accessing_itslive_s3_data.ipynb

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    "# 2. Accessing cloud-hosted ITS_LIVE data \n",
    "\n",
    "This notebook will demonstrate how to access cloud-hosted Inter-mission Time Series of Land Ice Velocity and Elevation ([ITS_LIVE](https://its-live.jpl.nasa.gov/#access)) data from AWS S3 buckets. Here, you will find examples of how to successfully access cloud-hosted data as well as some common errors and issues you may run into along the way, what they mean, and how to resolve them. \n",
    "\n",
    "## Learning goals\n",
    "\n",
    "### Concepts\n",
    "- Query and access cloud-optimized dataset from cloud object storage\n",
    "- Create a vector data object representing the footprint of a raster dataset\n",
    "- Preliminary visualization of data extent\n",
    "  \n",
    "### Techniques\n",
    "- Use [Xarray](https://xarray.dev/) to open [Zarr](https://zarr.readthedocs.io/en/stable/) datacube stored in [AWS S3 bucket](https://aws.amazon.com/s3/)\n",
    "- Interactive data visualization with [hvplot](https://hvplot.holoviz.org/)\n",
    "- Create [Geopandas](https://geopandas.org/en/stable/) `geodataframe` from Xarray `xr.Dataset` object\n",
    "\n",
    "### Other useful resources\n",
    "These are resources that contain additional examples and discussion of the content in this notebook and more.\n",
    "- [Xarray user guide](https://docs.xarray.dev/en/stable/user-guide/index.html)\n",
    "- [How do I...](https://docs.xarray.dev/en/stable/howdoi.html) this is **very** helpful!\n",
    "- [Xarray High-level computational patterns](https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns.html) discussion of concepts and associated code examples\n",
    "- [Parallel computing with dask](https://tutorial.xarray.dev/intermediate/xarray_and_dask.html) Xarray tutorial demonstrating wrapping of dask arrays\n",
    "\n",
    "\n",
    "```{note}\n",
    "This tutorial was updated Fall 2023 to reflect changes to ITS_LIVE data urls and various software libraries\n",
    "```\n",
    "\n",
    "## Software + Setup"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "%load_ext watermark"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Minimal\n"
     ]
    }
   ],
   "source": [
    "%xmode minimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import hvplot.pandas\n",
    "import hvplot.xarray\n",
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import rioxarray as rxr\n",
    "import s3fs\n",
    "from shapely.geometry import Point, Polygon\n",
    "import xarray as xr\n",
    "\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2024-02-15T16:18:56.585510-07:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.3\n",
      "IPython version      : 8.13.2\n",
      "\n",
      "Compiler    : GCC 11.3.0\n",
      "OS          : Linux\n",
      "Release     : 5.19.0-76051900-generic\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy    : 1.24.3\n",
      "xarray   : 2023.5.0\n",
      "s3fs     : 2023.5.0\n",
      "geopandas: 0.13.0\n",
      "hvplot   : 0.8.4\n",
      "rioxarray: 0.14.1\n",
      "json     : 2.0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark --iversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ITS_LIVE data cube catalog\n",
    "\n",
    "The ITS_LIVE project details a number of data access options on their [website](https://its-live.jpl.nasa.gov/#access). Here, we will be accessing ITS_LIVE data in the form of `zarr` data cubes that are stored in **s3 buckets** hosted by Amazon Web Services (AWS). \n",
    "\n",
    "Let's begin by looking at the GeoJSON data cubes catalog. Click this [link](https://its-live-data.s3.amazonaws.com/datacubes/catalog_v02.json) to download the file. This catalog contains spatial information and properties of ITS_LIVE data cubes as well as the URL used to access each cube. Let's take a look at the entry for a single data cube and the information that it contains:\n",
    "\n",
    "![itslive_info](images/screengrab_itslive_catalog_entry.png)\n",
    "\n",
    "The top portion of the picture shows the spatial extent of the data cube in lat/lon units. Below that, we have properties such as the epsg code of the coordinate reference system, the spatial footprint in projected units and the url of the zarr object. \n",
    "\n",
    "Let's take a look at the url more in-depth: \n",
    "\n",
    "![itslive_url](images/itslive_url.png)\n",
    "\n",
    "From this link we can see that we are looking at its_live data located in an s3 bucket hosted by amazon AWS. We cans see that we're looking in the data cube directory and what seems to be version 2. The next bit gives us information about the global location of the cube (N40E080). The actual file name `ITS_LIVE_vel_EPSG32645_G0120_X250000_Y4750000.zarr` tells us that we are looking at ice velocity data (its_live also has elevation data), in the CRS associated with EPSG 32645 (this code indicates UTM zone 45N). X250000_Y4750000 tells us more about the spatial footprint of the datacube within the UTM zone. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing ITS_LIVE data from python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've found the url associated with the tile we want to access, let's try to open the data cube using `xarray`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "tags": [
     "raises-exception",
     "output-scroll"
    ]
   },
   "outputs": [],
   "source": [
    "url1 = 'http://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "syntax error, unexpected WORD_WORD, expecting SCAN_ATTR or SCAN_DATASET or SCAN_ERROR\n",
      "context: <?xml^ version=\"1.0\" encoding=\"UTF-8\"?><Error><Code>PermanentRedirect</Code><Message>The bucket you are attempting to access must be addressed using the specified endpoint. Please send all future requests to this endpoint.</Message><Endpoint>its-live-data.s3-us-west-2.amazonaws.com</Endpoint><Bucket>its-live-data</Bucket><RequestId>B3FTXJHGW2D2SBNY</RequestId><HostId>sxMinJC+hY4DaO9Sbm5zTza7F0AIzCctsPTfMfBuHJCk6E+QEKW2EDTNoCy/MvemdPD6Dtgiai8=</HostId></Error>\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "[Errno -72] NetCDF: Malformed or inaccessible DAP2 DDS or DAP4 DMR response: 'http://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31mKeyError\u001b[0m\u001b[0;31m:\u001b[0m [<class 'netCDF4._netCDF4.Dataset'>, ('http://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '1ba57eab-c3b3-4453-a32b-db656f0cf856']\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOSError\u001b[0m\u001b[0;31m:\u001b[0m [Errno -72] NetCDF: Malformed or inaccessible DAP2 DDS or DAP4 DMR response: 'http://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr'\n"
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    }
   ],
   "source": [
    "dc1 = xr.open_dataset(url1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this doesn't quite work. Passing the url to `xr.open_dataset()` without specifying a backend, xarray will expect a netcdf file. Because we're trying to open a zarr file we need to add an additional argument to `xr.open_dataset()`, shown in the next code cell. You can find more information [here](https://docs.xarray.dev/en/stable/user-guide/io.html#cloud-storage-buckets). In the following cell, the argument `chunks=\"auto\"` is passed, which introduces [dask](https://www.dask.org/) into our workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc1 = xr.open_dataset(url1, engine= 'zarr', chunks=\"auto\")\n",
    "                                   # storage_options = {'anon':True}) <-- as of Fall 2023 this no longer needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                     (mid_date: 25243, y: 833, x: 833)\n",
       "Coordinates:\n",
       "  * mid_date                    (mid_date) datetime64[ns] 2022-06-07T04:21:44...\n",
       "  * x                           (x) float64 7.001e+05 7.003e+05 ... 8e+05\n",
       "  * y                           (y) float64 3.4e+06 3.4e+06 ... 3.3e+06 3.3e+06\n",
       "Data variables: (12/60)\n",
       "    M11                         (mid_date, y, x) float32 dask.array&lt;chunksize=(25243, 30, 30), meta=np.ndarray&gt;\n",
       "    M11_dr_to_vr_factor         (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    M12                         (mid_date, y, x) float32 dask.array&lt;chunksize=(25243, 30, 30), meta=np.ndarray&gt;\n",
       "    M12_dr_to_vr_factor         (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    acquisition_date_img1       (mid_date) datetime64[ns] dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    acquisition_date_img2       (mid_date) datetime64[ns] dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    ...                          ...\n",
       "    vy_error_modeled            (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    vy_error_slow               (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    vy_error_stationary         (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    vy_stable_shift             (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    vy_stable_shift_slow        (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "    vy_stable_shift_stationary  (mid_date) float32 dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;\n",
       "Attributes: (12/19)\n",
       "    Conventions:                CF-1.8\n",
       "    GDAL_AREA_OR_POINT:         Area\n",
       "    author:                     ITS_LIVE, a NASA MEaSUREs project (its-live.j...\n",
       "    autoRIFT_parameter_file:    http://its-live-data.s3.amazonaws.com/autorif...\n",
       "    datacube_software_version:  1.0\n",
       "    date_created:               25-Sep-2023 22:00:23\n",
       "    ...                         ...\n",
       "    s3:                         s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    skipped_granules:           s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    time_standard_img1:         UTC\n",
       "    time_standard_img2:         UTC\n",
       "    title:                      ITS_LIVE datacube of image pair velocities\n",
       "    url:                        https://its-live-data.s3.amazonaws.com/datacu...</pre><div class='xr-wrap' style='display:none'><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-6007efee-6e84-4e7f-a594-cd0c0dc2240e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6007efee-6e84-4e7f-a594-cd0c0dc2240e' 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'>mid_date</span>: 25243</li><li><span class='xr-has-index'>y</span>: 833</li><li><span class='xr-has-index'>x</span>: 833</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-76cae15a-9ce8-4ccf-a962-2d49fcd9f804' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76cae15a-9ce8-4ccf-a962-2d49fcd9f804' 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'>mid_date</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-07T04:21:44.211208960 .....</div><input id='attrs-463d0b0e-f0ed-44bb-8b08-73309cd721c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-463d0b0e-f0ed-44bb-8b08-73309cd721c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1f97c6-a0fc-40d1-b422-ea6aaa2259e5' class='xr-var-data-in' type='checkbox'><label for='data-aa1f97c6-a0fc-40d1-b422-ea6aaa2259e5' 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>description :</span></dt><dd>midpoint of image 1 and image 2 acquisition date and time with granule&#x27;s centroid longitude and latitude as microseconds</dd><dt><span>standard_name :</span></dt><dd>image_pair_center_date_with_time_separation</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-07T04:21:44.211208960&#x27;, &#x27;2018-04-14T04:18:49.171219968&#x27;,\n",
       "       &#x27;2017-02-10T16:15:50.660901120&#x27;, ..., &#x27;2013-05-20T04:08:31.155972096&#x27;,\n",
       "       &#x27;2015-10-17T04:11:05.527512064&#x27;, &#x27;2015-11-10T04:11:15.457366016&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.001e+05 7.003e+05 ... 8e+05</div><input id='attrs-11a7ebff-7861-4092-a2a2-b4ea2ed27554' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11a7ebff-7861-4092-a2a2-b4ea2ed27554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ce31b3b-2abe-4763-aa28-385163fc3986' class='xr-var-data-in' type='checkbox'><label for='data-0ce31b3b-2abe-4763-aa28-385163fc3986' 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>description :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([700132.5, 700252.5, 700372.5, ..., 799732.5, 799852.5, 799972.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.4e+06 3.4e+06 ... 3.3e+06 3.3e+06</div><input id='attrs-3cd0720b-32da-44d7-bd52-7c3e95601e13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cd0720b-32da-44d7-bd52-7c3e95601e13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17ad7e97-10c7-471a-b21a-a14318cf07a1' class='xr-var-data-in' type='checkbox'><label for='data-17ad7e97-10c7-471a-b21a-a14318cf07a1' 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>description :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([3399907.5, 3399787.5, 3399667.5, ..., 3300307.5, 3300187.5, 3300067.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-231f8338-130c-4fc3-b0dc-4145e65b258f' class='xr-section-summary-in' type='checkbox'  ><label for='section-231f8338-130c-4fc3-b0dc-4145e65b258f' class='xr-section-summary' >Data variables: <span>(60)</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>M11</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243, 30, 30), meta=np.ndarray&gt;</div><input id='attrs-1c7f0baf-efac-400c-a457-8cccc6b4a68b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c7f0baf-efac-400c-a457-8cccc6b4a68b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f54facf3-7c7d-40aa-8b6d-6c2ba66e545a' class='xr-var-data-in' type='checkbox'><label for='data-f54facf3-7c7d-40aa-8b6d-6c2ba66e545a' 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>description :</span></dt><dd>conversion matrix element (1st row, 1st column) that can be multiplied with vx to give range pixel displacement dr (see Eq. A18 in https://www.mdpi.com/2072-4292/13/4/749)</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>conversion_matrix_element_11</dd><dt><span>units :</span></dt><dd>pixel/(meter/year)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 65.25 GiB </td>\n",
       "                        <td> 86.66 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>M11_dr_to_vr_factor</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-5bf245da-5c3b-47da-9800-d0dc98959e9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf245da-5c3b-47da-9800-d0dc98959e9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cbc35c7-2f33-464c-98f6-d3db67c93f89' class='xr-var-data-in' type='checkbox'><label for='data-0cbc35c7-2f33-464c-98f6-d3db67c93f89' 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>description :</span></dt><dd>multiplicative factor that converts slant range pixel displacement dr to slant range velocity vr</dd><dt><span>standard_name :</span></dt><dd>M11_dr_to_vr_factor</dd><dt><span>units :</span></dt><dd>meter/(year*pixel)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 98.61 kiB </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 197.21 kiB </td>\n",
       "                        <td> 197.21 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 493.03 kiB </td>\n",
       "                        <td> 493.03 kiB </td>\n",
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       "                        <td> 197.21 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "                        <td> 197.21 kiB </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
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       "                        <td> 98.61 MiB </td>\n",
       "                        <td> 98.61 MiB </td>\n",
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       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U1024 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landice</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(833, 833), meta=np.ndarray&gt;</div><input id='attrs-4bd95c29-befa-46da-b440-7c3214059f51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bd95c29-befa-46da-b440-7c3214059f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dfbc889-e5df-4ca3-9ae4-7c80a11a2325' class='xr-var-data-in' type='checkbox'><label for='data-7dfbc889-e5df-4ca3-9ae4-7c80a11a2325' 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>description :</span></dt><dd>land ice mask, 0 = non-land-ice, 1 = land-ice</dd><dt><span>flag_meanings :</span></dt><dd>non-ice ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>land ice mask</dd><dt><span>url :</span></dt><dd>https://its-live-data.s3.amazonaws.com/autorift_parameters/v001/N46_0120m_landice.tif</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
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       "                        <td> (833, 833) </td>\n",
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       "                    <tr>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mapping</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff9f4f34-a672-4def-96ab-352a95c558ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff9f4f34-a672-4def-96ab-352a95c558ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0864ae22-8b74-4591-b27a-03289e330d8e' class='xr-var-data-in' type='checkbox'><label for='data-0864ae22-8b74-4591-b27a-03289e330d8e' 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>GeoTransform :</span></dt><dd>700072.5 120.0 0 3399967.5 0 -120.0</dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 46N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,93],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32646&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>universal_transverse_mercator</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>93.0</dd><dt><span>proj4text :</span></dt><dd>+proj=utm +zone=46 +datum=WGS84 +units=m +no_defs</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>spatial_epsg :</span></dt><dd>32646</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 46N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,93],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32646&quot;]]</dd><dt><span>utm_zone_number :</span></dt><dd>46.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=&lt;U1]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mission_img1</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-20e54fe6-31dc-43ec-869a-852678027760' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20e54fe6-31dc-43ec-869a-852678027760' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-361352e0-7e8d-4268-b9fa-dd09a801f7ba' class='xr-var-data-in' type='checkbox'><label for='data-361352e0-7e8d-4268-b9fa-dd09a801f7ba' 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>description :</span></dt><dd>id of the mission that acquired image 1</dd><dt><span>standard_name :</span></dt><dd>image1_mission</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mission_img2</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-69318fd0-4bde-4ff5-aaca-0abc0f674b41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69318fd0-4bde-4ff5-aaca-0abc0f674b41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81df666a-e5d4-4ca6-80e2-9ecf848482a6' class='xr-var-data-in' type='checkbox'><label for='data-81df666a-e5d4-4ca6-80e2-9ecf848482a6' 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>description :</span></dt><dd>id of the mission that acquired image 2</dd><dt><span>standard_name :</span></dt><dd>image2_mission</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roi_valid_percentage</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-033e8ef0-3683-48e3-ab1f-b2ffb9fc1bbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-033e8ef0-3683-48e3-ab1f-b2ffb9fc1bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08246668-bb79-49a1-b5ec-7d4e7de6967c' class='xr-var-data-in' type='checkbox'><label for='data-08246668-bb79-49a1-b5ec-7d4e7de6967c' 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>description :</span></dt><dd>percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity</dd><dt><span>standard_name :</span></dt><dd>region_of_interest_valid_pixel_percentage</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>satellite_img1</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-cff36952-62de-4069-b709-e4f9b7130658' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cff36952-62de-4069-b709-e4f9b7130658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74fb2c84-0c49-430a-a2f8-c75d145af4e8' class='xr-var-data-in' type='checkbox'><label for='data-74fb2c84-0c49-430a-a2f8-c75d145af4e8' 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>description :</span></dt><dd>id of the satellite that acquired image 1</dd><dt><span>standard_name :</span></dt><dd>image1_satellite</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 197.21 kiB </td>\n",
       "                        <td> 197.21 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>satellite_img2</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-6aeb4ab3-0d01-4ccd-83be-a95debc1d3c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6aeb4ab3-0d01-4ccd-83be-a95debc1d3c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f8d3546-9036-4c42-8f28-14f12f413406' class='xr-var-data-in' type='checkbox'><label for='data-1f8d3546-9036-4c42-8f28-14f12f413406' 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>description :</span></dt><dd>id of the satellite that acquired image 2</dd><dt><span>standard_name :</span></dt><dd>image2_satellite</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 197.21 kiB </td>\n",
       "                        <td> 197.21 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensor_img1</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-c723595d-55cc-4bc2-aa19-b0b3e0a8bd7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c723595d-55cc-4bc2-aa19-b0b3e0a8bd7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c557c250-32bc-4220-86db-90c365ecb9ce' class='xr-var-data-in' type='checkbox'><label for='data-c557c250-32bc-4220-86db-90c365ecb9ce' 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>description :</span></dt><dd>id of the sensor that acquired image 1</dd><dt><span>standard_name :</span></dt><dd>image1_sensor</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 295.82 kiB </td>\n",
       "                        <td> 295.82 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U3 numpy.ndarray </td>\n",
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       "                </tbody>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensor_img2</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-a4cb6dd9-7d50-4a38-b277-6cb2ffcd704b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4cb6dd9-7d50-4a38-b277-6cb2ffcd704b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f69ab13-cb31-4335-8b29-1a0f10d172cb' class='xr-var-data-in' type='checkbox'><label for='data-3f69ab13-cb31-4335-8b29-1a0f10d172cb' 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>description :</span></dt><dd>id of the sensor that acquired image 2</dd><dt><span>standard_name :</span></dt><dd>image2_sensor</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 295.82 kiB </td>\n",
       "                        <td> 295.82 kiB </td>\n",
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       "                        <td> (25243,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U3 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stable_count_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-ccb9a93a-0393-46b2-a02d-268eec212c5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccb9a93a-0393-46b2-a02d-268eec212c5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4215d943-e3be-4d78-b6d6-7631ef018ccb' class='xr-var-data-in' type='checkbox'><label for='data-4215d943-e3be-4d78-b6d6-7631ef018ccb' 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>description :</span></dt><dd>number of valid pixels over slowest 25% of ice</dd><dt><span>standard_name :</span></dt><dd>stable_count_slow</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 49.30 kiB </td>\n",
       "                        <td> 49.30 kiB </td>\n",
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       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stable_count_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-0bac97a3-0342-43b7-bda2-912d18a27d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bac97a3-0342-43b7-bda2-912d18a27d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c690d2e-213b-439d-8ce3-a04aeade37c2' class='xr-var-data-in' type='checkbox'><label for='data-6c690d2e-213b-439d-8ce3-a04aeade37c2' 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>description :</span></dt><dd>number of valid pixels over stationary or slow-flowing surfaces</dd><dt><span>standard_name :</span></dt><dd>stable_count_stationary</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 49.30 kiB </td>\n",
       "                        <td> 49.30 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stable_shift_flag</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-f367e9f4-c45b-4685-b202-ad13d95e877c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f367e9f4-c45b-4685-b202-ad13d95e877c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f8b494-8ca2-46a9-943d-60807ba28d5d' class='xr-var-data-in' type='checkbox'><label for='data-36f8b494-8ca2-46a9-943d-60807ba28d5d' 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>description :</span></dt><dd>flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity &lt; 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)</dd><dt><span>standard_name :</span></dt><dd>stable_shift_flag</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.65 kiB </td>\n",
       "                        <td> 24.65 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243, 30, 30), meta=np.ndarray&gt;</div><input id='attrs-20cb6ead-ec1b-44b7-ae21-2d906060b20a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20cb6ead-ec1b-44b7-ae21-2d906060b20a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d612c111-84e2-4a02-9263-8bffbe7b7303' class='xr-var-data-in' type='checkbox'><label for='data-d612c111-84e2-4a02-9263-8bffbe7b7303' 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>description :</span></dt><dd>velocity magnitude</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>land_ice_surface_velocity</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 65.25 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 784 chunks in 2 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 65.25 GiB </td>\n",
       "                        <td> 86.66 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243, 30, 30), meta=np.ndarray&gt;</div><input id='attrs-d92c81f9-c9fc-4f61-8989-f6ea3532531e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d92c81f9-c9fc-4f61-8989-f6ea3532531e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a029725-1e79-4284-bd15-6bf458c5afb5' class='xr-var-data-in' type='checkbox'><label for='data-7a029725-1e79-4284-bd15-6bf458c5afb5' 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>description :</span></dt><dd>velocity in radar azimuth direction</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <td> 98.61 kiB </td>\n",
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       "                        <td> 98.61 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vr_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-8a9d3b54-2e9e-4f62-b700-1d03d2efb403' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a9d3b54-2e9e-4f62-b700-1d03d2efb403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bab0ead-405f-4fc1-a472-f3a8c00c723c' class='xr-var-data-in' type='checkbox'><label for='data-9bab0ead-405f-4fc1-a472-f3a8c00c723c' 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>description :</span></dt><dd>vr shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vr_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_error_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-2c1cf945-1e4a-4eba-b04e-d1d4c20aa6de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c1cf945-1e4a-4eba-b04e-d1d4c20aa6de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f80f2c2b-0714-4b01-8bb2-c06d44176c7c' class='xr-var-data-in' type='checkbox'><label for='data-f80f2c2b-0714-4b01-8bb2-c06d44176c7c' 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>description :</span></dt><dd>RMSE over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vx_error_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <td> (25243,) </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_error_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-d35adddc-c513-426b-b623-a3704484419f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d35adddc-c513-426b-b623-a3704484419f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29678359-a90f-4bb5-8f89-c4604db02e07' class='xr-var-data-in' type='checkbox'><label for='data-29678359-a90f-4bb5-8f89-c4604db02e07' 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>description :</span></dt><dd>RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity &lt; 15 meter/year identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vx_error_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_stable_shift</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-4eb154b7-dc80-4703-8cb4-a89b4594eccb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eb154b7-dc80-4703-8cb4-a89b4594eccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5da20ef8-4115-4b21-bb2d-73e0b8ce92bf' class='xr-var-data-in' type='checkbox'><label for='data-5da20ef8-4115-4b21-bb2d-73e0b8ce92bf' 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>description :</span></dt><dd>applied vx shift calibrated using pixels over stable or slow surfaces</dd><dt><span>standard_name :</span></dt><dd>vx_stable_shift</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 98.61 kiB </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-df7cd14e-1095-4acd-881c-768493da4ded' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df7cd14e-1095-4acd-881c-768493da4ded' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b324536-51d2-4e31-b4ed-154fb4ea114d' class='xr-var-data-in' type='checkbox'><label for='data-9b324536-51d2-4e31-b4ed-154fb4ea114d' 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>description :</span></dt><dd>vx shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vx_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_error_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-f8556081-2e88-419a-a616-15867f03869b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8556081-2e88-419a-a616-15867f03869b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d11e1e84-d5a4-44e8-8f35-5590ae77dd1b' class='xr-var-data-in' type='checkbox'><label for='data-d11e1e84-d5a4-44e8-8f35-5590ae77dd1b' 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>description :</span></dt><dd>RMSE over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vy_error_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_error_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-f23c6d2b-7c6a-4102-963e-1671c2f805a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f23c6d2b-7c6a-4102-963e-1671c2f805a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7ed16c1-1a6d-4646-b9f3-734535218bdf' class='xr-var-data-in' type='checkbox'><label for='data-e7ed16c1-1a6d-4646-b9f3-734535218bdf' 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>description :</span></dt><dd>RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity &lt; 15 meter/year identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vy_error_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_stable_shift</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-d3380cf2-e6fb-4fba-a7bd-392910ea853a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3380cf2-e6fb-4fba-a7bd-392910ea853a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a688f827-aeda-4ad0-9b39-50f975436cca' class='xr-var-data-in' type='checkbox'><label for='data-a688f827-aeda-4ad0-9b39-50f975436cca' 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>description :</span></dt><dd>applied vy shift calibrated using pixels over stable or slow surfaces</dd><dt><span>standard_name :</span></dt><dd>vy_stable_shift</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-86522e87-4b1a-46f4-be22-2227e303ce5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86522e87-4b1a-46f4-be22-2227e303ce5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a03652a6-437f-4cc2-81b1-37b1b3727377' class='xr-var-data-in' type='checkbox'><label for='data-a03652a6-437f-4cc2-81b1-37b1b3727377' 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>description :</span></dt><dd>vy shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vy_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_stable_shift_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25243,), meta=np.ndarray&gt;</div><input id='attrs-88e529c3-3d5a-4ee0-8817-a8f647700c83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88e529c3-3d5a-4ee0-8817-a8f647700c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dacd24c-d6d4-4c79-a010-d4961e1d490e' class='xr-var-data-in' type='checkbox'><label for='data-4dacd24c-d6d4-4c79-a010-d4961e1d490e' 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>description :</span></dt><dd>vy shift calibrated using valid pixels over stable surfaces,  stationary or slow-flowing surfaces with velocity &lt; 15 m/yr identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vy_stable_shift_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                        <td> 98.61 kiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25243,) </td>\n",
       "                        <td> (25243,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d79da85-b441-4e26-bf23-80309c6f93e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-8d79da85-b441-4e26-bf23-80309c6f93e8' class='xr-section-summary' >Indexes: <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-index-name'><div>mid_date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cbde0801-66b0-4c71-8cff-1d29c63642f6' class='xr-index-data-in' type='checkbox'/><label for='index-cbde0801-66b0-4c71-8cff-1d29c63642f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-07 04:21:44.211208960&#x27;,\n",
       "               &#x27;2018-04-14 04:18:49.171219968&#x27;,\n",
       "               &#x27;2017-02-10 16:15:50.660901120&#x27;,\n",
       "               &#x27;2022-04-03 04:19:01.211214080&#x27;,\n",
       "               &#x27;2021-07-22 04:16:46.210427904&#x27;,\n",
       "               &#x27;2019-03-15 04:15:44.180925952&#x27;,\n",
       "               &#x27;2002-09-15 03:59:12.379172096&#x27;,\n",
       "               &#x27;2002-12-28 03:42:16.181281024&#x27;,\n",
       "               &#x27;2021-06-29 16:16:10.210323968&#x27;,\n",
       "               &#x27;2022-03-26 16:18:35.211123968&#x27;,\n",
       "               ...\n",
       "               &#x27;2015-03-15 04:10:27.667560960&#x27;,\n",
       "               &#x27;2012-11-25 04:08:32.642952960&#x27;,\n",
       "               &#x27;2012-12-27 04:08:58.362065920&#x27;,\n",
       "               &#x27;2017-05-27 04:10:08.145324032&#x27;,\n",
       "               &#x27;2016-12-06 04:11:32.294059776&#x27;,\n",
       "               &#x27;2013-04-18 04:08:52.932247040&#x27;,\n",
       "               &#x27;2017-05-07 04:11:30.865388288&#x27;,\n",
       "               &#x27;2013-05-20 04:08:31.155972096&#x27;,\n",
       "               &#x27;2015-10-17 04:11:05.527512064&#x27;,\n",
       "               &#x27;2015-11-10 04:11:15.457366016&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;mid_date&#x27;, length=25243, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a293e02b-5792-44a7-86f6-0bd09deacb8e' class='xr-index-data-in' type='checkbox'/><label for='index-a293e02b-5792-44a7-86f6-0bd09deacb8e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([700132.5, 700252.5, 700372.5, 700492.5, 700612.5, 700732.5, 700852.5,\n",
       "       700972.5, 701092.5, 701212.5,\n",
       "       ...\n",
       "       798892.5, 799012.5, 799132.5, 799252.5, 799372.5, 799492.5, 799612.5,\n",
       "       799732.5, 799852.5, 799972.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=833))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8784cfb2-78ef-4663-9b06-75b59a3f08a4' class='xr-index-data-in' type='checkbox'/><label for='index-8784cfb2-78ef-4663-9b06-75b59a3f08a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3399907.5, 3399787.5, 3399667.5, 3399547.5, 3399427.5, 3399307.5,\n",
       "       3399187.5, 3399067.5, 3398947.5, 3398827.5,\n",
       "       ...\n",
       "       3301147.5, 3301027.5, 3300907.5, 3300787.5, 3300667.5, 3300547.5,\n",
       "       3300427.5, 3300307.5, 3300187.5, 3300067.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=833))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd34b755-428b-48e1-98a2-c80475e5fb92' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd34b755-428b-48e1-98a2-c80475e5fb92' class='xr-section-summary' >Attributes: <span>(19)</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-1.8</dd><dt><span>GDAL_AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>author :</span></dt><dd>ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)</dd><dt><span>autoRIFT_parameter_file :</span></dt><dd>http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp</dd><dt><span>datacube_software_version :</span></dt><dd>1.0</dd><dt><span>date_created :</span></dt><dd>25-Sep-2023 22:00:23</dd><dt><span>date_updated :</span></dt><dd>25-Sep-2023 22:00:23</dd><dt><span>geo_polygon :</span></dt><dd>[[95.06959008486952, 29.814255053135895], [95.32812062059084, 29.809951334550703], [95.58659184122865, 29.80514261876954], [95.84499718862224, 29.7998293459177], [96.10333011481168, 29.79401200205343], [96.11032804508507, 30.019297601073085], [96.11740568350054, 30.244573983323825], [96.12456379063154, 30.469841094022847], [96.1318031397002, 30.695098878594504], [95.87110827645229, 30.70112924501256], [95.61033817656023, 30.7066371044805], [95.34949964126946, 30.711621947056347], [95.08859948278467, 30.716083310981194], [95.08376623410525, 30.49063893600811], [95.07898726183609, 30.26518607254204], [95.0742620484426, 30.039724763743482], [95.06959008486952, 29.814255053135895]]</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL), California Institute of Technology</dd><dt><span>latitude :</span></dt><dd>30.26</dd><dt><span>longitude :</span></dt><dd>95.6</dd><dt><span>proj_polygon :</span></dt><dd>[[700000, 3300000], [725000.0, 3300000.0], [750000.0, 3300000.0], [775000.0, 3300000.0], [800000, 3300000], [800000.0, 3325000.0], [800000.0, 3350000.0], [800000.0, 3375000.0], [800000, 3400000], [775000.0, 3400000.0], [750000.0, 3400000.0], [725000.0, 3400000.0], [700000, 3400000], [700000.0, 3375000.0], [700000.0, 3350000.0], [700000.0, 3325000.0], [700000, 3300000]]</dd><dt><span>projection :</span></dt><dd>32646</dd><dt><span>s3 :</span></dt><dd>s3://its-live-data/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr</dd><dt><span>skipped_granules :</span></dt><dd>s3://its-live-data/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.json</dd><dt><span>time_standard_img1 :</span></dt><dd>UTC</dd><dt><span>time_standard_img2 :</span></dt><dd>UTC</dd><dt><span>title :</span></dt><dd>ITS_LIVE datacube of image pair velocities</dd><dt><span>url :</span></dt><dd>https://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                     (mid_date: 25243, y: 833, x: 833)\n",
