subsetting_global_regridding-with-xesmf.ipynb

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   "source": [
    "## Regridding script for GFED4s data\n",
    "J.K. Shuman, A. Banihirwe 2020-08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeit\n",
    "import numpy as np\n",
    "import h5py\n",
    "import dask\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from dask.distributed import Client\n",
    "import cartopy.crs as ccrs\n",
    "import xesmf\n",
    "import regionmask\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Izumi/Hobart cluster configuration\n",
    "- must modify for Cheyenne (queue, resource spec,...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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   "source": [
    "from dask_jobqueue import PBSCluster\n",
    "cluster = PBSCluster(cores=16,  \n",
    "                    processes=1,\n",
    "                    queue='verylong',\n",
    "                    resource_spec='nodes=1:ppn=48',\n",
    "                    memory='96Gb')\n",
    "cluster.adapt(minimum=1, maximum=5)\n",
    "client = Client(cluster) # Connect this local process to remote workers\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read GFED4s global data into xarray dataset\n",
    "- use cluster to read from GFED4s hdf5 files;\n",
    "- create a dataset with group levels;\n",
    "- process NPP, burned fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (lat: 720, lon: 1440, month: 12, year: 20)\n",
       "Coordinates:\n",
       "    basis_regions    (lat, lon) uint8 dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;\n",
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       "    NPP              (year, month, lat, lon) float32 dask.array&lt;chunksize=(1, 12, 720, 1440), meta=np.ndarray&gt;\n",
       "    burned_fraction  (year, month, lat, lon) float32 dask.array&lt;chunksize=(1, 12, 720, 1440), 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-0733d5fc-752f-4dc5-92db-fbe19ae865c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0733d5fc-752f-4dc5-92db-fbe19ae865c5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>year</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-238307ba-bf04-438d-8a3f-4a9dbfdbacb8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-238307ba-bf04-438d-8a3f-4a9dbfdbacb8' class='xr-section-summary' >Coordinates: <span>(5)</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>basis_regions</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;</div><input id='attrs-eb2a7d62-b097-4fc8-9eeb-806315707d40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb2a7d62-b097-4fc8-9eeb-806315707d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22b72faf-df2d-4b67-b7a9-b077d7878b65' class='xr-var-data-in' type='checkbox'><label for='data-22b72faf-df2d-4b67-b7a9-b077d7878b65' 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.875 -179.625 ... 179.875</div><input id='attrs-9c9df765-40d4-4569-85ac-35e1face42c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c9df765-40d4-4569-85ac-35e1face42c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16d2ca29-2da4-4085-ae6b-a320e72a4903' class='xr-var-data-in' type='checkbox'><label for='data-16d2ca29-2da4-4085-ae6b-a320e72a4903' 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([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-ff91d0f2-16f3-4d9a-a61b-0aa565e4ff53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff91d0f2-16f3-4d9a-a61b-0aa565e4ff53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e271a295-7099-4899-b2e8-7c525684cb31' class='xr-var-data-in' type='checkbox'><label for='data-e271a295-7099-4899-b2e8-7c525684cb31' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.875 89.625 ... -89.625 -89.875</div><input id='attrs-8b95fe92-fd18-452c-8ee1-4b357631b981' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b95fe92-fd18-452c-8ee1-4b357631b981' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06c777c1-9f82-41ef-a5d6-b607221de4b6' class='xr-var-data-in' type='checkbox'><label for='data-06c777c1-9f82-41ef-a5d6-b607221de4b6' 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([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1997 1998 1999 ... 2014 2015 2016</div><input id='attrs-0dcd22e0-da71-4520-b793-dcd1ae3a78a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0dcd22e0-da71-4520-b793-dcd1ae3a78a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e31afd4-1409-4579-a179-0d470b3b853f' class='xr-var-data-in' type='checkbox'><label for='data-9e31afd4-1409-4579-a179-0d470b3b853f' 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([1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-10dfdb8d-0ebd-4af9-a78f-d89e8130a569' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10dfdb8d-0ebd-4af9-a78f-d89e8130a569' 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>NPP</span></div><div class='xr-var-dims'>(year, month, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-3463fc67-2b3d-430a-bbb2-a6443ba54651' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3463fc67-2b3d-430a-bbb2-a6443ba54651' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ca40047-53f5-4b3c-bbb2-cdcba10c1f8c' class='xr-var-data-in' type='checkbox'><label for='data-9ca40047-53f5-4b3c-bbb2-cdcba10c1f8c' 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",
       "<tr>\n",
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 995.33 MB </td> <td> 49.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (20, 12, 720, 1440) </td> <td> (1, 12, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 60 Tasks </td><td> 20 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </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>burned_fraction</span></div><div class='xr-var-dims'>(year, month, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-2a77be5b-7695-44dd-b547-b6dd71c4f3b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a77be5b-7695-44dd-b547-b6dd71c4f3b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e867d1a8-297d-4b25-9ba5-a0d8df7d9d57' class='xr-var-data-in' type='checkbox'><label for='data-e867d1a8-297d-4b25-9ba5-a0d8df7d9d57' 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:          (lat: 720, lon: 1440, month: 12, year: 20)\n",
       "Coordinates:\n",
       "    basis_regions    (lat, lon) uint8 dask.array<chunksize=(720, 1440), meta=np.ndarray>\n",
       "  * lon              (lon) float32 -179.875 -179.625 ... 179.625 179.875\n",
       "  * month            (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * lat              (lat) float32 89.875 89.625 89.375 ... -89.625 -89.875\n",
       "  * year             (year) int64 1997 1998 1999 2000 ... 2013 2014 2015 2016\n",
       "Data variables:\n",
       "    NPP              (year, month, lat, lon) float32 dask.array<chunksize=(1, 12, 720, 1440), meta=np.ndarray>\n",
       "    burned_fraction  (year, month, lat, lon) float32 dask.array<chunksize=(1, 12, 720, 1440), meta=np.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# process years 1997-2016 (2017-2020 not complete data)\n",
    "years = list(range(1997,2017))\n",
    "files = [f\"/project/tss/jkshuman/obs/GFED4/GFED4.1s_{y}.hdf5\" for y in years]\n",
    "\n",
    "@dask.delayed\n",
    "def read_hdf5(file):\n",
    "    import numpy as np\n",
    "    import h5py\n",
    "    import xarray as xr\n",
    "    import dask.array as da\n",
    "    groups = [('ancill','basis_regions'),('biosphere', 'NPP'), ('burned_area', 'burned_fraction')] \n",
    "    \n",
    "    ds = xr.Dataset()\n",
    "    with h5py.File(file,'r') as hf:\n",
    "        for group in groups:\n",
    "            primary_group = group[0]\n",
    "            tertiary_group = group[1]\n",
    "            data = hf[primary_group]\n",
    "            lat = np.array(hf.get('lat'))[:,0]\n",
    "            lon = np.array(hf.get('lon'))[0]\n",
    "            arrays = []\n",
    "            months = []\n",
    "            if primary_group=='ancill':\n",
    "                array = np.array(hf[primary_group][tertiary_group])\n",
    "                ds[tertiary_group] = xr.DataArray(da.from_array(array), dims=['lat', 'lon'],\n",
    "                                                  coords={'lat': lat, 'lon': lon})\n",
    "            else:\n",
    "                for k in data.keys():\n",
    "                    array = np.array(hf[primary_group][k][tertiary_group]) \n",
    "                    arrays.append(array)\n",
    "                    months.append(int(k))\n",
    "            \n",
    "                ds[tertiary_group] = xr.DataArray(da.from_array(np.stack(arrays)), \n",
    "                                                  dims=['month', 'lat', 'lon'], \n",
    "                                                  coords={'month': months, 'lat': lat, 'lon': lon})   \n",
    "    return ds\n",
    "\n",
    "xr_arrays = [read_hdf5(file) for file in files]\n",
    "results = dask.compute(*xr_arrays)\n",
    "ds = xr.concat(results, data_vars = \n",
    "               [\"burned_fraction\", \"NPP\"],dim='year').set_coords(['basis_regions'])\n",
    "ds['year'] = years\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.6.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regionmask.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regionmask.core.utils.equally_spaced(ds.lat), regionmask.core.utils.equally_spaced(ds.lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xarray.DataArray 'lat' ()>\n",
       " array(0.25, dtype=float32),\n",
       " <xarray.DataArray 'lon' ()>\n",
       " array(0.25, dtype=float32))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.lat[0] - ds.lat[1], ds.lon[1] - ds.lon[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our input grid is a $0.25^\\circ \\times 0.25^\\circ$ grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to tropics domain file within same folder as the data\n",
    "domain = xr.open_dataset('/home/jkshuman/sfcdata/domain.lnd.fv0.9x1.25_gx1v6.SA.nc')\n",
    "#domain = xr.open_dataset('domain_processed_GFED4s-burnedArea.nc')\n",
    "#domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon = domain.xc.data[0, :]\n",
    "lat = domain.yc.data[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regionmask.core.utils.equally_spaced(lon), regionmask.core.utils.equally_spaced(lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9424083769633498, 1.25)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat[0] - lat[1], lon[1] - lon[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our output grid is a $0.9^\\circ \\times 1.25^\\circ$ grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xesmf expects empty dataset with grid information\n",
    "ds_out = xr.Dataset({'lat': (['lat'], np.arange(-89, 89.9, 0.9)),\n",
    "                     'lon': (['lon'], np.arange(-179, 180, 1.25)),\n",
    "                    }\n",
    "                   )\n",
    "#ds_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Regridder \n",
    "(regrid algorithm for input and output shapes of data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_720x1440_199x288.nc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       bilinear \n",
       "Weight filename:            bilinear_720x1440_199x288.nc \n",
       "Reuse pre-computed weights? True \n",
       "Input grid shape:           (720, 1440) \n",
       "Output grid shape:          (199, 288) \n",
       "Output grid dimension name: ('lat', 'lon') \n",
       "Periodic in longitude?      False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder = xesmf.Regridder(ds, ds_out, method='bilinear', ignore_degenerate=True, reuse_weights=True)\n",
    "\n",
    "# https://github.com/JiaweiZhuang/xESMF/issues/71\n",
    "# regridder. = None = correction that addresses known issue #71\n",
    "regridder._grid_in = None\n",
    "regridder._grid_out = None\n",
    "\n",
    "regridder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regrid the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using dimensions ('lat', 'lon') from data variable NPP as the horizontal dimensions for this dataset.\n"
     ]
    }
   ],
   "source": [
    "ds_in = ds.copy()\n",
    "# build computation task graph for work-flow of re-gridding\n",
    "dsregrid_out = regridder(ds_in)\n",
    "#dsregrid_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 872 ms, sys: 2.92 s, total: 3.79 s\n",
      "Wall time: 6.31 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dsregrid_out = dsregrid_out.load()\n",
    "#dsregrid_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot originial NPP (1997-09) (yr=0,mnth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds_in.isel(year=0, month=8).NPP.plot.pcolormesh(ax=ax, robust=True)\n",
    "ax.set_title('Original')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "dsregrid_out.isel(year=0, month=8).NPP.plot.pcolormesh(ax=ax, robust=True)\n",
