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+   "source": [
+    "# `eReef` Direct Access and Use Example\n",
+    "\n",
+    "**Author**: Leo Li, Michaela Miller\n",
+    "\n",
+    "This notebook demonstrates how to access a public dataset from the RIMReP DMS that is in `zarr` format. We will use the [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (4km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr4.html) dataset as an example.  \n",
+    "  \n",
+    "Throughout this notebook, you will be able to:\n",
+    "\n",
+    "     1. Load necessary packages required for data connection and anaylsis\n",
+    "     2. Connect directly to the s3 bucket, load the data and check the size\n",
+    "     3. Priliminary data exploration to view data attributes and metadata\n",
+    "     4. Extracting and plotting temperature and salinity variables at specific locations, times and depths.\n",
+    "     5. Simple calculations using all data to calculate monthly means of temperature for 2022.\n",
+    "     6. Plotting a timeseries for a paritular location\n",
+    "     \n",
+    "There are also examples of how to do simple calculations to plot maps and timeseries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading and manipulating gridded multidimensional data\n",
+    "import xarray as xr\n",
+    "# Accessing S3 buckets\n",
+    "import s3fs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Connecting to the s3 bucket, loading data, and checking sizes\n",
+    "First, you will need to connect directly to the `zarr` dataset in the RIMReP DMS. This is using an `s3` bucket address, and no credentials are needed to access these public data. Following, you will load the `zarr` data file and check the result. As a result, you should see an `xarray.Dataset` that contains dimensions, coordinates, variables, indexes and attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 616GB\n",
+       "Dimensions:      (time: 4873, latitude: 723, longitude: 491, k: 17)\n",
+       "Coordinates:\n",
+       "  * latitude     (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude    (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
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+       "    zc           (k) float64 136B ...\n",
+       "Dimensions without coordinates: k\n",
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+       "    mean_wspeed  (time, latitude, longitude) float32 7GB ...\n",
+       "    salt         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    temp         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    u            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    v            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    wspeed_u     (time, latitude, longitude) float32 7GB ...\n",
+       "    wspeed_v     (time, latitude, longitude) float32 7GB ...\n",
+       "Attributes: (12/23)\n",
+       "    Conventions:                   CF-1.0\n",
+       "    Parameter_File_Revision:       $Revision: 1753 $\n",
+       "    Run_ID:                        2.1\n",
+       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
+       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
+       "    aims_ncaggregate_buildDate:    2024-02-24T01:47:28+10:00\n",
+       "    ...                            ...\n",
+       "    paramhead:                     GBR 4km resolution grid\n",
+       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
+       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
+       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
+       "    technical_guide_publish_date:  2020-08-18\n",
+       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai...</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-e14daa6c-11bf-498e-ab63-53a120387bb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e14daa6c-11bf-498e-ab63-53a120387bb5' 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>: 4873</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li><li><span>k</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8409c5a0-47d5-4677-b9a8-bfd9e07a844e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8409c5a0-47d5-4677-b9a8-bfd9e07a844e' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-91be7460-bb37-4ac6-8d1f-8f4f21e066f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91be7460-bb37-4ac6-8d1f-8f4f21e066f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86eebfa0-5d18-4482-b802-cb5b8a8cdf6d' class='xr-var-data-in' type='checkbox'><label for='data-86eebfa0-5d18-4482-b802-cb5b8a8cdf6d' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
+       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-56069187-055b-49a1-8975-68b36cf4d77a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56069187-055b-49a1-8975-68b36cf4d77a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e21915c-c878-4304-bc90-a90f2617e584' class='xr-var-data-in' type='checkbox'><label for='data-0e21915c-c878-4304-bc90-a90f2617e584' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
+       "       156.868788])</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'>2010-08-31T14:00:00 ... 2024-01-...</div><input id='attrs-4d599549-06c0-465d-8102-01863872ce53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d599549-06c0-465d-8102-01863872ce53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc176e15-4d85-4835-a206-a5f67740cb23' class='xr-var-data-in' type='checkbox'><label for='data-cc176e15-4d85-4835-a206-a5f67740cb23' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>coordinate_type :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/UTAA/</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-31T14:00:00.000000000&#x27;, &#x27;2010-09-01T14:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-09-02T14:00:00.000000000&#x27;, ..., &#x27;2024-01-14T14:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T14:00:00.000000000&#x27;, &#x27;2024-01-16T14:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0c321db-a7ad-441b-b99f-02218fe1e90f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c321db-a7ad-441b-b99f-02218fe1e90f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e962aba-73f2-468a-a774-234cc150fc83' class='xr-var-data-in' type='checkbox'><label for='data-6e962aba-73f2-468a-a774-234cc150fc83' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41cd896f-deee-4b42-8347-c03e80389b7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41cd896f-deee-4b42-8347-c03e80389b7c' class='xr-section-summary' >Data variables: <span>(9)</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>eta</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8cda7b5c-0590-4a48-92d7-47502c885e1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cda7b5c-0590-4a48-92d7-47502c885e1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83c48ebf-eb7c-4c31-9bc0-101d30d8b93e' class='xr-var-data-in' type='checkbox'><label for='data-83c48ebf-eb7c-4c31-9bc0-101d30d8b93e' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Surface elevation</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/ASLVMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_surface_elevation</dd><dt><span>short_name :</span></dt><dd>eta</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_near_surface</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#Meter</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_cur</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0b42981-5456-46d7-be42-72c3d1e57511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0b42981-5456-46d7-be42-72c3d1e57511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78dbb106-6d1c-4a8c-bb25-e1b8a106efea' class='xr-var-data-in' type='checkbox'><label for='data-78dbb106-6d1c-4a8c-bb25-e1b8a106efea' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_current_speed</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>short_name :</span></dt><dd>mean_cur</dd><dt><span>standard_name :</span></dt><dd>mean_current_speed</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_wspeed</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a0bbd8e-4913-4f17-a090-04aa72d58f94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a0bbd8e-4913-4f17-a090-04aa72d58f94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4ab6c47-0f65-4386-861d-40cd6ac968fa' class='xr-var-data-in' type='checkbox'><label for='data-a4ab6c47-0f65-4386-861d-40cd6ac968fa' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_wind_speed</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>mean_wspeed</dd><dt><span>standard_name :</span></dt><dd>mean_wind_speed</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-034a932a-89bb-4dc5-9c45-8dca31eb1645' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-034a932a-89bb-4dc5-9c45-8dca31eb1645' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-116c56ca-edad-43b2-8f23-60e7363318ac' class='xr-var-data-in' type='checkbox'><label for='data-116c56ca-edad-43b2-8f23-60e7363318ac' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/PSLTMP01/</dd><dt><span>qudt__unit :</span></dt><dd>http://qudt.org/vocab/unit/PSU</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/practical_salinity</dd><dt><span>short_name :</span></dt><dd>salt</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://environment.data.gov.au/water/quality/def/unit/PSU</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef22ae52-33dc-4137-9342-ac0b127e0adf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef22ae52-33dc-4137-9342-ac0b127e0adf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-983c4096-bd4c-4621-ba36-11d2d619ff5b' class='xr-var-data-in' type='checkbox'><label for='data-983c4096-bd4c-4621-ba36-11d2d619ff5b' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9552bcb8-5ea7-49c8-b93d-4a0ee9ad943b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9552bcb8-5ea7-49c8-b93d-4a0ee9ad943b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5220ef77-bbe8-4eac-8ad8-0ff2a63f5a5d' class='xr-var-data-in' type='checkbox'><label for='data-5220ef77-bbe8-4eac-8ad8-0ff2a63f5a5d' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Eastward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_eastward</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-91356b4a-f6e8-48cc-a59f-6a3bb7a07602' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91356b4a-f6e8-48cc-a59f-6a3bb7a07602' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c477cc4-b2e1-4871-a1b5-06fbcfb57fd5' class='xr-var-data-in' type='checkbox'><label for='data-1c477cc4-b2e1-4871-a1b5-06fbcfb57fd5' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Northward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCNSMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_northward</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_u</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef8f989d-4ed4-4a87-9ffa-f71972427c29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef8f989d-4ed4-4a87-9ffa-f71972427c29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-078639b1-5a03-4049-bc73-d4d02c58da6e' class='xr-var-data-in' type='checkbox'><label for='data-078639b1-5a03-4049-bc73-d4d02c58da6e' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_u</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_v</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8560ddeb-f699-4745-b84a-5fb74d02c99b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8560ddeb-f699-4745-b84a-5fb74d02c99b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fec16239-bc1b-4df9-8c9a-3f861038f81e' class='xr-var-data-in' type='checkbox'><label for='data-fec16239-bc1b-4df9-8c9a-3f861038f81e' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>northward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESNSMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_v</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-758e0ab7-5787-4f95-85d8-8079c4777ec3' class='xr-section-summary-in' type='checkbox'  ><label for='section-758e0ab7-5787-4f95-85d8-8079c4777ec3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1445af18-563b-4666-b3b4-d73a9b144d29' class='xr-index-data-in' type='checkbox'/><label for='index-1445af18-563b-4666-b3b4-d73a9b144d29' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
+       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
+       "       ...\n",
+       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
+       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7eb8d5a2-9718-42d4-ad30-bd5e4dfe38e2' class='xr-index-data-in' type='checkbox'/><label for='index-7eb8d5a2-9718-42d4-ad30-bd5e4dfe38e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
+       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
+       "       ...\n",
+       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
+       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-083c6b21-484a-4854-9d22-6f247cf76237' class='xr-index-data-in' type='checkbox'/><label for='index-083c6b21-484a-4854-9d22-6f247cf76237' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-31 14:00:00&#x27;, &#x27;2010-09-01 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-02 14:00:00&#x27;, &#x27;2010-09-03 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-04 14:00:00&#x27;, &#x27;2010-09-05 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-06 14:00:00&#x27;, &#x27;2010-09-07 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-08 14:00:00&#x27;, &#x27;2010-09-09 14:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2024-01-07 14:00:00&#x27;, &#x27;2024-01-08 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-09 14:00:00&#x27;, &#x27;2024-01-10 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-11 14:00:00&#x27;, &#x27;2024-01-12 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-13 14:00:00&#x27;, &#x27;2024-01-14 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-15 14:00:00&#x27;, &#x27;2024-01-16 14:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=4873, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e5970ed-1380-48f2-9f36-c6b0431643e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e5970ed-1380-48f2-9f36-c6b0431643e2' class='xr-section-summary' >Attributes: <span>(23)</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-1.0</dd><dt><span>Parameter_File_Revision :</span></dt><dd>$Revision: 1753 $</dd><dt><span>Run_ID :</span></dt><dd>2.1</dd><dt><span>Run_code :</span></dt><dd>GBR4 Hydro|G0.00|H2.1|S0.00|B0.00</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>aims_ncaggregate_buildDate :</span></dt><dd>2024-02-24T01:47:28+10:00</dd><dt><span>aims_ncaggregate_datasetId :</span></dt><dd>products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2024-01</dd><dt><span>aims_ncaggregate_firstDate :</span></dt><dd>2024-01-01T00:00:00+10:00</dd><dt><span>aims_ncaggregate_inputs :</span></dt><dd>[products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2024-01::MD5:9a148ec09083762a00e41c8444d3d4d5]</dd><dt><span>aims_ncaggregate_lastDate :</span></dt><dd>2024-01-17T00:00:00+10:00</dd><dt><span>bald__isPrefixedBy :</span></dt><dd>prefix_list</dd><dt><span>date_created :</span></dt><dd>Wed Feb  7 09:30:39 2024</dd><dt><span>description :</span></dt><dd>Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.</dd><dt><span>ems_version :</span></dt><dd>v1.4.0 rev(7358)</dd><dt><span>history :</span></dt><dd>2024-02-24T00:48:35+10:00: vendor: AIMS; processing: None summaries\n",
+       "2024-02-24T01:47:28+10:00: vendor: AIMS; processing: Daily summaries</dd><dt><span>metadata_link :</span></dt><dd>https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e</dd><dt><span>paramfile :</span></dt><dd>./prm/gbr4_hydro_nrt.prm</dd><dt><span>paramhead :</span></dt><dd>GBR 4km resolution grid</dd><dt><span>prefix_list_puv__ :</span></dt><dd>https://w3id.org/env/puv#</dd><dt><span>prefix_list_qudt__ :</span></dt><dd>http://qudt.org/vocab/unit/</dd><dt><span>technical_guide_link :</span></dt><dd>https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf</dd><dt><span>technical_guide_publish_date :</span></dt><dd>2020-08-18</dd><dt><span>title :</span></dt><dd>eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 616GB\n",
+       "Dimensions:      (time: 4873, latitude: 723, longitude: 491, k: 17)\n",
+       "Coordinates:\n",
+       "  * latitude     (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude    (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
+       "  * time         (time) datetime64[ns] 39kB 2010-08-31T14:00:00 ... 2024-01-1...\n",
+       "    zc           (k) float64 136B ...\n",
+       "Dimensions without coordinates: k\n",
+       "Data variables:\n",
+       "    eta          (time, latitude, longitude) float32 7GB ...\n",
+       "    mean_cur     (time, k, latitude, longitude) float32 118GB ...\n",
+       "    mean_wspeed  (time, latitude, longitude) float32 7GB ...\n",
+       "    salt         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    temp         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    u            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    v            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    wspeed_u     (time, latitude, longitude) float32 7GB ...\n",
+       "    wspeed_v     (time, latitude, longitude) float32 7GB ...\n",
+       "Attributes: (12/23)\n",
+       "    Conventions:                   CF-1.0\n",
+       "    Parameter_File_Revision:       $Revision: 1753 $\n",
+       "    Run_ID:                        2.1\n",
+       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
+       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
+       "    aims_ncaggregate_buildDate:    2024-02-24T01:47:28+10:00\n",
+       "    ...                            ...\n",
+       "    paramhead:                     GBR 4km resolution grid\n",
+       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
+       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
+       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
+       "    technical_guide_publish_date:  2020-08-18\n",
+       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Path to zarr files\n",
+    "zarr_path = \"s3://gbr-dms-data-public/aims-ereefs-agg-hydrodynamic-4km-daily/data.zarr\"\n",
+    "\n",
+    "# Connection to public S3 bucket - No credentials needed\n",
+    "fs = s3fs.S3FileSystem(anon=True)\n",
+    "\n",
+    "# Loading zarr file\n",
+    "data = xr.open_dataset(s3fs.S3Map(zarr_path, s3=fs), engine=\"zarr\")\n",
+    "\n",
+    "# Checking result\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, you can check the data size. To do this, run the following code, and you should get a result of ~605 GB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Data size: 615.837645316 GB'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check data size in GB \n",
+    "f\"Data size: {data.nbytes / 1e9} GB\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an `ARCO` (Analysis Ready Cloud Optimized) format, `zarr` allows us to access hundreds of GB (or TB) data in seconds `without downloading them anywhere, anytime and on any device`.\n",
+    "\n",
+    "For example, another eReefs dataset [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (1km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr1.html) with higher resolution, has 12 Terabytes (TB), yet we can access it within seconds. To achieve this, we can run a similar code as before, but make sure to change the `s3` bucket address and load the data as `data_1km` so it does not override the previous data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Data size: 12.681407857192 TB'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Path to zarr files\n",
+    "zarr_path = \"s3://gbr-dms-data-public/aims-ereefs-agg-hydrodynamic-1km-daily/data.zarr\"\n",
+    "\n",
+    "# Connection to public S3 bucket - No credentials needed\n",
+    "fs = s3fs.S3FileSystem(anon=True)\n",
+    "\n",
+    "# Loading zarr file\n",
+    "data_1km = xr.open_dataset(s3fs.S3Map(zarr_path, s3=fs), engine=\"zarr\")\n",
+    "\n",
+    "# Check data size in TB (Terabytes)\n",
+    "f\"Data size: {data_1km.nbytes / 1e12} TB\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Data exploration\n",
+    "Now, let's briefly explore the [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (4km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr4.html) dataset by simply calling these data. \n",
+    "\n",
+    "If you click the triangle, file or cylinder in the cell outputs, you can see more details about the variables or coordinates of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 616GB\n",
+       "Dimensions:      (time: 4873, latitude: 723, longitude: 491, k: 17)\n",
+       "Coordinates:\n",
+       "  * latitude     (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude    (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
+       "  * time         (time) datetime64[ns] 39kB 2010-08-31T14:00:00 ... 2024-01-1...\n",
+       "    zc           (k) float64 136B ...\n",
+       "Dimensions without coordinates: k\n",
+       "Data variables:\n",
+       "    eta          (time, latitude, longitude) float32 7GB ...\n",
+       "    mean_cur     (time, k, latitude, longitude) float32 118GB ...\n",
+       "    mean_wspeed  (time, latitude, longitude) float32 7GB ...\n",
+       "    salt         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    temp         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    u            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    v            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    wspeed_u     (time, latitude, longitude) float32 7GB ...\n",
+       "    wspeed_v     (time, latitude, longitude) float32 7GB ...\n",
+       "Attributes: (12/23)\n",
+       "    Conventions:                   CF-1.0\n",
+       "    Parameter_File_Revision:       $Revision: 1753 $\n",
+       "    Run_ID:                        2.1\n",
+       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
+       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
+       "    aims_ncaggregate_buildDate:    2024-02-24T01:47:28+10:00\n",
+       "    ...                            ...\n",
+       "    paramhead:                     GBR 4km resolution grid\n",
+       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
+       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
+       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
+       "    technical_guide_publish_date:  2020-08-18\n",
+       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai...</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-6bf90633-110d-49c6-bca3-6d11b4c0d3d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6bf90633-110d-49c6-bca3-6d11b4c0d3d1' 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>: 4873</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li><li><span>k</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-efc52d9b-61e8-4a4e-a4ec-f6d1a68e3924' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efc52d9b-61e8-4a4e-a4ec-f6d1a68e3924' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-7c9e6c44-00fe-4621-9ed8-0680ae02fcb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c9e6c44-00fe-4621-9ed8-0680ae02fcb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61aacd2c-4504-47af-8dc1-2c42730957b1' class='xr-var-data-in' type='checkbox'><label for='data-61aacd2c-4504-47af-8dc1-2c42730957b1' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
+       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-8891a0a9-3cc4-4706-8a4e-22b43b334979' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8891a0a9-3cc4-4706-8a4e-22b43b334979' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f555a2d5-bdd2-4ff5-9aac-2bec5327d25a' class='xr-var-data-in' type='checkbox'><label for='data-f555a2d5-bdd2-4ff5-9aac-2bec5327d25a' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
+       "       156.868788])</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'>2010-08-31T14:00:00 ... 2024-01-...</div><input id='attrs-05ce9850-1da2-47c5-942c-6bf20f72ae8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05ce9850-1da2-47c5-942c-6bf20f72ae8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ca4c02-31bb-477a-8c49-fda8324c6890' class='xr-var-data-in' type='checkbox'><label for='data-57ca4c02-31bb-477a-8c49-fda8324c6890' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>coordinate_type :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/UTAA/</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-31T14:00:00.000000000&#x27;, &#x27;2010-09-01T14:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-09-02T14:00:00.000000000&#x27;, ..., &#x27;2024-01-14T14:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T14:00:00.000000000&#x27;, &#x27;2024-01-16T14:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03be127f-7cc9-487b-9d2b-8ac8906d7021' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03be127f-7cc9-487b-9d2b-8ac8906d7021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7d07e3a-f644-4a3e-8290-021f8661776d' class='xr-var-data-in' type='checkbox'><label for='data-c7d07e3a-f644-4a3e-8290-021f8661776d' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-048b90a5-1d5d-46c4-9817-eaa41febd027' class='xr-section-summary-in' type='checkbox'  checked><label for='section-048b90a5-1d5d-46c4-9817-eaa41febd027' class='xr-section-summary' >Data variables: <span>(9)</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>eta</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9c40377-0f83-4c36-aac6-20ad9ff469ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9c40377-0f83-4c36-aac6-20ad9ff469ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0e1232-f1a1-4bbf-b40e-20ce4a17ed95' class='xr-var-data-in' type='checkbox'><label for='data-cf0e1232-f1a1-4bbf-b40e-20ce4a17ed95' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Surface elevation</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/ASLVMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_surface_elevation</dd><dt><span>short_name :</span></dt><dd>eta</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_near_surface</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#Meter</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_cur</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71077651-abab-44b9-b861-1ab82f621820' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71077651-abab-44b9-b861-1ab82f621820' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63ef2c81-1dc4-4656-83e9-23d70d782cfa' class='xr-var-data-in' type='checkbox'><label for='data-63ef2c81-1dc4-4656-83e9-23d70d782cfa' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_current_speed</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>short_name :</span></dt><dd>mean_cur</dd><dt><span>standard_name :</span></dt><dd>mean_current_speed</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_wspeed</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa34f268-e212-4c1a-89af-8ecba269b4ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa34f268-e212-4c1a-89af-8ecba269b4ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55856f74-826b-4a80-a1d3-0a454d2ae862' class='xr-var-data-in' type='checkbox'><label for='data-55856f74-826b-4a80-a1d3-0a454d2ae862' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_wind_speed</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>mean_wspeed</dd><dt><span>standard_name :</span></dt><dd>mean_wind_speed</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c778663f-d383-4e1a-b152-79b3b9f28d6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c778663f-d383-4e1a-b152-79b3b9f28d6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac13a8ad-ab38-4d7e-bc28-425dcfe4f410' class='xr-var-data-in' type='checkbox'><label for='data-ac13a8ad-ab38-4d7e-bc28-425dcfe4f410' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/PSLTMP01/</dd><dt><span>qudt__unit :</span></dt><dd>http://qudt.org/vocab/unit/PSU</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/practical_salinity</dd><dt><span>short_name :</span></dt><dd>salt</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://environment.data.gov.au/water/quality/def/unit/PSU</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-872e5983-09d5-440c-91c9-64121d9f7c65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-872e5983-09d5-440c-91c9-64121d9f7c65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37ccf19e-d1f3-4614-82a3-128c2b813caa' class='xr-var-data-in' type='checkbox'><label for='data-37ccf19e-d1f3-4614-82a3-128c2b813caa' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b36a1fa-1874-47ac-bc9f-55393343326e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b36a1fa-1874-47ac-bc9f-55393343326e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b741759f-4d4e-4435-a13e-33202ac750d5' class='xr-var-data-in' type='checkbox'><label for='data-b741759f-4d4e-4435-a13e-33202ac750d5' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Eastward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_eastward</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-969b262b-cdbc-4b8c-a316-d66d72ee8ba6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-969b262b-cdbc-4b8c-a316-d66d72ee8ba6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-680cc03f-0f7e-4300-ae2f-689d6bc03a85' class='xr-var-data-in' type='checkbox'><label for='data-680cc03f-0f7e-4300-ae2f-689d6bc03a85' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Northward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCNSMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_northward</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[29407975113 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_u</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b574639-98bf-4272-a36c-6277fe9e1def' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b574639-98bf-4272-a36c-6277fe9e1def' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8490d26d-9a6e-411e-9ff9-64e64964cc78' class='xr-var-data-in' type='checkbox'><label for='data-8490d26d-9a6e-411e-9ff9-64e64964cc78' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_u</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_v</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7686ae19-30a4-413e-9114-ddaf999c5078' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7686ae19-30a4-413e-9114-ddaf999c5078' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4af10071-26d3-49e9-964a-fa1b47f46de4' class='xr-var-data-in' type='checkbox'><label for='data-4af10071-26d3-49e9-964a-fa1b47f46de4' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>northward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESNSMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_v</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1729880889 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddee78bc-17ff-4112-9f81-664f3c179cce' class='xr-section-summary-in' type='checkbox'  ><label for='section-ddee78bc-17ff-4112-9f81-664f3c179cce' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e89608af-6eae-40bf-ac71-bfbfe58af9d9' class='xr-index-data-in' type='checkbox'/><label for='index-e89608af-6eae-40bf-ac71-bfbfe58af9d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
+       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
+       "       ...\n",
+       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
+       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7044cf3a-ae21-4b15-96d6-39a4ab837c8f' class='xr-index-data-in' type='checkbox'/><label for='index-7044cf3a-ae21-4b15-96d6-39a4ab837c8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
+       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
+       "       ...\n",
+       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
+       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f89b35ad-9758-485c-bd86-b681a567da90' class='xr-index-data-in' type='checkbox'/><label for='index-f89b35ad-9758-485c-bd86-b681a567da90' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-31 14:00:00&#x27;, &#x27;2010-09-01 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-02 14:00:00&#x27;, &#x27;2010-09-03 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-04 14:00:00&#x27;, &#x27;2010-09-05 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-06 14:00:00&#x27;, &#x27;2010-09-07 14:00:00&#x27;,\n",
+       "               &#x27;2010-09-08 14:00:00&#x27;, &#x27;2010-09-09 14:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2024-01-07 14:00:00&#x27;, &#x27;2024-01-08 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-09 14:00:00&#x27;, &#x27;2024-01-10 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-11 14:00:00&#x27;, &#x27;2024-01-12 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-13 14:00:00&#x27;, &#x27;2024-01-14 14:00:00&#x27;,\n",
+       "               &#x27;2024-01-15 14:00:00&#x27;, &#x27;2024-01-16 14:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=4873, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef4fdc81-913d-46eb-ab69-0313c88f349b' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef4fdc81-913d-46eb-ab69-0313c88f349b' class='xr-section-summary' >Attributes: <span>(23)</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-1.0</dd><dt><span>Parameter_File_Revision :</span></dt><dd>$Revision: 1753 $</dd><dt><span>Run_ID :</span></dt><dd>2.1</dd><dt><span>Run_code :</span></dt><dd>GBR4 Hydro|G0.00|H2.1|S0.00|B0.00</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>aims_ncaggregate_buildDate :</span></dt><dd>2024-02-24T01:47:28+10:00</dd><dt><span>aims_ncaggregate_datasetId :</span></dt><dd>products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2024-01</dd><dt><span>aims_ncaggregate_firstDate :</span></dt><dd>2024-01-01T00:00:00+10:00</dd><dt><span>aims_ncaggregate_inputs :</span></dt><dd>[products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2024-01::MD5:9a148ec09083762a00e41c8444d3d4d5]</dd><dt><span>aims_ncaggregate_lastDate :</span></dt><dd>2024-01-17T00:00:00+10:00</dd><dt><span>bald__isPrefixedBy :</span></dt><dd>prefix_list</dd><dt><span>date_created :</span></dt><dd>Wed Feb  7 09:30:39 2024</dd><dt><span>description :</span></dt><dd>Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.</dd><dt><span>ems_version :</span></dt><dd>v1.4.0 rev(7358)</dd><dt><span>history :</span></dt><dd>2024-02-24T00:48:35+10:00: vendor: AIMS; processing: None summaries\n",
+       "2024-02-24T01:47:28+10:00: vendor: AIMS; processing: Daily summaries</dd><dt><span>metadata_link :</span></dt><dd>https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e</dd><dt><span>paramfile :</span></dt><dd>./prm/gbr4_hydro_nrt.prm</dd><dt><span>paramhead :</span></dt><dd>GBR 4km resolution grid</dd><dt><span>prefix_list_puv__ :</span></dt><dd>https://w3id.org/env/puv#</dd><dt><span>prefix_list_qudt__ :</span></dt><dd>http://qudt.org/vocab/unit/</dd><dt><span>technical_guide_link :</span></dt><dd>https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf</dd><dt><span>technical_guide_publish_date :</span></dt><dd>2020-08-18</dd><dt><span>title :</span></dt><dd>eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 616GB\n",
+       "Dimensions:      (time: 4873, latitude: 723, longitude: 491, k: 17)\n",
+       "Coordinates:\n",
+       "  * latitude     (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude    (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
+       "  * time         (time) datetime64[ns] 39kB 2010-08-31T14:00:00 ... 2024-01-1...\n",
+       "    zc           (k) float64 136B ...\n",
+       "Dimensions without coordinates: k\n",
+       "Data variables:\n",
+       "    eta          (time, latitude, longitude) float32 7GB ...\n",
+       "    mean_cur     (time, k, latitude, longitude) float32 118GB ...\n",
+       "    mean_wspeed  (time, latitude, longitude) float32 7GB ...\n",
+       "    salt         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    temp         (time, k, latitude, longitude) float32 118GB ...\n",
+       "    u            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    v            (time, k, latitude, longitude) float32 118GB ...\n",
+       "    wspeed_u     (time, latitude, longitude) float32 7GB ...\n",
+       "    wspeed_v     (time, latitude, longitude) float32 7GB ...\n",
+       "Attributes: (12/23)\n",
+       "    Conventions:                   CF-1.0\n",
+       "    Parameter_File_Revision:       $Revision: 1753 $\n",
+       "    Run_ID:                        2.1\n",
+       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
+       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
+       "    aims_ncaggregate_buildDate:    2024-02-24T01:47:28+10:00\n",
+       "    ...                            ...\n",
+       "    paramhead:                     GBR 4km resolution grid\n",
+       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
+       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
+       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
+       "    technical_guide_publish_date:  2020-08-18\n",
+       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai..."
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that these data have four dimensions: `latitude`, `longitude`, `time`, and `zc` (depth).\n",
+    "     \n",
+    "There are nine variables in the dataset: `temp`, `salt`, `mean_cur`, `mean_wspeed` and so on.\n",
+    "\n",
+    "You can get more information about all the variables included in the dataset by calling them with the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Frozen({'eta': <xarray.Variable (time: 4873, latitude: 723, longitude: 491)> Size: 7GB\n",
+       "[1729880889 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Surface elevation\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    positive:               up\n",
+       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/A...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_s...\n",
+       "    short_name:             eta\n",
+       "    standard_name:          sea_surface_height_above_sea_level\n",
+       "    substanceOrTaxon_id:    http://environment.data.gov.au/def/feature/ocean_...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#Meter\n",
+       "    units:                  metre, 'latitude': <xarray.IndexVariable 'latitude' (latitude: 723)> Size: 6kB\n",
+       "array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
+       "        -7.036022])\n",
+       "Attributes:\n",
+       "    _CoordinateAxisType:  Lat\n",
+       "    coordinate_type:      latitude\n",
+       "    long_name:            Latitude\n",
+       "    projection:           geographic\n",
+       "    puv__ofProperty:      http://vocab.nerc.ac.uk/collection/S06/current/S060...\n",
+       "    puv__uom:             http://vocab.nerc.ac.uk/collection/P06/current/DEGN/\n",
+       "    standard_name:        latitude\n",
+       "    units:                degrees_north, 'longitude': <xarray.IndexVariable 'longitude' (longitude: 491)> Size: 4kB\n",
+       "array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
+       "       156.868788])\n",
+       "Attributes:\n",
+       "    _CoordinateAxisType:  Lon\n",
+       "    coordinate_type:      longitude\n",
+       "    long_name:            Longitude\n",
+       "    projection:           geographic\n",
+       "    puv__uom:             http://vocab.nerc.ac.uk/collection/P06/current/DEGE/\n",
+       "    standard_name:        longitude\n",
+       "    units:                degrees_east, 'mean_cur': <xarray.Variable (time: 4873, k: 17, latitude: 723, longitude: 491)> Size: 118GB\n",
+       "[29407975113 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:          mean_speed\n",
+       "    long_name:            mean_current_speed\n",
+       "    medium_id:            http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:       http://vocab.nerc.ac.uk/collection/P01/current/LCEW...\n",
+       "    short_name:           mean_cur\n",
+       "    standard_name:        mean_current_speed\n",
+       "    substanceOrTaxon_id:  http://environment.data.gov.au/def/feature/ocean_cu...\n",
+       "    unit_id:              http://qudt.org/vocab/unit#MeterPerSecond\n",
+       "    units:                ms-1, 'mean_wspeed': <xarray.Variable (time: 4873, latitude: 723, longitude: 491)> Size: 7GB\n",
+       "[1729880889 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:     mean_speed\n",
+       "    long_name:       mean_wind_speed\n",
+       "    puv__parameter:  http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/\n",
+       "    short_name:      mean_wspeed\n",
+       "    standard_name:   mean_wind_speed\n",
+       "    units:           ms-1, 'salt': <xarray.Variable (time: 4873, k: 17, latitude: 723, longitude: 491)> Size: 118GB\n",
+       "[29407975113 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Salinity\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         http://vocab.nerc.ac.uk/collection/P01/current/PS...\n",
+       "    qudt__unit:             http://qudt.org/vocab/unit/PSU\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/pract...\n",
+       "    short_name:             salt\n",
+       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
+       "    unit_id:                http://environment.data.gov.au/water/quality/def/...\n",
+       "    units:                  PSU, 'temp': <xarray.Variable (time: 4873, k: 17, latitude: 723, longitude: 491)> Size: 118GB\n",
+       "[29407975113 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Temperature\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             temp\n",
+       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
+       "    units:                  degrees C, 'time': <xarray.IndexVariable 'time' (time: 4873)> Size: 39kB\n",
+       "array(['2010-08-31T14:00:00.000000000', '2010-09-01T14:00:00.000000000',\n",
+       "       '2010-09-02T14:00:00.000000000', ..., '2024-01-14T14:00:00.000000000',\n",
+       "       '2024-01-15T14:00:00.000000000', '2024-01-16T14:00:00.000000000'],\n",
+       "      dtype='datetime64[ns]')\n",
+       "Attributes:\n",
+       "    _CoordinateAxisType:  Time\n",
+       "    coordinate_type:      time\n",
+       "    long_name:            Time\n",
+       "    puv__uom:             http://vocab.nerc.ac.uk/collection/P06/current/UTAA/\n",
+       "    standard_name:        time, 'u': <xarray.Variable (time: 4873, k: 17, latitude: 723, longitude: 491)> Size: 118GB\n",
+       "[29407975113 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Eastward current\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         http://vocab.nerc.ac.uk/collection/P01/current/LC...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             u\n",
+       "    standard_name:          eastward_sea_water_velocity\n",
+       "    substanceOrTaxon_id:    http://environment.data.gov.au/def/feature/ocean_...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#MeterPerSecond\n",
+       "    units:                  ms-1\n",
+       "    vector_components:      u v\n",
+       "    vector_name:            Currents, 'v': <xarray.Variable (time: 4873, k: 17, latitude: 723, longitude: 491)> Size: 118GB\n",
+       "[29407975113 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Northward current\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         http://vocab.nerc.ac.uk/collection/P01/current/LC...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             v\n",
+       "    standard_name:          northward_sea_water_velocity\n",
+       "    substanceOrTaxon_id:    http://environment.data.gov.au/def/feature/ocean_...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#MeterPerSecond\n",
+       "    units:                  ms-1\n",
+       "    vector_components:      u v\n",
+       "    vector_name:            Currents, 'wspeed_u': <xarray.Variable (time: 4873, latitude: 723, longitude: 491)> Size: 7GB\n",
+       "[1729880889 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:     Daily\n",
+       "    long_name:       eastward_wind\n",
+       "    puv__parameter:  http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/\n",
+       "    short_name:      wspeed_u\n",
+       "    units:           ms-1, 'wspeed_v': <xarray.Variable (time: 4873, latitude: 723, longitude: 491)> Size: 7GB\n",
+       "[1729880889 values with dtype=float32]\n",
+       "Attributes:\n",
+       "    aggregation:     Daily\n",
+       "    long_name:       northward_wind\n",
+       "    puv__parameter:  http://vocab.nerc.ac.uk/collection/P01/current/ESNSMPXX/\n",
+       "    short_name:      wspeed_v\n",
+       "    units:           ms-1, 'zc': <xarray.Variable (k: 17)> Size: 136B\n",
+       "[17 values with dtype=float64]\n",
+       "Attributes:\n",
+       "    _CoordinateAxisType:     Height\n",
+       "    _CoordinateZisPositive:  up\n",
+       "    axis:                    Z\n",
+       "    coordinate_type:         Z\n",
+       "    long_name:               Z coordinate\n",
+       "    positive:                up\n",
+       "    units:                   m})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can specify one variable by calling their attributes as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'aggregation': 'mean_speed',\n",
+       " 'long_name': 'mean_current_speed',\n",
+       " 'medium_id': 'http://environment.data.gov.au/def/feature/ocean',\n",
+       " 'puv__parameter': 'http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/',\n",
+       " 'short_name': 'mean_cur',\n",
+       " 'standard_name': 'mean_current_speed',\n",
+       " 'substanceOrTaxon_id': 'http://environment.data.gov.au/def/feature/ocean_current',\n",
+       " 'unit_id': 'http://qudt.org/vocab/unit#MeterPerSecond',\n",
+       " 'units': 'ms-1'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.mean_cur.attrs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `zarr` format has a `self-describe` ability, so anyone could understand data easily. Therefore, checking the metadata of the entire dataset by calling the dataset attributes as shown below will give you all the information regarding these data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Conventions': 'CF-1.0',\n",
+       " 'Parameter_File_Revision': '$Revision: 1753 $',\n",
+       " 'Run_ID': '2.1',\n",
+       " 'Run_code': 'GBR4 Hydro|G0.00|H2.1|S0.00|B0.00',\n",
+       " '_CoordSysBuilder': 'ucar.nc2.dataset.conv.CF1Convention',\n",
+       " 'aims_ncaggregate_buildDate': '2024-02-24T01:47:28+10:00',\n",
+       " 'aims_ncaggregate_datasetId': 'products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2024-01',\n",
+       " 'aims_ncaggregate_firstDate': '2024-01-01T00:00:00+10:00',\n",
+       " 'aims_ncaggregate_inputs': '[products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2024-01::MD5:9a148ec09083762a00e41c8444d3d4d5]',\n",
+       " 'aims_ncaggregate_lastDate': '2024-01-17T00:00:00+10:00',\n",
+       " 'bald__isPrefixedBy': 'prefix_list',\n",
+       " 'date_created': 'Wed Feb  7 09:30:39 2024',\n",
+       " 'description': 'Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.',\n",
+       " 'ems_version': 'v1.4.0 rev(7358)',\n",
+       " 'history': '2024-02-24T00:48:35+10:00: vendor: AIMS; processing: None summaries\\n2024-02-24T01:47:28+10:00: vendor: AIMS; processing: Daily summaries',\n",
+       " 'metadata_link': 'https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e',\n",
+       " 'paramfile': './prm/gbr4_hydro_nrt.prm',\n",
+       " 'paramhead': 'GBR 4km resolution grid',\n",
+       " 'prefix_list_puv__': 'https://w3id.org/env/puv#',\n",
+       " 'prefix_list_qudt__': 'http://qudt.org/vocab/unit/',\n",
+       " 'technical_guide_link': 'https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf',\n",
+       " 'technical_guide_publish_date': '2020-08-18',\n",
+       " 'title': 'eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.attrs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Extracting and Plotting data\n",
+    "Extracting and plotting data is easy with `xarray`, we can do it with a single line of code!\n",
+    "\n",
+    "Here, we use the function of `isel()`, which allows us to select a variable by using an index along the specified dimension(s). This function is similar to the `iloc()` of the Python package, Pandas. We could easily access the first date of a variable by `isel(time=0)` if it is unclear when the first time step is for these data. To check this was done properly, the title of the plot should date back to 31 August, 2010.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7a5190e46db0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting mean wind speed for the first time step in the all GBR area\n",
