{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "\n",
    "Here in this data preparation jupyter notebook, we will prepare our data that will go into a Convolutional Neural Network model later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Setup parameters and load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python       : 3.6.6 | packaged by conda-forge | (default, Oct 11 2018, 14:33:06) \n",
      "Geopandas    : 0.5.0\n",
      "GMT          : 0.0.1a0+36.gb1e7b75\n",
      "Numpy        : 1.16.4\n",
      "Rasterio     : 1.0.23\n",
      "Scikit-image : 0.15.0\n",
      "Xarray       : 0.12.1\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import hashlib\n",
    "import io\n",
    "import json\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "import tarfile\n",
    "import urllib\n",
    "import yaml\n",
    "import zipfile\n",
    "\n",
    "# need to import before rasterio\n",
    "import xarray as xr\n",
    "import salem\n",
    "\n",
    "import dask\n",
    "import geopandas as gpd\n",
    "import pygmt as gmt\n",
    "import IPython.display\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyproj\n",
    "import quilt\n",
    "import rasterio\n",
    "import rasterio.mask\n",
    "import rasterio.plot\n",
    "import shapely.geometry\n",
    "import skimage.util.shape\n",
    "import tqdm\n",
    "\n",
    "print(\"Python       :\", sys.version.split(\"\\n\")[0])\n",
    "print(\"Geopandas    :\", gpd.__version__)\n",
    "print(\"GMT          :\", gmt.__version__)\n",
    "print(\"Numpy        :\", np.__version__)\n",
    "print(\"Rasterio     :\", rasterio.__version__)\n",
    "print(\"Scikit-image :\", skimage.__version__)\n",
    "print(\"Xarray       :\", xr.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Get Data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_to_path(path: str, url: str):\n",
    "    r\"\"\"\n",
    "    Download from a HTTP or FTP url to a filepath.\n",
    "\n",
    "    >>> d = download_to_path(\n",
    "    ...    path=\"highres/Data_20171204_02.csv\",\n",
    "    ...    url=\"ftp://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\",\n",
    "    ... )\n",
    "    >>> open(\"highres/Data_20171204_02.csv\").readlines()\n",
    "    ['LAT,LON,UTCTIMESOD,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY\\n']\n",
    "    >>> os.remove(path=\"highres/Data_20171204_02.csv\")\n",
    "    \"\"\"\n",
    "\n",
    "    folder, filename = os.path.split(p=path)\n",
    "    downloaded_filename = os.path.basename(urllib.parse.urlparse(url=url).path)\n",
    "\n",
    "    # Download file using URL first\n",
    "    if not os.path.exists(os.path.join(folder, downloaded_filename)):\n",
    "        r = urllib.request.urlretrieve(\n",
    "            url=url, filename=os.path.join(folder, downloaded_filename)\n",
    "        )\n",
    "\n",
    "    # If downloaded file is not the final file (e.g. file is in an archive),\n",
    "    # then extract the file from the archive!\n",
    "    if filename != downloaded_filename:\n",
    "        # Extract tar.gz archive file\n",
    "        if downloaded_filename.endswith((\"tgz\", \"tar.gz\")):\n",
    "            try:\n",
    "                archive = tarfile.open(name=f\"{folder}/{downloaded_filename}\")\n",
    "                archive.extract(member=filename, path=folder)\n",
    "            except:\n",
    "                raise\n",
    "        # Extract from .zip archive file\n",
    "        elif downloaded_filename.endswith((\".zip\")):\n",
    "            try:\n",
    "                archive = zipfile.ZipFile(file=f\"{folder}/{downloaded_filename}\")\n",
    "                archive.extract(member=filename, path=folder)\n",
    "            except:\n",
    "                raise\n",
    "        else:\n",
    "            raise ValueError(\n",
    "                f\"Unsupported archive format for downloaded file: {downloaded_filename}\"\n",
    "            )\n",
    "\n",
    "    return os.path.exists(path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_sha256(path: str):\n",
    "    \"\"\"\n",
    "    Returns SHA256 checksum of a file\n",
    "\n",
    "    >>> d = download_to_path(\n",
    "    ...    path=\"highres/Data_20171204_02.csv\",\n",
    "    ...    url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\",\n",
    "    ... )\n",
    "    >>> check_sha256(\"highres/Data_20171204_02.csv\")\n",
    "    '53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b'\n",
    "    >>> os.remove(path=\"highres/Data_20171204_02.csv\")\n",
    "    \"\"\"\n",
    "    with open(file=path, mode=\"rb\") as afile:\n",
    "        sha = hashlib.sha256(afile.read())\n",
    "\n",
    "    return sha.hexdigest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse [data_list.yml](/data_list.yml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def parse_datalist(\n",
    "    yaml_file: str = \"data_list.yml\",\n",
    "    record_path: str = \"files\",\n",
    "    schema: list = [\n",
    "        \"citekey\",\n",
    "        \"folder\",\n",
    "        \"location\",\n",
    "        \"resolution\",\n",
    "        [\"doi\", \"dataset\"],\n",
    "        [\"doi\", \"literature\"],\n",
    "    ],\n",
    ") -> pd.DataFrame:\n",
    "\n",
    "    assert yaml_file.endswith((\".yml\", \".yaml\"))\n",
    "\n",
    "    with open(file=yaml_file, mode=\"r\") as yml:\n",
    "        y = yaml.safe_load(stream=yml)\n",
    "\n",
    "    datalist = pd.io.json.json_normalize(\n",
    "        data=y, record_path=record_path, meta=schema, sep=\"_\"\n",
    "    )\n",
    "\n",
    "    return datalist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pretty print table with nice column order and clickable url links\n",
    "pprint_table = lambda df, folder: IPython.display.HTML(\n",
    "    df.query(expr=\"folder == @folder\")\n",
    "    .reindex(columns=[\"folder\", \"filename\", \"url\", \"sha256\"])\n",
    "    .style.format({\"url\": lambda url: f'<a target=\"_blank\" href=\"{url}\">{url}</a>'})\n",
    "    .render(uuid=f\"{folder}\")\n",
    ")\n",
    "dataframe = parse_datalist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to autogenerate README.md files in highres/lowres/misc folders from data_list.yml\n",
    "columns = [\"Filename\", \"Location\", \"Resolution\", \"Literature Citation\", \"Data Citation\"]\n",
    "for folder, md_header in [\n",
    "    (\"lowres\", \"Low Resolution\"),\n",
    "    (\"highres\", \"High Resolution\"),\n",
    "    (\"misc\", \"Miscellaneous\"),\n",
    "]:\n",
    "    assert folder in pd.unique(dataframe[\"folder\"])\n",
    "    md_name = f\"{folder}/README.md\"\n",
    "\n",
    "    with open(file=md_name, mode=\"w\") as md_file:\n",
    "        md_file.write(f\"# {md_header} Antarctic datasets\\n\\n\")\n",
    "        md_file.write(\"Note: This file was automatically generated from \")\n",
    "        md_file.write(\"[data_list.yml](/data_list.yml) using \")\n",
    "        md_file.write(\"[data_prep.ipynb](/data_prep.ipynb)\\n\\n\")\n",
    "\n",
    "    md_table = pd.DataFrame(columns=columns)\n",
    "    md_table.loc[0] = [\"---\", \"---\", \"---\", \"---\", \"---\"]\n",
    "\n",
    "    keydf = dataframe.groupby(\"citekey\").aggregate(lambda x: set(x).pop())\n",
    "    for row in keydf.query(expr=\"folder == @folder\").itertuples():\n",
    "        filecount = len(dataframe[dataframe[\"citekey\"] == row.Index])\n",
    "        extension = os.path.splitext(row.filename)[-1]\n",
    "        row_dict = {\n",
    "            \"Filename\": row.filename\n",
    "            if filecount == 1\n",
    "            else f\"{filecount} *{extension} files\",\n",
    "            \"Location\": row.location,\n",
    "            \"Resolution\": row.resolution,\n",
    "            \"Literature Citation\": f\"[{row.Index}]({row.doi_literature})\",\n",
    "            \"Data Citation\": f\"[DOI]({row.doi_dataset})\"\n",
    "            if row.doi_dataset != \"nan\"\n",
    "            else None,\n",
    "        }\n",
    "        md_table = md_table.append(other=row_dict, ignore_index=True)\n",
    "\n",
    "    md_table.to_csv(path_or_buf=md_name, mode=\"a\", sep=\"|\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Low Resolution bed elevation data (e.g. [BEDMAP2](https://doi.org/10.5194/tc-7-375-2013))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_lowres\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >folder</th>        <th class=\"col_heading level0 col1\" >filename</th>        <th class=\"col_heading level0 col2\" >url</th>        <th class=\"col_heading level0 col3\" >sha256</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_lowreslevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_lowresrow0_col0\" class=\"data row0 col0\" >lowres</td>\n",
       "                        <td id=\"T_lowresrow0_col1\" class=\"data row0 col1\" >bedmap2_bed.tif</td>\n",
       "                        <td id=\"T_lowresrow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/elev/dem/bedmap2/bedmap2_bed.tif\">http://data.pgc.umn.edu/elev/dem/bedmap2/bedmap2_bed.tif</a></td>\n",
       "                        <td id=\"T_lowresrow0_col3\" class=\"data row0 col3\" >28e2ca7656d61b0bc7f8f8c1db41914023e0cab1634e0ee645f38a87d894b416</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in dataframe.query(expr=\"folder == 'lowres'\").itertuples():\n",
    "    path = f\"{dataset.folder}/{dataset.filename}\"  # path to download the file to\n",
    "    if not os.path.exists(path=path):\n",
    "        download_to_path(path=path, url=dataset.url)\n",
    "    assert check_sha256(path=path) == dataset.sha256\n",
    "pprint_table(dataframe, \"lowres\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x+Sak2+50+PN/93P83Ec+TGNjgxv2zrPR7lApFen2BylpJSSrqirT9TEsx6FvWiwtLzM7M0O3201d3uS8kyB+toh81OXdziWGiOBfiGLWa0bH9ULBC4m4zI1j/EXT52ClTt912KMbyILIJ5bO8ef3P4AfBHzfK17OA2fPsWushh8EHL1wib5to8oyG+0ORaOAKssM7GHqYiU3nyzL5FSFgWltOW5WmpC4T4nIM0tCyf6yN23W0sre1NkAfGItJedxx8EbuG1+D6+f3YcuSuQEkZ7v4QQ+F/sdLvbaHFtY5MzyCgBvuP1WHjt3gaNnznJo314sx+HJs+dwHAejUEDTNPbPzqAqMsfOniefz6OqKu12O9WAJcXXiWt4YP9+ut1uGshPzjVL1FnVf5a8RpGMJXGx5DgLd79wpBTXko5rB9cQzM4CyBVuqtQxJJkjRpWyrPCBJx+lmtN5zZHDGJqG4/v4QYAkipy4tIgqyxyem+V1tx7hu175DcyM1ZgZq7GrVuX2/XspG4UtbpDr+U+JcWXlDFkd1Oh7sOkeJY9sfCtZzo6NulqaprF/eordxTK6KJEXJRRRpK7kAKjmNCRBQJVlDE2jnM9zz0OP0rdt7jx0kNOXFjA0LY2RybKMqqo8ceo0Jy8uoKoqZaOAJAqoqkq/30dXFaZr1dQCe9ltt9JutyPdWUxyyedL9pl89qwlNfr5R7Ono69n3vWLV+338ULBTq3iCwTn201OD9rM50tIgsB+TcEKfE6ZXR7bWGG11Wa11aagaxzaPcvAdbGGDg+eOsNdh2/CDwJOXV7iwS/el+4zifWsx4Fl4CnWwpWygFmrYztrKtlXFll3apSssiSgaRo375mjquvMFkp4YYAZH94KfCzfw/I8/vn8YfpzN/CFtUU+c/JJJFFktd1BimUScxN1jp87zx2HbmK13UEURfbunsPxPIaOm9YfaprGntkZeqbFwtIyb3jZS7j/xEkWVlap1WopSSfi1qzO7EqJhux1Gl0nuQ6Ja6yqKnve/U6CIHhBWV9fD3aI6wUAs32Rnq/x4NoSF/UOBUXlUHmMS4MuG7aJ5blMViucuryE5ThIosjRC5cYL5e4cXYX9x49TqvbS/dXMgp0+4OUdAamRSGvo6kKjuengfYstiOh5MbbLm51JXcpsVq2U80ny9O1KrqqokoSA8/FDXzGc3kMScYOfNasActmjy8tXqA7MDmzvJKWG1WNAsuNDTRNY2mjyYHdc/Rtm4KWQ4qPM14q0bdtHM/DD/RUYNvtdnnNHbfxmYcfjVrv1KrYjgtsxrMSsk6es25i9jOP1ldmiTpbq5mULyVjM+/6xZ2OrewQ13WN3mCA5DZ4/5kLHKjUuHN8mgOFMl3fRRFFVEniXKeFriicjW/eG2d24QcBpUKeU4tLHD0fddK9cW6G9U6XVre3JSid3GhJPGtuYpzVjCuYxajbeKXXVwpKJzdnIi7N3uTApmVj20xWK8wWSrSGFpN6AS8M2fAcuo7NYj8ib0kU6ZgmU9VKepzOwKRSKqbLAHsnJ/CDSPUviSKO51FTDFRZ5vJGk55ppYLVR06fTTONSWmQ53mpLm30WmTJ6auR1qglm83GJo0QRUFgz7vfCfCilk/sxLiuUyx120gEEHrsKZb53prKJ848SdNzqMgqM6rOvYsXKKgqF9bWMTSNUiHP0kaTf/jK4zxy5hyt/oCSUaBeKXNhdR1JFDm0Z45d9THKeR1di+JFWcJZjntqPVNsR2LbPbJB7uzrRBSaWHGW49CwTQqySt916XsOT2ys8uDaEqoksWYOsBwHPV5fV1X6ls2h3bNMVSscnNmFKsv8+KtehSrLVIsG9XKJatFg//QUc+N1DE3jtn3z7JkcxzAMNE2jXilH8SxZwg9CgiCgaBS2fDbYPpaVuJGjRDUqi0jWy5Kb4ziIgrBFdrL3PS/erqw7WcUY11NW8d6VBW4qlNBFGSkwedx2+dTCGca0PC8d30XPdTjT2cD2PVb7fVZbbc4srzBVrXBpvYEkiuhqZJHctm+eR86cQ49jWX4QbnFtCloOy3FTMWeC7cSh2wWZE4y6e9u5idmbMlkvWVeRJVRZpmdazNbH2L9riv1jdQ6UayiixInWOn4Y0DBNVFnC8XzWu920RMnxPFRZZrXVTonKHjpoOZXxUhFNkrF9D0kQGdN0lgd9mv0+pxaX0qJxy3HShEbiMutaLk1UjCYgtqsE2K6MabtrkC0lyq6fdMbIXpvryfK6pmoVd/Dc4J7Fs/hhyM3FGoogMgx88n6fG/Q6/andqKLEQ+tLvGR8F4erE3zgwS9Hfd8tm4Mzu7i80URXVV5x0410TJOBZfPImXORJsm2kUQR14tiNklgudP/6gHmZH3YbDMDTyWo7eJWV3Ipk4ckChiajiSKWJn6REkUaQ0tFvtd1qwBE3qBrjtk2ihi+x7NfotyPk/HNHE8D3sYuVyGpqEoclowftfMHjZsE1EQKMgqtu/hBj6aLPPSmTlWW+2UuAAcz8Nyos6rWdJKrsEo+WY/09Ndv+2u8ahrGQTBU9r5BEGQzpp0PRHY14sdV/E6wUa/R0nJ4QcBTW9Izlkk761zwtO4ZA/4wANfYioXzWjzgYfu4wvLF1M3aW6ijiiJ6Q34t/c/yCNnznH04gJ928Z2XFzPTwPNCbLShq8V2607miHM7nv0eKOklQTMdVWNrR0BPwh47NwFHjx1ls+fPsV6r8extUirtWGbnFpdpTswafX66Kqaklar18dyHOyhQzmfZ1+lxvKgR0FWqOZ05golbM/F8lx0WaZhR6JbNSYjQ9eQ4vMKYssrOU/P89C13FMkItnP9tWuyeh722Ubk+ftrl0S+3oxYIe4rgO0+j3CMESTJGzfQ0QAuQZek5qc4ze+9Fleum8vs+KQxW6H9U6Xftyeptnr88CTpzm7FN3YVqz+ToLtruc/JdCejS2N3kTPFKPbZi2zp3M9t4OhaXEBdZTx65gmK602F1bXaPX6cUseP7WqHM9jYNn4QcBPvfwuDE3jxpldTFYrkRXme5TUKI6niiILgy6WH423bJuXjc/EmcUgqiCIg/mSKFLMR38SelxOlFMV/CBMP8to3Gr0dfb6ZJ/hqZUF2d74yf6TR/I9JZbeiyXuteMqXuMYDAbk8AhFlUcaK6xZAxq2yfzug+T0A3z4zHE+8Ibv4tFek0ftkC8df5KpaoVJw+CxcxfYPV5HlWUkSaRv2alrU8jrTFUrWEOHWtHADwIurTdwvag/1pViUl8LwWSxxc3Z5iZ9OkL0gxBJTJaD1OpKXifPkiiyf3qSak5Hk+TU+spu91/u/0LkHup5upLEhF7g1rFJ7luNOiZpdnQrdJ0hC80m9tDhb+OMXt+y04yjlDlfVZbTeGEnE1vLqQqWPdw2AD+aTRzFaJYx28I6eZ2VW2TrPJN1XgwF2zsW1zWMT10+hxDYgMjrP/RBxrQ8670ea+aAP7lwnPce/woPnT3H+4/9I3Nagf9476eZqlZQZZnPnXiSvm3zxMVLrHe7NHt9bts3Tz3u3ul6PgvrGzS6PU5dXubs8mpKWlk8XZwqeX0li2y0v1aC7M1cjjNyo9tnjymJIqoiR4+YLLIPx/U4Upvkrx9+hIOVemppAZQKeUqFPLvGakxWK4iCwN5SFYAVs48fhmiSHMknBJGWaaZkuNpqY8Xkpasq5UIePRdZP86IS5wkO1RZwrKHacnTqLWVkMxoJ4zkPU3TnnLNRq21rEbsSrGzubvf8ZSxFxJ2iOsaxT2LZ6mqOggi7z3+CK85cpgj5TFesmuWu6bn+NH5w9w5sYv/8JrX8217buDDF07wxOmzWEMHy3EwNI1a0YisKntIq9vjM195AlWWeeXhg+mPPumc8HS40g30dNbSqIWx3fqiGGmmpmrVp9QzZmNcqiynj1HSSvDBL3+RV9x0I59bOp9ORJvEpuqGQTWfp6zr2L7Hw8uL3Do2yZcuX6SqapRUjSAMadgmnbh9NLAZX8up0SOOd0miyHi5hKFrKZFlzyVLSqMWV3Y823gwGc9mb8WRfWZFqbCZedzuexBF8QVNXjuu4jWGn/ncx1naaLJ/eoqyrvOhXo8/+uj/5E/e9jYGfvQv33WGdH2X2yp1Pr+6QMuxefT8hWheQM+L4j8DE1WWGS+XuGXvHk5eXiYIApaaLdbjTgYAtvPsXL9kOcF2mbNRt2g7d9H1fJr9QRTwzpCbJEZ1hrqqYuhRbKsUzxjkuNF1SKwey3FQZZmFuOWO5Tj4fkRepUIeVZIoKTm67pCSkkOt1pjL5Xnd7n2cbDfwwxApztaNl0qsttrpdcySVbPfx3E9xsulNPbVt+z0j8JyXMp5Pe2q4XnitlnWbBF1IvRNurNmJ6NNkFhpCWllS4mypVBZJK/3vPudeL7/glPb7xDXNYS7Pvi7XFqMmtf92ZveghmE/PIDn+XRu38dQ5J5sLXGhm2iiBL3rl/mteOz+GHIA2fOstJqMx4rwhNXB+D86hoDe8hkpbx5s9lDFFnaoj96JvhaLK3scpa0khsWoGoUaPUHAKlFpatqqpdKiDchjmQs+Ywqm9m+JA51aG6G6XyRR5cW0VWVbtKOp9OhnI9qGw/o0byNNxoVlgc9GrZJN55kY73b3eIGlgpRe5+BZUfF6bKXlg/1LTsl1fVuD11V0vNzPI+cqjB03C0at2y8b1fcHqdcH+PS2vrTEjywrfsJW0uEktfZ90RBYPr/+A8s/9p//Nq/5GscO8R1jeCW334f7Xab73/D6/i1218O/cfJCyK/desEeBchzDOVL3O+10KKNUefWVug6w7ZNVbDGjpxgFhOA9fZGzApIgbiVL6ULick9nS4Ugr+SsS3HWlltwFo9QcospS6g7WigaFpFHQttZ50VaVUyEfkE+vQEiTarmYv6uNeLRooosS5dpNyPo8fBBRiCcNksYgaZ2XHVA09/vw31yY41dlIiatmGEglkfVuFy0XSSmSEinX9baQKoDjeqy2O9RLRQxdw3G99H1npIVNlnCM2DJLLLmkb39WBzaqwh/ViWVJcPQaQzSbtygIBLHI/IVEXjvEdQ1g7u53UKvVuOdnf559tPA27kOUFCRFJxQkEEQQNYooKKJEQVFZNnscro6z2N+0EHRVZa3dYaxUjNL9sS5LkTdnePaDECUuV0kwSlrJ7DWj2E5X9LWIKrPWwCgkUWSqWuG1N93Eo5cuUSrk+dZ9N+Ls9XEDn4dXl6gXClzY2KBaNCjndZz4fDumiTTYjEmV83kkQaBeKOD4fuoiDry43k+SqagaPc+FHHQ9Fy8IGLgOkwWDSk6j6wxZ7vco5/Pp/JH1cgnLcXBdL81sWsPIPTV0jXIhz3rcrDCxdi3HwbKHjFfKkXYs/i6CIKAeT4Cr51RunN3FwLK5/8TJ9JonTQuTa5dsd6XESPb7SK2umLSAiLyI+u2/UIq0d4jrecZdH/xd3v5938sP7L6R3Ma9WM4AVa8iSiohAiCCIIPboKRG2bD20MbyXD518UzU8WHopOn6sVIxLRhWZCnNMq53unFniOjHHD1vr4jfjrSyuFKaP/v+lSyuUSS9so4uL1EtGtQKBRq2yb5ilaqSQxEl3MCnOxxSUBRKao7pfJEN22QxY605nsdsqYwqSkwXilRVja475Fy3xXS+SNcZUs1p+GHImJIjh0dNUVlzbCZ0A1WS8IOA890WzX6fhbUGU9UK8xPjNAeDTcJyHDoDE8uJZCTRtRTTQm5dVbm80cQPAqZq1dT6SizhUl7H8aI42Z379lLJabxkfBdnllfiuJqfdo/NElFy/UZdxVFLa1S5n1hlqqLguBF5Tv7yv2f11//vp/2Or3XsENfziE9dPsfH3vov0fGwFv+WUC2g6lUEIb7JBTF6hB5IJazAQxIEbN/jrqndPNFcZbXVZr3T3aIzimJZIZIosNJqp8dLioI3M3YCfhBeUU8E28eztnMRR2NbTxcHS9aRRAHH81jvdCkXIrKcLBYpyCpO4KMIAvNGmSc7G9T1fBSryReZLRSZN8rsLVV5bGMFPww5UKqlgtLbSmP0fQ9VlCjUVBYHXfaWqkiCgC4r7FZECAM6nk9FUVmz+lTVAg+uX6ak5piYjqygerlm1QEAAAAgAElEQVSEKAixVktJ6x3375rCHjpp5jIqA3LSyTqSjC6w6ToqMlbsdpbzeQ7NzTKm6cwaZQ7qRX7j29/C2z/+P5FEkX4mFjgay8qKUUet2KwoNXk/G9iXJSl1G693y2uHuJ5HvErdgOEAAhtJLSApekpaYegjhAHIBiCClOfioE9dy7O8scbioMtfPfwIh3ZHE0MYWhTL6dt2SlqjyFpZW8fY4jrCVw/AbxdTSd5LSCnZ75X2NVWtUDMMHM9jfnKCPaUKmixzqd9GlcbQ4jhUQVE5oOZwfJ9qTicIYVKNsm931nexbPYoqTkkT0QRJc5bfcaUHEVJZq9WYOBF7asX7QHzuQLLvsd02MQKSvhhyN5iBUUQmS2UAFg2+5QKeSYNA8f3Kes6mixDfjNuWNC1NN5ViklXUWRc12NXvZaWGTnyZobXcT1qRYP5yQnefuTl/OXiGZx4Qtx3f/rv4usVpJnG0eqCrBWbuJKjlm32+8haX2mboCBAzBDY9YodHdfzhHtXFvhPCxb4fXy7iawWnrKOQBhZW6IGUomB67BqDajrefYVq0iiyNmlFSzHSUtgIPrh+kHIVLWyRV8EpJbWKJLxr/ZIsJ2FtZ2lNbq9riroqoIkCmlQfW68zht3H6A1tLixPMad9V1UVY0116bve+wrlFFEiZKaww8D9moFSn6DG4JLvLSQZ7ZQYjKXR5NkqmoOPwzp+R6TqkbeW+dIsYYoCFSUHCURpv0lQmuBAzTYpxlMKBpFKYofHi5H8y2Ol4pM6AYD10WTZVRRwnJcxktFDE3DiFtH75+eopzPU40TC/VyCUkU06SAKstMVSvoqkqtaESTe+w/RM5r8kNzBwD47o/8KbBZ0pRovLIlV9n2Psk1z05GAqStf7JzOSb9whJrLd2fJF3XtY07FtfzgE6/z6//w9/xX7/je0HJMdA8SmGfoHccAFGKfpxh4IOg0hUrXIy7eg5ch+5wyKcWz6Y6oySDBqSxLYhmtUlunqTebjuMktuVsFnkHP1bbxdnifYnZJbFdNvsc80wKBfycc8shZqa4wfmb8ILQxoMqckqmijhhSFW4LNLK6AIAtODRxGtyDIKvQHD9me4VSshU2A+V0bwFgkFAVDxW00c16KmlfCcAdP5OsLAx+6vASD7LvJwlbqkMhw0eL1aQBQEvmlqN6e6TQqKSl3P44cBfdehXoj+XKo5HT8MaNk2mizTMU3majVsz2Ol3d7yfSTX3o7jkPunJrk1XwQ/+pNpDy3ecPstfOn4yVQ7lu1dP+oqerHMYlTbNRoPy1plCZKp4hLrLAiC69Zl3CGu5xiNXpePLZ3jr77nn3NpaHLC6hKEcEAvkVMyQXFBpK0d4tLQpITDqU40n+3AdbEch6MXL22RPgBblkeV5V+NnJ7u/WS/iZYqS17AFtdwu32NkhdEanRJFPmhO15KQY5bPIfgBgG35ASGYo6c3yXoH0eUVARRYjhoIBXqhKIGkoEgiCiaiyCIhIFPYK3gDnsouSKCOESUNqsCAt/FNRvRnwEgCCK+a0Xb+S6yWiAMA4LBBQ7kd2MZZdZtk0PVcXRJxvI91qwBtufihyGtoUVBUVI3PQjDqAqgEgXpk5hX0sxQlWVeM7eXN9RnIDBBVPmLpfMsDrqcuryMoWtcWI20XDlVIesMJWLcrEYsOoa7bdA+Ia3tZCijWjItl2Pve36F87/ynqf9fVxr2CGu5xCPNlaoKzleN7kbH9gt9nnUVbB8jwcdi1dVbgc30i+tUuRYp8G0XmDJjkSarWHU6WBfbYxWr58G5RPleJZgtsOzIa+smDVZTshrdLun2/8oiUqiyJsPH2FKKzCt6liBz6QwgLANvkZucArHaqUutO9aKFqJUNRjeYgKUglJV3G65xElFzk/iSpKUUY2JihJzUHgIauFTPwwIAx8BFGKngURzxmgaCUC38Vpn6Bee3XkPgoCIWAHPoai0nVsznVbqJKEJsmsWQP2lCpc7Lap6joFJerp5Xges8USraGNJAh8x9wNzMtB9P1Kef5mbZnW0OLU5WU6A5OVWGcnikk5U/aPYlPnlsxvGWU43S0zIhlaLk0g9NlaEpTMVDQqTk0I73ojr687xiUIwpwgCJ8VBOG4IAjHBEH4d/F4TRCEvxcE4XT8XI3HBUEQfkcQhDOCIDwuCMKdmX39aLz+aUEQfjQz/hJBEJ6It/kdQYgEKs/mGM8Xfv3RLyIKAn948ivUlRzHzA4P2VHPp64zpO+6/M3qRZ5wFf6+M+Szq5dYHvR4pLHCg2uXudTrIAkCpVyOYyvLdOJi4KyLmK3hS9TmwJbxKz2uVAc4ujy6v2S77bbfzuqTRJFa0eBbDh3m1uo4FVlFEUUmhQGCfRHfXMLtPIkXy0IEUUKIyUiUVIJhE8R8FPsLbPD7KFopIjivjyDEmjVRRhAjDVzgO4iSgqga8X4iS0xSDURJQRAlVD2SmoiSgqKVmPZXmFdEdqk6s4pETc6hixJdZ0hdy3OgPEZdi+JqqiQxWTDQZQVJENCkWOkfhuwpVviRvTczL/aBAKQ8D/RNTrYbaWDe8TwMLRfFoWQpLdgGtsgpyvmoyDuRu0Ck0YtEvEL6ByaJm10j8vk8hmGkr58ug3w9tcS5GsF5D/j3YRgeBl4B/FtBEA4D7wA+HYbhDcCn49cA3wbcED9+EvgDiEgIeBfwDcDLgXclRBSv8xOZ7d4Ujz+jYzxf+PNzx/mJA7fwS5/8W/7NwTuQ/G40pb07xPY9Bp5DL14+222xOOgy8Fxs36Nhm9iehyKKUWthUWRhrZFqt7K4EiE9XZHy05HW6D5HX49ulz3OdueTjBmaxq1jk/hhSE4U0QkIehFZCYKIJGuEoR+9lguRnk2U8V0LUauD14bARvB7hP4wdveciKiAECEaF3PgO4RhwKB5nsH6SfobZxCkHHJ+EgQRUd+FoBSj7eUCgpSLyC8wwV1DGByF0EMTRbwwYM4oU1JzqKLIhG4giSITeiFKGpSqzBplgjCkoKpUczrfPrmHkrcEgQOBzRkn4H+cPIrluCy0WlGZUy4qc0raU2dJK8oQP5XEpqoV9k6OMzNWiwgtjnUmpDdRKTNeKQNbFffbJVGyy9cLeX3drmIYhsvAcrzcEwThBDADfDfw2ni1PwE+B/xiPP6nYdTs/n5BECqCIEzH6/59GIZNAEEQ/h54kyAInwNKYRjeH4//KfAW4J5neoz4XJ9zDFwHwzrO99x5JzVZAbfFhFpk1RrgBD6uv6lct+LWyYooUtfyiILIyXaDxiByF49eXECSNuv2EtcArq6LmH1vO3cxu90oOY2eU1IjmUw+8c8OHAagJqtUJIWw8wCB7xKGPkouCrwHvoukaAihGxGWWkJUSwR2A9+zUfIT+K5F4LsIooTZuhhZZ4JEzpjA7i2Tr+xGzFWwm+dx7S6aMYGolPDsDrIWZW1DpwGijKiWouyt38dzBkihj+CZkTvpdZGUCb6wcok767uY1Yt4YYAqiBys1CkpOQ5W6kiCgBJPTPs9u29kt9AF8xgQgFIHMU9VUJkfG+NAqcYnz55MLeWaEZFgs98nEbT249Y8AKqyeatKYtRbbapaoWOa6Lmo5U5y3Q1No9nv07dsdC3HwLS27e21nRL/esFVjXEJgjAP3AE8AExmiGIFmIyXZ4CFzGaL8djTjS9uM86zOMZzTlz3LJ7lVROzIAvcMjbEDUOUMODMoIMb+Fv0NH5mOSEt2/fQZZkWcGF1LSo1UaJ/6b5lP4U8RrN3VwqUjyI7nt02u99R0trykDZ7Y2VJLNlGEkVee/MhXjYxQ9d1mM7lqdvHUvKR1QLusBvp16R4diFJZdA8z3DQoDr3cgRCPGdAf+Ms1ZkCQ3MD3xkQ+C6ipGB1LqfxKsfcQFIL5AC7txzFriQVybPxJRXXaqEW6gz7awiCiF6dR/B7+K6FrEWWSugP8R2b0D5JLt/kJ2fqLAt5rMBHFkR2K7C7pINcgtBjiEzTc/gGPcBpfglPUpGUSG8m0IQw4Ki5SejjpRJufL0c10sV9b4fpAH9vm3juF6q05NEcUtfspphpDKYcj6PllNx3ajzrR8EW4q8YVPPlQ3aj9Y7Xg/xrqtGsYIgGMBHgbeFYdjNvhdbPv+kirdncwxBEH5SEISHBEF4aH19/aqf038/e4xbimPMOU+CIHKu20KxT4O7RjWnIwlR/3Q/DONHgCSIfMv0PA3bZNnssWZFWqek6Dj553Vc7ykuIGwSytPFnLZ9SNEj2cfofrME9JRjxTeS43pIkpgSa3Kukiiyf9cUe4oVSorKrFZgt38B1+5GcSvfIfAdZLWA5wxAkAlDH7OzSPvyE3SXTuHZHTorRxn216gd/BcMmudpXXoEx2rjuxaN0w8gSgorX/kCK499hgc+/EGcwQan/+G/Rucga+SMCRqnH+DYR3+Txpn7cO24vtCz2Th/L2ZnkcB3o+B/DEnWojhYMERwlpnsP8Je+yjz3jkYLuJ3j2Iv/R3W0ifJDR5novcQfu8UAJ4zwHcjqylwuiCqvKw8zt5ilYKsUNfzzI2PRceRotjfrrFaeuy58Tp9y2a8XKJUiNY1NC1q9aNpVIsG1Vgbpqtqqh1LZDB2JngPV+5ln11OAvr73/urz/p3/1zgqlhcgiAoRKT1oTAM/0c8vJq4Z7EruBaPXwbmMpvPxmOX2XT7kvHPxeOz26z/bI6xBWEYfgD4AETTk33NH/hrwJ+deYIbymOcNjvUlCKEHm+Z2g32BYi6xiNmbxBBwA9Bk2Xuayyl1pckiBypTfD4wuKW/ucJKWQzfVmkVpIfpIT0tSK7vu9v1SNl958QXtLmJQkcJ3BcLyIxVWWqUmZCL1CSFOpKDrezgqwWUsIKwyCKXcsanrWB3V3G7q1gjO9DzY+haCV816I6/1pw11h+7B+QtTznPvc3eF5Awchz6gufJggDxsYqGAWdY3/1+5SmZrnvQ3/A1K4JKnsPcfwLn6M+VqIwvpve2gk6C6eZOvJa8pXdDPtreHaXQhzId+0uamkPouwRBg6e3YncV0HCap9HL04jCGJ8/j6hP0wzl5Kix1ZkD1FSCMMAr3OCfGGGucIEMzmdZbPPhVaTernEjbumEQWB1W5U/lTQNUqaxqHds9wxPcOy2Wd3scxKu0NB15gslSipOQxF3ZKYOXV5ifFyiUvrjS1dP7JWFzx9C+kk23jD++7m9Dvufka/necKVyOrKAD/D3AiDMPfzLz1MSDJDP4o8NeZ8R+JM3+vADqxu/dJ4I2CIFTjoPwbgU/G73UFQXhFfKwfGdnXMznGcwbb8zjXbeEGPkgGlqCBfQnCaBLXkIisFElKm9hJgsgdtUleNT7DbKGUlqD8xYnHUZRoLsCsRZX8045aQdlMX9bqebpHgsTly+KKVpy09VjZ4/tBkL6vylFrZFWU0ESJ3OBxZLUQW1suYRggCGKU8VPyuMMuge8y7G7QOHk/zYsPMmiex3cG2K0zEHqMHbiDxolHEEWRyRsPISoqsixSKRfodfvIssjYvpsY9tpMz05jDwY4vTbdnkkQBKwe+zIn7/n/mb7l9XRXTiJIKsPeGpKi43s2CDJqcQ4QwTMJ/WEsqYgSAHpxOnJrRQlJ0Z5S+RAGUXIhstg25Rm4bW4WmxREma5jc/vULiaLRSo5jTfPHaAblwftHx9n1ijxjXPzdN2owLwgq8zVauwfi+JpSQeIcj7PrZPTVPN5DE1L2xtlv8fRgHxWXZ9V56uqmj6u5ZjX1bC47gJ+GHhCEISvxGPvBN4HfEQQhH8FXAS+P37vE8CbgTOACfwYQBiGTUEQfg14MF7v3UmgHvhp4L8BOlFQ/p54/Bkd47nCbx99AEWUkDwXTSqBt4buNkBUEfwBoVxGCgR0WY7KU8Ihju/zpul5xgSHc56PLsuc67boOsM0jmEPnS3EsB2yTfdGcaX4VjZ2lV03IZ/tkBBclriymqMkeZC8TiQCY4JN4Jqpi5hYMEkW0XMGOIMNhv01XLPP2vGH2Pu6t9C88BBWcy3SYIU+xviNVA8cYbC6wGB1keL0HnLFCmZjiYn5fYiKimdb5MenCRyH0uw+Fh57iBsP7GJpuUVll8XsS17N+qkv07l0CqfXRpAk1EIDOVfEbDxJfuwAvt1INV/gIslaFLBHAN9PiUwQpUjQGsaK91hmIclaalUGvgPF2wHo+R6vnJhFFATqWp4bjQqXrD7fffgWjjbX0pZF04ViGvQfeA6316d4ZH2ZMU2PFfwhs0YJTZKjbKaupTMSZZsuDmOx6naC1FF1fhY3/+Z7Ofbz115p0M5M1jGu1kzWv3f8ITZsE0kQmc4bvHVqjN7FeyjO3BVpj9w2fumlXBwOWB9aDOIs4m2lMcbCLothgY4XTSl/qd+hNbRoxJOaprWI/mYAPnEfE4wGxrNar+2Ia3S2nOz4doSWRXZ6e4ga+TU63U13VpbRclE85q6ZPdxeqTPOgKD3ZGplJRqtUC4TWCtYncsMzQ3MxiUA2uefpLL3JjoXT7PnVd/LytHP4dsmB7/zbhbu+0OCwEcUJczGMqKiIilRYD9XGaNx4mEAyrtvRFJU1o49iJI36K4uUahUyZXHaF48Q3F8ktLsftRiBUU30ErT6OUZPGdA4NrYvWVKU0cIA59cIaplDKUigt+LlhEQCLdkOaVcKbKuBZHA6UcZT3Us+g3IJXCb+MM2Un4XaPvY8BwGvsfRToNZo0xraOH4PjcUqwwDH9P3ojpVLeo59kRzlT3FCgVZZeA5lJQcHz7xGDdNTPLk2ioDy6bZ7+P7ASvtTvS7iV3A0enokveyKnzY2uf+xC9cHZnEzkzW1yjaQxvLcfGDgJ/bO4e9dj85YwLkCnhtwtBHss+xTyqR0wr4YcjucAWGp0Dfh+QL5ESRVuCnsyt35WFKSH3bBjkmlrgDwWiwPItEjpBd70r4WjOQQOqqAmmn0I5pptukk6hqGmU96vZpSAph9zS+Z29aLoFPqBYhDFIdltNv4NkWsqZTmJylOHUj+doMF+79CLlyDd91sNYfp7L7VnzPZv3EfRQmozCo0+sgKiqKbjB95zfhDaOyHklR2fPq72T18S9T23MAq7mGXpug6vvkx6dxem1cs4dqRCU7ZvMi+doeuksnyNdmkGSNQfM8oqRGWUe3TRBbiUq+Tkgk4Qh8BxEVz2ox7K+RMyZSa0vw+5Hi3+/jWRvRuLWC5HWouhY1UWJGdgn0u3A1g8uORUGSCAjZJedRRYkJNcdFa8Dh6gQAOUlCl/LIgsB4qUhJzdEdmHzjvn08vrzE2aUV1LhvmTWSYRztzJrNPGbbbD/T1t7PBXaI6yribffegyrL/MubbmfR6hEq9bSTKXIJSyqhh15cYCsyE6whBFHL4BAB3CbT6hSLYYgTBKiiRCCF0Y/RGabizcTKkkQRYklENkArJtlBP9gSvP9qeq1R2cN2EolkXV1V0XKbgeHERdHzahpjSYqNj9QmOFAooxOArCP6bhTb8uwosO1sMGieJ2dMIKkFihMHkXOLhGFAeeZmPGfA+c99FNca4Jo9pJxOGAaIkoo77KGVx/BdB608hiBFP+nETfOGFqHvoVZ3sXHqIURVRa9O4Jo9PNskVxkjF28vKSpWa43+8kW02gRHP/4XlEo6opIDPsKNb/7XLD/xCeZe9oPRMQQpIisn0n6Jkorv2XjD2BILfcIwKuoWRAk5ieVpE4Shn5Y0uXZ302UObNTBV5B9hz12F7Uwjp4/giKIDAIPCZG6quGGATlBpCKrND2H070WE3qBgqyyf3qSCd1grlZLZ+8u5/OAySAz3+N2fe2TOsdsP7AgCDj4G+/m5NuvnUzjtRt9u85wz+JZ/vWRl1AvFPhPD9zLyfYGHd9FLYwj5UoMQ9D9dvRP7XRxe+fw7WZa6JvARaLnu1GLlpyGJsmUlBzVnI6uKBi53JZp4UuFPIoSxY8URU6Xk9ejgfVRmUOC7QL82XWzgfkk9Z70nALSVi/ZomKAXcUSuixHYkxnCcIAWS2k0odB83wqGk3qBSW1wNj8K6nM3IHnDFh+9DNIioppDqnfdCfTd34TVmcRRa9A4COqOXLFyFKSczpqoUzgOYShj2v2KIzP45otKvOH0Mpj5OvTjN/8cpR8Eb02wWB1AXN9CdfsYUzMYjai5er4GI7jcerkRcxen4X7PoYgSbQW/pGhuYHVW95MKsTaMN+JhMKSWkDOldJ43bC/ht1djrOnNmHg4zsDzPYlrM4iZusSSJFV59pdBDlKXgiEFHoPow0eY699lFqwzrwiUpEVJlWNhjfkifY6U3mDH5w7yEyhyOHqOAC3jE3y0n172Ts5kX6vsHXKuNHgPETtcSZqVbKQZZnbfuc3rsKdcnWwQ1xXCWvWgN//ygOcXlnlwZOnsTwXNZE7hAFtz4XQw/fsKGsFqbvkezYEHhDQ8CILzFBU/DBMe8yX1ByVnIYiipRyuS2EoqsqirK1tCchj4S8gC3kpuXULWSUIEt0ybqj2cRkLLHsEmusXiiklpYqy0wbRQ6Uaxwp1sBZQXCjDheeM2BobqQWC6KcZt+SuNfGhS/Tb5yhcfIfWTxxnMreQ6iqTBD4OIMOgiAxaJ7fzOypeiaIDmphjGG/RXFyL96wh16ZRVIL1G/4BkRJwbdNChOzmOtLDDtNJCWH1Vyjdf4EhYk55JyOKEl4XkClXMDzfFqL51l84NNp2yFBkLB7K1i9ZVyrFWnQgMB38GMRbPa7Lkzehu9a2J1LOOYGxFZhGAbIWonB+gnC0Ke/cQarfT4iwjhulv7BuQ3oH2U87NBwh5zttSmpOaqyyjm7T3NoIQoit5THqKoaUtzBFTbdd1VVnzJZr5HXUwLTNI2uaVGOZ41KrK4rzRvwfGDHVbwK+OOTX6Fhm+wfH+eBM2exbZuDlTr50ILAIwx9Jr0FiOvwAj/ujmm1oh+tWgBRBmUCNRBRBBE39FFECWSQfCGVTOiSHKXHYxlEUlibkNVocD55lvxgiwUGm//AacGuIkOs5M6umw3yB34UdC/rOn3bTl3XxH3VVRVNltFkGUNR2aUblPuPEGRIJdE+DftrlGa+Efw+omqQUyMF+uUvfwitNsHlz3yUTrtHqaQTuA43vfmHIplEJyKEwO4iyiqlqYOp1ep7Nr4zwGovo5UnkdUCem4Gs30JNT+GO+whxdIFZ9BB1vIIkoyc0/GGViSZuHweI5hBNSr0Ly7SH9jUqhOUdx/g/GNfoVDbi6yP0bn8EK7VojRxKJJrxCTlDDYo1PYy7K8h54rkCnUEQYzKlWKlvyBI6bKiV9IsZODa6OUZlFwJs30J37MxJg5jtS+i1+8AwDVPwfAo07LGjG6AfgACi65UoiDJ2IHPp1cucmx1JcowmiaSJGIokUC1Y5rYcZZxc35NN51OzTRNVFVl6ATk83ls206ttFt++3088bbnf6LZHeK6Cji2vkqr10+zOBP1MQ4Wq2CfIfCdSIRYejmKu4w7jNTayb+zXp4jcCOdkDC8REW/ETvwccOAsXhGmlpOouPY+GGIjYcmRTKKIAzxZRlVljYnL5UyM/qEwZZOArDpBmZf+5mZchRFfsp6o1lKVZZRYpdRyj7LCrqs0HejtP2MbnBAFfFNNwqQi5vSAVFQyY/dGLlN/hBB1oGA1Sfvobd8gf7qAuU9N7C8fB/1XdOoxfL/Yu/Ngyy77vu+zz3nru++tV8v0zONGQxmMEMQgECC4CKKWkzJIqlEUWLJspPSZjmWE8lOxfkjcapSUVWScimpxKmyEjuxXJQsqSqOEqokU9ZGUSIlSKJIECRIEEMAAwxmMDO9vn77u+u5N3+cc+97PQBJAAQlOTW/qq7ufv3Wfu/+7m/5LoTrFxneeJLW6Uu6UgP81inSaES2GC5J1oXSiSieoNI5WTSsE1u4dp7BtU9hSYnfXkc6LoVSFJkma9t+QKO/VS8Otne2yaI5/fsfxm11+eYf/nayZEK6GCCkQ7h2ntngKnk6J4tGOin7bebH17D9Nn5zk2QxoNE9q9tEIUFlCMcnjydI2yeZHVAGPa2IEfaJZxr20dq4RBqNGN96kqBjdLyKmDyd0+jsUFqSPBpSzP4YgHZzG/y30BA2793coecFDJOIx1ekcKr55VciWQshaDVDVFHWFVYlbHinPdpfZPzleBb/Fsd//oe/RZSmbPW6qKLgmZdvcXF7i1DaqPmENBriNfpklIDA8dokiwGlynAbfZLZvq64CijjI6Tlcsa/l5ZyiApVn0GnlqglU4Rl6SRWbQstQacRAJqcXeO4ilfOrla/6iRktNKBE8nqToBqlQRf7W+h6+JLXWmBJpZf7DVg/nT9v6rwWmWhKMoMUSqddKIh4dp54tkBn/3VX2ZjQ89Xeucf4F0/8EO1tE2RxWSLKUVHJ61w7TzJ/EgLB1pCJw4hcYIueTKlLBRZNMP2Amy/raEWRv3UbXY1kdsNII0oC/28vU4ft9mtq6+yUMRxxmz3Os74mGQ8YOOBb+bo2U+zONrlnvd+rx6wRyPS+ZhsPuXoy0+y8eA7afQl0XQPafuodK7nXya55obupPKYdD6gyGIKldLoncPx2wjbJ57skiVTXUGOby11xCxJ6WyQDL9EkUVY0sUL+yTTW7QpuCrv45wXsmgpQsfl3q1NXtrXr7syzq3Cd536va0iy9UJ040qsfm+jxQW7/in/5jP/uR/8YaPmTcj7iaurzNUUbDR1jrj92ysc23/gLffe46CUq/6VYZwtSkDalK3iban5wd+c5NCpcsBfXoExYK2fy+BHTBVeU3E1hQhzWfUv1sEjoMjBL6way9BACnlCdL2V0LMV6oNw0xzIgP3JIVEz7cq0cDKDkxbeXWCoPbua7te7WNYlCVv723q7ak52EBv2aooVEpmKqJ4sovb6PP0r/yPXHj7OygKxdaD72Vw9XP0L7ybZH5EnkxxglVcA8cAACAASURBVC5r975dq0N47brVVnlMFo2Y7V6nd98j9UbRkg5u2CeLRqh0XsMSumceplApi+ENLCHxO9sk0wOKNEG4HkW6rL78Th+n0aSxvs1s9zrh1g6WkARrm2SLGeMbz9BY39avT+WMrl0hT2KGV5/GabRQaUTY10k56OzoltCSWLnUiSmekC2mlA3dSk/2nqFQis72W0mjIX5zk9bGJaLxLdL5EbYbEk33ENJhMbyuW+LWOrPDZ+mcfgRUyr3RZ7HLM9zr3cvt+bR2I6rGChWgeXXbvDwJaZDzbBGR5zkN3zsBaI7T7C8FPOJu4vo64u9/4t/oDZvrUpQlLxweMY+1rlZXupSlQtg+CB+vOtOm8/os6Ta3SBeaPF0N6gGsIqFcPIfjnmLN3SQrC6Rl4UpJqkAnL/27a24jLD0Hk9ZX3rcsh/cSaQlUWQCiBorCyRbRtbVIXpWcALC1sWpgO4Qrj9l2PTaDUL921+c4S9kO9Gt3/NIQqbOay1chzqUb0tp6K9ODK5x+1/uxhGS+/zK9ex4jT+dMD54l7J/HC9dRFXyiLOpW2/FaGoWfxaxdfJQii7UwYNgnGt/S7klG/TSLtFVbNL7J8KVn8DtrCMcji2b4na0T211RKJxGS/99MUU6Hp2zl2ifuYiQLs2tCzoxZRr9H48PiY4PaGxs09jYJuhtouIF3voZEpO0ijymyGO85ibpPNWvxwsNCNchXUwYXbtCY32buXeNcO08fnsby2kRzw7wm5vsX/kkza2zGj4SrusTo+OTxROOr/8ZvXseQ0itCvtSa4NY5aw1QzZaLZ566TqBt7Q3qxLZagSuyyxO6DRDImNiu+pZoM2EBd/24f+dP/zxn3ptB8o3IO4mrjcYP/5bH6EdNmj6RoLFstjqdVkfHNP3GySlwsNUGcJFlilFfERZFiSLAV5zk2h0vb4/IV2E5YKwKTEOP+kuJdB2Num72sy0Us2UlsA12k9VspIrCSYrinqj9ApIgyUQloUjbJTQVmZprj/QTc9jZuzopbDq1rO6/6alPQ83gxBpWQhLEBhKT9txadi69dh0PUhugyUoLVlv4qqw/XUscaCVSaVLNL6J3wYn6NHavMhk/wrh2nmzMQzrgX6WTCnMRtLx2ssK1tc/u2Efy5JaIsfIMksnoNE9q2dT8wHpYkD37GXi8SFes4d0Q9L5oD5xSMfF9gKyxZTo5otIxyVPFnid/lK00JI4jR6+7bP/9CeZ799EegFeq4vb6lIUCq/VxbIk0g9Jo2FNvB7vPY3X6J8Ap6bzASpesP3291OqDL+9zfTgWWy/jee02P/iH7F24SE69zzA7Sc+xrn3fb8eNcyPGF57ilIp1i+/h3i6R2vjEoVK6dsuM/M+XZ+OuGdjncl8UVfUVVTzUSkFUZISuIVZtDgGTB3Xc8xVutm3/Iuf5Y//47//dR9LbyTuJq43GJWcyHgRsdVq4UrJXGhb9axQeJYWtZO2D8kBIBDBKax0Tjze1Wj6QjHZv0Ln9CMnjB2A5dm/WBBYEEq9pYtVTlEWqKoqWh3GF1YtjwNLfa9VKWBXyjoZVdWSHuLrBNb1tH5UZFyPpVgmSFdIfNtBlQWh4xLaDk3bxRMC1xJ4QpKXBUlZsBFf0Shxy9Zf5UleX1nmSH8N8gWL4XVUOseSLnk8wWtuIqSLdEOKPK6TVqEyiixayt+Y+1J5rCuW+VGt7QXQ2rikcWJCMj18tm4hAfJE3z6LNKG7yJeYNOkGpPMx8VhTZePxgPbOBaTj1oTpQsVY0iGe7NLeuUBgkOwqSymylMapbT0qKFWN7cqiEVk0o3P6rWTJBK/R19I3uZ5vOWELx29riR7Hp3/ve8nTOdHgOTbe8hjpYkKeTDnzrg/WJO5G7xzDF58iTxZk0VC/tnSOdAJ6tst14Fyry835hIlZwrRDDQzumO+qKLRx7R2A5TvNOe7cRn8tJsY3Mu4mrjcQ/+WffIxOIyBKs5VqRNTI9r6/dOtJFwMcv41V5qjFbdz2OZobA/a++DFOPfxXWTv3btygtzRvAIPpMlHmoGa0ZIOkKMBvMEwiHKHbxNU5liMkqizIlCIrKjkaWT+/qp3UyUueSFybjZCsUKz7Ddb9BtenI+0M7Xgnrl9tNEPbYcML6EqXju0gKaAsyHBx0pcpVYKgpBTUc66lvLL52AkfZIFwAiyjBe82NxG2T5HHteqCTnSaEmT7bUgr8nJWo81VoXDD9Vpf3vFaRNM9nKCnTTbMiSCe7uokl43xOxtaYHA+PvH+JtMR2WJGqXJKpVvGxeEubquD1+6TTY8osgTheKh4gXC95W3HA4Sjk5uQDtFwD6fRwvZC/PY2ThChMk15qjB90vYpXf3+j17+Amv3aYuE6eGz6I9DjMo0aHUx2CWdXqF1+l7c5pT25gOcevg7GbzwZ+w++Qk2H36PTmpqjjz4PVrt93CcRPq981zmUVyb2GaZ5r9W1K00O5mgxosI7Ycp6IQNZtHSubuKv/IL/4w/+LH/9PUeQl933E1cbyAC19XA0NBlnukzdXXws32KG9MxYtuiKJWuGlSGLBZIr002u0k6HzC+/hzNU+fobH9TfWbmzpmnsHXiyic0vSZpZfLgWczyFFdJUqUMNKIwSUyQoQ9SV8pagWJVOqdqL33bJjCJSP/Nou16+NKh5/lE1ZDeqBPUWDLboSEkpxyfRjGG6EhrwJcKF0M6rkwrygKrzMhzfeChMgoj4Oe2z1Hmc61XpVLmw9vYp0Nsr0UyO8Bt9E8oLFTVlIYNrLMYXtdzKzMjKrK0nn/Fk12kG+IGXaQbMj24ot8H28cJuvXzi8daQFJlKSqOkH5AttCbSIDCvL+50gDQKgEKx9Nby8WM7GgXt9UlHg8o0pTO1g5FnuqNpt/Q952lOEEP6QR1e1wWqm53LSGxpEv79AN6MeCGuI0+6WJANNolnY4ospTCPAe9ZY3qOaDX6SP9m3Vl6jc3QfqUnFTWPd1fq52hMqixgKtQl6qVdO3lfGsWxfXipnIcOjSE+r+IuJu4Xmf8H1c+y8P9TV6cDGs4AixnTocTfVAKAOnieBLpr2uSdaYlUqQbahxR65T+AJo5jbXS9mkcUa7brCKGdI81Zx1XuNiWhSsEUaGIjKlGqhSiLKE6sCyL0HbqikoaOAWAY4b6lTu0tCxcU8EJC2xLENo2mbv8wHtmjpaWBZuOj7CgkR9q4woKo1panKgWVRYhpEIZhVNlBueFSvWB5axjqQWjW4/jt07p9hmw3ZDm+sWaTgOc+B707idfaBiJymPyeKK9FVWGZbZ2trEayw0EQbohpHMs6RgYhBb/8zsbpIsJpVKUjouKIwDaO5eZ71/DX9ukVApLjgh6myyOdvG7fYTtkkUzssWUbDEjjyMsqbeNTkNvjJPRgKJQtLbPEY8H9UaxLHX1KW2fPJ3XvMtkelBfJ0smZiOq278qacXHmve4ONqls3OZxfE12lsPkEYjzrzjg7Q3H2A2uArt00Sja1i9e2ovyHv7fVRRcDvWQNnVjWIlub1qyBGl6QkozMzcTgrB4XiCFIIz/TW+6xf/T37vR/7uGz6m3kjcTVyvMw6iOT0voO16tSRNtdmb53pL89L+AfMip+WGlJakSI4Rsgkq1uoCbsh8cFgfXFq9IDjxOIVKtfa6Jep2kSKm6awTuG1GecpU5Tim3ZNWRloUFKXRxCplLVRYhU6uAt92CG2Hhu1oYT9ThQksKuRDbgCuVfhCYmGxZjskZYFnAXM9u6uljoXm1oFOWtXr0DOcjMJUXdJUTY4/I50f4jU3SWYHOEGPQqXE0z0a3bO64rSEfowyRwpJKTyi4fNQaPJyqTK85qY2cxWylpWhAMtSugIzFZ4w8y1geR0hcRttkkJRFAq1SGjvXCBbDBGuh2sqL0tKCqMyId2AeHiA9BtaVsdxyeMFftjHkpL5/k28rpZk9o2aRWfnAVQW1/O3SkRRSNeAV4f47W3z/5rrtnB4w2D9hjS3zzG59SL+2iaN9W1UvGBxfAu32SVZDPBb2zXY1Ql6xJPbeI0+HemQN1pM0oRhGnM0nxOlaa3tVlVMlXrtnfCIqsKK0rRmSawyLm4NjvmLiLuJ63XGQ2tbHEQz2q7PuVa39kU8WMzRDs9aLvnzkwHva12A+XM6Oc1fqqEAXnMTL2xSqozSkpSoeiVenZF10qrenqqSiUEtkM46fWcNT7iMcwu7qqbylNTMsnzbRlqC0HZwDabLFYLQdrGFwBeShlEkdSyBbVk45rZVFGVJQYlXxjVqmyTX1Vd2ZPSm0Er/lk4yJSkW5XKLKGyTnON6e2YZxHkZa3nmZHaASiOcoFdDFlQW0dp+h0ncWteqzBdASqOzQzTZpbl+canpnsfkKTXfUThO3VZaUmheZLmEY1ThBD1UFpHNb9BY38bv9GuDC5WlRMcH2KZ99JodnLBFMhmgTAsZ9DbJEy3B43X6unJTiiJNcBrNWiYnnQ9wgi4qN9AHQxVKzesFmO49R+fMg4z2XsDvbaLSOSLoUhaK2e51GuvbxMcaSBpunefGH/0a6w88Srh2nnQxYHF8jebGJdz2ecg0GHfd8ZiqXLtvR9GJDXPFRb0ziS0pX3oDGZm/VxXYa1Eb+UbH3cT1OuJnv/QZHumf4q+sn+bjhzfrFmt3NkUVBec6XR7pn+LGxiYfff4K73v0MRA2dmOLIjnWq3yjhvnW/+AfEI1vEa6dr2c4KotreRMM2JQi1zZalXW8kBomUSxouqdxHI+ZynXyEYK0KMgKWUMVpBC0HU8nLikJTIXlCYltWbSlg0NmfP800RuzldQDdz1jo1z5m2kH62XCagiXstAHdVkq8mhi/BJlvUFzTdURT3ZRWYTb6ENDt4iyd5bRy19g550/pof3RazbbDUDWej7L3O8sI9lhzhlTpEtSKMR0iwAQOPDVDqv50nC4Oqq2dZy46eH4+v3v5s8nbM4voklJFmkh/OgE5jX7JAnEdJvUCqF3+0zvHaFoLeJ7QWkWUIyHhBu3UM2nyL9Bk7QrJ9LRT8q8riu/A6e/32C7g7xRKuKp9Mxhcro3PNQfRLTml49Gms7lCozs66EdHbE+gOPMnjuKRrrZwlap2qhwzIbgbdJmY8Y5inC0vNOKbSemyo0b1V7MeolU8U3rQbvcZKeSHKr1LFVHwLQldcHfvnn+J0f+jtvwlH22uJu4nodkRaKo3jBb8xGvKWzzqcObhLaDn/70iP84aF2UPvU7sv0goBLG5tkIsRWCSoeYwc9PLdJmeut0vjWUzhBu96CWULPNRy/bWSB05oeU/kFwgpMItV2V567iWe38YSumnJ3OSz1pY0rJLYQ2GiLjqa08SyJKyya+QGkVVIycSeA1Wjkf8VkZaohHQIsF4ukFtmrktZidAPLEjS6Z5kdXSVcO89icQPftHkqixCOTzS8wc6jP6grODXTrtWgk1iVyISPkAmlma1Vuu8AQmljCpXO64QB1Nu7imZTDdqLLKLIIqLRHK+1iZD6vpKxVrKw/QDbb+A218kWQw02zRKG167gNbtIR1eWtqefZ5EmNNa3yZOIZDLA9hv1QsB2Q6LxLfJkjrBd/M426WLAdFfj+ar7siyByuKaIlTmhQavqozm1llm+zew/QbC8fA7fUqVkkYjwrXzTPafwWtu4np6hhhKm57t8pn8NrBUq62SkS9tIpaJq1BLxdxVileULAf4cBIK8RdRed1NXK8x/pcvforQdrgxG3G5u86/eOrTFEoz73cXM0LHYZ5l3Nddq9u0l5I590sPS2VQFqh0RhoNzSZJcePxj3Lu27+f1sYlnMY6eay5jW7QQ6n0az4nS00p4xjcTZqySeg28ISgIW2SssAC7GoTKGxsyyIQkq6lILkB2eQEDceyNCewtCSV4pFVZnUVA7xy8wk6cVnLWVeJZeAPGRSKLJ3gBl0WwxsI6RL2zyOkUw/jyywj6OwgHZ+gew5kG+wuxfQZpoefBqB7+hFKu7NsSe2OSahxzd/TAFWFsLSefWmMOPJ0vlyAiKXJajUUr6RxRtefrrXnG+vbZui+wGm0cJvrqCxlvn+zpgBJx2V8/Xmap8/htjoIx8NttImO93DCFkI2mN5+ifaOTobFSsKsYA3B2iZes0Nj/QxZNCGLlmTx1aqxglfkyZzG+jbpbFRDNaa71+jc8wDpYlC3z1Y2IJ4dUKy/n3mZ0/MCDqI5plhaSiLZDnM7qwfyR2Od6KvZVu0fYNRCxnNNFTphUCsFShV877/6eT76N/987B3uJq7XGNKyCGyHYRLz0eeeMYqSer0cZZl2YXEcYpXj2zZt12OcpeB0sbIFFmVNtrUsyeb938HuZz9JHutWqlpjC+lSWg7ClZR5tEwmWPW8p2ody0KBsCA9ADvGkk02ZJOmDJmpTOO+qucuJK6lJXNId0EtTiQt0K2dhVzOrjDKrF8zxMpXsRzQm4SRJVPcoEu4dl7zCs1r1gqlU/z2ttZoR4Bs6HlaGjO4/qfYXos8mTJ46U9Yu/AB8/fYgFr11lU6gXbnySJKlZFnS79GYCWJKawVzS/LEiAFZa4Tl9NoEh0fkBzpaktlKb3zD5DHC45f+Dy2FyD9AOk3EIWiVDm9iw8hDW5LulqZtZLwkY7P1kPfriV85kdk0cgkdEVr+yJp6wivtUkyPTCKFhrrpSlBA6YHzyJslzyJcIImpdJ4uHQ6Ik8ivE6fwlTgXriuE7gdUKZzUrONbEmbnJJzrS6TNCEtFNdHQ9qe5iC2XY+jaFEP4n3PPeFpsFphqaLQQ3z1+syGvxFxN3G9hvhvPv0H7LTafG731it0r6o1cei6JrkJMqWYpInGeG2eRuZDPYQvU7zmJk5zh9nekzQ2ttn7/ONklzWaOlw7r5NRmYMlsKSnk1RVAVkCyGG1SqLULVM+MXOqlEA2CaRPaXukZaGH92UOZQIq1Qc+S7WGV42VFvCrXq+utswGsGQJpjXJ1TGEcifo4kCtQ+W3T+M2t/RtZVtDK4oUhE98/GX81ikavXPMjq6SJxN9vfwYilQ7Bbnavq0UnrYQKwtsoxQBGhlSFvGrPGV5YmZoSRcJ2GZ+1Tl7CdAKEtPbL+F3+vVgHiCPF5RK4XX6ejOcRfX9WJagfbrF+OUrmvqjMqTj0+ierZNp1a42Ny4yH1xD2C5OoBUxbK9dn6y81qapvgYkkwGWtGlt3U/enJMthhxdeZLufQ/ULWaWTMiNoKEb9HBa98H0szjeJtDhcneda9MhW60WbccjdNwakDwzWmqgqy0hRT20X0XOSyFIOQlUrbaOAN//kV/iI9//w6/+WXkT464C6muI49mMz964wXA6QwpRO9lo1r3kdKejCc+GkDzPMwbxgrRQLMoVbJYd4gRroBb4rW0ufMeP1WfQ1JyNM6NXrhOVbZIWrA7NV1u3ExVRmYKa6I1fdoCVHeCpEdL8TnqgvxfL21uGmlR9AXXFVFd35RJ4+coEZiotS8+3sGwNaZgdaOlk83rcRh+3uYXjtwn6b8Hv3guunsMg2yz2P60pQrLB8NrvaQBquF47UPcvfo9O6KVeVginASqmzOcUyUij0LNIK49Go7o9rNouTGKpwZ9lQZ5MsSxZ061sL8RtdQBobGzjhC1sA3mQfgNhuyTTkZ4vuZ4hhF8jmQ3rVs7x20xuP0fv3kfqeVmFJ6sYACqd62F9FhP2z9ecTNsNyZPJUrMtmerbJpF5foHGdZn3YvPh9+AETVqbl2v5pOHNJyiNYmo6fh7sJmW8i2cJjuIFXdc3UuA+240mvtTaaqsy3ZVf5526ba/2c1WR3emv+Y2OuxXXa4jhVEu+bPW69bCyMoRwhGSSJEbMTxE4msvnS008/tTogPd7y7YP6QJCb9eCLmfe+V3c/uwf4HX6WGckXusMepBUDZOqD4kekufxWA/zpdQ4pzoKDUsoUyCFYiWhlOb+ymJ5XU5WHkCN3q8P9tcS9Wxr+VhLpVNZJwrHb+tButPRW0JLoKbPMT18lu4936IBomqBpea0Ni5jBz2wbMY3P13Pfbzu/ZR5BAYqovJZLW0spFMnLf1aCoPIX86xQMMlnKD7ipeRRRPyeEGwtkk6HSPdgNn+TYK1zRrw2T5zH0JIsvlUV1O5RsTnSaTJ2oaG1Nw8S1kqLRft+CyGN2p8WQXR0Aktrb9blqhHBlk0QmURttfS6g9SUha22XZOcBs9nEaPIosQjkOWTGh0z9Yqr7BcRpSZpjNprquDIySDeIFvO7hCMytAJ56ewWmBSUiBD5FRda1EBVf8M7+Se/qfR9ytuL5G/PhvfUS/qa0m+8MRw+nshI77PE0NZaIkzXNmSaLxU85SjTRrPFwfPHk0xCo16DSeHdDonSNbzBhdu8Ktp36dPBrc8Qx00rHqA08nghNJqyyW278atmB+LlLz88mkdWdUB3ZdUZmEtEpz+epVV/WjhnPk8QTh+LQ2LmuKjdsHZxO8HdTsKtGe1pS3LEk0+LJu7yyb0u5odH06I53ewg26+O3tmqhcg3Ip6pawNBrvKo+1/ZvhOErbP+HdWNFsVDrHbfQ1TcjAU9xGD6/dx2m06N33CLbX0qoQsSYmS0cj5Z2whRO2cMO+5qBKSfvMRWy/XXtFOkFP48mE1GYZ4wHzwXWS2YGuls0ywQ16dZVbAZNtv0082dX6YHkMQhL2z9cng+j4QANWF0MmN6+SzHSlFU/3cIMek1tXOHz+T2ot+yqRB0LScfXmtWtAtaos2Y/mS1yWJYwZi1NXXr63VLm9U2Ry1Ul9dR7200988lU/Y29mvCmJy7KsD1uWdWBZ1tMrl61ZlvUxy7KeN9975nLLsqx/YlnWVcuyvmBZ1qMrt/lRc/3nLcv60ZXL32FZ1hfNbf6JZelV2Rt5jNcb7bCBFIKX9g5ohw3u2Vgny/JaMfR4Njth1hq4jtkqakJyaDvMDZSgLBR2QzuwVDgdYfucedf7yRZT/M6Gblvu3CiqVFNXzIFamWtULZ1FSakSSpXoy0u1rLJe8fXVo+YY6if8tf9B5Z3VoahBp/Fkl8Xwem2iqnFpMeO9p5kNXsAN+/jtbdxwXVdkAIWuFKQT4HbuJ+i/heapx2hsPFQ/H6tUlHlkXK2Llc1hVqs/WJbQMyczhLcsYbS7dFtYGu6jdAKS2QHC8Y2VnL59kccIx8VrdRFS0to+TzafMr7+nAamjnaZH94gXD+D7bXr9zQzyquO1zLLE424T8YDprdeJItGxNMjfXklyeOG9eMnswMNpZiOWBzdIhreJpkd0Nt5hOb6xVrN1fZCikJprJiQNPsX9OC/UGw98P4aAFydIPu2TSj1tvtMqGdcB9GMURIT2g7n2roK1Usml14Q1I5S1Yl6Vap71SUbqE1SpBA8de2lr/25+Trjzaq4fgH44B2X/UPg42VZ3g983PwO8CHgfvP1E8A/A52EgJ8G3g28C/jpKhGZ6/ydldt98I08xhuJl/YPtDfgvWeJk5SXD4+0Zrdc0iHuDGU8EX1pkxWK/TQ2MxAXypwSS8vzSgcv7NM58wiXPvSfsPf5P+LFx39Rg1HNnAOWMy2VTIzU7xFpNCSNhhTZAmXItiqL61lPqZKVBPa1Y3XG9dU2ia9adVWPUYFmZVNrpLPSjmYjED7ja7+BtH3apx7S86v2PchgE8vfpkgnFMmILJkwuPFnjG98gnyxj4r2UNGB3jYKI0uTzmsFVZVFtaxxpRiRp3ONyM9izaGEWqe+LBTJYlBXZMJse6Ub4gQ9SpVhe21aW+cBGF67wvDal7CkxJKSZDRgYYjVwpCmy0KRGcyYJSTx7EDbjx3dYL5/00AXmsRDvUFMZ0d1FSWcAGU4i4VKsT2tvCpcj6CzVVOaLOnQv/BuxjeeYffJTxCsbaLSiGh8i2h8C0tIuvc8RDzdrWV//I5eCqT7H8czHNRA6HnsPM/oej6B7bAdtthuNPXQ3tYVl7Cseva16vhUuTsBJ2Zj1Ril2jp+I+NNSVxlWf4hcCdp6fuAf2l+/pfAv79y+S+WOj4FdC3L2gY+AHysLMvjsiyHwMeAD5q/tcuy/FRZliXwi3fc1+t5jNcVH/jln2Or10UKwf5wdKL/r1bGUZrWDjcAaW7cZsqSQbzgKI7Yi+fQeps+iPOILNL/Ksdvk8UTbDcknuzir23SPXu5noWoZFLPjCqg6vj2U0wPnzOATn2AptFI02pKVc98KldlivwrJq8S6xXzrPr3ldvceZ2TreNKNVcWekDvrOH4ba1ammlzB+wGq7O9mkAtmyAaqPmNEzLMQmrKzvTwWRbD6/prcJUiOa7bV2UArpUeVwVBOIFNE7JObLqVNKt828fx2+b59DRoM+jV1U9uThIAQmj+4fj6c2TzGeHWDsGaxqBliyGTvWe19Ew6N22gWCYhv1FftzAVUTrVdJws1lLeledibnTppeNTFopw/Zw2jl0MSOZHFIbeZAlJdHzA+PpzeK1NprtXyRJ9X47f1qRz85kp0km95T2TX6dru7hmUN9yPNb9Bhfba7hCGC23ElfIJVXMkK6rjWNlpiKkqH+uNopVdMIGP/TR//tVP3NvVnwjZ1xbZVnump/3gC3z8xng5ZXr3TSXfbXLb77K5W/kMU6EZVk/YVnWE5ZlPXF4ePiKF/DA2R32hyPGiwXtUOO2KgkQIQWFKk4oi1bJKysK5nlay4k4QvKlaEHpbNRAyaq6cZtbuEEPN+hx8f1/DyFdXvjDn+fFx38egPHtzwNw7Y9+iZtP/ioAg+efYnr7OV5+4iO47XOMbj6J47W0iqYRpasoRCeS12oCWwGMVmYUrzaUX0Xtr0ZFmamrumq2Vt2nt4HfOkX39CPY4RmdoNQMr9EHIXH8Nnb/W8jce8ASpGaNX9l1Nft6bhSunacsC45f+pwWHMxjksWgxohVev1VEsjiybK6hVrPa9V4t0rsFaq/UmqoNOq9Rt/MqRxtpgE0T99LrZB6VwAAIABJREFUY+O0Bo8e7WIJSaN3FjfsE66f0xtCI7UspEupUpO8tClt5+xlTQlaP0NjfRthuya5ZLUAYjS6STTcY/D857QUjnl+Go5SIByf6cGzTG6+QO/iQ7itLovjm4TrZ7ThhspIoyHh+sUT6hPV+4XdpShLnpkcE9pauqiigwkzl+37DXzbITMzq7bvn3AIAl1lVZ/9KqTQbuaXzmxz6cw23/vgw6/4zLyZ8eeyVSzLsrQs6xW0tr/oxyjL8p8D/xzgsccee8Vtn7t5mwunTzGZL9gfjuqyOM31jCvNcxzHrlnzq23jqhJpViiOkgVf8Fp8E3v6oEcfXFapWIxvEq7fz/G1T2pDiAtv4/iFz/OlX/ufuPw9P0U82WX90mPc+OOP4nVvMr5xlXQ6onP2EvP9pyiU4vCFT7I42mXtvke1l2DnjLZFMwNoWYFY7/wfYC3hD1/x/3QnUNUkwAJKS4GVQilWlgACZBsRCEoDEtWE8YJw5zv1EF62iW59lGR2wOL4JoujXU4/8t0k8yPaW29FBpvYpi10u5dpn34nZTbWw/kir40yyrKgtLR+VTW/soyrdFkqkvlRPdgXgcZZVaR2MPxIw50sVaphCnlcJzUnCOldeIhkPMAJW3jNDpbU7W80voXthkxuP0f79CXi2QGVG1HVlgK0dy4wO7iBcDzyZI7b6Gl5nUwP38uyIItmTG6+gBCyNt/QT1rW1Wk82WVy8wXS2Zh0Nq7xZcIJkG5INNI8y9bG5Vq4sTIWsRtbIJvYhWArCGkZiW1VluTmBFuUBfMsJc4zHCHAWM0B9WyrogFV7WJ1OUCW5RrQuljw4mT4VT9TX298IxPXvmVZ22VZ7po27cBcfgu4Z+V6O+ayW8B33HH5J8zlO69y/TfyGK85fvTf/D90wgbzKGYWx3QaDaI0rWEQVeV1J1vetTWWSxVa2G+SxsR5Rt9vaL345luRi2e17It0KQE36HH80h/TOf2I1qpiWyeeaMR0/xmG177E+MbzBD2t9dS//2Gk47H31J+wcem9HHzhTykLDZwcvfw0/fseI57usRhep3P6EVMdaeNZ4xe0fKGWTkSryUuDWl8lya1UbHrwrBcFlkoRrgBRzbp0kkK4oHI9l7JsgzPT/MP49q8xP75GPD7k8Euf4Zv+xk8jGzs0LFuj44VPBbFQlovMR1iucQ4qYryqSjRtmdvo15ipQqWki8JUU8v2cHb4In57/cQw3RKS0swQq0Svq5dUbwoLvaV0W3p47YQtkukIoRT+1v0kswM27n+vhi8YC7QK3pDMhmTzKa3t88z2b1KqHNsz5h1CUhpAqtYSy/E7WjgxTyJOXfxmANJodML9HDSea+Ohd1GkCfF4QGP9DIGp9iqbs4q4XyWtMhtTJsd0u+/BtkIcSzBVGVGh6sQV5TlxRSwvy1oppOoqqlFJlahUUaBMAsuynPVOm4fWtrhmDzmKF3zq8Dbv2Tj9Oo661x7fyMT1r4EfBX7GfP/1lcv/nmVZ/wo9iB+bxPM7wD9aGch/N/Bfl2V5bFnWxLKs9wB/BvwI8LNv5DFez5M/ns4IPL0OPhxPOBxPONXrcmtwXLeG7bBRbxRXq63CKI7GKscV0tCAHOZ5Rst2uFQBI8uCIlsgvS6d04+wGF7H9ts0OjpPB61tsmRCc+My577ZwQvX+fJv/a/4a5vYfoMzj30Hu1/8OG5Lk33j8YB7H/5BRre+SLaYIoTEa24y2XuW3j1vw17Tw+aaFL0yVF9NXncmrSrxrUaFOaoVF9IJotKYl039Xfh3bDcBBGr6ZQ6ee5z4+ICNB97N2//uf8uktGkLKC0bK7qqeZgiANk0ZiAC7HbdcpaA05A1VUr6ayDbyGQP0EaxWTTS4NlSD+3dsEOhMrJoiNfcrOeLQD3cx1Rt6WKykhCXWmnC9vFaeh5WqJRm/wIIieuGpPMjSpUinAAKhRt2yOZTouEe3bOXSWbDGjWvN6d6libdkHR6lcnNF5gcHfKWD/0N4tkBrY3LuiJTGS89/isUWUrn7P10zl7i1p99jNPvfD8bl78doDbd0LI+uoqrnnc6fXmpjDp/Ci+4n/1C66pFhSJRikmW1ElLCoEszOdXypOCmXdAH1xby+JcOLVF2/UYxAtCRxuqVOKT34h4UxKXZVn/F7paWrcs6yZ6O/gzwK9YlvW3gevAD5qr/ybwPcBVYAH8LQCToP574DPmev9dWZbVwP8n0ZvLAPgt88XrfYzXE1Ga0gx8Xj44qi+7cXh0wp8Oo8F90jFF4dpa+sMxqHlXSOI8w7cdbswnXA7MgVAWCLcJxgzCb2uDhWR+hGsoINVMrCwVk/1nWH/LY0TDPdLpCOG4SMclWNvEbXVprO3w4h/8MtIPKNIU2w9QWUpr+zyN/kWisZb2tYP+csalRe7NzxX/UX8sKk7kquheFW7QNclOt03C3ySb3cAJTxslh0qPy9f3XaH1i4VufYTk0nf/FHTezaJQOMKCbA8rH6Hi4xr5rrFOQv+f0j2duOyu/g5Yjou0zX2bpFXNdqpBv+aITpCubqkWRzcYPPc5Oucuobywbt9sv11TcFbDsgS2kVh2gx7C8aFQRl8rpjBKDplBuk9vPMvaxUex/TZuo282horG2g5CukxuXdFVUucMs8Or2J7i4JknuPTv/C12P/txLCE5ePpxwm87r2dctuTMYx9kPrjO9OYLLI52ufShn6gXNndWwhXntJpvVf+PKklTLLCtLjYWtmWRsIQ1qKIgUstEVblKwSuTVvW59z2XKzdvce/WJpM0wbdtzjU7BK8HyPw6401JXGVZ/odf4U/f+SrXLYFXNWQry/LDwIdf5fIngIde5fLB632M1xqVcBoY+ybPhZyaJd9cQRlX119qdivzpiqKsiQmJy0Ubp6RuR6/Xa7xwcbxicqnxNKmGZYEy9XbM4P+ThYD0vkAv71dU2iE4+J3+jqBuR6lylHpHK+zhtPQGKIqae1/8U+wpMt8/xo7j/410tkubnP7juQFpQWgTlReVdJCLNUj9HVXta988rnuxMtsrOs1/15TcVVA2BjKlDLVs497vvW/YiE65CqnnVwlmVxnMjvg+MUnae9cxvFazAfXCPvndcs23cOSDl6jj3DjJcVIuKagiw0rQeO8bDekbPTrg9alYgYo3GaX6PiAbD4lm0+1uqlSSGPOakkbv72u1RosXZ25YZ90PqjnaLlxJXKDHmlxRJboVi7onMEJNEcxHusiX3svuhRqiIoXRMMD7ZgdbhB0d3CDLg/9wD/g6u/+ApaUNDcu6fcvj8FUu5aQtE9drivr3S9+nPve9yP69Rr4RRVOsEYy2z/hHGUJiXAaFCpBJLfoNjcpKEnKAlUWtdgk6JmsKku9PS9Pzj9X3ctXL+