{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calling the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from getpass import getuser  # Libaray to copy things\n",
    "from pathlib import Path  # Object oriented libary to deal with paths\n",
    "from tempfile import (\n",
    "    NamedTemporaryFile,\n",
    "    TemporaryDirectory,\n",
    ")  # Creating temporary Files/Dirs\n",
    "from dask.utils import format_bytes\n",
    "from distributed import (\n",
    "    Client,\n",
    "    progress,\n",
    "    wait,\n",
    ")  # Libaray to orchestrate distributed resources\n",
    "from dask_jobqueue import SLURMCluster  # Setting up distributed memories via slurm\n",
    "import numpy as np  # Pythons standard array library\n",
    "import xarray as xr  # Libary to work with labeled n-dimensional data\n",
    "import dask  # Distributed data libary\n",
    "import dask.distributed\n",
    "import multiprocessing\n",
    "from subprocess import run, PIPE\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(action=\"ignore\")\n",
    "import pandas as pd\n",
    "\n",
    "dask.config.config.get(\"distributed\").get(\"dashboard\").update(\n",
    "    {\"link\": \"{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Establish the number of task to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of CPUs: 48, number of threads: 24, number of workers: 2, processes: False\n"
     ]
    },
    {
     "data": {
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       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>inproc://136.172.50.55/32296/1</li>\n",
       "  <li><b>Dashboard: </b><a href='/user/m300876/preset//proxy/8787/status' target='_blank'>/user/m300876/preset//proxy/8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>2</li>\n",
       "  <li><b>Cores: </b>48</li>\n",
       "  <li><b>Memory: </b>256.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'inproc://136.172.50.55/32296/1' processes=2 threads=48, memory=256.00 GB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncpu = multiprocessing.cpu_count()\n",
    "processes = False\n",
    "nworker = 2\n",
    "threads = ncpu // nworker\n",
    "print(\n",
    "    f\"Number of CPUs: {ncpu}, number of threads: {threads}, number of workers: {nworker}, processes: {processes}\"\n",
    ")\n",
    "client = Client(\n",
    "    processes=processes,\n",
    "    threads_per_worker=threads,\n",
    "    n_workers=nworker,\n",
    "    memory_limit=\"128GB\",\n",
    ")\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Selecting data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a global pattern to find the files\n",
    "###cloud liquid water\n",
    "glob_pattern_3d1 = \"atm_2d_ml_2020\"\n",
    "## Define the paths of file 1 (dppp0016)\n",
    "data_path1 = Path(\"/work/mh0287/k203123/GIT/icon-aes-dyw2/experiments/dpp0029\")\n",
    "## Collect all file names with pathlib's rglob and list compression  -- cloud liquid water\n",
    "file_names1 = sorted([str(f) for f in data_path1.rglob(f\"*{glob_pattern_3d1}*.nc\")])[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Open data set_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset1 = xr.open_mfdataset(\n",
    "    file_names1, combine=\"by_coords\", chunks=\"auto\", engine=\"netcdf4\", parallel=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Dataset.info of <xarray.Dataset>\n",
       "Dimensions:  (ncells: 20971520, time: 466)\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.02e+07 2.02e+07 2.02e+07 ... 2.02e+07 2.02e+07\n",
       "Dimensions without coordinates: ncells\n",
       "Data variables:\n",
       "    ps       (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    psl      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsdt     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsut     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsutcs   (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rlut     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rlutcs   (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsds     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsdscs   (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rlds     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rldscs   (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsus     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rsuscs   (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    rlus     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    ts       (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    sic      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    sit      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    clt      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    prlr     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    prls     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    pr       (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    prw      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    cllvi    (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    clivi    (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    qgvi     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    qrvi     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    qsvi     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    hfls     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    hfss     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    evspsbl  (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    tauu     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    tauv     (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    sfcwind  (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    uas      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    vas      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    tas      (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:                  Climate Data Interface version 1.8.3rc (http://mpim...\n",
       "    Conventions:          CF-1.6\n",
       "    number_of_grid_used:  15\n",
       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/mp...\n",
       "    uuidOfHGrid:          0f1e7d66-637e-11e8-913b-51232bb4d8f9\n",
       "    title:                ICON simulation\n",
       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
       "    source:               git@gitlab.dkrz.de:icon/icon-aes.git@432d202fb95b6d...\n",
       "    history:              /work/mh0287/k203123/GIT/icon-aes-dyw2/bin/icon_dpp...\n",
       "    references:           see MPIM/DWD publications\n",
       "    comment:              Sapphire Dyamond (k203123) on m21281 (Linux 2.6.32-...>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset1.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.41 TB'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_bytes(dset1.nbytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Selecting variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_subset = dset1[[\"ts\", \"pr\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Dataset.info of <xarray.Dataset>\n",
       "Dimensions:  (ncells: 20971520, time: 466)\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.02e+07 2.02e+07 2.02e+07 ... 2.02e+07 2.02e+07\n",
       "Dimensions without coordinates: ncells\n",
       "Data variables:\n",
       "    ts       (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "    pr       (time, ncells) float32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:                  Climate Data Interface version 1.8.3rc (http://mpim...\n",
       "    Conventions:          CF-1.6\n",
       "    number_of_grid_used:  15\n",
       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/mp...\n",
       "    uuidOfHGrid:          0f1e7d66-637e-11e8-913b-51232bb4d8f9\n",
       "    title:                ICON simulation\n",
       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
       "    source:               git@gitlab.dkrz.de:icon/icon-aes.git@432d202fb95b6d...\n",
       "    history:              /work/mh0287/k203123/GIT/icon-aes-dyw2/bin/icon_dpp...\n",
       "    references:           see MPIM/DWD publications\n",
       "    comment:              Sapphire Dyamond (k203123) on m21281 (Linux 2.6.32-...>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_subset.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'78.18 GB'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_bytes(dset_subset.nbytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Changing time_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 466)&gt;\n",
       "array([20200120.  , 20200120.25, 20200120.5 , ..., 20201229.  , 20201230.  ,\n",
       "       20201231.  ])\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.02e+07 2.02e+07 2.02e+07 ... 2.02e+07 2.02e+07\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    units:          day as %Y%m%d.%f\n",
       "    calendar:       proleptic_gregorian\n",
       "    axis:           T</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 466</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7ea9c308-41ff-4e63-8568-877838b65d7f' class='xr-array-in' type='checkbox' checked><label for='section-7ea9c308-41ff-4e63-8568-877838b65d7f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.02e+07 2.02e+07 2.02e+07 2.02e+07 ... 2.02e+07 2.02e+07 2.02e+07</span></div><div class='xr-array-data'><pre>array([20200120.  , 20200120.25, 20200120.5 , ..., 20201229.  , 20201230.  ,\n",
       "       20201231.  ])</pre></div></div></li><li class='xr-section-item'><input id='section-b19d3e3a-1a33-4343-9122-31be5056968e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b19d3e3a-1a33-4343-9122-31be5056968e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.02e+07 2.02e+07 ... 2.02e+07</div><input id='attrs-3a0e17d6-4c1c-47c3-8087-412c1c07f698' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a0e17d6-4c1c-47c3-8087-412c1c07f698' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa9359ee-5ed8-4417-b51d-811c054d13bc' class='xr-var-data-in' type='checkbox'><label for='data-fa9359ee-5ed8-4417-b51d-811c054d13bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([20200120.  , 20200120.25, 20200120.5 , ..., 20201229.  , 20201230.  ,\n",
       "       20201231.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d4b8ee9-3a23-46ee-87ed-d712e6a61b8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d4b8ee9-3a23-46ee-87ed-d712e6a61b8e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 466)>\n",
       "array([20200120.  , 20200120.25, 20200120.5 , ..., 20201229.  , 20201230.  ,\n",
       "       20201231.  ])\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.02e+07 2.02e+07 2.02e+07 ... 2.02e+07 2.02e+07\n",
       "Attributes:\n",
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       "    units:          day as %Y%m%d.%f\n",
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       "    axis:           T"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_subset.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "time1 = pd.to_datetime(dset_subset.time.data, format=\"%Y%m%d\")\n",
    "hours = (dset_subset.time.values % 1) * 24\n",
    "time2 = pd.to_datetime(hours, format=\"%H\")\n",
    "timenew = pd.to_datetime(pd.to_numeric(time2 - time2[0]) + pd.to_numeric(time1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_subset[\"time\"] = timenew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 466)&gt;\n",
       "array([&#x27;2020-01-20T00:00:00.000000000&#x27;, &#x27;2020-01-20T06:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-20T12:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-20 2020-01-20T06:00:00 ... 2020-12-31</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 466</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d2cb354d-b920-4c5f-8c43-0659bbe4d5d3' class='xr-array-in' type='checkbox' checked><label for='section-d2cb354d-b920-4c5f-8c43-0659bbe4d5d3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2020-01-20 2020-01-20T06:00:00 ... 2020-12-30 2020-12-31</span></div><div class='xr-array-data'><pre>array([&#x27;2020-01-20T00:00:00.000000000&#x27;, &#x27;2020-01-20T06:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-20T12:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-17a5966d-964d-4af5-9fcb-d694e56a8438' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17a5966d-964d-4af5-9fcb-d694e56a8438' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-20 ... 2020-12-31</div><input id='attrs-165d55bb-2be2-424a-904e-209398f19cd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-165d55bb-2be2-424a-904e-209398f19cd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b30dcdd7-64a0-497f-bdca-a9a220c1527a' class='xr-var-data-in' type='checkbox'><label for='data-b30dcdd7-64a0-497f-bdca-a9a220c1527a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-20T00:00:00.000000000&#x27;, &#x27;2020-01-20T06:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-20T12:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-542b4d07-5e97-4d42-a721-ecc481e19110' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-542b4d07-5e97-4d42-a721-ecc481e19110' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 466)>\n",
       "array(['2020-01-20T00:00:00.000000000', '2020-01-20T06:00:00.000000000',\n",
       "       '2020-01-20T12:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',\n",
       "       '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-20 2020-01-20T06:00:00 ... 2020-12-31"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_subset.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Daily mean_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_daily = (\n",
    "    dset_subset.sel(time=slice(\"2020-02-10\", \"2020-03-10\"))\n",
    "    .resample(time=\"1D\", skipna=True)\n",
    "    .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (ncells: 20971520, time: 30)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-02-10 2020-02-11 ... 2020-03-10\n",
       "Dimensions without coordinates: ncells\n",
       "Data variables:\n",
       "    ts       (time, ncells) float32 dask.array&lt;chunksize=(1, 5242880), meta=np.ndarray&gt;\n",
       "    pr       (time, ncells) float32 dask.array&lt;chunksize=(1, 5242880), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c41e87b1-5b95-4c0c-89bf-3f81d812e975' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c41e87b1-5b95-4c0c-89bf-3f81d812e975' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>ncells</span>: 20971520</li><li><span class='xr-has-index'>time</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d3c15484-5171-488f-99c6-f908cc8d6f4a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3c15484-5171-488f-99c6-f908cc8d6f4a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-10 ... 2020-03-10</div><input id='attrs-1981492f-3c33-4181-8985-59b89a9695e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1981492f-3c33-4181-8985-59b89a9695e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cb3c133-1e53-499a-9e9e-0cc18945bb97' class='xr-var-data-in' type='checkbox'><label for='data-3cb3c133-1e53-499a-9e9e-0cc18945bb97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-10T00:00:00.000000000&#x27;, &#x27;2020-02-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-12T00:00:00.000000000&#x27;, &#x27;2020-02-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-14T00:00:00.000000000&#x27;, &#x27;2020-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-16T00:00:00.000000000&#x27;, &#x27;2020-02-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-18T00:00:00.000000000&#x27;, &#x27;2020-02-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-20T00:00:00.000000000&#x27;, &#x27;2020-02-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-26T00:00:00.000000000&#x27;, &#x27;2020-02-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-28T00:00:00.000000000&#x27;, &#x27;2020-02-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-03-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-03T00:00:00.000000000&#x27;, &#x27;2020-03-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-05T00:00:00.000000000&#x27;, &#x27;2020-03-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-07T00:00:00.000000000&#x27;, &#x27;2020-03-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-09T00:00:00.000000000&#x27;, &#x27;2020-03-10T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55440329-45f0-4412-96d9-b3f2ad31ebdd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55440329-45f0-4412-96d9-b3f2ad31ebdd' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5242880), meta=np.ndarray&gt;</div><input id='attrs-c97c9045-8be2-453b-a02f-44ee80bf43db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c97c9045-8be2-453b-a02f-44ee80bf43db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f7d75b-dfb7-4c6d-915a-49b254ac5bb0' class='xr-var-data-in' type='checkbox'><label for='data-50f7d75b-dfb7-4c6d-915a-49b254ac5bb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (ncells: 20971520, time: 30)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-02-10 2020-02-11 ... 2020-03-10\n",
       "Dimensions without coordinates: ncells\n",
       "Data variables:\n",
       "    ts       (time, ncells) float32 dask.array<chunksize=(1, 5242880), meta=np.ndarray>\n",
       "    pr       (time, ncells) float32 dask.array<chunksize=(1, 5242880), meta=np.ndarray>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "prmean = dset_daily.pr.mean(dim=\"time\").compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'83.89 MB'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_bytes(prmean.nbytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsmean = dset_daily.ts.mean(dim=\"time\").compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'83.89 MB'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "format_bytes(tsmean.nbytes)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Assign coordinates_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "####grid information\n",
    "filegrid = \"/pool/data/ICON/grids/public/mpim/0015/icon_grid_0015_R02B09_G.nc\"\n",
    "dsetgrid = xr.open_dataset(filegrid, chunks=\"auto\", engine=\"netcdf4\").rename(\n",
    "    {\"cell\": \"ncells\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                         (cell_grf: 14, edge: 31457280, edge_grf: 24, max_chdom: 1, max_stored_decompositions: 4, nc: 2, ncells: 20971520, ne: 6, no: 4, nv: 3, two_grf: 2, vert_grf: 13, vertex: 10485762)\n",
       "Coordinates:\n",
       "    clon                            (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    clat                            (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    vlon                            (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    vlat                            (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    elon                            (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    elat                            (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: cell_grf, edge, edge_grf, max_chdom, max_stored_decompositions, nc, ncells, ne, no, nv, two_grf, vert_grf, vertex\n",
       "Data variables:\n",
       "    clon_vertices                   (ncells, nv) float64 dask.array&lt;chunksize=(5242880, 3), meta=np.ndarray&gt;\n",
       "    clat_vertices                   (ncells, nv) float64 dask.array&lt;chunksize=(5242880, 3), meta=np.ndarray&gt;\n",
       "    vlon_vertices                   (vertex, ne) float64 dask.array&lt;chunksize=(1747627, 6), meta=np.ndarray&gt;\n",
       "    vlat_vertices                   (vertex, ne) float64 dask.array&lt;chunksize=(1747627, 6), meta=np.ndarray&gt;\n",
       "    elon_vertices                   (edge, no) float64 dask.array&lt;chunksize=(3932160, 4), meta=np.ndarray&gt;\n",
       "    elat_vertices                   (edge, no) float64 dask.array&lt;chunksize=(3932160, 4), meta=np.ndarray&gt;\n",
       "    ifs2icon_cell_grid              (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    ifs2icon_edge_grid              (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    ifs2icon_vertex_grid            (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    cell_area                       (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    dual_area                       (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    phys_cell_id                    (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    phys_edge_id                    (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    lon_cell_centre                 (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    lat_cell_centre                 (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    lat_cell_barycenter             (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    lon_cell_barycenter             (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    longitude_vertices              (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    latitude_vertices               (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    lon_edge_centre                 (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    lat_edge_centre                 (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_of_cell                    (nv, ncells) int32 dask.array&lt;chunksize=(3, 10485760), meta=np.ndarray&gt;\n",
       "    vertex_of_cell                  (nv, ncells) int32 dask.array&lt;chunksize=(3, 10485760), meta=np.ndarray&gt;\n",
       "    adjacent_cell_of_edge           (nc, edge) int32 dask.array&lt;chunksize=(2, 15728640), meta=np.ndarray&gt;\n",
       "    edge_vertices                   (nc, edge) int32 dask.array&lt;chunksize=(2, 15728640), meta=np.ndarray&gt;\n",
       "    cells_of_vertex                 (ne, vertex) int32 dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;\n",
       "    edges_of_vertex                 (ne, vertex) int32 dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;\n",
       "    vertices_of_vertex              (ne, vertex) int32 dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;\n",
       "    cell_area_p                     (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    cell_elevation                  (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    cell_sea_land_mask              (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    cell_domain_id                  (ncells, max_stored_decompositions) int32 dask.array&lt;chunksize=(5242880, 4), meta=np.ndarray&gt;\n",
       "    cell_no_of_domains              (max_stored_decompositions) int32 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
       "    dual_area_p                     (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    edge_length                     (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_cell_distance              (nc, edge) float64 dask.array&lt;chunksize=(2, 7864320), meta=np.ndarray&gt;\n",
       "    dual_edge_length                (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edgequad_area                   (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_elevation                  (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_sea_land_mask              (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    edge_vert_distance              (nc, edge) float64 dask.array&lt;chunksize=(2, 7864320), meta=np.ndarray&gt;\n",
       "    zonal_normal_primal_edge        (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    meridional_normal_primal_edge   (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    zonal_normal_dual_edge          (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    meridional_normal_dual_edge     (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    orientation_of_normal           (nv, ncells) int32 dask.array&lt;chunksize=(3, 10485760), meta=np.ndarray&gt;\n",
       "    cell_index                      (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    parent_cell_index               (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    parent_cell_type                (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    neighbor_cell_index             (nv, ncells) int32 dask.array&lt;chunksize=(3, 10485760), meta=np.ndarray&gt;\n",
       "    child_cell_index                (no, ncells) int32 dask.array&lt;chunksize=(4, 5242880), meta=np.ndarray&gt;\n",
       "    child_cell_id                   (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    edge_index                      (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    edge_parent_type                (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    vertex_index                    (vertex) int32 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    edge_orientation                (ne, vertex) int32 dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;\n",
       "    edge_system_orientation         (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    refin_c_ctrl                    (ncells) int32 dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;\n",
       "    index_c_list                    (two_grf, cell_grf) int32 dask.array&lt;chunksize=(2, 14), meta=np.ndarray&gt;\n",
       "    start_idx_c                     (max_chdom, cell_grf) int32 dask.array&lt;chunksize=(1, 14), meta=np.ndarray&gt;\n",
       "    end_idx_c                       (max_chdom, cell_grf) int32 dask.array&lt;chunksize=(1, 14), meta=np.ndarray&gt;\n",
       "    refin_e_ctrl                    (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    index_e_list                    (two_grf, edge_grf) int32 dask.array&lt;chunksize=(2, 24), meta=np.ndarray&gt;\n",
       "    start_idx_e                     (max_chdom, edge_grf) int32 dask.array&lt;chunksize=(1, 24), meta=np.ndarray&gt;\n",
       "    end_idx_e                       (max_chdom, edge_grf) int32 dask.array&lt;chunksize=(1, 24), meta=np.ndarray&gt;\n",
       "    refin_v_ctrl                    (vertex) int32 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    index_v_list                    (two_grf, vert_grf) int32 dask.array&lt;chunksize=(2, 13), meta=np.ndarray&gt;\n",
       "    start_idx_v                     (max_chdom, vert_grf) int32 dask.array&lt;chunksize=(1, 13), meta=np.ndarray&gt;\n",
       "    end_idx_v                       (max_chdom, vert_grf) int32 dask.array&lt;chunksize=(1, 13), meta=np.ndarray&gt;\n",
       "    parent_edge_index               (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    child_edge_index                (no, edge) int32 dask.array&lt;chunksize=(4, 7864320), meta=np.ndarray&gt;\n",
       "    child_edge_id                   (edge) int32 dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;\n",
       "    parent_vertex_index             (vertex) int32 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    cartesian_x_vertices            (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    cartesian_y_vertices            (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    cartesian_z_vertices            (vertex) float64 dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;\n",
       "    edge_middle_cartesian_x         (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_middle_cartesian_y         (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_middle_cartesian_z         (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_dual_middle_cartesian_x    (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_dual_middle_cartesian_y    (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_dual_middle_cartesian_z    (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_primal_normal_cartesian_x  (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_primal_normal_cartesian_y  (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_primal_normal_cartesian_z  (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_dual_normal_cartesian_x    (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_dual_normal_cartesian_y    (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    edge_dual_normal_cartesian_z    (edge) float64 dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;\n",
       "    cell_circumcenter_cartesian_x   (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    cell_circumcenter_cartesian_y   (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "    cell_circumcenter_cartesian_z   (ncells) float64 dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    title:                    ICON grid description\n",
       "    institution:              Max Planck Institute for Meteorology/Deutscher ...\n",
       "    source:                   git@git.mpimet.mpg.de:GridGenerator.git\n",
       "    revision:                 d00fcac1f61fa16c686bfe51d1d8eddd09296cb5\n",
       "    date:                     20180529 at 222250\n",
       "    user_name:                Rene Redler (m300083)\n",
       "    os_name:                  Linux 2.6.32-696.18.7.el6.x86_64 x86_64\n",
       "    uuidOfHGrid:              0f1e7d66-637e-11e8-913b-51232bb4d8f9\n",
       "    grid_mapping_name:        lat_long_on_sphere\n",
       "    crs_id:                   urn:ogc:def:cs:EPSG:6.0:6422\n",
       "    crs_name:                 Spherical 2D Coordinate System\n",
       "    ellipsoid_name:           Sphere\n",
       "    semi_major_axis:          6371229.0\n",
       "    inverse_flattening:       0.0\n",
       "    grid_level:               9\n",
       "    grid_root:                2\n",
       "    grid_ID:                  1\n",
       "    parent_grid_ID:           0\n",
       "    no_of_subgrids:           1\n",
       "    start_subgrid_id:         1\n",
       "    max_childdom:             1\n",
       "    boundary_depth_index:     0\n",
       "    rotation_vector:          [0. 0. 0.]\n",
       "    grid_geometry:            1\n",
       "    grid_cell_type:           3\n",
       "    mean_edge_length:         7510.64679407352\n",
       "    mean_dual_edge_length:    4336.344345177032\n",
       "    mean_cell_area:           24323517.809282698\n",
       "    mean_dual_cell_area:      48647026.33989711\n",
       "    domain_length:            40031612.44147649\n",
       "    domain_height:            40031612.44147649\n",
       "    sphere_radius:            6371229.0\n",
       "    domain_cartesian_center:  [0. 0. 0.]\n",
       "    centre:                   252\n",
       "    rotation:                 37deg around z-axis\n",
       "    coverage:                 global\n",
       "    symmetry:                 along equator\n",
       "    topography:               modified SRTM30\n",
       "    subcentre:                1\n",
       "    number_of_grid_used:      15\n",
       "    history:                  Thu Aug 16 11:05:44 2018: ncatted -O -a ICON_gr...\n",
       "    ICON_grid_file_uri:       http://icon-downloads.mpimet.mpg.de/grids/publi...\n",
