3. Geo_Filter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2771495a-a024-4e6a-9697-54ceb9ed9cbe",
   "metadata": {},
   "source": [
    "# Arctic Lake Bathymetry\n",
    "#### Filetering by geography - ICESat ATL13\n",
    "#### Melanie Frost\n",
    "#### 4/18/2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f65d44c6-0ffe-4464-bc62-12265fb8ff7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.colors import ListedColormap\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import rioxarray as rxr\n",
    "import rasterio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2ea7c91-0756-47ea-a806-aa60a6990b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install earthpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0a170d2-28b6-4aa2-a27c-f086b3d5a941",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import box\n",
    "import earthpy as et\n",
    "import seaborn as sns\n",
    "import earthpy.plot as ep\n",
    "from rasterio.plot import plotting_extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34f55cf2-f931-4b87-9e09-af39b5605dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'GeoSeries.notna', UserWarning)\n",
    "\n",
    "# Adjust plot font sizes\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92d2988f-5177-4044-84ab-dc10e257a66e",
   "metadata": {},
   "outputs": [
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       "      <td>2546</td>\n",
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       "   cycle    id  size  type  cloud_flag  bkgrd_flag  shallow_flag  wind_flag  \\\n",
       "0      4  2546     4     1           0         1.0             0          1   \n",
       "1      4  2546     4     1           0         1.0             0          1   \n",
       "2      4  2546     4     1           0         1.0             0          1   \n",
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       "4      4  2546     4     1           0         1.0             0          1   \n",
       "\n",
       "   rgt    seg_lat  ...  qual2  qual3  orientation    beg_lat     beg_lon  \\\n",
       "0  266  67.520551  ...      0      0          NaN  67.519020 -148.468245   \n",
       "1  266  67.520859  ...      2      0          NaN  67.520765 -148.468775   \n",
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       "4  266  67.521432  ...      3      0          NaN  67.521337 -148.468950   \n",
       "\n",
       "     end_lat     end_lon  sc_orient  quality_metric  beam_strength  \n",
       "0  67.519020 -148.468773        0.0        1.000000         Strong  \n",
       "1  67.520765 -148.468833        0.0        0.971698         Strong  \n",
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       "4  67.521337 -148.469007        0.0        0.953271         Strong  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/lakes_clean.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0aad1e4-7943-4250-a81e-2e62cb1169f3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.981132</td>\n",
       "      <td>Strong</td>\n",
       "      <td>POINT (-148.46886 67.52105)</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2546</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   cycle    id  size  type  cloud_flag  bkgrd_flag  shallow_flag  wind_flag  \\\n",
       "0      4  2546     4     1           0         1.0             0          1   \n",
       "1      4  2546     4     1           0         1.0             0          1   \n",
       "2      4  2546     4     1           0         1.0             0          1   \n",
       "3      4  2546     4     1           0         1.0             0          1   \n",
       "4      4  2546     4     1           0         1.0             0          1   \n",
       "\n",
       "   rgt    seg_lat  ...  qual3  orientation    beg_lat     beg_lon    end_lat  \\\n",
       "0  266  67.520551  ...      0          NaN  67.519020 -148.468245  67.519020   \n",
       "1  266  67.520859  ...      0          NaN  67.520765 -148.468775  67.520765   \n",
       "2  266  67.521048  ...      0          NaN  67.520953 -148.468833  67.520953   \n",
       "3  266  67.521236  ...      0          NaN  67.521142 -148.468891  67.521142   \n",
       "4  266  67.521432  ...      0          NaN  67.521337 -148.468950  67.521337   \n",
       "\n",
       "      end_lon sc_orient  quality_metric  beam_strength  \\\n",
       "0 -148.468773       0.0        1.000000         Strong   \n",
       "1 -148.468833       0.0        0.971698         Strong   \n",
       "2 -148.468889       0.0        0.981132         Strong   \n",
