smap_example.ipynb

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   "source": [
    "# %load_ext autoreload\n",
    "# %autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bcea6b68",
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   "source": [
    "# !mamba install xmovie -y\n",
    "# !pip install -e ../ocetrac/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9c6266",
   "metadata": {},
   "source": [
    "## Notes\n",
    "- This dataset is just **barely** fitting into memory on a large server. This should run better once we can compute out of memory with ocetrac. \n",
    "\n",
    "## Future Steps\n",
    "\n",
    "- [ ] Use the higher temp resolution, currently this only works with monthly data\n",
    "    - How to remove the damn climatology...resample to days before and then remove?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0c39bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import xesmf as xe\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f7db150",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_clim(ds):\n",
    "    clim = ds.groupby('time.month').mean()\n",
    "    \n",
    "    with ProgressBar():\n",
    "        clim = clim.load()\n",
    "        \n",
    "    ds_anomaly = (ds.groupby('time.month') - clim)\n",
    "    clim_std = ds_anomaly.groupby('time.month').std()\n",
    "    with ProgressBar():\n",
    "        clim_std = clim_std.load()\n",
    "    return ds_anomaly.groupby('time.month') / clim_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1f485ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.diagnostics import ProgressBar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908aeab2",
   "metadata": {},
   "source": [
    "We might want to replace this with the cloud data if available?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a0c65c2",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon                   (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * lat                   (lat) float32 -89.88 -89.62 -89.38 ... 89.62 89.88\n",
       "  * time                  (time) object 2015-05-01 00:00:00 ... 2021-04-01 00...\n",
       "Data variables:\n",
       "    nobs                  (time, lat, lon) float64 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    SSS                   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_ref               (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    gland                 (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    gice                  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    surtep                (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    nobs_40km             (time, lat, lon) float64 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_smap_40km         (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    fland                 (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_smap_uncertainty  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/64)\n",
       "    Conventions:                                    CF-1.6, ACDD-1.3\n",
       "    standard_name_vocabulary:                       CF Standard Name Table v27\n",
       "    title:                                          SMAP ocean surface salinity\n",
       "    version:                                        V4.0 Validated Release\n",
       "    processing_level:                               L3\n",
       "    resolution:                                     Spatial resolution: appro...\n",
       "    ...                                             ...\n",
       "    Source_of_ancillary_land_mask:                  1 km land/water mask from...\n",
       "    start_date:                                     2015-04-01 UTC\n",
       "    start_time:                                     00:00:00 UTC\n",
       "    stop_date:                                      2021-04-30 UTC\n",
       "    stop_time:                                      23:59:59 UTC\n",
       "    EXTRA_DIMENSION.time:                           1</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-540f297d-fa0e-44de-95be-b97783b4d2b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-540f297d-fa0e-44de-95be-b97783b4d2b7' 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'>time</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-feb87841-b442-45a4-8267-e7d98b3a27e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-feb87841-b442-45a4-8267-e7d98b3a27e9' class='xr-section-summary' >Coordinates: <span>(3)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-f3c6a217-c28a-4f8e-a80a-812c5380f9e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3c6a217-c28a-4f8e-a80a-812c5380f9e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2416b58-60fd-4448-8a17-ee2efc877236' class='xr-var-data-in' type='checkbox'><label for='data-a2416b58-60fd-4448-8a17-ee2efc877236' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>center longitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02], dtype=float32)</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'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-747b2c33-79f5-4986-a002-2423082e2324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-747b2c33-79f5-4986-a002-2423082e2324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcc3a83d-5c61-4655-8520-f908db3cdfde' class='xr-var-data-in' type='checkbox'><label for='data-fcc3a83d-5c61-4655-8520-f908db3cdfde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>center latitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-05-01 00:00:00 ... 2021-04-...</div><input id='attrs-3218267f-2bd0-45ff-94ee-82a09c99d26d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3218267f-2bd0-45ff-94ee-82a09c99d26d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3649ca8a-71ae-438f-9d9a-5377b0afff65' class='xr-var-data-in' type='checkbox'><label for='data-3649ca8a-71ae-438f-9d9a-5377b0afff65' 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([cftime.DatetimeGregorian(2015, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 4, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eeb6266f-ad1b-4134-a2b3-e7587652f02b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eeb6266f-ad1b-4134-a2b3-e7587652f02b' class='xr-section-summary' >Data variables: <span>(10)</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>nobs</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-76450347-e71a-457e-925e-8590010dd4b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76450347-e71a-457e-925e-8590010dd4b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42ed6bc6-c566-45ab-80df-69a0efb54e57' class='xr-var-data-in' type='checkbox'><label for='data-42ed6bc6-c566-45ab-80df-69a0efb54e57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number of observations for L3 average of SSS smoothed to approx 70km resolution</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>1860</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd><dt><span>standard_name :</span></dt><dd>Number of observations for L3 average</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 569.53 MiB </td> <td> 94.92 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (12, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 7 Tasks </td><td> 6 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<td>\n",
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       "  <text x=\"10.147358\" y=\"90.147358\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.147358,90.147358)\">72</text>\n",
       "</svg>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-99861d21-1bcb-4e2a-a898-0e4ba021acd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99861d21-1bcb-4e2a-a898-0e4ba021acd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db945aea-39f7-447a-a734-d42238e1e241' class='xr-var-data-in' type='checkbox'><label for='data-db945aea-39f7-447a-a734-d42238e1e241' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SMAP sea surface salinity</dd><dt><span>standard_name :</span></dt><dd>sea_surface_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>45.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 284.77 MiB </td> <td> 47.46 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (12, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 7 Tasks </td><td> 6 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gice</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-eba4bb8d-a1e5-4b71-ad59-842203b089a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eba4bb8d-a1e5-4b71-ad59-842203b089a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5271b1a3-de0b-45bc-baf5-9d8dd4faa28d' class='xr-var-data-in' type='checkbox'><label for='data-5271b1a3-de0b-45bc-baf5-9d8dd4faa28d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Average sea ice fraction (weighted by antenna gain)</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sss_smap_uncertainty</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-96aba6e7-cbde-4828-ac06-54d881d6a9c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96aba6e7-cbde-4828-ac06-54d881d6a9c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b4d3609-134d-4621-849e-d8602202f9a0' class='xr-var-data-in' type='checkbox'><label for='data-2b4d3609-134d-4621-849e-d8602202f9a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>estimated empirical uncertainty of SMAP sea surface salinity smoothed to approx 70km resolution</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>45.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd><dt><span>standard_name :</span></dt><dd>estimated empirical uncertainty of SMAP sea surface</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b96f884d-a08d-4a2e-a92e-66e9ddb2fe92' class='xr-section-summary-in' type='checkbox'  ><label for='section-b96f884d-a08d-4a2e-a92e-66e9ddb2fe92' class='xr-section-summary' >Attributes: <span>(64)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>title :</span></dt><dd>SMAP ocean surface salinity</dd><dt><span>version :</span></dt><dd>V4.0 Validated Release</dd><dt><span>processing_level :</span></dt><dd>L3</dd><dt><span>resolution :</span></dt><dd>Spatial resolution: approx. 70km</dd><dt><span>history :</span></dt><dd>created by T. Meissner</dd><dt><span>date_created :</span></dt><dd>2020-12-18 T13:10:32-0800</dd><dt><span>date_modified :</span></dt><dd>2020-12-18 T13:10:32-0800</dd><dt><span>institution :</span></dt><dd>Remote Sensing Systems, Santa Rosa, CA, USA</dd><dt><span>source :</span></dt><dd>RSS SMAP-SSS v4.0 algorithm</dd><dt><span>platform :</span></dt><dd>SMAP</dd><dt><span>instrument :</span></dt><dd>SMAP radiometer</dd><dt><span>project :</span></dt><dd>NASA Salinity</dd><dt><span>keywords :</span></dt><dd>SURFACE SALINITY, SALINITY, SMAP, NASA, RSS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>creator_name :</span></dt><dd>Thomas Meissner, Remote Sensing Systems</dd><dt><span>creator_email :</span></dt><dd>meissner@remss.com</dd><dt><span>creator_url :</span></dt><dd>http://www.remss.com/missions/smap</dd><dt><span>publisher_name :</span></dt><dd>Thomas Meissner, Frank Wentz, Andrew Manaster, Richard Lindsley, Remote Sensing Systems</dd><dt><span>publisher_email :</span></dt><dd>meissner@remss.com</dd><dt><span>publisher_url :</span></dt><dd>http://www.remss.com/missions/smap</dd><dt><span>ID :</span></dt><dd>10.5067/SMP40-3SMCS</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.earthdata</dd><dt><span>dataset_citation_authors :</span></dt><dd>T. Meissner, F. Wentz, A. Manaster, R. Lindsley</dd><dt><span>dataset_citation_year :</span></dt><dd>2019</dd><dt><span>dataset_citation_product :</span></dt><dd>Remote Sensing Systems SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly</dd><dt><span>dataset_citation_version :</span></dt><dd>V4.0 Validated Release</dd><dt><span>dataset_citation_institution :</span></dt><dd>Remote Sensing Systems, Santa Rosa, CA, USA</dd><dt><span>dataset_citation_url :</span></dt><dd>Available online at www.remss.com/missions/smap</dd><dt><span>netCDF_version_id :</span></dt><dd>4</dd><dt><span>year_of_observation :</span></dt><dd>2020</dd><dt><span>month_of_observation :</span></dt><dd>11</dd><dt><span>center_day_of_observation :</span></dt><dd>320</dd><dt><span>first_orbit :</span></dt><dd>30720</dd><dt><span>last_orbit :</span></dt><dd>31157</dd><dt><span>time_coverage_start :</span></dt><dd>2020-11-01T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2020-11-30T23:59:59Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>land_ice_exclusions :</span></dt><dd>discard observations if land or seaice fraction exceeds threshold</dd><dt><span>fland_fraction_threshold :</span></dt><dd>0.001</dd><dt><span>gland_fraction_threshold :</span></dt><dd>0.04</dd><dt><span>seaice_fraction_threshold :</span></dt><dd>0.003</dd><dt><span>high_wind_exclusions :</span></dt><dd>discard observations if wind speed &gt; 20 m/s</dd><dt><span>qc_exclusions :</span></dt><dd>discard observations if one or more of bits 0,1,2,3,4,5,6,7,9,10 in L2C iqcflag are set</dd><dt><span>Source_of_SMAP_SSS :</span></dt><dd>Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.</dd><dt><span>Source_of__ancillary_reference_SSS_from_HYCOM :</span></dt><dd>Hybrid Coordinate Ocean Model, GLBa0.08/expt_90.9, Top layer salinity. Available at www.hycom.org.</dd><dt><span>Source_of_ancillary_SST :</span></dt><dd>Canada Meteorological Center. 2016.GHRSST Level 4 CMC0.1deg Global Foundation Sea Surface Temperature Analysis (GDS version 2). Ver.3.0.doi: 10.5067/GHCMC-4FM03 http://dx.doi.org/10.5067/GHCMC-4FM03.</dd><dt><span>Source_of_ancillary_AMSR2_sea_ice_mask :</span></dt><dd>RSS AMSR2 sea-ice mask. Wentz et al., 2014. Remote Sensing Systems GCOM-W1 AMSR2 Daily Environmental Suite on 0.25 deg grid, Version 8,Available from www.remss.com/amsr/.</dd><dt><span>Source_of_ancillary_land_mask :</span></dt><dd>1 km land/water mask from OCEAN DISCIPLINE PROCESSING SYSTEM (ODPS) based on World Vector Shoreline (WVS)database and World Data Bank. courtesy of Fred Patt, Goddard Space Flight Center, frederick.s.patt@nasa.gov.</dd><dt><span>start_date :</span></dt><dd>2015-04-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2021-04-30 UTC</dd><dt><span>stop_time :</span></dt><dd>23:59:59 UTC</dd><dt><span>EXTRA_DIMENSION.time :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon                   (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * lat                   (lat) float32 -89.88 -89.62 -89.38 ... 89.62 89.88\n",
       "  * time                  (time) object 2015-05-01 00:00:00 ... 2021-04-01 00...\n",
       "Data variables:\n",
       "    nobs                  (time, lat, lon) float64 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    SSS                   (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    sss_ref               (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    gland                 (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    gice                  (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    surtep                (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    nobs_40km             (time, lat, lon) float64 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    sss_smap_40km         (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    fland                 (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    sss_smap_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "Attributes: (12/64)\n",
       "    Conventions:                                    CF-1.6, ACDD-1.3\n",
       "    standard_name_vocabulary:                       CF Standard Name Table v27\n",
       "    title:                                          SMAP ocean surface salinity\n",
       "    version:                                        V4.0 Validated Release\n",
       "    processing_level:                               L3\n",
       "    resolution:                                     Spatial resolution: appro...\n",
       "    ...                                             ...\n",
       "    Source_of_ancillary_land_mask:                  1 km land/water mask from...\n",
       "    start_date:                                     2015-04-01 UTC\n",
       "    start_time:                                     00:00:00 UTC\n",
       "    stop_date:                                      2021-04-30 UTC\n",
       "    stop_time:                                      23:59:59 UTC\n",
       "    EXTRA_DIMENSION.time:                           1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path = 'https://thredds.jpl.nasa.gov/thredds/dodsC/ncml_aggregation/SalinityDensity/smap/aggregate__SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4.ncml'\n",
    "path = 'https://thredds.jpl.nasa.gov/thredds/dodsC/ncml_aggregation/SalinityDensity/smap/aggregate__SMAP_RSS_L3_SSS_SMI_MONTHLY_V4.ncml'\n",
    "ds_smap = xr.open_dataset(path, use_cftime=True, chunks={'times':12}).rename({'times':'time', 'sss_smap':'SSS'})\n",
    "ds_smap = ds_smap.assign_coords(time=xr.cftime_range(str(ds_smap.time[0].data), periods=len(ds_smap.time), freq='1MS'))\n",
    "ds_smap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7dc8ae",
   "metadata": {},
   "source": [
    "I deactivated this here, because I was not happy with the 'stiched' dataset, even when I normalized both and then concatenated them, there was a distinct boundary between the datasets. You can just uncomment the below to see what I mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42854a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Aquarius data\n",
    "# path = 'https://thredds.jpl.nasa.gov/thredds/dodsC/ncml_aggregation/SalinityDensity/aquarius/aggregate__AQUARIUS_L3_SSS_SMI_MONTHLY_V5.ncml'\n",
    "# ds_aquarius = xr.open_dataset(path, use_cftime=True, chunks={'time':12}).sel(time=slice(None, '2015-04-01'))\n",
    "# ds_aquarius = ds_aquarius.assign_coords(time=xr.cftime_range(str(ds_aquarius.time[0].data), periods=len(ds_aquarius.time), freq='1MS'))\n",
    "# ds_aquarius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc64746b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bins = np.linspace(30, 39, 200)\n",
    "# ds_aquarius.SSS.plot.hist(bins=bins, density=True, alpha=0.5)\n",
    "# ds_smap.SSS.plot.hist(bins=bins, density=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24491c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#######                                 ] | 19% Completed | 17.6s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 51.2s\n",
      "[#######                                 ] | 17% Completed | 25.2s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  2min 12.4s\n"
     ]
    }
   ],
   "source": [
    "ds_test = remove_clim(ds_smap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5736e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd300456d60>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roi = dict(lon=450, lat=300)\n",
    "ds_smap.isel(**roi).SSS.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "125e00fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd308044df0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_test.isel(**roi).SSS.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256c4e2c",
   "metadata": {},
   "source": [
    "## Remove climatology separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fffb3dec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#######                                 ] | 18% Completed | 19.7s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 40.6s\n",
      "[#######                                 ] | 17% Completed | 18.1s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 31.7s\n"
     ]
    }
   ],
   "source": [
    "ds_smap_anomaly = remove_clim(ds_smap)\n",
    "# ds_aquarius_anomaly = remove_clim(ds_aquarius)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95236199",
   "metadata": {},
   "source": [
    "# merge both datasets (currently only taking SMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c28d022",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon                   (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * lat                   (lat) float32 -89.88 -89.62 -89.38 ... 89.62 89.88\n",
       "  * time                  (time) object 2015-05-01 00:00:00 ... 2021-04-01 00...\n",
       "Data variables:\n",
       "    nobs                  (time, lat, lon) float64 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    SSS                   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_ref               (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    gland                 (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    gice                  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    surtep                (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    nobs_40km             (time, lat, lon) float64 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_smap_40km         (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    fland                 (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_smap_uncertainty  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:                                    CF-1.6, ACDD-1.3\n",
       "    standard_name_vocabulary:                       CF Standard Name Table v27\n",
       "    title:                                          SMAP ocean surface salinity\n",
       "    version:                                        V4.0 Validated Release\n",
       "    processing_level:                               L3\n",
       "    resolution:                                     Spatial resolution: appro...\n",
       "    history:                                        created by T. Meissner\n",
       "    date_created:                                   2019-10-12 T10:33:24-0700\n",
       "    date_modified:                                  2019-10-12 T10:33:24-0700\n",
       "    institution:                                    Remote Sensing Systems, S...\n",
       "    source:                                         RSS SMAP-SSS v4.0 algorithm\n",
       "    platform:                                       SMAP\n",
       "    instrument:                                     SMAP radiometer\n",
       "    project:                                        NASA Salinity\n",
       "    keywords:                                       SURFACE SALINITY, SALINIT...\n",
       "    keywords_vocabulary:                            NASA Global Change Master...\n",
       "    creator_name:                                   Thomas Meissner, Remote S...\n",
       "    creator_email:                                  meissner@remss.com\n",
       "    creator_url:                                    http://www.remss.com/miss...\n",
       "    publisher_name:                                 Thomas Meissner, Frank We...\n",
       "    publisher_email:                                meissner@remss.com\n",
       "    publisher_url:                                  http://www.remss.com/miss...\n",
       "    ID:                                             10.5067/SMP40-3SMCS\n",
       "    naming_authority:                               gov.nasa.earthdata\n",
       "    dataset_citation_authors:                       T. Meissner, F. Wentz, A....\n",
       "    dataset_citation_year:                          2019\n",
       "    dataset_citation_product:                       Remote Sensing Systems SM...\n",
       "    dataset_citation_version:                       V4.0 Validated Release\n",
       "    dataset_citation_institution:                   Remote Sensing Systems, S...\n",
       "    dataset_citation_url:                           Available online at www.r...\n",
       "    netCDF_version_id:                              4\n",
       "    year_of_observation:                            2019\n",
       "    month_of_observation:                           9\n",
       "    center_day_of_observation:                      258\n",
       "    first_orbit:                                    24475\n",
       "    last_orbit:                                     24913\n",
       "    time_coverage_start:                            2019-09-01T00:00:00Z\n",
       "    time_coverage_end:                              2019-09-30T23:59:59Z\n",
       "    time_coverage_resolution:                       P1M\n",
       "    cdm_data_type:                                  grid\n",
       "    geospatial_lat_min:                             -90.0\n",
       "    geospatial_lat_max:                             90.0\n",
       "    geospatial_lat_resolution:                      0.25\n",
       "    geospatial_lat_units:                           degrees_north\n",
       "    geospatial_lon_min:                             0.0\n",
       "    geospatial_lon_max:                             360.0\n",
       "    geospatial_lon_resolution:                      0.25\n",
       "    geospatial_lon_units:                           degrees_east\n",
       "    land_ice_exclusions:                            discard observations if l...\n",
       "    fland_fraction_threshold:                       0.001\n",
       "    gland_fraction_threshold:                       0.04\n",
       "    seaice_fraction_threshold:                      0.003\n",
       "    high_wind_exclusions:                           discard observations if w...\n",
       "    qc_exclusions:                                  discard observations if o...\n",
       "    Source_of_SMAP_SSS:                             Meissner, T., F. Wentz, A...\n",
       "    Source_of__ancillary_reference_SSS_from_HYCOM:  Hybrid Coordinate Ocean M...\n",
       "    Source_of_ancillary_SST:                        Canada Meteorological Cen...\n",
       "    Source_of_ancillary_AMSR2_sea_ice_mask:         RSS AMSR2 sea-ice mask. W...\n",
       "    Source_of_ancillary_land_mask:                  1 km land/water mask from...\n",
       "    start_date:                                     2015-04-01 UTC\n",
       "    start_time:                                     00:00:00 UTC\n",
       "    stop_date:                                      2021-04-30 UTC\n",
       "    stop_time:                                      23:59:59 UTC\n",
       "    EXTRA_DIMENSION.time:                           1</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-48e1e4c8-9a7a-4e40-83c4-d0222a7cc753' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48e1e4c8-9a7a-4e40-83c4-d0222a7cc753' 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'>time</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2a621cee-d62f-423a-93e1-606f5c2cf277' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a621cee-d62f-423a-93e1-606f5c2cf277' class='xr-section-summary' >Coordinates: <span>(3)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-6d577376-ff2b-4690-b56f-95a4c95f1462' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d577376-ff2b-4690-b56f-95a4c95f1462' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-feca2652-7ec3-48bd-bbb5-b1cb8733c672' class='xr-var-data-in' type='checkbox'><label for='data-feca2652-7ec3-48bd-bbb5-b1cb8733c672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>center longitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02], dtype=float32)</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'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-5f0f937d-8703-4294-b173-980c1824f9a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f0f937d-8703-4294-b173-980c1824f9a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c7e6c37-28b0-4a92-a346-c298ffbf4a9b' class='xr-var-data-in' type='checkbox'><label for='data-3c7e6c37-28b0-4a92-a346-c298ffbf4a9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>center latitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-05-01 00:00:00 ... 2021-04-...</div><input id='attrs-40bce44f-dee7-453b-a2d4-47367fb7d975' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40bce44f-dee7-453b-a2d4-47367fb7d975' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48952a7-18b8-48cd-8167-97ea123a0f1d' class='xr-var-data-in' type='checkbox'><label for='data-b48952a7-18b8-48cd-8167-97ea123a0f1d' 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([cftime.DatetimeGregorian(2015, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 4, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f36562e5-c3bb-436e-a8e0-44dcf2f13242' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f36562e5-c3bb-436e-a8e0-44dcf2f13242' class='xr-section-summary' >Data variables: <span>(10)</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>nobs</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-f447696e-1ba7-4f91-bdcd-7112c782c1b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f447696e-1ba7-4f91-bdcd-7112c782c1b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d7c7f7-0b35-472f-8882-23f9bf1a1387' class='xr-var-data-in' type='checkbox'><label for='data-b3d7c7f7-0b35-472f-8882-23f9bf1a1387' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number of observations for L3 average of SSS smoothed to approx 70km resolution</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>1860</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd><dt><span>standard_name :</span></dt><dd>Number of observations for L3 average</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 597.20 MB </td> <td> 99.53 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (12, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 7 Tasks </td><td> 6 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "\n",
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       "  <text x=\"90.294715\" y=\"100.294715\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
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       "  <text x=\"10.147358\" y=\"90.147358\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.147358,90.147358)\">72</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-28d49abc-f59c-4868-bc8a-8a3b9ae4abca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28d49abc-f59c-4868-bc8a-8a3b9ae4abca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6b04570-26d0-4232-8b8b-2095f36ba607' class='xr-var-data-in' type='checkbox'><label for='data-c6b04570-26d0-4232-8b8b-2095f36ba607' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SMAP sea surface salinity</dd><dt><span>standard_name :</span></dt><dd>sea_surface_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>45.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 298.60 MB </td> <td> 49.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (12, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 7 Tasks </td><td> 6 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sss_ref</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-9ecd52b2-82b0-4d31-b5f7-0a38ccd3d528' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ecd52b2-82b0-4d31-b5f7-0a38ccd3d528' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce247b27-7744-40bd-a1a1-5cdd4224afc8' class='xr-var-data-in' type='checkbox'><label for='data-ce247b27-7744-40bd-a1a1-5cdd4224afc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Reference sea surface salinity from HYCOM</dd><dt><span>standard_name :</span></dt><dd>sea_surface_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>45.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gice</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-01a7527c-2e2f-4229-9320-7aabb81867f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01a7527c-2e2f-4229-9320-7aabb81867f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b7ffbf2-fd99-4dc2-8f37-0a91cb91a792' class='xr-var-data-in' type='checkbox'><label for='data-8b7ffbf2-fd99-4dc2-8f37-0a91cb91a792' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Average sea ice fraction (weighted by antenna gain)</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surtep</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-645b614b-469d-442c-a8de-f07d6019f390' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-645b614b-469d-442c-a8de-f07d6019f390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddac842f-1d13-4870-a94a-b1af1edd4627' class='xr-var-data-in' type='checkbox'><label for='data-ddac842f-1d13-4870-a94a-b1af1edd4627' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>long_name :</span></dt><dd>Ancillary sea surface temperature (from CMC)</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>313.15</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fland</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-d9a03772-63ff-484c-89a4-a3f30ca80b7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9a03772-63ff-484c-89a4-a3f30ca80b7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-292af063-a0b0-40ab-9fc8-e55c81bbd53a' class='xr-var-data-in' type='checkbox'><label for='data-292af063-a0b0-40ab-9fc8-e55c81bbd53a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Average land fraction within 3dB contour</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction within 3dB contour</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d714a21f-04f8-4551-84ed-2d6aee756e44' class='xr-section-summary-in' type='checkbox'  ><label for='section-d714a21f-04f8-4551-84ed-2d6aee756e44' class='xr-section-summary' >Attributes: <span>(64)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>title :</span></dt><dd>SMAP ocean surface salinity</dd><dt><span>version :</span></dt><dd>V4.0 Validated Release</dd><dt><span>processing_level :</span></dt><dd>L3</dd><dt><span>resolution :</span></dt><dd>Spatial resolution: approx. 70km</dd><dt><span>history :</span></dt><dd>created by T. Meissner</dd><dt><span>date_created :</span></dt><dd>2019-10-12 T10:33:24-0700</dd><dt><span>date_modified :</span></dt><dd>2019-10-12 T10:33:24-0700</dd><dt><span>institution :</span></dt><dd>Remote Sensing Systems, Santa Rosa, CA, USA</dd><dt><span>source :</span></dt><dd>RSS SMAP-SSS v4.0 algorithm</dd><dt><span>platform :</span></dt><dd>SMAP</dd><dt><span>instrument :</span></dt><dd>SMAP radiometer</dd><dt><span>project :</span></dt><dd>NASA Salinity</dd><dt><span>keywords :</span></dt><dd>SURFACE SALINITY, SALINITY, SMAP, NASA, RSS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>creator_name :</span></dt><dd>Thomas Meissner, Remote Sensing Systems</dd><dt><span>creator_email :</span></dt><dd>meissner@remss.com</dd><dt><span>creator_url :</span></dt><dd>http://www.remss.com/missions/smap</dd><dt><span>publisher_name :</span></dt><dd>Thomas Meissner, Frank Wentz, Andrew Manaster, Richard Lindsley, Remote Sensing Systems</dd><dt><span>publisher_email :</span></dt><dd>meissner@remss.com</dd><dt><span>publisher_url :</span></dt><dd>http://www.remss.com/missions/smap</dd><dt><span>ID :</span></dt><dd>10.5067/SMP40-3SMCS</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.earthdata</dd><dt><span>dataset_citation_authors :</span></dt><dd>T. Meissner, F. Wentz, A. Manaster, R. Lindsley</dd><dt><span>dataset_citation_year :</span></dt><dd>2019</dd><dt><span>dataset_citation_product :</span></dt><dd>Remote Sensing Systems SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly</dd><dt><span>dataset_citation_version :</span></dt><dd>V4.0 Validated Release</dd><dt><span>dataset_citation_institution :</span></dt><dd>Remote Sensing Systems, Santa Rosa, CA, USA</dd><dt><span>dataset_citation_url :</span></dt><dd>Available online at www.remss.com/missions/smap</dd><dt><span>netCDF_version_id :</span></dt><dd>4</dd><dt><span>year_of_observation :</span></dt><dd>2019</dd><dt><span>month_of_observation :</span></dt><dd>9</dd><dt><span>center_day_of_observation :</span></dt><dd>258</dd><dt><span>first_orbit :</span></dt><dd>24475</dd><dt><span>last_orbit :</span></dt><dd>24913</dd><dt><span>time_coverage_start :</span></dt><dd>2019-09-01T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2019-09-30T23:59:59Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>land_ice_exclusions :</span></dt><dd>discard observations if land or seaice fraction exceeds threshold</dd><dt><span>fland_fraction_threshold :</span></dt><dd>0.001</dd><dt><span>gland_fraction_threshold :</span></dt><dd>0.04</dd><dt><span>seaice_fraction_threshold :</span></dt><dd>0.003</dd><dt><span>high_wind_exclusions :</span></dt><dd>discard observations if wind speed &gt; 20 m/s</dd><dt><span>qc_exclusions :</span></dt><dd>discard observations if one or more of bits 0,1,2,3,4,5,6,7,9,10 in L2C iqcflag are set</dd><dt><span>Source_of_SMAP_SSS :</span></dt><dd>Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.</dd><dt><span>Source_of__ancillary_reference_SSS_from_HYCOM :</span></dt><dd>Hybrid Coordinate Ocean Model, GLBa0.08/expt_90.9, Top layer salinity. Available at www.hycom.org.</dd><dt><span>Source_of_ancillary_SST :</span></dt><dd>Canada Meteorological Center. 2016.GHRSST Level 4 CMC0.1deg Global Foundation Sea Surface Temperature Analysis (GDS version 2). Ver.3.0.doi: 10.5067/GHCMC-4FM03 http://dx.doi.org/10.5067/GHCMC-4FM03.</dd><dt><span>Source_of_ancillary_AMSR2_sea_ice_mask :</span></dt><dd>RSS AMSR2 sea-ice mask. Wentz et al., 2014. Remote Sensing Systems GCOM-W1 AMSR2 Daily Environmental Suite on 0.25 deg grid, Version 8,Available from www.remss.com/amsr/.</dd><dt><span>Source_of_ancillary_land_mask :</span></dt><dd>1 km land/water mask from OCEAN DISCIPLINE PROCESSING SYSTEM (ODPS) based on World Vector Shoreline (WVS)database and World Data Bank. courtesy of Fred Patt, Goddard Space Flight Center, frederick.s.patt@nasa.gov.</dd><dt><span>start_date :</span></dt><dd>2015-04-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2021-04-30 UTC</dd><dt><span>stop_time :</span></dt><dd>23:59:59 UTC</dd><dt><span>EXTRA_DIMENSION.time :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon                   (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * lat                   (lat) float32 -89.88 -89.62 -89.38 ... 89.62 89.88\n",
