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   "id": "592fc4b5-8557-44f7-ba9e-0ea652f6656f",
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    "# xcube_sh imports\n",
    "from xcube_sh.cube import open_cube\n",
    "from xcube_sh.config import CubeConfig\n",
    "from xcube_sh.sentinelhub import SentinelHub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36f2d65-6816-4819-a9bb-36afb92a7f41",
   "metadata": {},
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    "## Sentinel-1"
   ]
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   "id": "0c216641-be03-4045-bd55-7060899157af",
   "metadata": {},
   "source": [
    "Issues:\n",
    "- How to query by track number\n",
    "- bands 'localIncidenceAngle' and 'shadowMask' return an error\n",
    "- how to set the value for 'orthorectify' in the Processing Options (https://docs.sentinel-hub.com/api/latest/data/sentinel-1-grd/#processing-options)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b5125d6-1ea8-4685-9302-2f56eb2515ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VV', 'HH', 'VH', 'localIncidenceAngle', 'scatteringArea', 'shadowMask', 'HV']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " SentinelHub().band_names('S1GRD') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a404ed63-2486-406b-82aa-87832930fba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "cube_config_s1 = CubeConfig(\n",
    "    dataset_name='S1GRD',\n",
    "    band_names=['VV', 'VH', 'localIncidenceAngle', 'shadowMask'],\n",
    "    bbox=[11.02, 46.65, 11.36, 46.95],\n",
    "    spatial_res=0.0018,   # = 100 meters in degree>\n",
    "    time_range=['2018-02-01', '2018-06-30'],\n",
    "    time_period='6D'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e69b7cf-878e-4405-bc88-2e86886d8646",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:              (time: 25, lat: 167, lon: 189, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                  (lat) float64 46.95 46.95 46.95 ... 46.65 46.65 46.65\n",
       "  * lon                  (lon) float64 11.02 11.02 11.02 ... 11.36 11.36 11.36\n",
       "  * time                 (time) datetime64[ns] 2018-02-04 ... 2018-06-28\n",
       "    time_bnds            (time, bnds) datetime64[ns] dask.array&lt;chunksize=(25, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    VH                   (time, lat, lon) float32 dask.array&lt;chunksize=(1, 167, 189), meta=np.ndarray&gt;\n",
       "    VV                   (time, lat, lon) float32 dask.array&lt;chunksize=(1, 167, 189), meta=np.ndarray&gt;\n",
       "    localIncidenceAngle  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 167, 189), meta=np.ndarray&gt;\n",
       "    shadowMask           (time, lat, lon) float32 dask.array&lt;chunksize=(1, 167, 189), meta=np.ndarray&gt;\n",
       "Attributes: (12/13)\n",
       "    Conventions:               CF-1.7\n",
       "    title:                     S1GRD Data Cube Subset\n",
       "    history:                   [{&#x27;program&#x27;: &#x27;xcube_sh.chunkstore.SentinelHubC...\n",
       "    date_created:              2023-03-07T06:54:16.049322\n",
       "    time_coverage_start:       2018-02-01T00:00:00+00:00\n",
       "    time_coverage_end:         2018-07-01T00:00:00+00:00\n",
       "    ...                        ...\n",
       "    time_coverage_resolution:  P6DT0H0M0S\n",
       "    geospatial_lon_min:        11.02\n",
       "    geospatial_lat_min:        46.65\n",
       "    geospatial_lon_max:        11.360199999999999\n",
       "    geospatial_lat_max:        46.9506\n",
       "    processing_level:          L1B</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-952638b6-26ac-45b5-810c-69b7a3e77763' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-952638b6-26ac-45b5-810c-69b7a3e77763' 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'>time</span>: 25</li><li><span class='xr-has-index'>lat</span>: 167</li><li><span class='xr-has-index'>lon</span>: 189</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e9fc79e6-bcc5-4ce1-9b01-bd85e2bb0e1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e9fc79e6-bcc5-4ce1-9b01-bd85e2bb0e1b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.95 46.95 46.95 ... 46.65 46.65</div><input id='attrs-212700b2-2d31-4df5-ab43-78d663aee41e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-212700b2-2d31-4df5-ab43-78d663aee41e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413f1a29-5e81-47ea-a15d-30983dfaeeda' class='xr-var-data-in' type='checkbox'><label for='data-413f1a29-5e81-47ea-a15d-30983dfaeeda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>decimal_degrees</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([46.9497, 46.9479, 46.9461, 46.9443, 46.9425, 46.9407, 46.9389, 46.9371,\n",
       "       46.9353, 46.9335, 46.9317, 46.9299, 46.9281, 46.9263, 46.9245, 46.9227,\n",
       "       46.9209, 46.9191, 46.9173, 46.9155, 46.9137, 46.9119, 46.9101, 46.9083,\n",
       "       46.9065, 46.9047, 46.9029, 46.9011, 46.8993, 46.8975, 46.8957, 46.8939,\n",
       "       46.8921, 46.8903, 46.8885, 46.8867, 46.8849, 46.8831, 46.8813, 46.8795,\n",
       "       46.8777, 46.8759, 46.8741, 46.8723, 46.8705, 46.8687, 46.8669, 46.8651,\n",
       "       46.8633, 46.8615, 46.8597, 46.8579, 46.8561, 46.8543, 46.8525, 46.8507,\n",
       "       46.8489, 46.8471, 46.8453, 46.8435, 46.8417, 46.8399, 46.8381, 46.8363,\n",
       "       46.8345, 46.8327, 46.8309, 46.8291, 46.8273, 46.8255, 46.8237, 46.8219,\n",
       "       46.8201, 46.8183, 46.8165, 46.8147, 46.8129, 46.8111, 46.8093, 46.8075,\n",
       "       46.8057, 46.8039, 46.8021, 46.8003, 46.7985, 46.7967, 46.7949, 46.7931,\n",
       "       46.7913, 46.7895, 46.7877, 46.7859, 46.7841, 46.7823, 46.7805, 46.7787,\n",
       "       46.7769, 46.7751, 46.7733, 46.7715, 46.7697, 46.7679, 46.7661, 46.7643,\n",
       "       46.7625, 46.7607, 46.7589, 46.7571, 46.7553, 46.7535, 46.7517, 46.7499,\n",
       "       46.7481, 46.7463, 46.7445, 46.7427, 46.7409, 46.7391, 46.7373, 46.7355,\n",
       "       46.7337, 46.7319, 46.7301, 46.7283, 46.7265, 46.7247, 46.7229, 46.7211,\n",
       "       46.7193, 46.7175, 46.7157, 46.7139, 46.7121, 46.7103, 46.7085, 46.7067,\n",
       "       46.7049, 46.7031, 46.7013, 46.6995, 46.6977, 46.6959, 46.6941, 46.6923,\n",
       "       46.6905, 46.6887, 46.6869, 46.6851, 46.6833, 46.6815, 46.6797, 46.6779,\n",
       "       46.6761, 46.6743, 46.6725, 46.6707, 46.6689, 46.6671, 46.6653, 46.6635,\n",
       "       46.6617, 46.6599, 46.6581, 46.6563, 46.6545, 46.6527, 46.6509])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.02 11.02 11.02 ... 11.36 11.36</div><input id='attrs-789af052-3548-401b-819a-932b3aeae278' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-789af052-3548-401b-819a-932b3aeae278' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7537ae90-670a-462d-bbac-9e2ce1d81214' class='xr-var-data-in' type='checkbox'><label for='data-7537ae90-670a-462d-bbac-9e2ce1d81214' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>decimal_degrees</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([11.0209, 11.0227, 11.0245, 11.0263, 11.0281, 11.0299, 11.0317, 11.0335,\n",
       "       11.0353, 11.0371, 11.0389, 11.0407, 11.0425, 11.0443, 11.0461, 11.0479,\n",
       "       11.0497, 11.0515, 11.0533, 11.0551, 11.0569, 11.0587, 11.0605, 11.0623,\n",
       "       11.0641, 11.0659, 11.0677, 11.0695, 11.0713, 11.0731, 11.0749, 11.0767,\n",
       "       11.0785, 11.0803, 11.0821, 11.0839, 11.0857, 11.0875, 11.0893, 11.0911,\n",
       "       11.0929, 11.0947, 11.0965, 11.0983, 11.1001, 11.1019, 11.1037, 11.1055,\n",
       "       11.1073, 11.1091, 11.1109, 11.1127, 11.1145, 11.1163, 11.1181, 11.1199,\n",
       "       11.1217, 11.1235, 11.1253, 11.1271, 11.1289, 11.1307, 11.1325, 11.1343,\n",
       "       11.1361, 11.1379, 11.1397, 11.1415, 11.1433, 11.1451, 11.1469, 11.1487,\n",
       "       11.1505, 11.1523, 11.1541, 11.1559, 11.1577, 11.1595, 11.1613, 11.1631,\n",
       "       11.1649, 11.1667, 11.1685, 11.1703, 11.1721, 11.1739, 11.1757, 11.1775,\n",
       "       11.1793, 11.1811, 11.1829, 11.1847, 11.1865, 11.1883, 11.1901, 11.1919,\n",
       "       11.1937, 11.1955, 11.1973, 11.1991, 11.2009, 11.2027, 11.2045, 11.2063,\n",
       "       11.2081, 11.2099, 11.2117, 11.2135, 11.2153, 11.2171, 11.2189, 11.2207,\n",
       "       11.2225, 11.2243, 11.2261, 11.2279, 11.2297, 11.2315, 11.2333, 11.2351,\n",
       "       11.2369, 11.2387, 11.2405, 11.2423, 11.2441, 11.2459, 11.2477, 11.2495,\n",
       "       11.2513, 11.2531, 11.2549, 11.2567, 11.2585, 11.2603, 11.2621, 11.2639,\n",
       "       11.2657, 11.2675, 11.2693, 11.2711, 11.2729, 11.2747, 11.2765, 11.2783,\n",
       "       11.2801, 11.2819, 11.2837, 11.2855, 11.2873, 11.2891, 11.2909, 11.2927,\n",
       "       11.2945, 11.2963, 11.2981, 11.2999, 11.3017, 11.3035, 11.3053, 11.3071,\n",
       "       11.3089, 11.3107, 11.3125, 11.3143, 11.3161, 11.3179, 11.3197, 11.3215,\n",
       "       11.3233, 11.3251, 11.3269, 11.3287, 11.3305, 11.3323, 11.3341, 11.3359,\n",
       "       11.3377, 11.3395, 11.3413, 11.3431, 11.3449, 11.3467, 11.3485, 11.3503,\n",
       "       11.3521, 11.3539, 11.3557, 11.3575, 11.3593])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-02-04 ... 2018-06-28</div><input id='attrs-4ac190ac-b450-4c77-aea1-02241c7b3001' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ac190ac-b450-4c77-aea1-02241c7b3001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd365b51-3851-4496-9a86-130e2e32cfa7' class='xr-var-data-in' type='checkbox'><label for='data-fd365b51-3851-4496-9a86-130e2e32cfa7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-02-04T00:00:00.000000000&#x27;, &#x27;2018-02-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-02-16T00:00:00.000000000&#x27;, &#x27;2018-02-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-02-28T00:00:00.000000000&#x27;, &#x27;2018-03-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-12T00:00:00.000000000&#x27;, &#x27;2018-03-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-24T00:00:00.000000000&#x27;, &#x27;2018-03-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-04-05T00:00:00.000000000&#x27;, &#x27;2018-04-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-04-17T00:00:00.000000000&#x27;, &#x27;2018-04-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-04-29T00:00:00.000000000&#x27;, &#x27;2018-05-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-11T00:00:00.000000000&#x27;, &#x27;2018-05-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-23T00:00:00.000000000&#x27;, &#x27;2018-05-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-04T00:00:00.000000000&#x27;, &#x27;2018-06-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-16T00:00:00.000000000&#x27;, &#x27;2018-06-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-28T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 2), meta=np.ndarray&gt;</div><input id='attrs-5f974916-08cb-47bc-aa4c-c24dd3d60243' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f974916-08cb-47bc-aa4c-c24dd3d60243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-011c6789-d34b-4497-9271-b4c5a467f9f2' class='xr-var-data-in' type='checkbox'><label for='data-011c6789-d34b-4497-9271-b4c5a467f9f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> (25, 2) </td>\n",
       "                        <td> (25, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> datetime64[ns] </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "            </table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc7daefc-6ff3-4b5e-9094-8cd3847d0f57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc7daefc-6ff3-4b5e-9094-8cd3847d0f57' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>VH</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 167, 189), meta=np.ndarray&gt;</div><input id='attrs-e1a8a454-791b-49f2-9b70-488f538a674d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1a8a454-791b-49f2-9b70-488f538a674d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7246d0bc-cafd-4d93-b0ae-5da8ececd98d' class='xr-var-data-in' type='checkbox'><label for='data-7246d0bc-cafd-4d93-b0ae-5da8ececd98d' 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>sample_type :</span></dt><dd>FLOAT32</dd><dt><span>units :</span></dt><dd>Linear power in the chosen backscattering coefficient</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 3.01 MiB </td>\n",
       "                        <td> 123.29 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (25, 167, 189) </td>\n",
       "                        <td> (1, 167, 189) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Graph Layers </td>\n",
       "                        <td> 25 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
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       "  <polygon points=\"33.57868909589179,23.578689095891786 153.57868909589178,23.578689095891786 153.57868909589178,129.61043512763783 33.57868909589179,129.61043512763783\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.578689\" y=\"149.610435\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >189</text>\n",
       "  <text x=\"173.578689\" y=\"76.594562\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.578689,76.594562)\">167</text>\n",
       "  <text x=\"11.789345\" y=\"137.821091\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.789345,137.821091)\">25</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e48e7ccd-30d0-4d4d-a191-e7fabe67e2ae' class='xr-section-summary-in' type='checkbox'  ><label for='section-e48e7ccd-30d0-4d4d-a191-e7fabe67e2ae' class='xr-section-summary' >Attributes: <span>(13)</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-1.7</dd><dt><span>title :</span></dt><dd>S1GRD Data Cube Subset</dd><dt><span>history :</span></dt><dd>[{&#x27;program&#x27;: &#x27;xcube_sh.chunkstore.SentinelHubChunkStore&#x27;, &#x27;cube_config&#x27;: {&#x27;dataset_name&#x27;: &#x27;S1GRD&#x27;, &#x27;band_names&#x27;: [&#x27;VV&#x27;, &#x27;VH&#x27;, &#x27;localIncidenceAngle&#x27;, &#x27;shadowMask&#x27;], &#x27;band_fill_values&#x27;: None, &#x27;band_sample_types&#x27;: None, &#x27;band_units&#x27;: None, &#x27;tile_size&#x27;: [189, 167], &#x27;bbox&#x27;: [11.02, 46.65, 11.360199999999999, 46.9506], &#x27;spatial_res&#x27;: 0.0018, &#x27;crs&#x27;: &#x27;WGS84&#x27;, &#x27;upsampling&#x27;: &#x27;NEAREST&#x27;, &#x27;downsampling&#x27;: &#x27;NEAREST&#x27;, &#x27;mosaicking_order&#x27;: &#x27;mostRecent&#x27;, &#x27;time_range&#x27;: [&#x27;2018-02-01T00:00:00+00:00&#x27;, &#x27;2018-06-30T00:00:00+00:00&#x27;], &#x27;time_period&#x27;: &#x27;6 days 00:00:00&#x27;, &#x27;time_tolerance&#x27;: None, &#x27;collection_id&#x27;: None, &#x27;four_d&#x27;: False}}]</dd><dt><span>date_created :</span></dt><dd>2023-03-07T06:54:16.049322</dd><dt><span>time_coverage_start :</span></dt><dd>2018-02-01T00:00:00+00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2018-07-01T00:00:00+00:00</dd><dt><span>time_coverage_duration :</span></dt><dd>P150DT0H0M0S</dd><dt><span>time_coverage_resolution :</span></dt><dd>P6DT0H0M0S</dd><dt><span>geospatial_lon_min :</span></dt><dd>11.02</dd><dt><span>geospatial_lat_min :</span></dt><dd>46.65</dd><dt><span>geospatial_lon_max :</span></dt><dd>11.360199999999999</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.9506</dd><dt><span>processing_level :</span></dt><dd>L1B</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:              (time: 25, lat: 167, lon: 189, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                  (lat) float64 46.95 46.95 46.95 ... 46.65 46.65 46.65\n",
       "  * lon                  (lon) float64 11.02 11.02 11.02 ... 11.36 11.36 11.36\n",
       "  * time                 (time) datetime64[ns] 2018-02-04 ... 2018-06-28\n",
       "    time_bnds            (time, bnds) datetime64[ns] dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    VH                   (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>\n",
       "    VV                   (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>\n",
       "    localIncidenceAngle  (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>\n",
       "    shadowMask           (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>\n",
       "Attributes: (12/13)\n",
       "    Conventions:               CF-1.7\n",
       "    title:                     S1GRD Data Cube Subset\n",
       "    history:                   [{'program': 'xcube_sh.chunkstore.SentinelHubC...\n",
       "    date_created:              2023-03-07T06:54:16.049322\n",
       "    time_coverage_start:       2018-02-01T00:00:00+00:00\n",
       "    time_coverage_end:         2018-07-01T00:00:00+00:00\n",
       "    ...                        ...\n",
       "    time_coverage_resolution:  P6DT0H0M0S\n",
       "    geospatial_lon_min:        11.02\n",
       "    geospatial_lat_min:        46.65\n",
       "    geospatial_lon_max:        11.360199999999999\n",
       "    geospatial_lat_max:        46.9506\n",
       "    processing_level:          L1B"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube_s1 = open_cube(cube_config_s1)\n",
    "cube_s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "066f5821-f615-47e4-b52b-20454b3108a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcfae7c2970>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x3+edQG1v/4J2L0gVYmPQJPg6qvUVGZ/yzAv72xiIi0zsLfWq60n62XCcf2zHFVE/Bzbm+dvB7JKdnhxRZjiJzhNopPMHtvPY3bYdjU49Pi17/RdJXbWrmDlzJlava8DBQ+Nx3629Mf3p7fusLr9H7LMlv02bNmHo0KGYM2cO9tuP/jAddthhaslvy5YtyMnJwZlnnon/+7//U8edcMIJSEpKwr///e+o5RYVFeHPf/4z/vvf/8KyLHTv3h1HHXUUnn/+eVTLH2YNVVVV6N69O6ZMmYJrr7026j46Q1VeXo6OHTviqJxLEIpN9O3rJNIfkuqu9MITu71e/RZatRkAMOvHuf7yHXqYnDSCcnmFN9MfafDLhv6wBNw/RHYxvUChwf9AkkurH7t9Iv1Rz5i5ko739EtAHhr8MLNLy3zHNg7vTz8vXOM7BzwvrID/YSh/AJxGrt9OXgiC8keS2+7U1jW9s3beQCa9EG8d2wkAUNeKykjJ9T9gHf772+pH94FSMIr+sIbj6McO/9niP2ab/+FjJdAfspkL6eW474zLIqrX7V8b6NgKeqluHNCVjv3m56jNkZdLR2tXEzv7v8sLS8j/h2iXymripSuYzC+19fVcFv8h065ltBetiBfmJmAN6OX7HthK47lxa1HkvvLi2USb5FyB1vySVMMTk0S6Vk5t5Iu1vMRaOfSHtbEN/cGTMRK7vtC3v11OLxURY57PAQCbzvcnoP7pmicAAOOOPJXK3lzgPyazFQBg1sev+44b8de/qP+Xd6G2t1pF9T3oLpqILjsxx3dMOF97udGujVzLHUG/nlYsT7C4j1BKfRAu4cmb9xrLmIinl0p5/sh5A6nUv2uuoHuh650L6bgdPOvkZUuNqZiQv576C7X+Z01epD1LbZsvH+zbJXUDlZ322Xouku/FOqq3lcTPxhh//ZyEKInDt2ylY/hZ5lRWodGux9xtL6CsrEzlwtybsG0bQwYm4LzTU3HYoYkYftwmrFy5AZ06ddrrdfm9Yp8t5C5cuBCFhYU44IADEAqFEAqF8Omnn+Kxxx5DKBRC69atEQqFIpbV+vbtu8MovzZt2uC9995DVVUVNm7ciBUrViA5ORldu3Zt8pikpCQMHDgQq1evbnKfuLg4pKam+v4ZGBgYGBj8FvDGG29ge6mNv0xKw3794zBhbBLuvmngvq7W7wr7bMnvyCOPxE8//eTbdv7556NPnz644YYbEBcXhwMPPBArV6707bNq1Spf5vamEB8fj5ycHDQ0NODtt9/GaadFUuiCuro6LF++HCNHjmx+Q4IBOExlWzLT5aUWYaZCa/M9+9O+4/ofDgA442vqg0mpxQBctklmYGpmJbNhzwwrvCnPV6bM7OwabRbOdPjGm4cCALo+wS+OzCIFYt2lQ4eX5WR5LsD7VB9FEZgJ+URhRzAEPJuT2We4qNgtU5ZXZObaBGOhZqayrNkE+wW4M2ZbWyaQ87ad7581rro4CwCQlEfnTs6jMqu6um23Y3iGzF0+84v3AABjx55JG1KITSjvTceUd6F6DpxOzFR4f2Khel7jMhorpnQBAPR5hJjJTddQ4Z3Pir50ppgf1dBdX6ZTS6l6GbtSlvymLQcJe9kkyyVLgmH/d1997B2T4M6PdI8HZIkw4B8nvuOFhWuCiZKx71TyUgvXW9gRL2Og0INm6A2pdP5QGTOqy4iFlabpzEn4gD4AgODCFbR/ncv8dJjhf7YNK7gUAJDQlZ4RwWyqRzjef03GH3oiAGD92cQ6pSa4/WlxRYoHUD1mvXkIACBtOO3z1d9nAADG9hjua7NALSdHGwfaWJI+D6b6l7UsXkYMb/eztd5rFIgV1pLPI2OH72dhnBvStKVHLkOWVn311K67uq4RzDgv7ckzkesg46JuVH+1b+ZPdJ/UtqJ9U2cRa2zzkr+w9Vaaf4nOqa7xt6vE0xfCnImOV8ZERjoQrgO2YZ+goaEBt908Cbddl4F4HnN3Tm6N/Y7IxZQ7VqFXr147KcFgV7DPXqhSUlIwYMAA37akpCS0bt1abb/++utx+umnY9SoUTj88MMxe/Zs/Pe//8X8+fPVMZMmTUJOTg6mTZsGAPj222+Rl5eHwYMHIy8vD3feeSds28aUKVPUMZMnT8bxxx+PTp06obCwEFOnTkV5eTnOPffcX77hBgYGBgYGexHPPdoBgQBw7mnuykrPbrE455QU3DblALz+XsU+rN3vB/s8ym9HmDhxImbMmIFp06bhyiuvRO/evfH2229jxIgRap/c3FxfCGptbS1uvfVWrFu3DsnJyRg3bhxefvllpKenq302b96MM888E8XFxWjTpg0OPvhgfPPNN7vEfBkYGBgYGPxWUFNTg3se2YaH78xEKORfHbjt2gz0OXQjFi9e7PNpNNg97HMfqt8qysvLkZaWhqO6/hXBOI76EYq7kangsihv/RJBxhR02Qm0lPbVI0TXH7zkFN/urU5hYbQs/Xki3YQ6j1geFOGuUNe8lLLpz0R3d3xmqX8/zxKhEh8z/R7qTOG91b3aAADiv1juq5/Q88F2tKSmlvp2IIRWwlKm5wMsfIaIV6v9UUxKlL6DiDcRwKrNfC3kWBFTqyWAQRSl2JAeKSityqI+LjyC+qLj+7x8IAx/P/885OerSHAsyzVOpSvyrz6oCwAgYf4y3zGbL6VAjBA3NetZEuaqpanmCMv1fmlCrB6xrLeT4IAdoqlIsSjLNE2dRy3fiLBYa6NaRtpRVBqXrZam+LvtXdLzINCFxnP5fm3UtsQtNP5j8vzrMXZRia9+dhVdV1kGV8tLjKpRfdT/k76m5UK7nJaBJRJPRZ917+g7troj1b8uleq/dSTtlz3X/QNY1s2/TPzAn58DADx6zunUptuozfYb1LY2s9f59g8Xl3AdPP2sR9ZqkXVqaV2WfneyfEtFUp2DHCDiaM88waqpfv1Oz+u/9xfkFcbLEl8T5xdxujzr3Ehd/7JnoJs7aXYS+PqtpujeaM9YIIoUQa6htvxIO/v705KgjvhYNIbrMHfto3tdlP63O9vglbcrsOCjjghEkVtMvrMIK9bUY9Yn0e8Zg13Hryb1jIGBgYGBgcGeQ3l5OaY9tg1Tb2wd9WUKAG78awa++LY2wqvRoPn4VS/5/RbQ0DYVoa3MSOjut8yaOOWuD5WazfBsKP2HEt8h3wx+y/d9zKBJVPSSVVSWd8Ylol0Rm8pMXtgYZq4qjqCZswhl7RqNAfLM+pRHEAu0KwZl+fdt9PskyQw2LCHuOusAqDbLvkEWEktblDWDPvvcEcOhsS3i/RToydGc27hMzXJBMVWLSQgdnxCZxii+Hc3wW71BQvKA5vcVrCF2YctIP7tVn0Oh77VtPHkhuTsqx5AuMPVrmg13fJ6EzMikvih/n0TIySf6PZhklg+PF1a4gETvTYqMd8ZYRROg6zPraILwKGXoAQY7Yj90BNuQ7UdjwdZdqy9cBkWuYwTzAxHJa2JlaU8SXe+YCvf3hhQqs7E3jfW4z1icLD5kIoCX+1nYEmZn5TrEz1qoygxrFhYR1h9ryVPK6kBjJWEzsQNJ7Jt1wa38x81jt/fu6aMAAFuOpDHTOsCMFPsnZUyWfqJnSsGJZFQcjqX6Z8+lcWyv3ajKlLoHBmqiZGZtwk0wfTu8NzUrAyXQ1i052tE9222SxnqLpxSaZmUjbB1i/NYMajwIg8m+dahw22Nv3OwvQ6wXxH6GmSrlP6aNPUu8rTzBCpawWyFt1aCoBHB2blexp/HIPV3Rt2csjj0iscl9MlsHcc1f0nHz5KPx6dfVJvNHC2AYKgMDAwMDg98ZiouL8ciMUky9KXOnL0nX/CUdy1bV46OPPtpLtft9wjBULURMQSnQxMRDsTmeWbzTyLMv0Q2xq/mhV5GB35ePPgUAqLap0I/eeQkAMLYnOS1bHslbhEGfNmvbfpI/2bRoQHRn8kC8OwzEebp0hF+gH1PJszJmmZQ2yWarCF174dE0qVB1toQIFzKb1YQuSGdFLJnwxkXqnZSGq0M2bSinGWhYMySNchL68EoIhXlYv8m3j13i19bEf0/n7L6U2JHxL5B2avM5kbNAO47K7/FPMgsUs0ph6SxmQdIoqh7LHiddSe9LyT1aTCTFjgIAgq3SqXockr38oe4AgL6TyQ4j7GVEdwCvTivCqFO7zhHHMiMgLFNYWKZd0GWp8Scz/J1orJTGDlAzf0vc9zXX8KZsJ2RM2exkHVPpMq3pD9D1rjqDbRt4u9ITacxzhJu9nMp7/4krqG4rIfuC2a9KZlbFfTuJxvg7k44EAKw+2217u9787OCySm0ab+E4OramI91nVe2p3llz2TyUGZWCo+keSezTSpUZU842DnW0T7CSxlTBRYMBANkvkd4yYkzt4DpL/4R1CxhGoA2xru1fZeNP0Tuyu3jUPpNnlbBEjX52Vr8moqWy+L4RXaNT4dG1as8ZB/xdxp1c/wa+ljL2NEd1fyF+awiFYBCwd3DcL4Bpt/XEoQfFY+TBCTvdNzUliBuvzMDN10/AMcdU79Ncg79lmF4zMDAwMDD4HWHz5s148oUy3HND9FRq0XDpuWnYWtyIt99++xes2e8bJspvN6Gi/DpcimA6mwsWk8GbMthkdsFrCKiYG5lpsQbETuDvDxKz8lB30lINivVrfMb1c0UVEsmkUjvwpVSaHzE71CJ0RAsirJIwSF5sOY7zbP2PGBUnl6INVb4tTacjdZCyvBFQNqdsceqisx1N6XTUDLFPlKTVKyiCSdq89c8HAACy3ycXfdEZeU7ir5dm/AdEGqKq1BMyG5ZjZLt26wSzSQ9T1ceTcJQnt4kbmDEr9EeOKbB54MaTScfT6e+LqE6ajsdfQdaHcAqi9ed3AQB0vO/bpo8BVMocb87BnUVvNZVvsX40MWoxcxc1Wb+mEOzuZ0Ht9XTtJA+kinj09FWEpmdneema0IaVnzJUbUu9mBiqxnuo70NfcRTszjR9kipFxkM4si5qfIlZqaR04rEWSNHywbWhZ0l1l3QAQEKey6jU5FC/2LH+63r8vZTG6rnlZOjZ9Va6rnnjaBzmfESRt05MJENS1peeFUmbOdKxkM636i+kI3Sy6J49sCtpqopuI41i7Deke9qVNDZ6HkgZrxJ1uKtMNeCyXXqEpbBeDmukhInSn4nRlr6cpqL2dvKnUbG4Pl0r97GmNUUwQKlnSp7fK1F+f5mUhm2lNl5/uv3Od/bgqZfK8OjT2/HzimqEoqQA2hE+++wzPPTQQ1i4cCHy8/Px7rvvYsKECTs85tNPP8W1116LpUuXIjs7G1OmTMEll1zSrPP+mmAYKgMDAwMDg98J1qxZgxffqMDdU3adnRJccGYqGsPAC//I2fnOGqqqqrDffvvhn//85y7tv379eowbNw4jR47E4sWLcfPNN+PKK6/8TTNkRkNlYGBgYGDwO8HtU/bHWSeloHeP2J3vrCEmxsKd12fg5ntLcM5ldYiLolttCmPHjsXYsWN3ef8ZM2agU6dOmD59OgDK0/v999/j4Ycfxsknn9zcqv8qYJb8dhNeY89QmA0y43iJSmjlUCTFLnn+FJ3MJqDhtiQUre5EItTkpbTUVtuNZhn/e+FZAED311w6tPe0tVyWln+tggSkQrErSlpbqogGEXOuuIesFnpdu5jK0ETLEcdponTf8odWP7UEoNVDLRsm+EWUamnKI5S0WfQZZKrf7tjWdwyWrPCdSxezisA9Iu+ht16y1CT1ldBsPSRfD+HO8dgmFPtznilwfWYtngMAOHb82VTPCg4cYJGtva2UzukVuYpoV2vbyO/oXJ8fSn0RSGLRsiytiLCXTTDD25qom68xkpeN2xjNzBCe/qyuRgR2Ijp3tP4NtqXlpnB+QcQxTS5NypKu1FNfUmUEeKmloV8HtS33GBrzXe9c6Nu3SesFbQkranX4WLkGqgxtyUkfjxKsYqfRNbI8YmurUutbvu6bTyQ2of5QXh78mZYG2y6kY+vSWLR+Ki0721+6ovR239G9VdaF+iB9lf8c1dm0veAQqne3t2n/UDGdq6pPpt50JH3KliBsCeGso6Vc9UzY2TJtFKNXZRKa7O/P+g7Ulphv/Ma5ajlO//Pmvab6fRvcyYKNjGPeTz2nPMt7TmP05UsrLhaNdh0+yZvxiy75/fjjjxg2bDCWf94ZnTrE7PyAKLBtB/sflYsLzkzF1bcV7VYZlmXtdMlv1KhRGDJkCB599FG17d1338Vpp52G6upqxMTsXv33JcySn4GBgYGBwe8A0+8fifNOT93tlykACAQs3H5dazzyVCnKy8t9/+qa0MHuDgoKCpCV5fc5zMrKQmNjI4qLi/fYefYmzJJfCxFulYRgPYuVq1kEzkJzS1iFOA/1yrM0uzXNUMp7+jOZp3+1yfd73Fa/CPf8o+ar/7/Ufpjvt1WjXwQAjBt4BJWhhTrr6VnU7NkjPnRSaYbc504OwZcZXRNGjoJdEU+rlDMi5tXE9DKDdURs34/sAJxlzMR5ZoKKHcggsWmgyn+jqzmwsDmShkeMP6Wc1evh7ur46yX10TLbK4NHZg/EOkJh0xbokDaqFBk8Jrp+dCEAoF8BGXqq1D1SP65LwGtAKuJ4vn7lBxHbMu8y6oOYbLru4VQah1XDuwAAkmezsWtZua8ugDvbFmsNtV2upzKNVT/4t+8gzLqpMaN+VwEC9NmYl+/fYQcGpHoZEdBYpq0TewAAWv/ktrPzB5xShoXL9naNuVOMoGafIONZbAE855Ix4praan/gVP/50+s4CXycsDq+avhTo1iJdM1yPqQAjKo1JGhP/oGOFZYrthXtF/g3ta/gULeeFfl0vtrWdI3Wnkb7tmGyrtVPxGpVtScmKJzA4zGZ9hNDUoTcvlkxtS/VYyMbjq6g88bP+UFre3SRv+ozz3KTYt05eEOCO2K2kF2H3AsSqKMHwEQXo4d9vzmarYHLSDP7afHV0NJa+Y7RrTZkbNTV71TkvidQ2TgG/XrNa3E53bvEoKgkhLQ0v6nxHXfcgTvvvLPF5QsiGFt5Tv5GzUXNC5WBgYGBgcHvBA4c2NjJsupOYMNGQkICtm71ZzFojqZqZ2jXrh0KCvzL+oWFhQiFQmjduvmC+l8DzAtVCxGoqgdiaLYmqUeCNcTWVHclLUjcNjestmx/TrvCk6E2n/FsnBkesSWwajhkXDOsuzVzhfv/0fT/gxafCgB4rYLOv+pmSiPR40YOZReGiLVUYpQY3l5Kv3t0KZLKo26wn8mJ+fSH6B2gIao+aiepUNRsJJY1VKxBclYSexTM5sTLW7QUJQDsNRTOLaHYwuzJ7FYSskq96rKZ+Vu4xrfdV6akmhDmRDOYVIl3m9BY+domujCZuXJYfFXXdABA74upX8OOnw1TlgiiHSn3GBIKg8bGnnHbacyESlkPJmmA1rE+ZhRZBCT3JJsCdWXW5Lr15Fl3sEcX+i5sK7NZAqVnE82SaKfENDYaU7STpMyBND9La3EfiIGpT+vC/diopQ3Rx51oaBTb2JnCxydeETl7/+roTgCAsGbgquqjt0m77tCuWTS46aG4nkk0lizdMoCNftX49bKyfIzqR2Fh8omhSiphZo3ZxgCbnsaWlAIAYgqJsUoq8Pc3ACTnUb9tPpjGUnFb6rdWP9P26iyHP6lfO8z3Hx+Oddveain9X8yAq7KorKpzyNoktpLKKhpCZXe/h58twsBF+aOttI4eRtkLqwn7BhkXcpd7md6AZr0gjJ97PflO0ZmoHV1n0cSJhiuek0unJSEcrgMiyes9Dttx1PNk98ug439Je4dDDjkE//3vf33b5syZg6FDh/4m9VOA0VAZGBgYGBgYtBCVlZVYsmQJlixZAoBsEZYsWYLcXJq43XTTTZg0aZLa/5JLLsHGjRtx7bXXYvny5Xjuuefwr3/9C5MnT94X1d8jMAxVC2HHxwK1kr6BZjYN6TQLakhi3ZHtvm0nbKN9E7/bQBtEayHr68KOVPjfdccNolQUm87rrbb9dO0TAIA+GcTcnJFCM9Thpz5MOxBxhb+ccDGVuZn2E42SzJq9bJK9luoVt5Vmwyv/SaaaPT/latp+zYfCjmZtEo2mRzwJayOzyI7ERDmrqA6iQ2jIZuNU+QQQ+H65rx62sG2SckYM/eIlmo9m6+F4TvERLelrE9GIMqttGEh9EVrApoZK+8XaKo6e8yVLlRkx6zDqW1O/JnzKUUkSfSiGqfDrclS7oqTIQVcyX41dvNZ33u1jScOSMX8DAKDj/5EJaunz1P8px7nMlGorj7uAsJm8Pe9iMu7s+AYbpkrKIaWH48jBAT0jy1y2xrevG4nHOzDTI/qYQCLVT1ilaAyqwNKMWUXDp8rUGbOlVJcPHjwMABCqdctMqfiJ2xLdvNRtELOITaWeifGkmFJJ0JmRUol1ud9krGnJueXejKq3sf3MiTIH5X4TllsYnrouFO0Zt4H0RpLmKCav1K1zLTE7W8cQS5f1EvX95iOpD8p6U1mtf6b6FJ5A17/2J9qeuIXqYIXd+tqc0qguneqZupH6q7otXYv88XTOdUc/BwAYN/2YqH3gjcCNiAjW2CWJ+o3YT9NrelM4qWOFhVUsMV8bSZLcBKwoGlQFjvCWqO7g9nI49t5JjkxLfi3Ta+0Ov/X999/j8MMPV9+vvfZaAMC5556LF154Afn5+erlCgC6du2KWbNm4ZprrsHjjz+O7OxsPPbYY79ZywTAvFAZGBgYGBj8brAnNFTObhx/2GGHRdpUePDCCy9EbBs9ejQWLYqSZeE3CvNC1UIEautR14G0UpK4uDGRZiehWhqUiatK1P6WpEbg7+6M1D9jVQNTZmk80+r06jr35DQBwE8vDwAADAF9lh9CbMzaI58HAGw6Np2OfYdZCJ7Byjxc+RTB1exU7UfeNvFLeWatswS6Lkb3AfLM8gMDu/sOrehJ6/Kpc4jpUalxfvbrdQSNSTRME1a6GirRCARacfSRpn/R2QZJBJy0iNOMRPMzaiJNibQpdgNpk+RRI9qvYHtm1kTvUu/ObBuL3L4FgMBmiuazpV6iu9LOFdB1JN6IPGbCnDxmHIVp5Jlyq48pQtPJSOf9SPiZdi3ry7KIuQhv9aTnsfzM3cBvqQ1bZ3BSYY4+1Nkmu5IjSX+mcwY9iYxrjxwMAIibu4SPhb8MTfcULi311SUalJ6N09NYEkHLGjPFVGh6NumbtPNJexWa5P7BcJpIsSGawwhWtqmIV++Y0n5T2hqJKJNIVkkDJOeSsSPHe8p0wv5UU6osYRc54bbDjEvsMmqrKkHK8iZrz6T7J7mA6pewkfqx17+4qazTq+1M+8XF0/ftvaneKev5Wta5WrCsb2mfMCdd33gc7duKM/r0mMH7Hk0f4sU26jJi0huZRU6budStp6aRUmNI9wrT+lOx3xJdXRb5jJGy7HLuR2ENhQUVtpg1aSqZs1wrz/0uOjC1CsC/BZIS4Dh7h6EiDVXLGKqWHv9HhdFQGRgYGBgYGBi0EIahMjAwMDAw+J3AAfaAhsowVLsD80LVQtR0TEMcCzJtDh0O1hHtm7CUY2Q9tLQjQscmMqyLKF3ZJ8hyhFDXHqFkj3nn038OJSp6zeG0xDfkvsto+5F8Tmb4ywa3iXrK+uTO6v9tPqPloYQ8WsqZ9MD39MNZ9DFvf6L+lRBbC+8OeJZ8BNY2WkaoOCAbAJD6Iy0fqTBlXvKLWKZjqj1+EwnNxXQUAJw86o/SI2Q5kT7TP1lFX4XGF5qexelinBlNgKwbjwqUjQKnatl63hAAQOultBwbs5yFltwH3rQ7khZm26l0TPqr3/HJogucRbiv6sIWErbHJiDA9bO707JsYBWfn8dMzXAKXEhcoplDiu1EGi39BXPcTPTOdk5L0pOE7p8/QktqCx98EgAwrOZSXz1b/XsBN8P/4A17xP7x+bwcyMsw0qtqmVCJgPmHnaSooUpzOiV1n9Ayiq2FtusBFxIcUPMPGoNbrok8V48bv/efQ7diiGMRclPLIbZnGTHs16DIUrqCmMXy/ayCFzSDVK95ZCCF09GwsFqsNKx4PlaE0K3pHhUbl5gi7m+2V4DXZHQLbUsQY84gL6mW8NIYLyvG59H3lHeoHaFz6DlReSgX+YgbMFKTSfdtYxwdm/05B+3UUJ+IOej4g8YDAAqPIUF8XJB+rzidzlXWdSB0dP4PLaHbK9f6toey+NkmgSLybGHIMqj3ftNTCwlEDK+Wl9nwFVqaqmjGnhHBOrJsmJYKy64DSqOeco/ChoPwPhClG5glPwMDAwMDAwODFsMwVC1EoMGGE6KZTMJqEi07bLDn7CAMW2cmVFoYJaJkhkIEqFHCfXtdSwJnYbXmL6Tz1HHu0z9tOAwAMOR4CtFfP70P15nOkbyOZm2bj3aTpVYM9CcZfuVFUo5efeE7AIBgh34AgHA6zZYbk2i2G6pgxoCPC5S6aW9EoJn4AUVziCC76vghvEcXqs+sH6L2RbgJMz8ASHmTmBKV/JhnhHWHUFtLBvjF3Q5fipyn+FxeRk1YA56JFp01yHds2zepH9u9RSyY04HF6DncZ2yU6TWCrBi7HxXN1VA2DtFmt4AK1Q4PIZYptIpE9F5xshLWLqHPyuP2BwAkrSfGIvEnFr5Lug7pzwAzL2za6U2HohjGzTSGW28tBQBUc6j3vGmUwDQxQCLwsmnE/J05cBzVN4rY19aF6jnUX5akVWF2STfO1JmhYBs3Aa+tMw0SDKEnLlbMFDMVMVTvUCVd406zXXYi7mtOpC3Jo4V1VQaeknaHx4dYckhCY2EVvYlxa7mPxVi01s9uWMwSIobF9TIehP0Shk3YEcC1TRAGRcL2NVarqh+Nx7pUqn+rHymQRdkQRElgHSjgsSDMn7S9VRrXj65V66/IiDi8igMc6qmeWw9xx1LpAOqvzO/pGlR08P+ZiSulNpb0J2Y8fQ2N+cZEakf8f6TN7pj/85T3AQCP104AAKT2TqfPrzbQnmI4yyg5gZ5TrZYxi6fSLXn6amfmlzwewoXFvmNVwJBmyUD7+FlCxWo1ht3r9wvDLPntO5gXKgMDAwMDg98JHLQ8ys+8UO0ezAtVCxFXWIWYUprBqMSyTSVE9UDNylXYvMzSWZukaZEcTgnjeNOviP6CGYgpK8gQLcQylh/follaI0/SE9LpJgnW0cxq+1iaRVX1csN5K+kQdHqHw+i5CVO/PA4A0DeetDaBTaShkPmeJakCWAuy/C6X6Ur6merX8YWVvjZJst7akXxSLkNC960mwtmBSEZCJWdmHUbCz6Q56vAz7e8k02x+xTVUL9GsOF5mhWf6wh61/hfpnUSfseHS/gCATv9kI8iNPFvnMlRCXM9133oQldlpDodkMzMR4El4uJhTjahErJx25Ucyoqzhvond5mc4ACC0idik5C+Jwase2oXazroXS08QKyweJ831esZYzGbZmv3ESd1HUn05BUXHmbTfhivJyDMYoj4Icsh+1PQtYrbJTKP0QXi7JJfWHt7CHATZEsE7DqRt3BZb07Uo3YuYhAoTxIj/aRMf7xpDhiXsXTRdeoh+E3+cRFsHLVWR71h1Ej/jrOuf5Lt+LqvBrYuVwfpFTePXkJMOAIgp4DKZCMn4fJNvf0kr5dX+KJZL2iC2A8zKWVV+5sdune77Llqr9jPd1Egpm7Ol0QCAyhzqlwrOZmVvYtPQvnT9Q9X++zymhvqgrLvLJtksBLWYmKyQMk8l7WTOf5jJZZuH+G107g0nMLM/hcZrl7M9zyAtabu72fJt1w2JAzIepS9Yf0hN5rLEAJfTKlmVkazgLwUbLddAGQ3V7sFoqAwMDAwMDAwMWgjDULUUhSWwq8WMj99PxdxNTAY9s2Fl8KcZN7pslp/VUtoLZjK8kUQKPDPOnMQRMG9y1Nw1rHPIo0geYZHC2RSpk8HszSbL1Wk4B9CMc9MxxJAlcXCZVUVDZcUldGyb70nX0voD1p+A+yCdZmR9rlzttk0ibnhmVzOIIskG3Ec6ptwqml3WTu4CAGhI8aeViPueyrI92g+d1QhoKTwqDyR9RtJXxPTYmyjisttbNMu3OUWNmCECgL2tFNEgup2a3jzrZL2GaG4sTWsj2ioA6HEwJW/GC6yzkfQ0nMA4woBSNBjMdiUsoGimxt4d1T6hYtan8Zip7U/RfhJdWp9B/V10JOmwcp5cQueu5ohHZqiqOUEzACSxrirImhmb6yeMiV1OffBUh28BAOPzWdeUyAwLtyPo0aXYZcRaCIMnM39b6VlY06VFVwbS3WsC+PUxjs666eaacm4ZK/wpjK/FCZfDm/PdMlT6GtZIaYyVHnWqjhNjTY7ocxCO+E0xasJMqRRTbOSpjHsDvv2UPsejnazq44/SFeYpvpjLKKV+TViwjdvjT50idQpkueWInk40W7rxpSX3h+jGSplJK+Jk7hpjBQDJP3B0M/dr8mIqO4vHyraDiCXOni/zedZUDbCkYQCApC3uPf7g92OoPsP8jFnCzzTWN5xF90fnGWQWnLiZ7pFuq/zj5Y31n6n/nz70RABAWJJK61F9DHl+13ejfotdx/dGGWsUPYl8lZZQol35uR1IiN9rxp4OsAei/MyS3+7AvFAZGBgYGBj8TmA7QLiF70MtPf6PCvNC1UI4tbVAQJL8Rl95jup3oma/PHMVDYPMdiS6T9gtYUG80YEyw2ftVs0R5NtS+Sod23a9m7oB8KRBWM2pXnjW2bm6ndrHeolmVpX7E6Oy6Wh/3Y8cRrTW11soei2TGZd159MMMThI9AQuo5L5DJVVNJja1unDUgDAmrPZ/4rZkAAo+ss+uBcAYMMpVL/en9Ms2tePGjukfLuY2ck/m47pXErniFlGTFFsIc8cu3eh/T39GdQismwtiXTvS6g/c6ccRO34x098bpp5KrbLE+GI86nM8iHE7CV/+IN/36D/XG5UGI+HLh2o/utd7ZxEk4W70HULaE+/QC39nvUta0DaESNgFxBT6cRQn4nXGOCycyqVi6TAEf0TMz3HdhoKACi8iOqVuon6Jv6jxdAh6WHsigr/D1raIsXwyT3R6I+A9Say1tPAqGN1/ZKMFTkX+3mF2b/IO5YcOZ2eTklPMaOV6fAs3hc51gTU+bSIPBWBKeD2NQzoDB31HLWXupL6M1hAjLTc/3ofBDj1UEMOjbVQEV+HCk9icJ3xE4ZMGF9h1KSNKoqSv29nRsarF2U/LNERgceUxeNWIgXtNGING9OoD2LLOSl2PPX3pmM9FWOdVSCF7vNWadSGVp/RtWq4qxQAsHYwPXdiFif72tVmMd2jIx64Vm3L7COJnUnztekov3dVt1dIJ7ptKN0/rRYW+dsn+raghx1vIiWXXVML29lxsuU9BYrya3kZBs2H0VAZGBgYGBgYGLQQhqEyMDAwMDD4ncAGEEZ0beGuoqXH/1FhXqhainAYsHjZielxWaZTRnDeZTpNyIpwdKGiJSkUeD/5bnlSKojAXZYi5j/7DABg9F8ujl5XTcBrCSudu8XdyEsQIubu8xVvzyIR8ifxFMbf99gNAID8Q2lZJ3Ye7/c5CToTilzSOb6Qlhraf0Nl17emNsSv46UHFgqDlwQsXlYIbYvx1zuaQWpjg+83i5ddV416CQAw4q2/AADKupFAO1hPZWd8S3S+iK8BwOnXjcpYRkaIavmNlxPl+mb+yCaYmbSUYrPwtL6r3xQVAAKNVJ+UL6hMMUy0OUw+wKLjoKQPEfNIvrYNmRL+7y4rxBTQsmqwhEXf8SS0LxlA+7R7g0PD+ZqJADrA/WyvZqF8ry5uPbketb0pHU2wltv4nX/ZWMZ2/Hbqi8RvaAktrBlqAp6lPlmKkmUvbUlNN1i0eKkwXMjiX28Agr7Up9VLWQJI2VKfPLb5iJIaya0wC/CjGF9GLVNMeOXe3IHNh1puVylmLN92JWJn1KfS2I8td5eJ0n8i8bS9iuwnGsViQ9rE36tH0liP30oC7lAhL8txeL9vOUeE9tLHbWhMWzWa8axaWuX2xPMzjkXtjtc6gJf/VOCNPP9kmZCfi4Eyuv9ji+nYWEkXlUV16DPDLbK8J42J/JF03lKL6pvQlr7X/JvTWvH+VRSngbrWnM6mA9UpNdd9Fm+8WEsPFKLx2pDH/VlES6qp62n5sKI/3U/JbKALXrK0it22B/mZoFIiiUg9YEHlAPuF4cDnA7x7ZZg1v92CWfIzMDAwMDAwMGghDEPVQlgJCbBs/wxczTZl1h7F8E+gWCZNXKsnV1XfPQyAhFyXnCopUr4EAHz61NMAgIOnXAIAyJi50n8uzXjUCniGgTAkMnOWNBwbyD+hz3UkKJWUGO1Bs8zVD9LMO7yV2CcRgQJAoJr+XzbIb7IYt4UF0AXFfE7aL2EltbF1W07+y+fymk4q1kIXDvPU6ujTzgMAJC9YQr+LyF9EwCz+tzq4CYIrOxIbVDuA+jPzLRLgq5B7Pjb5Z2I7tozvwEdSPbM/9DB9AmFl+Fgr0W/vYPMsWJIgOyzgDbChYqiMmIL8kemeo6g+2f9HlhUxnLS5/QYWgYtIeSOloJHJpiVml8xyOivWeapJ4ytOEykXnnsgACDzRU6GzO0pJF0+0t5nWwIx2tSOp41+9splnKIbKoYzOWw9v8C3nY4N+MpQvzUREKJYJzGoFPPOKFN4sUUItGJrje3Ur7rlgbq/Y/m+kfvJc58rc9CAxsJJfcUeQ6tH/jkDAADpa+n+i1nmJsUW01SVVkdsKJhRKzmHrlXGcronhcF02LRVWTh4wvx1QbswUzZba1h6iiRpo5y7FVuzVLgBDroljM666myixaa7wpaJWajUAQBS1lObSnvQ+TrPoHrXt6X+jS2lsrf3lrRVXJcGqmdcBds+NLj9nfMK9cPWg+hz+cVPAAC6MattJdF9FtpEz6eUEr53S4WhYoNcT9J2q5xZdw4ICHJCa6emFgG7HijELw4Hllny20cwL1QGBgYGBga/E+wJDZVtXqh2C+aFqqUIhpRGakdMlILM0uUYpangY/UZPid1Vak2PCyNGPRtP4Zm3z3nnwcAmHno47S9L50r4z8yg5bZve2vg9ewUGaqIdaHiImlMGR8jOhbhAHoOYW+rr6Mwpb/NOMDVeRrW2jm3JptEervY5sGmb0LUyYH8LlazyfDz0ZmcXYEYU5k1hv4ii0NVJka8yZh4KWu/iGJk/amcIoOqZeaSTOzU9+RLBAyf6S+2XI16yVe4D7p6lpGBEpKqYwkf0h2ZX+6dsFaMgFNWLfNt7/dlpOrMivS5kfX0LAhia7XxospAXSnR5fQMcLsyCcnJ1bGlTrj4mFBpf/CYp/AY0J0duoYZhm6/Jfa3HAwaeriVnBC5q2RU3DdfFNP7aHMTIW5WLZGdowsS24x3cpAjCc7E1voeIw76Qe/cWY0nZTcD46m/dK1kTrjoo73pMHR9ZOKtdG1X5zWZNvhXQAA7efzWN/K6aw8zE9As1iwOpJuKO9Y0u6l5FG9QltZK1WqmXRKChWPzYPl8LjUyg5wqhQn5GfSHB4QVi3dR3YGMUZOd3fMBwvFKJPPI88XNnpVjKlYLYgBqezP5wxUu/2JSjpvl1dKffWM20ZtlfslsZCTtddQWXFLqU9q06nMhkT3WRdTzXpK3ueQHyh1V+tuVP/lN7htAoAuH9B+cYX0zLY2s5WJJEKGe5/X5fjNQWMXrNp7xp6OpVL17C5aqsH6o8JoqAwMDAwMDAwMWgjDULUUjfWwgsywtPLPSlR6Ce9MtlFjs1QEIH8KKyJpQiQqrEQzR4QbodZYzjqgGJrFnfgdaaeGHUERWiVPpviOc4rZEFAiCL2zU0lB0VSkk4J/pi2MVfe7aGb9+l3d1G9rnyM2ZvVhLwAADov9MwAgfzQxPVnfUZtLBlI9y3rygfy63/2eUqqTNxGuHhkm7IbOFmr7KQ0NR99JMlgAcOqpzcpIUqWUcbiN1LaQsIT8e6t/k+5FsTqlnmsl7AtHMIqZYUUOp2phTUfCWmYAeIardCStmQHwRIHFz/2R9ul3ADeetTGrNtB2jtgLN5GWRaU/8f4exXsWAFqt9JvASntCX3AdWG9U34/0ZAHuIyCSsVURmWoHifrSUtBoEWXCxAAAOE2Ixbq24lHESNWl+9va7sk8PqmmRRRtk5f9ksjBmCa0UsJuJfpZRjB7pLN3PggLKFG7PIYrx5AJb3kXamTOTGI77PWc0DiaGbDoxvr3AAAUD6bnjURcpv7IWkRhhKRdrAUSZsqXHFlYoTotIbTomsTEspKfR5Jmi58Zwe001m3ZH66mSPREwvipaGe+BqLDVMwUJ3tXqYa8oWZaBKVi20RPxua2Sbl0TZI0A9XkpazB2t+NxE3cSv2w5kx6/sQsod+SOQg2Xdozlhk3m9pVMJx+yZ7F7GGZe79Lv8Wz9k31Y1YmrHAdEPkY3+MwS377DuaFysDAwMDA4HcCBwGEW7j41NLj/6gwL1QGBgYGBga/E9hAizVUzl7yzPq9wbxQtRS2o9TUKh+avjTgpa7VMof8ZrvlAEqgGZHTLUErE1B09x2HvQcA+NuzpwAAqvej5YLR6atov4/p4+3jD+HjeAlDrANEbAug0WvQ50GE4F5XLYpoVSSnnqWWro/zfw6jj9IedF4x/yzrSaHFyVuo7XUZVJ8/XzATAPDmN5RpPmmDJ/dcLNUnWMXLB7y8UDCWhKRtXlxEOzaxlCVWCL5lREjVuQ22f/lQlqycMBsTcrclr+d6yX41roC8sRfVJ1TK+RazOYyauzOmlM61/SASp2d8TWLqqv1omWvjcXSN+/6j1K0g2x90/NdyAMCmyymvYsfn2B5DC3W36/1LbRZ3SiDNtbEI83UX40kJeAgsz/Wds/oQWm6Kz6f2LL+ErRjiqczeC7SQebjLbGoM8T2gTC7FgFTqm5vnL8ATNGHxEprNS3+tv6bfVvyV6tv3b2Rd4aRJHkG+NjtS2Wrid2XQKeJzEavL/e3NWwdPQIRnmU4J7WUZmJfdKkbQUrgdQ9c15wOyhnDYeNQXIAL/crzYhxQPos9gHbUpbRUvu6VQfwbzeNmVzWIlTyjqZenSb90BQJnqyhKziM7VkqUsx3mW9gB3KdsqcoNlwPeUI8EmXA9LTsvPHTUupI2ydJ3kP4e3fmp5UO5bGVM8xuvb+3P4xS9mE9TedB+mipkwgLoMOm/r7/3Ptpgq6teaTB6nbCIan09jyR5Ix5UPaQ8dCVupXg3dSc6QsJDOj6xMk3H4DwDzQmVgYGBgYPA7wZ7xoTLYHZgXqhbCrqmDHeSQbTHBFEG5mEnqjBU8Znsyw4rThKRyrH6glyliwejWBpqxxpZTPWI/p6ng419OBAAceMESAEDRSLIraLWSZqGhtcSGNHrSr+iwNCM/BU00qxslekPlQytyfb8tuZEM9A67iMTp8XMW0zHc5g6bOwMA/r15LB3AE9iK7q64PoEFpXVtqC2J31Abkk7mFCPzO8CLhixmYyR7SD6Jvp01G9AU9PB+ZSIqLIKwECuoDEeYv3iXAYjZTAEA1f2o70O1VFbmz/QpFgjCWFT3JmFs8QCaxffuzQrZrS7r5abioc9O/0f73L3gQwDAbQeNp99F5K0Zago753iYq2C3TrRNwvU1qwpluvr5Ct/2PpPh3+8DN4Q8eDZbMXBqniCblwq7FBbxP38Ge3Sh+jFzJcEBltcCgZkz/b5IyBf2iw0ndcG4ztJ5WSY9z4Y2tq2gpifRBPqqn6OkngkPpgiLRr7OiXlshFrPrNaWrb56S1kBsdmIcdnj8qEkwK/oKK2nz/TlLFbfSP2kGDOpp62J7ss8AnSxZeHzOTESRMHHSAoaYRPFBoQF+GLBYnlS+jQOJBauMcHP/CSsoHtTCckDfpG83VFL3eS5LmIOrOrj+PtemKv4TcS01mfz/c7XJCaXWTtP4EBiCbU5kdxFUNONWKXyrtQnYX7uxAaZFU2gDe2+dplyANg41mXFur1CbGF5N2pLIgeIYFsZLHvv2CbYjoWw0zINlN3C4/+oML1mYGBgYGBgYNBCGIZqD8CdBbNNgoRIi0ag3p2ZKHZDT0raoIUti5aKUxco7YBnhlXyJGkNPrjjCABAIudbCDPbkVBEZVzeljIXTxhKKVUW3Pui71xjux3snldCxrVQdlVPjamKYKai2BWIPkfOc83PCwEAScvJBFL4ANH6BNYS45K6khLviiliIMWdCToZxMrFsA7L7kJ6hsSb6ffaTjSj3vAn+t73Fpq913cjrVLe2CwuST6B9o9/F71twhp6tGZUca65slfgNrfxGP2J/iZE/dbIrGLidxsAAHHMDAgjJDqZrMk0Pho4rVGwTydVZnDxKq4Pa1EqacZ88f1X0Q4ncfVi6ZzZr5DWSrQqSpcX62EQRDtn+8P8FbshSb3FOLWbWx8AABssVtS7mp+UsgLfLo26NkqD0hGxXgsWm8p62CUxe3TimGGqIBarzY/U5+vPEWaSPjv8jxihUCmVJYmFfRYHYkcg96QwJ1oqKWUzoo7TLDk8ofr1B/elY/i6x29gu4dqv2YvrDNnKjUSjbXqg7qo38RiIYYdTbK+YYaPk13r1hYR9RJzUc89K+xbY7t0AECooJTK0kw4ld2ExubJvZl7Xg/o6DCHk3gXcZmSUJnTACl2i+sn2q/GjsRC2nEuixiooX4KZxLzFCxm01IxHuX6hpnlCtTxvZnOtiM8Pp1aT/9z2h1hlOPz5Hx0zPY+1Db7XdL4FQ/mX3OpLrFF1I70Ve44KDmYnidJW2mfjae5lh/hulrgEfzicGDBbiFX0tLj/6gwL1QGBgYGBga/Exgfqn0H80LVQgSS4oE6TXOhGwJ6Z4wSDddAM31HSzUjLIhiH7SExsvv6e7uzPKarEQ6X4Anu8mbaRZmx9L5r1lzGgCga29iaUZeQck/P//nU3ScGOwBsMv9JnyuXoh/lwi2KGlBgOiGhErDw7Pb9CBPsbVoNJXsVZu1SzqesIdVCNT6Z/qBrf5D4tvTTPWC/SkFzdd2FwDA6Me/9u33/vTD3fPr5oGqTVokZsDPWMisPZhBLMnK81tDR86n1C8J+awLEraoym/gKtq6det6AwBCpVSnHgsXR5SpjBK5z9u+uNhXr+LzyPhz1lJiKMcPP4H2l373MBWKgVARa1RGgKPlrFRXvwYADuuiJIoxhssc3NplodbIUNDNVZuA3Au63gkeU9NwAbGaalzybwnzyMS283zLV4boxRqZ4QsMIE1TYL2bdFidX/pTTzVj+5kpPXE5BlGZte3c6LTE9cSIhFNZe1RAGj/pcWWcK7o8ZooCHOFWM4j6taKDOyZDnColZTPVM/A9MY/QNV46NK2X9951Uon1Da5kQ1HRlsmnXBMtsbHcK5suJCbO8tz2mT9T3we3aSySFqmnTEWbSOUTs9WTOocZqGCVP4rUkaTjwnIVEBMYFL2YMOqcyDgQ57LMKjG0MNDb6XxxrCNrV+Q3N84dR/d3q9VhX/vit7nXPaba34bEAn7mraxGY2MtVuGXh4OWa6hMcuTdg+H1DAwMDAwMDAxaCMNQtRBOfYNrSMTQfWoi9vfuyxoE8UMK6MfK7C2HGJc+M9zcBSmP02x983yaITcwUxWsoLK2HplBO84kPUl1e5otdS3wz7y2H9ZV/T99LumW8iZRmdmPfc8Vlyy5O3kH34X9DmJthMyOw90p+iu4gLU+VqSXlQ7pL8WkyUxUIu9yyY9o3tWH0naaSGP23RRBWJdC9btkyruqzLf/S+lAJImwgp7QVkuSKwh3IO1HjzfcKKBR/yJd1qxvDwMAbDqG2MDOT3NEVpX/WgRapQMA1h/3DABg3JBj6AevZ5SKwNOYHK3vsz4mtuj8C0cCANq+Ru3aeDuxX/HrXe8gy/LPSC1mVMTfRxgp0f5JCpVQEc3Sl08hrcjMnP+6hbAFz7GdhnK1/NGGOlzNmiSwZtbOw+I6ut5J2CSJIFzvjyi1Oe1JaClXJoc1c+09EWX5zHrFR2qMAA9zEhD/LDp3mL2NGpNoPDckueNE9D+Bn+h+UlokaYvGTMkYKxznpmwCgIylbnSnpFcJrSZ2Te6OiFQ5ou3S0q+I75wvUXeBP8LXqfR7htk6gy4sbjb1X8cPaSyuuc1ljhJf57Ei11v8qMr90XGKeZR+L6UxFVzF15AjOn3nFZZLWMM6v25VNFNyLsUqSptDnntZRcuyZo49tqxqbnMh32c81mKqiKEKVvmTOSct82sFAaAhh569jXxaJxSAs5f4C9JQtdDY0zBUuwXzQmVgYGBgYPA7gQ3LpJ7ZRzAvVAYGBgYGBr8T7BkfKsNQ7Q5+NS9U06ZNw80334yrrroK06dPV9uXL1+OG264AZ9++ils20b//v3xxhtvoFOnTlHLaWhowLRp0/Diiy8iLy8PvXv3xgMPPIBjjz3Wt98TTzyBhx56CPn5+ejfvz+mT5+OkSNHNrveVigENGgZ7XXRqje8WkKwReQp6RaUyJZvBF0gvZGWsBr7u8tzP+SR0V8GH5KxlFMjxBPFnlhA5x1/03wAwNwp1L6CQ0ioe+wJ5CnQKt9dJll3KS31xUbPQLPLS3+WbgQKqKWUYzsfBACoHke0eMoiThcihpPacp6CZ4ktUvwuqnmxrCBaPu771fydlgZi+Nqkctj/vXNPVCV07yPWFbREGvMD2zawINsRwb4S92rLZGzwWXLSALXtX3NJ9J78J+rQmlxaklhxB/Vzn7vXULXF6JWXP8YNJCuMDZdGhqMvu2wOANeGIiK1jIj7WcBdcBht33r+EABApoStewS6Vh6r+mXc8VJ0RJoVXgIKtKZrF06hZZJWP/B4mOju2+/JywAAnYJkk6Evker1VUt+2tiKFuhgi30D95e9YTPX1y+aliUr+bQ2szWDx3xVtVmWjdTyUqOvLLShNov1QbCclz230rUt6+aGyNcNpeucucw/ht0lS/8SNbrT8mHm97Qsmzs+g7+7wReBCrpu4dIyX1nKd0Qz4VXPFvXM4R3ZnNN3jIjiYxJ9+waaGAfgMbTycnoGdXzB005ZxpTzy3nZ4Fgtc8tSao3fHsMSE9Fid0kaYpnCZemWC8q6RixrUuhZYrGthojsHW87WOiuyJgyXibk+ttcPwkUyFhGY6gxmcZYoDoyzZLFZYSK6TNzCW0v2j8J4XoL+CriEIPfEX4VvN6CBQvw9NNPY9CgQb7ta9euxYgRI9CnTx/Mnz8fP/zwA2677TbEex+GGm699VY89dRT+Mc//oFly5bhkksuwcSJE7F4sRsl9frrr+Pqq6/GLbfcgsWLF2PkyJEYO3YscnNzmyzXwMDAwMDg1w7xoWrpP4Pmw3IcPe/C3kVlZSX2339/PPHEE5g6dSoGDx6sGKozzjgDMTExePnll3e5vOzsbNxyyy24/PLL1bYJEyYgOTkZr7zyCgBg2LBh2H///fHkk0+qffr27YsJEyZg2rRpu3Se8vJypKWl4ah2FyNYw+aNEnoeaHow6qyLEqXG+GeArvUCz/LY2iD3NDelSn0KXbr6bD9D0f5jmq0VHs+z+I30AtpxLtWv8ACaWbX/gmZvMetcUWXZiC4AgNQlxG7YGzicWmbWOpvQlHB8BwyWzPgahhJLE7ea2BG7uCTq/m6aC89Q1c5rhbT+E1sCuSaamDla4IDThWbbte1odpuwsZR+kASw2rWTZLlKFMyz5A3P5Kh94uYTU1GfCv+xfHm7PE0Mml3OIeLMxgQ7ZPv2t1Pd0OzgY8RQNI7Z5jumyfQ/WsqcmtH9qcy4SBYxZQkn6+V9a7sSUxJTytYVLMCO27jNd1zFIBIpf/bE02rbmJMm0XkXr/TVU6VVkoTffC2VcaawDXzdbY9wXxgpFXxgaUyPlKVbcvC5JHG1N1WKw8JmGRN6InIxpBQGA2wqq4TRjVEYtHwa0+rxqqcv4vve6pTjO279WSSa7/pvuv+cja69g9OfLVN+WBlxPqq/FhwTJRUOgMg0UvCwQ9oxXkYcAOw46ufqLDqXmAfHbS71FKaNQ2aJHE6wLAygSj0jDL5ul+Gxg1AGx9p1hX4fSxCF3LNBP+Mv1zoq5JkiwRCcQkyeV/qqgRK6e/pTkrQ76ZycO4GDE6rq0Biuw9yVj6CsrAypqdoDYQ/htNNOQ2L3L3HCRW13vvMOsGF5DaZOKsS2bdt2vrOBwj5/Db388ssxfvx4HHXUUb7ttm1j5syZ6NWrF8aMGYO2bdti2LBheO+993ZYXl1dXQSDlZCQgC+++AIAUF9fj4ULF+KYY47x7XPMMcfgq6+a5mPr6upQXl7u+2dgYGBgYGBgAOxjDdVrr72GRYsWYcGCBRG/FRYWorKyEvfffz+mTp2KBx54ALNnz8ZJJ52EefPmYfTo0VHLHDNmDB555BGMGjUK3bt3x9y5c/H+++8jzDPE4uJihMNhZGVl+Y7LyspCQUFk+Ktg2rRpuOuuuyK2O3V1cByNZdI1Np4Zm5qZymwyRrsEogGScGUJX+aUC/+5/MGIOpx/yTUAgI2sXykeTOe9aX9KlvvsBxMAAOsn0rneGf93AMDN759NB8S7WoCiwRwK/E705c9oepboOzZteSD6FUmeGssmncIa2GUV0Y/z2hdookmpl2IwZDYszJTMVHk2LLYLvhn5WmLj4pZzKDafL5CmzSZZ4+F0IRbJyuOwe06ZEvraY3HA1Wm1kupXcx5pZP496HkAwLG9/goA6PaC/xSBxWt99XTa9Va/WZfybD1c5GuDbjzp9omkOaLt8cV+U1QAyB9BM+r4QgoNF0PE2G1s51FGLFFwI7E0Tut0AEBjK2pzyo/UB4OnXabKPPvZjwAA84bn+OqjDDzZ0qApGwVh2IKpbsohlTBZNFK6ZYDcg3Lt2GxV2A45t2+MCevKbGYgg9pmCWNWx6yHpBTyapDgMess8czmdVsEGWc6e831WHslsU+tlvG1ixMm1cM6LV3rK0tpNXXGSU89o8PLHsvzSKwDhNHtSpquYAX1V2VPYuXq0mi/uHIeSys5cbWehgcuS+TIs8DWrhXfR2rcNpb79/Oy9nwfCFupEs4LY8V2CSqdjPT3rjBTqsJ8JUU75WExAajnut0mnc6xla93pifVFDNT4PMF+LO+Yys0NsYA0cnFPYo9E+VnROm7g33GUG3atAlXXXUVXnnllaiaKJtv0BNPPBHXXHMNBg8ejBtvvBHHHXccZsyY0WS5jz76KHr27Ik+ffogNjYWV1xxBc4//3wEdS8hTVDsOE7ENi9uuukmlJWVqX+bNm1qTnMNDAwMDAx+cTiwYDuBFv1zWhgl+EfFPmOoFi5ciMLCQhxwwAFqWzgcxmeffYZ//vOfqKqqQigUQr9+/XzH9e3bVy3fRUObNm3w3nvvoba2FiUlJcjOzsaNN96Irl1JC5CZmYlgMBjBRhUWFkawVl7ExcUhLi4yqgOODSuozRjlxUxL4wF4tD0ys9ZNBGWGKBoRmUGuoRe4y8ZfpPYd9zqlUYnbTrO0zu9S2YPuITPOqV8cT21O4ASt+VRW5xDVa/2ZtM4ejnXr0OMRTo4gs3SeJTZuZTZE9AU701BFA8+CG3sQsxO7nWdvA8hsM249n0OfTUo/1kfOMpW2S09QLLPbRo4gatT0EayHCua0d0+Ty5Fi2nVW0VHSVkm/U0DMhS3aKjYT7Pi2+zCqGEyGkwlb6JiKuZRodUzeNVwmfayl7EAI1NKxfZbTJCPASXKL+rmz5XphbMaQ8WH7f5J5qKPrdIR50XV7HI1Y8Iqr38l8jMZQ8SBinK675g0AwP8debDvWEdm42wIGSr0p+HJedM1opz3BrEuhafTZ+bzkUy0v3B/BKlcW68eSlgDm6PMlGZK2iafHfi6ShliSCp9FEVHJBMqO53OEaji84ruTjRUzHqEC4t9ZUUkz0a0SFVJtE3jU5IKh+M4sTnr2gpGsimvfALIesqfvLtJnaLcL27D/PXzaJOEqbM0ptzazMazneletTkqMXkLjXlJ9myXRgkH1lg4xfSEJO0Ws8dlrNtiBlhFXgoTVOaRVYheTcqS6yjPKUk6Lfq3kF8Hpa6Rh/WSRMnqvMJQRVlZAOCOg1pNb1bljnm97TLu4jaUIGhr+q9fCMaHat9hn/XakUceiZ9++glLlixR/4YOHYqzzz4bS5YsQVxcHA488ECsXOnnSFetWoXOnTvvtPz4+Hjk5OSgsbERb7/9Nk48kcLjY2NjccABB+Djjz/27f/xxx9j+PDhe66BBgYGBgYGBn8Y7DOGKiUlBQMGDPBtS0pKQuvWrdX266+/HqeffjpGjRqFww8/HLNnz8Z///tfzJ8/Xx0zadIk5OTkqOi8b7/9Fnl5eRg8eDDy8vJw5513wrZtTJkyRR1z7bXX4pxzzsHQoUNxyCGH4Omnn0Zubi4uueSSZrfDrqmDbWkaAW22F0ho2uYB2oxZHSssiZaUVCLQAODlDeTnlM7HSmLOT18+EADQdhvPCJm8SdxK3w9+4ToAwEWnfBRRnSdzSJuW+j3V2T6SZqIdL+XqFvGsXGemZLa8Cz5V1rc/U9nDKdVL7GY6hzARwsqFtVQVPm8rpVGB7xhvomcAsHV2i69RsBXv52HD1IxTotE0xkFm40qbJB494lvDEUaSdgIAkpeTzqKqJ22TBNbpP9G5GjmAqLo99VtyLpVVOooY1bRP19Hx7SMZlbiDqGznsSb8mxT7ye3RxmX6k27C4xj2VDrlMmJ/z06hiMuzv5sJABh7zBm047ro2jqV+sWTyiTQhpJEt32HJkXhJrRSbiHRx5CX4Q2kcJ1ZS6UgjC6zHeEMTob742rfdhkfPr2TgNmNMEeyBar5nhSGjFmPxk1b/NV2oqTUkbGqMWGWxn7Up9Nnh0HEmMe+4x+/USH9JM8MXTOls7T8XaXQ8ZBm+phQ7AyP7cZ0eg6krCYmqzabBmw1j+dEZum8TJweeaki7iSRMdfLzuDE22UcGZco/lP0PIgapSisEd+/Vk2df7vc7xKZyWyuijSscdkkYaYimCqB+LRJfwpTzXou0Z6Kb54X0kbFtts2YO+CjmsPwHEshFtozLmzW9UgOn41xp7RMHHiRMyYMQPTpk3DlVdeid69e+Ptt9/GiBEj1D65ubkIeGjW2tpa3HrrrVi3bh2Sk5Mxbtw4vPzyy0hPT1f7nH766SgpKcHdd9+N/Px8DBgwALNmzdol5svAwMDAwODXCpt9qFpWhlny2x38ql6ovMyT4IILLsAFF1ywy8eMHj0ay5Yt2+m5LrvsMlx22WU73c/AwMDAwOC3Agd7IPWMeaHaLfyqXqh+k7AdwNJC8wViXOcRU8sykjL8U3SyJogU8ap85yWLgoNdwXT8ByRwLt6PvieU8DIC64YTC7lefPrUNUTbl/Yhqv2p2UcDAHrN2KrK7BOgZa3l19LQ6Pw0UeZOVR41ia0BbI3mbkoQHRWyZCEiYF5Csfp08+/30yr/d8/ShupHpvAD6a2i7msLxc/nDLWnPrMljYzXTFDK15cJ68X4kdOa8O6yzKGWGXkZKVDrllk0nITjEmaeVEBl5x1N1yb9Z85kXyHLXfSR/j0HTbCpYNfn1qkyq4ZQSHtu23QAgFj4WZq5pQ59STO+wF02C3AI+JdjaKlxXCotx45/91sAwIdzXgMAHHPyuQCA0PINAIDGvl185wh5lmkl9Y30k6WlRlH9J0sn0u+6garHqiPMZerLXoINF5PIu8tbHODQJtP3e2Nevu+cgMcugVMMrT+Rlv56P1bsOzacz2lrdHPbaJDfNMG6up9zKADmjpNI/H/3WxyVMJ4+ut33o+84ALBV4IXj+3TsRt+5ZCla9bNqwA4sT7heZcfRda/sQP3b4RVKjeRw2p2EjSxi51RFyozVuzyuDDK1JVxeMg2n0zMlWMxL6NVihcBpb9RyqefPkzwrulCwQaCGrSxEKF6qWa3wd4uXEdVypyeSWwTryoJBeyY7vAwuqacQ5ueB2H5IOiPPUqFantaf73GxZh3tDwDzQmVgYGBgYPA7gQNa9msJmhG7beCBeaFqKQKWSqHQpKGeN5RWZqw84G1hPySBqdgpiMiWhb013fizuxt6a8fQTDRlIx0T5kli9ue0z/ZetCFjOSf1TKH9ez9Os3QR7lYMdNMUpMwnVqjzf1xhNQAUnkbpSjJf8Ie+68yU2u6ZjenMiYRRBwpohlp8xv6+Y1u/+QMX6WcCgpmt3bYzG6SnKZG0IcJESEoaEd/qYmRv5iXFekkaDl2wq3YUMbCfJQlwKHxNWzdNTFIB1Ssxl0PAmX2pOY/6pGYTMSitVlDfxNTQp6RyiS/ipM55br3jt9KMft2prwIAxj98nK96wqREBAZoDEU4yWUVwt3a+X4r70qz7ukfUNmX/4nSNM15+0UAwP53U5RCAgc+iGh59bXdVRndb18EAJFmsNpXYRHVmNfZTW87xCZBSwwc5PskxKRbOI3GQW0PYi6Tvl7rL9KTskQYKmEset23gurVifrEWbqaT+JnMCPq6WUGpc5ST7YKELPYokPpuv/9IWKmBp5L5yir5xQ5kkKlzhNqr5uFyml1c1PZridp161F6GA6NpsYs0KKZ0H255zuScT/8inJh/Wk7h4BeQRTL0mR5T7ZzMwlm2Bawgh5bRIAWJnuM6ihfTqdZptcYGbGRJSuJ7gWAbmIwmVseZlJsdKQDdvZAkKubxIHAPG4cMSuRBIvC7vt6XeVnkpsGzzPkF02RW4hbCfQ8iU/40O1WzC9ZmBgYGBgYGDQQhiGag9AzRD1NXj+HvAaRUoouKQ3kDQmojOR3STkmBNwxm8hBiC1lcuC1eSzLoMnPhWdqOxWS2gWl7WVPms60kwwIZ++CzNlx9LlLxnglpn8CdUj4XOapQubtPUg0jdlasaYbidoyYp9aWL8pppK98LJhdt8Tr9X9iNWRjfwlNQjyqQPiEisq0K0eZYrjAU0rZeu/fI1QdjCnTBTChKWLmwCX+/4//2odglwUlcnh1lAZhW3b+Pr3pH6sf3X/tlrVTaVvb0nsV0dZ7ltr8uktu/3IAdVnEwf7b6iMRLYxjYUjX7tkiWpPlirEsh1NUJVA0mbkri+FACQwteo7EQqY7/vzvTVL5Yv0ba+zJpcQPWL+dIT9i8zfenPJs1gNbNYRoQdAOBhq/ws0bajaHwmbvVbmMSVsN5FUg1FM/SUxMmqv7hNmzlpt9IsNUYc22R9tTaKsazVmzRqbRaUUisS6R4uvb0TACBULjYAbKzp6xMxCvb3q25qG00vRIfzGPOYW0ri35JhND5jOtMzoogDnpO/ZPuUikr/uQIaA+dtq0pyzdeVmR+Lz6VS+EhqGn7GqdpKeqgUN0m1MFOilZJjnDhmoIWpsv3nFuuDcNtIO4pQMZdVrumvskj36JTSs8TJ8rP1Vh33rzCAXkaO+z6cle47JlhcDsvWkj//QjDGnvsO5oXKwMDAwMDgdwIHgN1iHyqTy293YF6oWgjLijSyU5AUCh4dRES0j2wXhkVmkazxsLbTLCl/As1sbcdN9eDw1UvYRscUHUafK64jVqnDf3kGG2YGrYpn6Xx8xRCalbZa6c6m19xCpqqrJ5FmZmwPco/v8l6pr35K36TLArS0IV4EEvwapUBrmqlufoRmmx3+yoaJ7Vm7kug32rPXbogoU9VHWBiVDNWf5kFYwoAn0S4VuoPIG51ZkyTEenoJ1scIG4X6yJlooJRm+OE2NFNOWOFv2+Yj6RyiXUneTBRQXSrtt+EkVz9W30fYKvrs8H9Ur0C91hdiKKuPS55FO63cJM6Ja9lMkaP0YriN8Z+29h16xeXvAACeCFEm7tghdNwXg2j74JmuFUnBpWQ82/6Fn+h8HlPFaAhq10Y3dqVCmIHgNomuLmPmcvqdmcnKfqRRStrALESjn12yu3Zwz5tPTF0jm1SqqMMG1nbtSlSfp258EH1qjJizjLRcRy4m49R5Jw2mH0qYVeTkviqVThQzW7cREhmsjVOBjFONmVJRbQAqhhEVlbKBng0pj7J5aTKzWGKAKylm9EhY1TCPFlH0oGAjT9EtCfNXrUUIM4vkJNB+kvrHqvdcM2GzklmfKFF8UrY3mg6AwwxVdU9/lGd8QbX7RVYFmDlT/SXsO2+3KlhDxc8jq5KvkTBrnvvdZjbLYkYyUEjXFR7N3i8NZw8wVC0Vtf9RYXg9AwMDAwMDA4MWwjBUBgYGBgYGvxM4jtXiKD0T5bd7MC9ULYTjwF1Ds/3Ue0BCeb1UvJ7tXsz5hDJn2lnlu2qdTr8zjV651bMskkxl5Y6lr62/Irq77HCiqFNW8pLJVhYfi1iUhebpX2+m7Z7lkLQ5vCwziT5kCc3+2W+yqS+1yXJINHNJ+c3m5YxQNofoc0hzzqRS+s4GeipfIKN8whCq28bNnvOL+NRvHugKcrVlORG6O7rwOcqDQ9rQiuh7ZzNbMIitgiZaV8Z+IqbtkOX+VsnLBSzMlZHQ5f/oe213WnbNPYbL5h0qOtFSRnJ+mD/d821voKWGPuPpmpRVktFnYDuLa8WIcGe2DxXu8kc4k3Ojace0n0sGmaWDaClt6qcnAAAkNuLroWSjMOS+q+l4z7HpJ5IZ7NF/oc+Ph3LQgSw5ypjhe8GuYpNWWUpRy8qedWW5rjLuOA+byqPJOduSl/KYS2MhtBZEEVzvGUu8HBxsTdfb3r7dVz91an1ZXoPPKiTA419udzbmRRYtQWUEOSciL/XZHEyxo2VFVaa7gY5ROfo00bw8h7rQ+JBxLGH/ABBf5F8azx9Ov3V8noJSVJ66pnJzyljyShhUjkE+JuQPSnDq2RBTliADPB4kH2c5P4OcyL6Q5TU1hqR60iZ5LvH1lvymtRkstWhwr53KJcjLc/pSvZMY52sjJLBAzhFtaZ+XB+1kbbm9MQzYe8k2ARbCLfahMkt+uwPzQmVgYGBgYPA7gYM9wVCZF6rdgXmhailsB44lZnIscozGTDFUmgtJV8JQ6UwkFJeZqm37k3A7fR3PzEKR4sbWP9OMb+4rTwMAZlfTzGrKmAsBAB2eK/BXWTe39My4ZaZ58PWXAAAqL/XfmB2mL/TXV59RR0l7ooe/22zoZy8jM0NhIgIyo5YUFNyfqavYDqC9y/w4IuLVZ7HSFhFmiwBX0kVI/4rRomeS6Rp78rSXGTUR86pziaiejQdLDiXLgVAt/Z725Ua3nlUeEaznu8zO434ipiJ8EqfdEWKDJ8c1cdT/VdnuA64xkc6zcAUFKqQNorLawC/AjVlBzJDTjtilxnSaNcfklVIdPOxcQ4ZfJB+sDXM9qB9DdVSx1t/T97hTyFJgv1euAgB0YDZq4xZXxH5W+58BANdmUNqcua16UZls62DxsIgwteV7w4kiSlcibbnOwn5y+pLtE8iANuNtsq4I2hQCH2bmTWxK4DGNVIaOfN8G2/tNTqv70ffEFdTmcF6+73edafNCbEcsZqi2HEbX6Kn7SdSfUeY3yt2hqWlE4bKv38pEWEYJkqjtSP0ZX0hCeJSUukWwAFzYoA5z6NngMlOWrx2qWiLct6OwOzozJfeNXF9+tjV0pmsTw/YdFo+xaAJuh4XgEPZS6iMsppyTP4VdaoynvkkoZCbO050+0TugzDgbW9HzJxxP9Y8tJNYzIGJ6jzko4IrpaScR3tf56mOnJcEO1wFbIppm8DuCeaEyMDAwMDD4ncAs+e07mBeqFsIKWm5iUFlb9yYK9WwHPCaCkhJBZnHCbnHoeE1vYmPKu1n8SbO2zjNdE7pALic15hnyYRf+GQAw/1/PAACuGEqzulVDicnImEPnbr2IGIKtw4n9avusO0t2eEac/galDWnNM/ktM9Jph7/5zQV3JRmypPmQmbOzLtf3u8zww6WlUctsTOcZ4LI1bpk6+6dbV2gzZyuZ+lXVkvtbDAsB195CpbgRo8QGv1bC7poNAEj6O003626jaxm3rshfB0AlVhWNjCUMGTNPFqd/6Xu/q+kBgKSf+D/cZ2suynbPH0f99eThLwEAHn38ZN+xgXJ/6L2EbgdjODF0Cs3Aazr4WQcAiKmkfinaj/ZJ20BtlxQ4KRzuj4/pIyWD2rWmE42lVhluf1YzzTZmOaWvOXveNwCAQXHEZt28HyXnFmYqyMxUON9N1g3Az9Jo1z3MKVFkrLR+b6lvv3DuZl8ZYsVgeQxehX0R41gxh7VS6Nol/khlVA0mLVLZ8fSZ9dT3tJ9UycPOKuNZRn0O9U8jd3m7Fyi9EhQryulvIhhfL4XqZ6ssWZaR+0WYKe5HYf7imGFxOtIzpT7Tve5xi/iekucQ3y+2Zq7raj+ZAZJ0QRpbAwBWcorWBj+zK9cmtIKeAxFt1ixPAMBKSfZvkPubPyy2VbDZCsRh0+KETeW+OihtEzymoHUNvmPkWRKsZQYraEUcC3hYqEaPNsrh5zjf5448+2rqYYW1Pv2FYETp+w6m1wwMDAwMDAwMWgjDULUQVkICLJtnirL2r5lzIkokmZrBMvsR7kQMRHAbzaC396CZa3whF1HHETJBTyJOMbHkmXXi1xT1Na7faABAxzeJmZjX/30AQNcKYrAyv6SZUnlPKrNtFK2X0oGxUV7t4oyIfejkkqxWmyV7NRcSYSVaE4mq4k/RvygNiEQJ8ew9dgvPMjlaCQCQzwlWZSYt6TRk5ipJXCUaSFgHaZeYdCa6iYytBmYJtOgd0Ws0ZBPL8OxrjwMALh12CgAgToQRUYw0HY46kxQfjkRtCnOmJ1OVFB8cOSjpL3o8487iJWFt4Dhq6zVvvQkAePCicwAAsZsK/Odgs8tAhd9QsWg/V8eXkkvl17RmZoebElNF9QtWUd/YrYh9CBSV0vYCYqwyPiIWNOZMN8HtiOSVvs+3t1Hm3dklA2kH0VDpmhk9+bCHmVFjRDRyWlSpjKWmYGnGlIDLHiqmR1gXZkps1sYlrSQGsiaTNHPbzzgAAFAylpMqX7bePZGMbTHE5CZ0+Ij1iz0o1Yy1hcqUPpDUSCoqcUcaKg2iPVTGrbzd2kSMX/mRpGFLWeGaA9sylpjVaqr/VFStfJeIwvoo9ZM287hTRp5y7wnjq1hlP7vs3qOe55IWQa0MPOXa8bMloKfXkXOxljJY57m3JWKQdVnhZLpmoSI2g9Wi/Bxh52LkWSdsuFtkgKN6hbUS5syJi4katfhLwIbV8uTIu7nk98QTT+Chhx5Cfn4++vfvj+nTp2PkyJFN7v/qq6/iwQcfxOrVq5GWloZjjz0WDz/8MFq3bt3kMb9mGIbKwMDAwMDgdwIH9ELU0n/Nxeuvv46rr74at9xyCxYvXoyRI0di7NixyM3Njbr/F198gUmTJuHCCy/E0qVL8eabb2LBggW46KKLWtgD+w6WExEmZbArKC8vR1paGo5sexFighzxUqvNxKJBZo8846s/kGaN8Sspcijc3s8E5R1GmoCc+TQTq2nnMipJCzbQfzTNgWJYeFZXcjr5OH03jdLJDHqY0oO0XRSZKLgug2Z2MZU0s0pYSuyLRAbaUfQNgJYAGpq2IlqSW8CN0BIdjByjtGgac9G+rfv/Ip7piyZJmLKw7TskIi2QpL+Ico30Y8GMVemYvgCA/CPYE6ot1bfDpE2+3ZXXkPeWkhQdzEypSKft5fBBoqakzcxMhYuIARKPJB/Y12bDZX0AAKnrqP7pyzgqMpf9s2L8/bjmcooojN/mPjSDzMJUEnGCjh/79R4q0om9opQehmftjTlUvzaPRD48v1pD5/v5iBkAgMQAsQuS1sjRIvaU1k6SVXs0iWqbsLMqKla8j4I73o/h9dtS7JC0TSDRp92oU+ra0/WNX02MT/549ndihqXNoip1aLCC75ONef4ihamUMSJMj4zHtKY9xBxmRJzllL5GRUdy21QEo+zP/aoiinn/uh5utGzcyuhhZ+IFp5h0jfnVEy97U/voGlJ1Dwa0e1F+l6jean8ErO2JkA2karosTcsVUaauY5V6erVhMu7knhQ9m1cTBahEy+EOFJUoflTCrOnML1WeGT1mGp2YEBrDdZi79lGUlZUhNTU18pg9gNNOOw3bOy3Dwef2bFE5W1eW4a2Ll2Dbtm0735kxbNgw7L///njyySfVtr59+2LChAmYNm1axP4PP/wwnnzySaxdu1Zt+8c//oEHH3wQmzZtitj/twDDUBkYGBgYGBhEoLy83PevrokJdX19PRYuXIhjjjnGt/2YY47BV199FfWY4cOHY/PmzZg1axYcx8HWrVvx1ltvYfz48c2qY319PTZv3ozc3Fzfv30B80JlYGBgYGDwOwFF+bX8X01NDdLS0nz/ojFNAFBcXIxwOIysrCzf9qysLBQUFEQ9Zvjw4Xj11Vdx+umnIzY2Fu3atUN6ejr+8Y9/7FI7V69ejZEjRyIhIQGdO3dG165d0bVrV3Tp0gVdu3ZtXqftIRhRegvhlJfDSU6nLzEazexoNDngUtMdSNgaU0EUdDibRHhVHYn+zjucduv2DtHJWw8iijihxLN0UeOnmpWgVKO/M9/lUHK+F36c/ITv9wGPXqb+33op2wnEEJ1dMIPClTMe9N8ooUWcikZlaGeBp4jBayJpcBHaBniJRU9BokwvxaxP6HkW5gbyPOH0srQjSw22tqQjfRBoYrlBlsE818YSH0K2tAj36QwAqOhI5w+yY0XbN2gZpP4gXq5dyqH5EnwQ8MxTLG1M6EuQsmwTz/vxUqZdVsFN55BtTocCuMswsgzS9Vkyzlx+YxcAQE1bEZvTZ85sEj5LWPjLf3oMAHDlrX9VZdohOk+Pl0t81XVC2pxL+kuWJnl5JJRH9d5+mhuMMOC/edxEOmY97/txFdWzcsxAX9HJH5FXhKOF7OvfqRqaUkHGUNi/1CfXIiBLQLyUptIFUQXpsycLxZeu9Z3XXk1i89j1vHzIZbX7nNp8zBvfAgDeucWdnRecIEvzbJeQTOOzz+1834T8IfqQJWGpkiw/epYq7XZUVvXY/QAAyV9voPpsK6Xf+Z7T0ymJ8aSE8jcku4/9hgOozck/0x89O5nq3dA3h9osS71sBlrf3W96GvqB+irgCe5AJtUTvFwNTQ4ggReBVtRmuxVbmiTz8mIpj/1E13pC7nNZDlS2DVy2k0jXU1IPqecR29CofvaYcNZn0/0Rt5Hq6YT8z00niccIG4AGqtgcmO8Jqyoy9Uw4nfohWEb1C7ehcwSLymDZjRH7/xIgH6qWi9ITEhKwdavfwiROu5Y6LH0p2HEitgmWLVuGK6+8ErfffjvGjBmD/Px8XH/99bjkkkvwr3/9a6d1PO+88xAKhfDBBx+gffv2TZ5nb8K8UBkYGBgYGPxOQKlnWmjsycfvqtYrMzMTwWAwgo0qLCyMYK0E06ZNw6GHHorrr78eADBo0CAkJSVh5MiRmDp1Ktq3b7/Dcy5ZsgQLFy5Enz59dqmOewPmhaqlCAbh1EqyT7Ea4Lf4ehYmxnlEtTwDLO+X7itGolyLhrAZXzF9butNP8RWsPllrHujVI/sDQCIn/ODv06aUZ7MXI/tfBBV6/BBAID/vfAsHV/s7p+4iWaHhQdz/T4g5ixmBRsAirVAT2JvsILYESUCjRLjYFf6U4g4ehJXLV2NI5pQZVgYmSRX9bXGTCl2SJgIMW0U4bvMYhr8JoMAVCi2nZUOANh8PZ2vcQUbT3YkBm3zxbR7j2tJuKtMWoVNSHNNCCVsWhgHm1PmWBl0Dkm0LEmUZexY8U2zNMJAOKxn2H40CVB737DEd0zJGRSMUNWDGIOtB/pn4N88NEP9/+gzzwcANLSh/opZRqybWElYGa38bdT6XbEgtW49l5bRAzHjfzTT738kMQ7n3E/ZvBc9TuLVobddCgBI0hIYC3MZ8AY4iNmqnuRaxoMmSldBAVJ2Nd+TtXURxwYLeGxkkfjYKXdNdAFEmvCW05j671+PBADEwB2f3V+loILaHBoLxZfQ2Ck7iljNtI9X+MvktDAyTqsPJ/YucZ3LTIqRb3yI+rWxG//B4c+AMCl5PC6lj4qJSWvo2wUAEFfitj1mPf0BbOhGzFNDaizvQ88M9zlFnylrqU/seGKKtv5pAACg7feee5yvUUPHHvAiLp/6JKCxw52epVRNuRfTMo2TRQEOgTJX5C/Xwk1+zs8Efp7KfeTwPWzp46MsMo1RTClbW6TRmK/LpPGZsJL6WcZyfTcaD4E6FvlX8jNQWFqv72xpte+3QNmObTx+L4iNjcUBBxyAjz/+GBMnTlTbP/74Y5x44olRj6murkZICw4Kyv27C7Fy/fr1Q3FxcQtqvedhNFQGBgYGBga/EziwYCPQ4n/NxbXXXotnn30Wzz33HJYvX45rrrkGubm5uOQSygt70003YdKkSWr/448/Hu+88w6efPJJrFu3Dl9++SWuvPJKHHTQQcjOzm7qNAoPPPAApkyZgvnz56OkpCRCQL8vsEsM1WOPPdbsgs8//3ykpKTsfMffOKxgEFas2CYwU6Wb8XnewmXWE19CDEl9Kv1W0YHezO+d+CoA4KYPzgQADDlmpa+ohfNcejPjPtJGOaI5apDko5EJigF3xh/7KWlVRDv18z2upmrsWDpv229KaUOYZwrMVGy4gpLPdnpoIf/ODIDGOvnClrX+iEg1oafUUKaNAV/ZXhNOFWKvWS0oc0BhpmTmytdGZxMtT5LqrUfSTL/8CJpVxiykMuTqNa6h7wlbub+rNvrqohKzsuko4DFXFAZF+ksYMwlL12fUcbFNfldh5WxqmL6cHx59yJ7AYmPPzC9ppr3ydmpjt2dpzN1/2Djo+PjfzwMAht5ObFGb73mmzX0flLQs1Zq1gPS3GC2muhqqmb3eoP/c6z+k9c9Uxq2FxMK0OovZsFfletP4DbX1J3v2nl+0ZZJKxsteen9XY0slVeZyPNFKygBVWKuAdv9KGwP8KWObNTfx64t9dQOgWKxEZkYS3iOLhWvvpfv7X0uP9Z2irh09Ky0eS4nfbwAAlB7ZXe3T6lMqK2YDXVe7TTq3kS0jGnhssfWCJcaVGXT9Q6WRbIlojIKV1PZAHdW7MYXuq4RC1pHFcuqeBBp7lZ2IdQxxkwsOjnzWp6+h88dt53tOTDXlnmQWZ87CDrT9L/7j+93rGpCiNWvRMqm+oe3+tojGS0w6bU76LJYGKoGxR2ejUsdwfeKKqEyl6eK+CJX69aDCftmsSbQ8Y8+JF4Nh/hAdVlI8wuFYwM2b/ovBdoBwi5f8mn/M6aefjpKSEtx9993Iz8/HgAEDMGvWLHTuTKsZ+fn5vui78847DxUVFfjnP/+J6667Dunp6TjiiCPwwAMP7NL5jjrqKADAkUce6dsuuq2w9kzYG9ilF6qrr74aHTp0UHTczrBp0yYcd9xxf4gXKgMDAwMDAwPgsssuw2WXXRb1txdeeCFi21//+lf89a9/jdx5FzBv3rzdOu6XxC5rqL7//nu0bdt25zsCf6wXqVDIZUt0Q7v0SEFf2ivEJiz7kV5O231OMwmJ3ksN0GxoyIGkWaq3ab/908jo7Ieqvqos0YXos/MIBkjqo6V0af8VTS+P/up8tc+GM2lG1X4IsRyJt9BMr3oE6bXq02XqJQZ/Grukp9/xnLepegmCPFsWg0VZR1fmjDUuA6D6OsjJRyVVi86UiXaGTQ8Vi8D1LhnhUssHXbwYAPD1i/v7yghVUT3S1tOMtqQ/lyXpOCSqSpg0rxGl5tuizD8FuoGjsCTSr3Zkwm1HTAM5Yay9lMaK1bsbV5jKbOAopg6vUx/lHs0Tos9I2/LoGc9BR3I+jyll3MlM0PZSXz1UFJ0wbtwHZQNcA9LxIyb4yp75xXtUr1RmJv4+AgBw860vAwCe7kraKov1RGHWm8GTeFvOH0hiZoWTdztbiSVSKY+EZZL6ic5NIkzL3CUBxXJqE0an1n/txOBRpRjiMiVqzWt6q1hWHpdtPiKt4XMfHUI7pFK9th9A+py4UvpemyFRwMRMBTzDefvoLryNNX2cjkpo0MRcTq8k+iKur4oYlDQotR7Gpa2kh6nzHRtbzsdoRp413ai/40s4GpiZqwRPcKjoQSs60RipT6N62D2IYU5fKSaxdM6+/ygFAOQfToxkWS8a3+vP76zK7PJvMqkNljPTLFqbeH42pHFqGY6us1hP5sRxKqwKjVkF3HtNrqfcizLe5D6Tvqn3p8wJlnF/elhZSzeHZTgxIQTC0T2c9jT2iCh9N1PP7E2MHj16X1chAru0UHrHHXcgOTl55zsybr75ZmRkNJH7zcDAwMDAwOAXAflQBVr0z2lhLsC9hc8//xx/+tOfMHz4cOTlkU3Lyy+/jC+++GKf1MekntlNqNQzmRciWMMaDJ7pWBoLsfxO9+Uy6UfaJzmPZj8xHHWUtF4T0fEs6LBXvgcA3NB6dUQdju06jP6jMT8RehIVERf9Ugez3bDW8gOIsdl6Ks1iOz9BN9bW6+h7x0v8qQjCxTw11XVSu7B+HepEXjcOJ1OVaESVekaLAPGlt5BISvEZYk8mYQvUfpLqQ6K+WAdTsz/NfksurYKOTumlVJ/L/alklCfTqg30qae70L2v4LJYwkx5WTYAbpJmYRXkdtTTCelpcby/cZmB3qy3KaRr5NXfAMD2PqxRCtA5Vlzkpog4evnx1KRj2XdGi7xUEZfM8FiaD1hEahAAKx8hTVqvy0k4Mmupn6IffyhF/4Q35/vb05QeKgoi9lX+Xv7xGNDuzXBhkfujpsdTerUmJA6yn0SHKS1OqeceFl2W9ixwxGtJrqekQuKIQYlaU/B4StlJdN6GFH8kW0wJaX/yjvZPYrO+p3HRkMhjTCzEtrkMVXCFX9Qj9RXWRUXHiWcc189O89cznOwyqLEb/V5mit3q2cZ/TBy1rT6FPtN/LKVTtaI+2nSU60PV/it/1GvCZo42ZB2TRDg6Sk+mMdUauwR4GDxhGtnLytHvaylL02mKhk7q4D9G9uX6VNfttdQzm3PWYvA5fXe+8w5QvGo7Prns62alntnbePvtt3HOOefg7LPPxssvv4xly5ahW7dueOKJJ/DBBx9g1qxZe71OzX4NrampQbUnI/nGjRsxffp0zJkzZ49WzMDAwMDAwMAgGqZOnYoZM2bgmWeeQYwnX+nw4cOxaNGifVKnZr9QnXjiiXjppZcAAKWlpRg2bBj+9re/4cQTT/QlRTQwMDAwMDDYu3CAFqedcVqowdobWLlyJUaNGhWxPTU1FaWlpXu/QtgNY89Fixbh73//OwDgrbfeQlZWFhYvXoy3334bt99+Oy699NI9XslfO3QhtoTqS4hvjxnussTGscS7V/EadYcP/MZkzibK/m7lkNHee9MoJPSGhyOX/Gavp5QXY3sMBwDYuohWqGlZ6tOXTiRdx3Y3PDm+kESq7V6j5YV6dhVo9SyHSw+lpZ2E2TwD0I0UZWkoyjKNMmoUgbiIeSVlhtDxIkLnJY2AmF0mu+JPWTJR6UE0E0ZlucAGnmK5UN+ZhK95h/M1W5GujknoUwoA+KDXhwCAcY2n0LFC+XNaC1uWHkWEzvWV7QGPFYMEJjhbPUtMgLskJUsRmk2CSmOkzuHOfZTQXUSz0j8SOs7Gounf0/JdYxuqw/33vQgAOP8/5AvT8yX3Xn38FDJ5fQRD/PXjpT8Re6v0RiL65utvS9ogzzLtuF5k67EmlA4A6PbJBQCAdUeRGH7ml+8DAI46i7bHfLPMd85oaDKwgevr/s7LxpK6R+otn54lQRVUIEvNmhmsWu6SZToZryJsl2XblEidqaQUcuLY6LGczy/LTGJHIc+QUm3pP8MdS2ISGcP3RSMvszVk0NjuMIvGWENbthYoEzE121CwbUJ9lrs8G96fAhRCnEYlWMTPgnrN4iDRk6oHQHBrKTeQl0tbuUtYEWJ4HisJS+nZpmwqUpJ8ZUhfxW6iJcPuz7vnk/NXd07jTzpfsJbaFlvMNiSy1KY9f1SdKjxL/FxnMQOVZ0qgps53zoa21F+NiVRGTGWjr96+8/BYkuXMUA2bgYYCsMN7R5ckGqqWoKWi9r2B9u3bY82aNejSpYtv+xdffIFu3brtkzo1u9erq6tVFN+cOXNw0kknIRAI4OCDD8bGjXvBZMPAwMDAwMAgKsjYs+X/fu34y1/+gquuugrffvstLMvCli1b8Oqrr2Ly5MlNWjf80mg2Q9WjRw+89957mDhxIj766CNcc801AChnzy8ltPs1w6mrhSMJcDuTyNoWgSbPYHKPdQWcaWvVkQA8RnVbaWYoVggWh4G3muMxt2sCdcNJgBg7n1PQaOyCgkzeWrXSGuHuF7OVZsgxBf7Z16q7aWbY49xlfAj/Lgk/d2DeKcmQ1UxUGBYReQqzJwdIyLlYBQgD4xHoOuB+kpBnMTGUMiWxKiedrW9Ns+F1J8WpEgAgdbVbZkWQ9h13AzFT4Vac6HT1Zl/bFCsn14pn4JaeBgeAVcmMRBKzHrV+k0DFEkoqHF3MryVRBiIF7soyQsrY7h8z9b2JlRvFXZXxI5WVts5lNB+cfQ4A4OFVtGx/Yx+h0vn8HF4PTj2iWwoII+C9Ro9lLwAAHNvpbABA3ztJ4Dr+zgkAXBuFT/6PGKtx+x1NZZf66x/oFSVz/CYSsiurApUcWROp8xhS26OwtcKEBttQG+s702ewmsa8JEvWE1urBMtiu+BlLEQcLSmF5FjZR+oT62o/AABitSKMSqmHeZVxxsfE1PL443FYn03HWmyrIGxNSAw12TYhNq9UFakMMEuJuWloz88GySleo6UDiqN6yT1tKVbeFXJbMsS9KYPgmmsKcyVsjrS1vp0wZ/QZt9EVRFvbaEwkyjNEBYDwR5HbJgDKzFQCSQLSB56Ey9ADPfj+reVUM8IySfqfxq7UN4Faf1LkQK3bR5U9OeHydtonhpOGh1ulKLNPgz2DKVOmoKysDIcffjhqa2sxatQoxMXFYfLkybjiiiv2SZ2a/UJ1++2346yzzsI111yDI444AoccQr4qc+bMwZAhQ/Z4BQ0MDAwMDAx2DTb2hFP6r5+hAoB7770Xt9xyC5YtWwbbttGvX78dWjztv//+Tf4WDZZl4T//+Q9ycnJ2af9mv1CdcsopGDFiBPLz87Hffvup7UceeaQvKeIfBVZcPCzOpi3pGgQlA4nhSFsTeZyY8sVsJN2DzlyEK/3h/JLY2DuzFg3V/176FwBgbM9DqayIkHvWhgizUsVly6zZm2qjwK/1KTqdkp/2vIjYL2cH+hbA1az4LA80k0XdDgGa1ku0VLKfzMh9pp1yjGzj7wHWsUh4d+kgfyh5qJLKzlhG5yrZz2UVerzGxoisbwmuYWNJro9d5tdpBTRDSNWuGvdaqtQ3ohuRMHlmrEQ75Yi2g20dXNsEZjI8feY0+tkWZVbKTJ69neotTNbG46n+/f9JNHjnbyITitqs8bl42Z8AABmgFBGid5Pw/UAv0ibYq9b5jrcsfzu9mP3fV33fB/3NT8eP338MV4KOVXotvgfslWvdneU6MxuuaxYiEm9rUAyaJzRebEVEM1XJD8+MecyC6Yafcg5hOLRxQRXjMczaPYt1Oa5xK997bGZb1yEdABCzncaO2AD4tHXMRAXYykA0R6ILsjtTGUFhTvWEwHzOcKZH4yeaI2aJHA7zD3D6H2GkLP4eLGf9WEW1rywnxr0S4Qw2Xd3G5xdmbTvdX2JMC04TY1VTmXFsnGun8z3sYTsl3ZI6b7b/vrZEa8b3rjDDwh6LxYVjRb4oVHfmhODlzLaFaJ+k5aRBLDugvW//hE3Uv+Ek6v+qbm5/piwp4E6gNlYOdo2DGxuCgJbH/pfAH0VDJUhMTMTQoUNRXl6OTz75BL1790bfvtFtI5YsWYLrrrtul3w1HcfB/fffjzrt7+mO0OwXKgBo164dKisr8fHHH2PUqFFISEjAgQce6IqKDQwMDAwMDAx+IZx22mkYNWoUrrjiCtTU1ODAAw/E+vXr4TgOXnvtNZx88slRj7v++ut3OevL3/72t2bVqdkvVCUlJTjttNMwb948WJaF1atXo1u3brjooouQnp7e7Ar81uG0y0Blj3QAQEwFzXBkllk6sjZi/7aziEUIx9LLZyPPtAI/+qP4FJskWgthhmx35iHGiBItJZGB8lprr+MgAS0CSn6POpvXNCZtvmfTTUkqrLQyTeR15Hr6ypbIv6Cms5IIISlLipTvmr7Ex2xxv9jC/LQj3YMwQqUDeQYrZoaVtH/KRvq9JpPr1OgyVPWtRXdFn7HzfqQiNF1OQNe96O3xTiy4zoEY7RjZRxgdub5NJdb29KckBFb1YRYpILobTrgsJo3rT3jat/9BWyi6r/I4l1lJfZdmbOULqV7frX8TAHBh7gjegyK08sf7U89I5Jtqu+c8En1aeuIgAMDXf5sBAKgdSoxKtzcp2rBPwyoAbrLnCHjHGo9PW9NZNXmNpN9F6yP11BMgA0BGOgAgZQNrfBQTaPvKUFXhjPaKxdOvHTxaMy3qTLR/Vb1ZryXRYMJMSZoYT2qixvZUvyAn/LUT6VpI78SvZ82RMG6pkiCcnyXcZi+bFODIP4lkE9RkUf3iSpit5STpwmQGLC47LvI5ECzh8ckRi8IuSYoXMSSFsHbMqNmtmXUsKef2ulrP0CZ/dGkor8TXRmGuVNuZ/apv62cirLB7v8dso2O296JjcubRdQ9uouTm4Q70TEneSON1e182D+b+rG9Fz/Kk5S6r7+jPLB6v8YW1aAxH/j34JeA4LU894/wGROmfffYZbrnlFgDAu+++C9u2UVpaihdffBFTp06N+kK1fv16tGnTJmJ7U1i2bBmys7N3viOj2bzgNddcg5iYGOTm5iIx0RVbn3766Zg9e3ZzizMwMDAwMDDYQ3CAP0SUX1lZmUpxN3v2bJx88slITEzE+PHjsXp1pM0QAHTu3FlNeHNzcxEtUYzjOMjNJclDx44dEWyKPIiCZjNUc+bMwUcffYQOHTr4tvfs2dPYJhgYGBgYGOxD7JHkyL8BDVXHjh3x9ddfIyMjA7Nnz8Zrr70GANi+fTvi4+N3cjTQtWtX5OfnRyz/bdu2DV27dkV4F9Kn6Wj2C1VVVZWPmRIUFxcjLi4uyhG/b4ST42BJBHShX0julBIl32mmK+QOx9MbcWI+0c2r/kJ91vsSv9hbX+pzbFk2c/ex80k0ObbbwQCAD9e97StDROoReeHq/XmxfEsVfD4rxNeS89ZFLPGpeskSihVZlgY9H50Vow0/MaqUJRRlqBg5sMV80ZJ9WLzbyMt1SQXU5o1jqawef6eX/SReqto+ohMAYMCZK9xCj6CPkjG8nCgiei0XomqHfIoIWETq3nbJEmpIll3lGF7qYwG7ChwQqwhddO/p16CWH05dC76uIupefxZR1f2e8IvAOy7jPGhBd5kn+y8UOVE2t6dv33918icZPWLIhQCA2M9/9m0PeE1XBdzXSfn+8XZe/28AAG8nU1DLQwtnAgCu630YtUP6Uxf7w11Kdhqii89lHAZa08w1vLUw6n7eeyDQlsPkOT9dTAEtJ9YOpDESu4CWJPVlQ7X8KEafXqG7bvYrppFszVA2hAJZ4kvYULNWaw8v39bmuNdIlgVlqa86m8Z66ibNHJiXuxra+20UYrZQsIJ3GTGcTm0O1FPZdW3ofokvii7EFRG7iLtFpO6zTVCmoBxMIve12oGDPFIkryEb+G6jcRlup9m6AGjsSNcoVEjXpiGHrm+oiI6p78JL/mKT0GD7PuX9IHaDZ3kuhdpe05brx9YKdls6v4jq88bTfdR2kZtyDQDi89iYOD1y7IuIP6aM+8K2m8ylarB7uPrqq3H22WcjOTkZnTt3xmGHHQaAlgIHDhy40+Mdx4mq+66srNylF7JoaPYL1ahRo/DSSy/hnnvuAUB6Edu28dBDD+Hwww/frUoYGBgYGBgYtBx7IsrPaeHxewOXXXYZDjroIGzatAlHH300Aqyv69atG6ZOndrkcddeey0Aene57bbbfARROBzGt99+i8GDB+9WnZr9QvXQQw/hsMMOw/fff4/6+npMmTIFS5cuxbZt2/Dll1/uViV+03AcNQuqzSEB5JYRHOLbQDOS+jR39ha/jWaiMrPr+zf67PE17bvmWJpViujWsXeB8WGG4tiuwwC4dgr5FxID0O7J77UDdXbJZY4UG6DNrCPO3dRsK5qtgsZaWbrRp4SDJ8mMlRkpTr8iNg+WhwGVVBINfTr6y+Y0G8WD6SYZdzSZS656lvYrGkHC/cwvKLx52yR3NrxpIoVH59QvpLJi/H0RYV7K25UoWbXPDXVXTAg/oGRGpMTRbDToSOoc6RudVWQTz2hwNCYvmElGnl2eIh2B3YEo7W370dgq704z6j4XLldlbKuj/up62AYALqtV04Xqf8eI/wBwLToEwo46knrG03YRx8etpr4esuAMAEDK81QPmdf3f4L2C3Sha2Sv3eBvn9eKQRhUZqJ0hjSQ4hdX6+NUrqnvGrFdQrBRxNHp9J1Zo0BrGiPhPA6Jl8AMGR8yy/UwqUqMLuH+POMVZip5gxZYUOM337STaH9hpQDXaNKOZcPJzdTnjTmtfWXZsczs8b0Qw6bBkoIoWOmOLfm/nUD1jC9kxkmalEjbg9VcP2aAlGGp1L/Cz974IM8SHuN1g7oAAOJ+Ip2KemYk0RgMJ0UGfYS28/nYFkH6QBi2utZ0PRM3+1cJqjvQKEv5nsx5vWOppj/d7+FUqt+KK2js9LuPmH9h8hpY1y4MX7g17RdO9lhaMMRuoiGVnlUxFWzjEHZ8gvhfEntmyW8PVeYXxtChQzF06FA4jqMYp/Hjx+/wmMWLFwOgZ/BPP/2EWM+zIDY2Fvvttx8mT568W/Vp9gtVv3798OOPP+KJJ55AMBhEVVUVTjrpJFx++eVo3779zgswMDAwMDAw+EUgovSW4LcgSgeAl156CQ899JASoffq1QvXX389zjnnnCaPmTdvHgDg/PPPx6OPPrpHM7zstg/V3Xffvccq8VtGaFs1YvI5bUMnYgYS8+mNN+44Wq/f3sFNd9D1Jv/M1KqlGczXTwwFALQuI3ZEzaw1xseJopPTExHv9xCxCz/c+ARtuJE+xh1OKVV0BsCXKDbWP+tyqv1Jb+0oxo16GRH1lgS7TK1KuhBdByNGjipdSFcOfPiZIzY8690NHf06i7pWNKtN+ZE0M1lfUb9Ov43YuXElNKvMfL8UAFB7ABlUVrV329t2oZYSR4fSlzHLoSXVVfCE5FvaWrwy+BS9lehLxOhT+lesLrivrCSXllapZiRxsm5/oZmrBrdRGHrmlzTLX3dOVkTTMuLoOv+wlfQiaes46WwZ9c/3+1H6l3sXjfMdt3rdCwCAcf0P5/a52iSHbRCsbDpf2gxhj2hsV1xGzMl+D9B4bb9OY1JVe11mUpIwq9807Z6cP9yUBYPo4DyMX6Ad1U+xhDwOY7jPlXFqQGOixIBW1715ITowNppNZG2fVaelyOHnQLhVku/3UIXLJgUa+HqWa+H3fB/V8T0Ru43rKwwwMy3BMtoeTnOfR8IGxW6l55IkWm5Mpu1xxWwhIdqkci6bzUStKGl3nDq/Zk6YykbWPcWvYx2T3D8xnIi9IzNtzOSESt12Wmw4XDKGNH5J+ax3YjZO7B1q2/Izhm1pkpfScZI8PeC5j+paUb+0/x99LxjBbWCWsLYH6bLiSlkrx5ovi60tApKw3EPnNKRTW2KLOC2UsHOxoaimoga7j0ceeQS33XYbrrjiChx66KFwHAdffvklLrnkEhQXF6u0eE3h+eef3+Hvu4PdeqH6/PPP8dRTT2HdunV48803kZOTg5dffhldu3bFiBEjdl6AgYGBgYGBwR6HvSd8qH4DUX7/+Mc/8OSTT2LSpElq24knnoj+/fvjzjvv3OkLVVVVFe6//37MnTsXhYWFsLVcrOvWrWviyKbR7Beqt99+G+eccw7OPvtsLFq0SNmyV1RU4L777sOsWbOaXYnfNLaVwu5AqSpicinaJi2Nlj7nD34rcv/P6OPw8y8CACT+TIaJqRtphhVh5CnYUfScMuykY3Nm06xs3OxTAQCz5pJJ49aHqYys8zlVgtLeuOeKak4JKC1IQAxHhTnR0sioaCaP0E/KtDVdkK5RkpmspEyRAS7pbrwpdWILaMa5/HpqS9/ricVSWp4OdA2u3HIgAKDHxzQDX30RzXDjl2ygz8UesYBEbWHHkDbqKXFkNmqXuEldVdoN6Sc+JlxATJpudqoYDWV2GpkcWelAbI3lYOZKZuOKQeF0PJI0udvDNE5/GOhan4RYRxf6JJ3qFytJaOn32av6+c6VmKSxJNEMXYUNWkdamcRi7hdOrzP/gI8BAD0rzqPNz3P9a/xl66yU93xWLLMGknanwp8GRtda6Swj4Bkz3E82RwY2ynaNAVbJsYWV0dIf+cpXhq1UhiRBV0mzRX/HLJNK1yLRnw0uIyyl1w/sQmVVsp4ohvpApa3hRMFOoqbtk3Hrud9D5cwWx7IhJrMuwVpJXM73RAIb1EpZov2T9Dc17r1phfg3SXrMhqSSeF1BS54cTqRzxG7jdlS4eqiKEd2pjBp+PsnYCvl1ZZI6J3kZRz6WlNL+fJ95U3ylf7QSALDhckpTkpDH9WiTzueg7wEm3IoOJAYw8wdmu1j3Vt3R1e2FRPMm+el5rFu1jer58EvDQcttD34LS375+fkYPnx4xPbhw4cjPz9/p8dfdNFF+PTTT3HOOeegffv2eyTTS7NfqKZOnYoZM2Zg0qRJyvcBoEaYZUADAwMDAwODXxo9evTAG2+8gZtvvtm3/fXXX0fPnj2bOMrFhx9+iJkzZ+LQQw/dY3Vq9gvVypUrMWrUqIjtqampKC0t3RN1+m0hGIS1gVgm8KwtaRHNyCXpa+WBndXunz5FaUDmPf8sAGDEX/9Ch27RZvw7YKSagszCwyuJqtT9ipKeTQcA2GUb6BQ6OwJPxJiWrgZNRJnJ/oF0TpJaQTNsb3JnFVmlJ0WWMmQ/YXzSiCmwajhCphv3X6Hrt7PhZNI3JLFFkM3pWAJpnB4ima6FMFIFI9IBALEDecY9lerd5ly3TMW6iYaL9TgRXlvK98mf8FjB50fkLwuSYiQpwX+M9I2k6eHZPYSd8Zbp0YEAgLONoo9UOhZJMSLXUkvTIr/XrHGTuiKbxp/MtZMK6NiaNnRNEpZwKo806oukA/xsw6xlnwJwtVQ+6CmH2pNW5qizLgAAdBed0Pt03QPg5LgnMNtU62E/JLUIMzrqGvGnG6XK10giMbVE1o6nTInyU7UUlpAZ34hIQUlYLfeoFclp2prXW0DGDqdjUaywltgawqBESQtlcULXGNEWScJi9oaycikKUaU/4XtCaby47wIVnmeNeEIl0/m9mi0vgsxkWVWiJ/NH+zkJHrZJ2CBOU6Mi9BiN7dJ93232sIrdzufgdm07NMctst5/DUT7GKql7amSlFh0Y9wHluaP5vPgyyTGKYaHWbtvOek0p+YRBjCunJ9fXIUwR0TKNU36xk3erdhgRvEI0iS2/q4IVnjHibv3FPZI6pnfwJLfXXfdhdNPPx2fffYZDj30UFiWhS+++AJz587FG2+8sdPjW7VqpZzW9xSa/Ve7ffv2WLNmTcT2L774At26ddsjlTIwMDAwMDBoPmTJr2X/9nUrdo6TTz4Z3377LTIzM/Hee+/hnXfeQWZmJr777jtMnDhxp8ffc889uP3221FdvQPLj2ai2QzVX/7yF1x11VV47rnnYFkWtmzZgq+//hqTJ0/G7bffvscqZmBgYGBgYNA8OHsgF99vQUMFAAcccABeeeWV3Tr2b3/7G9auXYusrCx06dIFMVoC+0WLFjW7zGa/UE2ZMgVlZWU4/PDDUVtbi1GjRiEuLg6TJ0/GFVdc0ewKCKZNm4abb74ZV111FaZPn662L1++HDfccAM+/fRT2LaN/v3744033kCnTp2aLGv69Ol48sknkZubi8zMTJxyyimYNm2aspO/8847cdddd/mOycrKQkFBQbPr7dTUwK6TZTH/sp0sBSV9vlJt+4bFngfHE82d+nOJv0Bepmsy7NsrVm9iWdDSw7sZyfM4zQoLYWVZLkJ0C0RN9+IDL4sEM4g2F2GvShviLTPsXzpxGmWpjJdWeLnDEiF7ud+cT9JwbLi0v9pU34rD+rdLSh5eZpClxhW07Gl1JuF13HbaP2MR9ffoKUsAAJ+2GqDKtFrREpi9aYuvvpGpZ6RvNFG4ZlgKuOH5atmlKRNQ6S9Z2pOxpO0PQC0TKcGy1EuWLBO05UQtAaiIL9t/7Y6lz/9JIcTdGmkZruwg2p7xGS8T8WpFPa8iV85j6wXyjsVD20g0fPjnuarM+Uf39LXJqeQlKF7KjV20xV+vC2gsFTxG42HhSgpwGXfkqe5OYrWRFD01hFXFYu4Nm+i7ZiGhRPOe+0gZxsq2CCNX7V6QZVm53nrgAQBLypIxxKJtWUpHmK6dWqqUOsjvUYIRVNmbt/rblMLLWryEha20jC3CcbXkKvemx9ZAluoCZSzAlyU9WX5nuwcx1FR9I/d0qrheum2v7En3UfJyDkKQ4BJODxXgpbS6bDo2VCXXhCULSbRf/LbIZ5Adov5ojKfP1B+oL8TqwLVi4L4RuQH3vzfd1bqTaem5wzy+RzUrCzue9k0opmsl1hJKgM/1rToocmUmccEGAEDmPLofyg7ugMaGWiB6zl6D3YRt21izZk3UKL1o0iQvJkyYsMfr06wXqnA4jC+++ALXXXcdbrnlFixbtgy2baNfv35ITk7eeQFNYMGCBXj66acxaNAg3/a1a9dixIgRuPDCC3HXXXchLS0Ny5cv32GenVdffRU33ngjnnvuOQwfPhyrVq3CeeedBwD4+9//rvbr378/PvnkE/W9ORmlDQwMDAwMfo34o2iovvnmG5x11lnYuHGjG3HLsCxrp8mN77jjjj1ep2a9UAWDQYwZMwbLly9HRkYGhg4d2uIKVFZW4uyzz8YzzzwTkX/nlltuwbhx4/Dggw+qbTvTaX399dc49NBDcdZZZwEAunTpgjPPPBPfffedb79QKIR27dq1uP5OfQMQ8CdCVb+xuBIe4ur2s4kBmPP2iwCAWfPIWmHM8uMAANY4f6h2RIqX5ojVNeZp1grybFBJk6OgyZQyAu3FU2aodhHPiqOxXapMFs8KM8XsjHxXRpAZ6fSdWRqbk9dWd3dn1qFt1Ofnnk6h9zidPj49igwoxbpAmIrGEVRG6SCalb4/9UgAwFefzVBlju1BIbh6cl43DU90pkIhClMhYfxKvC+HimmkiKqro4v+1aniXANSm/dVrIYIcB0/M6VE/g1+BkAePqnfblL79nrxUgDuA+Gg0ZSWZvEKsktI2URjOb6YWlDHWs4+z5Ap54o/k4ns+GHHqTIdSSHCdgk2sx9WvZ8JVkwL2z1ULiEhLw6gj6oekclyk75Zz31A/SLJcbeMp307sya1pkem77gAp2OJW+x6zAiLGObxFmQhs7L5EMJKxoPOAIvVSJQE0fb2UvpNmKYGvz2KMsZU29naQCaNHnG6N+QfgAqCERsEW6w4pD+lXhKkkOC3KQCgDEUdrifkvFKPUh6/bMGg2DCx5JDgAA/zk7SOhfdxmqUKWzOIGWdMOZ27UcaJ2JbEaNYNAEJldC0a2TgzeRnf3zxmFCurJyznz3AmUas1We49klBI54vN9SeXlmdcoI6fT5ziR0yOxQpBtXeRy8pKYmW7C/1tCeQSg5a2YAsa7eiC/z2NPWKb8Bt4obrkkkswdOhQzJw5c7dtD0pLS/HWW29h7dq1uP7665GRkYFFixYhKysLOTk5Oy9AQ7NF6QMHDtwtw6umcPnll2P8+PE46qijfNtt28bMmTPRq1cvjBkzBm3btsWwYcPw3nvv7bC8ESNGYOHCheoFat26dZg1a1ZEfp/Vq1cjOzsbXbt2xRlnnLHTNtXV1aG8vNz3z8DAwMDA4NcEp8WC9JZrsPYGVq9ejfvuuw99+/ZFeno60tLSfP92hh9//BG9evXCAw88gIcffli5FLz77ru46aabdqtOzdZQ3XvvvZg8eTLuueceHHDAAUhK8s/KmpMX57XXXsOiRYuwYMGCiN8KCwtRWVmJ+++/H1OnTsUDDzyA2bNn46STTsK8efMwevToqGWeccYZKCoqwogRI+A4DhobG3HppZfixhtvVPsMGzYML730Enr16oWtW7di6tSpGD58OJYuXYrWrVtHLXfatGkRuisACLZpDauEZ3Hab4qZ8RrpraYkncKGfLjmKwDAR30/oO0WJZu1m0g9sysMlbJP4LBpYaRWPMQapIfoo/eVi6KUqemCRCuVRQyALdouXq+2mQGCxub4LBK0JM6qX2R2K7NJmeVuK6XD+nUBAFRx6p72H3tvcirjhj+RKOGQyZcAANK2+YWELqtAH/WpVMa2AXT8scef7ZZYu8J3bFOMlNLl8KxYQvAV2+QJyY+4XlwfSbMD0UFpgkilz5G6ePV5oknR9Flqhib6ETmGt4thqqqvhxXrMW0pVWc/0kLlH0j7NqSKkSJ8n/E8qa9u7x/1y+5209r0uXy57zdln8HPDNVGYQCZjQlVU30Xsm7nkcf+qcq4beixvjY1ZhNVFqyifmy7kFlMZq42HUX9HV9E12HqRS8BAJ4efohbL2aJgpxYuWZ/sulI+JaTS9uSykV0Ucz4KIkVG7p6dI+BVLbv0Mw/ddNSsd4IcgJmNR6YsfKOHz2li2gLndw8rUzWZQlDKYaqjKpRfdT/Ez5aQvtyH8gdFnEuMZPV2DmlCfOMV0kKL1CJnreWUhEpnN6mFX3GFmtRVhZdu5j8Uk+juEzpDulHYV0lRZZYxYiehrVVgWpqT6jGZXpbrebr1oqulTJElXG6TbMbaev/26AsIzyWMlYesYRyr0E0krbz28k4/BvBsGHDsGbNGvTo0WO3jr/22mtx3nnn4cEHH0SKJ6n62LFj1QpXc9HsF6pjj6UH2gknnOCj2CTT887WLQWbNm3CVVddhTlz5kTVRInA7MQTT1QW8oMHD8ZXX32FGTNmNPlCNX/+fNx777144oknVIdfddVVaN++PW677TYA1GGCgQMH4pBDDkH37t3x4osv4tprr41a7k033eT7rby8HB07dtylthoYGBgYGOwNOE7Ll+x+rRqqH3/8Uf3/r3/9K6677joUFBRg4MCBEVF6uiZbx4IFC/DUU09FbM/JydmtADVgN16oJFNzS7Fw4UIUFhbigAMOUNvC4TA+++wz/POf/0RVVRVCoRD69fOnvOjbty+++OKLJsu97bbbcM455+Ciiyi1y8CBA1FVVYWLL74Yt9xyCwKBSIYnKSkJAwcOVBmroyEuLg5xcZEahNWXdECPD9lgbxFF8zmaKZ+X6Qgz+xLgGd0Jq+kF9T89Z0c/8Q4YqSZ1VvrvXJ+239CMNvEcLYotYEcco5tw2sUUHaeL/9RxYmDJszVvEmVdV6UbZKooMC3pbPEgYjLqMiJv7g6f+JdcW80iNsTWGD0x2mv7HzLfm7HgHQDAxSeSoarzg5+V2hHkmqm2CRMl7YlyHVT0ocykNVbJEQZNWCdJYyPRSnL9vf0ux0iZMt6EiWJ9lsUsic2GqColDcPxXiNuW8wqYjsyEmlGvyGO9Exl3fzjUKL+Aj38yb77/M2N0Ay0a+s/HzMljvJ9YR1ZFs38A2VsrMimoecsJL1h6/9zWfCUAC3Ni54otIXKDGelA3CT+dqs1+n2Lu234XhiEyck0Tme9qTtaRjo12UGmGGxOIIVlVrUqQZhQQMeI11hqwIcpakYH5155utrsyGuHpEJLXIJAKyOrDHjhMHqnpT7WdgiGScJ9Fk3gCaAXiNKZYApbJbOTEk95XlgS5Qia6xEJ1UbqQ9yqlmTJhq+kKRhoXPErPPfw046jdeYbcz4x3sStYsp6UY2L5XtMqmX+4TrH/HE4H6M2+I5p1yLQnm2ieaV2a8Yfz9aEn3M51Rpd7xtYK1ZfdsUeNGQHKIov816xfY8HFgtfiFq4jG/zzF48GBYluX7O3TBBReo/8tvu0LuxMfHR5XurFy5Em3atNmt+jX7haopZqi5OPLII/HTTz/5tp1//vno06cPbrjhBsTFxeHAAw/EypUrffusWrUKnTt3RlOorq6OeGkKBoNwHKfJl4G6ujosX74cI0eO3M3WGBgYGBgYGPySWL9+/R4r68QTT8Tdd9+tXNUty0Jubi5uvPFGnHzyybtVZrNfqLyUmxeWZSE+Ph6dOnWKyuToSElJwYABA3zbkpKS0Lp1a7X9+uuvx+mnn45Ro0bh8MMPx+zZs/Hf//4X8+fPV8dMmjQJOTk5mDZtGgDg+OOPxyOPPIIhQ4aoJb/bbrsNJ5xwgrJGmDx5Mo4//nh06tQJhYWFmDp1KsrLy3Huuec2tzsQ7FCFDcfRDLtHEc0cnfUU9SFMgXeN3VERYzQbaryY7SaY+Ptw3TcAgPuKewMAPjsgzXecj+1pyidHzsUzsAB7tzx6J2lR7h7Dvj7d6cU0vHaj25420TVkjvKs8r+sSuJaS0+dEoicWUdA6i8zRW6b6E8axpXSd9499FG6OrS2DTEOU4tJDzLsU5qtf3uEP3LTZkZQGJ/jpk8BAHQsJSYm7Ila1DVTSvOjRY+oKEUtuavoX6K9uEv/KJ2YlCHn4GVvYV7ctDZ8fKLmLQW42jRmF+r7kedW6KulvnPq0acqGtGr0+Jt0l9FV9P46wG67s+89SQA4JSfzgcAlP5A0XN/7U+RoxLt16GNVyPEGpk8TgicyaGBkk6HdTBWHkVCSa91/SsxGitu6AIASPm5SJWotD4aoxLI9SeblujTYBnVP76Q6rKEI/cu/Pp7VebfbusFAEjOZXYrifqtrhPVN1bYQvZ3Em8wSbMkLKhfO8csRidK0o21rDXUGVQpW54LWnJySekEAHYW1cdmjzX1W7V2D0pZfM86fSnyNX4ZjXkfM6lpH92T+T3X1H7wM6luYnBPu+QZJZ9y/+j+aWp/zXNL+sCbtB3McrFXHIop3ZKKcA34+1GxS1I/iVb0pp6J8Bvjr9rSkbe/fOCoyfrObiSpJGtWZXFy6YaUIBob9o41j4OWG3P+WkXpOyJTmouHH34Y48aNQ9u2bVFTU4PRo0ejoKAAhxxyCO69997dKrPZL1RCuTWFmJgYnH766Xjqqad26Be1K5g4cSJmzJiBadOm4corr0Tv3r3x9ttvY8SIEWqf3NxcHyN16623wrIs3HrrrcjLy0ObNm1w/PHH+zpo8+bNOPPMM1FcXIw2bdrg4IMPxjfffLNHL5aBgYGBgcHexh/Fh2ratGnIysryLfkBwHPPPYeioiLccMMNOzw+NTUVX3zxBf73v/9h0aJFsG0b+++/f4TjQHPQ7Beqd999FzfccAOuv/56HHTQQXAcBwsWLMDf/vY33HHHHWhsbMSNN96IW2+9FQ8//HCzyvYyT4ILLrggosN2dEwoFMIdd9yxQ9Ou1157rVn1MjAwMDAw+C3AQctfiH4LL1RPPfUU/u///i9ie//+/XHGGWfs9IVKcMQRR+CII47YI3XaLduERx99FGPGjFHbBg0ahA4dOuC2227Dd999h6SkJFx33XXNfqH6LSLz7QTU/pno5/yHSaR4Rz8SOj9w+58AAGnvLHYP0ESpDqc5UaaSvYmenz3zVQDA5zG0PcJM0FOGQBd/q1QvTJ3ftn4C/TCDKOyYsyKFpOGikqhl6WV6UzgAHlpct3nYURnCgGtCeBFP59xJSxynv0aO9m/8eT+3kMx0AMBr/0c3wslnfgoAOPB/tHz00tdkFdFvWj7tz+HTHZ79mc4hoeVBl91Uy296mhLbL57XlwRUqLswpQ2eoIQgt02Jz/0mp5Dlcdtv6KhS0oSavkXtDFoabUyhMtRSX2ysbz99mVGF+VfVQIekBcIC6qdQBzK36xSiZa2qWiq7IZPa+Nzj5O8mC5L1aW59E/O4fO7jumxaron9QQtHlzEty3h8bbq/SUuCWw9zxe2tVvvHbNxytgzQWfMyFuDzsnvKZurfy2+4CgBQfXap2jWQRsfGp9D5q7KoDZlf0b1QOJqsIJK30HJ40k80pmxZDpPlWY+xpxXiFFIr1/vbqEEZeXIf6ex/uL3HmHTZGt8+Yl6rylLBCWyMmU3L385a6iN3zEcuPSnz14C+/CVL+dqytxjNRlsOk7bKsqDedrFJ4WVstcy5nQXCsmQecutpyT3Ix9pa8EbEOeR3kV2whYykZ/K2KSAyB30MyTmTeHRvoWeL3ZWW1gP1dI6Y7Z77iOvX2JqXgWN57KdaCNf/+l9SfksoKChA+/btI7a3adMG+fn5UY957LHHcPHFFyM+Ph6PPfbYDsu/8sorm12nZr9Q/fTTT1GXxjp37qxE5oMHD26yQQYGBgYGBga/DPbEkt9vwSm9Y8eO+PLLL9G1a1ff9i+//BLZ2dlRj/n73/+Os88+G/Hx8b5UdDosy9o7L1R9+vTB/fffj6effhqxPBNuaGjA/fffjz59SCCcl5eHrKysHRXzu0HS5iqk3c0JRitoRv3wQDKLfPghSsdx7wI3YkAZYTKUSJJnlYFc/4to1bvUj0kn8nYPY6HbMyhoYnWZvQWPpRlWII3D6WUWHwU7tWJQ6UzEtFO7Ab1i7wZ/PVUZMsN3tO9smFmXSZ+b64mpcsPtgUAN/db5NYpD/mQdRWja3D19fi4FANR3oRl+zAqepddprJynndIC2UeYAGERlAhct4GQma2wTF6TQ0n4rM7HM2npE57hO8LSie5QmAI9aTKAhixiemI20PWMyWOWQD+XsKByjjCLwIVpC/rZCNoW5ztf/nF+r7WG1cS8tF8i/UbnSCiicwQaXYayMZn6IY6Nbwv357bt3xcA0OFVYlzEYNRSjAaVEVpJ90rW0shxvukiMqnttCYyITXgitcl3Ur6lxQoUnI4JVUPf5qh9k3dQuXH51E9NxyXTvVPpHuvkvOwW2EaXMWDaEPHx5lpk/HsETw7pdSmgFyTJhKWI4aeoY29iAmsaUv1FSG+mEcCHrPfGH/qJt0YV40/3QA2SRsfgBKEq2TOYRkzbFIr+4UlibOf/VSGnp57wrJC/mMFjRozxc8hlBDDH/HM8dhVCPsn7LUau/IMjNHqJWNpO18jvW/gjjObzx+Qc7T1pzqSZN5oS8+SYL4/VY0vJRA/nwNsZlo8gO6XlE2NaGxo4nn9C6DFS357qB6/JC666CJcffXVaGhoUEt2c+fOxZQpU3DddddFPcYbJbgnIwYFzX6hevzxx3HCCSegQ4cOGDRoECzLwo8//ohwOIwPPiC373Xr1uGyyy7b45U1MDAwMDAwaBp/FFH6lClTsG3bNlx22WWo5wlAfHw8brjhht1OHdNSNPuFavjw4diwYQNeeeUVrFq1Co7j4JRTTsFZZ52l7NvPOeecPV7RXyuslRsAkQ/xjDGFZ0HX3H05AKDkTm8oeToAoPvjNDsKLFjqK8/hkPK+T9EL6fK/EMuFKKkGxx96IgAgvInYF92M09UX8Ll4Bibmomq/JvRS/KP/e0TYd/RQYF94slaGPhNV9g4cji52E/F5pKl4adlBAIDucA0JbdZ6CTORtojqsXwyGbJZdjoAIP3jVbS/Jy1ItDpQW/w6lggGUNqhhVvbYT2Zsifcu96vFwpwOL+Ybnp1Ir76yYxaktfGu9c2ZlMxt0HTq8msXQw/azVmUCWjjmIWK+aqoomJYigJAOnsg9oYT32UvJnqF7s10vyyMZ2up51MnzmfkOanZAgzAJxoN6DZEAQ4NY2a+XvHGM/yO84kBmfLCcQWZX1F/Wwn+O+BEDNFditiChKK6Pj6ZJepaEzg8c8WDH0eIwuGus7EYpUdSn3RkE/MSu0gZko7E6tksYGlN0GwVaeZvsp2Zl/FAiHAtg7hOGpjST/6jC2jPopdsEodG+jhl1pUd0kHACStZFsJpWN0fPWJYKa8LJl6ZnAfsy1BoEZjcnUzy2oxv+TjveyL2HIoY0xNWyhWB2wzUd/Hz4KK1UCwyq2DGLWC08RYmwv5XNqzS9hlSZYsiaF5bNkelltBxlfIn/+toRWVEVdIDJawY4qbZduPgKd/A6xrtPl+jSulsVOfEkBjQ7NT5xrsAJZl4YEHHsBtt92G5cuXIyEhAT179twl2yYAOOWUUzB06FBfWjoAeOihh/Ddd9/hzTffbHadmv1CBQDJycm45JJLdudQAwMDAwMDg18IFOXXwjJ+C2t+jOTkZBx44IHNPu7TTz+N6gZw7LHH7nZA3W69UL388st46qmnsG7dOnz99dfo3Lkz/v73v6Nbt2448cQTd6siv1U4YRtgzYDNM67gVmJPMr+iGWObuZ6ZVhoxFB/OIeuGsd0OdsvxoMsDlOR37EMU5Wdlk55j1mfvqn1mfvk+AGBc/8Npg5ZQV0wahVUKayyN0th4JqxWiCOtlJGoP6GxSk3TREShW1AUfY4eGSjaD6UfoplFYxdq68ax1FexHCS56Qo3yq/DI2TMKGxL7ikU7dH1LerrGf9iweED9HFl99FR2+Gttx4lpXRtbKAY1qKqRKMWkUrH12i/iaZKqFwpppD+FBWK2dvGrIdcU8+1s5swIBU9mMycFRPFTEFAZyq8ejyefSujVm5T1isU7Tf2FdKofbeSDD67fkipnRoXEGPRtpT6vTHF1bLEFvpZKzuRfmvNUX52K7q+gUpmOziaSq6IxYlslQ4GcBM/b6YUJDGVpGtpaOX3vJNowwRmVuoyqKyk7zYAALaM6u52Qw3105qpdC26/Y1qUNGBjklLpetuO1S/XvdobJyMYw9L47DexqrREmdzG5UuhxGsYeasFV2rwgPoXujwrYft3OhPgpzIyXtrBhLDE7+RzS45ok0+FRsqmqpqD2OuJTNXKZKEiRKGNI7HL0fLNnak9gVqI3VBge2szRTtXhy3Wf5Ky3bui5hCHnup9L3gEGIT40rd8Rqqo2OTNjPj2JNEx6HlpI0TFqzsGNLnpX5IzL8yW41ybypGn1ms+m7EbscspTJDIYqSDGdTFKDTkdORfL/MX47XdFdLIp20hc5f3T4O1l56SXFg/W6NPfckKisrlQ7ci5iYmKgpaXYFzeYgn3zySVx77bUYO3Ystm/frvLltGrVCtOnT9+tShgYGBgYGBgY7C0MGDAAr7/+esT21157LSKH8K6i2QzVP/7xDzzzzDOYMGEC7r//frV96NChmDx58m5VwsDAwMDAwKDlcJw/hrFnS3Hbbbfh5JNPxtq1a31Rgv/+9793Sz8F7MYL1fr16zFkyJCI7XFxcaiq2nFW9t8lbMqcBLjLJPZ2pt7502u0GNAEwdVHDQIAJHy0BEDk0p8IO53NZJswbtRE9Zss/81aOs93zPgDjo1a1YhlrijLcsrYUb7bOxaUR1DpUqZne0S+MD13loilua3BYloC6PYyfUo7x/UZpY6xteXKuG1Ur/jFFAp79f7H07E//Y92EKsAsZLQDFa9ZQk3H2QBuVzPiP6LsmTqay8Aq283+mQ7DFlaE4QLOAedHtatwuz9pqLe8mWJUpb4ZMlP5XjTro0KExc627N0qJZ4Q7I8qOUO1K57qJj2SyyiMmvaRYbklw4n4bXFp2m1nJYTw6l0vavbUhlpa/zHWTU0BsNtaekn5F3iZOG4LJ22mctLPhImz0L2YN8uAICGVDpHfBFtrzqItrf7xi0zpoKX276k610ykNpaMpqWa9q+TgLxFF5uKhlKy10J2+i4pCX83POaYsrSWSIvRaZQ/1jltG99VzIrDdRRGSEe8wmFbA7Kl7/kDddPJ/NsGivqGcHXPf4nspdo7MYmh5m0xBcqoSW/+rb0XfLMxW5xl1BlCU9Vm5fdAyWlvu12ehLvz8+6Br6P2Li2qlOy2je1gCQPNYN4KZLtKBrapPrKjNlK9ajsT30RjqOysudS0EVdO3c5fN2pMv6pfv3u5uueTmVuPYL66ft7aEl63Gx6VlgS8MDXQ5a0AXjyZVLZtjybeXtgNVvcsMg53NWfJ1RJFXSjXwChFVS/hgEUSJA6ZzkanfqI/X4J/FGi/FqKE044Ae+99x7uu+8+vPXWW0hISMCgQYPwySefYPTo0btVZrOX/Lp27YolS5ZEbP/www93myYzMDAwMDAw2DMglqoF//Z1A3YRL7/8Mg499FBkZ2dj48aNAIDp06fj/fff36Xjx48fjy+//BJVVVUoLi7G//73v91+mQJ2g6G6/vrrcfnll6O2thaO4+C7777Dv//9b0ybNg3PPvvsblfkNwvHhptDpYldvGJVnlX2ncE+XfvTx/KnvwMAjDuMTEDt9TQ7UiJlEVNXuCzguCHH0D4cEvzhmq8AADMXzgYAHHsCpb6xlmmeCzLj1zLKN9k+L6KwWlG3e77rrJYI3RXTIv0T8lsF2NuIGTr6tPMAAB3muLYJBYf5BaVtX/mBjm3NJqAsRh3wDZmsds7m1BNiIhilzcL4KLEqfwY70OzX0YxQlcif+0jYncYRA9Q+MUVamLawmCIcV4yU3+JCty3wZrxXAncxr+QxIrYYjhYarhisHaQFUqyaZgaqmD3N0HH1OcQEjH35DH85nnrGVBFTJoaeRQdQ/WIq/OOhti39XtKP2pW1gOobKqvz1QEAkKgxZwKxn2AGK/jDajrnBYMBANmziPWIZVPMQIN7/Ss7Upll+9GxiWuprb3/Jv1In7XtiYWpa0XXsLod1TdpOfeNJzRKxOhyrznJxFDVdyFhc7CazrX1YOqj9v+jNnd8j8xatx1I+yXFuQEtVjKnM9leShuYfZFziNGr2GmI/Ucs90nZUBrHjYmuqakgcQOxRVadXzxvM7MmDGRCHj9/xOOWjUdTlrn1rNqfrCySfqS0Wo3ZwlSyfQuzcmJLYYf8bEhDGxonRYPd8PcUupxIzfWnKQpnUJ80pPrLmLXiMwDAAXddCgBo89zCiDar+zaebVqWkUmwjDGHU0rJ/WQtWk7f1X1EYylcsFUVqYxcOxCbFfMDPXudcBiOTmUbtAhPPvkkbr/9dlx99dW49957lZ47PT0d06dP3ycBcs1+oTr//PPR2NiIKVOmoLq6GmeddRZycnLw6KOP4owzzth5AQYGBgYGBga/CPaMhmoPVeYXxO7ouTMyMrBq1SpkZmaiVatWEbkzvdi2TY/q3jl2yzbhz3/+M/785z+juLgYtm2jbdu2Oz/od4ymQvGjhdMLm9Tl4SW0D9shgG29Np5M3zv9nUKkZe1fMRulWmJZD8YfRIlqZ343EwAw+z+v+H4//HwKdT/2YUokPH8YzYJ9bE1TDFQTaNLYM0qZqj8sf0LTADMtwkg5/SmkvXooGSduOpH22/KfgarIjg7POLWw73Bhke97zFxiAOwUrk8x3ySid/EyQZI0WMLghW3hayYQI0prP0q1FGRzRofrH7u5VO1rp3Fy1h4duUwqu6o71St+GzEC9ZyYN+nL1b5zCWumdFFw2aLGvC28gdoSLi1FcyCMmq9tYl0gSXuFmeLvg6cRs9rtVKpnwhOkl/n5qx4AgOzPXTa2vCNdg9pMGsNe3RIAxG3z6/WyFtDva8+mMRWXT/qYdl9H6rPitrNFCWuPHDFMZRbRSqHv7d/k9DZs+Gh3oRD4UIXLqJQMoPMlrqXPlE3U1u2D0gEA8SVUr4QtrEk6iPok5gC63uH5pPUp7eUmR85YwONQsa9UdqiSNWDVXP9aauP2wVSvtNV0jvIudE23rnF1O31ac9vq/FocS3RayqCSP5nNFrY27RtmvT2GtaIp2zqCzi+auLjtdG1sTu4brKftVV2pX5NXif6J9GTJn7rj1upKmjPRjwVLieFZew79nUhnr9JgPT8n+XkZz5o0m7VeCcXuX/XyLmx/kcpmumnU16LdquhK16jbW38BAKw75SkAQJvvmUVWmkS4EGNesQppz3/HOEmz6PQae/G9++3PvuOiJYpXz+sNxHbJc8iKjSVp5l6RGVt/CFH67ui5//73vysD8l/ClWC3XqgEmZmZO9/JwMDAwMDAYK/AcVqe3Pi38EIleu7Onf0ZBHak5/7hhx9wyimnIC4uDl27dsXw4cMRCrXoNciHXSppyJAhO6TGvFi0aFGLKvRbg2M7KiKnqYTCXtZHzD+FrQlU+vUuS/9KqWbGPsqGnk0cB3i0PhIJyBFOw6/xu9ifctscAECwjmaZMz4nI9A++DmizAhoOqcAaxdsMefbhTbrzJTSLmhJZTfeQilmOt9Hpp0p+Wy0dxBpMnI+dfvKHkoGfsElNDN2tKTHwjJl/yfXv13+I/oIb3oZ0QvpDny2nyVU0UHLSdMlKYdUig/PNZX+qunIqU82ltJnAV2rcCIbKvKlrBzdEwBQn8SzYI6iqkt3r1HOG3ReK4opnbe+ErGptGo7YE4FbtJrbnMWT5oKiHHJ/j/KPVO5iK6JHUP1bJgkbJNbz7hyNmNc4Pja0sjRXEWDiQHo9jRr/JgN6/20PwVI2JN2p641j3VhbPXos9RkRAU/v+rTOSVIvsuOBVli1uYH2taYSG2KY8akkg0+azOoXqnr6Fy1nB6m4BA6vqatO26S8ziFSwPrhqo5cowZynAG1TvzBza1jGM2rw2nOymlcroctVmV6YRpDDX0JuY2poRn4RX+Z4gYaIpuS0HGrUcPl/wz6X9yJ/C9No/q0ZDkH1tpq+hcwRpO/ltK9a7/f/auO0yKKvue6u7JeQZmyAxDjpJUJGNCxOxv0RVlEVxXXXN2zYpg2BXUFTEr6q5gXANiJIiiCCJBcoZhAjPD5Nxdvz/uva+qXvcMM8Ngou738fV0ddWrV++9Kuqdd+45MYl0Pd0t+5hwFnkVpAfcbqmrqS3kOWRyn5Sn0visZlHYxO3UISXtrbEUt1uzq+IxHbOLEL205YQ6LP/XHADAyRdNoeupJY7X5ufITLv735yinICNOykirDXMbzuHUNfk9dS+5vHEjRS7MHX/2VBueZZZxuoMifn9MH+hLL+jJZrC537qqadw2223ISYmBmPGjEFWVlazrrA16IXqnHPOUX9XVlZi9uzZ6NWrF044gZ4k3333HX7++WfXENkNN9xwww03fsU4WqxnmsLnTk9Px5NPPolTTz0Vpmli+fLlSEpKCrnvyJEjQ26vLwzTbFzTXXbZZWjdujUefPBBx/Z7770Xe/fuxUsvvdToSvweo7i4GAkJCRjtOQ9hXrbI0BEAjvr4RAZrnGx/gWZDW0a9GvJ8p3U6nv6wI0KCMNTBYxJk4oa1PwAA7nqQOFS+Sioj/p2VQceYmlaUQtLSaQbq37FHO+DQWYDqWjVoVVAubwahHTJD9OcRL0dHYPZdaXGo2v37Jzq9WHqIBlN4MC8IgA3F00xf68l8C7KNEdsTv2TCOWejEp4WKdYXQcCkj2Q2KxlaUg1uo/KuhAgJRyjAWWmGrd+rE5zXGL2K0oWV5k5ZhfPcOgIp/WBD9ZQNkKBtkkFY4+Q5WSgez8AZfRCOlRkWPEerTiU0pjaGjq1Ips/y1lwGV6+8o9PGpOP/mFuTZ1mliP5RbRzVL3xvvuMYZdUjmW6is8T9XtmrHZW540BQPQOJhG75ozlbU6GHNA4KO9P25E1UVs5gqkPLn2gcZI62+iXjbUJnirsQchKVR+1Y0J2OSVtBv3tK6NoqmHcUVkxl7Tg3mDfWsg/pUIXPpvEVXkxlhu9hXqCMQ+ZY1bal/QRw9RxkKxoZHwBMbp8DZ3RxnEueEYXd6dpbfUfnEoRV+ttutyNRy3pTvkzqm0ALGiMV7UKjh+EHqb4+Rtz8SXTtWcOs/YXXVtDL+XyJ2cf1PJGuKT6WPlvdxpprsc77LOc4S9vKL0mSQ4kP1n4GP6c4KzrvwmMcx6YuoO3q+RRiqUjXEZR7zzAM1JrV+KpyPoqKihAfHx90bHPEhAkTsDi6CClnDz2scip3ZqNo2rwmEbN/jWgon/v999/HFVdcgdzcXBiGgbpefwzDUFmDjYlGLx6+9dZbWLky+D/iiy++GIMHDz5qXqjccMMNN9xww41fPxrK5z7nnHNwzjnnoLS0FPHx8di8eXOzLvk1WtgzKioKy5YtC9q+bNkyREZGhjjCDTfccMMNN9z4pcI0jcP79zswR87JycEll1yCNm3awOfzwev1Ov6FihtvvBFlZWWIjY3FokWL0KlTJyQkJIT815RoNEJ1/fXX48orr8SqVaswZMgQAMSheumll3DPPfc0qRK/5wglGxAkZFlH5wIWwTljEonGjTrtcgDAx888CQCI9dBLqgjGBeyijYJIatCkOj9/XvnBVABA+4lZjv32ZhAJvN0j39sq6ySOi/VMgJ3uLduTGuf+6nCNjAkoaxa1LNeLiNeebbvoh0Ja/vCzJITsp5aheBmp/auWR4mQzkXMEmHO5UEh3oronsEyCYZm/WPW1rPkJ9citibKqoLLkiWWMOcyU0AEP2EtF5qSmi3LBPrSI2+PXsECi1JmiLHj08jGcm0iyaGW9Opa/pSlPlvfeZI51T2Kl/q0QxRhN5LbmcVXDSYe16SnBZ3GW0bHhBfQMkzEPvoeHUn1avnpQcf+Ar+LhcqBAdS3B7tb1+FnrceyHtJ+RKbu/CodG57D4qvcR6a0DbdZBC+P1bS1xC19BbTU5CnmPiqjY2o7OpfhWq6mz8pUqkTEQTpndTz1UVS29Z9QZSr3kQyRAlrai2bLnszRtOTT/n90zqi91I4BJuBnvE9tduAYq6/z21N7ZGTTb7JUGhCbIF6aNMNoWctTTTeeP4rbjyU8vHa7mRq69pYfkpZBIJ3aXpZWw0upLG8lC5RqS9CmbpVk+zuQQtcoS6bRO52SL9I3Mq0PxNN1GFV0rrafWstNpV3pP7gWa+i37CEi50C/P3v8awCAy74kMrpnmPO/ttQl9BxI+94a2dtvpH263URj5uNvPwAADPhB+Dc0PltfTvXwFxRSveX5KkvqoWgOsvRtX+o36xeAbq4wEXz//hFj8uTJ2LNnD+6++260bt26QYlzdlL6iSee+OuQ0u1x++23IyMjA0888QT+85//AAB69uyJV155BRMmTGi2irnhhhtuuOGGG40M8+jQoVq2bBm+/vpr9O/fv8HHHGlSepMEGCZMmHDIl6f//ve/OOussxATE1Pvfn+IUDMUBcXQZi1dnXbVBqqyLWFhSk4p/6iMZooXxtEsac+LbaBHuz8zYiP2FhoyJiiRr4zKzPqeyozMo+8dXiC7FgdGo5O0BanSkRx9VqYhWqFmbYGhRCr3HmAkJYFmsEKm9jLaFGDDYN0KxrDZjhiMxihbFf70JiUCAGr3Z9N+QqJuiNZImBgDMyrAxGx/FqNc3jpWyJm4bQhaZrNfgaBs+nI4p2YrAU3Zro+PEMRIJeyoo1wyQ6t1EoWFaC4Ec4OF7fypNljb47w2b16x47sZy22fV0ifknrPbRSWTdur21nIj+Hn8VhOiIoQxBVRXPqTfwdLHoiFSptsRstshtK6EW15f0po2D+MtrdbRGVXJdOxVQnO64rfxeOkyCK6o4CQE387FrplFEZELau5/aK3EVJhpFDZyZupjNwBfB22rss5juoTu5eRHJaXiNlL11rJxOvskwnZa7WEiM7+WEnF5+trY93TvnUsXlpT6DidEMMDnOBiVHE/M8rkVfvxc8JGSkcK/WcSiGZTZLbEKctIpM9WLE/A1aiJpXs2vIglJmLYnqnSGqdelogwI7ivxJg42okiV7WgdovMZgFSltXwVASP+bgN+Y5rjdvA8g4tqU1ufZSQ/ahTaKyUsz98n1aEyq9J7wYAaP+ldc/EfkP35K4/U1/0foqy1GOyqL4rppO9ElbTxwk3kxxNwnyNQ2x/ZgoiJfeiSvL57b+g/N6iffv2dZLK64rHHnsMV1xxBWbMmAHDMHDuueeG3K+ppPRGc6gaGn/729+Qk5Nz6B3dcMMNN9xww43mC7MZ/jUhZs+ejU6dOiEyMhKDBg3C119/Xe/+VVVVuPPOO9GxY0dERESgc+fODU5smzVrFm6//Xbs2rWrwfU755xzkJ2djeLiYpimic2bN+PgwYNB/5qa3dh8EqFaNPbN8fcaZm2NxQXQf6tL9DLEb8KDidlDnApBpiTWD3kDAHDSxVOtjfWUD1jr+J2mkdiqh+04tj5JEgiet9mWRTPT1SpGH7p5rn7uOrhUgIUOeUWIUCxe8p2DVqweFFoiCJ8gPjYBPZGbUGcRs1RJadbEKwOa8Kdw0ox2ra2NuZzmzeiXMood0J0+eTdfFveN1EuEFHl/I95KzZYQlCXIlFmfwfoYlRHkSvgx9vR0PTVbUDhpL7EiOcicFW7/QFvKhBFOTVh2CBsjuW8F+WMkyihmFEH6v1LMnelaBY0Iy7XQpNoUQux8hQJv1DqvWdpPkCo5t/ByBNGKtnhEyviZx1/Ud8T96ZBHY7oqha49em8Jf/LltKWxX96a2jf+oE0klpEpTyXXjz+j2Z4mwO1Vm0JlROQzn4xRp9SVwfdPTTwdk9/biagFItlWp4j5hLWM3nD75fVm6x++3SJtqhDhfIwgUIEYJpRFUR8JuhRgnps/jtrCl+e0TrIjmCYLihb2on4ML2PR0kSuZyF9r41i9IgPze9NZcfksnBpjTUmq1o40VhfOSNl3K5y/0RVMJIV5uQWBRil85ZoCCxgIb48VsTKJ/V7vvcW0TFZjPztLqHrqh5N5ypta6FkLdY4+23H/1G923xLZXT5DyFS3gq69s3/ZMTqn/Qx8EEyXk57dY0qQ3ifQeh6mA9GfRItzRhmsyz5Nf6YefPm4frrr8fs2bMxbNgwPPvssxg3bhw2bNiADh06hDxmwoQJyMnJwYsvvoguXbogNzcXtRrCbg/df6+srAydO3dGdHQ0wjT0ur6XIjsp/RdXSnfDDTfccMMNN34HYTbthchRRBOOf/zxxzF16lRcdhnpHc6aNQuffvopnnnmGcyYMSNo/4ULF2LJkiXYsWMHkpOJKpCenl7vOZrTf2/UqFHYvn07Xn75ZWzfvh1PPPEEUlNTsXDhQrRv3x69e/dudJmNFvZsaMTFxWHNmjXIyMg4EsX/6qGEPY1zEOZjQUVBbWQmUo/IpS4CKtlqm+8lS5XIXOexHd8nIcIur+1S2z5cQcaQ3W/gGVJD13y5Xt5WlN0QOJAXtItCSJg3FKiuCdrHUaSgI1KHULMxPq+vtTMjTFAl3d6mvqHplay0CubnMLrlb8Wimus2czU0JFCyb6T97byosNBZcTIjMpjzpc4pYqGMnJnlFcHHsnWLiClKtp9cm87tUrYgUhepr70t5G+dlyGzY+FKMRdMxEKjdhU697dnYgqHhsUf1XnFYihOyyxkxEoQLJMNeQNxdUuneAuobFPaXDIvyysd16W3o7L0sV2b2kfMsW38OgCKGyQIjLSVZJzZiYNiB6POJ/VhjpopKKG0idSb7VfEjLiiuzWuJTNQbHMEUSlpR+0cm8nim1F0bFgp1aEmXsYnfez+v+B7oHdnyrjd+14nAECr5VQPT5VwqdgehrlMQuwQQ2YUWYjVlltJ0LP9F7SvP8r53FEWSVHC8XSiSTWxfE+XB9/vEQWM8HGbh2/jLGMe29XpLR37h++iZ5xCWAssBFXdF/Js0LiGNa0IbfeWscUPo3iyelDWme7dvD7BGEK7xXRPViXTOYQ3JuE/w7laULwzEQCwfQLZ3Ay90bL6SlxA1jYKEbc9f2rNGiyqeeuIC3t+FVGCxDOHHVY5VbuyUPbwf4OW0yIiIhDBqwP2qK6uRnR0NN566y0HL+m6667DTz/9hCVLlgQdc9VVV2HLli0YPHgwXnvtNcTExOCss87Cgw8+iKioqKD9mzuWLFmCcePGYdiwYVi6dCk2btyIjIwMPProo1ixYgXefvvtRpd5xDhUbrjhhhtuuOHGLxtkPXP4OlQVFRVB2kyhkCaAlMr9fj/S0pyT5bS0NGRnZ4c8ZseOHVi2bBnWr1+P9957D7NmzcLbb7+Nv//97w26Tq/Xi9zc3KDt+fn5depQ2eP222/HtGnT8PnnnyPc5soxZswYLF++vEF10MNd8nPDDTfccMONP0qYhiVSdhhlREVFBSWWhUKn7KFrQZmmWac+VCAQgGEYeOONN5SQ5uOPP47/+7//w9NPP31IlKquFYyqqirHC1JdsW7dOiX9ZI+WLVsiPz8/xBGHjiP2QtWxY8cgktgfNXT/u/oI2kHSBtr37veTwGfmKyST0OphasOKDjTgtp9hpaW3HUblZl47yFFGu6d/ovMyPB4oLQtZ39pMguA9Mc7lHADwCMRvE6kMFb40TjWXpRg+V31LhP7cPEc99JBjPZHOmzfU/kYitYvJoqC7z2YpgLNJtLTjtBWOY80aXVLANkb18pWIKS+l8dKoyCPI0p9+vL2eBhPDTU1s06PfG/Jw4O21LDzpLeYll9jgB5mQeb2ltLwgSzrFQ8ivLnYX9UUYe77VJjNJPJ9J93aBxwqWYuDlK0VKV9dB48CMY2kIWULjse1PjHbUxR5CuJYlydo2NGZ8W2jpCpochVq+CyUDIUu1QpbXlkxNll5Qy4dC2OdlWV0OgsrXEgOkL+RY/t1U/zHwd16akiXCqI3WTNzfipYc/RF0bbLcmpzNy4QiAtspmevHvoW5VFZNPF1f99lWP5R0JoL1puM7UvVSTce+vgqqry+X+yom0nk9vPxV1c8iCCf1ZLmGbxKdTRJJ+x7sQf3tqaEyIoqp3jXRLNaZUw09hChek8ACqGt2UTWYRlA2uKNj/5gf6Pcq9lmM2MLtaL83Rai1xPksE5mRcFnuFt/NckmaoO9xa+lejd1gIReFA4nyUJVE7SfeiDFbWPy1FSeXPE7jcfsE2i823ZnMUR1rPd8D2n1jT+IxzQZSMn5D0dClyRYtWsDr9QahUbm5uUGolUTr1q3Rtm1bhyp5z549YZom9u3bh65du4Y87sknSfTaMAy88MILiI21PB/9fj+WLl2KHj16HLLOiYmJyMrKQqdOnRzbV69ejbZt2x7y+FBxxF6o1q9ff6SKdsMNN9xwww036ohfmpQeHh6OQYMG4fPPP3dwqD7//HOcffbZIY8ZNmwY3nrrLZSWlqqXoi1btsDj8aBdu3Z1nmvmzJlcRxNz5sxxLO+Fh4cjPT0dc+bMOWSdL7roItx222146623YBgGAoEAvvnmG9x8882YNGlSg65bjwaR0vVUxfri9+JOfbhhJ6X7PBp6UI+w56FCSNPeVJrFi31DZRpbZ9yWFXTM1v0004pfTjOphJ0004r+fjvtwOfX5RE8GTxTzbZI6UYSzxZ4phU4WOg8mUgaCBGbh4+HxTkFMQoV/kyavXjS6WYx+btKNdYQIoXsCeJnQ/qElK4EJnXX+3AnKlLTkVGRdeQYH1IqQvpLytKSCwTJM6uds/IgYnmUjZgdou4AYAj6JkgPk9are1P6f/h+tqCpDTGrFXQjnuoj6fGVLXmmXUj1DzvoRNBqkml8KATATkpnZEc/nyBQBqMLYnNSm0jnDDBJOSKrBEEh8g5CLpY2YMKuoUP63G46ChEKRTT11Gom/4OlK/zJdD2+XEEInZwKkR5w1EsllZiOcwXSkmEPz4FC2o1telTCgW1cCIoZYETPYyOCO4Lv94CgXSILIIR427WXd6OEC5FiCPAlpf1A+1a0pO0RRVTv6J1UT0EmDwyg8VI4wKpn1E5Gt/h2CCtlBJC7zsuAY3gpbQgrcbZ7VTKds7ylhTLK8ydmDSGQyrIpxtnfJt+7eUPo3kzaTP3uFQQ1FMrN4zPAiLTcRyKcq+yXdIHcNoSSbLzZQlw6vMfJOVV0bb4y57X5DtCYNoq5TB6fG26jsm4b+TEA4L2sAUHVNE5jcWJJmvB6fzFS+pdhpUgYP/ywyqnanYWKx95o1P/n8+bNwyWXXII5c+bghBNOwHPPPYfnn38eP//8Mzp27Ig77rgDmZmZmDt3LgCgtLQUPXv2xJAhQ3D//fcjLy8Pl112GUaNGoXnn3/+kOcbM2YM3n333TqVzg8VNTU1mDx5Mt58802Ypgmfzwe/34+LLroIr7zySoN4WHo0CKFqzlRFN9xwww033HDjyISQ0g+vkMYff8EFFyA/Px8PPPAAsrKy0KdPHyxYsAAdO9ISb1ZWFvbs2aP2j42Nxeeff45rrrkGgwcPRkpKCiZMmIBp06Y16HyLFi1qdB3tERYWhjfeeAMPPPAAVq9ejUAggAEDBtS51NiQOGKyCX/0cCBURhO4YrpgpsyoND6Wh2d1ZojZmuJIMZrkYXRo2ywSq+z4tKALzi4WcUFJ2Y7KtGbP5iZCcDx2lAVQs3ezLoSgnhDrE5XizsTG8iGUsh1xUBPdXMfImi4PYBNQVXIEFc5jJZ1e8XAEzUllTovGRfKs3Gh9kWusDc3/0k2u5Tr0trAjVh62wgkK5oSU92B+AV9j9Gae2ZY6ETQj2uoPmdlXtaOyfZweX9rByYVL/IHQTH8KcUFE9DIyS+QLrOvxHgyBMNnOJeFnyxRTQ6zFegY2hMvUrXEE8dPbsQ6upTKU1iURYGvzBLo2ZcejLD8YQY3S0GMeD3aESiFNzLup7uQ0S/WxwKSniMVNxQpHu1eNiBBEWLlWQcg0EWBTECo2LjZY7NKTX0i/19jGIqPHmeNb0T78U0QRXVP8Tn4OsClybSwbMY+gfg8PoeM67GIS/V3234F0fr7lIvPoj+hcMUF39nfkPmqDfacRetf+fxZ3pqg/IU4JX211niw5EYCFtIlSbi3zseI3sDwBX7s91H3diso2+P4QVDDo+agJ5m55lqxnPPut+6jDp3RsWJFznIpFj7GHr0nEORltrElzovCFd1n36ooBbwEAxmUMoWMY1Q6UlhFCVTX/iCNUX4SVIuH0EYdVTvXuLFT88/WjZsVJydgcpkXQYckmVFRUoLi42PHPDTfccMMNN9z4laI5bGeOEphl7ty56Nu3L6KiohAVFYV+/frhtddea3J5jSall5WV4bbbbsP8+fNDphY2xVDwdx2Gx+LJ1GUtEELgU+0r0CqbCis0R5AV5md4uhBsqgxHAWDHPgA2PhFHl+uDeVYAFHdFZlzhYlrawbJfyb10oOMQ4VDE76bZXMSPZMgclM3CocyI7ZlbMsvm2blkx0Ut+dlx7Jbp/QAAaelkoiwieYqbYuf87ONrFENiQSza0uxdeFDCSRF0QT7zRlAWR0LAygbxbdxFpynmGarOwwjVj7CheSG4cgrNkJk1Iyml/ajNY7YX0vZijTckiIagkEmWnU2AxSJNrk51AnOniuhao/Ywb4j5JMXH0blURpYIU9YDTgvnqKITja2wEurDsP1OkUPFpdMySek8GvoqNr2SqcfnEBsblVUnQqoRTmQTAPxsnyPIg9itmF5nxp2cU/GbxApE+irMevTJPsLHC8+ka6xuk0hF5XPWXInYBwUc9TKYn2MWWhNKg22elF2SblOUzLZPbBjs2SVm3nyPyD1j4wbuO4vGdvxuOm/mWCo7eidnjtYyElXCv4+ga0z7gTlV2XQz7zzbQvy+y6LnisFNLNYyYolTmUxlJG6kazM4G7S6NSEsHd5hnpSN6xWVR3Wv7pcOAIjYccDRBlUJ1M8FPamPujxNqLhf42uK8DBgE3flMoTbqS+wKBSZPwW1f/S4dwAAM+/4s9o3wNZBYtIcVsT2NTJ2eEwrMWB+ZvvknubnQXFZK+hRNZxUtiNX2gzsf7Esv2awnsFhLhn+DuLxxx/H3XffjauvvhrDhg2DaZr45ptvcMUVVyAvLw833HBDo8ts9AvVrbfeikWLFmH27NmYNGkSnn76aWRmZuLZZ5/Fww8/3OgKuOGGG2644YYbzRTNgTAdBQjVU089hWeeecaR0Xf22Wejd+/euO+++36ZF6oPP/wQc+fOxejRozFlyhSMGDECXbp0QceOHfHGG29g4sSJja7E7z0s2xXeoCNVDTDFVLwdfV/hZ/DMO/LZQvVT5V/ZOiaStVcyc0MeG+ClWMXtkawb5qZ49lr8h9ZZnPHHCEBVD9LDKmvNM+ljyEoofPN+57l4dh84wKilzchYzeh13pU2i+z+IBndKmRDEIsQAm9KW0sMieXasnk2zN/luWC2FL0fqkuLrzODyvR3puxDX76TTyQZeIKkeNj8OMAzVR2t89jMfKWegRaEWIg9iOj6KK4Pc+UCzPEyank7a03Vxlr8HNE2UudjNMFXwghUXiEAIOf8blwWbU5crZFo7HwBQQ95dh5IYiPgA04ul3B+FKojaJfwoGw8IlPQIh25C4gmmDbWRe9Ljm9L47u8o8U3idnGCJme/SjZknL+A8z9aOFEbxX3y4ZQQXhWeQcd16KsUDR7I0fWIWzIlB3BFP4YZ6Ep+xrJYGSekLG7zLmf3P8ynm0IVfv/EJJTk06IWIf/0TE1sXRM1AFGb3ysDdZarF84k+/nnQCAhB69VJkrL54HAOi9/CqqRj61fnUclZG2jM3GK9l4OY4zRbeR4KMgaYFkSwvIV0zn9cp9JP3P915VAiNTc3bTdg2ZFN6hg4PH95hwOHWzcxXyTIljU2S2tyn00z156bT/qV1fvP8cAIA/jM4bw/WMWMqSP2JzI+OyxOKa2iPjH9a1Yyl9VCVR/1WdQgh43Ac/0bO94dRTNxoQhYWFWLFiBXJzcxEIOJ8nh5I+yMrKwtChQ4O2Dx06FFlZdazyHCIa/UJVUFCghLDi4+MVaW348OG48sorm1QJN9xwww033HCjueKPv2T34YcfYuLEiSgrK0NcXJyDUG4YxiFfqLp06YL58+fjH//4h2P7vHnzmpzp1+gXqoyMDOzatQsdO3ZEr169MH/+fBx33HH48MMPkZiY2KRKuOGGG2644YYbzRBHyZLfTTfdhClTpmD69OmIjo4+9AFa3H///bjggguwdOlSDBs2DIZhYNmyZfjyyy8xf/78JtWp0S9Ul156KdasWYNRo0bhjjvuwPjx4/HUU0+htrYWjz/+eJMq8bsOM1Bnmn29ZHX+TRez1G1sfJzuX9ualy4usC3r1fAShRzLy18esUZJoqUSTyotd5nbCWJXopIMZQvxHbCWM2QZMzyHYG5vmZZ+LiKMsvzByx5mL1oSNDbuVLsGWcjIkiMTyCH2LALx62RpWRKwLbV45VhZMpNlGd2KRJYEhcQexktCQv61ncuzr9JZP82Sx5MQ7zhWxATlUy1Z2QQeK085BgDgq6B+lZT2iDyqr5C+faV0zpKOVFbiZmr32jiqb21ksMhcZDYtx3l4OUaWhaXfCwZRfbo/zyKtnJqvlmJs117TKpHL4iXJMl3ygInkYsPCS1jqnCIMWmVdu0g9BAoK6bssxykBTR7r0mc8wwx0pL4t6k7LNkmrbMKzfD6x5jE12xolxijJCkIG509FareFP4qX+GSJUq5RlpXV0o/f8bsIVSpRyfgYq1DuE0kikaQDU5aupD3l3pA2EfcqWUK1SUqYcdx/LOMgd9WB/nTeimSWIxlFYycmnPoy8TO6F2Vcp32216rng3xJMXT+Vdc8AwDo9iqtNqR+x2OddxdSukokkFizWf0py22FJ9OSc8KnJE0iRP3kTVT/qm6cQML9Hv5dITWF2F3Zl3A0uQlFhdASR9Q9ymOqIpXG3Mt3kVp35mm2MkfR3+0+c8rLGNrzSgm8cvt5+RwBTlKwyzwMX3selTGVlouX9XsXADDuwyFHAWb0y0ZmZiauvfbaJr1MAcD555+P77//HjNnzsT7778P0zTRq1cvrFixAgMGBIu1NiQa/UJlJ2qNGTMGmzZtwsqVK9G5c2ccc8wxTaqEG2644YYbbrjRTHEUIFRjx47FypUrkZGR0eQyBg0ahNdff73Z6nTYXn4dOnRAhw4dDr2jG85oAFEdALLOpcHS6g2SGAiESHUXZEfSexVhm1OLjQ5tHZ81aTTDCt9LhFOPjUyr0BUWzqxJsc26YZGq953FZfJlRBbw/mwUmhwZvAbt/Z5kEARBE7QhICiSJvioUt1FWM8uACnr5SLUqBPX5VhBRQT9kNlvKNd0QTtSEmnfrFxHvcwanp37Q/ediI3mD7fSqKU92rznJMHXdCCybGQ29ZUY33oEBGFkKqyAkCw7DbqqJSMVnEAg6ebeFiSYGPsGfT8W9Jkfl07n2smm1HydgkoBQFiupIJrFjncN0JGV4gPi24GWHAR0q42EU7dFkQFd7MRpvUB91VxF2qL+G08ju3m4kwg9xbQb362VfGUOs2dzUg2TxbUiesi6Fx1W4usHpbP5xG7F5EskPEg8gg8xpQptrSNEM4PWJIS6i5VYCCPGUGAIW3A6JegsyohQyyLbMiMJABwfcraUbtGMP8+9XxSob6p46cAgFnHkv2I2CwZ2j0DAOOPPR0A0CWCEdwp9FHTiq4t9yE2NF5N0hsZrxEZXURGxT7K072zKjPvWEIPk/9LoqEBQd25HmE8XiW5Q9Au2U+XjrH/LfeeR9BqaRMlU2E6PhNWUvJM1lN0z8R/laKKLO5J/SxGz1E5bN7dK52KXrWJyhaiO48h1Z5ST9uzPP7CfEcZeJs+PtnxHYpLAkjqhiMfpoGmKJ0HlfEbj/Hjx+OWW27Bhg0b0LdvX4RpAsFnnXVWvccvWLAAXq8XY8eOdWz/9NNPEQgEMG7cuEbXqUkvVCtWrMDixYtDMuuPymU/N9xwww033PgNBFnPHH4Zv/X461//CgB44IEHgn4zDOOQmpi33357SKkn0zRx++23/zIvVNOnT8ddd92F7t27Iy0tLYhZf9SF4bHJJtTRgfZ06jpkEfTtUmbqC6voZ0GfKmx8J5/M0jhlXDPtVafcwdwpnlF5d5MgqNTWjgwJGiQzT1+mJalg/71lDNnGVMfT96yhTnSpPM1a1079kWZ+MSyPYJYJn4R5T7FOFEylp8uslFGQWk4Xp3rxtFxm8IxMiM1KQOQGYqh+kastThcAmGLQG2elPMt5zL00q1X9qlvLxHB9uc+q+5I4YsBL/ZB/jPU46vof4rP429DMWLhJlak02/VVUB/6w+nYqHzmtbHooOkTM2rr/FHruf8EgeQ+2TUpnXb4mOvJ1eiwJ4vL4uuJ4jT6Umu8mNFivxKax6aQHgnhmQn/SeMyAbCsY2RMae0o41VQmv3jSbaixXoWs2VLHTM8+DFllLGEBXO+ZKzUJtLY8ZZWOa5VuD+BeJYKqbUaVPHA5P7RZR4YsVRWPYzGqb0KtbYALKNtSe+XY+UZIb+XaRZDjNIJYhVob7PBYY5PbSL1VVkrLoNP+2nPjwAAp51J0jUec5+jbEF3/DmavAoAb2tCVQc8RPIJ/77hJQDA+Ghqm56raHvmGbTfwhseBQD86cabqGwbitjif4TsQOe2aCbU/vyDIbcHtDYBAK88I8T6isefuo+1Z5+gtSajSikxVOa+eAuhOqYHIXrlrzqFOX0bdtGxdZilK2RKIoSgr3cNCXpelUkWNLPbfhe0jxuHFzqY09jYunUrevXqFbS9R48e2LZtW5PKbPQL1RNPPIGXXnoJkydPbtIJ3XDDDTfccMONIxRHSZbf4UZCQgJ27NiB9PR0x/Zt27YhJiYm9EGHiEa/UHk8HgwbNqxJJ/sjhjc+BiiXzDxtVs+okmkT5xObGOE56bYlBhuIBvItPgZgQ//sfCeZ7fJs2yNmvXVwfGR/HTGwI2v6PgoBktkAX1PYSjI+jWhBfIm4tXw8oyBFA6yZtcykY3iG50lr6ayXZjmhsqtk5trauT9gZXl5DzIKwjPWmnhqg8gthKyFCfeH+RrlxxPXI3oL8YlyTrRmp62+YDNhRqgUQYxD6l2VQZ+VyYSsiKXHwT6EfiRuso7J70MIWMpPhGpUp9H32A10/ooMuo7Igzzz5muOyCdkQGUe7bDQBhllHg0B6Pg8ZVod+yVd+zfXHU/HStYat4WnpAJBIWNUsh+F0yO8BE3MUolh5rKQqwin2kxqFe9KbIAkL03GMp9j5xTiCbZezmgDC376uS+95VaZynaHLVu8jPiJAKqngse08F7EBFm4S9ye3mwrc1AEG/WsTvW7XKNk+0mGns75s913yoJJzwDVTViZc6bQUSmDrWkqWll1it5FY0iQ0Ohc2rewmxMd9OZS1qy/zNnPoZ5Hyuyc0eI2H9M4u+2k8+mT93t78r8AAGd9TUhVax+N49RrSWx0wyKLM5m4wrJJAgB/Vo6zHro9ldaOqGJk2ob8BDRhT7kWj86FFE6a8OD4ufVYBpGZ/rzUSqra/BU9C2r+TtfeZTLzVFU9RbS2jv8mdWN72PisjIx98QXZaXXDAAQqKwHcGbqs5o4/KIfqySefxOWXX47IyEg8+eST9e577bXX1vv7WWedheuvvx7vvfceOnemsbBt2zbcdNNNh+Rf1RVNyvJ7+umnMWvWrCad0A033HDDDTfcODJhmEFz+yaV8VuMmTNnYuLEiYiMjMTMmTPr3M8wjEO+UD322GM47bTT0KNHD7RrR3SDffv2YcSIEfjnP//ZpPoZpu4ueYgIBAIYP348tmzZgl69egUx6999990mVeT3FsXFxUhISMCJ0RfCG9BMXbU1dskKAYKRn4DSXuL1eW22JrYsgSLW17FxVRTCFGINH7BptGimvqKbpThYoY6VWZnMpOvgh3lapzn2U9YzHduqfbJHE3rhK3fWs+W7lPWndHxEw6iOcxmcWQRAcXhkJio8HOEqySxdQvF1JFMngSFdexaVdiuY/FtNEtUvaygdm/ojGwWXMfJS6zzuwDEWqhB5kA1VK2mfmF2s65XtNBYv70c3tCBTnmJGLJjD5pgl6zN6RhwyvqSxtOVasrvwbWMumI4uCdpoL0f4TWKB4tPOwSFm02omLgirWNfYjjMqNC0rGbs8Vg6MJFujFis4WzFOywbk8Su6SwAQYM0oQfI8laE14Iwqpzk2BAGK5b7JzQ8+SNpD7tFaJ3dGcWV0xEIy82x2KDJWA4lsOcSm3Kp9ikodZeuZkIFkzoCLCL5HjQq65v0n0X31+LXPAgBOiqL75phHCUVq+xpBpYpvJPzLEJxOeWZZti+MkDKfsWBkewDA8n/OcdSlxwvB7hiJW6idvnuU9j2t43H0g5gOi03QIf7rsXNy69pX6q2j7uqZ0oYybz/55L9Bxx57J9W9xQ+0GmBu3hGynio0tF7Vyfb8NULo2wFA6Zn9UVtTiR/evxtFRUWIj4/HkYgJEybgC7Mc8aeOOqxyqvfuR+W/X1VOKH/UME0Tn3/+OdasWYOoqCj069cPI0eObHJ5jUaorrnmGixatAhjxoxBSkrK0UlEd8MNN9xww43fargcqgaFYRg49dRTceqppzZLecGpOYeIuXPn4p133sEnn3yCV155BS+//LLjX1NjxowZMAwD119/vWP7xo0bcdZZZyEhIQFxcXEYMmQI9uzZU29Zs2bNQvfu3REVFYX27dvjhhtuQKWm7Dt79mx06tQJkZGRGDRoEL7++usm190NN9xwww03fhMhOlSH88/VdW9SNBqhSk5OVgSu5ooffvgBzz33HPr16+fYvn37dgwfPhxTp07F/fffj4SEBGzcuBGRulCgLd544w3cfvvteOmllzB06FBs2bJFZSTKmuu8efNw/fXXY/bs2Rg2bBieffZZjBs3Dhs2bGi0SKlZXWPBuwwVBzmh24nk8pu2DKesaIScLlIITNBWxHGbvIK+1CfHCAxel4yCLPUpgqddNkGWM2SbDm9LiB1DDlksGEyu9aQQ6d7MsZZUWs+jvwMdiKju4SWLmj5ksh2WzctzsnwXoYl0yhKBLZ1ayR2IGKOX6unNKQx5zRX9qV9lqSi8uA67IACmj/apTKF2rEihvondR20Qta8k5HG1SVTvuP0W6Tcyl+qXO5iWGON/ZAFMXoYLxNH2yExeAtpNAqCmLMtJv9uXkaXfeZkj/5zuAADjLJKG8Jby0oUsP9RocgDSxx7bsl6AxwJ/1ZeeoaXzq6VrZcdS5TgegEW41kQq/Um07BYQ/UYR4RQyeizLOvBSnz/GunYpK2yvRSqna9T6k9tV9g+0o0QCNT4SrCUXRcBXEgdMRhaRUt0eSiZn3J6m30lEtocnv9h5LC81G7Lkp8kpFJ9AEhzhhTSGvJU2AjlLaRjhdMya22YDADotvAwA8OcBKwAAbT/OdtRPLfGKXYtD4sK5jFXLBHJvClv7cLsmf0dljvmZLFxu7kTiof+6iCbRW6us5I6XXzjdcc3eVFp2E7kG9STRnoF1ktQBeOoghstyZpDQryytcsLO6adcAAAo7WrRBk6/fykAINZL/fnZ32mpx/f9Rsc5xHJGno2K2iGfUVa/Bz37OaKyq1BbG/o3N/440WiE6r777sO9996L8vJgrZCmRGlpKSZOnIjnn38eSUlJjt/uvPNOnH766Xj00UcxYMAAZGRkYPz48UhNTa2jNGD58uUYNmwYLrroIqSnp+PUU0/Fn//8Z6xcuVLt8/jjj2Pq1Km47LLL0LNnT8yaNQvt27fHM8880yzX5IYbbrjhhhu/WpjN8M+NRkejEaonn3wS27dvR1paGtLT04NI6T/++GOjyvv73/+O8ePH4+STT8a0adPU9kAggI8//hi33norxo4di9WrV6NTp0644447cM4559RZ3vDhw/H6669jxYoVOO6447Bjxw4sWLAAf/nLXwAA1dXVWLVqFW6//XbHcaeeeiq+/fbbOsutqqpClW32UVxMM8+CCwfCG06IWcrrJMJp8ORIR5voS5j86Chf7SvpwBmEqAR27AkuQwtlw6DN7AyRUajWZu+CZElqbIiyFclThPOkbG1GrUjJgrCVhUjJb5FIpyliiQNGNcJlX5lJxzvTrZXNicw2bSiEIve2cL6EB2KoPrVxdO01sSzzUELHijlxICp46IetJwHU6n7pAIB9p1C9PEwob/8FHVvRnuoZvZlQkvLuNAMPZ4FHQaUAwMOoS1gJlZF7IpHPW77HNjxMxFdihrpshWp329yHU+vFnNdTw4KIxYScKRIy765m/nKviuCmza5HF1c1YzWUUJ2TSd1C8tZFT21lKmSK0c2dF9JEqLKd9CMdm7SRJQ0E6K3hPmIkxrvTEpc1NLK8Qqb8TkRPJz57igkRUv9PJFoIlSHoK6NaylJGu2/E7FmVzSRvs0qThYAN9RJ0i+VQDL42uSeMg/QcyT8p3XGu6H1i+WNdb0k7Kv+7x5zE8J2nveD4Pr6aUC6xuxHJAUuA2IZyc/3EVkVkXCwUTqydaLvvfkKu/l1yLgBg843U313bWWKhJV2ozLEbzwAAZF0oqD9dY8x+KjPh7Tr+r5D+sJPSa5zjTBHE6xDfFYspQyWjUP3jfrKeT298MQIAkNSdiNejZ/7kKGPDeUTER64TDVWWOVXBKwBKEoLvX0GsAuFeBDyhEz2aPY4CHara2lo89NBDmDJlCtq3b/9rV0dFo1+o6nuZaWy8+eab+PHHH/HDDz8E/Zabm4vS0lI8/PDDmDZtGh555BEsXLgQ5513HhYtWoRRo0JnMVx44YU4cOAAhg8fDtM0UVtbiyuvvFK9QOXl5cHv9yMtLc1xXFpaGrKzs0MVCYA4Xvfff/9hXK0bbrjhhhtuHOE4Cl6ofD4fHnvsMQWUNCUEFNHDMAxEREQgXFfEb0i9GnvAvffe2+iThIq9e/fiuuuuw2effRaSEyWy8meffTZuuIHE2Pr3749vv/0Wc+bMqfOFavHixXjooYcwe/ZsHH/88di2bRuuu+46tG7dGnfffbfaT89ONE2z3ozFO+64AzfeeKP6XlxcjPbt26PFJ9tQdnJvAIAnnnk9MktmtdWATbhSZBJ8bclsNFBI/CGzlGfQkpa+L9tZT0GMbFwmg6f0anYmnzo/S/bX7EHMEFwFJfQnBqAy4xK0SxArEdSrq80CNt6JiCjKbJ3FQINsN2q4LOHrxDNiIDydcIv/kHWK0y4icQehCQHmP0UcpNljWClzUSqcaIPiUq3dZaszW2JE0DUf04sQqx0fEGewoAe1X6vlIjxK3yOz6Tqyh1H94vYGyz5IanZtIiMAgiIJB0xLwZZx4GFkA7YZePYFJItQ2YKuIf0ZVhIVDo8qy8l/M3S5BLZUAaznZ21LQm685U6ExLM7h+ulzcoFWWXkyoy27mUR+dzxF5LQiORhEJlPiFRFKh0bll8IAPAnRDnK9G4JTj4JVDJiIuNO0FkRFhWLHA2pkjZR6Eyh7WEqqEwJ9avITBiaAa9qN1OMmBlNDpUCr/GuDEbIlPAkI4B+5nYlbmKLIpaFENudyrYW6lWV2DCGRiCPEBdBpiQUZ9KGSAsyJXwxk4/R7V8Ce4jb593HUhwZhILNGvo/AMA/Xpis9m29g86TvY2QqciD1PZlrem8na4mAdrCjyMd9VT3gLRdKDkYTeZBPYcinP/5KaFUvg7F/evQRu0Tv43q499GUiv/60CfyetZ9uFbQgLHdSUha488t6Qv5Vy2e1PqI1I5MjZMnwHTJXo3a5x88slYvHhxk11bEhMT6/0/v127dpg8eTLuvfdeeDwNu/eaZI7cHLFq1Srk5uZi0KBBapvf78fSpUvx73//G2VlZfD5fEFeOz179sSyZcvqLPfuu+/GJZdcgssuI6Jm3759UVZWhssvvxx33nknWrRoAa/XG4RG5ebmBqFW9oiIiECErsrrhhtuuOGGG7+1+IMqpdtj3LhxuOOOO7B+/XoMGjQoyC7mUGrnr7zyCu68805MnjwZxx13HEzTxA8//IBXX30Vd911Fw4cOIB//vOfiIiIwD/+8Y8G1alBL1TJycnYsmULWrRogaSkpHrf6hoqBHbSSSdh3bp1jm2XXnopevTogdtuuw0RERE49thjsXnzZsc+W7ZsQceOHesst7y8POht0uv1wjRNmKaJ8PBwDBo0CJ9//jnOPfdctc/nn3+Os88+u0F1t8fuy7qi83OcFaJlUfm1tXcA8DLi4M9iBKoHmQx7mCsliI+HOS0FY9IBAEmrqCxzhzVrD8rSE16WzOIE1eJZuXwqS4V6+lHN5AXEMp3ohoWKSbU1PpZ9dqkydPjYYi1LTlAY4a4wMrD/ZJq9t/rWKdIJAOGlVH4sc00qUumYsFKnCKNXE34U65Gw71n00J7hyEhpdTxtO/A0ZSGunUXZVMp0dt8BR5m1bQmZKupRy5/Wb13m0fkFmfKtJsseJehaR3glC41RnqqBGeq3Pn8hi4wDJzOayNuVqKGyxKB+9ggfS8uUMmx8t0AqcdGET2TsZiRC+lG4cjKp0NEPRhEN23XlnEx8sdooKqOUqQ4GAxBtlzIayyiYd9NuvniNr2fjzzgMiG3fTRa+1TENxXeSsS8G0YIm28o3JDOQsw6R5exnhYJxWxhBCJCNF6mZnqvzRzB/kZG0sp5UZsweQseE81cbTfXL62chLwG+9N5PkXDnvZe+AQCYEEv3x0U7x9C5ahldFq4k95mynglhTSXIFDQUS88kVs8cHjvjoqndHyiw9hMkrc1XhMoW9WAbnXZ03qwHCPEtvpDu2ZYvr+ILrGedSX/+CJIvfachbSpDUzL0OCvYH2m1Z+p3hFKWd6B+b7WY6isCs1tqGInme8KfmsgH0qexgbJp7aK7yvBds6WJXLMHtYHQWdfNHn9gpXR7XHklCbM+/vjjQb8ZhgF/HQLREq+++ir+9a9/YcKECWrbWWedhb59++LZZ5/Fl19+iQ4dOuChhx5q3heqmTNnIi4uTv3dHGKecXFx6NOnj2NbTEwMUlJS1PZbbrkFF1xwAUaOHIkxY8Zg4cKF+PDDD7F48WJ1zKRJk9C2bVvMmDEDAHDmmWfi8ccfx4ABA9SS3913342zzjoLXh7kN954Iy655BIMHjwYJ5xwAp577jns2bMHV1xxxWFflxtuuOGGG278qvE7eCE63BBaUFNj+fLlmDNnTtD2AQMGYPny5QAoye1Qupf2aNALlZ341dT1yqbEueeeizlz5mDGjBm49tpr0b17d7zzzjsYPny42mfPnj0OROquu+6CYRi46667kJmZiZYtW+LMM8/EQw89pPa54IILkJ+fjwceeABZWVno06cPFixYUC/y5YYbbrjhhhtu/PaisrKyXn3KUNGuXTu8+OKLePjhhx3bX3zxRZU5mJ+fHyTnVF802stvwYIF8Hq9GDt2rGP7Z599Br/fj3HjxjWmuN9tiJffya0uh1HgJCl6mbxYy6KXHruLPXvcCUHY3EXu7h5dMkCClzQ+XvExAOD0HpbPkBAfde9ABYMLuValS9fhkWcnp4s4qbacofbRZgUGI5dCYtf9rkLVTwknShq/iFyy71l1C2qvmjgnaV4I5wBQ3pLKiN8tfmv0IV546ty8vOEp4dTxMkl1l7R22/VoUhZoS8T3BZ/PAwCMP57SwFVKfBIty9W2iHUctu9Eay2/4/u0BG5k01jwFxSGPpfUl9vZw+0qy0y939yh9lHp3ByB7FzHd0VC57GjpC3kHEKcTbHI1J6DnBRRQks40v+e6GjHsYowrHk9qqW35ATosftskpVIGZ1FRbxA8gkJa3gZmwnP0CRYVH1DIeIypjXxWo8IvqrfeclXlqiFaK5LiQCWh5+ESBuU8piJc7aFEufUiO9UEZYRkXtUf9gziXrPeUSSLkundu30Hi9lcZGFXa17574bXwUA3DKfJrg1benad5z6IgBg/KDTAAD+Alq60n1D9eVSwHYfaAkqipRexziVcfHJ1m+Cfrtw54kAgOLLiORdk0L3Q0k6y8t8S3SHTVfT/dX5bWpf75ptdG6ReXDIzXB7as8wXxrRAhT5XGQg+Bks3qJBsgsAvG2IM7tjMt1PnZ4k6oa/Ky1Vl6Y7OTmJ3/M4lcQRXRAWIYj1Uv3wMNSa1fiy8LUj7+VXU4GEE0cfVjnVmftR8dzLv2kvP7/fj+nTp2POnDnIycnBli1bkJGRgbvvvhvp6emYOnVqvcd/8MEH+NOf/oQePXrg2GOPhWEY+OGHH7Bp0ya8/fbbOOOMM/DMM89g69atIZcVQ0WjhT1vv/32kGuTgUAgSNvJDTfccMMNN9z45cIwD//f72HJ8KGHHsIrr7yCRx991CFx0LdvX7zwwgv1HElx1llnYfPmzRg3bhwKCgqQl5eHcePGYdOmTTjjDJo8X3nllQ1+mQKakOW3devWoMw7AOjRowe2bdvW2OJ+9+HPy4fXdDajIFMyuwuU2hCsDURKFiTC276N41iTZ5eK/Muz4leKaVZfdWzXoDr4Sli8bjUR+NUsroqJsGwj4Yl2ijWKbYwj7Zdn8B6RSZB0cx0JqGOGZQlShnhXFzQolWauJs/sS7onAgDCi6ge5alOpCJhK7Xf3lOtc0blcL28VEbU/jLHMd48IpwGpTgLasOIgQgAOvZhYql+BSJxoQRGGc3xBUgCY9eFrblO1jE6MmXNujXUUGxBRBiTt0/96POg+q1NJY6h5+cdju2HRKb4mhXpessu9ZtfjtVsNaQeqmzdjkWQDSEDV1rjJHcUtUensTsBABtX05J69y94nFY4RWBDSZkAcJLpNTRIyMiCRIm4qSC+In5q6iKyNskIo4olLLg/1TUd4Nm5tGcBS5xw/5stQks1OK5J2lzusQSqV1V7On/8bjpm3Y3PAgBGf/xXx/Hlra2/z4ohFOauAmoDXxk/I9jX1WRSvYc/A7sJ/Vb3pNaX9t9U/UTIU5HSNXsYIbozQnV6X0KjNj7URe3TpSshkX96l9CrmfPOAQB0eoIQIDl76kpCqLw/hU7UsBPijTA+r8f5rFWSNJKAkZRIx4qwp6D0IgtRbo052acy3XleXy71cyJ/qrEjSQtF/IxmhMsosVliyR/cnmqMGx40Ab9wo56YO3cunnvuOZx00kkO/nO/fv2wadOmBpWRnp4etOR3ONHoF6qEhATs2LED6enpju3btm0LSlt0ww033HDDDTd+yRCD48OI34FsQmZmJrp06RK0PRAIoEb39qwjCgsLsWLFCuTm5gaR3CdNmtToOjX6heqss87C9ddfj/fee0+ZJG/btg033XTTIXUf/qjhS6c1eJEDUGKdoShLkkbNpp21OzlVXEuzlu8enlH9Zwpx07yw0CRfIf22/yTiqLSMIoHRsDWEXASELyQGy6VOQUoVtoGkhCa11GqRcVCzdRGRlBltRCJ9Dw/Bg5GZMYs+lmXQvkWdqKyYbPq9Mpm+RxTT96h9hC4FWOwweZPVoJVTmCfyDHF2KlvRjDnmJ+I5CFIhEhG6ZIRYaxi2+iq7Cr7GPWekOK9DE8YUVMTDqEz7L6i+yl4ENs6UhPQzz/Q9GiIl5959RU8AwD0vExr16NSXVBGe9dvp/BovxKPZxygx1pJSR33NIkb37EiFblskYpfCNZJrlzR0XUiRUaSiflab5Y+msVS0PB0A0OMJGpd+zQdUSRvoZt7CqQrBf1GIimaFFCTrUaZxgGQ7i4kCsNC1ROZ/yT3AaJKy8BGrESlDxEFDcb90rqEYlvP3yG3Ee8u6hJ4dYy4l3TyD95C09RbH5QQVfe6kJQCAD+cQnzLj7b8BAHYsJpRr/LFkTqxsZLj9Qhn3Cgqkxo48f6rlPqlybJdnmj8vj48jzlriOuu/km0GIU+ZbAtV0436e9ZPH1H95t0MAHh/Ai2l3PzuGEedHJxTDoWMBsRmiRFo4Uhy+9pFlGkDX0+YWCfZ7hHm+/V4gp+THRkOLGcRUBl33G4iaiu8URwMRreLhhIKG7+MUFmFOJsmjMNLSmt4HAVK6QDQu3dvfP3110HJZG+99RYGDBhwyOM//PBDTJw4EWVlZYiLi3Mg5IZh/DIvVI899hhOO+009OjRA+3aEXlv3759GDFiBP75z382ugJuuOGGG2644UYzxlHwQnXvvffikksuQWZmJgKBAN59911s3rwZc+fOxUcffXTI42+66SZMmTIF06dPR7SefNPEaHSWH0Az288//xxr1qxBVFQU+vXrh5EjRx76wD9QSJbfGN/5ikPl7U7ii2Kh4VnLWSshRBy9LCyoUCSZYWtZNXqGnj1jTtAMTyohVLVpNOMS8Up1riy2omjBM3DhzoTIntINYSVjUaEHYjUiCIU2O5f97BlF1Z2Y/5VCx+T3ot/idzuvNeIgXWPMeuJgCNenqj3NdE1fcH0rbiCkKvFczsDRbEIU+qGZpyokQ+PxANZsfcGGJUG/AcApf74UABC2mpAiQeXMVpxxtHVn0DFWu4idifNaPLoKP/PMKp6hNqp6wSLTJHy0jstktEhDBXVekbIcEnQsFIoo7SMGrjIOdTujVCdqV92GeG3FHTlz0FZ0/E665siVzuwtVU9Bl3R7GGmrEF5aCjnTzIcVAimcmbA65orCVbOjIHVkZqkxUkbIbqArWal4WdjVjKUyFIfKFsLVMURAlMdlcX9Cb2L2aShdNbVz9nAa6y1/pHN+9s6rQWUPX3ue4/sj3d4GAEz6hrKaul/DCHWpk1cYKlNOmbEL304QILGDOYQ4oleQrbQWalu3N526Pa3DCbG/LYW4Uqee/xfHtYm1C7T/ikJl5nmYE2cwMqbbVylrLBkHjBAJYo0Q/DGJ2j4k5OvLY6S5osp5jGbppM4ZIqPZ34qFctnyqjYlCrW1lVi67MEjnuX3ZVUlEkaPPqxyqjL3o+Kll37TWX4A8Omnn2L69OlYtWoVAoEABg4ciHvuuQennnrqIY+NiYnBunXrkJGRcch9GxpNsp4xDAOnnnoqRo4ciYiIiGYR+nTDDTfccMMNNw4zmkMpvXlqcsRj7NixQRJOjTl25cqVv+4LVSAQwEMPPdRk7Yc/XHi9MAxqxsA24kN5u/CaLusYeQ9a1ikGz+iq29NM32Aula+IZrSB7bsdxQta4pcy7PYMPHv07yddl/IBTsPg0jY0w279Os8YmSPkaclZdiEy3IL0sMTSQbKBhP+i6VIFWibS8YXMe0qwuAoVraiM/aNVxfmTyojbS2VEZTotaSRjrDoxeJjGf7sLAJBbQTPjRH0H0fnSvuvIlH0GrvhMfK2n9zsJALBg7ZeOor3FbJEj6E0n0hYTrhcGdLd2/p7tlQQZEbTI40QLhN/ibU2ZQ/5Ebr9/UZ0Slq61VcBpa6HqoVBOvmqZ6CgjYY9jP8cMPUqy4bh/ZYYvPKwEp9aWP5a25/ckZCN+L9WhoKdVZuS7Ox311VEQQaIUX09Qu/p000RvSMtoVOiWbr+jPp3IpZ1rE6TjpsrgY/gcCpkSVJM/KwYTMTbq50yrEDm2VviDbI1UQmVXJ9A5w0oZRdQmpZk3B6MznedTJtP2CaTufMxjZEEzrB+dK3od9eGB12kMtZhAWX7qXlB8IxuMKG1dhxWSbl+jUEZBMGWcVFjHbz2bzl/Tge7NWW87UTaTx2Gvby8GAHSo5cxnZV9VdzacIPq69ZE6lhFzpaMmXFRG8c1SJ6JF10Z94du8l77z8o96PurZshoKHmCDawAIhNFvvnw22ub6hheWwBOo326q2eIo4VDdeeedGD16NIYNG9akJbvx48fjlltuwYYNG9C3b1+EaastTeGEN/qFatq0aXj11Vfx6KOP4q9/tVJ8+/bti5kzZx59L1RuuOGGG2648VuKo+CFatWqVXjqqadQVVWFgQMHYvTo0Rg1ahSGDx+O2NjYQx4v7y8PPPBA0G8N8QIMFY0WxhDth4kTJypvPKBx2g9uuOGGG2644YYbTY2FCxfi4MGDWLx4Mc4++2ysXr0aF1xwAZKTkzFkyJBDHh8IBOr815SXKaAJCFVzaD/8ucqg1wAA5ZBJREFUkcKsqbWkBCQlfydBxypN2bDeWw2WVPDu20/feanFSGG/ICF9M1lUliO8CURitBNNdWmDuCUEnYtzfLzYMrAAHfYS2TuQ5yQaeiRdHLblDpFFqNUsFARaFeE6tl8xKmj5pjaVyipvZ0GwOcfS9Xu56vHM5S5Jp2sNL2FC8X5eUuHlm5petHQaeYDLjrGG68Z/pAMAOj4XcByjFk5k2UstjzntN3SCtD2EUOxJDu3hVNyNlkUTNvOGrbRMa3QnUivWbVb7mrLUJ/0p7SrbZcdWnFjACQ3e7dRX0Xn0vTbEkowSCZWyQpC4AVjCmBr5OlBlyRQoYrtGllfjQVLEeb/qDtTPbd6lziw9lvoq7QerXav7pQMAInZQv4oNiOoThtg9Uc7HkCzBKPuTEM8Vda0i26GJV4otk1ikSLuHEnJVy3O6HY0sc0s7Sb0lgYDvVVnqM+Nts2IWAfWnOsdQ7mA6tvVyXi5Mo+Wkg93oXH5eXUq/i855Ov6kju1+gO7vbmlE6v733593lF3WkUVCB86nDayzPP6EM6m6LOHhWN7TniGGNlYML8s9aG2jhH9FKNP2u8H2P0JnkBgzhRCBKE7WSecFjoAsAcN5DzuSLerKndKW8pUwLVMllAxMbh5X3PYsFrFP7l8lwipJEcmJjlMp+RGhXfC48eYWWmWyfZYZGeb49OSHGHdHKJTa+WGW8XsIr9eLE044AcnJyUhKSkJcXBzef/99bN++/VepT6NfqA5X+8ENN9xwww033DhS0RzCns1TkyMZzzzzDJYsWYIlS5bA7/djxIgRGDVqFO6++27069cv5DFPPvkkLr/8ckRGRuLJJ5+st/xrr7220XVq9AvV4Wo//CFDLGaqtBmuIAchTD7VoTyD8mflOMqSlGH/AZpZCSnUCGHposTjNBKyIFEenmnpJF8lcmdLMRfTZkUcjnSiHkX9CfWKymOrD7Z+CURQvcpbUllhNkHF1JX0d0007bNixjMAgOHXkCBh3DoSOdTTvMO3sXwCWz5U9LVSs2N30Yw0ajURn8XGpq4Zq4QicIcwfQ2I5QyjGrufTAzaBwBunzaX/phGH3OOO56KXE8IggORDJN6cTo6k1Rl9r15NhHYe1xDs3bPPieh3H+QBUw99TwgZczowpjys6AHQjCWNrEt2cs4tJBJRqYYRRK7FTAKE7mPEghqeeYf+TGPU1uZUtaGx0iktNcjPEa6k4xG1GpnAoYQjUV01cOorGlD0gLtUh2HeA8UOuprtxYBLORSyOwqucJ+L8ixOswvwq2aYKqkyYutjaA8hq2efq2eVWk0puL2yL1A9ShLo/aqTqB63v+nN+kA4mtj7pD+9hMDALpMo/a5eSxBPGtvng0AyHib++hc52VsmUFJKF2mEFJof4boKHeQJEC1E53zxjjNp2WcOtqd//YWOgngi15yImrjTr8IAODZzfe59IOcM5SBtYR2Pxi6RZJsP4QxPABlcg6Wv1Cm7ZLoIIgej53aLmQXZtZzT/qKqazAui20r9cLv3n0reAcyfj73/+Oli1b4qabbsIVV1zRICmKmTNnYuLEiYiMjMTMmTPr3M8wjF/mherMM8/EvHnzMH36dBiGgXvuuQcDBw7Ehx9+iFNOOaXRFXDDDTfccMMNN5opjpIsv3fffRdLly7Fm2++iXvuuQfHHHMMRo8ejdGjR2PEiBEhiek7d+4M+XdzRZOEPd2whD1He85DWLhmGFuHjUyoEHPkAEsfqHT6Gm3WHKIsmX0FoVY8wwrazpwqSeVVM7AoS1TSqAOZKulOXJDKRCozvJRn5ZI9XeRM8/aV2SxythFfrKYHKet/Pu9lAJZFhhLd41BoifCOGEkrPzZd7ROzLstxTICRPMX5UYgez1DrEOUL2GxQRDZBjt34GFkr7Tw9tHN55y9J4LPrpSy0Kfw3zcbFEWI5w+cY9Q1dx5JhrR31V/WrjztlbQBXIOQpVb3qOi5E/bwtWFqDeSOKbyLGwDp6INttiEcQIioikh3aOrZvuobGZY8H2exZawMjhBWJoFhIYv4fy4oodIHvI31shZSM0O1rNFFQHb0TRFjuG4Ov2QwhJlrVLsH5nSVAhFfYaoWzz/aNpe/pGYTaRv2p0PpRxrSIm+o8QP5ecCaZ13/32BzHz+NOvRAA8Mlnb1rbugylP7T7QiG8ck1h/Dxg+xV5PoVCgIS/pPfbx8veB2Ah0zUx1AYpH7NZtgjR+oLbUe5TJdRravdaHYg0NJ6hQ7xVfgtzPusMH8shMP+tLJ04k74KaqOKFozClwffb9Ef/eioj3281ZrV+Kpy/hEX9vyqrApJI0YfVjlVWZkofe23L+wpUVRUhK+//hpvv/02/vOf/8AwDFSFsFqSqKmpQffu3fHRRx+hV69ezVaPJgl7uuGGG2644YYbv8E4ShAqACgoKMCSJUuwePFiLF68GOvXr0dKSgpGjRpV73FhYWGoqqpqdlHyBr1QJSUlNfjEv5c32uYKT7iNfyQz1zpE8gBrJicGoH4WwPRWEyIQyKNMKEGXglAmW5aKhWIxWhTmzHRS5xKxTqkX7yfim4HI4GFQ1JnqF1bOiBVPuOL30Ay1MoWQgNg9Tt6TZxuLCdrREK6HcKIkPv5hAQDg9F40+FW7iWAlH1d1DFl+RG/Js649ggX8drOYoqlzPxhBCXM+GZRAIc94vWJ0ChuKxeft0ol4badspCypz3t+6Cir+7W7qCw4wzFb12bI3raERAViCWWY+18yP25ftcpZiMxspS1sgq6mjoSiHn5IqAiBTAmnSxAyMfiGJjzqEJYFbCbPdD2GzUJH2a/oPLZszuZkrkzPx5kvyOesHU5tUp4WnLWYsInFabN57Iv5tGRvall8gX6UkezNY6TKboos9dFtiTQDaEFBpGyvZLFp1ihmhO1ZwFmvvhL6FD5RUQZxqxKJWqMQ3/jdvH8RnTPKx3WwZ5oyRysgljdSv2IW0+XrSP54IwCg0yjiWE0+/hsAwJA3bOKwHJlX9gcAtHuJjlFiq3L/yD2hIZIqW1ZZvdgssURcM46eh3mDEh3HinGw4q5JNqegiqFQWU0Q19C4kirku2QISr01jhUAQEzMpR+7UqJVQW9CjyqT6Vyx+2m/yL1F/Em7l7PJe9SX61SRKqtX6iFom8dDbVl3crEbjYx+/fphw4YNSE5OxsiRI/HXv/4Vo0ePRp8+fRp0/DXXXINHHnkEL7zwAnwhUNGmRINKmTVrlvo7Pz8f06ZNw9ixY3HCCScAAJYvX45PP/0Ud999d7NUyg033HDDDTfcaHwYODpkEy6//PJGvUDp8f333+PLL7/EZ599hr59+yImxpl88u677za6zAa9UP3lL39Rf59//vl44IEHcPXVV6tt1157Lf7973/jiy++wA033NDoSvyuw+OBoaWoejSrAnv2lVnL2XE8K/PuYdRGk843GDlRSIFkaNn3ERRLy0JSv2tS+grdEu4UZ+gZNRbCsWUSnbfVt1wGIxLxPxFaE+DMpnBOSizrQrO56EzmZclMsNpm6cKzbLmWcd1HAAA6fEkzwy2zSb+p699YO0Taq29XOlcha8lEWuhH4GfWetJ0nvQwNc6M2IaE4mmo4Nlt2AXUR7r1jETJ6G4AgJiPVlOZoahTjJx52zEyxVpMB06ih0CHWT/RfsJFEYRA42MZHhs3KVC/Hq/Oa/GIWbLGabIbMsvM3duSLTqimaeTSdw+Ne7qyJZSej9lVraX0mWTDXItog0lKAdnCnoSaOyFr6Psv7CVgpZY90/exIEAgJgsGvPRu/n+YORJaSBJ/67jMcU2UIHOxN/SNZIAwMg8wBfDiK/oEnHbSNZhIP+g4zjZbjclN6OZ7ySbGKUrHOPMQoxcQ20Rk8OWJaW0X1EVnbvwQqedFAC0/4Su2VPIqJtwqjirV/q5522UOfo9SIcukEF8TXy4QZW19ibKEBz/BvmhqftDECHNtkYhwWInJLwum2aTjB2xoUrapCGNwtPUjI0l6jS2todmfaWQNOl3TU9P8eDsZXA7VQ6h+zi/N9Uzdh9tb72U+tkfQ9trk6ivfBt2AQAixQTd9ixRddc4aZ64WHgCPkCj9B2ROEqW/OzvIIpT14glvMTERJx//vnNWqdG41yffvopHnnkkaDtY8eOxe23394slXLDDTfccMMNN5oYR8ELFUDOLY899hi2bmXR227dcMstt+CSSy455LEvv/xys9en0dYzKSkpeO+994K2v//++0hJSWmWSrnhhhtuuOGGG27UFY8//jiuvPJKnH766Zg/fz7mzZuH0047DVdccUW9GlP2qK2txRdffIFnn30WJSUEH+7fvx+lpaVNqlOjEar7778fU6dOxeLFixWH6rvvvsPChQvxwguh08v/0BEIKKE/1BJcLi/3ikhsJ0xrYp+KRMufaolKIGshuovDvQ3SVETQ6hrnsboQoVgpKNsTKqMylSDswPUW2dvYQMsuEYV0/qif9tAPDGt75FzxdI64VSSJIGnzAamDjaQaKHAukQg8v28Cpctv+/YV2s6reONOu5Cvi9sgi5bJAgdt5eiiqWqJLPQcQUi2shyrltZCqIZIOnrum870/u4vXUm/813z38efoD8ep487e9BSpn1ZLHA8r++voGUWsflJfZ+WY8xu6fS5kZamdImDegUJOXTiu1oe9jhFLNVStCy52K9dlnZEZkCEMLkfKwdlAACitjltZNQSh9gv2eqlLD1kuVWRkMuc1yb1KSoJWV+7DEXK3B9om9wXvH3/1L4AgHZv7aLtvDTt4eXbmhRaCgzbH5w0Y4rkgkgwyPlEGkCWzmXJUpYChagfwrrGqOJlwiJn/3V6hlpo+wS2dNGWicMG0hgv/5CW+uyLZYV9qT6eknLneWW5Q9lVMSmc/4NQ4yK0qgYAYNvfMxzfM2asoaI14r2+VK4I/SW2/4CYLF+blkjX0obaK+M9kkvoXrmBqy1SJpIMwkutGgEdQBD5XC0nSx/VZZ0kgrRCj4i06BjFx5KMS0EPKrvlGto3ag+1m8H18xWx4CxbiqmxLsuddssnXZpEri06CjjEUn2zRXNYzzRPTY5oPPXUU3jmmWcwadIkte3ss89G7969cd999x2SfrR7926cdtpp2LNnD6qqqnDKKacgLi4Ojz76KCorKzFnzpx6jw8Vje7hyZMn49tvv0ViYiLeffddvPPOO0hISMA333yDyZMnN7oCbrjhhhtuuOGGG42JrKwsDB06NGj70KFDkZWVFeIIZ1x33XUYPHgwDh48iChxGwFw7rnn4ssvQ/NmDxVNyhU8/vjj8cYbbzTphH/EkNlSoDo0mlCf0GNIFMu+XZApRTS27SfIkwhSyoxexO80ywSzlFEHnkHu/wv9/kD6YlXk7Nlkxhr1I9uCiJCfpEczwbyyLRNxA/QZ9s16+i7nstugeJwEV7VPmZOgK/HJQhIePL3HSDoshJFxkEil2P9IHwixWFlk8IxWR6Ts5FGuZ+VwskpZNciJuLZZ5pyt3zDgAgBA0QIinLf2r6BT2WasYWxyHAiTa2aZCUamAms3h7weU5cnCBH1Coja99MMoaV+DgKnVpZuYxPxPeX5B3jGr8xnW7Ws87wmy3F49rFMQnsiRxtV3I77nA89Ncb067KjsrYHn72ebd8gDkVlP5LYiNhbCACoaZVIx/F9I6iJp9JCkzweJ8IkAp0i8AifkwBtskWSIFc1bZIBAN4SW7o/J3woTjqfz/Qxeuinz9oo2uP4aT84ruuDFPqP4sLzF6tt31xxrPNaWtO96NvLpP4kujZdzNTLgr4oonqfOGmq+i1qOx27+RuygyoK0D151vfXAQCiF/3sKEuhcoJM8X3lSbGSanafQ9IQHV7YBADYP6krH8yHRDjHnyVerBma29FZDak1dcNsRQbX7xv+nkp0lIr2ltCqIFBiB+Sppe9VbQjNjPqZ0HdJppGEB3AbeGJYciUiWN5Dia5Kvf1+INBIeRM36o0uXbpg/vz5+Mc//uHYPm/ePHTt2vWQxy9btgzffPMNwjVT+Y4dOyIzM7NJdWrQC1VxcXGjlF1LSkoQZ9P3ccMNN9xwww03foE4SrL87r//flxwwQVYunQphg0bBsMwsGzZMnz55ZeYP3/+IY8PBALwh6BU7Nu3r8nvLw0W9szKykJqauqhdwbQtm1b/PTTT8jIyDj0zr/zCFRVIyAL1vVYzOhRFzKlp7yrFPkA8yds0gFKRNHnnJ2Zpm1WZPsUdCbQkZCCLSPJ5LfLf69QZXZbutl5fYwiySxXBDW9VVTvPacSKtbpaz63iCKG2yQb9NRmda10beMHUsr2xz9+6vg9SHDR3r512KzoyJShzT5UWwhHzcYJEQHUzEtDmwyLuespEyYDAMrfIJ5L63mrnPWzISoB5pYoXhD3mb8OZKpeE2Spp6Tpa8bFuhVJ0MxfxoegTyGkI1S7Cd9KE3Q0RXxRODPyyWV7WySrff3MnQsI/842dgHAYESlqguNx/B9bAQtJuOMDJkldRNExRJHbFcUMtXaOQEMY+6XEny0IZP+loRaeHazFohwvfh3sxWhG0pqQdDGIqqXp5L3D7OQNU8Z1afoGJKhiMynfQ705zR+9mxKW0l9tHzlcQCApc88BwCY/jeyMDn+vr+rMlPzyY5GbKF8OYScKImL/Sxtwm3hFU6YGG3vIdHdcP4EAJORxn7/ugqAJaNQezXzKveR2CW2EmItzxx1b/IYyzy3g1XPVYw08jjtdtNPsIe0vP6s0027HQK5fB+LILK6r1Q9pNSAY7+azoQeV7bgNquw+j0yl+pZ3YnGkBi7pyyl9vGznIcuWaOjpLALkUY7VwsUCl9VDQR+GXPk5tCh+j28UJ1//vn4/vvvMXPmTLz//vswTRO9evXCihUrMGDAgEMef8opp2DWrFl47jm65wzDQGlpKe69916cfvrpTapTg16oTNPECy+8ENJsMFTU6HCsG2644YYbbrhx5OMoQagAYNCgQXj99debdOzMmTMxZswY9OrVC5WVlbjooouwdetWtGjRAv/973+bVGaDXqg6dOiA559/vsGFtmrVCmG6qOQfOepApkKhDUGGsZq5p84VqJcnI9kmIl6neC8iEsl8Ha0v/FHO791mbLW+SPaMjnZwVp/po9/z+tFMLJo9nb2pNBP35+TyASEyyGQWKTNQziSreKcOuQ2ZsYZAo+riGOmcKei2J8LTUUbIFoJV3p9m2VtG0lgf8/PZjmMX9f4fAGDuf/4NAJg6lsyRjdaEsASZ1QII5Bc46qN4TApB477QOSLcVp50ykRCji0T05apZC9Lwt+VjvHtIsSiuisJOoZtpIxNsYTREUPaxNs4Q69OdNFuqWGrf6DUsiJS6CCXoTIDNcPqsB8KaT8Zx7qliw2pVDxBaQPu3wCjSJ4C4g+F72BESvhPgioJP8rG3/Ps3O84HzzCoeJr3MPipvE0oQzE0b1QlZ7sOKwmznqcRmXT3xGc5XewO7WFdxSNh9T5iQCA6lhqv5hs5yTUy+Ml7Qtb3WrrEFXNon6WMeNtyfdTIiFE/h2cqSs8QvtY475o9z/is225lvpvSkdS9p26gK59ew0hQnfspXuiZByPD548F/Wy+qjtW8Q/kftbNzRWKLHcwx7n72IrYx/XiqcqQsdy/yh0no+RrNTedA9UtKD7K/IgWyp5redBdQLtmzOcju31CF2rP5ueYSrLlLlTgpQqXpwYhlfZEG2/9qxSQqhhjVrBcKNh4ff78d5772Hjxo0wDAM9e/bE2Wef3SArmTZt2uCnn37Cm2++iVWrViEQCGDq1KmYOHGig6TemGjQC9WuXbuaVLgbbrjhhhtuuPELx1GAUK1fvx5nn302srOz0b17dwDAli1b0LJlS3zwwQfo27dvvccvXboUQ4cOxaWXXopLL71Uba+trcXSpUsxcuTIRtfJfWV2ww033HDDjT9KsA7V4f77rcdll12G3r17Y9++ffjxxx/x448/Yu/evejXrx8uv/zyQx4/ZswYFBQE69IVFRVhzJgxTapT81gsH+1RB0FaCOXOJRknOTkofNpyg4S4rEdbUKTB4oUiiKjOqzvAa0uPYfkE659+8gQAwIK1VkbEwAdIvDJt7hrHsUYBQ+0MabeeT8s3Iqvg56UeJUwZqu7yXVLvmTi6uM/7AKwlNllak6UBXRgSCLGcqi2ZKnFAjYQeBLunJKk/C7rT8sAxjxBBd81tRNDttPAyAEDfz2n7uutpO3KpDYpHOVN08/pa/R3wUXt0eZmWVPwpnD2ydoujXjJGPK0o8cO/l5Z6hGjsWHIrs5bV7NcuJGDvViLVSl+Ebcl07FcyjmZu8Ut3qCKUUKLUhwn6skynhwiUWhfKy3oHC4P2laVoj3j4cb8bPC5UKr5co6TAe7hN4izupj+V+svLPnGy5OcpYVHdWLonAjF8f7G/njeXCO8mX4/pCZ5LyvKrTiiWpT4RCxVJhIgDtKS1/0SqU8x+6zlwYCAtC1ZwHs/Akzc6zvWfx0ga5Ng76X7LPs75PBBpgyhYS72yfBlgzz4Z2yKXYCby2MriZfccWvY0tGQA2CUxZHmVCfY3DHF6m01dtRAAcPrrt9A19y4EAHgn0TW3epHI8y1W2Ma8NgbUUr/HSTpXS4HavRkqYUSSY/T7Wk86kb7KHei85vCSYPpFRAE/J2uoX/28dBokDir3m1Z/U1/eg62tfRpVo6IyyN/viMbv4IXocGPNmjVYuXIlkpKsZ3hSUhIeeughHHvssYc83jTNkN5/+fn5QUbJDQ33hcoNN9xwww033PhdRffu3ZGTk4PevXs7tufm5qJLly51HnfeeecBoBf6yZMnI8IGbvj9fqxduzakYGhDwn2hOtwIRTTUtjnSf308C+aZjMy8PGIXIwKVmuilJzb4jTnA5MmgGZ2Q0HmmLUROZT0iqEdYsCBd9Dn822uMTNUxGzPZ1iKgpdXX5xQvs0khnxae3I1/+QqAhUx1WkCIEIj7jW5/XVVnWUGzXKmfX4jwgljxjFEQNyGDC/IGoO1/6O/KAZQqvorJpmE5tK+fD3mxiOQSzl9GFhrvHM/n5va95uHvVZl3rybUrSaNEJ39o7gfTxoEAGj3r5V8QVRfSW0XdMmsCCYiGxppW67Jw9uDnNelDH5wxC/mJARbOdXHdOYynOfzMhpiahIcQrYPsolxyGWYjn1U0oS2rxGuZQ9Xa7IJsZZopMH1q23plEXwlnHZ3GfeLEZMGV0I1DiRQHuo9pLxKTINHklw4DFTzhYkCc4EjdZLadxsvcTSrun6Km3zHqT7pOjlOD6Gy1xKH52msmCqSeeYnkdckJMfXwYAeOO/J6ky018ncrki5Iu8A4v/mqUacilEbU0Cw4i22lMRwis5WUIQWybAj914BgBg86Uk/Dn4bkLUSjtSm216hv4z6zGr0CpT3WtijcMCxNK+Ul+VOOJ8XhqhEnR04rpGdJdnX9FgSsCI3UfXXtaayi7sTGMtfq815ivSWPLlfacEhKpnCKSMNgj8w/Wz2V1J8o6KPJYCiYwIITp6ZKI5lux+D0t+06dPx7XXXov77rsPQ4YMAUA2eA888AAeeeQRFBcXq33tOpoJCfQsNk0TcXFxDgJ6eHg4hgwZgr/+9a9NqpP7QuWGG2644YYbf6T4lUjps2fPxmOPPYasrCz07t0bs2bNwogRIw553DfffINRo0ahT58++Omnnxp0rjPOoJf9CRMmBL1gn3nmmeq7YRgOAc+XX34ZAJCeno5bbrkF0fYJxmFGk16ovv76azz77LPYvn073n77bbRt2xavvfYaOnXqhOHDhzdb5X4XYQYAj1OGQM2w6hKfBJQtjKTFC+rgz+QUbTHzFGRKF7mEhQ5ZpqiMOMn5xSJBBozfiVRIzC+1+DDL+r0LADi94wTnNR2kt30xc1azN0F89DR6x8Gc2i714Wv6diaZT3Z540rH7i3JxQZJr63QyrHKVuiL8LMEsRJjWLEHqcOsNFBMKISy5bBdm1hOXHUP2W9Enl8IAEibSTPaN5eNAwCEZdH+RjILazKy8cJfzlFlbn7nVfqDnylyrR0+ZSSFhTA3PUoChN2vIV6TaRcLtF0nAARk7AjyYDqRoCCUUPpGxpA22wcA349bHIcoFKMdoXFGfqGjDP/BIsf+fk6rV3Wy16MfIZFGKctKCJKmdnT2USCZECujiq/H9jD0VPC1M4hkiuxFnpNcqlLyha+jox42dNbgcanS/JnjJabnSg4jgVAmfzRLIJRTH2aNJFRHxDoBYMtk2rf7y4yg7MtxlH3q//0FAPDZ268iVFydeTwA4OdrZqttAw/S2Gk1jyxddFTWQvwEYeFrZZ6jJdkRfI8GyWXwmNlf7EQCVz74jON7/4eJV+jJtaw6TBk7fF6rD4QHyu2rGZTLM0SX1QBsY0A3SZZnSzohU6b4KrONTKvv6DlQ1o5QiJoo69rjt9Nv3oOMYtYhNGrqfC15pniC0U65TxSin8Doa1mFEmf+o8a8efNw/fXXY/bs2Rg2bBieffZZjBs3Dhs2bECHDh3qPK6oqAiTJk3CSSedhJycnAafb9GiRYdV3yVLluC6664LeqEqLi7GOeecg6+++qrRZTb6heqdd97BJZdcgokTJ2L16tWo4odRSUkJpk+fjgULFjS6Em644YYbbrjhRjPEryTs+fjjj2Pq1Km47DKibMyaNQuffvopnnnmGcyYMaPO4/72t7/hoosugtfrxfvvv9/g840aNarxlbTFkiVLUK15lgJAZWUlvv766yaV2egXqmnTpmHOnDmYNGkS3nzzTbV96NCheOCBB5pUiT9aBCEFIfyCBHkq606CmDFLN/HBzHuI4cwiyZ4LkaWm0BfdUkSfxQlqIPUQk06eYb0y3uJpTFhCCNWCL5xeSKcfcwoXZjjroaMdOmcJUOiKoBeZl/TgH2gGsG0izXrHnXohlbVpG31K/WWGGyozUjLFGLUR9KYu82QJEfb0H7CyqLxs/BxoQYhd8jrmnn0t3BT+FH5WOSEapiBA3DZhew6oMscfSxYGH/9AE41uL/BvhYRu+dsQQtb1ceq73PN7OeqZ9iXN/P0tLKTAl0+8nJJjSFA09uttzmuVTEvJCGUUR/F38pnXYUcqdNRCxsi+bIQKL4/fgLSBEmm0ldOG6ucVc2QddWtJ6JzBSEognrP/2OLFE8o8W9At5vyYuYwe6dwoza1BCblK9o4NFTM1JEXdR3JNXE8zilFQHuubrqWywrM5m7LUKtNTJaK03BeCejEK5qmi/jx9tDOrLnM8tdl5kxdDjx/v4fvk2wudP2zf67gmhaxx30ifeG0ZrRJi4SIIkELpOLNyaNudQccAQJfFkwEA3edv53NZfaXQa7lfBUUSjpchqDIjaupA03m8De2En8eVPAsE5YqicxR3J0QwfivdG7XxtN1bTuMg4KP+j8qzUCKxCqpN5ezYbL43ZeyEQttt51bl2LhrphQvXELDNg5CCOkeiWhODpWdhwQAERERDhK3RHV1NVatWoXbb7/dsf3UU0/Ft99+W+d5Xn75ZWzfvh2vv/46pk2bdniVbmCsXbsWAP2ftWHDBmRnW884v9+PhQsXom3btk0qu9EvVJs3bw4peBUfH4/CwsImVcINN9xwww033GimaIZ3t4qKCkXglrj33ntx3333Be2bl5cHv9+PtLQ0x/a0tDTHC4s9tm7dittvvx1ff/11g5TNmyv69+8PwzBgGAZOPPHEoN+joqLw1FNPNansRl9F69atsW3bNqSnpzu2L1u27KgwQ25IKF6RbjQLa2090JG4KbEryXS0Vjg/fIxwfOrKUqOdJItG0BdNe0lmRMKdqAsxKyhUf57elwbYnueJ07N+yBtUVhrbWeyuDK6HvUypZwjNFZmlt3uTZrW4lT5GsQhb1NY1zv09TjTMtGUU6nY7uqWPhG5Rowx6eX9vyxbWvnE885R240+lTyPn1q009O12+xU+X6dPSVeoxVBCOVI/5+zOUicKk/oBZW5uvoOy7pK2ULuH5dhminye2M0H+SDaJ3MsoV3t/sfWH8WsT1aXubCd7yR1Z6TBrGGkT7SjNF0dMWRGFY/x/qTFZW8TTzbXT1DEDszHqmTOWQSNR380IT3eItYaOuicFQds9dcNdYOQKQnOdFX6WqJ1pXSMrPFpaM7y0n/SdzUtmAfDl5Y7OIbLpjJr2tIY9GVbvKyUnyUr0slNKhvS2XGumArmQbIOVNsF1P/X3sjZn7DQkLtyST9s0NyfAQCrhjgzyhTXSxBhm1E1YKFPDqRE02mT/tt/WR8AwN4302m/m5cDAPp+fxEAoNtVhFz55dliR/wEwa1xcvb0vlJIlMaHVFZV9vtd+k0ZKNP33BEk9BW/u9rxu4f5d/4YRhWFOlccvMTj3UwIn6k/s/R68XNWMpyVSbI9s1V4rfKCUKJlXv6OIioqKojTFAqdsoeu61SX1pPf78dFF12E+++/H926dQv6/UjGzp07YZomMjIysGLFCrRsafFow8PDkZqaCm99lm/1RKNfqP72t7/huuuuw0svvQTDMLB//34sX74cN998M+65554mVcINN9xwww033GiGaEYOlV1uoL5o0aIFvF5vEBqVm5sbhFoBxLleuXIlVq9ejauvvhoAEAgEYJomfD4fPvvss5DokaqeaWLPnj1ITU1ttO9ex44d1fmaOxr9QnXrrbcqafbKykqMHDkSERERuPnmm1XDuOGGG2644YYbv0780jpU4eHhGDRoED7//HOce+65avvnn3+Os88+O2j/+Ph4rFu3zrFt9uzZ+Oqrr/D222+jU6dO9Z7PNE107doVP//8M7p27VrvvoeKDRs2YM+ePUEE9bPOOqvRZTVp4fKhhx7CnXfeiQ0bNiAQCKBXr16IjY099IF/0JClKSXGyJ8egYJtKdoH/o8UXJO2MjzPNg264KC+hKWWLuxwvUa0VsR1sTFheFYJKyoI3rns4YBkeVmw9RNcZ9JLQ1UrWl6I3MPLmVy2pGIHLb2EgExl6WbzI30c2w/0p3N2+NSZ6q6WEeU6bYR8EQdUBFd9CVILWeqzxE/ZzsZOXmbLDilJCMwmz9LE6gdMrhZitB6mTTBTRBh73U+E15L+tOwVSHHO/ISIbbCIZfrHVC8R2qxJdS5LAUBlS7qmuE0kGZCynuq153wiVLb+VkjAmvXQPrZfkSUgAKa+pCxNL8RmaT+2t/G3J5jcu5NmpKYQyYtsSxwy/iVRgPcJxLGwJ1+bL4fI/ybfC/qzXJcHoBNxv2vcC1kCVveT3ItspWNqsgCARZZXoqQ83qq70bJ3TYyPP+mcMdlU7ydHvggAmLxsCgAgIs9q58h8XtaMojKru1Jaf3gRba9swfeXJmJqMBE/yRusjTMtlf4DOuHmKwAAifjJ8bu6ZhnbktAi5xChT7sVDS+NemKc0iptvyoEAGy52bnE024SLY8FNOK+aedv68+lGibma0v4QkFQtAY5IARtwBKQZbstpimkLuF9i0RHg8ehEPD5uKredFxMmPVc8n5HS6emyDmI3IQ8H+XZIM8hJrarpT8ZN4nWvRxoRcvvRo7mERcdZVE0jnT8Sll+N954Iy655BIMHjwYJ5xwAp577jns2bMHV1xB4/WOO+5AZmYm5s6dC4/Hgz59nP8PpKamIjIyMmh7qPB4POjatSvy8/Ob/EK1Y8cOnHvuuVi3bh0MwwgSjfXXQSOpt15NqgmA6OhoDB48GMcdd9xR/TLlhhtuuOGGG0d7XHDBBZg1axYeeOAB9O/fH0uXLsWCBQvUEltWVhb27NnTbOd79NFHccstt2D9+vVNOv66665Dp06dkJOTg+joaPz8889YunQpBg8ejMWLFzepzAYhVOJ905B49913m1SR32sYXq8l+KZBhmIf402zXjij8+itN+wAi8kJaVpmPRq6ZVmohFjv1W1vdBkFjXCqkwPVjNaGJslsNnx7rmPfr+bSbLzHsksAAJ2m7uIyRD5BS2sOQUQUdMP0Oac/G64i8cJxT2iKulrqux2ds2a9dayDKzFBPjdLCCjSdUUIdEmSCSTNWzdk1eQmBHkUVMnMd5Kw7XWW1oj7iRAdkQjw5DMBWwQqeVYesbeQjo8mhMDR07xPbDETgksIRYraRJ9ptcRZCIQ7UcLqREYEDJpF+0pspsOxdB5Br6S+So6iHSFS+cdQH7b8jvcT5KoomHwrKe3gT0lT9xRzuxY7yfJqjCshRSGWW9ehkhG4/xS6IWRZIcALUiWIho4m2lEUHRHh7/4I+szr6xTujc6hPu0RxsKQWXTuyHxrfPojPPzJ6fvVVHZlCpUVXsz1ZskIk9tC0I7xw2iZ5ONv/gc9Dp5D501eztwURkQDBTz+NKRajV+R0bAlahhyL4rUBk+OjWIaSx1f4WOYzpJ/PqEHyf8lU2T9mQNYzzCVnKMnl9SBPBtxLG0hkia5lvyIQtvFEF6QfUamlIGxoHDaszFtGaOyO2z/oUuSji6XoT8XdUkYMesWhM2eNMF/K8RX0MG4GJhNxy8aFQYOf8mvqQjXVVddhauuuirkb6+88kq9x953330hMwjriosvvhjl5eU45phjEB4eHsSlKigoqONIiuXLl+Orr75Cy5Yt4fF44PF4MHz4cMyYMQPXXnstVq9e3eC6SDTohcqeOmmaJt577z0kJCRg8ODBAIBVq1ahsLCwUS9ebrjhhhtuuOFGM0dzLPn9DmLWrFmHdbzf71eray1atMD+/fvRvXt3dOzYEZs3b25SmQ16oRLvGwC47bbbMGHCBMyZM0elFvr9flx11VUNzgj4Q4XXa81sJGR2ZDpn3AAQ+w0L5XEad81ISoUOX76RDmFukLKV0U19Q+h1WBwJ7S7SbFn0FGkVdvNmma2V0kzr9F6kRls1kLhfm14npGqcX3Pjltk9fwbsthFc5+zx7bledP6+s2gms+56Qqg+2UzqtOO6E1KlEAsNOQKs54UuixBkfiu/88zfo3NubIKERnIibWN+hmoLMXMVzpE2c1XGsso6wzYTlTaXVHzm8njYhsVMZG4Pozfl7Z0p+hIR+ZYopm/rPuc1aPIcEbtIrDSQGOsoO2IPz9ikrxx8PLbkEePfcGc75Q6meiZvZt6IiG4K+iBIS6TFFxTzYA+jHUaJk3uizi7In4ZMqbD3u4iDakioGtNybWIQHtTfXJdQJsl8HwcyiIMWfpDO1eZrur/OfH4xAOC1x0isdfhrNwMAIguoDqWnl6iyBLNImcfoZTnzMsLoM3IfWzgJUirjRFBO7stZB9NVmdcn7aI/NlG/lvW0CV8CiP5e+kSzvpL7SJ4Tdr6hMkdmJFIkP3j77muI8/dxOfPeuKs2P3EMAKD7dSR1EtLOppbPp9Ai7l/tWSYortwjSmzU1neKq7mLRUyl/wR5VEbQTp6Tukf3ZtHv4bbxKWNGkPAwbdzVsT0gciShBJsFBZT6ihF4TS2MP7j1zC8df/nLXw7r+D59+mDt2rXIyMjA8ccfj0cffRTh4eF47rnnmiwB1WhS+ksvvYRly5Y5dBq8Xi9uvPFGDB06FI899liTKuKGG2644YYbbjRD/EpLfr9WVFRUoEajiBwK4LnrrrtQxkvF06ZNwxlnnIERI0YgJSUF8+bNa1I9Gv1CVVtbi40bN6J79+6O7Rs3bjwiug6/+fD7rRmYtm6v+BsVFgoS0Lg7kduZJ8DIRchMHAB+jW8CWLNslS3DXAqLe+Ksl6n4BaGF9AAbJ8XjHBqROw44vhtsKxLYnYlDBpdZ2JPu0k5vy91KiEDXuWT6unUSWWvoYp0GhLNkvcRL1p6ewRg8K+ZLlJmtugBG72wzazObeGOK9ySzWZ6hKl6OIDvSnuEaGhIi49DgMgKxTlRBuEulHZzr/9E5LBZZyGbAe7JshWnlxzMSxV+r2yU6jy0qd9RXGQrb6xHDXK0a4fbQviWdaVyGcRFhBYygMvolHDCVLWlHa7MrQp5PUDowOij2NcoWRkMXHBwq6QuFTDiNdZVwp6AxIujJHBzF+bKhDmYcnzeHkD3PZhLbrelHs9SKVnTMNUm0/Z0cGmuJm3mcMhKH7617pvA2elD7p9K1RT9I7RUIF84Z38+C0uiG6tynCpWyRdJGuuaqBDqmOJ3On75We97oli8cDgRQmUU7Mxy9ScSz237BHMex/0imMru9VOE4PqCj47A9nzTjdEFSRbxY8UflnuxJ7W7stYlKqsxkvt9VlmFoZMoUwVzNPsYu3mlonBt1f4u1TGsSDUUujQvFZ7U9zwHAk2RZ+kjbqtbgcWjW1sIMBIuKHqmoP+f5yB//S0RZWRluu+02zJ8/H/n5+UG/HypLb+zYservjIwMbNiwAQUFBUhKSgrJAW5INPqF6tJLL8WUKVOwbds2DBlCOfXfffcdHn74YVx66aVNqoQbbrjhhhtuuNEM8SvJJvzSceutt2LRokWYPXs2Jk2ahKeffhqZmZl49tln8fDDDx/y+KKiIvj9fiQnW44CycnJKCgogM/naxKFqdEvVP/85z/RqlUrzJw5E1lZNGtu3bo1br31Vtx0002NrsDvPvRMO8CW+cIzmox21m8byHZFjIIDzJkI2DSBAFgza11nyZFNo1k7CB+CjxWOgLc1ZwMxNyggWX8aRAo4OQb285ma9ciCpe8BALq/SOhSxkOUEaF4T/YsKka7OvTkDLeetHn3XrJ9+eikx7msGwEAnUAZRDoC56hnW+J2YD/NYj1RkdoOGloo3AaZ4Yaop/BZxChWOBSyr5rZ12V7Iia7NnuGfRcT9yz1Ryo7gpE+QW3MeKp3zD763R9JZYavYoNomYnb+0W71kACzairUmh79FY2fGbNJZUBJRlctYwM2bSwPJU8VjhbL/8Eal9TxhYPO7GNEc0u+V21kQ3xU38rfhOPpTzmcmntqeqpm3vbUcSgtpfMy9BmxCp7UrTBxGTXzq06WOSsD48dsfvZdRW109Q9w2m3YicXqSaOyqpMtuqdcj+db+H/XgMAnBxBWlVxP2j8N+FKCiLN15V/PKEj5207BXoU/x9xeJLfYNSLq73lmg4AgETm07b4D92Tqv3kfoq1LGt0zk+Ax3wglVCXcaeREXPedOrfpC30e0UbOi56DZXpaxeshu1PISTSz3w8z49kAG8WcWYrt7O3RYrjOHO/Ew2nCml2NloonqOeDa2hnHZUSp6TkiEq7aLQZB4Ptcc4+TQy5iN2MSpis8hRiJni8Nm4pCFQPDeaHh9++CHmzp2L0aNHY8qUKRgxYgS6dOmCjh074o033sDEiRPrPf7CCy/EmWeeGZSVOH/+fHzwwQdYsGBBo+vU6DxOj8eDW2+9FZmZmSgsLERhYSEyMzNx6623Ntn/BgBmzJgBwzBw/fXXO7Zv3LgRZ511FhISEhAXF4chQ4bUq2UxevRoZXxo/zd+/Hi1z3333Rf0e6tWrZpcdzfccMMNN9z4LYTIJhzuv996FBQUKEX1+Ph4JZMwfPhwLF269JDHf//99xgzZkzQ9tGjR+P7779vUp0Oy+K5ubL6fvjhBzz33HPo16+fY/v27dsxfPhwTJ06Fffffz8SEhKwceNGREZG1lES6WDZJeTz8/NxzDHH4E9/+pNjv969e+OLL75Q3w/nZdANN9xwww03fhNxlCz5ZWRkYNeuXejYsSN69eqF+fPn47jjjsOHH36IxMTEQx5fVVWF2hAuDDU1NajQeHINjUa/UHXq1KlewtaOHTsaVV5paSkmTpyI559/HtOmTXP8duedd+L000/Ho48+qrYdKp3Rvh4KAG+++