+    "data.mean_wspeed.isel(time=0).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's do this for another variable (i.e., wind speed), and plot the most recent time step. To do this, select the variable `wspeed_u` and use -1 in `isel(time=-1)` to get the latest time step. To ensure this is done correctly, the title of the plot should update with the most recent time step available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7a519098e780>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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U1q9fL93vtBjU1dWhXVlL1MAouK9OnTphzZo1SnQViBJcRYBtRbN+/XpnSxaFQqFQKGRUVVWha9euDbKNGSOdTqMGBi7EvkgVED2UhonHN21AOp1WgqtAlOAqAsyNWFFRoQSXQqFQKCLRGCEoKWhIkQLclmovmqKhBJdCoVAoFHsoOiHQCxB2OogSXUVij1mluHDhQhBCpK+lS5f6ths7dqyn/jHHHNOIM1coFAqFomHQCKAX8NLUOrCiscdYuAYPHoyNGze6yv70pz9h/vz5GDhwYGDbk046CTNnznTOU6lUg8xRoVAoFArF3skeI7hSqRQ6derknGcyGbz44osYP358qJ+8pKTE1VahUCgUij2BorgUFUVhj3Epirz44ovYsmULxo4dG1p34cKF6NChAw455BCMGzcOmzdvDqxfX1+Pqqoq10uhUCgUiqZGIe5E9lIUhz1WcM2YMQMjR44MzY918skn48knn8Trr7+Ou+++G0uXLsWPf/xj1NfX+7a59dZb0bp1a+e1t+fg2llT67wUCoVCoVB4afKCa+rUqb7B8Oz13nvvudp88803eOWVV3DJJZeE9n/eeefh1FNPRd++fXH66afj5ZdfxmeffYa5c+f6tpk8eTJ27NjhvNavX1/wfTZHdlTnBJZJrdcuTnztUgJMoVAodivMpVjIS1EcmnwM1/jx43H++ecH1unWrZvrfObMmWjXrh1GjRoVe7zOnTvjgAMOwOeff+5bp6SkBCUlJbH7bkrsqqlFyxZlBfVhmBTbdtYAyK1kyVJAt09okcZRKBQKRX4U6hbUizeVvZ4mL7gqKytRWVkZuT6lFDNnzsSFF16IZDIZe7ytW7di/fr16Ny5c+y2zQkKyxVocvlVWpf7C6Pvq2o8ZfxyYYMCxC4zTOosVNAIXJYuJb4UCoVCsTfS5F2KcXn99dexZs0aX3dir1698PzzzwMAdu3ahUmTJmHJkiVYu3YtFi5ciNNPPx2VlZU488wzG3PajY5hUhgmBaXUOd66swZbd9bg+6oaqcDikeVmoci5Fim1+japJcbYS6FQKBSNh3IpNh2avIUrLjNmzMDgwYNx6KGHSq9/+umn2LFjBwBA13V8+OGHePzxx7F9+3Z07twZw4YNw9NPP92ge1w1Bdq0bIFtO2s8CYR5i9fWnTnRFTf5HetHs0dgFi8+sL5VizLsrKl1Fh0r65dCoVAUF4LCLCtKbhWPPU5w/f3vfw+8TmlOUZSVleGVV15p6Ck1afhfJgq5sCrkF86kVp/sczdBQKnlcuSD7gG3GOPHVEJMoVAo8kPl4Wo67HGCSxGdtq1aAIAT+C7+WkW1ajFRZUZwGTLhxQtfhkHdm7my8XfW1DrWMBmtlCBTKBQKRRNnj4vhUsRj+64aaPZ+WbpGYr8Icb+zvvhXHFjsF5CLBwPc1i9Wzl47qmuxo7oW23fVYPuu4NgzhUKh2Jto7MSnb7zxBk4//XR06dIFhBC88MILruuUUkydOhVdunRBWVkZhg4ditWrV4f2O2fOHPTu3RslJSXo3bu3E4vdnFCCay9il50bi+XK2lFd68pn5ggv+0VgvXRCnGPxxTY35UVbWN40/hUFXnj5WdF4i9n2XTXYttP7UigUir0N6/u8kKD5eONVV1fj8MMPx/333y+9fscdd+DPf/4z7r//fixduhSdOnXCiSeeiJ07d/r2uWTJEpx33nm44IILsHLlSlxwwQU499xz8b///S/e5HYzhMp8O4pYVFVVoXXr1tixYwcqKip293Sk7KiudYkSQnKxVIAllqy0DoSrE9yn+D/HZO7CCPMJc0PGsYyxVZH8uWweBDk3qkKhUOwuGuOZwca4scWBKCX5Z9Oqowaur/kqr7kSQvD888/jjDPOAGB9T3fp0gVXXXUVfv/73wOwtsrr2LEjbr/9dvz617+W9nPeeeehqqoKL7/8slN20kknYZ999sE//vGP/G5sN6AsXHsJrcvLPOKGt2oxsUWItbJQAwWh9ss0rBelrpcGCkLgvCyXYu4vIz+rGMvXxd7FF29dC3uB9SWxmMk0m7J2KRSKvYliuRTF/YODtr/zY82aNdi0aRNGjBjhlJWUlGDIkCF4++23fdstWbLE1QYARo4cGdimKaKC5vcQauvqYJjUsVCZnMWHHSU523BOrBDHkkUoBUwzV4fmjq2O3OeUaCAUAOF0Ox/0HrC6xaTWXNm77FpUrPrc+GYuBozAun+xN7ZCMijZq0KhUDR3irVKUdwzeMqUKZg6dWqsvjZt2gQA6Nixo6u8Y8eO+PrrrwPbydqw/poLSnDtBjLfrwPlRIpL2FATMA0AQLLTQVb9TV9aooYQUC0BaBqgJQCiWeeEAIkSK36KUlDJLxjv/nMJLOREFqGmR1T5Nmb1ieZuw+6L5OQWlfyys/nJvgjifjnwws6k1NlaiDDXouBKBXIWNrX1kEKhUISzfv16l0uxkO3tRG8EH95SzDZNDSW4GoHM5rXuAqIhVbmft97363JVqInst58C1MzJCaKBkIxThxINRNNBEymUtWyN9I4tTjn0pCPqSluUo66mOte36WO5YsJEJrqcuu77ADXct0bM3DVwVjC+TVz85mP3xQs7nRDnNgibrCvVhDdGra62FqVlSnQpFIo9DxamkXd7+72ioqLgeLNOnToBsCxW/PZ5mzdv9liwxHaiNSusTVNECa5GINmhW7R67fd3jrMbJZtn8xYoolkv0wDJ1CPz/TpHeKS4fgAgvWMLCKvP9yXgcSGKY4tiya5PXf3CJcQcAcbmTE13/QAC58ON7+oftgAjmk/gPScqmR4LG0ehUCiaKU0p8Wn37t3RqVMnvPrqq+jfvz8AIJ1OY9GiRbj99tt92w0aNAivvvoqrr76aqds3rx5GDx4cNHm1hgowdVESXTuYVm4eJedIFQINS2XIjUBSpw6me/XOeIt/cOmnHuPE0hSkRFX4NjjiX25Y/OZi5E4oi22wAmrb39G/NeC30pJJa4UCoWi4di1axe++OIL53zNmjVYsWIF2rZti/333x9XXXUVbrnlFvTo0QM9evTALbfcghYtWmD06NFOmwsvvBD77rsvbr31VgDA7373O5xwwgm4/fbb8dOf/hT/+te/MH/+fCxevLjR768QlOBqwiS69PSUZb/91HVOzCwAgFJb2NiWpczmtY7QokSz4r6YBYi3VonHYTABKGvD98/VpUTLKSDiWSoZPJ6Ye0Iq+qg9J5oTdTC89QJck+kdW5BqXRk8F4VCoWhm5JO81NU+Zv333nsPw4YNc84nTJgAABgzZgxmzZqFa6+9FrW1tbj88svxww8/4Oijj8a8efNc+xevW7cOmpYzMAwePBhPPfUUrrvuOvzpT3/CQQcdhKeffhpHH310/je2G1B5uIrA7sjD5RJeguXLCaQXrjPLlsylx5cHWYGk7kBNKHOtWvQ59utLQthqSenYsoD+CO1T+3SKNCeFQqHIl8bMw/WXNgejrIA8XLXUwG+3f9Gk80w2F5SFq5mS6NLTY+0CbBFDTbhSrFEDIMQRLn6CylqlyAWaU+p5d8VkMUy4xJRLSGkSwSWzhPkhruCUXfMTVs783NeCBGV6yzcAIF3UoFAoFM2NphTDtbejBFczhrkcsxs/z4kc5134JZEZMqOsRqQ5gcJcg1IrGckJNZeQMvjAers8yCImnYswT3GVJYKFpKsPn5WY7J5YwH1m89rIix0UCoVCoQhDCa49gETnHu7UE0RzB8ZLrET5BM3zbVztOQuTS4jJRBgA0BDXoijI/CxUYTnDnDLJPYs5x6hpreRk90M0K/8ZcvnQFAqFormho8AYLhV0VDSU4NpDSHbo5uTxYgLGEV3MukSp1/0WFh8VNV7KPg8MmHfauROpesSb4TOmZ27+1ippO+HdVY+JOo377IiVYDa78XMkOveQz0mhUCiaMFqBLsU4u34oglGCa09F0wDTdFu6nHgsScyTj9UokpsOXOyYELTuEVNcO48YkyRSDRvXY7XyOZaKK5lgs8sIMQFNA6WmldVfoVAoFIoCUE+SPQk76J0QE6Ca4N5jx1YdaS4uP3ejJ2bKi9M9H98lJksVguQ943CWOaeImt65RrTKOW1EgYVcOg1Pe8I+N0uwEg2AkRZvV6FQKJoFBaeFUAauoqEE1x5EskM3K+6IAiBUmuVdisxiZHqtPlKYuZnf9BpmLg5LtIIJZe6+3JniWV0SZJHjm/vNXbw/mTXP3r8Smr0mx7ZqUVPI0K9QKBTNiIJXKSqXYtFQgqsZYqxdIb9AiJUMQrOtNEIQuAuJgAmyCPniV8cwnDk504NEaGkSCxiEQHo/FyI/V3E+sjb8uy2wqJk7hmkAmu4+ts/NL96xRJhttaN6EvoBh3vvR6FQKBQKCUpwNQOMtStcbjJ+mx4XzCVn5tIbgBCvABMJim+S1YmDJ0ZLyL1lSGK2CLHEGcv/5ekzeG6+qxH9RBYAahj2vWdy1xiaDqppILolwEgiCUJNmF++69yHduBA2d0rFArFbkW5FJsOSnA1UYyvV1oHlFrxRoJwoJK4KmJbi4imu6xa1NSsMh4x9QJgCQ5ezAQlSJXBZ3eXibugOCyxquNS9K8TmIFecBc6n5dpSEQWQLNcnJZpgoqii42p6YCmgSRSIMmkVZYqhfnFO9AOPibwnhQKhaKxUS7FpoMSXE0Ij6uQmpbYMjL2qdcy46rORJVmOuILmu7EQLnddEJGeXs8nsi5umRCJ1+LGNuIOky4BcWk8ZYsds59Xo7QcuoZbpFlckLMECxhgPWZJpMgiZQlvurrAE2HivRSKBQKhR9KcDUC2eX/tR7MuiWI9L7DXdfNL94BgNwGCrw48BFZVOKK49tTO/7IKjNywguQJiqNJK5CBE6+MGFENA3UzHiuE5k1DsJn5Dcf0+BEE28h5EQVE2S8yOItYfzPAABJJC3hZdqLAxIpkJQO45M3QRJJZelSKBRNBo2QgnJpqTxcxUMJrkaAZtMgmg4zXQei6ZYAYzA3oC3GqGE4x+zcRUCKBsoEAGzxZRqORcZl9WJlYekWnDHdc5C5M516mp63AKPZtLS9cw9+yKx9vMhyCdhcOeVFVSaT64t3QdquRkd0aTpoNgOSyIBquiUSExmQbBqapoFoGszP3oJ2yLGx71+hUCiKDdEJiJa/aCJKcBUNJbgaAZquy21NqOkgmu48+FODzkZm6YtWJgdbVNCs18oTGSYK+MzptojLuRx1dxoHTQM1TY8lKdByJDv3KxOQWeestnbQuhSfcjvBq98ceIHlKuOFF2fR4oWW6JJ0zdsWh46LMVVqnafrrJiulfOQOHyEz70oFApF46DpBFoBgktZuIqHElyNAM1wK9/shzSzYqXffMoSDZoOiow3uD0OTDDZYxHOWkSZmBEsakDOguQIET41gux+ZIIpQnLUXF3RYma4RGjBiOJKHFd0HYITubzwYtYtLrjeUx+wPq9sGiZs96cdUG+seg3UMJTwUigUCoUSXI0BravOnThWJo071uFYcDghJF1JKMILNE86A04EaVwMku0Wc7vpcmIMYsyTSERhFCighL5pwMrAWLgEFie8PK5ZwaIls3BJVjFSR6y5569lE1bwfCIJolsWL5rJgCSTyC6ba1nBSkoBAHrvoYXfp0KhUERB13ziYCNCIuRjVERCCa5GgKbrcie84GLndhnRNNCsWC/Y4kX0gJguTmS5+nGEWMY9F77fKBYnFjMWYt3y7cfjosxvZSMf9+YSVgEuUHExgkd4cTFd1L5ODdMRWrzwIroGM5O1v9RqQXQNRN8JrbYapKQMprYVpLQcWnmFlRPNFmCJ/ifldb8KhUIRFaIRkAKSaREol2KxUIKrEaD1dTlhwsSNzgkqmQhDxmX58vyFYrsLqRnigmRiTjcdYUJ5a5ZQz5mzbyyVQNQ4LomYklqd8sQVl4XwlYueOp78XIYjFKlpwkxnXSKLGiZMYf6arsM0DGj2z1arS0NL7nKu6+UtQUrKQBJJS3DlfbcKhUKhaG6o7/zGwDQcl5QTGG9yViaZCLPfmdByGXV5FyRE1yDscVggPIs5Qi5Oy9UPwxZ2eoiAi4h/YHyABQoBKyCDiGBBkwowXmABnrQRvFVLtHAZtgWMdy0adWkQXXPeUVcPANYxgERdGloyAb00BZJIofaFe0BSpSg95Tfx71mhUCgioOkEWgEWLk1ZuIqGElyNAHt4WyLIDmiX1rMsYERo6xFU4opCSR3HOsOVRfm1KYq3PqoLERFWQuY5nsd6Janvdj96Y7V4McWOeauW417k3IyOFYwf39bG/M9MS5nQk0lQ00Ttfx5A2WlXhN6mQqFQxIVohcVwkSh76ioi0WySY0+bNg2DBw9GixYt0KZNG2mddevW4fTTT0d5eTkqKytx5ZVXIp1OS+sy6uvr8dvf/haVlZUoLy/HqFGj8M033xR17mYma7mgMtncK5txXjSbth70pgFkM6AZ+1VfZ79qQetrrVVz9otmM+5Xus5bZpc7bdJ17nrsWCyzX/x4sV4sDko2H+Fl3bO7bZR2of3yn1VGfFmfL7K5F82mYabrLPdhJmu5ELmfGTVMGOmM6zq7ZrBX2npla9PI1qaRqa5z3jPVdUjvrEG6qhr123ciU1WDbNUO0NpqIJtG7Qv3FPX/nEKhUCiaFs3GwpVOp3HOOedg0KBBmDFjhue6YRg49dRT0b59eyxevBhbt27FmDFjQCnFX/7yF99+r7rqKvz73//GU089hXbt2mHixIk47bTTsGzZMuhFcq/lAqpzsGBr/jiXcd29otCTioB3KcoC3nUxHgte96FznnG98/P0/F2TT8qKsKSpMaxhfoQmh5WkoRDnIgbEi9Yr3oXIXIym4a4nrmCkhglTGMNMJqClEk4MWNIwHRejQqFQFBvlUmw6NBvBdcMNNwAAZs2aJb0+b948fPTRR1i/fj26dOkCALj77rsxduxYTJs2DRUVFZ42O3bswIwZM/DEE0/gJz/5CQBg9uzZ6Nq1K+bPn4+RI0cWbf5mJusILMCOsTLk8Up8uXwVYsYj4Pj8Xd6UD3ClhbD6lYsnb6yYe9zIoiswJYQggILyekVYBSl3KfrHb4liitVxCSXDLcpEQWbYFjC/OqYT82W4+69LQ08mkNU1pCpawMxkkSwvg5ZMY9eTN6LlL64PvleFQqGIAdHVKsWmQrMRXGEsWbIEffv2dcQWAIwcORL19fVYtmwZhg0b5mmzbNkyZDIZjBiRS0zZpUsX9O3bF2+//bav4Kqvr0d9fb1zXlVVFTg3M22JLfYOuC1cAKSZ3oNwizLNJSqIpnlEiJhQNTA43RE5ktWMfJ+6Hr4VkUjQuEKGeCtfmL9L2O8eRCErCiu/Oq74LNNrvTINt7DyiDTXueG9nrZyfmRh/cyydnC9pmlIlJdCTyVQe8/VoIaJDpPu9b1vhUKhUDQ/9hjBtWnTJnTs2NFVts8++yCVSmHTpk2+bVKpFPbZZx9XeceOHX3bAMCtt97qWNyiYBpGLpkolzZAFF2yslywvVsguFx/gnhwWdKYwAsRZFa5JMN8gKWKWdLEzZ196wdklJeJpyBxRHSvhTCKAJMFv7O2siB4q65XfEWxZuXckdlcW4M6fQLV0FM6iK5B354A0TWkWpVDSyXw7c2/gZ5KoOO1OXf4hht+jX2nPCy9R4VCoZBhWbgKCJpHfvkRFV52q+CaOnVqqHBZunQpBg4cGKk/2SablNLYm2+GtZk8eTImTJjgnFdVVaFr167+/QlCwHUmEWBBFhq+jNX1iDbOWsbaukScpEw2Tz88KyQl+IofYfWf65rEEuVbzydNGN+HTFRJxxIEltVWLsBkFi0mtExOWJlCTBc1qF1GYdqiixomjEyuTz2lo+6H3K4EibIkvv/FqUiUJaEnEzANE8vPsKyx/V+YJ73/NZMucI673/WE/ENSKBR7DSqGq+mwWwXX+PHjcf755wfW6datW6S+OnXqhP/973+ush9++AGZTMZj+eLbpNNp/PDDDy4r1+bNmzF48GDfsUpKSlBSUhJpXoC/i4sJHoN3J3IiQQsJ2ucf/lEtZUHzEhHFH+8OjYNUMEoTocoFkQhLLiq6AMPG9hN0pkx85RGfxYSWu4w6ZUxcMSuXkTZgmkx82YLMyEXR8dYwjftZGhkDbxw9GKZBoSetcj2lQ0/paFFZBqIR6Ckdn//mZ0iUl3mE1/f3XI32V+dWRda+cA9ICyvGsXTEJdLPUaFQNE8IISAFbF5NTCW4isVuFVyVlZWorKwsSl+DBg3CtGnTsHHjRnTu3BmAFUhfUlKCAQMGSNsMGDAAyWQSr776Ks4991wAwMaNG7Fq1SrccccdRZkXkLN8AF5XoChieJFkiG7AIDhLWbHwBJsHWJ0ci1rYNj8BoiqKgGLt/CxlYVYyUxBUfm1E4WUK5aaQ+NSyVrldh8yiZY1LuXJqW8ioR2gZaSbirHqmfc6sYkbGgKYT55yhJ62fffV31UiWJx2BVrpPKbaeMhzJlikkSxPQUjpSrVqg7rpLHEGdqmiBVKty7Fz3HczX3gA1TKl1rOYZ+3dC00CSKZSNutJTR6FQKBRymk0M17p167Bt2zasW7cOhmFgxYoVAICDDz4YLVu2xIgRI9C7d29ccMEFuPPOO7Ft2zZMmjQJ48aNc1YobtiwAcOHD8fjjz+Oo446Cq1bt8Yll1yCiRMnol27dmjbti0mTZqEfv36OasWi4VjRRGsFaLlydfdFxCnBViCx7PVTMw5RrFc+boxI7hBAa+bD/B39QX1w6dhsPotTgC9n8CiAasW3cH0OasUE02i0LL6t8SWkTZcbXgrmGlQO1jfqmtZ0Giuvp2Q0LDfddsNrv+ggRoURCdIbK52xJimEyTLk3bcGEGqPAVNJ0iUJZAoTTg/U6Jp+PJ35zv/L4muo6RNSyTLS5EoLYGWSiBZUeHkDqOZDIzaGrS6cKr0Z6BQKHYfmq65njmx29Nmk66zydNsBNf111+Pxx57zDnv378/AGDBggUYOnQodF3H3Llzcfnll+PYY49FWVkZRo8ejbvuustpk8lk8Omnn6KmpsYpu+eee5BIJHDuueeitrYWw4cPx6xZs4qWgwsAjHTWdW5CcPnZD2FRiAFyy5YszstTRxBgmq6HuukiwQX/xyGqqAqySvH1/T4Dv778xhTzZOWOg3N1iTFagNs6xc6ZcGL1mNByXzeddqLYMtL8mBRG1h7LEVu5Y7CkHiwmzCBA2kDKdifoCQ31VfXQUroltEqtX389pduCi0DTNaTKkwBywbYlFSWo374TiVIrV5iWTKCkTSvrOJWApmtItCjF9/dcDS2ZcCy67L1Fp3ZItGlrbVWVsPouPelXUCgUDU/BaSGocikWC0KpyttfKFVVVWjdujV27Nghzff17c2/kYoDP2uReC7764QXYrIVKDILmIgruD6fPQx9CBJ2QSsLZbFUQe2DBJVYZoruUYm44vv0uBSFcXPXvRYt1k60ahkZPrUE507k2plpI9CylTGZ4LKEFvMsGiG/xo71i1jHOgGSmmaLLOKIMKJpSJQl7GOrTaqlJbSITqBplghLtUw610sqylz/31Ktyp3/23ppComyFEratIJmJ33Vy1uBpEpBSsuhlVu/L8mjzwicv0KxJxH2zCjmGPMGD0J5In/bSnU2ixFvL4k8127duuHrr7/2lF9++eV44IEHPOULFy6Upm36+OOP0atXr/wm3URpNhau5kyYeAhahQgAhsTN6GfklQW2i3m6XOP4pF7Il0CxJa7WDImn8msXZCELs1jJ+pT9HESBJV5n1izrultY+bkPWayWY+HK5OKzeCsWE1tsTN6NCIhWLfHYW6YTIpxT6IQgbRpA1rqeqjeg2aIsUZdw/iLWdIJsXS5/nJ603BP1VfUgOkGyNIG6H+pAdEuoAUD15p2OBS3XLoFkRTn0pFWWLC9FqwO6wPhhM7SycmRfvE/FhCkUDUBjW7iWLl0Kg/u+XbVqFU488UScc845ge0+/fRTl6Br3759vIk2A5TgagTiWo/4DZD9guX5HlkNMXt93FWF+YquKPcncwtKj0MEYFRXoGx+wQH7YbFd1FWPt0qxukHuQ15smZy44tuysV1xWkLMlhFgyBKFmF+Z9d1LOasXRdokYN/JqWrTuaYRgmwtJ7hS1ka4mv0lXm+XazpxYsWITpw4MUayNAFsrALRCUoqSpAoTSFdVYOSNi2hl6ZQt7UK+47yvzeFQpEfjR3DJQql2267DQcddBCGDBkS2K5Dhw6++yTvKSjB1QjkK2SIrrm2BPITYE7vIQH5keYaQxyKIkrTtUBhJSsLy40FRHMHxnIvRhRXfF1+taFffWb1kq0+FGO1+GB6vl9eYLnmLLFo8WJKFGJh53w/lptRrEtz7sc6ax5JTUO2Fo4b0gmyd8RWxsn3o6VygfpEI6jfYe3MoKd01FelHetXproWWsrKM7b1/muQLC8DAFRcdKN3wgqFYrch7qgSJT1SOp3G7NmzMWHChNB8mP3790ddXR169+6N6667TupmbO4owdUIxBFcpmlC0+JZpngMMSZMEGGh4xdgDTNChA7g7x6UWa6k7QPircRjmbCS1ROtV1b9cIElWrj8LFrOuU+MF5sfH7PF+qcGtS1bXstVVOKIL3bMW79YzFfaNKxyIxcLBthWMN0KkGdCjNTlzgErbYVpUCdeLFmaADW2Qk/pKN2nFMnyUuilKRh1aWipBDbf9TvopSlUb9gCANj/1pkAgK33X4NUhRUjlqmqgWkY2PHlBhx4z9+jfyAKxd5EgS5F2C5FMbn3lClTMHXq1MCmL7zwArZv346xY8f61uncuTMeeeQRDBgwAPX19XjiiScwfPhwLFy4ECeccEL+826CKMHVSPi5wGR4RJMgwsRge1lqCVc+L0lgfhhxAt8D+wkUOv7uwSgxV55+HAuSn9XMrTSCBJZ7LrzFyy3AxMSlfH0/q1bumhivlQuQd+pwYksWKC/GbAWJK7/AeoPmxFPuPGf9Et2P1jtxH5t2rFgWAKx4MP5LXtNzK3W1lI5MdRoZW5Tt3LgLRCNoUfkD9BTLH1aGRGnKtoLVYdUvTkXL/SqRsEUZNU3U/7ALRNeQ3plbdaxQKNxoxFrsUkh7AFi/fr0rxipK8u8ZM2bg5JNPdu1xLNKzZ0/07NnTOR80aBDWr1+Pu+66SwkuRXx4QdHt9sew5b6JSFfVwMxkQQ13wkyGKCp40dQYWVHiBL/H6UfmKozjIoxiwZKtIHTP3yugvOP4W7h4Sxbfv8yixfqQuQ9Z0DzvQvTGbAULLP7YLa7guS67Jqujk5wIs96915mWYoLMOnavhITB1cnAqaOlrc8rvSu3PxNzNbKkriw/GAAk7TQV2Tr2e0KRKEvASBtWyoqqeiwcOAhD31vivTGFQlEUKioqYq2o/PrrrzF//nw899xzscc65phjMHv27NjtmjpKcDUC3W638ofVvngfal+8D6XtWiNRWgIjnYGZySJTXQczk4WZznLiy23hIVweLUd8cZYvd133qkQxmD4KoXsZRu1HDE4PsGYFJiKN6R7k28gEllcIBtfhLVmuvn2sWawfmUUrd83tPnTqcQlN/QSWn2WrEKEF5EQWf433RuSuu68FWcJybfl2OdckACBjQK+xBBizjmlVdpzYllroKQ27Nu4C0TTHCka4gP1srTvXnUKhsCC6Vtjm1WZ+bWfOnIkOHTrg1FNPjd12+fLlzo4xexJKcDUypLQciVQp9NYZIJsGra9D/bYdMDIZZKrroKezMOrSQDLh7NkXBIv5YvVk+x/mk2MriqiKKryCVhRGSc8QHhDvH4NllQe7Cfk6oqtQ1j8vstzz9FrFZHFarK3oPgQgdSGyY+s9XGyFCa2gnF2ya7zLURRhvCXMKndbwmTWLwvqEmOszKmT9Yo1PmaMrYRkEJ3gP1364bRvP/S9N4Vib6Tgzavz2EvRNE3MnDkTY8aMQULIATZ58mRs2LABjz/+OABg+vTp6NatG/r06eME2c+ZMwdz5szJe85NFSW4GhFZnqHaF+9DSYckaLoOyfI6mOksMtW1MGyLl5nJWb0AryghuuYIGk1iyeLrxSWuVUwMeJf1IQa9i9asOCsIgWArVlDbICuWt653U2m/+C9xPFlQfO5e5VYt6z1MXIWLraiB9WH1RJeiBR/zRbl6rA0vwvxTU7C6cihnQSPuc4NAt12yLM9YnIUECsXeQsF5uPIQXPPnz8e6detw8cUXe65t3LgR69atc87T6TQmTZqEDRs2oKysDH369MHcuXNxyimn5D3nporKNF8ECs0aXPfSg5b4qK8FTdchvX0XjEwGRl0aBrN4ga2Eiy6CZO7GfIgzptwCJd+U2rIAya1ZYW5Cqy+3RSm4nbyO6CrkxxBFluuaz9ie2K8AF6JsT0SZoAq2eDnDB4otP8tVoYjf46J48vuel9Vj8WJ+Y4hWMq+7Ejhv80fRJq5Q7EYaM9P8m6f/GC2T+dtWdmWyOP7frzfoXPcWlIWrCVB6ym9Q++J9IElrC5VEeRakLreRMGA/zHUThBMpYULI77pMiMURVTxhAgsID4DnVxeGJRu1zt1KIY4VCyhcZLG+A4PsOfchux7VqsWO2TXZsVhHLMudxxNaYVsEeXErJCulRPBYYuZ7q0xeP8xd6W8dUygUwO6L4VJ4UYKrqaDZWbqTKecXhGia7y8LNazYrXyEUtw28YLk89tCxxsIHyy2gt2MctEQ5DIUywoVW35zE4kqiPzisuLEYxVXaLnbiWklGDKLVVgbvi1f1yvG3DnDAGBG2164ZNsncW9Dodhj0XQUGMNVxMns5SjB1UQoO+0K1P7H2thTSyRhalkkykqQBaAbJgxYcVxE12CCy2lkv+droeIJE1aybXM8dSLk3QL8c2b5BcCLQsvPXegZJ8CaJZaHiSzZ+DLXIX89bBWi9Q7nPNylmLtPeTqI4lu0/Nq7LVm89SlYSLG2fmPnAvT5Unf/ftcUCoWiqaIEVxOCaJr16NB0aKkEzHQWmq7D1DXoSIDqVlC5hgRMWDm8xNxchcZbWeXBwips1WNQPrGg5KRBVq0omd6dMQJWGPJlUWPD/EQefw/iBtMyoQXEi9cS6zH8XIhRLVphIiufYPso4itsjJxbUXQ3+gXg54QZP/6DbXrhN9uVlUuhAACiWdtrFdJeURyU4GpiEE0DkklopmEJrlQCJJNLEAlbZBHDnVmeEeZmDM4g77/xc5y+/ESWrL84YksmtPg+imHNirKVj2jNYnXcFjzJXHxSPrDzOMcihYitKALL7drzxk7J3H1+40UVYfI++LZ+5cD9rXti/I5PpeMoFHsTmlbg5tWGiuEqFkpwNSFKT/kN6v77CJBIAYYBvdSAmcnmkqQiCx0JGOKG1kLuLZnoiusulAmtoKzxIrI9GYOsWqyNf3oH74M7Ss4svizKptSyuclis8Jch+yauOm011qVuyY7D6sXVNfvul+9aG1o4HXAP94qrO8oIk4mvoLckwqFQtFUUIKriaGVV4Bm0qCpUtB0HUh9LYheY4mqVBJGOgOia8jaqSKIrsHkBVgBAe5AcAyWX1nQhtdxN422zt3iJ2ybHb4vvr8oQsuv3zCxFdd9aL0j9Fxm9SpEbMnI19UIALKftPj3r8ylGCTAoowvijiZazFX7p+OQqHY2yg4D5f6ZSoaSnA1IYxVr0GraAtaVwOaTYNqGqhmbeFDNM3JxwUAWiYLE9bDnkg2tuaJkroBkFuggvoJs2LJ2sSNm8rV9T6Mw7bbkV3jx5KNFxSML5ZFFVrWcfh50LHf5tSyNrmycDEWJrKiyHexDi/AZCsS/eYiEhQXJovpcm9LBNxb0RO/q1JuRcXeTcFpIQpoq3CjBFcTg+g6kEiCZtNAIgUCgKTrAABaykoMquk6tGQCRLNWLxLdEmNBYqsYLsMoAkvej/vpGlf85LO3oexa1PH4a/w9itvyiGVAdKsWj1+AvJ97Mai/fNNM8BS63pW1lwkvnrAcWkFB+bzljI/1Unm5FApFU0UJriaE3nc4sivnWSeaDqKboKbuxHQRTYNmZwwmmQyoYUJP2jFdWm7PRLaxtR+ybO7OtSKJLKssXGj5BcCLbeNuJC3DLz5MbBMmtsR6+Ygtr7iSW6PkLkZ/ASbrI6xuMdLsyMQO36/sb+Qw65qYXiJIdOWIYDpTKPYiiKY5XpB82yuKgxJcTRFNB0kkQU0jlxBVt4QXMU0Qw/oFyqWO0JyHG7Ny+a1W9BNbMsEkiq047kL/PnyETUi2eOc8gmVLNjY/btA9RA2Q90uuGjVeS9YmiGLEccnGiSK2ogakB6WDiBL75defTFiJgfJiHSbO7mx5CK7Z9Vn45BWKPRRNL3CVonIpFg0luJoYicNHILv8v9aJbeUimgaq6QAy0BJJS1DpOkzk/Ov8qkUe3s3ol+JBlpDUuRZTZAUJLOu6v+WK708aKxXDshXVjeg3pre+n2DLWbfiiK3gMvcYQfFOhcRpBYmtQlf9RcnFFRT7FdaXKKyCxJlCsVdTYAwXlOAqGuqTbGIYq14DTMNyCZpGLvbKNABNs6xeHLzgEsVXFPzEFjVNTxC9KFJEgSOKHFHohO2BmK/Ycrc1nfQS7MXGlsVsyVYjyvJs+c0ZgJD6wV0/rmvQ3VYUU9QOtpf3GVVsmfAXWwYnHP3n5X6FEVW8mT6voPmJc5CNdWfLQyKNr1AoFA2JsnA1MfS+w5FdNjdXYBqWANB0gE+A6gMfyxV2LUhsidet82BrltU2P4uWrH5Uy5Y4nzhuS5kL0dvOm9Q0KHO8eB60ubRYFtUVGd5ndPdhoZnnxetR90+MSlAQvsziVchYCsWeBtuTt5D2iuKgBFczgGiatWoRtgXGkIumMPyEmN8G0rkx5K46q42/G87bT1CKCLkwMk2vJY1vG7Q5dFT3YVC8Ft9eth8iIIof//kEBc3z16NYhFjGd+8YxRFahbjkxFirsDGjCqOowkuWof628h74Q/XnkcZRKPYkVNB800F9kk2QxIBTAY1baajpKDvjarQ451on1xbRNWi6ntdfLrx7UGql4q4HxUXJXIYyi1a4O1Ju1TI91rJoMVtin/y4njLOfSgLjvdL/wC43VtxxJMs7iqu6zEsLxdDJraC3IZx3ISyl19/YXFoUfpi+Lka+TEVCoWiqaEsXE2U5I9GSctbXTgVNU/famWbr66DjiQAwMhkpfXjbMfjbesvtnLlwS5Dv/6C2oZt1RN1bnyf0nQO0vQWbqtY2J6IQP5B8kHkK8CcOfr2KxdFwXOJrmCC3HmycYKSWIvjylY+itauIGuZsnIp9kasP9D1Atp7k2Qr8kMJrmZG7X8eACkpBTEN6KUpmJksTMNwpYZgaJrmZKOXiYsocVt8vULjs2TtPGkZIuyLGJZnK0pC00K26uHxE1tBddxlfD25YPMLCpf162fR8iPM8pQvUV2HYWJPlm2e70cUXWK/alcSxd6OyjTfdFCCqxlRP38mtNIWoGkNJJOBBsDMZC3XoqZZ54DjduTFTFAwvUxs+VmWGEGiKYrQkrWTia2wfv3EoFw4usVW0HyiJjWNU5a7htD6+Ygt7ziNL7SCiBOb5pd7i++H1eFju1TAvEKhaKoowdVMSL/5FEhJGWjG2vIHdiCjlkzkBBafaT58QaMvQYHoQDyxFZZaAQgWWlESm4ouziirEWXxWt57k4stnqjxVcECzN8VKRvH78fDf6qFZJ4PIt/4qKibShvCZx2UbV6Et3Yx0SbOd1qLHvhjjXIrKvYeNE2DVkDgeyFtFW7UJ9lMSB1/vnWg6fYyXx1Ei+aXz9ckHCVTe3D7eGLLb/woGezlAftysSVejyu2rCBwWT4oeRC9vMx9DYjm/spHbIlB61GD0/m2UYPpg/qR9RfWd1DerbD5N5SlTqFoTjCXYiEvRXFoNp/ktGnTMHjwYLRo0QJt2rTxXF+5ciV+/vOfo2vXrigrK8Ohhx6Ke++9N7TfoUOHghDiep1//vkNcAeFkVn6IrQWraCVlVtb/JSWg5SUQi9viUR5KRKlJdBLU9CTCdcvivjXCf/Lw7ZsEJf9EvvJz2/pQLTCXTRiugfRshW06pCvE23LniAhR0PFlkmp1I0odutXxtoFWZpkQiJM2OWu5crDxJbYTkYcESRvL19pGFXYyeYQNG8/V2uUJSHTWvSIUEuh2DNQgqvp0Gxciul0Gueccw4GDRqEGTNmeK4vW7YM7du3x+zZs9G1a1e8/fbb+NWvfgVd1zF+/PjAvseNG4cbb7zROS8rKyv6/AvB+ORNaK32AbJpQNOdYHiSKgWtqwbRdVDDhJZJIKvVO/FbJqyVixoAaq9S8YvjyoeosVuyun7t/FYjylM6uC1YsnHC4rb85sEoZsyW7Hp+fUQTGHFESxjFsBb59RE13kpchci7GHkXpMy1KKJciwqForFpNoLrhhtuAADMmjVLev3iiy92nR944IFYsmQJnnvuuVDB1aJFC3Tq1Kko82wI9F7He8rSS+aA1teBpEqtOiV11ruRi+MiBveXCRfT5ey7KIgTPtCe6ATUoNbqRx+xJAbmB8HX9YvZElcjBtUJCpKPErcVthLRL2YqSuoHWT1Zm3xEXFQXWpDYChNahYqrKEJOtvowd02+XyKrGyWuK4roUij2BggpMPEpURauYrFHf5I7duxA27ZtQ+s9+eSTqKysRJ8+fTBp0iTs3LkzsH59fT2qqqpcr8YmNehsIJvObfujWUlQtWQCmm3N4k3BLBeL3y9enF9IzSfYiAjlfvVk+IktWZ3gPGBeqxd/HrYgwA+/+Cy/umK9qAHxQX2I+LkSo7oRZWPmK7biuiHD4rbEulGvx3EvKteiYm9AuRSbDs3GwhWXJUuW4J///Cfmzp0bWO8Xv/gFunfvjk6dOmHVqlWYPHkyVq5ciVdffdW3za233upY3HYnJT+5CPXzZwKaBlJWDg0A0SwRliwvhVFnbwekmbaL0YsGt9iRWa2YlYtoxBE7mk5gGjSWlUvEL89W4PUAN2K+KxL9tuqJ49oLFlTR6gf1IVJMsRVdkEWqFqnvoLQPfDs/a1dUSxffn7JyKRSK3clula5Tp071BKyLr/feey92v6tXr8ZPf/pTXH/99TjxxBMD644bNw4/+clP0LdvX5x//vl49tlnMX/+fLz//vu+bSZPnowdO3Y4r/Xr18eeY7Eo+clFIOUV0Fu3g75Pe+j7dECqXTukWpWjtF1rlLRpiVRFCyTKS6EnE9CTCWippLOhqbVFkPViVi72Fw1vscoF2HsfWm5LGhHaBT/kZGKLD6DnrVqyxKbWi7rEFjXNWGKLJ64Vi68bNRVDmDsyyphBZX5z5K1KYcHsxQqgjzI3vznyfYl1/K6J5WGpMhSKPR1l4Wo67FYL1/jx40NXBHbr1i1Wnx999BF+/OMfY9y4cbjuuutiz+nII49EMpnE559/jiOPPFJap6SkBCUlJbH7bihSg85G+q1/WqsXEylA05EwDCCbcRKe5qxAJkgm606KalupnMSpZi5xKhNPvDBhli5m5WJ9sP6tc+IRM1GsYWHxWn5Z5P1ciKyOmNQ0aKsekagWL7E8akC8bKwg/LftkfcRxeqVp6fVt7847fwy0IdZvGTWLlm5XzZ6QAXPK/Z82B/UhbRXFIfdKrgqKytRWVlZtP5Wr16NH//4xxgzZgymTZuWdx+ZTAadO3cu2rwag9Sx5yK9ZA6QzQCJJEgyBWqazvY/eql1bqSz0JCAbpgwbQFEjdxeWUx08TDhpdmPLd69yCxYvPDyBt4TlwDSNALTpE6ffkH5IqLYEssBSyyGiS3nOKL7jy/nrwWJLfe5t34U65asfhyx1VBCqxAXpMzg6Se8wt2E8lWKvPuRF10AAOVaVCgUu4lmI13XrVuHFStWYN26dTAMAytWrMCKFSuwa9cuAJZQGjZsGE488URMmDABmzZtwqZNm/D99987fWzYsAG9evXCu+++CwD48ssvceONN+K9997D2rVr8dJLL+Gcc85B//79ceyxx+6W+8yXzNIXQRJJKwM9C6JPJp3kqJquQ0smkChNQUsloKUSjouR6Dq0ZNJyL6YS0FOJQBdjzgXpdh0y8SUzQUcJoBeD7uPil9iUERSz5ec283NhRRFVfLkfhbgSo1rN/PsIb8ePFSWoPswFGXQ9yM0oziXoOn9N1q94roLnFXsyRCNWCEneL/UHSrFoNoLr+uuvR//+/TFlyhTs2rUL/fv3R//+/Z0Yr2eeeQbff/89nnzySXTu3Nl5/ehHP3L6yGQy+PTTT1FTUwMASKVSeO211zBy5Ej07NkTV155JUaMGIH58+dDL2B39d0B0XSAExzOqsNEEpqdDJW968lErowTXlrSG9ulJxPWPo1OQlXiEl7WLzPx/FLK4sD8cCVYjSG6gvN+5axhLAu9mFzVT0DkGyvlfvD7z9s/birc6uVtE94+H7GVb8LSOATFbMURVux6ISjRpdhTaewYLllsdljapUWLFmHAgAEoLS3FgQceiIceeqiQW26yNJtVirNmzfLNwQVYP+SpU6cG9tGtWzdQ7ou6a9euWLRoUZFmuHuhpiGc2+kibHehJaAsEWkia53D2odRS9n7MZpMJOmgmgGqm05sl9WOx3TydAE5N6NT18itYHS5FmHHi3FuRW+sV3gOMO/954LhxQB56zjYlcgoRiC7vF60sqD+TUlZ1LHiXY8msooJ7waUzSUsdktso9yGCsXuo0+fPpg/f75zHmTAWLNmDU455RSMGzcOs2fPxltvvYXLL78c7du3x9lnn90Y0200mo3gUgST/NEo13ndvBlWZnrTAFKl0DJW1nmkLXGiJxMw7Ez0DM3QAE2D6ZONXgyqZxJAJo7EuC0eJ0jfFl1WfTv1hC22XKILpidIPw4ysRUnriqO5agYyG4xTGzFLfcfu2HuKfr41rtMeMUJmM+H28p74A/VKoBesWdR6ErDfNomEonIycQfeugh7L///pg+fToA4NBDD8V7772Hu+66a+8UXGeddVbsjh966CF06NAhdjtFcSCaBppIAaYJgjS0pG3F0jXoSIIaJnQkQHUTpmHCTGcdS5cscB6wLEaWAHKXieIIQE4gwW3lks41D4uWDFlurqgCLa5VJzioPnqfhRDl1qwg8ujziFK/2NYt7xwK78Obp8v/vBjjKRRNlWJlmhcTfAet1v/888/RpUsXlJSU4Oijj8Ytt9yCAw88UFp3yZIlGDFihKts5MiRmDFjBjKZDJLJZN5zb2pE+im88MILSKVSaN26daTX3LlznWB2xW4ikbLisRJJK4Be0614LRY0z2KyNDsPVyrhjt9yjnPZ6fnYLoZfrBYf08XX8Qus59sTpw5XprG4Mfc4LO9WY8APHddl1RAP9Xz7DGvX2O44nbhfsvnwcyrk59A4/1MUiqYD0XVr0VSeL2K7A7t27ep6zt96663S8Y4++mg8/vjjeOWVV/B///d/2LRpEwYPHoytW7dK62/atAkdO3Z0lXXs2BHZbBZbtmwp7oexm4nsUrzvvvsiW6yeffbZvCekKA5E10FNHdBMkEQSVNOhJQATGRBDg56y/mqgum3dAkBF07HjOszBrGSiFYxPHRHVSmX1Y7r2Vgyj0C16RKzUAdEtQax+tL7jWZcKgR8ryhxzLrnw/hqKKIKxscWfcisqFHLWr1+PiooK59zPunXyySc7x/369cOgQYNw0EEH4bHHHsOECROkbYjwe85ircXy5k4kC9eCBQsi7UnIePnll7HvvvvmPSlFEbDTQxD2nkw651rK0tmanjt3WbfsVYx6MuFYulh2+txqxZyli7V1huay0vMWLVYnzh6LfritX/79amLme8eN5LWY+D3cG8qa5VdPVh7XISBagYopXIphrWtosZVvW50Q3NnykLzHVSiaGsVapVhRUeF6RU3+XV5ejn79+uHzz+V/yHTq1AmbNm1ylW3evBmJRALt2rUr7OabGJG+x4cMGYJEInp8/XHHHdekMrHv9di5uKBpTl4u/heJiS5N16DbYoz/ZcsJL92p7ye6WNoIPleXO0eX1zWoaV4XYhBucee3kbZbBPqJLvHBzMpE91aYQBOnEVSHHzfOGJrPnGXt5XOS3a+0K2l/Udv51Q9yG4rj+t9j8BhBfQLeLzy/z//PrZToUuwZ7O6tferr6/Hxxx/7JhMfNGiQZ+/iefPmYeDAgXtU/BaQ5ypF0zTxxRdfYPPmzZ4VbSeccEJRJqYoEDGuiU8RYSe0s4LgdZhcOeXciMTIbddDdM1JGyEipo3gtwSCIWwFZDsj/dJEyFYrgst44d5OKBeIz+Zu9cnmQR23pXt+FBohzopF60HrXrXIXGq86y23Go66XJF8Gd8Hw6+O3xiia5Bvz1y5OXHgnTery/cRhHsLHe/c/TegDmoXfdVgFGuUX2yXeD2qZUsmtMVx/tzqEEzY+Vmk/hQKhcWkSZNw+umnY//998fmzZtx8803o6qqCmPGjAFg7UW8YcMGPP744wCAyy67DPfffz8mTJiAcePGYcmSJZgxYwb+8Y9/7M7baBBiC6533nkHo0ePxtdff+3KaQVY/lbDMHxaKhqVRNIKmE8mgfo6wA6eh24JL80WZFQ3gTQAXXdW9mmw82pxf9lYokhYoSjEeMniupw8XeD65mqyc+pKKeEWXc65LdyAXGoJK27MzvvFXIsw7TxgxBFd1lyIq5xZukx7xZqfwLLvxlXOiy7ATyjlWkcVbH6iix9DtopUFFZBMV2iKCsGckEUVD+qMApuH+aWFS2EsrZBFsN7K3rid1WfRpqrQtEUYX9gF9I+Dt988w1+/vOfY8uWLWjfvj2OOeYYvPPOOzjggAMAABs3bsS6deuc+t27d8dLL72Eq6++Gg888AC6dOmC++67b49LCQHkIbguu+wyDBw4EHPnzkXnzp33uKC2PYXUoLORWfoiYOogqVLQZAo0kwbNZoB0HTQARKuDmcmCaBrMTNZKHWEH0eua5snTBbitWcwCJl4XE6Ra5blget6yxZ+zpBL5pIgQ93QUxRUvunKzclu7/AQWjyiKgixgfHuZcPITbLLzKISJLtn9hPWR71z8+4/+fREmtvzKg2LggsRWXCuZQtEcaOw8XE899VTgdVkC8yFDhuD999+PNU5zJLbg+vzzz/Hss8/i4IMPboj5KIpI8kejkF3+X8A0QBNJuB4j2TSQSNmrE1kCU0vwOPm4TFnOLc2xdlFdBzUMjyvSsZBpvEuSOJJK5l4E7F9s05Dm9XI2zgaf+d7tzOTdlFYbtyuRPXZFaxcAaJxhVmbxAqK7E/3Lw0VXmCWNvxMT4WLIK+T83Y9BiDmsopCPcIljFZPFX8nKguK2xDJZPw+26YXfbP8kcN4KhUIRRmzBdfTRR+OLL75Qgqu5YRqghisYCtAMVxC9lkoA6ZxAIYY7/YOGhJPzihrelBF8ORNdrG/eZci7BnPTsQSQ35Y/QZnnefeiNU/7lkOEFy8neWGpCV5xmchiwsienaueX7k7Q3pwDJfMkia6G8V4Lr84Mpno4uvI4r5y9w4PDWUBimrRCltowJfz/0PZYgixjt+CB77OI/v0wq9+UKJL0fwgGinMwrWXbF7tl7IiiOuuuy5WBodIguuDDz5wjn/7299i4sSJ2LRpE/r16+dZRXDYYYdFHlzRuFi5ueLF2BFNAzjLltUPZ8kCE1m2tcsRPzmxxkSamKdL0wkMgxdTnGvRFl2OVcxwizAnEao9TxNua5rTnx3bxQLq4cyZOvfhcjVKYrwAfzFlQT1lQSKNt3YFxXAFnbM2vCCWuRBz43ktVGHCS9ZPQ5CP0BKvR4nZkrX1s2wFrUBVKJoTjR3D1VyZPn06Bg0ahFQqFan+4sWLMX78+OILriOOOAKEEFeQ/MUXX+wcs2sqaL5pYax6zcrHZeOycJkGYJqgpuGyWvEQ3Y7t4uKpmHgR67ksWMK5tI5t5RI3uGbwlq5cdJd9jbNb8MH41rnbzSgG1PutZGT37xfjxeMfXC93B8rKwlyMfiILoNIgejbnqMLKz80YRJDwyEeMFVNoieWFxGx521jvs9odirFbP5ZPWqFQNHuef/75yAneW7VqFbv/SIJrzZo1sTtW7F6MtStAWrQGMbOg6Tor5ipdB5qus9yJpgFodVZurmwGVDPsGK6s23LFCSyZkAqC6LZ1TBBpYkC8yyXIB7j7bG7NEFcwApzA0r0SjD+TpYpwr7h0X2duRj64Pkx4BcV9MeEU1Z3oHsPrzrS6zJXLxJffCsxg6x1c9fwo1Aokt0IF14niQpT341+HdzvKBNkTlYfigi1KdCmaD8Te2q2Q9nsDM2fOROvWrSPXf/jhhz1bEoURSXCx5ZwA8MYbb2Dw4MGeRKjZbBZvv/22q65iN6LrMBNJEGoCiVJo2ToryD1VagkvXYep6da2P9kMkEmD/VqxVBEukQTAkIgtTdOcXGyWyzJXR27lylnZiJZL6eD0J8RlaRrxrFwUE6Q6AfW28HKkliOi3EH1QFB8V058ySxeMuFltwLgJ6rkFjDRxWh/Kq66waKNr8e3RaD44tt4+xP75ImmquIG4efKguuEXZe5EMNchv7nuXI/K5pC0SzQ9Fzy63zb7wWwPGFRGT16dOwxYjtnhw0bhm3btnnKd+zYgWHDhsWegKJhoFoC0BKgWgI0kQTVUyCl5SAlZSCJlGXZSpVar0TSySLPtvkRt/vxO2bwWev5Mn6ja6tM/sCSZY+XZYrXJGX8NZbdnvVjbe3jzoLvGkNjG3h7s+HLtiNy2vumJvA/j2Jx8cN68IdnwHcLBa/VJ2y+Yp9iXXH84Ln6v2T9Bs0hbEw/sSUbTzZffh5y92Oun2c79gmejELRlNC0wl+KohB7lSKL1RLZunUrysvLizIpReEk9j0Ume9sVzC17TOJJIhWbyVEBQDTgFlXbf0Fk0hB03TQTBpEq7PSPNj5uahhwshkrK50E6ZhghomqGaC6iaIbcni83bxbkRZbi5d10ENChMmdDuey4QGotuxVKyqroMY1LKWcVsAOfFeOjz5ujRorvKg+C4I13TNsuyJPVr/2qW2FU5czQj4B79b18QyP0sVq8fiq2RWLLlly28811dmgDtRDKDn67nn5713vn2Uut56+Vm0xGt+gjaKVSuKJUwnwIud+2LUxlVRbkuhUOwhrFy5EkceeWTeseqRBddZZ50FwAqQHzt2rGuvRMMw8MEHH2Dw4MF5TULRMCQ7dgcAZDavBU2UAtQEJRqIkYXWCqB11ZbISqSs4HkW55VMWfmwtGpoqQTMdNZ6T2ZhGoYjwqhpwkhngSRgZrJI6CmY6SyQTMA0mSgzXKKMGiZMzbTzdgHEIB7hBV13uRmJYYIatmvPoJbw4eO7bIFlXfcG14uB9ax/Ju74ayzAngkvq32uR3YPYuC9N3mq250YllrCX2ABYSsi3W2C6/oF2ctWN+bayK1eMtdh1EB4eZ3gNt58Wv6uQ1lZHPeheCzW0wjB3K6H4dT1uRXcCkVThOi648HIt70ih7jDThwiCy4WTEYpRatWrVBWVuZcS6VSOOaYYzBu3Li8J6JoQIgGaAClGgjsx7CZAinTgEQ9SEkpaH0dTNMAKSkDNQwr0D6RArJpaKXWikYzXQcznQW1xZSRzkAvtYUVE2DJrJ001a6TsaxeVh1LfGnOuSW+kERuqyHDSgGhJy3xJAowS7hpVh1YAolqtjAyqWM5A7g8YLaoi2L10jnrmZ/Vi9hjBFm8NEKgUxajRV2CSixjMOEkii/vakZ3P2K5V3zJc4b5rW7k8QbU8/MlUotWEFHckTx+ViyxL5m7NizvVhyh5bhnSc7KqukE/z2oP076cnnwTSkUuxMVwxUZZljyY8eOHQXtrhNZcM2cOROUUlBK8Ze//CWvJZGK3UOy/f7IbF4LAJaFS0sACRPU0ECIBmTrAU2HpmlANgMzXQeSTFqxX9kMiGmAZjPQNA1ayrRckdkMtGTCsXYBgJHJuFyJRtreLsgWX1YdtwDTuGuO9UvPCSWrnIJobO9H4rJOWQLJfgjaT3+qmS7xZSKXdoIvAyC1qrn7zokvr9VLWNXIAuztNkyA5axfcoEEeEWSTHyxeqIAs8p5seXuI8zqBclfbHxuLz+CxJgfQf0FWbCsc/m14Jg2f5ElO09p8nKNSOL/CkgmqVAomh7//ve/ceKJJ/quPiw07VWsGC5KKf7+97/jj3/8oxJczYxkh27IbvzciueipvuingRBBrBXGWqpUmvlomlaf91k04Cm58RUNg0tkQTR6pxtgMyM2+1ITdMRZMyqZNoCzMxYbkhqmDnXo5/1C7Bdkm7xxUQSABhp65eAt1rpOrdVkeBaBOCyhDHLl7i6UcxeL26QzSxbQM7q5VwDXNd15JKpmp78WNQlnPgyrygTxZR3laNM2EWzhHFEMZvn8ZeeTKIUI+8Wf03uVsxd87OCyaxbfkKLL5vf50f4yeqlkjtTKJoAmlaghWvv+cPi0EMPxdlnn41LLrlEen3FihX4z3/+k3f/sQSXpmno0aMHtm7dih49euQ9qGI3wcQWNS0xxWObnUkSluWLlWfTQDIJ1NeBAiCmYf0CmnZ2+UwGMA3HkkU0zbFaUcOEoWvQmAUrmYCRyTpihsV2Uc2EaVsLzLRtJeOD8dNZznJmuoLiWfJUCyHZqe5OmeokTBUC7cV9G/kg/hzyM1msFy++dC5OzLEaISceAbhEGLNA5dx1bnegTChFiftyu//C27uFGaTwX+HyeC7iEnrSPiIIKNn1aNYu9/WoQfG82xCAs9KVlfErXNn1Bf2PxrDl/5Peo0KxO1GZ5qMzYMAAvP/++76Cq6SkBPvvv3/e/cdepXjHHXfgmmuuwYMPPoi+ffvmPbCicclu/NyK5WLYookSTlyxv4IMw1rRyLUniRTM2mo7Q70BZDNg9haasZPrcdsGMcHFJ0sVVzoCltXLEV6GCaSSnvxdLBUEv/MhL5J4y5YTBK8Tt+gK2J/RtU8jH3TPuQgtq4Y3yB6A447kxZfM+sWLL/f9+O/fGJY81brmLgtKqGrfOZuix/Llui3OOuS2wsnFlQyviPLWiyqswq/J6wSJLFlsFuC1ZmncYCyNCKtvpT8heOPowSAawfFL3vLco0KhaPo89NBDgW7DQw89tKBE8LEF1y9/+UvU1NTg8MMPRyqVcgXPA5Dm6FLsfhKdeyD77ac5d6JpetxGRNMsEWWvUoSuA4kkkLWEkt6qDWg2Y2WrT6SAumqUnnYFAKBmzl2ApkEvr7D6Sdc5wffUNJ2Vjlmt3nI32m5IKsTBiCkkrNWMLAmp5iu62GbYLNEpv5ci4N4qyBonl0RVFF1W7+LaRDaq25rmqcPFgvGzhSMAg61fYtyXN+bKP0WEPBM9Gz4nqPwC90UB497+h0iP8yFKji1/d2HUeuGWLCCay5C3ZFnX3ZsBW3VY/b3HGqBoJpACg+bJ3hM0z2dfaAhiC67p06c3wDQUjYKZy5MFSgFqWpnoOYiT6C5puRgBy2V4yLGBXbc4exKyK+dZlq9sxkozUV8HWldtrXjMpEHTdSCaBqMuDdNWFmIOLfeejLrvNeu6sP+iy6qU28Ca/cHCbxVk1feKLueaZL9Gq9xGTF0hs34JdWSxXaw2H3Nm3au4h6M87YPM4iWKqLANrf1dh4QTZfkvhfaDF0HuOLTc3P3q8+dR6rIy/jNllisxwa7MZcgnw5UJLD4J7/9OHIajX10Q/gEoFI2BWqVYEKeeeioeffRRdO7cueC+YguuuOnvFU2HxH65DNnG1ysBaKBEs6xexF6xyEHtc/3gI6P1f/gIGKtes9JMGAZoIgmTuRk1KxcMC64nht23YboTpuqaay9HHv4a7OvW/ou5zbCt/FjezbRZfd7S5WfREnGlmuAD79kKx1xFZwUlG0+WZsJqK0+wKooyPtgegDTVhOxcFpDPkIkbVtcLE2VucVQoMnEVJKCsMq+I4sv5a6KrEPB3F7rKBHchqxcmsvgyTSNYdvqJ0HSC/i/MC/wcFApF0+aNN95AbW1tUfqKLbgAa2nkCy+8gI8//hiEEPTu3RujRo2CrhKkNRuonrBiumwLF6FmLv6IWUS6HRG7X1JabqWSgL2ykbknAVDTsEVTAiay0FNJS4CZOecbMXNiyRFKpjsWzBmLdzXa+yjyAsgbs6V5RBdDc0dleQQYn1YCyAW885HjpmElb2VQLgYMgLMIwGmv6yAade7RElnEue64HHX/1Y7e+KrcfGRiK3ctWhl/zR0XFk94+VmxrGvBcV25GDR/YcUQY7GAnJiyyuXuQudcEFmAvecn70JMMcuXW2R52ukEK889GYf/82XvTSsUjYQKmm86xBZcX3zxBU455RRs2LABPXv2BKUUn332Gbp27Yq5c+fioIMOaoh5KopMYr8+VkyXbcWi1ESi66FF6ZtoGmgiBQJuVaMdMK+VtgDsHF5GXRqJ0hJkASvVg85ZpVhqCAAakq5Nr2VbBTkxWWxDbElqU/5MDGAHJ5RYDi+Gn7ji4cWY2I7PIWa1decRs+qyh7stwOzPwhRzjBm5fF+sTOOUj85ZqcIEkVc4MdEWrqQMCti6w9MPL6784sN4ogbSy6xWgFxcEcEK5bomiclibXiRZdUR9+DkBJuPyOLPAeDji0bh0Jkv+n8ACkVDolyKBXHAAQcgmUwWpa/YguvKK6/EQQcdhHfeeQdt27YFYO2j+Mtf/hJXXnkl5s6dW5SJKRqeRJeeRe9TO/gYAIDxyZtAIgWSMkASKdBsGrTE2iibZjOgddXWJtrYBaJrMNIZx8JGdM1ZvajBWsloslQS3FZBfMyTuD+jW27x+bf4fRitLxJTUCa8buL3cHRGkgky4TuJjw0TrWJMgCGpORY50QrGCzBm/bI/YS4oP9enYxXkxRc3P9HaBwDsK0QPSCkRJ3bLz4IVhJ8FLoqoyl1ziytxRSFfxvfj2Zhcc7sGxQ3TmSVLtqE5X+btR8Pnv/kZejz4bOBnoVA0CCoPV0GsWlW8PVNjC65Fixa5xBYAtGvXDrfddhuOPTY4sFqx96D3Ot45zq6cB5IqBdF0mABINgNqJ1dNJpMwa6uhs02yk0kYmQwMPQ3AysEFwJWvy0xb1jKiaznhpZnSPF26RlyWJT6wnkEMucuSGhQklXCOnXuDINKSrD4n7JJ2bJjJBBETVqbjvsyJJWsrIwAwMmwxAS/ANK69lX7CmaNgCWMiTCawxPvzvR/u/kXhE0TQ34CyhQk8oojKtXMHtPvVF4UVX99vRaF17o3HYuW8JcslqCQCS+zLEYGaZvel4cvfnY+D7n1Kep8KhaLp8N///hctW7bEcccdBwB44IEH8H//93/o3bs3HnjgAeyzzz559RtbcJWUlGDnzp2e8l27diGVSuU1CcWeTeLwEc5xdvl/rb+Y7NWMBgA9VWolS62tBtFroGUS0G3hxWerN+rSrjgoliTVRBa6LZj4rYE06GB7MwJw9mfUBYsVbHHkvNtQk3riwpyxpQKFWadygscRM3ZmfV6IiXs+AlysGNtLkhMQpkGlcWDs3px5MQGoiwKLW3npEZ3+91MMZNvgBIkw0f3HyphoZu1l/TMRxH5+VpnXxRhmwXL6ihw8rzkCi5+TlkzY5zrWTb4IRNfQ9eYZ/h+WQlFE1ObV8bnmmmtw++23AwA+/PBDTJw4ERMmTMDrr7+OCRMmYObMmXn1G1twnXbaafjVr36FGTNm4KijjgIA/O9//8Nll12GUaNG5TUJxd5Dov9JlrvRzvGla5rlYqytBkpbgCS3g2as/RwTtsjK1tWDpLPQdM3KOm+7HLXSEphcxnw+O70jFpJuQaOJGfZZW9vKFXTuru8WKKZhulyEVlnO9cf6cMSffV0mwnIbbFuWLd4VSQVxIrPW5XBb0nhcAg1wmaecxRNsSyR2HxLroAxNEImii4/tSMDGFYUUEOz6y/UlBMxHFFWAO/6K71u0iokCSxRXuXLNU26d65ZQ0zQ7YL+wAGaFIjZOqp8C2u9lrFmzBr179wYAzJkzB6eddhpuueUWvP/++zjllFPy7je24LrvvvswZswYDBo0yAkky2azGDVqFO699968JxLGtGnTMHfuXKxYsQKpVArbt2/31JHt4v3ggw/isssu8+23vr4ekyZNwj/+8Q/U1tZi+PDh+Otf/4r99tuvmNNXcDB3o/nZW6DJFGh9HZAqBWWrGbMZy+1YXwu9xM5Ob1u8iKbZQfb2g9swoWmasyejZUkyXALCT2Tx+FlzxGz5ouvREUemIPQguBHtlZR8zJboLiQGBdUsAebniuTdkAAXD8YQRB+PTCzxYtBb6nZ3AnAdRyFR6v2dJLr1tSMVWoJlyqqXE0u8RUy0jIWJKjamKObE8fkVicw96CeurDaaywqgOxYtt4ULADTbRf39PVeD6Boqr7zb8/koFIrdSyqVQk1NDQBg/vz5uPDCCwEAbdu2RVVVVd79xhZcbdq0wb/+9S98/vnn+OSTT0ApRe/evXHwwQfnPYkopNNpnHPOORg0aBBmzPA3x8+cORMnnXSSc966devAfq+66ir8+9//xlNPPYV27dph4sSJOO2007Bs2TKV5qKBkSVTTb/1TyCTAcmmQW3rVyKbgcliubRcqggjnQXVLJHF799ISlMeAWWaljBjx37wAksW12XVMWBljDegI2cRcostFltlOmKMbcBtXadSAcaC5R0LmMaEXi5dhOu+Qixvrtgzx6qVE4AOnDvVT7DFRbRAAfCImSABJavvlGteC5hVX26pYteC47c0wYXJWa24a0xcOYKKs15Z9625xtVtkeXqO5mApuvYMeM6tL7kZs99KxRFQ61SjM1xxx2HCRMm4Nhjj8W7776Lp59+GgDw2WefFWSMySsPFwD06NGjUTewvuGGGwAAs2bNCqzXpk0bdOrUKVKfO3bswIwZM/DEE0/gJz/5CQBg9uzZ6Nq1K+bPn4+RI0cWNGdFfEgiZe3xCOsdpgktVQqiWUH0JJPNrWa0rVymkbNuAbZoKnWLJR0565Tf14fL1QXetcbVkQgxR1wxKxvgpLHQXGU5EWa5P3Miimg5l6QsYJ7N2XQsXFZfvDUsNx9/wcQH7RODSIPrC8U/MF7zrScTUH4uRr4Oq+eK7QqM28qJIncd9ztfh3cLsjpiP3yZ5hJn7roaJ9aIrkEvTaH6Hzej/OfXeT8whaIIEE0HKUA0FdK2uXL//ffj8ssvx7PPPosHH3wQ++67LwDg5Zdfdhl04hJbcBmGgVmzZuG1117D5s2bPdaC119/Pe/JFIPx48fj0ksvRffu3XHJJZfgV7/6lfNFKbJs2TJkMhmMGJEL6u7SpQv69u2Lt99+21dw1dfXo76+3jkvxMSocJM8+gznuO6lB60HVzJpbS+USjgPKjOddQSXZphSS1Sx4IWXafhbs3i3IjVM53eDF2HMDQrkVmC6LXS5gHlZ2gjN6S8XnyV+HfLiQybQgjBN6tkCSYRd1yQWLB4/USQLludFFh8zBngFFLPQ+QkpJpx5McWEsp+g4vvhvy+81i0hIN6+JovhYuKKuRHZmC5xlrC20Kp98T6Ujboy8PNUKBSNw/7774///Oc/nvJ77rmnoH5jC67f/e53mDVrFk499VT07dtXGje1u7jpppswfPhwlJWV4bXXXsPEiROxZcsWXHed/K/HTZs2IZVKeZZ4duzYEZs2bfId59Zbb3Usborik14yBwCgtdoHNJO2HqAANE13stYTTXO5EaNgGgY021oh2/qHIevPFHaQNzNZV13R0kZtVyJ/zucSY+O6rF9sZaJp+q6wBCB1/7n3bITnWMze73efQfh9buICAx5ZugdeQDHxZPUndxPK3HyeOpyo4tvI3H9+7XlRxQs0TZiLLIaL/b8Sy4Gc4HL65108mgaSTIEkkqj77yMoPelXns9LoSgIUmDQPCmgbTNn8+bNUsPSYYcdlld/sQXXU089hX/+858FReozpk6dGipcli5dioEDB0bqjxdWRxxxBADgxhtv9BVcflBKA4Xk5MmTMWHCBOe8qqoKXbt2jTWGQk522VyQRNJyKyZgCa3SFqBpDUhY2wTptvByguxNt7VJfPj64VpNpunWhtu2dUSMy+KFFTVM6KmkJaKM3ByYpY21NdJZR2w5yVo1E0gmPEH+LIUDkQgwhhj8Lwbox3UP8rFcovVIViYeSz9TQTSJPwdRPLkFl1ew8G5ecXm6y4IWIKT4cWUWKtcchHpiH5og4GTWLy2ZcLthNN154Dn3kMil0CGa9X+baBrqFz4JoutIHX8+FIpioFyK8Vm2bBnGjBmDjz/+GNRO/kwIcbSBIfwBHpXYgiuVShUtQH78+PE4//zgL5Zu3brl3f8xxxyDqqoqfPfdd+jYsaPneqdOnZBOp/HDDz+4rFybN2/G4MGDffstKSlBSUlJ3vNSBJBIgpg6aDYNAljWINsiQDQdNAtrc+xMGsQO2iJMiJgGkLD2bGSuGpiGN+iTPZxN0/WXHzF16+FnmtaWRGx8WCknHDGn5USVBsv6JVvqHyT4NLi3J2JB+GJbd96tXAwY4BYvjmCL+N1obStk5/Di44oM4nq35prrVPONgOPuhZuXnkx4RJoroFwSD2XV0T2CTQwNiGbh8gosfp5E16AlJf25hKFXZPHtPWMyMcWLLEGAOf3b5SSR5OruvRYFhaIpcNFFF+GQQw7BjBkz0LFjx6J58mILrokTJ+Lee+/F/fffX/AkKisrUVlZWVAfQSxfvhylpaVo06aN9PqAAQOQTCbx6quv4txzzwUAbNy4EatWrcIdd9zRYPNS+EOSKfs9CZrJWFsBJVKgJaWW1SuTtq1bthDJpnPCikcsEy0OvBDj6/F9mwaIaVirEk3Tsaox8UU1ZrmyHpCGnYiVXdftXy+qe92NfM4wVuayfEF0OVqpLqhpQudWajL8Nvb2W2kpK5e18WvPrknLBcGQCLBMAblVfuJ1EmC1cgkxX4uYf5+iiPIcS+K4xGse1yC7T67cJay4/4OEbbdi/19k/++RSIJoOrLL5iIx4FQoFAXTyFv73HrrrXjuuefwySefoKysDIMHD8btt9+Onj39t5JbuHAhhg0b5in/+OOP0atXr9hTLpQ1a9bgueeeK3r2hdiCa/HixViwYAFefvll9OnTx7Op43PPPVe0yfGsW7cO27Ztw7p162AYBlasWAEAOPjgg9GyZUv8+9//xqZNmzBo0CCUlZVhwYIF+OMf/4hf/epXjjVqw4YNGD58OB5//HEcddRRaN26NS655BJMnDgR7dq1Q9u2bTFp0iT069fPWbWoaFxIqhSAlYtLKy23jtN1ltCyoem63HHWytPliCbTBBLJ3DkvqphFixdjzHXI+mf1TAMwNVBTB9EM67qmWVYw04CJ3PZCzOLF3IzQdZia4bKK8Ztzi+ILgCOm+K82fuWlqwxs6rkcZFFgdWVt+LKwPsV+ZItSRFElChYmjGSCR3Tv8df8RJHVLieiZEHyrA0fe6alEi4XsktMWZ2ym+bmwgst3T0PTbf+SGDCTxRautAf7+7hx04kYax6DXrf4VAoCqKRE58uWrQIV1xxBX70ox8hm83ij3/8I0aMGIGPPvoI5eXlgW0//fRTVFRUOOft27fPa8qFMnz4cKxcuXL3C642bdrgzDPPLOokonD99dfjsccec8779+8PAFiwYAGGDh2KZDKJv/71r5gwYQJM08SBBx6IG2+8EVdccYXTJpPJ4NNPP3USmgHWqoNEIoFzzz3XSXw6a9YslYNrN+Fsfr12BSgAUBOkRAMpaQFQ28pUUmYLItvdZ3DiyXYJer4kmIBg17kYMFd90wQyadAsrIehLbysawZYhBTRDPeDWre2GGIPe+YydMQFbzWyc4fJVj/KkOcCy6W30ASxYH1spkeQALkNrWX/u3XJsZ+Fi0+z4XHn8bFVQowUP0+ZMBJdeTLhFibYWD8ua5fHrSyIHEhcf1w9RzyZhhN/5YyRSLrqE13Pbf/NWRekwoqNIRF0JJGE+cU7VhX790KhiEtjb+3z3//+13U+c+ZMdOjQAcuWLcMJJ5wQ2LZDhw6+HqnG5NFHH8WYMWOwatUq9O3b12NYyndXHUJZRFiReeuttzBw4MC9ItapqqoKrVu3xo4dO1zqXJEfxtcrAfbfkpog1LTEln0OICe2uIeqVGQIrkXeFekItWzGchVmM46Fy+W6dF2zXIy8aOMD7PmgeW/+LW+QvzOvmILLcz8xET8nUTRFWcEoc/OJ5eJ4mq572vgJriBXHjWNXKwUQxZc7xvHJ3H58e/CXHj3HxNVrnFd7XS3qNKEuvwchXFd82Grw4gGqiWgdzsCij2DxnhmsDG2LfkXKloGW5YC+9lVjbaDfpr3XL/44gv06NEDH374Ifr27Sutw1yK3bp1Q11dHXr37o3rrrtO6mZsDF588UVccMEF0n2jGzVoPionn3wyVqxYgQMPPLChhlDsgRhfr8xZoQAAGijRckILsASYZi+1B0CJZpURu66I3ZYw8Qa4LV0ASBbWb4NpPcxh2A/WbNq6rhmApllB+5ruuCAJs4LBCrBnbkTrXHQXJnytVaLr0A+W2kJMUxEFvp0miasSBZd4HHad78vVdwQRZR3LxYnVThQmuqe9n6gS8YgsdszHUolziGKlYsLOjudiOxKIYk06B6LlBBas/9Ou5fi6juyGj5HY91DpPSkUvhQp07yYbzLK4jFKKSZMmIDjjjvOV2wBQOfOnfHII49gwIABqK+vxxNPPIHhw4dj4cKFoVaxhuDKK6/EBRdcgD/96U/SBXf50mCCq4EMZ4o9HP2Aw5H9ZjWQTOVEliC4KHfMrjkPKGYNYw8rTmxR07QsZ9TK60WQscRUMuXk+qKwRBzsoH0kUlbgPGA9kBO2JS2Z4lya9sPXju9iv1Vi2gqWAZ8dO/cjSWHhZ2HiXXlxYG14VyFf7ucelFm9/OoCEgHF8IuF8lzX3e5AWR9sHD4+T/JQ8bhCZNZPeEWUZ/4RRaC7T41zK+pu8WQfU7GMX4SkCXXs/9/ZjZ+DEg3JTgdBoYhEkQSXmPpoypQpmDp1amDT8ePH44MPPsDixYsD6/Xs2dMVVD9o0CCsX78ed911124RXFu3bsXVV19dVLEFNKDgUijyJbFfH+vBoqfcDyEGpVY5L8T4Y17s21YtamYBYlu4DLu+nrSC2bNwAktJ1hJd0HSQJCwrGHuwsiB6wFodCTs4OgEuCD8XV0Zg5ROTruUVV1X6QE3DE39ETQOkJPgLlLWTtYd9f7lj3qIIbzl/3S9GzkYqorhjl0XKzqsmTZMQJJ4kdT33yc3TI/x4F6PHtSqxkkncklIrGS+OkLO8eqxVhLjG9Qgvn2NefGU2r0WyQzcoFI3F+vXrXS7FMOvWb3/7W7z44ot444038tp/8JhjjsHs2bNjtysGZ511FhYsWICDDiruHzZKcCmaJInOPZDevtltqbIfOswtyB87MAsXNS2xQ01QIwPoKRAzC2pmQXTLSsaEkLV9UCrnXjSZwLLeaTZtWcEMw2pjGu4gewYnuggk8VUykcWvpmQPda6MiNcBt+VE0gac+CCy+CVZe+6zCI2L8+tLdgz/OClXzik+PkoqEIOD3wlXx+lbnI+PJUw6x9yF3DtvOYWPUBKsVFSsK74Lx1KXuCjW7PP0tm8BAKm2XbxtFAobomnRfocD2gNARUVFpBguSil++9vf4vnnn8fChQvRvXv3vMZdvnw5OnfunFfbQjnkkEMwefJkLF68GP369fMEzV955ZV59asEl6LJkmrTAfXVVtAi9XkoOSsZ+aB607BjurI5YWUa1oNKS1iii2g596GedIQT4cWLI2I00GzGzoBvAMjVh8HX5X4pWXoJkYiWrUCCRBRDFBkhwoqHF3SRBZvYv09QuWiF8nU7MuuUbIVUmJtRnLMolqKKJ+dcsFEK1inHiiW2jeJGDBrHrx2bw1685YoiBqRAl2LUbMo2V1xxBf7+97/jX//6F1q1auVsk9e6dWuUlZUBsHZr2bBhAx5//HEAwPTp09GtWzf06dMH6XQas2fPxpw5czBnzpz8510Ajz76KFq2bIlFixZh0aJFrmuEkKYnuJrSHouK5kldba0THE8JcTyFJqW59AwANKIDRLf++KcUBGn7eZoAzKzTHyV2XI0TL5MTXZaYsh9uWYAkhdgs+wuLslWMzNrEUgKIKSY4VyINC3CXiTBROMiSuPqIHRky4eJKXSAjQFBJx/ALKAfs3GiCiAoSTuIc+DoSVxtv9bQC3SKIGU3S1n1DoUINRIMpii6+vWzsIIuW7GfhZ/Wyy+urtgFEQ0mrNt56CkUj8+CDDwIAhg4d6iqfOXMmxo4dC8BKML5u3TrnWjqdxqRJk7BhwwaUlZWhT58+mDt3blG2EMyHNWvWNEi/Kmhe0WQpLStDbZ2V5JTSnNAyqfv/l67ZwgsEIASEaIAGwDCFhx73wHTikjS3EKMmSEq3LVr2AFzOL9gpIYggkqhMNPEZ6+04o0LgY5UcwRcUp8Xwi7fy6S8QP/HllxiUbwfBCqUnvYLGxiVEnEh/wbXGjc1/2/gKH/HYPhfbBoonVxnJ9SEKMX4eYgxcRCEnIp0b9/+7rqYapS3yX/6v2EPh3NB5t49BlGf/rFmzXOfXXnstrr322ljjNEdiC67a2lpQStGiRQsAwNdff43nn38evXv3xogRI5x6svwVCkVcykpLUVNb57JqsV9o0y6QSgRKHSHlCCpquMqg29YN0wTVEu70EUkudszU4AmQF5KuEjGvA+dOdGLFQuK1rHbBFiUivFvZ7/2D2F1tA6xcnq9Ufm78/PzOIYmDkliiqKdc7q7zjWXiF0mI71HdhGxMn8UXvHiSihzxsw6Nu9IAzSvIpPfp92C050z5/+3ivRLN+QOlrLRU3o9i70NctJFP+72ACRMm4KabbgrNhs+YPHkyrrnmGrRt2zbyGLETn44YMQJnnXUWLrvsMmzfvh29evVCMpnEli1b8Oc//xm/+c1v4nS3R6ASnzYctXV1jnULsGK2DNP9X5YQAp0AGiHWAjAjkwswN7OAkXYC6GEalhWLuRrtAHsmvKwy6hJffD0A8kSqIn4B8jJXYaB1Krpbj4/LCk0C69dv0BhhLjeIaQxyYorqCfeKPYlFKC8Lj6cdkZaHCiYx1UjMeXnwWLFITuDJCHmohQbTw+t2b9miLLBPxe6jMROf/rBiASpatcy/n527sM8Rw/b455uu69i0aVPk7YQqKipi5xqNbeF6//33cc899wAAnn32WXTs2BHLly/HnDlzcP311++VgkvRcJSVlmJXTS00QmBSCgJA14jjViSEQJM9w9gDDrAtDNzzjxIrdosVUNO2anAiy7QtCpSzdLGutZwIc+JJxdWSSEpEV9ItZkwD0CSWCD9BIqsnFkG+etN5YLP4JmoCCUn/Yg4zWSyUKByEVBHeNAiay3Xn6xqLIEqCXHeEmqB6wtPG9S4eCwQKG1ksls/PyVcchowvn5P385D9mWyauT9KFAoGJT4JoWO03xuglOKQQw6JHH9eXV0de4zYgqumpgatWrUCAMybNw9nnXUWNE3DMcccg6+//jr2BBSKMFq2KENNbR00+xfBoNQSWfa5FThPnKB5B0KsB58jNjRQIwswtyInpvjtgzziC3AEGFg7nwetS+howb9eVE95C8PElk/wtzVHTvQIbkZZOo3AL1JxHrIYI76MC1SXr9gjrjJPugR2H0HuUX4uYhnrUyKsfOcjE3dhrkmxH8gFEQCYVHcNE+RLMIMuctZdlpxX3gerbh1s3WntGduuVQv/vhV7PsqlGImZM2fGbhM3MWpswXXwwQfjhRdewJlnnolXXnkFV199NQBg8+bNe7S5UbF74Z9pbGtg9pCSi63cg5NqiZygsi0gzipHaloWLyainDJOfAGOAHOuQ5emfXBEHD/xIKuNVFzo4SvmxGuAO05I9AoKFiaECS4ZsjlEXYGneYWQ5xiwVkAI8ALRk3dNHEfsV+IezVdA8f+9XAJJWDXrEkRUeEdOGIXBLLjsOAixT8qVb95RjQ6tVTD9Xgsh/t8/UdvvBYwZM6bBx4gtuK6//nqMHj0aV199NX784x9j0KBBACxrV//+/Ys+QYWCBQIz2O+/zn0R8GKL31sRMAGiO4LK9cCmpqWriGbFdFkZUV17N7piuFgzZz9Gd7lrjkGCJoqbyadtoFjwFWReUSR7fIvCJlCQhaUvEI5D+/Rz27H2TDz7fN5iPzKrFi+k/PSLSanLomSVARqRiJoAEcTasOMwnJxwcAs2IsxFNoZf/5RSsE9r4/ZqdG6jRJdCsTuJLbh+9rOf4bjjjsPGjRtx+OGHO+XDhw/HmWeeWdTJKRSAFcdVV1sbvQEvKBzxhZwlC/bDjXMd5mK67HaUuMUXb10JinFizSVz8tSJIrICLEh8P5aYEVZKhrnmxGNqOm65sPkHpijwmbuzws7vs5CW225jSl3HQciEFRNSfEuZYBFFFLvGovGCRhaFjxEjmCpuGh0mpILGyN279b5u2y7s3zb/4GlFM8Xetqyg9oqikFcerk6dOmHXrl149dVXccIJJ6CsrAw/+tGPVLJTRZOB8q4kSawWSwPhuA81DRRaLuM8AOexxlu+XIOEWFsYfrFDUaxYERNhhsZjyer6uSllblAxfoqE5L7ymadVl7nJ3MMxZO46K+bOKvf7lqFcm1xffFfUU2YAHhHm6VcihiL+5Lk+rHcxI4Uw3Vj4zVV0Q5rONWDNlp3oXtkqvwEVzRIVNN90iC24tm7dinPPPRcLFiwAIQSff/45DjzwQFx66aVo06YN7r777oaYp2IvJop1y2PV4B+0sGK8LOHEuRa52C3H4qVrLlcjIFq++IDtkCShfkIHCM7lJI2V8gbAh1rIggLD4/SDaHFNgCWW2IpSUOSOXW3cgoi3MvlZmPJBbBpoyXLNT95fIXMRJxXFomWl5fUe89elQ5j855v7ebDSL77fCUqBHh2U8FIoGpPYguvqq69GMpnEunXrcOihhzrl5513Hq6++moluBSNDp9/CHBv/cMg9sPfsizpVmiXVRuAJhE33JZA7FFnRkjVwKNxliTXZPzdcJFEFPzdc2GWJlE4UehS0ZS7zg6iCyGDUsfSkpUsLHAJnwh9sr6iut2CrE/u/yfuMfLtS7RURTX0R1m9yCcWyfBiVtYfJEH7ADIsXQRzMarEEXsXapVikyG24Jo3bx5eeeUV7Lfffq7yHj16qLQQit0Kv/WPiGVFobBzzrs3r4aZy0YP2C7GXOZ5B8kKuuAUDt4vusCYJ2ti0mvS1AaQCCiZePIRFqLgEEVNUFB22CPbauMjDGRj+dxDbuyQuK0YYoy3GonjWWP6CxoZRLwQMUiebxtl9iYFKGiAMBXqC72KMV0fbtyBfp1bRxhZ0axRgisSZ511VuS6zz33XF5jxBZc1dXVzrY+PFu2bEFJSUlek1AoCiFon0WGCZITXX7uMX51oyikxNxPflvM8IixE0HpFIQ6UQQW7zqViQRWEkVY5Tp0tzWou33Yqjg/PJYiSuXJO6lYTe7602R9uruXfsSUuq1GojUvKM8Vm5/4GUgT70Yg7kpGVi8wnxc3e6+QdJdTCnzw7Q4c1kWJLoWidevc7wGlFM8//zxat26NgQMHAgCWLVuG7du3xxJmIrEF1wknnIDHH38cN910EwCAEALTNHHnnXdi2LBheU9EofCjtCy3RUldba1HdBiUOkJL5q4iADRQl+giGgEBZ+WyTQ18GxeR4pyEMjEHlCimpNd8Vtg5ZRIxGcGCBXACTLggszSJgoYJmCzLAxsgFoJEGRsvqoWLh/8cWLC7LJVCriP/OeT6lM0voD4oDGptlM7EDaX5LxZiqwxZX3y/snkFuSDd/w+88xavmZTCMIGl636AQSmOOaBtXvegaOIoC1ck+MSnv//973HuuefioYcegm7vQWsYBi6//PKC8o3G3kvxo48+wtChQzFgwAC8/vrrGDVqFFavXo1t27bhrbfewkEHHZT3ZJorai/FxoXtrwjYDw1ObMlcihqxRRextgVi2wMRSuFaqehaHZfnCkThWlg+KMBHWIGVQajrFVS8SDSofxC6nwstbBxPXeGcrxoWk+QeUxB+kFuYoooiEzmXcaTxI8Yy5buKMA7sHoMsXn6Cy891aPXDiVThB2f97tDcsQkcf2C7vOaviEdj7qW45bPlqGiV/wKJqp07UXlI/73q+da+fXssXrwYPXv2dJV/+umnGDx4MLZu3ZpXv7EtXL1798YHH3yAv/71r9B1HdXV1TjrrLNwxRVXoHPnznlNQqGIQ1lpKWpq66QP7DDXDG+ZoYSAUN6VyD+sQ/6qk8RaydyHYgoEWWJNNi+nH784K0mZpz7cVihZvSDBxCMNnpfMOciixa75xoKFWW0CRJHMZWZw9aO6+vJZfRhHhMmC6mWiSQNBxi9+jLWVjC0TVa4y1/8nuQAL3FpIodjLyGaz+Pjjjz2C6+OPP4YpWQgUlbzzcN144415D6pQFEqLMmtTax5mxQp7gLqew3w8lqtcnvGcIVtNKHUHchsK+wogiahi5VHcb9JcU+I8JOKUH8ebAJS7HuDiKhReALNzP/xEit/84iQezZewz4BwJjsnrozKRRMl/sJHtE6xNs71EFHlV9e6xupS/Ofj72BSilG9OwXfmKL5oFyKsbnoootw8cUX44svvsAxxxwDAHjnnXdw22234aKLLsq737wE15tvvomHH34YX331FZ555hnsu+++eOKJJ9C9e3ccd9xxeU9GoYhDyxZl2FVTa4kFEOjEFiiItuorMGN5hHQNsgB29mATBZYYX8bqQFaPr0PhPJ2D3ISsLj++U8dHSFntrQJDEAFR44Fk18O2jxQxnPsKd0nyix5MSl0WLVefZm5RKRMduiYvD4JvE6W+rL04J3/x5H/jcaxU7rqsnry9Vd/dh2FSzPnwW5zdr4vvfBTNCLWXYmzuuusudOrUCffccw82btwIAOjcuTOuvfZaTJw4Me9+YwuuOXPm4IILLsAvfvELvP/++6ivrwcA7Ny5E7fccgteeumlvCejUMSlZQsroH6nbe0ynVB4tow+Z/kihPi7mXyyrvvFYIkCC3CLLL8Afoh1+LGo+7qsjrueW1yJIikX1O0dW9qfzwq3sDggP3hhxOeP4sv5umI9f7zCQka9QV39Zuy5ZwSR4rdqla8b1Caoj4wZfM7aiwRZtIIsVN66Pn2ECDCTUvx9+TcY3d+d/kfRDFEWrthomoZrr70W1157LaqqqgCgKPFrsQXXzTffjIceeggXXnghnnrqKad88ODBys2o2G20alGGnba1C4RYrir7wcy2OskFzwenZAC8QsuVXNV+IIlCh3cRyixcQLg7UC7A5KKOr2P1TX1EW+662EbsP8xiIqufr/WnGIgWLy9UeJeRj+9RbOMWd2GI4tJPPLq2IQoQVO6fCYWuERgmFVyJ3rqe8Ux3v4+++zUuPeqA0PtRKPZUirlQILbg+vTTT3HCCSd4yisqKrB9+/ZizEmhyItWLXLpI3bW1DrCC8iJLYbjTpTEb/EbKfNB7zJ3oSiyIFzjY6m8mxt7LUpRrFYsbskr2Ow2gqUqioUq3xggkwKaKbk3+6EvwkSD7Fr+eK02xYIJF3bMxpHPXy7u+JWH7li53Ly9AgzQib+g8pSJ9Qx40n/4WbtkGJTCNK0VwA//72v8+mgluporai/F+Hz33XeYNGkSXnvtNWzevNmzatswDJ+WwcQWXJ07d8YXX3yBbt26ucoXL16MAw88MK9JKBTFZFdNrSOu2MMud05ygcy22HLSQvikbqAU3PJ5VpZzGYoCzBuwbr2LAssvDYSf1YpdN3hLlo+48luBxqcBcMoiiCqrnvxBzwsDfkzN9HcjaqZXsPhZh8Q+oiBzT4oWKL5fvzEynGjxOxbvX/eZpkxEuefoFmB8O/6+nGNbEIn3nWsnWCZDBKlskQHr7/4la2CYFL87Vn3HNzuI5t27NW77vYyxY8di3bp1+NOf/oTOnTs7XpJCiS24fv3rX+N3v/sd/va3v4EQgm+//RZLlizBpEmTcP311xdlUgpFobAHGS+0AC7+kxdbHPxfc0zAeARVgDWLtzp5LVxugcVn/pYnppQHrIvxVVHFVZC7MMh15ffAZ/UyQjmP30pBmUhhgoS18xMuUSlGH34xZVFEm1+sl/iZ8v0455xrzxtjRaET4lihcuXecUT4tlFpCMuhQtGUWbx4Md58800cccQRRe03tuC69tprsWPHDgwbNgx1dXU44YQTUFJSgkmTJmH8+PFFnZxCkQ8skL6mts4pc9JmcQ8aR2yJbkWi5bY/sYtkQkvmMuRjtfzdkHYfQkwVE1hBwevhq9KiCDD7XGKxsuoJ/UeMHXLqhLiyRHQC1NPcO1/O2uYrnLI+5ZpGYJoUWqhr0/6MPHXFm6KeY5lFKcgiaF13W7BYG76OWM8pM3M/L94VWiwMk+LORV/gmiEHF7VfRQOjguZj07Vr18Dkz/kSK9O8YRhYvHgx+vXrh9LSUnz00UcwTRO9e/dGy5Ytiz655oLKNN90qa2rk8ZrScUWi3UgGrI0J5LELPa8NQvwugx5yxjgFVJRBJZolfJapLxWK3k9e24xAq793Fl83SAxwI/P6jKBI4MJmSCXl9g+umCSo9sKnFl8wuDrsXvj2/kGvUvEpyi4LIuX2/LE/7z8jtk5L674+DIx1kysGyWOTmzPv/9x+CGh7RVyGjPT/Pdff4GKigIyzVftRPsDDt6rnm/z5s3D3XffjYcfftgTPlUIsSxcuq5j5MiR+Pjjj9G2bVtnU8fGYNq0aZg7dy5WrFiBVCrlCdCfNWuWb0Ky7777Dh06dJBeGzp0KBYtWuQqO++881wrMBXNl7LSUtTVVHsFFv+AJMS1CTW1E43mLEAsT1XOomWY4dYsUWTJLFy8wDJMuXDxE1+561x9n+X+MregX0yWGCfk215Sz68uTHcbJi50YkWRh7m4jIwQqG4I7zZRxJNGCDLIfa4yF2AUN6JnjuLPzkcMyUST7JoocPhjUVTla80KE13FXdygUDQPzjvvPNTU1OCggw5CixYtkEwmXde3bduWV7+xXYr9+vXDV199he7du+c1YL6k02mcc845GDRoEGbMmOG5ft555+Gkk05ylY0dOxZ1dXW+Yosxbtw4V0qLMm6zZEXzh5icY4nSnOiS5NyihC2nh8uqZQiix6DUa40SBBVvZZJZuDKGYK2SWK/kAsw+FqxR/Bz4flhdsVxmnRIDsf3ElczyJRMcsmOxTiHiwX/FoAXrL6pwCJpP2NyiWp9k9fz6iNJv+OpJf8T64rmfNcwwKaa88gluGNkr1niK3YByKcZm+vTpDdJvbME1bdo0TJo0CTfddBMGDBiA8vJy1/WGMjnecMMNACxLloyysjKXUPr+++/x+uuvS8WZSIsWLdCpk9rKYk+lpGVr1O/cbp34bEpNtYSTa4vCtjo4Aop6LFrsQcRbs6xrOSHmiqUCtS0Z9jmloQJLFFd8Xeu6RJBx9cQ0A85cQgRWlDgiQxBzQUIhivAKuybWCxJChQgQcQx+Xn6CK+q9i3MTyQYIMdm8ohBUNyG5H76+zB0pct3LHwMAbj750MhzUjQulJAC00LsfVbOMWPGNEi/sQUXsyKNGjXKtVSS7fmWb36KYvP444+jRYsW+NnPfhZa98knn8Ts2bPRsWNHnHzyyZgyZQpaFbC7uqLpUdKqDeqrODMw0VyuRCa2DNuVyFyIBrN0UepyHfJCy9keR/BW8oKKF1PMTegXAG/SaAIrzMKV4TZZDXP5OXMNEVhhIirqdf48ihUoqE0+RGkvCqCgOYRZrgB5HFhQ37JxrEL5vcgEkW7m4sJEd6shsW5lTYqERGiK8xK57uWPlehSNGuqqqocgxHLLu9Hvoal2IJrwYIFeQ3U2Pztb3/D6NGjQ92Dv/jFL9C9e3d06tQJq1atwuTJk7Fy5Uq8+uqrvm3q6+udLY2A8B+Ooomg6Tk1ZIstSjRQTYdhWiIqY1jvzGVocAJEJrTE2CzewsW7DEWR5bZwWWWiyOIFVpRkl0GrA6PkZ4oqsmQWLVmdoOuyOjKBE2TlCRNMYiB6PukQ4rg7w8RkvhimCT1CHiWp4OKtVNziDF0jMAy3EOPny7fjRZisb3Z/k+d+hFtP7R3llhSNiXIpRmKfffbBxo0b0aFDB7Rp00aae6tQw1JswTVkyJC8BpIxdepUx1Xox9KlS2MH5y9ZsgQfffQRHn/88dC648aNc4779u2LHj16YODAgXj//fdx5JFHStvceuutofNWNFE0HUAum7wJ4rjVXJYtk7dw8e5Ff6HlFhM5C5PcwiUXURlBkPhZuPzElTTmK0CIiW2CRJb4HtXKxV/L+rSRnvvEoAFwguiDYsMAb6b4UJFmixvD/tnxxzJBEscNys9BNheZJSnXzn+cXF2xXJO6Wj1jgBNYpvc6s3wB8FjAZKsYFU0MtXl1JF5//XW0bdvWOS5WslOeWGkhAOCDDz6Qd0QISktLsf/++6OkpCRSX1u2bMGWLVsC63Tr1g2lpaXO+axZs3DVVVcFbiN0ySWX4P3338fy5csjzYOHUoqSkhI88cQTOO+886R1ZBaurl277lXLZpsrdTXVzkpE3nqV5QSEQS3hw6xegB1fxQktt/CiuToB1ixeZAFucWVSflWk3OIUNS+TTGAFpW2Iaq3iRVZcgSWtHzHI3js/eRxeMeLDGGzeCU5sBPXdEHmvGGFxWEF1veea9JrvMSGB18Xx2Wf155/29Z2zonHTQmz+9puCxqiqqkKHLvup51sRiG3hOuKIIwKVXzKZxHnnnYeHH37YJZRkVFZWorKyMu4UAtm1axf++c9/4tZbb82r/erVq5HJZNC5c2ffOiUlJZFFpaJpYRLLwmXaYsqyItnvsESTO46LWa+s9kFCi4+ZYhYu3l0ougr5B7TMjcjmycpZvbB0DFHFVZzjqO9BAstvLv5uSNNTV1bPTxBFEUBh1q80Vy8fURUU9xREkOVIdo2V8UJRNr5hGtx5BFeRXcUwqMf1KOuflf3u+Q9x75n9wvtX7LH89a9/xZ133omNGzeiT58+mD59Oo4//njf+osWLcKECROwevVqdOnSBddeey0uu+yyRpxxjmOPPRZDhgzB0KFDceyxx3oWB+ZLbMH1/PPP4/e//z2uueYaHHXUUaCUYunSpbj77rsxZcoUZLNZ/OEPf8B1112Hu+66qyiTBIB169Zh27ZtWLduHQzDwIoVKwAABx98sCvp6tNPP41sNotf/OIXnj42bNiA4cOH4/HHH8dRRx2FL7/8Ek8++SROOeUUVFZW4qOPPsLEiRPRv39/HHvssUWbu6JpwVyELKGpZdFyB8YbFK7UD6LQ4q1ZTp+m15rlJ7LiWrH8gtr5Onw9IEjIFCa62HGQJUsmssL7dAusMGHF9xElaF28Fs3FGE9oUZOCaATUrk8itvVLxxB0LWgVIX/uF4PFiy/DzFm//EQi73r0E2A84+d8gPvPPszvlhWNxO7YvPrpp5/GVVddhb/+9a849thj8fDDD+Pkk0/GRx99hP33399Tf82aNTjllFMwbtw4zJ49G2+99RYuv/xytG/fHmeffXbec8+X0047DYsWLcL999+Puro6DBgwwBFgxx13XN6J3mO7FI866ijcdNNNGDlypKv8lVdewZ/+9Ce8++67eOGFFzBx4kR8+eWXeU1KxtixY/HYY495yhcsWIChQ4c654MHD0b37t3x5JNPeuquXbsW3bt3d9qsX78ev/zlL7Fq1Srs2rULXbt2xamnnoopU6Y4vtwoqEzzzYedNbUA4AgiJqyyBnWle8jali4/92HGyE9shcVkhQktvo4zdgT3IH8c3XUXLpSiWrSC+zJ9r8WJ+YpaJ851AI54aihIjOSjYYHxQfUTPuXuY/fDlY/jktYn3rHFn/+DPzvcM+e9ncZ0KX63aVPBLsWOnTrFmuvRRx+NI488Eg8++KBTduihh+KMM86Qep9+//vf48UXX8THH3/slF122WVYuXIllixZkvfcC8UwDCxduhQLFy7EwoULndguPqQoDrEtXB9++CEOOOAAT/kBBxyADz/8EIDldty4cWNeE/Jj1qxZvjm4eN5++23fa926dXPtj9S1a1dPlnnFng8vtihbjUh5cZCLr+IFQ4ZTOhlHJPgLrQwnMvjA+2JYs2RWI/68UCEVVs7HvLnqBVizvMduoRU59isP4RVHNAVtMeT0F+/vVADwD8Wwx/PbpohfDxXXuuV37Gf1cqLmHbxWL9cxhLHDNs5UNFvE1fh+oTXpdBrLli3DH/7wB1f5iBEjfJ/PS5YswYgRI1xlI0eOxIwZM5DJZDyZ3huLzz//HCtXrsTKlSvxwQcfoKKiItAtGkZswdWrVy/cdttteOSRR5BKpQAAmUwGt912G3r1srIOb9iwAR07dsx7UgpFQ7Cj2rJuUZpL/eDEbDErlW3J4q1aGYNyosp/xSETVaLrkBdUvIiSuQKjBMbna7XK130YZsWSzS3IkhV1nLhzB7zCKkw8RRFOzE3oN0ZUKJeWQWbVYmJFFGYyh04xsx0GpX1wL1CQu5ZkLkVenP3qnytgmBQzzu9ftDkromMlPs1/xR1r27VrV1f5lClTMHXqVE/9LVu2wDAMjwbo2LEjNm3aJB1j06ZN0vrZbBZbtmwJjKluCM477zy88cYbME0TJ5xwAk444QRMnjwZhx1WmIs8tuB64IEHMGrUKOy333447LDDQAjBBx98AMMw8J///AcA8NVXX+Hyyy8vaGIKRbHhH87WKkQ4cVoZ050h3qDUlUcrY5ouoSWzXsmEFm/NkgW+i65Cv4D3qBaouBYvv+Mg8cPPK9oY/kJL5jKMcy9BAstPTPHxVX7iySPUiuxWDIyKES6KizI1ybz9BFjU+DMmmoLTPrhjvqz+cxYwNh5/zjP27+9j1mh5qh1Fw8H+gCykPQCsX7/e5VIMWzgm/uHAcljFqS8rbwyeeeYZVFZWYuzYsRg2bBiOP/74vOO2eGILrsGDB2Pt2rWYPXs2PvvsM1BK8bOf/QyjR492srNfcMEFBU9MoSgWO6prQSmFRuAEyVtfQsydmEv34Agw26rFXIcsZos9RzL2E4cXSbKyIKEV1VWYr2UqigsukvCJucrROo9nzQq7B/GYFxxBIivIGmVk5eklcm0DL8eGjz0Osrx5xFiIAJO1cfb2llifwghyPXr7k7sgGaIAU6Kr+VJRUREphquyshK6rnusWZs3b/b1fHXq1ElaP5FIoF27dvlPOk+2bduGN954AwsXLsR1112H1atX4/DDD8fQoUMxdOhQnHzyyXn1G1twAUDLli1323JNhSJfmNjij03HJeh2IVopHXLuQ5aXy2pLPRYuQC60XK5HibswHzdh0LU4/chEj6tdxLlZ58EpHIJWGMa5pzChJRNYoW7FEGGVT7yWFDPgr/sAMSaKKQrqcUnyIky0gIkxYIUIsPBrpnCdt4JZ1y6Y/R6e+GW8ZNaK/DGpdwuwuO3jkEqlMGDAALz66qs488wznfJXX30VP/3pT6VtBg0ahH//+9+usnnz5mHgwIG7JX6rTZs2GDVqFEaNGgUA+PLLL3HzzTfjz3/+M+66667GyzQPAE888QQefvhhfPXVV1iyZAkOOOAA3HPPPTjwwAN9P1CFYnfhsXpQmntAIycseLHljttyuw4B7zkAZDkLF8BlmueEFi/AimmhitKP34o/13FEC1uUHFlRVxhGuc8oFi2/OrnrniJvnQgPlyhB9Qw+EN6vb0KI79yIJhFggqAiGnH17XFBCn36PSp48eR1J/pfC3JD2jPyjDP68aX4+4U/8pmJophQ+1VI+7hMmDABF1xwAQYOHIhBgwbhkUcewbp16xxDzeTJk7FhwwZnN5jLLrsM999/PyZMmIBx48ZhyZIlmDFjBv7xj38UMPP82bZtGxYtWuSsTly9ejXatm2Ln/70pxg2bFje/cYWXA8++CCuv/56XHXVVbj55psdpbfPPvtg+vTpSnApmjQaAWQLqVhAvGOhEqxa9UxM2efMwuUXCM8sWn4JSw2u73zdalHqxRFZYePna8XKd+5AsIiSWbU8dYpgvYojsKK2DRNiLG6Fzd/PAhYmvgghvhazsL/R41m2cvivgHRz3qx38fTYo0LrKZof5513HrZu3Yobb7wRGzduRN++ffHSSy85GQ42btyIdevWOfW7d++Ol156CVdffTUeeOABdOnSBffdd99uycEFAO3bt0dlZSWOP/54jBs3DkOHDkXfvoXvnhA7D1fv3r1xyy234IwzzkCrVq2wcuVKHHjggVi1ahWGDh0aulXPnojKw9X02bazxjmmAKozpvNgT5sUacMSQMzCVZflrVxWfFaY61CMzxJXGPIiC4D0XDzOR6QAhWV89x7758nixwqbU9RroZYqH/dhmNgK+6qLlA6iAPEVlm8L8E8NIQscFvNRim1l4/H9yMYS20TZ/kcsE68Fia+sSTHn4qN9r++pNGYernXfFp6Ha/8u8fJwNXdWrVoVSWC99dZbGDhwYOSdZ2JbuNasWYP+/b3Le0tKSlBdXR23O4WiUWjbqgW278qJroqUhjYtWwAAPt1cBQ0E0KyYE8CK8cowt6HpXokYFggvC4D3BKLnYd0KupavJct/DvGD3uPMVzzPR2RFcRv6iayGFlf59Ec0rzUKsK1Ywn2IbkjR9ShbyRjqepRYy8SfmYy4G3mLnPHoO3jh0mPyaqsIhw+hyLf93kZUa9bJJ5+MFStW4MADD4xUP7bg6t69O1asWOFJfvryyy+jd+/ecbtTKBqNNi1zosugwNadNWjXqgV6dqjA6o1VQiZ50yW2HBeiYNHycycC4UIrrkWLP4+Tgb2QZKRRxox7zIgqssS6Yn2Z21D2kAgSWWFiqFDxFcW6JYodBpu3zA3p53qMG/fFux7ZOHz6DCD3M2yIjbqV6Go4TFpYVpMi/6j3KOKK0diC65prrsEVV1yBuro6UErx7rvv4h//+AduvfVWPProo3G7UygalTYtW+D7qhrnF2XTjmonNQS/bQ9gPWBEoQXY8V1CgLxsxWE667YSBQmsuK7CwDYhliyZwArqL9Q9GeE4yurBsJQOQSIrqsCKI5yKaeGKYt0Kqi+zfomWL5nVixEmpngBFxYjxv9cRfEVVYzJ9oc8/ZEl+PevBoW2VSiaK7EF10UXXYRsNotrr70WNTU1GD16NPbdd1/ce++9OP/88xtijgpFUWlf0QIbt1dbIsgWWzohwtY97gB6MU5Lth1PFNdh3BguIJo1K2yrH/cc8lthGMf9GSXje1QLVq6Of3u/dkFCJ9Z2P0Vwq2gBCRxlAku87k0FQWNZvVgbZz4BVi/WR6HiC/D+nwwSZac89DZeumyw9Joif5SRqmmQV1qIcePGYdy4cdiyZQtM00SHDh2KPS+FokHp3KYca7bsBAAQwAqSt9NB1GVNR4yxmC3RhShL75CP4PIrA8JFDxs/qI+oMVlRxoviAo2yqjBS3RgB72Eiq7GFVZy+/USYaIHyK/NzN/LB8TLh5TemzOLFj6UJ8+Hnwrsc+fO4KNFVXEyqXIpNhbwEF6OysrJY81AoGp3ula2c4+++3uZJEMgHyIvxWrItd/INjo8T/G4ID+2oLsN8YrKixJXF2Von6qpCsV144lKZNUzeJo6gKnbQvCyOK0yExRFeQdYuqx2bR66NM2aIq5H1FWbxAuQWLR5RmMlQokvRHIi77VAkwdW/f//IHb///vuxJqBQ7G6WrvsBAGBwVhUnkzwV0ztw1i3T37olcxcGCZeoWd/FOnGtWQ1pxcpXYIlt/QSWnwAKEkZBAqvYgioM2XhBIkwmvPh2otiRCihBMFl9sTbcmIKlTCr0fNyNQW0A9/8fMZkqj2gZG/nXt/DK5cdCURhqlWLD0SBB82eccYZzXFdXh7/+9a/o3bs3Bg2yAhzfeecdrF69Wm1YrWjWsNWIGf4BQQCAWK5DKn+AN5Q1CwiPzfKWm9K+irl3IZBftvcwN6EZMJ5fGVAcUdXQ4stvlaLfykRALrz4dqLVK8jiFRWZ21CcW5w4L7GtzM3o54I0TIqf3L8Y88cfF/s+FDlMeHe8jNteIWfnzp2x6kcSXFOmTHGOL730Ulx55ZW46aabPHXWr18fa3CFYnezYsN2aIQ42/Dw8CkgTJM64ouli5ARZuUqZJWh2H+uLDxnVlQRyI5DrVMRRFYcgSX2I13VmKewYmKksS1a4hx4ZALMNy0EpYEB93z7MDej6GKUxXVJ48IiCC++Ld8+zLIXFus1/L438dqVxwfWUSgKpTE8ebFjuJ555hm89957nvJf/vKXGDhwIP72t7/lNRGFYneha0CSajBMExqhSGoESY3ApMSOGCXQfJJSMvzEE18mWpmiiCx/wRU/nUOU+LJiuAv9XIXeeiF9+gisfFYeBrodG0CIhVmX/NxvfshEl2zloqddiKXLL42EX9sg4eX0I7F88cisYIqGg1LrVUj7vYHG8OTFFlxlZWVYvHgxevTo4SpfvHgxSktL856IQrE7OGLfNlixYTuSOgGgQTcBwIRuECQpgUEINEphUgITlpXLpPknf/SInRg5s6zz+NvsBMWSGSbNS2RFiceKkgerEJEV9tBuCCEVlaguPT/hJQ2M94ntCnItesaTWKVybeWiiyGzeDGCBJhTJ0SIBTFs+htYcNUJsdsp1CrFqDSGJy+24Lrqqqvwm9/8BsuWLcMxx1iZgd955x387W9/w/XXX5/3RBSK3QUTXSUJDUlK0CKpw6SAoWvImBT1WQNIWE+ijGEiqRHABAxQTxBwnCXxQSkdZJYsv7pA/BWH4vyiCK18g96jpGkQhVZjCqx8goKjuB5krrliEOZijBLPJboXc23900cEichILtMCPmdKKYbeswgAsPDqIbH7USji0FCevNiC6w9/+AMOPPBA3Hvvvfj73/8OADj00EMxa9YsnHvuuXlNQqHY3Ryxbxt8uHEHkkQDIUCH8hRMSlGTMVCX1VCTMWGaGQAaDEqhUW+wL2/18luNJVrGgq1P+SUo9TsW32VCKsqehVGsWflYsuLkzwpNFdHAfhDZ6j8/8g1gL4RCRRcQHN/FiCrAgohikeMZcvdCLJo4NHL/eztqlWJ8GsqTl1cernPPPVeJK8UeR7/OrfHJd1UAgJIEQdoAWiR1zpqQRH3WQMakqIEBQEPGNKX5hPxyDWV5cWbIxVbU3FliHf48THxJLUghYisoNitXh0qPnf4iCC3ftkGxW7vxoeAXw8Tj55pz+ghwLYbFaUWNB/MTXX5zDrJ28X2GIcY/+sWFKRoGtUoxPg3lySso8alCsafRq2MFAODTzVVI6QT1FNAJQantUtSJnZXeoLC+ijSwr6Qg65Zo/fJf5ejuK19rlngeFhgftk2ObINoWb0gGkJs5SO0ogiJQggSMUC4ay7Stj4RVy5GHTsfa1ecMYPORRrbGrinQ1Fg0HzRZtJ8aChPXiTB1bZtW3z22WeRM8vvv//+ePPNN3HAAQfkPTGFYnfSs0OFc7x03Q9Iahq0ZO5BkDEpdANIakDGtPZhZEKK37QayAks0dqlmwTQgkVS1NgsWXv+OEpgfNxYLU9fAdatfF2IhVq1/IRikICMi58YCRJeQfFdUaxaUfDrJ67osvoqeDouggRc0M+cWcuOv3MBAODNa4YVd2KKvZ5sNotp06bh4osvLronL5Lg2r59O15++WW0bt06Uqdbt26FYRgFTUyhaCr8aP99pOV/X/4NdAIkkzqQBGoyBkyTojyp44fajCvGS8zPBQBpSZ+i2IqTqFQ8LzRZaVShJfYRJrZC3Y4FWrOKLQ6ijhckvPKxdsWfRzTrGBsXkLj3JJ9x3O1LouD3M4rrwjz+zgVKdIUgblmWT/u9iUQigTvvvBNjxowpft9RKzbE4ApFc2Z0//3w1MoNAICkZq9uNClG998PD//va1fduqyB2rThCKLatIH6rJnzSiLYWpVPNnggvzQPQQlLCw2ODxRmRXIbig/txhZgMoJEV1HH8RFdgM82QiGxZUDx4+OCPodiuDAVbigKcwvuXXLL4ic/+QkWLlyIsWPHFrXfSILLlGThVigUwPmH7ystP6htC2QMEwYF6rMG6rM6duhZGNTtckxnTdSkDUCzXI9+qxjjJCl1rkdw4xUrM7y07wIf1MV60Bfy8I4q1oLGCBIYMpEjCqO47kU/q1ZYcH1DpbHIhzjCS1m5FMXm5JNPxuTJk7Fq1SoMGDAA5eXlruujRo3Kq19C98Y1n0WmqqoKrVu3xo4dO1BRURHeQLFXsOjLLQBgp5YwkTEpsoaJDVV1AIAdNRmkEhrSWRPprIn6rInajGGfWxYw3irGJykNslwB/iu/wgLhw4Ln/frPJ7dWnO2DdidhoitMFMgEVxSh5Vfm9BFiMQsTanGEXEOIsKgWvyiiS9NIs0oV0RjPDDbG+19+g1at8h9j584qHHnQfnvV803T/P/TEULyDplSqxQVigZiyEHRFpnctuBzAJa1a3tNBmndxC77Wm3a+sVmYku2qjCqa86qK5xHFFiycYLKixH3QQgpWHTl404spjtrd4mtKMTZXqghVhZGdbMGrSrlx1X5uXygha1S3Bt9ig3l1VOecoViN/OHYT3wh2E9oGsEJQkNKfsly+8F2IkMbQFmZE2YthAzTQojS0FNeF6mQWEa1EmCyCdD5Nv7WbNk1ilZOVDcIFtCSKPEPvFEEWlEy71860QUW/L+CxNbcaxXuysPViEWLk0j0s9yyN0LC5yVQtFwKAuXQtFE+OPwQzDllU9gmBQpXYOR0JFKaM7KxnTW9GxsFiUZqUicLXDiPIwLFVpBG4Tna+3iH9b5xmMRLX4wdxyRGMu1F9Bv3FivYqSeAOJbt4rpSnTXt/odes8itf0PhwkKswAzVSFtmzPV1dVYtGgR1q1bh3Tavab8yiuvzKtPJbgUiiZEWUqHrhHLwlVnBdDXprPImlYerxqTwrRXNrpW/AVsGB2VqOIqH4tI1DZhogvIP7arEFdhlLaFBscHlRdLaBVLZAG7R2j5jVnM+9rToAW6FJtIKGWjsnz5cpxyyimoqalBdXU12rZtiy1btqBFixbo0KFD3oIrr6+gL7/8Etdddx1+/vOfY/PmzQCA//73v1i9enVek1AoFBaV5Sm0bWm9OlSUoG15CmWpBEpsF2MioUHTLDcb0Yj04cS7++K8otAY7ic/dxFjd7gZ/ebAvxhs/vzLaacR5+XqT1KuEeK8pHOQ9OPXZ1jdqMjuKQjZ5yOfa7iL1k+08vfFxho2/Y1I81MoZFx99dU4/fTTsW3bNpSVleGdd97B119/jQEDBuCuu+7Ku9/YgmvRokXo168f/ve//+G5557Drl1WeO8HH3yAKVOm5D2RINauXYtLLrkE3bt