       "Coordinates:\n",
       "  * mid_date                    (mid_date) datetime64[ns] 2022-06-07T04:21:44...\n",
       "  * x                           (x) float64 7.001e+05 7.003e+05 ... 8e+05\n",
       "  * y                           (y) float64 3.4e+06 3.4e+06 ... 3.3e+06 3.3e+06\n",
       "Data variables: (12/60)\n",
       "    M11                         (mid_date, y, x) float32 dask.array<chunksize=(25243, 30, 30), meta=np.ndarray>\n",
       "    M11_dr_to_vr_factor         (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    M12                         (mid_date, y, x) float32 dask.array<chunksize=(25243, 30, 30), meta=np.ndarray>\n",
       "    M12_dr_to_vr_factor         (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    acquisition_date_img1       (mid_date) datetime64[ns] dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    acquisition_date_img2       (mid_date) datetime64[ns] dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    ...                          ...\n",
       "    vy_error_modeled            (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    vy_error_slow               (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    vy_error_stationary         (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    vy_stable_shift             (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    vy_stable_shift_slow        (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "    vy_stable_shift_stationary  (mid_date) float32 dask.array<chunksize=(25243,), meta=np.ndarray>\n",
       "Attributes: (12/19)\n",
       "    Conventions:                CF-1.8\n",
       "    GDAL_AREA_OR_POINT:         Area\n",
       "    author:                     ITS_LIVE, a NASA MEaSUREs project (its-live.j...\n",
       "    autoRIFT_parameter_file:    http://its-live-data.s3.amazonaws.com/autorif...\n",
       "    datacube_software_version:  1.0\n",
       "    date_created:               25-Sep-2023 22:00:23\n",
       "    ...                         ...\n",
       "    s3:                         s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    skipped_granules:           s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    time_standard_img1:         UTC\n",
       "    time_standard_img2:         UTC\n",
       "    title:                      ITS_LIVE datacube of image pair velocities\n",
       "    url:                        https://its-live-data.s3.amazonaws.com/datacu..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one worked! Let's stop here and define a function that we can use for a quick view of this data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bounds_polygon(input_xr):\n",
    "    \n",
    "    xmin = input_xr.coords['x'].data.min()\n",
    "    xmax = input_xr.coords['x'].data.max()\n",
    "\n",
    "    ymin = input_xr.coords['y'].data.min()\n",
    "    ymax = input_xr.coords['y'].data.max()\n",
    "\n",
    "    pts_ls = [(xmin, ymin), (xmax, ymin),(xmax, ymax), (xmin, ymax), (xmin, ymin)]\n",
    "\n",
    "    crs = f\"epsg:{input_xr.mapping.spatial_epsg}\"\n",
    "\n",
    "    polygon_geom = Polygon(pts_ls)\n",
    "    polygon = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[polygon_geom]) \n",
    "    \n",
    "    return polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also write a quick function for reading in s3 objects from http urls. This will come in handy when we're trying to test multiple urls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_in_s3(http_url, chunks = 'auto'):\n",
    "    #s3_url = http_url.replace('http','s3')   <-- as of Fall 2023, can pass http urls directly to xr.open_dataset()\n",
    "    #s3_url = s3_url.replace('.s3.amazonaws.com','')\n",
    "\n",
    "    datacube = xr.open_dataset(http_url, engine = 'zarr',\n",
    "                                #storage_options={'anon':True},\n",
    "                                chunks = chunks)\n",
    "\n",
    "    return datacube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the cube we've already read in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox = get_bounds_polygon(dc1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_bounds_polygon()` returns a geopandas.GeoDataFrame object in the same projection as the velocity data object (local UTM). Re-project to latitude/longitude to view the object more easily on a map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox = bbox.to_crs('EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1047\"}],\"active_drag\":{\"id\":\"p1043\"},\"active_scroll\":{\"id\":\"p1044\"}}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1035\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1038\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1037\"},\"axis_label\":\"y\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1036\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1028\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1031\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1030\"},\"axis_label\":\"x\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1029\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1034\",\"attributes\":{\"axis\":{\"id\":\"p1028\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1041\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1035\"},\"grid_line_color\":null}}],\"min_border_top\":10,\"min_border_bottom\":10,\"min_border_left\":10,\"min_border_right\":10,\"output_backend\":\"webgl\"}},{\"type\":\"object\",\"name\":\"Spacer\",\"id\":\"p1093\",\"attributes\":{\"name\":\"HSpacer01547\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1008\"},{\"id\":\"p1006\"},{\"id\":\"p1007\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"callbacks\":{\"type\":\"map\"}}};\n",
       "  var render_items = [{\"docid\":\"5a58b1c6-1e7e-4da5-8f7b-59eb5484dc38\",\"roots\":{\"p1005\":\"c6fd5032-ac5f-43fc-837e-3a7e10b234b4\"},\"root_ids\":[\"p1005\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Polygons   [x,y]"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1005"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox.hvplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = bbox.hvplot(legend=True,alpha=0.3, tiles='ESRI', color='red', geo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"42ddfdd0-fca4-4a78-a173-41a5d4d4c539\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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      ],
      "text/plain": [
       ":Overlay\n",
       "   .Tiles.I    :Tiles   [x,y]\n",
       "   .Polygons.I :Polygons   [Longitude,Latitude]"
      ]
     },
     "execution_count": 18,
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      }
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   "source": [
    "poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see where this granule lies. We are using `pandas.hvplot` here, which is a great tool for interactive data visualization. You can read more about it [here](https://hvplot.holoviz.org/user_guide/Introduction.html). Later in the tutorial we will demonstrate other situations where these tools are useful. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching ITS_LIVE catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at how we could search the ITS_LIVE data cube catalog for the data that we're interested in. There are many ways to do this, this is just one example. \n",
    "\n",
    "First, we will read in the catalog geojson file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "itslive_catalog = gpd.read_file('https://its-live-data.s3.amazonaws.com/datacubes/catalog_v02.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Here we'll show two options for filtering the catalog: \n",
    "1. Selecting granules that contain a specific point, and \n",
    "2. Selecting all granules within a single UTM zone (specified by epsg code).  This will let us take stock of the spatial coverage of data cubes located at **working urls** within a certain UTM zone. \n",
    "\n",
    "You could easily tweak these functions (or write your own!) to select granules based on different properties. Play around with the `itslive_catalog` object to become more familiar with the data it contains and different options for indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import s3fs\n",
    "\n",
    "fs  = s3fs.S3FileSystem(anon=True)\n",
    "fs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting granules by a single point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_granule_by_point(input_point):\n",
    "    '''returns url for the granule (zarr datacube) containing a specified point. point must be passed in epsg:4326\n",
    "    '''\n",
    "    catalog = gpd.read_file('https://its-live-data.s3.amazonaws.com/datacubes/catalog_v02.json')\n",
    "\n",
    "    #make shapely point of input point\n",
    "    p = gpd.GeoSeries([Point(input_point[0], input_point[1])],crs='EPSG:4326')\n",
    "    #make gdf of point\n",
    "    gdf = gdf = gpd.GeoDataFrame({'label': 'point', \n",
    "                                  'geometry':p})\n",
    "    #find row of granule \n",
    "    granule = catalog.sjoin(gdf, how='inner')\n",
    "\n",
    "    url = granule['zarr_url'].values[0]\n",
    "    return url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = find_granule_by_point([95.180191, 30.645973])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, this function returned a single url corresponding to the data cube covering the point we supplied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the `read_in_s3` function we defined to open the datacube as an `xarray.Dataset`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "datacube = read_in_s3(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then the `get_bounds_polyon` function to take a look at the footprint using `hvplot()`. Hvplot is an API for data exploration and visualization that you can read more about [here](https://hvplot.holoviz.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox_dc = get_bounds_polygon(datacube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly =  bbox_dc.to_crs('EPSG:4326').hvplot(legend=True,alpha=0.3, tiles='ESRI', color='red', geo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
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      ],
      "text/plain": [
       ":Overlay\n",
       "   .Tiles.I    :Tiles   [x,y]\n",
       "   .Polygons.I :Polygons   [Longitude,Latitude]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1313"
      }
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     "output_type": "execute_result"
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   "source": [
    "poly"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now we know how to access the its_live data cubes for a given region and at a specific point.\n",
    "\n",
    "Let's take a quick first look at this data cube. The next notebooks will give more examples of inspecting and working with this data. To make it easier for now, we will only plot a few hundred time steps instead of the full time series, and we will look at a spatial subset of the dataset. We use Xarray's index-based selection methods for this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "datacube_sub = datacube.isel(mid_date = slice(0,50), x= slice(300,500), y=slice(400,600))\n",
    "#datacube_sub = datacube.isel(mid_date = slice(0,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                     (mid_date: 50, y: 200, x: 200)\n",
       "Coordinates:\n",
       "  * mid_date                    (mid_date) datetime64[ns] 2022-06-07T04:21:44...\n",
       "  * x                           (x) float64 7.361e+05 7.363e+05 ... 7.6e+05\n",
       "  * y                           (y) float64 3.352e+06 3.352e+06 ... 3.328e+06\n",
       "Data variables: (12/60)\n",
       "    M11                         (mid_date, y, x) float32 dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;\n",
       "    M11_dr_to_vr_factor         (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    M12                         (mid_date, y, x) float32 dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;\n",
       "    M12_dr_to_vr_factor         (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    acquisition_date_img1       (mid_date) datetime64[ns] dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    acquisition_date_img2       (mid_date) datetime64[ns] dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    ...                          ...\n",
       "    vy_error_modeled            (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    vy_error_slow               (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    vy_error_stationary         (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    vy_stable_shift             (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    vy_stable_shift_slow        (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    vy_stable_shift_stationary  (mid_date) float32 dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "Attributes: (12/19)\n",
       "    Conventions:                CF-1.8\n",
       "    GDAL_AREA_OR_POINT:         Area\n",
       "    author:                     ITS_LIVE, a NASA MEaSUREs project (its-live.j...\n",
       "    autoRIFT_parameter_file:    http://its-live-data.s3.amazonaws.com/autorif...\n",
       "    datacube_software_version:  1.0\n",
       "    date_created:               25-Sep-2023 22:00:23\n",
       "    ...                         ...\n",
       "    s3:                         s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    skipped_granules:           s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    time_standard_img1:         UTC\n",
       "    time_standard_img2:         UTC\n",
       "    title:                      ITS_LIVE datacube of image pair velocities\n",
       "    url:                        https://its-live-data.s3.amazonaws.com/datacu...</pre><div class='xr-wrap' style='display:none'><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-1a603555-9fa3-4f53-8fbe-e3a5eb69034a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a603555-9fa3-4f53-8fbe-e3a5eb69034a' 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'>mid_date</span>: 50</li><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-97c64dae-70b0-4596-8774-bc58ce4ce05e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97c64dae-70b0-4596-8774-bc58ce4ce05e' 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'>mid_date</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-07T04:21:44.211208960 .....</div><input id='attrs-fd0f4b6f-f54a-4659-ba34-3f3fc4744c87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd0f4b6f-f54a-4659-ba34-3f3fc4744c87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7efe88c9-b5fb-4c3c-999e-3a9d340ee38f' class='xr-var-data-in' type='checkbox'><label for='data-7efe88c9-b5fb-4c3c-999e-3a9d340ee38f' 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>description :</span></dt><dd>midpoint of image 1 and image 2 acquisition date and time with granule&#x27;s centroid longitude and latitude as microseconds</dd><dt><span>standard_name :</span></dt><dd>image_pair_center_date_with_time_separation</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-07T04:21:44.211208960&#x27;, &#x27;2018-04-14T04:18:49.171219968&#x27;,\n",
       "       &#x27;2017-02-10T16:15:50.660901120&#x27;, &#x27;2022-04-03T04:19:01.211214080&#x27;,\n",
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       "       &#x27;2004-06-24T03:52:47.830695040&#x27;, &#x27;2014-12-29T04:09:00.898055936&#x27;,\n",
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       "       &#x27;2022-07-02T04:16:01.220522752&#x27;, &#x27;2018-01-31T16:15:50.170811904&#x27;,\n",
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       "       &#x27;2022-06-19T16:21:55.211208960&#x27;, &#x27;2021-03-16T16:15:50.200914688&#x27;,\n",
       "       &#x27;2021-10-07T16:15:50.210428160&#x27;, &#x27;2011-02-20T04:00:25.059236096&#x27;,\n",
       "       &#x27;2022-02-19T16:15:55.210800640&#x27;, &#x27;2009-02-22T04:00:35.880577024&#x27;,\n",
       "       &#x27;2021-07-27T04:17:29.210422784&#x27;, &#x27;2022-07-29T16:15:55.220701952&#x27;,\n",
       "       &#x27;2022-01-15T16:18:40.211123968&#x27;, &#x27;2013-12-10T04:06:41.267568896&#x27;,\n",
       "       &#x27;2022-07-02T04:21:26.220122880&#x27;, &#x27;2022-09-22T16:18:25.220727296&#x27;,\n",
       "       &#x27;1987-02-02T03:30:57.169769024&#x27;, &#x27;1999-11-26T03:48:20.227965056&#x27;,\n",
       "       &#x27;2022-01-13T04:19:24.211129088&#x27;, &#x27;2016-12-26T04:12:57.674946048&#x27;,\n",
       "       &#x27;2022-05-20T16:18:35.220117760&#x27;, &#x27;1988-07-14T03:40:12.757213952&#x27;,\n",
       "       &#x27;2021-10-10T04:18:56.210806016&#x27;, &#x27;2022-05-15T16:17:30.220211968&#x27;,\n",
       "       &#x27;2021-04-28T04:15:49.210323968&#x27;, &#x27;2002-07-29T03:47:38.748616960&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.361e+05 7.363e+05 ... 7.6e+05</div><input id='attrs-6ee621d8-46f3-4b26-bb1c-1726af454b94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ee621d8-46f3-4b26-bb1c-1726af454b94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78846efa-7b68-445f-96e8-b966ee9ae631' class='xr-var-data-in' type='checkbox'><label for='data-78846efa-7b68-445f-96e8-b966ee9ae631' 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>description :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([736132.5, 736252.5, 736372.5, 736492.5, 736612.5, 736732.5, 736852.5,\n",
       "       736972.5, 737092.5, 737212.5, 737332.5, 737452.5, 737572.5, 737692.5,\n",
       "       737812.5, 737932.5, 738052.5, 738172.5, 738292.5, 738412.5, 738532.5,\n",
       "       738652.5, 738772.5, 738892.5, 739012.5, 739132.5, 739252.5, 739372.5,\n",
       "       739492.5, 739612.5, 739732.5, 739852.5, 739972.5, 740092.5, 740212.5,\n",
       "       740332.5, 740452.5, 740572.5, 740692.5, 740812.5, 740932.5, 741052.5,\n",
       "       741172.5, 741292.5, 741412.5, 741532.5, 741652.5, 741772.5, 741892.5,\n",
       "       742012.5, 742132.5, 742252.5, 742372.5, 742492.5, 742612.5, 742732.5,\n",
       "       742852.5, 742972.5, 743092.5, 743212.5, 743332.5, 743452.5, 743572.5,\n",
       "       743692.5, 743812.5, 743932.5, 744052.5, 744172.5, 744292.5, 744412.5,\n",
       "       744532.5, 744652.5, 744772.5, 744892.5, 745012.5, 745132.5, 745252.5,\n",
       "       745372.5, 745492.5, 745612.5, 745732.5, 745852.5, 745972.5, 746092.5,\n",
       "       746212.5, 746332.5, 746452.5, 746572.5, 746692.5, 746812.5, 746932.5,\n",
       "       747052.5, 747172.5, 747292.5, 747412.5, 747532.5, 747652.5, 747772.5,\n",
       "       747892.5, 748012.5, 748132.5, 748252.5, 748372.5, 748492.5, 748612.5,\n",
       "       748732.5, 748852.5, 748972.5, 749092.5, 749212.5, 749332.5, 749452.5,\n",
       "       749572.5, 749692.5, 749812.5, 749932.5, 750052.5, 750172.5, 750292.5,\n",
       "       750412.5, 750532.5, 750652.5, 750772.5, 750892.5, 751012.5, 751132.5,\n",
       "       751252.5, 751372.5, 751492.5, 751612.5, 751732.5, 751852.5, 751972.5,\n",
       "       752092.5, 752212.5, 752332.5, 752452.5, 752572.5, 752692.5, 752812.5,\n",
       "       752932.5, 753052.5, 753172.5, 753292.5, 753412.5, 753532.5, 753652.5,\n",
       "       753772.5, 753892.5, 754012.5, 754132.5, 754252.5, 754372.5, 754492.5,\n",
       "       754612.5, 754732.5, 754852.5, 754972.5, 755092.5, 755212.5, 755332.5,\n",
       "       755452.5, 755572.5, 755692.5, 755812.5, 755932.5, 756052.5, 756172.5,\n",
       "       756292.5, 756412.5, 756532.5, 756652.5, 756772.5, 756892.5, 757012.5,\n",
       "       757132.5, 757252.5, 757372.5, 757492.5, 757612.5, 757732.5, 757852.5,\n",
       "       757972.5, 758092.5, 758212.5, 758332.5, 758452.5, 758572.5, 758692.5,\n",
       "       758812.5, 758932.5, 759052.5, 759172.5, 759292.5, 759412.5, 759532.5,\n",
       "       759652.5, 759772.5, 759892.5, 760012.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.352e+06 3.352e+06 ... 3.328e+06</div><input id='attrs-5e2f51cd-e062-4ab2-ab0c-f15b6501d894' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e2f51cd-e062-4ab2-ab0c-f15b6501d894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0263518e-efd9-46db-9f72-db9c93620b42' class='xr-var-data-in' type='checkbox'><label for='data-0263518e-efd9-46db-9f72-db9c93620b42' 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>description :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([3351907.5, 3351787.5, 3351667.5, 3351547.5, 3351427.5, 3351307.5,\n",
       "       3351187.5, 3351067.5, 3350947.5, 3350827.5, 3350707.5, 3350587.5,\n",
       "       3350467.5, 3350347.5, 3350227.5, 3350107.5, 3349987.5, 3349867.5,\n",
       "       3349747.5, 3349627.5, 3349507.5, 3349387.5, 3349267.5, 3349147.5,\n",
       "       3349027.5, 3348907.5, 3348787.5, 3348667.5, 3348547.5, 3348427.5,\n",
       "       3348307.5, 3348187.5, 3348067.5, 3347947.5, 3347827.5, 3347707.5,\n",
       "       3347587.5, 3347467.5, 3347347.5, 3347227.5, 3347107.5, 3346987.5,\n",
       "       3346867.5, 3346747.5, 3346627.5, 3346507.5, 3346387.5, 3346267.5,\n",
       "       3346147.5, 3346027.5, 3345907.5, 3345787.5, 3345667.5, 3345547.5,\n",
       "       3345427.5, 3345307.5, 3345187.5, 3345067.5, 3344947.5, 3344827.5,\n",
       "       3344707.5, 3344587.5, 3344467.5, 3344347.5, 3344227.5, 3344107.5,\n",
       "       3343987.5, 3343867.5, 3343747.5, 3343627.5, 3343507.5, 3343387.5,\n",
       "       3343267.5, 3343147.5, 3343027.5, 3342907.5, 3342787.5, 3342667.5,\n",
       "       3342547.5, 3342427.5, 3342307.5, 3342187.5, 3342067.5, 3341947.5,\n",
       "       3341827.5, 3341707.5, 3341587.5, 3341467.5, 3341347.5, 3341227.5,\n",
       "       3341107.5, 3340987.5, 3340867.5, 3340747.5, 3340627.5, 3340507.5,\n",
       "       3340387.5, 3340267.5, 3340147.5, 3340027.5, 3339907.5, 3339787.5,\n",
       "       3339667.5, 3339547.5, 3339427.5, 3339307.5, 3339187.5, 3339067.5,\n",
       "       3338947.5, 3338827.5, 3338707.5, 3338587.5, 3338467.5, 3338347.5,\n",
       "       3338227.5, 3338107.5, 3337987.5, 3337867.5, 3337747.5, 3337627.5,\n",
       "       3337507.5, 3337387.5, 3337267.5, 3337147.5, 3337027.5, 3336907.5,\n",
       "       3336787.5, 3336667.5, 3336547.5, 3336427.5, 3336307.5, 3336187.5,\n",
       "       3336067.5, 3335947.5, 3335827.5, 3335707.5, 3335587.5, 3335467.5,\n",
       "       3335347.5, 3335227.5, 3335107.5, 3334987.5, 3334867.5, 3334747.5,\n",
       "       3334627.5, 3334507.5, 3334387.5, 3334267.5, 3334147.5, 3334027.5,\n",
       "       3333907.5, 3333787.5, 3333667.5, 3333547.5, 3333427.5, 3333307.5,\n",
       "       3333187.5, 3333067.5, 3332947.5, 3332827.5, 3332707.5, 3332587.5,\n",
       "       3332467.5, 3332347.5, 3332227.5, 3332107.5, 3331987.5, 3331867.5,\n",
       "       3331747.5, 3331627.5, 3331507.5, 3331387.5, 3331267.5, 3331147.5,\n",
       "       3331027.5, 3330907.5, 3330787.5, 3330667.5, 3330547.5, 3330427.5,\n",
       "       3330307.5, 3330187.5, 3330067.5, 3329947.5, 3329827.5, 3329707.5,\n",
       "       3329587.5, 3329467.5, 3329347.5, 3329227.5, 3329107.5, 3328987.5,\n",
       "       3328867.5, 3328747.5, 3328627.5, 3328507.5, 3328387.5, 3328267.5,\n",
       "       3328147.5, 3328027.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-248a7ac3-1364-4d5b-ae07-9e586b0981f5' class='xr-section-summary-in' type='checkbox'  ><label for='section-248a7ac3-1364-4d5b-ae07-9e586b0981f5' class='xr-section-summary' >Data variables: <span>(60)</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>M11</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;</div><input id='attrs-fe5789e2-855f-43c7-9a32-0ffa81160c80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe5789e2-855f-43c7-9a32-0ffa81160c80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95d51fa8-3ed8-44ce-ac89-4a029181a9db' class='xr-var-data-in' type='checkbox'><label for='data-95d51fa8-3ed8-44ce-ac89-4a029181a9db' 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>description :</span></dt><dd>conversion matrix element (1st row, 1st column) that can be multiplied with vx to give range pixel displacement dr (see Eq. A18 in https://www.mdpi.com/2072-4292/13/4/749)</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>conversion_matrix_element_11</dd><dt><span>units :</span></dt><dd>pixel/(meter/year)</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                        <td> 175.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 200, 200) </td>\n",
       "                        <td> (50, 30, 30) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 49 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>M11_dr_to_vr_factor</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-cea95a93-aaf7-4b4e-9ee3-424fbd06d68a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cea95a93-aaf7-4b4e-9ee3-424fbd06d68a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d214f943-bc8a-41bc-a5ce-3bf6746b7ca6' class='xr-var-data-in' type='checkbox'><label for='data-d214f943-bc8a-41bc-a5ce-3bf6746b7ca6' 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>description :</span></dt><dd>multiplicative factor that converts slant range pixel displacement dr to slant range velocity vr</dd><dt><span>standard_name :</span></dt><dd>M11_dr_to_vr_factor</dd><dt><span>units :</span></dt><dd>meter/(year*pixel)</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>M12</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;</div><input id='attrs-a0e155a7-d40b-4daa-8224-caeb4b7e77cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0e155a7-d40b-4daa-8224-caeb4b7e77cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c05b572-e5bc-45ed-bf86-d4172a69e3f5' class='xr-var-data-in' type='checkbox'><label for='data-5c05b572-e5bc-45ed-bf86-d4172a69e3f5' 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>description :</span></dt><dd>conversion matrix element (1st row, 2nd column) that can be multiplied with vy to give range pixel displacement dr (see Eq. A18 in https://www.mdpi.com/2072-4292/13/4/749)</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>conversion_matrix_element_12</dd><dt><span>units :</span></dt><dd>pixel/(meter/year)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                        <td> 175.78 kiB </td>\n",
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       "                        <td> (50, 30, 30) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 49 chunks in 3 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>M12_dr_to_vr_factor</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-488c289d-7970-4429-946f-f57011ce0dd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-488c289d-7970-4429-946f-f57011ce0dd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7457ce9-5a50-4620-aa16-43724577c697' class='xr-var-data-in' type='checkbox'><label for='data-f7457ce9-5a50-4620-aa16-43724577c697' 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>description :</span></dt><dd>multiplicative factor that converts slant range pixel displacement dr to slant range velocity vr</dd><dt><span>standard_name :</span></dt><dd>M12_dr_to_vr_factor</dd><dt><span>units :</span></dt><dd>meter/(year*pixel)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (50,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> (50,) </td>\n",
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       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "    <tr>\n",
       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0.98 kiB </td>\n",
       "                        <td> 0.98 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.63 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (50, 30, 30) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 49 chunks in 3 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 400 B </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>date_dt</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-8d0b0203-6d6d-4356-abcb-2815e9dcc04b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d0b0203-6d6d-4356-abcb-2815e9dcc04b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bec3dd2-06a4-4446-98ce-aec4b7715a94' class='xr-var-data-in' type='checkbox'><label for='data-9bec3dd2-06a4-4446-98ce-aec4b7715a94' 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>description :</span></dt><dd>time separation between acquisition of image 1 and image 2</dd><dt><span>standard_name :</span></dt><dd>image_pair_time_separation</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> timedelta64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>floatingice</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 200), meta=np.ndarray&gt;</div><input id='attrs-4330f17d-3d10-41d8-82fe-83d6d60c32fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4330f17d-3d10-41d8-82fe-83d6d60c32fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc28a9ce-0116-40ea-81cc-501df535703e' class='xr-var-data-in' type='checkbox'><label for='data-fc28a9ce-0116-40ea-81cc-501df535703e' 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>description :</span></dt><dd>floating ice mask, 0 = non-floating-ice, 1 = floating-ice</dd><dt><span>flag_meanings :</span></dt><dd>non-ice ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>floating ice mask</dd><dt><span>url :</span></dt><dd>https://its-live-data.s3.amazonaws.com/autorift_parameters/v001/N46_0120m_floatingice.tif</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 156.25 kiB </td>\n",
       "                        <td> 156.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (200, 200) </td>\n",
       "                        <td> (200, 200) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>granule_url</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U1024</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-b72eafca-4053-4632-ad16-6aebc4433794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b72eafca-4053-4632-ad16-6aebc4433794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4487e71a-166a-4365-8027-595908160dd1' class='xr-var-data-in' type='checkbox'><label for='data-4487e71a-166a-4365-8027-595908160dd1' 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>description :</span></dt><dd>original granule URL</dd><dt><span>standard_name :</span></dt><dd>granule_url</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200.00 kiB </td>\n",
       "                        <td> 200.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U1024 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interp_mask</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;</div><input id='attrs-635c261d-5cd3-4ab2-a32f-d690bf845ca3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-635c261d-5cd3-4ab2-a32f-d690bf845ca3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab84bb09-6ad6-4ab5-8e13-342f613c069a' class='xr-var-data-in' type='checkbox'><label for='data-ab84bb09-6ad6-4ab5-8e13-342f613c069a' 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>description :</span></dt><dd>light interpolation mask</dd><dt><span>flag_meanings :</span></dt><dd>measured interpolated</dd><dt><span>flag_values :</span></dt><dd>[0, 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>interpolated_value_mask</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                        <td> 175.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 200, 200) </td>\n",
       "                        <td> (50, 30, 30) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 49 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landice</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 200), meta=np.ndarray&gt;</div><input id='attrs-b4c08801-b1b5-4fd5-8353-d7ff73ef1421' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4c08801-b1b5-4fd5-8353-d7ff73ef1421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8269f9f-adc9-4de1-a1e6-f5f9adf4b990' class='xr-var-data-in' type='checkbox'><label for='data-a8269f9f-adc9-4de1-a1e6-f5f9adf4b990' 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>description :</span></dt><dd>land ice mask, 0 = non-land-ice, 1 = land-ice</dd><dt><span>flag_meanings :</span></dt><dd>non-ice ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>land ice mask</dd><dt><span>url :</span></dt><dd>https://its-live-data.s3.amazonaws.com/autorift_parameters/v001/N46_0120m_landice.tif</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 156.25 kiB </td>\n",
       "                        <td> 156.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (200, 200) </td>\n",
       "                        <td> (200, 200) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >200</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mapping</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4c8e3e6-805f-44c1-89ef-2bba52f45d48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4c8e3e6-805f-44c1-89ef-2bba52f45d48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65c599a1-1048-4e1e-a31f-d22b8ed11421' class='xr-var-data-in' type='checkbox'><label for='data-65c599a1-1048-4e1e-a31f-d22b8ed11421' 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>GeoTransform :</span></dt><dd>700072.5 120.0 0 3399967.5 0 -120.0</dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 46N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,93],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32646&quot;]]</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>universal_transverse_mercator</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>93.0</dd><dt><span>proj4text :</span></dt><dd>+proj=utm +zone=46 +datum=WGS84 +units=m +no_defs</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>spatial_epsg :</span></dt><dd>32646</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 46N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,93],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32646&quot;]]</dd><dt><span>utm_zone_number :</span></dt><dd>46.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=&lt;U1]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mission_img1</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-b85bf932-69aa-4e5e-9141-5aa73f259a10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b85bf932-69aa-4e5e-9141-5aa73f259a10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ff397e8-03d3-493e-8b0e-7c2ec7f61746' class='xr-var-data-in' type='checkbox'><label for='data-3ff397e8-03d3-493e-8b0e-7c2ec7f61746' 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>description :</span></dt><dd>id of the mission that acquired image 1</dd><dt><span>standard_name :</span></dt><dd>image1_mission</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mission_img2</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-c75fe557-1569-43c2-9de7-5ddf92fa4339' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c75fe557-1569-43c2-9de7-5ddf92fa4339' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8f7fc8c-0ba0-492d-9c68-dbefc9762859' class='xr-var-data-in' type='checkbox'><label for='data-d8f7fc8c-0ba0-492d-9c68-dbefc9762859' 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>description :</span></dt><dd>id of the mission that acquired image 2</dd><dt><span>standard_name :</span></dt><dd>image2_mission</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roi_valid_percentage</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-68aa1f3b-8d89-486d-bd58-cd16674861b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68aa1f3b-8d89-486d-bd58-cd16674861b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70ef3b86-fc31-496b-a5a6-eadfec1423a6' class='xr-var-data-in' type='checkbox'><label for='data-70ef3b86-fc31-496b-a5a6-eadfec1423a6' 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>description :</span></dt><dd>percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity</dd><dt><span>standard_name :</span></dt><dd>region_of_interest_valid_pixel_percentage</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>satellite_img1</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-b7b6e54e-becb-4526-9711-e40b7f64e1a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7b6e54e-becb-4526-9711-e40b7f64e1a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bac0044-c07f-493c-b254-bebae6b48839' class='xr-var-data-in' type='checkbox'><label for='data-0bac0044-c07f-493c-b254-bebae6b48839' 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>description :</span></dt><dd>id of the satellite that acquired image 1</dd><dt><span>standard_name :</span></dt><dd>image1_satellite</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>satellite_img2</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-9e47525d-5b05-459a-b8cd-da95617a0924' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e47525d-5b05-459a-b8cd-da95617a0924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d6c0a68-792e-4489-a5cf-2e3d7f1f8c67' class='xr-var-data-in' type='checkbox'><label for='data-4d6c0a68-792e-4489-a5cf-2e3d7f1f8c67' 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>description :</span></dt><dd>id of the satellite that acquired image 2</dd><dt><span>standard_name :</span></dt><dd>image2_satellite</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensor_img1</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-d93c74a8-062b-4d60-9b4a-b1e1ef0b0449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d93c74a8-062b-4d60-9b4a-b1e1ef0b0449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be6c12ca-066b-4b85-a3ce-f65813712117' class='xr-var-data-in' type='checkbox'><label for='data-be6c12ca-066b-4b85-a3ce-f65813712117' 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>description :</span></dt><dd>id of the sensor that acquired image 1</dd><dt><span>standard_name :</span></dt><dd>image1_sensor</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 600 B </td>\n",
       "                        <td> 600 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U3 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensor_img2</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-711fb070-8440-4c9f-8828-5e52a646f3f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711fb070-8440-4c9f-8828-5e52a646f3f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce5f201-6379-4d1f-b414-85f41f315ad3' class='xr-var-data-in' type='checkbox'><label for='data-bce5f201-6379-4d1f-b414-85f41f315ad3' 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>description :</span></dt><dd>id of the sensor that acquired image 2</dd><dt><span>standard_name :</span></dt><dd>image2_sensor</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 600 B </td>\n",
       "                        <td> 600 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> <U3 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stable_count_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-cf3cc694-d70c-45a9-a4c6-aa06541f00a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf3cc694-d70c-45a9-a4c6-aa06541f00a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7c7cd65-f479-46bc-9c85-c52d81094abb' class='xr-var-data-in' type='checkbox'><label for='data-f7c7cd65-f479-46bc-9c85-c52d81094abb' 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>description :</span></dt><dd>number of valid pixels over slowest 25% of ice</dd><dt><span>standard_name :</span></dt><dd>stable_count_slow</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 100 B </td>\n",
       "                        <td> 100 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stable_count_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-9ea0454d-c60a-4f4b-b3b7-090818ad9712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ea0454d-c60a-4f4b-b3b7-090818ad9712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfef8b52-7f27-49f0-9f2a-a133223f071e' class='xr-var-data-in' type='checkbox'><label for='data-bfef8b52-7f27-49f0-9f2a-a133223f071e' 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>description :</span></dt><dd>number of valid pixels over stationary or slow-flowing surfaces</dd><dt><span>standard_name :</span></dt><dd>stable_count_stationary</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 100 B </td>\n",
       "                        <td> 100 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stable_shift_flag</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-feb20edb-f259-4484-b4c8-bda6a6718141' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feb20edb-f259-4484-b4c8-bda6a6718141' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e39ceeb9-95fd-4234-8966-971239a94324' class='xr-var-data-in' type='checkbox'><label for='data-e39ceeb9-95fd-4234-8966-971239a94324' 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>description :</span></dt><dd>flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity &lt; 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)</dd><dt><span>standard_name :</span></dt><dd>stable_shift_flag</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 50 B </td>\n",
       "                        <td> 50 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;</div><input id='attrs-098858d0-1405-4959-9190-d267412ec16f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-098858d0-1405-4959-9190-d267412ec16f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e4c9568-d7bd-45cf-87a5-1616d0d86c69' class='xr-var-data-in' type='checkbox'><label for='data-0e4c9568-d7bd-45cf-87a5-1616d0d86c69' 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>description :</span></dt><dd>velocity magnitude</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>land_ice_surface_velocity</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                        <td> 175.78 kiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 7.63 MiB </td>\n",
       "                        <td> 175.78 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (50, 30, 30) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 49 chunks in 3 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va_error</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-0738a642-d615-4ea2-b603-e12e56c7d4c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0738a642-d615-4ea2-b603-e12e56c7d4c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-318d682f-92d4-4b92-b64b-4fc14f3485fc' class='xr-var-data-in' type='checkbox'><label for='data-318d682f-92d4-4b92-b64b-4fc14f3485fc' 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>description :</span></dt><dd>error for velocity in radar azimuth direction</dd><dt><span>standard_name :</span></dt><dd>va_error</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va_error_modeled</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-15981129-149e-4777-b833-570bcf69879a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15981129-149e-4777-b833-570bcf69879a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd3a8a85-110a-452a-ba22-bb4ad07103f0' class='xr-var-data-in' type='checkbox'><label for='data-bd3a8a85-110a-452a-ba22-bb4ad07103f0' 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>description :</span></dt><dd>1-sigma error calculated using a modeled error-dt relationship</dd><dt><span>standard_name :</span></dt><dd>va_error_modeled</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va_error_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-03e3fc24-00b1-4a1b-a068-8183af5d4f00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03e3fc24-00b1-4a1b-a068-8183af5d4f00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9948014-34d5-416d-a3ac-f52af3196207' class='xr-var-data-in' type='checkbox'><label for='data-d9948014-34d5-416d-a3ac-f52af3196207' 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>description :</span></dt><dd>RMSE over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>va_error_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va_error_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-cf0da1bb-e4c0-4575-bc9e-9bf656909024' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf0da1bb-e4c0-4575-bc9e-9bf656909024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a12b2f2-2528-4968-8291-fc6e4312760e' class='xr-var-data-in' type='checkbox'><label for='data-3a12b2f2-2528-4968-8291-fc6e4312760e' 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>description :</span></dt><dd>RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity &lt; 15 m/yr identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>va_error_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <!-- Horizontal lines -->\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va_stable_shift</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-4deb6829-e0e1-4791-aac3-3c608247ce7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4deb6829-e0e1-4791-aac3-3c608247ce7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1417fe70-918f-43eb-84e0-6bdf8f156cd7' class='xr-var-data-in' type='checkbox'><label for='data-1417fe70-918f-43eb-84e0-6bdf8f156cd7' 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>description :</span></dt><dd>applied va shift calibrated using pixels over stable or slow surfaces</dd><dt><span>standard_name :</span></dt><dd>va_stable_shift</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-d4b03b0f-2b53-469b-9f7b-a9cc1d8af5d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4b03b0f-2b53-469b-9f7b-a9cc1d8af5d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5da32fc-b935-4c10-8bb8-3bc75ccfd069' class='xr-var-data-in' type='checkbox'><label for='data-a5da32fc-b935-4c10-8bb8-3bc75ccfd069' 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>description :</span></dt><dd>va shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>va_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vr_error_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-7211580d-4ae9-4d63-91c2-cbd835b973d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7211580d-4ae9-4d63-91c2-cbd835b973d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-812b2196-086f-4a81-b00a-2d05cbc6d90d' class='xr-var-data-in' type='checkbox'><label for='data-812b2196-086f-4a81-b00a-2d05cbc6d90d' 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>description :</span></dt><dd>RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity &lt; 15 m/yr identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vr_error_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vr_stable_shift</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-7bcee04a-9cf4-479b-8621-b5e142c879b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bcee04a-9cf4-479b-8621-b5e142c879b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0172a70-7671-4cf8-9edf-9ed177b44fbc' class='xr-var-data-in' type='checkbox'><label for='data-a0172a70-7671-4cf8-9edf-9ed177b44fbc' 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>description :</span></dt><dd>applied vr shift calibrated using pixels over stable or slow surfaces</dd><dt><span>standard_name :</span></dt><dd>vr_stable_shift</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vr_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-8d74d64e-f6b4-4ae0-b85c-eab47fbe7111' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d74d64e-f6b4-4ae0-b85c-eab47fbe7111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb6faa26-027a-4ad5-a905-a4bb3857f2ec' class='xr-var-data-in' type='checkbox'><label for='data-fb6faa26-027a-4ad5-a905-a4bb3857f2ec' 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>description :</span></dt><dd>vr shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vr_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vr_stable_shift_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-6e3b6b3e-af9e-48ca-a330-1fa81a301758' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e3b6b3e-af9e-48ca-a330-1fa81a301758' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d73edec6-b2a9-43f2-9b41-ca9ac8d8811a' class='xr-var-data-in' type='checkbox'><label for='data-d73edec6-b2a9-43f2-9b41-ca9ac8d8811a' 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>description :</span></dt><dd>vr shift calibrated using valid pixels over stable surfaces,  stationary or slow-flowing surfaces with velocity &lt; 15 m/yr identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vr_stable_shift_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(mid_date, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 20, 30), meta=np.ndarray&gt;</div><input id='attrs-954b22a5-f6fa-4d73-a14a-b597b9b100ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-954b22a5-f6fa-4d73-a14a-b597b9b100ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d98fb34-04e8-42d9-bd81-b438cb444850' class='xr-var-data-in' type='checkbox'><label for='data-7d98fb34-04e8-42d9-bd81-b438cb444850' 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>description :</span></dt><dd>velocity component in x direction</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>land_ice_surface_x_velocity</dd><dt><span>units :</span></dt><dd>meter/year</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>vx_error</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-d84aa05e-11b6-4559-95cd-729b2ee82c96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d84aa05e-11b6-4559-95cd-729b2ee82c96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d85a813b-cf8b-45e9-a6d0-be218783bffe' class='xr-var-data-in' type='checkbox'><label for='data-d85a813b-cf8b-45e9-a6d0-be218783bffe' 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>description :</span></dt><dd>best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in &quot;stable_shift_flag&quot;</dd><dt><span>standard_name :</span></dt><dd>vx_error</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_error_modeled</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-15236215-1575-4ef8-a70d-e63de59402e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15236215-1575-4ef8-a70d-e63de59402e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2cfc1d9-d964-4d8e-b2b9-84143dd87dab' class='xr-var-data-in' type='checkbox'><label for='data-d2cfc1d9-d964-4d8e-b2b9-84143dd87dab' 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>description :</span></dt><dd>1-sigma error calculated using a modeled error-dt relationship</dd><dt><span>standard_name :</span></dt><dd>vx_error_modeled</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_error_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-8bf56f20-2664-4d99-8825-c1a293458e17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bf56f20-2664-4d99-8825-c1a293458e17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0395d7f0-78a4-48c6-a8ba-e7a4a572d37c' class='xr-var-data-in' type='checkbox'><label for='data-0395d7f0-78a4-48c6-a8ba-e7a4a572d37c' 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>description :</span></dt><dd>RMSE over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vx_error_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\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>vx_error_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-b8414614-e33b-4486-81a3-9eee470b0f0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8414614-e33b-4486-81a3-9eee470b0f0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c482d34-8f94-4618-b916-5058b38cfa1a' class='xr-var-data-in' type='checkbox'><label for='data-4c482d34-8f94-4618-b916-5058b38cfa1a' 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>description :</span></dt><dd>RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity &lt; 15 meter/year identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vx_error_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_stable_shift</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-563a33a3-3ad6-4eb5-90d1-5e7ccc7b252a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-563a33a3-3ad6-4eb5-90d1-5e7ccc7b252a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69f96e2f-5d15-40be-a444-73c4c087ef66' class='xr-var-data-in' type='checkbox'><label for='data-69f96e2f-5d15-40be-a444-73c4c087ef66' 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>description :</span></dt><dd>applied vx shift calibrated using pixels over stable or slow surfaces</dd><dt><span>standard_name :</span></dt><dd>vx_stable_shift</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-94be59b3-b715-4bf4-8b3e-f480e848c215' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94be59b3-b715-4bf4-8b3e-f480e848c215' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-642c0fff-a07e-4f71-8949-b4a59e0244b1' class='xr-var-data-in' type='checkbox'><label for='data-642c0fff-a07e-4f71-8949-b4a59e0244b1' 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>description :</span></dt><dd>vx shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vx_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_error_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-08483809-eda3-4dc2-b53b-665414a20a04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08483809-eda3-4dc2-b53b-665414a20a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b39f0f5e-6233-48e8-b7a5-320b95ee7e8c' class='xr-var-data-in' type='checkbox'><label for='data-b39f0f5e-6233-48e8-b7a5-320b95ee7e8c' 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>description :</span></dt><dd>RMSE over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vy_error_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_error_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-de3e26ae-a6c3-48d0-94d6-cd4ddd681811' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de3e26ae-a6c3-48d0-94d6-cd4ddd681811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b92b6d3-be85-44c3-bf0f-4f3b6be9a0ab' class='xr-var-data-in' type='checkbox'><label for='data-5b92b6d3-be85-44c3-bf0f-4f3b6be9a0ab' 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>description :</span></dt><dd>RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity &lt; 15 meter/year identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vy_error_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_stable_shift</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-6bdc9cca-0c36-4775-acec-535a68463eb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bdc9cca-0c36-4775-acec-535a68463eb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68f05797-c9c0-417c-8cba-42aaf0bb98b2' class='xr-var-data-in' type='checkbox'><label for='data-68f05797-c9c0-417c-8cba-42aaf0bb98b2' 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>description :</span></dt><dd>applied vy shift calibrated using pixels over stable or slow surfaces</dd><dt><span>standard_name :</span></dt><dd>vy_stable_shift</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_stable_shift_slow</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-87c71c2a-4ecd-4df2-9784-11883d213798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c71c2a-4ecd-4df2-9784-11883d213798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c33414e-01cb-4c9f-988d-092a8d7defc6' class='xr-var-data-in' type='checkbox'><label for='data-6c33414e-01cb-4c9f-988d-092a8d7defc6' 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>description :</span></dt><dd>vy shift calibrated using valid pixels over slowest 25% of retrieved velocities</dd><dt><span>standard_name :</span></dt><dd>vy_stable_shift_slow</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy_stable_shift_stationary</span></div><div class='xr-var-dims'>(mid_date)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-7c7d467c-d2db-45bc-b9b1-445ec2945e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c7d467c-d2db-45bc-b9b1-445ec2945e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d751a41-63bd-4971-bea5-e11e87768b84' class='xr-var-data-in' type='checkbox'><label for='data-7d751a41-63bd-4971-bea5-e11e87768b84' 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>description :</span></dt><dd>vy shift calibrated using valid pixels over stable surfaces,  stationary or slow-flowing surfaces with velocity &lt; 15 m/yr identified from an external mask</dd><dt><span>standard_name :</span></dt><dd>vy_stable_shift_stationary</dd><dt><span>units :</span></dt><dd>meter/year</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca063c87-d6c0-49df-a038-a02b37dcd8a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-ca063c87-d6c0-49df-a038-a02b37dcd8a2' class='xr-section-summary' >Indexes: <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-index-name'><div>mid_date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-715ceab8-d401-448f-9507-8f36bfd99c74' class='xr-index-data-in' type='checkbox'/><label for='index-715ceab8-d401-448f-9507-8f36bfd99c74' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-07 04:21:44.211208960&#x27;,\n",
       "               &#x27;2018-04-14 04:18:49.171219968&#x27;,\n",
       "               &#x27;2017-02-10 16:15:50.660901120&#x27;,\n",
       "               &#x27;2022-04-03 04:19:01.211214080&#x27;,\n",
       "               &#x27;2021-07-22 04:16:46.210427904&#x27;,\n",
       "               &#x27;2019-03-15 04:15:44.180925952&#x27;,\n",
       "               &#x27;2002-09-15 03:59:12.379172096&#x27;,\n",
       "               &#x27;2002-12-28 03:42:16.181281024&#x27;,\n",
       "               &#x27;2021-06-29 16:16:10.210323968&#x27;,\n",
       "               &#x27;2022-03-26 16:18:35.211123968&#x27;,\n",
       "               &#x27;2004-06-24 03:52:47.830695040&#x27;,\n",
       "               &#x27;2014-12-29 04:09:00.898055936&#x27;,\n",
       "               &#x27;2021-09-20 04:18:39.210712064&#x27;,\n",
       "               &#x27;2019-07-20 16:15:55.190603008&#x27;,\n",
       "               &#x27;2022-07-02 04:16:01.220522752&#x27;,\n",
       "               &#x27;2018-01-31 16:15:50.170811904&#x27;,\n",
       "               &#x27;2017-12-17 16:15:50.170508800&#x27;,\n",
       "               &#x27;2022-03-06 16:17:45.210706944&#x27;,\n",
       "               &#x27;2017-10-16 04:18:49.170811904&#x27;,\n",
       "               &#x27;2021-10-20 04:15:49.210423040&#x27;,\n",
       "               &#x27;2021-07-19 16:15:50.210706944&#x27;,\n",
       "               &#x27;2021-04-05 16:16:35.210226944&#x27;,\n",
       "               &#x27;2016-12-30 04:21:26.661210112&#x27;,\n",
       "               &#x27;2021-11-19 04:15:49.210712064&#x27;,\n",
       "               &#x27;2017-11-27 16:20:45.171105024&#x27;,\n",
       "               &#x27;2021-03-06 16:16:30.201009920&#x27;,\n",
       "               &#x27;2022-04-15 16:17:30.210929920&#x27;,\n",
       "               &#x27;2022-05-13 04:15:54.220319232&#x27;,\n",
       "               &#x27;2022-06-19 16:21:55.211208960&#x27;,\n",
       "               &#x27;2021-03-16 16:15:50.200914688&#x27;,\n",
       "               &#x27;2021-10-07 16:15:50.210428160&#x27;,\n",
       "               &#x27;2011-02-20 04:00:25.059236096&#x27;,\n",
       "               &#x27;2022-02-19 16:15:55.210800640&#x27;,\n",
       "               &#x27;2009-02-22 04:00:35.880577024&#x27;,\n",
       "               &#x27;2021-07-27 04:17:29.210422784&#x27;,\n",
       "               &#x27;2022-07-29 16:15:55.220701952&#x27;,\n",
       "               &#x27;2022-01-15 16:18:40.211123968&#x27;,\n",
       "               &#x27;2013-12-10 04:06:41.267568896&#x27;,\n",
       "               &#x27;2022-07-02 04:21:26.220122880&#x27;,\n",
       "               &#x27;2022-09-22 16:18:25.220727296&#x27;,\n",
       "               &#x27;1987-02-02 03:30:57.169769024&#x27;,\n",
       "               &#x27;1999-11-26 03:48:20.227965056&#x27;,\n",
       "               &#x27;2022-01-13 04:19:24.211129088&#x27;,\n",
       "               &#x27;2016-12-26 04:12:57.674946048&#x27;,\n",
       "               &#x27;2022-05-20 16:18:35.220117760&#x27;,\n",
       "               &#x27;1988-07-14 03:40:12.757213952&#x27;,\n",
       "               &#x27;2021-10-10 04:18:56.210806016&#x27;,\n",
       "               &#x27;2022-05-15 16:17:30.220211968&#x27;,\n",
       "               &#x27;2021-04-28 04:15:49.210323968&#x27;,\n",
       "               &#x27;2002-07-29 03:47:38.748616960&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;mid_date&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31f8ac4b-38a3-4bd8-91b0-36af262f92bb' class='xr-index-data-in' type='checkbox'/><label for='index-31f8ac4b-38a3-4bd8-91b0-36af262f92bb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([736132.5, 736252.5, 736372.5, 736492.5, 736612.5, 736732.5, 736852.5,\n",
       "       736972.5, 737092.5, 737212.5,\n",
       "       ...\n",
       "       758932.5, 759052.5, 759172.5, 759292.5, 759412.5, 759532.5, 759652.5,\n",
       "       759772.5, 759892.5, 760012.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ae78d7a-bcb0-49c5-bcaa-8d9999b51916' class='xr-index-data-in' type='checkbox'/><label for='index-3ae78d7a-bcb0-49c5-bcaa-8d9999b51916' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3351907.5, 3351787.5, 3351667.5, 3351547.5, 3351427.5, 3351307.5,\n",
       "       3351187.5, 3351067.5, 3350947.5, 3350827.5,\n",
       "       ...\n",
       "       3329107.5, 3328987.5, 3328867.5, 3328747.5, 3328627.5, 3328507.5,\n",
       "       3328387.5, 3328267.5, 3328147.5, 3328027.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ebfa4de-204a-4323-96c1-f9200495d183' class='xr-section-summary-in' type='checkbox'  ><label for='section-0ebfa4de-204a-4323-96c1-f9200495d183' class='xr-section-summary' >Attributes: <span>(19)</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-1.8</dd><dt><span>GDAL_AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>author :</span></dt><dd>ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)</dd><dt><span>autoRIFT_parameter_file :</span></dt><dd>http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp</dd><dt><span>datacube_software_version :</span></dt><dd>1.0</dd><dt><span>date_created :</span></dt><dd>25-Sep-2023 22:00:23</dd><dt><span>date_updated :</span></dt><dd>25-Sep-2023 22:00:23</dd><dt><span>geo_polygon :</span></dt><dd>[[95.06959008486952, 29.814255053135895], [95.32812062059084, 29.809951334550703], [95.58659184122865, 29.80514261876954], [95.84499718862224, 29.7998293459177], [96.10333011481168, 29.79401200205343], [96.11032804508507, 30.019297601073085], [96.11740568350054, 30.244573983323825], [96.12456379063154, 30.469841094022847], [96.1318031397002, 30.695098878594504], [95.87110827645229, 30.70112924501256], [95.61033817656023, 30.7066371044805], [95.34949964126946, 30.711621947056347], [95.08859948278467, 30.716083310981194], [95.08376623410525, 30.49063893600811], [95.07898726183609, 30.26518607254204], [95.0742620484426, 30.039724763743482], [95.06959008486952, 29.814255053135895]]</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL), California Institute of Technology</dd><dt><span>latitude :</span></dt><dd>30.26</dd><dt><span>longitude :</span></dt><dd>95.6</dd><dt><span>proj_polygon :</span></dt><dd>[[700000, 3300000], [725000.0, 3300000.0], [750000.0, 3300000.0], [775000.0, 3300000.0], [800000, 3300000], [800000.0, 3325000.0], [800000.0, 3350000.0], [800000.0, 3375000.0], [800000, 3400000], [775000.0, 3400000.0], [750000.0, 3400000.0], [725000.0, 3400000.0], [700000, 3400000], [700000.0, 3375000.0], [700000.0, 3350000.0], [700000.0, 3325000.0], [700000, 3300000]]</dd><dt><span>projection :</span></dt><dd>32646</dd><dt><span>s3 :</span></dt><dd>s3://its-live-data/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr</dd><dt><span>skipped_granules :</span></dt><dd>s3://its-live-data/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.json</dd><dt><span>time_standard_img1 :</span></dt><dd>UTC</dd><dt><span>time_standard_img2 :</span></dt><dd>UTC</dd><dt><span>title :</span></dt><dd>ITS_LIVE datacube of image pair velocities</dd><dt><span>url :</span></dt><dd>https://its-live-data.s3.amazonaws.com/datacubes/v2/N30E090/ITS_LIVE_vel_EPSG32646_G0120_X750000_Y3350000.zarr</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                     (mid_date: 50, y: 200, x: 200)\n",
       "Coordinates:\n",
       "  * mid_date                    (mid_date) datetime64[ns] 2022-06-07T04:21:44...\n",
       "  * x                           (x) float64 7.361e+05 7.363e+05 ... 7.6e+05\n",
       "  * y                           (y) float64 3.352e+06 3.352e+06 ... 3.328e+06\n",
       "Data variables: (12/60)\n",
       "    M11                         (mid_date, y, x) float32 dask.array<chunksize=(50, 20, 30), meta=np.ndarray>\n",
       "    M11_dr_to_vr_factor         (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    M12                         (mid_date, y, x) float32 dask.array<chunksize=(50, 20, 30), meta=np.ndarray>\n",
       "    M12_dr_to_vr_factor         (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    acquisition_date_img1       (mid_date) datetime64[ns] dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    acquisition_date_img2       (mid_date) datetime64[ns] dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    ...                          ...\n",
       "    vy_error_modeled            (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    vy_error_slow               (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    vy_error_stationary         (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    vy_stable_shift             (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    vy_stable_shift_slow        (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    vy_stable_shift_stationary  (mid_date) float32 dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "Attributes: (12/19)\n",
       "    Conventions:                CF-1.8\n",
       "    GDAL_AREA_OR_POINT:         Area\n",
       "    author:                     ITS_LIVE, a NASA MEaSUREs project (its-live.j...\n",
       "    autoRIFT_parameter_file:    http://its-live-data.s3.amazonaws.com/autorif...\n",
       "    datacube_software_version:  1.0\n",
       "    date_created:               25-Sep-2023 22:00:23\n",
       "    ...                         ...\n",
       "    s3:                         s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    skipped_granules:           s3://its-live-data/datacubes/v2/N30E090/ITS_L...\n",
       "    time_standard_img1:         UTC\n",
       "    time_standard_img2:         UTC\n",
       "    title:                      ITS_LIVE datacube of image pair velocities\n",
       "    url:                        https://its-live-data.s3.amazonaws.com/datacu..."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datacube_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `vmax` argument to set a maximum value for the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bf60eDP+q5K7HsgMMa2B4VDA9fZihhQxBwuy4sufNWK8Qchn8xZN5z7ADzmhiXcHU9+FprW67uB71dP0mfz7Ckfiswf2ZV0F6ipX39+Gvw2vX+/vh+kFdRV1kKFtpop8ZUmjH6QgpSjYhBBblkw8gfIz5Ub6P5zJMD31QcH6kHjNvk7pWv5/XD/o4zHZjmADb7YbK5wez0Xg9ll0kxGVoRqvbLjb689O1vjAH3ujFozV/XOW2g9A7/iWa4c31vu4NTvIzH3pmbHv+tm5/7eKydW576M9m+O2p/l4Mfat7AeE27POC2Wu+HWRNmKHLcGaQ9KCPZ7gQxwDMyjGMOUHYYKRPtWrOrKg8myxtRllFwkiDMSMyXmcIoXV9PMPoOKsSfUQQojpYX3F/kDb0aYUO5DXLCnC8Dfo09skgmdCGC+INgmMUjmcfa6vRx/P5uc26g/cnQ5cdhH8RvCsG745l4VA5ZgZSgRCbjZz0I2/ZJzLULBbKxrzn9dincQwZQrtqjITiod/IJvr3g2SYdR9lw7qFdps1oiyG0I45nk/xdSsIA2UoG2ckbW0rQ4mZj+wWQuwBJNsWQgghhBBCCCGEEKNCM5KEEEIIIYQQQohxRinPrMSojTFKhxDlaEaSEEIIIYQQQgghhBgVmpEkxpSkc7MlW1ckweoY6dQp/mC6WBhHP8DVKgD8HnQqBEtDIz2BR4D3d44F+CNaKi/RHsTRd0EKwKWT95/u97+w0m8HS0Ujjn0inoVLN8ONktB3EYvjR3qz1VhyHqvSBHlP6JSAsynwcZBglRjE1dNTgDj/ZIN3UOTlPhFcO0PZpfXwW7Bs6DzgijQE9yt0oi5xiflG733J2+FMYt5wtSnWZToJ6CZBXrKs6fcKXDAsG/gzcrhWCqtRNlhCPnAuYTljutOCtla2UltOD01s1TDAvAj6JPonAm9N5G8/dK3RgRTx6KR1WA0yWHGPy3zjemzHwWqPcCSxX0T6B18325/ejfuhbAN/B0E/xeOrqnzd6jtiW3mhx7Sc7XzIP2v1Gr+f1w5g3gUuELRDXI+rwpUafN5WwUXCvKAbJd2IMYi+LLpeIn1wMN6yXbIfoZcuWGkTN2C75V/OUdZBv1zWFoPl4nlvtns6idhHb668Qh9dXQnHf/YD2A5caPS8cfzkmMMxg2XDJeQ5BnBFwLrKnrvA2URPH9IXePxiS94HK5sivWUepMAtxncZ+qG4/Hykzw/aBdPCvGA9ph+THjzWTaSHPs7hg2b65Kzy7wPpKoiFMF4G70roB7Jaf3za7uty1ubfR7IJfjvthV8U/VzgYuN7+MaOLf8v4T1DCLFH0IckIYQQQgghhBBinLFl1baxXzFtPKRBjC8U2iaEEEIIIYQQQgghRoVmJImxpVgcmbKfdyAkhcuackpxLNwqQrB8cGR54SAcjUvKlk/Nx9RsLlGeckny4UiYQEeXP7/db/e99SC3XRj01yt2+qnwaRemsmO6Mqcfd8/xYQ+Gmf21G/306ZoVKEuGznG6NkNSZkz126t8aFzOpZwb/HTnwvoOfz5CFYL7cyntjT5/A8rKN0F4UrReFTFVnGlhCAjrGcOhahBKhqnfWTPCf7ZO/R65n897trukD1P9ufQ1w60Q1hFMxWfYAqf2oy4wlC5r9HWxNMm3rWqGJqA8ssmtbjsNlmtGWyzPD4SUsGwDssohGEnrBH+9Ll/vWBZBXeIy5ZFlvsOlolH3Kp8d7E8SlD3CsYL0oq7XLUdID9pdYUqr3z+I9DLsks/HthUJKU5Rt6ff/MK2a09Hn9Tsw2ur0MeyD0/YBzHEhTAElqHWWHY8wTLZKcOp2Mei7qQMkeX4x9A2hoTyeRDelPTg+FjbIexnOEYyvwb9/jyPhKVWSk5wLPoI9KGFNT7cNnhX4OVXr/f/gLzOEFodvJswtKwBYXvsJ9gnT2h126Vmn960B+2sE+djTArCjSL5lzDUnqF8CL3LY2GPCOcOQnDL60pODQCO5XslwwB5POs184YhoUEoNd8XkPcMzR6OhGGi7Isv+nepMLQddaXTh6YlTX68JekmvDuhnaab8W7cgroKhlt8XazqQd1gmObW9A3nZu0VLy2E2A3oQ5IQQgghhBBCCDHOyC2zzMZ+xbR8HKRBjC8U2iaEEEIIIYQQQgghRoU+JAkhhBBCCCGEEEKIUaHQNjG29PaOxKtHl6qGJyfwArTC4wMvQUKnAxnyUzbpqgmWhcUy5km5MwPHFl5Y5bazGVP8uZ3eKRTE1cMPxeWCqzu4rLWPSx9u83lRRFw8l1zl7NWhBn98Va+P6y/0+LLK6n0cf8Ky41LXhMeTtd4pkdL1wuWOJ09024zLD5wWIPAWlNUtLrVsWOI9oe+BSyHTcUAHApd6Nvgr1vrle4NlyXP/7IGvA/fP6M+gs4nLAZOY+4QOJjojmD9IT7oZToWSz++uv5jjthufh1+sp7JDKeh3ypdNR7uhj4rOouBZ2KdwWesaLm2MvKf/gp4Y+jaG8Kx0CsE1lrOPoz+D7Yx9MMs2RTvn0tR0w4B0xTr/D1iyPW9GXeCy5fD20FXXN8Xnd/0qOJ7Kzk/gEMpbsWw161UM9gsp6mGLT2vSi+sHrhXUBXjyrMmnNy+gLjP9HI+Rl4GPii6ZnK4x9Bvw6ARL1rOf4vXZDwX3p2sNdZHXL89P+KSCdksvXhXaLeng+O7bTdDuON4g74K8ZzsNlpxHu2/xfq/A+YTrlZp8uyrC6UhHYdqBusPnD/xf9PqgrJh+tnOCd7Og7vVuGyODsqWLDH1OkLf0sLGPRL3m/kLg/kQfjnaUVOO9ls8Wc4WyrjRyPMX9GpDXPfBVTWr129UoK3rtsL+wwY/POX2X3WiLjXCvtfu6lU3d4tvKBnfSwTbOKeW5lXbSA7un0iFEOZqRJIQQQgghhBBCCCFGhT4kCSGEEEIIIYQQQohRodA2IYQQQgghhBBinJFZbpmNfVjZeEiDGF/oQ5IYWxobzIovuyACV4yPlc5Xe29O4ESiZ6AA1wmcEoxVp/cgh8Mh8JdUihWGY8FqKzsUcvgrSi1wEjy3AsdP9WmDE2lwCuLekdSs1jsWshrfFXS93p9fqvZ507YEfig4ExgXH/isEjgiKGWKeHPokMhxfAr/Rd7uHUiFjR1+P7049AjwfuVeBfod6D6BAyFr8GljWdX85o/+fPoiGli28M4YdsNPkdTBMdDi21EyBGcEr8e6HXMiseyY12xX9IPAj5Gv9N6cAvxXDSvg/xrwz5Ov2+CvT0cS+xG3M5LX2GY9D9wmdCrR4xK4T5B3uGPgUIq4VPJe77sIHEpIf+DNYbtg3rHdsyxjdYcuGjqlWFd5/ybvGSqubPe7N8KTg/zIJm1ru0mnd3V1HzPTbde2w1v3J/id6EBq9n1+srEL2yhrjiGsG6xLdALSt0XPDMY76/VOqMDFEnEY5RhjkgG4UlrhgNqMukh3DUHZx1w3AXzeCucm8EOxXgftMlbPuc12S2cR310qJH1L+tBu4JManurHpMFWv792rS/7Ajx8fN40o3MJfTjHe3h/8jo4oDJ6gfC+ssG343TKZH891gWMWUE/XCGtQT1hWfH8oA/2z5Y2wlWG99AE43tOnxXfjfgeG4wRgO8nsf2s+6hLdLdldT5vU7wLBu+Gtbge3iUHp/l+qlTr01NbRFsdKrn/CyH2LPqQJIQQQgghhBBCjDMyMyuNg9lAWfwQ8RpDjiQhhBBCCCGEEEIIMSo0I0mMKXl1leVVW6a2JpzujWXMk+k+nMu47DrCGIIpyUOVl9IOl2GPhNxUWnYVU6cH3jDdbRexpGm6yodBFAf8s+RTsIQ7SDt8XvUc4aeuNy3z05U3vtFPn84YUYM/O0x8Ckuwd2OpZiyJGywVzTAK5GWGpbWD6d8oyxR1IV+x2l8Py/8GYZKvm+LT85I/n7Csy5f0DabJI1RseJKfmj7UgmsVEYYwZZK/HpeKZt5iGv/W5W9HTvcRoSFc/nczljlniAxCahKE3GQ9kZAYbu9sKN10lF23T1+hHX+160TIUKtvG/l6HyYRhJWUhbkEfQb7BIa81EdCWllPGdrGPi1Y4hzbDG9i3jFkhyGgXPYcf39kiEzQRzKkJwj5Qd1FeoI+mGEi3ObzMwyD98fxDCE2bKdd2/pNhh83L1rmL43xKEOYXTrBt8ukC+0KfWS+0YcTsZ0xBCWoS2xnfNvjEum8PkNqmddo50HZbsKS7+gng7JFH5/j+YLnJ6zbbAuEbbU8ZIj7GEYYa4esxwzt4rsOz+f9kFdshww3sqxyKHip3pdt/wRWDj8m1DF0ffV6fzj62CCENhJulXRhzOnC9fA8DM+OhWsxv1zdYj2n4oDhvxwD+F7Jeoe8CMZH1jW026zZ99GFDf5dLwjnZZ/PEFSWDcdHhIoHdYnv3bheodPnV17t8zer8fkzONGnLyn5+1V1+PtVr6/cp4+Etg0rtE2IvYE+JAkhhBBCCCGEEOMMybbFeEWhbUIIIYQQQgghhBBiVOhDkhBCCCGEEEIIIYQYFQptE2NLats+Z2IZUy4lnbR690ze6t0zwfLBXG45svxx4LoJlimv4EQyc8uY51ietubJF/2x9NLQjcK0wlNDb82m+Ue47Z5pPm5945/5Z81m+ryqrvXP2t/jn7X5RZ83xWVY2ppMQFnRswMKc/b3x1f5ssuaEOc/6OPf0z7vH8kntbrtBA4pw5Lw2QEz/PU2Ib8bEce/ucyxgJh/OgWKm+gY8puFbu93yFqxLHg/nAf0Z8BNksI3VZrpnUvB8rtQAqVcTpguFBC0q0ruMAvrAv0V2UzvQEo7kH9wVuSoa/RdsV3n7VjKmv6Rqgq+Ehyb0jFEnwZdKPRV0O0RLKleeY2UjHnJvCcl+K3oB6FnphbpIRG3S+DBiXhu6PuKLmWN/KXjKYmkr7ARdYvHl5cPl5Pns9ajT2c7oguMDg9sB/4pOo14PPOWYwqXDcfxebPvdzIuy428Zj8T1OUGOJ84BqCfDDx4WAZ9p2F+sG6R8vxhvQ1cX8hbOP2M43WkHQduNLq+6NGJQffYxg63Xar1zsaW5+GZg+cmqMv09kX8k4HTif0U63bE8UQCVx36/KCfLEt/zF/F7bTZv3eGaYdPivWQDia2m/aNfnuqd/px/Iv5qJKU7wuV3Wfh+Ij8ofsNvqq8E25TjGmFFp9/hQZfVkNNPn3VLI8i2kKJffyW/M/5nvQqp5TnVuL4NEbpEKIczUgSQgghhBBCCCGEEKNCH5KEEEIIIYQQQgghxKhQaJsQQgghhBBCCDHOyCywEIwJ4yENYnyhD0liTEk6eywpvBzLzNhuxnIjzj+IgObxjJvH/nzAx3pbCV0kHRj0l5CyWPnAb0G/BuPm6VpZuabirRiX3vaYP37TId7507DCn9835M8fbPLPXuzz6a3qgt+CjgT6L+gNgIslyB/kfV7vHQrFF9f6/bh+CXH7BcbH0w9S8n6PFOfT4ZD0w69R7ryI+SPgayj0eBfI0CTv18gL8Lj00JeBeglXSqnO36+qHb4OOJ3oPgnaDVxiBg8NHRJB+mpxPssG/oxkGHWhAW2j2tclOpRyOiFQt5JJE/zxmzr8ftTN8ucLPGoRH0bg7PFnh+4Q9EmBPwP3D5xI7GfghYmVVeAmQfoDdwtdMoRlTS8Q08vr0WtDzxDaGp1Pgb+LTij2WyzfqrJtOHwCn1VHl9+meyTiGgs8PPRVsc9sjDiAmL7IfjqRsjrkZerzphqOJI6nCcfzoM+v3O9kyK/At8VtumfoegHJhFacX5bfrLdV8Kz1eU9Nwj6RTiS+W6DfCBw/Kd8P0IeyHrNuoe7xWet/t9wnZ7J3DNp67+kJvDvwV3EMCpxOQbtGWwL0ZQXPg3e5oF1zP/rJ8roalGVV5T42w3jBukK3V9Dnoo9lvQ6cTE//ye/ns0bqVuCjGkJdoc+K4z3f3ZA/9IOxLQQeu3UbfPJQ16pj79kcM3fkTFS8jRB7BTU1IYQQQgghhBBCCDEqNCNJCCGEEEIIIYQYZ2SWW8nGfsW0bBykQYwvNCNJCCGEEEIIIYQQQowKzUgSY8/WGPOWZvfPg/u1uO3qP6335yEuP29t9JeF1yZwoUyZ5K/X2e236ZSI+EacP6W5yR+7wceB2xr/LIFzYPJEfzzdJXSbIC799d9Z57aHpiA98PCkgz6ufrjBdw2FHjiE6IZh3Hrgr4g4ouAoSuGGyRkHz/vjfhnyOzn4AH+/Fd4pFcT9s+z5PA1lHgGWDXwOJbpM6FQYgBNh0G8PTvDOgISOhSF/veJmXxfoPims8o6CwLdBX1U/PD28P70y9b4uB3U14tVJBuGKQVlkdb4uBy6z1paK+wPfRpPvNyxBXe0qc9/QRwHoRmHe0DUS+JuYl3w2pq22srMpT+A2oS8DBPen54fOJx7PvKTjidCnwfvRlUKfRzPux+shv4M+uzHyCsTyKgd+pcCLw3pOVwoInID0RzEt3Ob57MMI2zHyvrAZfT7qYl4Ht1nmx29jfnD8ZN2g74p1gWVngNnLugmXXd7lPXtJueuFPsWOTn8s+0z6n1A3gnbC8ZBeG+6nc5DX4/1ZNzC+ZXj/MDxfTkcg3W8YEwL/FdNPtxrbQquvO2kjxhCQw5e5s6QTypxQePfIp/l3r6QTZUnnD12bbFe1cPyxLNmn0YOHvB2aM81tFzfBJbahw1+f/kc4kBI4jugiDfoRlg2fh/0Q39X4rkVYt5l+eu3wrp2Ucvd/IcSeRR+ShBBCCCGEEEKIcUbJzMbDt7HKfw4Rr0UU2iaEEEIIIYQQQgghRoVmJIkxZXjGRBuu2RKKUnzJh2NVv4jp2ZjqzuWLk24sa9oXmX7NqfWcus+p+ZyyXGGZ0mzlan9sbIlVhmhgGn7vIZPddrHP/11goM1PB65b45+NYQp9M3E/0LD4RfwDpjMzRAZL6AZT22NLurJsuQQvpl/nGysv7Rxc/pml/noMv+LyxbUMXcDzbtoWCpDt58sm7fZ5X1yHZcE5zX/ATx0vTfHT/Kvbfd6mGzGVnEueV6OuoSy4DHkyEUs/My8Y6sYl2BmCMnOKT+8K366DuhML60A7LcRCehhSxHbOusbQtxKWjC+vKwy1Yqgbw2di4UVMO5etbmJ4DsKDYqFmDBdiOyZcJrur8pL2QZ/JpbHZZ07zbSUIo2DYIUNgWXcYyhaE1ERecWJhFOV1k9eOLEluVahXDMVmSEyQtsphiEF4UCR8OGv2Y85wo79+1SYsg47Q8KCdMq8IQ/OC/ZVDXIO2UCnM0Cx8frTFYD8p6yeCJdsjYXbWiz6V4x3TznZEmDexuoPwIt4/7/TtOGU4E0Nwcb2M/R5gmGLa1uoPYFnw+Xoj7w8Mx0IIbTCmse7g/LzsfSV4Nxvk+Idthmqx3bNs2UfxftAY5DUYzzENpdiJ8Yx5WYd3S7ZDtlvWzVi7ZXg1w5mpIeD7Quw9mvkVa0tB3Yr0m0KI3Yo+JAkhhBBCCCGEEOOM7OX/xprxkAYxvlBomxBCCCGEEEIIIYQYFfqQJIQQQgghhBBCCCFGhULbxJhS7Oy14lbHC30Y9IsETgjEhnMZdkKfR/umivuD2G26VLiccZm3IDg3smx48Ky4V90q7ywYavXXr9rs86Z/is/LwgCXaq6cnGzGJLedwrGUt/sl5BM4hPIJ3vOTM2/p22jf6PfTIVXjHQ5pZEl3egCy/f2SuTaEurTO3z9wLjC9ZffncvWlid4ZUOiE/6Gz2ypRWI6lmelxoccGToWk3S/lHDgF6DTAs+bIu6AsBuDdgZMh6YSrjO2aziJen20l1pYSlCUdEHTN1GKbTij2E+vby+6NJcjxbIG7g0sj01lEvwV9V83eZZIMw8+xCWUdLCVNlxn9GvTKVHY6Bc4ieoLgz8hL8F0wvVwKmu2a6QvyC+dHvEPM75xtkf1IeXmzD6NnJtYnNaAPjixxHjwb62mK+3GJdziRhlrgFGQ7ogeI/cpm1B3WFZYNvDqBVygG+q18J5cRD+pqcADr2rb0J4a8nDbBbaer/PhHV1nwbhBxLgWeHRD4Gll2PJ+OIWxndDrl6GO5BD09eXQmsc9Evxe44tCuU3p92Da60E45HmObzqagrZS9K+ZDGO/Ybjl+xt4z6VMEdBZmjb6e9u3nx5B0yN8vHfTPWtWFd7MJOH9dh08A221biz8f71p5lW/XaUePP38ztvH8gZuU+Ym2kcMXmtCBRF9Ym68rSd/gdo97tZNZYiVL4gfuhXQIUY5mJAkhhBBCCCGEEEKIUaEPSUIIIYQQQgghhBBiVCi0TQghhBBCCCGEGGdkeTyqcm+lQ4hy9CFJjCmlplor1WyJj097fdx9EnMq0BvAuHk4FPJ+HzfP2O3AQxDcr3IPWn49Xiuh54bXoq+B2xUcPWZmecFvDzUirh0en2QYjoYS0sM4/qZa7J7sj9/gfVPJhg5cH2VJpwFHp154hZBfGf1WdC7AVZP2VHan5JPb3HbSD48A3S5lzoWk0zsC0hVr/bEo+2zWVH/86na/fwp8HJuRFwRll09s2cGBW0i64DSo5IUxs3xSqz8fPim6UKwIVwodSCTmQqN7hfnZ2uS2Uzii8gbfzjcf5Mu6YZn3byTwZ5V7daLvUPBP0FFEV0rQ53TBqYRnsyF4Z1Bvg7JcuW7HaTWzBO6QDL4LulvYTpOJ/v75BrjO6OOiH4Pb9NokEUfSIFwo8GnRR8J+J0HVDdwv5eBaOX1MzBu6vdjHcT44nR7sAyMencDh1OfrYs1G1C3Cdsu6RF8VyxaeoBzpD8bAqsoeH3puAjMH6m5SX19xf+AVoiumvG0yr5/5k9vMmNdMOx1Ik3yfToJ2g/2BQ6kOeRnzVTG9dKfxfnSdGd9ncD7qBs8PnE3oB7M+1HVsBy46Xh8kdM3RWVXWzgNv2hr0mczL4D0TLjGMd8OTfR8+MMk/S3WHT1vtGv/sffv5el3V7Y/PquHFQz3PprRaJQKHUjXqGp631AYHZMxJSKdRE71/GG/pRGLdZtvi+8zW5x+u/M4nhNg96EOSEEIIIYQQQggxziiNE9n2eEiDGF/IkSSEEEIIIYQQQgghRoVmJIkxpbB5wAoDL09hxxTX0tRWf+wKv+RuEALDcDCQ1CLUAFP5g7O5hCxCE4KwlLL9nMafI4QjWLqYYQw8vs9PFy5iSdaBNj99urrb502x328PtPr7DzYjlK3oc6O6CyE7DJXDcr15E5ZkXY2yIwhDyBFikwdT9xHK1oCwBk5HH8DU9kaEwCCULa/DErY9KK/yusflbnMsCw4Yysap4+nq9X4/lufNEKrFEJaR5W+3wqnnERLmJUPFcL0gZIUhOgwrQPhWwuWSGU7FEBm0+5TLlmPp6dLrfBhmzUafP8PNvi4UuxBSU143uYp3kBeRkA+EaOQ9fhlukj+/zJ8/bYrf7vbnczn7ILyKIbEM942FTxEsy826wzDLpBPhVaybDD9KEV6FbsAYXsZ+lyEzgGEtSaNvu67uMTyIodFcBpshMGxX7PMj4c28fjJlkt9fj34BfVrW5p+td5YPUemb6POibYmvW8V1CO8lDEsM8gNlWVU5lI7hSVE4RrBfYTh1hZBatuPk9bP89iZf79nn8F7Zah/unKKeJS0IYWVesV3i/SAI3ULec3zM2AcjrzOGgLJfY9ggwfMk6Oc4nqcMpavwbmVmlg346zGUne9bQchqWdti+G3WgXqO8SboUwnfG5Nmt9kz1ed1cTPGM0YFDvi86J+Ed8E+v7/Y4599uAHH9yAEdYJPXx55jy5sQl3n8UGoO+pWk+8nSgjTrF7r21bW7EMF0y70m9QObB0DIioKIcTuQR+ShBBCCCGEEEKIcYZC28R4RaFtQgghhBBCCCGEEGJUvGY/JHV1ddmdd95pl112mc2dO9cOPPBAa2lpserqapsyZYqddNJJdu2111p7e3v8YhV44okn7Atf+IKde+65dsQRR9j06dOtpqbGmpqa7OCDD7YLLrjAFi5cGL3O7NmzLUmS6H+zZ88eVboWLVpk559/vs2ePdtqa2tt+vTp9s53vtPuvPPOV/S8QgghhBBCCCGE2Hd5zYa2LV682N73vvdtd9/69evtoYcesoceesiuu+46+/a3v22nnHLKLt3n0ksvtUcffTT498HBQXv22Wft2WeftW9961t29tln27e+9S2rpcdnD/C//tf/smuuucbFwa9Zs8bWrFljP/3pT+073/mO3X333XslLbap06zwcow4lk0tLMMy6vQExGKz4bAI4vq5zCidDHRU5IiV5xK0XJK3/Fj6I/gssSXPEe+d9vpnLSCOfrjOp60aS8QPNfj9rX/0ToVSDe5PmPd0SsDdQndJcDnmPfInoUsFS01bc1PF/XmTr1tJT2X/FR0Y2fSJbjtdWeYx4rLZdHUQOhb4bFzGOrKsd0LXB+8fWT43qIssK9TzhHnPJe7hzckP9H6RlO4UpofLKU/1Lhg6ooK2wSXg4WwqlHx+Dkzw1yv0YTno6dscSwn8VVzmO+hj6Bah0wfQ2RP0A4ReGfShMe8OnUCB34r9Evfz+lzamU4kwn4xRob7c9l1+E2C/GR62W/AdeP8XzF3V6yPhy8r6DfYrlhWvD7TinbHvElRlsWJ6GcmwRdFxxP7mQbUNbhhAnca/Ff0gwX+MF6P/SY9QvyzKMd3wOuVl2e+n3eR2RCWMIdbLHh3AGmL99AE7ypot/TUJHAasY9lPxHUFTgZg7yEUyjok7k/dj/28UFdrvxumQRjKu6P9GQRJxRtOeWOSvqt2Gfk9EXV+bIanuUdfMX1XW67sNn3+S1/8tcv9Ptn6Z/ir1+7Bv0M2yXe7XK8u1XBicTz6bukuywvoh3H3h8i5OiXEvQLWY2/X9ru8zMYM+C4GhmDhyPvYa8y8twsy8c+rEzqKUFesx+SzMxmzZpl8+bNs7e85S02a9Ysmz59umVZZitWrLB7773X7r//ftuwYYOdfvrp9vjjj9sRRxyx0/eoqamxuXPn2nHHHWeHHnqoTZs2zSZOnGjr16+3P/zhD3bTTTfZn/70J7vnnnssTdPojKD58+fbZz7zmR3ur+YvLuDrX/+6XXXVVWZmNmfOHPunf/onO/zww23VqlX25S9/2RYuXGg/+MEP7IMf/KB9+9vf3unnFUIIIYQQQgghxL7La/ZD0rx58+yll17a4f5zzjnHvve979mZZ55pg4ODds0119h999230/f56U9/asUdfLE/5ZRT7OMf/7i9/e1vt0WLFtldd91ln/zkJ+3www/f4fVaW1vtsMMO2+l0mJl1dHTYFVdcYWZmr3vd6+w3v/mNTZq07a/9p512mp155pn2gx/8wO644w778Ic/bCeeeOIu3UsIIYQQQgghhBD7Hq9ZR1IhFjJgZmeccYYdcsghZmb28MMP79J9dvQRaSt1dXV2ySWXjGzv6n1Gwy233GIdHR1mZvb5z3/efUQy25InX/3qV0fy5rrrrttjaRFCCCGEEEIIsWO2rto2Hv4TopzX7Iyk0dLQsMX50M+Y+D1wjz19n+9973tmZtbc3Gzvec97tnvMzJkz7eSTT7af/vSn9n//7/+1zZs3W2Nj43aP3R3kQ8OWl16OZYbDIWlt8QczTh++jhxx9MH5cC4ELhgG/9KngVhua4Bjobzs6NxJ4aOwyv6GIC2RwOT65zf6f0Be5PDK1LT6Zy90+jj8AvMqFhfP4yNulphTgj6OvM27TJINHUgP4vrrcTxdLfRr0DtAvwjj9NMK3+Dp3ujAuXCr5PTQwH+RTvZ+JoP/IXCr8HoMd6XHh+mthqOoF14f5HW2AXUvh4Ph+eV+N/0a9HXQgcB2TscCyi4v+v2s22zHdfCN8fxk5bqRn7Ne73VJ6Wdg2lk2DXAI9aEu4Hg6j/KNHX4/2xHrNb08gdMJ+1m32Efi/oHviy6THuR9I67PP+jQF4bnyxvhOmM/24Y+H20tcDzBj1KxXbPeMq/YB7WiDyqrR9s7PqjnHA8jnjl6cPJJrf78Ll93e2b447Mq3y+kPSgL9Bs5XCbJZnjxYn+sg6uMdZ9kdCqxX2O/w/vzeDqZZmz7w1oyAL/Kal92wbXZp7IdkoibLPDgBJ4etDu06yCv6Lljnx/xBEU9OGw3sbrKfoJOw25soy2w301Y9+FKS+FCy8u9gWxnU/wfWBP2EfTAsazo+Fu/yW2XZvi016zu8Gnt99dPN8E3hT49a0afOIDngQMp6cb1UFYJ3x3pTKr37SahPwzHJwP+eikcjyl8lXkd2infu/EumbUgv7a23US/3gqxN3jNzkgaDUuWLLHf//73ZmYjM5P2BP/5n/858vOeus/g4KAtXrzYzMyOPfbYii6luXPnmpnZwMCAPf7443skPUIIIYQQQgghhHj1oQ9JoLe315577jn74he/aPPmzbPSy3+tKA8/e6VkWWZr1661X/ziF3bmmWfad77zHTMzO/jgg6Orwz388MN2xBFHWENDg9XX19sBBxxg733ve+173/teuIJQGc8995wNvzyjJ/axqnz/kiVLRvtYQgghhBBCCCF2EyVLx81/QpSjuX9mtmDBArvooot2uP/yyy+388477xXfZ/bs2bZs2bLt7tt///3tvvvuizqV/vSnP7ntF1980V588UW7++677fjjj7e77rrL9ttvv+C85cu3hZfMnDmz4j1mzdq2XHf5eaNlxYoVFfevXr165OekudGSYtP2D+T08BouaYvp3pxlxbCGWLgYQ2S4bDrDRLA/KU8fpt9yud5kQqu/FqaeR8PquGQqwuzSbkxvRphC/Qsd/nyEKfTP9umrf77d33+TnzrOvImFKSRt/vpBaABCXBjKlnM/QmiSEupKrG5wGXFMlybJurLzsTQzw/KCkJTEpyXhVG1Mpc+xtDGnngd1BSGg0WW0E+Qln4ftDn1U8Hwzp/v7L/P9QRCOxWXFESLDsMYUU/WHWv3zVDNUjVPt2TYZxvjSWp+esrqZBH1QJISDoW5cvr4GdYGhV42xMArcnyEmXGac+yOhXvl0v7R1sMw3+1DUtXyKD8tMeHw/0sf9sfxln8znYVth3ePzsC6W1ZWgT0BRBO/XLCuE4zD8hu0ySZE2hmKx3RXQj9QhfBkRto0rfd4XehECM4z0M+yS4V/sV1j30C+x38jZJzPkhmXDUHf2g/UIPQzqHvq9oW3pH2714UJVfW3+Uhhvk9Xr/bXRroPQ7I0ojNia2s0IoUUfmW304VMJ+uggfLo3ElrPuoV3sRxlG4TC8X6x0Di042Qq+p1O5FedL58grBH9NN/lXP7gWkHZ8l2mE6Hna1D2E31dGZozzW3Xv9jhj2eIK+sx363YbjC+DTX7Z699zo9n2UQf/puuQ2h6i6+rgxN9O6pZ68eI4TaEliF0LckRytaNEFi+L7HfIKj7ae7LL2vcUn7ZYOUVrIUQuwd9SKrAkUceaTfddJO99a1v3WP3KBaL9i//8i92ySWXWHNz8w6Pq66uttNPP93e8Y532GGHHWYtLS3W0dFhixYtsq997Wu2fPlye/TRR+0v//IvbdGiRdbS4geL7u5tg1/MeVTubNq8eXOFI7dP+YcoIYQQQgghhBBC7DvoQ5JtWZ3tqKOOMjOzvr4+W7p0qd1999323e9+18477zy74YYb7LTTTnvF9/nZz35mg4ODlmWZtbe326OPPmpf+9rX7DOf+Yw999xz9tWvfnWHH3kWL15sra2twb+fdNJJ9rGPfcz++q//2n72s5/ZkiVL7JprrrEvfvGL7rhyiXclP5KZWU3ZX3P6IIMVQgghhBBCCLHnyfPEsnzsV0zLx0EaxPhCH5LMrLW11X2kOfroo+3cc8+122+/3S644AKbP3++3XrrrXbhhRe+ovu84Q1vcNvz5s2zj370o3bKKafY7bffbn/4wx/skUcesaamMNRrex+RttLU1GR33323zZkzx9rb2+3mm2+2z33uc+6DUW3ttum6g5GpowMD26Z813EK8SiIhcOtXr3ajjnmmJ2+rhBCCCGEEEIIIcYWfUiqwPnnn28PPPCA3X333faxj33M5s+fb21tbfETd4K2tjb75je/aW984xvtySeftH/7t3+zz372szt9nZaWFjv33HPtxhtvtJ6eHnviiSfsuOOOG9lf/nEqFq7W07PNyRILg9seMQeTo1gciZ/nsp/BsqL0exThUOiLxFbTqUC4vDPj+hm7Dg+C83HAZxF4YTgrDG6RMG2V3Scpl6iln4PUIX3wztRswtLPuF/OvMSSs0FekWDZbRzPssbyvfRb0CPE6wcen2DpaCwjDj9IVo+6WTZrj/6LwMnz3Et+m14W1pWBSGw/HUh0DGBZ8qTFh8zmtWg3WJ43qIssqwZ8XIYTKeHS07GP0SwblsVmn57B101w28Ue//w5HUjVvu2Vmv39huv9/qqqGW670LGtbiX0WcDrkjPv6eyh6wPPGiz53oHj2Q/A35FhBmnCdhT4N/A8g7g/3WRwvQUeG/ahTD+XPeeS8vB3JL3w2PTTYYTnQ78QentwPvtlevnKxsJkM8q6vnK95vgVOvXQzlk2XKK9hmnFDZevcZvp/r4eZ3CZbDjM9zvDWNZ7+qM+b6vpm+I2odePfjG28xo4CuG9CTw7HIPoGeKYxX6STqqV60Z+ruprrXivpAuelwbUax6/Fo5BppWevP28IyjdjPFugu/TU9Z79FN0FgZ+qiHUPbYDpDdFO6aHL3iXYt2OudD4PPSLcYl4rhCPfjmpgS+LY1gZPQf4vK2ja2wDfJf0K671zqRiu/dX2TRftvS6pSvhXMoq+58KOL+wYoM/H2NSumqd3892ibyrXc4+0e8vsk+O1G2OWdaIsmG/wj4bdTer9XVhqyNqqB/PJYTYI0i/HmH+/PlmtuXjyo9//OM9co9DDz3UDjroIDMzu/fee3f5Om984xtHfl65cqXbV/5xJybDLp9RJN+REEIIIYQQQux9SpaMm/+EKEcfkiJMnrztrwc7WnFtd97nldwjr7Dyxxve8AYrvPxXomeeeabidcr3H3roobucHiGEEEIIIYQQQuxb6ENShPKZPbsS5rWz93kl93j66adHfp4xw09rr66uHvESLVq0qKIn6aGHHjKzLdLtrRJyIYQQQgghhBBid9PV1WV33nmnXXbZZTZ37lw78MADraWlxaqrq23KlCl20kkn2bXXXmvt7e07vMaCBQssSZJR/bdgwYJomnp7e+26666zY445xiZMmGCNjY126KGH2uWXX24vvfRS9Px9HTmSItxzzz0jPx9++OF75B6PP/74yEykXb1HZ2en3XXXXWZmVl9fv90PQGeccYYtWrTIurq67P7777dzzz03OGbFihX285//3MzM3v72t29X/L1byfORmOrAcYTY8wSB8DkVDfw4FovLj8Vy1yN2m84FeHmS8uPpJmEcPX1QdJ8Q+ibgUgm8Nox7R9oTOpQYZ78Gq/XRYUDoRIA/JHAYTfaemyAOHl6evH2jvz69OsxvPh/LlnWF58PRkG7qtB2RbIIzgWVDJxK9LSnybvLEymlFWQfOJbpDhiJeGJCxXsMnFZRtrb9eMuSPz1sqfxwvNdKd4jeLcFJUr+zABVDWcCaU6JZBu8+Kvm6U4EwqdJfVjX6UBXwRgROJ9QrkdIGgHdFFlhSRV3ChBHWB7YrpYV2J+CxY9jyezxO4UeDPIgnTw/xkXSbsZ3vh66KjKUifr/vueeivossD7Spv9HkV5F0wXtEBCH9V4J1DQ5k1zW2W4HVL4cHL0IUmrLpDGG85ZsGnFTxPbLxlP0nfFvejbWTwjdHPRSdScH+OkeX5SxcY/Uzo84zjG8cfjp9sh6jX6boOv58+px6M9zEnEnxcOX1dzLsB9EuEfS7f1TjGoB3TfxV1KIFoPxF7Xyk/Hveu7vLX3urc2crARF8WdS/5dwP2wTnfVTpRtwi9ddxmH0i/JPN+Y4dPD8eUYBVnXI+eOz4fxyB6/4qoG4N4nlLlfsY6kL+p78N5v0Lflucr0P34KqeUp1bKx37ux55Ow+LFi+1973vfdvetX7/eHnroIXvooYfsuuuus29/+9t2yimn7NH0LF261N797nfbH//4R/fvzzzzjD3zzDP29a9/3b7zne/YqaeeukfTMZ4Z+1o5RixYsMD6+ysLjr/0pS/Zj370IzMzmz17tp1wwgnBNbZ+1bz66quD8xcvXmz/9V//VfEeK1eutAsuuGBk+/zzzw+O+clPfmJ9kKiW093dbeecc87IF9oPfOADVsOXJDP74Ac/aC0tW2Sm//N//s/gi26pVLL//t//u5VefoG7/PLLK6ZdCCGEEEIIIYR4pcyaNcve//7325e//GW7//77bdGiRfboo4/aXXfdZWeffbYVCgXbsGGDnX766fbkk09WvNZPf/pTe+qpp3b43xlnnLHDczdv3mynnXbayEekD33oQ/bggw/ar3/9a/vXf/1Xa2xstM7OTjv77LOj6diXec3OSLr66qvtsssus7POOstOOOEEmzNnjjU2Nlp3d7c99dRTdscdd9ijjz5qZlvCwm655RYr8q9SEZ5++mm76KKL7LjjjrO/+qu/siOPPHLEhbRy5UpbuHChfeMb37DOzi1f3E8++WS76KKLgut87nOfs/POO8/e8573uLR2dHTYokWL7Gtf+9qIIPvggw/e7kctM7MJEybY5z//ebv44ott2bJl9ta3vtU++clP2uGHH26rVq2yG264wRYuXGhmZu973/ts3rx5O/W8QgghhBBCCCHEzjBv3ryK4WLnnHOOfe9737MzzzzTBgcH7ZprrrH77rtvh8e/4Q1vsNmzZ+9SWq6//voRZ/C1115rV1xxxci+Y4891ubNm2cnnnii9fb22qWXXmq/+MUvduk+r3Zesx+SzMw2btxot9xyi91yyy07PGbmzJl222232cknn7zL9/n1r39tv/71rysec+GFF9qNN95o6Q5CITZu3Ghf//rX7etf//oOr3HiiSfad77zHZswYcIOj/nIRz5iq1atsk9/+tO2dOlS+7u/+7vgmFNPPdVuu+22iundbRQL26bicvozQlK49HMSLHmL6syp8SQSdhKEfXAKL+9Xvs2wBIRBJJ1YUp1Txbk8L8Mk1mNJWYZLcfleTo9m3kWW0c4HMB0b08GD5YQ51R/kq/0StEFIDmbUMcyBU+85NT5n2Emw1HR1xf1BqCApvx/rGesNp9lzKjnCIIKp4gOYKs5lrLkUM6eCM+wQy/v2vfkAt1273s9UZJhCXu23042oG3xePl81Q+H8/lKDP39oul8SvtCDsA0ss86p9L0zfNup3eDPr38OyyWj/HJO7S+/N+tRpC4EISdTJvnjGVITCZENQkoj4T1BSAuvx7AI9mMM2WG7wlLT1oRQMoQCBn12hvxjmATrfrCMOfazXwPZS3510xTLjLu2FQsxYZ/IUG0SjFfIW44Zw+jjGLKJ+6doF1byeTn1Cd/HrXuzH0OKy7BMOOsG84N9OLfZpwYhOigrhtAgvDhBPxOEO3NMYL+MMSRYwr78UvyH3mx7h+3w3sGzcsY485LtjGnjkunMi1a/hD3frRKkJ0e4Vc4l54PwpwgMMR2MhMqh6IKwRIZjoV0HoXLMzwb0Q+X5iT4m7fc6hyLyfmgS3sXQzjn+BuHFUyf7bYTx24TWHafVLOyTMaawDyTBu1ZQV9FO0CcG78E8HuHLwZjAd1W+e7Fp7TfVbZbq0Oejrg1M2vJ8g307WWfHOZkllo2DIKJsD6/aVmDb3Q5nnHGGHXLIIfbMM8/Yww8/vEfSMTQ0ZF/+8pfNbMuiU5dddllwzLHHHmsf+MAH7D/+4z9s4cKF9tvf/tbe8pa37JH0jGdesx+SHnzwQfv5z39uCxcutCVLltjatWutvb3damtrberUqXbkkUfaaaedZuecc47V05UzSt773vfajBkz7Be/+IX9+te/tpUrV9q6detscHDQmpub7aCDDrLjjz/ezj//fDviiCN2eJ3rr7/eHnzwQVu0aJH98Y9/tA0bNlhHR4fV19fbjBkz7K1vfau9733vs3e84x1hrPl2uOaaa+yUU06xG2+80X71q1/Z2rVrrbW11f78z//cLrrooh3GpwohhBBCCCGEEGNBw8sfh2OKml3ll7/8pXV0dJiZ2QUXXLDDSR4XXnih/cd//IeZmd1///36kPRaYs6cOTZnzhz7yEc+ssvXuPDCC+3CCy/c4f66ujp7xzveYe94xzt2+R5mZkcdddRuXz3tuOOOs+OOO263XlMIIYQQQgghxO4hs8RKe3g20GjTMdYsWbLEfv/735uZ2SGHHLJH7vGrX/1q5Oe5c+fu8LijjjrKGhoarKenxx555JE9kpbxztjPkxNCCCGEEEIIIYQoo7e315577jn74he/aPPmzRtZFOqSSy6peN6FF15oU6dOterqaps0aZK97W1vs3/+53+2lStXVjxvyZIlIz9X+lhVLBZtzpw5wTmvJV6zM5LEOKG336y4JeY5b4WfogOx44wVL9DvwUD7yHLDsSXjSWxJ2Zpt+/PAl4G0x9IGktXICziQAmdQMxwJvD7dKfDmBMczZJJxzPB7JM3eMxAuKw5nAvODS9oW4ICgd4D5TXcNvQC8H5cPjoWIlnsCNnsnQM5lwllvYs6DAu5N1weX2e6gywvdOvMK1P0/DKhTvbcnX7PebZcafV4WI0tRJ8NwJNXS+4NNLjuO/XmVv/7gBO+sKPRXXua8eoX3iwXLMzdgqe2y/AuWxabXhstwT53or9UFd1gF/9IWKi+zTfdK4MdAXUjYriJeHrrTgrrNdsb743p5C5Zu5hLyrKr1lT04ebsvy6QF/R5B20snwSfIpajLV0uN9Tl0mwR9KMebyBLmhPcbxPXgsaE7JBmEZwbOpNnzX3DbfY9Pd9tVK+Hl4xjCusBlv4fRzuB2sXrfjgMvDso27/X9btBnx/pw3K/cwxf4lIKywXZLZLwj6Ceyep93CfqwwEODMSegC+8L8FtlbT4vUzqW2C81oGyYHvqm8D4ReHk4RgVL0qOu0KuD5w98lRxz8a6Yl4fD4F2q2O7zru/1vo8YaPNlV/Mi2iXTwncl5G3w7sbzI++p2cwp/vC1aKfskwNHEgdYOovQJyZwJmK8TPkezbbD9x+2FfwewH6MjqR0wF+/ftmW/E0H0QbEbmf16tXRY2bOnLnL11+wYMF2F6DayuWXX27nnXdexWs89NBDIz+3t7dbe3u7PfbYY/aFL3zBbrjhhh1GJW1dwKqhocFaW1sr3mPWrFn25JNP2vr1621gYGC7q6bvy+hDkhBCCCGEEEIIMc4o5amV+Fe1MUrHVo455pjo8Tk/TO4GjjzySLvpppvsrW996w6Pef3rX2/vec977Nhjj7VZs2aZmdkLL7xg9913n917773W399vF198sSVJYh/+8IeD87u7tyxA0NjYGOwjDWUy/82bN+tDkhBCCCGEEEIIIcTe5owzzhjxA/f19dnSpUvt7rvvtu9+97t23nnn2Q033GCnnXZacN6ZZ55pF1xwQbD41NFHH23vfe977YEHHrD3vOc9NjQ0ZP/wD/9gp59+uk2bNs0du1XiXT2KFSvLPxz19fVVOHLfZOw/bwohhBBCCCGEEGLcs3jxYlu+fHnF/14Jra2tdthhh9lhhx1mRx99tJ177rl2//3327e+9S174YUXbP78+bZgwYLgvJaWloormJ922ml21VVXmdkW99Ktt94aHFNbuyVkc5Aqie0wUBaeWldXV+HIfRPNSBJjS22NWdWWBpusbff7GOce89yw46B7JuIXCWLZCeP2K/hGkgFcGz6IPIdTCPfOm+ASwbPmm+FaAXmXj8Pn9ekFYFw+XSgBgcMIecvOl06nyPNbP1w0dCTEHEid3X6bTgmez+dl3Wr05eGcDnj2BD6JfGOH34ZDIG2o9+fHHAYg8NYwL1lWbCfwU9DXYQfOcpuFbu84KLX5qb+FTp/XpRb/fHm1T99wg697VZt82QfOBaQ/pWcH+xtW+usNzPbOi1KVz9+ajf75CmV+kQQulHzdBr/d7f8aFSwZS9/VjMl+97qNVhHUlXxD5HjWa1bzWL0HCdphjnaaoJ0EDiO2I7ZrOqiCfgt1gX0y+52INyff1Fn5fuXHxtpZ1HEUcfAx75sxpT7mccP+YMxAu954iO+nzp/0R7f9o6Gp/vrow4M+n243Qg8fx3c+D/1aWOY5aUL+0NuD8T3qqis/NubcYT3keMy/YmNMYB+b9qLeBq4ynzfZpFZ//qp1/njmDepuuh4eHbZDut4i7YT9QlBXWLbsF3k+4ZgYa2vol/IejP/l+cF23OXfHer+yz97HdKaTW512ynb3Xq81zLvWlvc9vAs7yjkeGtwm6XL11a8vtFXyXYLL1w2obKjiP1K0Bb47sb3bNYFlG3gQJzkx7zhOp+eaui8spotxw/1V3ZDvtrILBkXK6aVp2H69OmvyIG0q5x//vn2wAMP2N13320f+9jHbP78+dbW1rZT1/jQhz5kn/rUpyzPc3vooYfsk5/8pNvf1LSl3WzeHHdt9fRs6/9HEwq3r6EZSUIIIYQQQgghhBjXzJ8/38y2fMT58Y9/vNPnT5kyxSZN2vLRdnsruG39QNbT02MdHR0Vr7V15tXkyZNfc34kM31IEkIIIYQQQgghxDhn8uRts7mXLVu2S9eoJAJ/4xvfOPLzM888s8PjhoeHbenSpWZmduihh+5SOl7t6EOSEEIIIYQQQggxzsgstdI4+C8bJ58NymcR7Uo42bp166y9fUvY6YwZM4L9J5xwwsjPDz300A6v88QTT4yEth1//PE7nY59ATmSxNjS12829LJ/oB5uGWwnA5W9O4EfgzDOnsfDFWP0JHB/WsGRkSGGH06eWJx5AkdCNqXV3xoOgwxx+InB6RDz5tBZMEgHEuL6OX2T59NRwLzD8+aD8FnQmYD9OfwWQX7yfD4vHRRwLgQOJzqXeL3yc+nLYN7xeLpXuL8OeQ2nQUqXCsuCZU9nQcSLk8FpVBjy6S3AC8S8TXB8Ql9HEf6POt/u0gFcD46I6pUdbnt4qndO9MzwdaFhla87VR2oq3Q4lPtOIm4O+q5YT0mCPs4oauxGfD5cJQnuFziAWBdR1ilcKtlk7xlI6F7B+YHPi3WJPg7uRx88vL/38hSXYso5+h16b+hsYn6wX0nafF0J/B7lbQd9GF1nCV0h9E+xHUZcZdaFsmfZTvRpT/p8Xia98FehLrW2+ry8pO1Ft/3T7qP9/RpQ1njcvMaXRdqDvGR+8PnhpgnqWtCPIb/Y5/N+rCst8BCWO1VYD+jUYx/LfgF5EfTZ9N7EnD+DKMs6n558OlxrGyGOYbtD+gPXGvIu8Pqx3bMfi7w75c3Ie6aX+c93PfovmX9wUiXI//J+J+qDZLvDu0DK91I6kvhuw0WgkLfFTfA5sez4rPUYA/LK/dDwZO9MotOQjqL+Kb5dVXX65ylujjw/32/ofOzx7w8c0zK41zK8L/RPxLv1y3VlsFe/3u7L3HPPPSM/H3744Tt9/s033zwyI2nu3LnB/pNOOslaWlqss7PTvvnNb9qVV165XYF3uez7zDPP3Ol07AuMj0+LQgghhBBCCCGEeM2xYMEC6++v/Ae4L33pS/ajH/3IzMxmz57tZg+9+OKL9rvf/a7i+Q888IB9+tOfNrMtq7NddNFFwTHV1dX293//92ZmtmTJErv++uuDYxYtWjSy4tvcuXPt6KOPDo55LaBPtkIIIYQQQgghxDijlKdWysd+7seeTsPVV19tl112mZ111ll2wgkn2Jw5c6yxsdG6u7vtqaeesjvuuMMeffRRM9vyseeWW26xYtkMyBdffNHmzZtnxx57rP3VX/2VHXnkkTZlyhTL89xeeOEFu/fee+3ee+8dmY10/fXX23777bfdtFxxxRV211132bPPPmtXXnmlPf/883buuedaXV2dLVy40D772c/a8PCw1dXV2Q033LBH82U8ow9JYmwpFrZNU8bU8yBUIAjZwTRDhpNxOjRDekAwBTkWisCwjfLrcyo1lwOuxXY/pgcjDCHF0sk5thPmTSx0jFP1Y6F33A7CBtCVFCuHc5Eg9C2ybHfC+zE9DIXjdG/WDYSqJZwuzmXJy9MTCQ0LlscFQT3r9uFIQd5h2n5YL7F0M+pSsIQ7puan7T7MIEXdzZH3nAqfbOzw5/diqj7uX4XQtwxtY7gF90dZ1Qz6ulPo8m2jZY1PT1BX+Hw1fL5t20NTfShYNULXuMx0sNw9+yCG12Kaf9DHBEuk43oMo2RdYDtAu0vXIcSFIazsN9jPgYTLiLf6sAqGERaegTSzGiFCDCll+BNDbGL5xX6E5VVWV4JwXvYJvBbbNesd++wGf++8Ee2UISfTcDxCPqq6fNlWP+vrZs3zPmzxzf/6/3Pb0wpYIp79GO5XQkhqgjGN4UX5MMKT2eeibQRjBMe07YQd+AugrmxGWysPp2L4UCzsvbFyiKkxxJWhWwzVImzXCPfNEX6ct2HJ92GEYaKfCkLrpyJUjulnqBpDcDG+xkJwg36FdY39JPudFvQrfJ/BmFre9mLvToTvTnnkvTJ4t4iNCUHYPd+NmF7UVdZN5HVxHcIIWffQToJ+ZDXOJ8jP4HlZ9giTTPBuW7PR7++b7MfgUo2/XuvzW+pq3o98E68aNm7caLfccovdcsstOzxm5syZdtttt9nJJ5+83f2LFi2yRYsW7fD8+vp6+9KXvmQf/vCHd3hMU1OT/fCHP7RTTz3VnnvuObv55pvt5ptvdsc0NzfbHXfcYUceeWTlh9qH0YckIYQQQgghhBBCjAkPPvig/fznP7eFCxfakiVLbO3atdbe3m61tbU2depUO/LII+20006zc845x+r5xwcze8tb3mLf/va3bdGiRfbEE0/Y6tWrbcOGDTY8PGxtbW32Z3/2Z/b2t7/dPvjBD9qUKVOi6TnwwAPtd7/7nd144412zz332PPPP2+Dg4M2a9YsO/XUU+2SSy6x/ffff09kxasGfUgSQgghhBBCCCHGGZkl42LFtCwaX/DKmDNnjs2ZM8c+8pGP7NL5TU1Ndt5559l5552329LU0NBgV155pV155ZW77Zr7EmNfK4UQQgghhBBCCCHEqwLNSBJjS5KOxHzTTRLEUjP2ug+egRSx3nA4BLHmTErg44gsXc04/fJtuk1iy9XCr5Gt9UvMJox730kfB+P4+axcJjxpa618PyxNnXOpasIlYOEySeg+icGyiUEnEmFZ0n9CP0rZ89BXFfip+OwsK8J6T99UkR4XpL2fS0XDtRLzUaEdZU1YVhvHl+p9XaoawJLqdE7Qr4H8SOE8KtX6ulLVCV8G/R7d8H9w+eYmPx062QxXC9tumYul6rfP+X3wK4V9CPKWvgj2C6yndIMwL5vhBsEy3vR5JHlln1a4jbyg2wVeu2DZc4P3bkOH3z2h2e+nSwX+jBz5m3BJ91rsH67sO+GS9QnqnvWVtd1WpJXeOvahTd7lwWcxnl/HZ4VziGlD3Rqqx/iY+fOrWbYYU6Y/sNxtZ5N8O047UfYYw6pQtjm8OhnqcsL8wPOwn8rZr/F8tg2O14F3B/10mSsuR/8f8yUGPkcej/E3GBPYb9CnxbxoRzvfSQ+NwYtHJ1Iwhk30dSHpw/n0/PD9hu8f7AdYVsw/jr/w7uXw8gUORfZT5duRdyWS1GI85HjKPjzo0/31MzxLwrpRB+9bFR1J/vp5PZ1DaLeA/cxgm3++pITrs49lvxS0O757YZv+Lfq14FCsX4vyQV1P+7ccnw6gDgoh9gj6kCSEEEIIIYQQQowzsjyxEv8QNEbpEKIchbYJIYQQQgghhBBCiFGhD0lCCCGEEEIIIYQQYlQotE2MLWky4kYIfBZ0vyD2Om+CZ6cbrprByrHuUT8IvQWMhac/pPx2jPsewrmM+ce106l+Wcq8AXHv/Xi2ji6/PWWSP56ulg2b/Dbj2OnviDkjCOPk6SyAQyFwRsDtEkBvAB0S9BwwPQRlGaQn4k2oRJBXaeRerGdN3n+RF3E+HAfpsjX+fDiSArdLCc8GZ0MKt0ne4r08VRvhy2rE/VhX4WZJN/v0lCZ6t8xAM9pZCV6cgj++GvcbnuzdNsVN3mWTd3T69NLTU5Z/yX7T/D64ObJunxcpHT5daHfTfTsP6hn7vEkT/PFo94FLja6UGHSz8HqD6GPhOgvuh/yhq4R+KkO7L7X5sh2c6OtW9Sb4rTCGDE1EP0KfxqB/3mIP0lP2/Fmjf9aUzj7WG4wBebNvxyzroCw7K5ddPZ61nn3cZt+Os42+7iUcg2ZO9cdXwV3GMS1ww8BVxn6HYwAIPHsYsxJ68TC+s4cPni/issnL+znWY27zWZiWYfSxOzl+JBNa/T8g7UkP3o34fsG6yHepoF3DxUb3WyfKhu8L7DeYP+zWMIZwTAocSxyDJrb58+nLxBgWUP5u1x957+R7Hp4tGcB4nKOs6KfC9RLmNX1OMU8e68Za5AXLmu2C7RZOpAJcQ8F4zn4H3r9sfTvuBwcS8wd1KUU/UgtnUjKAd7+XHU4F5turnJKlVhoHcz/GQxrE+EI1QgghhBBCCCGEEEKMCn1IEkIIIYQQQgghhBCjQqFtYmzpHzArvDyVd5BLYWMKL6bAJgxD4NT1yBK6wfRsRlNxyjOn8HIKdNn0d4YrBUuyc+o5pkvnmB4cLFON5Xe5NHXWiLxCGETCqeVc1pvTpZOd++YchKZxujbzg1PhI8stWx+mwkcThDAJLpeM0D2GnwXTw8tCFRJMs8/bGTaIvN/ZZas7u/35yJsgnIlhjAhxyTnlm3mPvMp7sDxxM5Y1z1DXmX7WLe5H2EeBx8/xZZXV+LIpMSrzDT6ss3aND2XLEHpnjTPd5mCrL5+a59dt20DoWhA2iGW1c9RTtvt81Vp/PSwZn7cgHKoDYYSstyBJI8uKM/yXYRzsM9kuGO7EbS7pzrrEdsgQlmmtbruqE8uOc2lqpG+o2T9/dad/3rQXIUlYOtvtq0GfnKFP5jLbbEcMSW31fXDajXAchFYH52NMSFdt8OejX0nbWnF99NEIGSmu8yGfDFlhWQZjBtpGENLL0L5IP8SQnJzhUAjvylm3I2Ge5aFzOftk1nuGxeU7F1rNcCa243w1+gWOXxxDNvs+LmFYPsuaodwtvt8J3q2YXoZ3Mb/4bsWQVoaiMX11ld83km7/vKzr+QD7iQrvknyvZJ/HesPxttH36QnGW4brsh0lHA8Z1sc+mXnN9MRgH/vCerdd/RzfR/x2FtMaxMKpOSbM8eMv+7nCJowZ6zb649nvvDyGJsOR8MZXGVmeWpaP/dyP8ZAGMb5QjRBCCCGEEEIIIYQQo0IzkoQQQgghhBBCiHGGZNtivKIaIYQQQgghhBBCCCFGhWYkibElSbbFVNPtglDtwLUSeAu4jCtizUGO2O8gNp5LW3PZdPpAymPVGffOtNLvwLj5XqRtI9LGZcWx3G2yqcMfT2cB4txj3pzAwxPz9DAvg/xAnDx8FkGUfays6WDg/hqkL1iyFvsD58OOfViBN4fQDcJ6HHMkIW2BVyavnBfWBM8OfRF0MtA5wP2BBwd1gdIiOh54f4L8aHtsjdse2s87qQo9qLv0kQzieZg+uFVq1sDBUOaCSdAn5Fzenu2CbjH6suBECsp6A65PNwqX/Y4tS84+j/WeXpvYUtR0u6DPTQw+DZQNl6RPp0z221jaOeHSz/QSoR+tX4Z+BW654cneU1TcAAdV9bbrpf3Ia3jnsha4Uga9GyXtgzeH7Yp9HPbTiRRALw77ePifmJeBh4/9FF0ydMHQndJQ2X9FN1rgVqE3B3UtaIvcj7YXjHEVXC+Bt4aOosj4mbAs+WxsJ4HnLjLegXAJdRxPlxr2B/0M+xH4qRL6p1iWPRGHId/1mD+RMSyAvi06KAMPUVlbxvgVvAvwWoR9PNshyLt8HxO4xHg+r896HHvXYt6y3vN4wvvzvTrwZ0auxzGoL/LuxzGOYyTH0I6ul9O5kx5NIcQuoQ9JQgghhBBCCCHEOCMzs1IeEZnvpXQIUY5C24QQQgghhBBCCCHEqNCHJCGEEEIIIYQQQggxKhTaJsaWQrrNa8FY9XrvSMhr4XCAP8M29/jj6d1B7HnCOHzGitMDEIs1rwQ9MfQ/EDqNGKcPf0UCh0EM+iQC4FDIS3BAtHi3SNR7w7yjcyHiWMrpQODxMS8A4/q5TefFQOR5yx1X9GegntEbExDZHzgU6KMYQFnRKQAfV/aGWW67sK7Ln492RJ9F4EYp0jGBbfq86jnswOHU4dPD/Il5c/Kib2tZXQ22/f2rVnpPTzbVO5jSjdvSn7fDn0TXSaxPYFnDfcK8p3slhTeHnpjAq4Pz2SfSiRTUNfo4uJ+uFbpd2K7ocuP1Ak8erk8nEvtV1PXQB+brTrEHdRnpycr7XbTzQheehe2OfRDyMpvg+5S027vHcnjd8hqfl6V6366qVyBvajDG5H671OTrTmED2h09foETCXnPPjhDXWe/BXK62XbS9RK44+huQ90LKKsrTEtQL/nskXafwDkUjAlsl7h+wrLk/Xg+PXY8P0ggng/jX/BuxvxB2Qb9In1W/T59Sa1vd3kP2xaeL6hr9AoivfR9lacn5ghiHxFp1xx/WXbs83K2s9Zmf3zEYRjUBeQ932Vi7xtBXScZ3GORMTCdOd2fj/zOOZ2BTslmuOcG6JBCerc6k4ZzMz+0v6rJLLVsHMz9GA9pEOML1QghhBBCCCGEEEIIMSr0IUkIIYQQQgghhBBCjAqFtomxJS2MTJsOprAijCEIa2D4E8O1YlN0Od0b05+D6zF0gWEcZfdLksIO95mZWR+mM0dCUrgcb7DkKadnczpzsJw9jk8jU+kJp87Hli+OLVEbm6oeWQI2WK6YS+hGwk7yrm6/zTCLvh1Pp86ZFywrhlBy6jZDMFv81PYA5mUktI5T39PnV7jtDEs1MwwjaUZYH8ORGDaAMISs1Z/PkJVg6WnWHYQnpb0Mu0AoW2SZ9GIH2h7Sny73yzNnM6ZsSytC29LX7eevxTCEbn+tIAyBIZSsxwTtMgiLAFwWPGFILUN02E4YOsd+iaF5DANBO03Zb7W1+G20pXRdh78+Qv+Cus5+oXkHS0Nvvd6kVn+9bp+f5WGfw9P9sSzbPK/cxwYhLsOVlzRn6Hah37erwib0I6gLOdLHvxoWejDeNaCuMJyI/Rj3c4xhWwiWkI+ETfL8WAhSEEKL+3EMZj9adr+k0b+L5J0YH9huayOh5Sx7tjP2Cxz/Ki1fb2aG+3M8C96EghBSvg9gfOzo3LnzGT7Ffod1FekNYNhi8P6BENou9LsgKb8f+ySmnfUefWbeg7IgDOVCu0yoLWDdYNkH77WVQ9uCUDfu5/sD62LwvoJw62q021g75fsO38VqK7+/hHVtB2Gosff/VxmlPLVSEAc4NukQohzVCCGEEEIIIYQQQggxKvQhSQghhBBCCCGEEEKMCoW2CSGEEEIIIYQQ44zMEsvCINUxSYcQ5ehDkhhbioURp0yyGb4PxkITuE2SurodHLj1enA40O0CuMR8sKx6peMZ181tOojoUqErhXmxk/6I2BKtwXLHQVw+Ji9yydVgueTI8sjR8/2mlSLLqrNsYumJeItSOhkqpZ9+KToXEtwbjoEE7pDAQ8O00zdBVwk9MfQ9YX8yodVt0xtja9v9Njw1Qd7Qi0MHEom5WLCdbMQy5ciPIl1qhHWb+QnSjjKfBv1VdBTBnUJvTbAMNrw6CXwXPJ55FTiVmHeBm6S38n72S+xnIu61IL10x7Cuow8uTZ/gtgvt8InQycS6CoYmeP9IaXabT95L3v2Stfh2n67ftn50Yckyf2/cK1h+nnkJ11fSDu8My5LtmOMV6zGW2U43diCB6HN74JuiE5B9KOtWzNtDtxrrDusG+xHuH0A/ESFnv8nyaPBul6RvW/ryNev8tVDvUvQDgbsraLfIG3hiMjgTg/EHBF497A/uT9iu6ZNhnxsZU6wG70pYwt4iziI6DQNPD70+MSfUtCluOxizysuTXjfWE+ZlP+ox2wEcShnf7UDgTGJ6InmRtMIzR+cQ8w4kdCDx+jye6SEoy3zdBr+fjkSWFfspvrezrvLxtj4P37uEEHsEtTQhhBBCCCGEEEIIMSo0I0kIIYQQQgghhBhnZONk1bZsHKRBjC9UI4QQQgghhBBCCCHEqNCMJDG2pMm2mOfByrHcQSw64/ZJzJPD83l9xorXeaeC0WNQfjx8GEGMP++FayWME+f1CD0ziPNPeD9uB3HpNIDg/sw7xs0zr/n89GUQOhOY9yjbqKOC6YUPJHBAxBxP5c9DJ0EnfA10GDAv6GRgWQY+i8j3/4hzKOvwbpbAt4H90XZGRxT9FRP98wcutEKkXSJ9eX+/vx7rch88OvQKRZxOQd2is2InCHwTwxFXGVwqCdsh/RAkcKuh7vBZ6Rxiu+Ozs+6xbhbppYHnhqDsCh24H9NH30ifrwtsW9Vr0W/AXzY0o9VtpxyDytLHPijr9K6uZBAOoUbvZzLsDsanwYgDiI4klhXPZ1nR5xV44+C5Yd524XlZd3n/tesrp5ftkA4mQncc/F55rT8/XbfRH78z7wP0yKGeBu4z+qA4noI8x/jDPpN5j7JJMGYEfRz7DXr74GYL2jHzmtdDW8gnwIkEgjEAfT7HzJz3Y7/I9xt4/gzOpMC3VV5Xd7Ye0jnIdw06fQieLce7CH1cQU2KOJNyvFvF3m1yljWJ+DeDdzOeTjcc62496n4VPIFwo2W1KPtqjt9b7pdbxOUkhNgt6EOSEEIIIYQQQggxzihZYqVxEERU0qptAox9rRRCCCGEEEIIIYQQrwr0IUkIIYQQQgghhBBCjAqFtomxZbhkZsPb38dYcDoO6ANhnD/PJ3AsBN6AEs6ny4VOiEqx8fTiEDoV+GxFxKkPVPbg0AEUXB95EziZIt6YqKeHcfdIX54zbh4OA+YXPUSR/AydEXCtwDcSuGyQXrptrLgt/xK4PwKnAJ1FERcX8z7wwvB4eHOSpLKfKng2tIMkqHtID/1SKLus1fu+0pVwpbCu0hFFHxifj3WF7ZD+DN6PPhK4XOgrSRrKXDcsS/gx8o0d/ly0k6wPbhXmPV0fcH/l9HMwL+hWYbthu6ALZgO8Muwj2U/Qj8F2z7pK9wnTR98WvToT2/zxHd7bE3h66JZBflatQnLbvEsmb93mfklwr6BeMW+6/b2Ces/xhP4r5i0dSRxvIl4+9nmB28zvDfqBoM+Etybo19gv4niaV4KAiYjrzdgvburw2+xXgrqH7bLnCzwu9EvFHIMk1kezLtTBu0NiXpsGnM93G7YTbtMpREdUC5yOfT499FUFpHj/oG+Sz0cPEesCyief7PuJZG27P768bbHPJHTmwQeVDKId0J+Fy9FhFPoo6cWL1JXY+0AzHI0g6UHesa4QOp74bsh+iO9egZMRTkmWLfupzXA80r/Vt6UdJ8ORNvIqI8sTy/KxDysbD2kQ4wvNSBJCCCGEEEIIIYQQo0IfkoQQQgghhBBCCCHEqFBomxhbBgbNSi9PQcVU9mAZU063joUKEIZR1FVemjpcVhxToBmKUFs2/ZrT8gmnI/Ne3Ob05xo8O0OvGPISXe6X6cV0ZYYJcqp5LAQHVw+eh9OfeT5D8Rh6V4+ltjE1PlhOmdOl+Xycyh+EFJWFPjAvGHIRCweKLfXMpZtZVkWGYFZeHnj48AP86U8vc9sZyjLIe6Y/CLFB2cbCLrnNcCqWJafGt2DpbZ7PJeyR3wwvY8iOC/tkWUTCIlxYnG0nxJL9AJfBjoQtBHWB+1kXgqWn0ccyHIghJmx3kaWho7BPZxfegfAwpIdLbyeTJvjjEWaSdfiwiATPn9ahLtRtq7tJF/oMhuPwWWKh2CyrLvQbDI2LhDvl7QhLZD/SgiXaWbciYYjM6yBULkIwfjM9HDNZ17g/QQhqgn6pEWPCJl/2Qd0tf39g/x8Zr7mcPcN7ctw7Nr6ZITyKec8+CmGEOftwhtIx7DCyhHvwvN3oU1lXKl/NDCG+2X6T3fZQi09Psdu300In7g+STb4fDepSWfkEYe1sl3hX4nL0DNmMagSaET4bGUNyjBlJEMqNdsDxj9uEZYe6HITWM39Q14NQf47/fF9h/vHdNGj3uH9Ms7CPkFk6LlZty8ZBGsT4QjVCCCGEEEIIIYQQQowKzUgSQgghhBBCCCHGGVmeWpaP/dyP8ZAGMb5QjRBCCCGEEEIIIYQQo0IzksTYUki3uQmGsWwpHRSEDgQu0RtZVjXwlRTxXbUHfhDGcgf+krLYb8aFxxxIdBrBcRA4DRhXTwcAfRxBHD+8Njvp2QnSFyFY8jZC0oola+nj4PUi/ip6fQIPUGwpbz5/pedhjD/zko6B2L1ZN2JEnAJVL6zFfrhf4AEKlr4GwZLyy/ya6jnrOvMy5r/iNsuadYPLDzP/4HwIfCW8X3n+ReoFXSZZF5Y25rPSrwWfBfuooN7RaRR4X3A/OJuCpa15fbpRDLtjXh32ExHvTeBooleHdRt+jJxLwKPfS+m4YtvkMuZV2/Znk1r9tXpR74LxgWUXqacsa7hUgnqOfiM/cJZPXzfHCDwrfFyBQ5BlS+cTXCpBHxob8zq7/DadSexHuU2YP3Qucj/dM1MmbTt27Xq/L+jDK3tkyq9lZma93umT0OsSeF9QFuyDmR72G2g3oTMJ94t56vg+ETidPDlcY6V6v11E3Up7fbur7mM795tZvX++NFZXWffL2l4yeaLfRwcS82oz/EysVw3o09nHgwTHJxivMrYTkLPseH30qTneJZOImyyphT8r5nxkenl9jjH0i8F3OTRnmk/eBtTlDR1uO3/ZmZgP7ZzDTQixa+hDkhBCCCGEEEIIMc4oWWKluMZ+r6RDiHIU2iaEEEIIIYQQQgghRoU+JAkhhBBCCCGEEEKIUaHQNjG29PabFV6uhoy1Hox4dbq8lyCIg6eTgQ4KOC2SjL4MOpdwPik/nr4IPhudBz2IuyeDiDPn9Qiul/d7X0bgsyDMOzod6IhAXtEzkw9GHA+E+UFXSgXngZmFjgT4NQLnBV039OrQUVV+PmP+Y/WQjp+YW2QQsf5M+876sejFYV4N0p+FskT6A+8O0pfDE5Sz3SFvsykT3Ha6Ak4nPi/rBupC3tHp70cPD/qZPIc7JS1LH54laBd49hS+h8CPxbJk2bMuGdIeeGsidSOv3IcluH8+GPHMxEjg52C7jrS7nGNAxE1Df0dKzx7bGq+PtpxuLMtfeGPyWrhG4Cph2vI5M3E8/FA99MAh75v9s5T7m8zM0q7KLjPb2OG32WeiHbGPpKcn6EM5PtKlQmch6zZdKnQi0RnF9NNP1gz3zLPLKqev0j62c/qV2M76Ua+mT/bHb/QeGfapASwLusXQh+bo45KUZYW8DVxnfFdC2fH9BfmV4HmKLDu60+AuS2I+K1yPTqakh644Oqkq9INBvaRzj/4ojF8tvp2mPShbPgt9kBi/05ivMhiDKs8PSNiu4EiMjkFsx7H39MBVCth2MKZVvbTB72e7x/NvHcM4lr3a0aptYryiGiGEEEIIIYQQQgghRoU+JAkhhBBCCCGEEEKIUaHQNjG21FSbFV6eKswpslyCFtOl8+FIuBanzHIKcGRJ+mD6OKcAM7ysfMozp37HQmI4lZz7eS9OF2bIDM8n3M/QuRpMZ+b9MfU8GirHJWd5fGz6Nqe6M5ypESFE3Zg6z9A2Pg+nQXPJ3kohPZzqHgs3Ylnz3sHUc4aI4PxYCCbDgWLL7zLcCFPtcy5NjesxdCzBst7BcsOYqs7lfLmccsL0c6o7CJY/5v2DEFhO3S87nu0sElbHvA3CCBl2iLQwZCXo01g3WG+ZV+xjmf4Cwpu4n+2AoXs9ldtdLDSOYY/BEvPMH+YHQ4AYhskbNjf5bYbclIU75wwNY7tG3gdp45LmIAiJWbXOX68W/QzbOcOZuSw4+viEz46yCUI8WReDEBPkbiy0hOln3WD+sl8kHK+HUZfZNpi+jrJwM9w76ENiS76zjymgnRCmpcTtSGg22znHpBqU3SaEmLIf4Tb6rQx1LUV4M+tesoFLzCP9fD9qQEgqQ/FYF4qR8C7er3xM29SJYyN9LOs9jk870YdwvOT4yPBhhKwmHEOafN4knci7IOSzWHk/xwTmFZ+/Ae9OPXj/oBYg6KPxPGvXW0VwPt9HgpDW+i35mw2iDbzKyWx8rJgWEXyI1yCakSSEEEIIIYQQQgghRoU+JAkhhBBCCCGEEEKIUaHQNiGEEEIIIYQQYpyhVdvEeEUfksTYkiTbHC705nA5ZTob6CyKOpIQq84lYwewLHngOUDsOJ0Q5dAnEfM78NoRb03gNGKcfeDpwfW4P0gv9sdcK4TLAUd8F4GPKrZsOl0qLGtcL6djgnWBS9LTccW4//L8pC+DvovAQYC0wnkQOAfoEqG3JubL4hLpkeWBg/Th+kFZNnpnQk7PDJcNp6sEfpG8y6c3cCKwnSN/Aj8HHQsoy4y+L9SV8qdPDM/OekL3FjxvwZLEid9mH7ezy5AHZc2yZLsidKmwbtFbR58G+2yWBepujraRVFf2WQVus1jdpyuG+c9l1yu50liPmRa6Ojq8eyUN+tDIEu5cMr7Du1CYV9YIdwrqeb6pwx+Pep7BtZawrnG8Bexj2UcnWOI9Cq8HL0/QJ9OHxX6K+RX0y2XPx/GPS7azXnE84pLk7fDwoE8L2gXHL6Q1ZTumO6yL/QB9Xjifnr3g/n47rUMfHvEI5bi9DWA8ntCK/RFvD716myP9MNt1ef6wj2S9oK+JfTTfE4M+G+9CEUdhMEbE3k34PsB3Q5AjL5PAH4V+qB6uscHKXj+mJ6/118vwvlBg/vH9h336xDZ/vXrfFoebttx/uH/fciQJMV7Rp0UhhBBCCCGEEEIIMSo0I0kIIYQQQgghhBhnlPLUSuMgrGw8pEGML1QjhBBCCCGEEEIIIcSo0IwkMaZkHZ2WpcPxAy10NuSMRYfXIJk8ETdDXH0P3Db05vB6vB+3y2PfGXfPlkY3B6+VUSoA6I2BnyOh44GOJt4vBn0VkfNz+iroLDDE1dN1EvEK5f24Pr02dCJFPDth+gDTV+4LYd6yHtGFEvFFBX4HeGbCvGfewTlApxDLktBJQKdSzDMDApcLnzer7KkJypJ1AV6gpKW54vWCfoL5Q2dFeXnxWeh3gP8ipz+LeRlz/KCsc3hiQg8MnoUwvfRrbOqsvJ9lx36G7SrmxWHZ0XuzubIzKtvsz09bmirfj3WL92fbK88Plg1dXyibtLWl4v5gjGC97Iu41VC3cjiZguNZl1BXWZc4/uV0KEVcZfT+0RkV1F16g+hkamRdZPoqjzl5L9oOr99WVl700DAvYzAv6JuiU4mOo6DPhnOIfSLbIaHTie9S7KcI+y167eDFCxxSHLOYnxFvUNDv4PS8ke8/yC86q8r7Qbbjfo5vqAssm8HKfX7Qp3AM4RjAsuR4HelzmXd8T074foC6Zbhc0gXnYfBui3bM8ZZd+BDLEtdD3eJ+OpFY1lUbtvTppSGkWwixR9CHJCGEEEIIIYQQYpyRW2KZ7eQfgPdQOoQoR6FtQgghhBBCCCGEEGJUaEaSGFOSmmpLCi9PWecUW4YrcRlULpfMsA1OlceU3rwBU2QHMGWY089jYRrl09kZjsTpyJzOy2flMt4RgunTbQir4LNwqnmwZD1uECwJj+nJDG/iVH5en3D6dl55+nQQAsOwRU73joX2cT9D4bjkb9l2sGQ7b9WApZY5lT4SGhZdyjgIA8D5zLtISEqwjDdDYiLLlgfXj5R9EGLD/CEMAUL+MvwrCEkisTDN8vxACEcQ7sPl67m8PZfdDsIWEHrFpDJcB/0MQzyDvGdYBUN4eDxDXmJ1IQjX4pLwOD4Wdol2yHCraNglxxQuxd3ol/ZmiE75/YO0dnRVTFuQFww7ZPgT2mmwTDfzJlKvgyXj8azB8QMIr2JdAWz34QHIa4bOcYxketh2DG2H+ctwrc5ufz6fn2Nu3bbnDZajZztgXWC4LcMMGYqGd4kgLA/7Ey6pzlAw9is9O67HW9LDMESEfjFvGYbId7FIGGXwPhAL8WX6Iu06CANlKNsA3v3KyiNhHxVrp0G7xXtmXSSElHkVhIIh79hH1VV+by21YDws+rwo9LCsUbZ8H+lhmCeOr8N4jbJMmF99KAs+Twnv9dU+v0p4by/04nq1W47PBir3X0KI3YM+JAkhhBBCCCGEEOMMrdomxiuqEUIIIYQQQgghhBBiVOhDkhBCCCGEEEIIIYQYFQptE2NKUixaUthSDfNsqPLBwbLkiNWmryNwpeB4np/T/YIYa8btk3JHBq8dLPONb7h0CvB8wiXmGYfPuHZ6dhgHHyzpGnEi0dHA54t4ZwIPQcyHVRVZsp7LDUeXE44sl9wEn0YXnFVD2543qUXe8tp0d3FJc+ZFpOxz+i7gQEhYT+nLICwrLjU9BA8Ql4SPtMMs4hEKXCtcnjgGHAqB98fgmmF6sZxwUH7lzxurV3SR0Z1Cp1GsrOlmoRuE1495emLLksM9FizrzbJinwzfR97S5PezrvajrCOuGJZtkH/oh4Jl0osoa/i4EraF8m3WCzxbwrKiS4ReHfYLXAIeDqagndH3NG2K37++3V8+4mIL/F7MOzqO2E5Zt+gopNcObrmgH03Q1pD+4HmYf7wfnEssj2TV+m0bXIKcHh3Ce7OPDhxFyNuW5or7WfaB74nPxna9YZPfxvtA0oz7R8b7YIn7tla/vxHX5xLygRMp4jCMORZZF+hya0NbLbRu22A7ZD2m45D1PuL6ij4L+1S+q8XqHkj78S64ybvCQn8VtvnuGHMMsq6QzfTooR9Dv1OagLrNMaXg09M/2bfVuuVb/GTpEEWfr24ySyzLx37FtPGwcpwYX2hGkhBCCCGEEEIIIYQYFZqRJIQQQgghhBBCjDNKllhpHMz9KGlGkgBjXyuFEEIIIYQQQgghxKsCzUgSY0q2uceyHX3ODOLm08r76SuhH4QOpU7vvcm7/XbC+zFWm+6X8uN5b3pl6NugY4Fx8/RrRHwPQdw+4v5z+KN4v8B/wbwG9FEkcI/Ezg+ux/OxnW/Y6I/P4D6hhyDmtyIbO/w2fVnl+U+PS4q0o14FDgZ6dej4ocsDzoZgNVbWjQY4B2rxLO3enxF4d2L+qpgHKHCN4Xp0MPD6zF+eD49P4AehT4xuFtZNtqVyHwieNYG/IofLJGW9pUeHoG4kTAvTSscPr0cfBvsdXC/r9D6NBK4YutgC6Giipy7w5KCdst9iP0pPEO4XlC3dNKwb9KNUcsnx3sirwE+FPj9v8fU46UPZoG4E9ZqeHDqL6LNiP8F+heNZxK/FvA2gE4ntNlb3WTc5nrMf5PkRV1swBjSiXypPH31MzJvA14i00gHE8ZWuL3pwYo4m9gPMOzqC2C6YfrY7tgN6cwjfhSLvN0G/FHvf4fMzvbwf8j/wBJW3ez4rx2f2eYFTqXJZB941Xp95Q9/TZvSh7PNmTPX3g3cueE+tR7/QGXEoDSMv+S5L2GcnlX1feQ36mcDJ5LeL3fRz+vwuNW1pO6XYWCWE2C3oQ5IQQgghhBBCCDHOyPPxIdvOx0EaxPhCoW1CCCGEEEIIIYQQYlRoRpIYU9LWFkuLW5Zm5RTc6FLXmN6cTpnsz49Nlef1sQRu3oXllyuFshFOV0YISrZqjdtOGQbA6cEMQ2CYHpeoZQgHp7pzejbyJgjLwFR8hjsFSzkz7wmnd4MgDKMfy4LXICyB5zMUgWEPwdT5yullKGB5uFTCqei8FsoiyPtgG+ezrLl0dJhcD9tVD0JmGG7E62eVl6hn3QrCEtkWEALDULkgRIUEYZxY7hihCkGoXSxsotL9uQx20A7QTlmPCeoG0x7kXUOr32bZRtp9APrAdELr9o/bSqTdBvuZt8NoR+yXGFLEcC2Gd6HfC8IoGeYZq1sgmzZh27WfXebTFskLjldJL8P2kBb0uW6JcrOwbnUhFDv2bKyr7JeYV+wHGCo3hPGQYxbLns+HJexzhtgy3An9ZBB+bdhGiEyQP6hb5WNGML7EQtcisF5Hj2dYI0O1mDcRGHoeHoB+gu2QdYP9DGF+sazYzzH0rwrtvAf5x/MJxxheryzULW/29TDZHAmf5bsFxxPmJesd96cMyURetzT50xkqz/BqhvNO9+/F+Uur/fUmTjB/QOXQ+2BM4/sLQ0gZesfr1aDsi77upAP++HR9h9uuGkDofmlLWykM7VybE0LsGvqQJIQQQgghhBBCjDMySy0bB0FE4yENYnyhGiGEEEIIIYQQQgghRoU+JAkhhBBCCCGEEEKIUaHQNjF+iHkH6Dxg3D99IYxNpz+jDtv0APVH3CqI5Q6WsHVpg+OHS6gyzj0Sdx54aBh3H/FhpHQqTWzz91+3wV8eeZfQgQAChxOdE3TDtLb4/VnlZc4D5wLzl2VF10xsOeLIEr1JUtjxsfQ30OFDNwg9MIHjJ+KnCJwL9HkgfWwXzIud9MiESzmzrCt7evKZ3k2WtsNNRtcKbx+pO0F+rm/3+4O2uOOlq4Ml0CP1KHD2sB3QuUT/Bjxsgbct5peKeGLoeEoM7YZ9GpeijrljWHfpBWLdjORf4GrhMuqTJ1a+X7D0tL9+1uDrQmH1trqSc1lv5j0dRvTGMS+GkXcsO14/0sdz2eyEfSzrOcoqGHNi/QpdLKwLkT43r4czqQqOw02dlc9HXQjGtBh0uZSnn+Mdz42UVdBPcDymw4d1hb4ppJX9ROBUIpV8jmZhP8F+i+nFu1Fwu8CXhbrMtsD8LHBMiYznA0MVt5OeHbvqks1w6bDPYJ9cG/EzEbYbvMvkNfw1DNcv4d2jH3nBdx+Ot3gXypnXfG9mP8E+Ge+KdI1ZfWWHIa+X9vi6VKrz+THchLY47Mf7ZNCXz9DkLe9XQwPDZs/ZPkMpT6w0DlZMGw9pEOMLzUgSQgghhBBCCCGEEKNCH5KEEEIIIYQQQggxJnR1ddmdd95pl112mc2dO9cOPPBAa2lpserqapsyZYqddNJJdu2111p7e3v8Ymb2k5/8xN7znvfYzJkzraamxmbOnGnvec977Cc/+cmo09Tb22vXXXedHXPMMTZhwgRrbGy0Qw891C6//HJ76aWXdvVR9xkU2iaEEEIIIYQQQowzsjyxbByEle3pNCxevNje9773bXff+vXr7aGHHrKHHnrIrrvuOvv2t79tp5xyynaPzfPcLr74Yrv55pvdv69cudK++93v2ne/+1378Ic/bDfddJMlDOMuY+nSpfbud7/b/vjHP7p/f+aZZ+yZZ56xr3/96/ad73zHTj311J180n0HfUgSY8vAoNnwlhjppKXJ7coCTw9ivekVaEXsNh0IJPB7+OPTOsSyM86fsejl6eO96SCgs6CGceC4F1sq4+7ht6BXJ2nEBeiX6oanhx6ASJx7zGMTuFDoUCD0DPH6vD99HUxvrC4E10OcP30k5dsxb0tTY+Ta8F9k8DvQDxE4meCPirlOcH7e4703wf1iziWWJcuC94eDId0ExwI9Ozwf7TZv9HUlGfJlldX6tpWybtC90g9nRll6Ej4bnp1usKCPYT1C3UmafR8Y+ClwfEaPW8RvldO7w/30beB56FTi89CFFrhUeD5dJ8jfBHU7GabnB3W5w3t1grbH5+ve7LYLfd5fVp7/ga/KdhK2yz7kRSMcPxG/VPBsdLvQv8W8R11I4W4L2jXHJMI+toT013kXGut2XoXni3gEg/3sJ5g/dLkkcM9UlV2PeRWMd8gL3CvoQ1Hv2M5C/1TlPs85+swsj42nTG/Mg8fnZb9G2A474XKjX4zHt2+sfH2WZcwfFnMiumvh2ei3asD4Sh8Tx3+OEbExAYTjFc6nN64OrrE+9Lno4/ieHbxbxcaQBoy3fH9p9v1Y0oO6Hrje/PmBI6nO51exG/1wlX/+6qVrzcwsG+428epk1qxZNm/ePHvLW95is2bNsunTp1uWZbZixQq799577f7777cNGzbY6aefbo8//rgdccQRwTX++Z//eeQj0pve9Ca78sorbc6cObZ06VK79tpr7Xe/+53dfPPNNnnyZPvMZz6z3XRs3rzZTjvttJGPSB/60Ifs3HPPtbq6Olu4cKH927/9m3V2dtrZZ59tixYt2m46XgvoQ5IQQgghhBBCCCHGhHnz5lUMFzvnnHPse9/7np155pk2ODho11xzjd13333umOeff96uvfZaMzM76qij7OGHH7a6l/8odfTRR9vpp59uc+fOtSeeeMI+//nP20UXXWRz5swJ7nX99dfbM888Y2Zm1157rV1xxRUj+4499libN2+enXjiidbb22uXXnqp/eIXv3jFz/9qRI4kIYQQQgghhBBinJHnqWXj4L8837OfDQqjWDX4jDPOsEMOOcTMzB5++OFg/5e+9CUbfnkW5le+8pWRj0hbqa+vt6985StmZjY8PGw33HBDcI2hoSH78pe/bGZmhx56qF122WXBMccee6x94AMfMDOzhQsX2m9/+9to2vdF9CFJCCGEEEIIIYQQ45qGhi2h2P1QEeR5bt///vfNzOyQQw6xt73tbds9/21ve5sdfPDBZmb2ve99z3KEv/7yl7+0jo4OMzO74IILLGX48ctceOGFIz/ff//9O/0c+wIKbRNjS021WeHlmG86EBALTo9AQpcKY8npMGLsej9i29lR0EtQ9LHY2cwp/viyjoieFpJEPC2BI4gulnr4IXj9du8KyZvp6YGPog/pYdw/nRH05MQcT/QC8fq8Hj0F8A7RE8D7B06KmNuFTgt4E+ieyYe2eQh4bbpGcjgKAmcBy5oOBB5PD82mDn8+8zLyF54k4vnJmRdBWaLd4f58vrwJDgX4uQJnBe8Hkq7KjqWE7boJLhi0vYSumZ6yulcPXwb8DvRFBO0aZZG0tVS8ng1430XgMEJdiTmcYn2ce1az0MvD41lX6aEJHFHIL6aPx9ODM4T9OD/od5BffUfu77brnlnjjw/8Jtv6qaAPYdkyLfDMlWZMctuF9R3+fHpfOH7xfoFHL/KXXI6X7NNi3p5Iv5LnPj1BnwroRBpu8nWpiu0S7TzwgbHtxJxOQYLK7sd23gXfCsef2HgOgj4/UhZBu+b1mB5635g3rAusa3S5sW7SU8d+imMin4/9BGHd2+w9fkFdjzmiKtVdjjcE76V5A/okOvr4LsWk1KBdFHze04mU9FdOX7Kxw6eP3jy2Q/aprBvsB3B84DzC8+R4nmyif/csdOH8Tf5dtfZpPwaVZqLf3IS6EOuXxT7HkiVL7Pe//72Z2cjMpK386U9/spUrV5qZ2dy5cyteZ+7cufbHP/7RVqxYYS+++KIdcMABI/t+9atfueN2xFFHHWUNDQ3W09NjjzzyyM4+yj6BZiQJIYQQQgghhBDjjJIl4+a/saC3t9eee+45++IXv2jz5s2z0ssfEC+55BJ33JIlS0Z+5kcmUr6//LyduU6xWBzxK/EarxU0I0kIIYQQQgghhBBRVq9eHT1m5syZu3z9BQsW2EUXXbTD/Zdffrmdd9557t+WL18+6nvPmjVru+eVbzc0NFhra2v0Ok8++aStX7/eBgYGrIarYu/j6EOSGFuGhsy2LnfOJXQxRTcIsanHcsmcMozp2zlD43oxRZZTYjn9m1OCQVazrTkVhrEkKpfx5vK3fDZO1wUMv+GzZVgG27jNqe9IXzp5oj+e6UU4U8KOk1P3I3kbTHWPhFUEYY0oG06t59T7dOIEfz5D4RBaxzAVK6ubQegUp9kzRGNnQ9kaKi8Lnkyb7Pdv2OS3GYaAkA9uM69T3p/pZdgHwxgQlpisw1LPXPIezTK2FHc2zZdl2oPlj6sYqoap9SifrM0vU56WPy+XLias5wzhJKwLCOFgmEIQGseQTi7lzLKe5PMq6fRlF9T7DM/L5w+WzmY7t4rwfoawkCC3Gc7EsEe2vWafP2Eomz8/W+ZfJsv7kZTjDUPZIiEkhQ4uP88l33fydSwIN46EdKAusM+mIyIMS0Q4MNPb4ttNvna9Px79ZIr0FM2HeQbh4ej3gjGI+c9waPbTDAcrD2NsRx/KPpzjHUPDkNZscyQsLwjTw/gVhDFWHmNi/RTLLmfoF4ksWZ8gnDtF3c7ZD+7ANzJyPT4f+zmez+fnuxrzrzx0MagnKAuOt0hrNO+RlpRKBcI+PBayGSs7jjFRBUTlkNSgn4qEkgWhaKwLECGzrhU6UT54l4uGNYo9xjHHHBM9JhhXdgNHHnmk3XTTTfbWt7412Nfdve2dprGxMdhfzlbPkpnZ5s3+vXvrdWLX2N519CFJCCGEEEIIIYQQY0qWm2X52ISVMR17izPOOMOOOuooMzPr6+uzpUuX2t13323f/e537bzzzrMbbrjBTjvtNHdOuXy7mn8sAOUffPr6/AfLrdeJXSN2ndcC+pAkhBBCCCGEEEKIKIsXL7bp06fvseu3tra6sLKjjz7azj33XLv99tvtggsusPnz59utt97qVk6rLYseGIzMCB8om91Wh5lxW68Tu0bsOq8F9CFJCCGEEEIIIYQYZ2R5alk+9utjladh+vTpr8iBtKucf/759sADD9jdd99tH/vYx2z+/PnW1tZmZmZNTdtUCQxXIz0920KOGcK29Tqxa8Su81pAH5LEmJL19Fr2cox2SvcJpgiGy/0ilhzLoNLZQC/RToNY81L9jpc9La72ceHBkuIN3imQ0DVCJwGW382Wr/Lnx+LaGWeP6ween8gSsbHlhgP/B2fktqJs4NkJlqKevZ/f3oz9MQcFfSA8Hg6GpAnLsvOvEmXPG/gl8CyBT4NlRf8Gl56mE6EB+7lkO+sOr09/Rany8sL0TbGu0vlEJ1KwbDpdLXBCBc4HzqWGQyJdA+cS6m6p1bttihs6/PHIn7QHTofyusGy4bLUTDvzBksdB54Z1kO4R4K6QJ8GfBfBUtAbkFesi+wnWHf5fFwaGr6KnHkJAgcU+xH6SXD9gTeiX2DyX6r8vFmzL8+f9t+xw7S+888+6dPSDecRPS5oJzlcYwn7FLpV6DahFw/ur2QAdYNLyMNBmHV2+f30b6Gu52yHTB/qNl0qgSuNziQ6pFgX29Anr9vgr89+ZqiyCy6d4pcVz8vrGus5x0d6WjgecvyJEDiU6HsK+vCk8n6220g75fMwrwKfFupugrwkCXxW2eq1fn/gDYy8z9DrE/FpBZR7ipgXSGuQFr6bxHxM7NPoY5yAdyHWez5rrG5y/Mf7QT4d9R5u0XR9h79dG8YgwuT0wmXWjj4Y/UjS6tt1jvxOImNIzKEo9k3mz59vd999t/X09NiPf/xj+5u/+Rsz84LtFStWVLxGuWC7XLy99TqPPfaY9fT0WEdHR0Xh9tbrTJ48+TXnRzIzG/vPm0IIIYQQQgghhBAVmDx52yIzy5YtG/n5jW9848jPzzzzTMVrlO8/9NBD3b7RXmd4eNiWLl263Wu8VtCHJCGEEEIIIYQQYpyRW2LZOPgvD9ctHBNWrlw58nN5ONkBBxxgM2bMMDOzhx56qOI1Hn74YTMz22+//Wz27Nlu3wknnDDyc6XrPPHEEyOhbccff/zoEr+PoQ9JQgghhBBCCCGEGNfcc889Iz8ffvjhIz8nSWLz5883sy0ziX7zm99s9/zf/OY3IzON5s+fH4QLn3TSSdbSsiXs8pvf/KblDFF9mQULFoz8fOaZZ+78g+wDyJEkxpSkpsaSdEtMKT0BKWKng1hzxKLniG0PvpsPIPadfhB0JIFHBz6PcieSmVk6uC1YPO+ACwUOgAz7SeAw4LMjjj4fpicHjgY6kbA/8ODQVzW1zW2n7d4DxOvnDf55kzXeZ2ETWv02ywJ+C1u+xm/D31GaNdXfD8+TboSDqcP7QZhf+cZNfj8dUeW+E/qtCB0KjOFHWnP6KuBUSAOPTWR5UtaljL4ryAHpiKCbhd4auF/yknceBT4tulbgywr2wzkVOCHYNtBuizyez9MEd00nXC1ldTGHlyWopxECjwu8bslMrIDCPifigUnoQsF2cD7bfawuBX1wafvHbQV+j6SAuphwP8qGHiGU3eb9fHoXL/gfldPzCsjr/b3y2sqvT+k6+Km64AJjvwCHUNDH1ze57aQXdWNTh9/m+FXv63kKhyCdSfTmBL4u+q04xrBq4Hz2czap1Z8/iLa13rtWAv8X2znrFvrBbH27319e9zh+st6zT42MAYF/CmWfMu0cE9hOAycTz2cfj/GVnhp4PRJeD/dLMj/+ZnXwRdLfNQTvTTuuh5WOcvb58G0F/QR9YHy/oIOp3G1HfyWdRJ2V33VsM+op6wb7SI7/K9e5zZx9Zi08e23oB+D8C/xYrHvd8Hf2Vh4P0/V4V+Xz04eJ/cNv3N9tFzt8P5fB0WTDqLudlceYvAX5sfX9pBBftl2MPxYsWGDnnnuuW32NfOlLX7If/ehHZmY2e/ZsN3vIzOzSSy+1W265xYaHh+3jH/+4Pfzww241tb6+Pvv4xz9uZmbFYtEuvfTS4B7V1dX293//9/bpT3/alixZYtdff71dccUV7phFixbZrbfeamZmc+fOtaOPPnqXnvnVjj4kCSGEEEIIIYQQ44xSnlgpH/uwsj2dhquvvtouu+wyO+uss+yEE06wOXPmWGNjo3V3d9tTTz1ld9xxhz366KNmtuVjzy233GJFfLx9wxveYJdffrl97nOfsyeeeMKOP/54+8QnPmFz5syxpUuX2uc//3n73e9+Z2ZmV1xxhR100EHbTcsVV1xhd911lz377LN25ZVX2vPPP2/nnnuu1dXV2cKFC+2zn/2sDQ8PW11dnd1www17NF/GM/qQJIQQQgghhBBCiDFj48aNdsstt9gtt9yyw2Nmzpxpt912m5188snb3f+v//qvtm7dOrvtttvsd7/7nZ177rnBMR/4wAfsM5/5zA7v0dTUZD/84Q/t1FNPteeee85uvvlmu/nmm90xzc3Ndscdd9iRRx45uofbB9GHJDGmJGm6bZoywpuC6c2E07U55ZbLL2OKcjBVn8uOTvThXEFYi/lwqkIXQhPKyBjCwrRjanuwLDafjduYDhwsicrp1rhfsoP435HL1XOJWFyfU89Xr3fbwfOs8fuTZoRXcXp3FaYpM3StB2EOXDKWYQ7Mfy53zPwq+Pxx4V3Tp/h9mOafbMDUcOYFpuFnz/uQiwJCUKwBS9AzrRZZfpR1g0vYk9gS832+3gchqbHlebE8cfB8DHtkuBPCRLJpE3x6kP95i69ryVqf36R8KeyEfRSOZYhGEPLCpaG5RPlKhHAGicEdUY+DEA+EdAT1mmXBkB6G2LCdMD3sV4K6WRlen8+bIz8n/uR5t/2O6r/xyWFIC9L3k01fd9un1J7njy8L20gOPsDvYr1hXgThuhiP0G6SIEQE4Uy8H8t2oq/3yQQ/fsVCQIOwSKQn70bIKsKLg2XJGSaJECL2wTnGsFKTv34B/USS434M10L+5AgdrPR39WB5e4R2BWF5gHnHvA1Dy3B9humzHTHvJvuyZthjUPYoy6Dd8/oId8oaUFcYjlTy90tX+PAtjllBGCbzK/J+FNRF9mscw8rKN+9Bn8a8QOiUxZajZ5/F99jZ+/n7sWzQjjieJUN4Fr7HcsxBXQveg/neG3t37PXPP3TwTLdd3OjLsmoNQmaRf8H92G8y9JD9wAYf8mpbQ/uGI8oBMS558MEH7ec//7ktXLjQlixZYmvXrrX29narra21qVOn2pFHHmmnnXaanXPOOVaPcO1y0jS1W2+91c466yy7+eab7fHHH7cNGzbYpEmT7Oijj7aPfOQj9q53vSuangMPPNB+97vf2Y033mj33HOPPf/88zY4OGizZs2yU0891S655BLbf//9o9fZl9GHJCGEEEIIIYQQYpyR5all+divj7Wn0zBnzhybM2eOfeQjH9kt1zv11FPt1FNPfUXXaGhosCuvvNKuvPLK3ZKmfY2xr5VCCCGEEEIIIYQQ4lWBPiQJIYQQQgghhBBCiFGh0DYxttRUjyzTSWdBjjj3HLHZaZN3nQTLpsNLFHgC4BOJuVwCBwW8AP0ztsXSDx/U6vYV+v2x9YtfsErkKTwwSHvoTIBjgc9G98jrZ1W8PvUTVcu80yhwEMADEDgemD66VxC3TxeKTZ9c8f4siyDOHkvWG71DGZfahgOiD86M8uftg4+iCJ8DnQSTJ/rjN3mHT/rGA/3+AZ8Xw23eiZDSmYBnSTdhOXu6WFp9XnAZcS4bHvNpWSPi1ulAii2dDW9Q0C5B3oz82Oj9H4F/g94cxtmjn3HOjNizw62S41kCLwxdKdw/QFcL6jXIueJx0C/4sghcI5Flyg3t2uqwdDTKMvD00M1CF1oNyob3oxtuZ2G/FeyvcH32Wcw7tKPgWnQEwdkT9Fmsp6x7fBa2Ey4pz/PpgqH7pRluGHqBsMx43u5dJQnG56Ddc4xAW0nRbeVNvp0HbhWmp8u7WZIG387pTCrPnwTOP3pmYuQcQAfhEKI/qoTjYzDvOtHnNXpn0HCbf/YqvB9wCfVSiz+f4+tQk09/zWrkNcfjBnh86AdjftAnxrrJusS2wvcf1sWy9CQ9vqLlU/CetwmOI7Yr9lE1aOd0BnahYrNPRztNe/0YwLIpTfBlW70cziA6iSb55+O7Dt+703Y4Htv88xQ34HkK6JfohGT+4X6l/fz7Ef2c1UtW4H7ox7aWfamyx+zVRmaJZeNg1basol1OvBbRjCQhhBBCCCGEEEIIMSr0IUkIIYQQQgghhBBCjAqFtgkhhBBCCCGEEOOM3JJxEVaWj4M0iPGFPiSJsaVUMrOXXQeI008Y201vAeLi6UAIYrPpUIJjIege6W6B46LYBUdGmRYgr/KT/ao3wZ0Ch0LgJom5TehWqfX3CzwwIKv31x+G86B6vc/7vkOmue0CnA/DtT799S92+PSu2+DTxzj9On//dI0/3tZvcpsZXCvJ1Cn+eJQVyWt93D09CIF/g3H4Zfk7PL3VXxuOgMLs/fx+zANNWc9eWu234Y8o0h1WrOwcoIcm8EfA0ZDUeQdDzFEUOB7oOML9A3cJ3TD0eRH6wjb669F9EniFOvzxgbOBXiCXNvQZQV6jHcLFxXqe0D/Bdjvo633gHkOfl9T7sgs8cYD9DMsqo5uE58Ojk7DsmD+N8NzAX0W3W8wJRVLkX8a6Dk6pPc//A71GZf10/ifv5gjqLe8VqdfR8Yd9GPOSziN6fOiTYh+G9CR0DLHseX/2wWw3qIuBgwnpCfrkAdQleuwmtfrj4Z7JDpzpb7cK7hjW1f1n7PjeLNtYH0vo6GNewfkTtCPUpYFZ3j1W6K/cZ/JdJfBDwXFY7Gn1+9FPFDagD+W71QT0e/W+3WfT/PXTQfixutEPTIRrjU4p+rvYr6GfKfcC5k2T/L0wngTtkH10F7xvEQ8bnX7JOtRL9hv0V6KupUM+LwZfB8dTyefdUDOuj36jdi3aKcfvVWv99VmXcT2+65YOfp3bLqJdFtb4MS9pg3uOPi22rZF+TwE3QuwN1NKEEEIIIYQQQgghxKjQjCQhhBBCCCGEEGKckeU2PlZt27kFLMVrAH1IEmNKPrHV8uotU1cTLmPeh2XUGbLCKcBYHnhotl8yvuoFTMlljxgs+Yvp02mh4v6kLOyl2O1DUNKByLLYXEoaYQHcDqblczlcph3TgQub/PTlQqfP26zRT9/um4ywA+Rd4wpMLWeICpff5fMyDJHTwzGdPOXUdeZHhrrEMJBV6/w26lKOqf5BOFXZ8xTX+2ODsAOEuiWoC8G1Ef6TtyF8qBd5N4y8Yugbwgq5n2GQpdchjJFhDFzCneFHbFcIQ0hqERrAsuNUfi7HPIiyZYhQZNnzvNnXxaQbYQ8MzSsP+alFO+O0ej4L6lHS68/P+307yWb4MIsgQJNhkOwD2UeyLjFEh3nT75+HoW9BqBlDTBg+NYj84fHspwjDnxiSw/AtlH0BS1UHy4AjP0qv8yGyhdXbwi7yTT4MMR+qHGYYhMBwvIiFqrGdxWC9L2G7CmXNZct5f8LxkXnP89mHEoZpcgxjSBFCUpMOXA/pKSzz4z3DyZIJrW7bpZ7tLDbeViO8h+2M7YLbfFbcP2tAyGaVrzvDDf7+hQFf14oY74OyRD8xPNP3QxzjDOFKQVl2ok9FqFtxk98/ONWPcfzFJF3X4bazyb5d522+7fH9huFZpdZtxzNUrNjJdoN3A7azyX68T1av98dzfIqFnLLP7Ea7muDLKu3zxw+2+T6u0Ovvz3fT4D24VLkfSKZBI8Dxkv0i3u36pvq6XNU41W93+edJe5EffBfmu+DWfnXIzPDqI4TY/Si0TQghhBBCCCGEEEKMCs1IEkIIIYQQQgghxhlZnlrGlVrGKB1ClKMaIYQQQgghhBBCCCFGhWYkiTEl2dxryVY/ABwLpVnecVRY5uPysw7vrEiwHHChN+KAaMWyonRWIPa6hNj0hLHkZecXe+BI6vPbORwFgSOI/orYMtiM2+ezYNnybKJ3KqRd3j1SqvPpm/BbBJtzyVsuEY8l5HM4HIaRl1XLsQRuA+LgI8t4B04L5gdIJk+seP2E7hbG5Ze5XrhsdRH1jk6kZENHxbQZl1SHEymox1zaGF6YYAn6yJLuaR/uR6cD6yI9O03egZAX0G5eWO63mbdNdBjRdwGnBJfeZvrY1ug1Yllzu9yPwrygB4deGzqBan3aE/gxCh0oS5Y1nz3Yj/RxaWbUlcBdRscS/R1wqQR5DccSr0dyutLQ5wZOJJAkeF64ZIL8Qb8aOKk2wAVTYSnvwMVFjw7Tzj6Sfiv2YYTLamPJc7bz4NlR7+kICvoRtmukP3As0QXDuoK2km/s8MfDDZPXoR+kR4jQW0TXDMcQ+r96yvKT7ZawrNmnxvxXHO/ZJ9GbN+jv1z/J151hNPOG1XiXavF9bIF1E31w2oOyqEEfirzM+tBvMH/QL+boR6s3+/zL4VRiWdKbk9Wi36iDNwfpL6xs3/G96NxB2SZsp6wrqId0oeUtGN/W+HerbKZ3EKXt3g1GX1XwXjrsy35ggq8rDct8O8+rfFlkdXzX9PmToJ1nuP9Am6+Mtet93Wh8Ac5FuuP6kb9wrQX9Dsb/ZKvTsFR57BBC7B70IUkIIYQQQgghhBhnZHkyTlZtG/s0iPGFQtuEEEIIIYQQQgghxKjQjCQhhBBCCCGEEGKckVlimY39bKDxkAYxvtCHJDG29PSZbfVwIG4+fX6FP7at1e+n42j1ereZwDVDV0ppoo9Vj/kxcjgacrSedGCbFyBDTH5W7c9N6dGhawSOJTqOAo9LC56VDqOIM6j3EO+jql0NXwY9AHQ+0PfBuPXNPl69CttGF0vMIcHj+XzYTw9C0u79WsHz0H1Dysor2ehj/gvddHVEXF1Ia9YMZxBINyDtfPZuX3YZ3GEpywo+i5iTqLQf3GUvrfHno67kuH9Cbw/TQxcKfB55n6/byTSfnqCt9KCuMf9JDRwZ5deD/yJwndELQ+8MHUet3mcVtGv2C0Nwj8TaId1fdCLRE0NfFX0grMukF66Tad5BZC+u9NuB4ynykkr3DJ1V7CdY1xrr/eWGUFfoBeosGxPY54CM/im0yxxll6Du5Cj7FHUpH0SfyLoIlwj7hWCMYR/LuoO8phcnNzqf0O/RCTUYGfNAAldbPrnN70e/GzwPy551jXW7fDvSR9PdldDPBOiXStjHRO5Hr1v9ar891OSPL/Yj7+lzZPoKKGs4mWytf7di+lK8m7EuBn0+xyyWxXI/prBPHpri33eKHSiPPvQDqBt567Z+MOmq7AwM2j3fC5vhPOK7F9sR3lPZjtMV6/z+Bowp8E0V0AcW6uHPqkc/hHfRQldkfMSQRH9kocPnXzX68L7pvs9NB/35tWswRnIMjI3XIH+5n8qzgciRQojdgULbhBBCCCGEEEIIIcSo0IwkIYQQQgghhBBinJGPE9l2Pg7SIMYXmpEkhBBCCCGEEEIIIUaFZiSJ8QPcKqWDX+e2kyG4VxDrXaTHBx4DulcKazZhP5oDj9/kY8ETuljKYsPzIlwc/fBjMA6ccfj0VdAh0ObdKlmNPz6f5b0xaa93BqR9/nr1/99atz1wgHebFOn7mOS9AAMTvQOh2OPzpqrLx6vTTUJPQTbRO43SdvgwkF+lyf74wlrvEUrWo6zp74g4LgLK709vC+sFHESW+Zh/ulVSpg1lTe8N203g44AfivW6/yDUlQF/fvVLG912Ydlqfz08Tz4MX8UyOCgmT/TbnSjbAfgs6Fahb4P5wfynH4NOKPrFKvm4eO4g/hZD9we3B3HtHGVHRxHcJbauw99/4gS3nbX4vEnb6UpBv9MDPwVhu+DzoK5lM6f49PXRiwM3DKBHKPCL0FXDsqezKvDc+fQPTvT9tGG7pvx6cJ/w3sGzwe2RwruW9VR2s+QRL1yQF7hf4O1hXtDzQ8cRz6ePiv0a+vQM7S5JOR6jrJD+rAMuuLXoZ1g30C8kdCaBoB8o9/BExoPAccTjmTfo04LxnnnL/Rgjav7or1fDsmMfyXaPPjvZVNkPaQnSx7rOvKRjkJ4b9NF5HvHioN+pema52w76Hb5fcbvcGYV3p8AvWYO8RB8evAeyHm/qcNv0SQVOQJZlhrxivUbeFODfrEKflxV92RboUtvg00tfZO8b/Lth3Yu+nQ43+vxMUJQ1a/EeTV8l84PtPOLxy0svb++kW0kIsWu8ZmckdXV12Z133mmXXXaZzZ071w488EBraWmx6upqmzJlip100kl27bXXWnt7+yu6zxNPPGFf+MIX7Nxzz7UjjjjCpk+fbjU1NdbU1GQHH3ywXXDBBbZw4cJdvv5Xv/pVS5Jk5L8FCxbs8Nhf/vKX7thK/1199dW7nCYhhBBCCCGEEK+M7OXQtvHwnxDlvGZnJC1evNje9773bXff+vXr7aGHHrKHHnrIrrvuOvv2t79tp5xyyi7d59JLL7VHH300+PfBwUF79tln7dlnn7VvfetbdvbZZ9u3vvUtq62NrBZVxqpVq+wf//EfdyldQgghhBBCCCGEEDvLa/ZDkpnZrFmzbN68efaWt7zFZs2aZdOnT7csy2zFihV277332v33328bNmyw008/3R5//HE74ogjdvoeNTU1NnfuXDvuuOPs0EMPtWnTptnEiRNt/fr19oc//MFuuukm+9Of/mT33HOPpWlqd95556iv/bGPfcy6urpsypQptm7duvgJZdx222129NFH73D/lClTdrhvd5Jn2cgywgmWwuYy4ukLK/zJmJ4dLJUd3KzydGmriUypXYfZaVhKuny6N0PJkm5M6+fSzwjTS0p4FoSQcLne/pk+1KzQ649nQy+sxLNgan7Nah9ulNX7qew9WNK1OODztlSLqegdviyTDj+VPmvz4VfJAEL5OH0cITAMZeN08GCqPqf65xWWgjYz43Tq8lAFhjHU4VhOq8fxKY8nm/BsDCtAvU86EaawzoemMWyi9lkf1pi3YIl4hj9FQmYSTk3v8XU/X+P7qoShe8Ey6ZWXJQ9CWBmGyVBBwlCBIKyjbJv1AiEkg9N9Pa76wwZ/KfZRDEPEs+ZouUkzwhQZZtGPvE8r92lmqEsMvSMMwWG4Vo1Pb5EhqQ1sGwixYQgQ08uQH4TEJAzBwf1ynF9AGOdgm8+P4Qnblt4urPLthOE7DJUyLqFuaKes1+zjhyIhnWxnDJNk3Uojf0nmeIrnCdoR6wJgqFrOfpBlzfGZ2+xnOH4HIba4PkPpgrZYlj6OB4T3jsGwQoaCFVG2TBvzgv0Q2wn63GAMYt2pw7sM+oGcZcW6wLrFupRWDlE1LAlPghBWXC9duR4noC5wTGkoe17mDcb64al+fCo+v9If3+XHW4akBqFsuH5e5/ucBP1GXoW8avBllVejXpfQpyIvSnV49yz46wXBxzi//nn/PjEMzUFVJ0LJUdc2z/Gh5E2s2y/50Pnhw2b76y9d44/f2bYohNitvGY/JM2bN89eeumlHe4/55xz7Hvf+56deeaZNjg4aNdcc43dd999O32fn/70p1bcQbz9KaecYh//+Mft7W9/uy1atMjuuusu++QnP2mHH3549Lrf//737bvf/a5NnjzZPvGJT9hll122U+k64IAD7LDDDtupc4QQQgghhBBC7B3GS1jZeEiDGF+M6afc/v5+e/TRR+2+++6z22+/3bq6uuIn7SYK/IvIdjjjjDPskEMOMTOzhx9+eJfus6OPSFupq6uzSy65ZGR7NPfp7u62j33sY2Zmdv3119uECRMiZwghhBBCCCGEEEK8csbkQ9Ly5cvtggsusNbWVjvxxBPtnHPOsQsvvNBWrPChS7feeqsdc8wx9pd/+ZfBiiR7i4aGLVPb+/v7I0e+8nuM9j7/+I//aCtWrLCTTjrJ3v/+9++xdAkhhBBCCCGEEEKUs9dD2xYvXmynnnqqbdq0yX0c4jK9Zmann366ffSjH7WhoSH72c9+tsvC611lyZIl9vvf/97MbGRm0p7gP//zP0d+jt3nscces6997WtWXV1tX/va1/ZYmvYWSbFgSWFLNcwbvXOisNG7WYKloeEZCpaAXQqnUnBvVH/G1Xf7ZWCTGVP98YzbL4u1T/rgGMBSxvn+M9x2qclHphfoc2j3y9fnU/wstNrVPq+CZbe74bmJuFroEGLcftOzHX5/B5bGbmrw+1F2OZbYLb3OLylb6MOStHS3RHwegQOJyxHTN8LrT2zz26x75dCxg3oTeGGY9/Q/0dkQuLwqLzNuTXAcwWUyPNl7djK4wYbr/XZ9J55nEH4OOBByOBzokknoTEJdSHB+4GqZ0Or3010TlB39V3S9YHYq20L52BT5g0ahv8KS4maWTZ/sttPVcHtgSfhgSXbCJdbp3QmWaMdSycwbli29NPBZZQ2+HdHdVpra6rbZpwd9KNspfCNsC4Frhe8RbLdwveVcdb3fl306uG07mQZvINwoCRbKyNEHZSzbiNsjcCLhWbJg/EHdRLun5yZFP5FxvIvM2qbzKKW7BUUXvOOxbrPPDm4Y+WMiHVNMH72Eg5G2UEY0L2IeNoKySFrhPmPesK6wbtATxzGEYwbdaYRlEZvBH3EiBf4qizgMWdbsx4J2H6k7YHjyNk9PEb7IvAn+x3aMf83e8RM4/2rgzTtwmr9eN94XwOYDJ/p/4PD+u1U4w79fZLh/sds3xN4Z/vkGp/i6Vt3qt+uX+34rRdkU6aeMuEqbOtEP9qPdNcO59CLGSMK6PtLv7VshWAptE+OVvTojqbOz0+bPn28bN260adOm2Ve/+lV76qmndnj85MmT7V3vepeZmf3whz/cK2ns7e215557zr74xS/avHnzrPTyC0J5+NkrJcsyW7t2rf3iF7+wM888077zne+YmdnBBx9c8WPZ0NCQfehDH7Isy+yKK654RR+3/umf/slmzpxp1dXV1tbWZm9605vsH/7hH+zZZ5/d5WsKIYQQQgghhBBi32avzkj6yle+YmvXrrVJkybZokWL7HWve130nL/8y7+073//+7Z48eI9lq4FCxbYRRddtMP9l19+uZ133nmv+D6zZ8+2ZcuWbXff/vvvb/fdd19Fp9J1111nTz31lL3+9a+3T37yk68oLYsWLRr5uaOjw37/+9/b73//e/v3f/93+9SnPmVXXXXVdmeJjQaGKJLVq1dX3C+EEEIIIYQQQojxyV79kPSDH/zAkiSx//E//seoPiKZmf3Zn/2ZmZktXbp0TyZtuxx55JF200032Vvf+tY9do9isWj/8i//Ypdccok1c2nnMp5//nn79Kc/bWZmN954o9XFlgzfAdOnT7f3vOc9dsIJJ9jrX/96KxaL9tJLL9kPfvADu/32221oaMiuueYaGxwctM9+9rO7dI9Zs2bt0nlCCCGEEEIIIbaQm1k2DsL1xsZWLMYze/VD0nPPPWdmZieeeOKoz2ltbTUz26Mrup1xxhl21FFHmZlZX1+fLV261O6++2777ne/a+edd57dcMMNdtppp73i+/zsZz+zwcFBy7