Saio6ODXqh8Pl/zoFIeQxG4VRqucOHEyoCX+QAbaVHInBpJXUHT3jBnmbKEZSM8B0koyNKJ7CsQsyZ2qJZeTElTDyYp6yEChOOHkr/Rx99+AADYPJWI5ONmsIxCCAFSgdSjL6CllYLTKaHBOJ7q0TucYPiMh9fyAeIo73SpV8sTgFq+VMsXLItgSFo9hynk9RxeatPS7kNea4JmCyPtWhWaVGolA1A75p7VWf1WwyoIvhI+VpZ4+FPowbUtaMeorbSU5q9yXodD4oDJ3EY7trGJoHEQtauQdpUlNUlPlwkIjyUzJZHOEW31u6+IyizrTgKeEcXOfoxdT0urIqioL48ZnGBgt1lSVRfSuSzdyj6qfs4EDLNaW4oOkZKvxrDalz+lX6VPhPQtyzu25S4JdR/Jp6Ta8zjcOvoVAMBP0ievfwkAmPTvGwAAaSup7SqTbAK5PHSn5fUAAIx+4lsAwCcPkgxJ4te7aAe5n/ncZf3I7qbi/+he2V7QQpXp+ZyW4ZKzaDxGHKRrjs6h9ik4jr6XF1L77rlhIAAgdTVtL21N54jfbY3jiLVcD79G7t61n3egvkm9mgnv4XTusu60TFc7tDd9woqyVnT+iCLe9yq6Ft9/BtC1ry8EAGy+kfqix7VbAQAB6SO+jwxbMkPtALIXCd9Px266n9qi+82UFBPg54Ee6v8qWTK031eaoKeiVfD5PQdpaTKcP0UuofYYur9rWifSdR206BpGeWgrLrOs3GkbdiTjKHmhuvTSS7FmzRqMGjUKd9xxB8aPH4+nnnoKtbW1ePzxxw95/LHHHovnnnsOTz31lGP7nDlzMGjQoCbVqdEvVPqSXE1NDVavXo2FCxfilltuaXQF/v73v2P8+PE4+eSTHS9UgUAAH3/8MW699VaMHTsWq1evRqdOnXDHHXfgnHPOaXD5L774Ii688ELExDgfpFu3bkWbNm0QERGB448/HtOnT2+y9oQbbrjhhhtuuPHLxQ033KD+HjNmDDZt2oSVK1eic+fOOOaYYw55/EMPPYSTTz4Za9aswUknnQQA+PLLL/HDDz/gs88+a1KdGv1Cdd1114Xc/vTTT2PlypWNKuvNN9/Ejz/+iB9++CHot9zcXJSWluLhhx/GtGnT8Mgjj2DhwoU477zzsGjRIowaNeqQ5a9YsQLr16/Hiy++6Nh+/PHHY+7cuejWrRtycnIwbdo0DB06FD///DNSUlJCllVVVYUq2+ymuJiJlX6/IocrpEcThAxFXFezMCGOt6SZaEAMYrVZupJCsM+wJd27hAXx8gucZcuMm2dnnlZEdPVnZjvqpRsx249VsgQH2Lqhp9MF/KKdtAZdcxxt9y3/mX8JRoDEViOygMrc8X/PAwBOP+UCql8LJzHfn8lSATopHDbURdKieWYohFc125W+0M2mQ6XUCpHUlupsP2+Q4KQqjGa2JSOJgB6735qvFwxnU9edNOOXiZ9KzeZZa1g2zeJlpi0z61DooQiQ1iRSGWF7OK27pMS5oyCUmuyDCH0KKgUAtUlUVnihE4Xz/UxCtELEFUS1zvrZSP6K7BtOTgtCOpdPZRcjKJeQgkOhCXWElKH6SqMDKAJ0Gt1fauJtkyFRyCeft6Ytodx5/ahNBj5AiRc/3kNo7GflbHjMQKZRS6X6qmzWMzG0z+LrCLktu5X6tzpZpEwMx7kDbLtS3oLuu4j3EgEAMdnWcyBzNJWfsIPrGUv7Rh+g79EfU79mnkXt1vMuus9fX/4WAODiEUQwLx7YWpUZoaOBbYlcLkhLgNFOkakoGdvLsbvJ6Hzssm1qW8BLzwJvDRPZp9O17TybrjnvGEKXWn1C17PlbkK5uvxjFZUpBHMb2uz9jvza/DxWetxK9SkbRD5xMRvZrmafTV4EsJ4P9ayqiIyDSmRgJLq2Az0vvbsJnRVyf9h6vic6kuF1VRs7ok1/hxVQ/TyVfM8dqIBp/oKyCYeJMP0eZBPsUVlZiQ4dOqBDhw4NPmbYsGFYvnw5HnvsMcyfPx9RUVHo168fXnzxxSYbLh8Wh8oe48aNwx133OFQVa8v9u7di+uuuw6fffZZSE6UaFqdffbZ6k20f//++PbbbzFnzpwGvVC9+OKL6NOnD4477rigukr07dsXJ5xwAjp37oxXX30VN954Y8iyZsyYgfvvv79B1+aGG2644YYbv0ocJUt+fr8f06dPx5w5c5CTk4MtW7YgIyMDd999N9LT0zF16tRDltG/f3+88cYbzVanZnuhevvtt4P4S/XFqlWrkJub61ir9Pv9WLp0Kf7973+jrKwMPp8PvXo5Z0Q9e/bEsmXLDll+eXk53nzzTTzwwAOH3DcmJgZ9+/bF1q1b69znjjvucLxsFRcXo3379sj7Tzra3uQ0JxUJhFDp/hIitifISW0mcxaU4KTToFPNwG2cK2XiK+acGuLkSUqkslvRp6TGGwc0flaIkN8UP4ivZcs1hEh0W0Ky/w8MJC7Vhf9Z5Dhe7GOAYN5K7iA6/yl/Zt0ypon4tpNworLdUanwbGcjaAMA2P+2h+KgeUN/r0fsTc2Iw5ztF8TpEfSLTVLzTqJZcgQb3vrKrXNEbaLJwu7LiUvT/nGahXtUu9I9Y3LqfpCNkfS7TYpBLDrC9jIPj8edMm3VjlWfjGh6cphjZePghZU40UHhPQVJGCh7HY0jIunptjGl+IHCSdLbXjPtDhzIc55LuFX1pJsrVEG0A0QyRFBNQSZYtFOJitp4W34WKRXJCJORiJSfqf4dHnM+F1aUd+bf6Rx5fQkdicm10LmqJKpPzG66NysWEPJTSaAGSgfTTDqs1HkPpvxUSPWspu01Kda9E5lH915YCY1Hb6XzWJHNGN6d6pvvp7ZJ8tJngIVrYxdYxucBDf01RA6Dx5unBT/TuQ8SvifOUslAupCaGH7m2GQTIr4lGy0PcxHlvu2WTYhP5ng6NnE9jcMr7idO2n/vJoTXrA2W4DCFlMb1ECQ/agmhsjkXUkJT2kJqm6IhZHMVt5UlMbbtcVwnfXHKSaj7X1YNdjCq3Ip4hYqfyaKsxj5CriJsShhKLkHGnYyziHAYR6Hu9ZGMhx56CK+++ioeffRR/PWvf1Xb+/bti5kzZx7yherHH39EWFgY+vYl+7f//e9/ePnll9GrVy/cd999CG8At1iPRr9QDRgwwOmpZprIzs7GgQMHMHv27AaXc9JJJ2HdunWObZdeeil69OiB2267DRERETj22GODTAq3bNmCjh07HrL8+fPno6qqChdffPEh962qqsLGjRsxYsSIOveJiIhAREREnb+74YYbbrjhxq8dIptwWGX8DhCquXPn4rnnnsNJJ52EK664Qm3v168fNm3adMjj//a3v+H2229H3759sWPHDlxwwQU477zz8NZbb6G8vLxJ5suNfqE6++yzHS9UHo8HLVu2xOjRo9GjR48GlxMXF4c+ffo4tsXExCAlJUVtv+WWW3DBBRdg5MiRGDNmDBYuXIgPP/wQixcvVsdMmjQJbdu2xYwZMxxlvfjiizjnnHNCcqJuvvlmnHnmmejQoQNyc3Mxbdo0FBcXN8lsscVDYTAraJakZu8eXjcXETq7eKSOQNQVun2JcJlsmSIyS1P2G1o2lHBQfLtp1imomMw6zbpQHlvd1cw1ncRJu7ajsrzX0sx5Lk4FADyfRrP8L9k8GTbRUEFQJFJ/pJlqTQy1V+RnJECo21foxscNCV9L4mf4JdOtrvYWJNCGIipbEjHcFaFJqY+0iWQOJtA1R+dwFlVbaqsKm8Bj7D66hpRVxEGTa1ToINdTz/aU/le8t737g+qpsg55hi38K08icZYk0y3A6IGnkGbWunWGI1RmqCYkK+NAREU1A3CFTNkmHTqKpUa0Jq6qkCuPE2GFjgzCQuqUXY0gY8xFqu2V7tjft5kthzxO028718rD6JwgU8Jn238aZVF+3sGJiJ8QQwjQ/2KIP+hl4KK4o3W9idtEPJcz8gqpni3W086S9Sfx7Z/4WSjm4okxXLaF6rVZqnHkOMQmKLyIxsPzHQjxOc88AwBwem+qpyFUsXIbGin9rAmmKnuYRCfqaewihCpmF9XlYB8aa/aM0TAes+oYMcPm50Cr7wglrE6la/zXMxMAALU3OA5D2yXW/efb6NQeVCbyfN+0/C+ZNPt5zMcv5XtyGPG5oiIJVfT8bGWhe3REV8pWf/DYYtSupifzc1rRNcs4cQh2yj0p/0fK89rrBYxfSJrnKFnyy8zMRJcuXYK2BwKBIF+/ULFlyxb0798fAPDWW29h1KhR+M9//oNvvvkGF1544S/zQnXfffc1+iRNjXPPPRdz5szBjBkzcO2116J79+545513MHz4cLXPnj174NH8mrZs2YJly5bVydTft28f/vznPyMvLw8tW7bEkCFD8N133zUI+XLDDTfccMON33IcNsL0O3ih6t27N77++uug/7ffeustDBgw4JDHm6apuNpffPEFzjiDJiDt27dHXl5efYfWGY1+ofJ6vcjKykJqqnMWkp+fj9TU1EMaEtYXduRJYsqUKZgyZUqjjunWrVu9mh9vvvlmU6rnhhtuuOGGG278BuLee+/FJZdcgszMTAQCAbz77rvYvHkz5s6di48++uiQxw8ePBjTpk3DySefjCVLluCZZyiLd+fOnUhLC7ZSakg0+oWqrheVqqqqJpG4fvfx81b4TWczGvpyXX0RygsQCCZPC+Hclkqsk3zNWiZzSz+wACQieMlRlhMOOJ25HcuQAvlLWjcvL/lmM7nzr0KSJWh98kekNv/w4xc562tHDbVlmegVBLsLmV4RrkWuoAEv5Xobe5j4rpP91f6y/KT3jc19XklEaOfyJtMyopHMyyAsEmpk0WfxcOeyckWadY6q7tQHSfN380mcy1yHulb/HmK8OkRcNThbyWSwnIIpwp/ch55859KuWRlCjkC1jyyFCiGc66ff92Yd47PcWk7UZSaUbx0vrenCtCrxQuoi8gk2orvIXXhELkHakfvdu4bS90WWQi3tyX61TiIyABgVvCQVxcuJ7I04YKKT4ylx64bz6Y/z6D4qKqH9o36KVvtE5IsXJ/VBbCbTAPj70ivJWD75UVrKqm5NBO6InTSmvAc4SaGwKLgCcr/wNUkyglFN22/JIqmGbdc4dfUyHibpAftSrOoDXvqWPjLZYxT86RHyv7TnfprBR3Rgkr/tvhLBS5HaCJcxIeORJURqj6f6tV4qS2fU3zv+RMef9Ky1LLpkDC+3yVI918cbazi3s5+myDyId2LZMW1pv1FWklPkAZaGCGNPwWJONslmL0EZ87ykF/Yz38O60KydWytjXugeLIhshIWp6zvi0RxLfr+DOPPMMzFv3jxMnz4dhmHgnnvuwcCBA/Hhhx/ilFOCPTD1mDVrFiZOnIj3338fd955p1o+fPvttzF06NAm1anBL1RPPvkkAHpIv/DCC4iNtdafJTuvMRwqN9xwww033HDjCMRRsOQHAGPHjsXYsWObdGy/fv2CEuMA4LHHHmuyFV2DX6hmzpwJgBCqOXPmOE4YHh6O9PR0zJkzp0mV+D2HJy4WKGaxQyVxwAiCoE+hUCghqpvarEU7xsPkUCVjYEOTPMrGRsrSCKYaAqDLF4gQqJ38bcB57JY7aUYX9iWXTdqA6DSLZrtP30iinJX96fe+3xNS1SE5eGbtF2SHZ8NCnhZUQ9AGHX0KRU6XbR6VlsyEctlBpCuCECkn6bq+pWGZvQvqJfY7SgaASf9p71Amam13StWuzrSQ2sodIm6pia1ylJ9As6KwMurf8B3URgEmvJtbd9ZdP7HIEakAqSe3r7IREXSGx44Q3U07IZ9n1FKGQqbkdxEHFdKtjNs6ZBWoDDPkbxbi5JQ2CLK0ErTUjnzLedm+Rh2h3x9cf7OMbUUitfuo2CJ4C7opKE1ef2rXhR2+BgCcscXSrQOAknWESJp8q3Y6llCQfZEWQlXQm/5O+5Lug8gSGuM1TGj2sOjlqmVkw1Q7gerrTSANke437XfUG7BQF0kmUdIQktDA/bx1OLVb+yE0LrZfwhVlZNM+5lWiRXUdSLkkPHB76Uk1UZ8wghVtXbuRRNcohHYziW2hGAlEPCdzbGOZmWi+R1gqos3XVM+eF2SqMr/sPgwA0GUWZW8tf4VsdVrPpwQBQT3NBEq4MUpkHFMbxayhsszEOKvyfD97GZXLH0qCp8nyHOJkhdo2VKYvp5COq08sVJAp+c3H90d1TTDKe4SiOcyNfw9ZfhLV1dXIzc1VfCiJQ4l8zpgxA3fccUfQ9rCwMFx88cX473//2+i6NPiFaudOerCPGTMG7777LpKSkhp9MjfccMMNN9xww43Dja1bt2LKlCn49ltnxqxpmjAM45B87lmzZiElJQWXX3652ub3+3HhhRdi/fr1TapTozlUixYtOvROR1Hsu6Q72nDb+74Wc986ZntAMCKlhc71UbNJfvu2IwAWMhVwnldMZwWpEGsNnln5cw/UUwFGxhg96v4v4jt9/OOnAIDRU//qOEfMD7sAANH7aGZtvMsIQLwNDduf46inSDCUd6JzRH66mk/N1ywyEFJ/I1gYUskdCM9GLGd0UdA69pdwiJtqUgqCjEiauT47V8gJ12vH1XTObtdYZtiRbHES0G5uTy+yNojexrIJcczP4tRyj/CkWFzU395KAtlzKs2yU39kc9xtxOUxhBsnSBCXISiHIFaBeEIT/DEW96M6ycl/jMyia/ZmcbaLCBYyQmWwN6agJopDU2O1pxL79AbzluyhUCO9jXgMOpArDRUwBTXUpBcEUZPxoOot94+N32WKhRMfW8LuSkv50m7t8AkAYMp3k2lDBF1X8lqqy+eTPgQAHPv6larM8lb0264LSMQy/T8k3+CP5LFVS/Xp/DaNj72nMHKyj37f+Ehb6NHzfkaUWaDTI+br2riUdhM+Vo+/szF4vUK+IvDJ40Da2eNEt/QIMmgHYPKY9whC6XUiOmIMbkbSubwi58H7icn3w3dNUmXGlxNCtm4GebQVnUOd4ysnhDd1EaNZ+XSsn9Fi73ayoqnqR+1Z1trq9yQWURXpkuRPLTsiwEKmfYwylfUhGY3ozSwSK4hbCG6UGlPCF6z1A6jn/4XmjKNENmHy5Mnw+Xz46KOP0Lp162CE+xCxYMECnHzyyUhMTMSECRNQU1ODCy64AJs2bWrye06DXqhuvPFGPPjgg4iJianTmkWiIS7PbrjhhhtuuOFG84cBU+mzNbmM38EL1U8//YRVq1Y1mbs9aNAgvPfeezj77LMRERGBF198Edu3b8eiRYuObJbf6tWrlVDWjz/+2Og3wT9yePxAzrU0W2q7jGcjHg0xsgt76ihkHWiWEgUNONEZB/Kic7Q8Ti6KyuZKZWNYMQ7VMsx0AUYA8HN2z8LdKwAAI68kWDT/Mpq1dVjC6EdL4pMYZTydL2SuhU08UCxcSk6hgZ+4kkxb/RFU77KzyH4o5uOfAADeBJrdiZBe+LYsbhNrluwXqxa94pJFF5C+4MyiFKctkj8rO+iag5AUXfSwjv3NdsS9yPg322IUlwbvK2UJz064UcITEqSNH4R+Fm1VmXq27LmOm5xXXXMMiRYijbgqvlLmSmUzYqXdr0YWbfe2bam2RVVQ29YkEZol5r4enmnnD6EHTIuvLV4LAKBWQ0Ht9epAbR6WzWNi9z7nDlo2n+LhpCRSmWwsqwvDAhZCplBP5u0Id07snyQUisOIS9WxndVvBT3EqJx34c+/zb0KADBj4lwAQK+2NGa2r6PsNEGyttdQf79532OqzPGv3AoAiJSEWr6PpW8q06i9vBU0ttJW0L1alEF18ZVRP6y5zXKfGH/7aVxWwHlNHMKpk/skkMeG5vXc50ERcCJVSkBVRIvlU/h5gvTZeZiM6AQJB0tmZho/M+Q5VGgZzQNW1mLSMkvMUzIw436mdupQSZSTg11pbGePJQQqKp/qXxtB2+MNQgj94fysaWU9b1NKeawwYqY4nfzcVO3Jgrkxa7nd42icmiJ2W2HLmpV77WAxX4uTt+pG80WvXr2arBclMXr0aLz22ms4//zz0bNnTyxZsgQtWrRocnkNeqGyw1+hdJ/ccMMNN9xww43fQBwlS36PPPIIbr31VkyfPh19+/ZFWJhTpiU+Pj7omPPOOy9kWS1btkRiYqKDT/Xuu+82uk6N5lBNmTIFTzzxBOLi4hzby8rKcM011+Cll15qdCV+z9HmP5uxN5XW9XV+VEiNIQ2R0nlDdWlLhUSyNHsQhWbwTGvj3ekAgB73UCaMWROaQ+HgJvlYk6VtK8c+YdfQ7Dz9r2xkK7NktkQRZMUrthNVNr0snjG/x8vBf/7LtQCA8EI2gI2nGa3oPcnM2wyTzB3nWAOAT9d95fh+Wsfj+GROJEg33lV8HT1L0HaMQgAE4dOMlWX2Lr/XJjHa8N3PQfVUCIrwmgSKl9m5nsEoKKOcU3FZbCgTZ0NJmWFrieemkAhGetS1prKOVhX3fzShH8okGVC8q4gDhc7zcplReXRsWU9CqmpiqW/it9BMvDKNZuJRGy3kL2wToVnlA1nJOIPqEbOROHX+LPqUTFYVbD4rLeOJt/pfGYGLQTX/plAFDUmRsadrYO0fZnHGosQrmC85nAGx8uMJwTgnhs75+OOEmNYcTzWrSaa2OfkTokF4Kqx7NNCOxnBVGvOCDhBvMGkV8ZliChkdYc2ryhbUBj4GeiucuskAgD0TnbpS7Z4huxXVz6JZJWNbxoGGZNmz/NRqg2RxsqWLzrdSfDgtU9OQ8W3PRpTzCReRyxYeoDefEUvNEFyhYpxRaETYeH2ircb3a8wGOiZqP6FZnmLmOTI6VsO6Xgd7UBsIpy1+l/WMLjiB0KuofKpX5F5uvwinwbEyPJY2KOYMQsngq7U9s2Wb/F8g2bNREcGrE0cwDjvLr3mqcUTj5JNPBkC+wPaoj5SekJAQsqymSi/o0egXqldffRUPP/xw0AtVRUUF5s6de9S9ULnhhhtuuOHGbyaOEoSqKcTxl19++QjUxIoGv1AVFxfDNE2YpomSkhJE2mYkfr8fCxYsCLKjccMNN9xwww033GjuGDVq1GEdv3PnTtTW1qJr166O7Vu3bkVYWBjS09MbXWaDX6gSExNhGAYMw0C3bt2CfjcMA/fff3+jK/B7D7O6GmkreDlBSJe25a66oqH2NEosNOCE3AFYKc2CMjO8XHAisWUjcuh3Sf+1lgjF+oMlEmIsQrFA9wEmpQ94iIi5vgq2X9i3SqufcylSUqaNFhYJfMPN9KJ9/IdkJ99zD8s25BJpNiKNSIAHxlK9W35BZNTwTKc46L4zrcyLTp9OBQDsHPsiNQET7/15mq2OLEWmEgE7wMsiQoh1pPLr6c/cvooszdcaKKIlCVlaDdtEZGt1tK1vDR+PierQaedBy8Q1TrsLkXBwLNfKmDCdS71qPEiZco3bqD1l0imWMKatjwwm6FqChD5HmTFrMx3fTV42BIu1hodRmnpNh2BCZ3ghLc+E7aVl15r2LLHRKpGqyfv5duc6jlNLRz6rjzxcZ7VcJLINcoy21AsPf492kufbLXYeBwC7T6O+armaCeRLaN9+0X8GAHz99BMAgHtzRjiOW/rCsQCAlPVWmTvOp7Ji06mepW2T+JPGcMpGGg8xq6hvoip4WTZAS1iRB4OX+NfdONvx/RSWcQjPpHvVv5+WW2VcSqjnkTamANvyeh1yI+q5IyvQkc7xLOPSrLESMeSZVdub7ufaGDpf5E+7aF/pI17Sk0+1jOjhJTa/dj/CtgwvkiDKaoqXHtmSJiybriOBx0FUAdUhdpv1TDG99JufJUuKe9HYisynssP3UbuKvIMkSSgRWamDPelDlkoTmdAu54oMs2RujnAYaIYlv98oQrV27Vr06dMHHo8Ha9eurXfffv361fv75MmTMWXKlKAXqu+//x4vvPBCk/jiDX6hWrRoEUzTxIknnoh33nkHycnWwzg8PBwdO3ZEmzZtGl0BN9xwww033HCjmeIPvOTXv39/ZGdnIzU1Ff3794dhGCHdLhoi7Ll69WoMGzYsaPuQIUNw9dVXN6l+DX6hEnht586daN++PTyeECTpozCM8HBEfUV+QKpjFYE8uEPrnAHWsV3/3UEolX01srkQiJN+YNFIDY1R6f596c38QB/Ll1EE8gwmgbb+zCkvENBERAMa8hIoYUKpDaXrcTMhD2Wn9qENQjoWMioLUrb4lq+R0Y/SnpReHbeG6lCRal17r/sJGTn2axJTPPGj5Y56zF81mC75IM12+xxHxO3cZ3oDABIX7YAeMvsOJBE/cNO1NNvtMYvqu/8kmkQU93Bec68HiZjvEXNie7q4drNbiB73gcxouW884Yw+1WOe7LGbsdr2UWhmlZOErPe7oE+GnUwrxPBa7XxSD41MD+5nJQS7K8t2VVwvtkQRyyP57l1byBeiPUPEgFcsfzqQHIVnp01ugVHE6oFdHIeG72fkkRFSZRDNiJQiRO9nVHSLNa5rGTHzR9FYKWtF7RVRSNf8r77zAACrq+g6luyjcwc4AaKkB+130/UfqDL/fd+f6LcsdpSQvAYB9gbRuTI2Uv0MljSJ3O1EMU4fea76e/PVhG5tn0AWX9knUHu1nUnKwt4WTpPuzD+RNETrRfQcMDIpCUDuUQDwaI4XgYNOiQo7mgUApiZHofaz2QPV9iXyvO9nkgYRaxeFeno56UPKlOeAJIgI4hNus0YSWQ5lkcSImIjx8u8KNaqk7eE5dK0Rm/mabYKuIjBq5FB94gupLUq6J9KuG9nKSeotchBVGqoc6r9CQXIj6XxZo5Lhr6oEmibA3ej4oyJUO3fuRMuWLdXfhxOGYaDEdi9IFBUVHfJlrK5oNCm9Y0fK2CkvL8eePXtQrWWQHApmc8MNN9xwww033GhsyPuH/rc9cnJy8Oyzz+Kee+6pt6wRI0ZgxowZ+O9//6u8if1+P2bMmIHhw4c3qX6GWZ87bIg4cOAALr30UnzyySchf2/qm93vLYqLi5GQkIAT4yZix4MDAADd7mKkSuNQOcyHvU4Eok4jYN1OJkToNjWKIyEcGS31WWaRwrGoaU8z2pzjLPG5NnN+pD9001vdEsevSQtoYedxCG/Jw/IHgqCoY+uw45GZoSetZdBv+kxZ8dc4rV64FPknpQMAconmgo6fOMdnUbo1p0jYSTPWqL2EMG26kmas3V9k1K3COXmobUHIXvYQTn0/ls6d/pTVZ95VmxzHSLsE0tjElcU3ZXbuZ1uRIPFcG5qjZszSJyJmqiGVqs+kbZhvZLRvzcfZxmVpBUKGkpngc2gGzOp4Fno07QiXSEDIbF2EWXU+mZQlzw4e83IfOWU9Qht+my25PRnpUWbImuRG5jhGqiqta2+1hLhdG29MBADcNZysZGa95NSsOf0i8gz74RYSos0cw9wfuVVCDOMuJ+wGABS8SJILBu9T3JGvkbs1/X0n988QFNc2zoVrJDZQp/cY6ThG8aG4XwXJrDzOyREJzw/mjxX2onZK/ol5Q3sIdVWWM+r+12RfGBmS+w0A/CylIuGJj3Uew8KdSuaBja6VFEaMyH7U/X+JQlsF1ZIxJttZYFgJawpqa+NJKqmNSCfiG2CEWZ4puiE32OwZYcF4hMHn9beiZ4cYbZe3NuCvqsS2R/+BoqKikBpJzRETJkzAd9sr0brX6MMqp/zgfmR++xIKCgoOvfNvLNasWYOBAwce8l1kw4YNGDlyJBITEzFiBPEiv/76axQXF+Orr75Cnz59Gn3uRq/bXX/99Th48CC+++47REVFYeHChXj11VfRtWtXfPDBB4cuwA033HDDDTfcOCIhpPTD+fdb5VA1Z/Tq1Qtr167FhAkTkJubi5KSEkyaNAmbNm1q0ssU0IQlv6+++gr/+9//cOyxx8Lj8aBjx4445ZRTEB8fjxkzZmD8+PFNqsjvNfy90tF9Ds1w/R/QrMQ3ka0fNBFCAMFZXXVwqOra34FYiaiewZwTPyMU1drvglB0oVky8mhmuHsczQQzHl1nlSkidtrbfUBmj5ohsI5UBPoxv2T15qB6CjLlYVShVkeZdLSOZ9oBziRDt3TrR7anwc5MR9lKyJPFAZMXkqhp0nqnN5NRTtdTmWxDv/hYfyy1V4uV1Nb5x7AlhQBCXM2qZPojbSXN+HO8jFjdYuehUKZT+2uoPoF8NrhtzZlubLOy4dpE3p/Qo14PE8cnwIiVAzEMssbh6isDaNP5e4B28IiVRoiMQzGzNthcVqFJnCUnY9gusglACUFaGYa2smuZByhl6OcVtJPHgUKbpC/F2gM2BWThf4lsi4gvllE9lDk2X6vBKITJbdPuAxaitWUOZp1MYyOKwCT8azchUxtuoKy6jLeuAACMjCO08fswgjsj8+jcxX00zpotti3nZYneXP0yOiZuj/N/rIp21K5XPzUfADDj0YkAgNT5llhsVU+yVxnXVYi0TmNoy4yar42vOfxrIu6EsgwTNCiZEcpN19D90O2WXQCAypOcFA4xzQ6wybNvI+3nz7eQDCWuqmkVCtoZyM1z7KeeUyLSyveuY8zbuE8AYDLqJfYvHkGkBKFiHqZC+OSesKP2guQLUi71FmRKypL7S8ZMCWcWik2PDZ0zY+nv7RewDVQn+j8g6qtY+KuPgreU31m0adMG06dPb7byGv1CVVZWpvSmkpOTceDAAXTr1g19+/bFjz/+2GwVc8MNN9xwww03GhmmGZQM06QyjpJoTj54o1+ounfvjs2bNyM9PR39+/fHs88+i/T0dMyZMwetW7dudAXccMMNN9xww41mClm2O4z4rWb5AcCNN95Y7+8HDhxoUDlHgg/e6Beq66+/HllZRPy89957MXbsWLzxxhsIDw/HK6+80ugK/N7Dtz0bBeN6AgAKfiBIPeMgIXUeJh46SOqS5uvhZTBNHkHfr94Q8q6I2tVBeBdvt4pW9BnFUHXHTwgO9/e1PMI8P2wEVyh0fdRSEH+XJRdeCtw/gqD4tqtC3JFcLyF9+tJJDNK/Z1/wviGuB5utNFlP545czYCzPgzX68uHxl5aQpO2kOWm+B3WsmN1Ih3ry6Ul0Zb8KU73Rgn7hfFyQll3IvVnjnJ60SW9aRHy+95IfmtLp1DiQtulhO6G7yukMllQs9f91CbFQ+i6Nt5HSy9xP5EHXNs3tlonqBGCrfSBLE1wO8nyiAglyvKYjIuyYAK6KSTogOZXKEt6smysE4Wlj2SpzWZQqpZSArzMIkuS2jKiENplVixLkxKBg4Xqb0+UMz1eLSPKspG2NKTEIaVJUqnsqhSLiOw/lZZo5WHY8ikaIxeNG0OHtqI+uvbDyQCAyP7U3hVtuK3K6XpOOd4SGlz0VX8AQPsvqa+8FeJbafOnA7BvNJ21PI225/vp/ln5wDMAgElTLeL5XW2echx7/elTAACBbbtog3qWcCJBdbFjfxHUtHsnquVW7pOe/2LBzvakKRi9hZbnqtsTneFgH2q/uL3Ud+VDSeQ5EB68nBi7nv5jMyp42VXGlkgcyNKuJLjokgx2iQPNT6+yXSIAICKblvryR9GzJHEDi+7u5/9UZYlPxpwtqUFJayiZBm3sqKQPblclNOv8b9Mv9AMAVal0bR0GZjr22Z8aB3/lL+iQ9wfVoQJIP+pQMXLkyEPuY+eDjxkzBu+99x5ycnIwbdo0/Otf/2pS3Rr9QjVx4kT194ABA7Br1y5s2rQJHTp0QIsWwSrJbrjhhhtuuOGGG80RTfHwCxVHgg/e6BcqPaKjozFw4MDDLeZ3G0Z0BCpSaMba8SOaYQl6EmChQG+sPaWYSdRainid5ddjURNK9NEenhROJee04KglRHD1JNNs0/w5J9QJ6TcNMRPrCYWGyQ88cwx0JsJs97O2AADKng5lb8Gk9G6EiAV2Z4Y+l5CVZUYoM0XbfoEtOxy/CcHVLNeI7oK0SPp/NQtA8mzUl2O1v4+bo5YtUWqjaZ/wPJYK0Gx2Yn/YzZ98nZIeXmSJxW070AsAUPknOn9ROvVFZKJTCkII7wZ3aecXqZ0/n0fE6P6Bq6BHmzeIJF08mtLiE77by9fIqA2neeskdCWrYbdtkRm8SC9wuwl65C8gFEehG0LcFSBTUDOf3caIhVKZZGwo8VDuCyFR68kImjCpkNX5IIQMDaUTaxqwrZE/hfqmNoauPXOE7dG3KZF+S6RjkyPoHBvmEfLs4271p9DFVrWgT18Lar+oKKrv58uPUUWmf8HIVCXXh6/RV0p94amlMlqspXbPPpm2v/TQWQCAOXHB9/3/RVPmUfwuKnPwXLKB2jyC+1nY03KfKARbtjMpPGAhlGI7JWNBSWtwuwmaGb6O0K6UHEJlJXEjrDBYgqG4G6OAHen5E7GW7HV00rnZjaUk9jGalKhJCeTbkjsEoWI0KWLtLvrOz6MURlgrehOyFnWQSeliuxVKgkNL7lDPEEG1NHTLiNbKlO01VpmRmVSPHfmJAIDAThq7cTnmL0ZKN0xLnuNwyvijx5HggzfohepQa5b2ePzxx5tUETfccMMNN9xw4zDjD2w9c+ONN+LBBx9ETEzMoXcGcMcdd+CWW25xWOVJHAk+eIOEPceMGdOwwgwDX331VZMq8nsLEfY8uCUD8XE0E8x4528AgKR19L3VAjaltc24q3vTWr936ZrQBdch6GloIo5ACJRLO9bblVL2zf1OJCqgyxWEEKgTkUiFHkiZ7WigiRGr1MtIJ65PWUZiUFlRC+ltX80Ae5MlhrFpF9VHLEp0NE5HpuzXXpfchNQn3MlVES6QMjrW7FsAwODZeiCe9jEqGdnhmbIqUxM9Vf2rCVkCsIQHNePlHX+hcdD+S+ZlyWXwDHzbn2lG3q27UyQRAHbnE8LY5jnhrdE5UocQtzHuCm16WqGhCGFOjhUAm/2Hkw8m9VGp7FEaH0vjkzgeJwoVYFRT7ENCGVMDQXYx+nZH/TTRWiXNIedqRQhgVQc2HfY7H3P7R1jXWRNHv7X5mupVFU/9lzOGzhG30TmWSnqy2XMuo7OMaM0++0W1zyOXT3IcE1ZCxwTCnAKUNXHUfotfeB4AcMqfL3Uc56myISpVTkS68CFq3792WubYPvNVkn1o988VfCCPRw2JARAkjyLojEckD3QuYpSzb9RxNrTTbEuz/gODEwEAqUvp+ZM7kuQpkjZTf3or6VyVaVRmzFoavzIWK/u2V2VGbaLnTckgQsJjN7FMQ6GTJ6Zfj0I0FQJrQz/rWh3gvlFyM9q4VN8jg58h+yYQ+p40nu7b/T/S8zJuJ+CvrsS6l+484sKeKzZXoG230YdVTlnhfuxZ9fJvTtjT6/UiOztb2c8cKuLj4/HTTz8hIyMj6Lc33ngDNTU1mDx5MlavXo2xY8ciPz9f8cEvuOCCRtevQQhVc61ZuuGGG2644YYbbjQlTNNEt27dQmqqhYqysrI6fzsSfPDD5lAd7dH3079g8dmUkRPB2UApb3LmUxwhHfvPszyH4nfTrCzvzuMBAB0eJR6Esg2pY9akG+DSF804mbO+1D4siKkyteQwDe2yoznKjkE31OUQZEqhRj4W0oymMqKyOWto/TbrILGQaUUz1wDPtAMaciKok4dnfop7pWdAhjjG0NCOIMsMyUaTGSvPqO0WOcq2RgRZPRqCIigOt1cQN4gdcO12PEG3PfOXOs1il1RBByVbkkUCez1Cmyu60qw++wRrNtx+KfVn9nG0LZwn6Uv7vkd/fEMfXV8n4+jO82gHMQZW5s16m8HKSFUim8IHCxNBTa6HX0PBRPQwyjZr53085U5+jvU784sk20vGmmQMyrnsY5ARBoMhf+GHBYrpmoTj5U+i3z0aqlMTR+2f8rO1PXcAle+PoD7IOdGJfrUcR1mo3ntp2WDsREKXP3r/BABAcn+6z6558zLroBPpQ7hxaSuo/eK2FFL9mZu0/UZCMDLeJnQ7+joae2WFhIb0nF6oijSq6ForeUyk/J32fRf0LNnxKKEeX131KABgdNwtAIBODzBCrKG2gIVO6xZS6pmhW84YTsTagfhweHIJ0Y3JYc5UDPVJ3D6uf0vOmg3wMyOLziVWWBJR663s30ArNkpfS2hXdVtCHr3xTsTMe4C4cwFBldmuR/EG67C5on3lucPPDnnmyr3Az3MksKgot8XmqZbBdCCWji3NpW1J22l7VYIBf9UvlOVnom6uYYPL+G2u+b388suNPiYtLe2Q+5imiaioqMPmg7svVG644YYbbrjxBwmxnjmsMn6b71P4y1/+0qzlvfjii5g5cya2biVZmq5du+L666/HZZdddogjQ4f7QnWY0eF/Jjr9H81Y/jngLQDAzSeTPkzHj2i24rdNDKuZb3XCeNKs+TGfjFZbv0UdKhYOgjp52xNnIJBFMzO74bGaXTK65dF0VPzFpY6ydK6V4kPY+D1qJsezNJWJJVlfjPyYMB37JT5BnIE9T5IuTWxED1WmbxvbfUh9N21z1EvxmvjcysbC0BC3emaXlg0Lox5K78tZhqAgRizPMossDoZCAQUZ0Xk6fI5AqQYjy2xOoXa220pczMU+h0O1s1i6MLKi6sMZepE/7QIApO+ycS64HdsscdbvhCyySDn+ppUAgK0XE3J68fDRAIB90ygbMGaNUyMHsNA5xUUSXlu0MwssUFDoPFDsNyQTz5bhaHDWluh4CXplCAIgmlYpzHPiY2XMKY0xO99N54PJGIp1klS92+kaq4YQX68yifqhpAP1f+I2ayx1fp37hvs//if+2oIQlv230VhpwejWwv8QMpVycrbjnLXpVt3iVlC7FfWkPvJW0TXlDSaUK3k99XO3V6ndt15P/V1RRp8ZHchyqLqdtfRQG+VEFPOHEbqV/ANpRcV+QvU86adbAQAJQ+m6Ct6lbLoWF+dx49gQP9GAkjFcBwcxiHMo95vsb9Mfk/shdjUjTIwSCY4bCKdjqlIiHZ9hJYxMMt/NTLbQY08OPxP4PgnbGswttP+uxoMg05F8PTYEVRA/0ZVTVjJiBK6j9aLVxvtL5mggzkI7vcW0b8cM5q0yDadwflvXeuY3FnfffTdmzpyJa665BiecQPf08uXLccMNN2DXrl2YNm1ao8t0X6jccMMNN9xw448SR4m58eHGM888g+effx5//vOf1bazzjoL/fr1wzXXXOO+ULnhhhtuuOHGUR3NYD1zNLyQ+f1+DB48OGj7oEGDUKuvTjQw3BeqwwxvlbV0kOKhpaC0FQQlH+xNSx4BG3r+8P3PAQDu//tUAECbjSTG6O9IxDmDPz07CdIuOIFg/YCXBOtSPtygylJyAwJri2BiXSRuXs4ROFyRL0OQky3yMcHZ+/7WFwAQv8e57Cbw/J4FVEYSn+Ozt14JKnPojbQkhUFETn/lEZL3v3TjJVT2n3jpRS3XGY56G7ZlB1Mf8JqYoSxJyDJe5UlkdBnzI0lZBAoOQg+VTl6jla3JN8hyXZ0yCpFWhweyaOlGlholrd/ULFNkGdGyfGHSvFjpVFikYTO/kE4rK04teRnpa7qmzeu7AACOHXwsAOCHabT0d2waCVWWnUpJEjHZ1nVGHuBU9jxaijIL2XZH2lVEDA0RO3S2r4gd2iU4Arl5jnZR5P0wWVZmQrPIK6Q6SclGQZFjf8D2nFf2StzfcbGOY6U+4QV0jtwBNObDeGXTV26N47Ku1H6RedTmvuxCKoP7rP3lNC73/iURANDhXV5ueperzwKW7e+wUszLi+haW35P7bZnnJxPpE3o2eCrpCsKVFO7hh2ga93rY6Lz36zlpM6PMLmfydC+Sr4fuC/K0+hzw99nO9ri9F6j6LhKZ8IBFcLJBjL2NfsiWcoTqx/1XSex2xMOeLlaCOFSpqeQGt/D94AvS47lsniJz5/AYrKx1v3uNazlPwCobZ1Ix8rKY4UmXivJCD5nUk1NoiWX4Stl8dUCHhRCc9BkZVTyici+8L3hkyVrr5XC70+ifmwZRWWu/5CoD2YKfjlSOlxz5IbExRdfjGeeeSZIO/O5555zZAA2JtwXKjfccMMNN9xw43cR55xzDi677DKcfvrp8Hg8hz7AFnaRcsMw8MILL+Czzz7DkCFDAADfffcd9u7di0mTJtVVRL3hvlAdZkRsyULPZ8kWJP09mqHGlBAqEb2SZjoxx3RQ+4++mmZno18iIb8+T9Cx66+jWeX4Y08HYCEDSZ9sBgDEf0Szm/0l3VVZMQuJ2F49kiwpZPoesZeQCnMvk8FF7FDkCPh77SAqK2yTlZ4s1i2mlrre4Q22euHvNWzPEoigGXjLNXRysdoY8fe/QY+YApqJVqbQzPXfB0bTDy8RYtXuCyJyLv38OMdxEQV07TU2ECL9g0Kq50bKSw42xeVZMdvvROYSAhMQ5CXEDEzQFkGFFBFXUsMFLQqRfg5YqAjsMhVCihdCfghjYsA2+xVBVU000C5uqULQBE4RNwVF2kv1aFlO7X36yHMBAKUTqR1fnzwLAPBe0SBV1PfXEPRd3Y36InIFo0M1TsFPk1EmhWgYXC9f8KPEI9IGIoSo2pXbj9tJoXW7aBx6xKIkjpEUW18ppM9Gfgeg0A0lucDn2vZnRiTY0yd1dXBigyA+/iiW90ikgebLJbSu/Nh0AEDKBiq7rDshEjHr6P4KO8CWU/cnqjLLrNsUAND9RapvRVu6pv0j6DrafUX9HZNEbdG/5zbHcd9s7qz+rmgdzvWkY+M2Ub8v+PKtoGsCgHHjiBtilm13Xq8kIIQIpe+j2QNJ/yrpEO5DU0/IsP2mjKt1AUzpQ0kwEMFR7lOfCOXqRtwAyvsQUi/IVFg51bM2js7hqeH+jWRrqTzntYaX25A1SYLQrJmsejpNvCGCs3J/c/163WslJxQ9T9f+ww5CgVtvo31K2nthOEG9IxZGMyz5/Vaz/ACgoqIC55xzDlJTUzF58mRceuml6Nq1a4OO1Y2VBw2iZ+D27XSPtGzZEi1btsTPP//cpLq5L1RuuOGGG2648UeKPzCH6tNPP8W+ffvw8ssv49VXX8UjjzyCYcOG4bLLLsOf/vQnRNWh5g8ceZHyBlnPuBEcYj1zYuQE+Awnl8ZgGQBDxA5tsOSuv6QDAH6+2slzeOogzWgWntEfACwDUZ49eVoQ0mLaUp6LBhLfKqKA9oncUwgA2DGRtkcyhaXNJ0RWKO5P6EPcgnX0g4hJhrBQMLX0dOG/CP+iugOlc4uVhvARzLBgPpavgPk1pYwSJRMCUdGGkICSDs73+pJ0+mz9Dc3ucgfS72mrrJnk7nPos+O7znMVdWLpBb6n2j1PApqBKmcKdEjemI6yCHdLfKM0ix6FSInIpfSN/ZYSjpE+C9asaOQ2VOiYhrg5ECpJ7xZ7EOGTybm0W1oJZio+FPV73jm91D7n3Oi0jHprzkkAgLQVLAqafdBZL0YRgsQ67WKoAQ1hEPQtwWm7EWChRM9BOpegiIrrY287rV2UyKqUUUj3TVUXugfG/nuJY/8X3jsVABBrgbIIK6V2CSunz7hNBY5jqlvxeG1B5074mdqiirdHZFJ9a1s4eVwAUJRBAzGykNoi9kc6sRlP17brQee91/5RloFg26Nd51oeZBFcLZOH4ZpbnM8QifEDxwKwuD9KDkRshOyIn6Y4rWRShFsVFen8nfvdwwK0IlNg2i1g5Hy6hZCYxDMHMMgmRkeZbWbJB4+j/jR5KEcU8T2gWQpFHmBeKUsiGCK3IGO/ygYT6efTg9tC+I0eXTpCniGtLGmLLXdTvwby2QQ927of/FWV2PbYP4649cyq9eVo13nUYZVTWrwfO9e9+puzngkVixYtwksvvYT33nsPXq8XF154IaZMmYLjjz/+F69L4xYg3XDDDTfccMMNN34jMWbMGLz22mvIysrCo48+irfffhvDhg37VeriLvkdZph+E4iSrC/JgOFsHN7Hjnykv7oLAHDa55TZtvB/rwEArknaDQCY8+fxAIAOH7N1wn6CmcxSzjyxiVsmfEkz47wziLDhq6AZc+eXbdNvWIKaRek0WwofTshExLIN0EPP/FPohnAoxIKEUZmaFLrmGraA8LB4nem1zXx5dhhWybPbYuaWyecmPjfPclvxzHTHJSRqGp1Dx/vKrRltl1edCI+vmLPiamiGmNePZogFZ9K1Jv+Y77zOfcR7MLtbtkDGFjazFrRIEBKZSQsCJJw0saoQv6hgyoeVdShooGTxCarE2VMeDQlQHBZBdULMptV5NZRB8bXElkUJFDKfixHJFh9bfJ1lHxPPr3xwOgCg5BLqm2GXkuDszgvYfV3agtHXimOJ4xP1vZRlawTNvFtHplRmoMalEoTXbOXM+gMATxEdI5lWSsgxj+4Xf3tCYX1lVNbrLxJaU96K+jSiN42xmkIrayz6APVneBG1dVVrqmdpW+qrmCwatwlr2bonirZH7HHO3n1bLMHU/HGUaenl+yEQxugQi0IirxAAkM5G1r53qH+LU8gQOHoXjed2iy1ksrSNWLY422R7DfGEru4zjs5R6cxgDRIAto01NQ55DHmEH8j3u1j6qLL4uRAQHhZnxBkhxGwVd46FW6vb0WcEPwcCBwudF8J8KP8AEgfOPj5a/RRxkNox+Wc6rz86NLrkzaI+UWbeqgCNFwlYCKpH6svXIve9GEVLVnSZ9rs8A1MtZNKUIV9D7VmVwpnVNQYClb/QYpAJB6etSXG4x//CsWPHDrzyyit45ZVXUFRUhJNPPvlXqYeLULnhhhtuuOHGHynMZvj3G4+KigrMnTsXY8aMQdeuXfHaa6/hsssuw86dO7Fw4cJfpU4uQnW4YQaUkahkwKgZoMzUbHwkQQf2nUho0oU7yUX1zU7EYVHcqqvp47SzCcnC2i10fIhsqpRVPCOV12OeiZYdQwhP1D6arUXm011SmEGzu9T8TlQ/W1mePaybJJk6whuSjBsu23eAyozlT0GshHdQ0dniFVS1oGsORAYjDgBgMqcnYo3YnzCXZTd9VqbQ77kDbDNrpiZ0mL+XT0JtH3WgkK7NT9lAYQcZLYxgFDGTNIXyz6PMyBUznlFlSlYUdhDCpyxmhO8k3BAmPZqS/adr+divTdAs1lgyOftIrtHYzwbWktnEYWn4hLDbET2sEOcDbKbTvJ9waJStTCmjXj6biTNfU/RyGmfdfqCy175B7ejp4XRfj/mZEL6ovYRg7LuUNK7av2eztdH1vAR103R+hHsmxtAqsvMd1wEA/naEQHmFwyXZfqzF5Smisisy6HvcWK7na3Rc7TZCn/KPt9q7hKqOtMXUN1UJnIUmElf7nNwz4eegxGlBVN2nvfq7oDd9dvofI0xcRC3b2fjEwoevbdsXGQCAFtcT39Fg5CpzhMVDSlvJWYatnP3+YsFQOkW1cwwpjTNBKAVdtmt2CdLUqqXjUHD9xDxccfg07l/Qd8DiEqYk0rHMmQoXnlU8892YF6rsb5hXdrA7XXO1TXoqYSftU96W0Uvpm63Mt5P7yHT2lQoZe3brLm1FwdD05hQfUOcCSn0FwbIZcHd5mj53nMtaYRX0Wdm6BoGKpolFuuGMb7/9Fi+//DLmz5+P6upqnHPOOfj0009/NVTKHu4LlRtuuOGGG278UeIPLpswfPhwHHPMMXjooYcwceJEJCUl/dpVUuG+ULnhhhtuuOHGHyb+2ErpK1euxMCBA3/taoQM94XqcMPwqOUSgb9lmUct19hIw362PKlKogG79wkiYHY6ici93Z+jJYuFH74BAPAU83KiwNE2CQYF8W/dSaeRFHL+PWYDCWXmjqGlP0kLl/rkH0PLH4XdrMtJWU9LEskfMGE9jZd6ZOmPbzQzhpbfapLo01cihHOqb+RuixgbiHemT3tzCh1lFg1sRdu705JJ2G6C71sudpLrN9zVSv0dns2k2SQWgZSUcJZx8JaJtYvJ9aQ6hJfTNbd4j4Tb+idcpcqc9IZz3f2rPCL77/mAlkbbv8WkdbHKkCU/WQLwO0UPAVhLD7zcYbAAZYDtacz2dE3VKdyOFbR8kHMsLW20e5/T7EMJgsoSnpB/ZelRxoqwl/XlDznc/tAUWQlZRuQyYifRslbpEFoSqryc+jXmcl7m2E9LxJ4aWtLMPKOtKrL1EiIIGxXcLkxGliUntYwkY0pEIyM18VCflXruYemNoqFWMgEAxC+je6BiAG3PGUTLORV7qF7RHZx00fAsi9ScwHz63FO5DQqob5LWU7uF5/CymCQWsLyDst1pS8uJZa2tfm/1PbW9t5TK3DOelrc6fMhEdkk24GXODgto6arwBLrWsp5031W0sZaJ9lxMf3d43XEpmJ5GAr+gJlCyCWIXo4LvZdOexJDI0huZLE6pkZFNPy+HCY1Bl+SoQ+QWgEUTSIhzbpd7NdJ5bFFvQhryBwQvI+YOpP5rvZzaIHoLa8LIsmtd4pwS4bys7LWdU5Il9EsQuRRls8N9IPeVLP3z0qZ3027r2NYsjNuNjinNprGeuCYM/mo/nE+0IxPNIuzZxONmz56Nxx57DFlZWejduzdmzZqFESNGhNz33XffxTPPPIOffvoJVVVV6N27N+677z6MHTu23nMMHjwYAwcOxNSpU3HRRRchISGh3v1/yXBJ6W644YYbbrjhxmHFvHnzcP311+POO+/E6tWrMWLECIwbNw579uwJuf/SpUtxyimnYMGCBVi1ahXGjBmDM888M0jNXI9vvvkGAwYMwO23347WrVvj4osvPuKCnQ0NV9iziaGEPWP+DB94tquLRfIMMWCbPQlh3d/fKZXv+5mnl2wsumDN5wCA4deQhUvC94xU2MjL/k6M7Oyk2aUhKdm1TjFJSeEOL2GiK3P3Wi2leUhFS+u9uponkx3fZlkBtq/Rw9OGzi0k2+pkOndEDqcW1wRrCBiC7PAMtaIzoQdCbA6EOwFTby6hISJQeMEPm9Vvz917HgAgbjfbfpQ6CblVrYjg6iujmWJ1IqN3EXTu2PVMYt2fE3xxmsTBxhnUV3GtqB7tLst17m43mwUUQR4A/r+9746Tqjrff6bszsz23oBl6SxdQQFFMVFBUGNLQDSKSjRGfxE1RjQYsRBBE43fGLEQeywxaowtFBVEBQsIFljpsJRdlu19Z2fm/v543/fcMgsCC6yu5/l8dJg79557yr1373nO8z6vISacTgNFEauLMFbYmgyapTf3IEZj77BoBiD/RfsDSs30hcnZ5WiTJDJ2pGWxGSs6EzwLWyTb+ZjasSSeLhtB10zvP6+3HdcytIf6d0wtjbe7kftDhPiWRM8Aok1kmYUVI0hXG8L8YDfqp9ht5bZ9awtTAAA1PajeXgexV3M8J0DeaYbPZ66hesVeSYLw7UU5tmPy/0fnj1/DARDCQPKYVo7KjaqfIGVtNQBg08VU397PsR0KJ82GCLOZwRSrCDHIre5nCsg/+fOjAICxv77Kdg5fOfXnolefAQCcPuVyAEDMWmZUuX+jEooD8GQyAy3pYKo4NZNiYRzPNGE7JQGy2HlYWETDab7qEO+rYB0Wodf3o77ZcYZ9t+S15vMgrYjaKMy33Lcuh9musimRQIcYe11srJ2zbcLOOYJNpP++61zWMkvPoxUHscsAyNhz3WNH3tjzizUN6FZwcrvKqa/bjc3rnzsoY8+RI0fi2GOPxSOPmIE+hYWFOPfcczFnzpwDKmPgwIGYPHkybr/99u/ct6mpCS+//DKeeuopfPjhhygoKMAVV1yBqVOnomvXrgdc78MJzVBpaGhoaGh0IrgMo53/UTm1tbW2/1paWto8XzAYxKpVqzBu3Djb9nHjxmH58uUHVOdIJIK6ujqkpaV9984AAoEApk6diqVLl2LDhg2YMmUKHnvsMfTo0QMTJ048oDION7SG6nBA2Ie2Qohhn9nIjM/9KaVEUUlxZSYVpNncmSPPAgCM/u9nAIBFz44GANQWmjOsgf04vH8yMxD19pmgaLsy3ueZaiLN4gO/pNllXBmzDgWmHYGbSYs9P6FUD5XH08VdeAsxaCq5MLNGXmFghOf00ju6m+0LaGfJ2kznDWURqxVTy6HvbALqkllxiMps6UNMgW8r9dnWFjO0O7mIyxdNGZ/X1cAMRCO1LRxH/esvp+1N2dQnDf2prDhL2p0ImwV6NnPoP+tc+t/4DazYcc0w+lmkIYXUF71uYOPHBEv4f209dwGzM5IsWc7rsGSQcHU/f3ZbTW2vG9NTFVk9inRKKZ+X8AaHiWFX6rf6vikAgIT3yTnVlUdj6hI7BSdzADOEPEqUyrP2pK/K+ZM2b7+qPwAg/22ayfq+3KoOEYPOYHe75ULMemqb9IHLmTFe+kaYKsvvkTj6LXbLXq4X3Q+tnApp1zi5B+kzsJ3GNL6E2hMoomu9+7htqsyqr9nU9FmyiJCrzH0RsUiR95k1SrNrNZq60gUQWy+WIeZvniDfF6wfCyXztS1sNZtdCmtryB+qTXSvto6kfo1pil5A+OCxxwEAff75GwBA3G5iesKs/fGuJOsL6UeVFL3enqIIAMJ7aTw9OaT9EQ2Q8Cou1u4pU0spSxJX8z1ieB1sDxB13yudE1/zjQUpAAB3C98bLVRGxmo6e3yp+azzb2OmhBk0dW2E7fePYszEKcTjuL+sEN2gaGD5IWa4+foLORKVy7OarSZcjoTRAOBmfWDSdjsbGIl1IeS0ETlSMGAKaQ8VEWKAnPqkWbNm4Y477ojavby8HOFwGNnZ2bbt2dnZKC0tjdq/Ldx///1oaGjApEmTDrq6vXr1wi233IJu3brhD3/4AxYuXHjQZRwO6BcqDQ0NDQ0NDRsCgQD27LHLB3zOfIYOtJUb0rmtLbz44ou444478N///hdZWVkHVc8PPvgATz75JF599VV4PB5MmjQJ06ZNO6gyDhf0C1V7EQ6bveicact3ywxGaVMkOsbJUEhqB44GXPtTWmvv4ioCAHS1MCpVTxEDUDqDLsB+czZT2bKm74ymYW1N5CsytSwZRZslrYMVHp4wd32H6rPjMop46/IgMWYRNlT08A3m2V1hP2e8JbKvklmtDJrteLfxTSqsnJjuiQGhzKwzOeUDzzrfLelnlnkf9WPrizQjSt5MM+kYjgLzVnBak1Q2AmQWrCWF+qZsJLW58K+mfqd2QAoAIGUH660caXiEDeny0CoAph5uw23kDPnt7xxaKgD95nMKH2aRVL+w2aFEBKpEstJ/cs0w65C43GR+BA3D7ZFu8V8Ss2bsIdYhkfu98QTqt8B2OqeMdig7Tx3rYZ2Ti800w92pX7177alHjArWtTEb6m3iGanMiNMtnjDMbsRuJaantYDZD5WsWaIQ6Xs4x+4nI5o6ax3czECKmW6oD2kl9hxH/ZfIaYyEPYyRjE1yKyZT67e9X2CeiCSGaEml+qSu5eTR64mrSsli3d1GLovZUEmFVJNL16doFAHAX879yQyKJ4m+b/g1lVnwFn33fkTsp2iWvPlduJ4SVWtW8/hbiZESM9rAHqrXr658GwBw9jCKjnIxQSpssooGbSOdiIupHNFOqe1yPfI9qnSCDiZKUlFZ2XmXMDES5adYZNq3qR9dM+5WOqZsON3vXo5CDpTTcYGtlhQ65XYtj6ndsidxVnoxYevlPhJm2KrvEobJqXN0as3knhRmSl4qJBowNjqVT9wXFPlXP7oAAFCX50U4eHT+3MqyXftAxx+o1isjIwMejyeKjSorK4tirZz417/+hWnTpuHf//73AZtz7tixQ6Wa2bp1K0444QQ89NBDmDRpEuLjo5/DRwv6hUpDQ0NDQ6Mz4fC8Tx0wYmNjMXz4cCxevBjnnXee2r548WKcc845+zzuxRdfxBVXXIEXX3wRZ5555gGd6/TTT8eSJUuQmZmJSy+9FFdccQX69ev33QceBegXKg0NDQ0NDY124cYbb8Qll1yCESNGYPTo0Xj88cdRXFyMq6++GgBw6623YteuXXj22WcB0MvUpZdeiv/7v//DqFGjFLsVCAT26y0VCATw6quv4qyzzoLHGanZwfjevFDNmTMHf/jDHzB9+nQ8+OCDantRURFmzJiBDz74AJFIBAMHDsTLL7+M/Pz8Nss55ZRT8MEHH0RtnzhxIt5++231/WAMyPYLj0ct3zlzaakwdWe4LWAK2fmCELG6crEQs0MxAOQyIhbhecsrFKIew12x5be0dpG0hb5nvEkh7UalPft8zqdUr9JRbH6YY65xtyYSjZ3xBX2P8PUqufNaTh0GAPC9t4a2N9rzGKrlOqsIlJe5XJUsnpalPseSD8RCoEmW7bhsXhZLnm5S8VXDSYRceSz1kydItHvqHjbQrKe1Hi9/ilA8ZT3nGsxMtNcBQPwOOl9rAS93VbCgPMDLcjtpqdKoEWNHOrbPnWQSKiLVqgnmbEnMLCt+WgAA2HsGLWt2e8Eu1I77hNwlI432PHeerEzuA3v4NwDEraBjwrzsFexN9Y7Zy9dIIy1/2JZOAITS2VJirylmF6PWSD6VIYaURoDGsyWX+su/jq0QfDQWuQ+vBACUXjUiqn65i+z0f8w2FnmzNYS7hvo3kkpCXrHakKAE9d1qm8D9GSy03/8tabxcVEbXcvo3dGxtd7pww7G0PdCf+qKh3lym8W6nf8eV0HXYwiknjTSJaKI+2P1T+iFzDV0nwWReDmtD7+wp5yVethnoezeNSdmJdC9U9aEyMz+ktrnZ4DPCS6qJS+n4SG8z/NuTZdYZAF787f0AgN8PE78Bfv5Irkd+ZsgzBAY/j7zR15Lz/lX3tSFidDZbZWsWV4v9XLDaEcj97Vy+ZojAfPdEspvI/pz6OZhC9W3IZnPTbDMIJes1lhSIbYMzj6XYkMgSIOcglCVKJSew2CiIhUJUDskabov0m+RwjAvYdjNaROTvyKFoga+Cyqrs50W47QC5ww8D0UElB13GwR8/efJkVFRU4K677kJJSQkGDRqEd955B927kzShpKTE5kn12GOPIRQK4dprr8W1116rtk+dOhVPP/30Ps/zxhtvHHTdjha+Fy9Un3/+OR5//HEMGTLEtn3z5s0YM2YMpk2bhjvvvBPJyckoKiqC3+/fR0nkvhq03MAVFRUYOnQofvGLX6htYkA2b948nHjiiXjssccwYcIErFu3bp8vahoaGhoaGj8EdFQuv2uuuQbXXHNNm785X5KWLl16aCf5HqPDjT3r6+tx7LHHYt68eZg9ezaGDRumGKoLL7wQMTExeO655w65/AcffBC33347SkpKlFjtcBiQKWNP/yR4vTxzYVYmIiHQktqgDYZKpdVwCtklfY0jlYLKFB+wCCA5bYlrO4XPC1NROprYhK7/2swH80xRQrSZAVLhwjkmW7LpUvp3KmeeiWmi+jSn2OtZ3d/g/ajsrCVsAMr1NhqiQ/JlNqssBGQ2yQyUuhSdaSSYubLOLiNp1MZNvyS2o+BNuzlkXXeaaae/tw0A0NqD2ZtiEmw3DM2DE80pNJtNX8YJInhmGs6k2W5LBvVX3Jc77M1i1tApqgcsAmwnePZ97dtvAQC2Ben72+eNpN/L2IKhKTrljCuZhaIp9ClsnCDUzc5+iempi6+tVmaoYsrrzZ32EdKtLBYC9ugeMeeM7K2wb883+7WxgOoXv5aZPWEg4+xluWuZRZRrnOsSSWRms9Q8h1wzNaMoTZEYjLr5kslfQGU1dKVjQ357hFHz+dUAgLodptjW8FIb/SVUto92USJpbzN9hi8kZiW0gPrXX0n9aRWjC+KKiWFyl5PYW42J9KcI2zc6jHP5mhNBubub2Z9GArXpf++8EHU+ADijx0jbOZRoWsTXbRikqtM608PIUorDdFXuRWXS2kbqGcNpHyNMPZ9j0zQS3ud+QuMcU0ufEhghaOxqiosTPtlGZcTYz6+E9wz1rFVidX7GyX1o6YOoa1qeXdLmkMNmwinql+vZYuSr7Bt8dssKAAhFgni39PEjbuy5emUduncd065y6upLsHHbCwdl7KnxPTD2vPbaa3HmmWdGqfsjkQjefvtt9O3bF+PHj0dWVhZGjhyJ119//aDKf+KJJ3DhhReql6lDNSBraWmJMjnT0NDQ0NDQ0AA6eMnvpZdewhdffIHPP/886reysjLU19dj7ty5mD17Nu69914sWLAA559/PpYsWYKxY8d+Z/mfffYZvvnmGzzxxBNq26EakM2ZMwd33nln1HYjbCDSyqkJeHbpltmJaKuseiKZwYgmikPLrTMZ6+/OZLW2GeG2XbZjPBtofTrbW2A7xpCUNKwjcoeJcREtEPaas5C8D1No01Bhg6iM7q+R/kX0RMEk2q+qkGZtmcv43VzOFWcyaS4Onw6n0wzVvYkYHqWtcCTvFU2DpKZQyWgbTCbGzTPOvg8TO1Q73M44iXYl2ItYvBhhAlgHEb+OQ/lzU9QxniaeyTfbxQ6uINXDv5tYh3AeaWlCiRzuXcPMlIzt7r2WijhmtaJv4XGfN4au443TybgzMEH6giwGMtfQteXjFCsAVGobVbLD58W7kxkd7qOWHsR++YppnGt6Uv3TrOl62FrD3chtEZbQYVobiefrNEDXR7A3MS+Brzk10m4zLU8cM02hLLvA1NNA5434+R5gJspdw0yf6HTYFNOaXFdYrIZLiZkILCXj2aTtnFT6eHvIdNweqnd9F+qLZjZ89aSbY5z4oWhjqEcTdlNZrfFu27GNm+hcg39BzO9XXxXwcdRHeX0s4z6DzhMRqxAZE0dalobjqAz/4i9t25UWsdmSaorTPI0//1IAwMLXSNzbe+llAIC+BaxJ20b3l8vreKYI69TWooSwMYqF4fEXiwC5R+VZ5nJomSxlmsm5meHtStdf1UBmLPmxFdhpYUgBhOPpGvQ00rkTv7Cwd8Lki7ZUmXLyc8bBvjmZLGs6KLWPJIsXpkkSkBv7MAMV9ku2i/GnlYUWOUpE0n/xeVtDMIx9a60OJ1wGVF709pShcfDosBeqHTt2YPr06Vi0aFGbmqgIP8DPOecc3HDDDQCAYcOGYfny5Xj00UcP6IXqiSeewKBBg3D88cdH/XawBmS33norbrzxRvW9trYW3bp1+846aGhoaGhoHDV0kChdowNfqFatWoWysjIMHz5cbQuHw1i2bBn+/ve/o6GhAV6vFwMGDLAdV1hYiI8++ug7y29sbMRLL72Eu+66y7b9UA3IfD5fmy6xRjgclUDUcMyibFBRMTS7cYvRo8cRjeIwj5PoL5e1TGeUHL8Qetdto0OlPlXVtFs6zbDFNFTpIBLNBKw+TtHS9T36LBth11aEEmgWmfMp1ad8CNW/9HRigiSJaeweSwQZG016RB/mSG+BcqofMlLok00wXRIFlk7bXRZtlbTNxZFsiV8RM1J8AUUOpa/lGTcberb2J91GzDpiUiRaqSnHfJlvSqV+zPqay5YEq5ykWUz7wn1Jqxb20e8xSmPBdcsxo5PcMusV/VqraMzsuqfe95Ej5Z5f9Of603E1Pal/67uYDFzq19X286XQGImZn0oXwhqRhlwaM289jXPaWurfliwzakkYPcNDDE/8RmYtnRowPockPI5kUhkqFYlF82VwAmA3Z8hxc+RVqAtdh60JxAx4G3mmL1F9jXbdjjXZ7sapdGzM51TftB3MBHA1fdWsreEnW2sca5KY2A2HeMdas0xJoFzD+cpdHNpaS/ltkbiF7+cY+vzqmwIAQGA37Zd3GjFCu6tNXcyu31K/FM4k1jjCSY/NhpDxo7+YmT1+LriFLeHrJdxssl5eHou3V7wJAOg/n8S/8TxU2y+QaDW6v/IfXENlOTVAhklf7EtfpQwwhZkSBsvNTLkYfMp26x9gYdslSTvXW1LJ7LiMjqk7mfZLfp/6KvstCk8O8/PKsOhFnVF96pklRp6Q+93T5v4IRE/aVeql1tao32xlSJnCigkBwM/bSI1F/iF6sYB9H1dcgK6ro6UUOco+VBqEDtNQnXrqqfj666+xZs0a9d+IESNw8cUXY82aNfD5fDjuuOOwfr09m/2GDRtUGOb+8PLLL6OlpQW//OUvbdutBmRWLF68GCeccEL7G6ahoaGhoaHxo0OHMVSJiYkYNGiQbVt8fDzS09PV9t///veYPHkyTj75ZPzkJz/BggUL8Oabb9rCLS+99FJ06dIlKjrviSeewLnnnov0dMfMEN9tQHYwcLnbWCZ0RINY19hd7PejtAk8e1SRJU52S2Y4MkuyRNCYUXH2dDYSIRhR3iusVeGIGEkjIakU5BMAXF/QdNedSKxHRqAAANDQh5iB+M12T6MkTj7bkEPn2DWW2pfxlckAJEoCVU63ohIGS7ShpLVgLZeRxWMm6Vl4u8s66xSPHU74LFFnYSZdnPWsHE5lplZx2cyGJH5r7ldzBumBdl5CVEWX9/n8QY46Y92QEUPjGVvFkW5+jkLkcfdUWNg50V2ILqiWZ8UO5sfgVD6Zq2gKG46jsUz7kurnaomOwgtzWh1vGR+TzP0Yx5olvlaSNzAjlUn7+0upDoGVW1RZZeeTd1baWmLOmjhxrcC/hxnSMLWxJY/1MN+yZkrSjCRaGE3WwKnIVb4OvCXUJo/ohDhKUbRpHk6ZI9qbxi6mT9CvJrwLAHhs6U+pTJ5v1XfhxLpfyX1DH5WFktaEq/cFe07tsaSJqaJ6NebQODZzsnt3d7pOW8uJDYnfTudoHkaFJa6gskoX0NJ/c55Z5tWnvUf/oOpiVwu1eekLxwEA8r41+94KSXek9Jj9eqnfJJqsx5tXAgAGPEJaroon7SxyykxqszuJtkc4kbkIa4yQ5RmiItk4IlAl7eZni1Nb5WUmU9K1CDtu9Xfi50xEnmldaVwlGXrei9RvvWdSKPHuXzPTIxpEYc0skafqKcuR0xFORROVgkbq40wfI7BsF5bNCDm0UqJXdfppybOataiuFgcbBZgaWU4MbvDKQqS2DpGjpKHCYUg9ozVUh4bvhQ/VvnDeeefh0UcfxZw5c3DdddehX79+ePXVVzFmjBkSWlxcDLfDemDDhg346KOPsGjRojbL/S4DMg0NDQ0NjR8mDK2h6iB8r16o2jL6uuKKK3DFFVcc1DF9+/bFd9lr7c+ATENDQ0NDQ0PjYNDhxp4/VIix5098k+AJ8bKcw8DTHRcXfaDQxkI9u+zhyMoyQATYjjBlq21ClOjdUZYy0BTBu8O80S0Z2q2pISS7vEVMCQAGC8MFram03cuh92FeDtv0a+qDvg85DAEBBNPpmMCmcnvbxAbAcSm6HClpgvl2w0rANAP07Cq3bW8eSMJxd5CXOVicHlMdXS8FXgqpHpwCAEjYae9fg5c7nIJNCfMWmlyWAgBL6L/zNpNlBElrw/tJCpimPEf4/ybT2kKML8MZtOwWZvuBmD1tK14jyXG2OpQPo6UgWd4DgK3n2K/VuD28DMOXdNq3bL7YQn3k315tq7/0nVuEvgBC2SkAAO9erpdcf7JMI+POy6GRJLZPaKL+q+9Dx+8dGj3viy+httR3ZXH6Ojq/r5rO0ZrAy3McaCDpYaoG0XHxO01WO/tTqvOO0+K5jfZzBfbyfcQrZXtHUWHde9JyZ83r0Saxvlrauao/1W/9FWQivIBtG/5v0DHU9n3ZaigzW7Ptm26mAJ3e962zHSJpVhoGUFBNxUAW+/NQ5L2x3ba/1SxWlqYiFbyEJmJv5/NJIEuDkirHkdaKfrNfuy5eImvpTkt/smzsreF6bCLhvixRtgVZtlRltzpS30jbnKbBSQn4Toihp0gkJMWMyCvkenUaDqvnqiVYhk1+pV/FPgEuF0KRIN6reOqIG3uu+aQGBTkntqucusZSrN/9kjb2PEh8rxgqDQ0NDQ0NjUOHy8Bh0FBpnuVQoF+o2ouIocSLLkfCY2V1YJnlqfBkp5mlQ1BupjuI2Mp0JhoFLBYLwkw5xJ3mOexmd0pkaQmZdqldeB8O73dJslQ+xrOJbR8yWOSdmQIA6Pcg1V9M/ADAX0nHBHbV2cuUmaDMJnl/ZSnAs8sIh+S7W03xaMTHSW/ZxgHdKFTcXU8MlKc5bGtrDCdadhoAOscBAFLWVlN9eCYdZgNP8TqMeO02Fa0pLHxu4D6xin45XYibDRpFPC82CmJ6Gsqk/ooEmG1qEDEwfdQNNNm5pE+IcfCUE/PjFhNVEeoL+8XMVZj7KhRP3/1VdtYOAPI+pnrUsbg7fg+nUYqx949iFxlKGL+HU4BY+tcrCYLFCiDRnlzWLdepmvnT7xEW5IcCbq6bSRmVD5Lk2/RdknnHl3KAAI+NMFXNqVRWAzNZMXVsLdJg/sGo6k9tSNgptgJcG75uxR5j7/l8XdZysuRKvsZPJvF697+brFdzFu3T/R2+7li1cEYc1fOMLZ8AACYOOZVOyWydpGFSrImlP3vN+YbafCwlQfcs56TcfC3F87Uet4q+75hK+xVfRNrQ/FfYKNNvstzBLinUL5ZtVghjqp4ZMXR9VPdnC47FPHbWFDUtDqaHf/PWcWLtOvpeN4iv6d6DAQBJK+i6VomaLYyV6lklAGcmX9IuMWNlNDnOLYLzNtJ/qVRH3MeK8ZfnJBvKqutTzq0YLTZ0tgry1XPTnnrLlZgAuOz2OkcOWkPVUejw1DMaGhoaGhoaGj90aIaqvXC74MmkmVak3JEoVlglawoDt0MLJQyUw6RTsUyOdBHWsiTFTZT5okDKcKS1ES2VlO0KtDFzcqTIcNo6qETQkpxULBn4XKmWGY7MastGUyLjnIWN9jYJI+XQkQnb0ZpB3z3NpgbM3WI3K3Sx7sbwUb3drGsCMxaSMsXd0GJvhzVkmsOoXY12EY3HoT2L5BIz0ZhFY5iwg1gIT220PiuUwgl+lRaOdWspNMNvyrXrTYSm81VKOhtOh7LVrFOoe5btEDHZlH1VkldmppRmhVkv+WxNNMfY08TpVhLokeDZQX3vYULU20C/l48h49S0L6vp3KyhExbKZU2MK3oWSXacwOwSM3gGm9pKaiHncQk7ovuzOYN+qx0oDB512KYpdP6uC9luoJWvLZHhUXVRM5Tq25Iefc0nbfDYjvG2CNtFbfd+TWMmD01PKzNvo+ja3zzFZOD6PkmslejBJp42CQDw38UvAgBimK0Y8z4Ze344iphepcFhKxErP9jan+wZYkq4vxwaSMNhiJr/MpUtqX8UI2NJwxLLKY1a89Jsxwob7K6lazuYR2W4g7S9coCLP8nWIXulhZnkayXwGdk6CFMPNuyUPZPYPkUxP8zOqeUmC+ulmDpmpFxiiJzGFiFsteEWtjvG/vxSer1UkzlXzwS5dpsdSZErWB/GCZaNJqqv6MzUOSxR5spCwe0wA/V64YpYnjVHEtopvcOgX6g0NDQ0NDQ6CwyYb63tKUPjoKFfqNoJd1wAzX0p7YqXZ3mejTv5R2ajLPsbXTm9zRZOORFqO/LO1UaqBABAs0Wfo1gs3lcYJ9nBkdzTmV7CyUIBpu4qImafTp2V264TUzMZScPAs07XeksiY55tZ/K+kiRZItkSVvM5ZBbJUUESQRZTSWW1ppmRaMK6uNnwUlgwlaBYdhTdFeuFZD8VNensE8DUWwhbKMcw++JlPVZSVZP9OImAsjBa3jJ7ZJBopQSBHaQzqi2kmbawXcLiSASfN2hh55ociYudECNNZi49dXbGLRInbJJ5+/vK2OyznPpcIsUE2Z/xGEkmkiS65jzMUKkUO9Y6McMQyUjlE9OHwf3qApfJrIGkngkzcyDfrTq3YKaMp2H/bOVExrk0Vsnb7P3ur+ToxCXU5gfvflj9NspPx/R78jcAgLR1VGbidjvj05zFqWaW0e+SoLl5Nd33XVdbxmjnXtuxkcpqAMDZ3UcCAEJjhgAA3n3hSQDAqaOn2fb3fbmN/uEy2Y+dp9H13+Nluh/qf0JpihI+YHdTiXyTY1hb5d3BddlXtBqAmG176CdOJo4g36tpzKBKjmxmPQvepvGuLaDrwL/XZL3k2lUJnh3RyPLcCjMDJOm3XAnE/LQZeM5pqUSDKIy5un8T2oiohnkPyHHKWBeAm9npmqG0wpDC13BzTzKU9RdRFmejno2I5f4WjSJHSxpdc8227+TyJUUPs1lGczOMyNFKjtx+Y0/NUB0atIZKQ0NDQ0NDQ6Od0AxVO2E0NSHmY4q+8eTx7C6FZtzBLjQz91q1Ncys1J5BnjIpn+22lycRcM7EnOE21t9lJiozTtFSqRm9x/ZdzfNlP5lxWb1alA9OrH3fRjsboxIti3bKoeOy+mXJedybmZXrTr49AZ7NiZYmwr5Fqio+jlITn6Uasx/dNdRPEkVn/sD14BlWc5d4PhdHnAmT1eLoM8tvUoZiSEQLFHKwWa0SgSlMVvTt1JrJuhv2vxK2yOXQZcWVsrYnlWb15UPsDFHOp2Y9Y5gZi3C/eCq5HzkFDjgyS9g70TUJM2SwrszbaF5TihUSAoiHO2sllb3tZ8QAdF3CnlvcF1IHsCbMU2b6EsmM3s1JriVZcziFygrH0HdPjSRzZk2Sx66DCsWbfdHrX9SmrT+j6ytuN6cBOpm0QEmvE9PXkkJj6aulMprSWQ9VQ3USVgoAbiujKLP0b3jfDNq3mRnREBPA3RbTuav6UZsTXkgBACTXRTM+4R70LNg4hcrofw+nmuH7S/zQ+j5DrBhOo48hYzYCAL5e2hcA4K8w2bnAsaTR3FVt92Pzl/cAAHj3cooZZsPM5L77njeL9xJyiZWRa6M5m+odU8fXUIjqXdOTOiN1PV0X6csp83XNsTmqzKQvyZ9L7muDvaqUt52klmF2PiLsZr09Ms7KzrklCbKw8ByBZ02bRRWiZ65ipKR9cs5mky0TFid52Vb6jZ+x/vVcrywqy81RnaIBE/83t+i5du0xz8+6VsViNUfrAI8K2s1QHZ5q/NigX6g0NDQ0NDQ6Cwxtm9BR0Et+GhoaGhoaGhrthGao2guPBy4XL03tLgVg2hl4JSO6xWBTqOrk1Syu5GWi+oFEucfWEN0cu6ua9hfRpSw7WcTqTsGlkczhvLzEIuLkiJNKl0zxYkC612L3IKJilbWdv7LJnqLpmcZX2yUDuxRTb4rSlVFfDNW9hW0QYst5H16SlPD/8pG0pOFtojYnbeK0E2HLrEmof07ZIktoBhsUyvKWCp/32pcCIwksqt5bY3ZLHLVFQsVV33vtIfZGHC8BSD828zIY5yaxLv3JUp8SiYpVBAvuJWVPaxIdU92T04Zw13ibeekyw2JxIKluxHiUhcNiarn3GOrfnOVm26znFrsE/y5zuUTql1TM/bTVsPVBz5cpVH/Dr3gJs4bKSOKVrPRv+BqzBjqIGJlTc7j408tLPBEWo0cSHQEYvOQX5qW+mCpz2aQ5hwMZtjuWmF8n24HWeF5m5HEXY9KYemkPovD2P06if/BKWvYn1NaqQroeavpSmVkcPOEK01jVdZc60GdisbkkHL+W7o++T7O9SB7d355qDtqoof4qeIPaWHIitevr3XnWItGYZ7nmv+Vl9nw6z+YLHwUATDz1FwDMJV+VwsUpExD5gN8S5s/PpuZcOqa+i93gMxJL/bd7Et1nsUX0PfM9emYYiXzfW6pZN4RsPZK+oOVACUpRpsWyBO1MhSX2BZJ+y7I8p1LKyHdl08LXjljD8LWlzmXYn3VKuA/AEGmEBLIExAyUDXrreCmSxeuuROojTwk911v70FjF7LYsc9ewtIC/W9PYuByKgSMKzVB1CPQLlYaGhoaGRmeBtk3oMOgXqsMBYUPEaJOhhNqGxepAjDCFHWL2KL6EhZxioyBJShPsxo+RZMt3ZihUehOe9bbmZ9qOidlayhWyh/sqlsdjZ2AA01jQqKHZujPpKDj5pyFGjmKXIL9bWTm/iJGpTTG1YgbpSI3DIvCUjVQ/b7mdabPv7LJ9RpK4PsxuuNj409PEiYQ5nYmkhRGDT7FwsKGcZpxqlpnK5oE8k21lY09vjQhe7RYSrjYCCIQRExZJCa/ZKNG3l1iYnN3C2nGRfA01djXDwusL7NdEcwqXxZdOcCyNWfgrarOH+1sYn9gqqndDb9PCQZgbESFX/J7GoOnLVNu5ui7iUPPLqwEAlQlUhqeF6pReZTKTIhCGzNrFUFKYVf4uNg6KceWE1pIqBwkmayK2DeL4IEafYE10Dd+LPV+ha6chn/qtZjAdmP41orD6D/MAAGN/fRWVmUn3U8pGtpKooOu37Bj7/e2rZPuE8XSuxGWmgFzu05Zsezh/bAw/K3jcPXwdpn1LZQ+cvAEAsKB8aFQ907vTdXlO/lcAgL7PkqC9dxnZJoQGUIoZJFP9YyqYNQzbmVOXxTbB4PumYqC9bd5GO6P3z1FPAADu/t2Z1D5OVixC7QTLvexii49NV3YBAPR6ntgsSb+kwIx+hOuj0m8JM2VhSVRKHoHEi0iwTKP9maaePy47M21NpqwCavZhBmpIG7kMl9cetBKzi5gqw2rZIBYQbDQqgRiI8cIItwClOPI4DLYJOpffoUFrqDQ0NDQ0NDQ02gnNULUT5T8fBF+EZiHpS3fYf2yDqTBaJfkxr+3LTEpC9SVdjSQjFv2TaJrKLMlpJQxZkuLyZq8kOuXfg73JeE6Sk7p2S/LkaGYqLCHOIUlPI7MzPofDpE+sGVwSkq9mm5aEoXGc3JhnrkZYaAb+ne0KQpnE4sSUW1gOa9utiYxlm6TRYRsEp9WlR1LSiIaKWb2mfGKdfBUWK4Zq7ms2ohQdmMFaOEHl+K4AgKxPmmxli6ZJ0s0AQDCJdS38W8Qjmh5O/5LELFILd4Ycyl93/pQtJWLNGWOvp3hmz/qVj99+HgBwzD3XAADi3ybWrSGXj8ml6zNpE7OIzAT6y8yx3HE6jVH8bhoLz1ucQoi1RzW9qI2lI+n3tH9T/yU3UEVzbiJmBb82+2nHw31o3w9Fi2LXzFQPt6fQaY1jBpUHMWULs4gWi4OYRta3cNqXuBJOdsxas4RdnGw6kRlJ1lIFc+k6KGEPxh5vXKXK3Pqzx/n8zDw0Upta0uysTcpmKjuYyFYN9bSfKBRV0mwALk6BJNeX6ANrC1NoX74OfDVUZvYtlKalnJm+rB7E6lR+ZbLNldX024qrKd1Lbz/THcygxu5kxkTuPbl3+R4J5rFmLSZ6Hp22jvq6Lj+W28b2DnydTlnA9g5/pI8+/6T9PV9uovbtMe+RSDZpvVLYb3TrL+w2DwWv8zOGNUlesR0RrZ0w5xZWSpl9yjUkrL/TaJghzzYpU55bEevzS57PslrAjJR6hkn/cT1NlovPzUyqq7rOrKeUL8/1amKLXYEAXBHns/MIQUf5dRj0C5WGhoaGhkZngQHb0uYhl6Fx0NAvVO1E8pYWbJ9K3Zj+kSMajBkEazqOcILdzNBVbU8MazgNNCWKTrZbWBqJklHRMjLj2k4pE1wSbbiFz+VIkox4TppqiQL0ZFC0lKRfUVErjvOriBiZyQhjJGZ+1mgv3ieYRRoqNxtieup5xiaszW5HVFobRplOSLScRNopSD/xuZXuiKsb2MPsh88cM5cYYu6tBhAdHSkz1pz/MRPpaVuf4fGYY+RjNshTZS+rsS/1c30etTF5MzEYXk7l4qkmdqzgVY6Is+iIpK8jATp22BxipjLX2Jk9MZRVyYhF52LY0/YAQFypPZlw1RBOs5POLNw21tRxdzVlum2fNa/2AwDknFWsykzeyIaeEgkmPzDLkLqUDBVb2QSzOYuuz5oerGnh6ySmIZrpzfmUyi4dTddUXBmzBpIaJ45ZDt7c/d/0Q1Vfui4bu5h/MYStyuG2BRPsDE59F9Y9cXflfkz3RD3rs5rL6ZrfdLF5TOEd2+gfbPLb3D0FABAopetuz030KXGWS3q+B8BMfxOKo/plrzHr2bib2cqMZFv9VDQvs2LCQEoEbPH5ebbdkyzRiL5qjipmJq32J3Sd9X6UTXi5zMIHuA59iG2q6k9tz9rKz6cG89pz7SYWPZ0/U7sSEylaOdEzevkeVeyTMO6iSbJqqOTZFmePAJR70nBqpCQaWpIoMwvlCZjssYI8Z8Q8VFYRhLlio1R3sl1vKc9AiWKmjQ72TxKVB1thGEcn9QygGaqOgtZQaWhoaGhoaGi0E5qh0tDQ0NDQ6CwwoBmqDoJ+oWongkledHuLqOodF5BYOa2IaN6YBlmKMy/OYCqLPmN5fYFzvUl+NnetPYeeIcLxNAfND8C1l8P7OV+UMv103AxilCcCbkWPi4WDxeIgLKJ3w25V4GaqHR5eyhNxqCwjCl3PYnuh9QEgIm3l5RgRavuD9qWc5h4kZvXtZvG0UwRqWTpVpposDC0fReLdjJUkjm3qRkstdd2obfGl9nN5msW8zxSlSzi56k825bMaswIWij+RlsFcbPwny4/uWsvSW4SXu+o43x4vG8Stp36OL2JzzlS7FYIsQ8hSn3ePuRwq+cvcSVS/vPfKbYeKyF+WawwP95UjSELlKoRpfJmygeqZwMtLsbVUlgjcWwPKWhEAEEym7/Xd6RzpfnNpc3cWKcADxXzdpbBxKot4W/PZwJVz0DUOpL7ytFDZFYOpDslbzOVcsXyo6U37Jm+lshvTqZ6JO+1Lv6F42h7Ly4YpW0K83RzTxK20TwvfYlLmrlNoHx/73qZubOVj+fqtoO99nqWyhz78lSpzLTg3Hl8TvhWk0P7f+g9t9RtfdBYAoMd/adnR25OeA/1vJFNMxJlLVGJgUTaW+jVtLd8nEftCw85xKdSeVHkO0Gf8LnuORAAqR5+nnK6vHv9hU1XOqykBF24O7oj4eHmebRWqTyoAAATjzXszazEt+6q8pHyvyjVsyDODc4i6GpsttYy2dQHMoBdlTSJl8PNR5cbk72JTIsvjIb6PPM0OE1kAnkpemha7Br5PZIlOnUvsE5y5Ea16JX5uGpL3M4uW9l3hCInS7QqPIwS95NdR0Et+GhoaGhoaGhrthGao2om4RV/Dl00zRsPTDYDJTAXuollm6KoEtX/MXvoMpbOVAAuDW3JoH7+kkXHOgiSUN2wyRzLjd7lpai2Z1Y1GR4bzCDMRktpBmBixbvCYsxE3zxrVLE3KlBkLC7UjjhmMpJdAWgq1L9FkqEIc9i6z22A8Z7TnlCnJm2kmG1vJM1VmWExDz+j3ftknmEKzx1l/fAoAcP0rlwMwZ+O5y6psx4lAW0KdjXiLSFX6ODHBdoywhMqaQVJjSFh3E4dNS6i2RUzvFmaKGUaXpMaQfbiNMksWg1Kw6aW3lhkfa3/zuLk3c/ABC58lRQbi/La2RoQtdBikRgJm4EDSVg5cYCYiyONZn2ef0buZAHp+1l8AABd8cSUAoDCV+vmz7d3NnX9JO8d/Tsxj1hd0Dg8HAoRZVF95SjYAoLan7VTI+4jTHMWa4x/xMWtUTW3xVdD16d9Dv+/8KdU780s6NmETsSLheDaXDbj5d5PJKp5A29JWM5vB+6Supd/3nkrn6LLULioWA8vqATS2r3x5rPpt8Ms0NlX/x0LmngMAAAMfGgwAWPtbMhOteZJY7XS2jGjIo/GPZNkNVQHA1Uj1yPiiGoDJHsm9sON0+3UrYxXh4U/ZSPUPbKs2y6ypsx1jTcUEmAalrWlUL3eQmcy1Zbb9Gvukq38Lu+XZxZYmYqnA94t6hjmYZ5WmRZj0JkuAjgjAJdBH0sHI85INNCPJ9HtLhl18HtjCdaiyUESchkYxY0E7u2nwM1AZNstzVBgrt0NEbz2W6+OS+zccBiJHSZR+OKL82nv8jxT6hUpDQ0NDQ6OzwDCiJBuHUMhhqcqPDfqFqp1wxXqVmVvCO18CgEofEzyV9tl+mzn1TtpCF2r6JzzD4xmir5lmpI197WljfHuJvfHsqaYNVqZCWA5JcyDbZeYkMylHgmVnolGbAanoFniGotJBSAJRLlMdK/tJODOzYDF7TR1RuCsnFWXzxfhamgnW9KCyQxxOHVNDDJXYAUgSYAULwyKaDrElmPm3K+j8rOXx8GSwNZW1QGWsN5EZoxipWmelUektwrZ9nSlmFBz6MSsUA6aSN9utKyJs1SAh7m5Oniv6E6ONMpXhoWjKHLYZKqWLj/VbwqzxOUKZrGVqtbCdXFbNoBTal5uYuJPKLp5EZQ/oTqzrRXfdRGWcXQ0AeLTnv+kAC8vU1UuMSe+SqwEAW0fQ9qSPiFETGwJBAjsuiIZpyZPzAQA9FvxK7dPtDXt6kOZs6k83664yv2J2uITGWZipCLOk3kZqh7feHPfsj4mF8bAez19J/VTXlcYuaRWVsftktjxhAjitiPVcbHqatcS8fmpey6d68V1Zn0fnF2ZqwDyyuihYQwKtDZcLI0X7l42i7xlrLAms2fZCTEKb8uyMVHMvZus2Uj3FeiFjNf0e2MpsrYWlcT4zFLtZz5pOTmfVyIakiVvpe7ArfY8tpeeXf495vwuL6BHGSRhxvvaFnTWYIfXsJNpeUhOpZ4wlFUwoj1hO795ae5kBu4ZTJTZPo3YE1u6mOkkaGcszRAyRRUOqtJJiLKqegVyPNoyQAftqgtJy7Szl+lmS2beVQuuIQGuoOgpaQ6WhoaGhoaGh0U5ohqqdcPl8ZhoZXudX7A3P+nr8fb3af88FfQGYa/1u0TBwQt5Ai32dXc3qmOlozbTPSgHAxYyDRG15y5ixkmSoMmsS3YGY20mknjUBM58/zFopT2aGvU3MgkRqJKUCJ/1NY60IRzRak2sGdtC+wUwxh6TS4iq4PjwTbHYkkg00BG3tsoHb0tiTtBuNHIUWt4vmCBLtJdF8isXhPgmntdGPnFDZLSlnZKYs7JJjhirtgOglWM+h+gKAS8wzhRHjsiQKyV3JWq46/vTZI4ogaW0s51bGgwKJeIyz60bEyNNdQ+2JpFD/1xVw8t+1pl4mxFGG/kpmV5PofI0ZVEbKCvpc5yK9YFwmt/0TYlJO+/JmAMC0CxaqMp8sOgGAyZD4q+RxQ+fYPYa+e+uprJYM1kX9jPQuPRZOAwCkfWIyFQ2crcbL8pqkbQ69ICMcsDNZouOTqM/spWbb05YRSySGmXuPT6FzMKPanE716/YO1UtSDG39OV17GV9FX5+1+XSeuL3UpqYse1Kk/P/R+SN+2q/gLbrWa3rQ+MeVS/Jky/OA7ykxB5a0NrtvpX1jvqH6h+I5cq+JE4dzt+85hTrP22Sm/EktoutONHyKvZIUWGyYm/I+pcYR9qh5KJmFejgRs2GJwFXavQRhX/kalgTqwto0iFkw/85aubai/KQfIvwcdEvKqVY7zSnPPF8ZM2bCMgs7ZGHjDfXvFlvb1PPceT85jIYlulcYYAAwmOED38dWM2XX0SKoItAaqg6CfqHS0NDQ0NDoNNBLfh0F/ULVXrhcpsZGGB9hF9pIjpz9Xqnte/3oAgBA/BZmfHaRtirSg2aAogkQbZK3OnpGLlqEsF+0CawbibfrdST5rzAValbaEh19olLQOFPLyGySI+GMTNI2uOvsKXMMv6knkSgklZyZZ7C+krDtHDLb3T2Gyu62ievFs0ur/qh2KDEjFYOpPoFSmiEHymlfSXTbyhF5Say5Ap9bNFiGO3rVW0XvyXk9dr2VzFSV3xTPzmV2qlK8WP7tbuBoJPaGkvQ2ajYuM0JOfyEzXOVr02QZI9F6iBZO2AE5lrUn6pySUoe/u1upT2r7JqkiI172H/JJyh76aGV/obqe3G9l1DfBFE4EnchjmUV98b/rT1FlJnahfRtyOYItl5MK13Ai44HE+ITfp2utuS9HXf2TdITZXI5omwAgfie1sSnH4bnGbGFrgv2RJteBJCFO3sZ+SvGW6MVEeyRjAkenVfZnjQ/bE8nY7TyFk/ry8PvLqd57jzEZDdGBubnuMewvd+xdnGT4OPrIYXbMw/dImK/xWNYZCoNlhfi6bT2PrzfK4IMkvgeC7KcV2Gvw/twuTopePtgsM/VbHm95Vjm87JReT5jVZLo3E9ZQFGPDEHtaGwAoH0L91GUpPV+8VfyckQTqotdyMwMlzA+z9S5hzK2+c6x9bOnDV0UWM6pbJGya2SLYIwslutYdiPafUppSvm+U1xU/a63pdAAAddIOvt9Fe2XReqmoSdFV8X1tBHwwwkfpJeVwJEfWovRDgtZQaWhoaGhoaGi0E5qh0tDQ0NDQ6Exo95Lf4anGjw36haqdcAX8cLlZOC4pSWTpRcJvreJGMaJrpKWecIwpEAWgjDFF3C1LE54SSi9ipKeoXUUULWaMHl7OCjuWMNyS4kWW61iorZay2liaFIGuhNorelzM+cQE0ykKddvFt7Z6SJoXofiF0mcRqIeXBHM+56UArl9rLi1NVQww2yVC4Ywvqe4hTonSkkLHxO+xLyeKfYKbheeisnc3mfV313GqjJCjP6TtElIuY+h1LFlIOTsspoc8XoZcC7vIgVIJYtXyBy8jiNjW6SNjTX/jsFJQpqtiASEpPZp57Fz2MUn5jELJg91MM0YxiQz7JOUR9+eFtHQiPd/6Kl2vcXup/pX9OUR+Iy2DbZ9qXfql8U76lK9/rnYwicpOfIGWbT08JoFysYOgD18NLyeWt6gSJXWTLAPG7q62tS04kOpXcgL1d96HnJomk+rpr6RzNedYAiCYp5clvvRveOmRu9xfxmLwuXSuHNBnzNW0f+mptAyVs8I0yRSh8u5T02z1E6sISYUky/KyBJ24kwNLOBWVGP4CQDCR2lQxiK+7HNon+UNe0uU0Mpmr6SStCWygm0qfnmYO5OhluX4W0b9rj6Wlu6RPttN2uaZkOUtE3fzMkfRM8WvZHqDeTDmUmEKBN2K666ngcXUEdTTl0zMmbmO5/VwOGxArvPU0NtX9JWUXW7LIEroE4oiwXGxS5B6wWBeoJfpkTg9TwYJ8PtbIo6VnCSxRS/5iwRCS52gbdgryfJQ2VVQdRWNPI8rE96Bx1CweOhf0kp+GhoaGhoaGRjuhGap2wkiMRziBZksi+gUciYybzBm2JLY1utGsNnk1i9DZRsGIodm8p4JnQTxbimTTTFdE6wDgyiZLAwlZFnsBdwMzQZIyhVPRRFKYuQjaLQSshpbhTBZximg7zmc7h7veLj5Xwm1Ju8L7W0OJFWQfZYTpCGmO0HYR3ougu/R46pOQheiL5aj3+i5UZjOTLWnruL+YYfE28XcOyfY2UL1krBSLY4FKNROyM3sifJUAAVVvsVVIYNuMDDNtiKuGQ8VlNivMkzBSMpMVBkrOFWyj//YBIyeHDuXAAGGslOkhQ9gQDzNxsTsq1G/lJ1MKlIYudjYr8Vmapdd3oXp1W05sgqTQ8ZfZGZhGC/Pj6k7nqR5Mbcn5gPuVs4A0pdvnc15OuNucQXWoGEKfOZ+YzKSv0t4vrTnMpLL4t6oPh83zOeq70PekrQ47Eq957h2cBDmSzaxLCd0PXZbY07JsWEcsTtoaFtcPp3MKqyRMsRW5y+hCDaZSG8qG0xhkrGFrDrckLKYy4jdXAwBKx9IF3ZJijoeIzGOZSElaSGX6q8K8na/tJkkATWMRqKBrri6f2pW60qxf1QC6ln21fK3zs0AJtYVVYkbQzUmU4ecxkWCKVPOZl7qShOLlJxBb2JBDz7qUDdRmYczFmgNsZixGs3LvWiFWFbFVVK+a3rQ9tp6eaYE4u02Gt5rvBWGmxOLEshTmrrcnZY6kExOu0lMp+wexMmEm0GmfUGeyc0p4L2J0WbWIHA6h+AHicIjSdZTfIUG/UGloaGhoaHQm6BeqDoF+oWonIn4vYkoq7Rsl7LaNZLnK/JPX/EVn5Wm2a5UiqZyeg9klmVG7/RZ9FLMf4FBmsT+Q2XpEkiEzgyJpTQRSv0iiaewpzJSUZQToN9FdNXcn9sVXZpmVwQwpF1sCI85kvdxirimhzQ6HO2VTwG1s7MaWDEIMMTPlst7j/FtTmv3Gb06lmaiPQ/OFofLtZmaQLSbaYqaEaRI2STF3nCDa5UhNI0yVMmnllCBKtwGYVhqCmFjbdtHXiaZOaT0cs2Bb4lbRY4kGrpSZJtZryPze4ATQzUPzbWUFSlgTZEnAm/ot9c+n9z6HtnDmyLO4HtxvPBOP2UaasNYexEKEc8x+jfuSrp2kcpV+lv4v0em1zDo0sNUF6+AkcXFtPrUntsbsQ9ENehpD/EnnK55IbZFLS8wsG9lQM7aOyhLbhd0nmezdZePfBwB88NvRvIXGu2IwXYdp62g88/tRW2NfJDZGbEokZZLhM7U0wqioFEl8X+S/VsX70nUQSbSnTtl1OjFTtQOY1VtqlilGo9KB8bs5XVFY+pX7poa1VV/Q+O+eQMxaQxdhuEwGqKEbp8L6mhNDZ1HbvOX7V4O0didWycO6SLEBAaCMZzPeom0utgARXaboQ5M2Uv1aU+yaT68Y7AZNDZVoouRZIml13K302ZImiZfp+57j6Z5MK6K6xNRQX3kazNUCZTzKSaeFXZdk4i1p9D2m3n4Ph7Pp2SwWHYHd5nNB2H/1fOFnB/w+uCL71pceVkSM9htzGrC4OWscKLSGSkNDQ0NDQ0OjndAMVTsRTPMjkpVC/2ZNQNI6moXKTMdTbiYjVQyEaGaExVIRd7x+zzObcDKbcO4qjz65MBUV1VRGQpytLEOMPlMoEiYi6TjEtI9ngoY1TQdrOSqPzeH60kfKBpqlx3K6CzG1VFF/fJwwVYbbEvmiEpnyvs2SLNWueyg/hmawLcwyJW+jMv1MAAbNjC4Iif8gF5G8gT7j97BWSWbr0kZhD0TzJdFAjRajVKG5ZWycaYDYtK/lGMoAHFPLRpmixxKtnDVCRiKAmNGJVJG+RJJOI+TYT84l2iphGWMtfSXGsaLH4uTczmg+FzOPMbXMnAnBwdoqMXoFgHBqvO3YMb/9NQAgeeVubhOzi167eezek+k6SdpO5+j6utkOP7OYxeOlbI7u2y7aIzavZNYpcT3dJ3LfZFTxPWBhfiTKL5hM9Yitps/WIXbG9Lz+lKj8kz8eDwCI30AsXv0AYoAkSTEAnHoJpbiRp6GwV93/S3qhkpPpujQWEmOV10rXgbuV2iwMi2+7yVSrRNmiF2y0s7LCGrv4Wtl2AekhW/oRu+TbQmXGNJgsTdwuuucSNtO2qkFUrwCncDIkYDTNPpYh/hpbQzv4K032Iphi21WZhgbz6Aerzg4wmXNvObPd1TRmVj5ERbbxNS3apBCzcRKZGeEou5hyfpawualoQK3RtnI/i0ltn+dobCR6tzGbr4sE2i9lExuRxkhaI9aCZpvMpDvIzB5ffwnfEAMpUdqu1G5U73hOkVTHGjUe2/hvebwt93tzL3vUtn8b919j01FcRjNMreahlmBENEN1CNAvVBoaGhoaGp0FxmFY8osYev3qEKBfqNoJ394mxJYTe9Q8tgCAGZ0mWgGJnLOBNRUeiZoRBkI8UCRaTpIR57BXStCyns8zuFZe048p5bIkNYpE6vHMrzWZZ4h7aTYvCYxdVl0Bs1WSdFb0WKJNEKYnnGqhiwC4OapPfKuack19TpyKqLNHQe09nvZJK6JZefcrNgIAqv9Imp+dP6X6tmRHR08lbOK0IJwANmUjR/VwhJBE90SE+WM2TDQf4inj9MYBzEhMBdFB5dpnnx7Wzoknj3qEZZn+Ti5ONms0cJ8LE+X02HFud/rA2B6QbSSLBmA4GDXRfMn4Swl7f0J6p+z3zTRI7kbqjxGzKDVKajlHhibb2Q7lccbXTuq3xC7U9iCmIHmzI10HgII36LosO46u09g6ZmH5mvJydFeQ04m42E+pOZPqHygxWcSYOhqLhlz6LZTN3lFv0GflQBr39xPJCymTmammnhSNmHMTJfmdtOVUVeaWX1D/dPsf94XcDhIZxh9Zq6geoXhO7cMaH2mHjWWUxL8yBszoCYPrYl+5bRdTdGX2ZzR2u33sjcR/zDzN5nUgmq2agXTvCRsrflMxdVSfrecSUx1TLxGEdHz8LsPWHgDI/ML+h7cxl+qZuInvAWGJwhJBzPeZpJbi1C5ynQOAIZpD1gdWD6R9Uj+nCGVh0EUfKlomTyXfK5wAGRaPO9XHO5lFYgY8ltPt+LZRGdXHk17McLwMxO2melcVmr5e6SuoLBVhLYmN+TOwltlZ0cTy9gg/38McNe22RDT7Som9DKVQG2SFwe2LgRFuAfbgyMPAYWDDtCj9UKDfQTU0NDQ0NDQ02gnNUGloaGhoaHQWHBandM1QHQr0C1U7EfZ7YbD4N/WDbQCAveN72PbJXLJT/TuUaw/vVnYDDnG6u0rC/ImCD7PQ1NNqCSVmOj5mD1HlIoQNc8qHlnRePuCViMWPPwoAmHjhr+iUHF7dkmXS4FX96FhJjeFnI0VvlWMpRyhlWVqTpQzeHldsEeI7UrnI8ov0gXcPLQnVBIkm33Ga3ZDSFaQ+SdhmLql4eEUlfjeb78Xw0p4sUYr9BBt6hl10bEwxL2VwaDf89nPRwSzq56U8Vx4tkbVwCpzYSjp5JDMFgCVkXJZtq01DSCPsXLrbx4NOxMsBHjMWxkYdbwW3QYw81XKifHKQgpgcShoTl2TfsVhHNPUmUbS/is7XxMttrfm8zGGPH0DGSgq8kJDztDIabzHaBIDWZDoozGPg4dMNn/EFAOCzB4fbykz9qpraw0sqroj9mgLMZbbE7dSmYDLVc9dP2MCTVvRQkU9tTerGS8A3UjBAWSMtl/2n8AVV5uSbaZmzKYfa2v1VXpfhe7LLIlqek+XwcAIv43nsBH+Qrw8AMLpQP/iKeVl4Jy2vulNoe8XpFNgQTKX+9u+h+yt/IQcMcLoTqwFpKIHO76+xp1WSpb5t5/F9kk33k/uDFPrkfhfzzraMM0W8LWMkYnll3+EwtxXjXjP9knmBiCi9cWAu1ZfT/ZSfQPdR5nvFdCwv7blKWLgtARi19gADK5Tgne8XQ54tvMyYsmwrtUeMkFngHsqisUneZD7HIin0TG3KtjgGA4jbQfevmJrK8qYE/3gcASTyHACApi5UZvyaXbY2IRSGK9KCowNt7NlR0Et+GhoaGhoaGhrthGao2omYPdUIdaOZWNMgmuUnbaeZiAhn1ewOJhsjzFNTL1PADACBrWy5kMpsQouE/7JFgt9iEsqiWUk5IhB2y8+i9DCHK5/58yuozmJ5wLNf/44adWx2Be279QKa0WV9QTOsllRi1hK/4dk6i5MlPYO8mkdkRmupZzCZWAEJ25ckra3x9tlm+X8oTFmSl6z+A4W2D733GjqFhehSgno+fWO2PYVGgGeZERajKysJYQSFUbOwNCrpqYRo9y+gT56t+zdbkh7DYtgqn/uj2dnQU5luStg8M1JK+M7iX5tNAhxMldrHnibEdLXkwIZtzIz2oH4N+6kPMr7kdlpsFuLWlgAA8l6h6+/67HcBANf8v+kAzMS7/lKHoasET4TsQnMA2PlTumafPI+Y0TuuInuCb24ZAgDYexndJz3no034ymlsIhaWRqUl4uAIdyunjYml8waTaN9kTldT2Y+OWz3odTrXv68GAPzkrZtUmV1KSXwswvrGXsRueJuFCYKtbbXM2iXslvqJ+Ntsu28PpxzicY4wg9JUSDYTKWvr+JP2r+/B9wgzrAkbqZ+DFvZYEjwLAmz0GVtJfdL/Uarv1H+Run7ukim2/es4DU+dJTlyYI/YtdB3D5MocTu5X9no01PPQnMR26uAFhapW9IcGcz6+ndSG8tOpGdHzhvEHhkNzBK12q1BFPj6Nqz3k9iI8LNEgndcbgcnwEEAbk50LCxzDDNqwe4Z5mn4Gor/ltjL5gIad4PLDKexuTIHAkliemHQVLJ0SzquOLZ6Ub9Z09JEHCa/RwiGYdj77lCgkyMfEvQLlYaGhoaGRmeBjvLrMOgXqnaiNScFXtEy1NIMpGIgzU6St0SH+4vmoOQUmimlbqCZn5dTaciMX4H3j6mkWV3EylCxnkrCd+W7i8Pn3Tw7EuNOlbxZkvxKGpuY6DJ7vlTF5+cZLNshiHllKI9TVHCyYZnRuFrteinAZEYas6gtKRtZd7OeymzuQTPDvLdJd7DhNxT63Ou9y6l6J9JMN2GROVsXhPxi5MczUbaOaMmnWbG3jlNOiIVEgl0vYUtBw8mmxeTS0+Aw9hTNB8/KhX1QJqFi7GmdNYfsaWuMCDMr3PeG0qI5ZtrCkrGFg2KyALhiqX6iH3Fzag+l+RJtj6QgYnYzbhvP2pnZFBYUMLVQm2f2BwBcNHgoACAmmxmTEmprUx6duzmVyiw/ho5P3kBlSmJjAAgQ6YVr5xHDmOHlNCBsNNqLvTUlKa4wZuGAnbEIppjfPc1241ZJliuGlO4T2fBxBY1/MNn+hyFxK/VN7vumCadoZNI+Je1UY1+6DmJYK+euoftIrEKSttFxMWxuKXqiUIbFSkTGle9FTw5ZbgSKSEslfR9KpmsqUMrtYNakKZ/ur12nRD+i43ZT22Oa6ByBHZKAl/rxz/dfSOfkP4qhONq/MZe+Z39i9kkoQP9uyqR96go4kXIBjXP6V7Q9UMGJmPewWbFYhkjKpEaTpYn0ISuIyoHUxuz3qM1GE+8jzxtJGO5MzySCaHcbzpKG4zdmfhXzvIv1b6JzYlZJWuytM3VMMq5iDRJYLzYKbIdQ79CNShoZNrdV1ivNZpkG66wUUojlMgK+o2ibcDh8qA5PVX5s0BoqDQ0NDQ0NDY12QjNU7cTrz72IMUUXAwASXqTZZtI2mnEVT6T31e7hTLX/9rNZC8Wak+wVrH9hJigcz0lTOTLKxbN3mZlbjT3FZDHi4yStJdW2ukkqGnet6BxYxyMRcHwcfOZl4JVEr8xiqGge1iJFkqiN7haJGOIILmaqWtic0b/NZAAqT6fZdvIWjiDLsicyTf5kh63ekVyuA+fSSE9iHcLPTT1C3WLSokjyUzH0FHZJyg7H2dkOSRxr+Bz6J0Cl0RGTS5fMUGW2J1F1zASp7cIQcTmueJMFc8WzIkyi+OQHZyLjZnsEkGhDDJ5p237fx+xTjBRV4mVm49z1do2d0p1YdBIt+awf4fHM+sJ+jEJYIsvoHN5mjrI7lcrq/a82WFluu7eG2TpmMSM8NpIyRcZEsbGcgiau2DT2VEnC+X7YfSrVO4k8YVEXYkagF9UjsMM+/unfcCJcSzobdwWfL4nGSiLu5F6UT0HsVtbSObQ/nkrTEFYicI0kO6saZGa3tjsbaDK75OU+2X2iPVGwf6/577g91Mc1vanD4sroM5TMxpjMqGZ9Wk0HiOarkM5ZX0D7N+SYbU/ZzP3Ema3iS/h64zHxVzEzmUFj4y/l+yqHGEDP7mgqQ/SKmR9zP5Xzs8DniN7le0C0i65EToreYr8X6ETM2KpUVw7NIR9idKFIwmC23XhYnreS9gaAMg6N0qBK0nmViopT6HAycXnGeZui2XgvR7sK861SitXUH70oP8MA2pl6pt3H/0ihX6g0NDQ0NDQ6CUiU3r4lP0PbJhwSvjdLfnPmzIHL5cL1119v215UVISf/exnSE5ORmJiIkaNGoXi4uL9llVdXY1rr70Wubm58Pv9KCwsxDvvvKN+v+OOO+ByuWz/5eTkHIlmaWhoaGhoHEUwQ9Xe/w4B8+bNQ48ePeD3+zF8+HB8+OGH+93/gw8+wPDhw+H3+9GzZ088+uijh3Te7wu+FwzV559/jscffxxDhgyxbd+8eTPGjBmDadOm4c4770RycjKKiorg9/v3URIQDAZx+umnIysrC6+88gq6du2KHTt2IDHRTgEPHDgQ7777rvruNGs7UIydPQ1NIkIP0kXoqyBq119C1PC2SSY17K4l6jppI73L1vWheu0ZaS+3z7Msrs7kvFxldXDCzUspYgLYUEhLi/Fb2AZBRNPKXM4u/hXDx+aupiGhdy/T7xI6zKLZply7mFtyjCkrBBa81/QiSt5Xas3qTp91Xdl4cBcdm/wNG/rx0pNQ6s+e+ITtXL9cehUAwF9sLr2kiKFnLC97sHtpyE+f/r2S24+XiFocAnL5bsnb56pzUP1JNDZGLZuUemWJ1BE4wEsUytrAumQh+8o2+R7m7yyAVcJWfpBFaqNz4gkidTRG7jheTpRr13EOQ8LSJQw93j6GbcHbZBcIe+rtSxeyTCxL0Anb6Rz9nuQDLLYJkQDvy30tyzauIO0jYyKQcHUx0JTlZG+j+XCXpRvZJ/tTWgbe/Bu+thJpebC+mvomfied67iZZN75+bOPAAAmTLzIPLGI4RPsppoilpdldhWKn0hlq/B5HsP6PilwQiwV5LOqkOqZ/Tn1WywvN0rZPZ6he6L+mC4AgLLh5iO6pYnvSZ/dMqShK42rv5yXTHnZ3s12Gonb6ByhgCw/mmO0/WyqV+YndA015NH3LsvoGA+XFQrIsiGVEb/LsSRseX7G7OYlPsktKc9rCcxQ1zrXg4NBJGDH1bifZ7GUKecTg08JxBCz4yo23+XrtSWD6uCttvztiLP/HVHLiGI7IhKJJnE7pXNZ5QwAELYEI4hpcYAtI1Q+QpfLZlPSGfGvf/0L119/PebNm4cTTzwRjz32GCZMmIB169YhPz8/av+tW7di4sSJuPLKK/HPf/4TH3/8Ma655hpkZmbiggsu6IAWtB8d/kJVX1+Piy++GPPnz8fs2bNtv82cORMTJ07Efffdp7b17Nlzv+U9+eSTqKysxPLlyxHDa/Tdu3eP2s/r9WpWSkNDQ0Ojc8HAYVjyO/hjHnjgAUybNg2/+hVl4njwwQexcOFCPPLII5gzZ07U/o8++ijy8/Px4IMPAgAKCwuxcuVK/OUvf9EvVIeKa6+9FmeeeSZOO+002wtVJBLB22+/jZtvvhnjx4/H6tWr0aNHD9x6660499xz91neG2+8gdGjR+Paa6/Ff//7X2RmZuKiiy7CjBkzbCzUxo0bkZeXB5/Ph5EjR+Kee+75zpe1tpDxRTUCQRJChpktkRD9tG9ptlR3nCmmjnxLLEz6umZrMQiU0mzJV8WzT56Jy2xIZkGulmghpDAQgZ18HsfdIDYKCiLEdknouSXsl20bavvRjDRhG5UZKKEZX20vzp7OLENgO7Nhuyke2GATvz0npaoyM76i8sV8M3E7zX6VKaBYLpTS7PzKp/4fHTiUZu3CTCVuM9vlZVNDT7O9rSKmFasAg8X9bh4Td3mVfX9LXymRrGxzsEZKjC7784xbmCs3syY2Uz0JCRdmTI7hcxjV1MZIDTODkgqH2Rqn0BwwRbuqniJCFybA+TCVVC7MoLX1rFQpUuL9tvqJMFwgjJUEUahZt7TZY87CPbV2Ea7Blh/CLilzSDEFlT7hCsrY1fZPUWUEE2hbahGxiZKO5eUx5MFw4QpiM71+Gu/n7/gbbZ9LRp4DV1AASZYl3YiPrxXvBjNFFGBeD80DiC3yCVPBY9Pcjw19Mx3GlDCvcUHxOGJ2kjfZWWzpCxezXK0ZxH7FVlP9sz83R6t0JF9nrPuvzad6hJiozPmUGUAWuAszXd3XHqiRWGyOS9oauge3XkDMirBfksopkiFBFfShTEz5flKDZQnuUNvkWpb0MMLgipWBBGpUmsbCABT7FEk3mXNhmlSQDj8rFMTclu8NdzId62qk7fG7mPXMSIYTwowqMbrHcW1XVnM9eTMzw8K0eSzmnb48WiVwSfqpjmClDAPt9j04yCW/YDCIVatW4ZZbbrFtHzduHJYvX97mMStWrMC4ceNs28aPH48nnngCra2tihD5IaFDX6heeuklfPHFF/j888+jfisrK0N9fT3mzp2L2bNn495778WCBQtw/vnnY8mSJRg7dmybZW7ZsgXvv/8+Lr74YrzzzjvYuHEjrr32WoRCIdx+++0AgJEjR+LZZ59F3759sWfPHsyePRsnnHAC1q5di/T09DbLbWlpQYtlKaemhh4CoXALQq32P7QhXs4JtXLur0bzuEgLU+gh+zGyAhTmF5WQuYE+QvzgbSO3m1rSAXvxhPlhF7bn/FIQy3HJw2cpU8qSNkXVs5WX1vihreppcD2D9v2pDPktbC+To14Mh4NwmPsI/DA0OJJM+obOz3VutfeHvFC5+ZzysJQOdkcc3lIRMypNXKpVbjyWGBrcNsPxkHE5hkIdb32hcrTNFeH6yMsD1ydi8PKc4XLs74kqx9zX49i3bUmkqieXZexHOmmEJeSO/7CGHG2Wa8X5h0Je4lzWfe0v/0bY4TekHMjtZRpcSJjPHbKMcZjzOso1FGIH9/o6Xgrka8bljti2y3Up92IoZHnx28e1IeMp55L95P4Jhbxcv+hJTvT97eV6RNr83eD7PBSSCEwup9V8RIeb7Y9rdwv3k0fOyWVIPbkM5z0ZClledHnfME8WIoYRvQ/MNkb4+gw5+yxiGXjn8ybivA7sEzqXo9/Fky0Stjw3ZdIQtt83qky55/hedalni3yPRJVpVpfvc4nCk1yecl1GXRceRx3Mtkv5Zlnmb6GIPEeOnOA7KysLG/FvbDK+aWdJBvpk9kGtyB0YPp8PPqfkAUB5eTnC4TCys7Nt27Ozs1FaWtrmGUpLS9vcPxQKoby8HLm5ue1sQwfA6CAUFxcbWVlZxpo1a9S2sWPHGtOnTzcMwzB27dplADCmTJliO+7ss882Lrzwwn2W26dPH6Nbt25GKBRS2+6//34jJydnn8fU19cb2dnZxv3337/PfWbNmmWA/gTo//R/+j/9n/5P/3dI/+3YseO7/jweMpqbm41NmzYdlv9mzJgRVfdZs2a1eV75e718+XLb9tmzZxv9+vVr85g+ffoY99xzj23bRx99ZAAwSkpKDkt/HG10GEO1atUqlJWVYfhwM+N8OBzGsmXL8Pe//x0NDQ3wer0YMGCA7bjCwkJ89NFH+yw3NzcXMTExtuW9wsJClJaWIhgMIjY2NuqY+Ph4DB48GBs3btxnubfeeituvPFG9T0SiaCyshLp6elwdbDYsLa2Ft26dcOOHTuQlJT03Qf8gPFjaatuZ+fDj6Wtup1twzAM1NXVIS8v74jVyefzoVevXoelrDvvvBN/+MMfospvCxkZGfB4PFFsVFlZWRQLJcjJyWlzf6/Xu8+Vou87OuyF6tRTT8XXX39t23b55Zejf//+mDFjBnw+H4477jisX7/ets+GDRvaFJkLTjzxRLzwwguIRCJw83LPhg0bkJub2+bLFEDLeUVFRTjppJP2WW5bVGdKSsr+mnjUkZSU1KkfYFb8WNqq29n58GNpq25nNJKTk49wbQ4f9rW81xZiY2MxfPhwLF68GOedd57avnjxYpxzzjltHjN69Gi8+eabtm2LFi3CiBEjfpD6KQDosCW/tmBd8jMMw3jttdeMmJgY4/HHHzc2btxoPPTQQ4bH4zE+/PBDtc8ll1xi3HLLLep7cXGxkZCQYPy///f/jPXr1xtvvfWWkZWVZcyePVvt87vf/c5YunSpsWXLFuOTTz4xzjrrLCMxMdHYtm3bUWnn4UZNTY0BwKipqenoqhxx/FjaqtvZ+fBjaatu548TL730khETE2M88cQTxrp164zrr7/eiI+PV39Xb7nlFuOSSy5R+2/ZssWIi4szbrjhBmPdunXGE088YcTExBivvPJKRzWh3ejwKL/94bzzzsOjjz6KOXPm4LrrrkO/fv3w6quvYsyYMWqf4uJixUQBQLdu3bBo0SLccMMNGDJkCLp06YLp06djxowZap+dO3diypQpKC8vR2ZmJkaNGoVPPvlkv8yXhoaGhoaGRtuYPHkyKioqcNddd6GkpASDBg3CO++8o/6ulpSU2Ey5e/TogXfeeQc33HADHn74YeTl5eFvf/vbD9YyAcD3i6HSODQ0Nzcbs2bNMpqbmzu6KkccP5a26nZ2PvxY2qrbqfFjhcswdNIeDQ0NDQ0NDY324HuTy09DQ0NDQ0ND44cK/UKloaGhoaGhodFO6BcqDQ0NDQ0NDY12Qr9QaWhoaGhoaGi0E/qFqoOxbNkynH322cjLy4PL5cLrr79u+/21117D+PHjkZGRAZfLhTVr1hxQua+++ioGDBgAn8+HAQMG4D//+Y/t9zvuuAMul8v2X05OzmFqVTSORDvXrl2LCy64AAUFBXC5XCpruRPz5s1Djx494Pf7MXz4cHz44Yftb9B+0FFt7QxjOn/+fJx00klITU1FamoqTjvtNHz22WdR+x3NMe2odh7t8QSOTFtfe+01jBgxAikpKYiPj8ewYcPw3HPPRe33Qx/TA2lnR4ypxtGDfqHqYDQ0NGDo0KH4+9//vs/fTzzxRMydO/eAy1yxYgUmT56MSy65BF9++SUuueQSTJo0CZ9++qltv4EDB6KkpET953SuP5w4Eu1sbGxEz549MXfu3H0+lP71r3/h+uuvx8yZM7F69WqcdNJJmDBhgs0P5XCjo9oK/PDHdOnSpZgyZQqWLFmCFStWID8/H+PGjcOuXbvUPkd7TDuqncDRHU/gyLQ1LS0NM2fOxIoVK/DVV1/h8ssvx+WXX46FCxeqfTrDmB5IO4GjP6YaRxEd7dugYQKA8Z///KfN37Zu3WoAMFavXv2d5UyaNMk444wzbNvGjx9vSyo9a9YsY+jQoe2o7aHjcLXTiu7duxt//etfo7Yff/zxxtVXX23b1r9/f5u7/pHE0WxrZxtTwzCMUChkJCYmGs8884za1pFjejTb2ZHjaRhHrq2GYRjHHHOMcdttt6nvnXFMDSO6nR09phpHFpqh6oRYsWIFxo0bZ9s2fvx4LF++3LZt48aNyMvLQ48ePXDhhRdiy5YtR7OaRxzBYBCrVq2K6otx48ZF9UVnQWcb08bGRrS2tiItLQ1A5x1TZzsFnW08DcPAe++9h/Xr1+Pkk08G0DnHtK12CjrbmGqY0C9UnRClpaVRGb6zs7Ntmb1HjhyJZ599FgsXLsT8+fNRWlqKE044ARUVFUe7ukcM5eXlCIfD39kXnQWdcUxvueUWdOnSBaeddhqAzjumznYCnWs8a2pqkJCQgNjYWJx55pl46KGHcPrppwPoXGO6v3YCnWtMNaLxvc7lp3HocLlctu+GYdi2TZgwQf178ODBGD16NHr16oVnnnkGN95441Gr59HAd/VFZ0FnG9P77rsPL774IpYuXQq/32/7rTON6b7a2ZnGMzExEWvWrEF9fT3ee+893HjjjejZsydOOeUUtU9nGNPvamdnGlONaOgXqk6InJycqJldWVlZ1AzQivj4eAwePBgbN2480tU7asjIyIDH4znovugs+CGP6V/+8hfcc889ePfddzFkyBC1vbON6b7a2RZ+yOPpdrvRu3dvAMCwYcNQVFSEOXPm4JRTTulUY7q/draFH/KYakRDL/l1QowePRqLFy+2bVu0aBFOOOGEfR7T0tKCoqIi5ObmHunqHTXExsZi+PDhUX2xePHi/fZFZ8EPdUz//Oc/4+6778aCBQswYsQI22+daUz318628EMdz7ZgGAZaWloAdK4xdcLazrbQmcZUQzNUHY76+nps2rRJfd+6dSvWrFmDtLQ05Ofno7KyEsXFxdi9ezcAYP369QCIhZLw+UsvvRRdunTBnDlzAADTp0/HySefjHvvvRfnnHMO/vvf/+Ldd9/FRx99pM5z00034eyzz0Z+fj7Kysowe/Zs1NbWYurUqT+YdgaDQaxbt079e9euXVizZg0SEhLULPHGG2/EJZdcghEjRmD06NF4/PHHUVxcjKuvvvqItLMj29oZxvS+++7DH//4R7zwwgsoKChQrEVCQgISEhIAHP0x7ah2Hu3xPFJtnTNnDkaMGIFevXohGAzinXfewbPPPotHHnlEnaczjOmBtLMjxlTjKKLjAgw1DMMwlixZYgCI+m/q1KmGYRjGU0891ebvs2bNUmWMHTtW7S/497//bfTr18+IiYkx+vfvb7z66qu23ydPnmzk5uYaMTExRl5ennH++ecba9eu/UG1U0Kanf+NHTvWdu6HH37Y6N69uxEbG2sce+yxxgcffHDE2tmRbe0MY9q9e/fvPMYwju6YdlQ7j/Z4Hqm2zpw50+jdu7fh9/uN1NRUY/To0cZLL70Ude4f+pgeSDs7Ykw1jh5chmEYB/rypaGhoaGhoaGhEQ2todLQ0NDQ0NDQaCf0C5WGhoaGhoaGRjuhX6g0NDQ0NDQ0NNoJ/UKloaGhoaGhodFO6BcqDQ0NDQ0NDY12Qr9QaWhoaGhoaGi0E/qFSkNDQ0NDQ0OjndAvVBqdBqeccgquv/76TnXeyy67DOeee267yigoKIDL5YLL5UJ1dfU+93v66aeRkpLSrnMdyfKO1HnuuOMODBs27LDVpyNxxx13qLF+8MEHO7o6Gho/KugXKg2NduK1117D3Xffrb4XFBR87/6Y3XXXXSgpKUFycvJRO+fkyZOxYcOGo3Y+DUptUlJSgq5du3Z0VTQ0fnTQufw0NNqJtLS0jq7CdyIxMVHlIDtaCAQCCAQCR/Wc31cYhoFwOAyv98g+ciUXoMfjOaLn0dDQiIZmqDQ6LaqqqnDppZciNTUVcXFxmDBhAjZu3Kh+l6WihQsXorCwEAkJCTjjjDNQUlKi9gmFQrjuuuuQkpKC9PR0zJgxA1OnTrUtw1mX/E455RRs374dN9xwg1p6AdpeVnrwwQdRUFCgvofDYdx4443qXDfffDOcmaEMw8B9992Hnj17IhAIYOjQoXjllVcOqX+efvpp5OfnIy4uDueddx4qKiqi9nnzzTcxfPhw+P1+9OzZE3feeSdCoZD6vbq6GldddRWys7Ph9/sxaNAgvPXWW6p861Kc9MGTTz6J/Px8JCQk4De/+Q3C4TDuu+8+5OTkICsrC3/6059sdXjggQcwePBgxMfHo1u3brjmmmtQX19/SG0GgLlz5yI7OxuJiYmYNm0ampubo/Z56qmnUFhYCL/fj/79+2PevHm235cvX45hw4bB7/djxIgReP311+FyubBmzRoAwNKlS+FyubBw4UKMGDECPp8PH3744QGN37p16zBx4kQkJCQgOzsbl1xyCcrLy9Xvr7zyCgYPHoxAIID09HScdtppaGhoOOT+0NDQODzQL1QanRaXXXYZVq5ciTfeeAMrVqyAYRiYOHEiWltb1T6NjY34y1/+gueeew7Lli1DcXExbrrpJvX7vffei+effx5PPfUUPv74Y9TW1uL111/f5zlfe+01dO3aVS2xWV/Ovgv3338/nnzySTzxxBP46KOPUFlZif/85z+2fW677TY89dRTeOSRR7B27VrccMMN+OUvf4kPPvjgwDsGwKeffoorrrgC11xzDdasWYOf/OQnmD17tm2fhQsX4pe//CWuu+46rFu3Do899hiefvpp9cITiUQwYcIELF++HP/85z+xbt06zJ07d7/syObNm/G///0PCxYswIsvvognn3wSZ555Jnbu3IkPPvgA9957L2677TZ88skn6hi3242//e1v+Oabb/DMM8/g/fffx80333xQ7RW8/PLLmDVrFv70pz9h5cqVyM3NjXpZmj9/PmbOnIk//elPKCoqwj333IM//vGPeOaZZwAAdXV1OPvsszF48GB88cUXuPvuuzFjxow2z3fzzTdjzpw5KCoqwpAhQ75z/EpKSjB27FgMGzYMK1euxIIFC7Bnzx5MmjRJ/T5lyhRcccUVKCoqwtKlS3H++edHvXhraGh0ADouL7OGxuHF2LFjjenTpxuGYRgbNmwwABgff/yx+r28vNwIBALGyy+/bBiGmVF+06ZNap+HH37YyM7OVt+zs7ONP//5z+p7KBQy8vPzjXPOOafN8xqGYXTv3t3461//aqvbrFmzjKFDh9q2/fWvfzW6d++uvufm5hpz585V31tbW42uXbuqc9XX1xt+v99Yvny5rZxp06YZU6ZM2We/tFWfKVOmGGeccYZt2+TJk43k5GT1/aSTTjLuuece2z7PPfeckZubaxiGYSxcuNBwu93G+vXr2zzvU089ZStv1qxZRlxcnFFbW6u2jR8/3igoKDDC4bDa1q9fP2POnDn7bM/LL79spKen7/M8+8Po0aONq6++2rZt5MiRtrHp1q2b8cILL9j2ufvuu43Ro0cbhmEYjzzyiJGenm40NTWp3+fPn28AMFavXm0YhmEsWbLEAGC8/vrrap8DGb8//vGPxrhx42y/79ixwwBgrF+/3li1apUBwNi2bdt+29nWmGtoaBxZaA2VRqdEUVERvF4vRo4cqbalp6ejX79+KCoqUtvi4uLQq1cv9T03NxdlZWUAgJqaGuzZswfHH3+8+t3j8WD48OGIRCKHtb41NTUoKSnB6NGj1Tav14sRI0Yo9mHdunVobm7G6aefbjs2GAzimGOOOajzFRUV4bzzzrNtGz16NBYsWKC+r1q1Cp9//rltCS4cDqO5uRmNjY1Ys2YNunbtir59+x7weQsKCpCYmKi+Z2dnw+PxwO1227bJGADAkiVLcM8992DdunWora1FKBRCc3MzGhoaEB8ff9Dtvvrqq6PavWTJEgDA3r17sWPHDkybNg1XXnml2icUCilB//r16zFkyBD4/X71u/UasWLEiBHq3wcyfqtWrcKSJUuQkJAQVdbmzZsxbtw4nHrqqRg8eDDGjx+PcePG4ec//zlSU1MPphs0NDSOAPQLlUanhLGPJRDDMJSuCQBiYmJsv7tcrqhjrfvvr+z9we12Rx1nXXo8EMhL3Ntvv40uXbrYfvP5fAdV1oG0IRKJ4M4778T5558f9Zvf7z8kwXlb/d3WNmnr9u3bMXHiRFx99dW4++67kZaWho8++gjTpk076P47EMh558+fb3sZB6CWMp3XkGxrC9YXvgMZv0gkgrPPPhv33ntvVFm5ubnweDxYvHgxli9fjkWLFuGhhx7CzJkz8emnn6JHjx4H01QNDY3DDK2h0uiUGDBgAEKhED799FO1raKiAhs2bEBhYeEBlZGcnIzs7Gx89tlnals4HMbq1av3e1xsbCzC4bBtW2ZmJkpLS21/eEXALOfKzc21aYdCoRBWrVpla5PP50NxcTF69+5t+69bt24H1CZrWdZzAYj6fuyxx2L9+vVR5+rduzfcbjeGDBmCnTt3HlFrhJUrVyIUCuH+++/HqFGj0LdvX+zevfuQyyssLNxvu7Ozs9GlSxds2bIlqs3ywtK/f3989dVXaGlpsdXzu3Ag43fsscdi7dq1KCgoiNpHXs5cLhdOPPFE3HnnnVi9ejViY2OjtHYaGhpHH5qh0uiU6NOnD8455xxceeWVeOyxx5CYmIhbbrkFXbp0wTnnnHPA5fz2t7/FnDlz0Lt3b/Tv3x8PPfQQqqqqohgKKwoKCrBs2TJceOGF8Pl8yMjIwCmnnIK9e/fivvvuw89//nMsWLAA//vf/5CUlKSOmz59OubOnYs+ffqgsLAQDzzwgM2IMzExETfddBNuuOEGRCIRjBkzBrW1tVi+fDkSEhIwderUA27XddddhxNOOAH33Xcfzj33XCxatMi23AcAt99+O8466yx069YNv/jFL+B2u/HVV1/h66+/xuzZszF27FicfPLJuOCCC/DAAw+gd+/e+Pbbb+FyuXDGGWcccF32h169eiEUCuGhhx7C2WefjY8//hiPPvroIZc3ffp0TJ06FSNGjMCYMWPw/PPPY+3atejZs6fa54477sB1112HpKQkTJgwAS0tLVi5ciWqqqpw44034qKLLsLMmTNx1VVX4ZZbbkFxcTH+8pe/AIhmM604kPG79tprMX/+fEyZMgW///3vkZGRgU2bNuGll17C/PnzsXLlSrz33nsYN24csrKy8Omnn2Lv3r0HPEnQ0NA4ctAMlUanxVNPPYXhw4fjrLPOwujRo2EYBt55552oJab9YcaMGZgyZQouvfRSjB49GgkJCRg/frxNP+PEXXfdhW3btqFXr17IzMwEQMzIvHnz8PDDD2Po0KH47LPPbNGEAPC73/0Ol156KS677DKMHj0aiYmJUTqnu+++G7fffjvmzJmDwsJCjB8/Hm+++eZBL/eMGjUK//jHP/DQQw9h2LBhWLRoEW677TbbPuPHj8dbb72FxYsX47jjjsOoUaPwwAMPoHv37mqfV199FccddxymTJmCAQMG4Oabb45i59qDYcOG4YEHHsC9996LQYMG4fnnn8ecOXMOubzJkyfj9ttvx4wZMzB8+HBs374dv/nNb2z7/OpXv8I//vEPPP300xg8eDDGjh2Lp59+WvVxUlIS3nzzTaxZswbDhg3DzJkzcfvttwPAfq8L4LvHLy8vDx9//DHC4TDGjx+PQYMGYfr06UhOTobb7UZSUhKWLVuGiRMnom/fvrjttttw//33Y8KECYfcJxoaGocHLuNQBCEaGj9SRCIRFBYWYtKkSTZ39O8zCgoKcP3113dIWp4fC55//nlcfvnlqKmp+V6Ymeox19A4+tAMlYbGfrB9+3bMnz8fGzZswNdff43f/OY32Lp1Ky666KKOrtpBYcaMGUhISEBNTU1HV6VT4Nlnn8VHH32ErVu34vXXX8eMGTMwadKkDn+Zuueee5CQkIDi4uIOrYeGxo8RmqHS0NgPduzYgQsvvBDffPMNDMPAoEGDMHfuXJx88skdXbUDxvbt21VEXM+ePW0WBZ0NAwcOxPbt29v87bHHHsPFF198WM5z3333Yd68eSgtLUVubi7OPfdc/OlPf0JcXNxhKf9QUVlZicrKSgAUCHE0czdqaPzYoV+oNDQ0Og2sL49OSLoZDQ0NjSMB/UKloaGhoaGhodFOdF7uX0NDQ0NDQ0PjKEG/UGloaGhoaGhotBP6hUpDQ0NDQ0NDo53QL1QaGhoaGhoaGu2EfqHS0NDQ0NDQ0Ggn9AuVhoaGhoaGhkY7oV+oNDQ0NDQ0NDTaCf1CpaGhoaGhoaHRTvx/a6/grFxyOeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " cube_s1.VV.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71678677-d694-4ea1-bcf2-1f15e978fd8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcfac8551f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " cube_s1.VH.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35902905-85db-49d6-a28b-625f37023be0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to fetch data from Sentinel Hub after 44.90783905982971 seconds and 200 retries\n",
