrXmbxaUEHISLB0HbLIiIiIiISl6K7+vwr6Dha4dTo7BoROQYpsSIiIiIiIvHssaADaKXrgw5ARIKhpUAiIiIiIhK3IqXZ6fjFYtOCjqUFW4Dj6ncLEpFjh2asiIiIiIhI3PIyi8uBJ4OOoxV2ARZ0ECLS+ZRYiREzK2vh/Doz6x+Dcc42s7fNbJmZpTZyfrqZOTMbd5Tj3GBm9x9NH9K1mFmmmT1mZiVm9o6Z/cvMsoOOq6OYWTj6OnrbzN4ys1vNrN3viU29xs3sC2Z2XQvXtun1ZmbPm9mpjRw/1cx+0tp+OkqDx7b+Z6SZnWtm/4iev8zMvtFCHyPN7JOtaLOyiXPfNbML238vREQkznSF5UBf9TKL64IOQkQ6X0fs8S4d61rgh8653zRx/hPAi8A1wB2R0uyeQLWXWVzdWQFK12NmBiwEHnbOXRM9NgUYBBQ3d21u4WwP6AXsX5J3V1ea+lrpnJsCYGYDgT/i34/vxHIQ59wvDj927jO3JeBvGbn3+Qt+GLP1mM6514HXDz9uZgnOuc78H72Dj22DGEbW/+6ce5KWv3kcCXwS/+9CdsG8JCAV2F+cP6vFx8w59+3GjptZyDnXlZ6nIiLiKwTCQCjoQJrwhJdZ/GzQQYhIMDRjJcbMbLCZLYp+S7vSzM5upM2nzOzVaJsHzOyI/0CY2QVm9qaZrTCzX5tZspndBFwNfNvMHmnkmgwgF/hsWqrdGCnNfhvY9dySigOTxidvS062v5rZKjN7JPpBGjO7JHrsRTP7Sf03yof1O8DM/mJmr0V/cqPHz2nwjfSbZtYjevz2aLvlZvZ/0WPfM7P/adDnXDO7pZ0Ps8TeeUBtwySAc24Z8KKZ3RN9Lq8wsxkA0dkHz6eN7L906Y0P1hZ//+87nXO7xn79Iz81syeibS43s0ozSzKzFDNbY2ZZZra0fgwzG2tmb3TyfT2Cc2478Dngy+YLRe93/fP48/DB/TazPx/+Woq6PfraftXMxkSvucPMbgMIpSaekdw/Y/vSLz9SXfKL53e8euNvas595rZro9cOMbP/mNlqM7s7em3IzH7b4PGf1WCsj0fHKa5/nzlsVsgdZvZLM3sa+F1Tr+MgWIMZOtH79xMzK4o+R66KNvsBcLZ53lt9cs55uXrb1v0bfjFvz/qf3FUVSklZY2Zjo+1CZvag+TOPnrboTL5ov1dFf19nZt82sxfxH7eLzOwlM1tqZo9H3ztFRCS+fYj4TarUALdHSrOHBh2IiARDiZXY+yTwVPTb2snAsoYnzewEYAaQG20Txp+F0rBNCvBbYIZzbhL+zKIvOucewv+W93bn3CHXRF0B/Ce8dWx40gnJw5YurxoPJJiRuGFT3cAVz48YBIwHRgO50XEeAC52zp0FDGjiPv0YmOecOw34GPBQ9PhtwMzo/TgbqDSzi4CxwOnAFOAUM5sG/IpopXTzl1tcAxyRHJLATAQaS3Bcif93nAxcCNxjZoMBLME7ffzcj4876Vc3eVXb9nkH3t7ca8B5428MpSefE732bGAlcBpwBvCKc64E2Gf+bBiAG/Gf64Fzzq3Bf08cCHwW2Bd9zp8G3Gxmo6JNTwK+SoPXUoNu9jvnTgfuB+47fIyEtOR/jM//aI+T77/Ws5AXspAlAL/sf/bYE/Ef5xnAJGCGmQ2PHhvqnJsYfS9oOFMtITrWV2l6ls0pwOXOuU/S9Ou4I6Q2SLoubEX7wcBZwKX4CRWAbwCLx373R+sGXHLF5H2vvZTce+o0G3HL7KTR3/jeoOO+fHuvaLuxwHzn3ARgL/59a0xV9H3uv0A+cKFz7mT8GT63tuteiohIZ/ps0AE04xfAHXTsf1tFJI4psRJ7rwE3mtkdwCTn3IHDzl+A/2HnNTNbFr09+rA2xwNrnXP1SzAeBqa1YuxP4K8/vWXGFRnusb9+MPRpJyUzZlTSieGtY4/HT/aMBMYBa5xza6PNHm2i3wuB+6PxPgn0jM5OWQLcG5150ju61OCi6M+bwNLoGGOdc+uAXWZ2Uv1559yuVtwnCdZZwKPOubBzbhvwAn6igR4nDLHkgT1TzTPSswZSXboPS/DS08cM7BFNIJ4O3Iv/3D0bWBzt8yH810gIP5Hwx86+U82on31yEXBd9Dn/CtAP/wM8wKvOuU3OuQgfvJbqPdrg36kNO+53xuiBFvL69Zo0NAVg4AUn1J9K6zVp2BXAM865fc65KuAdYASwBhhtZj81sw8D+xt0+UT03zcOi6GhJ51zldHfm3odd4RK59yU6M/0VrT/q3Mu4px7B3/5GQCWmJSC/7dISRk+gj0v/Jfdi56hdu+e5ORBg+tnvK2Nzq6C5h+LBdF/z8RPii2JPhbX4z/WIiISpyKl2UnAuUHH0YzLgU8DF0SX4YvIMUY1VmLMObcoOkPjI8Dvzewe59zvGjQx/DoW32ymmzZXEzezfsD5wMQhJ64ZmJhoIQPu+l+/lmZykgHU4idxwvh/+9aO4wFTG3xAq/cDM/sncAnwsvmFIg240zn3QCP9PATcAGQCv279vZNO8DZwVSPHm3yOeKlJSQcbeYaLRADoNfm4xAMrN3/EhSO1+LMDfos/dfe2aPO/4M+weBZ4I14SbGZW/9rYjn+/v+Kce+qwNucCDesV1b+W6rkmfqfv6aP6lq/d2ejYoeSE/o3165zbY2aT8ac/z8RfCviZaJvqhm2buFvlDX5v6nUcDxre94PPOS8pKS16LqXn5FNIGT6CivfeYfPvHggNuPiK0xq5Noxfh6Ux9Y+FAYXOuU/EKHYREel4U4H0oINoRn2CPhH4MPCnAGMRkQBoxkqMmdkIYLtz7kH85S8nH9bkGeAq84tlYmZ9o9c0tAoYWV+jAT8D/kILQ18F/M45N2LL8tF3rX9jVNWo4xJ58ZWqhm2SgRWHjTPaPigqOaOJvp8GvtzgPk6J/pvlnFvhnLsLfzr9OOAp4DP1NQvMbGj9fcUvjvph/BkPh3xglcA9CySb2c31B8zsNGAP/rKUkJkNwJ998ipApLquvLGOMsYNKXXhyC3AS865HfizPcbhJ2+Izsh4Cvg5hy5tCUz0vv0CuN855/Dj+6KZJUbPZ5tZa/6HbkaDf19qeGLoFSe9H0pNdPvf2QLA9udW1Z9ydRU1G5qIqz/gOef+AvwvR76ftEWjr+M4diBcVenw37eo3b2LxD796D11GunHT4hUlBTvaGe/L+MvhayvgZNm3Xj3KxGRbiIv6ADa4MeR0uzHIqXZ34qUZl8aKc0eHinN1hbMIt2cZqzE3rn4BSxrgTLgkG1WnXPvmFk+8HS01kgt/jfR6xu0qTKzG4HHzSwBf3nRETuLHOYTfFCb4GfAl6dfkp706MID3tWXZxCJEAb+7GUWb6j/Qtg5V2lmXwL+Y2Y7iX5gbsQtwHwzW47/nFkEfAH4qpmdh/8t8TvAv51z1dFlIC9Fa3qWAZ/CTzbVmNlzwF7tyhFfnHPOzKYD95m/DW4VsA6/fkcG8Bb+DIyvO+dKzWxc1abd7+LXZklr0FVF+qgBX8NPmCyKHluO//dvOIPjEfz6LU933L1qUWp0KUgiUAf8Hn/pEvizq0YCS6PFaXfg1zBqSbKZvYKftD5kRsTzF/ywLvtjy3/03r1P3xZKTbLek4eTkJ4MULn7lTV/wU9AHW4o8Bv7YBvo5ma6taSp13G8Wk44XLX2nu+W9Z56tufC4aQDb70BoRCh9IxIj4lTZranU+fcDjO7AXjUzJKjh/NpYfcrEREJVFdKrGTif8HS8AvLz6LZ2iLdmh36WUe6i0hp9ij8RMuHgAPAT4EfeZnFhyQ0zCzDOVcW/fA4H1jtnJvXETFFPxwuBT7unFvdEWNI58otnH0B8H3gBKAEyF+Sd9c/W7rO/F1yejnn/reDQ4wrZpZxzn+/9hHgO+sfeXl05cY9O8d94+Krnr/ghy8HHVu8yi6Y5+Enn78G9Mafvff14vxZ7wUZl4iIdI5IaXZf/C84uvJM+1wvs7go6CBEpOMosXKMi27fej2QhF9w9mbnXEUHjDMe+Aew0Dn3tVj3L11HdJeYLOB851zjRUe6KfO3q/4m/oyR9cAN0eVSIiIi0ohIafZVwONBx3GU+nqZxXuCDkJEOo4SKyIiIiIiEncipdkjgD8DpwYdy1HY6mUWDwk6CAledsG8NGAwsLU4f1bMv8iWYKnGioiIiIiIxJVIafZU4D9AV9++OCNSmp3pZRaXBh2IdLxzn7lt9JaXh1yMs9H4SZSGPz2BLcCoAEOUDqLEioiIiIiIxJs38XcUnBp0IEepB/Aj4NqgA4mF3MLZ3pK8uyJBxxEvzn3mNgNOBKYD051jIs6aqwf06+L8WTWdE510Ji0FEhERERGRDhEpzZ4FDAd+6mUWr23jtYOAV4ARHRFbJ8vzMov/G3QQRyu3cPYfgMuB3cCuBj/3Lcm765goxn/uM7eFgBz8ZMoVNJiBEq7xdm97Y3DfZi5/AzitOH+WPoR3M0qsiIiIiIhIh4iUZj8KXANEgLeAPsB+/N0r/wR4XmZxbTPXTwSK8Gd+dGWrgRO9zOKqoAM5GrmFs3sCTwNnHHbqFWDqkry7uuWHy3OfuS0ZuAA/mXIZMLCxdrXlCet2LB80soXurirOn/WX2EYoQevK25aJiIiIiEicipRmj8H/Zh/8zx0nASPxl078EX+pT06kNNua6sPLLF4J3NehgXaOscDXgw7iaC3Ju2s/cDGw7LBTZ+DPZGlUbuHsxA4Mq0Oc+8xtPc995rYZ5z5z22P4W37/E7iJJpIqADUHkre1ouu52QXzkmMUpsQJzVgREREREZGYipRmfx74CZDUTLMdwL+A97zM4jub6WsBcHVsIwxENTDJyyxe3doLIqXZyUAmsMHLLI6bD265hbMHAM8D4xscfhc/eZYFTAEmR3+mAGuAs+K9Psu5z9zWj2i9FOBCmn/+HqJyV8qbe4r7Hg+W1ormRcDHivNnqahxN6HEioiIiIiIxFSkNPss/K2SB7WieQUwzsss3thEX2vxZ7ocAMrxEw1d1eNeZnGrkkSR0uwewF+B8/GTUK8Br0Z/XvMyi3d2VJCtkVs4ezCwGD+RUq+OpjdI+dySvLse7PDA2uncZ247C/gLzcxIOZxzuLqKhPW15Uk795b0ngTWlpkom4ArivNnvdHWWCX+KLEiIiIiIiIxFynNHor/QfXwehz0zHqf/SVjGh56GX975W3AO6HBq38HnBreOtaAx4FfA0OBuUCTS4cOt/jlSr40ezuJibDk78NJTT20EsLCf5Vx1We38vaiEYwb2+rJCUf47YL9vPFWFT/9foufyW/zMot/1FKjSGn2QPzZPKc002wt0SRL9N+lXmZxeStDjoncwtmfB37RyuYHgF/iJ9xejafZK+c+c9sXgJ/Sxl1zK3envLXnvb6DgP5g7dlxtwr4DPAEcKlqr3RdqrEiIiIiIiIx52UWbwbuaWXzM4E7gJ8Dsxsc34k/Y2MS8H3akFQB+OMT+7n1i71Z+t8RRyRVAB776wHOOj2FBX870JZu26sCP0HUrEhp9kjgRZpPqoC/G80M4IfAImBZpDS731HG2FZ/b0PbHsDXgJeA9bmFs6/vmJBaJ7dw9um5hV/PO/eZ2x7Af961OTFStqnHAbDMdiZVAFLw6w2tAh7NLpg3vJ39SMCUWBERERERkY5yaXMnt26r49wrNnLyhes58dz1LH65EqDhLkFJd9+/+8UzL95w28kXrucLt28jHD5yxv0ziys4JW8Dk89bz2dnbaO6OsJDj+zj8b+XUXDvbj71pSNLWZSVRyh6rZIH7x3Egr9+kFh5vqiC86/cxMdv2sr4s9bxqS+VUj/L/1/PlDP+rHVMu2wj/5O/nY9+evMR/e7YWcdVn93CGR/ewBkf3sCSVysB+OMT+/8bGrz6OTNbZmZvmlkPADO7PTHBlvbrG9rw+et6rQJWffuuXWN/8uCeg33m37mTnz60t7mHch1woZdZvKu5RrG2JO+uLfgzZtpqGHBrjMNpUW7h7MTcwtkzcgtnv4S/k9G/neOy9vaXmFET6TF8X1FiRvXiowxtJJAI7Y9FgqXEioiIiIiIdJQTG/xeCSxtePLRhQe46Nx0lv53BG8+cxxTJiYDXNa3j9f3zjn9Br2+rOr5F1+tmrr4yeEs/e8IQiHjkb8cOrukqirCZ766jUd/kclbz42grs7xi4f3cdO1vfjoRenc9e3+/OFnR5Zl+eu/y/jQeelkZyXRt0+Ipcs/2An5zZXVzPtuf1YuGsHaDbUsebWKqqoIX/z6dv75x6EsenI4O3aFG73DX/32Dr76uT688p/jePyhwXzua/5GMT/82Z4JwEzn3BTg7BUvjEj/7ux+86Zfkn5L5cYxk7atHD1809a64xe9VJn8mU/25Hd/8u9nJOJY8LcyPnllkztOrwfO8zKL1zfVoIP9rZ3XnZhbOLtTZmjkFs7um1s4ezZ+Ed3H8GdIARZy2Hvt7bf36H3T0jPLJ9aWJY1vuXWr3JddMG9udsG81Bj1J52kvVOWREREREREWvJ9YC9+7ZT3vcziqkhp9sS6Ot4E7NTJKaGbbt1GbZ3j8g9n1CdWrEe61+PGT/R6609/OxBauryKMy7eAEBllWNg/9AhA7xXUsuo4QlkZ/k1Uq67uic/+81e/udzfZoN7LG/HuB/bu4NwIzLe/DYXw9w8okpAJx2UjLDhvg7BE+ekMy6jbVkpHuMPi6RUcf5x6+5ogcP/mHfEf0+s6iSd4trDt7eXxbhQFmEGZf3yNq5e98fr/1Yz1ffKxoxdMyopDN+82jEW7q8mlPz/PtXVu5YvbaGaVNT6dvX480VVWzbEWbKxGT69Q0dMRawAT+psq7ZO9ux/gYUtPPaK4EfxzCWQ+QWzh4P3AJcBzSarAg7m2y4SrPGz7dkd3HfN8HOOYowG0oAvgVcnV0w7/PF+bOejVG/0sGUWBERERERkQ7hZRYfUYzTyyxeWV1j1cCEaVNTb33uiWGf+9cz5SnXf6WUr32xD9dd3RMAg5BzcN3He/L9Of2bHKM9e3Hs2h3muSWVvL2qBjMIR/ziLXf9rz9OctIHpVxCIagLQ2s3/YhEXKOFcmd/pS+XXJh+3L+fKT/u/I9t5ukFQ3HOP/7563od0c9nP9mLhxccoHRHHTd+omdjQ23BT6qsbeXdjqncwtk9gDTgbfyZIKPb0c19uYWzP4Jfi+e/S/LuOuqdVXILZ3vAh4CvAhe11N78HZeGtmcsF6G2Zl9yrGarNDQGeCa7YN5vgNuL82d16hIvaTslVkREREREpNN5mcXrP3Zpj4f++IvM62/+VK+UiooIb66o5roGmxGff1Yq02/cylc/35uB/RPYvSfMgbIII4YnHmwzbkwi6zbW8f7aGsaMSuIPf97POVObn3zw53+U8emrevCLez7YDfq86Zt48ZWqJq8ZNyaJNRtqWbexlpHDE/nT38oabZd3bhrzf7OP277kz5hZtrKaKROTKVlXw6QTkpl0QjIvvV7FqvdruOjcNL5z9y6u/VgPMtI9Nm+tIzERBvZPYPrFGdxxzy5qax2PNLKUCbjdyyxe0+wdjbHcwtmGv4zmJvzCue8DOfizVma1s9u86M9buYWz/wXsAnY3+Km/vWdJ3l3VzcSWjj8z5X+A41s3tNsRski6GWntCdw8EgecuOPAjuUD6sAGR/usAcrBmp8y1To3ApdmF8z7KvBocf4sbekbp5RYERERERGRQGSNTJx7yoUbeiUmGhlpxm9/ekgCwY0/Ptm+O7sfH75mM5EIJCYYP71zwCGJlZQUj1/dN4gZnyulrs5x6pSURmeANLTgrwf4+pcP/dx75UcyeHThAa6+PKPRa1JTPe6/cwCXfGIz/fuGOO2klEbb/fh7A/nyt7Yz5fz11NXB2Wem8PO7B/HjB/fy/JJKQiE4ITuJi89PIznZY9XqGnIv3QhARrrH7+4fxMD+kJRknJuTSu9eIUKhIzZDWoFfK6RT5BbO7gd8Gj+hMqHBqcn4Wyj/mvYnVhr2NbmFOMppPOkCcDXQu/XDueoEi+ww46hmnCSm144eeNK20h3LB65M6lGzq/foPeN3rBi4IVIbamlXp9YaADwCXJddMO+m4vxZm2LUr8SQtXZKm4iIiIiISCxFSrOfxp+t0JhFwLRODKdFZeURMtI9nHN8+Zs7GDsqka9+PhYTE44UiThOvWgDC345mLGjkw4/fbmXWfxkhwwcFV1Scy5wM34tlCOCaOC30TaNrlmKP44Ei7xkxtSO6H3rq4PfdmFvQsst22wNcI6SK/FHuwKJiIiIiEhQbgaOrADrWwwcuZ9xgB76wz5OvnA9k87ZwL79ET736eZnxrTXO+9Vkz11HeefldZYUuUV4O8dMjCQWzh7cG7h7G8CxcAzwDU0n1QBuIEuk1QBz9yLHZVUAXARa3w609EbjV97pdG1YRIczVgREREREZHAREqzPw78qZFT/wAK6cBdY7qoC73M4mdi2WFu4ewE4MP4S30uBRrdgqi7CFnkBc9crHbyOUJtRcK6nSsHlLmwN7GDhngbOLc4f9bODupf2kiJFRERERERCVSkNPt5oLEPuufj1xIZ2KkBxa9nvcziC2LVWW7h7FHAZ/CLpLZrZ5yuyLPI4pC5sztyDOdw+9b0XlSxPe1MsOQOGOJN4ILi/Fl7OqBvaSMlVkREREREJDCR0uxEYBvQWLGSF/GXvdzVqUHFr6leZvHLR9NBbuHsZOBy/NkpF+LvNH1M8Yi8FPJchy0FaqhiR+obe9/vG6tCtod7Fcgrzp+1v4P6l1ZSjRUREREREQnSUBpPqgCchV9nZVmnRRO//nw0SZXcwtnpuYWz7wY2AQvwiwYfc0kVANdyzZiYqKsKbd77fp+OrIdyOvDP7IJ56R04hrSCEisiIiIiIhIYL7N4HfBloKrB4TLgdeBXwE7gPKCo04OLD2HgDuCT7e0gt3D2cPxiwLcD/WMTVlfWIUtzDlFXFdqyfdkgB9bRS6zOAp7MLpiX2sHjSDO0FEhERERERAIXKc0eg7/rybvAJi+z2B12Ph14ArgogPCCUgx8ysssfq29HeQWzp4KLAQGxSyqLs+VJHqRrI7qva4qVLp92aAanB3XUWM04t/A9OL8WdWNnYzOaqkozp+lBEAH0IwVEREREREJnJdZ/L6XWfy0l1m88fCkSvR8OXAZfnLlWDAfOKm9SZW8nIIeF1z03a8k/TXlR9RRGePYujB3IMEiHVbwta46tG37skHVnZxUAbgYeCy7YF5iE+drgLROjOeYohkrIiIiIiLSZURKsxOAR4Crg46lg6wCvuBlFr/QnovzcgrGAzOB64AMgHB23fLqaytPjF2IXZXbkWCRMjNGddQIBzb1WHJgY8/cjuq/Ff4E3FicP6ui4cHsgnmnAr2ASqB+dtjy6M+a4vxZkc4OtDtRYkVERERERLqUSGn2vcCsoOOIsWqgALjHyyyuzsspuAjYCNQWFuW/n5dTkASMKizKf+/wC/NyChLxd/qZCZzbWOdVN5UvjgyPdOgWw/HPVSVYpMKMvh05yt41vRZVbMuY1pFjtOB9/OTKi/UHsgvmDQeeBKY00r4cWAHcX5w/65FOibCbUWJFRERERES6lEhp9ieAPwYdRwzVACd6mcXv5eUUpAF34ydJioE64GlgBvB+YVH+wQ/seTkFg4Gbgc8DQ5obwCW7sspvlJXh0ZG71MQ9zyKLPFyqg2qDIWaM7ohx9pb0fqFie/o5HdF3KzngJ8Cc4vxZ5dkF8wxIx3/dfLSZ634HfLk4f9aBToix21BiRUREREREupxIafZ84EtBxxEjC7zM4mvycgpOA/4AZDfRrg7oiz/rYCbwMSChtYOER9Stqr6+Mo0QnV3/I065/QkWWW3GKR3R+57VfV6o3JkWZHIFoAT4DPAS8BX83bdOBW5s5prVwCeK82e90fHhdQ9KrIiIiIiISJcTKc1OBl7E/5DYFf0NeAf4yP4DSbd//PprpgL/C4RauG4TMKy9g7pEV1l9XeXrkePCx/iyoHouErLIi57RIUt3KnemLq3YkXagpiypj6uz4ztjq+cm/BT/+fUL/IScA5KaaV8LfAO4T/VXWqbEioiIiIiIdEmR0uyRwFKgT8ChtFYdfuHdu73M4neidVOuBL4JdGpx2boJtW/WXFWVicfgzhw3XnkWWezhcsxaTGy1W6TO9pe+PnhvADsG1VuDv3TsOuD6Vl7zJ+Da4vxZdR0WVTegxIqIiIiIiHRZkdLsy/Bnf3QFZ3mZxUvycgrGAJ8DbgAGBBWMS3H7q26sWOEyI0HuYhM3jMibIXNZZvSMdd/O4RZddK8lDfogjzXkk5+hdu9u9rz4PEM/fRNl766kZsc2+k67oMl+avfspnLDWnpObnr1Uu2e3Wz5w0OM+MrXjzi365l/k9C77997nXLGfuDaVob/e+AGzVxpWqvX44mIiIiIiMSh0qADaKWXPnTlddug4L9A05+cO5FVWc/Un6fn1lxatajutNogd7GJCw7vpDrn1iUQiZjRO2b9Oti5sv+Llph49oiZtx1yrnbv7oO/Z5wwEU6Y2GxftXt3c2D5m80mVprT74KLwS9euw5/q+UTAVwkgnleU5d9GijPLpj3peL8WZqZ0QglVkREREREpCsLujhoaz2BX0Q0LpIqh9A8hAZsJLC7pVZtEanz9taWJbdY02b/0lep2rKRgZd+jNInHsVLTqZ68ybqyvbT/6KP0mPiZHY9/Q9qdmxn/fwf0vOk00jLOp5tCx/FhcPgHIM/cQPmhXCRCNv+uoCqDetI6NmLwdd+Bi8xidInHiU9ezw9Jk4eufZH36PnyadT8X4xvc84Cy8tjd3P/gdXV0di3/4Mmn4NXvLBkjBfwE+u3K7kypGUWBERERERka6sq8y0WAj8M+ggGhMeHm5yqsKxxx0wo28sewwlRnqbF1nlamvHrZ//QwAS+/RlyCc/0+x14QMHGHbTl6nZuZ2tj/yKHhMn0++iSw8uHQLY/o8n6D11Gj0nn4Krq8O5COGyMmp372Tw1Z8m+YoZbH3sYcreXk7PKUfWebaERIbf/BXC5WVsefS3DL3hC3hJyexe9Ax7ip6n33kfatj8a0AZcEdMHphuRIkVERERERHpkiKl2SGgK+xus+JDV16XBBwfdCCNcf0iMU0kdGUGW+iAv1N6ZvkOS0gcd/hSoGavOWEi5nkkD8ykrqys0TYpw0ew54X/UrdvLxkTTiSp3wDCQGLvviQPHgpA8pBh1O3d0+j1PSZNAaBy03pqdmxj44M/9U+Ew6QMH9HYJd/JLphXXpw/655W35FjgBIrIiIiIiLSVfUF/gMMBnJo3+ebp4GLYhlUIx7D3/0n7tSeW72EZFS8Nspwezui354j9p9tIYeXEF4WqQtNaVUsoYZP58ZX3/ScfAopw0dQ8d47bH74AQZdMYPEPv2whA+uNc8jUlfb6PVeYtLB7tOyshl89adbE9rd0eTKz1rT+FigKV8iIiIiItIleZnFO7zM4mu8zOJzgPfb0cX/AvfHOKzGPApM74Rx2qR2WvWS2nNrcoKOI56YUdWR/UfqvOFHc72XlIyr+SDE2t27SOzTj95Tp5ExbgLVpVva1W/K8BFUbVhLza4dfpw1NdTs3N7cJfOzC+a1dsvmbk8zVkREREREpDsoBgqB3+BvZfyFFtrP9jKL746UZn+qA2NywA8+dOV11cCUDhynzWrPqi6qPb8mB8OCjiW+uI6efNDraC5OzhwCXoj1999Dz5NPx9XWcuCtNyAUIiGjB33PvYhIdXWb+01Iz2DQlZ+g9PE/4OrqAH8HoaT+A5u77NfZBfMqi/Nn/al996b7MOdU0FdERERERLq2SGm2eZnFLvr7aGA1Tc/Qv9XLLJ4XbfslYH4HhLQJuO5DV163AngKOLkDxmiX2jNrXq79cPXpmFYwHC7BwkvNOu5vVb497bV9Jb0ngyV11BidrA6YXpw/6x9BBxIkvZBERERERKTLq0+qRH9fAyxooumX65MqUUc1g6AJjwInfujK694DXiCOkioAtRdW91NSpUkDOrLz9IEVp/Ues2cFuA5dctSJEoA/ZxfMi79txDuRXkwiIiIiItId3dXIsc97mcXzASKl2SmR0uyPApfFcMx9wLVeZvEnP3Tldb2ARcD4GPZ/1JznIiTQ6HYv4sLAkI4eJW1A5Sl9snevAlfR0WN1kmTgyeyCeZlBBxIUJVZERERERKTb8TKL3wL+Hb3pgM8Av4uUZl8ZKc3+I7AdeBI4M0ZDvgCc6GUW/zEvp+B4YDGQFaO+Y8YNiGzA6C7LUGJtsxmhzhgotV/VlL7jdpWAa3wf5a4nEWh8T+djgBIrIiIiIiLSXf0AiADXeZnFvwH6AY8DnwB6xGiMGuDrwAVeZvGGvJyCyfgzVYbFqP+YCg8Pbws6hjiW7BzlnTVYSp/qSV5i5L3OGq+DLS/On9X2qrndhBIrIiIiIiLSXS0GzvUyi/8A4GUWb8bfOShWlgGneJnF93iZxeG8nIIzgOeBZrdSCVJkRLi71PboADYogi3trNHCtd6eSK03ubPG62BvBB1AkJRYERERERGRbsnLLHZeZvHiww7/pg1dbG/ieASYC5zhZRavBMjLKTgX+C/Qu41hdhqX6vaFs+s6tDhrVxdxdrZzdEpypXJH2iqwhM4Yq4M5/KVwx6zu8EcUERERERFprb/hF5ltzW5AC6Ptrmlw7H38pUUv1R/Iyym4BPgLkBLDOGOq7vi6t2quqRyIF1/FdOOLw8O9RCct40rtX5G9f33PCrC0zhivA+wEfg08UJw/a03QwQTJnHMttxIREREREekmIqXZRcAfgCnADfiFNxvzInA+fuHbPcBm4E0vs/jgbi55OQWX49dtaaqPQDnPhWtmVL4YPj48DcNi2XfN7jLW/vxZyopL8RJDJA/qxagvnk/qsL6xHKZTGG5ZyCIpZozrzHF3vdvvheq9Ked05pgxUAT8DPhLcf4sLS1DiRURERERETmGREqzzwDGeJnFj0RvDwNuBT4PHD5zYD/Q28ssbvJDU15OwWY6YYve9oj0D2+ourmijJTYz1JxzrHiq48wMG8CmZeeBEB5yTbCFTX0nDQ81sN1GMOtDllkvxmnBDF+pNbbt7u477Ka/UnHgzXYrtg5C7kVaQMq9qYPKct2dVa5f32vDdX7kieC9Qsg1DL8ZOTPi/NnLQ9g/LimxIqIiIiIiBwzIqXZQ6NFbA8/3h+4BfgKh9ZJGellFq9v2DYvpyAJ+BZ+IuZU4LwOC7idwmPqVlZ/qnIkRkZH9L/vzfVs/P0SJt77yUOOO+dY/+Dz7HltLWYw7JNT6X/uCex7awMbf7+EhJ6pVK7bSfrYQYz9xqWUvVfK5gUvM+4709ldtJri7/+d0xf+DzjHmzf9igk/uJr3Cv7G5J/dAEDl5t0Uz/07k392/VHeA7clwSJrgRyL8UyedkXjoHpv8oqyrRl7EtPqyBhcNi6UHD6iCLKLULfn/T5LqnalddYsl/34z/XfFefPOtBJY3Y5Kl4rIiIiIiLHjMaSKtHjO73M4m8DI4DZQP22xJMatotup/wq8B3gduC4jou2/Wouq6rqqKQKQEU0OXK43S8WU16ynSm/uIHxd81g3YPPU7OrDIDy97cx6osXMOWhz1JVuo8Db28mY+wgyt/3awTvX7mJ1JH9KSveyoFVW+gxbjApQ/oQSk+mvMT/c2x/aiUDL5p4FJG7fSELL0qwSH8zcuMhqQJg5m+/3H/8rmm9Ru6b1lhSBcA8EtL6V/buxNB6ArcBX8oumBcXj1U8UmJFREREREQkysss3u9lFt8NjAJmEi1ym5dTYHk5BfnA60DDLXKzOj/K5kX6RTa5ni6QpS37V26i/3knYCGPpD7p9DpxOGXFWwHIOH4wyQN6YJ6RnjWQ6tJ9WMgjZUhvKjbsomzVVoZ87FT2L9/E/hWb6DHRX1I06MMnsv2plbhwhJ0vrKL/+e1Z2eQiIYu8kGARPGOaGUkxvNudyksKp3fykCOBHwB5nTxul6HEioiIiIiIyGG8zOJKL7P4Z/W1WIB+wPfoAjur1lxSVRLrQrWHSx3Zn/LV21pu2ICXFDr4u3mGi0QA6DlpGHtfXYMlePQ+aST7397Egbc303OSvzlPv7OPZ89ra9jzSgkZYweR2DO1jdE6EixS5Jk7x6xVu0HFNS8x0mEzkVrwPc1aaZwSKyIiIiIiIofJyyk4MS+noCQvp2BNXk7Bx4FGl2bEG5fsDkRGh7Nx1OAId9Q4vaYcR6Q2zLZ/vXXw2IH3tpKQkcKuF1bhwhFq91awb8UmMo4f3GxfPScNZ8vC1+lxwlASe6dRt7+Syo27SBvZHwAvKYHep46i5CdPM/CiSc321ZiQRV4w46w2XxinQgnh3gENfTpwSUBjxzUlVkRERERERBrIyylIBf6Mn0z5cmFR/uPAkQVF4lBtXvVSPAZjJGGEWr6ifcyMcXdcwd6l63jj+l/y5s2/YuPvl9D//BNIGzWAZV/4LW9//TFG3nQOSX2bn2CRMW4wtXsrDs5QSR81gLRRAzD7YHLEgPPHY2b0PmVkm+L0LLLYM7radsbNshAp4ALa5th9N/cP+Zq1chjtCiQiIiIiItJAXk5BX2BX9GdIYVF+TV5OwQzgsWAja1nFnAOrSGJc0HHE2ubHXyVcXs1xN5zd6ms8Iq945k6PlwK1sbTlpSGlh27P3BEcg7J2LU7rVUViSm1aKDHSx8wNMeMl4HMLc+e/37Hjdx2asSIiIiIiInKofYDDr6vykeixCuDlwCJqhcjg8JrumFRZdcdCdvz3bQZPb0s9XrfOM3dSd0yqAJjnOnzr40Fjdi7qf9zes9N6VZ2dmBw+xfPcaDNS8LcXXzF9yczbpy+ZGfc1hzqDEisiIiIiIiINFBblh/GTKwDXR/99CugbTEStU3tu9cagY+gI4+6YzpQHbiSxV1pbLusL3TOpAmCeK+/I/tN6V77bb9i+qc00SQHuBl6dvmTmSU01mr5kZpv+aF2VsksiIiIiIiJH2gP0Bj6Sl1MwAD/Bkh1oRC0ILU3q7byaFyIjw4NJiu9YO571xN8a+9SgI+kISb2q91Xt6picRSghvH/k5M09zUhsRfOTgNemL5n5Q+D/FubOrwSYvmRmP2AWkAzc3iGBxhHVWBERERERETlMXk7B60D92pMfADOBHsFF1Hq1p9e8UvuR6jOCjiMoRR++h7SRA4hUVFeE0pLSBl00nmFXnoJ57ZvA8vK1D3LKz649YsbMlr+/hZecQOZFE5q8tvSplRwo3sbYr1zQqrGW3bqArM+fQ4/jDy2fcuC9UrYVvsOYL59/8FjFjtTX977f5ziwGO5Y5Rg7df1rSSl1p7Xj4veBW4GpwFeADGDCwtz578QuvvikGSsiIiIiIiJH2tHg928EFkV7GMf0t+deUgInP3DdYg93Ru3eCt79/j8Jl1cz8vrcmI4z5KOTY9pfc3ocn3lEsiVtQOWpSRmV+3a/N7CorjIxJxbjDD5+xwtJKXXt3UVpDPBkg9tPHQtJFVBiRUREREREpDHPAR8OOoh26baVRVrDHSDi0kPmzgZI6pNG9qw8ls58hBHX5UDEseahxex7ayOR2jBDLp/CkEsns3fZRtb9rojEXqmUr9tJj7GDGPfNSw5u+bzxT6+zd5lfwuaEb11C6tA+rHu4iFBqIsOvPo39q0op/tFTeCmJ9Jo4lN2vreW0h24AoGZXGcu/8Reqtu6lX+4Ysj53Di4c4b0fPc2B4lIMI/PDExl2lT9BaseiYlb/5BnqyqrJvu0iek8axt5lG9n4+OtMmjuddQ8XUbOrjKpt+0nsmdora+Z5OcvnPLWzrqy2P8CAi68gdcSoNj9y6X0q3ukzeP9ZR/83OGheDPuKa0qsiIiIiIiIHOlfwF1BB9EeCW8ljnd9I4vqTqodRBLHBx1PZzHcqpBF0vGsh3MUAT2BUalDeqfjHLV7K9i5pISE9GRO/tmniNTU8eZXH6PPKSMBKHt/O6f96gaS+mXw5v88yv6Vm+k1aRgAobQkTp5/LaVPv837P3ueSXOnHzL2ez/8D9mz8ug1YShrHlx0yLmy93dwyi8+jZcU4tUbfs3QK06idm8l1TsPHEy+1JVVHWzvwhFOnn8tu15Zw/rfvUTvez5+xH09sHobU+67hlByIu/O/Sd9zz5na8qISf1r9+5h88MPMPJ/2j7JKqNvxS4zQm2+sHHvAE/HqK+4p8SKiIiIiIjIkWqDDqC9rNJ6Jv0rZVrSv1IIDw2/X/3pikpSmRR0XB3H4Zlb5OGmAo6Ii9S5UE79ORwbIuHI0Lqa8Cs7Xyw+oXLLvl47Fhd7AOHyaio378FLCNFjXCbJA/wyOhlZA6natp9e0Udt4Hn+LtYDzx9Hyc+fP2T0urIqwhU19Jow1G9zwQnsemXNwfO9TzqOhIxkANJG9KN6237SRvanaus+Vv/0GfqdMZo+p4482L7/WWMB6JE9iKpt+xu9x/2mZhFK9mvL7lm6Hntn3wTsKQAi1VVEqqvwklNa/fj1G773pUjYi+WuwfctzJ1/zCxJU2JFRERERETkSIODDiAWvC3eGJLoltsw+xwJFnnJjGnOcSDsvDV41qD4iVG1de9xXkKIhP69cywlmZE3nrV80HnHn9iwl73LNuIlfvDx2EKGC0cadNNgfdVhS61a2g/GS/pgEoh5Hi7sSOyRwqm/vI7dr61j85PL2PHCexx/u7/yzEsMRdseFkMDoZQPNuxxznHc52/Z4iUmD2s+kkZiC0XKR5+6cUVyWu1U5w5uMX60yoA/xKivLiGWGSkREREREZHuIrPlJl1AqtuD0dqpC12QEcFqnWNn2HlbHHZIRdnavRWU/PhpMi8/GTOj9ykjKf33ilGRujAAZWt3bqotr36rpVF2PL8q+u979Bw/5JBziT1SCKUlsf+dLQBsf25Vi1HX7qvARRwDpmUz6oZcDry/vZX390h9TxlJxYp/HrxdvXVzo+1CieG9wyaUvjDu7JJ3eg3a/3pKRtWa489auz05rfZMADN6tTuIQ2UAj09fMrNbJCdbQzNWREREREREjhQOOoBYsAqvT8rP0jZUfbFiMyGGBh1PR4g4mxbBtoEdDxCpqWPZF36LC4exkMeACyYw5GP+7sGDLp5Mdem+9Ndv/l2tqwvXJg3oOXT8tz+6rsUxasMs/fIjEHGcMOcjR5w//msfovjep/FSEuk9eTgJ6cnN9le9s4z37nkKF53uMuqzZ7f5ftcb8+XzWP2TZ4at+/HcCrOEtJSRoxl02Qd1WRKTa0uHnLC9OL135almnAMwbPx2nKPOrMNyAh8B3p6+ZOaXFubOf6yDxogb5lqatyQSx8xsEH616TOBPUANcLdzbmFA8ZwL3Oacu7SRc+uAU51zOzs5LOlAZhYGVuAnqt8FrnfOVQQblXQlZjYPWO+cuy96+ylgo3PupujtHwGbnXP3HsUYZc65jFjEKyJyrMjLKbgQKAw6jlipnVa9pPaCmtjuN9yluQiYBxCy8AteNOHQXuHKGkKpSQBsePQVanaXM2bm+TGIsw0x1Hg7t72RmQTWEyA5vXr90BO2b0zJqD7DjMSWru9A1y/Mnf+7AMfvcJqxIl2W+Xuf/RV42Dn3yeixEcBl7e0zL6cgHTgB2FpYlN/4HDqRQ1U656YAmNkjwBeAZj8AZy2YOxgYBrxXMmNO4xXJ5FhSBHwcuM/MPKA//i4G9XKAr9bfMLME51xdcx1OXzLTgGwgFT/xJyIibTc26ABiqfbM2vSgY4gv1rAsxlFvUL3r5TVseOxVXDhCysCeHP/1zt+pO5QU6Z8xpGxxpDxhwJBx2/cmpdaeYcaITg/kUPuAvwccQ4dTYkW6svOBGufcL+oPOOfWAz81s38B33DOLTezN4GFzrnvmtn3gPXAOcCfnXN/A/8D8ejh59ckJ/X85I7dq7xIpNZLSb6nqq6u8sG6cM1Xo20uAv4PSAZKgBudc2Vm9mHgPmAnsLQ+FjPrBzwKDABeJQZv2BL3FgMnmllf4NfAaKAC+Jxzbrl53srjfj57bahX+oXrb/p+cr9PX1ybxdy71lyTPwp4GPgO8BXn3DIAM1sCfBHoA/w4OoYDpjnnDnTuXZMOtAR/5h3ABGAlMNjM+uA/f04A5pnZ80Au8GT093vx1zDvBG6Itn11zDWTb5n45dz7Xvv2U2P7nzy0dtQVE8vMs5CZzQUuBSqBy51z28zso0A+kATsAq6NHr8DGIVfuDEbuBV/ZuDFwGbgo865LrtbhohIKx25x20XFRkcLiHd/yJIGnXUyzgGnjfu4M5BQeozck+fnsk1wQfygTDQD391Qbel4rXSlU2gQSLjMIuAs82sJ1CH/2EE4Cz8D78PATcCmFmvkJd44cih0642LKm8YlvCidnXeDknfTXkeUmfM7PhZtYf/8PHhc65k4HXgVvNLAV4EPgocDaHFjn7DvCic+4k4EnguBjed4kzZpaA/6FzBX4C7k3n3InAt4DfAaSdlJ1YXbLpotpN21MSB/W1quL1ScCtXmpyHvAy/vPyhmh/2UCyc245cBswMzoz5mz8D8bSTTjntgB1ZnYc/uyUl4BXgKnAqcBy/GWOvZ1z5wA/AX4KXOWcOwU/iTfXObcvlJxwy85lW/6+6b+rj68tqwmNumJiCtDfRVxK34mD1jjnJuO/P94cHf5F4Mzo+9RjwNcbhJaFvz76cvzK/s855ybhP/+OXFwuItKN5OUUnAicG3QcsVJzSZVmYjcj7LyJzrEj6DhiIS2xNlY7+8RKX+Bf05fM7Bd0IB1JiRXpNsxsvpm9ZWav4SdPpuEnUv4JZJhZGjDSOfeec+4FYIyZDQQ+Maj/iXWehdIA+vQaTUJCCiEvMblH+uDkHulDTsT/pnY8sMTMlgHXAyOAccBa59xq5xcsarit2LT62865f9LNs7THsNToc+J1YAPwK/zn3e8BnHPPAv16nndKZvrUSaOqVq1Pqnx3HT3yTqdmwzbqdu9PTxjYp6dzrgx4HLjUzBKBzwC/jY6xBLjXzG7B/3Dd7DIQ6ZKW4CdV6hMrLzW4XRRtsyD67/HARKAw+tzLx19axkef+ZzXM6sfb927yE76xnkHO/cSPc7+2ZUjozffAOp/HwY8ZWYrgNvxE9b1/h2dlbICCAH/iR5f0eB6EZHu6jt0k9nGLtkdiAyPnBJ0HPHN+oWdbQg6iqNluP0hc/H4tx4LLJy+ZGbzFX27MCVWpCt7Gzi5/oZzbiZwAf7Sm9fwv+k9G//b2Tfxv6F9o8H1vweuBW4cnnnGwQ3iPWuwf72ZS0nq1Rf/P6yFzrkp0Z/xzrnP1g/dTIyqDt39VTZ4XnzFOVdDI/8jlnL8iIzUE0ZGqlato2rVOlLHjyLUM53yl1eScsJIAKJFbwvxZwhcDfwxevwHwE349TJeNrN4mt4psVGEn0SZhL8U6GX8GSs5+EkXgPLovwa83eB5N8k5dxFApDacWbZ+rxdKTqBmf9XBzi3BwzwbFr0Z5oOlwD8F7o/ORPk8HLIdZzWAcy4C1LoPqt1H0FJiEenG8nIKRgBXBB1HrNSeV/0mhuqrtMDhnRJxLAo6jqORklC7zOJ3a+1q4MSgg+goSqxIV/YskGJmX2xwLA0g+uF2I/6H05fxZ7DcFv233m+JFoTskT743/hLhg7jIrv3r9kS7SPXzMYAmFladKnGKmCUmWVFL/hEg4sX4SduMLOL8etkyLGh4d/+XGBnj/NOWZPQv/euyIEKakt3kTioLynHj2DvP14kadjAhkvaHsJf6vGac253tI8s59wK59xd+DNjlFjpfpbg1z/Z7ZwLR//2vfGTKy8d1vY9YICZTQUws0QzmwDw7HWPZfcY2YdT78jjzTufI1IX3SnUT4k0trNFL/yaKeDPxBMREb8Qfbf4nOTMUXdarZajt1LYeWc6x/tBx9E+juRQOJ630x7IocX5u5Vu8YYhx6bot6dXAOeY2VozexW/AOjsaJPFwLboLIDF+FPeFze4fhv+9ri/Ab4H7OeD5IoDKioqd70dDleHnXM78GtfPGpmy/ETLeOcc1XA54B/mtmL+IVx6/0fMM3MlgIX4S8TkWPDHcCp0efKD4DrS2bMiQBfSs4aGk7M7A9AyrgRdeE9+/FSk2+tv9A59wb+c/E3Dfr7qpmtNLO38Otb/LuT7od0nhX4uwG9fNixfYdv0R5NHF8F3BV9TiwDcswsu2zjvivGf+HMP/SfPKS835QhvPfwGwCVOCLAnxsZ9w7gcTNbjF8EV0TkmJaXUzAVmBl0HLESGR1eSYKWb7ZByH3whUOXkuBFVpiR1XLLwJwInBR0EB3FPpjZK3JsidZcWQGc7Jzbl5dTMBS/xsAF+AmSuwuL8rv0dECJP1kL5p4BfAN/reli4K6SGXPW1Z83syHA8/iJu0hjfYg0J7rV8ifxd5RKx9+d7P6FufMrAg1MRCTO5eUUTCNamy/oWGIl0i+yqeqW8mEttzzWuRrP3CsebrQZ8Tzro0k9kqqWJHgut+WWgfk7cPnC3PndMgGhxIock8zsQvydNO51zt0XcDgiAJjZdcBc4Fbn3ONBxyMiInKsyMspuBB/F8fUoGOJJYej8jtl2/AYFHQs8ctFEiyyxowxQUdyNNISal5ITgifE3QczfgPcOXC3PndcndLJVZEREREROSYlZdTcAnwBNAtdyypvKXsJdfPTQ06jnjmESkKeS4n6DiOjivvnVxVYxbXdR2fAy5bmDu/LOhAYk01VkRERERE5JiUl1NwBfBXumlSBSC0OqE26BjiXQTLcY5Xg47j6Fh6TSS0POgoWnAeMDHoIDqCtksUEREREZFjTl5OwQzgESAUdCwdKbQyYWDdmc3kVhxV1LLe9nq7vFKv1lsfSvF2eT2qr6sciEf/zos0SEYEqwnRtVdzVNYmnpzkhfebxe3uO5XA0hZbdUFKrIiIiIiIyDElL6fgOvwd+Lr9DH5vcygbRxkQoZqNtsfb420JRULrQ6neptBg221Dzdnxh1+XtDDljZqPVR0jiRXwcL2DjuFoOaxHXcRblBiKTAs6lia8sjB3fk3QQXQE1VgREREREZFjRl5Owc3AA4AFHUtncWmRPVRYH2vjXa7IP/A+iV27qGtrJVi4yIyuXmeF3slV75gxPuhImvA8cPXC3Pk7gg4k1pRYERERERGRbikvp+DLwBygDtgN7AfOCjSoLiLSN7K56n/K43Pr4QreSngj6YDtsaTI6HBleGJdDHbDceUJFtlhxsij7ysYCV54RY+kmklBx9GCcmAe8O3utPWylgKJiIiIiEi3k5dTMBH4IR8Uph0WYDhdTu206veB+EmsRNjmrQ6tSipMHuXtCE2uP1yZW/ZybAaw9LDztoSIVJt1zWLGaQm15UHH0ArpQD7wa2BtwLHETLdfUygiIiIiIseWvJyCZPzCtF3yA3I8CE+oGxx0DDjqbLe9kvREymup38sYkPLHtHO8HaHj6k9H+oU3ur7u9NgNZ2Mj2Gux6q+jvP+z59j0lzcO3l4++8+s+sE/yz1zZwCs+OkS3n9s2VGN8fe8Xx7V9a3QxZddHUqJFRERERER6W7mAicGHURX5cw52+ftCjKG0FsJL6Tek74v9ccZZyS8lXiaReyIz641V1Svw2L7mTbi7CzneCmWfcZarwlD2P/2FgBcxFG7v5KKNTvCFi2is3tlKX0nfZAXi9RFggm0eVODDiCWtBRIRERERES6jbycgguArwUdR1dmziz1/vSpdSfVvFZzWfVxeAzq1PFLvSXJT6S2XDelmg7IGBgRZxay+C3/0XPCUN7/+fMAlK/bSfrIfi68a39qzf4qQimJHFi/h5U/fZH+Jw1l14pSMnNHMuCkIay4v4i6ilqSe6dw8rfOJ5SSwAs3/4Uz7rqYHsf14bXvPM2AU4Yx8jK/9u07D7xMadF6QskJnPGDi0npm8bWF9dR/PDrROoiJPVM4ZTvXEhK3zTe/dWrVGw9QNWucso27mPSV3LY/fY2tr+8gZT+6Zx59yV4CYfsbK4ZKyIiIiIiIvEmL6egD/Bw0HF0FwlvJp2WeldGqm3xXuy0QR27Ux5OPaE1TZP/nHoSjt2xDsHMVce6z1hK7p+BhTyqtu1n/ztb6Dl+qKWOHrR/55tbdu5dtZ1eWf2whBC1B6o5+/4ryPr4JJbft5jTv/chzvv1xznuI+N455evkJiRzIm3ns3Suc+y6b+rqT1QfTCpEq6so8+ETM5/eAb9pgxm/ZPvANDvxEym/fJjnPebqxl64RhWP/LmwbjKN+9j6j0f4cw7L+aN7z7DgJOGcv7vriGUnEBp0frD78aJ05fMnHz4wa5KM1ZERERERKTLy8spMOAXxFPB1W7Aqqxn6gPpZ9VNqF1a87GqQYRi+Pg6aqjhfW+Ht8tbm2Ch1aF+3uZQltVZUmtjS3g5cVHd1NppMYsJMEiNZX8dodeEIex/Zwv7397CsKtOIXlgj35bl2+qSkmObOo7MXPY7re3MfQCf6fssg172b9mN0tmPelfHHEk90sDYOBpw9n8bAlv3buI838742D/XqJHZu4IAHofP4Adr20CoHJHOa9952mqd1UQqY2QNrjHwWsGnXkcXkKInln9cJEIA8/0y+H0zOpHRemBw+9CCFg2fcnMV4AHgQULc+eXAUxfMtOALOBs4HTg6wtz5x/RQTxRYkVERERERLqDmcDVQQfRXSW8nXhyaHVCRdXNFUVuYKT9yzgibEtYlLQ6tDphoLfFy7KIjT+auBKfTs6tO612HQmx2SbZ2+K9GFqcnOZyqxe7YeET8Ogfi35jrWe0zkr52p2kj+xP8oAebPrzGykVaYnDhp0zauXut7dNDKUmAuAc9BjVl3Me+NgR/biIo2z9HkLJCdTsryJ1YAYAluBhZv7vnkck7K+6Wj5vMWOumczgs0axY+lmVv36g1q/XmIo2t4OuR4D13SdlzOiP/OmL5n5ONATf0v0zAZt9gHfaM/j1FmUWBERERERkS4rL6fAA+4Evh50LN2d1Vha8h9TR1Z9tZ27+obZlDI/zXm7QmfFLKaIhZL+kby95orqkUfVUYStSX9J2ZqwMtGP7Z0kIqNq3w3fWJ6BkRKDUGOq14ShbHr8DVIG98JCHok9U6krq6J83U56zLqgJ/9652DbHsf1pmZvpV/UdmImkbowZRv20XN0X95f8BYZI/pwwufP4M07n2PaA1ceXgvlEHXlNaT2Twdg43/ei+Vd6gF8polzs6YvmdkDeLv+Z2Hu/J2xHPxoKbEiIiIiIiJdUl5OQTrwe2B60LEcK7w93hBqKCaJ7DZdWMealJ+kp3n7vMyWG7dN6M3E08mrWUa6m3LEyQg7KbcNJLhEUpnU2PXeZu/F5IfTJlu1nXzwsqzat8PXlw+Px6QKQPqo/tTur2Tg+eMaHBtAuLKW1L6pO4CD21J7iSFOK/gwK+5bTG1ZDS4cIevqyXgJHuv/8Q7nPHgViWlJ9JsyhPcefoMTPtv0DtbjPnMar/7v06QOSKfPhEGUb9nfofczKgn4UsMD05fM/MXC3Plf7IzBW8Oci99qxyIiIiIiIo3JyykYAjwJnBJ0LMea6qsrXwhPqGt51556NaxKvS99oJV7fTsqpvDwuveqr69MtL1eqbfRqw2tScjw1oWOswM2wDAcjvC4urdqLq+GNOcXTQ2zJekvKaUJbyeefHh/dVdUvOBOqWn9fYwrziV6kbdSEmrLQuZy67dh7mYuWJg7/9mgg6inxIqIiIiIiMSNvJyCUYVF+WtbaDMZ+AcwrHOikoYivSNbq75SXkUCo1psXMmK1PsyRliV9eyE0Fql7vi6t8In1+5NeiLlFKu2jMbahKdVFUXyqrr0lsCJXt3S5FC4rM55XkqoboQZw4OOKUZKgOyFufM7YLvt9tF2yyIiIiIiEk9uz8spGNDUybycgkuBJSipEhhvrzc49e6M/rbPXmuyURXvJLyauCj1Rxlj4impApDwXsLk5EdTz2kqqQLgBoZrOzOmjlAbSTi5rDZ5WlVdwlnA0c4WWgxsiUFYsbA8npIqoBkrIiIiIiISJ6IJlZeBnwBPAH0O+zkB+Br6gjguOHOu5prKReFxYX/JTB1rQu8kbEx8IWmUtzN0XAuXx63wOVVLIhdW5QYdx9HyLLIhPbFmY8jcaDMGH2V3lwBj8F+bQYsAExbmzl8VdCD1lFgREREREZFA5eUU9MRPmNwKNDmLQOJT3cTapbbXy/A2edldvZxH+IzqlyMfqTwd68rJO4dnbm2SF96SFAqbZ+50szZvXBMB1uDvwvMW8H/4RWTXwFEnaWLh0YW58z8ZdBD1lFgREREREZFA5OUUpOLv9vFNoF/A4cgxLjKl5vXwlRVTaHsSImYMt99BGlgbYnDVCV6kOMkL704IRdI8XJbZUS39+SNw08Lc+ZWHn5i+ZOZXiI9ZKw7otzB3/p6gAwFttywiIiIiIp0sL6cgEbgR+DYwNOBwRIicULMsfGXFpI5IqhhuJ+A57LBkhytL9CIliV54b4IXSfLMDTXjOOc4EHG2sjoccjXhhPEO6314f4mhcEmSF65O8CL9gGyzxreSbocDwNcaS6pEPYi/vfl5LfTzMvA68OUYxXU4A8o6qO8204wVERERERHpNHk5BUlAEdomWeJEJKv27fD15SMx0jui/4yk6kUJFpkacfZ6XcSrTfAiCZ65wWYt76rkHBEHK+oiob0hiyR45oab0ZH1a25dmDt/XnMNpi+Z6QG3AD8Akg87vQp/BtrfgB7AX4CJQGaM49y1MHd+/xj32W6asSIiIiIiIp1pFkqqSJyIDK8rDl9fPryjkirgyhIscrIZiSFzU0NeuE1Xm+EZTE4Kte26dnobuL+lRtEdee6bvmTm08Dv8F/Pm4HvAA8vzJ1fF226H8gDmL5kZl9gQoOfa4CjSYyUHsW1MacZKyIiIiIi0inycgqGAu9BR32I7fJqgXKgdwvtHHTxKrFxou4zBxa5UeFpHdV/akLNopSEjus/xs5fmDv/ubZcMH3JzETgcuCfzSwfauy6zwIPtTG+hp5dmDv/gqO4Pqa6cKVjERERERHpYu6heydV6vC3if4ocDJwJfA4UNXK638C9MXf1vanjZwvwq9ZoaRKjIR+nTHNViW8EPuenUsJ1S5JDoXHx77vDrGgrUkVgIW582sX5s7/c1uSKlEP4ydZ22vbUVwbc5qxIiIiIiIiHSIvpyABv4DlQPxlAccHG1HMVOMnSyqBfcALwL+BZwuL8vcf3jgvp6AH/rf6nwAuovGSDFuByYVF+Tui1wwDNuAnUUqAb+DXq3gauDDG9+eYF86rfDFydnXO0W+z7Ejywq+lJdb2MWNMbKI7ajcBH8YvOhuKHqvF3zp5dfTnRwtz52/uzKCmL5n5ceBP7bx83sLc+bfGMp6joRorIiIiIiLSUT4OTI7+PjjIQI7C94H5fJBIqS4syo+0pYPCovwDwB+AP+TlFPTbX7b5hpKNz3y9vHLnwMSEVMKRml0uEpldUbVrR4NrNuXlFDwBLAJ+UViUX5OXU5BHByRVdu9by4YtLzLlhE8fce7FpT/i9ElfICmxO080glBh6lnAi5Fp1WcdTT89k6pfCXnujBiFFQurgV8vzJ3/q+lLZg4HxkePbWhQCyUofwHeoH01l+JqxooSKyIiIiIiEnN5OQUe8K2g4zhKdcBPCovyY/Yh7r8v/e9u4CrgOxdO/d7fgfHPvHxHsXPhyxpp/vHConwHBx/Pu2IVhxzK4YicXHPU2aOws6oQcbUq5ImFufMdwMLc+RuBjQHHc9DC3PmR6Utmfgt4qoWmlcDHgOX4Cc76n7ihxIqIiIiIiHSES/G3WY1XNcBe/GVKTXkilkmVqPOBGufcL6K3N0M+eTkFvzDz/gPu68655Wb2JrAQ8r9rZt/L7H9iJthJA/tNYGDfEwBYufpxBvWbSG1dJTv2rCISrqWiejcD+45n7IgPAbBr7/us2fgsEVdHanJfxo+ZTkIomZ17VlO87l8kJqbRM33IBw9KbQUrV/+JmtpyemUMg2OkdETk3OoXyXBHNVsFXCTRiwyITUQx80TQAbSgEHgOOK+J87XAlQtz5/+n80JqOxWvFRERERGRmMrLKTBgTtBxNKEUuAXoVViUPwi/WOxU4Eb8GSF/BVbhz1b5eQeMPwFYWn8jL6cglJdTcB2wasSQs05ISkz/UNbw84clhFJS01L6XZuXU/B4rx7Dbxs17Nybhg48ha3b/Uvr6qrYe2AD/fpkA1BWXsqk7BmcOfnLbNu5gqrqfdTUlrN20/OcPP4GzjjxS/TMGMKGLUWEI7W8u+avTBn3KU6dcBPVtQcOBrd203P07jGCMyfPZEDfcVTV7OuAhyC+uB6RnZHzqyYdbT/pibWLzYinYrWb8Gscxa3obJqmZrY54FPxnlQBzVgREREREZHYuwA4PeggmvCtwqL839TfKCzK3wO8HP05KC+nIBE/udKhnn35jp+lpfa/ybMEL3vkxVRW7fp+z4xhdw8fPJXd+0oIh2uyq2sOkJ46gPTUAaxa+w9qasvYvusdBvadgGd+LdI+vUaTkJACQHraQKqq91IbrqK8cgevrXwQAOfC9MoYTkXlTlKT+5CW2g+Awf0ns3mb//l7z/51nHj8JwDo3+d4EkKpHf0QBK7uM2WrMaYeTR8JFl6V6IVzYxVTjCxcmDu/TfWAgrAwd/7L05fM/Bt+geeGPr8wd357i9t2KiVWREREREQkJqK7AM0CvhJ0LI3YgR9Xq7aULSzKr+2gON7GrxcBQDhS9/mzT7l9wOtvPzS9Z8ZQDpRvTdi7fx19e4+htraCzdvfOGSpzuABk9m6Yznbdq1gfNYVB4979sFHO8OIuAg4R99eWUzKvvqQAA6Ub6W5HZvtGNrNOXJK9Wv0jxxVUgVcdUZSTZJZ53y+DkeswjOXZi3/meJ9GVBDc4DL+OCJOXth7vwHA4ynTbQUSEREREREYuVW4G5geNCBREWAf+InMoYVFuUvKCzK3x5wTM8CKWb2xfoDBypK/w3geQkkJ/di266V9MoYRu+eI1i/5UV69xx58OLBA05m49YiADLSBjU7UK8ew9l7YAMVlbsACIdrKK/cSVpqfyqr91BRtRuA0p0rDl7Tp+dItu58C4Cde4qpC1fG4C7Hr3BeVdLR9pGRWPOKGaNjEU9zKmsS1ry/vf/it7dk8l7pwFfqwtbcOq2dwIsdHVOsLMyd/zbw++jNuxbmzr87yHjaSjNWRERERETkqOXlFGQD/xd0HFHFwK+B3xcW5W8JOpiGnHPOzK4A5pnZ14EdYJUTx3ysEkjt02MEuyNrCIWS6NNzBNU1++ndY8TB65OTMkhPHcCAaAHb5iQlpjNhzJWsXP04EeevasoafgHpqf05YfTlLHv39yQmptG7xwjKK/wavaOGncfK1X/ileU/o3fPkaQk9Yr9gxBHvJWJeyNn1LT7+ura0PL95T0jnvGC4cCwvukVQ9KSasfEKsa9FSlvbtnby9VFQieDn8CpCSec8c7WzC0j++3e0DO1+vD6MJX4WywHvZ1yW30H2Ad8M+hA2srcMVLlWUREREREOkZ0K+DngbMDDmUF8AXgpfptiruKvJyC3wGfbqldOFzDy2/dzxknfulgTRVpP5ce2VU3e38v2rGMxznYvLv3287ZhMPPJCfUFY3st3tkcmJ46NHEV16dWFyyo/9osCbic5GeKVXPHNdvz4uesRxYCaxdmDs/fDTjSttoxoqIiIiIiBytLxB8UmUdcHFhUf7mgONorwW0kFjZtbeEd0oWMmJIjpIqMWLlXj/2eK/QN3JGW68tr0p61TlrpEizWXVdYu572wbWpifVLDqu354JiaFIv7b27xzhNTv70XRSBcC8/VWpeSs3pyYDD5TMmBPr7cGlFZRYERERERGRNokWqT0T+DBwMXBysBFRCuR14aQKQCGwB+jTVIN+vbM4+5TbOi+iY0To+WQvfGXbask4R2RPeVr/5ltZYnlN8rR3tw4q65Va9cKwPntPDXkuvbVjbNjd50XnvHNa0XQvfmJuV2v7ltjSUiAREREREWm1vJyCQcAqoHfAoTR0VmFR/pKggzhaeTkFvwI+E3QcxxrnuUjdd/btxGNgS20jjorK6qS3D1Sm1NaGQzltHGlnv4zydwb32j/VMxKba1lRnbj6/R39RzU/W+WgS0tmzPln22KRWNKuQCIiIiIi0hb7iK+kCkDMCoUGbEHQARyTkignzIHGTjkH1bUJq3YdSHth865eyzbv6p24uyz9tLYnVQCs/66yjGkrNw/etn1/RpFzNDrLIboEKNLKpArAE1kL5t7Y9ngkVjRjRURERERE2iQvp2A3zSxZCcC/C4vyLwk6iKMVXWK1FWhhiYnESjjBVVXdUv5qUp+6afXH6sJeaXl10vsV1UleXdg7HqzN9VFawyyyeljvfXt7p1Wegp/DiQBu4+4+L+2rTG3NEqDD3Q/cWjJjTm1sI5WWqMaKiIiIiIi01VbiJ7FSCfws6CBiobAovy4vp+AvwOeDjuVYsePD9kpFuMc5tiuyMjEU3l1TlzAULAvI7OixnfPGbtzTh417Dr6UQtF/25NUAbgamI+/VE86kZYCiYiIiIhIq+XlFKQBGUHHEbUbOL+wKP8fQQcSQ88EHcCxoqYv6ytGkwPgnDexpi5xWjSp0hWtBqaWzJijpEoAlFgREREREZG2uA84LugggPVAbmFR/stBBxJj01puIkfLAVuvZCdmzRaR7SJeAXJLZsxZE3QgxyolVkREREREpFXycgpmADcHHQdQBeQUFuV3q2/n83IKPOBjQcdxLNh/Ii+H0+2UoOOIgb8D55fMmLMj6ECOZUqsiIiIiIhIi/JyCkYDv4xhlzvbeV0VcFlhUf6WGMYSL3KAwUEH0d1FEqjcdQ4jgo4jBv4IXFkyY05F0IEc65RYERERERGRZuXlFCQBjwE9Y9DdK0Au/hbJHwHamiCpAD4Vgzji0VVASdBBdEcOqO7H2h3ns2j9TazFs+6QwPoQ/mtIAqZdgUREREREpCVzgdNi0M9q4NLCovz62Sr/ysspmAj8hNYnS/oCaTGIJa7k5RQYsBC4NOhYugvnUVc+mhX7T+RA1RBGErJRwKig44qhfsCXgb8FHcixTokVERERERFpUl5OwcXAbTHoahvwoQZJFQAKi/L3AJ+ObjP8ADCwFX21dxlRXMrLKcgCHsbfbrer7koTF8JJHNh/Em8dOIGEup5MxOykoGPqYD8MOgDRUiAREREREWlCXk7BEOB37bjUHXa7DLi4sCh/bVMXFBbl/xWYACxoRf89ots+d3l5OQWXAm/gL486M+BwuqTKYbyzfyKvOGDvaSzbc6adVdfLzsQsXrYF7ygrgaeDDkKUWBERERERkUbk5RSEgD8A/Rs5/SB+EdnG/Ae/fsq1wF6gDriysCj/zZbGLCzK31lYlH8NcDWwq5mm1wJPtNRfvMvLKTgBf1eXXkHH0lU5YNsl1O28wM5YO5N1BybSO+iYOtGPSmbMOTyJKQFQYkVERERERBozHTivkeNFhUX5n8NfsvJz/MRJvbvwa6isKSzK/yMwEbiksCi/sC0DFxblP44/e+WvzTQb3ZY+45R2czlKFaN5K5JqJwKQYCMjKTYp4JA6y1bg0aCDEJ85pwSXiIiIiIgcKlpMdSZwL5DY4NTNhUX5DzVoNxqYA/y7sCj/zzGO4SLgqSZO1wCphUX5kViO2Zmij/FzwDlBx9JVrbuZNyNp3b6OSmPmlsyYkx90EOJT8VoRERERETlCYVG+A+7Pyyl4HfgTMByojP7esN0a4LMdFMblDX6PAG/hLxHaHf03FSjvoLE7XGFRvsvLKfgqsBSwgMPpcmr6suEYTaqAP2NF4oRmrIiIiIiISLPycgr6A48A2wqL8q/rpDEN2AAMix56oLAo/wudMXZny8speAD4XNBxdDWll/JCRZYdq7N9PlsyY86vgw5CfKqxIiIiIiIizYpukXwJMLsTh00CdkR/3w98uxPH7mz/i38fpZWc4SpGkR10HAG6OmvB3NuzFsydEHQgohkrIiIiIiISp/JyCgYAi4BfFRbl/zDoeDpSXk7B14BufR9jqWwsS7dfYicHHUccGF8yY867QQdxrNOMFRERERERiUuFRfk7gAuAnwYdSyf4KbA66CC6irLjORB0DHHg70qqxAfNWBEREREREfJyCu4G1gB/LCzK17KUAOTlFFwK/D3oOLqC/RN4deeFdnrQcQTsdeDrwPMlM+Y0+cE+a8Hc04FlJTPm1HRaZMcYJVZERERERI5xeTkFKcBeIBk4q7Aof0mwER2bogV7/wNcFHQs8S6czP71n6cHZtpNCZYA3wUKGyZYshbM7Q/cCWSVzJhzflDBHQu0FEhERERERKbiJ1Uq8XfikQBEt7ieBYSDjiXeharpabW8F3QccSIXeAp4KWvB3EuyFswNZS2Y+zngPeAm4JlAozsGKLEiIiIiIiLnARXAJYVF+RuDDuZYVliU/w7ws6Dj6ApSStkWdAxx5gzgn8A24AGgb/T4J7IWzNVn/w6kB1dERERERE4FLi4syn8+6EAEgDuA3UEHEe8y3iMt6BjiVL/Dbk8APhJEIMcKJVZERERERI5heTkFIeCOwqL8RUHHIr7CovzdwHeCjiPepZVwPM5Fgo6ji/hm1oK5h9Sj0SyW2FHxWhERERERkTiTl1OQACzDn20gTVj7Rd51SXZC0HF0EauBVCA9+vN9/JpKfyyZMac6yMC6OiVWRERERERE4lBeTkEe8HTQccSzrVfwQuUIOyfoOLq4q4BdwJXAH0pmzHk14Hi6HE39ERERERERiUOFRfmFwN+DjiOeZawiPegYuoHHgeeArwC3BBxLl9RiYsXMwma2zMxWmtnjZnbUBYLM7HkzO/Uorv+tmV11lDGsM7P+jRy/zMy+0YZ+Ms3sMTMrMbN3zOxfZpZ9NLG1l5mNNLNPNrh9g5nd34HjnWtm/2ji3ENmNr4Dxuzwx9vM7jCz26K/f9fMLmyhfZueMyJdQYP3/rfMbKmZ5USPjzSzlUHHdzgzG2RmfzSzNWb2hpm9ZGbTO3C8G8xsSEf1LyIi0sDXgNqgg4hX6WsYpzorR61h7ZUJh9dikZYltKJNpXNuCoCZPQJ8Abi3I4OKjpXgnKurv521YG4v4AKgjlAoRLhjtnZ3zj0JPNlSPNFjBiwEHnbOXRM9NgUYBBQ3N070WnMxehMwswRgJPBJ4I8AST369gwlpY455XPzPgQ888YvZ9U100VMOeduaiLOkHOuXX+8zni8x945b3Tq6DETXW3ttrF3zjPn3Ldbiqup54xIF9fwvf9DwJ1AINNss350bwg4F+gPvFjytVs3NzwffX3/Ff+94ZPRYyOAyzowrBuAlcCWDhxDRESEwqL81Xk5BT8Gbgs6lnjk1ZBhNbzjkon5l7rHoOXAh0pmzFG9kDZq61KgxcAYM0s3s1+b2Wtm9qaZXQ4Hv8H7q5n93czWmtmXzezWaJuXzaxvg74+ZWZF0Zkwp0evv8PMfmlmTwO/M7MRZvZMqEfahi3f/dXu2h17fgv8PmPqxGsyzpo8OXrN96IzWDwzuz0a03Iz+7/o+XQz+2f0W9eVZjajQQxfiX4Tu8LMxjW4D/dHf/+tmd1rZs8Bd5lZlpn9J/pt6GLgOqDWOfeL+g6dc8uAN83smQZ91z8+I83sXTP7GbAUONvMVkVnd6w0s0fM7EIzW2Jmqxs8LqdHH6s3o/8e3yDWx83s7/hrL38Q7XNZv+NP/2fmSXk/TMrofcHqf/3iHysfK6hK7tn/N/VxmtlF0W90l0b7yIgeX2dm/9fI43JO9NvrZdE4ekS7yjCzP0fvxyPRDziHzEoyszLzZ368Akw1s0+Z2avRvh4ws1Arn3/nNfZ4O+cWN/G3P/zxHt5YOwDzvPyEnr32bn7oZ6tDaemXp4+f+FngvVBq6p8sOjvKzC6J3s8XzewnFp2tc9hzZkT0b788+u9xDZ5LP4n+/dbYUc64EulkPYE9hx+MvsYWR98vDs5qiZ5r7jX5oJm9bWZPm1lq9NzN0fZvmdlfLDo7MpSR8cSBpUu3A08AD6791jc3Zf3o3ruyfnRvw29SzgdqDntvWO+c+2m073+Z2YnR3980s29Hf/+emd3U1nijr99TgUei72OpMXukRUREGlcA7GjjNcfMh+OULW1+bKRxw4AvZS2Ye8TKDmleqxMr5s+IuBhYAcwBnnXOnYb/YfceM6tf2zYRf9bE6cBcoMI5dxLwEn4iol66cy4H+BLw6wbHTwEuj37reH/ScYP+NfKhOf0zzp7i7Xr4Xz2AnnheQtop4273UpN/DAwEbgQuBMZGx50CnGJm04APA1ucc5OdcxOB/zQYa6dz7mTg5zSdAc4GLnTOfQ34JfAV59wp0fZzgDcauaYKmB7t+zzgR/UJB+B44HfRx2Q9MAb4MXAiMC762J0V7f9b0WtWAdOi13wbv3pzvanA9c6584FvAItPvvneWSPP+cQ5ZpZYuXtrwugLr08Yf9Vsz0XqPp2YmjHC/CVQ+dH7dTLwOnBrC4/LbcDM6DfYZwOV0eMnAV8FxgOjgdxGHo90YKVz7gz8okgzgNxoX2Hg2kauacxEGnm8zewiGv/bw6GP9/GNtTOzU7zU1C+M+Nq3EgZ/6jNe9ZZNCXheMjAqdfTYc6NjpAAPABc7584CBjQR4/3R8U4EHgF+0uDcYPy/7aX4STCReJYaTRqsAh4CvtdIm+1AXvT9YgbR53sLr8mxwHzn3ARgL/Cx6PEnnHOnOecmA+8Cn8360b2WOmbMhRYK9cFP7tQndGdy6GyUCfjJ06Yswk869wTq+OB96ixgcVvjdc79Gf9981rn3BTnXCUiIiIdqLAofx8ffDZoSQn+TM5jZvlQ2nqSg46hm+iLv833+qwFc+/LWjD3uKAD6ipasxQo1cyWRX9fDPwKKAIus2gdCiAFqH/Qn3POHQAOmNk+Pii2tAI/eVDvUQDn3CIz62lmvaPHn2zwP6lTh3zv868CoR5nT2H3H586ePGeJ55PTBzcf3L1ms3nwsH/kb8IeDPaJAP/f4gXAz80s7uAfzjnFjeI4Ynov2/gV0BuzOPOuXB0RkcO8PgHORJ6NnGNAd+P/o95BBiKv1wFYL1z7uUGbdc651ZE78PbwDPOOWdmK/CX9gD0Ah42s7H4mefEBtcXOud2Hzb+54A0gB5DxhJK8r8QTuk9iJTegy7Z8fbijfiJkCXR+5KEn/hq7nFZAtxr/nKwJ5xzm6LXvuqc2xSNf1k05hcPiycM/CX6+wX4ybPXoten4n84OxpN/e03cOjj3VS7Hj1OOjXBS0pKB0g7YWJ9vwlecnLfxIGDetdu3zYOWOOcWxs99yj+43y4qXzwmP0euLvBub9GlyK9Y2aDjrhSJL40XAo0FX8W4cTD2iQC95u/JC+Mn4iG5l+Ta6Mz+8B/jxkZ/X2imRUAvaPtnwImWSghlUPX/YKfrP0S8LfGAjez+fhJk5roFwCL8QuxrQX+CeRFZ8SMdM69Z2Y3tyNeERGRzvYb/C8XpjRxfgP+FyHD8D8cHxMiCVTuzkUzLGIrDfgf/C+mLw44li6hTTVW6kVnX3zMOffeYcfPABrufx1pcDty2HiHT02rv11+6GHrwaGJBACSs4a6yjeLR5tZ32hiwYA7nXMPHN7WzE4BLgHuNLOnnXPfjZ6qjy1M049FfTwesLfhY2FmF9D4m9a1+DMaTnHO1ZrZOvzkUyP3r1WP1/fwE1bTzWwk8Hwj8TXUi+gHES/0wd0y85yFQj2i5wqdc59o5NqGMR18XJxzPzCzf+I/ji/bBwVdG8bf1ONY1aCuiuHXQfhmE2M35238rcAO1+jfPvpYlbei3VfN8454jgHgnAslp6TUHvnBrrUaPs8bPlYqCCVdhnPupehMt8Nnas0CtgGT8d8jq6LHm3tNHv6eUb+M5rfAFc65t8zsBvyaKul4nsO5+jhwH9TX6tGgn7f5YOYLzrmZ0Xhfjx56DX/pzhqgEL9Wy818MAOuPfGKiIh0qsKi/HBeTsH/AC8cdmoL/kqBPwA/o/Wzwbs8B2z+BMtcok0NOpZuSjOBWqm92y0/hV+fpL6exknt6GNG9NqzgH3OuX2NtCnaft+j1UBF2YtvkTJuxMETaZPHhhMG9rkH+Kf59T6eAj5jH9QKGWpmA83ftaHCOfcH4IfAye2IFefcfmCtmX082r8BO4Hk6LedRI+fBowAtkeTKudFbx+NXkB9scYbmml3AP/DxuM0lnAxbO/a5SuAl4FcMxsTjTnNWthZx8yynHMrnHN34X9YGdfWOxH1DHCVmQ2M9tvX/CKTrfEsjT/e+2nkb9/I9Y0+R4BFB95aGo7U1lRHqquoWPX2wQtcJFJTtXnjNvzlWKOjH7Qg+vxtRBFwTfT3azly9o5Il2N+raUQ/lK+hnoBW6MzsT4dbQNNv9aa0wPYamaJfPA/hEsT+/aJVG/aBEDF2yuJFi6vBP7U4NpngRQz+2KDYwd3sHPO1QAbgavx3/8W4y9vrJ/B2J54699vRUREOk1hUf4i/Jkrz+CXB7gCv7TAX/BLHhwzSRWA3bksru2rpEoHGp+1YO5dWQvmfjlrwdwrshbMHR10QPGqNTNWGvM94D5geTTBsA6/bkRb7DGzIvzlNJ9pos0tFW8W/3rDl39YmzCwT3jglz4WAhzOhWtLd/2zqnjDT82sHH9Hlkvwd8N5KZrvKQM+hf9Gc4+ZRfDXGX6x8aFa5Vrg52aWjz+L5jFgOnCf+dvtVuE/FncAPzGz14Fl+B/Kj8bd+EuBbsX/ANGU5UDd0gdvnZV5Ut62pIy+Q/BnyoSB6qp9O96vObC70jm3I/qN8KNmVp+FzKf5nXW+Gk0ShYF3gH/jL3tpE+fcO9HH72kz8/D/JjPx6820dK0zf/vUwx/vr+LXPjj8bx8+7PqnzeyEw9s555aGUtMe2HDf3bMT+/YLp4wcHcKfYVNdtWnDi0QizjlXaWZfAv5jZjuBV5sI8xbg12Z2O36BsRtb/+iIxJWGy0ANv5ZTuMFSSPC/FftLNOH8HNGEblOvNQ57TR7mf4FX8N8LVgA9Sr52a/XQtWtn7nryb7/c/OP7LHXsWLOkJID38GteER3PmdkVwDwz+zr+a68cmN2g/8XABc65CvOLjw+LHmtvvL8FfmFmlcBU1VkREZHOUliU/5m8nIJQYVF+GCAvp2AS8A8+KM1wzCgfc+TKBompQcDXG9z+M/DxgGKJa+Zc/BeLju6j/WH8wq41wMMlM+YsCjaq+HbK5+Yl4k+NvwJ/Zs2Db/xy1luBBhXnxt45Lx3/w9SF+EsGHlj9zVlr6s+bWYZzriyaTJwPrHbOzQsmWpFjR9aP7j0B+Dx+Aeh/AY+VfO3W6uavEhER6b7ycgoGFBbl78jLKfgI/pe9GUHHFIR1X2BlJPmIGnDSfi/gF7CdFL0dwa9ptwJ/E5VlJTPmtGe1SrfXJRIrIvHAzGYB1+MX+30TuNk5VxFsVCIiIiJyrMnLKZiAX3j9h7S/vEOXt+YrbMazoUHH0Y3cgr/L6Vn4u9v+vmTGnLVZC+Z+DH+2ShnQs2TGHCURDtPepUAix5zo7BTNUBERERGRoN1G87UXuz0HYLRUE03aZkc0abKYD2rRgV+C4XmgFL+Qv75cPowSKyIiIiIiIl3LOUEHELSqIazCL7AvsbO9sYMlM+b8E/hnJ8fSpSixIiIiIiIi0kXk5RT0AUYFHUcQIomU7z2FN/dPobdqq3SIrUEH0FUpsSIiIiIiItJ1TAk6gM7kgMoRrNidw76aAZyE2VlBx9RNfatkxpx3gw6iq1JiRUREREREpAvIyynoDcwOOo6OsOdUivaewcCE/WxP3k5tymZSqwdReWAcx5Fgk1ruQY7Ct0pmzLkz6CC6Mu0KJCIiIiIiEufycgomAU8AY4KOJdZq+rBh06fpi9kxuW10wOaUzJjz/aCD6OqO2a25REREREREuoK8nIJPAa/QhZMqkSTKGjvuPMKbZ3BASZVA/EVJldjQUiAREREREZE4lJdTkATMA74UdCxNqTiOlbW9KevxLid6taQ11mb3mby49ww7i4jbGqpgc9IOygf8l4kJFfTbdjEvumQ75nc5CogKAMeIlgKJiIiIiIjEmbycghTgOeDMoGNpSm1Ptm68gWTM+uJcdaiM5T3eobrnck5IqKAfQPlIlm+7jBMwS2x4bfIWt7jPa/QuvYwJmGklRXDGlcyY817QQXR1mrEiIiIiIiISfy4hjpMqLkTtpk+yB7PxAJglh3tw2t4zYO/pznnVLE9bw56yE5h0eFIFoHowOaUfZaeSKoG7HLg76CC6OiVWRERERERE4s81QQfQnK3Teckl27RGT5pZJIUTy8Y304FZCGNQx0QnbXB80AF0B8oOioiIiIiIxJG8nIIewEeDjqMpe0+hqGpoE0kV6WoKgw6gO1BiRUREREREJL5cDqQEHURjqgdSsjuXKUHHITERAZ4OOojuQIkVERERERGR+PKJoANoTCSB6s0fJ4RZo7v/SJfzcsmMObuDDqI7UGJFREREREQkTuTlFPQDLgo6jsZYhBAhhgYdh8TMv4IOoLtQYkVERERERCR+fIw43WTEIiRYHWuDjkNiRomVGFFiRUREREREJH7E5TKgegn72Bl0DBITW4FlQQfRXSixIiIiIiIiEgfycgqGAucEHUdzUrZSG3QM0m6lwK/xZ0WNK5kxxwUcT7cRl1PMREREREREjkFXAxZ0EE1xQOVx8blbkbSoAPhOyYw5kaAD6Y40Y0VERERERCQ+XBN0AM3ZfjEv1PWyM4KOQ9olH/hR1oK5SUEH0h2Zc5r9IyIiIiIiEqS8nIIs4P2g42jKvkm8sut8JVW6gdeAK0pmzNkSdCDdiWasiIiIiIiIBC+uZ6scmKjaKt3At4Dp+IVrJYZUY0VERERERCR4cb0bUNo6qBkYdBRyFP4D/EAFazuGZqyIiIiIiIgEKC+nwIDKoONoTnoJg4OOQdqtDPi8kiodR4kVERERERGRABUW5TtgdtBxNCdpO1k4tz/oOKRdbi+ZMWdD0EF0Z0qsiIiIiIiIBKywKP9Z4Kmg42iKAaEKVgcdh7TZC8Avgw6iu1ONFRERERERkfjwDeBDQQfRUF06O/ZNZlXZCaSG05gcdDzSZitLZsyJBB1Ed6ftlkVEREREROJEXk7BH4Brg45j/0Re3X0WaZFkmxh0LHJU3iqZMWdK0EF0d1oKJCIiIiIiEj/+F6gJMgAH7DyXQUqqdAubshbMtaCD6O6UWBEREREREYkThUX5a4GfBRlD1TDeIWQjgoxBYuK7wGXaDajjqcaKiIiIiIhIfJkLfBbo0dED1aWzEwehCvrXT2vYcwY7O3pc6VD7gE+VzJjzj6ADOVaoxoqIiIiIiEicycspmAMUdOQY+ybxyq7zmIRZGs7V4dju1bI7ksQIzDo8qSMxswl4GH/ihAf8smTGnPeDDenYosSKiIiIiIhInMnLKUgH3gcyO6L//RN5decFdnpH9C2drhbILJkxZ3fQgRyrVGNFREREREQkzhQW5ZcDd3RU/5Fk6jqqb+l0icCMoIM4limxIiIiIiIiEp9+BRTHoJ8wUN3wQOJe0mLQr8SP647m4qwFcwdmLZh7b9aCuR0yQ6q7U2JFREREREQkDhUW5dcB32zn5WuAnwNXAH0Li/JTKvt5GftHJtwLUJmWuM/Wph6wdam7bX3KNtuQstk2pqy1TcmrbUvyKtuavNJKk96yXYkv4dgTm3skHejMrAVzx7b1oqwFc3tnLZhbgP98mQV8K+aRHQNUY0VERERERCRO5eUUGPAScEYLTQ8AzwJPA08VFuWXNDw59s55qcAfgCvTN9a+UD4sYRJmfVsTg0uKrHNDqxIwhrX9Hkgn+l7JjDnfbk3DrAVz04GvALOB3g1O1QJjS2bMWR/78LovJVZERERERETiWF5OwTTghcMOO+AN4Knoz8uFRfm1jV0/9s55/YEngantjcGFIjvdcVW1GIPb24d0uHVAVsmMOZGmGmQtmJsE3Azk03Rh5N+UzJjzmdiH130psSIiIiIiIhLn8nIK/gGchJ9EeRr4b2FR/s6Wrht757wxwL+BMUcbQ2Rkxft4R9+PdKhzSmbMWXT4wawFc0PAp/ALIo9soY8IMKFkxpxVMY+um0oIOgARERERERFp0ceBqsKi/FZ/Mz72znlnAn8H+sckAqNHTPqRjnQdcEhiJWvB3MuAHwAntLIPD/gGcENMI+vGVLxWREREREQkzhUW5VcWFuW7sXfOa/Ez3Ng759nYO+ddBTxHjJIqDgfQJxZ9SYe6OmvB3NTDjn2H1idV6lXFKJ5jgmasiIiIiIiIdB3XAH9s7MTYO+eFgOnAbbRc7LZtjApMWzR3AT2Ay4HHGhw70I5+3oxNOMcGJVZERERERES6jjvG3jnvSeBE/FoYB4AK4CPArcCoDhk15PaCEitxzgF/BQ6vjbK/HX0tPepojiFKrIiIiIiIiHQdg4FZ+NvkpnfaqCFX1mljSVvVAb8E7iuZMWd1wxNZC+YaUNPG/sLAihjFdkxQYkVERERERKTrCAHfAlI6ddQEV96p40lbGHBHyYw5O7IWzE0DTsXfWjsn+tPWOjvvlMyYoxorbaDEioiIiIiISNfhAcmdPmrIVXf6mNJaIeCRrAVze+NvyX20n/NVX6WNlFgRERERERHpOgLZ2dUluNogxpVWyzuKa58GTuaDmS1KrLSREisiIiIiIiJdRyiIQa3aS3dBDCyd4SvAauB44GxgcbDhdD1KrIiIiIiIiHQdgcxYoTw0BcdOrM31OnyO/Rg9YxyVxEbfkhlzHP5uQofvKCStEMyLUkRERERERNpk7J3zAvv8ZphHlfdOmy90bLJdiUVUhLS8JH71DTqArk6JFRERERERka4h0M9vtiex9bNVHLvZl7DI1qYOsn2JOaSE+3RgaHJ0+gUdQFenxIqIiIiIiEjXEOzntypvPI51zbZxVFIeesHWpyZ6u5KmGZbozFXjMa5zgpQ2ckCvoIPo6lRjRUREREREpGsIpHBtPcNw5aH1ZIRHHnHSEabaK7JtScdb2DvnkHOp4VUYkzspzGPdRuBOYAtQCVS18FMbra8iR0GJFRERERERka4h8JkFtjtxjEsP78H4YGlPrb1i25IHWY13dmPXuIzw3s6K7xhWgZ9Q+VHJjDmVQQdzrFFiRUREREREJM6NvXNeMvBY0HFYnTeUDSnb3dCqNzFCtj05ZJWhM5q9KDWs3YA61sPAnJIZczYHHcixSokVERERERGR+Pcd4JwWW3UCC3sD2ZA6EPzlQc1xuDrVV+kwRcBXS2bMeS3oQI515pyWU4mIiIiIiMSrsXfOmwy8QcA1VtrKJYdXu36120iJnBV0LN3MBuDrwJ9UHyU+aMaKiIiIiIhIfPsJXSip4lLDb7tB1Ql4HA+MDTqeLm4zfiLlZKAOv47KvaqjEl+UWBEREREREYlTY++cNwiYFnQcbeF61u3Ei49lS11cHfCxkhlzXslaMDcJSC+ZMWdP0EHJkZRYERERERERiV8XBx1AmyVGkoIOoZv4dsmMOa8AlMyYUwPUBByPNMELOgARERERERFp0qVBB9BmCa5Py42kBc8CdwcdhLSOiteKiIiIiIjEobF3zksCdgI9go6ltRwON6pyP4a2WG6/XcCJJTPmbAk6EGkdzVgRERERERGJT2fThZIqAITYpaTKUbtRSZWuRYkVERERERGR+PSRoANos6TI1qBD6OLuL5kx5+9BByFto8SKiIiIiIhIfOp6iZUqbww19lLQYXRRK4Dbgw5C2k6JFRERERERkTgz9s55Y4HsoONoK3OWYptSplIWeiHoWLqgW0pmzKkKOghpOyVWRERERERE4s8FQQfQXobhbU8+x3YmvoKjMuh4upAu+zc/1imxIp3KzJyZ/ajB7dvM7I529jXFzC5pcPsOM7utFdetM7MVZvaWmT1tZpltGHOkmX2ykeOTzGxZ9Ge3ma2N/v7fJvr5rZld1SCe/m2I4QozG9/a9nKk6PPw9w1uJ5jZDjP7Rxv7OeT5YGY3mNn9MYpxYfQ59L6Z7Wvw/MppxbUHn1NmdouZvWtmj5jZZWb2jejxVr1eYsHM+jWIv9TMNje4ndTCteOi7d40s6zOiLc9YvWc6mhm1tvMvtTg9rltibHhe9dhx4eY2Z9jFaeIiAAwKugAjpbtTzzDtiRvwFEadCxdxBeyFsxNDToIabuEoAOQY041cKWZ3emc23mUfU0BTgX+1Y5rz3PO7TSz7wPfAm6Z+/alhp8lPhPYDDw+Z8I/yuovMLMEYCTwSeCPDTtzzq2IxoOZ/Rb4h3Ouoz5kXAH8A3ing/o/FpQDE80s1TlXCeTh/81brbnnQ3PyvI+nAR+LXvs68FRh5PGImRlgzrkIgHNuenScc4HbnHOXtiW+Br4EXOycWxu9/WQ7+2k359wuPnh93AGUOed+2NJ1ZhYCrvBSUp4adcf31wDXjLlr3vNA0fuzZ7mOi7hdjvo5FUvz3r1oCPBxIBX4x6wTnl4ZPdUb/znxs1iO55zbAjSWcElwztXFciwRkWPI8KADiAWrDh3PhpQdbnjVO3joy8Hm9QeuBR4KOhBpG81Ykc5WB/wSmHX4CTMbYGZ/MbPXoj+50eOnm1lR9BvrIjM7Pvot93eBGdFvs2dEuxlvZs+b2Rozu6UV8SwCxpz3xbG9Hvmf17f+5PIXnpp/1eLvlby8cz6wccRJffPN7HEz+zvwNPAD4OzomEfch0bu07ej92Wlmf0y+uG5Mbeb2avRnzHRa0eY2TNmtjz673HR2QqXAfdEY4jbb/C7gH/zQUG4TwCP1p8ws3Qz+3X0b/emmV0ePX5DK54PQ8zsP2a22szujl73WTObl+d9fCywbpMr+WWxe+v/Kl3540vcv8tDFvolsJQW/gfKzBab2ZQGt5eY2YnRGSFPR2N9ALDo+V8Ao4EnzWxWLGfUHC0zuyAa74roY50cPb4u+rp5EZhBKHS7JSbdvvmB++cD/7f14Yde2HDP9/eY2dtm9rkG/V1kZi+Z2dLo3yijQX//Fz2+wszGdeDdau451dfM/hp9Pb9sZidGj98Rvf+HvG+ZPxtqlZk9FH3/eMTMLoz+zVeb2elN9Tvv3Ytm/Psn769/9lfrfgh8D3i118CUnWY2Ev85mxV9zt4TDS/DzP4cHe+R+vcpM/uBmb0T7bthImxa9L14jX0w826kma2M/n7I66Sp15OIiLSoWyRWACzsDbD1qVnUWFHQsXQBX81aMLepzwwSp5RYkSDMB641s16HHf8xMM85dxr+N/r1mdpVwDTn3EnAt4HvO+dqor8vcM5Ncc4tiLYdB3wIOB34jpklthDLpcCKPZsqH0lOT+h/y9/O8a6++ySeyH8rtbY63OvEjwz5EjAVuN45dz7wDeD/2bvz8Kiu8oHj3zN79hASAoQlEFIIW9mhUKAr2tp9MbVVu1jR2qqlbtW4+4uoVaO2WktrN1vb2NXa2koXC4WyL2Xfhj0JZN+T2e75/XEnkEBCEkhys7yf55mHzL3nnvvOZGbIfeec93wUPmdOGx7rI1rr6Vrr8ZjfHLc06qBSaz0DeAT4Q8OxwLNa64nA88CftNYfY444+E44Bm8bYhDNexG4RSnlASYCaxrtywI+CL8WL8ZMZEWF97X2epgEZAITMBN/Q8PnusbQoeeB/gUc8gwmVQFRddR4xjLNrbWerLU+1ErMTwB3ACilzgPcWustwE+AFeH3yBvAMACt9VeBfMwRWm15vXYVD/A0kKm1noA5evKeRvvrtdYXpv3q97nxcy9yx8+dr1K+cp8dsA+4+XP2Yd/5gWN41s/+AHwjnFRKBH4IXKa1noI5EuiBRv0Vh7c/CnTm9KczvaZ+BmwKv59/ADzbaF9Ln1ujMD8XJ4bb3ApcGH4MP2iuX5tdPQ88pWzKoRQOzOc2IiLW0e+W/xs7DfM16w2/ZhtWHZgM3A+MxUzEzVFKJQDXA+PCff9fo3gHheO4CjNR05zG75MzvZ+EEEK0rNckVgCUVm511DObKilq24pxwG2SXOlZJLEiupzWuhLzouLUESWXAY8opTZjXhzGKqVigDjgpfC3oTmYHzYteUtr7QtPMyoEklto97/weWKBxTVlvvmTrx1iB0gaGU384AhKDtYop9vePyLW+bHWuvTsHi0XK6XWKKW2ApecIfYXGv17QfjnCzg5xeTvmBcyooOEExKpmCMLTp1OtgB4MPwa+RAzETAsvO/dVl4P72utK7TW9ZjTtYZrrWts2FcWUzC5RlfaNJrocF7RQyQD1bC2Fip7CbgqfOF9F2ZyAmAe8Fz4cb0FlLWxP6vYgQNa6z3h+89gPoYGDYnSKcpma5IcrVj5EUf+8FBU/l8fzsH8gzMdc/reWGBl+Hd2OzC80WGvhv/dgPk77xStvKYuxHwfo7X+AOjfKLnc0ufWAa311vD0sO2Yry2NuRRjanP9OlxqcF1l4LSpN0phGzwmpqWRImu11kfD59kc7rsSqAeeUErdANQ2av+61trQWu+g5c/Yxu+TM72fhBBCtCza6gA6mkJhK3LPJ6DWWh1LN/d3YFlabvalkmDpGaTGirDKHzCnPjzVaJsNuCBcn+AEpdTDwP+01teHh7J/eIZ+fY1+DtHya/zixjVe0ucktdihsquzqmQe/tb6L8A0rfURZdaW8LTQXLfwc0ttRMd4A/gtcBHQv9F2Bdyotd7duLFSaiZmLY0zafY1GEXs3/M5dFsUMQxqdG1vb8fHsNa6Vin1LnAt8FnMGkMndre5I+u19hw2u7/Ou4+6fXtJ+do3sblcG7wPPqAx31MK80L+cy301/A7OdNnQkc502vqVA2/s5Y+txpvNxrdNxq1Ob1fpbA7FLrRKyLgM84U82nn11oHw9ONLgVuAe7DTA6f2r6lP/ZqTmlz2vtJCCFEy9IX5wwE/FbH0Wn8tjqcIauj6O7mAu8BK9Nys38KvO/NzOpJf+/1KTJiRVgi/E3mP4EvNdq8FPOPd8Bc9Sf8Yxwni0De0ah9FRDTEfFE93cv2/xGXgig+GA15QX1JI6I0gFfqKS2zF91ludsSKIUh+s9nFbYsZHMRv+uCv/8MeYFDZhFrFacRQzizJ4Efh4uPtzYf4GvN6o1MbmF49v8u6jUpe/WUl1/jCMMbDqyVxMecdBGTwB/AtY1GhGwHPM1glLqCqBfO/qzggdIVeF6QsAXgOaGBW/UhhFouGPU12OLiMDmctVUb/3kHcyRKgCrMaevNNQnigxPlbJCS6+pxr+jizCnJ1V2wPma9Bv067yIGIej3+AIju4wuz+6o5LSvDryd1X9iza+ZsOfWXFa6/9gThOadA4xtvX9JIQQ4qSZmFMveyVVZ3NbHUMPMgd4F1iRlpt9ppH7wkKSWBFW+h1m5esG3wCmhQsl7gC+Gt7+G2CxUmol5hSCBv/DLFbbuHjtWYlPibitvipQ/Kdrlxm5397EDb+YWOdw2cu3vJV/6soZW4CgMpdqPmPxWq11OfA45rD914F1Z2juVkqtAb7JycK+3wDuVEptwbzw/GZ4+4uYxW679fKzPUF4+sMfm9n1C8AJbAlPQftFC120+fUAoDH+EkeCz6lc1YAButog5AOy2xHzBsxpGo1He/0Ms6DoRsxpF4fb2p9F6oE7Maf4bcUcgfHXUxvt+96iUO3unbnaMHxATeToMSEdCoUO/fr/Qseff2YKZkIFrXURZtL1hfD7ZTVmTZIud4bX1E8Jf75h1iW5vYNO2aRfI6Q/D9w5ccGAuprygPHb61ex4h9HgtEJrtIXf7hjfXiFppXhgrgPnaHfGODNcL/LaKbgeDu09f0khBDipPutDqBT1dtbHi4uWjIbeDEtN1uSUt2Q0lpGEwkBEF5u+RLM2ianLbcsxLlSSr3pwv3neerq/sAIGi233I4+BmNOhxvTsDRzbzfq1zn9MKc+JWImVFd1w+WWu5UzLLcshBCim0tfnDMFsy5Yr6XRQT2iTqNobaEJcbr/82Zm/cjqIERTklgRQohOppSKB9YCn2itbz6Hfr6IObrlAa31Sx0UnhBCCCG6kfTFOX8HPm91HJ3NSK3di410q+PogULATG9mVq9OvvU0klgRQgghhBBCiG4gfXHOEOAAfWCREWNI3ce49Gyr4+ihtgFTvZlZvbfAcQ8jNVaEEEIIIYQQonu4jz6QVAHAZwu03ki0YDzwM1mKufuQEStCCCGEEEIIYbH0xTnRwBEg3uJQuoSOCa7VSf4ZVsfRw+3EXADgWW9mVrnFsfRpMmJFCCGEEEIIIax3B30kqQJAvW2g1SH0AhnAH4G8tNzsJ9Jys6daHVBfJSNWhBBCCCGEEMJC6Ytz7MBuIM3qWLqKRms9oq4eRYTVsfQy64CvezOz1lgdSF8iI1aEEEIIIYQQwlrX0IeSKgAKpVSeJ18djKgkqGSFm44zHXg3LTf7QqsD6UsksSKEEEIIIYQQ1nrA6gCsoPy2NGWoWHXUk4Ymz+p4epEY4J203OyLrA6kr5DEihBCCCGEEEJYJH1xzgygT48uUIaKVwXuKjSyfHDHiQL+k5abfZnVgfQFklgRQgghhBBCCOsssjqA7kDV28dQ7pC6IB0rAngzLTf7CqsD6e2keK0QQgghhBBCWCB9cc4wYD9gtzqW7kCj0Sn1K3HrOVbH0sv4gZu9mVlvWB1Ib+WwOgAhhBBCCCGE6KO+QR9Oqmh0CJdxgEijUEeEgriMOGxMsDquXsgFvJKWm32LNzPrldYap+VmRwIzgXnh2yHg197MrN2dG2bPJSNWhBBCCCGEEKKLpS/OiQWOALFWx9LVdGxgtU4IxKEYicLdVefd/7kf4RqWjA4ZuFKSSPrajdjcrq46fXcQAn4EPOfNzDrSsDEtNzsWmA3Mx0ykTAecpxyrgYe9mVnf7KJYexQZsSKEEEIIIYQQXe92+mBSxUjwf0Rc8EIUqqvPrVxOhvz6PgAKH/4nle+tI/4zfWrWkR34JfDLtNzsNcBGYAYwmdbrryqgqHPD67kksSKEEEIIIYQQXe8uqwPoShqNTvIvIyY03+pYADxjUvEfPkaoupaiv75GsLAU5XKS+OXrcA8fyJHvPMzgn96NLdLDoS//kv5fvJKYeZMpfOQloudPpvzl/9H/zqtwpw4CIO/HS0j80jUYNXWUPPOWeRKlGPyTu7FFdNmgnPaYGb61lQE81Umx9HiyKpAQQgghhBBCdKH0xTk3AZOsjqOraDR6oK/bJFV0KETt5j24hiZT9tIHuFMHMeQ3Xyfhlssp+svLAHhGD8O3+zCBo4U4ByRQv+sgAPX7juJJH0rMJVOpWrYRAH9+MToYxD18IBVvriDxrqsZ8uv7GPzTu1GuXjOW4T/ezKw8q4PoriSxIoQQQgghhBBdJH1xzkjgb1bH0VU0WusU3woiDcuTKtof4Oj3HiHvB4/iSIwn5pKp1O8+RPTcSQBEjE8jVFWLUVuPZ8xw6nYepG7nQWIun4H/8HGCpZXYoyKwedxEzRpP7cbd6GCIqg83EDNvCmAmZEqefZuKt1dh1NSj7L2mNvHjVgfQnUliRQghhBBCCCG6QPriHBfwIn2ktopGG3po/RrcxoVWxwIna6wM+fV9JN55FcrhgBYWc4kYk0r9roPU7zpIxNgR2GOjqFm9Dc+Y4QDY3C4iJoyiZv1OalZvI/rCiQDEXzufpK9ch/YHyPvRY/jzenRZEg38D/gy8B+LY+nWes24JCGEEEIIIYTo5n6FueJKX2HDIGh1EGfiyUilesUn9LvxYuq278ceG4Ut0oMt0oNRVUsgFMKZnIBn9HDKw9N8GsReMpVjDz2HZ/Rw7NGRAASOleAaNhDXsIHU7z1CIL8IV0qSVQ/vbG0EngdyZfpP20hiRQghhBBCCCE6WfrinGuARVbH0ZUUCvI8s/Twuk3YmWx1PM3pd9MlFD36Kke/+zDK5STpnhtP7HOPGoI2zBEtnjGplL74Lp7Rw0/uH5mCLcJNzEVTTmyreHsVddv3o2w2nEOSiJx0Xtc9mI5RBEzzZmY1P5RHNEvpFoY+CSGEEEIIIYQ4d+mLc4YBm4F+FodiCW3TlXp4XTmKYVbH0pGCpZUU/PxvDPn9N1G2XlNl4z1vZtblVgfR08iIFSGEEEIIIYToJOmLc5yYdVX6ZFIFQBkqVmuKUFZH0nGqlm+i9MV36f/FK3tTUgXgE6sD6IkksSKEEEIIIYQQnef/gAusDsJK2qbLsZFmdRwdKWbeZGLmdcvZTedKEitnoVel1oQQQgghhBCiu0hfnHMF8F2r47BcVGiv1SGINttidQA9kSRWhBBCCCGEEKKDpS/OSQGetTqObsGu/VaHINokAOy0OoieSBIrQgghhBBCCNGB0hfnOIAXgESrY+kWyh1zqLMttzoM0aqd3swsSYKdBUmsCCGEEEIIIUTH+gkw1+oguguFQhW45+FXH1sdizgjqa9yliSxIoQQQgghhBDtpJQaqJR6USnlVUrtUEr9Ryl1XvrinMuArI44R8l771D20f/Mn999m9p9e87YvmbnNsqWvd8Rp+5wCoU66plJQK2zOhbRIkmsnCVZFUgIIYQQQggh2kEppYDXgGe01reEt03yDEvNAJZAywsLa61B63Yv0dv/8itabROVMZ6ojPHt6rcraHQIpz6M2yim3laLIxRCYbc6LnEaSaycJaW1tjoGIYQQQgghhOgxlFKXAD/VWs9r2Ja+OOcq4K9lyz9Iqd76CToUJGrsBPpf9mkCZaXkP72EiJGjqD98iEGfv5PqrZs5tR1A6f/epWrTehxx8dijonGnDKHf3Is5/vILRI0eS/SE86nZvYPit97AHhWFe3AKgdJSBt9+N5Ub1uLLO0LSNTcSKCul8JVcQrXV2COjGXDTLTjj+3H85Rewud348o4SrKok8dNXEz3h/E57rjQ6pFPrdmFjXKedRHSUdcDbwHvAGqm30nYyYkUIIYQQQggh2mc8sAEgfXFOEvBH4HO1e3cTKClmyNfuB60p+PuT1B3w4ojvR6C4iOQbb2HAtTfRUjvlclG9ZRND7/sWGAZHHvkd7pQhTU5sBAIUvf4yKV++F2dCf469+PdmAyz696vETJlG7JTpVK5fQ/G/X2PQF+4CIFhVRcrC+wgUFVLw9791bmIl2b8CG/M77QSiI00P334M1KTlZi/DTLK8B2zzZmbJqIwWSGJFCCGEEEIIIc5C+uKcW4E/Af0Bavfupnbvbo488jsAtM9HoKQIR3w/HPH98AxL5UztDJ+PqLETsLlcAEQ2M60nUFSIo18CzoT+AMScP5mKtatPa1d/+BCDbrvTbDN5GsXvvHliX/TY8SibDVfyQILV1R3zZDRDR4S2EhmSIr49UxRwZfgGcDwtN/vfwLe8mVmV1oXVPUliRQghhBBCCCHawTN8xHHDV/8T4P6mezT95l9K3MzZTbYGykqxOV2ttitfuQxUi+VZThx7rpS98WVg5wxC0DZdqQf6ElCyYEoPUgYcAzKa2fdf4GeSVGmevMiFEEIIIYQQog3SF+fY0hfnfDVl4X2PK4czoWLdqhP76o8exub2ULlhLYbPB0CwopxgddVp/USmj2m2nSc1jZodWzECfgxfPbW7tp92rDNpAMGyUgJlpQBUbdncbKyeYalUbdlkttm8gYjUEef02NtLJ/k2oUjp0pOKs/U+cCsw2JuZNRYYBnwJyAWWAtO8mVm3ezOzjloYY7cmI1aEEEIIIYQQohXpi3PSgceB+UopBn3+TorffJ2yZR9gczhw9Esg8TPXYfNEcPSvfwRAudwM/OxtcMoKQJHpo/EXHj+tnSdlCNETJnHk4d+ZU4dSR54Wh83pIumaG8l/aolZvHbIsGbjTbr6egpfeZHyj/53onhtV1JF7kk6om43NkZ36YlFezwJZHszs/Y33ujNzDoS3vekJVH1QLIqkBBCCCGEEEK0IH1xjgNYBPwc8FgcDgCGz4fN7UZrTdEbr+Dqn0T8hd2vPqwxwLeM6FD3C0yEgG94M7P+YnUgvYWMWBFCCCGEEEKIZqQvzjkf+Bsw1epYGqtct5rKTevQwRDuwSnEzrjA6pCa5zTsVocgmlUkSZWOJSNWhBBCCCGEEKKR9MU5buCHwIPIl9FnzRheuxk7k6yOQzRrkDcz65jVQfQW8iEhhBBCCCGEEGHpi3NmA0/Q/Moooj1s5jLUoluagLkCkOgAklgRQgghhBBC9Grpi3MUcB0QBIqBXXu/v6jslDbRQDbwdaC1NY9FKzTaAAZZHYc44V7gE6AAKPBmZtVZHE+vIlOBhBBCCCGEEL1WOKnyK+C7jTavAeZhXvhPwayhchuQ2tXx9VbaYeTrYfWDrY5DnDDKm5nltTqI3kpGrAghhBBCCCF6s5/SNKkCMBMoAyK7PJq+wmUUA5JY6T6kkHAnsrXeRAghhBBCCCF6nvTFOd8HftzCbkmqdCaXrrI6BNGEXPt3InlyhRBCCCGEEL1O+uKc+4FfWh1HX6VdRtDqGEQTMmKlE0liRQghhBBCCNGrpC/O+SqQY3UcfZrTkAv57iVkdQC9mSRWhBBCCCGEEL1G+uKcO4BHrY6jz3PoGKtDEAD8D5juzczaZXUgvZkUrxVCCCGEEEL0CumLc24B/mZ1HAKw0c/qEPq4rcD3gHe8mVmyFHAnk8SKEEIIIYQQosdLX5xzHfAcMiq/e9DUoawOos/aA0z2ZmbJ9J8uIh86QgghhBBCiB4tfXHOFcA/kQKd3YdBjdUh9GFpgMfqIPoSSawIIYQQQggheoz0xTmD0hfnjGt0/xLgVcBpXVTiNIaqtzqEPswOTLM6iL5EpgIJIYQQQgghepJLgC+kL875B3Ax8Fnk2/nuJ6SCIKU9LHQBsMzqIPoKSawIIYQQQgghepJLgU+Fb6K7CirJqlhLRqx0IZkKJIQQQgghhOgR0hfnjACutDoO0QZBJfVurFMLLLY6iL5EEitCCCGEEEKIbi99cc6lwHog2epYROtU0Oa2OoY+ygBu8WZmbbA6kL5EpgIJIYQQQgghuq30xTkKuB/4LfLFcM8RVBFWh9BHfdObmfVvq4Poa+SDSQghhBBCCNGl0hfnDEhfnNPqtUj64pwI4Fng98i1S88SVLFWh9AH5Xgzsx6xOoi+SGktNYWEEEIIIYQQ5yZ9cU4KEAtUAzXhm3/v9xfpRm3swL1ANvAhcMve7y+qaaE/B/BfzFWARA+jbbpKp9bFWB1HH+IFxnkzs3yNN6blZsd6M7MqLYqpz5DEihBCCCGEEKLd0hfnRAPzgQXh25hmmgU5mWSpBlxAaqP964Cr9n5/UeEpfc8DngZGdHTcomtoNHpEnR+Fy+pY+pBtwFe8mVkfp+VmK+BbwHeBC7yZWV5rQ+vdJLEihBBCCCGEaLP0xTlfAz4LzAacHdClF3gbc7RLDBAHzEPqQfZ4xojaYygGWh1HH/Q4EA18Lnz/FW9m1k0WxtPryYeVEEIIIYQQoj0eBIZ2YH9pwH0d2J84R44aPtF2PCEPo8+pI02FJFYs8eVGP78P3G1VIH2FFIASQgghhBBCtEl4hR5Z7rgn0RBxnGWRBSyntckKGiPiOMsiCpkYWcCoiOMsw8B/dqfVAOpsjhUd5gngCm9mVrnVgfR2MmJFCCGEEEIIcUJabnYkkAP8xZuZ9ckpu+NAamb0GAY10XlsswWZDxCVz4qawcxBnZ7wUEGOR+VxzGaYbQG7s5b5jsPsr02mLhTBuLaeVqPRg30fYWNuBz0S0TY+YC2wElgG/NebmSW1P7qA1FgRQgghhBBCAJCWm20HXgauAwzgUeDH3sysUoD0xTmjgV2WBSjazBbgaFQe9UozqvH2kJNVNSnMQGFv2OasYr2nmJEKEprrS4MOeVhem8xMbHhaO7eR7FtGVGh+a+1Eh9gIvACsADZ6M7POaoSRODeSWBFCCCGEEEIAkJab/Xtg0SmbS4AfAH+z7Y+cg/lNuOjGHDV8ElHIMAX9mttv2FlXPYTzUdgij/Oxo455belXw+G6AZQFozi/pTaucr08NMivAwNDE1HNn190qOnezKz1VgfR10liRQghhBBCCEFabvZwYD8t12HcqEqcL6kK5+IuDEu0hwZ3KctdlcxRnByR0hzDxmalcSnN2PaeJujmo9qBTMZGdOPt9np2RRYwUoFL23VNXbp/vX9waByKxPaeQ7TJFmCSTPexniRWhBBCCCGEEACk5WZ/D/hViw1CbLUdipzQdRH1cZqAu4w1vngmYyOqlbbBqHxW2/1c2DWhkV+bTGkokvEAhKiMOUylgiFN2tl0XV16YJ0/JTgORf+uiK0PqQAeAv7kzcyqsjqYvkxWBRJCCCGEEEI0GHPGvTZG6FaXlhEdwV7LtphDHHFXcGHMIUrsdexsqa0KUhJzmB1dlVQBUDA4oogQABqi89h9alIFQBkqInK3a56rwL6tq2LrQ+KA/wMOpOVmfzctN/vMyTfRaWTEihBCCCGEEIK03OyJwGZaWSJXHfEcVgHbsC4Jqi8yqI08znp7PXNVo9+FhmAgmpX1icxv/Buy17MnsoAoBSlWhFs9hKOucryuas5YrDYYG9pbPd2X3lVx9VGFwK+8mVk5VgfS18iIFdHjKaWylFLblVJblFKblVIzrY7pbCilQuH4tyml/q2Uig9vv0gp9WYnnfOgUuq0Oa9KqZ8qpb7dzr6uUEqtV0rtVErtUkr9tuMibXMMr4Wfw31KqYrwz5uVUrNbaP9C+HVzapE+cZaUUgOVUi8qpbxKqR1Kqf8opc7rxPM9rZS6qZntg5VSL4d/PvEeUkrdoZR6pLPiOVdKKa2U+l2j+99WSv3UwpAa4viB1TEIIbrEr2glqQJARKig80PpmxzVbIw5RJmjnnnqlN+FAoermvnRh9mkghQCOCtYHVnAUKuSKgCRBRx3Vre+rLK90paOpqgrYurDBgBSA8kCDqsDEOJcKKUuAK4CpmitfeEkgets+lpzaEQU8HngMszCbY/NHH5gf4cF27o6rfUkAKXUM8C9QHYXnr/drhj9YH/g7rK6I5c6bZ5pCZHDrj5WtXulUsoBLOzqeLTW14N5IQ18W2t9VUttlVIDgdla6+FdE13vp5RSwGvAM1rrW8LbJgHJwJ6z7XfYYz8aZ4+O/DIwCPgP8OKB277vO9MxWut84LSESw/gA25QSi3WWhefbSd3r79jFnAXEAW8BLzxxLSnjfb2E/6dKszVQH55tvEIIbq/tNzsS4Ar2tJW9wu4qHaUKUPJii/tpIIU230cUQYBZRDCIKQMtNIY9nqc9gDNfhnUmM1gcvQRSg0XK7py6k+L8YSY2pZ2/iHB1ShmdXY8ghqrA+iLZMSK6OkGAcVaax9A+EJkiFLqVQCl1LVKqTqllEsp5VFK7Q9v/7JSap1S6hOl1Cs/eihxMPDJTxcVPfK7n5bcdPf1Bd+5/sKj+z51bfTPwu0HKaWWNxpRMje8fYFSapVSaqNS6iWlVHR4+4/D/W9TSi0JX5y0xyqafvMQrZR6OTwS5PmG/pRSlyqlNimltiqlnlRKucPbDyqlfhaOa6tSakx4e3+l1NLwMY/R6JuQ8Mif3Uqp94DRjbY3ea6UUpEAV4x+cCSwG/jJkfJPLj8v6aLoyYOvf+uK0Q+O01oHtdZ/UUrFKKUOKKWc4b5iw7E5lVIfKqV+rZRaq5Ta0+g59SilngrHvUkpdXF4+x1KqVeVUu8opfYqpX7TlidSKTVDKfVxuK+PlVINj20pMCD8O231WxbRJhcDAa31Xxs2aK03A5uUUu83ej1e27BfKfX58Gtgs1LqMaWUPby9Win1c3ts9N7A0ePrj9z/m2+WvvjOZ/N/9Ocn877/x1J36uDxjc47L/y73d8wekUplaqUanEu95lemx37lLRbEFjC6UudopRKCr8H14Vvc8Lbtyql4pWpZMJt43KB9z/88Ud3563Jv3X3v/a+8Obdb+eF3zu7lVI/adTnA+HPqW1KqfvD21KVOfLsL8BG4G9ARPh39HwXPAdCiC6WlpttB9r0/yoAdibr4XVBbdOVnRdV7+OoYVP0EYgsZHJEMTM8pVzgKedCdyVzXVXMb0tSpYGChO6QVGmrQEJoW915gclWx9HL+YH1wNMWx9EnSWJF9HRLgaHhC/O/KKXmY14INHxwzwW2AdOBmcCa8PZXtdbTtdbnAzt3bvU/Awyx2XCUFIZY8spA9funBqitG31Zaw6NsAG3Av8Njyg5H9iszNExPwQu01pPwfwgeyDc/yPh/scDEZijatokfGF5KfBGo82TgfuBscBIYI5SyoP5wZmptZ6AOQLtnkbHFIfjehRomNbzE2CF1npyuP9h4XNOBW4Jn+eG8PPVoMlzBXwpvP2PQD8gotpfTJxnkBOIDZ8PAK11FfAh8JnwpluAV7TWgfB9h9Z6RvixNVzs3Rs+dgLwOeCZ8GMFmARkAhOATKXU0BaexsZ2AfPCj/nHnPzW/RrAq7WepLX+qA39iNaNBzY0s70euD78erwY+F04CZCB+fucE35vhYDbwsdEKY9r17BHswZ4xozwANgi3Az+xb222CsudBj+QG6j/gcBF2K+z1peyaKRNrw2rfRn4DalVNwp2/8I5GitpwM3Ak+Et68E5gDjbE7bEX+1/0YgsmhbkRowIQllU57KI5XJVz915dOY76GblVLTwu/7OzE/G2cBX1ZKNXx2jgae1VpP1lrfSXhEnda64fcjhOgl0nKzozFHG7Zp1MEJBseVoWIb7ur4wMoODq1Xsfk4EFHIBEXfW3ZYo6k539cPhdvqWHq5G7yZWdO9mVnfsjqQvkgSK6JH01pXY/4hsBAoAnIxp/PsC1+0zQB+D8zDTLI0XECPV0p9pJTaCtxWW2NMB/PDft6CSGw2xYh0F+WlITtmImMdcKcyax1MCF+UzcJMdKxUSm0GbgcappVcrJRaE+7/EmBcGx5ORLifEiABeLfRvrVa66NaawOzqFwq5oXPAa11wxSLZ8KPs8Gr4X83hNsT3v9c+Ll7CygLb58LvKa1rtVaV9I0qdPkuWr0WC7j9M8QBcy5YvSD9kbbnsC8eCP871OtxHgh8PdwjLuAQ0BDjY73tdYVWut6YAcnn+8ziQNeCo9eyKFtvwvRsRTwS6XUFuA9zNFYyZgJxKnAuvBr/1LM9xtAaOjD3z/UuJOoC84HIHr2JFeovKpx3ZbXtdaG1npHuN+2OtNr0zLh9+CzwDdO2XUZ8Ej4uXoDiFVKxWB+rs0D5o28fMS60n1lqqawBnesG2ekOQAnZdZgNWB80qVa6zrM992F4dtrWuua8Gfpq3BijvwhrfXqzn2kQgirpeVmDwaWA1e391hV5jyxtKuOCm7Q/QIztd046ymMvZ12EO+PZZUmvIpOH6JQ2GrVQavj6AOk9pGFJLEiejytdUhr/aHW+ifAfZjf5H6EOU84gHkh13ARsTx82NPAfeFRET+rr9Mnag+4XKfN2qnRWi/HvHDJA/6ulPoi5sXiu+FvcSdprcdqrb8UHl3xF+CmcP+PA55TO21GQ42V4Zh1Yu5ttK9xPYkQ5uiU1qYXNRzT0L5BS0uBtbT9aRo9V5x8LPUNDaJdiVTWH2u4GwBOPJ9a65VAang0kV1r3Xh6RnMxnulxNfc8tOYXwP/Co4eupm2/C3F2ttP8N563AUnA1PBr/Djm70Fh1mNpeA+N1lr/NHxMvS3CXQXYm+kPmr5eG78u2jztrpXXptX+gDk6rPGyiTbggkbPV0o4ybscMyEyd9j8oRs98R7jwPuHSJ40oHF/Gqg85f6ZniuZny1EL5eWm30+5kje9k/P0ORT6Zhp/qiDeoA/AYVDJwS2d3CYvUZkATvdlcxVLf+/1qtF7nYlEGKf1XH0cpJYsZAkVkSPppQarZRqvGzbJMwRDssxp5es0loXAf2BMZgXfgAxQEG4nsJtRw8Fd3L6hUTQMDBmDj9QoJQaDhRqrR/HrDcwBViNOSVnVDiWSGWuftJw4V6szJor7SqgqbWuwPym+tut1HvYhXlROCp8/wvAsla6X054qoVS6grMqTwN269XSkWEvwFv/M1Vk+eq0fanCCdXRiTMYH/pKmr8JX7gH+/s+bVSSj3QqO2zwAu0bURA4xjPw5yutLsNx7UkDjMhBnDHOfQjWvcB4FZKfblhg1JqOmaysFBrHQjXzGkYafQ+cJNSakC4bUL4vdZgK+YfCRqgZvVWAGpWfeKzRXo2d1DM7XltdhmtdSnwT05OvQNz6uN9DXeUWRgYrfURzKHl6SMuGf508vkDgtue287AyScH7+StKeDw8iOvKqUigOswpw8tB64Lf3ZFAddzclTfqQLdoP6MEKKDpOVmfxpYAQw5qw5CHEMRANCJgZUoRgAQHRqhW/yepg/TGLZA3x4x6yi3Z8R/GDkqeq3H6yy0L8PgUOtHiXbQICsuWUlWBRI9XTTwsDKXJg4C+zCnBdVgTgloGKGyBfPCruF/+x9hfktzCNi6d6c/BtilNbeHQvjDfR3z+3RDAdmLgO8opQJANfBFrXWRUuoO4AUVLhoL/FBrvUcp9TjmReFBzGlE7aK13qSU+gSz7sORFtrUK6XuxJzm4gif56/NtW3kZ+F4N2ImYQ6H+9qolMrFnGZ0iKYXV02eK8xEC0AWZk2NOTHuAaHRSZc4N+a/Fqrxl87BrGvzVqM+ngf+D/MCtjV/Af4annoUBO4Ir/jUhkOb9RvMOi0PYF74i06itdZKqeuBPyilHsRMvB0Efgr8SSm1HvM1tivcfodS6ofAUqWUDXO0072YrzUO3PZ9PeL5xZ8BPkDrwdrv9+f/+M+uUGVNWai8+tYOCrs9r82u9jsaJVIwE65/Dk+pcmB+vn01vG8NYH9i2tN1E3LGLaopqn00+fwBVYBG6wh3nHvLu9/64AfAKOAfWuv1YC5XDawN9/FE+LMntZlYlgBblFIbpc6KED1PWm52CmaNq0swR7il0Y4RfqdxMEWn1h2g2FVMTHDKie2KYUSGPqHWcb52Ggd1f/8hXEYMAVuVqrPbqbcl4rONUFr1qVobjjq2KZhodRzdgaPKlubY6k7TaEJxxq76EYHjwX5GOjYGWx1bD1fkzcwKWh1EX6ZOXmcKIdYcGjESsy5LHrBi5vAD8gZpxRWjH5yAmWDZ/fbuX21srk14pZZrtdZf6NLgRK8w4vnF9kN3/jhv4I+/8kP3iJS3D9z2/bzWj2qb3vravHv9HW7MmiwR/7jinwPqiuvGaq3va+04IUTvkZab3R/4OWb9qtGtNO84IbYBBvYWEgkag5DaZDsc0b5iuT2QClLiKWabo46pyvwyUDRDo/END35cPyrQ5lWRxGm2ejOzJHlnIUmsCCE6lVLqYcx6N1c2KrQrRLsopQ4C08JLqndUn33itRkeWTdNEitC9B1pudkJmCufTbA4lBapPPce5bObhchbq/rUExjURB9hb8hDhT+OZHcZx+31zFDm6pCiFZWz69YYEXqm1XH0YAHgQm9m1tpWW4pOIYkVIYQQQgghepG03OxvYhbB7r7qbMttBZ55AI4aNhtOEgwXw5pt290TLxqi8lhlD3CB1aH0RIHE4Cc15/vPtzqOXuAQMMWbmVVqdSB9kRSvFUIIIYQQone51uoAWuUxJqqAPmDz4XWXUesp5iChJquXAWCvZ2/MIXa5S1ssrt3hovJYEXOAY45atrSlvbuU5ZJUOXu1owMBq2PoJYYDz6TlZss1vgWkeK0QQgghhBDnKC03+0bgQeA54EVvZtbxTjjHT4HF3swsXytNB3X0uTucIj6qUHttfjUVs5gusYdBQyWK49pGubYRsAWYocDhrmCMLciyuiTmo4AQVZGFbNIKbbiwhVxEG24SDQeDUeewpLGBz+ZnugK3s4rdwcgzN7fXss1VidQGOQdRW1zx1dN9VagTCySIs3cV8G3MxRtEF5KpQEIIIYQQQpyDcE2THZgrEgKEgHcxkyyvezOzak5przBX6Sr1ZmaVtPEcA4DjwNvADd7MrPoztH0fcwWgbs2zx7ncc8Q5rz3HBN185EsgObIAtzK/oW9CQygQw8r6ROYB2HwcMBwkYm/mol2Dq5JVIRdxITcjsBHhqGFzZCGTALRiR1UqY1uKRQUpiT5CQMHA9jwGcTr/gODG2vH+888pKSYahICLvZlZXTbKS8iIFSGEEEIIIc7V7ziZVAGwA58O32rScrNfA/4BRAILwrdU4EvAk208x4Lwv1cAr6flZl/vzcyqa6HtoXZFbxH/kGCa54izXcc4fMx1FLS8X4HdVcU8NMtsAZTdx4XA8bok9gajmdK4rbuMj9wVzAXQZiWXA01GTWgyYg6yQ9uo0Xb8hp2gdoDhwGE48HhKsCmY3K4HIJrlKnRMCR00VvhGBC+0OpZewA68mJabPdmbmVVodTB9hcy/EkIIIYQQ4iyl5WZnAHecoUkU8HngP8DLwELMpApgXtS30adO+fk/abnZLY32WAhcDfwbMNpxji5lROoUbdP7OqNvVzXzHT7mKbApGBRZxJSofD7CoAbA5mO/q4JpDe0VKAUjlDanJZ3YphlrCzHd7meOs475rirme8qYE1nEVJshSZWOFLHfdaGj2LbM6jh6icHA01YH0ZfIVCAhhBBCCCHOUnhaz0PAt86yi88D/wVKvJlZzf5hHi5GeQxIama3H6gGasL/Nr7VAPHApUD7hoZ0kajN7mXOEvv8rjqfhry6JI5FFBOjNOd11XlF22g0VRfUrzYi9SyrY+klkryZWcVWB9EXyFQgIYQQQgghzpI3M0un5WZ/F0gEbm/n4f8F/tFSQqWR6TSfVAFwAQnhW4/jGxro7yzpurIaClIii0jpshOKdtEOKg2XjrM6jl7kQuB1q4PoC2TEihBCCCGEEOcoLTf7auBfgGrjIT4gw5uZdaCF/uKBW4E7gant6LfHcZTadkRtcSsVUhlWxyKsYzh1eeXsumM4GGN1LL3I9d7MrNetDqIvkBErQgghhBBCnKW03Oxo4LfAV9p5qBu4G8hq1JcC5mMWtb0J8HRQmN1aMMEYWzG/Ds8B5wr3AccEhZIRC32M4dSllXPqSrBLUqUD7QLesDqIvkJGrAghhBBCCHEW0nKzR2MWpR15ll0YmKsFZQMXAw9gLsPcZ0VudS1zFTq6rOaK6B5qJviWBQaE5Pfese7wZmY9Y3UQfYWMWBFCCCGEEOLs7AdWc/aJFRtm8drbMKcG9YkRKmdSPzIwwlUolyh9jeHSslptxzqMmbQVXURewEIIIYQQQpwFb2ZWAPgC8Ldz7EohSRUAjCg9zHDorVbHIbqWdmrJpnWsj8KfT6KLSGJFCCGEEEKIs+TNzDIwR62IDuIbFqi0OgbRtbRTS2KxY92clps9yOog+hJJrAghhBBCCHFu5lkdQG/iGxGcUzmz7lAwLvSRRtdaHY/ofNpOpNUx9DIu4FtWB9GXSGJFCCGEEEKIcyNFNzuYEa2HV0/zza2cWxfQdr3D6nhE5wlFGHnYGGZ1HL3QV9Nys/tbHURfIXPZhBBCCCGEaIe03GwX8EUgD/CDXBR2Fu0iDk2R1XGIzqHRVE/1HUORYnUsvVAU8A3gJ1YH0hfIiBUhhBBCCCHaZwbwOOZSy+9ZHEvvZhDEINXqMETn8A0NrtJuPdXqOHqxb6TlZsdaHURfICNWhBBCCCGEaEZabvZ5QC5QC1RgjlAxgFFWxtWX2KttBxQq3eo4RMcznLqiPj0gv9vOFQ98FfiNxXH0ekprbXUMQgghhBBCdCtpudk2YB0wxepY+jL3IceqiH2uC6yOQ3S8qun1K0KxxoVWx9EHHAdGeDOz6qwOpDeTEStCCCGEEKJPSsvNtgM/AgqBvcAe4Eh4CeVLkKSK5Rzldr/VMYiOF0gMfiJJlS6TDNwF/NnqQHozqbEihBBCCCH6qlswCzv+GVgKHARq0nKztwHPWBiXANBoe6WKtjoM0bG0TftrJvjjrI6jb9HfXbjyC26ro+jNZCqQEEIIIYToc9Jys53ADqReirU0OEpsW+3Vtkp7tS1oq7XZbfUqSgXpj2aQQjmtDlF0rJoJvmWBASFZorwLnRd/fNmCYbuKgVsWZSwNWh1PbyRTgYQQQgghRF90O5JUsZaG6HXuFY4qu0wJ6SOCMYY3kBSaY3UcfcngqPLllw/d1ZDIejpn54LbF2UsDVkaVC8kI1aEEEIIIUSfkJabrTDrDaQC/wSGWhpQX6YheoP7I0eFfa7VoYiuodFUzqvbpp2MtzqWviLeXbvqtvPWXaBUk81PAAsXZSyVREAHksSKEEIIIYTo1dJysy/FrKOSCkidAatpiNrkXu4ss8+zOhTRdbRdV1fMr7OjiLA6lr4g0uHfePuY1RPtNt3cLJU/AfdLcqXjSPFaIYQQQgjRa6XlZl8D/AcYjSRVupStVuVHbXYvI0jtiY0aora4l0lSpe9RIRVtr7KttzqOvmJwVHlVC0kVgG8Ai3N2LlAt7BftJIkVIYQQQgjRK6XlZn8OeBVwWR1Lb2KrU8ccpbYdZ2xTrQ7GrPIoZ4l9ftzyiCJHmW0nQOQ21zJnsV0Kl/ZREXucA6yOoa/YV5F0YV513M4zNPke8MOuiqe3k6lAQgghhBCi10jLzU4AJgCXAD8C5BvZjmIQiNzp+th5zD4dcNaPCKz1jQyeVojUXmHbE73enaBQiQ3bNDqk3XqjzWeb3qUxi26nfH7tdhyMszqOviDBXfPxraPXz26l2ZxFGUs/7pKAejFZFUgIIYQQQvRIabnZKcBFwMTwbQKQYmVMvZXzuH1j5HZXgtLqxGiTiAOuOY4K+7KaSb55KDOB5SixbYva7B6mULGNj1cou/IpSar0UYZTl/mGBbb5BwdjsHO+1fH0BQ5baNfNozZObkPTWwFJrJwjGbEihBBCCCF6nLTc7DHA+8Bgq2PpzWx1qiBqk/uwvc42s6U2httYVzmrfqyzxL4ncptrtEJFdmWMovsrn1+7CwdjrI6jL3Go0J6vTlhxXhuaFgGDF2UsDXZ2TL2Z1FgRQgghhBA9USlwHnDc6kB6K88+54qYjz3xZ0qqANh8tulxyyOKIre5xklSRZxKo8HOMKvj6GuC2n5ebcBZ2oamScClnR1PbyeJFSGEEEII0eN4M7MKvZlZNcDjVsfSW7nyHTEK1aalcZVWqQplSZHgQG0lB95/lm0vZrPjpV+z750l1JcXWhEKvqpSSvdtOHG/ZM9ajqx8pdPOV5W/j33vPNHsvkPLc6krO9Zp524rw6PzUUjCzQL7KpL2tLHp5zo1kD5AEitCCCGEEKInO2x1AL2VMnBaHUNrtNbsf/cpogeNYvwtWYy9+XsMnv4ZgnXVbTpWa6PjYjFC+KtKKd23qcP6PBfD52US0W/gadu10XGPuS1CsYb12Z0+ak/5AH8bm96Qs3NBm5KoonlSvFYIIYQQQvQ4abnZg4GHgRusjqXX0ritDqE11fn7UDY7SWNPLnwS2T+FUMDH3rceJeirRRshBk+7kvjU8fiqStn3zhJiBo2ipvAQQy64lsMfvUz0wBHUFB4iImEw/UfPoGDDOwTrqkm9+DaiBgynpvAQR1f9CyMUwGZ3Mnz+LXjiB1CyZy0Vh3dihAIYAT9GyE99WSE7X/ktCedNx+GOwF9byb63H8NXWUJc6gSGzLwagMqjuynY8A5GKIg7NpHh82/B7nSz7YVfkHDedCoObUcbIUZedjue+GSqCvZx9OPXzQepFOdddS8ARtDH/veepq70GJGJQ0i9+DaUUux588+kzLyGqKShbH7qQQZMmE/l0d0MmXUNvqpSirZ/hA6FiBowjKFzbkLZOuc792C80XqWS3SKorqYoW1sGgNcCZzT8KqcnQsUsAD4AXAA+N6ijKV9YrqmJFaEEEIIIUSPkpabHQVsBRKsjqW3UvWqCIM4q+NoTV1ZAZGJQ07bbrM7GHn5ndhdHoL11ez+1x+JG26u8OsrLyJ13i0Mu/AmfFWl+CqLGXnZF/H0u5ldr/+Bsn0bOe/qr1NxaDvHNr9P2oK78MQnc97V96Jsdirz9pC/7i1GXn4nADWFB8m44ds4PFFU5e/j+JYPGfXpuwFzKlBdSR4ZN3wLZXOw46VfMWDchdgcTo5tepdRV34Vu9PNsc3vU7j1QwZN+RQADncUGTd8i6IdKzm+5UOGz8vk+JYPGTrnRqIHjiAU8GGzm5dytcV5jL3puzijYtnzxsPUHD9A9MCRTZ4PI+gnot8gBk+7grqy45R98gGjr/kGymbn8IqXKd23gf7ndc6iTaGYrh0hI04KaduICr+nIM5VP6gNzX+Rs3PBVZhL1DfcHlmUsXRtawfm7FxgA67DTKhMDW+eB1yfs3PBj4E/9/biuJJYEUIIIYQQ3V5abvbFwC5vZlaBNzOrJi03+z/A562OqzdSASpjP/bUK1Rbv+3ulvLXvUXVsf0oFP6aCoJ1VQC4ovsRlZx6op07JoGIBHNxqYj4gcSkpKOUIiJhEP4qs/ZnyF/HwQ//ga+yGGg6nSYm5TwcnqgW44gZnI7dZc6y8MQn468uI+Sro67sOHveeDjcX4ioAcNPHBM/YgIAkYlDKD+wBYDo5BEcXf0vEkZNIT51IvboeACiBgzDFf45on8KvqrS0xIrKBvxIyYCUJW/h7rio+x6LQcAIxTAERHdhmf07BiRRmzrrURneX739MD1Iz/ZPSiqcnQrTTPCt8bWAS0mVnJ2LnAAtwDfB8Y20yQW+ANwd87OBfcsyli6os2B9zCSWBFCCCGEEN1aWm72DOA94Dng9vDmh5DESqfQTmIr59T7Y9Z6Ntr8aorV8ZyJp9/AE4mHxkr3bSBQX0PG9Q+gbHa2vfALjJD5hbnN2bTGrrI3uiRSCmVznPi5oQZL/vp3iBk8irQFd+GrKmXvm38+cYjdceaavbYm/dvQhoEGYoecx4hLvnDmY5TtRAwDJ11K3LAMKg7vZPcbfyT9yq+Gm5zsXykFzdSNsdkdJ6f6aEhIn0bKjKvOGHdHsVfYKoKJRgiFvUtOKJowtG3YK95J/kmJR5fPGbR/nlLtOnx4cxtzdi5wY34Wfw8Y2VybZpTk7FxgW5SxtFcOYZLitUIIIYQQottKy832AE9h/t16sNGu5mqrGEABsBF4C/gb8BrQUOMhv5ljdEfF2ptot06svLBuilY6z+pYziRmcDpGKEjxrlUnttUUHcZfVYbTE42y2anK34u/uuyczhPy1+GMMmdGlexpeWaE3enGCNS32l/UgOFUHztAfUURYE7VaW0lI19lMREJgxk46VIiE4ee9cpHMSnplB/YQiA8gidYX4Ovqi2r8p6d6C2e+dFrPYfQlHfaSUQrlGtz8dB5z+2evtoXsle148Amy2Tn7FwQlbNzwf2AF3iMtiVVDgCrgH8Bt7bj3D2KjFgRQgghhBDd2Y85OcT8IEBabvZEIKtRm1rM+f0feDOzQqd2kJab7QYGAEeBXODm8K5nMQs2JnZC3D2fAiNCH7LXqhSrQ2mJUoqRl9/J0VWvc2zzB9jsDlwxCQya+imOfvwau177PRH9U3DHDzin8ySffwmHPnyBwq3LiBmc3mK7iP6DUTY7O195iITzZuBwN7/QijMimtT5n+PgB89hGOZImsHTrsBzhjgLty6nqmAfSik8/QYSOzSDmuMH2/1YIvoNZNC0K9j3n8fQaJTNzrDZN+CO6bySRY5q20jMpGd8p51EtKrCHznrnUNjl187cuu8Nh4yDCBn54I44F5gEe3/vBwBfDn881WYIw97HaW1JOmFEEIIIUT3lJabnY1ZEBHgEm9m1v/ScrMXAy7Mb0EvBf7pzcz6Xxv7U8B4YAiwHfgUMBr4VkfH3ht49jiXe44423oRJkSLyi+pLUZJEtNqHnvgk7vHfXx+G5sXY45MuQ86pJh1BTB8UcbSig7oq1uRxIoQQgghhOi20nKz7ZgjS24F0ryZWftP2R/pzcyqPcdz3Ab8FPOP/lNvt+//3I/iXcOS0SEDV0oSSV+7EZv7zHU1WpP/syfo//krcKed3WCQwr+8QuSU0UTPGn/WMRy+77ek/PIe7LFNC6/WrN9JIK+Q+GvnYy+37Y7Z4Gmt6KUQrSq/pLYKRYzVcQhd/bUJy6NsivZVW+k4+cA3gFcXZSztNckImQokhBBCCCG6LW9mVigtN/t24Ama1lhp2H9OSZVwH88Dzze3Ly03e61yOZ8f8uv7ACh8+J9UvreO+M/MOdfTtkqHQih719f7jJqWAdPMxUFCsUa6RtcoVJQ2Qiib1B8VZ81tdQACQEWX1kftT4yoaWvR2Y42GHgZeDNn54L7FmUsPWRRHB1KEitCCCGEEKJb82ZmBYE2TfXpBC9gGM8Q/rvZMyYV/+FjGPV+ip9+k8Dh42jDoN9NlxA1LYOqDzdSs34nGAb+I8eJ+8yF6FCI6o82oxx2Bj74RezRkQBUrdhM8dNvYtT5SPrqDXhGDaH0pfcJlVURLCrDFhNFwucup+ivr2FU1WCLiWLAPTfgSIxvEmBp7nsESypI+ur1VLy1kppV29DBIJHTx5Jw86UY9X6O//FFQiWVZqw3XEz0bHM534r/rqZ2wy50yCD5/ltwpSRR9eFGfPvzSLzragr/+oqtvMhV4y8vjopMHELS2DkcWfkKgfoabHYnw+d9Fk98cpf+QkTPUzvavxyFTCnrJg5WJRRYmFhpcBVwSc7OBT8G/rgoY2nQ4njOiawKJIQQQgghRAu8mVlaB4I+oFaHQtRu3oNraDLlr31IxLiRpPzyHgb96C5Kn3sHo94PgP/IcQZ8/bOkZN9Dae672FxOhvzqXtzpw6hevvlE39rnJ+UXXyHxS1dT9NdXT2z3Hcgn+TufJ/kbn6XkqTeJmTeJIb/5OtEXnk/x0281ia/k+XcIVVaTdM8N1G31EigoYXD2V0n51b349+dTt/MAtZ/sxdEvhiG/uY+hv/0GkZNOFl+1x0Qy5Ff3Env5DCreXNHsc1BfXehIv/Iehsy6lsMfvcSQ2TeQcf0DDJl1DYdXvNJxT7bolepHBFb6hwQlqdKNHK5KOK3It0Uigd8C63N2LphpdTDnQkasCCGEEEIIcWYRh+//fY3N7cIzJpWYS6aS9+Ml6A27TiQjjECQYEm52XjcCGwRbohwY4v0EDl1DACuYcn4Dx870Wn07Ilm+4wRGHU+QjV1AERNHYPN5QSgfu8Rkh8wVyiNmTuJ0n/898Tx5a9+iHvUEJIWXgdA3ZZ91G3ZR96DfzZjqvcTKCjBMyaV0ufepuT5/xI5ZTQRGakn+oiaMQ4A94jB1K7d3uyDj0ubUKxstoRQwEf18QMceO+ZE/saVrQRojm+wYG19SMCF1gdh2hMById/u5W2+R8YFXOzgV/AbJ6YnFbSawIIYQQQghxZnXD/vDAYuCXJ7ZoSH7gc7gGJzVp6Nt7FOU4+Se2UgrlsJ/4mZBBo51NjlXh+6qNhXHdaSn4DuQTqq49Mb0o/rp5xF4247S2KYu/Ru2mPZS+uJTIiaPod+Ml5rkaYrPZ0I1ja1DsK/QM6nee+Zg1dlcEGTd+u03xib7NnxTcXDcmMAklsyS6B12ZGlO6aX7K3owYl2++1dE0Q2Eu6XxDzs4F31iUsfRlqwNqD3mRCyGEEEII0bomNV4iJ46i8p3VNKyw6TuQ3+4Oq1dtBaB+10FskR5skZ7T2njOG0b1x1vM9is+wTNm+Il9EeenE3/tPI79+u8YdT4iJo6i6n8bMep9AARLKwlVVBMsrUS5nMTMnUT8VRfiO1DQpvjs5bZdrkDEieVx7S4P7pj+lO3fDIDWmtqSvHY/btH7BfqFdtRO8KejOLfls0SHGJeQv3zhuJX2q0Zsmx/j8g2wOp5WDALusTqI9pIRK0IIIYQQQrRuPVANRAPE33gxJc+8xdHvPgJa40zqx8DvfaFdHdqjIsj70WMnitc2p//tn6HosVepeHPFieK1jUXPGo9R5+PYQ88x8MEvEj1nInk/WgKAzeNiwL03ETheSunz74BSKLudxC9d06b43Ecdx8E2pvG21Itv48jKlzm26V2zEG7aJCL7n92S0aJ3CsYY+2sm+wahiGq9tegKCZ4al8se6km/j99ZHUB7qYYsuxBCCCGEEKJlabnZ/wGusDqOrmIvt+2K2eAZ03pLIUyhCCOv6oJ6O4qBVsciTnLZgtsWjl853uo42mgnMH5RxtJm5iZ2XzIVSAghhBBCiLaxaslnS4TijDFV0+r3+YYEloc8xlqN7nEFJUXXMVxGcdWs+pAkVbofv2EfURNwllgdRxu939OSKiAjVoQQQgghhGiTtNzsacA6q+OwjEbbK2x7Xcfsx5zF9gjlU6MVKtbqsIT1DIeurLyw7hh2zrM6FtGUTRkHbx610UiKqBlpdSxtZABXLspY+t9WW3YjklgRQgghhBCiDdJys+1ACRBndSzdgoHhPGbfGLnTNVGhpEhpH6XRVM6r26qdTLA6FtFUrKtuTWb6hnFueyja6ljaqQKYtShj6S6rA2krmQokhBBCCCFEG3gzs0LAcqvj6DZs2AKDQ9Pq0wLrrQ5FWCcwILRRkirdjTYy+hUs+8LotTN7YFIFzOT1v3N2LkiwOpC2ksSKEEIIIYQQbbfS6gC6G19qcHYgMbjM6jiENerTAj6rYxBNXZSyd/2lQ/fMV8rqSM7JKOCfOTsXOBtvzNm5ICJn54J+FsXUIkmsCCGEEEII0XY98dvfTlcz0T8/FGGstjoO0bUMpy41IvQ0q+MQjelARsKxqVZH0UEuBX7fcCdn5wIP8DqQGb4fm7NzwShrQmtKEitCCCGEEEK0XarVAXRLCqpm1k/Sdr3T6lBE16kfGdiKwtl6y54l5oOSdaouVGt1HG2VFlu0LMLh3wjgUMYBu9J2q2PqQPfl7Fzw1ZydC9zAq8AC4M6cnQuWAPnADyyNLsxhdQBCCCGEEEL0IKlWB9BdqYCq1Ipgz559INpKo/EPCg61Oo6zonWds0RvDkUQaUTZzm+8K+4/hasS/p4/K+GF/K2H/zR2lI6wR1oVZltMSTq8YvagA/O1hm2lg9YcqepXD71udaaHgS8CF4TvzwjfAGY1NMrZucAGJAIlizKWhroyQFkVSAghhBBCiDZKy80+AgyxOo7uxlFs2xL1iXuQQiVZHYvoGoGE0Laayb7xVsdxNpwlxuoRf6yZFYxUZfu/GxXEZr5uo5eXrk969PAkFR6AEOjvXHfkkXHTrY22JVrPSD708YzkQ3OsjqQb2AAMApIBO/AC8MVFGUuDXRWATAUSQgghhBCiDdJys11AitVxdCsaIna6lkd94h4nSZW+pX6Uv9zqGM5WpDfoA3DU6n4DX6s/DBC5oWJL0qOHx6tGszrslcFzL5KqqabEuRGfbTmaDir0q43ZA/evkqTKCVOBwZhJFYDPAc/l7FzQZTN0ZCqQEEIIIYQQbTMUkJkujbgPOla68x3zrI5DnDu7I1Rvd4R8/npXXGtttUNXhaL1lK6IqzNE7wgmNvwc+0lwqt9T/k7ccwfnKPA0aagwzulEGk2Zcxd1jmnUOcBuFNIvsAu3MQN1yrna3mlw3uB96ycm5s8+p9h6tyBQirlsc0lXnFASK0IIIYQQQrRNqtUBdDf+lOB4z35ntULJakk9UExC9aERE/IOpYwqjPJE+ccD0f565yd5+waUezcPSa0qjR7e3HH1wwObUczt4nA7htb+iEOhJjVIEt6pijMg5tSmKqAT4t44/nH1/IQxoThnQrvPVWNfTq1j/on7IdsAit0DsOkiEvxrz5hg0dQCVUAtmlo0PrTyXzhkn39iYr4kM89s+6KMpV/ryhNKYkUIIYQQQoi2SbU6gO5Gu4gL9jeWOUvs81tvLboPzZV3r9joifJPAZokT9wRgfNHTshj5IQ8QkF1sDiv3yHv5qH9jh3sP7FhwJZvyMkRHz2K1oHobcFVthBNX69OZ0RzzZUmsf8LBYkJLxRo7bHtrBsXXVh5aWK/ugkx43CoM6+841crKHc2/74wVBLF7iRsuhhPaCuGcmMoDwZRGCoagxhQkcBphXOrPDGrGdDWB9xntTrqqqNJYkUIIYQQQgggLTf7y5jTfTYBm4GD3swsHd4XD9xlWXDdWGBAMNJZ0ptWd+0LFJv/N9o266qtZ2xld+jU5OGlqcnDS1n3zrgNR3YPnBqMC+3BQUYXBdph7JXGhiHP1vV3FxqnJTuUw3HaaJUm+0GpeiMjakNlRtSGSjRUFt07bG/1hQlTmz3AYDeF7lmtzhw0VCK1jnYlqT45OHzW1LT9GyPd/h47FasLRHX1CSWxIoQQQggh+ry03OyvAH89ZXN5Wm72Zswky+XAuC4Oq9tTPlUcsdM1zOo4WrEemGZ1EN1NvnfApNKC2I8SBlW2aUrP+Rftdh3ZnUzdqMAxetJyviF9JPnfvmOxGwPTW0xzOOzx7elSQay2q5azJgHbcVCj29Nne86+48iQ2mmj9ndO9z3fGixIgsuqQEIIIYQQok9Ly82+DXi0mV3xwEXA/ViXVNkJjA+ffy6w0aI4muUssnsxn6fu6k/ADquD6K5WvD55smGovLa0dXmCE5LTiteH4oxJnRxWx9C6PmZrYNmo7OoBcWdKqgDY7QmAvz3d+1M8La4YpENo7TBWanQHrQJ0UlJsxf6pafsndXS/vcTPgTmLMpZ2+XteEitCCCGEEKLPSsvNvhZ4hu672k8G8BxwDPOb2HRrw2nKPyQ4s3pGfZ62651Wx9KM/wLfAmZZHUh3FfQ7osuOx7R56EPa/AMOFLGdGVNHcJYYq1P/UFM66KX6+bYg7tbaK6UUbvea9pwjmOwedOo2rSnWmtXagVMnBubo6ND69vTZFqkDigrqA06v1icTQSFDHS6vjlx1vCxuhdZUdvQ5e5ChizKWhqw4sdJaW3FeIYRFlFIhYCvmH5Ah4D6t9ccttP1Ya33aUm5KqaeBN7XWL3dinHcA07TW93XWOc6VUmog8AdgOuADDgL3a633nEVfPwWqtda/VUr9HFiutX5PKTUXc2h6ALgAMxN/JfAfrfV3OuJxCCFEX5KWm20HdmGOBNkCfAdwWRpU23wE/BBYZnUgzTIIxa70bLb5bc3XnOgYQdpeymA3ZkLFDhR3WkS9wFVfWbbN5QmOb2v71/dNXFFaH3Xh2Z9RBzz24PahsaUVo/sVDq4OuKu2FKXUltZHTgR1TkkbFdD7B75SXxGzIzi53VH5A3mhQ4eTaeNrTNvI0w5Vo122ev8QT2Fe1nnpwPBTc7SqxLlcBWwdvoqPQ4WqBsaVby6uiDvfF3CeeN6cjmDNjNF7N4xPPTzUYTdGdPR5e4BZizKWtitJ1hGkxooQfU+d1noSgFLqU8BiaFoZXSll11qHmkuqNDb5azljgO8DUzGTNYs3/WXRlk6JuptRSingNeAZrfUt4W2TgGSgxcTKvPe/M1Mb+vugRymb7SPg18svfehg4zZa6x83unsb8Fut9VPhc3wFSNK644eWCiFET5KWm23zZmYZZ3HoFGBU+HZ1x0bVqcqAT1kdRIts2DFUZyeoFOYXDEnAF4CWlnguA67esGRR+dSFOVd2ckwWaviC/BwGWymtne7gyPYc8unUHeP/sWtaMag2F11VGHmJETX7z+tX6B4RVzLOZQ9Natg3gGpGxpUQNJTfW560dmvx4FCl3zMJVLMr9ZzJ0CdrDU+e0e6kyjt7fkOMOyklSF2NQ9sdo5lEvEqkTtewmZVcoBac/pgMUpRfgz+EZ2/tYVDN1hrSCYE5FLo+UVqd3964WhKsquJYbq5vf+HxubaISJTdTvy8i4keP5FA0BG1cnvGvJXbxzBq8LGNc8btCkVH1E8/l/OtfS2f0XMSiBvQ/MrQ3Uy7pnR1FEmsCNG3xWL+8YFS6iLgJ0ABMAkYq5Sq1lpHh5MIDwOXAAcAFTssYyTwdMWhHRH5H79hc3gix0X0T7nJGfmTdYHaytlKqajwMRMwP2t+qrX+V3gkyjWYy8elAa9prb8bjuFOzERNAWZyojsnDy4GAlrrE4UOtdablVLRSqn3gX6AE/hh+HGn2iNdHyVdPGZg1a5jjrE/v5bCd3eMOf7u9oWO0j+uAQ4BG+DkiCDMOeufBT6llLoMiMGscr5GKbVYa53bhY9XCCG6hbTcbAV8CfhZWm72v4Esb2ZWSTu6OP0KqWf4L915VSKDoAp2ekFTO/AD4Cbge8DngXsw/9ZoEARu2rBk0d7w/V47DWhAQvmRb9/+UunTbyyw7fCmTmipXUJy+d70qYfzB44oSdEafWT3wIJda0aMqav2DIjrX31QKdo1qsHjCMbPHHhw1ZpjI1pNrAyLKV02JfnIsH7u2hFKkXKmtg6bdo1OKJwxOqGQQMhWu6s0+eMdpYOcNQHXJFDOlo5TAe3VdqJQDKiY4sz35PlGtefxANiVgznD70T7/eXHD67z7GObfRoXtb2DkE5ocZ/CrhP9Qyhy5SvU4PbGdiqtNcefforIjHEVg+74cqL3wQeIHDeeUEWFuT8U4uAvf4pn6HC44+4p+/IH0S+6+vCCqZuPJMZVzTmbc657PZ9B6VHnlFipqwyw8a1jzPncUAD2rS3lw6cOcfejbcuDvfCDbYydn8T5n0pusr2isJ7XfrmbO/5wPkAR8MlZB3kOJLEiRN8ToZTaDHiAQZjJkgYzgPFa6wOnHHM9MBrzD5dkYEfciAkjjGAg6uiylxl13b24Y/tz8N2/OzwJyQ1DSbOAD7TWdyml4oG1Sqn3wvsmAZMxEye7lVIPY/4h9DPM0S8VwP8wl7vsrsYTToScoh64XmtdqZRKBFYrpd6wR7hUqM4/JPlT40lftICqPcco+nC3fcpjtxtGfeDY6pv+Mv3U/rTWTyilLqTRtKtwsmtSJz82IYTozmYDj4d//gpwU1pu9oPAkw0jWNJysyOBuoalkk/RUxMrmzBH23RL9grbHoUa2wWncgAvAddtWLLo0akLc/6K+Zq4B7gZ+OaGJYs+aNT+gi6IyRKDkkpKoiPrJ913yxuUV0Wtf+LVK2L2Hx08GsDj9lV/avb6TfOnbUmqtzvU+roR00BFAYwYn5+eOi7f8NW6NtZVu2uhfYkVgLH9j12wrWTwupqAu8WREC5bcNulw3bPP8PaOS1y2o3ICUkFsyckFVAfdFSsPz7skz1lyadPp9HaP/LX1QPtfqK0jZDhot2jVRpTLleKzxHY5Qy6xpy6r07XsJ21hDBLeDSMagE4pHdH52d9rLVhqMhpY+l342UEisoofOhp3OcNx7f3cH9bZGTNoLu+is3ppHL1aqpWr0aHgjgSExnwuVuxuVwUvvgCkRljiT7fHNxy4AffZ8QvFzeJo37fXpTdTr/LLk3TIb1MuVzzg6VlxNwyE4C8v/wRe0QkAEf++Duixo2Hyz417NGsymEXTM47ctmXhgz94G8H+eS/xwn6DSZcOoBPfz2N0rw6lnxlEyOmxHNwUzlxyW7uemQSO5cVc2RbJc99dxtOt41vvDADl6f9y6vXVQVZ+cKRE4mVjhI3wNOQVAF4f1HGUgNAKeXQWgc79GRnIIkVIfqexlOBLgCeVUo1JEPWNpNUAZgHvKC1DgH5SqkPbC7P1fXlhbhiE3DH9geg36jJlOxcFTP5azkOzD9cr1FKfTvchwdoGCL5vta6IhzDDmA4kAh8qLUuCm/PpSct5XeSAn6plJoHGEAKkJx08ejEsg2HdOzYwQqgcmse/S8chd3jtNk9zgsxCxMKIYRo3U7MbyWTwvfzMOtpzE7LzV4ZTqbEA++m5WZXYi61ux4zeV2JeRHe0+zHvPjtrgV2cRbZu7KOiRN4derCnGs2LFm0FFgJrJy6MOfeDUsWVTQ0mrowxw7M7MK4ulTakIKqhp/jY2qmffv2lykqi1tdW+cODhtUOFkp5gJ4CDDOnbdhuy9lEig7gFLYPFH+KZ6os5s1oRRcOWL70Jf2TK4G1eyUrLlD9oXOJqlyKo8jGDdn8P55+8qSDhvYmky3cVTpLXa/uZS2MrDb64k5m3OEdJCVh57CMILUh6pGT2VeCHOE1Aku3ExmHnZlp1ZXsZW1zORSSvQx6qhm2Ne/lB8Y4E4p/P3fqd91AHv/eALHSki8N5PEu2+g8E//iKrevKkudvqMiKgJE4idZQ6mKn37barWriHuwjateo3/2HFcKUPMOzbmY2gjcvQYW+2unURPOB+tNc4BA9CBIMpuo26/l4Jnn6Ru3x7e3xE/0O0x6ooP10ZkzOtPWX49K/5xmNUvH2XWzUMoPlTLNd9J58CGMooP1bL4ipWkTetH/6ERoMBfF6JgTzXDJ8ZRUx4g94fbKTlah9Nj57M/y2Dw6BjeecSLO9LOxXelAvCbaz7m7kcn8+bv91J8pI7fXr+K82b3Z+z8RHy1IZ6+/xOO7a1myNhYbvvNeJRSvPn7vWz/oAibQzF6dn+u+a55SeBdX8ayZw5RWezn6m+lc/6nkinNq+OJezbx3Tdm8+IPt9c88Kp6CfO6I0opdTXNjKA/m9dIaySxIkQfprVeFR5V0fDHac2Zmje9Z1SjdXwzfYYwi+Iq4Eat9e7G+5VSM2k6xSfEyc+inlRNezvmUORT3Yb5fE7VWgeUUgcBT83+oiq7p+koVnXyr42iToxTCCF6FW9mVmlabvZYzBEKnwZu9GZmHTulTX5abvZngQ/DbRpU0DP//g0Bl1odxJk4S+1d/by6gX9NXZjzmYYRKo2TKmE2zOLvvU768KM7L5256bTRGUn9KmbRzCLAg5wVU2sM94qDgaRzKDrbVIzLN3Bc/4Ll20sGnzaSJMLh3zg8tqzDRlgpBcNjSw8cqExskliJWxPokHoaDVOBAMrq8tSWI6/7Z+tPNanxotHsYiNVuhyFopZqAEo4TgnHMRb/pb+OcqHrfQSOlWDvH48jqR/u4ebsH/eIFAKFhZVAhP/YMcreeZtQXR3a5ydi9OizC1yBNkK2qk0b6irXfBwROWYshs+HUV+P9vuJHJ1B5dpVxEybib8gn4SrbnS++YcniY5TBHwhfDUhYhJdzP38MJb+eT/9BrsZOCqa4sN1zLl1CFHxTra9X0R1qZ8v/fl8Kgv9vL/kAHc9Mon/PuIlJSOGux6ZxN7VpfzjwW18+7WWB4hd9UA6x/ZWn2izb20peTur+O4bFxA7wM3Dt63jwMZyktOi2fpeIQ++NRulFHWVJ9/CVUU+7ntuOoX7a/jbvZtPmxZ0YGP5dsyVwCZqrUuVUr+kmRH0WuszXfOcFVluWYg+TCk1BjMb39rc9OXALUopu1JqEHBxfdnxtzz9BtT6K0vxVZYCULZvUyhQU3lw018Wacy54F8P12dBKdXa0Mw1wEVKqf5KKSfmcN7u7APArZT6csMGpdR0zNE3heGkysXh+1TtOlYfqvVXYE4VIm7iEIpX7CXkC9TUH6v4Ez2rgKIQQljKm5lV7M3M+oU3M2vOqUmVRm3yMOtheRttjuuSADteOpAAPIQ5GrLbsdWqIRac1gP8e+rCnGZXXNmwZFEAeKFrQ+p8wwcd23f/ba8OUqrF4r3NGuUuvDDJXtmhq0pNH3horssW3Hbq9ouG7m134dnWTEk+ktbwswrqg4NerNvc/yP/OY9A04bRpKZfXCh+S4hAROCUUn+H2IMLN7O4nBlcim70VkxlDKNvvGNryi+/zpDff5uYi6aZcTobDXqx2dChgA+g6MUX6X/9DQz99nfot+BydNBMHiibDcKr9mqt0aHTVw52DUzGn3f0xH3lcDDk/kUuw+fT1Z9sInrcBPxFRYRqa/GMSEOHDEDhThlC5Kh0jEDQNu8Lw5idOZTLFo7gh0vnMv+Lw4mMd2Kzm1/6JaR4iE/2YIRg4KhoImIdKKUYdF40pXl1ABzYWM60a8wVp9NnJVBbEaCuqn15zGETYokf6MFmU6SMiaE0rx5PtB2H20buj3aw5d3jOBtNOxp/6QBsNsXAUdFUl5yWU/MWHawtA97VWpeGty0AHgyXQfiQpiPoO5QkVoToeyKUUpvDHzC5wO3hUSZn8hqwF3Pln0eBZcc3vv8vm8P15JC5N/j3v/mYsefVPxnaMHbVlxasDR/zC8yhuluUUtvC91uktS4AfgqsAt4DNp7l4+sS2lyr/nrgcqWUVym1HTP+/wDTlFLrMUev7Go4xldclQ+8DdRHpydXJM0bHVh365L6dZ9//EbMZTSFEEJ0IG9m1lHMWmI+zOk0R898RLd2HXAM8//hbkX5KVW6+RVRTvE+Zi21ucADmEkP7xmPaJ2DU6ZsnOKZc+y/WxmUWHLoO3e8FK8U8Wdz/ETPkflRtvqPOyoem0JdOXJ7dFJE1UdRTt86py20I8FTvWJQVGVGR52jQZy7fohbBTbuv+WH1H7thWExO4KTAAxt8L73T2zIe7ld/dUGKsiv3IFxrHC11ob2Hnh3z+aDLxdU5u8eqtE4cTdpHySAmwiUUhzjMDo80Lo/yeRzAJVnDq0IllZw/KGnKXzoaQIFJRz68s/I+8HDVL69gmBFmQvA8NXjiIlBh0JUbzz5J68jIYGil14iVFNN7fZtEApx5De/pvD556jZvo3yD97HMyqdQHExx55++sRxOhiw21wuSt58PRQ9ZSr2qGhCVZV4hg3H5nJRuX41nlRz8SeXx8aGfxcQChg4XDbKj9dTVeLHZlcNOR0crpNpAmUDl8dOfU0IZVMYoZOJn1MppbA7TvYDEPC1nAtuch672bfdYWNR7kwmXj6Abe8XseQrG5tt38zp08ZdnDSWpiPwG0bQTwrfhmmtd7YY0DnoiUMhhRDnQGvd7B8fWusPMTO5jbdFh//VwH3NHPbS0Hk3/Sbj1u8PDPl9B7b+7fs/x0zAoLWuwywqeOp5ngaebnT/qkY/PwU81a4HZCGtdT7mqj2namkc5DiAee9/ZxAwZNgXLth98OkVlc30e0dzP4fvt+vbKSGEECRjTht6DrMQ+0rMVfHOxgqgw6ZSnIUrCK/m103sBj6w1dn+h3ldER++xQBVmLGWhv8tBLZsWLKo4XJoRUMnUxfmJADTgOmNbq2tnrIZs4jtPzcsWbTvDO3WAzuAriis26kS4yvyf/DlfzhtNt3mZY5PpRTMjNg/46Pa8zYHtGNSR8SV4KlNvTptW2pH9NWaaR/lV+1RTqp9xbaQEcBuc1JSewCPo32lVQxtUBeooKBqB4MG33ihsTekDgd2nmcQoppixjHdnLLd6OJ9KGlsYTXH9VESSMIezuf1VwOp1pXseesfU0KfRGPzuEi857Ngs3Es+3FcQ5JJ/vbtVLz1EUZNXT+Nzk/49BWD8/70Jxz9+uEaNBDDZ46OiZk5i7J336XgsceIHGPWzx34pbtx9jfrGTLOLIsYNWky9fv2cjg7Gx0IUPjiC8TNu8irnK7h7oGD7e5BgwiVl2FzuYgYlU7V+rVEjBhJnXcv0f3d/mnXDo5497H92GyKLe8Wctuvx3MmaTP68fLPdqJsJ6eyj5zWjw1vHmPBPSPZt7aUqHgXnmgH/QZHsGOZOcv96I7KEyNc3FF2fDWtfZcLvpog/nqDsfOTGH5+PL/89IpWj2kwdn7il/etLX2l0aaGEfRf11prpdRkrXWnLI4hiRUhxDk5+tErNx396JXbARfmigWPWRxSt7f80ocKMJeUFkII0cm8mVnrgHXhu9vScrNvBN4AGk9VCGKuZvdrzHomgfC2w+H9DRflP8Mshvp/nRx2S/4FfMGic4P5fLyPOR32f+HpVqaFZ9/phiWLSoGl4RsAUxfmpNA00TINOIiZTHmplWRK47711IU584HbMb/wST/7SK0TH1Nd9OOv/j1ot+lznsZgU9oxO3LvyI9qRnsNbGmtH9F9JIyvjgFIihpJUY2XgTFjKKjcyaCYDMrqzAFpQcPPzsL3qPIVoTEY1f9CkqPTOVqxlaIaL4YOEjIChHSAGn8JHx9+2p4eP6fcUV7vKabAYxBiN59QpotIVxMZptPZrTczWk1iFpeTp/dTQxWzWMDH+r8kkEQ5pUxlnqf6ktGrKxYMOLHEd+LCm6j8jzkouXbDDhJuv9qt+wVLYmZfMLBq4wZb/+uuwxEXR+Hzz3H097/DPWwYjphYBn3lK5S+/Q7Y7Rx76kliZszAHhGB78hREm+4AZvbTfSUKcRfdDEHfvB9Bt/zNQhSqcLLUsdOm4lRZyY0+l95NaGaGgpfegHldJJy812h2bfu3l9bERjZuMis3aG4+9GTZXEatr/wg22knh/Ptd8dfaJQLMCn7k3jxaztPHTdKpweO59bPA6AiQsGsP6NfH57/SqGTogjKTUKgKh4F6lT4vnNNR8zZm4iY+c3nx/01Yb4232bCfoMtIbrHmx7/Rm70xY/5sLES3J2LlCLMpZqzBHzf8AcQa8wP0OuOkMXZ001N4RHCCGEEEKI3iotN3s88AonV5875s3MGpSWm61OXaI5LTc7CdgGvArci/kd9qM0MyqzCwwH/o45laarVwh6FrizYUnrrjZ1YY5qNNqlLe3twF8wR8rsx5xytB9z1NJCzOm8zhY76EaiIurKs7/+ZLHLGRrVkf3WGc6ClbXpTlBnPQKmq/nL7aXZc99OmDXs8+wrWcnEgVez+sjfGZN0KQfL1jI15Sb2FC8j2pXI4NhxBEL1rDr8LLOH38Gxqt3sLVnOnOF34bJHUFJ7+MQxAAfylu87XLNl1EwuQ2FjFf9lGhfhxMVq3uUCPoVN2VinPyCDqdhxsJK3mc7FxClzRIm2kbd/yfn9tdvuAajbsZ/K/3xE8rdvp3r5RnyH8un/havwbzu+tvi5V2YMuX8Rxa+/hj0yin4LFlD1yaZ9RX9/btTwn/0Me1Q0h7P/j5T778ceFU3VurUnEiul//0vNreL+IsuPvnkBFmuUM3WGzpVXFRN3m2XLHcrRY/53bfTvEUZS7t0mr3UWBFCCCGEEH2KNzNrGzCj0aay8PbTLty9mVlFmNN/vubNzDLCbe4F/tmoWS2Qj3kRXwoc6ISwN2OOtnBiLjHdlV4HvmRVUgXMkSftPGQY8GXgh8CTwDLgCPAm5tKrmzBHJXVrHrev+hf3Pl3Q0UkVgAhbYFC0zbe79Zbdhys+lADoGPcA6gKVFFTtIClqZJM2xTUH2V+6mpWHnmLt0RcwdJD6gDnzOjEyFZe9+bq6zuh+o2JsCccdyold2YkihnpqsSsH/RhAMQXU6Eo0mmhl1sH2EHkiqQKgDFIGPHF4TXP9R84cT92mXehgiOptm2ZETZyQB1C/fz/RU6eild4RtWDsSBXhMdpQ//B0qu2ra1bURKX8a9WMQq175apZ1cCGrj6pTAUSQgghhBA9Wlpu9ueBHwC3ejOzNrfxsMY1xzLScrNXAX8EXvFmZjW52PBmZu095X4oLTf7C0BDEdBV3systY3bpOVmpwNfBe6Csys02kgQs9Dr52i5jldneQ+4xZuZ1e2TEI1tWLLowNSFOYsxXxeNeYAOL6zaWb5522ubPe5Ap9X16WevCVUbns7qvlNopTWgBkSPYnfR/5gx9Fb8obrGLZg0+DqiXf2bHFdeX4Bduc7Ytzs6IYEqDgHDFQojvPJPCqkcYBdRxDCI1BPt7c1cTkevLptRdMfQSiPK0aSWk83tImLCKGo37qB2zVYG/eRrDl2vyyC8QLZmGOBXNuXR9uDHQPtWPFJEtj21AnnFCWMxk8I9YuRWO/xrUcbS2q4+qSRWhBBCCCFEj5WWm30LZlH0ZZir17XVqUPgZ2FOd4kEKlo72JuZ5cdMxLS0fy/wrbTc7B8Bt2COcpnSUvuwCmAn5opyDbf9mKNgYoD/tRZXJ/imNzPL13qzbumnmMutTrM4jrPmDzg6dZRQf3t1/JFA/9YbdiNKmdmDIbETcNjcxLiTKKk9fGJ/YtQIDpdtJGPAZSilqKw/Tqwn+bR+HDYXQaPpkr1K2Z32IYPrQkfzgzS6Vo5T/fHpOqooZxaXnzE+I9K+z1Hsj4pYXrK9bnjTRGj0RdMp/N2zuEenYo+PSta1wXWekSOnV2/aSL/LLo+uXrZth1FTN5YEPUXX6cO0Z2lgRUKb2wIeZ6BCqR67BP2ZWLLEuiRWhBBCCCFEj5SWm23DTETEeTOzalprf4qkZrZ5vZlZrSZV2sObmVULPJmWm/0U5vSjezGn9OzDrPvhxRy6/nnMv837YSYCLg//HNmR8ZyF24HvWRzDWdmwZFFg6sKcWzGn/URZHc/ZKCjqT/qw/E7rP95eO7L1Vt1MuJiFxxlLar/Tc2ZpCbPZWfQ+Kw89CUCEM+5EHZXGYtxJKGysPPgkKXETcNjMkTsqImKMio1ZTgVN6pUkM4QqynG2MurFVh9KGPbDXSnahvvY95rmLdwjUlARbqLnTTU3RBrT4z518cfFf8+dfTTn97hSBve3948HGx4dHyyhPYkVGNKOtsRG1RZCr0usbAfeteLEUrxWCCGEEEL0OWm52Vdjrg7U2AJvZlazf5SHkzgxZ0q8pOVmDwAivZlZB9sRx0