       "    NCO:                      netCDF Operators version 4.7.5 (Homepage = http...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bc258265-6ac7-45c8-9382-22c2fce7aa33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc258265-6ac7-45c8-9382-22c2fce7aa33' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell_grf</span>: 14</li><li><span>edge</span>: 31457280</li><li><span>edge_grf</span>: 24</li><li><span>max_chdom</span>: 1</li><li><span>max_stored_decompositions</span>: 4</li><li><span>nc</span>: 2</li><li><span>ncells</span>: 20971520</li><li><span>ne</span>: 6</li><li><span>no</span>: 4</li><li><span>nv</span>: 3</li><li><span>two_grf</span>: 2</li><li><span>vert_grf</span>: 13</li><li><span>vertex</span>: 10485762</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-71c57c7d-266f-49ee-9728-cc593424a206' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71c57c7d-266f-49ee-9728-cc593424a206' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-7dc7c5e7-d792-4f4a-a858-13e6907fb213' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dc7c5e7-d792-4f4a-a858-13e6907fb213' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbb9eef1-ef72-4b42-9385-1466500c4193' class='xr-var-data-in' type='checkbox'><label for='data-cbb9eef1-ef72-4b42-9385-1466500c4193' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>bounds :</span></dt><dd>clon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-5e4a4480-9cfb-4d60-a3d4-969c44d61236' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e4a4480-9cfb-4d60-a3d4-969c44d61236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3776e980-e55c-4551-82b6-20d3f112fdbe' class='xr-var-data-in' type='checkbox'><label for='data-3776e980-e55c-4551-82b6-20d3f112fdbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>bounds :</span></dt><dd>clat_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vlon</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;</div><input id='attrs-45f655b2-e87a-47ec-a8e4-35d939581c9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45f655b2-e87a-47ec-a8e4-35d939581c9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35167b7b-7883-4b7e-aef5-88e70451d034' class='xr-var-data-in' type='checkbox'><label for='data-35167b7b-7883-4b7e-aef5-88e70451d034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertex longitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>bounds :</span></dt><dd>vlon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 83.89 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (10485762,) </td> <td> (10485762,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vlat</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;</div><input id='attrs-1622252c-58a7-4290-82bb-63da48dbb16b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1622252c-58a7-4290-82bb-63da48dbb16b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a0b0082-dafc-4c11-aeeb-ec65d66f9a2e' class='xr-var-data-in' type='checkbox'><label for='data-9a0b0082-dafc-4c11-aeeb-ec65d66f9a2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertex latitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>bounds :</span></dt><dd>vlat_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 83.89 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (10485762,) </td> <td> (10485762,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elon</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;</div><input id='attrs-b94275ba-3899-42dd-b312-1558c8f13583' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b94275ba-3899-42dd-b312-1558c8f13583' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02416c98-11eb-499f-9fe3-ad068270fb81' class='xr-var-data-in' type='checkbox'><label for='data-02416c98-11eb-499f-9fe3-ad068270fb81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>edge midpoint longitude</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>bounds :</span></dt><dd>elon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (31457280,) </td> <td> (15728640,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elat_vertices</span></div><div class='xr-var-dims'>(edge, no)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3932160, 4), meta=np.ndarray&gt;</div><input id='attrs-380109d9-acdd-4f76-a550-b844db10fc1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-380109d9-acdd-4f76-a550-b844db10fc1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a5c7136-e13f-4b43-a14d-f0b4c84729e6' class='xr-var-data-in' type='checkbox'><label for='data-1a5c7136-e13f-4b43-a14d-f0b4c84729e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  <thead>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ifs2icon_cell_grid</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-9d6e0061-c930-4a14-a04f-373f25ecea61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d6e0061-c930-4a14-a04f-373f25ecea61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128b7964-a94b-4676-8e8d-730cb6d13074' class='xr-var-data-in' type='checkbox'><label for='data-128b7964-a94b-4676-8e8d-730cb6d13074' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ifs to icon cells</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ifs2icon_edge_grid</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;</div><input id='attrs-3b1512e1-6a67-424d-8ac8-ffb55298ca91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b1512e1-6a67-424d-8ac8-ffb55298ca91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e9de480-e9c0-4ea6-8b59-6d7db9902edb' class='xr-var-data-in' type='checkbox'><label for='data-4e9de480-e9c0-4ea6-8b59-6d7db9902edb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ifs to icon edge</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (31457280,) </td> <td> (15728640,) </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ifs2icon_vertex_grid</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;</div><input id='attrs-90cb7b5b-038a-45c2-8873-7c49a9e313dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90cb7b5b-038a-45c2-8873-7c49a9e313dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7795ae-b2ab-4f76-8854-7c11e663b607' class='xr-var-data-in' type='checkbox'><label for='data-1a7795ae-b2ab-4f76-8854-7c11e663b607' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ifs to icon vertex</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
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       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_area</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-a7c5e9df-10b8-4b7b-83d2-8b8d075a436b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7c5e9df-10b8-4b7b-83d2-8b8d075a436b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28ff2ff9-3511-4950-bab1-5ad7269a1ed4' class='xr-var-data-in' type='checkbox'><label for='data-28ff2ff9-3511-4950-bab1-5ad7269a1ed4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>area of grid cell</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>standard_name :</span></dt><dd>area</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dual_area</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;</div><input id='attrs-34ebbc40-6534-480f-9cd4-dbbf98127cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ebbc40-6534-480f-9cd4-dbbf98127cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ba014dc-a999-4e93-b3b7-ca30a8553b59' class='xr-var-data-in' type='checkbox'><label for='data-1ba014dc-a999-4e93-b3b7-ca30a8553b59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>areas of dual hexagonal/pentagonal cells</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>standard_name :</span></dt><dd>area</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phys_cell_id</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;</div><input id='attrs-8579817d-9e6d-4ec8-b0f0-5ed0bceede2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8579817d-9e6d-4ec8-b0f0-5ed0bceede2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4aee055-bf48-4157-a357-c54050e10f5f' class='xr-var-data-in' type='checkbox'><label for='data-f4aee055-bf48-4157-a357-c54050e10f5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>physical domain ID of cell</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phys_edge_id</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;</div><input id='attrs-e36ec1e8-4310-4728-8a68-8e547c20a1ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e36ec1e8-4310-4728-8a68-8e547c20a1ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90f13158-878a-41b6-9605-82d7607a6f49' class='xr-var-data-in' type='checkbox'><label for='data-90f13158-878a-41b6-9605-82d7607a6f49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>physical domain ID of edge</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_cell_centre</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-060bba69-d3ba-431d-8897-4b79b60d1804' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-060bba69-d3ba-431d-8897-4b79b60d1804' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adeff168-94d0-4bf8-8b0c-1902692fd7de' class='xr-var-data-in' type='checkbox'><label for='data-adeff168-94d0-4bf8-8b0c-1902692fd7de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of cell centre</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_cell_centre</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-bdbe153f-80e5-4a34-b646-abd7e3c24be4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdbe153f-80e5-4a34-b646-abd7e3c24be4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beee32a1-68cf-40d0-bec0-a2954f952aa7' class='xr-var-data-in' type='checkbox'><label for='data-beee32a1-68cf-40d0-bec0-a2954f952aa7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of cell centre</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_cell_barycenter</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-26ffd8c8-90a8-4ca9-9b6c-0a936cafb7dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26ffd8c8-90a8-4ca9-9b6c-0a936cafb7dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1425479-7730-4fcb-b6a3-8f508db38f12' class='xr-var-data-in' type='checkbox'><label for='data-e1425479-7730-4fcb-b6a3-8f508db38f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of cell barycenter</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_cell_barycenter</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-2cef1832-4796-45ba-8fa2-33236a517c36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cef1832-4796-45ba-8fa2-33236a517c36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e697afd7-804d-4760-9aa0-3fa1abb44b08' class='xr-var-data-in' type='checkbox'><label for='data-e697afd7-804d-4760-9aa0-3fa1abb44b08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of cell barycenter</dd><dt><span>units :</span></dt><dd>radian</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_vertices</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;</div><input id='attrs-089dd632-07d0-476f-8278-b3aed338b7f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-089dd632-07d0-476f-8278-b3aed338b7f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac5207d7-ca7d-46bf-8c7f-031241382638' class='xr-var-data-in' type='checkbox'><label for='data-ac5207d7-ca7d-46bf-8c7f-031241382638' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of vertices</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_vertices</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485762,), meta=np.ndarray&gt;</div><input id='attrs-897a2fe9-81e1-4eb1-80ae-c960ce0d7aca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-897a2fe9-81e1-4eb1-80ae-c960ce0d7aca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da070845-ade3-415e-b87d-a89031417941' class='xr-var-data-in' type='checkbox'><label for='data-da070845-ade3-415e-b87d-a89031417941' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of vertices</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_of_cell</span></div><div class='xr-var-dims'>(nv, ncells)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 10485760), meta=np.ndarray&gt;</div><input id='attrs-e2b6210c-5170-4f38-943a-b10026564eb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2b6210c-5170-4f38-943a-b10026564eb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94f1e58a-e56d-4fae-9f5a-bd1aba0d0a45' class='xr-var-data-in' type='checkbox'><label for='data-94f1e58a-e56d-4fae-9f5a-bd1aba0d0a45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>edges of each cellvertices </dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertex_of_cell</span></div><div class='xr-var-dims'>(nv, ncells)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 10485760), meta=np.ndarray&gt;</div><input id='attrs-d4973ced-4cb6-4bd4-94cf-491da5955a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4973ced-4cb6-4bd4-94cf-491da5955a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25da34ca-ba6a-422c-bb24-88d812eda107' class='xr-var-data-in' type='checkbox'><label for='data-25da34ca-ba6a-422c-bb24-88d812eda107' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertices of each cellcells ad</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjacent_cell_of_edge</span></div><div class='xr-var-dims'>(nc, edge)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 15728640), meta=np.ndarray&gt;</div><input id='attrs-82c2c1cb-b32d-4c4d-8876-48b0732ede8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82c2c1cb-b32d-4c4d-8876-48b0732ede8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d099e0fe-071c-41c9-be0e-5285766c15cc' class='xr-var-data-in' type='checkbox'><label for='data-d099e0fe-071c-41c9-be0e-5285766c15cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cells adjacent to each edge</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_vertices</span></div><div class='xr-var-dims'>(nc, edge)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 15728640), meta=np.ndarray&gt;</div><input id='attrs-ac8e8add-aba0-4e57-bd5b-75f15382cd90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac8e8add-aba0-4e57-bd5b-75f15382cd90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2e97f3f-33b3-42de-a17d-ba3704fcc1c8' class='xr-var-data-in' type='checkbox'><label for='data-c2e97f3f-33b3-42de-a17d-ba3704fcc1c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertices at the end of of each edge</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Shape </th><td> (2, 31457280) </td> <td> (2, 15728640) </td></tr>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cells_of_vertex</span></div><div class='xr-var-dims'>(ne, vertex)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;</div><input id='attrs-c8efe789-fa0c-4c10-91fc-09dee7436599' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8efe789-fa0c-4c10-91fc-09dee7436599' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c169e555-0119-495b-996f-e11457dff71d' class='xr-var-data-in' type='checkbox'><label for='data-c169e555-0119-495b-996f-e11457dff71d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cells around each vertex</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (6, 10485762) </td> <td> (6, 5242881) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edges_of_vertex</span></div><div class='xr-var-dims'>(ne, vertex)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;</div><input id='attrs-0c48d9e2-2cc6-4574-b514-aec7db2e9c39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c48d9e2-2cc6-4574-b514-aec7db2e9c39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7420c2f0-cd19-482d-ad1e-3111ac13d7ea' class='xr-var-data-in' type='checkbox'><label for='data-7420c2f0-cd19-482d-ad1e-3111ac13d7ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>edges around each vertex</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (6, 10485762) </td> <td> (6, 5242881) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_of_vertex</span></div><div class='xr-var-dims'>(ne, vertex)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 5242881), meta=np.ndarray&gt;</div><input id='attrs-470c093b-e37b-4de1-ad27-6725b2f97ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-470c093b-e37b-4de1-ad27-6725b2f97ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce04af3f-4061-452d-9f12-d814bbc40d35' class='xr-var-data-in' type='checkbox'><label for='data-ce04af3f-4061-452d-9f12-d814bbc40d35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertices around each vertex</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (6, 10485762) </td> <td> (6, 5242881) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_area_p</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-c6e2ec94-371a-450a-9d71-fa9960dad1dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6e2ec94-371a-450a-9d71-fa9960dad1dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f01685-25a3-4cc2-8f50-25255eb7a127' class='xr-var-data-in' type='checkbox'><label for='data-50f01685-25a3-4cc2-8f50-25255eb7a127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>area of grid cell</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_elevation</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-9db126ec-4fea-43f5-b365-6591683ee2e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9db126ec-4fea-43f5-b365-6591683ee2e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3d46c60-3a5b-43e5-87c6-fec9b1c1f8b6' class='xr-var-data-in' type='checkbox'><label for='data-d3d46c60-3a5b-43e5-87c6-fec9b1c1f8b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>elevation at the cell centers</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_sea_land_mask</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;</div><input id='attrs-03925a97-1fd0-4435-b5bd-d458fe80ad47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03925a97-1fd0-4435-b5bd-d458fe80ad47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e27952-2de4-4aa2-966d-471e1fb6d2eb' class='xr-var-data-in' type='checkbox'><label for='data-96e27952-2de4-4aa2-966d-471e1fb6d2eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea (-2 inner, -1 boundary) land (2 inner, 1 boundary) mask for the cell</dd><dt><span>units :</span></dt><dd>2,1,-1,-</dd><dt><span>grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 83.89 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (20971520,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_domain_id</span></div><div class='xr-var-dims'>(ncells, max_stored_decompositions)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5242880, 4), meta=np.ndarray&gt;</div><input id='attrs-54f01c24-4081-4a2d-9e8a-918eabb3ae37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f01c24-4081-4a2d-9e8a-918eabb3ae37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dfafea6-ab6a-4cf1-a6d3-0c740986ca25' class='xr-var-data-in' type='checkbox'><label for='data-1dfafea6-ab6a-4cf1-a6d3-0c740986ca25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell domain id for decomposition</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_length</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;</div><input id='attrs-927771f2-bbd5-473c-8952-edfb74b292d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-927771f2-bbd5-473c-8952-edfb74b292d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e0d9a8a-a540-47f6-bf48-5419add9542f' class='xr-var-data-in' type='checkbox'><label for='data-0e0d9a8a-a540-47f6-bf48-5419add9542f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lengths of edges of triangular cells</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_cell_distance</span></div><div class='xr-var-dims'>(nc, edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 7864320), meta=np.ndarray&gt;</div><input id='attrs-39f34d44-b1dc-4ec8-a39b-8f1e94b569f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39f34d44-b1dc-4ec8-a39b-8f1e94b569f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0b13d2c-6d62-4966-ba34-2d1a24726ba5' class='xr-var-data-in' type='checkbox'><label for='data-d0b13d2c-6d62-4966-ba34-2d1a24726ba5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>distances between edge midpoint and adjacent triangle midpoints</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dual_edge_length</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;</div><input id='attrs-a451df66-7a96-4ac2-b960-7af2ac9fbca5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a451df66-7a96-4ac2-b960-7af2ac9fbca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9ffef76-1c68-472d-89b7-f1238ec972cd' class='xr-var-data-in' type='checkbox'><label for='data-f9ffef76-1c68-472d-89b7-f1238ec972cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lengths of dual edges (distances between triangular cell circumcenters)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_vert_distance</span></div><div class='xr-var-dims'>(nc, edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 7864320), meta=np.ndarray&gt;</div><input id='attrs-9fe70bb5-a978-4195-a5f7-ce3f7301d98b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fe70bb5-a978-4195-a5f7-ce3f7301d98b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b16b70e2-8c88-47b5-82cf-936d62bfcfae' class='xr-var-data-in' type='checkbox'><label for='data-b16b70e2-8c88-47b5-82cf-936d62bfcfae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>distances between edge midpoint and vertices of that edge</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>child_cell_id</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20971520,), meta=np.ndarray&gt;</div><input id='attrs-c88779a1-5fcc-4bd7-a391-9b167c0dea5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c88779a1-5fcc-4bd7-a391-9b167c0dea5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52394ff1-8aa3-49df-951d-dacb0c380ec3' class='xr-var-data-in' type='checkbox'><label for='data-52394ff1-8aa3-49df-951d-dacb0c380ec3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>domain ID of child cell</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_system_orientation</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;</div><input id='attrs-5a540603-1c93-4fa5-bdb3-5c46a56da624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a540603-1c93-4fa5-bdb3-5c46a56da624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-072836a8-8c5e-4911-ae15-bec8438a7319' class='xr-var-data-in' type='checkbox'><label for='data-072836a8-8c5e-4911-ae15-bec8438a7319' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>edge system orientation</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_c_list</span></div><div class='xr-var-dims'>(two_grf, cell_grf)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 14), meta=np.ndarray&gt;</div><input id='attrs-3340d2db-b0f7-4701-917d-1fcc9d997534' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3340d2db-b0f7-4701-917d-1fcc9d997534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a30a69f2-6fb3-4e32-9fce-fd698caa1550' class='xr-var-data-in' type='checkbox'><label for='data-a30a69f2-6fb3-4e32-9fce-fd698caa1550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>list of start and end indices for each refinement control level for cells</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_v_list</span></div><div class='xr-var-dims'>(two_grf, vert_grf)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 13), meta=np.ndarray&gt;</div><input id='attrs-f2f8e77e-4046-4ec6-bbd8-178af8192635' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2f8e77e-4046-4ec6-bbd8-178af8192635' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-754ae516-7411-4d8f-b182-6d9111a57bb9' class='xr-var-data-in' type='checkbox'><label for='data-754ae516-7411-4d8f-b182-6d9111a57bb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>list of start and end indices for each refinement control level for vertices</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>child_edge_id</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31457280,), meta=np.ndarray&gt;</div><input id='attrs-10aa70fa-6135-4b86-91b8-f160acb6868d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10aa70fa-6135-4b86-91b8-f160acb6868d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a54b20b4-754f-4138-a61a-2eff7b4049f3' class='xr-var-data-in' type='checkbox'><label for='data-a54b20b4-754f-4138-a61a-2eff7b4049f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>domain ID of child edge</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "<tr>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_dual_normal_cartesian_y</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;</div><input id='attrs-b37fcccb-a187-45bc-b078-76ca191ee153' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b37fcccb-a187-45bc-b078-76ca191ee153' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4cbd542-f6c8-46bf-9913-36e6c93ee34a' class='xr-var-data-in' type='checkbox'><label for='data-d4cbd542-f6c8-46bf-9913-36e6c93ee34a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>unit normal to the dual edge 3D vector, coordinate y</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (31457280,) </td> <td> (15728640,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edge_dual_normal_cartesian_z</span></div><div class='xr-var-dims'>(edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(15728640,), meta=np.ndarray&gt;</div><input id='attrs-03f290b5-e13c-4df0-aa1a-00c1c0ffecb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03f290b5-e13c-4df0-aa1a-00c1c0ffecb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63104f53-d97d-4365-ba2b-96c540f71182' class='xr-var-data-in' type='checkbox'><label for='data-63104f53-d97d-4365-ba2b-96c540f71182' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>unit normal to the dual edge 3D vector, coordinate z</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 251.66 MB </td> <td> 125.83 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (31457280,) </td> <td> (15728640,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_circumcenter_cartesian_x</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-e4cc8638-6d4c-4c4b-a960-2b4587ccfff1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4cc8638-6d4c-4c4b-a960-2b4587ccfff1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bc08079-5af9-4fd9-aea9-7939fedb1b13' class='xr-var-data-in' type='checkbox'><label for='data-1bc08079-5af9-4fd9-aea9-7939fedb1b13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cartesian position of the prime cell circumcenter on the unit sphere, coordinate x</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_circumcenter_cartesian_y</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-fb99051e-64eb-4a4c-ac6e-ab1213323df1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb99051e-64eb-4a4c-ac6e-ab1213323df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3fd7a6d-0e78-4403-80f1-22092e312028' class='xr-var-data-in' type='checkbox'><label for='data-e3fd7a6d-0e78-4403-80f1-22092e312028' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cartesian position of the prime cell circumcenter on the unit sphere, coordinate y</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cell_circumcenter_cartesian_z</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10485760,), meta=np.ndarray&gt;</div><input id='attrs-4882e2f8-b6de-4d3f-8e7f-1c12d4038e41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4882e2f8-b6de-4d3f-8e7f-1c12d4038e41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350b6624-0d31-40e0-a5f5-06681f2c34e1' class='xr-var-data-in' type='checkbox'><label for='data-350b6624-0d31-40e0-a5f5-06681f2c34e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cartesian position of the prime cell circumcenter on the unit sphere, coordinate z</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 167.77 MB </td> <td> 83.89 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20971520,) </td> <td> (10485760,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-081b5893-4243-4427-a401-b81df42c4f3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-081b5893-4243-4427-a401-b81df42c4f3f' class='xr-section-summary' >Attributes: <span>(43)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ICON grid description</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>source :</span></dt><dd>git@git.mpimet.mpg.de:GridGenerator.git</dd><dt><span>revision :</span></dt><dd>d00fcac1f61fa16c686bfe51d1d8eddd09296cb5</dd><dt><span>date :</span></dt><dd>20180529 at 222250</dd><dt><span>user_name :</span></dt><dd>Rene Redler (m300083)</dd><dt><span>os_name :</span></dt><dd>Linux 2.6.32-696.18.7.el6.x86_64 x86_64</dd><dt><span>uuidOfHGrid :</span></dt><dd>0f1e7d66-637e-11e8-913b-51232bb4d8f9</dd><dt><span>grid_mapping_name :</span></dt><dd>lat_long_on_sphere</dd><dt><span>crs_id :</span></dt><dd>urn:ogc:def:cs:EPSG:6.0:6422</dd><dt><span>crs_name :</span></dt><dd>Spherical 2D Coordinate System</dd><dt><span>ellipsoid_name :</span></dt><dd>Sphere</dd><dt><span>semi_major_axis :</span></dt><dd>6371229.0</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>grid_level :</span></dt><dd>9</dd><dt><span>grid_root :</span></dt><dd>2</dd><dt><span>grid_ID :</span></dt><dd>1</dd><dt><span>parent_grid_ID :</span></dt><dd>0</dd><dt><span>no_of_subgrids :</span></dt><dd>1</dd><dt><span>start_subgrid_id :</span></dt><dd>1</dd><dt><span>max_childdom :</span></dt><dd>1</dd><dt><span>boundary_depth_index :</span></dt><dd>0</dd><dt><span>rotation_vector :</span></dt><dd>[0. 0. 0.]</dd><dt><span>grid_geometry :</span></dt><dd>1</dd><dt><span>grid_cell_type :</span></dt><dd>3</dd><dt><span>mean_edge_length :</span></dt><dd>7510.64679407352</dd><dt><span>mean_dual_edge_length :</span></dt><dd>4336.344345177032</dd><dt><span>mean_cell_area :</span></dt><dd>24323517.809282698</dd><dt><span>mean_dual_cell_area :</span></dt><dd>48647026.33989711</dd><dt><span>domain_length :</span></dt><dd>40031612.44147649</dd><dt><span>domain_height :</span></dt><dd>40031612.44147649</dd><dt><span>sphere_radius :</span></dt><dd>6371229.0</dd><dt><span>domain_cartesian_center :</span></dt><dd>[0. 0. 0.]</dd><dt><span>centre :</span></dt><dd>252</dd><dt><span>rotation :</span></dt><dd>37deg around z-axis</dd><dt><span>coverage :</span></dt><dd>global</dd><dt><span>symmetry :</span></dt><dd>along equator</dd><dt><span>topography :</span></dt><dd>modified SRTM30</dd><dt><span>subcentre :</span></dt><dd>1</dd><dt><span>number_of_grid_used :</span></dt><dd>15</dd><dt><span>history :</span></dt><dd>Thu Aug 16 11:05:44 2018: ncatted -O -a ICON_grid_file_uri,global,m,c,http://icon-downloads.mpimet.mpg.de/grids/public/mpim/0015/icon_grid_0015_R02B09_G.nc icon_grid_0015_R02B09_G.nc test.nc\n",
       "Wed May 30 08:50:27 2018: ncatted -a centre,global,c,i,252 -a rotation,global,c,c,37deg around z-axis -a coverage,global,c,c,global -a symmetry,global,c,c,along equator -a topography,global,c,c,modified SRTM30 -a subcentre,global,c,i,1 -a number_of_grid_used,global,c,i,15 -a ICON_grid_file_uri,global,c,c,http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0015_R02B09_G.nc Earth_Global_IcosSymmetric_4932m_rotatedZ37d_modified_srtm30_1min.nc icon_grid_0015_R02B09_G.nc\n",
       "/mnt/lustre01/work/mh0287/users/rene/GridGenerator/build/x86_64-unknown-linux-gnu/bin/grid_command</dd><dt><span>ICON_grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/mpim/0015/icon_grid_0015_R02B09_G.nc</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:                         (cell_grf: 14, edge: 31457280, edge_grf: 24, max_chdom: 1, max_stored_decompositions: 4, nc: 2, ncells: 20971520, ne: 6, no: 4, nv: 3, two_grf: 2, vert_grf: 13, vertex: 10485762)\n",
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       "    clat                            (ncells) float64 dask.array<chunksize=(10485760,), meta=np.ndarray>\n",
       "    vlon                            (vertex) float64 dask.array<chunksize=(10485762,), meta=np.ndarray>\n",
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       "    elat                            (edge) float64 dask.array<chunksize=(15728640,), meta=np.ndarray>\n",
       "Dimensions without coordinates: cell_grf, edge, edge_grf, max_chdom, max_stored_decompositions, nc, ncells, ne, no, nv, two_grf, vert_grf, vertex\n",
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       "    cell_no_of_domains              (max_stored_decompositions) int32 dask.array<chunksize=(4,), meta=np.ndarray>\n",