    "ax.set_title('Regridded')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/PQBIyCOoQ5ux4cBNTFgDH9IBvAzhwIlgEEPchKYtE5NY4hliAFvoZx0kgIkvtHZzdm0ml2IWs/qcr+2lYDgD4FSENPIyERmpmf1AVX8w4v1EavnYp/dkbabsq6qfBz4vIp8DPgr8E1ahkQJV7RCR1cDjIrJkjIY+Y1zRgnuAPgAyySeFDDwMUMNpBkNeIL6QkE0li0RS8DJEEzV4GKSBKrpoYx7LaeAMYHl7tNGIi5jQj2AQDwOA5aLnx48/NKYNO25iGaIfE5PDvA5AvCaxllsiMoGICAkks5ab6dYODrKdYl1Eocyf2QsFdGs7e3k5/L6Q+eEf+NXCgPZykNcYYoBsCuigmUIsE4ZDnOPaqyqnORR2u8ymgEH6cOAkhQy8eKinkk5aSSYdQvOrZNJYyroJBX+k5olciumnG5MgrTTQQCU27ARCs7wETcaGjW46sGHHRUzYZXS5ridZRkeXJ0oKG7mTDm3hAGcVvRwKcRPHSm4I+S7FnNOi+KXG1Olt2KH95dMUUqiHUVOpfAhpZNO3cUbQF+DXwFPAP4UqSnkBVHWfiFQC82HMtH+GmPKXO0Q/B/U15rGcOEmYquk4vDqEFw9dtGLHSRKpYWE3E4thqsoBXg0Fs7hCZpC9nH0wCgtZyQC9xJNEL53EhjTiPObSTzdu4sI2QYBciqjhFEGCdNGGHQclLMGGnQqOsIx1xBBHG40UsgBDDGr0FKc5RBwJuIjBwxDb+AMpmoGbWOZQQiwJ055zsqQR1CCnOYRHBylk/ozYPcHS2kYK7c28aUJBdikx1QxrgMOmqCTSzvu4ghoMLzhfx63s5gUWsIJ8Sia89v3aw2kOE8RPEQs5yGs0UzuqzcgF5lKWjlqMvlAKpDT8t6qOOkavevAwGI4BSCEdQ2yoKrt4jn28wgq9niqOE0M8wZBnsx0HDVQRTzIZ5IQ8iEzaaMCP5VHkYZAsnUMGObiJJUiAAAEMDNzE4T4Poe5XH9VhZ4oLQzGmjYzUyNwB9wDzRKQYaADejlU6byRPAh8N2bDXAT2q2iQibZP1FZF5qno61P9NwInQ9gygU1WDIjIXa8HzTCQHej5MKbgDIXerIhYA0wvuXu2iiuP00hXyLHCSQiYBuqjgSFhbjdE4HDiJIQ47Duw4MDGJJ5EY4sPueSKCTz14GCJIgC7a8OEJB40sZBVtNFBHBQYGBcxjgF46aAaUE+wPH9uwN0mm5oW0Zx8H2M5R9pBAMnOYRyv1JJNONx0sYz0mJkfZzVwWYxKknB1kkIdicpQ9uDWGeJJZxGoG6SORVNLJoZ9uumnHF/oMH17SNJtFrMIprkmv3wqu5wCvUkcFdVRQoksolkURfI1T8xp/GvW+izYyybvgcc8Fv/popSG8ZjBAbyiO1IEDJ0ECdPIaqHUdUsk6p8U1m9hYqutoo4G9vATASQ4SQzzxmkgXbWE//BjiqOAIAXysYjM2sTFHS0P3kY0U0pnH8rB/vkcHcXHxtNSxDxaXuHHhnrDdat1MFcc5xSFyKQxH4irgx8sS1hJP0qQ27CEdoJlaznAcPz5rgTxkb++ji2JdRAa5BPAjGDhxoZi4iAm5xXbgwh3+zTpxYRKkZoYEdyQ1Jc0IlgNVNSAiHwW2Yrn0/URVj4rIA6H9D2NVcb8bqMByB/zAVH1DQ39VRBZguQPWcNajZBPwLyISAILAA6o62k47g0xZc9IuDs2hkLkswinjbySwtIPD7MSGnW46KGEJ6eRMaIsz1cTDIAA+LK0iEDJAGBj008MQAwzSjw0bTtwMhIJUDAxSyMCOAx9eYomniRp66SKWeBJIpodO/PhwhAI8hkkji0RS8eOjk1Z8eADLxttNO2Bp4Q0jHpCCwSJWc4w9I8bJJpNcjrM/LPz9+MgkHxdu6qgArEW/TPLCGnObNlLODuJIZD23T6p9BzXIXl4KLx4mkcZauXnCtlPhU2/Yj3yQvlHBP7kUsVjWnPOY54tHB6nlNI1Uk0oWyaTjwo0TF8mkj7oWJ3Q/bTThJoY+unGGAnTSyCKd3PAaQj2VOHETR0JYuAbUTzfttNFIG43MYzkmJv30UE9lyMSRzhADKGbYjJZODmVspJUGDrMLgHXcNk7w2eLjCfb3z8xFEQPUDL81nE5M3+VPn9yi9ZzmUOhx6sQkiA8vBkb4XgfCcQqC4MNDP73Dbfep6nnfXCLyP29/5K73p89LmbJd3evNPPHXL17TNSen1LgNDFqpp5laUjWTFDKIJ8nSWkLTxiH66Q5NeUtYSi7FowJdRo0nBrHEA4T/NZxOEAPPzctwPmMJSVUretDLEMlkTGovnkPpuG1+9TFIHz10copyAHrpopuOsFkkiTRiiaeVBgQhhng6aQUs/2IvHhSTBJK4Te6jUasxMDjCbjwMksdcFsmqsD1xIStxiotB7aODFmo5zSnKKdONZEguLmKw42CIAV7mcXK0kFyKiSVh1LWyiY2Fuoo9vEgh85nDvKm+nnF4dJBj7KUntDjrIoYY4ljCWtLInlLbn2lUlUqO0EAVORSyntunNf0slFUsDPX14cUkyAC9tNPMAV7FhZtVbOIkB8N9sjSfAfoYYoBEUsJuf03U4sRFPIlkkkcSaRSIdT2HdIBWGmikOuxzHcPZY+ujmwSSRx3bjAltGCW0gXFCW2w2NDg60nUijJgYzKGhcdttyckEu7un7ryhDHaWj9qUJfmjFlBHMqB9dNJqmVgm+B6HHQUuFDOCkHdzyr3XBtOuTs1lCQkkU85rtNJAAsn00U2sJrCaTXTQQjo5tNNEJUeo5Ag36N0R22cH7llJwp56S2iHNBEROe8oPoc4SSKNJNLI1gI8DIZ9pgP48TJEeigUeTiAo5/uUBIoW9iGnUYWtpBQHXbTcqqbeippoY5+7WaAPtLJwR66jJnk4wgtbVZzKrz4l0AyKWTix0sexXTRzm5eoJhFlLBk1PEPa9slLJ3WVODRYX9k60HVSQtFLGQFN4zr61MPO/VZTIJkMQc/XkxMhhigm/YZ1cT96uMIr2NisoEt4x4YndpKL524iCGT/HEP5tMcohbLjJhKJrEk4MJNL128yh8By54bwE83HSxgBenkRGxaiZE4CplPIdYicL/2sJsXAWsxb6TwOm9NWwxLk/Z6zrlrJEIbwCjIwzxZMW77VEI7/FAYIbTt80oInK60xpxE+4+ThHFeUyNxS+yMeDSbKpEuTl7TTCm4/fg4wf6Qu1MceRTTRhMOnNixM0AvVRzHwMYcSumghUH62MvL3MDdo8aaTItI2FFFoMXSdsdqIheKU1yhHCAjGT0NM8RK1DSSsfZfVSWAHx9eOmghSIAeOskgh1yKEQwq9QhV1joFABnkkUJG+H0vnXgZCucQSSd7whlDFvm00sAx9rKU6yY9twat4jSHSCSVeBLJIGdKG3oX7QyEQsKH6CeBFOw4yCCXdHKo4DBpmn3BATld2sZR9pBJHqUsGydMq/Q4dVTgwMUAvfjxUTBmZjHfuYqAz/KN7qQ1PBsCWM1NeBkiibQZm0HEkkAZG4knadwCZCRCe0LhrubkQntYQbE70IDfeg/Y4mIJ9vdjS00h2Nk1cd8RBCYQ2iMxXO5xx6DBIEZMDJga3hesOrsoO5nJZrLfry9WODXwelh5uVCi2QEjY8qrPRykYmqQFhpooxE7DvrpwY+PZuq5kXtooJpGqsL2aw+D4+x4k2kRYaE98qDy8wjUN1zIec0YXh3iKHvopoNY4sPmFjextNFEWyhXxgJWEEsCbmIYYoB+unmB37NGb2KAPrwMkUYWXbRRwlLmTOLt4BAnS3QNu3iONm2cMKFUt7ZTxXGWErnfdyZ54ZlRC/V4saI3e+nCTSxzWXxeoedg2ZfbaaaJagbos2z8Mvrh16td1HCKllBcgwMXS1hL5kRTc3+QQuaHvIRgHsvw4iGLfBIlhbEP3wvFEIMMJk/cNVKQ2jPSCbR3jrq3xwrtabX0UF9ZMg8tPwZqYlu6gOCRk4jNNkpo27Mywe0Cj3fC38o0J3Z2nBFaddi8EjKX6JrFsKt8ohGsvlP8HgMDfTRTi8HMRAAHVPBPI7gD0SRT0z0mrS/eEBs5FJBDAUENcopy2mhkIOT/asNGkAC5FIfMAunWzZiURLCn55wPKlDfME7wR0L/29cT/4i1wBTWZi6AOq3gDMeII5FNvAHBoJUGummjNxQWn0x6ONPfEAMht6pYggRxE8NR9jAU8gUfDouPJ3FK90CXxFCm13OE1xnUPgplQXjfoPazl5dZzJpzCtYZTj4V1AAehvAyiIlJIqkXpLm2qjU7SCKNLOaQzRyMMaaPbu2gnNcoYiFgBYssZ/2U1yBOErmOWy5pLpKtjeVsyR0fTTpSkAba2qcdJ1LTilkeir4Wg+ARyytjpIIjNhtmVzemP4Bt6XyM3HTMA8dG7ddgcFJteKT9e1hojyJkLvHv3MMAvfjwkk4Ofrzs4nn8eLlN7hsntE2b0B5ooIcOOkP39Njsk+dLZEmmohr3Oc9vbGJjEatYxCrgrAtgEqksZKWVwD/0gxwrtDu1lf1sI4ciMsmlkWqSSCOVTFzEYMN2NqjhPMwmw0IbQAP+iBd5JqJRa6jlNNdx66jp8/ADrF978OElkVSOshsnLhJJZgErwh4LVRzHwyBpZJPPXProIZ3sCcw340mWNNboTezhRTq0BRt2sikIL6bmUHhe52UTO3FMba88Fzpoppipg4Y6aCKBZPIoBpR2mgkSwKse+ujGj5c4EkkhY5wwnymhbS+cQ6Cmbso2EwntS8KYez2s1YsRNl0ED5/AWGmlrB3W6DUYxD63iMCZ6nFasT0vl0BD49Tav2FQFzzFGY4RSzwB/FRzIhy+X8h8fOolSIAOWoghji7aaAhUkUASSaSxgBVhRcVK53BhRGLjjppKZiByci8vY2JVgnmJx1CUpXodMcThxI1g4JYYDulOWrFurC5aaaKadHIYYoCj7AklAArgVivfdSELiCuaN+7HZsTGYg5aJpmxN+XWxnI2P/hh3E9YUYwV/7mWkr/ZhedN1+F+cvc5nVc9lRSyADcTL7KOzMFdxkY6tYUaThJPcljYpGk2PXRwgO0sZg1xJPA6LyAI6/S2aQM63BLLBt1CF20EQkmhfHitB+QlzPOsakUMTiREY4gPm8j86qOOClLIIEXO2vctV8sq6qkkgzy66eBlnhg31g3uNxPnTCHY13fuBznNDG06oX2pmMwTZHif+gMEC3OgrR0NmKP7hbTtYH9/eEYZOFMNME4r7q4/Tjcd+Po84UIZgtBJKyYmxSzkTPAYfXSzgTuIk8SwYuUiBi9D1FNJDadw4CSNLE5ziAxyWcNNEwfkzcTiJDOWZOqq5oIF9828OXxjKCa7eZEjnBWS8SSRp3NppSHs7ZFKJimkh702hjHVZIBeGqlmJ1uJq96JXZzEawKlLEMMW1hog3UD2wvyCdTWA5bG5JY9YYFe8jeWBh730vFRE7lINPEU0jnDMQTI1eJpBWUTdeFINAMr+k9ESNI0sshnF8+iKPMpQzHZxyus1s3TCm+7OML215xLnPcDoEXrwuH8hTrf+h5GXIt0stnLywxqP310YcPOGY6xWa3oTFVlH6+QIpnM0XkYGMyhhDYaESzzTcBukhhIwu21o7YR34sYGG5rdjJS2I20OYdNYmOE9kTeEbaEBIJ9fWFt9GIw0b1lz8wg0NoGYqD+wKTCO7xt7+HJ94WYzAzoVx+VHKWVetLJxYULF+5wlaUiFlDJMU5zCAdO8inByxB1anlLLWAlnbTQxhDruQNXSPkSkXERnheDoNoITJNkarq0r9cCFyy4RayahhYG6/V2+umhiuO00kA/PeHAlh46mMeycXkWhjHEICFkbijRJVYEpgatKDcJMF9XYhvztB0W2mH7pJqWFj5CAxv7Y43EfDLfvpLMYAH79EVOcIA4tTw3Cpg/YVj2YlZzjL1UcnRU0QMRYTFrGNIBK0ow1NejQ+zmBTbpG2d1lZQKjjKXxZzhGDWcooiFI75va+axQe+giZpQxKplJjrJARbp6pAXTpCleh0iQqUeoTm0QJlODpkJpbT/+TJifmIVrzAHB7FnZRJoacVeXECgqhZ7agpGfi6B05XYUlPQgaHw96sBP0ZMDBLjRvsHMP0BxBDU1LCgBkvYS2jG0H57ISnH07E1d52XJm5fOI/AidMRbw+0tll/qIk4XBMK7Qtdk1FVmqilgsNkkMsGtkyaPmDkInSPdrKfbeRRzFpuJtZIImD6aaNxXHqKS3GfRt0BI2PGsgwN6QA1nKKes4sgcSSwgJUkk04nLeeUi8IuDlKxUqQmaRrl+hon2Mt8yqwxhqftIeG8JbcMe0kxgcqqCTWac9WwNBgkiWQ28SYMDProooFqXuUpEjWFxJA7XS5F2HHQRhNdtIUX4MYyVrPOoYA6KtjHy+RoEalkzmgujAslqAGaqMHA4AzHyKWIRqoZoDeUcOksLonBrVYJssFQ4q9m6uilG1CKWGglclIX9ZxhAStItmfjCBiYHi+pP99L7zs3kPBrS3gHWloRu4NgTZ0lnL1exGaEPS7sOdloXgYcPIH/pjKcrx3FnJtPMNaOvceL0d6N2d5pPbDT0gh0dGD29GKbNxc78Pq/PQzAsm8+SP632hGbDUlPA68Xs6cXc3CQrY3l3H3725C2LpreNo+M7+7AtmQ+ero6LJy3NpZz1xveibnfioYOnDg9SuOeyBtj2PfaiInh5DeWsfAzJwn29FyQ0O7VLo6xF0EoYyNJkjp9pxD1VDKXxeE1CjWDVHKEdLIvi0KhSNRUEgEzMucw1eQ1/kQ9lcSSwHI2sJ7bWc8dpEomhhikS855JxDqpZMAQfrp4RA7CagfW2ICqdsttzCxO0AMApVWQV1zaMiKyBxGDKtNhAwvAgHYxY4hBkmSxmJZzWbeRKF9EU7cnOEYXbRznH0cZTcLWUm+zI3oM+IkkZu4l1yK6aKN3bxAp56ju9dFolNb2cFWOmgJ+343Uk2JLGUf22jVs8JIVenT7lD6otG26SH6ceCkkiMc5DVe5SmSSCfBloY76MQmdsTpQGw2En69k4bPbsSWlGTZegN+WLOER+p3UvW3yyn8RR3mxuXIdctofvNcnnnylxiJ8birO/lT5S5OvycOe78PM9YBpknbe1eFj6PtIxsZfOMannrxUZ46/CJz//AhNj/wIZIrTIzYWPyr5hH4SRDv4jzE4UBvWME9q7egVfVoIED/jQNsbSwnePQUps/H1kbLG2NLbhl/+uOvqfvCRgyXlRJi5GwuUN8w+j7krO+1lBSw4OMH0aGhs0oIhP82Ys+urRgxMdiWT5yzxrZ8EZXOU/TTQyzxVHCYE7qfCj3CLn1+2u/awAin+vWrjx08A8AiLl1ahJEMZwec+nVZDm1WMSMatyEGN+ubraoeF+EpbeWc6CaeJGzYOcJuFvavRW+2wuYn0lZMn2+UuWSiNsbqpZj7jozbXvaTY+z/+Erk1QPj9tnERkYwiwzJglBh3oWspJ3mUQE3kSAi5FJkabNaw0FeY6NumbGsgOdLX+hal7A0nJscIEXTWWVs4oC5jQKdTzEL2S0voRrERQz5zKWEpXTQHE4jkEASaTGFMGTlT3GIc1TMsjk4aAk9MbB5ILi4CNNlo+DfT/HScQcpRizfevcPuT0mwJrPFLJ31e94YcjG3fOvp/pTi3CEsh1nzOsgGBvHs7/7GSv3/gWZb9phCb/0FFKPeVG7dV8e9HmIrbLzysMP8YfBWD7xwrs488YfWoP8Am7+yw/SscRB3p5uTK8Hm9tF4ktx3PnuNYAfw+UOe5/YkpLYklvGHHaET2nk4nmYjSuwVzYSLMpGX7fK2yEyygZvz8vlgVde4Tul88LXJXyNhobg0PEJZ5LBQ8dZyTp8rKDBUUOsP4Y2mqgPmSc9Ojjl/ZRCJs3UEqNxHGcfKWRyvdw1afuLTVANAubU+mTUxj1NkqlESdV1cuslPJzJqdUKGqliFZvYw4ukkskCVmKIge+e64jZdpxg/wBbGw6wJbds2gXIYaFtOJ1gt2OkJBNoaAznTokkVDmoAfbyCgaChyGu585xPsxjGWvLDKifBqpoooZ+eljBDaRLduQX5iKwX7fRSStOXORSTHUoInQTb8TLEPvZhiPGj3soEwcOlsn6Uf1HmQsy0gl2do32T7Y7rPdq0vOeDXTc5WHeX1UQ7Ouj5l83cuL+h/hVXyrZ9h7+4Z8+xNs/9wy5ji7eFt/DD3ty+FCSFfTUEOwjz3bhbo39pof/7lzB/9WUsX/1b7nzTe9G9x5ma2M5t773fssUM4knyDD2BaXM+UUjVdcNTtlupInPFh+PubgYW3071R8o5rv3P8zXbnkjgeqacd1O/3gt8+7fw+lvr2fex6xF9+G1gHFt9TA1nMKJk6WsC1dlmgifetnGH3DgZAErySL/vJWv5/XRC04ydcNP3vH+xJKJ18CGad9by+5PPXFNJ5m6YgT3SF7Sx8MRjDkUnrV7j2HYu2RCW+NwOLAY+O5eg+EzcVd10n59Fsk/2zlurJZPbCTrmzvGbTfVpIU6EkmdMme5V4c4STl9dOFlCBexxBBLP73EkUAX1gLWQlaSTcElKWM2GaZa2fNcuKnhFBWc9XJwEYtJAAM7xcYicsw5Ye+gcQtsY9YhRm63z8ml+e4C9n3hIRb98EFim2HfPz4UbnL37W/DPFnFM7V7ufFjf0XbCgNfepC/vvEFPp1aSelL76fi5p+G25f+5gEq3vFw+P2qLz3I/n84O96iHe9m7t9ZppyuddlkPVjFY6XPUvrbB1i0spo/zH9mymtyz81v5amXfj+tUrC1sZx+08N9RTeMuhbDi+f2jPRwEI/YbFT96zqK/v7sfWWLj6f3zqXENXpgx0EA+p8p4bXl/8eW3DLaH9xI+kPj78ORs8ud+ixuYumnhyRSWS4bpjy3ej1DLPFTCvhImAnBff1P3vn+hAgE995PPX5NC+4rcs6xms1hF7kmaniFJ+nTbvSGFaPa9f+fZbqYKFw3rFGvX0bsnipcLQN0bMjClyTYS4rH2cQnEtpgmYlypHBCoW3ExKCqHNHdvMpTtFLPEAOh5E792HGwgBV00UYh8xGEExzgZZ4goIEJPu3SYIjley8iZJFPDoXEkQiAN5QdcS03WZVlYhPCAnqcOUrNcUJb7A4Mp5NAbT0J9QHufPO7cbfDzz/7nwCc9lt++Y89+2ueqLEeoK9++/ucuP8hliyqxWNa38uw0L779rdxz41vpuRTO8MmjHs2/RlZr3WHP3NL3koK7jvMU9ufIHCmmoTf7OJLhY+zJbeMkk/u5A/zn+HW994fbn9X8djShODPPPv9TjWT25Jbxlvz16EBf9gWDnD3/OsBCBbm0PSpjdiWL8JISAgL7ZrfLbf29/eDwKkPnL3/Eu9t4Pa3vx/b8kUkV5w1r4jNxtbGcutzQtfZp148DIYy6wzRSgNDOjDp8QLky9wLFtozRSQ27qspAEdEbCKSKyIFw69I+l2RtasSJYUyNtKl7ewLVXap5TRLticDZ7WbmDuqpgy7F5sN3Vlu6e6tbaTWW/7BmpBguZlNZK88BwKDA7zCk+F0AMUs5Ai7SSSVUpbixxtOUdpO86iCtu00nXMR24tBjMSBCgP0Ekciq9iES9xhP2lxOmEaMwJiYIuPI9g/gBEXG/4+XH/cjZGQQHa5n3fLp3De3oLtP5T/9+Pn6Aom8JX/vYMvlH6Dz77Dxcf/28Mf/uKsVrwlt4zu928g+ehOa4GUbpr/NpF1OXkkUcng2zawJbfMMn2pL9yn9p83UvDPO/iLhz9FHjvC219o/DFbcsss84W3P7wdrPup4s9dbLnvfRD6vmzLFhI8fDap2ERsyS2j4uerKH3v/nCgmO49TM5exgWI31J8iu+FBf3o0PvKf1lJ8Wd2IqVzsT93HIDA8wU0vZLPPWsyCTRapiO/+jjAq8yhhNpQbviN3DmrvJWmI2hGYuOOTHCLyJ3AN7GKIfxIVb86Zr+E9t+NVUjh/aq6f6q+IvKvWBXfTaA11KcxtO9zwP1YX+/HVXXKUFIR+RhWvcoWzq78KLB82nO7Ek0lIynXHThxkU8JKZmlmPmZmAeOYS+di1lbP2WC+pFBGobTicTEoB4vEuOePp9xBAxqPzvZShkbSZccghrgGPvCiZaGuY5bEYS9vBxe5V/DzcRK/AUfw0wxpAMcYTepkkUJi6dsazidtAbr8QeGyKYAm8sFS0pRmw1bey/B+tCCp5pW2HbhHMy+fg50PBVeDC2NW0vFwNkiFt/4l3Q+/iHLi2j+zx4ksRqynqphzef/j/96oBvFxIadfnrYwJZxM6DJ8pBMhX1Badg8MqNMEOU5bM6re3QZc952FMMR0qkWlcLpalhYjB46hf/mMuzP78dwuzDyc6G3j0BLK6YGOcxu7DhYzGpe4nFMgixiFXkRejpdKDNhKlnzg/e8P75k6kX+zn01HPq7309pKhERG3AKuB2rtuQe4B2qemxEm7uBj2EJ7nXAN1V13VR9RSRxuACwiHwcWKyqD4jIYuA3wHVALvA8MF9VJ52eiUgFsE5VO6a+MuO5Ik0lI3HgJIEUkuJyCbR3Yh44hi0hgaGSNLBZC4X2eSVWKssxjBTqps8X9qcNdnePctGyxZ+fAI2VeG6Vt5JAMkd1Dy/x+DihnU2BVaVekrlZ3sxmeROb5I2zSmiDpXkXUMqge+Iq92oIzVpLv/YwkGbjYGAbR9nDbuMlTJ8P88AxdO9hcDmw5edar+RkGj67kdP/nsL2jt/gw8s6bgMYJbRB+NUXlrHsmw8CsP3dXyPt+zuozevjSw/UUcQCEkkJF/2dKBfMsPDt+KuN1ob1Uwvj+FczCZysGCe07UWF2JKTI7hiUzBBaH6gvgEjJoY59x1GQvet6fNhlh9DfX44fgYN+LE/t9cK5HG5eOqV/yPQ0opPveznVQRYyAqrL0FiiMPATrdOnxhrtqBYppCpXkSmcV8HVKjqGVX1AY9gacojuRf4uVrsApJFJGeqvmOqtsdxNtD/XuARVfWqahVWObTJ8zJb1AHnnoWPK9RUMpJ+ejAfvJP8h62b09y0EtlxBMfWPWjITh2sqsWWlUHT94rIuteaag4v8oxcaDKczrP2yzFpO0dlYjM1oiRYqspx9tFKAxnkkkomnbSSxRzyKCaZ9EuW+e5c8KqHo+ymz+jFqS5sasOGDS9D5AZKAOvcajhFD524cNEebMaGnSH6Wdq4jjgSMbDRZ3ZykNcoTVpHfJ/T8mMWA2PFQtpuz6PgO4do7DuBh0HWcRs2sXOd3ko7TWSRjwJH2c1eXqbkq23c9tUlPN90FHvpXGJ3WakVcigMu7+t5eZRC9WNn9lI7r+fXZ9I+37o713ltH1kI/s//xAr/+1BDvz92YVMGJ1wSm886xo6kcfHTDHsuaIB/6i0Hxrwj1s/6O2qozTHhpsqajhJBrmUsgwTk2Psw8AglyKOshsDG7fwZxftuGeSSAJwQvvLRGRkBfUfqOoPRrzPg1FaUj2WVs00bfKm6ysiXwbeiyV0h2sL5gG7xvSZrrDrGeBlEXmK4ZBjQFX/a5p+V77gHqSfZQ/VQMiOZ2w7YN30YoRvdg34CTQ0knXvWZ/krN8cIyjGqIWmicwqYz0Ihv+eLkTZ1CB99FiBK1hFf4MaxIdn1tscvQzRSStr7vg89mf2cN2vOtj+rlhASDGzQIQubaaSIyzhOnx4WEw++9lGDHGcYD/zWE6NrZIYEvAGPRzseZbY1DzWOm4l2NqOefAEmTVJSGoyzX215DEXBfq0myZqGGKAIQbIoZC13EIf3RxnH0H8lndGrlVMIdGRSaX/KKvYxCF28Bp/Yr6WhcuUjRTaY8n47g6Wux8k5zs72PIdS1B33r+R1J/sYqSzuf3A6RlKWjoxWxvLWfijByn8gnWsxsrFGPWtmL39+DcuwTYU4NRfupj/YetBVa0nqeAwmeTRRw/zWB7O296pLbRQi4lJLadJJ5s8Lo2pZCYYXpycitBDrXwar5KJBhlrF56szZR9VfXzwOdDNu2PYtmpI/m8sdSGXs7QK2KueMHttsXT+Pb5lPx6hOeIGFa+ipH5isYIWrN/APucXMzm1lEC23C5LQ0nJKDH/hsebzgJ/gSc0WOc4ew+G5bmbxOrNNpsx8oXbiPhlQqcKQUce3ciqaHbMhj0c4Dt9NJpuS6KtYDar9aMz46TJazlJOV4gv0oylDIddPTeZqhDX+Or6WS47qfno4OjA47Zmh/HRU4cTEndyNpvTY8DHKqv5wh+jFR7DjC1YoCjU3Yk5NZnfRWttf8hB46mE8Zr/M8pygnWdNDRRcmx9y0kpz/HC3YU388XtDPaL3JEPa5RVTcn0vR53ew8isPktivnPn6BuZ+eifB/Uep4Qy1nGLJiw3EkcD8XVbCrnaaqOAwa7llwtD24YrvdhwsZyNJpM7KWd1kBE2D4DSLk+Y0+0PUw6jV/XwYEU02dRtnBH0Bfg08hSW4I/m8UajqFwFEJMF6qxHfaFfONzoJ7qAb26+seoEj/YbHCtqJ/IsDtfWWV8QIGj62atT7YXvj2L/NsUJbDALqZ5++whmO4cDJBrZwm9xHgYwvUTab6aIdl8RgDAXQIc8oe39dqGL6zfzZqEWv4QooQ/STLOmsk1spY+OoBPsOnJza+QuOv8FKOJZOTlhol7KMW21v41MH7+LUvnrM36ynKGsj1y/7KDdwD7cu+TQ3cg85CQswXG7sWZm0vGMJDffPI44EeunEiRV2nkY2CRHk6zC2hcwft1/68O5gTR1Fn9+BLSGBzG/vwJMq/OQtD9Oi9ezjFeqoYIgB9vISr/Ak5bqDWk5Rzo4p85FYxTGSURQHjitKaAMwjX1bI3cH3APME5FiEXECbweeHNPmSeC9YrEe6FHVpqn6isjIOntvgnC9wieBt4uIS0SKgXkwIk3qBIjIUhE5ABwBjorIPhFZMlWfYa6wb3U8RSykhlMMptvZ2jA+RB2sdJ6jUBMjxvqRD9f4Ayv9Zt73DlqeDgtKLe17AjPJMCMFuS0liT3Gy+FAmht5w5QBObOZAH78+DAJYno9SKgElk+91FNJCumjoutUFRcxFDKfUpZiapCG0ic5ZRwOuzhez11s5E66kj2k/9FyX8skj3iSKExfR2neTTjmFrNnpZ27StaT+/98BM5UEzxykpj5i9j6/GM813SYYH8/ptdDoKWVrF8eovi7p+imnWWsxyVu5rIYO3ZsoRwo1b8tw7Z0wfiTDGGULbYW/C4xw/dS3QPLAJjzi9N8aW4ZZzhGN+1k2S133uVsYA6l4bKBTlwEJzDcBNTPkA5whmN48RBPYtj3/krCZLo8JRJRlXdVDWCZMbYCx4HfqepREXlARB4INXsay85cAfwQ+Oup+ob6fFVEjojIIeAO4BOhPkeB3wHHgGeAj0zlURLiB8D/U9VCVS0EPhU6jmm54t0BAWr0JG00sS737Wh3z+jwZDGwpSSNKj9lW76I4KHj4ffmTaswXt6PPT8Ps6PTSlIVGwuBABoM8kzd/vBi1bAL4bCP90gb+EBhPDurf8pS1oVNCFcqz+ujAKSQQTxJDNFPP73Ek0RKfAFtyf34vH1IWzc+8RKQADjsqNeLDXtIC7au+Ua2EBt6iDVqNcdCtSSv4xYEg/1sI9WRh9vvYJB+Msilnx6anc0sd99Ict/oXBtis1leQjYb6vNR4zlCtXkCl8QwpP2kkcVSGR9EM9sYzituLyrEbGxiaMsKeHIbx12HaPdaBXzTyGIhq9jFcwQJsIabOMqeUflETDV5lT/ix0eJbRlDwT5iSaBIJn9gXQxmwh1w8Xfvf39s8dTBQL0Hqjj1+Ueu+MhJESlX1bLptk3EFW/jBpjDPFpp5ETjs+jD9zP/wRGuZGoi9jEFG46eCv9txMTAy/uxJSURaGgKJ+5XrxcjKZHuuxZy99JEoMvSzP0B8AdGCW2x2UAMnG0DuImdtGrOlcQm3ohJkO08HZ5FGHYnnYFWjH6DOf1zcJOJgQ27OnCoC/WaVHKUfrpZIzcTUD+D9IeFNhAquSZUcJjdvIgdB6vZTI+/kx46iCeJk3KIoPrABx2+KpLHzB41GMT0eDHcLsTlojj/DnJPFdKvPQgG8WM1zUmq40xUbOFSMZyitvaLGyn8l9exzZtL7GunebrpJEtzMminlsWsJZNc7OJgka6mkiP00kWAAAENcJKDtNFAAD+33BBDz/bNmMEg9ZwmaxYEb50Ppsq0SaSuoiRTZ0TkH4FfhN6/G6iKpONVcQUMMVjOehqpoeWBf6aWSlQVe66VMD6cyD7EKE8SjxfD6STY04MR40Z9Puw52Vaiqd5+3B0Bnj7yMraEBLR/wPKpVdPyWgmNY2qQU/597Bh4ggxySSLyfMizlSABq1SVq4h1cgcruJ6FgTI280aWywayJJ8kSSNBkomROOxixyFOhugPm0fs4hi3QCgi5Eohi1mNCzexxBNHIvkylyWyNlSQ2kcqmcQZyRNeS1sosjXY34/Ex9G1JgOb2EmSNBIlZXyir0lcN02fj9aPbhw9dlLShG1nmuF7p+CfdlD3uXWoCARN7rnxzeHrl0pGOGeNCzdDDNBJK4XM52Uep4lqAlhrNzu3GxxlD2c4TiELwrnsrzSmT+kqM1Ehbbbwl0AG8H/AY6G/PxBJx6tC4wYrmf9m3kiHNnNKy7FjJ7th4jqJoxheyBQDVFFT0b5+bGkp6MAgMfW93L14MxDESE8j0NSCERvLnyp2clfxOgb93RwO7sSGjbXcMusCZ86HAe1jLy8zx7mAAm8xdnGQIJHZSwtZwFH2sE3/SCzxZFMQzlHu0UFOcAAPQ6SRyUo2ET9m3FhJ4Dbuo0XrqTVPkcb4TInq9WJbMp/g0VMEm5pJ+M34XDSRkvmdEQmeFs0nePzUFK0vjLGeTWJ3YMTHkf/CAMGUWEgpRnYfpTBhOZ5gkIrBwywJVQ5KkQxu4z5MDfIij40+B/JJJJkC5l95i5FjULVe07W5GlDVLuDj59P3qhHcw6RJNvNUOZFSSWXXEYIaYBWbpnQNGw6oUX8AW1Ii4nQQaG3HlpJE2/o0Un8cKkc1OIQ47JheH/dsfgsYjZQHt5NJHkUsnNUlyM6FBs6QRxFz/fMn9k6dglTJ5Aa9myEGGKSPExygW9vJIId2rCCdhaygnjPs42VUlXiSSCaNlvR+vO3NZJBDF+0UsWDUNR3O6Gj6fHD01Lgw9khqiU7F0Nxkbv2tl1eXj4+8nAk04B+VilUDfjzr5uF6qRwjIQEzP4s/1e5l7jP3M/dD/WzjMTppJY2s8BiG2MjXEuqppIQl5DEXp1yc470cROQ1coUnmRKR/1bVvxGRPzCBr7eqvmm6Ma46wQ2WO1hM1ym6GEIQqjnBcqZIbammVb7KENTrRYc8NK+Op2/PKzh+uBNb+jz2tD9mxWQEIIFkHKecFBuLGWKAXIquGqEN4MMT9pc+H0SEWOKJJZ41mhRO7N9NO6vYRLKkk0w6ppr48dFn9NBltrBoYDGDZNJKA3MoGadtj82RviW3jHo9wwn2k0w6a+PfCD2RV4gfXmAeuG89cY/uwv3cQV59yhKCkUbINv7dRnL/Y/IgH2uwszb2sfmzbUMBK0dOMIgeOcnd869ngf8I3oCPIEF0Ah+KuSwmiVSymHPFa9hjMXV6P+6rwMY9bNP++vkOcFUKbhFhoa5kZ6iyeisNNGo1ORROKWBNnw9/nI0Tgzvo3dNJDoV48XC0/TnAcmlzEUMTNQQJUG6+Rjo51FFBKcsu1eldNPq1hzoq6KaDEpbOyJhuiWUOJcyhZFyVcEMMXLhxqZt0ycJmj6cb5QQH6KSVak5yM2+ecvyBUD5zkyCV3a8z174cgpGlxO27ZznxVQPEPWpFKo9cqBzW3IM3r8b20r5JxxgltCdZBJ0ota0tP5dATR3GtgMEAb1hBY76LhgaQkwTw9OPm1gCjD8Xp7hCi7xXH5GYSq50I7eqDt9QK1T1myP3icgngFemG+OqFNxg1XQc+QUfYy8+PBQZi8E0R/3IhgsuiM3G4f6XiCeJddw2aTGDPIpRVSo5QmbKQk53vUasJuDHyxzmXZFaUJe2cYidZDGH9dx+UQo5TDcrCfb3kyDJ4e8tSIAB7ZvSH76AeTRSTTELaaCK9kAzpSylly4Uc0oTVtK2MxNWkBnJVEJ7HBHkrwEw3C4C1TUYLjeDd5YRv6MSc99JyMkKF0o+STkxxI2qyH4tYAnuaULer3DBPYL3YaWOHcn7J9g2jqtWcAOs53ZOsJ9uOnDiooIjOINucqVoXBIpsDQhX8BLRsgFaypEhGJdRE3XXoYYCPsmx5IQLvJwpdCn3RxiJ0tYS7pcfkGxkTvZw4vkU8JRdrNGb570YRgjcazQ69nHK+FEXqcopz+UdC2AnyRNI45EBAgQIEgAE5OMzIUwjeA+Vyazs29tLOfOOassN8Dh+81hJ/bZQwSG4wYGrNzv3WlBOpqbKeN6bExdCu9qIxIb95VeSEFE3gG8EygWkZHRnAlARCler2rBHS9JrOFmXtM/McQAyaRzmkPY1UGmjE7cJTYbZGfgqR6M2A+7gPn00k0Gbly4qecM6XJlCW0ry99JciicFUIbrHS4uVpEH10oSi2nKGLhuHbDM6VkrFJXbTSSSib2UG4YAxsBAjRSxUCoAr0NBzZs+PDSX+mhQAsiWp+wl84lUHFm8v0lxQQqq7AlJ2MODY0rwHFX6QbEqVYWJTFCtU2tzzVvWoX9cA3BOVn0Np/gUPNOMox8dpvPs5rN51yE+kpGQy5/U7Y51xXz2ccOoAlIB/5zxPY+4FAkA1zVgnuYFVzPTp4Nh0af5ACmmuOiG72OAAZGRNn7OrSZSo5iM5wkm6nYceAm9oq7pVqoo59eVoXyOM8WSljCizzGIlZTzQliNI6sMd9XWHMV4SZ9M4fZRS9dpJLJCm7guOyjWWspYSkr5IZRfQe1nwMDr9KwdJDrjpROO8MaK7SH0x00B2qII5H4SituItARUpjG2LvNIU8oGMyBPTMdHRgEm42ybX08daSNmA9W0rrnBVqoYz5lHDGtNBfxXBq/8lnF1W/jrgFqRORdQKOqegBEJAYrOVX1dGNcFME9oH1UcIQ0skkixbJbXkbiJJEb9R4aqCKDXGKJZx+vkKZZ4dzNGgwSrLNS8Jpqjpqam2rSTTuD9OPAiUmQo4SiM03oogWwvE16tINEUq8ILxNVpYoTFDJ/1rmUGWKjVJdxhmMUsYAW6qeMBrSLnZWMFs7LWEc3HezlJXK1cJRwjpV4NugdvHjkMaoJMEdLMTEnfWjXf34j+V+2FiJ7nf00eStooR4fHpy4uUHvHm3OGWvvVhPD6cTvHaSq+Qi23GzcbV6Or2iilkdxEUMW+aznDpzi4oQesLyXJiiCfTVzLZhKRvA7YGQEWBD4X2DtdB0viuCOJZ5iFnCMfZygl2JdwFyWXFZh5pIY5oZKbiWQTED99NBpmTZCPzKXx4abWFqoJ4ezNTtPUU4Xbdhx0EMHN3A3y1hHBnn48dJBC5UcpY9u9vASACv1Buw4Qnk7EmelIDcxw14Zs5EiWUCdVmDDThft9GgHSZIWcX+r3NrrFLFgQo3aEBsr9AaO8DrVnCSRFK7j1rC/+EgKv3vM8v7ISeVE44vYsVPMIhJJ5iQHaaSaPC2e8Hsetnv3r8znwK7vkUASrY2HAShmERvYQizxo/rewN3IFTd/u3CupQAcwB6qsAOAqvpC2Qin5aK4P4gIiZLKermdQuZTxQlaOf/ototBLAmWW5/77I9DRJjPCk5RTqe24tFBWrSeVhpYwlqcuMljLm6JJUssH1qXxJArRaziRgDcxOHEzQG2s4eXeJ0XeIHf065N9GsPAZ28+MKlxiY2CphHN7O3tFUcCeGgnYO8hlc903fCmiWd4ACxxDOXyTNlpks2N3A3q9mMGfKZDhfLGJH9MdjdTZe2sa3xp8STSBnXM0dKSJI0FrGaE+ynzdaChKouGS53uO/weGd2/YYMcsI2ebA8lOIkYZzAFwQPQwQ1MtfGq4VIUrpePXKbNhEJB9uIyL0Q2Y/xovutFTKPxayZdZ4Wa7kZO3a2Dz7GUd2DR62MgsmSRoks5Sh72MXzNDkbmccy4kikjQYyJnHPagjlhjEJkkhyePtwAEUNp9jFcxweVd3o8uMmliHOv5L9xSaPuZxgP3EkkUU+pyhnqoyWYNmv9/MKQwxQyrJp3TPt4ghlQBzAVPNs5STT+hxVpS2xh0PsZBnrWCxrsI3Ih5IgySxlHTXBY1YRDtVRGvuwME8ghR46seNAEEpYglsmXgg/xj52spVTka1VXT2ooObUL8yrZibyAPD3IlIrInXAZ4C/iqTjRV+cdIqbXIou9secMw5xsojVFOsi6qhgF8+SSCo5WkAiyRQynyTSSPKnYk9Po6u90uqHi3ZtIob4Uf7FJSwhiVRiSSCORPz4GKDXqtoiKQQ1SD89s64Czg3vrGLbr61gjmHPia2N5QDcs3oLgabmy3Jcw+HsP61p4C8K51PDSRawgt28SBuNZE5Qzq9XuzjJAXroJJsClrMBp7gnGH08DnHi1lj66SGRUHoENfGph1pO09BTxQpumLSAQQJJ9NDJGT3OGY5yk96L052A6fWEHwQFzKM9ZZDOrlY2cy92mfjnF9AALdSRSzFehiZsc9WiXPGLj5GiqpXAehGJx0qxHXHY75UXKTLDuCWWebKcG7ibPC2mhXoOsZM+eihnB7V6mkBHR9gv+JjspYrjvM5zVOvJ8Dg2sZMlc0iQ5JAJxU2qZIZzpNjERpKkzqpFQI8O8rNfD/LLP3VbGwYG2dpYztJvP8imv/4wuKxjrf/8Rmq/uJG+P5ViL507yoQAlpBtfGwCc8SGslFtzoXWYD9bG8v5TNE6ciiigxZqOU0scXQw+mEyqH2c1kPsZxtp5HAzf8ZSuS5ioT1MBjnUcnrUtiPspo9uVrFpUqENluthAsn00UUs8fRmG4jDEszDhTxEhLKuJQTdBtUcn3Qsb2gG1EgVeRSf0zlc6QwH4MxABRxE5E4ROSkiFSLy2Qn2i4h8K7T/kIismq6viHxNRE6E2j8mYnleiEiRiAyJyMHQ6+EIj/EerAIOnxSRL4jIFyLpd024A0aCXRxkkU8W+eFtQzrAAbbTpDXEla1hSflakskkRmKoNk7TF4zIV37WMsQAQQL8/efehUEFgaZmtuSWkccOzJtW8dSOJ9mSWxb2pgAmCMCGuxdvJrf76PgdOy1hHbx1Nd/u7qLnPRtI+sXOUU3sGelseKFhVGIn311rec+fl2E7XAn04xI31+ktHGE33bSTQgZe9dBBM6000E07+cydNpnYdBSziO08jVeHcEkMAIpSyPxpPaNiJI513AbAaT1MR/NRtHgVtr52kkfoUXZxUOwp4Sh7Jk2TEDtisbicHeTrXBaelSlXN5Fo3BFo5CJiA74L3I5VD3KPiDypqiNrDt6FVWJsHlYV94eAddP0fQ74nKoGROTfgc9hmTgAKlV1RSSnGTrGh4FYrErxPwLuY5pyZ8Nc8xr3VAz/GOexHEd5NUfZw2v6FKaaeIL9U5aGMjVIhR6mRk/Sq52X8KgjJ0UyiCGOvoO7sRedzX1hLyqkbYWbLW99L2Kz0fqxsx5LtqUL0BtXnm1bUkywuzv83nC5EZsN2/JFYS3b9sI+/rg4BXd3kL840czA29YDlhbuXV4YFtq1/7wRY9US6t4dYOvvfz6qSK9bYlnJjdhx0EUb23mKVhpIJYv13EEGeRgXeDvbxE4SqXRgRVOK3YFihvNjR0omebRQz4GqR9jLy5zR0fVJ3cROaQIRERJG+G/Xc4Yj+vo5HcOVynT2bTUj1rivAypU9UzIc+MR4N4xbe4Ffq4Wu4BkEcmZqq+qPhsqbQawC0ZoeufORlV9L9AVKhy8ASKrgBHVuKfBJjZSySSVTEp1GXt4if2yDUONCYMjBrWfE8ZB+o1efMGzi345WkgmeSSQTDcd9NCBHSf5zMV1jlP6mSSXIg7wKsuq/KSJlT40UF1D1n/XAJZyk/ntsxp38MjJUU5qgcrRBTuGF+VGloYb5uXv/5B7NryBuJpd2JYvYsH/bCQ1R2n56RoW/tcARV/Zj/PZZCrmbR3X13fnWpzP7GGuLuYU5SxkJQECdNBMFcfw4yObOSzlwkqWFbKAI+wmW/OxxybS3dMxygskEmKJx8MgTtyUsZEjvI7blkBezELMlHgqa/80pQvmGT1GOjl48eLDup7N1FGqyyZdzLx6ECLMJVwmIiOLhf5AVX8w4n0eUDfifT2MuzkmapMXYV+wCiH8dsT74lDx317gH1T11WnOYXgFe1BEcrHC3SOyjUUF9zlgExvzdRn7Q9+HFw+JmkovnfTSRVHIdz1ekikNLmE3LwCwqejDHK15kjqtoJNW3MTiCdkxE0gk84Ie2hdGsSwiXpM4wuus0k3nHSw1XLcToPODG0n90Y5RubKB0Hvr9xA8dJzio5Z/c+IvCZe/9X4qgw0LHiDxV5ZJxXPvOtxPvM5LP/kRW3LLmEMpPjw0UUs8SeRQyCJWUcWJGSkZlyIZxGgc7TTT02OZf7poOyevqGDIoJRCBkmSymJdw5HAbpq8dXhr+0gmjRVjgoWGadcmznCMXIrIo4iqcBFxJiwSfFUSmamkfJqakxNJ/7EjT9Zm2r4i8nksy+GvQpuagAJV7RCR1cDjIrJEVXunOMY/hGzkXwP2hz4jomLBUVPJOZIqWSxmDQ6cDNDLbp7nFAdppIpD7CSIn6LgPJLWb2YOpSxgBc6aTlZyA7mf+iwF//kVNhd+mE3ut5BODieNQ/To5bWVZ0gupSzjJAci7mNuHm1zHRba9tK5pP5oR9j0Yi+Y/KE07N+8tbEce4al1T7z5C8ZyBF632XlT3c/YZkIhh8CIkKpLGON3MRCWUm2zMGJa1JPk/Mhh0KOsocarGo45eyI2H8coAfLNNZCHR4dIkUyWMxqunyN5DOXxbJmwohIn3o5yGvEk8QiVpNNQTjvCjBu4fSqRCN8TU89o80O+UBjhG2m7Csi7wPeALxLQ76pqupVtX7IobStlcD8yQ5ORAzgBVXtVtXfA4XAQlWNaHEyKrjPg1wpYh23U8A8wIpATCObRNJYyy3Ep89BXz/EotgNzJFSzM2rkHXLyf/ZSeZ/eh/B3DRik7JYynWUmEvYw0t0X2bhnc0cPAxxUg+OCvrI3XXWju+7ay32fEs4ehMEMz99XIDIcE6PQLVlagnU1k/72Vtyy3iq/IXw+5yv7yDxVzsp3B2H+xWrmILhcmOsnjhHeCM1JJA0ZfrXcyGXIopZxEpuxMAgNb6ILiLPIjiyWHEFh/GrjzTJZhNvpEDmTdqvjy7mUMo6bkNEqOZUuKYkQNssC2K7GERi446wAs4eYJ6IFIeiEd8OPDmmzZPAe0PeJeuBHlVtmqqviNyJtRj5JlUN20JFJCO0qImIzMVa8Jw0K5mqmoxIMBUS/D2RnBhEBfd545YY5ksZm3kTN/IGlrGORs6A3QahSvGIsLWxnK75bvT1Q2ggJOR2lRNobcOdnUeuFPLtf8vgINvZrn/itYKXOKkH8eql9d+1iZ213IIPL3t4if7QDK92Yydd2kavdmE8vZN9b+nkgG5n5x/+gVfrfsJ2nqZPu8PjfLRieq3Q+1wRAH3vPFuVaFij3pJbxtCfraPnvRv4Qf4OPJub6f+L9VbJsn1Hxo0VUD9VHAunM5gJRIQiWUCaZJFIKt39dRH53w9oL9V6gn562cQbAaGfHnbzIj71TOkK2qmtHJG9NHCGcl7Drz4KKGUNN4W17stpUrtkzJDGHVpA/CiwFTgO/E5Vj4rIAyLyQKjZ01jCtQLLRPHXU/UN9fkOVvrV58a4/W0CDolIOfAo8IDqtF4Jz4rIW+U88mHIVFFoiZKq6+TWcx3zmkRVeYHfU/bufyXjV+WwvozWv/eS+zcegjV143I0i92BlC2Aw6cw/QG85gAe8SAKzdTSRA2r2Uy8XNrscKpKI9VUcJh4kuijmzgSCeBnkH7iSaSQ+aSTg10c1GsldVTiwEk37dzA3RMuoNmSkqj+cQFz7juMvXAOgZo6bMnJYY8U/x1rcDxrrTVtbSznjj9/H7L94JTHaiss4GD177DjZNFFcpezZhQyKlJyMpq1jiO8TgxxJJNO95p48gZK6S+pxv1HL6Uy8Yzhfd95lfs/0sMS1tJOE3VUsIE7rGIgWCaUHjrImOUpg5/XR/ep6prz7S8i/5P9T598vzN/6vTCnmOnaf3GD/9jGhv3rEdE+oA4LFu5B8u2rqo6bWXu6OLkDNFFGwBPfvLH3P+rNXQvjCX77acJjMnLPJzuUwN+Kt4TT0zDGnK/tgOXxOCSOMAkkRRs6qCCI6zg+kt6HiJCHsWkazY9dFr5rUMJmkwNYowRYHnMDYVwG8SRyGFeZy03jxs32NODc7tlygjUhBYoR7gRvvjTH3P3rW/j7556lLl/+BCL6ltG+YzbC/JHmV2eaThISe4hhhhg9fTJ1M4b2yTRjRORRT799NBkq2PIGcS+t4GBrBN88M4kfjyBF8mA9nJ4XTfbPtpr+aDH51Dev4MVXB8W2mCVKpttKSMuFqLWa0qu8MhKEbleVV8DMoZTup4rUVPJDODRQfazjUWs5oPrN2DExpL8052Yg4NWwvyRjEj3WfI3u8j92o4J9xVQSgfNdOvlSQDlkhgyJW9UVr2xQhssQZ8tBWRJPpnkTVjcdmVozTPrv0cX1bUtX4S9qBDb8kVsyS0jePwUX5m7nDeuPshTO55EbDaGnrW8o0YK7f/Zv4/s3H66aGMF10ekDV8KrIXTpWQGc/AP9bLxjiFa2oJ86RudNFGDT72AZd4xr3+K/Y5dJO3tZI1uxn//FnRpCcuuj6GNRoZ0gE5txatDfOgH22nQKur1DLvj/++cFkqvOEysXCRTva5wwQ18K/TvNFWmJyeqcV8gqspr/Im4NfPJ3VtkJRkKnF1QEqcTfD5L04aI6xI+U3OMmELLbBGvSRelBuRM00f3qKi/kWHud8VuGFcVpmtZMom/Gu3v3fLEIuz3DbKlugwIsm3p42zhrFth58Y8Slb+kTyKKWbRrKzvuUBWsF+3UXdiE3/3wgA/eusrFOY7MA5Zx9pNO6f2x/P4zx38x/ffj7N1kPs+9QyfSKkmJsePj6pw0jKAVz8EObiwYae3z6SLNrKZg1991mxnFqYMPm9mKHJyluMXkf8B8kXkW2N3qurHpxsgKrgvkAaqiHekcd3eZeEf0HD+ZbE7zkb/jRHY9pxsntq3dZyv87Dd9w2FZeSjNFFDI9WkaibZFJBNwawUVgBdMb3k+HMhCE2f3sg9KzJ46uDz3LP+HsQ2fsE89bmKsDlk8K3rcbf7yLp3f3ib76613PKBtThCRSv8K+dy9LVvsYAVZMnsXqgrYSkHa5/lDyfeS2pnJ/s7D7E5tM9FDClJXXzpoc+y7bc/C/c5eMSLHz8lLKWSswuxecxlAStopZ56Kokhjhf09yiKXZys0I0kkcYQAzhxXREP+Um5NpJMvQG4DbgFOIdq1GeJCu4LpJ8e+vzt+PHhssdbGndoIVID/nBSfltyMmZfH0ZsLMG+PisvSN5KxG7DlpcNg0OoP4B6vRhOJ6bPx5Lkm8mf4+Ir//wEbR0B3v9gBccD+5jLEhI1GTtOEkmZNRpX7JCDdhrJTV1Kztd30PzARlZ+dR6DXxug5IM9o+zUhssNKUkMbZxLwsFmXv329wG458Y3QyAAdjvuz9bh2WwllDr9qQU0f/2/yKFo1gttgCRJpVgX0fCPr9BUo1z/Bic+91YCe24hQZIJNGWTv+6/6ejMJC5WcLsN7rrdpITFzLm+kvZdGfQE23DgYl4op0kqWSxhLT68JIZSxAbURz1nqDSOM2D2YBIkyyggLZhFOtnjHvIB9VPJUYpYeFkjdqfkKte4VbUdeEREjqvqpNnXRORzqvqVifbNTtXtCmLYTUzRUSYSgKE/W3c2BLy72yqP1tdn5fOwOzAcdsTpgCEPgbZ2JDUZIzcbNRV7RjpPH3uF5E4b/5z4F7z1DQk8+1g8X/h0CoFYG5VZLRwx9nCQ7WHb6eWmhKUM0Mvpgd1sbSxn3xce4sBnH+LkjT/n6ZPbCdTW43nTddiSkghsWEz3NxWb1wSXIzzzSPy55YYYqKziiXlbsefmYC8pxvv1n5NOzqSeGbORfErQ9k6+8B8dvPh/+WSm28ImkDmU8ofHhcwlZ4grriQ1p4NW6kkli22v+egJWovdcSRgFweVHGEbf+AoeyjntXCQD1heSDHOZG4u+DDruZ2YYCzVWS2UpzzOUd3DoJ7N+RIkQB0VvMof6dWuS3tBIkElstdVwFRCO8TbJtsRFdwXSD2VrHTdgkvc2JKTATBWL8VwOol5bERioJE1LIdzNIuBOThIoNX6kQbOVGPWNaABfzgg5ak9f+LFJVbcwCce/yRf+FQac257Fxsdd3HDko9irF3Kbl7ghB645L7fY7HqPt5Ii/cMj/7RSon3ruqbeM1jmYnuO95K00Y76vfjrO3E+e00XvrxjzjzzkwADvo8OI0Ap/86HzaUUfYfD1L77mJu+Oof6KCVUq4coQ1giEEZG/ned4O896PNGAbhsPxkSWejbGEzb2ITbySZdCsLIckUjAi466adXu2iiIWs5WZu4G42soXlbGQNN5NBLjHE0eo5w4mObdhxUCQLyHnwI5Ss+SxG2aKQX34P7do0KoS+jgoqdYKsjpcRMSN4XeEa9zkw6RMqaiq5QGw4aNQzJNx+O3avibHtwPhAkTEVv4cZW9dwa2M5S7/zIPE3tPGpZhv/mb1/1P7dX7J8/V95+IfcVboBIyWB1W3LacfkVFErddWVl124OcVFiS7l3791gLfeE8+vil4GDLbklmFLTubUsYe4+ZUPEnQZbPveDyh95AEqPvQQfAjAzc8Kt8F7tsF7YNnr7+Twul/zj19tJ5XMK7JwrlNcrNPb2Lf1AC32KgLso4lacrWQPrpJIJks8pkny8N9UjSdLtKYQynNWME/DnGSxNl6m8OLwNWqDDHADdzNiYEDXP83tfzLZ9K4q9gy0SUSz8f/MYHP/Otzo46rhKXEk0g5O/DoIPNYds75y6NcdCZ9REU17gtkGevo93XQcWYvth1HsCUkIDYbVV/ZiD0zA9vyReOEdtUjZaNyeBhOJ6d/uhqAvH/bQdLdpzn0N8uZjLu3/AVGchLS2UuwoZkURzY//adOepkuUOvSkE4Oxw7bOHD4rAln4G3r0cJc1v39A/zyB99g2/esRG4Vb3+YZa+/E7CiJkcu1h5e92sAbr4hlnaaLuEZzCyGGCyS1SQFrOyL3drGMfZSRwVV7n28xOMc1314dJBOtULrF7CSOBLJoxiHOPGrjx7tHFe2rZSlLGcDboll+Q3dfPm/Ozlw2MOfql6nVzs5rK/zvX+1CkBkMYdVbOJG3kCxLCSdHAqZTzN17OJ5ntdH6dQ2zAg9ny4Gw37c072uEaIa98UiThJYpGs4VPFH0jd9DufOkzxTt58tuUECYkBrG7Vf3MjxDz0EwLL/fpBTmx5iZOnJu07eja3J5K6S9RDK0/zcIz+d9DOlvRszNx0tP4ktK4NAQyP/7wOJxM2S7HE2sRGr8Rw44mXVckuL2/7N72NihnJmn3UZ/HD9xrCAHnYfvOued/Cnp34TbpOXbcfAhqnmrPWoiYSVciNeHaKXLvz46KCFQU8f0E0DVfjxTVhU+47NsbzysuVKeR23ni2tBsRLUji98K7tNtzEsvGONlLIoI1XyKaAIQaII5FORx1Ov4sUMgDL73weyynVZQzRzw62sp9XSCA5XBTi0hOBDfsqsXFHwP9OtiMquGeAZEkjSdNofu1JCh2LuXvpTUAXqInhdJJYpdz63vtxvnqEgrQqltoe5MjHHmLjpx9gx9cf5k8LnoYFcNv6v+T5X/0kZBOeQkDZbMjJasyAH7Orm0atoZGaCSMWLxcxxFNZZS1+zd/2Xk5t+vmEhQ5+kD8+BuFPT/2GLbllYUGeN9dk0yaD6m0nKWbRxT3wi4xLYsjAqq4zshZrUIME8ZNHMVWcpJs25rKY5X/r5fGvVZJFPqUsI0bO5kzxqRcHzrBX0Ua5E7ACwlpiWnEOuUJVdpTD7GLA30sdFXgYpIyzxTFEhFgSWKgrOcEB+ujGo4OXJ/e3GXpNxRWucYvIt5niLIb9uFX13yZrc+WqL7OMuSymKniUhvctRlyucJpS0+fD7lFceyvofSKPQGMTRz72EJ9sWs2Orz/MlryV/H7A0kCf/9VP2JJbxvXuyb+WRT98kMCcdNQfADEYjA1ymnJWs2nUj/pyk0MBf3pxAIBTm35+QWPFG24+dn8yXUwcRdquzfjVd0GfcbmxiQ2nuEmTbNbIZm7gHgpZQNfXlrCO21gm60d9v01awzb+wMncJzijxzil5XhCi9NuiSV10RHqqEAQbGKnjOtZz+0sYAX5zJ3wGPKlhJu4lxji8HOZrmckZpIrXHADe7H8t93AKuB06LUCIps2n7PGPeyXHGU0CZJMmmbT9/3/YWjL/RhBxXVQCXR0EP/ILp4ejiIMZfXd214AOfvY2jA6B/ZkRXX3+XyUe+ZYJpcPwcZP/RUxj+5gX9tTlLBkVG6Ly42qckaO81e3nd+D5B1Vt2AVAzlLeqqNAOPvuz7t5iDbyaGIJZx3fqNZhztU89KGDQfWouxwpGQdFTQnHOaNG+O44boY9h2q5XdP9BNPErkUoaq8vt+Dy4ilzWwgizmICPEkTVi1aSR2cXA9d13085uUayByUlV/BiAi7wduVlV/6P3DwLORjHHOGndUaE/OHEpppBq27sD+yiGwGRhOJ3rjSj7ReDYRUq85xKvLHjunsTuDsfxlYgtzn7kfgB3/+X06zRZcxJA3iQZ1ueihA48O8sW/S5u+8QT8pvhFTv94dOKohHhjVG5qsDL3nUx6kQSSaadx3MLd1YJPPexN/D9e4UkO8hqnKOeR72fz+E9zufmDcRx+YgE27GRTAFi+2i6X8Lf/z8lhXqdTW877swe1n1a9hHnAZ66QwpVALozKPhYf2jYtUwrusT+UKFOTKCnkUcwunqPZX4W/pZXBu1Zi232cijemng0yMWKmHGfZNx9k4Y8fHLXt9hgrEDzhkJP1n3mA9Z99ACM+HgeuWRM5OUw7zWSQi80W+XGNDf0/c9ePRr0/csIXzksdUD+1H22ned7TxK6eSzGL8OObsgDvlcqA9vG66ylu3xxL8+Fi3veAh9s2xXJg+XK25Jax2unkk/9Zh2BQTyWqSpAgfq/BJ/4ylaR5KznD+PqfkdJDB4fYiW+KxFaqSpe2nfdnjERUEHOaV4SLkyJyp4icFJEKEfnsBPtFRL4V2n9I5Gxu4Mn6isjXROREqP1jodJjw/s+F2p/UkS2RHCIXwUOiMhPReSnWOXLJrVrj2RKwW3OEi+FK4l5spylXEcVx9nDiwS3bkPcLgJNzdiWzOf2d3xgwn7/3lka/vvwJx5i7u862XLf+3hq0PLKuGeFtcp/23tfp2MZpP7xBINDHbiZ+iFwOeinhyQjjbsXbx4nkCdj2ER02t8/avvpMz7yc/y8/8FO5oeSTd31j6/hf2I7X/y7VGp+AUNYfVyz8FpcCH3azU624vUqHU/fTUa6na/9UwZbf5vHZ9Mq2dpYzpbcMn736U3sfj6blFUn2cVzVCXWct+9MaSm2Ej42BvopQuPDk7/gROQI1YJum4mrtDkUy/7eZV9vHLe5zmKGdK4Q9VovgvcBSwG3iEiY6tt3IVVqWYe8GHgoQj6PgcsVdXlwCngc6E+i7Eq5SwB7gS+N1wRZ9JTVf0frCLEj4VeG4bNKNMxpeB2EnXIPx9SJIN13EYm+ewd2kpvt5WfwzxZhfGKFVSzJbeMOwvWhAXV8y0LAbjzTe9m+X89iJ6q5nO//Dn/VnE3JY/+Fb7Fc7jrnnew82trec+dr4DPT6u3elRQxmzBTSxdZitPH5v8x7zyqw9OuH2eIz7894vbB1l+fTMxxLKBLSSLteD73/8ax6f/OoW3viEBwxB66GIRq2fdzONCWfyO3dx8fQz7ny/gubqJ64EOP/DKlrh49cl8CpiHr7eDuic2siW3jIWfOE6aI492ms/7OG6T+8iUiet5ttNEF60UsfC8xx9JRH7ckQ11HVChqmdU1Qc8Atw7ps29wM/VYheQLCI5U/VV1WdDFXLAcurNHzHWI6ESZFVYVXWum/JcrRv2NqBMVZ8AnCIyZZ9hol4lF4lw+Suy6QlpKxrwY8TEcPf868PvKwNWcMRzi/4IgBljJ+Ogj7pPruKr73g3QdOg8r7vM5jlpOJdSSQf6uTpr29mqL+TPrpJI+vynOAUzGUxzdRyY04JA/etZ2P5W0ft35JbxoHPWn7t1YE+Nv31h8P7Plxvuak9/cIAd7+tjSVcx1xZPKrsVwIpvLbbmrrf+tZ30EnLhIUGWp64slwHVRWPDnLb51/ikO7kV3+woa/fxd/dfh0LfzLxgw6g/QHrmt09ZwWFxTexXDYQJ4mYm1dx0+Eh8os2UclRTuj+cTVCJ8OnHuq0ctr2ORSykS2UsCTi85ySyDXuMhHZO+L14TEj5QF1I97Xh7ZF0iaSvgB/CfzpHD5vLN8DNgDvCL3vw9L0pyUquC8yTlz4GZEESoRgfz/2hfOwl87lzpizblfzXn4/sr0cBI58/CGeeewX7FrxKAAxrX5Ov/NhtKIW0wan9AD5lMzKMHCnuMiliBpO0bpWSHpLwyiTyY2HrOux6SMfpsieQNzTB/hKx3y+3lkS9us+ctxLChmkyegHk6kmrkXHuG6lNRs8vPQ0SaROWM8x697zt+1eSoIapFUb2MmzvO54mq/+Zg7/9g0/dbtSsAcNan63nMJ/2MGdb3r3qH73rLbMqOkPn/WFD9bV8/t6K0eO8cp+/vilm0mr8LD4Xf9IZ3w/FYxJxzAJfnyc5AAv8fioJFVjERFiJWHGZjuikeUrAcpVdc2I1w/GDjXB8GONLJO1mbaviHweq+TYr87h88ayTlU/glW2DFXtAiL6QUcF90UmQADbsNdlKKnUkw17+e6z/4M/J4l7bjg7eyt5zyFsKUkMZViLcPdsfgtg2X1dZ1pZ8eUP05fop+MnP6CdZuZQcsnPJ1KKWEg7jaQ8Usnpf1sRtscCpDusBFTbvvsDVn3pQUyfj5eXxfDSrWe9Yz74riRaaRin8Q3ST1NLkAfeZ7m12X6w7Yovovsyj3OInZSylKGaUjq2D/C/P/wg7/7A+9jaWE7Bt6yfqXGiii25Zcx9zFIun9q3FYC6L2wMX1sNBnlr/rrw2PG/3YXeuJLuTU7WuG6jjgpO6MRml5HESSIruRGAnZF5qM0MM+dRUg/MGfE+n7Az7rRtpuwrIu/Dyqn9Lj3ryhTJ543FH7KDa2jcDKYPPwKikZMXnSH6yRoWLGqytbGcOws2IA47tsV+ntr+RLitPpdD268KSPvp66xMe5DcoRruKllPwDtITfBZGr9TjYGNOBJYxY2XJ7ItQhziJEnT8OzfR8mBVrZ8sixcxu2BpAYW/fBBXvrLr7H/Hx6Cfxjf3+WaWIOLIZagqZR99T0c/Mwv6KSVBay8mKdy0XHippRlZEoed+blhQpxnKL33Ru4/tBbWPPfx/hmbvmIaNKzvv4fbbyOYw88BA+M98wBMFYtwXz1AHmZ64mNSWfx2vdxbM/PyNFCkiR1yuNKkyxu0nsJEpl5ZSaYwZqTe4B5IlIMNGAtHL5zTJsngY+KyCNYi4Q9qtokIm2T9RWRO4HPAJtVR634Pgn8WkT+C8ulbx6we5pj/BbWomSmiHwZuI8Jfw3jiWrcF5kB+ogLuWoasbEsfvhBmj+6FtPjxag+mzjpPTWb6fO6Sfvp65z6zhpyf1eBmZaEd7CH14PP0U8vi1nDBrmD5bKBxGl+dLOBWOIZoDecUEtN5Z4b7uXuW99G1t4A75szvhCyqjIvx0FaSZ1V7YfRC/M2sSPd6bSV/5L8wj4SSJ69BQEiQFXxMoRrhCPAcCGO73zpW8TfWck3c/ew8qsPYl84j2eGnFaxCeCetXdx5lZ3KMfNxJj7rbStr377+2AzOPzkDh77aQ7lsoN+HV+VaCx2ceCSK89bJ7SA+FFgK3Ac+J2qHhWRB0TkgVCzp4EzWAuJPwT+eqq+oT7fwfK9fk5EDoaCZgjt/x1wDHgG+IiqTumWp6q/Av4O+ArQBLxZVSfNTzKSqMZ9EQlq0KqME3JT633TcnR5H6k/dLO14QBPDbp5oH4DdiPILwpf4cav/xXmxgQWPtSLpicj1Q3UUUEiKSyRi1fJ/GKRQiZnOBqueqMBP2Z9I6bPR+0nrmPR8bl8s6uHT6RUh/v09Jqc4RjruI2Esy6yo1jKOhpeO4ODPLJHzU6vXMrZwc28OfzenpbG3xetZXDrXLbkwoHGh7j7lzdxZ4yPb1RWcVfxOho+VkTO1y379rC23f7gRtIfGp3/JXjzatZ8cSMxa02WfutBZF0P+dkv0dRUG66uM2uYwchJVX0aSziP3PbwiL8V+EikfUPbSydoPrzvy8CXIzu6MKeBXkKyWEQKVLV2uk5RjfsiMkQ/LtyICLb4eJJ31HN84y/xftJKv3pPrIeH83fyndzdPFC/gfg/7MfYcYjg4ZNQ24Skp2KKhpPvX2kkk8Yg/fToWR9g02ctxv7sjh+CzeBHpzay/uB94f1dPSZOXFOGZjvFRbEsIl/mXtn1FbEW9xJIJs82Ovo10GFds7i7awC4/pN/RbCzKyygTa8nLLRHMlZoA9hfPURSpY+OJQYxbRD3WCLOmGQG6J3p07lwIlmcvEoiJ0XkY0ALlm/4H4GnQv9OS1Tjvoi0UEcmls3S9HihsYW7itaSXKCwzWpzx5+/j9Y1seT+6BDYbBgxMUhGGvT1011xkEbOsJwNl/dEzhOb2FmsazjETjbqndjk7O32lVWbCXZXkPeVZTzzuOU58+nqxfxsw04yyL3qfLKnYhGr2R/cRjELx3kJDZtN4n+7CyMmBnPoPKJDVyzEXV5N8XEn2j8AIgQWFzF05qWZOPyZ5RrIVTKCTwALVHXi6KYpiGrcF5EAARw4rYrvhlimAp8PM8HNnXNWcffizdi7Bsn5wUGrGrwIwd4+6O3D19zMMfYylyXhwJMrkQzJJZmMUSWzwKrBaTidDOZZs4llr7+T3204jAPnFb/YeK4kSgqxxNPvnlwoVz1SFrHQHlmkA8BW14I5JwvsNtTjhbxMBo/sJzmUl3s2cY0VUqgDpl9omICoxn0R8YufBE0Oe1OIzRYqsgDmTauwVXegtU1IaSEcPol6vaAmQy0NHGZXKIFU8WU+iwtnPsvZYTxHvjmXuOQcmt+1hISGIO4nXucb3/gO8372UYKf/SHdtLOGm67oYgnnSzYFNNhqSZnA5qyqpP7FS9TQTg+ddNNOAB/JpJNGNvnMHTWbGV5TAKu6UqClFbtpQlwcpteDpzSF7iM1JDMLFYJrS+M+A7wsIk/B2WAPVf2v6TpGBfdFROw21K+Y/kDYFXBLbhm2pCSMnccIeD08Ur+Tt5Vej2EGQQVV5TSHcOBiCWuvCpOBEzdB04eW5CH9dhK++wJD9PPRx45w56duJPjoVxFgNZtnZUDRpUAQ/APdVgEYVTzuAD5PL4JwmNcRDJJIJY0s5rEMB046aOEE+2mmlmW6nh08w43cg0tiMGJjMQcHMX0+Bt+6nrjH92BLtMrqtT7+S3rpZOEsnNkMB+BM1+YqoTb0chJh4M0wUcF9kQhqgPZAIyUsBDWxZ1rT0uZPbiT3u3vDi3TvKLyBlwK/BWCOluLHRz+9rGbTVaN5igg2tbOn8he4iGGIfmJJ4P4/C+BgO3MoCeeMvlZpo5EeOjm2xc7gM88z4OnDTQwmJpnkjSomPEwW+aRrDjvZyg6eAaCFBoqciyFw1vc69ve7UCBYXUvnW5ZT9bvHWMPN5/2QPKy7SCCFQubP/Hd2jWjcocCbear67mkbT0BUcF8kmqglWVPDQTKB1jZKHv0r5n9vv2XztjtCgRZBVsiNHNRXAYgjkQWsuOo0z828iWZqceIilUyMqROnXXOUsZE6Kuh75jWyyCeXolHmj8mwiY0Vej2VHKWNRk5xkBRHAQkDTmzx8Uhqcth0InYHXb/7FWlkEScJ04w8OQH8VHAYHx7m6fIZFd5XmQ17UlQ1KCIZIuIMJbI6J6KC+yIQUD8nOUAZGxG7Aw1Yec3nf6ac2r9dRf6XdiA2EIcLDQZJ00ycuEkmjSy5OvySx2KIMarGYpTR2MR+3hn24iWJMjZiqkkdFZzu38Uq22aCA4P4+jowMHDEJ9LWX00DZ1jKuukHnYIyruclHqOW05iYJGgy6WRf0JhhrhGNO0Q18JqIPAkMDG+M2rgvExr6L43ssNAGMIeGKPjafkygJVBLv3cAv3+QLtqII4GMaZOJRYkyOYYY5OtcOmjm5eDjJJJCJ63YsOPsd+PASQZ5F5wK2BCDAp1HKw100049lRf8MBhmRBKpKdtcJTSGXgajK+FMS1RwXwRMgtiwEyQQrtoyjOTnoBVnqOEUPp+HfEqYTxkpZFzTNt4oM4NN7KxiEz710kUbhSygnx5cuMkkf0bWTXq1ixpOkUY2JkESSSWDnBk4+hBXj0Y9Jar6xfPtGxXcF4Fu2kkhfcKovn0Vv6QdK0fJKjaRKpmX+vCiXAM4xUUW+RzU12inCRt2jrKHW3nr9J2noElrOMoeADpoJoY41nGbZY+fAYEbiY37arGBi8hLTHDVVPWW6fpGBfdFoIt22mmmQavIk9F+2MOZ1pZwXVRoR7no5FDIIH1kMYeqC6g9CdCjHZyinDmUUkcF+ZRSyLyZTTtwbdm4Pz3ibzfwVogsFWNUcF8EhuvzORif3N/DIHNZTGZkxZyjRLkgMsilllNUcTxcs/NcGdR+TnOYHjpIJJU6KihmESUyQ1VvRnINadyqum/MptdEJKLinVHBPcOoKiZBsikgdURIcZ92c5jXCRCgmEVRe3aUS4IhBmu5BVU9r3tuQHvZy8sUMp8S9wr2ep5lJTeScrHC5c+tWMIVjcio3MwGsAYic8+JCu4ZpoZTDNLHcjaMmkI2Uo1ispLro0I7yiXnfO+5Afrw46OZemo8pyhl6bhycjPJDBZSuBLYx9mzCWC5B94fScerIzRvFmEbkfhf7I7wv3NZzBADnOHY5Tq0KBMQ0ABnq09FGUum5HELb2EBZazlFvLlEpTLm6HyZSJyp4icFJEKEfnsBPtFRL4V2n9IRFZN11dE3iYiR0XEFJE1I7YXichQqLhCuMDCNCzGKg5cDhzBKjy8N5Jzi2rcM0wfPSxgBfGSGE7Jafp9nOEYSaQx7zztjFFmnn7tYRfPAXCTvhl7BJGK1yKGGBfPNDKGiPy4IxDcoZDy7wK3Y9WD3CMiT6rqSM3pLqwSY/OwSpc9BKybpu8R4C3A9yf42EpVXTH90YX5GVYRhW+F3r8D+AXwtuk6Ru/UGcaJk0FCVbHVugOPs48eOlnFjaPKQJlq0ksnfnykkBkVHJcYJy7ymIubmFEzpSiXkZnzKrkOqFDVMwChupL3wqgp773Az0OVcHaJSLKI5ABFk/VV1eOhbZGe0VQsUNWRmtxLIlI+aesRRE0lM0wCKXTTjket3Mn92k0j1diwj6vdd4BX2cvLlLOD6gt01Ypy7jjFzSJZRbFEF4tnC+eQj7tMRPaOeH14zFB5WPmuh6kPbYukTSR9J6JYRA6IyCsicmME7Q+ISLhgqIisA16LoF9U455pMsjlGHvZzlMkagq9dGHHwRAD+NSDc0Rh2xKWhKLaYkjl4i34RIlyRRGZRl2uqp+ZYv9ET+KxI0/WJpK+Y2kCClS1Q0RWA4+LyBJVHVcfTkQOh8ZzAO8VkdrQ+0KIbBEsKrhnGEMMNuqdbOcpeukCrMxvtZxmG39E1OBG7sEpLpIlfXYms48S5TIxg14l9TCqknQ+Vl6QSNo4I+g7+pBUvYSKIajqPhGpBOYz8WLjGyI4/imJCu6LgEvc3KJvoYcOWqjnBAfw4yWOBGJJHJe/JEqUKCHM0Gu6NtOzB5gnIsVAA/B24J1j2jwJfDRkw14H9Khqk4i0RdB3FCKSAXSG0rXOxVrwPDNRW1WtiegMpiAquC8SIkIyUY06SpRzIpLIyUiGUQ2IyEeBrYAN+ImqHhWRB0L7HwaeBu4GKoBB4ANT9QUQkT8Dvg1kAE+JyEFV3QJsAv5FRAJAEHhAVTvP6dzPgajgjhIlyuxhBnOVqOrTWMJ55LaHR/ytwEci7Rva/hjw2ATbfw/8PrIju3CigjtKlCizBkGRaQKiptt/LRAV3FGiRJk1zFQAztVOVHBfZlSVHjowsJEoKZf7cKJEubxcW2ldz5uo4L4M+NRDC/Wc5OCo7bfqW6OBIFGuaa6xJFPnTVRwX0IC6qeBKqo4TgCrFuUcSsOln6JC+8Lw6BC9dOJhEBs2Eki5aLMYv/pwiDOiY6rlFG7iyKVwVMbIoAbpoxsPAySQTJwkRvz5qopiEiDAUXaTQxHZV0uh6ajgnpao4L5EtGkjx9mPHx8ackRdy80kyYUVbr2W8asPPz5aaaCXTjppJZl03MRiEuQU5eRoIenkkBwqJTegvRjYcBMb8YPSo4N00gYoPjwECFDNCbK1gERSMAliYMMkiJtYggTx48PDIM3UkkMhPbRTxTHS1arN6MVDH124icVNLKcox6Eu4kgkiVRs2OmklU5acOBEMAjix4mbAH48DAJgx4GJSQ+dtGsTihn+DxTBwEUMc1k0LuXCbCRq446MqOC+iKgqXoZw4qaeStLJppFq5lPGHEqjGvYkeHWINhpJISOshaoqg/TTRSsD9NFNO4P0Y8MWrly+iNWjtOBiXUQDVmHmw7yOU10ECSIIPrw41UkMcZRx/TjtWVVpooYmauinh1QyQ0LQjWCwkhvopTt8DEGCGBj00IUDB3Yc2LGzgS24QmkOBtQ6bgAXbuJJwi2xAAQ1wAB99NNDH90ECZJCOgtYQQA/iokdBz482HAQQ9yowr8D2ksPnRgYGNgwQmmITEwqOcKrnCFbC4gniRhicRNHAD8GBsmkz5p78VqqOXkhRAX3ReQ0h6ijkjgS8DLEGspwE0ctp+mmnWW6ftb8YC4EVaWLNoIEcBFDAsn00sUQA2FhMywkHLhIJIUYiQv3D2qAIQbppp0BemmlnkTSqOAIKCSTjhcPXoZII4t4ElnAChJJnbJqeYzEUcoywNLOfXjDAs/UIN10cJhdNFFLAaUMaB8t1OHHxwC9ePFQylJSyJiwrmJaZMVKwsRJAnEkTLjPJnYSSSGR8aYdF2fz27iJnWTsROKY2NSSobkMYAn24X89DCAYmATx4SVOE3ATSy5FJJE2I9XgzwtV6zV1o0tyKLOZqODGsjUaGDMmRHu1i27aaaaWVdxIBUcwCWLDzlxZRJEuYD+vcIpyFrBiRj5zpvCpl3aaceLCyyC9dJFKFoo1exAMnLgYoBcPgwzQSz+9xBCLi//f3r3GxlWeCRz/PzOeGY899viWe0ISiGFJCiyXJGRpt13aLgStlHZ3keh+KIuQUCTQfoV+W61UiU8rbSuWFK2Q4MMKoa26WAKV0nRphSAiCSWFQAKO05A4dzseX+Y+8+yH83oY32ZOnLFzHD+/6Mhz5rzvOWds68nx897iTDBKmCbKlGmjgzFGvDQFEZQyeXJ8zmFEpZLnF4RmWminizaS3MkuktJNWcsUyLtVysOsYsO8f0YRiRLh66fqkIT5SP8AwGo2cF5P8wUfs4oNNNNCBz10s6qxC+FeJyJCgiQJkjOOqSoZJsgwQYphjnOEHBmaNY64f2GaiNNKG510s2rKf7oNZ0/cvizrwJ3VNJc4x3H+6L3hfiHitPKA7J5Sdlgv0Ewrcbxf2rkCSEYn+JD9lf3znKZEkTARTnKM27mHkIS4Q3fxPr/mFt123YNDWctMMMoVLnGKL2in0wXlMdZzM4MM0ESEGHGyZChRoJ0uOuhhHZtJkKx8hqIWGGeUNjoIy+xzXBc0T5kyESIwGR5m+X6GxEtNrGPzgnzuBEnGSfEJB8iS5k520SHLa4oCEaGFBC0k6GYVN3M7WU2TJ0vZ9c0rUiTDOH/mGMf4iO/xjwt3P2WQUp1C/uYquaEFOnBP5ojDNFGiSBNRQggF8nzAb9wTm7CN7Rzlw0q927ibBO3kyJJmnA3cQhORGcFhmEuVoB2jmQKFylPGQf0dGSYoUSJGnBJFyq7RKUYzrdpOlBhhmjjPaRIkCROmi5WsZgNjpEgzzllOIoRoo4O1bKpcO4KXX73I4JT3F9slPcsR3qeFNjro5hvsoFPmv9pJk0TooHaDq5/eGIvhPr5TyTlPNl4aaJaWSkqmqAVGGGKCMUoUuZtvLuzFrR+3L9c1cJe1zEXOUKJEExHGSVGkQJECaddQEyZSSTNM9siIEK30zACdErRDhLjMOc5ziijNhAjzHm+iKG3aSdw9NRfwcp6TcmT5G37AFS5TokiUGHEShAiRJ+vlRt0qKWnGyJImT44cGe7l2xQpUKLIEN6T+UrWkyBJjDhhCaOqFMgzrBc4w0kuMUiS7qvOkzZCWcuMk2KU4Upf8h08uOwCV5NE6GHN9b6NQCprmUFOMsBnxGlhJeu4l2+TkJnplkayxkl/FixwT+bOJlMLXrqgCRGhpCVGGeYkxxjmAitYC0Ar7cRppYkIa9lEgmTNp7PqRV4VnfNP7pxmCRN2DWbjZMnQQoIk3XSxkizpSmt8j8wMpFFiU/Zbmb0hKKVDlcalPDmucIk8WS7reSYYdXXb6GENW7lvQZcqK2mJy5xzjWwZ8uQoUyJLxv1cWminm21sp4e1tmyameIEnzLCEHfzAO3StYhX9tE4aXOVNDZwZzVNkSJxWsiT431+DVSymMRpRVXJkqaVJB10s5oNrJVN87pedZCWGpM9TnbH6mIlsHJe16pHVTnC+1Oe4tew0WuQZCttJKesfuNXVtMMcb7SI6Kb1bP+ZzauKa5wufKXyijDtNNFkm4SdLi0TpgYcVpIELZAbWrIknG9WYSSFhft98XXE/ei3EmwNfSn8V7VLIhJulnDRs5xig56SJAkwwTNtLgFBdpopzMw+c5rdZFB8uRIkCTNGFu4k5tkS806JS15/XNnSVGUtMQAnzHIAN2sppmWyjD5nfq9Sv9fr2yRA7xDlBjNtBClmZu4lVWsX/A/bc2NJa85znCCJiIMMsBFBmmljV08tCjX9zUAx2fjpIg8DPwH3pza/6Wqz087Lu74I3jzcf+zqn5Uq66IPAr8K3A7sENVD1Wd7yfAk3jzcf+Lqr7t706vXkMD93Ye5DRfkmKYPDnu5pts4Q4uMUiODE1EKFIgxTAnOEqRAlv0DjbJbY28jesiQZIeVpNimG5WM0GKP+sxEiRpp4uoxBjRyxziXaLEEELk8BYUvl+/Xwmw5/UrPnU5+1ba2cp9KOVKn+gCeVIMTenPK4ToYQ0phhlnFBjlMuc4yefs0r+9qqHUZvkqaJ5D/B9tdNJGB9vYTsx1BFw0io9USf3TiEgYeAH4Pt4SZQdFpE9Vq9d03I23Uk0v3go4LwI769T9FPh74BfTrrcVb6WcbcBa4Lcicquq1usjMy8NDdxJ6SLJzsq+qvIub0wpEyaMojTTQg+rXfpi6WuVNu7SB0gzzjgpCuRIM84pvmCMEUTFNW7KlHRKki7iJADv+1Vyw6cFIUeGzzlMiLDrjtfMBrZwhcsM6GcUKKAoRfKuMbWFCDEiRIi4Hi+xOQZsGDNdiiHSjNNCgnY6SdI9Z5fOheJrkil/dgD9qjoA4JYn28PUxXj3AK+6BRUOiEiHiKwBNs1VV1U/d+9Nv94e4DW39uRJEel39/BBQz7NNPMO3KpadzCEiPBd/QfGGKGfTxjmIt/i7xBCi/4LsRhEhFZmjo6b7FHibTmyZEgzzgQpRrnCQX5HVGOMMQLgjSyklTgJN8RaKFOmSJ4cWZpo4hvsdI2mQoTo9RvpZm4YPbKGB/WHnKaffj5hnFES2k4bHcRJsIaNlfaiBdWY7oDrgNNV+2eg6qly7jLrfNad7XoHZjnXgqgZuHOk+Uj/QIgwebK00EaKIfLkKFGkVdvpoIcWEpX8apxWosQIVQXmZuK008UwF/k9fV5g0gQRooQJVybFuZmtRCVW446WJhEhSswF2plB/SKDCEKSbi+NcgMMgzdLU0jCbOQ2NnIbRS0yxhUmGGWEId7jTURDxGmllTbyZAFvAM/kX43XrKRIqc4KOGUFuEtEqldQf0lVX6ouNkvV6Seeq4yfujNuax515q1m4A4R5iZ6KVEkQow0Y2zmL4gRJ0SYMUZIMUTGjbvLkibDBAVyiHpDZYsU3HBnr7ueoqQYJsXMdTQ3citw4wXuWkSEVay/3rdhzAxN0kQnK+hkBeu5hW26nRIlxhkhS8Y9iChpJsgw3piL+h+Ac0RVn61R6gxQPc/teuCszzJRH3Xnc72GqRm4I8Toka8HKEzPRyfpIsnMPp6q3uSS3mjHyJQ/48vq9SXOkSHiMrOzjWo0xgSLiNBEEx1MnRZgMgKc0i+u/Ro0bADOQaBXRDYDg3gNh/80rUwf8IzLYe8EUqp6TkQu+ag7XR/w3yLy73iNk71QNTKwwRakc6aIEHb/pgtJuDI3gjHGTNGg2QFVtSgizwBv43Xpe1lVj4rIXnd8H94q7o8A/XjdAZ+oVRdARH4I/BxYAbwpIh+r6kPu3K/jNX4WgacXqkcJBHyuEmPM8uKnH7ffSaZU9S2oGlxCJWBPvlbgab913fu/An41R52fAj/1d3fXpmbgHuPK4d/q/yzGfRhjlr7L13oCUUXqPHHbXCV1Areq3rdYN2KMMSj1n6gtcFuqxBgTHP6euC1yW+A2xgRHGSjb7ID1WOA2xgSHzcftiwVuY0yA2HzcfljgNsYEhq/ugBa3LXAbYwLEzwAcC9wWuI0xwSFlnZxEau4yliqxwG2MCRLLcfthgdsYExxl6g/A8Tnk/UZmgdsYExi+BuBYktsCtzEmQMoK5TqP1PUG6CwDFriNMcFhc5X4YoHbGBMYNleJP7bCrDEmOCb7cdfbfBCRh0XkuIj0i8hzsxwXEfmZO/4nEbmnXl0R6RKRd0TkS/e1072/SUQyIvKx2/ZNv14jWeA2xgSHuhx3rc1H4BaRMPACsBvYCvxIRLZOK7Ybb4mxXuAp4EUfdZ8D9qtqL7Df7U86oap/6ba98/wO+GKB2xgTHGWfW307gH5VHVDVPPAasGdamT3Aq+o5AHSIyJo6dfcAr7jXrwA/mMenvGYWuI0xgTGZ4661udbJu0TkUNX21LRTrQNOV+2fce/5KVOr7ipVPQfgvlavoL5ZRP4oIr8XkW9d/af3zxonjTHB4X+ukiOq+myNUjJnzfpl/NSd7hxwk6oOici9wP+KyDZVHa1Tb17sidsYExzlMpTqbPX6eXvOABuq9tcDZ32WqVX3gkun4L5eBFDVnKoOudeHgRPArX5udD4scBtjgqNxvUoOAr0isllEosBjQN+0Mn3Aj13vkvuBlEt/1KrbBzzuXj8OvAEgIitcoyYicjNeg+fAfL8N9ViqxBgTHEpDpnVV1aKIPAO8DYSBl1X1qIjsdcf3AW8BjwD9QBp4olZdd+rngddF5EngK+BR9/5fA/8mIkWgBOxV1WF/H/rqWeA2xgSHasPWnFTVt/CCc/V7+6peK/C037ru/SHgu7O8/0vgl75urAEscBtjgkPL3lavzDJngdsYExyTDZC12CRTFriNMQHiK8dtgdsCtzEmQGwFHD8scBtjguMqJpFazixwG2OCo1SGUKl+mWXOArcxJkD8PHHbE7kFbmNMcJR99OO2XiUWuI0xwaFaRuv247bAbYHbGBMcZfXRj9ty3Ba4jTHBUS6D2BN3PRa4jTHB4Ws+bgvcFriNMYGhqmidVEjdHPgyYIHbGBMcJR+TTJXsidsCtzEmOPzMDmj9uG0FHGNMcHipkvqbHyLysIgcF5F+EXluluMiIj9zx/8kIvfUqysiXSLyjoh86b52Vh37iSt/XEQeusZvRU0WuI0xwaH69VN3ra0Ot4zYC8BuYCvwIxHZOq3YbrwlxnqBp4AXfdR9Dtivqr3AfrePO/4YsA14GPjPyaXMFoKlSowxQVE4UfqUKNGahbJkAAp1zrUD6FfVAQAReQ3YA3xWVWYP8KpbCeeAiHS4BYA31ai7B/iOq/8K8C7wrHv/NVXNASdFpN/dwwd1P/U8WOA2xgTFs5cY/IXPsn8lIoeq9l9S1Zeq9tcBp6v2zwA7p51jtjLr6tRd5RYURlXPicjKqnMdmOVcC8ICtzEmEFT1CnDYZ/HDwM9rHJfZLuGzjJ+687lew1iO2xhzIzoDbKjaXw+c9VmmVt0LLp2C+3rxKq7XMBa4jTE3ooNAr4hsFpEoXsNh37QyfcCPXe+S+4GUS4PUqtsHPO5ePw68UfX+YyISE5HNeA2eHy7Uh7NUiTHmhqOqRRF5BngbCAMvq+pREdnrju8D3gIeAfqBNPBErbru1M8Dr4vIk8BXwKOuzlEReR2vAbMIPK2qdVaEmD9RG/dvjDFLiqVKjDFmibHAbYwxS4wFbmOMWWIscBtjzBJjgdsYY5YYC9zGGLPEWOA2xpgl5v8BWbE58Ns6GYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds_in.isel(year=0, month=8).burned_fraction.plot.pcolormesh(ax=ax, robust=True)\n",
    "ax.set_title('Original')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "dsregrid_out.isel(year=0, month=8).burned_fraction.plot.pcolormesh(ax=ax, robust=True)\n",
    "ax.set_title('Regridded')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set time to be a coordinate\n",
    "use for selection and sorting by month vs year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "regridded_out = dsregrid_out.stack(time=[\"year\", \"month\"])\n",
    "# view current time format\n",
    "#regridded_out.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1997-01-01', '1997-02-01', '1997-03-01', '1997-04-01',\n",
       "               '1997-05-01', '1997-06-01', '1997-07-01', '1997-08-01',\n",
       "               '1997-09-01', '1997-10-01',\n",
       "               ...\n",
       "               '2016-03-01', '2016-04-01', '2016-05-01', '2016-06-01',\n",
       "               '2016-07-01', '2016-08-01', '2016-09-01', '2016-10-01',\n",
       "               '2016-11-01', '2016-12-01'],\n",
       "              dtype='datetime64[ns]', length=240, freq='MS')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# update time to be in yyyy-mm format\n",
    "times = pd.date_range(start=\"1997-01\", end=\"2016-12\", freq='MS')\n",
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "regridded_out = regridded_out.assign_coords(time=times)\n",
    "#regridded_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store regrid file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store global regrid file\n",
    "#fout_gfed = '/project/tss/jkshuman/obs/GFED4/regrid_GFED4_1degree_1997-2016.nc'\n",
    "#regridded_out.to_netcdf(path=fout_gfed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select region of interest\n",
    "\n",
    "Using SREX Regions (Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) from Seneviratne et al., 2012): https://regionmask.readthedocs.io/en/stable/defined_scientific.html#srex-regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;region&#x27; (lat: 199, lon: 288)&gt;\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.0 -88.1 -87.2 -86.3 -85.4 ... 86.5 87.4 88.3 89.2\n",
       "  * lon      (lon) float64 -179.0 -177.8 -176.5 -175.2 ... 177.2 178.5 179.8</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'region'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 199</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6b4f2af8-9a74-473c-8d44-6f0d61ec97d5' class='xr-array-in' type='checkbox' checked><label for='section-6b4f2af8-9a74-473c-8d44-6f0d61ec97d5' 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>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-928c4034-0aac-4955-8ee3-62e13ef2de60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-928c4034-0aac-4955-8ee3-62e13ef2de60' 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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -88.1 -87.2 ... 88.3 89.2</div><input id='attrs-73b6c658-f6be-447c-a6b1-6ce6096731c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73b6c658-f6be-447c-a6b1-6ce6096731c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af9fc0f2-d7d1-4834-9dbe-7348f3a62f67' class='xr-var-data-in' type='checkbox'><label for='data-af9fc0f2-d7d1-4834-9dbe-7348f3a62f67' 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([-89. , -88.1, -87.2, -86.3, -85.4, -84.5, -83.6, -82.7, -81.8, -80.9,\n",
       "       -80. , -79.1, -78.2, -77.3, -76.4, -75.5, -74.6, -73.7, -72.8, -71.9,\n",
       "       -71. , -70.1, -69.2, -68.3, -67.4, -66.5, -65.6, -64.7, -63.8, -62.9,\n",
       "       -62. , -61.1, -60.2, -59.3, -58.4, -57.5, -56.6, -55.7, -54.8, -53.9,\n",
       "       -53. , -52.1, -51.2, -50.3, -49.4, -48.5, -47.6, -46.7, -45.8, -44.9,\n",
       "       -44. , -43.1, -42.2, -41.3, -40.4, -39.5, -38.6, -37.7, -36.8, -35.9,\n",
       "       -35. , -34.1, -33.2, -32.3, -31.4, -30.5, -29.6, -28.7, -27.8, -26.9,\n",
       "       -26. , -25.1, -24.2, -23.3, -22.4, -21.5, -20.6, -19.7, -18.8, -17.9,\n",
       "       -17. , -16.1, -15.2, -14.3, -13.4, -12.5, -11.6, -10.7,  -9.8,  -8.9,\n",
       "        -8. ,  -7.1,  -6.2,  -5.3,  -4.4,  -3.5,  -2.6,  -1.7,  -0.8,   0.1,\n",
       "         1. ,   1.9,   2.8,   3.7,   4.6,   5.5,   6.4,   7.3,   8.2,   9.1,\n",
       "        10. ,  10.9,  11.8,  12.7,  13.6,  14.5,  15.4,  16.3,  17.2,  18.1,\n",
       "        19. ,  19.9,  20.8,  21.7,  22.6,  23.5,  24.4,  25.3,  26.2,  27.1,\n",
       "        28. ,  28.9,  29.8,  30.7,  31.6,  32.5,  33.4,  34.3,  35.2,  36.1,\n",
       "        37. ,  37.9,  38.8,  39.7,  40.6,  41.5,  42.4,  43.3,  44.2,  45.1,\n",
       "        46. ,  46.9,  47.8,  48.7,  49.6,  50.5,  51.4,  52.3,  53.2,  54.1,\n",
       "        55. ,  55.9,  56.8,  57.7,  58.6,  59.5,  60.4,  61.3,  62.2,  63.1,\n",
       "        64. ,  64.9,  65.8,  66.7,  67.6,  68.5,  69.4,  70.3,  71.2,  72.1,\n",
       "        73. ,  73.9,  74.8,  75.7,  76.6,  77.5,  78.4,  79.3,  80.2,  81.1,\n",
       "        82. ,  82.9,  83.8,  84.7,  85.6,  86.5,  87.4,  88.3,  89.2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.0 -177.8 ... 178.5 179.8</div><input id='attrs-f9b38af1-93be-45b1-8809-5a3f20d21a62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9b38af1-93be-45b1-8809-5a3f20d21a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bf865e1-4ea7-44c0-b3c7-13eb8efc1100' class='xr-var-data-in' type='checkbox'><label for='data-3bf865e1-4ea7-44c0-b3c7-13eb8efc1100' 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([-179.  , -177.75, -176.5 , ...,  177.25,  178.5 ,  179.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d4e09f5-a4b4-4846-a8fe-8acd33e47781' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d4e09f5-a4b4-4846-a8fe-8acd33e47781' 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 'region' (lat: 199, lon: 288)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.0 -88.1 -87.2 -86.3 -85.4 ... 86.5 87.4 88.3 89.2\n",
       "  * lon      (lon) float64 -179.0 -177.8 -176.5 -175.2 ... 177.2 178.5 179.8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_mask = regionmask.defined_regions.srex.mask(regridded_out, lon_name=\"lon\", lat_name=\"lat\")\n",