u1mhzPZmy027Rdj7d21l8DavCNh1WW39CZ+b818dhjj5VPPPEEoJ1L7tTfVoWWsDm/tVkP4iu3lFXi9Z3cLaC2pQ9sm52wTd9v4ArJ27m+TBKWs6xUilSTrHNN1yhlS9N9FvvYfocsOq7bN8drMTm4gt/arkUI83TO7hd+W6/NhweUZrPVWN/GkjabD3yLPsAamyxVVtE8wDJ7Rau4Ov8qVzaMlqnQCrNBqw62slD4a28B7yzUBHAN0C0WNxHNi7XO1viF/5fZ4Ys0N+7T3MZopCscM19arfJqdLnX0moIwqjElgVWYbaG0tX3XSVMFZvEr9UZVB5T5GnN/ytVjspSvZlb6I1kkSbkSYRw3Np2rNE7W28gA9PKV5VwNL5lcHRRjX5PZge1GoTeToYk0wPyeMF09zpCSC5/4D+rvR799natmFHN6pLZAZahMlXA2tngBW3C4QUEnTO0Tr8TLJf53mdq7FxFsk4NVcrx2vWCxZLGkUi40Hgrt9KIUZ4xy1MmaWIgPIuTSawo60prldJTdR6qKIjSlMB1uXdtjbPNLveEeo7YtR3u763Xt7Es67NlWT72FQ7D1xx3KT+vEpULCiwrr8BzSXPFrcHxCTG2aru4qtfdM5I3S3iEqp1y5nnG08cHfH6wB3Z3qVJQZpTpwCgkxFj5WEuhxBOOnvso0fB5JgdXyKLj2mDBa24hfP2hyJMJTmMdS0iOrj5O+8xF+hceo7m5g9/paw2n6Zh0OtLo7HjySnqRZS/lc1Yvs5z6q94QWrZOHNJFOA2z+VrqRpXJYZ2s6spLNBCdRzUJHFjc+l1UOsdvr5MnE4os1jMl09JoRxxN1anAolVyzNM5VpHoSstochUq1UKNUdVStsEoNlTtlG2UKoo0IZ0uZZPzZI4TNOsKzBISFUe132Jz4y3Yfpt0MWCy93RN4E4rXqEZuFeh0rkREtQHsOYfpjTWzrD18LfitrqUpWLv6d/RphjxhFuf/dfYQQ/L2zDtaQuv0UdlMcIJSA2+zW+t09p6AK+5yWzvSShiws0HEW4b4TZxOxfAbuK27sFpntXvkXSx7GB5oipSyI4IpY2w9GfUlZLQcQkMhsuVGkRdqaeugk2rY6MKraba4KG1LQbxgmdGhwTyL3mr+P+3SHNF4OoyexZrW6a94YjzWxu4tn3Ci04KcUI18m1bpxkmEXOzdQRQhTaqmGcpWaEYxjGh4/Cl4gwPclTPJcpCUSQTpL9Okc+ZHlzhqV/9MG//wZ9k/0u/z8al9zI9fI7WxiWe/c3/jY0H30meRLS27qe5/S4oc2RznTOPfB+Wt8Hzv/2P2HnHv8vNz/4GAJaU9C89gtvo0+ide2WSgqVEDTlLKR2DiF81xjAGFtVl0glOKCDk6RynXNkyFinp+HOa3+f2YP5lhO0jHL/ehBVGtFA4AcJya1pUrZ21Qt4uVEqesuQrGp6gNuE10tcGv1YYtYRqA2p7Ya0Zr6vRGUWW6vat1WVxtFvDHo6vPs32299PPLlNqVKkG+I1N2vMVAX2FLZfVzbpfEAWTbD9BtHwNkHvdL1hzOMF2WLG6Ue+m7JQnH3n36S0HBwK2jsXSWf7uG2fNBoRtLcpLUnnzKPgbOJ1LizfBzWDIsdtnwN7TV9e81LT5VyxNK/TVO2Vhln1PTPvkS+dGnsY2g5OIciKghS9YZSVi8pKrXMCLW9gEp85uEVg26SF5EujIy61197wcfjV4m7i+goxXehWouF7jBcLzm6sczieaJ7WCnerEl+TQnDfWp/72j1+/YW9+g1dfWOr+YBv22RFwacPb/HgRpMyGxsBQTMILVMjeXKRt37oB8iiEVsPvh/LkjSDLre/8Juce9/3E66dZ3D9T4lGN8nT36Vz6iGkn2N5W5TJPvd+y3/E7S/8ppH5dckWM6LjA46uPMno9NNsP/QBLcIXnDKv2hwUsNwIrrjsLJMYKz9r6EOJRrpXlc+JMLM0IV2S+QC7SlT5Sa2sihBNsdwESlvWlmOrg+gKRFolrsp/0RJSbyPNAWxRatnjPEalEcJ2saWLkJJCSoTjEbge8/2XcRpN8jiqk5btemw9/F6t9b4wUs5GlVTaPgpqtHvVogO4oVZoyJMJyfQGo2tfpLl9ju6Zhzm+9gSt7XPcfOKj5HHEW/+9f0iZz7HskPW3/HUqXbPp5ndSSoeAvIaAjNxzhMLGKabA5rKSVQs977N9U7GvTJdKTcE6QcDHLF7sNtMkQqA1udJCV1jSspCmha6isrlbDSm0ZteDZ06zP5uhioLhYgGNBtIRDE07/o2Iu4nrjnjHP/3H5HlO4HtkuWIRJ9i2zbX9JbJeFQWdRgPHses2Mc1zvnywz4vHgxMWTqsmA9VQf91v1F6HHxnb/EDLEGIraIFlY/feQXH0p/TOvrM+MKqZyqkH/yq2GzLa/QLJZEDnzIP69oXSZ1mzcbPdkN65R7TawHhAa/sc0fAA4bq1W8xieJ1WcGo5pK8wWGWx/BleIV2j/ybAKowwoWnFjBxNrWYK+oCRTU1HMdu+imP4laKybSsLpfXyHb+SoaqH7FWVhVjqiWnlDGowbKFSDdy8dRWn0cT2IYpmpNMRthdQSoXtBfTuewiVRrWBK0DQO32StG2So34OBWUe4zU3TyhTeM1Ngu550vmA3s5j9M+9l8nBFZQRG9x59K+x/+zHWL/0mN6cZot6I7v39C9xz7t+GFTMht3gpcd/jmwxZeed30ewdplusTDVFcuFR2GwcRUMBZbtfJkC7vI9rL6beeRI6eXRII018FTlOBV/0rLqwby0VqotsXJyQ1N/9mcztppNVFny1t4GTx7uEuUZ3bDFKEt45viQt65tfMX3+o3E3cR1R4S+xzyGKE5wXRfHlmS5QhhwaWRkbKpeP0pTwmCJnK9mYydQ9Gb+FbguoeuiykIz8L0AaVl8puzyWAAivk4pPMpsSj7/dK2Y6bdO1UhsfXBq4nV78wHamw8AMNl7mqCzg0pnSPcUlvRQ6Qyvucl0//la4re9c8Fop7/MwbN/QP/CN6M/0JWHodH1qn6uoh7ir7aNmNtpyo+e1ymwXb1VLHNdCViunudlxzXos5COlj8uC4MDg8qtWkhAKS0vZgCltgqXlmfwCiMRIZ3a+ktIg+QHg9/q0zx1jnQ+xvZCsrnZzLoeyWiA3NheGlu4AW6jR7oY1nM7rWU/RVgnYRJArQmv510pRRYzP7wCQDI7IOjs4LdO4YYbunUVNqce+gHIZ0spoXTA8OUn2H74e8BZ49of/QzpdETvwkN4zUcIOvfok4WaLA1E1IzF8Ys01u4jnR9q3mV2S0NdhKvni5ZJWquyRiqt4S5dKRnlgqIsCGy7xnIJyyLO9XdpWdwpYiSFRZoX9Ql8FsfstDu0pc3V8TG+bbPdaLEZhLhCMl1hEbxZcTdx3RHDyRRhqqU4jtla6zFeRLi2ZKPT5tZgSNP3iNKUG4dHNAOf4XR2Yua1atukioIs0+1k0/dpu1oyd91vsDufIoWg75c8gcc7LfTAWXrYrt6iaQ5eVqssVPCEQqVI168TWsPouWsuoCYvS08g3SYtg/BeDHZJpiPWL7wbgPn+yzS3Dgi65/U2zhLmgF+emZcJbKVNNFScZWXVBOFilQPt1sPM/DerCs3MVZx1ZoPH6Zx6CL8ZkkYjrFQaaIGDELKm59REbhQCaoL06kwL9FOzpKy3mmVVDZrWqDLSENLB9gI9a1poonRZKMKtHVSW1hCSyrC24bex3VDzI41xr35cVUtZl4Wq3bQBkukSkpBMD5gPrjMbvIATdDVRenZAo3uW6eFzbF58PwBH1x5HSIfePY9hdx6E9Dbn3/fjlCqpWQClSkgmt+vHr05olnSIxi8bqZy9WiLbdkNNal9pDUFvgYtSgdK+neX4M7TDRwltPeqI8pxBvGBmcFw1LOIO5MES7rOER0zShMub6+wuphSG7jZJEwqnqHXA3sy4m7juiKIoKIoC27axbZvhbI4wM6q9oTErrdx/jS7RajuIY59QkajIp/q6kqxQzLOUG9Mxvm3Tc31SpYhFRumFWGqpu1UYizL9C/VlegisB7hZNCTo7NTKmtgN8tEXyZIJQe9+rDKi0T2rD0jbJxrvc/X3foF7v+2v4wRNZvsv0N1598n5VZWohLs8W5fpElRaHxA2q/ZhWoD1Bvin9d8tt95eUeZgd+mffTeL8U2C7nkcT9WUF9Bcwbo1qzZStZ7WHSoWteGFkV5eoQDpK9hk872aewjLZOh1+1qGeTKgVIpgTc/4yrKgyNN6npW7IZ3Tj9S+h9rIdXRiaZAsBrhBj+t/+hGcRot4PGB2+zqNjW3CzR1UGpHOx0xuvoDX7GqjFJO0kvmAtbPv1q/ZJJp0tg9APN1D2j62URCxhBZnlG6I1+gTTXe1AYjZ4hYqIzPGI1kywXHaKwm8AKMTX/0fs+gYx2vjC0HTdogKRdv1dLJKSoZJRGA7qFKttIoAQgOsjXDmehhyvtUjVjmBscMDbY13qtGkJR2Kr8GFfSNxFw5xRwgh6qQFkN4h2VFtV6QQNAP/FWC8SmRwFYkMGN86vVG8OZ3UiOTQGB08Pz7m9yMjPqdSfUZcUTmo2pGaZiM9pO3jNvr1VtJ2Q7Bc7KBH0NqmzMaoLDLidBOi8T5Fqi3mp3vPAdDculC9cp2YhLsyw6qiypqxma0sdMuSHethcHUdew3ZfkjryBv9eUqTuFKdvEpnQx+oZa6rJKPSUBlomBerK7BKydRoXlX/g+pLGA15y8AlhHS0Fn2u26Q8nTM/vlZrlRVKV6y2UQD12n383iZ5Msf221iWIJ2OiIcHFHlKthgyvv1UnUSS2YEWIMxig3p/EcuSpNGQoLeJNEyGxsY2nZ3LAExvv8Ti8DZOo0V7RyteDG89CWhZo6IWiXS1iKIT6O2nJUgMqLSiKYWnv1UTxxcDiiyuk5bttev/TxZPNE8xPjJshWUFXeHzqs+KymMaasCG43F7PjU+CYU+oXrBCVdzwJgMW6hCbxmlEOyEbWKVc1+7R8t2uLfVpe836HmBcUm36MpXtthfb9xNXCvxwP/8P9Q/Fybh+L5fJyv9hi3/ZVGScjheCv6tDuorva4KmFoRtMeLxYmKbJhEXBkeMkkTrk2GlG2Nzats7CsJYuG2kV6XPJ3rZGa3zVxHE3CdxqZWV7WE1mUHLKeFdALi6S5HX34C6bicedv30Tv/CFsPfBfzg5uGBmISVZVsKp6h8A063V8msnI5M7EodRLLj5dV2QrOS7/IBen8kNHuUxx88cNgd5FOUOPRLENqThcT0vlYy9KYraSorMRWqqsqykLVUji2G+IGPZLZAcOXPw3Sp4hu1/dBociiGUWWoCopZtvH9lp6/tRcNyeEgmDtFE7YQmVpLUyYRTPydE48OdLKE/GEaHxLSzsbAClAtpgy3b2O7QUMrn6O3Sc/yejal8kWMxrr28z2bxBPdmsAq2xeXFaMxQJKrc2VznVbqUnbN5nsPU0ajZjd+kT92m0jRKlNP7Qdm3RD0sWANBoRjW+ZjaOZiVUuTOb21fY3Gz1Nl4RzzTaqLMkKRZxrpsd8ZatYtYyrSPqtZhNpWfQ8nzXbxRfaAm/DC+g6Lm3pEEqbQEqGsylvZtxtFVei2WwSx6+0s8pyRZ7nNBvBCZS869hsdNr4nkucpCcE1SrNrtXKq0LiVz/Ps4xJktSo451mm985vMmHgqWJRaFSRHBaV0LZcf2Bs0RaG5p6LSODIhoU0W3SaITf3IR8QZ7HHH35CcKtHZLpiKNrj9MylJLW9nmjylroD7ht5JNlQe0aXSz0mVtok48KSEqZaska0H/PR+Cs1UlNmYNEGhBqdbBkx5/G6b2NcvRFLEsRDW8YsnFkbL/0fdqsYLOqreFKK1gafFZNQ3LapNFIJ2LhMj++hnQCg8afYHsB8XhAOh3h9za19lbQM/I1gsGLmjWxeel9JIsBi+ObOIH+f2ik/VTzQjtniMf7etMrJNPda9h+gN/tM9+/icoSAJxGi2BN0d65UFd5jbUd/NZ2XTGq+Yvk6Ry/uUk60/dZEeTH15+jf/ltzPdfJp2OcRr/H3tvGmNZet73/c6+3L227uqleu/m9MxwOJRISpRkaqMiS1FsQ7CjREksJIDhAIaB+IuBIAj8IUAMBEiQwEEAIwkQ24oFCTYsxVIcy5IpUqJIDrfhzPTM9PT0Wt3VXetdzzn3rPnwvO97b9UMZVscfSH6BQbTfftW1a27POd5/s9/aeOEMd2zl5WFc0c6ryW7atcNpdtSo3E6vCfAfr5vujgQF4wgXhUhdtCF7C5nw5vUHbg13DO0B99xjmkVs7Kk5Xl4CtfaiNqshjGrfkjX8Zg3FWXj4FoWniXJ6q5l4Vs2HzUV9XnhWjql6oRs2zY4V13XeK6D56p0YQ1KOjZ5IQx7TVKNfN8UIYBsnpvOCzCFTEskYJGe0osjZkVO3TQ0kSWMbyWWbaoxVAKu+tEAvC40NU64Iq4IqkMqJ/eO6dPETDBV2ylJqynSI+LBBWaH92itXYW6UmD7Eu3B7S7GRcunzPaE5qDB+iZfbKmaWm2p6gUIjzhk1DQ4Thu/tc507zat1Us8/tb/w8WfeAk3WmU+eSyFYLqL36qp8zlNJRpLy3agyBae8YrkubxZ1B5cdZVTjR8QdU5jexF1tm8E1lWR4QYtstEebhizfu2z4gkf9SlVUG0xn9BaO4vjt4ykpkikUHnxAC/qUs5laZCNd6iyhDIQp9LWxjmSgx2KZMps9xHtzYtMd7dZvfYKfqePF7VlKTI6ID3axb8mbqt6keB4IcOd14n7W+LGGnfxorZsOD2f9uZFvvPN38C2bF782b+I67eYHd6TABLLoalmlEYz2jLPjWbxJ4d35e8qZMVxQyWQF8wTpQDopW/Qbb1IYJ/i9aNdxrkU4Eo5nej8xfF8zloc49kOvm3Tdn2uhm0qoChrAvX5sC0J6Ahsh1zpIeM/zYfyu5znhWvpaDyrrmvacWQIpPO8wHVdHHvROeUqLGOaZvieSxj47I/GrPW6jGcJ7TA0+sZeHJsRcpnfBcteR7LBOdvqgqU2a5Yjm6B8qrqKHjj2Ylwz41tpsKZlFwDNl+qceoEyG9M9dZN0vEMTXiA+e43J/d+kc/pl+XpfWZ7p4mTZgIyJVZnh6dW9/pkauK8SJa3pLjaI1NiWS9k0NN4G2e5X8VurMvo5DqQPIdrCj2YUli1YT5kRDjaWKA62MN5z4T+JR/+iaGk9n+OGYg+t7GFAgG3titqooAotONe+WfJYPENncPyW6rLahJ01bMfBdn3B3kqJEbMdn/lkl2jltNjizCc4jk+8ukmRTnGCmGR/h97WNaY7D5hPh2SHu4QrG6xee0V+ruqOivkYr3ud+TOhvcwO7ynqR0E+HWI5LunhLvlkyIWXX1G/8yIAQ2s6bS8yKUe63/fCLo6yuWnqyrhZFPkRdntDkZ0dcdmwQ5riUF7DcsiVcAN3xebbR7scZInQIZZGxG4QkFeVAeRbjsu0LqkbmGlbHMsCyyJvavKqpu94fNRU1OdB+53YAAAgAElEQVSF68TpdztMk5RpkuK6LqHv0Ykj0lyAY90tLbuh+p7L/mhsvOl17qJ2jExzSQTW8ebZXN5inidaR71S7voBAz+A8CYktwCbxgLbiyXBRxNDjVUyYLlkw/smtszxImGuuzF2mZGOpKvpXPpL1JM3CAfXlVbQFoEuLLqrppbR0O2r2wBbAi3y2VO81mlVoLTeLYGgvSRqVo+tSnCaIZG7QlI3xKd+hIO3/xFH929RJBPS0TZRsIHlRrh+RTJ8SNQ7Z4qV5nMJFaDA8Vu0Vi6RjrYNFaBR3u51VeAqTaP2L5vnM+pSurVsdECVpYQrGwTtDRyFh+WzfWVDk5siEA3OUCRH8kEHqlwHsUpBzJMj/PYaXtBh9PgtbC/AdhyO7r2tRsMN0sNd3DA2fmidc1cYXLzJzje/wNlP/6wUlfgcjh0yefR7NE1NOtw2uslwsEGZpbhhhBe3aW3Ixni295Cgs2HGQO1AURcaz/Q5eO81oXeUGXFfROFBe4N0tI0XDfC0LMtyBA8tx+CUQhQu9hVuCRf9PpPugIMlsXXousyKHFssRCjqitUwJq1LZpVD0dSGeBFYNm3HYydPuew2pI1D9BF7RTwvXEvHtm1TsDzXoaobw5yH44GXGoj3XZe90Zh2GBIp6Y8moi5ruLQgW59WFB5zkqhq4b2Mi5wm6mLpwqHivo7vdxZdkdjfVFR5TuPJ95tPJUkmmzwVJnfvHNnTLxBu/rTgVuV48X2aUq62tg9VQj27L9hQdEWNoIDbx/Unx4olKs0HWMhJjA4ul59T7BO7fXC6rF7+Sdygy967X2H7G/+ca5+/CnaI7eSGRGt7yvm0nEpBmU8kn9HxxPGgtXYsiEL7zuezfVy/RZ4eHeOB1WVuila8co7WyiWKbGyCKQDKbEyRCu+sLvawHJem3iEbHmA5DtFAEnbm4wNsL6AuUqyojxvGlFnCfCRfWyQTopUNikTCYh9944+48EM/wdlXfgE3PsXqlZ821JFy+gA3GkhHWWaMt99fhG+kU0kPUnSQpq4o8xlBb1Ulb9um28TgoAVu2KW9eYGwu4mnRkEvkDAQ3YGbsbrMcOtcKBGdi/J+0K9pOQZsOo6oOwZByKyUi3bouGRVyac2zrISRNhYBLbDvK6wLYuiqQ11wtdRek6bqP4gbvy9nueFa+nkeW6oEHo8/OGbN3j97n0A0rw4RodY7r4AMz6mc3GTqOqalXbbJAHprwPBv7RfvWPbRJ5H3TRsz8acCmIunnxwuiNS4bCN5UCdGzdN+eAKr6t3+iXKfEZn/bqx/g3O/Bx75ZzAimkFXZxqKttAy1fSHBHuLuLEDoXWoPzjrdY1KUbL7HHDEVoSUxselWJvF2LdXMQv0l4b0l67yqOv/5MF7cJfw9aSIacr39NzsYOQwD+UfEKFp2msS2/7dPclOJ0A3sV8YqREVZETrmwQ9Tdpr16lyMaKrLmj/Mo0pcChKnJsLyA73KWcJ7hBjONEWLbD0d038dt9LLVpdHxh3xfJFC9uS7p1MqUMEo52doBvsPWpPwfA9OAO/WhAnU9xvDmN08ELuzRAe+0qe3e+QOvUedwgOobnHdugLpFe9f910bJshzIZ44aiENAFXRNvi1T8z7zIF5cJxydon8KiEUcLbZ3UWPKaWC4U++xXGybxp3Iasqqkaho82+FontH1AjEYaBpmTUXRNORVRc/z2S0yHmUzLkVyYUstn/AjdkN9XriWTl3XXD6zyTTLGE5n5HnOV995z/x76HuGVHoyklzjV7ow5UVJOwoZJYmR++j7gto6LnVvnm0TOtKOB/ayC4N5dAsKgrmpNFQJ3D7NdNe4NGhSYjbeoXf1lyC5xbrTxSr2GEUv03HaWLYv2Nj8CXW2i935mHxgdGxYnSyoEVb4IRISe/F3O1S8IeUZr10KqjGUh3jFPsPeZ+g7Dhd/6uNSGBu1tdSdmtbc2YrFb8eyzDwh2ta2M/p3BAwfSnuta7VBa/WC4UsBKgVJvqacpwbELrOEozuSZ+x3+jKuzSOyw12cMMZyHLJDMQDcefD7wtkKY7LRAU4YG4eJuq6Zjw649YV/ydpqh/lI8htbK5dogvMw36YqJAvR8Vv0Nl8inYhz60mnC1iA7CcJuJqYLFidpArVaukgFja2hNqqBHXN23Lc0JBP9fMGLCxvFGm1amqTr+jU1jES6r3xEYMgZC2ISeqKvKqUSLvivfEhXT9gK+qQ1RXvJGN6rkdkO7Ro8VGd54VLnVu7z7jguqZo1XVNr90izQvqusa2bfKyoh0GZvRbdn2ABXDv2Lby7yrJC8HANFC/XMBailrhu+J9JJyY2Egu5CzhWrCwVrZc8ELsIDQcqmj1OvPxI7LxjiFU9s58AtLbzCc7cOrzWMO36bZtKsAafp3R0zcp0iFBe4Oe3wa3vRgdURtEUHyg0DwOo1VsWHRhtr/YUDqxsl6RkcRxQ/pNLiLh8tAUI72ml4xFoWDUWYId9I+9Ptr/ajmyvrGqRYcIhtBa5TPy6RC/3TcZhbrT8qIB+WxEvHLOAP/lPDX2NpPxlFNxh/aZC0y27+KEEfO9J+TTEdPRhIuf+Rzz0QF1VTGfTemfv2wK6Xj7LqcuX+NbX/4qly+exrJditmUdPSYzta/x3T7X+FFAw7ufYX2+uVjv78uUHoEbGyMYmC5Ezv+nIg7huOG1GqELuYTU/gcT8wGtX+Zfh51AbODPvV8KNDA8je2fU65MQdhTFqVjPM5kespXzl5r29Px8pJwmZW5uwkc8MDO5pn2JbNRhDxxuEz8rriRn+NybH39fd2nhcudZqm4eUrl3j7wSNs22al22GazfFdh2mSY9s29pKd88mEH1jothzbNmNjVdek8xzfc4+5RXZbMZHvM80y6djiitSXru1ZNuNy/+M4029/8IHqooVtgHCctnROdkjY3qBUpFUhTT4hbG8QdC9QWTbD1c/R2/8S+cqPUh3eoy4z4sGW0CWaiibYEPeA6dtYbgvi65A/pZzcE0qEq7haqiMylilq9GvcPlY1XXRhlo0XdEknO8wefpXVi5/lyZu/TVOVdM+8gBd0RMbSisiTXcWAX7jALid6N1WNZS3UBFq32VCxnLFoWQ75ZETYW1ev7UKDOJ/uUudzHC/Cb68xeigLA9sLCHor7OzsM5gOgQv4nR5P3nqdweYmnc0LdM857L7xx0ynGWHok2U5d7/zOmHg4/suYeixc+c2vucRdXvG36u9dpV6epvho+/QPnWFsLNGPhMzRb3dBAzYrkdjTW2x8bGcD5M9VSZ8o6kKbFWkm7pS97cXIbPK6NALuiIvUjw9vcwResYYJ+jSlClzf5Nz7R4PJkMi1yMtCxzLJi0KIs9jP0s4mqfcXNkw9jUHWcKz6ZTD6ZSbL68zKQveOdjjfK/PbjrjzcNdPqrzvHCpY1kW33r3PVZXBqx3u2zvH1DXNWVpGz5XXdf4rnRcmrMFGJBen+VxUBNVl2/XPkb6z+tdadsj18WzHWzLZidPOX8ydWdZT6i5VLYa35w2UNPYItAuFKgt7OqKMrrBzjzhfPYGtbpy+60142tVKqmIHdrU03fUjxhjRzXl5B7paBsnOSA+/UOUkwe4nStSnJwY7L7Cv2osbWIHpkPL0yMJmTh1E8uNaK1d4P4XfoOmquhvfZx5cmDoAIPzPyiPNx3jtc5Iyvd0d/EhVqOPhZgJWtEF7GKXOh0qq5wMy/FYu/FDdE+/zGz/PaoyIxvv4LfEgz5aOS0Oo36L3tZN6iLl6N5bdM5c5CJQzVNmz7YFaPdduueukOztUM4T8rzk9LWPURc5fc/n8MEd8rxk88VXAdj9yh+xsd7Db/fxO33cICI5ekC8dpOwty7YnBca/aGkPTk0xdI2tcyPjYiN7dA0xzEuR0m/DB9L3df2fLNhZkm07ikr7EXGprxGy7CCPMe2XLCAaZHjWBah41I1NUVd4ys+Y1FXFMDr+0+50V9leyYXGN91eeH0aRzb5tv7Ozi2ze29Xb68pDD5KM7zwqWO69h0u13mivZg2zar3Y4RWWtC6iybq41jfWxUXPbiXi5iHxYsoP9rh2rtXtdstjtEjkvL9eh5AX3Xo6kHGPtjfTRQrRN5aoUPKWC7TB7heCFu2BPZkNrUefMHrAdbWM4NnPwJse3AyssC7A9e5bCJiKw55E8WMfV+myku7fYF2l5INt2lGL2D1/uYPJZqKmz5OpM/6wJajsHfkC7Q34CZeJl5QQerqYj7W1z+qV9m8uw9sslThvffpn/xBfx4lWy6y3wqnZcmaTZ1JbJvZQ3dWDZVnSpFgciRLFtcJspsTDmfsX71x0mP7jOf7mJ7IWF3Uzhfjm82rpZlkydjvKhNtLLBZOcB23feZ/P8GdLDXSZHQ3zfZfTgNvl0yNrHfoB4bdNsFIf33qGuGzrdNpbjsv3trxFHAb7vMtt9RJFMOPuZz8tIV6fMp0cEbdlkar6ajLnKpVRHpMFxlUBd0Vjye2ML0dR2fIpKBN9u2KVJFyoD3cXpcdr1W1hej2Z+KD8Ti2x4l7B3DpPibUuqtt2UEF3FKaBuavK6JlePybYs8qqhosKxLTbjDtvTMe+NDpnlOTdXN0hLGS2/+OQ++zNF2p3LhDFLP7rt4nOtojrzPOdvfv6nAUy3dTSVJ74dSsCAJqhqculygdJHj4IGB3PsY/8GS/SHmWBegyiibhpClS4c2DaRrUDxZTsZw1YvFwk8truQ7JTK78rrQF0uOZFKQTFcGjskqStARon0ye+zwoTi8Guk+7eoihSn8zFo3aRdDZnufEVEyoWkIh+9/88XcqBqqkTUT2VsnT+Rx50/ldst4ZZVRSpe+XVF0Foj7m9R53N2vvkHhL1VsiOxgUkO7zF5cpu5KmCa6uCeYIXbwQpWeWRcJ4r0yGwK2+tX5evCrilYgAm00KORG3apizlu0CEanMYNIs5dFdH56vVXuPnzv4RtW1LosoL9d77BfCSJea1T57n0k7/I5ouv0rtwjdmzR1z8zOdYOX2a1csfY+X6J2ifuUhdSoTYwcOv0jl17VhHZTou/XtZNrbr4/iREZRjf1Aso7eFtuNRqWg0XfCW76OpE5btkE8eMT14nzwdQl3iR8LV8+I10J7+trK8zu4T2g7rQUzLlW23ZztErvjP6/fx9nSMY1uCfdk298ZH1E3D4XTGrCiMzfnhdMrRZMpolnzgd/nTnucdlzq2bfN3f/O32Dp9CoD98UIUOs3mxGFAks0VSF8a4fTJTeFyOpDj2MeA+2UPI89z6cUxkeeRV7KZabkeoSM+4KOyYEWxmc1plsBNy6XRlIKmxKoFZyjmExql2XM7lxZMeGoodtFFMK5H4MSEp/4c2p/cW/tRisYhp8HPHxr94XTvDsNH36FIpgS9W5z5+C9KwWq9JIVKOZzSlNTZLlZ0FasEK39GnR+Jh371RFKF3D5NJUGs5z/9n9Ja/wOSw8c0lcZmfGNpk44e40V9CiUiBvAjZQ+d7eP4Kni2TrAsx3yA3aArRM9Q3EKFyCryGu3C6qkQi2hwWny3Zges3fg0u2/+IYNP/CjJ/g7D+2/TPXuJJ++KMWCr18OL23hxmyKZsPfW1yiSqXlJhvfeFg6WcokIeqtkowNxjvBb+PGqwbb0VlTjVk2jFAMsUSE+BJSvlY20ZdmSXK3TgOwPbh2bxjEjddPUtFYumS7O9tsUyb4ULuWvZlUCP+SzZ8SrN3g6T2l5Pi3XI63KD7VvruoFrL8aRhxkKSvtFtNszjTLDHdxOcrvozjf14XLsqyfBf5nwAH+96Zp/u53u29d1/i+z9b6GrcebR/TKoKYp4nsxzrGyVoeEZd9upZPXpQ4ji0g/VKgrAbmfdelHQSkVUlYlczKkrlb0dgxlkpuNoGq5pdzzRuOakqjxLCOG+K2zlJMHwrlwHIX4mdtS+O0pTvy1pji4j79l4rEOmPv3S/jBhH/7Nf/MZ/+/E+RDQ8IOn3G23e5/NN/harIJJdx5VXqo69ghxtLj2Euo6nqwhp3gOULsdULlCTIaSu6xDYAK1ufIepti/vB3l0mO3fobF5l9uwe3bMvGCufMhvLptRfpZw+FNKl36Icv4kTdPFbq0x23yVeuWCeB5BtpRuIpEhwv0Xsvc5GrErRM84nuwyuvEQ+HRKtbBD2V0kPd9m4kBgR9d6dWwZ07196gWR/h70777B26RoATVWRHu6y+cnPiR4yHpMnQnINFKZo7HmWo95OAO8f1mnJzZ4ZM9PRY+PS+sE39NJ4CSYPYDFj2aKEsFyqdGkpojzXIpXys5NMyOtKTAGbxkSV5eUiLAOgHQQcZCmHs5mZOrROVytMqqo++Sj/1Of7tnBZ8k74X4HPA9vAa5Zl/VbTNLc+7P7arePxwSF5WRkwXruh1nVteFyAkfPokXBZ3mMKU5rRjqS46G5rWS5UFIt8Ru3xPSty9rOEnudzZBWseGsmzutYvqEuRAoA18BtMR8LazpeI58+k1QdkOLlrigsygZ/DeqclhOSVTl3v/CrzHYf4QQxYW+Vz/8X/yW26/Pgi7/FytWXWLn2Cu31G5I005TsvfV/sf/uN3nh3//bakNVGi5Q5bRxnLY87toGyyeb7opFTzHBCs+Cu0KTPaapK6LeOfzWGlWZcfj+t7Esm3jtLFU+E/dQZRtjWQ5V+lR1V0PS8Q5he4NstM1k9228qEtr5ZJ8kB3lb+WG1J6SWIVQFam4xJrnUV7Psh7jx5IePq8O8OIByf5D3CCid+E6TVWR7O2wfvWm2dx1Nq8SdIS2kR3usvaxT4rzhMqFrItM2P62gx+vMju8Z3SP3/UYUH4RqfaB97YpesK894Ku6biWR0ZtewQSj6atcCzLFoDeCmSUD/pg2Vj1nMbyCVpr7FYFnmXR9QOO5qkZEc+1u7x3dGBA+mXIZJSmJih5PEuYZpkhWDu2fQw2+V7P923hAj4N3Gma5i6AZVm/BvwF4EMLl2VZJEnC4TQgyzLOrK2yOxxx8dQ6D/cOlOe8cLemWcY0E/W87zqm+Cxnz+kr0WgmIbJPj4aShs0CvF/eMhZ1RVoWcnUr5viOAzG0gphAB1TAwr5ZEz+LQ9LRI4J4FctxCOJVw9kJWmskRw+IVq6qsc8VjladmxHSanKiwUUu//gvG77P7lt/yOjhbaLBBitXXiIanMZ2Q2aH9zi49xXOvPjzVHnK4NILNGWKZYlTq6VY9M784QK4VwD97X/xt7j2+f+cumrhqPGmrnI66zeEqhCu4Ud9+ls3TJaiTvoBjObOb61Jd+CGNFVOpsak3ubHF+C24xl1wfLWDcQlofkQD3Q37Jr7tU7J2Nlav8j4yW28uMPs2SMjywFoqpKmyimzBNt26G1dJ16TQFY36CoHBnWR650lnx2IE22jxN8nH8MJrGv5/4AJ/Yj6oum0HBkj/WigRuHKGCsG7Q3FD1Pjs05GKtITha3CsufM1fjqRwNwIB3vMHKvcdaP2c0SItdjVhZkVcl+lqjshcK8d6u65vHRkaH36Ns1Frz8Xv+ozvczOH8WeLT0921123c5De12m1mS0oojDqczOnHE06MhK+0W64rXJRpF6Xx6Sw4SGvNaZtH7rkuvFfP0aHgsBVj/uaUcVJdHz6M0ZVbkPJgMeZRMeDRPzEjYYIHtHkuatmiEauCI46cIhwvpwpwOthctiKPatbTOhNFuh7IB9NaIVi5LxFY25sbP/E0Gl1/CCWPOf/oX5efYDntvfVnM+MqM/rlP0j/3SSx/lUff+A0efv03ZAzUhoT+Gcrxezy79Zvk+19Vo9NAPKz23uXt3/p77N76EuNnt0yhdfyW2ph55oOnt13lfGLSrfVY2ywVc223XFc5RTZmdnhPLFtU8o/+wLp+C9sNqRRfSgBu+UDPp7tkk6fkU8m6LOcTosEGdTFnPjrEchy8uEO8tkk2OhSTwWRK+8xF+hduko33CTubJlxDs/mz8Q55erRwO10+JwD45WKln3ed8BR0NqirXEZE9TVVkZqEI02nmCcHVEUmmlXjxa82tGobbf5sORJRp/hc89H7hN0znPZD9ss5oSMUHW0q2HI9Y6hpnFLU+10u6NlxK3N1Tm7Xv9fz/dxx/RuPZVl/DfhrAN6gT13XrPV7TLM5tXryl0mijm0p+Y7HSqfN06Mhvuuy0mlTVTWvXL7Ia7fv8KnrV/k7L3+Cf3FwyK9+42v4nmsIqfkJsDIMJAH4z5+/xs24y3/3xleYZvJvddOwFsYy1lUPjz941d006g1r3FDzGa2VS0BDY7liMlgMlQ+8jU5+Fka8smuuM3C6tE7/AK31FyjSA9qrV6ToAWuXfozk6AGnX/lJivSIo0evye3Xf54mP2Dz5Z+TkcRScp86gWIfL+yydvnHGD+7xWx3m8njuwT9Vb70a/+Qm6++jOU4zA7uYdkO7bAnRNXmsSFNoiQsji8dVpXP8FurhkwqRS03vvJu0MXxxWJ5dngPN+iI51a8qrCtwnRw+oOqgXqhTUQUyRFu0DJFcT46YD4dEa9tii7Rlw1zU5XiKLEiPmcSTTagmI9NSOx8eoQbRCoM41MGV8uTAzP+Lt6LHyxYcrujzBBnpgOd7W6L04UX4mrpTpmJd1tdiRc9wguzmoXSwHI8wb6WOlO0mWCVU8zHJnS2nbxJu3WTjuNS1BUHWcQ4n3OQpcemi5OZoQCzNDs2hWhDgo/yfD8XrsfA+aW/n1O3mdM0zd8H/j7A4Mql5tLmKd5/vGNY8rMkpyjlSv3C+bPce7ZrZvpeHOO7LoNOmzfvP2RrfY1RkrDSafPa7Tv8V0nC9VOnuP9sj67qzPQL2Q5DDidTUjs3V6p3Rvt8Y/8J+6MxoRJoV3XDOM8gWKICaFJqXVGVUyM4buqKoHue+fgR42e38KIBYT8Ct4/VHNAYWU6owP5sIYrWMh3Fdnfj67jFITQ5k+0vkY4ek8/2yUZ7RIPTxveqzvaZ7N3Gb63iRwOKyV3GT99k9YX/hOnO12if/1nyoy9Qzif0zr+Aq5jrr/zoZyXTsKrIhgf48YC90e8RdoQYqseobLIPtoMf9c0WrkiHxrZZkyrdsEsrvgQg3YbOOywzbKVdBIz8R3OhrMI246Hjt6gLGXOy0R6t9YvkswOCnozgTVUR9AQbE5wqoGlqswyYK5JscrhNU1dMn9zn9Cs/KZY10yH7xZewXZ98NqKpShFtq+LkqnQegOP0iONC63I+oypyWhvinaY3irbjLeyMgFoZMZb5zFBRXL9FnY3xW6sfIh+ymScHgg0WQwhOQzGG5B3m3jUGfsRWu8cwzxjnc6ZzUZRU9YdvCz3PZTxLjm3cn3dc//bnNeCaZVmXkIL1S8B//N3ubFkWo1mC67qUZYnruqz1e4yTlLqueevBI7NldOySdx8/wbFt3nu8Q13XTNOMj1/YMk4SdVXzhbfe5pNXLnHr0faxK9Djg0ODg2kP+i++f8e03t6StnEnmUJLRsNlcN6yHblSAi421XwIZWI86h0vlK6qLCV+vTyEYAvm21Angk1pXy9vw0h45k1N0dS0/dOQ3aez+aqxhNYGfdlkBz9elbitwRZevEaZHlGkQ1Yv/7j4zKdDmD8kWrkqThWnXmT7m7+OG8asXnuFvbdeo0gmJhYs6K0S9RwaZdGS7QnuUhcpqQrS8IIO89m+4DplRl1JR5jPDky6tBZaLyfxHOteWPh4NU2NpYqhHw1Isgd4YZdkXxJ0ynlK0B7ghmJHo0F5Px4wD4dEvbOKb6Y6K1WAkr0n1HXFdO8ujuez//Y3Wb3+CuFgAy9q43ghRSpdWVXIhvOYe+mJglVXhRgXpndwPF+6zqZabJK9rnlsplMc7wp8UBVgS3ybF3aNq4T5WTQ01HhBZ6FKqDNwY4rZU86t3TQJQDqGrBuG1E1D5ClDgaoyXdUsXWzKYdkos/xIi9f3beFqmqa0LOtvAP8fQof4P5umeeu73b9QV444DMhym9D3mCqWvO8GjKYzHNuiLOtjbW+/3eJwPOFwOuU3v/Z1dp4+I45jvvj6G7TbbfbG42Pdlu+KT/3To6HQJJa2jSvttunIelFES3G8aEp5g1keVqPy/eqKplEOl77Yhwj+sU3Y2RTLYn/1uFOqZUO4BZaPld3FKkc0dS6jndPmWTFnXldEjkPLdrGCc1L85k+w43O0vIgGSwU4vEvUO0VQFcb3KTl6QNQ7i2UndNavQ7CFld0TI7z99zj3mb/O8P7vKo+ws+y982WlExTe0/jpu/ixjG11MRev9ahNdiR5hNOD9w1WpTspze8q8xlFNibsbprXRndp+j56sVEvAeN1kRlHCd2l9bZuqrG0JxKipe7Eb69BXdFaAuK9aKA6YLHJ6W1dFxdT26GuKnoXrsvPKnPKearoHYlsKvd38OIO/Qs3jWZx+fjRwDi1elHXFDVdlMt8xny2L4VpqXPTeGKRHmHhKCxMpXErakS99JxUyknCclvgtJkfvi4k1cm38YKb3BmJ64Nn25yKWlRNQ15X7EwnxjhTm2VWdU1d1R/otj5KLtf3beECaJrmd4Df+be9vy4wWtIDEpSRqg3iPC8MPUJfRdI8JwoDqrqmHYb8hz/9E/ydFy7yK1/4Go8PDs3VR79o+spzUirUDkNsZxEma1sWnu1wqTsAt0WT7y4Y8zTG1kS0brVxWNh/76ucffUX8IIu+fgefu/aksFfDdldZVmT0XjrsvWrphTuOpCzFSjWe7NBgUNBQBRdlSdovsve3T8gOdhh7cpnBEBXH56gfYrh9jc5fPhVHL+FF3Qp8y8x3btN3N8iHmwxfvQH7L39x/QvviCMcs9ncOUlXNUJeLFswfLZiHwyIuiLtrCcC9s9O9rF7/RNlFmRTmmqkqrI6Z17QYqWIrGaPEoW3KeqSo2UpjmRz6y7tUo5hDp+y6QDCWXjlIDodUU22Tc0jniwJcVjusvo4S1sz2c+PALxuLoAACAASURBVCBc2cCPu9heZHAyJ4yxbIf9219ncPklbMcjXjsrfvYnWPSLdOwjbMfHCzq0Vq9SpEfGskc/bj0i2ku/t5ZIedFAPLksKU5+NBBNpy3C7CITMnBr5dJCx1jJ72xFF4Cas17EJ1ZP82g2puX5FFXFbjojVBhXXpaGBjFLs2MLq+dbxT/jU9UNoySlKCuquuFoOBKAfim5R/O6NO5V1Y0BKNthiO+6nO/1+Rtf+Q6Hk6nhdy1vGfOy5NajbQH06/qYa4TmdfWiSGycg0isnJWD5DK+pbEtcRRIcBSQfuaVnzO2x0FrDcohVjVZuJJqjEtvAAEsH6+ecMrzmNa1WDfXCV55QGxV5E1NWlfQ/STrN36BjY99jniwpT4YfaURHEkYxmv/Sr6lIkj2z75KMR8z3b8jDG/HJTnY4clrv8/g4iv0z75Klc8Ybb9LNnrGfHxAmSXUtRA5x0/uUCQTivmEw/ffJJ8MyZOxFLN8jt/uL0a5qhDmve2YsNTl52o57bppKgnfWOJU2Y5Pe/0GtqdGTcfDi9oy3vkt/KgvQm3PJ+qdor12lXx2wNHDbzJ88Cbx2qbgdqMDDm+/zmz/sXRunT627eBFbWzHIeitSifohuKM0dkwxbXMxsbfvsxnxP0t8tm+4WM5XoQf9Q3pVLqqIdnomUm5dhRcUGSyKKirgnT0jNn+AyzHIxpclG2r8gnTXaq+COHEhL1zymjSxZs/wLYk9SdyXPK6YjWM6HoBa1F8bCyUz8Vxl+CTReyjOM8L19LR0WRlWRKGIXm+sLPRDPqyLKnrmkmSUpaleXF02s8/fe3r3Nl5yjTLjGZxeeOihdV5WbLSaRuiaprnHE6n7I/GvL39mD9+/33+8MFduo4HudiBNFhGg1irqyWI5bF2/fSjgckIrKuc6d47wnWqZguqQnDOiJP1FnBqxaQNtCtFl6iVCWCVEKjEFnEsPU00uIIVbtJZvyHdiSccr7CzSTVPefClf4obdHD9FtP9O7h+i6h3jrrMOfcD/wFNVRGvbXL/i78ByAiVT4ZMd+4z2XlAPhnS2bxAPhmy/843GVx+icnOHSmGR7sUyYR8MqSuK8p5SthdM/y1uswMX6mcj409sqYB6GMr8z2da6jHR42VBfEq8eAC/bOvsn7lc/hRHy8SgqoXD3DDLslom2wiCT5FMiEbHcjG0/Npb15g9kzUAWF3jf6ll+U5CVq4YUx69PQYwbRSISdaW1lX+TGirO34ok9UxU47PQB01q/TXr9silEQr5KnR8wnu6qYebTWLhANzpCOHjN++oZ0bup5sSybdLJjljzkuwtlRlNSTh/Qd2XcnpUFa2HMRtSm5fl0/YCW55niFQZLgSYnfOo+yvN9PSr+u566rsmyDF9JdlzXpd9uMVRia9u2KcvSjIu2bS/GR51/ujTXH7/yNES+XCVX2oJJTZVaXr+wVVUzzTJT6MazhH/0/lv87Rd+EMZfl07CdsV3tCoIWqtCSvTb2F4sI6QdQZmZQhZ2NskmOwSds5iA0DrDqiY0/ibamjmvGyI9qVg+VEN58+ZPxX3BclXnVxs/sMaOcEMZXfWa/9JP/pISAQ9VAk9KNt4h7GzSP/dJgv4N4rU3KZIJ/UsvsP31f0Zv6wZ1XVFnKVUxJ58MccOY+eiAcp5hOx6Ht1+nKnLap84rt9E5bhBTZSle1CZPNaaUHzcbrHJqhXWhnjfQWr6apjzuWKC3lF7QNQz3qpDQXZTzAgimphOy3SCiKqT41kWOF3ekK1vZMM4XjuNTpENFEB1IMlE2pi4Vqz/qqkxI+RlOU2N7Yucc9c4ZgXqRHgmnS+Fxrt8inx0Yfprrt6irwnSDRTom2dshXt+kzBKC7qoapz3jTmF7MWFbui3tjIodUk4f4YY9HC+k53hCzQF8BdKLaaDoGcfz+THw/aQr8DJf8aM4zzsudeq6ZnNt1WgUdcZiVdemWOnbdPe1/P+irCRirK7N//XXAzi2ZTSM7ShkNEuOBcgCxyQR+k3wdDjkd3YfigsECMDuLhLqJD9QHAvqKicb3lVXfVmD58mBWvnXyq1BKBBNcHZBhXDauJYlAtqmXhQoV2U2lofKXkf9u9OW0