       "3 -148.468950       0.0        0.954128         Strong   \n",
       "4 -148.469007       0.0        0.953271         Strong   \n",
       "\n",
       "                      geometry  \n",
       "0  POINT (-148.46871 67.52055)  \n",
       "1  POINT (-148.46880 67.52086)  \n",
       "2  POINT (-148.46886 67.52105)  \n",
       "3  POINT (-148.46892 67.52124)  \n",
       "4  POINT (-148.46898 67.52143)  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icesat = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.seg_lon, df.seg_lat))\n",
    "icesat = icesat.set_crs(\"EPSG:4326\")\n",
    "icesat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "306d3f4b-d0fc-4453-ba39-39f03f9ff220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AKNS epsg:3338\n",
      "icesat EPSG:4326\n",
      "AKNS EPSG:4326\n"
     ]
    }
   ],
   "source": [
    "# Import data\n",
    "AKNS_shape_path = os.path.join('data', 'ns_boundary', \"nsb_boundary.shp\")\n",
    "\n",
    "AKNS_shape = gpd.read_file(AKNS_shape_path)\n",
    "\n",
    "\n",
    "# Are the data all in the same crs?\n",
    "print(\"AKNS\", AKNS_shape.crs)\n",
    "print(\"icesat\", icesat.crs)\n",
    "\n",
    "#set CRS of AKNS to WGS84 latitude-longitude projection/authority code \"EPSG:4326\".\n",
    "AKNS_shape = AKNS_shape.to_crs(\"EPSG:4326\")\n",
    "print(\"AKNS\", AKNS_shape.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58be3231-1ab5-457c-98b5-a0964ca3354a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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vccnndavR79aU5uQ6uh63U7//lYdtW1bp3dcsUEtDYzLUjnlOl8sh99hg63bJ604G23g8IcdIsPV73Wpt8o62b2yZ9e99aXNBbSHcAgCqhWALoObF4nGFwjGFI3GFIjGFIjENBSMaGAorFIkpFosrGkuG22gsGWzDkeT/zxeMxoYul9OhWdMbdfa8qZrS5BspLZ3Y02ZVNJYM2OFoss23fvmhop6nkiqxju1Y2V4fh6RbOtprJtjmCucpD2zuVN+Yr6dIWpP2OLfLof9+cEdZys6ttDHdy6926o2jp76+YIF0+SXnS5Keev51dRZYCU5ZMgCgWgi2AGpGJBLTcCSmcCSmUDim4XBMA4HwaIANjXw/FI4pbuHQZTUUpQcCv9elpgaP/F63mhs9amn0yj8yftLjdsrpTAbd0bjrkBJxKRxNhu9gKKqhYFhDw1GFIzFFonG9tvtNPflyaUvVVMq7r1qgB7ceUCRm3+lhxZKGCWNtayW0WuFySmvG9CSny/e7zGmQrrmqXYOBgDZtOVCOJkoqLNzmavOHblisHz1U+OzRkvR/P32dmvweTWn2jX5WAAAoN4ItgIp55fXj+sJ/PDP69RduuVSLzpmmQCiqvoFh9QyENByKjobYcAljDwsJTXMbpatX5Q4ETofkdjvlcbvkSrtYdyjZyxkZCbaZQrdJIU4aH5Dsans5nrMU64voEU71JI9VC79LyruunKeWpqa825Wzzb9z0xK1NHk1e3qzTp/eqLYWn3xe5qoEAJQXZxoAFZFpbOmXf/jC6P9fvWy6TpsxY9zPA8PD+uUzBxSOxuV1O3Xjivlq9Pttb9uhQP5t4gkpHImXFLZNsn1Hp5YuSQa49R3tenPvXj23M2TLc/f09eXfqEIKDbcJST/c3Dkabmsp1ErSL54+qBsvP11tU6Zk3abcbY7GEurpD6mnP6SDxwd03lnTNW9mszw5JqgCAKBUBFsAZWdlwqRHXjop6WTWn4cicW18cp9cTumD1xc+lnCyWN/Rrv7BQT2w9VBJ4zhf2S8tXXLq67MWLlR///jxmMX65XM2PEmJxq6tO7Yn2UqpcGJku4GB2iwtT/59j2YsS+4fHKxoW/qHwnpl1wmd6Alqwektmj7FX/SSSAAA5FKZtS0ATDqJREJDwYjtswDH4tKPH66tXrJ8Kt2r19rcrFs62mX36MaLLyzthsJ9NdS7mT7eN8Xq+NdNWw7o8Vd6bWyR/TK97+7feqis+8wUpgPDUe0+2Kvnth3VbzpP6OjJIUWik6PyAQBQOQRbALYKR2I61h3Q9re6dcsXHijLPmLx5EV7n009ZnMzzwdki0r3kKXCzGAgUDMz76YMSzpy7Jh9jSlSoTM0m6yWyr6HgpHRgPvyzuM61j2kKAEXAGATJo8CYNm2HV36zLefHv36zo9fqfOWzFAikdBgMKLuvmEdOD6gk33D+s7G1yvWrlKDSilhLZ9qhSWHVFKwvWiBRsfYjvXDzZ1lCcyVkJqZOdPY7ePHj+uZHcFqN7EsKjVp16rzmjV/7lzL2zc3ejRnRpPmz2rV9Fa/3G7utQMAiscYWwCWZCopToXcf7htlQ6fGFR3f0jRWOV7YO7Z3JkxnFqZGKicobaaSg2f6WNspfL1AlfC5Yt9WrhggX78cKfGvkVTY7dRukJCrSQNBiLaub9XR7qGNGdms+af1qJpU/xyuwi4AIDC0WMLIC8r42TTA2KleyrXrJijKS0tGX+WqS1Wlvixg+nlrbW2RA8KN/Y13LS5U+Uojrdyg+h4V9fIJHGnrF11am3glkav5s5s0rxZLZrWWt8BNx5PyOGQHA7HuO+Fo8l1rx2SnE6HnE6n3E6HXC7HuG0BABPRYwsgq3A0rm9vfN7Stse7uiYs11NJDzxzOOvFdb32ylYSobY+rC1i7d5crJYfZ9vnpi0HRtcGHgiEtWNfWIe7hjR3ZrPmntasJr9HPq+r5kNuOBLTQCCswWBEiURy8rxEIiGHw6FGv1sNvuTlVs9ASANDYfUPhZVIJOR0OuV0Sk6HQ5FITKFoXE+/8rqe2xYefe4rL2zQ1ZecL6/HJa/bqf6+kL7+k98oEonL53HqK79/peac3iy320UIBjCp0WMLIKP/2PS6fvr4noIeU+3evUoF2Ne3d+q1g6e+vmCedP7S+pyQaH1Hu360uVNM8WOmd1w2S9OmTp3w/VLfl4V81qzsKxVux2pp9KjB55bP69acGU06c84UOZ3VDWyJREKRaFyxeEJOp+RyOnWiJ6jdB3vVNxjSYCCs+JirKoekvuCg7n/q1GzU2V6TTD3aY81vlg5k6W53SPrzDy+T0+nQP//3ixq7NPe8Julrn7lJjX5P1f9+AFBOBFsAE3z356/rZ08UFmql6pcjVyLY5vqdMu3fyrqoQLmUcwboYsuPsxlbljyW2+XUWXNbdf6iGfJ77Sk0i8cTGgiEFRiOKhiKKjAckcOR7DlN9aJGY3ENBaNKJBJyOR1yOh0KR+IKhqKKxxMjpcIODQTC6h0IZdyP1eNFJY6Vf3Xr5Zo5tUHNjV65Xc7k7+t0yOlI9vC6nA55PU5Lvb3xeFyDwYiisYTi8WTvdDwhuV0OeT0uNaWF6MBwRAOBiIbDUUUisdEya6fToSa/e7Rnnp5mAKUg2AKQlLyIGwyEdbJ3WJ+4+4miniPThW4lw225g62V3yVTG0yeSRhmK/fSRvk+c6XsJzUz9+IFbTp/0XTLoTYWjyseSyiWkBKJuBIJKRZPKBKJKxSJKRSJqrtvWCd6gxoMRBSOxGz5fA4GAnpg6wHF4tZnJbcywZ2dfusdS9TY4JZrJLy7HA45RsKty+lQc6NXM6b41drs05Rmn1xpPbyRSEzHegI6enJIJ/uGFYnGFY8nFE8klBgJtn6vW9Om+OVxOzWt1S+Hw6FdB3rUOxBSMBTV2KvOZLD1qKXRo/mnt2jBrFZ5mB0bQJEYYwtMYqFwTIFQRP1DYR09OaTu/mE9+aK9M8RW6sLtPdcsLOvzv77d2u/w+vbOCWXJt1T44hWQpMvO8ZZ9H290durc9vLcUHplv/TK/k796Qcv1rY3T8rvdanB55bHnRxz63E79daeHv3tD07NA/DRtUt0WluzorFUL2JCW1/dNm7M6vnzpAuyDB0oVvrNK6tBeefu3ba2I5/v/2rH6M2IjZs7x5Usj7XiQr/Wrlqu6VP8yd5qhyM5ztmRHCe860CvsnWLDAQiOtGbXDrL63GqpdGrBp87eQMh7TGpnvOBQFjD4ZimNPk0Y2qDTb8tgMmGHltgEkkF2aFgRN39w+ruH9aLnXv0+At9tjx/ap3QTMod7Erprd2+o1Ov7D/1daY1XAtpf7l7yQArcn0mnn+xU7utVQgXvZ8jx47p8Vd6bd+Hy+mQ2+3U936xPe/2hQ4dKIZpFRmF3mx811VzNattqjxul+bNbJbL7ZASks/rVjwe188ee0FbXju1BvQFZ0pXLb9QPo9LDT6P5p3WrGmtfu051Ktj3QENh2Kjy8K5XA65nU41+t2ae1qzFs5ulcftGrf/cCSmwHBEw+FTwdjhSJao+7wu+b2uCY8BMDkRbIE6FY/HFQzFNBxOjiFLBdmhYET3bnpNJyLl2W+1ypGLvUgtxxi49R3t48oSXU5pzcrk+EHCLSqpWcmZkAsZ61qoStzISd1sevTxTh0L59/eqlLD7WQcQ7962XRdsHiBZk1r1Gf/5ems2/3nF9+hqc2+0bG2iURCQ8NRhcMxRWLJJY1cruQ45Qafe0KpeTye0KETg9p7pF+DgfD4MuaRYNvgdanR79GC01vU1uqX2+WQz+NmkixgkiLYAmXw9LMHdef/vDj69R+sXaoVl8yT2+WUy+mQa6SMzuUsfVmGeDyuUDiucDSWHD8WjmkoGFZ3f0jH+wK69+GdCgxH5fe6tfqyebrv8bdK/fVySr9QLOcFdb5957L/4EE9vX3I8nMWcpGebXxdauZXwi3qickVCufPlS44r/hw+6OHOxVnyvCsPvu7b5PL5ZDP41JTg0d+r1tej1Med7Kn1e91yenMPKY2kUho25sndbwnoMFgstIo2xWrz+NSS5NXHpdTzY0endbWqGlT/GppLH85PoDaQbAFbBSNxfXeT23K+vOb33GW5k5pldvtlNvllNftlM/r1v4jvfr2z3OU1d2wSGtWnSOHpFgsoVAk2RM7EAhrcCis4ZFAmwq2sXhC9z2+S8Phyl9xVXtm5CuXNmnBvHk5tymkTameop880qlIrNTWAfWp2hPHlaKUXltTfsdqaZN0Y1oZudfjksftlN/rVmuzV/NPa5F/ZK3i1E3f1ARSiURiZNbqqPqHQtp3ZED9gbAi0ZjCkeznN7fLodkzmnXO/KlKJKRINC4pua5wcuZrx2hJu8/jkt/nkitLwAZgDoItUKJQOKbBYFh9g2H92dcft/SYK5c2WeotTPe/b75QsXhCoXAs50ye1Qq1mcbYVuvCz85epPUd7RoOh8ve2w2YyifpfVW+qVUsgm35WbnhedsHLkre8PW4NHNqg6a1+jWl2Sef99T42cGRSabCkZj2HxvQsZMBDQ1nHlfjcEiNPo+isbgi0eT58lSwTa5BnArYDT6XZrY1aGqzX1NbfPJ6GLMLmIhgCxQokUgoMBzVYDCsnv6QjvcE1D8U1radb2rLtsLDaqHyXYRVM4DVWq9Nenuslh9ne55q3TAATJD+eTtw6JC2bBusUmusSw0RKMaDW3aqN8BllBWFDOtYOEW6/trzdfr0Rp09b+roskFjBUNRDQyF1TsY0ht7uzUUnBhw04fCrF42XafNmJF1v00NHp0xq0XzZiV7kRPSyDq9yaB8aq1jhxwOKZGQ4vHk970e58j6wIzvBaqFYAtYEIvHNRSMaGAouYzByb6gBgJhBYajo9tUKsAtO9Op9nPOyfrzX2x5UwMB+2eGam30qL+A533b2R6dfdZZFR1jmy69LLnY12jsBXu2cOv3Ogm9mNTWd7TrxMmTevjFrmo3pWBrVyUndytUOBrVvY/uKegx11w4RU+8as9M9CZZvWx6weeCay6cohUXn6Oz57cllxxyJ5cd8ntc8nhcOtYdUFdvUDv2dSsUHj9WpNgZsR0OqXlkPHA8kRhdp/f7972mgTFXzG0e6XdvvkTxeLLE2etx6mv//eKE5/v+394oh5LzaaTa73ZR9gyUA8EWyCISiWkgGFH/UEjHugPqHQhpIBDOOq6nkj2TuU7K5WpHap979+/XMzuCebYe/7ha6bW1I9hKyV7xR359UMFwVA0jk3L5vV79+OFOxci2gHFcTumD1yc/5z19ffrlc0dHf3bj5aerbcqUrI/91bN71d0fKmg/lDAX5mPrzpcSybGzHrdTfp9bU5p9Ot4dGF0zdywrf99CStCthORc29z2gYvkdJ4KtT6PS82NHrU0euX3ukaWLXLL46bHFygFwRZIEwrH1NUX1P6j/eruH9ZAIKJ4PPvH5Ojx43rs5Z4KtjD3CbkcaypmK9+yenFWzXBbarCd6Zauv87aBdBkXPoDqBf5jlO5jrtWwu3Y8CwxPrdQVoOo1SqhfGXJKXa9Tpna73I65PO6Rpc7am7waGqLTw1+t5r8HjU3eib1pFaJRELRWEKRaGxkrHRcsXhCbpdTHpdT7pGJxtwupyLRuIbD0eREmuGYEiOThbmcDvm9bvl9rglLSqH+EGyBEalAu+9ov46dDCgYiuZ9TC2EtXQPPfumuvrtLUXOtL8dO3fqN3utHT6qWZZczHI9mR5vVTluLAAoL5dTlqotch0PwtGonnn1qIaCEbkcMQ2GEopE4/K6nbpxxXw1+v0THkO4LYyV43Ehf9N84fYnmztl59nU6vnE63GqpdGrqS0+zZrWqNYmn1oaPPLUyaRWiURCw6GYhiPJIJpKIgklS7/DkbiCoYiGglENh6OKROMj/2Kngq079c8ln8elUCSmYCiq4VBUw+GYlBZsmxo8mj2jSVOafWr0udXgd1MSXocItpj0YvGEunoDevNQn45aDLRSbU2KNFYxY76K2Vehv3/68/QPDur+rYeKbpcVqTG2xV6cFDtbKuEWMMvVF7Tqydf6826Xryy5GIRb6xbPlJZfkvu4XOzM93Y9VzH7ycXpdKil0aspTV6dNq1RDT73uGDX6HfL467dwBsd6UmNxOKKxRKKxuLq7h8e7UAYDkeVSCRDrZScrCsaK88Z1OGQGnxuNfo9avS5NW2KX1Oakj3kyV5zF6XghqNPHpNaMBTV/qP92n2wV32DYcuPO3r8eBlblVv7nNwnMK/brWmtPstjvrLJdSd7+47ST/atzc0lP0c+qYmjCg21hZQfZ3JLR7sGAwE9sPWAYvFkb9CalfO1desBnWQMLlBzrIRaSfrlc0e1vsPeYJsedna/+aae323/BID1YOcJaXkZnveezZ0lLftUTvF4Qn2DIfUNhrT/2EBy/d2RMlyfx6WmBo+mT/Fr+pQGtTZ55fO45HRWLpwlh2olY2lyKcJkkA2GouofCqt/KKzhUFThaEzRWEKxWHykR7U0Xd3deuiFE6Nf33DpTM2YNi3nYxIJKTAcHZ34c/+xgeTNAZ9bjQ3JMc9Tm5NB1+91yetJ9gYz9tkc9NhiUkokEjrZN6zdB3u1/+iAogXO+FPNO+wfumGxnBYOsFYnNLnu0vkKhqJq8Lk1s63B0nPX8h3xTPsrZB9Tmrxac+WZ5WhSwW0BUHsqHYDq5Zhh11wLK89t1Bnz52f9ebFDXjLdzK2FHlurmkeCWYPfLa/bpdSpPPnf5PJEDknNjR41+j1qavCoucFTUGBLJBIaCkbUNxhWMBTRQCCiwHBktJQ4HI0lS4zDUYUip0qM7VbsWHirPG5nckIvj0s+r1vNjcmbB80NXjX5PfL76NmtVfTYYtIJR2I6eHxAuw706mTfcLWbU5AFbbIUPCXpHVcszFuWXKkLtLed7cnZhnwXDyuWNGjhggWjX5/s6dHm5zP3mqcv8VOopoZTbc3U61rMkiBjVXuWaABm6XjbaVmPd6aw81yz9Y2Azsiea0fCaeHB9pGXTmp9x/hg61HhFT/VMhiMaDDDWr7pHJL8Prdamrw64/QWzZh66oa20+nI2EMZjcXVNxjS8Z6gDp8YVHf/sCLR6pQf5Tt/2tH7nhrTOzjm1Xc6HGpqSI7VbWvxafqUBjU3etXc4JG3TsY+1wN6bDGp9AwMa8/BPu070q9QpPhSmGoHk0IP2i/+plM7T1XsWBqnlEup42szSV9GKD3MFqvQmYpvvm6RvG531nGyDiVLjUu1+eHOcWXJ051Sx/Xjn/fAsQFteeVwyfsCYJ9y3RAMhkLa/OwBhSIx+TwudVwxXw0+n6Tqn3NKUeoEfrmeMxe7KovKOStytblGxu+mMqxrZCmihpHg2+T3yOmUjnQN6XhPUP1DobL1wlqRXn6cjZWyZEnqGxjQA8+cOseuWTFHU1paLLXF5XSMLtl0+vRGTW32q7nRowafm97cKiLYYlIYDER0rHtIbx3u17Hu5AG60PLblFqZGKiaJ8lCTvTVPpkX0tZprT6944qFeV9ju8KtFQeODej57UcVyrJ+MoDKKeTCtxA/eXSnItFaOLOYwe6lf/I9b6nr2Fb7PFgsp8Mhp9NR8HCtcrHz2sPu16vJ71FLk0czpjRoZlvj6FhdVBZ/cdS1oWBEx7oD2nekXyd6A3rrcH/GHrBVF83R/Fn5L1YGA4GaCLVSsofTjh5NnPKOKxZaeo0