3R1lZGQ466CBMmTLFY+Zbt24dTj/9dJSXl6OyshJXXnmlp45IfX09fvvb36KyshLl5eUYNWoUvvnmmwa5D4UijEuPOgA6IagoSaCyZQn2a1uGWaOPxN8v/BHatEihTYskWrVIoqw0gZKSBPSEBj1BoOnWg0aTPFiLJbSKLbbChEBTE15RxEOQBaZQkRUmnoolrmSCMarI8hOhgW1CnkKysaWfpzCeEl0WJi38tbexYsUKTJw4EbquQ9d11NfXo2vXrrjjjjvw//7f/8u739iC6w9/+ANuvvlmvPrqq0ilUk75sGHDsGTJkrwnEsQnn3wC0zTx8MMPY/Xq1bjnnnvw0EMPuW7cMAyceuqpqK6uxuLFi/HUU09hzpw5mDhxYmDfV111FZ5//nk89dRTWLx4MXbt2oXTTjtNZcpX7DZ+c0w3lCR0tEjqKEvqTnnb8hTatypFh4pStLMtYK3KU0gkdaRKEo7wkokulm1alnU6TIBFdgeGuL3iWGNc/UYUXvk87KPQEMIuqtswTEDFEVj851iIqHKNn+dnHsWixeYZdR4yht/3ZuQ57akwl2Ihr72NZDLp/J/q2LEj1q1bBwBo3bq1c5wPsfNwtWzZEh9++CG6d++OVq1aYeXKlTjwwAOxdu1a9OrVC3V1dXlPJg533nknHnzwQXz11VcAgJdffhmnnXYa1q9fjy5dugAAnnrqKYwdOxabN2+W5g/ZsWMH2rdvjyeeeALnnXceAODbb79F165d8dJLL2HkyJGR5qLycCkakymvfAJdI0hnTXy9tdpJpJpKaNi6K20F2KcNmIYJI0thGCbMrAnTXt0oWlT84Ovla90KC6SP0m+UnGKNTSRrT8R4Iz+hFadPkYZKXpqv8IwbPxcnVivKatDXrjw+3gQamMbMw/X2p+vQsoA8XLt2VmFwz/33qufbiBEjMHbsWIwePRqXXXYZli9fjiuvvBJPPPEEfvjhB/zvf//Lq9/YQfNt2rTBxo0b0b17d1f58uXLse++8qzbDcGOHTvQtm1b53zJkiXo27evI7YAYOTIkaivr8eyZcswbJg3E/GyZcuQyWQwYsQIp6xLly7o27cv3n77bV/BVV9fj/r6eue8qqqqGLekUETihpG9pOW/+ucKtCq1xFd9SreSqaYN1NdmLdFiyFM4RHngs/O4wkvsWxRgfgKCH8cva7rfQzmOECtEmEQVP1GsWWHWvzCKLbAaS1jJaAqZ7hV7N7fccgt27twJALjpppswZswY/OY3v8HBBx+MmTNn5t1vbME1evRo/P73v8czzzwDQghM08Rbb72FSZMm4cILL8x7InH48ssv8Ze//AV33323U7Zp0yZ07NjRVW+fffZBKpXCpk2bpP1s2rQJqVQK++yzj6u8Y8eOvm0A4NZbb8UNN9xQwB0oFMXnkXOPAACM/fv7SCVMGCZFTUKDaVIQDUiYOjL12YLGKNTqJRN3MitY0NY0YdeL9cAudpqFqCIrKKYt8lwaKbatMQVWIXFpw+97s8lZuRoLtUoxPgMHDnSO27dvj5deeqko/cb+dZk2bRr2339/7Lvvvti1axd69+6NE044AYMHD8Z1110Xq6+pU6f6xl+w13vvvedq8+233+Kkk07COeecg0svvdR1TfYlk8+msWFtJk+ejB07djiv9evXx+pfoWhIZo0+Eq3LrNWNrUoTSKWsGK9ESkMipUNLaM6rEGSr4PIJ2uYDxWWvqOMV+xX3vsPuS2zv1ycQvNLRaRPwvVls+JirqPFXUYlikYwrtmR97q3xXGL8Zj6vhiDqYjiRsWPHev6/H3PMMUWd2//93//h888/L2qfQB4WrmQyiSeffBI33ngjli9fDtM00b9/f/To0SP24OPHj8f5558fWKdbt27O8bfffothw4Zh0KBBeOSRR1z1OnXq5PGr/vDDD8hkMh7LF98mnU7jhx9+cFm5Nm/ejMGDB/vOqaSkBCUlJYHzVih2J/effRgmz/0IANC5dRlq0gY27qjFnIuPduoMvWeRZaU2rGz11AQMw/RYrvK1asV9SPr17RdnFsU9WcjKysjxUnnuaSgVaSFjNoblqphbGxULvw25Af8/kGWbcQ+b/gYWXHVCw0xSEQt+MdzBBx+MVatWYdy4caiurg5NvXDSSSe5XHv8Ar5icPfdd+Oyyy5Dx44dMWTIEAwdOhRDhgxBr17ycI6oNJvNqzds2IBhw4ZhwIABmD17NnRdd11nQfPffPMNOnfuDAB4+umnMWbMmNCg+dmzZ+Pcc88FAGzcuBH77befCppX7DWc8tDbTqB9NmNY2wA1obxcUcdpqL/ERYopsFz9hqzEbGh2t9CK5TKNGDzvx+4WXY0ZNL9w9dcFB80P7XNAozzfxMVwMsaOHYvt27fjhRdeaNC5bNq0CQsWLMCiRYuwcOFCfP7552jfvj2GDh2Kp556Kq8+I1m4JkyYELnDP//5z3lNJIhvv/0WQ4cOxf7774+77roL33//vXOtU6dOAKxVBb1798YFF1yAO++8E9u2bcOkSZMwbtw45z/Jhg0bMHz4cDz++OM46qij0Lp1a1xyySWYOHEi2rVrh7Zt22LSpEno168ffvKTnxT9PhSKpkiHilIAlivyJ/cvBgAYWdPZs3HRxKEYdMtr0rZEI40iumS5xUQK2dDZb/FAFAoJeI+a2yp8DqFVmjy8yA/7XGQWL952EPaZ7U2WrkLdgqytuDisITw94mI4PxYuXIgOHTqgTZs2GDJkCKZNm4YOHToUdS6dOnXCz3/+c4waNcpJNTV79mw8++yzefcZSXAtX77cdb5s2TIYhoGePXsCAD777DPouo4BAwbkPZEg5s2bhy+++AJffPEF9ttvP9c19kum6zrmzp2Lyy+/HMceeyzKysowevRol2kyk8ng008/RU1NjVN2zz33IJFI4Nxzz0VtbS2GDx+OWbNmeSxoCsWeyqzRRzrH88cfh/NmvYuatAHDpKhPGzj2ttc9D0BTCGBv7A2Qo6xujEMcsdXQAsvpK0J29j2VKOIrzM0IBH+Ge5PoKgZdu3Z1nU+ZMgVTp04tWv+yxXAyTj75ZJxzzjk44IADsGbNGvzpT3/Cj3/8YyxbtqxoAvDll192LFsrV65Enz59cMIJJ2DOnDk4/vj8F1/Edin++c9/xsKFC/HYY485cU8//PADLrroIhx//PGhiUb3RJRLUbGnMX7OB0gbJnbVZfDd9jorxotSy/Jlx3r50djiqxgEPbyDKGbKhr3FkpUPobFteca+7S7B1ZguxfkfrkF5AS7F6p1V+Em/7li/fr1rrn4WrqlTp4au4l+6dKlrJeC3336LIUOGYMiQIXj00UdjzW/jxo044IAD8NRTT+Gss86K1dYPTdPQvn17TJw4Eb/+9a/RunXrovQbW3Dtu+++mDdvHvr06eMqX7VqFUaMGIFvv/22KBNrTijBpdjTuO7lj2GYFDvrsthWXY+atIHatOEkVM1mzEhxXvmIr0KW/zPh1FCiTwms3UchyWad65LPfHeIrsYUXPM+WIPyVq3y7qd6506MOKx75Llu2bIFW7ZsCazTrVs3lJZaoQxsMdzRRx+NWbNmQdPi/6fv0aMHLr30Uvz+97+P3VbG9OnT8cYbb+DNN9+ErutO4PzQoUNx6KGH5t1v7FWKVVVV+O677zyCa/PmzU6iMIVC0bx49N2vnePqjIGbTz4Uv3v+Q6QNE/VZE+msldeLR9NIqOiKkjC1EIHl11eYyzGuKCumqxDYu92F+WIGJLtlhFkqZSsalWuxuFRWVqKysjJSXX4x3MyZM/MSW1u3bsX69eudxXLF4KqrrsJVV10FAPjwww+xaNEizJ8/H7/73e/Qrl07bNy4Ma9+Y9/dmWeeiYsuugjPPvssvvnmG3zzzTd49tlncckllxTNnKdQKBqP2xZ8ji3VaXy3sx5ffl+NTzbuxOjHl+KbH2rwzbYabKuqR21dFpn6rLWK0cw9tKLuwVeMfF3FgB8z35xcDbmpczHzWzU01Mz/lS/FsKrKnDp78kbXhln4qyFgi+G6du3qLIbbtGmTJ+l4r1698PzzzwMAdu3ahUmTJmHJkiVYu3YtFi5ciNNPPx2VlZU488wziz7H5cuXY/78+Zg3bx5ef/11mKbpiSOPQ2wL10MPPYRJkybhl7/8JTKZjNVJIoFLLrkEd955Z94TUSgUuwe2L2Nt2kBN2sCOmjTq7fN01nTydInwwoFSt/Wh0H0Om/v2LnE3co5DFMtiQ1KIYOL7yFdYRg2oZ0jzs+WRELu5UqxVisUmymI4APj000+xY8cOANbiuA8//BCPP/44tm/fjs6dO2PYsGF4+umn0aoAt6kIW5lYVVWFI444AkOHDsWvfvUrnHDCCQW5gPPOw1VdXY0vv/wSlFIcfPDBKC8vz3sSzR0Vw6Vo7kx77TMAwK66LLZWp7GrLuO4EndWW5mf+YcY/9DjH8DNJK1fXhBCivKgjiI04gjO5i6+gMKtevkG1f//9s48PIoq6//fquolnYQEkgBJJCTsiCDrgIAOhDW4gKCAiizK5uvgBui4JzAqrw4MKo4jviKI4gA/EQfcWEQYFEQWQRaJBIlAFkHELCakO13390enKt2dqu6qXtLdyfk8Tz10V917695u6PpyzrnnOH+X9elWrM8Yrk2HTvsdwzW6V7tG9XybP38+Bg8e7LfAcsfnv+YxMTG49tpr0b1790YttgiiIfDU0I5IiDGheZwZrZpZ0KpZNFLiLWgRF4UmMSZEW4wwmg0wGAXwAu/ygHN+WAa6vIy30l/1eUjz0TRvlVI4asJCr4syXAiUC9Rfd2MgRGdDdisS+rj22msxfPjwOmLLarVi9erVPo+r28KVmZnp8Udnx44dPk8mUiELF9GQWL7vZ4iMwWoXUVJhQ1HJFZRfsTnyclWLKL9SjaqaItiS1cv9gRcoy0c4Up8Fm73hq9BQ+34CHT/my98DPXPwt/B1qHYt1qeF66NDpxET64eFq7wMtzYyC5cgCCgqKqqTTPXSpUto0aIF7Ha7T+PqjuHq0aOHy3ubzYbDhw/j2LFjmDp1qk+TIAgifEiKNsIo8LDZRcQYBVhMAsqvVMNaLeL3ShuaRNlxofQK7CKDXXRko3euwSiKrM5DM5ACLFKCyoHwjUXj+NrvJJifp/N9tLTVg7+fbWOJ4ZL+nfrTv7GhFjpw/vx5v3Jy6RZcS5cuVTyfk5OD8vJynydCEER4cFu3VOzJv4QBGYnYdKIY0UYB9ibAxT+qkBBrQqXVDpOBR6XVIcKqqkWUVdhgh+PJ6v7cVBJgoSBcxQ/gPe2EamHvmn6+WLrq6zsJ1n20pInwBUoT0Xjp2bOnHEIwdOhQGAy1Eslut+PMmTPIysryeXzdgkuNu+++G3379vVa5ZsgiPBnQEYiAGB0F0et0k0nihFrElBlF1FeVQ2TgUdJhSOwvrzGvWitCbJnIpMz0zOVh2KwAr19eQBLObnqO0WFHrzlM/NHeEUi/roSvdGQRBfzc5diQ94I486tt94KADh8+DBGjhyJ2NhY+ZrJZEJGRgZuu+02n8cPmODau3evnDmWIIiGhSS8AOD9786jRRMz4i1GVNrsKL9SK7gqrdVynJe9WpTNXe5CwVNtRm9IaRF0JxvVWAMxXFxNSg8653kqFvAOYGqOcEJXDcowFs6hwM4chz/9GwvZ2dkAHJnw77jjjoAX59YtuNyTmzLGUFRUhAMHDuCZZ54J2MQIgghP7upZmzPntb1nEBtlQKzZgHJUw/GTUo1KngPjOYhiTTyEUi4kDfmU1PC3th6gTVj5YjELlNBxz3NW57pGq1cg5xQKgmXNaky5uAh9DBkyBBcvXpTzg3377bd4//330aVLF8yaNcvncXULrri4OJe/pDzPo1OnTli4cCFGjBjh80QIgogs3j5wFkaeh7UmdktweuAJPIfqmtdqwsGbtcYbeh+wnh6ugYwF8jaWZJ3TI4KkufsivNznFEniS8/34ly2SXOfRiC6wjXxaThz1113YdasWZg8eTKKi4sxbNgwdO3aFe+99x6Ki4vx7LPP+jSubsG1atUqn25EEETDQhQZBM4hrgw8B4HjIPAcTAYeQk1JHN6tvfvDTRIQwXADeXuQBsJKJqFHMEr3Vbu/J0HkyerlPl8tli8t94w0AhWP11DiuGiXon6OHTuGvn37AgDWr1+Pbt264euvv8bWrVtx3333+Sy4dO8fadu2LS5dulTn/O+//462bdv6NAmCICIPo1MCVIHnnA5efu1ci1ApuaenJKN60DqGWnJRf2s8+lqX0dscPSVD9bZWX+/Z2PAUFE7JUBsnNptNjt/avn07Ro8eDcBR19HXwtWADxau/Px8xaRfVVVVKCgo8HkiBEFEFlN7p8mvs7eclP8nbReZLLgklNIEKqWPkAiUm8dfK5bghwBRswxocQEq4WmjgdZ4Ly33DXWtRud5+Eq47zqtT8ilqJ9rrrkGb7zxBm666SZs27YNf/vb3wA4Cm4nJib6PK5mwbVp0yb59ZYtW1ySf9ntdnzxxRfIyMjweSIEQUQudpHBZOBRXfOgrjTwLgLMvS1QV3j4snMxkDsVAf8ElqexlMSXvy5LNWHkKd7L+b5aYr78EV6+CLdwtLBFumuRdinq58UXX8TYsWPx97//HVOnTkX37t0BOHSQ5Gr0Bc2CS8pPwXFcnYzyRqMRGRkZWLJkic8TIQgicmnbPAYVNSkiyq9Uw84YLCZHmgipJJCa+FKzvgTi4atV1HgSWr6IMPd1uo+hNy5GLT7LUzC8FquXFmuX2vhKbfRcCyZ6xKwWa2okiy6ycOln8ODB+PXXX1FaWopmzZrJ52fNmoXo6Gifx9UsuETRsROpTZs22L9/P5KSkny+KUEQDYtoo4AogYeR5xEbZYDJwKP8SjUqjAKsdhHlVxxJUq3VtXVeJNej83utgd9K6HUheRNS/li7vFm39I7tPoaSlUqL+FILtNfyObuPH47WKIIIFIIguIgtAH578XTHcJ05c8avGxIE0fC4o/tVLu//viuvVlxZAZNBAKBu2VESX0Dgdi/qETiBdCuqjafXwqVmIfNkHVRzN/pq7XImXMWWL39f5J2yDTQ9hFhT+cGf/o2RDz74AOvXr8fZs2dhtVpdrh06dMinMTUJrldffRWzZs1CVFQUXn31VY9tH3zwQZ8mQhBEw0HgOVhMDpFVLTIINq5OIL2Es9iShIQ/LjhfBZPWflraeZuvvxYugecUrV5KFi8la1cgRJdePImhcAhyb6glbEQ/Y7gao9569dVX8dRTT2Hq1Kn4z3/+g3vuuQenT5/G/v378Ze//MXncTmm4W9ZmzZtcODAASQmJqJNmzbqg3EcfvrpJ58nE6mUlpYiPj4eJSUliIuLC/V0CCLk/GP36ZpSP3ZYq0X8XmlDpdWOSmu1HFjvHtOl9jpYBFuYqeHr2jxZB91REk7uwsvTT7+viWh9ST5a30iCU82i5elz2fnIoIDMoT6eGdI9lu86AUtsE5/HqSwvw+xBXRrV861z587Izs7GnXfeiSZNmuDIkSNo27Ytnn32Wfz222947bXXfBpXk4XL2Y1ILkWCILxhqtml6C5OpPfVbpYsdytXMNxwgSAQY3obQ22d7lZA5/PerF1AXYuXniSqWglHoaUkrDy5D73t8ow0KGheP2fPnsWAAQMAABaLBWVlZQCAyZMn47rrrvNZcOlOfLpw4UJUVFTUOV9ZWYmFCxf6NAmCIBoWxprkpyYD78g8z7m6FA1u7kUtQsY1uar3I9AEY0y1++jdNal0Ti3xqWLiVx8TzoYj/ibR9cTgpbsCOl59YGfM76OxkZycLCd4T09PxzfffAPAYXDyR4jrFlwLFixAeXl5nfMVFRVYsGCBzxMhCKJhoSaAvAmt+hI2etAbdB8I4RcI0SURjpYnCV9FUaCqFHgbn2h8DBkyBJs3bwYATJ8+HY888giGDx+OiRMnYuzYsT6Pq3uXoprv+8iRI0hISPB5IgRBNBwEDjAJvEsQvFT2xy7aXVyLkkvM2TWm5CYLFf4G06ud17I+vZ+Dp/Zaguq95e4KBHpdfKHGfW6RlpOLdinq580335RTYd13331ISEjAV199hVtuuQX33Xefz+NqFlzNmjWTFX/Hjh1d/hLa7XaUl5f7NRGCIBoWPOdwKVpMAsqvVMsCzGQQYBdr83FVu4mtcCCUaSSUxlfL5eUpx5fWz9NbxnoisrHDz0zzAZtJ5MDzPHi+1gE4YcIETJgwwe9xNQuul19+GYwx3HvvvViwYIFLaR+TyYSMjAz079/f7wkRBBH5zOibjrcPnIXA8TBG1QitK9UQrjhEgtXuSIJqrbYrCge1vFyBIhzclnpEpl7RpXZNLdFpIEr5BBpnIRjMvF++rDnSrFyEfnbv3o3ly5fj9OnT+OCDD3DVVVfh3XffRZs2bXD99df7NKZmwSWV82nTpg0GDBgAo9Ho0w0Jgmgc3NunNbbkXkC5tRpGwZGXy2ISaoSWI1WEwHOwVtf+H9rAc3UsXu7uRn8IhtBSyx2mBT3WKE+7FLWOIaGWc8uXepb+EurSQKFYc31CuxT1s2HDBkyePBmTJk3Cd999h6qqKgBAWVkZXnjhBXz66ac+jas7hmvQoNo8JJWVlbDZbC7XG0ueDoIgvDOyUwtsPFaEKIEHzAaYBB5WuyM/l5Q6wlotorrmTztjcoZ6SUhIlrCqmvNqRbGVqM+dhYHs72lteqxdnsSYniLWenEv/SO9j4SSQFrqR0YS/u40bIy7FJ977jm88cYbmDJlCtauXSufHzBggF/ZGHQLroqKCjz22GNYv369vG3SGbu9MXp8CYJQY2zXFKz/vhBGBghGDkaeQ4xRgM1SY8FiDEaex+x+6S79Fm7LhcBzcvLUS39YawSaQ3xVWj3/1mhNNeFOfWS11zuultxb7v2cr3tyz6qVB/IHd1ElvQ+k2PJ356We+pGRLLxEjf858dS/sZGbm4s//7muyzguLg6///67z+PqTgvx6KOPYseOHXj99ddhNpvx1ltvYcGCBUhNTcXq1at9nghBEA0XgQPMBh5GnoPZIMBsEBBtdBxNTAZEG4U6fWKjDLIb0mISEGt2vDcZBJil/F41D8RP7xuAT+8b4DEdg9ZcXaHM9aWG0v20rEPrWBLOubvCMZVEIOfmvlZPYyrlLhv66m6/50CEJykpKcjLy6tz/quvvkLbtm19Hle34Nq8eTNef/113H777TAYDLjhhhvw9NNP44UXXsCaNWt8nogn8vPzMX36dLRp0wYWiwXt2rVDdna2S0HJI0eO4M4770RaWhosFguuvvpqvPLKK17HHjx4cJ08LnfccUdQ1kEQjRWe4yBwjgeXwDkEmJF3WLuka+9/d96lz9wb2mHuDe0AODLXmw08TAJfI8AMsuhqGm3EXav349a3vgkLceQLWufpSUzqae/tmoSSKKlvURZO95KElyS+IkF0ObvgfT0aG7Nnz8ZDDz2Effv2geM4FBYWYs2aNZg/fz7uv/9+n8fV7VL87bff5HqKcXFx+O233wAA119/Pf7nf/7H54l44uTJkxBFEcuXL0f79u1x7NgxzJw5E3/88QcWL14MADh48CCaN2+O9957D2lpadizZw9mzZoFQRAwZ84cj+PPnDnTxS9rsViCsg6CaKyM7ZoCANhwtBCQ3EscB5vIYIBDgN3WLVWx71NDO+Jf3+QjNsrxc1V+pRoA5OB7Kahe4DkUlVwBAPxeYUVirLnmuii3rw6jB4gnIaSl+LWeeC1vuxnV+mlBixDy5r4LV2uaRDCLegcbf0VTuPx7qU8ee+wxlJSUIDMzE1euXMGf//xnmM1mzJ8/36ue8IRuwdW2bVvk5+cjPT0dXbp0wfr169G3b19s3rwZTZs29XkinsjKykJWVpbLHHJzc/Gvf/1LFlz33ntvnXnu3bsXH374odcPKDo6GsnJyYGfOEEQLkii6rOTv8DOAMmTKHDAltwLAByB9u78z3UZ8uv13xfCyDvEWrVdlIOxbSLDc6Oudun32ObjqKoRZZU2O8qvODb5eNpZGMwHjN4kqr7MxdtuRrVxg5kLrb4FlS8WTU9r17LBgGhYPP/883jqqadw4sQJiKKILl26IDY21q8xdbsU77nnHhw5cgQA8MQTT8ixXI888ggeffRRvyajh5KSEq+Z7bW0AYA1a9YgKSkJ11xzDebPny8XqlSjqqoKpaWlLgdBENoZ1bklbr669og1GdDELKCJuW4slzsTrk3F2K4pSLAYkRhtQrzZgLgoI5pZjNhwtNBhRQPw9115iI1yuB4Fvrauo1THMRAxXHoPvWidp97ratf0rCOUbtpgxNZp6a/kUh322le+LqNesIv+uhWDN7eMjIw6IT2PP/64xz6MMeTk5CA1NRUWiwWDBw/G8ePHgzK/6Oho9OnTB3379vVbbAE+WLgeeeQR+XVmZiZOnjyJAwcOoF27dujevbvfE9LC6dOnsWzZMixZskS1zd69e7F+/Xp88sknHseaNGkS2rRpg+TkZBw7dgxPPPEEjhw5gm3btqn2WbRoEdWNJIgAckPbRBw89zsEHjhc8Dt6XNXUa59hHZoDcFjGeA6wiQ43pYRksZCC6+1XGASeh8kACE45vtSoDiNrhpZdi0rnlfrrtXx5Gi/cY+N8xdvn7c6w177C9jm+JcMMNuHuUly4cCFmzpwpv/cmbF566SX84x//wKpVq9CxY0c899xzGD58OHJzc9GkSZOgztVfdAsud1q3bo3WrVvj3LlzuPfee/H2229r7puTk+NVuOzfvx99+vSR3xcWFiIrKwvjx4/HjBkzFPscP34cY8aMwbPPPovhw4d7HN/5i+7atSs6dOiAPn364NChQ+jVq5dinyeeeAJz586V35eWliItLc3jfQiC8EzvtKY+9XN2QW46USy/fjyzg/x64bZcxEYZUFJhQ4XV7shNJOX+EkXFh5JBRUx4EmJqffzF/Z6+ug29tVETUMHO/B8I/BF/WmLmtLYl9NGkSRPNIT2MMbz88st46qmnMG7cOADAO++8g5YtW+L999/H7NmzgzlVv+FYgCqUHjlyBL169dKVh+vXX3/Fr7/+6rFNRkYGoqKiADjEVmZmJvr164dVq1a51DqSOHHiBDIzMzFjxgw8//zz+hYBxxdqNpvx7rvvYuLEiZr6lJaWIj4+HiUlJZT4lSDCkH99kw8ANXFc1bBWi6iw2lFeVe0iuiQi4aGqJPw8zVvLmoKx7mB/loG2smmdr5JY3XL/QE196+OZId3jiY0HEBXjuzvsyh/lWDS2D86dO+cyV7PZDLPZ7NccMzIyUFVVBavVirS0NIwfPx6PPvooTCaTYvuffvoJ7dq1w6FDh9CzZ0/5/JgxY9C0aVO88847fs0n2Pht4fKHpKQkJCUlaWpbUFCAzMxM9O7dGytXrlQUW8ePH8eQIUMwdepUn8SWNIbNZkNKSopP/QmCCD/+57oMvH3gLOyMwWSo/e2otEm1HF3bB9Ki4WsAtzc3oVQGSele3pKi+tNGL5HmdtS6kSJS1hWoxKfuXpzs7Gzk5OT4MzU89NBD6NWrF5o1a4Zvv/0WTzzxBM6cOYO33npLsX1xscOC3bJlS5fzLVu2xM8//+zXXOqDkAourRQWFmLw4MFo3bo1Fi9ejIsXL8rXJFPk8ePHkZmZiREjRmDu3LnyFyMIApo3d8R6FBQUYOjQoVi9ejX69u2L06dPY82aNbjxxhuRlJSEEydOYN68eejZsycGDtT2PxWCICIDI88DsLuIEsEp5kttl14oHqzO9/QkgpxdmM7iy1/Xotq99RBqK6HeuXsrp+StTUNHycKlhJ5QIeeY8GuvvRbNmjXD7bffjhdffBGJiYmq/TnO9btljNU5F45EhODaunUr8vLykJeXh1atWrlckzyi/+///T9cvHgRa9ascUnAmp6ejvz8fACAzWZDbm4uKioqAAAmkwlffPEFXnnlFZSXlyMtLQ033XQTsrOzIQjed0sRBBE5iKyuwJL+FHgedlGstwerJO48xUx56qvURhJfeqxe7m30zMMb9S3W/BXGWixbkWLVckaKV/SnP+DIu6nF/TlnzhyvycMzMjIUz1933XUAgLy8PEXBJRlYiouLXbxQFy5cqGP1Ckc0Cy4pQE0Nf+oLeWPatGmYNm2axzY5OTlezZsZGRlwDllLS0vDrl27AjBDgiDCmbVHCmDkuTqFeAWeAxTCTgMhvLw9nD1d1/vwV7J6qQkvtfH0zlHPOHoJF2ETDCFa39T3LkU9oULufPfddwCgGtIjZRTYtm2bHMNltVqxa9cuvPjiiz7dsz7RLLji4+O9Xp8yZYrfEyIIgggk67935OWyKzw39OxOCyS1FjXlVIhSdny1uWgpZq0U46U2ntq4WmiM7rZIWnO4poXYu3cvvvnmG2RmZiI+Ph779+/HI488gtGjR6N169Zyu86dO2PRokUYO3YsOI7Dww8/jBdeeAEdOnRAhw4d8MILLyA6Ohp33XVXUOYZSDQLrpUrVwZzHgRBEPWC8wPE2cIliSBfURNPvrR3v+Y+L6UHvtI5tRivYBCMgPtgEgjrnftnfsube7F5Vn//J9cIMJvNWLduHRYsWICqqiqkp6dj5syZeOyxx1za5ebmoqSkRH7/2GOPobKyEvfffz8uX76Mfv36YevWrWGfgwuIkBgugiAIXxEZgygyiDWxLGqWIj1iS7+4qo3ZUpqD98Smde/nHHPm3EdvjFew0LPTM9h5rrzFzAVi/HClWmRyol9f+weDXr164ZtvvvHazj1zFcdxmkKIwhESXARBNCrUd+R5t3B5E1pa4rKUyuloHcfVosXXnBPlPt4sXoB3V2Mw0CNIArG7UM9n6m0uegVguFm5wtWl2BjRXUuRIAgikrij+1XgeQ5Xqj2LqWCKrUChXP+Ql+emtZagWkb8cLbUqBGoWorexicIfyHBRRBEg+eO7lcBqPvw1PogVRNb3h7y3oosaz2UxvU8X9c16rUw1afA8NcCU18EU9QFEynxqa+HGAHfTaRAgosgiAbNhqOF2HC0EE1MAgTOk7CptRRpQYvIChRKY9Z9r88C563uY32JikgTMBKe5n3Lm3vreTbq2Bnz+yACA8VwEQTRoJHSQfA8B75GcDmX9xE4x3tHTUXmIlz0BdIHP/Gm1M49Vqs2YJ53ielyH8O9r7dA+khKfxAq6DMitEKCiyCIBs2Ea1Pl1+8cPIcoA48r1aIj8zzPwVotwloT31Vhtde6UxiDXeQD4vLytJtQSz8tebdqr7kG/3sSaBJahBcJCs+Eq5WOgubDBxJcBEE0GixGAQIHxJoE2EQGnuPwh7UaAGCtFhEbZYBdZLIIs4sMFVY7rHbR5cGlZaeh2vlgPJj1CiJfBBRZcvRx61vf4KMZ14V6GiS4wggSXARBNAo+O/kLmpgcNVJtIoNRAKKNAgAzRndJltv975enYDEJct4myepjhTb3YjgIqmASTnMhiEiCBBdBEI2CUZ1di9t+/MMv8uvPTv4iX388s4OL6JIOgUWOhcfXrPmhyNFFBBeycIUPtEuRIIhGyc1Xt8TNV7eEsSaYfkvuBfna45kdYDEJiI82IjbKgKbRRjS1GBFrNsBiFGAxCjAZeE1pJgSO83poQasbU0tfT3jbvah3PCK02JkIu+jHwXwvd0W4QoKLIIhGzchOLQA4RMT2Uxfl83NvaIcmZgOSYs1IiDUhIdaEptFGNIkywGTgYRJ4l+zx8mtfxJRGEeZJdAVSBJHoajhQHq7wgVyKBEE0eiTR9dnJX/DxD7+gqtqOqmoRRp6Hze6obu2eTsEuMocw4uFRWHkSJp7cNdKY7nmQPKeFUE4RodbP0xzIveg/t729Dxvu7RfqaRBhAgkugiCIGmwiQ4XNLr+22UX8Ya2GXWSorEkZISFbtOC7pUcpXUSdNhwXNNGldk7Cm+iK5AB6vUI4Ui16dpGBpxiusIAEF0EQRA3OuxUBYPm+nxFjMsAmirK4qLTWWryk1BHOqAkUbw8uT+JLSXR5G8tZdDnGVC5yrXZOz72U5hzO6CmNFOlUiwDnx3fjpQQpoQMSXARBECrM7pcOAHjr25/RxGSAnTFUmUVY7SIqrXY5Q72EXWQwwVV8VFW7Zn5XQkkAuV93dzG6Cx2l9+rJUbWLLq2uRS3WunBBSvlBEPUJCS6CIAgvzOibjrVHCgAARp6HUXA8rKuqRfAch4KSSgCuokeygEnvnXEXMEoCy/26LKR0CC9P7kUlPIkupXl7GkdpHYEi3MWSJFINPIeJq77Fuml9QzYXcimGDyS4CIIgNHBH96sAALtO/wpbjaAa2akFNhwtxOOZHQAAbx84C8AhxKTs9CUVNgDe8yE5ixlFC5e7yHDSTnbGVMWSnlqLns4D+gPpAxnjFY4iy9NuTi07PesDElzhAwkugiAIHQxql+Ty/rZutbUa7+3TGhuOFsJucogu53qNzlYvpdgv5we0kgXM245Gd9GlJ5Ae0F6v0Rdrl7/xYaEgXAQT0XAgwUUQBBFAnAXY2iMFaBplhM0u4ordIbTcBYhWS5LWGC01l6Jrm7ruRb3CSI+1S4+LMVQiKxACy5trOBSQhSt8IMFFEAQRJO7ofhXe/+483HNMKwXbu6PFkuQe26W0k1FqE4yC1e4ixZsAi2Qx5Y6WtYSDG1RKfOpPfyIwUKZ5giCIIGIQeBh5DkaBh5Hn5ZJAzqWBPD2Y3cWCx2zzXjLbu2am5+WUEc7XFcsTeZmj81yD6YqTxtd7BALn70qvkLpr9f6AzIGIbMjCRRAEEUQmXJuKj3/4BTa7CLNBhM3usBhIcVzWarFOUlVveEp86ulcfeEpHk1Ln1Dhj0VKrW+oU1DYReZXHi5yKQYOElwEQRBBJtrIA0YeVdUirtS4EqsEO67Ya2O6lHYxBqq0jloAvT/jaKW+hFSwRY2v44farcgYA/Pj7xHTkXCX8Ay5FAmCIILMkPbNAQA8x9VxAboXwPb3Aa21YLbcXsW1qN4+9JYoZwLxmXkbO9zWrAdRZH4fRGAgwUUQBFEPDGnfXH5wGwUppovzKzbIG2rjKcdpaYvncr6mdgSa+rhXIMaWxKv7Z0kQALkUCYIg6o1hHRyWrk0nimHkOdhEHma7a34uAHViutxdi764BL2VAnKcc6276NxOz30jwSKkZ46+CCj3PtPeP4RVd/XSPY6/MMb8cguSSzFwkOAiCIKoZ0Z3Scb2Uxcd+bmqRQicCRU2R0yXJGrUEqT6ktrBU4C9VuGl1jac8S8IvmFYqZjoZwxXhHzXkQAJLoIgiBAgWbsAYOOxIhgEHrGMoaomOWql1S6LLsnyVaWQOFUJ95xcWnc1AvqEl3v7UBAoa5pvVqzwt+QR4UNESPj8/HxMnz4dbdq0gcViQbt27ZCdnQ2r1erSjuO4Oscbb7zhceyqqio88MADSEpKQkxMDEaPHo3z588HczkEQRAujO2aggnXpuKO7lfBbODRxGRAM4sR8dFGxEYZYDEJEJxySqnlyvLEmxN64M0JPVzaK8UqKZ9Tj0vyFs8V7MNXnOOtvIktrfdWuh7qwHsKmg8fIsLCdfLkSYiiiOXLl6N9+/Y4duwYZs6ciT/++AOLFy92abty5UpkZWXJ7+Pj4z2O/fDDD2Pz5s1Yu3YtEhMTMW/ePNx88804ePAgBEEIynoIgiDUkIpkL9/3M4w8D5hqrUjSn9VuuZ0UayYqZJ6ftf6w4j215vFSs3iFG766A/XFdUVGpnkmOg5/+hOBISIsXFlZWVi5ciVGjBiBtm3bYvTo0Zg/fz4+/PDDOm2bNm2K5ORk+bBYLKrjlpSUYMWKFViyZAmGDRuGnj174r333sPRo0exffv2YC6JIAjCI7P7pQOAYnZ6s4F3yW8lqL3mODlNxP98cES3AFDfpajdOlRf6J2PHmuZL9at+ti9Gcns3LlT0SvFcRz271fPzD9t2rQ67a+77rp6nLnvRISFS4mSkhIkJCTUOT9nzhzMmDEDbdq0wfTp0zFr1izwKv8ADx48CJvNhhEjRsjnUlNT0bVrV+zZswcjR45U7FdVVYWqqir5fWlpqZ+rIQiCqEtStBE2kUHgHPUX7SJzie0yAYoJU52xi0wWXXZWaxnTmpleS6B8uIguNQIlNP0ZM1SE6y7FAQMGoKioyOXcM888g+3bt6NPnz4e+0pGGAmTyRSUOQaaiBRcp0+fxrJly7BkyRKX83/7298wdOhQWCwWfPHFF5g3bx5+/fVXPP3004rjFBcXw2QyoVmzZi7nW7ZsieLiYtX7L1q0CAsWLPB/IQRBEB6IjzLCLjIYec6Rpd4uwiTwsNprywFVWu11+hl4Tk4lIQkDa7WIf93eXW4zZ8P3ssvRPVWE8zkJLcH64USg3YNa2ulNOlsf+BuHFawYLpPJhOTkZPm9zWbDpk2bMGfOHHBePkez2ezSN1IIqeDKycnxKlz279/vonYLCwuRlZWF8ePHY8aMGS5tnYVVjx49AAALFy5UFVxqMMY8fuFPPPEE5s6dK78vLS1FWlqarnsQBEF4Q9rJ+PEPvwAAeN7hIjTXuBbtIoPJUGtdcrZGWUyC/NouMsy9oZ3L2E2jjS67HwGg0mZXHMv9nES4CrBAWagiUWBFKps2bcKvv/6KadOmeW27c+dOtGjRAk2bNsWgQYPw/PPPo0WLFsGfpJ+EVHDNmTMHd9xxh8c2GRkZ8uvCwkJkZmaif//+ePPNN72Of91116G0tBS//PILWrZsWed6cnIyrFYrLl++7GLlunDhAgYMGKA6rtlshtls9np/giCIQGE2CODtIniOg8gYjDwPmyjCZnCIHqPAIUrgwfMceI6Ts9gDjrxfzrx94Cyax5ldrGTWahFWe928X96sXmrX9FCf7rmAiTEfxNZDG4/ilbHddPfzh0Dl4XIPnQn0c3DFihUYOXKkV+PFqFGjMH78eKSnp+PMmTN45plnMGTIEBw8eDDsn8shFVxJSUlISkrS1LagoACZmZno3bs3Vq5cqRqX5cx3332HqKgoNG3aVPF67969YTQasW3bNkyYMAEAUFRUhGPHjuGll17SvA6CIIhgcvPVLbEj7yJg5GGzOx6AFTY7bCIPkTlcRpLQEjhHzUajwOPmq+v+RxNwBOJLQfjWmtxeeoWIJ/EVbtSnyzDsPgc/BRdq+roLoezsbOTk5NRp7ovn6vz589iyZQvWr1/vdToTJ06UX3ft2hV9+vRBeno6PvnkE4wbN85r/1ASETFchYWFGDx4MFq3bo3Fixfj4sWL8jXJj7t582YUFxejf//+sFgs+PLLL/HUU09h1qxZsuotKCjA0KFDsXr1avTt2xfx8fGYPn065s2bh8TERCQkJGD+/Pno1q0bhg0bFpK1EgRBKCEVwN5/9jJsogij4IjrsikEzYuMwWavu59/7ZGCOjE5zvFbAsd53Luu5l4MtWsxECLH4y7FSBRaNYiMgfMj8F2s6Xvu3DnExcXJ59WsSXo9V4AjnVNiYiJGjx6te34pKSlIT0/HqVOndPetbyJCcG3duhV5eXnIy8tDq1atXK5JOyiMRiNef/11zJ07F6Ioom3btli4cCH+8pe/yG1tNhtyc3NRUVEhn1u6dCkMBgMmTJiAyspKDB06FKtWraIcXARBhCV/at1M8fzGY0UQGYOdAdV2EXa3Z+zyfT/DJooQOA5Wu4jyK9VyDUe7yOQ4LqX8XRLedjIqEc6WMF8tWsEIyA934uLiXASXGno8V4DjGb5y5UpMmTIFRqNR97wuXbqEc+fOISUlRXff+iYiBNe0adO8BtJlZWW5JDxVIiMjo84W16ioKCxbtgzLli3zd5oEQRBhgU1kuFLtauHK//UPAK7JUyXqiCInoeEuvvRatsJJcOgSSm5iy6s4C6N1OsOYnzFcQS5evWPHDpw5cwbTp09XvN65c2csWrQIY8eORXl5OXJycnDbbbchJSUF+fn5ePLJJ5GUlISxY8cGdZ6BICIEF0EQBOGZJuban3ObScCozq7xW9JuRsmqJb12RjG7vEbx5Ux9uBgDLXA8uQ3VE8Dqn8Njm4/j6UH1t6s93ItXr1ixAgMGDMDVV1+teD03NxclJSUAAEEQcPToUaxevRq///47UlJSkJmZiXXr1qFJkyZBnWcgIMFFEATRABjWoTn2n70MACizVsvn3z10HiJjiI82uoitaoX8XYDn1A/uosST6zHc8RaX5S3zvBYMEfA5hJr333/f43VnC5vFYsGWLVuCPaWgQYKLIAiiAfB9YQnMBseuxXhzbSyMwAEA51pnkefqiAE1q5SSuHCu16iGmhgLJVpTOaiV7vFEuIorUQQ4vxKfBnAyjRwSXARBEA0EkTHYRaB3WlMAwPvfnXc6z7zWAlRCa5C8J1dkMLEzFrB76XEdqgmscLPuhWtpn8YICS6CIIgGQIXNDpudocouYkfeRQxp3xw20ZEe4opTKSDn2otKoqFa465CrbsPgx3P5Y/Y8sVtqPSZhZvIIsITElwEQRANgJIr1bCJDFXVtbFZlyqsctyWVHNRqW6ihF1kdQSFuwCT0GLlUmsXSLQKOl8zzHtyFQYymD5YMNFx+NOfCAwkuAiCIBoAIzs5asltOFoIOwPe+vZnWIwCrDUJUKVgeaWdiJ6KV2uJTXIvlK1GMKxdgRI3WsVWIILp/7YtV/vE/EQUmZ8xXORSDBQkuAiCIBoQPMfVZKLnYWeiS9yWs7BSyhrvfk6rQHIWJmoWMef7KCHFmCmdDyaBch2Gk1XLmXBPC9GYIMFFEATRgBjbtTbj9ruHzgMQUMHXuhOlQykXl7uFS4vFyl2YebOI6XFRejrvDV8z3OsRWr66KetrQwERXpDgIgiCaKBEGx3JTs0GHkaBg83OYBNFuZyPJ6uSpp2INW30uBI9CTJP1jG9BCJXll6hFY5WLrJwhQ8kuAiCIBoot3VLBQB8/MMvNbUSRQA8YNAuDtQsYFrae+qn1C4ccln56jYMR7EFBK54NeE/JLgIgiAaMB//8AsAwChwEBkHkWMQOQ4mA++SIkJCiytRaqeE1kLWevN+ucef6SmYrYaWsYJxjQ9TcUYEFxJcBEEQDZhoI4+qahE8xzkOngPPXN2BagLFXYz4a+3S46Z0x/l8IAtJu7f1NS5L733rC3Iphg8kuAiCIBooe/IvQeA4GAUeAmd3lPnheQCibnGgZAnyZknSYoUKhEhxDt7XYrXyBb3WrHBxPzLmp+Ail2LAIMFFEATRAPnm599qrFoAzzHwHAeDwAN20eFSFHjYedfAeaVdh4HGV6HlaU7uwfuBEDT+WNG8XQtHSxgRfEhwEQRBNECk1AMiz2ATAaPgsGyJjIMRjnxdACAINWKLKVuJ1HJ3qeGr1cub2Au1SPE5BUSI581E5lfyUnIpBg4SXARBEA0QSVAZeR52HjDyDIAjTUQVREcQvQjYWW17kTGpiVfU4rv0CAylmLBAZavXOw93cenvPTyJL0HjZxwIqHh1+ECCiyAIogHSO60pAGD/2cswChyMoiQAeIiMIUrgYeMY+JoHqsMK4hSYLnCwqzxslcSWL0IpUIH3/qLXFak1aD7U1i0ivCDBRRAE0YAxChx4kYORZxCZCJExGAUeNpGBZ7WWLUHgALsIgeNkqxegLLx8cS8GMoeXtz6BQE/wfVgHzdMuxbCBBBdBEEQDhuc4gGdyHi7AUWOR5zgInPQwdQgAOT+U6Hq+drC64/u7IzAYqSYChS+B81qEls2/aelCFJnT9+ljfyIgkOAiCIJowAgcB45DTXoIh7VK4AAj7xBfImPg4XiwilKNP0lYqQgvKcDeHW9iSK8Fy5+s9cFETx6uULsVmWgHE+1+9ScCAwkugiCIBozDwMXJiU+NPA9RAGwic7gSJRciDxjhCKKvE0CvZvECPAbZexJgWkSSVnGld1xfCLS1i2h8kOAiCIJowPCco4Ki5FK0gdXk5uLknYxATcA8D2VxpSK8JMEGuFq9lHb76XUFak0PESgXoz+JWMNZWJGFK3wgwUUQBNGA4Wp0FFcjshwpCXgInAhRjtkCXEQXUCOu6sZwiaLDJVkzek1TZeEFqAsZX/J1BdO65ato8uYSDbUYY6Lop+ASAzibxg0JLoIgiAYOxwGMOfI/8SIHOxzCRHYdOiGd43nOo7VLEl6SCxKAk4vSNRA+UELJ3YLmaUxvBML1GGoxRUQWJLgIgiAaOIw5RJfdjjoCS4J3EkrOostZWAGQBZXSNTnFBOckplQsXs4EIrbLGb1j6BVfviRVDRXMbgez+2Hh8qMv4Uo95rslCIIg6ptOLeIgMofoAuAUt1UXwdmQxbmmihA4p3JAHAeB41yuSX1dY8PgIr4A77UEA1UHUU/NQj339GV+9b2L0hnG7HIcl08HI8EVKMjCRRAE0cCRLFciY/KhB8maBdR1Q0qiS8naJd3TOb5LIpB1E7UUttYSM+atjiS5EAl/IAsXQRBEAyX3QilO/lIKHhxsdlY3YzwHjO6SXOechLOliue5OlYsydIlXZf6q42hZO0KBFqsWXoyxjv/qbfsjxqhsnL5Zd3yc4cj4QpZuAiCIBogeRfLwKM2QF7gAZHVDZJXQikuy/ka4JqvSxZSbgH1zu0AZWuXVguUFryVHArkvXxBum993p/SQoQPZOEiCIJo4HAcYNe5u99TPJfzdaWYMKV27m31xnZpxdfahwQRbMjCRRAE0QBp37wJTv5SKr8XeACiFNDOZOvUphPFtQJE9LyL0XnnotI1Z+uVe2yX47znFBKOedZP6R69ecEiFbJwhQ8RYeHKz8/H9OnT0aZNG1gsFrRr1w7Z2dmwWq1ym1WrVoHjOMXjwoULqmMPHjy4Tvs77rijPpZFEAQRNPIulsl1FAG3eCzOYU2SYrHcr0kIbtrH2dIlxXRJh/M47rFdzvFdzmM5x4E593HHedeh1ngtvVYsPeNHElLiU98PSnwaKCJCcJ08eRKiKGL58uU4fvw4li5dijfeeANPPvmk3GbixIkoKipyOUaOHIlBgwahRYsWHsefOXOmS7/ly5cHe0kEQRBBpX3zJrAzJqeDcNYygtMvv7FGXHgSXWppH9zFlLPwUgqqdxdp7uM5xvEsvmrX4F0c+SOglESe2hHOiKLd7yNYPP/88xgwYACio6PRtGlTxTZnz57FLbfcgpiYGCQlJeHBBx90MbYoUVVVhQceeABJSUmIiYnB6NGjcf78+SCsQB8R4VLMyspCVlaW/L5t27bIzc3Fv/71LyxevBgAYLFYYLFY5DYXL17Ejh07sGLFCq/jR0dHIzk52Ws7giCISKJzyzj59dGiEhgFDgAPmyjKQsvulM7BOehcKQu9WtoHT+5D90SoSpnq3cervZ+Ce1HB5anFPagmjOojWJ9Qxmq1Yvz48ejfv7/is9put+Omm25C8+bN8dVXX+HSpUuYOnUqGGNYtmyZ6rgPP/wwNm/ejLVr1yIxMRHz5s3DzTffjIMHD0IQhGAuySMRIbiUKCkpQUJCgur11atXIzo6GrfffrvXsdasWYP33nsPLVu2xKhRo5CdnY0mTZqotq+qqkJVVZX8vrS0VLUtQRBEOMCDg8gxWXSJgiSsHLsXR3VoLsdzOYsuwF0E1Y7pHpOlJLycywI5x23JgfVOQsV5POc+zmgRYXris0K9czHYhHMM14IFCwA4QoKU2Lp1K06cOIFz584hNTUVALBkyRJMmzYNzz//POLi4ur0KSkpwYoVK/Duu+9i2LBhAID33nsPaWlp2L59O0aOHBmcxWggIlyK7pw+fRrLli3Dfffdp9rm7bffxl133eVi9VJi0qRJ+Pe//42dO3fimWeewYYNGzBu3DiPfRYtWoT4+Hj5SEtL82kdBEEQ9cU1KXHolhKPa1PjHTUVOQ5GgYOR5zGsQ3N8dvKX2hgsN1eZUqyXO+45u9TithznXNtKuMeMab23Nxek1p2L/rgIw9W1GKg8XKWlpS6Hs9EhWOzduxddu3aVxRYAjBw5ElVVVTh48KBin4MHD8Jms2HEiBHyudTUVHTt2hV79uwJ+pw9EVLBlZOToxroLh0HDhxw6VNYWIisrCyMHz8eM2bMUBx37969OHHiBKZPn+51DjNnzsSwYcPQtWtX3HHHHfjggw+wfft2HDp0SLXPE088gZKSEvk4d+6cvoUTBEGEGCPv+PkXeGBH3kWYDTwEzhHTJcdgKcQquQbHK4skd5TaeBJdauM6iy8lAeYp/itYpX4aC2lpaS6GhkWLFgX9nsXFxWjZsqXLuWbNmsFkMqG4uFi1j8lkQrNmzVzOt2zZUrVPfRFSl+KcOXO87gjMyMiQXxcWFiIzMxP9+/fHm2++qdrnrbfeQo8ePdC7d2/dc+rVqxeMRiNOnTqFXr16KbYxm80wm826xyYIgggnjLwUz+X4s1bESO41V5eitxgvvTinhXB2R9Zer21rV7iVs+hScz2quRuDlWoi7FyTdjsY74dbsKZ49blz51xceGrPwJycHNlVqMb+/fvRp08fTbfnFMQzY0zxvCd86RNoQiq4kpKSkJSUpKltQUEBMjMz0bt3b6xcuRI8r2ycKy8vx/r1631W38ePH4fNZkNKSopP/QmCIMKdHlc1xcFzv9dainjmyEIv/azKmQBY3dxZCqJCCn53FmGiW2yWRN3M9a65uJTyfEn3cMd9XAlP4webcBNdjNkBf2K4aopXx8XFKcZMuaPXkOKJ5ORk7Nu3z+Xc5cuXYbPZ6li+nPtYrVZcvnzZxcp14cIFDBgwQNN9g0VExHAVFhZi8ODBSEtLw+LFi3Hx4kUUFxcrmgfXrVuH6upqTJo0qc61goICdO7cGd9++y0ARyzYwoULceDAAeTn5+PTTz/F+PHj0bNnTwwcODDo6yIIgggVRsERwxVl4GHkeZgNPMwCDyPvOC/l6dLiMpRQsni5W6bc2/gjhrTOTc3F6LFPA4znqg+SkpLQuXNnj0dUVJSmsfr3749jx46hqKhIPrd161aYzWZVD1bv3r1hNBqxbds2+VxRURGOHTsWcsEVEbsUt27diry8POTl5aFVq1Yu15jbP9YVK1Zg3Lhxdfy3AGCz2ZCbm4uKigoAgMlkwhdffIFXXnkF5eXlSEtLw0033YTs7OyQbh0lCIIINtemxsuvvy8sqdlByByuRZGDTRQBnoNod03foEQdUeWHhUdvX6W5Kae0UM9qr2UXo7d2Sv3CwdLFRNE/C1cQE5+ePXsWv/32G86ePQu73Y7Dhw8DANq3b4/Y2FiMGDECXbp0weTJk/H3v/8dv/32G+bPn4+ZM2fK1raCggIMHToUq1evRt++fREfH4/p06dj3rx5SExMREJCAubPn49u3brJuxZDRUQIrmnTpmHatGma2nrahZCRkeEi0NLS0rBr1y5/p0cQBBGxnCgulTPS20UpmF4EwKPKLjrl0nLJ4CDjSWw5X9Ni2VISW56Enl7U3ItaRZXeckDhkHKCiX66FIOYFuLZZ5/FO++8I7/v2bMnAODLL7/E4MGDIQgCPvnkE9x///0YOHAgLBYL7rrrLjn/JlDXkAIAS5cuhcFgwIQJE1BZWYmhQ4di1apVITekcMzdREToprS0FPHx8SgpKdHk4yYIgggXTv5SCpEBjAE2UYRddPwpMgabncEmMthFBpE54rlExmQBIZ2TXiuJLW9Cy11kBVJgeQrq9+bK1COStLaV2l35owzP3tQrqM8M6bkUN2g+OIPvm7xYdRVKdy2m51sAiAgLF0EQBBEcDDwHxgARDAAPnmMQeN4htux28BxqsqO6ihR3sSWhZtWqT6El4WknpXNcl6/Z693bhoML0R2HS9F3tyDVUgwcJLgIgiAaMXKJHwYADJzIgTEOgAijyLnEc9kUBIXzrkRlAeYk0oLsMvQVtRQSLm00uB29Ca9QxHWFs0uxsUGCiyAIohGTnhgLAPj5UjnAO7auO4QHDyPPAIgQGQdAPY7LV7yJLTXrlKes81r6q6G3dmM4WrTcIcEVPkREWgiCIAgiuKQnxkLgOBj4moztvCN1hJx1nqvNNO8LWl2IImPyoTqWn9f1EKjs9QRBFi6CIAgCgEN0/XypHAaeAaJDTEi7Fm12h9iSM8NLRagDkHHeVwJ1X2kNSsW6nfGUvd7Z2uXJAlbfIk0U7eDIwhUWkOAiCIIgZATOUU6FgUEQORgF1GShd2SZd4/jCpXY0oMWq5xL8W0PJYMA9Zxe4ehiZHYR4PwQXHYKmg8UJLgIgiAIRdy1hrOgEL0EwwO1liC13FyBxFdXp9ZxfRGWkRTrRQQfElwEQRCEjAjU5N2qec8Y7IxBMnRI8VFSTi5JTEnvHa+9izH5egAsZMESW+738FajUc3KFUrhFahaioT/kOAiCIIgAAAFl/8AD6C65r29JvmphHPSUwAuYgtOfdyvu7eRxgoUzvFXocZb/FZ9w0S7fy5FiuEKGLRLkSAIggDgqE0rAuDg6k50tmqpIVnC5PceSvx4E1t2p7G0FrcOt1gyT+KKdjY2TsjCRRAEQcgwBjiybzkJJrcs867uw7piR2s9RSVc7xN8ESVw0r186atcm1G+HgaB9GThCh9IcBEEQRAAgFYJjiSoJ4pLAQA2u5vVyilHlpRZ3jluyx+hJaGU70oL3lyKQoiMSqEWXSS4wgcSXAFAqv9dWloa4pkQBEH4T1nNb5lNFFFpEx11FRlDpc0Om12ETXSIK5G5uhJdBJfTeFqKSPsqtCS0xHAJHq5pkRXehOOMP6VrGAUoLTXhcdQ+O4KK3Qa/7mK3BWomjR4SXAGgrKwMAJCWlhbimRAEQRChYp7O9mVlZYiPjw/KXEwmE5KTk1F8Yr3fYyUnJ8NkMgVgVo0bjtWLxG7YiKKIwsJCNGnSBFyY7JTxhdLSUqSlpeHcuXOIi4sL9XQCBq0rcmiIawJoXZFGsNfFGENZWRlSU1PB88Hbu3blyhVYrVa/xzGZTIiKigrAjBo3ZOEKADzPo1WrVqGeRsCIi4trUD+eErSuyKEhrgmgdUUawVxXsCxbzkRFRZFQCiMoLQRBEARBEESQIcFFEARBEAQRZEhwETJmsxnZ2dkwm82hnkpAoXVFDg1xTQCtK9JoqOsiQgsFzRMEQRAEQQQZsnARBEEQBEEEGRJcBEEQBEEQQYYEF0EQBEEQRJAhwUUQBEEQBBFkSHA1cP773//illtuQWpqKjiOw0cffaTadvbs2eA4Di+//LLidcYYRo0a5XWc+iBQ69q7dy+GDBmCmJgYNG3aFIMHD0ZlZWXwJu6FQKyruLgYkydPRnJyMmJiYtCrVy988MEHwZ24F7yta9q0aeA4zuW47rrrXNpUVVXhgQceQFJSEmJiYjB69GicP3++HldRF3/X9dtvv+GBBx5Ap06dEB0djdatW+PBBx9ESUlJPa+klkB8VxKR9JuhdV3h9ptBRA4kuBo4f/zxB7p3747XXnvNY7uPPvoI+/btQ2pqqmqbl19+OWxKFwViXXv37kVWVhZGjBiBb7/9Fvv378ecOXOCWmrDG4FY1+TJk5Gbm4tNmzbh6NGjGDduHCZOnIjvvvsuWNP2ipZ1ZWVloaioSD4+/fRTl+sPP/wwNm7ciLVr1+Krr75CeXk5br75ZtjtWsoOBwd/11VYWIjCwkIsXrwYR48exapVq/D5559j+vTp9TF9RQLxXUlE2m+Gt3WF428GEUEwotEAgG3cuLHO+fPnz7OrrrqKHTt2jKWnp7OlS5fWaXP48GHWqlUrVlRUpDpOqPB1Xf369WNPP/10/UzSB3xdV0xMDFu9erXLuYSEBPbWW28FcbbaUVrX1KlT2ZgxY1T7/P7778xoNLK1a9fK5woKChjP8+zzzz8P0kz14cu6lFi/fj0zmUzMZrMFbnI+4s+aIu03Q8u6wv03gwhvSJY3ckRRxOTJk/Hoo4/immuuUWxTUVGBO++8E6+99hqSk5PreYa+4W1dFy5cwL59+9CiRQsMGDAALVu2xKBBg/DVV1+FYLba0fJ9XX/99Vi3bh1+++03iKKItWvXoqqqCoMHD67fyepk586daNGiBTp27IiZM2fiwoUL8rWDBw/CZrNhxIgR8rnU1FR07doVe/bsCcV0NeNpXUqUlJQgLi4OBkP4lrr1tqZI/M0APK8rUn8ziPCBBFcj58UXX4TBYMCDDz6o2uaRRx7BgAEDMGbMmHqcmX94W9dPP/0EAMjJycHMmTPx+eefo1evXhg6dChOnTpVn1PVhZbva926daiurkZiYiLMZjNmz56NjRs3ol27dvU4U32MGjUKa9aswY4dO7BkyRLs378fQ4YMQVVVFQBHXJrJZEKzZs1c+rVs2RLFxcWhmLImvK3LnUuXLuFvf/sbZs+eXc8z1Y6WNUXib4a3dUXqbwYRPoTvf6GIoHPw4EG88sorOHTokGqcxaZNm7Bjx46Qxv/oRcu6RFEE4Ag8v+eeewAAPXv2xBdffIG3334bixYtqrf5akXLugDg6aefxuXLl7F9+3YkJSXho48+wvjx47F7925069atHmesnYkTJ8qvu3btij59+iA9PR2ffPIJxo0bp9qPMRY2MUJK6FlXaWkpbrrpJnTp0gXZ2dn1PVXNeFtTJP5mAN7XFYm/GUR4QRauRszu3btx4cIFtG7dGgaDAQaDAT///DPmzZuHjIwMAMCOHTtw+vRpNG3aVG4DALfddlvYuqi0rCslJQUA0KVLF5e+V199Nc6ePVvfU9aElnWdPn0ar732Gt5++20MHToU3bt3R3Z2Nvr06YN//vOfoV2ADlJSUpCeni5bDpKTk2G1WnH58mWXdhcuXEDLli1DMUWfcF+XRFlZGbKyshAbG4uNGzfCaDSGaIb6cV9TJP5mKOG+rkj8zSDCC7JwNWImT56MYcOGuZwbOXIkJk+eLP8P7vHHH8eMGTNc2nTr1g1Lly7FLbfcUm9z1YOWdWVkZCA1NRW5ubku7X788UeMGjWq3uaqBy3rqqioAIA6u6YEQZD/hx4JXLp0CefOnZMfcr1794bRaMS2bdswYcIEAEBRURGOHTuGl156KZRT1YX7ugCHZWvkyJEwm83YtGkToqKiQjhD/bivKRJ/M5RwX1ck/mYQ4QUJrgZOeXk58vLy5PdnzpzB4cOHkZCQgNatWyMxMdGlvdFoRHJyMjp16gTAYVlQCnpt3bo12rRpE9zJe8DfdXEch0cffRTZ2dno3r07evTogXfeeQcnT54Mac4qf9fVuXNntG/fHrNnz8bixYuRmJiIjz76CNu2bcPHH39cr2txxtO6EhISkJOTg9tuuw0pKSnIz8/Hk08+iaSkJIwdOxYAEB8fj+nTp2PevHlITExEQkIC5s+fj27dutURofWJv+sqKyvDiBEjUFFRgffeew+lpaUoLS0FADRv3hyCIETcmiLxN0PLusL1N4OIIEK9TZIILl9++SUDUOeYOnWqYnu1tBDOIAy2eAdqXYsWLWKtWrVi0dHRrH///mz37t3BnbgXArGuH3/8kY0bN461aNGCRUdHs2uvvbZOmoj6xtO6Kioq2IgRI1jz5s2Z0WhkrVu3ZlOnTmVnz551GaOyspLNmTOHJSQkMIvFwm6++eY6beobf9el1h8AO3PmTESuSYlw/83Qs65w+80gIgeOMcaCpOUIgiAIgiAIUNA8QRAEQRBE0CHBRRAEQRAEEWRIcBEEQRAEQQQZElwEQRAEQRBBhgQXQRAEQRBEkCHBRRAEQRAEEWRIcBEEQRAEQQQZElwEEQbk5+eD4zgcPnw4KONzHIePPvrI5/47d+4Ex3HgOA633nqrx7aDBw/Gww8/7PO9CM9I30PTpk1DPRWCIHRAgoto9EybNs2riAg2aWlpKCoqQteuXQHUCpzff/89pPNyJzc3F6tWrQr1NBoFan8vi4qK8PLLL9f7fAiC8A8SXAQRBgiCgOTkZBgM4V3etEWLFmFhWbHZbKGeQshITk5GfHx8qKdBEIROSHARhBd27dqFvn37wmw2IyUlBY8//jiqq6vl64MHD8aDDz6Ixx57DAkJCUhOTkZOTo7LGCdPnsT111+PqKgodOnSBdu3b3dx8zm7FPPz85GZmQkAaNasGTiOw7Rp0wAAGRkZdawbPXr0cLnfqVOn8Oc//1m+17Zt2+qsqaCgABMnTkSzZs2QmJiIMWPGID8/X/dn88cff2DKlCmIjY1FSkoKlixZUqeN1WrFY489hquuugoxMTHo168fdu7c6dLm//7v/5CWlobo6GiMHTsW//jHP1yEXU5ODnr06IG3334bbdu2hdlsBmMMJSUlmDVrFlq0aIG4uDgMGTIER44ccRl78+bN6N27N6KiotC2bVssWLDA5fvLyclB69atYTabkZqaigcffFDT2r2t69KlS7jzzjvRqlUrREdHo1u3bvj3v//tMsYHH3yAbt26wWKxIDExEcOGDcMff/yBnJwcvPPOO/jPf/4juxDdPzOCICKL8P7vNEGEmIKCAtx4442YNm0aVq9ejZMnT2LmzJmIiopyETnvvPMO5s6di3379mHv3r2YNm0aBg4ciOHDh0MURdx6661o3bo19u3bh7KyMsybN0/1nmlpadiwYQNuu+025ObmIi4uDhaLRdN8RVHEuHHjkJSUhG+++QalpaV14qkqKiqQmZmJG264Af/9739hMBjw3HPPISsrC99//z1MJpPmz+fRRx/Fl19+iY0bNyI5ORlPPvkkDh48iB49esht7rnnHuTn52Pt2rVITU3Fxo0bkZWVhaNHj6JDhw74+uuvcd999+HFF1/E6NGjsX37djzzzDN17pWXl4f169djw4YNEAQBAHDTTTchISEBn376KeLj47F8+XIMHToUP/74IxISErBlyxbcfffdePXVV3HDDTfg9OnTmDVrFgAgOzsbH3zwAZYuXYq1a9fimmuuQXFxcR3Bpoa3dV25cgW9e/fGX//6V8TFxeGTTz7B5MmT0bZtW/Tr1w9FRUW488478dJLL2Hs2LEoKyvD7t27wRjD/Pnz8cMPP6C0tBQrV64EACQkJGj+XgiCCENCWzubIELP1KlT2ZgxYxSvPfnkk6xTp05MFEX53D//+U8WGxvL7HY7Y4yxQYMGseuvv96l35/+9Cf217/+lTHG2GeffcYMBgMrKiqSr2/bto0BYBs3bmSMMXbmzBkGgH333XeMMca+/PJLBoBdvnzZZdz09HS2dOlSl3Pdu3dn2dnZjDHGtmzZwgRBYOfOnZOvf/bZZy73WrFiRZ01VVVVMYvFwrZs2aL4OSjNp6ysjJlMJrZ27Vr53KVLl5jFYmEPPfQQY4yxvLw8xnEcKygocBlv6NCh7IknnmCMMTZx4kR20003uVyfNGkSi4+Pl99nZ2czo9HILly4IJ/74osvWFxcHLty5YpL33bt2rHly5czxhi74YYb2AsvvOBy/d1332UpKSmMMcaWLFnCOnbsyKxWq+K61dCyLiVuvPFGNm/ePMYYYwcPHmQAWH5+vmJbT38vV65c6fL5EAQR/pCFiyA88MMPP6B///7gOE4+N3DgQJSXl+P8+fNo3bo1AODaa6916ZeSkoILFy4AcASap6WlITk5Wb7et2/foM23devWaNWqlXyuf//+Lm0OHjyIvLw8NGnSxOX8lStXcPr0ac33On36NKxWq8v4CQkJ6NSpk/z+0KFDYIyhY8eOLn2rqqqQmJgIwPH5jB071uV637598fHHH7ucS09PR/PmzV3WUV5eLo8jUVlZKa/j4MGD2L9/P55//nn5ut1ux5UrV1BRUYHx48fj5ZdfRtu2bZGVlYUbb7wRt9xyi9dYOi3rstvt+N///V+sW7cOBQUFqKqqQlVVFWJiYgAA3bt3x9ChQ9GtWzeMHDkSI0aMwO23345mzZp5vDdBEJEJCS6C8ABjzEVsSecAuJw3Go0ubTiOgyiKqmP4Cs/z8v0lnAPI3a+5zxNwuB179+6NNWvW1GnrLGi8oXQvd0RRhCAIOHjwoOwGlIiNjZXHUfuMnZGEivPYKSkpirFNUvyXKIpYsGABxo0bV6dNVFQU0tLSkJubi23btmH79u24//778fe//x27du2q853qXdeSJUuwdOlSvPzyy+jWrRtiYmLw8MMPw2q1AnBslNi2bRv27NmDrVu3YtmyZXjqqaewb98+tGnTRvXeBEFEJiS4CMIDXbp0wYYNG1xEwZ49e9CkSRNcddVVmsbo3Lkzzp49i19++QUtW7YEAOzfv99jHymOym63u5xv3rw5ioqK5PelpaU4c+aMy3zPnj2LwsJCpKamAgD27t3rMkavXr2wbt06OdDcV9q3bw+j0YhvvvlGtvRdvnwZP/74IwYNGgQA6NmzJ+x2Oy5cuIAbbrhBcZzOnTvj22+/dTl34MABr/fv1asXiouLYTAYkJGRodomNzcX7du3Vx3HYrFg9OjRGD16NP7yl7+gc+fOOHr0KHr16qXaR8u6du/ejTFjxuDuu+8G4BBpp06dwtVXXy234TgOAwcOxMCBA/Hss88iPT0dGzduxNy5c2Eymep8/wRBRC60S5EgAJSUlODw4cMux9mzZ3H//ffj3LlzeOCBB3Dy5En85z//QXZ2NubOnQue1/bPZ/jw4WjXrh2mTp2K77//Hl9//TWeeuopAHWtTxLp6engOA4ff/wxLl68iPLycgDAkCFD8O6772L37t04duwYpk6d6mJhGTZsGDp16oQpU6bgyJEj2L17t3wviUmTJiEpKQljxozB7t27cebMGezatQsPPfQQzp8/r/kzi42NxfTp0/Hoo4/iiy++wLFjxzBt2jSXz6Vjx46YNGkSpkyZgg8//BBnzpzB/v378eKLL+LTTz8FADzwwAP49NNP8Y9//AOnTp3C8uXL8dlnn3m1Cg4bNgz9+/fHrbfeii1btiA/Px979uzB008/LQu2Z599FqtXr0ZOTg6OHz+OH374AevWrcPTTz8NAFi1ahVWrFiBY8eO4aeffsK7774Li8WC9PR0j/fWsq727dvLFqwffvgBs2fPRnFxsTzGvn378MILL+DAgQM4e/YsPvzwQ1y8eFEWZBkZGfj++++Rm5uLX3/9tVGnwiCIBkGIYscIImyYOnUqA1DnmDp1KmOMsZ07d7I//elPzGQyseTkZPbXv/6V2Ww2uf+gQYPk+wP3UAAAAutJREFUIHGJMWPGyP0ZY+yHH35gAwcOZCaTiXXu3Jlt3ryZAWCff/45Y6xu0DxjjC1cuJAlJyczjuPksUpKStiECRNYXFwcS0tLY6tWrXIJmmeMsdzcXHb99dczk8nEOnbsyD7//HOXoHnGGCsqKmJTpkxhSUlJzGw2s7Zt27KZM2eykpISxc9ILYi/rKyM3X333Sw6Opq1bNmSvfTSS3U+D6vVyp599lmWkZHBjEYjS05OZmPHjmXff/+93ObNN99kV111FbNYLOzWW29lzz33HEtOTpavZ2dns+7du9eZV2lpKXvggQdYamoqMxqNLC0tjU2aNImdPXtWbvP555+zAQMGMIvFwuLi4ljfvn3Zm2++yRhjbOPGjaxfv34sLi6OxcTEsOuuu45t375d8TNwx9u6Ll26xMaMGcNiY2NZixYt2NNPP82mTJkiB8KfOHGCjRw5kjVv3pyZzWbWsWNHtmzZMnn8CxcusOHDh7PY2FgGgH355ZfyNQqaJ4jIg2NMQyAGQRAB5euvv8b111+PvLw8tGvXLtTT8crOnTuRmZmJy5cv10vi05kzZ+LkyZPYvXt30O8ViaxatQoPP/xw2FUiIAhCHYrhIoh6YOPGjYiNjUWHDh2Ql5eHhx56CAMHDowIseVMq1atcMstt9RJ4OkvixcvxvDhwxETE4PPPvsM77zzDl5//fWA3qOhEBsbi+rqakRFRYV6KgRB6IAEF0HUA2VlZXjsscdw7tw5JCUlYdiwYYpZ2cOVfv364dSpUwBqd+EFkm+//RYvvfQSysrK0LZtW7z66quYMWNGwO+jld27d2PUqFGq16WYulAgFTh33x1JEER4Qy5FgiAINyorK1FQUKB63dOuR4IgCCVIcBEEQRAEQQQZSgtBEARBEAQRZEhwEQRBEARBBBkSXARBEARBEEGGBBdBEARBEESQIcFFEARBEAQRZEhwEQRBEARBBBkSXARBEARBEEGGBBdBEARBEESQ+f+Mzdm4sYBB4wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.wspeed_u.isel(time=-1).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The more you add to the line of the code, the more specific your plots can be. For example, let's plot the most recent mean current speed (`mean_cur`) along the seafloor. You can do this with adding the `sel()` function to the code that allows you to specify dimensions such as depth (k= ). If you are familiar with SQL, this function is quite similar to the `WHERE` function.\n",
+    "The results should be a similar plot to before, but with the `zc` variable described within the title (i.e., -145m)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7a5190571490>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.mean_cur.isel(time=-1).sel(k=0).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of the above examples are resulting in mean values for either the first timestep, or the most recent timestep. It is also possible to extract data between a set of dates.\n",
+    "\n",
+    "Let's look at the mean current speed (`mean_cur`) for the whole of the GBR area but only for the first week of October 2023. Do this by adding the `time=slice()` function. Then, plot the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extracting the mean of current speed on sea bottom for all GBR area for the first week of October 2023\n",
+    "mean_current_20231007 = data.sel(time=slice('2023-10-01', '2023-10-07'), k=0).mean_cur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x7a5190866ea0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the subset\n",
+    "mean_current_20231007.plot(col='time', col_wrap=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's more specific, by adding on a particular location.\n",
+    "\n",
+    "This next example will show you how to extract the `temp` variable from the coordinates -19.5, 152 between \"2022-01-01\" to \"2022-12-31\". This will narrow down the data we need to handle.\n",
+    "\n",
+    "Yet, as we are now becoming more specific with our analysis of these modelled data, it is important to realise there may not be data at every resolution for every datetime or coordinate you are interested in. However, there are ways around this. You have the option to apply several different \"methods\" to the `sel()` function to indicate which type of scenario you would like to apply to these data:\n",
+    "\n",
+    "     1. none (default; \"method =\" is not in the code) - only exact matches will be returned\n",
+    "     2. \"nearest\" - uses the nearest valid index value\n",
+    "     3. \"pad\" or \"ffill\" - propregates the last valid index value forward\n",
+    "     4. \"backfill\" or \"bfill\" - propegates the next valid index value backwards\n",
+    "\n",
+    "We suggest using the \"nearest\" method for these data, which is what is demonstrated below. \n",
+    "\n",
+    "Along that same line of code, we have specified the lat and long that we are interested in.\n",
+    "\n",
+    "Next, the `slice()` function was included to indicate which timeframe we are investigating. The `slice()` function reutrns all values inside a datime range (inclusive).\n",
+    "\n",
+    "Lastly, the variable was specified (i.e., `temp`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tempTS_2022 = data.sel(latitude=-19.5, longitude=152, method=\"nearest\")\\\n",
+    "    .sel(time=slice('2022-01-01', '2022-12-31'))\\\n",
+    "    .temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that you've sliced the data (`tempTS_2022`), let's select the first 10 days of temperature across varying depths to plot.\n",
+    "\n",
+    "First specify that you are interested in the first 10 days using the `time=slice()` function. \n",
+    "\n",
+    "Following, using the `plot()` function, specify that you would like to see all the depths (col=\"k\"), and create subplots for each of these depths using `col_wrap=6`. The number here indicates that we want the subplots in rows of 6 - you can alter this to get the visualisation you desire.\n",
+    "\n",
+    "This will take around 23 seconds, depending on your internet connection. You should get a grid of plots (17 in total)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x7a51908deae0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1900x900 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tempTS_2022.isel(time=slice(10)).plot(col=\"k\", col_wrap=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's try doing something similar, but using salinity (`salt`) variable from \"2022-01-01\" to \"2023-01-01\" at -20, 152 and plot a map.\n",
+    "\n",
+    "To begin, you'll need to sub-set the data in a similar fashion as before, but specify the new variable (`salt`) we are interested in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saltTS_2022 = data.sel(latitude=-20, longitude=152, method=\"nearest\")\\\n",
+    "    .sel(time=slice('2022-01-01', '2023-01-01'))\\\n",
+    "    .salt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After sub-setting the data down to all of 2022, you can now plot the salinity for the first 10 days across all depths. This time, we've done a differing orientation of plots to make them larger and easier to see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x79e207e39250>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1800 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "saltTS_2022.isel(time=slice(10)).plot(col=\"k\", col_wrap=3)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Simple calculations using all data\n",
+    "\n",
+    "To conduct some simple calculations, it can be benefitial to create a new variable in the data that you can call to easily. For example, we will show you how to create a new variable containing all `temp` data for just 2022. Then we will demonstrate how to calculate a monthly mean and plot these monthly means at a particular depth.\n",
+    "\n",
+    "To begin, name your variable `temp_2022`, and using the `sel()` function, specify that you are interested in the `temp` data from 2022. You can check your result to ensure you see a `xarray.DataArray` just for `temp`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  margin-top: 0;\n",
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+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp&#x27; (time: 365, k: 17, latitude: 723, longitude: 491)&gt; Size: 9GB\n",
+       "[2202731565 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude  (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01T14:00:00 ... 2022-12-31T1...\n",
+       "    zc         (k) float64 136B ...\n",
+       "Dimensions without coordinates: k\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Temperature\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             temp\n",
+       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
+       "    units:                  degrees C</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span>k</span>: 17</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e8727d2a-5491-48d2-9452-53fb92295c6c' class='xr-array-in' type='checkbox' checked><label for='section-e8727d2a-5491-48d2-9452-53fb92295c6c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2202731565 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-6a6f53af-8000-4acf-8ea5-9abf48545b9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a6f53af-8000-4acf-8ea5-9abf48545b9f' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-5ef89b7d-9b2b-489b-bda2-9fefb676926c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ef89b7d-9b2b-489b-bda2-9fefb676926c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c08f050-6180-4482-a890-1601e234d19c' class='xr-var-data-in' type='checkbox'><label for='data-9c08f050-6180-4482-a890-1601e234d19c' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
+       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-0f4aa641-8601-416b-9e25-f14cc811fd9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f4aa641-8601-416b-9e25-f14cc811fd9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e5492c-97e1-43a8-8d13-77cb4d8092c5' class='xr-var-data-in' type='checkbox'><label for='data-65e5492c-97e1-43a8-8d13-77cb4d8092c5' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
+       "       156.868788])</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'>2022-01-01T14:00:00 ... 2022-12-...</div><input id='attrs-c1f43406-e4ee-4f86-9571-aacf678c58ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1f43406-e4ee-4f86-9571-aacf678c58ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b06c9eea-0ac3-4e6d-9f9b-b927a5f9dceb' class='xr-var-data-in' type='checkbox'><label for='data-b06c9eea-0ac3-4e6d-9f9b-b927a5f9dceb' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>coordinate_type :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/UTAA/</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T14:00:00.000000000&#x27;, &#x27;2022-01-02T14:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-03T14:00:00.000000000&#x27;, ..., &#x27;2022-12-29T14:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-12-30T14:00:00.000000000&#x27;, &#x27;2022-12-31T14:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-59b5be75-5484-44d8-b277-5aaa762dc497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59b5be75-5484-44d8-b277-5aaa762dc497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e8c6ad-3fdb-40a6-a321-3f480400f43b' class='xr-var-data-in' type='checkbox'><label for='data-69e8c6ad-3fdb-40a6-a321-3f480400f43b' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-614dd42f-605a-4148-804e-0b75a3eea8c3' class='xr-section-summary-in' type='checkbox'  ><label for='section-614dd42f-605a-4148-804e-0b75a3eea8c3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53d06c6c-7023-4453-b225-29e2be6ede68' class='xr-index-data-in' type='checkbox'/><label for='index-53d06c6c-7023-4453-b225-29e2be6ede68' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
+       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
+       "       ...\n",
+       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
+       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ef41301-ef1b-4138-94c8-cafcd1e58382' class='xr-index-data-in' type='checkbox'/><label for='index-9ef41301-ef1b-4138-94c8-cafcd1e58382' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
+       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
+       "       ...\n",
+       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
+       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dd15387b-1bdb-4b5a-a9d1-be5a8fc36c8f' class='xr-index-data-in' type='checkbox'/><label for='index-dd15387b-1bdb-4b5a-a9d1-be5a8fc36c8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 14:00:00&#x27;, &#x27;2022-01-02 14:00:00&#x27;,\n",
+       "               &#x27;2022-01-03 14:00:00&#x27;, &#x27;2022-01-04 14:00:00&#x27;,\n",
+       "               &#x27;2022-01-05 14:00:00&#x27;, &#x27;2022-01-06 14:00:00&#x27;,\n",
+       "               &#x27;2022-01-07 14:00:00&#x27;, &#x27;2022-01-08 14:00:00&#x27;,\n",
+       "               &#x27;2022-01-09 14:00:00&#x27;, &#x27;2022-01-10 14:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-22 14:00:00&#x27;, &#x27;2022-12-23 14:00:00&#x27;,\n",
+       "               &#x27;2022-12-24 14:00:00&#x27;, &#x27;2022-12-25 14:00:00&#x27;,\n",
+       "               &#x27;2022-12-26 14:00:00&#x27;, &#x27;2022-12-27 14:00:00&#x27;,\n",
+       "               &#x27;2022-12-28 14:00:00&#x27;, &#x27;2022-12-29 14:00:00&#x27;,\n",
+       "               &#x27;2022-12-30 14:00:00&#x27;, &#x27;2022-12-31 14:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adb7391b-94c5-46cb-b086-dbc627b829f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-adb7391b-94c5-46cb-b086-dbc627b829f9' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'temp' (time: 365, k: 17, latitude: 723, longitude: 491)> Size: 9GB\n",
+       "[2202731565 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude  (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01T14:00:00 ... 2022-12-31T1...\n",
+       "    zc         (k) float64 136B ...\n",
+       "Dimensions without coordinates: k\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Temperature\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             temp\n",
+       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
+       "    units:                  degrees C"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subsetting data\n",
+    "temp_2022 = data.temp.sel(time=\"2022\")\n",
+    "\n",
+    "# Checking result\n",
+    "temp_2022"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can group the 2022 data by month using the `groupby(\"time.month\")` function and then calculate the monthly mean using the `mean()` function.\n",
+    "\n",
+    "This will take some time as it is going through all data to group and calculate means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp&#x27; (month: 12, k: 17, latitude: 723, longitude: 491)&gt; Size: 290MB\n",
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+       "    aggregation:            Daily\n",
+       "    long_name:              Temperature\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             temp\n",
+       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
+       "    units:                  degrees C</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span>k</span>: 17</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f01883d8-0e3a-4359-8b7d-408e6ca16917' class='xr-array-in' type='checkbox' checked><label for='section-f01883d8-0e3a-4359-8b7d-408e6ca16917' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ..., 19.23143 , 19.228935,\n",
+       "          19.217936],\n",
+       "         [      nan,       nan,       nan, ..., 19.219215, 19.224407,\n",
+       "          19.191824],\n",
+       "         ...,\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan]],\n",
+       "\n",
+       "        [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ..., 19.755375, 19.752344,\n",
+       "          19.739822],\n",
+       "         [      nan,       nan,       nan, ..., 19.745625, 19.747873,\n",
+       "          19.714113],\n",
+       "...\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan]],\n",
+       "\n",
+       "        [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ..., 22.89508 , 22.895288,\n",
+       "          22.894762],\n",
+       "         [      nan,       nan,       nan, ..., 22.902071, 22.89537 ,\n",
+       "          22.898825],\n",
+       "         ...,\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan]]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-8fa135f7-a772-40e3-8352-6a4e63076788' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fa135f7-a772-40e3-8352-6a4e63076788' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-111339fc-8f5e-4785-a84b-591eb52f1a13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-111339fc-8f5e-4785-a84b-591eb52f1a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0023ed2-f871-4016-93d2-be0cfa18cb6b' class='xr-var-data-in' type='checkbox'><label for='data-b0023ed2-f871-4016-93d2-be0cfa18cb6b' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
+       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-7ae5f512-3df8-4278-b8f0-c8a72e9d7747' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ae5f512-3df8-4278-b8f0-c8a72e9d7747' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccdb8b61-b232-408f-82a7-3f758d574480' class='xr-var-data-in' type='checkbox'><label for='data-ccdb8b61-b232-408f-82a7-3f758d574480' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
+       "       156.868788])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef960d58-de97-4b51-a429-047fc5c7edfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef960d58-de97-4b51-a429-047fc5c7edfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c548e34-992f-4226-9e4a-74ad6ae1bc9c' class='xr-var-data-in' type='checkbox'><label for='data-1c548e34-992f-4226-9e4a-74ad6ae1bc9c' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-0f703fa0-9e35-4e1e-9a32-0c2399ee0c82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f703fa0-9e35-4e1e-9a32-0c2399ee0c82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf0fd9bb-1b22-4996-891a-ab522268d903' class='xr-var-data-in' type='checkbox'><label for='data-bf0fd9bb-1b22-4996-891a-ab522268d903' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91e993e0-84f2-4b16-a83b-b672ecac163d' class='xr-section-summary-in' type='checkbox'  ><label for='section-91e993e0-84f2-4b16-a83b-b672ecac163d' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4fbc7f29-9b5b-4b2e-92db-dbd2a4663f6d' class='xr-index-data-in' type='checkbox'/><label for='index-4fbc7f29-9b5b-4b2e-92db-dbd2a4663f6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
+       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
+       "       ...\n",
+       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
+       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a809b92-5787-479c-9888-dfaa5d8861a8' class='xr-index-data-in' type='checkbox'/><label for='index-1a809b92-5787-479c-9888-dfaa5d8861a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
+       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
+       "       ...\n",
+       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
+       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3e883895-aab9-4d21-b033-8af33c13c665' class='xr-index-data-in' type='checkbox'/><label for='index-3e883895-aab9-4d21-b033-8af33c13c665' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b7d0355c-b32f-4923-a112-681779350712' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7d0355c-b32f-4923-a112-681779350712' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'temp' (month: 12, k: 17, latitude: 723, longitude: 491)> Size: 290MB\n",
+       "array([[[[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ..., 19.23143 , 19.228935,\n",
+       "          19.217936],\n",
+       "         [      nan,       nan,       nan, ..., 19.219215, 19.224407,\n",
+       "          19.191824],\n",
+       "         ...,\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan]],\n",
+       "\n",
+       "        [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ..., 19.755375, 19.752344,\n",