       "  * time                  (time) object 2015-05-01 00:00:00 ... 2021-04-01 00...\n",
       "Data variables:\n",
       "    nobs                  (time, lat, lon) float64 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    SSS                   (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    sss_ref               (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    gland                 (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    gice                  (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    surtep                (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    nobs_40km             (time, lat, lon) float64 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    sss_smap_40km         (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    fland                 (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    sss_smap_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:                                    CF-1.6, ACDD-1.3\n",
       "    standard_name_vocabulary:                       CF Standard Name Table v27\n",
       "    title:                                          SMAP ocean surface salinity\n",
       "    version:                                        V4.0 Validated Release\n",
       "    processing_level:                               L3\n",
       "    resolution:                                     Spatial resolution: appro...\n",
       "    history:                                        created by T. Meissner\n",
       "    date_created:                                   2019-10-12 T10:33:24-0700\n",
       "    date_modified:                                  2019-10-12 T10:33:24-0700\n",
       "    institution:                                    Remote Sensing Systems, S...\n",
       "    source:                                         RSS SMAP-SSS v4.0 algorithm\n",
       "    platform:                                       SMAP\n",
       "    instrument:                                     SMAP radiometer\n",
       "    project:                                        NASA Salinity\n",
       "    keywords:                                       SURFACE SALINITY, SALINIT...\n",
       "    keywords_vocabulary:                            NASA Global Change Master...\n",
       "    creator_name:                                   Thomas Meissner, Remote S...\n",
       "    creator_email:                                  meissner@remss.com\n",
       "    creator_url:                                    http://www.remss.com/miss...\n",
       "    publisher_name:                                 Thomas Meissner, Frank We...\n",
       "    publisher_email:                                meissner@remss.com\n",
       "    publisher_url:                                  http://www.remss.com/miss...\n",
       "    ID:                                             10.5067/SMP40-3SMCS\n",
       "    naming_authority:                               gov.nasa.earthdata\n",
       "    dataset_citation_authors:                       T. Meissner, F. Wentz, A....\n",
       "    dataset_citation_year:                          2019\n",
       "    dataset_citation_product:                       Remote Sensing Systems SM...\n",
       "    dataset_citation_version:                       V4.0 Validated Release\n",
       "    dataset_citation_institution:                   Remote Sensing Systems, S...\n",
       "    dataset_citation_url:                           Available online at www.r...\n",
       "    netCDF_version_id:                              4\n",
       "    year_of_observation:                            2019\n",
       "    month_of_observation:                           9\n",
       "    center_day_of_observation:                      258\n",
       "    first_orbit:                                    24475\n",
       "    last_orbit:                                     24913\n",
       "    time_coverage_start:                            2019-09-01T00:00:00Z\n",
       "    time_coverage_end:                              2019-09-30T23:59:59Z\n",
       "    time_coverage_resolution:                       P1M\n",
       "    cdm_data_type:                                  grid\n",
       "    geospatial_lat_min:                             -90.0\n",
       "    geospatial_lat_max:                             90.0\n",
       "    geospatial_lat_resolution:                      0.25\n",
       "    geospatial_lat_units:                           degrees_north\n",
       "    geospatial_lon_min:                             0.0\n",
       "    geospatial_lon_max:                             360.0\n",
       "    geospatial_lon_resolution:                      0.25\n",
       "    geospatial_lon_units:                           degrees_east\n",
       "    land_ice_exclusions:                            discard observations if l...\n",
       "    fland_fraction_threshold:                       0.001\n",
       "    gland_fraction_threshold:                       0.04\n",
       "    seaice_fraction_threshold:                      0.003\n",
       "    high_wind_exclusions:                           discard observations if w...\n",
       "    qc_exclusions:                                  discard observations if o...\n",
       "    Source_of_SMAP_SSS:                             Meissner, T., F. Wentz, A...\n",
       "    Source_of__ancillary_reference_SSS_from_HYCOM:  Hybrid Coordinate Ocean M...\n",
       "    Source_of_ancillary_SST:                        Canada Meteorological Cen...\n",
       "    Source_of_ancillary_AMSR2_sea_ice_mask:         RSS AMSR2 sea-ice mask. W...\n",
       "    Source_of_ancillary_land_mask:                  1 km land/water mask from...\n",
       "    start_date:                                     2015-04-01 UTC\n",
       "    start_time:                                     00:00:00 UTC\n",
       "    stop_date:                                      2021-04-30 UTC\n",
       "    stop_time:                                      23:59:59 UTC\n",
       "    EXTRA_DIMENSION.time:                           1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regridder = xe.Regridder(ds_aquarius.SSS, ds_smap.SSS, method='bilinear', periodic=True)\n",
    "# ds = xr.concat([regridder(ds_aquarius.SSS).chunk({'lon':-1, 'lat':-1}), ds_smap.SSS], 'time')\n",
    "ds = ds_smap\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e15b7f92",
   "metadata": {},
   "outputs": [
    {
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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       "\n",
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       "\n",
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       "  display: block;\n",
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       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
       ".xr-header {\n",
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       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "  margin-top: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
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       "\n",
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       "\n",
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       "  display: contents;\n",
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       "\n",
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       "\n",
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       "  color: var(--xr-font-color0);\n",
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       "\n",
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       "\n",
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       "  display: inline-block;\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\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",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon                   (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * lat                   (lat) float32 -89.88 -89.62 -89.38 ... 89.62 89.88\n",
       "  * time                  (time) object 2015-05-01 00:00:00 ... 2021-04-01 00...\n",
       "    month                 (time) int64 5 6 7 8 9 10 11 12 ... 9 10 11 12 1 2 3 4\n",
       "Data variables:\n",
       "    nobs                  (time, lat, lon) float64 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    SSS                   (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_ref               (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    gland                 (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    gice                  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    surtep                (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    nobs_40km             (time, lat, lon) float64 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_smap_40km         (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    fland                 (time, lat, lon) float32 dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n",
       "    sss_smap_uncertainty  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 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-092798ed-368f-4789-948e-1012cb34c9a7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-092798ed-368f-4789-948e-1012cb34c9a7' 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'>time</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2b60a01a-6acf-4897-9e69-717508d8923b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b60a01a-6acf-4897-9e69-717508d8923b' class='xr-section-summary' >Coordinates: <span>(4)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-c4baf09c-e7ff-472c-bd05-0ce0caeb5be8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4baf09c-e7ff-472c-bd05-0ce0caeb5be8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cd8a731-e3c9-42d7-8bad-938da8c33707' class='xr-var-data-in' type='checkbox'><label for='data-0cd8a731-e3c9-42d7-8bad-938da8c33707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>center longitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02], dtype=float32)</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'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-d9a218a2-3e23-4f26-8f51-a2af7144dc46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9a218a2-3e23-4f26-8f51-a2af7144dc46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7eb1881-3b6a-4199-a4d7-c6bc9ebda9e0' class='xr-var-data-in' type='checkbox'><label for='data-a7eb1881-3b6a-4199-a4d7-c6bc9ebda9e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>center latitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-05-01 00:00:00 ... 2021-04-...</div><input id='attrs-c0d93a2e-d1f9-4376-8fb6-9a56c61996f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0d93a2e-d1f9-4376-8fb6-9a56c61996f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b57d90cb-9bd0-4548-8860-f9addb571305' class='xr-var-data-in' type='checkbox'><label for='data-b57d90cb-9bd0-4548-8860-f9addb571305' 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([cftime.DatetimeGregorian(2015, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 4, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5 6 7 8 9 10 11 ... 11 12 1 2 3 4</div><input id='attrs-ae79d96c-8367-4b90-a41c-7835d045785a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae79d96c-8367-4b90-a41c-7835d045785a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd2d7fa-e53d-4744-9d12-52a31333a495' class='xr-var-data-in' type='checkbox'><label for='data-4dd2d7fa-e53d-4744-9d12-52a31333a495' 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([ 5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e68e072a-b775-4907-a6d2-bcaa29f1e11b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e68e072a-b775-4907-a6d2-bcaa29f1e11b' class='xr-section-summary' >Data variables: <span>(10)</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>nobs</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-03959a40-db5d-44d7-9fe7-8a5e9b51c333' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03959a40-db5d-44d7-9fe7-8a5e9b51c333' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a1e54f0-8ece-4095-9895-4e53ef332574' class='xr-var-data-in' type='checkbox'><label for='data-4a1e54f0-8ece-4095-9895-4e53ef332574' 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",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 597.20 MB </td> <td> 8.29 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (1, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 607 Tasks </td><td> 72 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-90086267-da41-457d-9bd4-eb574b554ce6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90086267-da41-457d-9bd4-eb574b554ce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89122172-dcc1-45a6-bd24-7b9cbf1be516' class='xr-var-data-in' type='checkbox'><label for='data-89122172-dcc1-45a6-bd24-7b9cbf1be516' 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>sss_ref</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-00bc63d0-5d84-4aa9-819d-6f45b69c9616' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00bc63d0-5d84-4aa9-819d-6f45b69c9616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fd3796e-3dc7-4d06-8561-49128cd9f272' class='xr-var-data-in' type='checkbox'><label for='data-5fd3796e-3dc7-4d06-8561-49128cd9f272' 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, time: 72)\n",
       "Coordinates:\n",
       "  * lon                   (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * lat                   (lat) float32 -89.88 -89.62 -89.38 ... 89.62 89.88\n",
       "  * time                  (time) object 2015-05-01 00:00:00 ... 2021-04-01 00...\n",
       "    month                 (time) int64 5 6 7 8 9 10 11 12 ... 9 10 11 12 1 2 3 4\n",
       "Data variables:\n",
       "    nobs                  (time, lat, lon) float64 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    SSS                   (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    sss_ref               (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    gland                 (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    gice                  (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    surtep                (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    nobs_40km             (time, lat, lon) float64 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    sss_smap_40km         (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    fland                 (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n",
       "    sss_smap_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regridder = xe.Regridder(ds_aquarius_anomaly.SSS, ds_smap_anomaly.SSS, method='bilinear', periodic=True)\n",
    "# ds_anomaly = xr.concat([regridder(ds_aquarius_anomaly.SSS).chunk({'lon':-1, 'lat':-1}), ds_smap_anomaly.SSS], 'time')\n",
    "ds_anomaly = ds_smap_anomaly\n",
    "ds_anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63c8e02f",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;SSS&#x27; (time: 72, lat: 720, lon: 1440)&gt;\n",
       "dask.array&lt;getitem, shape=(72, 720, 1440), dtype=float32, chunksize=(1, 720, 1440), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "  * lat      (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * time     (time) object 2015-05-01 00:00:00 ... 2021-04-01 00:00:00\n",
       "    month    (time) int64 5 6 7 8 9 10 11 12 1 2 3 ... 6 7 8 9 10 11 12 1 2 3 4</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'SSS'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 72</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-028e86d4-5565-4f51-aee4-b37685d917e6' class='xr-array-in' type='checkbox' checked><label for='section-028e86d4-5565-4f51-aee4-b37685d917e6' 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>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 298.60 MB </td> <td> 4.15 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (1, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 607 Tasks </td><td> 72 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "  <text x=\"90.294715\" y=\"100.294715\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"170.294715\" y=\"50.294715\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,170.294715,50.294715)\">720</text>\n",
       "  <text x=\"10.147358\" y=\"90.147358\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.147358,90.147358)\">72</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-fc245913-5a18-4068-bd7c-4746b1237399' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc245913-5a18-4068-bd7c-4746b1237399' class='xr-section-summary' >Coordinates: <span>(4)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-dcb9f26e-61da-4609-a300-b312f69ecdae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcb9f26e-61da-4609-a300-b312f69ecdae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba33061d-b436-4bb0-b668-e0f8affd0895' class='xr-var-data-in' type='checkbox'><label for='data-ba33061d-b436-4bb0-b668-e0f8affd0895' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>center longitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02], dtype=float32)</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'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-2c7b2994-1dd6-4301-85d7-11f11f4096a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c7b2994-1dd6-4301-85d7-11f11f4096a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74b2639-573c-4c56-96d3-3d5f15e33ab8' class='xr-var-data-in' type='checkbox'><label for='data-d74b2639-573c-4c56-96d3-3d5f15e33ab8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>center latitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-05-01 00:00:00 ... 2021-04-...</div><input id='attrs-abfa0daa-338a-4ba7-95cd-9e6c0da8c5ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abfa0daa-338a-4ba7-95cd-9e6c0da8c5ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a6b5dd-8dd3-4048-9b3c-b323570e7963' class='xr-var-data-in' type='checkbox'><label for='data-11a6b5dd-8dd3-4048-9b3c-b323570e7963' 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([cftime.DatetimeGregorian(2015, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 4, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5 6 7 8 9 10 11 ... 11 12 1 2 3 4</div><input id='attrs-6a30add6-c49b-4379-abd2-07f87370f8d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a30add6-c49b-4379-abd2-07f87370f8d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9c146bd-0c24-4dd0-837d-0d601540c4c6' class='xr-var-data-in' type='checkbox'><label for='data-a9c146bd-0c24-4dd0-837d-0d601540c4c6' 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([ 5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf2889a6-8d81-4c66-8d23-5fc8d66ad12d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf2889a6-8d81-4c66-8d23-5fc8d66ad12d' 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 'SSS' (time: 72, lat: 720, lon: 1440)>\n",
       "dask.array<getitem, shape=(72, 720, 1440), dtype=float32, chunksize=(1, 720, 1440), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "  * lat      (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * time     (time) object 2015-05-01 00:00:00 ... 2021-04-01 00:00:00\n",
       "    month    (time) int64 5 6 7 8 9 10 11 12 1 2 3 ... 6 7 8 9 10 11 12 1 2 3 4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sss_anomaly = ds_anomaly.SSS\n",
    "sss_anomaly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6beb48",
   "metadata": {},
   "source": [
    "## Create mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d735a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd300111f70>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# mask = ds.land_fraction.sum('times') <= 0\n",
    "# dummy for just the first time step\n",
    "# mask = ds.land_fraction.isel(times=0) <= 0\n",
    "mask = ds_smap.gland.isel(time=0) <= 0.2\n",
    "with ProgressBar():\n",
    "    mask = mask.load()\n",
    "mask.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3bdaa1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2985984"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sss_anomaly.nbytes/1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77b22dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with ProgressBar():\n",
    "#     sss_anomaly = sss_anomaly.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7633df51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MemTotal:       26698836 kB\n",
      "MemFree:        12184968 kB\n",
      "MemAvailable:   20590016 kB\n"
     ]
    }
   ],
   "source": [
    "!cat /proc/meminfo | grep Mem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93bd8ee4",
   "metadata": {},
   "source": [
    "## Thresholding the SSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92068aea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fd2d8ff0070>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sss_anomaly.plot.hist(bins=np.linspace(-5,5, 150));\n",
    "th = 1.75 # in std\n",
    "plt.axvline(th, color='k')\n",
    "plt.axvline(-th, color='k')\n",
    "# for now just take 0.5 as a threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b4f66662",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7fd2d8cfd7f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hs2vnA2u3TdtYvBrJbuSswt2FEGOc7TG0jRDCdwD4/hjjGue7xQB+KcY4ULSP94QviX8DD3c8BjV4rTFRRXUreJjYsj6XFaPcfBifvBBjXF12PwMPvTOe+NCT06LINQpcoYKC9Woqv+3Ibrty2wn1zMruUy+8CqBm/K545IEmJ66S9bsfln7Wid7t1l4oAm2J/iOnCpkZtv5zOpyqLPMHZHT66TrWnQa9N6xLfPLZrTUnDjWHu5eBCcsCU13Yrs0wm5jOZ8fDnXRtusWduozAd0PokyEElY7vBDA+3QM4cHQseylujhxL1l5UuHdBI8I6b53CBg16kRmusssVPLDl+YLhTV3Jr60jIlTuKMv8zP71oAawjpmNB5R+1n/kFPqPnMK8JU9Uzts9gE7YDjMJld2nXng1Z7BrxpLyStmdLodqxSMPZI6bHt+DzQ5xuQP+1XXvnnrh1SyYomviTefaeDxer8DrPm/JEzh/ej+efHYrXn7+g7l7oQ5wN6hq3iqUwR3nwIUQ+gB8M4CT8vHeEMKlEMIYgG8EsGM2xrZ98wqsXrcDq9ft6LnyqHD3oFtD4vc/85Zr0HbqgFkHsJo8KhA0SJRGNF2ZX8qg0pWWDe92ab6eg+cZgjtPXuqoS2uFOx/MmGigdbpriGwAohX4XN2+ci6TUTpxs9VQanJkD1Y88gBWPPJAaWfRPnfaaEifZ9pFXm327Svnurabxq5dzzmLvXKoLFY88kBOp6w5cyvTNd3CBnuBuRl8qDD7uONq4GKMUwDebT5rrzCjJLy21q3WbblxYluuO1GFCsR0KWE1SrxsBEHZtdS0qr6zgoenXni11jUSyDUWAdqnNwL5RhDWKbOURiu/Vr69bYmsQ+bJS7ns290OOiwV3S3fNGV0OF0/2S1UHq298OKR1vYCm/Pwb7uNp6YDZTPTer79R04lA3/aJXZo1a6stk47y/J3L3eZFR+YmshRGmcSr7x0GOdP7+94e6VoToesVrj7cMc5cDOBovWI+J0q5hfj67VIT73A/+CFvRU9p0KGVpN4J7CKvUxNj5VdXSQWaK6xqFDBGpPdGBXa+ZeG9XhfPwamJmr1IwnZtdRIGjfLhnfXjDSp1eHSBhohv5uzb1pXWz23NWSBB9PNdLqcNyBtM3g6V8GmaXrv7lTbIRV8URx0MnVAcaOQdqDBi5kMR778/AfxVP19u01f7nt8a1ZLx+tROW8VyqBy4AzKZiFejK9nk6ddnPZOVcAVegsam9MNdvJrJ3KqNI2qoUMFBSPBXLMNaA4YdCrX2lESABag1gI95bxZ9B85he1Yn+naMouA362w65lNjuzBytcewhc+du9m4Ca2rAdW7cr0GjtK0+lvtbh2JyijNymfugbhvbgeoTZq0eDQ3fIMcymeMvdU19Lct2E5MDVROW0V2sIdVwM3nRi9fLwtZdp/5BTWnLmVZdyms7i4wp2D58LibHKeblpip/vvP3Iqmyyqhg41dLM2390CZgpSkfFuDIyvHHh/to8DR8eyfbcbeVdZnbfkibvC8OsEKx55AP1HTmG8rx/LhndjaNWu2R7SrOH2lXOZ7OqaXZQt2zW6G728ffMKDF692NG2lN3nwmL0Hzl1T8quZyfdDfT9fRuWt63LRhetxMXH3swaRJVp2lShAlE5cALP+fKKaseuXc+6LL38/Acr47cCgNqk/FxYjNHLxwE0Ot5N1yStSt7KYKqzFz9/LizGi/H1e1p29fpMbFl/19WutuOQshscUIuGa0CqlzU5bEqi679ZvasBEAVld2LL+ns6WMa5h+DafC8//0F84WOHZ2tYPcPYteu47/GmZVxdPPXCq5jYsh7jff3ZMhLarRFoDkJQP7cLzd7xmlv9OXbtuts4Q/Xuveq4AXk9w+zTsuHdd8U8tHpdrXdeO06c1vGqvXA3XI8K04+kAxdC+HwI4Y+d1+dDCH88k4OcLYxdu56L5hHszjTbxcYV5gbouBEzIRfdZN6qyaHh3LCV9bLh3di3YTluXzmXBWjuBhR1FKVRqedapoNeN9FhPhs3R44VGjred5Xc5kE5JQVaW7Pf6bhxonhxZD6nQK0xxoLhTVk3Rw/aJATovM6X2beU0zEwNZFbP41QxkOFfHCJsmsDE3ciWjVOYab4ubC4aSkqe+5WF1dZuQoWyRq4GOM7ZnIgs4VU176JLetz9QUeKoPi3kCqKDnVnCQrnMf6ruk6RUgt6MosYFGG4l6NAKew8OlDuPjYm8Bd0gjCLgJr8VxYnMnl4NKNAPLRYK3f9BovdItUi/TbV85h++YVSdm913WuGnm6XtdAvaFLqzlrJsAmIkD7HTG57XhfPwYcvXvf41tzzt2+DctrdZBDY7luk0At0+Y9A910J1w2vBvj9X1YrT527TpQsJjzva5zx65dxwCQq1+dxNxqmqXPlzIydG1f7/4+9cKrePLZrdkz6dkM7JlQBG3S1Ku64wp3L4oycO8qes3kIKcbRUbBvW4weLibMhRlUeZ8vVbn+reXOHB0DJMje9xJApiZLOCdCGYoNCI/uHQjnny2QdkamJq4K+h5NBas7GrggbLJrMKBo2MYXLpxWoyqG2+8kRuXvcbMqFQ614fNWlhHZGBqIkcdnA1Q/3TiJHHhdTWcreyeP70/W2NswfCmjMpISq4GzBiYUNjtyuK9774/o/wNTE00GfE875Tzdq9jYsv6msNWn5fG+/qbFi2fbVq0lTWPfbXz5KWc/UMWw9m183NOngVLcTyZ5OeDSzdOq81Q4e5DUQ3cBQDn63/t6/z0D236Qc68BaMg9iG6G1L83ULPf7oWyZxLSN1vzWAo7PpWndZbtEJKdvmdhdYe3K3gOaYyo7bzJjF49SLOrp2ftaW/05037fJW5IipfhtdtBKDSzc2dYm0v+sWKecNQM7AU9wLemZiy/qsfspzuO1nel89yt50jK+s/jh4YS8WDG9q6znSWvPU2n1j165jdNHKps8Hl27MnKvRy8cz/au6lwEvvm8XCx9+OHP6Uufl3Yd7wV7gAtyejNh55+bIsWzpEF3/rV2Hut3xlQVlhM7UKy8dxisvHcbqdTuypUk0y+ZtP7h0IyZH9jRdi5TcVdTICp0i6cDFGN8XY3x//a99vX8mBznT6D9yyn2o7tW6N2YtqJC4blMRWhWh3wkOhc1m2TF7CtkudmzpaTMF69zdK3LrBV1oWPA769TQcRjv68eC4U0tJ/xUo4K5Ai+6TSMLqBm5rQILs2VseA7nTDgoswnKG50W6lat87Lgb6webqVTO5Xd6aydfeqFV/HKS7XmK6whO3hhL9acuYU1Z27hqRdexX2P1+hpKn/sujl6+ThGF63MUSfpsPWKBszscSqDksJMLyY907h95RzG+/qzbBVlROdKrT0cmJrIyTkd77LH6gRlAgkTW9ZnOpN/VUdqcyDdH8+Nc4fSHw8cHct07pKhZ9zjelTJIh1bOXsVFC27UIYangkh/KP6/18RQvj66R/azCClFPhQ2Wj+3ULvoUH31AuvFk7qY9eu59L6jC5REXvXj9fMfscCXv2cHefmojPH5gAAMtoO1/3z2v2qIp4t/jrl017Tu6l5iTZr0HMcXLoRVw5+JPtfJ2UFZdgaV2oEWnnk8fQ5matyq9FsRrdJSxu7dj2TZYLZijKLx06nHDNAZvXGnZ4RVWhAQc9z2fDuLHO14pEHcsEyLyvqZag00KTwMnvT2eyk3aUhXn7+g7hxYlvmxFHPHrywF/s2LM99DiBbgoJ1doNLN+aMbc0kq0zbRb07QWo7zhVWH9xNlEqyG6weHKivX+bpTuoazUy98tLhLAunwRlPl9rmJt6zMx0YvXwc4339+MLHDhd2dlU6L9AsH/s2LMfYtetJmbPrE/KzIlROXAWizDICowC+AcDfrv//eQA/NW0jmkFojYid3Ih7ofB4xSMPJCkMC4Y3JTNJ5Hzf9/jWzGnj34uPvYnBpRtzLcFpiFm+NzHX6CaM4KoBMDmyJztvRnotpruTH9BoNGGdCHa3Au6eYIPCkxE+v6RCvhhfzzLAev+s052SeRqK2l2U15JGB7ddNrx7TmbjVC7ZYZOGsAeeczfGbRnQ4KERZvWuBk3uNlgDlc4qAwnsbHj7yrksu6Q1Q0OrdmG8rz9zxlev24GdJy9hvK8/08+pOW1gaiLbjvS1NWduTYvO9Wpz24F2itx58hLOn96P86f35zqXrjlzKyezGmiz1Mmiv+2Oi50uveDNzZFjbWWU7iTYcx2YmsgCZLz2L8bXm9aRJPR6axMaawfYjs4K20m0l8GzVEfRstvx3tvSm9FFK7O5IiVzmjlOQevqq/q4CkQZB+4vxhi/H8ANAIgxfg7AfdM6qjmA6TZkZhtrztzC2bXzcfDC3kwRjl277vK2PYUxObIHo4tWYryvH1tf+zUAflfG7ZtX5Ca9O+2aUg68a8BIrycrrZRst2sBFRm5ZTIpdyJoMHDS519+pnU3N05sa0lxpbFFA1rvo0ZGNdPnLXC9YHhT0uiYDXBceq4DUxO4cWJbrvYEaMiKRy9N6cAX4+tdB7ZSjtrd6rzRUbIUPLIZgOZMo6WYMXDEvzdObMPZtfOzIBufBTWqGVzwqHxn1853s0bdoNNMP8+d9F8+0wcv7M0FTahzlww909SGXTOVqXmrU7n9/c+8lXOmraPGLMzdlHEjKLu8nqqDCV5XO/fwPlCX8Llnza39nR5Ta3mBhs7gdjdHjk1b0Nc2VLElIfZ7ZsStvrQNzTwUNd0hDhwdy13jChWAcg7czRDC2wBEAAghvAfA7Wkd1QyBmSCgMYHMRUrUdODghb05Y2nZ8G4sGN6Um5g85ZiLLtVrD1hTw5dt5GEVkxqGqowmR/bMKUMYaJ5YDhwdywrprfEL1K6JZ0BbdHOe7333/bnx0GDiPlXZ381op9unR2+l4cw1pPid5+QRrHegQcyo8ODVi3NGd8xb8kTu2pC65FEnFTSQ9JzLGB7tgtdaOyZS19wti/oqaPxZvaAGsd4XZvlpFA6t2oWdJy9l8qo1ROwYqvqWFEPCUtXUmZtLGSPtBKu1REqdVAfBBs/aoW52Irtax7XikQeybOlzYTGWDe+eU9eyV3guLC7MpgH57JWnR7dvXpFdG2Xy2JdCn4mbI8dyGdfBqxcB9L42VheBV7Sq5+8/ciqj81rwOmh2LbW2WxmZvFuDsxU6QxkH7icB/DyALwshjAB4FcBdESals3G3Rn2LQCWlnaCAZlpZircNNDJQqmzH+/pzSpzvrRNnJ+O5SA0oMiS9sZI33w46NYJt1JmR9rs5c8zJlZOhbRhjgwEKO1EeODqWGcDsjEZZ1Wgxo/52nwuGN2XOznR3UesEniHLZ8zrMMnneNnw7tq6Wlcv9vx5XPjww7n/ta6FY77bdTHrDVXvWad12fBujC5amWtL/spLhzMK5Nm18/HKS4ex8+QlDF69iPG+/tw+Vda1s+fZtfOzTNZ4Xz8WPn2osKPeTIPj0/mDzyHh1QyVcdz09/1HTnW1TIbV8Swx8Dpk3i24OXKsZeOjsWvXc/O83ie9Nnrt7X3wMlaqW2+OHMtklnbHdMpuK+eNYCY9dZ2UraMouhYeUg32KtybaOnAxRj/DYBdAP45gDcA/PUY44npHthMwGuzzcngbmr6UISUwWRpE4BPMbP7GpiayEXZvOidlzHRfc2lmqKiTJYaTMzMADV6qpdh5P/87LmwuGMZ06gzUKtjHL18/J6R2yJoHaAtDue9Gr18HAuGN2WTLY0CzaDadaWA2kSsRfvsPMbjzQVDGKjJICPVNiMOND97N0eOYWjVrux59aLb2zevyOpcus2UaxaTmLfkibtSdtkEYeHTh3JOqjIU6MRObFmP8b5+XDn4kZzRq9kpgk4c6+DU2fGyenyRRXDjxLZcp0Zt5T8bUFqa6jeOOyW/lCWVqRT1t9MGGO999/3Z9bQNZMh4uBtZDy/G17HytYewet0O93u1n0jltU6YtbE8u8vLrupvDl7Y2xRQUxmYTpvhyWe3FjYyAfI10l4Q15uHOPewfq4o+EvZnSssjwpzA37eVxBCeALAb8cYf6r+/ztCCH8xxvib0z46fzyvo9ZI5YsAbsUYV9cXFv93ABYDeB3A36zX6rXE5MienNFwN3U8a4X+I6dy7dWBmqJRJWEVz+jl49iOPKUFyPPTrSHB/Vij2qO1EbevnJvz98Ke50C9McC+DcuxbO1u4Gi+8QsN/Rfj68DRxR0f9/c/85ZLk5zr16tXsBRBlVE6FmWpKbxvkyN7sPK1h3IF9suGd2eybpGS4dHhmhHeiSPy1Auv4uXnP9j2dinQCbPG0bLhhmza3w6t2gXU62MtDhwdw4tHau8z2TsaSo/nd67HLNChtUJ3o9NmMW/JE/jCx57IOQ8p2R0cqWWjxq9exOiilThbz+wrdfL86f1Nx7g5cgyoZ7B0PSvABHyONpgTHMPkyB5g1a6O9W6r7cauXW+7Psxm2QBgdLh2/tvRoD1PDm/Kmr4wa6nnadGpnuTx9Bmdt+SJu9JxU9B5Sa17RnnznLADR8dy9sLo5eM1mazfm/G+fmDkGHDUX9/Py1htRyM40U0mVc8pJRPnT+93O756mLek9ny3S+3cefIS9m1Y3rRMhrWJcnN+G3q3wt2Llg4cgJ8G8AH5/0+dz2Ya3xhjfFP+/wcA/nOM8V+EEP5B/f8farWTZcO77xmjtxNYRQw0shKDVy9idHgia4aia6eoI+dl27z9Kkj7mUuGnTUmtOsZ6x8WrtuRKfyDF/Zm18KeIxcB7+b8pmuB8DsBT73wKg62uY3K4eBILdI5IM73zZFjOA9gUGlQ4uTwXvL3lsplgxmdoJfOG59JL3CiNCfSfteIkbJvw3KMw38+u8m8ffWXvT1zmIF7U++O9/XXzv9oI4ugumX08nGgrv8WoEHP4/IYNCZZI0eHhTpzUo7l6llxamyAIlXHUwat5tGdJy+1Jd8ewyPXer4eCNh58hKwaldmAE/W61nH+/prGWjtAHh0ccf0s4WLl2TP/1yal2YSHi3da4hk36ucDS7dmDEDbiJPj0yxJrL9o/n4XEJiwfAmTKD9e9NKblc88kCOytwKntPJa0HnlTp3HLXnVve/bHi3u4+51hugwtxAmRq4EGOM/CfGeBvlHL+ZxHcA+Nf19/8awF9vtcFXDrz/nnXe7FoqSvdjlshOoEz5Lxvenfuck75HOyuqoaEDkppQu42sdQtLybDnQqeNxdkDUxNZVPzs2vktG5nw804U83vfff89K7u3r5zDKy8dzho3KLSGLYXRy8cz2hkXCiY1p6iGxdIpPWeN45ntxXsntqzHmjO3Mtm0wQc2G1Lazr4NyzNDomzEuV3ZvfH6lXs6aHbf41ux8+SlJjqg1nBZ6iNQa9rAhZKBGnUyRWlT/a2feWA3RV2MeLpkt6zzpuwPWxPJzIaO85WXDmfXg88yf6vt223GvG0q2p/9KYB713nzasG0Fl7/1/d0VoZW7cLNkWO4cvAjWUMe0s+9/drPvUyctUWmq4a+TOZYl0sCmsdLe4c1q9SxC58+BAC559lbnsjL1FeoUMaB+3gI4f8MISyov7YD+Ph0D6wAEcDZEMKFEAL5Tu+NMb4BAPW/X+ZtGELYFkI4H0I4f+0Tn5yh4c5tqLNmqZReFBTIdy7Tbnx2vwpr+Npud/xOv5/JeiJVwKnJnZMGJyw6ApMje7DmzC3sPHkJq9ftyNZqStVjaA1cWajsfvbGF9o9vbsKth5Bs8P6P6FOy3hfP86unZ85LMxUlZn8Pdqkt34a10iaKbAmjcuAHDg6ljWEsKBM0ohQZ40ZHdtMInXMMrBye686b0Cjjs0aqMpesA4Y78HK1x7CKy8dxtm18/Hks1uzfTHwwG6Unr5W3cusCZvVMHOn2a2Z1Lu6lqUel2Omg2lr3CjbWhu4b8PyQgeUTcvKPpsqu3/42T+6Z503oHadSVNV2Cy/QlkL+zYsx+p1OzL9onNjGd1rnwlLoaTsz6RzQ71LebIsDJ0TVG4HpiZy2e7V63bgyWe3ZvKd6kvQjuxWuDdQxoF7DsBfAnAVwKcB/EUA2wq3mF785RjjBwB8K4DvDyE8WXbDGOOhGOPqGOPqR77ma6dvhHcINBLsNRTxahDYCQ3IZxo8Y8/ruqTwjESrzHtRnFxmHy/G19F/5FTWEjpl/LLj23hff7Y48s2RY9l7GhRcNNmjOqqiL1s/UcluAzqJ6bIVKjs2qwHknRc1XosMCK8g395Ta1iQVtsLQ7is7AK1Tm8cizpKmmUhKN9shKHv+YwXrUukxy1CJbcNnF07H2fXzm9iNqjcUJ7VsFUnWwMQQMNQVGOZ+yVI39IMsaWAl826loVdSDwF7QjJMXnr4QH5xjqTI3uyMdOh1aUWipbLOHB0rJQzprL7nocXtfz93QyPRphyvii/GqTlguyqX8qC+9Pgruoy7q9XGbiyzUJejK/nAt20IXQ+IEtHqaIaMNFgDJBeGkFLAO72mssK5VGmC+UfxBg3xhi/LMb43hjj344x/sFMDC4xnmscF2rLG3w9gN8PITwMAPW/sza+OwVDq3ZlETXPILWRYCod1l7QAGYHNEAKzaXNu83u8S/pW1TGyqfXBZq7RTtGNH9LWgOjeYcf+yYAyGUslKKz09QP6f93e1v0mQQX8gaQ+2uDD6yN8LqdUXZTkfpUwMHWe3jbtHJ42oVts18EGyzRphI8XxoNxNm185uaYbzy0uHMWCsKvnRaS3SvgrLrLRpPfaeUVrYj5/809JjhB2q6RpeyANJGLIN1DE5p5mJyZE+2Lijrl7uJ9KvOLZsR4biHVu3CUy+8mqNCAsjpXY6bWXTK69m183POQapFeyW77WFiy3oMTE1kQUkATXO6dbIAZIEBew8B5OiTZe6Hdy8pq0uGnskFP7qR3Xa3VduF23rno+O/OXIsZzdRfvlsp65LihFV4d5FmQzcnEEI4UtDCO/gewBrAIwDOAXge+s/+14AvzAdx9eoYsoxuH3l3Jxqg2+hCop1FZqJs4pYqSzkaStVJUXVshFg7pNKFmiONtn22gC6Xltn3pInSu+D3Q2Vonf7yjlsfe3XMHr5eBZBJ0Xy5sgxPPnsVuzbsDwXYdu3YXlmbKXAazJTlA+2CH/qhVeT7cLHrl2f07ILNC+CbJuJMNPEek3WXxCkrqghaGtA7fH4udYnFWWO7W87xbwlT5TuaMbIrz5fWVfD+tIHQCP7QoeOtCbNZmirbg/c/0zReahri2S30/bwMwWlLR442lgf8+bIsSZ9yKyFrU/UhiWr1+3A6nU7ctF9fRZsUCNV56byzKCeLqXRCbgcQDvy7xmmXtt+m03X79ecuYWDF/ZmtXB6bva5nWnZve/xrW4dGeV2LlPjDhwdw+DSjbj42JtNjATKG7P8hC6rM97XnwsMUQezmUkKSvkF0HQM/d3Qql09CaD1HzmFmyPHSmfzbECR95sBRLtmnXXalAFhg2mt6gArVLijHDgA7wXwagjhIoCPAjgdYzwD4F8A+OYQwmUA31z/v22UNQBSv2OUda4t6qtgVoxK1FLPtC5t8OrFjAZACoQalFrnRdh1jmzdBaEGdBF6obQ4UZSZJLm0Ao/tLVYONCiStl5Is3Takp6wEcpeKeUyjlfRb3jOc1V2x65dzwxUKzNeQXvKUNXPrexaZ1DhOXdeVN/rutoN2I21jG56Mb7e6BIrncz0fCirWTe/Okj/1exbSj573SygjOwWnf/ElvWz3vSoCHQ6mR1iZo2LEvM6M0O6et2OpqwFMxnnT+/HeF8/bpzYlhl8pGnR8fLkkvrL3negIcdLhp7JNUvpBV6Mr7eUXa7/t2x4dzaGyZE9ucCLUviU/aHf79uwHDdHjmVBNs3U2IYovUKZ57KVfM9lloaOXZ97DRQdODqWUSPpZLEm/JWXDmNgaiKbC5X66zWZsX/1vhUFitn1uRdY8cgD2L55RVZTXASuu0o7itl01Zs7T17KOZ+UWwZ42YiHtkNqDprLOq7C7OCOcuBijB+PMa6sv74uxjhS//wzMca/FmNcWv/72U6PkVK2St8irPKmgpurxc46XlsjQKVjlQ2QV5w0MrSFtRrC2tkMaFY6LLYvm6GwjU66wcrXHkpOuLYgmcdmdI8UJ9Irb5zY1lQ3QgOY0Ua9LilHrZdKOSW7t6+cy7IsWj9jx7FgeFNOdudaVFjrhTL6Wb2rF9dxI1Rm921YnpRnYvDqxSxgYQ0Vhe7Hyi+NGjVuZgITW9Zn8quRcY8+qlhz5lbmBKjzRiegqD7QOgrdoojRwIy3ZqHsWIC5vw6iLrdCsBMqZXe8rx9PPrs1c/bYHn/fhuWZ7lHnTqmFqm/YtMeDp3e5eHg7urksivbFeiM6uaOXj2eZQOpQBhvVKWNWRzPIbCJlaZQ8J/5NZXJ6jbFr17N7cv70/qYMy+0r57BseHfmBMxFaEDPc66A5kzawqcPZc+qnrN1unkfGHgYvHoxm+9tXaQFP2fwopd2ApHqkgk07AVmT3OsIXnuJkf24Oza+Tk2jqfDbO28d9zZ7m5cYe4h6cCFEHYWvWZykNMFTh6kjrV6QDylYjtn3Rw5NiepPOqAbt/cWIPMGpqcDK1TpmAtnBfNLYrw2o6VNkLqjafdRTE9cBK6+NibrpFnaYzaaRKoXZPRy8ebso9AI6OjdJ8nn92a0Ta9ZRlsQXYnGLt2PZs8ytIetUbEUtG8erG5YlTcvnIuu55rztxC/5FTNcrM1YuZ4Ta0apeb8QRqMrTmzK1czeZ4X3/OOFG5LSqYt80f7DWjIdErg6KVoelRcPUZUmNVO59NjuzJnmGbdbXZS/tMduuc0uGk3JZ5xpXmaWV3Otvfd4vbV87lslr9R05hcOnGJsq1BoRstkkdEmY6LBVL5ZkOmDrglt5m9Q6dxOm4jp4xShkgKJs6LmYivSYm7Nyn8Go6U7WBnZwn9a1tclEU6KJTST2luppz0VwLlBG6zBBtBqvzyM7RJUj0Hqi+pTOuGTWdO0cXrcxRvZVqTHhy22swENZ/5JSr6zx7gfM7A4pK+5wc2YOLj72Z28Y24AFqztv50/vdmm5uU2XhKiiKMnDvqL9WA/i7ABbVX88BuCvaiWVNN+qKY+fJSxhdtBJPvfBqS6WamgCmQ6F0Cxo8qhionBm9YqYJaEymamQoV5vwrgELiy03HKhdmyIqWor60y00M9rKudZGLAuGN2W1fzSilApiF+Bk9GzfhuXYvnlFThZsjVb/kVMZdahT6ES44pEHshbg1qFTA1AzcXot2EBhLhkTT73wahZYodwsGXomG3cqo5gKOPAvodFezTqow60yyciqJ9vEguFNyTq5TqCd+VKya2tMaVCkqKL6v64XRx3o0ZuZVWSnNdY5dYrtm1fUFt/dsj7bTyoQYRevBvJOHKPbqfq42QDr9RY+fSgz4sb7+jF27Xp2jyi/XIpkydAzTU7cmjO3Mpq2ZknVaAb8BZE9WJnVIJoXfOsGqaCchTZvARq12TdObMsv3i2g0atrbwINnWBlePvmFZm+XfHIA6XW9kqdE/WDBkWLapn1vlmQsTKXwOAgQfbJ7SvnmmoQmQEFGnOiZkMZJAKayyasntROqguGNzV1VvWCZyq3vW4i1QosreDYFQyekCJtx6o1cARlmc+L7vPF+Dpefv6Dc55lUGFmkXTgYow/GmP8UQAPAfhAjPEHYow/AGAVgC+fqQFOG/7sT3PKgUbui/F1LBl6punnfADH+/rRf+RU1oJc90FjfC5x2ie2rM8mNy+zsGB4EwamJrLaAaXn0PBYMLwJN05sy9ZysdDIr430pppC8PcW3L6XdTbadtcqQJ14raHB6/Pks1tz2QtCJ6rxvv5MARetodULetJbN7+YO8bg0o14LizGvCVPuE41J9U1Z25lhsvCpw/lHLsFw5uwYHjTnIzwUb4Y4SQtZd+G5VmthUIzyGo8eN3QgLzc6r0pkkGdlPm/gmtzdQt18FOyax1NUkztOm+k6wHI6q+AZropjWaOfzqokqob6LjRobPYt2E5VjzyABYMb8LBC3tdKmWv2+D3ClqnBuQXo1ZaJGvY6NAByGqIbMMObQhBaGZEuwBbXcsGO14dqWYye3HPKa9ekEozgx5jg8820JBb/s9zUWeBY6f8qqPRq/mYuts210jVinK9Ly5i7gVBGTCZS8EHz7ax94jziW2Br2uccdkAALnuqQSvn11WwMpskU1gg769oHa/GF/PbIZWAVY+1+qwEeqALnz6UG4ZDwZkzp/enwvKKOaSHVlhbiLEGIt/EML/ArAyxvhn9f+/BMDFGOPXzMD4pg0DD70znvhQYwk5NeBo3HQS7WCUarboZ5wIOGnYqL03eRNUsMw8EZYioTUJNK71+rEbnjZS8L63Y7KGND+brmtpnTcd1+DSjdn7hU8fwo0T27Ix0ejiBMUGELoOnO1MqcaUXcslhHAhxri67LhXL++Px1YvddsNM4vK+98OZlt2x65dz0XGWf8E1ByiVE0PsxSsk0pR0bh4NcF75FEkvQycl71QI0Pb9FPOp2vNHi7YbeVWx0l608ELe7PzoOwCyMmqNR72bVhec0KvXsw941YntiO7q5f3x4/+/JHMMeT1HVy6EVcOfiS7X51kR6zem2k89cKr2bGfeuHVXMdInVOoMykv1KcqO1ZOAbhGHrMb6nDbrEWqeYfKNbfncbnf6Yr2W72rFHteAz7Ttrb1xoltWPj0oUz3ci5a+PShLNBmn0Og9ixYuepWdvn8Da3alen+duXvvscbFLrZkF12xmTnZe2iqPOVnRdtgIz/U35UTkkNtL/3ulBrnaJd90+hMqNyQEyn7Hodtm0A8MDRMVw5+JHctjpHAc3MAn5vGwl1K7uzjfeEL4l/Aw/P2PE+jE/eMdemW5RpYnIMwEdDCD8SQvgnAH4TwP8zvcOafix8OC9QpP4pWF/UTnSMLYxnA/c9vjWLzo5du55NlEo9uDlyDGvO3MLCpw810R2tM8aOaApGOKmAbSYCaESePFoBv1dYCqXWbvS6452F7fhkI4+TI3uyyYdGF3nrnGBIkeHv1HHwnKxujfobb7yR+5/ZDC4ozuPf9/jWtmVXO7/NJO57vLbsAutRJ7asb6rDmhzZk2UZ2dhCkVrSghRY77uyz2rKgNDvNXI9E2v2aE0lj6tZswXDm3INXCi750/vzwIOKqvshmYz8ZSvXkSErXMB1JogWAOsnSUtnnrh1RwVaaZBvWupZ0Cj8Ya39qBSqfT3BBsiaVYOgJuBA/KULkURG4DjsMvCTGdNoTWAbct1joO4cWJbJrdrztzK6gU5F2kmiA6yvQ696K5r96nlAJqJamcplhsntmXL08wmvAZnSiXX7KMFnSh13pQiaOsSgbzMebJmF74mbIZN5WC6oewA3nfSoa3dsH3ziowlkqLPEvbZ1v0Dc7czdIXZR5mFvEcAbAHwOQB/BGBLjDE5k4cQ3lXi9c5enUCn+OT4x7P3amTfHDmG0UUrs4ex3WgwF1edzUYmVKZsK26zW3Q2tAMU0Nx4wRbKp+oj7DFsjZDNVKRohPo5ncDtm7tbmLMVrINIB4aTllJ8tDUyQQWdcg68Ca9sAXwKDD7Q+WRUVOuXiLKG7VMvvJrsUDmTYMG/1xxkaNUujF27njXe0C51pDd7MqvGHe8Xo5zaWMdOwPZzzxBOUXa4/XSs88fsmwY5WPBPo1KNc0KzK0VrFfI60gjh+djz7ER2OUZ1NhYMb0o62GWhVKTZgAZ0vIym3g+rZydH9jRRzHjdn3x2a65203b24z7V6Na/Rbo2Vf9G2vx06F2l/epLx6r0R1uXzb+UY2aYFby+RRTmdp9LDZrxGSPtdd+G5XPCCWsXY9eu48lnt+aaP1EuWE87evl40xxpnW2Ve/0L1OTXdkr1nC1t/GF1alEAgkwBoLH22kxTD+384D1XOld5dG9+bxtx2WWegLnb9KbCzKOoC+Wfq/99F4DXUcvEHQPwyfpnKVwDcB7AhYLX