      "HTTP status code was 400\n"
     ]
    },
    {
     "ename": "SentinelHubError",
     "evalue": "400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2434\u001b[0m, in \u001b[0;36mLRUStoreCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2433\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mutex:\n\u001b[0;32m-> 2434\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2435\u001b[0m     \u001b[38;5;66;03m# cache hit if no KeyError is raised\u001b[39;00m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'localIncidenceAngle/16.0.0'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:645\u001b[0m, in \u001b[0;36mSentinelHubError.maybe_raise_for_response\u001b[0;34m(cls, response)\u001b[0m\n\u001b[1;32m    644\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 645\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mSentinelHubError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcube_s1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocalIncidenceAngle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2018-05-10\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnearest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1311\u001b[0m, in \u001b[0;36m_plot2d.<locals>.plotmethod\u001b[0;34m(_PlotMethods_obj, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, colors, center, robust, extend, levels, infer_intervals, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m   1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_PlotMethods_obj\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnewplotfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m   1310\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m allargs[arg]\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnewplotfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mallargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1175\u001b[0m, in \u001b[0;36m_plot2d.<locals>.newplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m   1172\u001b[0m yval \u001b[38;5;241m=\u001b[39m yval\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[1;32m   1174\u001b[0m \u001b[38;5;66;03m# Pass the data as a masked ndarray too\u001b[39;00m\n\u001b[0;32m-> 1175\u001b[0m zval \u001b[38;5;241m=\u001b[39m \u001b[43mdarray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_masked_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;66;03m# Replace pd.Intervals if contained in xval or yval.\u001b[39;00m\n\u001b[1;32m   1178\u001b[0m xplt, xlab_extra \u001b[38;5;241m=\u001b[39m _resolve_intervals_2dplot(xval, plotfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:3574\u001b[0m, in \u001b[0;36mDataArray.to_masked_array\u001b[0;34m(self, copy)\u001b[0m\n\u001b[1;32m   3560\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_masked_array\u001b[39m(\u001b[38;5;28mself\u001b[39m, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mMaskedArray:\n\u001b[1;32m   3561\u001b[0m     \u001b[38;5;124;03m\"\"\"Convert this array into a numpy.ma.MaskedArray\u001b[39;00m\n\u001b[1;32m   3562\u001b[0m \n\u001b[1;32m   3563\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3572\u001b[0m \u001b[38;5;124;03m        Masked where invalid values (nan or inf) occur.\u001b[39;00m\n\u001b[1;32m   3573\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3574\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# only compute lazy arrays once\u001b[39;00m\n\u001b[1;32m   3575\u001b[0m     isnull \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39misnull(values)\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mMaskedArray(data\u001b[38;5;241m=\u001b[39mvalues, mask\u001b[38;5;241m=\u001b[39misnull, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:740\u001b[0m, in \u001b[0;36mDataArray.to_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    729\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_numpy\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    730\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    731\u001b[0m \u001b[38;5;124;03m    Coerces wrapped data to numpy and returns a numpy.ndarray.\u001b[39;00m\n\u001b[1;32m    732\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    738\u001b[0m \u001b[38;5;124;03m    DataArray.data\u001b[39;00m\n\u001b[1;32m    739\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 740\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/variable.py:1178\u001b[0m, in \u001b[0;36mVariable.to_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1176\u001b[0m \u001b[38;5;66;03m# TODO first attempt to call .to_numpy() once some libraries implement it\u001b[39;00m\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchunks\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1178\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1179\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, cupy_array_type):\n\u001b[1;32m   1180\u001b[0m     data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mget()\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:315\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[1;32m    293\u001b[0m \n\u001b[1;32m    294\u001b[0m \u001b[38;5;124;03m    This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    dask.base.compute\u001b[39;00m\n\u001b[1;32m    314\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m     (result,) \u001b[38;5;241m=\u001b[39m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:600\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m    597\u001b[0m     keys\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_keys__())\n\u001b[1;32m    598\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m--> 600\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    601\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/threaded.py:89\u001b[0m, in \u001b[0;36mget\u001b[0;34m(dsk, keys, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pool, multiprocessing\u001b[38;5;241m.\u001b[39mpool\u001b[38;5;241m.\u001b[39mPool):\n\u001b[1;32m     87\u001b[0m         pool \u001b[38;5;241m=\u001b[39m MultiprocessingPoolExecutor(pool)\n\u001b[0;32m---> 89\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mget_async\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_max_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_thread_get_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpack_exception\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpack_exception\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    100\u001b[0m \u001b[38;5;66;03m# Cleanup pools associated to dead threads\u001b[39;00m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pools_lock:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:511\u001b[0m, in \u001b[0;36mget_async\u001b[0;34m(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    509\u001b[0m         _execute_task(task, data)  \u001b[38;5;66;03m# Re-execute locally\u001b[39;00m\n\u001b[1;32m    510\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 511\u001b[0m         \u001b[43mraise_exception\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    512\u001b[0m res, worker_id \u001b[38;5;241m=\u001b[39m loads(res_info)\n\u001b[1;32m    513\u001b[0m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcache\u001b[39m\u001b[38;5;124m\"\u001b[39m][key] \u001b[38;5;241m=\u001b[39m res\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:319\u001b[0m, in \u001b[0;36mreraise\u001b[0;34m(exc, tb)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc\u001b[38;5;241m.\u001b[39m__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[0;32m--> 319\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:224\u001b[0m, in \u001b[0;36mexecute_task\u001b[0;34m(key, task_info, dumps, loads, get_id, pack_exception)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    223\u001b[0m     task, data \u001b[38;5;241m=\u001b[39m loads(task_info)\n\u001b[0;32m--> 224\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_execute_task\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;28mid\u001b[39m \u001b[38;5;241m=\u001b[39m get_id()\n\u001b[1;32m    226\u001b[0m     result \u001b[38;5;241m=\u001b[39m dumps((result, \u001b[38;5;28mid\u001b[39m))\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/core.py:119\u001b[0m, in \u001b[0;36m_execute_task\u001b[0;34m(arg, cache, dsk)\u001b[0m\n\u001b[1;32m    115\u001b[0m     func, args \u001b[38;5;241m=\u001b[39m arg[\u001b[38;5;241m0\u001b[39m], arg[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;66;03m# Note: Don't assign the subtask results to a variable. numpy detects\u001b[39;00m\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;66;03m# temporaries by their reference count and can execute certain\u001b[39;00m\n\u001b[1;32m    118\u001b[0m     \u001b[38;5;66;03m# operations in-place.\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m_execute_task\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ishashable(arg):\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arg\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/array/core.py:127\u001b[0m, in \u001b[0;36mgetter\u001b[0;34m(a, b, asarray, lock)\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;66;03m# Below we special-case `np.matrix` to force a conversion to\u001b[39;00m\n\u001b[1;32m    123\u001b[0m     \u001b[38;5;66;03m# `np.ndarray` and preserve original Dask behavior for `getter`,\u001b[39;00m\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;66;03m# as for all purposes `np.matrix` is array-like and thus\u001b[39;00m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;66;03m# `is_arraylike` evaluates to `True` in that case.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m asarray \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m is_arraylike(c) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, np\u001b[38;5;241m.\u001b[39mmatrix)):\n\u001b[0;32m--> 127\u001b[0m         c \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lock:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:459\u001b[0m, in \u001b[0;36mImplicitToExplicitIndexingAdapter.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 459\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:623\u001b[0m, in \u001b[0;36mCopyOnWriteArray.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:524\u001b[0m, in \u001b[0;36mLazilyIndexedArray.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    523\u001b[0m     array \u001b[38;5;241m=\u001b[39m as_indexable(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marray)\n\u001b[0;32m--> 524\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(\u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/backends/zarr.py:76\u001b[0m, in \u001b[0;36mZarrArrayWrapper.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     74\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_array()\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, indexing\u001b[38;5;241m.\u001b[39mBasicIndexer):\n\u001b[0;32m---> 76\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtuple\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, indexing\u001b[38;5;241m.\u001b[39mVectorizedIndexer):\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m array\u001b[38;5;241m.\u001b[39mvindex[\n\u001b[1;32m     79\u001b[0m         indexing\u001b[38;5;241m.\u001b[39m_arrayize_vectorized_indexer(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;241m.\u001b[39mtuple\n\u001b[1;32m     80\u001b[0m     ]\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:807\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m    805\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvindex[selection]\n\u001b[1;32m    806\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 807\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_basic_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpure_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:933\u001b[0m, in \u001b[0;36mArray.get_basic_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m    930\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_basic_selection_zd(selection\u001b[38;5;241m=\u001b[39mselection, out\u001b[38;5;241m=\u001b[39mout,\n\u001b[1;32m    931\u001b[0m                                         fields\u001b[38;5;241m=\u001b[39mfields)\n\u001b[1;32m    932\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 933\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_basic_selection_nd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mselection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    934\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:976\u001b[0m, in \u001b[0;36mArray._get_basic_selection_nd\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_basic_selection_nd\u001b[39m(\u001b[38;5;28mself\u001b[39m, selection, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, fields\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    971\u001b[0m     \u001b[38;5;66;03m# implementation of basic selection for array with at least one dimension\u001b[39;00m\n\u001b[1;32m    972\u001b[0m \n\u001b[1;32m    973\u001b[0m     \u001b[38;5;66;03m# setup indexer\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m BasicIndexer(selection, \u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 976\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1267\u001b[0m, in \u001b[0;36mArray._get_selection\u001b[0;34m(self, indexer, out, fields)\u001b[0m\n\u001b[1;32m   1261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunk_store, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetitems\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \\\n\u001b[1;32m   1262\u001b[0m    \u001b[38;5;28many\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape)):\n\u001b[1;32m   1263\u001b[0m     \u001b[38;5;66;03m# sequentially get one key at a time from storage\u001b[39;00m\n\u001b[1;32m   1264\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m chunk_coords, chunk_selection, out_selection \u001b[38;5;129;01min\u001b[39;00m indexer:\n\u001b[1;32m   1265\u001b[0m \n\u001b[1;32m   1266\u001b[0m         \u001b[38;5;66;03m# load chunk selection into output array\u001b[39;00m\n\u001b[0;32m-> 1267\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_chunk_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_coords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_selection\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1268\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mdrop_axes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop_axes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1269\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1270\u001b[0m     \u001b[38;5;66;03m# allow storage to get multiple items at once\u001b[39;00m\n\u001b[1;32m   1271\u001b[0m     lchunk_coords, lchunk_selection, lout_selection \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mindexer)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1966\u001b[0m, in \u001b[0;36mArray._chunk_getitem\u001b[0;34m(self, chunk_coords, chunk_selection, out, out_selection, drop_axes, fields)\u001b[0m\n\u001b[1;32m   1962\u001b[0m ckey \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_chunk_key(chunk_coords)\n\u001b[1;32m   1964\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1965\u001b[0m     \u001b[38;5;66;03m# obtain compressed data for chunk\u001b[39;00m\n\u001b[0;32m-> 1966\u001b[0m     cdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk_store\u001b[49m\u001b[43m[\u001b[49m\u001b[43mckey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   1968\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m   1969\u001b[0m     \u001b[38;5;66;03m# chunk not initialized\u001b[39;00m\n\u001b[1;32m   1970\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2442\u001b[0m, in \u001b[0;36mLRUStoreCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2438\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n\u001b[1;32m   2440\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m   2441\u001b[0m     \u001b[38;5;66;03m# cache miss, retrieve value from the store\u001b[39;00m\n\u001b[0;32m-> 2442\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2443\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mutex:\n\u001b[1;32m   2444\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmisses \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:724\u001b[0m, in \u001b[0;36mKVStore.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    723\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m--> 724\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mutable_mapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:508\u001b[0m, in \u001b[0;36mRemoteStore.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    506\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_vfs[key]\n\u001b[1;32m    507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 508\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fetch_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:419\u001b[0m, in \u001b[0;36mRemoteStore._fetch_chunk\u001b[0;34m(self, key, band_name, chunk_index)\u001b[0m\n\u001b[1;32m    411\u001b[0m     observer(band_name\u001b[38;5;241m=\u001b[39mband_name,\n\u001b[1;32m    412\u001b[0m              chunk_index\u001b[38;5;241m=\u001b[39mchunk_index,\n\u001b[1;32m    413\u001b[0m              bbox\u001b[38;5;241m=\u001b[39mrequest_bbox,\n\u001b[1;32m    414\u001b[0m              time_range\u001b[38;5;241m=\u001b[39mrequest_time_range,\n\u001b[1;32m    415\u001b[0m              duration\u001b[38;5;241m=\u001b[39mduration,\n\u001b[1;32m    416\u001b[0m              exception\u001b[38;5;241m=\u001b[39mexception)\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exception:\n\u001b[0;32m--> 419\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m chunk_data\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:400\u001b[0m, in \u001b[0;36mRemoteStore._fetch_chunk\u001b[0;34m(self, key, band_name, chunk_index)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    399\u001b[0m     exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 400\u001b[0m     chunk_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    401\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mband_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    402\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mchunk_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    403\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_bbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    404\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mtime_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_time_range\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    406\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:729\u001b[0m, in \u001b[0;36mSentinelHubChunkStore.fetch_chunk\u001b[0;34m(self, key, band_name, chunk_index, bbox, time_range)\u001b[0m\n\u001b[1;32m    712\u001b[0m     band_sample_types \u001b[38;5;241m=\u001b[39m band_sample_types[index]\n\u001b[1;32m    714\u001b[0m request \u001b[38;5;241m=\u001b[39m SentinelHub\u001b[38;5;241m.\u001b[39mnew_data_request(\n\u001b[1;32m    715\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcube_config\u001b[38;5;241m.\u001b[39mdataset_name,\n\u001b[1;32m    716\u001b[0m     band_names,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    726\u001b[0m     band_units\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcube_config\u001b[38;5;241m.\u001b[39mband_units\n\u001b[1;32m    727\u001b[0m )\n\u001b[0;32m--> 729\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sentinel_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmime_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mapplication/octet-stream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m    732\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    734\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response\u001b[38;5;241m.\u001b[39mok:\n\u001b[1;32m    735\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: cannot fetch chunk for variable\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    736\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mband_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, bbox \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbbox\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, and\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    737\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m time_range \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime_range\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:432\u001b[0m, in \u001b[0;36mSentinelHub.get_data\u001b[0;34m(self, request, mime_type)\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m response_error\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 432\u001b[0m         \u001b[43mSentinelHubError\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaybe_raise_for_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror_policy \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwarn\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menable_warnings:\n\u001b[1;32m    434\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m response_error:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:655\u001b[0m, in \u001b[0;36mSentinelHubError.maybe_raise_for_response\u001b[0;34m(cls, response)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    654\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SentinelHubError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdetail\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m detail \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    656\u001b[0m                        response\u001b[38;5;241m=\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mSentinelHubError\u001b[0m: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process"
     ]
    }
   ],
   "source": [
    " cube_s1.localIncidenceAngle.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "400ecf6d-6b70-43e2-81e9-6298f0d17cf8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to fetch data from Sentinel Hub after 39.48806810379028 seconds and 200 retries\n",
      "HTTP status code was 400\n"
     ]
    },
    {
     "ename": "SentinelHubError",
     "evalue": "400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2434\u001b[0m, in \u001b[0;36mLRUStoreCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2433\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mutex:\n\u001b[0;32m-> 2434\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2435\u001b[0m     \u001b[38;5;66;03m# cache hit if no KeyError is raised\u001b[39;00m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'shadowMask/16.0.0'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:645\u001b[0m, in \u001b[0;36mSentinelHubError.maybe_raise_for_response\u001b[0;34m(cls, response)\u001b[0m\n\u001b[1;32m    644\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 645\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mSentinelHubError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcube_s1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshadowMask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2018-05-10\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnearest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1311\u001b[0m, in \u001b[0;36m_plot2d.<locals>.plotmethod\u001b[0;34m(_PlotMethods_obj, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, colors, center, robust, extend, levels, infer_intervals, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m   1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_PlotMethods_obj\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnewplotfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m   1310\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m allargs[arg]\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnewplotfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mallargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1175\u001b[0m, in \u001b[0;36m_plot2d.<locals>.newplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m   1172\u001b[0m yval \u001b[38;5;241m=\u001b[39m yval\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[1;32m   1174\u001b[0m \u001b[38;5;66;03m# Pass the data as a masked ndarray too\u001b[39;00m\n\u001b[0;32m-> 1175\u001b[0m zval \u001b[38;5;241m=\u001b[39m \u001b[43mdarray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_masked_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;66;03m# Replace pd.Intervals if contained in xval or yval.\u001b[39;00m\n\u001b[1;32m   1178\u001b[0m xplt, xlab_extra \u001b[38;5;241m=\u001b[39m _resolve_intervals_2dplot(xval, plotfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:3574\u001b[0m, in \u001b[0;36mDataArray.to_masked_array\u001b[0;34m(self, copy)\u001b[0m\n\u001b[1;32m   3560\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_masked_array\u001b[39m(\u001b[38;5;28mself\u001b[39m, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mMaskedArray:\n\u001b[1;32m   3561\u001b[0m     \u001b[38;5;124;03m\"\"\"Convert this array into a numpy.ma.MaskedArray\u001b[39;00m\n\u001b[1;32m   3562\u001b[0m \n\u001b[1;32m   3563\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3572\u001b[0m \u001b[38;5;124;03m        Masked where invalid values (nan or inf) occur.\u001b[39;00m\n\u001b[1;32m   3573\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3574\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# only compute lazy arrays once\u001b[39;00m\n\u001b[1;32m   3575\u001b[0m     isnull \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39misnull(values)\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mMaskedArray(data\u001b[38;5;241m=\u001b[39mvalues, mask\u001b[38;5;241m=\u001b[39misnull, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:740\u001b[0m, in \u001b[0;36mDataArray.to_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    729\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_numpy\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    730\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    731\u001b[0m \u001b[38;5;124;03m    Coerces wrapped data to numpy and returns a numpy.ndarray.\u001b[39;00m\n\u001b[1;32m    732\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    738\u001b[0m \u001b[38;5;124;03m    DataArray.data\u001b[39;00m\n\u001b[1;32m    739\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 740\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/variable.py:1178\u001b[0m, in \u001b[0;36mVariable.to_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1176\u001b[0m \u001b[38;5;66;03m# TODO first attempt to call .to_numpy() once some libraries implement it\u001b[39;00m\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchunks\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1178\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1179\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, cupy_array_type):\n\u001b[1;32m   1180\u001b[0m     data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mget()\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:315\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[1;32m    293\u001b[0m \n\u001b[1;32m    294\u001b[0m \u001b[38;5;124;03m    This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    dask.base.compute\u001b[39;00m\n\u001b[1;32m    314\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m     (result,) \u001b[38;5;241m=\u001b[39m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:600\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m    597\u001b[0m     keys\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_keys__())\n\u001b[1;32m    598\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m--> 600\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    601\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/threaded.py:89\u001b[0m, in \u001b[0;36mget\u001b[0;34m(dsk, keys, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pool, multiprocessing\u001b[38;5;241m.