+       "          19.739822],\n",
+       "         [      nan,       nan,       nan, ..., 19.745625, 19.747873,\n",
+       "          19.714113],\n",
+       "...\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan]],\n",
+       "\n",
+       "        [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ..., 22.89508 , 22.895288,\n",
+       "          22.894762],\n",
+       "         [      nan,       nan,       nan, ..., 22.902071, 22.89537 ,\n",
+       "          22.898825],\n",
+       "         ...,\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan],\n",
+       "         [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "                nan]]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 6kB -28.7 -28.67 -28.64 ... -7.066 -7.036\n",
+       "  * longitude  (longitude) float64 4kB 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
+       "    zc         (k) float64 136B ...\n",
+       "  * month      (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Dimensions without coordinates: k\n",
+       "Attributes:\n",
+       "    aggregation:            Daily\n",
+       "    long_name:              Temperature\n",
+       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
+       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
+       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
+       "    short_name:             temp\n",
+       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
+       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
+       "    units:                  degrees C"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculating monthly means\n",
+    "temp_monthly_2022 = temp_2022.groupby(\"time.month\").mean()\n",
+    "\n",
+    "# Checking results\n",
+    "temp_monthly_2022"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the `time` dimension has been replaced by `month`, which includes 12 time steps, one for each month of the year. We can now plot all monthly means for 2022, at a particular depth.\n",
+    "\n",
+    "Using the `plot` function, specify `month` in the `col` argument, which will create a subplot for each month in our data. Finally, we use the `col_wrap` argument to specify the number of columns in our plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x7a5191874500>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x900 with 13 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting monthly means\n",
+    "temp_monthly_2022.sel(k=16).plot(col='month', col_wrap=4)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Timeseries plotting for a particular location\n",
+    "\n",
+    "For this example, we will select a single point in the dataset and extract mean wind speed (`mean_wspeed`) values for a particular timeframe to plot a time series. We will use the `sel()` function to select the point nearest to `latitude` = -20 and `longitude` = 152. We will then subset the data temporally, and plot.  \n",
+    "  \n",
+    "We can do all this for the first 10 months of 2023 using the following line of code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7a51801f9d30>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the means of wind speed on a location of this year (2023)\n",
+    "data.sel(latitude=-20, longitude=152, method='nearest')\\\n",
+    "    .sel(time=slice('2023-01-01', '2023-10-24'))\\\n",
+    "    .mean_wspeed.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also do this for the month of September for 2023."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7a5167fde270>]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the means of current speed on a location of last month (September 2023)\n",
+    "data.sel(latitude=-20, longitude=152, method='nearest')\\\n",
+    "    .sel(time=slice('2023-09-01', '2023-09-30'), k=0)\\\n",
+    "    .mean_cur.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Save the data as a CSV\n",
+    "\n",
+    "Using the `to_dataframe()` and `to_csv` functions, you can the data as a csv file. The following line of code uses the `mean_cur` from the first week of October (from Section 4) as an example. The same line can be altered with any of the subsetted data we went over (e.g., `temp_monthly_2022`) to save these as well. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_current_20231007.to_dataframe().to_csv(\"eReef_mean_current_20231007.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "ac4bbee1919ea7aef9c6cfd22a25c3c68d6ebab6b5f3e31fb17a356d6f400124"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Python_based_scripts/02-Extracting_Spatial_Data_GBR_Features.ipynb b/Python_based_scripts/02-Extracting_Spatial_Data_GBR_Features.ipynb
new file mode 100644
index 0000000..ab762c4
--- /dev/null
+++ b/Python_based_scripts/02-Extracting_Spatial_Data_GBR_Features.ipynb
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting spatial data for Great Barrier Reef features\n",
+    "**Author**: Denisse Fierro Arcos  \n",
+    "  \n",
+    "This notebook will show how to access the RIMReP `geoparquet` collection for Great Barrier Reef (GBR) Feature from the Great Barrier Reef Marine Park Authority (GBRMPA). This dataset includes the unique IDs and names of all features above water, including sand banks, reefs, cays, islets, and islands. Since this dataset includes spatial data, we can extract the spatial limits of each feature included in this dataset.  \n",
+    "  \n",
+    "We will use the spatial boundaries to extract environmental or biological information for sites of our interest.  \n",
+    "  \n",
+    "## Worfklow to be followed in this notebook\n",
+    "1. Connect to RIMReP_DMS and inspect metadata\n",
+    "2. Load GBR features from data collection  \n",
+    "3. Get GBR features geometries to extract information from biological or environmental dataset within an area of interest\n",
+    "4. Export polygons as shapefiles"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Accessing S3 bucket\n",
+    "from pyarrow import parquet as pq\n",
+    "import pyarrow.dataset as ds\n",
+    "import s3fs\n",
+    "# Load and manipulate geospatial data\n",
+    "import dask_geopandas\n",
+    "import geopandas as gpd\n",
+    "# Plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "# Mapping\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cf\n",
+    "# Dealing with operating system, i.e. reading and writing files\n",
+    "import os"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting metadata\n",
+    "To get a better understanding of the GBR features dataset, we will inspect its metadata. This information will also help us determine the best way to load the data into memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TARGET_FID: int64\n",
+      "DATASET: string\n",
+      "LOC_NAME_S: string\n",
+      "GBR_NAME: string\n",
+      "CHART_NAME: string\n",
+      "TRAD_NAME: string\n",
+      "UN_FEATURE: string\n",
+      "LABEL_ID: string\n",
+      "SORT_GBR_I: int64\n",
+      "FEAT_NAME: string\n",
+      "LEVEL_1: string\n",
+      "LEVEL_2: string\n",
+      "LEVEL_3: string\n",
+      "CLASS_CONF: string\n",
+      "CLASS_SRC: string\n",
+      "POLY_ORIG: string\n",
+      "IMG_SRC: string\n",
+      "SUB_NO: int64\n",
+      "CODE: string\n",
+      "FEATURE_C: int64\n",
+      "QLD_NAME: string\n",
+      "X_LABEL: string\n",
+      "GBR_ID: string\n",
+      "LOC_NAME_L: string\n",
+      "X_COORD: double\n",
+      "Y_COORD: double\n",
+      "SHAPE_AREA: double\n",
+      "SHAPE_LEN: double\n",
+      "Checked: string\n",
+      "RegionID: int64\n",
+      "LatitudeID: int64\n",
+      "GroupID: int64\n",
+      "PriorityLb: string\n",
+      "Country: string\n",
+      "UNIQUE_ID: string\n",
+      "geometry: binary\n",
+      "minx: double\n",
+      "miny: double\n",
+      "maxx: double\n",
+      "maxy: double\n",
+      "fid: int64\n",
+      "-- schema metadata --\n",
+      "pandas: '{\"index_columns\": [\"fid\"], \"column_indexes\": [{\"name\": null, \"fi' + 4911\n",
+      "geo: '{\"primary_column\": \"geometry\", \"columns\": {\"geometry\": {\"encoding\":' + 1142\n",
+      "Number of columns:  41\n",
+      "Number of rows:  9612\n",
+      "Variables: ['TARGET_FID', 'DATASET', 'LOC_NAME_S', 'GBR_NAME', 'CHART_NAME', 'TRAD_NAME', 'UN_FEATURE', 'LABEL_ID', 'SORT_GBR_I', 'FEAT_NAME', 'LEVEL_1', 'LEVEL_2', 'LEVEL_3', 'CLASS_CONF', 'CLASS_SRC', 'POLY_ORIG', 'IMG_SRC', 'SUB_NO', 'CODE', 'FEATURE_C', 'QLD_NAME', 'X_LABEL', 'GBR_ID', 'LOC_NAME_L', 'X_COORD', 'Y_COORD', 'SHAPE_AREA', 'SHAPE_LEN', 'Checked', 'RegionID', 'LatitudeID', 'GroupID', 'PriorityLb', 'Country', 'UNIQUE_ID', 'geometry', 'minx', 'miny', 'maxx', 'maxy', 'fid']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Connecting to metadata file\n",
+    "## this is the location of the parquet file in the DMS repository\n",
+    "uri = \"s3://gbr-dms-data-public/gbrmpa-complete-gbr-features/data.parquet\"\n",
+    "dataset = ds.dataset(uri, format=\"parquet\")\n",
+    "print(dataset.schema)\n",
+    "print('Number of columns: ', len(dataset.schema))\n",
+    "print('Number of rows: ', dataset.count_rows())\n",
+    "print('Variables:', dataset.schema.names)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata is telling us that this dataset contains 41 columns and 9,612 rows. Each row represents a unique feature in the GBR.\n",
+    "  \n",
+    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `schema` not only gives the names of the columns included in the GBR dataset, but it also tells us the data types included in each column (e.g., `string` or `double`).  \n",
+    "  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will need to access four variables to create a list of all above water sites in the Great Barrier Reef Marine Park:  \n",
+    "- `UNIQUE_ID`, which includes a unique identification number for each area above water  \n",
+    "- `GBR_NAME`, which includes the name of each location above water  \n",
+    "- `LOC_NAME_S`, which is the combination between feature name and unique ID  \n",
+    "- `geometry`, which includes latitude and longitude coordinates  \n",
+    "  \n",
+    "**Note**: One location (`GBR_NAME`) may contain multiple values under the `UNIQUE_ID` column. This is because above water structures may not appear connected when looking at the surface, but they are part of the same structure and thus are connected under water."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to S3 parquet directory\n",
+    "We will connect to the S3 bucket containing the GBR features dataset using the `dask_geopandas` library. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UNIQUE_ID</th>\n",
+       "      <th>GBR_NAME</th>\n",
+       "      <th>LOC_NAME_S</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>09347110101</td>\n",
+       "      <td>U/N Sand Bank</td>\n",
+       "      <td>U/N Sand Bank (09-347a)</td>\n",
+       "      <td>POLYGON ((143.08510 -9.26657, 143.08702 -9.267...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>09347110100</td>\n",
+       "      <td>U/N Sand Bank</td>\n",
+       "      <td>U/N Sand Bank (09-347)</td>\n",
+       "      <td>POLYGON ((143.05491 -9.29340, 143.05465 -9.294...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>09355110126</td>\n",
+       "      <td>U/N Sand Bank</td>\n",
+       "      <td>U/N Sand Bank (09-355z)</td>\n",
+       "      <td>POLYGON ((143.10563 -9.39080, 143.10543 -9.390...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>09355110127</td>\n",
+       "      <td>U/N Sand Bank</td>\n",
+       "      <td>U/N Sand Bank (09-355a0)</td>\n",
+       "      <td>POLYGON ((143.09853 -9.39008, 143.09812 -9.390...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>09355110128</td>\n",
+       "      <td>U/N Sand Bank</td>\n",
+       "      <td>U/N Sand Bank (09-355a1)</td>\n",
+       "      <td>POLYGON ((143.09389 -9.39146, 143.09360 -9.391...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       UNIQUE_ID       GBR_NAME                LOC_NAME_S  \\\n",
+       "fid                                                         \n",
+       "0    09347110101  U/N Sand Bank   U/N Sand Bank (09-347a)   \n",
+       "1    09347110100  U/N Sand Bank    U/N Sand Bank (09-347)   \n",
+       "2    09355110126  U/N Sand Bank   U/N Sand Bank (09-355z)   \n",
+       "3    09355110127  U/N Sand Bank  U/N Sand Bank (09-355a0)   \n",
+       "4    09355110128  U/N Sand Bank  U/N Sand Bank (09-355a1)   \n",
+       "\n",
+       "                                              geometry  \n",
+       "fid                                                     \n",
+       "0    POLYGON ((143.08510 -9.26657, 143.08702 -9.267...  \n",
+       "1    POLYGON ((143.05491 -9.29340, 143.05465 -9.294...  \n",
+       "2    POLYGON ((143.10563 -9.39080, 143.10543 -9.390...  \n",
+       "3    POLYGON ((143.09853 -9.39008, 143.09812 -9.390...  \n",
+       "4    POLYGON ((143.09389 -9.39146, 143.09360 -9.391...  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Connecting to the S3 bucket where the GBR data is stored\n",
+    "dask_geo_df = dask_geopandas.read_parquet(uri,\n",
+    "                                          #Specifying which columns to read\n",
+    "                                          columns = ['UNIQUE_ID', 'GBR_NAME', 'LOC_NAME_S', 'geometry'],\n",
+    "                                          #Specifying the column to be used as index\n",
+    "                                          index = 'fid',\n",
+    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
+    "                                          storage_options = {\"anon\": True}) \n",
+    "\n",
+    "# Transforming to pandas dataframe\n",
+    "sites = dask_geo_df.compute()\n",
+    "\n",
+    "# Checking the contents of the dataframe - First five rows\n",
+    "sites.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When exploring the GBR features dataset, you may have noticed other fields besides `geometry` that contain spatial information. For example, `X_COORD` and `Y_COORD` contain the coordinates of the centroid of each feature in the GBR dataset. While, `minx`, `miny`, `maxx`, and `maxy` fields contain the coordinates for the lower left corner and the upper right corner of a feature. This is otherwise known as the extent, which is a rectangle covering the maximum spatial extent of a feature.  \n",
+    "  \n",
+    "We chose to use the `geometry` field in this notebook because it gives us access to fine scale data about a feature (see light blue line in image below). The centroid (`X_COORD` and `Y_COORD` fields) would have only given information about a single point (white dot at the centre of the image). The extent (dark blue rectangle), on the other hand, would have coarsen the resolution of a feature.\n",
+    "\n",
+    "![title](../images/extent_geom.jpeg)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting all features\n",
+    "We will make a map of all features included within the Great Barrier Reef Marine Protected Area in green so they are easily recognised. Note that the GBR features also includes a `Mainland` location, which represents the state of Queensland. We will show this state as light red."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7a7c24f6fc50>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Initiliasing the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "#Adding a plot to the figure and assigning projection\n",
+    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
+    "\n",
+    "#Plotting all sites in green\n",
+    "sites.plot(ax = ax, alpha = 0.5, color = 'green')\n",
+    "\n",
+    "#Plotting mainland in red\n",
+    "sites[sites.LOC_NAME_S.str.contains('Mainland')].plot(ax = ax, color = 'red', alpha = 0.5)\n",
+    "\n",
+    "#Adding a basemap of land areas\n",
+    "ax.add_feature(cf.LAND)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving GBR features as shapefile\n",
+    "We will need to provide a path to the folder where we want to save our shapefile containing the limits of all features within the GBR Marine Protected Area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Checking if Outputs folder exists, if not, creates it\n",
+    "os.makedirs('Outputs', exist_ok = True)\n",
+    "\n",
+    "#Save output\n",
+    "sites.to_file('Outputs/GBR_features.shp')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That is the end of this notebook. We have demonstrated how to extract data from the Complete GBR Features dataset, how to plot it and save it locally.  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rimrep",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Python_based_scripts/03-extract_NOAA-DHW.ipynb b/Python_based_scripts/03-extract_NOAA-DHW.ipynb
new file mode 100644
index 0000000..685006c
--- /dev/null
+++ b/Python_based_scripts/03-extract_NOAA-DHW.ipynb
@@ -0,0 +1,1861 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `Zarr` direct access example\n",
+    "\n",
+    "**Author**: Nick Forbes-Smith, Denisse Fierro Arcos  \n",
+    "  \n",
+    "This notebook demonstrates how to access a public dataset from the RIMReP collection using `zarr`. We will use the [NOAA CRW degree heating weeks](https://www.coris.noaa.gov/search/catalog/search/resource/details.page?uuid=%7BF77EF0B8-C12F-463F-B66A-CC922E50A39D%7D) dataset as an example.  \n",
+    "  \n",
+    "We will also include examples of how to make simple calculations to produce maps and timeseries."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading and manipulating gridded multidimensional data\n",
+    "import xarray as xr\n",
+    "# Accessing S3 buckets\n",
+    "import s3fs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to S3 bucket and loading dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 165GB\n",
+       "Dimensions:              (time: 14302, lat: 900, lon: 1600)\n",
+       "Coordinates:\n",
+       "  * lat                  (lat) float32 4kB -5.025 -5.075 ... -49.92 -49.97\n",
+       "  * lon                  (lon) float32 6kB 100.0 100.1 100.1 ... 179.9 180.0\n",
+       "  * time                 (time) datetime64[ns] 114kB 1985-03-25T12:00:00 ... ...\n",
+       "Data variables:\n",
+       "    crs                  int16 2B ...\n",
+       "    degree_heating_week  (time, lat, lon) float32 82GB ...\n",
+       "    mask                 (time, lat, lon) float32 82GB ...\n",
+       "Attributes: (12/59)\n",
+       "    Conventions:                CF-1.6, ACDD-1.3\n",
+       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
+       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
+       "    cdm_data_type:              Grid\n",
+       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
+       "    contributor_name:           NOAA Coral Reef Watch Program\n",
+       "    ...                         ...\n",
+       "    time_coverage_duration:     P1D\n",
+       "    time_coverage_end:          20240521T000000Z\n",
+       "    time_coverage_resolution:   P1D\n",
+       "    time_coverage_start:        20240520T000000Z\n",
+       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
+       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f</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-f1c37385-b5b2-4072-b391-cbb2b3fa2c0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1c37385-b5b2-4072-b391-cbb2b3fa2c0e' 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>: 14302</li><li><span class='xr-has-index'>lat</span>: 900</li><li><span class='xr-has-index'>lon</span>: 1600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ffee8b0a-7273-44f4-8242-11bcf7502fc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffee8b0a-7273-44f4-8242-11bcf7502fc6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.025 -5.075 ... -49.92 -49.97</div><input id='attrs-0d7e353d-ddd2-46f8-9b77-db4adb225edb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d7e353d-ddd2-46f8-9b77-db4adb225edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c3e7af-3a1d-4b6a-8c44-5c5149e35e48' class='xr-var-data-in' type='checkbox'><label for='data-89c3e7af-3a1d-4b6a-8c44-5c5149e35e48' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -5.025,  -5.075,  -5.125, ..., -49.875, -49.925, -49.975],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>100.0 100.1 100.1 ... 179.9 180.0</div><input id='attrs-009dbe3b-ee77-47bc-8e39-f1c88aeeb370' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-009dbe3b-ee77-47bc-8e39-f1c88aeeb370' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102c6609-3533-494a-89c3-a7043f8295a1' class='xr-var-data-in' type='checkbox'><label for='data-102c6609-3533-494a-89c3-a7043f8295a1' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([100.025, 100.075, 100.125, ..., 179.875, 179.925, 179.975],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1985-03-25T12:00:00 ... 2024-05-...</div><input id='attrs-d4f4d633-04aa-45f0-8c64-80daab3a264c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4f4d633-04aa-45f0-8c64-80daab3a264c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-078d7e1d-afd4-4eac-a354-b0c5782faad7' class='xr-var-data-in' type='checkbox'><label for='data-078d7e1d-afd4-4eac-a354-b0c5782faad7' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1985-03-25T12:00:00.000000000&#x27;, &#x27;1985-03-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1985-03-27T12:00:00.000000000&#x27;, ..., &#x27;2024-05-19T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-20T12:00:00.000000000&#x27;, &#x27;2024-05-21T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcd1d88d-0a25-49f8-984c-77516e2b6552' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcd1d88d-0a25-49f8-984c-77516e2b6552' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3db2e414-e2c8-4ee5-ba80-a0a4ccf907ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3db2e414-e2c8-4ee5-ba80-a0a4ccf907ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-739c8eac-0ce3-4c77-94b1-c0c9a5a3b687' class='xr-var-data-in' type='checkbox'><label for='data-739c8eac-0ce3-4c77-94b1-c0c9a5a3b687' 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>comment :</span></dt><dd>This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.</dd><dt><span>epsg_code :</span></dt><dd>EPSG:32663</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.2572021484375</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree_heating_week</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'>...</div><input id='attrs-5ff31099-0644-45ac-a7e3-2cd5e7d3cc08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ff31099-0644-45ac-a7e3-2cd5e7d3cc08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfce000c-b442-4e50-a4cf-ebb1e2f59de2' class='xr-var-data-in' type='checkbox'><label for='data-cfce000c-b442-4e50-a4cf-ebb1e2f59de2' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[20594880000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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'>...</div><input id='attrs-997364be-a1d3-4dde-93c0-720e13ae7948' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-997364be-a1d3-4dde-93c0-720e13ae7948' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f259d82-4898-4c4e-b7a9-fd679d827970' class='xr-var-data-in' type='checkbox'><label for='data-0f259d82-4898-4c4e-b7a9-fd679d827970' 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>comment :</span></dt><dd>A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.</dd><dt><span>coverage_content_type :</span></dt><dd>thematicClassification</dd><dt><span>flag_meanings :</span></dt><dd>valid-water land missing ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 4]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>pixel characteristics flag array</dd><dt><span>units :</span></dt><dd>pixel_classification</dd></dl></div><div class='xr-var-data'><pre>[20594880000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4dfe5ee7-c683-48e1-82c5-c824dca13c8f' class='xr-section-summary-in' type='checkbox'  ><label for='section-4dfe5ee7-c683-48e1-82c5-c824dca13c8f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b940337-311f-4ab2-84c3-88424297bfd5' class='xr-index-data-in' type='checkbox'/><label for='index-0b940337-311f-4ab2-84c3-88424297bfd5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -5.025000095367432,  -5.074999809265137,              -5.125,\n",
+       "        -5.175000190734863,  -5.224999904632568,  -5.275000095367432,\n",
+       "        -5.324999809265137,              -5.375,  -5.425000190734863,\n",
+       "        -5.474999904632568,\n",
+       "       ...\n",
+       "       -49.525001525878906,  -49.57500076293945,             -49.625,\n",
+       "        -49.67499923706055, -49.724998474121094, -49.775001525878906,\n",
+       "        -49.82500076293945,             -49.875,  -49.92499923706055,\n",
+       "       -49.974998474121094],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-86865bf7-1103-419a-9d53-e97b9a0a451e' class='xr-index-data-in' type='checkbox'/><label for='index-86865bf7-1103-419a-9d53-e97b9a0a451e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 100.0250015258789, 100.07499694824219,            100.125,\n",
+       "       100.17500305175781,  100.2249984741211,  100.2750015258789,\n",
+       "       100.32499694824219,            100.375, 100.42500305175781,\n",
+       "        100.4749984741211,\n",
+       "       ...\n",
+       "       179.52499389648438,  179.5749969482422,            179.625,\n",
+       "        179.6750030517578, 179.72500610351562, 179.77499389648438,\n",
+       "        179.8249969482422,            179.875,  179.9250030517578,\n",
+       "       179.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa5fce17-acc3-4e56-a879-eb4c7edcd56a' class='xr-index-data-in' type='checkbox'/><label for='index-aa5fce17-acc3-4e56-a879-eb4c7edcd56a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1985-03-25 12:00:00&#x27;, &#x27;1985-03-26 12:00:00&#x27;,\n",
+       "               &#x27;1985-03-27 12:00:00&#x27;, &#x27;1985-03-28 12:00:00&#x27;,\n",
+       "               &#x27;1985-03-29 12:00:00&#x27;, &#x27;1985-03-30 12:00:00&#x27;,\n",
+       "               &#x27;1985-03-31 12:00:00&#x27;, &#x27;1985-04-01 12:00:00&#x27;,\n",
+       "               &#x27;1985-04-02 12:00:00&#x27;, &#x27;1985-04-03 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2024-05-12 12:00:00&#x27;, &#x27;2024-05-13 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-14 12:00:00&#x27;, &#x27;2024-05-15 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-16 12:00:00&#x27;, &#x27;2024-05-17 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-18 12:00:00&#x27;, &#x27;2024-05-19 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-20 12:00:00&#x27;, &#x27;2024-05-21 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=14302, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0eca6e0-fd04-4b32-aea4-f2bd4d166218' class='xr-section-summary-in' type='checkbox'  ><label for='section-f0eca6e0-fd04-4b32-aea4-f2bd4d166218' class='xr-section-summary' >Attributes: <span>(59)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>acknowledgement :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.</dd><dt><span>contributor_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>contributor_role :</span></dt><dd>Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive</dd><dt><span>creator_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>creator_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>creator_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://coralreefwatch.noaa.gov/</dd><dt><span>date_created :</span></dt><dd>20180301T120000Z</dd><dt><span>date_issued :</span></dt><dd>20240521T141758Z</dd><dt><span>date_metadata_modified :</span></dt><dd>20201118T120000Z</dd><dt><span>date_modified :</span></dt><dd>20180301T120000Z</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.9749984741211</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.9749984741211</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.97500610351562</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.97500610351562</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Tue May 21 16:17:41 2024: ncks --overwrite -d lat,-50.0,-5.0 -d lon,100.0,180.0 ct5km_dhw_v3.1_20240520.nc ct5km_dhw_v3.1_20240520.nc\n",
+       "This is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).</dd><dt><span>id :</span></dt><dd>Satellite_Daily_Global_5km_Degree_Heating_Week</dd><dt><span>institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>instrument :</span></dt><dd>ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake</dd><dt><span>instrument_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Instruments</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Water Temperature,  Spectral/Engineering &gt; Infrared Wavelengths &gt; Thermal Infrared, Oceans &gt; Ocean Temperature &gt; Degree Heating Week</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.</dd><dt><span>metadata_link :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.coralreefwatch</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>platform :</span></dt><dd>Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.</dd><dt><span>platform_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Platforms</dd><dt><span>processing_level :</span></dt><dd>Derived from L4 satellite sea surface temperaure analysis</dd><dt><span>product_version :</span></dt><dd>3.1</dd><dt><span>program :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>project :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>publisher_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>publisher_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://coralreefwatch.noaa.gov</dd><dt><span>references :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>source :</span></dt><dd>Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>summary :</span></dt><dd>This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_end :</span></dt><dd>20240521T000000Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>20240520T000000Z</dd><dt><span>title :</span></dt><dd>NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week </dd><dt><span>uuid :</span></dt><dd>24c7f597-298c-4be6-a0c2-ad97b993f60f</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 165GB\n",
+       "Dimensions:              (time: 14302, lat: 900, lon: 1600)\n",
+       "Coordinates:\n",
+       "  * lat                  (lat) float32 4kB -5.025 -5.075 ... -49.92 -49.97\n",
+       "  * lon                  (lon) float32 6kB 100.0 100.1 100.1 ... 179.9 180.0\n",
+       "  * time                 (time) datetime64[ns] 114kB 1985-03-25T12:00:00 ... ...\n",
+       "Data variables:\n",
+       "    crs                  int16 2B ...\n",
+       "    degree_heating_week  (time, lat, lon) float32 82GB ...\n",
+       "    mask                 (time, lat, lon) float32 82GB ...\n",
+       "Attributes: (12/59)\n",
+       "    Conventions:                CF-1.6, ACDD-1.3\n",
+       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
+       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
+       "    cdm_data_type:              Grid\n",
+       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
+       "    contributor_name:           NOAA Coral Reef Watch Program\n",
+       "    ...                         ...\n",
+       "    time_coverage_duration:     P1D\n",
+       "    time_coverage_end:          20240521T000000Z\n",
+       "    time_coverage_resolution:   P1D\n",
+       "    time_coverage_start:        20240520T000000Z\n",
+       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
+       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Path to zarr files\n",
+    "zarr_path = \"s3://gbr-dms-data-public/noaa-crw-chs-dhw/data.zarr\"\n",
+    "\n",
+    "\n",
+    "# Connection to public S3 bucket - No credentials needed\n",
+    "s3 = s3fs.S3FileSystem(anon = True)\n",
+    "\n",
+    "# Loading zarr file\n",
+    "data = xr.open_dataset(s3fs.S3Map(zarr_path, s3 = s3), engine = \"zarr\")\n",
+    "\n",
+    "#Checking result\n",
+    "data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the above dataset has three dimensions: latitude (`lat`), longitude (`lon`) and time (`time`). This is a global dataset, with a spatial resolution of 5 km, and a daily temporal resolution between March 1985 and February 2023.  \n",
+    "  \n",
+    "We can see that there are two variables in the dataset: `degree heating week` (DHW) and `mask`. The `DHW` shows accumulated heat stress over a 12-week period, which can lead to coral bleaching and death. The scale ranges from $0^{\\circ}C$ to $20^{\\circ}C$-weeks. The `mask` includes the location of landmasses, grid cells covered by ice, and any grid cells with missing data.\n",
+    "  \n",
+    "We can get more information about each variable included in the dataset by calling their attributes as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'comment': 'A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.',\n",
+       " 'coverage_content_type': 'thematicClassification',\n",
+       " 'flag_meanings': 'valid-water land missing ice',\n",
+       " 'flag_values': [0, 1, 2, 4],\n",
+       " 'grid_mapping': 'crs',\n",
+       " 'long_name': 'pixel characteristics flag array',\n",
+       " 'units': 'pixel_classification'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.mask.attrs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also check the metadata for the entire dataset by calling the dataset attributes as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Conventions': 'CF-1.6, ACDD-1.3',\n",
+       " 'NCO': 'netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)',\n",
+       " 'acknowledgement': 'NOAA Coral Reef Watch Program',\n",
+       " 'cdm_data_type': 'Grid',\n",
+       " 'comment': 'This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.',\n",
+       " 'contributor_name': 'NOAA Coral Reef Watch Program',\n",
+       " 'contributor_role': 'Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive',\n",
+       " 'creator_email': 'coralreefwatch@noaa.gov',\n",
+       " 'creator_institution': 'NOAA/NESDIS/STAR Coral Reef Watch Program',\n",
+       " 'creator_name': 'NOAA Coral Reef Watch Program',\n",
+       " 'creator_type': 'group',\n",
+       " 'creator_url': 'https://coralreefwatch.noaa.gov/',\n",
+       " 'date_created': '20180301T120000Z',\n",
+       " 'date_issued': '20240521T141758Z',\n",
+       " 'date_metadata_modified': '20201118T120000Z',\n",
+       " 'date_modified': '20180301T120000Z',\n",
+       " 'geospatial_bounds': 'POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))',\n",
+       " 'geospatial_bounds_crs': 'EPSG:4326',\n",
+       " 'geospatial_lat_max': 89.9749984741211,\n",
+       " 'geospatial_lat_min': -89.9749984741211,\n",
+       " 'geospatial_lat_resolution': 0.05000000074505806,\n",
+       " 'geospatial_lat_units': 'degrees_north',\n",
+       " 'geospatial_lon_max': 179.97500610351562,\n",
+       " 'geospatial_lon_min': -179.97500610351562,\n",
+       " 'geospatial_lon_resolution': 0.05000000074505806,\n",
+       " 'geospatial_lon_units': 'degrees_east',\n",
+       " 'history': 'Tue May 21 16:17:41 2024: ncks --overwrite -d lat,-50.0,-5.0 -d lon,100.0,180.0 ct5km_dhw_v3.1_20240520.nc ct5km_dhw_v3.1_20240520.nc\\nThis is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).',\n",
+       " 'id': 'Satellite_Daily_Global_5km_Degree_Heating_Week',\n",
+       " 'institution': 'NOAA/NESDIS/STAR Coral Reef Watch Program',\n",
+       " 'instrument': 'ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake',\n",
+       " 'instrument_vocabulary': 'NOAA NODC Ocean Archive System Instruments',\n",
+       " 'keywords': 'Oceans > Ocean Temperature > Water Temperature,  Spectral/Engineering > Infrared Wavelengths > Thermal Infrared, Oceans > Ocean Temperature > Degree Heating Week',\n",
+       " 'keywords_vocabulary': 'NASA Global Change Master Directory (GCMD) Science Keywords',\n",
+       " 'license': 'The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.',\n",
+       " 'metadata_link': 'https://coralreefwatch.noaa.gov/product/5km/index.php',\n",
+       " 'naming_authority': 'gov.noaa.coralreefwatch',\n",
+       " 'ncei_template_version': 'NCEI_NetCDF_Grid_Template_v2.0',\n",
+       " 'platform': 'Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.',\n",
+       " 'platform_vocabulary': 'NOAA NODC Ocean Archive System Platforms',\n",
+       " 'processing_level': 'Derived from L4 satellite sea surface temperaure analysis',\n",
+       " 'product_version': '3.1',\n",
+       " 'program': 'NOAA Coral Reef Watch Program',\n",
+       " 'project': 'NOAA Coral Reef Watch Program',\n",
+       " 'publisher_email': 'coralreefwatch@noaa.gov',\n",
+       " 'publisher_institution': 'NOAA/NESDIS/STAR Coral Reef Watch Program',\n",
+       " 'publisher_name': 'NOAA Coral Reef Watch Program',\n",
+       " 'publisher_type': 'group',\n",
+       " 'publisher_url': 'https://coralreefwatch.noaa.gov',\n",
+       " 'references': 'https://coralreefwatch.noaa.gov/product/5km/index.php',\n",
+       " 'source': 'Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)',\n",
+       " 'spatial_resolution': '0.05 degrees',\n",
+       " 'standard_name_vocabulary': 'CF Standard Name Table v27',\n",
+       " 'summary': 'This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite',\n",
+       " 'time_coverage_duration': 'P1D',\n",
+       " 'time_coverage_end': '20240521T000000Z',\n",
+       " 'time_coverage_resolution': 'P1D',\n",
+       " 'time_coverage_start': '20240520T000000Z',\n",
+       " 'title': 'NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week ',\n",
+       " 'uuid': '24c7f597-298c-4be6-a0c2-ad97b993f60f'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.attrs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting data\n",
+    "Plotting data is easy with `xarray`, we can do it with a single line of code. Below, we will select the first time step of the `DHW` variable and plot a simple map using the `plot` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x72af31678d70>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.degree_heating_week.isel(time = 0).plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple calculations using the entire grid\n",
+    "Below, we will create a new variable that will contain the `DHW` data for 2022. We will then calulate a monthly mean and finally we will plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-border-color: #1F1F1F;\n",
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;degree_heating_week&#x27; (time: 365, lat: 900, lon: 1600)&gt; Size: 2GB\n",
+       "[525600000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 4kB -5.025 -5.075 -5.125 ... -49.88 -49.92 -49.97\n",
+       "  * lon      (lon) float32 6kB 100.0 100.1 100.1 100.2 ... 179.9 179.9 180.0\n",
+       "  * time     (time) datetime64[ns] 3kB 2022-01-01T12:00:00 ... 2022-12-31T12:...\n",
+       "Attributes:\n",
+       "    coverage_content_type:  physicalMeasurement\n",
+       "    grid_mapping:           crs\n",
+       "    long_name:              degree heating week\n",
+       "    standard_name:          N/A\n",
+       "    units:                  degree_Celsius_weeks\n",
+       "    valid_max:              10000\n",
+       "    valid_min:              0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'degree_heating_week'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>lat</span>: 900</li><li><span class='xr-has-index'>lon</span>: 1600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-602678f2-2466-4f5d-9b8c-1dc4e2977a4e' class='xr-array-in' type='checkbox' checked><label for='section-602678f2-2466-4f5d-9b8c-1dc4e2977a4e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[525600000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-a04eef21-b2ec-42ac-9daf-a130472c7ccc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a04eef21-b2ec-42ac-9daf-a130472c7ccc' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.025 -5.075 ... -49.92 -49.97</div><input id='attrs-3589bbe4-a232-405e-840a-3c0a18a16797' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3589bbe4-a232-405e-840a-3c0a18a16797' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e5f5af4-1cd7-44a7-8ee5-0a8e63ba014b' class='xr-var-data-in' type='checkbox'><label for='data-9e5f5af4-1cd7-44a7-8ee5-0a8e63ba014b' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -5.025,  -5.075,  -5.125, ..., -49.875, -49.925, -49.975],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>100.0 100.1 100.1 ... 179.9 180.0</div><input id='attrs-c3a748c2-c8a0-4030-bafb-938b59063f19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3a748c2-c8a0-4030-bafb-938b59063f19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c2f7622-febc-4c5d-92fb-414a25795d0b' class='xr-var-data-in' type='checkbox'><label for='data-6c2f7622-febc-4c5d-92fb-414a25795d0b' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([100.025, 100.075, 100.125, ..., 179.875, 179.925, 179.975],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T12:00:00 ... 2022-12-...</div><input id='attrs-c528283b-5c12-4787-bb3e-87b4f220289e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c528283b-5c12-4787-bb3e-87b4f220289e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c52a4e2e-08f9-4d88-a989-e218b825d3a7' class='xr-var-data-in' type='checkbox'><label for='data-c52a4e2e-08f9-4d88-a989-e218b825d3a7' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T12:00:00.000000000&#x27;, &#x27;2022-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-03T12:00:00.000000000&#x27;, ..., &#x27;2022-12-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-12-30T12:00:00.000000000&#x27;, &#x27;2022-12-31T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46a120c1-2f2c-4b8c-b22e-1ab158884955' class='xr-section-summary-in' type='checkbox'  ><label for='section-46a120c1-2f2c-4b8c-b22e-1ab158884955' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b0f3b88-6e58-4e1a-a490-94434b8555e2' class='xr-index-data-in' type='checkbox'/><label for='index-1b0f3b88-6e58-4e1a-a490-94434b8555e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -5.025000095367432,  -5.074999809265137,              -5.125,\n",
+       "        -5.175000190734863,  -5.224999904632568,  -5.275000095367432,\n",
+       "        -5.324999809265137,              -5.375,  -5.425000190734863,\n",
+       "        -5.474999904632568,\n",
+       "       ...\n",
+       "       -49.525001525878906,  -49.57500076293945,             -49.625,\n",
+       "        -49.67499923706055, -49.724998474121094, -49.775001525878906,\n",
+       "        -49.82500076293945,             -49.875,  -49.92499923706055,\n",
+       "       -49.974998474121094],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3e19835d-8ed5-45d1-b20b-cabc16885084' class='xr-index-data-in' type='checkbox'/><label for='index-3e19835d-8ed5-45d1-b20b-cabc16885084' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 100.0250015258789, 100.07499694824219,            100.125,\n",
+       "       100.17500305175781,  100.2249984741211,  100.2750015258789,\n",
+       "       100.32499694824219,            100.375, 100.42500305175781,\n",
+       "        100.4749984741211,\n",
+       "       ...\n",
+       "       179.52499389648438,  179.5749969482422,            179.625,\n",
+       "        179.6750030517578, 179.72500610351562, 179.77499389648438,\n",
+       "        179.8249969482422,            179.875,  179.9250030517578,\n",
+       "       179.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-672711b5-bd9d-45e6-80e9-9add75fef965' class='xr-index-data-in' type='checkbox'/><label for='index-672711b5-bd9d-45e6-80e9-9add75fef965' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 12:00:00&#x27;, &#x27;2022-01-02 12:00:00&#x27;,\n",
+       "               &#x27;2022-01-03 12:00:00&#x27;, &#x27;2022-01-04 12:00:00&#x27;,\n",
+       "               &#x27;2022-01-05 12:00:00&#x27;, &#x27;2022-01-06 12:00:00&#x27;,\n",
+       "               &#x27;2022-01-07 12:00:00&#x27;, &#x27;2022-01-08 12:00:00&#x27;,\n",
+       "               &#x27;2022-01-09 12:00:00&#x27;, &#x27;2022-01-10 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-22 12:00:00&#x27;, &#x27;2022-12-23 12:00:00&#x27;,\n",
+       "               &#x27;2022-12-24 12:00:00&#x27;, &#x27;2022-12-25 12:00:00&#x27;,\n",
+       "               &#x27;2022-12-26 12:00:00&#x27;, &#x27;2022-12-27 12:00:00&#x27;,\n",
+       "               &#x27;2022-12-28 12:00:00&#x27;, &#x27;2022-12-29 12:00:00&#x27;,\n",
+       "               &#x27;2022-12-30 12:00:00&#x27;, &#x27;2022-12-31 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80809eff-b474-4c28-9546-d0d2ee93231b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80809eff-b474-4c28-9546-d0d2ee93231b' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'degree_heating_week' (time: 365, lat: 900, lon: 1600)> Size: 2GB\n",
+       "[525600000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 4kB -5.025 -5.075 -5.125 ... -49.88 -49.92 -49.97\n",
+       "  * lon      (lon) float32 6kB 100.0 100.1 100.1 100.2 ... 179.9 179.9 180.0\n",
+       "  * time     (time) datetime64[ns] 3kB 2022-01-01T12:00:00 ... 2022-12-31T12:...\n",
+       "Attributes:\n",
+       "    coverage_content_type:  physicalMeasurement\n",
+       "    grid_mapping:           crs\n",
+       "    long_name:              degree heating week\n",
+       "    standard_name:          N/A\n",
+       "    units:                  degree_Celsius_weeks\n",
+       "    valid_max:              10000\n",
+       "    valid_min:              0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subsetting data\n",
+    "dhw_2022 = data.degree_heating_week.sel(time = \"2022\")\n",
+    "\n",
+    "# Checking result\n",
+    "dhw_2022"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now group our 2022 data per calendar month and calculate the monthly mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
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+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 4kB -5.025 -5.075 -5.125 ... -49.88 -49.92 -49.97\n",
+       "  * lon      (lon) float32 6kB 100.0 100.1 100.1 100.2 ... 179.9 179.9 180.0\n",
+       "  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Attributes:\n",
+       "    coverage_content_type:  physicalMeasurement\n",
+       "    grid_mapping:           crs\n",
+       "    long_name:              degree heating week\n",
+       "    standard_name:          N/A\n",
+       "    units:                  degree_Celsius_weeks\n",
+       "    valid_max:              10000\n",
+       "    valid_min:              0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'degree_heating_week'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 900</li><li><span class='xr-has-index'>lon</span>: 1600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-03388bd6-c65b-4b5b-a2e6-b9ec6a99e974' class='xr-array-in' type='checkbox' checked><label for='section-03388bd6-c65b-4b5b-a2e6-b9ec6a99e974' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b2ecc792-da34-494c-8bfa-a65b4a063df6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2ecc792-da34-494c-8bfa-a65b4a063df6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.025 -5.075 ... -49.92 -49.97</div><input id='attrs-1f8d537d-ddf0-407a-a5e2-8e05a58fdf0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f8d537d-ddf0-407a-a5e2-8e05a58fdf0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a0112f5-a7b9-420b-a39e-474bf2a78e38' class='xr-var-data-in' type='checkbox'><label for='data-0a0112f5-a7b9-420b-a39e-474bf2a78e38' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -5.025,  -5.075,  -5.125, ..., -49.875, -49.925, -49.975],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>100.0 100.1 100.1 ... 179.9 180.0</div><input id='attrs-5166dd92-a36d-4665-8901-2a538c2b757b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5166dd92-a36d-4665-8901-2a538c2b757b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f54dba-0c3c-4b31-8596-edddfa70181d' class='xr-var-data-in' type='checkbox'><label for='data-42f54dba-0c3c-4b31-8596-edddfa70181d' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([100.025, 100.075, 100.125, ..., 179.875, 179.925, 179.975],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-5131c5fe-0819-40c5-b69e-222eee68a249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5131c5fe-0819-40c5-b69e-222eee68a249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f7565e-d8f8-4b00-a177-fedb42c9f980' class='xr-var-data-in' type='checkbox'><label for='data-36f7565e-d8f8-4b00-a177-fedb42c9f980' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ddec254-93e5-4378-a8a8-22effd771784' class='xr-section-summary-in' type='checkbox'  ><label for='section-1ddec254-93e5-4378-a8a8-22effd771784' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb46536f-a174-4aa5-bc5a-bca084c83aa1' class='xr-index-data-in' type='checkbox'/><label for='index-fb46536f-a174-4aa5-bc5a-bca084c83aa1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -5.025000095367432,  -5.074999809265137,              -5.125,\n",
+       "        -5.175000190734863,  -5.224999904632568,  -5.275000095367432,\n",
+       "        -5.324999809265137,              -5.375,  -5.425000190734863,\n",
+       "        -5.474999904632568,\n",
+       "       ...\n",
+       "       -49.525001525878906,  -49.57500076293945,             -49.625,\n",
+       "        -49.67499923706055, -49.724998474121094, -49.775001525878906,\n",
+       "        -49.82500076293945,             -49.875,  -49.92499923706055,\n",
+       "       -49.974998474121094],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b2541e7-186a-426f-aff8-05fc4e1c6d1e' class='xr-index-data-in' type='checkbox'/><label for='index-3b2541e7-186a-426f-aff8-05fc4e1c6d1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 100.0250015258789, 100.07499694824219,            100.125,\n",
+       "       100.17500305175781,  100.2249984741211,  100.2750015258789,\n",
+       "       100.32499694824219,            100.375, 100.42500305175781,\n",
+       "        100.4749984741211,\n",
+       "       ...\n",
+       "       179.52499389648438,  179.5749969482422,            179.625,\n",