       "    dual_area_p                     (vertex) float64 dask.array<chunksize=(10485762,), meta=np.ndarray>\n",
       "    edge_length                     (edge) float64 dask.array<chunksize=(15728640,), meta=np.ndarray>\n",
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       "    dual_edge_length                (edge) float64 dask.array<chunksize=(15728640,), meta=np.ndarray>\n",
       "    edgequad_area                   (edge) float64 dask.array<chunksize=(15728640,), meta=np.ndarray>\n",
       "    edge_elevation                  (edge) float64 dask.array<chunksize=(15728640,), meta=np.ndarray>\n",
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       "    edge_vert_distance              (nc, edge) float64 dask.array<chunksize=(2, 7864320), meta=np.ndarray>\n",
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       "    child_cell_index                (no, ncells) int32 dask.array<chunksize=(4, 5242880), meta=np.ndarray>\n",
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       "    child_edge_index                (no, edge) int32 dask.array<chunksize=(4, 7864320), meta=np.ndarray>\n",
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       "    cartesian_y_vertices            (vertex) float64 dask.array<chunksize=(10485762,), meta=np.ndarray>\n",
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       "    edge_dual_normal_cartesian_z    (edge) float64 dask.array<chunksize=(15728640,), meta=np.ndarray>\n",
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       "    cell_circumcenter_cartesian_y   (ncells) float64 dask.array<chunksize=(10485760,), meta=np.ndarray>\n",
       "    cell_circumcenter_cartesian_z   (ncells) float64 dask.array<chunksize=(10485760,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    title:                    ICON grid description\n",
       "    institution:              Max Planck Institute for Meteorology/Deutscher ...\n",
       "    source:                   git@git.mpimet.mpg.de:GridGenerator.git\n",
       "    revision:                 d00fcac1f61fa16c686bfe51d1d8eddd09296cb5\n",
       "    date:                     20180529 at 222250\n",
       "    user_name:                Rene Redler (m300083)\n",
       "    os_name:                  Linux 2.6.32-696.18.7.el6.x86_64 x86_64\n",
       "    uuidOfHGrid:              0f1e7d66-637e-11e8-913b-51232bb4d8f9\n",
       "    grid_mapping_name:        lat_long_on_sphere\n",
       "    crs_id:                   urn:ogc:def:cs:EPSG:6.0:6422\n",
       "    crs_name:                 Spherical 2D Coordinate System\n",
       "    ellipsoid_name:           Sphere\n",
       "    semi_major_axis:          6371229.0\n",
       "    inverse_flattening:       0.0\n",
       "    grid_level:               9\n",
       "    grid_root:                2\n",
       "    grid_ID:                  1\n",
       "    parent_grid_ID:           0\n",
       "    no_of_subgrids:           1\n",
       "    start_subgrid_id:         1\n",
       "    max_childdom:             1\n",
       "    boundary_depth_index:     0\n",
       "    rotation_vector:          [0. 0. 0.]\n",
       "    grid_geometry:            1\n",
       "    grid_cell_type:           3\n",
       "    mean_edge_length:         7510.64679407352\n",
       "    mean_dual_edge_length:    4336.344345177032\n",
       "    mean_cell_area:           24323517.809282698\n",
       "    mean_dual_cell_area:      48647026.33989711\n",
       "    domain_length:            40031612.44147649\n",
       "    domain_height:            40031612.44147649\n",
       "    sphere_radius:            6371229.0\n",
       "    domain_cartesian_center:  [0. 0. 0.]\n",
       "    centre:                   252\n",
       "    rotation:                 37deg around z-axis\n",
       "    coverage:                 global\n",
       "    symmetry:                 along equator\n",
       "    topography:               modified SRTM30\n",
       "    subcentre:                1\n",
       "    number_of_grid_used:      15\n",
       "    history:                  Thu Aug 16 11:05:44 2018: ncatted -O -a ICON_gr...\n",
       "    ICON_grid_file_uri:       http://icon-downloads.mpimet.mpg.de/grids/publi...\n",
       "    NCO:                      netCDF Operators version 4.7.5 (Homepage = http..."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsetgrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Assing lat and lot to the output file_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "prmean = prmean.assign_coords(\n",
    "    clon=(\"ncells\", dsetgrid.clon * 180 / np.pi),\n",
    "    clat=(\"ncells\", dsetgrid.clat * 180 / np.pi),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "  display: none;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
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       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-var-item > div,\n",
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       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
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       ".xr-attrs {\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;clon&#x27; (ncells: 20971520)&gt;\n",
       "array([73.        , 73.05645347, 72.94354653, ..., 76.49297292,\n",
       "       76.54481611, 76.4503828 ])\n",
       "Coordinates:\n",
       "    clon     (ncells) float64 73.0 73.06 72.94 73.0 ... 76.5 76.49 76.54 76.45\n",
       "    clat     (ncells) float64 52.61 52.63 52.63 52.57 ... -45.17 -45.23 -45.23\n",
       "Dimensions without coordinates: ncells</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'clon'</div><ul class='xr-dim-list'><li><span>ncells</span>: 20971520</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-52952fc5-115b-40c3-a133-ba4e04ebf831' class='xr-array-in' type='checkbox' checked><label for='section-52952fc5-115b-40c3-a133-ba4e04ebf831' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>73.0 73.06 72.94 73.0 73.0 73.06 ... 76.44 76.5 76.49 76.54 76.45</span></div><div class='xr-array-data'><pre>array([73.        , 73.05645347, 72.94354653, ..., 76.49297292,\n",
       "       76.54481611, 76.4503828 ])</pre></div></div></li><li class='xr-section-item'><input id='section-52a12cca-921d-4e5a-8072-ef43eb3c3405' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52a12cca-921d-4e5a-8072-ef43eb3c3405' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>73.0 73.06 72.94 ... 76.54 76.45</div><input id='attrs-a15c93ee-1a45-40d8-b5e1-6e3f8014f05d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a15c93ee-1a45-40d8-b5e1-6e3f8014f05d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbf2515a-a3e8-4ede-ba22-9f713093945e' class='xr-var-data-in' type='checkbox'><label for='data-cbf2515a-a3e8-4ede-ba22-9f713093945e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([73.        , 73.05645347, 72.94354653, ..., 76.49297292,\n",
       "       76.54481611, 76.4503828 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.61 52.63 52.63 ... -45.23 -45.23</div><input id='attrs-2a6b5d64-25d3-4779-849a-88de3216ccbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a6b5d64-25d3-4779-849a-88de3216ccbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6ee4d16-fb76-43b4-ac60-7e7164dcfade' class='xr-var-data-in' type='checkbox'><label for='data-d6ee4d16-fb76-43b4-ac60-7e7164dcfade' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 52.60689104,  52.62663022,  52.62663022, ..., -45.17171677,\n",
       "       -45.23138792, -45.23383705])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b81f14c-b5c9-4775-a583-ec82a69eebd6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b81f14c-b5c9-4775-a583-ec82a69eebd6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'clon' (ncells: 20971520)>\n",
       "array([73.        , 73.05645347, 72.94354653, ..., 76.49297292,\n",
       "       76.54481611, 76.4503828 ])\n",
       "Coordinates:\n",
       "    clon     (ncells) float64 73.0 73.06 72.94 73.0 ... 76.5 76.49 76.54 76.45\n",
       "    clat     (ncells) float64 52.61 52.63 52.63 52.57 ... -45.17 -45.23 -45.23\n",
       "Dimensions without coordinates: ncells"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prmean.clon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsmean = prmean.assign_coords(\n",
    "    clon=(\"ncells\", dsetgrid.clon * 180 / np.pi),\n",
    "    clat=(\"ncells\", dsetgrid.clat * 180 / np.pi),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;clat&#x27; (ncells: 20971520)&gt;\n",
       "array([ 52.60689104,  52.62663022,  52.62663022, ..., -45.17171677,\n",
       "       -45.23138792, -45.23383705])\n",
       "Coordinates:\n",
       "    clon     (ncells) float64 73.0 73.06 72.94 73.0 ... 76.5 76.49 76.54 76.45\n",
       "    clat     (ncells) float64 52.61 52.63 52.63 52.57 ... -45.17 -45.23 -45.23\n",
       "Dimensions without coordinates: ncells</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'clat'</div><ul class='xr-dim-list'><li><span>ncells</span>: 20971520</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9d413bc5-c95f-45d4-94ac-545346714e69' class='xr-array-in' type='checkbox' checked><label for='section-9d413bc5-c95f-45d4-94ac-545346714e69' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>52.61 52.63 52.63 52.57 52.69 ... -45.11 -45.21 -45.17 -45.23 -45.23</span></div><div class='xr-array-data'><pre>array([ 52.60689104,  52.62663022,  52.62663022, ..., -45.17171677,\n",
       "       -45.23138792, -45.23383705])</pre></div></div></li><li class='xr-section-item'><input id='section-56c59c17-4cc3-4552-8390-348b36648044' class='xr-section-summary-in' type='checkbox'  checked><label for='section-56c59c17-4cc3-4552-8390-348b36648044' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>73.0 73.06 72.94 ... 76.54 76.45</div><input id='attrs-b22e1081-83c1-4eab-95e3-09e9cdad2f4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b22e1081-83c1-4eab-95e3-09e9cdad2f4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31b76829-0c8e-49b9-ba97-59005810664b' class='xr-var-data-in' type='checkbox'><label for='data-31b76829-0c8e-49b9-ba97-59005810664b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([73.        , 73.05645347, 72.94354653, ..., 76.49297292,\n",
       "       76.54481611, 76.4503828 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.61 52.63 52.63 ... -45.23 -45.23</div><input id='attrs-b72bd299-ca75-4171-b5a2-dc6c2aff540d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b72bd299-ca75-4171-b5a2-dc6c2aff540d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd9e90ab-cfc8-4190-ae04-c6e244d18aa5' class='xr-var-data-in' type='checkbox'><label for='data-bd9e90ab-cfc8-4190-ae04-c6e244d18aa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 52.60689104,  52.62663022,  52.62663022, ..., -45.17171677,\n",
       "       -45.23138792, -45.23383705])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b95b75a-07e0-420d-957a-abc2f9333fe1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b95b75a-07e0-420d-957a-abc2f9333fe1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'clat' (ncells: 20971520)>\n",
       "array([ 52.60689104,  52.62663022,  52.62663022, ..., -45.17171677,\n",
       "       -45.23138792, -45.23383705])\n",
       "Coordinates:\n",
       "    clon     (ncells) float64 73.0 73.06 72.94 73.0 ... 76.5 76.49 76.54 76.45\n",
       "    clat     (ncells) float64 52.61 52.63 52.63 52.57 ... -45.17 -45.23 -45.23\n",
       "Dimensions without coordinates: ncells"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsmean.clat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Plotting_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cartopy import crs as ccrs  # Cartography library\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "import cartopy.feature as cfeature\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter, AutoMinorLocator\n",
    "from matplotlib import pyplot as plt  # Standard Plotting library\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Using native grid_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree(central_longitude=0))\n",
    "cs1 = plt.scatter(\n",
    "    prmean.clon[::100],\n",
    "    prmean.clat[::100],\n",
    "    s=0.1,\n",
    "    c=prmean[::100] * 24 * 3600,\n",
    "    vmin=0,\n",
    "    vmax=14,\n",
    "    cmap=\"YlGnBu\",\n",
    "    transform=ccrs.PlateCarree(),\n",
    ")\n",
    "cb1 = plt.colorbar(cs1, extend=\"max\", orientation=\"horizontal\", shrink=0.6, pad=0.1)\n",
    "cb1.ax.set_xlabel(\"Precipitation (mm day$^{-1}$)\", fontsize=12)\n",
    "cb1.ax.tick_params(labelsize=12)\n",
    "ax.coastlines(resolution=\"110m\", linewidth=0.6)\n",
    "ax.set_extent([-180, 179.9, -80, 80], crs=ccrs.PlateCarree())\n",
    "ax.set_yticks(np.arange(-80, 81, 20), crs=ccrs.PlateCarree())\n",
    "lat_formatter = LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "ax.set_xticks(np.arange(-180, 179, 60), crs=ccrs.PlateCarree())\n",
    "lon_formatter = LongitudeFormatter(zero_direction_label=True)\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.tick_params(labelsize=12)\n",
    "ax.set_xlabel(None)\n",
    "ax.set_ylabel(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Assign land_sea mask_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "land_sea_mask = dsetgrid.cell_sea_land_mask.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "prmean[\"land_sea_mask\"] = ((\"ncells\"), land_sea_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "prmean_land = prmean.where(prmean.land_sea_mask > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KwozGYx2uakTUIA5VgANKcFUNvvRgMvRRFAkMCCoCZRgsRYVlrQqpwTygUYVUKSUoFSWQdgzQiqHVaLtSzCA4wAYEF8OQxVFUo1WYQAYBZT8/BZSgqCWQFVbYHkVTatOYCj/oIyCDIsqVV17Fgw88w2YzJnHO2RcxceLEz+ku/mKEiPDsgrcJkinqKyuZMWPGf/qQ/qPR29vLe++9y8J33mDNmrV4+Y8ZiEBySKCZOHETxjfVkUx3keqPsHL1eyxZtIJnn30R1/2sNVr/+kilUsyfP590Ok1quJtkaoD62lFMnTqbBQsW8OqrL9PfP4DrmoVOJjvANdedTSJRg1I+Qh5FDPM8CZoSAnE483vnM3ZsI6NGjcPzOxHRKFXEQH8bixe/TzYboaSkhCOPPJItt9zyP3sRPhGp4T4+/PBD1q1tI51OEwqFcF0X7WRpaBjDlMmbUVZWZqUQ6nNhn8xn6S/UomQEZI3E340VK1bw1FMPsXZtC+FwmLq6CiZM2Iytth5NRWU5nR19nHXWzzjwgAPYb//t0CoGxMh7HbS2reP999v50xOPMTycZ5NNpnDHHb9ju+225PobfsNPL7gUPximpqaCJ554njvvvJnamokE0guEOf+8SxkY6MDzImw7byu+evgegEZTwbPPPcWaNR9w/HFn8Oqrr7Jw4UJWr/mQ/fffg+LEKC659AL++OBNQIwnn3yEF19YTEd7F2VlxcTjxcTjCUpKSohEIqxfv4rOznaUimKez2ECASRGOp1ms80244STDqSitOnvsld/LdLpNK+99hqDg4NEo1GUmyIWLSUWKUVphXYdIm6Y7uQAkvPwc3mWLl3KkiVLyHtDVFQWc8aZJ1FdNQZFjLysJqw3QamAvN+JMIQmZlKkKoMmYkXxAIMEJG3dUhpUmTkoCUAVYWpSQijSQC0m618KrMcwVlEUZQh99vc+ILCyeVPVq4hSqGbUqgQvWI2QReGiVR1IET7dKEIojM7N1Df6iAzh6lFoyskHq2hu6eJnl/yOG399FZqYZe1G4r85crkcCxcu5OVXXmDZshUgAbGSHDM3n8ycWXNpGFVKxwbho6XvsHrFAKvWLAYJoVWUiooKJk6cuPFnzJgx/7HzaG5u5oUXXuCtt94il0tacBQhnRlil122p7RkFEVFcSIRj5YNH7JsaQdbbDWbudtMoKpiLEIGTQM+64EwmmICUpgUfco+HyEED4VHIJqAJA6lKK0xz1sKRR5IIAj9/es4/rjz+PG5FzJ79mfPDvyrYnh4mK8fdShf/vJcJjbNJFaUxfOK8bxustkh2toyrFq5gf7+fgJJobXLgQcczs47f5l77r2DstJ6qqqqqKuro6GhgUgk8p8+pf9zjICskfirISJ/c0Vg2KA8N974W156+WkS8QRahyjkxBcuXMg777wDwIwZk3n51Wf405OPMX/+u7RuGGLq1KmkUkMkk11kMnnC4QRXX30F0WgJgXShCJPPx0ilUhQVFbHnnnsya9YsolGf/v4szc3ruevu67ng/F+yaNEiTjvtNKZNG8Mvb7wB142wycSZdHV1MW/eXMaO0zQ0jCbkTiCZWm1Wm6kMQ4OQzQqNo6C6phhHTTLnxiAQoChjOLuUW25+gHcWLuG2397zV6/HvzJEfB597GF+//vbOOCgfdhlr2kkopU4qhJXlRCQxA+ShPRohGF8WlGYdGLBdgIcFAkUHlCM0IsBVwFKVaBwEHKYFGKxrUL0Mem/DIYNK6QPK+1rATCMUGYZM2uRIQ4BwxaUgaIYTQKP9WiqcHUNvj+IRxdIxmq4fBxVwruLn2K/vU/k2GNP4JzzDkGhiLmb/fsu9kh87nH++eezcvUy5s3bjk23rGbS5FEUhSK0tw/y7FPzeeO1NwmFHDadNpeJk0ax6bStvzBC/6VLl3LVVVehlMLzPBoaK9l+hzlsu812RKJhfGnG0WPQNp1unqUUnnQBw2hGo5RjwARVBHSiiNsxJoJSgqNqCYKhjal4RaWxhyCLkAQBrasxz9cwvq/46MOlzJ+/inffWURquJ+JTZtw7rk//UKwOB9++CEXXHAuY8c1ctCBR1JdE2XZikU0r2vF930cXYLrJtBao5TwxBNPcsEFP2Uo2cNxx51AcijD2LFj0Y7HcCpHT08fFRUVPPTQQ9TV1f2nT+8fihGQNRJ/MW677be89vozBIGyFHbBL8qjoqKKiop6IpEIoVCIcDjM7DkzmTVrBo7+2KvsyCOPZO3atVa4nCcSSTB16hSmT59GXV0DiUSCRCJBPB4jHHZQf8NjpxB9fd3kcjnKyio3rm66u7t54403WLx4MStXrmRgoJcDD9qDyoo61qxpI5fL0dfXRXt7JyIKc29nARAJbaSpRXIoFd44SImI8ShiiKYJkzj6qBMZNWrU53iV//EYHh7m8ccf58GH/sB1119OSUmRFecHBAwRUmNxdMxqnBQFywaT3stgAFIIRYCQxVg5lJs6Q1WGSC8FHzClslaHFcfYWpRhbB5SGNF8LQakdSHEEMmjCBOQMat7iaEpwqMfV1UCYXxpRVOG61TjBR0EMkxADhHj5fXkI4tZvGQ+b721iFUr1/Pmm69SWh7FUf+vI8xI/DfE+++/z+9//3s2bGjh2z/6BmPGVBJWLvff+zgvP7eEutoG9tlnH+bNm/eFSv0VYs3a5ey88y688PwLNIxy8EkTVk3kZSXG96oUkRQh3YRSLnlZC/g4KkogLoYRzmNS5q61ZUngSYtd7NjFjyosavIE0oa2IEvoRKkiC8o0r7z8DIccfDLjxjWx//77suOOOzNr1ixCIQiFiv7qefw7I5BBFMavsLW1lQcffJDe3l6amsYxZlwprluM+C7gIAJBELDJJptQW1u78TMGBwdZuHAhbW0rGRzMU1pazj777ENx8X+HV94nYwRkjcRfjHXr1vH1ow7nlzdeyPSpOxHQjNHvFNPT20JfXzu5XIxcLkc2k+GtNxfwyqtvM2OzTfnOd86htLT0M+9raGiIyy+/fKPZ4nbbbcfee+/9ZyuyV199lTvuuINcfgjXdXG0wveFTEb42c9+RldXFxdeeCHbbLMN48ePx5d2fE8zNOSwZPEC7rjjXp544gnmzp3Lhg0tPPLIIzQ3t9DV1UVb21q++tUjOOCAQ1i0+D0++vBdPvpoHV1dXRz21f2pqmzkxhtvwHWVTY8JY8eO4qADv87UqVP/IyvHlStXcs4553DpFd+muh4ieiza8VESx1GleNIOJFEqgbLGFI6qx5cBhBQm5ZAEMPorIiiiBLRZndUwWkUwgKwMkQGbVkyiEEyThkqUyiESQshZ6wsHJIaiCJEMWscJZJBAUkAEVIaQHg0EeNKOq0YxnGkhlW4mCBzefr2f8y84h4MOOpjTvnXmn5m4jsR/VzQ1NbF69WrWr19PRV2WQPpp3ZDh4vN+zW677caRR37tn352AsngBV24ugyt/vEJ+M477+TFF1+0vxXMfbOYtLcHaHL5QQ776sHsvOtMIEZI1yGBb5la48OtVQ0awaMZiOPSSEAHxneuyLBRODYVWEJAM4oSNOX45IFWFGW4ugFf2jHPZy+QsM9mGGGYDRs6eP21V+jqGmT58hWkUlkCSdPd1cNXvnIoixcvZnjYeOVprRERioqKKC4u5qCDDmLzzTf/1DV46623uPHGG4jHE9TU1FJaWoqIoLXPDjvswIwZs1FKPnPK3gta0SqB/n8WRi+++CK/v/MWQBFIEt+H2bO34thjvk5xovEf+t7+m2IEZI3Ep0JEuOiinxIKZ/neGSfgOo1WFB7HGGUOoMjh+VHefnsBd/zuEd5770OGhgYoLy9h8pTpLHpvCWd+/1tkMwO0tQ2z0047M2fOHNauXcojjzzNbrvtxtq1a7nllluIx6O8/PLLlJWXsPNOe9DZ2cntt9+O1pq1a5fwwYdL+e53zmH27C24447b8WQNImnu+N2fuPaaO3j44Yf5+c9/zoUXfY/y8jq0SjA4OMBzz/+RJUs+5KOPVnDuj37MtGmzOffcH5HO9HPwIXvQNH4GiUQ12267NbfcciuXXXYBu+22M5OnNjF16jhKS8Zz469+wfPPvgFoDjpoH0aNrmTd+rUseHsFr706n9WrV7Ns2TLq6+v+7WXDXV1dnPqtU/jmKbvxpXk7E9KjyQdrcJ0K/KAXJAeqDEdVENBtfqcYRQAIAf1oahB6USqGph6PlfbTPRxqQAkiRZg+8IJSEZBSAtZjKg5jKFwCOoGw8c0SD02pFd87aGXNUSWEo2JWp1UGBOSDfi695Gqu+tktHHLo/mw6fQ6jRtVz4IGH/Jlr9kj898SqVav42VUX4fsBxx9/NDNnTUR8zfXXX8OKpV1cdNHlf8Za/F8iCAJuvfUW3pr/IsUlYRobJ9BQN57J00Yxa8YOKKVYvHgxPT1d7Ljjzn/+XsmQ8zfgqApOPfkH3Hjj9eRlLSI5XF2JwsHRZQSSMV5xVOCTBdqBIsvsO5gCkjg+69BUoFUIX/pQRI2QXaIoHJSKI9JKgGD842rsoiRpK2/DCCk0o1AqhUg3KGNKjKSA0ZadHsIAvyRICHCtOXyKtWsGeP/9Bey00/bE42GMcXCSQHJkhkP0DbTwg7MuIhGvRCllWfoMrhti1ap1XP6zHzB58ii6u10GBnpRSpPNJXn+uVdYtOgD9t9/Fw45+IR/6js79thjWblyJcXFxYRCDqGww5Ili7nl1quZt81+/9Rnf5FjBGSNxKfi4ot/QuPoUo782iFofFCmtYqptouTyaS59prbWbjwXdY3r+JLX/oSBx28E5tvvhnhcIjAH+bii37H6DGVjB9fTV3dJJ55+gXmv72Q+oZSDj54Xx57ZAGRSIQl7y/ijt9fgOMU8fTTL3LxRb/mqT89u7FdyJ133sRZZ/2ELbecg+tG+cM91yOqF4B0qoLnn3+KG66/lVNO+Q4vvfwM3zn9O6xa1cyvf/1rjjzyMKZNn0bThAloHeGGG26gtExx2OG7Ai4ONQg+H3ywlJtv+j3n/fR4ioujKFWGIouQRURYsngxV/7sXkaNGsPyFctIDiUZPbqSru5+clmPaZtOZP/9d+XL2x9s22L8+6Kzs5Oddt6B51/6LWWlo3BUhEB6yPiNuLoFVyWAEPmg22iuVB6HUrQGxEGpmC03T+HqRgIZIpC0Ff/7CDmUKkaCHEKGsJ6IkCMgbS0uhjAu7lU4qox8sJJAPFxdBxKQlWU4VBLSdeSCdYZtUB5ImvN/cguXX3odm262CaPG1BMJx3noj0/8W6/fSHy+sXLlSk4//TQuu+ZbjBs9FV+6UCiOOeJSTj75FHbdddfPZT9XX301ZRUehx62M+lUHR3tPaxveZMbrr+T++/7E9FolJ+cdw4ffbSII4/4JnPnzuXV115gxfI1ZLJJ0uleiksStLemmTl7NF8/ei9cqvHoQKNMtweyKOWgiGCKPPJo2wzFeNGVoiRqC0pK7OIzilLlCBsw3nLFhtVl2FTxSjtGD5lCCCzIErMPVWKr47owC5gqhDWYyrtSjLVKEsNyZTHWlMMYj7sKTNUvQAeGkUtgfPESCOvta0bvZbRe68jlIJstIp5wrOdegcnLg4wC1U4uX8zOO+7Fs8+8TFHRP56SFBH23XdfNpsxmW222ZL0sKKnp4funtV09/QznMraohkoyDiU0pSWVlNREaeioooddtjlv9Yv76+BrC9ecvzvxODgILfffjvjxo1j1qxZ/9HcbaG/2H9jRcTKlSt55JE/sdMuW/DUky+xzbbbUlZWWL31EQQRDv/qsRx77LHcf/8fueLKc9hm2+0IhXwopJkch/POuxDTfmYIqGLylDqUfNNUzKhettpyWy44/+ekh7NkszHi8Ri7774r9XVTueyyy7jooosAOPLIE9h338N4+umnee655/jmCWdy3AmHssUWs0FiRKOlaK2YOnUS199wFQcedDCRcIw999ybbbfdnurq6o3n1tq6lj323h0j5Pbx6SIQj6nTRnHNNb/ADwYJVAdCBiVFCGkCyVJbX8MOX96Spol1nHLarjQ2zkCrNIIZgNvbO3n8sYe56dcHc8SRhzB61BTWrVvFhAlNTN90PLpQyfc5x/DwMLvtthtn/+hkXn15MRMnRNl0sylk/AzrU8toKh6NJ50oQjgqjieDVj9Sj6MS5IJ1uJTiyWpAQ9BvXNkJcKSCgH4UcUK6kRxrETJ40oXCRxFHE8aTPIosjlOPGbg9hDz5oA1fPJAIvg6DdKGUxlGlBJKluWsdx39nP3bba0cWLVjBAw/fxxZz5n3u10hE8Hwh5H5xzAn/V2N4eJivfOUr/PjH5zJ29CbG+V8pok4joXDAVnOn/9P7WLduNVdffQ21tTUc+fUDAQiHU5SVV7PgnQGaJk7k2Wef5YEHHiBREjB7zub85Cc/Ydtt57L7HnM48KBdCUcDIpEIvb0DPPXEAu664xFmTN+B2VuW4FAOBKb7gKQRiWFWHMNAFMFBq7gBQyIEdGBaQTkYrSJACwrBmACngSFMC6thmx4ttaDIMWBLNhhwJEmb2C/0CO1FqxJrHDyIohYotUCqBQO4DDtmwFE/ZryNgQVwwoCVOBR6oeZtwcsYRIyOKxRSlt0O2fcWAwN2kRnwwQdvEY1GaW9vZ8KECQB4nseZ3z+BfF6x6y770dTUxLJlS1m+fAVlZWVMnTqVpqYmXnr5WXp6OsjlUsyePZu99t4RRYBWtfjSYhk8B602sVXGLQhpJIiQSsbp6Wmjp2eAX/7yl1RXl3L44Uf/19i6eJ7HunXr/urf/+uYrKamJtl///157bXXmD9/PkuWLGHTTTf9l+9XREyZ/fvvUhQr5rXXX2L9+hWUFNfheQEiwlFHHU5r61oi0RIS8TA77bTPF6Jq5m9Fc3Mzb7zxBo8++igTJ06ko6ODTCaJ40TYb7/92HHHHXnuuee4/PJLeOON+Rvft75lIQ0NcaDK2gXEMQ94K4Zij2MGo2Jg2FDqysH4KvWhGMPJJ53IbrvtzW677f4psJxMJrnx11ezYvkyIuFSttlmHhMnjuOyyy/ihl9eTVV1GEjy4ZIkV155DYceejj77mt6gH344SJOOfVbtLd1cvnPzmb3vbdBE0YRQxiyaaxqFB6ubiTjL0bI4WB662ldRiCtaGpQRBA1ZFepaYQkwymPyy+9mbr6CkaPqeStN1aQz2fYfffdmTZtCg31E9Da+T9bQPylePfdd3n1tefZ0Lqat958l0xaOOKIwzjwsHlUV5aTzg+hVAyl+gnrMSgCfMmjlBDSFWS9DjzaCelxiCRRKkwgvThWy+WRJKzG4qhS8kEXnnSgSVi/rQiOKiXntxHWY9EqRi5YR0iNIuO340kSRyfI+gO4yvRiLI3MxpcUBx5yIF5O+ObJX2WPXQ+hJ7OKKy/+NQfv+3W23Xbbz+36PPX6Ot5f2c0ZR8353D7z3xXL2gepLYlSGgvxyCOP8OKLLxIOhwmHw/T399M+OEBIaSoSCbbeemvmzduW8eMn/Eeryx566CHenP8CbR3N1NSW8vWj96K4uI6PPljOPXc+SV1tE8cdd5xtiP6PxcUXX0xX92pOO/0oxo3ZHJ8O8wcRIMoF511PWVkx69a2kyhW/OjHp/HAA3/kkQfns9/++/L1rx1EoAJj6MkQEMXVFeSDVstq5whI2bR/gEMZgQU4UI0BWjkcmkD1U+jViRR8mRwM8Akw9idFQK+tuo2D6sQwNXHba9QArUD6gAxaJVDUEUir0TraamCj+zL6MBNRFCmgAZNqHMT43q3E2EAUjIPDCD0g2MKXCErFEGm17FzCpi3TaGXsICCOo8rwgnYCuoAQDo1096zjop9eRTrtsNVWW/Huu/M5+NBt2XzWFrz0/PusXbeGSZNGM2nyOAb6cyxdupaVK5rZcqvxNI6qoqtzmMqqODNnTgN8tIpjQGYvRqs23rJtPhBCpNAo2wBGIcWi95bz+GMvsWb1eqqq6th2223Je0lSyQypVJahoSGSySTp9BDl5dVUVZVTX1/JnnseQDT6cUHWvyMe+OMdnH/+pRx80GGcf/75/xvpwqlTp8rVV19NdXU1M2bM+Jc1UA2CgEceeYTnn3+aZNJ4pEyZMp3pm9aSSqWYs8Usxo6tRlGBo2oZGhripluuZOqUMfh+ggfufxjP84hG4yilCCQHElBTM5pJk6ZSW1tLcXExxcXFjB8/HsdxWLt2NUVFCcaNG4tS/7zR2uDgIMuXL6e2tpbRo0dz3nnn0d2zjuNPOJZZM7cHDArv7u6mtraWDz/8kAkTJhCLffpGfeSRR7jzzjspKnJpbm6npWUD7e3tOI4mFArR0dHJFlvM5K35D2F6hGcwD4+LGYRaMMJQRaHSZng4y3PPvMszzzzBUHKYqVNmsssuuzB79uyN4FREWL16NePHj+f111/no6Uv8I1jj8EMRB6aYnzp5NJLfkNJoobvfvcHADz11FPccMN1jBlbR21tCfX145nYNIUxExzq6ytxdR1CkpCeQNp/D/AIMZGALhQRmptbqawsJRYPW6F4D0gEraKgtN3vBjRxtKpm7dqVvPHmMyz9aD1tbW3kcgGHHXos++zzaZfv/2s89dRTPPL47Wy19ab09QSsXbOeltYurrz6HCoqlRmoVQ8O1YScCnKBGYwjeixe0Isn/YSdWnJ+OxAQ0eOsiFiTDd5FcAip0WSCNZjmzdVoyskFa/EFQlSgVA5PcuSDFBFdzUC+hZADJe4ksn4X2WAATxxcGc0vrriZ22+7jWNPOp7Fb77HW/PfpLQ8zpixo/n2KWey7777fm7XJpXO09WXZlzDF1tAH4iglaKvv58X35zPg68twc8r2ta30rpyIacc91Xm7bU7Q1mfhpDD6JpaPkiuoyRcTr0T4Y233uS8C85l/ao2LrvsMr7+9a//x87FGPt6PPTgH1nywTvkMi6pVIp0Os2aNWuYPXs2l1122SfOPWPE3X9nXDv99NM56ZRDGN/UgFIptKrFIWYF6ILQwyknXsrVP7+RX/7qMoqKYpx00vEE5DCidt/oA8W34vMhFBohjaOqCaQXIYtWo1B4aGL4+AhdmMVhzrLC5Zhm7sMI1SjloRjAgJQIhrHKYkCXho3+ccOYMU4hksU0QxnAADONAXLtgGsrfx0U9UYzSRozbhZt/Byj9dK24KQC411Xahe3CiGDAXTdtpglsIyb2GriSnxpxYyXJRgGL4oEmkAlKTSLd6i1mssA30uwYMF8XF3OFluNwqQjHVNdTJctsilD4dvvJLCVzBEUQwiCIgPUYrzA+oEkWm0CDBFI0p6jD+QI7PXSeBjDYw3Ko6crxJtvPUU4nCeeqKG0uI5YPE88IUSjMZIDVXR2rWb16ne44/anuPXW3/9ZVuMfiSAIaG9vp7KyjEjks6VL29tbePiRh9lk4lR23nnn/w2Q9e/QZOVyOY444gj2339f9t1/BvFEua0WiRAwCOTQNCAMGbpY5dHUE0gPQmBpXxeFjzGhKycgQGSQzq5WVi4fpLu7heRQjoHBXtasWUsQuIwdW0kqlWPNmtUowjhOnMZRlYwfN5kJTXXMnrX9p3Llg4ODvPTS83R2dlNdXUMul+WFF55leDhFWVkJEzcZy4cfrGBoKMcuu2zDnvtsybW/uJeuzm5mzd6K55//ExDijO99nyeeeJyWFtNfblTjeHbffW+22mqrjUA2mUwyffp0dtllHjvuuAd1dQ3kvV6y2SEaGsYxfnwDlZUOxgIgBWg7AJRZmroIoc9aKyjMgJMAPJa8/zrPPbuYe+95mO9+93QOOfgInn76ae68804mTJhAS0sLvu+TTPVQVVXDSaccymabTUYziiDI8pX9D6avv5dzzvkBX95hd6v18kmnc7S1r6WttZc1a9Zw9913s2rVMlDmGCLhKKVlRYwdO5aiWAWBZAiCNMXFFSSHPLTWlJeXc/r3DqOiygVxUbiY5k95HFVCwAAuVSgVAzFl25lcLzf/5h6Sg5rzfnLp53JfXnnNhZSUeOy83864OszKD9bw1JMLePP113niqatxdSlekEfhEDCEVsWI5AhIolWCsFOPJkLGW4+QIuJMwg8GyAcduLrMpBb9Ljx6iOgxRJxx9GTfIubU05nuYCgfIeokKI+UsjbZwrhEBVrF6MuuYlR8LB/0eyR0lqSXI6JC3P/MG2gvx+CAx+svvMmHrz3HpjNncNE5P2SnnXb6XK7Jf0Os6kzSNZiho2s9P770FzSqNG3dSbbaf0dqK5uY21jMS0sXMn27OfjFYwjIkfE04xOD7NiwiamgDdJ4kiHmjuHWm//IU48/z9y5c1n84Xyqays5/qhvs/nmm38hvJP+UmT8tdxw3Y28v3gNWkN1dQNjRk9i1KhRzJo9icaG0ZaJ1bzzzgJ+c9NVXH7ld42ZL0W2a4CHopyAdk456cecd96FnPn9b3PrbdcQdceRl+UAKAkbU1w1DJInIAPKt0L0tJ3wzbj0cXpNMPYJQ0AWhzF40osihKsSBAyiVN5uV4JSxRhAl+Zj8BTGAK4shsEPY1J7ppWV6aIApo3VGgygM89QsJF1G7SvF2HGzyxIv2GwVBdGazUWoRlIY3y20oCDkiILNPswoEiDChBJgAU+ptClAigjL6vRxIAiRA2gKUdoAxJopS0LV4JJVbooPPsZphoS8mga8ayRsSKDJgoqhohZaBd6pBrbCg9H1Vt5RisQt8deYOkMK6eptgx6H0g5qCQiPppqhLydU4bttg0EtAKDnHPOb2hsGM0286bS0wkrV31AJq3wvDxah2hoaKCuvpSS0hLSKVPtPnbsWGbOnAmY1mjxeIyBofV4+TBKBYhoW33tmeKgQIhEokyZMoPp06czffr0jcUd/zPC9/Hjx8tTTz3GxImT0fqfl5SJCK+88gr9/f3kcsN88OE7rFi+llO/dRxbbbW52YYUxuMkj4gx4TQgwXgTKeUgJIAukGICWjAGkT5QbsWPeTRFQIggUNapO4rQg6NqgIx5iKWGwLpl+36c5pZlrFvTy6pVi1iw4H3yeYeZM2dTVVXFCy+8QDQa5ks7TKG+oYKebkEp+NIOkygprrSGeC1ADE0ZAe0oatBU0N6xhEWL3mPX3bbmjtufx8uHmTV7E2bMakChWL+2i+eefZ8FC97C9weBKDU1o6itq+KFF54nm83haKitbWTOnKmceNLBaF2B2OozJQ0kU8309beyatVa3l/czkcffcDwsOD5w5SVltC0SQMTJjRSX7sp991/L2+/vYBzzjmTL33py7S19nHqqd9kzJjRbDJpFMkkTJ5SyW23PsAhhxzG2rXtlJYHfO+M04A+OjryNK9fz8svLSYcStDeuZzOjk6+850zmTljm43f98knn8wVV3+LcCiMQx3d/YtZuGApD/7xebaYPZcTTzwNXwbxgm5cVY6jy3nvvfeYNWsWS5YsYsJk+0EKNGFcVY1HGw5VCD6B5PDpoqfD4dnnH+UXV93JNb+4kR2/vMs/fa/2D/Zy//338sab83EdmLnZbLbZZh5XXPlTbvnduSiKcVQEz/cIVApNGK0M06eIEnKqyfkd5tx0LVFnAlm/hUywmiJ3MhIEpIMVuFTg6lJ8GSbvBwwHIbrSeZb0pUiEhGK3ho/6OhhDNa19DrEKoTYd4tHeVr7cVEVfyuWdxz8gObyUzJr3aRlIs/vuO7PlLl9il002Ieb+a9jnL2qcfu0fePTu3zN92lgm73EotelhHnm/havO2JmQk2dSSYiuzBpSnk/MqaUqOhZhA0P5QYpCHiWhmXiBmSQDaSXAob8nxfBALXXjhmhvG+QPdz/O8mWrKAqZVbwX5KmsKeeAfQ5m7ty5n4tsIee3ARB26je+lskOsOT994mEitlss80Max/4FuwF9A90smZ1CzM2n8yll1zOunVrufiy08jmknRsyNPS0seChfNpaW6muLiUvfbai7raRh555E88//xTnHzq0Rx44N44TswyKcMEDHHkYT/giit/xjdPOJFt523LkUccxYSmBiSIkuN9a9abBjIoiQNxAjYYywXVb/VVpsF5IG0E5FG4OKrajveD+GKYIEfFQcURGUKhTLcIxLJGvmWz8hjGqg/sfGHSfMP2/wNsbHlFDSLNmNRgCQZcKfhEQ3cjsM9grFUStpdhGsNSlVgwqOz7NaZzRRYDAgUhYT241mDsIRJmgUuApgJFhLwss9spC/QABtGq2IK3VntehlkzvV+j9pyMjEBTa+0sPBwqQfUDCZAkYr30CgUFSBGiusycSRjTemsAw3YNYUDlEKaoIIIBr9UYUKdRjEYkZZlJ3x5bORpFQJJs1mHBgtdY+M58ams3YcLEMsoSk1BOJ0rqaG5dQXtbK4P9gxQnaigtrWPR4tdZvaqZb3/7TJ5++mneeusl7r7vZ2jrZWYMnE21trnGaTLpMGtWZVmy5A0WLX6Hjo5+5s37Eied+O3/DZA1duwoqagoYXAwxYyZm3LZZRcwedKnzuszxyWXXMirr73EEUccRUV5lHETKpk4qRFNAqWiaMrwJYlSXYDCUBh2klChjayMQoHqASpQUmwdfX2gEaEfYRhNBdCLBHGUNi0aTPopD+QQ8VCMwbhnD4IVRpoHpAiRTvKew7KP2ujobGPOFrMoLfUJ0BiK28Hk37vt/6OYG6UURQ4YRKlii/67MANChS3Vb8Y8rKUo+lDKNH82q6o+kDCdnX2sXtPC+nUtKOUzMDDETb+5j+23/zLt7RuIx0uAFCIOWscoikMiUcxLL71GRXktlZWVVFaWIORpae5k+x3mUFMzjtYNnQwODnLooYf+mYbDD/p5+pnnuOnXt+G4QiaTYvz4qVxw/sUsWrSIhe++xumnn4zpx2cexMWL1/Cjs3/OpT/7Fo2jSjj+mMu45hc3Mm7cOFpbm/ne977D7Xedj6mUi5pVriT4za/vZjiV4QdnnWf2LYNo4ijl0NPTw1lnnUV7xzprjury5R3nsNdeu1JaWg1oPGlHyCD4IAl++P0beOqpP3HgITtx8EGHMnOz7VG4aP3P2xWICNdffzULFr5BJBzl+2efxJgxFTY5kEVRjFIuYWcMeb8dT5oJ6U3IB1340oNIQNgZRyq/nlyQpEjVUxQZRyq/Gj9ow9WVaBUjH3QymNd0Zoz2pD0V5dZ3+jh0k0ZWdiRp0mmyeY9nF3zEWw+/QlmFZtYYl0x6mP6UywnHHsLuu+70X1kY8s9EV+8wv7lnER91vsPSlx6jom4M2x10NJlVLbzRkuLgPSfRnu7nK9sVUxYpprGonpy0kPNTBBKiMjqbrN+Nq2Kk/PdwqCPsVIPkyQTNhHTcMJFqJll5x2iBpJKs3wXapG5bUh+yYUM7C55fyKKF69Fulh13nMc+ex35KW+7ZcuWccJJJ1A/pp7GukomjpvKdl+ay4xN/7w/ni9DpFIp3nrjXV56+U9saBnACeWZ/+Y7RKIhLrrwMh548E5835jb5nI5iouLaGwcxZLFyykug5rqen50zhWo8FJENI4qQSmNyDB9/UM89eSrvPjiG4ScCnbaaSfuve92dtl1G4474QCMHUgSCLF48Qouu/g6Tjrpu1x33XVc98sfUFvbCASI+NaHqtW2sXFQFNmJOYoiZ5gp4oSdTfD9ITw6MUxLuU1vDWC5bhTFQKdN7UXtpBuxzIugyIMCxXgC6bdjbx+QB1VnDXj7wWY4TBeFbvv/0UCrBX0ldh8DQA2KQYwbfIFMKPh7aXuMGfu7qRYMJM3HbbJ8TBGTSWsa/65CKi5sn+8WFGUoBK0SlrnzjKO9qsQUNJliKGUBkRC1AC9AUYNSfUDC9jGNoVUIKAHBevHlMKyaa1OEeVw1DvCsv5g5HgNALWOmCno2U81p2Li8ZdGG+Vi4b9LDIhpUzrKP/UA3Sqos++iAKBxVTi7owtUJUzmqfJRyEUkyMNDDr258hqI47LTzLKZNm44BfAIiOKoCselkkQBNLVprAsnZlGiIgYEhKium/G+ArDlbbCZvzH8YCTR/uOtelixZS2dnP3vssQv77DeX4qJ/zDSyra2FF198ildfnU82O8y3v30aUzctt5RwFnNjmslLBPugm+aUqKztF5dCUQdqwN6Mpfb1XozHiRX2SQLoQ6lCFUiMj/Pvpo2JWFBkHrQcZqVifFqgBE/W2t8jhkIX0+4E+6AjNUAOU7nSh1IFoCR2H7Vou6ozxz3BGkj2AHG7vUehMs+UJvdbUWU55mHw8IM0++93PAveXsIhhxxCQ0MdQ8l+UqlBshnI+wNk0rDvPgfy2muv8d6id3jx5ftQypj65fMOt978exYtWkUikaCvdxA3DNOmTKW3L0ln5wYkCLF8xTLOO+9MttthJt3d7Rx1xDnEioqZNGk8P/zxNygujmAqerDHOwxU2ofb46gjv895P7mQbC7JT396PtfecB41NY04FBEQAIPk8j6HH3oGP/rh+Wy99dafukfOPvss6hojNI4q5dqf38uzz77CCy88y22/+x3fPeMgZm6+qR1YK/FZZ6wNaMSXTj5aMsDTzz7OiuVrEfEpjo/mRz/60f+5ZYSIxxVXXUJNTZwjjtwdRxfb7y9kwbIRmGoiCDl8SWPS25V2IvHR1BHRjQzm19OfXUE8tAmuitKWXkN5qIR4qA5PVhHS5fRkQ/Rn+2gqncJdTzdz9xPv0Pbeg0xsiLHt5lNxHIfK6loyEideXs/he21JLBb92yfxPx5/fOhRjv/epUzcfmc2rR3Nm/M/YtLkTfjqSbuSoYNDNx2Fg5APkigClDZNu/3A6BgdZZrp+iTJB4MgUUK6GkUST5KEnBKiznjS3iqEAFeF0SpCXlIoXPJBH37goFUdCp+S8Gja+xfw8gvzeen598GPcv75F/7ZPXjsCd/gwK/tSMPoSh747XPce8+D3H/fw4ybFOLJJ57n9dfmM9jnEy8qY9ZWtWy33Vyaxs0B8tx1912sWbeM8pJGjjh6d4piQkiNwacLhwm4OsRgajW9fa3U1dfgqioC1Y7DeAwYCOMF/YSdBjxZhsIhn6vl2Wdfor+/mcOO2N0CGxdjASAMpxQnnvBdrrnmV1x22WXMnF3L4UccCla35TKagCFbRavREkeUgxkDzXhrGJtSAnrQUmYYEpXGgJgEiozpHEEJqH4UjRgheghI2rE6QKmI1V6Vo1Uh3Ze2P6MxzNYQZgyutb8PYNgR9xPpL1vxiCnqUTSYtmNqyP4N+5mCWRAX2Z8+REoR1mFAX5E9NheHBls44AExhB4KDJhDHUIfvgzgqgYC2uy5F1uwV2DkXBzqCGyVo9CBYbEqQaWseD1NoYMEhHEYi886BBAJjKxCcgjDKBWgpAxUBiSGMIBSeRQNQB6FMqQCrXys2/L4eC7WFIClCAjDONQjZK1MIgvUowlZw9cwemPBgmmhZq5Pxn5OBgN8Cw72jQagi0kta+pRatiCYB+zkA0QKbap51K0EkJOw/8KyJohr795J1qFUSqBppxMvpknn3yGP9z1OE0TZlBZWUmi2KG0pJ6yslK2334bEomKv/vZ/f39XHDBBYTDDgcevB2zZ09GqRKUEkQcoAexfiQwbC40AKUWnAwhEkbwcdUohAErstRACE0RSoUQSaOUESuaB3bIfukTEFmLKXetw5dhlMrZlUUxiDatSRjArM7A3DRVKHpAuShqKBiJmhVQKWZVZUz1TC59AAPeCj4sQ/bfGIpSe+MEGKrWRxElkC5L5SZQVBDIBgLSiDj0dgup1DDxuBCNaxKx0aD6UDRw37330tnZzjHHfJWieNSszFTWgsY4pqIkCSJks4OsWN5BZXUlNTVxXKccRRpBI7by5xdX3coJxx9LotTDVAmCkLYP6AAF2l2TQFFC78B7/OTHV+M6Luf/9HQSxS6aSlzdQD5YB6To6/e46/an8X3FGWec8an74uRTjiGQFMMpzfhxE/npT43txAsvvMADD9zDFb84DYWDqyvxgh4CBnGpw6cfQdCECUSjlMOGDa1c8OOrmT1nc2Zsuh1u1GPO7K1IxMo/td+/FB8tX8gxxxzPfY/9isbSSaCEQGz1FRX4VtRrrm0WRKzYFfJ041JJPugikHICoCPdQ85XaFXKQK6f9UMBm1XG+bA3y1C+koZ4gua+DO+3J/noxddoe+l+fn3jNWy16SRcZ8Qu4ZMhIvz85z+nvaOLt2Q2O84Yx7TqUvpr08x/upUrvlVLJOQSdusR6UOrMvygx4p+I/hBP4oSfDpxVCkRNYm8dJIP1uGLoFURgeQJOeVoBEcV4UsKh3IcXUTGW2sqP6WHkCrDk36EPBFnDH7Qb7ZVCdo3DHH5pTdx029u33jse+y5Cz8492jqaho47ZRLuPiiSxg7dgy/u/MG8v4wRx65O1VVxRSFNiPjL7djXBWG3XfI0wqASwmCssa47UCEiDOGvN8HDNkqvghmgVdmjXJL8KSXsG4050i3mcAx2QMhjmEWBoAy3nv3TX526e/5yXk/5I03XmH9+ha+//3vEkv0I7avoGEcIiiVIKRryAVrEBm0rWtCGzMLopJA1PzfMjRGa1RtAcgQiLbZhjAhPdr4zTGAGS/TGK1pgd0xvTuNmLsfUGg1ygIT384dyjJLNo22kcnJ80lvLMOAdQEJo/e0zJlIr2F4GKDA6gR2/+a4czblabRhprG1WVz5tNvvzLMViD6CZ82DA7TSIAWQ5SL2OzSsnYOmGp9mFNoCo9EgfZbpSRi2BzDZnRJMMUEEV9UYmxXpQlk2zKHKzmeGcdKUmMwCWcvCZTAynCEUoKi2qcIkrpqBFzQDgQV/1fZauWhlCqLMtYnaucM48hsgGKBJENBnc1CjbPYoY69lBo3gi/EScwgQlUeRICCNQuGqseSDtfbpKUWrHCFn3P8KyJopL77xSwiKcLWLUmYy0Urxyitvc8iB3+bWW2+htj7P0KDQ19fHE088z+RJszjllFMoKyv7s8/L5XIsXryY119/nSVLFpL3slRXjaamtoS33nyTX/zi1zQ0llrEnLQPlzI3GZWAZ9HtMOaBKUPowqEBURqRVnvTeUAjaiN1qzC95gbQxAjI2S++16SdxPqtAODgKCPaNOCozbJKvZgHu5KAARDfDlwp21Mrg1IaTSnmgYkirMU87GX2vVlQpRhTujRQDWoIc8MVWk6UoSSKT4u9+XOIRIABtBqNUgo/GDTgSQDlYlYbAYoKPFlv95cytLiK41BiQBpDtoQaIGmE7KzHpD9jmNWXYWNMuLaQoJUCY2VSoQ6Cb3UIA3y8MhwynyMO6XSGb3/7bGbNmkN1TTkbWnr48MMPiESiHHzwIXx5h93/onbF8zyCIE84/HHV5bXXXkvLhhWcf8FZOKGC5q5gFBgC8XnnnUV8sGQF2azDIUduQTxeYehlCfHcS4tZ37yGO255iNvv+C2Txs/8zM/AiaeeyI5f3Z0Dtp1DMjdE1PHQOmFp9x7rDu2iLLA3PQOVoc+lGE/SDOaGGfKgJSV4QZi0F6VvuIjHlrbTGI2xtmuQmRPL+aillfKV77Ny4Xyq6xu47tILqSuNf+ZjLYSI8Nxzz/HO0iWk+ofYbc99mDdntvnWk0neeecdpm86jniihBeff4NHHr+TXMZFgiw77LA7Rx31jX94n//OWLGqlXPOOYv99t2TZS0JHnyjlROO35aZo12kOktVtJrJZUMocji6goA8ImkKxpEivTiqDqWKyfhLUWijD5I8AYP4pIg5U0jmPsTVJaByhFUdvvTjqlr23P1wli1bys677IBSUFdfyeRpdWw2fQ4TJjUScqGjfT0fvL+OB+57hmlTp6Ioo7W1lbHjGpm79bZcfMkF/P6+i3nogWd48bnFFJUI06dsyeFH70PI8UD1o5URMGvRBNYrzaEElCIgh5I8ShXh0UGUyaCsTx15wmoMoMlLO4EMoykloscaHyXROLocVIyA5RjdTgQKKTEJAyFuvvnX3Pn7h9l9991YtWoFTRNH8f2zj0bhYuwLcnb8jBpRtxompMYT0IsvQzjEjeJDjM4UBYpRKJKYoqUOQMw4JAMY3dWQHePFjD3Si9bFIEX4tOAw3ji/049hYmqtFMFo2JQqx6Q5XQrVjyYUEAExGQMDijSQAuVY4XkGaMQsnA0bBd12bCxEHGOREMIYmw7YRbtJ1ZkxPIJCI6LsMYJxsTeMopGP5HB0CCUVtsBL2/371o6mjo/9ugqZFg3Uo+zCX2QAaMCnx47LAVCMo1xEBgjwQOKGPaQCnxwGVLkYJ/ycrXAstBjKYmw26uz1NKA0pCfgBa1m3gsERQ1KpwlIofBQlKJVDBGPgCyGGay1YDiDYhSBrLPfT3zjMQTiYfTXvp3jXByVNIymSoDk7DGBEMVgjyJCesz/jvC9oaFeRo+ppbFxNL/93RW0d66ioa4RN6zRStPfo4jFHaJRBwlcBgZ7GOjPcPttT/Loo4/zzjvvoLXmoYfu5ZFHHsdxwsyatSlbzZ3OZpuNo79/kJ+cexWuG8V1w+y081bsvc9WaDUJkS7MTWdcgo1YskALF1gp3zI+jWiKUcojkBYCsmgaDbKXcnw2oIijVcjQ0fRjcnIOokpA2s2KRBIofFxdZYGU2VaUBjFMjqPG4Uk3pgQ4gqBRUkqgNgAaVzUSSBdmxdWNotqU7ksSlBkUUF2IZDCpN4WQsrSxMegzK4oejD7BMGKKXlBxkMIqM2EZumIUcQIZQkjZdJb1b0HjqlH4bMAA1BxqYxpSMAydb/+vMECpA6M1M2JHZZ2UPzbny1sKuFCOXLinFVCMVsWk092cdOK51NaVsen0mdTUJhg1uo6JTU0URcf9zRSzGeysG7TAggVvce9993HJZachDINELbAcxjysJYDDXnseyVnnHI9LPb/61fUc8tU92G/fndC6jJXdy7nhZ7dQWzGKH//wH6s+POl7pyHlRXxpn8MZV51helWCfJBBkacoFMXVNfiBoeBdNQGfNQQCihBprw2Fy/qUIuVFaU2GeaN9iCnFZYQ7erjlqTdINq+k1EtRGg3TnRO+cejBHP2VPf8pu5QzzjiDXC7HqHkzKK2p4Q/3PU9pbzMlcZeiUBlbbz2Xdxe/RHJomC9tP5fd9ppOVfkY3n77Te65+2Wu/cWt/3aX/c8aq1at4uhjTmGXPb/Gbntuw3X3vMjqQYc5203hB/t4lIaqKQqF7ABfZHUrOSBGJpPiN7+6hw8//IBAAnK5gNlbNHHQobtQVzUVBeRkAKGXqJ5OTtYbQK80ETWFbLAK8MnnIzz8x2f5430vcvfdd9Pb18mS999m6YctfLB0AV7ep7a2kmzGobu7k1ixzzeOOZCmSU0s++gdnnhkMSefegollWkCBonqeob9LpRECTnlpL12IlrQOopIEq3jhFQZKMj5XYR1A46Kkw0+IqSaLPgoAxVYdkKI6c3QOkraW2AsbYgSdZoIWG/GLMIYmcMAZjwdRtuJUcRFSPP+kmW8995Kamri7Lb7djhUgdL40omg0LbCzyxeTfNlU2UnsLG9TSkFOQFkLWum8Bky+5aIZaCrMbokMIu9JCJGeqEVNv2VwqR4qwzQUwOWpS9IQHL2XEwhQOF7N/MGGL3tAIKi4MVnFuARw/AQxQjq1yK4GPG/WVQavVjCapsKLu4FeUjevr/UvlaEwsWXDRR8qQSNQwhHVeHJOgSfkCoxf1cG3JpUZmIjIfBxpWE/JkOTBamx19mkY7WuQCRnfcqyCDELmEMYi5xy+31H8aXLjtu+3Y+Zb8w8Y6+RqsGhHJ81GDKgAlfX4kkXQhsEJTi6EqHHLkoyaFWCVuX4gQGPWsURHPygG00cR48iH6wElcNlHJ5sACkmJ2sJ6UpcVYMvvShcHB3gBxGUCmPmQgP8DJDOotBoVU3Iqf3fAFl1dXVy4403smzZMlpaWqiqqqKtrZXq2jhnnvVVYrEKw84Q5ic/uolsLk1VVTmTJ81g//2/snGiePmVP3HTb37LrFlb8+3vfAOl+vGDnLlguhxlWxcEQdI6SRdj8vDaskUmx66lAkghatgOEq69uTWaMoSIeQjERatyy+wsIxCzQnBUKVpVIrSZ1ZdyMWW4Pi6bkA822JVTFMjgqjH40mf30w9gKx4FkSSOqjbUtkQoGMBpFccPUogK0CqEqyqtUDEPqsQIARkAwihVYenfgjarYBJn9CImHTmAogHUKgp0tUgaUxVSYfp6SQk+rRh6PGSN+ARHxQms2Z9WNRgBaNheKzNQKKsFMMdQAGilmNVgKQrZeP3Ng5nEMIgDfCz0T9vjDsjl0xx39IXMmDGHqmrFKy8vIgg0U6eN5uCDDmPKlNl/8V5rbm7mrLO+TzxRMPkrADjNNttswdePPgDIgETNtcUBpe2KcYAHH3yJe+95jP2/sg/77n0Q99zzAK+/Np+mpk1YsmQxp59xDFtvtQ1hp/IfegZEhPseeIArb7+LYy79HruNipMIDRDRo/AZQugnputRKmlFm8PWysFDRKEpY9VQjkW9Hl2ZPta8soyOl55n++23Z7Ckjs1mzCBUUkZJ2GWbxs+Wxvx78Yc//IE777yTN954g+LSSmYc/hMmbV3H17cI2DBUQjKryQ567LdFLb4kyAdpSiO9rOhJcONNN7H8jfd4+tGnvnAWBXfefS8P/vEhxk0+mGUbWpi0RRMddZXsOTZgcuUw00bV46owKI+Ci7hhkl02bGjjjO/+hNO/823mzp2HoyEIwlx8ybl0dW/gZ1edT0CvBSS+acZrtZiBZAiszMBMtEO4lHDDtY+yz14HMnXqVFatWsWdd97JVlttxXbbbUcikWDXXXfl0st/zKgpGWK6CkclNpp9KiKEdAMpfxURXU5/vpkQpaAcHBVCo1HKJxf04OoIripB6LPpyhARp4m09z4GdCQ2PueOMsVDDglCThRfckAOV9WjVRl5WYGZrKvssz24kcVy1Xh8SRLQiXFPV4h1MzeAqQRHxW0FLWhVhsKkHM2CuAQzaZdsBEyaegyA6CTAQRFYj6j0Rj8pRYV1fO/DF6ylgUnlaRXDkwEKbL9W5Qi9OFSZfoEEIEP2OzeCBiNhKFQMZuzdU47Qh7bpRpEYJo1ZhqiC15ZgFngF9qsMs9hNmGu2keE3lZKmPVaUQNrtd2qc5w3YKSwYjdO98RGLoJRtFG01a4LYps9Gr2R0xgWriJQ9BtPmB6IGUBV0SmRw9XiCoM9+V0b3ahaeCUxGpc/OXS4aY+ngS+/G7xYULuMso5UGe98HDBjygGGUCmH6Pboowsb+gxAiA/YamHRrIL0oFUJThaKIbPARSkWt6XKnIToow2M9LuNRKo1DCUoHeEE3GtcAKZVAETPWE0rhqAaUEvzAJ++3sm5tJ9Mn7/W/0Vanvr6eAw444FOv33ffffzwrJsZSibZYcfpTGiq5/0P3uPBPz5OKPTpiq7tv7QH239pDx566CGOO+Y7hEJQXhFmYCANEmf8+Ho+WrqcB//4KMlkipy3CjCEcWBBiFYhPOkwA4K4+PTgUo8oD1QMn5SlTz0Qn5ysI6orKGgSTAWFxmfYCiyTKGVF3JInz2o8SaOVJhDTU9CTJKIUBZ2VES8OG/SuK1ESxgtMThlVYvVPhgkT0igVIaDH5OMB01y0UB0ZWCF9oQeXRqtRIB6i2im0gDC3TQtmACgD8ihVirKOxcKQbRoMPhm01VQoHPJBL5DF0WWWtTOJQqNfiKKpwuTHCwLHQmf7FCZFW0LAcvu3EAqjlTB/j2Lo/R6EKAqXxYtWcsXPruFb3/out912F2ec+VOOP97o1D74YAn33fd7Vq66nKrKUVxxxRV/xpacfPLJHHjwzkyaNInBoWaGBtM0N7fxh7ufZPbm82hvb6OmthiXEgLM+SqJ4Kgq8vTwlQN2Y++9v8xzzyzijO+fRnl5FbfeehurV6/m7LPP/j9X3SmlOPTgg7n/ySeYXTqKsG5D46HUAFryCA5CFpcGwygyiBAyjXAlQSbIMuxrerI5KiOlvPjsn3jw/gf+pUzR1ltvzZtvvkl3dzfJdEDrmnXkVSXDDU3cdNsrhIrCzJ47g9/fu4bF2TTvvrSE3afV8lG6j9YFH/Cj7531hQJYPT09nHzqd1mTj7PPcWcTtA6h+1PMmVbB2PE+jRUJKmMBDgnQDsigTSdrO+128vb8Rey66958abvdyefz3HnnH3j0sfvYcZe5HH/yXmjlIhIipKqt7iTAdC0YtgxwjJBqxBPz3PlAeVmIxx57jPLycr575ul8addZdPVt4JxzzqF/YAOlZTEWvvcmTdN2tCxMBEe7ZP1BEB9hkGJnOnkZIkQJYcejyJlCyImT8TtJekuJqGpcFcOnG1OA4+BQRtZrRcjhqkZcFSXPEEi5qTSTAEeZ6UYpbRkWhR/02gkxZM8rZPVRxpPJk1bMeKPApp/MXWBkC4U0n6PqwBpeolJo8exzYGQdZlKPAGnLrA1ZRiiEoPHoBTH1a6qQlRCFL56d0MutnimDL0m0HWt8GUZJCWjBlzRKbFGT8lGSAGVsPs1Ym0ORRSSKKboxwEKrCjxJb2SoAlIoKcJVtfi02ao814r/+8yxWXE8tjrQaKYKQu4BNIJiMqbAyYAfIWylBFmQYox0JcD4cIGmFh+FSIAvnaAidg6IGimJ9Qkz34NJ/xlCI4KyZ6hVhCDotkBYWbBsvytVcLPPIhK2AND8bq5xYHVjbfg027kgD1JkTVPDiBpExDO+WUpwqAN6bJPvJFpVg3RiZBt5hBK8IAmyAUdrQnosPh1mLsQ1dj2FSk89iFYxlOqz7w2RZ4gQpnuHqcYsRZHgqT+9xA3X30htXTVBIOy1925/daz4r2Oy/p4ZaRAEPP30kzS3LGPqtKk0TZjCn558kpqasUyYMIGpU6f+xff5vk9fXyelpWY18sEHHzBq1Ciam5s55dRv8sorz+BLF4H4GPPJ0Yjk8eiyuqcQWhkDOYO+PTtwZNCU2hLUDIHk0DqKRuFLgCKHVmVoVYqp/CisXoqseV0OlAYRlNQiahAv6LK9rmzVoeojxCbkpYN80IcmhqgsjjLiQYcitDZ+Kr70oZSH8UopwafNagICC/C0fWALbSNiNgXoYgBVocojbf91LUAs6BUyFlwOITjWo8b0ugvI2O2iuKrKuDKrFAUKGjIYf5KCK3wKkyYcwujSavGl3+47sINSDId6C9LydHR0c+nF15FOZ1FKM2b0JE7/zik88fjz/OrX13H77dcyesxEBgbauPYXv2PBwnc5+qj9Wb5iHSuWt5JO57nyyisZPXo06XSaBx78DclkhtLSIspKJlJSkmD6ZmP5YEkz991/Cz09SXbdbTsO+ep2Vq+SRlPB44//kVlzNqWhrgkv6CEvfTz84Bt0dvZwxmlX/B/u/E/Hq6++yuOPP86Mb+zF7JoSSiIxkDyJUBIjTC5Dqyg+nWipwBOhP9NOKgizcjBMXzZF13tLyK4c5JxzzvlcjunvxUMPPcQ3jj2OmvGzmDpuGiokvPVeO3vvsR3rk8O8/84CptcGrGvuYOKEsZx28lHstttuX4j2VJ3pJB+2t/PwTb9lXXML02bMpWTCVIbq61n91HK236GasZuE2ao6RNQtRdOFVhVge8uZFNAwK1d0cu0vbqKsvJIffP9CHn3sfp588gkOPexAdt1zMo52bLqjCE/azeLBGlkGQFg1kA3WIniEdZ2ZdAjw6SeVTvL6i2t547UPWbnqIzbbfFO+fcbRhBzHpFICA9AiTgnZYAAkRMgpQ6PJ+h0oVUlIhfBkkAAMi04JSkFZZEt6Mq/hqjBeYJjuItcUxwT2GTfVZwEh6hA1hCdpkBwRXYnWEQM+CiJkSVjGKYymkYA1mErYcgIJDDOtoriMMgutjdXeYYwQ3rGf5WJSgQWtoIcZP0zKsQCdCosxqEQkiZFEuChVTiDdmDHFYaMkRArptbBd4PmgilAqiUgETdRavYRR2kEkZwC0ilAQ3ysUri4z94AMIyoK0o/Yz3TVWPKyCiGDImr1pl0Yx/lqPGnB6IJKMZqxLnu96sB2pzCpvQoomGPj2O8isHOQmOPZ6EZfDFJovdNtFveY+00kS6GXKaqg5fq4st5RcUS1AS5BAVASoIijCJlFvHRi9EppFMYJX6mUTQGaVjpG7J5DpAqs95mpQmzAkzZMOlRj5lPDzom4IMP4DOOQQOs42qbwgsCAIs8CRocEPkNmkSkODnE8aUbs9mbeCOPoMvJBB5oSUIKDg6MFIQ4SJSemYtNVdfjSQqEAIJAI9917L2+/tZLrr70N4H9Hk/WPOr4/9cxDHHbI0fh+QDqdJhqNMnXqVN5+++1Pbfv2229yxx234bqQzgQolcVxXPb/yk58afttUaoIX1oxuX9jc6CVxpccQeCglfHfMLqcqLmpFARByg5CAY4KEUgeV0XxySJBBq0q6c2GEdVPTdT6oKgKjANxsz26CtRGijYPoshLN6BM5QZRc2xSECu6ltkxrsRKxQiryeTkIxwVR6kQxsE3iwFP/Zj8f2AfPtMJXgjZ1JuDVlUIEcA8ZKaTvGNZNkGIYiqKHEPd2geNwDj7BuRQuDjEcXQUIWsGOlVkr2kJxojVx7gPhzeyWsZhvwex/mFGv9CPGTxLCIIUHR3dXH7JnZx40jfYdPpMlAqjlCKXy3HddddRUlLME088QVVVnFCohOOOO4ZoNMbTTz9Fa9tqerrTzJmzBUcffbR1jAfZKIT8yyxKZ2cne+y5K6ed/lX2/8rexIqM/8oTj77FGd+9kMZRVey7327stucsxjeN4htHXMJDDz72me/fvxd33HUXN973CDdfezVLvQ6qox7FoSEUFZSEQ9QXNZAL1pP2Bkl6WTJ+hKX9OVasz/LmXQ+QUBluue5WIn+B7f1XRW82x9quHmJD/SxdtoJnXniTgb4eimurqR6/GT86dn+i0S+ODUQymeSBRx7mrkcfMiUjO+7P6E3HcfjEOA1FtdyzbIDpxb1MqQ1REiklpFMUuRPwacFMtmWI9PHaK+9zx+0PU9/QyOmnfZ+KijJOOfVE5m23OYcdsbPpaaewk5WyIltlFzE5tEqQD7pxVSXZoBOxiz1HOQRqEIc68tKOH0DOH8az44BIBlc7uJTgyzCO9ki4m5P1+xnyVxBTteRJElIaIcALsri62qRn8HGpIsDD1RpHVZD11+CJj1IRoroCj3YQIazqcJxiMv4ajE1MGqMyd4g5tWZsVJ5l4wzwMfNPr51EC/fgEEjEsvDVKGUmRcM+FarxwKSyCsLuIpuSGuLjFF3eblNtQIekEdKgYihJ2Ik9BgyYMUiSaCbg8QGCgyOV+PRYNkUZywfloylDKYeAJMagtMSm/ATUIIhrmcYiCi13zPmm0CpMQNKymspuZ6CpImx0tzaFacTwRnuqqTHVqCwDBJPOLFgZFKx+BjGAx0VsOrEwHwiFmr8yjGE0OEzCpx0jK4nY8V8ZhkpVYbIZBrSZoqgCSI5hKv26MXNdGiFu5RIKM0elDSgiMMerlL0eafM9q4L0IoJI2rKYUTRRIGKZqX4UlRZwFuPJGsMwicJVdaCStk1VgqzfjOm1Clk/h6tdHCXkvH58XEOABEmU9ghroyFzdWCAJdpWWQ4TiE9I1xMEWRwdwwsGMBWwMYyXgOnioojhSSu/velZurt7Oe/cn+M4of9/gqxPxqrVi1i+rJl587anpOTPe5y98eaz/Pzqn3HdjeciXhGp4QHqG2M4qhFP1qMpQWuFEgcowZcOfHKEqCMga6plVMKCp24Cu7o05nc+Wd9FqwhRx7A2rgZffIIghUeOjBfG1VGKQ3UE0gsqsHRqDuNqG9k46CllSlODII8nvYbOJWvBTjWaLKaM1wVJkJVVuKoEhcKXPFG30ubgjXZNU2mpcEGrmPlsKVTUxDEAzMHoCqxnGGUG8EgYv1AuLKX22DKIrZIRCRBCZpWnQpbadi2NXm4eJFUCMoCjGgjoNhMKDQgtFJx2HWqtMHUYKLHATyPk+eijFi766bVM2mQzmpo24eijj/6rqa+uri7i8TiRSJhddv0SM2ZM4be33s+JJ57EFVdcQTab4fHHH2T77XelrKyMm2++luXL1zFlynSmTp3K6NGmmvLFl55mm7nzmDRpGp6X4fEnHuLJPz3GcMpn0qSxbLvddEaPHsXNN9/Fa6++x5Zbbk53TxfbbjuPk7/5/f/T/ftX7+tVq/jaad9hh68fyUE7z2Zdqh0lAeOKiykND1Pkugx7ebJ+lqhTzbvL1nD5mZdx7pXXs/9WW/79HfwXxcrBJApFU8k/XgH5/8b6lvWc9eOzKQrHKdp8HDO22Y5YfCwLPuznsDkeTRUuJaEi1gxliLoBo+IlaCUosig1nkDaMEUZjZx99rcZPXoKxx5zykYA/4tf/IJxE2PsusfmhJRpTi74xvNHdWFAgmMXW1lMkYVGCJMPkgQyZErflVldO1IFgCcpkt5aYk4DSqJkZR0hVYqjo/hBGp88GgeFSyboI+6OJ+t3mglVDeOqOIEIPgOGhSdERCfIBS1EdB0oh7TfhkPYPM86hEMMpcKmNZNsMKBDAgoGmo4KEdLFRkphxxDDUghIha3yS9vUmGldI2StdUHGAE+KMT5wZqIzr8cwGrcKTI/R9ZiJ3YRhdCfgB92YqkOxbJVnmXBsSkvhyzAhXUsgSVNdJwXH8jgOMTwxKUZX1wMpgmAQlENINWDsAFwggs8AygrTTbVjDsEzVZjkDeBTxhgUMcJyR8XwbWsZw9z7uEyy46HxFjQdPLosYMGO1QGFKkWz6M1hUrGe3XeAyQaEMF6I4U9cPx9j3xOmUI1s9FtmEW1kIHFrcmqg68biJJUAMWN8YHWBZoE+DDazoVTCfv+GfVQbC8MKLvIFB3yHQHowgLTQTqhgT2EqtZWKIKQ3LjgcGvGljYzXh6OrGch1E+BRF53EkNdCxAkIgjDdXUlu/vXd9PV2c9K3D2HC+EnEHGPZYCwoxiGsNXNUEOCRJBBwVDEiHj6DaISQmogvHSQz3Sx8ayWvvfIh4UhA/agqnnpsPhddeAVTpvxlM9LPrMlSSn0VOA8Yg+lu+Q0ReUUptTNwg339Lfv6OvueauBeYBJwroj81r5+G3A0sLWIzLevTQRWiKkx/ZdE04SZNE34dKn84088xKNP3s8vbzqfaLSKmFtHLLeYfJAGNWxIZBVFk0BUznpfKRQxMrIeEZ+QqkapMHnbAFYRwQ+G0dponCJa0Zd1yLtJIo5nmu+qBKI8HBGKXJeM77KsfzW1MU1JOAKqmEDyQApjjFqgxrEPcdKCligGK3sIHbh6EgF95II2zEpICCRESGNSiVIQS4Yxw1Az5gbPE0glamMvLgP0DE1t0pgBhfYWg2YFYM3+QKF1Fk2CQHw7WBRYPRd0lECGzaApAxin4bxdRaRQFOFJF1jqOlCddj8ZNLVmPSQCKowmZN6jinjg/sd5+aWF3HzTnZ8Czn8pCs1D8/kcc+fOIZ32ePXVF5gxwzwbF1zwI95e8CYrVzbzta99jbcXvMWll/+YFcuXs3TpG7zw0gPkc4p587bi93fexKpVLRx91Anst+9h7L/fVxERli9fzquvvsDTTz5Jd7cRhHb1dHDTzTeT9Xv+8Rv370RTUxM3/+42jvnGkWyz2RiKS4vpHlY8vTbDnEqXzuYSuisV29d6bBjuZX2kAYpK/+cAFsC6ZBoF/zTI6uvr46STTmHrY4+naeJMVqVbmV5bzZRyxRFNNeT8fpQKEIZoKjUTqqNqQGVQdANJTH+7Xtas+RDfi3Pat85EqY/B/+67785vb7+KXfeYgSk8wbA8qg8jkA7sM1CwfImiqMJnHa5SBFSaAhAxk5Yxf9SEdBMJJ0yeXltg4yHKwwt6yUuAo6J2ovYoD89gOL8GSKNVDK2qMUU/LhBCKyGia/CCHsJqFIFKo4IAVxkzZocKIk4DOb8TkRQ5euxkGuAQQ4jg04OjJhjNJxrDVJmUVgEsKDS+DNprUIIxdi4QAC4iYlly0KoIRR1Cs01ZmRJ9B22ZFAfjKZjBSCSyKMps6o+N4MqwR9augQ4UGi/oIeJMIhssw5cUASk0nu1fZwppXFVJLug1GiwVIi+tVr4xhBmI04gqxbBqIRRhIqqJnKzC9BUNWWbIFD9BFyIRlDLVgmIXkAVPMcP8ZawkwvYkxLeAowbopFCtaN43RCFdaMbhPrtgL8awXVGUPTbs4llZZ3isZMUUQJkG14UWcQWDU/AQydkUW9pAZbGFSGKq+ArehwZ4mU4k0G9BrtEWomJ2/0OfAGk+vpTBRkuKkAXHOVBVGKlIFk866c9m6e7qo239Ctav7WOLLZqonTRM1AnhqBh92Q5+dNYN/Pjc8zn7h99hdGMdDiFElBGyKw+kmcCmOyFD2huiL5OnN+cwNhElGHZ4+dUXWLH0YT744CNikWLmbbs1xx3zbTzPY+26ZRx91OZ/1qnk/43PBLKUUrsClwOHAfOBevt6FfBH4HjgUeBC4B5grn3rd4CbgYeBZ5RS94ip68RexYuAv64Y+zfFgrffo7S0lCEZIJ/PM+z1EdYKhxrydIMEeDJIzs/haCEgZ6tnelGiMOJij1S+BS8YRhMj7Dg4fiV3/f5xPlq2iEsuPwOtHYZyYTw3x8KOAcKhYWZUxEl7leSDToz+SbFysJ9ZlZuR8VO4KoerxTJDJo3nS58RxZLF1Y14QYelgwMcmxIIxMd4y3i4lBIo01InH3Tj+1nizmRTeUHxxty7cXnvIpC8LVctdHSvtKurPKaaJEpAp/FcUS6KaowRa5pADVmavxiHGjw6zIoU1xjIkjaCfARja5Gwkwh83FQ6CaJQqp6CtUMgrYhlBc3EkeXRR17j3YUbuP6636L1P6bZCYXCXHrJ9Z96/eyzz+NPf3qYr3zlMMLhMEEA5eVRttp6CltuvRmQRsk4AprZcZetyGWT3HLz/dxz753MmjOV0kQtu+yyB8cdd9LGzzSaGeOGLBurhD7fmF5dyV3X3sCZ3z+T8Yd9jb7yOgZ6FCsffp8x24xlmwnjeHJdP8l+h+VrWhkK/d861X/RY+eGz+e8AjdEf1EpHZRQFbQwPlFFeSRPPujBlxLCbi3GV6cbs/hRKDVsVtuEUAzadEmYe+5+kn32OeDPABbAxIkT6e5MmcmKqAUIGYzxcc4KjxOWyTBsTUAPpgK6iIiqIhesJx+kLYuTxLQDy+PoNKlchpwMEncqiTrl5IM0Ob+dsNSTV+24xMkFvaAihJVhhwMGCFSAq0owFWUpHFVKlrU4tv2NqxvRxK3OdAgv6MZRUfKSM8crWctIhUEPm0URgpEmmEphZU0mjfljJ4bKypqUE4pAwkYLKlGrxSoIriMEYqq3zRhlWrVAEp8kCBgX905EsAau/fh0YtiaahR5fAbMeCUO0IGvOoFSMyb66/FJmyrOwCcvCldHENqNwD9YjgQ+WpUTUlFTuLTRPqcPpcpxqcKz1goKwZOUYZqUWL2QsZRwaLTnH0Zbdq4gUQhUNxBBSWB4JJVGU4RmNJ6swWhYh0EZ004zjhqvPgPeFIGY1KNhEAsZiQLTZ5pmm5SoMb3WVOHTgkhhoZ3CVK8WwFaR/S6SdvyOARNQqvkT6e0YGzVXZEHKMa2HyizA7TNMlRSZW37jojxEIIM45AgwDbiNZq6KQPqRoBOfPIOpXq64+C42tOVoqK+kacJoJjXN4uLzbuTW2y9iQ0czd976FuvXNXPqKady7323c8wxB1IUrScfdOEFvTgqhCt15AMfpTJohkC5aGrIBmvp2NDFrbf/gfRgjr333Jn9996Dc74/Dcf5c8g0ceLEvzuWfFYm6wLgpyLypv19A4BS6pvAByJyn/39fKBbKTVFRJZinqjCT6FEpBC/A45QSu0gIi99xuP4zJH22ggkTzw05q9uk0qleP+DJWz1pc154K7HeH9+B1tvM5O89OIHIUR1EnMERyUQhJ5sN1pHKA2XkfL6Cak0ro6jtSIIenFVDVqbXoLvzF/FofvvBSguvuw0AknycmuEu56cz/Czf6S6op5kuo95287GK6pl+mbT2GxaER3rU0wYo1k1uJyY4xIL+awagJBfxqa1Hlr1E4jCV4NAFi39aIpBOeSCFkwqcz0Kl4geY3LVtkok5w8QYESZ+WCQgF5bmm1WX1qV2TYsVs9gqVmjMwso+MfkpcUOnB5aSlHKs6vGACUhUFkC6ce47RpGTFOCzyDGbbcK0+1d7CoihrKVVAXhrFmNmjYMPim76vRNpRJpuruGefjBZ7jttrs/16qzkpISDj306xt/D4VCDA72UlxchaHffYyGwUOkBzfscuLJX2f9miHaupeyZPFH/PHB33PqKR+nBJVyUXY13tq5gcaqKLHw52ON8MmY2NTEnXf8niuvuYbk+mco8wPWt3fSslJRedIxRLMNPLi4D/f1e/nVZRd87vv/X4q08gm8Yc7aZQbNwx+R9fqojVURkMBREaAfo3txAEEEPOmxz2LBODICCKedfjgnnfh9+gfa2X//w3DsgqC7dyXr123gwfvfYP+D5uCqYsscw/rmLt57dxm93f2khnOEQ2UcdtgeFJf5BKQJSzWBzqEI4SqPfJAjwHj35PxeAkkRdoQI9YScmJ1EA8LaxRhBmmroEMVEdT0p/0PDOEhAyCnBVTFygTHmzPjLLZPjg9LkpAXTvaLAcMdwVCUePWYilmJ8WoEUBJqwjqNUr9VZGU1WQAuKKky/V2cjWxJIFCxzJ0EU6KdQlWl0m2X4rLNXqdiyNMWIpDDNijuBdrTUI3ThST8u1aAittdcNyHVgAhkZQhNiDxdaCnCl36McWUPiGHAcmRByvCRjzVQkkErcHQpuaAHAUI6YcdKF1+67cJSEdFjycoKjCl1QVLhgxjjCK1SG+8hU+Vd0P2YszVmmgGyEdCYjINIxoyFykfEQ6uAglhcVKHtjtGBBfjmvrSZgY+NmgcpiOILTuhmvDbNmRUNCNqmaEMUipMKrvjmvg+hVb+5LqrYVPJhqi+NhYdGCvoqKbbVe1gtnIdIQaRvpCvmOWrH0Wbhbvwokxakw9tvLeWaq+/hrO+fwdytv/xnz2w8Hme/vb/J5OkTOeqIE3j+uT9xw68u5aBD9mLPvfdBxOiQfenFUTHaUu28u2AhC15awpe/vDNbbDOWx19cwjW/epSZY8Zy1ulnsUnTpH96fvm7mixluL408BMMYxUFHgK+D1wGhEXk5E9s/z5wnog8oAwVcR/QBJwvIr+229yG8QDoBA4Vke0+a7rws2qy8oHRFIV12V/d5vDDD+cPf/gD02dNYctZc5m9+Wxef+M16jYppX19B+PGNnLq6V9DhQRUgt5MjrDWlEXKCIJefAaIujXEnUqG/WZyfkBzzzAP/u4B+lpz9Pb0cvPvzmXZYDEvvPEG3Us/4pWXF/K1fb7Cqaecgu97vDV/Pvc/cj9tLa3gCEMDmsqSGCt7+xk9YRRzt59F6fjR3LMswalbVbHrGNf6zKQxSL+IvnwHJaEGHJUhHwzgqtFkgzYiusKY40kYpTQBYUxLhiICXEQ6CKmJOI5naWEHIQUqhKYMkV5QhXx9mMB2ejcPqpkoFC5hPZpA8la0HsYIOgtVgwoRjUO9BWNGvGoqcWIohkFFTBsOGSLA25iidCix7E+SguDdURX4kuT6a3/LDtsdwJZbbvt374V/Jp559j6WvP823zrtaxQ0dq6qwZM0gXRbvUIUB5Pa/WjpezzzxBrOPvvT1Xp/uOcufn/PLUxoGsvPL/8lrv73iLuXrl/PET84m9TaHoZCEb6663b8/Mdn/Vv2/d8aKwa6OP6E47jml2dTHA7I+VGaSsrQyrHpsCFAm3SVUp/QHxWhVBS1sU2KRojg+4P88OyfM23aDI4/7rsAiOTJ57Pceec9PPf8E8SKQnheGiRBWVmMLbaYy7jxY0jES1i9ZiXfOuUs/vT0nTRNjtlJLCDEGDxpJx8MGLsXwoSdWvLSZnRXqpRA0obBEIVPirBqQhgmL2uBKPnANF02LXECo8vS9XjSCYIp6MEBUoT1KPLSB+TRKow2PA2KKK4qIyBPLuhARPCCnEnEOI2EnBKMxMCYFRu7hQSKACSN0uUEAQg9aKJoxuDRYqqqlbYFAaW2bVQKh0YruPdARdC4OKrKeghm8GyD94ImLAiU3bfRuUWdcWSDZgJySOBhDDI9xFYpasJ4VsPkB/UM5HrIBsNURWsoDSdAxLqaGxmDsaUQHGWKDxQVaCyzQx8O9XhsQFktq6m0C0AVtE4hCg7psrGyLoFWWXyxVguAQzW+dBomEVNBHjBsvbqsOztiMhn2eIzowxQXYMduI9QvVIkXmXEYjfGxKkSxhTYdfFxsUG7vfZvyI4OxNTAt5fyNJtbm2hkWP4xWGWvbEKB0gJKoXVxjj0MjuDSv7SEc66Gqsh7XNfrnodx6tHa54sK7UUQ599xz/6r9je/73HbbzTz/wp847lvfYM7sOm674yGyyYBEcZhXXlhM2I1TFC1lOJ1kiy23Ys/d9+Spp57iwaf/RHFlFRdfdDnTGmr+4THj/1xdqJRqwDBXC4F9MXfpw8CLGJ/9LhE5+xPbvwbcJCK3/Y3PvA0Dsi4EVgAn2n//IsiyjNk3AcaMGTNn3bp1f/OY/5Fo72vj9XfeZJetd6YkUUIymeSjte8zrWlTLvrpxey431TGTChnqKeMdImHp8LUxXJUx6oJBTEyai0iPn4Q5/n5Uc4/+VCu+/UN7L3jHuy7317U1Cdo6wuYPnsWB+z8ZWZMn0GiqPjvHlc6m+OjDz/isddeYdWixawdSjPva0czqWkssyqaGVUiZIOAjF/GUN7HUTGqI4LSKcK6mHzQR9xpIqCTvAyhKcLRoMTBI0/OK7BwEFYaIYppp2MeNEdFbeVPBqViVhsWI6xr8YJ+QzGLEFKjQWXxxFQFmpVYFFQIxLOiSNf0AEPZ6pFC1/qIoZFx0Mr0NTSNVmOgjDFswXXetLvQhJiJRxsXnHcFJ55wJmPHjv/c7oX/N5qbm/nOd0/kkst+yIQJTfjW88xMqGYw9CVlK0+yiOQIJMO3jr+Z2377O1zXDASB+Dz/3HM8/fSzHP/Dr3Dn7x4lli3m7LN++C879v83RIQgEF5e08P2E6pw9BfHc+qLGqd+9wQOP3E3mkbXUOTGiblRlHLx8lFuuOFG3nxjMVf94ic01I0BNYTR0kRRBfCAueehkva2Fs444wJuu+0eYtGiT+2ro6ODK6+8gu7uDkQlSRRH0FSQTLWjCBOKCCefcjKTJtdbAAfGBiGMxzqQCnJBB47SKGUE1yE1Cq0cskEbInlCug4kIEcriMa0VUkjwTABDlHdSE66EckSVrVE3UZS3ru26jFMWNWQCdahUFbTpWwl8jAoD00cRQKfPvwgiR8Iji7CwSHklOLouLVgwSz07PjiyzDGuT1NIANoVYbxKUqipdSkATe22FFo4vh2TAswVWGCENLVgIPvJ/HpRas4eT9FzJ2IL30EMowvpqLaVTW4FJOWFoIggyhFVDcScSoZ8hZhpBdhAkkQ0iGG8jkGc4YxLAmHiYcSlIdGk5dVmEVoHI9uI4ugBJ/ejYJtTdgy4AV3+RzG8DPMRimFrSw0hUJxOy5qtCrGGJTG8fHI+R6uHrTMH7jaGomSxWcYrRqANF7QD/g4OkYQaIQ0LnUEKoVjRFT2fo0YwKcKYv2EZbOMPEQIWx2VRpFGq2qMdKRg72CE+8YuImNAp4pi/CHDdHUOMX/BB/R3aVrbl9LR0U562GIO5VFaXE1paTnaTfHh++uZOHEKOa+H1199h1t/ey1jx49lfffbrGvOcuEPrmXul+Zw6Xk//4vP69KlSzj//PPZ/8B57LLPzrzZlaWi3eP3d9zCN446noH+Qbbffgf6+/tJJBIbC1AKMZhJEyj4/9g7zzDLqqJt37XW3id3nJ6ezMwQhpxzkCQmgoqoSFJAQEVAVEAFxQSKgiKKgqIIgpgToBhQQCWIknMcmJw6n7jDqu/HWj3gp74vKq+Rui4v6dM9+6S9165V9dT99BbLf/b4/1v8PUlWH14/daSqXh4eOwh4P/BrIFbV45/19/fhq1bf+x+OeRmwRFXfLyLH4pOsQ4BHn69K1vMRl112GT/96XWUKyUa9RaPL3yE7XbaijUrl7N0yXJ22X0z3vvBo8nyLn78SJNf/qFA+yfncuKJJ7LejttTbLbp6+6mXP7bvrRnx4oVK/jYeeexcOUa9njZHhy23+Ys6kxl4/4eugu9fPjW21m6qsF+m/WwyZSMvpLSykqUo1UkuRBLL5HJQMaJmEbHtRGaGLHE1mLWOq/7BTCSbl+5YgxPKvYu6x4LkWC0HyerQnLkvbecmwBp4vk3TSx9IILDkbshjBQxBGsWsWtvFoINIuE02L9EQejvWxT+ou4F9W1DK33c/Ntb+Na3fsbE+CivfvVreN1r3/R3f8b/fxx55BHstvv6HP7GA0D6ICAjnHYQCr6crsOkoU1idRDHKN/+9nX86vpbWGfW5rzu4Fdy2RVfptXI+OSnTmU8W87qdoFffvNnpBNlzjj9zOf9df+3x1jyBCXTz9U/uJ5bb72BQrHC6OgQZ330XAYGBp7zcW657Wa+fOnned8ZxzBrVi84xw3X38Nll13Lsce+jk023ZZ3vfN0rrjyfMT68XmortVEGDFMTMAxbz6BGdOn877TT2HmjD/l9KkmtJNVvObVR/OJcz/M3PWUKIoDgdsCVXJdgWUg3KwTrMQhQTKhktGFc5nXRJIgVImYQsZqVMFRJ5IBLDVSXRPaYjFGS3gPUaVoptF2yymZmXTy1WEaOPXtNUnI3FCoQhTCtFcjaKAixHiQZoH1cDRI3FCYlitTkDLWDDCZJDnaeBp3RmR6vSWYekq8aiPUv0tBS1SAte2qIk4n4aPdWOmiky8BXHiNQpavJtMWHpJaoJN7v7ySmeEr5NJBHThJKOgcmroIaHtNmsGL2nVRGCQqkromtWgKkNPMO7TTjLZz9BSm0BN3h6nsMkUzl1SfDmuVYJlFqiMo48QysFanKhRJ3BJUHSU7N3z23vrH2/Y8Q4j3lj/esquZpojktPNxuuL1iWQ0IGo810sohaq6CZ9RGW/87LEfhkIw9M69dlAJHYRSaHO2QcsIOUZmkfM0Xk/WjegAGYvDMdbFsSSI8CtBT+VCcueHEBxDpFnKxRd9l/vvWcYBBxzAtGnTGBycwrRp0+nq6gE807JerzM6OkqzNcyGC7Zc25q76qqruP7667HWUioLc9dZwOw5s9hgo3XZdssd/7/rR/nCRZ/gvvvu48yzTiYvNlE3hR/88Kc8ePNjnH/++VQqf7qxeb7j7+Jkichi4AxV/Vr4eTLJugh4k6ruGh6v4q2wtwmarL90vMt4JsmKgIeAS4BP/CslWc+OPM/JXcbDDz/EZptuwWFvOpCzLziauvYwvMbwtSt/wa9/9Wteucs2tFsZdr15nHz0m1mvp/d5fR2rVq3iZz/7Gb/87c38+ulV9JcjRJSRZavZ7KDjOWin9SnMTNiw1zKl4DCSsKYzQV/cS0/RG2C2c8FIylC7wFAnZ4sphli6ic0UMl0BxMQyk1SX4iF53XiYYjcEkbrXVdTDjipCSb0hNQW/Q9YRslDlsVIgNlNx2iJnDOijIF046TAJFgUDmgQxazcShJV+ycl9SVqnkPM0BusFrbRwecqbjvgAV339R0TR82tgkGYNLv7i+TzyyCOc+6kziUwJp52A6PCVuMnPQsnIdBSlQ6zzyVjNb298kN/cfDuvfP2LmDtvDrHtYLSbSjSHsfQuvv/1P9AYjTj11Pf+iSj6hfjbYqTzOM30aX5xzf08+dBy3vaOV/DD7/yBG2+8ifef8QG6u3uZO3eD53y8u+66iyuu/ArDwyPEsbD9dtty5JHHE0URTz31NLfffiu5ruT1Bx8QWuQmtFE8hfsXv7iZ0WHDG95wCE6bqLaI7B8nek899SjdvbB40RAvfckrufWObzE4rRZakzWMVnC0yXQNBM9SDZ6dTh2ZyzGmiBXBOzZMKlgyrPQE71AB+nEs9a1DisQyjVRXgXaAbjLGsBJ5M3ht+utYmgg5lgo5LXqjHWjkDwa8Q+mZ61OUgqzjEwtt483au8l0mKLtp2g2JNXHcWT+dTufbFkqQNH7xInF0IdzHXJaiAiRlMi0ReYmKJhBclVEcjI34ZVBuWJNhKGLxI0ixBjJ6YrmoQLNdDmJTmCpUDQlcqljpEDuElLV0MJqkKkQmwoV20dOi9ylxMaS6hBVOxcRQydfjlBiNGlTtDmxVUT7yHQNFTsQ2qo+0SnYdX3V0C3BUMMYYRJ5k7k6Skpkq1j6yHWS7eWTYW93Uw5aKoNIRjsrYE2FgmkTyQBKwyfXAcwKZSJTxrkJEh0iYi5OVns6vXqfwtjEIAnqHCo1DAWM8XBpDbwq73U7FU+IH8dPtcZ4DMXkZGNgZhHj7dRcGNLwE4033/wgF372Co5+85t4xctf87dcxv9rOOe45JJLeOCBB1i9eikHvGonXvf6lzGRVHlodJyVY2s46/B3cNmlX2ezTbf8h/ie/r1J1keAVwD74duFV+PbhZ8FHgeOBn6MF8jvoao7/fkjrT3eZYQkK/x8WDhW/79qkjUZw8NDvOf9JzBn7lR2esMxfOp959DV1cXB++zNhV88j92OOIZf3T1Mcus1/PSaH61FBvxfxIpGmysfX8jsaoU19z7Ald/6Ievuuh8zNrC8fLNNWdA7SjsbZlEdBkqGdWo9ZK6JIyeSEkYqDCeWnsIIJeOIbU+oIrVgrRG0hikpL670k09FXw6H0P6r4W1lFEMvTjPS4CUWySyEDo4Gk55tIgYj/fhpmE7QJ6TAZPXKC0y9dVEBz4Bp4FsxBSYJ8H7Tk/HT637Hb296jLnzZrB06VNsstm67L7bS1hnzibPy+f8ve9dyRNP38kJJx4ahgMm8AtNN8/YD1VwzpLhacUGg9MMR047T4hlGjnjOI0oGQt0qNgFfO7C8+mqzuAtx73zeXmt/+3RzNbQTpdzzJHv5CuXf5RSVOT97/0q3T2GSrXKTTf8jmuu/mnguf31cccdt/PAAw/x4598j3vufpgHH3yYQw99Axd98aPUuiYBxWXQJkrERz78Wd589InMnTufLF+G0iS2fiKp1WrxtuOPZtq0Pp54fCkf+MD7+MQnPs0XLzsJyInM1LXaRkNX0OYkiFpyfMsaHEneohRN9xPPOoYLejGjXXgIcIRPvBphwxKFCkkhDJckCP1krok/rxWVetDtlMBlvvIszmNqco8fMBQpRdMCKkaxZpAkX4bQhZEWIiWa6QQFEyGSUTDTPBndtdbeuMGz+1IdpcA0MtaEmzlYppCyCkcCar0HosvJNSd19fA5R8Smm2a+nIIpopKT5YqVmGo8SDsbBoWUOlYKFE0Pia6hZKZgRHDqGE+HcFimxOuhMkruDC1dSVH6vO7Wlv3UuBhi6Q/VMD+9530MhynKIE4aGJn0ChzDJygVJnlVIoKRIrkLbhZisUwNeAJPiPdTj1MQjTy4mmW+YrUWatrG0B+8crOwHnqLMW8z00ceKPqCkOkYJnQEfHuyl0xXEDMTMRMhIQ7AUUpYyhjjAQBOa/hNtZ9wddr2058SBT9NwcqUsI4bVq1ayUc//AXmzN6YU0459W+2DXsuce21P+TXv/kl7zvjrVSqHiEEdZ4cL7G0WSViMYvvuZ9ffvd2DjvkMF7ykpf8n72WyfhLSdZzTe8+CvweeBRfdboLOFtVVwMHAWfjTZV2BN7wN7y+b+AJaP+ysWjRIk477TROPvXdbLPnFixdNsEn3nUWRxxyON/94hc58MCD2GC9zbnjmuvYb70p3Pabm/5PEyyA6dUSe9LL0G9Xc8Jr9mXd+Qv46SXnc891T3H9w+NcesMQy1slMnWUbI3VnYTHxwcYando5uNEZpwZlZx6WqKRdUiyUSRY1ai2ccEKyAsjA9/EeCyEBG2EHzUfD7vaAqnzuycRxUgXRjrB7FTwEyhFvLv9KH6371upljlMWihM8rsE8b5gazcCCUgdYXDtrkmYwstfvjN7770X2267Ice+ZV96eiwXXXwBhx9xMB/+8JkMDf19bKqDDjqce+95lJz2WuGtomjQl3gD7BYqwxgpYMUQySzyAB6MpQcjSpqnFIMdkpWYXMY5+piD+fGPrw1Yihfi7w0rberD3oezkU6QaZuPfeJk3nfGUcS2yuGHHfU3J1i33XYzl156ITNnVfj8Fz5Ad08PTz/9NB/5yFm859SPhWQ7iKtFabVyHnn4UWbN8a0Ka6YRmblrj3f++edzzLGv5SNnn8R5n3k3P77ue7zh0Fd5zp4UvAm05gFsHLSOgDERBTMVKwWsRJTj2QhNnA4T0Y+l5q9PMSB+PkxIsJSx1IhkcgM1DgKGKpEUsTbDmA4iGRF9GCJKZpCMFpkmGK2R5x1yUqwZJNWUTj6EpYdMhXa2CqeWzLX9DVorGMpkDqCA02YQjE+QMoL3bo3J1Fu+ODHkasidkrgOuXo5QYWNMPSS5J1A+65gpEjZDiIS08hXgRbJNSWin1hqZNohzTpkmqPimYb9xU1pZqPkTohNL05jmvlw0JJFqDRIdIhqPOiTDQyxdIFmnvnlNAi7c2JbQ6RIbAaoRP1EUTVUS/zNHgEkwkofk2Jybx3fFThlMaL9OB0PMo3YV/K1QppH5CxFZLJCOQUjMZb+oNkSomDX5Ct3YMUi5ECdSGqI5IikFM0MItOLMZM6wSEiSiirAsG+C4PBUsGKImYsaGA9ToS19jxtPKLCA6Anpx+driHJ2nz5Sz/k9Pd8nvecdhZnnPGXhenPR1x11RX8/o7f8OGPHk+5oqhr4VxCJ3eMJyOsW+0wpdxF9ybb0Zk1j1uHk//9oP+H8Zz6K+qNs44P//v/f3c9sNFf86SqeuT/97MDNvtrjvGPiEcffZQf/vCHLFq6iEXLF3Hux87lihuu5ptf+xmXfuFi1lvnGUZGFEVc+PnP0cwm6C9N/4e9xg3m95Oknr108ZknceHOW/Gryy7klt9vQKVosJRZsGnGExMdirbDSBLTUxxkNBkidY6KzalFTVzw/eq4cYpmPr7PnwAJquOotLCU8GPcbXAlck387gwXIKMWKxp21BYnnbV0aSszA6umiWK9OFQ7gCDSCgtNBaijOo7XJAhOxnx6p2F6hwg/Pp+irgiM42jxon283kQkYsbMmbzkZTsCEQ8/uJgTT3ozCxZszC4778mLXrT7X62Ru+jiz7Dd9puianDi/RpRXx2QICBVSUAnPcbGA9fFEZmpa6c3u6MZOBnCL1xl0nwZwyMNal1lTj/jdE466WQvon4h/uYQisyYPptNN96KP9y4nD32XkCpVOUH37uWS770NT75yfP+5mPfe++9HHbEa9hhh125884bUZdy+dcuYWxsiPvvf5BbfvsQu+y2CZ/+1KU8+shSxsfHec97PoQVv9mStRorH8uXL2TTzfdHqDNzZjfvPvUYVC05KxEth/fjK8ciDiutAK308Eev2fKmxyoxlqLX1OCdFQyxv+7ycXJy7wwhdVQrePZULWgklcQtZtKyJpIpfoJQOxhtEkns25KsDCP7bQwOQ06uY1hKGAypy3FEdJwjynMi06RolYwUkSne81AnPB7BCdAgkyGfYDlFjfWVcNoYUZSIzDlaDJG6DkaKZDpB2RSJTC+tfJFvyhpDJF04rZO4lUSmTEW6SdwYiENpEkmJZrYSI0Ik3SRugla+GkNMIYI0j5hIV1GMLEqTnkItrE+KF+m3/fWvBSKJg1xhER3XoGC7vCG1eqSGn7B2QE6uEzjqxNINkuIYQ6TkRfvaRAPlX+ggJkJdk8g4inYOfgoxAsZ9gi69pOlyv7ZQwhtz94CM423Gct/OkwJWm2EMKcZP+mU4+hAmECmgAJqu1f153IKHiXrBu4Zzrz8kWFUQD0BFJyu2A9x9zw2c8/HLOeyQQ7n88qv+5mvrr4mfXPdDLrn0g4jpoM4FP+EmVqo8Nap8Z9UEC3oK/OKcz9BudXj5kUf9Q17XX4rnV8TyHxBJkvCT637KD675Fs16m55aH0+vXEy1t8aHzvogu7/4xVz9ze/TU/tTungpqlKK/n47j78merqL7LLdbACeGG8zb/4CitM2ZNlNX6W02yu48QFBemtE5TZbDhgWdCvdcU7qlEzHaeVF+mNHlsOKdkTq2syrNTHisFIh0yZOh7BSJnETftcnHq7nuVgARSIp4hgJi7cgYsNusIbKOOjYM5wXSddqM7zItRC4OoZcc/x+sAiSBLaNNz/1OoHpZG44KBgyHOP+uNImZwQJ4k3UjylvtPECvnzZ2Tz11NP87ra7eevxF1OtTGVwcIByOaa7p8qcWQvYb79X/REPRVV58MEHOefc09l6u3kc+eZXef+rYHrqJ64MqRvF0Ua0gm99FkA8xDU23vYiU2/aLaaBISLXDKc5ijJ1+gAXXXoKjzyyhlNOO5mNNt2YF+22C9tvucefTL+8EH85ctdg2YonueOO25k1Yy5bbrod7zzpdI4/4WBOPvloDnjlPuyyyy58+lNfo7+/m5133uOvfo6ZM2dz3733s/0OW/L+0y/gZ7+4jH1efARfufRsli5dyemnf5oNN5rJnDnr8OUvf/kvHkc1o90ZpVwtcuedd7LbbtuDelKOJxUNIOI8XkBDS0i9gbuXsE4g4jlRHua7GrSAGA+cNNQC067h68E2IaYb0RpZGHbx/KUWSj/CgNd7kWClSK7jJHkDIzFZvoSC6cFQ9V6pUiV3LVpumIL17cTITifJHqMY9ZO7XoY6jzBYmsFI8gRdVIlNl38uKSFaJc0VFUtHm0RMI6JCR4fpuDFi6SIyBdCUDiuAAkiglktO2fSTMuLbiVKkk2dUo+l0FeYw0rkLMULJlEldnb7C+jTTFTginDqcyxBRIhORuTpWBCtQtTOo6wiNrE7BDfLrZbDllCK9RcHaKupycgySZ6SuQ8e1iKWPogwgYtbqMZ0zOBIKJsZI5NcEMaAlciqgoxgc3gMQjOSgvoInYVrPoIjJMLTINfZyCWnjgdiLKUUDnicoDVRLePs1P6XplXgJor79ZwJ4VAPgVWQE7wE5CcmZQLQLRw+qQ3jiejt0KNp4fI7/DoSI3PkkTiSmWW/wsbPPx9gCX/3KVWvF7P/XMTw8zJT+GYjx2JS2a9DOYnoKVZwathm03LMk5YrzruSgF7+Sk9/8tzTWnt94Icl6Vpz0jnfwlUsvpVzpZacdN2OTTTbl8EPfyKabbYYAS5uLmFaeQcH+35VC/56IjdBJYKuXHc7rejK++pmzmDX/5Sy7ZwFLY8cd02KOWNBmyjoVugoRaV6iHDXJndBxoxRNN6Md5ZqnW1Ros9OsJZRsldhWEY2DvqODpRsrGULR7zylBRQwkvkLH/XCVzEg1aD78EkVYhCc13ow6YheBTLy4FvmK1qCU0VJsbTR4IfldCUSPK18y7KEI4UAMfTsmAjF+psUHZCUufP7mTf/5bz+kD1ojpcYHltOqzXC+Fibsz98Afvu+8q1SdY5n/wAd9/5ABtvNp/TP/x6pk+fRsUuIHHLPHeIIZQJctcGWqCWTEeIzSBKjnOd8H69EaqoCYJRi5UunC5BA+MszWPqaYN5683lYxe+g9tuu5df/+7XfPVrV5C3DBd8+rN/1UTcf2Okacr5F36Q++97jH333Y1rfnwny5et4pPnfpxvffsy3nPapzj66GPZZJNpfOzjb+GNbzyDxYsX8+pXv55C4bkbY++33/4cdtjXOPqYNzF79ix+97uFXPX1b/HZz32MLTbfhK9+9UTKpcG1QwwTExP84vrr+OWvfky93saajNe99ijuvucPPP74g+yw4xZsutn6eC1i2SdKTARNksVbztSYhFP6hMpPr2n4vTfRjUEkbEZGwzlvPfwTEzYkKRosVfw4fhV1wygxLbcQRwuDF8+nzlGwXTgnJA5K1oQhmCJWYhLnwaA98SbUs9/T0glSF9AxtMmcY1lzjGZWpK+gIA0iKZHqSqCImjZOfXW6ka+iIN2o8bR2FW8BJsEap2JmoeIth1QzYtuN5BU6rMSpkGpCMxunGnvNUyxVPBqmxUT2BDkJ4so4SSmIxQSSu5FeWlmd0WyUSrVJOWoTiWF1u8FjI3189/YhDt/Bst3MIkXjh1us8e3otlOenFiK0ybrdPUQ0Y0Vi7XieYGaY7XEJLcqkgFyVodKoQZUg2CI8LtUnyg5bYMIBTMT70U7igmpjq/0ZwgZxuRA7Kc08TBTpRC0dRkafB/R4CkrJgxE9IA0mbTHETLPJdSYSfG8MDk0MQmktkABRxqwGb00JjK23/ZVLNhwHQ56zRuoPavg4DcDf6xCyrLseRtK6u3tpV6vgxZx2iHNIwri5S4PrWiyfHiCsQfvY8nD9/DAzN7n5Tn/3vivTrI6eYNWNsZwZ5x1auux4w47sN72W7LxrAW8ePedsfaPrVrm1Ob9c17oc4wnbv0V13/n23Rcyr5vfwcvP/YtXHzueezcdRTz+6Yh3d1ItU6uY5TMNNAJ2nmD7ngasakBDUq2QprB9U+lDHQJ3ZGyTlcBa4LtgvgEc1K0irSwJvVAQcp4OrzXJCkrEIYRctREoGUmAXgGB1IJyqtOWFi9eawve8dMQk+92HwolOIrZKwBlIjpfnRdh9FQ7rb0EuHHsL1VRTe5+J2vECEYat0R1e5BlClYepk6+CPP58FT2PfZ58X8/g93ctw7X0wl6iPTCXKXk+kI6urEpkbqmkCOMTVyB63ApcE2MTJA6p7GUMVqFUeKSgunEc41fcvFDJK4xRjtw9EhdU1S12a7HbZiux33RqizfOEyzv7YWXzs7I8/LxiQ/8S46dbv8JlPfYk3HrUvx7/9cIxRXv7Sbo468q3sv/8+vOrA7Rgd6uad73obZ37wWDZYfx6XXPIhvvudn3PhhRfwrnc9N7PusbExJiYmKJdrLFx4N2ef83Z++pM7eN97P8CWW25EqzPGSSedSHd3P1tusQ033HAz5apjnxe/jA9+6Dh6ekuMj03w7W/ewoxZJd55ynswa2G0CaoWI0U0uBw4Ct4ii9FA845A6qA5hPPYD6j0hBtoHS9a78UTyzM8XDjDOQ1VK3DqtVi58/y2tltMwRQQFRx+ArHjngZnsVKgFs/DSpuOjvl2oIxgpEQsFTq6nJwcdTHlaBaJGyVzdTK1jCfjTC0N0hvPpuHuCG2xCEuMwU8RW9NNriuIpBIq1zmOBm03QSQ1wNDSRQg5VkpY6aeRLqflWhTNdF9Z1wgVb+Oj6kg1I5VhIjwOwlLFmCptt4ZUV5PkMU5TMl2FpUhsYta02zgVugsxs2pw+MYr6Nd+fvjkGPP7GkyvFIlsD07aRC5GXELiHI1UWdW0FGwH1ZXU4h5qhR48bLlApqNYCqgZC+LnyUEZxdIf2oUToSXY5duwEiM0yV0TKz1+iSX1rV5SRBSnvhpFMK/2m9JRoIO3HhuG0LJUykHT2vLrqVo8Ii+oxDQJybfxFU6KwDCq+doKmH8eITKe79jVXeKBh25kzehj/PRHj/HOdx3PPi/dja9f8V0q1YhO2/HBM89igw025q677mKbbbZhxYolTJs262+7yJ8Vxhhy1ybNhzEmo5kpE6mjnhW45fGU6z57Gccd/nIOvOJy1pv+r2Ed9l+ZZI12mjw0upzRzjgb95apxd1YiTjssMP+2S/t74o4jpk6awaPN+pQrvCq3bbg5ifewS+/eA5d6+zF9NYu9PfOZrQzyo7TOlSjDlYMWbAZSFyTSjSFmV2Gru4GqjOoxMKTE6NMLyu9Rb8YI61Aey6i0g669FGsDCIh2TEyhp8KdCC5T4ZohvZi2U8MMY7Q7ZMXhomYT8ZCCFqCSTBq5sb8zeJZJqh+0kbJdBTCDhFyHMMkWsd7uWWB8zXpVu9QIryoM/UcL5QNFszk3vtuY7PNt6RgZ7LdNnuy9dY/5777nmb7rfqxlEl1GUIviGB0AMcjWCqopiAVhBKdfA3QQ8FKWBQ7OEkxanFYLCVauhxDGeeUVlYkdS2cQltjJpIcR4JjDf3FiPU2mMe6mz3JEUe9kQNe9xredNAh/6Az6d8nnnpiFfu/eif2fskOmKBJWbFyJZtvthOl4gCnnPIxxsfrrFy5KlR0KtS6co486nDefPTpDA8P09/f/yfHXb16Nd///ne47Xe/wRhPbO/qrrHhxuswd+4MjCnxxjceyqGH7oONYhwx0OCJx5fy6COLufiSszB2jLWTprTp7unnuLceSuZW4VEnvXjCeYyTFA31Ti+C9rwhQwUhD5O0cQCNBl0gHbwnXBwaQAlOJ/Ck7jpOkzAh6MiZQFWx9OBZW55bV7W+lZerv546bgRvPgzWeOPlJG+gJOSuTCmagphl/mZPhSz32qAsywJeIaEolp5CN12FOkPpA8TGEUscEooaqv3kOorqBJFYUtZgVLCmDyijLsXaHoq2SCNbg5UMYyukbjkFWyBRxdCgbC2ppBhTYjx9EiuOXFNQxYlSNDVSnaCR5rTzlLhYxkqZWKoYOog4SqYPE8yMVQu+nakTxN3jVNoGIzmJS4iMrxyKsVSjXrriDksbQsdNsF5XIVTZ2yS5w5ESyxRiqZHTATUYMw3vzTqAlz4MhbZcoOlLB6N9iOmQh0p47oaJbFcQtXvMg+oUvFGKQwL+wvsL9oaJ1hZICdEIJfNrTah+yloEQxVoeiG7eL7ZZHUUJoXv3urHa9J68OvnCKDkahBbYHBgkCOOms2DD03jzt8/ynnnv4uBgR5GRsY45V3v4SUv3Ykbf3kPhx3+aq74+iWc8q4PPS/X/GabrccD9z3K5ltuSFcc8+unO1z38BjliUX09XRz2MHHPS/P83zFc0I4/CvF84FwaGcpT9W9Bcx63X1E/wCGxj8qLv3Wt3nzGw7msHMu5Mr3vJ3bH1/DL276NRd+5iI2e9FrmbvjLOYs6Ga9KTE7TWuQOkt/KadsuxByxjqwsm0Y6iSkrocNajmLmzl9pYRZFUM5ToikgpVJs9A8tC46eExaHZiKMIZiMN71K/xNjjeVLYXKk/NiUhK8H5dvgBgZQLXuy9+SY5nqNQ90EErErEPCQwhVLKWAhxB88uWREEpzbdnaA1S97YmhH8cYfgR9CoJlxepHeN8pX+Srl3+WUuQJ8vc/cjNfuvQLvPP0w4mt93e0JsdKASOe1RNLv19EHYjxWIrMLQ007L5Qjm+F1xAhFGnlq1AM6FRa+bCHG+aQa4GinUri1lAwQjWOaWQpSxsZ61Tnc8bJZ/D9r33jH3ou/TvE6pF7edtbT+GyK87FUERkgs+c/1022mA7DjjgQA488FV8/4efxxiHP0dKTGIKnlo4zIc++FlspBz/trezww47A769ccghr+ftJx7MjjtthLF+6lWoeYwBHhYpk0MQTMHpcnxy0oufnC2gtHyCRFcY7ogQ7UcZC+emX3uNRMGuimBDEto5Oul8oIh0heqx3yzI2paOTx1VDWgczm3fkBf1xu6KRZkIOsAc1OFNrGMcDVwQeBvpxWiVjq7wwAAThTZ9RuJy0txSjXNEDLkLs8FmDvX8SXLXjWpMR0fJXYV6GjGlZKjYEazJg3+qp4d3XINYpiJiA4+rQiNdjkhEV2E6jWSCnBYF4ytNSgtDD0pKyVRIGcXb8eSkmoPGqEYY41uB3hpHiU03Sd6kkzsyZyjFoGpJtUlJplKIoqDvFIpmFm1dQu4yMo0Y7eTE1jJQLFMwXoekWiJxbVQT6lkHKNETl4lshmpOkneITMnrl2hStrPD9GCE4GGivgqJR2Lgq/5KjmgX3lA5QbWKYMhZjWgZa2p4WUUxfOcJk7BSf/5l4Wow4YxyvvWK8cfUCD88EUHALfh2tDcVnzSTfkaqkYTv3eM+HC0EAeKwQU3AKRhD7ibdB2JEFCu9/rXna9h/35M48KCXcfSbX8fbjj37eRPG33//Pfz4J9fw9ncewPLmMs497zp+/4cVbLH9VhzxmgPZZ6s/Bf7+I+IvIRz+IypZj4yuYm6tj1L03EazS1HMRr0z/o9f1T8njnz961jeynj/UYfxicNfzQ7rz2La1FfQ6p3KdRd9Gmu25fH7p/KbCOa+Y2/mlHMKMk5BaqS6ir5igXIkOG3z25Xw+zXCLlOrjHRysjxFJWJuzdBbVAx5qOK0gCiwrHJytxpoE5kZpLp6bdvi2R5fSGEttNDfwArh5iJMyjIlLCAakjACGTnnqXDRJ+TBfsJSxcoAGY8DDURzCJUmRwsrJTxqYZUv52NRRohkClOnrMPL9tuRM8/4FB/60EcwBeWJJ59gzswNyREKYeHxSVwLpRc0I9EMEb/rtK5MJqsQsd7mQgyi03D6ACI1jBiSfDi8xy6KtovENRBJcVRp5Y6uQk7HGVq50szKTKQ1Rjsj/Pr6XzBY+ccIS//d4oST3sORb3kFIxPDXP/TG/n1Lx9gy6225tWvPoAvXfJlWu1x7rvvcbbacgFev5Lgb1ZN5s+fzVcuPY/TTjuDTlJfe8wvfOELHHPM0eyy27ohD4oQqaE01tJEfBVqJV7r5M9x1g49+MRHtAtvT+JvXkIHZBRLT0iGrG+ThU2CUETEQyv9DbCD38SUw1WR4pOsUP1Qf69T1TAdVsfbsziQmJwl4UY5yWpSPAk98iJ2SigdX3XVEqJFWroMr+spYangEDLGKZkyXdFUWvliUEtkYrI8o+3GENdL0ZSxVOmkqzCU6Ym6aKSLqdkykfSg4it2mTYpSBnVKBDRffUxU4tojqFALe5nLFtI5mq0c0Mrj6hFnvqemQalqEjBDOCYoCCKcxGQ4Cj56V6NwmdnKUfziOxi0tzRyTtU7HpkbjE5dTLniEwJD+XMsS5GRHCul5JZTiXqwZoSIh5ZEEkPCRMUbMxANAXVBkas1zVJhZIVMk3IXAMElAmMRGHtS1FVck2xUsRojKOMSDsMMURkLsA/pRGqmmW/ngSfWn+OlIJ2tIyQolrESzBq+A3Eaph07xDFJ1fwTGLumVr+v4sInbAJ7gNWI7TD+hi0YGpwZBgKeP/IkDKIX3cjMxtYjaq37vHfZwNHRNKJ6O6poFInip8/G69NN92CT577UUQP4tZlFX5780Nc/fXvsf6MPx1G+1eIf/skS1VZ0Zqgr1h5zknWf3IYEc448lDOOPLQtY/N7Slz1kEvYurEIn74uyeYMIPMqVh+/OBSXr1xwoNjZXafXiQ2fX63SM4GPUW64yKJE259MuWe5Rl7bmoYTzv0FCI6LqU3yinHUdiNatidT0VZ5qtTbhyH8wuNTrY3ChAWB3+xp76dpzUcq/ELSR0jxWALssYXr6ULUW9d4nVcZZQUQzG02MZRbYWdlq9W+BuY9wZTVZAWIjVyNwLBlDRxoxixvPq1OzNj5oMcevjr2XHXTbnl1/fx5Ss+SyILsdLxY8IuohTNwhhH6tb4Ej+zSRnGUCPNh1AyIqs4N0YzG6dgBasdMCkZCZZ+WrnQTlcxkbbJVEldiWbWZlUzo+MKPLQm59F7/8DqBx/mrpt+Q6fVYXjxIj7+qU/SV/3XXEj+GaGqbLHt1vzkmnv4yfcf5hUv25eLLzp9LaMnimJKpQrbbv0SPnPBmZx44hEQbGn8DaRFvbmKO++8h0+c83luvvVafv6zm6nX6xx/wstQiiAlVNeg2oYgLvb/Pw6aB/GyFzn783E0VGQNSIZvF2q4EfqKApKCxvjzNAKtB53MJOzX4uG/lVDx9fwjKHqWEpHX+RGTMxxuir617g3WFdFSeO6CZ245iyEBcRgtYKVCrg7ViFzb/n0wRsnUyF09QDXLOIbx1HHxSY2toM4zvMQIVupBDL8cR0TZFsjU0BUZUrVYG2GNkGspQDcN6iJabiXg+U6OFlU7FScpqY57QrmLaeZjGCyRlGlkPtmoRorNja8cSkZZ5ga7MFAt+eqies9BxwQ5K4llJsgIuYHErWGonTK9UqVgChjTg6GN0sYSYY1H0YxpN0VbIzZt0IjI9CPSoMv0olr1FWqJfHKthWBp470EDdlab1BHO0xkl0Fb5DSBGql63Zw1ZW8nxJjfCIoGn9gUods/j/qKvJEMAlPLa7EaIE38IFAznHd+7VM6iAoqRYSS/1sSJrsP/jwZQ6SKaI5jAi959wMHz+hhqxgFkXY4D1OEMk7aeMbhRHht03AsQ7VBhuXjZ13MO995Eldf/UNefdD+JHlKq9V6XrSlIsLg9F6u+uZ3uOI7t/K+U9/9L5tgwX9AkiUi7DFjvX/2y/iXj3a7zYN3/p6Fv72JBQvupnverjz61AZ8ZnEfXQMFxlqOTaZMpVpYyMxymVhialGZUjzO1rOLPNYoMFiezs2rlrHHDEs1UlZ1RhmUlKIt+Q2/CsoaRGKsdmONwZB6rop0Yc0MVDs4XRZK+mF3IxYjMZF2+x2c1FBSHMvDyHMUkrPisyoJZV8FEzBaxuF5OF5z0MbfREcR+nzJXlK/M1fPVck1x9t6JBRkDk5Xs/1Om7Pdti/i5tt+wZuPPQATDWGdIZYKiXqERMoKrNqAtxgn1yap1kFSMpd7Tr1CwbaoRl3kan2DJy/RzhOKMoV2Pkw7S2lnGSmGglRZ0eqwstXi6u9dT/uOP/Dyl7yUan8P4xuuS2H6TGb2D9L7HIzF/5tCRDjj5I/9xd9P6Z9CT0+VW277Bs7F/OhHv0QVHn5oEYsWLQQso6MjbLHFlrzxTYey24s24rWv352NN5rNJHBRtemTLcbw52Hu8R30hgpDAadj/qZG17MwIB2EGk4jcjqhWpHhDZGLofLlJwkV5ydQJYXJgRJMSLAGEHUgw3ij3syT3wPAU4hCJaKO1+AQNDQeNaJomAyu45OuEhlN0AqwBiUikj6ENo6IiG6MiciZIGc11vhJRKSFwSL0oHbMTweSh45lTtGWcSpU7FRSN0yqiyiYCGtinDZwLsfST2wcHZZQtL3BI3ENiVtBSoLRMu0sp2yqlC1Yo4gKOUquOVZ7MZIgUsS5CYxRIttLok9j1GLEz8aJeGsdXAnHKLkOodKhbOcynub8bk3GlFLErErEiuYKXjanh9xB6mLKtoSJldnxXNJ8KUIZkQJGvOwBcYgkGPWWRkgWXmMLSxFLN84sASpMGkP7SmgdRCloH0ox1JVKQeiOnwYNGz80x+EQAcuA56Zh/KSfJKgWQMKUqYIjxVLy55j6qp4REzaczXDeRvgkKUMpQwAl+zW7FZ7b+tYrLSY7CuATQdUM36L0Njyohk1EGWjhtO4nK1FiGWTZsjE++uGDuOueW7nrgdvYY98tuPSKC3j7ce/9O696H6e8+0N85ZLL+Pl3fkCpVPrf/8E/Mf7tk6wX4rmFqnL77X+gUZjJXU+OEd39VZJvjrPXa/Zj98PfzYMjwyxsLmXHnl5GszqzZ7cpR4pIi8FqjRfPG+KhsTEGSgVWNw1FM8jy5gSGBrOqFc9zUQmL/hhifHnbMIjKcqARROIKlJFgy+C1GilQxxjFM1k8g0s10OWlgqonU6sUyHUEFd+a9C3KKJS/Cbs2DTv7Jk5H/YKlOUI/TleEBcdDBoVuch0DLaEUkKjNri/aKrRd6sTG4agTGYuVbu9PqAmx9HnROhOgBa+ukDIlKZPrakR7QErkOsZXv/QT7rt7ITmWLIPFixbT0z+FKT1TqGtOKhEjjTY9GDbZfic+f83VjA4Pc8zb38Zur9iT0486gS988aJ/+Dnz7xxf/vKXOfvsj7Djjtvy5S/9jGnTZrD11lsRxTEHH7wP66wzgzzv8Ksbr+ezF1zEt7/3SYrF4rNsQrxuC0w4Fzy0U8TiqHuxNEWUfmAFon46C8mxFMhpgE6ganE6jjU1T5oPqBHfIp9FokuAhFimg6YgDqetcB47lBUogrosCOizQIB3GKyvaKkXxhtM2JzU8dNlXmvjGA0teIMLbgUiFqtVjPHISsEPtWSs9klFSBigCVLE0Saim4wJvOlzH6k+DWZykCQncr1kLEOMUlCIpC/csA1Kh4wloAYxhlggZyGRzTGml8yNYaWL1GU03HIsQlfcA9IAV6SdQ5In5C4jN2XaeebrM/ljKClFM4dcx7FSCkMnCdbEpOpQbeKIcSxFdYK9Z/ST5dO55s7HmDkXkqyPFa2VJK7D/C4lzcaJrVKNu3Dqq20+ucjxbbcuf3yJcdQw0sFqBU/Zh5hpCB2sEZwWwmcAk5VQQ4lYqjgRRDSsXClOfRKPGIwKqnUyvObJSBQ0gIIRxeNqPI/QqGUS3yDicGpDcmfDOerwyBjvQ+hbhALqh2yEblQ8JkSo+gGltVKNJsIsX7kFD4lVD7ZVUpSqHzTQcRBHxCxyNxRsh6BWiyjZmAP225m3vOljHHX4Sc+LWfP0wdmcccb7/+7j/CPiv1L4/t8ay5cv59s//AkXX3Yl/fO3RWdvwMrrv8R6g/3M2XEbNj/wVSx8OOLB5cMc+8ppJHSYVUnYdiBiLB1hab3KWGrIVFivu0xR6kQmo6dYpmgrfrclZTy1PWKtOFgn+/geVOo9CKNnWiFB9wIWpwmesZUzuUvy/+93UrkmeFBelcmFz5fJ2wge3ukYgeB/pmsNWTtEphfBkukETtNwE8nJQ5XCSA+5a6C0iOlFBZQORntp68qwMxWK0kuiQxjpIlPopC06KlRipWKr3nTX1bjxhnv47jd/yk477spue+3LqqE1zOmZxq4v2o1clWnTZ7Dlrrtw9oc+xKbz5ocWjU+IX3Hom1hwyGtg2aO8eNPNePub3srLXrs/48tH2GWPPTn5zcf9ETz1hfjjyPOchx+5g4032gZjntlLHvHGgxHJ6O6aSr0xxO57bsH+B+xFX896AYng7aNYa4zeZnJ6VdeKjLNn6VuC7krLGMlwTHLgItB20OdZVGKyvI6VARxrcJr56ozxk7Je32jD5OooBI9Pj3ToQVmNKqEN1EfGaiKZRs5K33oUT43zZ0TCWt0WxQAeHUGokSuIZhTMdBKWAg4brK08ODjz+i4F73cX4yGVXsPjj13CahcZq3AEM2M37q99nWzdZb7IhQ06xchvkqjjkfUpjk7YnNXouGXekiePaLsiVVuhHOehuucY7+AF66bg5QfiKekFU6Nsp+CdKJw3uMa3DZWY3I15hZEqmRunlbWwpkQ1nsFvHlpG1F+gFvfSSBMqUZ0NugeJrUdlVOI+RCrh++4FhvEpUbBp0Zhc28haw/rJNl85YDe68e4PiX99Wgmt4hIapgW9GN3bBeXaxogX51sp+39DQq5CbLrDOeG5ghJad884CKRM3smdTvpClvEGzn5z6zeV/kxWzbxjB1HQbHlel2EuOQv9dx0Mo60p44JezidvOV6xlaEKuXa87lWLqFgeeughrrv2Ts48/QLe87538Oa37M2TTyxl1apRFj2R8f73n/k3XdONdDWtbJSB8nM3eP9Hxn+08P2FeG4xY8YM3vG2N3PCcUdy7bXXct7nvsQ6m72YHdaHhx66nyvedTO7nnwa2++4LreuynjRzAILepugJUp2KsVoGJsZuuJpLG+sYKAYkbiZtPIRZtdSLyAV39MXHCK9KMtBYvwC7Vsw/gZWBMo+0ZKwWOik91cOlHi2V7i3JPGWIaBhItFi6CeXYXKXY2hjpA/VNaFiFiNSIdchHI52Ou61XbSwUkZNA2+FUfbrvmuEG6TFicFpCyOGlhsl1wgrkKuj5YaIjENoEVlDpVBgvNn2+hLXZmhNkw+ecgGDA3MQV+Fnt9zNrU8tZfbUMkvufYoLPnMB0zffgsV33sG3f/JjGhP1tQlWo9Hgbe98N0MzNyTvn8rcOb10ui3nXvcNVi1awZy563HGG45k2wUbs/vuu/8Dz55/r7DWsukmO3DNNdfws59fQ6uZUiqVWbNmNW8/4WC223EL+nrLazWCIhNMTrv6KTDw+hevU/FJVdP/HsVhwwaijohDpUWuCmI9toQSInlAncRAgdy0EBlG1GCJQTIiKQMlMh1D8w5t9ySxLYHGFMyGJDyKsCbcDB2qZW/tpOJ1QGIxMvk+RjHMDDotr5nJddy3fbRIRhPVNt5WJSV3be+hSQfVxE8BTqZq0oOGqpVIJyQGkxTwnFyX+YoYBawMkFNHNAIinPOtUiEi1TGMCNb4RMP733m8i6FKzDTaujxoljKKURXN8HZaGiESkbsMkZSKKSGSkGtOUbow0o0IJG4IQxmVjFwcBu+7KlrDWgkVphKYxOtGNSPJ6+y0IEaJGG4P01uAntiRuFHG0jrTyzPC+pNhJMVjE3zC6afzopB0JyEtr+MHfEDpwbnVGDOOJ+8raIL3g235qhQZfjDH+bVSy0jAgHg+VoRHM8TEYf30m4DJyv/ktGwBL48oIzR86i85Th2qdfwQg0XUrN3kqhbwLLbgYBFcBYSMXJbgNw+Z19MieCD0OjhZEs7tAFDVngBengib1yZCiV/+/C72ffmBACxfvoTxCeWqr93COvP6eXrRYlI3QWz+eulDyfaE+8C/V7xQyfovjnq9zsc/cR6fu/ALrDtvNgcd/nIeWbSS2/9QYWDL+Zz67r148RywkiNMYXV7IcubMdc96Vg1BodsmTHSUXriMv2lMvNqGbH1u7jJiUKftBQw0o3fCU6yWtKwK5yclgqE9LXcl0IwqJ4ce47wDvRlch1bWw4X+lHWkDu/vJnQ2vCtFIdgyFwTQxetvEHiFEMHY5SiMWExU4zG5HjivGE6Th2pGwZ6aLshkqxIHEUURUhcC6clkJyy9Ytf6qCdGlYuXcoJbzqHUrnEi/fblVe8bi8qPVMYS2FsySouPvsSPvOJz7HRRn/e7vMXv/gFJ5zyPqYfcyYz1utjwVRhfHlCpRSz/vRenk5GWf6rWxj9wx1861vfeqGa9b/EIYcexJkfPop582bSbhV5/ImHmTNnkN6+XiJ6Q2stY9KexKkfVYeOr+yQ4ZlFKbk2iWU6YpKQrNhQXfI3I5HpXiRPB2990sFKF1YExAZxtAH1E1eTlSF1GTkRqs1gV9UFOo413sLKqJ80FCKKZiPaejdO89D68e4KItHaSq7S8ViGgEfJXYOcgk/sSHyrkF5StxprilipkrkxIjuArG01aqg0W++goOCRDyVgHMi9PykdjEaIlAOzDtBauJZCgqNVjPVCat+2Allr3eJw6t0jOm4CQ0zRzEBkHKTg21DUgrl0AaeGiF4St5xyVCCnjdOMyFQRSmQ6HNAsHnDcdHVy5+gtzA1Tex3SLCUygpUCHdcicd3kOkFPoYyiNNIxnJYpRylF20UkXVgjiFRR9egZ35bzYntH3UshwgSphnXHYzcmNVF95G4pYENV0IXvCnxlClR9JdVIHDaenm3lW4FesG6IvDBeXFhHDSLeZ9Dr9DzXKnHD+InIqXj/xkkrsgK5U19hI0OoYOlCGUGJ/dotka9OhvXXkOPCa3Faxw9YQO7GIGysUzeGpZ+nHp/grI9cyDe/8R2MMRx59KGUazmnn/Zx5sxelyQfITa9/5Hr1guVrBfiT6JWq/HaQ47iom/dyLR9juWbP/0ag6U2627yCtadBtsPDlEws8l1iDR/EkippxlTamVmdZWBIQaKBUZTR9KsU7COeTWftMAA6Bq/W5IYdBQVz2d5prTtky2/qIe2BI3weILBNzMINzoPGfTThV4n44X2EBGbim9FkGPo9omWFlHaWFMChYJNEAxJXmZFs8k6lV5yGQexFCjitSklUh3BqSXVCKcjqFYp2iIFqdLKV5Or595EJqfjUsT14Jjg0bse4tgjzmLBpnP5xBdPo9rVR0djSqZET7WH25OVbLDZgr+YYAEsXryYWqnKoovPZ7xaRTYa4N4HVzCaRszZYltW3PMj1j/mVEqzxrjtttvYeeed/8/Oj/+EOOWUd3LpJd9g+Yqn2H6HbXjrW18fKlUZTtqoSxFjUY0DxV8w0sRgQbz+yamvbKlmJKyGLCJTR9HWGE+WYySlElVI9QFyJ6gzWGuoRANAupaKDhWs5CBNnBN/c550OdAGwgyMtLHEZFokdwmR9UJ1JcHSRaIL8Y4KRQxd5Dru9TWAnyDzbXJL5Ktt+Mmw4RVjJIlj1pxZKCNk+KqOc2BMFlpG41hm4ViBZ9z1Y4hRGhjB3/C1FTY4BS9CxyDSQ6ZrQjWtg5gw6RbE6JOieRdwDUZKIUEoho2YAY0oShdeUzZMFNp+LkzFRVRoujFyTclkFGNKiJlGQRIcDSS04AyTFbec2NSoGiV1DVL3NMtbgE7FSoWZtQ7WRFRsREmLTGqWAMq2B2si/141Q8PnhIwHjEFoyalDaCJBzuA/F5+g+2Qn8ygZdSCrsCb2654Ww/sK1jX4xEUEP1GqNgjNfRXKt+eeSVIn18LJNrZoEacpRjwnLQ8oCCs1HMtCK9tXWPMglYAyRjwmAhkJgvcEpxKq/YoVRZmC6mp8d6GFR/QkYXtsmUga9BZ6KMgU7rzvUT7zia9z2Ve+gTEGVSVxLXoKZR58+A7mzF6Xgu173q/xf/V4Icn6L4+tNl6Hl++8gFZJ2PeQE3hk4X3M224XBqdm1NNeplZM8LbyXJSl9ZjHVglpmjLW7mafdXoQVoBkrG47nHNMr9aoWg9Y9OXdZihu+7HySfd3X8mK8SL1Sa3DZDk4AgFLEQ28IH+zSzBiw6STQ7UW/l0TkS6MG6SjDwL4tp/W8TwgfwOKbE6qhkgsbZdgxXmqsxisiJ8Y0ioGhzIOIhg8JLDhRlAs1tSIJKNsCrR1nHre4tc33c2HTvgU+x/0Ut7xgSPBRmRO6OQJqTM02qvpnTOLB+97lN322oPzzv80O221LQCtJONlx57OGw7Yk+OPPpqjjz4aAOcUY2StNcWLdpzPXvu9gjsXPc6qh+9niy3O+EecIv/Wse02u7HtNruh6vj+D77NCcefy/mfPYE4jhFaONqIK/kqjTZQUdAqke0HGjiXgaRBdF4lyRpYKdHK12BNQiVSOrmfJCvbblraBJtTtC4k7RVfSdImkXE4FZ/c0Ao3S+MrNjSxrCaWWqiC+Y1G5hpAESt+mlE1x0rEE4+v4IH77+JFe65HT08FIxFoCUebZqPFr2+6k5UrV3D9L/7AD777S1SVt530Gj58zlHeqYAOuXaIpeCte6hiKSCSYqh5rZC28DYt/ga9aNEirv7Rb7jzjgexFvr6uygWinzk7BPXcpQsgzhpYJicPHNeg0UTz31yWHpBAoyYcphSA8tcOvmTTKrLHBNYyji8vqtkK6ROcIxjJUF0FGO68KL+DkIVI0UytwYvFUiJRCmYuaS6nIp1tPNVFGzOSLvKYNnT7yOqZIExZo0hNv7VZGr9Rk8nvH5JisFcOsGYIoRK3trqEzF+0MAjQlxgY/k2qXgUBpNcq0mYbOzbrOIhs7lGwBqeIbN73pf/rELVMkgvvAel+Mk+klBVy8NxLSL9gIfk5s5z/pxG+PZ4GygFDW0TJUfVhPVusl0OhgaeaejZiWDJ16JyMmqRQTVjaHwVn/7YlVx++ZV01XwrUERQTdj3gD35wvmXsf78bVhvvf8+EsALSdZ/eYgIg9PW5/eLljG+yc6sHh1Anmyw6ZwCs2opQgtrijw14bh9cZFNZgyzaZ/luicsv37C0BhZwc4b+RvKul1d9BQMi+vjTC0+Qa2QYVAiiv5CdR0cHa8DUQmtjhQPYZysbhVCO8GEceXJU1QxxmsOhCqeWh0mbLRExjg4P3buFyDfZnRY0AaTBtMili7Tjykto2D8hGCmGWhOHkbgUxcmpqQSxJ+KoYuCtFBp01vsJnfDdNw46gqI9rLxDlvz03uuJnUdnMkxGjHaSWjl3lZoTbtDwQjrb70h1155LTtvvR19/f1stOGGbLH/QTz+8H1cU3+Kw1+6G93dnvliAmtn6623ZrKtX6/Xuemmm5h1+KuoVqv/wDPl3ztEDAe95g309XXx7neex2cv/ABQIFfvHWcmbzI4nLTJ3ZBPDsRhqJC5BNVVxNagOkFsUgzdRNYSmyqe05ZStlVybWIpARmZNsjcEEqRIhvheNJvPogRunDiKwQR3SG1MN5wXTPQIkibyZF8r8sSHn90KWd/5Ar22uulvP0t5zA4vZvtdtiYg9/wcu655zHO/+S3ef3rDmHjDdbl0NefxuJFe3LBJSey7vrTQwvUb1pi00Mk3V4/JYqShNajIrRRsajWaTTGeesxZ7PJpuvx8pe9hONPfB3qMibqLT53wVUsfGoh8+fNRaQHndSASTEkV2lILEpExDhRVMdC9ce7JfprugSspGC7ELpJdQU+eenC6ZBvA0pMwdTQkKx5tIJFteKTY1KctinYKk7LSLAeyllF6jzTarDcAS1QjCI6+RjWCMWoG9FBfGWoGc4YS2z8NKihhpMhjGRelG56gDKZrkTJiE0ZLxTPEJMjatHQDpwcHvB+hnjkgxSxZCFJS0FyUAmf1wQ+qeoNG0NBwlqGdiBgJCa9Yz2OoYY3BvdNPS/Wj4EVeA1XEUTD0NCkS4AX9CtDoWcpRDINZZVvNUuY7EbxJtENjPSS5qMghlbqKJouYpuRZgkfev8XOe29b6e79kylSlVxecrlX/k5K1cMsWrVqheSrBfivzP2fcm2PP2T25nI/Kjv07ffyyav3ZZGpjSyhJH2GIvq8MRwzIbTlb6iY8uZQqEAbrnjjqGp7D59NQXboGSnMLMyQScbo+2EosSkjBJJ7MvT0ktOAuqFucokAb4XdMiX/sH/Tv0i4G8+NuzS/A7fG98KTifwfoQtnHM48aLNiD6SMFnkdVpeXxIR09HliLRRRrEmwrmM3JXJtEXJFBhN2kSSEVuLNcUwCdUiNmUcwlB7FZYyicto5h1SN0rHRYymE/THJWIbsbLZZCwR2q5K0mgyurrB/Utyyhvszus+ugV3XvMzVjy1CDdrBg+vXso2L9uJZbfdzMqVK9cmWX8uarUa++233z/kvPhPjL332o8rr/x2mHRrEYUWlZ9Oy0FTcmdouoxa3OWnvUhY0croL5YRHEgH1XIAiMY4HeLSL1/Hrb99kJ/99Ffc+eDXGJgyDatlcvXapFgqHiFAhDfjzcg0Be2gJBjpI9Mh8txriTyXyZuyxzIV1ToqOY89vJKzPvwVLrnkCnp6ejj8ja/hNQe+jttv+x77HbA7IjGlYi/Llw8xUW9y1z0Xscce27Hx+huSk/sKmuYUzFR8BaOO90OMQNLQri8AhhVLHaef/n4KhQLvP/OtbLTxfKwM0O4s4ZZb7mXFinEee/QpBqZOAzEo4xiZwiTlflIk71uW3rMPTUAi1uqWcKHN6BNhAKHl237aCloixYqnnxMm70TEtyJ1MpnxlRoPAY2wkpHnLRIdQ9Uw3mnRyWN6pEhs/PMZ8VZEuWv57x7fEhXxU82eX9VBpBOqR5NDEZ7BZ83k8I7nqTnNMWrw78KbPlshbPjKiCShIRnjnSIUSxGH+kEdLSMyCtgwFdgB+vBtYD8pCklo92moVE2EVqJf91AlJ8GQghQxwTrJ1/WnhKR+JLw3QdXgdAIjXeQsQYhRjT1uJEg5RBJQX9nMdQwHxCZDZJykOZ2TTjyTY9/8bnbefs8/utZEhPXX35yj33wMc2dsgPkz9nWqjsWNBlOKZarxf2Y68p/5rl6IvyqGh0foLdSQp4fJ4xqbbz+dmbWM1A3x9EQ3ixrK4kaBmdMSBkpVPn+/sNVAncEu2HoetPNx+ospkRhGk9XEkTc7riLYuEjB4KdQpIhKIyRbkNMOJeoU0aFw0wrmzyrP2g36EXAXmDG5etBirr4MvrZErjm5EyKJySTBA0p9qT6SLiJqtPLxwLQqkOooMI0kz0ndMDkFnFpyEtLcJ2xTogE6bjUGaOcjGGrkmpMyTlG8lqxia6RxgqHDqs4EhbSHsp3KonQNP7n8uzx16x3M33hLrvn2N5m95Ra87JST2fg1r8Pd+GuKK1dSX7KYtx+2BweceQal6IVL8v86RJpkOh7aY17BZFGMqZFritMSmRsnd00cZTqZcteSFlvNSukrOGLrGUHtbAQrNYxJufW39/L1K3/Aqw98Ke9424WsM286p37kCKpRRCtv0xXHxARvPcnJcm+dIhQYTSaoRTmxGBTjheaixJSxpkCmI+TO8ZUvfp+f/+QerrzyW/T0eJslK13s/qK9+dHV30NEefTxpxgZXcN3v/td5syZwVNPP8E5nzsRY1PUTdqstH2yGDRbnnZexrf3vDHw6jWrOPnkT3LB509jcGp/EGvD04vv4/qf3criJSvY9UXb8OoD96a71oOviqQIOSq+4uLTjRJez9PAo1kivAWLT2iMUV+xIw2vLUddE+/76bVJQkTB9PgJPsnxANc2iY4QaRVjbKielYEOufNJRzODNZ2UrthQjaG/VPVm0xQ8ckHqKLm34RNhLVNKR/EVdQkJ4jMWX0GRFZAZPqFjskqFb485BJEckWEUL2cwWHx1KQ1TqXGoKE7aJRmQ5lrphMfIVryelNQPWIifflW1iPQC3kdSJQLNceqNyIUMoTtsXnNwFs++Gg3fr/XPp6lvs0oJoebb0aR4orwNnYUOSg1hnFwzcs1QihjTYeWyDqefehpnn/VRNt7oT/TerFi5hDvu+T13nXgXF3/2MtZZZ521v8tcjlNlIl3Gg6Mr6Yrns+u0gb/5mv5XjhdW9BeCu+99mPtW9FEZGmbRXXey7XF7MZbklLVErkol6menqU3W6xkjkoSt+/r43m2Wl2/RxZSZExSNoGKxRDzdjLjqgQpTS+O8fBb0FcuUrCUBOi6nYHIcAdWgBpFuNMDv/GRXILxL7knXgayd6wRKTpqnZK5DbAcQekn1EcDQylpE4if+UvVm0BEFjGmhQRvWdi3WJC2sRFiKtPOUgh2mOx4kcSuJpEQ9G8bi6C70EYuh44ZpphlIgdRZjOTBBLZD4lLqqaMWDxPbiAhHwRR55OmnefCmx/jNb29nm+2240s/+jEHH3wwb/vyFxgbH+c3F17MrPnzed2rX81J+7zkP3LS5p8RznlK9mRF5C+F+PQfpy0veNdRDDVaqaUSF2jlGdW4Qq5tYlOkk4/Rj2HV6jE+fO6VlLSPHV+ygF332AKJ20wrO5rthBWrlvDNb1xDqVRip1224aZf/o6999mDRhZRkAJq2ygTiEQkroFTGO4oVlJiKTDucjLnqEaGcgR1N47NCyQuRRRu/93D/PCH11IqPWNNUiwWOf30D1Au1zj1pK+y7/4v5Yc/uJpCwVOwf3f7bRx37DFMjDU45J1Hs92+u7Ld1AK1woS/EUsBAjB0Ek/gtM3o6Dh5rkzp74e1yYRy2MHvo7u7yhe//Elmz+5+BhiMt3DRtVOFRTw+wISfCS1CRcUhWgqm3QYnHQitLsjIFSbSMe4aqrHjoKVgFJVO+M4m8TAVVMfINMU6S2S9Rkq1GFrAFQpRyqAZpCARbbeIWHrIGfaJCW1Egy+h5Kh6HIUGDZSERMrhK9mGcTyWw+EN4EOCqj5Jd2Ix0sZXmwK7TwRP/vf4D/85Bs2ppIhOGjqbtY9bqXnYp/HJlBfFg0jkhfYS+6lWJpi8fYtGfqhAEiwVIPXA0smWX2h5EzAN/jlbCDGo8X+rPlGMZABk4lntyxhVbwieB9h0ZAz33LWUCz99LV+86GtMnTr1z15nv7jhRg56w8vYfZetOP2D76RaK7Lj9jtzzfev57TPnYcUYrafOpu9ZwxSsP/a1Pa/J15AOPyXh6ryxqOOYWTrQxh7bDWrHnyEV56xJ1vPduw9q8xQMkxvYQoj7TGmV2OQNo8PGz55a5Wd1rMcMLfNYCnGSIKRmHZe5bcrJljSyHFEvG5+iYFSwQszfU0JCWPb/mfFUMGYYEqqeZjoshgphLH2lNR5I1M/ul7y7b9QBXNqSZ2jaLwTvLpJAnuKlQKZTgAV2nmbdiZ0RX0kOk7qUmJbJqaLejbi0zsphP2mZwLFEuPUMJKkjCQ5jdRQi7qZVnEsbylVW2RqCR6fGKdn1HHB2RcxY52ZvOZVB7HXbnuuTaAOOOAAHnfKluuvz2WfOOdf3gri3ynGxkf5xPln8dgj92HjiGqplywXtBgxb/0tKRYtUdahUHCseHo5s+cVOfKoAzHiyHF+kktzzrmlgtaF9bsX0TO6lIHpERutvw31pMW5H/8ijfGU933g/cyfvjHf+M6XufeBuznp/W+mbJs8/dQQnz3vW+y/z0EcfvgRrFzzCMcffxIvfsW27PKS3ZjSU6SepiysK7sMTmMiXc1EnlOkgjUdxpIi7bxJOSrjNKHL9mCtC9iBlG994yYaQxEfed/Zf9NntGbNGnbcex9q/SU+f+VHWa9XaaWOajxIpdhGnU+IVBIiKSAUueGXd3PXXXdzyqlvQqigNGg2Orz1uLP4ylfPoliMgr9iB48vaOKZTGX/mDgmHfwg9pNsdPzfSAB6Is9iL3l5N2pp5yMsaRSZErdY3qpTjMrMqVkItTcJxxrvdMDkFEyVchSR546cBpHUiEwJ1KCSehCsKdPIWhRMBdSS6gSVuIYlDomX35z5qpWEZN3LzX0C6BEHznnkhF2LbJoEtU5iaDI8BLbJZOJKSLX81KANGz/8f5MHvV0c9Fd+olqEoMUUlNhX9QS8w8S4T5LwlC51Sk6LWKprcSS+RZkGZIMXrfsVN2eSW+glGTG5dkCTkMRBZLohaPcmJhoUyiYMBTl+8ZO7+fG1N/OFz3/tz3oRjtbH+dGNv+Ciz13K+z9+JJtvMIWJtENj9SgP3PUoS59czZ57vZ7td9iZkv3P8Rt+AeHwQvzZuPWuJ7np9qd43UG9zBowXLTiCQZrOV2x0MzHqUaGRtqhFCuNrE07i5nVrRy/U8ZQR8nVEpuZtPOnaGYGp0JPQZlIIpY0cy65t8MJWxeoxVX8RJOjkzUoRUUvKKVOpsNE2u9p75rjpIMjRVzVj2y7CfIw/m2JscaFknkJYyqA0MkmyLQURuE7qOQoMW0Hqn5yDK1Si6okOuaTNbHkrkArHwWxWPEmuymK0xJOHcYk5FgiA4MlZZVGDHcmcAhWlGLUwomwYa3I6e++gEu/8tW1rZxnxzXXXMODa4ZY0N9H9Ge0CS/E3xbNZpMX7bEb7z33GE4+9RUUbMRYq83KlrJ8xHHZTUtZUIvZYLBBlxTYcvOZDG40wIMjdQbLJeppi+nllFQdpbzDN8/5JHvvuAN77rw9d/3ufn541depN0d5x8mnsMWmOzIxMc4ZHzqNk45/F9f+5BeMDi2nMlhi9txe3v7x4znv1EvZZZedWXfdDfnGlVdz3c++x3nvu4x5G8+jb/c9qfQXmVJsMb0M9URITYHEOTJtkLpuKgpOU8azUWqmRskMcv9Df+Dxu5/mnE9/7m/+nAYGBnji3rv51tVXcdY7L8SVZ9OZtw8zZtR52Z7KToNNekpFUuenissWNlgwjWuvXo1ICVXvC1qpdnH82w/muGPOZMMN1+Xdp76NcilmkoAvxIjJwhDBZFVxslXWxLcRM5QSvsLVCgmOz1hEM1QckTHMriY00gkcZZY1ItY0LVsMgBVDKx8jdR0SdRTzEhkTtDJvdm2lSJJDwTTxQFhflVEiCraAqMWalE7ewrkKhciglDF0PPuKLCQrkKsiIfHyAnbP9hIxZBkgTURK2GDwjST4BEYRmZQ7qAerkgEOR06uGaKGyBRDspSHhFRYS5TH+M9GFV9tLHnxuzgsFqcawKITIMZXxsSFNq3x7T+dTF8njaN7gVGePa3omYBjoBCJx4+IwkS9yGfOv5DHHl2CiRM22XgD7rn7cfbc4yVc8qWriKI/nyC9933voWfudA45cjem9QpGC8ytdHPhD6/nkUeeolgSBgdq/1EJ1v8Uf1UlS0Q2AO4Dvquqh4fHXgx8HlgH+B1wpKo+HX43Ffg2sAB4v6p+NTx+GfAmYEdVvT08tj7wmD4b8/1n4oVK1vMbV333Vt72trfw6mNOZIuXT+fGny5h38M3ZkopY5PeiJmVIsvbCUWTsbRuKMdQMGW/+GmJyLR5ZKzI/K4WS5vCE/c71h/spjSjw5b9HR4bKbLTzIhILKpdtPMRWnmHnqiCGi84dpoR0UPHDYfhly6M1HEozimRFdCSN2FV63fClGm7OlaE2AwwkSwHKWFFsNLGiiOSdWi6leC6yHSEtlM6WYHMVbHisDgyaZK7nMhGOKeoczgDRmuMpC2mlUpkmpA7RzM3WC2T0qIWpZSism8p5hntZoMvfOj7HPnGI9lzzz3/yd/qf1ccfewRvO8jhzNrYBatvM3jYwnzu3IikzDSyRkolclpoU6xUqTtWqR5hRtXFJhRTpnbJfzkhpTvfuc29t6iwIffc8pffK63n3QsA7MKzJu+ObvuvjvvOfU0jn3H/my51TwSLVAfGeU9p3yJA191MC/b/cXMnj0bYwy//u3NnPWFLxL3Rrzl1LeyrFVnVjVnu4FenpgYpTtOqcVCLF4nlro2sakCMRdeeh2STOPjp5zwJ68nd0rqHPc+dD/JeJ3VjTYbzZnJxhtv/D9+Zid/+nx++puF7HPwYSyqpDRGU169laVaVGZUDNtMsfQUSnzm/G8yMFDjiCMODAlEHK7RAhecfynz5s2gr2cKe+29O97ypQ8jE77NFgYKTKhXP2N+7fVPvqpVCElEyiRqxblO2FwltPMmIx3H42NdLGs6plVz9poxlVY6wZqkSdnkFGyVVp5QjYRqNEAlMqQuZyIdwYhSjorBzzDFqRKbAXIdRjUnMmU6eUruJijHfXjD5AKRsX6aEF9dmqzCr+VhaTHorzoY8S1Cb0wNvnI1KRyv4gcBQNUDlV0wcfafQQsjPUGXNYlOMKAlkBY+GQqi9smWrloyzTGCry7hwmfogobLgvpk0Bf+IpApwPJQNfSbSQ/Ntd7kPstpuxbFyNsffeeqX/Cdb/yWc875OFtvvQ1Llz7F0PASNtt0R6wt/MXz6uGRJ3n3W9/KJy86l0s++RWGVw5h4jb9U8psMH9r3nbcu31r9j9QIvGXKll/7Zb688Dvn3XQAeD7wAeAfuAPwLee9fcnA18GNgSOEz9bOhnDwFl/5fO/EM9zHHLQTkzpr2BJiW3MljvOY36XITIFMs0ZTZoMljK6YyhYh3PKoomcB0Ys40mHshUGSy1mVQps0QdLHx/n13cuwUhGLRZ2mKG0szojrTqrO0tZ1lAeGLE4aeIXDq9ryLRNpurF5yTk6rzZqi1jKGIkJ5YpdIL4MnGjqBZIHDTyIRwxogkFYyhYQ+IMq9qrqNi5rOmMs7RZ5smxCsOdmKfq4yxrtplwHax0IxTIMqGZWUaSiDXtmKEkxROkhdQV6LgKNRtTLTSYXoZyVAJ1LB1vc9fDS7n2x3fwqxt+xV577fXP/UL/C6OnNkDRxDSzlYx1mtRij2WIbIFp5UrQnEwhU0PilFYGmZZZp9qik+esbjp+9+hjPHrz1cydNvd/fK4ZM6Zx162PsOnGW7Ng/iZcdcV3+MkPbuPTH/8WrpVjuwZ46ZnvYdno0xx97GEc+5ajUFV2321Xfn7V13jdi3fnk2ecx0OjMStbQpK3mV2BwUKJ7rhE0ZbI1QYW3DiGcXq33ZXvfO/b/PznvyBzOQsnRmmkCY8tWcL+x76ZXV/zKj5/5Wf5wS1X86Ubb+bMSy7htQcfzHkXfYHx8fE/eQ+qSi2tkj/1BA/ffhs7zmhzwm4l9p8HW01xbNyT4HSENe2MQ996II8/8QTf/s61+Dad8+0oadHpdDj/05fz4x//iu9974eIRIg0QEpBPpAFbZDDIxpCAgD4CeE4kNK96B0mcK4TBlksxkRUo/UYScqMtJVblgmpg4dHlvLjRTm/X9XFE+MxqRsLMvuMXOt0gldp2cZU4i7PrVJ8i0+8pMBbACmZa+K0TmxqAdYJRoJVjk5W5J7tCUmYzAMxEqpbIQHSDKeTCZGvWvmW2+RtdtK9wo9aeEaft89x6hNApw6nSsaoF5lrmHhV/3oAci2ANsldigbnDCMS2o9FlGaQVHn+m5ECllE86kFDu9DzDwXjK/3SoZlCO7OcddG1NCcM11xzDdtssy0iwuzZ89lyixf9jwkWQHehi/GRDueecQHLF67ka1/9Omd/+FPsuNN2HHP02/zn9x+YYP1P8ZzbhSLyBnyd8RZg/fDwa4AHVPU74W8+BKwRkY1U9WGeGWWw4bme/eleDhwqInuo6k1/5/t4If7GEBF6ursY6J3Ogzc9za6vmUuujr6i8nQdtpkCZVNGxLB+d4tlzZT53QVmdzl64oj+kmXjvi4y52hkTV66d4kbFjqSvMiy5gT9BSF13jdr0UgXT66wzJ4RERmHpYoiqIx4uwyxWOnG2ha4QXLW+JK7xqSujlPvZ9bRyJfJNSFxDmssQkJkLUhCkhtaWReNbIKVzZWsbiujSUaSF1jV9tNKC3r6GU2GsVKkqXVSFXInpCpEpsJE2mKw1PLTighiIKNIl+lmLBnhe5f8iHM/8Pm1n+OPfvwj9vv2vqy/8X8fB+afHfvttx/XXPNbXnvIDnTHju6CpRQBWFpZzJ1DKUbGmF2FsinyVD3lphV15tbgNz+4kZV3P0qlWOSqqy7gxbvs+Bef58knn2Tp4sUsXrQEldUAlMtlLvz0Zdz+h1/xiQ9dzIqVQ7zx+AN5+SF78spXbc6VV9zAaw9+Na946f4cedQb2WaDbdhryz/w0vkNppXK9Jf6yXWY3KUMDzt++6sbuOueR5k2OIWNt1yHmZttxKJrr6ISx7zsZS9l3W22o1MbZLNZXbRdi4PfciBucD0W9DSYUhLm1sqsbMYsHHf8+Oe3cNgJx/PwijHi3FIQpVqOqXfabL7hVnz+KycxbXpCoawMlDzte6DsW124IkYM9bTJqWe8gQ+c9mXWnT+PHbbfDJFuoMGxxx7OrbfczcGH7Mtpp3yCWq2XV7xiF55JMAxrEwxpg04KyycbY1kQVyuqLXIXpoEl9rpLN0Sia+gttumL+9i+D/aYHtHOLb9YbhhNEn6ztMg7tmgzo9rGSESuvjrm6BBZzx6LTR9rNVFhmtDRCXiXEgVb9DR2TbBiQq1t0nzZTypOEuod7XA2eBG/X58mYaH+WXwzZhIKmvCMAXNIsiRhcrpSwuMuPOukvU6u3o/Q4CexHV7LhZaZFMNPVgaNmGfp2vJQcTMYybnzzge58YY7OfHEN2LjNEw7eu/YXFNfedMCljK1GFa3hHtvvoOjPnk+xWLxr7wSYWZ1Knvs+VJuu/sOaNa578H72HyTzTn4VSf/1cf6T4nnlGSJv6o+ArwYePOzfrUpcM/kD6raEJEnwuMPAxcA3wE+AXxIVRvP+rdN4GPA2cBuf8d7eCH+ztCK8Oh60/jQHl1EJqWelWgm42zYLXTFHnip6ihFhvld3aQu8QuEePPSTHPaeYPHxxNGBe58vMNem1YoRRWs8bs1xXHnopinhho8oFUGS2U27HVkjGPoxTGENQZD3Zu74oikjJEi3rC5GSZeIvxkjOCMUrRFrJRo5zmxKeIUhjodlBYijoyUgqmBNugvtjGU6C4KuRunFkesbme0MkvuOkQ2pp0reSakLiK2fvHrK8xk4cRKmnmLVeQ8tXAF537g85xw+slsv902HLDn/vT1/ffZRfyrxOIlC+mb1k0zF2KjtHIhSpssb1YY7rS5bXWBV852lE1MPeswkZYZbyVc/4Pf0T20ih9/8xsADLVbrGo1GCz/ecjrlClT6CRNrrvhs5x43Kf4zrf2X/u7Hbbbmx2225uRkREu+crn+cG3LuLQd7+B7V6yE4/f+Sgf+/hZXPb1L/LpT5/P8sUrmFEWjPEcuCWr6rz7redw4Gtfzmc/dTk/ve46RseGOekdJ3P33Q/R1dvN3K22Z/bOe7P3249llwU97LNRP410nHqqTK8WSd0EVgz1dILBcpnYJKQv3ZIVO21J+aki08goRDlbzKqx6fQxYmvpKfrrOHcVVDN/LeeGFe0WM8olrMnpLnajdPOhs45n/TmvYsutFvCrGy7DmAhj/KTdrbfcxbtPPZZ99tkpJBQpQNAD5UFsHfNM8uWF4r6i4ieKlYxc/fGSNGE4GaMW5zjXoqcQs8ss2GeuHxZx6th3dpMHR1o0ahF9pRJl69lZivM8u9AKtFLy2iSczx0R0AQ1FksFg5cfGBwi3szZ68gmE0Gvl1IE7yFI+F0DnaxsiQXN8fZhIdmSSkjUUiZVUT5CsgR4mn4nJFz+s5C1tmHBXocWTqtYKZEzTs4Ihu4gik8RLaJMQpuzcAxh8eJlfORDF7Fgww3JMuXCC6/gHe88BEMhtB+bIHGoemVortz8699xxaU3cOjrXsf82fP+pmsR4A93/oGjznk/EyuGOPeiL/K1z134Nx/rPyGeayXro8BXVHXx/1fqqwGr/7+/HQO6AFR1Of9zAvVF4BQReQXw2F/6IxE5DjgO+CPWxgvx/MQue76cB277MUu2fxlb9Bu64jZHfGIxL9q4mzPeNB1rUhRHpoZM87ULqUiHPDi8R6ZCwbaQSImqJXaZ7YisEEnOSKfJqlaMqeVs2t9D2Y6R6ATKeqgbQWU1xhhiZuFYgwuCT5EY9XV+CraCMwCGXDO8NqRATI1chhBTRNTvGvvjGi3XRkyFlmvTFQuJ815rJVOlla1hNFesFBhPmrQdlG3ERGZJnWFh3TG1mNHOLCtbOZ2KsqzlGBtrEY+PsHL5EO/+yPs47wMf+6d+by8EdDodfvCDb/OFy99FOY6wpkwkEySuQ2Qs63cnbNHvp7wmspTVbT+5tZmbwg0//xl/uPVXa4/1+9VPU7Axe5f/fDXSDzTkdIxBi39etNvX18dpp7yfW2+9lQvO/ixzNxhk3vrr8alPXshZH/8wW2++I5tsvC1f+vQ3Oe5dr+euNVCzwqrVIzxwzyLe8tZjmTljHuvMWZ8XvWwXzMwZHHnG8Tw+GvHEkhZ7rSdsNCeiu9iiqwDNLCOWAoYYYyydvMl4p4mVApv2Ftm8t8ne6zSpRkUWNyJWtlrE1jPmktyi2ks9G8WpMFguo8SsadUpmQa9BSjH81AdZuXyNbzp6Fdz+ulH46fj/EResVBggw3WYd99dwfRIMbOPbYO3yoEb7HlTbR9U0OCjcxkwuUTsRZJPoYjJnOwcNwxtRyxpqXcv3ice298jJMO34wbxsaYWnJs1p/SV+hmaqmECDitEJkMiLDGV3S8+NsxSUM3Yv2QC95XEkL9SJ7NvFL8lGDgV5HjXA7iyJ1BJAukeS9zqGcg2iRXb8lTsJaiyddOJ/rw04dOPevPc7n81OXaFmzQXxkpYcSDQFXjYGNjfMVMklCNm0zOUlAX/CAVJOUPf7ifz17wLT71qY9hTJHjjj2Oc897L+8++Vzq9Q6tdoP+/h6OPe41bLzJBqiO8s4Tz2ennbfn4s9fzLTBv+8eGxcrDLcbDE5zLF+99D9Wg/Vc439NskRkK2AfYOs/8+s68P/jqbuZVPr9L6GqHRH5KD6JO+R/+LsvAV8CL3x/Lsd+IZ57fOjUIzj8TW9kVmUfRDLKNmObDaZxT1Rg0USH+T1+F2qxWFPxXmKa0HYjjHdyisZhpMOq5hQGaqt50y4FinFEko+Ta0o9t+RUWbgyYUp/zr7zIgbL0HGrUTXEphtfVm961hW9KOOgLgBIM6CGCcbSQoRqSupapGQUI0Ms3oYkdznFKCXPoJ51yJ1STzOWNgyxqeE5PAaRqTwy1kS0g4pl875Bfr+6w6KxNrNrZVp5m426u8jdCHesGeJr7/k4Lz769Zz3urey64t35+qrr/knfmP/upE7DdTtf0zceOON7PvKbSmXSlgijERUopmQLWJWpc54ViDLeyjHTcYTYVHd0FdMqXYvZ9Z6PbgwqQUwq1plevlPJ0MnQ9UxPjHO8Ud8lO132+l/fF0777wzN/72Z5hKyneu+wnnfnQel13ydQDe9fb3ccxxh/HUI0vomrc5g+Vupq63MX0zN+SA/Q8lCkDaebPnsXjxCmbWcraa6qhuJJRiQ9mWyfMUayzV2JLma4iMb3/F1uC0hDUp3dagWiBXQ8EqG3bPoGgW8fBoieFOymZ9GRv0NphSthhycq1TiaaxcV/gO0kJp2tIsxbnfvJrnHDiYfT2d+PwSUVPn+XQI/blG9+4lh123Iop/f1hajfCBTNhNMJI8PIjQ7QQ0Cze888jDDr+7+jDkNDKx7l/uMDUco3IOO5bmfGVOxVdLpxz2zg7bpjTSZUkj+jk47TyChNJh2kVD/M0eK2RSI4G6KeSh99NNvU6GOKQBHpmn/+NT35yl+AUPBU/9YBY9TAHpy1EugI6ZoJ2Oko56kZoAxG5QseNU7BdeOG/Z7J5TpX3DvTJ22QCmCFYdC3fLQvP1Zw88chpIxAqUVnQlQVNKwan45x5xhcZWjPB1IEuLr30K5SKfZx00nFcffX1rDN3PT7wgXOYOnUQ1HDVNy7nrjufYKNNN8BguPfeh9j/Va/6uxOskfE6T64a5+lxoUeXUSsomebE8t8LMngu73xPYB6wKGSjNcCKyCbAxfgpQQBEpAqsBzzwV7yGrwKnAQf+Ff/mhXgeY7A2lb323IMrLvoh7z/tQMDx+gP7+MYTLZ5qNphRMxRsFTFK5lqo1omkCyGmZHJazoAqzXyEOTXYblpCyXZjtcqyZoenJlI27004ZlvLb1ZBI8voLlisdHBOiY3gd47eBFpZjdEYMUUmFRKGKilDgVZTI6VBZDIMESJtnItADa08JRKIpZvEjWKknyUN4fdDTSJy8rSIMUVGOhlTCjXaFKhnCWvqKQvHEjodx1izyanb95PJMKNpzCP3PMJd1/2KTV+0C9u/Yi+++JWv0V/5y9Y3/63x+Ko6n7/pCc45cDOKkf3f/8HzEHPnzuXCz9/Ftrtuxfy5A7gsoWQbFG0Z1Qgrs1jdfpJaATbt62agkJLS4JY/PMhAb0w7axAXegHYvH/O//hcIobLL/s6KkKlOOWPfrdkyRLueOIB1hmYzspVS3jsqUe55be/o9Npcckll1CI/1gw/LkLvswb33goF3/pJbhoIXvssxcXXXA5tzwyxPVf8fNA1UIXm244mw26qx6Mai1l20HET7i1Mq/XiU2ByBhyl+NwlOMiqeYkWsdolZ5CN4mboO2W0FtMmVHtZcOenNvXdPN0I2PvmREF2wxNM+/CYKSIEUE1Z2yig4ksm2w2D9UUhwNyRIvs/8q9uPOOB2i0hul21quLNMZpgjURFi8VmKSIi3jiPLAWlwCeau/ooFqglZfZsK8LdJjhjmO1KzFDOxR3Xp+4L2VFS1jWFOZ1CwPlKk4ncIyjTEXoAUl9e08nraZt0DwFD8AwcON9TSfZVh2cOg86NlNJ3CoyN05kS8RMDZWnHDECGiPGm4YnzlsQRdLE2CKRiYNgHnLnrXMECbDTJpGd5EqVmKzkdfI6RdvDJEPwGQNpEEoY6aB04VuUElqWnvTuBwraqFMeeeQJLv7Cl5k5c/7a8+zMD57Bpz/9aaKotvax3OV8/cqr+OKXP4IRoWAr3PCbS3jf6ZewulPjba977f94HfylUFXe9M73kg5swYuqNT7z/iu46IsXE5v/3gQLntt04ZfwidNW4X8XAz8GXgb8ANhMRA4SkRJwJnBvEL0/p1BPZ/sQ8J6/5oW/EM9vvO/E97Ju/wK+cfnNKB0W9LQ5ZqMKsWSsaoGlhKGMIcZKBSWmZGtU4zJojpWUrackrFP1QMDUpTTyMWpxQsVGfOPJIo+MtHArHRv0dDHcyUjzFIeQuRy/0NmwK0tQASMZVow325UGEV6ToWIomBJFM5XYlrCUA1DQEVuHSuonjiRjZStnRatJzQgTnZixPGHlWJWJVs69yxo8sDRl2ZBl8VhCI3WsN2jp71ZuXd1gSaNE0Qgzp/chxnDl6Wex1TabsdH0mf/U7+pfNeYPVHnzLvP+YQkWwEYbbcR73ns6v/zZnaxpx6xupQx1mtTTjFbeJHVP018sUIpqQM5AOeGR257gB5f9jK9+4bt0hQTruUa1PI1KYYAszf/o8RPfdQKXff2rXHTV57n9vl9x069vIneOd73rBLbe7E8tR8rlMkcccRTX/PSH1FNlSnmIyHZImgtx6o+9+eab8dtf3cvyZUt5ZDxhaSOhmUKa5yxp5CxcndBImp77JAVi043TXsbzFuNpwmOjZX6yJGVJvcVY0iISoZFaBosT1AoZU0se7PuNJ3KuXQT3j8DCiRY9hX4qdh3PvJIqA33dTEy0uO6nv2V4rEmmHd8WBEAYHa1T66p44LB2IDg4pG6C8XSlh2Fq4ttigcukLiJ3BuccWT4eputynLbpihPmVDOmlQ0DhQIzq8qLd41Zt9Rm5NEmVgxbTYFKnAPdlEyRKcU5GKpAO+QnOZO4BRHnfRC1g9OS1zqpgVAlV/Uw1sxNJlorsUaJbTexVMkZx4Vpviw3IOBcBpIRG6Ea91CMeinaGlAA9Ql1phm5a5C7CVI3wkS6gtRNkGub3DWZNJ8v2l4g8kgIDbY2Ugim4H7WEm1hKZFrGoT1ikyyxbAYU+LcT53EeZ/6MKee9g6yLOOT557J29/+Vr52xeXcfffNTOKanHNMn9HPAfsdxbvf+Qk6WYo1EUed9i6u/fa3WLFixV91TQA8+dQy9t7vDayq9FLdfFd+deut7PaK17DRzA3/6mP9p8VfTXwPE4TrP4uTtQ9wITCXZzhZT/0vx7gMWKKq7w8/G+BeYNMXOFn/vMiyjNe8/qV86bL30VXqpp07mvkEThMKto/B0lQUQrXJL2ZOHfVsGIDc1RDJaaRteooO1OunmlnEg6OWG+7LKLRSDtqjyqyKoxA5DGViY4JWw08jRVLD0UJCdctIwQs01VfSEBMWmByokLkxnHbIVXB0mEgMK1uWdgbfXej1GmtaylgLyo8MsfTucTbaZz3uG0uICoaBOUWQnPndBQYqHuBnjFI0MKdSoZnm/Or7P+SbZ36C0dYYPaUXqlj/SvGDH32Lr1z+BTbaZiMWPrKUrbddwEsPeTGRCOoss0sxxbhMmne4+MJrKBV6OPyIQ5gxbYO1x0iShKcW3cWKlUtpNyqMT6xk1swFXH/9z1j41CPkuWGzzeexatUwy5auIMuE3r4qU/rm8LGPfYzHnniMr17+VZ544lHmze9jyYpR3n/ah9l4wSZ/8XV3Oh2OeNMhfOTCt3DYfqfw6U9eyKbbz8DamL6ir0YsW7aE0854Gwe890TSLGW9AUtPbHh8TcbHv5Iwc/MKHzvAMliKGGrDz+9dxp4bVimWCjw8EjO70mZmtYtq3I3Tuh/gd9203ShWcqDE0xNtRpKcla0C/cWIl87uo5MnjCej9MQ9IB0WLlvJLTfex9VXX8/+++/Mxhuvy+DAdB57fCFXXv4TTn7XIWy4+TpExhJJwfPI8g4umD138g651ugqtLEU8TDSCRBvou2c+DajjgGCaAGVBHX9LG1NMNR2XHqHobWsxZ67llin5pjb7ZhViXHOMJYJc6p9IL7ChngEjL+dmLVaKJ/ApGGyr4bT8TCRCN5g2aJkGFNAVAOBfhJCqkFY3+WrE2JCpd1BoLXnDjJt4DTBUAQRGmmDoU6D/kJEMfLt0lgijCkhFIO+Kg1VKuN9DsV7S1oRMhXQFNWcjAkMNV/NDO9NxIOafXIe8/nPfpPf334fRx71enZ70cZcfPH3uPSSH/C7391Od7cf0HHOoer43Bc+zvKVj/PeDxwBWmDxogZfuvgaLvz0F/7X667ZbLL1jnuy0047c8eDD1LZ/TDm0M+snfpY8sgNTCv1cNFJ7/irruV/5/hLnKwXbHVeiD+Kn/7yu9z+u19xymlHgOI1VUlGqhGzatNC6Tsi1zEvZtQizXQViLCm1SIyOT3FUhDHC50s8YA8bQJVfr0yxVJg71n9/D/2zjrAbmrd4r+9kxwdr7srbSlSWqS4u7u7u8ultLgXhyKlpcDF9aKPUtxpC5S628x0/HiSvd8fyZk5nU4LtNi9nPUet3OiOzk5ycr3rW99kgbvbVmCpywQfvm28EWr2TJo37hQa78Nihe6V1kPGmXj4lKRdHCUoDKpmFFbyOcVUJOyScah/bIUlcuqWf71QsKlbXH6tKahVZiCoEnvtgay1GBAKxctXAxhUBKUrEik6VEgiVhpighyz+W38u4Lb/5VX00ea0FVVRVLl8+ntjbG8C1GMPrGK2k/sC3SCPDs+LfZuEdnXCeDNARbjdieo48+GYCVK1dy+x03sLJ8AQUFrWjbLkDbdiWUFLchGrWYN38xW2+3Gf0GdCWRESz8eR4d2renfcdCyisrOf6o6znlxPM45phjVhvPjJ+n8vSkFxg9ejTyF9z9P/vsU+4YeyNzZy3hzTf+Q8dO7QF8/yRwVA3PvfgY77z/LXZCkCgO07d3a75881MIdqY2Xc8ex+9Fr3Ats6aVM2/hSkoCgvGPPseXy1+nXdSkJBjBkBJLRPzIThjP1ymDreMo7ZJxAyScBCm3Ld0LbGxte67kRDGET0K0YGlDHV9N+ZqKZSuprXbo0asVgwb1Y8igARhYOFphGGFQcRylsXWDv60QsUyaNmFAGxjS9EiFDuLoOpJuGq2KcIkTlBa2yiCF9rzzdJiatMGjM1y6hzOkzBCtQwb7d8sQTxbw3bIGVFSwdTsLIUK0DgZxVALLCKOUxjBkjvg624/Pa2iNb83g9Ua1kQT88x9Aa9evKDSQohjld56Q/mYMEUTqKJAGIfzeqzYppx5DBPx+fAJXm9SkV1AcCKNRSBFGEkKLNFq7vjeXQuD60UOQwgL/RdJVNoawcHUGL8Xote7x7o+WT7KiXrQO7Yvyoaa6njNOu55tt92OzTYdyciRI1e7HrXWbDVyCKNvO5MBg7sSkBJXw4VnPcEz45/7xd/dgSdfwPKa1sRS1Vx2404YQcmWHVwanAiXnXknzz72DMUFBb+4nf8V5Nvq5PGrsPtOB/PRB1/yystT2G+/nTzCIwIUmDZaK/9H6mKIAFqBq1NIGcBRcUpCGkkIU0T8NzGJ0jaWNhGyGEO47NDBQusAtltH2Aog/UvQo/oWMqvhEAKBg9Z+CbUWKK/uEIXr3WC0xNEplLZxFSRdiGXSfLC8mE/npoivjLNJl0KmvfwFH/68hI0GbASBUorKCllSniBaGmRQxyDRVilKoi5By6XIsqhLG8TtFKUBQVVaE3cNRMgkGIzgui6G8eelw/L4ZbRq1YpWrZo0UtddfROBgKd9mTV7Gm3bdCUSiWBZFg0NDVw3+lIWLVqENASXXH4k3bq2xzPG9B5ytq7DpBSHGgyKiburkNJgs828qJTQYdq1CXLA/jsTitirjUVrmx59TEaPuQb5C42qAbbaamu22soj7q++9jqvTJbsOXIQpcVFlJaWYohi9tpnL/bcZ29KQv35fupUyleuZPTp1xEKhSgvL+fRZ8ZTFXApCUbZZcRWnH7auXwzZxlKuwQME1OaSN9yRRL2K+lsFBpDRxEiRtjUREyvwXNaeVETKSxMmbVfcEG4dC9pRed9tkdho5RB3EnROtjGN+UMIEQ9WqVxdAIhLExhYuogQbMbhcZSbOoxRRhTBHBVhrTKkHQ1aVcRt5OETIvajMaSCrTAkJLlCUXGTbNHtyARM0xAmvxY5ZKyTUxLsVWPIqbXpPm+KkOHcDFBmcIQXq8+KU1fH2UjhYWUnu5JConSBgjt9+wL+X0YXd/1PRud8lJzUqSQhoXUpucvpUMIodHEcXTaj9pnEERIuBnChkAqjSGDmBJahcpQ2iHhJAlKQUrFCBsGhjA8awZlYEivxtJRGUwpcJWNEIYX2cIjfkIIX8vmIrWBd+cMIbCRwvBJqeLBe59j2tTZjL37Ibp1a7laVgjBXvvsRzItSdiapBAUBkC2EHf5fvp0Pvx2Kt99+QM/LFjKXjtszsJV5exz9GHMqapk6sIwe20uaXDSaG0h04rCSGTNDf0DkSdZeayB68fcwllnn0Tfvl3o0a+MgCEJSC9FpnzDQHw5q61TIFwkQRoch4C0iBjFaBlHKUWBVUB9pgFcKA5GCBgFuDpbx2N5b12+X4xAeuH+Rv8cr8oJpN8eQvseNNpra0EtjkqRsF3m1Uf4qsKmojLI4pRF+aflWMuqeWfZdOpjcYqLi1iVDGKHBdHOBURW1JD8+DO+TQ1lm63KyKgQK+KanzOaNiEIGhbdCw3KE0naBcLE5i+g2Az+YmQij78ez784gUeeuJpddh3GXXfcitAhGhrq0ErgOIqzzz2EoUOP8VM0XrWqxEVjoEgjtImtqzGEZzIZEBaRQAgpQmgcDGFgihDbbb8Fkz/42iNnwtfHCIuQ0R+xHtVUb7/7Pg/dfw/n5EzTWlMcamqRs8nQoaut8/PMmbzz5tuUFBfy+ouv8+XXHyKEwChohSs6EDKFHyVRHgkQaU/jiNdiSBoApZ7vnPYKT9CAtHwhtkZioYVoFMQHpK+NlC4FVrHvu1SCq1ciAEOaCF3s9wLNareqMQ0Tk7YILDJuNSlVg9ZRlM6ADhA2BY6ysQREDAO0gYtLaSDAqpRJ1IrQvVCyPF5D66jBy18KnGSCTTaVvLpA07FQMtuoRQibiAHdihSS7IPe9dvfeCajQhje/QbHq5bEoLEhs19xKNF+dWL2OjEwpItUIRABIOVXLAZAaKQoQOkAYSNCyAjgVS1G0SLpR8UML0qnq6i3wZQhAqIIr/m2xnbjGFIjCCGIYkrfy0rbXpxfeCP2/MCCaASujiG19IiXcNEoXnjubZSK8uT4Fxqvy7UhnU4RjlokbMUXH3zPtx/OYaftdmucn3Fddjr2LIqiIaKDurHNwUMZJoeTqq7jrN0OplebFIUVIZavEnxSHqZLzQI+fWkKI3camb9X+siTrDzWgJSS88+/kIsuOpeufTpwyGHbsdnQgaTcBGgImQGUb94XkFGv4lAmichiEipOZXoBRVYZAcNAigDRQDFa+eF1YeFV1tQiMTw/LJzG0mmvsakny/PSgv6bJBLth8FdlcbVCRxlsyqlsB3B8qTBnGrJirmaBd/MI1pbzpI5P2JKSWE0SkFJXxpq40S6lLFgbg1SasyuA0nFBDMqoLN0cbTnGh8x03Qv9FIl87+dyn9ef5ctBg3h4Qce/kf7vfw3oKamhldfeZlx4y/D1g5XjjmCqFGGlKb38EehCfrNdF0MwiidxtE2ChtQXnsYBIgAhgDLLMRxHWw3QcCIAIqKimpuGD2O2++4dY0H2foQLIAH7xvLHXfdylffvE9ZtDMdOvxygUXvfn0oatuKVsVF/PTTTwwcOJAZS8oR6SQFVoqAYfkVaApDWKCF9/DzoyBSgKu94JYpAmjtVVFqbSNF0CMeyvV1QqYXeZYuAo3G9KvzFFCFQQApTD/d5YuzhQFaozR+/0CvClgQwdU1GAQwRQrTDJBWKQKGjauKQaYICY1pmJQGJN0K8fRehGgXVgxWbbjk69nIdIatjEK6RKBN2GBgiSRmQ9KGDtEYkCZqtcKUQVwdR2sHU7T2SZFCadOPSDkobTT2HvTMPb1rwhCe83nWWNUzRNZoLdHY3jnyCZshIGRagEIpxyPl2gQRQuiYVxyApsCQuLjYKgnCpTppYhlJQmaCgBReChFA274vqhdZRGdfbz1ibxIlo5Jo7WC7aQxZSI++bXnzzVd58qnHOf6YU9Z9AQVc7rltEo4rOXi/Q7hh9Em0bdsWgMmTJ3P1HbdRsMnODOtbyIwpb/PoxJmUlJZxyEn70LtdJ2Z/+BPvvfAmZUVlrBQWmV49ue7K6+nUqdNvvPr/d5HXZOWxVmitWbJiPo88fDfffTOdS686kqGb9fXsGWTYpz8aVzm+DwyknAQplSEgQQqDiOkJLYUwvBsw0r9JSL96pvn156UjPQND/6FHGK+xqtffy1ZpbNXAqpTJikSatxYVMK8mxNLqBNYPMeoXV7Bi2jdEywL07tyGhUtt2nfuyfKKFXTarAdJbdJvo1JWSZdW4ShGa6hKKfqVwICSMLauZunc+Xwx8Q1atW/Pk3feRziw7p5defy1SKfTTJj4CO+++wajxpxEh25dSDmKoJEmYrbxzCkboxdeWs/RLiYhNDa2TnleRShMYeEJBV2PmGCitPIiXkIBUF1dx2GHXsLlV57Drjsf9tcdeAvY95RT6bDxCG46aQBBw8AyQoDl/4Yc75iEH5kRAY+ECS9irBrbs3i2BloLEvYyTBnFkEEMLF/XJdGY/guS4YnARdBv7+K7oOP33sP1rQzAMzJVKBySdj22SiGwCJiecNxWNoIIaRUnJEsJmQYZlUZrSVCGkFKD9l7ObJWiIZNiQczmh2qbVWmDfTtLolaGsnAIqVshZQbTj0iCROH1JDVkY0cfT9Cus3ci2zMR1drvh+p4ASm/sbKUGqVchBA4SiFwkNLw1/dNkv1G1JIQLmk/GuhVTSsktorRYDukXE2rgNc02tFBDCEIGCGUTmDIgJcuFSYIkISwZBBHO/79EyQGjjKpTC1B6QAlAS8HYLsNIIJcdNpYHhs3nnCozVqvleWxSmK2pm9p28Zptm3zwDOTuP2664gUF9G3V29m/fQ97Tu3ZeO9dmHKs29T1K6M0o6d2a7nAC666KL8yyd54XseG4iamhr+de1l7LzrJuy5xzZIEfDD6srr5O5X/bkqhas1hhAobWFKP/QuhOeRQwhN3KuiEWHfMFCQ9a7JttnwHgqeiaHWXqpC4aJUjKRSLKqP88YiwfeLA8RUgHBQsHjyMtILyomvWoYISgp3GElZfQMVS6opKS6m9cBW9N+sFYsSDaAMNm0dYV48QftwAEfZuKQpsgziC2bx/sMTef+F1yguzFcS/t3x3XdfM+aGqzj6uN3ZdfdhZFQQrYKkVS1loQiWLED4Je9eLCLtkwCNVgKXjN9FIAPa8Mi/MADLf0B73QeybUs8EqJROsTpp15Kmzbt2HOvQwgGDbr1KqVNSV/kr/AGSqfTTPlpKpv06keb4pLf5VysrFjJ6RedwM33XEjXaCEajWUUIoSBJuVX5QpPWK+1X26iPf2Y8CrUvMITv5ZOg6tSXoscYWOKkP9AzbaG8SNk0PigzZ4hAKU14OCqMIZwEMJrTqw0jVEaRxko4kgdBGGRcit8U9ki/zxrDGH5MgXv5c2LiGlcnWZFIsOqVBxDOEycHWCLNmH26J7CQGLKUoxGfZX2o1bSH6vyj8O7jynlVSdLEcBzoc/4TvKur+kKIITy7Rv81CLCj1y6fkrQRSkbRye9+5t/LpTWZFSSkIjgaIfKlAIylAYlSqQwiRIxC9GYpN0qLBlEC+FHXMESUaSQuNrGcZPYOoEUEWw3Q8p1ULqAmFNPq6DnDq+0xWeTf2DhnDgXXfCvtV4vk1dUsiKR4sheTR5xw0ZuRf/dt6L/Lrsx7eU3sBavov9Gffnksy+pdTXXXnoRVdIkVZ/glH323OBr9n8FeZKVxwZDKcVRRx/Aw+MvIigjWEbU/0GnfeWFlwZAp/GiUQYI380Y13NiFtlIgkLKCN7NyYtYebcjr4+49j8LFI5OgZIoBAknxoJYks9XKj5bHKBrtJBq12FBlcOyL1cRcmKUdimmuNAkUVRCfTxDbVWa0q5hoiGDiOmlQlwJylE4Goa0jpIWafoXh/nP/Q9QbFk8dP3NFBXlCdbfHfFkJUcdeQSPPnkZoXAEdAhXxRESNKavjbEwZRC0wktV243RAEcnEdpr/CtQCAIY0gItAQspbSCEEF6fOqWA7LUuPJ3NsiUreOutKcSSCebPX0BtTYxhm2/FmWdcTKCFCKhSLjfcfhXffz+dXv26UF1eSU1FmiuuvJBNh26/QYUVo26+gk22aseIYZsQNbzosWVE8RJapldQ4gu+Pbdxz+2dbGcZv/2LR8C84wSBoxzidhWOW0hxCEzh93cUGim8Z0jTk8SLYnkvSCaaDEp5ejXpv5gJXxagcT13cy0xpDcmR3mO5h7Z9cw+PYLkbVtr09NtCtP333JRWtCQqWZBzGZVUtKt0KFTxPNGE9LEEhGkMDBkdpyeiacHr5wG3w8+W2jjpY5B60Dj8TRqRbVCEfD0ZCRwtHefc1QKTQhNjV/NqTBEkLhTh+16lYlKC95YougU0WxUWkChmfAb0xteOloJHJ3GEMK/jjUWQaQMYCuFo2vRfmRVaYGjXWwlcVTS693peMdkSMWoix7l/HMuYePBW7V4vWhf5ypzIlF3TXyU115/mZ3OPYOvPp7G7m3b07BqFYcddhjdu3dfvwvzH4A8ycrjd8ENN17DVjt0ZNNNN2L5gjgfffQNW2+zGf37dyVrTth4Q9Sg/TdnL6WgV0sPNoaYtabJriGrb8iu74k5HeWytKEaJTLMqDV4ZUGQ4qDG1ZKMEqxYbrCoPE2Hjia9WgdIkaFAhplXF0dkNMWFAWzHxDI1sytThAMS5QpiaZc+7SwM02H2w/dy/jFHc8w++65208nj74sXXn6ahvhKDjpkKIYIeteOyGBS7NsEuEgRRYqMH1Xxohde6sZ7aEotyTYFFkI06oqyMRmBwNW2Lw7XuNrBFEE/kqs9c0syCILeZ5XmlVc+ZdmyBs499+I1qgxfeHkii5Z+z0mnHklAahSKihV1PDH+3yxeUIdtZzj++JPQpOjapS/9+w5dLR1TXV3NwkVzKC0ronvX/o3zlMqw9/478dBTV9Mm0gYhwCCAIYNkozXeMQKNekfDT3PRSGLw+/w10SYTV9mUJ6uJ2Sm6FbTCkp5NS9NPOCsewNd1ec2OvXNt+2lDI4c4OUAYrWN4RM6zK1A6k/PC1qSRyr60ycbvhdXGp5SDqzNkVAOuMsmoFCm3nmKrBNMIYomIR54bT2M2kuUVPjSdH88R3rsHKX9Zn/Rqx49h+i9qOuNXpOKX6jgo7ZB0bEwjQEhaQACFi60acByHlFI4WrMopukUtSkwowSkH0lUkowW1DsJtIaQ4cnvA4aLJaL+0BVa2kgKMKXEdlIgHFzl4mpFRpkknASFZhhEmkQ8wYVnPMDj416krKysxd9Qc6xKxTj63AvZ+Ygj+PiRh3lu/JMEg8Ffte4/GXkLhzx+F5x55jmcceaxPPz4QN597wtSCc0Lz31KfcPL3HLbeT6JEt6dtvEN1GisHGyqGlSN/3q3Skn2pu9VFPp9wpRnCFidqiGtHUxdTMSIc0wfTUUKGjIus2sFyaIgiYiiNAB9Sm3q7DD1dpxNO0iCwkVKl0TapdLWDGhnkmkIUCFT7NLJpDioqJlfTWHvnnmC9V8ER8V56eUJjL3vMo8kCa/NjCCKIS0EIEUE4VeUSSF9PVDQJ0XKszaQEim8li9eAwpfZ5QTuWiMxgqJFIUIbBQBBNmG6QUIYXsvBkaQAw/ciWuuuYOTTjqYHj36sNfeuzF0420wZICAFaCoMELQ8JqUIwSdO4cYdfWlKKWZtXg6H0/5lrBp8vnnHzNvTiVlpa3ZdschfP3ljyxfsZBBQ7qxcFEV06ctYNgmGzFocB9m/FTBrrtsRdtwewzp/Z5MafhEQPgvPD6hEVnrCdfXqWUJR5ZYZImNJ+gWQtMu3I5WoTgSga09vzuhbZrCYBqBxNVpbx4Sr/FxwE8T4u/D27cm4wvjTYSwPXJGwCMrON44G5uSNBXBNJEyhdZ+Hz8JhrIIylKUCIAoRxEEXeilOKXw034pTBmm6WUwNw0MHsHS3naJeKafeG1yQPjXR7aJNDgqiSnDWCKAEEGUBtPy0oZJdxVBWYYhJVKGEYaLQwrbsekSdQlIgcBGSgNXKTLaJmnb2K6LoJBVdhpQuNqlLBgjYmoMoZDKQIlaMrYg5Wg0QTJKYEqbuC0JSBMtkihtEoqWMXTzfsydP50tyrb/Vb+r1qECupe2ZsJtt7DXLrvmCdYGIk+y8vhNKC1pyw7b7ckHb09jyy0H883Xs7nssis56aRjSCXThMNhGvuDCUB7DzZPvJmNbGXJVlYk65d6+yajAuEJPLVC4eKoDPV2Cq2jSCNOzyJBdTpF2hEUmFASkCyJ1tM1bdAm5GJKiVFdzoovvqfAlbRtH8JNpFhYnSDVdxsirdpipzS9W7ls3zVG+0iAnxb+QGizPnmC9V8EQZBgMOJVu4mgX+JehBQeUYICjwR5xfh4ztyAjvmP7AhCWBjSIxPCf8DiR1+bIiVetZggiGjctp9mAz/i6pMWIRpfNMaMuQTIMGfOIt548x1uu+UOttlmBz6Y/D6jRp+MJQo9fZHIanu8CHCvLn3pfUxPfyQmSMGqVXHefvs9DjtyJH37Ho0lA2iifDX9G7p3aMvcWUvZZutN6dGrfWMqyfB1Z1nNkHdluzlEavWzuTqypMgjFNOmzmLo0P4I5UX5UnYFlhHGlEGEFhiy0D/DMc8qQfjlLb7RZhNZUo37E1kdpvAiSUKoRsIntNfsWwovpekRsKyGyot6exooBX51JFIglARiBGUBljTRyiHpVmDpKAYShIWn87Lx+hp60cuma8QAbJ+CuTRpRb0v2iOOXpdHR2WYvipDOubSNqzo1q4EIdIEZBRXu1gy4hcDmBjSQaoElpRYAROES8YFyOAqi59qBElH0b0IEq5JVSqJIQ3aBCHmapQWxB3HF7srMipARrvUpVyWJ22UlhhYlIQcOoRdP7Xo8tmH3zDtuwVcev5mLf+I1oIHb76BVVWraNN67aL5PH4d8iQrj9+Mk08+i8OP2I/7H7iCW295nGOOXsUJJ5zAPWOf4fIrTmy8GXqhde8h55U5a8g+jBBNpn+NeqysXgvPPRoHrZO4KkFZ0CCWacDBICjCJJMJvvlkDvNnzKR/l9b07duWogaTZO9+1KTDfPPovzlkn/3o26M385csJNw5TNuYzZ1330+HQf3pULWcSgHdbj+ebsWFBLfoy0N3vcVhB5/8V5zSPNYDUhgEzCDVFQm6dW3r24EE/Ie1gRSOr6nBf1Brsh5shvAahWcJU1aTI3z/oyZ41MFLU+lG2pVt+QSZnGlZ+H5MQgFB+vbtxfl9uoF2+ejj7zjq6DEUFRX4YnNfu0i2RYpD0Az71Wo2WQOT1q3g6KP2xyNjQf84MhSaYb79ahb77rsjoFFK+C80Rs6Acvssipx/s6Qid372uLzz9P77n3L77eMZOnRj7rxzIoWFYe69/ypMI4j2qxO1UJ6li7+O53vlH1FWd9m4v2yL41yZSrpxnhReqyxBsnEZgdn4ItZE0nwHd2355zvpEWehfKNTT2cljCCumyLjVmHKYoIi6FdQWn600q+CbDwHfmRP+Pcqna2G9qOaQuOlNb2x1TYEeWNWiqM2SZGpDtCnzMJ2FaY0