VOlXTeWMPaj3lk8JXCWbZiMq8IrQ0ihtRgXtk6dJ1CC5KP7t5hsv8/OHQlBrQvBv9LxiPbGWd7+5/+AwZWX6GxewIvbuEEkxSGMxKF0fKAsmXPy2YHhVenivcyR0lFey6OkiJAXHYwO4SjzGcMnr7P73hdIR485vP8aB+9/VXhjykJnrqyb3SCiqSvBtjo9UTH0Vll/4YeZPn0gXc3RLkFb2Pe261OXOVH/HL3NlxSBVPnoFymWDrYopCvy41UjDE9Hj8mTAxX0IQXbdkVArrMmAYKO/Czp9hZby7rKqcvMFOSmmkvn7IbKz76kmUuWZlPNaeqKOHuPTT/kE901VoMIz1bWTpYUscjzTFAyYBQkGiJ53nH9GR3LshiphJ/pdIrruti2zd6hhA34vm8KlR4dT46Rom+U7+fYjhJUyxV+Wdt18dSGsfjQV6XI95mmGVVVG6tnrWe8dbTHz61coZlviwNDPqRpKvJ0qIDbxRtav8Fnh/fw41VaK5cUbtGGXAoctotOo9bFp2vZjKtaJEHFvhQtp00aXiYqnqqk6xWolSGcHS9cJ6rSRKhFvbPGOjgZPhQMp30KLJuVyz9DnT6lvfES7Y2bWI3wscZP32Rw8Sb3/vU/ocwSyizl8PbrBK023/zya2ydW+fJt/6IM6/+CJbj4DoRliMeXNOd+6IFbPeFMpCNsb1wIRpmgd1ofpg24TO0iFq5qtoi/dF8s2y8Q3L4mCKRgNgqS6nritb6Fo7fkizHMKa9fpks2KF75irZZJ+gLQk+btCRIldIEnb/vCwi/NYaZTZmsvuuFB0vMhSHUo//YCyWAemaVfq07YXkswODzy1Y9B5FNibqbhoNY+4eEPXOqoL29rGlhQ7dqKsCJ4iwlBzJeHqp0boqM4r5A9ZPX+fuPOVgnuLYNv0gwnMcqqZmX9F69GZRk1BzBac8d4f4MzovnjrNcDik3W6bDstzHZIkIQzDD6RcnyxaJ8+y3Uet1PR6DHzz/kMTZabHRQ3mn2Qhe56ExT6sfLbU9zMtvuNRqyuzpcFmFeWl/z05eiBXeTsGZypWJr2PIQVHuU64YgYY2REFEV45ln+LbxJZNhSHNK2XsPKnpgs7rBpW7EK+3hOcrCmemt/Z8SK8aEDQPQ/+afKDbwDP8Fd/QL5HsU85eQdv8An6jk+Rjbn4ub8IwGya0Asj6iLnhY9doH1G8K756ICmVrYx6nmoipzZs238Tp9GAfqSdlMoUmYhXYkGsy178eG1HWzbwfZClQqdq65NvMNmewu1QlNVtE6dM2GrjidBuf2zL8tGdyA4WTvsUmZj4v6mvA5Owcqll/GivgjAQ7H2CfKnxIMtMWdUm0Pd8WlLGgDbi4z8SY/DliUyIR1dZju+QAa9c1JQ1W1+a5WwK49DM/W9aGAyCaTDkoWF1VSg5D8nvb2aWvSeDTZFXdPzpWC6lsVOOuPO8NB0VONZYnCu5U16Vdf8s7/8V7H4lT/5g/hveZ4XrqUTxzHj8Zh2u00UCuEujmPCMCRJEmN9AxiSqi5e+u+gO6+alMU46buOKUjaQUK31steXb7nHsMF9Hl9uMdWvy9jG4LfGAxmKc1Fdztu2MXxInEECFaMnY3rp0pkXQqWZfuGx0UDntVIJxVdlduVTjJvagKFbyWNxWGZseLmUuCa3HjDO24I0RZN/lRwNTsEO6ZpaoL+Nbn//AlF/CK+36Jx2jjhCnWV0zvzCvf/6P/myo9+nrd+77cJA5+bP/9LpEe7fOl3fo8f+7mfYvLkvhkxLcclnwxxPJ9M+ZnHa5tmTF5OxaaujONCUxSE3U2zyQPlY1bJmJin4oHmK+cH2w/wojZ+a3WREl5kqlvJF5s8VUikq1qldfYnDMAtiUeJioKTk013aaqCdPSYsHPabBJtx5MgECWS1gsEU2SU1Q9gvPY1gVX/ThKBJptjJ+hz9PA1WquXiHrnRMOoeWs6mzI9EjNBy8YNW2bMtoM+vrL+KZsGx7KILEfi1RyXXSs5huUud1cf9Xi4fJ4XrqUThQFJkjCdTlnv93jv/gPK6rjFjS5E6ysDdnb3iOP42MZRn+X7hr73oRbOJ1/YZePBkyvmo3nKd6pNXmn21JUxNPwhnQIdxKvMkwOi3nkqxXK3Ha1FjCE/VO6o4sVlTApVJJWn6RHR9YVvfL4L/mlxqFBmgZblY1sWc6dP0GxLF9c6A+UUbBdr/ljY+jopqNgl6N9Q2JkUTY+Cxo5oLB8LW9kLd1h/4YexLIcXfwqykYDO3/jt3+Rj188Sr21yeLhLWuRGhiPi6LHCvKQouH7LGBC6futYUo7thgStVeOrblXZUqdTY1kOUec08+RALIEcj6Aj3Y8eyTWJ1XI8kQ6pwqKJnHVRMNm7TVxkhP2Lokyoc3ToLnXG5OnrpnsK2gu9I2CoDwCu0xKJaV4cI9S6YVf5yS8VGdVVOl4oSxod9ea0OfXp/wYruwtNyfrlz3H48KsMH3+Lzvp1/NY62CHF7InhsxlianaI075MdfQWfpNiLwW+1k3Du8N9ukFAVokKRE8N+s/f7b3+vZ7nhWvpHBwekec5H7t8iVGSUFYVcRSZwgULrGtnV3zUT46QugvTm0ctzJY/fzDODDAg5nJxsx3bGLE5ti3BnHVN2X4VZ/ptbBwctzIres3adv0WWDZxXwZLx29LkWpK8ahCZzbacrvTNok6lEMadwWLUlwjyqHZLOF2wfIprACbhhXXZ1qVBP5pvCrHqlPqpqLOFd8oUmCwtwY6IblOMMLuKgG3izV/KFtOrwPBOfrnfwQa6Qb27/whT7/zJV784R/mW1/8EvkXf5fNq9fZu/8+nUGfIp0RtLu4gdgO27ZDXeSko2fGD6t2fFxt7GhLwbdceY5oSvO8ac2fzil0/RaFcojQWsIynwnzXHcmqssRHaHEjOWzA4PzpaNtRk9eJ+xumqQdbbNjO76ImuNVA8KDggEqWa5Yli3EVBwZK8c7xrpGXtuWwATKN972Inn8lrPAL41DbiYFNH+KZTusXPsFM7ITbkHyjhJ010KnwVoUvuyJPH9NzYbq8veLnKNibrCtNM+ZpRmjJDHvaf0e1xvzj/I8L1xLx3VdfDW+pdkc3/OI49jwu2xbEn80v0sXsWV6BHDstgXW9cEX7mSx0uz7ZecIfbtj28SOS900OHZIA9i+h6vU/a5Ot9YMeccXPaK7JgXDWs5JFLtj3P7Cx16lVFt1otKDQvBP4w425M1bjincVWN42HM8rKYkaVaJuxs0xT7WfBtHJy1Xuqs7VJFmOSY1W4+qmgxruXJbsQ/BGch3Cdc+wbmVm4zPfInWyiX8Tp83fv9fkk+HuK6N4/kU6YxH9x6xdeUiyf4OluPQOiUC5OneXQWS51SFrcBpKUhWU9BUlcKz9pc617PKr0uFpNrSDZXZ2NjtGNa6goE04G82mI6H5fiU6ZHRB1q2g98SbadlOdSFiMC9oGt4ZlJwbBUh1jGj3zKbXhNFXb+F5bao5kNsp286JFPs9NGOt3WycMOtpuJTlu2QHH2V1sbLVIdfNiOvZdlURSrwghLeN42MnpSHtINzzBuYVAVvHD7jcm/AW3vPjO72u73H0xOhvt/reV64lk5d16z0ezx4+ozpdEocx8c6Kn2m0ykbGxvkeW6AfPvEKLi8gQRdwP5kpbx+kSPfl27LhV4cm06tpsGzrKVcw4V7ZYN0ENnoPqEaPQjWl4wDc0xSse0rfCuUAkMt99Fs+iY3nK9y/B51leN3zuIpnMypxoDN4e1/wsr1v6QoFbFkMDY1lrL/ldHQNgxsLJWArSPMqgScLlZxIFd47c7qxMa2p3v2MwBsffqv8OyNrxL0VkWY7DgE7S7drMANY7LxEZbjUCRTkz5dJBPaG1sK/1PjfD6jrmRELtIj+bsqJFHvnPH2WnbdsL3QjOZaO2grbaLewgF4UV+FYaiuU9ELmrqiqhfRYpbWTirXWt1FaazMdjwDnC87oeqQ18Zp0eRHJoLOjwbSDepgXR2ggmCPxsOsyGiq3ADzs8N7RL2zpoPT3w/A1woEyzNjNe4Kw6qiZbtcjTok/YJ/+O2vG6fTqhIidjsKj42G2srpozzPC9fSKcuSs6srjFUkme/7JEliik+73ca2bTY2No51VcAxDOxkEfuws/zC6uxF3V53WzF5WZLNc+MJtu957KQzzvoxXVBvTPWG0CEali1SDcuXIA3LF4zKVgGr1GJb4iheV3moPOC7Uiw04RRbWT33cXsvku7+sQmlgNp0SitXPi9dktOFenfRYTldKVpud9FVNaV836aU/7KH8rhsn8btLQpoNQZbjZeA9hArsjGd01vMnm3z6NEzLt+4ghNE9DeEN7W2ssHw3tvEa9KVFMnUOJZqrpZVyQa2LjLTPWlxNLnytlcFoGlqAd8bRWhdThpSG1utJdQ2Mk1VGKsdGQUnxqmiaWoz2ml5lqtIrlqi5UcDKT7eAsDXryvYUGWmq86mu6SjxwTx6iIFyQmXLjy1WZhkkx2me3fIhgd0Ni8Zh9bemVekW2tqE0SrTRoB9XzsG7KzZdkMi4K+m1LbbWZlwalBn3xv33RbVV0b8wDfc00Y8kd9nhNQl04Yhrzx/j2zWdTdlt4W5nnOdDqlLEuR+ygC6TKPSxev70aTOAlULidf69uzufh0dVsx690uvusyKwp2ZhMe54tNn+5KFsde6qjUG5haioIqHMbwz12RwtaU1JNbysrZBf+M3DffhTqnmrxD2N5QRfBwMUaiwPpl6dB8G/zTixGlHCp9pG8wpWMjo+XKfZpSCqUdyr+VY1XI9K8VEvbOsfXZv8i3vn2HV3/mz7P/ZIcimfL00TajB+8Rr21y6Sd+kXwypMwSvLhNnYs4Xtslp0cPxYYmOZBCorAmy7JVmOzC2liPa140wG+J9Ea7jDZNLXiZ32K0/baQXlUcmOV4onX0QvkeXijbXVVcvLCrLGQ801np4pMrATV2KGO8fi3dvvwXnDa8u6izSdQ7S3vtKnbQByckObjNbPcNRW1wjbvrfLqLF3Vpn9qiKjKK9Ih5ciAd53wiv3uZGcPFsLNJ0FqjmE/MosDyB1R2zIqS/gQWrIWxsWXKi9LQffTJC8G+pmn2gQCZ7/U8L1xL562/9V+bwpPnuSk+GuPK89z8WVMjyhPEupN//24FbPlosTVggM5sLtpH3XHpzc2D6Vi0hiwXLXtBbQBMQGu9IJiKTfNUzPhmT6iTh+Tje1jliF//b3+ZZvYujS4k3hrk+zIW9j+JFV+Sr48uql9yvDRmojqqWorP7LbcpkdUfX9dZJtSRN+FKk61GmGrZGFsqImtGgsDsEPaa1f587/yV40G0PF81jZW2ds7YnDuFSzLYbi7q553ETub5YWS6Eg2ZIdCuaYCxhI6GT6UfEZVmADj62Wr0UkXOhAqQjTYMNmFnEjucUOxvLEsW3VS4lhqK5qKHw2ku3J8vGiFqHd+0dXpcVpfHLwVeU69NXC6jHclMq1pvcj4yTfYv/O7THbflbCUugI1NvrRgPb6DeUO21K2Q8IBqwrZqDb1ImjFdjzc7jUaW4wJw/YG4foP8sy9gNPkdOshYDOta944eMb+aGzwrXGSGt3usuQn8n3+8V/4j/6Nn4N/l/N8VPyQ4/u+KWC6OGkTQS390ULsZUBe32e5A1uOOJPrxPENY5rnpq0GEWF7nrhKaOZx5PvkVUXouGRVyV5tsQ4m8EJODXXJsQ6sqU3eYjLapszG4ozaOwdOG9+f0rh9ful/+D0oxyR1RaydI9o3F92SBtABY/Xc5PJBakqoc6zySCgQoXh4laO3ceN1pXVUj7HKwY1ViEUOVYXtNDTlePH9xZBlUbz0eGnZWP6A8z/wl9n6lMOV0TZ+NOB3/8e/zqVXPoHjt/jar/1vAEz3ntIGelvX8YKOURjUVaEwLuE9ObZ0Qo7qhjRRU28BdeCqH/WxFTFzOU3HsmXbp4FxXdTEFTk85vtlmcKnNoIqgLXR7rPmdczMc2HVc+pigtW6tijwaivb3fq8jOWT7xhgfXD+U1RFyujpm/TOfZrGDsBxCbpt6iJlnhwwfvou8co5s6ywvdCMziDjIeWQej6ULrJ1jZ0mZlYVeFZA5K5Q1DW7ecZuOhOzgHab9W6XUZJwatDn9vaTY/mifxbnecd14iRJYkB3kC5LUxza7TbdbtcUsCzLjhW05fNhYH1ZlsyVBGhZMe+74kmvW+tsnnM0kfRfkC4sK0uyqiQrC+4mE8GRFt99sR1UekOrqWiq+WLj5IZ01m8QtFYXa3KnvRhJLJe8qWnmzyC9I12S05b/u31h3utj+YsuqakFp/KUk0E1xioOcNvnF+OnYvajQGkZY3zlSCqOF1QZVJncr9Egvi7ES8XYbdNYDlFnk3S0zeVPfor2xjlsx+f9+884f+MFtn74p9l/9JBkf4eHX/mnwkxXVjV1mTOfHhkSp2bBaw2hpcYr6UYKsZjxImWPHJqRT1vLOG6IFw0Un8vGC7uE7Q3joWUrrpe+r6u8ymwvXmyBseUi4MTq9ZCi3diB0CSyh9IJF/uLMdxyIThHOtqmrgr6Z18lT4+Y7t3Gj/pU2aF+J0KTGylT59Q1gtYafmsNv7VqOklAjatDsuF9AMLTPw7uCoHlsOGFfHH/MVE9pduM+fLuNj9z7grpPOfp0ZCHe/s4ts37T57Kxdix+ZGbN/7MSKjPC9eJs9xBaQsbzagfDofmtpNeXfp2vWHUlIllNwndgRVKhK2vSIbn5djHWu00lwKmLXLG8zlHecZOMuWxLYnbpmDpY9lYNGYEkIzBEL9/YyHUrjOefPtXpShlD3nwh/8LWK64nkYXuP9H/4cUsme/S/r0DyG5Kx8cjVPVyUKI3eQLGxQF3Df+JlY5WsJn2gsZifK+byxvASarD48eYcR3rDZFWITfOYttaghel/bpT3L5z/0K/a2P47Y2GfRaRCsbHNx+nTgOcDxFJykyksNt8mSMG7TworbxrtcgvRdvECidZ1VmpkgJfSFVnVoh2z4N6IPZENqOh99al62e4+OEK9hejB8N8DtnVZJOKNmTOnhVb2MNu75cbGLtGLw13O4NoYigxe8sIIAqIVp7idWtz2B3XyIaXCNSxoYm5RsguorXOoPjhmTjHbGfjvpCxyhSbDck7G7iRSvG9lurB8ifsOJYTKqCgyzh7927xz94sss4n/P/PnqPdhQa+dpolpj3dOT7/OvvvCVeddGfkCP5pzzPR8UTZzyZ0O0owqEaEbXVzebGOkfjyTH3iOWCdTJEVn/98n0APCX/0anWWvqj/2uHIa0oNLeneU47DGh5Pr7t0PUDDsucszrl+sTRRcLxIrmq2yHMd43jQVXusnrphyizEcXqj3F4+3UufNbFn32bJrzA6pUfhPJQcva6F8jHD/C9rhQvDaBXU1XI1IiqP1R2DMWubAotX32Nr4qUveimAIxFjktjqQ7LcaAR7ZzEsC2N2XqTqruUpsRtbdLxW1CM+exf+Evk0yGdzYscPH4stjL33mZw5SUm2+9jez71ygZBdxXHb5mACDfsUiS7+K018vToA6nSJlS1GuIFnQXFYYn2YDvShTZLX6cvIDperLE8heGpj53etNrqebKE5S7fNJa/61ATb82QdqlzRTHxwVujCS/KNtY/TbTeXxR4MFtdq07Bdoh6Z02nNZ9JGInmheHENM0hZT4jGlwhIcDyTvPObERalWzGbULXw7Esvrh9n/2Rknmp97XODdWTQzsMmWYZ673l6eCjOc87rhNn77//n8xo6LouruuSJAllWXI0nnygYC2D7ycpEXobqQuY67o49iITcdm+WZNPl40GR0nC3nhMVddMszmzIse2LLKyoKxrnrkqxMhsFxcjo+346oMvL3E23WU+3SVorRENrhD0roil75Pf5uKP/2URSUfXoBzSWb9Bun8LvC7p/i1hge/fUmC7bX6WVc1oipEUJc3BMrpHVZT01nG5q6CmKWeCeWmaxtKoq3MjLZqlrqsUv/2l72F4aZ58MC599j9Twus513/i57j/B/8/e28eJEl23/d9Xl6VlXV29TE998xes7tYYAVwAdCgSEI8DOigQYikBUi0SIoSdVAKK2QFdVq2FNIfokLBkGXRNhVkmBZlQgw6eOkgTYogAQLEscS52NljdufYnunp7uo6s/I+/Md7+Sq7pmexBxaAwvMiOqo7Kyurqyrrl7/je/wap9/5nRLt7nok8ymG3dAMgworVfV7zIbU4Wq0t3R5pz8r1cgOpze1rA2gG/em5db6VEAWaMFHUeZLClTVbK/Av5ro3lfwkRpIN1dsg2wis+Mq69JTYZXlZpNlKVk5OBWRPH46gnRIvBjq/zlPI+LFUEseWZ6k/IhsqlgBJlgDkrLgU5N9doM5RVlw3Z/y9Gifzw1lOWioCqHnedx/alteiGNZJoaxrEYGnTY/+8e/7zV8E19+3Qtcx6w6fcfzPAzD0I34evm3iuNava2CW8trYlsmg3aLQbutG5aVAmqdfF1NElMFj0gVeM+xLFqq9AnzjN1gzn4aSTI0LL/46stTTZYqaIQ7eJTm9rtkUCgL8vA2zfWHcZprdE88SnB4BZLblPEB2H3i4JDg4Cmc1gZue4s4OFR9KF9PA6vMTpSphEEIC7IZ8fiywn1FywxNN90t2cdCqSDkMaKItQnIkS9/9Xet/D3SA9Ov2wBbqkNc+KY/zdrFN5GnCesPvhm3e5JovM+Ln/q4DFiNJok/WX7Wlqt0sKTHoO1KbFVZSu/GSjrGMG3NVazQ7nmywGn2sZprsvwra8MRy9MQiTSaSfxcEclAks0UBGRjCW1RqHZy6K/w4AAAIABJREFU6UlJcnsZiHIfkR7Ikjy+Bem+ytAsebx8JkvzIpA/6v0pghuk/g0JeclTzdmssFqG6eC0T2gITGnKSsPtX8C3NoiLgsc6AxZZysd2XyLJc6I8Y5EkOJZFkS9dq6rpYpV1VSDUr7QtWbXulYrHrEoBogpMnufpzOu4VQW51d+rx6dZTtdranvyKoUeqeZ7z/PotZbNb9Mw9GSx2XVU38CUjj+Ak5k4hsk0ibhhdjivHIAAmZkY5rLxW0EMyox08pTKLkrMRp8s2FsaLViuhCmYDSgT3M5J1esYkyyG2slnsX+ZyuYdpFR06ZxY9rwMl8bgzepLNVwGGNMDITO10mhCNYZXqqWwzBK1tr4wqU9iJQI9lBACUWWZtS+G1cYGBufeiek8xdr5b+HqR36SRm+dM488SufkeYoswds4pyk+bvcURRooUUGp3S/MhiyT1f9UrUp6RurCSxBpkaeQ+BJLVWaUWVjTuM912amNKKyeDkayv9cG4S2z2dxHlKnGdLnqfQ6mOwhhYrtdDHOBaJ5HRFdluac+B3Q2n5FHQy3tnU93cNtby2NuPgHpkHD8wtGynwKjuU3gnGeUxjRNk7CQ02wAUwhS0I4+I98/om6qzZMVL/HtDz3Ap5+7cux35vWuexnXMWt4eIjneXieh+/7OouqE6iPy7hWMVtVZtZyGxr28MDJbR3ApCu2Ixucqp/VbXmsddo6M5M4mGXJYgpBUsgrX1IUTPOUsv1WfeJJn7zqBJYBS6SHlPEBdvchhN3TJ7RpuzidpWduEo5BGKSL27iDh/GHVwjHN5jtXSbPIsKpdFB2OxKdbpiOdAqCZUloD1QJJAnVokxlNpXNljgzYSHs3pGgIIRszEtIx+oyKIWJ4bTlBKzIjjbsqyxHWGDJHtHa2XdAmdHorXPj9/4jzcEWsS8NPSikQ7aUdo6lpE1ZyJ4ggjQcySZ1o68zLbuh9KuUzleFxo/9fQnUjEYIFagqWWenuaZQ8zlpPCec7ZL5L2mAqDxxIvle5T4kQ7LwUJf1yWIo6T1mhzSckIZjZntPMz94jnT6NKVy7knDCZG/TxZNKfMYhCE/q9Y6jda6xpk57RPYbpdsepl0cVsGO/ecLElR6PwiYZjGGEJwO4kYZwldp8F9vTXSQqr7gtTdeuTsGd2nrXsntpvSOObzV6+9IY15uJdxHbtu/sN/yqUf/0fAMvhU2K1V44z61HB1VfuGSYppCJIs48ru7ZqXYnoEz9V0HA3cq4+Rm46tr3pJnpPkOabqdQVZyovxgvucU4jo6sp/IBvnVUZDNgH3ApbiLQpnDYpE8+uEYRIcXuHpX/4XnPyGb+XgqU/RHGyx+8xTzOYBT7z/g5y49O0ACKRKgm7Yw7JBX+MjlkbjaDO66ssoCWqhekHCPNoQP+bdlCWj1aSehcn+GMhYLUtjozGgTKdkyQi72aZ37gGyKMByPSn2t/kQWTyX0jeKn5cshkvJGKcFlkcRT3RmBUj1UcuVgoOKCmQ6Le1lWSiVhiJPZTPebJEthmTxXL5nwiCYHNJevx9D9ZGqwJuHt7U9WjC+ruWuy2SMcNbobj0i3bsVyVtSfNo0e2dBGGThWPMpbatJZ/OSxqwBhLNd3PYWZvdR4uGnZVaczRTFC8WyAIqEnXTBJA6xDZNJEnHSk45U626TQwDPY7YIeGH3NomiybkNh9kiwFfKp6ZhkOcFv/B9f+bLfK6vbd0LXHdZ3W6XIAiIooi218Q0DPaGS9R8FazqJeJx6hAVbKIqP/Mip+nI+06vy3KkgkT4kaRGpGmGq0bMlVKE7ZqkRU6YpLQVij7MMxZZStOymVlteqsvQvU6pGKmhShT8tkXVGaxFBJ0e2cIDq9Ic4tkwX3f8UGC4Q3KImfw4OOsP/RWqVrQXCNZDFXjWorLUZMXlnw9xTlUGl8S82UsAZRaEUJd4S0Ps1YuyjdNHkuUOWUVkGA5TdRg22rn2j4qeAlnHVuYDM69k97Jt/Dr//yvsbHe5ewT30M039WZEUAWzaQMjrJ7My2XPBppUT7t8IxJo7UusVWxtP4ikdliMLkhRfmyBNvtsnb2CRASuZ7Fcy0QaDc6Mhtz2jLoW7LENBtd6REQjuluP6bldORzZ2C1cZrIz5FSltFmVw9FrOY65BF2c6Dwd1PMRpd0uqOhJlmywLKHNDqnyf0XZXA15Gs1G1I08aZzCTNaYAiDWRoTZinDSBliBDIA+1FEt+WxN55wan2g+1thkrDZ6y79RmuOP1/p9bpKRSHE9wkhviSEKIQQT6zc93eEEFeEEM8KId5T2/5ete2KEOJv17ZfFEJ8Um3/d0JIwI8QoqH+vqLuv/Ban+PVLN/3JRg1TQmiWE8UV8Gmx0naHEfzqZr1APMgJExSiXvJC6aLQAeoCsNVNefDJGEaBLw0GjHyFziWha2eK8okINVPE+Z5Suk9cuxrkQFF8vYMpyuVN7PFknKjCMBlmWM1OjS8dabXn2fzTW/H65+jtfEA3uCidEpWdu4VtCIJx4TTHQWEVMHHkKN8UcTLZrGhGvSF6hNWqH9hKQWCpmpmV/rpijeogaislIUVup7l8ap9qimr5Wn9+D/8g3+d9uY2lrdJe/0BaUIL0ovRUEa04RjTaR/JnADtVyisJsJqYjXXsRtdFexkuUiRH9Ht2n/+txm9+OvM9i/j9c9Jqo2Qwd+0m2ThuMZCUJxYp6V11CrHHimXo2Aktpw6lkZDXgAqgnv1PlT4sGREKWzKLMTtntKgWav3iNTksgf6syvjPUlJKlNm7sPsRAvGcUSUZzRNi7QoOIwCojzDNS3CNCUvChaKf2gaBifW+kckmapJ41daZ76+Xm+P6yngTwIfqW8UQjwKfAB4E/Be4CeFEKaQZ+W/Av4o8CjwQbUvwD8FfqIsyweAMfDDavsPA2O1/SfUfq/1OV7xGvR7DA8P6XY6R8rDKIruMM1YnShWv69OGA3DwDSEDnJVUAqTRCLnayVipQ5R/8mLgpa9VFPNy4JFlrJIE3xFBSrbjy+R8avgVADT0x6EEm4gy7dG76I+wYVh0jv/IE67f0QPqloVhqlymZG+hQXh9OaSV0ch+YDxjGyxSx7cWsFw6YMthwimq8GoGtFdZEtA6p3/yMqG4uit4VEaDZo9qdG1dvERdqyLhNMdKvNVx1uXNl4VxKHI9HNr3iBLvS1KCeXIlMtPWRbYzb7mNqbBnCwO6Wxdks3/zjbhfBd/eAVv7ZwOipbTWlKnyoQsHGPYHrG/TxbPabTWMRp9jOb2EkJRQUHKbEmcFwqs6p7TfcYiT8jjCcKUZbrltEjW3kW4/0n8l34d/+ZHtCR05O9TlgWH7mNci2XQckwTxzAYx7LC8NMEP5UXpaoZ31JTw9ki0EGskm2uhk9v1EQRXmfgKsvyclmWzx5z1/uAD5VlGZdleRW4ArxD/Vwpy/LFsiwT4EPA+4QQAvg24BfV438W+O7asX5W/f6LwLer/V/Vc7za1/axP//X8DyPjcEatmViWRau62rvxSiKjhCxq8Z9tarAtRrUKspPteo63WG8RMyv6nevddpsdjtE+TK45WWJIQSmYRCXBfM84zDLyL2HwXsYmveBe0FO6ip6TTaTpN5GV2dIokwhD0ijGUk4IU9DLNfjEx/6GcLpjvLnm2iScpknoDSeykLCBqxGB29wn/wypUPy4JYU0ROGxkNlwQFHMqY6ELXiJ6oMjFrjXps5HAlURi3rqmdf9X3kcS2nRcNb5+w3/jCzX/whXvjt/4twukMaz0mCwyX1RZhquxxe6PfHbCylZopMScFIff+qL+a2t7DdLo3uOo7XZb7/LOH4hnQ6siRBPEsWOO0T2kpNl9MKSxZOX5IeAZV8cjpfvlfHZZeVAGMRSGUOVZobjYHSaZP9xNLexBr+jna1rpthNNcexGx0MQSsWw5Ny8IxDHaDJbB56PvMkpi8LPVAabvXI8kybOVIVbfaq4LX//M9/x1v1HqjQuJp4KXa3ztq2922rwOTsqxqAr39yLHU/VO1/6t9jle92i15xXUUH3EynVKUS2x0Hd/1ctnW3QjYeVGqn2IZqNLsjqAF8mS4PZniR+oEKkscw1Q/Bkmes8gzpnnKbhKylwsOygYHNAm8N0u6j8JDyelfH2E2SMORkuk1tC+faTdpdLZ47Fu/nUZb2sPbzT7h9CZpPNNTsyJPabS3FHdvjTzxZbYlLMzmFqUhv/Cm3dS6VJJDtwSVHv0dKqwXGDLgqv7M0VXrZ7HyhT6SaRr6tbY2LzG58VFG16+w9eZv5PYXfheKnGi2KxvaqnQ0lDNQnoZyEGB5SzxcWWhakuW0cLx17Tpd8Rvbmw9J+ozbJQkq955U31IWmC0pbqgxaBRa0rkqlU1L0YNq5bzmiB6RCaplsQq0mkdyOpnHEzA8Als6/Zh2U8tPg5wqR5MXIU8IixxLGLQsh2EU0nUaAMySmJ7XZJEkHEaBnnK3bJuNXpem49BSU8TpIuC9b3rsDbEjW11fNnAJIX5LCPHUMT+vOov5eltCiB8RQjwphHjy4ODgjvuLomDmL9jdPyAIArxmE7fRoN/vawWJuqzzy0nY3K2krJD0dWeUPC8IlYhgfbpYccD+yOmLrDkubg2UmqirqIFglCXcigNuJxHTLCUpC5l9Vaj0MqNMDinSQPaVjhBV0MHovnf/Rdrbb6PZO8PNz/wHWSKqgBVObxJOdqRjTJFLs1dQIMl9haqfywzKlPSaIk8x3YHcr4JPyHeEI+h50FlGiZAYqyP0n2KZhdQpNLASABWvkQJUifYN3//3Ccf7pIs5872rHFz+JMFI9ucqZVBgqexQSfOo/63IE22WYdouGBW2Sk4j82SBabk4rXXsZpdgdJ1wuoPptGl0z0s8Wx4sAatmG1Hm+kJQKUhI3TSVdZYrsI+KFnREBkjeFomvA2tZ5JBOaBqmyrZkpiyENJDN0wh3/S3QfYK8LBmmMYssoWXZhFnKLJV90cobdBqEmIYgTFKujUa0XVfDfPKi4PT6gM/e2pHo+TewTFSf7suvsiy/oyzLx475+ZWXedhN4Gzt7zNq2922HwJ9IfQZWG0/cix1f0/t/2qf47jX9lNlWT5RluUTm5ubd9x/9e//Y0ACUqsy0TAMTQG6WyP+bqj641blgA1L/7m6Pledwzhot7ivt8an929imyZdu4EpBHYltYLUAvfThEWWEOc5aVmwyDNSo6OmexmilABJ//AFZYVlS19G05HI8UZX6psLh2R2nRc+/NO0T5xh9MJTAGTKPad74hEtqdLeeADTabPY/yKRv48/vIKwmkxvPglFsiwZM6UoUVFXAMqCNBhKZH7V95HvpMwS6xnXcX275Tu9/LUKaGYbDE/rx1/96IcYXv6M+pxMnE4ft7OhydOwlFzWeCsVQASllD8ucoo0JFkc0vDWaZ94C62NB3G7J7EaXSpdLru5htPewDAd/INnNItAFCFlFqosTpWjSta5UL20Ik9J5i+p9yRRNJ5Ku8xX6iAVrcpTeLAEw+litZQ2vXcCnAEi3qE1uCjJ1cprsVJwpYjwi4yTTpPzrsdD7T7bXputZps15ygGK1fQnnpFcHqwpsvEJMvoeR6bvS4/911/6mU+p9e/3qiw+KvAB9RE8CLwIPAp4NPAg2qC6CCb679almUJfBj4XvX4HwB+pXasH1C/fy/w22r/V/Ucr/WFVKqnldpDvSFfd/55JXiu+qoeb1vLL2VVMgLkuZzSVFcu0zAwhcFe4JMUOY5h0lRQjCiXkjd7gc8oDpklMX6assgSFlnGLE8lPchw0QoRwOjFz8gru+FSmh1Ke53SbEnqS2sbygTLaXHqbe+hs3WJ3rmHGD7zpFREMEzi4JDFwTXKMufmF3+VPPGJ/X3c7inKMiccvUh74wHSYJ8kOMRwpHFEGuxLKzPNNzQoi1yK4OVxrZ+lEPPCvJMKVCsp78jWjhCzM3UMKZ+czCfYXhvTbtAcbLN56V002ltE/r4uS7XmVh2DJgxKYetS2lBl12J0VQZj9xymd4pm9yTN3lmlttCnu/UIrcFF2ifeUsO2yelkhX8rjSaW26NUcIw0nOgGfpHXVDGEwVF12QSRTRGZdFtHEb1Fvlh+zkoJNwknWE5LmsY2ulKxwpNDi3YxwywTRmnCzSigBNYa8tzrOg3ajYbO/CtP0Apwevmlm9qNKi8Kbh2O3vAyEV4njksI8X7gXwKbwH8QQnyuLMv3lGX5JSHELwBPI4XRf7RUpnFCiL8K/AZgAj9TluWX1OH+FvAhIcQ/Bj4L/LTa/tPAvxFCXAFGyEDEa3yO17TqZOl6NlUJDa4GtePWaq+rOma16g7WdTBqJXk7aLUYLRY0HYdLgw0udPpYQlCUsBctGMchUZZxstVhkSWYwqAoCwlYLRxSu+S01YU80JnFubd/n3RWrr4YCole5jECyKIp84NnabQ2aLS3pMnp+nn9P8f+Po3uOodXP06jvcboxifZeuDbmB88A4C3dh5/KCkfdnNNBizQ1ly2YWmD1AorlafStRnT5mWvq/VmfFkrNVdLR83XNGh0TvPAf/2D2gqsknQG6aBT9eEksLRdex4VHLOZPqyUPZ7R3rxEHBzSsPtAQelehCLCNGc0VZPdaq7JaV80ZDG6Kh2tm5IfKPKFpEAZUhMsWQy14KHTlFNdyYaQq6IUVej9EmSQKjL1/o2lnZzpLc1Pkn0VBG3M/ts0zzGbXpayOYaHXxoS1Jyn7IeyGR/mGeM4JC9KnWHV+YeVumkdAnF7PHlD8Vv6Yy3L8svv9f+D9cQTT5RPPvnksfdd+vF/dCTDWg1SxwFP69vr2+o8xtX7TUPok8GxLJoNR2NlKkDqdr+HKQxats17T93Hbhzw1GgfP014dG2T+zt9vjQZslDja9eycQyDlu3w9t4m+2nMBRsCGnjRs3f2hDDIo6GSYTGlCKACL5pOm2h2C9N29ZQxi2bSETmLmL70FCff/MeI/X05HTNt7M4F0vk1hUmKZBmjyhoM1WjGUEj3hdKDai65l7AygaQWnNT7WmnZa819VeIYjgZpFosXObz+++x9/mOc+6b3E0526J16nLLMSRaH2uQiSxaS1tTe0hlMZYQrJ6bS1SeNZLmcxjNpae+0qCRi7Na2JJ2nI5bWcBBNXmR0/dNYjSZbj/63+r0X+ZxS2BSJnOoWWSS1602HRvesVELNE4TZkLxKNc2sgMV54i//Nh1onFsqRhguZTpVJGsDo3WfImMnJPOXuNH7r7jotpnnKTfjkFvBnLTIGccRiyxhlsQkWX5EjhnQVJ8oltnXyPel009RvGyZKIT4g7Isn7jrDq9w3eMqvoJ1HD+xrr1Vz8TqprCrPbC7WZhVKy/KJV1CXdmqE6UCpL6wu8e1/QP2fJ9zZsRHd68zDAOKspTI+jzHNkzystRXzArr9VIc8ImDm/z64YjPzIbEzYclv04rLmSIIpQ9HhW08izCH17BbHR1E99prmE3+7KRbZjM95/FbvbZvPSt5FlE6+Q7tQgfeYBhOoTTm7IsLRImO58+UrICGoBqOO2jjfhVmIPOgOr36YOoPpIKwtlMwgSykWyoZwm9cw+ShmN2PvGbpOGYxfCK1ORqdLCbfWxXOuDoVWGm1ASzKiOLPCGY3CCc3qSzeQkMkzScsBhdZbH/JbLJF5f/kwqshmnTWj9PWeTsfOqniKcvkC1uyoyLQk/88jRcaoJlgcaQVUGrAsdWODdTNdrTWHkBxDe0Rr0k0qeY7ob0SrSXJimG6bBhN1jkGY4waKjSsBr42IaJKZayS3XV3uq8rM7RDYWY/2qte4HrFazV4FP9VNlTHepQFxmsr+OI2dWx6wFu0Gnr+/wwOgKVKPLiCJr+i4k8uR3TpGXbGMIgzDPSItdE7LTImSUxe+GCj+/vkKsAl5clTy+mHLqPUVprWvuqjvHBdGtCe0OScIxh2gTTHdJwQmvzEZxmn87WJYLRVW4/9ZsKWJlgVjgxq4vZfxtuZ5vprc8zv/15TMslnL5EkQbL8s5wZTMZjganI1CJlbXqcqT7QKrkTaeQ+USTa3z+Q/8Te1/4fRq9dQnj6PQZPv9JYn9MNJfuzuH0puQiKmYAFROgGhaYbUpDErFtt6u16pNwfKS5n2cRweQG0fg50sVtiZNT9B+ntY7TWsdyPQ6vfoLxzpPk0RCQnonRbFeWrbar+34VnKKCYqRqOJJGMyUMuTS9yBa7YA24mdtQyGCZRjPlRG5p+edwchWrdZKeaTPP5eS5Z9n07QauaXG+08MUgrwsjvBnTcNgtgg0KBpk9hXFia4UvhrrXuB6BevZH/sHr3jfOgEb7syyjttWh0ncHk+OXNmqXhdwxDVltgj4yU99jItdyXc0hYFjmjw7GbIfLqR6RJ4TZhlhlkodpTTRjfxZIkffB2nEjnGC6/YlDtxHoP2ovJLniVR2MBs0ehex2+eU2oFDw1vH658jjyfacTkNZ5x/5wfkl2lxk1x9uUhHEF2jLAt6px5X5qcOaCnnlX7UMZCII/I8RzBcLPevwytU+SnMBnFwSBbPuPQnfpT969e5/ru/ym/+638pHYC8NmWekYY+84PnpIJCs6+9DWXLtA5wLfTxhd3RBhuVQF9v+7ElDAEYXvk9FqOrkhNpD8DypNKE28VuduluX1KWYDaLg8tKdiZhfvAcsb9PrvwCQJq15mmk+ZVpNFu6C8HSNFZNCk8bMUUsM0138LCSGEq0x2Wzc5KZcx9hkWMIwSRLiYuCIJfn7W7gs8hSwiQ9QkOrSkNYGr7U+14/80e/547z/Y1Y90jWr3CtOvfAnZSfuqPPcf2t44652h+rrm6n1wf4YaSJ18fRKfKi4E9sX+Cfj57k8Y1tTjZbXJ2NyVVTPlUnfSEM0qLAUJI4fpoQZilJkZMUBS3LliVBWfCRxYwN9ySPdQbcTiLeZEgUvOmdktOv7gkqDSeR35JAzN4Zmr3TsoelApnZewuEL8pGv7OF2chkM9nJddM5T6PllbNIau/MSlA64vzDym2lNlHreVX3Ga529CnSiMff/2dJQ5/e+WuMXniaaDpicPHNkrfnD/HOSJ6gVIQopTZZdSzlZqQHGaUs0VqDi8RqIlmWOW7npMyS0oj2ifslrGExpGm5EsyKhDrUbdMWo6taRVUYJmUq0e12a02XhHka6TK2wphVAoiVOmvDW5cBLJVCiUk4kY5OsFRclfdAY4vnghkbjostBGlZUCDwTIukyI/gBzVTo3a7ev8bjdtaXfcyrle4nv/b/zPAEdBp3RyjWnXViHoPbHUdB0aF5VWsEhmsQKemYfDuRx7W+ziWRavp8vc+9Tu0HIenR/t8Znib7zx5gZblUJQVKr/UUjhpkRNly8xrkSbsBz43/CnPToY8NdrnMArIy5J5nmEJwZeKDUkfKSKlW64AnUVUQ4RXoMwEy9uUU63oGghDTvBynyKRr6fRWtePs5zWsgys6YndSdtRqz49PHaKeCeI1bRcPBVY25sP4XY28DZOYlo2ttdmdPWLTG88RziSGljR/Dbzg2eXgNq6aQccwYZhedLQtb11RCHDbnT1LUjZbP/wChSJNGP15XQ1WRzSaG9R5CnxYkgwuYEQJk5rXb6faaiHB6D00urvTJ7ooCVxYLL/hd3XUBatW5aONC+yTMZ8PGoyjkNmeSp5rnGIn2fEZaEa8ylRDQBdKfGu9rvq2MM3QqL5bute4HoVy3EcLMvSgNS6QWy16gGr0pmvCxDebdXvr58UpmFoIvbvXH5Gu6jUgamOaeKYJuM4ZMOWkje2InQfPcnypfxumuqRd5Ln7AZz9hY+YZaxu5izE84xheBWMOd/2yt5VpyVnMHourx6F5HmKkb+PpG/T55FRJNrpOGIxN8jDYay3CoUMLK5jrBaqgSTq64jL2rbKzT9KqZLPshY9r6OqELUfoqIg2d/jRd+5/8kmO5gONKAVX7ZEzYeeRuj5z5PkcasXXwTvTOXCCeSfC2EidXoSAoTLMtQ7RBeqN+lCYjpDpT5xKEcZNguXl+qrFZcxjSa4R88Qzi9SXC4q0s+y+3S2Xxoef44rSWf0DAlT1QZtbrtLc2TlH2tVN239GwMpztQJogiljQmZ1u9nRllMgZ7i9/nPg6jQPU/I676U/bDBTv+lFkSMYlD8rLAj2NdJgJHqDzHBbCv5rpXKr6K9aW/8Xd5/H/5cXxfntDtdpvZbIbjONpjEaTUc5IkeN5Sjnm1JDyO3yiXuQxISqerri5ZZV+Gaej0fG8+14/5ueuXCbOMDddjHEv5nFVvu+rYMguTZaUfybK0u7nJjj/DMU1enI2Vl2PGF8b7HHhnGYcRa9mYP9zwuN55QPZIPMF1f8KjvQ0ulPuU7gVE+Bxl8yF+8dYL+MOEDdfjTf0Nzi8+KzFbRQ5G3RXHAOqBS3H4ENyxdCBTF4IiWfa/tAaYw+al72LzkT/FfOd3NURi8/53E/u/xO6XPk0WRwweWsdpbRBOpG7VfP9ZHG+Nssx1OWdaiVKu8DRnUf4fmc5A80ySz6u+l63YB1VWBFLuRpg2rY3TTG88TeekVKpo9C6ydu5d5PHSMs20m7VMShK6k8pSDTTCXihMnBCmvL8spJmrgrDYVg8MqZ12u/0NzNJUiwTO05ikKMjLgijLMJ0G4zhilsYkea5LwGooVMcZrpaKbySh+rh1L3C9ylVJ3bTbbYbDIe12G8dxNA2oCkqe5+E6NqPJ9Igz9t1Q9PVsq+ppVSsvCpoNh7VOmyIvaDVdFmFEbhRHgptpGMySGNsweaA3wLNsfuHZL+qRdb0XUQXCELQaBcC1w0O2+z12/BnX9g8wTIPtfp9nDg/YC3zysqQoSz5nGOzNP8c3nbnA1fmYKMt4cTZmFsec6USc9Dqk8xv89nPPcjCdsb3WZ3zhAgf9h3lns4DwhswGikBbrGmn6LIAwcvQe6qel4VYY7TQAAAgAElEQVTW98pkuVoaDbTSquFCEdHZfjNQyJK3TOluX2Kydpn+fY9gN9vS4dtbIw3GRJNDrIY0RrWba4jKfCRbIJQHpCyVE7QUtVJ3EMLQnMMkHON46zKgFdJhpywLKSlkOvTPt7S6ajh8Wh7HMGme+GaK+dP6lVaqplUPKw4OKUtVFqp+YpkmpMkSHJtnEXZrG8d0yIJbmM0tzP7b2A9kAHUtmyhLMYRgHIcUSmVkGMn3MslzFumyKV93o0qyTJ9zq1zar+a6Vyq+ytVutXSZWPW7ZjN50lQoepDeiZXV2N36XNVaLSFXm52V4eZ47msRt/oJozWSbBvbMCnKkt+5eY0gS3W2Vp1oizDSgaqCVSzCSNM2QJJpD+ZzbFs6uSRZJk0QypK92YzRYsF93TXetHmCmwtpn3Z7OiUtCrqNBossZTeY8/T4gFbTpd10efiU5M9dmY740N6Uj5fnmZkbaJOISnlBKVXcMV1cXYrKIspc8v5gSY/RZWRWA5AWEoyJoNHe4tQT0rYsnh5y42O/Jks6y2HjwXeyef+7pTmsEgasyjEqIcS6qoUKkKXqMVXZpNM+ieVt4ra3aA0uKiFBgyJPcJp9TLupLdIAgskNYn+f6Yu/rIKdLFkr6k9FB6r+thodZfCRI1S/0DAdmr0zsrdmtilbb8Yc/GHKZMzzScFQqTsAWmurKEvystA4wFkSs0jTO9ymVqeH9fPua7HuBa5XuZ79sX+gy0PHcej3+wRBwH1nTutycTabLU0DHOcIRGIVgX9c/+uIzI0KeJWLys3DESPfPxKM5AkliPKMcRDgxzGX1jb49y88c0TbK1WSOYswYhFGGotTBbE0lfeN577+G9Co6GpKeaLT4VO7O+yFC57avUVaFFxYX8dVpfIsjtkPF/hRTNNxeOy85LyP44hcBaRJHHItXpA2L5GFh8T+0nFIvjkSSKq9Fauly7SELDjQvSJABZcV/FflL5hVrjqepDC1Nmi012hv38/mY++QmlqdbaxGV075bJfY35e2bLD83/Rx1d96mmlIkK5SU53sfFLubjQx3C2aaxcosojF4VU1RbT1MfMsorv9mASyIpv2kS8NfA2njd0+hze4T/MkTadNo7WhSko5iTSdFo32lu4fprMrckCy+AL03skokReeWRJpw5UdX15wJWwmJcxkW6DqqVbnyxG+7DGB6pe/7wfu2PZGr3uB6zWsbrcrtejbbZqOzWAwYGf/AH+x4PTWJq7rsr3W5/Z46d9XZVz1srB+W/3kxbLro5uiVQCZ+5oGVJ/2NB2HaRAyDULCJKHrurypv8F9g3Ud2KorZz3g1QNjvRQAdF+jWpUe2CKMeOHggI1Wi1kl6SOEKi8SFkradxqEOJbFQGmaVSXm7kKSxMdxxPX5hBcjn3z9m2n0LyGspnKpsY4Bna6cqipDqzSspCWbfSeHsTJHtTxJgQFKs6MzliKLcFo93M42zd4ZHQTyZKlyqrFnsFQjrTSxQB5fNe4tp4W3dm4ZRFU5mSc+wnRobz5A79TjclsaMd19GtNpaRVWy2lJAKvCf2XhWNrGWX1wz2hcV6ZKT7s5wG50SILDOwQQi2gf3AtcjwOals1aQ2Z3++GCoizpOg1mSUxaFKRFwUL5elZBqwpUXwu4w5db93pcr2F99q/+Tb7xp/4FYSRF1oIgYGtjHcuy2BuNSZKE6SLAD0JtlOF53rEUoePWauPTsS2SNNOYLtOTJ9NwOqPneYx8Xz+myAuGvs9/fOkK14ZDHYDq/Yj6rZaBVr/X7aaqZZgGfiSztDWF7L81nTKe+9x/8gTjINClRKXRVH9O2RNZ9tjGcUTTspilMVGeEXQHbDonGDhnEULQFIY0Pc19yKa1d2aJ1xJlvJRmgWUTv2rc1wGj2ureldxFwyXPLrN29u0Ypk28OJRS1PEct7NNHBxKuIbXoSwLhFKvkM4YkYJg1CaMOtAalGYLA2iv308W7GF17gfDkiqo6/fjdE4TTa7heOtYTovemXcw3fmU6oHJgUA1UZTshEtk8QyzyMGS0s6VlJAQhsTJ1YxMKt1+y2kh7B4HZYNMme22LRlE98IFeVkwjiUP0VEKJdVnVuQFhmno4PVy66vdlK/WvcD1OpZhGEyDEEs1K6tS8dypk7JHoPpdlmVhGgLHsomS9K6k7GobGORFruEMfhjRbCzpFH60tICqgkR1ZQQYz31mi2UwOY5rtrpWA9hqU7bCk43nPq2mS8/z2Oh18aOY4XSGbS9PJceydJBdDZKVcKJtmJhlySyJ2Q3mhHnGyHZomxbbtotnDcDeoExHkNxaAj9B0nkQMkMri6UQYi4hGsJqoseVRaYa/jmlsZS5cZprXP3Yz7H92LsxLVeqV4Rj8izC651hMbqqqT9pPJegWSwlTeMtQakVut/wZFYU3aAUJmk8l/pjVh+iF8kzBc4VzpLAbW/pLM3tnpTPqUQA2xsPSHpPMNSTxXR+U2eDRlNphCmQrOW0cNtbhLNdLZVdplNCU2qybdoNbschV2Yjid9S5Gl9MazxYg3zaNviuNuv9fr6yv/+C1qf+JH/no7XpO02cByHMIo5tbUpe1xByEIFNMMwcB1bN+pty3xZI1mj9uWGSrwtYaT6TlUg88PoiItwHU9TlyB5pUFrFRW9CjCsnruS2amC43ju65F59Xg/inR/BOqEXIGpgo/UMJej+L1wwZXpIdfnE9JCaufPCigxyO0NcC8oJ2wZHCX3T/1eRSjlH6mdgWpL96dUj4vcJ09DvI2T5GmkuX6zvctEs11NvamCQcNbXyqiVp6RFfSiKmmzmQR5OltgdnEVsJRspB6W6yliHBxK4UQ1UbXOfw+N9bfKJrzqUZneGS2RrYMxyKBnmFitkzRaG+SJT5EGmE6bUpi4aw+RxnMN8p3nGbYwmGQJoXLqaVkOI1/RlBRkpz5BTNPsyLBmdVXnyi+8/43xTHwl617geh3rI3/uR/GjmF5b9nEm/kIbaFzYPqEb+NWVLcly4iQ9EqzqAoRS1kbcYajx+H0X7nhu01w2TCtbqOMC2HEI57ut1X4XHKV3VMepglJ1Za5foatmrtu409w2yXI9wSrKUvEoJXcSZB/sMInkl6zImecZYaHUWxvnNAq8FCZVD6uarFVfeMN0auj7QoNk5WMMSEeU0U2c1iZ7n/s9GagWh0xufpbO1qUjWvp5GhIvDsGwJCzCrAwzkmXAqnpowpCO0EUEVp9SmDR69yv7MEd7NJbJIcliKD0Zs5nUCaMAs00wuq57a/7uJ1gcXlmalCQ+Tues7usls+vys+o+itF+SJKonVMA2I0OZnObsvkgTdMkKHJmqdSMvzIdsRvM9edaH96sZtn1z3T1s/z5933wrufRV2PdC1yvc81mMy6dlidMv92i3W5z/oSUgR6NRkf2razJ6nzHSk2iCmSmmjQ6lqlLw2u39/V9Oojky+B0MJtpJYl6mfdymdZqH61+uzpJutvxKnWA1atz/Tj1L0e1klxxKFUAA5T5hxQ/nGepVm1NipJFkTEpTWLv0WWGVcuqquAE1CAVlpaoNpzusqFeRMz2LnP5P/wz1u5/jOu/+ytaY9+wXDqbl2RWU31mtgtmW0rClNkyeBnKw9DwlpSgslDu0LdkiagJ4gaG6ZCEYxIViNJ4vkTkJ7egLDjxjr9P7EtaTzTbxVBQiVIJC5bplBKBaTe1/I1UQg00PASrj9G6ALnPx/2QURJzfT7hqdE+V6YjDCE4mM3Z7Hb0Z1MPXlVPdPWzXs3gv9brXuB6nevZH/sHfOrZ57l48gRJlrHZ7UjHEwVIrfpcIPXlAb29rulVZV1RkuJYJpHS9q4oP3D0yhcmCWGcaL1v01z2oo7LtI5bdzsRV6+49TF4fX/HsjBMQ//o15kexf/Ug1+F5M9LSfquDEZNIQiVasUwCtiLFkzShN0kZJjGTDJlzqr08SmkUmupghOwnPyBzMwK6ZyjA0guv+CN9hZub52dP/goht3g2kd/gc6JRyjzhPnBs5RGQwI4ew9it7bJw9tojfw8gMYZGbA0ALaQ24Qhg0eRgOrRYXoU0ZA8i0jDifR0rDS14pE0F1Hu36JMOPXEXyac7NDsndGQh7LIyeK59K0sMuWI3ZX6ZskIkR7KKWuyJwNZmTFy38QZt8U4DnFNS1N4oixj0G7L7Lc4StWpD3LqWVh1DlSf/9eyRKzWvcD1FVhvvnieME44vT7g5vBQB5AglFPFRDmh1GEPTbdx5BhJkpBmuTTjiGJcx9bNd1hxAaql+aZhSEcgBZmoGvfHBaQ8vzNA6fuOKQ2Pw+3UOZLVqhr3RV4cOfmrALYIj/I5HcskL0rGQSB/L0sttbMb+EziUGvmG0L5RwKN6AoVDagKWEf1wxytTy8oZVPeUvpmFaZLGDRa65x49N1c/JY/xic/9TSHN15kdP2ThPPbrJ19B5OXfp8ilgqilbdhZZ4bz29KccLclxlTPYiBvLX6aF343CfPoiO4re7WIxi2VHot0oBw/LwMYKrsbPbPYCokfiUoKEwbx1uX9B6rqUvGKttMps8rF5+I1DlLqnw287JkP1zoLBekftveeMJsEejPaHX6/Ep6o1/LdS9wfQXWz33Xn8KPIp6/ucsDpyVCvOk4GlG/2e+RF9LNuuc1KYqCMIo1NKIOjzANQRAEnNvcANBN/co4Y7VxPl3IbCwvlooSo7lPnkubsyTN9O3qOq5cfLlVZU/1v4u8IIoTjeY/rrkPMqBWj/GjWKP2b0+mSrEiZRJHDMOAYRSwH/qYQpCWJfPKYk3pcglKnWVVOlRV4xsMqdyahsteWJFAOqNIZuThPk/+zP9AvBhy+7O/x/pamxs7B7zwn3+J+e4Votkt+ue+GcP2JPbKamE2upRWDwxPWowJQzbdgxdlgLKkPDSFJDeTTeQ2sw3hiximQzC+QRIc0tm8RDC5QTC6SuTvYzhdgtFVmamlQ4j38dbOKZkc+doqyEcajmWZGQxJo5k2qRWGidM9Lxv4podNSljkXPcn3PAnXJ9NmMUxjmXhmCbTMMS2LX1RPC5A3e18+FrBH1bXvcD1FVqOZeG5DUZzn5uHIw5mMwaDgYZK6GmcmuZs9ntMJhONnvdUBuYo9Ym8KLT2VpVdwZ1Tvzqvscq+ei0Px7YwTUPShRQh27GtOzKo1VKg+n317+p/u1twOq68qN+fphkj32cayEnkeO5T5IXOKkeLBX4cy4lkUWjV1mEUYAqBIYQsv9TSI/8iVxM1Za5RZkcR+KAnjlmyYHr7KeIo4uanfp0z3/iduA2HP/L9P0iSZEyuXmZxeIXF3ufBkoKCZbaQz1sWsvlOoTTkDXA29HNqfXejAYWSezZcMFwprbN2Dm9wkcnNz0jVCGUkO9v9LO3NS+TRiCK8fUQ5o/J6FOq1Gio4VwYc0eyWpBgZTTkEMNtQJMRY2ELQsh26tjyvqnNkGoZaK776bFdLxvp5sXr79bLuBa6v0PrwD/5lQH65T68P2F7rs7G+Lk1lg5D90ZiGY+vsand4iGVZNN0GtmWSZLk226z6XdMgIE5S3Y9YpV5U7iv1VZFg/XCJ9QLJdxwp6EL9GF+uXFzdXletqALqccFstdyssjNYkrpbTfdIwKueI8lzWrZD07RYc1wWWYKfZ8xEW07sjIYGYBqmXWvYV32vYtnvKjPC+S4Attulf/ItuK0Wz33+i0yuXuYtf+Q78AZnePx7/xK9cw9JMUS3S+Zfx2gMEMIkX1yT5WelzZ8ra7WKu5gHEgYBkAUycKZDLX8jDJPI39e6WtWS+LE+sb+vZZiTUKL7G9669LlsdCT3sMhx21ta4yvx97RsdNWYL8LblN7DhEXOLJeyRTuLGSPf14YW0yBgtgi0GOXdgMn12+qz+XrobVXrXuD6Cq6P/fm/RrspSzvHsoiiiLPbJwiCgPV+T0IXVKlYcRhNw2ARhEwmE52VXTx5Qto8OQ6e28A0BKlyWqkHjbreVhUg/ChiNPdpq6BQv28V81VHx9eb6PVtq0FrNVitKk+sPqa+H8jGfaspy96FwqLV+3h74wkHsxmLNOG6EjhMigJfuRZRRIB0uDZsb2muUWYy0wEFXXA0qr3ZO8vw6u+RJQv+77/7AU69/ds4f99ZbK+D6Tax3S6twUW8jZNMrl1mdvsp0nCspoiOVJXIA0p7fWn5ZfUBQ/amhCXLUWcLnA2MRl816aVnZZ6GWgsMVMByZQCSdCKbspQNeC3XbFiYTktzJU0lCW0rt+2lBpc0vsjmVzG8M4gywUCwGy7YDxeESXrEn7Pin1bn2pcrEav7v56CFtwLXF/xdTCdMV0ETBcB/X6f569dZ9DvKQdgmTlt9Ht0VAALopgoinAq9HKS6KA18RdagytJEnqexyKK6Xkeg3abzW5XlpbWsl/Rdl0GnTah0gZvuy5Nx+HU+oBBp02SZpxaH5DUCLSvtAxYzaSqbfW/jysxq1X1uaoSEe5U1rxwYovTa2u0bIeWZdNvNFmkCX+oM6BLtFRmqHBVhif13BUnUOpZOUsX6jIji6aURc7nf/4fcuHcFnuf+z3Ovuu9CNNkfOUpTKeF7UqC9OD+t5AnIbO9yxTJTJWFhRI8VJAHVZJJm6/sKCRBNebLZKyf31AejW73pORDmrZ0vFZLZlZdDMtVJWQqMzVhYDe6WG5PBihngyJPKPJUSlJ7ZzDdDcp0iu12OTS3uZnmfGF+yGEUMI5D9sYT1jyPa3v7zFQ/tPpsXrZErPqjX2clYrXuBa6v8PrIn/tR2Z9quly7fp1+vy+R5UVBkiTs3t7T/nOV4GC/3+fM1iZ7o7GeNlbN/HkQ4kexBLMqJD6gpWbaTZdey5NBSWU+t8cTmX2paZ5jWcwWAWGcsNnrSkeWhnPXILT6++q21ayqWquaX6v7VBlemKz0oGrHDJOEME01KNUxTd61fhKvDFWgiGQD3OziN+5j5lyQkz33AqgsRBKu1TSulK44Jx76Tt76/f+EN333j2A2mgyf+QxWo4m3eZKrv/tvGd/8LOMXn+L25z5KPJ8wOP9OJrtfoEynUjrHVDLTKuMS+RxRxKonpfpeRaLgCIWcBqrsEGFpKo/d7FNkipyujEccb500HMtJaRZJHfr9L2nKUZ74WK3TlPHeUeiH6ckpYjSjtHo0DIMSSae6tZgzDULyouAlhSespGrq08MqY14t1/VnkhdfE/WHL7deV+ASQvwzIcQzQogvCCF+SQjRr933d4QQV4QQzwoh3lPb/l617YoQ4m/Xtl8UQnxSbf93QkgXTSFEQ/19Rd1/4bU+x1drffgH/zJNx6Hb6bDZ7zEPQk70e/TaLd76iJQu8YNQo+urkq+CRPQ8j7G/4P7TJ+l4TZqqN1YvQwGdWeWFpAFt9rpsr/XptTyNrK+fkO2meyRotF1X476OW3drzN4t27rbWg10q1izKE70OL7qyw0XstTZXcxpGqZsjKcjQFrYIwyKErKyZJYXxEI2wo0KEgFAQRrNcJprfPqn/zpXP/ZzdE88iuV6jK5fYfjMZ2j01glH+wzOfSPpwufB9/xFbj75EW5+9tewnBaRv0+8GC7R8aXMtEqjSWn1EI1NhY5XJG7lEk3zHBLdryaNVGWhQ+/k49pUBMuTGv1IoKl0GHIlXMJauvgkMzWFNG0ZAN0uJPtk0RRn8Dg7xkmGacLtOGRnMSNSWL+8KO7grVbnRf2CUg9m9aHKr33gh172s/1ardebcf0m8FhZlm8BngP+DoAQ4lHgA8CbgPcCPymEMIUEpfwr4I8CjwIfVPsC/FPgJ8qyfAAYAz+stv8wMFbbf0Lt91qf46u2mkqfPkwSTg7WuLC9hWkYbHa7nNqQ00Y/iljvdnT/6uz2CU4N1kiyjK7XlH2gKGYRxViWpWVy6sEoVCUkoPmLFQQC0EHCbcjp44m1Pq6ySK+ArNU6LhAd169a3e+4q/XqqqPx6/tUzX7bXhrhjpQ09plWV2qMZep/tFR5ZTiMzJMsioy4zFkUGZYwoHGO0r0oAwmA4hsWeUKjN+ALH/7P/Kcf/wusP/Q4Z9/xbtzBFrbXwds8ye2n/z0Xv/WDpOGYh//4DyBMkyyaaesxCXpV9J5KKtrZlkRps63gEAF4Dy8Br9lMlo2mFCSUqg42Vus01tpbsbwTlOmcZHGAYTcldkuJAjrNNRmgPNn0d9oSZiOa57G6l7TztTV4J7m9RVTk7MUBV6aH7IcLbh2OtLpDhY6v+lp3+8x1j/JleIpfL+t1Ba6yLP/fstTCSZ8AlBcS7wM+VJZlXJblVeAK8A71c6UsyxfLskyADwHvE0II4NuAX1SP/1ngu2vH+ln1+y8C3672f1XP8Xpe52tZ//o976fb7dJ0HJoNh0UYMei0MUyDW8MRa+2WNn9da7c0iLMuKVP1tja6HYqiYHutz6Dd0sHj7OaGPuGqTKvVdGk2HHot70gm1fM8ep6nQYfVc9Sb6jrorUwwj8u8qitylSEdlbBZgmOrY9R181ePt6pGEKkscmcx49Z8xtOzEeSBRIYrbflZnmqNL0cYmMlt9nLBiBYT+zSjxiOU3Scoy5zY32frsXewtdnjTe/+TnY+8Zt0Tz1Ee+sMp97yPbS3zhAc7OKtPySb9IOLmLbDfO8qwfiGzOIQS5qR4S75iqYnA2oFmVDmsen4c8teVxFpizH5hrRZvPQbhOPniWa7WvEU0E170zsDtjRxNRvyttm/CNGO/Gnex27rrXwhinlyNtSGF0/efIlrh4d3/YxeCWe1euxv/dm/+IrO9a/F+kr2uP4c8J/U76eBl2r37ahtd9u+DkxqQbDafuRY6v6p2v/VPscdSwjxI0KIJ4UQTx4cHLziF/pK1x/8lb9B03E4u7nBaO5z/8lt+ffWBqfXB/qL7lgWE3+hpWsqik+upo+bva7O3hzLotfyaLsuJ7pdmiqTqoJDdZWdLgJOrw/k/jXTjq4KaK2mbNpv9LpHpou9lkcYLzFjwLFN2ur/rgYAg3b7yFSxekxlwlEdr44JqyhD1QDh/o0NwiTRgSwvSv7k/Y/ySHdA2XyA0jkps6nGKVqmSVIWnHKa9C3ZQLeEICwywiInp2CapwirSbN3hsH5d/KHPvA36Zy8yP3f+WcwTIcsDimSGWe/5X/k7Du+h8///N/SMApv4xxlLlHp84NnGV37mHzhRbRsxMc75NVXKLkttye3FSRCuhcZpuQ05mmI296SpO1sor0e7eYaFDllfeLobUAyVFQgQFiSHuRsEbTfStl9gitJwX4SseNPOYwCrs7HPDsZygGO8uOsm1roZvwxNmOrtLC8KPiN7/8Lr/Gs/+qsLxu4hBC/JYR46pif99X2+XtABvzbN/Kf/Uqvsix/qizLJ8qyfGJzc/MNeY5f+8APcW1vn0GnzUsHQ2aLgBdu3dbTwmZDBqa3XDzPaO7jRxE9zyMvCi3aB1L+xg8jnrl2nRd39zANg48/8xwXTmzpE7Db8jhQ4oKbvS4Pnz7FNAg4tT7gpYMhJ9b6+uqbprJMfGl/qMuD6jnbCq6gjTjsoxSiKvBVQbBOoq5/OY5b9eDlNhy2+3022m38KOLy7du0XVerXUyDgA/fusqToz3+YD4id7blBDEdsWE1iIucLwZTFnkGzjZJWZCXJXlZkhQFsywl9d6M6Q4kutxbZ/3Cu2gNLtLZfIhwtM/zv/W/s3jpP2FaLmff9V1Mbz+FYdp0tx7BcpvSRCOc0Fw7R+LvSkUKpXGP2caMd2o4LoMyGRMHh1JfPlWBx/A0ybrRWkcUIabt0uydJg3HeGvnafbO4DT7pPGMTCHki1QOI8p0zmHnG3g+97iVhDwVTCmBcRIxjiNenI15aTJh5C8YTmcatHzkM8ky/RlXrYTjhjN5XvAf//QP8/W+vqyQYFmW3/Fy9wshfhD4E8C3l2VZybfdBM7WdjujtnGX7YdAXwhhqayqvn91rB0hhAX01P6v9jm+ZuvEWp+m4zCczriye5vT6wNMw+DEWp+xoumESjb3mx59mE888xymYfDczi1sy2Q099nodnjk3BmajqMVTx86c0qPuKvJ4fZan7WODARPvvAiZzc3OJjN6LY8htOZ7nVMg4CeJwNdHai6pyaSdfzVaO7L0jPNaHoyI4ni5Ah27MRaHxTtZ6PX5dZwpB8fxvK1DdqyVKZGwD6YzfR7cHZzg+F0diRgL9KUZ8dDoiylY9lccpvE9haNdMgJu8coi/nkeI9zLVli2YaBpc7CjJKbSUjLuZ9N72Hy0cePSB9f/OYPaKccgP7JtxAHh1hOizSes/3oe7j2+z8PwPqFdzF88aM0+2ewGx0a7Zw8uyUxVY2lS3QSTqTooDClXHQRQxFgtU5D7iMoKc0OdlOQJz7N3hlKaw2yCXm0QAhprGu7XUTzLIfGBvM8w8hzrRt/czHjdKtLlKUMlT9ilTVNlRptHVxcBS2owVbMWrCqQSC+XuEPq+v1ThXfC/wY8N+UZRnU7vpV4ANqIngReBD4FPBp4EE1QXSQzfVfVQHvw8D3qsf/APArtWNV89jvBX5b7f+qnuP1vM7Xu/6P73gfl2/skGQZP/nd38dap02YJFzb29cYrNHcZ3utz63DEdtrcjjbdBzObW7gRxH7kymfeOY57j+1jR+EDDptPnPlRWzb0hzFU+sDnf2M5z5Nx2Gz09Hlw4m1vpbBCeOEke/z9oceONKj2uh1ZRmqgm31f1TZFyx7VhUiu+d5pGlGt+XRbcmy1DSXGdtmr0vP8zBMgyhOtFaXaRgMpzParsv9J7cZz/0jTfpBu6V6ZYKdxYyrvpRxNhCU9gYDI8cSBqe8DllZSm5jURAr5YnbUYABzPOMy6HPzdbbiHrvxGmuabljkHzHSnyvIjU3vHUA7v+WH+KBb/srTG8/xYlH30/35OOyiW6YRLNdaReWzqmwXo3OaRkcLQ/MLqUwKaIhpBMwVK+sCMD0ML1Tso+llDKV5mYAACAASURBVCeq5r3Tux/ReZyJuckwjbkVLZjmKY5hcmM+kZPCJGYcRzimiR/FJFnGbBFobuuqaTDcCXMAltmXClr/JWRb8Pqlm/9XoAH8puyX84myLP9SWZZfEkL8AvA0soT80VKRsIQQfxX4DcAEfqYsyy+pY/0t4ENCiH8MfBb4abX9p4F/I4S4AoyQgYjX+Bxfs/Ub3/8X+OCv/Dz/5KP/WUvlOpaF23BwVW9rrdPm1uFIl5E39g9oug0OR2Nc1yVQdI2surr6C6I4od2UuLAqEF6+scNjF87JjGY+ZxFGdFuy/Ky4jRe2t3QGVsn3Nh2H2SJgo9fl8o0d7j+1za3DEY6SZe61PFxllVZlWWmaMZr7bPa6GhXfdBzSVE4yuy1Pg0yLXDbtqwlnld1VwbTaB6pJZa7wavKLeHMx4/lOn/vcNtfjgHme8sJszGEUcK7d45TXIcwzmqZFQxisOy4nnSbX4wVZWRKXBU8vppx1H+VEM5a0nCIBCpL5TZ3ppPFc24bFi0Pc9hZrp99KFuxRlgrgKkxag4vaUdoyHYn1KiKp/1XxKq0BhjK3raSeyyxEiOVkr4I8ON3zYA2YGX1mWcookyDSjYbHHwxvYQqDvFTGFkrmJ1L9zUUY/X/tvWmQHOl55/fLO7Ouruqu7upudAONBgb3YA5iOMObXPGmubRCoihpV6REmQpZsryyP2yIlrxreWOtkOSN8NJWSKTW1FJrWpRmuRSX0tIURZEiOcO5TwwGGJwN9IHurq7qOjMrT39430w0oLkHGAxm6h9R0dlZmfm+VW/mU8/xf54n+6FJTe3LordXaFiXpX5pQit+rfu1tkK5ZN29sXHkyJHkoYceuubjfPqbX6VWKXNhvZ5pLSAezPVWm/3bZ2j3+jQ6XUxDZ2F1nYnySKaZ7d8+wzOLy4AwL3dPTWZ+McPQ8QaCeW8aOrVKmeV6g/GREtWRUubc73oeh6e38e2nnmKuNsHyRoORXC4riZOalZViIfOBpVHKVPta2miwfbyKbYk69LPjVZY3Glmk8+jCeWbHq0LjKhVx/YBWv8+5i2vMTlRZldkBrV6fkXyOSrGQCbj0s6WCPSXsFmyb+ZEKu0ujnOk0s3ItcZIwUyhhazqaolC2nMuE1qRps+J7+FGEpijoqkpe0ylqOiXNQFdUtLTeFkCwRuKK+I6i5zOiJ4C3eY4kkWRSqyS6TasaiinTgcKGIKOqsvN12BaRRtWWFU+Foz2R1Vs1w0bR87TtfWyGPiEiUroZ+Cz22szkS5zYrGNqGm4YsOG5lEyLsmXTC3weXVq8jIuXbqelhNJI4rM557dqY68WX0tRlIeTJDnySq8zbJbxKuOLH/oJPvqVP2FucoLV5ibjpVJmQo0WC5nQ0lQRbZufqmVaSXpTtvp9HNNk99Qkru9nAmayIlj6o9LH5Zgmb923hx8cO87BmW2cXl+nVhScoocXFhgtFFhtbtJ1PeZqE1SKBZ5ZWqbV7zNeKhHFMdPV0Uxj21pHfnyklJl1SxsN8o5NpSiuZxg6e7ZNU2+1L2NnR3HM7EQ1M2NTgZSalynSaGvagCONtLq+T9sf8NjGRdr+IDu+ZFo0By4TTkH2DhygKQqOqjGIIsqaSV0ZECmKqLSKgo6CG0X4cYypquRVAz+OiRNwjElUYxIrLV+jFYQw6j2RaVxpGzFVM4RwA6HBpR20FV32dJR16SWnC0UVJqkqOvEs2gdZ9z3MQR9VgY2BSyTLWpuqyonNuuzI4+NHEYYqupXX3X7mN7ySiwXQ4/JS3FemYqVBFcc0byhNK8Uw5ec64Bs//QuXqfapg70lhdao9IGlJmPX9YgiofW0e332bpvObsSzq2uZtpSafanmNJLLce/xZ9i/fYaZfInJ8ggnL65mvqebpOAbLRYwdZ1nlqQmN/Ap5XPZr7amqrR7/cz0a/WFOZnOfffUZPYw1CpltlUq2YNRyuey8bbSMvwwxDB0qiOlyx6sUj6HpqqXdQ3aWgF2tdeVvQCjrEGtK82mlX6Htu8J0urAoxUFHMiPUA8HxAn4cZQ1Q21FASEJCdCLIurBgHYY4MYhjWBAI/RZDwcMtDKRIvMg84dxqrdglXagGEXRANaqyZxJU1Q81WWZ6Mw0lK3LIo/Qa0ESojsVFEVlPXcz674gDa96Pda8PnXPRVMU1twuzYFHL/Tppc1aI1G3zI+iTLhX8/mMzpL6NlOmfLp2W2ktjiQfpz6wG1FowVBwXTd88UM/kWlPqRlYkOTR1HzqeqKPoaaqWbrPugx3a6rKequdse5vmZ/LtJhZWYTQD0PesX8vH5rfy0Jnk2888Aj7pqcyikSzL/IXD2+fzUyxmiSyFmybVq/P/snJy3hijmkyV5vINKhKsUClWJAmYSnzq71nxy4Ozs5kLO30M/akdrdn2zTbKpUsOukNBH9rKxkWyN6DS2H9n9t58LLv8sqUow2vz4bXZ93rs+y7nO61aPouXhTixxG6qjKIItwoohUGDOIIN47wkxhXbqfNOurBgFYYsBH6+ElMW58WXbFjX6b6yMRqoyqoGvropaaxst48SSxSe8w86GWi/GEeN/az5g9o+h4nWxuym7TQsk62GgRxLOYbRXhhSBBHoqWbqmBqGqau4ZgGmqJc5nxPTcOtGm3mqJe+LNPQMQ09K8V0I2IouK4jCrYwr1IzEC53nm7VUKbHRjOhcmjHdgAmK2Vu2bkje/hty8zIniO5HO+cmeOJ8xe49+J5vEjUw+8FPnNjY9RbbR4/c453HzpAxXKot9pMj42y2txkrjbB0YXzFByb5Xab2fEqlWIhM/3SNKPU0R/FMXNVEf2sFQqMFvKc2KzT8/0syjVdLmcPVCoEu4MBtUqZekvQNdLMAcPQL3MspybpnvEJqvk8368vifpliioeYlWUfwbR9qztD7JS0Be9HuNWjpW+8CWOmTYJUNQNXCnIBpLK0I8jBvElLlicJLhxRDcKaIY+5wd9NkOfQfF2Ir97eWFBxSRRtnS3jvpCgIEQaGGfqHSEU8kox/ttVvodVr0eUSy6Hm14fTYHLnWvn32O5kAIWzcQlUW8MERT1Kxe2Y5iOSvJnHLztprZWzMZLmtW8jw8uxsFQ8F1HfHHH/hxgMsEF4iifyM5QS1odrqCAiHNv4Jt861HHmO0WGBpo4Efhpxfr2epQGl0EESr9UqxwMXNFnfVZkRN/EaT9mBAo9vllvk57j3+DF975JHs3FqlnDn7a5UyrX6fVr+faV2pX8SRvSJTxjzA8kaDer/PmJ3DC0NMXcvM3dV2G8c0MQyd5Y0GM4USq81N7pic4fCO7dxz7Dj1Vpue6xFHceao/+TB23FM8X387I59HBidQFMUKo5DybTI6waqomQPsKlp7CxV8OOI5sClOfC46PVQpX/rfL/DY/UVBkmMpQmBpwB+EqMAVcMiRlIrJKG1F4f0Y+EPc+OIC4M+Z3O3QuQLATVYBP8iSiSc76f8kHOM4qrSN6aV6Bdu4xm3zWbg0/SFHytlvXcDn3YwEC8peMV9ERHFsguS/EFzg4CJXJ5e4HN8Y52m62bcrTSiC9BzvUxYOaYpKuPmhBk+ks+9Jis+vBQMo4oSr1ZU8dnwmW99LStT45gmecem3mpn2sme6SmOnr8gfBojJU6vXGSXjCauNjezKg+u73PnrnlO1+vUW232z26j0e2x2tzED0PevW8fTywuZg9B3rFpdrrM1USgIO+I6zQ7XWzLzDS59Li8YzM7OkpblkcRpqPBhfWNzFflGAZREmeagalpovFrnLDebrNnYoLldpvV5ibv2n0TPzxzml2TNRo9UXsspXSkGt6uqUmquRxNzyNvGOwolvnoSMSfrA6Ik4TmwKViORiqShDHuGEgI3AhcZIwZjuUTJuD5SpPbdap2g7NgUfeMJnLlwjiGENVGcQRvTCkbJioikIvCtEFxSfzj5mqRkSSacSGouCoOrqiYCgqvThkXWpJhqoxY+dxNA03EmbnqtejF/jZ+6k/DkTLtmw7jC77kUi1XNcX2nIvCLi4KdY9NcXTwEn6SjWtreeD8KNezxzEqxVVHGpcrwGkmlfqpE4d95qqcnBmG88srzBXE9Ulnj6/yNmVVZY3GoyXStnN6/o+e7ZNc67RwNR1dk1NcmF9IzO/qiOCLpB3bC42N9k1VctIqcsbjax4YbVQyATYuuR5aapKdUT4r+qSte+YJgXrUqeigmVRcURN9K4nNIa8YWCoKudWRR7o4alpLmxuMl8ZpZTP8ecPPshbds5nLcoAjszvzL6Dgm3jGAZuGFJ1cji6wZ7SKHc3EvaWq4zZOUqmhRsGOLoQmI5uULHEPD44u4uqncMLAx6ur6ApCgudFh8ta/yjyjhFTWevETFhWIzqFjucPJOmjamo6IpCLwwJkwQ3CtFVNRNavgwKeFFE3fdohj6rgUcvCnF0A1vT2Ry49OIQP47ZjAKW+x3ZvTvBDQMWOpu0/QG9IMg0LFGKO7osST3Vdrue+D5OXlxlvd0mjkQeaNqoJM1L3FoFAi6Vr0nLeb+WE6dfCoaC6zWCL33k41kqTmqy2ZbJ8eUVZser2S/m7bvn+ehdd1DK57iwXheVTQsFapUy51bXmB4ZyegDNcnAH8mJyN63nz7GrrEqP/PmN5M3xC9xu9dndrzK/u0z3HPsOAC7qlV6rsfsRBXHNDPeWJpXCCLSFyUxjW6PuYlxzm9sUO/1sHVhCvpRJEw541KJ4DW3JxLB45iDtUm2jY3yw9OnuHV8MiPQXmg2M3rExeYmjV5PpLTEggpw//oS/3hqJ+OWg6mqaIosSR0J57UqG2sUDJPHN1ZZ6LYYs3Myf1H4hu7eGPCE28NSNBK9TCHu0otDqrqFtvJNNEWhZjiYmkZj4NILfUKpzbUD4T/LfFHBgE4gqArrnuhQ5EUhqqJyotVg0etlvioALwwI4lhqoqkZmNAeDC5LeN5aKijrIyD9l2kgIzUHt1Z8SP2OV5YcanS6N2wE8dkwFFyvIXzpIx9n/+yMiPjJNJ6DM9syjaqypcppGilKj0v31Xs9HNMQwi8v+FzTY6NZlYo//cE9PLy8yISTzygNKWv9vbce5szqGjP5ErVKmSiOMQydHzt0QLLcFf7msSeYHSlL+kPE3OgohirqjO0YKXN6fZ2PHryZ0ysXOblRx4tCDs/OsN5uY2s6BcuiG4gQ/7t238SplYsca66zZ3yCO3fvYm5sjNFCgbfP78KRwYauNyCvG2iKyh3j0zzjdpjvP8aYnWN7YYS8btALfYI4YqWbajYx7WBAybC4tVzlI9M7qVgOmqKws1gRQjsKUMJNToY6KgoPdxpsTryPOEkYpcO4YTFh58jrJq7sUVgyLAxVw5NCDCCQ/rS8buCFAc2Bl1E1VvqC1iCCBV6mrW3F1ioN6f/wDzuBp+ag6/tZhsHW1J70Gum2O/Cz1+tJaMFQcL3m8Dt3/Ri/feRdGIagHrQHA5Y3GhyZ2X4Z36nd61MpFmh0u5lmZVsm51bXhIYRx9iazmpzk2peECRFEvdebqqO88Di+cwcKdg286NjPLO0zL7pKb50/31MFYpMlsvsGh/ne08fF6RYRWVnbYKjK8uZVudHEReaTZY3GpRMm/21Sdr+gP0z23j8zDkhVIKA7WNjBHFM3jAopPXkTZuP3fEmojhhZ6kiNDZNZypf4ExLtKI/NDVNJZfjTLPBQnuT+9aW+PvlczzjHObxjYss9toEcUy936ftD8ibptDQooidxQpT+SJ/tXSG84M+K71OpuXoqoqfxDzkxpL6EHDEATeOaIQ+i7HDVHCO/UqDsmFSMa3sPE22/oqShJ7UtrwoFEz+OBZCzR/QC3xMVRUCK0rNSxHJzPIGk8uTmq9Mydn6I5Wes7XHwLP1DUj9Y5qm8l9+9hdvmPzDl4Kh4HqN4vPv/ViWE7hn2zQL7c0t1R2MjNhZq5R5/Mw5ZiXpc9fUJGcaG8yPjvHAwjmmx0bpBUJAzVWr+GHIj06f5vCkYLbP1yZEk9ok4V179rDcanFobjsPnD1L1cnRdF1u37WT2Ykq33jwYd6yc57Tyxcp2BYjuRwPnTmblcyJEtGNR1MUbF3nPTcfZKG1yZjtZA/uodEafhxRMm1W+h3yhsneSpWz7SY/t+cwD5w9i6aozI9U+Mihm3l7bZblzU3eP3cTXc8jThJ6QUBe1cnrJhteHzcMpLYhxkjH6kkBkqbNfHR6nl2lCiv9Dv0wYH3g4scR3cAnr+nc24uY1wYMIsHpWjd3MrC2s9tApAUpCuteH01SHkwpxDZ9D01R8CLBt7KlBtgLBdO/aucyUxHIksGD+B9GDVNs3U41L2NLX8yt9JkrSy6nJuWNHjl8PgwF12sY/+btHxD5ejLV5/DsTMayHy0Is3GyPMIHD9+MqWnMVavZDb/hCY1MVFgImC+LpN8gCHnfvv2c2Wxw5+5daIpCFCfU3T63VqeoFgqUbFt0w05i8qZJq+8yXirxnsOHONmoc8ee3Tx9YQk/DHnrTbs5tGM7Xc9jvlhhplDiidUV6rLB676xcY6vr7HebjPh5Pne4llsTacX+Di6aHS60NmkZFr81YWT/Pztb84e8sVum9OdJr96+M184f57su+l63l85+ICzYGoixUlCQXLomRZlEyLt01up2RagpypKBxtrBHFMV9dPMm618ePI3phQC8MGDEsTFUjThIqpsX/1xS+O0fVqAcDTrgd7u95nOu28KKQimnTHLiXCce8btD2BxgyWbrte5QMCz+KMooDQHMgytL4UZRFXp/NTNxaVihFHF3qS5ket9WkLNj2Zby/17PQgqHges3jd+76MYI4YsRxWG63s+igreuMShNwYbMpTBPfp2BbVAsF1tsdbp6YBCT7PAppSsb8Q0sXaHS7zBRGWG6JUjEzhRJffupR5ksVmv0+P37bbXzl3vvYUSxLuoVGaUtn7UPbL5U8q+ULzI2N8c2Fk/QCn8O1KWqFAndNzeKGAW/etp3xkvCnmbqGF4XkJe3A0XRqTh5VUdhRLPP1k8f4wPRO3Cjkluokhqpx7/oSf/y+jwn/ThhQKxZZ2Gyy2ukQxQmOrmd0iF7g873lc1SlQx5gZ6lCyRRCbcPrY6oabX+AqaqSUR9wvt9hodvG0XWCOOLiwKVmCE2xoJs4us67LNEtZ1exTMVyWOy22VuuZt9DJMvpiAhnkgUmmr6Hoao4ukgC31EsiwiiTN1JnerpWm3NBIhiIbDSqrBbhdrWtJ6u59Hq99FU9XUvtGDI48pwPXlcLwa/89g9BHFEo9tjvFikOxhkdISSafHo+fPctn27SMj1RJ0mW3J5bq1O8v2lBWr5AnndYMzOsel73H9hgf21SdwwYL5U4cTmBmO2Q5QknKyvs298grxh0vYFZ8qQAvCZtTXGSyVKlkXTdYVQMy00Rc2u5UshkjeEOTdm51jpdygZFqYmBEfZsgmiCEPT0BSVvHzgjzZWOVAZZypXJJJ1tqIkoWyY/OXCCTRFZSonE6qDAe3BAFvXiZMkq5wgWOZKZpZ6YUDZcoiTGDcSDPTUVwUi2udLv2AQR1QswfhPHfK90MdNKzBoGhO20G6ObdYJoogxO0c7GJDXTTYHrvAzhaKGVjsYoCkqhqqy4bmM2Y7wh4WXBNeI49By3cs6TIPQkNOgTOrPSmvyp5paWjHXD0P+n49+4tW+NV8ShjyuNxg+e+vbsDWdcVndIXW4g0gPmR2v0hy4nGs0qNg2JdOiPRAmypONNUGYVFUMTWMyJ3IL375jnrJl4+gGda/P3rIonjeVKzI3OkreMFnpdYiThLJlkzdMKpbD3NgYpqax3GoxP1Lh4mYLQxXCa2+5St0T5taEk6cttQ1NUajaOfKGiR9F3DI2SV43KUmnt6mqrPQ7BHHE7ePTNAce7UBEB/04oqgbbPge75jaIQitScJTaxcxVY2SJcxCVVFY7XVZ6XXohX4WwdMkkTSII1RFJa+baIqSOdajWAgzEM5zTVHoSn5VXjflPBImcyJLIK+brHl9vCjilsoEfhyx6XtU7RwbXj/TJkumja0b5HXJZg8DJpw8a26PKE4u06z8KMo4WH4YEkdx1uk7ii+1cKu32tm+2fEq7sAnL9vOvdaF1tXEUOOSeK1rXFvxR08/nFW/TLWRVOvYUSxnaSRVO8eZTUFIncgJQZdqTiAepDvGt7HQ3czy/UxVxY1CvDBkwsmz2BNUiSAWUbrFXhtT1ah7fQqGKfxg8uH2o4hbq5P0Av8Sb0o3qVg2vTDINC9TVTN/T8m0aPuDjLRZluTR6XyRsmYQA1XD5OtLZzhSneLo5jpBFNH0PU6vrzM7OsrecpUgjugFPnWvL78X8RmFiWZQNISATJOSI5mTWNvyGQ1VI05i/DjOvtfU6V427cu0x0BWmlAVNRN2Z9pNpnJF1txulkeZmsO9MKA5cOkFAd3BAMcURNV6r5dFd01dp94Sc7EtM0t72pp8PpLLsSrb1NmWSRzFGYH5RsBQ43oD45f3vynzl4zZOfwoYqZQIk4SHltdpu72M+f0jpEyecPIHMR53cBUNe4c30bJsHiysYqj6dlD6kYhed1kplAiiCOqdo6K5bC9UOZ8tyXNt5iak6crBdThsRpVO5c97M2B4CtVLEHi7IUBJcNid2kUWxPRsLykRERSSJRMi4lcAU1RmM4XyakaR1t1LFWlFQZULJunWxuYqkbT99hdGuW983sI4oh7Fs9xYrPO3nKViuUw4RQYs3OoMnq3vTBCL/TZ8PqZc9yLQiqWTd3rZxpZGhTQFAVD1TJz0VCFX25z4DLh5NmQ5NO8bmKq4sfD1nTeOjFDR5qFpqaRlyx+X85hKldke1EQhDVFpRcEWXTWMU2q+TzTY6O8Zec8IzlRXHF2fCwzBbeSSvOOTc/1biihdTUxFFw3KH790J2UDKGt7CiWaQ5cYfaZJvMjFUETkL6cvGFiahqOpjNTKFG2HJ5orjGVL1IyLWzdEMX0DDPz56T+n7RIn6lpbC+IjsszhRHawYCZQglT1fh3j9xP0bDYUSyzKOvCm6qWRd6qdg4/jnCjkAnLwdaEtiGEh0PFsgniiHE7R9XOoQAJcLg8TiPw2Qh9VEVlwsmTN0yarsuG12c6X2RnqcLH995MFCfct7bITKHEhJOXJY4j3DDgaGONIIrYWRLfy0q/kwnZzIE+cOkFfkY0XXO7rLk9Jpw8JdOiYtmZdjhm5zCkhtocuIxajjBZdYNDlfFMaJZMi4ppZxqmH0fUnDy2rov3LYsJJ4+paxQsi+VWi4rjCOKsbTPiOPhhlGliKadremyUkVzuDWUaXomhqShxI5mKV+I/nHoy0ybagwE7iiOcaTWZKQrBkv5ST+UKlEyb1X6XdjDImOR1r88tlQlZReGS6ZOaQmOmzUWvx0qvw0yhlBEvQYT4bx6r8eDaEjtLFVZ6nUzb0hSFqVwRP44YMSwpFEQ+oSVNuQ1fcI4qpkWQJCx0Ntk3Moa7pUjgmtvLtCI3CtkcCL/ZU6sX+eTB21nud5jOFSlqOp977D4+vv8wM3aeRuiz1OsQJzHNgce7a7Ns0yK+urZGybDYni/RCQO86FKhPkNqUD2p5dVlFLJkWvQCH0PVqOUKKEAYx+iqioqYm6mquHFETtVw44jT7SZt38ORAZH0fICznSYV08aPIxzd4PiGyOdMywtVnRx53WCh00JTxfXX2x0c0+T33vq+V+Guuja4WqbiUHBJ3MiCC+CPjz9KL/Qvi8LNyJZdXhRmDuiSaeFo+qUcQunXUhWVWPqrXEmkHLUcbFWjFfh4kagy2g0CvChkh9S6/EhUMri1VOW+xgqGqmUPPZAJPy2lCcgQ/phh0YuFaRYmCf00UXqLoxyEQHt8Y5WSaTGVK3Km06QX+Lxvao5G4KMrCuOGzXrgsT7oM27l2JTO9AnL4aLXoxsEvKkyzvfXl1jpdajlChQNC0fXsTWdxV6b+WIl+zxeFGbRxdRMFKRaAz+KcHSdiuWgIDoOxSQUNQMVwbwvaDq2qnHvxkpG/i0aFp1gQNGwCOIo84XZusEj68vCn9hpU83l6AUBS80mR7bNsur2qDl5FjpCk/3tI+96NW6na4ah4LrKuNEFV4r/cOpJeoFPEMdM5QrkDVNEz1LOjzQBTRkdS4WEo+mMGCbrsobVzsII/SjM8vHW3B77ylXWvT41O0990MeWvrHUob3NKUg/lEqUJJkTe8LOESdCWzI1DUtRCZOErmSWT+ZE4nYnEP9XLFsU0AuFoN0cuPTCIHN0u1HIVK5InMSUTJuFziZvG5tmM/K50O+wp1CmG4XUDJvVwMu0tZbUEk+1GxS30DI0RcnK48zkSzRlik7VztEcuARxJAimmpaNm9dFys+IYRImMSoKeU3HT2JGNINeJDpqd8KAU60NDE1jvljJxq5KOkUv9KlYDt9dOkvBMKm7fSq2TZQkHF9e4Zduv4t7Vs/z64fufFXvo2uFoXN+iGfFz+2+mZJpZxURUpNOUxQqlo2hasKkCcNMO3M0HT+OWHa79MIAR9fpS4GWstwnnDyOqlEyLJq+yzankF3XDUOmnDyDRAjLCafAXLGMIx3xK24PgLJhYigKgyQmJsl8TGEcM4gi4kRUTXB0QwpfITDcKOS20Rolw2Kx1+aO0UnW3B6LXRGB21Ma5YzbIYhjqlaO9WDAj9YWaYQ+OWma6SgUdIMxwxLXMi3eXB5nV6mCoWpMOAUqls1Ct5VVtvCigJrki03lixn3S1XUTOB3w0DU60piZgwtY9ynVVUtTePA6EQWVEhL8bSDAaamCV9ZHEveWkFkSUjf4FvmdvL1s8dfN0LramLY5ed1iJ/dJWqyf3dlgbY/kA9hmJk5bpqYK/1NabMJEILIjyIwhBnpRSETjoj2dcIAS9PwoiDrplO1cxwojdKPI6qGgWbm2AgFYdXWhdAYMyxCEtwoEtVFgaRdzwAAFJFJREFUEdHLomFysd8lTmK6QSD9SB5eGOBGITP5EmfbTUqmxf3ry9i6jqFq/PGxRzhcm6JsOSx2W1nCs6VpLPU6VCybmcIIuqLQCH1GNIOCZjAIhXZFIoIHSwOXda/PzsIIlqpxpifMsVTLK1sOZ9rNLCfRk4TSdCxTEYnaI7ohSjv7sn+kbDEWyNpbgSynY2u6oJDEsYjkpik7MnqbBjA8ST799N5bX7V75kbDK+1k/a8URXlCUZTHFEX5G0VRpuV+RVGUzymKckq+f/uWcz6lKMpJ+frUlv1vUhTlSXnO5xTZYVZRlFFFUb4tj/+2oiiVlzvGGw3vmdrBx3bsEZqNbmZRQ0fX2eYUGLcc2tI5LqoWxJfy6eIoqzkleEg+ZzpNWvL4p1sb1HIFcppONwrxYtF4IpANJ8IkwYsjEaHzPVoyYjeIIpq+SzsYcHyzDggB6eiCx5VG60xV43y3RdlyqEtSpxeGrPV7HK5NcabVxNEFP2qhs5lRFLwoZJudpzlw+eHaIhXdZGXQJyGhrBvYUgPL6zrjhs3eQhk/iVl0O9xSGmMmX8LRdEqmxZhps+H1uWt0ijPtJlGSUNQN/DiiMXAJk0TU6YojbFkLzE/+YaL0uCWKGR5trFLQdHRVpSCzBExVE8nWss79npExPrPvNj6z77ZX5ya5QfFKTcXfT5LkcJIktwJ/BfwLuf9DwE3y9UvAH4IQQsC/BO4E3gz8y1QQyWM+s+W8D8r9vwF8J0mSm4DvyP9f7hhvSLx3eo53Ts5iqqKxxJjlsOF7rA/czFfTHHicam0AZGH/TZnEfKHXJq+bVO0cf7twGlVRKZkWJ1sbbAYDNnyPBJg2HVYDkcuoKwr9MGBUmkU5aY7mpdbkhQG2pmcF9dwwZLHXphMMMsZ7XTaQWJOmpqaIDjeL3TYly8qqiB5fX8MNA1q+x1SuwJdPP8X7Jrbz5vFp7j53PGtV1olCLFU0g60ZDnlNox0FjGiC59aKgoypXzJtwiThw9vmebKzwc2jNY5Up+iEASv9DlUrx6rXQ1dVLEXNIokgSj2H0nfsRSFnZTHDveUq31o6w4hm4MURjqbjRQHjVi5bpzvHp1/NW+OGxSsSXEmStLf8m0fQbwA+BvxpInAfUFYUZQr4APDtJEkaSZI0gW8DH5TvlZIkuS8R0YI/Bf7rLdf6ktz+0hX7X/QYr+Rzvl7wzslZYhI82dE5LSWcRtGmckUWuy2Wem2iJGF7scxj9YuAECJuGHLTaJWiYeKFAbF8OFf6HZZ7HR7rNLAUlabvUZCO+1bgM+Xk2QwGjJk2a14/I8Om0bpTrQ1RzcJyss7UtqZnvK0dxTJn201s3ZCpRyL6aKoafhxxcGISLwo51lznWHOd0ysX+Z9/9Hf8u6MP88n5g6y5Xf7Xv/82AEsDl83Qx9E0Trgd9jklVEXh3dVttAKf7U6BbXaebabDqGESxDHTToGibvCX506wLz/CnhGRGnVkZBxdphoNEtGtJ0oSHE1DVxRMqd2lcMOQvG7wtfPPkACqAiXTRlUU3jk5yxAvHq/YOa8oyr9WFOUC8E+4pHFtAy5sOWxR7nu+/YvPsh+gliTJity+CNRe5hhDAHeOT/O22oxwgIcBC93NzNRa6XeYKYzQDXzOdprCTKrN0At9TrUbHG2s4scRf798LkvTeaS+QhClmkZMXwqjv146zaSdoxf6NGUD15SmkJqlAHVPRPMW2iKNxdREOtF9KxfY8Pr0Ap8zrSZuEPD3Z08RJwmr3S61XCEryLfm9mSgQWVz4GV9Io+eO8//+dRD7CiW+WdvfRc/Wlvii0cfZs3rs+p71AybB9p1/CTGT2LGDIs4SfCTmGXfpR4MKGhG1mfxbVOznPO6mIpKKxiw5PdpBz6hFFiGqqIqCvN6ktWqB3B0XVSj0DRmCiPcPFbjyY1VemHIXePT3DJWe/bFGuI58YKCS1GUv1UU5eizvD4GkCTJbyZJMgt8GfjvruVkpTZ21fgbiqL8kqIoDymK8tD6+vrVuuwNgbvGp/nZXQdlpYViplFteH12FMv0Ap8Tm3VW+l1qTp4JJ8+hUZHakyZvp9HGVbfHmttl1e1lzPNDozXuPvM0bX/AiGFlAstUVfw4wpPF/wSr3OX2iWkeXVmiK6OgFcfhdL3OyeYGJcvKejKeazY4t7rGoytLrLk9HN2g5bo0XRdXUiYCWTnhQ7fdguv7/O25U/zWN7/Bf3zoIaI4FknYUcgfHnuYKStHVRfz2wgGuHHEQPqwGgOXB5qr7HWKtHwPWzPohSFVw+JoYxVDUSkZJp0o4HRnk4teHy+OOOEnHMiNZLy6NHezJ+keXhjwmX23DbWsV4AXjComSfLeF3mtLwP/BeFfWgK2rsqM3LcEvPuK/d+T+2ee5XiAVUVRppIkWZGm4Jrc/1LH+AdIkuQLwBdA8Lhe8BO+DpFGIGEvf3DsITRF4Wy7yaHRGku9NmfbzaySQsp50lRhDqbM7rxh8NTqRd4yO8fTzXXyusFMocSB0XFW+qLuV8mwWOl3APDCUDrbRX12Nwx5urnO4doUhqYReBFPLa9gGKLF/Peeeppb5uco2BaNbo/psVHmR4W5ttLtUCsWWe10WJa9Jx3TZN+2ab5yz30UbAtT1zk0tx3HNLmwXqcXBvyn08d45NQZbq1NE8gqqP90+z7ODXp4UcSOXIllr4dmKHy3vpwlY5dMixO9FodGawzimPpABAV2Fcs8sL7MjuIIfhxjSTPRVFUiSWL9xPyBV319X694pVHFm7b8+zHguNz+z8AnZeTvLqAlzb1vAe9XFKUiHebvB74l32srinKXjCZ+Evj6lmulkcFPXbH/RY/xSj7nGwW/euAIv7z/TTQHLg9eXJS1uxyONda5tTrFYrslS9MY7CiMcKbZwNZ1Hjpzll89/Gb+7MEHhM9pdIKH11fIGyZjdo4H15b4Tyefomw5Mn9SYaXfyVKACoZJdzCg7vVZ7Lap9/tUR0rcObuDeq/Huw/u5+kLizx+doHxYpGCbbPS7XB8bZW8afLM6mpW7iVt5XVy5SLvOXyQT9z5Zt52YC9RHPP0+UXu3DXPV350H1Ecc/vueb70o3v54ZnTfPupp/jThad5uL7CYxsXOdlpcrbd5O6jj3OqtUE7GHBbZYI9hTKbA5elXpsznaYo52NYPCbN6HErx8HiKItuJ0vx+diOPUOhdZXxipjziqJ8FdgLxMAC8MtJkixJ4fN/IZzifeAXkiR5SJ7zaeB/kpf410mS/IncfwT494ADfBP4tSRJEkVRxoC/ALbLMX4qSZLGyxnj+fB6Yc5fTXzxxGMs9toy/UU0umh6Ho1uN+uKnBa0e+TUGf7pXXfxB3/3d9y+e56375hnpd8himOqdo7txTJfuP8e3nrTbsbsHN8/fYo3bd9Oc+BlpXbSRqd7ajUeOnOWt+7ezXJHCKQL63UqxQLNThdTF/0hQbReW5YdvQHyjs3yRiOryd6QXZAOzW2XnYpU3rdvP//3D37AztoEc7UJHj8ravN7A5/psVH2jlZZ7LZ50/gUJdMmimOONdelwO1iyIT0xXaLA9UJDpSrfG9lgQknTxBFHBidYN7OM1EcuT4L9xrGMOXnKmMouJ4f//zeb3P03Hnetn8vR89f4Mj8TlHgLgpp9V1MXefBZ07xk3cc4T8/+hjbxkbZNz2Frems9XtZW620Q01aV+qmyRoXms1snLRJbRTHLG80mB2vZiWJU2Hk+j5BENLqCzNtvdXOOjePj5RY2mjQ6vb4Nz/5U/zb739XtDlzPUxD58L6Bod2zHKxuYmp6+zfPpP1dKxVylxYq3N+bR1d15kdH+OO3bv4H266mR+2NlAVlT9/8lHmaxN4YShq3BsWa26PpWYTU9f5P975oeuyPjcKhoLrKmMouF48fv3732S1uUmlWBAdrWU3bU1V+csf/ogP33UHAKdXLvKOfXvxwpBGr0e1UMDRdVa7XR585hS3zM9xeuUie7ZNU83nsxSkcxsbaKoqalI5Ds+sruKYJiO5HK1+PxOAaSWFVPNLhc+Pjp2gXMhz6twCv/FTP8n/e++Pslb07b7L7PgYSxtNto+P4Q58HEt8BhC1rtZlCRnHNJmslLlpahJT0zhQGefuJx/DtkzeNbeLY411Wv3+UFi9BAwF11XGUHC9PHzqr++m63lZt+1toxWOLlzgrTft5lyjwTOLy9yxZzcgmOSOYaAqCseXljm/XueufXuYKY3w2IULWd30Uj7HSC6XtZhPkXZvdkwTVVMJgpBTKxe5ffc8PVcQX6sjJe45dpy926aJ4pgTS8vcMj/HfcdOMFoqZv0g926b5sTSclakD0TLL9PQGS0UyDs27V6fvGNnDSwurNd5x9493HvyFH/x4//kunzfNzqGgusqYyi4rg5+64Hv0ur3qbfatHp9PnHHHfz7e+7h9t3zTI+M0HQFG9/UNR4/u8A79u7hO0eP8Y9vvZVHlxezOutztXHW2x2WNxrUKmXavT6mrmMYeta9eySXI+/YXFiv0+h0GR8poakq69JRv3/7DKau87Xv38Nte29iaaMhIqLtDntmt7HeatPzBLWjWipm3XIKto1p6NkYtmXy+fd+7Pp8oa8zDAXXVcZQcF0bfPQrf8LsRFU40IOQPTPT+GHIaKGA6/ssbzTYs22ac6ui92Epn7tk1vX62JaJN/Az8zDv2KxK/9SebdM8fvYcmqpybnUdz/P4xLvfziOnz3J6aYXp6hiNbo8Ds9v4uwcfZuf2WVrdHo5tiW45foDneeRyOUxdy6gUq5stHv6V//F6f3WvSwwF11XGUHC9evjUX99NKZ+j3euzZ3qKpWYTb+Cza7LG9596mrnJCYIgZHa8yoX1OuutNiP5XNa6q+t6zE1OcHr5IqPFAksbDfregIM7Znny7AJ7Z7dl2tXGZotyqcjuqUlOLC1j6jo9b4ChawRhhG0a3PPf/Nr1/kreMBgKrquMoeB67eBTf313Zhb2XA8/DGn1+rT6fd5z+BCPnDrD+Egpaz3/6ImT3Lb3Jk5cWGL7xDjnLq5yhzQNJytlvvSRj1/vjzSExFBwXWUoirKO4Im9GFSB+jWcznAON8b4r4U5XO/xX+ocdiRJMv5KBxwKrpcBRVEeuhq/GsM53NjjvxbmcL3Hv15zGJZuHmKIIW44DAXXEEMMccNhKLheHr5wvSfAcA6vhfHh+s/heo8P12EOQx/XEEMMccNhqHENMcQQNx6SJHnDvIB/BTwBPAb8DTAt9yvA54BT8v3bt5zzKeCkfH1qy/43AU/Kcz7HJe11FFHn/qT8W7lijCbgAc8AXwPKW8aoA6vACeADW8b6oNx3CviNLft3AvfL/X8OmHK/Jf8/Jd+f23LOZ4EVYIAoR3TkijE25Dyu9RxOvdgxnmMtX/SxW875IqIQ5dEt+15ova7mPXEWaCDq1j0F/DN53BmgA5x/FeZwCmgBR+Ucflsedw5RHurCtVrHl3OvPOdaXm9h8mq+EA050u3/Hvgjuf1hRA0wBbgLuH/LYp+RfytyO72pHpDHKvLcD8n9v5cuFqIj0e9eMcb7gbfJBfpd4N/K675F3kxngFuA04AmX6eBecAEHgcOyGv+BfDTcvuPgP9Wbv/Kls/208Cfy+0D8vzDwD8CXEQnpHSM9yMekCeA913jOVjyRn7BMZ5lHV/0sVec907gdi4XXC+0XlftngCmEELid4Eioh/C9+X7fyDviWs9BwXRG+J3AQMhRO+V6/hbcg7XZB1f6r0yFFzPfhN/FvhDuf154Ge2vHdC3mQ/A3x+y/7Py31TwPEt+7Pj0nPl9hRw4nnG+DTwQ/neZ+UrHeNbCGH2FkSV2K3z/qy8AeuALvdnx6Xnym1dHqek5265VgP4hfTcLddOX6/GHJ53jOdYuxd97LOcO8flguulrNfVvifOAf/blmufQPxovSpzAHLAOkJg1eU6nQD+q6u9ji/nXnm+dXzD+bheY12Jfh5hHlzY8n56rReawxiwmSRJ+CxzyM6R77fk8VdeawCMb9l/PebwcrozXc1OTleri9RLuicURZmTY31vy/uLCC3oWs9hDdgt/7aAR7m0jouIhjRXex1fzr3ynHjdCa7XQleidA4IX0NRbr8TePuWw+aACHjkWswB+LP0syMahvwA8Qt8+/Of9sZFIn7yr2mYfcsYX0X4mPrPdtg1nkMEtBH3RRlRFv2GwutOcCVJ8t4kSQ49y+vrVxz6ZeAn5PbzdQx6rv3P2ZUI+LkkSQ4h/ESn5PafIXMhFUX5eWAa+MUtY6R/Z7Zc//nmsIFogqtfsT/9PP9cjnsrwvF7APjfEaZBCkv+f63mMCs/rw6MyONf6nf9bHgpx74QVmX3KF5CF6mXfE9sGWMW4fv5MvCwvFb6/gwQvApzmALWkiTZRAQM3s6ldZxBmHVXex1fzr3y3HglfqIb7QXctGX714D/KLc/wuVO0Afk/lG5sBX5OguMyveudIJ+WO7/fS539v7eFWN8UF7nkSvGSJ3zZxHC5gzC0anL7Z1ccnYelOfezeXOzl+R27/K5c7Ov5DbB7ncoZo659Mxtjrn3/8qzeEFx3iWdXzRxz7LuXNc7uN6ofW6aveE3P8I8NAVY/w+wjn/wKswh3Hgf0E46x3EPfeQXMffkudfk3V8qffK867j9RYmr7Lg+qpcqCeAbwDb5H5F3jinEebdkS3nfBoRpj2F6CSU7j8ir3Ua0W0oDTuPAd9BhKn/dssNlY4RAD7CCfqYXMB0jA3Er+0JZDRInvthBH3iNPCbW/bPyxvtlLwpLLnflv+fku/PbznnNxF+lnQeqwjnaDrGhnxd6zmcfrFjPMdavuhjt5zzZwgqSIDwsfzii1ivq3lPXECYgU/JtX8McR+eBboIf+e1nsMCwkx8Sh73L+QYC1yiQ1yTdXw598pzvYbM+SGGGOKGw+vOxzXEEEO8/jEUXEMMMcQNh6HgGmKIIW44DAXXEEMMccNhKLiGGGKIGw5DwTXEEEPccBgKriGGGOKGw1BwDTHEEDcc/n8ZP4qCS2mArwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with rasterio.open(\"lowres/bedmap2_bed.tif\") as raster_source:\n",
    "    rasterio.plot.show(source=raster_source, cmap=\"BrBG_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download miscellaneous data (e.g. [REMA](https://doi.org/10.7910/DVN/SAIK8B), [MEaSUREs Ice Flow](https://doi.org/10.5067/D7GK8F5J8M8R), [LISA](https://doi.org/10.7265/nxpc-e997), [Arthern Accumulation](https://doi.org/10.1029/2004JD005667))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_misc\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >folder</th>        <th class=\"col_heading level0 col1\" >filename</th>        <th class=\"col_heading level0 col2\" >url</th>        <th class=\"col_heading level0 col3\" >sha256</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_misclevel0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "                        <td id=\"T_miscrow0_col0\" class=\"data row0 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow0_col1\" class=\"data row0 col1\" >REMA_100m_dem.tif</td>\n",
       "                        <td id=\"T_miscrow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/100m/REMA_100m_dem.tif\">http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/100m/REMA_100m_dem.tif</a></td>\n",
       "                        <td id=\"T_miscrow0_col3\" class=\"data row0 col3\" >80c9fa41ccc69be1d2cd4a367d56168321d1079e7260a1996089810db25172f6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_miscrow1_col0\" class=\"data row1 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow1_col1\" class=\"data row1 col1\" >REMA_200m_dem_filled.tif</td>\n",
       "                        <td id=\"T_miscrow1_col2\" class=\"data row1 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/200m/REMA_200m_dem_filled.tif\">http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/200m/REMA_200m_dem_filled.tif</a></td>\n",
       "                        <td id=\"T_miscrow1_col3\" class=\"data row1 col3\" >f750893861a1a268c8ffe0ba7db36c933223bbf5fcbb786ecef3f052b20f9b8a</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_miscrow2_col0\" class=\"data row2 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow2_col1\" class=\"data row2 col1\" >MEaSUREs_IceFlowSpeed_450m.tif</td>\n",
       "                        <td id=\"T_miscrow2_col2\" class=\"data row2 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Ice%20Flow%20Velocity/MEaSUREs_IceFlowSpeed_450m.tif\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Ice%20Flow%20Velocity/MEaSUREs_IceFlowSpeed_450m.tif</a></td>\n",
       "                        <td id=\"T_miscrow2_col3\" class=\"data row2 col3\" >4a4efc3a84204c3d67887e8d7fa1186467b51e696451f2832ebbea3ca491c8a8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row3\" class=\"row_heading level0 row3\" >28</th>\n",
       "                        <td id=\"T_miscrow3_col0\" class=\"data row3 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow3_col1\" class=\"data row3 col1\" >lisa750_2013182_2017120_0000_0400_vv_v1.tif</td>\n",
       "                        <td id=\"T_miscrow3_col2\" class=\"data row3 col2\" ><a target=\"_blank\" href=\"ftp://ftp.nsidc.org/pub/DATASETS/nsidc0733_landsat_ice_speed_v01/LISA750/lisa750_2013182_2017120_0000_0400_v1.tgz\">ftp://ftp.nsidc.org/pub/DATASETS/nsidc0733_landsat_ice_speed_v01/LISA750/lisa750_2013182_2017120_0000_0400_v1.tgz</a></td>\n",
       "                        <td id=\"T_miscrow3_col3\" class=\"data row3 col3\" >99eb934702305e2d27afa20dfc211c1d45ed762727bda29e0f251976a1877a92</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row4\" class=\"row_heading level0 row4\" >29</th>\n",
       "                        <td id=\"T_miscrow4_col0\" class=\"data row4 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow4_col1\" class=\"data row4 col1\" >Arthern_accumulation_bedmap2_grid1.tif</td>\n",
       "                        <td id=\"T_miscrow4_col2\" class=\"data row4 col2\" ><a target=\"_blank\" href=\"https://secure.antarctica.ac.uk/data/bedmap2/resources/Arthern_accumulation/Arthern_accumulation_tif.zip\">https://secure.antarctica.ac.uk/data/bedmap2/resources/Arthern_accumulation/Arthern_accumulation_tif.zip</a></td>\n",
       "                        <td id=\"T_miscrow4_col3\" class=\"data row4 col3\" >e6b139801bf4541f1e4989a8aa8b26ab37eca81bb5eaffa8028b744782455db0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row5\" class=\"row_heading level0 row5\" >30</th>\n",
       "                        <td id=\"T_miscrow5_col0\" class=\"data row5 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow5_col1\" class=\"data row5 col1\" >GroundingLine_Antarctica_v2.shp</td>\n",
       "                        <td id=\"T_miscrow5_col2\" class=\"data row5 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shp\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shp</a></td>\n",
       "                        <td id=\"T_miscrow5_col3\" class=\"data row5 col3\" >2d8f84e301c4e33ad1cb480aa260b8208b3fcaa00df0b68593ffe7d5aa6c9d7e</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row6\" class=\"row_heading level0 row6\" >31</th>\n",
       "                        <td id=\"T_miscrow6_col0\" class=\"data row6 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow6_col1\" class=\"data row6 col1\" >GroundingLine_Antarctica_v2.shx</td>\n",
       "                        <td id=\"T_miscrow6_col2\" class=\"data row6 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shx\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shx</a></td>\n",
       "                        <td id=\"T_miscrow6_col3\" class=\"data row6 col3\" >01eaff4a35b4cd840a3fb1bdb63f7e51b2792c2ccd92d4621cb5d97cb3e365bc</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row7\" class=\"row_heading level0 row7\" >32</th>\n",
       "                        <td id=\"T_miscrow7_col0\" class=\"data row7 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow7_col1\" class=\"data row7 col1\" >GroundingLine_Antarctica_v2.dbf</td>\n",
       "                        <td id=\"T_miscrow7_col2\" class=\"data row7 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.dbf\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.dbf</a></td>\n",
       "                        <td id=\"T_miscrow7_col3\" class=\"data row7 col3\" >1cfbe90b262a7fb81cbe61af4f75b23e03213a078ed866e66c6467ab422e017e</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in dataframe.query(expr=\"folder == 'misc'\").itertuples():\n",
    "    path = f\"{dataset.folder}/{dataset.filename}\"  # path to download the file to\n",
    "    if not os.path.exists(path=path):\n",
    "        download_to_path(path=path, url=dataset.url)\n",
    "    assert check_sha256(path=path) == dataset.sha256\n",
    "pprint_table(dataframe, \"misc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download High Resolution bed elevation data (e.g. some-DEM-name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_highres\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >folder</th>        <th class=\"col_heading level0 col1\" >filename</th>        <th class=\"col_heading level0 col2\" >url</th>        <th class=\"col_heading level0 col3\" >sha256</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_highreslevel0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
       "                        <td id=\"T_highresrow0_col0\" class=\"data row0 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow0_col1\" class=\"data row0 col1\" >bed_WGS84_grid.txt</td>\n",
       "                        <td id=\"T_highresrow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D\">http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D</a></td>\n",
       "                        <td id=\"T_highresrow0_col3\" class=\"data row0 col3\" >7396e56cda5adb82cecb01f0b3e01294ed0aa6489a9629f3f7e8858ea6cb91cf</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
       "                        <td id=\"T_highresrow1_col0\" class=\"data row1 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow1_col1\" class=\"data row1 col1\" >2007t1.txt</td>\n",
       "                        <td id=\"T_highresrow1_col2\" class=\"data row1 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow1_col3\" class=\"data row1 col3\" >04bdbd3c8e814cbc8f0d324277e339a46cc90a8dc23434d11815a8966951e766</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
       "                        <td id=\"T_highresrow2_col0\" class=\"data row2 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow2_col1\" class=\"data row2 col1\" >2007tr.txt</td>\n",
       "                        <td id=\"T_highresrow2_col2\" class=\"data row2 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow2_col3\" class=\"data row2 col3\" >3858a1e58e17b2816920e1b309534cee0391f72a6a0aa68d57777b030e70e9a3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row3\" class=\"row_heading level0 row3\" >7</th>\n",
       "                        <td id=\"T_highresrow3_col0\" class=\"data row3 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow3_col1\" class=\"data row3 col1\" >2010tr.txt</td>\n",
       "                        <td id=\"T_highresrow3_col2\" class=\"data row3 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow3_col3\" class=\"data row3 col3\" >751ea56acc5271b3fb54893ed59e05ff485187a6fc5daaedf75946d730805b80</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row4\" class=\"row_heading level0 row4\" >8</th>\n",
       "                        <td id=\"T_highresrow4_col0\" class=\"data row4 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow4_col1\" class=\"data row4 col1\" >istar08.txt</td>\n",
       "                        <td id=\"T_highresrow4_col2\" class=\"data row4 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow4_col3\" class=\"data row4 col3\" >ed03c64332e8d406371c74a66f3cd21fb3f78ee498ae8408c355879bb89eb13d</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row5\" class=\"row_heading level0 row5\" >9</th>\n",
       "                        <td id=\"T_highresrow5_col0\" class=\"data row5 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow5_col1\" class=\"data row5 col1\" >istar18.txt</td>\n",
       "                        <td id=\"T_highresrow5_col2\" class=\"data row5 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow5_col3\" class=\"data row5 col3\" >3e69d86f28e26810d29b0b9309090684dcb295c0dd39007fe9ee0d1285c57804</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row6\" class=\"row_heading level0 row6\" >10</th>\n",
       "                        <td id=\"T_highresrow6_col0\" class=\"data row6 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow6_col1\" class=\"data row6 col1\" >istar15.txt</td>\n",
       "                        <td id=\"T_highresrow6_col2\" class=\"data row6 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow6_col3\" class=\"data row6 col3\" >59c981e8c96f73f3a5bd98be6570e101848b4f67a12d98a577292e7bcf776b17</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row7\" class=\"row_heading level0 row7\" >11</th>\n",
       "                        <td id=\"T_highresrow7_col0\" class=\"data row7 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow7_col1\" class=\"data row7 col1\" >istar13.txt</td>\n",
       "                        <td id=\"T_highresrow7_col2\" class=\"data row7 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow7_col3\" class=\"data row7 col3\" >f5bcf80c7ea5095e2eabf72b69a264bf36ed56af5cb67976f9428f560e5702a2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row8\" class=\"row_heading level0 row8\" >12</th>\n",
       "                        <td id=\"T_highresrow8_col0\" class=\"data row8 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow8_col1\" class=\"data row8 col1\" >istar17.txt</td>\n",
       "                        <td id=\"T_highresrow8_col2\" class=\"data row8 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow8_col3\" class=\"data row8 col3\" >f51a674dc27d6e0b99d199949a706ecf96ea807883c1901fea186efc799a36e8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row9\" class=\"row_heading level0 row9\" >13</th>\n",
       "                        <td id=\"T_highresrow9_col0\" class=\"data row9 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow9_col1\" class=\"data row9 col1\" >istar07.txt</td>\n",
       "                        <td id=\"T_highresrow9_col2\" class=\"data row9 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow9_col3\" class=\"data row9 col3\" >c81ec04290433f598ce4368e4aae088adeeabb546913edc44c54a5a5d7593e93</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row10\" class=\"row_heading level0 row10\" >14</th>\n",
       "                        <td id=\"T_highresrow10_col0\" class=\"data row10 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow10_col1\" class=\"data row10 col1\" >2009_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow10_col2\" class=\"data row10 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2009_Antarctica_DC8/csv_good/2009_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2009_Antarctica_DC8/csv_good/2009_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow10_col3\" class=\"data row10 col3\" >1b9fe0faf4ef217794c2a1de9ef8cfa45f5949efdc4e925930d31c0554cf0ca2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row11\" class=\"row_heading level0 row11\" >15</th>\n",