+       "        179.6750030517578, 179.72500610351562, 179.77499389648438,\n",
+       "        179.8249969482422,            179.875,  179.9250030517578,\n",
+       "       179.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85602eaa-90d3-476b-86db-3eee024bbc7b' class='xr-index-data-in' type='checkbox'/><label for='index-85602eaa-90d3-476b-86db-3eee024bbc7b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3e5fadc-a6f8-4c38-bfcc-9d52c73ca245' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3e5fadc-a6f8-4c38-bfcc-9d52c73ca245' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'degree_heating_week' (month: 12, lat: 900, lon: 1600)> Size: 69MB\n",
+       "array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 4kB -5.025 -5.075 -5.125 ... -49.88 -49.92 -49.97\n",
+       "  * lon      (lon) float32 6kB 100.0 100.1 100.1 100.2 ... 179.9 179.9 180.0\n",
+       "  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Attributes:\n",
+       "    coverage_content_type:  physicalMeasurement\n",
+       "    grid_mapping:           crs\n",
+       "    long_name:              degree heating week\n",
+       "    standard_name:          N/A\n",
+       "    units:                  degree_Celsius_weeks\n",
+       "    valid_max:              10000\n",
+       "    valid_min:              0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculating monthly means\n",
+    "dhw_monthly_2022 = dhw_2022.groupby(\"time.month\").mean()\n",
+    "\n",
+    "# Checking results\n",
+    "dhw_monthly_2022"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the `time` dimension has been replaced by `month`, which includes 12 time step, one for each month of the year. We can now plot all monthly means for 2022. We use the `plot` method, specify `month` in the `col` argument, which will create a subplot for each month in our data. Finally, we use the `col_wrap` argument to specify the number of columns in our plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x72af3185c5c0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x900 with 13 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting monthly means\n",
+    "dhw_monthly_2022.plot(col = 'month', col_wrap = 4)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple calculations at a single point over time\n",
+    "For this example, we will select a single point in the dataset and extract `DHW` values for all timesteps at that point to plot a time series. We will use the `sel` method to select the point nearest to `lat` = -20 and `lon` = 155. We will then subset the data temporally between 2020-01-01 and 2023-01-01, and plot it.  \n",
+    "  \n",
+    "We can do all this in a single line of code as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x72af242db920>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.degree_heating_week.sel(lat = -20, lon = 155, method = 'nearest').\\\n",
+    "    sel(time = slice('2020-01-01', '2023-01-01')).plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating time series over entire grid\n",
+    "We can also calculate time series for the entire grid. Below, we will show how to calculate the maximum `DHW` values for each pixel over 2022 summer months. We will then plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x72af1c5201d0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Subsetting data for the period between 2020 and 2022\n",
+    "dhw_sub = data.degree_heating_week.sel(time = slice('2022-01-01', '2022-04-30'))\n",
+    "\n",
+    "# Calculting mean at each time step and plot results\n",
+    "dhw_sub.max(dim='time').plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "ac4bbee1919ea7aef9c6cfd22a25c3c68d6ebab6b5f3e31fb17a356d6f400124"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Python_based_scripts/04-AIMS_waterTemp.ipynb b/Python_based_scripts/04-AIMS_waterTemp.ipynb
new file mode 100644
index 0000000..0bc056f
--- /dev/null
+++ b/Python_based_scripts/04-AIMS_waterTemp.ipynb
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AIMS ocean temperature dataset\n",
+    "Authors: Leo Ghignone, Denisse Fierro Arcos  \n",
+    "  \n",
+    "This notebook demonstrates direct access to the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset in the RIMReP `geoparquet` collection. This dataset includes sea temperature data from 1980 for tropical and subtropical coral reefs around Australia, and it includes sites within the Great Barrier Reef.   \n",
+    "  \n",
+    "The `geoparquet` collection is a compilation of datasets that have been converted to the Apache `parquet` format. This format is designed for efficient storage and retrieval of tabular data. The `geoparquet` collection is stored on the RIMReP S3 bucket and is accessible to all users.  \n",
+    "  \n",
+    "The workflow in this notebook is as follows:\n",
+    "- Inspect dataset metadata\n",
+    "- Subset the dataset to include sites within the Great Barrier Reef (GBR)\n",
+    "- Aggregate temperature data per site\n",
+    "- Create a map with the location of all sites where GBR temperature loggers were deployed"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Accessing S3 bucket\n",
+    "from pyarrow import parquet as pq\n",
+    "import pyarrow.dataset as ds\n",
+    "# Manipulating dataframes\n",
+    "import pandas as pd\n",
+    "# Load and manipulate geospatial data\n",
+    "import dask_geopandas\n",
+    "# Plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "# Mapping\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cf"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting metadata\n",
+    "The AIMS Temperature dataset contains over 150 million records. We will use its metadata to check the structure of the dataset and to determine the best way to access the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "deployment_id: int64\n",
+      "site: string\n",
+      "site_id: int64\n",
+      "subsite: string\n",
+      "subsite_id: int64\n",
+      "from_date: timestamp[ns]\n",
+      "thru_date: timestamp[ns]\n",
+      "depth: double\n",
+      "parameter: string\n",
+      "instrument_type: string\n",
+      "serial_num: string\n",
+      "lat: double\n",
+      "lon: double\n",
+      "gbrmpa_reef_id: string\n",
+      "metadata_uuid: string\n",
+      "sites_with_climatology_available: int64\n",
+      "time: timestamp[ns, tz=UTC]\n",
+      "cal_val: double\n",
+      "qc_val: double\n",
+      "qc_flag: int64\n",
+      "geometry: binary\n",
+      "fid: string\n",
+      "-- schema metadata --\n",
+      "pandas: '{\"index_columns\": [\"fid\"], \"column_indexes\": [{\"name\": null, \"fi' + 2827\n",
+      "geo: '{\"primary_column\": \"geometry\", \"columns\": {\"geometry\": {\"encoding\":' + 174\n",
+      "Number of columns:  22\n",
+      "Number of rows:  176703852\n",
+      "Variables: ['deployment_id', 'site', 'site_id', 'subsite', 'subsite_id', 'from_date', 'thru_date', 'depth', 'parameter', 'instrument_type', 'serial_num', 'lat', 'lon', 'gbrmpa_reef_id', 'metadata_uuid', 'sites_with_climatology_available', 'time', 'cal_val', 'qc_val', 'qc_flag', 'geometry', 'fid']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Connecting to metadata file\n",
+    "uri = \"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\"\n",
+    "dataset = ds.dataset(uri, format=\"parquet\")\n",
+    "#Checking contents\n",
+    "print(dataset.schema)\n",
+    "print('Number of columns: ', len(dataset.schema))\n",
+    "print('Number of rows: ', dataset.count_rows())\n",
+    "print('Variables:', dataset.schema.names)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata is telling us that the AIMS temperature dataset contains 22 columns and over 170 million rows.  \n",
+    "  \n",
+    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `schema` tells us that the dataset contains 23 columns, which matches the information we got from the metadata. It also tells us that the data types are either `string` or `double`.  \n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we refer to the original site for the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e), we can see that the `qc_val` column refers to the quality controlled temperature obtained from the loggers deployed as part of this program."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to S3 parquet directory\n",
+    "We will connect to the S3 bucket containing the AIMS Temperature dataset using the `pyarrow` library. We will only keep sites that have been deployed in the Great Barrier Reef Marine Protected Area (GBRMPA), so we will provide a `filter` that contains the minimum and maximum coordinates (i.e., `lat` and `lon`) of a bounding box around the Great Barrier Reef Marine Protected Area.\n",
+    "\n",
+    "As we saw above, this dataset includes 23 columns, but we will only select a few that contain information that is interesting to us:\n",
+    "- `site`: name of site where temperature logger was deployed,\n",
+    "- `lat`: latitude of the logger deployment site,\n",
+    "- `lon`: longitude of the logger deployment site,\n",
+    "- `time`: date and time when loggers recorded temperature data,\n",
+    "- `qc_val`: quality controlled temperature data\n",
+    "\n",
+    "Let's get data from a few reef sites near Townsville: Geoffrey Bay, John Brewer, and Davies Reef. We will filter the data to include only these sites.  \n",
+    "The following code chunk may take several minutes to run, as it is loading over 170 million records. The DMS team is currently working on optimizing the data partitioning strategy to make it faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>site</th>\n",
+       "      <th>subsite</th>\n",
+       "      <th>time</th>\n",
+       "      <th>qc_val</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1991-923-2786</th>\n",
+       "      <td>Geoffrey Bay</td>\n",
+       "      <td>GBSL2</td>\n",
+       "      <td>1991-11-20 14:00:00+00:00</td>\n",
+       "      <td>28.58</td>\n",
+       "      <td>-19.155</td>\n",
+       "      <td>146.865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-923-2786</th>\n",
+       "      <td>Geoffrey Bay</td>\n",
+       "      <td>GBSL2</td>\n",
+       "      <td>1991-12-07 13:00:00+00:00</td>\n",
+       "      <td>29.79</td>\n",
+       "      <td>-19.155</td>\n",
+       "      <td>146.865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-923-2786</th>\n",
+       "      <td>Geoffrey Bay</td>\n",
+       "      <td>GBSL2</td>\n",
+       "      <td>1991-12-07 14:00:00+00:00</td>\n",
+       "      <td>29.9</td>\n",
+       "      <td>-19.155</td>\n",
+       "      <td>146.865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-923-2786</th>\n",
+       "      <td>Geoffrey Bay</td>\n",
+       "      <td>GBSL2</td>\n",
+       "      <td>1991-12-07 15:00:00+00:00</td>\n",
+       "      <td>29.96</td>\n",
+       "      <td>-19.155</td>\n",
+       "      <td>146.865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-923-2786</th>\n",
+       "      <td>Geoffrey Bay</td>\n",
+       "      <td>GBSL2</td>\n",
+       "      <td>1991-12-07 16:00:00+00:00</td>\n",
+       "      <td>29.92</td>\n",
+       "      <td>-19.155</td>\n",
+       "      <td>146.865</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       site subsite                      time  qc_val     lat  \\\n",
+       "fid                                                                             \n",
+       "1991-923-2786  Geoffrey Bay   GBSL2 1991-11-20 14:00:00+00:00   28.58 -19.155   \n",
+       "1991-923-2786  Geoffrey Bay   GBSL2 1991-12-07 13:00:00+00:00   29.79 -19.155   \n",
+       "1991-923-2786  Geoffrey Bay   GBSL2 1991-12-07 14:00:00+00:00    29.9 -19.155   \n",
+       "1991-923-2786  Geoffrey Bay   GBSL2 1991-12-07 15:00:00+00:00   29.96 -19.155   \n",
+       "1991-923-2786  Geoffrey Bay   GBSL2 1991-12-07 16:00:00+00:00   29.92 -19.155   \n",
+       "\n",
+       "                   lon  \n",
+       "fid                     \n",
+       "1991-923-2786  146.865  \n",
+       "1991-923-2786  146.865  \n",
+       "1991-923-2786  146.865  \n",
+       "1991-923-2786  146.865  \n",
+       "1991-923-2786  146.865  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Selecting a set of reefs to filter the data\n",
+    "filter = [('site', 'in', ['Geoffrey Bay', 'John Brewer', 'Davies Reef'])]\n",
+    "\n",
+    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
+    "dask_geo_df = dask_geopandas.read_parquet(uri, \n",
+    "                                          #Applying initial bounding box filter\n",
+    "                                          filters = [filter], \n",
+    "                                          #Specifying which columns to read\n",
+    "                                          columns = ['site', 'subsite', 'time', 'qc_val', 'lat', 'lon'],\n",
+    "                                          #Specifying the column to be used as index\n",
+    "                                          index = 'fid',\n",
+    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
+    "                                          storage_options = {\"anon\": True}) \n",
+    "\n",
+    "# Transforming to pandas dataframe\n",
+    "df = dask_geo_df.compute()\n",
+    "\n",
+    "#Checking the contents of the dataframe - First five rows\n",
+    "df.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see above that the `site` is repeated multiple times. This is because the temperature loggers were deployed on multiple occassions at the same site. If we print more rows, you will also see that the coordinates for a particular site vary slightly. This is because the temperature loggers were not deployed at the exact same location each time, but they were all deployed near each other.  \n",
+    "    \n",
+    "Next, we will calculate the mean deployment coordinate, mean temperature, and total number of temperature records for each site. We will use the `groupby` method to group the data by `site_id` and then we will use the `agg` method to perform our calculations.  \n",
+    "  \n",
+    "Note the use of `compute()` at the end of our code. This will trigger our calculations and load the results as a data frame into memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tempMean</th>\n",
+       "      <th>latMean</th>\n",
+       "      <th>lonMean</th>\n",
+       "      <th>dateMin</th>\n",
+       "      <th>dateMax</th>\n",
+       "      <th>number</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>site</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Davies Reef</th>\n",
+       "      <td>26.571945</td>\n",
+       "      <td>-18.819999</td>\n",
+       "      <td>147.651407</td>\n",
+       "      <td>1996-05-02 14:00:00+00:00</td>\n",
+       "      <td>2024-03-03 13:50:00+00:00</td>\n",
+       "      <td>3193598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Geoffrey Bay</th>\n",
+       "      <td>26.027825</td>\n",
+       "      <td>-19.154884</td>\n",
+       "      <td>146.867632</td>\n",
+       "      <td>1991-11-20 14:00:00+00:00</td>\n",
+       "      <td>2024-02-14 05:29:59+00:00</td>\n",
+       "      <td>3278860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>John Brewer</th>\n",
+       "      <td>26.899369</td>\n",
+       "      <td>-18.627434</td>\n",
+       "      <td>147.069898</td>\n",
+       "      <td>1996-04-23 14:00:00+00:00</td>\n",
+       "      <td>2024-02-06 13:50:00+00:00</td>\n",
+       "      <td>2430204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               tempMean    latMean     lonMean                   dateMin  \\\n",
+       "site                                                                       \n",
+       "Davies Reef   26.571945 -18.819999  147.651407 1996-05-02 14:00:00+00:00   \n",
+       "Geoffrey Bay  26.027825 -19.154884  146.867632 1991-11-20 14:00:00+00:00   \n",
+       "John Brewer   26.899369 -18.627434  147.069898 1996-04-23 14:00:00+00:00   \n",
+       "\n",
+       "                               dateMax   number  \n",
+       "site                                             \n",
+       "Davies Reef  2024-03-03 13:50:00+00:00  3193598  \n",
+       "Geoffrey Bay 2024-02-14 05:29:59+00:00  3278860  \n",
+       "John Brewer  2024-02-06 13:50:00+00:00  2430204  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Grouping our dataframe by site_id and aggregating the lat and lon columns by mean and the site_id column by count\n",
+    "counts = df.groupby('site').agg(\n",
+    "    #Calculating mean temperature\n",
+    "    tempMean = pd.NamedAgg(column = 'qc_val', aggfunc = 'mean'),\n",
+    "    #Calculating mean lat and lon\n",
+    "    latMean = pd.NamedAgg(column = 'lat', aggfunc = 'mean'),\n",
+    "    lonMean = pd.NamedAgg(column = 'lon', aggfunc = 'mean'),\n",
+    "    # #Calculating range of time when data was collected\n",
+    "    dateMin = pd.NamedAgg(column = 'time', aggfunc = 'min'),\n",
+    "    dateMax = pd.NamedAgg(column = 'time', aggfunc = 'max'), \n",
+    "    #Calculating the number of rows (i.e., deployments) per site_id\n",
+    "    number = pd.NamedAgg(column = 'site', aggfunc = 'count'))\n",
+    "\n",
+    "#Checking the contents of the dataframe - First five rows\n",
+    "counts.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mapping sites\n",
+    "From the summary table we created in the previous step, we will make a map with the location of all deployment sites within the GBRMPA bounding box. We will change the size of the markers based on the number of temperature records collected at each site (`number` column)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x72eeeb2951c0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Initiliasing the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "#Adding a plot to the figure and assigning projection\n",
+    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
+    "\n",
+    "#Plotting the data using a scatter plot\n",
+    "ax.scatter(x = counts.lonMean, y = counts.latMean, s = counts.number/50000, alpha = 0.5)\n",
+    "\n",
+    "#Adding a basemap of land areas\n",
+    "ax.add_feature(cf.LAND)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have successfully plotted the location of the AIMS temperature loggers within the GBRMPA bounding box. We can see that the number of temperature records per logger is not the same throughout the GBRMPA. The largest number of records are for loggers located near the coast."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rimrep",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Python_based_scripts/05-Extracting_Water_Temperature_at_Site.ipynb b/Python_based_scripts/05-Extracting_Water_Temperature_at_Site.ipynb
new file mode 100644
index 0000000..4af4c0a
--- /dev/null
+++ b/Python_based_scripts/05-Extracting_Water_Temperature_at_Site.ipynb
@@ -0,0 +1,925 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting temperature at specific reefs\n",
+    "**Authors**: Leo Ghignone, Denisse Fierro Arcos  \n",
+    "  \n",
+    "This notebook demonstrates direct access to the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset in the RIMReP `geoparquet` collection. This dataset includes sea temperature data from 1980 for tropical and subtropical coral reefs around Australia, and it includes sites within the Great Barrier Reef.   \n",
+    "  \n",
+    "In this example, we will extract the coordinates for all sites sampled in this monitoring program and then extract the temperature data for a specific site of interest. This means that we will not need to know the exact location of the site, but we will need to know the name of the site of our interest. \n",
+    "  \n",
+    "**Note:** The `geoparquet` collection is a compilation of datasets that have been converted to the Apache `parquet` format. This format is designed for efficient storage and retrieval of tabular data. The `geoparquet` collection is stored on the RIMReP S3 bucket and is accessible to all users."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Accessing S3 bucket\n",
+    "from pyarrow import parquet as pq\n",
+    "# Manipulating dataframes\n",
+    "import pandas as pd\n",
+    "# Checking patterns in strings using regular expressions (regex)\n",
+    "import re\n",
+    "# Load and manipulate geospatial data\n",
+    "import dask_geopandas\n",
+    "import geopandas as gpd\n",
+    "# Plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "#Color maps\n",
+    "from matplotlib import colormaps as cmaps\n",
+    "# Mapping\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cf\n",
+    "\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting metadata\n",
+    "The AIMS Temperature dataset contains over 150 million records. We will use its metadata to check the structure of the dataset and to determine the best way to access the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the dataset URI you obtained form the metadata catalog. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uri = \"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pyarrow._parquet.FileMetaData object at 0x71b5086f6610>\n",
+       "  created_by: parquet-cpp-arrow version 15.0.0\n",
+       "  num_columns: 22\n",
+       "  num_rows: 176703852\n",
+       "  num_row_groups: 371\n",
+       "  format_version: 2.6\n",
+       "  serialized_size: 1043554"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Connecting to metadata file\n",
+    "md = pq.read_metadata(uri + \"/_metadata\")\n",
+    "#Checking contents\n",
+    "md"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata is telling us that the AIMS temperature dataset contains 23 columns and over 150 million rows, and it is made up of 50 different files.  \n",
+    "  \n",
+    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pyarrow._parquet.ParquetSchema object at 0x71b498fcb440>\n",
+       "required group field_id=-1 schema {\n",
+       "  optional int64 field_id=-1 deployment_id;\n",
+       "  optional binary field_id=-1 site (String);\n",
+       "  optional int64 field_id=-1 site_id;\n",
+       "  optional binary field_id=-1 subsite (String);\n",
+       "  optional int64 field_id=-1 subsite_id;\n",
+       "  optional int64 field_id=-1 from_date (Timestamp(isAdjustedToUTC=false, timeUnit=nanoseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
+       "  optional int64 field_id=-1 thru_date (Timestamp(isAdjustedToUTC=false, timeUnit=nanoseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
+       "  optional double field_id=-1 depth;\n",
+       "  optional binary field_id=-1 parameter (String);\n",
+       "  optional binary field_id=-1 instrument_type (String);\n",
+       "  optional binary field_id=-1 serial_num (String);\n",
+       "  optional double field_id=-1 lat;\n",
+       "  optional double field_id=-1 lon;\n",
+       "  optional binary field_id=-1 gbrmpa_reef_id (String);\n",
+       "  optional binary field_id=-1 metadata_uuid (String);\n",
+       "  optional int64 field_id=-1 sites_with_climatology_available;\n",
+       "  optional int64 field_id=-1 time (Timestamp(isAdjustedToUTC=true, timeUnit=nanoseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
+       "  optional double field_id=-1 cal_val;\n",
+       "  optional double field_id=-1 qc_val;\n",
+       "  optional int64 field_id=-1 qc_flag;\n",
+       "  optional binary field_id=-1 geometry;\n",
+       "  optional binary field_id=-1 fid (String);\n",
+       "}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "md.schema"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `schema` tells us that the dataset contains 23 columns, which matches the information we got from the metadata. It also tells us that the data types are either `string` or `double`.  \n",
+    "  \n",
+    "We can also obtain more details about a specific column, by calling the `column` method and passing the name of the column as an argument as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ParquetColumnSchema>\n",
+       "  name: qc_val\n",
+       "  path: qc_val\n",
+       "  max_definition_level: 1\n",
+       "  max_repetition_level: 0\n",
+       "  physical_type: DOUBLE\n",
+       "  logical_type: None\n",
+       "  converted_type (legacy): NONE"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "md.schema.column(18)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we refer to the original site for the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e), we can see that the `qc_val` column refers to the quality controlled temperature obtained from the loggers deployed as part of this program."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to S3 parquet directory\n",
+    "We will connect to the S3 bucket containing the AIMS Temperature dataset using the `dask_geopandas` library. We will only keep sites that have been deployed in the Great Barrier Reef Marine Protected Area (GBRMPA), so we will provide a `filter` that contains the minimum and maximum coordinates (i.e., `lat` and `lon`) of a bounding box around the Great Barrier Reef Marine Protected Area.\n",
+    "\n",
+    "As we saw above, this dataset includes 23 columns, but we will only select a few that contain information that is interesting to us:\n",
+    "- `site`: name of site where temperature logger was deployed,\n",
+    "- `subsite`: contains a shorten version of the site name and an indication of where loggers where deployed (see below for more details),\n",
+    "- `geometry`: includes latitude and longitude coordinates in [well-known binary (WKB)](https://loc.gov/preservation/digital/formats/fdd/fdd000549.shtml) format  \n",
+    "  \n",
+    "Based on the `subsite` information, we will create a new column that we will call `deployment_location`. If the `subsite` string in a row includes `SL` it indicates that the temperature logger was deployed on a reef slope (depths of 3 m or more). Thus, our new column will be labelled reef slope. If a row contains `FL` in the `subsite` column, this means the temperature logger was deployed on a reef flat (areas less than 3 m deep). Therefore, we will classify this observation as reef flat in our new column.\n",
+    "\n",
+    "Note that when we use the `dask_geopandas` library, the `geometry` field is automatically recognised as coordinates in decimal degrees and used as the `geometry` column in a `geopandas` `GeoDataFrame` object. This means we can use it to plot the data in a map easily.\n",
+    "\n",
+    "The following code chunk may take a few minutes to run, as it is loading over 170 million records. The DMS team is currently working on optimizing the data partitioning strategy to make it faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>site</th>\n",
+       "      <th>subsite</th>\n",
+       "      <th>time</th>\n",
+       "      <th>qc_val</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2903</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>1995-12-04 16:30:00+00:00</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2904</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERFL1</td>\n",
+       "      <td>1995-11-25 04:30:00+00:00</td>\n",
+       "      <td>26.83</td>\n",
+       "      <td>POINT (151.91820 -23.43840)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2903</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>1995-12-04 17:00:00+00:00</td>\n",
+       "      <td>24.93</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2904</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERFL1</td>\n",
+       "      <td>1995-11-25 06:30:00+00:00</td>\n",
+       "      <td>27.67</td>\n",
+       "      <td>POINT (151.91820 -23.43840)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2904</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERFL1</td>\n",
+       "      <td>1995-11-25 07:00:00+00:00</td>\n",
+       "      <td>27.56</td>\n",
+       "      <td>POINT (151.91820 -23.43840)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       site subsite                      time  qc_val  \\\n",
+       "fid                                                                     \n",
+       "1995-130-2903  Heron Island  HERSL1 1995-12-04 16:30:00+00:00   25.06   \n",
+       "1995-130-2904  Heron Island  HERFL1 1995-11-25 04:30:00+00:00   26.83   \n",
+       "1995-130-2903  Heron Island  HERSL1 1995-12-04 17:00:00+00:00   24.93   \n",
+       "1995-130-2904  Heron Island  HERFL1 1995-11-25 06:30:00+00:00   27.67   \n",
+       "1995-130-2904  Heron Island  HERFL1 1995-11-25 07:00:00+00:00   27.56   \n",
+       "\n",
+       "                                  geometry  \n",
+       "fid                                         \n",
+       "1995-130-2903  POINT (151.90810 -23.44350)  \n",
+       "1995-130-2904  POINT (151.91820 -23.43840)  \n",
+       "1995-130-2903  POINT (151.90810 -23.44350)  \n",
+       "1995-130-2904  POINT (151.91820 -23.43840)  \n",
+       "1995-130-2904  POINT (151.91820 -23.43840)  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Extracting data only for specific reefs\n",
+    "filter = [('site', 'in', ['Heron Island', 'Hayman Island'])]\n",
+    "\n",
+    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
+    "dask_geo_df = dask_geopandas.read_parquet(uri, \n",
+    "                                          #Applying initial bounding box filter\n",
+    "                                          filters = [filter], \n",
+    "                                          #Specifying which columns to read\n",
+    "                                          columns = ['site', 'subsite', 'time', 'qc_val', 'geometry'],\n",
+    "                                          #Specifying the column to be used as index\n",
+    "                                          index = 'fid',\n",
+    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
+    "                                          storage_options = {\"anon\": True}) \n",
+    "\n",
+    "# Keeping unique values\n",
+    "dask_geo_df = dask_geo_df.drop_duplicates()\n",
+    "\n",
+    "# Transforming to pandas dataframe\n",
+    "sites = dask_geo_df.compute()\n",
+    "\n",
+    "# Checking the contents of the dataframe - First five rows\n",
+    "sites.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating `deployment_location` column\n",
+    "We will use the `subsite` column to create the deployment categories as explained in the section above. If no condition is met, then we will label the row as other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>site</th>\n",
+       "      <th>subsite</th>\n",
+       "      <th>time</th>\n",
+       "      <th>qc_val</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>deployment_location</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2903</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>1995-12-04 16:30:00+00:00</td>\n",
+       "      <td>25.06</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2904</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERFL1</td>\n",
+       "      <td>1995-11-25 04:30:00+00:00</td>\n",
+       "      <td>26.83</td>\n",
+       "      <td>POINT (151.91820 -23.43840)</td>\n",
+       "      <td>reef flat</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2903</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>1995-12-04 17:00:00+00:00</td>\n",
+       "      <td>24.93</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2904</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERFL1</td>\n",
+       "      <td>1995-11-25 06:30:00+00:00</td>\n",
+       "      <td>27.67</td>\n",
+       "      <td>POINT (151.91820 -23.43840)</td>\n",
+       "      <td>reef flat</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995-130-2904</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERFL1</td>\n",
+       "      <td>1995-11-25 07:00:00+00:00</td>\n",
+       "      <td>27.56</td>\n",
+       "      <td>POINT (151.91820 -23.43840)</td>\n",
+       "      <td>reef flat</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-130-498651</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>2024-01-06 04:27:00+00:00</td>\n",
+       "      <td>27.7697</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-130-498651</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>2024-01-06 07:02:00+00:00</td>\n",
+       "      <td>27.4415</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-130-498651</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>2024-01-06 06:27:00+00:00</td>\n",
+       "      <td>27.4935</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-130-498651</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>2024-01-06 06:17:00+00:00</td>\n",
+       "      <td>27.6004</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-130-498651</th>\n",
+       "      <td>Heron Island</td>\n",
+       "      <td>HERSL1</td>\n",
+       "      <td>2024-01-06 06:32:00+00:00</td>\n",
+       "      <td>27.5044</td>\n",
+       "      <td>POINT (151.90810 -23.44350)</td>\n",
+       "      <td>reef slope</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3899268 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         site subsite                      time   qc_val  \\\n",
+       "fid                                                                        \n",
+       "1995-130-2903    Heron Island  HERSL1 1995-12-04 16:30:00+00:00    25.06   \n",
+       "1995-130-2904    Heron Island  HERFL1 1995-11-25 04:30:00+00:00    26.83   \n",
+       "1995-130-2903    Heron Island  HERSL1 1995-12-04 17:00:00+00:00    24.93   \n",
+       "1995-130-2904    Heron Island  HERFL1 1995-11-25 06:30:00+00:00    27.67   \n",
+       "1995-130-2904    Heron Island  HERFL1 1995-11-25 07:00:00+00:00    27.56   \n",
+       "...                       ...     ...                       ...      ...   \n",
+       "2024-130-498651  Heron Island  HERSL1 2024-01-06 04:27:00+00:00  27.7697   \n",
+       "2024-130-498651  Heron Island  HERSL1 2024-01-06 07:02:00+00:00  27.4415   \n",
+       "2024-130-498651  Heron Island  HERSL1 2024-01-06 06:27:00+00:00  27.4935   \n",
+       "2024-130-498651  Heron Island  HERSL1 2024-01-06 06:17:00+00:00  27.6004   \n",
+       "2024-130-498651  Heron Island  HERSL1 2024-01-06 06:32:00+00:00  27.5044   \n",
+       "\n",
+       "                                    geometry deployment_location  \n",
+       "fid                                                               \n",
+       "1995-130-2903    POINT (151.90810 -23.44350)          reef slope  \n",
+       "1995-130-2904    POINT (151.91820 -23.43840)           reef flat  \n",
+       "1995-130-2903    POINT (151.90810 -23.44350)          reef slope  \n",
+       "1995-130-2904    POINT (151.91820 -23.43840)           reef flat  \n",
+       "1995-130-2904    POINT (151.91820 -23.43840)           reef flat  \n",
+       "...                                      ...                 ...  \n",
+       "2024-130-498651  POINT (151.90810 -23.44350)          reef slope  \n",
+       "2024-130-498651  POINT (151.90810 -23.44350)          reef slope  \n",
+       "2024-130-498651  POINT (151.90810 -23.44350)          reef slope  \n",
+       "2024-130-498651  POINT (151.90810 -23.44350)          reef slope  \n",
+       "2024-130-498651  POINT (151.90810 -23.44350)          reef slope  \n",
+       "\n",
+       "[3899268 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Creating new column with deployment location information\n",
+    "sites['deployment_location'] = sites.apply(lambda x: 'reef slope' if bool(re.match('.*(SL[0-9]{1,2})', x.subsite)) == True \\\n",
+    "         else ('reef flat' if bool(re.match('.*(FL[0-9]{1,2})', x.subsite)) == True else 'other'), axis = 1)\n",
+    "\n",
+    "#Checking results\n",
+    "sites"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extracting data for sites of interest\n",
+    "We can use the information in our `sites` variable to extract data for a site of interest. All we need to know is the name of the site, as shown below.\n",
+    "\n",
+    "Here, we will use data collected at **Hayman Island** and **Heron Island** as an example. We will calculate monthly means at each site, so we can plot time series that will show us how temperature has changed over time."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data frame does only include observations for the sites of our interest. This means we can remove and the columns labelled as \"right\" from our data frame because they contain the same information as the \"left\" columns.  \n",
+    "  \n",
+    "We will also calculate the mean monthly temperature at each site and deployment location combination."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>site</th>\n",
+       "      <th>deployment_location</th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>qc_val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>05</td>\n",
+       "      <td>25.171466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>06</td>\n",
+       "      <td>24.397491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>07</td>\n",
+       "      <td>23.204402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>08</td>\n",
+       "      <td>22.742272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>09</td>\n",
+       "      <td>23.614451</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            site deployment_location  year month     qc_val\n",
+       "0  Hayman Island           reef flat  1996    05  25.171466\n",
+       "1  Hayman Island           reef flat  1996    06  24.397491\n",
+       "2  Hayman Island           reef flat  1996    07  23.204402\n",
+       "3  Hayman Island           reef flat  1996    08  22.742272\n",
+       "4  Hayman Island           reef flat  1996    09  23.614451"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Extracting the year and month for each observation\n",
+    "sites['year'] = sites.time.dt.year.values\n",
+    "sites['month'] = sites.time.dt.month.values\n",
+    "# Ensuring months are two digits - This wil be important for labelling our plots later\n",
+    "sites['month'] = sites.apply(lambda x: str(x.month).zfill(2), axis = 1)\n",
+    "\n",
+    "# Calculate mean monthly temperatures for each site and deployment location\n",
+    "sites_temp = sites.groupby(['site', 'deployment_location', 'year', 'month']).mean('qc_val').reset_index()\n",
+    "\n",
+    "#Checking results\n",
+    "sites_temp.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting monthly means\n",
+    "Before plotting, we will add a few extra columns that we will use to label and color our plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>site</th>\n",
+       "      <th>deployment_location</th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>qc_val</th>\n",
+       "      <th>date_mean</th>\n",
+       "      <th>colours</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>05</td>\n",
+       "      <td>25.171466</td>\n",
+       "      <td>1996-05-01</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>06</td>\n",
+       "      <td>24.397491</td>\n",
+       "      <td>1996-06-01</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>07</td>\n",
+       "      <td>23.204402</td>\n",
+       "      <td>1996-07-01</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>08</td>\n",
+       "      <td>22.742272</td>\n",
+       "      <td>1996-08-01</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Hayman Island</td>\n",
+       "      <td>reef flat</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>09</td>\n",
+       "      <td>23.614451</td>\n",
+       "      <td>1996-09-01</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            site deployment_location  year month     qc_val  date_mean  \\\n",
+       "0  Hayman Island           reef flat  1996    05  25.171466 1996-05-01   \n",
+       "1  Hayman Island           reef flat  1996    06  24.397491 1996-06-01   \n",
+       "2  Hayman Island           reef flat  1996    07  23.204402 1996-07-01   \n",
+       "3  Hayman Island           reef flat  1996    08  22.742272 1996-08-01   \n",
+       "4  Hayman Island           reef flat  1996    09  23.614451 1996-09-01   \n",
+       "\n",
+       "   colours  \n",
+       "0        0  \n",
+       "1        0  \n",
+       "2        0  \n",
+       "3        0  \n",
+       "4        0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Adding column with dates for which means were calculated\n",
+    "sites_temp['date_mean'] = pd.to_datetime(sites_temp.year.astype('str') + '-' + sites_temp.month)\n",
+    "\n",
+    "# Transforming site column to categorical data\n",
+    "sites_temp['site'] = pd.Categorical(sites_temp.site)\n",
+    "\n",
+    "# Changing site categories to numeric values for plotting - We can then change the colors based on this column\n",
+    "sites_temp['colours'] = sites_temp.site.cat.codes\n",
+    "\n",
+    "#Checking results\n",
+    "sites_temp.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we will create our plot. We will have two subplots based on the `deployment_location` column and the color of the lines and points will vary based on the `site` column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_77961/46233019.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  groups = sites_temp.groupby(['site', 'colours', 'deployment_location'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We group our data by site, colours, and deployment location\n",
+    "groups = sites_temp.groupby(['site', 'colours', 'deployment_location'])\n",
+    "\n",
+    "# We select a color map for our plots\n",
+    "cmap = cmaps['Set1']\n",
+    "\n",
+    "# We initialise the figure and axes. We also define the size of the figure.\n",
+    "# Axes refer to the subplots in the figure (1 row and 2 columns). They will share the y axis\n",
+    "fig, ax = plt.subplots(1, 2, figsize = (15, 9), sharey = True)\n",
+    "\n",
+    "# Now we use our groups to plot the data\n",
+    "# Element here has the categories of the groupby object (site, colours, and deployment location)\n",
+    "# Group has the subset dataframes for each category\n",
+    "for element, group in groups:\n",
+    "    # If the deployment location is reef flat, we plot the data in the first subplot\n",
+    "    if 'flat' in element[2]:\n",
+    "        # Plotting dates in x axis and mean temperature in y axis\n",
+    "        ax[0].plot(group['date_mean'], group['qc_val'], marker = 'o', ms = 10, \n",
+    "             # We use the site name as the label for each line and the color will change based on the colour column\n",
+    "             label = f'{element[0]}', color = cmap(element[1]))\n",
+    "        # Title for subplot\n",
+    "        ax[0].set_title('Reef Flat')\n",
+    "        # Adding legend\n",
+    "        ax[0].legend()\n",
+    "    # In all other cases, we plot the data in the second subplot\n",
+    "    else:\n",
+    "        #As above\n",
+    "        ax[1].plot(group['date_mean'], group['qc_val'], marker = 'o', ms = 10, \n",
+    "             label = f'{element[0]}', color = cmap(element[1]))\n",
+    "        ax[1].set_title('Reef Slope')\n",
+    "        ax[1].legend()\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you would like to save the figure, you can use the `savefig` method, as follows: `plt.savefig(file_name)`. Here `file_name` can contain a path to a directory and a file name with the extension of the file type you would like to save the figure as. For example, `plt.savefig('figures/temperature_timeseries.png')` will save the figure in a directory called `figures` and the file name will be `temperature_timeseries.png`.  \n",
+    "  \n",
+    "If you would like to save the data frame, you can use the `to_csv` method, as follows: `sites_temp.to_csv(file_name)`.  \n",
+    "  \n",
+    "That is the end of this notebook. We have demonstrated how to extract data from the AIMS Temperature Logger Monitoring Program dataset and how to plot monthly means for two sites of interest.  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rimrep",
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Python_based_scripts/06-getDHWmax.ipynb b/Python_based_scripts/06-getDHWmax.ipynb
new file mode 100644
index 0000000..4020916
--- /dev/null
+++ b/Python_based_scripts/06-getDHWmax.ipynb
@@ -0,0 +1,1745 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the DHW max value from a specified year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*E. Klein  \n",
+    "2024-04-05*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Degree-Heating Week (DHW) product is a widely-used indicator of ocean heat gain. Particularly during the summer season, it is used to keep track of the heat stress on coral reefs. \n",
+    "Although the general seasonal pattern is well-known, the maximum value and the day of the year when it occurs can vary significantly from year to year.\n",
+    "\n",
+    "This notebook allows you to connect with the DMS NOAA DHW product, extract the maximum DHW value and the corresponding day of the year for each pixel, and save the results as a netCDF file. \n",
+    "You have the option to specify a spatial extent and a temporal extent (typically a full year)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## imports\n",
+    "# Loading and manipulating gridded multidimensional data\n",
+    "import xarray as xr\n",
+    "# Accessing S3 buckets\n",
+    "import s3fs\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You need to know the URI of the DHW-zarr product in the DMS. Please visit https://stac.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-chs-dhw and copy the item URI. Below this addrees has been already assigned to te variabl `zarr_path`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 165GB\n",
+       "Dimensions:              (time: 14302, lat: 900, lon: 1600)\n",
+       "Coordinates:\n",
+       "  * lat                  (lat) float32 4kB -5.025 -5.075 ... -49.92 -49.97\n",
+       "  * lon                  (lon) float32 6kB 100.0 100.1 100.1 ... 179.9 180.0\n",
+       "  * time                 (time) datetime64[ns] 114kB 1985-03-25T12:00:00 ... ...\n",
+       "Data variables:\n",
+       "    crs                  int16 2B ...\n",
+       "    degree_heating_week  (time, lat, lon) float32 82GB ...\n",
+       "    mask                 (time, lat, lon) float32 82GB ...\n",
+       "Attributes: (12/59)\n",
+       "    Conventions:                CF-1.6, ACDD-1.3\n",
+       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
+       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
+       "    cdm_data_type:              Grid\n",
+       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
+       "    contributor_name:           NOAA Coral Reef Watch Program\n",
+       "    ...                         ...\n",
+       "    time_coverage_duration:     P1D\n",
+       "    time_coverage_end:          20240521T000000Z\n",
+       "    time_coverage_resolution:   P1D\n",
+       "    time_coverage_start:        20240520T000000Z\n",
+       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
+       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f</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-640ba3b9-56b3-4fee-8706-1a93039950ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-640ba3b9-56b3-4fee-8706-1a93039950ab' 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>: 14302</li><li><span class='xr-has-index'>lat</span>: 900</li><li><span class='xr-has-index'>lon</span>: 1600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aeff3ea1-889e-4c75-8905-9292539adf7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aeff3ea1-889e-4c75-8905-9292539adf7a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.025 -5.075 ... -49.92 -49.97</div><input id='attrs-7edad069-12b5-43ab-9979-c2936af08d5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7edad069-12b5-43ab-9979-c2936af08d5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-161a787b-d73b-417f-b342-3d6979810043' class='xr-var-data-in' type='checkbox'><label for='data-161a787b-d73b-417f-b342-3d6979810043' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -5.025,  -5.075,  -5.125, ..., -49.875, -49.925, -49.975],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>100.0 100.1 100.1 ... 179.9 180.0</div><input id='attrs-661a9ed0-0067-48b2-bdb9-f592f8dcc0f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-661a9ed0-0067-48b2-bdb9-f592f8dcc0f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeaafde5-d775-4220-92d9-d38b2a4d5a7b' class='xr-var-data-in' type='checkbox'><label for='data-eeaafde5-d775-4220-92d9-d38b2a4d5a7b' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([100.025, 100.075, 100.125, ..., 179.875, 179.925, 179.975],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1985-03-25T12:00:00 ... 2024-05-...</div><input id='attrs-92f02c75-8789-4d99-b2f4-94362063f5ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92f02c75-8789-4d99-b2f4-94362063f5ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0f8e8d2-06db-40df-9a11-911c29a635bd' class='xr-var-data-in' type='checkbox'><label for='data-f0f8e8d2-06db-40df-9a11-911c29a635bd' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1985-03-25T12:00:00.000000000&#x27;, &#x27;1985-03-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;1985-03-27T12:00:00.000000000&#x27;, ..., &#x27;2024-05-19T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-20T12:00:00.000000000&#x27;, &#x27;2024-05-21T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fab367fa-70cc-4480-a784-eabb6bd95e25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fab367fa-70cc-4480-a784-eabb6bd95e25' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92666837-e4d8-47ff-b049-48f793e47d87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92666837-e4d8-47ff-b049-48f793e47d87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13bbe70f-c418-4c24-aaa5-fc52a8686bd7' class='xr-var-data-in' type='checkbox'><label for='data-13bbe70f-c418-4c24-aaa5-fc52a8686bd7' 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>comment :</span></dt><dd>This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.</dd><dt><span>epsg_code :</span></dt><dd>EPSG:32663</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.2572021484375</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree_heating_week</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'>...</div><input id='attrs-10ad132c-faea-4e43-9e7b-57f92ab77f4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10ad132c-faea-4e43-9e7b-57f92ab77f4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7875253-86da-403c-b31d-13156c6e11a7' class='xr-var-data-in' type='checkbox'><label for='data-e7875253-86da-403c-b31d-13156c6e11a7' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[20594880000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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'>...</div><input id='attrs-24b14608-3c37-46d3-8a46-2ed039304565' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24b14608-3c37-46d3-8a46-2ed039304565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eb64060-bde9-46c1-adfc-0a4dfd989ccf' class='xr-var-data-in' type='checkbox'><label for='data-1eb64060-bde9-46c1-adfc-0a4dfd989ccf' 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>comment :</span></dt><dd>A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.</dd><dt><span>coverage_content_type :</span></dt><dd>thematicClassification</dd><dt><span>flag_meanings :</span></dt><dd>valid-water land missing ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 4]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>pixel characteristics flag array</dd><dt><span>units :</span></dt><dd>pixel_classification</dd></dl></div><div class='xr-var-data'><pre>[20594880000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62b8e19c-fd75-4c7d-88d5-bd78ecf044d7' class='xr-section-summary-in' type='checkbox'  ><label for='section-62b8e19c-fd75-4c7d-88d5-bd78ecf044d7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c61ef787-d0fb-4e24-b404-d3ec41acf1c6' class='xr-index-data-in' type='checkbox'/><label for='index-c61ef787-d0fb-4e24-b404-d3ec41acf1c6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -5.025000095367432,  -5.074999809265137,              -5.125,\n",