MUSxf+xJ0CFSboaKZIyigKQ4UIxppsmoDA1pF1sJMsrkmwqoShsY0qAqBR3CmohlIVwLy4SvKg06FwQoCSRxVZS4E8R2awlIiWUqlseCLKy3iQYsSixF60EDyMj/45NPPmfXXXbl10IIkSdYvxPyJCuP3wzDMLjm6hs45+xr2HPPnTjppON55JFbef75WhYtXEH37l3xHkxZzYfOMWxsehTpxjdSkXMT9UuqUWTlF4YMIHQMhGBunaBuwc9MvPNVttl9Zw7Z70hUVR0LZ8+gPm4zZeLtdOzYly/fn8L5J53O8OHDGT58eOM+z9xrT/7vs7fZdbs9Wb5sPhdddB4PPzGKbh27E6tLU19fS1FRyZ92LvNYP6RScW64cRRDh/amW7dOWCJbVOG1SWnU1miFxovkZB+ghgj56atsNVz2wa3wHvjQdJ1KsgaW2SirJxZONPphCZHV9WS1PJ5Ivkk/ZPgO4y7bbzfC324IhJOzXe8hrjERwkWIkJ+iUo1RN+0vJ33tlEISChVwwP5nYzs/IaWgf7/dmDDxdrbccmjjuWquYFr9U/Y85H52G5dp27YdO+64G5deegUAo667mKnfz2Twxv1R2rOEEMJCkUBiIAk02hZkW2M11Rzmkrmg/3l1LZSHTDNLgDRkXecbx+kTMJH9fgACftTMQRgarQMo7RIyC9A6Chhe4/nGqKbyv/umNKS3V0k28uddGcr/brz4m1eBqQCbJQ0ZqpXk2blhQvEMx2yZQogYrQKKgBFBkCBitkELRdSK0kaHWBaLUZ6op2uBQdLNMC9mknA0IcOgVoOrIKM8i4hp1YKwGWU6JvV1UFqiWBRzWRUP4wiXgkCSuB0iaijq0hEcV9GrOEg05JAJRwgZaTbfaxdWVVaSx1+DvPA9j/WG46Q4/YwT2H//HXjwgWe55dbbuf76f/HII9dTVOg5LWcJlucUkzWnyQqGs6TK8reo/Nud8rUP+IZ+niZr9sJl3Hb325QYBg/f+SDRaHS18SjtUlW/ApWyaNOmza9yHH75lWdYVDWbgw7biWVzFvLS81O49cZxv9MZymN9oLRNxq0iZLZfbbrnVSRYvnwu5553NmeddRzbbjcEsDCkJ372Um5ZnY3pu2X7gm+y1W1eheGaER7pE3+zKZXVqFdqqkZTGjxzSCMniuVpoLIRES9qJvwXi9z9eNtbPfpl0UQUcsiQpnF7QkRAe+X5QjStrbUiHBrCXXdfxdFH70tx0abEEz8QDgfWSACufjy5aXvN6kSr6ZwoJTnqqIt5+mnPPTwWW8VJJx3LhIl3gOF6qilpeTE+7ffNQ/tRavzvI2uTkKVbAnKIb8vjy9VfNSdhzZF7jrPbtsn6X+E3jgd81/csEbZ8cb70K0/x15X+dAelPcKltRfFkoCjMiScUZfGhAABAABJREFUOEoniTsOi2NRPl6UosiUtC6yGNzKJWLahKwgQWkTlKVIaWLKAFo7fFFRQ0DW07VA4ShYGg+gNKRdi+pMkmXxIBnlknDgp9oAHcJBljTYFKkMhYWCsBHhm/IMpWFB0BCkFJjCIlYHS+NpisOSjlHBgPaCDkGXd+5+jLGjbvhVBqE16TRp5dI+nG+J81uRF77n8bvj/fdfI9YQp3//3hx+xB58+80nXDfqek484Ur22mskx59woL+kAO2ihUT4+qymG7qngfDSCNl2F957sNKOFw7QNovmVXDPTS9yzy130bNbjxbHI4VBm+LOUPzrj6G2JkX/rl1oHSigzaDB3H/X6znNZPP4K+DqBLaqJcTqJOupSXfy1lsfUVJSwL33XkH7dl0QMitadpEy+51lH7jePM9EFLKGlN703IeyJpuKEo2EJ7ut7MM307isaLxt5lagZQs+/PY6WQuTNQhV9m+BR64yOfvTjcvp1UiG5+2lmqX6vEBZiG+/e5nBg/bmoylfseWWm3DFFfdw113NDSKz28uON3v8TXYLLUFKzTYjh/Dmfyax117HUFDQmjPOPJPTTr2KU049gi233NgjUmTF736aTmTJpGhMxTad++w4csXs4BHZbCQxS/yy480afZpAMmeEzclXkyTBI7l449IRX0+nIee78VZROCqFlCHQjmezoMDrlapROChcpDZR2iTpVgBhMkrQKtiOdmHoXmhyy3SH1rEMce1SICUbtcrQOpTBEmkMgv55MtmsdRtiGUFSVSN1lKJAnBILEo6nf0sG0yBMKhLQKuASNdP0LHToVeSQ0QaVaZdhHTKEDJNvq0y08pKfFQ1pwsolU2kzr8EgaAk+euBuDth551/twP7wzLl0ioQ4pk/L/Q7z+O3INxfKY72RyWRoaGggGDA58oi9+fDDKaRSdbzwwht88slUKsqryKZLlNYe0WqMnGYvPb/zPOQYBCq/PFx62hICjH/4DR5/+Mm1Eqz1xVZbbcX7736OrRMIIejSPcoPMz4AIJVKsXLlAhwn+QtbyeP3hCWLKQz0X23at99+xPRpM5k44Tbuvfda2rVv77MWI4e8eIQqWw+IrwX0Ij+5wutc4Xd2niQbAfGwNq8qA+FrvbKEy6MJXoo72/LEG4Ru0hzqbOQ2V0yfaTaOJniVbxLpV0wCjX8L//+85VwGbdSH888/jkMOPZhPP/uYESO256GHXmTtt3cr5+/cyrrm8MZ04okH8957k5k06QkAtt9ub2644Ta+/WYWp592DbGGVOOYFi9ayhOPv8CUKd/hEa6mKHVuNMvbb3MBfmqN87D6MTg0kbDcabnIPRZf44nyCVbA/76sxjOY5WCmDGEIn/JpvKiV8HqmCrx2PtkihYAsJWqUUWiVYEqBwiFqOlyzSYhDehjs0N57cVwWl0ARhvTtIxBoDZZ0CRkQNcMETIfWIYuYI0npDEVBQUlAEjU0xQHF3l1chrbKsGdXmx5FCstQCA0LqgLMr4VUg2bJzCSzv62ioMam/ttV1Ly7BGpcPhn/IkcfewJnnXXWWr7fNbFjx44c0L3br14+j19GPl2YxwZhzpyZXHPNZUQiQf71r9MZNeoBrrjiKoqK2nDRRWdz6GG7s+++O6K1Q0bFAc+92DBM3zvHfyT6+pXsAwuUT8y8EubTT72Oxx598Q+JMF1++bkccth2DBjUk6+++Y6fvluF44Q4//zzKS0tpqamjlQqlS9l/oswdeqXXHfdv3hq0q1EIpHG1i2A787tXUeNqTX/msr2k2vS2QiaNFbQcgqqpbRZLoI0kaOmSIteLeWXtX+gaWzaJduEmMbx5JKM5tV3axtPy+NbvHg5d9/9FHfe+SgA5513Gqecuj+DNtrwiIQGXNfljjvGU1zUntNPv6BxzqxZ33DLLbdhGN75LStrS9++3Zg8+XNuv/182rfvRhN50kDE/9z82FuqQMw1DM0igke03GbriZy/mxXS5OxrzbvHutKQueda0pQKthBC46o0jmrwqLbwjE/TqgFbCWw3jhBBSoKlXoWoX/ijtI3E9Hplui4IG1c7NDi1aO2ScUPU2Q4pR1OTFsRti4x2cZSkLg0/1QeJZULUxhRkYNHP9ajqNOkFVZS0jxCLuYSjBiVdNTVfv873L09ay7Hl8Xsjb0aaxx+KJUsWc+GF53DBBUfw+uuTuemmsSQSKc4//3Qefvg6XOWS0Q1o7WLJAiyjOWHJuf1pjcqKXLVJRtVx841PcPABpzJ48Ca/+9hjsVqOO/4o7r3vKqZNn05lRYr99jmB8vJyOnbsSGFhIUqpfArxL8DChXO55ppLeeCBqyksLGok4FmIxhRTtlJVAFEE8Ra2liU/TTYCLROq5hYOGi/60zyKYvnbaknblK1c8wiZl4LObjdXh5Sd5uRsM6cCd43xNdd4NeGii27hlFNOoX//EdTW1XLmGcfyxPgbCAZ+zcvBL5FLWLmykjvvnEhNTYzNNx/Oaaed489xyWQcUqk033//Idtvvx+vvTaRKVMmc/TR+9OnTxei0Qhe5WIGVjP/zHphZYsLvIrMNclVrrKlOSnKkqzm5yX3+25OavH3k875O9PsHKyun8tCawvt6+PQnvmtFCZKewUQtm4g4yYIymIMqXxynR2jvwvfV8xRKRyVxFFpHO2QdmwcDRmlSTouNRnJZ+UhpNAYwmBmXQjbdehRZPHdSpt4DOyKDMkySThtUBrUxC2Y9fDNvPXYOAZ260wefw7ymqw8/lB06dKVk08+hqVLl1Jb28CCBTPp3n0g7dp14Myzrufmm8+loKAER8UAG6UCvjA9V+iq/D6FTTdLpR0EJsuXrqJTp7Yt73wDUVBQwh2338upp5xEJBzm7rEPUlxcTCwW46WXnsdxnDzB+ovw+usvcOGFJ1FYWMDqWp7s/zbX8AgEzdO7udqf3MiHzpmXveaaGpQ3RUQEucL0JuROy9UYZjVfOWSwxesnu68sVq++bZlstEywAK699ixOP300kyY9T0lxCaeceiq33voY11x95lrXaRpHrnIsS/RyU/uCtm1b8957H7PDDsP5+uvPckiWQSBg8M47L/Cf//yHY449mL33PhjDCPDJJ3N47LGXSSTiGIaka9d29OzZg7q6OlaurCCZdHBdRTKZxLYVSrkMHNiDiy46IedcZHVULRHO5unfpvF6pC57LTTXhUmy5NyDjfK0+56GTJi+JYjnw5XtFmCrDKbM9oDMyho8mwohFGgXAwNT+Aa3jd+p58WVtbLxel/YGNJCCgtTuigyCGpwSZNJeUo2S0JAeq787cNpSoNp5tYEcJRD71KLJZaGYosM0Dps0Lcow9fPPs9RB+3NwG6dufWdT3j16ed4aewNtCsp/IXrII8/AnmSlcfvhlatypg4cQJXXHEat912F/fe+zhjxtzJ7NkzOOfsq3j44dEYgRDeDSmbGlSNHe7xw+lKO0i8MnuNi53JoLVFWdmvE2+uD7p378n99z1KIllLxw6eJuGW265g2vc/s9dee9Gq1R9D8PJYO2pqqpgyZQqnnLIXTWJv76Go/ciVaHxI5pJ1aIr4mDQ9pLNoXr3W0rzmYuqWojxZgpb7kM/dVvN1cpfLXS/XAyp3/eaRs3VHm4qKCjjssN25/fZbueSSK9lh+5158IH7UUojZZaQtBQJyx5zNl23uhA/O04pBQ8/fB3jxr1CQUEZTSTU4Ikn7mPRotk88MC1PPzwC7z11vvsueehq+9FuSxePJf58+fTv38r2rdvSzhchGEYhEJhAgFPt/TYY/cybtxznHLKYTR9P7mET7Hm90bOfPx5zYlxlpBlI2r41YPeedda4yrb0/CpDEJ6JqiudhDCwdUKpdPghrF1hohV4FeRZmjqx+h1tzBEwG8hJHxNYNaktjGIhefdZvuRLs+ANWSWkHFjBI0UWggkipHtM8yoDbMopkkrQXFIYytJSdQlYjlet1ehGVii+G7yFxSmXK479VQ++3Ea40aPplpHefmzqZy+58iWLps8/mDk04V5/I5wWbjwe6666gbq6+spLW3P448/iWkafP/9h9x44x3cdvu/6No1CsjGEvgmQbDv/N5o8eCF6n/6cTb/9/4cLrrwsj/1aOYv+pZBA0by8ssvs9tuuwBxoIC1C4Xz+L2QSCQ4/vgjue660xgwIFdXlGuVoIGg/5BrenCuHS2ljLII0BS9aYmI5K67Nv0UNEWB1qX1gdXTis29qpqnHlsiV2s/ltNPH80pp5zDZpttwYcfvs4DDzzCJZccz7BhQ9YxjrVhTV2UUpJttjmUAQN6YxgSrQ022qg7559/IuDiupqzzhpD585d2Gabrdl6612wrN+iZ9QcccS+HHXU3uy5505I2ZycZqNDuRq4ptTsmsg9V4Km/oQaVzm4KoMhAzjKxiXmR7MA7dlRGCKAYVhoDcpvKO7oNGGzNEdjp3zy5LVfylp6NNltrA7lN6D2dqZxtWc5Ybsxj+zholWamKOotw2WNLhUpkBhEDJAaQNDeOL4TpEwS6vreer2J+nbYyOuvOQqpJQM329veh17GoueeIxPXn/5V1na5LH+yGuy8vjT4Lo2559/OoWFQdq168l5510MeI1tTz31eB566Epat/aalTZdfdp3uc4alHq6LInks8++Z+6cBk484ZfSHr91nBnmzZvLDz9+Q239CsqK+9C+fTuGDx/hv/nD559/zqBBgygsDAG1QCvyRbl/PG655Xq2264/I0YMoSlilH1YqqyBAY1WBi2SjuZEpyUh9S8RKvCIdaKF7a9tP7kkLBulyr3Prk1XlRuNW1ukJlez1TLRGj36AY488iR69x4MQCYT55hjDuHJJ28hFAo3W6c5ecydt7p/V9P3YDSbnksUm0jbzz/P5csvp/HGGx8zYcKzRCK/Pl0Vj9fz4ouP8+67k7n99stp375NztiyPmhZAppND2a/A5Hzb0sEzfG7AEDGSaCFBjKeZQwgfGG7IYO+c772rzKFqzQBI+gXMkSQsqU05i9DEwCd9uKX2nPM1yqNo9No7aKwQQtsZZNw0p5vvGNQnxbUpCFggGVoWgdc3n/9cz56aypjrruT/v29qtyly+ZzzS1X0nOXnan9eiZ3jL79N48xj9+GtZGs/NMij98dhmFx/fXXMWvWAvbff5/G6WVlZdx9942ce+4NuG62EqxJsZCbxlG+maQQkvKV1bRp+/vqCd588zWOPGp/9tp7Lw495ATuv/cJtCjnq29e55BD9+a55x7j6acfZeDAgRQWFuLd2NuQ/8n8GVBMm/4tw4dv7X/OPvybyv6z9YNee5vmD9Om7XgI+P/mEqzsg7glTVTzbcVY3eoh196hpeVzdUK/Xri+OklpqRUOrE5uWiZ9hx++P1dccQWzZ38BQCAQ5fTTT2L8+FfWsk52+83n2c3mZ7+H5mm45hYZHgYM6MXxxx/IqFFnc+yxh7Nq1a93HY9Gizj22PO59daxnHXW9VRWxnLmNnd+b95j0MyZL3P+9fRYWbMNrW1cHUfrBAKPUAltYMoACokUBQhh8MbrH3PEoRdz7TUPYcmIp9USAT9KtTaCte5otyDjkStt451PB41CigBSWhjSM9i1hEHEMikyI7QLB2gb0qz4eiY/v/cFD11xL9ecMxbDLuLfz7zaSLAA2rQtoX5lJfPe/pCzTvx9X07z+G34xSeGECIohHhMCLFICNEghPheCLFHzvydhBAzhRAJIcRkIUS3nHlt/GnLhBAn5EwfL4TQQogtcqb1FmuLrf4NsLBmBUIIXnp9Mul0mkSi+RtxHrkoLu7Miy++Rbdu/Vab3rnzIHbffS/eeuvzxmnaF5YKJOiQr3MwENpBKUVBQZiqVfXrNQ6tk43u8VncdtttHHroEaSSkmv/NYoZM75mo4368eYbk5k7dymXXHoCSsUpKpaccsrxfPbZh+vYfmyN7eexYXj00YcZtvkAoMGfsi7tTTbFl32oWTQRoeztJDeNlCumbr4tN+fvbNQj+3dTFG1NArS2CFd2P7n7yuqicpH9nCUPuZG73LG25N2VXc4ie2x9+3bkscdGM2rUDSxevACAAQM2ZdmylS2MTvpC7NztNd9Xrh1F7jK5x5R7XLnHbDBoUC86dCijvr62hfGvGx06dGPx4uUIkfsd5to9tASV82/T3xrXI1gKlMrgaMczPxYhjzCJEEJq0hmbBfOWc9PN93Ha6aO4/4FJXH/jVXTo0JH33/8EEEgh/NZMLZ2P7DlYG7KFGzJL9zxZvDQQWKxYVo1WFpIQphEhKD3t1wfvTOXS0+5kycwKQqo94x99gecnvcWZp1yOlNZqewhaZeyz5xEUiGJ6du+5jrHk8UfjF9OFQogocAkwHlgM7Ak8AwzGe8WbB5wMvA6MAUZqrUf4694AzABeBd4DdtJaJ4QQ44F9gG+11rv6y/YG5mit1/kK8FelC9OuzTOvf8DIoRuz7z57kLHhiMN24JJLL6Uw2v6XN5BHI5LJBCeeeDTPPHMz4Jv/AQj/BkgKjcRxk4CB1JKjj7qCfz/7GoYRWOt2s1i6dCmTJj3FjJnfYJkmWitqqh0mTZpEOBxmyZIlLFy4kOHDhxMI5G5Ps3jJIv7973HM/HkurVoVc+3oM7lh9CO4rmSnnXdip532wpABv1VQLZ6ZahTvZ5LH74FTTjmGceOuYd0aKlgzTddcX5QlW/Y6ttVcC5V9eJus2cfwl/yqcqM5LbXT8RoZtzz23x8//TSHN974jssuuwbbXsWpp57G44/f5P/ePL1QIz0Va4u9tKQLy424rc0Co6la85VX3uX77+dy3XV3sLa9rAuffvop48aN5aGHriEYDPD11zP54IPPuPzyk9axVi5B1Y33GKUljk6SNUE2/EbarsrgkOH8s2/FEEV07dqTvn27M3z41igFXbp0pr5hEddcM4p7xo5i3f5qEi96miI39at9Q1ohZGO1Ij7BFULz5Zc/cPedz9Krd2eWL19Gh46t2HrroTz/3P8hTc1OO27H/vseRWHhry/CyXev+POw3hYOWus4MCpn0htCiAXAZngClZ+01s/7OxkFrBJC9Ndaz6RJkWj4+8r9tp8EjhRCbKe1nrJeR/UnImhYHL//bgB8+slHfDf1J8beexVFBR344ac5DBrY+y8e4X8PGhqqse2E/6mpSszj117TXFc5xJwEQgexDMHmWwzgq6+nsOWIXda57Xg8zmlnHMkl/zqKg08/mU4FHVCuyzFHXtu4TJcuXejSpUsLawu6dunOJRffAMDoMRdyxunXsOeeOzJkyADuu288d955J0ceeShHHXkiQmoErX0PnDx+L0Sjxbz33qfsssvWv7BkS35Jue1icm0UcvU7axOa65x5zd3Yf+lBlZvuptk48OflWkusL8HK8Vtaa9rRLyDRgkSiGgDLak2PHr34+ONv2GqbjRGNHl/Cj6PkHmPzdkE0m96cXDXXbrlAENdN8u67U3jrrc946KEJrA/BAth6660pKAhy6qn/QmvNppsOZ9asRTQ0xHxrj+Zjzn52/OOjscBGkcAQHuH1Gth7373GRjsutdVpnnvu0RbHUVzUg9qaBiorK2jTpoym6ya34jEbPctmOppSv1obIFxcrXFUPWBgySKEyHDjDQ9jZ+DJJ58mFAqzatU8Rl33L5595j2eHP88pvnLL5ctQQiBoyoQBDBkyXptI48Nw29+Oggh2gF9gZ+AjYBp2Xk+IZvnTwcYC5wKzAQe9ednkQBuBG5Yr5H/hSgpLmTH7Ubw/LNvcN/jL7Hjrjtx1IWj/uph/dfgp5/m0L+/F8JWKttA2hOAOjqDq5KknFqWxVLE3XoaMtUcetzO3HzbnawsX7HObS9Y+AMjthrMoI16URwI4roJHrj3BU477UzC4fBvGue/rrmTww49kUsuvpVXX32P/gO6sffe23HN1TfxweS3effdj3jm2YeZPPlNHhn3MK++9Wyj1iyP9cddd93D44+/yuoC5ix+7S2rueA5wOpap1xNzxp2k80+N0/TZR+uVrPpuX5TWWSbIK/LAqL5tOy2Wlo+m65c13XmPfCLiiIYRlM684orRvHSyx9w2y2PN44n274qS0aa1m8OvZbpsKYI3iOpjzzyLNOmLeCGG27a4BeRjTfenAkTnmfixBe54IIrGDKkH0uXlufsM0t21ky1enIE5Vf9GUgCGH6P1Ox4v/16DiefcAPnnnvBOkYhuPnmOzn77Ov54Yc5OfvJRi7XHnXVgJDZ+5yLq21cncbRCcbePYGuXYcwevQ9hP3GzJ988hVTv5/F4sUrmDnzx/U8ax4cVYOrazdoG3msP37TlS+89vaTgCf9SFUBUNdssTqgEEBrvUJrvY3WuoPW+uEWNvkw0DVX47WW/Z4qhPhGCPFNZeWvF0/+0QiYYXp1COMaHXn+ofv48psZf/WQ/iuw/fbbU1MT5403JntRLOE9xDSOp8USFhllUmiZLGyQ1KajmCGT8686jrvuvGut29Va89SkJ9hm20EIosyvT2HKMD/+OJcRw7dcr7H26zuYf/97EgfsdwJbbnEww7fYh++nfsdOOx3AmNG38MYb/2HGjK/o3TvC/MXfM3yHrRkzZgy2W7Ne+8vDe+iHwyGabk/ZYLjAqxBsbtqZhUvLhEw3m5dbJZf93Jww5W6nJZKUdSnPTSWqZuvlPuybbzNL+loieL+k8/o1cOncuT0LFiwiS8gsK8Lddz1Ku3ZdmTDhjaaxaa8Z+68rNP+laFT2XGnmzl3C6adfTOvWLUWN1wcm2e9sdVVJdp+5UbZsJFKC39LI6x8pABev6bOivqGak068gi++mMUj4yaxzTbbr3MEHTv24NZbx3LqqdehFDR9T82vvaaRaEJ+pN5AKxdNAssoImgU0lBfz8yfF3HsMU2pzzFjLmNV1XxefPUOdt9zC2bPmfXbT1UOQmY/Akb3DdpGHuuPX02yhPcqMhHv6j3bnxwDipotWkSTYnWd0Fqn8XRcY1jHr1dr/YjWenOt9eZt2rT5tUP+UzBixAj2GNmX1197lWGb9v/lFfJACMHddz/I8uVVHH/cZVRU1qK1g4FFwAgSkAW0CbehU7QdfYqK6RSNIoRJn34dKCxyOO20E3jhxecB+HnuPF59402Ov/BSDj/yIDr0aE2vIX3QaHoUWfz041wS8RgFBb8tigUwq7ySOQt/IBAJ061HJ4YNG8awYcOZO2c+jzx8F1dffQnz5y3hwIN2ZuTILdhqz1045aaLqW6Yxz7778W0GZ+wYOlyXnvjFT7+5D9kMr/k45RHFmVlrclkspqobGpN02SlkFu6n3vryI1Q5VoTNDe0bE5wsrqt3PQerBnFyjUObUmX1XwfzVv4ZLe3rmthbWznl8XUuZ+llEQiERYtmrlav8cTTjiT9979yBNDauGrgrLVd2tDVlmyNg1WUwSutraek0++msrK+C9sc/3RtWtPFixY6n9qIsxKe/81pQld8BtrS2F6KULfDTSdTnPGGaO5+JKLufCCayks+HUVzN269eKKK67m/vufp+ViBGjsm6g1SiVxVMqrZsT2tGDCU8/E4wky9upRyYULl3D4EbuyZHE58+dVcOABR/ymc5NFMpnk3fdeY/mKBZSXl//yCnn8IfhVPlnCU849DnQH9tRaJ/3ppwLHaa239j9HgUpgUz/StbbtjQeWaq2vFkKYwM/AOOCWv6vwPY8/BosXz+eqqy6jpCzMpkM34phj9gFhoUljSMPvByaxVRJbOYSNIKmUw4QJbzL5w+ksjJZBsBMDt9iMc3dvTVGwgTQ2bUMmAQM2GXAsBx60P3fc9sBvHtuRo2/m2xfH02OjPnz02v8xc8ZMunbtyoQJ4xkzZgwrV5YjpeTAA/ekY8c2fDt9JiWtW9OlXSmLFy/irrvv4eiLbmPx1I+JrVrJtdedyxmnXvvLO86DUaOu4uSTd6Nz52xRSUuEJhe5mpxf8qFqyVh0bRGk3FYsWYhmy7ZkKNoSckXkWeLXfNxrS8v90vE1zc/1nqsor+LyK+5g73135aD9T2uc89RTD6JUnCOP3gPQSGEhyDZqXxuKgHqaCFWA1V3pBZ9//j0vv/w+u+12ADvttM4ExQZhxYrF3Hffzdxww/nknnfP5LOJBAshQGcNPzzzT41LbU2Kc8+9nosuupJNN11Dq/yrcOKJJ3D33edSVJQtevFNTv0+lVo7OCoNIoghlBfJwvULDTQCE1vFeOqpd7HTcPqpVwDw+ecf8/Aj99OlS1suufgaiorWL7DwwIP3UN+wkGVLy3nzzU+YP2/Rem0nj1+HDTIjFUI8BAwFdtZax3KmtwHmAicCbwLXAdtlqwvXsb3x+CTL/3wUcA9QlidZ/0QoqquX8tFHX/Pss49zznnHMGyLQQgM6jLVCGppsAuxVZqyUJAiqxilXOqcBp6a5fDsA9/Tv0chI/fuydadFabQhA2DkOly5gl38dwzb69XhU3GVXw4+WU++uRrNt14ODvusAMlJSUAuK6LNLzUw5zZC5j41ARGjx6NaPbmPnd5JfWrlhINSnr06kLA9E1YtcLVKzBEa4T4LW7Y/21I4KXimqfj1o2zzz6R22+/gFAo1GxO8wo/kzX9nLJExmLNiFFLVYZZPc267oVZcXe2qXBLhCjXRiLbCNlZy7JZRPE6CTQnTi2NKXeZ7DbFGsutdhRa47iK4467jMcefZpwuMCfrLnqqkvZZJPOHHjQLl6fPZGrMfqlys7c4/Wq5j744CtefXUKBx54ONttt9MfXtV20kmH8NhjWUmvhVfBt7pgX2OgySD8uiutNO//3xQeHfcWt952Gz2692phy78OTz/9EG3bhth5561Wm+64aTQKQwRBCBxlg/bMTT1y5ccORQClUrhkOOuMm7j4wlH06zdgvcfTHCeeeDS33ncJYdPl2KOv4vln38y7vv+BWO/qQt/36jS8O8vKnB/OaVrrSUKIg4D7gKeAL4HD12N8zwBXAGXrsW4e//WQlJV1Zf/9u7LLLjvx4IMP8MB9z7DJJhux5bYbs9GQTmgNhVYBIRlCaUnMdXHcKIf2tCk6th9dW2kGdPbeqoNGCCFMXAXCsNb7Zh8wJLvufBC77nzQGvMMI5smkPTt25cxo69vcRu9O7aBji29iQqkiPBbycd/H7KRjl9/nJlMhpqaWkKhAlb3pcqm4HKjT7kEqzlJau6PlZsizI1GuaxJYJpHirL7ye2b1/yBld1edr/ZRsS5FWjNka2ybU7Wmu+/+bTcyJleY8ksNAIp4YQT9mH8+Hs444wrvWWE4LrrRnP44Qew9947Eg5nHwW5FXItWWI0EbBYLMZrr33Ap59+RyyWoqSkNddeez2tWv05ljZSmriui2E0Ee2mn7rpVy2bCN2UKl6weAUTnnyLZ555DtPcsNa9VVW19OvXl2wl5c03T6Bt2ygnnXyI1+TeF/sbUmK7CVw3iWVEQBhIAeB6nSWUyc03X8Rpp1zKAw88Stu27TZoXB4UhmETcxtQBGnbupiGhlqKi/OP2D8bv8bCYRHr1ku9D/wmMZLW+vhmnxUw6LdsI4//TUSjJVx88ZUo5TBjxgzeeON5HrznaTbZchCHHLAj8aDg0UcmMnfuStq1K+akkw/imBEdcJRgSbwSR0lChkXSTfHEg6/Qtl17PvtsMl26dqS8fBUzfppFg6voNXAgu24xAvkX2S94DbJLcXUDyo1hGR3+knH88Sj9zWsopSgsDLNm1Vr2wZ/briaLX+Nh1VzAnutrlSVNzdfN6o2y6cK1Oa63VCmYu0xzL66WqhxbOpbcaS0RtbVVLTZVD2ql2Ha7LZjw5LWceprXpw/AssLss8+BfPPND2y19SYIYSBF88bYuecj1+RTcOKJV3PiiUdzww3HUlLy52tlBwwYwA8/zGXo0H5en1MtkDL7PfhpOTIgJO+99ylvvf0pdbVJRo0as8EEC+Dzz79ihx0GMW3aT9x008OcdNKpPPnkYxx77AGYFmjt+OlKjdLKv9dIr1WP9vVi/nVTUBjhltvP4fwLTuGMM85n5DY7btDYxo69iaVLV9A52ppXX3kPpQVFRSUbfMx5/HZs+JWWRx5/AKQ0GTRoCIMGDcG2U3z55ZeMveNZbDvD8ccfy6ZXbMfChbMYPWYUe+09gn322ZmgEcAQAb5cqpkTD7PAbE+0+ns+/+pd3n4nQbQgQM9+Xfh0WYI3JjzGww/ezUO33Eu7dr/Hm+P6QWDi6nqkjmKI5jUk/0yMGzeWAw7YudnU5vqlliItuc7pzdFcM5WNAOWSluY6rdxpzVORLZG6dfX9y91v8/HlNqfOQrImAfs1BGvN6YY0QWh232s4F5x/Prfcckejncn8+fPoP2BLHJ3AIIQUuRHHlsnqokVLeOyxl9h8863Yfff1E2X/Hjj66NO46KLTePLJWwBP0N5EZZvMXqdOncFrr03miiuup2PH36vSEW666U7+/e9JpNNpHn10EgUFBUyd+i0LFiyhZx8vmue17vFeDKQIIYTEUUnAxBAShUZpG1s5tO1YzKOPX8tJx4+id+/udGi//k7tSllcf8PFGNLk5Zc/5Mknn8v7+f1FyDeIzuO/GlprLr/8PDYa3ImDDtue2pRJxJLUpzMgHNpHwggBhoiitcOS+Cq+qkyyKBakYu4iZr/0Nq9NevYvPYa4PY+0W09ZaJO/dBx/B0yYMIFvv53M2LGjaC6qbnrgB9cxLxe/1CeQnPVaErivTWCeJWgtESGR85/Lr9NlrUv/tK5jyJ2XK6rP6pH8kWmF1hpHOfwwbR633DyOyy67mM0334Fbb72enr1asd/+OyCF8ghZC6ivj3PrreNYunQVnTt349hjT6Rv399PP7R+0DzzzIMkU3Ucd/z+SNGUws+aq2qtGD/+RXr3HszIkXv/4SN69dVn0NSy594jkCKIo1IoMkgdAuHgqCSGDGCIIFIE8UJairSKk7KTBM0AU7+bx/dfz+ecc9a/SGb69M8ZM2YMp59xGBMnvM5jj9+LIf9Xo+V/D+QbROfxPwkhBDffPJbyFUmuvuwhvvj4G+YuXUarkKJNOADC9G9o4GqHsqCgwDJpF3IoKQrTtuyv1yhIwuh1lvT/c7D55puzalUtnrtLLjQeYbFYnWBl52URpolAreldtPo6uetlG1CD58WVXf+X1st+bq5pyq6baz+xtrGsjWC1pM3KRW6fxTXTjlqDq7wIoKNS2LqOTTcdwJNP3si4cQ8zefL7nHPOKbz7zodoNCnXydmuN9avv/6Jp59+g5NOupqjjz6d8eP/zfXX3/Y3IFgAgsMPP4OPpnyHbDz/BiLbrkZrbJWgd5+uzJkz508Z0dZbb89HH32NozIIIbBkFFSUOjtJQ6aWlGsjtIWjbAQuQmikNAnKIkKWiatNhg7dlKlTf96gcQwZMpzCwjJefPFtevbuyAknnMcxxxzFy69MwHUTv7yBPH435NOFefzXQwjBJZeMYvac6Xz55cc8ftezxGIN7LDDVuyy8wg+/3waBx24C4IgQjj0K3YxhOaN//uSq08+7Zd38AfDJYCtfp1Hz/86Bg4cyODBGzF9+kw23ri51HNtzum5SDb7nF0+t1dgc8+qrPA9W6WnWlgn+3dzYpY7trUJ1ptbQ6wt8pb1e8oKz39JZ5ZNR7a8nNaKmL2CoCxFCgdThBFAOBLl/gf+xZlnjCESOZ1MRiGEQAqJUoqXXnqbZcsq+fHH2XTs2IltttmR8eNPJRot4O8GIQTRgkKSqSThcBGCTKOpqhAaSYAu3drx3rvTfqc9ajyS37zq1UPr1u0oL6+iti5GUbHDa699yuyfl7PnUdtRUhrFFIqwCY5KYAoTKQMIDDRpTFlE2onj4JB2MmidQoiW9/PLkPTo2Y0XX3iJgRsNQLkZuvfowccff0BBtIBddjlwfU/ABkHpWgShDTiu/z7kSVYe/zPo22cIffsM4Zijz8J1a3jvvfcZ98jrjBlzBx98MIHtthtORLSjTaiGqaviYKcpKNjwB4cnYLVBWzi6ElO0+00VjQVWa6Jmqw0ex/8MBASCudqg3J55zRZcLd23LsuB5oLu5tvMTbtlWDN1uLY0YnYMuWL2rB6rpYrGNasBVx9X856Gzcnd2ir+vMiVENk0WZZoBam3a4EwZcECQCBQmIbJ/fdfzZlnXscnn3zF229Ooba2jnfe+YzddtuTHXc8kFNP7Us4/EuNz1NAFVCM1wDkz8eQIQP47tsZbLX1xmgtcrRHEiEslC1XM2PdMKSBcqAzLRuRSk488TQemfg+hxy7H2++8imBYIT/vPEFA0dsxuZ922JJA4MiFBKpBUq4xDMJHBy0lthaE7RMPK3e+qNTp/aM3HYzwuEiTj31LN54/R2ef/5Zaqo1XbsOoFevPr9LAcBvw7qu//9N5NOFefxPwjBK2X33Qxg9+nYWLfqJBx74N4sWL/GSNsJlWBsTo3bVb+5n2Bx1dRUcc/QhHHbooWTsBI88PM4r3/6N+KM9hf6bsOWWW/LSS+81myr92/PabtAtPURzH4LNz29WOJ/7kDFZu8g891aZ+/22RNKaR5ia67+aY13ffXPvrObbaBqn1hpXpb1+oHjpcUNopAiQcBSLY9WrrW1ZFo88ciOdO3egpsamY8f+3HrrfRxzzKkMHrxJiwSrvHwpb7zxYs6UFF708K9LQR180BE89thLeOdH+qdTo5SXjnvssRc47rj1cRZqCSaet9navzPbrqZjWQEBQ2KZBhedfxU92m3Kk/e/zj1jn2ZZvI4liVqU9qKHWnutxSQuMSeFoBKE2kCheh0nHH8CqaRg2223plvXzpx1zgFMmHQTqXQ9l15+FpdfecYGbH/9IEUpQmzYPfe/DXmSlcf/PLp2HchDDz3KJRffweJF5ThukBXJDLHaGB07dvyNW1OkUjE++2wKkyaN4/QzTmbQoN5kMvDxR+/w4P0TOfvsM3jzrWf4bysq+btg25F7snJFDTfe+ATJpMBRDo6y0UqjPdNuVo8gNbVVWR1uzvTsd5EbIcum5bJoHkXKRVZ3lUvMch+0gZzlcpFLjHKXzwrtm0fjcpdpHrVaO3lX2kWjQJho4TVdF0gsWUDUNCkNgCUV6NVv+ZMnf8qqVdUcdNBh7LTTvnTs2Hm1+clkkhdeeIGxY+/g1FOP46qrLuT+++/LWaIE6A20XevY/mi0atWZTTfdmNdfn+wfO7jaxcUBDatW1dC79+9VVGICrVnXo3Pjjbfgq0+mseDb6YwYsQ2DBg3iuEMP483xE1m2pJavZy2iyCpConG1gxSCsBkgZFoUmC6LZpWj3fWPMDmOw9y5ixHCYty4iaxcUU3Xrr054fiL6dBuc7bfbhfmzF7C3AVL8veoPwH5dGEe/wi0atWeO+64mz322JPPvvs3DZkgC+uSlDfU067w11kn2HaCSy89n8rKcirqSukyqBsTJ97MffdO4quvvuTjj3vy+puPEMuU88xT/2Hx0oXss8cxVFUtobCwHT179kRrzUcfTaGmppxEMs33074iFCqkpCzKRede/Qefhf8OCCG4555r+e67WRx77LncfOsFdOnSBkd7/ecMaWGIbJ/BLPHw/lUatHIwjBAeafKm+65RiDWIlMqhMdIXTTd9Xt3nKrturpg9i0wL6zSNa83ls8vkWj20JKpvTrRa0pb5YxceqRTCQAMZJ0mDXU6BFSVsFhCxDBA2GonwydyOO27JuHFjuPLKf3H33fcza9aPfP31FyxevJyZM3+kqCjMLrtsx447DuCMM/YhEIBTT/0XXgTr76OrOeusiznmmEPZdY9hGNLAklGU1r45adbnbG19Bn8L4v72ImtdQopiIpEIL7/4ITffdP9q8669/GJGXfMvht54FiWdumAIA1d7xQnJdJxH73+bpYvruO++R9d7hOPHP8z770/m2WdfAKBL184cf+JBvPbq/7HliBEUlxTx84y59Orbi1gsRmFhXg/6RyJPsvL4x6Bbt14MGNCLL7/4ngUlHdl4t3254MJrmfTInb8qXffBB28zaHBvTjj+Mn6eW09xG01S1VFT00C0IMzFV55MxDKpTBqcd8mxPDL2dcaNu4PSVhF+njGPoqJ2JFJJXB1nxx2Hcfsdj1JZUYVlGYzYatifcAb+eyBENzbbrBs339yKW2+5i5vvPJekaqDQCmNhoSn0CJMQSIIgvKiF1hqE4adgGrdG1merKaaUS1CyxCW3J1+GNZs7t4QQHuHIwqSJcOWmCbPkq/m2sgTLomWrh+b7bZm0SSFQOqs90iglMAwD4ZgknQSBYBHg+gTLaDR5EAi23HITli0r5+RTDqd3755steVgtt56Z7p3P3yNNiz19TE/kvj3agUlZZDNN9+az76az4jNeyEtGwE8/MgLJJNpft9H3bqjP0uXTadTp87MnbOE4uLi1eYN6rUFl15wLeMffZLFC8u56+7LadWqhCVLVnDhhXdx7jkXsd1lO6z3yJYtW8bpp5/HlVde0jite/cB6A8m07p1MR9N+Zbx4+/gu29/5PLLLsUI1QF5kvVHIk+y8vgHwWDvvXdn9x1OYY+9d2DA8L2YToDDL7uKh8aMojQYwLZtHnnkEd566z8cceQe7LXX/pQUd8a2bV586QXOOfcQXJ2kT0+QIoytBNN/+Jk99tqJmQvm06dHK0qChQgUl15+EgEZRGkAlxUrqkilE3Tu2g5DCDYf3p277nqOdz6YwYrC/tiuwjLyGXwPHkHp1WtzDtj/GM4781bGPnAetpbUJBOUhUBqE8swUSgQXjVd2k0TNgvQ2vUpBDSSnEbSpZtpu7KRIdePHTW30zBylmkeqWpuJ9GSASmsTppaEuln12spJZgbsWtJ/E+jDituJ6hO1SBFkIBpIIkStTwDTCksTCw/ReSCligECM1BB+3OQQftjtdbL5u2XFPnNmXKV+yxx16sntb8e+CQQ45kzB2X03/jk2lvWdTXp/loylccdsSR7Lv/XkwY/2xj79H1xy8VAkAiUU9lxQo6dirDcZw1xOVbDNuSYcNGMGnSg3zwwZcMHdqfq6+6i3GPTKCsbP2d85cuXcDgwZtywgkncN11NzZO79e3PyC44ILj6NqlC0cctSf77ncI++x/GBf963wO2PHQ9d5nHr+M/B09j38UTjjhApRS3HnHaL5671nOP2VLCkQ1u+68HVdedQlHH7s/lZXlRKNhysuXcvJJJ/Puey9w0slHMnKHjWnbvSMJJ4mjFXV2BRmVYM8Dh1MVryXYqoyAjGCIAgwZJmAEPSNUCUIYdOrYlh7duxAwTMCgQ5suXDXqZG57+g6Wz/6WT+d8hVL2Lx7DPw277747Rxx+NOMefIWUY1OVEpQnqvh4ZTULGhooTywjbtdQn6lG4+KoFK52sd0krnLQOChcBIZvqJDVVkm09jQ8We6ilEIpgfL5hV7DQDT7ty+ybiRNWazNDyvbTqe5eP3X3IJziVd227laM881XOkMGZWh1k5RY6cISwXY2CpFQBZiSS+FqrWDq1xc7aBw/HpDf0u6uY1F7n4Fb7/9GTvssMevGPOfj06d2rFqRQXpWJoVK6s499w76N17CO+++wGXX3kK55538u9YZbh2/Oc/n3PKGYfjqjRnnnVKi8sIBA0N9Xz5xQ/cftvzPPbYMxtEsCorK+nSpSf77rsnDz744BqR+SuuuJJJT73H6aefSirRgYsuuoRe/Tsz9u6H87qsPxh5kpXHPw5CCPr33YY999ybc4+6lOTSZZx1yv6ccOKuDBzYjfqGaiZMmMTjT7yF7rQ7i5cu4LyLDmTP/TdlQUOMqlS914ZEuIDDvgdux71jz6Us7KWrhEghhIHXpNZ7i5UChJAY0vR0QyiEdCkJFrFd9yjXXrArT95zM8edfgxLly/5S8/P3xG77rYLn33zM8viaWKOzXdVIaZWSz5a6bI84ZJyMgSNEJYMoXGxdQMuSVxstBagBSknjuPaOK7tPVi08mNdgqSdwXFdlPYImcLGdh1faJ/rm5UL1ezfLJprf5pSgBrbE6ivsY0smhO05ttqqmbMckONp1VzccmoOAlH8OlyzYzaFM/OFyyO4VeqGWiEH+XTaJTvkq59h3TN2isyNZWVVUhpUlr697QbEcLihjFjuPScu7nyivu49l+jGT36BqprK+m5USu2GDGAt99+8w8fx4cffsgTTzzP2ZcdSTBssHLlyhaXO/HEC9hmmz145JFHNqivoNaaa665grvuuoMnn3xqjciZ1g7ffPcVtbbL6Jtu4uRTj+aUUw9mv0vPRha1pqq6dr33nccvI0+y8vjH4orLruXjKZN56MGHWba0lt13PYk5s1aw3bY7EwwG2W6XrSko1ITbDqTC6sG85Q7JOMxYJJi6LMZTswIknBQRow2FgVLaR1oTNguQBJAotM6glYOjTJT/sHYVSGFhCBO0F+EKGWF236k/191yOoefsS8nn3E8i5bO+KtPz98KkihtO/Xg8ylfETZcOkUTtJEwb2WK2rSJrdMkHcPvFZfCdm3QCq0NFDYpJ8nKxCqq0pUk3BjaJymOTpNyqpHCT4/pAFrbxOwUSidR2F5bGkCvZkzaUnQp12k+d152/WzUy8qRuAu0pum/1SJhq7fU0VqwWtAhq0HTBgiNJcJIXcYLnwtmfW+Tqqqif1EDSVdTb8dI2Elc7ZBRKbxeegKlnUbLEc+QdHVilYt7753EGWecxd8xVZhF/z7DueP2hzjn7Evp3bs3AFoV8tG0ONscsDkPP3kvSafhDx3D4YcdRDAQ5vrRjzN06+FM+Whyi8sFA0EOPPDA38G+xeb+B6/lvPPOo6XvprJqAffdfz9th40knsyw48Ej6TKgHcUyRtos5t5HXt3A/eexLuRJVh7/aHTs2Imioo5ceeWNPP7EgwwYMJgdd9wJgJuuvZG5307hzie/5bxH5vDcV5KldogaM0q7IoOwoVhc4zCzvBaFg6McMiqNRqG0RmkXVyfJqBriToqUW0dDpo6Mm0EgCRitsGQBpgxiiVa42qBnt2KOPnc/zr34IpLJ5u7l/1wIIbj/5nuY9uUizj/zMT56ZwkFVppeZRpSiu/np1kUq+CL8hgpx2JVSpJWNg32CiqSK0i5cSoTDtUpg9m1GVylyLgJMk4MV2VocMpxtYOta3C1S206iSZEwnbIKIVSOusdgdbNKxRzLRqkv4z2I2BeWlFnCdFq/Q69dTUCWzm4KuNP8bajtEJrr2JSqazpbdMelXZJuUlclSHjxkg6daxMNtCxe5SjR7gEDZNOhZo+RS4N6RgZnfTidiKAQCKwvEeyyP7dfGxNj4eamnqWL69n4MDNNuRr/FPQrVs3hg1rKiTRIsnMJQ4WJqFgEEtuuGjfVRW4qrbFeeeddwWjrrkVqUx6dw7w0ksTeHLCWGKxug3eb0v45ruvuPrK+4nHW/Yq+2HaPBbNX8bBA/tyw3U38fEHXxOQkutPvgbmTWXnbfv+IePKw0O+QXQeeawDL7/2HmePvpt2m+7JdhuX8kNGsd3WHdmyuIGPZ7i0c8Ns2r+IzQeUknTrCcgACE3CyaB0BrTNGx8tp0IVsufwMroUhHwxfAYhQBDFlAIhDFyVZFVyOatSBjO++JYXJ0zhzrF30qXDRixfvpxkMkmPHt0RQv6jzUs/+eJHrhx1DsecdwgVBT3oWqBJNriU10oGDSoj5iYoCyo6hTRFQfiq0iJha5LaZb8uFiuSKbR2aRUyaRMKs2BeA59++j1LFtRgZ9IoHWObkZuyw25bklYxiqxCLMNCaRNDeKlejYMlIyCaERM/GqW1QJPBEBYIgVbKo1PCoklgn/0OFWhwdQZDegTIi565q9kQCARCWAjRZDy6MllB1DCQwkQDS2JJquLQo1ghDTCFRXEwQjxjE7IsCswSFBaQRAgTicD7/2z7n6ZjUdqLpEkBl156K8cffzYDBw79E77h3xe33XMF9U4aNxHHdKKMHnXnBm9T6wRgIsTaXdlraqo44aSjGX33hXz15U+88/y73HrbnfTo1rxd1PojFlvFkccexlHnHMh7z33KuAeeQIgmElmbXk6B1RrlQCAQoHLVKo46+XjuuvMCrrnsNspXrOKjKZ9hGBvmLp/H2htE56sL88hjHejXuxOypJD68gp+mB2nYsZSBuy4M5O/rmTu/Hp2PnkIQ9tG0ChMGSCtYkhhYAoLW0NtjcuEp2czaI+BzKhJ0jpkYlgGChcTyRcryulcVEjXgggaTcgy6WaZdNtlWwb06cnoG0ZRX5WkVetCwiXFVC1diRWIsMceO7H/vscj5e/h/fPfhW1GDOLFZ5/jiqsvwY5Oo/txh2NXJ7DKJIvq46QwEUKzNG5QFtDMbwiScTN0LxSM+0Ewe5rm/F1SPPro2yz4sZKNBvZl++335sB9+hIKhYAMd429heW1VRx/5F4eSUIjpSJu1zNtSQObdG3j0xIHW8WxjLAvrJee3k5ohLYA5RMu6aUCta8HEx4ZM6SFUqYf5RK4yvWJnIutUwgs39XK25bSLkJLEC5CmLQNtiKpVqG0whSSjmGJoSxSrqIm7dC/2ERjUxiwMH1SKHTGSzGiQARyvMGafLmUFkAagclPM+YAkf9KggVwwZnXMvbBu9h0h13Ybsudf5dtCuH5ZLmuy6QXnqZj69b07NGdLl16Y1me4W1paSu69OnL4vI6Bm87mGGb9eDs809l7N330rvbxhs8hpUrV3L86Sdw7IVnsNUmnZj95VwOOvJQ9tjrAI478nBcZZN24wSNQsIBz6ahTevWjBy+JyefMIZO7Ur55OOv/9EvbH8G8pGsPPJYBz75djr7HHk+oc7D6br3CPT06ey03xaUdbLYqb1B+7IgrYMRbJ0h6dRiSHBdT3Pj6hR3PzaPWYszDD6gPwUFkv27atpHikmrJKuSab5bHmCP3iEKrBIEDq4WQBKJR9ySTgNplcKSUJ0J0ikcIRaLMfHfb/P1F7N46J7HKN4A0ex/O8a/8G+efuFJtjvlOGYtCdOhfYj62mXMe+cdonaalAsFJmSUJl3YmuKBA1jyyUx6RmzOP/sMNttsc79IYXVorTn4iH24/b7LaF0UxhABpIJvZ/zEnIo2lHSE4b00RWYBWjgEjVLff8ozjnB1xtc6WUg0Upg4Kg3CSwEGzSJUNu2oARxcbfvrujg6TtAo8UmbRmiBkCZKJzFEAfV2grBpIHFIuQ6WMFA6RU0mhtIFBE0DU7gEhERIi7CM+P5hDraOERAFngarkaSv3sha+ZE0O6059thLeWTcREqK/56C978SqXSKrUYO4+RTD2HW7CXM+nkB1/3rOoYP2xqAK0fdyEIrxO4HbEbn4iTTP1nOBy++wmvPvbZB+3184iSef20S+19wNAN7tKJnQSmFAcEFd77GW48/ww/Tp2DrFO0jvVpcX2uNUgrD+Oe9pP1RyEey8shjPVBgWZQaNr0GllGbdNhyhz70KM5gRiQpS1MWjCKE51MUd2wKzACOzmBJi4wK8eFX5XRoG+T9H1227GIzQwQo7VPN5G/j9Ozdjp16pnFUA0IUghZeKtHvoSeFCSJKUIeRwiRqubgqjQg67HTItvQY0otjTzyE++97iM7tW76Z/q/j+IMPo6BzdyY++BBOqooVjsvWm2zJJWPuoGfnTo3LOa7DosWL+HDKx+x4y5X06NFjndsVQnDtlTdy1imX0q1fFxbMW0nbaIBp037mmX/fSrfuHXFUxqdUUTJuPaaIIkUQlxSucrFVAlOGUEKjVQbHTxlKwpjawXbrEcLEwMTWCSQBTGkicDEIoJSFot7rQSiDCCUQSFLOKqpTSdqGSwgaBg0Zm5JgBoFFcaAErVMEjahXUSg8LZmDDdr2I2kuwjA9vZcChALdZGmhsHFUhoAR5vLLb+PiS67KE6y1IBQMcd45J/PiW5+zyO7PlRftwXW3jOaOMXczYMAArrribLY69Gg233MY0ZQmGWhFXdpGKbWG0euvxePPPM2Ylz9g2+Mv5Iek5J2vJdv1aKBXwzJmvvMW733wLq3Cnda5DSFEnmD9SciTrDzyWAeWldeRjK+k47CeNMyrYeRGxcxKhdm3nUJKm4ZMElMKViZriRgmpgywKOZgqAZUupgLzhnGshS8tyhKWaQQiCFEgmG9iwgVKsqCHZDCqzgTQiCEQmIhUCgcTGkBhlcBh4mrHWKORhJBdOjJfifvz6hrr2bcQ5M2sKHsfy8OHjGcg0cMR2mXRnLaDKZh0qtHL3r1+PVkdMiQIYwfN4HyVZV079mLwkCI0259jKOOv5EzR53IdsN6EDE1UijCVpiYW46rTIRUFBqtsWTQSxtikHQEhtQkHUnQaCBhOwihkTqNoyWmYaK0ja1shAjgak3CqSJsmZ5+TykQCkmUpFtPadAkKC3SboawaWMrF0NoLFmA1p7xqhAKx82AkI2u+AqFIUob3fAF4CgXQ3o6MIWN0gJDhrj3vokM2XhjtvCjMnm0jOOOOY8v59Sz4LPv+OzDMnY/+Giuv2EUxx1/CLvufDB9i0sJNlRR3MZiq+EmXzzn4ug0AbxGyVp7BQ/r0ndl8eob/8ctD0yk74mXUoegZ9imQCs2L0hx26hn+Pfzz9Cp9bpfIPL4c/HPvCvnkcevgOu6PDxpAnaHbZj62SJ22bErZW1asVEriesqCixN2k0wvaqauXUGVWnF/Hqb196qYuKzcSavSNCus8UnlSE6tdcUlzbw1kqD8oSLFS2kNFSGKQJIEcSQ3sPaSzHZTZVk2msJY4oAlowgMbB1FCFcOkQyOF37sqQhRl1D/V95qv4WkMJokWBtCNq2bsvg/htRGPD69G29ySBuuv1BXn7sRdIqw/KkpjLtsCphU5V0STlJUIKq9Cpq0kmUdqhOx6nKxKlIpvm80qEuHSHhQMJxSbsmQnhRJa0g4dRTn6lD4xIwJGnHIGa72ErhKgNHpwnKQiQGjk6QsBMk3RiOyoBvthowwpgiiCSEZUQRWoFwMYWFIYJIoVHKxfUdVw0ZBgxcbaO0Z0/x5msfUV3lcMLx5/6u5/N/EVprrrrsXMZeeR7Viz4nTjFl+57MuBfe4PEnH+L+u27hmbGPsiweJO0KHDNEPNXUVUDpOpRee+VhfX09jz/+OEN2PpTTrr2Jop57YX4zg6CrEfWSQwbAJx9+hxPsTOc2fdZbY+UqRcrNmyH/3siTrDzyaAEZx2GLXXbh8y/mYtol7LNLX6plmIW1kkXzVlFVn2FefZB6G1qFJPOqXRJpi6qUYF5BEXQpZHZDlBe/tdilp2BoiWJJeYzhZdU8OMXh3R/qMJAoHM9PS2tcpXG0g1IK23VQOmsHIHG1ZySJkMRtT1SdcTXdCmzCvYcx/uXncf8EN+t/Oo7dZTg7b7ERDRmTT5eazK0Ns7ghzAfLYGlSMLvWIunY1GTSpFWMtG0zrz7NyqRkedwmaqYwjDo0aWrSiikr65lXX0d5MuV1V5RR36cryYpkHdOra0i7GkcZNNgJUk4ageNXKkLQdDBFlKBR4Pt6pXDxI3oy4JufCgwMlO/mrnUGR6VxdRJHpRFkUNpF4+DoOsaOfZLvvpvHdaNuyYuifwUa7DRfVy5hxLZb0bFLJ155YwrfzUgjtz2CZz74kHPG3MvGvTvzn1vupaYuzaa778LJ557XaHIrRWukaL3aNheubGBZZQyAnY4+jZdn11Hdcwe67306yXAYGQwy9T8/MH9RLTqgWVYwhE8+/Y7Ppv+8XseQTCb5sWoZX1cu2rCTkcca+FNIlhCiTAjxshAiLoRYJIQ4MmfetUKIlUKI50Vu7WkeefyFmPTqv2nVoYQO/TZjj71GEDRjdAtmmFGeoaJ1K2bEonwyP83imGJhzOXreZoH389wy/2VlNVXcsQWmkR5goyjefHHDP83w2Hajylm/pygJByhVxeLz8qrSdoN1GeqSTkuQoCrUsSdOEk3hdYplAaBIuWkSThJKpIxBLUk3Qw9iyU/rwyzNDyEpyc+w7Nv/+evPm3/CEz+6GNqkoW89VU9X5cbLIu5GNLFEBZTKkzeXWzx8eIAGRVk2tIMX5UL5tcJlsRMwlKwsEHzY02GhfVp3l8aZlk8zPyGJJ9XxJhd28DimOfALnQRnSJeW6aYnSYgNVrbpFQSKSBhm9SkbQzh4LlrBajLJLFdG0elybj1pOw6XJ3B0SkULlq7SGkipYH0JPloBEqnWbq0gtNOvI0eXbdgzJhb8wTrV6IoEGK/bhsRMQPcfN1tnLD9RtTPepVIockWhx/JsmWLKCmUrFhSwX0XjUU5goRdz7IVcwF8mUDTuVba4bpbr2Gn3fdk7/0PZVUmSXrwIAo6daRy9nKSVQ4lncvYpHuEwb3gxfkGnXoXcNwJmzH7xw9Wax3kKnuNtjm27fDiiy8Tj8cbpx1++CEct9u+hOvzEfHfG39WJOt+vNb07YCjgAeFEBsJIfoAWwDdgO+Bo/+k8eSRR4twXZcHHnuU0Xc8itF/DxAhUv0iLGgwmfhGLdpRNDQE+b+vbBpWZvh6ucXMOosdBitO2SnK8M1L2HZYKZM/rqNbYYqiTIBCV7F1P5N+g0uZVlFIv+4h2oQF8+tSLI2ncLSLrapJOnWknDi2ipNxm9qw2DpJ1ApjChOLIO3CIWoyLmlHsl2PBIHF87HD/Xh+8od/9en7R+CJd/+PwXtvyQ7DC8k4KarjIITJkphi2/aKLiUuhRGTT5c4fD4jyd6dXaoTEHdgVdriwxUBljYIWocd2gYNFjY4lAQVHSNJigMKWzusiCsWx+MoBGlX80m5Yn6DwNWCiFGGrSR1mXLC0kVpi+pUDFsLBAauiqGxacikfWG+9xB3lO1VQGoQ2kCjsFU9DZk6nn72P1w/6mluu/U+DjnkCP7Oru5/R2RJkhCC0046gfZuHHfFMgb3jnL93ccR2eMgDr72HMJBi7qp32Jm0phGjDUbfsPSVcuZMWc2ycJtSLbbmJ0vPovSIsnQ9hqn3qWoWLKswubbt5bwwOhPKHRSDCpTbLXXTjzy4KM88sTtgMMjE+5lj/124ejjj+OKq27k0ace5YEnHmfo9vtw9DHHUlVV1bjPwqIgrbtvwa033PUnnbF/Dv5w4bsQIgocBAzSWseAT4QQrwHHAOPxfs1Gzn955PGX4fup07jl3mdov9+52PEE0YBk8/4B6jNhMgUGdXUp6t0EG/eFaCgElsHsiigjOyt+rE4SD0umL5ZMnVuAsDL0GxpDYvHa5AqKOxVQ0jHInCqXRcsUyYikU4FDRUrRPhxkSayOIsuhe5FJ1PKcxhsyDkVWAE2SgBGgIAjoIClHEHMSxB244NhOnHvUJHbc4hxWVtTQvm3pX30a/6fRPRRg8MY9KS0MsqBeIwyFxqI+E6Z9MM7ylCBW2YAhAzQUFLA04ZAUBn0KBaUWRFq7xGzNtHKLrkUJ+pcISiyFRmDKKJl4nBpHYhgWprRZGleUBSVKCSrTNogGXFdRldG4lqQkEKTAAkmShKtJ2EE6RCOETXDcEJXpOjpEAgSEV+louykMKUnYcd5blubDhyfRt3MvHn98/D+2eOL3hBCCow47gMdffofXCg9jxADNsLbQ4LRn92P34stX3mHM9efTtm1/WiKzixYvoaBdT445fgi2gsTSWqZPUxjJWjq1DlESjLJ8QZw+mwTo0HEge/QWOCrNrdc+yBmnn83gwUN48oXnGX3dHey2z84s+GEln306AWGm6dm/D7FYDbfdfR9du3Zt3GflKujQw0AGPI8v2/GiYZaZvx42FH/GGewLuFrr2TnTpgEbaa1nAtOBRcBmwMSWNiCEOFUI8Y0Q4pvKyso/fMB5/HdiQz3ftNaMG38nOx64L9GM4NADC+m6ZS+2KBMUNThUJkx+mKNpmJPGtQqJBqHMyhCNpPhkZZCMthjWXfD9lDnMW7GKwrZhWmvIzI4R7VRKSVmY3TYJUJGUTJnj0j+kSDgwbYnLuHdrEcImoxVLYw5pJ0NDJsOM2noa7BS2rqchkwYcliQy9CkOAN4NMaAMYg1JHrjzRvoP3oYnJ/4b284LWP8ovPn2J3xsF/HqUsmyhZpP5xhUpzXRRB3T5tmsSoX5v/frWbwkTre2UO0EGN42w7K45v+WCuptxaJVBnMrBX2LXTpFFF9URJhRE2DyUs30VQGilsbQktl1Qb6ttCgLGHSOZugQKkQg+bRCMHVVa7pEO1AaKqYwUEzQKKEsEMYlTkWijnq7GiljWNL0fdtctM5gCImj4khp8e64pxg8eBMuOO+qPMH6HXHwPrtS7NaxaspbjB07lxc/N4nbUao6bk7Z0G1587VPkb6haXP079qXQCxFSbsyIrIV38836DHYItW6iBF79CRYkqJq2UpmzqinfqXDe3MlX/8QY6dtNuaYo05i6JBhXHL+tbTrOoRbrr6Zd95+mZ9+nsqL777O5Redw8fvvc7Zpx7XuL+Js5ZSlUoyZ+oPXHHhJQCc86932PeUF1i5Ks7Hn3zGteMmMm9Z/tm7PvgzLBwKgOalE3VAIYDW+nLg8nVtQGv9CPAIeGakf8AY8/gfQNqdjRSagLF+bSt++mkaifp6RuzSARGP8slHi1g4v47vFxXzw0KXVT/8TLSsGynbYMoSTaEtKal1CHW2CRYI6mKSuLY5+Ige3PqqS2WNwc8ZRaqugY0aLLr2CfHZfEVhFHYfZtCrg8uzMyKUVzhUztVUGpJhvSRlhWGKA0nKk4KMYxB3MrjaxlY1GLqA+XUmrUIZz+ZBw+JYkPZDDqFi8UJalZhcceUVXDv6FjbbuA8/za1hxNY78cR9l+Y1Nr8DXnrzHcrjmsGhKkRxMQtdzdylAoXLd9Vh4lOXU9PVpLhfW0p6G1TMq6L9kEJC0qUiLqlKaaaulNhKUFpsUpNx6BwV9ChMIQjy41KbQAQWxAR1KYfpiwSDuwtW1mgSSUGPVimiVpRliQT9im0so5hs/0NXa2K2YHadgSBOxBRs1iZMUUCjlI0hBVobODSQURZjH/+AoV025cxjz/mrT+v/HAoCxTw/4SlG7rQ7bYd24vMVGTq0yfDzMsmc92fRqYNJXUMdxYXFa6zbpk0bllQu54Uv4uy7SYD+A8OEOwQoaaNZsiiNocIMGNiWeKve2INL6dve4dmnp9E/VATArEXLcEPF1FatYPnKeVx57Ti+m7mIbz94n+eff56uI9uvtr/BrQq57aYx7Ljl5vT0/fb22rY9l19zHzsf/iSBhKRKFjDxnsc5aPfN2HTL/Ri2SW969+jwx5/I/wH8Ga8uMaCo2bQi4I9thZ7HPw6W0RFTdlzv9SdPnswBhx7M7Xd8zvatKtltq7a0HzqAlQSI9i6kx2bdKetURLwwRnrJMiJtgtQYBhVVDmkkKxfFeOW1FM99IDAVxBtsVghF5x6FxAqDLFmUoH2Ry9xqky8rTN762aK2PkOkyKTrJhEsLUilwyyOOXxcblBjSzYqM6hPKr5ZEUFg0pByEYkGBpWG6ByNoLRFv35FdOnVi82O2pMdD9kauo4g2n0PkiOPRvXbmldff4khm47kuRff/h3P9j8TQwf2pU//Ulq3C9K3JENpQx1yZYJMnSIRNUn3LqV+mUupm6ZTKMMCUcSCeICViShbdxRs18lgo2AKVR2jIaFZ0BDk6VkhAoamtj5N5zITw1YsWhnhxyWSVqWaZVPjvPZVhhcXRfihRvLiQpt2YU2vQtdzlkfjuEkqknV8udJhdpXJ4oYgPQs1VakkScdFC41AEnckDRmX/3w3iy/e+4Kzzzr7rz6l/7MIWUEef/xBxOIviM9fROtgkh7tFH0PO4J2G/fi7MsuYVlsJS/MnU7aXb0y+KZrr2TlSw/w4Qzo2NVi1eJ6TDL0bO+gNmvP0qSkIWUwt1xw+93/RzczwlUXXAvApIkv0Kb7SAbvewhHnHwJZkk3VpbX89Zbb7HbbrsxfX4178wtZ0UsBcDQ1sU8/s0SDr75GX6atQyAPXYeTDQSJhNTNOh2ZAr7UTR8f2Ys+on6eBW27fLx3BVc/9DrPPn8K4y68S4eevLfv6nCua4+wYrymt/pbP998WdEsmYDphCij9Z6jj9tY+CnP2HfefyDYIjCDVvfcPjp5xkoeyUVtsFXq4qoCUt+qla0KVXI0tYMLEgj7CLKCwqprdG06xkkVq+prA/SpSDFMdubrIg5iKjCal2CoV0KggIRhG9/TFBgF5OOSOrrFJ3rUxSEXQqKIqSSAebVKYaFHTK2w5xVgh49HAwRonVUEQ26pLVEGSG27mmAcEk6mg8WGRQHbI4+th3vzLPZuBN0G7gPdQV9mDwvRqD/Zmy1w9bEZn3PuRddyhcrqujavogTd92BgoLIertO/1PRs0cPdhjYnS8/nsvWI/uQJECnzvDZRw3031pQI0ziFXWUtyvlqWlBhvXQuNrgx1VpRrTPUGQpygMWhWGbN8bPot9mJbTq15qnZ4foFYqQUvXMWCSwzAaipqR1yCLcHpYscRlelKY+I5E4hBC8/MZMTtlvEwojBTg6Q8R06VmkmVNl0akIFsdNOkYNHOWgdAglXAosi7ETv+addz/l1rvuzEc3/0BorenetT29OxVRXD+dV5/uTahTgGjHDrTbfFPe+s9kHpv0NKrLRmxR2oOurZruX/tsuw0f774LX/40gx/qShi2Qw+UzqB0mGVfJTn0kFa88/ZKrLgmUr2Um8Y+jZSeY/+0qe8jMxux4Lsahhx/Mm/cfytPPDiOXUZuCcBHH/xEZUTQrihMhwLP/+3sPbZg5swltGvjRdZM0+SrKS+QSqXoM2QkicppzPuuCqdbN0Zu1pcBfTtzyu2P88r999Jjh72ojwdo9dULPPfCBDputRubbrYZx40cSKvw2vWhL//nSypW1XHp2fs3Tvt+ZT3LGlJs3yFCQUHBH/Ct/Pn4w++wWus48BIwWggRFUJsDezHWvRXeeTxV+GQQ45l7k+z6RyYzw6bFhCbu5J2IknHVpqQraj+sZq3Pm1ghR2iWNt0KHLoXRYkvjzN0jm1fPZKLS/OkMy0LcylyyDjkBGCb75NI8JhOncK0LFMsFNXm2hYUNa7hKV1QcKJDD+Xa4QBrcOapGEwsJVD66DNrFVxKmqgMiUpMIIEzDrKkzFidgohYN9egq4lDnNVIQknwKwfkhgixIqKKlQsSSSUon8nh4NP2IxNtuzMm19+wv3/9yndhu5Ix95bstkhJ3DEiSdyzqWXsHxlxV/9FfxX4OqrRvPxs+9Qs7KOQV0VBw032WubKJuXpdDKYrNtOxIzTIoxWTW3ji6hGEED3lsQ4OeaIFMXCD76JMWAbdvww/w6WocF3UKCD15bRv2P1VjFAWRJkIZlCWbOSFFaFmW/rQzKVJyULVheA598H2f6zHq+Wl5BVX0dGTfKN6ssZjUoDuqn2LRI0VbUETU0lpEkKIMEjAKmTP6Obz/+mfcmvcxm3f+ZrZj+LKRVkunVU7ns6tNJr5hO701aYZtB2hNgzjcJ+m21L58uaOCBi6/jnXe/BuC9KdOZM38FAFts3I/Mks9Y/OH/8c7tb7OwWvL+M18TcR2UCNG/cxF9iiQyZTe+LP28aD4VQIfNetAxMoMfJz7KdkedxN333tc4rrP324jrdhnIkNYFTPnhC378+UdKjRDPP/oQO+9+GF16bcHOe5/Gz3OWEQqFKCwpxApFKB6xE1avjRl9/c18P2MG70waT/v9zqQmVkSb3huR2WgfqtvuyPfL6rjn+jvZfPeDOfKCS1m2fGWL5+eYQ7bjvFP2Wm2aKQUzv/mcwsJCdt3jUN6ePPMP+Gb+XPxZbXXOBB4HKoAq4AytdT6SlcffCuFwiIytOOmco7GkRixKs1WHepbXmMxebrJ88mxkhzDtSgrIyHrszgHaRjrRLr2M1JIQkW4lbDNC8+4z3zH/i/lsriU19RkK2nZh8XKbdjFo0yZA/1KHhlSGgICyiMmipGRQF4U0NT/UBzF1mllf1tN1qzDLM4KaQIpOAYOU61CZNAgIxYI6RcKVdIqaJGyXLbs20EYngGKWJyNEtc0pW8OUHyw26iWZHTfod/z5hFaZmMWSOVYPUotWUrzpAGLxDLFAA7sddBxDRg7jrmuupG009Fd/HX9bBAIh3njqMY47+Ti2P/c0tuwVpqjQ5v/mh9mrj4sr4tTNEdTVpVi0IkFmY8HS+ijti21m1Cm26mXQVUaY4QY4fI8wyhCsqNEMGxagd/de1C+uoTBgUjeoFTO+q2XSAoON05V8+/4iNt5zMD/ONan9Oc6+Zw3l41WC/h1