       "                        <td id=\"T_highresrow11_col0\" class=\"data row11 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow11_col1\" class=\"data row11 col1\" >2009_Antarctica_TO.csv</td>\n",
       "                        <td id=\"T_highresrow11_col2\" class=\"data row11 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO/csv_good/2009_Antarctica_TO.csv\">https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO/csv_good/2009_Antarctica_TO.csv</a></td>\n",
       "                        <td id=\"T_highresrow11_col3\" class=\"data row11 col3\" >7a90c5955fa881b4fb88e45ff11629e60ff9ad045c07bf4c6e3aa1f7d1a9361d</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row12\" class=\"row_heading level0 row12\" >16</th>\n",
       "                        <td id=\"T_highresrow12_col0\" class=\"data row12 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow12_col1\" class=\"data row12 col1\" >2009_Antarctica_TO_Gambit.csv</td>\n",
       "                        <td id=\"T_highresrow12_col2\" class=\"data row12 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO_Gambit/csv_good/2009_Antarctica_TO_Gambit.csv\">https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO_Gambit/csv_good/2009_Antarctica_TO_Gambit.csv</a></td>\n",
       "                        <td id=\"T_highresrow12_col3\" class=\"data row12 col3\" >93da613223733a4850283b700060afdb14f1002fe5613b8d78c6d3be83e34072</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row13\" class=\"row_heading level0 row13\" >17</th>\n",
       "                        <td id=\"T_highresrow13_col0\" class=\"data row13 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow13_col1\" class=\"data row13 col1\" >2010_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow13_col2\" class=\"data row13 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2010_Antarctica_DC8/csv_good/2010_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2010_Antarctica_DC8/csv_good/2010_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow13_col3\" class=\"data row13 col3\" >f725a8dbc21d31601b99ccaf9f5282ecd516f2ff966d268b4e735ea1af2014e6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row14\" class=\"row_heading level0 row14\" >18</th>\n",
       "                        <td id=\"T_highresrow14_col0\" class=\"data row14 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow14_col1\" class=\"data row14 col1\" >2011_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow14_col2\" class=\"data row14 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_DC8/csv_good/2011_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2011_Antarctica_DC8/csv_good/2011_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow14_col3\" class=\"data row14 col3\" >38aba2a39b0d58b72827f25cfcd667fc943f25c0024d3c52cb1b9e65e9e76163</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row15\" class=\"row_heading level0 row15\" >19</th>\n",
       "                        <td id=\"T_highresrow15_col0\" class=\"data row15 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow15_col1\" class=\"data row15 col1\" >2011_Antarctica_TO.csv</td>\n",
       "                        <td id=\"T_highresrow15_col2\" class=\"data row15 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv\">https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv</a></td>\n",
       "                        <td id=\"T_highresrow15_col3\" class=\"data row15 col3\" >4bf37750b9986ce582c9fd1f3a6ac622fc17f3b3ecb07b7a7132eb3797ee31d1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row16\" class=\"row_heading level0 row16\" >20</th>\n",
       "                        <td id=\"T_highresrow16_col0\" class=\"data row16 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow16_col1\" class=\"data row16 col1\" >2012_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow16_col2\" class=\"data row16 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2012_Antarctica_DC8/csv_good/2012_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2012_Antarctica_DC8/csv_good/2012_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow16_col3\" class=\"data row16 col3\" >5c6701b8c34bd57517b93e8e18f32e4579d6e2f56e4796bd7140b3e338544007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row17\" class=\"row_heading level0 row17\" >21</th>\n",
       "                        <td id=\"T_highresrow17_col0\" class=\"data row17 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow17_col1\" class=\"data row17 col1\" >2013_Antarctica_Basler.csv</td>\n",
       "                        <td id=\"T_highresrow17_col2\" class=\"data row17 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2013_Antarctica_Basler/csv_good/2013_Antarctica_Basler.csv\">https://data.cresis.ku.edu/data/rds/2013_Antarctica_Basler/csv_good/2013_Antarctica_Basler.csv</a></td>\n",
       "                        <td id=\"T_highresrow17_col3\" class=\"data row17 col3\" >56609027b4af04ba078ae093772916341bd1d6ab5f110de11b21294507733cc8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row18\" class=\"row_heading level0 row18\" >22</th>\n",
       "                        <td id=\"T_highresrow18_col0\" class=\"data row18 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow18_col1\" class=\"data row18 col1\" >2013_Antarctica_P3.csv</td>\n",
       "                        <td id=\"T_highresrow18_col2\" class=\"data row18 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2013_Antarctica_P3/csv_good/2013_Antarctica_P3.csv\">https://data.cresis.ku.edu/data/rds/2013_Antarctica_P3/csv_good/2013_Antarctica_P3.csv</a></td>\n",
       "                        <td id=\"T_highresrow18_col3\" class=\"data row18 col3\" >9de95030f49ce0bbf107eb72418db2845c39822872a6c9aa10f023148262f658</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row19\" class=\"row_heading level0 row19\" >23</th>\n",
       "                        <td id=\"T_highresrow19_col0\" class=\"data row19 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow19_col1\" class=\"data row19 col1\" >2014_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow19_col2\" class=\"data row19 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2014_Antarctica_DC8/csv_good/2014_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2014_Antarctica_DC8/csv_good/2014_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow19_col3\" class=\"data row19 col3\" >bd8c8674ba66508c64303725bfe45b3365467d01f69cfa8ec4258a3ced05e5bf</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row20\" class=\"row_heading level0 row20\" >24</th>\n",
       "                        <td id=\"T_highresrow20_col0\" class=\"data row20 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow20_col1\" class=\"data row20 col1\" >2016_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow20_col2\" class=\"data row20 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2016_Antarctica_DC8/csv_good/2016_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2016_Antarctica_DC8/csv_good/2016_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow20_col3\" class=\"data row20 col3\" >ec3b514dfcae265f5b8643eeb3503be8a0a6531e563faf9f12cb67f2b618a741</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row21\" class=\"row_heading level0 row21\" >25</th>\n",
       "                        <td id=\"T_highresrow21_col0\" class=\"data row21 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow21_col1\" class=\"data row21 col1\" >2017_Antarctica_P3.csv</td>\n",
       "                        <td id=\"T_highresrow21_col2\" class=\"data row21 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv\">https://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv</a></td>\n",
       "                        <td id=\"T_highresrow21_col3\" class=\"data row21 col3\" >9208a64fefe2f4a6e7f08d44c0af0c35400cd814590c32b8eb02f1545bfc8bec</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row22\" class=\"row_heading level0 row22\" >26</th>\n",
       "                        <td id=\"T_highresrow22_col0\" class=\"data row22 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow22_col1\" class=\"data row22 col1\" >2017_Antarctica_Basler.csv</td>\n",
       "                        <td id=\"T_highresrow22_col2\" class=\"data row22 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv\">https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv</a></td>\n",
       "                        <td id=\"T_highresrow22_col3\" class=\"data row22 col3\" >c97d0d92f3095ee8c3941d915028728423758594cc95e7b819889b51693f0712</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row23\" class=\"row_heading level0 row23\" >27</th>\n",
       "                        <td id=\"T_highresrow23_col0\" class=\"data row23 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow23_col1\" class=\"data row23 col1\" >WISE_ISODYN_RadarByFlight_ASCII.zip</td>\n",
       "                        <td id=\"T_highresrow23_col2\" class=\"data row23 col2\" ><a target=\"_blank\" href=\"https://ramadda.data.bas.ac.uk/repository/entry/get/WISE_ISODYN_RadarByFlight_ASCII.zip?entryid=synth%3A59e5a6f5-e67d-4a05-99af-30f656569401%3AL1dJU0VfSVNPRFlOX1JhZGFyQnlGbGlnaHRfQVNDSUkuemlw\">https://ramadda.data.bas.ac.uk/repository/entry/get/WISE_ISODYN_RadarByFlight_ASCII.zip?entryid=synth%3A59e5a6f5-e67d-4a05-99af-30f656569401%3AL1dJU0VfSVNPRFlOX1JhZGFyQnlGbGlnaHRfQVNDSUkuemlw</a></td>\n",
       "                        <td id=\"T_highresrow23_col3\" class=\"data row23 col3\" >dfb20a4ff133f361f64b5730e7593a46cb3d599ae62b0dc874685794990c8d91</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in dataframe.query(expr=\"folder == 'highres'\").itertuples():\n",
    "    path = f\"{dataset.folder}/{dataset.filename}\"  # path to download the file to\n",
    "    if not os.path.exists(path=path):\n",
    "        download_to_path(path=path, url=dataset.url)\n",
    "    assert check_sha256(path=path) == dataset.sha256\n",
    "pprint_table(dataframe, \"highres\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Process high resolution data into grid format\n",
    "\n",
    "Our processing step involves two stages:\n",
    "\n",
    "1) Cleaning up the raw **vector** data, performing necessary calculations and reprojections to EPSG:3031.\n",
    "\n",
    "2) Convert the cleaned vector data table via an interpolation function to a **raster** grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 [Raw ASCII Text](https://pdal.io/stages/readers.text.html) to [Clean XYZ table](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#table-data)\n",
    "\n",
    "![Raw ASCII to Clean Table via pipeline file](https://yuml.me/diagram/scruffy;dir:LR/class/[Raw-ASCII-Text|*.csv/*.txt]->[Pipeline-File|*.json],[Pipeline-File]->[Clean-XYZ-Table|*.xyz])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ascii_to_xyz(pipeline_file: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Converts ascii txt/csv files to xyz pandas.DataFrame via\n",
    "    a JSON Pipeline file similar to the one used by PDAL.\n",
    "\n",
    "    >>> os.makedirs(name=\"/tmp/highres\", exist_ok=True)\n",
    "    >>> d = download_to_path(\n",
    "    ...    path=\"/tmp/highres/2011_Antarctica_TO.csv\",\n",
    "    ...    url=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv\",\n",
    "    ... )\n",
    "    >>> _ = shutil.copy(src=\"highres/20xx_Antarctica_TO.json\", dst=\"/tmp/highres\")\n",
    "    >>> df = ascii_to_xyz(pipeline_file=\"/tmp/highres/20xx_Antarctica_TO.json\")\n",
    "    >>> df.head(2)\n",
    "                   x             y         z\n",
    "    0  345580.826265 -1.156471e+06 -377.2340\n",
    "    1  345593.322948 -1.156460e+06 -376.6332\n",
    "    >>> shutil.rmtree(path=\"/tmp/highres\")\n",
    "    \"\"\"\n",
    "    assert os.path.exists(pipeline_file)\n",
    "    assert pipeline_file.endswith((\".json\"))\n",
    "\n",
    "    # Read json file first\n",
    "    j = json.loads(open(pipeline_file).read())\n",
    "    jdf = pd.io.json.json_normalize(j, record_path=\"pipeline\")\n",
    "    jdf = jdf.set_index(keys=\"type\")\n",
    "    reader = jdf.loc[\"readers.text\"]  # check how to read the file(s)\n",
    "\n",
    "    ## Basic table read\n",
    "    skip = int(reader.skip)  # number of header rows to skip\n",
    "    sep = reader.separator  # delimiter to use\n",
    "    names = reader.header.split(sep=sep)  # header/column names as list\n",
    "    usecols = reader.usecols.split(sep=sep)  # column names to use\n",
    "    na_values = None if not hasattr(reader, \"na_values\") else reader.na_values\n",
    "\n",
    "    path_pattern = os.path.join(os.path.dirname(pipeline_file), reader.filename)\n",
    "    files = [file for file in glob.glob(path_pattern)]\n",
    "    assert len(files) > 0  # check that there are actually files being matched!\n",
    "\n",
    "    df = pd.concat(\n",
    "        pd.read_csv(\n",
    "            f, sep=sep, header=skip, names=names, usecols=usecols, na_values=na_values\n",
    "        )\n",
    "        for f in files\n",
    "    )\n",
    "    df.reset_index(drop=True, inplace=True)  # reset index after concatenation\n",
    "\n",
    "    ## Advanced table read with conversions\n",
    "    try:\n",
    "        # Perform math operations\n",
    "        newcol, expr = reader.converters.popitem()\n",
    "        df[newcol] = df.eval(expr=expr)\n",
    "        # Drop unneeded columns\n",
    "        dropcols = reader.dropcols.split(sep=sep)\n",
    "        df.drop(columns=dropcols, inplace=True)\n",
    "    except AttributeError:\n",
    "        pass\n",
    "\n",
    "    assert len(df.columns) == 3  # check that we have 3 columns i.e. x, y, z\n",
    "    df.sort_index(axis=\"columns\", inplace=True)  # sort cols alphabetically\n",
    "    df.set_axis(labels=[\"x\", \"y\", \"z\"], axis=\"columns\", inplace=True)  # lower case\n",
    "\n",
    "    ## Reproject x and y coordinates if necessary\n",
    "    try:\n",
    "        reproject = jdf.loc[\"filters.reprojection\"]\n",
    "        p1 = pyproj.CRS.from_string(in_crs_string=reproject.in_srs)\n",
    "        p2 = pyproj.CRS.from_string(in_crs_string=reproject.out_srs)\n",
    "        reprj_func = pyproj.Transformer.from_crs(crs_from=p1, crs_to=p2, always_xy=True)\n",
    "\n",
    "        x2, y2 = reprj_func.transform(xx=np.array(df[\"x\"]), yy=np.array(df[\"y\"]))\n",
    "        df[\"x\"] = pd.Series(x2)\n",
    "        df[\"y\"] = pd.Series(y2)\n",
    "\n",
    "    except KeyError:\n",
    "        pass\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing highres/2007tx.json pipeline ... 42995 datapoints\n",
      "Processing highres/2010tr.json pipeline ... 84922 datapoints\n",
      "Processing highres/201x_Antarctica_Basler.json pipeline ... 2325792 datapoints\n",
      "Processing highres/20xx_Antarctica_DC8.json pipeline ... 12840213 datapoints\n",
      "Processing highres/20xx_Antarctica_TO.json pipeline ... 2895926 datapoints\n",
      "Processing highres/WISE_ISODYN_RadarByFlight.json pipeline ... 2700265 datapoints\n",
      "Processing highres/bed_WGS84_grid.json pipeline ... 244279 datapoints\n",
      "Processing highres/istarxx.json pipeline ... 396369 datapoints\n"
     ]
    }
   ],
   "source": [
    "xyz_dict = {}\n",
    "for pf in sorted(glob.glob(\"highres/*.json\")):\n",
    "    print(f\"Processing {pf} pipeline\", end=\" ... \")\n",
    "    name = os.path.splitext(os.path.basename(pf))[0]\n",
    "    xyz_dict[name] = ascii_to_xyz(pipeline_file=pf)\n",
    "    print(f\"{len(xyz_dict[name])} datapoints\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 [Clean XYZ table](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#table-data) to [Raster Grid](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#grid-files)\n",
    "\n",
    "![Clean XYZ Table to Raster Grid via interpolation function](https://yuml.me/diagram/scruffy;dir:LR/class/[Clean-XYZ-Table|*.xyz]->[Interpolation-Function],[Interpolation-Function]->[Raster-Grid|*.tif/*.nc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_region(xyz_data: pd.DataFrame) -> str:\n",
    "    \"\"\"\n",
    "    Gets the bounding box region of an xyz pandas.DataFrame in string\n",
    "    format xmin/xmax/ymin/ymax rounded to 5 decimal places.\n",
    "    Used for the -R 'region of interest' parameter in GMT.\n",
    "\n",
    "    >>> xyz_data = pd.DataFrame(np.random.RandomState(seed=42).rand(30).reshape(10, 3))\n",
    "    >>> get_region(xyz_data=xyz_data)\n",
    "    '0.05808/0.83244/0.02058/0.95071'\n",
    "    \"\"\"\n",
    "    xmin, ymin, _ = xyz_data.min(axis=\"rows\")\n",
    "    xmax, ymax, _ = xyz_data.max(axis=\"rows\")\n",
    "    return f\"{xmin:.5f}/{xmax:.5f}/{ymin:.5f}/{ymax:.5f}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def xyz_to_grid(\n",
    "    xyz_data: pd.DataFrame,\n",
    "    region: str,\n",
    "    spacing: int = 250,\n",
    "    tension: float = 0.35,\n",
    "    outfile: str = None,\n",
    "    mask_cell_radius: int = 3,\n",
    "):\n",
    "    \"\"\"\n",
    "    Performs interpolation of x, y, z point data to a raster grid.\n",
    "\n",
    "    >>> xyz_data = 1000*pd.DataFrame(np.random.RandomState(seed=42).rand(60).reshape(20, 3))\n",
    "    >>> region = get_region(xyz_data=xyz_data)\n",
    "    >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)\n",
    "    >>> grid.to_array().shape\n",
    "    (1, 5, 5)\n",
    "    >>> grid.to_array().values\n",
    "    array([[[403.17618 , 544.92535 , 670.7824  , 980.75055 , 961.47723 ],\n",
    "            [379.0757  , 459.26407 , 314.38297 , 377.78555 , 546.0469  ],\n",
    "            [450.67664 , 343.26    ,  88.391594, 260.10492 , 452.3337  ],\n",
    "            [586.09906 , 469.74008 , 216.8168  , 486.9802  , 642.2116  ],\n",
    "            [451.4794  , 652.7244  , 325.77896 , 879.8973  , 916.7921  ]]],\n",
    "          dtype=float32)\n",
    "    \"\"\"\n",
    "    ## Preprocessing with blockmedian\n",
    "    with gmt.helpers.GMTTempFile(suffix=\".txt\") as tmpfile:\n",
    "        with gmt.clib.Session() as lib:\n",
    "            file_context = lib.virtualfile_from_matrix(matrix=xyz_data.values)\n",
    "            with file_context as infile:\n",
    "                kwargs = {\"V\": \"\", \"R\": region, \"I\": f\"{spacing}+e\"}\n",
    "                arg_str = \" \".join(\n",
    "                    [infile, gmt.helpers.build_arg_string(kwargs), \"->\" + tmpfile.name]\n",
    "                )\n",
    "                lib.call_module(module=\"blockmedian\", args=arg_str)\n",
    "            x, y, z = np.loadtxt(fname=tmpfile.name, unpack=True)\n",
    "\n",
    "    ## XYZ point data to NetCDF grid via GMT surface\n",
    "    grid = gmt.surface(\n",
    "        x=x,\n",
    "        y=y,\n",
    "        z=z,\n",
    "        region=region,\n",
    "        spacing=f\"{spacing}+e\",\n",
    "        T=tension,\n",
    "        V=\"\",\n",
    "        M=f\"{mask_cell_radius}c\",\n",
    "    )\n",
    "\n",
    "    ## Save grid to NetCDF with projection information\n",
    "    if outfile is not None:\n",
    "        # TODO add CRS!! See https://github.com/pydata/xarray/issues/2288\n",
    "        grid.to_netcdf(path=outfile)\n",
    "\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gridding 2007tx ... done! (1, 266, 74)\n",
      "Gridding 2010tr ... done! (1, 92, 115)\n",
      "Gridding 201x_Antarctica_Basler ... done! (1, 9062, 7437)\n",
      "Gridding 20xx_Antarctica_DC8 ... done! (1, 12388, 15326)\n",
      "Gridding 20xx_Antarctica_TO ... done! (1, 7671, 12287)\n",
      "Gridding WISE_ISODYN_RadarByFlight ... done! (1, 5320, 5292)\n",
      "Gridding bed_WGS84_grid ... done! (1, 123, 163)\n",
      "Gridding istarxx ... done! (1, 552, 377)\n"
     ]
    }
   ],
   "source": [
    "grid_dict = {}\n",
    "for name in xyz_dict.keys():\n",
    "    print(f\"Gridding {name}\", end=\" ... \")\n",
    "    xyz_data = xyz_dict[name]\n",
    "    region = get_region(xyz_data)\n",
    "    grid_dict[name] = xyz_to_grid(\n",
    "        xyz_data=xyz_data, region=region, outfile=f\"highres/{name}.nc\"\n",
    "    )\n",
    "    print(f\"done! {grid_dict[name].to_array().shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plot raster grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grids = sorted(glob.glob(\"highres/*.nc\"))\n",