TSr4XSi1LtmL+rBbNPa1ZJ8bcjGn0JfSLpw/mfezaVcnSaSsXA5RIm23tqmRtJusolxeh1izDw9aHG61eNj3rz9Z3tE+YXd8j6f1jAlC2Y6ipoVaS4omE4mWaobiaylHOPDQc0dBwREdPBtTg61Nbi09zZjaPjs31eSlXrgSCLepSYPhUoD3eE1Q0Fs9Z1rnllcOWwu0DW2vnovGZHUE9s6NT1y+foZnTs5+Qy2HxTI0rbc61nWmsBoNNWw5U7ILF6XBo1rTxIdoh5e1VTgXvfKE19XtcdX6Lnnp9oMTWoho2bTmgtavmc4OijMrxeQ+GQjUbalO/74FDh7Rl2+Do91ed16z5c+dKqv0S6ae3D1neNtd6ttL4EJuNySF2sugbsHaO6xsYKPpGVjCUnBX6yMkhtTR61dLo1bQpfk1r9avJnxynW8tLNJmMYIu6EhiO6ERPUG8d6dOJnuDo5AbxRCLvWMUtrxzOO+NwjVTjjPPwi12Suip6Ql1+Sbt2WrigKWUcL3K7paO9oHHAVnoT5s2ZI71e2xeqyG7TlgNiZFdp1ne0lzTurlA/e2JvWZ7XTvPnztX6uZl/tr6jXdt3dOqV/ZVrTy2Fx/RS51zL5KF0N1w60/IY22zGfrZzeeCZwyW/1xIJjS55dOjEoDxu5+js1W2tPk1p9qnRlxyX6/NWdpmmekWwRV0IhqI63hPQ3sP9Ot4TmDBb348e2mnpeX700M6cBzKXszbDrVT5dfis9gJW25JZ0o5j1rYzTbb1crOVSFt5TejxM1tt9v2ZYcWSBknJcuNyH7/iiYS276qdCiCrxh5vxkoFunIfO2rlvCJl7q1OhtyTNdXOepKcECp/sLUycVQ1RKJx9QyE1DOQXJPY7XKq0e9Wo9+dDLstfvl9yfVzve7k7NRej5Pe3QIQbGG8490Bde7r1rGegMJlnmCnVkNtyomTJytalry+o932GZftdslF7dph4WLrkotqp82FaG5s1Aevt7ft6zvadfDw4ZosS146W9p+pNqtQD2q1JwFpsx2nn6zNNekeqlAV07FhMUrlzZZKke+cmlTQc9biSVnkJkpN9WtiMbioz26R08GJEluVzLMet2u5H89LrU2JUNvo9+tBp9bDX63XE5nlVtfm5gVGUY72RfUK7tOjB4QsrFjJj1TerHWd7Rry9ZOHTg1JErzm6VVK8052JeD1ROh1Vk0J1PJWa2891NrFNdKe1CaixZIS5dYqxAotZJg7ar5OcfPV+pi2JRQm5Jahqxanzk7Xher7y+rrJ4jlsz16pLzzrT8vCbrHxzUA1sPKaHkUJg1K+eqtbm5rPtMX/rH6hI/P39yt4aG8y/32OR36d1Xn11SG+3kdDjU4Her0ZccoztrWqPaWv1qbfTK7bYeck/2DusT33hUQ8GomhrcuvuT12n6VH8ZW15ZBFsYq28wpFd3ndCB44N5ty3mpNzxttM0va1twvqqJst28n7+xU7tHnOePnu69Lbl9ReEf/NK57iy5CWzMvfU2n0hVA9qIUyO/ZvXQntQvFle6bprrb2eqdf9jc5Ovbyv+H2u72gv23rZVsQTCd332C6Fa3SyqFpj5zHWzh6+Qo49+ebtqAem9Z4Oh8O67/G38m733mvPlN/rrUCLiuN0SC1NPk1r9WnOjGZNafaqtckrZ46e3Js/syljZaPX49S9d64tZ3MrhmALI4UjMb26u0u79vdYGlPGRfAp6Sca005KlcLfZaKnnu7UQeuTjNoq09/8ldc6KUsuE4/boXg8UbbhF5leTytLpZRyLK/25/ZYd0CPvmDeuNpKO3+udMF59r9W+w8eHFeWnKoAKVQh78H5bdKqt9Xv+cLUm8D3Pb5Lw+HsBze/16n3XntOBVtUGpfToSnNPk2f4tf0KX65XS65XA65nA65XU65nA597CsP5RyuVy/hljG2ME4ikdChE4Pad7S/7kNtS4NLA8H8JTOFGDv2h3FC2a3vaGfGyzTVCLW5Lj4vuqBdF12Q/P8mf85rzXuuWagGn6/if9NyH2uqvfZ3MBSt2r5NUc73wIJ587Sg8BxbkgM9ld1fJfUP5q+WS21X7rLkQr332nOyhlvTQq0kxeIJdfcPq7t/WLsOSA6H5HI65XY55HI6NRAJ5Z2DJhyJ62TvsPFlyQRbGKdvMKQ9B3sVCucPfKZe7Dolvfe6RfK6kx9Ru8dl3bO5U2dbnGPq+Rc767Is2YrTZszQ+o7JG2SrbbLeVKkmn6QGn09SeWeBf317p85fWtjre+DQoZL2+cyOoBYukJ5+plP7x8yLtqBFunLF+LYUO34vlwYfl1zZjF0bt9atXjbd0hjbevfAVmufxwe2Hpqw/FwtyNZjm6sn1xSJRHJiqmhMkmL60eZdlh730S/9Shs3rCtr28qNKbVglEgkprcO9+tET/4xr6aGWkn6UEf7aKiVpPmzWrTqojm27mO3xfOy1e0AmO99Yy5A16ycX7b9vHaw8Mds2WathyiXezaPD7WStH9g/Pnins2dE9bKfOiFEyWfU2a2Ncjrru/xloVY39E++s+UUCtpUlftjGV1HGMtjne0Uq02GZkf6Qm2MEwgFNXR7qG8B0qTD0rZLnvmz2rRh25YrGWLW8d9//rlM+jZQk1pPy150XrZOeMn3kj/2k7ve/tZZXvuWrV21Xy5LJzFnRazVPpxpLmxMevxqB7ds7nT0gXvPZs7dbyrq+Dndzocuuy82cU2DzXE6rrnJq6PbpXVY0OtHUMOHrZW/WZ1O9QW6mJgnjyp1uRQKyV/vcFAQM2NjRN+5nQ41L5wttoXTrw4mt+scUv8AHYq5HO17OJkQFp05plalLbaxa93lefz6fN41Nzg1mBwcoxjXLtq/rg1jH/0UKfiGY6NTof0oRvyj6nPdnPslhKX2alXqXVbC72pmKq+KWZoSaZ95VpbNsUhTSgFrfZravrN2HpfH92KNSvn6n4L5chrVtZWb7zV9dmfen1A6+0tlKt5tXYTohjMigyj9A2G9PSrh9U7EMr482qfrMvF6rTz5fj963XpH1j3wkud2mWxgyrfBev2HZ16ZX/+50mtcVqoTU/tqftwmymoSNJQMKgHtx5QJJaQx+XQTSvnq6mhYdw2O3fv1otvnpqfYPlZLi0+O/9ajeU4tqS/V7LNcu10KGNorxXFhLR4IqHtuw7otb3WlpLLtY/BQEAPbD0wOh469fdyOZPl5JlukqZU45w5r0m66sr6OKeYOnt+PJHQY1t26viYt9+ZU6UrLrN/6aNa+zsU8p6vtbYXq7e/Xw8+m38JgW9+8u06Y15r3u1qGT22MEpi9H8m6unrq2RTKuq+x9+q2kx9hFpYDbVWLF3Srlf257+wKCbUStLaqxYpFInoiRcPKxCKKBiyd1bxWpBtIpamhga9f/XinI9dfPbZWpw/x06w3uae2xmu5H/TZx7PpJZDrZT8HQodd+l0OHT+4gU6P+3l+uXmTo2dSLdN0o15Lq7H9tyboFpLhpXD+o52y+ujV1u+z+9bvdJbBa6EkO+4UC/B0HRTW1sl5Q+2podaiR5bGKZ3INlj2zc4sce2lntr37/6bG168k2F8ky3nk++cFvI38Ar6WZOSrCgHHe4K/W++8kjOxWJ5T/NeVwOS9tVW7U/k/nu/FsNwHYH5Wqr9utSrOFwWPc9/lbF92vq38tUhX7WCn19+gcH9cDWQ0ooWVGyZuXcmlviJ+Xg4cOWypGvOr9F8+bUVy1yrvfBJsNnQ06hxxYGmnjxORS0Vs5VDXNnNsnjcunGFfO18cl9JT3XcDiu4XA4a1lyIReLN4+cuNZ3tOv5FzvHzX5M+THKbX1H+4Sy5GLLj3OxGlYjsYR8HqfC0bi8bqdm+eMTZs8th3NmWO8Rr4UwMLW1Ves7ct/Vt9KLU0+h1mRWhrjAbMV81p79dWdBZcmtzc01uaRPJvPmzJFez/83qbdQKyWPvek3J7/+x1frnLPaqtgqexFsYZRoLK5MNQY/f8rCoL0qmDuzSVdfklwRvtHvt2VdyEd+fVDvXJV9BlgrF43pF8hvW96ut5XWLNQxq+HrnAJXwVi6pF1LlxTXJqsK6YlNVVSEInHtjxS3jmu28Hng0KFxy9WMXbfz0rRtX329U9vGzC103hzpwvNr/6Ix33Fnhku6YXV7UTMKZ+JySh+8npBcqiWzNK6UttzmNVVuX5NdsZ+Nt3qlK+xtSk2ZzCXUY29Ozp7RpPnzWqrcInsRbGGMaCyuQycGNRAIV3S/5y9q07Y9PQWvxfb+1WfL43KN+94Hr2/Xjx/uLCncBsP5J8bJdtBOlR9PBmMnVLEyiQqyu3RZu3ZZuEC6dFntvbduWjm/6Btfpd6EGmv+3Llab3Fy0AvPb9eF59u3b7tl6mnPNSFY+kVivjG1Vtn5+pRi9bLp1W6CZT/d3KmxA3l8Sq5dbGWGX7vUy8RRMNv6jvYJZcn1WH482RBsYYzj3QHtPdKfsce2nC5YdJouWHRawXc+00Ntygevb1dgeFgPPL2/qDF9DV5rH9ti7jhamVXVBOlLYMTi0qYtB7LOJov8TL3D3dTQULFZdWv1b2CnTO+BfLNc31PghDRWWVnDN928JvsnL3rkpZNa31FguUIVZHrtQjr1+lSi53syfEZgjnlz5ky6JX3qHZNHoebF4wkd6w5o21sndbw7kHEbK2v5FWP1sunjZrss1zTxhTyv1aV/CmVlHUwT5HsvEG5Lk770zzkzarOnNl2297cVPo8z78Rvk+GCvdTgk/ob2RWgUmv5ppd5Z5Mq/y53gHNK+lCG98Pho0f1xKunZu+/5sIpmnP66WVtS0q1y7XraYkfk5Tyuk+GY9pkN3tGk1ZcMFt+ix0mJqif3wR1J5FIaDAQ0ZGTQ9p9oFe9GWZCTnnnlfP0i6cP2t6G9CUcVixp0DM78k9UtWJJ+Xo5KxlqpWRP148e6jQi3A4GAnlvcCRGtrOzLPnhRzt1YkyF+Ey3dP117dp34IC2vnHqZszKcxt1xvz5tu23Gi5d1j5hTKgJPnRD+4SKhGgsYemGWDga1/qO9qzr+U6GC8DtO+wLRquXTbelHHnTlgNa39GeHKu8LX/7UmOa7ZjrIJe4JvZSZwoYT7zaJ73aV/b3z09rYAwyodYsMzIXnKHe1GHXZhGFPED5DQTC2n2wV89tP6qXOo/nDLWS1NJk/2wUmS42Fi5YYOmxVrdLsRqEyxGYh4LBvD1Z8URtzzyd8sDWA7ZuZ8U9m8eHWkk6EU1+f2yolaStbwSq3nMymaXWeV3f0a73r14sr8faKdDrTm536bJ2re+Y+G8yyFduXIhC13zN5dCR5Oye+V6HsT+v1Njc1Gc932e+3MeE3GfP8jNpDHK9Kfb4dMPqyXFcgyo+vK/c6LFFTRkMhHX0ZED7jvarqzeoWAG1g8WOATze1TWu9yC9/Niu/eSycMECPWOhR6TQwGzFgxZD3oNbD+j9qxfbvn87Wb1gtevCttgL0nKNOUR+jz3RqaMFXunfuMLsXvZaZNeYzidf69f62bNHnzPX7NMp5e6xHcvq73j46NGKlSVXmp03MlC4Qj9rnJtgMoItakIoHNOhE4Pae6RPJ3qDihUxqZKUPCAPDA3pga0HFU8kx4euWTkvZ4/uaTNmFDzxx/qOdu3dv39cWfKKJQ0lBc9qTc5TyDqftc7qBWsxk86ke/jR0i7K9x04YHxZsmmKCVIuZ3KpLthvfUf7hBuLpbIy+/SalfO1aYt9VRt2eOLVPq2vw2BLSKoNVsJtajkuTB7WBuOYhWCLqkokEuodCGnPoV7tPdKvcJ4JWqxoaWqqyHjQhQsWaKHNHajlCMz5WF3n0+NylK0NdrF6wbpmZemBMr38uFBb3wjoDHJtxRQbaj94fenHEpaeyh5wxt5YLHUpNKsm29++EHbOjjy/2ZangU24yYDM6ivcEmxRFfF4Qn1DIZ3oCWrfkX6d6K398ZuVUo7AnIvVdT5vsiEMlltzY6Mcyn2YdogL28nmsSesX6g7HMkxtTeumG9LTy1LTyXXuc2lb2BADzxzuDKNkfRGZ2HBrVJL4dSK9R3tRZXspzswyLALAJVFsEVFhSMx9QyEdKRrSEdPDql3YLgi60siOyvrfDodMmY921s62rMu+WNXmJhMF7m1LtuM1GMVcoF+i43VHrmWnkqM/NyUcHvRguInkFq6JPvvWOpn6cbLCy/ffXmf9W1ToWyyhdtSQ+1YhFugltV+NV4hCLYoq3g8oWAoqqFgRP1DIR3rDqqrL6ihYKTaTcMYH7qhvW7WsZWS4bVc5Z+T6eK2GIVOxlaKTK9Fakbqal9IV2vpqXJZuqRdr+wv/L2f63Ww47PUNmVKyc9hRVd3d1GPu/qCVj35Wr+l7cohFLF+rn3HZbPK0gZJ2rK1U6tWmnUeAWAegi1sl0gkFBiOqrs/qOM9QfUOhDQQCCs4HK2zSv76kmmdz5tWzjempzZdc2OjLeMjxzrelWER0yK12fZMtSNTUEmG3GTQXTxTWn6JPa+JlSVUqhluC1l6yu73abkU0mt50YLcPbV9AwO2tKcSig3go+2zEGznjszsbLcnXrRe4v2rXx/T+o6pZWnHgcH82wCorESC5X6AjKKxuIKhqIZDMXX3B3XoxKBO9g0rEq3QmgqwRWqdT4wXDIX08yf2ys538411Vppn5eJ/5wlppw2B0+qM1A8/2jmhLLlSKr30VKWs72jX9h2d48qS84XYTAoZU3vj5afrl88dHfd1KT21F59hrRz54jNK61VO3Vyp1oz3khQIFVYd1dXdrRnTpumyc7z69a5wmVoFAOVBsEXB4vG4gqGYhsNRBYajGhgKq7t/WMFQVIFQVIHhSN3dAao3mS6yql26Wat+8uhORaL2vqHr7W9daE92qb2pVmekLnXm6lJUcumpSlu6pF1Ll1Ruf21Tpmh9h30lx+e2t+vlffkD68yZM6V9J2zZ5/qOdh06cmRcWfLVF7SWrac2pdHnUTAUs7z9Qy+c0PqOaVp05pn69S6GXQAwC8F2EorHE4rG4opE44rG4oonEqNBNDHyf+LxhGLxRNp/4wpFYuodCGkoGFUwFFEgFC16zVlUR7aeg2qXbtaiUkLt+o52/XJzp3rGfK9N9ddTK6modUhf/E2nbWXJtaiSS0+hcFZ6Ue0eTz939mytL0OQPXLsmB5/pXf062svmqrZs5LjZS9dMkW/+vVwUc9r5W+0ZWunpTJjlv4BalV9XcMTbAsQicYUiSYDniQ5HQ45nZLL6ZTT6ZDL6ZDDUdnZxRKJhKKxZJtiI/+Nx6VYPJ78fiyu2EiQDUfiyTA6HFU4GlMkmgy3iXhyieZkpk3+/3haqI3H63EZ58mn1scl1pJgKFR0qF29bLqk+gyxdtl5QlpeoX0tmibtsTD3z6Jp9u2TpadyW7NijqVy5DUr5pStDes72vVGZ+e4suSLz0j26Na6cDSqZ149qsNdQxN+lgy5vbbsZ31Hu/a89da4suTLzvFq0ZlnSpJWrbR2A4CJowBUAsE2j0Qiod7BkE72DetI15DCkZhisbgSklxOh5wj/1xOp5wOyeN2yeN2yuN2yu069d/Udk6HI+1xjpH9JCNlciD3qR7UVG9qekANR2MKh2MKRWKKjvzs1DbjA20qnGJys9r7QLhNemDL3qIfW65ZgGtBpvGVlTbTba3MeObIGe6yS9u1x8L7/7JL7X3fV2LpKVNNaWmxdbtindvernMNexl+9exedffbuB5PmrHniqvOb9GiM8/UojPzb5sN5xMAleJIJBgNmUv/UEgv7TiuoycDipfwp0qF2GQv7/hwK430liZSPacjAXfM/yegolSFlNVxIWLDTKh1yO7SzFL+VoW25YJ50msHy9OWfMq19FQ9qNakSlZ0dXfroReKH2O7dpX9r3O5Q202mV6LF17q1C4Lw+vPmSFduqx+j4uAqWZNa9SKC2ar0e+pdlNsQ49tHi6nU4mESgq10qnSXgD1qZzrtZbCrlBld6hdPLO0xxc6BjIVatPLkhdNs7+nNl05lp6qF+s72tU3MDCuLHnNijll76m1Ysa0aZKKC7bFlpnnmtgvHI1WJdRKmSt5rITa1HaXlqFNAEpTj6mEYJtHU4NH82c161hPgGAKTCKzpvt17KS1SVfmN9dm+XF6GWwsLm3acqDgMtjtO+yfHdWOiaPWd7Tr4Uc7C5r9eE939XsCMd6UlpaafU2KmUSqkM/Xrj179MKe3G/gVKjc+sqRgtpht4OHD2venPKNeQaAUhm40EDlzZjaqClN3mo3A0AFrbporvVta3BilGxjO6XkXdofFnCxPnZMrV3ue3yXLc9z/XXto2uFXjDP2mNe384yJrBufUe7brh0fInBDZfO1PqOdq1dNX90ySaXM1l+bDXU3rO5M2+oHbtt/1B115V96vWBqu4fgM3qsL+OHlsLkjMMW1iQEKhhVnsearXnpNK8bremtfrylv7V4t9rMBDIe75KjGyXrVwyFInoiRcPKxCK2N4+SRoOx3Xf47v03mvPse05c42hTd/u/KW27RaTwIxp07S+Y+K02cWWmRdT2j80XMWFmTM4Z4a1cuRzaq+YBcCIeptpiR5bC4KhqIbD1hc4B2pVvhBWiyGtmt5xxUJNa/Vl/JnHWbt/rwe25l8/Ndd2m57ao58+9qZO9g8rGCrfsW84HNdwuLq9UECl7dqzp9pNsIXVCaGYOAqoVXWWakWwtSy1LA9gumxhrFZDWrW944qFuvm6RZozo0lTmryaM6NJN1+3SO+v4cmArBaYZNpu01N7NBgsrmfoogXJ91EhSwA98muL3axAnbBaflxrrjp/4oRe3CwFUEsoRc6jbzCkPQf76LFFXeFiozBet1vXLLM4gLMGuJzWwq0r7dZmKBIpOtRK0tIl7aP/3X54pyLR/HeDg2H7LvLzLekzdjugXl170VTNnjVLkjQwNKRfPG3PzaNsE0et72ifsPQPS/wAqAaCbR6RaFwDgbCe3NKpQ4FT35/bKF29ioM2gNqzZuV8bdqSvxw5Fj811m9OgxTy+IveZ/rNkgavW5Fo/vG5DV77TkPnL23Xawfzj108fynHbtSXBp9Ly5fM0vxZ43tVW5qaMt7ILHSMb76boZcua2dJH9hu/8GDenr70OjXVy5t0oJ53Jm0S/0VIkuORKLehg3b60RPQB/98kNZf07PF4BalGtW5HLwe53jJoIaDod13+Nv5X3ce689U36vvbPO57po55iNarN7TeiUQt/br23r1OuHTn19/lypra1l3OzHV53fwhI