WY37AObOsxXWwX5vNyxOpAztbUhTl3AX+zOo6zMWfStrW3feaDGa23PHvvV2cc1NhSO/McHamuwJW34d7zUjrzHFprQgcObSQUOjHKbLNewTDSSVCnj35pyZFfjNnvS408kbwKllVyLPtxUn6zCGULZ4g0flXo2q0TApE4dCrq5DRFVexcoYK2tk8FC3JModr0WXLekLz1l0/Z0mNHc52iDnPVtpxFGUv9rTXuDDJiRQghhBBC9EXvAZ8A54fvF2EWNz1NOGHyLHB5Wm72esxvRN/FrK3iD7cZibkM8fC03OzDmFOTlgHLgX3NrDZkw6z/8TO694ISY4DlabnZE7yZWdVWB3OWngKuBG60OpD2yitM7NfZ54hQgYJa7U7t7PN0FM9AfwroclDxnXUOpRT2YUOGhQ4cKg5of+I6PiCauHYlVQBi3y86UvSl4SMBqj/aSNlL75Jw25UnkyoACpdOCMTg0MNRTVf70gmB8RS6ilXbV246ThuTtAnR1XWtt+oxDgMPLcpYalmBbUmsCCGEEEKIPsebmVWXlpt9E7AOyAW+783MKju1XXhEyT+AQeFNM4BxQDnmSj0N345+DnM5ZDCH738hfAMoSMvNbpxo2Qm8CNzckY+pExXStNhvj7JhySI9dWHOQsw6Op060qEj3bzgw+XHixPcnX2eeHutvzbY6afpOGaKstOvY5XDkWgbNPATZ8ExYzafHnA2fURvqIgr+lL457lTiJ7bQpklp05tdruNeB0TWqWqHG1NrLR5SuSevMFD+8VUL0tJLO3vcgQzlOq573HMZdSvBV6zKgCZCiSEEEIIIfqstNzseG9mVnkL+2Ixl2JubkTJL4EfNoxEScvNHg7spfUVNg5jXuD/Hbj0LMPuSquAKzq69owVpi7MuRh4H5qOCuiOIj31lQ89sMSlFJ2+ZM/xQOzGrb6hrRVW7jZqDroPbnogPbWrzqcNo8YoOL5e19ZeAJy5wMqpx0LwwGPn1xuR9uizDwBCXt+m40senwwQqqoCpbBHm12mfOObKEc4zxRkp0I1WfXKX3ic4y/8HVAM/PztOPufnqNxOQJVIwcd3zV6aF5dcr/yQU67kX7W8VpnHTBzUcZSSxIcklgRQgghhBCiGWm52Qqop+WLqX9jrtRzIHz7LnBrG7quw5x2dF4HhNmZNgHzvZlZVVYH0lGmLsz5FT2gGO9Nly9ffsmMzfNab3nuQlrV7/ENXHMsGJccwj6mK855Lo6+nvjxwWcHtm8p4g5g1NRuM/ILxrf3uMI7hq6pvDRp5jmdPEiBKnFV6JhgVelzb6U4YuMGx190cdM2mjpCuBTqxMgTbRiUL/8fOhAg4fJPt/l0mfNXrEyMq5pzTjFb49JFGUs/sOLEMhVICCGEEEKIZngzs3RabvZK4OIWmlwdvrVXBN0/qQLwRG9KqoT9GHPFpW48QkNz4eStg5vbMyv1IJ+7O5Zv/tBcWff5JRXU1hh8eVH7S7Hs2e6juDDE7IsjPRmegvkrcnYQsjlK53wlY2thMGZ4SwVtf7vgA9xRDpSCqP5ublp8PjGJbRtYU5ZXy+HNZZz/maYzso7tqeTl738CQEVBHe4YB55oJ5H9nNz1xKwmbcs3Rwe3HHuLAVFpDIwZw4f7H2X28Ntx2du2gNbx6j1EOROIdrd1do3JFhU53nA6VxMIzGq99Umxy0r8lZc2twhZOzgYpAf4B6FAJTiCRq3vWN3ePQNL/v1vtGHgHjqUxOtu3Gu3uyYe+tUviJk2g7q9e4i9YDYVK5aDTVF3wEvKwnspePZJghXl6ECA+DnziJ1prghdu2c3pe+9gwrWB998X8/+3C/H4Y5y8IvLPmL6tYPZ/mERoaDm9pyJJI/stotsfR+z1lWX686FsoQQQgghhLCUNzPrEiAZyAQew5zu01e8bXUAHW3DkkV+zFFFtVbH0pKMkYe3uZyhUc3tc7ngw3dqKS8NnfN59uzw8/H/mtYvjYkwEiZ4js4f6SrKO9Oxdz05i/tenUfKuDiWLfG26XyhoEFZXh1b3jp9+eiB58Vy3ytzue+VuYy5OJlPfyuD+16Ze1pSBaD6gOeclgg+Xr2Xan/xWR1rH5Q8EGjXk+8+VJtOR8wSaZjAppRDR4biC//xj9CAL3yRod/+DhgG5R+8fyLboZxOUu75OjGTphI78wLiL5xPysJ7ARhwUyZDv/4AQ76+iPKPPyJUU0OoppqyD95l8N1f5d4X5388dEKs+vCZQydOHdXPybdemcWcW4bw4VMHz/2xdJ7LcnYumGrFiWXEihBCCCGEEGfgzcwqBP4ZvpGWmz0UcxTLpcBlQLOjC3q4eiAVc4pTr7JhyaLdUxfm3A8saeMhxUBLwxt8wB7M1ZNaq69zRkoZekzqke13XLM00FIbu0Nx3eeieeFvldzznaajVMpKQvw6q4Tj+UEA7v9xAudP87B9s48//LwUX73G7VH88KFEBg918HhOOb56zSfr6vni1+IBOLA3wD2ZBew/UDDtwrtqueDzI84Yc+q0BFY/f5CAL8Qbv9hG/vYKbHbFFd/NYOSMRDa+foTdywsJ+gz8dSEC9SGK9lfzyI0fMfnaFOZ8ceQZ+//g0b3s/vA4AV+IYZP6cc2PJhCssp920IHStZTWHgZg4qCriXL1oy5QwdZjb+MP1eKyRzJh4JXUByspqt5HWe0RvKWrmDzoOiJdbR/to9zuVNzuFfh8bV4CWRkMdHtr9/hGRXXYKDVlUx7HgHi/MymxCBgYM20a5e//r3/D/uiJk1o8tmLlR9Rs3wpAqLycQEkRoZoa/IXHyf/LH0L/eK5mrhHUDJ90Mn814XKzdu+QsbFsebewox5GRysAfg5sseLkklgRQgghhBCiHbyZWUcwl19+Ni03OxnIp/eNBPcAd6flZi/3Zmad+/CI7ucJ4Arg+lbavQPcBDwIXATswlzVaQuwHzi0Ycmi0NSFOVcBr9DO4qaN/fxrzyzpH1/1ldba3fTFWG77dB5f+ErTgRs5Pyvlli/FMmm6h2N5Qb75xePkvp9CapqTR/85EIdDsXZFHY8+VMav/jqALy+KZ9dWP9/+uXk9/niOn0PeAI/8YyBvHx9V+cdrlifNyByO3dnyS3v3skKS02NY84I5uuHrr82jaH81Ty9cw/1vXQTAkU/Kue/VuUTGudi/toSVT+/nC3+Z3qbnZNatw7nkHrOO6ksPbmZTbnEhqNNW6HHYXFww/IvkVW5jV9H7TE25iR2F75ISO46UuAkcrdjCzsL3mJJyA0nRo05MIzob9kHJ6aGDh+uh7YWF494vKijsyMSK2wUOu0vHBfNVhXMgGpTTeWLEis3V/MuwzruPun17SfnaN7G5XOQ99md0IAgaItPP40t/GLNieHLx/FOPc7jM14DNDkao29ZozQWWWLXksiRWhBBCCCGEOEvezKzjabnZH2COXOlNHgPu7aVJlYYlmG8DPoO57PVVwKlFOnKBL4anD/2olf7enLow5zrM5V7PZu3igMMR+j/gTlpJzkTF2Ljyxmj++XQlbs/JBY7WrazjwF7/ifu11QY11QbVVQY//1YpRw4GUAqCgZYvjGdfEoHPGeGN7u9Ji05wUV3iI25gxGntnrxrNcqmGHheDJd9fTSv/vATZt2WCkDSyGjiB0dQctBc+TftgkQi484u33RgbQkfPbmfQH2I2go/tsr4+uRm2g2KGRv+N4Ndhe8DUF6Xz+TBZt5scOw4dhd9eFYxnEo5nckqMnKZrq09LQHRkqhNFe0vgnMGOhAgWFxGwHs86E4cQsXqVeURaenxrR1n1Ndji4jA5nLhLzyO74iZEPMMG07xv17W7urIMXpAZHF9bShUnl9P8qgoG3COBWK6zP1Aas7OBZ9flLG0zctOdxRJrAghhBBCCHFu/kHvSqw8BHyvYSnp3mrDkkV1wMvAy1MX5kRijmC5GbMg8XPA1zYsWdTmxNKGJYveDidXXqf9yZXDC8ZtP7rm0Ii/Afe01jjzrlhu/0w+V90cc2KbYcDjrw3C42k6wuR3PylhygUefr1kAPlHAnztlmMt9utyKfKD8XlAmrKrFkcn3PXkLKL6tS1Z4oqwt96oGQ3Ti+7JvZD4QRG8/+c9HH3XppObG0CjWrzT6uazYRs4YFJo/8EKoE31Xmw1oXG26mClEe2I7YjzK6eTxLtvDBU+/cIUHQzUeDKGx0RfMv0j/MwF0Noo0A7jgAramqyeFDl6DBVrPubIHx7CmTQA99DhANijo0m6+Vb1x3veTNZBcypZwoIruPkO3zKgzQmkbuA6YASwratPLIkVIYQQQgghzs1rwF85h2kg3Ugl8GBvT6qcasOSRbWYU3lembowJwKo37BkUbufgw1LFr0zdWHONcC/aMdUEU7WsvktbUisxMXbufSqKP79zyquujkagJlzI3j5mSo+H54itGe7j/PGuamp0iQNNJMbb71cfaKPyGgbtdVNZ00EQsqfH+g3qB1xA5A6NYFP3swjbWYixQerKS+oJ3FEFPk7K5q0c0c58NUG29Rn0GfGFtXPha82yPZ3C+hnT4VmfivHqnYxMmEWx6p2ER9hljyKj0ihoGonKbHjya/cQT/PEMCcNhQ0/Kd30g7Kbo9TMTHLdFVV25IONo7ZakO2jkis9LsxnMMNqMMpv/xmAJdumGI01ygLLh/68+9r3S94gYE/yZbnPpxw+aeHnYjb4WDwXQub7TdyVDqR9y06GbIyQsOT/5vxo/fmntg2dHwc9z4z7VwfQmcKYVGB8d42F1QIIYQQQogu5c3MKgf+Y3Uc7fQXYCZwI/BN4DeYI282A8NaPqz327BkUd3ZJFUaHb8Uc9RLXWttGzmw5tCIeOCRth5w691xlJeeTIw88NMEdm7xcdun87jlsjxee95cKfvzX4nl0d+U8eUbCzAa5VGmXuDhwL4AX7gij3f/XYPWythbEV9oYEtvR9wAzLhlONqAh69fTu63N3Fj9kQcrtNHqgw8LwabXfHIDctZ+ez+M/YZEetk2k3DePj65Tz/jfWkjI8ncbovCfRpWRFDB1l1+FkOlq1nTNKlAGQMuIy8iq2sOPgk+ZXbyRhgJiQGxWRwoGwtKw89Ra2/rL0P9QTbgMSZwPHW2hku254Df54YExzgbnfC6oycekSjpAoAul9wnk4IzkfhQuHQccEzru50Ko/TX2FTRghgzNC8DTbFafVsurmDizKW+qw4sdIdsfSTEEIIIYQQfVhabvYXgWe68JQlmImRFE7WQOgHXB7eNgU4vTjGSb/2ZmY92KkR9jFKqSzMpZxDgDFw0mVLBk+/8reYv4e3gd+Ff47AHM1y4ueJ6fs//upn3/wWcK0VsWsNq+vSPq4xPLNbb22tzd8ZuaLaG9nmVXk6U6i45CNdVj73TG18KZ6VR341dk5XxdSEBlu+e4/Sqk2Fc2+56KOVcVG1A5dumFQ9b8L2QHSEr1sPT2nBC8C9izKWnn3W7CzIVCAhhBBCCCHO3fldfL7D3swsL+A9ZfsTAGm52UOAxcDnG+1bDXwCFALvdUWQfYVS6gLMArhTtNY+pVTisc3vuQZPv3IH8GXgrg1LFjX7TfqaQyP6Y9boeRVzGe8OqcPRHpvrhy2rMTznXEvjRxPfIjk9FiNk0C8lkpsWTyIi1tnu1YDOJP3evCGbHjg5qObD/Y8ye/jtuOxNaw/vLV6Bw+ZkRMLMNvddVONlb/EKQtoPGpKi0xiTdMlp7XzBanYWvU9FXcFcdCgQSbTzPM4nSsWc1la7be0ayVCY8xyBojJ0vY9QVQ2OpASCJeXEXXEh8ddd3KRtsKySgp88ii0qAvfo4YSKy0n+9u1ULd+A/0Ae/W+/BhTVzU2fOlVyv7I9CTHVs5VCXTljY3tC7m4+B6icnQtuXZSxtMtGkUhiRQghhBBCiHOQlpttAzK76HQG8BLm1J0WeTOzjgJfSMvNfhj4PTALuMGbmVXQ2gnCo2/uB/4LvAGs7a2rA7XVmkMj7IAxc/iBli7UBgHFWmsfgNa6WCk1Y+PjD3xDa32DUupa9fgDL2IWO7UBO1YfTP3q80sq/vremzUjAn7NkFQnP/ld4sGIKFvsz79VRFSMjV1b/JQUhbjv+/245MooiguD/PC+ImqqDEIh+O7/9WfSDA9rltfxeE45fr9myHAHP3wokcgoG3/7Yzkr3q/FV6+ZMNXNg7/sj1JNq7geC8RtKAnFdEiBUqfbzn2vmAM4Xv7BZta8cJCLvtLumUVnFJXqS3Un+df6ilwzOrLfKl8ROwrfY2rKTUS7+mNog6MVm09rp7VmY/5rpMSOZ9KgazHKy9dXFHov8FNPFCcTK9qsV+QwXLY2Lf+rDQNlszFgkZkLrduxn8r/fETyt2+n6LGXcQ5OPP0gpUApUhZ/40T70xjEt+X8sZF1VUp1ZIlfSxQB3wb+3pVJFZDEihBCCCGEEOdqLub0m85Ugzka5Y/ezKwDrTVu4M3MWpuWmz0XmNWWpErYh5jTmiYDDwJFabnZN3szs5a1M+be5OfAgDWHRnxt5vADgWb2LwV+rJTagzkaKBdYifkcgvka2QZMBxxDUh15wJtX3RztvG2hWWz2r78t498vVad+9g5zwEpJYYjHXh7IIW+A79xdyCVXRvHff9Uwc14Ed94XTyikqa/TlJeGeOqRch5+PpmISBvPPlrBC09U8qVvxnPT7TF86ZvxAPx0UREr3q9j7mVNR3YUBmOr6QTDzu/HsT2VJ+77aoO8sGgDx/dVMXhsHDf/ahJKKbyri3nntzsxQpqUcXFc8+PxOFx2frvgAyZfM4Rdy45jBDW3/G4KSSOjqS33s67o72OrDtmI8zQtW+It+Zi8yu1EOGNw2iOJc5uLMx8p38yRik/QOkSkqx8TB16F3eZscuyB0jWkJVxAtKs/ADZlY1j8FIKGj5UHn2LuiC9jU3aKqr1U1h9n5tBbWXPkH8R5Bl1QzO76EEGPQ5t9GhjoOI8RtAfttoOhKa5/TqJ+m5dQRRVKKewDEwkVlREsLiPu6vnUbd1Lwq1XUvToP4maNZH6Hfsx6nzY483CxEZVDaXP/Jvix15GhwwSbvk0sZ+ew7HsxwmVlJP3g4eJnjfltN+BUefj6K8eGjHsOz9A2e0Y9fUc+cNDNNxvbG/e4KlTRu1fmRhXZc20pXOjgUeBXwF1XZ1UASleK4QQQgghxLn6XCf3vxgY5s3Mur89SZUG3sws7c3MWtWO9oeBP3JydY2Pwrc+ac2hEQuA7wN308JIIa11NTAVWIj5rXku5jSsfUqpDGAG8PuBKfZrLr8m6ie33BU7G3B6dwf4ys0F3PapPP77eg0H9pysyzpvQSQ2m2JEuovSYnPA0NiJbt56qZrHc8rw7vITFW1j2yYfB/YGWHhjAV+4Io//vFLNsTxz5Z0Nq+q569p8bvtUHhs+rm/Sf4NKw3OmWjxnxQhpvGuKGXNx8oltBbsqufJ7Y/nGv+ZTdrSWQ5vKCPhCvJL1CZm/nczXX5uHEdKszT184pjIfk7ufWkuMzKHseJps9jtB3/Zy5jLEqLnjf3itgFR6dQHzeRNRf0xCqp2Mmf4HUwedD2V9SfziMkxo5k9/HbmpN5FlKs/Ryu2nBZztb+YWM/A07Y7bG4SIodSVGPOusuv2k6MOxGbakhMaOYMuzN/ZMSUrShVPlNdVtzfPqiozlcRP+iP33LH3HNtdOXbK0l+8C7ib7yMUHUdUdPHMXjxNyBkYI+PYfDPvoZndCoAtgg3g3/+NSKnjSWQVxje5sF93nCGPf4T+t9xDWUvvQtA/7tvAIedlF9+Hdew02vj2tzu2oi0Uap21w7zMX6yiajxE09LqjR4/eOZEwxNy+txd19B4EvAYczpdF1ORqwIIYQQQghxbj4ApmFeWHeGf3gzs0o7qe9meTOz7gfuT8vNTgDqvZlZbZrO0NusOTRiEPB3QAH1mMtqN0trHcIc7fOhUmorcDvwUWy87frIKJW05JVB3/j/9u48vMri/P/4e86SfScbCRJCANkhigRcQKW21qXaWpfWWrW1y89+baW1m9Tu1La2pXZRW5dibWutu9VaUYtsSqwIAmEPIUACJED25ORs8/vjSUIICZDkhFD5vK4rF8kz88xMTnIZz/3cc89Pv31gRkNtiMIi5yTmH92xn5//MZPR46N48ckG3l3p6xgvKsp0Gtv5t7Aohvv/kc2K/7Twg6/u5/rPJ5OY7GL6ubH86LcZdNbqC3PPXQdY+EIOWTkeHlxQQ2vrkQ/yW603py+vTXcCrSF+d9UyaiubyRmfzKiZh9Y0bGIKydlODGfo6UnUVjQTHechdVgc6SOczIzCK4ZR/PgOzr4hH4AJH3CCBTnjkyl5zXm/v2PVQT756zNpLdjbHPpDAV6X81rWtOwiK2FMRyZKRvyojrkbW6vZemAZgZCPUDhAenx+r76vYclTKDtYTFbCGGp9laTGntbRlpUwBhMTPTI9eyKby4t5M/xKa4vL57XBMKG6RkL7ayBs2fO9+7D+ABgI1TbginLW6c09/OCd+JlOuaaY8QUdW3tsKESwupbKO38DGGec4xEyVYlnzRhRu+Q/xE+YRP2qt8n42DU9dm8NeJNeXz1560VnrD0ywnRya08/eht4ajAWoIwVEREREZF+KL123j9wtnjMwHkTfmRaQP/ER3i841Z67byDpdfOax6s+QdTW12Vv0DHkbM/BgqLy/OfLy7Pf6S4PH9OWx+MMacbYzoXE5k6PN8TmP/7jAtiYs38iz+aMDYz2zOjribEjtIAI8c47wObm8IMyXQTDFheeb7pmGvasztI6hA3V34ikcuvSWDz+lYmFkazdpWPXTucN9u+ljA7twfwtwVRktNcNDeFWfzykT/GgHU1WMywvr5GXbXXWLlj0YWEAmGKH9/R0eaJOvTW07gN4ZCFY1RVdbfd43IZwsFDfRvrovfuZGhzw8j4FWETDrYmuN7yG//OnsZZt/dfjMu8iHNHfJaCIecQssEj+iREpVPv6z5ZIzV2GC2BOg4278RtPPiCdR1t7ZkrVY1bCdswk868ZnHM5FEuV3wsNhAELO6keDJv+wQpV32AhHMLSb3qA+03H+MlcIJr/h2VRA3PIvent5P5tU8firQdjcW6Dnj3xo7IJ1hzkJbt2yAcJjr76Kc+b9mde2Z1XeKKY09wUvr6YGwDAgVWRERERET6rW27TXHptfM+DQwD7sRJS4+EQQusnOLuBC7s8vXjwEeAm3FqqewqLs//1cdvTCwEHjXGbDDGrPV4mfyHp4aef86c2PNrDoQonO5kVYwaF8WocVEdBWQ//9UUPnvlHm771F7yCg6v+dGdd1f6+PQllXz6kkoW/7uZaz+TROoQN3f9Ip3vfrma6y+u4LMf3cOO0gCJyW6uuC6RT32okm98vopxk6OPGK8uFLejX69QD2ISvVz67QksX7idUKDnZKddOwuXVW4Khf9297jtLy0857//vKduX8A1atu6twqW+VuMf39FwtaWxugqa7HhIKF1bxUsC7rH7H/hVyZ717ah5+9x7TonFPJ76gqHzPTMmj68smljcygcIBhu7di6AxAM+4l2xxO2IfY0lHS7lvy06Ww/+BZNfic5zFpLWc3bHe05SRN5b88/GZ5yJmEbYlftmo62Ot8efMF6DIb9n8nLdKckEa5zStd4MocQqmvEFe9k69hAkOD+nk8Cblq5DgDfxlJc8c7vjQ2FcMU7tXEal67q8d4OFmsP+veZoGsGQOIZZ7Hv8b+QOO346v0+/2bRpHDYRGRLkLXgC3vKqv0JK/b5E5dHYsweLGYQtyxqK5CIiIiISASVXjuvGri74In5wZ5L7AAAKtNJREFU9wAfAtKBRpwCtI1AAvAw0NMWjLuB7cDwto+GgV6zHK64PH828P0ul+O66ToUmHvHD4Z8+44fDJkN5AEFwNVAKsCyLSM6On/77sNPdrnqhiSuuuHI05W/+8vDt/Us3pAHwKUfT+DSjycc0X/a2bH86YUjS6V88Y5UvnhHajfLdhwMxdf22NhPOeOSyT49ibUvV3ZsAeos6Df+nRUjxg+/8BOuslceH2nDoZFxmcOJzb8ia/O7nlGBYDRv/btwtCc2geaqneED+/a4Nr874rzsogzKX32MzdvXkpBTgDfB+f7iMoaRMn6aWbH+T+FYb7IrNfZQIs7o9PNYufMxYrxJJEZnEAwfmVSWGJ3J2Iw5vLfnBUI2ABgy4gsOfT9J49l6YBk5SePJShzNpqrXqfNVsmbP8yREDSEvZRo7alax+7u/GeMZPxwTG82+n/0JV7SX5I/MZt8vHyPc2IQNhEg4fxqe9B5+LsEgld+7j3Czr2ObkHdYFo1vraF1205ixhd0f187C+wLbHVFx45pv5RQeAYHF/2LhClHFrjtjrMlaNLWi87s/ZagQNhV3RiKKWsIxbQ0h7yJIVyjwOQD+WBtgrt1U7zbP7a34x6HC4DyBRs/ePPccYteH4Dxj8rY40kjEhERERGRiCl4Yv4w4CVgcjfNF5deO++VE7wkaVNcnp8BrKHnwFd39gIZQPdVQU9SbzfnL6sPx503KHO/Nn7Jzs1DI3LMc2fxZY0rErc3Rfxkm70Nm6hq3MbkoZcdtV/DWM+aimvip/Zljl23/5ycH30Jd2Ifk9QsuKqjlhm/67CfaeO692jasJ6sa6/v1XBXz1qxPDOl/tzj7V8XjHl3d2vqZDA9JnC4CK8fG7d3ohm4g53/PHfcohsHbPQeaCuQiIiIiMgJVnrtvN04R/D+u5tmZagMguLyfFNcnj8VZ7tPbwu6ZvM/FlQBaApHp3VzeSfO8dFHFiKJEL/PU79zc/bUgRi7aUT8OSGvWRPJMTdUvcqW/UsoGHL2MfvGlwbHYu1xVpeNsIDZht8ctsjq55/hwL9fJPXCi3o93PNvTp8SDpvjOqa9ORS1eXdr6pijBVUAwrgm7g8kvNnrxRy/yxds/OAJ35mjrUAiIiIiIoOg9Np59QVPzL8c+BpOXZYYIArYNKgLO4UUl+d7gdnAFTi1U4YP7opOHGtpDuOqAJYA69o+1uNsWXucAXyvuH5lwRowswZkcGM4eEZaWnrxgVYDRxaW6YPxmccflHAFiHH57LpwrJnU23lO+/U3envL4aLsKJsYWm4aPB1ZJhlXfKxXQ8TGtla4PaEWa43bWuNavun07TPGbjVuE05wG3vkPjSgNeyuKPMNSQXTbXtXVYHEgjRvU5Pb2IGoH5WKE7RePABj90iBFRERERGRQVJ67bwg8LPBXscpLBF4mffP+yILlOIER5qAZiAfp+5LuxLgfmN47M4JL9V3HWB+yWUPAx8f0EXagdsHAhBK8Az3Zccsid3ri/hWo+MRVx462Dh2cDaH2OTguTZglhifu1ffu8cTrJ80tey95OTmrlvD8ra2ZGGw28fH70kAsBa7z5+0PMHjS4txBXNLWzL9YHKPfzaTtbs1dUlezMGB+vlcyQkOrGgrkIiIiIiInJKK8soO4mx7eT9YBkwryisbXZRXNrUor+ycoryyi4DfAgGcLJRZwKSivLLfF+WVHRFU6TTOgEpIbh7wQFbduKTzfOnRSwZ6nu4krQ8cWZH4BAoPCcy2ntAR221iYlsrphSWLklKbtpy6JxnS+6w/SvPmVXi6yao0sFiRraEPKWBsKt6c3PWeweCCeeV+9InbG7OclmnOG2vNIaiZ/rCnkidnNbVRwZo3B69XyKzIiIiIiIiffEEcMlgL6IfdgBfB54uyivr7mSSx4DHi/LKqo5nsHkTXlw4v+SyC4BPR26Jh4tPbunuhKXIchlX7ZSU2TF7W1Yll9TnG+iunsyASNgaGIu1QczR640MGANmqG/y5PS9r23ZOGyCzxc9FCAcNt7UtMbZqWnbCIXMtqp9KRWpaY1xMTGBGccz7K7WtAMB604GM7XTZH0MIpmocl/avtPjqgZi+13TAIx5VAqsiIiIiIjIqex5oJUI1eM4gZqA+cCCorwyX0+d2rJyeutLwHQgosfith9Im5DUcsIyOnzZsWf6k6P2pr99YJ0raHtd96QvXAFiXa2sD8cw8UTM152QdSdUtCQNKTp7U3J1VfKSTRuGFwUDnpT2drfbjhqaUzOqN2MGrGd6/1ZlW4Z4m97xEHK7jPW4TdhrLWFjIraTphW4E3g2QuMdNx23LCIiIiIip7Ti8vxnceoy/C/5aVFe2bcHavD5JZdNBN4GYiM15ra1w1a+t3z0aQnJLTsbauNnRmrc4xK2odS1tcujD/hPSN2VXdfFLWka4x2UGi+dnTm8fGlKrG9WOEzl9tKh5QWj9kw1JnI/095IcPsGsq7K28CNc8ctGpTi36qxIiIiIiIip7onBnsBfTCg9UPmTXhxPXBbJMfctS3Lb60r94QHVQBcxl0zNXV2KNr1zomYLnld4LhOyBloJZU5Y6wl4HKRM2r0npmDFVQBW58bXTN1AAb2A98CzhmsoApoK5CIiIiIiMg/cU7QGfjaH5ERAlacgHkeAcbhHAneb8lpjRzYkxKJofrMFQinnoh54p06KyGMcZ+I+bozNKnunXHZe08zBu9graFdsrtltcfYSGertALT545btDbC4/aaMlZEREREROSUVpRX1gS8ONjr6IVVRXllDQM9ybwJL1rgm0Bx26VQf8YrnL15VkJK01v9XlhfWWsJM+xETOX2E+9qZeOJmAusHZ+9Z8nk3N1LdnzqTmr+8oJ/et6OFeOH7p22/i/rs97945oTs4ye13eg5JF3ByJL6b6TIagCCqyIiIiIiMgprrg8PxsoPIFTrgPC/bg/WFyeHx+pxRzNvAkvhoCrgV8DLf0ZyxiYc/V/p7jcoW2RWFtvuVtCleYEFike+fuGoVHVRx57HEmxXn/leaO2rR2aXD87I6FptsvrssHV69xeX905Aznv8bLWkmya1i9+eEdUhIduBn4a4TH7TMVrRURERETklFVcnj8Ep17JhAGeyuJsOfoVMAv4YT/HWwd8tCivrLS/Czte80suWwqc199x6g/G71z0eFFK34/q7ZuYvb53U0rqzjiRcwLUTfKu2nNFbA4uMzTSY587snRVtDd4ZvvXj876K1NvnkygJcC0W89g3WPrCbQEOePzU2mp8fHm3W/RuNc5jXjG16aTNSWTZ657nksf/DBRCV7+etETFM09i9GXFvDGd5cx+tICmqqaKX9jJyF/iMbKRkZenM8Zn5sKwLq/lrDlBSdOdvoVo5n4yfE0VDbyyldeY+iZ2exbsy9YMDHWteqfe1xDRyeQPSqBT90TkcOZ7p47btGdkRgoEpSxIiIiIiIip6Ti8vxk4BUGNqjSAtwPjC3KK7sCWApk4hTdPJYwsLmHtknAW8Xl+QN68sz8ksvM/JLLhs8vueyDQGUkxkxKaxo+7cKNWyIx1vFyNwV3JW+oyziRc7ZLXhc4M31p6/aBGLs54G3qem3c1adT+u/t+BsP/xVb+cu3mfDJ8Vzx58uY8/PzWfZjJ5kma0om+96roqa0lsScBPau3gdA9fpqMic5L1l1yX7O/9F5XPnXyyl7rZzqDfvZv/EAW/+5jY8svISP/OkSNj+3hf2bDwBQV17H6EsL+PzTF771ybsnurzRbu54dmakgir1wC8iMVCkqHitiIiIiIicym4DhgOntf17LU7go7/2Ab8F/lCUV7a//WJRXpkFbisuz/8JcDvw/4DEbu4vAW7GCawsAaZ20ycD+Elxef65beNGXFudlZ3zSy7bAwSAPTjr7tZdk18ia3QSWItxGy6/cwLDC9OO6Ddi3J5pT3z5+fqCK799RNZK+X8eJzlvPCkFUyLyPZhguCl95YFo0+nnurtuHfW+vYzPuigicxxL4sZA7v7zYyI+7rbqjOzCYbs3etzhce3XohKiGHVJASV/34gn+lDt3Mq391C7vbbj60CTH39TgKypWexdvY+E7HjGffx0Nj27haaqJqKTovHGOXVvc4uGEpPirH/EBcPZt6YKDOSdPxxvrLfteh77VlcxfNZpJAxNIHNS+u5M756zI/Bt1gLb2z7KgP/MHbfoYATGjRgFVkRERERE5JRUlFdWB7zV9gFAcXn+94CfAZ/r5/BXFOWVFffUWJRXtgf4ZnF5/t3AF3GCFVk4BWJ/CvyoKK+stW1NHwbOwcl+8XX6tx7YN1BBlc7mTXgxACwGFs8vuWwk8JHu+nmj3fzf085uoa0rqll072ZuWXh43dJwyOJyG76xaHrSvx71vdPSFDNtINduPa74sNdsdQdsJAJmfRJVHR6BtQcwZkgkx633xY5Zsm0Uw1Nr3irIqD4NnOK8Ez8xnudu+CejLx/V0deGLZc/cgmemMPDANmFWWx8chNNQ+M58/+dwY7FOyl7vZysqZ1eLmMOn9jgbG7rQdscSWFMsxvbXeDwaJqBu4F/AWVzxy2q6eX9J5wCKyIiIiIiIm2K8spqgM8Xl+c/BvwB57jhvjiubSdFeWW1wE+Ly/N/DdwAvFuUV7aqS5+9wNN9XMdA+AE9BFY6a20MEpvkZDNsf/sAi+/fQmJGDHs21fOVF2bzo+n/5muvx+T+689nU7H8GRoqthGdmIbt9I69vnwDFW++gCcmntiMXPz1Bxl5yS2EAq1ULH+WlgN7wIbJnvYhkvMncmDT29TvKCEc9NNaf4CU/EnkzLyc+nHJpmnZUrYfXEm0J4F4byouc+LeDhvAU2dLgymRDay0j76zJm3mrtqUQDhkw9bij06Ojsn/wAi2PL+NMR9xgiu5M3LY8OQmJt8wEYADmw8y5PQ0ErLj8dW1Eg6GSRqWSNbUTNb/pYSZ3yjqmKGyuJLWulbc0W7Kl+zivLvOxrgMS3+wgik3TcJay443djL7h+d2XlfS7tbUJTlRtePdHpMRCoRxe49ZjeRJ4I654xbtjOhLNMAUWBEREREREemiKK9sWXF5fiHwDeA7QG9PNelVdkRRXpkPeLCXcwyKeRNefHd+yWUP0k1WT6A1xO+uWkbQH6KhupXPPDyjo233+jpue3YKacPiOq6FwyZUV7YOX201Y6/5OsGWBjb+/WcMGVtEOBhg15KnGHXll4hOGsKOVx/ruG/fqtdIyB3F8AuuI9jawtanf03CsNEAtOyv4PSrv4Zxe9j4+E9Jn3QugTj3lK0HlgbOHf4Zr8cdzdu7HicpOmsAX6Uj5T3SONKf6V4fSDLNB2dGB/0Z7khsk+lgrcsLJmwMMQCTrp/Ahn9s6mifecd03vxZMc984gVsKEx2YRbnfNvJJsqYkI4NOwGt7KlZvPP7d8macuhXOGtqJm98bxkNuxoYeXE+GePTARh9WQHP3/gS4BSvTT99CA2VjR33NYZiZm9pyWbUlafzw4tXNo8+IzGuhzorG4Hb5o5b9HokX5MTRYEVERERERGRbrRtxflRcXn+EzjZK+d36bIN+AowEyeDY3KnthP7rv3Eu59uAiudtwLtXFPD03eu4bbnZgEwbGLyYUEVAL/P29xYWUrqqEKMy4U3PpnEXCdA4qutIiopjegkJ8kjdVQhBzauBKBh92bqd5RQteYNAMKhAIHGWgASho3GHR0LQExqFv6GGoK+JuIz8xqjPHGpANmJY2n2n9gdJt5Gm+5tDKYDuFrt6sqrj/fEbFsBJIBJPlqv+KjWnTctvz4FSAKIHRLLTcs/1dEekxLDhXd3X+v4/B8eOuwpa0omn337xsPaY1JjuPDuGV1vY9L1E5h0/eG1nxNzErjqiSsOuzb9y9OY/uUz7di4vfVgO9fVaQS+D/x27rhFx1PQ+aSkwIqIiIiIiMhRFOWVbSkuz78QuBH4JZAGrAQ+UpRXVo1TC+Ku4vL8XGBa20fFYK33RJg34cXV80suex64oqc+w6em0lTjp+mg8345KvbIt59+n9cHHFnDA8AepYiHhREfuomY1MMTg5r2leNyH5rHuFzYcNj5IiEuNewxa11BO5lBFl8WLDj8im02hp1uV/ig1xsMRHuD0VHeYJrXGxruMuS2+Lxr99cmTATT7V4alwm3nJVX7jeGE3qEde+Y+L3+pKW50XWz2i78FfjG3HGLInLa1GBSYEVEREREROQY2grELiwuz38JJ1Pj10V5Zc1d+lTgBFSeH4QlDoYfcJTASvX2RmzYEpfS8y6q1pao1oScAg5seIu0MdMItjTSWLGN1NFnEJOaib/+IK31B4lOSqO2dE3HfYmnnc7+9cvIPfdjGGNort5NXMawHueJz8yjYvlz7J91Eelbg+xr3Exi1MDUsrVYi4u9xFBtk2kwVeSakBnRuY/LZ8PJnubFnoRQSpQnNNTtDmcbw9iexoyNCUyOj21dCtgobyimpj4uD0x2+4xn5ZWvdrtsJLcWvUFbhtaYy0cxplMR3P6oDcaNyomqW20Mt88dt2hpRAY9CSiwIiIiIiIicpzaMlR+MtjrOBm0Za08B1zZfq29xgoA1nLV/Cm43N1ko7Rp9XkDyfnjaKzYyqZ/3EN0cgbxOU4yh8sTxbBZV7H9pT/iiYknLnN4x33Z0y6iYsVzbP7HPWAhKimNkZfc0uM83vgkss/6ECUrHpwc3eptSo3JibdHy4jpA4tt8H01XBEewSg8DAWGAri2syXmF66AwXgBrLH7fV8P1yal+y7ozfhpyc3tmR7ExfgbqmsSlvoDnvNGZ1QvS4j2zzravb2wBpgLzOLIrW/9EQb+CeYPu1tTX/lV4RPhCI496Eykf5lERERERETk1DC/5LKZwJvH0zfgdzfv2ZG+0dcc5fc1Rwd9zVHhqt1pCb6m6DN7uicUaMXtjcZay+5lTxOdnEHmlO7rhByXsA1kLq0udYVsj9khfeW/NLw8cKk9t7u2qEfNEm+xa7Y1dm/LnWGfzWVEJOZs9btLJqbvTTSG4cfufUz1OKdZRQM7cLa89ddunKLMDz80beH7dnucMlZERERERESkT/77+rhNVbvT/puZW9M8bNS+1IxhNad7POHorv2CAZfv5cfO3uL3RfUYROnOgQ0rObj5v9hwiLj0XNLHz+zXelPX1L7lCtlIZXd0CKfZtwOXdB9UAfB/yp7n3mHf8n0hPMxmRyaoAhAdFZqwvSFt/4jEmhK3sROOfcdRLXpo2kL/Le/clI1TQ+iSPo5jceoO/QF4+aFpC4P9XNdJTxkrIiIiIiIi0meFty7YAIxzvrIBT1RoS1pm3YHcguronPzqgqjoYNK//nz2+taW6DMGc50xe1pWpWyo71Vg52hsnF3r/7BtIhZPcLIdQwJHPbVnIBls6/CEmlXR7lB/6qzc/NC0hQvbv7jlnZvOAO4EPgZ03s/VipPV0lUl8DDw0EPTFu7sxzr+5yiwIiIiIiIiIn1WeOuCB4GeC5xga8GknKDldMvlC1VlrNjvNjAkUmOGk+07LXeHp0VqvP6zZMc2LEmKau3tXqkq4KvA3x6atvCIAMEt79w0Fuf48L1tH/XA6cDngRuAd3CyU148FbJTuqPAioiIiIiIiPRZ4a0LbgL+NNjr6JG1NmNZ9Rp3wBZGdFivLW2+N1zQ9br/YBOl9/2Hxs17MV43MdnJjLz1AuKG9b5kSfmjK3DHRjHsmrPYsXA5yZNOI/XMPOrW7Wbbr1/FeFxM+c0nKV+4gpq3y0idns8Zt01fmRHTNNUYYo71LeAERO58aNrCml4vDrjlnZtMd8GYU41qrIiIiIiIiEh/rBjsBRxN8ob6pe6A7UfF2x4EyMJy2CYZay0bvvccWR+cwLjvXA5A47YqAjXNcIzAirUWLBhX96cojbjpUAmXqtc3knv1NLIvngTA3pfWMuOpW3FFeaj1M6M15Nk4LL4uzRiyephuDfDFh6YtLD7O77ZbCqo4FFgRERERERGR/tgGVOOcKHNSiTrYWhKz19epqKytA+JoO/q4PwwmgWbqiD9UW6VuzS6Mx83Qy6d29EsYlUmoxc/ar/+DYIMPGwwz4uZzGXLOKHx761h/59OkTDmN+g17GP/DK6h6bSNVr5YQnZGINyWOhNFObGTzz18mbcZIQo2t7F+ymZp3yqh9dyehFj8hX4A1t/2V064rIuOCsbSEosaVN6buzEuo8XXJXGkE7gJ+d6pu2xkICqyIiIiIiIhIn62+b64tvHXBm8AVg72WQywfLlz79pzs9UTfFNw6JLM5JTmtZYg3Kpzsa/G0rHkrd92KRSMbNq/LyvA1e8aCcfU0kjH2QEJS667G+ujh1prD0k5c+9gXHnkosNK0Y39HIOSwflEexn//Cjzx0QTqmllz299IO9vZRdSy6yBj7riYUV+5iIYte6levInCBz6NDYVZ/f8eO2K87EsmU7e+grQZI8mYdToAKy67lzP+cGNHnyhXcMfwhJpAl6DKU8Dt7+djjweLAisiIiIiIiLSX69wkgRWpo8qXfutK//lSohpnd5de0xsMHbGheVnzLiwHICmhqj6d5adtunNV0f6SjemD/W3eka393W7w7t//NCL4dwRdVOthYPVcVVb1mXu2vBuduO2DRmeipaYUBPeMcdak7WWHY8so27tbozL4N/f6GwPAqKzkkganwNA/boKhpw7CneMk1AzZOYRJVyOKTWq+a30mKZJxpDQdqkM+NJD0xa+3OvB5LgosCIiIiIiIiL99SDwKaA/x/32y+jsvaXfv+a5moykxl6d1BOf6E+afUnp9NmXlALw86/PWbL+nZzZUdHBLT9/7LnUtIyWDABjYEhmc+bMOTsyZ87ZAYC18G5Tzrq/VU2p29GackZc3pC4/Uu3HDFH9esbCdS2UHj/Dbg8bt6+/o+E/c5OnPYgSjtjuq+xcnwsQ2KaU9qCKgHgZ8BPHpq2sKUfg8ox9JjuJCIiIiIiInI8Vt83N4hz9G7jYK3hljlLdvc2qNKdufMXnz1qfPWye598Ors9qNITY+DMhMpJvxz58rmPjXkq/Nk59cvDzb6WPS+t7ejTsGkPvn31eFPicHnc1K7ZSeu++m7HS548jP3LtxJqDRBs9nNgZWkvV2+oakloAt4AJj80beFdCqoMPGWsiIiIiIiISL+tvm/u9sJbF3wFePhEzOd1BwOBkKct3cPS7I+OzLhRYe93f//v83p7X5w7kHB99rpzz3/CYz9w27am3X8vjndFeYjOSiLv02dT+vv/sPrWx4gvyCR2ePcnBCWMziLj/LGs/sKfic5KInnisN4u46X6QPSCbBr+oxN7ThxjrV5rERERERER6b/CWxcY4BngyoEY3+0KhT40Zf2q6897y6QnNo77zcsfKHlt3YQpz9zx2yqvOzx8IObsi79XT1r+5P5J5x67Z0S0AAuBe5fOuWfzCZpTOlFgRURERERERCKm8NYFGcB6IDNSYw5P37/zc3OWlJ1VsGO8y2UP257z7zUTlxTml8dlJTecFan5+isQdgU+ufmaA2Fc2QM8VRUwY+mce8oGeB45CgVWREREREREJKIKb11wLnAHcCGQ2E2XBuBl4Jqexoj2BnxXnvXuqo/P+G9CcpxvytHmCwTdO7ye0Ih+LDniHthz1pJXa0fPHsApfMD5S+fcUzyAc8hxUGBFREREREREBkThrQu8wAzgg8CHgGk4RzN/YfV9c3cW3rrgYuBROmW3nJ6z579fvGhxy/hhlVONIWkw1h0JzSFP0w1brg6CSR6gKT6+dM49Tw/Q2NILCqyIiIiIiIjICVF464IUoG71fXNtp2tZsVH+B04bcrDp07OWr5o+esdPgJhBW2QE/Wz3eUvebjhtILJWvrl0zj0/H4BxpQ8UWBEREREREZFBVV2Za4CbgAcB9+CuJnJqgjEHbtn60Xgw3QaKEt2+965NX+d7aN+0CWASjnPYR4Bbls65R2/mTxKuwV6AiBxijHnDGPOhLtduN8bcd4x7pkVg7rHGmDXGmNXGmIIubQnGmPuNMaVt7auMMZ9raxthjFnf3/lFRERE5JQ2DueY5vdNUAUg1eMbMja2+u2u16NMcNuXc95850+jn5ny4bStRddnvLf2OIesAuYqqHJyUWBF5OTyOHBdl2vXtV0faFcCz1trC621pV3aHgJqgNHW2kLgYiCtujJ3zptLMp8bM9ozrroyd3V1Ze6lA71IY4xnoOcQERERkcg43geHGTkVG4CvAFz58f2sec/f77m3bgtwwUVVXPjBKsp2BA9ra2wK8/Vv1XLW2fu48INVfODiah77axMAO3cFmXVhVb/nb3fHsOXjP5Hx3orL0jYtnZ28fcn1GWve/MvpTxbMTt4xzRinz8fSN5x9WlTtm8cx3HeWzrmnPmKLk4jQGxSRk8tTwI+NMdHW2lZjzAggB1hujLkfOAuIBZ6y1n6v683GmA8CPwCigVLgZmttY5c+U4EHgLi2Pp8BZgK3AyFjzCxr7QWd+hcA04FPWmvDANba6urK3DXAC1HRxLmcEO3U8p3BJydMdZdXV4db2m7/P2vtm8aY89vWtQ+YCjwDrMP54xkLXGmtLTXG5OGkNmYA1W3r32mMWQgcBAqBd9v+EP8V54nGy8BXrbUJxpgE4HkgFfAC37HWPt/2Or4MLAfOBiqAK6y17esUERERkYHR/uDwlU7XrgO+3rVjRk7Fb6src5ut5UHA9Hfil//t4+IPxfDNO46sf/vVO2rJG+6heHkmLpdh/4EQf/t7c3+n7Faqx5f+8fSS9J7ag0GLx2P4yYhXJ9+05apdIVyn9dB1Lc7/K8tJRoEVkZOItfaAMeZtnIyQ53H+6DxhrbXGmHnW2oPGGDfwujFmsrW2I2XQGJMOfAf4gLW2yRjzTeCrwA+7TPNn4DZr7RJjzA+B71lrbzfGPAA0Wmt/0aX/BOC99qBKJ/fgBGc6ZGS4Yt95Kyt2eEHlOGPMaJw/pO3blKbgpHgeBLYDD1lrpxtjvgLchhPY+R3wZ2vto8aYzwC/wcmkARjT9r2FjDEvAvdaax83xnyx0xJ8wEettfVtr8dKY8wLbW2jgU9Yaz9njPkHcBXwl64/AxERERGJqF49OMzIqXh40ybX/4VCjABSFi/x8fNfNOD3w4g8N/cuSCEh/vCNF+vWB/jGt2pp9llG5Hm495cpvLPKzx8easLthpUr/Tz71KG4RtmOIO+uCfDA71NxuZz4TfoQN1/+0pGnQu/cFeRLX66ludnZeXP3j5OZflYUK95s5ee/bCAj3cX6kgCXXhLLuLEeHny4CZ/PsvDhNPJHeNi1O8jtX61l/8Ew6Wku7l2QwrBcD7fdXkNKiov16wNMmuTl5hvjufX/qhMONt3nCZ411VY8vcqc8+JXCLX4KfnucwQbfPgqahNDLf7LsPfoweFJRluBRE4+nbcDdd4GdI0x5l1gNU6wY3yX+2a0XVthjFkD3Ajkde5gjEkGUqy1S9ouPQrM6s3ijDHzjDFrJhXundS1LRiAr32jNs8Ysw54sssa/2ut3WOtbcXJlFnUdn0dMKLt85nA39o+fww4t9P9T1prQ536Pdn2+d869THAT4wxa4HXgFwgq62tzFq7pu3zVZ3mFBEREZEBYq09ALQ/OIRODw6BedbaacBkYLYxZjJATa2t+/0DjZ+uqgptXHBvI089MYTXX8lgyhQvD/yx6Yg5/u/2Gu6al8SS1zIZN9bDL37VwAfmxHDjDXF84XMJhwVVADZvCTJhvLcjqHI06ekunnzcmf/B+1OZ9926jraSDQHm/zCZJa9n8uTTzZRuD/LKSxlc/4k4Hn7EWee359Vx9cfjWPJaJld9LI477zq0i2f79iBPPTGEH34vme98t47P3RJP8eK0mILk+m3tfVxRHsZ//wrOeODT/wy1+KcDvzSmfQMRo4HfW2snALU4Dw5lEChjReTk8xzwK2PMGUCstfZdY0w+cAdwlrW2pm1rTNfK4gZ41Vr7iQivZwMwxRjjstaGrbXzgflxca4wXVI0H3iwkdQUVwtOdooLJ4OkXWunz8Odvg7T83+LOhflOvKv6JGux9lGdKa1NmCM2cGh16nz/CGcJyMiIiIiMvDaHxy2Z2R/pu36NcaYz+P8v+BQnIdyawFefMm3Z9Uq//caGu1fLrtifxRAIADTzow6bOD6+jD1dZazZ0YDcO3VcdzyhZpeLW7BvQ288GIL+w+EWfdu9mFtwQB8a14tJRsCuFywfXuoo23qFC9ZWU6t3RF5Hs6f7axh3Fgvy990asS8syrAnx5KA+Dqq2L54Y8PBVYuvywWt9u09fPz6CNOvz/eVjNq3MPhMNiAtTZ6x8PLwntfXjcGPTg8aSljReQk01YT5Q2c/ZPt2SpJOIGFOmNMFvDhbm5dCZxjjBkFYIyJM8aM6TJ2HVBjjDmv7dINwBKOwlq7DXgHJ4XT3TZ2jN9vA8BhG1Fra21g/4HworZtQzfQ+6rub3IoW+d6nNTG7qzkUES+c7HfZKCqLahyAV0ydkRERERkUDwHzOnhweEca+1k4CW6PDjcszfsa2y0zyx+NbN48auZLH8jk1//MqXfixkz2kPJhgDhsPMMb+5XEln8aiYNDUcetPPAg41kZLhY/GoGr76cgT9wqE901KFnjC7Xoa9dLggFuz+0x3R6LBkf133GjNtYE+0Oux4b82Rw+OJ/lgY3la+3gdAka+1UnJqFPT04VOLEIFFgReTk9DhO1sffAay17+FsASrBCbis6HqDtbYauAl4vG0rzEpgbDdj3wjc09ZnKkfWYOnOLcAQYJsxZhXwWijE14Cvh8PUWosF6j54UfSCZ59vGWmMWYlTE+V4skw6+zJwc9vabqCtMnw3bge+2laPZijQnpP5V2CaMeYdnMDMpl7OLyIiIiIR1t8Hhw890vgLgOaWMKWlh5/uk5TkIjnZsLLYiTE8+XQzZ8+IOmKgzkbme5g62cvdP2sgFHICID6fPTxXuk1DvSUr043LZXjy6RZCoSP7HM1Z07w8+7xT9uTpZ1qYPr37tZ15RhQvvuQke7f3j3MH4yfEVRWMSGo8oAeHJzdFtEROQtbaZ+myzcZae1MPfc/v9Pl/cAqAHW3sNTj1WLpe//5R7qkHvtB9a+4DyxZnJgP1V193IGSv45udGr/ddv8bOH9Mu1tzR5u1dgdwYTfz39TlUgUwo62o73U4GTVYa/fj1F/pzsRO43Ut0CsiIiIiA+txnJMhrwPnwaExpv3B4XZ6eHBojLnpzrvqf3b/H5rqEhJN8re/kURBweFvY3/769SO4rV5wz385lcpx1zMgl+k8P0f1VN0ThUpKS5iYgx3zTvy9KCbb4zn5s8f5J8vtnDOOdHE9ZBl0pP5P0rm9q/W8vsHGjuK13bnxz9I5tYv13D/Hxv5wJwYkpKcea76WCwLftOY2/bgcA16cHhSMk7NIBGR/x1tW5l+hxN8qgU+07ZlSURERETeh6orc/Nwggpd6wy+LzS3hImNMRhjePb5Fp59roU//ymtvXl2Rk7F0sFcnxydMlZE5H+OtXYZzlYpERERETkFZORUlFdX5v4KuHOw1zIQ1q4N8K15dVggOcnVtZZM9eCsSo6XMlZERERERETkpFddmZsIbAGyj9X3fSYzI6dCwZWTmIrXioiIiIiIyEkvI6eiAZg32Os4wSxwcLAXIUenwIqIiIiIiIj8r3gUp4jrqeJgRk5FL88ikhNNgRURERERERH5n9AWZJg72Os4gfYP9gLk2BRYERERERERkf8ZGTkVbwAPADWDvJQTYe1gL0COTcVrRURERERE5H9OdWWuAYYCE4EJbf+2fx4/iEs7HnuAvwDPABnApE4fpwPbcOrJPJuRU6E37Sc5BVZERERERETkfaO6MtcF5HFkwGUcEDWIS2sBnsWpE/N6T7VTqitzo4FQRk5F8EQuTvpOgRURERERERF536uuzPUAozg82DKp7Zq7n8OXAMXAcGAakNKlfTcwISOnor6f88hJSIEVEREREREROWVVV+bG4Gy/mQTMxwmOHEsJ8Ebbx9KMnIqqTuMZYCRwJk6QpRB4KCOn4omILlxOGgqsiIiIiIiIiADVlbmnA8tw6p501mMgRUSBFREREREREZE2bRknXpx6LF4gnJFTUTe4q5KTmQIrIiIiIiIiIiJ95BrsBYiIiIiIiIiI/K9SYEVEREREREREpI8UWBERERERERER6SMFVkRERERERERE+kiBFRERERERERGRPlJgRURERERERESkjxRYERERERERERHpIwVWRERERERERET6SIEVEREREREREZE+UmBFRERERERERKSPFFgREREREREREekjBVZERERERERERPpIgRURERERERERkT5SYEVEREREREREpI8UWBERERERERER6SMFVkRERERERERE+kiBFRERERERERGRPlJgRURERERERESkjxRYERERERERERHpIwVWRERERERERET6SIEVEREREREREZE+UmBFRERERERERKSPFFgREREREREREekjBVZERERERERERPpIgRURERERERERkT5SYEVEREREREREpI8UWBERERERERER6SMFVkRERERERERE+kiBFRERERERERGRPlJgRURERERERESkj/4/a5UcYQv30jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axs = plt.subplots(ncols=2, figsize=(20,10))\n",
    "[ax.axis('off') for ax in axs]\n",
    "\n",
    "final = new_df[['code', 'geometry', 'metric']].merge(geodata[['LAD20NM', 'LAD20CD']], left_on='code', right_on='LAD20CD')\n",
    "\n",
    "final.plot(column='metric', ax=axs[0])\n",
    "\n",
    "for x, y, label in zip(final.geometry.x, final.geometry.y, final.LAD20NM):\n",
    "    axs[0].annotate(label, xy=(x, y), xytext=(0,0), textcoords=\"offset points\", ha='center', va='center')\n",
    "    \n",
    "    \n",
    "original = geodata[['LAD20CD', 'LAD20NM', 'geometry', 'centroid']].merge(new_df[['code', 'metric']], left_on='LAD20CD', right_on='code')\n",
    "original.plot(column='metric', ax=axs[1])\n",
    "\n",
    "for x, y, label in zip(original.centroid.x, original.centroid.y, original.LAD20NM):\n",
    "    axs[1].annotate(label, xy=(x,y), xytext=(0,0), textcoords=\"offset points\", ha='center', va='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}