+       "        -5.175000190734863,  -5.224999904632568,  -5.275000095367432,\n",
+       "        -5.324999809265137,              -5.375,  -5.425000190734863,\n",
+       "        -5.474999904632568,\n",
+       "       ...\n",
+       "       -49.525001525878906,  -49.57500076293945,             -49.625,\n",
+       "        -49.67499923706055, -49.724998474121094, -49.775001525878906,\n",
+       "        -49.82500076293945,             -49.875,  -49.92499923706055,\n",
+       "       -49.974998474121094],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6076b6ec-1e09-408f-be79-26abd0f119e7' class='xr-index-data-in' type='checkbox'/><label for='index-6076b6ec-1e09-408f-be79-26abd0f119e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 100.0250015258789, 100.07499694824219,            100.125,\n",
+       "       100.17500305175781,  100.2249984741211,  100.2750015258789,\n",
+       "       100.32499694824219,            100.375, 100.42500305175781,\n",
+       "        100.4749984741211,\n",
+       "       ...\n",
+       "       179.52499389648438,  179.5749969482422,            179.625,\n",
+       "        179.6750030517578, 179.72500610351562, 179.77499389648438,\n",
+       "        179.8249969482422,            179.875,  179.9250030517578,\n",
+       "       179.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a5c4963a-7e43-48c7-9d58-217562750b33' class='xr-index-data-in' type='checkbox'/><label for='index-a5c4963a-7e43-48c7-9d58-217562750b33' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1985-03-25 12:00:00&#x27;, &#x27;1985-03-26 12:00:00&#x27;,\n",
+       "               &#x27;1985-03-27 12:00:00&#x27;, &#x27;1985-03-28 12:00:00&#x27;,\n",
+       "               &#x27;1985-03-29 12:00:00&#x27;, &#x27;1985-03-30 12:00:00&#x27;,\n",
+       "               &#x27;1985-03-31 12:00:00&#x27;, &#x27;1985-04-01 12:00:00&#x27;,\n",
+       "               &#x27;1985-04-02 12:00:00&#x27;, &#x27;1985-04-03 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2024-05-12 12:00:00&#x27;, &#x27;2024-05-13 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-14 12:00:00&#x27;, &#x27;2024-05-15 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-16 12:00:00&#x27;, &#x27;2024-05-17 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-18 12:00:00&#x27;, &#x27;2024-05-19 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-20 12:00:00&#x27;, &#x27;2024-05-21 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=14302, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d48c924-3639-405d-89fd-61f509e28ef0' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d48c924-3639-405d-89fd-61f509e28ef0' class='xr-section-summary' >Attributes: <span>(59)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>acknowledgement :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.</dd><dt><span>contributor_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>contributor_role :</span></dt><dd>Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive</dd><dt><span>creator_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>creator_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>creator_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://coralreefwatch.noaa.gov/</dd><dt><span>date_created :</span></dt><dd>20180301T120000Z</dd><dt><span>date_issued :</span></dt><dd>20240521T141758Z</dd><dt><span>date_metadata_modified :</span></dt><dd>20201118T120000Z</dd><dt><span>date_modified :</span></dt><dd>20180301T120000Z</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.9749984741211</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.9749984741211</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.97500610351562</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.97500610351562</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Tue May 21 16:17:41 2024: ncks --overwrite -d lat,-50.0,-5.0 -d lon,100.0,180.0 ct5km_dhw_v3.1_20240520.nc ct5km_dhw_v3.1_20240520.nc\n",
+       "This is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).</dd><dt><span>id :</span></dt><dd>Satellite_Daily_Global_5km_Degree_Heating_Week</dd><dt><span>institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>instrument :</span></dt><dd>ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake</dd><dt><span>instrument_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Instruments</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Water Temperature,  Spectral/Engineering &gt; Infrared Wavelengths &gt; Thermal Infrared, Oceans &gt; Ocean Temperature &gt; Degree Heating Week</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.</dd><dt><span>metadata_link :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.coralreefwatch</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>platform :</span></dt><dd>Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.</dd><dt><span>platform_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Platforms</dd><dt><span>processing_level :</span></dt><dd>Derived from L4 satellite sea surface temperaure analysis</dd><dt><span>product_version :</span></dt><dd>3.1</dd><dt><span>program :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>project :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>publisher_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>publisher_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://coralreefwatch.noaa.gov</dd><dt><span>references :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>source :</span></dt><dd>Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>summary :</span></dt><dd>This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_end :</span></dt><dd>20240521T000000Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>20240520T000000Z</dd><dt><span>title :</span></dt><dd>NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week </dd><dt><span>uuid :</span></dt><dd>24c7f597-298c-4be6-a0c2-ad97b993f60f</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 165GB\n",
+       "Dimensions:              (time: 14302, lat: 900, lon: 1600)\n",
+       "Coordinates:\n",
+       "  * lat                  (lat) float32 4kB -5.025 -5.075 ... -49.92 -49.97\n",
+       "  * lon                  (lon) float32 6kB 100.0 100.1 100.1 ... 179.9 180.0\n",
+       "  * time                 (time) datetime64[ns] 114kB 1985-03-25T12:00:00 ... ...\n",
+       "Data variables:\n",
+       "    crs                  int16 2B ...\n",
+       "    degree_heating_week  (time, lat, lon) float32 82GB ...\n",
+       "    mask                 (time, lat, lon) float32 82GB ...\n",
+       "Attributes: (12/59)\n",
+       "    Conventions:                CF-1.6, ACDD-1.3\n",
+       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
+       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
+       "    cdm_data_type:              Grid\n",
+       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
+       "    contributor_name:           NOAA Coral Reef Watch Program\n",
+       "    ...                         ...\n",
+       "    time_coverage_duration:     P1D\n",
+       "    time_coverage_end:          20240521T000000Z\n",
+       "    time_coverage_resolution:   P1D\n",
+       "    time_coverage_start:        20240520T000000Z\n",
+       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
+       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Connect with CRW DHW product\n",
+    "# Path to zarr files\n",
+    "zarr_path = \"s3://gbr-dms-data-public/noaa-crw-chs-dhw/data.zarr\"\n",
+    "\n",
+    "# Connection to public S3 bucket - No credentials needed\n",
+    "fs = s3fs.S3FileSystem(anon=True)\n",
+    "\n",
+    "# Loading zarr file\n",
+    "data = xr.open_dataset(s3fs.S3Map(zarr_path, s3=fs), engine=\"zarr\")\n",
+    "\n",
+    "# Show data structure\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here you can define your data slice:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Slicing parameters\n",
+    "dateMin = \"2024-01-01\"\n",
+    "dateMax = \"2024-12-31\"\n",
+    "latMin = -26.0\n",
+    "latMax = -8.5\n",
+    "lonMin = 140.0\n",
+    "lonMax = 160.0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With these values, extract the data you need"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
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+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;degree_heating_week&#x27; (time: 141, lat: 350, lon: 400)&gt; Size: 79MB\n",
+       "[19740000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
+       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
+       "  * time     (time) datetime64[ns] 1kB 2024-01-01T12:00:00 ... 2024-05-21T12:...\n",
+       "Attributes:\n",
+       "    coverage_content_type:  physicalMeasurement\n",
+       "    grid_mapping:           crs\n",
+       "    long_name:              degree heating week\n",
+       "    standard_name:          N/A\n",
+       "    units:                  degree_Celsius_weeks\n",
+       "    valid_max:              10000\n",
+       "    valid_min:              0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'degree_heating_week'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 141</li><li><span class='xr-has-index'>lat</span>: 350</li><li><span class='xr-has-index'>lon</span>: 400</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e71e9df6-b469-4014-97ba-eaf3c344c3a2' class='xr-array-in' type='checkbox' checked><label for='section-e71e9df6-b469-4014-97ba-eaf3c344c3a2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[19740000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-ca823886-557e-4b39-aada-5abd7865fa36' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca823886-557e-4b39-aada-5abd7865fa36' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.525 -8.575 ... -25.92 -25.98</div><input id='attrs-bb975b5f-8550-45a2-a68b-f3d47b4eaeab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb975b5f-8550-45a2-a68b-f3d47b4eaeab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c49998c0-8df5-428d-8d21-2905a8e315f5' class='xr-var-data-in' type='checkbox'><label for='data-c49998c0-8df5-428d-8d21-2905a8e315f5' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.525,  -8.575,  -8.625, ..., -25.875, -25.925, -25.975],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>140.0 140.1 140.1 ... 159.9 160.0</div><input id='attrs-542d22c6-8452-41cf-963e-68ec5e40a7a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-542d22c6-8452-41cf-963e-68ec5e40a7a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac3a1c9e-32c0-430f-bdf6-0fab6ecc55ef' class='xr-var-data-in' type='checkbox'><label for='data-ac3a1c9e-32c0-430f-bdf6-0fab6ecc55ef' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([140.025, 140.075, 140.125, ..., 159.875, 159.925, 159.975],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-01T12:00:00 ... 2024-05-...</div><input id='attrs-c812a7ba-dc88-475f-b9f2-a87283c2d0aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c812a7ba-dc88-475f-b9f2-a87283c2d0aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ca91a2-4407-4acc-a4b8-d931d9544cd6' class='xr-var-data-in' type='checkbox'><label for='data-50ca91a2-4407-4acc-a4b8-d931d9544cd6' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-01T12:00:00.000000000&#x27;, &#x27;2024-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-03T12:00:00.000000000&#x27;, &#x27;2024-01-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-05T12:00:00.000000000&#x27;, &#x27;2024-01-06T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-07T12:00:00.000000000&#x27;, &#x27;2024-01-08T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-09T12:00:00.000000000&#x27;, &#x27;2024-01-10T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-11T12:00:00.000000000&#x27;, &#x27;2024-01-12T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-13T12:00:00.000000000&#x27;, &#x27;2024-01-14T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T12:00:00.000000000&#x27;, &#x27;2024-01-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-17T12:00:00.000000000&#x27;, &#x27;2024-01-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-19T12:00:00.000000000&#x27;, &#x27;2024-01-20T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-21T12:00:00.000000000&#x27;, &#x27;2024-01-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-23T12:00:00.000000000&#x27;, &#x27;2024-01-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-25T12:00:00.000000000&#x27;, &#x27;2024-01-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-27T12:00:00.000000000&#x27;, &#x27;2024-01-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-29T12:00:00.000000000&#x27;, &#x27;2024-01-31T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-01T12:00:00.000000000&#x27;, &#x27;2024-02-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-03T12:00:00.000000000&#x27;, &#x27;2024-02-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-05T12:00:00.000000000&#x27;, &#x27;2024-02-06T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-07T12:00:00.000000000&#x27;, &#x27;2024-02-08T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-09T12:00:00.000000000&#x27;, &#x27;2024-02-10T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-11T12:00:00.000000000&#x27;, &#x27;2024-02-12T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-13T12:00:00.000000000&#x27;, &#x27;2024-02-14T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-15T12:00:00.000000000&#x27;, &#x27;2024-02-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-17T12:00:00.000000000&#x27;, &#x27;2024-02-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-19T12:00:00.000000000&#x27;, &#x27;2024-02-20T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-21T12:00:00.000000000&#x27;, &#x27;2024-02-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-23T12:00:00.000000000&#x27;, &#x27;2024-02-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-25T12:00:00.000000000&#x27;, &#x27;2024-02-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-27T12:00:00.000000000&#x27;, &#x27;2024-02-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-29T12:00:00.000000000&#x27;, &#x27;2024-03-01T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-02T12:00:00.000000000&#x27;, &#x27;2024-03-03T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-04T12:00:00.000000000&#x27;, &#x27;2024-03-05T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-06T12:00:00.000000000&#x27;, &#x27;2024-03-07T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-08T12:00:00.000000000&#x27;, &#x27;2024-03-09T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-10T12:00:00.000000000&#x27;, &#x27;2024-03-11T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-12T12:00:00.000000000&#x27;, &#x27;2024-03-13T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-14T12:00:00.000000000&#x27;, &#x27;2024-03-15T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-16T12:00:00.000000000&#x27;, &#x27;2024-03-17T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-18T12:00:00.000000000&#x27;, &#x27;2024-03-19T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-20T12:00:00.000000000&#x27;, &#x27;2024-03-21T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-22T12:00:00.000000000&#x27;, &#x27;2024-03-23T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-24T12:00:00.000000000&#x27;, &#x27;2024-03-25T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-26T12:00:00.000000000&#x27;, &#x27;2024-03-27T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-28T12:00:00.000000000&#x27;, &#x27;2024-03-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-03-30T12:00:00.000000000&#x27;, &#x27;2024-03-31T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-01T12:00:00.000000000&#x27;, &#x27;2024-04-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-03T12:00:00.000000000&#x27;, &#x27;2024-04-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-05T12:00:00.000000000&#x27;, &#x27;2024-04-06T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-07T12:00:00.000000000&#x27;, &#x27;2024-04-08T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-09T12:00:00.000000000&#x27;, &#x27;2024-04-10T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-11T12:00:00.000000000&#x27;, &#x27;2024-04-12T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-13T12:00:00.000000000&#x27;, &#x27;2024-04-14T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-15T12:00:00.000000000&#x27;, &#x27;2024-04-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-17T12:00:00.000000000&#x27;, &#x27;2024-04-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-19T12:00:00.000000000&#x27;, &#x27;2024-04-20T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-21T12:00:00.000000000&#x27;, &#x27;2024-04-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-23T12:00:00.000000000&#x27;, &#x27;2024-04-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-25T12:00:00.000000000&#x27;, &#x27;2024-04-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-27T12:00:00.000000000&#x27;, &#x27;2024-04-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-29T12:00:00.000000000&#x27;, &#x27;2024-04-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-01T12:00:00.000000000&#x27;, &#x27;2024-05-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-03T12:00:00.000000000&#x27;, &#x27;2024-05-04T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-05T12:00:00.000000000&#x27;, &#x27;2024-05-06T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-07T12:00:00.000000000&#x27;, &#x27;2024-05-08T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-09T12:00:00.000000000&#x27;, &#x27;2024-05-10T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-11T12:00:00.000000000&#x27;, &#x27;2024-05-12T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-13T12:00:00.000000000&#x27;, &#x27;2024-05-14T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-15T12:00:00.000000000&#x27;, &#x27;2024-05-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-17T12:00:00.000000000&#x27;, &#x27;2024-05-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-19T12:00:00.000000000&#x27;, &#x27;2024-05-20T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-05-21T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9120a98d-f25e-4b9a-880f-b1faa850f109' class='xr-section-summary-in' type='checkbox'  ><label for='section-9120a98d-f25e-4b9a-880f-b1faa850f109' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6af8eb5c-0ae1-4cc7-9e90-a6d63983b161' class='xr-index-data-in' type='checkbox'/><label for='index-6af8eb5c-0ae1-4cc7-9e90-a6d63983b161' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.524999618530273,  -8.574999809265137,              -8.625,\n",
+       "        -8.675000190734863,  -8.725000381469727,  -8.774999618530273,\n",
+       "        -8.824999809265137,              -8.875,  -8.925000190734863,\n",
+       "        -8.975000381469727,\n",
+       "       ...\n",
+       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
+       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
+       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
+       "       -25.975000381469727],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=350))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ed0d5dd-d491-4c3b-ab0a-53c01c90e546' class='xr-index-data-in' type='checkbox'/><label for='index-9ed0d5dd-d491-4c3b-ab0a-53c01c90e546' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([140.02499389648438,  140.0749969482422,            140.125,\n",
+       "        140.1750030517578, 140.22500610351562, 140.27499389648438,\n",
+       "        140.3249969482422,            140.375,  140.4250030517578,\n",
+       "       140.47500610351562,\n",
+       "       ...\n",
+       "       159.52499389648438,  159.5749969482422,            159.625,\n",
+       "        159.6750030517578, 159.72500610351562, 159.77499389648438,\n",
+       "        159.8249969482422,            159.875,  159.9250030517578,\n",
+       "       159.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74e96530-954c-4302-914b-7b109bf12680' class='xr-index-data-in' type='checkbox'/><label for='index-74e96530-954c-4302-914b-7b109bf12680' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-01-01 12:00:00&#x27;, &#x27;2024-01-02 12:00:00&#x27;,\n",
+       "               &#x27;2024-01-03 12:00:00&#x27;, &#x27;2024-01-04 12:00:00&#x27;,\n",
+       "               &#x27;2024-01-05 12:00:00&#x27;, &#x27;2024-01-06 12:00:00&#x27;,\n",
+       "               &#x27;2024-01-07 12:00:00&#x27;, &#x27;2024-01-08 12:00:00&#x27;,\n",
+       "               &#x27;2024-01-09 12:00:00&#x27;, &#x27;2024-01-10 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2024-05-12 12:00:00&#x27;, &#x27;2024-05-13 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-14 12:00:00&#x27;, &#x27;2024-05-15 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-16 12:00:00&#x27;, &#x27;2024-05-17 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-18 12:00:00&#x27;, &#x27;2024-05-19 12:00:00&#x27;,\n",
+       "               &#x27;2024-05-20 12:00:00&#x27;, &#x27;2024-05-21 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=141, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a033226-a792-402a-99f1-1935fcf65f7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a033226-a792-402a-99f1-1935fcf65f7f' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'degree_heating_week' (time: 141, lat: 350, lon: 400)> Size: 79MB\n",
+       "[19740000 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
+       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
+       "  * time     (time) datetime64[ns] 1kB 2024-01-01T12:00:00 ... 2024-05-21T12:...\n",
+       "Attributes:\n",
+       "    coverage_content_type:  physicalMeasurement\n",
+       "    grid_mapping:           crs\n",
+       "    long_name:              degree heating week\n",
+       "    standard_name:          N/A\n",
+       "    units:                  degree_Celsius_weeks\n",
+       "    valid_max:              10000\n",
+       "    valid_min:              0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## get data\n",
+    "\n",
+    "DHWyear = data.sel(time=slice(dateMin, dateMax)).degree_heating_week\n",
+    "\n",
+    "# Clip region. Note that lat is sorted in reverse order\n",
+    "DHWyear = DHWyear.sel(lat= slice(latMax, latMin), lon = slice(lonMin, lonMax))\n",
+    "DHWyear\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, you can extract the maximum value of the DHW for each of the pixels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Get max values\n",
+    "## coords\n",
+    "lat = DHWyear.lat\n",
+    "lon = DHWyear.lon\n",
+    "## max value\n",
+    "DHWmax = DHWyear.max(dim='time')\n",
+    "DHWmax.attrs = DHWyear.attrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the plot of the DHWmax for the seleted area/time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x76226f2dcf20>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "DHWmax.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and the day of the year when it reaches the maximun value:  \n",
+    "Note that because the argmax function produce an error when find a full vector of no data, we need to transform the data to eliminate those NaNs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## day of max value\n",
+    "## need a trick to avoid all NaN slice error in argmax method\n",
+    "mask = np.isnan(DHWmax)\n",
+    "DHWzero = DHWyear.fillna(0)\n",
+    "DHWyday = DHWzero.argmax(dim='time') + 1\n",
+    "DHWyday = np.ma.array(DHWyday.astype('float').values, mask=mask)\n",
+    "DHWyday = DHWyday.filled(np.nan)\n",
+    "DHWyday = xr.DataArray(DHWyday, coords=[lat, lon], dims=['lat', 'lon'],\n",
+    "                       attrs={'long_name': \"day of the year where DHWmax\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the plot of the day of the year when the DHWmax was reached:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x76226f1616a0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "DHWyday.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, with the two data arrays, create a dataset and add some global attributes. Remember add your name and email if desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "## edit some attrs\n",
+    "globAttrs = {}\n",
+    "globAttrs['title'] = \"DHWmax and time of the year when DHW max was reached\"\n",
+    "globAttrs['creator_name'] = \"YOUR NAME\"\n",
+    "globAttrs['creator_email'] = \"YOUR EMAIL\"\n",
+    "globAttrs['comment'] = \"Data extracted from the GBR Data Management System https://stac.reefdata.io/browser\"\n",
+    "\n",
+    "globAttrs['geospatial_lat_max'] = latMax\n",
+    "globAttrs['geospatial_lat_min'] = latMin\n",
+    "globAttrs['geospatial_lon_max'] = lonMax\n",
+    "globAttrs['geospatial_lon_min'] = lonMin\n",
+    "globAttrs['geospatial_lat_resolution'] = 0.05\n",
+    "globAttrs['geospatial_lon_resolution]'] = 0.05\n",
+    "\n",
+    "globAttrs['time_coverage_start'] = dateMin\n",
+    "globAttrs['time_coverage_end'] = dateMax\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
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+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
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+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
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+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       "\n",
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+       "  display: inline;\n",
+       "  margin-top: 0;\n",
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+       "\n",
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+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
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+       "\n",
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+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
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+       "\n",
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+       "  font-size: 11px;\n",
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+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
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+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
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+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
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+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
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+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
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+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:  (lat: 350, lon: 400)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
+       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
+       "Data variables:\n",
+       "    DHWmax   (lat, lon) float32 560kB 7.32 6.44 5.61 4.94 ... 13.82 13.55 13.38\n",
+       "    DHWyday  (lat, lon) float64 1MB 68.0 68.0 68.0 68.0 ... 68.0 68.0 68.0 68.0\n",
+       "Attributes:\n",
+       "    title:                       DHWmax and time of the year when DHW max was...\n",
+       "    creator_name:                YOUR NAME\n",
+       "    creator_email:               YOUR EMAIL\n",
+       "    comment:                     Data extracted from the GBR Data Management ...\n",
+       "    geospatial_lat_max:          -8.5\n",
+       "    geospatial_lat_min:          -26.0\n",
+       "    geospatial_lon_max:          160.0\n",
+       "    geospatial_lon_min:          140.0\n",
+       "    geospatial_lat_resolution:   0.05\n",
+       "    geospatial_lon_resolution]:  0.05\n",
+       "    time_coverage_start:         2024-01-01\n",
+       "    time_coverage_end:           2024-12-31</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-56531d12-5ec6-46a1-8d34-d974b4232326' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-56531d12-5ec6-46a1-8d34-d974b4232326' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 350</li><li><span class='xr-has-index'>lon</span>: 400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cececa41-7ab6-4129-9de5-291c642949fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cececa41-7ab6-4129-9de5-291c642949fd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.525 -8.575 ... -25.92 -25.98</div><input id='attrs-a96475d7-f1f7-417f-985b-229ab4fb1edb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a96475d7-f1f7-417f-985b-229ab4fb1edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5efc1983-7230-4f18-899e-32337c353b45' class='xr-var-data-in' type='checkbox'><label for='data-5efc1983-7230-4f18-899e-32337c353b45' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.525,  -8.575,  -8.625, ..., -25.875, -25.925, -25.975],\n",
+       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>140.0 140.1 140.1 ... 159.9 160.0</div><input id='attrs-ce389558-70be-4cf8-a03d-b0975a7cd373' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce389558-70be-4cf8-a03d-b0975a7cd373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4c770bc-6185-446a-87f0-d6654d644db7' class='xr-var-data-in' type='checkbox'><label for='data-d4c770bc-6185-446a-87f0-d6654d644db7' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([140.025, 140.075, 140.125, ..., 159.875, 159.925, 159.975],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-603b49ce-4a1f-4629-a056-869b0ae7ed79' class='xr-section-summary-in' type='checkbox'  checked><label for='section-603b49ce-4a1f-4629-a056-869b0ae7ed79' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>DHWmax</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.32 6.44 5.61 ... 13.55 13.38</div><input id='attrs-6624ab44-a2b1-4cb8-8919-c9d215dae847' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6624ab44-a2b1-4cb8-8919-c9d215dae847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-405efaf0-a615-4394-a81a-68b63843ca6a' class='xr-var-data-in' type='checkbox'><label for='data-405efaf0-a615-4394-a81a-68b63843ca6a' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([[ 7.3199997 ,  6.44      ,  5.6099997 , ...,  0.45999998,\n",
+       "         0.45999998,  0.62      ],\n",
+       "       [ 7.25      ,  6.2999997 ,  5.64      , ...,  0.62      ,\n",
+       "         0.75      ,  0.77      ],\n",
+       "       [ 6.91      ,  6.41      ,  5.64      , ...,  0.9       ,\n",
+       "         0.78999996,  1.09      ],\n",
+       "       ...,\n",
+       "       [        nan,         nan,         nan, ..., 13.67      ,\n",
+       "        13.349999  , 12.91      ],\n",
+       "       [        nan,         nan,         nan, ..., 13.84      ,\n",
+       "        13.53      , 13.11      ],\n",
+       "       [        nan,         nan,         nan, ..., 13.82      ,\n",
+       "        13.549999  , 13.38      ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DHWyday</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.0 68.0 68.0 ... 68.0 68.0 68.0</div><input id='attrs-ea51a9ec-9cd9-4245-a809-914cd1eaf316' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea51a9ec-9cd9-4245-a809-914cd1eaf316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1376ed76-2197-4e77-a224-cdc5f72c1363' class='xr-var-data-in' type='checkbox'><label for='data-1376ed76-2197-4e77-a224-cdc5f72c1363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>day of the year where DHWmax</dd></dl></div><div class='xr-var-data'><pre>array([[ 68.,  68.,  68., ..., 116., 116.,  17.],\n",
+       "       [ 68.,  68.,  68., ...,  17.,  17.,  17.],\n",
+       "       [ 68.,  68.,  68., ..., 117.,  17.,  17.],\n",
+       "       ...,\n",
+       "       [ nan,  nan,  nan, ...,  68.,  68.,  68.],\n",
+       "       [ nan,  nan,  nan, ...,  68.,  68.,  68.],\n",
+       "       [ nan,  nan,  nan, ...,  68.,  68.,  68.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2982e736-38a1-4b74-ab2b-64f3d02d6aca' class='xr-section-summary-in' type='checkbox'  ><label for='section-2982e736-38a1-4b74-ab2b-64f3d02d6aca' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-71df8b6e-ae93-452f-bb2c-2f9e8f1759d2' class='xr-index-data-in' type='checkbox'/><label for='index-71df8b6e-ae93-452f-bb2c-2f9e8f1759d2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.524999618530273,  -8.574999809265137,              -8.625,\n",
+       "        -8.675000190734863,  -8.725000381469727,  -8.774999618530273,\n",
+       "        -8.824999809265137,              -8.875,  -8.925000190734863,\n",
+       "        -8.975000381469727,\n",
+       "       ...\n",
+       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
+       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
+       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
+       "       -25.975000381469727],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=350))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a7f4c95c-06b5-456a-b0b7-b037bd0c3ea6' class='xr-index-data-in' type='checkbox'/><label for='index-a7f4c95c-06b5-456a-b0b7-b037bd0c3ea6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([140.02499389648438,  140.0749969482422,            140.125,\n",
+       "        140.1750030517578, 140.22500610351562, 140.27499389648438,\n",
+       "        140.3249969482422,            140.375,  140.4250030517578,\n",
+       "       140.47500610351562,\n",
+       "       ...\n",
+       "       159.52499389648438,  159.5749969482422,            159.625,\n",
+       "        159.6750030517578, 159.72500610351562, 159.77499389648438,\n",
+       "        159.8249969482422,            159.875,  159.9250030517578,\n",
+       "       159.97500610351562],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a0f952e-eb7c-431d-b613-9a6a84c72ebc' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a0f952e-eb7c-431d-b613-9a6a84c72ebc' 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>title :</span></dt><dd>DHWmax and time of the year when DHW max was reached</dd><dt><span>creator_name :</span></dt><dd>YOUR NAME</dd><dt><span>creator_email :</span></dt><dd>YOUR EMAIL</dd><dt><span>comment :</span></dt><dd>Data extracted from the GBR Data Management System https://stac.reefdata.io/browser</dd><dt><span>geospatial_lat_max :</span></dt><dd>-8.5</dd><dt><span>geospatial_lat_min :</span></dt><dd>-26.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>160.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>140.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05</dd><dt><span>geospatial_lon_resolution] :</span></dt><dd>0.05</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-01</dd><dt><span>time_coverage_end :</span></dt><dd>2024-12-31</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:  (lat: 350, lon: 400)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
+       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
+       "Data variables:\n",
+       "    DHWmax   (lat, lon) float32 560kB 7.32 6.44 5.61 4.94 ... 13.82 13.55 13.38\n",
+       "    DHWyday  (lat, lon) float64 1MB 68.0 68.0 68.0 68.0 ... 68.0 68.0 68.0 68.0\n",
+       "Attributes:\n",
+       "    title:                       DHWmax and time of the year when DHW max was...\n",
+       "    creator_name:                YOUR NAME\n",
+       "    creator_email:               YOUR EMAIL\n",
+       "    comment:                     Data extracted from the GBR Data Management ...\n",
+       "    geospatial_lat_max:          -8.5\n",
+       "    geospatial_lat_min:          -26.0\n",
+       "    geospatial_lon_max:          160.0\n",
+       "    geospatial_lon_min:          140.0\n",
+       "    geospatial_lat_resolution:   0.05\n",
+       "    geospatial_lon_resolution]:  0.05\n",
+       "    time_coverage_start:         2024-01-01\n",
+       "    time_coverage_end:           2024-12-31"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Create DHWmax dataset\n",
+    "DHW_max = xr.Dataset({'DHWmax': DHWmax, 'DHWyday': DHWyday})\n",
+    "DHW_max.attrs= globAttrs\n",
+    "\n",
+    "DHW_max"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write the results into a local file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "## write results\n",
+    "DHW_max.to_netcdf(\"DHWmax_y2date.nc\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Done!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dms",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Python_based_scripts/07-stac-metadata.ipynb b/Python_based_scripts/07-stac-metadata.ipynb
new file mode 100644
index 0000000..dc251ff
--- /dev/null
+++ b/Python_based_scripts/07-stac-metadata.ipynb
@@ -0,0 +1,4028 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e06a27bf",
+   "metadata": {},
+   "source": [
+    "# Metadata API Introduction\n",
+    "\n",
+    "This notebook shows basic use of our metadata API using a Python library PySTAC-Client. It demonstrates how to fetch all collections, fetch a given collection/item, and perform simple searches."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1e16077c",
+   "metadata": {},
+   "source": [
+    "# Connecting to metadata API\n",
+    "\n",
+    "We first connect to the metadata API by retrieving the root catalog\n",
+    "\n",
+    "To do this, you will need to go to https://dashboard.reefdata.io/ and copy your Authentication token.\n",
+    "This can then be pasted into the password prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "98942e75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'GBR-DMS Data Catalogue'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from getpass import getpass\n",
+    "import pystac_client\n",
+    "\n",
+    "# Metadata STAC API root url\n",
+    "URL = 'https://stac.reefdata.io'\n",
+    "\n",
+    "# Go to https://dashboard.reefdata.io/, copy your API key and paste into password box\n",
+    "\n",
+    "# Create the client\n",
+    "api = pystac_client.Client.open(\n",
+    "    url=\"https://stac.reefdata.io/\",\n",
+    "    headers={\n",
+    "        'Authorization': f\"Bearer {getpass()}\"\n",
+    "    },\n",
+    "    #ignore_conformance=True\n",
+    ")\n",
+    "\n",
+    "api.title"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f6efbb86",
+   "metadata": {},
+   "source": [
+    "# Fetch all STAC collections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bb7693fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<CollectionClient id=bom-reeftemp>\n",
+      "<CollectionClient id=jcu-tropwater-seagrass-mapping>\n",
+      "<CollectionClient id=jcu-nerp-effectiveness-inshore-monitoring>\n",
+      "<CollectionClient id=abs-spatial>\n",
+      "<CollectionClient id=imos-anmn-moorings>\n",
+      "<CollectionClient id=abs-census>\n",
+      "<CollectionClient id=aims-ltmp-mmp-coralreef>\n",
+      "<CollectionClient id=aims-temp>\n",
+      "<CollectionClient id=seltmp>\n",
+      "<CollectionClient id=coral-sea-boundary>\n",
+      "<CollectionClient id=imos-anmn-nrs>\n",
+      "<CollectionClient id=qtmr-vessels>\n",
+      "<CollectionClient id=des-slats>\n",
+      "<CollectionClient id=imos-anfog>\n",
+      "<CollectionClient id=des-qlump>\n",
+      "<CollectionClient id=imos-nrmn>\n",
+      "<CollectionClient id=mmp>\n",
+      "<CollectionClient id=aims-weather>\n",
+      "<CollectionClient id=des-wildnet>\n",
+      "<CollectionClient id=des-wq>\n",
+      "<CollectionClient id=amsa-vessel-tracking>\n",
+      "<CollectionClient id=bom-auswave>\n",
+      "<CollectionClient id=noaa-crw>\n",
+      "<CollectionClient id=ereefs>\n",
+      "<CollectionClient id=nasa-jpl-mursst>\n",
+      "<CollectionClient id=ga-gbr-hr-depth-model>\n",
+      "<CollectionClient id=imos-satellite-remote-sensing>\n",
+      "<CollectionClient id=gbrmpa-admin-regions>\n",
+      "<CollectionClient id=des-coastal-data-system>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for collection in api.get_collections():\n",
+    "    print(collection)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bdb8721b",
+   "metadata": {},
+   "source": [
+    "# Fetch a given collection by ID"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fef20a46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<style>\n",
+       ".pystac-summary {\n",
+       "    cursor: pointer;\n",
+       "    display: list-item;\n",
+       "    list-style: revert;\n",
+       "    margin-bottom: 0 !important;\n",
+       "\n",
+       "    .pystac-l {\n",
+       "        padding-left: 0.5em;\n",
+       "        color: rgb(64, 128, 128);\n",
+       "        font-style: italic;\n",
+       "    }\n",
+       "}\n",
+       ".pystac-row {\n",
+       "    overflow-wrap: break-word;\n",
+       "    padding-left: .825em;\n",
+       "\n",
+       "    .pystac-k {\n",
+       "        display: inline-block;\n",
+       "        margin: 0px 0.5em 0px 0px;\n",
+       "    }\n",
+       "    .pystac-v {\n",
+       "        color: rgb(186, 33, 33);\n",
+       "    }\n",
+       "}\n",
+       ".pystac-k {\n",
+       "    color: rgb(0, 128, 0);\n",
+       "    font-weight: 700;\n",
+       "}\n",
+       "</style>\n",
+       "<div class=\"jp-RenderedJSON jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div class=\"container\" style=\"line-height: normal;\">\n",
+       "        <ul style=\"padding: 0px; margin: 0px; list-style: none; display: block;\">\n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"Collection\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">id</span>\n",
+       "            <span class=\"pystac-v\">\"aims-temp\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">stac_version</span>\n",
+       "            <span class=\"pystac-v\">\"1.0.0\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"Reefs are under threat from climate change. Elevated sea temperature is a major stress to reefs, capable of causing widespread coral bleaching and mortality. Small excursions of temperature outside normal summer levels can cause stress and bleaching in corals, especially sensitive species. Temperature data from this program are used to correlate with bleaching events and derive local bleaching thresholds. Data are also used in a wide variety of other marine research programs, including fisheries, ballast water, turtles, sea birds, seagrass, coral disease, oceanography, process studies, validation of proxy climate records etc.\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">links</span><span class=\"pystac-l\">[] 9 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"items\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac.reefdata.io/collections/aims-temp/items\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"application/geo+json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"parent\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
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+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">\"EARTH SCIENCE>OCEANS>MARINE ENVIRONMENT MONITORING\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">2</span>\n",
+       "            <span class=\"pystac-v\">\"EARTH SCIENCE>OCEANS>OCEAN TEMPERATURE>SEA SURFACE TEMPERATURE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">3</span>\n",
+       "            <span class=\"pystac-v\">\"EARTH SCIENCE>OCEANS>OCEAN TEMPERATURE>WATER TEMPERATURE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">4</span>\n",
+       "            <span class=\"pystac-v\">\"Oceans\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">5</span>\n",
+       "            <span class=\"pystac-v\">\"Sea Water Temperature\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">6</span>\n",
+       "            <span class=\"pystac-v\">\"Temp Loggers\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">providers</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"Australian Institute of Marine Science (AIMS)\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"AIMS Sea Water Temperature Observing System\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">roles</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">\"licensor\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">\"producer\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">url</span>\n",
+       "            <span class=\"pystac-v\">\"https://www.aims.gov.au\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"IMOS\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"Integrated Marine Observing System\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">roles</span><span class=\"pystac-l\">[] 1 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">\"host\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">url</span>\n",
+       "            <span class=\"pystac-v\">\"https://imos.org.au/\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">summaries</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">proj:epsg</span><span class=\"pystac-l\">[] 1 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">4326</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "        </ul>\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<CollectionClient id=aims-temp>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "collection = api.get_collection('aims-temp')\n",
+    "collection"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "47a540fe",
+   "metadata": {},
+   "source": [
+    "# Fetch all items\n",
+    "\n",
+    "The function get_items return iterators, where pystac-client will handle retrieval of additional pages when needed. Note that one request is made for the first ten items, you can make a second request for the next ten."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "17d6de4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First page\n",
+      "0: <Item id=aims-temp-loggers>\n"
+     ]
+    }
+   ],
+   "source": [
+    "items = collection.get_items()\n",
+    "\n",
+    "# flush stdout so we can see the exact order that things happen\n",
+    "def get_ten_items(items):\n",
+    "    for i, item in enumerate(items):\n",
+    "        print(f\"{i}: {item}\", flush=True)\n",
+    "        if i == 9:\n",
+    "            return\n",
+    "\n",
+    "print('First page', flush=True)\n",
+    "get_ten_items(items)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f385a94d",
+   "metadata": {},
+   "source": [
+    "# Fetch a given item\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "deafc5f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<style>\n",
+       ".pystac-summary {\n",
+       "    cursor: pointer;\n",
+       "    display: list-item;\n",
+       "    list-style: revert;\n",
+       "    margin-bottom: 0 !important;\n",
+       "\n",
+       "    .pystac-l {\n",
+       "        padding-left: 0.5em;\n",
+       "        color: rgb(64, 128, 128);\n",
+       "        font-style: italic;\n",
+       "    }\n",
+       "}\n",
+       ".pystac-row {\n",
+       "    overflow-wrap: break-word;\n",
+       "    padding-left: .825em;\n",
+       "\n",
+       "    .pystac-k {\n",
+       "        display: inline-block;\n",
+       "        margin: 0px 0.5em 0px 0px;\n",
+       "    }\n",
+       "    .pystac-v {\n",
+       "        color: rgb(186, 33, 33);\n",
+       "    }\n",
+       "}\n",
+       ".pystac-k {\n",
+       "    color: rgb(0, 128, 0);\n",
+       "    font-weight: 700;\n",
+       "}\n",
+       "</style>\n",
+       "<div class=\"jp-RenderedJSON jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div class=\"container\" style=\"line-height: normal;\">\n",
+       "        <ul style=\"padding: 0px; margin: 0px; list-style: none; display: block;\">\n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"Feature\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">stac_version</span>\n",
+       "            <span class=\"pystac-v\">\"1.0.0\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">id</span>\n",
+       "            <span class=\"pystac-v\">\"aims-temp-loggers\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">properties</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"AIMS - Sea Water Temperature Observing System\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">themes</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">scheme</span>\n",