qWRlXFJgmpjQY9akkMW0FmxTZnHZiAUoXeEajKsm///0Wd11/Vz6C+ScgZEQY2mpzImaU+++5hQsvu5L+2+7Jj0tDVCypQBS1xqnJYEUj7LuX11d40dJKCqMh+vTswIH77EO7srb8Z/LHfPLpR/z88ByiVgHlsz/k2ddN4oleqPhySoNNJKZPl+5Ur4yx0plDP+HghCyqE2FmrSinIpGibaTpd11XX8chu+yHFQ5z0AkXMW3aVKLREgyp+ebz1/+fvfuOsqsqHz7+3afdXqb3mUxm0nsPKUBCR5ogUkQE6QIWbIiiCKiABQugIk0B6UV67wkB0nufZDK9z+33tP3+Efy9FtSENkTPZ62srDn3nN1u5ua5++zzbA7/zA4WvfQQN//6Z3znikswq0ppeXMn/roqDv/06ZRPPQiltQ0zmWNn6058hiCm63S/3YsenkD9YbPYvnUjBxx7LoGwZP782Xz3vDOoqtq9r6KqKqjq3/87nFAaYcIpx/KZ/ZpYsPAQPnP0DLZv30ZpaenH8I59NMQHfSLr4zZ9+nS5dOnSoW6G57+M4zqceuoJTPr0EQwqVdSXpPj9dSuprysgV1PPYQdGWLkiw+p3UgwkM4yZVUJdtUOZ38eqpdswCqsQqs7IOhdfbZy+PoWVT26ju7kTVy/GX1jIwEAndQ1+JkwbzebBPCNKXV5fkqd4TIiZ9QZ6Po8RUkkNupTGTZI5FRHUGR61aO+BjNBozivM0rrYMajSUB/DEYJhYZP2tGBLwoflQH3AZGmHxn6VJm+tSTNpYgxLarT1S0xHMDCYJ/XScvRgkDELamlz4xh+l+HpLp5dupnBVRt59s934Pd7gda/09/fz1lnn84Pr/8i/bqft7sDjC9weK5JUBCCfIfC4nVZps3WKPW7KD6djKlSELCIGS6FPoFfifB6c46sY1MRkgxaKgHyZExB3KezYUuObN6hKtBPvLiAycVZetIKby6DWUdGaU8KDq6x0VWFnhy0JHU2doLsytE/YHPioRH68rCwVqO7Jcltt7zAtdd4uZCGwmtvvMxPf/MzYjOPZcXSBKHqKkQ6g7P5SZa+8PAelSGlZFc6wRU/+x1P3HYX40aN5fhj5nPxxf9/bZ3ruux/xFHUlAZonLMfm9ctY3NnGeVRePrWX/5deZs2beL1VSs56qijKA+G/u/4hk3b+OZVN/HgbdfiNzSOO+kU1m3YhR4pp62/m2BoGMV1NeT7s4iSIjLZLOb2NsJBP719SWpHDqe4uo50Z5K+vEVkZj2+/ibklnc47ICRfO9b30dT/vUcTy6X44zzLmDJkneoKS3k9ddf27vBHgJCiGVSyun/dNwLsjwe2LBxKXfedQu1p5zCopeTbH/jTerHVjJqVDlJn44TC5PqyrFtWZJdy7qYevRIKsf52NHah6KG6WxJcMpcHcVn8PKTnXSLOCJhQsCitd/ByOj4hmvE+loIL5hM36YkxapEixtsf2kzxvhypo4Kki6IMDaep9DN89LbCSbvV0Z5ALalNbqygtExMC2bSWUWpmmwcdBH3smRlUG+0DDAml6BIl029EcY58vyao/gjZUpRlT7KCiNkLUstndK1P5+9IyLbNvMNy+ZhKYFaWpOsCxTwlvLtxFddD+nnnYisYrJHDlzHIbxnzev/V+0fft2vvP9i7j8l18jYe1+enB9n8PcSpdUzuXZbSE29mjUFWU5oVHlgfU2U4ZpBDWFpx5Zj6rq7Lf/MERIZcl2SUOhy35lJovXS4YXuTy3QmXidB9drVnqhoU4ZqRGczKFoRjsymhs7hOU+SSj4nnyQmF9r0tLQgUUPj/CZMdgiKnVNkHN4YunXMONN/6ayoox3lqsIfLci0/zze9fReOhp9DWJshl/HSuuI+2tc9/aHXk83lmH3oU9ZUBbr/tZibPPYLOpk58usqq5a9TWz3sfZXrui5SCO547S2euft22rZ3ksiYhGYcRsvbW9EVQcAnqKkqYv3GDtKJNLbtYvh9VNbX0N+ToKCqiHB9hliil6dv/u0/1fHgX17CdQQtyT7ufOw+3M5+RlUVcv9999HT04Pf7//ErtX6V0HWx3W70OP5RBs5YgrbtnRw+YgY04v8/KVoMqovQHMwgJoxCZoBkkGJVpvH1+GjdVMHGaHQvmuQhikN9DS18We9jrpgkjWr+whPCFM5v5RkIkf2L29TOWEc4Skl+NwiakoF6W4DEx3TzuMriVDu87FVC1Npu7ywSWNyVKOy3mB9j8qLG7JoNQaOFCgdWSY2Kry9WiUTF5T4smxr11gwSvKzh0xClmDufirLux20Mg0Xm6rqCEcPc/AVu/ziwW76twvGH1dMXVhSqs5kTbdgVkmIY6dFmJ3JY/jrmHn02Tx1zyI2/2UR11+7i1cee8b7j/k9DB8+nP1mzGXNsk2MmjqGxc1QEtm9FdKAozCoC4ZXg1/RWNFskhuAEkPw7OJdOBVl1FcF6c87tLU7fGGqi3QN9LzDrl4FOxRCKh28sDjGeccF0FWJae9e9t6VcWlOCbalNEoCOXpTgjZXI+86jCp0eWWD5OTfrqRmWgPfPUynbet2Dj/8ICoq9t3bLv8NDj3oCGbNnsXXLzmPeHE9FQ0TeGGHjmXZ6PqH89/x60tW0bqjF3tQIxYo5/cPPsxl559Db8cAPfkt1Mi6vfpd7h/I8dBj6zntsxNpa2sm3LGTB27+PQDbmnfylR9chx3ayYhTz2fLS0sJja5ATSo0HFpPLNdH57YcuayFIU2Su1roScZQ+7fzk+t/y5fO+Sybuvr46lcup7GhjIfvuA+EhqIVUTG6nKu/ci4nnngiANdeey2vv/46ixcv3qdud+87LfV4PkKqqrJrVxu5tKQw4HDE7CBbXt2MP5OldX2exkiSZF6hsKaUqaeMJjI8QnHMT/38MXTnLUbPrqd/Vy+b3jaJF4QwwiHWvrATJ5EnrDu0Ddhk13UREybZnEJS8SHkIKGubo46vh6z1UFfn2HHaz1kTMniDpW8o+HzC4xig11vtZHutNmxI0vz1jQPL0mzskvF75c4HZ3IVDcNtTaJbIJrbu7F3tRJWVShvkhlxigVrRRe2myS6tYRUrDyhpUseiHFG3/pJ60F2dJv8+QGm0Re44wxIWJaAMOOcspJc4mObGT16pVD/RZ9Yp140sn85b7n6coZVBcrzCxXydgG/VaIar9ChT/PhAKFHlPgD0FzxmHY6FLMbIC6IhVH8ZN1VAbyPiyhsDWnc/ICP2XFKl84rJCDpqkU+vKUBxyCeprVb7Rw801beGKbQkDkSdmCVVmD7S152lf3MbNcMqpYcvSR9fRYfi6/s5W7H3iTww4/BkUUe8HyEIuFCvnVb27DNhNsee1pNm3fyswjjmHqUUfz5cuu5i+vrf2/cx3HwXX3buP3gw+YyWMP3MxTjz8EwCH1w3jxgYdY+vKzTKzff6/ff11TCIZ0FFVw5Gcu4Iof//L/XmuoreOJ22/ks4ctILFmOcOmj6FpWwJRU0+goATf8BpmHTaM4SNK0CMxMlmB2ptAU8YhjQzHnnAij973O0oLNd5Y/CbjTvks4QnTqaiIcvcffvN/ARbA2WefzY7mNo494fNYtvOBk09/XLzbhR7Pu1atWsrVV/+QSy49k81WJeNLu8hqfra0uywY42NXyuXutRoZxSWQd9jwRguN00vZr8rkiXWSUtVlS7uffFsOM9dL//bt1B0wlYHtFm7EoKBAY3ijwcKZRaQyKe57NcG8sRp9ST+vP9GDOb6QClUj3Zckl+xi3oJa6isKWbsjSUE6QUNZnj+tUHGEzrgiFavET0o1GOl2k+nLoYbiFBb6eX1Nikh2AF2PM1hZjJPMMmsESGyWrMwTD+h0tKQoG1VEz6vrOPicsRzVAEu7DLpMm7IATC/xE5cZsorNijU7aXtnC5d9+xdD/RZ9IjmOw4mnHs3XfnA+W1QftUEVXZUkLJfBHDimQ4FP4+2BIG81u0yqsJhbobCzGzTDYlmLSnkhNBarzCy2Gcw7vNMkaU9Lsn0282cUsKnPJmwIRsaylKCTStjsTJiofpfysgCvtkdIpXNsXp/mrMMMYn4HO6Vy2zKX5M4sn5vYx1N3P8rvb76TaCQ+1EPmedeF37qMh95YRnn1EWTdfkRXFwNNi2icMoKieIRtW5spqSyiOGAwc+Yc5n7mWBL9KjUFIcZV/GP6yQ9fSzJL2nYYVRAmkTUprxvBuCkHsuSp2/9u0frAwCAz9j+SQ04/k9eX5HHryonrGlPmSAp1lx3ZEE3L2smtSxH1t+OKOEdeNI4HfnAddWVB7rnzEXb2DvLC668SDpdy6iHvHQwedMRprF72MmMPOAw1n+D5R+5DVT8Zu/H9q9uF3kyWx/OuSZOm86c/3c91P7qdl9/s5ME3+tAUi1dXD5DNWazcluHoBocDysFo7WN4vR/NTLGiK8qObUm0eIgxU/yEShSEkWDsvEpK0jZqiZ8p4wpQS/yYAZ2nViR49bUmptYorNql0pxXmfKZSuZWaHR05ygoDBMwQuhhnQ3be1h173Z0xaW0Okr56GIyisqad3YQ3bqBaiWNrsKAGyJvBejb1k/cESgpg/1nhfBJSV25oLUfNFtSOaaQ+WM1XFMhn8uilhr85fdbeHBRDrUnRbY5wzvtKqblEg7D6n6NfMVw1qzbNNRvzyeWqqp873s/4rmHnmNCPEzGgeFRg6qgRpHfpaFIoTBsUxrMMrvG5pBqSUS3mFSVpyslyeYkPZafwazN6+0uy7rBdFysdpPeVodNfSY5F5oHJM80BSkM+giVq0wdqbJtl8UTz/SwZFOSjZ0qReUKG/qhp8elfVChPGgSM5JM2a+SQNSPZQ4M9XB5/saPLvsWV37jQrLb7mFwzcv0d7ZQVD2X0fP2xywajZj/efxHnsFA5RHc+uJ6fvjDq3ng8WdYvXnnB647b9q8tbr9X75uOr2s62tnbe/uvOHRgMG9r77Gq0/c8ncBluO4nHPxj8G2WfX2LuYdV8qUiQF80qJMU7GiMfy2JLmtD7PSJn7IJMaeNI2t2yEYCHPV93+OpvhoKCnlvONP5HOHHvAvZ9ue/ssdvPP2Ys485Uw2r3ibSy65hCOPPJLbbrttr2f8Pi7eTJbH8w8WL17MQ4/+ie3uCKxAhF5/iLJsAttOctqhlTQlgjz/4g5C8TAiolFaGmP9W02ER45j5LAAq15cz7GHVRIZFmTzG61kSgpI+6Ns6FRx8g7lus2We59HNS2i9WUcd+FkhheqbFpq8dJyk+r5BXQNOowmx+jqHI+/rBEcFgTXwQrqZPIWIptD6emleUUzlZ9egG4m2PLHF8AuJj5yOOUjXcrHFNDhRNg/luellRZBI0PZ2EIaVYvl7SppXUHXJStufxtfMMrVl41l164Uj6yxOGquzkGjQyRshSUb2tn21Kv84ec3D/Vb84klpeS004/lppu/x5+X9TFnVICkbWIoJsLVsG1JUmqkXcnz21QCmoovl6G+SiFhlrG9u4e8IYgKl6wraIw49Joh4iKNP6pR5IOmQR0rb1KhZpkzIsTOpI+n3u5FKDYtKZ2WHoVvHAJ3LBXsWNfO/DqTbN0oRhQJxpY73PWDm3js7gcQ4pPxzd/z/7muy7oNy3jqqRdYv3mAksIw76xYyrbOJGq0nHDDOND9aC1r6EmmUGWeAsXmjDPOxjdxKmdNrOTkc68gEnH59kWnU15Txwuvv0ZDcSnTpk3jjvufpKezjW9++Vx6cv0Yis6OpjR//stS5k0VTJo0mZqSv98tI+90AhKfWv7ejX7Xho3bmLbfERRUTCKb2EnV5Bkc9oUDWbZGoaFSJ1fo5427Hie14nVO+sIxbOtspnlLK9ULTqEsbLHi0fu4+urLOe6ABXs8Xr+941kqyuLMmjKSHU2bmDdvLqFwjCdffBbHzXDgzAPez9vwgXgL3z2ePTRnzhyeevoRRlcFYXgjqx5/E7UgwvSjxvHAym7KK0McdvIEti7tJ9O7i7KZ1ei1U9i4yead9QPkuhM88KduiivKcUZUEGnVmTvHYFCRJDvyTBsRouK7h7Px2fXY4RqWbAzQO9zB9buUjDDY2Wbii2qoZWHWu0GKh2VYvGQrU0YPp6AIaO9BhH0EJtaTFCHaX1lOQW0p8brRDHZ0kNRT2E4R257tJ9ogcGr7KKgrw4qWEe5oprm6jIKKKLqVw7cpRXFYo+KgETy5zSUYjbL/nAyq1HijxWZGlUP/+vUsXDB7qN+WT7xU0iaRT1FWpNCVT+M4OprQGBG3SViSfA4M1U9fTqEhZlJSDEu35Vny4JOIWdP54lyD1U0O69YN8vraVo6+aCrhQoHhOPiExKfrHFln050JIaVKSyLDhnWtnDC7GH9ZlBIzy1sDQQZ6BigtKaB6SgFvb0iyrs3hpdeztDQnyOfz+P3vvTGxZ+goisKEcTOYMG7G3x3v7e3nzWUreOyV5WxcvgVfeQn+sEUo57JxYzs7tq9ixiiVYSMPIyWDBNUErzz/OJ8794vcfMNN1BYUsHLFcgC++OXvArCuo4UfXf5L1i7fiGCAa7+9nj89ehefP/Zzf1e3Ty3bo7arisMlF51KX3+a/Q88m5Xrt3LXdb+l+NMX8uarTTiDCQbXLueBu+/j4P1HAfDS2o1c/O1vMFCwgMbPfpmrv3ctsR9HOWDulL9b1G6aNt19CarKC/+uzgvOOIwbbn2aex7+E0ceNIMXXl3Bd77zbd58622+fdFFNO/aRU119d69CR8RbybL43kP/f09fP2bX+Lkr36ZJSt3UTy+lHXb0mQCPpykYHyNQsvOXqIFMbauyBIo9rEtkUOtLiC9pgknK2gwguwkR4HtQ4wNYGsGk2U/6apSOiyDrl4LpzcLtsbkSRrFikbLAKx+eDVTZxbTVlNFY1TQoKXYtLKFrWYpKAb1kXaSrS6l06sIGioPXPcO1QdPZrBlgOyWLfhHVlM+qYL86g7s0jAOGRomDaNjwKF30ToiDRU49RV8tsEm22oxf1KAFtNlS3ee9oyKT7okVphszLn85HPws2/8gT/f/YCXN+vfyGaznPel87jiV2ewM5Wn2CfZ0KWyutNgV05waK1JZUzQl3fpTMPkAocXOv209rtEWloJNdSQTkkGMy6v3/gG/okjmPKpRmqiKUpIkXX9zKvX0IXkx0v86Lpk1+t9OH0ZRi6sJNfdxeRSh7Ubu+luDdK8tY2cqjDuU2NpXzNAyX5FWM2bOKgwwPe/4m0I/d/Ath1UVbBu2wZu/OUdFBZVMXvubG6+5T662zZQOnIsX/ncp1jfm2T46BF8auI4zrnoh9z3yP0oikGsJMrCYxZy7dfOpiRa+aE+EHHhlX/imaceZHRliJ5kHyNrG/jjH274uwDqvvse4Po/3U3Jp85m43NLaXnpVo469lTmzJ3D184/FoCHn3yLZau28qPLPvdPdViWTWd3knDIRzwW5KJvXsbjz75DLteLYSfYtX3rh9afPeHNZHk8e6GgoJiiokrWtDSTFJJYLofT1YseCdCzymLNBpPWzl50GSdTU0FRWMOfEUQ1SeOcsSx+ZB3r09uQlk3FtCralncxbfpwDtqvgnuaVPq7JfrWbcRLyskIl22bbNozg8w/IEzh8cPY3BFg62s7GagMs6MiRFGogNEjFJYvyrK8t4DhhkO+O4kyrJBAJMSmB56n7tj5pAcrMPJZrB6JEi4kmm1h9MGjoTVFfdQgetoIVq42iYdt5tdk2aEpNHdmKa9SScZVKgsEQhhUDrdZsjbLdd/8Pdf8+GovwPoPAoEA3VaOjW1pgoEQ/dJCUx00aTK1yIeVg53Ch5AZWrebLG9xOegQH1OiJp2BEn72o7V07VpHKBjBzgiCvjCjjAyJjE3tMB9vdkV4rSPH6vV5etd1YdcUkdq6BVOq7Nroooo8WZJMHR7k+ZSFDITQ3T7WPbyEkvETOWKiwZuB6fzh+qs58rCjmD66fqiHzPMBadru277jG8fx2xt++n/HX37xJTp2LmPxU49z2MyJXHz+GQAMJtI8dN+t1NaNZcaMsfz8l9dQ6Pto8t/d+P3T4fun/9tzTjrpRDbZGf7yx18zasR4xJiDefCu6+jpP5VjjphFQ105xx0xg0MOmPie1+u6RnXl/98b0VdaTzb/GiOrGygpNtgxkGFYfOhnbb2ZLI/nX+js7OArl36ZktGfITLSz7ptJq2rOpkyIsChRxWQw8c9D+5k8vxqFq0eIOgUMmpykCU7HMx1beiGhVoeZfb4CC+8maYuPYhbECUy3E9JiZ+Tai3+tFRja3uGA/cL40+mWbRJsnBcjvYmhYdXuRRGFWpH6ZQVh3Ash1wgTKY7QW5pN9szPcw6fDRb11jQlSYZ0GkMa+hFA1BUycpOyZw6l+07XTJvt5BRuiicPIlpI3wcN1clmRM8/JzDmpY8px1p0JLSGchJ2hOSz4/JceuV13Pdj3/NuNEThvqt2Ce8sfwd7r/zN/TMPYOI6dLhMzAsk09PsdEEdOXD+JUsTT0qazp0tK4kwWSeAw4xeOrlFItf30xo0Ka8sZhRJ4ylQtF47O0UVQWgNxRwYKVk5audtLf20GdaOAmDgGnQsaWJwcQujj5uOjM/XUfLDpfXVmcQ1SH6HniFgw6t4Oij6rjxHoem9h4q0ot5/r67hnq4PB8h27Z5/oWXmTF9CsXFxQD0DaS46KuXc/5Zn2H+vDl7PHPVmclTEjBQ3sdM166WNn5+/W/45c9/8p6vSynp7e1i2eqXWb+unedefYumLTu48kc/4bNH7fkarb9yXUlTcweTZ84k09dBLptB1/W9Luf98DK+ezzvw+WXf53KkeOIDy/gqTf6aVnWRS6XYP+vHEoi6TKm0qEi6mPxmm42L97KghNH88xylXxrmgFV55ADw/iSNh1mhGA4Q7q1mzVL2xg3ro6IZbKyXScSHKRx/3rClo6LTVHU5IkbFhGuHElzJki8XhDOQ/eOFEVzqkis7kczHOYdFKVv6Q7eXtvN8Fkz2Lq5D18gQs0owUBORRQFcFauoXJ0Of6Ccrbv6qR6ZDWqHyqEyeKXtlKXSxCcM5EF9Q71JXDJnS7jJus03XojP/3h15gy7sChfgv2KaeeeRoN536R5laFfERHqi41ukI0lIecxeo2g0zSoqBcZ3snVMZc6soF7dtzvLWomwIlTHR8gPLGKFbKJbxtkBUbUsQPrCTiV9m1pJ1RE6OoER9v3LuKwvoKtG6XUK2fY48qZ8XaHpzWDINZl10d/YigpCZsM/WwUWx6qY8dG3sZbHmGTYufGOqh8nzC3f3ga/zxvud4/sEfMXrWXP7ws+uYN2/OXpUxZvLB9Pd1sujlR2lo+Gg3JF++ejs7dnVx/Kdm09raTm9fDxPGj//Y8sJ5tws9nvfhG9+4nIu+fAY333IVbz73EoN1IbJbJMmmbl5fm6T8M/XMkw5vZ3zMPGgMejjIuYc5+LVCrn/JoqEAVq/roFXJUGj5OXLWWJyCYlo6XQLNTdSPKaKgehg5RaFp+XYsvYCDFkY5/itzac8GifS6BGMSPz58FYOk/Rr+4gATpxkMNGWRVpb584extb+TmpHl9KezdCUDFJQFKUsMMnxhDXZJIas6BLH6IrIDWaygj3VLB1GNAiKlOmqynec2F9FIhMqpCrnW9YxqGMHksR//Ezr7snw+jypNwn4/ZWUuXabKtg6H1TtSnLFAp7s5h6IKCuMKVUGF8uEOkaBkZZtOyFQRmkussYhEyyCDS1di+f1Ya7JYRobMg4P4bAWrRLCuO0a+zaRi3igiqW7S9ZW4pkt3NsP+4xTWFEbZtTJHpL6SARVSuqRXBNi2fiM9SUE0sPcf+w899BALFiygsLDwP5/s+a9QURKgNCY58byvs3jRCk48/Rv4A5L6+koaR0+hv3Ud5WWl/OoX773h+FvLN9Oyq5mSmqlc9aOfccdt/7yNzkelqqqCqqqKj62+f8fLk+Xx/BuxWJz9Zu/PY4+9zJEnD2PEoY1cft0MupfvQna1s2F5F48+t4pkLkmn6mf1SsGSjTpb+jUWjNEZW2xxwMG1BAw/qYzGsxu7UQvDaHEfNdMakPkc/Vmb/r4c7Zu7CKhp7v3DRlK2TlvGpS2t4s/mcMhTWFtMNKAwdqrGQMJh9TvdvPFOni2lw3FGDidpmgxu7WXXllZk0mLXmg7eerWVnrRgfNyg1DBYccciNj26isSObtz2TlpbLJy8RqTYh5pKUb16Cy/98m5+8J0feJnB94KUkm9+80tc+KXP0ChNRLofJ5dGsxxq44J2y6YrFKQQm10vtrH8jQRuV56iADQUmcT9WY46upKKQIqBxU0MbmpBU22s6hC53gHc/hz+kI4WUlBWbSe/dgu9z71BqqKSijofZFyeeyFFpxNk8U6FoCpZOD5IYXUErTxCV7dJtKGW0oUTiYUdHNf5pz64UnLH0l20J3J/dzyRSHDp1T9n+qdPJ5G1Pq4h9QyhCy65luM+cxqtra0senk9RaXT8ZcehH/eubSJSbz0Rje5iiN5c1MTvb2971nGsy8sQVENBrM+7n/kRV5btOIjbfPUicM5/lOfvKeg/2uDrFze4t5H3mYwm/vPJ3s8/8b5532ZRx54gYpghK63dtKehcYjx/CTCxq55JgQ0w9uZOkrTTx163K0Yonht1nf76PPNujq0GgzNU6eD4GIy/D+br45JcEXZrssmOlnwuhywrEQsVCM4rGN5FIByvZrYHtS0jsIeU1he7eCZYLTP0C0t58ld61i5zoL1fUzcVoliaxE1zXKauNkezNYrR00r9hBjxFHmTaJ/pYcj/1uJWuf20EwFiZSX8HIQxsZf+BIOtxCcvgp0QLkUzleevAG/nzzryiNR4Z62Pcpt9xyC+OnjWTMhEaqqvJMqPIzpkxnZINgyrQQu/o01rRq9Id8HHFQiKpROs2ORlC3aYw62Dr40wKr32TYUSOpLiujfdVOwuUKujpIb9cGNOHQt347HauXYXbuIjBxJLk1faiWjhuF+ZNV5lXbVJl57KBOt5AcPNzmi9NMSmPQMCzGlGF+DENHEe/90a8qoCj/P7ju6u7niM9/m550CBkq4u4XF31cQ+r5GEgpaU5m/+6YaVr86bYbiZTPZmdPDCc0lqS/nLJxw3F78mT7U5RPmsaKVx8kFKqkpKTkPctub2kCvYhYNMqEyQexauX/5jKf/9ogK5Mx2dHSy31Nm2lJp4a6OZ59mKJoNDY24su6DNNtlmxXGVR8OGVFvNwcZk1nCaHhYyirK2bTWylWNbsIkWdUoUNZjcv4YhdsldraIEUNNfx2aZiVPSodGRe1QMPMOqx7qxvpUyibWM1J03VGhCSm6hLWwGrPsmNzPz2r+1m5xsIMVWLnLPoGLFatSaLksvS9s5HND7xCMN1Kpa6Qa2pl7lw/o2ocKmt15MwRCD1MciBHy/L1bH9yO6seWE6obTuJ9jRdjp+1qzdw9oXnc8DsKUM95PsUKV1effUZph4xnx2ZQQoNP7NrXWZVmEwuMhkZzzClSnDWXJfDGy3EsBhKSYBZoyCb13h7nSQW8/H82iTtWRt/iSQRswnJAnrfXI+vZAyxcJy+nh0EnSCjGhow0jnSm5tofetNQjuTqK0Z1q3spWsgw5ypArfYT2mRgSUUMlIhl/Wx5Y00FTkLR7rvOUupCMHnp9ZQFvb937EvnH8xm1e9jWI0oskKtqx66+McWs9HqK2tjeNP/xJz9j+EH179I2zbBuDTJ52DPzaSeKyYZDZHqL6WqnH1WAUFDDu4DkvV8Mdd4sEcrz74u39ZfjSiEY0VkMk6xKrrWdvSwYYNGz6u7v1Hjz+7hm07ej7yev5r12QVFoS49OIj6M/niBu+/3yBx/NvfP7zZ/GLX/yUq675Gl97qpvWLpfr3hgkFjHoL3IYtn8hmUwps6MZhlcJdvWk6VrZSXRMCdVlQX6/QnDOHJPm5hQnjw6yql9j/bos/dttlKDBhMYwwQqdlLToyEgcDHIJF9O1cNMaqaYmEutbmHPOcYyNDvDIUyliRT7chM3Amx10bl6L09WHoioo7X2E6xoIxlX6MoJsu4MvoDG4NYnPCRPyZVHsPHVVRaQaS1ErY6zZ3kdxWqMsHB/qod7nbNy4mokTGxkbj7N+oI3yQJ7mtJ+KgI+sNUhd1KYjYBJQfSQlOLbk0EqVJR0aybxGY6NCNOhw4DiNPgpYtzNP1dxRFE5V2PjyGsIFESpmz8TOuQws66bVJ4gfNR433Y9P7eTNDc3IhCDgV1m8xmIAhfnDDA4ZLrlvucMT2zUS7Sn2O7eOKl+OwAuSdzZvYcbIEf+2X6u3LWFT+w5Kxh+Ia4VRrAzdu7Z8TKPq+Sj9/tZ7uPx736WwfDLhyH4s3mEx99Mn85NLLuK1V1/FV3oQaRljxCkLiI2IUKo7rFo0SN+rm5EDWbY+9We+9qVz/20di5dup6e3jymzJtHplLPi3t+R2rWZeXPncsEF539MPf1n/QMpLvzKt1l48EnU1xZ95PX91wZZf1Xg8/L7eD64UaPGccIJJ/D1L1/JRRddwOpNFm+6g5j+cvzJFD1NIfxRyYiJOrqT562ntvPm0jb2myoJi130ywgPFlUx2OQwOJhEBgs4cnyAN90E48ZplBTBhl4L04SRBYKWnRnKC8NEAiHaDMlxh03l+ceL6V62ndRohVC6FTseJDqzlI5H16D0J6mpK6WlL0dxTTU1x87gpXV5IrkWRsyqxHixm3x3B6X15VgDefrVPObo4TiWJLktwbo7X6S8UGCNqh3qod7nvPDCa+y//xyEMCkLGiio1IVMBi0bRyoM5gWxgE6JL8/qPp1+R1AbMzms3qXAZ7M1oWEAr7eBUiFR4n6iNX6iKYH9qSnEkOxc20N4QjWaFsDXuQvXkESMCOGpBfSlXKzODuKaxfIdUFJegF9V2Nmdpa/Zoqg2zPg6nYpClWzKZuumQVTxrzcXtt0cr735OD/7+U1c9KOvctNPlzLrzMnYPc2seuQtpJTeer192LMvvMrXv/UDgqULITyM+tlFmEYcIx7jjC9dgyUKCesKTlEcEfARCwikcNErAgQjw5hYVsimt/u55e4HOOcLRxIMvncuqlAwiMCirbWXWIFKxeRP805bO4uvv5mzzvoihvHR5Oj6TzZva+P+e+4gGAhx9uev+8jr2+eCLHffyjjh+S9y6KFHM2nSDH5wxbepqy3nuks/SzBg0JbqYdGOOOtTA3RmLcYVqIw5ZCpMnYw/M0CiJYW5Ik1mPx+5WJw7H2/GGmzjzGMKyLt5dlohUoMgTEhkBf0GrDP9HFCaobbCJVNv80qTw6QDSnnhiU4eeGAj9Y2NpBN5tG6LeFEhMtdLV0+S8lE1TBwRoazQ5ok1g/jSg3QYAVoWrUTBwl9bQ3drAsvSaFk6QCimkUoOEApJzDFFbOhOD/Uw72Mky5cv4qKLDsN0MhT5AliuAJmh2JdnVNyg1B/E0FT6MklGxCQ+zSXnCEbFBKbrYGCxvt9g+v4R1nUo7F/u0rE2TaYqTL69k76sS3pHiikLK2hSAwQaR2G6golFGezeAc49OsqzG+pJrO1F6Dq9K5LUTlG5+U8pLDdFmT/E9GEuGcelc8UqTjhhIVNH/POWKdu3b+fXf3mEt195joaKECOOOocly1JIAowtKOCnX78Qv657AdY+SkrJDfe9xi+u/Dbl045Hmg5KUYD9jh5GxIXebTbL5VHkgzG6+tO4rkF0xwBbA0U4W5OkNmwifGAD2rgqon0H0de2hpqxB7HftLHcdccNxCOBv6tv0/plxEpGADbd3d0UKeX0tA0QsA06O7upqakaknGYMLaOAxceQdOWFR/KF4ZczqSpadu/fH2fC7JakzlcKd9XYjSP54MqKyvnd7+9g1deeYaLz7+ao4+ay3EnHsrYSoelqyXd/QZuxGJkZQZT9xHMKmxxykgsW8GmVzajDyujdv8KJtcaLFqRoWRSmERSZWOfyuxIgvHlkrhfIdKq8Wa7wpIBh/68SsuGHOMnFjNhoaAp6iACCgNvbKG2cQLV82r47IxJvPFGK4M+g9JpxWzrgJqaQlqfb6Vt03aECfHyIvpzIUYfO5ddrVnSm1pJ7XIwzQGKSopJde1g1plHDPUQ73M0TUUIgSoMTDcDSCQOEKXYl0FTTCQqQtEZzEHCtBlXAJqSZ1dGUBmSxP02Pbkgm1WBVDT2a1BJFQQoGFbK5nSE3MF5RpfC/sUK9zzdQ3RSEcKvE6sqpN82mDkOlqyULHlyI/NPquaVVQ5dWzs5/qxqiLhEVYdFz75GftUa7r71zn/qQy6f5+ATT2bqacdzyg++xoginftfHSAUyuOmB5D+BOPmTOWJ227/2MfX88F1dHZz1NEnsLmpg0D9TKqrx7Lf4cVoRSo7Uyqdz63l6KOrqJxazrJtkqCjU1eqUe3L8/ZWk47uNBnVhy/ux9Et0oPdaPFq/JUL2ZR0mTjzUK649Cu8taaHK755EsWFYSwrS3llKa5UyFhBzLSNSpAxk+Zy//0P8vWvf2VIxiIY8HHz735DQ30lf3nsaY479sj3Xda3L7uKX/7yF8ydPe1fnrPPLXwvC72/zLMez4dHcOCBR3DPnx8kGCznrNOvILV+LZ8dFySFxqvNCjlH4gNGVRocP1fBHWii/Z01mMkEjXUqIV3iHxYmYMNZY21ml5jssHRs16U+bDI85hJs66Jlay/RjElJwqGz16SqIkJZcQGpUAETPz2LAw4pRjcU8gGITy6jd1kzzz24mszi5Qw+uwrdVIhEYqh+jdaOAbK9O/HXhonGHTIt7RSXB1Cz/fS1t5NZv5HDp00e6sHdxwjAh5QaQuj41TACUIWGRGBKB0Vo2LZKv+kQ0BzmlEGx30YhxM6kj/UJDcURyJTCpEqbT9dLDpwmmVc1QFVUckhVnvGlgq0JhV2ay4QDCzAySTYtSRD3udz6cIpntyrMPrWEk86q4pgDirCcCBOPLKG6EHqfe4zHfngTh5XUce9tf8an//Ma1Z/84Y/EDzyE4NiZ2K7Bqj6BUxzjU/PifPP7c5hdblKtBymIvPeTZJ5Prm07Oxk75QjaM8VYmR4qp81g+v5FVIwwGFaiomQUdooIrQN+Nm622LDFYnAQdi7dRdO2PJPrNIoOKqfikBEUDdqklrajVwSoHF3M6PljKDnqEIqnHcYv7n+IZ565i8F8hs+f/U0so4zOgRx5JYZrdZFOtaEogp5EnKuuvZFMJjNkYzJ8WAVNzd3UzJlDSyr7ny/4B9093Xzr5z8j5Qzy6c98jnvu+fO/PHePZrKEED7gJuBgoBDYClwmpXz63dcPAm4EaoG3gDOklDvffa0EuB8YCXxPSnn7u8ergV8BBwA60Az8XEp5x79ri6Huc3Gh57+Uoqgcf/xpHHfcqRx//Kf41rfP4tJptSSsFMs6AizKuAQMSSxoc9Z3D2fNNpvKuhg71nfT1ZZh/sn1uFKjPJhhfo2ksF9jR5cg5QTYZCtQW0mhrjNWJnjHbkPp0ukshkljIPV6mvIpEXb0Z2kfTKO8mKLDihE3qsikBmhxfThWhky2FX/5CETKIa7myGzdSccLfpre3sT4ifX0uBlyGQctZBAvCmMYH88WFP9NXDcN2DiujSVdDDWAlAo6goAWx3RTaBrUhFUMJUDWypC0TbKOzUFVDjsSCv0ph9VrB1hwoMb2pE1mwKDUp2JLl7qQSV+3oNjw82azznEjBEUBsHqT+II62kCC5OsuS/MhJo+qwIi6VI5W2bLV4dHrfs+l3/wSC+cd/W9vi7z+0luEGxay7MkW3ClFWIVBhkcLcPwuSmGQHTu3MmvWPyWz9uwDYpEgqqJQMm0B8cZZzFs4gsNn+Mg4DlsSgpoqP0vCZbzdqhF2TDRpU65EaU5CX0eaFc9vJzplBFWlfqysQ89qEyeRwRldSeeaLmr0AQbRGX3oUTgywenf+ibd6/oJDJ+CogexRzRSYJaw7bk3qagtIy/9oJfx0KNP8/lTTxiycRlWU8zy7W0IoDoc+I/n/61zL/oqgxUVbH/hOdYue4fwv3m4bk8jFg3Yxe6AKAZcDtwvhBgmhCgGHn73WCGwFLjvb679KnALMAo4Vwjx11Vyd75bZh1QBJwOdO5hezyeTwxFUbjnngf4891PsGbZBtrTIUzX5jMjcsyr0ogaNmY8RGVNkAYSLJgTpXpOOdNLLeaVJ2lJWKTyLuXCpXtpgqAOU8ty1AWzjCDBa6+0sH5FG9vXt3Pg8DxZ18/8ST6OmqnQ2edD6RYkOwUVRT6ijWFClRFCdQGO+8Y4YmX1BAvD+IorcGNljJy/Hz1ZiR0MICbWUDurkkBQxx9McsmXzhzqodwnzZu3PzfffD+KoqIJgUBHYpI0+3HdHJow0IniU0K4Mk9AD1HkK6PAp6IpNmMLXEojCg0TNFQEJUYRy3t01u6waGp32ZoUVFdqpFWI+m2Kw2lKYxqz5kcxURhZk6TPZ9M26LI8obOo00d+ywr6H/4jd95xJwfNP+Y/rjs58dAZnDGnCJFPs3XVNra/vI4n3+zg0begLJZh7aI3mLf/3I9pRD0fFst1+cqVPyYQLmDE2CpKF04mYYTZ2O3Qk/AT1QSL2mDUlHIcHyx7q5ldazZiqmkyBVH89UGyiqRl6SZWb7bZtl0hUhZg7Lg4J+2nUlps0NvTTd2Jc6ieXMOChSOYft55NF5wEaHCYYQbKwj5DPRYKfGasYxaMJ3u9k04LrzyxsqhHh6OH17J1JL4Xl/3jS9fSHEyydfOveTfBliwhzNZUso0cMXfHHpCCNEETGN3gLROSvkAgBDiCqBHCDFaSrkRUP/mj8bu+XWAGcDX3i0b4KNNB+vxfIQCgTCnn342K1a8ymmzGmmIurSkBEUBi+KgS2uryf6NDsu36vR3GnxqmKTMn2dNvw9FOIwvkFgByX5HxshKwehCF00z2Nyjc+gpozBOG4GmQHtWIatLGuv9LB/0kRq0OeDgQjblfZSVKgzDx6ZNJq3be9ADKtFjxyEcm2SihYbqQja8uYrB/j6kEGxa3Y4QDopUSO7YSVmR92Th+3HmmRfx5S+fz47tLdTVl2K7OSw3Q8ZRsKRO2FDIOlkUkQMUBDmydo4B00XgI2dLdqQgFnCwXGjNJphTatE3KMlkVJJplwnleUZEFdLVEtPRiBh5TEdle78PX6wQJe0yrNBiTImfW677M6OiGg/dfT8Fe5iS4+ijjuLA879B1YLTmVOTYnOnQsvOHur3q0QTgvVrdzLjir3bt84ztFzX5be3PcBLz75OrHIWq99pIza9gXfeGmDFjgFyuTSlcxvoX7WK2Z8aT5eZh8FBho+poC0ToL7SZlI1tJaMIZjI8MabfVROKie702K/I0sYWQEXn19PeypHc86mKysQimR8oaDNTRMQkiM/VU3HBpdcCBoKaxg/I872d6oZbBI8/tijTFu5ml//4kfMnT1+yMYpkcyw6O2NHHHQ1D2+Zu6cOcyds2e/D+/r3psQoozdt//WAeOAVX997d2gadu7x2H3LcFzgY3ALX8TVC0BbhRCnCyE8D7dPfu8hoY6tm3bgSUjgMaIuEJAU9BFiH5X5b77d5HNOmxYNUhT2yCK4iNnh3ExGDR10raK33ApMGxyjk59RGC0ZXFMk4Kwj2EFKhVhFc006eq0WdmvMW5qAYM5F9dv0JHX2ZXQGDepGLPL4rWHd+BKF5m3GXvIOJhRj1oQIixctGySzNpN6AMOkWCUkD7AEYfMHOoh3GdddNEF3Hnno6iKD1UE0ZQAg3mF3nyCXclBunJpFFQsxyZj53GBvAtdWQfT1XGBkjDURQzCGvhUsFSdeQ0mBw1zqQ0Z+FQX6Zr0ZxxCmkGR32ZWYYZDRzmk32ll2cYs13//btyODDdefc0eB1gAlZUVFGgZfnyyj7HD48hd7fTv6OKVZRbbN+1g7Jhx3lOF+wApJZdf/Vu+f8VVVA+fwfe+9xNUYxR9HTmyrZ0EpUtJTKdv22Z61y+n+bnFGK5O565uwhEfsQMn0ZsIk1rdzY4+eHODTULXWGMG0PYrp2SMn1lHFZPLw2DaR8YxifpVRkUtjqtTmVasU1+QZ+HcQsrnjGDpBo1NXS77z9C44JQI7Z1ZYhNHkdD8RMceR07GOO3zp/P88y98pOOyfPlyUqn3Tkre1NzFOyu3fmR17/XThUIIHbgb+KOUcqMQIgx0/8Npg0AEQErZDsx7j6JOBL7N7tuMo4UQa4BzpJTvvEed57I7UKO21ovHPJ9M8XghiUSasG5gywIEvWhCEtMdJtUKUgVjSff1kS2O0K+pRPUAh1QbdGazVAZDNCUzgIImBDHdJuNE2N7cTU3AZXSjTdK2cBCMGaWQ6zc4ocylLWvT3eIwIiRZtj5LpDZEQJOMPGwC3duyGM15HMOlHZ2BThMlUIYe7sN1bJx0gsSWtbR1LuOeu3+Brnvrsd6vkSMnsWNHD/m8iWaA6hqMiPvZmU6TMh0KNJVBy0dIC+O4CYSUGEJSFnDoNfPUhCWGou3eKaBA0JlxOXq4gyJcJNBvSrKOxaApaEkrNPfAp2qiCJGiIxCgZHQVJVaERG8LTz18L8VF/zoP1nt56LE/cOCMRp5cYzEsZNBfWEllY4S8rrPsmaX84OJL9qic7nSegoCO9h4bBns+fK6ULFmzhDcXPce6pgzPvrIURR/O6OpaBhiFP2qQMx003UCPlJP16RSUQjbfS8m4BmT9MDqXbSEQkJRXhZkyNUTxTAOfrTOqWPLqxiBOzsIJKxw7QaMtn8dRQAof69MOEwoNVNK82h7kO1NcNJFhh9TY0uxQEIXqUtjgV6gIOQRVg/FRk23+Aqpmz6NgYhn5DS0k3yzmm5f/lJ8Chxxy8EcyTueddzaBeJTvfvsyDjv4UKR0kNJCUfxMGjeMSeOGfST1wl4GWUIIhd1rqUzgoncPp4B//I2OAsl/V5aUsh+4FLj03XVdPwMeFUJUSynlP5x7M3AzwPTp071MWZ5PJFXVcF0XVQgimorpGvgE9ORypGzBa30uc4IGWaFghiV5K02/tXu/OEdKqkJ+0o5DkQ+iup/V7SZjFsaYXpyjKydwXZX6sEtY8/NqxqU95xDQYPiYCE19goOnSZ5fnkRWqMTCCtuSDlplkGJVEq1QsNQg3a8ux81mKR0xmnRPgpoZRVidKgfs/17fgzx744tfPIebbryLr3/9iygo5Ox+onoAXWSIGRqqcDFUFdM08Kk2JYEAPbkMARU04ZCxDBJCRxOCuGHhUxQQEYJqFlda+JUghpZnfJFCR4skqg+wYdAgFAwycb6PrC/AjKIF/OL31/CLy3+yx+1et20Tv/r1bZzxwyt5frtg1NQsh0/0Q4nGsHiO+55roaFh5B6VdeFNi5k+ooRvHTd0t3/+V5imyYFnnUs8aHLY4VPpliMou+Aw3JRN89OrqKxtQGZ7CRRXYLkh0v1tKJkC1FA1R31uPkqkitVv7WTM7HIaRgQYUZenV/XR26dTWWuhalBR5DBmmGDADDC2IE+jpRE3bNqzJhV+h7jP4v6tfmYWO9iuhV+XTIwHGH6Qyeb+PAFd5dRpPlSpMmApHDAdOhWoKw8yUK4Sb6xkS3mETOdYzr/4O6xbOQ+//8NPIH7bn37FBV/+Lj/62fW8s2ot3/jKcUiSBJRJH3pd/2iPv26I3XPFtwJlwAlSyr9ux74OmPQ354WAhneP7xEpZQ+7g6xKdi+e93j2OabpoGm7kzVqQsNQomRtlYwjMVSNs0dnEMLHAcUmscwgS3odknY/fs0maWXIOeC3DJ5t0nmpzUVRXNKmhqEK7n9wC8/u0tmaDLOmP8DGLo2qgJ/hIZf55SYTi2FajcZpMzS2vZNlsNOheFiYeLlKuFIh0ZNiUr1ErwigaH6Spsvw42eQyO/itKMPIerb51LmfeIccMBCduzoZPny9ShCQxExfMIgqBmYDjQlbRLmID41jRQ2aTuPoQbIOwqbBlSKAjZr+6ErZ7M9abB5UMV0bFxpE9X9FPklKdOHT89xwYQMHXmDlK0wuVjy2VkuY0ptzBEzeeXVxZiW9Z8b/K4fX/crDr7oIpKGYF5VntXLbRKuQDgOnVubqRu553cPvn70OM48sOH9DJ9nDz336qNc9LVzOOZzx1M0YwYTzvwS6ohZDBtdzVEjFSaM9tNwxAQ0X5xQOE5nX4pwWTGT549n+qxqZhfb1M4qYVMYxh1UyNQTRhOaUEl/MICtwrAqm1FRwYADNdUwJq5xRI1FgeHQGM0QN7KU+QV1YZ2srXLyCJM55TkKdIEqVLJuFssxGVvkY3gkgJB5fJqDIhRKQ/CFWZKvH69QXwT7VUVobJSI0gj6/gfzvZtu/b89FD9ME8bM5xtfu4J0f5rH3lzMZ794Oa5V+aHX81725pP1t8AY4GAp5d8mlngE+KkQ4gTgSeD7wOp3F73/S0KIa9k9K7YRCAAXAFullL170SaP5xNjx46d1NdXAwIh/ChC4tfyRB0F3bYR0uDeJ1oYFrRIxhwKqwt5YZvkkAaTrpxEdZK8tMrHtFEuRX7BhAKV7k15qkZLzjyhHFV3ea4lS0PM4iszNRa1qXRJiRR+ogFQFYNwSY7x08OsaHJQLEltmZ9SLcNgLMzKt7o5YEycZCSMFugku+YB5lcP45zjjxnqofuv8bOf3cD553+R226/CkPVUZUwpqMwYFrk3Ty9OT+9ecmEQoe4IejK5unKucR9sHlQQ0gNXXGJahYxQ0NTcvTlJSUBG79QOLRK452OEKGozkGVklJfnvaMi65A0nGZWmyw2h/mjeXLWDhr9h61WbFTFNeUsyHhQ1F8bO9NcfxEhV4pee2Zt/nKmefscf9njS59v0Pn2UPX/OjnaEHBdbd8h+++EaI9DVNKTMaOyrO+N4w0XCYfYJDdGiKr+zjmwAbmNeoYQsUVJpqiENVtZtdISoIaOddByhxp00fAyJK3JYoiMISfEr9FdSiPJV1CmsCRDgHVT9RwQEjqI4KkrdKXA10NsXkQwnqGATNIBSZFAY0qNcpAPojt9NKRFjiuQUDzc0RNBoFDw+QIm8sVds6Zz+s/vY7pd97Abbf9kalTPtw1oscduZCjDt2f7//sWpa8/CodbQkaGj76vG97mierDjgPyAMdf7MA8jwp5d3vBlg3AHexO0/WyXtQbJDdAVoFkH33uv/aT/ulbYNIJDMq40PdFM9HZOvWt2hsrAYkiuIgXQ2/GiOnphkWT7IzaWBoGRYePoJx1SEG7Sx1w4MYap7OpJ+GuElZvaS6wGV7UmWqZrNgUp7mFpN+N0SBnSam6WxP6EyMaxT4JLmkg1Bd1vT5cV0oVRz8rp+z5zq8vNWmOWvz5uY0WiSIvXYxW7u34xoWJWHJmDEj+NkPrxzqYfuv4vOF0HUVgUAICRKEUIj5JDHDIGPZaCroIoYrTZK2xdRiP8gkvXmHuJFlV0rnhU6dmWUOmiIQSLYOOBQHXVSpkXVcXGFhS5MRMY3+bpf+vMLIOCjkiFVUEI2E9rjNs2bPZumiZVRPnkY6oZGqiLA95VJd6CJ7upk/ec+CNc9HTyKpqIhz7W/OYeOAn94+F8VSeFFX8OMjLyUxn0K538+RZ46lKeVSGXJpiFkgbdK2Q2fWR3nMwZUqRb4ArRnQFfD5bVQVhoU1fKrJ1CINKVykdMlafmwgY2dxkJT4NQZNE1sXSCmoi0SxpWB41EUhgq6k0FUFRRr0mwkckqQc6B0MckC5gemmqNcMwpokFVEJxwo5KmRx/r1XseydNTz7yrMfepAFoGkaP770u3Dpdz/0sv9lnXty0ruJRf/loyVSyheA0XtTsZTy4r05f183kLNxpbec7L/Zli07WLBgBrt/rWwEYKghIobFmEI/pXoFP9mxDDOXoNCvoVkOWTvBig6F+phFUPcR90n8qsP+ZRaPr9BYsk6hOxZhVKXOcC1AY1ma1qTNM02S2XU6L24VdCsG/pCfzQN52gwfftWhy4KFI338+PFWxNY36Vy9kasvuYCFC39IKBxBVbwnxT4qUgpA7v5buggcdEKgKKhKLxUBlT4ziU8xqQlpSOknL7XdT5W6EDUku9IBZsss5QHB4jaVhO0yRYOglqGhQEETKrZr4kiNuKES0R2SpqAto+Imk8SMPV/Xcvbnv8jzZ53IhBmTUSoNxvaDm+7jjZ2SjBL56AbKs9e2bl1CaXEBhurj3q0wslYwPKIxsyKLjkNL1iaoqZQG8gyLOAhFpzLoUBaw0ISPjK2wtU9DIDBdl5ybxK8a5BxBRAeJQsJyieIjoAVQhYtEx6/k0BUftmtguwqDuTyaCpowUISFlA5IgRQ52rMCn+oANilHoSuXoyIIEwt9mI7ElRZJ26XQ2P0wh081qI1IspZC3slSO2Y4P77uYY459NOMG7fvr+3zFmJ8TA4eXjTUTfB8xLZs2cm55x4P7F5TIBQBqPiUCBkbNqQTfOOyGVRoGV7eJfDrKtURi4PrHFpTBj1Zk9qwJKAp5ByFYLHJqOk+Juoutq1QXZhFojCmAB5PqTzfYjG61mCEMGlK62xPSsrDCgWdKV7eZrHr5TupCkT5wimfZcGv5qOq6pCOz/8KXdfIZvMEAj5cqaKLAJabASnwqyFMJ83GAYeaYJzKSBbXtZFCJ2OqdGctQprLeWNsInqOjGOwvN9PRBM8sM3iyDqL0oAk50rCmg8JjI4rvLLD5PcP9OFzN5Pr76Outm6P2+vz+fALwf6NKps6FVwzR11DnFUPPMoRBw1dRm7PP3v7rVXst98EOjJ+dvUL5tUKSoJZSvw5erMGNSGVnC3pyfgYGc9wYHmOnWmNpqSfhogkoNocO1yCUPErgp68gUSjKqgSM3R68ykiegDbdRjID+C4FoYSQREaHdksYR1Kg4Kcq6ELQda28asADobmw5YGxX4TKQPoSg5FMSnzBzGdDD7NJar7ybuSmB7FJzSkGEQlRMaSaMIhqCvEDJ3f3vINfnzppZz1hUtYuHDhUA/7B+IFWR7PhySXcwgE/Px1Jgsk4CCERmc2j+nkyJkGKcNHzlVoCCmU+S0CWhhLOkwpsQlqGnlHkrJUikPQb1rYrsqB1Qo+TVBgQNKSnDHCZXmPYEaJYPOARrBjkHJLQ+lzaOsweO73P+fmX/+Qw/c/YGgH5X/Q1Kn7sWLFFubMGY+UNrY0UfHh4NCT7SOgS8YXGEQNB1cqSEzSlgP46c8rBDUXBRMXhQe3KeRMwYzSDGFdElA1enOgCoFEEDME76zO8v2fPEA00c7nzvw0X3/8SQzD2OP2CiEYMaaBP/3idmZ/9gQWVJg8evPTlLqCi7/w6Y9uoDx77fjjP8/pZ5zAr4+YwjX75ykLqPTl/QQ0hZjh4lM1urMuPjVBiV/HlQojYgHW9dsU+FxSFmQch7AWIe+mEbj4FQdV+HClTYFPxa+oSASuG2ZFXz8NUQcbQVCFoObQbwqiOrgySMIyMVQNv6biV4J051KkbElQkwzmQwjSxHSJ7Wj05AyC6u4ccIX+IIrQsS2FgXwGnybwqQa2tEFIKuJhbvzdt/nGV36FZmSYP/cIhNg3vyR6QZbH86Fx3/3bYfeNYYHrChyZIqzrNKUkjuuSTenMK89T5HPIOKAiKQ1IVKGiSoOcncRyQ9QGTcp8Cmv7DFK2haoo9OQcdEVSHlCYWJRn0HLoNH2U+cIs2Z5lx5I1+Lve4qm7bmD8+H1/qn1ftGDBQdx11y3st994FCWA5qpIbASCYn+MHjNNkS+KIIcjdwdaQVXFUF1qw4LONGSkQsiVxIzd67JaUgolAZV1vS6uojKtKE9FIMvK/hA/veqnTBt/Anf++HzCoX+/xce/cvV3r2fR8qe59Q/3UBgN8J1vXEtl5cfz9JVnz0hpsXTZczx4/7McffwcDj5qBq7rYGhpLBnEdjJsT0qqgjZhXSOohxDEgATjCzUECoOmjekmybhZgoqGS568o7FhUDKvLIamqLiuS14m8WkwqzSOKgwSdhcqOi4OqnDJmA5+3aQiKN9NQaJhyywxwyKsh8nYebYkLAKqgQg5ZC0NhEZx2MBQdi+dyVh5Ng72MSIawFA0dEXFdUGgYcoBcIOc9v0L+fEl1/HknANQxb5569oLsjyeD4HjOKjq7l8nKeW7QZYLCNKmRTqnMq1A561Wm+KCPGUBge2CX/OhKGGiej8pyyElwRUOGdumK6szoShC2jXpzZs4rk0qJZH+YpqSSfYvE/SnDN7aCaWKTfnwELm+4WTaVnoB1hBqaBjGjh27M0grSCxpAQ6OdEAIinwGqrBRRTGO7CBlOxgCcrbNil6XEkOl0KeyOeEj6WiMjGbYlnBRhI3lqAjH5v5NBgvqVGaX5ChQLH5/+efed4D1V3OnHsHc3x7xIYyA56PywANPALB50y4WfGo2edfAUCyEyNJrQUA4hHUNXSioQiVtDxJUHRzXwdBC6KrNH1fq1IYMPjvGwnQ0dqYlAdUiaw+Qc0KEdQUXHwJjdzJcKfApMXw+jbTdRcpyCapRIrpAComUKrYrsRwdn6ZhKAqWAgeUSzRFJWeb9Ik8Ic1EFy6K9NOeMSnyOQwLRQhqCpa0yNo2aQfSVh5VhIgaLmGfRjxgoIi928D5k8QLsjyeD0FvbwvFxbs/CFxpY0sLx92dFytlZ3mmxWbbLoc+fBRFBV05jfJAmKihI2WejQnIWwphwyFpafgVjY0Jm0ErR19eIao7vNITZGwsR6WvC6QgZUPClIyMCTb3aDQUmxxxsM4f1374eWY8e0MjEonQP5CkIB5BV4PknX5URcWRElyBFAq2m939WrYXoez+D2lMXJJ1XBKWRWVAoywgGR4JETayZGzIu5Jkv8LypT2URgoIqBoVpSUUFRQMdac9HzEhdK668hcsX72ak8//FIaiowkVx3VQhE1tWKFLBU1AzBfElTZZK0lYLQGZZiCfJ6LpTCoxscmxtkPDF5RUhwQlfknWzrNpEGKGj0KfIKf1ENb9aIpEF1E0RWA6MXYk0kQNm7AhkFKiCgVDlSCymI5AV3305DJUBQMIBAFdp1ILIN0cOVugqQqSLHnXJKgrOG4xWTtLULfR3SxB3cdA3kQxJcW+DIbq8j53APxE2Hdb7vF8gmzbto6amgpAQRF+cjb057PknSyKFOxsg3zKYV69zgGVPupCOhsGUnRlB/GpIQp0P6MLoDGqMizspz5ic3iVCTj4lTzr+3TKgy6zy20ytiBuhEjmDarigpm1Pk6eYtNYIlid85NVvScHh5bgnHMu4Jprfg9i96ymXy3Yve7FdbBcCwcX0x3ElQoFvgKyjkJPTqUnq7K0NUh3VuKQZ/OAxZbBFBv7XW5bLEjnQiwd0PERoDoIW/oVBlzhPdTwPyIWi3H6aZ9n+4pWInqIqOEn6oviuEF6ci66IkBoZG2XpJnBkRHyMg/CRRU2uuJjVJFDbVSysydDKudS7HN5pd3HoBVieomgIZonqKtEdB3btchZLoNmC2lrgJDuZ3ppBEsKck4eW1qkLRtNqPg1FUNVsN08OcfFBVRFRyGAofhRRRhN09CESqFPR0qFQctk0EySsrMIbDTFJaILXNdlwHIQMkJtXQ1L3lo81EP/vnlBlsfzIbj99vv57EnHIHFxpYMrLfryFgKVgmAAzVYpC2vsX1aIoTjk3QyTCzUMxWHAdIj6UvhVG1Vx6cw5bBg0CWguDZEURX6XuRUuWcfFdPxs6tXY0OPQkpas61XZlpD05QSLm328sXgL06bMGurh+J83YcJMaqqH8dub7kUTCqAghE7eyZB3HTQUdEVDShvTVdiciNCU0kn32kwty+LXDNb0+Xlzu8rjWzQ60gopS/DSGpOuRb3UVPqoCmuobW3MmThjqLvr+RgdsvBo7vnjsyAlmvADLiFdJaBKBvIuizod+rIZek0IaiZIgSrC6Iog46SoCCgkbYVmEaA2qtBvQkhX6cnnSJoKKcdBF3lyTg5FKChKAFUIdqTy9GYtQrrD9OIgQVXBlTqG6uDK3alLFOES1AKU+wswVD+mE8Bx/bjSRFX9BNUgflUnakQp8BcT1aLEfQZSmrjSQCWITwQI6YKygEbSUTj6jEO57Y4bhnrY3zcvyPJ4PqBMJoOiSOLxGEiJI3OoZIgbBTiuIGvlmTNeMqaoDyPbQkC3AJeca6IrElf20p/3kXcFKUtS4t89Zb+2RfLLZxX6cxYZW6UmbLEtYVIYFPgUA9OUTCxwcdhdzoTMRnzvPM9l554x1EPiQXDxxZexdWsP27Y1AwoKgohRTEQPoykGhhpCEQppu5+x8UGakgornCBS1VFx0DNptrzawvLtCt25AJrpUByEsePCTBoj6Nqyjmduvp2Lzj17qDvr+RjV19cza9ZU3ly0HiEkKpK842BLhbJAgHFxlw0JyaJ2eHmXy0u7sjgkUAQkTYOwEaAuJJhfWcyqfo32LIyKpjAdMGUWpELOyZKyJY6rs3kwzaBVTHNaZWsyQUfapieXR4gopuOSc/NknAFsmQVh4GIRNFK4MotKEk1JAwZCuKgiAEJ5d49XlaAeQVOgKhTBljnStovpOnTnJP15jWKfid9xKIjvuymQvCDL4/mAAoEArivpy2dY3dcHCBRFJ6hlEcKmL29xRG2QQ8dVUVocQSDozTsMmC4Jy0UAUuaQEop8EYp9Pkr8kpJYkMZyga4qbE1AytTJOlAWEBxYm6EyKshJH9VBHS2f5pHbH+Ghe+4hHC4e6iHxvOuyy37ANdfcihAgpQsSFHSkFDiuSc42yTsaeVelIpSlKyd4pV3BdHViMZ2y0ghu0maw16WuzsdAf4ZscYDOHbt45a53ePieJygpKh/qbno+Zued+zXuuvMJVKEhhE5AMwjrOlHDIm6AKzUStkZGCraldV5slTQnVcqCGrrQqQ0bVAZ7UYBkPk5tOMCYuILtGuiKhu2GsVwbXTUZEVPwqzlmlBjMLCmgMhTCp7noisWWgQA7BhVyTg6VEJoIIoSOX42gCgMUF4SKoti4MotLGlfmcYWFRLB78xgfQugYSgF+PYyi2BT6XUI6GIrCS8+8w2GHLxjiEX//vCDL4/mAcrk0QnHxqy4Fxu6MxioBhKLw5C7BnVt3JyUtCBeRcrN0ZzXaMwa70oK0FUYXKqV+QdZxaElncWWWoKpREwvwuZk6YSPI/FKL9rRGW8aHgkrY0JhVrlARylNsODxx45+49sqr8BnhoR4Oz98oLi5m7NixvPLymziYSGHhvvvEli1zuELDdVX8KiiuyuE1Jio2O9I2TRk/pROjyM4uensyDCJQhGBGcZZXbvsjv//d7wgG93z7HM9/D58/CFLiyt2zQ4bw41f9uAhsV6ck4LCwIkbYCFDos5BIevMWpp3CBfyaSnlAR1MUlnWZ7ExmSViCqOGSMjOs3TnIuladnPPuHKxIYggby82jCIHK7uAulXd4vCmIIaIowiBtddCXy4DwoQofqgiioKMIHQX13W1jBIIwjmtiOSkstxcFBV3JE9Z0QFKgV1HoC6CpLquWrmf+3AOHcrg/EC/I8ng+oJdfeYUD9p9LQFUo8gu2DCZpSg2gojCzxOH4OkHWStGe7mZ5T5aEZRM1JFFdpSKYxmb3h2BY1Sn1q8R9QYJ6kPX9gzhunvEFAgdBbTjN2LhLddiHio+AarC+V/Dw4yuoLKlh0vipQz0Unvdw8cVf4Xe/ux8rq6KLMIq0UAQoMkhvxqTXzLJpQDAy7mdaoWBWqUVQc6mPmDQUCdRkAmfxcqytHYweGWLpH2/j0ks+j9+/51vneP67CDSKSwtYu3ENlpNGChvLhd5cnk0DeXQhybvdbB6UlAUlI2IuRT4LxzXQFBAoGIpKeSBArwlvdevo7wZquhKhtkzwVtLg7W4N002jC4HAQREKSA1NsXGcAuZV6/x4vyi6IkjbHaRtCwUbXBOJiyPTODJLzh7Acf+6bktDkEUIA0X48KvFuCioIoyUOgKXoJ5CVwS6KEAIA0Pfd2fnvRQOHs8HFIvrNO/qxJY5dCXA8GgUySC2IygLmJi6hSvhweUKy95OcOFpUeJBl7heRN4ZwKdqxHwOCj4MNYYrMxiqQ0T3s3nQ5sBKDVzIuyaayFHkCyJFjrwlaH75GdLrW7jmF38Y6mHw/Au6HuV737uca675LVdd9XV0ouScBDvSadKWACl4p1dntpLEkUFqwi5ZS2HVimbUskoqZw2na+V2phUq7HzkVxwyfyqfOfT0oe6WZwgJIfjKV7/OVVdezrW//DKOYyGEIGnqLO/WmFYq6chJSvyS1rTKil4/rmNTFYLKsE1l0KbEl6U146cuunvbnbvfbkfb+CabVm5FFZLtCYXmQJD8KdOYMbORWERDSBtXZAhoPvJ26t3bfbvzv0kZAgkRn8quTB8RrQBDzaIIG1UIENCbT5O1BokaQfyahoIBqAhMXNcl5wwS0EOYjoIrTXK5DJq257sXfBJ5QZbH8wGNHDmChx66B1e6uNLFdnenIs05Fhk7h664mK7ACQY4ZmGceEAnqjuoIkPSsXGBnBMhYdrUhTPkXBNFGAR0l/nxGI6bwXRcApqDLvwkzRwRPcpVV/yG4cPqufEXd6Cq3qzGJ9nIkRNoaenfvVBZGJiOoMTQMUSasoBBiV9n02CeJV0WybyKX5qMGxZjbd5gbG2Og0YN46lf3sTFZ57B0Ycfi7KPbjHi+fBUV4wln5cYih8pNXJOhuFxg9N9haC0M1UV9OZTbBlU6Mr56cxD0rFYPwCzinM8/uoylr70BpoWZUNvhuKKUr5+8pF887wrMAwdRQTI5/Pcetsf+MmT99HX18dJpx7MwYfOQlV0VCWFih/bNXFlFtPZncfNdA1ieoCQZoCIYLs5hNCR6IQ0dv+sSBR0bJkhZ1uoioah5lFEDNtNY7o5JCq/u+kxTj/9C0M91B+IF2R5PB9QS/MA5eUlONIiY4Etc1iuxY5mSUmlgyrKaMskmFFtEtZdBBLbNVBUgaro6IrAkXmCuoGuGmRtia5BbciHodokTZOdKR+dOT8qMRRlgKUb1iDdMF+78AdD3X3PHli58jXq6koAiSNN/JqBrviRwmAwnyOmCyqDGnlHpbosjyIdpBqHdIatyzfyxJ2P8r3LL2XhTC8ju+f/cyW7c/EJHUMzEBiE/d0kTQdXRtCUNDVhh6gvSX9epS0juP/nf+aF9i5mHXAId93+MFF/CNt10ZR/Xj3k9/u58EsXAxeTz+eZPmsS/oI4EybVoyuSpJWjeWsLO7e28ZlPH4jt5nFcB1VIIIvtupiOg0EeVXExVB9lwQqQJo4E00mQspPoSpAAkrydA/IgBDf+9GmQAebPPexjHtUPlxdkeTwf0JNPP8wRR09CSsHqvhTNCY2Qz4/rJinJaUQLbOKWj5ztoIgAhprDxSZtW7T06uTyNrPqdSBDynLQVI1MNoPPHyBtZVDQsNwgkGXNQIp1W7p55Bd/5q4/eLcI9xWzZs3iD3+4BQCJg6qAT9HxaRLddNnV3EFf2wCjKnWwXDraBnj08UXsaOln/qxJPHrvXURDNUPcC88nyWWXf4m64WX0ZvL02UlGxIpRhYV0YdCE/r5enJ4+hKFRWVtGqj1J69vrWThiGF++6XfEffr/lfVeAdY/8vl8vPTy85x7/jmc9a1Tqaorp23zTm78yf0Ul0b51DHTQULOCZJ10lQGJbZMoYgYIJHSQCJwZAYhHBxpI9AJqwUk7X6ydh4pdRKWy85NTTiuy49/+KOPcAQ/Hl6Q5fF8AK7rsHLV21zwtcNwXIe8azAibmCoKs342ZK3aet2aclAWBNMKdqd4C/nOMR0P1G/RtOgQ2cmT9wforerm6Yt3Xzl/B/x/asupHJYgPGTRjAyDu2ZPF2dCe79zS08fNcfKS+sGurue/aQEHHeemsdv7nhLk7/wmfJZbPcd/8jrFq1GVVVqR9eQXXFKFYs3Uo2N0BJcT1fPvfLjB41GU0NIYT3jJLn773zzlo+d8GRNKUV2nI6hpKhNiR5a8lafn/DYwyvH0VD/UjS6QxNO1+guKyUaDzGly/5BgFD/88VvIeSghp+9+vb+O4PvsZ+B85n+eL11NbVUTWujP6khS8QpSOTxK/CjmSWqOGiiEEs10faSlHq19FVSd61UBFITEx3kAEzRFNC0pL2EdA1Hrj+Ke679c4PecT2zuq1r1FSUk5F2cgPVI4XZHk8H8Brr73KnHkTSFkZblobZEyRSUVhmpAmKS3RyNsOaSdPoV+gCrBRKNEVYoYkb2fpShWRTktcTPrzKbqSftKhMoZPncCdtz3NgQtncvNvHsMwdNKmTUYJ8Zuf/cILsPZBq1ev4bEn7uf8c68kGNI555wzOOfsywj4g0PdNM8+6L4HH+X6G6/liQd/Sd2Y0aw1XDYsWc3CeQfx2xvupqKi4iOpt6i4iEhBhHvveICbbryZk07+DNYih4TjZ9yh+zMmrlDgM7AdgelYIAQqOn4tQ8ouxDL7cKVNwgyQdwURw6Eno5B0VMbGDbqb1jBq3Dji8fhH0v499eMfX8eu5g6uvPJHHHjAwe976yohpfyQm/bRmj59uly6dOlQN8PjQUrJGWedxtU/PZVu18/DTQpjC3UOqcqTtXxAElMGeKpFpyao4Mg01SEfJX4T09UQIkeJrwaVFC5JUnaOHakAJT6Jrljc+utHOWTeZ5l/4AxUDGwbNN1AEd7ehB6PZ/dnUGvXFrraknQM9lPT2MiE6mEfW/3PvvA467asoK/VZNvOHRxx1kHsP3UYQd0la2fwawZC6liugUOKtrRCazpIZTBDn+ki0TBEMW2ZbixXUhMU/OobP+cPv/o91ZV1H1s/3ktLSwtnn3sK8+ZPIOAr55KvXY74N5+9QohlUsrp/3jcm8nyeN6njq52XCNDAo3rVgcYX6DRGHFoTklcmaE0oCDdNI4bImW57Eqp1IdNdMXCdqJUBAIM2i0Idu/5ZSg+hoVdXFdBEYIxYyt55C/3sGDBAoQQGPv2k8wej+dDJoSgumwk1WVDU//8A/dj1vxxGE45W7du5Uc/+z4FymHMmlGHrigE1RiSLLrMgxtmWNiixJdDIigP6bgySF+2lw0Dgo6swfq1O5k+adaQB1gA1dXVfO/7X+WmG/9AcmALXzyrh3i0aK9v3Xs3+j2e98GyLK75+TUcfeJRvN7lI6hIZhanGLDSaEIjoqkENfCrOtOLfRxY4eegKoWigERikLAHybiDWA7YUmA7kn7LAhxURRA1NI44cgE9vbtIpNuHursej8fzT4JaMXHfcILBIBMnTuSmX93Ew3c/RdYGx43iuIK0lSNj5bBI4dckpcEgJYEoYdXBEL2gWCyo8HNghUnvqnc45sijh7pb/2fe7BM4/viTsaVD/bBGLrvqq3tdhhdkeTzvw8uvvcz69hZ6yht5dbVk+6ocpYbKjOIwdRGdoC4RxIkbEWYWhyj2G4yMwaAZBukS0FySlotfVQmqBq1Zg0UdfpKWghAGadtl0avvMGxYHdHQEH1N9Xg8nr1QVFCJmRV0tWTIOhls2Y/pajhopC2XhClQBKiKJKDFsN0S1veqvLy8if5NTbQtX0Z97bCh7sbf+fSxpxIOB7n25m+xfNkaVqxYQTKX5Fe/vZKnn7/nP16/10GWEGKEECInhLjrb44