LM2tvb7dFHH7Wvfe1r9pnPfMaee+45++pXv2qNjY3bPffiiy+2/v5+O/vss+2d73znLt3/6KOPtmXLllkVPBpvfvOb7YwzzrCPfOQj9o53vMM6Ozvtc5/7nJ1zzjl25JFH7tK9doVk3Ua3ncOPkdTj4xmeo1SL2OxDZ7vN4vLKsdb5BP8hL6n2XoGBGd4PUvNH+D7KgROAjoVkQ4c/vMH7N9JOXHuyj5tPVngvTOAWYZw6HUAFykHgHNjo05fAp9F7iE9vfRccTRtQltO8Aynb7I+v+iNmsTU0VNymcyqhD4ROhV7E5fNDLGcC9tPJBKcDPD8O+ijgm8jhSqHbI6jnLEt6Y+hgot8CLpXievSl8OxUM++YV0hfjvT1HNTqthuXeAdF4L/YD+6ZjT59pdneMVFY431h1uKdFVHfF/0ifF56hsqvx7TDq1JY5es961XQrlnv6EJhWTJtcBYZfFPB9SKOobwYeU1kvzZIJxTcbgNsN6j7sVmvfF66V9BHB2WJuk1PTlbjr1/V548fbti2f3iyr/f1+NtWVu/rVYq0JYGPCu2YdSHw3PjrB94eeHcCpyDbRb13paRsF+y30Gcn7JfoyYmUbZKgzza0U9ZN5Ae36ZwKfGGsu0h/3lfWp/PerFc7Cd8lgnpLzx79WpPRzkHgqIC/K++Ez4tOJtbFnfRRBe8PdCLx+sH16AWs7FgKxkyOAfQOwv9VePL5bWmjjxJlEbyHNsL/2EK/la9X1cs2+P14VxvYz58/2JRi2+fFQMtMt92y1L+LpEM+Lwur/ZhUW+3LqvF5f36p2fdzXW/w735ti/27Z2mSz/uk5Msqw/24XdWOfoVjFutO4PlD22TdEULsUfbqh6S+lwfqBv6SWIHNm7cMALUQWe5OWltbRz5YmW354HLuuefa7bffbhdccIHNnz/fbr31Vrvwwgtf0X3e8IY3uO158+bZRz/6UTvllFPs9ttvtz/84Q/2yCOPWFOT75gXLFhgDz74oDU3N9sNN9ywy/eP5fsxxxxjN954o/3t3/6t5XluN954o91yyy07fZ/ly5dX3L969Wo75phjdvq6QgghhBBCCCGEGFv2qmx78uQtK9bEPjSU89vf/tbMtoRk7W3OP/98O/vssy3LMvvYxz5mmzZtip+0k7S1tdk3v/lNMzN78skn7d/+7d/c/vXr19vll19uZmaf/vSnbcaMGcE1difvfe97raVly1+/HnrooV26xsyZMyv+NxZlKYQQQgghhBCvJrau2jYe/hOinL06I+mYY46x7373u/bjH/94VKFipVLJbr75ZkuSxE444YS9kMKQ+fPn29133209PT324x//2P7mb/5mt9/j0EMPtYMOOsiee+45u/fee52b6Otf/7q1t7dba2urTZw40e68887g/Mcee8z9vHX21n/7b//NpkyZEhxfiWKxaG94wxvs8ccft5UrV+7iE+0EdbVmxS1TaYPp15x6zinGmJJc2Izp4VzmOzbVvhfnI+yhZkWHTw+W3M0P2PaRj9N7k82YZo+0FF9EqBqXQOf0XYZPMbyp208Xzvbzy45nCAMsbkLICUPlENpV/6QPRcsnYlnwaX4J9vTpP7ntwgHekVaa4GfLFV7wS9wGS2czDAPTzbmMOetKMF2aoQYM/eMS9HnZ9HGGTMSWgmbYQGxpaYSSJciLrBFlhesnKOtkM5YNZygdl5Ku9Xkz3Obr5lCTv37teoQGINwpr2/16WHYCNp9YQVCA5hehgAh/dEQHC6D3uLrUjpQVpfQR1nCv8UgLZFQuCD0jM/Cafx9FUIqbTshm7w/+0BsJ60IoUFICK+XF336e6f5dlm12Zd9XQ+XrMfzMmyT+cX7M8SlJhK2iDDPQjXqJsqjUB6ah5CJ4UkIQ0deZg2+3RRfQmg1+xzWa4ZoDCAvehAmGYR2oc9DH5lNQPoj4Uxpo2/3zCuOOUkQTo1+KhIulnAMZPhXsOw3wsf5/Gz3wQ13399Vg7DDWAgnj0e9TrvRboo8HnnB+zH0ux1/FEUfGaSfeR2E4GLMQ96nba1+P+sa2n3OMM0E9w9C4ZBe/mZTod9gyWS4dwqtQBiC6q9dmuJD1dI+vIt0+j61er2/Xm0d3wf8du1Gn9dVz+I9nXmLPr3Y5Z+v9wC/v2+Cv1/1Zn+9tf/Nh5oPo5kOo5m3/MmXVcNKX5eHJvp+qXqFr5v8vaD8PdvMLF2DcPKX3/0YOiuE2DPs1Q9J73vf++z++++32267zT74wQ/am970ph0em2WZXXzxxfb0009bkiT2t3/7t3sxpdvYOovKzHa44truus9zzz0X3GPg5UGto6NjVHlw00032U033WRmZgsXLtzpD0lmZnksPl4IIYQQQgghhBCvSfbqJ9uzzjrLjjvuOBsYGLC3v/3tduONN9q6detG9idJYmvXrrXbb7/djjrqKLvtttssSRJ75zvfaSeddNLeTOoI5bNydiTC3p332ZP3GA3Dw8P27LPPmpnt8TA6IYQQQgghhBDbJ7OxD2nL8mRcrBwnxhd7fe7f9773PTvkkEOso6PD/v7v/96mT59uycvTcN/85jfbjBkz7MILL7Q//OEPlue5HXbYYXbHHXfs7WSOcM8994z8fPjhh++Rezz++OMjM5F4j6uvvtryPK/43ze+8Y2R47/xjW+M/PuufHy78847R1bImzt37q4/lBBCCCGEEEIIIfY59mpom5nZpEmT7IknnrBPfOITduutt1p//7Z42YGy2OSqqiq76KKL7Atf+MJOrfI2WhYsWGDnnntuxdXgvvSlL9mPfvQjMzObPXt24GlasGCBXXTRRWZmdtVVV9nVV1/t9i9evNiKxaK9+c1v3uE9Vq5caRdccMHI9vnnn7+zjzIqNm3aZH/4wx8qflxavHixffzjHzezLbPDLr744j2SFkdVlVnVFlcEl4hN4OkJlkWnC6bdLwkfOCdIzF8SOBbg44DDISlzNHEpaKvzPowcbo6UzgHG9DfjXj04nsvR01HQhSViO+FEivgq8hY/U67U6J+vwOv/8SW3HbhL4FwoLIc/hN4hLKmbwLmQI7+TbrhP8HyBM4kOCNYN+EpcergsNV0e9C2BwOUB/1OCJcmDJc03+HqfN0FaAF9X3oDrD/p6nU2b4K/f4+83MNGf3zvZOxUm/RaulAHUrVrk5SD9GnCjNdKJ5DcTVl36veDvyFr9qpgp3DN0XJW3VS79HLhB6Cpju2Q9Y7tjvWMfyHYE5w+XgA+8MHSr0TVGrw2eJ0ef1z8VbrNBf7/qjXCdsJ2u9u0+aDvMDy7VzWXa8fxJZLXXpAt1tUJYdwH1MmuFcwgusAL7WHpq6ARkP0HPDbxvdJ8EHj2MVznq/XAz+vC+ymMAq27gNmOfGpRdZQ9Q4L2j84nOpIjvK9rWXgm8V8TdFRtfErZLAi9e1FlE9xnqLp1MTC+XVOf4GfR78NFEHUvMP3rs2I/FoDsO+RGWR1n+I6lps28nQVo3w02GejvU4rfTWt8uqlj2m33aG57xz1470b97dc/27WDzsa/35z8L/9XqdX57P6+7GGzyGTBc75+3Dk6mhjW+LBO+KmEMqF2Ffo7uUsL3IaQ3g7s0gccvaXnZUTWMsUwIsUfY6x+SzMzq6+vtK1/5il199dX205/+1J544glbt26dlUolmzhxor3pTW+yd73rXXs0tOrqq6+2yy67zM466yw74YQTbM6cOdbY2Gjd3d321FNP2R133GGPPvqomZlVV1fbLbfcYkW+KEV4+umn7aKLLrLjjjvO/uqv/sqOPPLIEefSypUrbeHChfaNb3zDOju3/CJ48sknj3yY2t10dnbavHnz7IgjjrAzzjjD3vKWt9j06dOtUCjYSy+9ZD/4wQ/s9ttvt6GXP6ZcfvnldtRRR+2RtAghhBBCCCGEqMx4WTFtPKRBjC/G5EPSViZOnGh/8zd/s0dWQhsNGzdutFtuucVuueWWHR4zc+ZMu+222+zkk0/e5fv8+te/tl//+tcVj7nwwgvtxhtvDGem7GaefPJJe/LJJ3e4v1Ao2Kc+9Sn7l3/5lz2aDiGEEEIIIYQQQrz62Ksfkl56aUu4y3777WeFyJLXW8myzFas2LLU+Ote97rI0aPnwQcftJ///Oe2cOFCW7Jkia1du9ba29uttrbWpk6dakceeaSddtppds4551h9fX38gtvhve99r82YMcN+8Ytf2K9//WtbuXKlrVu3zgYHB625udkOOuggO/744+3888+3I444Yrc92/aYMWOG3XPPPbZo0SJbvHixrVy50jZs2GD9/f3W0tJiBx98sJ100kn2wQ9+0GbPnr1H0yKEEEIIIYQQojKakSTGK0m+F9d6T9PU0jS1J5980t74xjeO6pylS5faQQcdZGma2vDujHEXY8aKFSts1qxZZmZ20qTzrbbwcgw4QwdZ3m1wQmyCE4nQC8A4/eB43J/p4cdPxt1Xuhbj7Lt8XHfgdenyceVBWuBqyddt8LdrQZw/fFF5ITIYrPRx9YGLBHHrhrj3pBseAbpXmB+xD8vMHzKA68OxQOdD4Dyqg9uGPpQJ3lOQtndt2+iFnwHk9NhE/BGBh4d1ic/K61XDx1UH9wgotfkP5YMt/tnTYT9E9E+EC6bf7298octtB883DKcUfR5sp/QMoWyzGaiL1IXQycAhj3WxwqxQthu6x6LXjnlcWNYE5wd5B+8O6wLrfeBYYp/GukVPDbw3Q5N9O8mqfV6mA/76VX/y/Uy2qQPpQd2BsylwneF4OqHyfv+8wfl83vJt+q+Q98OtPm+GG9GH1Pi8qF3jvTdZVeU+MB30fVja6/uBvAhPzWb/rIOv8+4zuk1y1I1iB/KKdZteILSbtBteH3p44D5L+tCv0XNDv9badn88y264sucnGNPK2kIec+bRPcbxhNfm9VhPc2zj/BR/zAycRRECBxN9VOwn8LzZqjV+f+T5Y44mpien/4vPx34Px6etzVaJvNIYjT434R+O6W+iIwnvJqVpbf56bLd9Pu3sww39QM8B/tk2T/f7i30Yf1f569es9mPC4FTfR68/3NeFHM2o9QU4kl6A84jtCs6nvMO/DwQuVHj/6EYN8p+w7rzcb/QPd9sv12xZiGj58uU2c+bMytcZh5T/rvS2Oz9ktZObImfsefrXd9tvzt0SxfNqzVexe9nrq7bt6nervfi9SwghhBBCCCGEEEJshzF1JI2GrR+Q9rQ7SAghhBBCCCGEGDfkieXjIaxsPKRBjCvG/Yek1atXm5lZU9PYT+kTu598aNjy0stTUyMhQsF0Z06v5tR5TEFOWzD9mVNmMd27fNlvszB0IN2IMJLyJXC5jPUwlwnH9GmGXjEUDUu6M1yHIRo8PsOS74W1HX7/JB82mDJvOP36pdV+ezrCiyJhEMH1GK7FqeqcTh6bociwRk5/Ntx/iPs96boOXK8svVySnVOzg3sDhoigHQThNwxTYF4wzIL3x3K5XHW8WFU5PKm624dhFDdjansvwgprfVkEU/nJANLLBCK/Ux6PsJEc4VdsCyThsuSlfLs/m5nldb4sgmfH/iDko92H52Zot3kNQlwQzpSs90s9Z9Mm+f18Fob59aMsgiXYI8t45ygb1MXq9T68qX+6r1tVKJs0WNIeZcu2xX6W4PwEy5QH/USFfitn6BnKMkXe1q5A6BqWrR6Y6su6ZoNv90Mt/lmLGzDegIRhini26lW+rpUm+BDatN+34wzLlqdd/vpBKN1g5XAkhmcHoWw8nsukdyIcnPD9gKFuHGOSyBLxFdKWlxgmhz6O4bzBcvSVQ9kIw4EY2pUwPItjEu/HbT5fDcYcvmuhT+bzJXzXQjsMwr35h2Kmh2WLUMAgJBZlmzLUv6xulahIgHYgYdgeyipBvUoRypXg3Yj9SCwktGqz7yca1vrkVHf5vKle59+VknUb3XYNQl5nvoi8x/tHNs2HxKabELqOd9u8zed1ENaIPjvv9c+bI3SdoewBfHd+uXzyrH87Bwshdjdj8iEpif0iYWZDQ0O2dOlS+9d//VczMzv44IP3dLKEEEIIIYQQQgghRAX26Iek7a3Mlue5HXbYYTt1nSRJ7K//+q93V7KEEEIIIYQQQohxTWaJZZxJP0bpEKKcPfohaUeC7J0VZ59zzjl26aWX7oYUCSGEEEIIIYQQQohdZY9+SLrqqqvc9jXXXGNJktjFF19sU6ZM2cFZW2Yg1dbW2vTp0+24446zOXPm7MlkijEkaay3pPiyNwNx80EcPWKhE/o8JrT689f4paVjS8rS4cBlWOm4SIMl5ctizbm8bbP3UTBO3i0nbxY6lbjUdBv8Fo3epzEwyTuWij3eUVBA3Hq6qbJ/Ip860W/TF7VyvT+BZVOFJe0zPF9dZf9I4BGKOBa4/HIC51IOTxCvF4Tf0uFQln95q/e+BP6qKd5bYxs7/L3pAKLjBz6IhN4YppXemIivKhn06S3QlVLl63Kxx++vaoe/CnU3afcOhIFD9nPbNS+gnXImK5wOQTvd3Ftxf2miL59iO8q+WNlPUr62aU7PC4+FqyRnPUTel2bCaTSAskBag3oI11qKpZcDF0of9sPlkk9q9enhUs4Nvp/ZfKB3GtHXMTgZbje4VgI3WxfShz42b8b1NqJus22wH6X7LdL2rKxuJMPwssC1VaqHJ6fga8dgqz++do1vN1kdnEbrvBMpQ96nPWgXgQcH/QD65MJy9Nnw6hR60S5ivimMj8Z2xXYa6eOD/fAEBWAJe7pXguuRsv10/gSHot3lzJvY+ewn6B5jWXJ8w/nsMzlmBPfnuxBdatRJvc732Vkjxvc/rfLbHIPoVILrjONv4KtCv5FjO/AYkUa8f5Rfn44kkkTeLdinIG2lZv+sGfxThR5kNq5f6MOYAGdhijGDziXmtbFdcLzF8wTvdoTevx5f9tkEP/6m9IeV4CalE4mOx1g73lq2fMcUQuwR9vqHJDOzj370o/bGN75xT95aCCGEEEIIIYR41ZLliWXjYMW08ZAGMb7Yq7Ltb3zjG2ZmNnPmzL15WyGEEEIIIYQQQgixG9irH5IuuOCCvXk7IYQQQgghhBBCCLEb2asfkoQIGBwyK70cI07/CGK7S4hlL9R7D1De4fcn8ADRoRDQAU/ROh+7XkDsOZXxSUvTtg04eKzWpyUZHr2vwcws2eR9GcVViFtHXtXReYS494H929x2zQp4AlAW9Hek9NYwzp7OBjijkgF4AZhfDXAasG7Qh0GnAu/f6OtKaT/vfCo+v9IfT+cFHQtlToekzz8LnTyFNZv8uU1wKvXAMVTn0xr4puh3Ql7SwxP4Jab4Z0/6vJOgd7q/f8MyuFrgQklYF+DhyeEuq26v7GjI69BO17f7+9H5wHYOH0dx2VqrBH0ngYOhzNOT5JUdRHmTrycJ6mXOvINPK+1Hu+jF/ehmQV4lG9GH4Vmy/Sb7+23w7T7ns6PdJANwrfVVdrH0TYb3p9M/78bDmtz2hCfhzxhE3WC/SDcK+yH0O4F7Bl6gwJlUVh5ZPfJ60HtmCnAoDbdh/IIHLoHjKHCH9Pu87jvI+6TqlsNzUw/XSFXlvCvQH0VvDvulFu+ZC3xU9OLw+uyXeD968FD3E/QjQVkCOpzylN4h1J3y69FxxHpHL054c7/JPo5OIvYTkWcLXF+8PT1y3Obz0UNTRFntpPuNnpsE72ok8OIQjv8xryDyM1u5BjfcdjzLhn1AkDb6nlDW+QB8i/9vqT/dAMt6mu+jk8X/n9uuecuhbnuoyfdLpTqfVzXsVwLHIFxtq/14meB9JMf4nk7x6eX4n/Shj2Xdpe8SJOyjI07DkXe3UsR9+CojzxPLx0FY2XhIgxhfjOmHpE2bNtkf/vAH27Bhg/X19UVXc3v/+9+/l1ImhBBCCCGEEEIIIciYfEj65S9/aVdddZU98sgjoz4nSRJ9SBL/f/beO96OslrcX+/sfXovyUlPIICEC0iHgFRRpFwxeEGKQKjqRUQvggUUFAso/sSLIE3KpV0pIhcQ5auEQCRSBIUQAqmkJyen93P2nvn9cZJz9nreZE8CyT6HsJ7PJx+ZzOyZd946TmY9yzAMwzAMwzAMwzCMISTnL5J+85vfyMUXXyxRFMV+gWR8BAiCwc+s+YkxQlgS/MS1BGEX/EQ4Lk1oqw7Z8cKjmGK3L+ZT/NbBkCMvPe8qhKLxXAzVQqpjL10uwwKQ0tX1MSWtrquCheuwH5/WI6wgeGepPj/vz6trhFkU4tP0mFTPfpgirlepwywcPs9m6meHz9OTSFHrhZsxpChA36wZDA2MkvpT98Ry1K0H+hn7dSlCreLmSaZwZ9gEzt9Xp+81f6nua0Wr9afmvTUIp0JUQRJRAwwnco3Ner+XbhjHM4U9Q9nYV6p0eJQX3sXrcdyybzEkqHxw7ARrEKaI8B7286BJ90vXpvtlVIa2xrzQN3mULtoqhKAyAqZC1wXPF6xtlmwErHuEEbCvJzv1+RlW0VOhj2/6mN4uWa4vt+B0Hb415gXdlwvrETITYtwizNQLv2JIDLdb9JqQGYbBMEAvXAj9Ptnak3W7d5wOL85fvFZtNx2ik5K0j9FlHbNOj+sQdZ9cp/tewHBiriEM4eGc7s2JqDuGrDBcivtxfS9El+OeaybO74cgxaRpZ/kzz4VreesPQ61YVwyjx3oufNbgHB/pgR12YD1DWF6E8GWGawUIl/LmxDD79SOEOwWreDgmIoaaYX32wskQhsj6dpV6XmBfYuiaF/IKMtdIl8weJseyeedCv/1T611Zj/9M+Tn6Lxi6jr6XGDVS78fzRQLzTu+Ean08+mbfaF2XQZ9eMxjyymdNL9SMMFQ8gbZoRsgs+w7huOVY4zy2nRJGwyNjGqcKw8g+Q25l3n77bfna174mURTJHnvsIX/4wx/kqaeeEpH+hXjhwoXy6quvyi233CL77LOPiIh84hOfkLfeeksWLVqUy6IahmEYhmEYhmEYhmEYIKcvkm688UZJp9NSW1srL7zwgnz2s5+VCRMmDOzfYYcdZJ999pELL7xQXnnlFbnssstk1qxZcvHFF8vEiRNzWVTDMAzDMAzDMAzDMAwD5PRF0syZM8U5J1/72tekrKws67HOObnuuuvkqKOOkhkzZsidd96Zo1IahmEYhmEYhmEYxtCyIWvbcPhjGJnk1JG0fHm/EGFD2JqIji3v6+uTPKRyvvDCC+XZZ5+V++67T84999zcFNTIHUEwEK/POPqIaUGZdrVFu1DcmDq13TdC+zM8vwjSmjqmiu5AGnPG9QebTvHrlZ1x3ThXSIcA4sI9JxJhivc6pHjvyJ5ilU4EL10vfRJ0EPB4eHECpoZmCleMe8eU7vh9VKL9GQ77udR5qaTp3yhE2/N+mP44w6vgkPbb813EuUKSmz63iEhftXYO5KNueS+pHcfo0y/Tfq78dyG4QFsk12lPTKpUOxcK1mhfh+cooP+DsC8VoD7acX7PkYR5oAeuldpKvZ99Hw6MsCy788H1ZNyPl3Zabwet9MigJ7ahH8Z4Z/JWojDoS65Zz4Fe2m3PVwHnUVu72nZwhXmOpBrtJsur1/NWski3ZQH8Va2T9P2FaPqK+bq+ipfr86fQVg59J8BY9FxsdCaxr7Hvuozr5TOFOtoWXSMsgC8L/TT/XTQu5oWqV7QzqbIQawLKw3nIwavjeXsw76RH6XGeaMe4YV1yTo9Z4yQVMy9wbGEOZl/11jy2HdOec97l/WS2p+c8ivG00HNDpx49MDEpz+k84vrMJPKec4h9k+spy8Nx4ZUHdck5PMbxJPg/n57HD/UXoTzeOI6DXiM+OyYyrkcnEk/l9WOsd1lcWxs/IZ4b6VqLe9bi0w3qMu+NxfpoPOd2Tx6vtulEpC9SWuEcpKOQz65YrzkPsW295yPWd2kJ9mfve2792HKpUKRZDMPYxuT0i6Tu7v4BPmbM4P/RKSkZnCSampq83+y0004iIjJ37txtXDrDMAzDMAzDMAzDMAwjGzl9kVRd3f8vXh0dg2+wR4wYMfBV0rvvvuv9Zt26/gwFzc3N276AhmEYhmEYhmEYhjEMiCIn4TD4Y6FtBsnpi6Rdd91VRETmz58/8HfFxcWy8847i4jI//3f/3m/2fB3I0aM8PYZhmEYhmEYhmEYhmEYuSOnjqRPfOITMnPmTHn++efl7LPPHvj7k046Sa699lr57//+b9l1113lC1/4gnR2dso999wjt912mzjn5KijjsplUY0cEXV1DbiHosnj1D737nv6YHhqGPstTdqBlNeD2Gt6cLrpZIITiQ4IQI+P8hwx7h1x+MGoSn2t3l59POLEXXWV2u4bXaG28xZrn4Y0a89NWN+gr1+u3SWeQ4jOB8S9R61wq9AJQacBnUgFOH8fvAMhfBd0VCxerrcZ18/r59FDgPJ0o/5ZXvg9XKZbhb4JL8YfbhN6afTRElVoJ0CyDf0QdRfW6b6R6NT3Eo6sVNtBuz5fWA5XGOqiYLXuSx50i8EhEZVinKK6HPqSlMGJAKI8jI0+tE0X2pJOh7Ls5QmLdF9JdmfcD10jdA4Vw/WBsgbsx/RDwDfheWjYL+nPoLuFfZPbdH/BZ+HgpwhWaN8WXXAB5onKtc1qu3jlSLXdVafrK12gy9+4p3YyVb0NHwfbnvXJ+uC8TNg+mf4Rem3osStBXXXrsgRtMd48emLQd8ISvb97pK7rhin63kpWaz9W+WJ9/QTmAUfHEO+XfZHrL+dcukxwvqiHa172f9ekqyYStH1M23peHsyjQeYayzmNzqJOeF/oOmNd4Nreeh8HnD/evWBaEKfrIly5OvvpWbeetwbX53rNeYjPLzw/64N9i32BY4O+sD121j+Hiy5cgueFzPJmcV2K+G0VIFHQ0/W3yJZA3+WWur88FxhI1mrXGf1XdCL11MI7l9ZzboJrDh1HcXMqnU/5mNeK9PUTTbo+2Nbeeo7tYEP9Bdmf3w3D2Drk9IukE044QUREHn/88QFfkojIpZdeKtXV1dLX1ycXXHCBlJeXy6hRo+Rb3/qWpFIpKSwslG9/+9u5LKphGIZhGIZhGIZhDBmR9L+fHvI/Q10RxrAjpy+SDjzwQLnrrrvkuuuuU2Ltmpoa+fOf/yyTJk2SKIrUn5EjR8pjjz0mU6ZMyWVRDcMwDMMwDMMwDMMwDJDT0DYRUSFtmey7774yb948efbZZ+Wtt96SVColO++8sxxzzDFSXFy80d8YhmEYhmEYhmEYxvZIKE5CT4QwNOUwjExy/iIpG3l5eXLMMcfIMcccM9RFMXJE1NMr0fpY5mBNk95ZpT1A6ZVr1LaDTySoQWw4PAZeHD9jvRH373mC4DkK4RNRfhB6cuBMiBpwrzi351CA4yi5rlHvpwOoQse5B6Pr9H7E6Tu4U8Jx2mVCApRX6JcaqduifWft8Sl7Q7dluhZx+Wt1/YTN2n9FH0bUCo8PHQ2M4++Ai4Ywrj+VxUvAfkLPSosuG70z9E8F7fA70DEER0KqVPs8oqTu5121ujwVc5v1+VK6LdccWqu2q+dpp0Jfma7L4kX6fK4N4wp16XlsivX9Rflo2wQcFr3wc3h9UZc3PUr3RXp1ojyUL42PtzOPpy9C4NPAvYbwO/RV6LZOduhxnmC/o6cmzvmT0HUTYo6kF4bzjDfn0fUS5x7Lh1sGc27+yhbsr1SbrZP09SvfhbsNbR/Qg8f6oWsF4zgq1e3hetC3MMfrH6MunPa0Bc2YYzgvlMPrxn6Mfpgq1r9PdOnjC7EkMLlOmK/rIl2j/4GucNE6/QP4wFKjsb6ibROtaKtW3r/uG+xrEf1g7PucR+gtYt8FXK/pwsn07NF1xrZw9OrQ/0RnEoHzKIxxJgWouyi7Jsdb33k9iehKQ9/ks1IcbAtAn1Xc8V754VByGOeOa0Ye52mQ+Xtei44klHVLnUgfFEc3KOeRnSepza5R+vmicKmec3srdV9q2lmff+wSPhfTlwXHUYWeR1JlGNd4Hsmb9ZbaDnaZpM/PeZBeP7Y910zDMHKKjUDDMAzDMAzDMAzDMAxjsxhWXyQZhmEYhmEYhmEYhiESRU4ifmY6ROUwjEyG7EVSQ0ODzJ49WxYtWiRtbW2SjklpKSLy/e9/PwclM3KJmzhGXN76sKZOpOvEJ7yJGh0e5X2CzLTqCO/ywpUI98d8fh2UwN2V+Qku+3PMtYMKnVLW+zSe52PqaYQLuSaEkPBzaGyHY3QoW28twh7eXqWvx5APhnmsWKs2yxpb9X6E2CQaEZqG1NBBua4fL0QFn+o7fsrPkCQvVTX2My066iMzBCZq1GF4DuE0IcL+vBTweWhbfMYfMGwPacbzmtBX8Cl4wRKETaAvRuU6tKz8Pd2XEp16O3+BDktkquyoDHXFUDSGG6EtHOaBqESHH0UI53Kp7J/CMxV030jdl/h7pm2XzNC6bn0vfaP0HJNXr+egsAThPAxhDRhuhPCodtxre/aQGS9sknWLMEqG94QMB2aab44TELVgnE8aq89fpPt+T5Xern5T9/XuEbpvJgr09QtW1GcvD0N4K3W4tBfKli2cimF8qJtgiZ4jvTkXbZ2q1f0w0YHwHYSGFSxC26Mt8xv09Vp21X2zbbzui+WLEI5VhL6Kukm043iEE3lrXMA5FfsRMsOwS96f1zYMl2JYJecZhs5lCSNlOJEXMsn9abQ1y8pnFc7xPB+IUgzhxBweZg+980LZeH6GuHrhXZi38DySqK7UJ2SYYQdC3zEuef2gXJ8vwhrqMK8FTAmPFPNu54m6PBmhio5zHJ69Aqw3xxSdqc8d11fi4HMm2wrd3BXpOTEs1NfvqtXbvWU6JDWvXZ+woAl9B+ul96yEeS3NkNtZ/9LniwkVlIVL9TZDWBMjsu73xvmGvsmQOMMwtgk5f5G0du1a+cY3viGPPPKIpBibHIO9SDIMwzAMwzAMwzCM7YvXXntN/vSnP8kLL7wgc+bMkbVr10peXp6MGTNGDj74YDnvvPPk0EMPzXqOu+++W84555zNut5dd90l06dPz3pMZ2en3HTTTfLwww/LggULpLe3V8aPHy/HH3+8fO1rX5MJEyZs7u1td+T0RVJTU5N84hOfkIULF0rEt9yGYRiGYRiGYRiGYYiISBg5CYdBWNm2LsPhhx8uzz//vPf3vb29Mn/+fJk/f77cc889cuaZZ8odd9wh+fwSdRuwcOFCOf744+Wdd95Rfz9v3jyZN2+e3HHHHfLAAw/Icccdt83LMhzJ6Yuka6+9VhYsWCAiIp/+9Kflv/7rv2TfffeV6upqLwuDYRiGYRiGYRiGYRjbNytWrBARkTFjxsjJJ58shx56qEyYMEHS6bTMnj1bfvGLX8iKFSvk3nvvlVQqJQ888EDsOf/85z/LmDFjNrl/3Lhxm9zX3t4uJ5xwwsBLpAsuuEBOPfVUKSoqkhkzZshPf/pTaWlpkZNPPllmz54te+655xbe8YefnL5Ievzxx8U5J8cff7z83//9Xy4vbQxTosXLJXL9Xg83brTetxbpiOkBYIp3pBGXHjiXYmKmXZn2k3i+D8+LgPNlxG5HTJmKMM4ALhM6jjynAs7n/b5Vu1k8cHyI9Ma9I+C1QV2H8AAFqCvPicD7oQPKS9ONbXh3HJ1RSFfM9MteSt8UnVXMjR3TN+AvkcLB8rkRNXpfs+43biIWqbaYNNf0YcHrEhXy3rVfgn6IsFK3fdCiPTgB3CdFSKnumtG3mOo6xosjDn0Jnhymag464ctAedIVcESg7/D4sAQ+rlY4mODd6a3W58+vH6yvqAI+iA7UPfwNiTZdVwHuNVWq+3mqRM8xTGieQFtLiH7J1MicEznu4KEJ6BLjuOacx/38fR9cZmVwNCGteudYPQ8luvX1eqp1/eePqtXXWwpPEcZ51Kzdca4QHh7WT+a8SS8cPS90m2D9cZhjkkz5jrag889zqZRjTse8UPae7nsh0nDnr9XnT5fq6yfgCguLdV8NMCemy+Czwjzm6B6jQ6mUacOxvsOb5/W1mL4bVFXq81drh5Srz1jjOEfzX71Dimsw7uiNo3eHLjPUVRo+pziHkgcdgcTz8GCc8n6w3nreHI6bLV1v2bfpQOyIccOxrwT06sG7k1l+lp0OHs93mf0fvemPCqq129PhWS5sas76e7alw/5UKdxoO+ryzfvBf2Ut78Gn/EJtp0fqcRHieSJvne6b3hq49276Amhrzvlcr+moyuqtE9m0c3EL1SnG8GDXXXeVn/zkJ/L5z39eEvj/QQcddJCceeaZcsghh8i7774rDz74oHzlK1+JDXPbZZddZNKkSe+rPNdff73MmzdPRER+9rOfyWWXXTawb+rUqXLkkUfKYYcdJp2dnfL1r39dnn322fd1nQ8zMavN1mXp0n6p2kUXXZTLyxqGYRiGYRiGYRjGh4ooGj5/tiVPPvmknHLKKd5LpA3U1tbKL34x+PLzkUce2WZl6evrk1/96lciIjJlyhS59NJLvWOmTp0q5513noiIzJgxQ/7xj39ss/IMV3L6Iqm0tP8rhrq6ulxe1jAMwzAMwzAMwzCMDylHHHHEwH8vXLhwm13nueeek+bmZhEROfvssyXYRPbtTFH373//+21WnuFKTl8k7bHHHiIi8t577+XysoZhGIZhGIZhGIZhfEjpzQgL39TLna3BCy+8MPDfhx9++CaP22+//aSkpD9cedasWdusPMOVnDqSvvSlL8mMGTPk3nvvlRNPPDGXlzaGKUF1lQTJ9f4cxELTPRPVN+gfwykh+Yjjp1OBMDY+JhabTiZ6epTXCHHeEc8N5wIdBJ4fA9+T0onkWBf0dzQ06u0K7e0JeuGzwOervQfruPfCt5brA3h/9H3kZ59qfGcSDkhqp4RratP7u+GKYdw8t1lfdL/wfCxfppOB9w5cg/ZLedcq1V4Yr61Xr9X76ctgv4SfyvHey7UDgW4Rt0q7ySK4XFwFfk8XTIP20LC8QZPeT39ItMtEfb4evb97JF0uuj4LG7P7wtLlcKeg72c6kUTg0+hGXaT0tT0XB7ww9EEleuDT6NPno4fGI+47cz5k0U1C3wfcLp6rpFK70dIVuu+Gefp6eet0WyQb9HZpPfo6PD+tH6tU2+3j4KDq0e61YtSHWw3PXjLmkYdzeiaoS89lQi8c1yN6YxLYXqfnCUfPDYZ5tGKN2k7i+PRkLRfNX455CP6sxFrsxxzoSvXxEeoyuRTzlOcwQt0X6PoJ4WgKVmG9Z1/nvMc5m/XNea5Vu17CjDXXcdx469PGwy4GDo+Zo/ksEXbCW8f1idD/EudE4jin9mfUSJwPbrHGZr2b6zuftehcoi8L9x9UV+rfcyzRdYY1t3v/ndR2wTq9ZoScd1sG93vGI66X6Ash+llAfxb9U6x7+h0L4JnDehr26bYOu/S9JTr0/olP6br59E9P19dD3VeMh4QY4yxB3ycIOC7pu+I8yXkRa6o3br1xj/bhnL3heYLPWR92IifRMMjaJsOgDDNnzhz471133TX2+OnTp8vbb78tTU1NUl5eLjvttJMcffTR8pWvfEXGjh27yd+9/fbbm3WdZDIpkydPljfeeEP95qNCTr9IOuWUU+S0006Txx57TK699tpcXtowDMMwDMMwDMMwjA/AqlWrZPny5Vn/bG3CMFTvD0455ZTY38ycOVPWrl0rfX190tDQIC+99JL8+Mc/lp122kluvfXWTf5u2bJlIiJSUlIilZWVWa8xfvx4ERGpr6+XHiZ62s7J6RdJzz//vJx//vny3nvvyRVXXCG///3v5fTTT5ddd91ViouLY39/2GGH5aCUhmEYhmEYhmEYhmGQAw44IPaYaCvbuX/5y1/Kyy+/LCIi06ZNk/3222+Tx+64445y0kknydSpUwde9CxatEgeffRReeSRR6S7u1u+/OUvi3NOLrzwQu/3bW39kQ8b/M7Z2BDaJiLS3t4uBfzScDsmpy+SjjjiCHEZn8z+4x//2GzDuXNOUpbOcfsjDAc+fQ35+XQ3Qt2Qvpc8Pf9navvT+fqT3gTTtDNsA58kO36Cy0+Ymb45I2yEn7a7AoQDcXJl346L+40Lq2BabqRoTxdj6PNTdly+p0ofnzd2hNpOLF2ttjt3H43y6vstek+HNwUIuUlX4sVyH0PvEBLDtivKHi7m1ZeXchef7/L3meFZDOHAvfAzfFej0wGHCNn0UkOzLzE0LSaVswc/teen5CBC33T81xaGrLDu+rL3bTdSp3CPUtnvp/g9hDWibcIKXX8MJwuTm0gXvJ5kFz/VzygPv5ZH3TmmMkZdBQxbGFGpthPtSNmOcB/eS8B5g5/zc45i27CvMEyAx3MbhPm6bvtG6Aew7ho9h5bNQ8gtQmDTBUhDzoieFOYB9i30TW8sMqSGIUuZx8eEeHi/Zb9HKBnHdcT1juHOKKtrwTjAPBR0IySmBunuMadKhW4rh5TrwXIdSueFQbIvob4iL/wY9bUEfV8A5zmCtmZ4mtfWHCsZ84AXisU5jvv5bEEwx4ecQ2NC5SKuV1zvsv9cJIx5nogZ17Hh05UVej9DTDmvMPSP8xTXPN4vzle0UNdvVKyff5KtSCmfWf/sBwz/RVsz7HDxt/dS232V+ve7XrdUn49zNuo2QmhYzwmb/j/KIiKFT7+uth2e/QL2TdxfWKbvx2He4LOWF5pGEgwDRd/DHNw3Xpevt0rPKyVvIWSWoXNkQ3m3dXqxHBMNk9C2oSzDzJkz5dvf/raIiIwcOVJ+85vfbPLYadOmydlnn63eOYiI7L///vKFL3xBnnzySTnppJOkr69PvvGNb8hnP/tZGTVqlDq2e/2anM//77cRMl8cdSH8dHsnp6FtIv1vJ9/vH8MwDMMwDMMwDMMwhoaXX35Zli1blvXP1uKtt96SadOmSSqVkoKCAnnooYeyZoCvqKjwXiJlcsIJJ8hVV10lIiKdnZ3y29/+1jumsLD/JWsvX3RvhMxwtqKioixHbn/k9IukGTNm5PJyhmEYhmEYhmEYhmFsJUaPHi3jxo2LP/ADsnjxYvn0pz8tTU1Nkkgk5MEHH8yaRW1zueCCC+R73/ueRFEkM2fOlCuuuELtLyvrT+bR3p49gYuISEfHYPKGzQmF257I6YukrdHwhmEYhmEYhmEYhrG9E0ZOwmEQ2pbrMqxcuVKOPvpoWblypTjn5M4775Rp06ZtlXOPHDlSamtrpb6+XlasWOHtHzdunLz00kvS0dEhzc3NWYXbG76+GjFixEfKjySS4xdJhkHCllYJg/4Y9KC2Wu2LOnRK3HCVdjQwjekxJWdl3R82aB+Hl/K2XacDjnUikczJA36IEE6huNTGrhIeHPijvNTTdAggDj9CXHyqBKmbkUKW6XILmvX+vio9UUb5OoUmzyeITO0boT02+Wv1G//M9LwiIu0f030jMUI7kOhI8JwPbDt6CZjCvkZfz/MmZPo+mKq5Df96AadA1Kx9VY79LJ3dB+G5WkL6ofBZbSlStNO7g7oKinR5gkZd3qgcLhW6YOhEiHEkSat2vQTYplvGdaEt0basr87R2sGQLtTXL1qjy9dTp/tm4cKMFPKe/4lOpOz9jASr9ZzEvhTmI211G7wzlWVq26ubOK8g+zW8NxFSvtNVli5AWnM6i+DLcPBbhCWYR9B3kl26fKVUD+D83WN02xUjtbQryZ7UIypEau7MeQHuEa4vnuOIczhTtONevTTg9NA0NG2syBkFwBrSouchpmynqyRdV6mLl+T9YY6kay3GQRS1r9L74zxBdKHQgcTjPe9OjNMqLm155qF0InFOZllwvPd79h3OkRGOj/PSxN1LjF8qXF2v/wJtG3A9xFiI6DTiPMjycD1GX2R9ukL0XfZljqVe1meWtqXPJC5tPPaXoFuH9bos6TW6btn2AdZTOpjym3RdRWwbHE+fFl1hJGjQ67vnciNsKxBxvWbdYzuvXs9TvZXoayh/VIBxjttzfevHSsr+7+2HnXXr1smnPvUpWbRokYiI3HjjjXLWWWfF/GrLyKbN2W233eTRRx8VEZF58+bJQQcdtNHjUqmULFy4UEREpkyZslXL92Eg544kwzAMwzAMwzAMwzCMTFpaWuSYY46RuXPniojItddeKxdddNFWvcbatWuloaH/H6HHjBnj7f/EJz4x8N8zZ87c5HleffXVgdC2Qw45ZKuW8cOAvUgyDMMwDMMwDMMwjGFGFA2fP9uazs5OOf744+W1114TEZErrrhCvvWtb23169x2220DXyRtTL1zxBFHSEVFf0bKe+65Z5NfL919990D/721wu4+TGyTb//OPfdcERFxzikT+oa/fz/wXIZhGIZhGIZhGIZhfLjp7e2VadOmyd/+9jcREbnkkkvkRz/60RadY8mSJdLU1CR77733Jo958skn5ZprrhGR/uxs55xzjndMfn6+fO1rX5NrrrlG3n77bbn++uvlsssuU8fMnj174N3E4YcfLvvvv/8WlXV7YJu8SLr77rsH0u5lvvzJ/PstIYoie5G0neLy88UF670cdDIgjj6A+yWK83/Q4SDwGCDW23MuMI6/RPs3WF59Kf2xnxeHjnHg+Sc4TmL+GcBzLsAvFUzQDqOiZbo8vXU6Tj8sQPl76WDS5UkV66kkSOnj81e26POX67ZMVcFdwrB6VAfP3ze6AuXT+xPN2oNAh4PrxVRIfwe9AZ1dG/9vES+mn34HulQ8JwP8E9612S+T2cV+EcsDz403I9ObAweDa9EOo6gCnp4U/F3s6/RplCHDBRxS0g5X2kTtngma9fVSI3R5Ej36foI0XDKo/8Jluq8qz1CAMU9XB/d3wLvGcYy2Dcv0+RIdaGvWJR1E9FPRCYR5ht4ZukxcO/o2rp/Xovtyx0TdlgUNuvyJXjqUdHlDOJd4vUKcr6cS/jHM+VES9VGgj08XaydUGu64oGKwPYI+9KMuuEh64ORphSuNc3qMr8mbB9jX6BLjeki3COrCcc3BvMDyOfSVCE5BR3cKz8/1FW44rvee1wfF4++9NTAZ4w3CPKQcjSiL5ziih65Zzxl0HnmeGs+vhbriHAknU0Q3G/sWLheUV+IvMO7gYIyFPq44RxM9RFwjaqv0zwvh6WvTa0BqJPxjuFyiQ9dn279pT16mqy3RrftZXrtuu17MMX0l2QM5umuz7pYolb3t6ExMvPau3k9/1BidAp3rb1RC7x76fQfXBH1/fSPhcMKzYKINz1a9aFv6vzjuUZ78ZszxE3XfSBXBG4j1Pb+hv73CXhFZLsaHjNNOO02eeeYZERE56qij5LzzzpM5c+Zs8vj8/HzZZZdd1N8tWbJEjjzySJk6dar8+7//u+y1114ycuRIiaJIFi1aJI888og88sgjA18YXX/99TJ27NiNnV4uu+wy+d3vfifvvvuuXH755bJgwQI59dRTpaioSGbMmCE/+clPJJVKSVFRkdxwww1bpxI+ZGyTF0kTJkzY6AujTf29YRiGYRiGYRiGYRiD9IeVDf3/f97WoW2///3vB/772WeflT333DPr8RMnTpQlS5ZsdN/s2bNl9uzZm/xtcXGx/PKXv5QLL7xwk8eUlZXJU089Jccdd5zMnz9fbrvtNrntttvUMeXl5XL//ffLXnvtlbWs2yvb5EXSphp1U39vGIZhGIZhGIZhGIbxfth3333lvvvuk9mzZ8urr74qq1atknXr1kkqlZKqqir5t3/7N/nkJz8p559/vowcOTL2fDvttJO8/vrrctNNN8nDDz8sCxYskN7eXhk/frwcd9xxcskll8jEiRNzcGfDE8uPaAwtYSgi/Z+mRkhv7JhiFp+bh536c2fv03mmW2aoG1P4Eqaw5efupfrz6khdX39OHDTg03eEA0XrkL4en7ZHDDfiPwswNTJTMTN19OgRajN/jQ7D6Jisf59sw+fSebpuO0fpuqx+BemEEcLSPkl/Ll02F2nQEbJT0IgwDYYO4nPqEOXj59pRgM+rqxBehfr10rRnhDp46Xbr9LftXmgZ+1V+TLgUQufCpmZdNoaGMeyA6YGR0t1LGc/ysTxIV+zYNxmiEhPi0rvTKL1/TKXazlu8Vm0HLXrc943Vn7637KjLW38gUsgv0tcf+1d9vnQpwsuQQj6TEMd6QQ9tjLlAWzPkhZmnWbcMbUNYQMSwRRadXwQzRAbhumGVDhklTLNd9rYeJ1GePl+yA9fD/THMsAghqgx9iwsz6RtRmnU/Q2BDzDt91YPzRICIjbwW5p3WfSGPIR0MPeMczlA37vfaKpl1v5fWHOHOUqpDtYNO3Vnad9cP2CWL9BrGecUPucWa5M0jDIlF/WB9DlfoPOsuH+s358EiPcczpCddrH+fbBqsH4Z0OrYl2ybuS3uGsvUxRTt+z/NxnHAeiftMgMdjnPNZSxDKF7Xp54MIIaZBJeYJlDc9Sc/xiWV4PsC4D4t02wQ9etshdK5zjF7T8pt1WzP8KdnBOMmMY9v1elWIZ4u8Ip4b/agz+5wUFOtxHmG9ZZgjwyoj0X0nwRBa9hWsMQFDWAn6XqITYZpeGKPejPA8ExXrvhVi3HHOdzhfokNfP68ZoXhcIwcunAMrdA6JxA2PL5J8GcLWPf9WaLeysjI544wz5IwzztgKJeqnpKRELr/8crn88su32jm3Fyxrm2EYhmEYhmEYhmEYhrFZ2IskwzAMwzAMwzAMwzAMY7Ow0DbDMAzDMAzDMAzDGGZE4iUnHBKGQxmM4cU2eZGUYDz2VsA5J6m4dO/Gh49EYjBldjp77LYboVO4Jlq0A0EqtbMoXYO0pUjXLPB7MM24l/KX2006/bJK0RviXuhAWLlGb9N3wbjxmDEVIfWyy9f+CfomXKNOsR6OrFTbhfU6Dj25ulmfD16g/JXat8HU19EI7bGhE8lLKV+ry5O/HI4numXoNIIfQ5jyHXH1Xlw2jqezKtMjwLTYTHfrpaVGWusI6XcDpMf1fBa8d6b7ZT+lqwRt79VlGdqSqZrRVt64ZfkJ6jrRA78VnVEofxo+q54q3be7qzHWinV7FNVn/xCXfSOsGKyPACneHfxJXop1tJ3nc+C14AZJrtPjOqL3BS6wgG4VrJkR/Vb05sBjk4YPJMI8lsD1Ao4jOIicYGx0Y06G+yys1X0xXajrs6he/37dHrp+8tt0+QvXbdp3JSJSUK/ro7tu8PpJukJSnFMwDpgCne6TJjiH6KPiOKLzj3MS5wm6zrA+Cvpy18RKfbpO9M1iXR4H/1XQhXmOac05T6Evu3XNunyYN4Oxo9V2z3i9piQ79P06b17BPEj9WOaczzmdDiE6k/Qw9TwyEdcEevMq0DbsO3x+IPQ98voY564g+xxNJ2NstuWY/cE7S/VfcE2EgykJh1RqTLXabtlZe4Yad9PXz2/V569+W/eFIHNeinGycD3qqtV1l4R/qfw9Pc7XnbO/2i5qpLNPr8fB2mZdAO/ZJJ19m23R3qF3F2uflOfjhEMpQYciiArwe8wLEdqa9ZnCPJAu0mOjoFH3hWQDnFBwSG5Yw4I+uJQMw9gmbJMXSVtDlmUYhmEYhmEYhmEYhmEML7bJi6Srrroq6/6nnnpKXn31VRER+bd/+zc54IADpK6uTqIokrVr18orr7wic+bMEeec7LfffnLcccdti2IahmEYhmEYhmEYxrAkioZJ1rZhUAZjeJHzF0nXXHONvPrqq/Lxj39cbrvtNtl///03etyrr74qF154obz66qtywgknyPe+971tUVTDMAzDMAzDMAzDMAxjM8mpbPuvf/2rXHXVVbLLLrvIrFmzpKSkZJPH7rfffvLCCy/IPvvsI1dffbVMnTpVjj766ByW1sgJqZRIsN4fQJdLofZ1RM1wStALAAdFYh09A3BM0FeC38d5BrxY9IxYc5YtwrnTUyap7eTydfpcvYhLL9fOo6i+QbIRNcODA/cI6zZoQJw+XCmSr+8nrNCOgnSJjnPPQ13Rl0GXSlSl78/BXxWWFWfdn67Ucf99Zbq86UJ9/yWLUT8Mx0VX6d6lTm0XrBz8vWtHjD77UYzbLajUbpSoS5+PfclzHACHtvYcCnSVRDE+DPo56N2h+4VOBvhFXBk8PXAy5K3S/ix6e4Iufb1UkV5H8jBMd7xXbwe98A7lZ/cAhQWDTockVV1N6Eeckzjn4F6ikuxOoqBct7WDcyhvna5rrx/zenAwRcWVehuOp1Qx5mScPtkORxTqMujEPEY/V0x504X6fMkO3ffq99LHV7+r96cw7ht21/NIfru+ftFafb2CpsG+xronPbW6rQp66GWBJwcePG/cJDGPFOuye54e+K9Yl8I1AZ6cwpXafdYzWs/JvVX6fMkO+K1QPUEL+iZcLK6lPet+rjFBq54X6UTyQF8W/kM6PESpysHrRUmsV92YM3Hu5Fr9bOLdK5yCdBryWSJKwcWWHzOvcJuePDqR0tn7ctzzi7cmwGnkPRthnIfo63w+4fNNEn6w8gT8W6Eee4kejOvVur5TJYP3V7gE6w3K0lur+2FBix53yTa0Fe61dDUcfUt1vw8a0HeItx6jrqq1X4uusahHt53jvEAfJ5+L2bZ4fnB9cJ0V6u3IZX/+6SvXYyWvXZ8/fw2cjHwOhwtuwzwahehjhmFsE7JbR7cy//3f/y3OOfn2t7+d9SXSBkpKSuTb3/62RFEkN954Yw5KaBiGYRiGYRiGYRjDgGgY/TGMDHL6ImmDF2nPPffc7N98/OMfFxGRV155ZZuUyTAMwzAMwzAMwzAMw9g8cvoiqbGx/xPSlpaYTzkzaG3t/xy7qakp5kjDMAzDMAzDMAzDMAxjW5JTR9KYMWNkyZIl8uijj8qRRx65Wb955JFHRERk9OjR27JoxhAR9fVJ5PpjnF0ioXciNtshTl5K4IxoQSw1XTJ0xwiu53kE4EWgt2jKTmozqB982Rm1oiy88rtL8Re6LBEdBzgfPTNRi/ZfMC4+qKrU10PdRAWYCtgWiJsP2rRzIFiT/UVvZt2IiOfvcBGu16o9AvX/vqParn1N10eyUcfDJ9rhqOjSbdc7rlL/vgXOCpDpROq/QEZ56cUhiOFnP2Zf8RxIcAzRJRY1Nev9cH94vgyc3yXQ79F3IrhY6Otw+XAkJNGXR1br88OJFKxt1vtLdPnCYn3+dKmuj/wWfb5kl+6rqWJdnqJm3B99G326/pLdcERkEtf2bMsYd0hes/bAhIVwlZTAdZbWv0+0w4cBl1iwbI2+Ph1KldqLkxqty1+8XPsp6K0J4XTyPD70c2Deov+jYA08Oqi/onr0hWbdVgXwGhWv1ufPW6vHXt8IPa9mOp7of3I9+lrJDvihVq6VrGCcSDfcYyHdJKgr0p19DnOlWicQ1mi3Cus+6NV11zVSj8MEHVCoD3qCYunUfSnget+BOR59IV2t7697FJ4PMC964z41uD/K0/uS7XQk6f29NSPVdn6TnvMDrD9BCzwvfHahp4YqCNYtnze4fsfgrTnoC64SfQXj2vNHEtyP53yiM5H336nPn7dM769q0fUTsfzwkyXXZcwrHIdwBiW69G8Tnbqf81koKtLjpA/PqcUxzkSv7TlnEq4pVdq56OjjIljvfScT6geePT7fCPVf8Gkl4Fgq5ryB+6df09EHxueRDf+b3rIxMOwZJlnbZDiUwRhW5PSLpM985jMSRZHceuut8tBDD8Ue/8gjj8itt94qzjk57rjjclBCwzAMwzAMwzAMwzAMY1Pk9EXSd7/7XSkvL5cwDOW0006Tz33uc/KHP/xBVqxYIX19fZJKpWTFihXyhz/8QaZNmyZf+MIXJJ1OS1lZmXznO9/JZVENwzAMwzAMwzAMwzAMkNPQtrFjx8rjjz8un/3sZ6WtrU2eeOIJeeKJJzZ5fBRFUlZWJo8//riMHTs2hyU1coXLyxMX9H8KzNAxL2Ut0gVEDTpcKsTn1V66ZYYE8ZNekswe7uWWrdL7M8JcXAHCkQg+FY/W1Otz8/f83JifK6NsQQU+RWcYIH6fHjFCnw6pldNl+vPqBEJqpEKHxDiEpoV1Vbp8CInx7g+f7tf9ZaXabt9dhxKUzEdIz1qk9EUoYP7yZr2fKW4RriXN8Lpl1C8/ZWf6X4ZqSYe+d1dbo7ajVoSwNOqyuhodKuYq9afs0oZwIIYt8NN6hkmwLhh2gBAUjhOH+3UMDWOIKT5NT1fqvpouQnrgRn39vhLdtsUr9DzA8C4vTJN9meFYmWnTcW9RuQ6pcAjbk3aEfGBO6xmly164RM9pPB/HYYAwC4Yxsu28MAmmfu7Q80LZWwhbYHgvQ1QRShaW6voKOjHncpyhb/RVoy8U6P1F61LYr+fB7jF6HmXYo0vr+mcYi2TcT9CG8KFInytvjU5Z7oUncz0hTIHOlOsxMGW7t37ifEEj7gfly8O4SXSgr/GfIZlSnvMExz3XOPZNjh32FYTISPaM9h7pfKw5mdtYj4I8hDv1YA7pzX5xhqAydIzhy17b8Vmli3XL8Kx09v3FfB7Q59uQQn3wL3T5GQrnhf3H4I0Fhi8xZJjHMwU9Ut57KexJ5vkZZoey5K3Gesx+V5A9TK/iTf0s4vUFhhevXae3R+vnA86xXM/CEvQl1i3XNz57cRwHXB8xD/D3pJfrP9ourq34vMI1jb/fEEq3nYVgRZF/60NVDsPIJKdfJImIHH744fLGG2/ItGnTJAgCiaJoo3+CIJCTTjpJ3njjDTn88MNzXUzDMAzDMAzDMAzDMAwD5PSLpA1MnDhRHn30UVm9erXMmDFD3nzzTWlqapIoiqS6ulr22GMPOfLII2XUqFFDUTzDMAzDMAzDMAzDMAxjI+T0RdLzzz8vIv0Z2HbeeWcZNWqUnHbaaXLaaaflshiGYRiGYRiGYRiGMayJhknWtuFQBmN4kdMXSUcccYQ45+S3v/2t7Lzzzrm8tDFMcaUl4hL9ngqHuP6IsdFMa86UsnS9xMVuMzaeseEoTwA3DT0CEWPHM4tCRwE8Nm5krdoOy7TDIOiGgwAOImmje4T3RmeCjmpNvrtcbUdjtYMo6EFbJJFeF+mN6eUJOpByHWnNPbcM/SAVOu6/9C3tlIqK4dugY4J+DniKIrYHUmV7fS8j3bLrg+OAjiJ6aOirohuMLi/2S6YrZr+jM4n+CqTrjZB6mqmZHfwdXpA8tznu6DBAqmqOm6AE9UnHA9IFFy/UDop0FcYOXC90SPD8QQPqK9i0OyWCH8O1wi1ShrTU+H3hMri3WrSPg3Wf5Pnpv9pS4oQHHDf0XSBFu4O3zksrjvsJayvVdtAUk+Ie5DfreWL1VO08GvGaHudtk3TfKoBKzauPzHkuxPrE+R9zFtNUs6a5JniOI+yPPNfIljmVPO8Oz0cPThfGBccx1zT2lbi+yfqLcSrx/gLMowE8dgWJCfp8bNsR8PxkXC5y+tgEHEh5jdrf5KWbZ1026n4d1ug5OqB3h88+SKHuOYPY9kgB7/mqPE8e53ykYOf5OWfCueT5LuNS2LNt6EjimsK+xt9z7MFJKYnB87l6PWd56xXWfs+tucM4vZ/+K28bdcs1ZLyOxAja4MOMWW8dn9Vijk/XVartRAOeX9j2fN5gX+uJcSJy3mBfpAuNfZ33g58PHO+GJODGMD5y5NSRVFra/5C3xx575PKyhmEYhmEYhmEYhmEYxlYgpy+SJkzo/xeizs7OmCMNwzAMwzAMwzAM4yNM5IbPH8PIIKcvko4//ngREfnLX/6Sy8sahmEYhmEYhmEYhmEYW4GcBpF+4xvfkDvvvFNuuOEGOfnkk2X33XfP5eWNYUjU1S1R0B8z7Tkc6JaBuyWCNyCgy4Wx1fQE9eptV1Gm98Oj44HrK7cN4sajNev0sfQ5wVcRrGtW2+nRNWo7gbI5eHGiCu1m6R2p7y1K6LJ31+ryJHp0HHphvY6Tz1sJVwvuJyzUbRl06t+ny7WfItGK+4FzIc6BkIb3pnecvt/C1bq8QZveDnfWPg16dVwHPAWZfg/4LTy/Eh1HhI6AuOPhLAgbteMhqNA+iKhLl93VVOnzwVclTdphxHFDd5nn/6LvA+X1XCj0iayE/2rKeJQne/2kynTfS6xpVtsBf4/68cj0ddD1wX4JL0xEl1g3/BHwRXlzFt1c9FPQX+GNG9wr/Fje9eD3ikZpd5uDPyRqatb74a0Ja3RfpO/DoS9FlXrcJjr0/eY16u2uCfr8o2ahPkHxav37RKfepn/LpTLuN4DTiA4j9CvPPsW2oleHbjO6yjiOulGX9MoQ9lWuh4TOJ56frjbPmUSXG+Y5rHlc3z1wfc9JVa77juvW1+OampevPYCZzxPJdjh+uB5wHBfpOcdhvfN8i3SBwQsTFen1zLHu2JZwsUk7vvrnvEFvD719bAv2XXqEgKPnhmOD8xLnZLYtfVsJnJ9rKNcUzluZ98/1mnVFnyLbcrV+tovG6X7l9Z3VDfr8KHt6t0lqO5liW8b0BZafx+NZLdEY48nz5g20VTP6MteUOM8e5yH2tThHEueZDW0b5fQ7CcP4yJLTkTZq1Ch58sknpaysTA455BD5yU9+IkuWLMllEQzDMAzDMAzDMAxj2BNFw+ePYWSS0y+SdtxxRxER6e3tlba2Nvne974n3/ve96S0tFQqKyslwTfLGTjnZOHChbkqqmEYhmEYhmEYhmEYhgFy+iKJXx9F619ttrW1SVtb9k/SXVwqd+PDSeAGPn1lyIyfRhyfK/PFY1wfYZpUhrIhxIWfMHtp0BEGErYPph92DCfi59MkJkQm0YLPmxluhPS27bvo8KV0vr73yn/qz7EL5yHcCCE8YTHunXWNkJCAadIRBsH76dhJl7dold6faEQoHVJHJ5GSN1mvP80PEUrH+vVC2bAdMa35iIxQQ4ZG8VNyhhEQhsgg/Ih9h6FsXsgJPuOPS73s2tH2LC/HAcvL++XxBeg7+LSe5ef9572zXG0/veY3avsz1Reo7QKG1rF8CK2Laqv1/mUrdfnqRgwey7TXHfgMv0mnIGcomZcCniElDI1jXfHe4v55kOVD30hNGKG2E+0ImyzU1w9GICySfQWptAOM87BS92WG/iXW6PoLynR9uC5dn/kNurwMa3RM3Y1w5M7xpWq75F2kAs/sO15K9uzj2lufYtJweyEeaZwfc6hDyEeURtgi1wjs9kLJGXLCkBSGMzHChiEuhPfnhZ7jfEgh74UrxaQRDxiOjN/nrWzWl68dfB5QIY0i4tp5LuyPS0dfrvtZXDiQQ1ukq/TvvecBznEMbyIMXWOYYi/ub4QOrY8am/XxuN9o8jj9+3ff09tck+LWlJhQOq8vMYQX83JmGGSE0O7UaB1impyzRP8WZU/vOEZtB10I2YRCIT1ZHy+4tURH9n7vzTtx8wrHNX/PZ7V6HXrnytB3MYfGrkEBPxDAQO9hW+vzhQj/DhDC6ofUbqehbZFsJF56CBgOZTCGFTl9kXT22Wfn8nKGYRiGYRiGYRiGYRjGViSnL5LuuuuuXF7OMAzDMAzDMAzDMAzD2Irk9EWSYRiGYRiGYRiGYRjxRJGTKBp6xctwKIMxvLAXScbwgWlFGbsNZwKdRUxzzthpOiHodPCcDCBasUr/BWK5M9Ouh63an8Q018JUzy1wJgTanZKGKySgLwNOgdJ58Ogw5TrTDcM5RP9GCuXNhwuF6YfDAu0dCOBQipyOXy9Z2KzPxzh+umdw/2GVdkSFJfB/gGAN6ge+K4EXoG/PHfR2+eD9lSzQ54ry0DZrkO6X/TrGZ8G2CeBM8txiTHVM5xL8GBwHfqplukiyO5Po1/L8H2xbtKU3LtF1jyk6E+VDGvUu/IB+DZaH3iH09Whd4+DGhNH62Eb0mwrdDwW+Cc8Tk5c9VTLHbVQGpxK9Mb0xKd1R94lmfX6H8yWY5pxzJL1x6Nt0uzh6exzdLOirrdnThAc9hdiGWwbzQKJPl694PlJ3NzXr7XDzhRCeQ89zDOFc9MpwDuYcjXHOOdpzo9AJSCeS9/sY/1acE4rzSsx6TNeKK4IPi94cznMcS1VYUz1XWvZ5NrF68Pw9k+vUvgL0qy310NAFJnSNsS9w/YSzzxuHJC5lO8vLvktfJByEXttV4X76sEZwXuScjPryvHq833yu79ifQn3j+Pbdagf+u3OkHgclq3Vdd31yV7Wd6NFlL3ltmb4W65JuT9RNxH6ewPNBiT6f5+9i32DdlmA9j/FXyhjd96Ure9t7fq5uHM9xSziWcP/ByFq9n8+y3jwW6P81DGObMuQvktasWSNz5syRxsb+h/Xq6mrZfffdpa6uLuaXhmEYhmEYhmEYhmEYRi4ZkhdJURTJbbfdJr/+9a9l7ty5Gz1mt912k4svvlguuOACy9hmGIZhGIZhGIZhfPSwjGnGMCTn3/41NTXJoYceKv/5n/8pc+fOlSiKNvpn7ty58pWvfEUOO+wwaW5uznUxDcMwDMMwDMMwDMMwDJDTL5KiKJITTzxRXnzxRRERqampkVNOOUUOPPBAGTVqlERRJGvWrJGXX35ZHnroIVm3bp28+OKLcuKJJ8rMmTNzWVQjV6TSIuEmYqjjHAm9Mc4GOiHoDcCXbgG8OGEzvDx0PMCBEXZ0ZpwMcej0Oaxao7cnjtHHM+6bug3EzYvo7eYp+l4iamwadHlKeK/wSTg4FqKqMn0867KtU21HbIt0zD+tLNc+ql44ivLeWKyv19ymtsPi6uznp5enQ5eXcf7JCu2myV+e4UXib+m/oDOIjiE6DYpQV/BDeK4UugDol2Dfg2PAc62UwMND1wmIIpQffcNzQpTq+nDw8HhjBXhOJo7rSvg4vBNkdzxE9JVlXM/zXeVpl4k0Nme/NstOV0k7xg3cJI5tQR8HCHEvAT00DXCd8X5AVA7nEese44puF/rDoiTmSfov2Ndr9NgKmrRXiH0r2aDrk/cbxniJMq8f0QVCnxV+641Lr9ti3NCpRLifnpoA4zjOicT9XF/hVqPDifOK5zBkXdLZxPJwHuT99mI/CBdrV02AvuK1H8deRvny/4V7hZfOcw6h7ui9czV6PUqN0HNkckV2j563XnrOI1337Nc8nj4roQMJXh/PkcS2x7zkdpqo92ON9K7POZ/1zeM5R7MvcRvnz2sb3C7toxcOzzrohnlt6EecA9mv4Cii0ygsj5lz8zGndmGc0QXGugxi3GT8PcYdHU3EsW9iDfOeH+hsopOxG74rzpOEz1cbys/50DCMbUJOR9oDDzwgs2bNEuecnH766XLzzTdLWVmZd9xZZ50l1157rVx00UVy7733yqxZs+TBBx+U0047LZfFNQzDMAzDMAzDMIwhwbK2GcOVnIa2PfDAAyIicvjhh8u999670ZdIGygtLZV77rlHDj/8cImiSO67775cFdMwDMMwDMMwDMMwDMPYCDn9Ium1114T55x89atf3ezfXHzxxTJz5kx5/fXXt2HJjKEiSqclijYeysI04bHpjnnunuwpch1Sb3shQ/y8muATZu/TeXUtfL6MT907dqxU2z0V+nPk8gX6c+FEG9KC5+t7qf4n0tsjfChVo0NUmPK8e6IOC+is0+UvxefRrTvqz4vz2vXnzkVr9afx+Usb1bYXGiDZafmMTslb+eJytR30xKScZYpagjALt3iF3p8ZPuZ9Wh7zqTv7XV2NvhbS+0YJhBO1I/wH9xJ14tNy9stiHboWIazRMU020wUjzIGhaMFqnVKdn657n9JjbCRGjdTn5/0wnTLDxUDEtOVIL+wQ9+ndT5bwsahVh1Q6fqbPcCH0q7iU70yDHaEveXMi7jVg2m3OmQxf6kVdE4ZxInzIC19q1CEorlT3vRChbl79MWyEYRLoO64bfcEbGxg7hGEhmffDULa4EMsYvPT2LGtMKHbEccCQVPY9hooxPKlF92Uv1GwL7494oebcxv0wZIV9naHnDsd74cqg+8CddXHWDfYNzsGpUj0OE00I1WpGP0eYfM+ESrWd14h+yH7J8/HeMScGmNMD9gWGqqHtXbmeJ6KmZr2ffVUA55Uler30tAT8OcobFSPlPZ5f+LzijVuuuXheKXgvI5QQYXOdk/WcVtCkzxX06muFI/WzXFSItsrPPk9EKFtYqssTIKTTpdAWeJ7wQuXZNlgjomT29dNTTGDOFoSucxxSk+CNc87pMXO+vwZuIlyczxmGYWwTcvoiqbGx//887rDDDjFHDrLh2A2/NQzDMAzDMAzDMIztnkiGR9a24VAGY1iR09C2ior+f0VYuXLlZv9mw7Hl5TECVcMwDMMwDMMwDMMwDGObktMXSbvvvruIiNx1112b/Zs777xT/dYwDMMwDMMwDMMwDMMYGnIa2vYf//Ef8txzz8ljjz0mV199tVx11VXiNuG5iaJIfvCDH8hjjz0mzjk5+eSTc1lUI1dE0abdC4z1zmNKV8SOx6TC9hwUTIPKWHDG2dMpkc2JxDhx+iDgROgp19fuLUPcfIHe3zdef6FXtFCnD45KcH2kB06u0z6MHjiRInhkkt26jfoqdBx/9RtIM75ah6Ku/A8dzlobaC9Q/qK1attVV+r981dhWzR0PiDFrmuPSZseF6dPx0TG8Z6bA44Eqa3UZWmCV6cvJt09016j73j9kL4pjqO2dr1Nz06KqZ319Xi/Xl/vgO8jxnHAuqZLxfMEMRU1xrE0Nutt+jPGckk6ZAABAABJREFUjVKbYQkcE0xbnuHhSU2qU7sS7ShLA7wtSaZixrimI4l1SecQ3CvevdMLxzmTqaCBY11x3kLdeH4Ousfgr+C8wnlNyvX9RXw+WKXnCW/coj6Zht2DvhJ68jLWBM+JRNcI5tisviURzz3izTkcl9wf5yyKGddsG447pnT3yst5ZQsdhh50OMW45ThOPXcb2oPHF744T+/n2MsgWQJfYofuZ4I5inXVMknvL6jW62dFJ/rOyjV6GynTA4x7r63g/fE8eez3DVBHcN6gH0zgJoOjyVuP6UhiX2FbtcW42uicpAdvUynh15M5r9Avmd+ky5J4c6EuW90Itd09STuSwqRumwDOxGSHroswD2VD2/aV04HEZxO9GdABCN8X5wE6C+lM8uZgwucXrkn0dbGt4kBfcXx+YV9e79eK+mI8mB863Po/Q81wKIMxnMjpi6QLLrhAfv3rX8u8efPkmmuukUcffVSmT58uBx54oNTV1YlzTlavXi0vvfSS3HPPPfLWW2+JiMiuu+4qF1xwQS6LahiGYRiGYRiGYRiGYYCcvkjKy8uTp59+Wo466ihZvHixzJ07Vy6//PJNHh9Fkey4447y9NNPS5L/gmEYhmEYhmEYhmEYhmHklJw6kkREJk6cKG+88YZceumlUlFRIVEUbfRPRUWFfPOb35R//vOfMmHChFwX0zAMwzAMwzAMwzCGjmgY/TGMDIbkM5+SkhL5+c9/Lj/+8Y/lH//4h8yZM0caG/tjtKurq2X33XeXfffdV/LpqjC2P6Jo0D9DRwIdEIzzp9OIcfmFiJPn7xlrnswe+0sXTVCqvQnK0RBXdjgVqv6pHUdhCbwwcXHlrfDu4Pqp0TqOP7msXm13jdDOgbax+nopqFlq5ujtKInyoS1GzdLumBVHVajtcW/AFcMvEOGGiQrhSEBfcJ2Ij4eDIaK3SABdMyxPctOuGdcNz4rXb1FXTbpu6BCiu4R9x9EXAf+T572BY8lzFsB94hwcDhxndCDxenTH0LXC+oD7zNvuowMKY42OBvqt4HzoHq3HcfEaOB3KBvf3Vum2yMc4CxY0q22343i9DfdHQLeK5+bCvbOu6K8og8sli8dto7/3PDgoD10prfRt6d+HlWVqm/6wZLPuO6nKYuyHKwUOJX9OR9+FnyNs1mPNc1Bl8RbRn+Q7kOhhQduxbfF7rifs9964oienozPr/ri+QC8dXWt0lXltsWI1ro++ynkszr3COTfMfv+e49BlX5N4/kyHoudbpBOJcwpdZ3AMjfjdG3p/hXYcho1Nej/LxnFGr9zIWr0f61fUh+cDOoTom4rpS1wTPH8k15gYX5ir0c8nUqjLExaj73DepasO81T3eP28kWwfXGNdnx5nQRfWqwmj9aVXaE9bIZ69hM+F8FXSyRShKlMleJZJ4VmuiG4zvZ3s0CdMtMJxiAvy2S0qgP+KjiU6kfh7Tnv0Z2FecD143sH1IgdHUhfqLy+7988wjG3LkMaL5efny9SpU2Xq1KlDWQzDMAzDMAzDMAzDMAxjMzDxkGEYhmEYhmEYhmEMN4ZLWNlwKIMxrBjSF0l9fX3y2muvbTS0bZ999pE8hmwY2zVeiA4/fY9Jf+wcPm1nmlB+UouwB+94np8pcUHm5+IhPg13aXw6jhAL16fLkmhGWYp1+l9hCnSmoF+7Tm2HE/Sn4+98c6LartZf3kvBUfr3XW/VqO11p+lP+Qv/G3UzSadYb9hdh0FUv4PwpElj1LZDOuSWPfT1Sxfr64dF+vp5+FzaC7NYtExvM616Eeob32tHDRmhCOynSD8fMeyOYQVMb8vzMcSDISJMuc6wvDB7iIwXRsiQFdyPw6fmseFBDDmJsofOeWGDDO9iGnCE3HjjmOUBxa8u0Zcbo8NE+qrRFzKvjfCjAP0oZNtzDuM43tIU8TyeYYBxeNeLCaFlmnHO2QiHChBimkYYRtChzxc0IPSMMOSI4e9McY8wDWFoW8yakjX8KqZfsS24fjjM6Qyd88ZFTCiXsKswnIjHMxwJ48abF2LuN9pFryleSMwaHb7t9R1ejzEycaHpnIdY3zFjKTN0nW3DfhV2IvQL144Qzst5QfqwPsWF+TGMsQvzRgNC46oq9X7URcRxzL7CuunGmoK2Y+ldhQ5pDUfo8rhetOVq/bwRlevnIYY/RQz9y0fboj4Ll+j6UfMYQ0A7EJaXj36K9TscpZ+tgtWNuij4fViEbYRm9ZbpcV/59Dx9/bg5KiaENkJ5ej++o9oOMG7zVjbr8zHEtzC7hoHPPwylSzBUThjOjfvDc7vrxlhaP7ZcCu1oGMY2YUheJHV2dso111wjt99+uzQ1NW30mKqqKrnwwgvlyiuvlGLG2BqGYRiGYRiGYRiGYRg5J+cvkpYuXSpHH320LFy4UCK+Oc+gsbFRrrvuOnn00Uflr3/9q4wbNy6HpTQMwzAMwzAMwzCMISQSkSjmy8VcYKFtBsjpi6S+vj459thjZcGCBSIisuuuu8o555wjBx54oIwaNUqiKJI1a9bIyy+/LHfffbfMnTtX5s+fL8cee6y8/vrrkuSnz4ZhGIZhGIZhGIZhGEbOyOmbmTvuuEPefvttcc7Jd7/7XfnBD34gAeJpP/axj8lhhx0m//Vf/yVXX321/OhHP5K5c+fKHXfcIV/+8pdzWVwjB7hkUlywvhsyTt87GG/jGYsd9/sYD4Ar13H9dM/Qi5Dt3waC6ir8hT66Z0/tkyh8DyGe9MLQpUJfBOP4kXac/4pQ8y+9Xf2mTmEbPKN9GYUHaQ/AygKd4rZ5F90WaahMuF05X8e1d4/U4auFq3SByxbq8q09QKfzHTm7WW0zTt+VwHmBdM1e/dGBAcdFlCWVdjBOpwuW5tZNHisivhOILi6+QKejgM4l+i+A5z6hXwvpiWWkbnvXiPvhuGJd9iK9L10l9NzALxLrkOI8wBTwOD5YqdM3s23pdlm3x2DnzW/R/bJoKfwMcGsFTagr+qtwb55vYyVcZ5V63Lku1C36JVNPOziJvL4T5wzidpwnCPNeolGPu6hQ95WoHPXXjHHKsUInFL06nqcIfZNOKByf6brx0suzn9HDFuNtIZ4TiW2DOYmeG7rC6BBydKsRrjmE4xoumYB+KvYV7uc85LnRUF95uJ80ypPG79keMf6vzHkihC8qGDlCH9yFlOoxfscQKeI5B7NvuRo9D3BOdNQ9cA7m+sVxQJcbPHgkqK5U2xGeR+j38tbXddob5PmxKsv1Nrw5jn3T0fuj67N1F32+ZLfuO/nNg/Nm/tylWcsSVui6XnqsfvYowKPb6D/puk9V6zk7VazL2lem255l9RxOdArxeSGNcVKr/ZKebwsOouQ6uIX4PMFxxb6l94pD3wzox6LDkU4kziOcJ2Ked7YXosiviqEqh2FkEmPW3Lo8/PDD4pyTz33uc3LNNdd4L5EyCYJAfvjDH8q0adMkiiJ5+OGHc1hSwzAMwzAMwzAMwzAMg+T0RdKcOXNEROTcc8/d7N+cd955IiLy5ptvbpMyGYZhGIZhGIZhGIZhGJtHTkPbWlr6U++OGTMm5shBRo/uDxNpbY0JDzEMwzAMwzAMwzCM7YVIhofoejiUwRhW5PRFUnV1taxdu1YWL14se++992b9ZtGiRQO/NbY/onRaomh9DHVI/wY8N3CpRPQG0NeB2HFXWJh1v9BRgfMH8BLw+ip4GK4SV6rj5AvWaodAWKr9FkEb4r49Tw4cAaXaLbJ2qnY05bfqOPSa15p1+Vq0E6JzL50lMb9F388ud2oPQMcO2i+V1444fuoumvX99VRrL0HXeHhrEPc/6o/wGtA/0g5HBBwPDPSOWrTDwvMQwW0TZHoUCuEmgV/D289z05Ozao3adjtpn5brRT+HqyTu/Oz37DserFu6WOilgVuM7rIBJ9qmiHM+cZ6IMA9M1H3XtaI9eD/chrNhxD8Hr99dnZf12Ki5Re/nvbMfYs7x/FQles4JWtCv6Vzi9eilIewrdCx1wgXDtmHdUaBAt1sFPHQY130V+vx59NjBX+W22OmENYWuluTmPyU7OoPYL+k24X7UXTRSu0zohXGoy7APbYO+4jmRPAcRyoNxFvXAwSSA8wq9OKx71gfXz1Ksr1y/4VqL6F5j36Q3iPdLMlQL7Feck+N8S96psf57sC+U6TnZNaCu+lAXmHM9BxPHHdsObeN59NgWeq9EaHvv+rx/PEuFa7S3LqiqlGx4bR3o7b4SXcKCJj2v5S8cbE/WndTi/2ugbepe0W2R14a2KNbrY1ed3uazUaJLj/NEX4yrjM+5MfOKN8ej7xa+vjj79aq0E4p9z9vuwhqUxFhB+Vw3fk8fFhUonMe8eWb9fj7PG4axTcjpSNtnn30kiiK56aabNvs3N910kzjnNvvFk2EYhmEYhmEYhmEYhrFtyOmLpNNOO01ERJ577jk599xzpaOjY5PHdnR0yLnnnivPPfeciIicccYZuSiiYRiGYRiGYRiGYQw9kRs+fwwjg5yGtp1xxhlyyy23yIsvvij33HOPPPXUU3LKKafIgQceKHV1deKck9WrV8tLL70kDz/8sNTX14uIyCGHHCKnn356LotqGIZhGIZhGIZhGIZhgJy+SHLOyRNPPCHHH3+8/P3vf5f6+nq5+eab5eabb/aOjdbHvU6dOlUef/zxXBbTyCEumRx0png+C8RCM9Y7zocBog74Reg5SCM2m/4T+kfo18g4n2N8NnwVIVwo6RJ9rjy4QzzfQ5E+n2vSjp+Rs/ThrguOBdRlWKMdRfkN2huQ6ISHZk2D2u7bXcfRpwv0v1oUNujrte6ijw/zsx9fuBTuGTgS0pXauZBohscHbevoLWLfqavV2/SHlA1ej3Ub0VuDuqbLK2Jb4t5Y155ziY4C3gv6ce/Oo9R2/gL4P+h+Yd/zHA26r4djdN0FjfBP0UlBVwpcaJ7zAbZHjktZU6+3y3XflhDHw8HgevX+vDWDjqX8pbqs6VrtHkkUwzfFvsC2gT8isXydPrxV150r0+4wto3n+4DjyJvzYrw+Xl+lr4r3w7agawWeH3rr8vNi/BdwJEVsOzqhvDmd9Z/9Echl/D7iOEbZI7jRok60Bb0xdBjx3uEaiUaP0L+HyyWii4TQL0V3GP1R7Gt0CtLTx75H1wn6kuNYQds4l72tHMvjuVVi+nJfFndM3NovIBHjdYnzZ3F9Wq3nAa+fsu/g2SakQ6lFJ6sJPraDvh7ndJafbcs5mm3B+6NnBwTleh7ls5rnbOIaifrK68D1sIZ07jGY8KfoRT0OHL1w6AsF2A5W6PUmxDgtXIdnJziQgi64vuLm9Djof8zm8hSRoCa7fzYsQV33Yhxy3K1r0vvr8XxCvxXhGuP5vNA32dc2jOPIHEmGkQtyPtKqqqpk1qxZcuONN8qUKVMkiqKN/pkyZYr8+te/lhdeeEGqqqriT2wYhmEYhmEYhmEY2wlORFw0DP4MdUUYw46cfpG0gSAI5KKLLpKLLrpIVq1aJXPmzJHGxkYR6c/Otvvuu8vo0aOHomiGYRiGYRiGYRiGYRjGJhiSF0mZjB492l4afYSJ0mmJov5Peb3Pq5n+mGnHvVAzpDXnJ8H8JJZhD3Epfb0QHxyfmY6YZUWISqJZf2qeYLgSPsVOj9WpoYN2fv7LutObcWGCwUr9aThrwgvXQl1XLNDi/HSBvl7TrjqMoRtfU499Tv8+uRLhXAxDQOrnRJMOfWMYSogwjsRYHd7lpYqNS5u+avBz9oj9iKCsUqNDRlwPUq7jXkP048BrS3wKz34q+vr5y5v1bnwazhT2XoiLFwaBT+WZop6fpicZ/oTzEYZpRAxTzJ72O2pqznp6V4Xrd2YJq+AcwrC7ctRVI67NfszwWISssO6jah2m5xrQVkjzHTF8idfH/UTdGOdsa4YnsW0ZFskQGKR4ZxiDa0F5GdrGsAiGDHmprtlX0V5sPy+8OqN+0vrYsEGHcPDcIeo64BzDtl+6Sv+e4bR77Ky2HUPhuF72oi4Y+sbre2GZWD9ZVxy3MSGwXsp2rnkMkeH52HYE86Arwv0gxMcL08xy/oBljQmJZDhR1MUQU9QNnxcotOW47cye8t1xnuK4RF/zYFtzm3UHvLDBlTp82sXMC9QGcN7z+npUok+3VNd3ohn1nzEWXKU+d2qMfjhJ1iO8GOMqHKtD2eStBWozb/xYvd8L3ULIZfumkxD1H8DQdSoeMI4474DUBF3+MA/h1t2cU9k3Me44xyNUkCHArlS3nTcvMTSvRB8flaPvDIQU27czhpELhvxFkmEYhmEYhmEYhmEYIBLqIYeG4VAGY1iR0xdJ3d3d8tBDD4mIyLHHHisjRozIenx9fb08/fTTIiJy+umnSzLmX4EMwzAMwzAMwzAMwzCMbUdO38z88Y9/lOnTp8vYsWPl9NNPjz2+qqpKrrjiClm5cqVUV1fLCSeckINSGoZhGIZhGIZhGIZhGBsjpy+SHn74YRER+cIXvrBZXxclk0k57bTT5Prrr5eHHnrIXiRt7zD2G3H9dKN4P2fseVycf4puGqRhZWppz+G06fTEXrrgBOK+i+BEoG8JDoHEMqQ0L0NceQ9TzOLeanQ6XMc4fC9dMRwEo/D1IFwqidXaF0Ln04g2eH6QMpdOIzoWnOeo0G0RwkFFR0Vi4njZItgX4bCI6gcdTryW10+YtjrGWeCQKtmxX9MBNLpWH9+6hY4F7h4PZx3Tnjdqv1fElO5MJxznHiNMo16n/WCuSbc17ycchfqgK4aOB/rGmCa8atA75OrRz1vgf6CrpBi+CHplWDecU9DvHXwUnostLuW5Pjo+hXoeU65jHmNfZ9/iNr0/LfCP1CBDK+eFmBT3nhUDa4iXqpr+DfjOMuclrg90kbgk2kL0uIhS+vfp1WtZWkUA35ZbtFyfj36pyRP0dorOIdRlKdJ6d+P+gux+qagY91up/Vxhvu47Ccz5Xkp5tjXdcjF48y5B33V8vsi8Psah471y3NKTR8cPxwH9T54PiuMO/kfUDf1T3n6uGZzT+TzOFPId8MZx3kF9eWsCnmdCOKOCWox7nG/dvpVqe8Qz76nt+mMmqu2idfp+S5fieSPjfrr330ntW32ALmvdq3q7cKn20gXLMY7L9Dig48/zxoVoe/Y9z/MGrx3nMIl5TsU4DuBoDFG+iGtKml469H06Htl3+KxJH6W3JmGbz+kddJ2tP//2pkiKnO9OG6pyGEYGW/iE/8F48803xTknhx122Gb/5tBDDxURkX/961/bqliGYRiGYRiGYRiGYRjGZpDTF0nLl/f/i9r48Zv/dcC4ceNERGTFihXbpEyGYRiGYRiGYRiGYRjG5pHT0LbU+s9pe5hWNgu96z/b7ERKZsMwDMMwDMMwDMPYbrGsbcYwJacvkurq6mTJkiUyZ84cOeiggzbrN2+++aaISGyGN+NDShQNxONH9PIwzr8cDoZmHavuxZJH2WPN6S3wPAH0FsSREUvulcVz/OBajBtH3HvUqZ0Cjr4nno8OJc8joz02wcp1+vCRer9b16zPxzh2Xh9x8kEjfCFwOnlx/vQEwAHBuHo6lYQeIrYl+pZXv2ivsBleoC3tG+pkdCLEuLx47/RH0fFD5wDqWsrQN+HzCHrgdGjQXqA4d0m0Vvcl+kRcNXwYBPfrOZEK4JjA/QV0JsT0LSnVHqOwWrtpUmWDfSEfjiTPiQQfBNsyojOJcxB9TvRdsG34jzLsl3Qs0dHE6+Pq3rhi3+K8ReCziLqze+zCCl0/rht9vUu3JY8XzFvpMt2XkgtX6vLQy9OBeSBznqMHB8SOC899ovcnJozVf4GyCcoWco5t1m4xb97g+tqJvsNxQQ8Q+k4EB1JYqOeRMKnb1jMY0R1DhxHHDuZoz8/Fvs15lPNAlueDKOQ4zE5I5yCJ8Tv6J4RzCOVxpVjfMc6j0iK1HWDekip4fN7T48LzR3VlX+8i/kOv55OEe4xjhfMa2nLELHiI0LfLlurf92GNiyr1nB6OHfTuFcyep/aN+4u+l4DPbpN0RAUdflGevna6RK9XQbeuywBzvjcHwyPHfh2M1E5A71lvjV6PvefguQvVdpJrGtxrYTHupwV9H9cPynTd+645Opf4fISxw77FeWFD301tmWPNMIz3R05D2w4++GCJokhuv/32zf7NrbfeKs65zX7xZBiGYRiGYRiGYRiGYWwbcvoi6fTTTxcRkVdffVUuueQSP5NFBlEUySWXXCL/+Mc/1G8NwzAMwzAMwzAMY7snGkZ/tjGvvfaa/OQnP5Fjjz1Wxo8fLwUFBVJaWiq77LKLTJ8+XV544YUtOt+f/vQnOemkk2TcuHFSUFAg48aNk5NOOkn+9Kc/bfY5Ojs75ec//7kccMABUl1dLaWlpTJlyhT55je/KUuXLt3SW9yucFG2tznbgKOPPlqeffZZcc7JPvvsI1/72tfksMMOk9Gj+1NOr1q1Sp5//nm58cYbB14iHXbYYTJjxoxcFtPYhixfvnxAuH5Y4UlSGPR/ph0g7MMLd8InrV7qaqZF5Se0DE3gp+/AS+vOMA4vPC0jtI1psr2QEHy2j5AUh8+Bvc93WRYOY37+y/TARfrzZC/1M0K5WF7ee9/kUXp3C1JTtyDsAmEarGumD/bqB+mHvbbhp/lINc3PrXk/6TVIF8zrx6WazrxWCcJ1Ro/UB7Th03B8St83plJt563SIZ1e32AICsIuvLAIhi/x3hguVazDJpjS3RtX3GZfQltGKL/j2GH4GENamJqa9cNP/+t0qF3PSF0/hS/NHywLw4XQtiRCP+rbd2e9H3VTsLxZn4Btw1A29nO0jfPaNiZtOduSacARmuaFA6NvRQhDdAyzZGhZGUPbMC/FhEOFlfr6qRK9P/G3N9R2ULr5YRdMYc5jOQf9qfUuycan8/U/jiWqKvQBDPtj+BTbjuOKITDjx+j9rZiTOW4K0LfqG/Tpqyp1cWp1uFRfma6vgtd0CI0rxFiKmTeiFoQXMzyKIbQIyWF4NkPlMtOUO6Y8Z0gq12+Om5iU6yR2PWHdxMzR4W476Mt36HnDtaIv8XkFdcM1xHveAN48GdOXgyKsKVifwyo9roN2PB9gDQjL9fU7x+jzFzQN9p3kyzq0jeMqbu13O0/Uv8