/KJunnu7UwVO5VfOapKuuTL6POY6X38y2Bq28YI6aGjzVboptCLY5RKIxve/Tv8i7HR8woLIGAwE9sPWAYvFkOe2alfOzzu47mVU63KaH1Pse36XhcPaa6PQwbKfXt3eOK0u+YB49taiuscetcuF6BKYo9QYP7/XSzZzaoJUXEmwnhb7BkO763i/1cv4OB8qSgQrKFtYckuX1IyeTSlxMp/hc0vtWj38NsoXbcoZaQJIeeaxTx8dUw5/mkVa/vTrHiEreZOKCv/xq+eZqd2+vfvXrY6Nfv+OyWZo2dWr1GpSBHVULlCWXbubUBq24YI6aGwm2de+1PV363L88bXl7TiRA+eW7OCTc5lauEsixMh0Lh8NhPfLrgwqGo2rwurX6snm2lx/bYfuOTr2y/9TXFy04NSHWZLNz9269+OapSROXn+XS4rPPrmKLClNLZYyVrpzgeqS8Sr256nY5tXhBm7we+xYmOXp0UP/n3pcVz/FG+4fbVtm2v1L8y39v0b5ee56rVn4nUzmdDi2Y1aJGf/0EWyaPyuA7G1/QxicP5d8QQMUMBgJ5Lw4TI9vVyp1zJPm9Xr1z1VnVbkZOmYLQK/ulV/Z3TrqgkOlv8eKbMb34phl/i3w3cO7ZXLnfw8pxC+bIdZMiMfLzfOHW63FqzswmndZmz3lq7e0bLW336W9u0aYN68Z971hXQLfd9YhCkbh8Hqe+ecdqzZpRvvOn1bZatfTM6bY+H8w3qdexjcXjCgxH1DsQ0rHugPYfHdDa2zcSaoEa9MDW/LP8FrId7Oc09IxiJQhNBrv27DH+b/HIY9baZ3W7UlmZndxub3TW9mtkqkJurlZKoUFxz97e0f+/7i826mN//5CGI3ElJA1H4vrY3z+kdX9hb/hMsTvUApnUfY9tIpFQNBpXKBJTOJL8bzAU1WAgrIFAWMPhmEKRmELhmP5907ai97N3/34tXLDAxpYD9SMYCmnzswcUisTk87jUccV8Nfh8BT2H1TGi5RpL+tPNnQpl+L4JPViV8q4rzTsGbt9hLQRs39FZ12XJhQTWnbt312xZ8vH8K0wVtF0lnXu69MbR0p/n5X3SufX7Vq2aQm6ufvD68r8Ah44OFvyYT/zTE9q0YZ3W/cVGxbOcK+PxZOjd+LV1mTcowmvbT9j2XEAudRlsE4mE+gZD6g9EdLI3qP6h8Gh4DUWiiozcnRrrpZdLu8P5zI6geno6dclFnE2AsX7y6E5Foqc+cYFQTD97Yq88bofef91iy8/jcloLra4y9Brmuui/Z3OnZrikG1ZP7s++0yE1NTRUuxkFGzumNt92S5eUty3VUmgv7ItvxrS4NnOt0S6+sF0XXzj+e6nx6f2BGkzik0y1b66m+8OvPVLU4451BbKG2pR4PLmdXWXJn/vuVlueJ93a2zdOKK8eq9Kl1qg+QwvHsguFY9qxt1u/3nZUz7x2WG/s7dahE4Pq6g1qIBBWOEOolaTO46Xve8ex2i/TAiopPdSOFYkmCvq8rFk539btrLLSxq6YGZ/9cvUuOx3Sh26Y3MHeVA8/WvvvW1MVckxoa848eUtqfDqVIdVn9aZpOW6u2umPvvqwpe1uu6u44Fxp2UqcK11qbZIntu7X2ts3jv57YqvFO7wGqLse23giocFgROFoXE6HQzEl5JDkcjnkcjoVTyQUjycUyzV1XIkqOTEFiuf3usr23OFILOfshNk4HJLPU3y7orGEopW6XTyGx+2Uy+kY972BUChrqB3rns2d+nBHe96xS82NjXJIeWdFTk0cdbyrS4+8dHL0Z6uXTddpM2bkbc9YPy0wrJrw2V/f0W5pxtjnX+zU7pNZN5MkeVwO3bRyvpE9tTDjZkyhTvNYKzM+rcyTgPb29xe0/dvflv+G3MVnJMuMrWwH+61ZOd/SmGm7b65K0sZHd+o7978x+vU5hZ3KxonErF2chCKVv5YoVnrPbaVLrU2S6UbAXff+Rnfd+5ucvd+mqLtg2+Bz68KzZ2ooGFEwHFUoHJPL6ZDb5ZTT6VAikQy18XhC4UhMQ8MRDQbtL/H5zSuUJdey6VP8Ou+s6fK47Q+3iURCnft6dOhE4eNfTmtr1LkLp2nz83t07yN7R79/8+qFuumK/Gt+dvcN6zc7bSg/KIDDIV10zkxNaR4/ZvZjf/eQ5ef4gcVAeEtHu6WlFjJdsCdD7smCgmemMbX5PPRIZ82XJWcLt2P/Nm9b3q49eZYpicYSxofaixZYK0e+yLzhwzmVEmqXn1W+m4KlWv323Dduxm5XTg8+e8Tyts0Nbvk8+ZP2ue3tenlf/t/t3PbaPv6YqtCbq3bJFER2ddm6i4x8Ni5H9JVbV5atHDnluz9/Sbe+e1lVSq1NkW8Cr3yl3Saou2ArST6vS74CeuPi8bjWXTVXG5+ybzbkHcekS2x7NtjN6XSorcWvpgb7b9vH4nHtOtBb1GOdTof+4M6J5T/3PrJX9z6yN+8BJxiKFrXfUjgcDjX43Jo17dQJ4qlnCp8J9JebO3WjxXA7GAjoga0HFIsny77WrJw/ejFR7aU+umL5t6kF+f4Gk2V5paVL2vXK/vxhId/EUS+81DnuYvOcGdKly2ovYOzas0cv7CntOFGrE0elWK1KqBVrr1pkeVvTfrd6Y/Xmql2qOZPwN+9YbdtzXbB0pm3Plc3PnjigW9+9zHIJ9W13PaL/uXNtmVtVO6yWGz+xdb+uWWnundy6DLaFcjqd+th7LrU12JrK4ZBam3xyOPJva7IGX/Fv/bf29+lP/8/jo1//459dqzMXTBm3jdfjVFtLYbP+StLXf/BSzp9buZtWzH5L4XROfLN89Se5f49MepS/7S/u2KHntoVHv778PK+WLzk1o8++Lmu3sftDAzqjwLLkQlT6NUiJxRPqHwrn39ACq8uUVGoG0HIqNSxkeuyuLmlXjZSm21lyXAu/jxXrO9r1yGOd48qST/OUv6e2EtZ3tOuNzs5xZckXn0FPbaXku7larCe27tdd9/5m9Ou3nV29wbpOp2zvzdy0YV1FgrrVEmqTSq3tMPa9lW87gm2dqNSHrpa1NHp1yeKZlnsyH372TX3vwZ3jvrfyXK/+9y3XlaN5tvIWMZY10/sjFXJTgdMhh5acMa3g537g6V2Wttv02C6tfXvmsuSZUxvUduGcgvddKo/bnhPwyhxt/+0v/nLC957bFtZz214teD/3P3FY//03F+bd7l/uLfy5pdy/Rzn1DIT0/PajikRLO2EfPmp9zZEqDOkui/Ud7dq+o3NcWfJFC/L31Fa7QiCfyRhqU6oZYm+6YralcuSbrphd1POf297Okj5V1NzYaOsNvUzXFs/vrs7B1elU2cafbtqwTq9tP1HWsmSfx6lhC6HVzlJr1A6CbZpNG9bpP37xsn76mIUZGnJYMsumBlWYQ1KD3z1hvGQm2W4CbH0jrK1f/KXxdfrprI5NcDodWf9+mx7bpW/9YntJ7fjWL7ZnDbblKK2upGx/t3LccLLyHi/GNRc2l+258xkatme+gCde7bO8ba3PAFqIpUvaC1rS54WXrIXGF17qrEpZsp2hts6LeGw3tbVVUv5gm9wOk1mtdKj4K7QczgVLZ45eH77vUz+3PJlVPu+5Jjlp1zfvWK2P/X3+OT7sLLUu1bYdXfrMt58e/frOj1+p85aUr6qsnhFsM/i9d12s33vXxeO+99mvb9TrBVQq1/vEUVYOxPUwCD3lrf3WLvR/5/aN+q8sv3OtnLwq4VPvX1ZwOfKq8zJPQnTvwzvsaFJRiqniuON3SztZ3vOrbfrB5t2jX3+442ytf8d5lh8/a1pjWWd9T/db71yiVr9PJ/uGK7bPWmF1ApddXdKl5W3KBHbPeGz32MHJgPGwtamYyoxyqaVlVr7+p9eWPdT+5tVj+qv/fHb069vWXaBvbnzNlue+9d3LJCVLqJ1O5ZxAqhyl1sXKdI2RCrl2XkPfcfMllsqR77jZ7BmCHIlEonJXQHXgjZ0n9al/25Jzmw+/o12m/lWnNHm18qI5amvxZ93mV0++aflAdO1FLbr9f9V+WXI2+w7260++8VjeSXTSpR+M7A616c8/EIjoS999Rid6gprZ1qC/vHWFWhqr23tb6O+c7QBejhsChd4NtdqGTRvWlXTnNdd+rJzgItGYLaH2A5+93/K2P/n7d+pw15Cefe1IwZ8T0xUSHisdYiZzCXKt6e3vH1eWfNMVsyvaU1uO8aCmqoUbDY1+t1ZeOEentTXW1A1vh0P6+V3l64wo5Xf1epwK5ygvznR+zLXkT/tp0l2frkzHy1PPHBh3o/9T71+mq1Yke5et/E3sDLeV3l81EGxL8L37X9G9j+4d/fo9V8/Tu68+V/2BsHr6QxoKRjQcjiociSkSjSsSiytmU8lFuVgJtnYFllpX6gnnq3+wSucunm5L+XG6sX/T3//KQzpyMjBhm9nTG/Wtz91g634LVUggLPU5ClHIezI9qGZz58evzLndhzvOztkTW0snnBdfPqq//q/n8m73179zuZZffLr2HenX1lcPE2xzMDHYlmOWV1RWJWfwrXVWPhOV+JzWarCVrJ9jrEyiKUl9g2H99hcfLKlNXo9T947MXvzdn7+knz1xamLD91wzf7SnNpNjXYGcZcnlPqfmen