9KZVSuC9774fAJqMIGtE3L5yrm1qgy3Knil84WONtZw4qalS0YJwTvqsERjv68foopUZt52/oUJhnUGRkcVj8D074HFSsI4kkI++pQzk6YAaEjZLZhUxJx06Q08+uzVnNFj6kY2YednEbgz7G2+8kSumB2qTxZKhZ7LJZOzadVx87M3couRFoIzPVi3R+dP78YWPHcbFx97EwQt7MW/JE5lDag1jNt5YMLwp+59QJ4XnQsdUOy5qlpn/6/3x/vfe62e2fm4msnBAc82IUp/4bLO1tz7H1uFhlkPXIVPHYHDpxqbmBu2ARrDWtHD/dMIXDG/C2LXr2HnyUung2cvPf9ANXswUSJ2jLl3xyANujZrSHQmyGLjGJmVWM2IMyOmzqbKs+tWu38fPU8YxPyNmq+6m6PnSrptK8weQrf2mDTRGF63MgpNA41lUw7fT51LnC2b6+HdyZE9b2Tdgdpe+0OdrcmQPJrasz9HnB5duxJozt/BifD03n1vdqdD6zaLPvexbkc3E4+pvSLNkQyRl9kyHk6N0SdW5lC8+h5yf1A602WWrf7WWHmhed5T2AoN3FSoAxRm4f1v/ewE1h4wv/p/CRIzx/THG96VeAD7Tk9F3gYUPP4z+I6eyglWv2JtG2cSW9bh95RxuXzmXa3/tgR2uOu1y1S1oqA6t2tWUBfTqGrSRAddzYR0B4NdjWAfQy5YopUyzbva3RVk9/p1OhWWzkIyErXztoaZoYSrKT+fXc97s+arT1akTt3DxEsxb8kSu2BpoGCW8X8uGd2dUNH2lMG/JE7NWQ7TikQdw3+Nbc/da6bqr1+3IDOODF/Zmhi/pZJ7RByCjWNFx0d/ZiLCl9uokqlRj7ldhZXsm6JNqpHPsaqhzXS8g31BgYGoiVwvnNcZgG2+g0RSFx2ML+E7Okd0rqXt1AVwaadQJlFeuz1jEapjNDm0vP/9B3Pf41qZ6SlLQFz59KKP+2pohzUIy05Za04z7pNySDk9YJoSty/GcJMr/TAUcUwsUU8/TSNd5xzZ20bF6jXg4j1kKNtCQ3U7mFK7XRr2rS8SoUzMwVesGzC66XmdgRbcdXbsBn3kbDARq11HnAyu7hHakBBr6RGEzqKmghN2njscbA58DLzjca1CGqBs1aELHjlk2RdHzpVlLZvFt8NCyOWZyjdEKcxtFXSi/PYQQAPyVukP2fnHM3l+wz28ocdwyv5lefMmX5v7lmh+2cxcjTwTXnprLmBzZg4MX9maKb82ZW02ThBoQ1mgGmh0rXW9Is2weLYJKqJNoro0YA5gWGiUpIpqFJBYMb3JpSpoJ0vEtGN6UyYzKSoqWZ+vuegG73pPWgai8zlXZvX3lHJ58dmvuuvB6r3jkgSyT6HX4tDVuq9ftcGmuQH7ip5Gosm6NGDpGXqG6V89F2dVnaropL2qg00nVjDGQL5gHGtQm0kuV6sT3msEgBpdu7Lg+deHiJU2feTQ6IF8DNXj14qzVZpaBV2dKvfiFjx3GFz52GE+98GrmpGlwgRH7oVW7cgatLkni0aApt3qdLHPAfk6k9DLH462/NR1QJ1KzGsysA8i6cNq269bRswby4NKN2XPNeb2bQGBKdi302qrOma3scBnYAA2vE4PQdD6ZCU2di1frRrQKEti6Sz2GykmZc5ku2eWzwayffm5p0R4jQLulMqg4MDWRdSelvFjqr5XbubI+a4XZR2ENXKy1qPz5dnYYY7zB9yGEt4UQHgkhfAVf9jdzBbevnHMj6F7UfbbrhIqwYHhTLipIZw6oRXCeeuHVrNCbkSvtKgUgV09ku0rZWgkgT4sYmMovPOtFhPm5RVE0+MDRsbbpKSmoQa0Rf0I7YOm56XnZyLhV4HafStPk/eGaWt1i8OrFpjXKNCKcalM8lzBvyRM54+z2lXO5ZQ+eeuHVpnWIxvtqSzdcfOzN7BwPXtibUQEtiowoOvG6HqIGIazsWkcyVUtH9MqJY/ZAHXZr4Kw5cyuLhnMsWqupeszqMhoUr7x0OBfc0aJ9ZiB61WGT42cWjtdwLhu9CmYp6FQwU6iyy+9txzxmQtVRs443G84o1DnQwAKNbKtLvWCDhTo8RC+DD5b+pWMi7Yx1b5RNUnptcEEzdN7aeHZO43lpBq1b8DqyntOemzrks1FSUQbUY0OrdmHZ8O7sfnNeYpdKZpY0A8ZzsmsSKrxOwPZ7vZ9Fsus5RAoNvh04OtbTpRm45p9tFEQ7wGaIVSZtltEu7XL+9H688tJhV9/Z5meV81ZBUWYZgZ8CcDTG+D/a2nEIQwD+CYDfB3C7/nGMMc6J/O/q1avj+fN5JigbJNh2zLqGDx/Y2aKatQKbUVhFqXQUpdocODqWUU6sgtWaLhq1drJcvW5HVr+hfG+PLqT1SQptrUtoYb46gb2gmlgHjg6tF+Vne2iOm+2t7TlZR5dGmjXm2ZJZz6PtZQQKZJdQ55vjpBFYti5upsFzsLLDYAOvv22frjLL5QZspNjKNbuj2fpFu18dh5Xn1P+An+XodvK1BrBSKFMGYuqZ5ne69AKviRrGGqyhwdqp7Hpye9/jW3MLNk+O7MmWP7A1IsDc1rscL2nUQ6t25XQFryXnELYNV7zy0uHseqxet6NpqQerY3i9AGS62N5j+5x4hrSOz2K8r7/rkgA6tfa5sMGH1LIsBHUYa7P1nHgt7X3gcbpaAsORXZ4Tr+/g0o3ZXErMdbkFarLLpUZoE5BxwPkOQO7ZJFK6hNAlh6ytwCCTXaBe96syXzTvenXnVw5+BAcv7O2J3vXA62PHbsdp5dheI3seau/webHnUC0jkEa1jEAe3wjgv4cQfjeEMBZCuBRCKMNF2A5gWYzx62KMy+uvOeG8pcBJavvmFRjvqy3YzQmGkRVGp+cqXn7+g1n0zxbO7tuwPFvmYHJkD4ZW7cpNODq5UrmuXrcjW+Tb0tIGpiZyEVGC9AAtPuc2XpQtRdvU7MKBo2MY7+vvOgtHZWzb9FrnzWYntFsfDX51YoF8LZGetx7LOm+9wopHHshF371GCnMdGsW0GQN2KrSRShpPbOpA541yxv3oPQIadB91zNSwVaNXo/2eY+g5iiq300npARrPG6HX0WvwYrMCuu3qdTsy2V+9bkdW+8ci/V7L7hc+djjb53hfrfY05SzMZSOYYEbD3nNLn6SO0GeUdTDcD0EGhW3Rrs18CNssQmXDu+/KrFDw2ell5shSw5QGqeOzTpuC2fXxvv4se2mzHrp/S4nuJViWwMDD4NWLs1b73g1szZZmfZYMPZO91zpxoLEwOfWMdri190Lp2qqTtEkaZZtyTio851UrJ6q3tPSCQdKza+f3VPey8yihz4bS9r1nRu1GS/Endp681FR60i31t8LdjTIO3LcC+CoA3wTgQwC+vf63FT4FoDectxkEjQlOivOWPJEzhlnTMNexb8PyHPWAhua+Dcux8OlDuagtocpJI6Cc3KmA1ThIUbAIT9l6Bcx2HwByBvjo5eOZod6tE2edQp63N+ko2MlvYGoCBy/szSYaNTgYAee2N0eO5WiTRa2tu4XtJMlmOvx/rssuO/mxxlDr2thp7slnt2LNmVuZEcDIr1KwgMa9tBO4Ugnt50BeNtU5pJGTqtWwGTtCs2QTW9Z3RUlbNrwbg1cv5ujddu0/m7nSphjec+mBvz9/en+2GO10F87T8NVFjfnZikcemPPOm1Iauc4m/wdq2TjVtfb6a80btyM1WGVYjW3KsWZF9m1Ynuu6qvvz2txbWi1/q3K88+Slttbl80BWi63TK6LJamYm9T3PgZ1T7e+9Nvi9xrwlT2Dlaw/lanapd19+/oNzXnaBfP0kSyuAfCmJzRYTes2VUsiAsf5G7QEukeEFxanrtUaX0GCyt/Yn2VN8PfXCqx3TKdk4hKUmGqDxqKPq1Ho61upmbWJCm432iO0qXKGCRRkH7vPO61rqxyGEnSGEnQA+DuA3Qgg/zM/qn895sNuUokxnyVbdpmYSA1MTOHB0rIlaMDA1kdEYgMb6Rfwd6w2oQKmEbYTTUgDI7R7v63cjqEpzUIWt+7F1eEB+TRVidNHKluvs8Xt2AeOLHTJZtE0D/ZWXDmfZB14HpU8SbPVrjaZU1y2LmTCErcFQxojo1kDrJSgnSr8hdL0lO3GrM2WhgQggb2jYeit18mgEaDBBMxk2Y6dQw5uOu62V9EDZvX3lXNZ9kTUYPDYpTrxWO09eyr1X48J2mKSRz/d6jSjvfF57HcUugie7ZTIac0F2OU7NIqUcJr3+vE+21gto1Mx5mVJmovmd0rS0FpJOjcoGHUl17rwMHGs9z66dn6211QpWdhmwYGbcazJRFMzTbIU+l5YeTmfA1hjN1LIImkkmyjhus7leLGGdd8Jeu7Nr52PB8CZX777y0uFsbU6rh1UOWwVJU06P2gvjff1ZR+hUhliz4N5xPfBeUG7VebO1zbbOTaH6luM+f3p/FtxVx9R75vmsztQyNBXuXJRx4H4LwB8C+B0Al+vvPxFC+K0Qwirn9++ov/4/AL8K4D757O29GPRcxWytoeNhcmQPRi8fz1pN0/GwUTTytWksnD+9v8m4owGgDUxUIdKQ0M+oxDj5k9t9du18nD+9PzMUU3RUbS5iu/7Zdu8WNBq0RsFS8jxDWutH1AklVZS/IZTuoVSQV146jIVPH8LCpw9lEfJWY65QbMyo7HmZX8qxpfNy4tXJXg0MdVD4PWVd6ZA0YDWrrZk6Qp06BbMPA1MThedpZVePyfOkcWI73jEbZx02rWsjtZrUOpuZU8zWepZ3IuhAqk6jPNJ41SCCOk10qqhj7DzCAJuyKp58dmsWeFD9o9uQdqayq44jf6dUYXUWVe9SfxXJLq/B7Svnsv1w/bVUUxqVr6LSBGY2LOjg8Xvr7PLZnsvNH+aCEwf4tbyEOt3Up2TpTI7swfnT+3NzKvWtDWLwWdDP6dAwc0yQ5TK6aGWyKZVXksHz0CU6ihgHDPICzc3s2OzGBh30Gun6sPyr9X0eLZryahsUKSOImMuyW2F2UcaBOwPg22KMD8UY340apfLfAxgEMGp/HGP80RjjjwL4n3wvn3XVViyE8GgI4ddDCBMhhN8OIWyvf/4jIYSrIYTX6q9v6+Y4rZBSuP1HTrU00KYbVEBauwcgy7pphEwXz1WjQSfbgamJnAHgRYMJGgOMKNv96lpaRRExz9HUsaU6/Xm/XTC8KRc91sVX1QimkmUmjg0daDTxnDRazL8HL+zNrasFNCJuPNZ4Xz8Gr16cdWVclKmYbdkFGnUAOhnbLK1O/jRKaRDzuwXDm3LZ59FFKzP5VENWQdkl1VUDCKTBMgjhbWtr5XSMilayqzLOydyrq7Kf0aBRqjM/02wyaUs24EJoVFif4dmW3RQNiveJHfNmAymquMqKyil1Ao3Wi4+9iSVDzzTVA2vTKeosyjnrElkjx/URKeM3TmzL9qNMhouPvZlbW02Px3ErdVMbPaVk96kXXs2Oy/2RdqbHZlBAn21LoVN6nBrAHJvOQayJs00ieLzBpRt71i21U3AdwyL0smNiu2BwV4MLCnXkWQrA+3bjxLZazVk9SKuBL+rfgxf2ZvJDUPYp/1z2iPYG52xdomfB8KZM1gnPjrAyzzEU3QPLpuCyFhoETlFxKaOaKeb/NrhAfaDyyj4CHDevE7umVqiQQhkHbnWM8Vf4T4zxLIAnY4znAHxJwXY/XPKzdnALwA/EGPsBPAHg+0MIX1v/bn+M8bH665e7PE4hJkf2JJUBlYBGdWYSqojYQcpSZfQ3zEYxes+JV5W41vEobVDrJ9RwVTqXBxrXpHMuGN7URCXQc/HWN7o5cqxw0kutB8PGNHTeSKnkuTDSx2j5zpOXmiKLpC/R2RtatSunsJUewXEvGN40pwvcObbxvv5Zc+KYxVVjgk6VGm02wsqJXyOuBIva2ZjI1iDS8WZwgY1QbLtooNEunC8aIJbapufjUYCBtCNdVCPkZcNsnauXxWDwQbMY1oDQAAaj4cweA7PvvBVh3pInsg53s2UIqyF548S2nM5NQWUWqGVpuQwMZYzyyvX3+BmDCkAzVVd13+DSje7vqPMYlLOBklQWZtnw7sIgkF1aQ4NtXOsOyHcbpPx52QjPQOe2loZPvUydcXPkWFNXyNkAgz8pMFg5G3r3vse3Zg4E9R/gyy2ZJJQnZoF1/TgN/tpz1kCAzURbebUyy+eCAQyO0wYeFPocFF1/tVc04Kq6WxtkKfT4GjijztXPCXXsCBu4mam1GCvc2SjjwH02hPBDIYSvrL92AfhcCOFtaCwPkCGE8K0hhIMAFoUQflJeR1FzwDpGjPGNGONv1d9/HrWM3qJu9tlrrHjkgZwBOhtKmdErjf4q/cyDUtRsTQQnbo2aaaSXEWEqOkbJrALSZh50oBhVZnZAHTkvy2e57R54DCpwpS9qO2vt4Ka0MtstixQMrRsk1HEjOC7SmlLO5GwhZYCxo9p4X3+uBmCmYGsnlGJmqYpaTK6w15r0LzrRNAxo6F587M1cho3fFd0vlS8gP/nqgtgKraNQQ1bB+6LURRrwNkhCw9vSP22jId1Ol8MognZ8m0s4u3Z+Up8qxWrs2vVpXzxdoWOyTUaY3VDwvmlmDShuWqBGrXUI1AlkbSRlTZtQWBq5ZvRU5zJgor8vMuwV3EbnGQYIGUz0Gu3wf5txA/JBEUvvV2OYyy9oBqgokDhT6D9yKkmhn7fkidz5s3ZwpsHrSnlQp8oD6Y1As3NEm8BbH4/HoDwuGN6U6TguacSgkepYNgOzx/FA3W6piEOrdrlzn+pvbVCiz4xtquXtwzaJ0sCCBnRswIy/Z4CR2b/p6FZd4e5CGQfubwP4cgD/sf56tP7Z2wD8Tef31wCcB3ADwAV5nQLwLd0OmAghLAbwOIDfrH/0fH2Zg8MhhAcT22wLIZwPIZz/wz/8w14NxYVOYNqMYDoduttXzmFgagKDVy+6RgPQHOkB0o1ELJRSZhWbQhW/XQiYhohVvjQsGb3W+jKPgsaJ2Srk21fO5RS9KkV7flYZ2+wFFS+XGODERAOHmQprcKjDwY5e3Xaf7JXsqqPugQY/7xdld7ozyryPq9ftyC2HAOSzvSykB/JySGPY1oZ5/wPIUSJtxzUrn9YAUWMaQGaETI7syWTGFqZrTRCNH89Qs40YtHZOnQNv4XjrFGoN68DURBZY8WpTNAihzTB2nrzkro9YFr3UuUXUac1ua/BsunUukF/KQoM+Oh595pSK6Bm4RUj9lvvzvlc59wIelFvAnx/0mmsgqwjMTNNR1GCMDbyp7FodzDlB6b/c3mY4tDERjfFuMse9lt0ix2znyVorfl5ryu1MOHMeJdybczVjrA3SqA+VLgvkdS2fDaVY0rHn/gevXsxl14gDR8ey+2l1gC3poONInalBkVTAV+0a79z5GWme3vGBfBMoIJ9dpg7gsTRgoQ68ZxtVqJBCSwcuxvhmjHEoxvh4/TUUY/zDGOMXYoxXnN9fBPARAK/GGP+1vE7GGD/Xi0GHEN4O4OcA/L0Y4x8D+GnUljp4DMAbAH4icS6HYoyrY4yr3/Oe93R8fE4KKeXKbmrzljyRRTKnOwND6tDg0o2FBbt28qUx5xUwq7LV31uajEbvNGoGNJS70ixoZNj6HaWDWUUHNBtwqegqI1g0HujQenUc3ph5XGbQqGCHVu3KNX8A0FT7pq2t9Vp2W4fRK9nlNWwlu0DNeaDzW8Zo6xSsnwFqWctUy37CUnyV6qKOnIKf67OolEjdj25fZFyr3DDCTqNS779mmtW48LDz5CUMLt2Yk3tLRaPBO7poZWZY0bDV7bQGjus/8tmic1ZUi8rvu2mD3iu5LQPKrg2WDC7dOG1dKseuXc9lh5V9QOfD3jsgL1cexdF+RyglsGhO8b7z1mHjX1JsdYwedf3s2vlY+PShJqqqzgG8Fq+8dBhrztzKlmshKJeUTatDLY0y1QyDtFnWKxNF3QnbQa9lN3W/KLf6nOl8NF0BiNtXzmXyqWBQgXO91aekpKfWKONntg7Y3sPV63bkWBGq7/UY+gxo4MpeT953DdZ6/QCs7Hpzxtm185vqj/V58Jws1b+aaea4tJu3Mn10bHOJqVNh7qNMBq5txBi/CODdIYT7er3vEMIC1Jy3fxNjPFk/3u/HGL8YY7wN4GcAfH2vj9sp1IijciobVStLYVMFv33zilyWgrBOnS6ybR0b60zZ8/Fqj2xdBhtBqILXLBy3519971HlgLxyU268vZ42ksao3s6Tl5pq4HR/6nx57Y2ZXVGjV+s2bCZuLtLQykakX4yvY7yvv+OIYFmjY+zadezbsDwnj0C+KQgNPW1+UORQqqHqZSbs+dg6Nd2ewYeUI5eiXdqorudQelm0s2vnZxFrSzPuP3IqO386CtY4BvI0M7sOE1FEU9Zauel03NsFn91WsvVifD13T7l+Uxm0YyzTedPF5TVL4GUMLU2SASRFyohjJqII2q6f2xD6LOdoa/X3qr+sTOl52Vo1BQNca87cyrILvP6URdLtacyq8+UtQWMbl9hGMQSzfmWzhHMRlHEvyNkK7couoXOhBrhsppiOnTIVGJBSUM6ow3QxdX4O5GnPrEVX6LF0Ww26cl/qHCp7g9seODqGJUPPFHbd5W/5l3rXgwbDtKRE5U4X7iZUXr3g2Vy0GSrMXUyLA1fHJwH8txDCP+rVOnAhhADgJQATMcZ98vnD8rPvBDDezXHKogy1iIaajRwVRYRvXznX0nnj2kfastk2/LDOkK0jIor47tYY8Sg/Gi0juOCwRurUufOOx+1tDZSOxb63kbN5S55w983Pbl85lzkL/OzKwY80UUxtJoPNHewaSgCa+Oxn187Pfpe65rOJmyPHSsnuguFNGQWU59CKklaGskbZtV0DvWABr7M3SS4b3u12aWQ2TOWAi7tSFtVAUofNyo4n6wobJfYoql6NCGGvlTWY9LzGrl3HikceyPbPhgJ2iQDAl2OtndPvbAZD36dqZmcLuoh7GVB2x/v6W9KAtZV4Cqyvu33lXK7DrzpNKpf832Z3Vc5SGQaln1PGrAOoOHB0LFu6RLcHmoMW1ggHmnWeziscexGYZdHugmWhtHnS6G1TFZsxpoPIDsuahbZG/GyDwZeyYNMl3SYll5THVvAoxapngHyQ1crpsuHdWQbY+4770/91X0BevlVO2SxsdNHKpqCvJ8eUWQafrYNvHX2gmSWh0ICHFzDmdVr52kOlApo7T17K1nbUumjWxdpnzVKX53LTqApzB0kHLoTwY/W/T3e472sAfql+jHfIqxv8ZQCbAHyTWTJgbwjhUghhDMA3AthRuJceoewExcYcKUpYCmXWDlMqlBbMq4OiykPHrk1LdH+q0K2SpiGhWSxvnFoQrI6bNpEgrJK2hoN11nTysJkbbkfHQ8fPrMW+DcuzZjN6Lez9VEORUWd14mwkj8sOaB1MmaYRMw1bT+Vh7Nr1jILK69+O7KYmOQYubIMS3U6XmyDUmNHvbEaYBoJtwpB186sHFoDmjDD3ofKs36mMWoPHk1/+bvDqxZwsWQoyYYMP/UdONZ3r7SvnMG/JE5kDvmx4d64+UDOU6szRAB5atStnQLBOA8g3kFCDei6hzJhuXzmX0fLKOntlDWCFUg4t48E22fEycqpj1bBloIFySCfr5sgx15jltts3r8gWtFa9m3L2dBwqL6QsMpNms8kp3aE6mQ0YuA8AOedqaNUuVy9q8C51n1OZi9XrduS2n4t6txX7xpPD8b7ynYGLnDxvv172zGavVGZZ12jtBBvAULD0QJuGEbpG6s6Tl1zdy8+8TDXHo7rO1nNyH3TwNSOm4PgpuzqvHDg6hpsjx7KyCjsX6fw0MDWRW+fNNt6xDqS3xuFsNLKpcOehKAP3bXW6Yket/2Xtt30AfkL+7xgxxldjjCHGuEKXDIgxbooxLq9/vj7G+EY3xymLVgqZypSLYxc10CC4PyovTyHrZ1yzjJGegamJnHNko5haMG4NPf6GtJrBpRubeORUpjYq61HEvMgtlbB1+jQSbK8NDRwaAqosPbooMd7X7ypjXsOBqYms86K9Rh4svcfSJ9VR5sKmc82IABr3ppVRYOWVAYiUI0fZ1Yndg9KpgMbExv16tByFGg36P/dN+osNDDDSC9TkT2Uw5YDZ7wg1aii7XiAByD+DqefFOz++t87r2LXrOLt2fq5LmXdP9PjjfY2131RWbbabuqSojnY2UdYwp5FE48gLVhHt1hgtG97dVLdVdD89p43Qe6SUMAXlb+fJS+6zMTA1kVsKRo/p1SrZ92yeYeVFn0s+rzz2k89uzWXQWe8NIFfDqvtQmWK9F+cj60AqGBTTrBzHo4Eg3VbHPFfQSi/a35V5BtX+KAqwqR7Re+FRZe1f1be2YyM/V1bEeF9/05qvDCKRKsnf0cFX5x5oDqqlKOwatLJLpFCO6UDyM7vciGa7vFp9ywzS9wxi89h8jmgbaEAXQI6ZY6HLF7RDna1w76LIgTsD4E0AK0IIfxxC+Lz+bbXjEMJACOFjqNEZfzuEcCGE8HU9Gvesw0bHU2DLcH2IU4p27Np1rHztITcypuC6R3TaNGquUSB+tm/D8mxSpRIuMmjYUYrrEnm1al42cfTy8abMF51JS9kpmsw0i6DX2Lbp5SRwc+RYzghjRzrtTKdj0rGrEaLXRLNUGvW3aw95NRt2X53UkE0n+o+catlemwbV0KpdTQ6Th6deeBVDq3Zl92Nw6cYkDdgzrHh9KT8Amgxafa+Gmtfh0xa/870Wp2vEmB3UNJprO6fqfrLaN6EUKdSoSZ07s49Kp6ZTRqOCziiNEc2K3r5yLvteZVqPy/ushoPqAxpP3NZbj2sugY5CioJOw8wunWAdCA/qxKScOjbdsItHE97zzgCUdcZ1nPwd/27fvKJJru0ixvwtj2cdIYL6PPV/is6l7/dtWJ6Nf+HThzL58Nbd06AZ6edehoHziR7fBh/V+d558lJGpaZxrCwKG5iYa9ljoCYfqaCvXkt1pssELFTm2sne2OwqkG8OlqLPplgvpDQCPl1SHT/eI71P6hgR+p5zjH5GOfHkjMfQ9Tr51+qQ/iOn0H/kVG7eGly6Mbf2nccC4Xky+A3kmQ0WdoxsxONlKCtUKELSgYsx/mCM8QEAp2OMfy7G+A79W2LfhwDsjDF+ZYzxKwH8AGoNRu4JUIl6tShA2hgmb7oVXU0NCNawUJGlqJdexo2wDpV3fFIotXFCykHh99bA9bjzrTIudr8aPeQ5j/f155wxb9FsGgcaMdv62q9ln+m47TmlJlEaEtax47Weywq5Fc0RaM6OAenGF/qdbbVP6H2ho6C1bZ1kLHWBVcqx0m9S3dIsaMBTptWp4zkptNmInrvuj9i3YblLtwFqzpXNZlgsGXoGQM3hGlq1q6n2j80jbGBmaNUu1xnz7qdSj5jBmosZ5BTUcNWW80VyNXbtepatuTlyrNDJ45pd2mmUjjDpr6nj2ICXOh6a2QCQjUVhm1oo7dLT27buTvejbAaVYS9YsvPkpSwja+cEzj90PFY88kBOdpkRZGdZdQyVCq/HHb18POuuqqChr5R5rXUqcgbmCooCZ9S5GiDl0h4p1oPKO69jSqczGGZrZL39UtaU8ZKCZWmo/lcKri4NozrWy6DaAO94X3/OiUoxQVjCADR0mQbSbAkJ0JDdVM2ZBlJS10HljGO09Zr8zOs8ybHzObS01QoVUiizjMB3hBDeG0L49vqrbD/dL40x/rrs5zcAfGmH45yToENjI14TW9bnJk2vtTfrWQhS+oB8VmdypHj9GNuAgNvwb2oSs8YEjUC+10iYGhM0zHW/jMymQGWrBrBuwwlDsy/6O04kdG51YUydtJ98dmvWIMOLDNMg56KykyN7mrosptr5rjlzy+026S3crHVd1hCbK0jRNCa2rG9yAloFE54Li7PGA0A+ypqSXd431uwAzY6LyoiOiY67Hac9Hw000ACwz0RqouRE6u2b99TW22ltEOlC3H71uh2ZQasRYyBvCNsmA3wmtNHEwQt7c+fALJpm2fWa8Ri6RpGOQes19B7aJTfmElJ6jc+1/o4yk6rdUiNSW363olaS9qgZQZsd0KCArffU+0QHSZ051r1pMyjvWaSR6jErVP/cHDmWW4ZAx8httKMmgKzu12vapHjqhVfx1AuvZtdB5zPun86LZs68cQC1541On14n29yEKFpkeS6hiB6nS1BMjuzJUQGLoHoIgGuT2CYqtEGsQ8k5EUBOV6gjpPMCZddmkqnrbP2X/lZplABy6wXasTDDp+MnrKOk2S91DLUBmdLErc61dc+2QZV3DymnZEhYKroGeTVQpvMB678rVCiLlg5cvYnJRwE8jdrC3R8NIXxXiX1/vN6BcnH99Q8BfKK74c4tzFvyRFNU3jpvFqnJSzNZnIg1CmUV8opHHshFgy28jFqKZsXvvTFpFE6L5tUQtpQ40roUNGi9Y6hRkXI6OVE8+ezWzJHzQIV58MLe7JrNW/IE5i15Imcg2PNV6oONluvk5NHL7P/qVKozM5dAZ0knLq1hI7zM2L4Ny3MOsjrBGtlNnTfrX2yU0nP2iowya5AAyJwmNoHQ2gR2GOMkaRtH6H6swWAL9K8c/EiOruVRjDgx8zm1hg5RlJ23WQ2P8mS3t5F2a4DbjJuNCu88eSlbFmEuBiC0ARExsWV9LhBEaLYGyMvT7SvnMifVdk306IqE7USbYjQAjcyIGnL6P5BfgJoZLzr83AfQMCytvtUufPzLzxYMb2rq3Ll63Y6sdsgao08+uzW3XhXnF9W3dOoIr+ENdS7nAfs86VgJDdhYHWR/r3rYq7NLZa3mIhg0S2VNCb0mSvOj8a8La6eQuiZeDTv3XQQvq0zYjG7K5qCjR92t18HWkVv7yT4LnmPH39vAqzqYQCML2n/kVNO6rUpV1vGrnOmxb5zYlltH1mbfgLwza3V41YWyQhmUWUbgHwL4CzHG740xfg9qa6z9oxLbbQXwHgAnAfx8/f2WTgd6J6AoU8boklL+FEpjoSJO0Q492NqWVN1SmSilGj0aIWLGcefJSxkX3TOM7LiARoSUTiG3tfQc5ZOrA6mdzKgMUwaxnr/Xpl2vL1/eemcadfSiZ6r8lc6qxg6bwcx12HWBLCxtSaFUKK110IYxFpQtNc7KOrq2Tsej1I5ePp6jiTH7QGqkzazpWkUebM2FvS72OdPf8Lm3kVfKDK+BNn9otRyDjsczjpSGwwybR6X0sin7NixPNhSai3jqhVezZ1ifWS+zBjTkkUEuNc40yu5hxSMPNN03fm4/08yWpUi2AhtSae0jx+QFAe1zScPXZkKoO1UfW2dHn0v+z20V6rTpPKC6hOuaAc0ZlhR0SRrqUt2O58L7oPqWNXJzFRqMYft/rmmmGX+gOIigThqdn1TdGOEF45YN726q508t80OatgfVbaoTdb7XZiVAozs3qZaEDcylgstEq6CYBtI0I2j1uGVFAQ1ZtE3QdHz6l2DQhOOw2X9m5OZinWaFOwdlHLh5McY/kP8/U2a7GOPnYoz/Z4zxAzHGx2OM22OMn+t4pHMULHzlg6/GgCoZNicB/CwPoZkzqyyscrEF2zrZE6qwiyKSVglaI+OVlw5j4dOHsmyZnSjVKfOokHafPF6KJpIaqxexStEpvOxl1gVz0cqsU5YaSN7kbwuS6Tzq+Wg2Q3+r3P25BmYlGQG2dWJ0wjhxt4rGqtzbjFWrwvoyMkp4lBkNJgB5WWNtE4MQejz+Vum1eq76nDJDQNqhHb9S5ezYbC2syrENUij0mWdtkC6+nAKNsIGpiSyLos2O+L81Ivh+LmaNFSseeSAzgLW+0D6/vM5eZ80ig5f7IyVbYbPMluZuZV+30zHYZ8pmRG3nRY30p54XpaMRKsM2a2Wvl6dX7ZjZVdn7jjJMSqXuSylzKUcXaNRFT47sydbY0/HoPKrXS+EFNeYK5i15omku9/TSsuHdWRaHoJxxvrILWqvtYOXWq60F8jrGNn9KORhWdosCqlb3MTCRYg8BjYztmjO3co4ej6375otybhkIhCfr3nzD62HXtrXPM/dnm75wiReP7qv6/8aJbdmSLnw+5qqtUGFuoowDdyaE8CshhM0hhM0ATgP45VYbhRC+OoRwKIRwNoTwa3x1O+C5iOfC4pa1Ilor5WXggHT0X+E5cZwUqRyKqIJexNVOwnb/HmWQESbPmAXy0TpLlVM+uO5TDRVLM+J29n8q4SJKqM2ycJKiskwVlqccQ5uNA/KUHj2XuVpDpNBuixpt5CTK66xRRHsvPMoTjVs6Gyq7KcPAaz5TJsvVCior1iC0n6dgnTOOwW7nUcoAuLQzG8ggPLmx64JxTKntGNVXObbjJ7XTfu/pibmIyZE9GF20MjNodczW0AfS2S+tkfECcAobuad+oYFcxJjw5IKwDrZuozrO3hePmqh0xIVPH8oMYNtivej+8phn187PKGc2Owc0MmBaT8lxKy17vK8fo4tW4uCFva7OVtnTa5gaq5XldoJAcwGWGptiPlBPlGHiFAV9PYo835OyV+RAeHOgpWMTnp607CPvN0Bz5lxlz45Df2/fKzh3efXutFW0QQ4zo0DjmmgWzqP6KvicqI7nZ2y2Mt7Xn9O/KvftLm1S4d5EmUzaDwL4MIAVAFYCOBRj/KES+z4B4GOoUTB/UF53HVTp6WK8Xu2QrbWwUTJOvJ4hpmDkWfet9B8aE2WikKnsANBaMQPNWTT7G07qKcMl5QB6Y/TGOTCVX1dMJ3Jeu4kt691sGDOo9horBZDXlZ0/2fZXF0DW8Wnr8jtFIc9b8kQT796LqOpEmmrlbuk4QD4z7bXLbkUr8wIOreTJGhnW8LYZM+981bAnKAM6nk4yh/zMOscevEZB85Y8gRWPPJAZFLbuldsBaOoCmHIklDLNa5G6z7MNntuL8fUm/WgprN5SKx4tSqncqjt5DcauXW/KXul9Kes8pJx8O3aPrmvl2RrRKo967npsu289H/3fc4J1LGpgA8gtXOzNFdS/DNRYxoiXbWTTHmbuaBh747Ljm6uyS5mjvcA1Vy3sNbRyrh2cx/v63W67mrUr2ldR/Z3NaHlrovL7TgJrFilbwpPZVgEXDdAp1V+dLEI7gOuSMbwmtBd43BT1XqmVZFkUOZprztzKNY26U4IQFWYXZTJwiDGejDHujDHuiDH+fMl934ox/nSM8aMxxgt8dTHWOQs+1P1HTjVRxbyovYdWnSNVqd6+cs6NYCrsODz+u6XIeI6XQh0WiyIut22SoBFbfp9aM0X37TkTqWugzqxG53WSUoeF908XpAX8CJtOZjaTouMF5r4i1oLt58LirF4MaK4jKHKy7DVWedVlLWgAp+6b5+ha2fWceQtLe/WeQZXF1Pek+uhzXEQr4nhT5+B9T0PZixKvOXMr5/BySQQruyseeSD7bLyvP6OEqkFAY0IdkFSWWZ3KuUxF4znbLJTN6Hs6xBqspKulshzaoZX75T6LMiNWz3rUxCI5tkgFxFLr0nnOTspw9mQz9d5mds+f3p9l14iVrz2UC4Sp3mUAgg1PeG4pqPPNzCCApm6Veu5zFZxrgAaFG8hnf0nXVngBGr0mXgZt2fBu1y7R+k+uy6djsAyFMmhl69iMlPedzjNFGWvCayDF8Q9MTWTraFow8Eu9yGtNJ46URq8+nrrnxfh6rs6T+7L1jPa4QKMjuZ4H76dXj1ehgkUpB65D/GIIYTCE8HAI4V18TePx5iRshAdoPLg0EFmEn6JIkNbmTVCecpsc2ZPs+GjpY3Z8NpOkLw9lWx17v7GUMg/e2GwNSEpRes7XeF+/q5AVRQarUh/IzefvGYn26J9AenHguQK7Hp9GKYF8hJ8vXb+ME49mM/Va2wY1GlHXDFSRA10UiABq98fSAvm5hxRtzaJIvml46D6KHDb7DCo0i2YzOztPXkqur6fba+ZEx2PHpePX+6yG1FxexzAFPT86O9Zo5PpvKR3Bv971sjLIjo7aOt/bn5UPdaxsVtQ+a3wPNDtqRdk8btcqY5WSRWvMWwfWygx/o9T6myPHss7FRXr37Nr5ufUHU79V2aas22xoUTBlrmHJ0DOYHNmTBQJI6dPgkmXt0Mlpdx5LdZf0bI+UDlkwvClJaSwrizagktqXd3z7V++5ZyMtG96d25etXR/vq3VrJcPmyWe35rJhzLIVdYZkPT31r64pacepVE29ZpYRUqFCK0ynA/e9qFEm/18AF+qv89N4vDkLz0la+PSh3Gca/bJF8NoRzivWZ5SJWDa8260lssaIp0SsYZGqceumg1KK0mNrrJQWqoa+pTbq74FazYftaqULHdvsm0Wq2NsqZF04NJV1uxMMCEKjiJ6ceU78k89uzX5ru6da6ESmkUprhFB2iyhu3J86K+rgWyPCo0mmjA0v4NLKMClz/zm5pxw6LtLN/7nmIXF27fyWssvvPGPGq1NMveezSKd7rgcfgHz2wi7q62Xg+F4701KXevdJ1/VjXScNPf5N1cak9IA1YClrOn477iLaWUruLawTausGFTZ4qP8zS8Tj6CLhQENn8PqW1bt6/bwGHwQNXvvMWYdnrmJiy3p3bT4FnWM9R10zjktGWIYJ4Ne76fb6XVGwTJ0mbfZl7RoN7lkqtkXZoK/V2VZWi5gROj/wPfex5sytrNsrgGz9NgAYWrUrN4951FSFBtUokzoeb41OBn74P8+FNs6dUD9fYXZRyoELIdwfQljWzo5jjO9zXu+XfX5zu4O9GzC4dCMGphqd4egksXORQifKFC+dhoMqmPG+/sJJy4tk8b1SOAem8vUTRfVw6sikjFzdngaKF2Urcnq8Oh9L5eGxaHDZdZvaBZWpZim8iLSdzOgIaaR/rhrClmLj1VxZB19/a7fx6iko+7wuvHb2fmpHV+97hdekgw6iOkBlajn1Oy/C7GX12I21KOPmwWbJ7IR/c+RYIa24CLrfVq3Fi8DxFGW55wKY+fLOT9kDeh93nmw0Z2L2gY6HXRgZaHZ4tPGEZi9Upovo1wSNXjo4GsQqm0X2vudYrY71HDZbd0Z4rBCvZs1e3yIaZCtwTkvJqrcUgWb/dD8c71yGnicdCxs09bKJyrBZNrw7y9gxsKBBYM8Z5m9S3+lv9Lf60nth5/8iGeDz1y7rwbuXGkCwzmIrSq3WBaucM+Bj7aoi9B85lauVs1269Xqp46bnye84HmDu2gsV5gbKLOT9IQCvAThT//+xEEIvVhn8sR7so2O8dfOLTa2Ou0Wrh40UKP2fE6BtYqAKpRW1Ydnw7oxDXzRhpSI6NtsyObIniwLppGwXsbZZiVQmw1swmMdLGb2eI+c5bx682id1vsqAHHe9B94Y7XitoccIabvGcxEou72GzXQNTE1kdI+za+fnsrxeRkozGTrR07HTWjj+xhoYreTX3gMvOq/OUcoJ8oyHouCDt43NZhdlMXSsqefQyxLqd2WN0RWPPICXn/9gcptW1DReP81EkeqtaDez8dbNL2Ls2vUZMUo4ZtsZUXH+9P4cXZWGFYNivA5eXSxl2svW22uu+7NjpJNlm6YADQOzFdvBOzfvvnv6TwMyrWo47b49GU05nWX1H/WudRQ9UEctGN6UNTpR6puOq1vZxZ/9qbukRC+gWa2iMgOl2NkAEABcfOzNXOt7z0HTOVSzofY7bq9/Lazu1XnPLhFgl68AmvWdyk/KAUxl93T/Vu+ltvGWb7lxYluuY3g7eDG+jnlLnsgxWAi156gLOF96tc/chvWJirmcUa4wsyiTgfsR1Bbv/iMAiDG+BmBxD44derCPjnH/7RvZ+14+EK0mHWaFNAKcmpwHl27EguFNuTWfPEdicmRPzpBI0bSoEGxUTY3oZcO7szGufO2hLFtoOy6SeuBlv/Sc+Ftr8Otvu6EZquFM+gEpSATH2kk0NkWnTMEqYb3WPP7tK+cwdu06Jrasx3NhcdtjIsauXe9qe4VOLpZaW8Z4I1ivyWik1r3Z43mGhCcLngHhUYNojPK5URnQ90X1Gd733rlSnkjFSf3OA41/NWA0U6h6gUETftYOrebl5z+YPD7hZdfU2WBtx9m183F27XxMbFmfvbrBc2Fx1/tQeOfB63fjxLZcgCxlHPK8bUYtVdvmybQaZ9zngaNjuYxJ2YAFj1HGoPScJ60Ns8ejkWufdf5GgxFeYMJmUOySKvq7TuCVABDUUVwX0QY79d5pFvL2lXPZq128NW9hdi69cuS0cQjPC0jXOCq8uj8FdS+DkF7zk4kt65sy9NZpK8rgF1GymU1qFRSjbrMvfqd/vaydDQ7belFeGxt45l915ijrDJosG96NwasXW9YdW8xb8kTSiaY+8K4vv9fteO8Y+KoychUUZRy4WzHG6XD54zTsszR+63f/MBfZnImohqV6aNML0uzUkGYnudHLx0s7HzZtD6TrGhRe4wRdRJXjt7U+aojamrUFw5tyESaOj/vxjIQiWFqfx4H3jJZUfVJZcJmBFOykNzmyJzN82TUQaCzErJkqm5Uqg/tv38C+Dct71mDi9pVzGFy6MUnTVSfFg3dNB69ezMmGopUTopOebcbDa6v7OXB0LDe+Kwc/kqPDqENUVB+k8qufFWXedF8202Plmv/zM90vKcWk/Kmu4LIF3QQ6FB41kFF9K8tsbKMGRadg0KyXtR1s6MBnwRpuGt22108j9UrBVGPOAw0wL7hDeVU61fbNK7JrS9nlXzU47TO25sytplppL+vlgQ5dytC3zV08WHqlF6DjvhS6xMorLx3OuvqVpaPp8b0skl5X1tzZuUTvny0R6ET+VOe2ovl3Aj5b1DfWiePzCeQdbaC55o+NowamJtzukkAjMJmqkbOfUeaLHI7UPoBmByvl8Ft41Hd1wBhcTrF/vGNQ7m2NMT9Tp271uh0d6bwih486lYFyvoZW7crV2SqoPzw9VuHeRRkHbjyE8LcBvC2EsDSEcBC1xiRzCiGEtSGEyRDClRDCP2j1+w981Xtw48S2nj0MGhkpimi3AjNldFasITFvyRNNE5pXGN4qa6GTqTp6Oqnr36ImAPYYjJB60VC+p3NXZOB63wHl1gzzjJNuJt5UTQu/079AQ4Fz7TldRNzWK7aLt+YtBFAzFlsVwJeFdovkdaIBRdA4tfJh98OaDBoWHuWX8IxgL1pMcBF2vmegwx6HTWs4wZc1fItAmQWa75sarXp8/jZ1n/XZ4vVigwF+busp2oFtCEOocTw5sidr/67r9tn7QGN52fBuDK3a1faYbrzxxrQYH7b2tCyscWgd650nL2Xyqddr++YVTTqX15cUPq2R0yAEP9fnVqmedEbVebfrENLQtMuxeI4U4OtLuzhyCt611SCdZzgr1Z4BiYGpibaDVepUt/rOc86BZofOo1yXAXUu0MhK9xL2PJW5AjS6JQKt14bz9qsZn1asEutoq85NBX5pr2gg2MvuKlJya7tfp7pVW8ZEUc2yXke+NFh28MLebMF6O5bxvn63q3cZFDl/HmWef6l/1TbrZRlGhbsDxavH1jAEYBjAnwH4WQC/AuD/6sGxX+/BPgAAIYS3AfgpAN8M4NMA/kcI4VSM8X+mtnlr3kI3W9MJUs4b96+TRmrCHJiaAIZ3A1PNXZMIKl5VykVdvewEN1l/rwqZv6PTY5Uf/65etyObmFe/VNtOjYudqCnCZcO1RUlHh2sRo6FVu3I0PJ0Ahlbtwj40jFiPIqGTlhrJNaOkdWv3V146DGzY33TsoutWBK9OwFJQvUilbdFP2RjvYAyf+txbHY3dw1MvvFqLLDvfUSbXMLuEhtFn79V4Xz8GRC7PmvOn7HINQyBNk/KoOZ6RYqOcOmFTVjjO1et2YCe2Nkd0659x6QHNwln9MKnbGeNgJ/IRXh5fn2Ev46fPRna8+jUnxvtqdVk0rF484ly0AvDeqFyqPgBqOmF0eCKTU47l5sixmozW22K/8tJhrKlfszVIR849xEXvA1B7Rraju2weUG9ewn+Gd2MczV3nFJMje4BVuxoZThk/KXmjl4/n9vHUC6/WqKh1hsbNkWNNcquyX3ROuedfPrdjuTlyDBAZKlqqxQYO9DfM2jJzpPfVIkUfLoOipTh47MkL7evceUuewNi169m1tVCHm/d+YGoCC58+VPvBhv1N5zXe1w+MHMs9X2WgJRe9gNoM9rnUe8S/N05sy2wDzmmWtjiJZvlTm2G8rx/zRHZ13VqrX71sqdodHmNH/08FyMo0uPHkqCjTnPqsSJYps2fXzsdk/dmw3VMzdhSKKb0e+o+camrG1S6DIWdb1B3IyomroAgxTg+TMYTwNIAzMcbPhxD+IYAPAPhnMcbfmoZjfQOAH4kxfkv9/x8GgBjjP09t83UrH48/+59+I1OY7T6gCstLTqXAU9+VBceox9NomtJA7bIChGcIK3XIi14pihTw+dP7cw071ClU45gTFBWmNUKA/NpIWiMENJQrnUePQgEg970uP9CJ82YnXA9qIKWccO+zlYveeSHGuLrsWP7cV3xN/KNfOwoAXcsui/tT67CVrR1k/SHPXx1aHSPrLmwUmNdXKTtFNBSVMY4xlSkGynfGs+tQWUOFcqsUR3tMz8G1AQg6jQrdRrMufK4OHB3LFl1vB2PXrjfdE8CPptt73eo63nztSGnZHXjonfHEh57MHacb2dXzSj17FpMj/hqZAHJOjmLFIw9kxwJ8nTu6aGVTUAxoXvPP6gXVcR4oM/qbVrJMueIzSaSCYapjrS5VPW31t8LuG6jp3YMX9jYCD13ILo9RpFv5uzVnbuXOxZORyZE9+Nqjv1hadr9u5ePx0s/9dE+CgM+FxU26TZvYFMmv1XMDUxMYXLoRVw5+BEuGngFQy+6qrNJJs3r39pVzGc3fjsc6cF4Asgg2mFYWes8ItRP0N01BtAKn3OpivX6EXlvaT93ILoOVqSAkg9s6Jj6zg0s3Ztdc3xNvW/oNbdkMs4n3hC+JfwMPz9jxPoxP3jHXplskKZQhhF8MIZxKvUrs+x/VnbcPAvgWAP8awE/3auAGiwB8Sv7/dP2zHEII20II50MI5z/3mc9kn7eThfMK+C3/3kPRd2XRqk5PqS2EGmspx4OfW2XioVVrc3UGdZ+p/VkaRSoDqE1U9PMUdp68lBurpXS2C+1ERxokmxMo5VXranhci1ZRSg8quw9+sSYHWktXBrbhydi16y3rA4uCEQrSv1jLo9dkYGoik11ef32GNCtHKE1y2fBul9pnDboiFC1Gr7/R1thWVnaevNRUu+RRzPidXidrxKjzZik06rzpmNsppFeknKSipgSebGrNCevyWkHl9rM3vpB93i7zwQbJ1LDX8/DogpQPLwKumQ6Pkse/rZ4zz3kjdd7qBB0TUKwHyuo7r8bn7Nr5uU57/HzfhuW4cWJbE4XSez6Kxu05+6T+nT+9v6mzcrdQ51EdYK1ZAhr0Q25DUF+WpT+q7P7Jm7/X1DClDFJdV9VBWjac7nDsjdXOpaSQs9mGdgJuhVRdoi0BmElwDmENqNYxWzplKutLWL3LjHRK/xy8sBeji1bmSh06cd6A4vmZ9Y4KfcaVsqqsCGDuL41RYeZQVAP34wB+AsAnALwF4Gfqrz8BSjG/vlj/uw7AT8cYfwHAfZ0PtRBeR8um1GKM8VCMcXWMcfUjD35p7iHutLvPxJb1ucm+jGHSLW3Ta07C8c9b8kRmZFpqH6H1WNbQBprXdLFOmO1KaZssjPf1N0VxLd2DvyljWOuxgXxjCsKrzfMiyt3ULqx45AHMW/JENlGyOYGF5banjK+iicRCZfftD/15AO3TKWwNit5vvf/eeG2k0o7b0qiKDD2bBeH3liKsv7ewY7D0ZCu7dpypOgubOdFj79uwHINXL+b2rc6avW4aSecxtK29yoa2jC/rHLUD79pa3aCGpM0wev+Xgcrtuxbelx23neDDc2FxkwxoRlFhnWg1erznxQbfSFe1x9ffF3V+JRWLzgTrC3WfKrcqS2WuaSqb4Rm4C58+1LTmFI/hdU5NHV+pvjp26wylaPBAd8EH22EZgDtf8XMdM/8vqqtNQWX3HX/2VjJzXQTrvFN2bEZFUTQn2CAtZbbVPMJjWSbJ9s0rcvOCOnTbN6/IdcDutAEM0DrwS6h8W9aN1m2mAkz6ngEnhaWn6jn1opxG0X/klJulVbvM2gcMhNIus8Gg6RhnhTsXLSmUIYRXYoxPtvrM2e6XAFwF8BSAVag5gR+NMa7sbsjusdqmUK5e3h8/+vP5YpJ2W8YDyLXiLeKGK9qlU+rvlYp2s16X8vLzH8w5cPxOjQby3fmXipoTK39fRJMoysxZA99+5n2Xyu60ovCxHg/IGw0albW0TKVFdEp9USgtxZ6bpUWlOP38vF0KJek8im5l1xoBNPAsBauIpgjkGx3Yfc5b8kRG2+FxWecC1Iw10ou8pjGUXa8w3zsHL9NpJ3+en8qlV9eov1F4dXcpCqU9tpVRSxHS/XOcndxnhSe3QHndZdGO7JJCqSjTVMHCtj8nWo27lW5RPUIo7fq5sDgzdtecuZXTu6SisTmJzaCwJsZSi1Nj4HvAp4d5+s07Ly/wYp9jYvW6HU2ZYfs8233qdbL0U+tkd6t3ed+9TKqXefVocjrediiUSv/thopvnf9UY5dWTpkGwVK/43WiTaA6F0BmB4xePt6k8wieo6V9lnn+bCC1HRp7iubbTo2m9zxbWGqtOqzafKzTLByhzz5xU+qK7bOs+sPaagDakt3ZRkWhnD6U6UL5nhDC+/lPCOF9AN5TYru/iVrDk7Uxxj8C8C4AP9jJIEvgfwBYGkJ4XwjhPgAbAfRisfFCKA3MixDzrxc5KWNs2N9aKhoVpC5SevvKuSySfnPkGIZW7cKy4d14LizOlHH/kVN4Mb6ea2fLfXoNJLxIZ2qs9py939proy9v4rLj0EicpUR4v+Hn3VAoLfqPnHLpJ0URbR1jOxORxac+91bXNApO3ilDj+t+paCTaWo9Q2t0ellupQXfvnIuM2hsx1SVXe3EOrRqVzZm/rV0ZS+TbDMeKpeakdJrYs+Hv+d18lp/pzLDCv7vZd241Emvut55UeF2nbdWmeUi6LXt9FnUsaaui6eDUgGjds+FzgLlmdk9Om+2/pjXWx3VMp2JPZklKCv7NizP5M/TrRYqx5Yp4DVDIZ3XZtTtSzPq3J9mv3qhd/nsA83zbSpI5gUAu3mWdNterGPIjs1Ac+2xHs+eA+d/0g1bPbd6/W9fOZeNXQO4qWwlUHNgtHTAy8apvOnzpEukEJb9oNRsIB84TAXE9JjeM+yxQXTctnkMofR9nnu34AL1FnYusA7nsuHdGdun3WU4Ktz9KOPA7QDwGyGE3wgh/AaAXwfw91ptFGOcAvAHALia7C0AlzsbZstj3QLwPGoO4wSAfx9j/O1299OuQuYDpVGbIhQp2VYGhDW0Sc2x8CY2fmYXgtT1zTT65Dlx7cIqTf2sVXaSxo3NVKa2s79To4b/cxy9bP/8Ynw9aymtxwbyE4rWNPWK/uDV1bQDTgyekZsaozX8gEZdFNB8bnrdCX2vMt3quthnkwvUkiZl12dqFShodUybfRuYmsja+et2lDPrFOoak4RH/QXydU5eG2s7pl5AnYmyzpvWUHUSgFj48MPZ8fgsdnJOfOaUMWB1BpDOCKhhP97Xn2stzufKOh+3r5zLltqwwQ06bAuGN2F0UYNgwnNT2dXAj1I2OXalKnoyTKqtyiO/T8Grt+P9W71uR0bdtb/xoM67RxkemJpokvHBpRu7zhwrbAbe3md9Fm1gx+6jLOKi92WyQCemV8HAMnOCpUxqmUKrzFZReQhlcWBqIqv9UsdGM4aeI6POkI5T74F+ZqmQfJYo+8qI8BbntnJnKbP8PvVcaDDBa2bEAOJ01v3pGLSpld7Hoq7LvZK7Cnc+WjpwMcYzAJYC2F5/LYsx/kqr7UII/wTADwH44fpHCwB8pPOhthznL8cYvzrG+FUxxpEy29DwLorkFsHjjtv9cP+tHDRrECn9SxUS0MiyAY3aGS/KyOjyzZFjSYV04OhYZoCogrBGd5HB2070r8z2rWoNvHoMIN2CuZ2xtAtGJosmYV36QCe6Tp25r34g5CijkyN7OlqI/sDRMQwu3ZgZxFqX5NWwFRntngxyfDdHjjUZ64NLN7rXwAuGMPro1R4NTE1kY+a2DAKUqQ1JwcqJDSJ4+9XPdO05GrUabT67dn4u4+ZlWjzaZS+hmcxWwRE7NjoQnYLX98DRsbYDZ6OXjzfpW9KginSRXmOgOfNEmpdmkFK6ibAGrPc5HUCChrDKbhFSjASgWPemMiSkk6sMWgoX5xWtbU5lOLhvzXbRGGWgqNMacw/zljyRM4KLGCJ27MyWdgKe2+DViz038CdH9uRkjlk1Ohla62bPibrGo5Fa+fSCJvyMmR5+xmcs5cSlbB4bfFCdaXWI3Y7gIts2C63gWog2oOjNRaxL9YLBOv41Z251vOZbGWgWjtfDW+9O9RlZUtqvoEIFoIQDF0L4HgB/C8DK+utv1T9rhe8EsB7AnwJAjPEagHd0PtTeQ1PSN0eOdfzgquIDfCqEV2ht4dGtLA1AnU1SSuzkpBE0jYB5x221QCe3KVPsa/fvGfTe9qn96QTdihrE8VnqiY6F0cVeUF+84yuKnB2dTDt1MAamGu29h1btatvIT8lgK8fFox8CzcaddXR0v6TwelH5wasXmwq4lUZiwVoQb7FZOnJl0SozrH9bfa8R5vG+/sxps3RffsZtPOzbsLyn9F8LS6uzUW4vG9Tt8YDGfe0UyoBoVeRvdatmMIA8zYv3jQEwXbh53pInspfqWd6f1PlYObR0KE+3dptx9epzz66dn8tcqgNJ51WhmX4bZPAyIXSIrTHaa1gqpT531nnQzKZu0w50HbiBqQkMrdrV8VxCGbE1ZV69ljrFQHEAyToulEmthUs11iC0+2LRwuvaFVjhBbra1R0DUxNNMmSZDrZpj+otPYZ1alev25F1ZCU8BoLOOb22F7ygmT5TNuhfOWy9RQjhcAjhD0IIbjPGEMIPhhBeq7/GQwhfDCG8q/7d6yGES/Xvzs/syJtRhkL5F+T1vwH4EdQcs1b4Qqx1SIkAEEL40g7HOC248cYbyS6C7cAqMF3PBWiunbHcbQs1njyjQ4+pXHY9HvnsNGomR2oLavOY3EYXmvQme3u8ZcO7myLGVsl4UWv7mTqF7fw+ZdxotFC/s1TQogmpFyiKUPM7UnD4vlOowbRvw3KXTlsEj1qSOoYi5Sjaxgup7ZmNUDpvkeNvx6Xb8v6StuY1HtF6jSLDrYima7dNZV0IG2Tg/zbAkKo3VWNUr+fg0o3TEnzwjqVQ2lZq/GXw1ryFrp7opLZDHXpvjSpLGyxyjvU7a0SfXTsfo5ePZxFwlT+vVpLQzxhkAPLL0Fi9xc/4alc/2KxCyinU/bJemtk2Oq42k6YUTi8LRNhgRJa1Kllm0C40U2EdNxuE6CYj/9a8hdl+x/v6u8o+axdo7lPHmHoOU4HYoqwWUGwveONSx3L08nFcOVgjTnlLKHk6oOg6e/rV+0wDW9rtlPtlzZxHU9cOq2fXzm+qx7PH9kogCHVoew0b3OdzyLF7Dn0ZNleFljgKYG3qyxjjv4wxPhZjfAw1BuF/iTF+Vn7yjfXvZ71RShkK5ZC8vg/A4yi3HMC/DyF8GMA7QwjfB+Bl1JYhmBNY+PDDGFq1K2cIdwJrMHoRTAsvypZqfuEV71rjacnQM7lIl65lpWNTaDc63Zc6egrPwStzzawjpgacZtgsjVXPMzUZpAxINay8MU6XQrYKlx2m+N0rLx3GeF8/Ri8f7yqq9ta8hbm6up0nL2XNPNqBzRZYOfGynSnDReuj9DML6/xwsreRUlts7o3P/r/w6UNZJp3X1yv+TgUI7P9KYUkd1zp2kyN7Mlqq9xtvG2tg83s7iU9XXQbvsxcEsNFqHXu7DsbE5U81Zfg6zcKpYdWKPmmNsiKqXQo2E2Ix3tefW76F0CZR2oCKTifPfeVrD2XXVhtFEEXBAjsOyqyOgwEET49bGVQD/MaJbe66YqwFtNcyxY4YvXy8pzRKdYLVseE5LRjelMtoKDqhoX3qc2+5z0G7c4kGHUYvH2/K/qSYN/a9/ob2QytqLceswQelI6dYDtwmhZWvPZTL6BY9KykGTdFvGTyxQS51zngtmEnn54NLN2bfWWeO+9J9A83zVq/1rt5za/vxf73/vLfdZuQr1BBjfAXAZ1v+sIbvBvCz0zicrlAmA2cxhVpNXCFijD8O4D8A+DkAywD84xjjwQ6ON22w0Y5OYCdrz1jwGmoURVI8J1AdASo0wjPEiyK4KbrosuHdTQa6p2ztZ2Wc1iKsOXMrU6S6f3ueQD4rqM6nza4phXQ6oVlMnbj0Grzy0uHc0ge9oESwXqfb87PrABFl7rvC0qpaOc56rydH9jTV01hYo0vlgIvN3jixDaOLVjZ1AbTPR8pYVdhjpY5vf6PUGx2Hl7WzNSR2HF40uJewRhn1F4MNHo1S0a4cv/2hhwA0n1cnmejRy8czA7jdcRTRYPkMt9qnF3BSg5zQe0vqIlkS6gTcOLENQHNjhaJMpw2Ied+nMLh0IxYMb8ptz/81W9yqhooynjIwu2W4tILOATR+uY5WEdul3TnqT958s+m4nQQfvGCSwmaEU/qU9+ns2vmZM01MjvgLluucae+rrgmr+xnv688Fduz4Dxwda8pqFZUyWBTJL5HKPAL5ZlEMdrG+k9lzABmV/eCFvbmurd4YlYrK+9tL5oPVHWoTci5U2eX300WhvwvxUAjhvLy2dbKTEEIfapm6n5OPI4CzIYQLne63lyizDtwvorEo9jwAXwvgRIzxh1ruPISvBLA0xvhy/WK8Lcb4+S7H3BPoOnBrztzKHhKus1YWbMeeWk+NitbSAO3/XgGyxcELe3MUTU66ntHgOT52HPyd/au/L9qfomhCpEJKORs2isbx0cn11uqyY1CKTOo8xvv6MbpoZZaV6UV7YCp23pfxvv5sPSU63tppip9rE5JO14HTIMC+Dcu7kt1lw7UFiGkQePKr1zHlrFoqntJg9DlJ3R9Pnokiag5papQXKxv8jT12avwpWfWcmDIOoX2GbCbLPm88F8oJ6XbbN6/oyTqGQPr+qz5MPZvE25Z+Q2nZXb28P77z7/xM7hgDU7Xut+08i0qfVYdHHaQinWuh97sM1c77vkhfttK5WnPEZ5q6nudk5YXzRVEAJ3Uu3vg8mqX+5XF1vJQb+wzb50mf6V50o+SaZJ4+YbnAKy8dxo0T27Dw6UO4cWKb+3y1o3fn9T0Uv2nvf8zOSZ/JdmRXG4J4ulDhPXsWVicXzf2KVveY73W/RQGkVo5s6nlQXWPPKYXU/K7zF485uHRjts6dnp/OFfo/0JgDlgw9kzuvXujd21fOZfMcx2PnKl4TncusTdmuzTCb8Nb/nE6UWSMvhLAYwC/FGAcKfvO3ADwTY/yQfPZIjPFaCOHLAPwqgKF6Rm9WUCYD9+MAfqL++ucAnizpvH0fahm4D9c/WgTgP3Y2zN6DnHagN1kaUmWUbjK4dGNWn1OU0Rjv6y9sBfzKS4dzUSMvik+kjIcVjzyQNTzhcbVlcBm6jn7XKhujULpfq22oRBc+fajJkLbOrq23Iu2S58/f8Rqo89ZLsAslOyvqGk3k6VtHbvW6HR1n4lhQr9G5bsD7f/DC3myiTtVoFY051YzDdjkldCKbHNmTa8GuoNGnVFsbReY9t2tiWSpxqygmZc5r5MEx6/j5V40S/U0r+iczMZQfey7WeQOaFwPuBNq1NLWeIQ2IVMOKdnHjjTdyzQh6QQ0dmKq1P+8k0+PRfu25eZknL9tk5YxQh4X6yGY/7PmTZaGw9XaqL73GM3ou9q/V91a2vWNbxgepd2vO3MplmvU66DIHM0H/4jHOrp2PJ5/dismRPZnO1eeyk0zG2x96qCk71uk8YtcZIzyd1Sro6TEFCFuCQJvEYwPoNrzflF1mjL1zAJBlnrVTYgo6XnVUPNnli5lAT+949F7NNG7fvCLHjgAa8k1dRPaMrYXVc+yVzcB5ztKb+Xe8rz+nI8sGCCtMCzbC0CfrzRgRY/wDAD8P4OtnYVwZyjhw3xZj/C/113+LMX46hPBjJbb7fgB/GcAfA0CM8TKAL+tirD1FLxZDJrZvXpEZ74Bf+Fr04LFzl4VdIJjRGuWbA+nJkcqbipgLfo/39ScjoVTgKYOkzORnlbHXKUrhTVLKZVdlToPLZk/oJNpasJ0nL2V0kl47b7Z1v9a2LRjelKvDsGvaaJv5TqHXoxOlbruwFhVs8/pbYy8VJVZKpU6eGim38qsTJj9LyWnRsh36t8y52DV4bPMg0rLsMQil3tnjaH2WNzZen/G+/ozuY40ydd6mo36T65tZA4bRX+vU6jPZDuKi9+X+79SgnrfkiUxWB5duzPQvsWB4U04f81hFdS6tnkUvKEaoDOp3VnZ5fYvuoRrwnRpqRc4codF8LyjBeSB1jymv+zYsb7p/dPptEGhypDcdgO3yPeqQqnF+c+RYNg6g+7UzSdHU+9IJi0Pvv9d8iygKzqW+s/dP9W1qHBpk4rXUoJn+taDu0OPb/XnwqKHqlNo62RTN3WMO6Xd229SzqvrBOtG91rm8ZnYu1LHr9dF5pMLMIITwAIC/AuAX5LMvDSG8g+8BrAHgdrKcKZRx4L7Z+exbS2z3ZzHGL/CfEMJ8NKiYcwJlMkOtYDs36cPOWhjlpKuhoZOnl4GzRmNqEgb8WglvYlWkmliUzcYpdEKhQWwjlgrPgFLet5f90ONo5NxSdiwdtUxksBPYrIR1arQOg2Pj2ku83504cdrNr1PnzcLugwZyqrYFSDfooWPmFf0zm+RNiq2W8SgzgVmjvQw0M8q/nux6WTnr0PIv6zrt2HRMer7URTQ4NEPLwEMvDYmBqYmmmkRmsA9e2Juj31HGbG1Ou/rz/ts3cmsx8fp0SquzOpcOna1NspRKoDlbzHO099Ojpw9MTeQ+ZyZKz8lCg3texpGf2Wtjg2eeTNuFyImUbrGULa1j4/96LP19mQyLl8nrpexyDuX8yiAHz0H18ILhTbg5cizH2ihTrmDx6IP3Z+/1uewWL8bX3bmbsPJkg6DWKfXkRv/3nhmC4yBVUJ3tIl1qs0kpGVFHOgWPfq/71uNZGqJ2UC0KqFHOrS4f7+vPgsPe+fYi+JBaAN6eUysqf4XOEUL4WQD/HcCyEMKnQwjPhhCeCyE8Jz/7TgBnY4x/Kp+9F8CrIYSLAD4K4HR9nexZQ9KBCyH83RDCJdROckxenwBQRhv/lxDCbgD3hxC+GcAJAL/Ym2F3jz958008+ezWLNphoy7twlJMgHxhMF+ccIpA41FfVDa6aLEqfc/45XpFBKPsNlOo/HbrxBUp4lbXjAZeilrnZeI8w8oaNfxct7cthb06HiDfBaxbhczoM5ej4Pm+8tLhzKizjUYob0qzbBef+txbAJDJrUZMO0HR9pZqZZ1UL/BgZYYG07Lh3YWGnPL8ucahxcDUBPqPnGoynpQqxN+p85mivA1MTeTorYTNLOqzCDTTJ5X+5NEtPYNC909dxO1YK8lrYs+3V5mM0cvHMbRqVyarQ6t25RpsqCO6et2OLAhkFyIvg9+5HpuCSJ1Q6/TcRy8fz2VBmI1TGfQyqkXNITzYcer/2zevyM5L14kjVNZejK9netfOD/rbFFSOUrqjSKd452ezxNYgV3nW7SdH9mTXz+oJz5HgOXdLAbbLRqxetwODVy9mcxnriJiFG5hqXm+x3cAZgw+KXjml6kyRLkjo82edGw2uWIcEKG6cBOS7Ti4b3t20RpyVA7uItwYdihhGyr7QQJAGHhT2efUCCV5Witk/PiM2wGop+Hxm7TVPoZc0SoILh3N8tOMosx6qTFx3iDF+d4zx4Rjjghjjl8cYX4oxvhhjfFF+czTGuNFs9/EY48r66+tijCMzP/o8ijJw/xbAhwCcqv/la1WM8ZkS+/4hAH8I4BKAvwPglwH8w65G20P0L300pwSLomApcCIiX1wnba+7H5WORq+VUgf462nZ33L/NpqrhoBn/PYfOZXrTqVjVCqEpV54mb/UhE1YyqDCy254Roet71DnQZ0lu43uL9Xxjsq4G0N4dNHKTAGvOXMLTz67NWtgMnr5OM6unY+Lj73ZRM1gpLCbtYS4favi8hQou15TDLu+ltJQUveTY0o5Kdaps3LLvyl677wlTzTRWlrRPe2+CSurdEQYBGCHT1sj5Rn7atzqM6HGAb9Xx0GNQQ022CyRDQyp0dWNIcznWoMMpC0DDWNLZfT86f1Zhq4TI0KzGKlMQRmowWt1rkIdnXazLtQ3nBeKxtnqHOyi0xyrzRKWRUrXevDolDye0u20jo1ZUj7P1kC3z5Y6fdzGLj8xHUtgDK3ahaFVu3DjxDYMTE1kx1Dj3AZ1mF1uN3D21ryF2bw1MDWRNcfoBi/G17P5R5sJAfn6TOuM8HsrM5Rz62Tzbyr7m5JfLlbvOWbq+FkH3zs+kQo+eM6cV3ersug5jWqb2GyaZoZtZrssbbkX9cecU6n3WW7hBaqVnt8ttbrC3YciBy7GGF9HrZbt8/ICVyVPIYQwD8ClGOPPxBifjjF+V/39nKFQMothJ6+y0BbcqrDU6NXvdbIEkEXYiVZUQyo2rl3HSI2m5Ms4oGqs9x85lW1TlNrXrJ93HFXK2riDhoU6oJZquXrdjux7KiZOGikam0IzbztPNpZb4H5tsbc6r72gpR28sDc7L56z1gjxmtrzKBv180BDuFMqT2pNH7s4MmGdCpv59H7H39rv2PIfyNdclMlA0KAAasaP1jSqMamTXmo/QF6OSW+147eOnEe74fF4Lrag3qKI5mYnadUdmrnpVTSYjhvPkcYEz0MNQHtvOtGbNEbVOWoHE1vWZzJkGylYFDEEbEZVAxTUPaxPBNKMBDUOi7Lg1nnz9sHfFekFz6im3qEupKxaKrBHS1dnTOt2CXUAizLI1tFL0ed537o1hMkm0Rb3RSUFfM8a6U6MYN3G1n6VgepdKwO2o2cqaERYuSW41plXTlFUMpGSXdoI3rmmHD8dk70n+h0X4/Zgs6V8MdPI68RxqRPEjpJar2hrF4F0Qyp7DkD+fnUa9J3Ysj5jCJCuqZRTq69UF1TOWwUPrTJwAHABwPn63wvyfxIxxtsALoYQvqIXg5wOPPrg/V0/DJyMyuxHFTGVrBdtouLRegZOzkDD6QHyxnA75+K1w01NSNY59X5jj20nblXSXjaRnyvVzjMW1JlllsTui8fSa1ZEmemWSrlseHeu2xqNJhq3qoBtlqYb+Rvv68+6Ni4b3t3WOagT603C6hywyxyddy/oUDS5WCdOnVrPKG5FBU1lC5XW2woTW9bnaHbW4aNBzLX7aAyr7NKIsJFwL3ij8CinXpDCi7gWLbTbDTxHLFXHkmrAUAYTlz+VrdGk9SvtPn9WB01sWY/JkT05Grk6oEXZY8Iac+rgcN1M1Ut8r3W7RbD0NItUwMGeqzoB6mwp1Z5zhwbQVq/bkX3vBR6IA0fHmhw269B55+tlSwjeFy0t6FSO7bItqkts4w79nCBrod3gGTv/0jlqt6aaMupBr4U6JZ4DbO+FBnhUFw2t2pWj51oaJQMeZc7Byq7VuXotrVyk7AX+TgO9BOXX1nTaY3nQ/QINpoOWYqTGapG6Nt0Ezyj7XnDJOzfKQydU8wp3P5IOXIzx2+t/3xdjfH/9L1/vL7HvhwH8dgjhP4cQTvHVq4F3CypkRasmCkQqgwGkFxymQrHHsLVu+luNotLwo2OiBpCi03oorU8qUsj2WPrXKqUimoo1ZL1MjkZ/7e89Cob3v/1cKXXdGsO6voxHMwWQo0YAzUs3dOLEfepzb2FgaiJr39yOch+7dh03R441nbsuSK4Zsn0bljf93l57e+6W8mOpsIQ1vtqFV4hvx2QzrwQNGHtsz7CwRoRmeb0MhCLlPNA4scEFRmYJNcpsIwKgM0rPxJb1OdoQgxDaVluvi2be6fQPTE1g8OrFto77ga96T+5/PgtlQdnlGAlrTGtgglSlojobIO9A6XtbK6eOnVLc2nEI1Bi2jrreF0L1rKVzau2TbqcyyjmDAQjSG0l11PFv37wiVyetsAwKSwu218kGIKyj2Ck8R92DlRF9jjrRu6QPLxvendWgtxt8KDPnsMyCKMrYE0rDpr1gqeQHjo7h5sixXLdkflcWrOH0KOk2e5SiHtv7xWUfAL+hFGEdOg0G6nlY6rt+pgEM7qPI3umFnWDhLdNTRAfVztqVM1dB0bILZQjhP5f5zMGPAvh2AP8UjXXkfqLdAU4Xfud6zBQAJ5N2KUGWS01jyotOpqgoKeVMKh7/audCQqPMkyN7WkZ5PfQfOZUpFJvNs4ajrR9TZ4SwxrulTFpFbWG31aUU1Im1NXCp/T357Nammge7vksnxoRGVEnDtfddJzZey8GrF7FseDcWPn0oMy7bxaMP3t8x/RJotK7n2IkiZ5CF1oRmHQm992oE8h4xSEGZUflKNS0pAovqt29ekRl1aoRzLNYA1vcpShxlRuVPZTC1bqOlAXN/Ss/0os6E0oT4W6VnKrrJyNmMJbPDvI6q2+gQ8TnpRGaBWh2R6hI6gO0YkN61SNGehlbtwsKnDwFArmmQBsQ0m2r1hDpBPI59BoDaGpsrHnmg9DkADSfOe4Y8h9DSAbWbsY6ZxrCnZym/WmtkAwikUurx9Hu9FtyeDUJ4/Zg10OBJUQ1su/AMXUJ1CuXZk5d2jeC35i1sarvfztyhv/WYD97zrTpAZTYFz5FSurIGrYAG7bcTvWuDt9xfSm6939tMHNC8dBLhsW5sqYI+E/wNUNxtXDuw6riKKN6dyq5deN7KgK2L1mPr/NtJ3XCFuxNFXSgX1mvdHgohPBhCeFf9tRjAI612LGvH5V6dDjSE8C9DCP+r3gnz50MI76x/vjiE8FYI4bX668UWuwIAfPUDIUdXoHJtN6KtD5qu45QyKnaerDXgsJ3vNNPE6JFGxQemJnK/VwPrwNGxth03hXaX8iJDnuGofxUDU/nGB6lILtBQ0trNTrtq6Wdq9FqnzToIuk9tYNCp4VkEO4HfOLENALLotqdsx/v6u+pCef/tG7k6JW8cKaiR6m2j49UOaKOLVuaimpwslZa18+SlrEmA0rR4TXjflbrYacaY8BbD5fOnDTn0ldqHypxGRLkfPrNqHHuZRR5TM6wcD7fRGheVea69ZpswqBGsAZROMxkaBWYAgs6bNqCg4zbeV1v36+bIsWxs7Qa8yHrgMUcXrcxa/3cDz6Cn/lWjTym/GgQC/GVNGDTzMrGU3XYdtzLnwpf3bNv7bXUz5cyrQSvqtqpypmNRaua+Dctx48S2pvoiPbbWIXkskW7ok9ye8jo50mjgRMqgngMzba1o22XgMXaA8sa8rdFUncdzopFO2bUOG5sMedTC1DzCe8dne8Hwpmx5mG5k19beq/Nou8Dq851av3NgaqKpmRTf869d5kW35XqQAHJ2k+6Dsqz1cxqwoKxoKYeO2Z5DJ/DWB7YsrcGlG3F27fxsvuL8oIHXChWA4gzc30Gt3u1rkK9/+wUAP9VqxyGEz4cQ/ti8PlV3vspQMC1+FcBAjHEFgN8B8MPy3e/GGB+rv57zN8/jrXkLm5RI2QdTC8m9tti2qxChCtlOpFQu3gLWQIMiwRcnx+lY2Fehhp0XSVW6lUcnVIOC56bKlwatRoXV+eV11mtto8q2nblHRbVROI61kyzGxJb1mfFsFwcfXLoxu/9FsLVT7eCteQuz92rElw0+aE0VoctS8B7T+SVdk+PeefJSRsOxtZxsskNoVzSNzveKCkJKj80aWDqPfa/Gh1fb6dUkWTmz9GbdP41Lyi8ztB5VWqGOI68dO512I7MEZZfQdvY6RoUanx6lsxPY9TPLQJ0aPb7nxAD5rGdRjRahAR5rQCp4vG6CZoBPIVVql0eZTcHWtQHFmQc6sMyepa6JlQnV5zyu/vVoixoE1AxEO5jYsr6JrqjO4L4Ny7Hw6UNZhjZ1PimZKQOr6zmGMijK2tn7q9dbdQuvsQ1cErz/KYYPddLo5eNdBc3smFXPMdCjzrX+1mbzOS6FPns2eOadlz0G5xbqZja68QIPdDaVlku9obTibnD7yrkm2dX5dufJWvOonSdraxSqvtV7efDC3opCWSGHohq4AzHG9wH4+6YGbmWM8YUS+94H4AcBLALw5QD+PoCfAXAcQDEXwB/P2RgjZ9hz9X12jHD1EwA6N0J0ciI8CoRG0+jAeNkXOxF6TsfqdTuyCbrd2qdWsIv6UiEPrdqVG4d14vR/zazYLBrBSJKeu3cdFDZKxUyGLdwHGhQ0G13XCJ6O1Z57K3idxOwEpfvn2JXOwQnai2KXQbj6idyE080E49VQjvf1Y2jVLpfyC+SzmqRsacZLjUkgn2WydQu9gBpWvNZejQc/U2fIy+ApLLXOZnkpW1o/ZuHRh4iUEwc0mh0BDYPF1v1p9rwIY9euN8kuDWCt5WT2jffYBlM86mxZvDVvIQaXbswZdu08e56u4ftU7SHl85WXDjdl9dUQtrVdSmMlPEe9G/DepfallFbCe9a9YCGAXMBMA2i8HsyMA8h0vWZWeK9sjRD1FnWFDTQSlCUrL+1mXVPdG/W9nSu9AJnqpXZ10CfHP5697yRoymdNlwDRJQQIpfRp5jcV8FOnhL9RnUGZtU5Lt9AO1qzFtsGHVvOSpbGr82l1LuBnkG3ggNsfODqG0UUrcePENiwb3p3ryMqAgjdGjoHzdLdzVVG/BCDPStESA/vM6dxVtldDhbsfLWvgYowHQwgDIYS/GUL4Hr5K7HttjPHDMcbPxxj/OMZ4CMC3xRj/HYAHuxz3VgD/Sf5/XwjhYyGE/xJC+N9SG4UQtoUQzocQzn924Z8D0FBmuh5aK0xsWZ/RRahwb185ly2CqxkM/j1/en+urb3WDHgYmJrAxcfeTDbxUPSiyJbGhK0n0PExpW8j8JYykQInGGsM0wgmvOyVOrgefUSXZaARoddaz2nlaw/lti97/cauXc/9ry3seV3sNbLjtXTRsrCyq4telwUnEzXMFz59CM+FxbmuZNbQ5fhJTeH92XnyUhbp1PPUiVwNCM9Q7TaLQdCIIGjUpI5LePUoKQdFDSNrSDBTqUYJ6x11wlWnVv9aZ8kLWrCZTKeF9YyK6z48Y1SdHa2hSdULFkHl9k/e/D0Afgv1Vs0gUoYQa1GVEsWx6fV78tmtTXVdzD4prVUNYTX66KCvXrcjScXtBrYmUZ9vz2klOBalLZcBgy5apwbU7oVmHjQb4FFVOVbqBF5DBSmVQLMjUxapc+fYtSU9x0/9pDRm/dsKKrsLvvRtAHzZbYXnwuJcB04LvcZay8vgg9KtKac2AOGxBdRp75Tt0Qre+ej8naIhUl+nAtEaAATyXaWVEqkynKNtXr2Yk+WUfcG52soI52l1CDtdrsdmn1X+D17Ym7MDSJMkLXRgaiKbW9h5tUIFokwTk38C4GD99Y0A9gIo03rpdt3pm1d//U35zl0PLoTwcghh3Hl9h/xmGMAtAP+m/tEbAL4ixvg4gJ0A/m0I4c95+48xHooxro4xrn7w3e/G0KpdWSt+5VAXgYaGKgEqJ6U+pqIkaiTY1ux2Ar45cixHAbCTLtDbBVI1i8HORzRWi+rH2IzAo1BaZ4D79OqGeH5s7rHmzK0sE2QjvdyP916jzPY4XGhbo5BlnQg9l9HLx7OJl/sijY+RZxp9lm6kBkbZidXKrtKr2okI631cNrw7V7OnMqvUQI1aWjkgfdGeJz/TiXDZ8O7MqEqtO9gJ+o+cwuDSjU2NHXRi18i7Rm1TBgbPQ59JrQWy3dH0HFnLYptDcMF30oE1E63XjvJja+U0895uFqCI0qv7U2dHAyxDq3Zl11cpca2gcvued70zM9w60Vtq3NoCf6C5tT9/y/PivbZ6x4I6Wo2+NWdu4fzp/bmsVS/AJlKkodksMR1TpVsW1cFZA8/W7fH8+B0zVzdHjmXXiEakzWxaarQ6R7r8ABvH2Hot0pW9c0jBOu52XlX6L7MXKutDq3Zl19XW+7WCyu4j7/tK9zftPoeWekjwvPj8A8h1T/X0hJYUeDWc/B3/tkPJLQNbz0Z9qbKqAXLb9I2wDagI+yxbaPaRgTOeJwMgmonXIKOyMOyx7TwNtD/PKnSOKQoYUob5/Fw5+JHC8VWo0NKBA/BdAP4agN+LMW4BsBLAl5TY7n8HsAnAHwD4/fr7Z0II9wN43tsgxvhUjHHAef0CAIQQvhe1zpb/OxcFjzH+WYzxM/X3FwD8LoCvbjU4FiXTcO3kAeXvB69ebIoYKawSYhZq9PLxpjo6Khkq8fOn9+PmyLGmQvrpiKhpF0rNECr1gODkrso3df30uqgBrJEnpUDqcSy9ktfLGtH2d9bA1sYnanS0e8+VDucZsQeOjmXj4+9oUGhmsJUR2QrqnDCi12pithlVOsSDVy9mE+F4X3/mQOgCz1pAzXt+du18N0LPAIAajLyvuvAu0PmiqBbMFpA2ZK+xRyHSgACvnWdEAA0HyqM1Ag26I40OzazZCVopknY/9h552Qw953ah2ywb3t3UXIHrtFm88tLhrO7ROsJlobWbijLPoHVS1GljUMDSx+ig8P2aM7dydV/cl/e8an1KyvBvRY9qBzTsVq/