c8kEYoGGPXErgXhu96z45dWM8Yesfyt2Gc837qoA3hnI85nCGtcaHw3liortS7EX7scD7XgzWMc/QmNBHdfa3y3OLfiIjIsmXLBhI3fZjI/P9KY398hSQx5w4FqaZmWXHFj0Vk29Tr4YcfLs8//3zscWeeeabccccdko91OZMoiuTLX/6y3HbbbZs85sILL5RbbrnF14VksHDhQjn++OPlnXfe2ej+iooKeeCBB+S4446LLff2SE4dSSIiDz30kBxxxBEyZ84cee2112T69OmbPDaKItljjz3k0UcfzV0BDcMwDMMwDMMwDMPICRsytI8ZM0ZOPvlkOfTQQ2XChAmSTqdl9uzZ8otf/EJWrFgh9957r6RSKXnggQc2ea4rr7xy4CXS3nvvLZdffrlMnjxZFi5cKD/72c/k9ddfl9tuu01GjBghP/rRjzZ6jvb2djnhhBMGXiJdcMEFcuqpp0pRUZHMmDFDfvrTn0pLS4ucfPLJMnv2bNlzzz23co0Mf3Ia2iYiUl1dLS+99JJccsklUlRUJFEUbfRPcXGx/Nd//Zf8/e9/l+rq6vgTG4ZhGIZhGIZhGMb2wlCHs+UotG3XXXeV3/3ud7J06VK54YYb5POf/7zsv//+ctBBB8k3vvEN+ec//ym77LKLiIg8+OCDmwxzW7BggfzsZz8TEZH99ttP/va3v8mpp54q+++/v5x66qkya9Ys2W+//URE5LrrrpOFCxdu9DzXX3+9zJvX/9Xiz372M7ntttvkqKOOkqlTp8p3v/tdeeaZZySZTEpnZ6d8/etf38q18eEg518kiYgUFRXJL3/5S7nqqqtkxowZ8vrrr8u6df2ZBWpra2WfffaRI488UioqKmLOZBiGYRiGYRiGYRjGh5Unn3wy6/7a2lr5xS9+If/+7/8uIiKPPPKIHHrood5xv/zlLyW1PnT+xhtvlCKEzxYXF8uNN94oU6dOlVQqJTfccIPceOON6pi+vj751a9+JSIiU6ZMkUsvvdS7ztSpU+W8886TW2+9VWbMmCH/+Mc/ZN999938G94OGJIXSRuorKyUadOmybRp097X79Pp9MBncBMmTIg52hiOuERCXNAfcx6bUp0+DcZ6I1bWMe6eHh76Q3g+eneYFpWOpoz9nmuDKduZlpt+CvovGAfOumJ8r5c6WntlHJxBXppu/N5zoaBuEl3Z03R79wO3C50UbNtonyn6+Dfm6/1suz74QdgX4DkIayv17xuR5p3lydimMyExDi4SEL63XJetBl9coi3zlur0vZ4TgT4KtG2si4R9L4Rvo06nF3bdaFu2JZ0OXop69AW6yDpR1+y7MW40D/rBVutU0nRYFa1t3uTv6UJhmmwvhTmcQS6l7z3Rh7qge4R+C16P5RFAhxBTNzfBfeK5WOCdYV9CXwzhOErDk5Po1OUJOjEnY55x3bj/GD+GQyrtBOdsrhE12o8lazHWsngTgopytR02NG7yWBGRY0rOyro/hANIXEy/jnEieetXY7Pa9OZM3ivdY3CphIW67hP1uvxBJ9wn7DucUzHOvfJlcRKKiMjOk9Rm+47a2VSKseWN3Y8NPkMGjRgXdCBhjvX+kR5lC+ilA3T4xTmIiCvX95pYsCJreaQke3noOhM6kugJYtvAixO263Hsrad8HsK8F/D5pFqPvQh9nfNK8RNv6/NnriEcNyhLAO+cP05Rt3w2i/FjeU6iYtQFn6VCOGG2sO9EoV5zHJ8V2bY833sr9faIGl288XVqO1iNeRHrfdCJeQrPymEpvH9pjFu6URP99R324rnmw07k+v8MNcOgDEccccTAf2/sS6IoiuTxxx8Xkf4vnDaV9f2ggw6Sj33sY/LOO+/IH/7wB/nv//5vtQ4+99xz0tzcLCIiZ599tgSbeNacPn263HrrrSIi8vvf//4j9yIp56FtW5N58+bJpEmTZMcddxzqohiGYRiGYRiGYRiGsQ3ozXgZubGXO4sXLx74yOTwww/Peq4N+5cvXy5LlixR+zLD5rKdZ7/99pOS9S/nZ82alb3w2yEf6hdJG8hx4jnDMAzDMAzDMAzDMHLEzJkzB/5711139fa//fbbWfdnkrk/83dbcp5kMimTJ0/e6Dk+CgxpaJthGIZhGIZhGIZhGD4u6v8z1GSWYdWqVbHHjxs3bqtePwxDufbaawe2TznlFO+YZcuWbfb1x48fv9HfZW6XlJRIZWVl7HneeOMNqa+vl56eHimgMmI75iP7Iqm1tVX++Mc/yiuvvCKvvvqqrFixQurr66Wrq0sqKytlt912k+OOO07OO+88qampiT/hJnj11Vdl5syZ8sorr8jcuXOlvr5eGhsbJT8/X8aMGSMHHXSQTJ8+XY488sj3df6bb75ZLrroooHtu+66S6ZPnx77u9mzZ8vNN98sL7zwgqxevVqqqqrk4x//uEyfPl1OPfXU91WW90UiIRIkNr6Pnywitpux3J47hk6k2Nh1xIbDxUKPgedYyjwfHAFeWbm/F+6PILsPyvPMxDiSHPwWdD7wXjx/RFmp3obPImjR23SrpMfqMRR043rNbXo/vDbBfO0VCum7YP2y7ejhofuF5yvWrhc6IKIM74/n4iJwBEV77KT3r2jQ2/BD0Lfl2O/oDOIXmhwXdCIUoq1Tum7CAn3+ZGuX2u4bpX0ViU5d3qA+xuGAvhzVIMlCl76eVFfq4zlPJNG2rdrv4dCXo1Vr8PtNL4vsR/TOeH4J+JfyltRv8twbvTb9VyXoa/RjdKKukmgLul7oHOK8gnHiucXgRApL4O/gvITtsED3vXQ5H74q1VbQBD8J50WsEUGXrp+Q/g34suhHUfvgLIrgjeEcfUzhGXp/GDNnRaj7LYROJG/OpzuNDiT2Pc6RPZhDuR7TadSBvsbrYWxEzbptPY8gXWxJ7fFpn6jHdXcVPH7j9TwVpLRXKNmWMRZY1gLdryPOSXF+xyyurf7CbOGzzuiRepvjvopzKNoijixOQBG/r9Gp6NVHTN/mWIhz0bluzFv5mHfTeqwF6EvqeSdCWVnXfJZiv2yBxw3jiJ42Qdkc247rI8dlXnYvX1QCN9kKzHF8tmvP/uzGtvD8VuVwGNGhWAbfFvsirh9V6nHMOT+sgR8rH568DR7CODeV8YE54IADYo/Z2hFDv/zlL+Xll18WEZFp06YNZF7LpK1t8P9PlJaWevszKcnwxbXD5bbhPHHn2Nh57EXSR4CXX35ZTjvttI3uq6+vl5kzZ8rMmTPl5z//udx3331yzDHHvK/rfP3rX5e//e1v3t/39vbKu+++K++++678z//8j5x88snyP//zP1JIIWUWVq5cKd/5zne2uEw//OEP5Qc/+IGEGQ8rq1evltWrV8uf//xneeCBB+Shhx7aorIYhmEYhmEYhmEYxtZk5syZ8u1vf1tEREaOHCm/+c1vNnpcd/fgy8p8JNggmS98uvAPBBvOE3eOuPNs73xkXySJ9H+KduSRR8q+++4r48ePl9GjR0sYhrJ8+XJ55JFH5Pe//72sW7dOPvvZz8orr7wie+655xZfo6CgQA4//HA5+OCDZcqUKTJq1CipqamR+vp6+de//iW33HKLLF68WB5++GEJgkD+93//d7PP/dWvflVaW1tl5MiRsnbt2vgfiMgdd9whV111lYiITJ48Wb773e/KHnvsIStXrpRf/epXMmPGDHniiSfk/PPPl/vuu2+L79cwDMMwDMMwDMPYCkSykfSQQ0BGGV5++WUZPXp0Ti771ltvybRp0ySVSklBQYE89NBDUldXt9FjMz+C6OVX5KAnI2KgCNEFG84Td46482zvfGRfJB155JGydOnSTe4/5ZRT5A9/+INMmzZNent75Qc/+IE8+uijW3ydP//5z5LcRKjEMcccIxdffLF88pOflNmzZ8vvfvc7ueKKK2SPPfaIPe/jjz8ujz32mIwYMUK+9a1vyaWXXhr7m+bmZrnssstERGTChAny97//XWprB1N7n3DCCTJt2jR54okn5P7775cLL7xQDjvssM280/eHy0uKS/R/asvwKi8ciW+FmTYVb4GZbpjpjSN+qs9PfgnDVvg5dsYn0AyDcwhJifipN0JKHD6lZygcy+qF2fHzbIQNeJ/ux30GzM+RWT6mfMd2oonhTDien3/zc1imU67Un+5HtZX6dEgDzhCkrr0mqu2it5DSluFPVZtOl+yQativK/2Ja7pYt10CbRGV62t7n74zDJJ1T1p02CDDEBx/z+vHhDvlrcX58am5Vz62PT59d426r0ajEaKTQnkQFeI6Ee7F+kIoIFNnS0+WhwaGNTDcieG0CAF1qFuGroXFelwF7QjNKsR+zAsRwiJjU0PHpfGO+SydqaPTBZjnknrcJlsYWqf7Qt4q7Oe8wLAOhqS2IUwDYdMJhHulR+uQW7dIh9Bm9p2IYXmduBb7AsOXSVwoG/c7dnSkLUeoGPsxQ0ZcO+YVpnjHHB/W6jk3QIhrWK9DdIOqSn19nn+iHndupwl6GyFA0SqE6OizSX6L7ovJLl1/fSW6L6SK9RkKlg/OO9FqhKAynIdzCtsGbecdT9iWfNbheo053T+fLk9qvO73yZW6rcI12f8h0mEN80J8OU/wfvB/rAI+P/B5hdsMg0S4dThvkb4en2c4NjPa03uWwpwWdTBMD3POCKgvetg3UBcxz34R5hUvdJ5hesVYk3h9hkcz1D5OAcFnU66fmAe8Z0k+P3Cb6z/XdzzbUqMQsS9tOA/Dto2tzujRo7e6A2ljLF68WD796U9LU1OTJBIJefDBB7NmUSsrG1xbGK5GOjoG1yWGsG04T9w54s6zvbNdZG17PyS4UG2Ez33ucwOm9ueff/59XWdTL5E2UFRUJJdccsnA9uZcp62tTb761a+KiMj1118v1dXVm1WW22+/XZqbm0VE5LrrrlMvkUT66+Tmm28eqJuf//znm3VewzAMwzAMwzAMw9garFy5Uo4++mhZuXKlOOfkzjvvlGnTpmX9TebLreXLl2c5Ugu2M8Xbmefp6OgY+P/OcecZMWLEVvMj3XTTTbJu3bqtcq5tyUf2RdLmskGglRlzua2usbnX+c53viPLly+XI444Qs4666zNvs4f/vAHEREpLy+Xk046aaPHjBs3To4++mgREfl//+//bdabWMMwDMMwDMMwDMP4oKxbt04+9alPyaJF/V8c3njjjZv1/3l32223gf+eN29e1mMz90+ZMuV9nSeVSsnChQs3eo4PwsUXXyxjxoyR448/Xh544AHp5FfQwwR7kZSFt99+W/75z3+KiAx8mbQtePDBBwf+O+46L730kvzmN7+R/Pz8TYrGNkZvb++A6X7q1KlZ5WEbPhns6emRV155ZbOvYRiGYRiGYRiGYRjvh5aWFjnmmGNk7ty5IiJy7bXXqgzl2dhhhx1kzJgxItIv6M7GhiigsWPHyqRJk9S+T3ziEwP/ne08r7766kBo2yGHHLJZZdxcUqmU/OlPf5IzzzxT6urq5Itf/KI8/fTTko7LFJpDPrKOpE3R2dkpK1askCeeeEJ+9rOfDTRWZvjZByUMQ6mvr5e33npLbrzxxoEvhT72sY9lzQ7X19cnF1xwgYRhKJdddtkWvdyaP3++pNbHfsf9LnP/22+/LUceeeRmX2dLiVJpicLURvc5xnYTeoPoKaAjgilqCVPaxsRYe7HkmWVhGmnGhfNcvBbTbhPWTR++HKMPyot7z74dMS6erhKGhrINef64eHW6T6hMGqXDMNOlun6TC7XjyPMAVVep7aKF2hGRHqs9B4lG7fMI1jTpAmU4G0I4CoJWtEUR0gG367qg18bRa0NHAlNFx/Vr4IpxPrYV/BuJNrhN2FYhUkMzYUVM+SKkgPecTRw6HXCh0ckQZPdreD4P3k8WP4mX8pyuEp4b27076rTdPVX62kGfPj6vVfetRDfqshmeN4RS051G14nns2DfiklLzjTcea2676bR972U8PSJcZ5h2nVse+1BvxXn3Zg1xY3EPLNgyeC+mHB4+pfo7PNSmse0hbeesa0YNh/j+HNdqBt6Y+rgeoG/ymE7NVKn4U42tWS/PlwyTAvN8oWVcKewrUv0/mSrrt+eEXrcd47M3n7lGeWJvHFB7xvGFccd5zzOsRxXXK/5+4ieGnh98C/VUVOz2k6UYc5n36PHh30d5c/mh9wovD/PGaW3Y32V6Rj/WIyzKnPNoOvMJbEe5WX/v0lcv4TbGDdhCZ4XMG5cKXyMfB6gUzHQbevoGm3RzkHPv4W+GFTocc3f0zEorVgD4Zf0nv04x3djXuL5eTz6juPYWv/84FJ4bjE+NHR2dsrxxx8vr732moiIXHHFFfKtb31rs3/vnJMTTzxRfvOb38i8efPk73//uxx00EHecX//+98HvjQ68cQTvf+fc8QRR0hFRYW0tLTIPffcI5dffrn//4VE5O677x7477iwuy3hpZdekgceeEB+97vfyerVq6Wjo0MefPBBefDBB6W2tla+8IUvyOmnn77Re8sl9kWS9HcC55w456SkpER22WUXufTSS2XNmjUiIvLNb35TzjjjjA98nUmTJolzThKJhIwaNUo++clPDrxEmjhxojz66KNZnUo///nP5c0335Qdd9xRrrjiii26dmYcaJwcLTNONPN3m8vy5cuz/lm1atUWn9MwDMMwDMMwDOOjhBMRFw2DP9v4Pnt7e2XatGnyt7/9TUT6P+L40Y9+tMXn+frXvz7w/6cvvvhi6cI/7HR1dcnFF18sIv0u469//eveOfLz8+VrX/uaiPR/VHH99dd7x8yePVt++9vfikh/NM/++++/xWXdFPvvv7/88pe/lOXLl8szzzwjZ599tpSVlUkURVJfXy833XSTHHLIITJ58mS56qqrYsP4thX2RVIW9tprL7nlllvkwAMP3GbXSCaT8v3vf18uueQSKS8v3+RxCxYskGuuuUZE+gVcW5pesK1t8F8N4ozymc6m9+NIorDMMAzDMAzDMAzDMDbGaaedJs8884yIiBx11FFy3nnnyZw5czZ5fH5+vuyyyy7e3++yyy7yzW9+U6699lp59dVX5ZBDDpFvfetbMnnyZFm4cKFcd9118vrrr4uIyGWXXSY777zzRs9/2WWXye9+9zt599135fLLL5cFCxbIqaeeKkVFRTJjxgz5yU9+IqlUSoqKiuSGG2744BWwEYIgkKOPPlqOPvpoueWWWwYyqz/99NPS29srixcvlh/96Efyox/9SPbee2/54he/KF/4whdk9OjR26Q8xF4kSX92tv32209E+t9SLly4UB566CF57LHH5IwzzpAbbrhBTjjhhA98nWeeeUZ6e3slDENpaGiQv/3tb/Kb3/xGfvSjH8n8+fPl5ptv3uRLni9/+cvS3d0tJ598snzmM5/Z4mtnSryz+ZFERBnn+RbXMAzDMAzDMAzDMLYWv//97wf++9lnn5U999wz6/ETJ06UJUuWbHTfj3/8Y1m7dq3ceeed8vrrr8upp57qHXPeeedl/eKprKxMnnrqKTnuuONk/vz5ctttt8ltt92mjikvL5f7779f9tprr6xl3RoUFBTIf/zHf8h//Md/SHNzszzyyCPywAMPyPPPPy9hGMprr70mr7/+ulx++eXS29sbf8KtQE5fJPX09Gy1tHgi/UKtGTNmfODzVFZWSmVl5cD2/vvvL6eeeqrce++9cvbZZ8uJJ54ov/3tb2X69Okf6Dp8a3rkkUfKRRddJMccc4zce++98q9//UtmzZolZWVl6ri7775b/vrXv0p5efn7fuNZWDjohojrXD09gzHZW/rlk0h8ONyqVavkgAMOEJH+WHiX6H+xFcIVEzBWnB6CNrpo4L9A7LeL8AKNPg06GOBg8qDjoapycIPOADoX6IHphE8DcfFe3Ho74r/pZiG8tzjinEiETii0TTRCO4pcJ+L+cf9e2zW0YLc+n6B+6AkIy3U/Dpp1/SXWIc6fDgvG7We0Z8AYf/ZD1F3nOO3uSFZP1j9/E+lKOV5ZFjoIGB7rbaMt27UjIuyCswnld9WV+vccR5wz0DcieIXcirX6+BI4H9jX2dc8X5ceCxHqyzXAdxVHZv3xJXyFnqtdO/pNVYXeD7dHkMK4xbALC/S9JVswbuCLiDCOttiJhHnCc7fRT4F5J+D2OsxzrL9y/MMJnUZxQknOU5zn2DfQ94NezNONzfr0rK8tKQvhvXD9cCgrPXkx3hrhso71M4RXLmiDs2mN9sZ58wbqNtmLtuO8wDmdfTPGbxU0wNu3gw7JT+F+Eq24Htojr1PXd9l7+v7DDLdNgHET9ehxRyeSP44wB+H3dPg4zHn06nigb6hnDxGRPp0yOlq4VO9neTnu6IukGw3jmPMAnYz0g8URdaI89A5GW/g8k2WNCGL+YdXtNFFvs21W1ett1i2fzdZgzqFTiM+GdPiBoB7rmffsibak7wprmOcYnDRWH9+6hc+ebHu2BZ9vOI9yvUf9RKV4XuhYP9ZcjGfrw0bk+v8MNcOhDJtJEATy29/+Vj7/+c/LbbfdJq+88oqsW7dOamtrZf/995cvfelLcuyxx8aeZ6eddpLXX39dbrrpJnn44YdlwYIF0tvbK+PHj5fjjjtOLrnkEpk4cWLsebY2lZWVcv7558v5558vK1askAceeEB++tOfSnNzc05l3Dl9kTR69Gg57bTT5Jxzzhn4AuiDUFxcPJBhbFtw5plnypNPPikPPfSQfPWrX5UTTzxRqqqq4n+4BVRVVck999wju+22m7zxxhvy05/+VH7yk58M7K+vr5dvfvObIiJyzTXXDJjot5TMl1Nx4Wob7PMi8WFwGyPOwWQYhmEYhmEYhmEYIn4Shq3BcccdJ8cdd9wHOkdJSYlcfvnlcvnll2+lUm095syZI/fff788+OCD0tKSPenFtiCnL5Kam5vllltukVtuuUV23313Oe+88+SMM86Qmpqa+B8PESeeeKI89NBD0tHRIU8//bScfvrpW/0aU6ZMkZ133lnmz58vjzzyiHqRdMcdd0hDQ4NUVlZKTU2N/O///q/3+5deekn994avj4466igZObI/S1Dmy53ly/HFA8j8osh8R4ZhGIZhGIZhGIYxtCxdulQefPBBuf/+++Wtt94SkcGXcMXFxXLiiSfmrCw5fZE0bdo0eeqpp6S3t1fefPNN+cY3viGXX365fPazn5Vzzz1XjjnmmI2m1htKRowYTOn73nvvbdPrzJ8/37vGhjCz5uZm+eIXvxh7ng0v6kREZsyYMfAiaZdddpFEIiHpdDrW7J65f8qUKVt0H4ZhGIZhGIZhGMZWIFr/Z6gZDmX4iNLY2CgPP/yw3H///fLiiy9KFEUDL48SiYQcffTR8sUvflE+97nPqaRZ25qcvkh69NFHpaGhQe677z65++675V//+pf09vbKo48+Ko8++qiMGTNGpk+fLtOnT5fJkyfHnzAHrFixYuC/30+Y15ZeZ1tdIz8/Xw444ACZPXu2zJ49W3p7ezcp3Z45c6aI9Eu9tkYIYjbCtnYJg03MTIjbj+Bu8ePeY/wYiB1nrHhc7K+D+yZsadX7M7xAEWL46ZmhR4fnZty550Ri2YrhWPAcCNn9HBGcQl7cO9woUoKpo0l/Tsn7l+Wrs14vQtt4Xp5xOvuAW7JCbUcTdcgn3SZuvnZEsMe5saP0/uWr9P5K7bqRnoz6CGNikVH3ZW+swbngCKDjgNBxRDcJ25LngxPJ823QqcDf0zEEX4iD8yGib6QeLhbPv6HdBhHrp7ZSH5+iVwfnY1+jsyFmLLgMx5KrxdezLBu9bhgXyQJ9b0GXvlaqAu6RPsyBhfDExfgnIvgnIo5jQi8P65LzVIwPLM7B5M0z3I/7c159a9+Y93u0R98IrK+4fuIduNIyXTZxvqQY96ArRlkxL4Sdelx6Hh660Xh+/CNcVKT7Uoi+53no6BJb16iP5zjm77swr6BvRMX6954nj32Bbhi0bZinrx/k6/IXvdestgvW0bWCsZWX0Xd5b/QzchxxfeU44hzF5wM4k+gS89ZT1hXLS6dig25L79kHfdvbH+d84v44Pwfvh84obx6hCw1tX4K25fUxNsIszqYATp50me7HyTUIHeH/YeO9cQ6ju6wGfscu9K1V2iHoCvDczjmf6zX/cZ51ybrAOHcd2M/nC9LB+sGzKd2j9PjxeYrzANccw9iO6erqkscff1weeOAB+fOf/yyp9eN7wwukAw44QM444ww59dRT1YcvuSSIP2TrUlNTI5dccom8/vrr8o9//EMuuugiqaqqkiiKZMWKFfKTn/xEdtllFznyyCPlvvvuU9nGhoKHH3544L/32GOPbXKNV155ZeBLJF7j6quvHnjruKk/d91118Dxd91118DfH3HEEepcn/vc50REpLW1VZnxM1m+fLn85S9/ERGRT37yk5742zAMwzAMwzAMwzCMrc9ZZ50ldXV1csYZZ8hTTz0lfX19EkWR7LTTTnLVVVfJu+++K3//+9/l4osvHrKXSCJD8CIpk7333ltuvPFGWblypfzud7+Tz3zmMxIEgURRJDNnzpSzzz5bRo0aJV/5ylfk5Zdf3qrXvvvuu2NfUv3yl7+UP/7xjyIiMmnSJPnEJz7hncM5J845ufrqq73fv/zyy/Laa69lvcaKFSvk7LPPHtg+88wzN/MOtpzzzz9fKir6v6z49re/LQ0N+suAdDot//mf/zlge98g+TYMwzAMwzAMwzByTDSM/hg54b777pP29naJokhGjBghF198sbz00kvyzjvvyFVXXSU77bTTUBdRRHIc2rYp8vPz5eSTT5aTTz5ZVq5cKXfffbfcc889Mn/+fGltbZXbbrtNbrvtNpkyZYqcf/75ctZZZ0l1dfUHuubVV18tl156qXz+85+XT3ziEzJ58mQpLS2VtrY2efPNN+X++++Xv/3tbwPlu/322yXJT81jmDt3rpxzzjly8MEHy7//+7/LXnvtNfDWcMWKFTJjxgy56667BizrRx99tJxzzjkf6L6yUV1dLdddd518+ctflvfee08OPPBAueKKK2SPPfaQlStXyg033CAzZswQEZHTTjtNjjzyyG1WlgGcG/j01jFNOD9P5qf3/CSWx/OTXYY78RNZfq6ejxAfpLT3UuZmnpufqvNaCNGIOhhuhE+7Y8IaNlKA7OdjOBHO74We8XNthNo5fH7sEvg0H3XH9MHep/osb73+NN/7vDsmdbWX8pahA6uRwpfXZ/hT6WB7hvisPmjFp+zNOgQyqtEhLgwl8/oKw3V4vJcuGH2FoXC8N7QNUz2zL3l1yf2NOvTNCwvRZ/fDGrHflSAkaF2z3o4JG/X6FtvSC4nddLpkh/Aj79zslwhncu06RXlUp0MmE50IdSvR4yrRjbpCGGAYEwLrwTAHL2QmJiyCx8el+WZacKZV57zJvsuwR4QzMcyzd5T+oranGmESKH9JSq8B3hqTDYYBMiSDaaxxbdeNcKEs64uIbCTcB9dv0eM62YjQsBEI103qug8n6HDfYJkOyXW9uq/37jBSn65N30+6RB+fyNNtGzA8ukaXjyE2SYz7sBhjoUD3jUSrHnvCabU7Y03u0/0g6sRvub7y2cR7lsB6gzDI2LBGjrO4Z1GUP8A8xHmC6z+J0gzb5PoYE7rHOZq/J17f5hxO7YFuH1fEcCqUJ+P8vYfvqfY1T9ZtVdisf1vegWe3PIbh6c3ECjybQEMQtGHOhDLBC1XDHOit9xX6ywQvHJjrPZ+b2RdF952oQs+pbi3uj/Mc5rGwTme+dr1Y0xjyypDhEGONY7Fwffv1oZ4M40NGSUmJTJs2Tc444wz51Kc+JQGf/YcJw+JFUiZjxoyRr3/96zJq1Ci58sorZc2a/oeXKIpk7ty5cumll8oVV1whF1xwgVx99dVSWVn5vq/V2Ngot99+u9x+++2bPGbcuHFy5513ytFHH/2+r/Piiy/Kiy++mPWY6dOny0033bTNO8qXvvQlWblypVxzzTWycOFCOffcc71jjjvuOLnzzju3aTkMwzAMwzAMwzAMwxhk7dq1UsSX4sOQYfUi6YUXXpC77rpLHnnkEeno6P8XkyiKpLa2Vk4++WR58803ZdasWdLV1SU33nij/OEPf5BZs2ap1Paby1//+lf5y1/+IjNmzJC3335b1qxZIw0NDVJYWCh1dXWy1157yQknnCCnnHKKFFOSuZl84QtfkDFjxsizzz4rL774oqxYsULWrl0rvb29Ul5eLjvvvLMccsghcuaZZ8qee+4Zf8KtxA9+8AM55phj5KabbpIXXnhB1qxZI5WVlfLxj39czjnnHDnttNNyVhbDMAzDMAzDMAzDx0X9f4aa4VCGjwofhpdIIsPgRdLy5cvlnnvukbvvvlsWLVokIv0vj5xz8slPflLOP/98mTZtmuSt/0x8/vz58vOf/1zuvPNOWbZsmVx55ZVy9913b/F1J0+eLJMnT5YvfelL77vsGzLMbYqioiL59Kc/LZ/+9Kff9zW2Rjk2xsEHHywHH3zwtimQYRiGYRiGYRiGYRjbJUPyIqmnp0cee+wxueuuu+Svf/3rQJYxEZGxY8fK9OnT5bzzzpNJkyZ5v915553ltttuk4kTJ8r3vvc9+etf/5rj0hu5wovbZ1py+DUiOCYCplmFFyDA214vVjwmnXNQjlTSmXH8jPGn/4J+DHhgPCcDPS6e0whuDx5PJxHjypnCncfzfKyrFN0nuu7i/FcO3gAv5Ss8Ad790sGAtnMT8dUiUuoSV6lT8qZHVKrtTJ9GMH+Z/jF9DnW1+txMO12o7zVdrusiXaTvLdmGe+vTdRl00ikQ809I6Aues4l9BdDh4KWk5+U4NuDFYd+nT8Q/oT5fENM3/fLg+kxvnOFM8vo1XSWely17XSaXaO+Ml2J9nPZJdI/UfaO4Te+n/yK27QmPpy+qO2bOpc+K9895hNfLpy8M8wLHdbeeB1I1ek4OMDYKG/T1eivgoJo0QRdv+aqMnUhRHpOi3RXpsoQVej3y3CCc8zDHxfYt1i1hine4XVJlcL316vOlpoxX28lmOIvgROqu032hbQLquke7zGi9DNpxv5hnArpUEtn9XfSTea60jOeDqAPHJrIrB7w5xHPy0P2F9Rbrl9e36MmhC60MzyJcD1N0BGb3x3hzOsapi1EcuWSMn4b1GeMD854FYxyQ3prBvpBRv/mNup/VdGO9wG9TlbrfRnRv8lpNzfp86+B7LMFzKPpeMLpO/56ePq5vcJ1JhLZAX3Scg/HsE5bC6dSkHU6C8nttg/U0WKMdivQKRthmX428ttfbPaP7HU7d3SKyQLYfhovoejiUwRhW5PRF0iuvvCJ33XWX/O///u+AYDqKIkkmk3L88cfL+eefL8cee+xmeYI++9nPyve+9z1ZtWpV7LGGYRiGYRiGYRiGYRjGByenL5IOPPBAcc4NfH200047ybnnnivnnHOO1NXVxfxas8FbFDGbhWEYhmEYhmEYhmEYhrFNyHloW0FBgUybNk0uuOACOeKII973ecaOHTuQqt4wDMMwDMMwDMMwtisstM0YpuT0RdKvfvUrOfPMM6WysvIDn6uwsFAOP/zwD14oY0hxiYS4oD++3dGV0gsPDvwbcR4gOpFI2JXdmeD5T7DtOZUy4vwd4755L9gftrZlvzaBU8A7HnHvEe6VdS0xzgQ6kWI9OHAiefvhghHUjwi26WyCm8VrazgTAnoA6FyogBOpWvtMBF2tt27QSVGwap0+tFO3pazR+2XsSL2NUN4A3pfkmhZ9PD01ldqPka7U4ySxSjsZvN/D+0O/h0fMV6B0h7Fveg4kXI/+q1hQnpBOCvpIGDpNnwjvP+P8dAKFae2rSEwYm7Wo4YrV+tQYV4kx+stcl4qybgs8NwFcKWxbz20GIsnuB4n1ZcGH4fkt6EbjvEPXC8dpB8Y5PTd59IPo/YlZb6rtQs5ru++itoOxowb+m21H5xDntLBKzyFdY/W9JXp0Pwt6dFnyGvS9enMm5zz067BGz2nB2mZ9/Rasf6JdJy6t6y7o0uMyKoCXJqm304W6PIkefb78Vtx/ix5LURHm/ADrv9f28Ax1674YVpfp0y2HJy9z/Ua/cxn9QEREGpv1tbEeBlhPIrrL6Dzi+oa6lJ7s653D+by+Qi8dxxW9cFgvA6zndBp65YnzzhH6xzgne8djP+cV7h83Wu/PnNP53JfmnKvP3Veu665rhN5O5+u6Lo92Vdv5K7Gec86srFDbUbM+3mtbtn0Hns3Y1vTYEToHV9Xr/eV6HHGcOrrLuIbw2ZH1z7GBvubwrBgVwyu4ofjZlyrDMLYS8TKircjFF1+8VV4ibWDJkiVy1FFHySc/+cmtdk7DMAzDMAzDMAzDMAxj4wxJ1ratRUdHhzz33HPiYv6V1DAMwzAMwzAMwzA+TLio/89QMxzKYAwvPtQvkoztC6ZPjk1nzHAuft7Mz7HxubP3OXZcynt+ostPoDPC1Ri+w7KG+NTd+xSbn9YzZIWf1jMtNj6tdzGhaHF17aUfjks9zZe7rDuej2nGGerGz7fjPrVv0SlqQ6TMDfB5tqC9+nbQyaiZIjfZllHfSJfrhxmgX/XqfhoWo60RQuKlRk4yhEMfn+jAp+X4NN9rS37KjpTrXipohp7FJTzA+b3wJoa+xZQvNlW0FyaCeYIhSRx7uF4afSnbb73P/DFH/Lnr3k2ea3P4dN6p+vyjkKSiSKem9v6RxUsLjrZlCA7bqjgmLIJtx3msXI8VL+SGbRvo7fSOY/TlSnVoXLIFoXwgQFhH2KHnBdeKcLGMsRPU1ep9eQjLY4gGQqsKGnXZwqS+91SZPl+iW89xiR6UDSEu6ZGYhxAqF9bokJmwmOXXm4km9AWmVGf4MubIsuU6pLe0FWseQ26YYj7MHkIjCPNMIASYoYVRPq5XU6W3V2WEuiHk0gsXbkNbxIWMcn2MG4dx4cMjavTxDTqluhdKxrqOgSGnDvNK3JzvXZ9zLscO1n9vHmD9xq0RgBqEqGDw+i6l9yXa9LOSa0G/TelxVP9xvV28FnXD0OsyzNF9mGNxPY4LTwNQXSlZwfrP0Hbhes625jjH8S4uFB19wVv/eT1shwW6r6Tq8OwGBl502AsPw8gJOQ1tMwzDMAzDMAzDMAzDMD682BdJhmEYhmEYhmEYhjHciFyGSXyIy2EYGdgXSYZhGIZhGIZhGIZhGMZmYV8kGUNLEAz4dhzcLJKHOHjGySPO3ktpH3dppFkN4R3yUtgzDp9eg0wPEdNaI649NgU5HQHt2uURC+PWWR46Bug8gJPJ+zeIVp3i3nMi8Hx0IvD+kd44Nv0vvT9p7e9g2zLuPkKKeFeljw/64FRA24f5g+UL6MVhCnM6euAaYa/1XCB9qIsk96PuUZdRXN3SnginEx0GLiYVNVNhe+UDng+Lvow44FbzUtCz/vlzzivsu5nnj5ljovlL1HZIv8UHJIo7n+ciw72Vam+M58ugpwa+Kc8JRa8dwe+jcjiW6MvoYRpyfX56hxxS0CdWNejroW+ybwXlGKv0eXQP9mVvfZLsbhCHuk2in6drtesjxL2kC+nJ0+MwgEstsRYurxBtj3EQtHDc6zk6oostAZcJxy3r2m2ZiyycCN/XvMW6vFWVen+Mm8Whr4Xe+o207ntPHvhvzvd5zXAe8lwYN54jkWWjxy3GEeQ5llr0+uutMfT0cT+9NIRtxZTrcA5690MnEnxe0Qjtp+qp0/NSokuXP/Hau/r8nksN7cG+h+NdT8b9cA7kNp5dgkbtMKp9E+npqTRsw/nw7BMl0faVcIOta9Tnx7MNxykfKFwKdcO26oH7LGDbYz+vx7bguIRXjw5J73pcs7jkcujl6QPaxvW3V2+H/d9bw8gFNtIMwzAMwzAMwzAMYzhiAnFjGGKhbYZhGIZhGIZhGIZhGMZmYS+SDMMwDMMwDMMwDMMwjM3CQtuMIcXlJcUl1seg0xPQh+84GdfPWG/6QegV8OL84THg9enn4PXzNj186H2h48BzCsTguUjoGIqDZWXd0KHAuonz2MBR4Hlq6HaJcRoIvQEsH+Lw6XCKarRHwK1pkKzAQZW3Bo6FDr0/rCkfvBZ8EuwnnmsE/ZSOBOVvEJGoQN97WKrPn2iEm4RtRUcR6zKB2uM44vHoOwG8O2x7Opo8f0URXGSAv/9z171Zj4/jmMIzUB44K1LoW5ntG+NI+nP3/R+obOQzVeer7UQ5/Bnw0rga7R7x3GBwpXHceD6sOFcZ/Rg8Hn0/Ql9z1NxwXqJDCX6NZLs+X2r8CLUd9FXr33dq3wfHNX0hmW6ZCF44bz2ortTbHHecs+B7CuhEoq8qjXGJug9r0DdQ14kG7XZJjYZHDqfvK9d9Jb8RrrU03DEC6F4pgR8La1L0hvbgePMKHUxcU+G5+9M/f8gSZeXfvvXLgf8eM4vXyv5bh3sJ0a88J2KMYykWjosRNXo/PT/02nBe4BrANa0VfRHeoIhtzTWoUF+vr1Jvd9bhfIHerpwN7x/HBssbMw9J22D7Rl0xPit65TAHFb40Xx+POZjOwygf4xzj2vXi2alWz2GePwvni+BaC95eos9XrJ1OfbtNUNt5i9bo69Fz19yi99N5tON4fb1G7W6L6nRfdd143inWzwOpMr2dbNN9jT6zivV+y+5u9MkPOS7ydZZDVQ7DyMS+SDIMwzAMwzAMwzAMwzA2C3uRZBiGYRiGYRiGYRiGYWwWFtpmDClRKi1R2P9prCuICanhNtN2x8G07EwBz8+1mcaUYQ8IE1HhbPx0nTB8KA5+Wo/QMleYPZ2vFwKDe2WKV4bm8XNoQTpfaUXqaYZv8VN8wafmSHkrSCGfmjhSbSffWabPz8+rFy/X+yUGp6/ntV7RplNvhyX4FLtG97MAn6oLt0mT/nScqZtdof7UPCzXbRMgLNExjJCf/TPswkshj/IyTBK/Z0iOF4aA8sv4UWqzd6QOJeiu1n11xwd/on9fj9TSjFAt1INnxBn76v1JhFthrBXXD95/0R9f1zsZcrq14RyHeYVhfy6s1McX6Lp2TK0cl0YbeOOMYYqYE9mX/DAOzLEM8WHa7rVIhY3jgwrMI6w/3j/hvJVZvnGjdVEZUpKXPYQ1aMW45JzIe+lD23ghsQhhqW/Wx3McVqFuUNUOoWIF61Devux90ZtXMGemq/W47qnW+4tYfwyJadOheR4IJTz4C79Q2y/+7tKsP88M2Uh0YK3nnM05kuOAoVYxoexe+G9cqBa2o8ZmfTzDLpFineFArle3bYTihhW67RzWPNeD+kFbRd16fU0grDPZpeuzt0yXr+t4PWcH0B6E+XrST3bo+ylYpfuSywj9c+Xlap8g1C1au07v5zhkCGtDk74W5qSoFmH3HQit45zMbczpEUJiA4Tvct7iOPFCZuPAGuH4XN2sQ4C949t1XxA8D7Bv5tUjzJTFwRoS9PT3zXRv9pDFDx2RDI+sbcOhDMaw4kP9IqmqqkrOOusscXH/p90wDMMwDMMwDMMwDMP4wHyoXySNGTNG7r777qEuhmEYhmEYhmEYhmEYxkeCIX2R1NbWJosXL5a2tjZJb0aY0mGHHZaDUhmGYRiGYRiGYRjGEDNMsrZZaJtBhuRF0u233y4333yzvPHGG5v9G+ecpOhnMLYr6EBwBdm9Ap6jgd4CvpykT8RzMNFDAPdLXMrbzOvFOBM8eC0eTzcKxwLLQjcK4tjpVIjDc7HQoYTyhkg/zFTOIdsabRXB2ZRcoO83bEMcPtKyB6N0GvCwvkEfzpS2qE9HPxbKI5l9oQAp1ZNIJ08HUV52R5ErRppsOBtcD9Ll0pdRDmdBTEoFl4pxMvDeOc7gVglR3oAp6UGKjqlifT+dI/QNhH2ov3w4K/Lh1enVvy9s1vtTRXp/V40ea5k+joDeF/ZDcGzdV/Rf0FtDNwrGebo9uxeG4y69fKXeD58Vx6F3Ppe9s9Ab56U1pz8LTiJ6bzjveX2P9UO3G10wcWnP0Ve9eZXzWobziSnS6UTqqUVZsDwly/Sck2zFHNmVwjbuhUX3XGbZ/zHOS7Ndqd1qLTvoeadqDtqK52tB3/TWX/rD4Nnh/yFZtlptcg1hXwmQFj2CZy/OifTp/NPV9rjddhr473SxbqsE/FBe3dNjB6eg51ukG4zPImO1E1CWrtLnZ7/n9QqxJnGOj0uzw3GBlPLcdvCFBRxnSBnPNTHRq48vXg1fWAoOpQp9vdbx+nxFjXAmtev6STYNtm9E3xOcPZzj+JwaVFeqbaEnjs9CDRhXhfBNsm+h30fF+vigVTuH0hX6XhNwEoX5uu8kmjr19Thn8vmjCL5MwmerAowNzEMCp2PQBidSqZ6XQrYPn30Nw8gpOX2RlE6n5fOf/7w88cQTIiISbalw2DAMwzAMwzAMwzA+Cphs2xim5PRF0i233CL/93//JyIidXV1cs4558i+++4r1dXVEvBfNg3DMAzDMAzDMAzDMIxhRU5fJP3P//yPiIjstttu8sILL0hVVfaQB8MwDMMwDMMwDMMwDGP4kNMXSW+//bY45+R73/uevUQy+kmlRIJ+N4QX909PEL1A9AqUwf/Rjthv+jjoWAKeE6lFx7azvJlffDrGyTOMk1/g0SmAOHPeu+dQ4vlYl4wjp8uEPgs6nuBOocPI8/xgm04kEnXpOH56dcKGJn1+eITo04rKdV+IRlTq869cpwsQ43ahcyrKcD4EbWirZvw2JoQ3KoEDoAr9uEJvB03ohwH8VDiePgu6VoJu3ZaO4yKF36PtQ/gvghE1ej/cJUG7diIkm/U4Tdbr+ytaoeunr6xCbbdP1mOjsFL3pZ61+vdFq+GUKNRt31UDZ1OmQ4n9JA66RiL0DY5jzlGx54c3Bn4sb56goymCT4pzapzrjfVRrtvam3c4z9GNNiL7c0GE8oRFcMHQ15Wvyxv06f1BF3wdfbr8UUb10jPDaxWu0W6P5n/T/TS/VZc9fzX8Wpxz6EahW60V6xvXR65fDY1qOwmPTsV81E2bHifSru/P8+YV6HFDl0wCHp8k3CkRXDJRjDcvwPFdY0er7f3P/f/UdneVrv8x/7az2nYdg/ebxBzlubfoOOJ6iznfc49hzvfavgV1PXGM3tZHi2vQc3B6ZLnaTqAtPWcSxyXKH3TquqeXz3MuYZ5xdCRiTQp6cb3e7E4njuP8NjiS1uqxkGhDe2aWrTdmveazFZ/t0JZ0GDk820hf9nHqnZ/etnr9LETHUoD1zGvLDpQH4ypqxbMaxqGj04nzDNcEOqZQPxJiDqdDCQSdmCdK9Nhz631mLsYZ96HDQtuMYcqQxJN97GMfG4rLGoZhGIZhGIZhGIZhGB+AnL5I2nnn/n8BamxsjDnSMAzDMAzDMAzDMAzDGG7k9EXSqaeeKlEUyZNPPpnLyxqGYRiGYRiGYRjGhwoXDZ8/hpFJTh1JX/va1+T++++X3/zmNzJt2jQ59NBDc3l5YxgShaFEG6Qt8BC4fMxYiNX2HEmMbfeOh8cgqeP06apxbdpT4MXKA+VFojOBDqKAHhqUlc6COCcSt+l0iHOdwG9B34bnq2DcPM7n4MNwuP+Qvg2cL71WO4x4fvo5uC3N8AjpvRLtPFHvb4c3IILHgI6qskEXTVTfgJ/it6wr+CIcPDKuUvstPJcXvTsluu1WH1aptru1smiDkmyAulf6sF+3VcF7cDKE8FtU6PKG1dozExZoB0LQAwdCi3bFRJ3wWaxaozYnNOgbSsN/1TlOj+OCZn295DLdt5IYa7XpUWq7fp/Bvvy3dbepfceO+LLa/kz1BWrbG+f0Y3iuND2H/b/wYcnGpxJfUNuep4Z+Drpb4P7yfB08HuNaivR2WKHrPmiLccFhXkiX6vKHSV1/ec1wqcFxRGcSoWMpVa6vl+jCOO/LGLucY3Cu3hpdFyUr9LjmuTt3qFTbBfX63hKNelx4zg+40ehKYV9zI2v1/pX1+nqj9f6IczB+7zkN6a/CvJiA8ylM6u08utXgdApwve6P6zm8r0TXR+ly3Tcq38G8k8A8muFucfRP4VlB8rWLTDDHCT0zIMKcT/+T43qD/ekdtA/K5eu6663Q4zqZB5caXWH0BGGcujSerdB2YR5dZLquo0pdXz3Vur7y2rBGhnyW09vF7+k1omQ+6gtriqA8UjK4BveN0utXslmf23E9YtkwLl0v5vg41yd9kw7jmM4k3gvOHyxfK1sExzHLx+cNzivweUaYg0O40Hor9fFFK+hkwjxLzx7XpA6M1Q3lTeHvDcPYJuT0i6SCggJ55plnZN9995VPfepTcvnll8s///lP6e7ujv+xYRiGYRiGYRiGYRiGMaTk9IukRMab8yiK5Be/+IX84he/2KzfOuckxTflhmEYhmEYhmEYhmEYRs7I6YukCJ8kctv4iLOlqbD5yW3Ml22uHZ+6JpiyHtfn7xGS5MGwjUwY4sJwpTh4PMP24sL+GLLCumXIDUPTmIKW5/NCdFAXDCNkebcUfl6ONOZMi+6F4i1brY9HOFnvTjq8KX8Bjs+sH6bXrarURW1q1teu1GnBw0rdr4JWfEqPNNqpNfrT9bjwpzg+nX+62k6M1fcuDI/iOEBfCNboUDhvFLMv8x8IGFbBvoXyJNbpMMayZoSk4vioC6ELCAfLa9C/Hz0Dn9pnwQvBjAsJxe9TH5+stpnCvPb5VWqb4T5hY7PeX1OlL1COkByGS7VlD8nxw4k57nBHnAfYlpgXGAIrSCWdLkYoHudVwDTjUaE+PtGD9kG4U9CaEVbB9Qbb6UJ9b8l2fe6+cj1OGMqmwuhE/JAOhCFGpQyf1vNQWIY05Dh/UF2pf8/ns53G6+JU6evl1+v1NGjDNkLF0+X6910jEd70dyRhYX3vkj2UrWQ5xjVCdBMtWP95v4UZ5WGommQPmfSI+0dP7A84p/LZuEyPW/a9sFiXL4HQNYaKcdtbcxjGiTneYVy6/Oz/VyJVre8vwrjtK9NtmYfItEQ36gt9LWpA+DXri88bZYPrd8cYPU6KMaXktzD0CnMG13eGCzPklHDOjFM0oK+ofisirgNzLOf4Wr0m9I3Uoeh5HegLvB8vlE3fX7C2WW+XIBwauA6GX2MN4BqSF6O02PAsa///0jByQk5fJF111VW5vJxhGIZhGIZhGIZhGIaxFbEXSYZhGIZhGIZhGIYx3IjW/xlqhkMZjGFFTmXbhmEYhmEYhmEYhmEYxoeXnH6RZBgkKCyUILGJGGrGltN1ghTyjv6OLSRqRcp4pLb2Ytnp5yjLiF1vasHJ8RqfLhWkmHXwS/BevbhxnM9LzUxHUR78GUxxu6XEOZIYpw/o5QlX69TUjJv3Us7GeXXow0JK3faP63TKRWt0XwtH6/TKme0ZpLAPdR3AS7P2EyPUdmGTLnuyQ7e9iD5/0Szthzim5CxdNPiuPC9PjFeGKdmZQp517/m24P+I6Ndi34SrxfUgdTRcMH2V8Hel9NhKrtRpxz2/B1N5cywW6vtLleF+M2Fdot95NR1kd3elC/UcxhTm0q3dJZ6rDJ4epnZOV+i66x6h9xc9q11g3vkTaMtC/XumvqY/RNAX6FwKkiP17wPOwXrTm1djPEZhAVLOt+r6XLe39oWUrhy8fsk/lumy5ut+k87T47awQzuOgi59733V+vh1H9fXHv0cvDYNek3x5ji62jr1vbvO7K6vsEiXJ4UU8kwZ3z1au1owqsXV63HI+y9ZifJ43jvMWwuX699HY/V+OKA89wrShEdwULkM711UrZ15IdxcQQfGYb129HAO9vopn1WKMed3wueEcZ8q0+My2YGU7HQkYY5MNOJ5guWlqyyFusTjguOzEcqbt1ZLjxafqHtLzb/07/Pa4PPC/dDl5ir02KHHx/Vh3snoG2VLUNdwjXnPXiDCvXrPGg4jA46fsFqXPaDLi23RjefePjwL1Vbq41dop6Jj2yyAA4rrI51E3n5sw4mULtPbiTY8xxfSt4ln00b9XM41JBxTq3++of7onDMMY5swpC+S+vr65LXXXpM5c+ZIY2O/aLG6ulp233132WeffSSPD7GGYRiGYRiGYRiG8RHARf1/hprhUAZjeDEkL5I6Ozvlmmuukdtvv12ampo2ekxVVZVceOGFcuWVV0ox/7XGMAzDMAzDMAzDMAzDyDk5dyQtXbpU9tprL/nZz34mjY2NEkXRRv80NjbKddddJ3vvvbcsX748/sSGYRiGYRiGYRiGYRjGNiWnXyT19fXJscceKwsWLBARkV133VXOOeccOfDAA2XUqFESRZGsWbNGXn75Zbn77rtl7ty5Mn/+fDn22GPl9ddfl+QHdOAYw4+or0+i9HpXATwCnl+kVH+Z5jmR6N8gdLkwrp/Q84PtCHH3LtMlUwC/U4+OS6cTyTt3jDNJGPNPHO6VTgEez3tl+bmfzgfuJ6j7RIV2UEQt2qFAh5HnYJo4Rh//3kq9f4J2Hkm+7isRXC50IrluOCdwvCQGazAq0XVFx07bDtqR1FmHU/XC4wLnQAQPzyaMYpuETqQILpKALjK2LRwJnpusgM4k+rrgUADd4yvVdsEa3Vfnn1mltqve1r+vWAh/B8d1lN0nFjU26/2rtVPir+33yKagC8Xx3HAg0eVF/0VeM+aJJPo95wF6X+iz0lUnwdzFartop/H6gJ0noXy6/H212ovTW6HHafGymHmGbUN/V0rXR6IV8yD9JTifoyutF76OYt2Xe6q1v6TqHV1/Qfdg+SJ4WFjWZA8dRdrBQ/JXwnm0O3xTXfr3EdpaulA3WA9dO+oa4zRCX3Joi2Sbvj5dYQXrUFdwEjECInx7odpOYKyEvdnrK8n6b9DulKgMYwP3Q+eiwzyXWb/0T7m8Cr2dyr7eRbiXgL5FeGT4fEDHj7ffuyAcSJ1wDNFdBs+Nw/pML51r0uuz4P48Dx7quneUXu8F1Vdx30u6vLx/kEbbJujXoleIa1rG80bQQL8V7qVMr98CL5y06rppOnZXtV39KnyPIFi5Tl+fHrxR2qkoUDalxmiHYqIFz5a8d5y/e48Jarvwjff08d6zKuahdVjj8LwR4PYdI0zg8YvwWB/C+RSWoG+w7Uv6zxc3n3wosbAyYxiS0y+S7rjjDnn77bfFOSdXXHGFzJkzRy677DI57LDDZJdddpGPfexjcthhh8k3v/lNeeONN+TKK68UEZG5c+fKHXfckcuiGoZhGIZhGIZhGIZhGCCnL5Iefvhhcc7J5z73Obnmmmsk4FcGGQRBID/84Q9l2rRpEkWRPPzwwzksqWEYhmEYhmEYhmEYhkFyGis2Z84cERE599xzN/s35513njz22GPy5ptvbqtiGUNJEPhhS+uJmPI9JoU8P90XpPeVUh2W4aXSZthJXKgcyQyzYGgWQk68MDvCz5F5LwxHYshIh/7+mZ+ee5/95zNFOsKZWpCCFeFOjp+SMy06y4f9ThAShBTy3u+X6zTlXspdfMrfPUaHReS16vqMDaFhaFxGaFuqXH+andeoP/1O9Oq6KX8PoWaMuGTTNCN8Cp+Ch006RMbDMawQ4yDm03dXjjBEhD1IGz51Zypp9lX0nd4KvV34rg6PKtxZ943//Oxf1Pa9yw/U5/uhTgec/54OHRCmcx6tU84Ha3Xa8mMnfH1wgyEgXdnDeeLGgUNdBG0II0BdRgzDQFsF1ZX69whLCD82SW0n1iE8CG3LcdO8s55jy5fq8gXNqFuEL3lhIZz7mUq7HH21A+O0QM9b6QodMtRdp7fX7Ie+V6nHZtVcfb7qtzL6NooaoeyFK5FGG3NIZjisiHhtO/rJZfr8bGuGATKMkmGWY0fp6zFcmqFduJ+eEbruCtbo3yeYppzzBkKCXMx6yhBcx/DqdoRxsj64xmLeiSoRGsfjM8Ym17uAYTLs11j/HDIOe88y7OdsC4bC4/hEN+6d4cpdaAv2RcKQWIbG834YZol5rXtPHS614nA8mwXZyxOlEF7G+ubxDF9G2Kc3T2aEdUY7jtXHMrS8A23F0O1iPU6qn9Xhw15f4RrCcYMQTm9OxDhJrkPYITQGMkKHvgmeFwrnr1Hb6R20FiDRoM8XFSNEtB113YyQXYR1Rnj+4LNViDmdYaRhHp710PddS/886Pgc8mEnkuER2jYcymAMK3L6RVJLS/8EM2bMmJgjBxk9un9Sa21tjTnSMAzDMAzDMAzDMAzD2Jbk9EVSdXW1iIgsXrw45shBFi1apH5rGIZhGIZhGIZhGIZhDA05fZG0zz77SBRFctNNN232b2666SZxzsnee++9DUtmGIZhGIZhGIZhGMMHFw2fP4aRSU4dSaeddpo8/fTT8txzz8m5554rN954o5SUlGz02I6ODrn44ovlueeeE+ecnHHGGbksqpEjXF5SXGJ9TDRTvtJLQG9OjDPJ8xIwdpzEXZ9+kyTj/jPeyyKu3YvhZ1w3nUrYT2eClzYbcfp0j3gp2nEvTHkbNTXrwxFnH63V3hmmWmVcvMA/5fkt6EqJSRMukt1TEK3Ucf8FJTquP1WutxPM5gznBdOMe+XL3AXPTV6HnuOK39P9MCzSbZ8q1duJHn3vHQftiLLh+ljo85vQNkjrHTTBk9OKFO7ou2l4dAI4DtyiFXobbZOq1vXRVQOH05469LnsYd3WP/z0v0s23OfhXKrXKe7zoZSqXKTnlWL6PzLnBfRj9vvkGO2lSa/RuY8To7SPyfNlYByES3Vdtn1+X7Vd+Zo+f9u/aT9UXrvuOwWrsoeIO/iuCpfpzlQVaV9WfgOcTpz3OM7hWvHm2HLtBwmL9byVgPssxLhu21H3rVWH6fLn1cDh1KP7VmGjvp7LbG66TvqQYr0Lc2gWr5qI7wpxy1bp/ehrwTgoAejfgkNIGpv1Np1BLA+2C5fDjUJ3TAHGfYA5n+4XrHFc0+jB8RxQ6Ev0k3lrIs8Hl0tWb18K66/nGIRTCM8OnueGjkOuxx1MsY7nA7Sd68ue3t7re/poCdrgdoOPix4+qanSx3fqvhbsoJ1IqSI9j5VRG8RHK7aV55ajoEy3RzrGE5iogOevbvB5hj5FzyfFZyWMk7BGu8ASnNPY9vnFatPB0edB1w+fgwnnKY6TmOPpIEqN123fV6aPz2/R4z6f46pVO5xCPD+0j0d9UC3Xg2cxdIVEt96f3NA+4aaf0QzD2Hrk9EXSGWecIbfccou8+OKLcs8998hTTz0lp5xyihx44IFSV1cnzjlZvXq1vPTSS/Lwww9LfX3/Q/Ihhxwip59+ei6LahiGYRiGYRiGYRhDh8m2jWFKTl8kOefkiSeekOOPP17+/ve/S319vdx8881y8803e8dG6/+FZerUqfL444/nspiGYRiGYRiGYRiGYRjGRsipI0lEpKqqSmbNmiU33nijTJkyRaIo2uifKVOmyK9//Wt54YUXpKqqKv7EhmEYhmEYhmEYhmEYxjYlp18kbSAIArnooovkoosuklWrVsmcOXOksbFRRPqzs+2+++4yevTooSiakWvCDHsbPT10HsScynMiFWivAP0mkkT3Z2w7PAWSxHvXQp4/43jGscc5kUrhCkNcO++ddUNHgudgyOL0ERFxdCz0amcDnUje7+FEcqjbEE4FOhgcHAieEwrnc3lwQHTHOCveW62286sqdPnK4ffo1fUV9MCRkXmu5Wt1WTp13RWgX4a1+tpBHz02+veuXvsr+vaZpLZbJ+u+FKAbl8A3UQxHktBbQ+A6oQvGee4ybGNcJxdo78+Injq1TU+QiG6bnW/T59/hhnfVdltKj8u5D0zR14cygo4pEq1rHPxvjMsE+lHUpdsu4LiGE4letpY9dXbSChxe+p4+f9cO+h9ZyuZoZ5LndCrTPoqoQo+jYLl2i3V/TLdN4TLtWHJdGKdoe8+VhnkpKtAumXQF5hG61TAHd43R9duyAyos1GMrtRo+j2a6V1BfGb6QVN3GfY4bCOAyS7brceZ52dr1uAgwDl0x2gpOIs/ZxznfG4fZXTAO5ac7TejNaYNvKgk3CxyG3prleW9ivD9cn3F8hPuNWtBXY5xMah+de/TcoGwhxr1gm24wbz0fpR2EskKvKWy75EI9h0qlnoe8eYbPB4TPQnz2QVsHo/W8EBbqvle0Ut9/8Yrs13cl6OtYM/7cfk/W35PPVF+gz8/66RzsC1Ep2hrjKuhEv6G/shl+JjzbhdjvcG8c51KC8hSi7/SibeEgigXjOMK8kejQfT0q18fntWGe6Il5FsV6HqFvsj7DPF3/XTXom3Qo9eq/KFy9/vycXz7kDBfR9XAogzG8GJIXSZmMHj3aXhoZhmEYhmEYhmEYhmF8CNi+XtkahmEYhmEYhmEYhmEY24wh/yLJ+IiTnyeSWP/pLlLsep+e8xPZPqQ1z8v+6b8HP//mp/upmE92mRI4MwyDIR5xZWOKVoYl4PNjhqJ58Pw8HuntvXT3CClhqJiXepq/ZxgBf49P9Xk3IVNHJ1AfTDWdZlr27Clv3UgdSsDU1kGz/pQ/XKVDftxOEwf+u+XIyWpfxVNz9G/rG/RvmWoZn/UzHW9mqmIRkTyExBSt23SIhohImNRt1Y1woOIFqGv0tbBNpwF3DPNgX4hLP4xxHKzR9SGluj5CpAdu2F1vT0SgSH6Accsoj5jt1v3HbXJ/x2h9cBIZ16ve0SEdyXUI/2lGuBDGQeXs5Xo/Qk5SY3Qa64J1DKnRdR2O0qFyksK4ZrhvrQ6VK3prpdpm6F40RqeuTo+qVNvJRfr3DAf2Qtli5rV0GY5HmvZShNAU1eu+WNCs75djqaBJj4XMMJdUMdNkIw14uW6rYi9Fu950DPViGCRCxRzajinevTmf52PdIgQ34DjlGsEU9TyfNy/E/DslQ9niQlF4PRwf1KCvIxQuwnodCeo/437Dboxb4NCPHeqK+705kaHXPdnXw9gw/3aUNyaktm+0DvVKZoTvbvR6DH/G80nQjnkB4V1Roe6r3WNK1XZe0Q76eGoEYtjxl/+f2h5xgg5nrnxLh5cFTYP15VqwvsU9W1E7wJBSzNkMA+S48zQEHIcVuq6kU5fXG2cMZ+aaw+dePJ+w7RMxygj2jbBv0xoAERH38ltquzTm+aHxlL3Udm+5Pr6vTG9HCaf+d7vBsrYZw5Rt8iLphz/84cB/f//739/o378fMs9lGIZhGIZhGIZhGIZh5JZt8iLp6quvFrf+X8cyX/5k/v37wV4kGYZhGIZhGIZhGIZhDB3bLLQt2sTnoZv6e8MwDMMwDMMwDMMw1mOhbcYwZZu8SArpXYj5e+MjTDotssFVEJM+WAoQpx/nZGD64A6koKe3gB4jOix4fqaoz/Qe0FFAhwGdA0y/y7IQ/p4vaPOzp3RnilumOw5a4elhXcPtEks6LhU0Uu563iCkjC3T3oAIHgAv7TppbNbHB5V6Px0QddoFk1l/lf9cp/ZxnvPupUZ7aPpG6Htp3FW3Bb07qbLsbZvXiev36O38FrhDkFaczqaAfctzleAr0wKMK7YFnAjRaqSsr9Ip4SMc3wnlxPN//rg+HpevbNXlLV6j27arVt9/iKGX3xZtcl8KWrW+EvgxupDyvQ99gb6MAIVHauT8dZjD+jCO4HkLmrRPg6meHeZE4s2Z6BtrDtH+rpp/YZ5jX4H7LLlWu0t6x+mxwb5e/LZOi55cpe8/rx0+sWXa/+F57crhH4EHKKoedFIlinTbJqAycWl97lSJLntfqW7bdAGcf6P0OEl063GbRFruoCu7i8RzHHFNiXGXeesxHUZ0rXBNiHOlFGT3CEUsXxy8Htd3wX6WN6N+guLyTe4TEe/ZIwr1Nr1y3hqAug3LdNsHiVp9/qUr9Da8ea5Jp4CP2HZwDvVW6r7ZfeTH1HbRaniA6ICas1CXt1I7l7wU9y16nBZ36vrqGaN/nyrR5f9M1fn6fKjP8mnasdSpq09KkcI+v2HweSHisxnmXM9vSeIcRav084Hn8uIagGejcKSeExN0ebbFOJPoIPLWc7Qt1+cQ56MPM4XyxxDAH+b5MOEuK1kFj12L7hucJ6P14z7i/GQYxjbBRpphGIZhGIZhGIZhGIaxWVjWNsMwDMMwDMMwDMMYZrio/89QMxzKYAwvcvpF0lFHHSWf/OQn5b333tvs36xcuXLgd4ZhGIZhGIZhGIZhGMbQkdMvkp577jlxzklHR0f8wevp6uoa+J2xHRJFAzHbEWPF6fHBT+n18RwJcS4XbiNWm46mqFeXz+Vr30jUNbjf0QfGayFOXOgq4e/hNmEcfFQE90kv/A/0ZeTDRxHjyeH90FvjChG3j/uNWrXDwdEJBZ+F17Z0KsGf4Vg/9GNwG33DrWtW2yGcS/QkuAyvQN8ELWTo2XUPfWnE8NOVkuzU91bzlu4LEV73N0zRZal6V7d1fpP2TyTa4FZpwfxL9xjGQQRflCtC29DXRScDacf97ThWbXeO196atnG6AtL0ElXp+gu69PEBhkKUQN/F0AjQ1XrLB4/P191CIuon8vRf9FXBkbS2Sf+A/ZzzRIyLrK9Wu1WSaOueWn3+okWNart9b133JYu0s8jz2GDc1f1pmdoOK7Xfqmf3iWq7YJWuwK6Jlbp8S3T95C/R1+udNELvX6SdSXnvLFfbEZxInFfoZilYBF9Hw2B9FNSj7egq4RyDOS9/rC57qkzXLT00HLcR3GPpMviw8jCHwp/l+bTYtr0xHjyuWakYdxpwnrslu8vFm3NZ3xwrXDPhZuMzZNis+3pQNyLzYH2umHHIfuXgKKRj0LsX6kMxzujVSZdgXunC+blesylX6WeZ3mq0LZqyaYqek2vepj9L11eaXp9GPRakCXUPT2DQi74AJ2LPzqPUdvGa7P7VZDv6doYHydH9VYznum64uuhQynpl3x8leLbis1uqIns/T9TrOY3PzY5OQpSXbrOI98Nx6DmWstc1/ZcumZd9P32emEcKZ7+T9XpulPZXpmpK11/HzC2GkQtspBmGYRiGYRiGYRjGcCMaRn+2MWvXrpUnn3xSvv/978uxxx4rtbW14pwT55xMnz59s85x9913D/wm7s/dd98de77Ozk75+c9/LgcccIBUV1dLaWmpTJkyRb75zW/K0qVLP9gNf8gZ9o6kDV8vFfKLB8MwDMMwDMMwDMMwPvTU1dXFH5RDFi5cKMcff7y8847+Om7evHkyb948ueOOO+SBBx6Q4447bohKOLQM+xdJTz/9tIiIjBs3bohLYmwLor6UROvTJnufvOLz76gHYQdxITT4RNZLOY+0r46f7vOTW1zf25/5OTo/s0eYnBcGwLAIflrP0DR+fsxQNp4fn+Y7ft7MULcSXfeuBaFpDGciDBNk6FpFWdb9XmhcMdoG9evSuF/WF9uD6ZuZrjgmrCJqbB747+59xqh9ZS/rcJ/0WJ2qOXL6XEuP0/ee16zrdtwMXZd5iExLdiJ0rgJhjghBiQr1p/aJdQg7YEgpXuLzfhZ/TrdlHrIRj3+qWZ+vQYdXBU36ft1YHcaQRGRBkY5mkrw2pNJGlGO7bh7pK9Ftm9eR/Z/YSpdvOo15ohvhrxhXiS497iKEdLgKpBnnHISwwwjjJH+ZDreKCvXxeUiVvOGz/4HfIwyyrxppyjGPJDoR8so5ln0HYRNppOEuWtigr8d5Etv5y2PCyzAnO8xDYZ0OuSlYuCb79TPClyOEQnH94PoSIOQiWKavlc/QLs6BTBE/olJffqK+90Qv0mondVsUrdEDKczTfa8AYYIeXO9iQsMZVhhxDub2podZ/+VYXwh38tYcpnXHmhUwBChzbFYhHClEv2BoNtrKC8UmXJ/mL9HbY0frbdyLN2cj/JjbDn0rwfChLl136TIdqlf1Nib1HfA8jtDwxIp6vZ91jXHLNaqgHqH+CLtkCG9eoe4bbeN1Wxc26LFRkLnmMKSTz6Ecl9AAhKP1eih4FgkaMG+ksoecsi4c+x5g6Ln3nIpnRY5LL1QtgQU0zP4czdA0agtYf17oHcoTctwCXr99d60WSBX2329PZ4/Ia1lPZXwIGD9+vEyZMkWeeeaZ932OP//5zzJmzJhN7s/2fqG9vV1OOOGEgZdIF1xwgZx66qlSVFQkM2bMkJ/+9KfS0tIiJ598ssyePVv23HPP913ODyvb9EXSueeeu9G/v/LKK6WysjLrb3t6emThwoXyyiuviHNODj/88G1QQsMwDMMwDMMwDMMYpnxEMqZ9//vfl/3331/2339/qaurkyVLlsgOO+zwvs+3yy67yKRJk97Xb6+//nqZN2+eiIj87Gc/k8suu2xg39SpU+XII4+Uww47TDo7O+XrX/+6PPvss++7nB9WtumLpA0xiplEUSSPP/74Zv0+Wv+vJtXV1fKd73xnq5fPMAzDMAzDMAzDMIyh5Qc/+MFQF0FERPr6+uRXv/qViIhMmTJFLr30Uu+YqVOnynnnnSe33nqrzJgxQ/7xj3/Ivvvum+uiDinb9EXShAkT1Iuk9957T5xzMnr0aMnLEhrjnJPCwkIZPXq0HHzwwfKVr3wl62dphmEYhmEYhmEYhmEYH4TnnntOmpubRUTk7LPPloDh1euZPn263HrrrSIi8vvf/95eJG1NlixZorY3NMIzzzwju+2227a8tPEhweXniVsfk+2lhKcjAg6EuBTyjH2nY8FLGR/ncPBS+qI8Gdf3UrLS5QEHj+dMYNx7TGplL6U5lUxIF0y/RZivyxN0wxtAdwu9Aqx7L4Us7pcpd3m/XmpnxOGn6NfQvw8rtMslaINoB3h9CeULx2nfiXtv1cB/l72iU46z3wZd2gFQ8Jd5anvxHx/IWja5Um8eOu16tZ0qgf+iG2nEW3RfjIp0XXbtpB0DRW+t1MfXaF9Ib6XuC5/69D/U9rIO7aFpfGeS2i6HT4OOhpJXlqjt4hd1/XXvvxN+D7/GrLl6N8Yp0x3z+kxXnOnLcvlIDc05huOc6efpdYGbi+MsyuO40pt0h6VLMU5BolX3hQAOplR59jTgTCvePVr7LnoqUF60TdXLcBxxnBfq+g1rtH8rhJ8kWQ9XDOch1G+wHB4gPhhyTegcdLUwxbtgO12ryxq8p51IET11Mc6hvgl6XCY64LMqwRwe8zTXMU73zbLF2kOTrqvUxYG7JURfSbTpviRN8IFxbBTovuW5Z+g83MRD+8D5G5uy7qeLJaITka6WzH/cpCuL/ZROJK5nhE7CLoxD+LSkQ7dN2NKqL9eF9QzjjPNMepSek7kmuHa0Zbnu2+wLHkhpL/SJwWsTVuqxkly8SvQBWN8r9RrEeSjZofteYRPWRLjqMvGdQajLSu0Si0oxD8CJxDk7qta/d1z/8OyTxPXTCxbrbQHse2lKFOEs4rwTB54tA/6enjw+G3IcYyy1HarX8/w2XR/5ry7QP8f1Wifg/yesL15f2/aVlNxF/X+GmuFQhlzxwgsvDPx3Nr3OfvvtJyUlJdLR0SGzZs3KRdGGFTkdaYcddpgcdthhUlJSEn+wYRiGYRiGYRiGYRjGFjJ9+nSpq6uT/Px8qa2tlYMOOkiuvPJKWbFiRdbfvf322wP/veuuu27yuGQyKZMnT/Z+81Ehp1nbnnvuuVxezjAMwzAMwzAMwzCMrcSqVatijxkOGddnzpw58N8NDQ3S0NAgL730kvziF7+QG264Qb70pS9t9HfLlvVnYC4pKYlNEDZ+/Hh54403pL6+Xnp6eqSgIPvX4dsTOX2RZBiGYRiGYRiGYRjGZhDJ8MjallGGAw44IP7wGC3HtmTHHXeUk046SaZOnSrjx48XEZFFixbJo48+Ko888oh0d3fLl7/8ZXHOyYUXXuj9vq2tP3S+tLTU20cyI63a29vtRdK2YtmyZXL22WeLc07uvffeWIH2ihUr5KyzzhIRkf+fvTcP06Mq8/efqn5737OSkEBYAw4gCkFAxpgBZUSGzQ0HHeCr4jKizgj4dRwRRf0pOuPChSK4oIAoizoy7simEL8BRVBZw5p09qT3vfut3x9JOu9zn3SdbpJ0d+BzX1eui6LqPXXqrJXKee5zww032KxZs3KvF7shZWWh12ILGfwWCf0hdBSx48JXwh0EDcL3rMd7CUhCQXzg1dmWfgIngtGN0sk4dl8Gw3OnueOytW2j3svMwjh0xv0zTh6OobQXZdkfcxZhcojEwQdEyjrwFMCPFbhMkJ90XVvu9VlnF34PJwOeJ21Z789XlbQtOgFQ1oPN3qnw24GIEwm8Jn2TO67bD9ugwv1BX0UGX0Uybw93XP0I/lUJZbVqifdrHHqmdxDtWdnmjg+o9h6aq8+a7o7rf+29NoFXCHX/yw1XueNj3/Jf7ri/wdfVnV3ftZ3Jayv+edvBcMQVQidSg3eBBP2CYxbHPKYPh9Cmw33d1D/r3SkZxqG0z7fNjYd7f0fjk76fDVf5can1IO8qqezw/aZ6HZxEdJnRewd3W0aPHdwsZf3e5TLcjLaO9Muq0e9R/mk7+g7cNZxDHPS0rPP9jG6UrODH9MB7h/Sygi+Lrn19en3N8NBl/rjfN43Am1fR6Z8to2qlwv+PQrdPoArut5TzIXxigQOJcyR3+O2nawXn6UxE28na4c9i3+WcUnrMd5LI/BDzOXF+SuBIDLw7PMYYTg9NOg2VDcpW+PmruIdviynGrcKzcIkhv8Xpvi0O7NXkjivZtuF0SvB8w3vP9ueH8//ymQz495Py9b4fpwPwBNLnVeL5ofss6468m6BtJHAcpf1496Jvku2UDqVe1G0Nxji+9/I83WSBiwz3p6cP8H5GhyDHSLjg6IbLWtvccddc/z5T1u/zO+th9AVQQNWmHZvbTto9Fb66iMni9NNPH/neUMqiRYvsLW95i/3v//6vnXHGGTY4OGj/9m//ZqeccortsYd/N+7r2zxuVXAu2w6lH456+R7xAmdCHUk33XST3XnnnTY4ODimXdj23HNPGxoasjvvvNNuvPHGCcihEEIIIYQQQggx+WyVbU+FP1tZtmyZrVixIvfPZNHY2BguHijh5JNPtk984hNmZtbT02Pf+ta3gmuqtvyD8QD/sXg79Jd8cK2uzv/4+UJjQj8k/e///q8lSWKnn376mH9zxhlnWJZl9tOf/nQX5kwIIYQQQgghhBB5zJkzx+bNm5f7Zyrzrne9a+RjU6lHaSv19ZtXlHd1dQXnSHf3tlWRYwmFeyExoR+SnnnmGTMze/nLXz7m3xx++OFmZvb000/nXyiEEEIIIYQQQggxCrNmzbIZM2aYmW13B7etH8K6u7utra0tN62tq69mzpz5ovIjmU2wI2mr4T1mPy9l67WrVq3aBTkSk06hbJtTppweHzRPegjoPGAseMSrk8EtkySR76oxaVypI6LOx61bD2JmmRbyXram1Z+HY8CGvQMgYXqI20+41LISce50CAS+C/ikWHaMo+eSUuQvY93RuRRx0fB5s8DBUOuODd6DhM9HXwfLE34uK/EqDDfAxQUvTP903+6Oeat3/Cy94cOWC9plcWX+ThnF/XzYcIFl257/rytZjX/WmnX+eR676mB3fM/RB7rjaXt6n0f3JvSFveb4/D7llz//qvt7ufm794eR8trJJKVuFo4RGbw2cKkEC6s5JsHpE7hSMOaVrdrgjuvqfduqWIlxA3VfbPb9gk6kIrw4KVwls/7g06f/q7zVjyN0mXDc6t7b/8td1Xqfn/J1eB56c+bTrYLy70J+DNDbQw9eqTsGYyDnKz4bx/Rh1FX3PD9u1Kz2z0ZHUYK6bHzGj5F0GHXP8fkdrPPPuu5lPv/1z/r0Kzr98wzW+eeppjeI7pjmJn8eLpbiJl+36Vzvp6DzqNje4c+zroI5BW2B7ji6cEryn7Cu6Tjkb2MOJeQtENCuXGN5pJFQieE13mlUtuceo1y5GfbLgb28M2mg0T9/RTscSp2+rVasg2uMBB4dXx5BCAr1WZhTswo4m2rx/sT3GbTNYuW2GyRl3mOXYEy2Lrw74Dh4N+C7BMb8DO9m1pS/eiF4V4qQst8B9ju6RYfZz1B22ZBvC1kX6pJjKsgG/PV7fO8h//tG+LcOwPtChc8PPX1bVXHD/S8wR9IUlG2/EMiTgb/kJS+xW265xczMHn30UTv66KO3e93Q0JA9+eSTZmZ28MEHb/eaFzITuiJpq9V848aNY/7N1mvHIrsSQgghhBBCCCGE2B7r1q0b+cawPW/zcccdN/Lf2wt928r9998/Etr2yle+cifncuozoR+SFixYYGZmd95555h/c8cdd5iZ2V577bULciSEEEIIIYQQQogXA1ddddXIiqTFixcH51/96ldbY+PmnWq/+93vjrp66Zprrhn57/E4oF8oTGho2wknnGB/+tOf7IorrrD3vve9NmfOnNzrW1pa7IorrrAkSeyEE06YoFyKCaW8fNt2ogwNKMcSWWwjGhAJPWM4VhBOhRXNQdgDltwG4WalWz7S8s+8YZk9l/0HcKk8783l1AzdKmJ5dG9kuTRDcJB/bpkbzT/LGgRhB9g+MwhjYBgFQ9m4lTbzxxCYMqx4RChDhm3Ek75t6ZWt2uR/ixDKmhaECz2c73v7n6cOd8e9p73NHdf+7AGfN5RtWRfqjmGHXCrPukXd19dh6T6qsmueL/tk2XR3fOAjCAVowdbSU52SvhRsMc4xiWELKNsNJ/swwBl3+LC+wRm+3TGcqYD0Ktb5Lc6Ddtrpyz7t9ONGiuv7m30/K+vDmIy2U/7gU/5+05p8ftAXGA5Wt9yHQZIM4wK3mmYIS8b6YXls8mEbQQguw1pKJwXcOyvzv2VYHes+RUhHwyM+L4PTsCV6lX+WoWp/XP806rbPj3HVy0cP3TIzaztipuVRxPybsClgXCgMIESnE/Mt2k6wbXgnQm5RN0GIDbc5j3kphnB/Xl87evgYQ06D0DeGrmNcSBj+xPmc8/UQt1D3dcuySDl/bmrzx9yiHukVyn16DF0L3l84ZzCElaF+rPtgFyS8DwGGsg3V+X5d6EBoO/pasWWlOy4N/8qmN/p71WPMYTgt647tmO9iaHcuXNbMkh7MzxgjU7zbMIw/6Ad8t2Q/QltjKFtKxQTCKvluxvcPkjb58i22Y85C/otr1vrjg/1KkXDc9fer3rS5fNO+yN8XdjcU2jZmnnnmGWttbbWXvexlo17zv//7v3bppZea2ebd2c4999zgmoqKCvvABz5gl156qT3yyCP2xS9+0S688EJ3zdKlS0d2fFu8eLEtWrRoJz7J7sGEfkh673vfa1/60pesra3Njj/+ePvBD35ghx122HavffDBB+3MM8+0trY2Ky8vt/e9730TmVUhhBBCCCGEEEJMAL///e9t+fLlI8cbNmxzQy5fvtytADIzO+ecc9zxM888Y0uWLLFjjjnG/umf/skOP/xwmzVrlmVZZk899ZTdfPPNdvPNN4+sMPriF79oe+6553bzcuGFF9oPf/hDe/zxx+2iiy6y5cuX25lnnmnV1dV2xx132Gc/+1kbGhqy6upq+/KXv7xTnn93Y0I/JO299972mc98xi666CJ77LHH7OUvf7ktXrzYXvWqV9mcOXMsSRJbtWqV3X333XbXXXdZlmWWJIl96lOfsv32228isyqEEEIIIYQQQogJ4Jvf/KZ997vf3e65e+65x+655x73//ghaStLly61pUuXjnqfmpoa+9KXvmTnnXfeqNfU19fbz372MzvppJPsiSeesKuuusquuuoqd01DQ4Ndf/31I7vMv9iY0A9JZmYXXHCB9fb22ic/+UkrFot25513bteZlGWZpWlqn/zkJ+0jH/nIRGdTCCGEEEIIIYSYNBLbzq6jk8BUyEOMI444wq677jpbunSp3X///bZ69WrbsGGDDQ0NWXNzs/3d3/2dHX/88fbOd77TZs2aFU1v//33twceeMCuuOIKu+mmm2z58uU2MDBg8+fPt5NOOsk++MEP2t577z0BTzY1SbK8ve92IQ888IBddtll9qtf/cra2trcuebmZjvppJPsggsusJe+9KWTkT2xC1m5cqXNnz/fzMxePe+dVlXYsv1qsH0yYs1jjiRusUvPD0jg72DsOWPV6VTI6HQo3T54DganPsTZI8482F6YeUccecY4ezgTrNFvacu4/mIt4/r99JCua/Pn6YQgyE/gTKIzgXH1rIs+1AWdRnRKwEERbO1M3xXuH5QfmdHsj7tL6o/PSi8M2s0QPDjd87yDgNvbv27Ov/rfr/NbwAdOg4X7+vu3eOdA8OxsK9whE/1wuAGemna05Vbvvek81uen0Ovrsm1/37Zm/tG7VYpVvm5+e/tHLY+j3/bf/veo2t7p+VtNl6Nbz3hw2/9IsG11uslfXFy33h3T7UXXSTrNt6uh+TPcMX1XG47010972JdV2SZ/nG30/q7hg/zLznClL5zBBjiMHvZtLav05zO4VdJebPFe7Z+fjqasyp9POuDVoRuN4yL6WrEJPhHmrwfjaAH+sla0vcZt6RVrfF4KT632adGlsifmAAyBbHdZpU+/d65/lrJ+n0Daj7Y46M8XWv0YWKzxZcX0EwzJFZuwbTnKqtCOMRokbd6FEoz59OTQQUjP3Ua/bTnH9CTJ3zsmgysucCSVjoOcD3AvlgWdPMF8R+jZ4fV0LtHlxWMCh2G23u+UTJeZ0R9J6Aerz/fwkGQjXGh0ydFJiHGhZ98md1z7iB+XDJ6fjE4m1meJt4djZFKFZ8W7BP2QxVnTfNp4l7K/PWm5ZPltped13vNS+5Qvy7ZD/ZywHlqYQo/PTzVeB6Y95vtFxTq0hWda3OEv27/tjl9b8c9Bnh14PjqVkiP+zh0PNWJM57vpMDyBq/w4k2wZJ/qGu+zODdeamdmKFSts3rx5+fmcgpT+Xemgd15s5fVNk5shMxvsbLNHv/kpM9t9y1XsXCZ8RdJWXvayl9kNN9xgWZbZ008/PRIDOWPGDNtnn30siU2UQgghhBBCCCGEEGJCmbQPSVtJksT23Xdf23fffeMXCyGEEEIIIYQQLwa0a5uYouSvBRZCCCGEEEIIIYQQYguTtiKpo6PDbr75Zlu6dKmtWbPGenp67Nvf/rYTVq1atcra2tqsqqpKK5ZeqGTZ5j9moSeAjiTG1VdEvDoIj0xqEdcPv0YQTlnnHRKBnwOUenuyld6fkcAxEHV/sCyoMps32x8PRZwMXXAGwZE01OB9EWmFd7UU1nsHQQAcRUF+4YbJhn1dJvCLBP4Klh99Vmwb9GX14370ABXgUGD+2+BgKPEmJCW+hc3/w7ejYoN3KhTgsWmEa+XEqrMsDzqRSLLOOx8yusdQNoHfimVLP1Wlb3v9c/3zV3b656t/1OdnqMn3w9l/8D6QdLX3eTC/dDIk8Jc075Ufs5/RKcW+xvIqJeIuS+k6I7wXvC307nAMmnkXziM/RXhk1rzr5e64e75v1w3LfdmVDfjzHf/o67oAdcu0h/24MlTv+23fDN/PGv7i8zvU7NtCGVwm6YY2f8N6OJDoz4CzKnDLzWjy13fggTCOpHTb5UDnnqEf0ouTzZ7ujofhYKp9eJ2/vs6PIy0neDfKzAd82abwU3FcGqry+altyffyJJhjkvVwFmF+LU73zqPAjwWvTcIxF+WVcD6m945jOvsq+loGd0tScj86eorwVyUcQ+k8Ylmx3/Ndg+86bCt0GEUcTEE7n+nbWgbPXjAn0IkIL1DSBxca5jjOnwnLB+ezGj9uDNf64+rb/uJ/PwfvPxHnIX2ZWd229JN1viyLbd5BFOSd/YLtFr6sYsTXFDiD0K5rnoEDqNv3o9oW/2wD9f5dqRMOYLrQylv5LuXLg04k8uuB7+eeD+ZrvGqVdfpxp2yTf96h2f79IkXbSzow5gshJpRJ+ZB0xRVX2Mc+9jHr7Nw8YGRZZkmSWHe3nyzvuusuO+uss6yqqspWrlxp06ZN215yQgghhBBCCCHEC4ss/Ag4KUyFPIgpxYSHtl1yySX2gQ98wDo6OqyiosKOOOKIUa99y1veYnPmzLH+/n675ZZbJjCXQgghhBBCCCGEEIJM6IqkBx54wC699FIzM3vb295ml19+uTU2NlrKpaNbSNPU3vSmN9lXv/pV+81vfmPvete7JjK7YiIYLprZlqW/DNnh0nQuXWdoGrcTZogKl5NzOTe3G25tw3ksQeYWu6Vb9gbbB+cvjw6WqvN63ovpD+f/M0HCbcdbfVhBGbfJZkhHZNvtIBSMz4dQvtIwQDOL1kUYUsRQOfye4Uv+bHB/bvGb4f5cjp5MLwkrQShYxvAY3Nt4b4b1VSOMgGGDDHvAVtHBNuRBGIV/Fi7lT5ubfHq9ful4utpvcV+5Hs/T6a9naECKMJEAhqx2oK3OnunPo+7YFouNNbnnbQBhj9ymfLDkPNt9JKRzeEa9Oy5r8WF7nUf6MLy6pxBCye3pu9EP2M6b/f3mfNeHhAwfup+//94+DIJhEu37+/NlbFpDvt+Vd/gLyjehX1WgH2HcYgjQ0PyZuedZHsXp/vmD0DWG1FSjPjHmZ7Oatv03x2S2BYaEcov1VoTMtPjQtfI23+/7D/DhO8mgf/bZ9yEkFOG7DMdiKFjdCl83aRfGsSr/fH17IHStcq4/Rlst70BbRfhWEO4dmzNZ3gxn7vAhMRxng3Bp9t2ScTIoO5AMInSa80OK+WsIYwq3fOd8h1DrIGySZYVQtKQf82MlygJhghnGjQRtyRCuzLrMyn1Ib7Ec41Y7ypPzdSxcjHCOa/JhlOyLQXjaU9u2tOd7XFLuy6K4AaHifC9EODHfRRiqxvN9r3uZO04RXlz9wDP+92j3FXg3mtnmQ17rWnzdZHwhiYzB//h3H3PHv/zbZ2w88N2JFOv8OMAxuwzHSSfme/bjui3j1GBihghOIcTOZ0JXJF1++eWWZZkdc8wx9r3vfc8aGxujvznmmGPMzOwvf/lL5EohhBBCCCGEEOIFQjaF/ghRwoR+SLrrrrssSRJ7//vfP+bfLFiwwMzMWlpa8i8UQgghhBBCCCGEELuUCf2QtHr15h1nFi5cOObfVG5Zjtzfn79jlhBCCCGEEEIIIYTYtUyoI6miosL6+/ttEL6WPLZ+fGpqatpFuRKTyvCwjTiSGPfOLWjpERhHO9pueoG3CHH13KbV4EWAm6bUyRD4H3hv/pbw9yBwGNHrwu2E6RBAXD23XA0cDHQ0EG4hT4cCPQRwqwTeGmwNHZynE6KYH4efwZeV8cM00xuEI4JulVIvAtsJtxyn34nuLjgUAn8UCPJOB1Lwg4i/CsfcQj7webDf0EdBlxmej36uoO2yn+N84EQC3LqbniFuYx7bijur2tZ2hxrhc+gbxjF8V/AvcTv6+vtX+vNoh9kabAEPH8TgYfv4n69s8+nh+rJHn3XHdWUL3PFQtW/Lle3wTaFpds/z3pzKdl/3hU7/vEU4koZq/fOmvf76QhscS2W+robr/biSwl1TrPPPz/qgC4bjRLphm9eo51DvBKpEXsqeWePTYj+Z7bdgt5a17nBwAXxQ8OoMNPmyqmj3ee/ay3tvatb4frniBF9XA83+Wff/oc8vPTcVrb5u+mb4uu6d7uu2bpUvnyr4rLI9/fOmrfkeHnqDgvcFOKnYVpJ13nUTpF8yTibw2GRwCCbcgnwQ7RTzXRHb29MBlHTke96sBp63mGcO0OGUzfYenaQX8zfHYIL5MemDFzDD/arps0T59WBOq/IDTbq3d8l1vMTv4Jwgu5WbfNuoeNL3taR+W/mxX1evxLsQHEmx90Iexsgw/QzVoF024t0Nc4IVUBcrVrvj2pVIrx4+q0Y/bvTt1eSOqx73ZTdeftV7rTt+bfmZ7rjtJd5JVf+sb9uFbrSV2Py9tW3xHfOFgMLKxBRkQlckzZu3eTL429/+Nubf/PrXvzYzs/3333+X5EkIIYQQQgghhBBCjI0J/ZD0D//wD5ZlmX3nO98Z0/VPPfWUfetb37IkSew1r3nNLs6dEEIIIYQQQgghhMhjQj8kvf/977dCoWD33HOPXXLJJbnX3n///fba177Wurq6rLKy0t797ndPTCaFEEIIIYQQQohJJsmmzh8hSplQR9KBBx5oH//4x+0Tn/iEXXrppfaLX/zC3vCGN4yc/+Uvf2m33nqr/frXv7Y777zTzMySJLHPfe5zNmfOnInMqpgoKsrNClvi54fgDYKnwOheoSNhe2nnpQe3SwDcLIyNN/hEXKx2zFvDZ6GDaCjf0WPdkbKin4M+CNw/q/RDQTKM+9HpxOejU4HeHDgRAs8P3CTJrBn+fFu7P670zgljenU+7p/5T/j7Ou+gyNrhSaAzo/R5WPaBrwqujlrvzwigz4mOJZCg7WToFxldYmhbSbXPT8Z+CJifBB6eDOln6ze6Y/ouAmcU2065b5sZ/RrwkdAHYkg+cNdUYBpM/Q+K1dvGEfaLwSb/7BWt/ny6vs2nTfcZXCMb/t7PczPuwDiQ+mcbaIRPotWnP7Rnk8/fCu/7KH/4Of/7PWe549XHepfK3HvgPCr4tj1QX8B5X9bl7XQm+d/3zPf+jppnuyyPDB6fvmbfF9JBXx+V6+Dh6fa+rqS5yZ8vGTdrlsOxw7zMhHcGHp3u/fyYVlnr2075Rp+34QaOE75s1x7p63r2/b7fFDp8P93nJnh8noafi3fb07fF3n388608wZf93Lt8WQ9V+/P0WZV1ReZfjnuxORVk8AgV9/HPk/bg/aGkbyeD+Y6kIC90HtGrU453Efqe6MUrw/Wx+Z/zO+43NMv3q/KHnvb5m+7rttjo58NkAHMMyiPDGJrBizewh/fgVNJz0+/7yjAcSQNNje640I/yHsx3OhVnNbnjUh9XzZ+8Ny4oa84XqKugVbJtcH6DRKnmN3/xP+e7CeomafZlwX7C99QinIIp34vLfNvomeXPrzh+vju+9omj3fHbD/iD5bHfDz/jjms+8AqfH0xxNetQvnwPH8B5+LW2vm9kRYz1QohdwoR+SDIz+/jHP26Dg4P22c9+1u677z67//77Ldky8F544YUj12VZZkmS2MUXX2wf+MAHJjqbQgghhBBCCCHE5JHZ1JBtT4U8iCnFhIa2beVTn/qU/eEPf7AzzjjDqqurLcsy96e8vNxe97rX2e9+9zv7xCc+MRlZFEIIIYQQQgghhBBgwlckbeXII4+0m2++2YaGhuzhhx+2devW2fDwsE2fPt3+7u/+zqqrI+Ef4oXBcNFG3S+VISoMIeJ5hoONti3oKOkxZIYkhvshpMfdn8uhuUyf7ZvhUFzqHoRPIe8MB2LZpJHl1uV+eXMAn5VhgVyqP4xjLNdOWDf8fReWJfP6Xizt53L0DoSmoT4ybGmfcDtjbvGLtpWWbDVdRNhdOtNv853xWVjXqJuEdQ+Yd9Y9nzUIq+B5hgEiTJGha2EYBkPJ8PtGH9ZgHfnhSkE/rvX5S+rz+3XS7tNPuRV3gw8JKmKpfzKAvlVSPwy5KOvjdvIYQ1j2BO12xq99yEkQAgrqHvXpM8SkfJ0vi6zOl8XwbB8mkeLZ593Rl3u+ay+fHrfhrv7LBnc8NNNvZZ32+x9UrkUYRhf6Odsu2kr5OoQl1vm6DrYZj4VL5Z3HuaFmHw40WOfrova+Z9xxELI6fw932Dvb5711oe9nh536iD//Kx+WGOQdZZcijO+xy2b769f5+1du9Onte4tvG0M1Pn+VG/35snbUJbfnxrhmDMmNjENBuBXn4MiO9klpX+R29ghpzarQ7+owvw36MSt7aoVPb4bfvj6oKx6zLDguRMKpKh5r8fnBuJMhFC/FfDw4078fJEP+9wNNPvSta54vn7rVvjyLNQhBRf7L+n3bSAf885R14/kRKpcgnCt839mWvyLeFYLQMs4PCdpVLcIA+S6DsPoi5z++D4DhZpQ93gXTlvX+B5F+HoRBom2XIWwwHfR1M70Qmb9BbY2vq+FyX14MbcvQlMt60PapUUB5bw39S4ZSs/FlVQjxPJi0D0kjGSgU7LDDDpvsbAghhBBCCCGEEFOGqSK6ngp5EFOLSQltE0IIIYQQQgghhBC7H/qQJIQQQgghhBBCCCHGxC4JbfvUpz418t8XX3zxdv//86FQKFhjY6MdeOCBdtxxx8mj9EJgaMgsG9r+OcZCE/pHsKV94FRAHD63JQ+8QoBumsDTU+LZSapwbaN3gwTOpEheY3H0CbcVj7k/uGUstpAPyoqeAHp56LvgFrWd8ARhS/egLrHNeeDPoM+KXh1CLwDLk84IOii4ZW5J+bDsAycS/U30XSHtbADbUqOskircb5DOhUi/4bOwLbAfxZxITK8T+WHd0FcROBtwnunFrqczgfnN0Ne4dTe2/i5btc3zU+CzsJ3Df8G2kOzpPThDs/y4UHhylb8+2DYcdcF+R4cQ3F/0dRS83suyeu+vKCvDNt7Nvu1VbfTpV/6/x9xx77EH+fN3+q2uqxbu4++P+wVtB32NPivjVtcbWnN/H8wZcM9Zye1bF3kHUe1q308H633dFPEsGT03ZMUad1gP78uGQ2e64+f+a6E7buinK8Xfn16abLbfVry6xj/PtDt822v5B9+2emf59Br/4udT+rpaXz7D5/dJ3zfSToxDbLtBP/eH7MdlrRg3OKfV0HFYUj/0vPXC84J+W6yEm2xNh//9HPinYmMiPXit6KggmeXLlo7AjGXJOYLzH8aRsl7/+6E6X/cDjX6Mrejy6dc86tvmEN1sXb58q7+w1h23fmFvd7z+BO+gmv5X32/rnnCHlqzxbdPqS37/kv3cqZ45fgysXQZvHeoua23z98KYwvk8qYWzLzJnpGh7geuLYxrGeKvzz1Os9e1+uNbnt7LdP9+sP/m6fe+cf3HHZf/z3z79Cvispvv7NW+AgwlNs9Dn65LzMaEbLSluuV+a7xfc7dCubWKKsks+JF1yySUjMt3SD0ml/39Hqa2ttUsvvdQ++MEP7pT0hBBCCCGEEEIIIUQ+uyy0LcuyYGeI0v+/o3+6urrs3//93+0HP/jBrnoEIYQQQgghhBBCCFHCLvmQVCwWR/6M9v+fz5/h4WFrbW2122+/3RYuXGhZltnll1++Kx5BCCGEEEIIIYSYNLbu2jYV/ghRyi4JbdtVJElijY2N9upXv9o+85nP2Bvf+Eb761//OtnZEjtANjBoWbolhhxhjwkcD1HvD6+nhwh+kaSx3p+nS6Ufse1015DS9Lkaj3HrdCTQWRA4knCenh3mvdbHxQfX01FE51El/BGE+WPZ0OnA/NFxFLhfUD4sT5RHxudj+pG2k/H5I/nNnUsjeSUJXG/ZoPdbBP4Mlk1KZ1C+U4B+qCyFDyRBejH/VOIdC8V53iWTtntvTbZugzsO+v3M6f48HEkZ+iWdSUkl3Hl43rIW+DJqcD3bgvsx2gXdX3SJwV1SrPbnC2t9P8mme2/N4Kw6n3xLm7++yqc31Oz7ffmKTT5/kXad9Pu6LmvzZV9e5p+HTqPsgL3ccdVzGAciTqTBJl8XBXh2ytp8W7I2pM9xhP4u1u0MX95ZpS+/pHdbW2u++zn/W/TzCoy5WQXcHdOn+d8j78X53qOToV/M/41va32z4UYB/XO9h+apc/z5+kY/JzVeB18XvDh7/B7enAE/bvTsB+fSCj+ONf/J9/tiFfxUaAvFmT69Ifi5Klp8+bHtBvM/fGPJRrSd0nGOYyooW+PdW2UYk4p7+H6SDPi8JfC4BXmNeOvo3ck2wgVGOIZzzMV8PzDTO4h4fVmfT6/uWT/nZOUoP7xvrDvC95XGp/3ztX3N+8Dq1/l+P++2yLvgc6vdYZHj3B7b5pihWt8uBmt93vluwHeNX3V9Nz8v4MSqs9xxSsci0qdrjE4ga/C/L9uIMQ+euLTfzynJoB9zU7jUkiGfXsNffVupeI3v1313+rbfdLu/vrwL71J4by90oO134t2Zvkx6BLe+b8VckUKIncJuu2vb/vvvb2ZmXV1dkSuFEEIIIYQQQgghxM5gt1qRVMpLXvISe/rpp+MXCiGEEEIIIYQQuxvatU1MUSbtQ9Lw8LD95Cc/sdtuu83++te/2qZNm5fgT5s2zQ455BA74YQT7LTTTrOyUbZkLxQKtvfee2/3nBBCCCGEEEIIIYTY+UzKh6Rf/vKXdt5551lLS8vI/9u6w1uSJHbvvffaVVddZfPmzbOrrrrKTjzxxMnIppgIkmRb/D0dR3CbBIGY9PLwoyNjpHlMTxDvF3iK4JZBrLbzDNCxQG9OxMdkdCgwPbpAGGcPd0pC7QudDIFjKOKHinlz+Lyx69s6IsmhrKv88yYsn1E+QI/AuHqUP+8XUOotQjvJWLYgpZsLZRX4MPr7cZzvCAr9F3CR4H4ZfVLlaPfIT9CPkB6dSEG/oreGDqgu74JheTK/SS0cR4FTCs6JPt8Z2LaD+itpW0kV3GHsh2x3GGOSIZ+3odneY1PW5e9dsdL7LYZmeo9NWZd/lkIryr4bZTkIV8se3kXSu5fPT0W7L5vCc+t9etN8foZrUT6V8HsUfFsttHn/ReXj3m1ibLts++jHxdY2ywVtMcU4O3DIfFy/zeVS+TTGsB6UNTx4yaDP26bj5rrjtv29E6l2lU+u/jn0SziEynp9u+7dxzuFipW+rBse8HUxXO7rqmoj2t467zgqFnzbWPFan15FG5xOm3zdFdrgfoOLpYi+VYRnJ+1HP0bbCsbRAb4P0CsIF05p35vr6ybB/JR1QKmA+ZNOJEO7t0GMiV1oSxzTOcZxzIr4G5PIuMR3r/KNftwoVvu6CfrxeszfGMM5Ls79mW/b/ft4L155J11tvnyKHGf4robnS+u88ykrGdPpc6K3LWn0Y9wvn/uyjYfD3/8ld9z06sN8+nw1hJssGfIXFCt9WRYrkP/Ev18U2JYwPyYdmCPqfNkyP/3T/flPL/yZO77o/rP9/ft9/ivXsi79OJD2oC1znOX8zveprXVL16MQYpcw4T3t2muvtZNPPtlaWlosyzLLssz23ntvO/roo+0Vr3jFyCqjLMtsxYoV9vrXv96uv/76ic6mEEIIIYQQQggxeWRT6I8QJUzoh6Rnn33WzjvvPCsWi1ZTU2Of/vSnbc2aNfbUU0/Zvffea0uXLrWnnnrK1qxZY5/5zGesrq7OisWivetd77LnnnsufgMhhBBCCCGEEEIIscuY0NC2r3zlK9bf3291dXV299132+GHH77d62bOnGkf/ehH7aSTTrK///u/t+7ubvvKV75i//Vf/zWR2RUTDLegZUhPYliezfAphmcxJIchPdgyN+E24EEYC7ZzLuZsQctQLobAMBSrMrKd7SDCHBr8ct5iI7at5tJ6Ln1n6ByXuseW2keeJ+Ny5AhJtS/7rB2hBAynYogO6y4WjsX64RbyCPtgKKALJWC7YpgDQrky3DvhsnzmBduKB2XNMAKQoe0kkSXfQfgTQ9PYL7hdcacP+2BYRXB/hGEyDCIIJ2O4GMIcgzCHoG5xP5RnUodwrJLnYVhhEDLCMMFIXRcQVsB2HWy7jVA2bgXNfp/N8aFr3FKe+Svv8PlneFEZQs2SDW3ueM1r9nXHAz4ayubf5sO/hpp8vy1f47eSZujawH4+5CjFFvRpbBt0tIViR+coF24mKenbGcJ7kl5sS40xptjow2n6pvmyr2jzP69d6/tRFbY8Tzt93Q9N99t4bzzM36/xKd9WZz6AtoNwpuEq9Ev0i6pVvq3O+62/f+1ylD3a8sAC3xYZopOx2/ZhPgfDddj2HH0jGt7MENmmkhCmTvRLhgtzvuF8znBa3nqWL7sK3o/zPd6NgncfEIyZHMMJ84/pMu3tzz3PsE4+fzD/4n6Vz2zC7zEOIr2UofDBHJwfml46bg5VI7y1Hg0xEjYYYxjTE0PnijiuWEetgC+LFCGhySDeNza252eoDu8T5sfYIDwaZbvgf3zZfvH+t7nj2cM+v9XPtPn8rffjRAotQwDnLL67kq1thX8fEELsEiZ0RdKvf/1rS5LELrzwwlE/IpXy0pe+1C644ALLssx+9atf7foMCiGEEEIIIYQQU4Akmzp/hChlQj8kbQ1PO+GEE8b8m9e85jXut0IIIYQQQgghhBBicpjQD0nDW5YalsWWHJew9doil8YKIYQQQgghhBBCiAllQh1Je+65pz355JN277332lFHHTWm39x7771mZjZ37tzIlWK3JMtGYrCzXu5RD2LfH2POJBC4Zwh9JfQCcWvq0vzzwycdQ0ybeaXHJeJ7SnrhVOB2w4HXJn8L9+D6CIETia4YxrmDYl+bv5zeGuQn8NywvGNeA+aHjoXAf4LzJc9HBxKPg7piOwqcPcgb+kXg6YGvI3Ai0SEEEjqOkD6dTxYpm4T9gm2L/3zB61k+9JPQn0G/V9C3Imux+fxou+756Y+i/4relfH6H1jWbDut3umz6fi93fH0nz3uk9t/T3dcthIOIqRfaMO25myb8NbRzzXvhqf8/ed7L07bQu+G6dzLP/+84Xk+P2va3DF9XQn7fZb/D05JCv8Vfl/552f8+ZL66H7Vge5cRTs8Ny3Mq0+7eoPP+zCa/VCVL4v+mX5+Kq/weR9o9gnUrPdtZ6DRX9/wV1/3Pfs0I78+Pzbk/8fgHt7BVPvYRn/5DF8ehbXe1VLxzHqfPsatoab8+TiFC2ZgGhxJ3fD40CVXmT9/u/qCE89q4UTiu0qKlxPUfdLv817xHNLn+0GCxtGd7xxM8S4TjEMcY/E+kU3zW9z3z/J1XfXQs/76WdN8etyCnfM/xomsybcVW7nWH+85yx3SxWb1/vfZet8Wg/eBBn/9wLRt9dnX7Ouud4btVMpQ1XSR1T7lx/S0B2NsxAlUrPLtOp3uxXSBFw9OJjqW6K0L3o3QVgs98FehrWcVdBpG3kU5/8MHGtQt29YWX1dWzHdH7pYorExMQSZ0RdKSJUssyzL73Oc+Z6tWrYpev3LlSvvc5z5nSZLYP/zDP0xADoUQQgghhBBCCCHEaEzoh6Tzzz/f0jS19evX2yte8Qq76aabRsLdShkeHrYbb7zRjjnmGFu3bp2laWrvf//7JzKrQgghhBBCCCGEEAJMaGjbIYccYpdeeql97GMfs1WrVtmZZ55pTU1N9rKXvcxmz55tSZLYmjVr7IEHHrC2tjbLtiypvPTSS+2QQw6ZyKwKIYQQQgghhBCTRpJlllATMEn5EKKUCf2QZGb20Y9+1BobG+2iiy6ynp4ea21ttTvuuMNds/UDUk1NjX3hC1+w9773vROdTTFBJOUFS8q2eDjooYELJaOfhNJ2umUCfwYGQPo/eEzo36AHoBT4H2wIK+/4W7pU6ExgHDlW8iXw2liGZ2GcfA88N3QwMY6dzgg4iJImH5fPuHVjXfH5mR7LD74Lo4eI+WPd8/fMD707IPAglf6e/ib6mfgs9FPQgbB63ej3MgvbOfNaibobjLQ15Cd4UaAjgQ4ikLFtIf+Br4Ppsa+wbtgXWPdMn+Xf5X0j9Hsl0+GOKb0f+yVIOrzvInAe0f/AftcJRxHrGmUx7YFW//O/398d193xqDsuwm+Vzp3j00dbyTo63HFS7csyaUa/R1mXrdrkjmsbfdss7/HlMVSD8pnl00/7fHmWdXlXDVc4B30H5c+2nrHvlpyve8h7XPr29/6nrIbuD5+Xpr+2+fMYo/rneBdIocvXRee+3oNT1u/zXtHh20Z5K/pFpx+Ta/7kHUbFPf3zFGv982QF3xaHG31+ChvQdun14RjMcQZNPRn05ZOirqvgRBrYA+XX6u831OTHvUIXXHCl94ZjMGO/rfNp0TOTDGDMwpgTOJdQVYGwiuMI3Wz0vNGTx/mSc8LKNe6wajX6Ta13JmXl/nzGMX6d93FxTko2+XElYz98eqW/HuNOtsGPK8GcEtnQp2L9tvoodPuyGa70zxrMN+OkdrWvm+rVvh3TUUj/VoK2kqAfFGswf69B2SP//YctcMfs10U4lPrhkOK4U+hBP8WcNVyHcTFt8oetsXEj352Wcf7f6kwcHvumTkKI58+Ef0gyM3vf+95nb37zm+073/mO3XbbbfbXv/7VNm3aPDFMmzbNDjnkEDvhhBPs3HPPtRkzdrL5TgghhBBCCCGEmOpkNjVk21MhD2JKMSkfkszMZsyYYRdeeKFdeOGFk5UFIYQQQgghhBBCCDEOJvRDUpqmlqapffazn7WLLrpoIm8tpijZ4JBlw1uWWW9HvF5Kgi1ug6XysaXzTB/hXhmW8ifcNpwhO6Q0VIHhOQybC7ZEx7p+LqXPsBSdv88LszMLQ8GC8CUs3+5HWCG3fGeYIe+HpfDBlvHcepnPz/Ji3TEcikvZ+XyRpe5cvh6ExICspLzCZfWRPQy4NfRGLPNnWeA4qUQ/wJbsQdtj3bNssZSeIaUMOQ3CKoMt7pF+HdoCwx4Zhsj8c6vnNh+Sw7aX9ful8gmWvmcIAw1D7dCXSvsizxGWBcMi2DbQL4qdPjQuYdghx0BQ0Yl+ybJHWRdX+3CtdM5s/3uEXQ7u5VcIl3X5sh2q92NoWYMvj6qHnvPHkecZCVPYCssToYTst0FIDe+Htl5kaGHJ+QQhkFUPPuOOgy3ROYag7BkeVOj0bauw2ofv1KMbbjwUW7Rv9Okz7I9bvA83+JCZzgX+OB1ECEsvxq16tE08XxoLLUeoXYEhKiw/hmeDiqfX+/+BObQc90/W+7DQ0i3iB2f5MLmyDp+3tAPzWRCCGglp5Xn+vj//XYgkiR9XGGoehBtHyAxjPNq+Nfu2l67y4diG/ARjPLZ0T1i33NJ9KH9cK8724cjpSuQHc1zav62vpV0+rzN6fD8sLvDhv39/2hfccVmff7ZCj89rfZsv+6QTZRmZf4OwRJC2+PkwNn9XrmxzxwNz/Lgw1Iz5EHWTDqGfD/FdEoeo2uFq3xeK5b6tFtrwvJt8fpmf0TQISURXIITYOUzoh6SKigobHBy0v//7v5/I2wohhBBCCCGEELsVSbb5z2QzFfIgphaRfzrfucydO9fMzMpiqwOEEEIIIYQQQgghxJRjQj8kvepVrzIzsz/96U8Tedvt0tHRYT/4wQ/swx/+sC1evNj2339/a2xstIqKCps1a5a9+tWvtssuu8w2bty4Q/e5//777b/+67/szDPPtMMOO8zmzJljlZWVVl9fbwsXLrSzzz472LWO9Pf3249//GP76Ec/aieccIIdeOCBNm3aNCsvL7fp06fbscceaxdffLGtXLkyN50777zTkiQZ059LLrlkh55bCCGEEEIIIYQQLzySjPtu7kL++Mc/2jHHHGN77bWX/elPf7KGhob4j3YRt912m73mNa+JXjdjxgy77rrr7MQTT3xe9znuuOPsnnvuiV73pje9yb73ve9ZVRV9I2bLly+3Aw44IJpGbW2tfe1rX7N/+Zd/2e75O++805YsWRLPtJl94hOf2GUfk1auXGnz5883M7PFjW+1qrR2u9cF7hV6gGJeodjKN7hRuH0zt/QNt6yP5KcUemboWmE3xJbywTbhdA7A+xLA9HF9EFdPZwE9PSjbDGXBLdQzxrmDwOVCJxKfl8fc8p7lx+ePeXpYX6zb0vP03jRiXOOW4vQ7sS3E7k0XGHwTWQU8Mdgm2+i74NbLzB8J/FPwc6AfBm2D/Zqusj7vjGA/i3mCAhdNzL3Ctk2HVE7aARyDmBfWdays6fxh3lH2GfpdsL19i9/mO6hL+DgS+KmG94CLBB4eemeymcjPAPp1O/xgMX8VxyWO4ejXdMcE266nGMc2bsL5bb9PYmNKbL5q8GXJfk5XSUVLmztuPcr7qxqe9M/adpBPf/o9vq4H9mxyx4UuX9aDjT7/fTN8WyjHNt8Vm3y/6p/uy6dyk0+/rBNtZRWcRnTBcJwr5Hvwsla40+hWm+5dLMVqtPXS+T7iY0p78ufbrMKPA0kPxiCmz3GF5zmG0YsX8UXSjzU0zc+35c/5LeMzuMKyvb0nKHAQMb8cQ/l+gbox3K/Y5seFFOMa71fc4Ptt0Ffp1Slxr2UzvNssq/FjSor5k26zpB15n+HbWfqs74cZ647vUpgj0mo/ZvFdrdjt5/OyPWZZLhgDB2f6uiji+Qbr/XF5px/Dy3qRn0p//XAVno+vM3AsVbb4ure1GCdA0tzk/8eWOaRvqNPuXP1tMzNbsWKFzZs3LzedqUjp35UOe9PHraK2aXIzZGYD3W320E2XmtnuW65i5zKhjqQjjjjCLr/8cnv/+99vixcvtiuuuMKOPfbYicyCY/78+bZkyRI74ogjbP78+TZnzhwrFou2cuVKu/nmm+1HP/qRbdiwwU455RS777777LDDDhv3PSorK23x4sV27LHH2sEHH2x77LGHTZ8+3davX28PPvigXXnllfb000/bTTfdZGma2g9+8IPtpjNr1ixbsmSJLVq0yPbee2+bM2eOlZeXW0tLi/3sZz+z66+/3rq7u+2cc86xmTNn2ute97rcfH3729+2RYsWjXp+1qzIZCSEEEIIIYQQQogXHRP6Ien//J//Y2ZmCxcutAcffND+/u//3ubPn2+HHXaYNTc357qTkiSxb33rWzstL0uWLLHnnntu1PNvfvOb7Sc/+YmdfvrpNjAwYJ/85CftlltuGfd9fvWrX1mB/0q9hRNPPNHOP/98O/74423p0qX2wx/+0D72sY/ZoYce6q7bd999bc2aNeHOFls4/fTT7bzzzrPjjjvOBgcH7T//8z+jH5L22WcfO+SQQ8b9PEIIIYQQQgghhHjxMqEfkq655pqRjyFJkliWZfbcc8/ZihUrcn+XZdlO/5A0FuH3aaedZgcddJA9+uijdvfddz+v+4z2EWkr1dXV9sEPftCWLl1qZmZ333138CEp5bLl7XDUUUfZ8ccfb7/85S/tT3/6k3V1dVldXV30d0IIIYQQQgghph7atU1MVSb0Q9Jee+016qqaqUpt7eZY8j56O3bBPXb0PqXp9Pf37xYfkpKGeksKWxwvVfBhRLw6dJ8k1EvFvD/wEpTGzZttJxaeGUBcv6MWzp8Cfh3zQdB9UoCHpg33pkNhpo/7t55ef0xlA50CPO6CV4fOATxPts47F8LrUdcsDyoo6EyKOSbooqG/BN6BAPo4WL6l96P7BG6UrMbfK4H/IXAEDcEJxLKJ5Z2wH9At0osxJ/LxO3BCAXpuogT+CjwvPTc8z/zweQiePynQ54G2VgrbOV1b5fjtnJn+XptQ9zXwPSHvGdp9lmHMYlZXrvbX8x9MMMbRZxV4fzBulK2m6823rQzXJ13od0Hdou2j34Z+K1/XadGXX+DL6Oj06cGjQ4JxqfS39MrBfZbM8mNu0u/LZrjR99u0z58vX4+2hLw3POHHmcEmP0aXd/m2MTTLe3EGG33Z011S1ucbU/Vaengw5qLuyjvgdunFOIDrh/ff09//qVX+erYVjlvtvnyS2b6vse0mfcjfEPpSaVvmGIBn7znYh/7XPOndYEkH5kvMJ/TYmeFdA3klHBcSePI4/2WVGLfK+D7i2z3H4GRdm7+eHjyWdTDf4pjlC0diSjfaMOZ3lGc6e0b+9RxnSt7dknV+Q51sL+8iG5jl+115K96lImNosddfn9Lxx7wN5Psr+T5QVod2j9/3vXRvd1y51o8zZd1wmeFdtHKFb8vF+ojjsNz/vljuz/c3+bqrfxbutE6fv2Lwnu/HvYxev63ln+a4DoUQO40J/ZD0zDPPTOTtdphHHnnE/vznP5uZ2UEHHbTL7nPDDTeM/Pfzvc+6devs9ttvN7PNgvDp06fvlLwJIYQQQgghhBBCbCUeM/Uio6enx5544gn77//+b1uyZIkNb/mXnw9+8IM77R7FYtHWrl1rt99+u51++un2/e9/38w2u6PGsztcf3+/Pf3003b11Vfbsccea62trWPO63/8x3/YvHnzrKKiwpqbm+1lL3uZ/du//Zs9/vjjz++hhBBCCCGEEELsPLIp9EeIEiZ0RdJoDA0NjXwEaW5ujnqFdjbXXHONnXvuuaOev+CCC+yss87a4fssWLDAnn322e2e23vvve2WW26JPvudd95pS5YsGfX8WWedZRdeeGE0L1udTGZmbW1t9uc//9n+/Oc/21e/+lX7+Mc/bp/4xCeedxjiypUrc8+vXl0SfpEVN/8xC8OveH8sZ04K3MoZ30WxJDjYhpxljVA6hjkUsX1wyuXapVvQM9yGISPY+jljKFWBz4Kl2vUInWMYAJb9B+FNTdiinmXPkJhpTUgfITqAS+0zbFEbbMPNrbIZyhYLwWFb4dJ2brOOEKHg/t1oOwwlqCqpe5Ydlu0nLHtuCc/zbDsxRxpDbJD3aFlGxpwgtA5lG4RbxcI2WVd83iKOqxn2GAmdY3gSQ914f7alvC3n0ecz9IOUIaEb2nIyGm7LzWX7CfpNEIbIHdHZTvlssX6EcSSb0eSOB6f5fjvQ4NOrexghrQzTqEFYQgPCPnv9/RgGGjw/65rPw7BSA6xPhlOX1H0y14e8ZCt8KNYwQz4Q8VpY7bcoLzYhHIlbxjNcCd1ioNFfX9bv++FgPbe392XDULZCuw8hGa7H/FfOfurTK3QhdKyVYZ/IL8M8GQ6O8KyNi+e748p2n/+6pU/hfhy30DcYDl0yTmUdPm8J5sOaZQgtRzsL+hnDd/sjYfd8HzCEnnFMzwuzN7N05TqfGsepIFwYW8Dv40P5yp/26SV8foYhMqSWIaudDK9GKCBD59g3gufxh4YQ5GRmyYp9juEI40ur/bNTM8D5YqgeIacMxWLdISQzwRgXvKdyPuW7IELDqh70f+fIZvtohbQd7y+4f98hfnv3qifWu+PitHyFRopxp2YNQ2DRF6glIHj/CPrx1r60m2lUhNhdmbQPSX/729/syiuvtNtuu82eeOIJy7ZMPEmS2AEHHGAnnHCCvfvd757UncUOP/xwu/LKK+0Vr3jFLrtHoVCwiy++2D74wQ9aQ0ND/AejsGDBArvyyiujK5rmzJljZ5xxhh133HG27777WqFQsOeee85uvfVWu/baa21wcNA++clP2sDAgH32s599XnmZP39+/CIhhBBCCCGEEELsdkz4h6RisWj//u//bldccYUVi8WRD0hbybLMHnvsMXv88cftyiuvtPe///32X//1X2Pauez5ctppp9mRRx5pZma9vb325JNP2o033mg//vGP7ayzzrIvf/nLdvLJJ+/wfX7961/bwMCAFYtF27hxo91zzz329a9/3T796U/bE088YV/72teiguxFixbZX/7yFzPbHNr23HPP2U9/+lO79tpr7dxzz7VLL73U3vGOd4z622effdbK8a9PL3/5y+20006zd7/73fba177W2tvb7XOf+5y9+c1vtsMPP3yHn1sIIYQQQgghxPjQrm1iqjLhH5LOPPNMu+WWW0Y+IP3d3/2dHXXUUTZ79mzLsszWrVtn9913n/31r3+14eFh++pXv2qrVq2yH/7wh7ssT01NTdbU1DRyvGjRIjvzzDPt2muvtbPPPttOPfVU+9a3vmXnnHPODt3nwAMPdMdLliyxf/3Xf7UTTzzRrr32WnvwwQft97//vdXX14+Swuad2UpXaR1xxBF2+umn29ve9jZ7/etfb+985zutpaXFLr744u3+No+jjjrKrrjiCnvb295mWZbZFVdcYVdfffU4n9JsxYoVuedXr15tRx111LjTFUIIIYQQQgghxOSSZFwStAv5/ve/b29729ssSRI77LDD7KqrrrJFixZt99r777/f3v3ud9sDDzxgSZLY9ddfb2eeeeZEZXWEt7zlLXbjjTdabW2trVixwpqbm3f6PR555BF7yUteYmZmH/3oR593SNlHPvIRu+yyyyxNU/vb3/72vHaAGxoashkzZlh7e7sdcMABu0S+vXLlypHwt1fP/BerKtuyCotentg24oyLpxMJsdb0HITbrmObVXoDEAufNfgPc0nJdsdZlf/tUDO2qR6Cr6ILcfFwIgXb9eJZkn46fhD3Tq9M4IVBWdb5/AZegJVr/f3pSGCcO3waQd3RpQKnUdKAlXr0U8ELENQtttJO6pEePAPZGu8BCPwapY6GWCw+2yXbNR1JgUMIdY2tjrNVqAv6rNjuScSvEXhomL/A20PHEbcL9uezBvQNbp1d5FbcHCfg24g5kFgf8HVwHEhrSvIH/1OGeyd4tsAFhrLM1qKdsa5Zd9yCndtw068Rq/tm730L6q613R9jm+7n3jjXHe/1Y98WmV7/HB++Tc9PseCfr3qdL19uvT1c58eRsjaMe+VwrdBVg7oefni5T69xW34TPDvHrJ4jFrjjoWr/7OXdfkwv6/ftur/Jt62alb4fDDbBnzUMRxG28R6qpVcn//5FbEnPOWe40p8vdMN1MujT757n+3XVRp+/QrtvqxkcUSnnxPWt7jjwEGGM5vxfPMCH3Afpd2LcKYVjNtpCsc7385TzMR19dASBjPMdNXNtkTGbcD7m/MjnizgIAw8exxlucU+PDZ2EfD9BetFxthH/+Bq8b2DcLWlrAzN9Xot416n+09M+LbwrZY2+LTz+rmk+vSr/7At+5I8rN8D/9FSLvx/qInAmcY5BW8swvwfvapwv+W5EpyM9d+i3wzVoG0g/hR8s7cO73kY/5wR+TZCw7rfQN9Rpd678pplt/sftefPmbfe6qUzp35UOP+PjVlHbNLkZMrOB7jb7848uNbPdt1zFzmVCd23burrlwAMPtN///vejfkQyMzvyyCPt7rvvtoULF1qWZfaNb3xjorLpOPXUU83MrLu7237xi1/sknscfPDBdsABB5iZ2c033/y809ma12KxaD/60Y+eVxqFQmFk5VRLS0vkaiGEEEIIIYQQu4TJ3qlNu7aJUZjQD0kPPfSQJUliH/nIR6JhVmabQ7E+8pGPmJnZgw8+uKuzt11mzty2o8JoO67tzPvsyD12Vl4ncJGaEEIIIYQQQgghdiMm9EPSwJblq4cddtiYf7P12sHYlpC7iNJVOTER9s64z47cY2fkdWhoaCScbe7cuZGrhRBCCCGEEEII8WJiQmXbe++9tz3yyCPW3t4ev3gLHR0dI7+dDG666aaR/z700EN3yT3uu+++kRVEO3KPnZHXH/zgByNlvnjx4uedl7GSDQ5aVtz8kdB5Z8zCuH7GbjMun34QxuUztpw+EsaKD8HNUo5YdTgVik2jS9LpsyjrififIr/PUh77b8IJvTR0q9DRwLKGcyjB+Yw+CjiIgrKFXypwIqBuAusQHA5ZRyTun34s+k2C9OBQavLumOLGTf73Pdu8BsGz0f9ExwH9D3QkEa4QbOvw6dFhROijYD+gC4wwf+gXbAus2yB/cLHQiRD4NAK3Gc6X43l4P7Z19g2UT9rc5I6zvm3jCttlMt37MKwLri54aYIxhf2WDibWHX4eeGHoGou0vYR+rCbvMOo8dl93XLXBl+VeN6/y91u/0aePsq6Af6S8wz8vPT30aRQr/fU9c32/r4bXJx3w5bPpED+H0Mn0p798xh2fWPsvI/+d9fjxPt1jljsersp3IqXw3nFMr1kNV8qgz3tZX/44Ufb0an+M+WzwwD19fnp92xqs9/2mWA4vDvLLsu2f7uu6rB9tH96aoQa0jSd8/ofnz3THZQO+bYbuM3jsLJ/AiVQ6J3AM4rsB5o+UY2TEQZj1oq6DfolxhHMMx7Aen16xtc2fxzjBMY79lF4dPn+xC89fjfk3hTcvBl1vffBncQznnNXm55DAgQiGGrbVB51IxQr/rEMHeAdMsZzCKn+4xz3+mG6x8g74ngY5qIPAZ4Xf892ObZXzPXfKphuUzkP6sbiDNtIr7+zLPR/0LcK+xOfDnJjxfWjr8w7Dl/cCQDumianIhK5IesMb3mBZltktt9wy5t/cfPPNliSJnX766Ts1L9dcc4319eVPdl/60pfs5z//uZmZLViwwI477rggjSRJLEkSu+SSS4LfL1u2zP70pz/l3qOlpcXOPvvskeO3v/3twTU33HBD9OPbjTfeOOKRamxstFNOOcWdb21ttTvvvDM3jWXLltn5559vZps/qrznPe/JvV4IIYQQQgghhBAvLiZ0RdK///u/2/XXX2/f+MY37FWvepW9+c1vzr3+5ptvtm984xu2zz772AUXXLBT83LJJZfYhz/8YXvDG95gxx13nO23335WV1dnnZ2d9pe//MWuv/56u+eezf+0UFFRYVdffbUV+GU/wsMPP2znnnuuHXvssfZP//RPdvjhh494jFpaWuyOO+6w73znOyMfiU444QQ799xzg3S+8Y1v2HnnnWennXaavepVr7KFCxdaY2OjdXd322OPPWY333zzyAevJEnsK1/5ik2b5v+lvL293ZYsWWKHHXaYnXbaaXbEEUfYnDlzrKyszJ577jm79dZb7dprrx0JIbzgggvsyCOPHF+hCiGEEEIIIYQQ4gXNhH5IamxstNtuu83e8pa32Fvf+lb7/ve/b+ecc44tWrTIZs2aZUmS2Nq1a+2+++6z7373u/bTn/7UjjzySLvxxhutsbExfoNxsmnTJrv66qtHdpPbHvPmzbNvf/vbdsIJJzzv+9x7771277335l5zzjnn2BVXXGEpl41uoaury6677jq77rrrRk2jubnZLr/8cjvrrLNGveahhx6yhx56aNTzZWVl9vGPf9wuvvji3PzuLJIkHdnKNVuzzp/jVtkb/fa/QTgTt7BnOBZD37g8nMuzsbw7Q9gK758MbFuSm1UjRAUhFwm2B2ZoGuEWq0G4FMONClgqX8RScG4Ry7KJfTRluBHzw+2BYzA8KlIewVL8Zh9WmLT7usra/fJnhkMlDD1ozw8fK11enjC0iqAubBjHA1i6HdkCPljmjy3Wk1gYALd4D0I6I6FsDEvk83OlPpemox8FW0/zeWqwlTTDxbD9cRDmyaX1bKsMg+DS/lI4ZnBrYp5nuy4i/IZjGEgQ0RKEunFbcf6e/TgSlsCyrej06Zcte9j/HGMm88ewiXRakz9fxi3pEZKKcLAMbbWyzRdQAWEjv/zzp2xHSBZs2zI+QQhFce0Gd1x/L8IK0TaGDt/fHad9CDfqRsgK64bddgB1z/kPlK/381n/3Px3qsqNvh8MV6EtMfoLxxmnCPTrDGGFXUfOd8cVbb4ttL7ShxLWP+vLu2INwpPR9srWYw5Ic8ZVhN8EIayV+VucWx/GNLQFzjcBCF8qTvd1la5DqDW2QE+42r4YiYlhv42FnmNOKSJUL+WYT20B5xiOuchvijE6QyhfFnOo1o2+5X3vDP9sfFXqnu3nn4pO3zbqn/TzWe0Axqyy/CBLzlcp5xC+a6FtBGN6rc9vwjmIIaCsa8K6BByjg9A1ztdMj/nn+0+RcwDaDsO7t9RtFguh293IsnC+nqx8CFHChH5IKisZILIss1tvvdVuvfXWUa/Psszuv/9+23fffUe9JkkSG4o5RrbDb3/7W7vtttvsjjvusEceecTWrl1rGzdutKqqKps9e7YdfvjhdvLJJ9ub3/xmq+FfZMbIW97yFps7d67dfvvtdu+991pLS4utW7fOBgYGrKGhwQ444AB75StfaW9/+9tzBeTXX3/9SF4feughW7t2ra1fv94qKipsxowZduihh9o//uM/2j//8z9bc3PzdtOYO3eu3XTTTbZ06VJbtmyZtbS02IYNG6yvr88aGxtt4cKF9upXv9re+c532oIFC57X8wohhBBCCCGEEOKFzYR+SKL4cyzbzO+qrej3228/22+//ezd7373807jnHPOsXPOOWfU89XV1fba177WXvva1z7ve5iZ7bnnnnb22Wc7l9J4qaiosDe+8Y32xje+cYfyIoQQQgghhBBi15NkU0O2PRXyIKYWE/oh6ROf+MRE3k4IIYQQQgghhBBC7ET0IUlMLpUVZmVbPBcMUWSsOLZ4Da5nLHnMNUNXC7eQxf0D7w/dNyWx8EU4jbhVcrEG8pNge2HE/AdbHefHpSdteLYZTf48vDQJ4uqD/NArg+uLTJ8OB3p0urE1K5wTdCAEPis4HZINbfn5peMoQ9w9t02ni4bentIw3cHI9rl1tf6Y59lu4RBgXhOmRwdSN7aOpienh2UPXwfdApwlMvgy2K/o46Czib4NtjXeH06IoLzZ9tlW6M9gfuBwCMq7qiQ9uE6CdkofBfsVgaeFDqXAXcYxKKOfCv6LBF4a5o/+CjxP5dP+PK0T7Ddpk9+ivchtxLt8+aV9yH/EdZJgzK1cibpbs94dv27h/3XHv3jsc7npk+zZldv+O+KBK8KhxDGj0Ia20hfxuuD3aXvOdvVmcScg6mp4f+/9qVwL7wy2OS+0+36Trlzrjqs7fVh9hjG9b4733JTBEdV5oB+jK+t9XTc85Z+nvM0fFxt8vy/jOBdzxcwoKQ+2Q84P9fAjcr5DP+DvjW60yLtP2oq2xTET/kjOj4TusiA9wnGiLOKfqcUcxb5RhTmnEeVVhXGuE32Hv+c4Q3fcaj8uFErmrKZHfN20L/Q+p5o1nG8QWVHw/aQI91dZP8fcfPdZMB/Tx0XHEOuGdcn5H+0+OB/Mx5ElKLyex8wv217M44f3m+B9YZT8J0PDZvmbXQshdgIT+iFJCCGEEEIIIYQQYyDb8meymQp5EFOK/K2RhBBCCCGEEEIIIYTYgj4kCSGEEEIIIYQQQogxodA2MbkMDpoVN8fzZ3SXEMZCM3YbTgi6TkjoC4EzoQZ+k7JI7HZPSf7r4TjohdNnEPfug0MBrpQAlgWhH6odDoHYboiMa2fcOuLUUzqdcD7r8PdPKuEsos8CBOfpLMrG+U08EvcfuGj4/CVeHnpiWHYJ/Q1Ii2VBMrpP4OkJnEroR0HJwBcVtDU4hwIPUL13nQQ+KpZt4ExAjvh89FsETgjkl89DBwPzw76ToW8mOd4h+qDoImFZ0EfBumZe4f5I6FJhv2VZxIj4t4pr1/nrk9HdYJv/h08v5gkqbvIuF7pOAv8X28IA2maDv955bswsq/D5PfHIS/z1A/CfYFxOS9wtGVxdLAu6uYJ+E/HaBc4jto0G726xOsxPKHuWLdtq5a8ecMdls2f69OgaY1vmOIA5YGBuozvunen7VVm/L79Z9/txMu2Bl6jcX9+1r/fq1P/Ft92sDh6jLswhrK8Sj1/gYUnzx0irRj/FfBdzDhabfN2mPXgXQtvImv31SaefEwLjEcaRDHOS88BtD+64zLbJyzvQFjGOBOMY5tCE71pVHDfxrjbd9/tg3OAcUtJWs8SnXf+MbydpD32JGPPgcxqq98cpxqyywNGHsuCYWYsxku8PfDeK+a44bvBdDcdB+vDUZfCBsnzCcS/LP2bdEzohR5tTx/tOOMVJipv/TDYTkYd169bZsmXLbNmyZXbffffZfffdZxs3bjQzs7PPPtuuueaacaX3y1/+0q666ipbtmyZrV+/3mbOnGlHHXWUnXfeefaP//iPY0qjp6fHrrjiCrvpppts+fLlNjAwYPPnz7fXv/719oEPfMD22muv8T7mCwZ9SBJCCCGEEEIIIcSkMXv27J2STpZl9p73vMeuuuoq9/9bWlrsxz/+sf34xz+28847z6688srwHw9KePLJJ+31r3+9PfbYY+7/P/roo/boo4/aN7/5Tfv+979vJ5100k7J9+7GC+uTrRBCCCGEEEIIIXZb5s+fb6997Wuf12//8z//c+Qj0ste9jK74YYbbNmyZXbDDTfYy172MjMzu+qqq+zjH//4qGl0dXXZySefPPIR6V3vepf99re/tXvvvdc+85nPWF1dnbW3t9ub3vQme+ihh55XPnd3tCJJCCGEEEIIIYSYaryIdm27+OKLbdGiRbZo0SKbPXu2PfPMM7bPPvuMK43ly5fbZZddZmZmRx55pN19991WvSUEedGiRXbKKafY4sWL7f7777fPf/7zdu6559p+++0XpPPFL37RHn30UTMzu+yyy+zCCy8cOXfMMcfYkiVL7FWvepX19PTYhz70Ibv99tuf72PvtuhDkphUsqFhy4qbY5oDLw09BOW+udJxFPg7AOP0kz1m5aZP5wNjw43um5L7F1Zs8Ofom2DMPn1O8D0F0DMD70s2zfsjknWb/PUsKz4780sPDTw92Zr1/n5Ij3VLj09Cbw1dLnTBRBxHQRz9ML0EaDt0s7B86UEqzW9XN65Fu2mEP4OwrOlWYT9I4KugXwLPHvQT+ikiHp/A4dQd8VnBn0F3DPt1kc6nYX8+Zd3A/RLUDdsyYX5InoeIfopa1C3aJR1ECdsx2ymXV/M44kQKXCQEbTPoVxEnUtCWkHw2hLYPVUlSYL9E22bbYl3Dn5GxPtj22tE3OK6g7wajSkn+OGYFDj6OqWgLCceFYExDehyD6ABs9P0imzvD/3xdm78/vXbsV3S3YL4rTvdzSrpirb9+WpNPbjocgni8hkd9/uhASvpQXqDuyXb/P5DfYM7jHNkIx1OpH4tjIjxygfeFDh8wsPd0d1zW7Z+NjsFinW/3aUenv38/OhZdauhH2aa23PxZGco65vFDW6SPK5if+b4T8f5FLD8hHPeQfnBcMu6mKKtkAN61TszvfE9t8tdXrodnrRPvOr3oZ2hb9L4VK/EuVfDPmhboTEL6dCCxbjnuIL1gXGPdD+B8zPXGvhXzcfL+fA8f7d0w9o4opiyf/OQndziNL33pSza0pe1dfvnlIx+RtlJTU2OXX365HXPMMTY0NGRf/vKX7fLLL3fXDA4O2le+8hUzMzv44IPtwx/+cHCfY445xt7xjnfYN77xDbvjjjvsj3/8ox1xxBE7nP/dCYW2CSGEEEIIIYQQYrclyzL7n//5HzMzO+igg+zoo4/e7nVHH320LVy40MzMfvKTn1iGf0S88847ra2tzcw2S75TfuTcwjnnnDPy3z/60Y92MPe7H/qQJIQQQgghhBBCTDGSbOr8meo8/fTT1tLSYmZmixcvzr126/mVK1faM88848797ne/C67bHkceeaTV1m5eSfj73//++WR5t0ahbWJSSaoqLSnbfqgJww6CMAhezyWu3LKWIUFYkhxsac/7McyEIUSloW7MS5tfhp9Mb87NaxBKxvAdLiVHXpMNbf58EAqHkBUspzYs3U+4FB1hfcGW7YatlbF1NgnCsxjqxlA5hmUEYZHcYhbPwy1mWV+xEKDSMBCGXDJsj0u7GSbA7XVZ9wzFCrZYR0gH88O64dLzWKhXiqXkDFNk/hl+1I78oq2nWHIctBWUV9aHsE88X8IwD/ZbhqSyn7NvlJRXEvRL306SWmytzH5NGFbQh7piPyA8X5bf7rNO1D1gCGo6DeMU+xn6Rca2GQnFC+qmEVvcc9xieZYxZMaXX7jNObbSRkgMwzI5LrlzDF3Lj6wOn4VlGQmNC0DZDdX7ZysfbvSXt6MsIuG8WbVv6+kGP4dlc2a64+Fa9A1GrAxi/sW24WkHwrEqMK5tQtsi7EuROTbpwv1qSsqPYzZCWPvn+rKtfBJhfpgDKp5BP6zz6SXYAj7pxJjKusIYNTjTh+mVM5SNbY/jAsKVipzDeIw5huHJaT3CBjmGd6AtMuywHaF87BsMhyKxEOPStsDt7v2tw3bFZ2U/ZZgjQ7lYdjXsh/6wsIEKBX9BsRohpOzHDPnkezFCStkvk16MgXyfYMgt3y/Y1iLv6ZxjgzmV7yNBiPGW58+0TuLFyiOPPDLy3wcddFDutaXnH3nkEediGms6hULB9ttvP3vooYfcb14s6EOSEEIIIYQQQgghoqxevTp6zbx58yYgJ54VK1aM+f7z58/f7u9Kj2tra62pqSmazkMPPWTr16+3/v5+q6yszL3+hYQ+JAkhhBBCCCGEEFONLIuvcJ6ofGzhqKOOGsPlE5/nzs5tywrr6upyrrSRkDQzs64uv/pvazqxNLaXzovpQ5LW/gkhhBBCCCGEEGK3pa9Ef1BBHQEo/eDT2+tDiremE0sjls4LHa1IElMHxqLHvmTTcxPx4gRuFjgZAqfSBmwfzNjuwHGRbv+/t5O3wN3BbcAZV86to+lmobeG3hg6FuhU4lbJwZawhdzjwFjAbcIxEGfcjnc4/3mDuP6i92fx+UPfSL7vKsgP21LgZikb9dqgLuksoP+BvituX09nEB0+kUkroXeGjgPWLZ0DhE4mOAwCZ1KwnS+O4bsIHEdwLgRemzQybnC7YO4qzm3O8/omfBDBluL0Q/DZuE21YUxh2bBd0inEYz4r2mLgOuG4wPuxLDkOIb/BNuH8fexf6TiO0V+Ctpv25Lvj6AuJzQkJ3Sul4xjVHPRDBZ64fDL6nVB2Cd0qQQI+74V21O0A8hNzMA2hn7Etol/SiVSs9OkXen1ZFwv+eXvn+HGjbJp3tVWt8E6moC2hr9Fnlc2Z7o/hgrFhumK2PW+B82Gdz2vlilZ/vjvyFwfOZ0PoB6wL9sP+/PmrnFvUg8Azg7IrYg6hk4hzVOABpGePbXcov3zoJgvOB14djAsRh1KSYI4rfd/gkIz5Nrg3+znLgvMXxxyWbSWcSeV4V4KrzAZ9fjgG0olEAicSxshg3OAcw/ToSOIcEYPjDIm0jeC9fOvzDI9vPBbjZ9myZTZnzpzJzkZAVdW28Wggx3NoZtZf0t+r4evcmk4sjVg6L3T0IUkIIYQQQgghhJhiTJUd00rzMGfOnElxIMWor9/2j6cMVyPd3ds+wjOEbWs6sTRi6bzQUWibEEIIIYQQQgghdltKP26tXLky99pSwXapeLs0ne7ubmtraxtTOjNnznxR+ZHM9CFJCCGEEEIIIYQQuzEveclLRv770Ucfzb229PzBBx/8vNIZGhqyJ598crtpvBhQaJuYVIodnVbc6jihA4F+DLpcYgReHXiB6DuJ3C/wdTAOtjRWnnHf9Vjq2BlZKknvDp+dTiA+G7010xr8+U0d+fen14dlxbh0Ood4nnHudEKApNaXLb1DWXePP2YCGXwfMXfLcMQLlONDSeg0olOAdcO6o8clcPwg/ZiLhWXPumP6rJtG31YsFh9OzwzLg34wOpj4/Dzm89O/RXkNn4flQZ8G+zWdVSUE7ToG23l5vm8iaIeBTyrim4Kvg20lcCLxejqCgn4bcadFfBqB74v9umWNP4+6GnrJAn9c6+9f3urTL9vkfWSh5w7jQhu8PCXPkzR411hZA+qWdcMxB96YhGXFuuQxf9+LMW4YHhq6SjjuxByEHGfQr8vaICY9oCk/PVDW59tS1Upf9sF8C/fL0F4+pKKs29f92qMbc+9f2e6fv/GJbXNyNrPZ33sI7Z4OINRVsQsOP4x5ySCcQqwbzqfwztHpl86a6X/PfkroEOJ8H8yPaHtwIgVzGts25whkh46kwOvHOYxzCvs1nVTTfX2Wphfcmx47jsns5+OF9+uH9y02hpJgHIk4jQoRJxLbAhxOyQB9VPnOw7Dt4N2ObQcEziU6GDkHbbl/Noy55oXAFAht2x3YZ599bO7cubZq1Sq76667cq+9++67zcxszz33tAULFrhzxx133Mh/33XXXXb00UdvN437779/JLTtla985Q7kfPdEK5KEEEIIIYQQQgix25IkiZ166qlmtnkl0R/+8IftXveHP/xhZKXRqaeeGvzjzqtf/WprbNz8DxLf/e53ww/sW7jmmmtG/vv000/f0ezvduhDkhBCCCGEEEIIIXZrPvShD1lhy2q1888/33qxM2Vvb6+df/75ZmZWKBTsQx/6UJBGRUWFfeADHzAzs0ceecS++MUvBtcsXbrUvvWtb5mZ2eLFi23RokU78zF2CxTaJnYfguXWOMZy7mC5c2zJMLfcBcEWt7i+NGQnKY9tc42wh2Dbbyw35rMzfIjXM5yqZZ1PLhJ6FoQBcvlypCxTLF/m9sIptlMOlkNzeTVDcBieFGwPjKXvXE5ehaX5XB7N8kAYhgvfQl4YdhCEDzFkg8v0+/3W0gnFfXhWlnWwDTaXgnP7YT47w4+4tJ9tkWEUDOdCSEoQisYQoBTnmR/CtsCwkypun5y/xX3SgNC+kjDULEU7YBgE23FszGFZcpl/LLSMdYNQsaDtsd9xTIuEUwX5YQgOyjYoH5D1+ZCVYJtxjGtl7T6ktbCJYZqR/HKcZP7yQm6Dso/MLyxL1lURv2fdBSE1SJ/bbqOfhG0zEjrHfheE6GKcqkJYYbd/no69fb9L8Dg1q3xdDjf6cSzt8M/X9bI9kZ7PT027H+f2+N0mn5+DmtxxZatvC8Mlz5P2Ym7n9FfJfuqfNa3P7xeG0Le+l+7tjis2+PRTjJEJ56fWNn8eoe1sq8V2hLZjDkg4pmJcSpoQNshxhvRjTmLf4btbK0JMAceJYE6pwZwWC+92p9BQ2Q84pgTzD+qGoVmc/zFfJx0IxyWRUPAgjJ/368e7FednlhXzz1B3visGbQdtl/njuzDfqzmuRdrqSNt6gYWBTcVd23YVv//972358uUjxxs2bBj57+XLl7sVQGZm55xzTpDGgQceaBdccIF97nOfs/vvv99e+cpX2kc+8hHbb7/97Mknn7TPf/7z9sADD5iZ2YUXXmgHHHDAdvNy4YUX2g9/+EN7/PHH7aKLLrLly5fbmWeeadXV1XbHHXfYZz/7WRsaGrLq6mr78pe/vMPPvjuiD0lCCCGEEEIIIYSYNL75zW/ad7/73e2eu+eee+yee+5x/297H5LMzD7zmc/YunXr7Nvf/rY98MADduaZZwbXvOMd77BPf/rTo+alvr7efvazn9lJJ51kTzzxhF111VV21VVXuWsaGhrs+uuvt8MPPzz/wV6gKLRNCCGEEEIIIYSYamTZ1Pmzm5CmqX3rW9+yn/3sZ3bqqafa3LlzraKiwubOnWunnnqq/fznP7dvfvOblkYE+vvvv7898MAD9vnPf96OPPJIa2pqspqaGlu4cKH927/9mz300EN28sknT9BTTT20IkkIIYQQQgghhBCTxjXXXBOEr+0IJ510kp100kk7lEZtba1ddNFFdtFFF+2kXL1w0IckMXVgLPd4t1llrDjdLyTmH4ldD9wW9XCPDM3029kWVmwwfwGcAXx2ukzojaFHh3H+3IIVrpTi+o3+9tgCnnHqsW3FM8TZJ4iDD51J2JacDoTodsaI+4eTgXH0xY3eQ8Ty4ha1dFKVns+6veuDz5rQZ8W6pWMAvofAEQA/RYa6I4GPIuZ24dbV7Ed0IPBfqOheYX7g0wjqlm2d5RXph3QyZbHnISjvUr9W4HdgWfAYXhv2syAvKXwUbBvYgj5W1oF/ir4Lli29PJFtvINxBV4etj32w8BvwXGF59nPKWzgHBL4vHiMvlBfh9+XlAedRNxinP2cZcm6ijmMWPYR3xa9b4FHL7ZtdhnKJoUHCPlPN3qPTWW3L5/a8unuOCunq8WXPZ1LxVo/B9Q96vvO4ExfV717+3Gxf5pPbxiPU78cjqa6bfdjO0uQd+tH3dJtFnOFoV2X9fp2X6z2/TYrR78Z8vfnKJDVYf6CIzGp8fM/vXqBR4/jGuY8Uizxym2P2LtdzNEYjBOcM9hXchyKwZjFdye6yWLzRwyOSUE/xPzGvI/3dtV0ENLHGfHKcX6O+DqDtsLnC96HcD2dUXQi4f0qaLtb81eMvCcIIXYK+pAkhBBCCCGEEEJMMV5Msm2xeyFHkhBCCCGEEEIIIYQYE/qQJIQQQgghhBBCCCHGhELbxKSS1lRbWlYTv/D5EHFKWDm8BoyFRxw+vT8Bpb9fu96fqkUMPxxF1gXnQCwunVTiWeChYVlkm9r8acSdRz00jHOnm4TOAqbHsub9UR6B5wflkTQ3+ePAmYTy4P2pIYAPJHAmlXh4kgbv6qBLhY6AIC/0tiBvCdppcfVaf33ELRbULf0T8OgUOzp9fpk+8hN6ftDWIs6FoF/RPVOLvsJ+DW9Qtsb7QAidCoFzIa+f8970TYzXnxE4fNDv2c+q6EIb3zrzDGWZtHX4C8br6Ql8H3Cr1Pp+E/iqinTR0KeBcY33j7nTmB7yF3XTlbZlus94b7ZzenEInUlRR1/EdcayIhwHOEbTNcJxic4njmsbNrnjGjiZskbfT4sN/v7rX+7H0drV/vlqViK7zf55UniLhvG4Net9ekO4f1LyvJsO974lQ7Ob/v/ga5o/zR2Xr8OY8swKn9wQ8j7g81bWgTGIZc+2Qo9Mqx/D6UoLHIKxfhbr92hbKebLYA6IePpi+Qmch+0Yx0CQXp5bDu0+cP7QlxW8R2JMC/xO8NRxvuF5zimxMZD9lu9+OE75+9h7bzDOsa1E5hC2ZRyzbQZth/BdcMu4lgwPmXVv7we7KdmWP5PNVMiDmFJoRZIQQgghhBBCCCGEGBP6kCSEEEIIIYQQQgghxoRC28TkkqZhOMFWuJSfYQnbS6sULtmNhKFkDQj7aPdb2AaBclyeXRoqgNCspNUvvS7ObPLny7B0exOWanPpNpc7DyM0LrIUPQh3Ytky/IihcliOHGxvzBAchFEk3Qh7QMhLLLQumdbk/we37I0shbdK5I/hZHW17rg4w4c6pO0l5Y2l2GxnGbfdjpBUIYwOZZEijI/pB9vhRrZg51L7hGWD+/N6HqezZvr0uFS/LL+8gn4f3A9tY70PMwnKP7KdcLHXl18QulcSFpJh2+sghJMhE9zamGEHFZG6YLtlP2SISWyLeLZVbls+wLrC/Xm/SL/KsIV7NIQG4xDDMLhVdcK2EgsV5JjN/JPS8uYYxzGHcAwLyo5jENIfbVvrrbAsI/djaFlWg3CkVZhDUjwfQ3IjcFwaRkhuYdYMdzy7d7o7Hpju5+Oeef640Ovz1zvLl0fzw37+Hq7y/X6o3l9fGho34/9tcOeKKCvDfJ0OYr6sRtrTfehbcaafT5IhlDX7YRByyS3cI+FGvJ5zQj3Cs8c5rgRjNPpGEM41SjjStttF/o0baoA0FqYJXLhWLJyX8DxDo6sx5jGMkHUbC2XDu0jG8GH28wJC6zhmZpFQMY6JDFWPhOBmPZgjWReRtkiNQTAH4jg4/wJFu7aJqYpWJAkhhBBCCCGEEEKIMaEPSUIIIYQQQgghhBBiTCi0TQghhBBCCCGEmGoUszA0frLyIUQJ+pAkJpeysm0x4YzLj/kvgm1Z0Zz5e8JYc3qJYnAb01IvEmP2sYVq+tya3KSzPbw/wlbgesaRR7Ydj26h2pPv4wi2sGVcPvPD+3ML93r4qDa0+evpLAqcRpG4eD4vt+Sd4Z0VQduC1yDd0D76/eFrCPxYTQ3+/KY2f8yJmc6EwIfB8/k+qAS+KjqGkirfVrNOuMHYlunjwP2tG74rbj9MR0SwPTDaEvKf1GBr6T7ft5I6+D7asRU2HA8p2ibbsus7dAqx33Gr5GBraJYF+l2kLAIi7TYYh+B4io6pdD7Rfcb70TdFjxDrniC/WZP3g+T2w+1RRPkFfS3iMSrNLz1yo7n9tsL5iKMi+zHHOG7DDedQ6Inj/Il+3usdR4FbBW03YVvnfBchwZbzZc3eCxS4TuAkrEDbH2hsdscpvEJ1K/zzDTShPNG3Vh/t62f+b7aV71AzxgQ4kcrbfT/q38c/W0UbHEro52mnL+vhafDgVPuyS/rRTtkvy+Dpoe9xAG3BAMcZOpPo+YttQc+2EmwZH+k7EWcjx1m23eB9hONwadvj/FXMf7ehGy2BOyxheoFTKX9OCL16uF9GVxve1XoxJsdcbYTjEmBZBnMYyy/27jlOJ1XMibT1/Sbj2C+E2CUotE0IIYQQQgghhBBCjAmtSBJCCCGEEEIIIaYa2ZY/k81UyIOYUmhFkhBCCCGEEEIIIYQYE1qRJCaX4WEzGyWGmh4AAldKENvNOPsK7xGIOpiYfhfcL4xtL4WulBne72Bt8DEhL4GviXHhdJ8wTj0SBx84lRI6kPKdB4GHh/lDfhJ6gfC8vF9Ch0Kdd0hEoaOBdcW21ec9B2H50WNQctzh3R6BN4Z+h6AdRhwCcI0wr0lzkzvONmzyx/TS0PlAlwrbPUjoFIIXh/dj/ujJYdsJHEV0OtF3Rf9FN/ODtoj7FeENCtLLczjgWQJfBMcBwrpgv2Q75BgX80vA3xE4gjiOxFwnhJ4g5ofPw7bItkaXXDd+Tz9WAW2BLhnqPoYjfYHeoNJxaRBjFD0yrGuWDdtV4FACEedRtn5j7s/pFgvGmd5I3QZzRKSttufPWcGYzrbHfo26rHvKpz/U6H8/XIlxBGP+YJ0/P+uP/nnSgW3lkfbne2KyGl+2Nc/4vNFJxPmrWOvzXra2Lfd+LMusAE9NuT+f0nnI+Y1tj/2cTiTMaYE3KPAMoa/Q/0WvD58P9ws8fTEHYuz9J8+RlOSMAbaddkxYNnw34hgZOJHy5+fou814PXB5TsDtpRdxFmXJOF1q9G/G8hNz0438UEtnhJgI9CFJCCGEEEIIIYSYYiQWfOOcFCL/dCVehCi0TQghhBBCCCGEEEKMCX1IEkIIIYQQQgghhBBjQqFtYnJJ020xz/Rz0IuD2OxinfdlpJ35sdyBRwix4xl9InC/BF4hUno/uDsCjw6fNYjT98eBs6gzll6+u4Rx53QYJEwPeo/ALxUjzz1iFsT9Z1jDm7S1++tr0TYiDqTgeelsYluh14CU+kICz03EZ8XYfR7TocSy4vV0JtEjA99F0tjgf89nr0Bl05GAfhL4sehEgjsmaYBbhvlHv2f+o20ddZ9On4b04ETi8wX+j5L0cO/ApIW6LtKrxrKmb4P9BHkJ3B/sN7ze6IlD3XJMozuF3h8e16Kt1Xt/VtKGcYp1xfJBfrIK5GcIdR/0W/7bGH4fOKMi5VdSP/RfZe2dPqn95/vfIq20E2XHdsy65fyHsgrGtIhrjPmJjkN0k7CseZ51R7dMU6M/X+XbYjKM+8OnVURbSNAWypD/wRl+Ttt0sG8bZWgKrQduK+89/p+vq675Pq1pdzzrf0z/FecjlFXa5ttOMOazHaOtJXAIDe6/h0+OjiSOoaiLIpxPaSt8V+bzl9DXRS8gxhF6hdg2Ag8evYCxthjxEnFOca41ljXnqy4/X8Te1YJ3Ix7zfjEPXcyTx3Ek1k8J78/5MFL2gReQc1rMdQqCcYzv6Xz+0X7PctndyWxqeJ+mQBbE1EIrkoQQQgghhBBCCCHEmNCHJCGEEEIIIYQQQggxJhTaJiaXJNm2VJZhD9yyFktak2osf2ZYBsPJgpCdyHbOXG7O7Y3zQt1iy42LkRAWpB1sSd6MpdptyBuWhmd9DKvA/RliwiW0kVCvYMt4Pj9DSCLbogdhhLw/wpNi6QVbyka2H44tjS8Na0nY7lgWDEPkttwMhSMMS0BbyDoQJhHZVpzXJ9Oa/AUsi37cn7BtM79BGEIkrJF1Gds+mX2DS9+x9D4Is0gxDpSjfkr6ShDexDA/hLalDPNjSAjHOI4DDFNkOFMNQskYXrTBbxEfhPUxpJXjBsfcoG2gbLlNOM9jDM4QvsStoJMelA/ZhHGL+eOcwm3I2ZYQRlEc2NaWg2dnXfxtub/1zOn592bISzeehf08Eo4ctMVh5JfhP9zGOzbmEo6R1QxzxDFDYnj/IYTIsK8gXDw4LvPplXf5521+3J8frPXHM3/x9Mh/F+f4upt2vx8zsya8K6xeb+MhmM8jISvB/AwqHs0fV5IZCO+tQN2xbRDOUZHrg7YUhGdh/mUoG9/F8O7FcSXD+0XaUO9/j3HUhfKh3aTsl3x2vtvEQkbZj3ieY1AsfInnxxvCFQmHjtUtx9SUofJ8t8S4xLqKkfB9KRKOvHWczIqRkL7djWxq7Nqm0DZBtCJJCCGEEEIIIYQQQowJrUgSQgghhBBCCCGmGplNjdVAUyEPYkqhFUlCCCGEEEIIIYQQYkxoRZKYXLJs9JhwbinLbUSDrbRxDF9JBqcSrg63LWUsd8wZUeLRyRBXn3RjC1n6K7jFe2SL86GZPi69AA9PxvsxPW7PWwmvD3wXAdwSllvABj4RuF/onyKoy2IPnweOBbhXAmcTnBFFlE/gP2F5BX6S0fNfXLfBHaf0PXB7XTIIpwCcQaxbemV4PZ8tqfVenahzAf0uuD+dTPTS0CsEL07Qr2NOCR7TwRA4H/KfJyDYUr7kmP0Gxxn9ULG0Af0RwdVsO9zOno4luEK4bXfg62I/ZTun0yniAwm2cqYTaXqz/z19YsxvsEU9/BhsmxwH2JfYNnP8IDyXzvVbrtt676Oibylw6nHM5hjDuuuBz4r9hOnH5ke6Xwr03KGsWTbcBj3mekPdpV3oK5zvUTeF1Zt8cvV+HMvK811qZf35c8761+0z8t8z/9f7rgweuYR5px8KTqNkzix33LtXozuu3Igx+8mVPv1gvoYfi2M++u3QLP++kPZH/DEo26QPYzbHCfazyDgXOKLq4Xzc1JabXuBu4zgWmYPS0rbIdsv5ge9CHHM5RpHxvkfu6BbvEYdQ6CiMOJE4ptM1ync55p9zUjCORN672Zbo0cP9RubQSBsUQuwc9CFJCCGEEEIIIYSYYiRZFnw0m6x8CFGKQtuEEEIIIYQQQgghxJjQhyQhhBBCCCGEEEIIMSYU2iYml4FBs7LN8e6BI4LOh0Yf51+sgfNoEA4L+EqSAfg66AHogj+DTiXGltPNUrUtdjzphEOBvgzEnRfnz3bH6UC+G6Ts8ef8efidArcJ48XhOAji0nuR/4gLJXSXRLwBMU8NyyuIk8dpxOkX2zpwvf992gBvEeo2qYOzIcelktCtFXOTsKwjLrAErq/AaYC8p/BVZEyfTgKC6+n7YFnSwUAPT0avDp1KLHv2+1hbjjgZgutjziQ6H0p8HEFdM29wd5DQV0HXV75vIvBT0DUScyDRj8UxrBFensDVRpcYnod1TY8OdRxsW0HZ43noUkF+aMWgO4YEbTenb2TIS7bBO3uCukHaGbwv9DOl9OxsbB01L9uFYzLLnnVJ0JZK5zMzs2QAlRcLcaC7BA6jwK3GvpGhPDmf87jJe4foKUx5/yFfHjP/Z9ucGrSL9k6fF9Y1+lnCdor5dqjW52W4yvfLumdQl3A0BZ6eHtQF5wxq8CryX/1Tjkvl9NRhXGHdsS1yDGa/7IIDEdA9x34ajMuEXr7SOYAuTTqTqlG3GPMy1EUwf8Xmp5gjiPD6wHEYOSb0S6IsM/o7ScRpFPRzHkfm55jHLm/MfkFR3PJnspkKeRBTihdJDxRCCCGEEEIIIYQQO4o+JAkhhBBCCCGEEEKIMaHQNjG5VFaYlW0JZ2BYBpfmM+SnD6FwXDLMsA+EJgzP9GEcZbHtQiOhAkl3yXJtbpnO7XkRWsVQtmQ9whq4jfY8bD29sd0fM6SFITc4HYS6MTyJ23ZzJ2Usn44uhyZcfh3b8h0Ey8sZekAYMoPl0cH2zc1N/vqObaFzfFYutQ5Dy9COkBeG1fHZgrxzC3rWFZfac5k/+10sdIwr2Xn/SHgX65p1FYTCsXyrGLYxzqX8Qagh2i63oC/py8GW6yw7hpSwbpGVjGXB0LPYmMRn51bVDD2L9KMkFlIaq9sa5B/3TyrQeJgej/k8zA/CHgJYX5GtroMw0tL7cQxnu2U/5/zALdLRdooccyJjWBYLLUN4EkPzgnEBYYUZw58YTsQt4TkGs27q2TaQf4Y1MlyJoXK8H8Of0FbKOMeRGc3bslaPMMMK344Ka/1827v/DHdc3u6fnWF0w5U+b+XdeDa20zXrcs+nHDfQ7lOE/RcrUbeoi2INwp0YTpRFfo/8knSOD+UPQt3YNtmPCfsKw62CkOKSY8zHSRnqvoB2yLwx5BVZC8LuOP8T5j2YzxgaN85t7pk+31X57sZQM85ZzB/zEwv1J+N9HobSbRmnkhfYOgnt2iamKi+sniaEEEIIIYQQQgghdhn6kCSEEEIIIYQQQgghxoRC24QQQgghhBBCiKlGZmHc5GQwFfIgphT6kCQml/4Bs7LNPgE6JAKv0Hq/3XLKbbtjW2HD8ZB2e49Bsdr/Pu2FA4LbluZ5CejH4Hbz8GkkdBzRCYC4/KQN2xETbnFOKiFJKsf1dBbQmRTxgUSJbXnLuHc6ori1Nsub6cc8PJEtZbMSJ9Lm7G07HzgFWFds130o28h2uMG21us3+uvptWFZ0bHE9OlGGUZ6Mdjv6GDi8+E830sCP0jMaxPZ9jw4T2LbKZf+ns/GNb0xfwZ/j3ab9cLzQucQxzzmtZJjYGTRMfs5j2uxjTkdQvTOBH4L1g2OmX+OK2y79O7AqRT0ReYn9fWTwiUXtD13McqSYyz7Fcqq2OHHbLbLYJtsnucYRhca5pgix4nAIYhxCnNCMP/xeVk3GFM5RwT+LT4v2wZ9WRzj6Z5B2yh2+y3l08YGyyObuc2RlPbCv9Tq8zI4twk/9u24Z66v+6qN/veND/v53vkVzWx44V7uOH30WX89+w3HYDqD6JcqQ1uF56asA+MQHE9Bv4cLLaXnj+MK+gLzzzmL3r2EDkh6dwqRMb80Pb4LgITtku+VnF845vN8xu3uWZbj80PSdZY7hm2P8ToF2ZboMCzSCYX0Ik7Dcfs1OS5vzW9Rf70VYiJQaJsQQgghhBBCCCGEGBP6ZCuEEEIIIYQQQkw5sujqtIlhKuRBTCW0IkkIIYQQQgghhBBCjAmtSBKTS2WFWWFLvDu+tgfOCFIP7xAdDoyTx/l01Tp/nq6Y6d5NU6zx59NeHzvev+c2B0PlM97nFPPYBFT6ewXPxrxWwncxgDh56CayCvg3+vGvDJG4/XHH5QfemTT/mM8bcShF/6WGPhE6JZh/pg9K4/hZFkFe2K5ZdvTC0EfVlu9UClwhaDtZj/ddBNR5D0/w5PBbBJ6fSN0GfqtCfltLq+GUojMB9y8W4TOJ+TPG2XacrwP9OLhXe8RdRs8L+nFQd/XeNRK4Ubq8ByZ0FqFtoC0E/ZbjFOsaJDOn+/SqfL9KOrr9D/i8vXCd0T8CVwp9Y0F+2BcI3TH8fc65jC4Plh3yXqRDiL9PMB/Rucf7Y5yg5y2jPyoY0+gUQt12dvnjnkhZYowO6o4euy7fFoK2zzkABM4ljrt003GMZ3rT4Ex6euW239KnhLTL1/uy4p0q+a7Qg7oJXGm+bdCJFIxZ9GXhWZI+n37akT8H0IEUOJH6OebjPNsC5zR6dTjO0FHItprmzykBHAfZFkuPOabH3iNJ7N2H57OIH5JwzEdbCMahwEFEbxzftfD7mHMwyT8fzimRd7NY+cXqns+7Nb3iOF1RQojnhT4kCSGEEEIIIYQQU4wk2/xnspkKeRBTC4W2CSGEEEIIIYQQQogxoRVJYlLJmhssq9iyLHsQy5uxBa91I0wism1rsCVuJbchz19+zS15swYfVjHU7EMRuvfYtpy9Yh1CXja2+rRnz/DnuQUr84ZnLc6b5Y/L/XLfsm6EhCCkJWnlNtsIm+D9CZbWJ9xmO7Kla7A8mc+P0Ltgq2suz44sD2fYRcKtuisj24DnbJEbhHpxKXiwNbMPewhCPAhDRmJEloonDJsk7FfM72hLyUchCGMIQuFQ13zeSFhHsGV9EJqADMXaTs4249zKme0kDMng9vP+PLe5Zqhc0unHvAzhR0F4TyxMgttoc9tt1hUIwjLX+PDghOHGrEuGY0XCoRhSFIQ3BW05EkLLtsO2zn5e+nvWJdsZxxT260j4bLSfsK0hxDUYs9k2ANtu1os5gv1knGTsR2z7wZyAusHvg+djCG1knOQW9Amiz7OS8gzC8Bhmz2OknSK0qm/BNHdc2dLh0+9GiCpIIiGZtsmnF7QdzG8MXcs4xuJ9gvNlEDrGcYv9nG2B8z/qMqiryPycjHcL+bz5PGiXkfky1k9iY3KYAX//6qrc+2UY0/keHQ11i8GyTVE+RfRDzIGZRfLD0DWOW2z7HLdGC7WfEmJqIV746EOSEEIIIYQQQggx1cimyK5tUyEPYkqh0DYhhBBCCCGEEEIIMSb0IUkIIYQQQgghhBBCjAmFtokpw9AMv9V1+dORraPpRGBsNf0YiCXPZvutq41b4EZ8IdRx1LVs8wJkiPvuO/oAd1zz8GqfFLYND7YVp4MA2/GmkS1ig7Kg34POo9j1TJ/g54yjD6+PpBe7H5wVjLMPtvSNEGxZT79J6TFj/LE9bgb/RcJtvlnXzMsgPDb0T0A3kRTp5aG/CmXBthPZVju4nk4H9NPiJvjBytnWIo4kEHiI6P8I/Bs4jm2XTOdFyTYlgQuMZcN2yrLh9fQ7ALpaAq8OfR70Z7S1+/Nse2y7HGPRNoNtxnvhrWn19+PW2Wy7hD6QwH9Bb07gPktzz3OL+6At0ceR0xaDsuS1dKPRZRIkmD/GhS4U3C+2JXrgSmG7x+UcJyLzbeBU4pbv9ORwTGb+MaanNZFxk20hVj+43/BBe4/8d+GZtf5alh2cg0FdwyFUuQbzO+uOZcm8x7aA74Mnjk5BPCvfH6wifxzLyuD/MtQVPXXo94n5cSVww+F5OQ7FXGp0PAXjAttWSXnzXSuY62OhPIE/KuL4Y96jXjukxzko9p4ag22DZRV5NwvKi8TKI+LLCq6PvNuN5Cf2zribkRQ3/5lspkIexNRCK5KEEEIIIYQQQgghxJjQiiQhhBBCCCGEEGKqIdm2mKJoRZIQQgghhBBCCCGEGBNakSQmlayQWlbY/D2zsIlOJHgH6MugRyAS251VIT36S+A1yKp9nH4y6M8XWn1++/ebMfLfww0+7r5yAxwCcAAkdd4PlTXX+/OrN/hjxIUnKKusBv4Iuk8YPw5nQeCRIfRh8PqYCybwc8ScS3A0VFf784yrH6LTCC4XXk8CDxA9CNvOZwNIO+I8CJxJdJ80+LoP/FhMj96ZBnhsevz9At8Vj/nsdBzU0T3CsvbppREvT/D7wG2G+1ei78CrE7TF4H4+PToe6HBy5U8XCNt1LC+BZ4ZemIg/inml44fuEeYn8N4gfzOn+fsV4FDqhBsm8jz0+iSou5j7JQrGkSyNCBx4fUaXCsqz9PrASYS6j7SzKCyL+jqfN3p5MAYmqNvipjZ/PZ8tQ90nkdfBmEuF5/FzjpP8d+2EcxDh+0Ck78QciXSrFZ5bX5IZ5B6+puHZTe447Ua/29Thj/H7wLHEZyEcQxM6iwDrhnNU4MHL99Ikw6gt9gV6avpxP75r1WD+Zn4Ix1GOi6xb9nOIXdIcn1jQruk0iviaYp68oGw5v3NMB/TiBX7I4D044i5jXY2TwEuXjeIs2no965rvfuN1UtHHlW4uz2R40Mwr8YQQuwB9SBJCCCGEEEIIIaYamYVf3yeDqZAHMaVQaJsQQgghhBBCCCGEGBNakSQmlWTFGkvKNq8/DUJKYtt2M0yDW8ZWY8nwEJb+1/mwj6Fmv3y8sMGHriW9WHofhEFs+8/BWt+18GQ2XNPo710/wx1XP4tttGv9UvCM2+Vya2Vuy82l8wwt41LwyLbkSVlV7vmAWOga8xMLPWNb4XLvYEv3yJazQX5Qniyf0t+zHcaWZvN6PjvDHmJhhtz6uANhFZGl8kHIx2jb6W5NL7KUP0P+g3AmPj/qOimLjAMkGj6GaS7W9rGVdlL6fMw7+xWeJatHyGoVl/VzDEEIRi/Kkvfj7xGuE/QLhjEynKjPjxtJEWUbbEWN52Uo28zp/vpWjGvMP2AYRHRrbobgxLbWDkJ0cX3puMHwJLYji4S0jneb7ta23NOcLxkymzb40Dg+a7ETcR+xEJxYyExsjOVxLMQ0Fu4USz/yfhCEe3EcLAXhR2UdCHFl3XL+ZJghwhazjZvccYLz1tHpj1H3xY2t7jidM9tfzzER40LSh/yyLPB8GcaNILya/YjzOcd8lK8NIhSOoWysu8h8H7xblhL0YyQ9HAllG28oVmTMC8qK293HQucIz6NtBmGBrJtipJ+zrHk9+yXzw/Ivzw/FC8pztHEpFqorhNgpqKcJIYQQQgghhBBTjCTLLJkCO6ZNhTyIqYVC24QQQgghhBBCCCHEmNCHJCGEEEIIIYQQQggxJhTaJqYOjB0f91bQ3CIW7pbA0QAfyQC3DfeHAwu876PQ6j0JFSVOpaHGfIdQWb+/V7HS521gtnckwO5gw/U+fTp8ytbCRUKHQWyLd8bB0/nAbdBBsAUs7p/WeXdMbCuIwPPT7f1Voa8j3zMQ3aI3SG9050NCA1bMaRRsv4u6iWyDzS3dg62lgx8k+ccsG+ok4F4JHEhNDf6YWzuzbdXCGwQXTBJs84388TzbJp+P94/5Oeqw1XZnSVvDtVkdnhVjTBFOpAztKh3weRts8ulVdtX79Lt8u8/QD5PYlvP0UcC9ZpUo203oZ2h7QXr0a61Z78+z7Ol0ijmXIm09cCjxfvQK0UcW+D9Kfs9twANXCn4blE2+WyWjH4tjJPsBHEgZnEeBS4WuFToF4Z1JYi6XmH8KBPdj3XKMj7lQ0BaycmwDTu9PzD2XF7IR80nRaYQxhHVD/1XQliL3SyrhOGI/JmxrGLfoj+T7AvsJ6y6thsMxVrb00rXDAUXYFmJb3kcofT8Jtq9n3mP9lm6vmEuMYxLbfeB945g2TmcgiOZ3vLAu6eWLvcezPGPjQswT+EIly+I+rInKhxAlvEh6oBBCCCGEEEIIIYTYUfQhSQghhBBCCCGEEEKMCYW2CSGEEEIIIYQQU43MzMYXTbxrUGSbAPqQJCaVJEm3xUAztpsOhpiTAbHYjPvP4ExKnmnxv1+wp79drfd3DNX67lK+nl6hbQv8hmryu1axyfsiBhp83HftSrpP8OwczBm3XEWrEq9Heogzp8/Cyn3cOp1HrBvG4dOhEI3zH68fK+ZIYvnweJweodKzGV0o48078zLesqGXBnVLL0wA3ChB3UbcZYGbBfmnxyfwa9F1QucRnQ48T+jxofNoU5u/XzM8RPSRlLpo6CQaht/B0O57ImMW7lWxAWMg6iZwg1Q2+vPwa2X0V23Y5M93wMXCY5DUom0WMKbW5rthErpk4MOgzypou5E5IPB90N9B/wa9PWj7pecDn1OkHwR5473YT9Gui/BhsZ8nHd4rEziVImUXuMxibpaYs4h+KRDUTVm+OybqPulHXQ5zjow8T5DBnPzHvCB0kw3hXoP0tPmyCOou5rFjXXJM5LgBt1vSj/zwevYz1E1aQNuBty5BcoE3hwznz6Ghd4htEfdjeee0zYx1RxcayyLie2JeAjgmWb5rjf7LoG5i7xvsF7F+AGIuOI7RSUbnVOQ9PpafQmRcCN6Hhrd/HyHELkGhbUIIIYQQQgghhBBiTGhFkhBCCCGEEEIIMcVIssySKbBj2lTIg5haaEWSEEIIIYQQQgghhBgTWpEkJpdC2TZfAL50Z909/phx/PTWMHa6t9cf0zHR3OSPe7wfo2jwCgzCRcP7l8Rkl3f5uPveWT6tdMg/a9VGf/26I72DYN7PvA+jrDvfUTRc5z0xaUW+YyHwwtChQAdCJRxKcEIk9NTwXzHoJYg5jcbrBWD6sX9FoYNhHJ6jwAlEHwMdB9G8RL7vM6+BgwD9qN/3mwSeneBZ6cuCXyPwf8SeN8gvfs/8E/ou6O+KuFmsD+MGPD2Bf4yumsZt3p8UdTfcgH7WiX5JhxDGmKzaP0sygGeNPXsRZQmPT9Luxw0SeHLoLCIcU+k+K/i6DpxI/Ry32FYiPgy6YmJ9iX4t+DsCdwx/X3qeYx7Llu2c8xnbfaTd0puT9aDsYx4dliXH5Nb28aWXRrw1Mc8cCM7G0mP5sa2yLfB5A08f3xdK/GecPzgGMm+BEwl5QzvO9p7rk2tDW4rVNV1kvd59lvD+QdtL88/zXSnmHoM7LkM/D94HYsT6dcQTGLzPVOL+pb/H/Bj0U6Yd6bfB78fpWOL8nPHdKxBC5ZdV1E/F3wd+KbQVJhe4TMfpJiPB+8g4nZMjbTUmqxJC7Az0IUkIIYQQQgghhJhqZBb/wDoRTIEsiKmFQtuEEEIIIYQQQgghxJjQiiQxdQiW5nOraYSSRUJcEm6py/QZ4oMluek6v1wci9ttuN7fv/zpddtutanDnasZmI68IjGsDt7zFwg7wHJjLvbNsEUqhXgZt9PFdrvB9sGRcKOsEeFBff73w41+6X3Z6o0+gdh2yRXcupohMNh6m1tlx8KzuFybx8HW2TnL1Vn2wVLvSOhZsBQedROEFYxzqTpCOIoIGWVIaBCGwPszxIf9KkgfddU3vu1/WT4Mx+KW7UE4E+uS+Q3CULA1d09J6AP6RdmaVp8XtMMgtIv9Ev0k2Pa6Ab/v8mUbtIU0EkLKfoSwjiAciGXDsmSoGkLrLMP9CcMWOKZzHAjCLPLDEoMxPhYyNHOaPy4dl9Cu2a/YzoN+zhCTyDiQdXZZHmlDvb++z4cXBeFJDGNkyMxQJDQtFoJKOA5FQv2iASwsL7ZtwpBehIPlbiPOMYLHhPMX+zHKPmn17wdBWbMfEYx54Zjo20JQtug3WTD/R0LVGRaIuhhvKFsQKh9rK7FoKZYnx4FS+kc/tV04X0UUC0G/5rscxzjmHXWTFSLvBxyH0G+DfhMJLw7aDp+PoXDjJZb/5xsqN7yD+RJCjAl9SBJCCCGEEEIIIaYaWTZFQtumQB7ElEKhbUIIIYQQQgghhBBiTOhDkhBCCCGEEEIIMeZJwRsAAHP8SURBVNUoTqE/u5gkScb059WvfnU0rV/+8pd2xhln2Lx586yystLmzZtnZ5xxhv3yl7/c9Q/yIkGhbWJyGRo2y7bEPNN3wVjtYXpt4CGgv4Ox5rFt07F19dA+e/jzdNcMYYlnqaegw2/nO1zju1pZj4/fTnsRqF+OLeUL3jmQ9MJtQs9Nv4+rp8MocJ/E3CF0PjDOvtY7GtJuPE/El8G6DLa6DtoCske3Coktxx1AeRYYt5/jCaIfA8cZ02Y7JLFtw+FaoSMhgbMh8EvEyoruELYVenUi0MkQuFroH6HziG2NTorpcJ/QZRPzm/B56OvoKMkP3R+o68DLFtnS3PmXzAIfRhEOpXSYDiOUDeoqcJ+AwK8R+LUwDvXAf0WXSy/6Lcf0WpQPoUeIbpNun37WBFcb2kq2qc2fZ/0lbOt0t5Ucs1/E3B4gibk+mPdgG26ffhFzTEofV+LLssiyYNmyX7Gf2vicI4GLhc4m9svYuMRxKMOcgLqkkypDeRHnUOIYwLrjs9EZlLFs0VYwxgVjJNtlHcY4jpHc7p6/j7wLJThfpOst4pfk7wOXGuuOcyZheXNc5JzGcYh9keVVkl/Oz8F8Geknwb3oQIrNAXR3gWAM5zjBcSR4z42UNd+tgnEt0paYPq+ni41tM5KfANw/43y/5XxWHK/8SryQyLLM3vOe99hVV13l/n9LS4v9+Mc/th//+Md23nnn2ZVXXhmOX2Jc6EOSEEIIIYQQQgghJp33vve99r73vW/U87U5/zD1n//5nyMfkV72spfZRRddZPvtt589+eSTdtlll9kDDzxgV111lc2cOdM+/elP7/S8v5jQhyQhhBBCCCGEEGKKkWRZsBvzZOVjopg1a5Ydcsgh4/7d8uXL7bLLLjMzsyOPPNLuvvtuq67evMJ20aJFdsopp9jixYvt/vvvt89//vN27rnn2n777bdT8/5iQo4kIYQQQgghhBBC7LZ86UtfsqEtIaCXX375yEekrdTU1Njll19uZmZDQ0P25S9/eaKz+IJCK5LE5JIm22LM6flhLHoNYsnh5Qni4Hv7/DEcDcVZTf48vrQXy31+0