+       "            <span class=\"pystac-v\">\"Topic Category Keywords\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">concepts</span><span class=\"pystac-l\">[] 1 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">id</span>\n",
+       "            <span class=\"pystac-v\">\"oceans\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">url</span>\n",
+       "            <span class=\"pystac-v\">\"https://wiki.esipfed.org/ISO_19115-3_Codelists#MD_TopicCategoryCode\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"Oceans\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">scheme</span>\n",
+       "            <span class=\"pystac-v\">\"GCMD Keywords\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">concepts</span><span class=\"pystac-l\">[] 3 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">id</span>\n",
+       "            <span class=\"pystac-v\">\"ca154e02-a226-4cc7-8e4a-4474e7eb1eeb\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">url</span>\n",
+       "            <span class=\"pystac-v\">\"https://gcmd.earthdata.nasa.gov/KeywordViewer/scheme/all/ca154e02-a226-4cc7-8e4a-4474e7eb1eeb\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"MARINE ENVIRONMENT MONITORING\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"EARTH SCIENCE>OCEANS>MARINE ENVIRONMENT MONITORING\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">id</span>\n",
+       "            <span class=\"pystac-v\">\"bd24a9a9-7d52-4c29-b2a0-6cefd216ae78\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">url</span>\n",
+       "            <span class=\"pystac-v\">\"https://gcmd.earthdata.nasa.gov/KeywordViewer/scheme/all/bd24a9a9-7d52-4c29-b2a0-6cefd216ae78\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"SEA SURFACE TEMPERATURE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"EARTH SCIENCE>OCEANS>OCEAN TEMPERATURE>SEA SURFACE TEMPERATURE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">2</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">id</span>\n",
+       "            <span class=\"pystac-v\">\"46206e8c-8def-406f-9e62-da4e74633a58\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">url</span>\n",
+       "            <span class=\"pystac-v\">\"https://gcmd.earthdata.nasa.gov/KeywordViewer/scheme/all/46206e8c-8def-406f-9e62-da4e74633a58\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"WATER TEMPERATURE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"EARTH SCIENCE>OCEANS>OCEAN TEMPERATURE>WATER TEMPERATURE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">sci:doi</span>\n",
+       "            <span class=\"pystac-v\">\"10.25845/5b4eb0f9bb848\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">updated</span>\n",
+       "            <span class=\"pystac-v\">\"2024-06-08T15:59:58Z\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">proj:epsg</span>\n",
+       "            <span class=\"pystac-v\">4326</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"The data provided here are from a number of seawater temperature monitoring programs conducted in tropical and subtropical coral reef environments around Australia, all led by the Australian Institute of Marine Science (AIMS). Data are available from approximately 80 GBR sites, 16 Coral Sea sites, 7 sites in North West Western Australia (WA), 8 Queensland regional ports, 13 sites in the Solitary Islands, 4 sites in PNG, and 10 sites in the Cocos (Keeling) Islands. Data are obtained from in-situ data loggers deployed on the reef. Temperature instruments sample water temperatures every 5-10 minutes (typically) and are exchanged and downloaded approximately every 12 months. Temperature loggers on the reef flat are generally placed just below the Lowest Astronomical Tide level. Reef-slope (or where specified as Upper reef-slope) generally refers to depths 5 - 9 m while Deep reef-slope refers to depths of ~20 m.\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">end_datetime</span>\n",
+       "            <span class=\"pystac-v\">\"2024-05-13T13:50:00Z\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">sci:citation</span>\n",
+       "            <span class=\"pystac-v\">\"Australian Institute of Marine Science (AIMS). (2017). AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program). https://doi.org/10.25845/5b4eb0f9bb848. Accessed on [DATE].\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">table:columns</span><span class=\"pystac-l\">[] 22 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">499410</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">2612</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"deployment_id\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"integer\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"Yorke Island\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"100th Site\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"site\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">2</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">3820</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">1</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"site_id\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"integer\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">3</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"YORKSL6\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"100THSITE\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"subsite\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">4</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">15516</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">2612</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"subsite_id\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"integer\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">5</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"2024-02-09T00:00:00\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"1991-11-20T00:00:00\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"from_date\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"datetime\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">6</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"2024-05-14T00:00:00\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"1992-06-21T00:00:00\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"thru_date\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"datetime\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">7</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">35.2</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">0</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"depth\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"number\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">coordinates</span>\n",
+       "            <span class=\"pystac-v\">\"z\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">8</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"Water Temperature\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"Water Temperature\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"parameter\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">9</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"Vemco\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"Hobo\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"instrument_type\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">10</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"SU-12887\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"10048399\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"serial_num\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">11</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">-2.6651</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">-34.3725</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"lat\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"number\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">coordinates</span>\n",
+       "            <span class=\"pystac-v\">\"y\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">12</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">159.2372</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">96.7556</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"lon\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"number\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">coordinates</span>\n",
+       "            <span class=\"pystac-v\">\"x\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">13</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"23-079\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"11-055\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"gbrmpa_reef_id\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">14</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"fe1301f9-6d81-4ffe-9d68-112c1bc48b32\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"02132000-49ef-4fae-87b3-c45ffae4b988\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"metadata_uuid\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">15</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">1</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">1</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"sites_with_climatology_available\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"integer\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">16</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"2024-05-13T13:50:00Z\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"1991-11-20T14:00:00Z\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"time\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"datetime\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">coordinates</span>\n",
+       "            <span class=\"pystac-v\">\"t\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
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+       "    \n",
+       "            \n",
+       "        \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">17</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">228.7883</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">5.65</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"cal_val\"</span>\n",
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+       "            \n",
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+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">18</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">33</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">16</span>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
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+       "            \n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
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+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">4</span>\n",
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+       "            \n",
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+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">1</span>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"qc_flag\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"integer\"</span>\n",
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+       "    \n",
+       "            \n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">20</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"geometry\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"any\"</span>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">coordinates</span>\n",
+       "            <span class=\"pystac-v\">\"geometry\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">21</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">max</span>\n",
+       "            <span class=\"pystac-v\">\"2024-990-498874\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">min</span>\n",
+       "            <span class=\"pystac-v\">\"1991-923-2786\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">name</span>\n",
+       "            <span class=\"pystac-v\">\"fid\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"string\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">start_datetime</span>\n",
+       "            <span class=\"pystac-v\">\"1991-11-20T14:00:00Z\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">table:row_count</span>\n",
+       "            <span class=\"pystac-v\">176703852</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">datetime</span>\n",
+       "            <span class=\"pystac-v\">None</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">geometry</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"Polygon\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">coordinates</span><span class=\"pystac-l\">[] 1 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span><span class=\"pystac-l\">[] 5 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">159.2372</span>\n",
+       "        </li>\n",
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+       "            \n",
+       "        \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">-34.3725</span>\n",
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+       "            \n",
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+       "        \n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">159.2372</span>\n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">-2.6651</span>\n",
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+       "            \n",
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+       "        \n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">2</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">96.7556</span>\n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">-2.6651</span>\n",
+       "        </li>\n",
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+       "            \n",
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+       "        \n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">3</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
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+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">96.7556</span>\n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
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+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">-34.3725</span>\n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">4</span><span class=\"pystac-l\">[] 2 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">159.2372</span>\n",
+       "        </li>\n",
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+       "            \n",
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+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">-34.3725</span>\n",
+       "        </li>\n",
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+       "            \n",
+       "        \n",
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+       "        \n",
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+       "            \n",
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+       "        \n",
+       "    </details></li>\n",
+       "            \n",
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+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
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+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">links</span><span class=\"pystac-l\">[] 12 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">0</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac.reefdata.io/collections/aims-temp/items/aims-temp-loggers\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
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+       "            <span class=\"pystac-v\">\"application/json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
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+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">1</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"collection\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac.reefdata.io/collections/aims-temp\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"application/json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">2</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"parent\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac.reefdata.io/collections/aims-temp\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"application/json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">3</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"root\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac.reefdata.io/collections/aims-temp\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"application/json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"AIMS Sea Water Temperature Observing System\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">4</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"describedby\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://pygeoapi.reefdata.io/collections/aims-temp-loggers\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"Link to Data API\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">5</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"describedby\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://pygeoapi.reefdata.io/openapi?f=html#/aims-temp-loggers\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"Link to Data API Documentation\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">6</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"describedby\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://portal.aodn.org.au/search?uuid=4a12a8c0-c573-11dc-b99b-00008a07204e\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"Link to the Original Data\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">7</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"describedby\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://creativecommons.org/licenses/by/3.0/au/\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"License: CC-BY-3.0-AU\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">8</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"license\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://creativecommons.org/licenses/by/3.0/au/\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"CC-BY-3.0-AU\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">9</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"describedby\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"Link to Original Metadata\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">10</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"cite-as\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://doi.org/10.25845/5b4eb0f9bb848\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"How to cite this dataset\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">11</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">rel</span>\n",
+       "            <span class=\"pystac-v\">\"describedby\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://www.aims.gov.au/cc-attribution\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"How to Acknowledge AIMS\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">assets</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">data</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"application/x-parquet\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"AIMS - Sea Water Temperature Observing System\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"S3 address of the AIMS - Sea Water Temperature Observing System in GeoParquet format.\n",
+       "***\n",
+       "\n",
+       "### Connect to the data via Python:\n",
+       "```python\n",
+       "import pyarrow.dataset as ds\n",
+       "\n",
+       "dataset = ds.dataset(\"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\")\n",
+       "print(dataset.schema)\n",
+       "# See https://arrow.apache.org/docs/python/dataset.html for the use of pyarrow library\n",
+       "```\n",
+       "\n",
+       "### Connect to the data via R:\n",
+       "```r\n",
+       "library(arrow)\n",
+       "\n",
+       "bucket <- s3_bucket(\"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\")\n",
+       "df <- open_dataset(bucket)\n",
+       "print(df$schema)\n",
+       "# See https://arrow.apache.org/docs/r/index.html for the use of arrow library\n",
+       "```\n",
+       "\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">roles</span><span class=\"pystac-l\">[] 1 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">\"data\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li><details>\n",
+       "            <summary class=\"pystac-summary\"><span class=\"pystac-k\">metadata</span></summary>\n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">href</span>\n",
+       "            <span class=\"pystac-v\">\"https://gbr-dms-data-public.s3.ap-southeast-2.amazonaws.com/aims-temp-loggers/datapackage.json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">type</span>\n",
+       "            <span class=\"pystac-v\">\"application/json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">title</span>\n",
+       "            <span class=\"pystac-v\">\"datapackage.json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">description</span>\n",
+       "            <span class=\"pystac-v\">\"Frictionless metadata for AIMS - Sea Water Temperature Observing System dataset\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "            \n",
+       "                <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">roles</span><span class=\"pystac-l\">[] 1 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">\"metadata\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        </details></li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">bbox</span><span class=\"pystac-l\">[] 4 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">96.7556</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">-34.3725</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">2</span>\n",
+       "            <span class=\"pystac-v\">159.2372</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">3</span>\n",
+       "            <span class=\"pystac-v\">-2.6651</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">stac_extensions</span><span class=\"pystac-l\">[] 4 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac-extensions.github.io/projection/v1.1.0/schema.json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac-extensions.github.io/themes/v1.0.0/schema.json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">2</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac-extensions.github.io/table/v1.2.0/schema.json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">3</span>\n",
+       "            <span class=\"pystac-v\">\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">collection</span>\n",
+       "            <span class=\"pystac-v\">\"aims-temp\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "                \n",
+       "            \n",
+       "                \n",
+       "                    <li><details>\n",
+       "        <summary class=\"pystac-summary\"><span class=\"pystac-k\">keywords</span><span class=\"pystac-l\">[] 3 items</span></summary>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">0</span>\n",
+       "            <span class=\"pystac-v\">\"AIMS\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">1</span>\n",
+       "            <span class=\"pystac-v\">\"Temp Loggers\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "            <ul style=\"margin: 0px; padding: 0px 0px 0px 1.75em; list-style: none; display: block;\">\n",
+       "        \n",
+       "            \n",
+       "                \n",
+       "        <li class=\"pystac-row\">\n",
+       "            <span class=\"pystac-k\">2</span>\n",
+       "            <span class=\"pystac-v\">\"Sea Water Temperature\"</span>\n",
+       "        </li>\n",
+       "    \n",
+       "            \n",
+       "        \n",
+       "    </ul>\n",
+       "        \n",
+       "    </details></li>\n",
+       "                \n",
+       "            \n",
+       "        </ul>\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Item id=aims-temp-loggers>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "item = collection.get_item('aims-temp-loggers')\n",
+    "item"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c9609c32",
+   "metadata": {},
+   "source": [
+    "# Inspect an item for assets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bc7927c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'href': 's3://gbr-dms-data-public/aims-temp-loggers/data.parquet', 'type': 'application/x-parquet', 'title': 'AIMS - Sea Water Temperature Observing System', 'description': 'S3 address of the AIMS - Sea Water Temperature Observing System in GeoParquet format.\\n***\\n\\n### Connect to the data via Python:\\n```python\\nimport pyarrow.dataset as ds\\n\\ndataset = ds.dataset(\"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\")\\nprint(dataset.schema)\\n# See https://arrow.apache.org/docs/python/dataset.html for the use of pyarrow library\\n```\\n\\n### Connect to the data via R:\\n```r\\nlibrary(arrow)\\n\\nbucket <- s3_bucket(\"s3://gbr-dms-data-public/aims-temp-loggers/data.parquet\")\\ndf <- open_dataset(bucket)\\nprint(df$schema)\\n# See https://arrow.apache.org/docs/r/index.html for the use of arrow library\\n```\\n', 'roles': ['data']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect assets\n",
+    "item_assets = item.get_assets()\n",
+    "data_asset = item_assets['data']\n",
+    "if data_asset is not None:\n",
+    "    print(data_asset.to_dict())  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5292b0dd",
+   "metadata": {},
+   "source": [
+    "# Inspect an item for link to data API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "50430e73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'rel': 'describedby', 'href': 'https://pygeoapi.reefdata.io/collections/aims-temp-loggers', 'title': 'Link to Data API'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect link to data API\n",
+    "link = item.get_single_link(rel=\"describedby\")\n",
+    "if link is not None:\n",
+    "    print(link.to_dict())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "62e26114",
+   "metadata": {},
+   "source": [
+    "# Search for items by spatial and temporal extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d8af6334",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "des-wq-gr-buoy3-env-nearrealtime\n",
+      "des-wq-gr-buoy3-nearrealtime\n",
+      "des-wq-burnett-nearrealtime\n",
+      "des-wq-el-buoy1-nearrealtime\n",
+      "des-wq-el-buoy1-env-nearrealtime\n",
+      "des-wq-el-buoy1-current-nearrealtime\n",
+      "abs-lgas-2022\n",
+      "amsa-ais\n",
+      "csiro-seltmp-baseline-surveys-jul22\n",
+      "abs-postal-areas-2021\n",
+      "abs-lgas-2021\n",
+      "gbrmpa-geomorphic-map\n",
+      "gbrmpa-benthic-map\n",
+      "des-qlump-gbr-nrm-2021\n",
+      "des-wq-dempster-nearrealtime\n"
+     ]
+    }
+   ],
+   "source": [
+    "geom = {\n",
+    "    \"type\": \"Polygon\",\n",
+    "    \"coordinates\": [\n",
+    "      [\n",
+    "        [\n",
+    "          162,\n",
+    "          -33\n",
+    "        ],\n",
+    "        [\n",
+    "          162,\n",
+    "          -3\n",
+    "        ],\n",
+    "        [\n",
+    "          136,\n",
+    "          -3\n",
+    "        ],\n",
+    "        [\n",
+    "          136,\n",
+    "          -33\n",
+    "        ],\n",
+    "        [\n",
+    "          162,\n",
+    "          -33\n",
+    "        ]\n",
+    "      ]\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "results = api.search(\n",
+    "    max_items = 15,\n",
+    "    limit = 5,\n",
+    "    intersects = geom,\n",
+    "    datetime = \"2000-01-01/2023-03-07\",\n",
+    ")\n",
+    "\n",
+    "for item in results.items():\n",
+    "    print(item.id)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "98229925",
+   "metadata": {},
+   "source": [
+    "# Search for items using query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2b773a87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Search for items using a query to filter by keywords\n",
+    "# Currently \"contains\" operator is not supported, so you can only \"eq\" operator find exact matches\n",
+    "\n",
+    "results = api.search(\n",
+    "    max_items = 15,\n",
+    "    limit = 5,\n",
+    "    query={\"themes\": {\"eq\": [{'scheme': 'Topic Category Keywords',\n",
+    "                              'concepts': [{'id': 'oceans', 'title': 'Oceans'}]}]}}\n",
+    ")\n",
+    "\n",
+    "\n",
+    "for item in results.items():\n",
+    "    print(item.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06f157d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "d95ed3e0c57c5648f0aae0a872a2f6da817c91393ca776950b237d8871ec68c2"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Python_based_scripts/AIMS_waterTemp.ipynb b/Python_based_scripts/AIMS_waterTemp.ipynb
deleted file mode 100644
index ee334cf..0000000
--- a/Python_based_scripts/AIMS_waterTemp.ipynb
+++ /dev/null
@@ -1,557 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AIMS ocean temperature dataset\n",
-    "Authors: Leo Ghignone, Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook demonstrates direct access to the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset in the RIMReP `geoparquet` collection. This dataset includes sea temperature data from 1980 for tropical and subtropical coral reefs around Australia, and it includes sites within the Great Barrier Reef.   \n",
-    "  \n",
-    "The `geoparquet` collection is a compilation of datasets that have been converted to the Apache `parquet` format. This format is designed for efficient storage and retrieval of tabular data. The `geoparquet` collection is stored on the RIMReP S3 bucket and is accessible to all users.  \n",
-    "  \n",
-    "The workflow in this notebook is as follows:\n",
-    "- Inspect dataset metadata\n",
-    "- Subset the dataset to include sites within the Great Barrier Reef (GBR)\n",
-    "- Aggregate temperature data per site\n",
-    "- Create a map with the location of all sites where GBR temperature loggers were deployed"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing S3 bucket\n",
-    "from pyarrow import parquet as pq\n",
-    "# Manipulating dataframes\n",
-    "import pandas as pd\n",
-    "# Load and manipulate geospatial data\n",
-    "import dask_geopandas\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Mapping\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cf"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting metadata\n",
-    "The AIMS Temperature dataset contains over 150 million records. We will use its metadata to check the structure of the dataset and to determine the best way to access the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.FileMetaData object at 0x000002B230BE0BD0>\n",
-       "  created_by: parquet-cpp-arrow version 10.0.1\n",
-       "  num_columns: 23\n",
-       "  num_rows: 150663521\n",
-       "  num_row_groups: 50\n",
-       "  format_version: 2.6\n",
-       "  serialized_size: 155051"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Connecting to metadata file\n",
-    "md = pq.read_metadata(f\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/_metadata\")\n",
-    "#Checking contents\n",
-    "md"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metadata is telling us that the AIMS temperature dataset contains 23 columns and over 150 million rows, and it is made up of 50 different files.  \n",
-    "  \n",
-    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.ParquetSchema object at 0x000002B230BE5C40>\n",
-       "required group field_id=-1 schema {\n",
-       "  optional int64 field_id=-1 fid;\n",
-       "  optional int64 field_id=-1 deployment_id;\n",
-       "  optional binary field_id=-1 site (String);\n",
-       "  optional int64 field_id=-1 site_id;\n",
-       "  optional binary field_id=-1 subsite (String);\n",
-       "  optional int64 field_id=-1 subsite_id;\n",
-       "  optional int64 field_id=-1 from_date (Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional int64 field_id=-1 thru_date (Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional double field_id=-1 depth;\n",
-       "  optional binary field_id=-1 parameter (String);\n",
-       "  optional binary field_id=-1 instrument_type (String);\n",
-       "  optional binary field_id=-1 serial_num (String);\n",
-       "  optional double field_id=-1 lat;\n",
-       "  optional double field_id=-1 lon;\n",
-       "  optional binary field_id=-1 gbrmpa_reef_id (String);\n",
-       "  optional binary field_id=-1 metadata_uuid (String);\n",
-       "  optional double field_id=-1 sites_with_climatology_available;\n",
-       "  optional int64 field_id=-1 time (Timestamp(isAdjustedToUTC=true, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional double field_id=-1 cal_val;\n",
-       "  optional double field_id=-1 qc_val;\n",
-       "  optional double field_id=-1 qc_flag;\n",
-       "  optional binary field_id=-1 geometry;\n",
-       "  optional int64 field_id=-1 hilbert_distance (Int(bitWidth=64, isSigned=false));\n",
-       "}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `schema` tells us that the dataset contains 23 columns, which matches the information we got from the metadata. It also tells us that the data types are either `string` or `double`.  \n",
-    "  \n",
-    "We can also obtain more details about a specific column, by calling the `column` method and passing the name of the column as an argument as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ParquetColumnSchema>\n",
-       "  name: qc_val\n",
-       "  path: qc_val\n",
-       "  max_definition_level: 1\n",
-       "  max_repetition_level: 0\n",
-       "  physical_type: DOUBLE\n",
-       "  logical_type: None\n",
-       "  converted_type (legacy): NONE"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema.column(19)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we refer to the original site for the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e), we can see that the `qc_val` column refers to the quality controlled temperature obtained from the loggers deployed as part of this program."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to S3 parquet directory\n",
-    "We will connect to the S3 bucket containing the AIMS Temperature dataset using the `pyarrow` library. We will only keep sites that have been deployed in the Great Barrier Reef Marine Protected Area (GBRMPA), so we will provide a `filter` that contains the minimum and maximum coordinates (i.e., `lat` and `lon`) of a bounding box around the Great Barrier Reef Marine Protected Area.\n",
-    "\n",
-    "As we saw above, this dataset includes 23 columns, but we will only select a few that contain information that is interesting to us:\n",
-    "- `site`: name of site where temperature logger was deployed,\n",
-    "- `lat`: latitude of the logger deployment site,\n",
-    "- `lon`: longitude of the logger deployment site,\n",
-    "- `time`: date and time when loggers recorded temperature data,\n",
-    "- `qc_val`: quality controlled temperature data\n",
-    "  \n",
-    "The following code chunk may take a few minutes to run, as it is loading over 150 million records."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>time</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>149842221</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>2002-12-10 02:00:00+00:00</td>\n",
-       "      <td>31.043</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149696096</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>2003-07-17 18:30:00+00:00</td>\n",
-       "      <td>20.547</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149696097</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>2003-07-17 19:00:00+00:00</td>\n",
-       "      <td>20.567</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149696098</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>2003-07-17 19:30:00+00:00</td>\n",
-       "      <td>20.629</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149696099</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>2003-07-17 20:00:00+00:00</td>\n",
-       "      <td>20.649</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              site                      time  qc_val      lat       lon\n",
-       "fid                                                                    \n",
-       "149842221  Karumba 2002-12-10 02:00:00+00:00  31.043 -17.4935  140.8302\n",
-       "149696096  Karumba 2003-07-17 18:30:00+00:00  20.547 -17.4935  140.8302\n",
-       "149696097  Karumba 2003-07-17 19:00:00+00:00  20.567 -17.4935  140.8302\n",
-       "149696098  Karumba 2003-07-17 19:30:00+00:00  20.629 -17.4935  140.8302\n",
-       "149696099  Karumba 2003-07-17 20:00:00+00:00  20.649 -17.4935  140.8302"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating maximum and minimum coordinates for bounding box around the GBRMPA\n",
-    "filter = [('lon', '>=', 140.5),\n",
-    "          ('lat', '>=', -28.5),\n",
-    "          ('lon', '<=', 156.0),\n",
-    "          ('lat', '<=', -6.5)]\n",
-    "\n",
-    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter], \n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['site', 'time', 'qc_val', 'lat', 'lon'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) \n",
-    "\n",
-    "# Transforming to pandas dataframe\n",
-    "df = dask_geo_df.compute()\n",
-    "\n",
-    "#Checking the contents of the dataframe - First five rows\n",
-    "df.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see above that the `site` is repeated multiple times. This is because the temperature loggers were deployed on multiple occassions at the same site. If we print more rows, you will also see that the coordinates for a particular site vary slightly. This is because the temperature loggers were not deployed at the exact same location each time, but they were all deployed near each other.  \n",
-    "    \n",
-    "Next, we will calculate the mean deployment coordinate, mean temperature, and total number of temperature records for each site. We will use the `groupby` method to group the data by `site_id` and then we will use the `agg` method to perform our calculations.  \n",
-    "  \n",
-    "Note the use of `compute()` at the end of our code. This will trigger our calculations and load the results as a data frame into memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tempMean</th>\n",
-       "      <th>latMean</th>\n",
-       "      <th>lonMean</th>\n",
-       "      <th>dateMin</th>\n",
-       "      <th>dateMax</th>\n",
-       "      <th>number</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>site</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>19-131 Reef</th>\n",
-       "      <td>25.747975</td>\n",
-       "      <td>-19.766148</td>\n",
-       "      <td>149.378946</td>\n",
-       "      <td>2011-03-12 14:06:51+00:00</td>\n",
-       "      <td>2021-02-09 13:50:00+00:00</td>\n",
-       "      <td>721871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19-138 Reef</th>\n",
-       "      <td>25.757545</td>\n",
-       "      <td>-19.805137</td>\n",
-       "      <td>149.426347</td>\n",
-       "      <td>2011-03-14 14:03:50+00:00</td>\n",
-       "      <td>2022-01-05 13:50:00+00:00</td>\n",
-       "      <td>929087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19-159 Reef</th>\n",
-       "      <td>25.929941</td>\n",
-       "      <td>-19.663895</td>\n",
-       "      <td>149.915001</td>\n",
-       "      <td>2011-03-20 14:08:10+00:00</td>\n",
-       "      <td>2017-02-17 13:50:00+00:00</td>\n",
-       "      <td>324720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20-104 Reef</th>\n",
-       "      <td>25.845430</td>\n",
-       "      <td>-20.028918</td>\n",
-       "      <td>149.694016</td>\n",
-       "      <td>2015-01-31 14:00:00+00:00</td>\n",
-       "      <td>2021-02-13 13:50:00+00:00</td>\n",
-       "      <td>526896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20-344 Reef</th>\n",
-       "      <td>25.206312</td>\n",
-       "      <td>-20.771892</td>\n",
-       "      <td>150.891808</td>\n",
-       "      <td>2006-09-05 14:00:00+00:00</td>\n",
-       "      <td>2011-10-05 13:51:48+00:00</td>\n",
-       "      <td>141552</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              tempMean    latMean     lonMean                   dateMin  \\\n",
-       "site                                                                      \n",
-       "19-131 Reef  25.747975 -19.766148  149.378946 2011-03-12 14:06:51+00:00   \n",
-       "19-138 Reef  25.757545 -19.805137  149.426347 2011-03-14 14:03:50+00:00   \n",
-       "19-159 Reef  25.929941 -19.663895  149.915001 2011-03-20 14:08:10+00:00   \n",
-       "20-104 Reef  25.845430 -20.028918  149.694016 2015-01-31 14:00:00+00:00   \n",
-       "20-344 Reef  25.206312 -20.771892  150.891808 2006-09-05 14:00:00+00:00   \n",
-       "\n",
-       "                              dateMax  number  \n",
-       "site                                           \n",
-       "19-131 Reef 2021-02-09 13:50:00+00:00  721871  \n",
-       "19-138 Reef 2022-01-05 13:50:00+00:00  929087  \n",
-       "19-159 Reef 2017-02-17 13:50:00+00:00  324720  \n",
-       "20-104 Reef 2021-02-13 13:50:00+00:00  526896  \n",
-       "20-344 Reef 2011-10-05 13:51:48+00:00  141552  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Grouping our dataframe by site_id and aggregating the lat and lon columns by mean and the site_id column by count\n",
-    "counts = df.groupby('site').agg(\n",
-    "    #Calculating mean temperature\n",
-    "    tempMean = pd.NamedAgg(column = 'qc_val', aggfunc = 'mean'),\n",
-    "    #Calculating mean lat and lon\n",
-    "    latMean = pd.NamedAgg(column = 'lat', aggfunc = 'mean'),\n",
-    "    lonMean = pd.NamedAgg(column = 'lon', aggfunc = 'mean'),\n",
-    "    # #Calculating range of time when data was collected\n",
-    "    dateMin = pd.NamedAgg(column = 'time', aggfunc = 'min'),\n",
-    "    dateMax = pd.NamedAgg(column = 'time', aggfunc = 'max'), \n",
-    "    #Calculating the number of rows (i.e., deployments) per site_id\n",
-    "    number = pd.NamedAgg(column = 'site', aggfunc = 'count'))\n",
-    "\n",
-    "#Checking the contents of the dataframe - First five rows\n",
-    "counts.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mapping sites\n",
-    "From the summary table we created in the previous step, we will make a map with the location of all deployment sites within the GBRMPA bounding box. We will change the size of the markers based on the number of temperature records collected at each site (`number` column)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x19a0c2fb1d0>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting the data using a scatter plot\n",
-    "ax.scatter(x = counts.lonMean, y = counts.latMean, s = counts.number/50000, alpha = 0.5)\n",
-    "\n",
-    "#Adding a basemap of land areas\n",
-    "ax.add_feature(cf.LAND)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have successfully plotted the location of the AIMS temperature loggers within the GBRMPA bounding box. We can see that the number of temperature records per logger is not the same throughout the GBRMPA. The largest number of records are for loggers located near the coast."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "rimrep",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/Direct-accessing-eReef-zarr.ipynb b/Python_based_scripts/Direct-accessing-eReef-zarr.ipynb
deleted file mode 100644
index 0861ba9..0000000
--- a/Python_based_scripts/Direct-accessing-eReef-zarr.ipynb
+++ /dev/null
@@ -1,2304 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# `eReef` Direct Access and Use Example\n",
-    "\n",
-    "**Author**: Leo Li, Michaela Miller\n",
-    "\n",
-    "This notebook demonstrates how to access a public dataset from the RIMReP DMS that is in `zarr` format. We will use the [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (4km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr4.html) dataset as an example.  \n",
-    "  \n",
-    "Throughout this notebook, you will be able to:\n",
-    "\n",
-    "     1. Load necessary packages required for data connection and anaylsis\n",
-    "     2. Connect directly to the s3 bucket, load the data and check the size\n",
-    "     3. Priliminary data exploration to view data attributes and metadata\n",
-    "     4. Extracting and plotting temperature and salinity variables at specific locations, times and depths.\n",
-    "     5. Simple calculations using all data to calculate monthly means of temperature for 2022.\n",
-    "     6. Plotting a timeseries for a paritular location\n",
-    "     \n",
-    "There are also examples of how to do simple calculations to plot maps and timeseries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Load required packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading and manipulating gridded multidimensional data\n",
-    "import xarray as xr\n",
-    "# Accessing S3 buckets\n",
-    "import s3fs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Connecting to the s3 bucket, loading data, and checking sizes\n",
-    "First, you will need to connect directly to the `zarr` dataset in the RIMReP DMS. This is using an `s3` bucket address, and no credentials are needed to access these public data. Following, you will load the `zarr` data file and check the result. As a result, you should see an `xarray.Dataset` that contains dimensions, coordinates, variables, indexes and attributes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
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-       ".xr-icon-database,\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 4787, latitude: 723, longitude: 491, k: 17)\n",
-       "Coordinates:\n",
-       "  * latitude     (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036\n",
-       "  * longitude    (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
-       "  * time         (time) datetime64[ns] 2010-08-31T14:00:00 ... 2023-10-08T14:...\n",
-       "    zc           (k) float64 ...\n",
-       "Dimensions without coordinates: k\n",
-       "Data variables:\n",
-       "    eta          (time, latitude, longitude) float32 ...\n",
-       "    mean_cur     (time, k, latitude, longitude) float32 ...\n",
-       "    mean_wspeed  (time, latitude, longitude) float32 ...\n",
-       "    salt         (time, k, latitude, longitude) float32 ...\n",
-       "    temp         (time, k, latitude, longitude) float32 ...\n",
-       "    u            (time, k, latitude, longitude) float32 ...\n",
-       "    v            (time, k, latitude, longitude) float32 ...\n",
-       "    wspeed_u     (time, latitude, longitude) float32 ...\n",
-       "    wspeed_v     (time, latitude, longitude) float32 ...\n",
-       "Attributes: (12/23)\n",
-       "    Conventions:                   CF-1.0\n",
-       "    Parameter_File_Revision:       $Revision: 1753 $\n",
-       "    Run_ID:                        2.1\n",
-       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
-       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
-       "    aims_ncaggregate_buildDate:    2023-10-13T06:40:10+10:00\n",
-       "    ...                            ...\n",
-       "    paramhead:                     GBR 4km resolution grid\n",
-       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
-       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
-       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
-       "    technical_guide_publish_date:  2020-08-18\n",
-       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai...</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-ebcfd916-8136-4b92-b773-6d4f5203b792' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebcfd916-8136-4b92-b773-6d4f5203b792' 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>: 4787</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li><li><span>k</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-68950887-980f-41de-af37-be775ea3134a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68950887-980f-41de-af37-be775ea3134a' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-a6dc8b42-8d7b-4dbe-99df-e1a9ab725d21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6dc8b42-8d7b-4dbe-99df-e1a9ab725d21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a4e7427-a0a8-4f69-93ae-3988e6e5697e' class='xr-var-data-in' type='checkbox'><label for='data-9a4e7427-a0a8-4f69-93ae-3988e6e5697e' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
-       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-865ed27c-ade9-4b46-8174-16be7c50a449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-865ed27c-ade9-4b46-8174-16be7c50a449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe0e61d4-9343-4051-89f6-fa182393416e' class='xr-var-data-in' type='checkbox'><label for='data-fe0e61d4-9343-4051-89f6-fa182393416e' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