bkbvGvN+aIbfPumVNcNxAnrnhzRm2EY/KX4qCaOuYaZju21CrzVaWga0b5e/bgaWUpepXuS7mgaNjuTEqHTx1HVrhxhtvZO+tvi2jwzRgxW2Veqj6QgNFev00C8xz0Hk0RW8mxXZyZE/T894LWIfEHsOjSNp7quwDIJ9tVCoooXM6a4S13T73yfmM/5PJYJ8vHYOOoxfO0uTInixZYB1HPTZ12fnT+5uYSt3aCxXuTpRx4N6qL8x9q57Z+gMALZuQxBg/HmP8UIzxoRjje+rvr8QY34oxvtruQEMIawH8EID1McYp+fw9IYS31d+/H8BSAB/395KHjcrQiWvXqOT27B6kFDOguWsiHRFVLGpMkqqmBojuh5/1msajkbMiOo51yAg18IA8LYzb0TFUyqOdqLzMmnVcbYRcKVHjff0Z5YL79taZ6wSciBlN5Lo6FkrdI7+d41Tns5MJguvAddJV0x5PneJW4+Gkopkh7bRJB83eKzWAtf7Jo+V1A15vrf2ZHKm1XNesjY2Cppw2BSOj1sECkGWoNdug9DvCUmD4rHgOE7fVmjQgfw3bgQYrmOEG8tk3BY0Jha4fZrPhZRCufiJnuLXTyIT3S3UK7yk7tnnQ1vlA3qHQ/TBrPLRqV66jIdC4l8xATxcdbbyvP5ex1oyCV0dps1F6PpQXddAUXn1Rqxq/8b7+XI0Wx0WqsKV52Ywf0U7ggTrC1iNpVsPK0eDVizkWgJ4jAzHtIi56X1PddLu0Ok/fkSZnjfRXXjqczfP8S3nQuZNrwmrjDRsMZdfJXtfNs+yCDTnUWSK8BlFFel8z5kCjtpEdYVPrveqcY59PPrv2ODbDZ3Uzv+t0jtKMuc6vy4Z3N3Xx1DpWOw5eA9t5ukKFMg7c+XrL/p9BrQvlbwH4aKuNQghfXV/Ee7z+/4oQwj/sYqwvAHgHgF81ywU8CWAshHARwH8A8FyM8bOtdvbWvIVZqlqzAq0Usm0iYGk5OuEQqTWfgIbxYCcVUphsx0X+pYLrZT0GDVs1sjR7QKdEjYt9G5qbrSjsZK4GgJ3YbVYNqClonZzUgOR+UvQwdZosFaVd0KnXCcmjYFj5sVE+nahbreuTwldM/V5muFqDztbpEbevnMvunzoDGhH1oBFeXmsdt+3YZes/7Xu7Dk6vJiM+j54zpNkL/a3C1kIQfDYZMKAxTMcglQnW+lXKq7dvGj0MJNnf2Iypnk9ZR25iy/rsupPeqU1XmBGw90Qj+rYJEMffDhY+/HD23sskpbqoUqb1eLwnagQB+bUjeX/On96fOUbMXtisOCPghG3Sw2Nppq5XsqvMhyJ9pvLrObSefBFam6ZyRt2owUWgEWyw+yQ9jce2WQ/dr17n1DmXgZetUX2r7Bk6Kxyrnj/PsZN7p8sItENFnNiyPqd/1HnRDKJHzda1X5UCrHMnP1c5sF0qdeFu7qNXzAfuH2gwDPQ4no7y6KLUf6lME3Uu9ZBnUzED7G1rgx3s+kgdp3OhOpG9CtZYmUt1PrbHpMNeoYKHMk1MBmOMfxRjfBHANwP43jqVshV+BrVW/zfr+xkD0HH7oxjjkhjjo3a5gBjjz8UYv67eHfMDMcZfLLO/T33urdz/jIoU0eqK2rSTEklDkJGwIufBGr92PJ6B1E3mpiy0KF4/s86pbQrA8+BkQmWkk4s6NDa7RiVN7NtQW3dozZlbOHhhbzZBaOZuvK/RlIT79RxKNeIGpiYK17JLwU6C3JdeI14Xq5y9Gj3W/LULGsKeEdIqg8b7p4603Y86vnaivDlyrKkOSJ0TOoi8B14UXmsnehV8GL18PNfOutVvec7eAuxAPiuuz7MFr5HNIgN1nbBoZe4YvFbM5ujCyGxGoIaMGhlAQ7Y6aQRBiqmORSPmNiCh560NFTql85BC6V3vVs6o51RaWp01xOyaZWrE28Yyuh0z+dTPZ9fOz7Kv6hz0mgFhnTHqSn1OlPZqKb1W/ynNjvvnGqTUz9qNkvrI6nfKL7MEqsO57qN1sgi9zt0secP5pRW1TZ9tABkjRucMoDeLIes+Ug6R7eLJbZjltY4Fl3egnN44sS1XpwzU7otmZQjVGwcv7M3kn+h15pj2ECnpKdvAC9TxeeK4NWts75XCLujN36le0L8LhjdlMqrfDa3alQvcW5uEv6Vstyu7lAdLWyUjhNBsHLOxenw9b5u5q1Ah6cCFED5gXwDeBWB+/X0r9MUYbaau9yspd4hHH7w/Z0CP9/WXMoq2b16RKd3tm1dg4dOHcjU+QH6haho+SoVQOpc1RjS6aBWuF3miAdbLegw9rm16ATTaW1Nh3zixLZnZosGg+169bgcWPn2oqcmH7kOdW0uRssciZQ5oRKzsb+xE1o4SvH3lXI6GQiifnfsfWrUr1+XSo8BpBLVTR9wzjIporzr50yCkAU9jwqOe6LlxnSV1Quz+Gbk/f3p/UzRUJ65e00A4Uer90TUMSUHib9l8RY0L63Smap+APC3LOjUHL+x1o/SMtKszQh3Ba0bZ0BpUPoO6MHI72TeluPK50mYqdEaYnWNDCs2+aaScDnq7BuH9t28kmzuldK/qtaLMiY7FBkteeelwbX2lRSuzQBCfQ2axlTKlQQDu14uC90KGJ7asz3Qp7w3XUtTuc/Zaq/PN89P7xTboOnbb9GLnyUu4cWJbLuPA55vPhjpsSo+kYawLaetzYinZO09ewpWDH8nkzGbuUteGSM0vlN0rBz+SWxBbrxPln85uJ23Yf+d6re8at6WjWDSPTGxZnzX4Gr18PGv8AjQ6R/L+qg6xsqbPHsH7xsZILLtYMvRMVucFNDfQAnrjvAIN3cbaLrtuJedBvU587qxz6S2ZZGuMgTwLya4By+PoX/1OZXvJ0DM5Hcrroxk57/nevnlFqaVbNBFAGdQ5Qf+3QQcdM8d2/vT+LHDXa0e8wp2LogzcTxS8frzEvt8MIXwV6h0nQwjfhVrHyDkBZuDYFhpoGEaeIcFok10vjUYX0DCgU46JdkZMZXEIy88+u3Z+00LYpNF5mNiyHs+FxdkikmUWeeaYNILr7Z80GkbRdKyWGmmVrGZ1PEWkBfSkVQCNQmUalnzp/zSObRTNUlNoCLeTwWCjGrsP0lfOrp2fRRHPn96f1dIAzcXlPLdOKRq/9bt/mB2bVDTeO49CMnbtei7rxmgfs8PeOFSGef1shF4nZSC/XpEawzSYy15vLto7sWW9uwiqxe0r55JGiT6T+px78Lq8qiwppUm3sVBnmMYqawX12VC5Iez7lJx000mOcqrBGd0/G1IsfPpQ1hhCa/JS510GNoBFeNkIQuW2DNiam8YaM4es1eIzyzGoM6EOrh7bRsZpPFvcvnIu9ypDVbO0cB5DayCBZn2p84ruS8H5ZvW6HVhz5lauBbs6ftZBogzvPHkp5xxyHOrIaPbEngcNZzoYduxl4N338b7+rMMk92drkbVmU4/XadCM8qTZRv4tauBla/EmR/ZkAS7Vj7ZmludAx5oBDOvQ0xlkEEvZM1o3pWP3rinXpnsuLC5lL6TAOujzp/dnQTUNvtjMMJDvpMm5xqOP6zaaWQOaa0LVSWImlLYXm8iwjMbLPOt1Vn3b7pInul+buV9z5lY2BsovbRplc+n5TSf7qsKdhaKFvL+x4PVNJfb9/QA+DOBrQghXAfw9AH+3N8PuHXR9IC4l0Cq67RW+0mCwWRCreKxisvuxdTCc+NQxsccoikxzMdZWBt/tK+eyfaihbo0G/mbZcK0FvWaUNAvJc/Hq3JTKtPPkpWwCU+qEpUnqfuxEwM9YD6D1HjTEUu2/y2QylNZE0JnUiZyTNQ0Wuz1RlCkrg/6lj+YcJKBRSG7Ph7VvQKN2ZryvH6OXj2cTJe8hJ3iNVGo2+ZWXDuc69Cl4r/XeadtoLegGyhvjZdcY1DGRKsYJ2Ium0um1lDA9HyBfB6G1b0Ajw24zrITqBpUHPR81rJRqqUa70n4Ura4Ls29erYWC95z3m/ulAcbx8Dw5tqKlCVLgdaJsUCY9maIRaWUIQJN+0owZZWz1uh3ZNfOosva+0xC+OXKsKXA13tefObM8nt2fGnZ8HluB+orXkowFGnWAXwdJMAigepL6dbyv0RTFshJI09M1xEiR1POyTkWKamZ1v92POu26blwKGpShfPI51nupgSvVf5Mje3Dwwt7cfaQzxO/bwaMP3p9l77QOkIa2dR6fC4tzTcE0m6psDmvQe3JF8LcasAQazo92hOb1ZibI6gorR1yrjtdv9PLxlqwevdYqv8wm8/m0mUrKig1IA43yghSDht+RUmjlDGg4OrbmjGOkTcT9eTRynhO3oQ114OhY6WWH9HoyAK3Q7L5mMHVZGs/WrDJwFYgyTUw6Qr0L5VMA3gPga2KMH4yxRO55hvDVD4ScYbVgeFNTQb2+tGZq34bluSJUKhHNxKmCBRq0CHUuuK0XkSb0c603oGLR7zWCZsHumqnokSpZratYMLwJo4tW5rjbNE748jIWXlRXoY0G9Lhq9PL43B8/V8OL+7k5cixnFNi12GxEnZNbkWPLKLruV6+3pc5ZZ0AzfzSuekG7uv/2jez62JrN7ZtXZDKrPPw1Z25l56pUHTVo1JjQqDEN9yef3Zqj46ncWgeGVDVCs4VA3jG7feVcLltsz2tw6cZCY4L3kmPiWDUYcuDoWM6o5r5ptKoTpplJfa75mdJ/vCy1Pp+6fSrLqffP0tm4Pw0K6HOYgl4vj3LJznU0LKyRpZQm6klraLWLt+YtzF0PDT5wTJoB2L55Ra4ujFRfpTjagIJmes+f3p+NW+UWQK4BkKVn6e/USLSNByZH9mTPGeXWc9xSOlcXIef4yFggs4M1XPa6qTPpBZhsjZB1ynShZ+u4E3pcPQZ1uzre9vn3nBD+vqjOfOza9axpjQYLdR+8J8xQakaJa2epLrNzit1nGWgTE8Keg7IGdBF0zbwPTE1g8OrF3PzplVNwLtR7Z7ObSrMG8nWCpMB6S04MTE1kz7dm23Q+5LqaqUycfn5z5FhGA7342Ju555pyymecgVagQZPUzJu1G/gM88X5iAEKZog1oMP9abCQr4MX9mLw6sXsHvA6chvN2CnojBXZC0+98CrGrl1vYjPw3DhWIF+awgw5kO+urYFxwH/OK9zb6LkDF0LYqS8AfwfA98n/cwaWClMmG3Pg6FhuvRkvQ2WdCz5wulYUjV8amYzKWbqUOgR2fClKWCqLWHR+dlFO26VOf2cXY7UTZNkmB6RZ6HE12grkW6/zc0YzFwxvyjlmej80+qqGkNd2uh3oNdSuWzwfTiA8H81S2qwgx9Ypxvv6sxoLjo33RzuK0oCgE6MOh05c6pBRpnaevJQVe1u6nzrDShXS7RntpKGi188a8EXO7eDSja4hrF03eY1Xr9vRlLEdvXw8ey5YS6CGk2ZBaNjqdeYC8DZ7qrKpzoSXNbJQw1Mj+/b5Se2rlfzaBXLprOpxNSPGpRB4P/RcKdc6tm6MYF437SKozyWpVzRkDxwdqxlfdcdfHSx7fXgdeT90zDbzo/ePxpINjukzqnpSOx/qs+d9ngpAaJ2MrQPSoJmORfUux6znSL1qn0kgb6yqoa+60mN02ECEZsDUsFTnmp/zM+6zTB2RzsupjL+ej9dRWrez16iTQJrWCPI8CA2mUEbtnJY6rs1Ac47je+pl7QSt+9TxaTt6IB/00HFefOzNJn2ashuKKIPbN6/IgtWpucx27qbcaQbOlk5wnzazO7poZU63c57XawbU6rVtbRnppwreU50TPWq0PYciaNaO56TMDepZPu/KSGLm2LMjvSBLhXsbhQ5cqOHRNvf5jvprNWqUyUX113MAvraTQU4H2BHNTpo2m6Gf22gW+eYaWbGRXoKK2K5bQoeNyiZVc6HjsQaLUgIUKQOvFTVCJ15SknT/dg0c63QpndTCUkTU6PUmN14Pm6VUx4TjpTNjDTDdTg35VnQI66zQEfAiwzxvyhMjnVYOhlbt6ih7YUH5GV20MieXWg+nzhODDnRcaDAqhWrnyUu5TJY17HhPtVU3kG/mMjmyJ5cV1TFZGaVDyd9oxLRd51odMmbfOBHyOLZ+1d4/dVpXr9uB1et25Awla0wAzXKp++U11AXcaYCpcWqdcM1Ep2rGysiuZtF5ffn8cX80kjTLOnj1YraYrI6j26U4dDFkXis6rvZ+exkKALkOjDxPvm6OHMtlwEifpI6w626mau7oAKq8eA147ILO3YBOF41wW6erGWTqMK2PYzZKMxj6XPM7G6zQ80tRs2zmR/fNWlp16DwUZYsVKx55oKVDQOj3yg4AGvODjl2zWe0awlZ2WduqssD9auZN586FTx/CsuHdmSGeshWA5uVQlAbpbcdzU3qnOgPcx/bNK3JdI4vQSra1aRRljudsnxEF742ylNQOs/Km58Nr69F6qTcPXtibBeDYEEgZBnQg7bqcmu3XpVpSNqHFy89/MBdg4fUgOKZXXjqcW5tWx0C5YIkGgxNecL9ChUIHLsYYAfzHdnYYY/zRGOOPAngIwAdijD8QY/wBAKsAfHmnA50OMDIC5B80nTQ9w9j+nm22qXiomDStz4mOkdGDF/ZmBoQa/VRsSs+k8rEGh0asGbW2kWCrcFS5EkqHoNHjOVOp1vtq9Otixlr8zuvAF+sRlg3vzrpV2SwgrwmvoxrDNvLLidHL/nUatfIMCGt8A/nJRGlYev5qRHDfnWbfGHzYefJS1gGM8LKxQM25pQyzY5xOIKQkjV4+jgXDm7Iug3bi4z55HdRgsMfW8xvv68/JjU7slFsNCNjxAzX5sJQeG+0cXLoxi+jSmFBDW7NwQCPzxKY8Wmuh7btZe6FRUzVa+FcjpzYzrZRF3j8aEdQX6mDb61gm+kuQPqyZKrselDrbpC/pQuw0/NV56wYLH344l9FTWOPUozABjaCSdWr5PKlRyWwMgxdsZFNE8yNujhzLjjne159R8ywdzcqYhX6mgTPWSPF7GuA7T17KdLCn8+11UtYDnTieG+VLHTrVpQQz+Trv6DG4f25PPTs5sgdXDn4Er7x0OHdf7Hh5nmX0MClorYIE6uCQWsdGQTw/nqt1DoD2M3ALH364KYCzb8Py3L3ifvVFut54X3+yDlu35xj1OaFc62fqxKXkmPLjZYftNfDuT4rq6mXkmAGzbCGbWbdzvL3PdK60ezWfATvn8zMvUMprAzQyVxqMs86+bq/2mH2ui3Tw7Svnsj4KFtZGUMo+5Z1NhTQww9/r+ConrgJRhkJ5LoTwFzrY91cA+IL8/wUAizvYz4yAWbBUhMVGYTT7ZSMpqjg0O6QUDDWKGZnRKLHSh/hbr+4tlXlLKRqen62T0sgWo1NUjvaa9B851ZSJTIHnrxFgjerr+dL40sipOsHjfY16IxoSOjnbehdG3whLVWlV/6bg9aCjqE4jz4UGMBWzRv7L0krLQNcw1EwLo/I0ZNShty3ziyaEZcONBjXeROcFGnSS5T4Hl27M7unA1ETOKE4ZEgrKsWaogEatyXNhcc5AVIpOalK3sgvU5GZo1a5cTSDrFTl2fu61kx/v68fCpw/lqIf8XOWfYO2HyrJG0PUa0ukjyjhxE1vW4+CFvTnjzhrXvI+sSWHGzWvgwe957t3IsnXq9dmmXtUshj53dj+eQ6DZY80Ae+NQg5hj4TNLHajRfaD5+pcxplQ/ao0X2+q32hcNSdIO9V5q8yCLVM2ibk/59p4XPu8eVW/fhuWZc/jks1ubWvfrvnj+vLcpR44Nl+ggFjUwooPkMVY4Bp6TbYqhQal2oPvQ7JHqQOoje55qC3A/DOZ4tFW7Tufg0o05eqTOp3RMNLN84OhY9hvNDloWD9Bw3nh8ldftm2vLJk1sWY+xa9ezABqfVTtuay/QAXoxvl4rP5F9j/f1Z7qQGSkGBi8+9mZu3Ukr8/pX7zW357XR7qcaANZrxt8zeHXxsTezY6lcF2UkWc+qQW6+9xr2MNCd6iL+ykuHMwdWg+K8xhUqAOUcuG9EzYn73RDCWAjhUgihTCj4GICPhhB+JITwTwD8JoB/3c1ge4n7b99oUgQ6YVhamioejbYBjci6VTC2CQK3sY0vCCocKiJ1YtTpK6J/Kco4owpmCZm+V+dK90XDmROWRmUJjTDxGvF81REj6Hh4Do/Wf3BbGlt2LT8tYNZjcKJRylbqOqQ4/+pg8/6wtk6bIuik6hlYOvF0UovxJ2++CaBRU/nKS4dzHR8ZyVOQ7rNseDeWDD2Tc0pSEWoWsvPaWRlTZ06pfjx3Rp71PFPF4fqdDZZo5o9OKf8fXLoxt9YSI9UauVZjnsYzI+eji1ZivK95kW51fPReavBB9Ye2zCZ4XTlRs05HAzZ2cvcyb8wcaabde7bHrl3HxJb1OboVgFzGT0F6qWak1EjUJjw6HluQ3wm8a0kZtrVL1rFQZ8PTE2y6lILKhx5fOz/qNdOaVqCRVbDH8LLf9nyI0cvHm2pKVVesfO2hjDpFQ39iy/pc4MrS8e314TXW87TXgX89iq7uyzZs4ndAfu0snes8ahj1vIU2LuE12L55RWZg63WyCyGrk22zV9TNOqZO1oF7a97CHN2Y14T3SmnqaisoPdnaAfze6lYbnNDgmDomALJ1PLkd/45ePp7LyGrQWd8rrFNHHWMdBnVQ+d7e0+2bV+QCbxNb1mf3k9sB6eVIlg03Gr9QvijPSiXkM0sWBa+V2mNqQ/F/hQbVNQBskcpIMtirdpkG4Va+9lA2dtuNnPYg5xCC10X1r9oNFSoA5Ry4bwXwfgDfBOBDAL69/rcQMcYRAFsBfA7AHwHYEmP85x2PtMe48cYbWcaGiiKl2ICGA0BDWI3IyZE9mVGjytVGNW0Rqu0uprDRO314vQnXRsJ0Px4FQOlk3H775hW5zlZUMHY8/D2NX4Lccxup5Ljtmkbs6mfXibPb0qHU7JGnxLSpCA15pUzQWOWEaY1gdkPUepPxvv7cBLlvw/JsoWZO5JZGo06e7c4G5GmUnRgTBK/B+dP7s7EcvLAXN05sy2SQTg2AXM2G1osBzYtW3xw5lm2bcnTVUGbXSZ3E6JywnmjB8KZsLPba81iafVFQVpmpyW23aGXOoFaZ4wSvNU2cqNecuYXDj31Tdh1oJLCNO+8NaZbcRgM0/KvPOsdhs3VbX/u1zBDmNqk6K5vNb4WJLeuz/dGxVRo3Zde2P6dRxeNbB9TKx8ELe7PlE9oFqb9aiwYgFyUnPIqV6io6Nhwb92UzAJRHraXl76iT9m1Ynq2zaR1L6kEaqlZutXuxrYcCatdUn3Hu2+pq62RZ2aGuUIpvqg17au04ZhgYWCBtWI/N66XnNLpoZW5eG120MrcPK/d6fQkNCthsoVeTrU6MZrvG+/pzQRkvCMJAhHUmNbDWLsh6oEHN/S0Y3oQbJ7Zlz503DzPIpxhatSvLsnjsBSJj+tSdIc8JoUPD39JJ5j3zath5vNQ6barHqRf4bI1ePp7pKi5QzvuqFHHVuXx+GPjZvnlFzrlevW4Hzq6dn1uexwYMtF6bzpDSEdVRIjTQQx1o7z+vlWby9DfKHLHNd1R2GfQkeP3orFFfaeCFTCteA4UuWl6hgoeWDlyM8ZMAHgXwTfX3U0XbhRB+S7a9EGM8UH99zPvNbIH1GOpAaWQoFW1hhEYN26FVu3IRFVuwbWuhGCm0XeIIXY/LOlM6VqC5+QPHTqhR4RXdUyGpgrWGsHdOALJ6KSpaXWhbqQ/8/uza+bkuYawJ8ChZnJQ0gmmdHVXImqHkNVfKqXVcyhjGdPh4DHVmNVrP/ZG6qNt755aKUrcDuzi4UvAINSToLKtxppMggKblImgIZHV0ppGDZpx0wuc+VB61nki3U6Pc3iM9Fveh58wmHkrLtEakrTsZmJrInJuza+dnjpsuXG4Xn6ccqePGmjFrMDA4oM+QBhXUUdbfsBkNgyIaQS4LW3/h1ZdoxgJAjgZsI+5KDedzu2x4d86Qagef+txbWeaC0GCMfcYoGzRsLP1LM4T6rNO4t86S1ff8q/eXekd/s/PkpabgnkdFs+Pms2bHoY676gc6kjq2yZFGIwqOjzXXvDZWn/D6MqhIPapZDY6PDAb9jnNTZsDXnQedS0i5pfywQYfeVxuMayUzmhHWLKmVGTs/eetrMaim2TvNdHWTPeYxeJ2UOgfUrqs20+HxtT6ReoZUdau3dF8DU34dnwYwNDiszhShNW6qz1Xfe/KqnRTH+/rRf+RUpvsJDQzpEkQ2GKfZVX7Pz5RJo7qYY9Fz5XhsAEOZDfY54m9svTa7WlJerVxQXxRRbvXecW5Tx9HqYRsA43hvnNiWaxSlczPP0zKdKtzbaOnA1emPPwTgh+sfLQDwkYJN+utUy9TrEmoNTmYdOqlQiXrUOj68XraEE58qm+2bV+QW36aCUEfAi7BrFE2NQO1KZttK22gpj28NDDqkrWrj+FudVDxqm0KVEZWVzejQkNBouN2GTob9zstieY4Pr2sqi6qGvre9dkQk1JhTegaVsBpMtmmE3Q/H3S0F4u0PPZRNqHpt1AGiscJj0VHh5/qdUpaA5ppGnfh1ArSZFP29dguk8UBjRJ01RnT12RpatStX+2SzVJRjXXxex20jqHoOfMYtfY3vSU2j4a5RUpVNGgu2JpPXdWjVrpxjQNmikW6PzYCQpV8plLqX0lH6jKtzyWyy7cSoeonGro5Ng002a9tuY5NHH7y/yaAC4BqMQIM+q86CGuT8jhkyQvUr93ngaG35Fw28cEkN3jMan9yW587gkWan9Dpbyir1L49tgxOUQTbcsaDcnD+9HweO1rqNLhvenWXQaOwOTE3k3nP/vL7egsj2GDpvqZPAIKLWaPFzrrlJXcZAiF0ehc+G0qrVgdb7Zbt+sjaKULqyDUJ48wKDDAwwMjPUqe599MH7s/O0x9S5VZ8T2gaWeaPOrWXTpMangSirF9TxJtTx0SybpfhRnu01tcGK0UUrMbpoZUbRVnkeXbQyFzBh1s8L/NKWIathaNWu3PnYZRCsPtq3YXmWEQaaAxj2WtiMqz1PNu/R7XWOVjaNR1v3Atu8v+rEAcgcMsqmOo46T9s1HBmI4fPZi4ZSFe4OlKFQfieA9QD+FABijNdQWyYgha9BjWKZen07gL/U+ZB7g7fmLcwZ/KrcaFTamhad3Enl4uTJzBUXtSR0oXBOilQiNtVvs2uew8isVyuqmTXStZ6PsF39GA3TNsi6XwUVNH+ja5Xwf3YnUydsvK+/yfhU2hApbPyME7mNmtMgpcIfXbQyN17lsvNa2AhkyiHl51wmgsfSfWuE00bFeK43R47lqLWKbpy4Rx+8P+cgc8LhPdHaRAsv68vxUXY9upMaqZxUGaTQiLlO7Pzfowap8UsDTo+9et2OXP0Gf8v6L82I8HM1OHUCZy2fve+k3Ookr4YSZdgD5U4NW0sD5LU+u3a+68zz3BYMb8oavthrbw1zPt+p+lZ9ztW5pNFtKUSWameNmfG+/mxJBV0rjo5Eu0itgUg50Qg25YEy7lEmuZ/BqxdzulczIJa2a2lpOjbPGOYxVA9okMzqSxutL6Kv6f2l3NIJoJM1sWV9ZigqgwHIZ+KpA5W5obqHTBC23Oe15fVgUIK6QO8RnWf9jNvrdVP6pl1ah5i35AmkwGvKAJlmZSZH9uTmGb1Gyhrgmo07T17KURU5dh1vWUxc/lSTY6E6nzYDazlJLSTsAs4MVnKMvOeUbU936zHJIGDQwdb5UT/zGVa5siwNr4uxZZLwHDVQxGuuHUw1QGG3536po3kN+KKOITSgwIDEwNRE9rxpMEPrE1WvqMxq5laDCipjdPY1wJEKCPOaWDuCjAW1hVjjpgFuS8Vf+PShzMnT+mmlUmpWrkKFMg7cF+rLCUQACCF8adGPY4yfLPH6dC8G3w3uv30jm8TU2Vk2vDu3dsh4X62OZmjVriYlycmS0RRGk5RSqJMMPwOQtWlX8Ph6DCper8Bft1HKixq3ts26vldj2BroQD4i3mrNKSpRThhAzSCwDjBQ676mRhgNZSotVVB8z99qhouGBrM8HIdmevRc9Nxt5Mxb1FS3sQssa+bNq+vg5MAouB1/0UTdCp/63FtJQ9Nz+ulEc9w8Lxo66ogB+do1zUxbp4/Qz9UA8Kgn2iETyBu/Sr9NTVLWANHaEz0/rc/TugWtESPNiuBaWOyGNt7Xn1tKgOB5MZCy5syt3PNFZ06dPI5ZI6k2q+M5Ufb+cvz6PLIDGr+34DF5T3gMlWH+TznQyDYppfy91li2A9YRefVZ6ryrE0BDXbPNStPimKl3eU/UWR/v68fhx74JB46OZc4LjTRLawOQLSeh90ODA54+9rLQhGagNUvMv9RZdLBY0wkg696noHFo9YdduFjPiQseqz5slU238406bnx26CjYAIbNYHBfdv4au3Y965pITI7syRxVzdilGDC6ncoz31N+ec06kd3+pY/mDG4GQ1jXS/tB9bJlaKjTdvGxN7PPgMYanDp+0qjH+/ozx5RzDbM4msmhXqFjzTlOg6F8ztacuYXBqxex8rWHMjuD10cD2rZpkjpnSrnW+dRj++j2vJ+8J9RPN05sy+aA0UUrm+pvdR6lXKlO8IIIqj9UnrnAucLqIy97p2C9PM8ZaLYr7Dqr6sipHiM4H+syNgqboatwb6OMA/fvQwgfBvDOEML3AXgZwL+a3mFNP96at7BpErWUDU4YtgiV4PZq3HOiBBrrDwHNC/yy+JZKVydHO8nagmxGwpl54njVmdPf2swTkF5nSTM6VODbN69oWn9LaRceL1sNAhtpUn66Gom8zpzoBqYmsGTomdz15rEs7YTnODA1gYVPH8oyUjYbo2uOpbIYen3G+/qzdt9eRkSNPCtD/I1eH6WQdcpnf/TB+3P/a0aB2WCb0dVoJmsV1TlRI47j5GRvJzvSo2xdAvdFA0CDIBwPZUAzdcuGd2d0Ml5PGnDq8DHLok48kTIYgIZznqNiSvaF+yRlT7Pmes5AQ3a1WJ9Za54v96dNJ3i+nJxVN/BcVLbojNB5KMq62XPThi8cnw2ETI7syYxC1T08txsntmXy7DVh6LSGyAt26HNj98tMOnWFfV4on9yvNoSwLAWu5cZIN7MzlFE+I1r/qPpDP1Onzhp6vLZAXk+qM8Pf28z9eF9+ORurdwG/5svqQ3sd1WklbdRSClUf6xxg92Mdf45Jm1nwfIB8NtTub2BqImsEoxkepfMOLt2YjV2deV5XPrvU96Tc9hIMPlhKpDpEdD5sgIL/c87n9dfsytm183M6yY5fM1DqnDA7rvocaFAnVe8zCMNAK5sUUX713DQQRx2tgeLBqxdzQV8NmKie0lIO/l6brFBezp/en8uMcn1BXhuFlTN9lrwSDGVlUE6127dm7VhfapdssvXVFurUcm7geemcwvFqllQdO83acfwEndQqA1eBKNPE5McB/AcAPwdgGYB/HGP8yekemEV9OYKrIYTX6q9vk+9+OIRwJYQwGUL4lnb261GG9KFRZQnka0l08uXDZdcNsQ6VGkKMZtuxMPpmMwQ2+wDk65WY1dM1vzJ6jayxRgPBRovo+Kjxb7F984pMeeuY6XjpuXlUHlIUbFbKRpXohFiaqK7potdN98VrSqXK66FK2DqwXuaCx7CNEjjJ8fysE86/B46OZc4os1y8tjp5dQIbGdRJ4ZWXDmfZn50naw0Gzp/enxmWpKkqVYTb8dzY6API07b0GtIQ0e+ZnfKoOHY9M/1OG3wweKIUQN2/PkOeQ26fEaCxkDl/z2ug4HZ8zmic632z2SA1/DXjqPdY98u6K1t7QfDcqUs0C67XLvW/jY6rDuA5MLjBwnm9th7Flh059Rp0ksVg4MHqVDW8+KykOrNpRsmDpXLxsxsntmXBtfOn92dZRLIEBqYmsqwIO+JpoMg2RWF3SWY1bECHzwah56hBOupGO34AObm29bsaTFK5o2NgjVut99NxWjqqHR9Qe26zIFHdGaM+5/NgaXw8ttUdds7xGCU67p0nL2Hw6kUcvLA3oxrajqIEM5AMfhC9yFhw6RYNNFgaHLvX8ndas6Q0TkUZWbdBQRrxr7x0GOdP78fFx95sCopyfmW2R2X37Nr5GftC5x91zDUA69UO2qCPOnKW7UNbQ1kGHJ9lfVAe1fnh97QflJ5omTx67ez19PQtda3SiC2jQs9FYQNqvAa6rAbQqAG1pRe8frQdef1ph2nzO7WXOrUZKtx9KNPE5MdijL8aY/zBGOPfjzH+agjhx2ZicA72xxgfq79+uT6+rwWwEcDXAVgLYDSE8LZWO2JETR8ma1iQsqDgZM8FmzVKafnXdKJs9NpO5hqx08jMvg354nxtZ08loYqVET6NDnLCtNkQSzNaNrwbFx97M6eYdXJVZaUTqBoCPL4XKVcFpE0iPDATxzoor2MT3ys0U0nKGK+DpX5YGpptD8xJRyO6OpHZ605o0wd2a+PvtXmKjc62A9ZjqKGitEieM4Asg6mOASdHOheaeeBktn3zChy8sDcbry4Ka6ljKv+EyjKhBozKkxqIlG2dyEnRscamZh7VaVS5tYGGyZE9mUGoNT8cqxqqNgPHc9Vn3Eb8NcOVithaiqf+XgvWeZxWNZuasdFmCgxcAHnjRlvf8xlWw1l1Ht+TCcDoMmvh2oXqPtKmeY9VD9LxPH96f2YUa9YWaNCVdHvN9NisMq+FdQCUak16m2Z5rEPAa07HEMhTrmwgQK8jA2wemGGy0No+rbvmNvpMaFTfgw2GUC6YUdcmL3zeXnnpcM5AXza8u6mTsup3DXZqFnTw6sWmukMLBhE4b3KeYz0xry+fWQ3o8NpxmRcFAzYMIKYClCm8/aFa3zXPGfPmOgBZfZatYQKQyZfqPg0gaA0UcXbt/KxMQ4O/rZqz8Jw5J06O7MnmasIGbvW5t5lZoMFmocwy0JRityjDR9knvBc6N1m2Cs+R14C/9ZgfahudP70/p7d4fjYbZrFvw/Im20iX4/FgewLo/vX5ZaCDY+E5qAOr98YmFCpUUJShUH6z89m39nogXeA7AByPMf5ZjPETAK4A+PqyG1MRaNSVUMqDhXbiIs6unZ9b+NgaaAqNDmltAh9sZkdStCk1uPiQaw0HlZlO+Gp8WnoM/9cMEdDcAISfDV692FQXpvtSqhydOu5TO5bxPJXvTsNO2+oqlILBa6XntebMrSzjREPAdo2zNCSPqmRrCfVeM9s53ldbe02N4AXDm3INBThZcQLQsXcCNSYs1YVUVBo2lB9vErDHT41Hs2FKD7RGg9ILgWaZp4HC/eh2NAr1M3WsuDCtlTc62TTC2aWS10QdFcoxHRvKpdZB2Uiy5wgA+edX76VG5Xn/lYKkQQQ+b6TiLRvenWWGLj72ZlMmhvB0gjp32hxD74Nmdzz6s2ZT+HsaFMx+KOxaZa0wcflTuWt248S2TMcqfcoGObQLJcdOo0oz4Mz26LVg5pv7owOq14T3iYahgpFz7UCr14H6jQESQvW5fQ44d3BbHlPP7+bIsSwwwfvA8/ay73pdCX0O1fjUOUYDjFbX8nmkLtUxelBZ0kAijXubPdfrwWeN47HMFWusc7+TI3tySwbo+HRdUKDWoMhmmMri0QfvzzJfHoVN67c18GtpfaQsEpb2y8yYfq/ZJM3W8D5yHvZkzdozdqkbK7d6bfS3zDoDjayc6ierozx6u3bH1cwjWQyUcyuHnC/t0irWLqMc6LOtv1W9Yu0tjm31uh2ujHoBuVa09qJ7QdgkANB4Dr0MbCcB3wp3L4rWc/u79Zb/y8wyAJ8A4IeCpx/P18dwOITwYP2zRQA+Jb/5dP2zJoQQtoUQzocQzj/4xevZ5xrNspzjs2vnY+HTh3KKXx0i66SxJTonW6+g10bD6WxpEbKlCI339WPJ0DNNqXWgMXGqUWknWo6bE6Q6aFYJ6cTI4+l+aeQtG97tOgYcDxUTlZRSAJQyZZUXJ8nJkT0ZpUGjVaRZcaxWkdPQ14mR9Dmg0Z5c4XXZ1Ih9ynDhdxzfeF+tJod0HkbE9X6mMohFUNl9++2p3Hfcj0bDdW0fbX2tzjTvLdciItRp16isOp66kC/pRHaCZQ2YNey0YxyQn+hsNoGwtE0gP4FqdlO34TM4umhlFlyxWRGVT8osM6gcpxoVqfuWagtv6yc4JqBRJ2uzCNzOo+2oLGoDE+3OSUeW56MGGI/DaLjnGPM3So2mMap/W0Hl9s+/qy+XWbfR5dFFK7OMklIWtRbQOyZ1N+8bnz2+lNYKNJo56PXm9bCBLjYw4v+qd3hM1WeazaP8D63ahYGpiaz9OY+hUXmgOTtmDVD+JvWMALXgHVkLWqeq0CyLyiLQWG6E++Y4aLDbTLOyTiyFTX9vG6wAjaCZBposNZ5YMvRMFgCyTcE0W8RgiMoQO1ryWlh9nILK7sc/9Ubu3PQvgCwYwWtoA7+69pedS/R82MjDOomsSbXrj/IaLBl6JgsaegFCjlWXxuDnqqNV19l5m7ABXqChlxj4oi2kAStd9JtBWL0+GrCx7A2OnddLA9eA79iQ9aL17AwY2+tDCr9tvAY05hGrd4FmJ446R50yfT5sQAlAjo1BdoyeUyd09Qr3BooycP8Wtbb/p5BfBmBVjPGZgu06Rgjh5RDCuPP6DgA/DeCrADwG4A0AP8HNnF1Fb/8xxkMxxtUxxtVvf+jP5wwEnWxUwdOJWDC8CUuGnsl1lwMai3aqoQQgN3HZ9tWa1dEIF4tYSXXQF7NpjCpqhyoaWaTAUQHYSJRmGFQxW4XFY6rSJ61OI7aeIcuaL9JdvO8JzdgwssguTHZcSqdTh0IjgaowbVRbr4d9D/h1RLrmmGbz2IhCryehFA1me/jei+KVpUeo7P7eZ6cyw8Q689yfZp4sJVHHqtfDOkz6v1JZ15y5lUWaSW2yUKeNlKNlw7V6OUuj4j1U2qKVLRpjqUyApcdShnVtP37O55X7s/Qg0tw086ZNQNTAUkeBRoAGJezi2txm9PLxpu94zppF9OiTKSfPBosYqPCyV3ouQC17xe0oL3qN1ZHzDNgUVG7/ZF5fdu10qRA6QKzT1bW7gOamHeqIWgPTMge0jpF1YHT4bBBOqZkauCBsgMI+z5b6pY4S9acGCvT8bct9zabw98w226yxjosZXL0uK197KNuPnhPnMY6L1HU+E1xUXs/Zy07ofrUboF6DwaUbm4xg1qMCPj1Y74uX3bFyoVQ1Ba+vBg9sfbkHld2vW/RgFrTSZQr0fD3qsWZ+7G9IzbWBYNWnvN+UeS/rzU6YvI/q7Nu6dGXucGwHL+zFkqFnctRPW05iHRXKnz6LqrdStE47Hn1vs5W6lIw6X7z/6rRpEAVoXhPXjoXPDAOKGghO2Qb2c8uKUP2rY9c5htBgKjPtBK8Dr4llRVWoQCQduBjj9Rjj6zHG70Ytq3UTNcfo7SGEr5iOwcQYn4oxDjivX4gx/n6M8YsxxtsAfgYNmuSnATwqu/lyANdaHetTn3sre4CVHuQZPNZQt7QVm9HRh00ViUY7b5zY1jQRcXJktM1mvzhBedEmKl01gK0BphkG7kMNAVU0NBbUWdVJ1pus1HnUfet4tTBZM3JqNFCZ2lbB3EYdbd0X98GxEzqxKDzapD1PIE+nAxqZCRuN1joXjsfKjR1rJ3j7Qw9lE6fSb21mlZ/R+WK2h86ZngczWFb++XuOW6luto4CyLd8trUi3K8XsVeocWsxtGpXgy5Zb6ZDihbPm04aHSRmXvmcaQMRdfo9o50TqQYPmAHWLA3HbQMXXt0EmzlY54xUHm1cUURNAvK1m1p7okEIGvmDVy/mjJAFw5tydUXqWJMWTIeLHeu6ofB89QMhkwc6wNoAhk6KBo+o0zjugxf2ZoEk+wzxObBZNaVcqk61GVca0qztoXPD32rW0QbXaMzbZi88N+puypAa/x5tTDOydGhVDlLZEYW9NrzG1uFdNrwbo4tWNtUa63Oiy9GwHlWNfN2vDQwBeTo6wWeAmRs+s3x+dQ7mX+1oaVkDhM6DZAdQnrSFezu48cYbmczeOLEtk1sG0PisWAdWr4HeA+oROpI6fl2AnZmqwasXc9dcZV2DmMqEGJiayGVzrBPAOcBzerkNQT3G547UTzqOvId82ZIDnh+D4eqwUb71+WKQ0Dp8Ogdrt0h7LD5rtLk4Ho6b+pyBI7KNWG4BmK7Fjg1hA2fZb69exJPPbs10KJkQmnVUtobNNmr5gMppp51/K9y9KNPE5HkAvw/gVwGcrr9+aZrH5Y3jYfn3OwGM19+fArAxhPAlIYT3AVgK4KOt9vfog/fnjDGgMRFpRk6VbyqTphE0VTCaeufnqggVrAOgMwM01pPi5GiVItCI5ACNAl8+/AufPpTLIugEoJMMi9f1PJTyoi3Ph1btwsr/n72/j7NqPcvD8eshQDj4wkkkpgdMJCmETDIMIFMlLdKqiCgGv+UjivagvFR6OkIRrFgZ+2K/3+HXnioUQYxYXlryVVqU1JOiiPhGaYv5gucwjI4ETE4a4dTkxBMS5ZAD8vz+2Pta+1r3fp61196zZ2YPPNfnsz8ze++113rWWve6n/vluu/npZl1E7j+BWq0JjoP/K539c7cBKwGEY0nTmraRIPb6ySk94cYmp7vXKnRW5utKAM1okN0rGnrjtRNiqRK2cgqna6RRtLeM8NlNWEKezyVO14HS9mzBrl1SrkfQusxmG3l52zBTCilSGudgHz7cf4fKnTXsWoGsftehcbJ6CVbxNPA04wb75sGKWytKp8vaxBbo4m/t0ENBkW0aQ1f3IZGgDqVeo0puyHqJEEZ0nULY/JNpwWoUNA0ezK/f0+WmdF7og58973aYrs0fGn4tIKP3/V1GQS9XmQZ8NraAIBlJdhnsSj4wHtMo0h1lnYU1vvZfW84W0KDwSwN0ig0kEI504CNjpPvSYdjQx3+TjP/fCkjhOD90iU3Qssw2GvM/ehyNAq9ftxfRvmtGuxs4qWZWXt+dn03grJLWbcZZ84DdOgVOzb25MatzhpQy14onZzZXt5DPa+y8LPfBaC2rpc+D+q8aVDBXlfVG+rEhVgZqpf1GdAmR8yST+nfkMmpNq2y+9V7TceTss3gjbavVyaN2jfq8NFuUGg9o4LXyTqMDNiE7AiCn7G+n/vQ4Jn+1WvNuYGfq55TqF6L6V+7XmwMSvekQ0tm14ETg1n9ruol/lV2Fc+dc1+iUyYoyjQx+REA87337/feL6i+4pWbo4fnnXPXnXODAL4JwE4A8N7/EYD/AuCPAZwD8MPe+79utDN3+5OZguEEHmqDrEaCGoM22quUKY3q6jah7YFa5oQRTxq1mhXU39hordIHdPLgxD13+7O5KKQqOf7Wcu51zBr5ZettLaa2UIWjmSpG8bVeiYrbXtfQOAhVbtuX7K4zutRBZAROz0GNhUYKmb/lpKQGRfe94Rx3n5+pcW6dYj2nUManDF6fNC07lip/ZqI0Y6Fj48TBcTFgANTWRaOho5OoZviAfDMSO+kyOsv3lCtLx9UMiqUE6vuh6V25oATHfmNgb44qrLUFCl6T2DOoz2FofS2dNClPmmFRirSetw028NqHAjhqYITOoZExEfpe16WjzD4YOJmjjukaVNYxYIMLXWyXshHKtDSLkM4C8uwGtvvn5zcG9mbj4rnZ+xobk3WmrO6inrLBH0LlEKjR7nUOAZDTZYQ+T8oeoNOmY9NMBqH0LO3QyH0xeMAaGzXSOSa9zjrHULb7bl+rcy50ziC9NTR/qYPIudTKvjXyH926jElzl+bOkdtqRppyzPvB/WjgSOeNUIdBOlu8h61mkXnPFHwu+Awx28LrxzFb6iTPB6jJNuc1zss8P9L1dZ7WLN32Jbuz82Zgj8Fc20qf10UzuKrfKTPMimlbfaUuqn7XzDlQc8JtEInno79XmdRtGDhSHcWMIjtxMjPJ33MfGsgYmt6VdSXVc70xsDdrnKL0Sdavh7po8rPhTWtyHaztdty/yrzOQbpvXaMudJ84B2nd30hYEAmPF8o4cJ8GcLfhVqMM7/0GOo/e+zXe+1fkuwHv/d/03s/33v9Gmf39+edeB5CvU6DytIadnUA4SXE76+yEIsKc7BmVVIWtTiInZ/09H1pGZnQSUTqLKmSl4OzY2JOLrOmxNFUfyiwSlmLFManioVEUcoRZN3bx6LFsP2pMqWFMI8RO+qGI2cGrz9dlM2ik8lqE1gqjUQTUZzOsAaUZOEaAeY2s0a4dvHgteR7s6qXUq5DB3gik//KYKre63p86iMrj12ww778WUmt0WqknzNiwKyXlkJPstUWv1t0zm6WLrQOlTrWldMUmuBhNh0agOu/q5B84MZiTwe1Ldtfti4aeUvzUuLHH47Nk6494Tgp1FC1dkNtbKqRCjdzhTWui26tM7NjYk5v46WjbJTL4YhZDjUler9D/ZfCOtzxVV0Olesh+znFqMyJlGRCk3nF7fmZl0QYmAOTqmnWf3M/Bq8/nas+4H32Oeb1s9kefI15DzSbTwKcxqU04bNCJWVt1+AjNTNtrycg/dbVG/LWFvTWoNQimAaEcm8QsRs/z5Fyq7Adm2XhfbgzsxaNbl7NzszWfGnigw6kBTRtg0gZg+nlo+1bAZYcoB9yfBlN5z6b0b8C0dUdytWihrqPUO9TJ2k1Sj2OzfMy40iZgjZ8yd7gPOnoaMOF1YdMgnhefL4774NXns3usgSc+M9yGNXhATb8yYGUzftY+0KCBNksi+BltH2bBrc4s+t42C2LtvF2GYeW5h7nmNmpX2CCZLb/QJjxaqmCfVWtXhuSSWVaVA+qZ5LwlKMo4cJ8A8HvVxbJ38TXaAxttfG33u3OTr4IPs052QD4DRqWqrXX190BtMlVKSfe92lpaQL61N2Gjt4QqPiq2HRt76mpyVCmrMcHIKIvTLd2TEXpLk9D15xRqmAP5omMFI2L71i7IumzpdtyvraWgcU0HVScmbhsrWteoojoJanRbuppGuvneOofKrVcnTg1fHb+lH/Jzwir4MuCCyNZZB+obKzACqTVV9hrba6cTIOlKjMZShthwhJlYPTcNijDLkDmKcs313G0nL3Ys0wgyj2kDDKxfyGiiN09l9V4hB4iGsM1OKtTQ0EADo9H6uVI1gdqaabpvraVSfaItsumc6O+KqL4xOrDWiPF6su5DMzQKjYortA7V6slWYJtHWDlUJ9I6FY2gDYdszRV1eZZpE8OU2QJuZ53GvtvXcnperx+NctVhGtjSWkLqA6W8U9cu37I5Z2iGuuLSqaEzZNkG2vadwS3bil8znJZqqKC82HlCneXDN0/l2r/zGGp0AvXNHnSfocCD1lQR6mAPTa81ZtJMkMqvjoP3nb9vhYb2nq/+ckyauzTLGvJ6aXMjjpv3k3JlHV29/lb/a9mAfU41y6cBWdUnocBn6Jp03xvOsS70eCEarjbZ0fKK+f17srpkgjpYA8/cv31+9Fm5cnZ/3fp62r2zd/XOzB7guHgf6Hjp88XgE+0GDbLoeKirVS6oz21At0gnU7a1OQ9BO0JtFnvdKau6Pql+H1s/MuHJRRkH7n+jUv82FcBXyGtC41NDn6hTVDZFrZ3v1CBi5EbbH6uS5oOmnZg4cdqMm81IaLaOjiQjbxqRIbimEFBrWECqDbdX403PVY+phpV2A1R6D/nn6iDypXUyHHPIINQlGbQQnAhRPrh9yBmy2U69RurA2vpFzaiFKEs8njab0OPfGNibu2Y8X9YmsFYsZJDyerZalPzUo/vZOdMoowOj4+M2jIqyNb4aQyqD2siE5wTkO8sppYqwAQ4663Rk7MRtjVObOeP4Lx49lqNr6n2wDnTv6p2ZU6jPHF86geq14b1So4RGBQvpNUt0ftXkbBz6nPN4avxzf0o/U6ObhihQMUBs9oRyqdc6RK3jGDQwpOMhdp25jmnrjmQyo0Z3yDmzz2/o2WwWDH7pM6pOhUKXjCD0GhUFP/Te81lVmee+smxE9RpntV6zF2b/2/U4re6kbmb2ghk9jp/XVw1JnVcAZN8DtYWoQ9RvlQkb4WfjJ8o4jV0NQBAMjmgtrGVUKNVPnyNdIoUUOmVf8DvVK6pjlX6qmXE+Jzwmg036vGjAgwY8z8HOCba2k//rc9cKJs1dip5ZM3LXHUBuzuW9v3Xww7kug3zOORZ9Pu06djwnvTcqExn9vdoQQzsSq6zZ7DKD0+oQqJOlXZTZWTej2pqlWHhOQO154nUIzf+qc/X8iNii58r4YF2e2hg2MKEZLK3Z134HvE7U10XOL5HZe6KT6bAxi8rP7HzA8+e9YTZ1Sv8G9MyaUaf/GTDomTUDk+YuRdfxF3Bh2zJc2LascIwJTw4aOnDe+5/y3v8UgJ/m/9X3Expv/6pKFsPSZnQRTjUUQpRKVbhsC65GAxWnKkpS1TjB6QQdUo58yPXhHprehfunt2bNMhid5cTKZhU2Da8GpU6KGtEEavVQalx03xvO1kghTcA6IXReAORoHkCNFkAlrK34NZp45ez+7Lx0v0De0bOTA8/TRqnYdpgRXTXmQ12ktN6N19Jmi6atO1LnvJXJUGh2ju9boVC+PmlabrIiQpOP7t8GFiiHlvOvTR0oQ+qcUmbUWVKnT40qfheKKqus24j8/P49Wb0DZd8+h0qpun96axb1VSoSj6nOvVJseV00MkqHUIMeti02J2YbtaYhYRd71u1su3N+ZxuQqNyFajL4l10TFdy/6igNsmgghQaPZjU0ys1rHGov3gzc7U9mHS1DWUAeh2DGRwNoGuDSYIwaVuq4Uk6VIh/LzPbNW5/9Xp8J1emkPXJ8fI4oQzcGKnV6C1+amY3f1tvZZjJATcdlFN956zM9zG0shV2fP37G+7XwpZnYt3ZBjhJmnTnN+gH57nc2iBMLDqn+4ecMKtrsWqgWjp9z/KoT6DzwGvTMmoEL25Zh0tyluLBtGXpmzUDPrBmZYcv3/J7b6qud4Hh07rEZJ3XWeR5KAda5Zkr/hrqAroJZM1LZbSAutLadZkJ1ntX7rs+4jpVNdjhP0cYI0QKpZ2z2C0COGmnnv5g+oXxyH8rGYF2eniO34b3Q39n6fn3+rHMF1PSxBhZDbBGtXVabig7yjYG96Dr+Qs4B41/9jPV0/J5ylZDQCGW6UH7AOffHAIar7xc65w6P+shGGf97+t+oW2+FvGitU1Auvk190xnjRLpjY0/OCWMEUdciUgWoEXS+B2qKU2kxGplUGpnuRyOa3JeNolr6GYDMoOL10OwbFSKVmjqBBCN/2pmLWb+QY6O0FoXWABGclLgfTjAhOqJO/mpM6DlbiqE9nn1PClWIzsJrp4avbY9u6VU209AKhVLP177nZKOTj8odHQyN1AIValvf7Wt1SzfQmSG0oxuhk6kGAgg+R9bBfjBwMmegKwWJMqUF6Lo/e98V9pqGou8arVaaLkFnxWZPuYaU6gJ9xuwaYurgsnZQx6TOKY0APltad8IAhNb0KWVQr4VGgvnZrjPXMwO3Z9YMvPHisdx7Grv71tYaCKjjEZKLZp04P/td2fOsTTiso919r7ZQvMqtOvuhbIVtHqS6izLPrnnKggAqOpcBADXglEpPR1DlUR0vRvtJFQcqRl5s7TTKOdcVY8YfyC91QbDbIMetNWIauKCzTcoZDVplBQB5w5rdFfV+KI2a1wKo6BQGVlQGuA2DJErHt3qZ+wGQzZtatw0gc8Ro2LYDNJBjAYRWQGOcz7el8FtoBlKDBGzLT32puoXPJ59ddQg+5F/OAko671mobDOwSflQujhtF7If1AnU4IW1Q7Rpji44zqAt5c/SX/mdvu9dvbOu+yahQWFea822Ul+HAqs8f52n9B6pzlC9q9lknr8uaUJa8/z+ylqn6pglJIwWnPfBNa9rGzj3BwC+G8AL3vvF1c+GvPfdYzC+UcP7Fy723zj4Wm6iVMNLMTS9UjOhtS5Ff4F8IxDulxFeNepUAWrKXWkidtvtS3YHo226H47BLoCs3/PcCKU26IKZaqDrcdkMQql21miatu5IVjt18eixjNPO79Uw1P3o2DgW1gnp2Pmdvab8TM+jUbYrRFmiUV2kjB/duhykHvF8Q8auTioLZz991XvfWzg4wVe+873+0uU/ABBuWR6SYaVy6DUhrKPO84gZ6lokro41DUxd9JtQ2cmyChJx5rVf+NLMTE7KyC2PbaOpnMgpbyrfKnuhc9Nor70WF48ew5Wz+7Ouqvo7vYZ6H6ys6vNtrzuhgZSV5x5i7vZng7VRWsfFJjuq05Tu1QpWHLqU/c+6KsrEb2//xtKy29vb65/e+O8B5LN++ryqrGm9ntKfKGf87fYlu7Osr9VZdNZDjRPKXJcVhy4F6XZqKGu2UuWtDDRjzftHR852xOQ1osOnzrU+W3wOelfvxP3TWzFt3ZFsbVFrNFOGdd1NoKa7KOvcX0ifqV6nvuf2Ok5mFTkXqvPC8+PnY2H8Oueakt0rV640tf/hTWtyz7tC76fNOm9fsrttGRiOQZ8BtVc0EGXndmvv6BiB+kCZPqtWr9mMvn1mdD7U9yE50/eULyt32r3SjrHIRtDzaFRzRmfuQ/7lrKmJrss5mmhGdscb3TOf9qc/uHzMjve+Ex+dMNdmpCjlwHnvv8E596I4cNe89/UtqCYQ3r9wsf+Vr3tH7rOFL83MOulxUqRxACA48RBFWQBrbCpCETPrsDCapUYtFasacJph4z5pUGjhOcdkqR3W8NT3VHS6rRpVBMfMyUEncZ0wQpQ669CpI6h/lQpo6UjqtPL62u2I2PICBK9V2ejvikOXCmuJ1PC5ePRYFqV/07wPNO3Aff3u/wAgXJCvDkLo+5BBq5Ot1gXoOehzwAmT17nMNQot2fBg4GQWHAFQZ9Ro8ITfqyGocmwz5EA4U2SNU70/nPzV+aYzZ41X3kNruBTBOmuh4JE1/lVn8DkO1UZZg6rdlDEAGLxzN+fMNuvA0QieunhzjiJlgx4Ack6FpbWrzKoeso76SJ1XgoYwryk7KCpUl1tHXsepziUdMi78S12lS34QRXJm9R2Qd7gU1MmxoBKDFECtJknf20CGjqko8GgDaTxHdVbbEXAoi9F24DoZj25drgvqMSiiOlWXKADqgycAGgY3gfD6bFZPK3j8ruMvYPDO3Vz2WQNytNFUl1NnUzfrs6FOm9oiGkCwTVoI2n783VjJaQjJgYsjOXC6gXO/AmAfgEMAlgL4JwB6vfdxsvYEwFe+873++z99P5s4bMQWqFcqChs55UOvSsFG1xgJBfIKjROeGpL83EbibFQv5NjYrCChrX25f1LUiJghqWNT2p2NkqvhS4QM7H1rF+Dw7IXou30tc1g5ZmvgcB9E2YggvwtlIXk9LI2VKJN5C2Hq4s3ZxGENKHt9WsliAHkHLhTRBOJOuH5nYY1ixWhx8oc3rcllnOdufzZnzNnxWmfTnmco0ssJXSf4ogwcUL/WIoDc72POWswosVHf0H5Czl1R8AeoX0tK/x+tLAYzcvvWLmgqe1xkBA/euZu7J2pIEvZ+2WAWUGxMjiYYnNBMOFAxjHVMg3fuNmX4rTh0KUcjjgVXLh49hjderJdZi6mLaxkMWxsUcsisnPMeWT0Wct64PWEdN7137cw6lcWT7MAp+OyFGEXtfp6Y0bb6mk5i2aAT7aBp647kggwMKtogQiijDeQbbzEDycA3F90mxiq7VgbJgYsjOXC6gXMzARwAsAKAA3AewA7v/edGf3ijh/cvXOx/+Td+r87gsgaDGn9ALfug0Z0Q/YzbKuVl15nrbZmklBahY7c0o1A2g7BUJesUqQLUiCybjJA62YgeaA1q/dxeb0uDA8KOsMJOOjRybDTRRtYI27yE/4904mKGQR0Bzfoona4VCqU6cOoYWjAgYY0qKxf2s9HI3pQBo60PBk7mDN1Hty7nKMjNGsG8DjbrBtSee0v3BVD33OtvNSthaWShzAf3E8o6W9pdKINjI8lAOFs3Vvdu8M7dtjlwoX0D+XO3xloZh+VxAjP8GmRpx3yiDrmlrIUyzDaTaLeNBZOsUQ3UdH67zqUZJAfu8cHUxZtxbdGrWQabMqU2hqVPEhpApwzaheY7DcmBiyM5cE8AWEdks16hjAVQT4ML1VQcnr0Qh2+e6ugHvxFoJIQyGTZCS4Syh9PWHckt7GypTUTMMdPfkFoUyirxva0vCtW/hbJ2lkY5GhF8GkhK3SOunN3ftAPXjDFhI/hA/rpRlieyzHYK6LQToVo6BZ8pZuWBcEDC1qYqNJAzHp3LkhH8eIGUN5sJDQXibP2c1iRaaIBiPKlniiS7CRMVyYGLIzlwuoFz/xGVjNvnq+/fAuBnvPf14f4JhPcvXOyf+aGDAGqR9GuLXs0ahNg6hlA0h58z9f64dh3SCC0RK0IO1fQAqMsW2kiYrV2zv4tlM2yNm61/A2p1U0UUCmD0s04rDl0KZuQevHR81By4EJjBHU2aXUItiwTUZy9CAY1QRlSh8m6pruPVdjoZwU8mNEM6UfVIkt2EiYrkwMXxJDlwZVaz7KHzBgDe+9ecc4tHb0hjg0+/9jpuVjMiSsWZ6+YAG3syQ0uzSQe217I1u6oUvfOrJmPStok3eTUDGodTF2/OKJTnSUe8CqDahl3pmzAtjfetDdMh+X77kt04H6nTCtEg6mqDDF1Uu/KxNbvSKMei4YPFhW3LADG0tR5lLKHrziSMHphpYFaDzlYlKFHLYIfk+cbAXnSb5RNiSGsGJYw1alm0pUmPJCQkJIwDymTgrgH4e97716rv3wrg9733xe3WOhwpojY+0GYFWp8VM2JtUbVmH7S2Iuc8CmwjF37G3/HY40khbDaalmR34kFb8StiGbgQTTuUZR5v6mvKYiRMVCTZTZioSBm4OFIGLo+fAfA/q90oPYDvATAwqqNKeGxRidwuBXCptlQDWDNRWxdqaOAkukNr7fVvAKqL5gIVw3bfWmBSNSLMaDBrv2LriN0Y2IsLWeQ41X8ljC5CWbIVhy7V1RZVghGhzprXcRAVGZ6UPUMJCQkJCQkJTyJKNTFxzr0PwDej0oXyt733fzzaAxttpIhaZ4HdB4H8mni2/XqrNRdcbJuNTcY7c6FIGbiEiYqUxUiYqEiymzBRkTJwcaQMnEHVYZvwTltC56Jn1gwg6JTVaix6zPtmMGnu0srvU71GQkJCQkJCQkLCBEYpB64T4Jz7zwDmV98+DeDz3vtFzrk5AIYB3Kh+d9l7/9zYjzAhISEhISEhISEhIWF0MWEcOO/99/J/59zPALgrX/+p937RmA8qISEhISEhISEhISFhDDFhHDjCOedQaaTyzeM9loSEhISEhISEhISEhLHEpPEeQAv4RgB/7r2/KZ+9yzn3onPu951z3xj7oXNuq3PuinPuymc/+9nRH2lCQpuQZDdhIiLJbcJERZLdhISETkZHOXDOuQvOuaHA67tks+8D8Mvy/hUA7/TeLwawC8AvOee+MrR/7/0R732v9773bW972+idSEJCm5FkN2EiIsltwkRFkt2EhIRORkdRKL33K4q+d85NBrAWwBL5zZcAfKn6/1Xn3J8CeA+A1PM3ISEhISEhISEhIeGxQkdl4EpgBYA/8d7/GT9wzr3NOfem6v/vBjAPwCfGaXwJCQkJCQkJCQkJCQmjho7KwJXAeuTpkwCwHMC/ds49BPDXAJ7z3v/FmI8sISEhISEhISEhISFhlDGhHDjv/cbAZ78K4FfHfjQJCQkJCQkJCQkJCQlji4lGoUxISEhISEhISEhISHhikRy4hISEhISEhISEhISECYLkwCUkJCQkJCQkJCQkJEwQJAcuISEhISEhISEhISFhgiA5cAkJCQkJCQkJCQkJCRMEyYFLSEhISEhISEhISEiYIEgOXEJCQkJCQkJCQkJCwgRBcuASEhISEhISEhISEhImCCbUQt7txNWrV//SOXdjvMfRJGYCeHW8B9ECJuK4x3LMX9vMxkl2xwwTccxAh8ruBJVbYGLKwUQcM5Bkt92YiHKQxtwYTdkMCY8nnlgHDsAN733veA+iGTjnrky0MQMTc9wdPuYku2OAiThmoKPHPeHkFujo6xnFRBwz0NHjTrI7RkhjTkgoh0ShTEhISEhISEhISEhImCBIDlxCQkJCQkJCQkJCQsIEwZPswB0Z7wG0gIk4ZmBijruTx9zJY4shjXns0Knj7tRxNcJEHPdEHDPQuePu1HE1wkQcdxpzQkIJOO/9eI8hISEhISEhISEhIeExQvfMp/3pDy4fs+O978RHrz4p9YhPcgYuISEhISEhISEhISFhQiE5cAkJCQkJCQkJCQkJCRMEyYFLSEhISEhISEhISEiYIEgOXEJCQkJCQkJCQkJCwgRBcuASEhISEhISEhISEhImCJIDl5CQkJCQkJCQkJCQMEGQHLiEhISEhISEhISEhIQJguTAJSQkJCQkJCQkJCQkTBAkBy4hISEhISEhISEhIWGCIDlwDeCce9o51yfvZznnfmWcxzTdOXfWOfcnzrk/cs79G/nuzc65/+ycu+Wc+wPn3Bz57pxz7vPOuf9m9nfCOfdJ59xL1deiyHHfVd3nzeoxplY/f4tz7iPOuUHn3Mecc92R3zvn3M9WxzbonPs6+e6Yc+4zzrmhgvMuOrcfrI7rpnPuBzvx92ONx0V2nXOLnHP/q7r9oHPue+U326rbe+fczILjxmT3vdV9f8k5908Lfp9kdwyRZDd33Jjs/oPqPgedc//TObcw8vsku2OEJLe548bk9ruq+3zJOXfFObcs8vsktwkJBUgOXGM8DSBTyN77O9777x6/4WT4ae/9ewEsBvB3nHPfXv18C4DXvPdzAewH8G/lN/8OwIbI/n7Me7+o+nopss2/BbDfez8PwGvVYwHAHgAvee97APwAgAOR3387gHnV11YAPy/fnQCwKvI7Inhuzrm3AviXAL4BwNcD+JfOubd04O/HGk/j8ZDdewB+wHv/flRk5N87556ufvc/AKwA8KkGx4zJ7l8A+CcAfrrB75Psji2eRpJdIia7nwTwd6t69/8GcCTy+yS7Y4enkeSWiMntbwNY6L1fBGAzgP8Q+X2S24S2o5Hz3yBwsMo5d6P63T8bu1GHkRy4xvg3AP5mNVr075xzc3jjnXMbnXP/1Tn3UVfJYG1zzu1yzr3onLtcfVDhnPubrpL9uuqc++/OufeOZEDe+3ve+9+t/v8GgD8E8DXVr78LwH+s/v8rAL7FOeeq2/42gC+2cszqPr65uk9Uj/F/Vf9/HypKGd77PwEwxzn39sBuvgvAf/IVXAbwtHPumervLqJiTBchdm7fBuC3vPd/4b1/DcBvIazcx/v3Y43HQna99x/33t+s/uYOgM8AeFv1/Yve+5eLjlkku977z3jv/z8ADxoMPcnu2CLJLhrK7v+s3jMAuCxjsUiyO3ZIcouGcvuX3ntf/fzLAPi6HdTGluQ2od04geLrHQwcOOfeBODnqt+/D8D3OefeN6ojbYDkwDXGPwPwp76SmfqxwPfdAL4flUjKAIB73vvFAP4XKtkooBIZ3e69XwLgnwI4bHfinPsmV6Mw6ut/Fg3OVaJiH0TVgQIwG8CnAcB7/xDAXQBfVeI8B1wl2rDfOffmwPdfBeDz1X0CwJ9VjwUA1wCsrY7n6wF8LcLGRDa2wD7KIHZu0f065/61c27NeP1+nPHYyW5VvqYC+NOGZ19DkeyWRZLdsUWS3QrKyu4WAL8R2UeS3bFDktsKCuXWOff3nXN/AuAsKlm4EJLcJrQdJZz/WODg6wHc8t5/ohoIOVXddtwweTwP/pjgd733XwTwRefcXQAfrX5+HUCPc+7LAfxtAKcrwRcAQJ2DVI2QLWrmwM65yQB+GcDPeu8/wY8Dm8YiXMRPAPg/qCjpIwB+HMC/tocr2O+/AXDAOfcSKuf9IoCHge1bGVuZ30f3673/F+P5+w7HhJLdqhI9CeAHvfePmjlc0X7HaB9JdtuLJLu1fX8TKg5csJaozD5aHEOS3eaR5BaA9/4jAD7inFuOCv13RbP7GMEYktw+3pjpnLsi749472P08hBiDnbo829oeZRtQHLgRo4vyf+P5P0jVK7vJFQiUYuKdlKdhPcHvrrnvf/bkZ8dAXDTe//v5bM/A/AOAH9WVdgz0IBq4L1/pfrvl5xzx1GJ+sE595sA3g7gCoAfQiUSMbkaTfoaAHeqv/8CgE3V3zhUajM+6Zz74ervAOA7ZGxEto+SiJ3bnwH4e2a/v9eBv+80TBjZdc59JSrR2p+sRsUKUVZ2C36fZLezkWS3sm0PKjVE3+69/1z1syS7nYsktwLv/UVXoYzOBPC9SHL72GHaM89gfv+esTvgiY++6r3vHcEeJozjnSiUjfFFAF/R6o+rzs0nnXPrgKxAcmFgu9/1tSYi+goqY+fc/4OKQvgR89ULAH6w+v93A/gd4ZsHUY2y0fn6vwAMVcf0bdUx/MPqPn63uk9Uj/Fr1d897aodpgD8QwAXvfdf8N7/nJzHnerYfqB6DZYCuCvOYxnEzu03Aax0lW6YbwGwsvpZp/1+rPFYyG5Vtj6CCq3hdMmxl5Ldgt8n2R1fJNltrHffCeAMgA3e+4/L75Psjh+S3DaW27lVWwOu0iBiKoDPJblN6BDEAgcjDSi0H9779GrwAvBLqDg1/w7AHABD1c83Ajgk270MYKb9DsC7AJxDpVbsjwH8ixGO52tQ8fyHAbxUff3D6nfTAJwGcAvAxwC8W3733wF8FsDrqAjjt1U//x1UKBxDAD4M4Msjx313dZ+3qsd4c/XzDwC4CeBPUDEo3hL5vUOlCPRPq8frle9+GcArqDST+DMAWwK/Lzq3zdXPbwHYJJ//awBrxuv34/16HGQXwLNVuXhJXouq3/2Tqrw8REWZ/ocmZfdvVH//BQCfr/7/lUl2x/+VZLeh7P4HVLr7cb9XIr9PspvktpPk9scB/FF1n/8LwLIkt+Mvt6P1WtL9Xv/XN//XmL0Q0YNGNrLnMvDdalTqiR2ApQA+Vv18MoBPVJ/PqdXn8/3jeW1ddWAJCQkJCQkJCQkJCQltQe+CLv+xjxwfs+O9ad4HrvoCCqVz7pdRobDOBPDnqCzpMAUAvPcfqmaHD6HSqfIeKg72lepvvwPAvwfwJgDHvPcDo3cmjZFq4BISEhISEhISEhISHmt477+vwfcewA9Hvvt1AL8+GuNqBakGLiEhISEhISEhISEhYYIgOXAJCQkJCQkJCQkJCQkTBMmBS0hISEhISEhISEhImCB4YmvgZs6c6efMmTPew0hIwNWrV1/13r+t7PZJdhM6Bc3IbpLbhE5Ckt2EiYpmbYaExxNPrAM3Z84cXLlypfGGCQmjDOfcp5rZPsluQqegGdlNcpvQSUiymzBR0azNkPB4IlEoExISEhISEhISEhISJgiSA5eQkJCQkJCQkJCQkDBBkBy4hISEhISEhISEhISECYLkwCUkJCQkJCQkJCQkJEwQJAcuISEhISEhISEhISFhgiA5cAkJCQkJCQkJCQkJCRMEyYFLSEhISEhISEhISEiYIEgOXBMYvHMXg3fuYnjTGjy6dTl7Dd65m/0FgBWHLmXf8Xf8fsWhS1hx6FL0GCsOXcr2k5CQkJCQkJCQkJCQoEgOHGoOVwz2u/n9e3Lvu+8N5/YV+u3Q9C7sOnM9t93UxZvrXgo6fo3GTqdP/+dvhzetwfCmNYVOY0JCQkJCQkJCQkLCxMDk8R5AM3DOzQfwn+WjdwP4FwCeBvBDAD5b/XyP9/7Xi/Z1/+VbGN60BgCwb+AkhgBM2bQGXcdfCG4/vGkNplT/vwHgwInB3Pc7NvZgCoC51c/7AvuYC+DYom8uGhYuHj2GXdiMOO3lMAABAABJREFUC9uWZZ/RiZs0d2n22YpDl7Bv7YLs/a4z17Fv7YKckzg0vQvddDbPPcwyez2zZhSO4UnB4J27E/paTF28Gcu31Jx+lRmCjvv5VZVHXWUoIWE8MXXxZlw5u39CP4MJTx6oU0P6NiEhIWGsMKEcOO/9DQCLAMA59yYAtwF8BMAmAPu99z/dyn4Pz14IoOKETV28GW+8eKwuG3b/9B7cGNgLoN55i31m0Xf7Gm6duY6LR49Ft1m+ZTP2rV1Qcdqmd2Wf89h0MM+vmgzcG8b5VZOz7+av2pN9DlQcuJXnHmb7UOcOSBMQMH5OXKPMahE+/pm/xOCdu7hydj+A2n3luQzeuZvLCiusXC/fshnnV03GkMhaMqgTRgsadLh/eiuGUDGIky5K6GR8/DN/WcdiWXHoEi4ePYY3XozP5wkJCQmjhQnlwBl8C4A/9d5/yjnX8k4OnBjEjo09OHBiEAtfmgmg3si9cnY/bvRvGMlYK8eYvRA4+OG672jQMKNG43tK/wagf08wa6KfPRg4CQAYqv62Rtesz8o9iXh063LuetHB6UbFyQ3VHI6GE8OMr6XgtgLe031rF+QcsEe3LmfnxawsAExbvTOXrQvtj9s+unUZNwb2RrPRCQlAPV28Ef7wj1/G5Pk9OL9qchZY6l29EwAwNWAID965i10m4JWM5YSRYvDO3UzumgXn06IgrEUZpkRCQiOkMpgEi4nswK0H8Mvyfptz7gcAXAHwo9771xrtgFkz/r226FXM769l2vj/gWqGrhns2NgDoJIxm7p4M469BCyvOm9U5hePHss5b1OqTuKN6u/UgC4yptXZWHHoIQ5e3YB9Ayex68z1nKHffW84Z5g/KVQQ6wBrdqr73jBWnnuYo6MC7c/M0XkDatnUVvGeGS5z2nLOPoChgZPorTprIef9/umtAJAZ0PvWLsgytnT+h6Z3AdWgQEKCBR2rfWsXYOH28r/78pkzcR/AtHVHsHzLZqw89zBn2A4Lhd0yEAgNrl1b9GoKMiSUBue7fWsXYPmWzfjt7cdL//Ydb3kq06f71i7ALmzO5lSydhQMEk5bdwRAfq4PbZ+QUITkvCWEMCEdOOfcVABrAPxE9aOfB/B/A/DVvz8DoC7d4JzbCmArADzzZU9lTpbSH/vmrc8+vzGwtxQ1UrFjYw/m9+/BynMPcWHbsuiDt2/tAmDt/uw9aZwf8i83dTyL86sm48bVikG/r2qEd98bBqrOG1DLsPB7ndimBDKNzBhZGmenQp0vnuv8/j3ZvbUZMKWd5tGeerEQXZKZ37LIye7sd9R9n92j/g24tgiYv2oybvRvwPn+PRVHb9XWimNWdVhpUNAgOb+qIh/d1e0TjTIhhOFNa7Brye7MeG0EK7dfL8ErYvmWzZX3iyrP7pT+DZk8q55k5mP5ls04ePX5Np9ZwuMMZVmUZaSo7E57y9uDvx+a3oXlWzbnssUMnvE7gt9tful3khOXUBoMWl05uz8LsjYTOEt4fOG89+M9hqbhnPsuAD/svV8Z+G4OgP/mve8u2sfb3Jv9xY3flnPQ1KBWaqWF3Y6fza8avwCyWiSChnK31Kd1i1MFtMcxsg1P+J5ODP/vOv5CkNJXlB3S7GQRuN14O3pFTVusU8UsnFIJY79tBsOb1tQ5wEA+aPAL+NRV731v2X2+f+Fif/1Xfz54L6xjaJ1V2w3VyuSuM9cf+4xsQuuwdOOFs58uLbvvX7jYP/NDB3PZCKBm2F48egxXzu6va8jE55HvlYKZZDWhEWJB1N/e/o1Nye7NSYuz4MGDgZOZMU3Qgbt/emuWZSZUhhm8SA5cQhlYCi7QnOyON3oXdPmPfaR8tnukeNO8D0yYazNSTFQH7hSA3/TeH6++f8Z7/0r1/50AvsF7v75oH90zn/bLPvd09j6UmdEMnM3WWedux8YePKjSFoGKYaFLCAA16t5YdgJUB46wjpXS+xoh5MSpg6Lfc5IDxqf7YaOum49uXc4c6aHpXVldhDYHGYmBuOLQJRy8+nz0+hDvO/HRphRO74Iuf7J3Xu4zG3AIZfdCzhyhxkjKviWEwM63GuRoxYEDkMtUAKgzhqf0b8j0h+qTXWeu4/yqyZi27kiiUCY0RMh5Y9CqGUPvK9/5Xn/p8h8AQDQIawOhlF8yHggGK5IDl9AIj25dzjWiAyry24zeHW8kB270MOHWgXPOTQfwrQDOyMfPO+euO+cGAXwTgFIVyqFMBTs3Dk3vwvz+PTh88xQO3zyF+f178GDgZPZef2+N5X1rF0SjfiOtf2oFzIbZujqiWSNofv+e7GU/1+850fXNq/nSvC6h9evseny6nh3/bwS7UHoRXWbS3KXomTUDK889zOrG+Bv+zja0aRZ02HjfGRTgayT7VTAAsWNjT5086nFuDOzFynMPs0mh+95wnfGckKAY3rQGj25dzpytg1efL0WfLMKVs/vrmjbpPtmYiUEPOm8Xjx7L6oqS85ZQBC0N4F/+b43iRnjHW56qm0vovFG/Us9TZinP+9YuyGqPAdRloBMSQpi6eHOm64CaHLfagCfh8cOEzMC1A8zAHb55KouWWcpOKBPBGg2iEUUNqK3BBUycdbhCWblQ3VajjopF58tCb5sJ4u9WHLqULZPA4xTtTx23ZjNnpCmEJtdWsnA281nkrDVLoeye+bQfvHwOffPWZ8EEi9B1UjqnOtXcR8rAJVhMXbwZ909vRd+89ei7fQ1T+jfk9EAz2eP3L1zsf/k3fi975tVhK6qvVT2hgZZEn0yIwWYuQvT4ZrIYzCJYuVWEdK4GJO14kp5NiEHZDgByctts9ni8kTJwo4cn2oE7/cHlucyb0h8bgcawKvQbA3uxfcnubJsiR/BxgDoEITTjrI50kfGyReE6sdv7zHV9gEq3xlabtjRy3pSOu2NjT8sUSq25JMpcv+fcnMxpU3pPovQ8Xhjp+mp8JhmwCjUCamaypAMHIOfENRM4YFBnogTCEsYelBEbTKWeI0W+FQcOyLNoygQWgXwdUwo8JBSBrJ9QlpbJgInkpCQHbvTwxDpwbGICIOsaCaAp4yBUXwY8edQem60bjyYmZR04dRTt+nDcD4Ac5aWMPNimKFYmipriNJuBo0Jcee5hy9ndR7cuZ/SM5Lg9ntC2/M1C60MJreEciQOnTUoe9yBXwtiimXbrzTSCIOtBMVG6MidMDGjPBNtoTJvg3RjY23TQdzyRHLjRw4SrgWsX3v5VT+HAiUE8GDiJoeld2YLIzRjB3FazUE+iMrfn3MryCyNFWSeEBmmsno77GZreVbpekUaD1rqFYCmooXq1Mnh90rRMZpm9aLamY9LcpXjjxVRInxBHaEmRdtTw0mk7v2pyVoOZkDASPLp1ua7mrez/ZRFq3jXW81zC44dHty7jOTcHQH59Vu1cyv/ZmyEhAXiCHbhpzzyT0cgY7WjEYSeec3MwvGkNnnNzMGnuUkyauzTaIORxB5uLdB1/IdfcZKTr2Y0W2Lhk15nrQadnxaFLWfaN2YaQDHBbOoJKZWR9mTaOidWqtWIAPPXofmb4koJmaTnPuTnBBiwrDl3C1MWb08KgjzkG79xteqIf3rQGUxdvxtTFmzOdpjrRdtlr1pmzcgtUnptEh0xQsLFVGbDxFfXvhW3L0DNrBi5sW4YL25bVNXygYRwKThTBz35Xnfz3zVvfsfNcQufj0a3LOfbSpLlLM/mljQLUBxtSw7EEIurAOee+6Jz7QuD1RefcF8ZykKOBTw19IthwRDF4527wYfmQfxldx19oKXvyOEGzWPyfht3wpjVNLU8w1uAErwh1r2xkBHffG8bBq89nbc/ZCdL+jk4ds3CHb54acfS2FUV+YdsyvPHisVxb6ycZdhJ9XED5Lercyg6v7DIZA+WM1MlWI8D3X3kl+5+dUMsuqpzw5KCMTAzeuZs9tyvPPcSU/g3Yt3ZBnRxfObsf51dNzoxgOm7N6t6nHt3PvT9wYjA5bwl1YMfsImhHberSwzdP1cluaI5O+jJBMTn2hff+K8ZyIGONr+1+d9bR7OLRY+jZln9YyrSsfxIzbhad3oJelaIuVK10G9bfXNi2DFMXb8ZKVNanGkJtwrf3mtmtG1dfzT47PHth9v8OVDqb5j4Th58O3Y6NPfiFE59q6pxenzQNQI1aYeuHVhy6hH23r+FDBXVFTzp10naTfZwweOduJr8hTF28OWvkoHVtB04M4n41Uzy8aQ22L9ldoTlWf3fgxCAO94+MRkldsW9t5X2qfUtQUHYBBGuUhzetwYETg+i7fQ3z+/fgYFV+h9aexJT+Dbgh23IpCtss5/DshTh88xR+Yd4Hmh4fZT8UvB1p06CEiY3BO3fRjYpNNHinK9rFHECwDhgAuqtN1vatXYArZ/dj15nrWd3b/P49lZr3RDlPqKIoA/fWotdYDnI08KmhT2RrEcWM2V1nricDI4LhTWswpX9DNjlaSuL8/j3j7uAWUR+BWnGwbqeLePeu3ontS3bjwcDJoEO/+aXfydZzs1mJAycG65y30NpvrVIoY537eC4pUleM7nvD2fpi8/v3lArYTCTo/Y+dm9IYmTkm2E2XBuvQ9K6MBkx5bzYT52e/K9MVId3KjGBCAhBnGFjnaWh6V7a8BevaCepxzlNc27VZsO74wcBJPBg4WTe3PW76I6E5PLp1GbvOXM9kNlTXO3jnbo7SSx3du3pn9n+orEPr3jo5WJ4w9iiqgbsK4Er1r31dGf2hjT6m9G8IOmgrDl1C973hXIc/oPKQPu4GBs8xRi1TaqRGONkEhhjNupYyk2XsPu1buwAXjx7LuOaMful58TM6cyFcW/RqbiF3tlfXz9TQ0AYmI6VOvj5pWpZJLAut13vSoTUz3feGHysq36Nbl9F9bzjapIHrumlUl/8DtczwwavPY9/aBVmTJ5tpb9WQGJrelT1/ZRGq5XwSMbxpDVYcuvTYPst6TqFmU1y2Rh00bqvU9Cn9GzK5ZiZDZRxoLYscCzwQ1l5IeLLA+0/dqDYIg8bLt2zO6L4Eab67zlzP9C7nI34OVGS20xlPCWOLqAPnvX+X9/7d1b/29e6xHORo4O1f9VT0u5hxMWnu0ify4SGvm52SNPpuo0f8vJGjS0OkFbSSFV157iF6V++sK2rvmTUjm8zn9+/J0epY38aJm2O246ZDxghwbLmAdnYsi9Ww3RjYi/OrJtfJcCtd1x4HsDvd1MWb8Zybk8kl7/ONgb11wYdG6OT6zmnrjtTpqAqlp5bd4vfWECb65q3PnmNmzHRJgb7b13L7KYunHt3HrjPXg+sb8VihfW5+6XeaOs7jBAbSQrqyFSduJHp3rMFnlAFFlUmF6m/9a7flupkq32Vha+ASisEgA2WXepiftxII72S5HZrelelIlbnQeW5fsjsXMNTMHYC65MG+tQuw8txDHDgxiBsDex9b6n9C82jYhdJV8Kxz7p9X37/TOff1oz+00cW0Z57B/P49UaUQiqIAj0/NBpWoKlagpoiGpneh6/gL2fl33xuua87RfW8YV87uz5yJfWsX5CKb9tppBJmgQm93RNl2ySvbtIORWy4EC9ScHz3PEJrNsLW6jMCnX3s9+l3X8Reyzpl6/dnZ6kkCz3/f2gW4tuhVHL55KpNt1n0B+Wj/1MWbg7JIQ1odtzIF62MNOkdqICgtZ/mWzTlHSZ8T0st2bOzJjBGuNah0y5G0/r94NN5AZ9LcpZjSv6FOJz+pzSIoW9QnWQ1MFd33hks5ZNT1uh27jXYyti/ZnZMzyiwpk6rfY7TIGwN7sevMddwY2Jtl2vtuX8syzc3g/KrJ0UCPOptPOihnpKyeXzU5CyxyLTOgFggrumaUb8oqOyh3mt4FkGV5h6Z35XSuljWwCyptCP7PbDGDDCvPPcyu05T+DTi/anJW95mWEUggyiwjcBjABwB8f/X9FwH83KiNaAzBh0wVBFCbOB/XFtdW+VGJDG9akxlnPbNmBBfoBvJ1MXTigBqVRaFZD9YNMONhMx/tVMo8VhmqDLedNHdpzqClEUzKw7VFr+L8qsm52jaCWbfDN0/laoUsrVL/cptm8Z4ZDkDN+VXDTBeXfxxl14Kt77msB7Hi0KVsLb9dZ65nVMDDsxfWGX+854dnL8TyLZux68z1zJGjTOq1ZKaV++iUyPDgnbuYu/3Z7L1tQ02qs0KNgb5567NzooxzrUEaz6x1bSUSfP+VVzJdoUsWEMyyPOlUtKmLN+ee48M3T+XWfAzdz+FNazJdQCOX15YRfms8Xjm7v6OcuCn9G3LBARr+QN554/PM+lXKsMovt2WWmQ15Lh491lIGQzuo8vra537luYdPJENHwWty8Orzwe95b3mfeH80mKwOHeX1/umtuHj0WC5730ksCAZHKYtWb/KZU/lWZxZAjukQWjqA37djLc6ExwNlHLhv8N7/MID7AOC9fw3A1FEd1Rjg/iuvZHxjoL7e6XFfYLb73jD65q1H37z1Ga2QyofGqi3UZtTywcDJzPm1RbuHb57C/P492WRGZ4VR4LFc08Q6nFT+y7fUr4PGc+V6divPPcwyBdqQ5MbA3iwTaZ0vZi4AZI5czEEb6VIU909vzQzdJ5UeSbCZzK2DH84+u3j0WC4CTKON946yofdnx8YeHLz6PA5efT5nKNAwVoosf3t49kKcXzW5IyLvPbNmZPRGgk5ckT7jtdAMu2Y0NPu2b+0CXNi2rKUGRX/+udcxpX8Dri2qdG5dvmVzHZ3SGuFPIrgOJeVXo/n8nzqbNG+gfuH15Vs25+RS1+C7f3prx1Kx1Fmz2YYieVbZ1+y6LiPwxovHWm6udWNgL6atO5K9V73LcT4utbQjATNJvAestVWHRumwIUeHwTN2YQRqc97c7c9mdY6dDI5dz03tJUtNZ72brTUmK+jCtmWPtU2a0DzKOHAPnHNvAuABwDn3NgCPRnVUY4A//9zr2YPBOicAuTqRxzGa9ujW5SwDQUOUBpWlHerkf2Ngb26CtFQeCypiKu4YqMi67w1jSv+GtkbVuBixgstGlKVUbl+yOztvGvAHTgzmlgGgo3bgxGBmFPGc1UHTTn88z1YmIY0G8/rpPoEnZ4kL0v6s47J8y+aMiqKR+CJ5VKeOtEGNlvJeA7X7qrTMTqD18Fmy2Rarz3QReABZNkONLgBZp1nuWw3UVrLHdJ7n9+/J1WkO3rmb6aQnRXYtfZ2w98rWwwC1gKMaxAxW3D+9FfvWLsgcu4NXn8/oXfoiOiWD3HX8hYw6xowDKWkAcs8igDpZpcML5BtsqUMIjIzmeP/0Vtw/vTWTXToaQ9O7grXHTxpCwcRQ7aLKH3WRzbayqYc+C0r3vjGwt2NkF8jLIcE1ClV38vnTDBzlVO0Sde7sMRISgHIO3M8C+AiAr3bODQC4BGDCS9Hbv+qpbHJ4MHDyiVO8DwZOYuFLM7OoOxVFyChjBO3w7IV1SlYjaGowUylTgemkrEsOaEQTaM2haYSu4y/kIv3Lt2wuXAdt0tylOL9qcubsaeMTUiKVGskOlHTkeG20qYtmbXQ5gVDb4GZxYduyzChppUB/ouI5N6cuI0HQkFBHjIELNRZIfdUGCDs29uTra2YvzE2koS54nYIL25blDAk6dKHMgHaXDD3/2qWSmYyRLgBvA0SUXTW+nwQUORFT+jdksqq0PMowwftCRoTW3pBmqDpIQYedS+l0iiG8b+0CTFt3JKsX0mfN6koudaHnp4EHgrLPv5PmLm2J+aABDuDxqYdvFZbuqDIU6vhJ3QuE5/kHAydzdV/M5HEZCP6O944Bi05gPwD5sg0NolFOqTtJ/7XBMaDGEqLNRFD2n5TgVkI5OO99442cey+AbwHgAPy2975zLJYW0T3zaT94+VxucWegVof1uCrnR7cu48bAXix8aSaWb9mMudufrZvM2LxEDTvlrPMzNjhQJWR53by+ygHn9lxInW1zGTF+MHBy1K7/4J27pfeti26eXzU5V/iunSZ5/XhteF0INcB4HUnF6zr+ApxzV733vWXPoXvm0/70B5fXUV6bObfHAbw/vOZAxYEZvHM3kzMacnqvtGaG2LGxJyfbaoDYz5WaS6OONTbA6C6hURY0anhd+Ixpq2t7rqzhBGpBCSBvbE2auxTDm9Zki9T/Aj5VWna7Zz7tf2nwU5jSvyEzzLXW9kkyTlYcupSr8eK5c4F5GnjqwFi9ahvL2KUxbIRfnXVtUkPd3Lt6Z2Fga6ygy3wQobkFqMiuPp9APoOj8xNlrRXZ7V3Q5Y/9Zr4pFFC7X0+S7AK1Nc2unN2fXQvL2CGss6Z6OeTI8Z4CFVmetu4I7p/emv2OOlf/Dk0PL5w9HqCN9WDgZHDBbv6vNFAr7/b6TJq7NKvx3rGxB+878dGmbIbxRO+CLv+xjxwfs+O9ad4HJsy1GSnKdKFcCuC29/7nvPeHAPyZc+4bRn9o0fG87Jy77px7yTl3pfrZW51zv+Wcu1n9+5Yy++JDo9m3x71bH2mFmpGiccuoLBWQtlonRWdK/wb0zVtf1xCBE6jltJOiyuixGh5c/+Tw7IWYu/3ZXCakVTpao9913xsuve+eWTPQfW8YF48ey+hdoaYkADKDi922ANRFz/vmrceBE4PZZD/SZQUmzV2acxYeZ7kNwdJVAWR1E/r9gROD6Lt9LXPebgzsza3bx/vI+6c0nfn9ezIqF1C5z/r9ynMPsX3J7tyz0AlUSma2tf7HFtYToQyGDepYg6xn1oymu0NOe+YZAJXMierZruMvPHEG8IVty7L6lu1Lducalxw4MZjTpxaWWkU5V2eNOndoehemrTuSc/AYVAM6e+0yZhjVCA5RIgHkarK1pkj3Q7Qiu69Pmpb7vf7/pMkuUHFCtG+ANt2xlHUAWfMNzcJZUFZVHw1N78rZKjYLymN2ErR2M0Qp1fpsbUqi9X663qHqZNbOJyQA5SiUPw/gL+X9X1U/G098k/d+kXjZ/wyVzOA8AL9dfd8QsYW8nwRc2LYsq+tRY83WVGjGAagYsNrggMaA0ls0sm8L0unI6HYE64lGgkb3s9n92wkEqDm6pHMwAkxqBFA5dxpRmnkDarVqT2p79HaDdBtSVJjZULm1Re9atwjk1za09WCk8lDG1SjhBM0mN51SN8saQCDsBGhTHuu02WunUW4Gd1rCm78s2GHtSQUzYBePHss1LgFqWQilo8eg8kjHjLSt7nvDuLbo1dx+1NHTY4aaO40nVp57mJtXNINBMMtxftXkXP0cgJxxzBrV2NqHjfDUlDe1ehqPLehwaOZt+5Ld6F29M5sPgWKdqEFOOoW8j6GsqqXRqp7qhMAZULFBYnWbQE032+uyb+0C9M1bn5NhoHKOg3fuPlEU84RyaEihdM695L1fZD4b9N631j5vhHDOvQyg13v/qnx2A8Df896/4px7BsDvee/nF+2ne+bTfujVz4/qWDsVOkmTtmhpYaQrkFZlaTqkT9hW5Upd00WtNfvB43GfMbrFeFLRHt26jElzl+aa2hTRPkIFx0TM4O06/gKec3OaovIAT7bsKp5zc3D45qm6ltRT+jfkKI1D0yvLB1AGbVbUNpoB6p2YkBFi6bL8zcpzD8e1ppYBgoUvzcyeUaXxKJ2HUFrdjYG9uWeW10gdhZ5ZM/Do1uWm6CqkUD6pQTMLS9EmlEJGnaOOiqWlA7X7Z6laQK1duaWmWRom9z/eepdQh5TnQMqnnXeA2pxEWH3N86Veb0Z2SQPrBHp0J0BprjqH67wO5Gnm/B6op/RqWQdl3NKCQ9BmYgdODHZMUFSdSVt/fPHoMVw5uz9nK1FX2+AKkKdEt6J3xxuJQjl6KJOB+4Rz7p8456ZUXzsAfGK0B1YAD+C8c+6qc25r9bO3e+9fAYDq368O/dA5t9U5d8U5d+Uv7r8xRsPtPLALo7bJ55ICtmPUjo09WVSInzNSxglfnTddgJLQejFCm0lYh4hZgbFc50ULslX5WkpdDLaZi42w8fw166PrljWCyu4X3/xU6d89CVB6L3Hw6vPZvZvSvyHnpJHOG1rMl7+h48LPLKWNdC4aG5Y2M5aF9VxDiYuNE8zqKIVSGzrEGptoowAAOceXMl1Wdq3OTc5bHrYLneoMdUC4mK8NJGimgtto0xl1cMgU0FblZEkwM8os61iBy8vwpeOyVEj9PxSAIFMilL3RrFzZeUVl97N/8fkn2nmz2S3r/Ct0LqTe2L5kNx4MnMS0dUeyLJre4wcDJ3H/9NZMb6tTqNR2PdbQ9K7cuquHb54aV71LueJ6urYjMJCXW7WVKLvagIr7mLbuSG69x4QEoowD9xyAvw3gNoA/A/ANALYW/mJ08Xe8918H4NsB/LBzbnnZH3rvj3jve733vbPe+77RG+EEAZUGjTWuo2UNUgWVya4z1+s60llFrlRLOoJ0YLTrHw1m7eDI79qhsMrs48K2ZZg0d2ndRKV8c3L7OZGEaBDa9S9kIJOySieubMRQZfdtz8wu9ZvHEY0cB06INmBgG5colcrKJMF1/UhHswuA65pUusA1DZh2GBNlKG1qWNKZtXUSISqPBl70L/cD5NfkokPBv2UM2qRza7D3knJiHWsbjADqW7HTUbPb2CUCrDOjTUv4G83eFVE1m4HtUBiDOvSUNcoVW61T9uwagczKqWzT8GcWGchfA/5tVnafZJ0L1K8xSH2nNVuqP60cUcaZYWMNWGzdR8qCUt9VZ7OuU7dtFx7dulyKjqlyCtQ3YbLrvu1buyBr3KaZcrJFeE2AGrX0/KrJWbBcu6gmJAAlHDjv/We89+u991/tvX+79/77vfefGYvBRcZzh+NCZXmDrwfw51XqJKp/x218EwnnV03Gg4GTGV2Ka67Y5hB0xG4M7M3RJsnl5sTPhiUEHTZV7rqWloJNQlibow7eSEBFXMaYsPQd4sCJwUyB0sAC6lv9ahRba7IIOqkJrSE0qcbot0r5C33ffW+4riGNNSTYmCZk8Or6atrpEUBdLU6r4PmWceJCwYRJc5dmzxOQXzNQ32utkY5bDabDsxfm1jkMPcMJcdh7OHXx5iDtmhkm6gptdKDGYOh+h5gCoYweUAs+6H4vHj2WGY4jqSfS35bZD8+v+95w5tBZhwCoLdRNvatLLWhtlOpZ/tUaqlbr4J5UDN65mwWxiPOrJueojkBeB9nlhgi7rltoGSN2CQWQ1YGymRRl2NaRacBpJLLbbNDN1kUrNGjAplIMmtM20AAZg4t03CzNGahfwibhyUaZDFzHwDn3Zc65r+D/AFYCGALwAoAfrG72gwB+bTSOT7oHEDeqNJXeiRi8cxdXzu7H8i2bM+XTd/taLsKrClULkVVJkRZgW5NbaGMEwv7P/WrdTTuhdU+NoIX+pEfQub149Bj2rV0QpVTaY1w5uz93fpayt2Njz5jJCuW20+WzEXQh9MM3TxXKCifNAycGs6i8tsnnd/qXUHqPdqEk6MR13xvO5ILOvUZeR4JQrVpZDG9ag0e3Ltdl1fnc0vgKLfbNLIZG1wnNVI5VwwsNwkxU2S3q9qi1iXRGQk2htCuwZpVC+kg7/qlDHgoqcQw0LmPsgWbQzDPAOWfluYeYungzpi6uNY1SGmnv6p2Zc6Z6VbtNAqira1UqHmV9rGRXafkTVXaBesqq6opQMIE1m0RIlxAq64dvnsrZHtwHdRX/131SP7djTVU73kbQ60J7QeuolSLJOYNZNZtN59ykzyw7d6ueTkggJpQDB+DtAC45564B+BiAs977cwD+DYBvdc7dBPCt1fdNo2z0pch5A0ZnMep2gRM/FQlQo4aFukYpTYxG7/lVk3NF5FTgh2cvxPYluzNnr+/2NWxfsjubQGlwW6qPGsecnImRGsLsCKXrTcXAtvx2bLyfLJDfvmR3bmLhX71W3feGM4OD2UUAdX/bhTJRx4lsQHBtN6CSvWBkVmHl6NqiV3Nrm+k22lyHdRRan6jQBYVVNljzqVlpoL5ovVWwEcqFbcsaUkfZlp8ZlaI21mr48L3WToW2tZnKdtKWyshupyzW2wrsumbDm9ZkBh1Qo/5RJx+8+jzmbn82215/G6oZ1uyDOjf8n7q7+95wrsmH1sJRTka6UDtQk8VdZ65nTReKQL178OrzuH96a9Y1U+V4aHoX7p/eGq1bVTnns83nevuS3Tg8e2H2DLfr+QSQczZjeM7NqaMgEoN37nZM98QQhjetyVFrVxy6hBWHLhXSF3UuBGoOSajkQgNeqlcO3zyVW/uPWVfVayq77cpMURaZyWt0b7iMBOVV6/tUdlnbxyANn73QUjhKTbVrHCYkKCaUA+e9/4T3fmH19X7v/UD1889577/Fez+v+vcvWj1G7IHVxYGBykRnHTk6J53KUQ4tkEqOObNuzFpQCduJkddAI8pZ5LNKcaFyomFIhJwiBSltutZWO5XX/P49UWPiOTcHz7k5dfffZu9IE1WqqJ2w+BlQiy5qR07ut92OfsxBe3Trco7OGkMntRAPQR203tU7675nMx4g76BZmiXlT++HOtncjgjVGtnxUM6nrTuSBTjaaSgWOfzMTGn0Vw1dW/+jyyOEsous9eT/er10n+0w9vUcQuBCyba2SzG8aU1HG8E01CgjC1+aibnbn81lyZRypovO83uloocCX0B910XKNzPPtn4MyDsz+9YuwPItm9se6Q85XQRZLcOb1tTpJsomz2vluYc4fPNUsCkWULke2oiL1/f8qsm57MZYYfDO3ezcQ/qe+raTG/vM79+TOR9ARc/N3f5sXc2kDV4yY8TvWftF2GZnlHFmnJh1CmXu9Fjcd+/qnW3VRzquEB7duozn3BysOHQpa1oCVK4P51qg3jbQIE3oOJpF5G+nrTuSzSfJmUtQRB0459yuotdYDnK0QIOXE0ijqDIfqItHj9WtDTZRQAXQM2tG5mhqQw4W2BK6eKql6GgUTqPJVLw0/gDkDGX9a0FHMlSQ3yo0+hlyrm12Q5XvynMPs0waMy40prQ7J5CvgQJQJyPtpIgyEkraBmuUQrDOCVAfqOhkA5gRXzoVb7xYWTerd/XOOnkKGXeh97p9CCHK7/Ylu3NRVaWfKdpdVA/Es/rW8bkxsDdX98bxaD0Fny06FTZ6bY0JXZSXa+QBwLVFr6IV0Pjh2IuMEl2MnWOw59zJdSHDm9ZkQTJezytn92fBLgWN35BM0gErow+5zYOBk9m+rLxSZmn09q7emWuk0k4nLnQ+oUYRofOj48MxMbhw+OapnKFMuVZHVZ97Xns2cWn1/Abv3MVzbk5mO9w/vTUa+LJybeuzLmxbhu57wx2re7nmI597ou/2tex+MJukoP6j7UCWAlCzG0JlF7YezgY2+FeplOyIvXzL5lx2eaTnzexxSJdb/WODvAwyWOfNLsxN8BqpbIaOmyiUCRZFGbivqL56AfxjALOrr+cAPDbtxIY3rcnogz2zZkQVqk56jFLuW7ugTnnfGNjbcXSfFYcu1Z0TDX8WJ/P8WM+jES2et3bZezBwMqe8h6Z3ZVkRKl5rSOqEGjNIyWWP1ZmNBKEuk8SOjT2ZUaW1VfvWLsjasdtoro1qh7IuVLp9t69lNL0bA3szqobtXNUspq07gr5567NulkWyp1RC1vhZdJoxoeOhkcbPrpzdn3MueH5l6aqhZgc0Ijgp6za2BsPSzygrlqI8EmjnsVCGNVRPyky21kqRvhMybtXQVONLvyO9iYGfdsju0PQuPOfmZIGemOwy8EGsPPcwJxekV3WS7IboV+dXTQ6yNpT6aGuCCb1vNpDGv+rkMuun7Ao68Jy7mHFj/Zs6Nu3IHmuXvRgom3pOoS6cB68+X0fxJf05FOyjLuC1OHj1efTMmoEL25ZlFM9mQfkkLfPRrcuZ0R1rsKTPIs+300HbQMsqdp25np0/7w3r37lcBTPlpDwSZCTsW7sgux+hujkgv6wFs6kMNsUCY7Q72sV4UIZRjPpqt7eyqY4og2QcX8imUeds15nrWUCFa8ZRbjuV3ZUwPog6cN77n/Le/xSAmQC+znv/o977HwWwBMDXjNUARwuvP/jrOpoUF2/uXb0zyG3n2mkXti0LGuuT5i5tqmHGWODRrcs5KoLixsDeLJXProms52FEiwpalU+IXgkgtzilGiExugthFZp2YmsH1NC0EzebYWgkl+Nj1ysaB6zvy5q/VCeY0LW1kTcaKm2pffvSX+WKoQ/fPIXhTWvwIf8ybgyE18/jtYzJKA27TgKNIo3WhmoNOelr7Q9QT1sKNZ2hccX7osagdly0E3SoQJ9gi+t2ZI91QXDrMA3euZuj1KnTGQKvozZ5iGXEaZRp1q4dBhIdGA2C6PWzThydt67jL2TnZ1vo00HoNONYa3aUtqoGbkxGaPiH5Ffvt95/vacMnlGWaVjb+mbrtNnPRgLq2tCi9hyHnv+DgZO5gIxdw47Pee/qnbnz4P+8PqGMcpET2QzIwrBZkpDsUQ9RdmPyuevM9Y6jr7Nbp9VjPGfaBfyMsq7yCOTrLGPnr8ewlHbq9KLrx8xbuyiU6iiFglTMlh2+eSrINgJqXSU5Jju/qjxSH2gtLNk7E5XplTA2KFMD904Auur1GwDmjMpoxhDu9iezqCWVRt+89RjetAb3T2+te3AubFuGN148hjdePJa956uTEIr8WsUXa317//TWYDMGbs820/Z7BZW5dVa0ZXsIVNI6gbfDAC4CHZ1QdPTBwEmcXzU5G9eBE4O4ePRYXQSYbbdDYI0RfxNzZluBpZnM76+tmWeP0XX8hSxrAlScOBvN64TIXihoEpq0dSLkuTMrTKeM3wHIaH+skQHqo/z2WbBGYN+89XUOpGafNStH3TGa1D7W5Gr2wgZFNNrN4ANpdSqLSlOiMTJt3ZHs+vDcR2rUv/7gr7MurlrAz0h/37z1dfVSH/IvZ0aUyq3K64Vty6IR/bGCBk2sMc7W6EC9zqSRbBvt8P5ZGmuoq6+Fre+MIbQmoK5JN1pYcehS8Bjd9+qXueD5k8auLI9QQIbfAXnGwYgd0i/9VXYMm6VkkM8GH6zsWrkF8jVP4wHWzhL8n84ZZVGDOSEqoOo/3hfL4NFnlAExq8Mo33b5IcLOB/a+jrbsDt65m7GUOB6ek+p7PtPLt2zOrlMom8fzs+fZzqZCCY8vyjhwJwF8zDn3r5xz/xLAHwD4T6M7rPEBqREAMlqEbWvcCFoPNtZYcegSelfvzLKJobo+Zt00iqu1bEBFeVNx6MKv909vjXa10wWslc7TDCw1rd1NPopw+OapXCcsu2jzjo09mWF+ftXkXESd4DULRbC1ucZIqWdAfeZEm5RwEtRlL8pA19kZa3CcfN5Y92bllzRn3ic6bbqdTorW0GV9gm5r5SwU7WUDH23eofWempGP1Ya0E7w22o2TzoA6QVP6N2RUJ46TWS2li9prsHzL5szJbdeab089up9lQ9QA53sN8gxvWtOw8yZBh2m89C6bx2hdlw308H5RL2hWIUQXZ5T/4tFjdfrIUitDlEvVA3wfc3BVdknbGm1DmM8NnVu7liJ1MZ8lrvsF1JZn0fOhDGk2nnT8UJOpVsC5nXMss9RA/t41W0Ix3i3iY/RlrZcF8s3J9LfcFqisF0mZtLRcZTEUBVu0DlSDSyHWCMelWavRkl1eoytn9+eeRw2m6phpO2lQWucInd80I009rRTnhIQQyizkPQBgE4DXAHwewCbvfTT855x7a4nX0+06gZFAWw1T4VtFyoxbs+iEeoyu4y8Ela3Cfsbzp/JlJI4ROE72/NzW+ej1U0ORKKp9Yy0LFRsN0dG6lnYsdF5i10w7R2kzB6U96GS168z1zFnWyGI7z0cpg323rwW7D5YBI4tj6TRbsNZQYR1pBlPs4uoaDdcocIiCRqPE1s6EamloEPI7a0TYVuxaSwqMTmdP6yTY2lNSj3iOXPONNZyhNbJ0EVltGKHXzxoSzcrx65OmYdq6Izn6GeuTQk5Mu5faGE2EZKMIWgekzhVphPvWLsja6gPhReq146J+F6On28wxYee85Vs213XJaxeGN63JLVJM59ZmNCjbpJbpGOn4qUPBzC6DDjSaaSjb82mlTp3UTa6hWpSRKvtstKNOtlUw8KBBIEsFBGqBIUtlZ1Mndcq0OY9mKvnMs8u1/kZlVBfuVh1NWOqsDTyMdcYqZFNRHmzdpgZwQs4bHeah6ZUlF3pX7ww6953WYyFh/FDUhfIrq3/fCuBlVDJxJwF8qvpZDHcAXAFwteDVHh7ZCKGGDilFrB3ggzd18eamecjjReW5sG1ZXWc4jexojYA1cHm+VIi2S5Q6A4wK6/ehiaiR4xb6jDQ3pRWNRmSdzWtCY1IniNHhWB2fbfZAA37luYfZNdEGGZzgdGHtVsBrNzS9KwtCaMe+ZvfbM2sGrpzdP26yy4n92qJXcW3Rq0FqMjuO3RjYG6VHMRLPWkWl6mn9oq0dAypOjNJgti/ZnXsfqwOjAanR1dGG0jmZLYsZ7nyedJkFzbyxJu7w7IXZubAuQ5//UBOOZvDUo/u5TnG8F9uX7M7ROWlYlg0mjKcRTKghS0qn1s6uPPcw1w5fAw9sqc6/bGBw4MRgMLNM8Bng/BVa71BRRJO0MsuMYrtBA53dffX5sguYK2zN+eGbp+ruO41fzfDSyXswcHJE5/P6pGlZNkQdE9Uv8/srDZaadeSB8ckec81Tzv1cskPlbGh6V65uXB0SOmWWFsn7oNsRXP+Qv9PlYGLPO58te43U8afjduXsfhy8+vyoyK59FnW8tlYQQI4dwuc/VIdK2HIMzShS73ZCYiChc1CUgful6t+rqDhkfPF9DMPe+3d7798VewH4XFtGP0J0HX8hexEawSR14/yqyVltWSNaGjtcjdfaLrpwti5+TAOv6/gLhSl5O5FrRsxGShU2iqRNIYiiujZGnuf378llRHXf7YZ1YvlXO5jRcNXOlJz0mN2g0aHZSj2GZt8Oz16YTfhlKWJ1ePOXZfUUVs60MxvBrmKh1t2K8ZRbzXSTxqxBh75563Fh27KcgRGSVUINLTZAUPojo7zWYd915nq2fzUQOREz00qaGSleQL5ewdKH2gldE4hrCtFIVceezxtbWittkoGSvnnrcw1F+KytPPcQC1+ame2LdDSg9cWHP37X5/Qjm0GdXzUZfbev5YwiGjM81tTFm6PHDNV0jiX0uDagc+DEIOZufxYXti3LOtEdvPp8dt8oYwz4qH7kfdEARIzyq5mP0PdqUFNubb2zym8oE9NOnF81ORcY5TkqhVLZBRahOUHPUZ9XdRIoS83OKZ9+7fWs5p2yC9T0hdLp+KK+5ZIvIfBZGC/ZVX3Ru3pnzha6MbA3mw90PmagQYO+vN56HXg/7PIUep+UGstt1GYJUScJjplySwd7NFkkpM+G+gjoPMOgGc+F9fSx59D+HwJrR8e7bjKhc1DUhfI7nXMOwN+tOmTvFsfs3QX7/ECJ45bZZlQxbc7c3Hu2YeeEQf4y6UfA6BfItgNs1Wtb/Gvbb9JN+J3+BeqLk7kvfs7CXNvd0k74SoOwn+ukZ39HYxSoUbvaHVHj8TVKpvUTjH7zM81k2kWO1eiwhpE9NzpzPK92NTUhaHQTMcpJp0Ej13bx967jL2TPJydJTpRALTukE5ttIBMKHvB4Ozb2ZFko7idUb6HGCKHGiV0Emcdud9Q0VocK1ORKaxm5QK59prTFOY2LmHzwGSYtiK2um8F7vvrLg5/TqNHnjDQ7vkL02k7Bo1uX63QB8SH/Mj7kX85lxFUWeb40Quf378GVs/tzgTKyANQ5sbJsjV519KyO10wcUJNhdai0RrHdUJaHbTdvcfjmqZxMcoyUF9vlcGh6rUspn/8Qg6ZZPRiSXXW+ixoWdVp3Xws+f2TvUD/QXlDZVRmnnrRdYbmdrfsE8g1N6LSoXNsgRqOaueVb6hlSoxXwJQMEQB0lVx0zyxBRTOnfkF03fQ5jzYMsjfL8qskd1zgvYfxQWAPnvfcAPtLMDr339/m/c+5NzrlZzrl38mW36RQ8unW5UkNUjQTzAbXKQCeMTgbb/OpYuYCurs9mU/pAvCOSZj5CE5YaDMqrLwJ/E2vLTmpg37z1bTOE6QzaZhXk+jMLCFSM39AkogrbfqcKVx0025WTxt1IofVOy7dszuiIOjZrvHUa2JyExi2zkzQimEEE8vQV285b6b1A/j49GDiZMwD5+fYlu7OsJRtHEDQCeQ2bqbHQcbSrFo7PAOvGbBAkRKPTdvTa8ILXkWuQabMT2xSABsf8/j2YNHdp1pV3pLCtyDWab1+dCmZPWH/16NZl9M1bHzSAGSBQvWcNYqCml7kkheryKf0bcvRKDS4RRdfL1o2GwN+3M/hARohS75TWzwAM6bschxqvFmr4aiAgtM3Kcw/RM2sG3njxWFuMYDoz1glRUM90qt4F8p2MH926jMM3T2UyTdmd378n1/re6kkgz16w2SbriGjG114z2g42SGEzT/rsUFdpMLWdsrvi0KWsCzXlls8nx28p+AR/w2usja/0XBR2GYHue8O4sG1ZR3SLTugclOlCedk597ea3bFzbjuAPwfwWwDOVl//rdn9jBX4YKgyI+xk16lZjBC67w2jZ9aM3CKk6khQyerEp7UIOgE1otRopjIUnbcGBxU1i/btuAFpsiAUoXZAx6KRfmss6rWyUemV5x7mFkIHkFEk1bnTSY21lu3oREnQSVSKX6cbvSHEaFvDm9Zkrew1EwzUP4tXzu5H7+qd6F29MxcZtk6dvqdRzSYfnDRVRlQHXDx6LBjVV4OG1K1Q1q5VKCVaQUdM6zCsw8YmQaRUMvPGzKNSF7UjoI59NDqUkmoE1GewJ5qeBWrZtR0bezC8aU3muGtmXNe55PmSqq/oXb2zruMi/2r9m953bQal26vTFsqIKLQDMWujRgq7DwZG9NxCBjJQq3XS+UnPSfepmU1L1xutzIXOGxrgVR0zHjVuZcA6OFv3xoCZBsvOr5qczTUqQzx3dcx53Xmv1BHh7++f3orue8O5gLLec31vj0nEGBFEO5g7fIbJPOq+N5wFSXUe4DOpTpztVGn/16ZX6uDaetXkuCWEUMaB+yYA/8s596fOuUHn3HXnXBne1w4A87337/feL6i+OrqtWM+sGTllxqwA0clRtCI85+Zk0XrbxCGmKEPnqhkrzUwQnLxo4NLhUYNZ6YdAvJaOx5vfvyenEFtt+qG/5+RCJazcfqBiOIU47jpJE1oLBSCXyeF77ofHaUfWTaHrDTGLMl61bCNBz6wZdTWTw5vWZHWEzEQANWqvGvxKa9H1d3jPVHZtpofZpSKnl3KtVEl9VliDwSYUbArTzlo4m/llDR8NYUvJtY1NbA0V5VPHyWtKJxioBS/abUh8yL9ct081tLWmrFONYKA+O6BdEB8MnMyoV6HASiiru/LcQ1xb9Cq679WWXdBMstWn6rBbOdbaTwsrl5RxGpVK3RwJNECmAUGdU+6f3pqTb3VENZBImQx1HeT1sB2FR6PRTdfxFzI9G5vH+LeTDXDbhVJBu4H6RTP8y7dszgWp1KHWRl5KlbQ2x8pzD+u6zTLQpBk/6u9Q5phyoOvOaVa2XbDNWPQzpfRqIIHsBqWp3xjYi2nrjuTWk7MBQT3H0eoImzDxUcaB+3YAfxPANwP4IIDvrP5thE8DmHAtc2gIc0ImXYhGTScu3q3omTUjo+JQeamCPHj1+Yx+o5THXWfya4BpBswqd36ndXZqXLPJA6GTKRWxnfD4mW0bbw0RdvpqFaQ5aWSaDheV6ZWz+zPHTNtFa/RQ60asETE0vStTuqwnGM2GAASNCZ6bbUoynk1KyoJyqY6vyio/I1QWSYFlDZFux2YjlsbD+6gyqjU03A7I32sbRQXCBqVmdxs1kWkE7sfKEoMG3feGc01K2NREa075PKsjx79Wxul02I60owErl9ooIrR4dydCrw9rDm2GQeWG+tPWTlKeuPi6Nsrh91aP2nujBuDQ9K46g9MGFbQRBKGZ6OfcnBEFz+zcYdkMdlsyM5QeqtdAz4lj12fT1gKPJibNXZrNEWxyQnkezwYlzUCdKzojSvW1tgJQvxC8Bn9YwsHvuE+9p9338ss72MCU3T9/F5JbC91G6ffNYvDO3Wxf2kDLzgMamOD4gRp9XQO4dEz1mQwF+HgtR7MpS8LERhkH7ouB153Yxs65Xc65XQA+AeD3nHM/wc+qn3c86LQp1KCIodXubO2GdjPSSY+K4PDNUzkHzab3GUHSyZZ/NWNBUNmzXsMqVY2y2kioTtB2nDomoGLw9M1bj97VOxvWFFFhc0Fzvjgp0XDifrWgXr9TOpE6fXO3P4t9axfg4tFjmaGr142OB6lqpE6ONmhAKMo4bp0iuzQkSO+zGScgb0Qo/Uq/04lVmybwGEV1NaxtoEOmRi+NbR6XtR0KdfT4bOj5NQJlVzvfrjh0KUcx5TOiHTT1c0upJLSmk+Oz/zOQorUbZcc+EoTktEywrBNk98K2Zdn1tq3xbYaB21k9Sd2hWSaVXZV1OnWql2zTEiBPe1Var+6bsJkM/t+7emeOZlsE1bv2vlCWNDNuM5IhShwDDuoI8HyunN2f6V+bxdA5brQDaCFHrUywbDTWiRwJdI6zulEdN/6vy4IQ1jbQzGtI91i50ufHliPEmn0A+ftvs3Blgk/WZhi8czd3LHXCNKOu52UpyrYXAL+/tujVXCDRUu2L5qeEBKKMA/eHAD4L4OMAblb//6Rz7g+dc0sC239F9fW/Ual/myqfhduQPUbohLoNttZVQ4Ag5QpAbiIFkKMs2A6Smhmjwjp49XlcPHoMU/o3ZI7MjYG9uHJ2f249uqyOzRw3lInTrpBA/ULJh2+eargunxrAPJciUFnqxMGoGGkialBxWYEp/RtyjR5Il+L42WxA14FLKIY6KKx7tJF3O0kCtfWFDs9eGF1c3tao2AYoNDC771U61105uz+jk9losu5XM3Q6HjV46MjdGNhbGA3mQuVq+IZ0inaStNdO39vMZaj+1dKCGSlnfRozQaOZgZvoWHHoUua8acCLOsEak9uX7K4zOLcv2Z0FwVTm1QkjNVdlUB0VPa59z8BU7BkiQlk46rUiR1kNYAUN4V1nruf0oTXOgfrMBs+Z+jSWabTPCOcfPgPtrDluN8Z7YWbNjvF/7QapDaN4H+0i1dxPSBZtkFi3jwVrgXxTFAaV9Du7bEvR+TUKPlnZpS3AAJLNrIUcUQa2NdANIJNbzSoywEbGh6Wb6r4nQgY3YXxQxoE7B+A7vPczvfdfhQql8r8A6ANw2G7svf8p7/1PAfhj/i+fjSiE65x7h3Pud51zw865P3LO7ah+/q+cc7edcy9VX98xkuM0QoxKwojbeCpkPTaVjFWSutadLUpmZ61QZF6j95wY7cLhrNcg95tgYb81KrWJgSJGsdSxFoEGEBWpUsZ03TbS5UIdrqhM75/emvsNo96qkJULzxcN5r7b1zJKYLvr35pFzACjATSemQztMslJXyc0Gp7MCui1p4PFNc/YHaxo8tbPbS0RgFwjHl37iPulQRyiTnJcQE2WykZTNRPG87cyr2sLElpjoR3R1MCwi0NzfDyefsd14fiMjDf9NqZXO0F2LWVXDT3KrK1rOb9qcnZvlX4O1BwZDQrxvd4npQDr/ab+0WeJx6NMsc4xRkPjWEJZFotHty7XBVpsI6vzqyZn61Raajx1sC4Xoc++0td4TfV7nkPv6p3Req7xQtE6cBz/eDtxQP3zH6qNUwck1u6/+95wThZj2yo4T7MbLvUOUKPPkjkT0rehz2y2Lma3cRkQgkEUfmfHqKyE0PmEyj70vZZm2HIRm5UbbdZDwsRGGQeu13v/m3zjvT8PYLn3/jKANxf87idKftYMHgL4Ue99F4ClAH7YOfe+6nf7vfeLqq9fH+FxGiKmDEh10Q5kYwkqD06+GkWjwtAFf4FadIsK8/DshVndBrua0VBkwwSCRsK1Ra9iSv+GjNN+6+CHc1k8daBs63PbQQ2odcNjpE+34dhi15fKv3f1Tkzp35BNCDcG9mb1jAT3S2Wv9DogH9nVJi38X5tWcNw2W9F9bzjXVrwTwUWVgfEzhLXQe+FLM7MJjROe3gu7ZhVBOdUsBZCn/3ASpbGh9W8qD2q42qAGP+NYtO2zzXzre55fzGBjxheoGBIhWdQlAIB8904aDH23r+XOUZcZoLG868z14DFUnnkNOyEKHFvAVmmW4+nEaYbXGmjXFr2a6cbDsxdm987SpkiNtHVxITAAxfpPS32nvLJFvxrhoawx32s9k4L6MTb3aaa5d/XOLCs+pX9DMIOhDa5CtHyg4rjN3f5sNpdoWYBmhNTZ7F29MzP2ty/Z3RGyG7uXev/Hw4lbcehSpgtCQVv+H6I/hpgtGuCy28ay+LyfdNJt92B2qebcq9D7bus8+T+PW7RWn57zlbP7c817lFJug+LWcdXrEKKw6/rC3I/Oc0A+cNEJspvQuSjjwP2Fc+7HnXNfW33tBvCac+5NAB7ZjZ1z3+6cOwhgtnPuZ+V1AhUHrGV471/x3v9h9f8vopLRmz2SfbaKGC1v0tylODx7YUZHHGulPKV/A+6f3poZC7ZoOFYbwwlSI/r8XwtyLWL7oSPL99bx47a6z9A1tUXUzWJ+/55c9Ht405osGm2hRrpGwG1k7MrZ/bnfqxGkx7LZxk5A7+qdGU3PgoZw971Kw42xDkDoBMvMrtKBWXNIaK0bDdQYTVXr3zSrZI8LIKuzJDS7amtDNYsSK0ZXkAoWWy6BtN0QNezi0WO5Z+bGwF703b5WZwwrNKqux1WKMFDJCNG4sBmXTslk7Fu7ICq7QO1ajaRpQStQp1GptkCFkcDsnNWjZDtYqIFHOVRZB5BjDWjjCKtvaFhaJ93WdQL1NUShcRXVktnmFnRGHwyczDVyYSOdIio9f/9g4CT6bl/LdKk+Z3z+VD8oVZXP5njjwrZl0UXvL2xbljm9ALLaq7GE1alaJ9l9bzjryqzyZoMEBOXNrkUaguoiOm+UC80wUz5o2yi9XfWlddwsuu/Fl8RgIEDpxRpc0OfK0s25b/0b+p1mFfUz+6xRjpPzltAIZRy47wfwNQD+a/X1jupnbwLwPYHt7wC4AuA+gKvyegHAt410wIRzbg6AxQD+oPrRtuoyB8ecc2+J/Garc+6Kc+7KZz/72REd3zYPUHCCphM1vGlNZlSMpmExeOcuHgycrJtklfaoBd2aEbPnoxQt0h8PnBisM5CVHqHHthEpQh3EUM2OGtOq7KgoOea525/FxaPH6gy60PUlzW1+/55sjLo/HSsnC3YytIq5yBAC8nQjvb4jrX9rp+wWwdJopi7ejBWHLmHwzt1RlV06izw2qbg206ZRVu2OyHvFAIXWS6gxQAcstsQAO2DqfQxFU7WeM9Tu3FLceCyl1DRzPRk4IEgp0mNZiqca3Ppc6dhovC18aWZOtrV5z0jqetsptzEjGKjQ1/V5BWrUtbEIRFjnCsjrBxp0vP9W14Ui9dRDQM0Z0u85v1gHzUIzKHR69DfnV03OHafofhcFpEK/5Wfafp5jtoETzQJpEwqdvwhSJYemd9UFHEjDHJreNSLabztld/mWzYVyePHoMUyauzS7JisOXcp072hC569QVnZoeleQ7cB6Xm7Dv9Q/vCfqnNt1JNV2iHUZPnj1+bpnw7bVjzlulKFG+st2eLX7YOBLM8Uhx9WyboCazaWBBWV8qN6w64gmJDRCQwfOe/+q9367935x9bXde/9Z7/0b3vtbge2vAfgwgEve+/8orzPe+9faMWjn3JcD+FUAP+K9/wKAn0dlqYNFAF4B8DORcznive/13ve+7W1va/n4XcdfwOGbp6IGWNfxF3Bh2zJ0HX8BNwZq65mo49BucJFfNRBpXNouXJbKGOuOyAV/9TekbylCylcpmNwXjWM6Tdo0xSJEp7GKnJG4EG4M7MW1Ra9i2rojOHj1+ax4304iNOj5nRpaaohpFi7GSy/iwo+UPtku2WXXtlgkctLcpTmjxzqyo4FHty7nal3sJKoZKS60bSPAdnw2I8XsEuWThlJRbZqd+FXOD988FQ2UKGzNUiNwH5oB5ns6MLrwvaU/heovNNPIOlaeN58fUs943vpcjWTZlHbJLVHUrEA7AVpK3mhlNUI1x0DNELQBIMIu3WDf69j1M+ovLgej2zB7YTNbIblk7bF1+PnXZhItipYUUPnhddCaZFI6bVZRn0Nm1oAak2F+/57cGouh4/IYlPGRoN2yG0PPrBl448V88xY15kdLdrlfPlN6zRnYUr1rZYG6xeofZvBsNk3Pj3Ks+knB31PGtRHagRODGS039rxYaDDA2m26rqBuq/qWlFx+H5KtELOJv1cGD8sDNPNKmebYi4JVCQlEmQxc0/De/zWAr3LOTW33vp1zU1Bx3v5f7/2Z6vH+3Hv/1977RwB+EcDXt/u4Fn3z1hdm4YhQxLLdUTUq4lBhbAgxx43v6WTZz+nQhWrXlJr5YOBkYcapb976bGJXiqRV5jaqRTqo7tsuKcDJ+8HASWxfsjuLxjLCSEeKjq2NApIyotD6OJ2s7IQWo691Eo2ybESahjAnyGnrjkRrkEIou2ZUqNuirR8jRSoUAbVGYKhlOJ0VrZ8hBS1GCwvRZIqOEbr3aiDb/dnCfiDf7U27m7LGh7KstCYdrxru9tmhwRRqIEFoFobj7KRI8P3TW7Fv7YKG+lOpR7EGHTE85+aU3tY2OOBfvW50VGwmM+TQKy2Sv7XZJwYeqGNVB6veC2UICKu/baZLnbqy0O0vHj2W6UrLULB1xna+0mdGAzec20IOvDWmOQ91Ugv2snI4EtpcM+v0Pbp1OdMjNlhmM7RATX4ox6qPgXw30OVbNmeyG6s7m9+/p67pDaFrr1o7gjbI/P7Keq5amqFBPaXz8hULonEctoZO61D1t0WBXK1LtgG0WN2p1bHJeUsoi1Fx4Kr4FID/4Zz75+1aB8455wAcBTDsvd8nnz8jm/19AEMjOU4Z9N2+hlsHP9xwO2sEkcJSpGxJuSwyJqYu3oypizdn+6FSpaFWtOaYdcjsy26rWTh1RrRznyo4FpFrTZ02QTl88xTmbn82R1sDapN5LLql1LgdG3ty2QmCDgqvM5U7G6wMb1qD7Ut212UNVRlrJzQAuQmLUEdA7y+XHLDGUCdBm1QUQa8fMXXx5oatxBsZElyT79Gty3VNCbRrHq8bI5OaTeDfkINh5RGo76LG87Pb0DkL1bPZGlEey9aVKiVZOwCGmorotbJGgqWLDk2vtJRmkwv9jvVPanyrQcMmFyFn0kaW+YxN6d9Ql7EeT9C4LeNU7jpzHYdnL8w5wqQCxxBrg2+34YtBDc048Fg2c2HliY4MjWDNVIT0xYOBkxlVV+swCQ0IUNYUNjugQTqlqVH+dXsbICkC5ZbBET0+nwn7LKou5nXQ5/vBwMm67JylnvWu3plR6+xyNZ2AN14Md6oNgeemsgvEM3FlSzNIgx+8czeTXc2UUg9rwxugfuFsZr6oK9gBkvOk6is9H9XL6hDSKdS5X/WOnas16Es51uciNjfE5jyOWdf8jEGfa6WMkr2jdgGv8Y2BvTlHWZ9PygQ/uzGwF8u3bM4ysgkJRYg6cM65f1v9u67Ffd8B8N+qx/gKeY0EfwfABgDfbJYMeN45d905NwjgmwCMegiDRllZtJKJsXUDIYT2GXPeQs1IYmNS6iPfc3Ll/tUAUZoW6YqxfdoxADWH6MHAyRyVgUXV3J+lwGkHwNjxSB1jDdz5VZNz9QYWNnuh41GakWbjbOZQ/x8plWc00MiYYPCA95p1hI3W4AOQZQFCoMPCa68GAaETPQ1dG6UH8jUXfG8XTlXnibA1G7ajmFKDtDak6FnRbK4GJLQwv5G+6Jk1I2eca8dUjnnFoUt1lMYQdc6eKw0IG3AAags8KxiE6aTsMRCnLyke3bqMg1efx+Gbp0obzTSO+25fiwZcaCTb7xnw4dhshN52sLNBvV1nautd2XoeNQo1+8a/Ku/8nM4Tf6fPDuXT0tJj9zlEAQ1ta++JldGV5x6i7/a17HkKZc65bz7TB68+X1cLx+ujTTKUSr9v7YJS8+Z4oagJD1Braa9BxItHj5WiUT4YOFno5BHqhPSu3hnN+Nh7qtTVoeldWV25LqZufxMKAIWCaQzecT6wTn3IprFBB63Bp5OvMhZzznTMu85cR8+sGXWfhbbnvMQGLErHZ7b44tFjeDBwElfO7s9qjvl86tiU4r9v7YJx7aabMHFQlIH7jipdsaXW/7L22z4APyPvW4b3/pL33nnve3TJAO/9Bu/9gurna7z3r4zkOM2gDG1BJ6tGUcHn3Jxsgl157mGQLjS8aU3muFgHIVSfZj+zbcjte0vX0cVXgbzzpUqSkTRSaEjz0c55pHCFjAgi1NpaqTRUlkqTCGF+f2UB1+57w1nNEpUuKSST5i4NUu6UkmapFSEqYei+0kHoJCoPUDufRlQ0Nl/hZG0jwha6nk7ROXPB2BsDe+uynZqFVYPNdvOyFC91pGPUL6XCAqiLmvI4Nwb2ZhOqPdcQNdg+N7ZpjT771hC2z+bCl2YGx65RZdYMatdQBQ0ara/Q5Qm4H14DIE8X4iLT9tp0AmJr7oXA+9HMb4B4jasFnwdLm6TMMOBlZTd0DJXn0PFVrlU36fFsVN8+O5Q3Gp02KKa1ZvxcAwOUFb5Xhgi7KcagBqvKrnZG5bGmrTuSBRBCTWCYpdB9c7wabCmqsR4vlAmA7TpTWd5D5TYWuAg5Zo2gdW72MyCsuzVoSVw5uz9nf9D50sCm2jKWGaCUW80+6dhs0Flr4bidzdCFapPtvKDXLVbja58jMh2Udq/Pi63jBvK2i9VBPF8NDNOG6sSgb0LnociBOwfgVQA9zrkvOOe+qH8b7dg51+2cexEVOuMfOeeuOufe36ZxjztChr+FRlE4AYUWDCboDHK/IRoXEfqOCspSITUrEDqu1lLodtyfjWhSadkJm2PSiLTuj9Fia3BworaGiz0/a0zq9+qM2DoCrVXUe7by3EM8unU5qPRjdUB27DY7Q4VsqXKdhAvbljU0JPSe60QbM4aHN62pk4cQrYe1Cwq972rk6f0OTcqhjBMQXy+MDgwNa9IKgXxWjoYfs1Zq0MQyFdbwtdldNZL1ebwxsDcXBHrjxWMYmp7vnhcz6Ic3rckZ4nqsAycGM8OIzQmWb9mcoxmpPtJaJaVEdSKunN1fGHzQGl+7xEco28ClRdSJCsluqPYRqF/LjX+pQ608hu4n76Mdh+5b9RgbelBGbS2SnifnHpUTjk0zxLq9bsdOxIpQR2IeWw1iNUZJNZvSvyGbG0PPiWZ0eH4x2jcZGjruoeldWfOqiYh9a+uX8mgUhNB5KiS7em00EMd2/Lr+H7+zNXCWkaDjBVAXJLLZL3XsCDZS2rd2Qc7hjpUe2Exco2YoQE3fFwUYuu9VOr5e2LYMg3fuZveATAy7rZ0HeH3oUNrsIq+L3leeb4gR0Um1xwmdi6gD573/Me/9DABnvfdf6b3/Cv1bYt9HAOzy3n+t9/5rAfwoKg1GHivEDDrtCklY2kgIbNcPxLMJzCpx8tIHPmRgKKVraHq+CQgVjm3mYZWdbWyiBfZ989bXGRA6obAmiJMwDQ5GsrSTnjWw7KQVKkamkWqdODpy6ozQIBma3hU0zK3DpufE49uaFnXYLCylr1NQZAyoAaAUyljHz2YQKtKPTa4hpz50nTWrwfsXos/QcFGjkdeBhoDeaxsNjt1HNexDE7siZhCrExdy3njeIYdNqZ/c7+Gbp+oWg2Y90bR1R3LUSoLGtq6x1GmGxBsvxh1L1l9Sn7FOtkhulRbZyGFVvQzkdQHvmUbTuV9LhSRs7aelA+sxbFZ3aHpX5ujYejAgnOmxASWtd9MAhH2+bA00nbf5/XswvGlNlom7sG1ZTnbJxti3dkHmuNmssxrDnHfsM7jrzHXcP70VF48ew7R1RzIafCjLwc/vn97acOHmsQblMESjZH0mZYILoRdheNOaXL1aEfug+16+OZdlUzBgo7VwQL75jP7V/SrdnHrDsg6YwbO02VDg1Mqp1tHHmqPpHGJr6vleA4cMnFHv2u7LPFfaCUV2m20mZdG7ememU0P70XpYOnupkUlCGZRZRuC7nHNvd859Z/VVtp/ul3nvf1f283sAvqzFcXYkqBhsxItKQZWKUgAJ/Z3SUWxzlBAf2tL77JgsODGGaoG4P23ZS0NRHRD9q0YJHaSQcudvdmzsySJtuq0drzU6rfGl56AUSipaFmnbCD3P78bAXhy+eSqbqLR5gB07DQAaBZYXb9/zuuhEVZaONR4IOXGkQbKmgJRXXoOQUUGjOTQ5hxrx0NFQ2WLtgWaFgDyVRmt67HaasVDjOURZo8HB89GaC22frnIYahzBz+3ahpYqpBia3lUXDNHraBsS2CJ/LdjnddFj2+umUMOA/3P5ABrbWlRfZBCOJ4qyEQzOUL4AROWWUOqdwtLjP+RfzhmH+pupizcH9xG7fjbQEHLKYzpE5ZKywGPb9QvtdkA+i0Gd2Eh2NKjXd/taXSaEsstrtuLQpcygp5PGGiWO1xrp6rQSGmTTOiulx1p0WtCB6Jk1ozBwpst4KJghC831oYyYzeoD+a7CzLLrdWK9FmHnaf1Mt+M9Y3bbOm/6V6FBJ92/Ho/0Wg1qawM2u4yGddC4lFFMngnKLqF2BZ9NtX10SYyV5x5m14PX1J6vLtESChDboLRmoBMSitDQgas2MfkYgHWoLNz9Mefcd5fY9yeqHSjnVF8/CeCTIxtuZyGUTSAlz06+ahCGFAojSzQ6tHEH96voOv5CYYTc1uWoUtJIvSpS1jtx3ROlD+hYNFtx8eixOi79tHVHCmt9lJ6hk7Y6ZjGahs0oEFS0WsTMazZp7lJ0HX8hF73jtbOGtDVabXQ7FO2290FpUzHDf7zBLlcaDS4qnNaJ2v6OsBNXLJgwae7S4HeNavK0RisGNfh4L+lU6X1SI0YNjhsDezNZVHm0GWodv9ZnqIGv0PeaAVPYNY+ASlaDlDS7b1v7oeOymaCYEajgd9YR7zSEOrQN3rmbBWem9G8IZssshjetqaOrUwbKnHcRnYywAYQYNSy0bQiqtynbNOQp+wxKcH+WsqyU+RDKZhu4j1BWhNk47UaoGTjVvSvPPcStgx8OGu9AvYFLup1m1UPj70S9GzLMOU/xXC4ePYYrZ/fn6o6t46eyy+xZI6YHa9b0WNaJC8HSKe12mg3mex0LgwSU11jNOZk0CjtP7NjYE1y+SXUvv6dcUg5oW8Web94Hyq6VI6WR8n/eIw0S0XbiNWOgTPcF1HROKNs2kgXoE54clFlG4CcB/C3v/Q96738AlTXW/nmJ320G8DYAZwB8pPr/plYH2qlQRWXXY2MxcqPfKbrvDWe8cCo8ncwsbI0B96uZARv1IW2SSp+v7Ut25yLCGiXmsTQzxQlUx0AjkZkDPaYaUzpZa9ZD226HqBd2H5aWUcSFDyF0H6zTG6P3EToBARUDXSNxnWgEW6jztOvM9UwWNEPWqAul1rfN79+DvnnrsWNjT7TRjxqWvOc2q0nYZRt4jzUaWjQu/lafKYW9R7pw7b61C7LmOc0gdBxO9CqjNoMH1Gd/NFCitUuh87b0ZZ6PRt2V0lOmsUInw2Ys5/fvyVgB2siFCGXnrQ6JQTMZQE0PFLX91uy8ZWQA9bTgmCxbp1T1i3UMNRBlKfGhhlQaPLBOrP39lP4NdRkQ/j+/f08uEDRp7tLouamDBuRpfBY6v/A31P+hQGZZJ3ysYQ1z0iY1q6wOQYwSS0dYqZaxzDwRcmx3nbmeq1nUzJsGfRWhuc/uV0G6rULvjc759hyHpndFqZP2M95z/dyWMHB/Krsa7I7VvhIcY+hcGeBV2bNBhlD5R0JCqyjjwE3y3n9G3n+uzO+896957/+J9/7rvPeLvfc7vPevtTzSDkXPrBmYNHdp9uDfGNibc0gsJzyGMsZhbK2X2P6tgrNOHKGKWilbfGmWzFKH1Mi1Cthyx2MR5iLjm4owRj3gd0px0PeDd+7WZZbUYNOxcFFQpeZNW3ckMxQ0gqkTrM2+cQLmenPNGv5jhTderDhkUxdvzkUCizLFRdDsQFGNl24LhNs0WwOXBpul8xK2fijm3GikdvNLvxOk8ui+YjRCpaNZB4xt4/UzDXwQoUiyjkUpaXS27BpNFpYqrUGcK2f3Y0r/hqylNVBrX817zu6TRYZgJ+CNF4/lqNJ6vanDlC7WyBDmPphp5XXTOhki5Biw8QGh8hfK1NkxW73Kz3ScSr2NOSc8Z9WbdBBVVkOUtNA+Wb9MmYpRAPl7HsvS0hg0VMrng4GTuc7FQI0ey1o7QudUwjoSPA6f8U6X3cE7d+sCn0BxEwvKO2Xj/umt0Y6/ZTpk71tba1kfyyiH5nvCykMR7Vp1r6Xg6u9UdhksAFD3VxFzypRFBFSCD323r9U1edPfWLlV2FIYvWexQJjaSNZ5tr/rVNp6QmeijAN3zjn3m865jc65jQDOAvj1Rj9yzr3HOXfEOXfeOfc7fI10wJ0IPvCkWnFSIuwDye9CE6aNFgF5I8NytW12ikaIIqQUtLuXHVfMKQvVfYX+52+tk6jnrc6dUtVshPbAicHc+K0jp9E/e54hh9Ea6jpZENoogg44F+vVRixa48HsoXLiQ3z4TsWuM5XC/6K28bY+kaDRa41LO9Fag4Lba1TTtnS29Y4WjZxNKwPaMMGOT8fPmgqbJdDzsxkIfdlt6NjRoRia3oW+29eycYQWCFfZ0do0ADmjmgvG8jqpUUXDOlRHRZm1mVAAuXqlToUaU0PTu7IlGKxM2E6UBB0IQs831iyhCLp+lM3Kx5wNoKa7bMCryJDTOYGywu31fmrWecfGHhy+eaou66YIySHHzyAW15+zcmsz8EDFseV58j5p7ZbqUYvYPQgF32IOT6dCZaNoyRVbm6agc07atzoioUy+Zmrt9bqwbVnQUVP6pJXp7nuVzo2hJh+xOVefL6vjGEDlb5QKrY6fddbI9OA+bVMcnV+KGDo6FtpZlGfreNlaadpTZABYG0x1lbWbdHx8VsoszJ6QUCaT9mMAfgFAD4CFAI5473+8xL5PA3gRFQrmj8nrsYMWtpJiQzqINeJVcc7v31P3oHKyZeQo1NABqChmKs6QkdV1/IWscFyjwVRIZYpkQ0azOnVF2Tf+npM2DdfYPvX81REjFcpmbHaduZ4ZxDGKDimfQ9O7ssglnUIaa13HX8hoLXbtLuWwE7Eom45Br22s0U2ngMbBhW3LMGnu0iiNMXQtWAdHo4wZpZBhGApCWLltRFVVFBm3IUc+tG8aECFHxb7ncxbLmlnEsh3M0NJB1O93bOzB9iW7g2NlZgyoPMOHb57K6uMob+qM8FnjdQ85cerUWseBxk5IR3UKqHfo9GuTpFBDhtCza6mmCq0rZJZieNOauuYJzUbMlSIOFGeTbSZXZZWyqMETQnWcnptmF3Vb/WtBvUh9yjHp79Ro1mPp+HWRY+pcfq/1e5RnzaLwb1EWnrANKjoZXD8vFCwF6jM7sQCalRO9Bnx+tRaNKHKC7ZgsXTVW5xsKPCs0yBXKKKtjZ9d/I2LOfUhv2+fTHsvKLp8ZngdtMAYYqW+s4927emfQ2S7KasZYUQkJZVAmAwfv/Rnv/S7v/U7v/UdK7vuh9/7nvfcf895f5WsEY+1Y9MyakT3Yj25dxvlVk3NKVxUAUC5auPLcw2hkzhrAzPqpAmD3P6u8tMkBkDd6yo7NoohiYY8F1K6Hrf3guj06AVjjW7fXZhYx5acRL9vBquv4C9l2PbNmoOv4C7nPAGR1X1zbTmuj+L0e3y7+GauB7BS88eKxrH5HqTSh+xiTDV7fEMVFHYgbA3tzxfcHTgzW1U/oGHTfRQhlX3Wf9n9CJ3LNmhXVa4SyZUXQa8H/NahzY2BvLisC1C/UTCOUhv2DgZO5Bkqqf4Bwg4KQga3QSHXM4e009MyaES3219pcNZasvgPyiwGHjE4bFdfmCJRrPUYjfcjfhv4v+oywmQtFUQaRhnPsO/2dNsxhvbSeC6ltzOrFxhE6H94zBo1YhgDUaPchJ83WZNOhszpiItQe6/Nq5SWWobGBTv7V+ZKwNNLhTWuCtWicH0nnbEbv6vF1LtcxWWcJQC5bRmimLXbfrIwSdr4pgh2PlS973dRW0W25TBF1kDaJsrqTwWs6ePb+xuo+Oz34kDD+KOXAtYiPOuf6nHPPOOfeytcoHq+jwIcWqCm0GD2NRqtS0UL1ZhaqrGwmqu/2tWhROxBuE87JI1aT1Ei5hyJNFvxM137j+KhQ+bnW89hzVXA7XZtODSpSxRQho8OCFBEbpdfzZMMWnhOzKzYiCnRuFq5ZaHDC1v4QdiLV9zqRA+HOfCFZsscpk/0I3QublQhlEoqgjmpRcX2Iomnrmgh2ftUaIN3OUpJj0Hqv7nv5RaSLIuZF9OiJgKLx8nroNtS5ZWVI6bT23jwYOIl9axdkWf5QJsVSzxqhkfNhKcCWshvKyKm8FhnBegw+m5YmZwMC1mC3tYW6TRG0xtjWtNJhY12yNv/Q69woWNFp0LmbVDygfrkMniOXerEOU0y3hGoCrdyHqOpldEAoU2zfq2zEauAom1buWQtpg6+hmjc9v1BW224bykaqA8lrxGe3TGdItW8Iay8QseVQQg55QkIIo+nA/SAqlMn/CeBq9XVlFI/XEbCOGiciqyBDyrHIiKXC1glSlbZVmKE2uKFj2IYejWiR/K7s50XZnDLZPm4Xishah/Pi0WOFNVzqtIWWgCA0C6cTHRWqOr/WqQ2d00SoJwKKl2/g90CensZrE4q0WhkNZTfoWOtxQ13RLEJyHII6bKFtQtm0kHNXhJDxqw0nQpFvXhtLkbat/tUI5qvIkAh1Q4xl1KibYovG6m8mQvAhZnBqK3aNeDPTyfutHeusYxJzCtThKEJZfRcCx2iNVkKpX7E1Bg/fPJUFCfj38M1TmU60+9Xros946DnXbbhdDI1kVxsp2WtqG0cQdlFqK+udnMlgIIFjDpU2hOYWdYpDjptmIUPy0CjLPtKaQmazDpwYzNZj03HQWVN7xjpk1I2WoaCBiKKMuc2uWYSYFnrdmp2vqV9Un9JGaLSEiyI0pyYkhOC89403cu4pAO/03t9o24Gd+1bv/W+1a3/Nore311+50n5/kgqZa3+o0WsfYK0JsBEsOn2Mzg1N76orEI9RC2NUMH4Xis7pOEJ/y2TXCDvhFHHAQ9txHZxG+47t166fw31pcb+lSlo8unUZK889zGidDwZONoyc6/UCkLt2vNesu1M4565673sLdy4YDdldcehS7rpZx01r4FhYT1opJ2pLv2KHQ3vdWLvDRcLtMew91GcAyBsvRdRIjiNUt6ZF9DFamWbEY+D3NrBCMOii+7oxUOl0qnJB6Dn1zVsfzBQXBR+AWm3i/dNb62RRYR3yUIt2e33tsZuR3dHSuaHMF2HPMaQL7D0DUKf/uIZZ6J7EnGOOyV77RvILhNdUbEZWG8mtjruZGj4b3LD0NQ006nseq1EWY+rizdkSOiwj0Gun11Vb6FvwunO8IX3fCbLLbBpQy5jH9OD5VZPr7qsNWIbKLkJzPP/ncSxCTUzsM6HQ7+yx1TE7fPNUIVU9JrOh54561CJmwzSb+ea+eQ0ayS7nUCCvZ6jrVa8CxbX1eh9D+r5Zm2E80bugy3/sI8fH7HhvmveBCXNtCOfcFADdAG6brv+FKLOQ9wcBvATgXPX9IudcsfVbDv+2DftoHV/6q1yb7naAhoRm2ZRKaf+GEIuK7TpTa0tvtyvKsAH5aJIt1NX/bcZOFZ81jjRbE6IJ8T153/qyn7GzI2HXllPYSHboWrJbolKneE6xKLbFpLlL6zoiamRPa9vY3IIGAzMlek0ZkWwHXn/w16OSEbGTiFJn7b24tujVnGOkzhvPs1FNlb0esXtOhOhuoX3bAIWOL0Q7ixnEdl+hY+p22had49X6Kj0mnynuS9eO5Gc6rjL0M4J1jaQUhzLxCq4JZ2thlcYWQtO680t/FWzL3w6UYQUs37K5bokHdd5iepSG242BvXWyQuPKyq7VU5pB1c94r+11DnU1tbRJixBNuQgcj47Bvuy1UISyIOo08W+zstsza0ZG24xh15nr2WLXQO1663zAZ29+/546mWtWBu+/fAuPbl1uu961WSKec/e94ayWXpeqUVaDZaOEgkGha1/Wmbayyr9FNgqQb66j1F0rL9oohJ/HsGNjT921Cjlv/I4UWz3fsmCGmudcNht5YduyOgaEnpN1hoE8O0AplUXsh07OKCeUg3PuQ86591f/nwHgGoD/BOBF59z3ld1PGQrlv0Jl8e7PA4D3/iUAc5oabRiuDftoGfdfeQUAgsp9JNCUuRq+oRQ6jXo12hTWoGI0kk6VGgR2gVlLpSCKuujZbTVqxfM6v2pycOFNjXRbZVQGun2MblREP41tq+dC2lAzYDfPkFOmICUkFAEn5ZLvaQg85+bUrVFXBsM3P53bTzsQuqc2M7rrzPVMhnXy5IRs6xtCDlcowhkyJJZv2YyDV5/P1d6EJuJQ5lkNCCDvvNEIDq0ZZBFzuIsiubyGoW30Oqmjz9/pX5WXVsEOd0B+bTRbH2rHb/9XQ37FoUtYcehSS3L7+qRp2f/tlN0yBtbyLZtxftXknMNtnTd7T2L30CKkj+gohgIOFnY72zQnBvvcheS1qKEHAwd2LHpO1onTY9rARMgJVTRTV6k6IXQdec/pxOk8y2PZAMRzbk7LcjftmWeyQKeuP9huhDJkrKu294tzstZo88X96HWMZVytHlCda3VA6Pe8zna+p64NyUuRYxn6nFksXoNQ0DgUQGm2ljcUIG8Wb7x4LNOz2uBIx8v7qNdMnbne1TtzYxjetAaDd+5OCCp7Qil8o/f+j6r/bwLwce/9AgBLAOyO/yyPMg7cQ+9987N1YzTmbo4ipj3zDID2NphQ48s6b0C4tfX8/tqaOKFJhwhldEIoyvI06s5lDYFGmT2Ot1mHTTs22c9tXZtel5BCtuvk6GSkx+hdvTPLcjS7xlOIwmCXCrATVIxfT+Od0fxm6EvEl8+cme1zNFBk1Cs0Ksq/ZccUuwd6L+01jNEllQ5nKV7a1MHWYIScuFBNRQhF9y30jMaixyEqkn3uGmVCimCdtaHp9WtA8Xt91uzvlUZNg5nrrpXFp197ve2F+YN37gYzBQqeuw1e0YgKOU/62aNbl4NyEdILmu0PgYY4s/Yqu1wHy9ZO2hrKmIwWUdJi1z3WECokI6HMj+7fZjb7bl9r2oCOHc+Oi/slDU2ZHaFz0+xlUfamCGV1Q1kUBUH0fIsykYQGbuf319bj0xo7vReT5i4NOqGxRlHcv+rX7nuV5j7nV03OBfdCtNoQrMxYhCietDd0vDb41Wh/jVBkhzWDonGRGltkCxHUWyFGR8KExRvy/7cC+K8A4L3/P83spIwDN+Sc+34Ab3LOzXPOHUSlMUlHwTm3yjl3wzl3yzn3z8r8Zmh6V+nOc43w6NblnOFGRUMFE3qIGWUJRcSt4tR15qyRF6uJscZ0bJFWCxspU0W28tzDrAtYM07blbP7sxehtTeh9t4KXVcPqO/sZOlK51dNjo6tlfvN62DX6VKaUCiLGqKv6v+tOGHvvPd/MqU+0kkGqC05UYTQxB56H8pyqpEK1Bp8aIS4yGmMnSMXej1wYjC4Bl3MSaSRYxs+WLkIUR9DsFlxO2b7/ADh6xdz3FTWm73flgZsoRkXS2umcUyKsz6vRXWqZXBjYG9hw6GyUJp3I6qodrlTR5bXOuak0eHScRfVIcaMLO7DPg92AWMuTFzGSYsdP7RtiB5JqOOuOlbpwCorocBC6Lgxh6oMrDOsmW3KKJAPCOo8wrEz+7x9ye4se9OsY/n6pGm5DNZIm3wUgcdRGnMo0GOh9EptyGMRCzyUBQPJWt92ftXkrF7cIhQIDo0/9txaR6jIVlB5DdXxNtKf7ZhPAWRsh/n9e+qeKyBfb2yDztSrvPd9t6/lZLddY3yS0chfcBX8bPX7Qefc15X9bUl83jn3nc65xQD+DmolapMBPFV2J2UcuO0A3g/gSwB+GcAXAPxIs6MN4OU27AMA4Jx7E4CfA/DtAN4H4Pucc+8r+s3rk6ah+95wVlQ7kmzG4J27wSg5m5jYCYM1XkBx90JSIYoKsa0yjkUJbRtpoJhOqVFPfZWp47PQaJKd/CzlxTYdCEHpftZxm9K/oY5qsm9tZf0n2wq7WYQi/TbCaPdPPr1SM7ThQCuRYGaP24HBO3frHC5LRbEIdaFslBHWtZ74vKnsarbaZlpDdRe2OF4RWnutVRQ1BFLodQg5XnoOpNCojGq9ho2Wx7JjZWGp0CGQ9h2KCtu/fJ6afZbe8ZanMvnXNRxbhTaAKANLm7QIZZOHN63J0dC2L9ld5xQXGfPW+LZyaxfRpvPGbVuZl0L3JZbpDW1bJGPMuNj96l89lur3Mm3YFY2CD0RIH1kKXVlWQQyffu11AKhbb7VVFDF+QlnwUEkAEXsOec3VidB7YLv9lrkmocCjldMiXVlU96n702cw5MQzCGIDDEonVTltVMMX0ruKVmSXOp1lJzGEKKlav2+7BI8kq51Q2l/4dgDzqq+tAH6+id+WwT8CsA3AcQA/Ipm3bwFwtuxOGubmvff3APRXX6XhnFsH4Jz3/ovOuZ8E8HUA/h/v/R9W97u2mf01wNcDuOW9/0T12KcAfBeAP4794KlH9zPDZn7/noad3cpADaVGRfU6scQeyPn9ezAEYOFLM7F8SePjU4mGOpUVrVfFY/EcYmjGaVPYbEqMWmrfhzp32jFaug+v2b61C3B4++9gx9VXKwtYALhR/b7Vez00vQvnVw1jSD6rdErLOzHagY3vOTZ2J8XASWAEzmQWob83DKB12Q1NaGUm8qHpXehuIZNJOYvdg9DzoMYhI78hFDWnKeuIxcZbFjr2i0ePYRc21z3zQLhDq94HW+zO/YWWCWgF9rkJ6a2YsTMScJ/72qn9DUJjtucboglaJ5eBFoU1xJSGpo62NqPgmPi87ECxUxbrNNkO6D3V+UpxftXkTIcCFaeNupO/U1hmBKm6luaIkg6ZgmO0xrVdT4uG/Y2BvTm9FKrpbAXveMtTI94HMXjnLlBwDRtllwjrPKv82vUf962tdxRIr2SnRN3ebmv1ZygIYevAQ7D7jm0fowtq5q9voKZj525/NgtEdt8bRt+JQRwusFhD5zjajhEdM8tg0IZotiQkYVRQxl/4LgD/yVfa9F92zj3tnHsGlf4fTfkaIXjvPw5gVeDz3wTwm2X3E83AOec+6px7IfYqse9/XnXelgH4NgD/EVUvdhQwG8Cn5f2fVT/LwTm31Tl3xTl35c5rf1VoDMYweOduHXe9KJqpCDkjsXoDpVoxK1G2Tq9M4XsIRRPzSJQK6QyWpqO0AUKdttj1snVxOn69bqHrUJR1LIJdV49RR7Z2Jlaee5hz3naduY4HAyezDoWafW3GOFbZ/exffL6lc7DNerhUQtmJi/dMl8cgYpk4ylpRFzjbBj5UOG9RppPoaCMURbbZZaVB2ayxzUbH7sPQ9PoGAc2A0WM13q2jrJRCbsu/I8laq9y+9rnP5fbZzH6t/CjrIUbnjRloMee1KCsA1LrNhmAdhRCFNkZtbISRUvxDxqp+ZrNUsUZNMWObv6OjZmujRwK9l2ReALX5iPqI0Bpjmwm33Y4bISa7zWCkzXpUJ5bJknF8eq0asSQIzWZplixkI1k2D1DTyUU2lb1+MZpwLLjA7w/fPIXue8PZvdflCTSLHWLNqAyQCWF1gv7fbPZN9xG65mQ7KJQ6GcJEdupIPx6rF4CZfG6rr61mSGX8hdg2pXyNRnDO/ZBzbl71f+ecO+6c+0KVrrm47H6KKJQ/DeBnAHwSwOsAfrH6+ksgl4SI4a+rf1cD+Hnv/a8BmFp2YE0i1NGyrkmK9/6I977Xe9/7lq/6qpyiKduJ0tIZnnNz6lry68RRpsDW0in5V9uxN5o4OH6lWcZa5hd117NKrh1RqVCti81U8K913mLZOSCfxehdvbOOQmGd1ZEa/ZPmLg1eOxup54RgDRo7bqC8QaCy+7a3Pt22QuZQJkjRqC4BqC9sB4qNhZj82YnS1jPwPRsSxAIVscWOW71mkUkiuJ1i39oFURkIZRUUet3b8QyylsvSkWw2uwghJ6ARrM4ltO6rEZ5zcwrvHWlt9t7YjGZIFzfKMs7v31NngMdqRkMBpZgz1EgWVYZbpffHnEgNICnNC6jdY0uJC2XfdI6wNC9t0jASTJq7NMvWU3451qI5VRtk2CYbZR05K7tZhq9JJ06dfpUl+yxaxLLizULvpZXlXWeuZ3RmXid+rn/LYLSyxkBtDmANPp/13tU7665PaByqHzSoBtQ6YLc7A9cza0ZuTtNgg2byWdNnmUkWE9mJG2O8yue2+jpivi/jL8S2KeVrlMAO1MrIvg9AD4B3AdgF4GfL7iTqwHnvf997//sAFnvvv9d7/9Hq6/sBlOFC3HbO/QKA7wHw6865Nxcdb4T4MwDvkPdfA+BO0Q/c7U9m/zczQV7YtqyQm28nju57wzh49fmsdkAVsF002UbbOGlao1jpZ1TIsWYMpNrRsStLHyzLiS+Doro5KjWuRWW/s9cotND0+VWTcf/01rrJdaS1jSF0HX8hdw3Z1EAXZlblrJFTm31pJQMMAB+/63OGVNkIb6PFy62DATSunbFR+VBWJGasaiZbOf/WEKaRqW3xiwzgdhsSoYwODWE1hq2Tph3y9N7TeLAGg8qGyrLWp40EPbNm1LUhJ2JMAN02lKFvBp9+7fVsH7xHZWT3Q/7loM4LyYrNHIYM3kYZQD1P6hD77MQCfnp8Zt+HpndlGfyi513Xy7IyHPudze7ZsRRlMtTp0v2wble/4+dA3mEL0RqtvrfNq1qBOnHcp72HId0QcoIaNcwKgSUXdk4uM+4P+ZebOhbBe2fltUhurRysOHQJXcdfCMp7jJLJxcS77w3jwrZlOdnXMo3QGm+KkQYebFBGbQCO/f7prXV0y6Lacs0Oa60ZEbJRRrpkhC7xZO+jvtRGKNL17bLLnmCU8Rdi2zTta0Tw0Hv/oPr/d6JC1/yc9/4CgC8ru5MyDtXbnHPv5hvn3LsAvK3E774HFS7nKu/95wG8FcCPlR1Yk/j/AMxzzr3LOTcVwHoADWmeC1+a2VITCWJ405pgtzDl7QP5CCbbnscoEY0MDzV4qCDVmOD/DwZO5orhn3NzMkXcdfyFukxdKIvSLtgukUA+82OPpwXoeo10DTpeIxYz6/jZnTCEdhn3XB9ufv+eLHJpMxo0WvR+cn2o0XAumwEpaLHMYFl6p3Y+CxmToSy3TrCcHJdv2Zw5dLbOaN/aBVnQRJuhkEpjDYjYvR/J9bYOjMpm7BoqLIWYf0MGA2m4HG/I0GgVF7YtC1KCihYBDjlumglpBSPNiALNGzJlaZs2+GADYiHnUwMelBWOj9dbndCh6bXlTIqyyTqOkM5oZNTzWBZW/iw13cq6lVsbSIs5Rbr/kYIZDSuXRXMms4z2+K2Mx+6/lbUQQ/uJbVO0XaN92GdE14gjrTNEcbVBJqLr+AuZzg51Gi5Cq3rXUrxD91CzyKF5SO8/dbYNMDA4xqyxXoNdZ66XbqZTBM0kW8ZW6Lm2tmE7AngJGcr4Cy8A+IEqvXEpgLve+1dK/rYMHjnnnnHOTUOlcckF+a6tXSh3Avg959zvOed+D8DvokQXymrzk8+glq17COBm2YE1A+/9Q1Q6uvwmgGEA/0UWyQv/Zva7MgN7+5LdmN+/p2mFrE6Z8r/tpMoHVKkUMQcNaM54JqwCoGOj2zzn5uScvTJOnEaEqEA0mhpTLEqFtJO85ZorVVTpopY6Gsvu2I5YFlpk3U6oQqYhr/UkRdQpGoatGLHvmVHJ4vN6tDJBWtpTEZWS5xW6F0oX1m05eYaCG7HjcByckFVObBS0lUY0ZVuwl3n+9Hrx3utSF9Y40N8A+YXrtXW/ZvTseNtJoaEhHIvM86UZO+qUkUaAy17jGHidYjSvEJ3S1vXoS39vDVeCa8EB+WYeRdRMXld19Khz+V1ZndTsM67nX2Tsx2QzdG01kGDPW7MIIbTDCNZx8HhFtTBs5W7H26ozGaPnNgPbXETHc3j2Qkzp31BpGmOMfP1N7PmxbACgcu8H79ytm2s4Dp2fQ9dKbSJ2tLRjAorXINTPGsmxpT1b2BKKkFMXklmek6X6hrLGZQJyrcKWdsTsAMu+sM9aQuuI+QvOueecc89VN/t1AJ8AcAuV0rG+ot+2MIx/AeAKKjTKF7gP59zfrR63FBo6cN77c6i00txRfc2vdkophHPuXwL4cQA/Uf1oCoAPlx1Ys/De/7r3/j3e+7/pvR9o5rdcv6TZB8NSNmJrc4XWlwpFBe3vYsoulIWL1Uzw/1iU1zo3qmStcaccbkt55PsrZ/fj/umt2Ld2ATa/9Dt1vHqbiQAqDrS9FjpBh94rQvRRng9pHu2qGbOYNHcpHgyczKiydjIE8nWRCttKvhnY69Fsobylp9pJj39tFDSWmSma6OiMsJ01KToh2l6RY2cDLCqrVo7tIt0hYygGNQz1HOw49TrpRKtGgo1yhxy7EHVYjzlalBk6cerIxK7/tHVHMjnn2Jp1Kt7xlqey38ccpTK4cnZ/0JHgfbOOsP7lsS2KdLHug3rEMjfUGdZryvcqu5pBjq1DapcXUBQFrELXNHZvVe40I9eI7kgj1zr6PPZoZwsubFuW6VRLA9ZXTLeMpDygUQCqDKjzLWWd8xSbXhUFaPn7WBBDxxuis6v+p0Nc5BQSZQJn1PXUz2Xn3iJ9YO+nrgPI7/X+KlVSKZexNQMVo1EPR4TKGOx5q47Ue0edN5LgV0IFIX/Be/8h7/2Hqv977/0PV79f4L2/UvTbFvDnAD4AoMt7/0POuR9wzv0agH+AyrIFpdDQgXPO/QCA7wWwsPr63upnjfD3AawB8FcA4L2/A+Aryg5srNC7eueIaJSWXgPkuxSq4uX3Sq+xShwIZ88aoR3ZJdv0hOOPRVaVBkpKFc99Sv8G7NjYE5zwYtHQkNIsU6cCoG7CI0j1oHMdakowUugYtc6F107rIhWtGhKvT5qW/a91Ns3ALibMcehirByfOiD2fhXdP2ukD03vymh31vAIUWZtPZHdPw2FAycGMyplbLH6RsaQBi0aRdobOQAx41WNZBtJZgDEOtM0wEbLKLYBmlC95q4z1zMjiONpxcB56tH97H+e/42BvaUbSOmYQ06GOhCE1a+hcVvKtu7PZvHYREH3bZ1FzX6UeS5VHunQcZH5WPApls0I6Rn7XNlzt+hdvTNXIxSi2vEz6oeiWqLe1TtHXEdkwbouIH9f7XF5/Wnst+q4UefSWaUea2UuidGu7RqF+n9o/cJY9lffFwVctDShKIATu3eUzZidoo4cYZk+ijIBdA16kfZoZc+eQxkar9Wv2tik3bIbCgaq7ogFdpdv2VyaBp7Q8fgFAH/pvX/NObccwL8B8J9QcewOlN1JmSKGvyX/k6/5h9WDFeEN7713znkAcM6VLswbS9AQaCZSRNBY12gpHami9rgKOj85ykvgtzaSpmPWNVps9zBup98/unUZk+YuDXZT00ycHseOSccToutkx+7fgH3GQeZaaFP6N+B89ZooFU0NR60vU1i6qdJVleJ0o6Aerp0IyU9lrZq8AchxjUQRc1FZoHWDZH7/HqCaCQNqRt6DgZNAZKIL3QdFzClnbWDNkMz/rvveMFYaQ9saz/zN1MW1dsvdyNcSze/fU7fGlsq+nrs+OzH6ClCTbb13avBY9K7eGXS0NPMWW9+wdq41ncBngMccvHO35bbWIVgHxX5eNkPayvGA1lvk6ziytRWroB7hvdAgARCXYZVfZqKsntm3FsDa/bntdX8r5ZmyY72wbVmWieuufnf45qls34dnL8zW0gLyC493m9bqVj9bWVbn0cqTgtcoRM/VNasU+hmde/5Px4b3w1LW2gmrH7rvDWMlNuP8qsnZki06Zo4RqK/5bISnHt3PApm7zlwPzollEHO4YoGifWsX5NYMjdH7ODaVV94PyxSaNHcpdsnSLUNyrN7VOzP51gwWgJzshmoy1d7Q5/qBjN8GJOzvQzqI47ROesyJ02VuYnpWnwm7/6LftQs3BvZmjc+AcMfyXWeuY9q6I3mac8mgUELH403e+7+o/v+9AI54738VwK86514qu5MyFMrt8vohAItRbjmA/1LtQvm0c+6HUCnS+8WyAxttfPq113PKopUsHB/CwzdPZRkA7ifGVY8VnbO5hSLGdbfQid4qR+Vc6/c26k3nM9aCPYZGlEY9N57f/dNbc5kjZu/UiQPiNSWh68LPrJOsjS6A5tqXl4Uel//fGNiLY4u+OTf+EC2vFQrle2a4Oqe5Vfqvonf1zmh3uWaaEcTqaUJg9FHXLbJOuY6PsM+uGilFDrvNyIWeF/3fPpexrEqsPi1EleTf86sm51qb6z7UsCirB1oFI+88hq6lyOyGZjD4GglzgdHkVmAZDHTSGsmmzZhppkzf6/bWgY+xBPi9OgbWCWYkn9vqGlQA0Hf7GoCwvg1RQXVbSxFm9j80rzRCTF5D2xD22dXtuG07MxmPbl2uy6gOTa+sl6jyoWNrVw3pSJr3xBgLscwwodeVmU+gnkqvukLZDjp3Dt65W5eRVeaF1jPaexcap23SZm0g1S3U1aEMsv3Myq2ON+ScE5o5Vtg6t1CQioGH0W7Zz2vE+8Drb+VWaZ7KgEiY8HiTc46K5FsA/I58V1rBtNLW/x4qNXGF8N7/NIBfAfCrAOYD+Bfe+4MtHG9U8I63PJUpl5FkQ5SKFmpfbDNShJ1kgNqkW6auwf7OImSgqvJll0pLvaQT16j2InS9Qp/FnD/SLXn9QtE2LeS1DguVGQ03G5kOTWikJLUTapTYyY0tjhXWGWgFnxr6RDBL1CpsfYB+HjOKQ1TK2LZar2QR+sxmKhU2KxI6Zt/ta9EaOP4uFmAB8k2J+D40PgXpj7HIraX87Vu7IOe8N4r4srlBOydv0r+sMWcNxyK6dzN4fdK0Ol07kg51GrG2Yyy6F9wuZMjxc6Bm6JGKF5Pv2LPHcy26VkrVIt0aQM4I5r4UrJOy2Y4DJwZzzbLsnGSDK6EOwUXGq26v11eDcDre0arfpBOgzpR15rOMUhWszWaGbjwQe86AOD3d/tX/Q3Q8fqd1iepMWVDueP3sXKzbXTx6LLt225fszsmpZb/Y+kTNOIXYKoStw9fz5XMdWtRdUbQkkf1NTFbVCWw3jZLHDen0w7MXBu8/baaUgXss8MsAfr9a9/Y6gP8OAM65uQBKd1MsUwP3UefcC9XXfwNwA+XbZn4cwG967/8pgP/hnOu4GjigpgRZ21AWLEi1UU8iRHkk7EQXasSh24VgnTS7L6sk6QgBFSUB5BWnddy0s6Y9B23M0Sg7FlLqIWqbndw4KagzrJFurq3HbdUAIqwBFctAjgQ6Aeg9KGOctmLgfHXP+3KF8K3Q2npmzWhIJdNsS8gYbhTZjhl6IYM2ZKhysuJvSI0pMkamrTuCKf0bshqiUOfAoigyX6FmKDpWGqw8T0vBIZpZJJZZqdi2NNjbhRsDe4PGob7nX7vYbCtGhNbAcR+tUCjVqAsZxEW0vVgtGPdn6VmMgodkttE9tddInxM+v1r7aHUq/7fHsQwD29nO0uBtZkOfyyL9o+O1DY80CxRqrqBjp9PULmeOVD6yHtRhO3j1+exYtsuuOjOj5Vg2QlFAL/aZlXGrT/Wv6iJ1qEP7IsgEiQXT9P5SZpjtBJBlkbcv2V3XZZNBA923OnE2+BACdW2RblSE5qPY86vUYrt/6gDKVDtgayYti4Fzl45R6Z7aWTVh4qLa/ORHAZwAsMx7z8XAJwHYXnY/ZTJwPw3gZ6qv/x+A5d77H2/0oypt8ldQKdYDgNkA/mvZgY0V1OhudbHRAycGg/Q9QjNAMepfyJhRIzH20JYx6Iamd2HS3KVZ7czQ9C4cvnkqpzCL1iKyhce21kLPw56z/h9ypPg7S7PkpBy7ppYyZJtyxNBu+iQNL6C+Lk8pojpGu10r4IRWZJA2QszhUMPYOohqxMcmU6Xe0diwclIm0MHtJs1dWpcliUWr75/eGsx26/6sYWPpaRpcYM1GKCrM8ej/NB5j9DMavlrPodHlWOSZE/2DgZMtrz+lGLxzFw8GTmb6j2PSaxTLvoXel4E23wHyDX+aASm3HIfefzveMlQodQDun94a7c5qn7MYxU33y659Ol7dVp81rQGyWRB1iPT547yh3YaJUBDJUj35PpattB1UNVuv/4foaIrRyBqorHJsOl+EGAXaobQZvD5pWp3j3OqYY+/ZWZHPXUzeQswdoP5ZKNKZ3D607+57lWY9tBdIoVe523XmemYvxcZlz8/qXAZogbD9EaJZ8rqUkSe997o9O1AqDl59PnNKtcP2vrULsPClmaMmv/zLZ596Xpc+sPNtysA9HvDeX/bef8R7/1fy2ce9939Ydh9lHLjv8N7/fvX1P7z3f+ac+7clfvfDAP4OgC9UB3YTwFeXHdhYoNXshcWOjT2ZMcIJMVRrFaMm2G3UGbJ/FbEIq90fFfGjW5ez8421A6YiVWUay0YwKhRTKGp0hM6jqOGKpW5o5k4jzlbxacZGwf3H6KGtYHjTmiB1k+v4hJzuRjTUMnjq0f22KXEr/40oVWooWzoWv48FMWymVr/T/9U4Ujktul5Ks20kkzYCr88u70+jxcBj1DZrKOj4aBzYKK9mM+b376mjV44WLFVVnThF7+qd2XdFxlozmLv9WQCtGcJc/kAzxEBNn8e601mokaaBPN1nyIhWaqTNgCjKrH1mncAQqyG0vWVwAPklZYp0nDpxauSHOp2GKJOWEh3Krljw83YEHxR0AmiQU5ZDDn076pps0KHZ9Sj1vtl7zOtfRJ9UB5S/5b22z4M9JoN+itD+gMayy2O0OofxmDHWj/4fmjN57Zght0saFdH29fjcN+0Hq7OVltsOMIO268z1jAVF8Flcee5hbp7g3FbE9Eh4MlHGgfvWwGffXuJ3X/Lev8E31YI9X7D9mKNdNAqtO1Bnymap9H/7EDarCIsMeGtk6m/0nOmY2c6ZlkZG5abbhbIRsXGq0mlF4ccomfwuFuUjeE/anX3TzCSviVXKnPBtXRXH1eoE2C4aRWjSbxSljlECrYFknaSyiDnhMfojUB99DtUKxMZgsxhAfFkO3nPN+Mb2y4xc6HOFZil5jtZh4jm2C6GoLo3cXWeu5yi6IcqW/b8MlEKpS6q0sii7jp0oci5DmUSVexuIKUIoQKDvrexye+2E2KozYQMoyt7QtRC1m2XReXC8BB05NYobOWUWlF1LZWUdZzvAY0/p34AHAydzQQY7HygNWI37ZsAulO2Eyn6zY9J7X3Sv7bNgM36h7zgm1n3ZJU8aZZ5DiD13fbevRden1c+Llh6Y0r8hc+RCsIwH/t23dkFmS2gWPpaZbEcdnDpjh2+eymSKNNRG7BY7JyQ82Yg6cM65f+ycuw5gvnNuUF6fBFAmhfH7zrk9AJ5yzn0rgNMAPtqeYY8cbMWuUciRKOiimhmgPpNWJpJijQIAhY6TZqeASqS6UcvxRk09bGtfHsdGzssYl9bw4b5jk3ooyhercbPUIDoBsQnmwInB4DIKIwWbwMQyC7a5RquwstFKa3m9vrYmztKlGq23o9H7kCxY+be0Uvu7SXOX1hn2/L5MZqPIwI7B1mSEMsShZUJita9KzdZzK3IkdQKPUaja4chZWh7BRg/71i7IjGK7lEfRM1uE+6+8kv0/Etknk4DZsJDs64K9QOOOf7FAUCwTZjMx3IZyG3JKLx49hgvbljVcRiL2rADhekt+X9SACqjVLescojpSjVV1FG02w2biQlkiAHULJR84MYiFL82MnndZ6L3nHKLGuzqOpFfqOELNpRpB6b9l65tDiBnfzHTaIEQZY93qLS29CFHFuY3NuIbsBaWjqt61826Zccb0lx0/UE8FBvK2TyhQrrTO2Fyk38fYIkDNjtA1EUcLIbp3qOszM47tDiQkTFwUZeB+CcAHUWlY8kF5LfHeP1ti3z8O4LMArgP4RwB+HcBPjmi0bcQ73vJUNnmR890sbWl405pc+302NQGQRcViNQjWQNPaIFsrpdDasVCzEtYAhAyImJEfUqCKkCEbQqgFfQj2Ous5AWGjSeuT9P9Q+2wdd8jQ1MlhJE6c7of3g8tK6BgA5KLj7cwEtqrMVxy6FKTTagRe5VONtlA3r1AE2dKYQgapTr7cR0hOe2bNKGyEEKIO8ZiNrhEn8thkHrpfagxrpp3H0sJ/zQDwWNZAY9ZTDebYc3R+1WQM3rk7YjrarjPXc9fUUhHZvEPHy2vUigM27Zlncg5EO6CyaymejYzKEIU+VD9ZhtpYlB0mQnWRsd+E6OdFoP6xAbnQki5AuNOfjkcdgFizk0bnodf34tFjOLbom3H/9Na20SiVckZamtVDuh4c51adQ1rFSAIQfKZ6Zs3Imlpw3Gof8EUnQh1T65ipnlN5DQXdQjoo1PhHHbZGOmnXmetBObLbhKD3IhRQ1jnUBqn5ezo3RIj9EQuahxwoPs8aGLh49BimLm7f+nC8T6HgqK0vJJLzlqAocuC89/5lVGrZvigvOOfeWrRT59wkANe997/ovV/nvf/u6v8dQ6EknUcNlGZS049uXc6l+T/kXw5uR4cqFu20sBOLKlylAqpj0jdvfS5KVRQd75k1I1MC6nDytxxDzEHUbdUAIIVBaVi2C5ga9HSYtdWxGhd83zdvfS4ira2IHwycLFzXiw6Vwq511+5lBWgghIwEOna2KUYr0Mla24aXwQpZxDUGpZSoUwEgOOHwc27LZyrUCU2hhkajJkKT5i4NZuHsWOgMUQZJT+Gx6CBqrYyO0XaktO3atfYSQN29Hprehfunt2bHYBYgBjrNHEdu4dYq7MQ90oiwNXh07KFtLf2tVfll1oRoNpM3eOdutGNeEV2VMhtbG8qO0bbEDzluNtMVywz3zJpRt84fx2aNSksF1s8126jnrwGx+f17cgEkfqZ/9XM1/nneU/o3ZM9vyHDXBguEXvvQulzM9vBYthtfM7CBT3VEGHw6v2pybm0te+9aMYRDz3kzoN4N/U6DJQTlVbPJIadZHVP9nPvlvjRARHnitXnjxbB+6pk1I3dcDeTo3G0z+nZ5B5tdVL1LO8HO3aQFx9gPGjhTGdYgGscVqomzmTV7bW3GmbLVqhMX63wdysaH6JwjDTokPH5olIEDgKsArlT/XpX3UXjvHwG45px7ZzsGORogJaLVOqRQG3yF/TyU4QtN1DHsOnM9c0a4b92fbexRBJud03Mpcz6qXC3oyHEyDfHJQ+e28tzD3PjpQPbdvlZXN0ilbCc8Xg8bDQ+dk1JeR7qkgKWZ2uvIyLjNEo6UQsnzPHzzVEtR7UayxwkkNMnF1oMqoqrRaIhlunedud6QChoyki1dRhce18whv+ciwEPTu0q3h1ajQe9j6B6S4mOzNxaa8VLngn9tLWI7jFCCsmOzojZrym255hPPrYyeKTo2UFv0dyTGPFAxjC3dukhugZpxfOXs/pzDr/eLxq2OV0FHqYwOf+PFY3VGshrRReD+h6Z3BZ9zXR/KUtT1f8ukCM19VqZj62nZ7JouwdOogcxIDNGQrGjtqB5Xu6xqtrxVCiTlvxXQ6LcOOq9bmXkSiAcigNr9jlGv+Te0OHQRLmxbFtS7sYwW5+ci+8Y63XoOKn/qtD0YOFm41ADBY1v7Q4OSIVw8eixHP+VvQk28WoEGk62+BfJNlez9TZm3hBCiDpz3/jurf9/lvX939S9f7y6x72cA/JFz7rdlHbmy68eNOj4z+MeZk9CsE/fo1uVsIWwgH4nl+lN2goh18tP3QOVBZZZJo4uEtkk+v2pylgXk5zcG9jbdFCBUz1ZE3yQsTbToGoYyHUA9bUGNEKW3UvEya6JRN26rUcAy9XNEq10p6cRaw4j3QbMNWpuix2vFmNEFkfWcy0CjhzoBrzh0KbfPEGyGjJHTGP0DqN0XHaveG06qjdakCoGGjM3yqSOkUWw6cYwe6/iAsDETirjrPbWwzwPlL5SV4MtevxDVzjq9zGK3Ulj/6NZlHJ69MNdKns+SpamGZGukgQdek2YNYsou5dbWHdqx2qyxvQe2XsbqIiAe4LOOXjOgMayGLs+B0GfT0utC9HJ77pZ5wLnOOnXcn7IuLItDsychAzZEqQ5lkZUu3K4uq3rt1LnUJjxFz3Az0GvC/Yw0+Me1H+18xXMJdQW1XRcBZGuwUaeo7gmVZPD/UGfKItgAW8hZLHJyNChI/V+kb6zTo82PYgEyPR86ZZxfiurZlm/ZnAXa9TxsfWI7oLTP86smZ4uTA3FbiWh3M7aEiYsyC3n/dpnPAvgpAN8J4F+jto7czzQ7wNHC13a/O4smcx23kUY5tJ7KTpZUAvpwhrIVffPWY9eZ6xn9g7BGHh2FWwc/nHPqLC2yEbqOv5BTCCGnEQjXm9nCaSA/WWgkS414zTjYicgaKSvPPcztx9KnQpRTRSgq3Q7a5PCmNbgxsDejZFqlyntCCujhm6eybenkjxSh6G0jaHbK1j3Q8SQFxhoTQGXCZyc3dTRsp0JLo+mbt77u3tp712wjljdePJZNuvYcY7DrGVmEHAEagZqhuTGwF323r+V+B9ScOq3r5HdsCkLYDKbSnWzjBe6rXWD2KKTzlIoUc+qLMosxkPVgf9vMflRvWgdKaWRkAtj/gcqaTyGn4/7prXXGr63PDUGpjWVxYduyukCeUvltZJ605NhYLNVKr5H+jjT0IpC21n1vODOA9bqHMgT8POa46fui57MRNGOsQSG97zSK7Th0qZlWnqUHAydzTJpm6L+2Pb3+v/Lcw1xtHtdEDV1D3g9CHVS9znp+DwZOZvKv95HB3mYDvpT1UKA3lklUp03P2+oV1b8hdo2tN7ZzCeVA1+Octu5IQ8cyhBDlExiZ/Cp4r3kdlBGijcPsfLV9ye62HD9h4qOoC+W0aq3bTOfcW5xzb62+5gCY1WjHsnZc7tXqQJ1z/8459yfVTpgfcc49Xf18jnPudefcS9XXh8rs7/4rr2BoeldmcLH9eysRNXUI7HICjIoRsSgOP7f1CxrBV2XNyDAf+Cn9G1rqREiUXZ9MnTiea6gzpKUhEZZbrhMTFzHV8yPshGwdbk6mPJ6Ogfd2NJYSUGqdnXC67w1n95PfaQQRaI0a8enXXsfQ9K5c9LbsftR4tUYFM4bszHpjYG/2uQ0o2IyZ1ptoEGBK/wbsOnM95+wA+dq3GEVnJAhR5vS9QgMKoUBObCK3dagA6hrYaKSbdS4hGg+fbzrG3DYUMeZ+W40K6zPcqKstETKWWoU9VllD2K7bFsL8/j2ZzNmMQ6wOhwhRJanndp25HnTKWzGAY9BrbLPISgcLBf7s+SjN3J6npZtZZ1rnsytn92fPM4MKzJjrOJdv2ZwFhCx1jfpXdXSr6L5Xa/Kl92H7kt3Z9YtRt2PMkjL49Guv113rBwMnGzrDhDq83feGM30X0gU6n55fNbmOYUCD39YnqlNbdJ48XrPB3hD0Hth5nucWqzO1gdiQXuE8Tt2sdXeh7a2TbTPHGsAMQW0RoD5DGTufstBnnDKkz66Om4HRi0ePYeW5h9nfhASiKAP3j1Cpd3sv8vVvvwbg5xrt2Dn3RefcF8zr01XnqwwF0+K3AHR773sAfBzAT8h3f+q9X1R9PVd2h4dnL8wtXB1aqyuESXOX5hpuxJpi0Bi2yjYUXbeTrypxLcDldra17kiUcdfxF6JZyFC2LZY9UsOQ2c1QRgOodwDsukMPBk5mhoMamYxOEhopt4Ygv7NOMRFatLwMWIdh1//rXb0T09YdqZtsY5G/Vg3hd7zlKQC1KCYd+zJ0Ok4Y+tLxWCdX5c1SU4CaUc2oIOuJ9L4cvPp8Jg8WbPbRKtiW3T5PSpsMQSd2zSZqls062Wp0PBg4mZ2z0qkJ1ttoQxt9FmL1GKxPUYNDx8vjtYrBO3ezZ6soC6zf2yxOq+DSLUC+rqtsNtrKq80gayZcAwihcRdRuWkE8reHb57KHZfyMJKAGcfI54XBKxpodr0ynT+UhqbGZsgxsgwGIH8tdp25nulUfb4ptzbTYRtTaRAu1jmv7/a1TKe3isE7dzP9plkYDWRSTum88vx4Hfh3JHVMQEWmqNOaCT7EAg9T+jdk56R2hVJbbbZe/9eOt2rgh+jg+9YuwJT+DSMOOlg2AVBzLGnv2M6ZOgZmiS0TKaRndp25nmXHKfvKiLC/tdc51hxL74naaMooCfUtaHbOenTrcq7umnMBncPQPAAgKw/g/0Q7qZwJExtFNXAHvPfvAvBPTQ3cQu/9oRL73gfgxwDMBvA1AP4pgF8EcApA0yEM7/157z2fpsvVfY4IIcO9HfQ67QoJ5DnNSjMBauvU8Hs19NTgs5F4Gtsh+l4r2LGxB933hnMdDW0kOvQb/U4dWdtenWMmLJ0UqJ2jjVayCyAQXhyd38VqalaeexiMlrIoupkaOHXeeA1Yg8jMCSevaeuOZBFUZrcsWjGIaQhbClYZxR5qF02EaICaeWNGGahf2wyoGRQHrz6fa+Kh2Ul1hrRWZSQIZWZizlujdthEmRorHkvvbaxRQtHSIJZqBNRnnNXQUT1R1omgEaHHCT3Tep9CWeJQ9qcZqLMRc65iUCqfyrEGB9QQ5r2wdNAHAyeDjSB6V+/E3O21FXL0eT08e2HbM/js8GezBLHnKia7mv0C6mvprDOjOL9qch0VPlQ/x+MwwGAzmVZuGzV+KNs8CKjpXAasbKDJZpE1w2GPWbQUSSPwd6Ha5zLgdRqa3pUtA6LXwc4PdOL4nMSeNxsM1e30mlgnfSR448VjQV1gHTIF5/dYdk7HZ9k3SrdkhlHZNpaNE9oP5TZUbx1yzHgepGDyHJqZs7TzaFHJgz4/muXmMhjqVI40AJHw+KBhDZz3/qBzrts59z3OuR/gq8S+V3nvf8F7/0Xv/Re890cAfIf3/j8DeMsIx70ZwG/I+3c55150zv2+c+4by+4k1FSiDCzNko6M/tVambnbn8W1Ra8CqEWuqMBIhSNoWKlxu3zL5ky5WKoMnYeRghk8ZiL75q3P2vTTCFfDVFv82ja/Cmv0cPyWQqG0HL0ejLTRqLD1ekA+I2EpFrzOmhGi00ZqT1nQkGDtmz1PTsaaTbQRUTqj7ZhIGaELRdBjsB3k7HmxDbleLzWO1fAGahMO7yWjnbwnGowAateD+2wHdTKWZYvV6BX9NrYvnoO9xup88Jy0JkefDz4LoUi8RoJ3nbmOaeuO5OTXohX50SYHF48eC3aSpFxY45THU6OpFbAmxQYSGnVRfXTrcs6Iu3J2P66c3Z8LqGjHOnvN9UVdQ8eH/19b9GpG9WVGh/tpV92qhW2yQMQWw+ZvCA1UaFZTqYQ20EOHt8gBoe7XhiYhp2vXmVqta6g5iQbkioz7Rgg5Mno/QudiA1s2q9MM/vLVV/O00Cqro0xW8dGty1GnUZspWYSCwKH3ahPsW7sgc3g0Q6n3u11yrOcUCiLaQLVCA9IhnatzuMqUzj2WCkzQZuF5qw7V/23wQY9jM3IEl6goA+om+0zQCbT1epwrOUbNDMaytwlPNso0MfmXAA5WX98E4HkAZQrFHlWdvknV1/fId8H14JxzF5xzQ4HXd8k2/QAeAvh/qx+9AuCd3vvFAHYB+CXn3FdG9r/VOXfFOXfli29+qs7xKoNQG+MDJwazBiZqPDAyr/u/f3prbsFrptKVdrB9ye5MEQP1iyWTmjCSVt6NYDNydoK8MbAXh2+eyhlGPH91tuwYrZGhsBE2a3hYupVSKThhAahThHSKqdDt/Y6t4RcDz6nv9rW6rCMNDXaWAmpLSGjmoFknTmX3wV9+PjNCNcPVKJNBI1kNOTUEmm2vbaORtvaC+9ZsDoMWdAbbgTdePBa8lqRGhgxgawTrX67ZpecWou2Gjhlr3a5ZTRvACNH5WN+o33N76oWR0BmvnN2fMyKAikwy4zq/f0+wdlQppmXun5Vbntv901ubptSpfKnssm7L1qGq7rF/GfywmVq7dIO9xvYYIwWXFtAIP8Est9aWEbHOpXpN+D5E/7ROLhGrzY1lufg8s8ZTxx4KUjQru9RZqj8YfAidhzotPPfDsxcGx9IIKrtvnjY5kxkGCQ+cGCxNoYzpes2AKmvF3htbimGdCKXjAshqxpQuzf/bKb86RqDm0CsF2FIYLQuJv4vVwOm9CmUpQ1A5V3vCXid97mhX6bitbmg2cKVUbgZP2XHSzrcq46qDUuYtIYaGDhyA7wbwLQD+j/d+E4CFAN5c4nf/AMAGAJ8B8OfV/591zj0FYFvoB977Fd777sDr1wDAOfeDqHS2/AdcFNx7/yXv/eeq/18F8KcA3hPZ/xHvfa/3vve1N9VTj8o6QzQ8GKVkh8GFL83MaGbd94YzRU/6niqGg1efr6O3XDm7H9cWvZpR7tTIH5relTU20IwcqWrtQGgCCdWPhSYc3Qc5/ZY6R+WunH0qaEupVOM55AioQWEnY5t5O79qMrYv2Z1Tlq1SZe1kFat9AvKUPtJ37p/eGqTYNYLK7rvf8UxwmzLGBMdDQ51UT15bvWeh+gIGJTRj2ojiw9+yTpH1PrvOXG+pDX7sGDoph4wEItQ2Xt+HMnYaCdbrQidH5ZLXSLdTOWF2xzq7fNHpJHVHaTVEK1Q+jeYSC1+amdtGqWE7NvbUNSqywZVGsHLLc9PzbyUzQ6fBZhWoO5gFpUxrrTINTMsMAGqyMTS9K0fxDNUTtVN2CW0nbhf/DnXuDdXJERogsNkKG3iLNbSx84H93p4D5YNGsgayVI+Xhep8joFZXH3mNJDG+83rGJrDyoxBZXfKlz+dBcy43x0be/Bg4GRDOdDGFGrQE9RdXApAP7fOqXWMixzRWNauiEbfDBis1EAM9RaPpc+TykEsw8X9Erw22n1TM4o6j4ZKNQhdGkA7gnMszOiTPROaS6kzmrG3elfvxPlVk7Pac6DmfLPUhjaT0kFD91WzcgkJQDkH7vXqwtwPq5mtzwBo2ITEe/8J7/0Hvfczvfdvq/5/y3v/uve+6ZnPObcKwI8DWOO9vyefv80596bq/+8GMA/AJ5rdv1IFG3WiDE1EDwZOZjRJmzlTRUrnTmszdLJhlIxjCsFGpNrFa9d9AvXRWCpKKh7NKtix2loanWzpiHI7do3T81Pjg9dRKWoPBk5mTgjXtGLBvkadqXD3rV2AAycGM6ebuHXww01dGzUUqJiBmlMfunaaQbTOUav3TmvriDIBCBrflPf7p7di80u/A6BWN6SOtB0n3x+8+nxdPYnWiBC8v5rNjRWVjxShzK3Sle3zaX8bcuz0GuxbW2uow2NoO36bDVAjIPadBjB0LEAtA6ONGoCa/DcrOwz+6HHZZVCxfcnunPEYkqtmapjs7/QesSFE2f2RgqryRB0K1O65zRRZJgQ/4zWwtTJ8RjhWHsPSE9sBfd4oY/dPb80ZlEB9YMFes1DdJ6+HOswhuaFe02upFPVYdoTXxmYtGWDUZ2ff2gU5intZ0NDWgB+vQ4gZAsQXKg8xP8rgHW95Kkcft9nxIoQyoA8GTmb6ls8276c9J9Ud1KWql+kwK+NDZWFK/4bc9u1qgz9p7tIco8B2y7x/emv2Gc+bAdxYHVhINviMhq4LoQFvO1ep7tQscIyarllRdfa432aXj6DNx9/RSefcYW0GjkOzhgByzK2EBKCcA3el2rL/F1HpQvmHAD7W6EfOufdUF/Eeqr7vcc795AjGegjAVwD4LbNcwHIAg865awB+BcBz3vu/aLSz98xwubou5UwXgTVtnCD40k5SBA0wa5yRujQ0vSsXhdEIDI/VN299MEIVypKMFIy+2/oIS13su30NQ9Mra5v1zVtfV+9jnUptH62LlFuagzVktaaM21ER0mDmeGjY0Vmzx+++N4y+29eyzCgNFtsePwY69aSN8ry77w3nqDRaZ2MneXVUY7VGZfDUo/vBz1eeexitJWKUmMGCXBapek4EMxvWIeN5KTUnNqnouWuNZIhC2E5oRtuCTvbKcw/rnMjue8PBbIc1NrgPypp1ymO1RfZz1R9KR7Nj4jPD47RifFJ2h6Z3Zeej97939c5c1o11lSvPPcwt/2AZAc3iqUf3M91HGSM7Ydq6I1HZ1c9VD2n9L3X44dkLMz1uaWiWEUAHkNdD6VZ6jtYAZxamVSfWQrOrU/o3YOFLM4PGJ5B32mIdK5V2GavxsfRSXc+Sf0MOn60nZMMiq3Pt+dnxNyPHmq1RdkUok8TnSjOvejzev1abJ3HeoHw1koNHty7XPW/aZMPW8dmmMjGnxcqoZuZI9aQTzXlTA8vtzB5bvbV8y+bcvKq0T6s3yjqTGnQEiqmUqldVP6ttRWg5gT0vXjPq4VZ0HhkPQ9O76tZv0zEzIGcZRhqkvXJ2f11WPuHJRpkmJn3e+8977z8E4FsB/GCVStkIv4hKq/8H1f0MAlhf+Iviccz13r/DLhfgvf9V7/37q90xv857/9Ey+7v/yisAwoZVDKx/0wJmcsvZelqjKdaIZF2LZm5oILG5hUY/ObHqQ0wjUZ1N7Rw5EnAS1+ugSouG5K4z17Oo3rFF35wpVltTo9FWjV5xsg85oTaSy3O3WUB+T/qfwmaldNJTx0TPtxWorFgKj21rzQlfa0aYEWwWlF3CUkaLwEkkFEUM0V7VMdW6Nd4n2zpcJyDNgnL/uq0NbowE9rzV8VIDmZHpWCQ61EiHsBSkKf0b6jqeqRywyY8GDEI1pWos22edx9PIO8fRjueelCLqMTV6KU/6XLcScCDuv/JKMGsNNM7K8rwZNLD0KYJNmPgboL620xp1lr7Nc9fsJ8erOqtdwTObhaXsHZ69MNOhuh4Uoc+Rzb5ZmiWZDHbc1iEmuKyMdYD0nulcodkk3ice1+qaXWeuZx35GoHHDy2ErYE96vK+eetx4MRgnTHcN2999lmrrIfue8N18hUL2BA2qwLUN03TTKciFISKHYu62uphPr/8TKmMIwWdQHWUKMeWQq7/09mMjcM+V9S3WudO2OuhlG+VXX1uuUSI1d/6fWw8IbZRCCsOXcpda8swGprelT1jQLxLsT0/zcgnJBQt5P119gXgrQAmV/9vhOnee5up65hVCP/39L8BIN90o8ykTMWkBgUpDBqV16h60YShxp/SEi2VhUpFi9QfDJxE7+qd2LGxJ9hcpVkcODGYdVsL0U+AWhdNZlIIGuM0crjwJDOTOknx3EJOBK+XOmxqPFoFSmXGLJiO2Srmvnnrc1Gwss7bo1uXcw6tRvZvDOxF3+1r6Lt9rS7ir4hFTlsxJvzsd2X/66QWo6AA8VqJUERTs5tqVMTk2mZcKZvczk7mo0EB0f3TOFBnKHT+51dNzmpOdbINORPM0LFWNTbRWlqM0qO1UyKxfcnubFvtiqr7X75lc+4+8LzK1HGyuRLvnTZqAfKUIs1K0QhmloW6SbMzzVCJLKw8hKLjQCX7RqeA27KOlOPktWdjJ4LjZ1aJn/GltVF0bsiQIPS6Fy0F0So0u6jP3ZT+Ddn6czyuXXNNx6HybdccJUKGn16fkGFqdZrKKJs4WX2nzAk+/6r3ymYQVO9ael7RPaCTxes5v39PFigd6b3j/pQyv33J7mBGa/DO3Wxe1LEfvnmqLtOi860GUGzwIaRXyAbh3MpmGepUqf3QzuyxDSoROldoe36lBhK2ZEL/AvE19ELzrF2H19oaymqw+9Igmv2cr5XnHra0hp4yjKze1Ky3jp1/Of7RZK4kTEy4ai+Q+i+c+92C33nv/TcX7ti530ClWclp7/3XOee+G8AW7/23tzzaNuIr3/le//2fvp8VIgP5jIBdX+k5Nwd9t69lXPMQHUB56ED9+iWaJbFZiVBkUGlAhG53Y2Avti/ZnSkcVSzPuTlZVJJKrdFi38+5OZlRo8ex/xOcDKatO4L7p7dmf4mV5x7i4NXnc8pSo6ChY/CctBue3Y4KkHTMudufzdEAbQt7LtjO73gcGnWNulA+unU5O65eb0KphMu3bM7Go9eLxd69q3dm16hv3nr03b6GhbOfvuq97y0chKB75tP+9AeXByO2+9YuqJPdFYcu5WrB6vYnlDMrc/q+b976nNEbklnWIfLe24yebsP/7VICj25drpvMiibNFYcuBbvbHZ69MKtx1MYhPH6IWmc7/oUK7XkNm3Fg9Dmk3KnMhuh+1B10rGwzhhsDe0s909qhUSPyIWqgnpM+K6HzOXzzFN407wOlZbd3QZc/9puX64w+1Yn2fNTBoUNi12vi/VQdorLcN299bokBoF6Pqs7g9/q82M95PPuscby8zhePHss6TYbA7XeduZ6jnXH8rJWa378H09YdwZWz+3N0fZV3SynU+h2gJsvaACsE3lt+b9/rddTPKOM2YMPrwfHG7rXF8KY10fogS5FVR8eO7cCJwYw+r7qoGdntnvm0H7x8LnsfunaNZBdATieq/On9Dn2u51UE1WOs+dXfrjz3EHO3P4u+29fqZJdBYGZHi+QWyOtdPXZZJzmUJQzNUzGdbZ8PoDZH2WfeygKfYdWrvN7Uu7Qb9J7wvhXNR7yOVmZD1ydk4/FzoJ5ayXNtRnbHG+9fuNj/8m/83pgdr1l7aiKjaCHvbyp4FTpvVfwwgF8A8F7n3G0APwLgH7dn2O2BbWDBKF+jDAGVlqbgp/RvwLVFr2Lu9mez5g6MkmnkRB9GnWhCzpvy7bVgPzSWEDRK3QiawQtlcTiGUKSNhbr3T2/NfstJIKScNOuolFCCkUfu326n2azzqyZnEVdmM+wCwXRklepDhDqUWWj20EbnOA42GNi3dkFWI6jZse1Ldtfdux0be1rKYvz5517PjgvUaipC2d7BO3ezz9UYtcelUcEMqtaR8BoofcjSePkZ5Z6GCpCP2pM+SoQcSmsolqGqaVacOHzzVK4xAO89I9KMpqv8qeOmzpvWcdhskaUEhhaN57kAlftOg9gam9pkRzOINEZ4nmWuCZ037p9Rec266VIB3feGs7Hr+HmdNPtQ5rkJgccj3Yv3f9q6I3XXbOriGv2IGVDtakc5jelAGmO3Dn64LmtuA1J8hvSZtrLJ5/ng1eezZUEUrDUsyoRbMLK+b+2CjB1Ap9PqMjXI++atz1Fobdc/gjUzzNrp9VPdrMyEkPPG7e16nJxbVCZs1oLHU13eiDrOzLG9Vvp7pfZaZgQpavP78+tatpr9n/bMM+ibtz7T60o5DsFmjoFa06uV5x5mC8MD+QZqxND0rrpmQjFoNk/1mG2sxTEcvnkqKrv87ZWz+xuuzah0Tat7+WzqfMH7QmhAj9AMXMjWaHQtOEdZdgJ1Gj/XMgqOD6g8V5wvyUZS+wNAUxm4GFNBbQp9r2Ox56r15AkJQLkmJi2h2oVyBYC3AXiv936Z900uuDWKePOnP1ln0FKRnF81GYN37mJ40xo85+bg0a3LOHzzFA7PXpjRq2y2h6BCoKNhDQxN51v+vH1vlZAaLTp58zccKxWvjY6uOHSpsHhZDXQb9SEYIbOUBHVqbwzsDS54GYrMMaLEc1IFZSkQoYgrFxzn2DSazjGyRoL1EYpGdUSkwSg90957vceceGiU0WhhVkoNLKXMNoO3f9VTOSfWKvXhTWsweOdu5pRrtpPGpS2o5nY6Np1gOXko1YMTqnX21Omj46dOO7dn5pLP2qNblzNDwlK6yhTdK2VMjXHem9jkR6eVToJCa+LsAqzqoOq503DVz5Q2ZcdhaX+EOmqUE+36V3Yyp5yq88nz3bGxB7vOXM9loKgPs0Ympm7PPgfNQCmmdF6UlkrZ5f3W54jNOmgsWtnnefGzaeuOZHQx6onQMxcyolhbqw4Un2Hun/PHikOXcgEwBu3YSCLkjBDz+/dkOpXrmw1N78o1j2EUnrJFedL5gbRh24jHUmaB+mYmei6a2QBqDav0+eV2fLb5OfUyv9Ogm3aSLBMk1bGF5IxrbcacKFJQ1Wngflql/vKe7Fu7IDdvbF+yGwdODOLRrcvZi7LIrKpl4ZByb8/NBjJttsbaCIRlxhDWqQXyDAjOEwwa6Fj5fQgrDl3KZe1twEPnXh2vOvQa4Iw1kCLUltDAdPe94VygW59vqxv0GeJxbYdV69za611Gdqye5Gc8bqiWz+pUW0rDwFHs/ic8mWi7A+ec26UvAP8IwA/J+47A13a/u+4hYkRYqURqeDFjYh9o6yhZp8A+cKpgtK6EnzH7ZtdkAmqNEjRyZCcwjl0NlR0be7Bv7YI6upqOUZUfnSZ7LlP6N2RdONVBiVFmgIqBq4qPkUyNetlrpcZrkaGq0VV1KneduZ4peL0GavQwExIDO4hxEua4OU41GIem12o77ERGpRtql97KenTTnsmvA8fJ2Wb77L3RiVsdHTZK0H3o/dm3dkE2edDw2L5kd26SJtTA0/c6zlDzCksVYjAh5lgRSkEDkNV/qJFKg1QpLJZyyet0//TW4DpwagxrVpBjJTRDZjOaZahFMSNtx8aeXOMH3o8YmH3jteZzzXHbzABQazqk+923dkGQitSKEfH6pGl1v7NLUXBsdr0z6+hpQw/7TE5bdyQLIvH7Wwc/nN1jSwdUZ5j39v7prdk2mkHWJkR0OLWjJqFdE2M6htRs3gtLDaVOpg6xLfj1eCr/lu7Lz+g8hDL1NuDAbCy/G5relek/ykFonlHjVfdJg5y/ic1Dw5vWYHjTmtz8ZilsGvSzup9jOXBiMNc8iOc10vohnhfnHWb06dzps8/zt8vkEBoMiB2L91wzfhog47nzWMywH7z6fN0yJvzN3O3PYuW5hxjetCa3jeoxyzIIgbLLtST5G322lOkQAq8fUL8Op8ox77tm9jhHaFM5vuf/9rghh1mdQKA2f98YqLF6QoFSCwYhbTdvtalIz6Qu1+we5wtms23wwQZIExIKHThXwTua3OdXVF+9qFAmZ1dfzwF4XyuDHC0oFWN+f629OR9WKkROoEoXobNjlZNGRvWh42SjBp+m+jXjoFQrfgfUNxnQznYhZ0j3AVQUPSfIEHTyzbILEjG3NXUaVaWy53moEap1EYzahiJZ6qTSCdOIlFW+sQgtnSX+b6kXOskX0SE4Geq1trKhDhIzBDRAe1fvzE26tosdUJ/BLQs9Z6vQWZ/DCY/XktRB3isuqK0tuTWiailWlorDa0DZ1jHFDE2gJjeWZgXUDC/7m/untxZm4ZhpCTnRbHBjs1bqvKkzoN0AtWnEvrULsoXPtZkQ98lFyvWcmMUJ0aSA/z97bx9n1VXeff8uApGgMdGS2gS1iYLj6PASGW1sI0+1SFFqbHlKxVZqQu5SS0MptKWV6d1qn3topQp3SoqKhuCNd6Wi2MZiEWPVNL1LLZgwjJ1S8K0mcKvxBbUEA3I9f5z92/Pb66xz5swwAzOZ6/v5nM85Z5/9svba6+y9fuu61nVVR3h5P2J70VHZtq71ODzn0co8qWZziPif0/8ZrxM7EenIeBpsQcugyxq5+LRCamHXdsLrTvHGumfgJEYPpbs2z4H/QbqcMchJOtrNz+l/UK3nrAstKy3I/C+ldZK7rkz6zN+bBZniuS3Yd66MkJerd6A6YMj/Ef+7FEmpu6T+F3R9tnm1oqeDk9qO1SrINtrWtb600umzML0XMKedJjTOoW2a4o3XRYWlDpypqCR0n1brP89pqOQSu7PfsHbPkXKKAe9t6bNH7y1s0yQnqLl+I5dcHkM9Qdg+c1ZlFZ+cpqD32TRgFT0vcv0Fvc9qTj7tJwFVy5kOInEQhvconYLAelEhRaHYyHKrYj79rOeU/k8pkLce21WXPkNzzZJm97z2e+6tCMq0H6cDCMy/mbNOalvgczjnMRMETQWc1yKc/M1gdujub3X3twKYCuBF7v477v47AOYCeOZQCzrcMBS7uujQRYY3aH3AAaj4RAP1/tM5073+QdOR9fQP3mhUO7Ua6SsX2S63jMfKzc+hCwV9xPXGwpse3Ru1rLnIUGqx1DC5QL9lLB1JzIWvBlCKJ+5X60gfornz5Db60OAx2bkdaB4PXSe1vtNrxE5hx+laJFKO+nMeAY+dmzOjwnewpCJZR/JYdp0ArclLJy/ZVhfeWL9z9FL/A/yPMAS4ih8tE9D/ANWOR871jOvSLZHXRl0etdO0afHMuo6wTnhnR5Ej/TwP/k/SRLnsbDHYhNaHugXrsYi6DGtnTq85j5VaI9OOTs5ayX2klhFe94HEk663ds+Ryhw6Hiu1nNAtLdcR1/dGAycDofkLNQfb2j1HsHXa7LKzREuUBpRZu6cWKn/ykm1lW+R5sp7Y2aUQolDl9aX45sAK2yjPhVbN3OADw9Cn5VfrUNpWCf+D6o6mwU64Pc9f58IpbHPambv/7u04uHdzNp8l66NRNEoKYBUBqagmKl5Tjwm62/J7Cu+NrKNmIkoHafR+odeFAyUpaXumy3xaptyyVuB/ioM0rLv9Cydiy6GNWPnI4ewzke1BB6w00Xlu4Ct3DdLldAtOBxwoeHIDMCyz3rfSZwjhoFjaX+i77ZayDWly6pxgST0K+M7pBL1T2ksLHgdo+F8GUAo3zWuXG+zmsXJzd7WO2XZz9asRuFkfun3af0vRqSvsx+g9nvWu9x217qXXlv0utjHdTxAArblQHjCzFw9h388G8Lh8fxzA9UPYz4jg024oR8EGchdIRZj+0dih4Uik3qj0z6svJV3G7Xkz4W9pZy8nfkhuGW8IHaf7sHXa7Mq8I6DaWdQRKN5oVOzoKKlaIviulg6OBvLGmea5onBMLX4UDiqa0rpjZ437WDV3XcObWzoiN9DN+PzxA2WuNl4HzevF47MzyZF/FRQciQWqcyDUgnOh8MbPMrEONGBOaiVlFFU+hPWByXe99kA1GfvhOY+W1g19MOr/gqIx11nrON1XWgm1Pet3lpnLtS45b+P88QNlWOb0gUzhou2qd0o7Jky/qbTU8Hxo8dFOYW7Um51gjp5qPaUDCmmnKiUdZEnvAek9JqXZ3M2eE6fK/wVFjN5LeI31P61W5maW7qFY3QgHzWiFB/pdKOn2pJZcFRpMTkwxR9g2gP52RSuUtumcWyxQE01q+eR+0oE2DmyxHlh36QCGzu/KzfXiHD+Wnah1KhUsvH/SW4CRfxV2gvW4tHKolYQs2HeuTLugglC9CvRac9mCfefq3LtWzV1XZxVL0evWaN4vn0XNvBLY7jlQknpqKHSRv5A2SzT3JoUE24wKKSaS1yA0KnJyFjEONKX5xzTVB2EdLth3rpy/mhvM5MCFPpfTNp3ztGC5uA23n3/XA+g5capsu9NXvaFpLtF0oKn9nnvLgdNGAwpEvUZ4b2ZZKJR5rPTYqRhaOWNpJXCS/o/TgZZGAxcA6gadU9KBB7rYpu1P78G5Pict7Lk+HAclgoC0IuBejpqI+4KZ9ZjZETNrJfLCTgCfNbO3mNkfA/gXAO+7kMION0ysTTTkPzu1KmZyIyRAvxhgh5EPsjRRtI4a65+6UchbvemmkcHSgB+KLmcUMd0+Damduhwc7a75fqc3Wm7fd9stlZE/WqrqRqllArxaADg6rp1kdqI0kEw6ryHtQLJzQnRSOzsBaaTR3M005U12ffnA0Bv+8S3vx9HuDWUgBaD2kFW3pP0LJ5YDAyra1LKjD6z0od0K2pngNWFbpQUFqFr49Lpoziu1rGnHk/sC+gPN6D44KphLEaFzD9NRdB3l1Y6yljXnOsR2qOtzHyybClR9OKsoBPoHEVJXSb7rOvqiG5h2orSD3sy9h+/pAzgVbjyfXK4g3Sb3gGcAI712KuSB/vmo2jmjIKHIVSs4y6fXudGASivwHNXFSy1Y+v/X//a825dX2rrCNqzCjx1AbRfNhBzLwgEQiia1OKXznYH+Tjlf6sbL8wDy0ejowsyReS4DUNcGjm95fylwjnZvqMyX49xArQ+WTdsrf9MAEXr/yQ1kss2unLEUk7qWlfcOlo9pVbSsat1lfakFIef5QDc9HeDLlYkdYl6LtJ3mAn/puXCdCxF1vNY6N5LJ5fkfUpHBQZ6c66gOnKb/VV7T9PnBAbpVc9fVeTOkz8jUOszy8Brq9ddncXoN1MK7au66St7TdHv9zDrXtqsu6do2G8075iAdy5/bv9YB64yDDamHTzonMe0Dcj9qdeYxc8/rZtE6cx5XLB9QPxCXWpHZVrUfGASkFQH3KgDPAfAKAK8B8HPFe1PcvRvAcgDfBvAdALe5+58OuaTDzFe/XQvFnk6EJ/zzqjldg2Tk/kh8KGrwj/SB0qzDTteOVGTkrG60TOnvGqQjJ9w4Wqg3QVoKaarnDV5HbvnA0psLHzR0NVs1d13dKLXe+PQ952bSuWhN2Xnhw5DH0zps9IDS7zw2g3ak5WBdpNecVsl03xzhpThKc4RphwlApdOkI+UqDuguMpRJ9T7thqadaJ6/juDyGrKzl1oHNLiBrk/rDY+XRgMF+kOI87ipu2uKXrN0TgyvCTs8/M8xRQXz/uSuae4Y+vuWQxvRd9stZbvPdarTcvBBrvvRDrqWPZ13qZYMndennVh2kFPrBTsarCNeAx0RVjTiaGpdTt2XNDgFrcp6fukcVf0vs/y6v1Zh+gugGnBIO+EcFOid0l65zkQ7fanAJrTu0iU+3Y7z6tiedH/aUdU5X7n5zjpIoG2J4opWEJaB7us8L/4PacGa1LWsIsTUIpi687IjzMiTdN+mJZkCOK0XABU3b6C/3eaegWwrFE6pBVQt6anQ4jnn9q3wnqtzuXmOfE+tdrweeg9NByZZd9y3ut5diCVDA5wBtXaiYfsBlHPiCOs4JxYUHUzTdbWdcfA0vb7p/5LBdfRZoYMF6TXR/1H6/Ob9aOu02VnrUSrGGnk06X0tHWiiez/vCWd2rygHB9JByUb7571M+xj0nlGvBz5bUg+iZhGLJ3Utq3NVZsRRPVbObVQ/p/0hLqeFUL0h2CccSrTq4InPgALO3b8C4FkAXlF8Pt1sOzP7nGx7yN3vLF4P5ta5VDzraVeUn3lTSHM8pTe4NAdO+sfSiHpANaAHgPLmx31z/zqXLe2YpRYHjg6rm0IqrjgSmN44mLTyaPeGcvSQ7pDpPLx0Arx2JglHudNk5fffvb288dClRB/wagnRzgyPN+/25Zj90NSKGyk7CdrZVHGZigK6WwCoi/ikYeNzaLTR1MWPDwdN2s75OewwNhNmLIO64Q0We+RL5WcdceT15jXSvHxcJ1eu9LpovdAKpjnONLKY/l9Yr+ncUbYTfbjRcpG6w2k5OR9Pg7Fw1H/BvnOYMP2mytyb3intZedVLU66b7Uc8/+rneNU2KqrEc+DkRDZkdlyaGOljaedWaC/Y8B0Fjz2qrnrsPXYrop1R130UpGUs6hq/ekgDNHP3C/rJWfJywm0dHS9WQ6sRjzjR64o96uR2lR08jvrkWVgwAN+zt2jc1Y9nS+nFnsKEbU6p3XFcmnevtRKwY4270864Mfja+e7c9Ga0sqXE2Y895yFgYNvmlKCsE0SFQdqadd1aIlr61pfzusiar3UQcAthzZmI8KmgwSKWqrTwQWieeT4XduXDirwmmidqUjhtdDndRqBeLBMvvba8hhpdOOcmGK+tUb3OArw3IALB/jSeze3b3YPYB2kv2nbTMvNe3buWcp7Lp/F7yoyQXGbs939uVHTpN58Ruh/Sr2SdHAlbbscYMlF6tRnjLrD87d0fmTvlPY6S5xaPbk9B311nbQOc4GjUk8MHahL2yHZtLg/UTjv2fo84H9BrcXatoMAaEHAFe6Pvw/gzcWiSQDe33gLtBeulo1eR1ALcHJJueL8mYrVIX1gpqPfXKadBAb94DrpzVj/nOl+gWreotTKxAe8RkZKRzq1s6C+1uncO57H1mO7ypFIjc7FYB9p7i2KL12PsLNICxw76HR7yFkoj3ZvyIqEnGikwOFLO7h8qGjHKB3V0nrWwBI6Yp8bgWS9aRoHjqSzw8ZOvIohtcapEFFrHcVueryhwGvLzheAch5gmv5BO6p0+dTObM71hdtxnc5FayritHdKf1hxrXMKmrRTwm20TapQ0/rQuUu59sKgAeePH6g8LCmq9TpTMG2dNrvp3BoV3an7por3XNLo9AGddvK1PmkZB/oj5elx1C2Ly7iPRnPUUtQNSM9PBz/SgDz6f0hDf6sw1XMZygBEztWN7ULvP6xvtZDxeGmgGP3Me0xqNVWL19o9R7LJ5Hm92rrW182r0nyTmqYjN8DVO6W9tCTw2NwX7xv6TNDBCi0vy8+5bxRSakXYcmhjXdqA3D74HahPOUBrh7Zbzg1MLf1nu3fi4N7NWLDvXDl3TsvONqYDS1qm1IrJfTaax6ZtQ4OP5PJRavumcEoj2qbBuFrlzMmTlXLr/TUXoEqnCfB6p4OMQL1VPIX3JbYR3Ye2cU0MzsFaHWDWQTigvh/SyJqnn492bygtTgDqnh+5tpbWV1r36fOG/w+6QWvuTQ6AKBx017bD+wMH/lL3S72fp/+JTYtnlv9hvhp5aXGZDl6nlkseP/1vA/1Wbbbl1bfOKv93KtT0P38hUVSDJx6tuFD+AoBbAPwXALj7CdTSBDTi+ai5WDZ6/RyAnxx6kYeX9CHXaHRDxVMa4EEFFd1k6COuDzZ1yeHNhfvRTm06Ys4bTio0G7kypeKJVgTOtdMbaN9tt1RGKLUu0iAL6n6hHTq1dmmicx1V5Xd1M0stOURv0lunza4Eo9AOBYWcdjx1P/qQ14d9TkzntsnNp1BBQ4GmOZ+A6sNAhVI6L+5C4Y2eIdW1s7T12K5sx4LkEvzqflneVLCyDaTb5TpfrUZ70/xQQPXhz84PgEpnTK177BxoUBaeAwcrOKk8vebpfzI9vj7o046CjtjqA1c7Bdq52bR4ZtkJT92Rue3xLe8vgxpom+d/V5ensPwUJ3RX0nKrdYsDTzxHbStqbUqDL/C6DBZaMbQjpeh9i+2O80l5X+H/TjtKvAZpxzJ1RWOUzdz/kR1B7RSf7d5ZisqVjxyu7J/3Gt5/2C71f5E+VyYv2VYJapGziqTbrZq7ruEcG7Ui6DnS9YztVlOE0K2c9Un3y/T6UqTmXMJ0sCMtN4/JTjAH93gf7J3Snk3dsuXQxkp7SP+LuRyeWg/p/xJAnRtpW9f68v83WCsGc29SgOSiMaf3f6A6GJQKnY7TfZU8Yxwg5O/allJBxHrm/3vlI4crAyAsX5pGKCUVMLynaXnVxZu/6cAu/2eNnjUsX0rOapkOzqYDpWd2ryjbnw5U0RKXiuR04Ip1l1rf00HaRvWVwv1wIJvlSb1TciKOg6Bp2gFFDQhDabfBE5tWBNzjRToBBwAze3Kzld39Ky28Hh6Owo8EKhDU7SN1K2CnnS5lmktOJ6iys6EBPlbOWFoGCdFRrLau9ZVQ0SrW0tFcPljTkby0U8COWC6PiI4qa+dTc9Twhq0P3fSGqO5xAMrojakrGt/VHYp1p+JI55AxNHO6HwoxLbe6CVFE8zdem0ajjFqHQL+rh7YDtV4B/R3ANOEw39XNkiOKqavIcMFyqbUi18nSQB38npYb6Hd7ZPS/tN4O7t2MlY8cLuuUgoBtih2d9MGqbjN8gDNvV6PzSttH+jBOOwf6+9Zju0q3F/7H0gehdthJKgLUSpbrdOSswJosnB0T3idoGWTnKI3SmhsBV9JOsJ4z7zW5hLbsxPdOqQZh4P+H9yT1NEjvBVx/qKSiJReCniHM2WHj6/CcR+vaAUVCWjZ2qlJXUM5PSs9B77O8DpqbMrUoA9XnRNqB12dB75Sa27RaoNL7Ms9By8WE16lFMT0f3ue1k839acRG3iObzf1kG6DrOUWGlpt1sGDfucr8Ln1OcV0K8PT+C9TmEDGtA+sztT4A/WKa6yi5+WzpHEt9TjYSzs04c/JkKWL1XpcOxKigo5ucWs8aDe7wf6nPQFo6U7SN8FzYVnunVKdTcJ203lnPGqZeB23oUstolyQd6GBb0cEQ7lMHTTmPTcWN1pvCwYOURu63HMRIvQJSt1PtL7G8vF48Nw5G63NQAxEpaV5SbWcqfoH+wbSBBJi6+POYad8udx8IxietCLgPmtm7AVxtZr8G4D4A7x3ZYl0c9GGTdqx5M9VRSL2Zph1y3gymr3pDeQPQhJn0h6dVasuhjeX+05t0o7lKQHWkH+ify6IjpTqqTu6/e3tpzePDkFYJfaAd7d5QmajOyb59t91SurHwhkiByge4TlDmqFLaqQVqDyc+NICq73x6LbiMri88R45oqWUyHcFkWXRy/IJ957KWoTfZ9aW1hp2V1JKpQk2vv1oIaHXjOqy/1E0rPe/BwNFgwpFuHo8PEX24smxsc9pZ53d2eNkmuU/WXzqamZvbkVot0851Oucg1zHVEVKNmMcRaXZYNZ2DQqFNNOeWtvN0lJTlTYWAjqxqe+Z3dnT1PFXs6HIN+sB5GZO6llVEaGrxOtq9oS6tiKK58fi/ZtnUGqJoh4//L14frbucNSG1gLUK3dA0KAuh8Ge5Wb88f32nK6paF7cc2ljpIPG6pmJdB4Z0Pd6bU0sky8bOn7YXllMtCbxHpJYDHit1s920uD9ReTqHaO2eIw0TgOs50Xql92wVkXqdOLCkgxUqrtStWSN/8npwPQ2QRLdtzb+ndcvrpFYT5fCcR+vqPG3/KqZT63QzaxzFgz4nh4JPu6HynQEn+AzNPbMAlINcei4K2w7rh9s2ekaoGAFQeS5q55/3Cv2fpUFcJnUtK6M0a52mAySaKkWX62Cmnn8udUPO6s5t+J9XgcZ2pOfDKQK5ZwnrSq2WrDuWXYM3qRW82X2M5Wq0jv5vU28qRsFM3WN532AbAvrz326dNrvuXq33/YHKG4wvWgli8nYAHwLwYQBtAP7I3f9ipAuWUqQjeMTMHiper5bf3mxmx83sqJn9bCv744haSu7PxlFzPoD40NYIg/xT6fwEPoTpw62dH6Kjbioc9fg6qqojp5yDp6OWZ7t3lq46QP9oVqPQ2TrCSSguz3bvrIzS6wOLN6dNi2dmw46zjtKRXj6AWGZ1XeDv2iniw0oFsNb5pK5ldXnV2BFJJ8JvObQRWw5trEtqrKTWRRWamgcs3V6PTyHFjlBb1/rSTYlzydRaOFjOnDxZOXbaaeH5U4x1LlpTCTygI9ZqOUoFFYUhO7tsgxRIdFHSY9NdV0mDhBC6eKqVTa0W/B+kke/uv3s7Zj80tSJM1bKXc2vV8vHFc00FaXpNNIqgihe2Lc4j0uAa7JjreWhb1FFVHlPPIbVK6Wh5et/SjhbdLLUz18jaoiPd7MhppywH23VOFLaKCisdLGk0Kp+Kvf0LJ1Ym96+au66M5KjiN424qx4O2nkkFCcU2SxjGjI9J/LSoCHcnz4HaHHT+zjvzToIxW11v5peQO9Rei9Vt0nNhcdlTILO/1PuOmu71QiBQDViLEWXdrC5LsvBlw4o5bhzR095H9E64DXT6899pVa31MLGzxT1x7e8vy7K5WC44vyZbFlS91J2sHns9Jx5f9RohmrNoksu7zF67Xh/4bnzuaiurelgB9uJCl31jtBUMPoc1mioQH2k5dwgD9u41k864KX7YLnU1VH/Z9p29V7D/w4HBrTN6Pw3Xhc+B7W9aiCa9FnKwCy8Pw50r1NLpN5radXsOF1zIVUByX4B2xNjEtBVO0Ra0AqtBDF5m7t/wt1/z91/190/YWZvuxiFy7DZ3ecUr48V5XsBgKUAXghgIYCtZnZZKzvTB2FqrUo7Y5qfCOifcMsHp7pWkPTmnYaoTd3WeCy1NAC1Gx7d41KBmQqOrdNml646hG4B2llN59PwRsVRLi7TyJg6ssQ6WbvnSNn544i+3lDVnUMfUOkNkfvizY2deoWCTefucFK9duZ1fR5Hy8Eb56zrriqXvcu/XLFIsN7U5SIVwRRjely16mr9s5NGcUcRNJROsE+7obzJ0x0szTfITtSCfefKsObcZuuxXRU3KR1RZ5tjW03nFXacrrmxsT3xwauWDA448D196KdWAdZdOq+OVgCgPycQUM2PpO9pWgcAFSsNgEqHi9vxYc/ttPOT5iMD+v8HdMPj7wwE0WhEXq91OkKddsBZDt5TGAkut20u3H4q2vT7/XdvLzs27FhxsIHzoxp1IBpZ51qBVozUxZvnz/NNA4LwnFW8pO6uHPDR+zYHffRYvBeS9P6pwkg7XOn/NG0TA1nTVSzxPk7RriKP5VTLOdA/iJHeO/U723TvlPbS9VSfIdyHzlc+s3tFKea07ekUAbZn/qYDmul/JTeQpm0pFY29U9orniDpgBLRZ2vOvTi1sKXvKvpTq+xgYTtU0ZEOztDtNBXB6cCNDlhxuT7n+duZ3SvK+56696f/JXqR8HmgZVTBqazdc6QUc3r/4r2N10EHAtPpA7z+Cu9LbANtXbU0Ro28i3gOvOem97WO07UAWbkooKlrYe66aD8u9RRJ98f/JQVYK/cAoDoQzmdhOvCuzxq9l2neydw9MghSWnGhfGVm2auGuyAXwGsB7HL3H7j7lwAcB/CSgTbyaTfU/SE1GIKGeeeoLP/EORdH3kw7F62ps0KoWxtDzbd1ra/cDOlG03G6P6R++kBksAe1xvGdZUvd/jSPSvogzsFOHFB1M8uNfE5f9YbKRPE0YtzaPUcqN0q6k6bH48OA0Qu5rgpePnjYoaC7gQqmXNAOtYzmkqqT88cPVNwT0k6ElpPtQYWRnreev9apHrORRbRV2HG7/+7tWPnI4TKiIUUuH8T7F04sr5lOTmfbOrN7Rek2qw9hlpWDExyV5EOJczi1PHTL4Xa0pKbiOie0gP78Wfw9t17a4VHRp1EG6f7Fa57Oz2MZgWonkHWl11RRtx0mqtc60HcdxaaVOG13fICn1r/cPJZmuatU6PCd/yV2JhXtaJzt3lmmX1BSaxDQX2c6T7ZVrjh/BkDVlUsHkID+e05OCB/t3lC5PlyWrtcoCuequeuwddrsimhT96j0fgvUzz1Lo9EqbPu5dpNavXLujqlLWKMyqShKRY7uW10ZOZc1vWb8j2pi8MNzHq0EOUot1dwuLSPdrgczt5f3bd2Glqs0mIZec1pF2V40AJcG3Ui9Ay6E3GBvamHivTLNF6aDSGx/aT9C647PuHR6gZLeD2gt1rJpO+c6fKm41OdT7nrz2MwzyP+cCvJUnGp9q3BKxVD63+dAhFpS2U5oredndQ/V/dHriefD+2+u/bN8ev7qNZEOShO9Z+sgpN4jFuw7V6a+4XlrH0bdLssUT+KCqoMNufYXjG+a5XP7jSLkf1uSBuBLAC5VVsE7ijJsN7OnFcumAfiqrPNwsawOM1thZgfN7OD3H/2/lRtHKjaA+sn1/OOkNwF9SLBDTLcQoL8zyPV4oz64d3PlBsgE1kD/zYHujEzSumruunJ0avZDU0vBqBYGvUH2Tul329HRN1rZ0tEdjsSmN1CKgtW3zio7HbnIUhpI4/67t5ejfkQFZ9oxSV0etc750NFOMC1/RBPSar3Sinh8y/vLumokYtORPI6GqVuUMn3VG8rPfKgxL1juPNIOW6to2/3iV0+Wx0pdXdhx6Vy0pi6Z/PRVbygFHjtbXD9XFrbZNHx9oweIupqllrvUxZT70OvEUU+ek1ra1BKXCrzcMdj2OKBBy2NqjWP4bXYCeQ7pSLFaKPn/Uiso0N/x1DpSQUnxn4u4qeeSDsQ0G3DhwIPWoYqz1F1b61I7i5pTUcuf1i2vKy08A6Ht9hvf+g6A/vln+l/rnVILvsD7BeuYVmSgv5OjkWfVqtA7pb2sZ3aU0oE0dpJ0DgzvdzyO5o3Uc1eXSObqIvwf5eB2Ok9V65/r8HgcAGQdbFrcP69WO4vcR+q2xUEX7fxrsKK2rvWVe5SeO+uN53lw7+ZKu1arDPeVs0zwPp2ma9CBq/l3PVCZMsBrTWtQOiDF+5m275xYAPJ5S1OBMRC5tst9a6ee7VUHSpjrT88v7YhrvQAo78n8zntTI9dp7lMtTZO6llXSBOl/NBcsiOT+7+m9DOi/T3GZtmd1xVQBxUE8ii4VlalVUPtIPA63yYkvnneamoCD5rw26XNSB4ly9aD3+Vwdzb/rgbKdUxyq63EqAtN5cvycCxDGeki9Rlq53wbji2YWuL9CLez/vaimAZjr7m9ost2QMbP7zKw383otgHcCeC6AOQBOAngHN8vsynP7d/dt7t7p7p3XPP3qSseApG4DOVek1NxOgaQPi9W3zqq4pvA4fEjxGByZpdWP++RNQF1Y1Jox+6FaKj1GqlJLGR/qegPvON1X6Yhz4nt67nSfSvNTaV3wd6C/s6NWB1oUD+7djM5FazD7oamVToDekFXk6ki41jdRVzytW44SU9yyPtOHEi18uQd4+oBkuO80uXpqoeLIL90tdGRQH8ba0dfOSas3ZW27k55ydXYEU+vrzO4VFReyM7tXlBYjfaB0LlpTmcvJ+gT6LaY6dyI9HrdJrXHafnLWGl5DfbA2ErRqweNAhr5yFjudV8QycRBDOz3qVsXAGL1T+iNbakclLaN2NljvKvT5n+V8JNabztWiINL0G6nVqZHbj9Y30G9p0ZyJaf2xnnQ0upmLaxpoiO281flE2m6v/MFjleOwzPpfYj2yTW2dNrvOMqiWcn7nvlRcAP1umOlot94D2D45WEZrFLfR+tKOGtuYno/+39O60/+sehjkBF0qjNg+uI669Ksw0PmaKqAoyFTcpmVOB9Y6F62pWNTV80GFiQ5CpAM3jcSKkgpkdvYpAni9NeotO848hnbGec/mMhX7ZChtlxZ6licVidpO+XxNr73OTc254aoVUVMBpDlagf68gDonLrXcK2qxTC1sKtAZdEsHG492b6jce/WclNQdNLUC63cV3+rmqYOAhM/KdBCdz231MEq9GRQdwOMgA9sd23/nojWVKQJ6/BQ+a+h6npab7upszxxkZttVK7Neew6wa92EK2WQ0lDAufspd/+yu78eNavWWdSE0VPM7NkjURh3n+/uHZnX37r719z9h+5+HsB70O8m+TCAZ8lungngxEDHemzCZAD9f+Kce58+6PlnTi1KQL8VJr158c+qI7epIExFTHrz0huOjpjyRspRHC0TrQ8sN9DvdpK7wbNjm35XlzKiERw5Ep+b96Dh8zknTzuxQL9VI+1oE4ohLldBxM68dn7TDhG344i2JjUFajnwiF77rdNm17lk6HXTkVfWM91UdT4kO+M5Nxje1IfiDvG8q/rHLOguwn3ynfvnnMHUvZTrch6MPvjYMQVqDxq1fOrIOts0O878TecGsi3wAZheQ+0Qc33tLKfraiePcBQ4dUvNzfVgwB0V7DqSy46Atk0en595ztrJZueDcxvZCaVI1XOkYND/LttT2rlS62hu4IHpBGhFmXf78nIeK6PN6v9LOxpptM7UsqPbLNh3rlw/nYc7WHhfZMAlXlfWIffPgEN6vWhpU9Ga1o120PR+o/9D3R/vYamFn3BQiu2c7vXskKoLIa9p75T2rNtsKjB1Ocucdq4BlC6u+r9IO34sH9snLe0k7YymHU2KHuaG4/9JRTBQExSp5YDtiu1e5yLmrpeeM//PjCKo66SeIvsXTqwbcFJLkf6X1VKunWNg8O6UGoVy5YylpaVQO/l6zdLnr5Yh553QcbqvnAOlz5qchQlAJXhJOp9dBx3TQVgePy2T/ve5rQ688TqoB0Rafg52qAjP3adVFKZWrrSMSs4KlfbRgKrbcTroyOtGrxq9L3Nd3kO5z1wZ1UVW/3O5ND16v6TXVFqXvAf2Tun3cmp0DcMSF5BWgpjcAeBrAD4BYG/x+rsRLleuHNfK118A0Ft8vhfAUjN7kpndAGAGgM+2sk8dZeKfNCey2KnThwVDE9NnueN0X8Wdjg8StapxX9oZVOuaWgl0G8LlvHnoKK6y/KF/KD9z37wBp3PkeONZNXddRazRDYMPB12m22p5UnclvtM1NBV6aXRB3QfLzY4k60IfKrQ0pJZGHjd9GGrZ0869PgDTOuK66URkXZZ25jUCnl43Xl+N6DZYHpswOdsRStGHwKbFM8v2ybJpW+cgBkWWWob4G0dmcwmv1+7pD47AEUmOZHL/ui47iVyfy7Ujy/e0U6cuqCrm1NUs9wDcv3BiJWACYTtibjYA5eDAQKPMFF4q1oD+dsqOrlqEKWg3LZ5ZCmRakFkeDgho2wf6R9HfZNdXyqDCWAWw5jHLtRl2ItQiq/udvGRbKVLo8k3L9GA7wZOvvbbS9lgXvIY8V3Xf4jXUTlDqCpZazlX0pvWnbnTp4BDfNcqdDhxoPXJggu1t+qo3VDqu+jvXVysC11NybZ/vqQtYDq0/1pkODmpbZhnS+y/rimWnp4eWh/Wog27aueZgCgWsDjwCqCQmT5+/OYv80e4N5X+DdaapRWghVWHAcvE76+X4lvcPadDMHvkSVj5yuHzpszcddEytRdp3yNU3237Owq7PJQ443Lmjp/wf5YQZByy5f60Trp+2Z6LnonO81bOFz20uJ7zu6iGjSeS1vanQVpdovbenZdL/69Zps+u8PvQzn2Hp4DT/o1pvfKax/8Vnl9Yr7/Nsu5ymoPer3FQLfR41iu6bPis4kES0b5qeazC+aSWIyW8DaHP3F7r7zOKV9+UZWTaa2REz6wHwcgBrAMDdPw/ggwD+DcA+AL/p7j8czI71oQs0nh+m4X/TxKP6p2JeHB3dojVE5xqkDxigGsZWbz6adFujRDbyjWdULu286o1aQ/+zjCsfOVxxHSSNRgG5f3ULWTljaUXQqYUs7bTQlTDtZKUdAqBmCeqd0h9djRY1umlwm9zNTd3mWG/clla43Ag+ULvOsx+aWtk+jZZI9GHJEVK9fnywpi5gg+WK82fqrntqRWJ59AFNFzXt1Ov5sj3xIZbCB4vm6wL6526wHfCaquWVZQRqQlbdHzlqC9Q/+ID+6I4UKTq6nz7o1NqlD1iOcPN8tUNDt522rv7cf3wg59xx1C2a7Z9zothx0evC8O1E2zwFsnaG1AqVig8ddVcRp/9ztUTnQmDnOpIknZd7ZveKyv+W/+XBijdCN161/ql7NUftU/c8wmvFYCQsC8+R1mYNCqWj6OlovX7nPbutaz0mL9lWXgMKXL3mLB+vn7Yb3v80cuqmxf0u1ipqNCCEotdH793pwIZey9RLgMt6p7RXjqsDkawjBgCiFZedcf0/aftU1Bqt5eF+GOmWdbh2zxH0nDhViQKcdsC5X7Zh/ufTTrDWr1qqOxetKeuWZUtD9w+GyddeW7kGdOFUQULrK59VPAfWvQ446TXg84zl5EAp7wPqGtnWVQtUooNNbMOMtMmoxOqJoe1cy9A7pb3M55reN1NLoZI+H/R8eD0Il+vgq1qm04isegwdhNU6pZUqF9gpZ6XT683/BAVp56I1padC7nx5HG33E6bfVAl0p88/tgOgv302SuXCdei1kvYled7cb8yFC5RWBNxXAZwacK0Rxt2XUTy6+y3uflJ+63b357p7m7v/fSv7u2LSZZh13VXl6747bsZ9d9xc/tFUaKmfNdBvkVv5yGEc7d5QChUN6qGR5XSZmvd5E1NXDHUrSP+oejPjjY43dr6zg643eM2JQtiJz1lTeKNK/e5zHaYUFQe0Fqzdc6QUnXxwsG5J+qDhg4s3cc29xk4Wj6V1qnMU004aH4S0trA+zh8/UCeCaWHgsRnZDEBdmgYV0jr6q9aDXAj4Zh3pZpw5ebLyPWehIel1SoXqnTt6ymujD0Ode8WO39HuDWXwA33gsWOUa6+p9VRH00katp5uao3OgeWb1LWsHC3VDoF2OulupXMOdH6BPhDVrTXtgHD52j1HSgs32zYtVeo2rHWQnosKCq03ra90QEPbclvX+rKTd/74gbqRfrVK677S0WiiFlB2ajQiHutZyzqU+RhnTvYH39GIo7QUaT3kOik6UJSmCAD6R8rTgAF6j+N/VAcrtAPNOmSdqMgm7EypCxb3c//d2yuuWLzfafLsdM6mWh+0Y6z3NR1YURGTCpd0nrC6vgGoiA7936Sdby2L5tZi/aeDUOouri+2J56nWtnn3/VA5bteMwpeHoPt5czuFZX/GQPDqChmgLDUpVrnpA2lE5xz61PrnwYnU2EH9F9LdTfkeS/Yd658LnWc7qtL/pyGpif6DNP7Op//3JZu2nov1rKsfORwaWGf1LWsnFefDrhqW9NBAd6Dte3qdec2Z3avKO8lbGOpSFTBy/sV79M6t5f3Ar0PpM99HkPbHf+HHBzYcmhjJQIrUHXB1jrlsp4Tp9Bz4lR5n9q/cGIZgVkHP9M+1ZndK+qOpalxdNAqHfTkfgfKSReML1oRcF8E8OkiWfZavka6YJeK9nvuLcUcQ6VrdKnULS51Y9BOrv6R9WGtrgQ6YqtzmXjj4f41kARvmOp2QYuPlodRGtPycR2KrJUzlpaddz7wKTpSkUV482Iof67HumKnkw+Q1C1BrTe80aWW0NkPTa10eHXUWUfTKfRSyynPj9DCosJKr6sKGC5n+VbfOqucvJ8ePwc7O7qfBfvOYcL0m8oX29lgmXzttXUuYHoN+JuG9Wc75jVh+Y9veX/pQqKi9c4dPXVzCmmtAqqdh1wksbTTrA8zPsA5ys9zoHWNI8Bpx47nN+/25WXEwt4p7Tg851FsWjyzklZCO3r8rhZkbss6YX2wLhj6WcvLMh3f8n50nO4rhceCfbVce9oxVxdMdpS0TlQc8fxTKwRf/D9oe1c3X1qwdESbn9P/rLZZ7WCx/tUVk9c/deVM73mDQQfGaIllZ1I7PzwPLbtCi5nWAa0f7ETzPHiN+Zn1wqA1hG1467TZFXf4tXuOVKJCagdPBwt4XMLj631PnwPs0Gl7SNfhu4YeVwGjlhPeB1Xg8b7LclH06LHoKsr12c71vquWrFTEs3y8f3Kwi4MmOhiggkfrmNeQ7/x/5awR6v5LoZGKdlrCKTR0YLaZNaQR/3HKy7rUuUsaHVfnlqXuzPyNwlTPZ/qqN5Sh49P/PO8btMjptSCNBlPSaJ204AD90a3TZwdRK3Ij6xrfJy/ZVsmJq/dtHQjRQU1eH7UEsp3Q4q39Ew5aaRvUwSrWZSO3ZLpGq7hORTYtp2e7d1bmsqnoSyNMc9BPBR5hWTQYkJaPHijqRszypGXk8rC+BUorAu4/UZv/djmAK+X1hEc719ohUmucLuMNJ+eWwxtHamnYtHhmmSOEIyzsSKpbUXo8nWDOmxxH8XTUj8eruGAklpbVt84q5waRSV3LyvX0xqsWHgpInruGEdYbD2+M6XxAPb90pBioWjL44NZlPFeeEyOyqQsrX1ovHG3X/aycsbS0oPIhsWDfOUxf9Yays8dOM2/iPHbvlHa033Nv3YtCTS28I4FaLrS+Vj5yuBJ0JxdMhcx+aCpWzV1XPmCYaye1hmi74L75cGL7Swc52PYovDhZH6imVeCoLTsDOetkoxF7jiTzIUe3RW0vbV3rywdmGsqfAyJcj+JMB2FYH9rx0c4a3Q/VWqzra72wg5RaZFXwsqObzh1RAa4dQz0f7icVWvyf6JwMnr+OiOt/Z7g6DpOv7Z/GzAEFjdSoFrLU4qViBkBdh4j/QwoU1t/kJdvqAg7owE0a1Ols987+QbFijrPOmeE9nvfujtN95ZzGXO4viife/2kZ0uuZuo2yHWnqCj1XPRegf04l24WKCt53NXCCiueDezdXLOI6sJAGseqd0l7WidY52wa9PvgsSaPyqVXm/ru3Y+uxXWUd6oBIOviU/ld1kCb9b7BedD6WwmfEYK0Yz3raFZXvqXUvF6WWYo/3AB0I42eKs/R/xu10UEb3r4NnqdcF26oODlFM89mo0a3ZT0mFIa/XpK5llXsFX+l89PQeodF3daBW8+3qs4vnemb3inLAs3dKv3ty+V5MD+F58FnBwXB9JvA6aL2wTriMlrw02BbFM9sR77m0Yh/trs1l14EI3s9439i0uBbVlsfW/7i+q1utzuvUa8Fyh4gLyIACzt3f6u5vBfB2fi6+jwvY6WbnQtFRpdyING+I+ofTDpfeLPibPrR0Yj3RETQd1d96bFfFgqLrU2ix85zCjvfKGUvLXGHaYSGM3qQPQB0V05DTirq4aOARfajxAXFw7+ZKpy4tgzL7oanlNaA7K0nnafB8VJTqb8ybw99YPo6MslOnAnPWdVeVAu1ik3ZoU/RBy/QKulxdJMmWQxvLNsdOpQpgAJW6SB/4jcLcczmjCupoJet8Utey8kGYDnIA/R0abseQ4upWotdPO610IaKlbO2e/oiDPMfUDYsPUrUsqvuvoq49OvGfbTMVwKvmrqskdtVz1cESFVlqxSDaCdFj8jd27PR/oXO1NCS4jjir5WWobr45HpswuVKH/JymWlH381TM6XnTuq+kHSjuk6SdSK7LEX7OrdN2mrP+aV3zmMw7R4GuA20qxNXjQu//aTtKA5dwPbaJ1J1ey6Tz7ygcuYztn9vwf0eXMp1ztX3OKyplYJ0wjUTH6b6K2NLk9loWLWfqHqbJ2fV5lta93vNSt1Z2qtVVkXWWirahziNKXTNVxOmzTMut11A74bzX8F6Qotulz3Qu4z1E3Qx5fukxKXa1raTrpgHDuD4tUix7WsZ0SgG31W34XJnUtQxbj+0qB5F4fJ0nCNSeFTlrFwWdtlGy5dDGuuBgHaf7yjnU9GzSY+Tu73rPodjWwXC9V26dNrsS0In3aF4b1v/kJdvKdsdnhLqN83nDessNCAzXfTh44mDu2ZRp/SuYvRTA3QCe4u7PNrPZAH7d3VdejAKOFJ2dnX7w4MEhbcs5UxxB5k2PIzLspOlIv+Yo0+Xt99w7qGO/ya6v6yinI3C6HKjmhymFXBGwBOiPLqnvGqaY8Nz0e/pgYUCLdEJyOr8DqM6J0LDVqfuJfk7rT0Mep/7lPEb6kNOHedpBSIU6rRwsx0iINTM75O6dra7fObPdP/uRewC0NiKXuslwRFSvD1CtY6IPfL1eQL0lE6h350lHPXUbjtan14ZtKD0HLWMqtvl/ZOeYnSLmY9Ly8X+bs17xGPxd96fWtVy74fkxnQCtQanlN217eg3Tc9c6TwUf96nudfrO+5EeR89RR8eb1UMzLpvx0pbbbufMdt/+8QMNz0MHGlIatTv9P2sdajvWfTZzn0vvo7wfatvMkc5Dyv0n1aKs9zElFfy9U9or7o9sU2knnPtWFzSiIoMCjh1dbQP67OK5qFcC6zy9N6ZzkrgfbqPHSe/njdrWQMfI1RE76VovjeC1HEzbfeHsG/0Df//p8hqwLnN1rfcWDdyi587fgGp7VeGtfQu9t6TnyPrR+5/+J7j/3ICS3pt0nnruP6rXUZ/xuQFq7ie957HsvPdrGbmNrsfzY9vM9XVSt8pUnGq71j5b7r6q58IyKvpfz1k9dR1dT6/b0e4NmP3Q1LoBmvQ+nT4ftA5nT7t6UH2GSwn/OxeLsVQ3F0orAu5fAPwigHvd/cZiWa+7d1yE8o0YFyLgFE5mVZibib+TkbLU9N12S8NORu5mp6PPjawmOTc4rqsjXzpPjq4Nah3J3ViBaoccQOWBmOZS0fXI9FVvKOcFNOugph3+dD1Gs1x966xKyOzHH8yH/B0JhkvA5R5eaXtI60gFBpflOtF6nSiUc67EpJVOrw4UpAMffKirWxof1LlyA40fgOlx0/2l566dZu1cpMfM7T9Xplw9sd1qJznX8dD9NOqkqHDIHTcdtOC50jKZdrJJ2sFOxeXR7g14wY6PDlnApR1SvW5pWYGqAMndn1Y+cjjb6UmFClG36LQu045aWs5G/7n0fqQDTbnzzYkbFSS5tp6K9NSCo/tLB8a0zXLuEvfJ5NFaRnZiNbE06yWts/T/qpZUtjVu2zulvXxeEB5bO+qN7h+5/2Gj/0Vu247TfUMScIqKOdIs3UMq4BW9ZhRybAOpCEvbmZ4rr5Oeu/5vG9VL2ha5fqNng37W/2s60ALkB/EIRaeK87TO0rbD82i0jf7PdTAh1zfSgQe9H+j2W6fNrntOAfVplRQVeOnADpfPfmhq+SzNDVpqnfG8+HksiZQQcCNHSwLO3X/CzB4UAXfY3Wc33XCUM1wCbjQy/64HAPR3ONOcUnpjUbGZkguxD+QnTTcTCkB9B403TA2ioTdzffCrtYid60ajc0DzB1Y6Qsyy68O/WZ2MBEMRcDs7ZzQV7Y2uh3Ycgf48fXyQpNeNsBMxkEUmV4bcNWj2gEutpvwtHc3NWRD0HIGqQF81