    "fig, axarr = plt.subplots(\n",
    "    nrows=1 + ((len(grids) - 1) // 3), ncols=3, squeeze=False, figsize=(15, 15)\n",
    ")\n",
    "\n",
    "for i, grid in enumerate(grids):\n",
    "    with rasterio.open(grid) as raster_source:\n",
    "        rasterio.plot.show(\n",
    "            source=raster_source, cmap=\"BrBG_r\", ax=axarr[i // 3, i % 3], title=grid\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tile data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Big raster to many small square tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def get_window_bounds(\n",
    "    filepath: str,\n",
    "    pyproj_srs: str = \"epsg:3031\",\n",
    "    height: int = 32,\n",
    "    width: int = 32,\n",
    "    step: int = 4,\n",
    ") -> list:\n",
    "    \"\"\"\n",
    "    Reads in a raster and finds tiles for them according to a stepped moving window.\n",
    "    Returns a list of bounding box coordinates corresponding to a tile that looks like\n",
    "    [(minx, miny, maxx, maxy), (minx, miny, maxx, maxy), ...]\n",
    "\n",
    "    >>> xr.DataArray(\n",
    "    ...     data=np.zeros(shape=(36, 32)),\n",
    "    ...     coords={\"y\": np.arange(0.5, 36.5), \"x\": np.arange(0.5, 32.5)},\n",
    "    ...     dims=[\"y\", \"x\"],\n",
    "    ... ).to_netcdf(path=\"/tmp/tmp_wb.nc\")\n",
    "    >>> get_window_bounds(filepath=\"/tmp/tmp_wb.nc\")\n",
    "    Tiling: /tmp/tmp_wb.nc ... 2\n",
    "    [(0.0, 4.0, 32.0, 36.0), (0.0, 0.0, 32.0, 32.0)]\n",
    "    >>> os.remove(\"/tmp/tmp_wb.nc\")\n",
    "    \"\"\"\n",
    "    assert height == width  # make sure it's a square!\n",
    "    assert height % 2 == 0  # make sure we are passing in an even number\n",
    "\n",
    "    with xr.open_dataarray(filepath) as dataset:\n",
    "        print(f\"Tiling: {filepath} ... \", end=\"\")\n",
    "\n",
    "        # Use salem to patch projection information into xarray.DataArray\n",
    "        # See also https://salem.readthedocs.io/en/latest/xarray_acc.html\n",
    "        dataset.attrs[\"pyproj_srs\"] = pyproj_srs\n",
    "        sgrid = dataset.salem.grid.corner_grid\n",
    "        assert sgrid.origin == \"lower-left\"  # should be \"lower-left\", not \"upper-left\"\n",
    "\n",
    "        ## Vectorized 'loop' along raster image from top to bottom, and left to right\n",
    "\n",
    "        # Get boolean true/false mask of where the data/nodata pixels lie\n",
    "        mask = dataset.to_masked_array(copy=False).mask\n",
    "        mask = np.ascontiguousarray(a=np.flipud(m=mask))  # flip on y-axis\n",
    "\n",
    "        # Sliding window view of the input geographical raster image\n",
    "        window_views = skimage.util.shape.view_as_windows(\n",
    "            arr_in=mask, window_shape=(height, width), step=step\n",
    "        )\n",
    "        filled_tiles = ~window_views.any(\n",
    "            axis=(-2, -1)\n",
    "        )  # find tiles which are fully filled, i.e. no blank/NODATA pixels\n",
    "        tile_indexes = np.argwhere(a=filled_tiles)  # get x and y index of filled tiles\n",
    "\n",
    "        # Convert x,y tile indexes to bounding box coordinates\n",
    "        # Complicated as xarray uses centre-based coordinates,\n",
    "        # while rasterio uses corner-based coordinates\n",
    "        windows = [\n",
    "            rasterio.windows.Window(\n",
    "                col_off=ulx * step, row_off=uly * step, width=width, height=height\n",
    "            )\n",
    "            for uly, ulx in tile_indexes\n",
    "        ]\n",
    "        window_bounds = [\n",
    "            rasterio.windows.bounds(\n",
    "                window=window,\n",
    "                transform=rasterio.Affine(\n",
    "                    sgrid.dx, 0, sgrid.x0, 0, -sgrid.dy, sgrid.y_coord[-1] + sgrid.dy\n",
    "                ),\n",
    "                width=width,\n",
    "                height=height,\n",
    "            )\n",
    "            for window in windows\n",
    "        ]\n",
    "        print(len(window_bounds))\n",
    "\n",
    "    return window_bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: highres/2010tr.nc ... 164\n",
      "Tiling: highres/201x_Antarctica_Basler.nc ... 961\n",
      "Tiling: highres/20xx_Antarctica_DC8.nc ... 19\n",
      "Tiling: highres/20xx_Antarctica_TO.nc ... 989\n",
      "Tiling: highres/WISE_ISODYN_RadarByFlight.nc ... 19\n",
      "Tiling: highres/bed_WGS84_grid.nc ... 172\n",
      "Tiling: highres/istarxx.nc ... 175\n",
      "Total number of tiles: 2499\n"
     ]
    }
   ],
   "source": [
    "filepaths = sorted([g for g in glob.glob(\"highres/*.nc\") if g != \"highres/2007tx.nc\"])\n",
    "window_bounds = [get_window_bounds(filepath=grid) for grid in filepaths]\n",
    "window_bounds_concat = np.concatenate([w for w in window_bounds]).tolist()\n",
    "print(f\"Total number of tiles: {len(window_bounds_concat)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset tiles to those within grounding line, plot to show, and save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tile_gdf = pd.concat(\n",
    "    objs=[\n",
    "        gpd.GeoDataFrame(\n",
    "            pd.Series(\n",
    "                data=len(window_bound) * [os.path.basename(filepath)], name=\"grid_name\"\n",
    "            ),\n",
    "            crs={\"init\": \"epsg:3031\"},\n",
    "            geometry=[shapely.geometry.box(*bound) for bound in window_bound],\n",
    "        )\n",
    "        for filepath, window_bound in zip(filepaths, window_bounds)\n",
    "    ]\n",
    ").reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load grounding line polygon and buffer by 10km\n",
    "gline = gpd.read_file(\"misc/GroundingLine_Antarctica_v2.shp\")\n",
    "gline.crs = {\"init\": \"epsg:3031\"}\n",
    "gline.geometry = gline.geometry.buffer(distance=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a3d4d5fd0>"
      ]
     },
     "execution_count": 21,
     "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": [
    "# Select tiles within the buffered grounding line\n",
    "gdf = gpd.sjoin(left_df=tile_gdf, op=\"within\", right_df=gline, how=\"inner\")\n",
    "gdf = gdf.reset_index()[[\"grid_name\", \"geometry\"]]\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving only 2347 tiles out of 2499\n"
     ]
    }
   ],
   "source": [
    "# Save subsetted tiles to file in both EPSG 3031 and 4326\n",
    "print(f\"Saving only {len(gdf)} tiles out of {len(tile_gdf)}\")\n",
    "gdf.to_file(filename=\"model/train/tiles_3031.geojson\", driver=\"GeoJSON\")\n",
    "gdf.to_crs(crs={\"init\": \"epsg:4326\"}).to_file(\n",
    "    filename=\"model/train/tiles_4326.geojson\", driver=\"GeoJSON\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do the actual tiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def selective_tile(\n",
    "    filepath: str,\n",
    "    window_bounds: list,\n",
    "    padding: int = 0,  # in projected coordinate system units\n",
    "    out_shape: tuple = None,\n",
    "    gapfill_raster_filepath: str = None,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Reads in raster and tiles them selectively.\n",
    "    Tiles will go according to list of window_bounds.\n",
    "    Output shape can be set to e.g. (16,16) to resample input raster to\n",
    "    some desired shape/resolution.\n",
    "\n",
    "    >>> xr.DataArray(\n",
    "    ...     data=np.flipud(m=np.diag(v=np.arange(8))).astype(dtype=np.float32),\n",
    "    ...     coords={\"y\": np.linspace(7, 0, 8), \"x\": np.linspace(0, 7, 8)},\n",
    "    ...     dims=[\"y\", \"x\"],\n",
    "    ... ).to_netcdf(path=\"/tmp/tmp_st.nc\", mode=\"w\")\n",
    "    >>> selective_tile(\n",
    "    ...    filepath=\"/tmp/tmp_st.nc\",\n",
    "    ...    window_bounds=[(0.5, 0.5, 2.5, 2.5), (2.5, 1.5, 4.5, 3.5)],\n",
    "    ... )\n",
    "    Tiling: /tmp/tmp_st.nc ... done!\n",
    "    array([[[[0., 2.],\n",
    "             [1., 0.]]],\n",
    "    <BLANKLINE>\n",
    "    <BLANKLINE>\n",
    "           [[[3., 0.],\n",
    "             [0., 0.]]]], dtype=float32)\n",
    "    >>> os.remove(\"/tmp/tmp_st.nc\")\n",
    "    \"\"\"\n",
    "\n",
    "    # Convert list of bounding box tuples to nice rasterio.coords.BoundingBox class\n",
    "    window_bounds = [\n",
    "        rasterio.coords.BoundingBox(\n",
    "            left=x0 - padding, bottom=y0 - padding, right=x1 + padding, top=y1 + padding\n",
    "        )\n",
    "        for x0, y0, x1, y1 in window_bounds  # xmin, ymin, xmax, ymax\n",
    "    ]\n",
    "\n",
    "    # Retrieve tiles from the main raster\n",
    "    with xr.open_rasterio(\n",
    "        filepath, chunks=None if out_shape is None else {}\n",
    "    ) as dataset:\n",
    "        print(f\"Tiling: {filepath} ... \", end=\"\")\n",
    "\n",
    "        # Subset dataset according to window bound (wb)\n",
    "        daarray_list = [\n",
    "            dataset.sel(y=slice(wb.top, wb.bottom), x=slice(wb.left, wb.right))\n",
    "            for wb in window_bounds\n",
    "        ]\n",
    "        # Bilinear interpolate to new shape if out_shape is set\n",
    "        if out_shape is not None:\n",
    "            daarray_list = [\n",
    "                dataset.interp(\n",
    "                    y=np.linspace(da.y[0], da.y[-1], num=out_shape[0]),\n",
    "                    x=np.linspace(da.x[0], da.x[-1], num=out_shape[1]),\n",
    "                    method=\"linear\",\n",
    "                )\n",
    "                for da in daarray_list\n",
    "            ]\n",
    "        daarray_stack = dask.array.ma.masked_values(\n",
    "            x=dask.array.stack(seq=daarray_list),\n",
    "            value=np.nan_to_num(-np.inf)\n",
    "            if dataset.dtype == np.int16 and dataset.nodatavals == (np.nan,)\n",
    "            else dataset.nodatavals,\n",
    "        )\n",
    "\n",
    "        assert daarray_stack.ndim == 4  # check that shape is like (m, 1, height, width)\n",
    "        assert daarray_stack.shape[1] == 1  # channel-first (assuming only 1 channel)\n",
    "        assert not 0 in daarray_stack.shape  # ensure no empty dimensions (bad window)\n",
    "\n",
    "    out_tiles = dask.array.ma.getdata(daarray_stack).compute().astype(dtype=np.float32)\n",
    "    mask = dask.array.ma.getmaskarray(daarray_stack).compute()\n",
    "\n",
    "    # Gapfill main raster if there are blank spaces\n",
    "    if mask.any():  # check that there are no NAN values\n",
    "        nan_grid_indexes = np.argwhere(mask.any(axis=(-3, -2, -1))).ravel()\n",
    "\n",
    "        # Replace pixels from another raster if available, else raise error\n",
    "        if gapfill_raster_filepath is not None:\n",
    "            print(f\"gapfilling ... \", end=\"\")\n",
    "            with xr.open_rasterio(gapfill_raster_filepath, chunks={}) as dataset2:\n",
    "                daarray_list2 = [\n",
    "                    dataset2.interp_like(daarray_list[idx].squeeze(), method=\"linear\")\n",
    "                    for idx in nan_grid_indexes\n",
    "                ]\n",
    "                daarray_stack2 = dask.array.ma.masked_values(\n",
    "                    x=dask.array.stack(seq=daarray_list2), value=dataset2.nodatavals\n",
    "                )\n",
    "\n",
    "            fill_tiles = (\n",
    "                dask.array.ma.getdata(daarray_stack2).compute().astype(dtype=np.float32)\n",
    "            )\n",
    "            mask2 = dask.array.ma.getmaskarray(daarray_stack2).compute()\n",
    "\n",
    "            for i, array2 in enumerate(fill_tiles):\n",
    "                idx = nan_grid_indexes[i]\n",
    "                np.copyto(dst=out_tiles[idx], src=array2, where=mask[idx])\n",
    "                # assert not (mask[idx] & mask2[i]).any()  # Ensure no NANs after gapfill\n",
    "\n",
    "        else:\n",
    "            for i in nan_grid_indexes:\n",
    "                daarray_list[i].plot()\n",
    "                plt.show()\n",
    "            print(f\"WARN: Tiles have missing data, try pass in gapfill_raster_filepath\")\n",
    "\n",
    "    print(\"done!\")\n",
    "    return out_tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "geodataframe = gpd.read_file(\"model/train/tiles_3031.geojson\")\n",
    "filepaths = geodataframe.grid_name.unique()\n",
    "window_bounds = [\n",
    "    [geom.bounds for geom in geodataframe.query(\"grid_name == @filepath\").geometry]\n",
    "    for filepath in filepaths\n",
    "]\n",
    "window_bounds_concat = np.concatenate([w for w in window_bounds]).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile High Resolution data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: highres/2010tr.nc ... done!\n",
      "Tiling: highres/201x_Antarctica_Basler.nc ... done!\n",
      "Tiling: highres/20xx_Antarctica_DC8.nc ... done!\n",
      "Tiling: highres/20xx_Antarctica_TO.nc ... done!\n",
      "Tiling: highres/WISE_ISODYN_RadarByFlight.nc ... done!\n",
      "Tiling: highres/bed_WGS84_grid.nc ... done!\n",
      "Tiling: highres/istarxx.nc ... done!\n",
      "(2347, 1, 32, 32) float32\n"
     ]
    }
   ],
   "source": [
    "hireses = [\n",
    "    selective_tile(filepath=f\"highres/{f}\", window_bounds=w)\n",
    "    for f, w in zip(filepaths, window_bounds)\n",
    "]\n",
    "hires = np.concatenate(hireses)\n",
    "print(hires.shape, hires.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile low resolution data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: lowres/bedmap2_bed.tif ... done!\n",
      "(2347, 1, 10, 10) float32\n"
     ]
    }
   ],
   "source": [
    "lores = selective_tile(\n",
    "    filepath=\"lowres/bedmap2_bed.tif\", window_bounds=window_bounds_concat, padding=1000\n",
    ")\n",
    "print(lores.shape, lores.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile miscellaneous data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: misc/REMA_100m_dem.tif ... done!\n",
      "(2347, 1, 100, 100) float32\n"
     ]
    }
   ],
   "source": [
    "rema = selective_tile(\n",
    "    filepath=\"misc/REMA_100m_dem.tif\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    "    gapfill_raster_filepath=\"misc/REMA_200m_dem_filled.tif\",\n",
    ")\n",
    "print(rema.shape, rema.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"bounds\": [-3174450.0, -2816675.0, 2867550.0, 2406325.0], \"colorinterp\": [\"gray\"], \"count\": 1, \"crs\": \"EPSG:3031\", \"descriptions\": [null], \"driver\": \"GTiff\", \"dtype\": \"float32\", \"height\": 6964, \"indexes\": [1], \"interleave\": \"band\", \"lnglat\": [-143.20725403435895, -87.64224776983792], \"mask_flags\": [[\"nodata\"]], \"nodata\": 36159.75, \"res\": [750.0, 750.0], \"shape\": [6964, 8056], \"tiled\": false, \"transform\": [750.0, 0.0, -3174450.0, 0.0, -750.0, 2406325.0, 0.0, 0.0, 1.0], \"units\": [null], \"width\": 8056}\n"
     ]
    }
   ],
   "source": [
    "## Custom processing for LISA to standardize units with MEASURES Ice Velocity\n",
    "# Convert units from metres/day to metres/year by multiplying 1st band by 365.25\n",
    "!rio calc \"(* 365.25 (read 1))\" misc/lisa750_2013182_2017120_0000_0400_vv_v1.tif misc/lisa750_2013182_2017120_0000_0400_vv_v1_myr.tif\n",
    "# Set NODATA mask where pixels are 36159.75 = 99 * 365.25\n",
    "!rio edit-info misc/lisa750_2013182_2017120_0000_0400_vv_v1_myr.tif --nodata 36159.75\n",
    "!rio info misc/lisa750_2013182_2017120_0000_0400_vv_v1_myr.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: misc/MEaSUREs_IceFlowSpeed_450m.tif ... done!\n",
      "(2347, 1, 20, 20) float32\n"
     ]
    }
   ],
   "source": [
    "measuresiceflow = selective_tile(\n",
    "    filepath=\"misc/MEaSUREs_IceFlowSpeed_450m.tif\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    "    out_shape=(20, 20),\n",
    "    # gapfill_raster_filepath=\"misc/lisa750_2013182_2017120_0000_0400_vv_v1_myr.tif\",\n",
    ")\n",
    "print(measuresiceflow.shape, measuresiceflow.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: misc/Arthern_accumulation_bedmap2_grid1.tif ... done!\n",
      "(2347, 1, 10, 10) float32\n"
     ]
    }
   ],
   "source": [
    "accumulation = selective_tile(\n",
    "    filepath=\"misc/Arthern_accumulation_bedmap2_grid1.tif\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    ")\n",
    "print(accumulation.shape, accumulation.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Save the arrays\n",
    "\n",
    "We'll save the numpy arrays to the filesystem first.\n",
    "We label inputs as X (low resolution bed DEMs) and W (miscellaneous).\n",
    "Groundtruth high resolution bed DEMs are labelled as Y.\n",
    "\n",
    "Also, we'll serve the data up on the web using:\n",
    "- [Quilt](https://quiltdata.com/) - Python data versioning\n",
    "- [Dat](https://datproject.org/) - Distributed data sharing (TODO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(name=\"model/train\", exist_ok=True)\n",
    "np.save(file=\"model/train/W1_data.npy\", arr=rema)\n",
    "np.save(file=\"model/train/W2_data.npy\", arr=measuresiceflow)\n",
    "np.save(file=\"model/train/W3_data.npy\", arr=accumulation)\n",
    "np.save(file=\"model/train/X_data.npy\", arr=lores)\n",
    "np.save(file=\"model/train/Y_data.npy\", arr=hires)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quilt\n",
    "\n",
    "Login -> Build -> Push"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching a web browser...\n",
      "If that didn't work, please visit the following URL: https://pkg.quiltdata.com/login\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the code from the webpage:  eyJjb2RlIjogIjg0OTA5ODJlLTM0NWYtNDljNC04Y2Q0LTUwY2FlMjhiOWNlZSIsICJpZCI6ICIyOWI4YzUyNS1lZmM1LTQ5NTItOGQ4Yy03NzQyYTg1YmI1MmEifQ==\n"
     ]
    }
   ],
   "source": [
    "quilt.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tiled datasets for training neural network\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/W1_data\", path=rema)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/W2_data\", path=measuresiceflow)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/W3_data\", path=accumulation)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/X_data\", path=lores)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/Y_data\", path=hires)\n",
    "\n",
    "# Original datasets for neural network predictions on bigger area\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/lowres/bedmap2_bed\", path=\"lowres/bedmap2_bed.tif\"\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/REMA_100m_dem\", path=\"misc/REMA_100m_dem.tif\"\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/REMA_200m_dem_filled\",\n",
    "    path=\"misc/REMA_200m_dem_filled.tif\",\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/MEaSUREs_IceFlowSpeed_450m\",\n",
    "    path=\"misc/MEaSUREs_IceFlowSpeed_450m.tif\",\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/Arthern_accumulation_bedmap2_grid1\",\n",
    "    path=\"misc/Arthern_accumulation_bedmap2_grid1.tif\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching upload URLs from the registry...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0.00/6.74G [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading 13 fragments (6739537494 bytes)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 6.35G/6.74G [00:01<01:40, 3.91MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fragment 2b994ae9d13f6c01ce00c426f52c6dce0c4681f8c8aaf8a96608fd3d62f3a269 already uploaded; skipping.\n",
      "Fragment 28e2ca7656d61b0bc7f8f8c1db41914023e0cab1634e0ee645f38a87d894b416 already uploaded; skipping.\n",
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      "Fragment f750893861a1a268c8ffe0ba7db36c933223bbf5fcbb786ecef3f052b20f9b8a already uploaded; skipping.\n",
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      "Fragment 4a4efc3a84204c3d67887e8d7fa1186467b51e696451f2832ebbea3ca491c8a8 already uploaded; skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6.74G/6.74G [00:09<00:00, 688MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading package metadata...\n",
      "Updating the 'latest' tag...\n",
      "Push complete. weiji14/deepbedmap is live:\n",
      "https://quiltdata.com/package/weiji14/deepbedmap\n"
     ]
    }
   ],
   "source": [
    "quilt.push(package=\"weiji14/deepbedmap\", is_public=True)"
   ]
  }
 ],
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