kH/+7JOpN9fcv/mRneqYvkaf22D91kUaxHjD7dI2oU4/hp/fbHClxWdTBniziv/9pw/H/FxZCjbtLHBX88YY36ijv0rRuBwQAKBzyr/Gzj9GEkWiYGmk4HlQccFr8+DzoDgPPOW7/7K6JsIPDW+XSZwFAT+CpY9vTZ0NmVwIkTcJ4FrhenzeehUYPkwPbSVrLrJ/35TR276USdDaV/tj/iiMqRNWPZ0hTBvbDv0UdC/Bd9GgrLO4D7J6FYxtCXWDfsh2w6fh2VLhxJdNEyP5U1/SVuXPw8HUkJvEMsz4jVyzxPzKLBuOeaxrQROQI4x47OaBl4benCqfN1mqIvAjcK6Hy8RJxTnpMBTwbop4nlYvvT4JXgeuuOCvluSHttxRt9TxEsTeGTy6zp4dvYLPivqMjjPdyHen9fT/xh7npgTifkP+llO2W8PllfMMwjyfF3BOcJ2Fbl3xjEmMp8G/aQQcR7G5mfMCTGHYtRfGRv3gucp5h5nUUck3ifoVOK7KeeEmBNRvKDJssz+53/+x8zMDjroIDv66KO3e93RRx9tCxcutMcee8x+8pOf2Fe/+lW5kp4nWpEkhBBCCCGEEEJMNbJs6vyZwjz99NPW0tJiZmaLFy/OvXbr+ZUrV9ozzzyzq7P2gkUfkoQQQgghhBBCCDHp3HTTTbZw4UKrrq62+vp6O+CAA+zss8+2O+64Y9TfPPLIIyP/fdBBB+WmX3q+9HdifGgNoJg6cMkrw4m4xLcSITBB2AW+kyK9dM2m3OxU9GBHgNZ2d1icM9PfrjTEiKFR3OIVX/XTDh9mUKzD0nWQ9HL5sl+SWd7qwxKSdXjWhnp/vGadP0aITTpzuj/Ppf8bW/39uM02iWxxz+XYGesWFBmmwa2kuZo6tn1zsBQ+ssVtTloJhlmG3ZFgKTdDxbh0nOFNDCuIwX9hYr9jyCnDGHh/lg1D71i2sTAN1j3DJNAWk16E5MSW0rOtdCJcqrT82U5YVny2SHjS8Jxp7riszW9lnLZvf2vjbRcgP6wLhjWwXzK9aF2gHyD8KejHBlh+Odtym1k8tI/lHbQNnGf6DIMo5tQfww4JtwnHHBBsP8/rQXQb7hiREJMEoeJBCCwIQsEioWjhGJ4fEhvUbYbnZXkFYZWou9g4kheuxXbN+ZztnP2GjDc8iPfjs7NtBG0FxxwX+vO3vA/aWqwfkVi4GOeQYJz0v0/SSNuMta1Y+Lb7bX7YYlhWkbA/zp9Ir9jjx/jg3YWhYWx7TJ91x7YRC4XjmDjO0L/oipXou1QkdA0vcymVF1uvGx7Y7v8XO4/Vq1dHr5k3b97zTv/hhx92x8uXL7fly5fb9773PTvttNPsmmuuscZGrxFZsWLFmO89f/787f5OjA99SBJCCCGEEEIIIaYcUyWsbFsejjrqqPjVzyPPNTU1dsopp9jxxx9vBx10kNXV1dn69evtrrvusiuvvNI2btxoP/nJT+zUU0+13/zmN1Ze8mG6s3PbP9jU1dVtL/kRamu3LRbo6urKuVLkoQ9JQgghhBBCCCGEmDRaWlqsqakp+P+vec1r7Pzzz7fXve519sADD9hdd91lX//61+0DH/jAyDV9fds2m6ng6j5QWbJavpcbgYgxow9JQgghhBBCCCGEiLJs2TKbM2fOTk93ex+RtjJ79my7+eab7eCDD7aBgQG7/PLL3Yekqqpt4fsDDOkF/SU7bVdXV+dcKfLQhySx+zAA709PxBkBh1JWDl8I4+QZuw1/B7eJTXvg9ygZlAKHUOAMynebpD3cBjt/O9+U23rjuMg4824fl88tZQPfFMo+64GTiP6qDr9MNPBl8HkYl1/IdxoETqTY9sDcYhcUe7B1Nusrb3vk2PbxdIckkWGXZU+PzHgdSDG3Smy73HL8ntuC81996AuhY4H5R/kF/bQTniD6Q5g+z9fgBSHmMWL+Sq/ns/Janu/t88doV2UbsZyavgmMC8N7NPvfP7sW16Od04XGtor8Za1tPjvsV3SbsJ9xi3f084S+rJgHj/2c5zknsPwi+QvniNHdbskgftvpxwwr5LtEErZDuk84RgXem4iXhr+P+aWYXmwcYFnH8hfz6ARtHeMK2wZfV2NeHG4Tzm3F8zxDsS3bUbaBQykWzhHxSwUOpljZsO6YPsuKBG0r4r3hX9CCthdxnUV9mHTPsa7zPUFWhr7Fd7m88oCjMOp9o8MwaFfIW8yHhfkpN68WvruRhGMy04u8G0UdSOMNXeL1EZdb4KiiY3KU/MfKZbdjquyYVpKHOXPm7JAD6fmy77772mte8xr72c9+ZsuXL7dVq1bZ3Llzzcysvn7bO08sXK27e9scHguDE6OjXduEEEIIIYQQQggxpXnJS14y8t8tLS0j/136YWvlypW5aZQKtkvF22J86EOSEEIIIYQQQgghpjSjSbxLPzA9+uijuWmUnj/44IN3TsZehOhDkhBCCCGEEEIIMdUoTqE/U4CHH3545L+3hrWZme2zzz4jx3fddVduGnfffbeZme255562YMGCnZ/JFwlyJIlJpe/Q+ZZVNZmZWTrkR6iKPz7pjpNyNNcK+DoYyx07jsFYd3oKctw42bQGdzxc590bhTY4iujPAAniwIt7THPH6XNr/PXTmnz66zfmpk+ywIEAvwQ9OHAUJKwbOofa6KeC04A+Dv6+P1+iF/gwqvB7enL4+27vZkkb4ZoZT6x64KvAszKvdDTwPJ+dZV1X64/pYKiCxwfPkhXQrlFVCZ1NdBHQoRS4X/LLLulHfmP+D56nKwW+jazaP3/Si/JMef22th6UPfrtcKM/TruQN5alP7SMro9CjTss29Dpz3OMYr/kGNUN1xn6QdQtVhFxGKHtst/TVZKYr4sMbSep8c+f9fTgfMR/xWM6rOhewfPZUM5bM+cjtjvCumW/oVcmz8u2HbJh9LuYF4feu2B+gx+L4wyJzK9BXXEMjT0fvH5B3UbyG5Qfx43InOATi/mqIo4l5oX+LRD86/sQfp/4cSl4V4p58tgW6O/C5aEfC+mjfIK2QwdSzG0We5djemzLmXfBlTqVYg4i9sPM4EyKucLolWPZ0LeF2wdzBPMbeRfhGB/zcwVzAsdMto2Yr4oE9x/negbOSYH7bUt6U8EnJHYJTz31lP3mN78xs82+pD333HPkXJIkduqpp9rXv/51e/TRR+0Pf/iDHX300UEaf/jDH0ZWJJ166qmhX1GMGa1IEkIIIYQQQgghxKRw66232lDOR/21a9faG9/4Rhvc8o9O//qv/xpc86EPfcgKW/6h4Pzzz7de/INXb2+vnX/++WZmVigU7EMf+tBOyv2LE61IEkIIIYQQQgghphhJllkyBVZZ7eo8nH/++TY4OGhveMMb7JhjjrEFCxZYdXW1bdiwwe6880678sorbePGzREWxx133HY/JB144IF2wQUX2Oc+9zm7//777ZWvfKV95CMfsf3228+efPJJ+/znP28PPPCAmZldeOGFdsABB+zSZ3qhow9JYlLpmlNuA3Wbl/6mQ36Amv4AltwiRCdYvs0wBV7PmwdhENgmtR1hJFj6mJVhiXLpUvnYcl3cKwm2OvZkzT60Km3D1tNcHo28pwx3QhhGwi3YGVoWbNkeWdrO5d0MaeFyb0ZNcPl0sNwaP+ByZ4bCEW7Zi23Jg+XqCLGx0m3NuV1vbHt6hmBwS/PY9rblkSW4Pf5fX4Jl/30Iz0LdJwzRiW1fHNtmNwjLiISQ8HmL+f06tlQ+CBeLbOUdhOaV9PNio28HaYcv67J2lD3LGnWb1SHch2EPDK1iXoNQNtZ1f+75bP4cf7/1rf56hvIxxDWvX5hZ1ouQEoY3NfhxLWlt9+cx7iQMuR3vcnSOOyQnlI3tKBgj2K5jYxDTi/Ujph/0E7Qt1hX7RST8KijZBHPAeMs+9heQSD9leQepcc6JhBQH5VU65wVhhpF+yC3WWfaxUK1I6Fsw3/H+fPchQcgqY2wx7sT6CYjNYRnGkSCMhPdj2CGPY/nDuBeEX5XO90Hd+sOg3UXKLiCo2x1r57FeF217vD4W2keYXqTtk2hfiIT4BowWMhwbT8WUZdWqVXb55Zfb5ZdfPuo1b3jDG+yb3/ymVeLdfSuf+cxnbN26dfbtb3/bHnjgATvzzDODa97xjnfYpz/96Z2W7xcr+pAkhBBCCCGEEEKISeG73/2u3XXXXbZ06VJ76qmnbMOGDdbR0WF1dXU2f/58O/bYY+3ss8+2Y445JjedNE3tW9/6lr3hDW+wq666yu677z7bsGGDzZgxwxYtWmTvfve77XWve90EPdULG31IEkIIIYQQQgghphpZNjUE4rs4D4sXL7bFixfvtPROOukkO+mkk3ZaeiJEsm0hhBBCCCGEEEIIMSa0IklMKsMVm/+YmQ1X+tjowUP3ccflf37S/7ibLhjfnIvN3gs0VO9jaSuWd/nr27yfo/Okw/z9u30seDrov8yXd2yLy0/7vM8iHYh4XehvYNx8h89r4N+IOIqCrZ0Z104HUcxJwH+V4PX02sQ8AnQw0c3C/NJ5AAdUxvIiKZ0QkaEQuz6452cMP2P8g+3p88ueROuC7q6u7tzzQfoRh0LwPDHnEB1GbHt0NrHtM790FsUcFfR99MITVOXbWrHGjwsp3Skl5ZOgX0fLotLXVbGGzh+kh99n5XCl4PzwNLjPQNmGfL9UumaDT2/BHv7+KMvC6k256QVOJI5TrCtcb/V17nBoVoO/vwG6UIIt7Ok1olsG4x7zV5o++wHbLf1PIKuGhy22LTfbAvtVZBvwwInE30fIErhWYlu0s5/yem4jTvj7wGWGfm1wydAth/oJt03PcagEY/ooHpZRCLeEj/i1YvMP5wCO+Zwv4ZsM+hlhXfH5YlvcM7nAg4O2SGciHU9Mnx4Unse7YFAeOX0r2N4+1s9jrk72u+4ef55jENNnO6Ufiw6lSL8J3pXo34q1RfYFjluRFSpB+pG2lKCtoJsH/Twp235+k2G0ASHELkEfkoQQQgghhBBCiKlGMYuLxycqH0KUoNA2IYQQQgghhBBCCDEmtCJJCCGEEEIIIYSYamQ2RWTbk50BMdXQhyQxqXQsHLZC8+YY7nTAx0o3LffB0RWzZrjjDK6VhLHoCD2vWNnqT8OJlMA9U9mGWPhhP4KmQzmuFjgG0rX+XkGcOYEzIPA/9HgnQEI/B5efMu4+EicfxrVHXDB8Hl5Pgt/TkZDvEaA3KHAiMT/wDCRVEXfLAOLrmd/S6+lUgDtrhyd/lmXEh7WzCdwiPEZbS2owrcTaDqGfg84i+DXodAicUt1wRvX4cSKlxwfjQNpf4qCI5T3ioUkxRLHssgrf7+hUKjb6vBZafYLJmo0+fTiHgn6Bdl/W5tMrwicVuNn6MIZOb/bn6Syiy6wK/bLPX1/WATcZ235djf89/VusL4ybQVumr6R0HOCY01zv7835gL4otkvCvMb6HX8e8fYE4wTniMAxhNNwsdDXFXOxBP16R8fFwGXD5CP3yyuuYEzN97AF/YrtlGNgcIyyir0fMHf05gR1ifeJIj0zEU9ehKhjkR4c9iXMt0E/phdwML+vZf2YI3KIll2sX8X6dcwPGXGPBc6giogjiY7AiK8rIBgXcMz84efB+wLzE/OBjTN0Khh3WF5CiF2KepwQQgghhBBCCCGEGBNakSSEEEIIIYQQQkw5sqkR2qbYNgG0IkkIIYQQQgghhBBCjAmtSBKTyrQHU6uo3fw9s36lj/UudPrjwdneSVFog+8CcfT9e3gfRyXunZbNccfFOn9FeZuPs3euFDNLBuEdKNsWC07XB507xfYOn1aNd31k9GvQTYJ/mSi2tvnrY86DiIMhQ/pJF+QudBhUoXSR/8ABwWN6AmL/8gLHRFIJl0u/L296D7I+5I+eH8bx5+Un5hQgERdKBs8O/RHB/VgW43QiBE6DGLHnY9vK80uNJT0Q+MDoz4A3J1u3wZ9nXaPtFhuQfokbLe2Gi4N+pu1luBT2i5T9yD9LhrJK4QbJ6OfYc5Y/pguF7ZquFuQvpXukFk4i9jv6KXg/ulDoH6H3rh1+K44zXZgD2PboiGLf4fV0q+T4T6J5IzH3B8uC/STSj+ksIrF+zjmIXpvATZZijqP3poiy5e95/4qK3PMB9O7QdRN4AVF+efXBc3QmRbwx0TGZdRvz8NA1xvywLAxlkYzPuTRuOIfx/HjHfJYH2w69eezHsforyW+Qk4jLK4FvMeiX0WeLuNCQXvBssX7M+TA2rsTcbCTybhl1wUXeX1gjobMq8v60JX9RZ5wQYqfwov2Q1NHRYT//+c/tvvvus/vvv99aWlps/fr11tvba01NTfaSl7zETjrpJHvHO95h06dPf973uf/+++2uu+6y++67zx5++GFbv369bdq0ySoqKmzu3Ll29NFH2znnnGNLliwZNY3+/n77+c9/bsuWLbP77rvPnnvuOduwYYN1dnZaQ0ODLVy40E444QQ777zzbN68eWPK19KlS+1rX/ua/e53v7M1a9ZYc3OzvfSlL7VzzjnHzjzzzOf9vEIIIYQQQgghdgLZFAltmwp5EFOKF+2HpGXLltlb3/rW7Z5bv3693XXXXXbXXXfZF77wBbvuuuvsxBNPfF73+dCHPmT33HNP8P8HBgbs8ccft8cff9y+973v2Zve9Cb73ve+Z1XcTcrMVqxYYWecccZ209+0aZMtXbrUli5dav/93/9tX/va1+xf/uVfcvP0qU99yj75yU9aseSL/Zo1a2zNmjX2q1/9yr7//e/bjTfeuN28CCGEEEIIIYQQ4sXLi/ZDkpnZ/PnzbcmSJXbEEUfY/Pnzbc6cOVYsFm3lypV28803249+9CPbsGGDnXLKKXbffffZYYcdNu57VFZW2uLFi+3YY4+1gw8+2PbYYw+bPn26rV+/3h588EG78sor7emnn7abbrrJ0jS1H/zgB9tNZ9asWbZkyRJbtGiR7b333jZnzhwrLy+3lpYW+9nPfmbXX3+9dXd32znnnGMzZ860173uddtN55vf/KZ94hOfMDOz/fbbz/7jP/7DDj30UFu1apV95StfsTvuuMNuvfVWe+c732nXXXfduJ93vEz/fxusqnzLsmUsRR2e2eCOkz6EafT45c5ZnQ9JKZZHAk24HByhajzml/isgCW5pUuGuY00lxNHtr8lPJ9iG+0Ex0FeuxGaFlkazuXbwfLifpQNt9VmSAvT5zbgDJnp9XUbhPaNc3l2kmK5NwlCByL6uKGSpfEsm2Cpdj7B9r0kFnY3XqLbhOe06+0ds62hLIOwjjL8PrJ9MZ8/6+OW8Ggr3Kac9y/Pr5+UW86X1idDOvksA+MLyTA2y8hW0VkQzoPjHpTlUP5WzIYwyrAf+wwmG1uRIYTYdnS543RaE9KLtF2Mm9k0zAEILQyeB+NIEErIcYLhVHnjdBA+E+lHDDHh7zlOVDC0DOFRTB91y1CuILQjCMHN3yLeUoRtMnSPYyafLzZGcw4M7p+/ZTwJQoBAEPJDSsqLY5gNRELDYqHkMWJhiUFd+cNY2bDuEo6psfAh5o9h/aw7jisMLQu2hGc4Fu6H95/gfrFwrLzQO4b/cv5j2fJeQchn5L2TZRl714i8T/BufJ8InicWas7rOY7w+fn7ILQeIbL8PdseiYUEjzanaOGMEBNCklGG8iJheHjYyiKT/09+8hM7/fTTzczsjDPOsFtuuWXc9xkaGrJCziTf29trxx9/vC1dutTMzB566CE79NBD3TXFYtGSJLEk5y8Zy5Yts+OOO84GBwft5S9/uf3xj38Mrmlra7N99tnH2trabK+99rI//vGPNmPGjJHzw8PDdvrpp9utt95qZmZ33XWXvepVrxrX846FlStX2vz5883M7NUL3mtV5VvcR5EPSaWuEjOzsk2d7pgfknr28k6l6hX4S04fXk7KIy+DsQm45DjpxF/IevyHnGIX/BqEH6LwMsEPScFfWnb0QxI/3JBYnH/kL4zj/ZAUexEN6obPG3tZ418c+MGA5H1IisG/LPNDCuseRD8kjffFkMQ+JJFIvwg/JI3zxZblxY+u/As3/8LL5+Xz0fPDjwul+UXe+DE56UXeoh+S8JfpJj9mDdfwQ4c/TPt9v017cP/YhyTmp8OPqdGPITv6IYnps3xr8BdgfkiCC83oL+H5cYzhZja+D0l0d/FekQ8d4V/A0E47fdkGH5Lgdwo+JHFcwbiVVKLfxMYZjplknB+SAqbSh6TxemPG661j2XD+jnlxYmXJ9Hf0Q9KOOooiH5KC54l9jEB90TMYfKworQ/mZZwfkgK3F/Ma5GWcf+XifMl+HPmAHDzPeP2U43UNRT4kRdsGiX1IGuX3fcNddlfbDWa2OapjrNqPqYT7u9L886yqUB/5xa6nb6jT7lxxlZntvuUqdi4v2l3bYh+RzMxOO+00O+igg8zM7O67735e98n7iGRmVl1dbR/84AdHjrd3nzRNcz8imZkdddRRdvzxx5uZ2Z/+9Cfr6uoKrrn66qutra3NzMw+//nPu49IZpvL5Gtf+9pI2XzhC1/IvacQQgghhBBCCCFeXLxoPySNldrazSsn+rjL0y64x47epzSdfu4+Y5tXWJmZNTQ0jOpcmjdvnp1wwglmZvab3/xmux+khBBCCCGEEEII8eLkRe1IivHII4/Yn//8ZzOzkZVJu4Ibbrhh5L+f733WrVtnt99+u5mZzZgxI9hpbmBgwJYtW2ZmZsccc4xV5Gy1u3jxYvvVr35l/f39dt999+XuKLejZG1tlpVtWfpLt8rqtf5iLHEtcrkyVn8N7+9DBAOw5DgZbxhI3hLdXoS2gRShXNHlz5EtaaPHIAhl4wo9Lp/mcunAWeTbUxZzJoGES9OrEdLC50FYolWhPbejLmMjHUNiuFSfy8NLrg9WCzKvfLYEziNu9xurO9ZFLGwiFpq2kwnyE3iFdnArbP4+1vYZ0sL8MCSpE2Gn9SVhmBgjEvTbrAohGqwLfuBH3jKGWTDct8Zfnw7ke9wCWLZwCGV0CvEYYyxdakEoWyxMhCE8rW0+/T54g4KttyNtm2GLCHXLmn2oQBCaWJr/yBiWoV0F80kkXJntOkiP4cxMj21rnCE0QTgXxsC0BnWBUDiG1gUhp6w7toVgDkL5RRxMScLyjLQ9wjDU0rRjjj6G3zKvHIP4rLg+FsoWZjDiqYm8TyRZfvhU6K/Kn2MSagLGGT4VlF/MO8i65Tg3tANbwXOMQVkNt3e4Y85/QSh6bP4DWU673C5sSzwfGYOjoelBqBz6OfphNCwzomV4vqH1US/m7kZWjPukJiofQpSgFUmgp6fHnnjiCfvv//5vW7JkiQ1vGQRLw892lGKxaGvXrrXbb7/dTj/9dPv+979vZmYLFy4c1+5w/f399vTTT9vVV19txx57rLW2to6a1yeeeMKGtkwwsY9VpecfeeSRMedHCCGEEEIIIYQQL2y0IsnMrrnmGjv33HNHPX/BBRfYWWedtcP3WbBggT377LPbPbf33nvbLbfcEnUq3XnnnbkrhM466yy78MILg/+/YsWKkf+OydG2yt34u7GycuXK3POrV68ed5pCCCGEEEIIIYSYfPQhKYfDDz/crrzySnvFK16xy+5RKBTs4osvtg9+8IPW0NAQ/8EoLFiwwK688spRVzR1dm7bjaeuri43rVLX0vNxJJV+iBJCCCGEEEII8TzIsvHv+Ler8iFECfqQZJt3ZzvyyCPNzKy3t9eefPJJu/HGG+3HP/6xnXXWWfblL3/ZTj755B2+z69//WsbGBiwYrFoGzdutHvuuce+/vWv26c//Wl74okn7Gtf+1r0I8+iRYvsL3/5i5ltDm177rnn7Kc//alde+21du6559qll15q73jHO4LflUq88/xIZmaVJe6D3ojrZ4fJbMQ1RPcLvUFlC/CBCtshc4v76nU+tjzhNqnVFTiPAXJjqz+OxXKXph/Z1trgXEjoAOC96WPiMd0f3NoZTqbAPxHxUwS/Z5w8SBqwTWmwDTrLjlu0I0GG2dOh0EG3yTi9O5HtkQ3bvLvnD3wZLFs4BJg3lj23t485A8brRKKzgWVPxussGC90Fg3nPx99WYFfLFY+KVw0dELAq5OVlZwvo4vDHw43wLeEtMs64VDq9ONrQr8FXCOFTrSNGBgTi/B5pE2N/v7ot8PrNvjr6/D8LOvYcdCvIh4gErhaIv4OEDiROn35BH2hdIUwPGysm6QPjiE6gwInENslygpjThZzfmzAceDNYb/luJQ/jgRzSuBCi7xOBmN+xEkIoq6V2E68g6gfjiOl6cfmC5ZNzGkUyxvyEvU9xbxy9HPRFRbzUbEtwsUWdSzG2sI4nUvRLeB34C+3SeR9OCDma6JDZrzzZfBeme9WCxyLrIvYuxbhODGGHa53CLTVYJxhX2DbHM2/pQ8eQkwI+pBkZk1NTdbU1DRyvGjRIjvzzDPt2muvtbPPPttOPfVU+9a3vmXnnHPODt3nwAMPdMdLliyxf/3Xf7UTTzzRrr32WnvwwQft97//vdXX14+SwubVQocccsjI8RFHHGGnn366ve1tb7PXv/719s53vtNaWlrs4osvdr+rqtr24WKAf1EFpTu+VVdX51y5fWLhcKtXr7ajjjpq3OkKIYQQQgghhBBicpFsO4e3v/3t9qY3vcmKxaK9//3vH5FZ70yam5vtu9/9rpmZPfTQQ/b//X//3/NK5/jjjx+RbH/yk5+0Rx991J0v/TgVC1fr7t62a1FshdT2mDdvXu6fOXPmjDtNIYQQQgghhHhRUcymzh8hStCKpAinnnqq3Xjjjdbd3W2/+MUv7J//+Z93+j0OPvhgO+CAA+yJJ56wm2++2T772c8+r3ROPfVUu+yyy6xYLNqPfvQj+4//+I+Rc6WC7ZgMu3RF0UT6jnpOOCT3fN1jm/z/aIJTCuFThT8v9+exBLa4cIE7zsr9d9W0GmEo2EI2WbvR/74kvIyLmbmtdhJsV4tvuo14Ni7TbfMhKiTYUpXhTLx/bPthhgVgMglC37jUnls/R8I6kn6EKzE95o/nuVSeS/15f5ZvbFvx0rYUSysW+pagLLHUnku9dzrj3Jo5RrA0vTI/3MsYmkZ4Pdsuf8/88xjhuknJh3MzM6vx+S0Nec0KDLPzz1rYhLTY7hhOU1+de55hBlkwsniKtX5L9hRhDMG/HCEkliEwKfp1sJU16yIWxlFEefRiXGGYI8MauJqW/Tyy2jbheb4U83xp30ZdJhjTg7pD2WQ1vm6i4wS3aO9CmDnafRCiOd6Q19i23pEQF4aExvphsMU722Is/di25By3BnxbC+6fE56WDXD+Qxg+Q8GZt8j8GSXWVmKhZLH0Im03qRhnaBnLlvnDOBJsGR+b8xiexb6CtlDk+0pJrHwQTou0GN7Lug3qmu8OOzifBrAfMb1YSGss5Cv2LhTRLGSBhwBQMRE5DjQRkdDCkbYUC+ETQuwUtCIpwsyZM0f+e7Qd13bmfXbkHnl5PfDAA61sy4TH1Uqk9PzBBx/8vPMjhBBCCCGEEEKIFxb6kBShpaVl5L+fT5jXeO+zI/fIy2tFRcWIl2jp0qW5nqS77rrLzDZLt7dKyIUQQgghhBBCTCBbd22bCn+EKEEfkiLcdNNNI/996KGH7pJ73HfffSMriHbkHrG8nnbaaWZm1tHRYT/60Y+2m8bKlSvttttuM7PN3qU88bcQQgghhBBCCCFeXLxoHUnXXHONnXnmmW43M/KlL33Jfv7zn5uZ2YIFC+y4444L0jj33HPNzOwTn/iEXXLJJe78smXLrFAo2Mtf/vJR79HS0mJnn332yPHb3/724JobbrjBTjrpJGtsbAzObeXGG2+0b3zjG2Zm1tjYaKecckpwzTvf+U777Gc/a+3t7fZ//+//tde85jU2ffr0kfPDw8P2vve9z4a3xChfcMEFo95vV1B79+PuePjgvf0FdFJg6+ekyjsohhDbXkaPD/0j8J8Uq+BIGvQx12X1ftVX0lCSHlZ8BU4krghjXHjggYHfgU4GuksIHA/B9XAYBZ6eeu8RSOhoijmMqkfvZ2YW+K0C6Jigf4R+jci/miQc+mJbypLS9OnoiW1tzJj/iI8pifgg8twem8/TH0WHQv7PA+hEYFvETo9ZR77cP/Bjxbw7kecvtsNHhnEhoa+L6XOcKWmbgXcmVrcxp0+172eD030/Swfg/qALZCjix4j5M1gW3NY75qVherF+FPwebberxx/j/lkf6pblTScV6j4YZ2L/ulraFtkueO8ssj096ior5zbdqNth5C3mZIq5W8h4PHDbO2bbirlfYl4guk84bgXboOeXX+BiYV/Mcc0E/Zxp8V6RdhQ4luizYj8LypaOJG7pTu/eOL04hA4oOosi5RO9P9p2xufZUQJfGN71SsszyBvms1g/mWjYFrgcIOJKG2/bDe8XcSaRwBmV7zIL+t44x5lsy+8z+viEELuEF+2HpEsuucQ+/OEP2xve8AY77rjjbL/99rO6ujrr7Oy0v/zlL3b99dfbPffcY2abw8KuvvpqK4xTaPjwww/bueeea8cee6z90z/9kx1++OEjHqOWlha744477Dvf+Y61t7ebmdkJJ5ww8mGqlG984xt23nnn2WmnnWavetWrbOHChdbY2Gjd3d322GOP2c033zzywStJEvvKV75i06ZNC9KZNm2aff7zn7f3vOc99uyzz9orXvEK+9jHPmaHHnqorVq1yr785S/bHXfcYWZmb33rW23JkiXjel4hhBBCCCGEEDsRhZWJKciL9kOSmdmmTZvs6quvtquvvnrUa+bNm2ff/va37YQTTnje97n33nvt3nvvzb3mnHPOsSuuuMJSfo3fQldXl1133XV23XXXjZpGc3OzXX755XbWWWeNes273/1uW7VqlV166aX25JNP2v/5P/8nuOakk06yb3/727n5FUIIIYQQQgghxIuPF+2HpN/+9rd222232R133GGPPPKIrV271jZu3GhVVVU2e/ZsO/zww+3kk0+2N7/5zVZTUxNPcDu85S1vsblz59rtt99u9957r7W0tNi6detsYGDAGhoa7IADDrBXvvKV9va3v90OO+ywUdO5/vrrR/L60EMP2dq1a239+vVWUVFhM2bMsEMPPdT+8R//0f75n//Zmpubo/n65Cc/aSeeeKJdccUV9rvf/c7Wrl1rTU1N9tKXvtTOPfdce+tb3/q8nlcIIYQQQgghxE5iqoiup0IexJQiyTK1CjGxrFy50ubPn29mZosbzrSqdLMXpO8VB7jrqp9t8z+kIwLHxW7v16CXIIUvIzsQDqaIAyLtgWOhu9cflzoYYn4KOoro9ojE7Qfpj7KSbQQ6luhCgdcmcBrRL0EvEOHvmV+mx+dHXWSd3rMTeG5I4E6J5DfmcskjVleEZcO8EjoB+Cx0h5DYs8ecBKybiDMii3hoMrYduFHo5wrg86CuMvq0Yr4s1EfQF0r9JfRBdHf74xr8lnXLMQz3HprhNzdIB+Hr4BhE4HlLelAXkbKjSy2oa7a1Ap1HdEjh92wbrCu2Rbpj2HYivo+M5U2P0HT8w0tHpz+uLXFWVSEvfZG6CMoaz8ZQeZQlnX2BD4ttsa09/3zMXxX4riJzCsc59rOYOyVC4OUJHEkoH7Y9phdzJJU8T+CXijl/eD3zFktvPE6+7RwHY2rM0xdxmzG/9OAFdRHzY43z/uOGbS0YB/CuVnou0k4DR1KknUVhuw5v6I/H6zIL7ke/Vr5DKTgfm4/HSdCvI3UXOBOZ3igewr5it93V8QMzM1uxYoXNmzdv/JmdZEr/rvTq2edYVdmu2zl8rPQNd9mda68xs923XMXORbu2CSGEEEIIIYQQQogx8aINbRNCCCGEEEIIIaYsxaJZElnNNlH5EKIEfUgSk0oyrdmS8s3hHJXrsfUzl2tzi3csyU0RksLhjsuz0zWb/PlpDT5vvbgfwzI4oJYuAcZy2yCUjEvFuXw3FmISCwHB/bJe3D+2PJvLhVn2saX9vJ7hXLx/ZIJMauEpi4XEMOSGoYTMfyy8jPcrDffi0vJICAbvlVXmb3ef9OHewVLvSKhbLESFxML6EOoWLFUnOxi2EWxFze2CUR47vN0xcVtFI62mRn8cC/chGFMK6yNlEWyzjbqNhQMRhi2UIX2GNbIsE/Qb9juGG/dzDEUYQyyULRYiw3AyXs+Ql9Y2JIfrOQ6WEhtT+KzBNtisW7Tr0e+8GY4rQXjuOENiYmUbG/OD8Otx9ntmr8h+nB/WmMXGufFsWT/eUGeGhgXhSTiOpRcr+yB0jWWNEE6GgnMOYfp8/4jlLwi1z697lg/nkCAUMHb/nDDF7eavZNxJ0sgYmfPbzTcbZ6jaeEPXgvQibYOXjzdMk3CciYTKjTvENXh+HENJQRh6OBLmGXvvEELsFBTaJoQQQgghhBBCCCHGhFYkCSGEEEIIIYQQUw3t2iamKFqRJIQQQgghhBBCCCHGhFYkicmlvcOsbHOMebIBzqI5s9xxEPHcha23Ecs9dNRB7rhidYe/ni6aTjiaerBlLL0ClYjdLnFiZHAMBI4C+DCyIb9NdbDlKV0ndIEwjpy+DeaVv4/FtcecQrEt6uFKCeLX6SCKxd3T7TLeePjxegLy4vz5rDG4vS239Y7ljfeLbcU8XvcJz7NtoS4TPj7rht6cmMuE7hP4LoLtiiO+sYR+snpsoRtzKJW6bngtvWnst/RLxESVTC9GhsJn3fF+9GX0+nEnuB5lm8F9lvD6hnp/DLdZUuM9dhnG/KDsY+4zulwa6bnD89FbFCNvXAm2RMcxvXA8zzmhL+KhY15YFs3wdfHZu/18VuxBP454cxLWbcTxFMD8Ej5fEnEyxcZslneMnPSizp6Y5ybmqeE4QSLeuyB/zG4wJqOuA7cY5xikx+el6yxwJrEfY4wO3Gv0DmIc4vvCuJ1OpccRB1DM8RODZTVeZ+B4zwf9aJyutBjjTQ+utCTLbztBecVccBnfrbe0FS2cEWJC0IckIYQQQgghhBBiqqHQNjFFUWibEEIIIYQQQgghhBgT+pAkhBBCCCGEEEIIIcaEQtvEpFKcN8uKFVu8Foh9Tje0+4tjTgbEVtM9MzjTu1HSQX++DOn3HTDbHVc9tcHfL8eNk5TnO47ofYmRZXSzwPsyFPFv0FnA811wGDEunb/Hs9NZkCT4fRpxIMTi7mNenRgx3wjj9Isoz4Ecf0nMoRD4HiJOHl4PL8243R90HMU8PPRV0LWC/AZtmf1igM6D8blOkpiPi/D+421b43FGMG9sJ33o9zif0F0Cr07W5sfAwO9EWBcsK7pE6KOI9LNgXAMZPTgFpIcxOZkxzZ+PeXY4DkZcKlaJ8uXz0mM0SDdMSXrBvVEWbAvsNxxTeZ79kv0WY2qGuoi1Ws4hdCJFXWQxxusJikFHU3lk3EHdZYHvK/99oXQcD9Jm2UT6QXS+CnxXSC+YjyK+qPE6/4JnxzjFthiZoxK2VeaXvquYu21Hw2fG6/3JI9aui5ExE88WtMsdJJp+pCxj+Ql8V+N9/4gQjEuB4Cv4gT+mL2zLOJykGI93d4pZ2I8mKx9ClKAVSUIIIYQQQgghhBBiTOhDkhBCCCGEEEIIIYQYEwptE5NK33/0WDZz81LWlWub3bmFH/PXZu2d/nj/+blpl6/vcsdD031YSLHCL6FNK/1S2Mq1/vfBkk4u/y5dOh+EhmEpOPKaMVQssp0tl55nZf56fiHOsvxwpmDrZy4NZ37q/LbeQfgVV6oj7CBhSEks1I3HXG7OsIXhyPLbYLvlyHJzLu8uXQ7OpGLL9Bl6Fdv6mNczPdYd6fHbfkdDw2JhEwjhCRb+x8I+QDTMk/llSA9DimIhsLGwClL6/HntYHv3RjtPEPqVsd8glI1ln3V1+/Pz5vjL123018fC/FD04bbduD9DfBji0o0Q2ZpqfxwLWamMhIdxDGZ6sXGK13f4MT7D/VwIE9tZLAyPBGMaBg6mzzA7tJ0gnDkWIspQNm7ZHutXMWLhXLFxLPZ7hrAg/8HTB+mjrbKtlx7G5huOubFxgTC9WD8NQt9Y9+MMFee4w2OWNdsOQ3h5P/ZjwjmCbZ0wPwznKsufQ4MwzdL6iYV+MW9BOPD43l2Cdsf7cYzl9eynGEcSagsI85N/dTz0PnZ95P5h2Cau5zjHuuS7XLa1fMYZSjvFybJiqLiYpHwIUYpWJAkhhBBCCCGEEEKIMaEPSUIIIYQQQgghhBBiTCi0TQghhBBCCCGEmGpkNjV2TJsCWRBTC31IEpNK++2zrby+yczM6jhAIS6fsd9Ja8RhhDj9wibvFxmcUeuOhxr8FraFVvg+GPc/gO5Tehg4hugQQlw7u2LMTRJst4u4Zfo0iuPcmpnOBgI/SOAsiP0+tuV6bMvdGONNL+Y7GY/3J/A1jdMBFHP8kPJxbm/P41hd0WfV1uGPuSX9YL7fiyTB8/ryokcoqUJ+Ym6VYBvwSH3kbaXNc4GfAd6WHj+G0DXCMS3oN0wPeU83bPLXc7t7pBf4Nli3cATFXG2WRLZY5++Dfonyo3cu4m6J1mWs73BO4ThW6qyiP6mhPj9vhdg21nR15XhczMY/rsRcLXSpBH6syBhNIn6twHXC/Mf6MUnzXWccV6Lbrue60PLHqOgYy+tZFmwr452P2XZi7Z5ORh5zDI955WJtI3CzRdpyrDxjnhwyMHr5sV3EPHHjJuab4rtdLL2YU4hlGfNfjtdpRGK/j10fK98dTV8IsUtRaJsQQgghhBBCCCGEGBNakSSEEEIIIYQQQkw1smxqrLaaCnkQUwqtSBJCCCGEEEIIIYQQY0IrksSkMufWFVZV1mpmZmtO2cedW794D3fc8PQ0d1yxEf6Rfh/Xn5XDmdDnzxfa4QMp0FGBzDJWvN47llzsej9cH4iTz+ACCXwVFd51ktBZUBlxKtBp0NRgudBZ0An/FMuGLhbG+fP8eOPyaQqI/R7nsy7vw2IcfuDZCW7PuP1xeBLou2BdjNfpQ2IuFUK/0zAdCxGXC/0cdCLR9RJzw7CtsS7L4a2hD4x+EfrHIl6hBOkH9887Rr+M+R0S+irw7IGbhL4O+jVw/wzensDjU1ONDOW72zLUZeBwor+K17NtclxrbXeHRYwzrJtkhh/zs9Y2fz7Jd1RZMA5EvD8s/8aScZPPNhjx1vD8ON1hgdskNm6wn7KfkPF6bZgfMl6XTMThFMBxjq4Y3h9tL2VbyfPqRMYQ9tvA88ayYt3wWWMOvphkl/fj9ZH5luNEQv8jyyrWNmNjKse9wIWG+ZpjNkFfY33lwTGWz5JOa/bn8SxFjEkBbDsxfyOyHsxfzG/kXYyeOY7ZLLuE3jwes63GnI4RR2FQt0Fbjrwv8PqtXj6tnBFiQtCHJCGEEEIIIYQQYqpRLFr4r9uTQOwfLsWLDoW2CSGEEEIIIYQQQogxoRVJQgghhBBCCCHEVEOybTFF0YckMflsiRnf45ct7n+3L5rjjtNBxJrTgdTrY8GTLvhD4C1K6JCohouF6/V64bjI8/bQUUD3B700jDuHzyHr7fWnGRfOOHb6OehSYd4Z114Ntwrj6mPpBXHzKOuYl2e8fhC6TehmIYE3iB4jOiyQXl5+Yn4IlhVVK1w6TD8F02NeYi6WiBMi+qLAsknYdvl8rHukFzxPxJVCr09fvo8s5sEJrs87T/9TzOti/fmn8Xs6kKJ1wX5CH0ePHzeC/KIfclyKukY4zjA/nXAUwXeVdHvPXYa6TTo6/XGl/z0dUUlKtwuep73DH+P5Yj4UB/1R8D0F7RJpBXXNtsD7sS7YTwJn0A4uOI/1Q57n/ThuBf0w320WEPFzkWCOjbR9Bx1InI/p+OH8EfPA8DjwvmEMjzmWIh6aaFvg/YIxHsd0GtFjxzmIZc3nZd2PN3om1nbY9/J8X7H5l/1svP7H4N0o/30hcCIF7rL8dp6xLmL+LNZVMMfQsYS2wL7BvhRLP+aA5DHb4tbnj87NQoidgULbhBBCCCGEEEIIIcSY0IokIYQQQgghhBBiipEVi5ZNAdk2d5YVQh+SxORSlm5bytrjwxwKvQhXwvjVN8uHX9UwlG2cITIJl8oHW0vnb5vqlhhzS/Jgi1Ms7+Vy4X6EssWW6TPULNhSlnnHcueaGqSHkBjC/ARL02NbvEe2kAUZry/kL3+OhoUEW0HzhuNYvh7bxjqNhDGW414MR2JdxcIamHeGLRBuHxwjFsISoxPPF2wNHdsGPBLCE9Q1lvozdIDpMUwjb+vp2JbkDF8CwZMi/Da6xXmwfB/hVgz9Yn4YAsp+lLFs8bxMD6FqTC/r8M8XeyktdvnQuHRPH+4c9BWWF8d41i3bCvtaTkgQw41joWxhODHuHQnnDcIUGTJSV4vfY9wpRsIUY1vMj5dgHIyMEyz72Jb1HMNj831s3HRpMa+RvzwFodv4Pes2L7TKLHx/iG2JTmLhy8EYzveRSOgZn4/hxgztC/KXX1cZNQS1eD+JzIkMJWR4V27ILp+1td2nHRvTGU7LMTUWYsnjINSaoW1oGxW+boL7RebHYEznu2ostD4SLh0ja0c4c0xTQGq2/L1gKPLeI4TYKSi0TQghhBBCCCGEEEKMCa1IEkIIIYQQQgghphratU1MUbQiSQghhBBCCCGEEEKMCa1IEpNK21FzrbKmyczMKjd550PlRh8nX9bt4/CrehGXX47Y7P6IW4bQeUSXDR0YgVegJJY9z6tiFm5/y7zVeP9TkDc6CoLth/OdRRm2TA22w41tORvb8pbeHfo6/v/27jw6iirfA/i3s5N0QgKEsAqENWyOsgwImkRGUVwHFHmgBo7igoo4Ij5ZBHGQp+KCgxuIiKIiuIGCOIIEUFmSgD4QcVBACAYIAUwIkPX3/shL2f2r7q7qELLx/ZyTc5LUrVu3qn51u/t23V9ZtF/C1WPCC9T+6+2pv0Vv3yo2dH4QnavG1+OMrY6Fzq1lynOjjoXO46JzFOhYsIodHbdWOZVM59rPWLBilStFP+paU+fWoY6nnFaPCdf5Nkw5IHTOKh/Xro4DzeIx4KZjr+M0QuW50cfCKpeYyinkiHT6rk+fi1CdH0odmz9y3ben8/TonEr1wtyX6+vMKheL7reyc9yX635EX1u6j1b7axW5pceO/7lqdH210L2tASpHUWneSfdtqeta51TSbTE9ttvqm2Ad1/rYqOvCFKtWjyWHxXWpc5mY6vP9GG8dS1blTaxys1jliHKry6LPs3xEuZ/f2uu41dedPhZWfbB+zdB9nC5vFWvqOvX3XIlart9v6Dw4plxm+vjq5TpHpOLQ+cb8SBhsyqek3xvo9x7+JiO2ev1VsWCKa6uchfo1xiLHkYS6b09C1PYLVR9q9Xpu9V5P75/ux1TuVMvYL39vW6zyjBHROcGBJCIiIiIiIqKaRqTyH4hQ0XYQueDUNiIiIiIiIiIisoUDSUREREREREREZAuntlH1cgBGWg2daibIdx6CgGPuOSh85rEBzHPJ9dx3q3wkOs+AnnvuWp9VPiUrOg+OpnIcmdpWqPJHqfpMOZH0vuv2WuUB0nkB/M2bo+fJn1DnNsI9709puD72Kl/JGbX/FukxJETtb71I9+UqFh1FLhWqfXcU+pGLA4AEqmNlygGk88BY5P7Q+Sp0nOrY0teBjg3NIj+VZazo/B9WeXN0fTqHgj4+he79gEPl/THlvFB/myLXNceVzm1mlXNIb0tfV7o+HdenT7v/bXGdOVSeHlN5vVy3T7O6jd2Uu0X9rWNJ50yy2J7ku+fHcOjY0XR+K6v8HRYColz6AX2sdF4bnfdFxZ1lbhMdh+Eqv5XOX6WOpSkvjqav+0KLfF8qt4lY5aHReWusXgPUuXboXGyqz5VCi5wnOo+Pv1MwXNqr866ZcmuZcolZ5IfS7030+wNnuPvfVu8XdPt0H2qVR0b3W7ofssijp8+VOPzr4819sPufev9M146OXas8Qfra8tEvWOU40m035fSx4mcOJdN1oc+lysVmqt/0/sH33w4J9b1cv7/RsayvU4v30aZrSdN5/vS1pN8Le9tubScCwM/8W+dCXTuudNZ4RxIREREREREREdnCgSQiIiIiIiIiIrKFU9uoWoUeK0bo6bLbrOv9rm7R1bdTn7F4RK2+pVffDq1uiZXGDdzrP3LMvby+fdrqkcCuU4iCVVv0I2P17cV6Cohp2p3vaRWipsCYboc2TcNQx8bfqXemR9Ra3Nqvb8kttpgCo8+l3n/Nakhc3xGspwTp9qipco4CdWu+yyNxHSfV9CM9xUM3Xe2LfiR6abieNqEea12g4rJEP+rZ/W89dc5RoKY16FvFddzr5XoqndWjpTW9XJ87q1i0eoy3xfZNU3A0H4/CllPqXFtMMXGo6UeiHwnfMMZ9fT0twPQIcx2nus+z6AOtprKpfkoiotz+1lPn9P6ZYkFPO9D9oOmx6irWI9SUH6tpk5puj2n6s35UtooN1/bqaXmn1bbVvupHmpumdKim6unHpulHVlPHdCxYvT5axK7oKaJqCo+ealb6R657ffpc6ljR07GspmtpFlMDRZ9rxTS9yaW9pulLVn2avs70sbe67vS513FreR2r7enXS/1+wGp6sooV01Q0C6b3H6bDo2JRn2vTVDiLc6mPl35/BPXez3X/TPuuj43FdCJ/p/H7O93W6n2npo6FqD7XdG401e84rFI+6NcMPQXW9F7RYpphpJp+bdXH6/eeFlNwayspFfMU0upoB6e2kcI7koiIiIiIiIiIyBYOJBERERERERFRtdu/fz/Gjx+PhIQEREREoEGDBujduzdmzZqFU6dOWVdAVYJT24iIiIiIiIhqGilFzXhqW9W0YcWKFRgxYgT++OMP43+nTp1CWloa0tLS8MYbb2DlypWIj4+vkvaQdxxIomoVdKoYwV7mv7s9Yh0w53jQeX6K1Q12FnO7HYdz3Bfr8ro9+gY+nTfANSeDbqvOvWF6JLqa5216XK/OkWSRH8LqUdVWORx0/VaPDzblOHL/E/oR9zoPj9X+qdw0AerxwqLyCJVGuB/vgHw1jz/U/Xg7Cv18NLZr+6we/2txLEXFcYDKgaT3Tbdd0zmRTNsLUblazqi/dWzox/vqQ+OwyOtjaqBV7hbvuUsAmK4lffxMuWnUuRT1TZYpv4bOU+SaM8N0nVjk5tD5KXQ/cNK9LabrtkWc+/o6l5c+lsf/cP/b9Bhv37nIdA4nOaHqs8jdYjr2+txZ5TjS517HXqi61nQOJr1c58mxyi+iz71re3QeG91n623pnEC6T1Hr6zw8phxF+lgG6Lw5FjmQdL4pHWv69dGUZ0flWnNfaop107G2ynmkWeXi8PMx6ro+02PeXY+HzpHU0D2fouXro1q/9GS+z7ZAL/czX1RAXCP1H/f1S49k+96+RQ4k0bnfdPuUUlU+QOVWM+2fRX2OQN1vqXOpPtw6HKq86pfctqavG1Of6fvY61xhDn1d65xF+oO47lf0dX62+SsV0/ZNudV0fk/VJ2pW7wWtrmO9PFvlKo2p7/63bq96zTK9BlCt88MPP2Do0KE4deoUnE4nHnvsMSQnJ+P06dNYvHgx5s2bh59//hnXXHMN0tLS4HQ6q7vJ5zUOJBERERERERFRtRk3bhxOnTqFoKAg/Pvf/0bfvn2NZZdffjnat2+PCRMmYNeuXXj++efx+OOPV2NriTmSiIiIiIiIiGoYKZUa83MupaWlITU1FQBwxx13uA0ilXv44YeRkJAAAHjxxRdRZPGETjq3OJBERERERERERNXi008/NX4fNWqUxzIBAQG4/fbbAQDHjx83Bp6oenBqG1WrwDPFCCwtm2Otc7uYcjToudo6R0W9eu5/67nbFnmAHOHuOSQkN9f9b1WfQ8/7d12ut6Xbotuu80vo8oFqzLdU5wTQeWXc/zTNi9f0PHw9b920fb09nbNJbc+UE0ntX4SPYwmYchyUqjxBOo9Q4Mkz7uvr/B6n3WNJQlWeIJ1vS+dBcI2d02pbOneXzpWic5HouNZ5Z9yXoqS+e5yL3jed48EiL40p747OvWKVv8tXrhHAHFumXGcWORWc7telzn9Vqs59wGn3+gJOuufXcug8OKacTSr/huv+6H1Rce6oH+m+XB87TfcppuUqh5HaV8cRlU9Cs8hHJTo3ixWdZ8aUU0jFou4HTNWp5afVudL9mo4tnZNJX2t6+/pJL1Y5m1zPtzr3+tiZchjp/Fd6X62+2bXIG2OVA9AyN4ned51bRPcDuv26n1PXsUPnEdJMeXos2mu69nz3M6acU1bHy/V8mPJRqX3R+65ZnTt97K2+0tXH3ipflkW+R1Ps6etM5xyyytOjr3udGk1fp5pV/fr1P9s9v2WAfu+n8xSpfsDn+yFffYAnFq9/+vXZFBsq36Opz9P1W54L9b7WorzpDhOdU9AqZ5TaP1P+Sp1vTO+fFqvyken3V7p9uv7yu1O85F6lmm3Dhg0AgIiICPTo0cNrucTEROP3b775BldcccU5bxt5xoEkqnLFLm9yCgrz/lygEzQW6cEVPTjg34c00wtyqU5KrAYnSlQSP8U02OA2kKRexEwvvhYfnktU260+8Fh8KLFMRmnVHv3OUL+JNy23ePOh908PZpje9Lsf69JCnWzTfXmgjh1Nv7FUSZMdOgFmsfrA7HD50KXjUo/iFVkMXOgBUz1Ip/4uUQNPZzuQFKASjTv0/hRbJCy2TNxqETt6uVbkvj+lhe4feEtL1UCS2p8AvT8lFolv9UCSr6S+pkE5tdzfPkpTsSOiPizrPspq8EAdaylVAyumhMT6Q4BeX/Urqt+z+tBgNcBtWl/3M+KjDwbM/VCJ+0CVZSy7Hi+1LSlVg14WowGmfbU6VboLNr2m6A//ZznlIFANJOnXFFOSXvfYLC1VA2v6eJT6mYRXMw1g637E9wdwKbW41lzbo94bBBTrt8r+PVBAHxur8ubl+r2L+74GFOe5L1fXgWn7Vl8smdpjMTCm30spAb5zecPyeKrjr/cnwPTFnXrNLdH9qMv+6zjXcaX7HEVKVZ9nGfc+q7Pu86yOlT4XJb6TZes+Huo68XsgSb+3shpIKtEPSNDvBdV7uRLdPvVAi5Ky43PG5XW+2Op1thYokFPW121VtAN/vp/KysqyLN+iRQu/6v/pp58AAO3atUOQ/pLRRadOnUzrUPXgQBJVuezsP58gsnnXG9XYkrN0orobUIfkWBchqvFyrYvUaaetixDVOoequwEWDlR3Ayzo71oqG/udmqMGnovs7Gy0bt26uptxVtLwdXU3waR3796WZcSPLwzOnDmDo0ePArAegIqJiUFERATy8/Nx4EBN7wDrNuZIIiIiIiIiIqIql5f3552VTqfTsnzE/6fEOHnS9+wROrd4RxJVuW7dumHLli0AgNjYWJ+3L1LdlZWVZXyjsWXLFjRt2rSaW0TnO8Yk1TSMSaqJGJdU07jG5HfffWd8tujWrVt1NqvCmjRpUmPvtsnKyqr0z29nzvx562JISIiPkmVCQ8vyZZ4+XQNvgzuP8BM8VbmwsDD06tWruptBNUjTpk39nktNdC4xJqmmYUxSTcS4pJqmZcuWtT4mg4KCauw+nIt2hYX9+bCMQv0gDw8K/v8hRfV0sn2qUpzaRkRERERERERVLjLyz6fe2pmulp9fllDdzjQ4Onc4kEREREREREREVS4sLAyNGjUCAGRmZvose/z4cWMgqWXLlue8beQdB5KIiIiIiIiIqFokJCQAAH755RcUFxd7Lbdr1y7TOlQ9OJBERERERERERNWif//+AMqmrWVkZHgtt27dOuP3fv36nfN2kXccSCIiIiIiIiKianHjjTcavy9YsMBjmdLSUrz99tsAgOjoaCQnJ1dF08gLDiQRERERERERUbXo3bs3Lr30UgDA/PnzsXHjRlOZ5557Dj/99BMA4MEHH0RwcHCVtpHcBVV3A4iIiIiIiIjo/DV79mz069cPp0+fxpVXXomJEyciOTkZp0+fxuLFizF37lwAQIcOHfDwww9Xc2vJISJS3Y0gIiIiIiIiovPXZ599hltvvRW5ubkel3fo0AErVqxAu3btqrhlpHEgiYiIiIiIiIiq3W+//YbZs2djxYoVyMzMREhICNq1a4ebb74Z999/P8LDw6u7iQQOJBERERERERERkU1Mtk1ERERERERERLZwIImIiIiIiIiIiGzhQBIREREREREREdnCgSQiIiIiIiIiIrKFA0lERERERERERGQLB5KIiIiIiIiIiMgWDiQREREREREREZEtHEgiIiIiIiIiIiJbOJBEVAslJSXB4XD49ZOammqr7lOnTiE+Pt5Yr3Xr1rbXe/bZZ9G7d280aNAATqcTCQkJGD9+PPbv329733788Ufcc889aNeuHerVq4fY2FhcdtlleP3111FcXGy7nsWLF2PgwIFo2rQpwsLC0Lp1a9x2223YtGmT7TpycnIwdepUXHjhhahfvz6ioqJw4YUXYurUqcjJybFdz/miJsWlP22xg3FZO1VGTL711lu2133rrbcs28S+8vxWk2KS/SSVOxev31u2bMGYMWOQkJCAqKgoOJ1OtG3bFtdccw2ef/55ZGdn+1yffSVRDSdEVOskJiYKANs/AQEBkpmZaavuhx9+2G3dVq1aWa7zyy+/SMeOHb1uv379+rJixQrLet544w0JDQ31Wk+fPn3k6NGjPus4ffq0XHvttT6PxfTp0y3bsmXLFmnatKnXepo1ayZpaWmW9ZxPalJc+tMWK4zL2qsyYnLBggW211+wYIHP9rCvpJoUk+wnqVxlvn6fOXNG7rzzTnE4HD7r+OSTT7y2h30lUc3HgSSiWmjPnj2yfft2nz8ffPCB8eJ0xRVX2Kp369atEhgYKGFhYRIZGSmA9Qf2vLw86dSpk7Gt0aNHy5o1a+S7776TGTNmiNPpFAASHh4uP/zwg9d6Vq1aJQEBAQJA4uLi5KWXXpLNmzfLF198IYMHDzbqv+yyy6SkpMRrPcOHDzfKJicny6effipbtmyR+fPnS9u2bY1l8+bN81pHZmamxMXFCQAJCgqSCRMmyPr162X9+vUyYcIECQoKMtppdyDkfFCT4rL8TXHPnj0t2+QL47J2q4yYdP3Q/uWXX/qs6/jx417bwr6SMSlSs2KS/SRjslxlvX4XFBTI1VdfbZS79NJLZd68efLNN9/Ipk2b5IMPPpCJEydK+/btvQ4ksa9kXFLtwIEkojpqwoQJxovbO++8Y1m+uLhYevToIQBk+vTp0qpVK1sf2KdOnWps55lnnjEt/+6774wXyOTkZI91FBUVSbt27QSAREVFyS+//GIqM2bMGGM7Cxcu9FhPamqqUea6666T4uJit+XZ2dlywQUXCACJiYnx+gY7JSXFqGfJkiWm5UuWLDGWjxo1ymMd5FlVxWX5B6TExMQKt5VxeX6wiknXD+179+6t8HbYVzIm7aqqmGQ/yZj0h53X7ylTphhlZs2a5bO+wsJCj/9nX8m4pNqBA0lEdVBJSYk0b95cAIjT6ZT8/HzLdZ577jkBIB07dpSCggJbH9gLCwslOjpaAEhCQoLXb3Tuvvtu4wUyPT3dtNz1BXTmzJke68jPz5eYmBgBIF27dvVYZtCgQQJAAgMD5cCBAx7LvP/++z7f5Bw6dEgCAwMFgAwcONDbrsvAgQONbR06dMhrOfpTVcWlSOV8QGJc1n12YrIyPrSzr2RM2lVVMSnCfpIxaZ+duPz1118lODhYAMjIkSMrtB32lYxLqj2YbJuoDlqzZg0OHjwIALjpppsQHh7us/xvv/2Gxx9/HADw6quvIiQkxNZ2UlNTceLECQBASkoKAgI8dykjR440fv/4449Nyz/99FOPZV2Fh4dj6NChAIAdO3Zg9+7dbstPnjyJNWvWAACuuOIKtGjRwmM9gwcPRlRUlNe2LF++HCUlJQCAUaNGeazDtZ0lJSVYvny513L0p6qKy8rCuKz7/I3JimJfyZi0q6pisrIwJs8PduJy7ty5KCoqgsPhMF67/cW+knFJtQcHkojqoLffftv4/fbbb7csP2bMGOTn5+O2225DcnKy7e1s2LDB+D0xMdFruZ49eyIiIgIA8M0333itp2PHjmjSpInXely3oevZsmULCgoKLNsSEhKCPn36GOsUFRV5bItVPb7aQp5VVVxWFsZl3edvTFYU+0rPbSGzqorJysKYPD/YiculS5cCKOvH2rRpAwAoLS1FZmYm9u7di9OnT1tuh32l57YQ1UQcSCKqY06ePIlPPvkEAHDBBRcgKSnJZ/nFixdj5cqViImJwaxZs/za1k8//WT83qlTJ6/lgoKC0LZtW9M65e3NzMy0rEMv1/XYbYvr8uLiYtM3UOX11K9f3+ebj6ZNmxrfQOm2kFlVxqWrXbt2oVevXoiMjERYWBhatGiBG264AW+//bbpzZ5uL+OybvM3JoGyb4zj4uIQEhKCRo0aoU+fPpg8ebLxTb037CsZk3ZUZUy6Yj9JvtiJy+zsbOzZswcA0LdvX+Tm5mLcuHFo1KgRWrZsifj4eERFRSExMRErVqzwui32lYxLqj04kERUx3z00UfIz88HANx2221wOBxeyx4/fhzjxo0DAPzP//wPGjdu7Ne2Dhw4AACIiIhAdHS0z7ItW7YEUPZmo/wbHgDIzMyEiACA19uGdR2u2/b0d2XUY1WHaz26DjKryrh0dfjwYaSnp+PkyZMoKCjAwYMHsXz5cqSkpOAvf/mL1zdrjMu6z5+YLLdu3TocOXIERUVFyMnJwebNmzFjxgy0a9cOr7/+utf12FcyJu2oyph0xX6SfLETlzt37jR+r1evHi6++GLMnj0bx48fN/5fXFyM9evX49prr8U//vEPj9tiX8m4pNojqLobQESVy5/b4h955BEcPnwYffv2xejRo/3eVl5eHgDA6XRali2/BRko+7YoNDTUrQ479eg6PLWlsurxZ590HWRWlXEJAAEBARgwYAAGDRqECy+8EA0bNkReXh62bt2K119/HT/99BN27tyJ5ORkbNmyBRdccIHb+ozLus+fmIyPj8fgwYPRt29f443+nj178NFHH+HDDz/EmTNncM8998DhcOCuu+4yrc++kjFpR1XGJMB+0lMdZGYnLo8dO2b8/uKLL6KgoACXXHIJZs6ciV69euHMmTP44osvMH78eGRlZeGFF15Ahw4dcM8997jVw76ScUm1BweSiOqQzMxMpKamAgD69OmDDh06eC27fv16vPnmmwgKCsJrr71m65tP7cyZMwBgKwly+Qs8ALd58uV12KnHWx3noh5/9snOvP/zWVXHJVCW8NLTt5mXXnopxowZg9GjR2PhwoU4fPgwxo0bZ0qQybis2/yJyb///e9ISUkxxWKvXr1wyy234PPPP8fgwYNRVFSEhx56CNdff71p+gL7SsaklaqOSYD9pKc6yJ3duCy/YwkACgoK0KNHD6xZswZhYWEAyu5SGj58OHr16oWLLroI+fn5ePzxx5GSkoJ69eoZ67KvZFxS7cGpbUR1yKJFi1BaWgqg7GkX3hQUFOCuu+6CiODBBx9E9+7dK7S98jcIhYWFlmVdbzt2fdNQXoederzVcS7q8WefdB3krqrjEoDPW+KDg4PxxhtvGDkNPvnkE1M+EcZl3WY3JoGyvBa+BjSvvfZaTJ06FQBw6tQpzJ8/31SGfSVj0kpVxyTAftJTHeTObly6nkMAmDFjhul/ANC+fXvce++9AMqmpK1evdpjPewrGZdU83EgiagOeeeddwCUfaNxyy23eC03Y8YM/Pzzz2jZsiWmTZtW4e1FRkYCsHcLruu3Va6395bXYaceb3Wci3r82Sc7tyufz6o6Lu0ICgrCHXfcYfy9bt06t+WMy7rNbkzaNXr0aOODvY4lgH0lY9JaVcekHewnyW5cup7DkJAQn09aHThwoPF7Wlqax3rYVzIuqebj1DaiOiI9Pd1IdnjttdciJibGa9mnn34aAPC3v/0Nn3/+uccy5S9m+fn5WLx4MQCgcePGuPzyy40yLVq0wObNm5Gfn48TJ074/HazPHFgbGys2y3ArskHy5+yYVUH4J7Y0FM9PXv2rHA9hw8ftmyLaz26DvpTdcSlXZ07dzZ+19+0My7rLn9i0q7GjRujUaNGyM7O9vi0LPaVjElfqiMm7WI/ef7yJy5dj2P5UwTtlD1y5IjbMvaVjEuqPTiQRFRHuCZDtLotvvz22gULFmDBggU+yx49ehT/9V//BQBITEx0+8DeuXNnfPTRRwDKHh/cp08fj3UUFxfj119/BQAkJCS4LXM6nWjZsiUOHDiAXbt2+WyL63Jdj+ubXbv1BAUFoV27dqZ6MjIy8Mcff+DQoUNeH9WalZWF3Nxcj22hP1VHXNpV/lQXTxiXdZc/MekPX/HEvpIx6Ut1xGRl1MGYrNv8icv27dsjODgYRUVFKCkp8VnWdXlQkPtHUfaVjEuqPTi1jagOKCoqMu7OiI2NxdVXX10l2+3fv7/xu69b59PT0407Sfr16+e1np9//hmHDh3yWo/rNnQ9vXr1Mr4B89WWwsJCbNq0ybSObotVPb7aQmWqKy7tcn1ccbNmzUzLGZd1z7mKySNHjiAnJweA71gC2FeSu+qKSbvYT56f/I3L4OBg9O3bFwBw+PBht6leWvkAEAA0b97cbRn7Ss9tIaqRhIhqvWXLlgkAASAPPvhgpdTZqlUrASCtWrXyWqagoEDq168vACQhIUFKS0s9lrv77ruN9m3ZssW0/IMPPjCWz5w502Md+fn5EhMTIwCkc+fOHstcffXVAkCCgoLkwIEDHsu8//77xraeeeYZ0/KsrCwJCAgQADJw4EBvuy4DBw4UABIQECBZWVley53Pqisu7SgqKpJOnToZ7du/f7+pDOOy7jkXMSki8uSTTxr1Pvnkk6bl7CsZk95UV0zawX7y/FWRuJw9e7axzrvvvuu13MiRI41yGzZscFvGvpJxSbUHB5KI6oAhQ4YYL2IZGRmVUqfdD+xTpkzx+QL63XffSVBQkACQxMREj3UUFhZK27ZtBYBERUXJL7/8YiozZswYYzsLFizwWM+aNWuMMtdff70UFxe7Lc/OzpYLLrhAAEh0dLQcO3bMYz233XabUc/SpUtNy5csWWIsT0lJ8VgHVV9cfv3113L8+HGvywsLCyUlJcVo23XXXee1HOOybvE3Jvfu3Stbt271Weazzz6TkJAQASBhYWGSmZnpsRz7yhSPdZzvqism2U8yJn2pyOt3Xl6eNG7c2HiNPnTokKnM2rVrJTAwUABI165dPQ4Usa9M8VgHUU3DgSSiWu7YsWMSGhpqvChXFrsDSbm5udKhQwfjBfCuu+6Sr7/+WjZu3ChPPfWUOJ1OASD16tWTbdu2ea1nxYoVxjc2cXFx8q9//Us2b94sq1atcntD079/f9MLuathw4YZZZOTk2XZsmWSlpYmb775pvGmAoC89tprXuvYv3+/xMbGGt9CPfroo7JhwwbZsGGDPProo8YbmNjYWK/fUJ3vqjMuU1JSxOl0yvDhw2Xu3Lmybt062bZtm2zYsEFefPFFSUhIMOKgcePGsmfPHq91MS7rjorE5Nq1awWA9O3bV5566ilZuXKlpKenS1pamnzwwQdy8803i8PhMM7fnDlzvNbFvpIxqVVnTLKfZEx6czav34sXLzbir2XLlvLyyy9LWlqabNiwQSZOnCj16tUzzs0333zjsQ72lYxLqh04kERUy7366qs+v7mpKH+mEO3evVvat29vtEP/REVFyWeffWZZz9y5c41vUT399O7dW7Kzs33WcerUKRk0aJDXOgICAmTq1KmWbdm0aZM0adLEaz1NmjSRTZs2WdZzvqrOuHT9Ft3XT7du3eTHH3+03Cbjsm6oSEyWf2i3+gkPD5fXX3/dsj72leSqOmOS/SRj0puzff2eM2eOz1hwOp3y6aef+qyDfSVRzceBJKJa7pJLLhEAEhgYKAcPHqy0ev3NRXPy5El5+umnpWfPnhIdHS3h4eHSsWNHeeihh2Tfvn22t7t9+3YZPXq0xMfHS1hYmDRs2FD69+8vr776qhQVFdmu591335UrrrhCGjduLCEhIdKyZUsZPny4fPfdd7bryM7OlsmTJ0vXrl3F6XSK0+mUbt26yeTJk+Xo0aO26zkfVWdc7ty5U1544QUZOnSodO3aVeLi4iQ4OFicTqe0bdtWbrnlFlm6dKnPbyA1xmXtV5GYzM3NlUWLFsl9990nf/3rX+WCCy6Q8PBwCQkJkbi4OLn88stlxowZcvjwYdvtYF9J5aozJtlPkjeV8fq9Y8cOuffee6Vdu3ZSr149cTqd0r17d5kwYYL8/vvvtupgX0lUszlEKuHZoEREREREREREVOcFVHcDiIiIiIiIiIioduBAEhERERERERER2cKBJCIiIiIiIiIisoUDSUREREREREREZAsHkoiIiIiIiIiIyBYOJBERERERERERkS0cSCIiIiIiIiIiIls4kERERERERERERLZwIImIiIiIiIiIiGzhQBIREREREREREdnCgSQiIiIiIiIiIrKFA0lERERERERERGQLB5KIiIiIiIiIiMgWDiQREREREREREZEtHEgiIiIiIiIiIiJbOJBERERERERERES2cCCJiIjqFIfDAYfDgWnTplV3U85aXdoXqvmmTZtmxJwnrVu3hsPhwMiRI6u2YURERFSjcCCJiIiIiIiIiIhs4UASERFRFeJdHUTnltWdVURERHR2gqq7AURERJVJRKq7CZWmLu0L1X779u2r7iYQERFRDcA7koiIiIiIiIiIyBYOJBERERERERERkS0cSCIiIq90rpETJ05g6tSp6NKlC5xOJxo0aICkpCS8++67XuvQOYEyMjIwcuRItGnTBqGhoR7zmGzfvh133XUX2rdvj/DwcERGRqJLly546KGHLKfX2H3S2c8//4yxY8eiS5cuqF+/PurVq4f4+HiMGjUKW7du9bluuX379uHRRx9Fjx490LBhQ4SFhaFNmzZITk7Gc889h/379xtlk5KS4HA48NtvvwEAFi5caLS1/CcpKcnvfSktLcWiRYswaNAgNGnSBCEhIYiNjUVycjJeeeUVFBYWel1Xn98zZ87g2WefxcUXX4zIyEhERkaid+/emDNnDoqLi20dEzsKCgrQvXt3OBwOOJ1O7N6922vZhx56yGjjvHnzKq0N5VauXIlbb70V8fHxiIiIQP369dGlSxcMGzYMH330EU6fPu1xvbM57uUKCwvxyiuvIDk5GbGxsQgJCUGTJk0waNAgLFq0CKWlpV7XHTlyJBwOB1q3bg0AyMrKwqOPPoouXbogMjISDocDqampbutkZmbivvvuQ3x8PMLCwtCsWTNcf/31WL16ta1j5Su/V2pqqnGeyre7ZMkSDBgwALGxsahXrx46duyICRMm4NixYz63s2nTJkyePBlJSUnGsY2KikLnzp1x7733YufOnR7Xe+utt+BwOPDEE08Y/9PXmMPh8NiHiAg+/PBDDBkyBC1btkRYWBhiYmLQu3dvPPnkkzhx4oStY0RERHReECIiIi+mTp0qAASA7NmzR9q2bWv8rX9uuukmKSoqMtXRqlUrASApKSny6quvSlBQkGldV0899ZQEBAR43U5oaKgsXLjQa5vLy02dOtVrmenTp3tsR/mPw+GQxx9/3OexefbZZyU4ONhrHQAkMTHRKJ+YmOizrC5vZ19ycnKkX79+PutMSEiQffv2eVzf9fweOnRILrzwQq/1XHfddVJSUuLzmPhj+/btEhYWJgCkV69eHmPnq6++EofDIQDkhhtuqLRti4gcPXpUBgwYYHlOFixYYFr3bI+7iMi+ffskISHBZx39+/eXnJwcj+unpKQIAGnVqpVs3LhRGjVqZFp/7dq1RvnU1FSJioryuq0nnnjCLR48cb2WtbVr1xrrrl69WoYPH+51W+3atZOsrCyP21iwYIHlOQkMDJSXX365QusCkL1797qtd+TIEcvzGRcXJ5s2bfJ8MomIiM4zHEgiIiKvXD9Y9urVSwICAuSee+6R1atXS1pamsyfP186dOhglHnggQdMdZR/+OzcubMEBgZK69atZc6cObJx40b55ptvZObMmUbZl19+2agrNjZWZs2aZZSbNm2aREREGAM9K1as8Nhmq8GXKVOmGGUuueQSeeONN2Tjxo2Snp4u7777rvTt29dY/tJLL3msY/r06UaZ6OhomThxonz11VeydetW+frrr2XWrFnSr18/SUpKMtbZs2ePbN++XZo1a2YMjGzfvt3tZ8+ePbb3pbi42K2tiYmJsnTpUklPT5fly5fLjTfeaCxr27at5OXlmepwPb+XXHKJhISEyNixY+Wrr76SjIwMee+999wGO1577TWPx6OiZs+ebdQ9efJkt2U5OTnSvHlzASBNmjSR7OzsSttufn6+dOvWzdh2jx495PXXX5dvv/1W0tPT5ZNPPpGHHnpImjVrZhpIqozjnpeXJ/Hx8Ua5G2+8UZYvXy7p6emydOlSt0HHvn37SnFxsamO8oGkhg0bSrNmzcTpdMqkSZMkNTVVtmzZIvPnz5ddu3aJiMjevXslMjJSAHi8htu3by8ApGfPnpUykHTJJZcY+/Xxxx9LRkaGrFy5Uq655hqjzLBhwzxuY968eRITEyMpKSny5ptvyoYNG2Tr1q3y+eefy/Tp040BM4fDIWvWrHFb9/jx47J9+3a59957je3oa2z79u1SWFhorHPy5EkjxkNCQuTuu++WZcuWydatW2XDhg0yY8YMadiwoQCQmJgYn4ODRERE5wsOJBERkVeuAw0A5L333jOVyc3NNe5kCQgIkP/93/91W17+4ROAdOvWTY4fP+5xW0eOHJHw8HABIM2aNZP9+/ebymzdutUYTGrevLnbB8JyvgZftmzZYtztpAcuypWUlMitt94qACQyMtLU3oyMDKOODh06yIEDBzzWIyIel/n6MO7PvsyZM8dYfvvtt0tpaampzMSJE40yEyZMMC13Pb/BwcFud7CUy8nJkbi4OAEg3bt3t2yzP0pLS+Wqq64y7jLZsGGDsezmm282Bgy++OKLSt3uuHHjjP2+7777PB47EZGCggI5dOiQ2/8q47iPHz/e6wCaSNlxGTFihFHmlVdeMZUpH0gCIE6nU77//nuv+zt48GDb13BlDCQBkH/+858e9+vKK68UABIUFCRHjhwxlcnMzJT8/Hyv+3LixAnp3r27AGV3bHlidWeVq/vvv18ASP369SUtLc1jmX379knTpk0FgNx6662WdRIREdV1HEgiIiKvXD+QXXvttV7Lbd682Sg3ZswYt2WuA0nr16/3WsfTTz9tlHv//fe9lvvnP/9plFuyZIlpua/BlyFDhhh3oHgbPBApu7MhNDRUAMi8efPclg0bNswY4Ni6davXOryprIGk8rsoGjVqJLm5uR7XLy4ulk6dOhl3U5w5c8Ztuev5/cc//uG1Hf/93/9tlDtx4oRlu/2RlZVl3GXSunVr+eOPP9ymKI0dO7ZSt3fs2DFjwPLiiy/2eLePL2d73M+cOSPR0dEClN2l5237f/zxh3EnTOfOnU3LXQeSpk+f7rW9v//+uwQGBvp1DZ/tQJKv62vVqlVGuWXLlnltjy+ffvqpUcfRo0dNy+0OJGVnZxvTK2fPnu2z7CuvvGIMuPoa6CIiIjofMNk2ERHZMmrUKK/LevfujS5dugCA18S9LVu2xKWXXuq1jvL1oqOjMWTIEK/l7rzzTtM6dhQVFeGLL74AANx0000ek3yXi46ORrdu3QAAGzduNP5fWlqKVatWAQASExNx0UUX2d5+Zfr999/x008/AQCGDh2KyMhIj+UCAwON83b8+HGfScRHjBjhdVmPHj2M3/fu3VuRJnvVpEkTzJ8/H0BZ8vLhw4dj7NixAIAuXbrg6aefrtTtrV27FqdOnQIAjB07FoGBgbbXrYzjnpGRYSRuHjlypNftR0VFYejQoQCAnTt3Iisry2u7fJ27tWvXoqSkBID9a/hsDR8+3Ov15RpLe/bssawrPz8f+/btw48//ogdO3Zgx44dCA4ONpb/8MMPFW7nl19+iTNnzgCAcay9ueyyywCU9SMZGRkV3iYREVFdwIEkIiKypVevXj6X9+7dGwCwe/duj0+s6t69u8/1d+zYAQC46KKL3D4oanFxccaTqsrXsWPnzp3GAMJjjz3m8WlOrj/p6ekAgEOHDhl17N271xgEKP9gWR1c9/uvf/2rz7Kuy30dr06dOnld1qBBA+P3vLw8O030y/XXX4+7774bALBixQrk5eUhNDQU7733HsLCwip1W9u2bTN+9/ccVsZxr+xz53Q6ER8f77WO7du3G7/bvYbP1tnG0tGjRzFx4kR07NgRkZGRaNOmDbp27Ypu3bqhW7duuOaaa9zKVlT5NQ4ATZs29dkfdO3a1Sjr2icQERGdjziQREREtjRu3Njn8ri4OACAiOD48eOm5TExMT7XL38keHk9vjRp0sRtHTuOHDliu6yr8sEnwP1Da9OmTStUX2Vw3W+r41V+rPR6Wnh4uNdlAQF/vl0ov7ulsj333HOIiooy/p4yZYrl4GNFnM05rIzjXtnnLjo62mcdrtei3Wv4bJ1NLGVkZKBTp06YOXMm/vOf/0BEfG7r9OnTFW5nZfQJRERE56Og6m4AERHVDr6mggGw/MBndwqR1XbsbMsT1w+tzz77LK666ipb60VERHj8v512VoWzPS81xcKFC5Gbm2v8vXr1ajz22GNuAw81SWUc98qow+q6cq2jpsdKYWEhhg4dipycHAQHB+OBBx7ADTfcgA4dOiAmJgahoaEAyqbEtW3bFsDZtbm8TwgJCfFrulqLFi0qvE0iIqK6gANJRERky+HDh9GyZUuvy8u/3Xc4HJZ3H3nSoEEDZGVl2Zo2cvjwYWMduxo2bGj8XlRU5DZVxa5GjRoZv//+++9+r19ZXPfb6niVHyu9Xk2ya9cujB8/HkBZbqDc3FykpqbiueeewyOPPFKp23I9h1lZWWjTpo3tdSvjuOs6OnTo4Hcd/nBdz+41XF2+/vprI2/Syy+/jNGjR3ss5+mOx4oo7xMKCwvRsGHDar3LkIiIqDapmV/zERFRjZOWlmZrefv27RESEuJ3/eUDO9u2bUNRUZHXckeOHMFvv/3mto4dXbp0Mdr173//2+/2AUCbNm2MQbL169dXqI7KuJPJdb83b97ss+yWLVs8rldTFBUVYcSIETh9+jTq1auHb7/9Fv369QMATJ48+aySKXty8cUXG7/7ew4r47hX9bkrTxoP2L+Gq8uPP/5o/D5s2DCv5VxzG3li9xpzTZZf0T6BiIjofMSBJCIismXhwoVel6WnpxvJgP/2t79VqP7y9U6cOIGPPvrIa7n58+cb01n82VZ4eDgGDBgAAEhNTXX7kG5XQEAABg0aBABYt26dW+Jmu8qTRxcUFPi9brlmzZohISEBALB06VKvSYtLSkrw1ltvASjLUeU6iFJTTJkyxXiq2axZs9C1a1e88847iIqKQmFhIUaMGGE8WasyJCcnG9MV//Wvf/mV86kyjnuPHj2MvEYLFy70uv28vDwsWbIEANC5c+cK3y2TnJxsTH+zew1Xl+LiYuN3b3mISktLMXfuXJ/1uCZo93WdXX311UZi/xdeeMFt+0REROQdB5KIiMiW5cuXGx9sXZ08eRJ33XUXgLKBlvKnb/lr1KhRRpLehx9+GAcOHDCV+eGHH/DUU08BAJo3b44bb7zRr21MmjTJuFth2LBh+PXXX72WLSkpwXvvvYfMzEy3/48fPx4BAQEQEQwbNsy03JWnZeUDAr62bcd9990HAMjOzsYDDzzgMVfME088gZ07dwIARo8ebeSYqSnWr1+PZ599FgAwaNAgjBkzBkDZnV8vvfQSgLK7VB599NFK22Z0dLQRoxkZGRg3bpzXPDtFRUWm6V5ne9xDQ0Nx5513AijbtyeeeMK0vojg/vvvNxKD33///f7upqFp06a44YYbANi7hqtT+/btjd+9DXo99thjxsCjN66Dbr6us+bNm2PUqFEAyvqWu+++2+dg0pEjR/DGG2/43DYREdF5QYiIiLyYOnWqABAA0rNnTwkMDJQxY8bI119/Lenp6fLmm29Kx44djTIPPPCAqY5WrVoJAElJSbHc3ssvv2zU1bhxY3n++edl06ZN8u2338oTTzwhTqdTAIjD4ZAVK1Z4rKN8/alTp1ruk9PplAcffFBWrFghW7dulY0bN8r7778vY8eOlWbNmgkA2b59u6mOJ5980qgjOjpaJk2aJKtXr5Zt27bJ2rVr5YUXXpBLL71UkpKSTOtOmjTJWHfmzJny/fffy+7du2X37t2SmZlpe1+Ki4ulb9++RpmkpCRZunSpZGRkyOeffy6DBw82lrVt21by8vJ8Hgtf1q5da5Rbu3atz7J2nThxQi644AIBILGxsXLo0CFTmZtvvtk4319++WWlbFdEJD8/X7p162bsU48ePWTu3LmyceNGycjIkGXLlskjjzwizZs3lwULFritWxnHPTc3V+Lj441yf//73+Wzzz6TjIwM+fDDDyUpKclY1rdvXykuLjbVkZKSIgCkVatWlvu7d+9eiYyMFAAer+EOHToY17ivePB1LfsTI97i+uTJk9K4cWMBIEFBQTJmzBhZtWqVpKeny+LFi2XAgAECQPr162fUoc+PiMju3buN5VdeeaWsW7dO/vOf/xjXWVFRkVE2Ly9PunbtapTv3LmzvPjii7Jhwwbjep4zZ47ceOONEhISIj169LA63ERERHUeB5KIiMgr14GGPXv2SJs2bYy/9c+QIUPcPqCV82cgSURkxowZEhAQ4HU7oaGhsnDhQq/rWw0kiYi88MILEhoa6nUb5T8hISGye/duj3U89dRTEhQU5HP9xMRE03qZmZnSoEEDW+Wt9iUnJ8ftQ7Wnn4SEBNm3b5/H9atzIGn48OFGncuWLfNYJicnR5o3by4ApGnTpnL06NFK2baISHZ2tlx22WWWMeBpoOJsj7tI2eBOp06dfNbRr18/ycnJ8bi+PwNJImXnsHwwydPP1KlTLePhXA8kiYisWrVKwsLCvLYzKSlJduzY4fP8iIgMHTrUax179+51K5uTkyNXXXWVZSwAkOTkZJ/7RkREdD7g1DYiIrKlTZs2yMjIwMSJE5GQkIDw8HDUr18fl112GRYtWoQPP/wQQUFn/zDQiRMnYtu2bRg9ejTatm2LevXqISIiAgkJCXjwwQexa9cu3H777We1jXHjxuHXX3/FlClT0KdPHzRq1AhBQUGIiIhAhw4dMGTIELz22ms4ePAg2rVr57GOxx57DDt37sS4cePQtWtXREVFISwsDPHx8RgwYABefPFFLF682LRe8+bNsWXLFtxxxx1o166dWz4XfzVo0ADr16/HO++8g6uuugpxcXEIDg5Gw4YNkZSUhDlz5uD7779Hq1atKryNc+H999/He++9B6Bs6tf111/vsVyDBg3w1ltvweFwICsrq1KnXzVq1Ajr1q3Dxx9/jJtuugktWrRAaGgoYmJi0LVrV4wYMQLLli3D8OHDPbbrbI9769at8cMPP2DOnDlITExEw4YNERwcjLi4OFx11VV45513sH79+kp70l5SUhJ+/PFH3HvvvWjVqhVCQkIQFxeHa665BqtWrcK0adMqZTtna+DAgUhPT8ett96KZs2aITg4GLGxsUhMTMTcuXOxZs0aI8eVL4sWLcIzzzyD3r17o379+ggI8P6Wt0GDBvjiiy+wZs0ajBo1Cu3bt4fT6URQUBAaNGiAXr164b777sPKlSvx1VdfVebuEhER1UoOES+JAYiI6Lw3bdo0I4dLbXi5KC0tNRILP/nkk5g8eXI1t4iIiIiIqG7hHUlERFRn5ObmGr/Xr1+/GltCRERERFQ3cSCJiIjqjO+//974vUOHDtXXECIiIiKiOursk1kQERFVo6KiIuzcuRO//PILJk2aBACIiIhA//79q7llRERERER1DweSiIioVjt48CD+8pe/uP1v+vTpthLyUsUdPHgQx48f93u9iIgItGnTpsLbzc/Px969eyu0bseOHREcHFzhbRMRERERB5KIiKiOcDqd6Nq1K+6//36MGDGiuptT502aNAkLFy70e73ExESkpqZWeLtpaWlITk6u0Lp79+5F69atK7xtIiIiIuJAEhER+TBt2rQa81hwb1q3bl0rnihHRERERFQXOITvvomIiIiIiIiIyAY+tY2IiIiIiIiIiGzhQBIREREREREREdnCgSQiIiIiIiIiIrKFA0lERERERERERGQLB5KIiIiIiIiIiMgWDiQREREREREREZEtHEgiIiIiIiIiIiJbOJBERERERERERES2cCCJiIiIiIiIiIhs4UASERERERERERHZwoEkIiIiIiIiIiKyhQNJRERERERERERkCweSiIiIiIiIiIjIFg4kERERERERERGRLRxIIiIiIiIiIiIiWziQREREREREREREtnAgiYiIiIiIiIiIbOFAEhERERERERER2cKBJCIiIiIiIiIisuX/AFUqN3Qjnwc2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 448,
       "width": 585
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datacube_sub.v.mean(dim='mid_date').plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the mean of the magnitude of velocity variable along the time dimension. Note that you could plot the entire spatial extent of the data but it would take quite a bit of time.  The rest of the notebooks in this chapter will demonstrate how to subset this spatially-large data object to specific areas of interest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "- This notebook demonstrated how to query and access zarr data cubes from the ITS_LIVE dataset. We also looked at reading data into python using `xarray`, introducing `dask` to our workflows, and some preliminary data visualization.\n",
    "- The next notebook in the tutorial will take a look at the dataset in more detail and demonstrate ways to organize and manipulate data using `xarray`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
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 },
 "nbformat": 4,
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}