3zXTOM3c7OsuRSb6DXOoJtmSQSCUWicQ2Ho8lQG40rGosrHIkrGIoqGIoqlm+hrSpwu5xaNHeqpuaY1XUyBNtaO9mkSx3osoXalFoIt+l3K8dadV6DPv3RjpyPL3dvdz7lfi9s2rBuQvlxNoWWJZeikKC9/9iAjnYNlbtJEwwFwznf/+WWvsRPNtVY+qeUYOuS9MFJFnrqUbZQm2JHuN1/8KCe3n7qs3/l0iYtmDevpOcsh/Ty42wKKUt+c+9ePbfz1Grnly/26ayFC/M+zvRgazUY/c4XH1TvoPVZ+m9bd4H+7Revj4659Tikb3/hHZo+NXtHSyltTSnXNaqdr63dbUyfgfuOmy8xeibksQi2dainP6Q7/vFx9Q9G1Nrs0V1/eq3aWu2bzMauYJte1p3q4ay2fQf7dds3Hqt2M/L6wZfW6MN/+YCl7apdllyKex/eoe89WHrvU7F3PU288LBLrd/Z3X2gRwdPDFa1DRu+n38s+e2/lb1HoVR7jnXpZw+fumJ/z/ULtGjWDEvtyqac7Z3sHBWahqtrIKDv/fyNvNt95N3nakZLcWXJd33/xaw/u+O3lhf1nOWSq63prLS9lN/d7XLonAVtOq2tcULAqLZ8x3WrQbHQUGt1/4W44x82qvN4/u3sLkvO10tcjFo435qCYFtnPvT5+xUYjk74fqPfrR/93Ttt2UchF/rZxtjW8gXzu2/faERJ5bkL2/TG3h5L2331T66uQIvKp9hwWcp76VhXQLfd9YilZQMqqdKfj/Sy5DOmS//w5+9Uk7/6cw9GIjHFa+DT+v7PZB+T/JM77TnuFrPfXD/P5i8/fKkuutDQaf0x6rf/8kFLxy6/x6n//tJNWX++a3e3PvudZ0a//vuPrdA5Z0+z9N66dU27brr6bGsNLrNCPgv5PrNWnivfc7icTrlHppQv9ebpnR+/Ur29w7rzf06F7c98YPm4rwuR7RyTXn6czVduXVn0cj6VHk9q935zjestRbWvi01S/SsT2CZbqJWkwHBUH/r8/ZbDbbbe1G/d93xBbSo01KZ+Xs0PcfUvk6050RO0dbtaVszsxP4S1qgr18nJRN9/fPys1PtOSrd8fuKF3eKZ0obPVPZz6yliLepy2LRhXcFjv0qV7/Pw/s/cX9Tn5rLlc0tpFmpEyOINuVAkLp8n86VgpvfO2JCbz3cf6NR3H+g07qI809/jn364RZufP2n5ObY8d1CrVy20tG2hn9Ns68xuumJ8Cfj/WzpT/+tvJq7/nk+2azAroVZSWdeorWXlum648+NX2v+kdYxgWyd6+kNZQ21KYDiqnv5Q3rLkTAfYfDNBZ5LpwPjGTmsnhjd2nqxaWbJDtR9u7/z4lfrPh7ary8ISKzPbMi+jY5pNG9YVVJZc7Bp1tRxqP9xR2d6PP7/7ce06YG2pq50nqn9TqppuffeysgbZsbbtsLbO0LYdXdq0YZ3+4AsbddjC/a1iX7uvfe9hPfnaqXGWV1/QpL/4yPVFPRfs4fM4LfXY+rLcALRzCEYtHBc+clO7pXPHR26aOL62mL/F3fe9YjnYSsnPXnpZ8kfXLNEPH9mlQCg2+r3vfPYGy8vUtLX61Oh35702zOREd1Azp1X22uFvf/eKiu7PTse6AmW7bmA928JQilwnbv3yr3S8J3/IOa3Nr+9+4R1Zf17uwe7VKA3JJVN7vvnJt9f8GNtNG9ZpIBCZFGNsM8kXPrPd0c6n2LExVmc3LFUlLw6HhqMZe2atqPZFbKU8/9IR/e33fz369V/91mV627LZZd9vqcfR9LUk//Z3r9AlRZYf1/KwksnM6rEsU1Dq3N2tO/7vU7a25w/Xnqd3XlvdsuRiJhIq5ZqoVt7/uar5smnyu/TDv3vXuO9VYiJFO1VyjO0HPrOpqGFLn3r/sqyTa0q18x4ySfG1eqgp/YORkrez2ptqinffvlFrx/x7d9pBOdtBuhyh9qt/sMq250od6FoaPZo9Pfed29nTG+sm1Hbu7h59LcsRaiXptrseKWj7Oz9+pTZtWGfLHdV8J7BKn+C+8YMXin5srU24VQ5rb984LtRK0t9+/9dG/O6XXDhLmzasG/1XjlBr5econ1kzGuXMc4XndCpj75/doVaS/nXTNtufs1CFHmP/6YeFV6rVoh/93Tv1/754Y0GPGR7TS5zyj392raXHfuXWlQXtSyrP+c1qWLVj4iirpf/prloxX5s2rJtQbpy6tkDhCLZ1orXZWnjJtV0x5cbZrF01x7bnKmr/GSaASujUhValL7jOXTxdmzas0/vefkbebb9y60rLB7pvfe6GrOG2Fpb6scva2zdmvdjye5xyjPz3O5+9oehQKxV+cpox5qKwlJPQtz59/ehzpJcbf7jj7Kqc4I6WuIzOxkd32tSS2kOgS5Yf27kd7Lfxa+uyhttSbgCabNOGdRPKjT9yU3vGY2whY2rTfeK9FxX92HJoa/Vp0dxWy9v7fRPnL8i0Tm0mFyydqanNXsv7Kuf5rVI3jLOV9Fvd93lLZoy72Uj5cfEoRa4TPf0hS5ME/L8v3ph1jG0l1txKn5Qqm1KW/qm1i8pMpV7HugL6wzsfUnTk03fROTP02Y9cXvRMswOBiL703Wd0oieomW0N+stbV9RNT20l16ErtJzI73Hqf0YWjk/ZtqNrXFlyvjHbDof087tq7wLzy//+rJ7bdqyk57DjdfnQ7Rs1NmI3SvpRFe9kp5cfZ1POsuT091g2xS5xZUWtDStBdqkZ3pMTRTn1zTtW5xynWa5zqGnvg3ooQx7L6vAlSfr3z3dkHWNr5zq2lfo7pZclV3OJn0+9f5muWjHftn1jPCaPqhNWJglo9LttXc82m1wHKqthtdBQm74kSa3IVuo1a0aj7rMxzLQ0eoxf0ieTzt3dlrdrP3tayfv75h2rCxpjm6mHN3Xndax337FRmW4h1mqolaRPfvjSosfY2iXTBVRA1Z2MxkqoTW23aVl52mg1rHLXH1LyfJN+Ay6Xu/7oqrKMsS3WV779oJ7ZcSogrVji1ec+nn2JomqrxVArnRq+dCRPNY7b5cg5cdSmDesmLP3zj3927YQe3f/6m5vUNxjWb3/xwQnPMc0h/WcFz312hthMUqX/5RomBevosa0zpaxja7U3NZu1q+bo99/7Nmvb2jjhSLV6aO+4+RKFYzH9489ezfhzDmKlq/V16DL12GZz5PiQ/viuRxSJJeRxOfTPd6zW7NOaSmhp+RUyK3ImpbwmleypL0Qt9VRWc+KmWvo7wH52n1eLfQ9U8z1e6BI/n3jvRQXNhFwtv/+Vh7KGW7fLofu++u4Kt6h+5Lp+8FuolkDpCLZ1qKc/pDv+8XH1D0bU2uzRXX96reWe2mJPZoVMQZ+Sba3cQlR8rOzCtoxlvoWWesGaal08r7tjo+IWjozFvO9NU2y4/dg7z9W66xYXtc/08uNsqlGWXGuBLr0suZzlx2OlL/GTDUv/mKsSw5Ps2v/df3KNFi2cWvA+7GiDiTduBgIR/eW/btGeQ/2SJK9L+tfPZC8/hnWp68Fsw5ro9Cgvgi3GKfZEVo0DezXKjz+6Zoneu3riOncoj2qGiHz7nkwnp6HhqL7xgxd09GRAp09vtDT2tty9tXbspxi1MMa2VlTion+y3zQce6O6pcmjE70Tl/XL9Te++/tP6JGXeke/Xr1sqj7xW9dY3r8dS/+855r5Ba/xnF5+bFU5jgcsaTW5PfPrQ/rKj06tEvC5D12qFZfNzfmYci1JiPwIthjHpGBbrRJkTmSVY/Wi6q4/usqWMbbpsp2cOCmV92KvloOtVL+9OMUo5/tgsn/+Cll/1ClpYwFrsOZ7bT7xdxu1x9oUB3ll2le+CQ9rbeKm9LLkjrdN15/cYt8yfqhNxXyGSllDGqUj2GKcTY/t0rd+sb3wx9XoxWU5TJYL1lpR7RCRrcfowOEB3fb1RxVPSE6H9OWPrtRXf/S8hoJRNTW4dfcnr9P0qf6ytasWbHx0p75z/xujX5dSfjxWrQdbiV6csdLLku0oP57sPR6FhNqxUu+9Uo6b5S5BzjbGc+wSdaW0wc6y5C//2/16buep1+HyxW594Q9yz1eC+lDsZ6iY89dkr0yxE8EWExR6QilXb1k+BNvJo9ZChNX3ntfj1L0FzEaKpFoeYztWelnyZCg/roTJ3uNhdfm+bFYvmzqu/DjXdullyXadV7OVH+eauEg6FW5LbYcd54VaO++gctLLj7PJVJZcaLCd7JUpdiPYIiPTe0zKhTG21ZNelmzKDRXCbXGq3VOP6rG6nnQhs5Kb5NYv/0rHeyaOpS2HsZ8hO8qPc30mra6j+oMvrdE/ff/hosbYWmmHFVaOPxcvnqk5M5r0kbXnq8HrKml/qC2lXAMX8liWCLIf69gio00b1lkqS67mheV3PntDQWuO2oFQWz3tZ0+repA5cHig4MeEI3Gd7B2u+7Jku23asI4ek0kq0/rQpWxnmkqF2nR2janN5kvffcbydl/9k5uqVpX15X+ztn73yztP6OWdJ/TA1r2SpM98YLmuvGJeGVtWun/+8Vb98rkTo1/fePlM/fEHV1axRUmZXuvJcIzPt7RgPJ6sYKnHypRycVa7Aahda99+TjLgblinu/7oqnE/u+uPrqr6QafSH