dJITYKITICCFeFkLU/c1rJe8eaxVCnPk3x6uFEA8JIXqEEINCiDVCiDP2tj0ez1DQgnFCldWowiXoUwkaktaEgl/VMYSOpoAqHNqzEkuq5N3ds1T14SCaIunOaiRMlX7TwZU+qoIKh1VaSGmQTGV54qHX+dPtT3PFD65DeIknPR7PPuJX19/IDdc+xE+/dzv337uYnuZBDHTamjo58zPf5SdX/RkhA2gizDvPr+TJH95KZmUb/VtNrvnpFRQXf7K20FEVA8PvY/+FM7n8p+fxxHN/4uxzT0YPuNx/76P/8fr3sybrRuCdv/4ghCgGHgbOBh4HrgLuA2a/e8pXgVuAvwDPCyHuk1JmgDuBVUAdkAcmAN528p59wqTx40j/qp2urIYeClDUGCCl5pFIhKJQ5KtDkiFl2wyaNkV+P2krj+km8WkWVUGVsBLlu5deQ1lJAVY+Q29PGsf1oSohjj/uWO6682J8emyou+rxeDx7rLiohj/ecQ8d3VtZtWIdTzy4ks1bVqJoFjfd+GvuuO1ezvnidygpqqKmtpJ77/4zyh7k6Roq9z/7GMmMCSJEpDDPcWcfzFGDc3jxqeUcd8yp//H6vQqyhBAnAwPAYqDx3cPHA+uklA+8e84VQI8QYrSUciOg/s0fDfjr8vwZwNeklOl3f16xN23xeIZSQBWolsPGhGDL+haEEuKAOSVIcmgiiOl0IoTCuIIwQrgIfPjVOK40saSBX9Pw6ynS/VnO+ua3CAXjlBSXo6kBhPANdfc8Ho/nAykvaaT80EYOO/TYvzv+kx9PJ5FI0N7ezqhRo4aodXtGSsmlX/46V/zuu6TsDK3pIK1NzTx5/d3Mmz+XY4455j+WscfhoxAiClwJfP0fXhrH7hmpvzYqDWx79zjAr4BzgY3ALX8TVC0BbhRCnCyEqP0PdZ8rhFgqhFja3d29p032eD4yX7r2OvwHH0I5grqYwYUHRtC1HhzpYEsTofgBFVdmwHWRUsdQBYbqw6/EKPYFwfETCRczYvhsKstHo2txL8DyeDz/9aLR6P8FWFLa5J1dfJLSSUkp2ZlIIoTguSef5u1H3+HVvzxPV85m28pNfP5zZ3LZt674tykd/mpv5uiuAm6VUu76h+NhYPAfjg0CkXcb2y6lnCelrJBS/v5vzjkReB24HGgSQqwUQsx4r4qllDdLKadLKaeXlJTsRZM9no/Gjk0bKBkzCpq72PrkThI9CVRRjJQFOI4Frg0IXBfyjgM4JFJprvvJH3nt1VW0tO3itRdXY+btoe6Kx+PxfCzuvu9WPveF47jh5utZs3Y5v//D9Xz5a2fw+hsvAM5QN+//XHnzn5k8eQZPvfIy7e3tfOH009m+aRfj3G56Vjdz8kmn7HFZe3S7UAgxGTgYmPIeL6eA6D8ciwLJf1emlLIfuBS49N11XT8DHhVCVMtPUkjr8bwHRUJRKERfdzuKlaM0qrIrlaDQl8RQ/EjhoAuDlJVHIUiRovD668uJxarZud3hjdeWMHJULTff8tuh7orH4/F8LPoGOjngsP0IhuHeR3/HjJmTmHXw0fzuusc4+IAz/3MBH4Pe3j5u//kvOPQHP+F7P7qasWUV+Hw+guEo13z3dm695U97NIP1V3s6k3UgMAxoFkJ0AN8AThBCLAfWAZP+eqIQIgQ0vHt8j0gpe9gdZFUChXt6ncczFLLZLKiSTff8Gbupn4jfx4Dlook8IDFdF8txcF0/ET1EyPBhuSZjRk3klVefJ5XpYf/9D+D00y4k4IsPdXc8Ho/nY3HB2d/knntfoHLqBE6/6ETm7j8OTRq47icjL7pl2Rx59GcpqNqfVc90cvGVv0BRFK6++mpOPfkLPPLgM5QWV+5VmXsaZN3M7sBp8rt/fgc8CRwGPAKMF0KcIITwA98HVr+76P1fEkJcK4QYL4TQhBAR4AJgq5Syd6964PF8zH7/9Iu0ZSQDu1p4/LE7aB9spqI8QsywEATRhY+gHkcoCj7Nh8DFcrOU18KFF5/Cn++6h6eefGyou+HxeDwfK03VmTlzGkvfXseGgRD9A4P86Ae38d3LrhzqpvHm0o2cfd7FDGTD5JQQw6ZXs+TOP3DmmWdSUVHBfvvth6btfTC4R1e8m3Ih89efhRApICel7H735xOAG4C7gLeAk/eg2CC7A7QKIPvudf95qb7HM8SKDZX8rh1EJ0yloEJn+hlHE9Jz+PUQmgAhdFR0lHfzW/k1H46r8dZby7ntlmd46qkXKSvzEox6PJ7/Pd/48lc45bSTEV1pbnnuWerrGqit/bfPvn3krrv+Nn587XWMnrWAGx/8Cu07Ezz56EPMnreQBQsWfKCy39ccnZTyin/4+QVg9F6WcfH7qdvjGWolkSDzP/sZcqvXMfH4k6gtcigLSHRhINBRhYLEwpEOAgnSB1g8/ujb/PpXf6CoqGiou+DxeDxDoiRawT23PsiaNWv4zOEnMqyuca/WOH2Y/vKXv/DQQw+xtCXFp7/7dUbPGcvWdW/z7D3PEFAKGVk3/APX8cm4Eerx7ENu+sMddA2bQP8ul4qaHMeXRAjp3eQciV/TkAik1LHdNIoQ5DLQ2t1EW2uPF2B5PJ7/eSUlJSxcuHDI6rdtm+985zJeW9rMgZdfxEkRhwkFFiEtx++vfZE7bv8TsQ9pOzMvyPJ49tLOzkHGHDeHKdWj6a0K0Ecf0g3iV10EAnBBquQclXde3c6ddzzGmLEj+OKZ5w110z0ej+d/3sJDjydWM4nMsKN4/b5Wxk0oJjC/kM2PP8CZp5/7oQVY4AVZHs9eOe97PyMYL6dMS7N8yVKmfX4u8ysNSoIqCjqgINAwXUibCf70xwe5955HPtHbRnj+u0npIoT378/jAWhra2PFyrU0BKqY++UDaN4OS6TK5gdfoK61k6O+e/SHWp/3m+fx7KE/P/k8f7r5l/T1uiy56SV+dOF0vjnTYETcj8AAVByS2O4gmmLhV0BRLB57/E5cNz/Uzff8D3KlTUt6FXkn/Z9P9nj+B1RWVvLFs85h2MhSXvnlr0m/cCuD91zD4ZVx7rjllg99fZjY1/J+Tp8+XS5dunSom+H5H+RKybUPPs8tl32VWMVcrvrBYSzcfySqAq7UcKWLS4K0JXDJE9aKQKb5w+8f4I1XNvPFL57HUUcdNdTd8PyPydqD+NXokC0u9ng+idbtGmBXX4bDJ+1d3qt/RQixTEo5/R+PezNZHs+ekpKbrriKMYd+DVnQyJv9Pl7ZMYDp5kFmUYSJQCWgCWJ6ET41BIrKF885keKyEKPGVA91Dzz/gwJazAuwPJ5/MK4m/qEFWP+OF2R5PHvoul/fhUsV/myOyoIIlhNnVInAdjNYpHGkhekqCASqInDlIK+8tIhzz7iSIw47hsbhE4a6Cx7PPuWF1x/g+l9fSW9v7ydqA2GPZ095C989nj20fkMbwVgpPr9g28ZtnOarpCoyHMu1EAAij6a46ISw3SS5bJ47b3ue++590ptJ8Hjeh9tvu49PfWoyV1z5bVIJh9tvv32om+Tx7BUvyPJ49tApn55GJt3N5q3tpFJZykriCKmhkEcIA9OxcckgFFi3diPX//Q+vvqVS70Ay+N5n6ZNmMPzT7+BkAEuuuiSoW6Ox7PXvCDL49lD3QMOG9ctpbJxIQNWAK3GoM9Mo5LEr0XQFAPLTbJ40Wruv+cFfv7zn1FVMX6om+3x7LMuueQSwAuuPPsub02Wx7OHTjnhYCaMqWXq+GoE8PIrzdhuDEUoSAm2G0ZKSV9PiiMOPXmvAqzevl6+9YNvccMNN3DhJedz/oXn0tHR8dF1xuPxeDwfOS/I8nj2kK5rdHb1MnliI+eevBChumzo66UnJ8g6g2TsrdjSZc6B47n9jlsYHBzco3Lb2tpoHNHIynUrefHNl9AMg5bmVpYtW/YR98jj8Xg8HyXvdqHHsxfOu+Aibrj+hwSLJtGlDGNbSRXnT5RowmHl2xv53S/uY/v2Vizb5Tc3/Ibvffd7/7HMiooKLr3iUmZNmUl7TydRf4R535lHLBb7GHrk8Xg8/7vW9vexM5XkUzV1H0n5XpDl8eyFk044ksu/fylbXv8VRaU1qKGTWX3+YYz2J/n5VXcwd+5+3PCnH/Hm4pVseKdlj7Y0EULw7Yu//TH1wOPxeDx/VRcO41fVj6x873ahx7OXVi5fzvd//FPKyyvZ/swDfPvTX+CkE74Jqs7kL36Vu5t1Fh40A9MZ5Jrrv08mkxnqJns8Ho/nPUR0g8boR3fXwJvJ8nj2UtCn8cPvfJ1wfARvvngvbSk/vsLhFM6ayWC/TtqJsqg9yPnfupAXX3mZY79wImWRIqaMm8Spp55KRUXFUHdhj2TzNk+/vYtj5tShqd73MY/H88ljOiatmXbqwjUon8CN0D95LfJ49hHfvOAYHv5/7Z15nGVFdfi/59639nu9792zMcPADIOACiiiuKCiuEbUGDVGjcEYFdG4xR9G3JIoRKNijEs0iqJEREFFjCTiyo7sDMPs0zO976/7rfee3x9Vt9+bZmaYgenpGaa+n09/urtu3brn1q1bde45p6quuoLXvurPqMutZ8N1/8GVX/gU+Q23cu9oP5uHp7n3pvXkJqZJZzNcc801iy3yATFTrNA3MkOpEi62KA6Hw7FHFCXUw7ePchtEOxwHiVv6J2gLi3zzim9wx90P0tPQwFv+4i942tOeRi6Xo6GhgVjMGY8dDofjicbeNoh2SpbD4XA4HA7H42BvSpZzFzocDofD4XAsAAekZInI60TkQRGZEZFNIvIsm362iKwXkVkR+bWILK85p92m7RSRt9SkLxGRH4nIiIhMisi9IvLmg3ZnDofD4XA4HIvIfitZIvIC4DPAW4B64Cxgs4i0AVcDHwVagNuBK2tOvRD4BnA8cL6I1Nn0y4EdwHKgFXgTMPg47sXhcDgcDofjsOFAonA/DnxCVW+2/+8EEJHzgftV9Yf2/4uBERFZo6rrAb/mJwaIPf804L2qOmP//9PjuRGHw+FwOByOw4n9smSJiA+cCrSLyEYR6RORy0QkDawD7o7yWqVpk00H+AJwPrAe+EaNUnUz8GXrglx2cG7H4XA4HA6H4/Bgf92FnUAceDXwLOAU4MnARUAWmL8T7iTGpYiq9qvqM1W1W1W/WpPnNcDvMG7GLSJyl4ictqeLi8j5InK7iNw+PDy8nyI7HA6Hw+FwLB77q2Tl7e8vWaVpBPgccC6QAxrm5W8ApvdVoKqOq+qHVXUdRom7C/iJiMge8n5NVU9V1VPb29v3U2SH44nDrzcMc+WdfYsthsPhcDgOgP1SslR1HOgD9rSo1v3AydE/IpIBVtn0/cIqbZcCPZjgeYfjqGWyVGDT5CS/H6jOA1nSlObY9swiSuVwOByOA+VAAt+/BbxbRK4HyphZgz8DfgxcIiLnAT8H/hG4xwa97xUR+QxmhuF6IA28A9ioqqMHehMOx5FItBBwIZimFBQJVFk/OcSGyWnq4200JOrm8q7uyC6WmA6Hw+F4jByIkvVJoA3YABSA/wY+raoFq2BdBnwXuAV43X6UV4dR0Lox7shbgJcfgDwOxxHNT37+AEOjOV766gweSZJ+lp66NlZmupjYOcpoYYDR1npakvXswYvucDgcjsMct62Ow7FIjIzNkMuVWL60iTAMufvhe7jlxpv53//7FT1L2rn2mv/hK9/7HM94ytNoTPQstrgOh8Ph2At721bH7VbrcCwCQRAwMrSDLTt28LJXfpD7/nQXAMesWs6pp57A9lyRj37pMs4+7bn4XmJxhXU4HA7HY8IpWQ7HIURV+erXvsp1N9xAPJ3i6su/x19/4G1894cfpbdnLZlYE3Gvgf680pBIkPCdguVwHM7kK5NMlwfpSB+3x+OjhXGakg344lMKZimHs2TibajqERkGUKlUCMOQRML1TfuDU7IcjkPEbbfdxqWXfYGnnbmO8y95J1d8/gfc+eC1LF+xhJj4pGPtKEVifoalLs7dcRgTaoiijI2OcNeDtzIzGuB5Hm1tbSxZsoSlS5cekQpEOSwQ91IHdE7CSzNdniDpD5MrDeJ7Pp3pNYSaY6o0xc6ZHXiylvzoDB//9IcJFV75ihey9unLaUsvpyHx6GtxqyqVcIDJcejvH2HdunV43qMvDhBogIc39ywCDSkERTKx9KNeT0QItYTgIRJjplImJh7X/fSnXHrppVx66aWcccYZ+1dJRzFOyXI4FpjbbruNj37mM6xZdwp/+5H3kY0rt/32f4hNj3PJp7/GV77xUXwvhlIk4fcutrgOxx5RVUYK27n1xnv5wY+uJF8s0NaaZcWqDo5ZciwEGX77py1s/vGVjG3p4zOf+SyrVq1abLF3Y7J0P75kmSn30ZBYjSAk/GYmig/jewnGi8Pc/H/3sP7+rfzjhz+1myJTDvNUwjLpmFkWMtQiniSphEPUeQXCcDuBTqNBGmWWUvgQCb+F7GySf/viP7NzVz9ve/8raG1Yya+v+yXfeNt3ePVr/oy/ePXb8TwPVUUp4klVyXvwwQf57nf/k76dg/ixMrNeghHppmlsCy8598W85s9fg6hQV1e3R6V2w+QmGuP1TGwbZ8uWLeRjJep7mzh7zbOYnJxk69at3DM8xdOPXc6xy5cSi8XYvn07t917DVs2j1KYGUeDOJWwkRs2bKCDAuTH+MlPruLv/u4CvvnNb5LNZoCQik6g6uNLEt+re4QsRysu8N3hWCDK5TIf+MAHaGtrI3b2ixmbBbnl+2y8/z7Oe81Z9C5ZwXFrl5L0Y9TH1xHz3CxCx+HH6OgoP7zme8S1jtvuuon6TBN/feEraW9cguBRDscpa4GJUo7hfAeeTFA/VeBfP/lNjjtuDW9961/T29s717YXs43ny4Ps6LuDwZFpOjuaGR7dzshOn239m0mn4gz0lXh4032c9bzTuOHnt/DiFz+PZctWEobKhm13UN/QQW/rKsrhEAO7+mluWMu6U7JkG6Bvxwga1LGxv4/tu3Ics6SdjXdt4KH163nPhe/ghBO7mCxVSHkhqVgHheIIV111Hb/6xX1kMlkq4ThKGZ9mlApQYeWq5bzmNa9k6fIsiM+v+qa5fWeKM5e3svOmX3Dj/9xIXTLG2Ng0icYWznra6aw5dgktra0UCyF33nMd//vLu1i3bg1r1hzP+GQfmzdvZ3Q4R1NjB829K7l9eIDWUoF4rky5XKant4VVa7rpWNJMa1OKRDwJNNEnvZy2dBxhmobEGjY89ACXfOY/8GIVYl6GQGcAH0/i+NJIoGOIJBCSVEJhOF+iLZ0m4QnHH388f/u3f0s2+8Qx2e8t8N0pWQ7HAvHrX/+aO/90Ixdc+E6u31bgkgv+juf/+ct426vOxtMSMQmZCUYRQnozz8T34ostsuMoJl+pcP3D6xn90x3cedMdjPsh9YUySIXnv/QUOhtOZN3aE2hoTjBS2Ag6QzZeRwXBo5FiZYZ0PIFPAQgRybL+wS386L9/w9DQTnIzU5z5jDN56cvOpb0jwY5ts1zx/cvZvGkniXiGzq4Wli9fxspjjqenp5eGhnrwJuntXonvP77BOJfLccUVV/Dr31xP79IMHe1tjI8VaGxupKe3jcb2FFKO0dnRyQlPWkE5HKcwHefOO+9mcFeRUHN0dDdSzjcyMTFFRYdZ2nM8hXycm2/+LTOzoyxZ2kvM82ho6UAb6pkcGuSMk57CGc94ElACmcFnDeWwDxEPZQbVGCgIcQJmAR9kFp86PIkRaplQPYrhILlSB1tzg+yc7eTMrjoaE4MQZshVBgi0g4cHCuy4/xYmB4YYH50lXZdg5cp2XvCCZ+L7GSDAkyRKHiHDVGmQXEUYztfRkJihMbmSrnQLxeBhqwiHxGiholOIdKCMUA5m8UTwJU2oBXyawCvjk6QU7sSXHkImSXqrKIXrEYkzkJ9hquRzw9Y6ntvbyYlteT70D5exrGcJbzn/TSSTMdKxxsfdfhcbp2Q5HIeYQqHA37z99fzHNz7Kd+6Byz70Ad7yiYtY3rWMqdIQz+zK4JFDJE9b8jiK4RApv4mk30wq1rzY4juOIIIg4N5772XFihU0NTXtO6+WQGEmV2D9+vWsX38vt9z9W7bvnGLKj3HuC07hJc9/PfcMrOf0JStIZEKgn47kkyjqNjwJAR+ljCcxErKUgBxKGRR8CUAUoQFfmimFO1At4Es3t958N9df/yuGh3exZGknL33ZC3nSiU8hpED/wHb6to2wfXsfw4MBo+NbmZyYpLNzKR/84DvwpAvP278IF1VlZKSfL33pMrZt30R9tpUXvugsnva8FhrjJxAyju9lCMN6CsFGkBJQJibNxL0uSuEAKa+TgGGS3omUws0geWLSiWgrZe4CTVmlYhSlAPhAFlQQIGAcjyaUCXyS4IV4tBEyiWgcxcOXLKWgDxDifheqQqCDIB5ohoBBJGwiYJy0t46p8t0k/DgJr4OyjoKmKYUjxLx2KgqoT8wr4hEDyRGTVSjDQIwgnMTzGvDJUtJ+SsE4qk3EvHqmy+PkgxKtyXpiUiAkR51/AhWGEEI8MgRM4ZEB0niSoBIOE/OaCHSaGO2UdDsxWYYQUNGdCIKwhF2FB0l5nbSmlCAoc+vIDL/flWTlww9zw40/YzY/TmfLSp76lNNZuXIlhWQrq1csYUlbCg8TRhFqiamJCi0th++GME7JcjgWkK9dfhVX//Q6Xnzmybz73e8mCALe+94LeekrnsnZz3shG6aUG+/+NTd99yomZwNOOOtULnzDS1DJkw/ypLxGKoziEyMT76Q5tWaxb8nxGFENKYd5fC+JLwc/7HW2MkbKb8CzZQ8NDfHud7+btetOYnR4gFy5wt/99Vupr6/H931Wr149d+7tf/odP772CjY9vIv6+jTrTlzJ6tXLWL22ld7OFzMTbGSsMEZ7uonGxAn0zz6IkKU+XkIZwZM4MYkhNBBSwCOFsVqFAMS9pQQ6gEcXoQwDBcLQxyOOWAVJyBDqKAoIWTzxUSoIBUSWEoSbrZJRBqnnUx//Ks9//pN5+jNORaSC0IwncdCGvQZ/3/fA7/noRz/Opz75SVavaQB80CSFyhgJrwHxFCFAJAOqhFqhHE7iSZqYNAEVQqYIKeNLHVCxsjeBTBPqDD5L8KWJQPsJmUDIWGXLw9MlhAyglPCkEV+yKGOYWirh0UDIDEIMSKD4eNShDBFq0boNlxDqJEoJJQQC4rICpUCo40ASlTwe9VTCKVC18Vwxk0cmidFDyIhRiGlGmSHuLaUUbke1gqoSEkMRYvRQ1u14xEEmiXs99r5n8aQLUY+QQRQPpYiQxScBkiXQYYQECb+bUrAR8EDqiNHOeOk+6mLtxL0EFS0wVepnutRGXSxGQ2IKJEVpqpfb7/4ND264m2tv6Uemc3RmSsS8GHE/xtDkCM0tPZRzE5x8yim88rxXsXJ5KzOVOgrBNF3pbmQB3rUDwSlZDsdBphKOoWGeH1/9B+65/y7+4i0v5IHbdrJ+/WaSySTrTj6Gpzyzizq/i1Q8TiHoR8MsQ7kZ/vdn13LnTdv5wL9cgO81kI2nKAY5uutW4XvxuQHUcXBRrSASoxj0oVoiFVuJqlIKR0h4LYj4qIaIPHLwnp2dpa6uGtAbahmlgi9mplY5HKdQGSFfyVMIpvGknq66E5iamGJsapSxmTFa6lppb2mnoaHhUWOTgrCMovgSQ8SjEBTZMLEBmRlg0/2bGNkFGx/ewPYdW3ju2z/CZZf+D696zhoGlyaYvP2P+B6Mjk+z8d5NvPevXsWW7Q8wsKufC99/HqtXHYNIO8okcWkkYBRf6qjoFKEChKT9E8kHfST9NkrhFjxtAglAZkh6KykG2/AkRswqVkqelH8KYVggYBiz1e0YZqOQGDCFkEKkERgE4kAKY/cJUWaANEKB0I5LAwMzfPlLV/Dc553K2Wc/F5gAmgl1BqXI9q0jrH9oC/mZBGNjO9m6bRuDg/14vvLPn/kwjQ0diMygxEHzKDFrbetAyVlFMYtiFSdpJNAZhGZCBhBSeOIhNABqNu+VHB49KKN41BOwA9TDl+WETBPoCEIaj1aUUUR8IAlM4dGOUI8yRcgkEKDUATmEBIoipMAqMVBCEVRLtq01Y6yIeWLSTTF8CFSBBKjie+0oo4QaIMTxvRaCMI9IHiFFqCV8L0sQTlIhZ2Yeaisl3UxcuimEO0n5y4hLhoA8qnmQEI86PEkShmVCKvi0EMg2hGYED9UZhDielyHQSUCAMkI9lTCHSAWfbpRdBHhMlzKUgj4y8TIpfwmp2DGEYcBI8X4mS7N0pNaRTWTIle8i6XXw8OQwMU/pShe4845t/OynNzM+PMh02aMSVujM1vOmt76Epz7lhWTii2PtckqWw3GQKIeDlMNxhBAh4Ev/9iPOOed5HLc2i0cPn//8F7nrTw/zzne9jROe3ETMzxL3fFTNwAtxKuE037/8RsaLs7z2L19Ib906EJ+4W3j0gAnCSRRBBDzqUUqEmifmNc3lKQdDlMNBVELi0kox2E7COwYkpBKMUNEYdfElqOap6BilYBrKnezcPsodd97M//zPL4jHGgnCCuoVCcsxPnDR21i9cjmFfIl/+dTXGRgcwI95RkHQAtMFIZVM0N7aSLIuQ4GQBHEK0+NMTuWoizXTN5njne89n6efvJTb108TK1WgPE6+MkUxmCYozzI7Dv3b+9nVN8bgWD/trVnWnbKMVauO54Rj19DV3ch4sZW77t/BiSsb2FAY5qZ7Syxp7yETj/NvP76PzoYB3n72Ks5YsxoYRsBacUomOFnMoOzRCpSp6CAmjiiBp42I1KFMo2oUk7jXjZA2gygPAYrQiE+nqX+2Ac1AEYghFIEAo1e2IZQxu6k1ATMYJaQIJIAKlUqFd73rIurrk5xzzgs4++xT8bxeVCcwVqkY3/3u5dx6620857nPIJNZQWNTjt5lbXR2NJHwjifQMUKGEWlGEFQ9IAcioD6eZFAtG2sTbQT0I6RRCnjabFyIIiiTeHSiTAMJPOqBgJAhq4yNAAl8WgiZBjyUnFn6gBaj3DCGkAdarHI9C4hZKoF2lCmMQpVDaARCW0dJlBSqiicZQqZQcmAtXQE7UDXtHikDKVRzQD0eRiEy9zSOJ20EOoiQsdfO4csSQiaphEOIpPFoJu51UNGdeDQQMDCnFAKgSZQA30sQah5PGoFJzC55FZQAT1sRSVLRrUAdHgl86UW1QpkHELLEpJ1c+UFCCtT5q/CljkAnCbRIKQyo81uIeW1Mlu4k7jUaxZsCSpyE10jc66QQ7MSTGJ4U2Tpa4AufuYzR4WmWd63i/PPfwspVx1MMCqRjGWupzJH0TZ+wEGuUOSXL4ThIVIIZpgvr2bKpj5jfwL9echmXfP6DNDV2gwpBGHD+W9/H5NQ0X/7KZ2nrSKBaQCkT946nHGwE8UHjfPmLV6PeDBe8+52kYsvc7ML9ILI0lYNxAp3CEyEMi4Qyw123TXHDDdfT1ZviLX/1PkJNUdZ7gSLT09OMDfl4kqCxtURzUyehlhgfm2Hrw7Bly0Y2b72bnX1D5It50uk0K45p5aQnPYvTn95FS+PxlIJJ8sEIg4NjfO7TV5IvziJUuPDCv2Hdk55KzPNRHeHhhx9m6dKzKHm7GCuW8L16JgplulIB6XiOmN8BOsrt28v88OtXMDW8k1J9F70dnTRl8sT9GC3JTgJ/kK6ODlatXMbqY55FPBkQaJFCsAnfi5OOraQU9OHRgC9FkBSVIEGoAyRjy4l7HdzUv5HfD4xxUnuZs3tOpxxuRVES0kYh3IovDST8VlSLBDpOzOsi0FGgSFzWAIG1TIlxh2kOT3ptfNEulBIQxyOBMoHQSsgoQj1CCGQAY5UTCRHSmO1vMxhlwwR8iyiq9UAM1SLveMffs3nTZi5871/xkpe8CghRzTM2NsLnP3c5vp/g4x//BJAg0D5UQ0IKmNiq5YT0oxrgy3JEINBdxhpEN0pubgafzxJCxquKBK2IloEEyKxVfEwMlUcjSgDkAEHIzilIQtZaeZZZhS2PiCC0I6RQRjCKiGCUyhHQTpRBjJISmHJkJUIRZQSh3db7LtAUSh4oA+ZjzSg4itCNsXAN2v+7EHwChoAiShrBxyOD4qM6gzIB4qMaoDb4XiSGLxlrZfOM3BoDKZhyNAQ8fKknJI9xfWZRWoHNQANCMzBl3YrjgFjL3zSqRTxpRZmhEg4apV6wym7eLqmxhmLwAIIQaJG4v5QgHLVtpABk8ahDJE6oI+Z5aD1T5YcJiZEb8vjWN37A9l0TlIIcmXiSICwTakjSb6MczjJZmsaXOo7tWcG6des444wzWLpiKSKQ8JOPqV9ySpbjsEZVueeee9g1sItlS5bR3m5cKslk8rBSPB544G4u+deL8bw4a44/AU/qWXVcmhedexo+PYh4eDTwncu/Qizmc+01N/Lt714MEkOklbjXQEU3IzTbL9J2/uZt5/OvX34fyVgnDYnVjybCUUcYFgEPz4sT6jRhOMCll1zF/Q/ci8RKFPMB605YRVtrO//76xv553++iN/+7jf87Po/MRQUWVkfI+7FqK9voK29mVw5x+YHttDS0kYhn6ezo5W1JzyJpcuzLFu2jM6eLA3pJzNd2sVseTN18S4aEi1AkZA0qE+oM5R1ioHZCr6nNCcSTJXKFIJGlmVjVMJpkv5yisEGZsttFDXF7/s3sKapm95sKw2xEoVgPQJkE2dQDMfZNbORmHTTn99JY7zC0kwHvpdEtQJSIuEfy3TpXuJeAl8aiEkXniSZDW4F4iS9ZkTqUQI0LFBhElWP2co4oilmgzztqR5iXgPlcIRQZ+2gmjXWG9LGyoOHqI/KmHWXtaEUgRCPRgJG8EgCORQPiCMU8OggtC5BxMe4CusxFivPPEOaMdaqAsZVlrfHE4j0oDpkn/os1157Ez/96fV84QufIpNpBJ3g/gd2cfHFl/CJT/w/1qzpxMQfeYQ6A8SJSQ+hjmHinIYQloLsNNfXTvvOTQL1KD4wjVCHMovQYY/nERJ40oJq0boxA2P5Io3qDozicDzIkFFSrBVOZAahzroXE4RMYQLi81bRygEpWy99QCNoDKVkr7/ZKmZJe16drbsc0IDqLptWZ+LYZBxowqOdkD5UfbCRX560EeoEShFfOkAVlVlU84iUzccK7XiSJdQRjHvXXDNkEqGESD1CzMx+1CE8FBUPaAWN7nkSiFvlMw32g9IonQEwDvjWrd6FSJkwHCLUAMjieWlUB1GN4UkTIkUbi1Znr50mYDtozF7HxKfFvG4C3Qm0mHg+rceTFiq6Hkjiy1IKlQ12/AgIKRGXJpSA2coMsJzKdI677vk9t928lfs23EspyHPhBe/hWaeffcD9lNu70HHYEoRjVCoFTjnlFF7zunPJZlsYHZpiaGiIycIsdck4EgjjuWn+6Uuf57XPf/FeyyqHMwRhkVSshUpYIbafs5H2RiWcJdACvii+NPHww1sJK3GeduZT6NsxwIMP3MM3v/kga9b+B/077uD+B+5j67ZtbNq4me9877Pcfvt6BgfzdHW3oMxSDmfwxExHFxLEPJ+ezuPY+NB2nrSu/XHJ+kRCNW9N+inTwRKgwQr+8Mc7+NrXLuPVr3457//gq5kuK5nYEh568CGmpjbyivOeQ2dbE8euejcvfW3IAxP38PTOJwNDzFbKtCaPJVdZT0yV2ZxPS/PxqI6ClAhC37rNAmCCmAjpWAupWIcZXG0QsEiIR0hCkizPZhktCoWKEDJIV10zZd2BJxVEAsqhT8gW2pKncFLrNKUgSf+s0NCYxPMaiSMEuh1fWuhMZ/ClSHv6qQzlb7UTIZbje/XEvAby5Z1UwkFirAR/mgp5EqxG8AHBk+UoO/DIEEoBT1N4XgMN8TiKUC/taAhhaKw9PmkT4yMlotgoRYyby2s3yhpF6xZL4NGAkMGXSVQDG19lrFXGahHg0YBRIiZsBJPYGKMUxlJTBhkFWoEA0WYzmMoQMInQaBWWMvfe8zDlUpn/95FPk0zFGRmeIZ1WvvnNL1Jf34BqHKN8zCL4CI2E7ALxEJoQOhHqCOkAdhlrjE5YmQrEvFWE2ogyhdAKzCBUrPstDUwgkkXwUUxcl5ABSdh7mQRiIEk8AoRW20byKD5CEo9mQvqIrElGASrb8wUo4kk7ofbbuKlOjNJZsG9CCWix53iIxDEWrDRCDqUZmEEpGMVOQqNMMUaofTZmKgBmQFrtPRVMHUgWEw+nIGlgDMED6vAJMcpzCyYOrowvPkoCIYFIDqRi31WzeKoJhUgAdSCTQN5Y/mwgP2SBfvPcRPClB+MiHQNSeNKNyAwm0L4OSOJJB6HuwNN6EycpBVTrrBtW8UgiKEoFz2vGEx8vbEaZRRkkGcsgLDVWTA2ACr40kk0IHkVohec999k8/7kvRmniHz75Xu64847HpGTtDWfJciwqDzzwAG956xs58cTVlEol+vuHmJ4qIF5Ipq6FTEc9vSvaObZnJaVKjJ6eet702vMZLTxMKZihJXUck9M7iUuKhoYWymGeXHEr//jhb/DQ1od49Stfw7NOfzaJTJ6x0RzHrTyZdKOS8puYKu2gokNk/aWk4j2oKpOTO8lmO4nF4lTCcWbK2wi0RCbWRMzrwvcaGBwcYMPGu1i54klMTEzws59fSamkNNQnaGvvBC9P/64h7r5rKyc8qZcPvPdjlLWPmJ+wrhLzpaxMowhjI/V85CMf4ktf+SAJfxkx78hfM+axUAmHgTIxr4dy2EcuN8zttz7EnX+6lw0PbSQMlNNPfyavf8M51Nf3AAMoSatkpIEyqqCM4bGckCEq4SxxbymjhT4KQZ5MLEVDAkTq8KULJUeo4whpPOnBBGAbpS7UGEqGmLQQhDttLEovZhCt2OsVgASe1FEOpvDEp6IDQBMJr4tyOEWhMkZdrIWx0gNk/FYyieMJw1mUEr4Xt26hNGHYSKCD+NLKbBBSCbYiUiLut9iyhqiEo/g0kI4tJ4gGRfURiaMSInPxQMYqERIgQExWoMwS6iTGJdUMWgFJEOoUquW5mWmCGYRD8ng2WNnc41JgGo3ce8xgvtPjGOUhUj48opgjqCCssutCGZeP6ihmkG+0eWYwSkUdaNq6mJqILDK5mRFmZ6dob1uNyDhKN8K0dVMq0IwQB0bsOQWg27r4yvaZDltFKoXSDxTxOM4Gt9chJAnpx8ym67JtatzeU9la3Brw6ECZsHWQQ6jDKA+BrYsExoI3DTQAmTmXmdBlLYZ11h03Yd2Bassv2fIiy1XS1mPkVk3YtBlTV+QxMVuhtWSNAhOgzdaFGUcki4l1i6x2aVt+3N6rCcg37w9zdWcUq2lE6q0sCftjnruxJiXn6scoqFmMQjhNFKMFk3PKtsk7DWQQmqzla5ett7Stx1HAuC+h18wY1AFTllQItRlhAmjDaC6RuzlAWGHWFgvN81ZrRRQaEWkmDCftc5tGNY7nNdu2HqLqc9H/+wKnnHIKr3vd63gsOEuWA4D+/n56enpIp9OsWXs8LzrnxZz3mpdz0slrmJzcyTOe9kqe9ozTaG5oY8WKFSSTSS655BJ+85vfsHz58keUNzU1RV9fHyeccMJerxlqHtUyvtfwiGNr167lmBXH8LnP/wvpTAloxiNOqGPkZxq58lfXcMUVP6SUF1Z1N7Fz6xAXvvcCJqZGqWieyZKQzsRpiiUp5qeJ+XEqZXjbX7+T5rU+99/8ID//nx8wNZmjva2F//rPK5iY6SebaiNep3R1NXHcMSdwwom93HPXZq75yfVk6lpIJusItUA5nGDZ0qW87rWv5/i1CYLKKJNTE2zdei9Xfv+nFArGneD7Pi0tDYQap3dJE887+7m8611rCWUrZfoR8fClBbTeugyi2VUFWlonyU0XzFe3Fg/asz4SUA0IdBhfouDfAFXlB9+7nhv+9/949nOfxDnnnM57L3w34o/isxqRislH3A5Ko0AZkTZE8qANQBpPG4EcAX3UJ+pokk5CZvCkHqVARfvwiOFJAkiB5DFWkTiqdXgSYNwyo4gU8MTDzJBrwFhqQjuAzKDaSMzLEuiMDcZNAiFxL0Us0UGo47Qk1oCME4bTZikCacCTegLNmXgUrwNfEwge2XgjZR/CYBARxReIxVZQCeoIGANP8dRHiVn3kglONmsiNeJJuxnQdRqRImbAK9pp7iVgGk+WoUzgSQHoQIiBmNmAZsq/h8wN8s2YNmssJiJJVNOYAb+MGSQbbWD7BKpJRGxcEyOYGXSKGfCb7XklVMuYWJtmc9wuqWAGTTO412dWUZ8xFhejNI3a33Xmuc8pDJG1pAIUMIHqYp9ZHcZaN0tkVTPxSyWUBmAY8z4aK5dI0pbTgeo4JmChZM8pINTbe0jY30WMCzBl78+4SEVC0G5gCBhCKKN4pq6tS9EoHnkATBB8zj4jo/QapS1BFBdmrhO56kpWoR4FrUNIozJl5RvHxGdFrscGRFrtLMtZq5BPmXuWyCKVB8btOxYFtceshatEpGyZdyDAKGUlzAzShJXLs8emTB2Qt88hCXTY++23/ycxSm2AUYbTGOXOY06ZFLXPBjwp2raasc/Ct+2wDPSh2goyYfJrBo8WVHbYNiaINFAKBjHuRJ/tW4ts2vQwV1/9E17wghfwZ3/2ZxxsnCXrCKJ2RkQQBGzatIn7778fVaWrq4t4PM7g4CDj08OsWrGaJb3LCMOQYnGWwcERRkZGyGazqDfJjr6N7Owf5g833s0f/3Azf7ztCrLZDN/79g3cee99hOUifTsGmZnOc9ppp5KuyxBUzLoqrW1ZisUC01MhqXSKrs5Odu3q47OX/iPJpMe2HdvoXrKUCnW0JNsoBf2ETJHyjzeyh5MEOk7CXwHA+vXr+fjHP046U6GhPsOF77mY3mU+lXAIj1bKYYyBXduYnN5M0m+nd0kvDdkufK+dqdI4MW+alN9MJZikpDuAFCm/ixAlCMtUwgmSfrfZV0xL5Ct5Ul4XI5Mb2dW/k5Edce6+97ccu3opL3/5C0nHT2C2tBnfr6NY2Ur/jjjf+87P2TWwBU9g6dJeTjv9yZz2tFW0NJxEwDhYC4qSAnLEpMcqTYOINKMM40kzYOIOVANysw+xbdtGvviFq3n6087irW9966K0q0OBCZgtoCih5jAWhzwiS1EdtUHUoyh5POr44Ac/xoYND/OFL17MsmUdKE1m4VaWgxQJwiIew4gXR6QJVaMciDSATqBkge2AoprBDAZpG4eUxcwAG8VjCSK7gBQi7aD9KI2YwW0IM9DkMIpGxd5Nwh43QcehevjSTsiMlcNYNszMKzP4qu4EejFum7wtK2u/6KOv8YrNn2JuQFOANKpmQAyZwafbxK1QQgkws1yNcqFMI9QjkrblVTAD1AxmwG2xykcME5RuY6rEWKBU03hSh7LFzsarswpH1t5b2Q6Aai0z/baMGFXLyKitr6It0zODtBgrjHkeZczAOw1k8aQTs8ZU9GMUV6NgZK0MBRv/FFncAqqWnmKNDB5GWUhhLGJjmIqMYo6iODFsnhn7f52pB23BBOlP2vsJrQJiZkia5zsFJK17c9CmB/Z5jmEUwGbbX4+hOotxkY5b+XoxVpx2YNhaZZIIJStXJHfW3mMBkTrMexMCPfZ+xm19JeZ+zHOLjuXtEhK9trySra/E3D1jLZemHtqtfFGdVKxLscXmnQVrSWJO8erHvCeRVazeXr9i6ztm0xts22ilqoxFVrYU0exEk57GWCbriSxjYq2IxiLXZS2sKczkgzSwFWhD55aQMEqoaTOdhNqHRzt//OOd3HnH3dxxx52sWLGCE044gac85Skcd9xxPB5c4PsRxsjICDt37iQIAnKzk2zedTdDA2Ns3zBCbmaM6SDE7+zgjWedQ1EnmBwuUCzlSTZXSNelmBgosmHbCDF/nKa0z4olp9PYHDIxNc7wyA5yk8r4RI6Yl4CYR9xLUgnGqVRKaJimHORIJurJ1qd4wfOeyymnriQeF2JkGBweIp1qIJnpYfP0dlqSPgMPb+RLX7gKT2DJ0h42bNqEn/BZ0tXEsqXdPOPM0zn2mFOZzU8zPLKdjRvXMz4akEgkSaUa6OisZ+mSNcRi8LGPXcyTn3ISb//b14KC59WZAFI780ikhHnpPAIdADxi0okyY332HqYjmkZoJaCECedtINAxIKASKsoMKX81gY4SqJl6nfRWIBIjH9yDoPj0YBZKLIOU8aWX0OYNmSbudRHqMMzNmDLuCeN6mrRPs97EemBWyn7ggQ184hOfpbMjS3dPB69//d+wfNkKe2/+oWxmh4xQq245nRvwxjGdaAVTf0MMD8MPr/wFt91+M8euWslHLvpbkKJZX0gCQvVRxsiVJ/FFqPMb8P0eq8TZwFu7/pDYgddYUWYQUaDVKiQQ6k6qA0Hafh2bgQmyVvmJA614lDDB0qa9mWsp2OnwZtCybg2yGBdTK8JOoMvWQgwzSEUKQBQAPmPzjBrLFCnmZuMhmIU/xzBWgTRoBmXEuq1i1rWVxMQL5agqa3E8lgAxQjZjBiTFtNEWmyePGSCnrEKQwQxqausyUsSaqQ7S0YzBcO46hmZbtlGizVICXVStGglbV9ESBr4t27dus2lzDUmZMrUNZRsAQi9IzJSvw9YB1W5dkAl7z5HiFClRRqk1rvlRqgN7G8Ya1Wyf95Stg3GM4utbJWsGoxBg7ykw52oKpc/WxTGYAHaj6Ag9ILswLrH2GiXUWJLMtSLF0Lj0oo8AEbHu54ptK902PWfLiFx3FXtskGr7jZZ+iaxWU7aNRUtlpG2ZgU2P+qrIFWniw8TmU5IYK2GUr8vm2WVlSdn/66m6/BK23bTZtjFgr91cUwdRXU/Y34GVuwnm1larB+tari7xMYtR8jqBnfZ+8qad2JX2VafN/5JnLv4Pn1CziNYzOraZT3/6C3R2LOElL3kJxx9/PMnkY5tJuCecknUY8uMf/5hrr70Wz/MoFou0trYyOztLEAQUU3FOXn0sdcmQVKqJeH2R1s5m1q5ZS1NDnNmSct9kyDm9pzCQv4uGeCvjpS0UA2V59hnEvATbcvcRapHuunbQuHGPSIZ07Dhy5R0EWqQhsYxceRceeXxJETKO2Tahjpi0UA5HqYsdS6g5yuFOAp0iJu0IWYYLG8gmUsS9enxiJPwlgFAJxwh0ilJllpGRCfq2jnHLzbfRt2OIukyaluY2jj32BFpaA0rlMoXZGLsGNjHQn2PXzmFmZ6e44MK/4qRTliJaT8xbTkV3ABUTI6A5QjUzkzwxnb2Ih5C1HWqJmNdFOdgEhKiIWVSPZoRZPJoR0pR1u1lUj358uojHugnCMSBOEJZQhvC9TlTLhIzhiZnp4ksvgW5FpAWhZL+uuowpW31CKhgXRT3T0wm2bLmbbdsGuPXWW+jr20FPTzvv/8B7aG5qJ9ScUTm0gJDB8zKIpgiZxpdW9rQo5pGAWTxx2g5ko5jBoAEhJFSzBUmxVOLee+/h9ttu4t57+igUc7S19vDqV7+GU0/rsW6CEmIHZ0+WAEVCDQh1AjSJeIrvmfWETEfcgOl8c/bvdkyHPovpwCPlN4vqFNhZc2bQUBtoGylaGUz8VeSSasV8tUcdvODJUmPB1GmiTt0M1Fk7gBcRGqyVJho8FWO9aSLUQaqzyCaouo9iGOU8Yy0jZjYegLAMo+TvsNfswQRBm8HbKAl1doAs2uuFVK1azZhgdZ+qm8zGaUVLC1Cx58Tt/RYxA1xUZ80Y5aLeHg8wFrst9v9WYIdtDUsxA2VkyYjil8pUg8CbMEpcGjN4Z2295Oyz67R57XpbKDpnjUkg1rJi6jyK0Wm0FqLIymXirITliHimDTFpy03aZ9xiA/NT1vo0a1xsc1YXo96ZD7mUra9+cz2J7jtagLVoj0VxVhnbLrbYc9vsMy+x23phduFR81GQYc7aSN66C2fs86mvqZvouWRq3NlFqhbDJtuuPMwSCwVbt1E7j5SmaFmIVvtcIsUnaY9FLuA81Q+khG0zdTZPHZFLumo97LTXHLVpkZs1+khJWnk8204iq2HFPBPyVBX/YSJrnlK0z8kn1Gk8yeJJ1ngKyhtJxZqoBGU+/cmvMDI0w4UXXsiaNQuzm4aLyVoEAi1TCKbJxKor0IYaMDC7hfZ0Lw8++CDPeOWriK1dx+uWdhMEAZlMBlXlwhvu5NTj6jljiZD0VhAyQSkIyFWGEG0kF+zi9PZWlAL18TxxL6QzvQ7I4YuxHHSmYwiKSB0BJeLSSsxrohJuJUaOZKyTcjBIykugVAi1QCzqUDQk5qdQAsrhNjtrqIgnjST85VTCKerjik+FpHSikifQKUIqlIOthEA6vprerlZ6u5fxzGecS0WHEAJi3lJUIWALHnYarp5pg3QDkrFjCXWaSlAgZBizfopn4mm0CaQJYRghjYiNQ2DSzH6SGEqAahmRBpRpfFqpsBUoGBeOKqFM4NOIas50uOQAUC1S0V0oATFpxaMRpIRYi0HIJErOBgkXbSdeACbQsJmf/eyX/PrGG5maHENEyGSzHLOyi+VLV/C2v3kLS5d2YywiZvsMT5rtmkSmrithPx6d9msssiJU14YyMSyxw2pZi1rCcJpSqUJ//wB9u+6jf2eemdlJSqUZJiamGByYZXJyGFUlmWxk7bpeTj31ZN78lreSSFTwpANP6gjCAftV71s3U45QR/GkDZF+fAkQSREpTKaeosBgM6W82iFHg/gkpuMHGLJ1GMXWlEEFtQvMQgBi9nYTzGbHquOYwcgj6vyNAlREpGLiQcgCEyBToAmqX+tFjDWghPnyD2piSaK1j5LWhdeNiU8SjJUnCzpt3WQ+SAnVKatodOBLnS1r0JZTwqNiB2RjPa3WE8A4Jmi8y9ZLyVpLRmzeEKOcRtPvJzGD4RBGuWrAzESMXKmRApfFDLBRHWds3YaYOC7fKthKVdnsMBZou5yJWUx2p6lD2m3ZSXtfkRs1D6wAa90zbqZJqlYRs26XEgdr6RKrEChqFDGNAsBTzCks0ojqKKJpM0tvLiC7kapSYi1vEsnRSNWS1Gljl7ow8UUTRNYd06YiZSRpn6tpr2bGXmThGbMz/SKFx7QNsw5Wva33caqWVBNnZhSqBkxQeMxeU6jO6vSpKtvjtp4iy2+Hva8JW3d1mPcmep8it2JkfUtRjX2bwChkMapWysiqWE/Vqjhsy+601y9R/TDqsn+r/T+0xyPXeTTBoo5qG28gimk17c3MMkSNBVjVI+Gl8Wjj85d8gWc8/WzOPfdcFgOnZD0GrrnmGo477jjWrl07lzZaGCE/lSeuCVrbW/HFJ1+ZZLYySNKDickh7rx9A7/9za1s3PYQ2VQa3w8486wzOK43SyJZwvcaKQXbEJJ88Iwukv4UCa+LQAcIKZDwumlKmNiNZOwYkn4BZICk34svWWvNWUGg46gOEvfMV5CZemtM/yIZCHMkY234Xg+hFlAGqYQxPAIb3Ju1U3AD4tJNoOM2oBhEAgIdJOZ1kYotRTVHKJPEvWUAlMMd+NJOTMATiHm9lMOdlHUzJo4iQxCOW7eYEDKKRxbxGvGYNcs5hGatnJjXQqAl+6USTTX27BdLNx5pG8czRfRFVdF+jPphYlKMMjRGjHYgTcgIIgmEOhPc6bUguhxlmkq4E0/aiVEhtF/DITu46P9dSv+uUZLJjHVLKaGWEAGzmvMMHkkKxRme+7zT+ehFH6KhybcK1E7MCs4lfOkm1EErb9kqg6b9eHbXe+zUcRPQbF7PUKcIdQKfpZTDLXjSjKdJe9zs+2YseocWM2COMDWV5Qc/+AE33/S/+L6QTGbp6VlJV3eWJb2r6e5eRjw5TVNTlu6utWQyDYhM4kkXITlUc3bQB2WSQAcQaQIVO03crhous0RxI2YAGcF0wtMYZcEE+SrdRDFI0QKPc9PtmcEMZpGrIQoingExwbfmmmYgMS6jIsYCYp4j0mQH6ZSVoRET5OxhgpOnMZaNY4m+wIUma6EYxTzjZqugpc27oNEX/RAmxicEjdvZXTMg9ZgA5gAYs0HlXZhA6lmQGTvANmKUkyIyNyh5qLYhtJhlDJhGVWxAfBKjPEUuz1ZbRxP2WCvYDYbNQG2CkXXOQmJco5HVRuz6TVXrUauVcdwqCpFiZhQU4xqNnkXS3rtHNUZo0p4TuSnjttyC/XvMPgeoxmZ1Med6jKxuMgOawiyzYNqFJw2EWqkpc9rKVodZlX7U1oFn6zGaxRpdv8U+j6SVbdLeu4nNMm7cyIU6iVH4QmtJiqyIGXudMkYBaccoDBWMpTBS9EJ7Px1WOUvbss07KHNxUNGMv5G5+jduu2TNM44st1H7jxZhzdj7n6kpK7Jogvk4qLN1GgXnp22ZvWBnbZoyaz8eIndy9P51ULVaDmPaXxPVdxWqFs12mxZZNLHyRB8MHiKNqDbiyajpUxkCaSPmmdi5zZs385GPfITFwilZj0LkThURAp2lEozzzW99g1NPPZnNm/qIxWKE5MmVZ+lsXUJQKTM0vp3u9iUUc0KuOIDvxWjI1nPaaafx5r8+h2OW/wNBOGE6XGnAF59QxwiDwCzZJz7dmYQNdh03gyo+Zn2eHmCCmJfETKf28b06E49BHmUMby7QMY2ZcVTBNFKzxYMnDfbepow1iF7iUkFl0n7RmhkbZqbKrFVUhLisMIsNagMhw2YvKzFfkpWw306Jz+P79ahOGUuElIBZlAJx1uD7acrhLrtooeBRj+e1mbrQIr7XTNVdEuJJygSs25WkY14LpnMDs8Gp2V9LqEM833iYiONLK6o656oSSdlZSwk7WOVBEpi1fsqEar5yEbMNhY+P2aZCefnL/pzvfe8HFIvjBEFId3cXr3/jyzhh7VqqnVgvquMoRRPEqxMofXjSbOMcUnOzwIQJ0CbMqsgpzPYbWZAR0AbMlP6QUCfmXE1mRtwYvmRQnSWUKUQbMV/nRZQpfOnevzZtv4LFvv5q4zqEPVvHzDtQBqlQLpfYunUn9913L7//3U2Mj/dTX9/Cy19xNn/zN/9pB+5plCbzZWldRMaV5WHipExw89xMLfHxWWZcampXFcfH97LoXCyM+bo1/0+gtIOmEfIgGVTHrBUzPmfZrFpZok6/DTNwRUHII5hBIZpxFrlBollUUQAuQMa2h7y1JiateygK8DWDvs7FvAiRhXNuoJEUaNYqixOYgbYFmAIxH0+qHVbGKIyjruZvG1SuFZB2u+5PyVrkpq3MLVbRi+JnzHIgIiOYQTmFseIlMe/YOLCspi6mqSqeauUs2nqIAvSjevIxgfShPa8CkjbXV+x5BZQOYNJOeJi092xi8oy1x8ZcIbYtYq9htocxQc6BkZtmYClqJzVE1pLIUmRWVS/ZOohhJkNY642kgSZrbTJ7LZpBvxWRBDCJWYeq1Tw7iWYs5qi6vSJFod3+P0A1vi1SyH0ia1p1JfeoHpdStSxGLrPI3e1TnWnYYO8tioeL2nOWavyVUF3qAapLMZQxSssURolpojpTMIrTiqzkdqFWklStR5Hbr2Jlb7blR9Y9MwnB/B2jaslstfc5g3nXpjCKW9LeyxZ7jQaqkxMii3MUCxjupqQLteESkdIXzbIcs31MErP21wyCj2odJug9g1Lh2NVLuOyyL/Kud11gy7cfCGpco6YPTtqYuJAoDEVVzcQl6vG8x/4Ru99KloisAP4dOMPWwFXAhapaEZGzgS9j3tZbgDer6jZ7Xjvw38BxwEWq+i2bvgT4AvBsTE1vB/5VVf/r0WQxswVMnERtoLCxMsijulJUS0QBgLV5TacFAwND/N///ZLWtgTNbSmOO2419Zll5IP7gQRnPfvJ5AtjfO7z/0RjYz2VcJRQ60j4jUyVNjM5OUCdt4LmpqUMFe+nMdGDL5M2xqYEoiglKmEzcb+RMDSrR8fErGgt3jQxqUekEeamZyfMAE697TxM4zezUcYxDbLOGnyKqMzA3C7v0YtTb2NHrCJFHLGbtCKj1mRdb77CJImZ3t0GNBDoZlQmMIvAJahurpqyViHzkpqNUc2ApWIUBcTDpxVPknZhOjPbR+hBMBa4IIy+JG3sgpiOUmghpISQwaeLINyFGYA7TbskhdmwNWndDiG+NJkXxs68EZpNRyuKr1l7/zFEEpjtHCDmdVhryhDm6xtCAjZtHGb79m20tzcSj/fQ0BjwwAMP8+pXvZ0//PGXNDebwU3wEa+FKNjTkyYCzWM6GhvgygwmGHYWBIQOzDTqEUyshpkVFtJGFCBt4mbMdO0oJk1kCrQXkVnMVOwGos7j2muv5dprrsNYLUuoJhDxicXghec8j3PPfRXJZDS1vcXeb4GqyT9SKgxDQ0Ncdtkl7NzZB5IkHvdYsaKTE044nosueg8tLSvnZg6KpOdM9cYimrSzLqPOstG25chaUbCdZIvt4CZBfIQAs/hjE2JX3Y5iYcwXcAy127wIbfa9nbLvtdr2Zaalm86bmnv1qMZpGeuJGbiioHSPqmIzhtBt37chY/2xcTW2dqjGnTSZ94jtmJisdvNFTcmW3wTsrImzqSeaYWkG1wlbhyVguW1HUB3MjNUpsqSiFStvFjQJErXrXajd+85cdxpjOYhmfkXT6aM4o3YzMBGtpxTNjIum13tUB+ERe27Uf+bseVF81ZS9riDSilksdKeVIWpXZiD3pNVaQk3gvNrZn1Wl1bh4zRpHYmc2llASeCKoRvFd5qNFo9mc2mnahpTtc4qsSD1UFaYohixvZpSSJ3KnmufWR9X11Uo1HiiJURCMS1UoYNbsGqQaDxjNiIzbc6Lr19nyIqtRpIznbFuIrE+RdTCq41abp8DugeyNtpwM1RjEyCUY1WuGqnIXxX0VYE4pjhTtbqqK4QRRnFv0MVu1ikWxUVF9RLFm0YSIFkxb24F5N6KJDWAWTE3bfJE1ecCOXxlbfrP9nKi2F/MuB/bDOLrXKExgCmWZteBGSm3aWn+VKK7rQx96O5/9zFe56KL3s2bN8bS0pjnppNX09DwV06bGMX1+A8osQTiMeCsIdNg+rxiQsf2M1ngYckT7Wpp73TP7HfguItfZmvtbzNP6FfB14ApgE/A24KfAJ4FnqerT7XmfBh4ArrHnnK2qsyLya+Bu4P/Z2ngS0KWqv9iXHKeeepLectv/ojqNSApfeuwXUuTPTVglYBZPzAraqtEgVSJkxHZQpjv2pNWuVAtBaALq2lqP553v/ktOOGEVgwPTPPjgBsLA4y/eeC7POetleJLg93/4KVdddR1Tk0VOOunJ/OVf/jnp7BSxeBplgriswPcajTVFox3XjTk75vUQhEPGcmK/DkKdIO4dSxBuBwmtZUqtht6CUaQa7aAcfZEYn7bR/EtAK740EGgRGLQm5wRmanITaJHQdubGjbjc1o9i4iBmgZ6abRbMcgOAXcRwGOP3b0MpobqNaDaU6axKNphUUGaJyRIgRUVNYKgvTQhZG8ReINoI1ZOGuQZc3Sw2ik3wEJK2Ux43U/BJzrnHlJJVuI1lr/qyp4k6B1OHxsRuXqRdmBeyaOsojfnymrT3Y5SWMBzk3HP/gve//52sWbOKSiXJ5GQfmUw9q1adZpWcaDq5Av0o7db8H9gOo9FeK4FZfLDODigzCEvt13Q/5pWatbJPEMWrmEJmrR6/xWUAACMPSURBVCurBNoGDIE0gNovYqlnYGAbH/mHT3P6007m/PMvxBMwK3WbINDZ/Ci/uO6XfP/7N3D55Z+hrq7DKtPjVonwbYeXoBoLMsv73ncJb3vby1m79lhElhK5q6pfvcdgBoZRzGBp4jGEXpStMLdA4SDGypIk1B0YC0wa1axtPwlUZ0CGrRWkA5lzH9m1lOglCmw2Fg3Taetcm4lmFJmYIKEHkaSdRWgmLZh6TNt7AONaGqE6Aytr37UO8wzUx1hoSqgat4tICjOdfJqqG9K3bShSuqKg3hFqF9Y09dZt3+PIfdRuLTpFqrFT9VRjZsq2HDO9XjUarCMLX2RRApFpYNYq13atJO22FuWiaTtz8SzRdPnIzZazv5vsPTRTXQYiiVEgY1SVggAz0EYzLgsYSw3AgFV6o5imCaK4HiFm+zCx78M41QUsowkqcfv+jCMswcQcmfdWpMdeM6pTD+g07mUKMLfUgVJ1haapLsERBVZHMVYp+1OkGk8UKR2dVGcJZ6nuuxiNNy2YPftimDaTtteMJjK0UZ1VCdE6VNXZyFEM3iTV2ZqNto4ja1fkWqsjGgeqbrnI6lNmdzdq9Oyie4ncrtEHFlSXZKhn9zaQqrmPSI6oLUbu2FGM4tRJNNtWrCJprNYDthzjPTFpw1SX1Yjckr32A8zIZPoME9RuAv0ji/IEkcvTTNrIWLmi/jayOtZTtfwZK3UYJujrm+EXv7ieW2/9Ew88cD+7du3iwfW3U5fuJAh3Edq1y4QAoR3P80ysrOZNeEAUAjC3dplP5KExs3jr8byGxx34fgxwmZpo1AERuR5YB7wKuF9VfwggIhcDIyKyRlXXW2minyg6DuA04L2qOmP//9P+iRGz1pCqu8MEkeYx6xENotqA2Wtp2HTUMm7dbAEmvsG+VDoLGhJGLjlpRJlh566buPTSb/PL62/htNNO57WvfRm7du3kPe+6mK9/vYunP/00nnHmiTzjzKfi0cZvf3sbn/3sZ5maHqZSjlvrWAVV31pHSghJWlt7OfXUUznttNNZuiyFiVUo4ckSPDFr3HheDBE7nVujNV/qMFsZ2H3MsMGblPHoMn/rDMgAZsuBgh2825mbVqt5RDL4dq0gpWI7+SbEbhJa/YoHsz5KaAeSvLFU6UqqX0EzIMZN6REz7kN6jOuTAqLNhDqGUsGs2hIj1BmEImZD0bgx5+qkiTeSerBbcijYevPBdraBjiN41hqYItQ+IiVT5jrQDGY2WwGzxk10L9HfMdtuok44i+mEquZ8sTFR0ESlUk9DQws///mvWb26mxUrWoCziGaiVdeQiQKMo7V7AsxsIOMqNS9+ZCUyx4zlKfpK6mBoaJp0epb6+gaqA2ccZIpo3zgAkQlrPTBr+qgG/Oynv+KKK67k859/P51dbba9ZfDsNG+hiWxdD6959Xl0dq7m29++mne8440Yd3Kz7eQil1c0Q64MQDab5YorriWRMC6K2dkK4+PjqIoNxo9TKEzyile8hPPOewNmII7qvcu2vymiQVgJEOkgmn7uSYOxWmkes3/bEqoB12mY+/ptr/mYyCCSIZzb5y5prV8FqutZ2Y8P9e1zLtg6bbD1WaJqlanYv+uozsaK2e580LR/ujFWhQqRdcxY5aLA4ihgt0zV6hZZC5owijRg3cOGaJAFs77SOFV3UOTSKWD2rDNxUkKPTZ+lqmTUoezCWJlbgVbbD0ZKcz9oJ2ZQW45IHNURoin8Zv+7Dnu8xz7/Ckaxm7Eyd9sBNYrLGsJY3CLLWCdVF1QUC2UsUYiHasa2Z7MohardCkfMR4hZxiCK3ZkG6bIfw2a48CRFqFEweGRZ8akGYxv3k7EWR67T6INrhmo8V63FIZpVF82Ei/KERB9l1WUMWmraxwRGYYmeUeT+Eph7/yM3X6Q4RTFmkXJqFITqjLs01SUeonED21aMsqxWuRPbo1bPC21/X0/0rkQW8mqAvbG+mmdoloEwAfPGPS5zs/x8qu+F6dN0bp21Dsw7oPbDIFLamzAxpkbxMG0+ilkcQ6MPljnLV6Tg19tlPhKovaZZw66CcecusXGMBWuVHrPPocX2uzkgh6pxl5rxf5aNG2/h4os/RzqVxcwOFXp6O1l97JN485vfQlsHdLR3k0r5tm11obqZuXW/pAWzbZTxJEXLthjPS4KQPnv9rB1LYlTjAh/JgViy/hZ4BsaS1Qz8Evgo8BwgoarvqMl7H/AxVf2RiHQDPwRWARer6ldtnhvsk/gS8EdV3b4/cpx66lP1plt/hJAh5hlLVRjm0DnfdNR5Yr+GCiBNeBi3idlQMvoSzhEFPZrF8GaNsmI7mTBs5fbbf8tDGx6gtaWdltY069Y9mfqsNUlb5Ufnvi4mMA1ximiNHdFWlCIiMwwO5bnzjlu54/YH2br1IdaesIa/f9+FqJgG7dGOWUV6HJlbNG8CNEvIDjyWmDWjNJqd0WzzeWatJilaTdsEUJv7n7V5Y4hEbqhIyRglsl6Z4PKcVfCiL+UkZrCLOrsOe94EaLd9icEM1hN4NFnlzQRLmmUJPJASHssw02xH7cA0bIPpO6ybaZJoQcqo/o1FKYoZGrED8ATmpY5MvJ65B4lM3Bmq67FESmPByj+EmQEUUDXJR7EzpkMw9xe5S4zZf2AgxwUXvI/jj1/Fs599Bp2da2htbaW1tYVkMorhiQI1o6/NaNaSsWJdf/0NpFJ54vEE8bhQV9dKNuuRTgtXXvkH7rrrHmKxEscdt5L3v/8fqM5IiwKDTac9MrKNn/zkOrZs2cHQ0DCl0gxnnfVs3vzml+H7aq8XuVLSKBlUEwwPb+Puu2/lD3+4maAS45Of/CyI+bI0dThFdbD07PkFgqCd/v5tc3WYTrfQ2FTG9wWzr1mW3Mwgbz//As455yW88S+fYz8yYrZ9pmxnPGrbio/Z9iNAxMNsjrvTvj+CcfHM2Pc5ZjtfReZcBdMISzEz1Uw8SXW9JI9oirppm0p1plXGyqF40mwtKcNEQetmyY1jbHsLiQZFM6MwayZJzE2fj5ZBSGHcGZH1LGvbXwHz7vZTdUGN2TLrqa72H83gi1wrtS7ElI1Pi5TAyNLQRBRkbeQp2/bWbvu0PFH8WzWwOHINRu272dbHJEaJaLaWrmF7rSXm45QSxiLRTHVmVwfViQRd9ngBWGGUJ6uUmvxp+x5FIQpN9m+z3AJatM8kibHstmCWYYjqtN64xxHMVP2dmJl9PtWZaR2YGLxI4YxinGJUXXPRDLkltjVN2PTA1nkU2N9gB8/IcuPb55Sh6qbL2J/I8hR9REWutwmb1kbVypdkd2teFF+1AuNyjdtzmqxc01Q/1CK3YNE+1zF7/ehDJWqHE1RjWTsxbW3Q/t9INMuyGvcVULXSlW3fir2GCfg3Y0mDbQej9poDtpw2W2+RZcvUbDXOaoK5mbZ02/Oi2YYVojXydG4sa7dy2PguMaEiqoOY5SyamBuv1bjzA52yXqjIVZqlWBzjjW/4a7729U/R0rwKs6dmHtWAUIv4Uo8n9db7VbTGg0bMNjyCULa7JYyj0dhiF7kVmuxC0nlCLVq58qiCJy34XvbxrZMlImuB7wIn2zv6NvAW4BvAsKp+uCbvH4Cv7yu+SsyiLB8CXgasAe4F/kZVb9tD3vOB8+2/x2Ai6ByHhjaY06YcC4+r70OLq+9Dh6vrQ4ur70PLiKq+aH7ifilZYlZE3Ap8FbgUo6J+E3gIu0qZqv5dTf57MVarH+2PZGJMLJcCLwCW6D6EEpHb96QtOhYGV9+HFlffhxZX34cOV9eHFlffhwfeo2cBjCN0KSYmq6hmG/VvAecC92OsWwCISAbjGrx/f4VQEyBwKSYgoOVRsjscDofD4XAc9uyXkmWVoC3AO0QkJsZB+leY2YE/Bk4UkfPELMH8j8A9Nuh9r4jIZ0TkRFtePfAOYKNV4BwOh8PhcDiOaPbXkgVmFuGLMNFuGzHRa+9V1WHgPODTmGi2pwGv24/y6jAK2gRmDuxy4OX7cd7XDkBmx+PH1fehxdX3ocXV96HD1fWhxdX3YcARt0G0w+FwOBwOx5HAgViyHA6Hw+FwOBz7iVOyHA6Hw+FwOBaARVWyRORdInK7iBRF5L/mHXutiDwoItMi8oCIvLLmmNjA+VH781mp2YRQRF4qIttE5FYRWXno7ujwYG/1KiJPF5FficiYiAyLyA/tYrHR8QOuVxH5qoj8e02euIjM7CXt6Qt+84cJIvI6235nRGSTiDzLpp8tIutFZFZEfi1i9zYyx9pt2k4ReYtNO0NEpqRmk04R+fpe0v7jUN7j4YqItIjIj23dbxOR19cc+5iIDNi2n7RpD4nIa2vynCkiuoe0nEQblzmAg9fObfp/iUjJ1nP0c/di3Ndicyj7cJuu9hnW1v0HD9kNP4FZbEvWLuBTmDW35hCRXszCp+/DLF/9AeAKMftxgFmY9JWYpSNOAl4KvL2miH/EbtsDfHzhxD9s2WO9YpZu/hpmqeHlmKV/v1Vz/LHU628xm3xHnIrZW+WseWkAdzyGezniEJEXAJ/BLNZbj6mLzWLWg7sas1NCC3A7cGXNqRdiFvc9HjhfROpsHh94Sk2+Z2GecW3aWZhn4TCb1Ucbz70B+IqIrBOR1cDpmLb/J+CNNv/8NnwWsH4PaX9U1QoO4KC384jPqmq25udkjk4OZR8ecfK8uv/sQbmTo5xFVbJU9WpV/QnVPTcilgATqvoLNfwcs1/CKnv8r4B/VdU+NXst/Cvw5przo42tot9HFXurV1ufP1TVKTX76FwGnFmT5bHU62+AtbZjBaMA/ADIzEu7SVXLB+seD3M+DnxCVW9W1VBVd9r6nNvn0+4BejFwsoissec9Yp9PW2c3Y5VW+6GRwAxatWnH4ZSsaJ2+84CPqmpOVX8PXAv8JdWN2WrrGUy91X4UPAujPMxPO+rrdx4HrZ0vguyHNYe4D3csIIttydobtwMPisjLRcQX4yosAvfY4+swa3RF3G3TIv4JuA34POYFd+yZs9h90dgDrldV7QO2YQahqMzfAX+cl3ZUDFDWhXcq0C4iG0WkT0QuE5E08+rXbo6+iWodfwHzJboe+EbN5um1SsBZwO/tT23aFvssjnaOAwJV3VCTdjewzq7ddw+mvT4VuNwe/w2wzroZPczzuxJoqkl7BkdJG94fFqidOw6cx92HOxaWwzK+QFUDEfkOcAXVbdJfU/MyZjE7W0ZMAlkREWv5ugq46pAKfYQhIidhTMevqEl+rPX6G+AsEbkG4465GTPYRWlnApcswG0cjnRidkh9NUbJLAPXABdh6nd4Xv5JjKsFVe0HnrmHMn8DXGBjK56FUWJvAr5bk/abg34nRybz2zDsXscfBj5ce1BVt4vIdkw9bgceVtW8mD1Yo7QUcMsCy34ksRDtHOD9IvKumv+vUdW/OohyP2E4yH04wJ0iEtb8/+eq+suDKvRRyGFpyRKR5wOfBZ6DcY08G/iGiJxis+QwsVoRDUBuX3seOqqIyLHAL4D3qOrvag491nqNLC1PAjZbM/bva9LSHD0DVN7+/pKq9tvdEj6H2YJqfv1i/59+lDJvxnSeJ2IthaqaA3bUpDkri+Gx1nHUhiNLLFTb8FnALapaPIhyHuksRDsHuFRVm2p+nIK1BxagDwd4yry6dwrWQeCwVLKAU4Dfqurt1td/G2aQfr49vtt+ifbv/d4r8WjGzvK5Afikql4+7/Bjrdff2rwvoTpA3Y/Z7/IlwG02NuMJj6qOA33Anjq1x7TPp6272zBBrN01W1b9zqadhFOyIjYAMRvkHrE/7ThSsiJLIfZ3lObqt4aFaOeO/WOB+nDHArHYSzjExOx36AO+iKTsFOnbgGdFlisReTKmo4tisr4DvE9EekWkB/h74L8OtfyHK3urVztr8/+AL6vqnqb7P6Z6VdWNwCDwHuwAZb+cbrFpR9sA9S3g3SLSISLNmNlUP+Mx7vNp+a0t5481ab+3aQOquungiX/kYkMKr