dx2mr3pDKZjYlnIdK+3A5EaRcwMDKekIbnp+OcGVtmWtr0bCtFH98nPa3hUVb+lxG11v/f8PRsB1TL3ad79mHoDqNU/PKbXMqwUKQOWa6f1CR9bTzxTKqbjKtcPc4EczQdDoPFLLEdfLjfhrFFOgcW5Hdu5ZJ2nbbSbKU4urWoC0E62imOKtEdqB5f2bVg5te+o6qp4eOdT7o9E9pBUa3WfYFgbT0ZswZao/dPwLAKr3jTRgUcfp/oi0OUuWDkYA9S6WOQ+I3HXMiax0gILnnBuMSD1YcgNNqUeBin2K/1SQsF5S0aXP8EYWwLTcSnofTj0PtF/A65AbqE7beO54absBUOeVpB5N6Trcx0CkbTsd4Gj0rFi75wg+ueplY0akhIAbOVpKI2BmPwnAzexyM/tdADGLchTDgBn0uQZqNwnO2dIgG83Q9egrrjf5tq715ZwV3mB6p/RH0Uo7M7xJswzv8i/jvjtuxqzrriqjf7bfc285aZo+6dpR1k4OA7dsnTa7DKih5dC8X1yuk7c1It2krmVl0JHRzpmTJyvXQOFDIH3nOeuDQFM0cP4DkM9zx+ug2/dO6Z8v0DulvZx4znLxe85Klz7gtH3wWutDreN0dV5mx+n+iea9U9rLOQcsl7rMqIjaemxX2RHiA187y5rzS0U9A6/wu86JaIQGJ9DzIh2n+8rJ//xf6XnyXPS8c+TceXjc9nvuLVOlpO7aqeVGI3LyHEnOXWiwHWpuo+KN9wn9j+qxNUol0c5rOj9IBZFuwzmubLNbp82uBN1he2V9N+rQ6SslPQ+9blyfwZV030e7N5RzlDgPlefMNsfPGm0xzaulaIeYc3hY5+l8LZ2Dp+1N0+Jsn/OKyrxA1qnCuWYUzDx3Xq+0XnUfW4/tKl9ad1r/+n0gcvcZ1jvzYQ6G9hnPqmuDQH0KFC5rJMBzESx5rbcc2lh3703FXOrCyHsU18u1XS5PBYnSO6V/7q3CgYA0aIuKJcI0ASn6DNd5gKTjdF82sJA+X3JovbN8GsmbwdlokWzrWl/OM02PBfTP6+RnTdGUG1Qg3DcDuqX3ykbr67raP+G56X70XBu1rWD80YoFbiqAOwHMB2AA9gNY7e7fHPnijRxPZAvcxUJdN9ObfzpXbu2eI8MShfH88QMA+h9Mg5lD2HfbLYOec3gxuBALHDDw6HQzd5NGc3EmTL8Jl9+4vDK3hOurayxQnd8D9I/0p2JYLcNqJeD3Rq49jYRLMwuenkuj/TWyhrRKK2Vu5H6XK2M6VwXIR0zNWRWUVgYhNH9cjlbqYrAulGqBA6ourLnjpXPZgPoUDdw2dQFTV2x12UzrS62M6ToTpt+E+Xc9UOd+2apwTTtyjSx9KaknAb0VAJQeCyS1BLN9lCH9G7iVst5Sa61GBs21b7ropS71qftjWgcsUyr60nmG6f05N0UhR6P7RG7ggfX5bnyl5bb71Gc/3x848C+V/ypQP0dY6zQtE+cnplMEWnGtTD1RdKCC9+jUZTG9hwzUfht5zWgbYzTLw3MerROFpJH3QOoxwUETtdZxe7Wypc8WbWO5Z5MeG6if48/904U3taw1chnOCWAdHEzvu/Rg0m1S91atI31Op6ReCWcfumfMWJnCAjdyDCjgnqiEgAtGC4MVcNfYk/xrxz7T8v5zrhlANfgBHyI50XX/3dtxeM6j2SACuU7XhcCHnrpeAfXzd/TcNGBQ6uqYC3ihn3Mdv2bun62ineXU7RfIT/bPuQJvObSxMkqcBhnSjsxg5tjq3FySuo/mSOtjKEFMeI7svOVENT8DjYVvruyp2yV/a9Q+WGcMTHW2e2fTeuw5caocjNI6bCTYU3fFZkJDXbDS/xiAuk6e1p+Wo5FLba6eGw2CNOtMpuVWUkGWdp4V/Y8NdA/Rgbu0rAOVTwWmnvNgBh8o4HIBnpSce6C2W3Wh1Daq9wpCoUfrnAYZYRvkoBjbd1qPHGTVMqWDq1oGFVRp9OI02IoGDVMR0sw9Or23pu7deq1ScvNFte7SwYxGboj6v+J/Ttt9zj1SSYVYswEzDkA0+p+l6PJcoDHWQ7hQNiYEnK5g9j7ULG7fKb4/DcA73H150w1HOSHggtHChQQxAeofVFxG0k5Fuu5AFpueE6cqARiAkQvIM1ywo52LvpZ2iHMCjR0YoD+pbRoBLu0QNBMiuU5Hs+hwWo5G8y30+4WIaAqRNABB2oHPWQAGK+Cu/vX3VERHs3MHGkd05HatiC2gKkp57NHWhtPOdmptTaMep3WoHXMVyDkGEj/N/hdpAA09ZrpuSk7Itdp2KeJY/lSoA43ngWmZWKeDDcDz2Y/cU4nymc5ZzLXl1DJMeF1addm//Mbl5SBQo23OHz9wyaYA9N12S0Ug5wbAWDcaaIWWMIooHdxpFG1V53I2mleXE5y54GjpoFCjSJcpjSzGOdLBspzIb1Yefe7y/MeSSAkBN3K0IuDK4CXNlo01QsAFo4XBCrgXzr7Rj3z4nQ2FAVCdgA70j/xqlLjR6E463LBjoTSLlJa6A6fuTQz2AORHlnOuermQ4rofkrPYqAWB5dTOxaq56y7YNVnFbqOoreqepO5jg3lYPvXZz/fv/MOOijUi7YClwiFncWvWiR3r9N12S9bCSlLxlnNpTtfVDnNun41c69JBoNzgTzOX3rTtpuQs/gNx/viBSpAQpZElPScoFuw7NygrBi1wAOoEQbOARvyvaKTKixnh+GKSDkAoubl4GminGamoaWSB42egGuX6vjtuLp8DOqCTm07QzAWT8D4/lAEguq7n/qtp+dWFNHWhHUsiJQTcyNGKgDsM4Kfd/dvF96cD+Iy7j+mZlCHggtHCUATcB/7+03UuMzpfRIXIaLM0XCrm3/VAJQQ9H4hpRDYAdeI3fbDnxJp2UgbTMVUBpeQshvodGB4RnlrgcnPFiI5uAxh0J/gl695byXWlI+TqZqoicbjmz45F1Oqkkf+AeitaIyuzficDzZkFmrctupw2EoWpiyaPOVyDD1ovQHW+4EAuz+wsD6aj98LZN/q1v7alEjSm0Xw2/T4e7719t91Sei9MX/UGbD22qzJXjxY3tRDTspnm2VQhxt9zKQxU5AzUpnL3Wx3gbDZHdTjm86eW5GYu441ydI4lkRICbuRoRcD9KoA3A/gQAAfwSwC63b3eSXkMEQIuGC0M2oUy2u6Q0TkjQOMgLmmghxzqGkTBN5wdNp0EP9Luq+l8LnXbY/3kjjuYthvtduhwLipJ51Q2a6+NrFAp7Cy22jnV/1LOKpKbiwcMn+WfbbaRO7J2/jmHbNPimWU7Hmzb3X7vJ9G5aA0Oz3kUd+7owbv8y3XrsU7G64BDyuU3Lq/LlQfU2m+aFB2onxsIIDsvEKjP9zbU+2IaKCf3XxmptksaeTyk8+CG0nYvNSHgRo6WgpiY2QsAvAK1KJSfdPd/G+mCjTTRmQhGCyHgLi58eKbzC7gs1+kkQ3H7eiITAm7kmX/XAxX325y7XrM5bRowR13HtGN8cO/mYR8Y0AAOwxmJOAfd99QqPtAcpWi7F4fzxw80jNqZirNU6KXMu3059i+cWEbDBIbfJTXNfzvc4o2kg4m5YDdbDm3MHjcEXGNCwI0D4oYcjBZCwAVjlegEjzza0UsDOgCNrT101YoBhzzRdkceivhcRMrR3i5VeI60RZWWSrUQNyMEXGPGk4BrHC96lGFmfw2grfh6NYDvuPscM7setcTiR4vfDrj7my5+CYMgCIJgeEldfFsRb8Do7yAHT2zOHz8ATGmvc2kdK+1ywvSbcN8dF+dYT9TANsHIMuFSF6BV3P117j7H3ecA+DCAPfLzF/hbiLcgCILgiYCmQkhzkMU8q2C0wmiLnYvWlO121nVXjRnxFgRjgTEj4IiZGWqBVD5wqcsSBEEQBMNN32231AU6YFCDtXuOjMvohsHYgC6/a/ccwZndK7B/4cQQbkEwAow5AQfgZQC+5u7HZNkNZvagmX3GzF7WaEMzW2FmB83s4De+8Y2RL2kQDBPRdoOxSLTbocHABWp1owtao+TcwfASbXdo3HfHzZgw/aYyrUOItyAYGUaVgDOz+8ysN/N6raz2elStbycBPLtILL4WwF+Z2VNz+3f3be7e6e6d11xzzcidSBAMM9F2g7FItNvBc/74AfTddgs6TveVrpJqcYsO8cUh2u6FMWH6TcMeuTEIgn5G1VCeu89v9ruZTQSwGMBc2eYHAH5QfD5kZl8A8DwAETIqCIIgGFNMmH5Tma5iliyfdd1VMe8tCIIgADDKLHAtMB/Av7v7w1xgZteY2WXF5+cAmAHgi5eofEEQBEFwQYSVLQiCIGjGqLLAtcBS1AcvmQfgT8zsHIAfAniTu3/ropcsCIIgCIIgCIJghBlTAs7db80s+zBqaQWCIAiCIAiCIAie0Iw1F8ogCIIgCIIgCIJxSwi4IAiCIAiCIAiCMUIIuCAIgiAIgiAIgjFCCLggCIIgCIIgCIIxQgi4IAiCIAiCIAiCMUIIuCAIgiAIgiAIgjFCCLggCIIgCIIgCIIxQgi4IAiCIAiCIAiCMcKYSuQ9nBw6dOj7Znb0UpdjkEwF8OilLsQQGIvlvphl/vHBrBxt96IxFssMjNK2O0bbLTA228FYLDMQbXe4GYvtIMo8MIPqMwRPTMatgANw1N07L3UhBoOZHRxrZQbGZrlHeZmj7V4ExmKZgVFd7jHXboFRXZ8NGYtlBkZ1uaPtXiSizEHQGuFCGQRBEARBEARBMEYIARcEQRAEQRAEQTBGGM8CbtulLsAQGItlBsZmuUdzmUdz2RoRZb54jNZyj9ZyDcRYLPdYLDMwess9Wss1EGOx3FHmIGgBc/dLXYYgCIIgCIIgCJ5AvHD2jf6Bv//0RTve7GlXHxov8xHHswUuCIIgCIIgCIJxjpktMbPPm9l5M2soAs1soZkdNbPjZvYHsvzpZvYJMztWvD9tJMsbAi4IgiAIgiAIgvFML4DFAO5vtIKZXQbgLwG8CsALALzezF5Q/PwHAD7p7jMAfLL4PmKEgAuCIAiCIAiCYNzi7n3uPlC+x5cAOO7uX3T3xwHsAvDa4rfXAnhf8fl9AH5+RApaEAIuCIIgCIIgCIKgOdMAfFW+P1wsA4BnuPtJACjef3QkCzKeE3kHQRAEQRAEQTACfPXbj2HtniMX85BTzeygfN/m7mWUUDO7D8CPZbbrcve/bWH/lll2SaJBhoALgiAIgiAIgmCs82izKJTuPv8C9/8wgGfJ92cCOFF8/pqZXevuJ83sWgBfv8BjNSVcKIMgCIIgCIIgCJrzrwBmmNkNZnY5gKUA7i1+uxfAG4vPbwTQikVvyISAC4IgCIIgCIJg3GJmv2BmDwN4KYC9ZvbxYvl1ZvYxAHD3cwDuAPBxAH0APujuny928WcAXmlmxwC8svg+YoQLZRAEQRAEQRAE4xZ3/wiAj2SWnwDwavn+MQAfy6z3TQA/M5JlVMICNwBmdrWZrZTv15nZhy5xmaaY2V4z+/ci6eCfyW9PMrO/LhIM/ouZXV8sn2Nm/1ys32Nmr5Nt7ijWdzOb2uS4NxT7PFYc4/Lk9xeb2Q/N7BcbbG9m9hfFsXrM7EXy23Yz+7qZ9TY5fvbcit/eWJTrmJm9cTRuHwRBEARBEAQXSgi4gbkaQCng3P2Eu2cFykXm7e7+fAA3AvgpM3tVsfx2AN929+kANgN4W7H8NIBfdfcXAlgI4H+a2dXFb/8EYD6ArwxwzLcB2FwkKfx2cSwAZXLDt6FmVm7EqwDMKF4rALxTfttRlKsZ2XMzs6cD+GMAP4Fajo4/NrOnjcLtgyAIgiAIguCCCAE3MH8G4Llm9pCZ/bmZXU8rkZndamZ/Y2YfNbMvFZastWb2oJkdKDr2MLPnmtk+MztkZv9oZs+/kAK5+2l3/1Tx+XEAn0MtEg5QTST4IQA/Y2bm7v/h7seKbU6gFh3nmuL7g+7+5WbHNDMD8Ipin0B9ksJVAD6M5lF3Xgvgf3mNAwCuLiL1wN3vB/CtAU49e24AfhbAJ9z9W+7+bQCfQF4MXurtgyAIgiAIguCCCAE3MH8A4AvuPsfdfy/zeweAX0bN8tIN4LS73wjgnwH8arHONgCr3H0ugN8FsDXdiZm9vBCJ6ev/NCtcYUV7DYBPFovKJIPFZMtTAH4k2eYlAC4H8IUBz76fHwHwnWKfgCQvNLNpAH4BwLsG2EezBIit0OjcGu7XzP7EzG65VNsHQRAEQRAEwXASQUwunE+5+/cAfM/MTgH4aLH8CIBZZvYUAD8JYHfNWAMAeFK6k8KiNmcwBzaziQA+AOAv3P2LXJxZ1WWbawHsBPBGdz8/mMM12e//BPD77v5DOcfB7uNCytBwv+7+R5dy+yAIgiAIgiAYTkLAXTg/kM/n5ft51Op3AmqWqznNdmJmL0dtXlXKaXf/yQabbQNwzN3/pyxjksGHC4F3FQrXRDN7KoC9AP6wcGFsitVCqD4DwEEAv4aay+PEwvqkyQs7AewqxNtUAK82s3OoWaF+rVjn1WieALEVGp3bwwB+Otnvp0fh9kEQBEEQBEFwQYQL5cB8D8CVQ93Y3b8L4EtmtgQoIzHOzqz3qcJNM31lxZuZ/Q/UBMRvJz9pIsFfBPAP7u5FxMiPoDYHbXeLZf/Zogz/zd0dwKeKfQKSpNDdb3D36939etTmhq10979x97+U8zhRlO1Xizq4CcApdz/ZSlmanRtqgVMWmNnTiuAhC5APpnKptw+CIAiCIAiCCyIE3AAUeR3+ycx6zezPh7ibXwFwu5kdBvB51IJhDBkzeyaALgAvAPC5Yq7cfyt+vhvAj5jZcQBrUZvDBwC/BGAegFtlft2cYn+/ZbXkhc8E0GNm721w6N8HsLbY948UxxoMHwPwRQDHAbwHEt3TzD6A2rzBNjN72Mxuz2yfPTd3/xaA/w/AvxavPymWpXPYLvr2QRAEQRAEQTCcWM2AEARBEARBEARBMDw89dnP95esa2QTGH4+ueplh9y986Id8BISFrggCIIgCIIgCIIxQgi4IAiCIAiCIAiCMUIIuCAIgiAIgiAIgjFCCLggCIIgCIIgCIIxQgi4IAiCIAiCIAiCMcK4TeQ9depUv/766y91MYIAhw4detTdr7nU5QiCIAiCIAhGP+NWwF1//fU4ePDgpS5GEMDMvnKpyxAEQRAEQRCMDcKFMgiCIAiCIAiCYIwQAi4IgiAIgiAIgmCMEAIuCIIgCIIgCIJgjBACLgiCIAiCIAiCYIwQAi4IgiAIgiAIgmCMEAIuCIIgCIIgCIJgjBACLgiCIAiCIAiCYIwQAi4IgiAIgiAIgmCMEAIOQN9tt1zqIgRBEARBEARBEAzImBJwZtZmZg/J67tm9ttm9hYze0SWv3qgfZ358nGcP34A548fAAD0nDiF+Xc9MOLnEARBEARBEARBMFTGlIBz96PuPsfd5wCYC+A0gI8UP2/mb+7+sQH3Ne0GHO3egN4p7TjbvRNr9xwBgIYiru+2W9Bz4hR6TpwqP8+/64Hye4i/YCiE9TcIgiAIgiAYDGNKwCX8DIAvuPtXhrqDs9070XG6D5O6lmH/wokAgP0LJ9YJsr7bbsHZ7p0AgLV7jqCtaz0AYNPimQCAtq712HJoY4i4oGXOHz9QaVdBEARBEARB0ApjWcAtBfAB+X6HmfWY2XYze1puAzNbYWYHzezg9x/9vwCA3intaOtaj6PdG0oRd7Z7JzYtnllxq+w43YeO033YtHgmeqe0o+N0X7nfBfvOAegXdEHQiPl3PYD5dz1QWn61HQVBEARBEATBQIxJAWdmlwO4BcDuYtE7ATwXwBwAJwG8I7edu29z905377zyB48BqAmzo90bynWOdm8oxVrH6b5S1HEdLif8PPuhqQAau2AGgbYNtpveKe2XqjhBEARBEATBGGTipS7AEHkVgM+5+9cAgO8AYGbvAfB3A+3Ap91QEWJ37ujB1q7aZ4q1O3f0AABW3zqrspwulLr9wb2bsXbPEdx3x81DPqngiUvPiVMA+q20vcXysMAFQRAEQRAEg2FMWuAAvB7iPmlm18pvv4D+/nFD7JEvVb6vvnUWVs5YWrcexRtFm0JBRytdEDQjXGyDIAiCIAiCC8Xc/VKXYVCY2RQAXwXwHHc/VSzbiZr7pAP4MoBfd/eTzfZzjT3J/19cWwo0cueOnsqy9nvurfzOtAMK3eBmXXfVIM8mGC/QAgfUrG5sM5O6luEFOz56yN07L1XZgiAIgiAIhpunPvv5/pJ1771ox/vkqpeNm/7UmDMduftpAD+SLFs22P0840euwNYDuyrz3wBg67FdmDD9pobb8beeE6fQcboPk5dsw+MPbh/s4YNxRueiNTi4dzOA6ry3s907gR1XX6JSBUEQBEEQBGONsepCecFMvvbaumU5N8lGzLruKvROaS875UHQiMtvXI55ty8vcw12LloDoGaJ47IgCIIgCIIgaIUxZ4EbLs6cPFkJStI7pR043dfU+pYSLpNBMy6/cTkAYN7ty8tlk7qWYd7t68rv+xdOxGUXvWRBEARBEATBWGXcWuAUCrlWQ7r3nDhVyREXBAqFG1CLTnrfHTdXcgwCtYTwK2csrXPhDYIgCIIgCIJmNLTAmdn3UAsKUvcTAHf3p45YqS4RqUWNgSfS5bOuuwo9J05FVMFxAEV6o/QQGpykc9Ga0tp2+Y3L6+ZG7l84EQsKl8lNi2eiY+GubOTTIAiCIAiCIGhEQwucu1/p7k/NvK58ooi3s907cbZ7J9q61teJtL7bbmm67azrrgoXyic4FG+NhDp/5zw2ird5ty/Hwb2bMf+uB0pLbe+UdvROacf+hROxf+HEMhLl1mO7LsKZBEEQBEEQBE8UGgo4M3t6s9fFLORIMPnaa8skyo3mvXWc7guRNk7RxNsdp/uy6SMANAxC0nG6rxR+fNf0Ab1T2jGpa9DBU4MgCIIgCIJxTrMgJodQc6G0zG8O4DkjUqKLxGMTJqN3SntWoM2/6wFs6t6JXgCz6jcd1zB9wmCCvYxF1u45UrG8HeVql8YAAGZBSURBVO3eUMkJ2HPiFO6/u99Fci1q1jduk8skT/E2ecm2MnppzIELgiAIgiAIBkMzF8ob3P05xXv6GtPirRXW7jkSId4L5t/1QMVdcMG+cxccyKXnxKmGVq3RQGo1O9u9syxzz4lTWLvnCObdvrx8cX1tMx2n++pSBazdcwRndq9Ax+m+QaWtCIIgCIIgCAKghSiUVuMNZvbfi+/PNrOXjHzRRpYrzp9Bx+m+7Fy3++64ueyQa5CK8YiePwO3pHPCKPD4alRn548fKF+6z54Tpwacc3ixoYDPRSbtON1XWt+0Llg3dI3ktiruNi2eWXGjDBEXBEEQBEEQDIZW0ghsBfBSAL9cfP8egL8csRJdJDQPXCoq5t/1QGk1iTlwjYN48Dd9MVx+3223lBY61i3dLjn/q3dKO9buOVIKntFmkdPzZntYsO8cFuw7h3m3L8f9d28vk3JzndSy1julvZxruWnxTHQuWoOO031YsO9cuTwIgiAIgiAIWqWVRN4/4e4vMrMHAcDdv21ml49wuS4KZ7t3Nu1Ej7c0AY3mt3UuWlPO2aIVia6FWn+MtAjU6nbtniOloKM44zYL5q7D9GmzgS3vH/A6XCootBSKMEaaZH2U1jXZlgJ1y6GNWDu3lrz74N7NWLDnSJmWYLRZHoMgCIIgCILRTSsWuLNmdhmKnHBmdg2A8yNaqksMrUnjZQ6cznHLMe/25XXzuFS8qXBTIUZ3QXVDXCt50LYe21UKIqAW0GO0uKzed8fNmLxkW10kSc59S8V9TuyzLs5276z8fv/d20fNeQZBEARBEARji1YE3F8A+AiAHzWzbgAPAHhChM5TUUKrEzvWHaf7SuvRE5U0CElqBaPV7P67t5dWN0AiLSbiTPfTcbqvdBdUoUdRx2W0Zi3Ydw5nu3cO/0leAI8/uL1OxKdzANlG0uAlrJtUuAEorZlBEARBEARBMFjM3Qdeyez5AH4GtZQCn3T30efvNkheOPtG/9CLnlWKBs51o4AbD3Pf5t/1QGlp3L9wIo52b0Bb13pMmH4Tzh8/UGc5279wYlawkWZukOqKSFEzqWtZ6WqpQmc0pSnQemhmkVUL3f13by+tdOqGqfWnOQjN7JC7d47keQRBEARBEFxMnvrs5/tL1r33oh3vk6teNm76U61EobwJwCPu/pfufheAh83sJ0a+aA3L82UzO2JmD5nZwWLZ083sE2Z2rHh/2kD7ueL8GbR1ra9L1j3ruqvGhXgDam6CFCUaETENJkIXx8lLtmHtniPoXLSm3I4WtlS8MdgHX5sWz6yz5LH+c1as0exiyHNJYflVvE1esq38neJt1nVXNbReBkEQBEEQBEEzWnGhfCeA78v3/yqWXUpe7u5zRGX/AWqWwRkAPll8b8pjEyYDwKix9FxKthzamLWeUZgx4uK825dj/8KJmHf78vJ3ABUhoqHzKdjuv3t7OXcMqAnBzkVrShHDYB+jMWjM0e4NZT3o3EieC89R5/YB/fMEKeZ0Od1Wx8tAQRAEQRAEQTB8tCLgzMXP0t3Po7XolReT1wJ4X/H5fQB+vpWNxqsFhLnXgOp8NgBlaoUF+86hd0p7+Z1ChNY0Ws20DukquGDfuXJuW8fpPhzcu7kyn5ARHAnFkVoDmVrgUsHokGe7d9ZZyzRlwsG9myviTn/nZwCl1fKJPq8yCIIgCIIgGFlaEXBfNLPfMrNJxWs1gC+OdMGa4AD2m9khM1tRLHuGu58EgOL9R3MbmtkKMztoZge//c1vhgUENfG0au66MvCGBhJZu+cIZj80tbSiqdCiayTX08+bFs8srXXMd5YKIHXB1OXkzh09FzXEft9tt5Suo3rc1DKZfqfQ5HJ97zjdV/5Owar1lOYfDIIgCIIgCIKBaEXAvQnATwJ4BMDDAH4CwIqmW4wsP+XuLwLwKgC/aWbzWt3Q3be5e6e7d173Y1mNNy6guOi77RYs2HeuDK7B+W2Mnkghxjxw6uqo7oSbFs/ElkMby2Wc19Y7pb20OFH86We6GabBUtbuOYKVjxwGgKbpDVqllX2033NvGbxFmTD9prK+0nl/QG0eH62UmhsvZ91Vy9z9d2/Hgn3nwoU3CIIgCIIgGBQDCjh3/7q7L3X3H3X3Z7j7L7v71y9G4RqU5wTLhVp6g5cA+JqZXQsAxfslK99YgO6RKXQJpIjSiIqaQmDtniN1KQUYkAToTy+QzpFT65UmBE+XcZ+riuTXFwKtaa2IuL7bbkHvlHbcuaOnDOgC9LuVslxHuzdgwb5zmLxkWxm5k+soqTClGyWFcS4QShAEQRAEQRA0oxUL3KjBzJ5sZlfyM4AFAHoB3AvgjcVqbwTwtyNdljfZ9dnlo90tru+2W7Dl0MYyhD/ntWleNqBmIaJ4u//u7RVBRmiRopsgLXRAVayl26X50fg7g51wHwwQcqHsXzhxUPtZfessHO3eULpVUszpeexfOBGH5zxafm/rWl+xvtFiR5dUDf5CYQwAl9+4/ILPLwiCIAiCIBg/jCkBB+AZAB4ws8MAPgtgr7vvA/BnAF5pZscAvLL4PmgGstJQnDUSb/PveqASNn40cueOnrplaYARCjddBlQjTKpVjQKH1iW6RirqKqnuiLqeRnbUSI8XQvs99+Jo9wbMuu6qAYV1+z33VuZFUpSlbp5tXeuxcsbS8rzV+saolTyPSV3LSlGXph+Yd/vyYTnHIAiCIAiCYPwwpgScu3/R3WcXrxe6e3ex/Jvu/jPuPqN4/9ZQj9EocMb54wdwtHsDVs5YitW3zgKQt8IdnvPoqJ3XdP74AWw9tqv8TnFBl0qKFBUV6tII9IuvSV3LKkKO6zaKtJjmi0tdMfVYFJEUcsNF75T2hvnl3mTX4012fZ3IY/kmdS2rnCvbANA/D67jdF8lCMyZ3Suwau660sK5ds+RSvTNIAiCIAiCIBgsDWOam9naZhu6+6bhL87Fpe+2W9B+z704f/wAeqe0Y8uhjU3Xv3NHD1bfOitrxQJq+dR07tRopa1rfSkyKVgo5DRfGYAyHcCmxTMxqWsZVu7owf5ju4CF69Er+zzavQEdXetr2xdC7Wj3Bty5o6cMSMLjkE2LZ1b2occF+q2BuOPmCzpfiqqO031Zca2ivXdKOyY12I+K07PdO3G0a1lppZuEWh2snbuunNtXC+JSe6+dZ3VeIet69qoLOr0gCIIgCIJgHNHMAndl8eoE8BsAphWvNwF4wcgX7eLAwBWTupah/Z57AdTypKWWuJUzlgLoF3EAsPXYrqzFbrTNgeu77ZbS8nS0ewOOdm9Az4lTOH/8QBmFMseZ3StwcO/mSqASzg+jVWlS17KKC2E6300tVY3mxeWSiNMKOJwuhhOm35S1wLV1rcfWY7uw8pHDdVY0lq+ta32lnrhs5YylmFQIubau9WVSdM6D07pRS+Z9d9yMWdddFaksgiAIgiAIgkHR0ALn7m8FADPbD+BF7v694vtbAOy+KKUbSX7wXwBqlpSO0304iprwar/n3qb5x1SQ5CIPchnF4KWm58QpdHStx4JiftokFHO7it+3HNoILKy5AG4SS9WmxTNxtGsZUNQPXSy5/X70n2suCiNdTdu61gMZgabuiPyswUvImd0jm7Gi58QpdGTKRUuqusymqJhPz5/ira1rPY52LcOqwjIXkSeDIAiCIAiCC6GVOXDPBvC4fH8cwPUjUpqLjIa+Z4e977ZbSmuK8i7/Mt7lX0b7Pfei/Z57K58JP19KN8o0Cma/K9/EOutS75T2Slk17H+a4Ft/pxUvDeKh71uP7WoYXn8gNLhHLt3BYFErl37WoDU8Z52nd7R7Q5140/qja6guo3st65aWW3Jw7+YIXBIEQRAEQRAMmVYE3E4AnzWzt5jZHwP4FwD/a2SLNfI8NmFy+TmNMgjUOu8UQ4NxibyU4o2WwwX7zmH+XQ+U7oJqQQOqYoXf9Z3kQu/z/Dj3S7+ngi39PBgxR0vVcAYxScmlMuid0l6xLCoq+IHa/EAu4/mvvnUWFuw7V65DK6QmNL/vAuf0BUEQBEEQBOOXVhJ5dwO4DcC3AXwHwG3u3rAnbmZPb+F19XCdwFD56rcfq0s4DfS7A7LzPmH6TXVWlGZQqFyqeXCaY02tTYw4qXnWgP65fTxf5oEjGhZfBRhzvmnd5FwqVeDptkSFJIA6F8P9CyeOWF1O6lpWzuFjeTWnnZ4DLWtpW+id0o4F+85V9sEgJqllkjSKhBkEQRAEQRAEA2Hunv/B7Knu/l0ze3ru90ah+s3sDIATAKzJcS9z92cPtrDDyQtn3+jX/tqWSsRAjZyo4fYBtJwaQMXGpUgnkB6/58SpMsgGhdOs665C3223VOZwUWBpMmqKGa2PlTOWlp+1znKWx9xy3T/R76nFjdEvh3tOodYLy7V2z5EykijPXz8D/XMmAZQBYBi9M3VBZR2k2wAorXDnjx/AZTNeesjdO4f1BIMgCIIgCC4hT3328/0l69570Y73yVUvGzf9qWYWuL8q3g8BOCgvfm9En7s/x91vaPQC8M1hKf0FwkiAFFp0CTy+5f11654/fgB9t91SieiYY8L0m8rXpeJo9wb0TmnH+eMHKkJi67TZmHXdVeg5caqSCoGf1VJEwUK3QKBWPyrecu9aBp0blgvyQYZjnttQONu9s2JpA/LWw1zETJaZIk6XayLz1O1y/8KJ2L9wInpOnELPiVMVd8sgCIIgCIIgGIiGAs7df87MDMD/Uwiy54gwe06Tfb60heO2ss6IcsWkyyrfZ113VcUSxI49xRBJQ8yPRlQ0rJyxFHfu6CkDrwA1caaWt63HdmHBvnOVc9NALrRC5YSMvus8u7au9bW8cTOW1rkSpoItN8+NERvX7jmCs907R8SNkonEeZ77F04srYZaf4Tijm6l+xdOLN1V2U64nOep56yf6c4aUSmDIAiCIAiCwdDQhbJcweyQu88d0s7NLgPwDEi6Anf/z6Hsa7jp7Oz0gwf7DYnnjx+osxptnTa7zpWSXEoLWzPoGpgmG3+Xf7kWMl/cRDXCorpYqvUtdXUk6nK4au66crm6ElKwqAumCsFG1qdNi2eWoo4ulCzPcNS7BnfRZONp3jaSupACKEWvnnMarVPPl26Y2r44R7H4j40Lk38QBEEQBOODcKEcORrmgRMOmNmL3f1fB7NjM1sF4I8BfA3A+WKxA8gn1brEUBhoR3zrsV2VTnlqhRqNdJzuA7rWY2V3/xyuCdNvwvy7HqjldisEigrT3intONq1DGvnrivngKWWx47TfZi8ZFuZl61z0Roc3LsZbQvXY5PUC/PLqRBi8m9athrNmctFvexctAbzbq/lUMvlkxsqLO+queuwBRvRdrqvYm1bfeusUgSz/On3Td070bloDToWrqizKqZirpF4C4IgCIIgCILB0IqAezmAXzezrwD4L9SCk7i7DyTEVgNoc/dRMd+tFdrvube0UvVOaa+JusJac6nmaQ0WDVwy+6GpODilHZNuuwVbAJxdvLMuWAlQcyXc1L0Tm4DaDMcC1gNd/ebdvry0JJ3Z3Z+ge+2eI6X7YUcm91t6zLPdOzG5EIBriwTjaXRMwpxpLOv8ux64oDD8On/xaPeGWr1078TRxK0UAFaj/zxohaPIu3NHD7Z29eHg3s1lPVCkad2W7aYIZDJ5yTY8/mC4TQZBEARBEARDo5U8cK8C8FwArwDwGgA/V7wPxFcBjLl46bSMsAM+67qryvlxo936prR1rcfBvZtLwXS2e2cluAbFGSMoMs2ABvFQ0Trv9uVZC5mmLUi3SRN9b502u/ydwozb6j5y88IW7DtXuiwOx3w4LSctlSwv5+3pHMCtx3aVib0Z2IVz4TTVQu4YKuZowQyCIAiCIAiCodCKgPte5nWi0cpmttbM1gL4IoBPm9mbuaxYPurJRZHU6JKN5mH1nDh1yfK/5Vi75wgW7DtXCggNuAHU3BMZKGTV3HV1VkaK1v0LJ2LLoY0NRaxazjSpNTnbvbMWBfPYrnI+m26r1rdWg3rMv+uBpr/zOvCanD9+oLS+pWXQ+YIUZ5oPUHO83bmjp5LAvJFllqJO59Wt3XNkUMnMgyAIgiAIgiClFQH3OQDfAPAfAI4Vn79kZp8zs1xwkyuL138C+ASAy2XZU4aj0MHA0FrFiIhAvyCj+Jh3+/LSErZ/4URM6lpWihMVLio6dC4YkI8gmUtgvWruuvJ7x+k+bDm0sRKoJKWRkNu0eOaAoff7brsFACppFHJC62z3zuxcvFS8cR0N/KLnoqjI1Tl1XJY7XhAEQRAEQRC0SisCbh+AV7v7VHf/EdRcKj8IYCWArenK7v5Wd38rgH/jZ1l2QT6IZvYsM/uUmfWZ2efNbHWx/C1m9oiZPVS8Xn0hxxmIRla2WdddhaPdGy6pFW7+XQ/UCaN0ThZdJymSKPI0fD4FSpo2Qd0HgX6hpekXVPDw86bFMyvCsK1rPe6/e3tpARwM+xdOHNBSpykg0mAsmlgbAFY+crhue0bgTCNxklzuPD12W9f6cr9nu3di5Yyl2LR45qiNXhoEQRAEQRCMDVoRcJ3u/nF+cff9AOa5+wEAT2qy3ZtbXDYYzgH4HXdvB3ATgN80sxcUv2129znF62MXeJwBaZTMmx3+gVz8Rpr7795ecd+jaFNXRVrfchEU79zRU4qUtq71ZXAOwvM8uHdzZZnOs1v5yGEs2HeudFlkLrSz3TuxYN+50gJIIZcTZWqd0xQFQPNrwDIz3xpRl9KO031lnaT5/7QuUjpO95VRKtu61pdpFCh21crGfa2+dVZEngyCIAiCIAgumFYE3LfM7PfN7MeL1zoA3y5yvJ1PVzazV5nZFgDTzOwv5LUDNQE2ZNz9pLt/rvj8PdQsetMuZJ9DpVFAkwnTbyoFQ99tt5TufBcLJsCmODtbhLpP4e9Avxvk0e4NpaBZ+chhrL51ViWtggqhNBm1HmNS17JKjrS2rvXoXLQGa/ccKa18mxbPLBNhsyxaJv2s5eR+592+vGlQGZ7Hgn3nSndN5q3jPhjABUBF7KrVjdtRpKn7KHPB0YJJQcj6Yf1zn0EQBEEQBEFwobSSRuCXUcvn9jfF9weKZZcB+KXM+icAHARwCypB6fE9APVKYoiY2fUAbgTwLwB+CsAdZvarxbF/x92/ndlmBYAVAPDsZz97uIpSBzv0Rw/VOu49J06V4fKBkcsBxuMQpgCYd/vyUmgBABZvLsvSuWgNzuxegclLtgGYinlz+61eq+auw/5iX2X5xXpVEylHymiXtYiUtXQFSNwiD+7djEldy3C0aBEdhaCZ1LUMmLuujHK5FvXCjZTWuCKwClBzZ1W3xJ4Tp9C5ZFt5zLV7juBs907cL3nr9gNlWWt55mrpESDuohr4pUyTIPPfVs1dh/uXbMPhOY+id/HOcpv7796OBViOTYurZe+d0j46EyAGQRAEQRAEY4oBLXDu/qi7r3L3G4vXKnf/hrs/7u7HM+sfBvB+AA+4+/vktScnqoaCmT0FwIcB/La7fxfAO1FLdTAHwEkA72hwLtvcvdPdO6+55pohH3/C9JuwYN+5hi58jFTZfs+9pSVmKHO9BgMDduxfOLEUQ7R08fgASjEH9IuUyUu24eDezaU7ZOpyqN85v4uWJu5XA4tobrfORWvKOkitUCtnLEVb1/pKoBVa5vg5V2ec19YoouOZ3SsqaQooYhWee2qJpCVOz2f/womVc2jrWo9Ni2fWBKGc0+RCOOaCsoT7ZBAEQRAEQTActOJCOWjc/YcAfsTMLh/ufZvZJNTE2/929z3F8b7m7j909/MA3gPgJcN93JRcJ72VbdbuOTLsQU4uv7F/LtvkJdsqwid1M2S5U3fENPAJrUl8cZ+r5q6riJtUGKXH1TlyKatvnVUXLZPz5VIBSnJuk6mbas7lk/ubMP2mUgBSaGp99U5px9nunXW57TR6J9F0BB2n+0oRTCGoidKDIAiCIAiCYDgYEQFX8BUA/2Rm/3248sCZmQG4G0Cfu2+S5dfKar8AoPdCjtMKmrusGZO6lmHtniNlx55CpZH1DqhZ0/puuwVvsuubrsMXULP+0B2QwmTBvnOVxNn6G1Cznh3cW3WnZG44DTKiAk8Djej508JIscdlzRKg0/qWJrzmZ02QTQHGdSd1LcOdO3qa5lXLlVVTC6hw03JzfQ14QtGnQU3SwC4UbvsXTsSCfefKsqmFMQiCIAiCIAguhIYCzszeVrwvGeK+TwD4u+IYV8rrQvgpAMsAvCJJGbDRzI6YWQ+Al2MY59o1gpabgbhzRw82LZ5ZBvmYvGTbgHnMgP5w/c1gmHygmtONwoWip1HIfc5dU0sX99Ps3BgohZ/X7jmCM7tXlNsD1bQCQH0eNM0vR7GWRnzkuvsXTsTWabMrgq6taz22HtuFs907K6kO1FXxzO4VpaCioOWL6+l5sy5YH2qNpNCjSyjdQ4mWPQ2OEgRBEARBEATDRTML3KsLd8Uhhf6X3G+bALxDvg8Zd3/A3c3dZ2nKAHdf5u4zi+W3uPvJCznOYKD7YjO0E8+w+Y2sd7SoMQdZzt1y/l0PlL/n5nepxUyPSdGVHltdKZvBiJF6TN2XWpoobtbuOVIRZxRtGs2RyyZ1LSvzxKW/qZhNLXUpLNOCfefKACdqHdy0eGZpiZt13VWlYEutbEwvoN91bp0eT49J4UwLXSuCPQiCIAiCIAhaoZmA2wfgUQCzzOy7ZvY9fR9ox2bWYWYPoubO+HkzO2RmLxymcl9yWglK8Sa7Hse3vB9HuzeUIorkrGI9J06Vnf1WLDe1SIoz6yxium9N2J1a1nQeHLclFDWpNU/h+ip+uF89XlvX+nK+WFvXety5o6cUZEe7N1TEHNfhb4RWtpUzlpaCUIWfzoO7746b6+awqfhkuH/m68u5cKYiV4Or0PrG+X05ayXLRpfUIAiCIAiCIBgOGgo4d/89d78KwF53f6q7X6nvLex7G4C17v7j7v7jAH4HtQAjTygaWa16TpzC6ltnYf/CiZj90NRKbjZ1d0xJ567lEknfd8fN5WcKDZ3LpvC4qcVMRYW6+1EQMvok96vzz3SbXPl5joqKMibBzqGulbqMrL51VpmTTffd1rW+IuJmXXdVWU86Nw3ot5Kl+fF4zppyISdaNXeduldqPfJ8mwVxCYIgCIIgCILB0koagdea2TPM7OeKV6vx95/s7p+S/XwawJOHWM5RCQXP/LseqCynkKALXSPBpoFMckFNKFxyycDTuWskdW9Mj0tXymbukqkgSaM5chmP38wyp7/1TmnHnTt6SvHGz+oe2SjhNfezau46bFo8E5O6luFs9846a1yjADC0EG45tLHcXzNRq/XDY6uQ1AAoaT2pRY8iOAiCIAiCIAiGgwEFXBHE5LMAlqCWuPuzZvaLLez7i0UEyuuL1x8C+NKFFXd0MWH6TXWuduePHygtRCpyUnLLGLGSwkfFTCriGA6/kXhq9H3e7ctLQZFGXqTwSEPrM1Ikj5VuB/SLnDRqJC1qGoAkR2qRy0WXZH3ofuieye15DisfOVyK4vvuuLl0eWUOt9SayAieen5p9MhVc9dV5rtpHehnuotqxMwgCIIgCIIgGA5aSSPwhwBe7O5vdPdfRS3H2n9vYbvlAK4BsAfAR4rPtw21oKMVDQ5CwbB/4cSK1Sa1YDUKK6/RFHPzslLS39LjpEm8uZzvaRLrNDUC16V4Y5kY3fFo94YyOmQjgaYidOWMpVh966zKizSaE0f3yLRMbV3ryzKoFU/FXA5GkARQmdOm+9d6oZCksNZAMI3cK3kd6bap1zUIgiAIgiAILoRWBNwEd/+6fP9mK9u5+7fd/bfc/UXufqO7r3b3bw+5pKOUxx/cjscf3F66UaaWHU0K3Sz6pFqudG6WWqJyrpS6PY+jwkRd+NIAJqkFia9c7jYNna/nyPlsbV3rKxYniiugX8SlaRFUeKno4rLUnZJlUJfJs9076/bL7x2n+yq58rTuVcSpSymXM8ALo2LSIsnALJq/TrfhcYH+axepBIIgCIIgCEYvZrbEzD5vZufNrLPBOm2SxuyhIrjjbxe/vcXMHknSnI0YrQi4fWb2cTO71cxuBbAXwMcG2sjMnmdm28xsv5n9A18XWuDRiM5fo6UqZ0Fj53/BvnPoXLSmTsxxXYoJFUAUM7m0AimMLJmbH0c04Ebq/ggg6565f+HEStkoulbfOqsiNNNUACmMRAmgso9WYLnVytWINHVBDkYIBfotlRTATGjOY6XuphrFU+s2Fa65QDRBEARBEATBqKEXwGIA9zdawd2PMo0ZgLkATqPmZUg2a5qzkSxs3pdPcPffM7PFAG4GYAC2uftHBtgMAHYDeBeA9wL44QWVcpTDqIVrsbwW+VAEXS3iYe2zui+uRU3M9Zw4hVnXXdXvqlcE6eg43YejmWMd7d6A9nvureSL4zvF46bFMzHruqtKq2BqbVNUXKSRL/W3dN5XW9d6YMfS/s8AkFjd0oiStLilkShT61vO8qaohets905MKpbTCthxug9HJafc2e6d6Dlxqs7i2Atg1j334j7UrJt3PtSD41he1CHQeXdtXZ0fp3UBoGK9u//u7cDizZXyH+3egI6u9Th/HGVOuiAIgiAIgmD04O59AGBmrW7yMwC+4O5fGbFCNWFAAQcA7r4Htblsg+Gcu79z8EUae9x3x81AEbL+/PED6CiWU0yllracG+XZ7p3l8kldywCxuqUWnZ4TpzCpe0Od0KlZg+oDbPCYjURcIygI1T1z7Z4jpfugzltrlICb5T7avQFbj+3CyhlLK8egkGsk3GghVIGq1sE0QAjPsyLqkt+J5vJrv+devOue2me1qKbzBzXFwJndKzD57uo6aT13NIiqGQRBEARBEAwrU83soHzf5u7bRuhYSwF8IFl2h5n9KoCDAH5nJKeOtSTghshHzWwlaqbFH3Chu39rBI85qtDAHzkBpN/XFrnJAKBtYdWCo+KoFC8SkTHnotdorl1uvYGsc6k7KK1K6KpPuA3Uu1CmedxSt0mKt9x5pBZCLUMu5YCeS7qO1knnojV4/MF8gm0m6ebcQc5JTN1hmSIiTdStkUS13OePHwgrXBAEQRAE44LvP/poXR9phHnU3bPz1wDAzO4D8GOZn7rc/W9bPYiZXQ7gFgBvlsXvBPD/AfDi/R2oBXQcEUZSwL2xeP89WeYAnjOCxxwV5Oa/aRTD3sX9ecE436qRiFI3QMh8NT1GTsTdd8fNdfnpcmKtFaucngfzqBGKs0aCiueg66xGZo6YJMTOlUvdGBtF8VTOdu/E1mmzsRobSivY/oUTMXlJbSCmkXjjbz0nTpX1enDv5jpBnEb4pJCjS+VQLJ5BEARBEATByODu84dpV68C8Dl3/5rsu/xsZu8B8HfDdKwsLQk4M7sCwLPdPTctK4u73zDAPl/p7p9odX9jBbWs0eWOkSXXZoR4Tvikliagft5X7phKKt5yYkKFXyNLXiPSuW658xlI0KVRHNXdNJ13pp8X7DuH/WKxaxbI5Gz3znI+XLqfgdCUA5wzB9Qn5m5UTp7Hwb2b+8saVrggCIIgCIKxzOuRuE+a2bXufrL4+gtAZQbPsNNKIu/XAHgIwL7i+xwzu3cYjv22YdjH0PnBf6HnxKnKfKcLhfvinLFJXcvq5sDROsNl6jrI5QzBz+UqhJj/jOunImgoNMsnx8+pmyRTB1CwVSyFyEei5Lku2HeuThyp2E3JWQ0biU2Nqnl8y/uxds+RskyDEaizrrsKs667qpL7LbWoalqB1CJ3/93bS9fLNL1AEARBEARBMHows18ws4cBvBTAXjP7eLH8OjP7mKw3BcArUR8bZKOZHTGzHgAvB7AGI4i5e/MVzA4BeAWAT7v7jcWyHndvLe574/0+yP1dCjpntvv2j/eH5NeAFkNFBRyhUNFohUA1jH/OEtVxuq+IYDmzbp/pvhl1MhWjqSCqC7DRwvw5CtGz3TsxSSxZzVwmByInOjkvsJGrYq78PAfuU62Usx+aWoorrseQ/0AReKZF5t/1QFkP3G+KBjjRqJTpNWed67nMnnb1oWY+20EQBEEQBGONCVOm+sS2xjmMh5uzD90zbvpTreSBO+fuw2em6qe5chxhHpswuWKtaSW/WitoAmtFO/VqlWtmFUpFnuaX0+AoOetbblkjN0rCiI7pehRvZ7t3lmVoVbxp0m0tdw5aLpkom3PdNMk5v6fJxlW8rZq7DkB1rtyCfefKOWqD5b47bq5Y8nJQkJ3ZvaIspwpIWuSYA1C3C4IgCIIgCIJWaUXA9ZrZLwO4zMxmmNkWAP9nhMs1aMxsoZkdNbPjZvYHg9l2sPO/cqTWL527pp14CjkVXnTHU3c9bkthkloI1Y2S1qRG1itN1N1xug+TupZVwu9zOd0zJ3Utw/6FEytCTl03m9EoaiTrRM9JXQ/ToCy6vi5P95fmmiON8rZdKDr/LRWDWjatP61HtoH7795eSUkQBEEQBEEQBK3QioBbBeCFqKUC+ACA7wL47WE49peHYR8AADO7DMBfohYV5gUAXm9mL2i2Td+xr9ZZtYaKBgyhdSoN1AE0DqChQubg3s11Qulo9wb03XZLOS9LxY5amZpFPUyDovCzfk9ztE3qWlYnmvQ8dT+5ZWqNZL2osOVr/8KJDS2XraLbsh50GYXTYIKYpGh59y+cWIk8CeSjj6akaQcupDxBEARBEATB+GPAOXBD3rHZEgD73P17ZvaHAF4E4H+4++dG4FgvBfAWd//Z4vubAcDd/7TRNvTLpZvbhcyBm3/XA9i/cGJlzhppJZy8JqpuBqMX9pw4VVqVdC4XhaRa9zQfGZBPsE0aJdRuRivlZnATijTuf+WMpWUuuLau9Vg5YylWPnK4Iua0LI0iX6akCdDpUglgyNc652KrgpXWtDRCJdB/fRtZAj+56mXjxmc7CIIgCILxQcyBGzkaphEws4+iyTw1dx/oivx3d99tZjcD+FkAb0ctyd1PDKWgAzANwFfl+8O545jZCgArAACTnox5ty+vBc8YxIH6bquddvs91UCctc78wPPO0tD9unwgmAiayb9zpIItJSd+0sTag6HZscrz7FqPBUWZVZBtPbarsv7qW2ehrcH+0oTmjdDImPy+H9XAKYMJYEImTL+pTsSlAUnWYnlTd9xcpM2YAxcEQRAEQRAMhmYulG9HLYv4lwA8BuA9xev7aC23wQ+L90UA3llkOL986EVtimWW1YlPd9/m7p3u3nnljz0TwOBCywM1sUPBA/Rb33SuG1E3QXVF5DwzoD48f648OdGSLmOkRLpYMhBIGn0xt4/Vt84akngbiHROWHpuuXKllrdcKoIUvR6aWDxXlgtJrp3L39Zxug9bDm2suLN2JEnJm6V6iGTfQRAEQRAEwWBoKODc/TPu/hkAN7r769z9o8XrlwG0YsJ4xMzeDeCXAHzMzJ7U7HgXyMMAniXfnwngxEAb0SLScbqv5Xxw7/Iv413+5coyRlpMrVE6xy4VL83cFAcSlWpBokUwZeu02ZU5bROm34Sj3RswYfpNpeBp61pfET8DlWuw6Hks2HeutE4xx10qXhodmxY13Z/OM2xE+nuj+XyDgSKukfDmuVE8M0BNI0tbWOCCIAiCIAiCwdDQhVK4xsye4+5fBAAzuwHANS1s90sAFgJ4u7t/x8yuBfB7Qy9qU/4VwIyibI8AWArgl5tt8KynXdFvNSs79vUWlmakLnUqMHRem4b+TwVEK4JJ16Eb5dnunYCkQKAraM+JU5jUtax0T6RrZ8+JU5hVuH1SgJ4/fqBcT90OWwnGMVg2LZ5Zmm3butaXn9M6ydUHzzWdG6cWxHRbDZ4y3Am0J0y/Ce333FsRz5O6luFoUVa6uDJ1QRq4ZC2queFmrxrW4gVBEARBEARPYFqxiK0B8Gkz+7SZfRrAp9BCFEp3Pw3g6+i31p0DcGxoxRzwWOcA3AHg4wD6AHzQ3T8/0HZp516jSbaCCp1Vc9fVWVOahcDP7auR2EhzvwGopAFoNC9swb5zlTlXPSdOVSyNtMoB/W6IqfUvzePW7Hi57XMWptwxFuw7V2fFautaX8m9lkb4VMGmOerS4CIjRfs992atfJsWzyzdQxllUqNNUswNNF8xCIIgCIIgCFIGtMC5+z4zmwHg+cWif3f3Hwy0nZn9MYBOAG0A7gEwCcD7AfzU0IvbtJwfA/CxVte/4vyZ8jOjR25aPLRjH+3eABSRDjX6I9BakI8UbkNBl0appOVPrVHpvo52b8CmJomnCa1bOgeO8+boBohBuFVq2YGq5Q1oLKgqAT4KK1aNIyKIjtSt21Gcu+530+KZFQvlSFgUyYTpN9UFV6GISwOadCxcUQq7++/eDizefMEpLIIgCIIgCILxxYAWODP7VQCvAzC7eL2uWDYQvwDgFgD/BQDufgLAlUMv6vDy2ITJWLvnSOnmNpT5UWngEuZzYzCLtXuOVOZ8kVYtWLnypLnb0jlshK58nOPH/aX7zCXazpVPLVsaYKRR0JBm5AJ3MAx/Ol9s0+KZ6Fy0prKd/k4rI4AyJ1vnojXltdX9X2w0eAnFnJ57WN+CIAiCIAiCwdKKC+WL5fUyAG9BTZgNxONeSzLnAGBmTx5iGUctFC1nu3fWWdwGmtfViojLuS8qbV3rsfKRw3XLNe+ZQkFDN0p1p9TcbEC/WFLhloqgNAiKCrpGgjgVbxRddCvUuWIASvHG4+u6LE+jlApcr3PRmkqy7cG6yg6G9Px5bNY9LYfzbl8e1rcgCIIgCIJg0Awo4Nx9lbx+DcCNaC0dwAeLKJRXm9mvAbgPtTQEo4Irzp8pxcRQO9MUPc2sd52L1jS1vlGE5CxEHaf7Ku6R6Ryw3Nw4BtDQsPnqYsh1mQxcrVRtXevLoCaTupbV1YnO7Vqw71z5OTdHToWMvuu+gOrcMHL/3duxafHMOjFHctvsXzgxO8+M7/rbcIfu753SXs7VO9u9s7S49k5pLxN7a3L1XMqJIAiCIAiCIGgFqxnJBrGB2SQAPe4+oOIxs1cCWIBanraPu/snhlTKEeCFs2/0Ix9+Z53LYi7XVyNowaI44bwx0julHZ2L1pSd+FTEqHiqzOtK5rxpsug0ETjX4zw+osFLdJvUPVH3m5Juk4qOzkVrsPyhf8DWY7vq3DrTvHKp6FRYPrW2pZEbc6JN4TlMXrINB/durrhVzrt9eV3dzbruqqb7axXm30vn/mmbuP/u7ZUypdf4shkvPeTuncNSoCAIgiAIglHAhClTfWJbK057w8PZh+4ZN/2pVubAfdTM7i1efwfgKIB7W9z/f6Am3H4XwD+Z2aiZA8cgJpO6lg05P1gqAtTtUEmta7TOaDRCFVzs2KeWLQqcVCzoPnI5xzQYih5TBVOjfHVcN2cxmnf78roQ/irecgm2tfytQvFGy1yuPDzng3s3lxau1ApHkTgSc89yKSSOdm/ApsUzSwGvvwNV99AgCIIgCIIgaIVW5sC9HcA7itefApjn7r8/0EaF2+SHALy7WDQNwN8MrZjDz3+ccizYd25YElez854L7X9w7+aGwo6WIYq3tXuOVPZxtkEUSbXyMFCK0sj1kPBYqZVL99/IpTQX7VHXXX3rrIaBVVhmfVdU6NBypuj3NHAJXUVT4cttOhetqQimRgnQB0s6/46up3R/1fNU0dk7pR1ndq8YljIEQRAEQRAE44dWBNyr3f0zxeuf3P1hM3tbC9v9JmopA74LAO5+DMCPXkBZh53hmIekc8vautZjwb5zZeddxVij7XTO2v6FEyuCUl3+GPhC6Z3Sji2HNpbfKSbm3b68/Nxxug+zrruqdA3lPu674+bK95ygys3La1ZnLDstcKkbZRpBUy2fFKOaNy2XBJtCTIOZ0D2RrqRnK2kIUDlPvg+HcO85carOZfJs987KdaObLC1/FJpA4zQSQRAEQRAEQdCIVgTcKzPLXtXCdj9w98f5xcwmoohIORp43lU2LK50GtqenXV2zHMWtEbJugkFTrPoj5oaoK1rfd3vublv6W9A1VKnURJzbpiNytHoOEDNhZIvfm/G/oUTseXQxtISl4q3Rm6ctKwxTD+jVeo6uq0mDb9QUvdbraPeKe11FjoK1UZ1HARBEARBEATNaCjgzOw3zOwIgDYz65HXlwA074nX+IyZrQdwRRHMZDeAjw5PsYeHweQuG4gthzZiy6GNZS4zFURAf8c9RS0yRAVB6oKXWna4HOgXMGTC9Jvq5unp/tT6plESlVRoqPjgsdJIlkC/WGs2Hy59pxVTz2ve7csrlkd+n3f78orQY9nS9bR+lEldyyppFIZK75T2yvmnLqmTl2zD2j1HyrJyXc0XGARBEARBEASt0swC91cAXoNawJLXyGuuu7+hhX3/PoBvADgC4NcBfAzAH15QaYcZFQvN5n01oufEKXSc7itdJdvvqcZ2UbGk+252HE1NQNTKlwtQQvS3XDTNCdNvqqQUSIOm5HK0MWCICrb0ePydddjWtb7OfTIlJ54HSjkA1Kx0LFf6e5r0O43KqUKQ9Xz++IGm5RwI1oFa9TS5uM6B1EihkcQ7CIIgCIIgGArNBJy7+5dRm8v2PXnBzJ7ebKdmNgHAEXd/j7svcfdfLD6PGhfKMydPVkTbYCNRpsmgVbzRuqIufRqoRKNBKpwjpuImJ7h02zS32EDnoRY5ftZAKI1EWvq5UcoCXWf1rbPq5pqlbpSpFQ6oCp30OMytlkttoIFZNGhJKj6Zz+5CRRSFOyNedi5aUx5fj8drQotb5IALgiAIgiAIhspAFjgAOATgYPF+SL43xN3PAzhsZs8ejkKOBD7thvIzrSeDtcA1i6Soc90YcIMBSxbsO5eNANnWtb4UOLm5cun3VJi0GpaeAUyAvKWKZR4o91qKBnQ5272zFG9bj+2qBDZJRZ0mJwdQBnXhKzf3LhWMKpoGKjfridd9qK6UZ7t3VgLHaOAVdTVN2xfnDEYQkyAIgiAIgmCwNBRw7v5zxfsN7v6c4p2v57Sw72sBfN7MPil55FrNH3dRaZTIuhGX37i8nI9GsaJQeBzt3lDnCqnzpSj2UquVijiKPgBlXjHuS0VM75R2zLt9OXqntA8qGTnLm6K510hq+ctZyRQVKXSrVAucCjetl1TY7F84sanroUb0bMRIBAzRslBA0mUyrbdUiA4192AQBEEQBEEwvmklkfcnW1mW4a0Afg7An6A/j9w7BlvAkeKr336sbo5as7D/Si5oBlCbE6fLKOwO7t2Mg3s3Y1LXsqz7YyoGVz5yuFxHhYvmFWNZKaToQpkGLRmIWdddlQ2IotYsllMtWzmrXc5tkBzt3oC2rvXYemwXAJQpF5jwGugXfBS/R7s3lGJOI03ymM1EGS1iOXGp+eYmdS1r+bor548fqFjU5t2+vGIx5DEYiGXykm2VuYJDtfoGQRAEQRAE45tmUSgnF3PdpprZ08zs6cXregDXDbRjyR1XeQ21oGb252b270UkzI+Y2dXF8uvN7DEze6h4vavVfTLYhObwaiXBM0VYziq0afHMivAAaiKB66h40G21HBoYJSdm5t2+vMx1pgEyBive0nI0mu+mqDuonnO6fmpRa+taj5UzltaJls5Fa7Bq7rrKtqlVk/PeOM8sZ/nLuU3qnDNa8VTsArVr3ihh+kCoK2QakTM3/y5tJ5FKIAiCIAiCIBgszSxwv47afLfnozr/7W8B/OVAOzaz75nZd5PXVwvx1YoLZsonAHS4+ywA/wHgzfLbF9x9TvF6Uys7+/6jj9Yt2zpt9oC5ygDg8Qe3l4mZNQdaGv6f4kuZ1LWsIiiA6tyxRvnd+D3dH9fXeW2D5b47bq6kPdCyacJsomIpJ/q4jzTyJq1vCoVZzlJGi6OK3zO7V9TleVPSfG9atyxbOr9wsCJKrW9A1dqolkuN4Hlm94rK8VqdrxgEQRAEQRAESrM5cHe6+w0AfjeZAzfb3e9qYd+bAPwegGkAngngdwG8B8AuAPnedxPcfb+706xzoNjnkGmf8axaIcU6svrWWVmRkYPzzXSeW/o5Fyqfc7405Lx29PW3dBuyf+HEOpfCC0VFlAqfdB19V+tSKqhSgUPW7jlSWhdpIWtklQT6o1OmYpmRKHNRMzkHLXXpzCU87zjdhy2HNmZqJA/TDjA4SW4em0alTNMM8Frn5soFQRAEQRAEwUAMOAfO3beYWYeZ/ZKZ/SpfLex7obu/292/5+7fdfdtAF7t7n8N4GkXWO7lAP5evt9gZg+a2WfM7GWt7oR5wzR/WSto7rCc+51Gk+R3oiIvZ72iQEmTcitHuzcMOkLkQDz+YH+UTKAalAOoWpb4na6UmmMtJ0hUzG1aPLNSZ+lcOYo0vqfXJBeOn6JILZqck5bmYMslz27VhVKvO+snN48tZ11L64Uus8N9HYMgCIIgCIInNq0EMfljAFuK18sBbAQw8EQx4Hwh+iYUr1+S37L54MzsPjPrzbxeK+t0ATgH4H8Xi04CeLa73whgLYC/MrOnNtj/CjM7aGYHv/jVkwBqgkStWTp3K3tShftcGrkyTQidC5cP1MScWua0Y68CREUABcL+hRPLyI2NxMiF0julvdyvijMtjwY20XI3I03S3chy2MyiqPXWittjGpyFYl0teb1T2gc1d/Bo9wYs2HeuFLa5VA9ndq8oA9ekrrJa7oHaWhAEQRAEQRCkDCjgAPwigJ8B8H/d/TYAswE8qYXtfgXAMgBfB/C14vMbzOwKAHfkNnD3+e7ekXn9LQCY2RtRi2z5K0wK7u4/cPdvFp8PAfgCgOc12P82d+90985JT7m6tNBoBMFW0blUFBYa/j8lXcZ5XKkFhnPkuA6AiuBQci6aF4JGSVS3xrScOWtSul4aMp+wzrZOm13Zhwq83HXQhN+pdbKRmEyDh2iOvkldywY99y2XMkLn0zVym6WlLRW64UIZBEEQBEEQDJZWBNxjRWLuc4Vl6+sABgxC4u5fdPfXuPtUd7+m+Hzc3R9z9wcGW1AzWwjg9wHc4u6nZfk1ZnZZ8fk5AGYA+GKr+6VLns5Rmn9X4+IxNxstcBphUffJdZU0WfWZ3SswfdUbStGWc62kVZDCissmdS0rxcRwRTLsON1XiTBJgUHxQQHE+Vup+GzkDkg3QxW8TOxNGolRzjNkYnBNpcAyp2JOrZOadkHFYZlkPBGSzUgDlaTlZ/l03h7hPLuhJF4PgiAIgiAIAtKKgDtYhOx/D2pRKD8H4LMDbWRmzyuSePcW32eZ2R9eQFnvAnAlgE8k6QLmAegxs8MAPgTgTe7+rYF29ryrrBQH6hI5kBjSwBkUbb1T2iuChB34SV3LSqFAAUbBx3UauVqqJYuCRJODa5TKVlwYW4FlolUoN0crdflsFq1SLYh85/mnwVwozPj9aPcGbJ02u5JOgaTRLTmfTMVcug5TFaQulKmQbETPiVM42r2hDEySJg/n+ah40zKkETWB/lx1QRAEQRAEQdAqrQQxWenu33H3dwF4JYA3Fq6UA/Ee1EL9ny320wNgadMtmpdjurs/K00X4O4fdvcXFtExX+TuH21lf/9xqjYNT61YC/adw/6FExvOLZt/1wN1c9+AfnGx+tZZZQeeFiN+T7fjb7oOxY3Om1JxxvVThssC19a1Hgf3bm7oLplLk5CWkd9V3KmQ0WAtqdXtaPcGTOpalrXGqXtisxQAmkOPx6EIzu03zUHXDNaPni/bSpr7j2VJl2l70O2DIAiCIAiCoBWaJfJ+UfoC8HQAE4vPAzHF3VNL3aiJ2vCsp10BIO/m1gpr9xzB6ltnlQJCXfx0v5wXp5akNOpluk3vlHZsnTa7tLrl8skB1c5/z4lTLZe9GWlY/ZybZCqgGOiErpYMwpJaqdKgHbRK8dzS+qB1LI3iCaCSiiA3j0yTpwP5hOGDsVym4lUtonrMO3f04M4dPaWg0/lxes5kuNJABEEQBEEQBOODZha4dzR5vb2FfT9qZs9FEXHSzH4RtYiRo4Kvfvux8rOGpj/avQGTl2yrW//88QN1HX5ay+jWqAKFHfY0r5zmDmuUK21S17JyO12HYnDljKUVa1ROiPScOIW+225Bz4lT6Dlxqi5KZg6Wn4IpFVwaoTL9XQOvcN4c59OptUm3V/fJFFpGz3bvrIjds907sXLGUty5owdbp80uUyr0Tmkvr5vuTwPVUOgNVjSldaeusywfhZui4k1dRnn84XJ9DYIgCIIgCMYPzRJ5v7zJ6xUt7Ps3AbwbwPPN7BEAvw3gN4an2BfOs552RSUwyOQl20rRcGb3iuw2tCpxzpx2xo92byjFSTofCkBpldHIiLloiwv2nauzFFEobT22qzY3rMVk4zrvaqCIlT0nTpUCh4KUbpBqCUzFD9090zQDzfLB0TKVS9jNa9Bxug9nu3fWWbA6Tvdh5SOHsfXYrnL+4P6FEzGpa1l53Rj0JD3ng3s3l+Wkha5VF0ZN2k5XWbprdpzuw9Zju8oysVypmyjPW9NEDCbyaRAEQRAEQRC0EsRkSBRRKOcDuAbA8939Znf/8kgdb7Bccf4M9i+cWLr3Hdy7GWv3HCk73uePHyitVz0nTmHykm1185/oPqiuk5yrpd97p7RnLXEqaFQMpkIKqLdEsZwqhmh1O3/8QOliqO6aPJccDFiyYN+58ljNYGJsknO11ND5Kn7VAtko9L6ux/PV5Vq+o90bSoFMGKWT61PkaTk1cmgj+m67pdyH1v2queswecm2hsnfOZ9PrXJr9xzByhlLS4vgcM1dDIIgCIIgCMYPwy7gzGytvgD8OoBfk++jCrWI0R1SYYRDdvw1oiKtUxRjdKXLdeopotSCx+XcH8sw7/blpRUpTRpOQZizYgH9bpYUeNw3z6tR0mq1DvKc01xpR7s3lOVXyxWtSSroGJUyTV7dKD+cLmNZVWBRFHec7sPKGUtLQcXlW4/twp07euqiWqpYTK/LykcOZ+uC9N12S6Wc2jb2L5yIg3s3N03+3ta1vu4YW4/tKvc3fdUbmh4/CIIgCIIgCFKaCjir8axB7vPK4tWJmsvktOL1JgAvGEohR4LHJkwGUOucT16yreLuRgvPpK5l2HJoI3qntGPLoY3oXLSmModNoeg5vuX95bJcUBNa/QayvqQCRNMHpG6Rao1i4A8uo+BiORpZ4fi7BgVp61pfRm/UfWg6A847oxtlo3ldtMbR9RSouq0yl54GKVl966yK9UtTL6SWO3UtpTWOApDrMcpo6p7Z7BqoK+adO3qwcsbSSt12LlpTEbOsI5aBqSQokDUKZaspDIIgCIIgCIKANBVw7u4A/mYwO3T3t7r7WwFMBfAid/8dd/8dAHMBPHOoBR1urjh/BkBN6KSRFVU0nO3eia3TZldCyB/t3lAKORVPtMSkc58Y4EQtNRRJFH60qtE1Ud0rtVw510aG3te8c9x3mr4gJ1p6TpwqBSvQPwcwdWME+iN1UnjRMpkmpZ53+/Iy8bemRaB47Z3SjjO7V1RC/hMNWJJav9I8cirseH4UchSAtEqyXhfsO1ced8L0m+qOT1I3Te5TxTOtcDoXEqhZ2jiXsVxWJA2n+E2DngRBEARBEATBQFhNozVZwewvAexw938d1I7N/h3AbHf/QfH9SQAOu/vzh1rY4aRzZrt/9iP3VNwMSTrPivOpuJ7O/0pFHIUM59TROkPxtn/hxEqUSwognStG4cMy8XjqDkhXwkYBTTT/GdDvupiLAsnfNRKnumBSGFLktd9zb5kTr3PRmvIcGH1SP99/93Yc3Lu5rCdGqTyze0XpJqp1mJ5jWtat02bXgphMm12xYJUROuW3s907y2vHuuB3Xsf77rg5W3/z73qg/MzUCHSXZT2l7WTrtNlloBk9dqPzo4h8wY6PHnL3zmxBgiAIgiAIxiATpkz1iW23XLTjnX3onnHTn2plDtzLARwwsy+YWY+ZHTGzVkwHOwF81szeYmZ/DOBfALzvQgo7nJw5ebJhoA52rFOrkiauTl0bAZT50CjAgJr4mrxkWzlfjhY/oD7wR8fpvlL4Af2ihXPJ0nKouyQtfCoUKDxScvO2aFlLXSXpbqiCinPD9BxoUeS7nqNG+9y0eCbO7F5RibaZS1eQ+07rGiNBKvp967FddfnWKGQpxJvRc+JUJRm5pmrgO687RSnLxoAqOlcynVfJumTuwCAIgiAIgiBolVYE3KsAPAfAKwC8BsDPFe9NcfduAMsBfBvAdwDc5u5/OuSSDjOTr722DAGfugmyU00h1ta1vhLVkXQuWlOJ2KhWOQ3soRYoHkstbxRaC/adq1jNtPOfC3fPcjYSady/pgRIc89tnTYba/ccKYVN7jwbpTxg0BUG9GDkSUXnxKm75KbFM+tSDaTWTA3dT1IxpAJo9a2zSndSjQaaupCqEE/pOXEqW5/chvtXKMwX7DuHO3f0lHP0WN5wlQyCIAiCIAiGiwEFnLt/BcCzALyi+Hy62XZm9jnZ9pC731m8Hsytc6k4c/Jk2bFuZPFRSxiFmnbu1dVRg5Rwflfqvjipaxl6p7RXhIuKCRVuueOk5dNAHrotoWVImdS1rBJs5F2S2aFZWHwej/PPNi2eiQX7zpVlUGsl950m8ta60O8UsOl10ND9dHnk+Z/t3olVc9eVgUpozVo1d135OZePLze3kNCymLNcal1rcBMV5psWz6zMfbtzR09tDuWxXZjUtawi/jhncaD8fEEQBEEQBEGgDCjgCvfH3wfw5mLRJADvb7wF2gtXy0avI6gFOLmkTL722vKzCjXt2LMjzxxmnYvWlOIrF+CDy4F+0UCBwoiLdJNsBK1k6vKnViXuU8tJ65wG6SB0q+Q5MhiLllEFCyNO6u9Ec7EBNYE2+6GpONu9s5K8W+tFo26m4pCRKTkvLgejPlL40hpKwcSk2RTHjDJJKDpp/UsFrqJBU2i5THPRqXBj+dRdUq/d6ltnlddK3SVZljt39IR1LgiCIAiCIBgUrQQxeQjAjQA+5+43Fst63D0bA93MfryF4/7Q3R8eZFmHlY6pV3vPgX2VZb1T2tG5aA3O7F5RZ4UjtLBV9iWufzmLE4NfALW5ZmkIew04kga5UNHDABoaSCMNOMKyMLgIBRL3nbpGppExGYEyDZzCwBzp+WqgFQB15zDQMXk+PHYuqAzLkgskwuMwuAnQL5BSwagJ1olGoew5carO1RXoz0dHsdhon+n1SRO6AygDquxfOLHc77vxlXEz6TYIgiAIgvFBBDEZOVqZA/d4kU7AAcDMntxsZXf/SguvSyreFFqc6NLGsPCpa6MGISG0BGkgixRuw31QBOlxc+56dB9MLYK0MmnC6pUzllbC8zOYiqJWOxVYen7cJpcAvP2eeyvbKprYO51vxnQLPEeNxslzZWATtWypq+OWQxsr+73/7u0VS6MGNyHNhFYzt0W99rT8rXzkcJ14YyoJXbbl0MbyfGh102PTEshzbxRBNAiCIAiCIAga0YqA+6CZvRvA1Wb2awDuA/DekS3WyPO1bz5WdrJp6WrrWo+1e45Ugm2oe5wKhkldy7Bq7rrS2sU5YXQZJLoNt2MEQgYP0flvmu8sFROcR0W47spHDpfBRADUWQD1c+eiNeV+c5Et+ZtauNR1U9dRUagibuWMpaXro0ZvTPer5dLol6wjimq6LXJfjNSp8/B43DRvHJODc9+a3Jufgf7gJWv3HCnFWSNUnCl37ugpg6Z0LlpTnoMKx1RYhogLgiAIgiAIBkMrQUzeDuBDAD4MoA3AH7n7X4x0wVKKdASPmNlDxevV8tubzey4mR01s58d7L5TKwqTQzeaa6bukDqnjZaxnDUK6O/A09LEvGQqEhuFlqdg1MiTFDYsJ8WkCjMKO77UMqdz3yi2KPC4jDC0vp5XLnDKgn3nsPrWWeX8L4oYdaWkeyEtkjwuj8nzOtu9M+tuSiGo7qq9U9rL6I+cqwagdKukxZOWNEaLVGhlbetaX7mGag3NBbwp88AVYoxBbICaqGN70flumvIhCIIgCIIgCFqllTlwb3P33x9o2UhjZm8B8P1CUOryFwD4AICXALgONQvh89z9h8321zH1at/9mnkVS4q6QxKKMlrgchakdP4ZrWS677V7jpS51gDUuePl5rHl5pLlEl1PXrKtEhiFQUG4jHOucnPRGDxE88/xvBudY+6cgWpwDkJRo+eolilNRp5LkJ4mQ2cdakAVWuk0yffWY7vqypVCt1By/viBcp+sC4rsNICL1mGajkCX59pNmgx89rSrx43PdhAEQRAE44OYAzdytOJC+crMslcNd0EugNcC2OXuP3D3LwE4jpqYa8rka6+tuLulbDm0sSLegH7LEdAfVn7BvnMVKxqAOlFISxEtSme7d5bueytnLEVb1/pyrhhhVEUVPDwuBQatOLSq6XkwgTZQn+9MlzN3G4WSWrQ4z0znsdFlcVLXMmydNru0rtFK1da1Hse3vB9bj+0qRRRzoXE7AKUlUq1WaWASWto0MqamB2A9sZ45D44JtbVcvCaNLJyNxBuPqdeCrzTqZWrNZNlUPOscxvSaB0EQBEEQBMFANMvn9htFyP+2JA3AlwBcqtjndxRl2G5mTyuWTQPwVVnn4WJZHWa2wswOmtnBb3zrOwBQCiC1+KilLI2iSOFBIZETHbQIUXhpPjgVexpMY/qqN9QdR4UCBQiX6z5Z3jO7V9QFX9HE3LkE1mkKAn7WdbVsmt6A7oipayH3lUZzVMsc57xpegRF3VfTc6Yo5O+6X53zpiJ567TZ5XxH7lOhGOZxtH5p5aM1jS6R/C0tP8+Z10ytsnotctbcIAiCIAiCIGhGMwvcXwF4DYB7i3e+5rr7G0aiMGZ2n5n1Zl6vBfBOAM8FMAfASQDv4GaZXWX9Qt19m7t3unvnU6b+GABUhEHH6b4yzD9QtXZpJ51ztFLxkUZg1Bxk7LCr9Y3QEkXxpwJFA4boXLec0ABq7pQqDDQRNtdL55RxOV9czkAcnCumwi+1HKko5TF0He6Dok3Ll7OokclLtlUEogY2oWsj576lCbt5TL43cqVsdm6cJ8jk3Fym7qdsL/y+YN+5SkAUoCYyJy/ZVlkec+CCIAiCIAiCwdJQwLn7KXf/sru/HjWr1lnUhNFTzOzZI1EYd5/v7h2Z19+6+9fc/Yfufh7Ae9DvJvkwgGfJbp4J4MRAx7ri/Jmyc60uivyuERE3LZ5ZCg+6TKaueLT80F0Q6He9A1A5FoN8EApCogKFSbyBfjHBZSrGGFp/3u3LK5EvgX5LEedlUTyoaygFDAOzMKVCWi6dA6d54XjuapXktgwaosu2TptdCjMNcpLOG1R4bBVTKpAILXZtXesxecm2mjXukcOlWGT99N3W75etdZlG6WT9qOC6/+7tmHf7cnQuWlO+ANS5466auw6di9Zg5SOHcWb3CuxfOLEMlKPlD4IgCIIgCIJWaCWIyR0A3gLgawDOF4u9USLvkcLMrnX3k8XnNQB+wt2XmtkLUbMWMojJJwHMGCiISefMdt/ZOaMUDBqoIhUFKrYo5O7c0VMJlMHAGWmHnOupmEktUHpMXWfV3HWVeXiMzNho3pQGM9HgH0A12EcalIPBPzoXrcHBvZuxddrs2jlJiHsNpMLzpQslLYqsj47Tff3ui48cLo+n56j1x3Pj+fJ4PA9NRq77UoHM8mh0Sa2rRvXPQCY9J04BqIp5TdAN9FvpGBGUFrdm6PXIRe0EgMtmvHTcTLoNgiAIgmB8EEFMRo5Wgpj8NoA2d3+hu88sXhdVvBVsNLMjZtYD4OUA1gCAu38ewAcB/BuAfQB+cyDxRlS80cVNO++0lN25owd37ugp11drDzvj6iLIdVS88Xsji4tantIcZCqc+FldLHUbDcCRm1/WcboPd+7oyYbE5xw6nndqlZy8ZFvpGsnzpVBbfeusylyvrcd2YfWts8rjsW7UZVJFYVvXemxaPLO0ZObm66ll9Gz3zlJ8UrylqFDKifO2rvVl8BIyecm20rqq58+0BbkAMEougbq66fK8w4UyCIIgCIIgGAqtWOA+BeCV7t44s/EYpGPq1X7zN6/GykcOV0K9qwihhScNj68WNQ1WklrnclY17kcDgDAQiYbCz6UiUMsWt9fk0JOXbMOZ3StKMadRM9Nyct2cZUrLl7MWKrQKstxAv4WqUdoEdd9M5xY2srBpHXAfOqctLatCK6Gek56jngePofWi1kCgP1cgz2Pykm3lsQ7u3YzORWsw7/bllfmJ6WcV5ZFGIAiCIAiCJxphgRs5WrHAfRHAp4tk2Wv5GumCjTSTr70Wx7e8HwAqc8No5aGVSi1IQM0lcMG+c3WCJM19lgt6QquPuiZ2nO7D/XdvR1vXeqzdc6QUlLQC0Z2Rx2F4/jSPHFCz/jBMfZo6gNunQuds906smruuLJ9Gu1QRo66SXJ66L3LOHlCLqgmgMvctlxaBZeC8NZLmxdPgKlouFbhnu3dWroNGgaQllcKQ31fOWFqX4y51oVQYiIZQvKnl7eDezaVI02iT6tZK4Z7O8wuCIAiCIAiCZrQi4P4TwCcAXA7gSnmNaR6bMLl0i7v/7u2VjjStNVunza5YbihSKPYYYRKod6FUaJGiYFSrGVBzXQRQ5hSjBY2CLRUwGhhFRYvOedPz0ZQEPPaZ3Ssq4ez1/ChA1Tp1tHtDxa1y5Yyl2aiOFKk8LsWmih6g3wWS9aNl5TE1kqWmNrhzR092HuCkrmWlMON3FYapVZFunlqerdNmZ8vLMqTHO7h3cyneeB01/YOuu3/hxErut1bm0AVBEARBEASBMqCAc/e3uvtbAbydn4vvY5orzp8p3SaB/s45Q/qnc+CAfksULWQqcnI5zWjJ4/wudSlkQuxGbn+pYKPYBKqRDjkfLZ1TxaiJtJxxPyqQVOSlIfjTqI76DqBSL0C9eymtjMybpqkF0nx3GoVT3TjV0kfR03G6DysfOVyx5qkYVDRlA4WuRgnl8bdOm12WTQOrkEldy8rya0LunMjrXLSmzCmXimi19K7dc6RuvlwQBEEQBEEQDMSAAs7MXmpm/wagr/g+28y2jnjJLgJMUH14zqPlMnbye6e0Y9XcdaUVSa1immA67eyngi4NBsJj6PoUcuoeSIsTrVNMCM6O/4J958qynu3eWQnIAqCSU45z04D+hNUM989jaoARWrJSUcWya3ROCl1F3R9Tgcwy0qrI+qPAoehafeusypw0BjehBUvnwqV1v/KRwzjbvRNr9xypCDKKSu6bdb59zivqrlEuyEh6bBVotK4RnffGcmveuJyLaxAEQRAEQRAMRCsulP8TwM8C+CYAuPthAPNGsEwXhTMnT5bzzTj/jMx+aGrFVZLiiUKgc9EarN1zpOywUxTREkRRxLldarUDagJj0+KZlaiWLAOFlaYN0ETSmxbPLOe4UYiwPCy7irRmsPy5ACCau03nsWkOO84TBKqpFmhhUhHH82G5aFVU8bvl0EZsnTa7dDXV/HJpSgMmxSY8nuaIo6jS9fi7irCDezfXuW6qeywtqBRdaQAVFamc/6burHfu6Cl/47HpuhpCLgiCIAiCIBgMrQg4uPtXk0Uthekfzfi0Gyodeybr3npsFw7PebQyd4oiioIidX3TOXQqpCgqmERbLU7qIknxo1EjVbylc6XUdTAVMYx+yOiI+nvnojVlWXuntJcWvTS4Ca1TmletkRslxY4KIIpQFTlcvnbPkUqaAIo6ja5JV1Na5DQvHn9TMZTWPZfzHGlppGgkauFLSecA5