/Rq/76ovtu+/mhRj/vEN4p73GS3acM6pX/KG1W/n8XP371Ra28/9e/zd1fnwr7afB5rlyZWtzPJ73+lsjdrK+lET9DW7cpl7JjaQtz5Py9WLYxbsfb2jeNCrST98rkTVW9ztv1Xo13bdnSVvN+//p3LbWpN0m13PWLr89U7emxhSS30lmVSiV5byo+RYmVt20yGgsVdKKG6Y2grKdPF1KsHkt+vxWNvOX3zjtWWjuvfvGN1BVpTOQOBSM7xp6ab2dagrr78vdEz25Jrqa46r0FbthUecq9f3lbwY+xUi5/ZfGGtWm0uZ7vSl6n75IcvVZM/e+yxK0gvv/h06b/yb+eQZOWSol4rU8ql/m53YlKZNaNRzjK/i198fUd5dwBjOB3FPa6pgXuIyM7Kxd1kYuW47nRWvmqn3P7m29ZKdctlUYlTFuQLIH956wpLz5Pa7tMf7SiqHX/24auLepydnn72YLWbMOqff7zV1u3sYvW4Vszx78/vfly3fP5+PbftmPYdHdBz247pls/frz+/+3Hb9pFLvs/Cpg3rJnVlSjnx14LxNn5tXVnD7Sv7y/fcMMs3//y6oh539yeLexzqn9Vy48lWlpzruF6vE6ocPF74GP5MrPZwpW939+eL/5ta2WdLo0ezp+e+GTF7euO49WwL7a2zo9fx8sWl34i8839eLPk57JJeflzqdtXwxFbrF2J/fvfj2nWgL+PPdh3omxBut+3oKqVpWdu2acO6CWXJf/07l4++R61WnNRbZUq5EWxRF+rxIge1Z/6cloIf4/U4mTgKWb16wN7t6snGr63Tdz57g/wepxxKzoL8nc/eULfHe4+7yJKQMVIXzVZ6jAr5fjHPlcm3PndD1nA7dh3b9OdfdV5Dzue9fnmbbaW0rFNbe+669zeWthsajmYNtSm7DvRpaMwa0Z/59tNla9vyi08fnatm04Z1yTLlEZO1MqXcqI8DgALkm613LJb6AUoza0ZjXS7pk8maq87SDx7sLOqxTkkb04JdtmOVldCb7xhXSoj81udu0EAgoi999xmd6AlqZluD/vLWFeN6atN9+qMd+nTReyxcIcd51I5v/CD/2rOp7b7w0SvK3Jr8Nn6NdWztRrBF3fjITe36XpEXBblctMD2p4ThNm1YpwOHB3Tb1x9VPJEce/vlj67UV3/0vIaCUTU1uHX3J6+jpxaAZTdfu9hSsL33H9bK67ZWcFdsAN20YZ3+zw+e1MMv9ox+7/rlbbaNX21p9Oirf1L9sbC5bNqwTl/+t/uLmiX5Mx9YXoYWFefGy2daKjO+8fKZFWhNeR21OPma1e0qYePX1ulYV0C33fWIQpG4fB6nvnnHanpqi+RIJBJFzvMJ1J5y3GGttdkNAdSPz9+90VKZ8YXzpb/7BMeievcfm17XTx/fk/Xn77t2kX5v7fkVbBHG+vK/P6vnth3Lu12tXTdYuTaqdJvX375Rgxa3vePmS3TNyvy9DFZfn8vPmzXaY7ttR1dJ5chW24bKYIwt6ordB+ZaOzkBqC9WwyqhdnL4vbXn633XLsr4M0Jt9X3ho1fox3//rpzb1OJ1Q7FjrsvpngL2aTU4fvLDlxa83XlLZlhuRyaE2tpCjy3q0r0P7yipLPmiBdKX/6z2Tk4A6lOuHpVavFBGeYWjcT349Fs6cnJIs6c36aYrz7RcfozKePrZg+NmP/7MB5bryivmVbFF+f3zj7eOK0u+8fKZ+uMPrqxii/L3Jhd6/Ms1K7IknTN/ir7+iWsLbkcmHJtrD8EWk8aHPn+/AsOZx8pwcAJQbellyZQfA5gMMpUll1Limy3cZgu1KZnKkt915WwtOf30cbMfU35cuwi2mFR6+kO64x8fV/9gRK3NHt31p9eqrdVX7WYBAADAJkPDUX3jBy/o6MmATp/eqE9++FI1+Zkzt94RbAEAAAAARmPABgAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARiPYAgAAAACMRrAFAAAAABiNYAsAAAAAMBrBFgAAAABgNIItAAAAAMBoBFsAAAAAgNEItgAAAAAAoxFsAQAAAABGI9gCAAAAAIxGsAUAAAAAGI1gCwAAAAAwGsEWAAAAAGA0gi0AAAAAwGgEWwAAAACA0Qi2AAAAAACjEWwBAAAAAEYj2AIAAAAAjEawBQAAAAAYjWALAAAAADAawRYAAAAAYDSCLQAAAADAaARbAAAAAIDRCLYAAAAAAKMRbAEAAAAARvv/PD1p4arAhOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "AKNS_shape.plot(alpha=.5,\n",
    "                         ax=ax)\n",
    "\n",
    "icesat.plot(ax=ax)\n",
    "\n",
    "plt.axis('equal')\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99c31416-f412-4e31-b4c9-432f76a26280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90837, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clip the data using GeoPandas clip\n",
    "points_clip = gpd.clip(icesat, AKNS_shape)\n",
    "\n",
    "# View the first 6 rows and a few select columns\n",
    "points_clip.head()\n",
    "points_clip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01625717-41b1-4995-a37a-288a0e7d3de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "AKNS_shape.plot(alpha=1,\n",
    "                         color=\"white\",\n",
    "                         edgecolor=\"black\",\n",
    "                         ax=ax)\n",
    "\n",
    "points_clip.plot(ax=ax)\n",
    "\n",
    "ax.set_axis_off()\n",
    "plt.axis('equal')\n",
    "\n",
    "# # Label each point - note this is just shown here optionally but is not required for your homework\n",
    "# points_clip.apply(lambda x: ax.annotate(s=x['name'],\n",
    "#                                         xy=x.geometry.coords[0],\n",
    "#                                         xytext=(6, 6), textcoords=\"offset points\",\n",
    "#                                         backgroundcolor=\"white\"),\n",
    "#                   axis=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cd5afb0-f9e1-4e34-b4f5-7da88789a93c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/explore/nobackup/people/mfrost2/.nccstmp/ipykernel_1650539/4193631941.py:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  points_clip.to_file(\"data/ICESat_clean.shp\")\n"
     ]
    }
   ],
   "source": [
    "points_clip.to_file(\"data/ICESat_clean.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ebd48b4-af66-4410-826b-048bf68a37ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90837, 30)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_clip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78488889-8387-453c-bffa-437ac2188e5c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ilab-pytorch]",
   "language": "python",
   "name": "conda-env-ilab-pytorch-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}