\u001b[39mpool\u001b[38;5;241m.\u001b[39mPool):\n\u001b[1;32m     87\u001b[0m         pool \u001b[38;5;241m=\u001b[39m MultiprocessingPoolExecutor(pool)\n\u001b[0;32m---> 89\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mget_async\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_max_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_thread_get_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpack_exception\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpack_exception\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    100\u001b[0m \u001b[38;5;66;03m# Cleanup pools associated to dead threads\u001b[39;00m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pools_lock:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:511\u001b[0m, in \u001b[0;36mget_async\u001b[0;34m(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    509\u001b[0m         _execute_task(task, data)  \u001b[38;5;66;03m# Re-execute locally\u001b[39;00m\n\u001b[1;32m    510\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 511\u001b[0m         \u001b[43mraise_exception\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    512\u001b[0m res, worker_id \u001b[38;5;241m=\u001b[39m loads(res_info)\n\u001b[1;32m    513\u001b[0m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcache\u001b[39m\u001b[38;5;124m\"\u001b[39m][key] \u001b[38;5;241m=\u001b[39m res\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:319\u001b[0m, in \u001b[0;36mreraise\u001b[0;34m(exc, tb)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc\u001b[38;5;241m.\u001b[39m__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[0;32m--> 319\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:224\u001b[0m, in \u001b[0;36mexecute_task\u001b[0;34m(key, task_info, dumps, loads, get_id, pack_exception)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    223\u001b[0m     task, data \u001b[38;5;241m=\u001b[39m loads(task_info)\n\u001b[0;32m--> 224\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_execute_task\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;28mid\u001b[39m \u001b[38;5;241m=\u001b[39m get_id()\n\u001b[1;32m    226\u001b[0m     result \u001b[38;5;241m=\u001b[39m dumps((result, \u001b[38;5;28mid\u001b[39m))\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/core.py:119\u001b[0m, in \u001b[0;36m_execute_task\u001b[0;34m(arg, cache, dsk)\u001b[0m\n\u001b[1;32m    115\u001b[0m     func, args \u001b[38;5;241m=\u001b[39m arg[\u001b[38;5;241m0\u001b[39m], arg[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;66;03m# Note: Don't assign the subtask results to a variable. numpy detects\u001b[39;00m\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;66;03m# temporaries by their reference count and can execute certain\u001b[39;00m\n\u001b[1;32m    118\u001b[0m     \u001b[38;5;66;03m# operations in-place.\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m_execute_task\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ishashable(arg):\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arg\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/array/core.py:127\u001b[0m, in \u001b[0;36mgetter\u001b[0;34m(a, b, asarray, lock)\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;66;03m# Below we special-case `np.matrix` to force a conversion to\u001b[39;00m\n\u001b[1;32m    123\u001b[0m     \u001b[38;5;66;03m# `np.ndarray` and preserve original Dask behavior for `getter`,\u001b[39;00m\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;66;03m# as for all purposes `np.matrix` is array-like and thus\u001b[39;00m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;66;03m# `is_arraylike` evaluates to `True` in that case.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m asarray \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m is_arraylike(c) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, np\u001b[38;5;241m.\u001b[39mmatrix)):\n\u001b[0;32m--> 127\u001b[0m         c \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lock:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:459\u001b[0m, in \u001b[0;36mImplicitToExplicitIndexingAdapter.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 459\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:623\u001b[0m, in \u001b[0;36mCopyOnWriteArray.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:524\u001b[0m, in \u001b[0;36mLazilyIndexedArray.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    523\u001b[0m     array \u001b[38;5;241m=\u001b[39m as_indexable(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marray)\n\u001b[0;32m--> 524\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(\u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/backends/zarr.py:76\u001b[0m, in \u001b[0;36mZarrArrayWrapper.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     74\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_array()\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, indexing\u001b[38;5;241m.\u001b[39mBasicIndexer):\n\u001b[0;32m---> 76\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtuple\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, indexing\u001b[38;5;241m.\u001b[39mVectorizedIndexer):\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m array\u001b[38;5;241m.\u001b[39mvindex[\n\u001b[1;32m     79\u001b[0m         indexing\u001b[38;5;241m.\u001b[39m_arrayize_vectorized_indexer(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;241m.\u001b[39mtuple\n\u001b[1;32m     80\u001b[0m     ]\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:807\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m    805\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvindex[selection]\n\u001b[1;32m    806\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 807\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_basic_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpure_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:933\u001b[0m, in \u001b[0;36mArray.get_basic_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m    930\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_basic_selection_zd(selection\u001b[38;5;241m=\u001b[39mselection, out\u001b[38;5;241m=\u001b[39mout,\n\u001b[1;32m    931\u001b[0m                                         fields\u001b[38;5;241m=\u001b[39mfields)\n\u001b[1;32m    932\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 933\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_basic_selection_nd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mselection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    934\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:976\u001b[0m, in \u001b[0;36mArray._get_basic_selection_nd\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_basic_selection_nd\u001b[39m(\u001b[38;5;28mself\u001b[39m, selection, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, fields\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    971\u001b[0m     \u001b[38;5;66;03m# implementation of basic selection for array with at least one dimension\u001b[39;00m\n\u001b[1;32m    972\u001b[0m \n\u001b[1;32m    973\u001b[0m     \u001b[38;5;66;03m# setup indexer\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m BasicIndexer(selection, \u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 976\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1267\u001b[0m, in \u001b[0;36mArray._get_selection\u001b[0;34m(self, indexer, out, fields)\u001b[0m\n\u001b[1;32m   1261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunk_store, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetitems\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \\\n\u001b[1;32m   1262\u001b[0m    \u001b[38;5;28many\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape)):\n\u001b[1;32m   1263\u001b[0m     \u001b[38;5;66;03m# sequentially get one key at a time from storage\u001b[39;00m\n\u001b[1;32m   1264\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m chunk_coords, chunk_selection, out_selection \u001b[38;5;129;01min\u001b[39;00m indexer:\n\u001b[1;32m   1265\u001b[0m \n\u001b[1;32m   1266\u001b[0m         \u001b[38;5;66;03m# load chunk selection into output array\u001b[39;00m\n\u001b[0;32m-> 1267\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_chunk_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_coords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_selection\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1268\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mdrop_axes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop_axes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1269\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1270\u001b[0m     \u001b[38;5;66;03m# allow storage to get multiple items at once\u001b[39;00m\n\u001b[1;32m   1271\u001b[0m     lchunk_coords, lchunk_selection, lout_selection \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mindexer)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1966\u001b[0m, in \u001b[0;36mArray._chunk_getitem\u001b[0;34m(self, chunk_coords, chunk_selection, out, out_selection, drop_axes, fields)\u001b[0m\n\u001b[1;32m   1962\u001b[0m ckey \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_chunk_key(chunk_coords)\n\u001b[1;32m   1964\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1965\u001b[0m     \u001b[38;5;66;03m# obtain compressed data for chunk\u001b[39;00m\n\u001b[0;32m-> 1966\u001b[0m     cdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk_store\u001b[49m\u001b[43m[\u001b[49m\u001b[43mckey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   1968\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m   1969\u001b[0m     \u001b[38;5;66;03m# chunk not initialized\u001b[39;00m\n\u001b[1;32m   1970\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2442\u001b[0m, in \u001b[0;36mLRUStoreCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2438\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n\u001b[1;32m   2440\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m   2441\u001b[0m     \u001b[38;5;66;03m# cache miss, retrieve value from the store\u001b[39;00m\n\u001b[0;32m-> 2442\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2443\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mutex:\n\u001b[1;32m   2444\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmisses \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:724\u001b[0m, in \u001b[0;36mKVStore.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    723\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m--> 724\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mutable_mapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:508\u001b[0m, in \u001b[0;36mRemoteStore.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    506\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_vfs[key]\n\u001b[1;32m    507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 508\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fetch_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:419\u001b[0m, in \u001b[0;36mRemoteStore._fetch_chunk\u001b[0;34m(self, key, band_name, chunk_index)\u001b[0m\n\u001b[1;32m    411\u001b[0m     observer(band_name\u001b[38;5;241m=\u001b[39mband_name,\n\u001b[1;32m    412\u001b[0m              chunk_index\u001b[38;5;241m=\u001b[39mchunk_index,\n\u001b[1;32m    413\u001b[0m              bbox\u001b[38;5;241m=\u001b[39mrequest_bbox,\n\u001b[1;32m    414\u001b[0m              time_range\u001b[38;5;241m=\u001b[39mrequest_time_range,\n\u001b[1;32m    415\u001b[0m              duration\u001b[38;5;241m=\u001b[39mduration,\n\u001b[1;32m    416\u001b[0m              exception\u001b[38;5;241m=\u001b[39mexception)\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exception:\n\u001b[0;32m--> 419\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m chunk_data\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:400\u001b[0m, in \u001b[0;36mRemoteStore._fetch_chunk\u001b[0;34m(self, key, band_name, chunk_index)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    399\u001b[0m     exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 400\u001b[0m     chunk_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    401\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mband_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    402\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mchunk_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    403\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_bbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    404\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mtime_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_time_range\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    406\u001b[0m     exception \u001b[38;5;241m=\u001b[39m e\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:729\u001b[0m, in \u001b[0;36mSentinelHubChunkStore.fetch_chunk\u001b[0;34m(self, key, band_name, chunk_index, bbox, time_range)\u001b[0m\n\u001b[1;32m    712\u001b[0m     band_sample_types \u001b[38;5;241m=\u001b[39m band_sample_types[index]\n\u001b[1;32m    714\u001b[0m request \u001b[38;5;241m=\u001b[39m SentinelHub\u001b[38;5;241m.\u001b[39mnew_data_request(\n\u001b[1;32m    715\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcube_config\u001b[38;5;241m.\u001b[39mdataset_name,\n\u001b[1;32m    716\u001b[0m     band_names,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    726\u001b[0m     band_units\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcube_config\u001b[38;5;241m.\u001b[39mband_units\n\u001b[1;32m    727\u001b[0m )\n\u001b[0;32m--> 729\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sentinel_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmime_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mapplication/octet-stream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m    732\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    734\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response\u001b[38;5;241m.\u001b[39mok:\n\u001b[1;32m    735\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: cannot fetch chunk for variable\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    736\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mband_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, bbox \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbbox\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, and\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    737\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m time_range \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime_range\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:432\u001b[0m, in \u001b[0;36mSentinelHub.get_data\u001b[0;34m(self, request, mime_type)\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m response_error\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 432\u001b[0m         \u001b[43mSentinelHubError\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaybe_raise_for_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror_policy \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwarn\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menable_warnings:\n\u001b[1;32m    434\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m response_error:\n",
      "File \u001b[0;32m/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:655\u001b[0m, in \u001b[0;36mSentinelHubError.maybe_raise_for_response\u001b[0;34m(cls, response)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    654\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SentinelHubError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdetail\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m detail \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    656\u001b[0m                        response\u001b[38;5;241m=\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mSentinelHubError\u001b[0m: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process"
     ]
    }
   ],
   "source": [
    " cube_s1.shadowMask.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2591fedd-570d-4d08-8838-1367e79f2f61",
   "metadata": {},
   "source": [
    "## Sentinel-3 SLSTR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fb2162-81fc-400c-b514-afc70d8f3638",
   "metadata": {},
   "source": [
    "Issues:\n",
    "- S1 band have only values of 0 and 1"
   ]
  },
  {
   "cell_type": "code",
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   "id": "1a17aadb-87d9-488d-ae55-83c7e5cbaa38",
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     "data": {
      "text/plain": [
       "['S1',\n",
       " 'S2',\n",
       " 'S3',\n",
       " 'S4',\n",
       " 'S4_A',\n",
       " 'S4_B',\n",
       " 'S5',\n",
       " 'S5_A',\n",
       " 'S5_B',\n",
       " 'S6',\n",
       " 'S6_A',\n",
       " 'S6_B',\n",
       " 'S7',\n",
       " 'S8',\n",
       " 'S9',\n",
       " 'F1',\n",
       " 'F2',\n",
       " 'CLOUD_FRACTION',\n",
       " 'SEA_ICE_FRACTION',\n",
       " 'SEA_SURFACE_TEMPERATURE',\n",
       " 'DEW_POINT',\n",
       " 'SKIN_TEMPERATURE',\n",
       " 'SNOW_ALBEDO',\n",
       " 'SNOW_DEPTH',\n",
       " 'SOIL_WETNESS',\n",
       " 'TEMPERATURE',\n",
       " 'TOTAL_COLUMN_OZONE',\n",
       " 'TOTAL_COLUMN_WATER_VAPOR']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SentinelHub().band_names('S3SLSTR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d41ab428-5aa0-404f-835c-c35d508e2232",
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    {
     "name": "stderr",
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     "text": [
      "/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.\n",
      "  time_tolerance = pd.to_timedelta(time_tolerance)\n"
     ]
    }
   ],
   "source": [
    "cube_config_s3 = CubeConfig(\n",
    "    dataset_name='S3SLSTR',\n",
    "    band_names=['S1'],\n",
    "    bbox=[11.02, 46.65, 11.36, 46.95],\n",
    "    spatial_res=0.009,   # = 500 meters in degree>\n",
    "    upsampling='BILINEAR',\n",
    "    downsampling='BILINEAR',\n",
    "    time_range=['2018-02-01', '2018-06-30']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "011bf2c2-16a1-4e5f-9bbe-e7da146473dc",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 230, lat: 33, lon: 38, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 46.94 46.93 46.92 46.92 ... 46.68 46.67 46.66 46.65\n",
       "  * lon        (lon) float64 11.02 11.03 11.04 11.05 ... 11.33 11.34 11.35 11.36\n",
       "  * time       (time) datetime64[ns] 2018-02-01T20:13:30 ... 2018-06-29T21:16:59\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array&lt;chunksize=(230, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    S1         (time, lat, lon) float32 dask.array&lt;chunksize=(1, 33, 38), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   S3SLSTR Data Cube Subset\n",
       "    history:                 [{&#x27;program&#x27;: &#x27;xcube_sh.chunkstore.SentinelHubChu...\n",
       "    date_created:            2023-03-07T06:52:28.574972\n",
       "    time_coverage_start:     2018-02-01T20:13:30.178000+00:00\n",
       "    time_coverage_end:       2018-06-29T21:17:14.559000+00:00\n",
       "    time_coverage_duration:  P148DT1H3M44.381S\n",
       "    geospatial_lon_min:      11.02\n",
       "    geospatial_lat_min:      46.65\n",
       "    geospatial_lon_max:      11.362\n",
       "    geospatial_lat_max:      46.946999999999996\n",
       "    processing_level:        L1B</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-78fd11bc-d74e-4c45-9093-ac7f9dca9a75' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78fd11bc-d74e-4c45-9093-ac7f9dca9a75' 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'>time</span>: 230</li><li><span class='xr-has-index'>lat</span>: 33</li><li><span class='xr-has-index'>lon</span>: 38</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5ac34deb-089d-43a1-b1e6-3974e3cf55de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ac34deb-089d-43a1-b1e6-3974e3cf55de' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.94 46.93 46.92 ... 46.66 46.65</div><input id='attrs-68bf3c3f-f610-47e9-9fac-0962d3ca9c3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68bf3c3f-f610-47e9-9fac-0962d3ca9c3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e319be3c-f875-4185-bba6-fe2935b6f716' class='xr-var-data-in' type='checkbox'><label for='data-e319be3c-f875-4185-bba6-fe2935b6f716' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>decimal_degrees</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([46.9425, 46.9335, 46.9245, 46.9155, 46.9065, 46.8975, 46.8885, 46.8795,\n",
       "       46.8705, 46.8615, 46.8525, 46.8435, 46.8345, 46.8255, 46.8165, 46.8075,\n",
       "       46.7985, 46.7895, 46.7805, 46.7715, 46.7625, 46.7535, 46.7445, 46.7355,\n",
       "       46.7265, 46.7175, 46.7085, 46.6995, 46.6905, 46.6815, 46.6725, 46.6635,\n",
       "       46.6545])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.02 11.03 11.04 ... 11.35 11.36</div><input id='attrs-166587ee-5a99-44fd-9c64-5b1b8be414f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-166587ee-5a99-44fd-9c64-5b1b8be414f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40dd8a15-2d9d-4f05-a98d-9a8da13b4ac8' class='xr-var-data-in' type='checkbox'><label for='data-40dd8a15-2d9d-4f05-a98d-9a8da13b4ac8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>decimal_degrees</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([11.0245, 11.0335, 11.0425, 11.0515, 11.0605, 11.0695, 11.0785, 11.0875,\n",
       "       11.0965, 11.1055, 11.1145, 11.1235, 11.1325, 11.1415, 11.1505, 11.1595,\n",
       "       11.1685, 11.1775, 11.1865, 11.1955, 11.2045, 11.2135, 11.2225, 11.2315,\n",
       "       11.2405, 11.2495, 11.2585, 11.2675, 11.2765, 11.2855, 11.2945, 11.3035,\n",
       "       11.3125, 11.3215, 11.3305, 11.3395, 11.3485, 11.3575])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-02-01T20:13:30 ... 2018-06-...</div><input id='attrs-738455ad-70a2-4837-8715-37b45f1e89d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-738455ad-70a2-4837-8715-37b45f1e89d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6683064-68e4-4863-a11a-413ab24fc35e' class='xr-var-data-in' type='checkbox'><label for='data-f6683064-68e4-4863-a11a-413ab24fc35e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-02-01T20:13:30.000000000&#x27;, &#x27;2018-02-02T10:05:23.000000000&#x27;,\n",
       "       &#x27;2018-02-02T21:28:18.000000000&#x27;, ..., &#x27;2018-06-27T20:28:22.000000000&#x27;,\n",
       "       &#x27;2018-06-29T09:54:05.000000000&#x27;, &#x27;2018-06-29T21:16:59.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(230, 2), meta=np.ndarray&gt;</div><input id='attrs-79a9a34d-d501-4498-bb37-f919daed6437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79a9a34d-d501-4498-bb37-f919daed6437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70468098-1638-4a9c-83dc-0f6f52ef6104' class='xr-var-data-in' type='checkbox'><label for='data-70468098-1638-4a9c-83dc-0f6f52ef6104' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 3.59 kiB </td>\n",
       "                        <td> 3.59 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (230, 2) </td>\n",
       "                        <td> (230, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Graph Layers </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> datetime64[ns] </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "            </table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f1f70c-1c3c-4838-84d4-a54d0b71d065' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d5f1f70c-1c3c-4838-84d4-a54d0b71d065' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>S1</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 33, 38), meta=np.ndarray&gt;</div><input id='attrs-ac7d4d9c-6400-4bb7-aca1-aaad0625a0de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac7d4d9c-6400-4bb7-aca1-aaad0625a0de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-052811b4-0af6-4778-a40d-c8d4cf31e25b' class='xr-var-data-in' type='checkbox'><label for='data-052811b4-0af6-4778-a40d-c8d4cf31e25b' 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>sample_type :</span></dt><dd>UINT16</dd><dt><span>units :</span></dt><dd>reflectance</dd><dt><span>wavelength :</span></dt><dd>554.27</dd><dt><span>bandwidth :</span></dt><dd>19.26</dd><dt><span>resolution :</span></dt><dd>500</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.10 MiB </td>\n",
       "                        <td> 4.90 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (230, 33, 38) </td>\n",
       "                        <td> (1, 33, 38) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Graph Layers </td>\n",
       "                        <td> 230 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-36cb5286-bb51-471d-9d71-8378238bb9fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-36cb5286-bb51-471d-9d71-8378238bb9fc' class='xr-section-summary' >Attributes: <span>(12)</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-1.7</dd><dt><span>title :</span></dt><dd>S3SLSTR Data Cube Subset</dd><dt><span>history :</span></dt><dd>[{&#x27;program&#x27;: &#x27;xcube_sh.chunkstore.SentinelHubChunkStore&#x27;, &#x27;cube_config&#x27;: {&#x27;dataset_name&#x27;: &#x27;S3SLSTR&#x27;, &#x27;band_names&#x27;: [&#x27;S1&#x27;], &#x27;band_fill_values&#x27;: None, &#x27;band_sample_types&#x27;: None, &#x27;band_units&#x27;: None, &#x27;tile_size&#x27;: [38, 33], &#x27;bbox&#x27;: [11.02, 46.65, 11.362, 46.946999999999996], &#x27;spatial_res&#x27;: 0.009, &#x27;crs&#x27;: &#x27;WGS84&#x27;, &#x27;upsampling&#x27;: &#x27;BILINEAR&#x27;, &#x27;downsampling&#x27;: &#x27;BILINEAR&#x27;, &#x27;mosaicking_order&#x27;: &#x27;mostRecent&#x27;, &#x27;time_range&#x27;: [&#x27;2018-02-01T00:00:00+00:00&#x27;, &#x27;2018-06-30T00:00:00+00:00&#x27;], &#x27;time_period&#x27;: None, &#x27;time_tolerance&#x27;: &#x27;0 days 00:10:00&#x27;, &#x27;collection_id&#x27;: None, &#x27;four_d&#x27;: False}}]</dd><dt><span>date_created :</span></dt><dd>2023-03-07T06:52:28.574972</dd><dt><span>time_coverage_start :</span></dt><dd>2018-02-01T20:13:30.178000+00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2018-06-29T21:17:14.559000+00:00</dd><dt><span>time_coverage_duration :</span></dt><dd>P148DT1H3M44.381S</dd><dt><span>geospatial_lon_min :</span></dt><dd>11.02</dd><dt><span>geospatial_lat_min :</span></dt><dd>46.65</dd><dt><span>geospatial_lon_max :</span></dt><dd>11.362</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.946999999999996</dd><dt><span>processing_level :</span></dt><dd>L1B</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 230, lat: 33, lon: 38, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 46.94 46.93 46.92 46.92 ... 46.68 46.67 46.66 46.65\n",
       "  * lon        (lon) float64 11.02 11.03 11.04 11.05 ... 11.33 11.34 11.35 11.36\n",
       "  * time       (time) datetime64[ns] 2018-02-01T20:13:30 ... 2018-06-29T21:16:59\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(230, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    S1         (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   S3SLSTR Data Cube Subset\n",
       "    history:                 [{'program': 'xcube_sh.chunkstore.SentinelHubChu...\n",
       "    date_created:            2023-03-07T06:52:28.574972\n",
       "    time_coverage_start:     2018-02-01T20:13:30.178000+00:00\n",
       "    time_coverage_end:       2018-06-29T21:17:14.559000+00:00\n",
       "    time_coverage_duration:  P148DT1H3M44.381S\n",
       "    geospatial_lon_min:      11.02\n",
       "    geospatial_lat_min:      46.65\n",
       "    geospatial_lon_max:      11.362\n",
       "    geospatial_lat_max:      46.946999999999996\n",
       "    processing_level:        L1B"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube_s3 = open_cube(cube_config_s3, api_url=\"https://creodias.sentinel-hub.com\")\n",
    "cube_s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "83f26262-a518-4e03-bc59-a52f174e2bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3be5b837c0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cube_s3.S1.sel(time='2018-06-25', method='nearest').plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6950a9fe-8d03-466f-99a1-faca119ba887",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b7bf7a3-e5c8-4a89-a9dc-2cd8712a09ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d08ff9f-8ce1-460a-aff8-a7e436cb2d0a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "default *",
   "language": "python",
   "name": "conda-env-default-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}