    "output_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
    "ax.coastlines()\n",
    "ax.set_global()\n",
    "\n",
    "output_mask.plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False, label=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alaska/N.W. Canada',\n",
       " 'Canada/Greenl./Icel.',\n",
       " 'W. North America',\n",
       " 'C. North America',\n",
       " 'E. North America',\n",
       " 'Central America/Mexico',\n",
       " 'Amazon',\n",
       " 'N.E. Brazil',\n",
       " 'Coast South America',\n",
       " 'S.E. South America',\n",
       " 'N. Europe',\n",
       " 'C. Europe',\n",
       " 'S. Europe/Mediterranean',\n",
       " 'Sahara',\n",
       " 'W. Africa',\n",
       " 'E. Africa',\n",
       " 'S. Africa',\n",
       " 'N. Asia',\n",
       " 'W. Asia',\n",
       " 'C. Asia',\n",
       " 'Tibetan Plateau',\n",
       " 'E. Asia',\n",
       " 'S. Asia',\n",
       " 'S.E. Asia',\n",
       " 'N. Australia',\n",
       " 'S. Australia/New Zealand']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regionmask.defined_regions.srex.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "zero-size array to reduction operation minimum which has no identity",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-068f964bd1c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mregionmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefined_regions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msrex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'abbrev'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    449\u001b[0m                 \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 451\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mtight_layout\u001b[0;34m(pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   1488\u001b[0m         \u001b[0msubplots\u001b[0m \u001b[0marea\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mincluding\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1489\u001b[0m     \"\"\"\n\u001b[0;32m-> 1490\u001b[0;31m     \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_pad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mh_pad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_pad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw_pad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrect\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mtight_layout\u001b[0;34m(self, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   2613\u001b[0m             kwargs = get_tight_layout_figure(\n\u001b[1;32m   2614\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplotspec_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2615\u001b[0;31m                 pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)\n\u001b[0m\u001b[1;32m   2616\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2617\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/tight_layout.py\u001b[0m in \u001b[0;36mget_tight_layout_figure\u001b[0;34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m    306\u001b[0m                                      \u001b[0msubplot_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplot_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m                                      \u001b[0max_bbox_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max_bbox_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m                                      pad=pad, h_pad=h_pad, w_pad=w_pad)\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m     \u001b[0;31m# kwargs can be none if tight_layout fails...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/tight_layout.py\u001b[0m in \u001b[0;36mauto_adjust_subplotpars\u001b[0;34m(fig, renderer, nrows_ncols, num1num2_list, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m                     \u001b[0mbb\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfor_layout_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m                     \u001b[0mbb\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[0m\n\u001b[1;32m   4201\u001b[0m                     \u001b[0;31m# this artist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4202\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4203\u001b[0;31m             \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4204\u001b[0m             if (bbox is not None\n\u001b[1;32m   4205\u001b[0m                     \u001b[0;32mand\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    284\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclip_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0mclip_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_fully_transformed_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m                 \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_extents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mget_extents\u001b[0;34m(self, transform, **kwargs)\u001b[0m\n\u001b[1;32m    601\u001b[0m                 \u001b[0mxys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mdzeros\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m             \u001b[0mxys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mintersects_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/tss/jkshuman/miniconda3/envs/analysis/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     41\u001b[0m def _amin(a, axis=None, out=None, keepdims=False,\n\u001b[1;32m     42\u001b[0m           initial=_NoValue, where=True):\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_minimum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n",
      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regionmask.defined_regions.srex.plot(label='abbrev');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to select the following regions for south america\n",
    "regions_of_interest = ['Central America/Mexico','Amazon', 'N.E. Brazil',\n",
    "                       'Coast South America','S.E. South America',]\n",
    "\n",
    "# Thus we first need to find out which numbers these are:\n",
    "indexes = [regionmask.defined_regions.srex.map_keys(region) for region in regions_of_interest]\n",
    "indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_mask = regionmask.defined_regions.srex.mask(ds_in, lon_name=\"lon\", lat_name=\"lat\")\n",
    "#input_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask for original data that matches indices for regions_of_interest\n",
    "ds_in_msk = ds_in.where(input_mask.isin(indexes), drop=True)\n",
    "ds_in_msk = ds_in_msk.assign_coords(time=times)\n",
    "#ds_in_msk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask for regrid data that matches indices for regions_of_interest\n",
    "regrid_out_msk = regridded_out.where(output_mask.isin(indexes), drop=True)\n",
    "regrid_out_msk = regrid_out_msk.assign_coords(time=times)\n",
    "#regrid_out_msk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap='turbo' \n",
    "bounds = [0,0.1,0.2,0.5,1.,2.,5.,10.,20.,50.,75.]\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds_in_msk = ds_in_msk*12*100\n",
    "ds_in_msk.isel(year=0,month=8).burned_fraction.plot.pcolormesh(ax=ax,robust=True,\n",
    "                                                             cmap=cmap, levels=bounds)\n",
    "ax.set_title('Original')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap='turbo' \n",
    "bounds = [0,0.1,0.2,0.5,1.,2.,5.,10.,20.,50.,75.]\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "regrid_out_msk_test = regrid_out_msk*12*100\n",
    "regrid_out_msk_test.sel(time='1997-09-01').burned_fraction.plot.pcolormesh(ax=ax,robust=True,\n",
    "                                                             cmap=cmap, levels=bounds)\n",
    "ax.set_title('regrid')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## South America Custom region\n",
    "- Path to domain file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to tropics domain file within same folder as the data\n",
    "domain = xr.open_dataset('/home/jkshuman/sfcdata/domain.lnd.fv0.9x1.25_gx1v6.SA.nc')\n",
    "#domain = xr.open_dataset('domain_processed_GFED4s-burnedArea.nc')\n",
    "#domain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input Grid for region\n",
    "- pull size of region (height=nj,width=ni) and coords xv,yv\n",
    "- create empty grid for subset\n",
    "- subset lats,lons from regional domain \n",
    "- provide corner coordinates\n",
    "- select lat,lon inside region of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-85.        , -39.10994764],\n",
       "       [-30.        , -39.10994764],\n",
       "       [-30.        ,  14.60732984],\n",
       "       [-85.        ,  14.60732984]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pull coords of region\n",
    "ni = domain.ni.data\n",
    "nj = domain.nj.data\n",
    "lon = domain.xc.data\n",
    "lat = domain.yc.data\n",
    "#define width and height of the target arrays (match domain size)\n",
    "width = len(ni)\n",
    "height = len(nj)\n",
    "\n",
    "# --  Create vertex matrices  ----------------------------\n",
    "lonv=np.full((height,width),np.nan)\n",
    "latv=np.full((height,width),np.nan)\n",
    "\n",
    "# pull coord vertices for custom region\n",
    "lonS = lon.min()\n",
    "lonN = lon.max()\n",
    "latW = lat.min()\n",
    "latE = lat.max()\n",
    "\n",
    "## coords for South America\n",
    "    #if tagnum == 2: # S.America\n",
    "    #    ln1=275. = -85\n",
    "    #    ln2=330. = -30\n",
    "    #    lt1=-40.\n",
    "    #    lt2=15.\n",
    "\n",
    "# convert to -180 to 180\n",
    "if (lonS > 180):\n",
    "    lonS = lonS-360\n",
    "if (lonN > 180):\n",
    "    lonN = lonN-360\n",
    "\n",
    "# name and create array for region mask vertices    \n",
    "SAmer_mask = np.array([[lonS,latW],[lonN,latW],[lonN,latE],[lonS,latE]])\n",
    "SAmer_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<regionmask.Regions>\n",
       "Name:     SAmerica\n",
       "\n",
       "Regions:\n",
       " 0  SAmer  South_America\n",
       "\n",
       "[1 regions]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names= [\"South_America\"]\n",
    "abbrevs = [\"SAmer\"]\n",
    "SAregion = regionmask.Regions([SAmer_mask], names=names, abbrevs=abbrevs, name=\"SAmerica\")\n",
    "SAregion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = SAregion.plot(label=\"abbrev\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mask data for custom region\n",
    "- create grid with nulls outside of lat and lon of region\n",
    "- select only points in this region, drop others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask for original data\n",
    "# grid with nulls outside of mask\n",
    "mask_SA = SAregion.mask(ds_in)\n",
    "ds_in_SA = ds_in.where(mask_SA.notnull(),drop=True)\n",
    "#ds_in_SA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask for regrid data\n",
    "# grid with nulls outside of mask\n",
    "mask_SA = SAregion.mask(regridded_out)\n",
    "regrid_out_SA = regridded_out.where(mask_SA.notnull(),drop=True)\n",
    "#regrid_out_SA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zg0T3AsCywBLdC6gvW0vankwA5s+9H4AH5xzmoalWvV/kznLasicABFyvlJLan3ybc0v/DzdExpMUdatdAHwNYKDKtxc7TepOl5Abbwnrd7/VDV283iS/JqV8+0LPdSXhokQiHQ4Hr732GqNiUkkZcgdpsRl0yA4+bKrgnNFaNCxiOPGRSXx89ohVYhFEpK2+Zo43f8QHX+ynrbOVr65uYPwNWYyKuZ1wEY63s5mqpvc43vxRj2OllBxrPsoJzhLlbaHrg//S2yJ3lgfsq2J+Co2li4h74W1i/mwOjTcn0uCtQ6Snkvz9v9P7NCQVE7uiTMfUtvhyiV1RFrBW7IoyGpKKacueoAktdkWZvj9ueZeOza1KkKxKkDQ9n0ZRdhbVUyoQm0px5RdqwgOLEIUQ3Pr3v+HOuEw87Wdo9oXONtuwYaMbF618a1oOkWFR3BJzO7fEpPFxUA9lbMQNRIa5OHq2kvfP7OHztnqa2k9zrPkoNWc/wOkI5/bYcdw69E4OL7mDqqb3ONPWyJDwOG6JSWNs3AS8nS18cq6Kzi7LiuuSXXxy7kM6ZDupzpEkRadoCzF2RZmO8amkAHSLWOpr3llO9HcfQlaX09rZTEpdt0duJhPk3ByWVFnlHZE7ywMSEGCRUPWUwN5Yda7gfbfv9zHZu455ydNI25NJZY1lDa5KkGzf72NxgyAjtZiEE3nErijD6QhnaHgcLZ3N5/nfsWHj+sNlkfMO66Ug9bahX2H0kDHcNmQsn3vrOdV2EqcI5864e7gpahSRjig+a/0UKbu4dcid3BAZz8dnP+SU9yRhDie3DR1LuCOcj84d4eOzH1LtqUQIweghYwgzYnlmkiA+Z7UVhF9RRlv2hB5E15Y9gZseeJqvnn2K8f/UwHNnrE4IFRtT+zWWLiIjtZhE94IeGdXIneVkpBazMn0hixsECSfydPbUlV+oLTt1XfOSp5HoXkBljbWfsugWNwjKUsJZv2k3DUnFzHaW6KRHlHMI55KSafDW8UX753bLlg0bveCiYmoKwdOkfF0dVHsqcYVFMcI1EiklXXThCovGFdazGV5KSVPH55zyfoZP+kgdOoZwf/zM29lCtaeSm/zZSSEEXl8LrZ0tgGRYxAhtLcb768qqp1RoolBQ8TW1j8L8ufezftNugJAWmHlsqLXM/WNXlGkXcosvF7BKQCAwDmeS7sr0hSypWhMQvzOhrvfzJZP4rPUYAgfexJtoTxDc+ofOASdfbFw5+LJiapnDnXJXXly/+z19oJn1VW3XTEztshQ/OR3h3BF3F2faGvj47BG66CI2/EZafGdxOiKIcQ7l5ujbNBmd8zVR1/Ixia6bGe4nLgVXWDRDw4dxpq2BEf4MpcsZjcvZsw2rsXQRsdlWLEuRgSIj9bexdBH1y7qD/us37e5BgirArxCcbFCP27In6LUUaa1KkCScyNNkBhbBLW4o0edLSC3EW1SAK7+Q9Zt2k5FfCP5EQ31ZHuOWd3FomYO0PZk0LnVDKYQ7IhkVYxU6j8j6Bz4Jr+CT6ucYcu4kI6NTCHdEnPf/yYaNaxGXraIzTIQxwjWSYRHxtHaeY2j4MLpkF95OKylwur2BcBFBRFgkEY5IpJREhkWFzO51yk5GuEYOKPOnXE1FBgqmtZW2JxPSredd+YUk+q0jHWtz954xDUaiewFqiqtnqZuEE3lWW1V6N6nNdpZoy82VX4icm0Mk5VROySMjv5DZzhKWVK0hY1Mx1ORZru6mPLz+jGkwTv32rxgCpDtS+MxxjGpPJbfHjrNr2WzY4DKOyFNwOpwMDR9mnUw4iHYOJSl6NCdbavno3GFOttQSGRbFzTGpfHzuQ9o7vT3WGB6ZwOdt9QOOIynyMjOUiuzU/SVVa9jiy9Xunoq9BVtlZlIh1HkSTuTp/erL1uItKgDQ6wO4azvYULdDJwTasidwaJn11s92lrB+024S3QuQc3NYmb6Q2BVlyLk5Oh7XG8KEk+To0SR95xlqPB/g6+oY0Ptjw8a1jMtOaqEwJDyWcTdM4s64TJp9Z6lvPc6Jlo+JC78xoHhXQY3a+7S5esBN8iahqSB99ZQK/Xyie4EmtFAZSgWTDM11FYmpNaqnVOhaNPXXW1TAqgTJvORpOjmgkLYnU5eJKLRlT2CLL5f5c+9HbCrV5+8Pt+T9NX8xRlKXNKzffW3YuNYxKKSmEOmI4sbIBDqlj6To0aQMuSOgaFfBIRykDv0KAsGJlk/6XVcRT2PpIp31TNuTSaJ7gSYLE9VTKnrUnplrzZ97P7EryljcIDi0zKF7Q1emL6S+bC2LGyy3WFl6nqVunT1VwX8zAaD22VC3gyVVa3QpSOTOchqSinX201tU0IOcQ8Gz1E3R6tdwnPt/2nK0YeN6xaCSmhCCpOhbSYq+lWH9zDNwCAdJ0aPxdJzhi/ZTPb7gZkaxsXQRh5Y5iF1RRvWUCiJ3lmuLR87N6bF2onsBnqVu3RsKliWWcCJPk2BRdhZgZStVkmFJ1RrS9mSyxZdLRqpVpKusOpVJDc5mmihLCbdasYJiZ+pag7OxvVmTADc98recPiH58I6/5UcPf9rrfjZsXOsYVFI7XzgdTpKjb+WU97MeX3CV8VRI25OpLaD+iCHhRJ5uXFfWXfWUCq2jtsWXS1lKuL6vsDJ9oS7h8BYVhIzJBcN0ZZdUrQkg3OBr64vETMTnrOam3J8zbkgmw2e/xbO7Gzk68yt2LZuN6xJXFakBRDuH0trZHPILq+rP4nNWUz2lgozUYsYt7xmD6y34bhKaKqad7SxhQ92OHvuaJRvQsyWrrxidQnAhb28wrzfUtTeWLqItewJhDief/cAB//oGKf/v37hxeo1NbDauO1x1pBbuiEAg6Ohq77FNBfWVy+gtKtBEF9waFZ+zWrcxeYsKdBDflV9I2p5MZjtLcNd2ZxPdtR24azs0mblrO9jiy9WtUwO1qvqD6UYvbhA6XqdifooU68vWhowDpu3J5MX7nmfRW+PpqvIixO85/SeS9uXxMP8FHL9MpXbkEA4lnOJwaxVV7Z/yafNRTrc10N7Vdklegw0bg4mrTnlQDUSB0CoclgsIlOXpolplfSkN3oA2J1YHWHiVNQ5WGo3litiU+wmWC7ol2bq/Mn2hVRpiXINS5RhI5rI31JetxV37uHW+/MJugvbnORLdCwjV3j5/7v24azuY7F1H6qxxeJbcTcfGOk52VuPt/B+cN3iJa0kh3jmE//n7RB74aTjnwppoSc/gdO0bjIp00pz5V7jK3iQqLMZWBbFx1eGqs9Q8HWcsGaKwyH5JQ9V8eZa6e7RNKSjBSFXWEVxmochMWWoKlTV5uiYtOBmgztVbtjIUVJLBjA2WpYQzL3kalTV5ARZa8HH1ZWsD3N/8nXvJSC3G4QwnNuJGEqNuJmXIHaTH3UXm37XTuf9Vbm34Hl/95hainDHEu5K4Z889ZBZ6iZy9E1FTyxcjqjg9pJxG70nbhbVxVeGqs9RinENpavm83/0yUosDgvq9QZFe9ZQKMvztTVt8uTqO5q7tCLDSFFamL2RLaq6/bzSQXONzVtPGwAP9EBhrUx0FS6rW6NfCiTwaktyMy1lNY9BxiaCfM1+zaZ2CRYjxyy1BywYgbUWBtvYsYrbI+QZgmLwHx1+s5fSbf8Snh88yKiY1ZA2hDRtXGq46Sy3aORRvZ0uPYcnBBbKVNXlaJqgveJa6tdSPipfNdpaEJLKBwuw5vVBU1uQFlI/Mdpawfb8Psam0RyzNvK86EWY7S3pYp2ZiQqmLhEJ8zmoST34LuW4hbRtLaf3jiVR7KmkL0e1hw8aVhquO1BzCQURYFK2+Fv1csJZZW/YEnb1sy57QI3to1pMtbhA9LDplIZWlhFOWEh7gdgK6V7O3GrRgsuir1SkU1ICYJVVrtILIFl+udkWrp1ToNivoWbJSWZMXUp3XhEluobap13bTHy9l2N//LaPGz6fac5AW39nzei02bHzZuOrcT19XB+1dXqKcMfq5uorHyPn1evDXmZnyP8GSQYAu2Zg/V1iElRKoqLHP9TjUdsfTJnvXkeErprImj4xUqzsgkdAxutgVZbRBQDP9+VptqrQkfnk5jaVuqqes1QkJ9ddbVBAyUXC+GIiLfNP9C3l3bQt3FK/hs1f/htuGjr0EZ7Zh4/LgqrPUznY0ES7CdfB6cYMgd8Z0Di1z0JY9gYQTebhrO5i1zVKJNcf2meSiCmuVJaasMeV6lqWEs6RqDasSpO4TNftFFUwhSXWui3U91RqNpYu0a7ykag3eogJdQqIQytLqLSkSDNVBMZCExj0Loon+zRbaYuM4eHofZ9pODfSl2LDxpeKqI7XYiBuIcg7h6NlD1JetZda2Zipr8kh0L2BX1ARKXnudUXvb8RTX0pBUHDC2L3ZFme7ZXJm+MIDMzI4BNUcglNBkMIItnUtBaAr1ZWu1+5mRWqwtzIzUYt7YNYKEE73Xxw0089pYuqhPV9SEQ4ThfuBVMr/+j5xsrbWzotcRhBCjhBC/E0IcEUJ8IIRY6H/+RiHEb4UQ1f6/N/RyfK4QokoIcVQI8deX81qvOlITCNo6Wxnhn/x+LCuCuorHenwhH3n5qR7DUSJ3lmsrx8xqlqWEs8WXy+b8l/rs1eyNsHprcwqF82k4V6Sqkh6qw6GyJo83N4/t81ovpBh4IMec+u8n2f/oBsId4Xg6Tp/3OWxctfABfyWlHANMBv5CCDEW+Gvgf6WUacD/+h8HQAgRBvwTkAeMBX7gP/ay4KojtZOttUQ4XIxeXc0+1+OM2tvOPQui9RfyngXR5M6YHmClgUU8ZnB9XvI0/VfpqgXPHugLoeSI+tsPeta09QWVoVyZvrBHicqqBHledXAXg+Dz5M6YzqgX7ueLdpvUrhdIKU9KKd/13z8LHAGSgYeBzf7dNgPfCXH4JOColPIjKWU7sM1/3GXBVUVq5zo8nGlrZFRMKlGz/46ylHByZ0zXBanJmRt5d21LSOJQGUWlqlFZk8cWX64u4zBjS/E5qwMIMBSC68oGst/5QqmNQHev6fpNu/WkqTlFT/Yoxr0cCH59DUnFOJ6rx9NxGjlAfTsbVzweEkIcMG7ze9tRCHErkAn8HkiUUp4Ei/iAhBCHJAPHjMfH/c9dFlwV2U9P+2mafWc53d7IzTGpWnPtwTmH2fJyLonuBby7tgVP4QMcez4t5Brqi+lZ6iZy52rSsjOpBEgvCSg8Vfsm9pRduygEzz0YCOJzVhO/vItVpVITbUZ+ISWvvU7ujOkDKi6+XLi5/jt8EvMh53werWxs48pCWFwEsXkp/e4X0XgcqhrfkFIu7W9fIcQQ4HVgkZTSM8A2ulA7XbaA7BVvqTV3ePi0+ShCOLg5erQ1GDlnNa8++mJAXOlYVgRTi8fw8L3987RqjUp0L+i3nutS4XwJDbqD+MqFVWP1prZadXCznSUhVUhMBLuOFysiuX2/T587LmI4X7T3391h49qAECIci9D+XUr5n/6n64UQI/3bR0JAG7TCcWCU8fhm4MTlus4rntQavCe4KWoUN0WNIs4vJPkrhyX0aBbFTvauG3CcayDbrxSYKr5gTXtXCY9VCbLf4L65fft+H68++qJW/rgQqB+NWdua+Y8dw3CKgc+OsHH1Qlgm2cvAESnli8amN4A5/vtzgO0hDt8PpAkhRgshIoCZ/uMuC65oUpNSIgQI4QggrEdefoo3N4/VXzDVCK7iV8EzB0JZJ5dKKigYl5osQwlHNpYuuiDLL+6ZairfaeLBOYcv+rqOZUVwfOpQurySupaP7U6Dax/3AflAthDiPf/tW8DfAw8IIaqBB/yPEUIkCSF+AyCl9AFPYJWkHwFelVJ+cLku9IomtWrPQc51eAgXEZqwEk7ksaFuh67fAgK2mY8VzGLVy43LQZb99a+eLwYSZ+kPqjg55W+PEPX1fGrOHqa541L0ONi4EiGlfEtKKaSU46WUd/tvv5FSfi6l/IaUMs3/97R//xNSym8Zx/9GSnmHlDJVSvmzy3mtVyypqYb1eFcSsRHd9Xyq4j+YuOJzVgdkPU2Lqb+4k4LKJJ5vr+blhjk74WIQm5fCfc+OZ+vMmP537geztjVTV/EYNy55nTtv+B5DnLF0SHtEn43BxxVHal2yi7bbp1LV9SmusGhujOzOEG/f79MV/wpKT+zQMkfAF3+gA0tMKJcuVBnGYMXggmNqChdCvIrMgpMjF/rats6MQc7NoS17Am1TM+mSnRe0jg0blxJXFKl9kXg7RzqqOf7uP3GrjOeWIWmEOyK0qkZZSjgNScUBvZaRO8u1sKOaIHU5arf6I8a+5IAuBkqV11xT9bGebyZzVYLkngXRPZ4fCOmbdXyxK8qY2lrO5vyXaMuewKl3/xPf/+xmiDPuvK7Hho3LgSuqTu3Yh//ObUPHMvKf3wuIk812dqtuQDGepW5LiFHpliVBor/OzAMBwoiXEr1ZTdDTursUsbX4nNU0hijyVX/PpzvhYmH2h85Rcudz4dkbzvApDdw2dCwRYZFf2vXYsNEbrhhLTUqJQ4RxY9bTmiDic1YHTG0yv8Sq1uxyItjaMpu/FS5X/K2+bO1ly9BeDA4tc+hSmuXDm6k99wfGzN/G2b3/esXFIm1cn7hiLLW22+4j8v0P8L6zTg/7aCxdxKoEy1qz5gFcXhILxkBI5XIR64WUbFxuxK4oA7ebx3J+7n/G6qRpXPdnRP0ihs8c4SR+/WfI3G9y7m++MXgXauO6xhVhqfm6fBx9dzXJ0aNJyF0TsE2NsOuNPC5XDO1icEkylVeY1VNfthZXfqEe+NxYughXWDSpQ8fh7WylaUgsHyV5+f2uebz1lzEcku9y6IaThC34l8G+dBvXGa4IUjv+k2kMS5qs1WzVPM6EE3m48gv7rNNS7U6hZmAOFi7EbQy+9osdr3epYLZoeYsKery2KGc0KUPSGNUexriGEUxeVceWlsnc+9NahoTfyulV30NKyedt9XzRfgpfly/EWWzYuHQYdFKTUhLxj/+D68yJgEC8Z6mbypo85NycAa3jWeqmLXvCBX+hB5sQzUD8xRJ0b67r+Vp/wfubRGtmQk1UT6nA4RD8xYLvkp5xkE7ZyYm0WzkzIo7P2xo4cu4QHw13UDP7bmuGq91iZeMSY1BJrVN2cqTpXVqdzQyLGK4luVUJR9qezJDB+d5gWm3niyslKK9KOIKvZ1feEb7l2ce3PPsAeHdtS/ChPRBMjJE7y3l3bUuv74/5/Pb9Pv9gaHdIyW/VaN+bRemu7eAP2x0cb/kI6Wvjrqd3M3nGf/OVoRnc8q2fcEPHuxwdUs/R6LM0tNbRYU+Ht3GJMKik5sBBR1c7tzaFcePKfQHbzOlQ5ws1nHiwra8LgSIS6ElKy385HoB/HL6PZT882O9aeoCL3/pry55AcuZGqqdUEJ+zmu37fVp1Y/t+H9VTKjSxPXyvU9/v7YcleKhNfM5qPdKvLCWcaGcMyXlPM+ZHrxAWEcXG5U8w7IU93Dgul1vvfpspzx5jpEfi7Wyhqv0oNWc/4Exbo13Ea+OiMKikJoTAFRZF2I/+AVd+YY/p6K78wgsOmCt3dDBwsWQan7O6hxs9tXgMOb9ez/Jfjqf4Y3j6+wMrdFUDXBQBpe3JJCO1OEAEc1feEfJ37iVtT2bA+SN3lod0500XWf115RdyaJlDz3fYnP8SqWtq8K2cwdGvvU/1lAryd+4lcme5Jm7XrgpGP7iJW4akMTbyTlImLuazzBEcaakKGIFow8b5YNBjalFhMdx982ygu/E8cme5/oJdDJRF8mXD7NU0Z4wOBEphZNzyrpAKHceyIvhR1UReeKVJu6IKwW6iul9ftjbAXVXv82TvOkbtbeehqdZkKFd+Ia78QtqyJ7DP9Tht2RNCxufUdc0pelI/rqzJY9zyLn3Ojcuf0Na2+rGK3FlOW/YEbR2accRhL+zhxj8r4MZf/CPR/+dJjsY14Ouye0ltnD8GtU6tU3Zyzudhe9jfcC9naS+vwPv5cMieQOTOcrwUXNT6ie4FtK0oI57VAV+gUAW0wUHwC4mxqXUu9Hig3+OV3FL89/yEVrqI7ft9PHyvMyQJxq4oo54KLSwJgYmEqSvKidxZ7rfm1LPFPIyT/uoC12/aHWABik2FyLk5xK4oY7K3gle/M4n7nh3P1D3l1EVN4NjyCABG7W1HffRKvz2fY1kR5O/cS1F2lnZ7a2OqOF5fyq1D0vt9z2zYMDGolpqKnbj+4wMm//5Vzjz2l7z/P3OpnlKhXUdTI+1CoNzQ4ONNi0aVjJgxpAuBctfU8X0F0vtCX+c3M8RqP0V02/f7iM9Zra2y2BVlVE+p0MKSEPh+1pet7TV+FwqmtWfG/sCyir1FBRxa5sCVX8iRwge4pWQiAF/7c0t+PX/nXvJ37iU5c6M+57GsCD/JWa8jPmc11VMqiHCNwIGDcx0ePEljqfZUUu2p5LPWYzT7ztpZ0+sAQogwvy7bLeo2oOP6+nBMnDhRHjhwoN9FMod/beBXGoRO6eNYcw1ftJ9iVMztHG/5mD/69Gm6Sn6mLYi+cKHEcTGoL1ur3UPTqurrWi7WijNnHCgC0r2vdA+LUW67IjMVO+utzCPhRB4lr70eYMmZvbW7oibgKa5lTMFvSduTqdcNfs3b9/vI37mX6ikV7HM9Ttwz1Xq9NzePxV3bwbzkaUwc8iwA+7+3j9Jvz+eeBdH84/B93FIykfyde/Uxh591c+qzPTjvTCf3W2fZc8dPeCz5n6n87RkO/e8XHK91EjlpIqMfHsKptEdJnvWCvh7Z1UlXRxtSdtLyrJb0umJQ8flbA9pPCFEupZx4oecZnzFEvrl9XL/7PfvcJ/xyS/3XpJRvX+i5LjWEEH8JPAvUA0o7TEopx/d37KC3SYUJJykxdzAyKoVTbSe58e6H6CxejhADs5jOR0BRWS4X04KkWoVQGUWw9Dzpv2BW7W+6w+r53o6tL1tLRmox2+seJ3+F5aJN9jpIdC9g+3If7LfcuamtExi3vJyNy7OY7F0HWKRXV2FZvPfQU50D8M88KOhBuIeWOThSOAIPMKbgt+xzPQ7Z1rlyWK+t2sid5cTnrGay18G+7CzinqkmNi8FD5ZCMYB3UwEP7hrBq7zImPffASx31VNcy648gDgeMggNQGxaSmJMNHdN3Ec7sNa5DbiBv7hzCPzlKN6IkHzwv8fZXhpF+/IZHDzdBRERdM13guyCyEhEWwdDw+NIir4VV1jo12/jisVCIF1Ked5DMAY9UQBWFrS9y4tH+Mh4cwyu37074GPPJ8Npul6mfNH5FOxG7izXx1ZPqTgvK7F6SoXOEkJ35b9nqZv6srUB16GyiirIrqwg5aq9u7aFuGeq/YRWrsldZTEVcU9tLeeeBdG9updpezJ1CYwqXo5dUUZdxWM8NPVUgEquch1Lvz2/x2upq3hMW2dvP3eQ2LwUNtTtYEPdDlz5hTzy8lM88vJTHCl8ALCSEi+80kTlO018r6uUouwsfR5XfiH1jc9y18TuRIi7toNZ25oBq1zkhpERfO1PE2DZvxL1v3v56uoGsl5swP3PXr458x3ujplIxg1fZfjDP+EPjo853lxjJx6uLhwDmi7kwCuC1DztZ/iURu748b8RMTyaQ8scIcniUvRDjlvehWepW2fmGksXkbYns9+1zXibKpO4EIvPasxHFxeHOo+KdYlNpXiLCvAWFVA9pYK6isf0frkzphObl6LjU6pwOfh6FYKt3uDp9dVTKtgVZWU9ATzFtZpo1Hkjd5ZrFeEjhQ9Q8trr7HM9Tum3A0dEFn9sHZ+/cy/zkqdR8trrAPpv2p5MNtTtIG803PfseMSmUuYlT8OVX6jPmTtjul6vsiaPspRwts6MoSwlXAuFbs5/CYDxt5cw9Mf/TPTbHyLCnDh++d8AJOauZVTOYuJ3vEb0o7dywvEuDa3H6bJnlV4N+AjYJYT4iRDiKXUbyIGDSmpSSj5rPcax5qOMffx1Rv7dO1TW5OkvVjCURRH83EBRX7ZWf/FNQjK125TldjkaytVwmET3AqqnVGjXTRFc2p5MIneWk+heQHzOak1mRdlZ1rHFtdyzIFqTYc6v1+v7imzUa1GWm+mev7u2hXfXtrB9vy+gTq16SgUZqcUkZ27UrqsaN3jPgmjuWRBN/s69mhjN+BtYlhlYcuGV71g/ri+8Yv1V5SMb6nbgKa7lzc1jAWsITMakON5+7iA/zXiWktdep7Imj/yde7WFV/La6/pHYIsvV0seVdbk6f9l2p5MZjtLkHNzdJKisiaPhBN5+vHIh57l1sw93PZkJYnfOMtHSR/R3ml3MFzh+BT4LRABDDVu/WJQSe14Sw1nO85wR9x4xIa/xltUQNqeTCZ71/XqLp3vEJJQrUKh4FnqpiGpmLbsCTpbeCn6MM31TSS6FwRYVmY2MXZFWUCAX2UFlSsYubNcX6s1nLlcW5BqLdOiVGsq6yd/514yUovZlXeEXVETtOV0pPABMlKLQ3ZzqLXN8yz74UE+zT2g73uKayn+GPJGW8fMn3s/dRWPBcxEeHDOYd7YNYLYvBReeKWJ4o8ta81TXMs+1+O48gsZtbddu9lgEfZsZwnzkqexpGqN/kFSPwwAYpMV2FT1cpU1eaTtycRbVEDsijJc+YVE35TOyb//b5LyMznq+IxWX/PA/4E2vlRIKZ+TUj4HvAj8g/G4XwwaqbV1evmi/XNuG/oVwh2RWpobYJ/r8QBLwsT5Zg7NVqFE94J+j1dE0Fi6iDZ/vdzlEqSsnlJBY+miAKLtLUbYWLqIexZEB8S+TKIZiGW5oW4HyZkb2RU1AW9RAQ9NPcXWmTF4lrp5+F4nD009RUNS8YAG1by5eazublAdDrF5KbqVCywCe/u5g8za1qzjbcqCW/bDgzz9/TjyRlvj9pqeT9PXqCzBqa3lzJ97PxuXPwFYQ3eOFD6gf2QyUosRm0pZv2m3JjFAf3aqp1Ro4lMCo0IIzk77GdE/fYKasx/g6TjT72u18eVDCDFOCFEBHAI+EEKUCyG+MqBjB6uk43jzRziEg6ToWwOeTziRR2VNXp/xqoGURShhyWCr40oSX0w4kRdSkjt2RVmv5Sy9DZcZ6PmAft/fga5V8trrLPvhQU1knuJaKt+xrC+F5b8cr93T2iNZHHbtZaw3C3dtB6P2tuMprrWypcZfsDKuGanFzHaWaLdTxdMakop1Wc2hZQ5Wpi9kSdUa/ViRm1nuYhK/6mxoeeVl3v/933BTVyzDIy+XCHxP2CUd/UMIsQf4Gynl7/yPpwKFUsop/R07aJZai+8c0c6hAcND1IcubU9mny6frpT3ZwzNgSTq+YakYh2/SnQvCMgIXi6cr+yRSWgqI9sXoUF30e1ACc18b5Q0+kBHBvb1P5jtLOFYVgR5o63Y3taZMTqe9vT343Rvqqe4lvueHU/GpDi2+HI5cO45Drv2EvdMNceyInjk5ac4lhXBm5vH8vZzB3nk5aeIzUuhruIxKmvyWFK1hnnJ0yhLCcdd26EnzK9MXxhAYIBu61L9pRA49lD9wClruG7NPfxs783Utx7j47NHaGo/jbSTCFcKYhShAUgpdwEDmu04aHVq8a6RNLQeJ27kbxBC6Fqt6ilrGWdU+Pfl9imSaltRRsKmPKAUT9KigMEr+sPs/hLmGVwkafb1Ws3i2vNxhTcuf4KHS520ZU/gwTmHWT91N57SgR3fG3Fu3++jjHBG7W0nblIcczY/aU19n9TdZF/5TlMAsVW+08Srj75oWXJYSYXYve28WvwisXkpLPGu48ikB3j10RfZunksW3y5rHQu1Ospa03FFddv2k0aVvkHoJMssSvKAobVqELh+mVW8bIatxy7ooyM1GL+h++SHheOd9qjnNj6VxxrPkr0bRNxNnchRiYRW/0hcRE3Duj9snFJ8ZEQ4qdAkf/xnwIf97G/xqBZasMiRtBFF2f9MQ1V/Gn+8vbVkK4+wGpcnLeoQFs+gyGFfammR/UGFQgfyL4mVAuVZ6mbh6ae6lXSKdR7FtyMr6zgyd51PDjnMMt+aNWjpYzZywuvNOm6M7DibOo+oFumFDzFtdrVBHTGOzYvhQfnHObVR1/0Tw+zCE3F5I5lRehjVKbTXduhG/GDoax683OlXlNljeX+35T7c0a6H2PK93aSFjue9NGPkvT9/8PIv3BQ19nI5231Id8zG5cVfwbEA/8J/Jf//tyBHDiobVJn2ho51fYZabEZIbfXl63tYZmYo/HML1yoTgGV9YIvd5xcf0g4kadLEJSF0RcpKov1fFrCetu3r64K9X6pmJtJnG3ZE3DlFzLbWcKDcw7zyMtPsaFuBwCf5h4gw2+lmTE1lQVV2c3Kd5q4pWQicc9U0/R8mo6p9bgOI7amHoNFaMoNnZc8DUBfz5ubxwY02Ae3r6n32Gwxq55S0SP+Zr7XkTvLObtwE+VTXmZs3AQ9EOhiYcfULi8GzVKTUvJF+yminUN6bDOHqSjLQsXLVCZSfTnNzKH6wC5uEDpW15BUfEURGljXpIp3Td03RdJqPoN6PWapBvQfu0s4kadLUkJhZfrCkJJI6r1emb6QxQ0CsamUOUVPMqfoSYqys3TxbNPzacx2lvDwvU4evtfJ8xP3MbV4TI8kgcqMKoK679nxulRj1N52jmVFWG6o/9b0fBqxeSn6eQXTqlNWm7oWsEjPXdtBUXaW/pxA9+dBEZoqlVGEpl6zZ6mbxQ1CW/3qvW4sXcTQu0biGBlJe5e3z/fcxqWBEGK1/+8OIcQbwbeBrDFopObtbKWp4zSusGituKC+aJ6l7oBgtlk0G/xFV89npBYHuB8NScXnXdN2PgglcX2hMK3OhBN5WvEilPW2fb+vz+b0xQ2Cypq8HsmE+JzVeu0tvlzmz73fakcyCFIVsiqY98tSwrXrp6wlsP5npd+ez668IwGEppA3Gu2WKnJSBbgB70FxrXYxzRo1RW5Nz6dRlhKunzuWFaFdTwVVIBxcnyc2leofOWWZqWJntc+qBBnSfW37t7/hu5FeznZ80fPF2bgcUDG0VcA/hLj1i0EjtShnNLcPzeCU9zOqPQdp9TXrX09Tvkd9+Fz5hbquS1X9r0xf2Kd7eTFxrt4Gi5hrX+j6ochQZey8RQXaggtGwok85iVP69HipMhMzs1hVYIMID11/YeWOTRJzXaWMGtbM7OdJVp+G6xi2S2+XLb4cllSFTiqMBQWNwh2RU3QvZ5Pfz+OH1VN1G6nKvVY/svx3PfseJqeT+PNzWN5cM5h7UoGI5TbqTBrW3OAy6qu3VzLzPaq1wRW/Ex9VsB6v9XjYFkmE5E7yzn004e4d+x591XbuABIKZUlcreU8v+ZN+DugawxqB0FQ8JjuSN2PMMjEzl69hCur/4prvxCxi3v0sFdRXSVNXm6ubuxdBEr0xfqL6bCxRTIBncODLSg9ULOE0yGSgl2cYPQX8JQaEgqZkPdDuYUPamJrCg7i4akYlYlyADrRKF6SoW2UlYlSP0ebp0Zg7u2g1UJ3THVzfkv6QleaXsyNcEBuq7MXduBu7ZDW2vHsiK479nuWrRRe9vJmGSVdHiKawMSBKP2tjNrWzNNz6cR90w1s7Y14ymu1cdCIJEpyzA2L0Vbb6qerSwlXDe4AwEkbL7HW3y5ulPF/OFTfbWmjFKoH5u27Am8tuN/+N3hc7aG25eLOSGe++FADhz0hnYhBMNdN3HbkDEcaz7KoRs34v36PfrXM7imyvzgHcuKYIsvNyCj11+8yYxbBSOYbMxpSpcKoVziyd51OrtpkoyC2YuqAuQqpqSym6HOoY5pSCombU8m9WVrqazJ00QVbCmpOJ/Z5aB+NOYlT9O9ofk792qSU6g9kkVsXgrLfniQNzePZUzBb3WXgMKxrAgtCmmSl0oyBFtmKu6mLDNFcubjspRw5iVP01ab6iZQCSXTQlPBfwA5N6fHHIveLO/EKAejhofT0nku5HYblw5CiB8IIXYAo4Piab8DBmQuDzqpKcT4rbZT3s841L5Wx0YOLXMEWGuhoIjPJLRgclMua/WUCurL1jJ/7v0BLmYoBVhFJqE+7OdTaKvIZXGDoPTb8wMKQmNXlLEyfSGVNXms37S7xzUHr6Peg9wZ07VKRTDU9SpBR/W6gzFrW3NA8XPwGsoCXJUgtZWjXGPlBsY9U42nuJYH5xzWZDNrWzN1FY/xi3Qrc67ITEklBZOTgnps/lXEqchQJRNULG2LL1cX6arZFtVTKhCbSgM+N4rYFOmFUkMOhv4BzEvBM32CLj+6XiGE+EQIUSmEeE8I0aMsQlhYK4Q4KoQ4KIS45wJOswcrdvYhgbG0vwJyB7LAFUNqABFhLm6KGkXHov+jP1DjlndplykjtVh/Yddv2s285Gk6KA7o7gFliZhQEkNqnS2+3B61X2o/IEDLLBSCg/WLG0SvRKcIc/2m3eT8er0l8Ljfx/y591M9pYJZ25p1fNBcY+PyJ9i+30dGajGu/ELdIbE5/yWdQe0NZq/ruOVdJLoXkJFarFuPwHJBTfe9P6TtyWRxQ2BZQ+U7TVqdIzYvhR9VTSR3xnS2zowhbzQ9gv/LfnhQE2HI9yqI2BRUB4I61rQSQ03IUlA/eGbLnPrbX7jCs9RNUXYWW2fG8MDZGuDSlHRc5fi6lPLuXkpN8oA0/20+8M/nu7iUstbfPfAnwO+NeNoR4OaBrHFFkRpYRbnezhYOx/8KIOCDqqwUZVmpoLoJJdmjyhJMqF9y9UXeULdDl4aYJQ4qUVFZkzfgDGootzH4ulz5hTpWN2pvO+s37WZl+kKOZUWwftPugOJhFWeLe6aa2c6SgHjQQBIUZk2WisE1JBXjLSoIkPFR5xsI1HtRX7ZW153d96yVDFAu5C/SD+j6tVtKJup6NEAnE/pD5TtNAYW70K2iq1xaT3Etv0g/QGVNXkC5CVgkpz4roYhO1QYq9KXE8vC9Tty1Hby308MQZ2y/136d42Fgi7SwDxgmhBh5gWu9SreMN0An8KuBHHjFkZpDOLgpahTnps9l2NefZ9zyLuTcHMCysExLJtRcUOW2qi+u+YFVZLCkag3eogKtHab6BQHdV5hwIq9HmUhfiF1R1mdvqXLnYleUMX/u/RzLimD+3PtZv2k3ZSnhzJ97v17Ds9St412hyh96Q6gp6ur1KlJU1okK9M/a1jygBEt92VoOLXMEBOQzJsUFWFSe4lqe/n6cTggoCSFVymEmBKBnDM1cNyOo5arktdf1e6KyqGO9ls7cZO86jmVF6Cy5K78woGQDul1xVcyt3E8VN+3rh+LcuU4qjkminQOS87qWIYH/9itmzA+xPRlLsVbhuP+5C4FTSqnNcf/9iD7217jiSA1geORNDHUO48D27yKlDHAdobulqrImr0e2MHZFGYsbhLYW5s+9v4dumdIMM0snzGxZKPe1L5hV6P1ZPeb1qnqxVQmyRzxNkQL0bwUq9JaxDVbYVVbNqL3tbJ0Z0+c1q20r0xeyMn0hdRWPWc3mfkuq8p0m3n7uoHY/obtYVgX51fNm65TZNWCuFWyhKY22exZE68JblSyZta2Z7ft9pO3J1Mq5s50lAS61EjIInqMaPA1L/fgFvxfxOav5/qblDL3hThziivy69IpzkYKylPB+bydiHQAPCSEOGLdQpHWflPIeLDfzL4QQwan6UP75haaMG4UQD+mFhXgYODWQA6/I/5IQgqToW/HJDj769Q8B60O4oW6HzuKJTaW67MC03lS8RH34zbS/ghISDO5rBCv2osoaKmvydElEXxlQNU3pjV0jtBx18P7b9/t4d22LJqhRe9t59dEX9RfTs9TN/Ln3k3AizyLJ4lpddtFXvG4gCLbE7lkQrWvFZm1rDpgPoKCSJOoHRBG9Wd2vRB6h22ozrS+V5VSuat5oAiwwEyaZmeQWm5fCW//codcbU/BbGpKK9fWr2Q3QnalV16qeD072KAIL9T81E0bb9/toy57Awx/PIfJsY8jrvobwhpRyonFbH7yDlPKE/28DVj/mpKBdjgOjjMc3Aycu8Hp+DBQIIT4VQhwDlgI/GsiBgz5NqjcIIUhwJVPfepzY8Bv0xKJxO8tJYwKVQOJSN1BGRn4hDaALcsFy22ZTwrIfHqSoKss/nNdCRmoxlcvySNtjPTYHqSg0li4i0Z9HkDmrrap08vQXZ1WC1Me58guJe9RSm1BfzUPLHPp4hWNZEf5sY0mAfti8GU8Rn5PD+k1W+86+7CxioyxyKEsJt+rLThTT0M97ZmZxTb2xUG7x1OIx1BesZeVMVXwb+IO6Mn0hS1hDov+9WOwUAXE4i5yayJgUF0BAgO7xbKI7QRBMZqaVptRyzY6EvNHdnQORy6zQwNaZMdRte4ypK8p5CNi1t7t/sxLYkkqAWGQoRZPgx2YvsYnG0kWw3Np/taeVxKjhPd7D6wlCiBjAIaU867//x8DzQbu9ATwhhNgGfBVoklKevJDzSSlrgMlCiCFYPepnB3rsFWmpKdwQEU9HVxvh91jN+WbNWnBmUjW/L6lao+NpW3y5jPVmBcyThO7hJ9Dd4K1mAyjtNbVN9QsqCw4sRVcVyDfXO5YVoevG9rkeD6j2V32SqgHbtGBmO0toLF3EG7tGUFfxmCaCWduacdd2sH2/jw11O3RPq7nu4gbB9v0+XaaiiHaf63HEplL2uR7X24Ndq75igKsSZIDbujn/JZ3JVJZU8ceWtaYIK7jH89PcAz2ymJXvNGlFDwXVgWBCraniaIsbBLO2NZM7Y7p24ae2lmviUtLdCsF9wb2hL1XjecnTaP3/fkxHV7udJIBE4C0hxPvAO8CvpZQlQogfCyF+7N/nN1gDU44CG4DQw0YGCCHEt/1rPCmEeEYI8cyAjhvsYcb94ZT3JJ6OM9w21AqYKxJSMRLTyhq3vAuxqZTZzhKtkAqW6xeqSPV8oVQszJiNu7aDX6Qf4Onvx+nMnFluAIHqEuaQ32DFV2XxKFWLqcVjtPWpXo9SBgZ0V8WxrAjmJU/T12UqxSo8OOcwgK7mVwOGy1LCeXDOYZqeT9OxKjk3J8Bdqy9bS13FYzrQnzEpjhdeaQp4zcFKHSor6imu1fuqv6HiacriMy06lTld9sOD/Khqon49KlkRSmEjcmc5c4qeHHAcEgKn3r+7tkVf21d33MLRs4e4feg4opwD0iccEL4slY7bJg6Vf/v7u/vdb8uiGv775yevKJUOIcS/ANHA14GNwAzgHSnlo/0de0VbagA3RibS4jvXY0iGIjTTZVQV5MGB/lA9hn2hr8C56ptUt7hnqnWPo0lYuTOmk5y5UY91e3DOYR1jUhnNZT+0SEJ9iTImxWlVC0UWyp2e7Swh4YTl/tZVPBbQ9whW8F+1MI3a265JVGU439w8NiDeNWtbM5/mHtBkp0pHNtTtoPTb8wPeA7M/FAggJ1VQe9+z4/X6qm1Kva6nvx+nY2r6PQ7RUXBLycQA1Y5fpB/Q75H6oVhStSaAzJTmnopLNpYuCiC04LhZ8HxV6O7jVbHM2LwUpv10Lh+fO0Jy9OhLSmg2BowpUsrZwBn/wJUsAuN1veKKJzWHcBDvSqLeexxAK3iY0t8KZgW52X0wkOZsE73pkLnyC9niyyU5cyO5M6b3cK3e3DxWj5VrSCom0b0g4AumguzKklI1W6YShSKLspRwEk7k6RmXZsIj59frmbWtWROlWtu8r7apolVl0ekJ6v6+zDc3j9Uk++Ccw8Q9U83WmTHMn3s/u/KO8O7aFk18inQVOS374UFeeKVJJzXM15IxKS4gxmZaYL0V3oZyV39UNZEfVVnGivo/zp97PyvTF7Ir7whgxS/zd+7VklXKRVcwiU2FGEL9cKmug4OvfsS+sT8n3pXMjZEJIa/VxmWH0npqEUIkAR1AiEBFT1zxpAYwwnUTZzu+oK2zFSBg2LGKnahqeTMmphBqaPBAYab5vUUFumbOW1TAIy8/pS2qNzePDdm2FKCo4dcMUxabaa2Yz3uKa0kZs1fPvlzcIKwZAH4rVDeu+483eynNQld1XcqCLEsJJzYvRQ9CVhakIsym59Noej6NJVVrWL9pd7fEj182SCFYBPKFV5oC2qTM8ysEZzdN69S0XmPzUrSVp1xONYYP0BnvJVVrmFo8JmD2hJpf6i0q0MrIbdkTQtaghUoYeIsK2PjT0dz7ZiuJrmTiXRdaN2rjEmCHEGIYsBJ4F/gE+I+BHHhVkFqYcDIiciSftVp1fepDrNqG2rIn6BIMJfSnkgqKVEK5Hf1BHVs9pUJPDxebSjlS+ADz597PbGeJtoQ2578UUr9sTtGTWpkCLM2vexZEA+iBI6qvcevMGD2bU1k2bz93kAfnHGaLL5dxy7toSComPme1nsxu9lGq2jCwMoUmyRzLitAW4oa6HeTOmG4VJ/vJx1TCUEkYFQs0M5x5o62b+RgIqFNTsbdQFpkpJGnWtT0457AuC1HnU0W8tUe6J8VXT6lgQ90O3WoG1v/WlV9IRmqx/tFR4w1NQYJQvbQKjaWLaFw8kZ+MrSTRNYoRNqENGoQQDuB/pZRfSClfB1KAO6WUA0oUXBWkBpAQlcTZji90bE1NkVJEpaZ1t2VPIOFEHmJTKe+ubdEfbqBHbVp/ULGWfa7Hme0sYdTedjbU7dBWhbu2g4emnuKxZT8P0HmLXVHGxuVP9OirzJ0xXVsIOb9er8s1VBJj1rZmSl57XTdtf5p7gNojWUwtHqMzpOq1JroXaJfPnJCuuiQAnQww1TK2zoxh1N52Sl57XROyqXqhMsXz597PvORpuGs7tPuo/ipF22CLzbS+gmHWsynhSLPeTWVUTff17ecOapLOnTGd3BnTyUgtJn/nXhJO5PHwvU5NZmBZz6YAgoqTqeLrYIkhk/C8nS0c9RzipqhbGOG6KdTHwcaXBGmN9PoH43GblLKpj0MCcNWQWphwkhB1Mydba3U7kZLKCRb7k3NztDWiBnKk7cmksXRRn8NceoOaN7l1Zgxxz1Tz4JzDOpOphu3G+2vZVqYvZP7c+7X2GFhEojKLZqZN1brpL5n/S65aixQ5JJzIC4gLKmJclSD5NPeADtJvnRljSXXnpbCkag0PzjmslTKCoazET3MP0PR8Gm8/d5BRe9u1BQowccizWh8NrASAKsVQg4iBHvEy0/pTx6n4oSrYNTOn0E16pnWXMSmOuGeqOezay6uPvgh0K25sqNuhpZTM51V7lClnHuxqBssNDfv6cmrOfsBN0bd8qfM/bfSJ/xZCTBcXMBjiqiE1gBGRN9Ha2cKJp+4KIKbZzpKAaUJt2RN4+F6nThiYNUvjlncNuJ9TwV3bob/oj7z8FGMKfstjy37OQ1O7uzbmFD2pS0mCh++CZUWEkujeFTUBsalUu51bZ8ZoJVmlElvy2uu67s1EfM5q7nt2PMeyInSWUGHuXd3F3qoHUxGtkgxSsbS4Z6rJmBQX4KKqGj/obiJXFqFyP5W1ZpZwmAhVrmLOBFWxMvM5BWW9mbG5ktdeZ2X6QmY7SwIy2t6iAqug2kgONZYuChjQo2KjwQNpYleUcfjXf8pQ5zCb0K4sPIXVwN4mhPAIIc4KITz9HQRXGak5hIORUbdwsvUTGkoWamLbnP+SLrw0a5ZU7M2ULDL3GSgevtfJkqo1LKlaw2xnCWl7MhGbSnHlF7IqQWqXzyS/rTNjWL9pNxvqduCu7WD+3Pt10ahS4di+38fU1nLdr9j0fBru2g4yJsWxdWYMW3y5mlB+kX4goLgWLCJV7uTT34/TJHUsK0KThPprapopgjmWFaED9U3Pp/HYsp8DBPSh5s6YrgnEdCGh2+00OwGCkx/m3+AmdfU41HbVUqWa5N/cPFa3jZmdDUo/TXUSmAW3yr1UpR7mkGP1f2hcfC+ft9UzMjp0c72NLxdCiPv8d+OllA4pZYSUMlZKOVRKOaAK6KuK1MDqMuiUnXg6zuiYiSIsV36hHi4CVtHtuOVdAZXmFyrTnbYn02odqlqDK78QOTdHq26o7KqqDVMk4Mov1MF384voWerW9V27oibomjewrKo3N4/VUuXKFVW1cOaXUpWLqNiaCribVoyylJSlBt3kYZZ3lKWE01i6iFF72/WYPED3p6raNAi00kLBnM2piDQ4zmaq3ZqdCuZ2041V7496bVt8uaxMX6j/1xmpxT1+rN5d2xLwY2b+79UP4snWT4h3JRHuGJAAhI3LD5XN23OhC1x1pCaEYGRUCidba5FS6g9y5M5y7eIpKJns3gb4XgjS9mTqDJspXbPFl6trw5Q1oeJAisBmbWvWbU3QMzuogvRKu1/Vh4Gh1+9XITHnDqxKkDw09ZS2eGZta9YlI0pBI9T5VBxOJT7UmhvqduiC29i8FJIzN7Lp/Xe0HlowmSmLren5tIDsq6qTMxvUQ6lwKJgdCQpNz6dpl9x065UKiyrlqazJ01PHlD6eyjJDzylkAHVPZdDsO0uCKwkbVww6hBCbgJv9KroBt4EscNWRGkBs+A04RTiN3jogsCl5/tz7A3oa1dANhfic1VrS+3xKPFz5hdpaEJtK9XBfsDKxr/ob2oEAqyhYb0xV/JszLlUpx/y597N1ZkyPuZeV7zTpGJgrvxB3bQfeogK27/eRcML6MisNNrNMYuvMmAACqnynSdeiQXe5iKpVa0gqJnfGdE3AW3y5LPvhQeoqHmOf63GrVu6dnkkodQ71utVQlOBsaGxeik5qqNcfCsG9pGB1G6gY5ZKqNQFj+oKh/t/Bg3QU6svWcuw386k99wduiUnDIcJCrmNjUPAgUAq0AuUhbv3iilXp6AtCCEbF3M4fPO8TFzHcKqdgNdDtknmWukkE8Lsb2/f7dOlFYml3A7za7lnqpr5srSZEk/CUJXDPgmIWN9xvEVPU6/AasBY8hQ/ooSOqIwACezsVPs09QNykOOKK4Z7iMQC8u7YZd9YOPbl8XvI0Zs8swZ2VplU/Kt9pIoNaXi1+kabn05g/937KCGdD7Q5w7aCsITwg2+rOSiNlzF5m/bI7gH/fs+NJ9q7jSOEDAe+nUuzIOFFMyWuvs3VmDJvzX6KU+SzDP5OzGNg8toeVNtabxRZfLh4/qau417GsCOsYAi2vt587SMakOEaRotvBzPay4FhbMHltqNtBWXpJSELLSC2mku44G1McjGN1j+TM8Pv+nD+cPcLI6BSGhg/rsY6NwYOU8hSwTQhxREr5fm/7CSF+IqX8u1DbrkpLDSAyzEWi62aONR/tc3SZZ6mbyJ3lPLbs53omQeyKMi0UGSqxoKBaalTLkwlV3Kpahcw4kqqIV8+ZYomqvxHQKhue4lome9cxtXgM85Kn4S0q0LE5FY9SBajq2M35LwWUeagsqyrKVUWzKkHgKa4lOXMjr35nEk3PpzGm4LcB/Zr7XI/z6qMvamty4/InOJYVwY+qJmqr6sE5h7Wrqf4+OOcwrz76og7kh4I5qV0RlrJGze6K2LwUXQ+nrstU0AXLlTcJTSVmTNUWVdoRPIkM4HRbAx+dPcywiOF2tvMKRl+E5sf3ettw1ZIaQLwriS7Zxem2+l7lmFX9WFv2BLxFBbr6XMXezFiLmUAI9YUAdLHs5vyXeGjqKcZ6s3RvolKiMAPxQIArag4dUa4fwJHCB3TdlcqSqtiY2letAVar2D7X45SlhDPZu07HmPa5Hmf9pt26W+DT3ANalWOf63Fd+1VX8ZgmvF15RwKsJUUicc9UE/dMNZ/mHuBYVoSuMVOZSZU4UKUn5kxQVZcXql1KudPq9QSTocr+qqnw6gfEXdsRIG+urLjKmryAGBsQMo7a6mvmRMsn3BAZz8goO9t5laPX+rWrmtSUG3qitZZO2RlyH0Vm1VMqdG8gWL/mSnNMzTpQ5QDxOT1dFhPm9G9z/qU5WCR4KG9v8SMTSjJ71rZmylLC8RTXcs+CaKYWj9F1a8pdW5m+UNd5pe3J1AQ0am871VMq9Ci6jElxmghULA+6M6bBMkmmq/jm5rE88vJTOp6lWqSCSzOU5WRmepVkuCJhs1DXbAEzM5tmDM3MdqquhlBqK5O969jnepzJ3nXaalPKxhAYSzvdVs/wyETiXUlcQE2njSsLvbpnVzWpAXTIdpwiHEcfLyVyZ7ketacKcr1FBeTv3Ku/CGrSE3R/Ecz5nCbML8q85GlMbS0PIAczpmaSmarGNwUSVdAc0JZOcuZGq9vAOFZl8spSwgPq2RShqGlLnuJaVqYv1G6oapVy13aQO2O6LuNQsklmzM+MZ22dGaNdS2V13VIykYxJcQH1Z7VHsvg094COp0F3R4RaMxgqWWFqrpkFuuq5Lb5c7X6GKu4FyzIrSwnXYw8VDi1zBEzUklJypv2Urbpx7aDXX6UrXiSyP7T4zvLxuQ8ZEzehz8EYyvoydfcPLXMExGIqa/JCjtzbuPwJ5iVPCxhTp1BftpaM1OKA7KeqzTJFI1V8LVgkUpFVwgnLhVJ9oKZWnGreNo9VtWwqMK8yjub5Td2zt587yH3PjmfrzBidmVTPm9lJU55IITlzoxaJNC0/pUQL6PKV4NesCC5YfsiESqi88ErgHAN1PQpmjVp/80rXb9pN5M7ygB+g907vYfwNkwd9gMqXJRIZNyFZTt7Tv/jsh0+9yafr9l1RIpH9QQhRIKUsDLXtqsx+moh2DiUqLIbTbfV9KisEu5MqC+o15gwonS2zlqmxdBH52Xt1+QZYRKfUVRPdC5jdEPpHQ4+HA53tU2Sx7IcHeZpa6st+6699K+fh0mK9vsgvBINkFaEp91MRkzurO0OoCOAX6QfIG013wa5fO+0YFhG9/dxBKzb2zkEt6Fh7JEuT4guvNMErVkvU09+P40jxA7yt1TW63c17FkSzq9gi82P+6zLJPO6Zajx0W4Cm4q1533RRQxHsm5vHWkRGSa+TvkKp/YpNpcQWdf8/HcKBlF1wlU2Fup4ghPhH+nAtpZQL/H9DEhpcA+4nQGLUKOq9x/F19UzzBzevB+vWq+JZOTcnYJhx8D6qe0DNLJi1rZl317YAVibSbM42ddH0GkYfJKAtqH2ux/EsdTOn6Ek9cWrj8id0v+f2/T49lFnJ8Sj3VWmwmaPjVMuUajFSlpQinGU/PMgtJdaPv9n2pPTQRu1t1/Evlfk09y3+uJtgt+/3BVhmKktqWmdqffUeBL8fppaaOUJPQa2vLLPgEXjBbVPu2g6dFTZ7QQEcOOgidALIxhWDA1j1aC7gHqDaf7sba6Bxv7gmSC3GOZQbIxKp8rxPi+9cwLZQFlooKOFJ0yKDwHiaZ6mbouwsxi3v4lhWhI5bNZYuYl7ytJBfWr1OkGUSm5fC1NbygNkJiiyCB8U8OOcw3+sq1US1/JfWDIN5ydN0I3vw0GOVVJg45NmA55/+fpw+z4+qJmryUYTkKa7VqhqKCCvfaWKsNysgCTIveRqTvet0QkNldk0po97cTfM9AkgZs1dfm7LolNWp4nlqHmvwzURZSjj5O/f2KgrqEA66ekko2bgyIKXcLKXcDKQBX5dS/qOU8h+Bb2ARW7+4JkgNYGT0LSRH30rN2Q843dYQsnatN7FAsOJW8TmrkXNzSHQv0C1DZh0bdH/JTOKJz1nNbGcJj7z8VEBHgCIvZW2YJOcprtXtPGCVijzy8lNaLmlqq2VRKlL5lcNqzVKWzJubxzLbWcJYb5Z2uVRc65GXn+ohYa5IT5GXIi1VOwf0ULlVk5/ue3a8bq5/+vtx5M6Yzmxnic7UvvBKkyZRdQ51fGxeirYszcSJ2fkQnARQ51ftUCqGNttZQmVNnr4/21nCvORpAbE2CCznMN9ji9RsS+0qQRIw1Hg8xP9cv7hmSA1gWMQIbh86jgbvcT46d0TLfyuYIovQk9yUKCTQo45N6bApIUIFJUi5pGoNcm6OrmPbOjMmoJncLMaNzUuh8p0mjhQ+wK6oCbqXsyGpWGvB7YqaoC0qNb1J3Vd9kKp2TbU5jSn4LceyIjThKFdSFc5WvtPEL9K75wCogllVV6ZEKRXhmPMINtTt0PEt6FbyOJYVoSWKVIuUgqmyYSKY5M04myJdpXxiEpZqYjcfq/q0spRwLUGlOgqUtppKDAnCbPfz6sHfAxVCiF8KIX6JJendaxzNxDVFagBRzhjuiL2bIc5Y/uA5yGetn/b4dVYEFaxWGwqKAJUopRmTU25OZU1eD3kj9aU33U5zloAZZ3pwzmFmO0t0AkI9rxBcIpIxKU73eZo1XqoYVyE5c2OPvsvlvxyv1wluPwKLLNS5l/9yvCYm6LYAZztLqJ5SoY9XcS2ziNbsEoBuIgvlpkL33IJ5ydO02KWyvEzLDAIH6SjL2YQiNECLRTaWLiLyhlQrUWDjioeUchPWQOT/8t+y/G5pv7jqSzr6QntnG3UtH+HtbCE55jZiw284r+PVjE3lmoaKx5lKtqH2iV1Rxq6oCTp2pUQdTdmfYKhSC5WRNC0Z1V+qyjUq32kKyFwqa1DVzQXPHDXXMMtCzDIP83xmbGtD3Q7yd+7VHQ/Q3emg6uEUyQQH89U1KZ24DXU7KEsJZ0nVmgACC4YS3lTrqf0qa/J6jO7b4svVPzBmkiBtTya7oibgmneM2yNSiAhzhTzXlwW7pKN/+BVv/wS4TUr5vBDiFuAmKeU7/R171Zd09IWIsEhGDx1DU/tpjjfXEO0cQlL0aCIckSH3Dy7naEgqJm1FAZH+ViuzPEBBSd3UfbuFqa0TdGO9qd2VO2M6s2dak9mVNTNLlSwUBxa9qsb1jElx3GeMxWt63mpuX/5LqwRDZU8zqIUxe/Go1qziwEZ6RWij9rbjwbKUzPmbnuJaHiyGrZvHMgs/iWWl6Toz5QJveN4iIdVFoSzCMQW/1bG1rTNjmE03mW3x5VLy2usBPbAPzjmM+/luCy8jtZglVWvISC1mtrMkYG21j5sd3XNM/fdJ7x7crPY3pb3VVCmFr7++Ap83j3DXHSH/9zauOKwDuoBs4HngLPA6cG9/B15z7mcoxEXcyJ1xmUQ6oqhqeo/jzR/R0dXe/4FY7ouaVmVKQyt4lrpJ25OpyWrj8icCjlfZ1CVVaxhT8FsALamt3FFFaKpvVLltW2fGsHVmjC7dUFCN6GZhqglPca1OWkB3VlWtq8ozZm1r1sH8lDF7tWto9qcGD0FWBBmqmV7dV32gCop0VNmHir1N9q7DW1TAkcIH9JBoJS+kbmYf6WTvOiZ71/Uo41DnVTE06CY4pXZ7erSDqLAYuz3q6sFXpZR/gX/+p5TyDDAgJc/rgtQAHCKMkdEp3BmXiUDwYVMFdc0fB5BbKJnvxtJFWg7alIY24Vnq1jG0/J17AzoXVLZO9XWqLyoQkDxQE8xfeKUpYLq5+qJDd5W+ugX3baoEBFjDZ8wByGq95MyN2lLbOjOGW0omsnVmjG5/UiSmyjbMolblXubOmM4+1+Ns8eVqi0iVt6jOinnJ06isydNZXOjudDiWFaGPLcrO0m1fyjWFblJSj/VA5dTigIynCTXKUAlHVk+pYOPyJ4hdUUbz8feJCovucYyNKxYdQogw/IW4Qoh4GFiW57ohNYVwRwTJMaO5My4TieTDpgpOtHwSsnA3GMFzPc37qohXEaOy6hLdC1i/abf+cjYkFQf0daoK/FC9jSrOtaRqTQ81C5VRNVVBjmVF6AJWJbNkjsjzFNdypPABvb/qqwy2upQyBlhkpvpAy1LCefXRF3XpyIa6Hcx2lrArqnuQzSMvP0Vy5kZN5GqqvVL+UNeiEhPqHIo0S157XR+roOJp5nPu2o4Ay07NjlC9vWDF0vJ37iVyZzlf/N8VRIWFlkaycUViLVaCIEEI8TPgLa7X7OdAEe6I4OaY20iPu5su2cmRpnepbz0esE+oejYVJwvWvlfbYleUaVVdVS9VPaWChqRi1m/a3aNbwdToVyj+uNtiA0uW6IVXmkgZs1dbKcryMqv+P809wNPfj+O+Z8fTkFTMPQuie5RZmFAWYUZqsW6IVwOawZrepOYnqGxpbF4KYwp+G+AWKgI03VBl4Sm1DpMk1fWoGQzmoBdzuMqGuh2a/MziYm9Rgc6SqnUUMSsXVCmzeJa6ObTMQceQ4bicNqldLZBS/jvwNPB3wEngO1LKXw3k2Gs6UTAQRDgiuTkmlXhXMlVNFcS7knTDc6hsp2epW9eUhdoGkAjdUkY5q9nuepx5J6bhLereNzlzI4kLFkAx7PIn6ixZHgKGnKhM6I+qJuphyvk798JmKwP54JzD2m2cta2ZY1kRfG/Zz4nPsXoft4Zo/DYzoGBZTcqVTck9AJPidLN6j+B9Sjj7ah9nnm+azmACeiiNcv0m16xjJZZlNdtpJUmO+d1MRWyztjWTnLmRlen3W6MF2REyUaD2M8UHoDvbqVA9pUKPQHTlFzJubg5tK8oY+lYXb7V+gisytNKHjSsW1YAHP08JIW6RUn7a30HXraUWjMgwF1HOGI41V9Pe1RZyH2W5KUJTU+JNmIkERYr5O/fqYS1pezK1bHh92VrdP2mWduSN7pbnSc7cqPXMVHmGcumUhaMGLIMVTP+VI4e27Al6PJ8SV1QCjeZ0qjc3j9VWj6e4Vhffbp0Zo91M5Rqq4L2Kl0Fg72XanswAV3FWUPa2LCU8YMhz7ozp2qVUxFqWEq6LiaE7jrjPZZUmKBFNdZ4jhQ+QkVqs1zFnR6gaQ9/213F6OwlzXPe/4VcNhBB/CdQDvwXeBH7t/9svbFIzcNvQsYQ7XFQ1vcdnrcd69AkGj9cbt7yLQ8scgVPW/YkEM+kQPHdStWGtTF+oXT2zzaj2SJbOQia6F/DwvU7Wb9qtp5WruFP1lIqARvq3nzvIkcIH9AwD1Zs52buOWduatR4aEJCIUK1Iaq1jWREBhLR+025d4Q/dQXzVQK/cULN+TamG5M6Yrtcys74Q6DYqPDjnsI43qjWUZadibcrN3VC3Q7eLuWs7AmJu6hr/kLmH90r/jPgoe2LUVYaFQLqU8itSyvFSygwp5YBMbZvUDIQJJ0nRKdwRexetvmaqPZU99gmWJUp0LwjIjvaFYHdWWSyf5h5g1rZm5iVP00WssXkpTPUPZlHnbXo+jR9VTdTFsnUVj2nRyDc3j9XJBjMrqlqLVPzJLNFQFtlk7zoqa/L00GYzXgVQlJ3FPtfjWlxTYbazhGNZETrupV7PbGcJD9/rDKj2n5c8jXnJ0/QYPTUVyhR3VOdUiYrgWKCyFBVhqalXqrBYWa5qsDFAxB/NxSnCiY+0Se0qwzHQc4fOCzaphUBkmItbh6Tj7WzVMuH9Edb5wltUoFud7nt2PPcsiKYhqZjN+S/x8L3OgJmVCmqC+hZfrq5hUxlMZQWZszePFD6gA/VmS9OxrIgeLu8+1+MBSYwlVWt6yCmBVXc3bnkXaXsyWb9pd0DgX62/xZfL9v0+3dp0LCtCzxFQLijQo/J/SdUa3tw8VncaKLIzFTkUYal2rq0zY4h7pppHXn6K6ikVWuVYlXe0dXqJdg6x69MuEkKIfxVCNAghDvWyXfhncx4VQhwUQtxzkaf8CNglhPiJEOIpdRvIgTap9QIhBJFhLt0UH2xlhSK5UImFUG1T6lhFTqrGq7c1zG0P3+vUhaqrEiQ5v16vv9gqk6pEJE2JbWU1BVs/7toOHa9SmcOGpGItk60st6mtFllU1uRxaJmDXVETKMq2GtnXb9qt3UiVzFClFuq5lekLA8QszTYqpbYxbnkXm/NfArrLN5TbqVxusCzHktde55GXn9IFxK8++qLuv23LnkBdxWN4iwpo7/IOelvUNYJfAqF72SzkYckFpQHzgX++yPN9ihVPi8BS61C3fmFHTvtApCOKts5Wop1DLmodsye0ekoFiUsXkbApT8eU6ioeI7lso87chSoCNrE5/yUoepJ3aeaeBYtYlQCrGBOQSVVYv2m3RT4pJZpUVDD+kZef0gQJFlnEPVPNQytOsaGuW9JoQ90O5uWDlwLGLbc6LHJ2rkfMKKWyKg+mVFixP5yAM0AqfYsvF2Z2N6JnpFqzRfHLCpWlW5nRJQVrYJllvakiX5P4zMLcspRwlnjXQQ3A6/xt5XOWW1wEsSsKiNxZTg7rIRvau9qIdNikdrGQUu4WQtzaxy4PA1uk1Uy+TwgxTAgxUkp58nzP5S+6TZNS/umFXKtNan0gMsxFW5c34Dk1Db7RX1wbPAQ5eN/gx7F7MonMWc1s55M6uH3MFUFGcjENpcXWbOp+cGiZQxNb/K9fovTb89k6M4bDrr1UvmMNSMFvub2xawRvzh2rA/A6CZCXQkNSMbPWtjC1tRxXfiEb6nbw0NRTVE+p4OF7nSxuQFtxcm4O1csqSMPKLnopwEUp+1yPW9lQNRwaOGRM7aqsydN9msr6Uu6xnuQOgfu7dhAMs1kdul3X5MyNZFDMrqgJJGdu1NcgNpXiSVpEe6eXiOuU1Frb4nqo/4ZCU1MlsO8hIYSZtVkvpVx/HqdLxoqDKRz3P3fepCal7BRCxAshIqSUA+tnNGCTWh+IDIviXEd3rFLLFbHauu+3qNL2ZOIJcXwoVzJyZzmHljmsOFEyVFZZX9QNdTt4GGePpnoFNYglbU8m71ZM4O0DB3mk6h1WFi1kK5aLqXXLgop5U8bs5c0jWQH9kp7iWmJXnCJ3RiHVNRVQk8fD7mLATcaJPErWtsA2aw7B4gbBoWUOTRhybg5Fy7Moc1qWnLeogCO7RvDIy0/hLSrQzekAsypeB2MEp9mvqucqZKVpNY6M1GLKnOE9GuOVKsf6TbvZkm+5rLMqXid3xnQqa/LInQGJSQvAn52urMkjsr0IIRyEOwbUMni94w0p5dKLOD5U0LJ3CaD+8QnwthDiDUCn4qWUL/Z3oB1T6wNOEU6n9OnH5gg9k7D6cheDrbW27AkcKXwA6J5PKefmaFnv3tZSpSCepW7uWRDN31Y+py2fUA3eSqnjzc1jyfAX06ouANUkXz2lQtfPmWhIKmZqaznJmRtJOJHHkqo1mtDUPIXHlv1c18ap0pINdTsoys4KaIECAlQ6wMr2pozZS9wz1fqazBIS5Waq16TWMzsPtvhydSxyn+txrUen3r+i43M53vwRtw5Jt5MEXw6OA6OMxzcDJy5ivRNYdWkO7JjapYNDOHodkjxQhLLWlEWTuNRNY4hjTHdWWYdKXjxUJwNYE6QArbGWQfcUeJUwcNd28OAzL+qWo4zUYrzZBexzWQH//BXdU7Mqayp0lwBYNWirEiT1ZWtZUgUsg0dSrZjcShYyz9eto6a6AFRB7yisEpJ5ydMoee11Rj07vscMh+TMjVBjWb2u/EIdf9tQayUdVrKQJazRbmdGajF1FY+xNd1KkszKS2HJMgeJpeDraOHbedWMjE656HiojQHjDeAJIcQ2LHHHpguJpylIKZ+70GNtS60PhImwXgd1XEiJh9JeU5ZHb2uMW96lyyuUdHhDUrGeaKUIbosvV1szwZPh1XwBsCr3zaHHqhSj5LXXeWPXCEbtbefhe51E7iynsqa7BWzWtmbd9qQIDaCu4jFWpi9kQ90ObWEFT3lS5Sam6ORsZwlTW8vJnTFdt3+5azt49dEXtSVozheorMnTWVRlLaoYkamCq5ro0/ZkMuKPX6Lije8SEx7L8MjE8/0X2egFQoj/APYC6UKI40KIR4UQPxZC/Ni/y2+wyjCOAhuA/tUp+z7f74QQO4NvAznWttT6gEOE9Tqoo6/Si95gfmGVm7R9vy9gopRaOy27jOqytYxb3kUjlgpvyWuvk5xZQYZ/P/XFVgKQithU10Dxx01aiki1NmVgyf1Q61fFnWnN1IxtLcBqDqugvmwtK9MX4imsRf7aarnavn8Hk72Wq6czkSklEKJAlhTrukxiUwmKLX5LcOvMGLbMeE4T/M9eCOeR//tOQCJAr+d/7N2T2R1XS81lFq/3eH8dC79PW6eXtFi7z/NSQkr5g362S+AvLuEpFxv3XcB0wNfLvgGwLbU+4CCMroGNGhwQ0vZkkuheoItoY1eU9SA06J47Om55F3OKntRTrlRTt6rZ0oq2QZLgL7zSpCdG3ffseEpeex05N4eMVGuwiynSqGYOFGVn6UREonsBqxKsGG9j6SLmz72fecnTdK0ZwE8zntUxNQjsJkgZszdgMAwQ0BoFloWl1Dbe3DyW2iOWC2yq16p9VeFu9ZQKtvhy2RU1QWu1rUxfqI/JSC3m87Z6bo5JHfQp7DYuDlLKcuP2tpTyKSy3tl/YllofuBwj1VRJCKXd1lqo2QaqbGKLL5dfOXIYU/BbEt0LkDmr2VpUohU5FJSbp6S+t86MwZ1lSYDHPVPNxuVPkF+0l+plFXgKa2Hm2IBeybKUcBITFugsbn3ZWqb6e1RnbXudLTOsMX4JJyyCGeVvyQpOUIDVu5rhVw9R51eKt7O2NePO2sG8VKgEVjq7p64H6KW5djDP569VSy9hctU6xi3vYnZRiXY1VWN/XcVjJGdupH7kb2jr9NpikNcAhBA3Gg8dwETgpoEca/+c9QGf9BEmLh3vV0+pCNBeSziRR8KJvB6EZta8NSRZWmdK1WPj8icC1Gi3zozhzc1jAwYIq7iaso7e3DxWN6RnpBYzpuC3bM5/qcewk3fXtujJWonuBSxuEGyo20HujOnIuTm8u7ZFdwuoZvVgqDVNCXI1K8GUF1ftTspSrKzJC2iHUp0E6rl9rsf1KEL1XoJl+eXOmM6Rwgdo7/IS7gjHIcLO919j48pDOda09gPAHuAp4NGBHGiTWh9o72wl8hK22KTtySThRJ5W62hIKtalCGbfZeyKMipr8vQgXjk3R2+flzyNuGeqmdpaHlDiYI6iO5YVgbu2g+TMjVqSOyO1GFd+oXbV2rItF25J1RpNFFtnxpCRWsz8uffrZMRk7zoakootbTZ/87u6jtnOEl2SYQo/BmuiKYtSDZ6JzUvRRb0ZqZZ4piktZPZ8Kqj19rke18kL6B6X98amMZxpP4XLttKuFYwF/gl4HzgEFGMRXL+wSa0PtHV5L2mLjarab8ueQH3ZWnblHWHWtmat9gFoxVz15QWLgMYtt9xgb1EBsXkpVE+poHpKRYD2mEoKKOnsuorHdFyq5LXX2VC3Q69bPaVCu3vKYoJuUqmsyWP9pt1kpBYHEK4iIeh2PYPJUfWZBg+GUXNKt86M0fvIuTlUT6nQQ1hMlLz2uo6tKSVcs3WrsiaP29++m8bSP/Bn4xrwtJ/hpqhbLuI/ZOMKwmZgDJas9z/67xf1eYQfdkytD7R1Xrpm6NgVZXiL4OGlxSxuuJ9Z25pZ9koTP3q++4sfn7OaeqzY0uSqdaQVZTJ/7v0sqVrDvmxrSlU+e3XCwFtUoKWKwJoy5XmmGowyiln+GrG3nzuos6K8bLm4S8rWWsSZWkxaUQHr9+xmX7bVKbAlFTCUZrW8EDuI2/Uib861at2CXVhVXwbdAo+qLMOdtUO3RjGzhPUUWLG+nXv18Rmpxbz66ItWszqva+Kd7F0XMHn9sZ05rCxayE8mfUZ963Fujr6VuNgMu9D22kG6lPIu4/HvhBDvD+RA21LrA+1dXiIdUZdkLc9SN9VTrHIJZVkt/+X4AM0xZbGp7dVTKrRVNC95GpO963RxLFgFsWpfVXyroCZCeYprrTkEitCwLCAldqlq0iJ3llM9pSIgS6luqoYMuqfMq1o3JdyoFGjNFillka3ftFvPI1DPL6laQ/WUCk1oSipoQ90OXZycnLmRVQmSVQnSakXzJ1Zc+YU0li5iVYKkKTKcm6NvY1jEcJvQri1UCCEmqwdCiK8CAxq2bFtqfaDtEsbUVDM7AFHdgogl214n98R0GpKKdRZ068wY9tU+HmDBALpGa6XfeiO9RI/KU/LcTc+nEWuIRJpV+2pK/C3PR1DttQLtKna2cfkT1vi71Ol4NxUwf+79ekpT9ZQKrfChxSM3WX9U/OzNzWMp2fY6yZkbWUJQE7W/FGMWr2sFDmXNefdkUpSdpWN0k72WhepZ6iYRkFLS1tXKqYW3c87nwTvezW5j6S7ZydC4Cdi4NiCEqMTqGQ0HZgshPvU/TgEO93Wsgk1qvUBKSXtX2yVReIhdUUbjUreWG/LMsaaif6+r1GrtSbLiaarUY1WChAZrGrqy1Epee52tM2M02SiJnji/u6lgqt6as0FVW1IGtRbpZVqxtAb3AuJzVjNvEzrbWJSd1V1OQbcckGlVKaJSTflpezKxJB0qdLmF6oBwGQW3ALOxYmSTvevIyC+0Sk2mVLCEPBLdC/hCdnK6rYGzHU00+zw4hIMYZxxDw4eR4EpGGL3T4Y4I20K7tvDgxS5gk1ovaO9qw+mIuCRFnJ6lbhY3CLb466Fj81KsaVLuBSSWdtepRe4s1yodqiZMCTHmzpiu66k31O0IcFvNPkp131NcC1lpPZ/zP145cyFbUnOpnrKW2D2ZuCjVCYJ5qVbw3+3aEWBBrfQTKoYEkDkJHdDSRJU1eVCWR+ye7vdBTaiqrLHIK21FJpVYqoIqUXK24wuONdfgCotmWMRwkqNHExEWedH/AxtXB6SUtf3v1TfsmFov+Mnqc5ck8xm7oozFDYJVCVILMm6dGcPK9IW697MtewKLG0QAsYEV73r4XqeOXansoGp5mpc8LYDMgpvEzToxfT3+WJtZNKtiWSaU8q3KgpqFsQrm0GAT3qICraIbubOchiSrk2Fqq3VfZXKrp1SQ8LW/pGHxBBq9J/jo7GE+bT5KcvRobhs6hhsjE2xCs3HeEFbLVmhMnDhRHjjQf2lI5vCvXcpruiLQ6D1BW2crN8ekXvRaygVTLqRSo52XPE0H6hWRKYtHNbIHW0QKqq5LQWUazcHIJplBt8WmMpIqHqYymHJujh4xp4pjPUvdAUq23qKCgO3BUJPp1Wtpy55A5M7yHgXGvi4fJ1traWr/HCEcDPW7l7ERNxJ2jRfPVnz+1oD2E0KUSyknXuh5IsaNlSNe/bd+92sqfIGWf3/la1LKAQXir3TY7mcv8Ha24gq7uMynEnasp4KV/gEkS6rWkLjUDfstN7KsIZz1m3b30FFLy7bc0NkNVrzI7dqhB4zMdpYwalt7SMtMtTkp8lLPq+cUVN+oGyClxN/rWUw8q2lYWkzaioKAa0rbk8ns9IVs3pmDlwLSICThmSMEg6XJlZvtaT/NseYa4iKGkxY7/pIWONuwYbufvcDKfF44qdWXrcWz1E3ansyAMXBpezKJz1mtBRLBcsPic1YHEIIiDLOo9c3NYwPcRJO4oGdsTZHZsayIgP3M54EAgUY121RZjLEryrT1tqRqDXOKntSFv7qPlZ6WpIL5uj4reYJPm6s53vIRtwxJ4+aY22xCs3HJYZNaL2jrbMV1ETVqie4FxK4o04F+QLcYzSl6EuiuyK+reIw5RU9qIlFQdWt1FY8FPK9lsAkkKHOeZ7DrqfbxFNcGZEVVHycETrpSj6unVGhVDEB3GaxMX8ihZY4AOSVFdiYOLXNYbmhnKx82vYdAkB53N0PDh/X3FtqwcUGw3c8Q6JQ+OqWPcMeFB6mVdTIveZo1f+BeJ+NychCbrPIGc6bm1pkxbM5/SZd0qGOr/VOapraWU4RFjsq6UzVjysqKMzoJzNF4ys1UxKXOq7Zv8eWyxJvHuJwuPUzGKtlwkObPXObOmE7da49ZmUsgragA5pb0EHbU8T/QyYDEpYs42dXBR2cPk+i6mRGuAQkt2LgEiDwXHTLuGYyjJ+Np+RKu58uCTWohoFzPi6l/UkFyb1EB+RSwuOF+3LVPUOYM10Rkjn1TLp5nabfrmbjUkvGurLEq781+TXOGJkDZTKtdSkln6/alEEKOs7Y1gz9BMNtZwvo9BViiCBbUF8GVX6ilgVQphsKSqjWkGftXT6kIyIY2llqxtC7ZxcfnPiQ24kab0Gx8KbBJLQS8FxlP0/po/nF3i/3B/nnJ05hNiR5n95sFkwGrk4BsqxQi4YQqtH0SELhrrdmYpFtktDU9RmupBU9GNy0ys/nbhHJ5ddP6HqtMpC17ArH+5ISyFL1FBazH2q56LhUS3d2Tm1z5haQVFfTYR0rJseYanMJJUtStF/x+2rBxPrBjaiHQ0dVOxEW4nqZm2uIGwZKqNWzx5VLymtUmtMWXy4+qembq58+9n5LXXtfSQYqA1P1jWRFavmfU3na9/cE5hwMITcE83vy7dWaM7ul05Rdq99GVX6hVQoJjYyaCY3/eImuAsEloCSfyOOdrosV3lpQhd9hV/za+NNikFgK+rnacoqeVM1AofTTPUrc1dBjLcto6M4YyNZncsKLasifo5nSVtYx7plpLCCmoHksgQL7nzc1jAxIH5jEKoRRqq6dU6FYoV34hs50lOi6mXEoTZjN9rGGlqdegdOKUVlyz7yxxETfaoo02vlTYpBYCHbIDp+PCSU3VbYE1KTxtT2YAiSkra/t+Hwkn8pg/934d+A9uZwKLzNTxptSPOb1JYevMGG3JQSCZBa+jSk2UhaXmBpiEZlptapbBoWUOXPmFRO4sx+t3OwFd3qHQ6msmKizmgt5DGzYuFDaphYCvq33AU72VyoWJRPcCGpKK9bai7Cwt5mgG96G7vkvp95u1ZsF1aCqwDz1dSiCk2qypSqvWUPG0ypo8TUreooKA9iVAtzqp++s37dZlKWARNnRnOoMLiLvotN1OG186bFILgY6uDpxiYKRmDhc2a7zic1bjLSrQOmJNz6dp91NhXvK0gHkEwYSmyi7M2ZrBMNudTCgyU8eUpYTrIl5TYdaMp6lY4LjlXSFLAVQiQM3fNLOhKs4WUOcWfgOe9jM91rFh43LCJrUQ8MkORh4JXSHfG8wBKvVla7WLFipTqXT9K2vytGikWTyrmtNVRlMNI561rVnfoDuBEGyJQTcBqsdq5F1lTZ6OgwVkKufm6PvqdQTvpwjYtORMBJO6Kyyati7vwN9EGzYuAWxSC0KX7KRLdnJm3K4+p7D3tS3RvcDqlfT3eyrSUVlQQCvGQqDuGQT2aObOmK5bo1RHgGnxmccpHX8zc6o00hY3CD2fwLSwxi3vItG9QBOZmdlUWU11i89ZrVu/FKqnVNCWPSHgFruijMbSRXg7W+xBKDa+dNikFoQz7acY4oxDCNHnFHZzW3zO6gA3VEFZWorI9rke16SjtkN3q5LZoxmbl8Kove16YpPKapqdCGaLE6D7SdVtiy+X9Zt2B1hhfUERtSIvz1K3fp1t2RO0coiCmQ2FnqUedqLAxmDALr41IKWkofU4o2JuP6/jGksXhRxIDP6aMLqnqUN3EL8sJZyVWNZacJ2Zynw+WExARpSsNB70DwpWayiYQ1i6ZY2KdfFvWUq4FppMdC8gMuia+yLxyJ3lJC61Xic7yxm3E2YXWYOGSVcdBlb2s3pKBeNyVlP16lSG210ENr5k2JaagS/aT+F0hBPjjB3wMabQo4Lqn1SZQlWrtnVmDMeyIpiXPE0PFFHZSOWCKnJrej6NNzePDSQ0P0xrDbrjZiq+ptRqobubQUHpoCkX0XwNvaGxdFEP8mvLnmBNiPIXE4NVPAxWrO3zNCe+rg7bUrPxpcO21PyQUlLvPU5SVErIMoTeLLHg5+JzVtPmv5+2J5Mtqbk0li6irChcW1ENSd2zNOtr/Du7dvSIrYFFhA8anq3KdoaCqY/mru1gy4lpQHfGVNWkhRJt7Asm6bVlTyCe7tdYlhLOBn8rlyK3zrZm/lA4lRGukZdEDt2GjfOBTWp+eDtbrMlE4TeE3N4XCai5AtAtjJgIROasZvbcEmgQekiJFd9aFKBwsc/1eACZxT1TTdPzaWzx5bKhboe21mLzUvRcTTXgF7oznbkzplsL+GBLcvf1Lalao8+nGu3NPs82IJ7VAW60Kf5YWePoUYtWv2wtGXRbh0plpHHxRD4+9yFRYUOIdyWdx3/Aho1LA/tn1A+HCEMiL6hYVH3RE07kBbQWbVz+BFt8uQHlFGa5hHLXVMBflXKAZXVtqLPUbhWh5c6YHpC9nOxdp60jVVAbjC2+XMYt79KBf7CI1MxmAjprCd3CkOqaFSEqAcnYFWU6uwuWxlpj6SI++6tMqj2VjIi8iVtibretNBuDAvtT50e4IwJfVwd9zWxQ6C0GFazbr4L1iizMQtvZzhKWVK1hZfrCHu6k6iRQg4MVoZkwS0O2+HLxFhXo1qzZzhJNcA1JxZqMlBWW6F5gWVWG9elZ6g6YKQCW+6wIsS17AvVlawMynpvzX+Lhe514lroZ/sAqPm2uJin6Vka4RtqdBDYGDbb76YdDOHAIB53S128zuxJzNBGfs5r45V1aRwy6uw0sK6l73+37feRv2stKv6WmZ3PSXaOWv3Ov7gkFAnosFXmZE57MYSjr/TMEvHsyUdJGjUYLU33ZWmL3oGW7VXwsYKZA0OuL3FkOU7p/A7UbWraWRPcCDr8xk3ARwQ0R8X2+dzauTgghcoE1QBiwUUr590Hb/wRQn/JzwJ9LKd//cq/Sgm2pGXCKCDq6eipcmNDKsEEIzhD2h2r/MGDoLvF4c/NYts6MYWqrZSmt37RbZzNd+YWMW96l42kQurndlV+ob4p4gq8r0b1AW2YKZo2aep3mY1WjpgpyTeuzrdNLg7eOm2NSbQvtGoQQIgz4JyAPGAv8QAgxNmi3j4E/klKOB/4W/LOtBwG2peZHp+ykQ7b3qc6hEwL+zKVy5xJO5AUU3wY/NhGfs5qylCfJ8Fnbt/hyrWlO9zphgXJ9u2N0iri8RQVUL6vQSrQK6r7q6wycxVkccJ29QWdDDetMqe8qq9RU7jD3lVJyvLmGBFeyPUTl2sUk4KiU8iMAIcQ24GHgsNpBSmmMrWYfcPOXeoUGbEvND0/7aW64aRLDV/6+15iZsnzGLe8KIArVIB7qOKWtFruijHfXtvArR07AMWpYcCg0JBXrxnFlVSmlDvW8stDMWJ6a16nO3xuhKevSjLmp11BftlYnC5RwZCicaW+kQ3aQYGc6r3Y8JIQ4YNzmG9uSgWPG4+P+53rDo6hf1EGAban5cbq9kRs8UlsoJoKJoS+SAFW2UayfS8u2XLV7FkSzfb/VEbB+ardVVZSdxcM4tWtrJhQS3Qs0waxMt2JwpqUG3S4hU1TpRbm2shqD5ICgO5YWnCgwoToO2rInBAw1NmOGvq4OTrR8wuihYxB2pvOKgzjb0qN1LRTCjjcCvCGlXNrLLqFiCiEzakKIr2OR2qBNOLc/iX50dLXp5utg0tq4/IlejzPLIsCq4A9VqV89pYLt+336cVF2lrau8nfutVQt8gvJSC3WFlN8zmp9jLKUzNKNtD2ZrEqQVE+p4NAyi9CUdRU8SNiESWi9xdIUVOwtmOh9/glRN0YmEOMc2uv7Y+OawHFglPH4ZuBE8E5CiPHARuBhKeXnX9K19YBNan5EOqJo62wNue3he50BX/r4nNW6/aixdJHuDoBuiZ/t+316vqcqsJ2XPI15ydOIzUshf+deoDtoryZPAbrcoi17Ag/f69SlFWbphiLE+rK1WuNMEVpj6aIehBYc+FdQ5NZbt4Q6zkyEtHV6qfYcZGj4MEZG9ZwvauOaw34gTQgxWggRAcwE3jB3EELcAvwnkC+l/MMgXKOGTWp+uMKiaOsKTWomIneWa8VX9YUPJahoDkbJSC1msncdG+p26FkFSs5HQVlE3qICTUjK5TPPCT3LOzxL3QHy28HWllrL7Pc00ZuVpl6veUyL7xxHPZWMcCUxMjp0S5mNawtSSh/wBFZ66AjwqpTyAyHEj4UQP/bv9gwwHFgnhHhPCHFgkC7XJjWFyLAovJ2tIeNpENqiMcknGPcsiGaydx31ZWvZULeDV78zCaCHQi0QYA2ZjwGtUVZZY2VUTSI0g/eJ7gU9RtQFvxZ1rGmBBW/vzWW1FEzqqDn7Ackxo4l3jQy5n41rE1LK30gp75BSpkopf+Z/7l+klP/iv/+YlPIGKeXd/lvPcWlfEmxS8yMyLLT7Gcqy8RYV6BIKheDY1Pb9Pva5Hmdl+kJG7W3nvmfHA5Z7ujn/pYCso6r7UiUjprXlWeq25mruyaS+bK12LZUQpULsijIruO8nJrPVKdRrMRMi6jp6c0E7pY+jZw/R1HGaO2LvYljEiL7eShs2BhU2qfkR4YikvatNN3P3hflz79dV/QqmJRefs5q4Z6oBtFS3mjegiKm+bG0AqZi9mcrqUpBzc5g/935Wpi8MiI15lroD5g2oJnSzwT4YKiaoSM+MB/aGU97PcIpwbh86zq5Fs3HFwyY1P1p8ZwmbPKHfQlVFKqGGoChsXP4EsXkplKWEkztjulahNdfISC3WfZYmiaoJVInuBQGkpyS658+9P8B1VC6lUqk1a9TMYSi9ZTXVeUJlOMGSN2/0nuCmqFvs+JmNqwI2qfnh6TjDiPfq+7TUEk7k4Vnq1q5nqLgUWEmCrTNjmLWtmZLXXteDUvJ37tWV+WZCwHQTTcvLJNfHlv1ca6kVZWchNpUSn7M6IIFgusCmO2lmQ02JbvOvyrYGv/bP2+qJcQ4lymnPGrBxdcAmNawguKfjC1K+vqJPS81bVEB8zmoS3QvYnP8SrvzCHu5bfM5qjmVF6PmeargwoF1Ms7hWWWvKQjNjXSrOBhbxrEqQOtGwoW5HQP3cu2tbAshLWWm9EW8omIQXn7Oa8Al/Rn3rcRKjRvV9oA0bVxBsUgNdyhETO7pHcN2Ekt1RxNeQVBxAULEryji0zKEHn8xLngZ0Tz4325BM8mrLnhByRF2obOSqBKnXVeoeCSfyNHGq49UafcXWTARbaNU7/pSK//kxt8SkEe0c0ut7YsPGlQab1ABP+xkSRn+L9m9M7LOtxFtU0Kcahzk+zl3bodVpM1KLceUXasI0Y1xKTcMss4jPWR1y8rsinoakYh5b9nO2zozRtW8AD77u0Xpw6hwmkZrrhBo+DBZBftF+irqWj0kd+hViI0IrAduwcaXiuic1KSVNw+O4YdqPgL5lu3tr6g7e58E5h3X20xyCAgSMmTu0zIGcm6PJp7u/cpG23Mw4WXD/6aoEyWTvOspSwvll8zdZnn2Ij899yI3fsKSuepMeUvEzc5v6W3fT/+V480eMHnInUU57aIqNqw/XPak1+zzIpi8I2/w8rvxCTTChYLqavSFtTyZjCn6rJz4phVvVrzlueReNpYt05hHQvZvvrm0JcH/N+70VzCa6F7Ckag1frJ2JEycO4eD3//VtPnzjBzR6T/DhGz+gU3b2OC6UW9rW6eWTc39geORNRNv9nDauUlz3pNboPcmNnWEIIaisyevVGgvu/Uw4kcf2/b4eI+iKsrNYmb6Qyd51uk3KVKhVUJnGxtJFjFveRdqeTO5ZEE1RdlZA4eyhZY6AOJx5Lep+onsBbS/+kIgwFx1d7bjComju8NDW6aWp/TQftlRxsuVTfF0ddEofzR0eYu//afe1dLby6blq/uB5nyHOWG6KGjQpLBs2LhqiL03+iRMnygMH+m/hyhw+aCojF4W2Ti9/8LzP2GETCRNhAVX8/UF1AswpelLXoDUkFZNwIk/HuCZ717EyfaEuAclILdbikUoayDxXcEuWmUgwXdPeIKWk0VtHvbeOkVEpDI9MRAiBt7OFBm8dX7SdAvzdE11eIhwuIh2RnPN5GBE5knjXyD5FMm1cGlR8/taA9hNClF9Mu1Hs8LFycs6Wfvf7sHwVx/7wyteklG9f6LmuJFzXltqptpPcGJlAmAjrcz9TAsgsvVBxLW9RgZ7lKefmMGpvuy69WL9pNyvTF2prTZWAKEvMTA70lXntj9AAhBAkRN1MWmwGp9vq+ejsYb/lFs0tMWl85YZ7ybhhMulxd5Mx7KuMir6N2IgbGRM3gZHRt9iEZuOawHVLap2yk9NtDdz6TPdYuFCZT1Wm8diynwOElOlWVlSiewGNpYvYOjOGJVVr2Od6HFd+Ies37Wb9pt1U1uTpOJpZTxZcKKtg3lctTabrGSrGFruiDFdYNFkzfotE0tTeLWsVJpy6K0AIQUx4LMMjE3E6bK1QG9cOrltSa2r/nBjnUDpWze7h9plQxbLB20zBR3Vs7Ioy6svWsipBsjJ9obbW1CCU4H5RsGJmihSDC29N681MLCgyC0XCiiw//+1iumQnrjA7g2nj+sJ1S2qe6CHcGJmoH5s9lAOpwn/43m7rpr5sLYsbBPPn3s+RwgfYlXdEbzNnCcx2lvQowA3OqAaTp5m4MGN+waoawRlbKSXezpaQOsw2bFzLuC79jrbOVs6dOcQtw6wYbDCRDCRRYCLRvYDNfoWMJQVWQqCyyprBmbjUrTOkqxIksSv8cbPSQCJqy55AbHZgo7pZ09ZYuojY7LIe+0HPeJt6nPZnm/jo3xcS7xtGvCvJbki3cV3gusx+nmyppVN2kvndbkXi85nZGQqKSNRfJf+TcCIPOTeHOUVPsn7Tbj0FHbrJszfBRtP1VGjLnkD1lAr/gJXATGmo19DW6eXo2UpujUknJjz2ol6jjUuDLyv7Ge0cItPj7u53v+PNH3Gq7eQ1k/287iw1KSWn2xu4bYg1i7WvASXng0PLHIwjkFhiV5RRWVNBYukCllStJSO/EC8FWiLbJCKTuPQapd3rmJPTE4FG//Nqn1CWmkp+fPTK/TjEdfevtnGd4rqKqXWNmUZdy8c4RTi3fHsDcP6upglToSM4NqYUOdSMUJUkcOUXaiFH6O7D7GsASvA1qixob32qpqXYWPcWXXQSGRZ5oS/Tho2rCtcVqX1Q/jMARsWkBshp94W+lGEVkfU2xFgF8w8tc+j+z9nOEtL2ZGoiMxvk+3KBzXOoUXjB2m/B1zH0a3/NobKnSYlJx9FPLZ4NG9cKritSc7S3E+9KIto5VBNAb7VpCn31e6rJ66FI6dAyh15bKXdsqNvRY7bBQBE8rs68zlAlKV2+Dt79zSziXUkMsWNpNq4jXFek5hROOmWHfmzGnkz0NmYuGGqWQKh9VeO6Wg8IqFtTVpYpBNkbQrVvKfdWWXrBa/3hydtwiDASXMn9vg4bNq4lXFfR4zDhxCd9lnUVRBAKsSvKiN3T04LrbZiJORMgGIo0E90LWOVfI3GpG++KMhJOQEPSooBkQPCMzVDXFwwdOyu11uroaud0WwNn2htJj73bLuOwcd3hurLUIsIiaets7TXAHp+zmuopFQGy1gqhFDLM54NhxsqUFeVZ6qa+bC3VUyoClG7VOsHnDNUSFWqASqfs5ExbIzVnP+BI07t4O1u4bchYu5fTxnWJ64rUYsNvwNNxptdKfGVVgUUeodRnQw0c7vOc/pibIj9VXxZ8brVvl+yi1ddMXPnX+NNN3YkA2dVJ7P3L+Oyv7qap/XNOeU9ysqWWT85VcfiL/Zxub+DGiAS+MuxeUobcYQs82rhucV25n0Odw/j0XDWdspMwEdbnfE9XfmHI5vXzQXzOatr62B5c0wZwtuMMH5+r4uPJR3nr82YcYX8M7c34/jyKMCkId0QE3IaGDyM5ejThjoiLulYbNq4VXJKOgqsJbrebv/7rv+bb3/72YF9KSJw7d47k5GSOHj3KjTfeyKlTp2hra+Omm24iIsImrusJdkfBheG6cj8PHz7Mhx9+yLhx4wb7UnrFkCFDmDRpEm+99RZhYWEkJiZyyy232IRmw8YAcd2Qms/nY86cOfzsZz8jJSVlsC+nV3R1dfHee+9xzz33DPal2LBxVeK6IbU33niDyMhI5s2bN9iX0ifee+89hg8ffkUTrw0bVzKuG1IrLy/nm9/85hVft3Xy5Eni4+MH+zJs2Lhqcd2Q2vvvv89dd9012JfRL775zW9SWVlJbW3tYF+KDRtXJWxS+5LQ1tZGQUEB48eP52//9m/59NNPA7ZLKTly5Ajf/e53SUxM7GUVGzZs9IfrgtROnz5NU1MTt95666Bdw+LFizlw4AAvvfQSJ0+eJDMzk29+85t897vf5a677iI2NpY/+qM/4utf/zqVlZV2TM3GFQUhRK4QokoIcVQI8dchtgshxFr/9oNCiEHLdF0Xxbfvv/8+48ePx+EYPA4PDw/ngQce4Bvf+Abf+MY3ePHFF/n1r39NV1cXqamp3HbbbQwbNmzQrs+Gjd4ghAgD/gl4ADgO7BdCvCGlPGzslgek+W9fBf7Z//dLx3VBau+9996gx9Nuv/123n//ff3Y5XIxffr0QbwiG9c6OmUnx5qP9rtfs88D0NHHLpOAo1LKjwCEENuAhwGT1B4Gtkirmn+fEGKYEGKklPLkhV7/haJPUisvLy+/0rOF54N169YN9iWwfv36wb4EG1cPTl3Mwe1d3tGft3mHD2DXTmCSEML8gqyXUqoPazJwzNh2nJ5WWKh9koEri9QupkXDhg0bgwsp5SfAJwPc/T2gt1/cUJZNcH/lQPb5UnBdJAps2LBxUTgOjDIe3wycuIB9vhTYpGbDho3+sB9IE0KMFkJEADOBN4L2eQOY7c+CTgaaBiOeBtdJosCGDRsXDimlTwjxBJZOcxjwr1LKD4QQP/Zv/xfgN8C3gKNACzB3sK63T+khGzZs2LjaYLufNmzYuKZgk5oNGzauKdikZsOGjWsKNqnZsGHjmoJNajZs2LimYJOaDRs2rinYpGbDho1rCv8/SW7mTncX0EgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap='turbo' \n",
    "bounds = [0,0.1,0.2,0.5,1.,2.,5.,10.,20.,50.,75.]\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds_in_SA = ds_in_SA*12*100\n",
    "ds_in_SA.isel(year=0,month=8).burned_fraction.plot.pcolormesh(ax=ax,robust=True,\n",
    "                                                             cmap=cmap, levels=bounds)\n",
    "ax.set_title('Original')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap='turbo' \n",
    "bounds = [0,0.1,0.2,0.5,1.,2.,5.,10.,20.,50.,75.]\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "regrid_out_SA_test = regrid_out_SA*12*100\n",
    "regrid_out_SA_test.sel(time='1997-09-01').burned_fraction.plot.pcolormesh(ax=ax,robust=True,\n",
    "                                                             cmap=cmap, levels=bounds)\n",
    "ax.set_title('regrid')\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
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       "\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",
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       "\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",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
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       "}\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",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          ()\n",
       "Data variables:\n",
       "    NPP              float32 40597.914\n",
       "    burned_fraction  float32 0.91219974</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-fed2f7c1-55fc-461a-b831-c8602d492b48' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fed2f7c1-55fc-461a-b831-c8602d492b48' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d09f0d50-6efd-492b-aae0-380cb32a54b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d09f0d50-6efd-492b-aae0-380cb32a54b3' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-48d95882-560f-43f1-9a1d-59471e426db8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48d95882-560f-43f1-9a1d-59471e426db8' 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>NPP</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40597.914</div><input id='attrs-b74934ef-1b4a-4306-bd23-5905ecdc1f81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b74934ef-1b4a-4306-bd23-5905ecdc1f81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d853d5ef-a6fb-4a61-b1b9-91a10db89381' class='xr-var-data-in' type='checkbox'><label for='data-d853d5ef-a6fb-4a61-b1b9-91a10db89381' 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(40597.914, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burned_fraction</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.91219974</div><input id='attrs-9bd95a66-3c6b-4cfa-a794-1c4f94f674f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bd95a66-3c6b-4cfa-a794-1c4f94f674f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd006eb-0604-4cf4-917e-89a6ee95a590' class='xr-var-data-in' type='checkbox'><label for='data-6cd006eb-0604-4cf4-917e-89a6ee95a590' 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(0.91219974, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e7de691-16b3-47fc-a34e-c0285887f590' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7e7de691-16b3-47fc-a34e-c0285887f590' 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.Dataset>\n",
       "Dimensions:          ()\n",
       "Data variables:\n",
       "    NPP              float32 40597.914\n",
       "    burned_fraction  float32 0.91219974"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that means across orig and regrid are reasonable\n",
    "ds_in_SA.mean().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "  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, 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:          ()\n",
       "Data variables:\n",
       "    NPP              float64 4.029e+04\n",
       "    burned_fraction  float64 0.9225</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-f365f6e5-0adf-491c-b80a-4336f91fa22e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f365f6e5-0adf-491c-b80a-4336f91fa22e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b461e1b1-bf37-43b9-abd5-1008a78836fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b461e1b1-bf37-43b9-abd5-1008a78836fd' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-297d14ec-ae0e-446c-85c8-2fb1aad63c4e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-297d14ec-ae0e-446c-85c8-2fb1aad63c4e' 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>NPP</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.029e+04</div><input id='attrs-56b885fd-65ec-4370-a79e-60f5f7a98fbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56b885fd-65ec-4370-a79e-60f5f7a98fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81e44acd-256b-4db7-91ac-2d874086683e' class='xr-var-data-in' type='checkbox'><label for='data-81e44acd-256b-4db7-91ac-2d874086683e' 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(40292.51382199)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burned_fraction</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9225</div><input id='attrs-079bc535-b52a-4005-ab0a-1bec694b7af1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-079bc535-b52a-4005-ab0a-1bec694b7af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3b5d1b1-5d0d-4817-8618-fd4cbc5ac948' class='xr-var-data-in' type='checkbox'><label for='data-c3b5d1b1-5d0d-4817-8618-fd4cbc5ac948' 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(0.922473)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0dddf118-b933-45aa-a07c-8a300901b16a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0dddf118-b933-45aa-a07c-8a300901b16a' 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.Dataset>\n",
       "Dimensions:          ()\n",
       "Data variables:\n",
       "    NPP              float64 4.029e+04\n",
       "    burned_fraction  float64 0.9225"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check regrid mean against orig\n",
    "regrid_out_SA_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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