tw/dV4eobslWbDvHI5veX+lnGl6hiAIgiAIgiBolVbSCHwIwCYAdwG4CcBvAeh096UjX7yR44Wzb/QP/P2nAfR3/O+/e3sltD5Q7eSrqNLk0oTh4ym4UqGnSbnTOVJEkz1zzhT3fXDv5kqY/dkPTcXhOY/izh09pdVKSYVfozlXqRBJxQtQnwZAc6/lIlvyHOgOCfRbv9Jk3czDR4soxdr9d2/Hwb2bK/XN8jKiI+uIQnfe7cuz+08Tl+eSlGt9E16rdB+NRJ+eOy2ceh0Oz3m04u56tntnpBEIgiAIguAJR6QRGDlaEXBTAdwJYD4AA7AfwGp3/+bIF2/keOqzn+8vWffeOiGmHXPtqGsusJxA0046RVzOUpeuw2iQs667CvPveqAUUytnLMW7/MvZsr/Jrsf2Oa8oxUCaZwxARfDw2I0EHIUSxYqecy5fm8Kcd4oKwEbiKF2mopY58XLlZ1kplnmupHPRmjIaJOed8buKZxVmmiOPx73vjpvrzlWvD1BLIcAyUJynolytoYfnPIpVc9dh0+KZ6Fy0prx+IeCCIAiCIHiiEQJu5BhQwD1RSS1wau0BkBUbQFVI0F0xZ0nLCYBLSc+JUw2FiTL/rgcAICtogX4RpAnL098oZFQc55JZN6pr7otWR426CaCSAkFFFPeVy7emv+esaRSdE6bf1FI9qiDWtsDzS5eRXFlesOOj4+aGEwRBEATB+CAE3MjRigXufahZ3L5TfH8agHe4+5iOgf7C2Tf6kQ+/syIUKCx0rpO66qWWt1wnfrQJtwul77ZbKmIH6BdZGnVRhUrOCqj1mhO8tGAxZ55argBUXFNzok5p5MbaKGCIitSBBJzSc+JU5Xx5DC0bXTppgZv90NRyOcsTFrggCIIgCJ5ohIAbOVoJYjKL4g0A3P3bAG4csRJdRCgm1A0uDTSyafHM0tUv54J43x034747bsbjD25/wok3AGi/594yqibD/1NUkZx4S5Ojdy5aUxcA5P67t5cvBnnROWS9U9rLYCVk3u3L66yCOVfVebcvL7dN5wLq9rSuTph+06DEGwDMuu4qzLruqrpUCSwP33untGPe7csx+6GpOLN7RRmpUtNEBEEQBEEQBEErtGKBOwzgpwvhBjN7OoDPuPuYjn/OOXDauX/8wf7PnO/EOVJArcMetMb8ux6oWCi3HNpYseSlLpUagGSwQip3TJ2rpsdL5zTSnXKox0yPT6vjlkMbsWruurq5cdNXvQGrb52F9nvuBVCbz/hufGXcjBgFQRAEQTA+CAvcyNGKgPtVAG8G8CEADuCXAHS7+86RL97I0dnZ6QcPHrzUxQhGALo2qnUrF0FyOERbI84fP1BGCj2ze0Vpxc0NApjZuLnhBEEQBEEwPggBN3K0FMTEzF4A4BWoRaH8pLv/20gXbKQJATc+OH/8QDlXjxa4BfvOjSp31xBwQRAEQRA80QgBN3JMHHgVoBBsY160BeOPCdNvwn13FBaxrmU4CuC+wn0xCIIgCIIgCMYaLQm40YCZ/TWAtuLr1QC+4+5zzOx6AH0Ajha/HXD3N138EgajmQnTbyrnnQVBEARBEATBWGXMCDh3fx0/m9k7AJySn7/g7nMueqGCIAiCIAiCIAguImNGwBEzM9QCqbziUpclCIIgCIIgCILgYtJKHrjRxssAfM3dj8myG8zsQTP7jJm9rNGGZrbCzA6a2cFvfOMbI1/SIAiCIAiCIAiCYWRUWeDM7D4AP5b5qcvd/7b4/HoAH5DfTgJ4trt/08zmAvgbM3uhu3833Ym7bwOwDahFoRze0gdBEARBEARBEIwso0rAufv8Zr+b2UQAiwHMlW1+AOAHxedDZvYFAM8DEDkCgiAIgiAIgiB4QjHWXCjnA/h3d3+YC8zsGjO7rPj8HAAzAHzxEpUvCIIgCIIgCIJgxBhVFrgWWIqq+yQAzAPwJ2Z2DsAPAbzJ3b910UsWBEEQBEEQBEEwwowpAefut2aWfRjAhy9+aYIgCIIgCIIgCC4uY82FMgiCIAiCIAiCYNwSAi4IgiAIgiAIgmCMEAIuCIIgCIIgCIJgjBACLgiCIAiCIAiCYIwQAi4IgiAIgiAIgmCMEAIuCIIgCIIgCIJxj5n9rpm5mU1t8PtCMztqZsfN7A9k+dPN7BNmdqx4f9pIljMEXBAEQRAEQRAE4xozexaAVwL4zwa/XwbgLwG8CsALALzezF5Q/PwHAD7p7jMAfLL4PmKEgAuCIAiCIAiCYLyzGcA6AN7g95cAOO7uX3T3xwHsAvDa4rfXAnhf8fl9AH5+BMs5thJ5DyeHDh36vpkdvdTlGCRTATx6qQsxBMZiuS9mmX/8Ih0nCIIgCILgouCPffPjZx+6J+uKOEJMNrOD8n2bu29rZUMzuwXAI+5+2MwarTYNwFfl+8MAfqL4/Ax3PwkA7n7SzH50cEUfHONWwAE46u6dl7oQg8HMDo61MgNjs9xjscxBEARBEASjBXdfeKnLoJjZfQB+LPNTF4D1ABYMtIvMskbWuhFlPAu4IAiCIAiCIAjGAe4+P7fczGYCuAEArW/PBPA5M3uJu/9fWfVhAM+S788EcKL4/DUzu7awvl0L4OvDfgJCzIELgiAIgiAIgmBc4u5H3P1H3f16d78eNaH2okS8AcC/AphhZjeY2eUAlgK4t/jtXgBvLD6/EcDfjmSZx7OAa8kndpQxFssMjM1yj8UyB0EQBEEQBMOEmV1nZh8DAHc/B+AOAB8H0Afgg+7++WLVPwPwSjM7hlokyz8b0XK5XxLXzSAIgiAIgiAIgmCQjGcLXBAEQRAEQRAEwZgiBFwQBEEQBEEQBMEYYdwJODNbaGZHzey4mY1olvTBYmbbzezrZtYry55uZp8ws2PF+9PktzcX53HUzH72EpX5WWb2KTPrM7PPm9nq0V5uM5tsZp81s8NFmd862sscBEEQBEEQBMA4mwNnZpcB+A/UJhc+jFo0mde7+79d0oIVmNk8AN8H8L/cvaNYthHAt9z9zwrB+TR3/30zewGAD6CWFf46APcBeJ67//Ail/laANe6++fM7EoAh1DLPn/raC231WLEPtndv29mkwA8AGA1gMWjtcxBEARBEARBAIw/C9xLABx39y+6++MAdgF47SUuU4m73w/gW8ni1wJ4X/H5faiJIy7f5e4/cPcvATiO2vldVNz9pLt/rvj8PdSi8kzDKC631/h+8XVS8XKM4jIHQRAEQRAEATD+BNw0AF+V7w8Xy0Yzz3D3k0BNLAH40WL5qDsXM7sewI0A/gWjvNxmdpmZPYRaosVPuPuoL3MQBEEQBEEQjDcBZ5llY9WHdFSdi5k9BcCHAfy2u3+32aqZZRe93O7+Q3efA+CZAF5iZh1NVh8VZQ6CIAiCIAiC8SbgHgbwLPn+TAAnLlFZWuVrxTwzzjf7erF81JxLMY/swwD+t7vvKRaP+nIDgLt/B8CnASzEGClzEARBEARBMH4ZbwLuXwHMMLMbzOxyAEsB3HuJyzQQ9wJ4Y/H5jQD+VpYvNbMnmdkNAGYA+OzFLlwREORuAH3uvkl+GrXlNrNrzOzq4vMVAOYD+PfRXOYgCIIgCIIgAICJl7oAFxN3P2dmdwD4OIDLAGx3989f4mKVmNkHAPw0gKlm9jCAPwbwZwA+aGa3A/hPAEsAwN0/b2YfBPBvAM4B+M1LFBXxpwAsA3CkmFMGAOsxust9LYD3FVFJJwD4oLv/nZn98ygucxAEQRAEQRCMrzQCQRAEQRAEQRAEY5nx5kIZBEEQBEEQBEEwZgkBFwRBEARBEARBMEYIARcEQRAEQRAEQTBGCAEXBEEQBEEQBEEwRggBFwRBEARBEARBMEYIAXeRMbOrzWzlMOzn02bWORxlkn1eZ2YfKj7PMbNXD2EfbzGz3x2GsjzJzO4zs4fM7HVD3Ed5PpnfLrj+zOx6M+stPv+0mf3dhewvCIIgCIIgCAYiBNzF52oAgxJwVmPEr5W7n3D3Xyy+zgEwaAE3jNwIYJK7z3H3vx7sxmY2MTmfIAiCIAiCIBjzhIAbBGb2q2bWY2aHzWxnsewaM/uwmf1r8fqpYvlbzGx7Yen5opn9VrGbPwPw3MKy9OfFur9XbNtjZm8tll1vZn1mthXA5wA8q0m5Xm9mR8ys18zeJsu/b2bdRXkPmNkziuXPLb7/q5n9iZl9X47Za2aXA/gTAK+jBSy1rBXrXV987jKzo2Z2H4A2Wee5ZrbPzA6Z2T+a2fMzZX+6mf1Nce4HzGyWmf0ogPcDmFMc/7nJNi8u1v9nM/tzsYLdama7zeyjAPYnFrIrzGxXsd1fA7iiQV2+2Mz+T1FnnzWzK83ssuI4vEa/3uhaBEEQBEEQBMFIEgKuRczshQC6ALzC3WcDWF38dCeAze7+YgD/L4D3ymbPB/CzAF4C4I/NbBKAPwDwhcKy9HtmtgDAjGKdOQDmmtm8Yvs2AP/L3W909680KNd1AN4G4BXF9i82s58vfn4ygANFee8H8GtS5juLMp9I9+nujwP4IwB/PZAFzMzmAliKmsVsMYAXy8/bAKxy97kAfhfA1swu3grgQXefBWB9cb5fB/DfAPxjcfwvJNvcA+BN7v5SAD9MfnspgDe6+yuS5b8B4HRxnG4AczPncjmAvwawuqiz+QAeA3A7gFNFfb0YwK+Z2Q2N6iQIgiAIgiAIRoqJl7oAY4hXAPiQuz8KAO7+rWL5fAAvMDOu91Qzu7L4vNfdfwDgB2b2dQDPyOx3QfF6sPj+FNQE3X8C+Iq7HxigXC8G8Gl3/wYAmNn/BjAPwN8AeBwA52UdAvDK4vNLAfx88fmvALx9gGM042UAPuLup4vj31u8PwXATwLYLXXzpMz2N6MmfOHu/2BmP2JmVzU6mJldDeBKd/8/Uv6fk1U+IddGmQfgL4rj9JhZT2adNgAn3f1fi/W+WxxzAYBZZkZ3zKtQu0b/0aicQRAEQRAEQTAShIBrHQPgmeUTALzU3R+rrFwTLT+QRT9Evr4NwJ+6+7uT7a8H8F8tlqsRZ92dZW50/FY5h6rFdrJ8blQv33H3OQPsN1f+3P6ara80q7Nm++W+c+sYapbEj1cWFi6kQRAEQRAEQXCxCBfK1vkkgF8ysx8BanO3iuX7AdzBlcxszgD7+R6AK+X7xwEsLyxWMLNpxRywVvkXAP+PmU01s8sAvB7AZwbY5gAKqxdq7o+tlPPLAF5UlPFFAOhCeD+AXyjmmF0J4DVAab36kpktKbYxM5udOc79AH6lWOenATxKy1cOd/82gO+Z2U0DlL/ZcToAzMqs8+8ArjOzFxfrXWlmE1G7Rr9RuMDCzJ5nZk9u8bhBEARBEARBMGyEgGsRd/88anOnPmNmhwFsKn76LQCdRXCLfwPwpgH2800A/1QEAflzd9+PmhvgP5vZEQAfQlU4DVSukwDeDOBTAA4D+Jy7/+0Am/02gLVm9lkA1wI4lVnnU6i5hjKM/4cBPN3MHkJtPtl/FMf/HGrzxh4q1vlH2cevALi9qK/PA3ht5jhvQVF/qAV4eeMAZQdqc9K2mdk/o2Ydy5U/5Z0AnlIcZx2Az6YrFHP/XgdgS1HmT6BmaXwvgH8D8LkiKMq7EdbrIAiCIAiC4BJg/R52wXjBzKYAeMzd3cyWAni9u+fE1ajEzJ7i7oyc+QcArnX31QNsFgRBEARBEARjnrAijE/mArjLahP1vgNg+aUtzqBZZGZvRq39fgXArZe2OEEQBEEQBEFwcQgLXBAEQRAEQRAEwRgh5sAFQRAEQRAEQRCMEULABUEQBEEQBEEQjBFCwI0hzOx6M3usiASZ/vYWM/vdS1Csi46ZzTGzV8v315nZcTP7u2bbBUEQBEEQBMFYJwTc2OMLLSTHHjJFLrnRzhwApYBz978G8N8uWWmCIAiCIAiC4CIRAm4MY2ZdZnbUzO4D0CbLn2tm+8zskJn9o5k9X5YfMLN/NbM/MTOG4v9pM/uUmf0VgCNmdpmZ/XmxXo+Z/brs+/dk+VuLZU82s71mdrjIb/e6JmWea2afKcr2cTO7tlj+a8V+D5vZh4tUBzCzJcU+D5vZ/WZ2OYA/AfA6yVEXBEEQBEEQBOOCSCMwRjGzuQCWArgRtev4OQCHip+3AXiTux8zs58AsBXAKwDcCeBOd/+AmaUJx18CoMPdv2RmKwCccvcXm9mTUEs8vh/AjOL1EtQSaN9rZvMAXAPghLsvKsp2VYMyTwKwBcBr3f0bhfjqRi2NwR53f0+x3v9ALVn3FgB/BOBn3f0RM7va3R83sz8C0Onud1xIHQZBEARBEATBWCME3NjlZQA+4u6nAcDM7i3enwLgJwHsrqV5AwA8qXh/KYCfLz7/FYC3y/4+6+5fKj4vADDLzH6x+H4VasJtQfF6sFj+lGL5PwJ4u5m9DcDfufs/NihzG4AOAJ8oynYZgJPFbx2FcLu62O/Hi+X/BGCHmX0QwJ6mNRIEQRAEQRAET3BCwI1tckn8JgD4zhDmyf2XfDYAq9z947qCmf0sgD9193enGxcWwVcD+FMz2+/uf5I5hgH4vLu/NPPbDgA/7+6HzexWAD8NAO7+psKKuAjAQ2Y22PMKgiAIgiAIgicMMQdu7HI/gF8wsyvM7EoArwEAd/8ugC+Z2RIAsBqzi20OAPh/i89Lm+z74wB+o3B5hJk9z8yeXCxfXlj5YGbTzOxHzew6AKfd/f2oWfVe1GC/RwFcY2YvLbafZGYvLH67EsDJ4pi/wg3M7Lnu/i/u/kcAHgXwLADfK9YPgiAIgiAIgnFFWODGKO7+OTP7awAPAfgKam6M5FcAvNPM/hDAJAC7ABwG8NsA3m9mvwNgL4BTDXb/XgDXA/j/27tDlYiCKIzj/w9MFl9BQYNg8wV8AMGq4AtoVdBiNmwRg69hXzCLxaD4AlaDRRAUHcMdEJYVFtnlOvL/xQsXzsTDfOfMbbqs4xPd7dgwySpwXSOQL8AusAwMknwC78DeDzW/1VjmeZ2TmwPOgAfgBLipZ7nnu0EbJFmhu727qud4BI7rcwqndQulJEmS9O+llHEpPP1FSRbpZszWfvn/PPBaSilJtoGdUsrWNGvsS5IN4LCUstlzKZIkSdLMGKFsywewMO4h7wmt082R3QH7wMG0CutT3WZ5ATz3XYskSZI0S97AaSaSXAJLI5+PRhejSJIkSZqcDZwkSZIkNcIIpSRJkiQ1wgZOkiRJkhphAydJkiRJjbCBkyRJkqRGfAHqPl1/JGi3sgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x648 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "da = sss_anomaly.isel(time=[0,1,2,3,4,-4,-3,-2,-1])\n",
    "da.plot(col='time', col_wrap=4, robust=True, levels=[-4, -th, -1, 0, 1, th, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d92a77f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sss_anomaly.quantile(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44193396",
   "metadata": {},
   "outputs": [],
   "source": [
    "sss_binary_positive = sss_anomaly.where(sss_anomaly>=th, 0)\n",
    "sss_binary_positive = sss_binary_positive.where(sss_binary_positive<th, 1)\n",
    "\n",
    "sss_binary_negative = sss_anomaly.where(sss_anomaly<=-th, 0)\n",
    "sss_binary_negative = sss_binary_negative.where(sss_binary_negative>-th, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aff5bf8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd2d82a7550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sss_binary_negative.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b7c4cd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd2d8247dc0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sss_binary_positive.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d806f39",
   "metadata": {},
   "source": [
    "## Ocetrac Magic 🦄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "767474e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                        ] | 0% Completed |  1.7s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 33.5s\n",
      "minimum area:  1848.0\n",
      "inital objects identified \t 1791\n",
      "final objects tracked \t 193\n",
      "[                                        ] | 0% Completed |  2.6s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 34.7s\n",
      "minimum area:  2013.75\n",
      "inital objects identified \t 1530\n",
      "final objects tracked \t 158\n"
     ]
    }
   ],
   "source": [
    "from ocetrac.track import track\n",
    "\n",
    "with ProgressBar():\n",
    "    labels_negative = track(sss_binary_negative, mask, radius=10, min_size_quartile=0.75)\n",
    "    labels_positive = track(sss_binary_positive, mask, radius=10, min_size_quartile=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7acbfabd",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon       (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "  * lat       (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * time      (time) object 2015-05-01 00:00:00 ... 2021-04-01 00:00:00\n",
       "    month     (time) int64 5 6 7 8 9 10 11 12 1 2 3 ... 6 7 8 9 10 11 12 1 2 3 4\n",
       "Data variables:\n",
       "    positive  (time, lat, lon) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "    negative  (time, lat, lon) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "    SSS       (time, lat, lon) float32 dask.array&lt;chunksize=(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-b098ae52-e06f-4190-ad34-d175dd7cef3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b098ae52-e06f-4190-ad34-d175dd7cef3d' 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'>time</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c63dc48f-cf33-46c6-bf42-e3a26950d6ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c63dc48f-cf33-46c6-bf42-e3a26950d6ee' class='xr-section-summary' >Coordinates: <span>(4)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-9812f759-3ecf-4cef-a153-ee5b1c6b9ce3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9812f759-3ecf-4cef-a153-ee5b1c6b9ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158b8744-d2c0-4e60-83b7-31f0927e2c63' class='xr-var-data-in' type='checkbox'><label for='data-158b8744-d2c0-4e60-83b7-31f0927e2c63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>center longitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02], dtype=float32)</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'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-35fd04b3-6423-4027-be44-79f6064a3d39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35fd04b3-6423-4027-be44-79f6064a3d39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3de59557-050a-4e08-bfc0-d6e251500cd1' class='xr-var-data-in' type='checkbox'><label for='data-3de59557-050a-4e08-bfc0-d6e251500cd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>center latitude of grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-05-01 00:00:00 ... 2021-04-...</div><input id='attrs-70f6831a-9bf8-4147-a018-276d633f79fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70f6831a-9bf8-4147-a018-276d633f79fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-384b95b7-7836-4ef8-9fa0-75b0733cc0ba' class='xr-var-data-in' type='checkbox'><label for='data-384b95b7-7836-4ef8-9fa0-75b0733cc0ba' 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([cftime.DatetimeGregorian(2015, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2015, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2016, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2017, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2018, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2019, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 4, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 5, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 6, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 7, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 8, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 9, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 10, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2021, 4, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5 6 7 8 9 10 11 ... 11 12 1 2 3 4</div><input id='attrs-18c3271f-46c4-47fb-a964-80f8dd762df2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18c3271f-46c4-47fb-a964-80f8dd762df2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc14368d-30b2-4fab-8b45-86fd2f80f82b' class='xr-var-data-in' type='checkbox'><label for='data-dc14368d-30b2-4fab-8b45-86fd2f80f82b' 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([ 5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44b44ebc-04fe-47eb-a03b-9767f48e13e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44b44ebc-04fe-47eb-a03b-9767f48e13e6' class='xr-section-summary' >Data variables: <span>(3)</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>positive</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-714603bc-ff21-468c-aeb5-24393bcb0a26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-714603bc-ff21-468c-aeb5-24393bcb0a26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17ce3d3e-68ba-4ba1-8786-fa270447a77a' class='xr-var-data-in' type='checkbox'><label for='data-17ce3d3e-68ba-4ba1-8786-fa270447a77a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>inital objects identified :</span></dt><dd>1530</dd><dt><span>final objects tracked :</span></dt><dd>158</dd><dt><span>radius :</span></dt><dd>10</dd><dt><span>size quantile threshold :</span></dt><dd>0.75</dd><dt><span>min area :</span></dt><dd>2013.75</dd><dt><span>percent area reject :</span></dt><dd>0.3300758874350736</dd><dt><span>percent area accept :</span></dt><dd>0.6699241125649263</dd></dl></div><div class='xr-var-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]],\n",
       "\n",
       "       [[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",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [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",
       "        ...,\n",
       "        [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",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>negative</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8c0a1ddc-5b37-46e7-8ac2-323fdee06647' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c0a1ddc-5b37-46e7-8ac2-323fdee06647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70dbffdc-a299-49d8-8576-45a0b166cb63' class='xr-var-data-in' type='checkbox'><label for='data-70dbffdc-a299-49d8-8576-45a0b166cb63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>inital objects identified :</span></dt><dd>1791</dd><dt><span>final objects tracked :</span></dt><dd>193</dd><dt><span>radius :</span></dt><dd>10</dd><dt><span>size quantile threshold :</span></dt><dd>0.75</dd><dt><span>min area :</span></dt><dd>1848.0</dd><dt><span>percent area reject :</span></dt><dd>0.3475870809663174</dd><dt><span>percent area accept :</span></dt><dd>0.6524129190336826</dd></dl></div><div class='xr-var-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]],\n",
       "\n",
       "       [[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",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [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",
       "        ...,\n",
       "        [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",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-c74f0b79-5337-4964-9872-b77816565a2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c74f0b79-5337-4964-9872-b77816565a2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-133bfc5a-6f42-418f-8ef2-156b8abdde72' class='xr-var-data-in' type='checkbox'><label for='data-133bfc5a-6f42-418f-8ef2-156b8abdde72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SMAP sea surface salinity</dd><dt><span>standard_name :</span></dt><dd>sea_surface_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>45.0</dd><dt><span>coordinates :</span></dt><dd>lon lat time</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 720 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 298.60 MB </td> <td> 49.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (72, 720, 1440) </td> <td> (12, 720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 7 Tasks </td><td> 6 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"200\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
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       "\n",
       "  <!-- Horizontal lines -->\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-26d783ad-57b8-4ac9-a162-81768285c483' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26d783ad-57b8-4ac9-a162-81768285c483' 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:   (lat: 720, lon: 1440, time: 72)\n",
       "Coordinates:\n",
       "  * lon       (lon) float32 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "  * lat       (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * time      (time) object 2015-05-01 00:00:00 ... 2021-04-01 00:00:00\n",
       "    month     (time) int64 5 6 7 8 9 10 11 12 1 2 3 ... 6 7 8 9 10 11 12 1 2 3 4\n",
       "Data variables:\n",
       "    positive  (time, lat, lon) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "    negative  (time, lat, lon) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "    SSS       (time, lat, lon) float32 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_movie = xr.Dataset({'positive':labels_positive, 'negative':labels_negative, 'SSS':ds.SSS})\n",
    "ds_movie"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382934c7",
   "metadata": {},
   "source": [
    "## Movie time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ac7e9508",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xarrayutils.plotting import map_util_plot\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cmocean.cm as cmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c82f1291",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_plotfunc(ds, fig, tt, framedim='time', **kwargs):\n",
    "    ax = fig.subplots(subplot_kw=dict(projection=ccrs.Robinson(205)))\n",
    "    ds_frame = ds.isel({framedim:tt})\n",
    "    pp0 = ds_frame.SSS.plot(ax=ax, transform=ccrs.PlateCarree(), vmin=31, vmax=38, cmap=cmo.haline)\n",
    "    pp1 = (ds_frame.positive > 0).plot.contour(ax=ax, transform=ccrs.PlateCarree(),levels=[0.5], colors=['r'],  **kwargs)\n",
    "    pp2 = (ds_frame.negative > 0).plot.contour(ax=ax, transform=ccrs.PlateCarree(),levels=[0.5], colors=['b'],  **kwargs)\n",
    "    map_util_plot(ax)\n",
    "    return ax, [pp1, pp2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "855a1487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd2885833a0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RhFChUUgMo/AZBsFYT/DF5qnxSreyqtQ0KOKaQxgJxYu4VZ1Ui9cz4kQ9mKqu9Xfrttla30rDpGlykLSQlPCWeQuw7dW6FlUPRZPS6BjPCbzdhe19p1/UlwQRdy4tbTy1gHYn/m4/TzEyY0jbXrWXgfTcCluw3Lpz2q8XRlY/T7pV13KGXZmTd6+/yFV7K2t1Y0j3Squaw5qtNrT9QPXhhNAbg5AYwsQySNcTymh1zWEuqVmp0ScyqYfUECaMfjYt1fYfei/VWMZ3Yk+9so5Os9JGvQwkhG6r4uQ63gRRH0MnF70n2vWHqpuROn2orcr9p+4zhis5tI1Wybsk/Xuenypph+6HFkKYqAbhRpFqL1CnmkOZaVCUieRo4MXAO/L8QuA7XYsohC7o9Tfufadf1PE+J0qtoPYEcqMxHKp4ynlQDdsT0mWec9jR9raS/gJg+++5R9QQBkYv7/5p17RUPMlPlBN+WfUJoZc9IvdzwKCRulup4ClJk8jPPEiaAizualQhdCBOxoOv3yO5jWX/lT9gN0BNRmWUSQ7fAk4D1pZ0GPAW4ItdjSqEAdbN2km/7oiqWr+TwVh1qxZT9RjSvdA2Odg+QdJc0ngJAt5o+9quRxZCnWGoFQzCHVH9NqyJoZsMPD1kNYcydyu9CLjT9nds/w8wX9KO3Q+tZUyTJP1F0hl5fk1J50q6If9co5/xhTBew5AIR023r32M4t1K3wUeKcw/mpf10yeBYu3lIOB829OB8/N8CEOt1TMRgypqDU2U7JG1bNOTpNUl/ULSdZKulfTiqkMuc81Btpd0wGd7saS+9eYqaQPgdcBhwAF58V7Azvn1scBs4HO9ji2Eqg1DQqiJxNBcbbCfCh0FnGX7Lfnu0WdXWTiUqzncLOkTklbI0yeBm6sOpAPfBD7L0ndMrWN7AUD+uXajDSXNlDRH0pz77ruv64GGMFEMcmIYlNiqqjlIWg3YCfghgO0nbT9YdbxlksOHgZcAdwLzgR2BmVUHUoakPYF7bc8dy/a2Z9meYXvGlClTKo4uhNCpUX3grV7Fg/1sDNwH/Chfe/2BpFWqjrltcrB9r+19bK9tex3b77B9b9WBlPRS4A2SbgVOBF4l6XjgHknrAeSf/YovTADD1NTTC4PyzbyRQYnNiKcXL1dqAtaqtXDkqf7L+PLAtsB3bW9Dug5c+XXWwbk0XoLtg21vYHsasA/wO9vvAk4H9s2r7Qv8qk8hhgliLN1jhKUNWq2h+4P9lO4+4/5aC0eeZtUVNR+Yb/uSPP8LUrKo1FAlhxYOB3aVdAOwa54PIYTB4OqalWzfDdwhadO86NXANVWHPLRjSNueTborCdt/Ix2gEHoqag9jM2i1hm6rXXOo0MeBE/KdSjcD76uycGg9EtwBzd4DsP2NqoMJIQyXfo2PMIyqTA62LwdmVFZgA61qDrUxpDcFtie16wO8Hriwm0GFEEbLRE8MRixaPFyt+K1GgjsUQNI5wLa2F+b5Q4CTexJdCGFgla019DMxdDL6W9e7zxigsRrKKHPNYSrwZGH+SWBaV6IJIYyMiV5bKLIrv+bQdWWSw3HApZJOI11XeRPwk65GFUIYWoOWFOrj6dezDx615GD7MEm/BV6eF73P9l+arS9pzRL7XdyNx71DCKGdRk1N3U9oIzSeg6TVbD+cT/a35qn23pq2H2iy6V15anUkJpGaq0IYOa+78BNLzZ+507f6FEkYJKNUc/gpsCcwlzxEaKY8v3GT7a7Nj3Q3VRuPOoRRU58YwmDqddOXDYsWj0hysL2nJAGvsH17B2WW6Ve88r7HQ+iHMsngdRd+ImoPYbTuVrLtfCF6u7IF2v5H7bWkScA6xf3Yvr24TgjDYjy1gkbbjmrCWHz3JgN3UbrfzPA1K5V5KuNiSdt3WrCkjwP3AOcCZ+bpjE7LCWEQdKO5aNiboCIBdKLakeB6oUxyeCXwJ0k3SZon6UpJ80ps90lgU9ub2/6XPG05vnBDGC3DniCaGZSusgeJXW4aFGWec3jNGMu+A3hojNuGMDC6fQKPaxKjz4bFo9J9RsHCksuApTrsuxmYLelM4Ina+9FhXxgmo/rNviqddE8x0Q1Sk1EZZZLDZcCGwN9Jt7GuDiyQdC/woQZDdtY67Ls9TyvmCZa+JTaEMAIiQZQzSE1GZZRJDmcBp9k+G0DSbsAewEnA0aQxpZcodNj3VttLddAn6a3jCVbShqSuO9YFFgOzbB+VH9T7OanPp1uBvW3/fTz7CiGEKo3i3UozaokBwPY5wE62LwZWarHdwSWXdeJp4NO2/xl4EbCfpBeSxk893/Z04Hy6MJ5qCKG5uHOpNSPsctOgKFNzeEDS54AT8/zbgL/nZxgW168s6TXAa4H1JRWvsq1GOrmPme0FwIL8eqGka4H1gb2AnfNqx5JGiPvcePYVAqRnEbp93WEUL0ZHsljWkLUqlUoO7wC+DPwyz/9fXjYJ2LvB+ncBc4A3kLreqFkIfGqsgdaTNA3YBrgEWCcnDmwvkLR2k21mAjMBpk6Nrp1CNcaaQEYtKURCaMHgUek+o8b2/aTxShu5scH6V0i6CtjN9rHjjK8hSZOBU4D9c+eApbazPQuYBTBjxoxhS+ShT8qcxDtNEKOWGEJ7g9RkVEZXbry1vQh4bh78ulKSViAlhhNsn5oX3yNpvfz+esC9Ve83hHbO3OlbcdIPTY3iQ3BjdRvwR0mnA4/WFo7nOYfcEeAPST2/Fss5HdgXODz//NVY9xHCeNUSRHTdHWqGsW+lVuM5HGH7c41uSS2pNq7Dcjzz7MN4vRR4N3ClpMvzss+TksJJkj5AerZiXLfMhlCFSAZhCQOjkhyA10r6Iun2046TQ+F5h1XTrB8ZW4hLlfl/NB9E6NXjLT+EELqlyiYjSbeSbvJZBDxte0Z1pSetksNZwP3AKpIe5plBfkQ62a/WqmBJW5DGn14zz98PvMf21VUEHkIIw0PduFvplfmGoa5oekHa9mdsPwc40/Zqtlct/ixR9izgANvPt/184NPA/1YUdwghDBeXnCqm5F2SvpTnp0raod12be9Wsr2XpHUk7ZmnKSVjWsX2BYVyZgOrlNw2hBBGh6n6CWkD50iam5/fauVo0uibb8/zC4HvtNtB2+SQ+0O6lHSRd2/gUklvabcdcLOkf5c0LU9fBG4psV0IIYye8jWHtSTNKUyNTv4vtb0taUiF/STt1GLPO9reD/gHQO53ru1jBmVuZf0isL3tewFyzeE84Bdttns/cChwKuk6xYXA+0rsL4QQRlDpWsH97S4w274r/7w3D+W8A+kc28hTubsjw5Jz+DJdH9UrkxyWqyWG7G+Ua476OxCd4YcQApQ4HZcjaRXSeXlhfr0b8JUWm3wLOA1YW9JhwFtIX/pbKtVlt6SzgZ/l+bcBv2m3kaRNgANJ3Wgv2Y/tV5XYZwghjI5qn3NYBzgtdxu0PPBT22c13bV9gqS5pNv9BbzR9rXtdlKmb6XPSPpX4GW54Fm2TyvxAU4Gvgf8gHQvbgghTFhVPedg+2Zgq7LrSzoK+Lntthehi0p1n5H7MDq17YpLe9r2dzvcJoQQRlP/+k26DPhibs05jZQo5rTbqJsjXv9a0kclrSdpzdrUxf2FEMLgsspNVe/WPtb2a0kXrf8KHCHphnbbdbPjvX3zz88UlhnYuIv7DCGEgaT+97j6T8BmpOvA17RbuVRykPQsYKrt68tGYXujNmXuavvcsuWFEMLQsqBPg/1IOgL4V+Am4CTgP2w/2G67Mg/BvR64nNTXEpK2zt1wj9cRFZQRQgjDoU/dZ5AePn6x7T1sH1MmMUC5msMhpLaq2QC2L89DdI7XcPVfG0II49HjZiVJm9m+jtTDxVRJS42NbPuyVtuXSQ5P236o7FCcHeh/C1wIIfRK7894BwAzgf9q8J6Bls+clUkOV0l6BzBJ0nTSU88XdRplt0naAzgKmAT8wPbhfQ4phBCSPgz2Y7vWJ9NrbP+j+J6kldttX+ZW1o8DmwNPkJ6SfhjYv7MwG7q1gjIAyP2GfIfUCdULgbdLemFV5YcQwnjJ5aYuaPRlvu0X/DJPSD8GfCFPpeXeXM/K/X98EdgW+M9aO5ftf+2kvDZ2AG7MTw4i6URgL0rcrhVCCD3R+2sO6wLrA8+StA3PXOddDXh2u+1bjSH9a1p8HNtvaFP2v9s+WdLLgN2BI4HvAju2C2oM1gfuKMzP79J+QghhTPrwnMPuwHuBDYBvFJYvBD7fbuNWNYcj889/BdYFjs/zb6dck1CtP6XXAd+1/StJh5TYbiwaNeYt86vI/aLPBJg6deoyG4QQQtf0/prDscCxkt5s+5ROt2+aHGz/HkDSf9guDiTxa0nN+g0vulPS94FdSI9rr0T3uuuYD2xYmN8AuKt+JduzSMOXMmPGjLhbKoTQG917hqEpSe+yfTwwTdIBy4Rkf6PBZkuUOVlPkbSkywtJGwFlhgrdGzgb2CM/dLEmS3elUaU/A9MlbSRpRWAfoIoH9UIIoRq9fwiuNizzZGDVBlNLZW5l/RQwW9LNeX4a8G/tNrL9mKR7SV193wA8nX9WzvbTkj5GSkaTgGNsX92NfYUQwlioosF+yrL9/fzz0LFsX+ZupbPy8w2b5UXX2X6i3XaSvgzMADYFfgSsQLpu8dKxBFoizt9QYhCiEELoiz41ZEv6GvCfwOOkbpC2AvbPTU5NtU0Okt5Tt2grSdj+SZtN3wRsQ+pLHNt3SWpblQkhhFHTxWcYytjN9mclvYl0ffatwAU8c5NRQ2WalbYvvF6ZNNTcZUC75PCkbUuqDWq9Spv1QwhhdPX4bqWCFfLP1wI/s/1Ame6QyjQrfbw4L+k5wHElAjop3620uqQPAe8H/rfEdiGEMHr6V3P4taTrSM1KH5U0BfhHm23GNNjPY8D0divZPlLSrqTuNjYFvhTjN4QQJqp+NSvZPiiP6fCw7UWSHiX1INFSmWsOxSellyP1XXRyybj+mmLzeZKeLWlV2wtLbhtCCKPBvb9bqUbSCsC7gZ1yc9Lvge+1265MzeHIwuungdtszy8R0IdITyOvCbyA1MXF90jXLEIIYWLpX7PSd0nXHY7O8+/Oyz7YaqMyyeG1tj9XXCDpiPplDexH6hDvEgDbN0hau8T+Qghh9FScHHJv1HOAO23v2WLV7W1vVZj/naQr2pVf5gnpXRsse02J7Z6w/WRtRtLyxAA/IYQJqgtddn8SuLbEeoskvWBJHKnHi0Ut1gda98r6EeCjwMaS5hXeWhX4Y4mAfi/p86TuYnfNZf26xHYhhBBakLQBqVPTw0gjvrXyGeCCul4u3tduH62alX4K/Bb4KnBQYflC2w+0Kxj4HKlN60pSdxu/AX5QYrsQQhg91babfBP4LCX6SCJ9mf8+z1zv/T7wp3YbtUoOtn2rpP3q35C0ZqsEIWk5YJ7tLYhnG0IIE11ndyutJWlOYX5W7lEaAEl7Avfanitp5xLl/YT0SMF/5Pm3k55Ve2urjdrVHPYE5pJyXvGROgMbN9oIwPZiSVdImmr79vaxhxDCiCtfc7jf9owW778UeIOk15J6rVhN0vG239Vk/U3rLkhfUOaCdKvxHPbMPzdqV0gT6wFXS7oUeLRQbrsR5EIIYaSI6h6Cs30wcDBArjkc2CIxAPxF0otsX5y32ZES143LPAR3vu1Xt1vWwJi6iQ0hhJHUv3s1dwTeI6nWijMVuFbSlaTLB1s22qjV3UorkwahXkvSGiw9OPXz2kVTG0kuhBAmvC71ymp7NjC7zWp7jKXsVjWHfwP2JyWCuTyTHB4GvtOuYEkLWTZXPkR6aOPTtm9edquW5X0deD3wJHAT8L48whySDgY+QLp39xO2z+6k7BBC6Lo+dZ9h+7axbNfqmsNRwFGSPm7722Mo+xukcZx/Skos+wDrAtcDxwA7d1jeucDBedS3I0htbp+T9MJc9uakRHaepE1st33Io960nxy+1Pyt7zmoyZohhNCZPo7nMCZluuz+tqQtSB3urVxY3m48hz1s71iYnyXpYttfyQ/HdcT2OYXZi4G35Nd7ASfm0elukXQjqduOtvfxhhBCzwxZcmjbfUYe7vPbeXol8DWgzB1HiyXtLWm5PO1deG+8h+n9pAf0IHXod0fhvfl52bjV1yRCCGFM3ME0IMr0rfQW0pN1d9t+H2n80ZVKbPdOUu9/9wL35NfvkvQs4GONNpB0nqSrGkx7Fdb5Aql32BNqixoU1fAQS5opaY6kOffdd99S70UiCCF0Uxf6VuqqMr2yPp4fanta0mqkk33TB+Bq8gXn1zd5+/+abLNLqzIl7Ut6MO/VtmuHcT6wYWG1DUjXOhqVPwuYBTBjxowlv4ZIDCGErhugE38ZZWoOcyStTuoGYy5p/OhL220kaRNJ50u6Ks9vKemLYw1U0h6k/preYPuxwlunA/tIWknSRqRR6trGV1YkjhBCFbS43DQo2iYH2x+1/aDt75G67943Ny+187+kO4qeyuXMI91VNFb/Q+pk6lxJl0v6Xi73auAk4BrgLGC/sdypFEIIXTOE1xxaPQS3bav3bF/Wpuxn2740D0tX83SH8S1h+59avHcYqevaEEIYOKLxxdFB1uqaw3+1eM/Aq9qUfX8eYMIAkt4CLOgsvOEXz06EEICBqhWU0eohuFeOs+z9SBd/N5N0J3AL0KpzqAlh2k8OjwQRwgQ0SHcilVHmgvSY2L453300BdjM9sts39qt/Y1Ht07WcTE7hLDE4pLTgChzK2tHJDUcsq527cH2N6reZ7fFt/0QwrgM2DMMZbRMDkpn9A1s39FqvTq1Yes2BbYn3WoK6ZmHCzuOcEhFrSGEsJQhSw4tm5Xyg2a/7KRA24faPhRYC9jW9qdtfxrYjvSA2kCqsmYQiSGEUG/YnpAuc83hYknbj6HsqaTutWueBKaNoZyeaZUg4oQfQhiXUXnOoeCVwIcl3Uoa7lO0GD2o4DjgUkmnkT7ym4BjxxHrUGiXROLaRQgT0yDVCsookxxeM5aCbR8m6SzgZXnR+2z/ZSxlhdBtmx3y30vNX3fIp/oUSRhJZqDuRCqjzHgOt0l6GTDd9o8kTQEmN1tf0mW2t83bziX1x9R0nVEykZqe6k+m0P6E2mibMtt1QzGWSASh28Tw1RzKjufwOVI/SQArAMe32OSfJc1rMV1Julg9dFqd/CdSYmim2cl/PO91Q/3+Gu2/1zENq12Xe+uSKbQxgtcc3gRsQ+qNFdt3SVq1xfqblShz6DvGm8jJoOyJs5MT7GaH/HdH3+A7/eYfJ/tqRBIYO3mAzvwllEkOT9q2pFofSau0Wnmsg1kPilvfc1DUEMahHyfhThNLv8sdRpEUxmnAagVllLmV9SRJ3wdWl/Qh4DzgB90Nq7/ijqL+6EaNJIRBMXLPOdg+EvgFcArpqecv2f5WtwPrt+hvqT82O+S/4+QfRlJVg/1IWlnSpZKukHS1pEO7EW+ZC9JH2D7X9mdsH2j7XElHdCOYMiQdKMmS1iosO1jSjZKul7R7v2KbKKKpJYQxqO6C9BPAq2xvBWwN7CHpRVWHW6ZZadcGy8b07MN4SdqQFM/thWUvJI0wtzmwB3C0pEn9iK+sqD2016z2UHWtolWiiyQYKlOySalMs5KTR/LsCnmqvEGq1UhwHwE+CmwsaV7hrVWBP1YdSEn/DXwW+FVh2V7AibafAG6RdCOwA/CnPsQXKhTNS2GklD99ryVpTmF+lu1ZxRXyF+C5wD8B37F9SSUxFrS6W+mnwG+BrwLFBviFth+oOpB2JL0BuNP2FXVDj64PXFyYn5+XhQCMLclEraE76u96OnfxyX2KpLc6fAjuftszWq1gexGwtaTVgdMkbWH7qnEFWafVSHAPAQ8Bb89Zap28/mRJk23f3mzbsZJ0HrBug7e+AHwe2K3RZg2WNfw1SJoJzASYOnXqGKMMoyRqJ+1VdRtro3J2Xe6tEydBLK7+ViTbD0qaTWpS701yqJH0MeAQ4B6e6R3EQLuO9zqWR45rFMO/ABsBtVrDBsBlknYg1RQ2LKy+AXBXk/JnkYYuZcaMGQN001joljj5h4FQ4XMOuQujp3JieBawC1D5TUJlLkjvD2xqe3Pb/5KnyhNDK7avtL227Wm2p5ESwra27yYNJrSPpJUkbQRMBy7tZXzt1N8WG89RdO66Qz7VcVNPq8QQzUatVd0tRjxEV92trMB6wAX5WvCfgXNtn1F1vGWekL6D1Lw0kGxfLekk4BrgaWC/3B43brWTeBXdcI9aQrjukE/19Ft5lftqF3skjmpFYsgqqjnYnkfq0qiryiSHm4HZks4k3V8L9Hcs6Fx7KM4fBhzWrf2N2ol91HRyMu91UhtGYzmZT5TrBuMxSE8/l1EmOdyepxXzFHpox3cvnYMvOe6APkWyrPoT7aCfeMskkag1hK4wMGod7+XxoJG0iu1Hux9SGCb1J9P6+UFJFrW4BiWeiSKalJ5R8nrCwCjTfcaLJV0DXJvnt5J0dNcjCyOh3TfxXn1Tj6QQ+qn2nMMwdbxXplnpm8DupLuCyA+h7dTNoCaq+iakiaJbzVH15UaCKOfcxSf39Bv/hHjWwR66ZqUyt7Ji+466RUM/WM+wGsYE0qx2UFxeu1W16ppE2esM3dj3MOvkZF3FiX0iND8NW82hTHK4Q9JLAEtaUdKB5Cam0B87vvsbS6Zh0c8Tb5z4w0AYsmFCyySHDwP7kformk/qIna/LsYUOjCsCWJQTtaDEsegqeKbfKdljHrtYdhqDmXuVrofeGcPYgkTQNlmnnhIbXg0umYw1hN9bbuRuwZhYNEAnflLKHO30rG557/a/BqSjulqVKEjw1R7KKvMdYoQhsnI1RyALW0/WJux/XdJXX90O3SmliAG6SG58aoyEbQaPCgSzuAZyTuYRvBupeUkrVGbkbQm5ZJKqMglxx1Q+qQ/irWI0HudnphH/XpBFYat5lAmOfwXcJGk/5D0FeAi4GvdDSvUjFJNIEwcI/etf7zK3qk0TMnB9k+AN5PGc7gP+Ffbx3U7sImoPhFEYgjDZKKO8laGAC1yqWlQlGoesn0NqUvsME67b/flpebPnnvoUvOtEsIlxx0QzUZhoEXzUnMasmsOce2gh+oTQ23Z2XMPbZs0Qui1XnejUb/vkTJgTUZllOo+Y1BI+rik6yVdLelrheUHS7oxv7d7P2NsplFiKPNeqEbckTQ2I3eS7hs/079Su2lADE1ykPRKYC/SrbWbA0fm5S8E9gE2Jw2yfbSkSX0LtCKRMKoXCWJsIkFUYxTvVhoUHwEOt/0EgO178/K9gBNtP2H7FuBGYIc+xVip3bf7csdJIi5it9Zu/InQWC8TxMgmoyGrOQzTNYdNgJdLOgz4B3Cg7T+T+ny6uLDe/LxsJDW6KB0JoTOREELPmYG6E6mMgUoOks4D1m3w1hdIsa4BvAjYHjhJ0saku8TqNfwtSJoJzASYOnVqFSH3RSSDMEpGtqZQb7hyw2A1K9nexfYWDaZfkWoEpzq5FFgMrJWXb1goZgPgriblz7I9w/aMKVOmdPvjLCXuPgphYpNdahoUA5Uc2vgl8CoASZsAKwL3k0ao20fSSpI2AqYDl/YryFbOnntoJIkQCiZMrQEqu+YgaUNJF0i6Nt+5+cluhDtQzUptHAMcI+kq4ElgX9sGrpZ0EukhvaeB/WzHSHUhVKgbzztMrMRAauuoxtPAp21fJmlVYK6kc/PDypUZmuRg+0ngXU3eOww4rLcRhRA6NaESQoGorsnI9gJgQX69UNK1pJtwJmZymGii+SkMm0ZPVE/UZNDQ4tJVh7UkzSnMz7I9q9GKkqYB2wCXjC+4ZUVyGCCREMKwqiWBSAZNdNasdL/tGe1WkjQZOAXY3/bDYw+usUgOfRBJIAybVv0sRUIop8o7kSStQEoMJ9g+tbKCCyI5hBBCL1SUHCQJ+CFwre2uddMcySGEUErUEMaj0q4xXgq8G7hS0uV52edt/6aqHUAkhxBC6D4DFXWfYfv/aNwzRKUiOYQQQg8M0tPPZURyCCGEXojkEEIIYSkGFkdyCCGEsJTBGquhjEgOIYTQC5EcQgghLMXAoup63uuFSA4hhNB1BkdyCCGEUC+alUIIISwl7lYKIYTQ0JDVHIZmmFBJW0u6WNLlkuZI2qHw3sGSbpR0vaTd+xlnCCE0VNEwob0yTDWHrwGH2v6tpNfm+Z0lvRDYB9gceB5wnqRNYqjQEMLAsGHRcJ2ShqbmQGq1Wy2/fg5wV369F3Ci7Sds3wLcCOzQYPsQQuifqDl0zf7A2ZKOJCW1l+Tl6wMXF9abn5ctQ9JMYCbA1KlTuxZoCCEsY4BO/GUMVHKQdB6wboO3vgC8GviU7VMk7U0a7GIXGndd2/C3kMdhnQUwY8aM4fpNhRCGmONupfGwvUuz9yT9BPhknj0Z+EF+PR/YsLDqBjzT5BRCCP1n8JA9BDdM1xzuAl6RX78KuCG/Ph3YR9JKkjYCpgOX9iG+EEJobtHictOAGKiaQxsfAo6StDzwD/K1A9tXSzoJuAZ4Gtgv7lQKIQwUGxYPzom/jKFJDnlovO2avHcYcFhvIwohhA7EBekQQgj1HDWHEEIISxusZxjKiOQQQgjdNoQd7w3T3UohhDCUDHjRolJTO5KOkXSvpKu6GXMkhxBC6DbnwX7KTO39GNijuwFHs1IIIfSEK2pWsn2hpGmVFNaCPGQXSaoiaSFwfb/jqLMWcH+/g6gTMZU3iHFFTOW0i+n5tqeMtXBJZ+V9lLEy6Vmumlm5659iedOAM2xvMdaY2pnINYfrbc/odxBFkuZETO0NYkwwmHFFTOV0OybbXW8GqlpccwghhLCMSA4hhBCWMZGTw6z2q/RcxFTOIMYEgxlXxFTOIMbUkKSfAX8CNpU0X9IHurKfiXpBOoQQQnMTueYQQgihiUgOIYQQljHhkoOkPSRdL+lGSQf1MY5bJV0p6XJJc/KyNSWdK+mG/HONHsSxzKP4reKQdHA+dtdL2r2HMR0i6c58vC6X9Noex7ShpAskXSvpakmfzMv7dqxaxNS3YyVpZUmXSroix3RoXt7P49Qspr7+TQ082xNmAiYBNwEbAysCVwAv7FMstwJr1S37GnBQfn0QcEQP4tgJ2Ba4ql0cwAvzMVsJ2Cgfy0k9iukQ4MAG6/YqpvWAbfPrVYG/5n337Vi1iKlvx4o0pvvk/HoF4BLgRX0+Ts1i6uvf1KBPE63msANwo+2bbT8JnAjs1eeYivYCjs2vjwXe2O0d2r4QeKBkHHsBJ9p+wvYtwI2kY9qLmJrpVUwLbF+WXy8ErgXWp4/HqkVMzfQiJtt+JM+ukCfT3+PULKZmevI3NegmWnJYH7ijMD+f1v9M3WTgHElzJc3My9axvQDSPz6wdp9iaxZHv4/fxyTNy81OtWaJnseUuy7YhvQNdCCOVV1M0MdjJWmSpMuBe4Fzbff9ODWJCQbkb2oQTbTkoAbL+nUv70ttbwu8BthP0k59iqMT/Tx+3wVeAGwNLAD+qx8xSZoMnALsb/vhVqs2WNaVuBrE1NdjZXuR7a2BDYAdJLXq/6efMQ3E39SgmmjJYT6wYWF+A+CufgRi+678817gNFK19R5J6wHkn/f2I7YWcfTt+Nm+J/+DLwb+l2eq+T2LSdIKpJPwCbZPzYv7eqwaxTQIxyrH8SAwm9S99ED8TRVjGpTjNKgmWnL4MzBd0kaSVgT2AU7vdRCSVpG0au01sBtwVY5l37zavsCveh1b1iyO04F9JK0kaSNgOnBpLwKqnViyN5GOV89ikiTgh8C1tr9ReKtvx6pZTP08VpKmSFo9v34WsAtwHf09Tg1j6vff1MDr9xXxXk/Aa0l3ddwEfKFPMWxMuhviCuDqWhzAc4HzgRvyzzV7EMvPSFXqp0jfmD7QKg7gC/nYXQ+8pocxHQdcCcwj/fOu1+OYXkZqWpgHXJ6n1/bzWLWIqW/HCtgS+Eve91XAl9r9bfcxpr7+TQ36FN1nhBBCWMZEa1YKIYRQQiSHEEIIy4jkEEIIYRmRHEIIISwjkkMIIYRlRHII4yJpdUkfraCc2ZIqHeBd0vMk/SK/3rrY62YHZRwi6cAKYllJ0nm598+3jbGMJZ+nwXvjPn6Spin3hCtpZ0lnjKe8MNwiOYTxWh3oKDko6frfnu27bL8lz25NegagX7YBVrC9te2fd7qxpOXrPk8IXRXJYQKT9J7c6dgVko7Ly6ZIOkXSn/P00rz8kNw52WxJN0v6RC7mcOAF+Rvx1/O6n8nbziv0nT9NadyBo4HLWLp7gvq43q401sVVko4oLH9E0mE53oslrZOXvyDP/1nSVyQ9UtjnVflp+K8Ab6t9c6+vEeT1puXXX1Dqx/88YNPCOi+QdJZSZ4l/kLRZg9jXlPTL/NkvlrSlpLWB44Gt8/5fULfN9nn9P0n6euHb+3slnSzp16ROGovf7J8l6cS83c+BZzU5lttLuigfs0slrarUCd3XC7+jf2v2uwgTWL+fwoupPxOwOenpz7Xy/Jr550+Bl+XXU0ldM0Dq+/4iUh/3awF/I3V9PI2lx13YjTRYu0hfPs4gjc8wDVgMvKhJPLOBGcDzgNuBKcDywO+AN+Z1DLw+v/4a8MX8+gzg7fn1h4FH8uslsQHvBf6nsL9DKPTlT3pydhqwHemp2WcDq5G6az4wr3M+MD2/3hH4XYPP8W3gy/n1q4DL8+udgTOafPargJfk14fXxTy/8Lspfp4DgGPy6y2Bp4EZdeWuCNwMbJ/nV8vHdGbh2K0EzCGNW1Asv2m8MU2MaXnCRPUq4Be27wewXRs/YRfghdKSjilXU+4HCjjT9hPAE5LuBdZpUO5uefpLnp9M6pvmduA22xe3iWt7YLbt+wAknUBKLr8EniQlAoC5wK759Yt5ZnyAnwJHttlHKy8HTrP9WN7/6fnnZOAlwMmFY7NSg+1fBrwZwPbvJD1X0nOa7Uypz59VbV9UiH/PwirnFn43RTsB38r7mSdpXoN1NgUW2P5zXu/hvM/dgC0l1ZqonkP6Hf21WZxh4onkMHGJxt0QLwe82PbjS62cTohPFBYtovHfj4Cv2v5+3fbTgEdLxtXMU7ZrMTfbf1lPs3Sz6sqF182Oy4NO3T630ml3z60+L7Q+Zu36vmn2OxbwcdtnL7UwN6uFAHHNYSI7H9hb0nMhtZXn5ecAH6utJGnrNuUsJA1RWXM28P78TRtJ6+c297IuAV4haS1Jk4C3A79vs83F5G/rpJ52y8R5K2koUiRtS2pWAbgQeFNu018VeD0s+dZ9i6S35m0kaasG+7kQeGdeZ2fgfrcY98H234GFkl7UJv5W+9mC1LRU7zrgeZK2z+utKml50u/oI0rdfSNpE6XegUNYIpLDBGX7auAw4PeSrgBqXT5/ApiRL1ReQ2rDb1XO34A/5gu6X7d9Dqlp5E+SrgR+wdIn5XZxLQAOBi4g9Vp7me12XZfvDxwg6VLSuMoPNVjnAlJzWe1W0lOANZVGB/sIuUnFadjNn5N6OD0F+EOhjHcCH8jH62oaDzF7CPn4ka4f7NtgnXofAGZJ+hPpW32j+Ot9F5ic9/NZGnQp7TQU7tuAb+eYzyXVkH4AXANcli9wf59oRQh1olfWMPQkPRt43LYl7UO6OD1IY4O3JGmy8xjHkg4idR39yT6HFSa4+LYQRsF2wP8oXRh5EHh/f8Pp2OskHUz6f7yNdJdSCH0VNYcQQgjLiGsOIYQQlhHJIfRNfuL38XxRuP69Svo0Ggaq6/cpP8F9o6Jvo9BHkRxCv91U4tmBMcu3ww66rSn0++TU99IH+xZNCERyCAOk0z6N1LxPpZ0lXSDpp8CVrfoSUuN+oFaRdGbuj+gqtehFVdJ2kn6fYztb0np5+YdyuVco9VX17Lz8rbnMKyRdqAb9PlV/ZEPoXNytFAaCpO1ID4BtQ/q7vIzURQakvpo+bPsGSTsCR5O6/zgKOMr2zyTVP4+xA7CF7VskzQQesr29pJVIz2WcQ+oyYnpeV8DpknYi9et0l+3X5dgadn+RHyL7NrCX7fvyif0w0t1Sp9r+37zef5KeZfg28CVgd9t3Slrd9pOSvkTqF+ljjfYTQj9EcgiDYix9GrXqU+lS27fk1836EmrWD9QfgCOVeoQ9w3bxQbiiTYEtgHNzbJOABfm9LXJSWD2XW+uq4o/AjyWdBJza8oiE0EeRHMIgGU+fRvWKfRI160todxr0A5Xf2450HeCrks6x/ZUG+xBwte0XN3jvx6TeZK+Q9F5SL6fY/nCu/bwOuFztuycJoS/imkMYFGPp06hMn0rQvC+hhv1ASXoe8Jjt40m1kW2blHs9MEXSi/P2K0jaPL+3KrAg7/OdtQ0kvcD2Jba/BNxPGteivt+nEPouag5hINi+TGnQmstJTwnX92n0XUlfJI0hcSKp36X9geMlfRo4k+Z9Ev2ANFbBZfkp6vtI3+rPkfTPpH6gAB4B3gX8E/B1SYuBp0h9LzWK+cncVPWtfF1ieeCbpH6X/p3UieBtpPEhaif/r0uaTqp1nJ8/x+3AQfmW3q96DCPFhVC1eEI69I1SF9Fn2N5ijNsPdZ9KrSj16Hqg7T3brBpCV0SzUuinRcBzGj0EV9J2pHb7eaRxrD9dVWD9lO96Ohr4e79jCRNX1BxCKEHSaTwz5kPN5+ovcocwKiI5hBBCWEY0K4UQQlhGJIcQQgjLiOQQQghhGZEcQgghLCOSQwghhGX8f67JHaz/WuiPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_movie.positive.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b9b5fa7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<GeoAxesSubplot:title={'center':'time = 2015-08-01 00:00:00, month = 8'}, xlabel='center longitude of grid cell\\n[degrees_east]', ylabel='center latitude of grid cell\\n[degrees_north]'>,\n",
       " [<cartopy.mpl.contour.GeoContourSet at 0x7fd2883d37f0>,\n",
       "  <cartopy.mpl.contour.GeoContourSet at 0x7fd2883e1b80>])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "custom_plotfunc(ds_movie, fig, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a55ac06b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_movie = ds_movie.chunk({'time':1})\n",
    "ds_movie = ds_movie.resample(time='5D').interpolate(\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3b9df00",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xmovie import Movie\n",
    "from xmovie.presets import _add_land\n",
    "\n",
    "mov = Movie(ds_movie, framedim='time', plotfunc=custom_plotfunc, input_check=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed85919",
   "metadata": {},
   "outputs": [],
   "source": [
    "mov.preview(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e84aa13",
   "metadata": {},
   "outputs": [],
   "source": [
    "mov.preview(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ea34634",
   "metadata": {},
   "outputs": [],
   "source": [
    "mov.preview(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19103b75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ddf232e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5e26bd48ece42aab80d5bfaa8397e28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/116 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1478: UserWarning: No contour levels were found within the data range.\n",
      "  result = matplotlib.axes.Axes.contour(self, *args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie created at ocetrac_sss_smap_aquarius_full_contour.mp4\n"
     ]
    }
   ],
   "source": [
    "mov.save(\n",
    "    'ocetrac_sss_smap_aquarius_full_contour.mp4',\n",
    "    progress=True, \n",
    "    overwrite_existing=True\n",
    "#         progress=False,\n",
    "#         parallel=True,\n",
    "#         parallel_compute_kwargs={\n",
    "#             'scheduler':'processes',\n",
    "#             'num_workers':2\n",
    "#         }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d33b7e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}