-       "       156.868788])</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'>2010-08-31T14:00:00 ... 2023-10-...</div><input id='attrs-68861f2a-4c26-4517-aa2a-72758b7d4e99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68861f2a-4c26-4517-aa2a-72758b7d4e99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c37e19a-b5c7-4c0f-9718-b644778ecda6' class='xr-var-data-in' type='checkbox'><label for='data-2c37e19a-b5c7-4c0f-9718-b644778ecda6' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>coordinate_type :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/UTAA/</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-31T14:00:00.000000000&#x27;, &#x27;2010-09-01T14:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-09-02T14:00:00.000000000&#x27;, ..., &#x27;2023-10-06T14:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-10-07T14:00:00.000000000&#x27;, &#x27;2023-10-08T14:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4bb786b-266e-48f6-a967-963eb08161cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4bb786b-266e-48f6-a967-963eb08161cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a45c8536-714e-4087-ac89-c235b4aebf21' class='xr-var-data-in' type='checkbox'><label for='data-a45c8536-714e-4087-ac89-c235b4aebf21' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0d18d31-6dc6-4147-80e0-4c4d3bfea7f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0d18d31-6dc6-4147-80e0-4c4d3bfea7f0' class='xr-section-summary' >Data variables: <span>(9)</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>eta</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75eb42b2-48e6-45a7-b98a-3cad02e831a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75eb42b2-48e6-45a7-b98a-3cad02e831a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b4b9c68-f808-46a5-8612-acec53a3d247' class='xr-var-data-in' type='checkbox'><label for='data-3b4b9c68-f808-46a5-8612-acec53a3d247' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Surface elevation</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/ASLVMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_surface_elevation</dd><dt><span>short_name :</span></dt><dd>eta</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_near_surface</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#Meter</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_cur</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a9b4eb7b-30ca-43ec-a325-50d74364a9f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9b4eb7b-30ca-43ec-a325-50d74364a9f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54e6b3f0-05a2-484f-9254-8ab0049caf2c' class='xr-var-data-in' type='checkbox'><label for='data-54e6b3f0-05a2-484f-9254-8ab0049caf2c' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_current_speed</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>short_name :</span></dt><dd>mean_cur</dd><dt><span>standard_name :</span></dt><dd>mean_current_speed</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_wspeed</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-813d15e4-ad9e-4c52-bb10-6f30ec0a0df7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-813d15e4-ad9e-4c52-bb10-6f30ec0a0df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a80bbc-a91d-4671-b458-b493c53a5e00' class='xr-var-data-in' type='checkbox'><label for='data-20a80bbc-a91d-4671-b458-b493c53a5e00' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_wind_speed</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>mean_wspeed</dd><dt><span>standard_name :</span></dt><dd>mean_wind_speed</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce7eaa2c-ad5f-404a-bf65-2ffc56f6dc04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce7eaa2c-ad5f-404a-bf65-2ffc56f6dc04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5acf328f-64f9-4c2a-a663-440ba13c15cd' class='xr-var-data-in' type='checkbox'><label for='data-5acf328f-64f9-4c2a-a663-440ba13c15cd' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/PSLTMP01/</dd><dt><span>qudt__unit :</span></dt><dd>http://qudt.org/vocab/unit/PSU</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/practical_salinity</dd><dt><span>short_name :</span></dt><dd>salt</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://environment.data.gov.au/water/quality/def/unit/PSU</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ee65cf2-bd06-4c30-9b4c-da59f0edc3be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee65cf2-bd06-4c30-9b4c-da59f0edc3be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0d1cba3-694d-4841-8376-18a5ca7a0ddd' class='xr-var-data-in' type='checkbox'><label for='data-b0d1cba3-694d-4841-8376-18a5ca7a0ddd' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5358297-69f9-48cb-8853-22ea23ffc988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5358297-69f9-48cb-8853-22ea23ffc988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cd10a46-94ca-444d-b0d5-0a03578150df' class='xr-var-data-in' type='checkbox'><label for='data-0cd10a46-94ca-444d-b0d5-0a03578150df' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Eastward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_eastward</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b868bc9-67a2-4e6c-9418-a8ae2648ec28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b868bc9-67a2-4e6c-9418-a8ae2648ec28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47ef0b66-acd5-4329-9b0a-5581780a6199' class='xr-var-data-in' type='checkbox'><label for='data-47ef0b66-acd5-4329-9b0a-5581780a6199' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Northward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCNSMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_northward</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_u</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1163fb31-f9dc-44c3-ace3-5526eaf5058f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1163fb31-f9dc-44c3-ace3-5526eaf5058f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1de1f4-b78e-498a-b229-ab86883ab314' class='xr-var-data-in' type='checkbox'><label for='data-4c1de1f4-b78e-498a-b229-ab86883ab314' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_u</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_v</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18a51945-cd9a-439d-8aff-5d4b8c8e9359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18a51945-cd9a-439d-8aff-5d4b8c8e9359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c38c24-8433-4396-9d46-1c415579f81e' class='xr-var-data-in' type='checkbox'><label for='data-97c38c24-8433-4396-9d46-1c415579f81e' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>northward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESNSMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_v</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6db37a6-e339-4e93-8be4-4c87063de724' class='xr-section-summary-in' type='checkbox'  ><label for='section-b6db37a6-e339-4e93-8be4-4c87063de724' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-307302b9-0d60-44b5-b2e5-acb422c3303e' class='xr-index-data-in' type='checkbox'/><label for='index-307302b9-0d60-44b5-b2e5-acb422c3303e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
-       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
-       "       ...\n",
-       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
-       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ecee958-cb0b-480b-b4c3-ab23c1b9f101' class='xr-index-data-in' type='checkbox'/><label for='index-7ecee958-cb0b-480b-b4c3-ab23c1b9f101' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
-       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
-       "       ...\n",
-       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
-       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd363f62-d661-4c70-9b50-5cc8b04f642a' class='xr-index-data-in' type='checkbox'/><label for='index-fd363f62-d661-4c70-9b50-5cc8b04f642a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-31 14:00:00&#x27;, &#x27;2010-09-01 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-02 14:00:00&#x27;, &#x27;2010-09-03 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-04 14:00:00&#x27;, &#x27;2010-09-05 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-06 14:00:00&#x27;, &#x27;2010-09-07 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-08 14:00:00&#x27;, &#x27;2010-09-09 14:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-09-29 14:00:00&#x27;, &#x27;2023-09-30 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-01 14:00:00&#x27;, &#x27;2023-10-02 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-03 14:00:00&#x27;, &#x27;2023-10-04 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-05 14:00:00&#x27;, &#x27;2023-10-06 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-07 14:00:00&#x27;, &#x27;2023-10-08 14:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=4787, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fbbc9ae-a009-4a29-af0e-2a0b1cdb18c2' class='xr-section-summary-in' type='checkbox'  ><label for='section-2fbbc9ae-a009-4a29-af0e-2a0b1cdb18c2' class='xr-section-summary' >Attributes: <span>(23)</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-1.0</dd><dt><span>Parameter_File_Revision :</span></dt><dd>$Revision: 1753 $</dd><dt><span>Run_ID :</span></dt><dd>2.1</dd><dt><span>Run_code :</span></dt><dd>GBR4 Hydro|G0.00|H2.1|S0.00|B0.00</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>aims_ncaggregate_buildDate :</span></dt><dd>2023-10-13T06:40:10+10:00</dd><dt><span>aims_ncaggregate_datasetId :</span></dt><dd>products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2023-10</dd><dt><span>aims_ncaggregate_firstDate :</span></dt><dd>2023-10-01T00:00:00+10:00</dd><dt><span>aims_ncaggregate_inputs :</span></dt><dd>[products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2023-10::MD5:b7b3c689f1fba9c481e9be7d90bf3677]</dd><dt><span>aims_ncaggregate_lastDate :</span></dt><dd>2023-10-09T00:00:00+10:00</dd><dt><span>bald__isPrefixedBy :</span></dt><dd>prefix_list</dd><dt><span>date_created :</span></dt><dd>Tue Oct  3 22:10:03 2023</dd><dt><span>description :</span></dt><dd>Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.</dd><dt><span>ems_version :</span></dt><dd>v1.4.0 rev(7358)</dd><dt><span>history :</span></dt><dd>2023-10-13T06:04:53+10:00: vendor: AIMS; processing: None summaries\n",
-       "2023-10-13T06:40:10+10:00: vendor: AIMS; processing: Daily summaries</dd><dt><span>metadata_link :</span></dt><dd>https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e</dd><dt><span>paramfile :</span></dt><dd>./prm/gbr4_hydro_nrt.prm</dd><dt><span>paramhead :</span></dt><dd>GBR 4km resolution grid</dd><dt><span>prefix_list_puv__ :</span></dt><dd>https://w3id.org/env/puv#</dd><dt><span>prefix_list_qudt__ :</span></dt><dd>http://qudt.org/vocab/unit/</dd><dt><span>technical_guide_link :</span></dt><dd>https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf</dd><dt><span>technical_guide_publish_date :</span></dt><dd>2020-08-18</dd><dt><span>title :</span></dt><dd>eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 4787, latitude: 723, longitude: 491, k: 17)\n",
-       "Coordinates:\n",
-       "  * latitude     (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036\n",
-       "  * longitude    (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
-       "  * time         (time) datetime64[ns] 2010-08-31T14:00:00 ... 2023-10-08T14:...\n",
-       "    zc           (k) float64 ...\n",
-       "Dimensions without coordinates: k\n",
-       "Data variables:\n",
-       "    eta          (time, latitude, longitude) float32 ...\n",
-       "    mean_cur     (time, k, latitude, longitude) float32 ...\n",
-       "    mean_wspeed  (time, latitude, longitude) float32 ...\n",
-       "    salt         (time, k, latitude, longitude) float32 ...\n",
-       "    temp         (time, k, latitude, longitude) float32 ...\n",
-       "    u            (time, k, latitude, longitude) float32 ...\n",
-       "    v            (time, k, latitude, longitude) float32 ...\n",
-       "    wspeed_u     (time, latitude, longitude) float32 ...\n",
-       "    wspeed_v     (time, latitude, longitude) float32 ...\n",
-       "Attributes: (12/23)\n",
-       "    Conventions:                   CF-1.0\n",
-       "    Parameter_File_Revision:       $Revision: 1753 $\n",
-       "    Run_ID:                        2.1\n",
-       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
-       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
-       "    aims_ncaggregate_buildDate:    2023-10-13T06:40:10+10:00\n",
-       "    ...                            ...\n",
-       "    paramhead:                     GBR 4km resolution grid\n",
-       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
-       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
-       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
-       "    technical_guide_publish_date:  2020-08-18\n",
-       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai..."
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Path to zarr files\n",
-    "zarr_path = \"s3://rimrep-data-public-development/aims-ereefs-agg-hydrodynamic-4km-daily/data.zarr/\"\n",
-    "\n",
-    "# Connection to public S3 bucket - No credentials needed\n",
-    "fs = s3fs.S3FileSystem(anon=True)\n",
-    "\n",
-    "# Loading zarr file\n",
-    "data = xr.open_dataset(s3fs.S3Map(zarr_path, s3=fs), engine=\"zarr\")\n",
-    "\n",
-    "# Checking result\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, you can check the data size. To do this, run the following code, and you should get a result of ~605 GB."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Data size: 604.96917894 GB'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check data size in GB \n",
-    "f\"Data size: {data.nbytes / 1e9} GB\"\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As an `ARCO` (Analysis Ready Cloud Optimized) format, `zarr` allows us to access hundreds of GB (or TB) data in seconds `without downloading them anywhere, anytime and on any device`.\n",
-    "\n",
-    "For example, another eReefs dataset [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (1km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr1.html) with higher resolution, has 12 Terabytes (TB), yet we can access it within seconds. To achieve this, we can run a similar code as before, but make sure to change the `s3` bucket address and load the data as `data_1km` so it does not override the previous data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Data size: 12.301155749192 TB'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Path to zarr files\n",
-    "zarr_path = \"s3://rimrep-data-public-development/aims-ereefs-agg-hydrodynamic-1km-daily/data.zarr/\"\n",
-    "\n",
-    "# Connection to public S3 bucket - No credentials needed\n",
-    "fs = s3fs.S3FileSystem(anon=True)\n",
-    "\n",
-    "# Loading zarr file\n",
-    "data_1km = xr.open_dataset(s3fs.S3Map(zarr_path, s3=fs), engine=\"zarr\")\n",
-    "\n",
-    "# Check data size in TB (Terabytes)\n",
-    "f\"Data size: {data_1km.nbytes / 1e12} TB\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Data exploration\n",
-    "Now, let's briefly explore the [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (4km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr4.html) dataset by simply calling these data. \n",
-    "\n",
-    "If you click the triangle, file or cylinder in the cell outputs, you can see more details about the variables or coordinates of the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (time: 4787, latitude: 723, longitude: 491, k: 17)\n",
-       "Coordinates:\n",
-       "  * latitude     (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036\n",
-       "  * longitude    (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
-       "  * time         (time) datetime64[ns] 2010-08-31T14:00:00 ... 2023-10-08T14:...\n",
-       "    zc           (k) float64 ...\n",
-       "Dimensions without coordinates: k\n",
-       "Data variables:\n",
-       "    eta          (time, latitude, longitude) float32 ...\n",
-       "    mean_cur     (time, k, latitude, longitude) float32 ...\n",
-       "    mean_wspeed  (time, latitude, longitude) float32 ...\n",
-       "    salt         (time, k, latitude, longitude) float32 ...\n",
-       "    temp         (time, k, latitude, longitude) float32 ...\n",
-       "    u            (time, k, latitude, longitude) float32 ...\n",
-       "    v            (time, k, latitude, longitude) float32 ...\n",
-       "    wspeed_u     (time, latitude, longitude) float32 ...\n",
-       "    wspeed_v     (time, latitude, longitude) float32 ...\n",
-       "Attributes: (12/23)\n",
-       "    Conventions:                   CF-1.0\n",
-       "    Parameter_File_Revision:       $Revision: 1753 $\n",
-       "    Run_ID:                        2.1\n",
-       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
-       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
-       "    aims_ncaggregate_buildDate:    2023-10-13T06:40:10+10:00\n",
-       "    ...                            ...\n",
-       "    paramhead:                     GBR 4km resolution grid\n",
-       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
-       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
-       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
-       "    technical_guide_publish_date:  2020-08-18\n",
-       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai...</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-70c922e6-3369-4a30-8142-940e6bb82991' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70c922e6-3369-4a30-8142-940e6bb82991' 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>: 4787</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li><li><span>k</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7fe09e96-bd8e-4cfc-8008-d9fdf66fc552' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fe09e96-bd8e-4cfc-8008-d9fdf66fc552' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-e6c77821-9a8c-42f6-99d6-1d3c69c8b1c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6c77821-9a8c-42f6-99d6-1d3c69c8b1c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31a53269-ab86-4072-bdff-7d1d0c679661' class='xr-var-data-in' type='checkbox'><label for='data-31a53269-ab86-4072-bdff-7d1d0c679661' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
-       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-75fe8525-e33a-4c30-9b0c-93e279027eff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75fe8525-e33a-4c30-9b0c-93e279027eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f4a994-8034-464f-933e-f2730c7824b8' class='xr-var-data-in' type='checkbox'><label for='data-d3f4a994-8034-464f-933e-f2730c7824b8' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
-       "       156.868788])</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'>2010-08-31T14:00:00 ... 2023-10-...</div><input id='attrs-48213553-228f-4b2b-a4ba-6c6433f7e2eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48213553-228f-4b2b-a4ba-6c6433f7e2eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4307a01-c0cc-45f5-a8c3-727b3e810341' class='xr-var-data-in' type='checkbox'><label for='data-b4307a01-c0cc-45f5-a8c3-727b3e810341' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>coordinate_type :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/UTAA/</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-31T14:00:00.000000000&#x27;, &#x27;2010-09-01T14:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-09-02T14:00:00.000000000&#x27;, ..., &#x27;2023-10-06T14:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-10-07T14:00:00.000000000&#x27;, &#x27;2023-10-08T14:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35b7db74-47d4-4451-894e-a0e0eacbbfff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b7db74-47d4-4451-894e-a0e0eacbbfff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7ceb4a4-afde-4a9a-8330-23cf4202dab9' class='xr-var-data-in' type='checkbox'><label for='data-d7ceb4a4-afde-4a9a-8330-23cf4202dab9' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e9da454-ed2d-42aa-8fcb-59fa71770d92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e9da454-ed2d-42aa-8fcb-59fa71770d92' class='xr-section-summary' >Data variables: <span>(9)</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>eta</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3268c11-524e-4324-9c71-9986aa293d3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3268c11-524e-4324-9c71-9986aa293d3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67bdbf92-6492-417c-892e-b9e2a02e8af9' class='xr-var-data-in' type='checkbox'><label for='data-67bdbf92-6492-417c-892e-b9e2a02e8af9' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Surface elevation</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/ASLVMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_surface_elevation</dd><dt><span>short_name :</span></dt><dd>eta</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_near_surface</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#Meter</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_cur</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c6062a0-71b4-4b02-8ebb-92a485b791de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c6062a0-71b4-4b02-8ebb-92a485b791de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b257ca3d-b66d-4c50-bb4c-99e78a7ec556' class='xr-var-data-in' type='checkbox'><label for='data-b257ca3d-b66d-4c50-bb4c-99e78a7ec556' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_current_speed</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>short_name :</span></dt><dd>mean_cur</dd><dt><span>standard_name :</span></dt><dd>mean_current_speed</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_wspeed</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2cec9b51-5a03-4bb9-a73a-6329b3408a83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cec9b51-5a03-4bb9-a73a-6329b3408a83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b2dc45d-912f-4889-9d1a-981a69192195' class='xr-var-data-in' type='checkbox'><label for='data-7b2dc45d-912f-4889-9d1a-981a69192195' 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>aggregation :</span></dt><dd>mean_speed</dd><dt><span>long_name :</span></dt><dd>mean_wind_speed</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>mean_wspeed</dd><dt><span>standard_name :</span></dt><dd>mean_wind_speed</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba206fb3-3e60-470b-a153-9c895c0439ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba206fb3-3e60-470b-a153-9c895c0439ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-078deaab-d384-4eaf-8c18-4823a407548c' class='xr-var-data-in' type='checkbox'><label for='data-078deaab-d384-4eaf-8c18-4823a407548c' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/PSLTMP01/</dd><dt><span>qudt__unit :</span></dt><dd>http://qudt.org/vocab/unit/PSU</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/practical_salinity</dd><dt><span>short_name :</span></dt><dd>salt</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://environment.data.gov.au/water/quality/def/unit/PSU</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e55098db-d929-4c72-9145-0fadf4dbdfb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e55098db-d929-4c72-9145-0fadf4dbdfb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c349968-0c33-4856-9c5d-22bb6c35227d' class='xr-var-data-in' type='checkbox'><label for='data-0c349968-0c33-4856-9c5d-22bb6c35227d' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e6692da-b7f7-4490-a0b5-1f6daf324ec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e6692da-b7f7-4490-a0b5-1f6daf324ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d9816d2-e4d6-4836-9837-6926565087de' class='xr-var-data-in' type='checkbox'><label for='data-2d9816d2-e4d6-4836-9837-6926565087de' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Eastward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_eastward</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, k, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db20e4d8-e61d-4c60-a216-dd5ed2bd8018' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db20e4d8-e61d-4c60-a216-dd5ed2bd8018' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea763002-7470-47e8-8ecd-675028a1799e' class='xr-var-data-in' type='checkbox'><label for='data-ea763002-7470-47e8-8ecd-675028a1799e' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Northward current</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/LCNSMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_velocity_northward</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean_current</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#MeterPerSecond</dd><dt><span>units :</span></dt><dd>ms-1</dd><dt><span>vector_components :</span></dt><dd>u v</dd><dt><span>vector_name :</span></dt><dd>Currents</dd></dl></div><div class='xr-var-data'><pre>[28888975347 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_u</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d2adf7a-303c-4901-a28e-e3695710c289' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d2adf7a-303c-4901-a28e-e3695710c289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0c539d-5838-43b9-a14a-35dab7993391' class='xr-var-data-in' type='checkbox'><label for='data-6c0c539d-5838-43b9-a14a-35dab7993391' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>eastward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_u</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspeed_v</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc99171d-8ade-4e34-b4fc-773c5b55a63b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc99171d-8ade-4e34-b4fc-773c5b55a63b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d3d815b-ff51-4f14-8d32-4bb6e1a41127' class='xr-var-data-in' type='checkbox'><label for='data-4d3d815b-ff51-4f14-8d32-4bb6e1a41127' 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>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>northward_wind</dd><dt><span>puv__parameter :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P01/current/ESNSMPXX/</dd><dt><span>short_name :</span></dt><dd>wspeed_v</dd><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[1699351491 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c1405a2f-c30a-4845-9c70-124b0e30204c' class='xr-section-summary-in' type='checkbox'  ><label for='section-c1405a2f-c30a-4845-9c70-124b0e30204c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-22edf52b-ae5f-402b-bc96-315b1724c827' class='xr-index-data-in' type='checkbox'/><label for='index-22edf52b-ae5f-402b-bc96-315b1724c827' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
-       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
-       "       ...\n",
-       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
-       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a8789a50-bbf7-4348-96ec-1f8b9937094a' class='xr-index-data-in' type='checkbox'/><label for='index-a8789a50-bbf7-4348-96ec-1f8b9937094a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
-       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
-       "       ...\n",
-       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
-       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-36f260ef-a63b-47ae-9cf1-374dc63afd40' class='xr-index-data-in' type='checkbox'/><label for='index-36f260ef-a63b-47ae-9cf1-374dc63afd40' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-31 14:00:00&#x27;, &#x27;2010-09-01 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-02 14:00:00&#x27;, &#x27;2010-09-03 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-04 14:00:00&#x27;, &#x27;2010-09-05 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-06 14:00:00&#x27;, &#x27;2010-09-07 14:00:00&#x27;,\n",
-       "               &#x27;2010-09-08 14:00:00&#x27;, &#x27;2010-09-09 14:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-09-29 14:00:00&#x27;, &#x27;2023-09-30 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-01 14:00:00&#x27;, &#x27;2023-10-02 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-03 14:00:00&#x27;, &#x27;2023-10-04 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-05 14:00:00&#x27;, &#x27;2023-10-06 14:00:00&#x27;,\n",
-       "               &#x27;2023-10-07 14:00:00&#x27;, &#x27;2023-10-08 14:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=4787, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c287eae-2ff4-4a62-8fda-3a0d026da24b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c287eae-2ff4-4a62-8fda-3a0d026da24b' class='xr-section-summary' >Attributes: <span>(23)</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-1.0</dd><dt><span>Parameter_File_Revision :</span></dt><dd>$Revision: 1753 $</dd><dt><span>Run_ID :</span></dt><dd>2.1</dd><dt><span>Run_code :</span></dt><dd>GBR4 Hydro|G0.00|H2.1|S0.00|B0.00</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>aims_ncaggregate_buildDate :</span></dt><dd>2023-10-13T06:40:10+10:00</dd><dt><span>aims_ncaggregate_datasetId :</span></dt><dd>products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2023-10</dd><dt><span>aims_ncaggregate_firstDate :</span></dt><dd>2023-10-01T00:00:00+10:00</dd><dt><span>aims_ncaggregate_inputs :</span></dt><dd>[products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2023-10::MD5:b7b3c689f1fba9c481e9be7d90bf3677]</dd><dt><span>aims_ncaggregate_lastDate :</span></dt><dd>2023-10-09T00:00:00+10:00</dd><dt><span>bald__isPrefixedBy :</span></dt><dd>prefix_list</dd><dt><span>date_created :</span></dt><dd>Tue Oct  3 22:10:03 2023</dd><dt><span>description :</span></dt><dd>Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.</dd><dt><span>ems_version :</span></dt><dd>v1.4.0 rev(7358)</dd><dt><span>history :</span></dt><dd>2023-10-13T06:04:53+10:00: vendor: AIMS; processing: None summaries\n",
-       "2023-10-13T06:40:10+10:00: vendor: AIMS; processing: Daily summaries</dd><dt><span>metadata_link :</span></dt><dd>https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e</dd><dt><span>paramfile :</span></dt><dd>./prm/gbr4_hydro_nrt.prm</dd><dt><span>paramhead :</span></dt><dd>GBR 4km resolution grid</dd><dt><span>prefix_list_puv__ :</span></dt><dd>https://w3id.org/env/puv#</dd><dt><span>prefix_list_qudt__ :</span></dt><dd>http://qudt.org/vocab/unit/</dd><dt><span>technical_guide_link :</span></dt><dd>https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf</dd><dt><span>technical_guide_publish_date :</span></dt><dd>2020-08-18</dd><dt><span>title :</span></dt><dd>eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (time: 4787, latitude: 723, longitude: 491, k: 17)\n",
-       "Coordinates:\n",
-       "  * latitude     (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036\n",
-       "  * longitude    (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9\n",
-       "  * time         (time) datetime64[ns] 2010-08-31T14:00:00 ... 2023-10-08T14:...\n",
-       "    zc           (k) float64 ...\n",
-       "Dimensions without coordinates: k\n",
-       "Data variables:\n",
-       "    eta          (time, latitude, longitude) float32 ...\n",
-       "    mean_cur     (time, k, latitude, longitude) float32 ...\n",
-       "    mean_wspeed  (time, latitude, longitude) float32 ...\n",
-       "    salt         (time, k, latitude, longitude) float32 ...\n",
-       "    temp         (time, k, latitude, longitude) float32 ...\n",
-       "    u            (time, k, latitude, longitude) float32 ...\n",
-       "    v            (time, k, latitude, longitude) float32 ...\n",
-       "    wspeed_u     (time, latitude, longitude) float32 ...\n",
-       "    wspeed_v     (time, latitude, longitude) float32 ...\n",
-       "Attributes: (12/23)\n",
-       "    Conventions:                   CF-1.0\n",
-       "    Parameter_File_Revision:       $Revision: 1753 $\n",
-       "    Run_ID:                        2.1\n",
-       "    Run_code:                      GBR4 Hydro|G0.00|H2.1|S0.00|B0.00\n",
-       "    _CoordSysBuilder:              ucar.nc2.dataset.conv.CF1Convention\n",
-       "    aims_ncaggregate_buildDate:    2023-10-13T06:40:10+10:00\n",
-       "    ...                            ...\n",
-       "    paramhead:                     GBR 4km resolution grid\n",
-       "    prefix_list_puv__:             https://w3id.org/env/puv#\n",
-       "    prefix_list_qudt__:            http://qudt.org/vocab/unit/\n",
-       "    technical_guide_link:          https://eatlas.org.au/pydio/public/aims-er...\n",
-       "    technical_guide_publish_date:  2020-08-18\n",
-       "    title:                         eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 dai..."
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that these data have four dimensions: `latitude`, `longitude`, `time`, and `zc` (depth).\n",
-    "     \n",
-    "There are nine variables in the dataset: `temp`, `salt`, `mean_cur`, `mean_wspeed` and so on.\n",
-    "\n",
-    "You can get more information about all the variables included in the dataset by calling them with the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Frozen({'eta': <xarray.Variable (time: 4787, latitude: 723, longitude: 491)>\n",
-       "[1699351491 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Surface elevation\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    positive:               up\n",
-       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/A...\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_s...\n",
-       "    short_name:             eta\n",
-       "    standard_name:          sea_surface_height_above_sea_level\n",
-       "    substanceOrTaxon_id:    http://environment.data.gov.au/def/feature/ocean_...\n",
-       "    unit_id:                http://qudt.org/vocab/unit#Meter\n",
-       "    units:                  metre, 'latitude': <xarray.IndexVariable 'latitude' (latitude: 723)>\n",
-       "array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
-       "        -7.036022])\n",
-       "Attributes:\n",
-       "    _CoordinateAxisType:  Lat\n",
-       "    coordinate_type:      latitude\n",
-       "    long_name:            Latitude\n",
-       "    projection:           geographic\n",
-       "    puv__ofProperty:      http://vocab.nerc.ac.uk/collection/S06/current/S060...\n",
-       "    puv__uom:             http://vocab.nerc.ac.uk/collection/P06/current/DEGN/\n",
-       "    standard_name:        latitude\n",
-       "    units:                degrees_north, 'longitude': <xarray.IndexVariable 'longitude' (longitude: 491)>\n",
-       "array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
-       "       156.868788])\n",
-       "Attributes:\n",
-       "    _CoordinateAxisType:  Lon\n",
-       "    coordinate_type:      longitude\n",
-       "    long_name:            Longitude\n",
-       "    projection:           geographic\n",
-       "    puv__uom:             http://vocab.nerc.ac.uk/collection/P06/current/DEGE/\n",
-       "    standard_name:        longitude\n",
-       "    units:                degrees_east, 'mean_cur': <xarray.Variable (time: 4787, k: 17, latitude: 723, longitude: 491)>\n",
-       "[28888975347 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:          mean_speed\n",
-       "    long_name:            mean_current_speed\n",
-       "    medium_id:            http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:       http://vocab.nerc.ac.uk/collection/P01/current/LCEW...\n",
-       "    short_name:           mean_cur\n",
-       "    standard_name:        mean_current_speed\n",
-       "    substanceOrTaxon_id:  http://environment.data.gov.au/def/feature/ocean_cu...\n",
-       "    unit_id:              http://qudt.org/vocab/unit#MeterPerSecond\n",
-       "    units:                ms-1, 'mean_wspeed': <xarray.Variable (time: 4787, latitude: 723, longitude: 491)>\n",
-       "[1699351491 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:     mean_speed\n",
-       "    long_name:       mean_wind_speed\n",
-       "    puv__parameter:  http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/\n",
-       "    short_name:      mean_wspeed\n",
-       "    standard_name:   mean_wind_speed\n",
-       "    units:           ms-1, 'salt': <xarray.Variable (time: 4787, k: 17, latitude: 723, longitude: 491)>\n",
-       "[28888975347 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Salinity\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:         http://vocab.nerc.ac.uk/collection/P01/current/PS...\n",
-       "    qudt__unit:             http://qudt.org/vocab/unit/PSU\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/pract...\n",
-       "    short_name:             salt\n",
-       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
-       "    unit_id:                http://environment.data.gov.au/water/quality/def/...\n",
-       "    units:                  PSU, 'temp': <xarray.Variable (time: 4787, k: 17, latitude: 723, longitude: 491)>\n",
-       "[28888975347 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Temperature\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
-       "    short_name:             temp\n",
-       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
-       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
-       "    units:                  degrees C, 'time': <xarray.IndexVariable 'time' (time: 4787)>\n",
-       "array(['2010-08-31T14:00:00.000000000', '2010-09-01T14:00:00.000000000',\n",
-       "       '2010-09-02T14:00:00.000000000', ..., '2023-10-06T14:00:00.000000000',\n",
-       "       '2023-10-07T14:00:00.000000000', '2023-10-08T14:00:00.000000000'],\n",
-       "      dtype='datetime64[ns]')\n",
-       "Attributes:\n",
-       "    _CoordinateAxisType:  Time\n",
-       "    coordinate_type:      time\n",
-       "    long_name:            Time\n",
-       "    puv__uom:             http://vocab.nerc.ac.uk/collection/P06/current/UTAA/\n",
-       "    standard_name:        time, 'u': <xarray.Variable (time: 4787, k: 17, latitude: 723, longitude: 491)>\n",
-       "[28888975347 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Eastward current\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:         http://vocab.nerc.ac.uk/collection/P01/current/LC...\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
-       "    short_name:             u\n",
-       "    standard_name:          eastward_sea_water_velocity\n",
-       "    substanceOrTaxon_id:    http://environment.data.gov.au/def/feature/ocean_...\n",
-       "    unit_id:                http://qudt.org/vocab/unit#MeterPerSecond\n",
-       "    units:                  ms-1\n",
-       "    vector_components:      u v\n",
-       "    vector_name:            Currents, 'v': <xarray.Variable (time: 4787, k: 17, latitude: 723, longitude: 491)>\n",
-       "[28888975347 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Northward current\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:         http://vocab.nerc.ac.uk/collection/P01/current/LC...\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
-       "    short_name:             v\n",
-       "    standard_name:          northward_sea_water_velocity\n",
-       "    substanceOrTaxon_id:    http://environment.data.gov.au/def/feature/ocean_...\n",
-       "    unit_id:                http://qudt.org/vocab/unit#MeterPerSecond\n",
-       "    units:                  ms-1\n",
-       "    vector_components:      u v\n",
-       "    vector_name:            Currents, 'wspeed_u': <xarray.Variable (time: 4787, latitude: 723, longitude: 491)>\n",
-       "[1699351491 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:     Daily\n",
-       "    long_name:       eastward_wind\n",
-       "    puv__parameter:  http://vocab.nerc.ac.uk/collection/P01/current/ESEWMPXX/\n",
-       "    short_name:      wspeed_u\n",
-       "    units:           ms-1, 'wspeed_v': <xarray.Variable (time: 4787, latitude: 723, longitude: 491)>\n",
-       "[1699351491 values with dtype=float32]\n",
-       "Attributes:\n",
-       "    aggregation:     Daily\n",
-       "    long_name:       northward_wind\n",
-       "    puv__parameter:  http://vocab.nerc.ac.uk/collection/P01/current/ESNSMPXX/\n",
-       "    short_name:      wspeed_v\n",
-       "    units:           ms-1, 'zc': <xarray.Variable (k: 17)>\n",
-       "[17 values with dtype=float64]\n",
-       "Attributes:\n",
-       "    _CoordinateAxisType:     Height\n",
-       "    _CoordinateZisPositive:  up\n",
-       "    axis:                    Z\n",
-       "    coordinate_type:         Z\n",
-       "    long_name:               Z coordinate\n",
-       "    positive:                up\n",
-       "    units:                   m})"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, you can specify one variable by calling their attributes as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'aggregation': 'mean_speed',\n",
-       " 'long_name': 'mean_current_speed',\n",
-       " 'medium_id': 'http://environment.data.gov.au/def/feature/ocean',\n",
-       " 'puv__parameter': 'http://vocab.nerc.ac.uk/collection/P01/current/LCEWMP01/',\n",
-       " 'short_name': 'mean_cur',\n",
-       " 'standard_name': 'mean_current_speed',\n",
-       " 'substanceOrTaxon_id': 'http://environment.data.gov.au/def/feature/ocean_current',\n",
-       " 'unit_id': 'http://qudt.org/vocab/unit#MeterPerSecond',\n",
-       " 'units': 'ms-1'}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.mean_cur.attrs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `zarr` format has a `self-describe` ability, so anyone could understand data easily. Therefore, checking the metadata of the entire dataset by calling the dataset attributes as shown below will give you all the information regarding these data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Conventions': 'CF-1.0',\n",
-       " 'Parameter_File_Revision': '$Revision: 1753 $',\n",
-       " 'Run_ID': '2.1',\n",
-       " 'Run_code': 'GBR4 Hydro|G0.00|H2.1|S0.00|B0.00',\n",
-       " '_CoordSysBuilder': 'ucar.nc2.dataset.conv.CF1Convention',\n",
-       " 'aims_ncaggregate_buildDate': '2023-10-13T06:40:10+10:00',\n",
-       " 'aims_ncaggregate_datasetId': 'products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2023-10',\n",
-       " 'aims_ncaggregate_firstDate': '2023-10-01T00:00:00+10:00',\n",
-       " 'aims_ncaggregate_inputs': '[products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2023-10::MD5:b7b3c689f1fba9c481e9be7d90bf3677]',\n",
-       " 'aims_ncaggregate_lastDate': '2023-10-09T00:00:00+10:00',\n",
-       " 'bald__isPrefixedBy': 'prefix_list',\n",
-       " 'date_created': 'Tue Oct  3 22:10:03 2023',\n",
-       " 'description': 'Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.',\n",
-       " 'ems_version': 'v1.4.0 rev(7358)',\n",
-       " 'history': '2023-10-13T06:04:53+10:00: vendor: AIMS; processing: None summaries\\n2023-10-13T06:40:10+10:00: vendor: AIMS; processing: Daily summaries',\n",
-       " 'metadata_link': 'https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e',\n",
-       " 'paramfile': './prm/gbr4_hydro_nrt.prm',\n",
-       " 'paramhead': 'GBR 4km resolution grid',\n",
-       " 'prefix_list_puv__': 'https://w3id.org/env/puv#',\n",
-       " 'prefix_list_qudt__': 'http://qudt.org/vocab/unit/',\n",
-       " 'technical_guide_link': 'https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf',\n",
-       " 'technical_guide_publish_date': '2020-08-18',\n",
-       " 'title': 'eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation'}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.attrs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Extracting and Plotting data\n",
-    "Extracting and plotting data is easy with `xarray`, we can do it with a single line of code!\n",
-    "\n",
-    "Here, we use the function of `isel()`, which allows us to select a variable by using an index along the specified dimension(s). This function is similar to the `iloc()` of the Python package, Pandas. We could easily access the first date of a variable by `isel(time=0)` if it is unclear when the first time step is for these data. To check this was done properly, the title of the plot should date back to 31 August, 2010.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x11f5ea5d0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting mean wind speed for the first time step in the all GBR area\n",
-    "data.mean_wspeed.isel(time=0).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's do this for another variable (i.e., wind speed), and plot the most recent time step. To do this, select the variable `wspeed_u` and use -1 in `isel(time=-1)` to get the latest time step. To ensure this is done correctly, the title of the plot should update with the most recent time step available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x11f5aa990>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.wspeed_u.isel(time=-1).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The more you add to the line of the code, the more specific your plots can be. For example, let's plot the most recent mean current speed (`mean_cur`) along the seafloor. You can do this with adding the `sel()` function to the code that allows you to specify dimensions such as depth (k= ). If you are familiar with SQL, this function is quite similar to the `WHERE` function.\n",
-    "The results should be a similar plot to before, but with the `zc` variable described within the title (i.e., -145m)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x11f80a1d0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.mean_cur.isel(time=-1).sel(k=0).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All of the above examples are resulting in mean values for either the first timestep, or the most recent timestep. It is also possible to extract data between a set of dates.\n",
-    "\n",
-    "Let's look at the mean current speed (`mean_cur`) for the whole of the GBR area but only for the first week of October 2023. Do this by adding the `time=slice()` function. Then, plot the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the mean of current speed on sea bottom for all GBR area for the first week of October 2023\n",
-    "mean_current_20231007 = data.sel(time=slice('2023-10-01', '2023-10-07'), k=0).mean_cur"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x1223ad310>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x600 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting the subset\n",
-    "mean_current_20231007.plot(col='time', col_wrap=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's more specific, by adding on a particular location.\n",
-    "\n",
-    "This next example will show you how to extract the `temp` variable from the coordinates -19.5, 152 between \"2022-01-01\" to \"2022-12-31\". This will narrow down the data we need to handle.\n",
-    "\n",
-    "Yet, as we are now becoming more specific with our analysis of these modelled data, it is important to realise there may not be data at every resolution for every datetime or coordinate you are interested in. However, there are ways around this. You have the option to apply several different \"methods\" to the `sel()` function to indicate which type of scenario you would like to apply to these data:\n",
-    "\n",
-    "     1. none (default; \"method =\" is not in the code) - only exact matches will be returned\n",
-    "     2. \"nearest\" - uses the nearest valid index value\n",
-    "     3. \"pad\" or \"ffill\" - propregates the last valid index value forward\n",
-    "     4. \"backfill\" or \"bfill\" - propegates the next valid index value backwards\n",
-    "\n",
-    "We suggest using the \"nearest\" method for these data, which is what is demonstrated below. \n",
-    "\n",
-    "Along that same line of code, we have specified the lat and long that we are interested in.\n",
-    "\n",
-    "Next, the `slice()` function was included to indicate which timeframe we are investigating. The `slice()` function reutrns all values inside a datime range (inclusive).\n",
-    "\n",
-    "Lastly, the variable was specified (i.e., `temp`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tempTS_2022 = data.sel(latitude=-19.5, longitude=152, method=\"nearest\")\\\n",
-    "    .sel(time=slice('2022-01-01', '2022-12-31'))\\\n",
-    "    .temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that you've sliced the data (`tempTS_2022`), let's select the first 10 days of temperature across varying depths to plot.\n",
-    "\n",
-    "First specify that you are interested in the first 10 days using the `time=slice()` function. \n",
-    "\n",
-    "Following, using the `plot()` function, specify that you would like to see all the depths (col=\"k\"), and create subplots for each of these depths using `col_wrap=6`. The number here indicates that we want the subplots in rows of 6 - you can alter this to get the visualisation you desire.\n",
-    "\n",
-    "This will take around 23 seconds, depending on your internet connection. You should get a grid of plots (17 in total)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x11f5aad90>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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2zk/vqGyw6R9f7tVLa/MkSUnhgfrLFeM0c1hUN1+JvvT1vkrd8MJ6tbY79P2JiXr8yvEy9tP1hrlWu8c5cnE6nfr2QLVe31Coj7aXqLnNLknyNRp09sgYXTU1WbOHR5PQH2AcDqfufG2L3t9arCB/Hy25ebomJIf19bSOiGsVwGDTq7c6Zmdna/r06Ud9ferUqcrKyjrh8a1WqyQpIsL1H36bzSaDwSCT6VArp4CAABmNRq1evfqoCU6bzSab7VCrq7q6uhOeEwD0FWIZvFFVg03ZpfXKKnWtz5ddVq89ZfVqaXO49xmXZNFNZwzVBWPieHNgkCCe9R8b86p152tbVFDdLKNB+sWcdN0xb/gpSTBGBPtrZnqUZqYPrqTF0OgQvfOLmbrxxQ3aUlCrHz23To//YLwuGp/Q11PDcSKWnbyKept2FFm1vciqHR2Pg+tsStK101J03wUju6zXi/5nVnqUnr5mkn7+yka9s7lIwSYf/fGSMQP6JraBhHjWWXldi97cVKg3NhTqQGWje3t6TIiumpKsSycmKtpMm/mBymg06LErx6umqVVf7a3UT19YrzdumUHXDQDoB3r1fwWNjY3H/KOnvr5eTU1NJzS2w+HQnXfeqVmzZmnMmDGSpOnTpys4OFi//vWv9ac//UlOp1P33nuv7Ha7SkpKjjrWwoUL9dBDD53QPACgvyCWoT+ztdu1r7xBu0vqlV12sAVlvSrqbUfcP8DPqGHRIdpX3qBthVb9z38269GwQN0wK1U/PC2FNzIHOOJZ32u3O/SPZfv05LK9cjhda10+8cMJmprqHZWE3i4yxKT/3Dxdd762WZ/uLNPt/9mswppm3XLmUJIBXoRYdnzK61q0o9iq7YV17oRmaV3LEffNjDPrN98bqTOG0xbQm8wbFau//WC87nxti175Jl/BJl/de14mcc0LEM9cLfWX7y7X6xsKtDy7QnaHqwFesL+PLhyXoB9MTR7wnWdwiL+vUYt+NFnX/OsbbSu06rrnv9Xbv5ip2NCAvp4aAAxqvdqidsKECbrlllt0yy23HPH1p59+Wv/3f/+nLVu2HPfYt956qz7++GOtXr1aSUlJ7u2fffaZbr31Vh04cEBGo1FXX321du3apdNOO02LFi064lhHuhMtOTmZ8n2gH6PNRlfEMvQHTqdTxdYW7e5IYO7uqMzcX9nofhPgu4ZEBikzzqyMuFCNjDMrMz5UKRFB8jEaVNVg0yvf5OultbmqamyVJJkDfHXNaSm6flaq4i2Bp/Lb8wjiWVfEs76VW9moO1/boi0d60d9f2KiHrpktEID/Pp2YoOQ3eHUwx/u0uKvcyW5qtUeunh0v61mJ551Riw7urK6Fm0vPFSZub3IqvIj3PRkMEhDo4I1NtGiMYkWjU20aFRCqMzEI6/2n2/zdd/b2yVJ95yboQVz0/t4Rp0Ry7oazPEsp6JBr28o0Fsbi1TZcOgcTBkSrh9MSdb3xsWz9u8gVtVg0xXPrNWBykZlxJr1+i0zZAnsP7+jiGcABpte/Y18zTXX6Le//a1mzpypcePGdXpt69at+t3vfqf/9//+33GPe9ttt2np0qVatWpVp+SmJM2fP185OTmqrKyUr6+vwsLCFBcXp6FDhx51PJPJ1KmtLQB4I2IZTrWWNrt2Ftdpd2mdqzKztF5ZpXWqb2k/4v6WQD9lxpldj/hQZcaZNSLWfMw3BCJDTLpj3nD9/Myhemdzkf711X7tr2jUs6v26/nVB3TR+ATddEaaRidYPPVtog8Qz/qG0+nU6xsK9NAHu9TUapc5wFePfH+sLqY1ap/xMRr0+4tGKzk8SH/8cJdeXZevEmuL/nn1RN5M9QLEMldcKe1IZu4osmpHsas680gdHIwGaVh0iMYmWjT6sGQmXRsGnqtPS1GjrV0Pf5ilv36arSB/H90wK62vp4VjGIzxrKi2Wb97d4e+3F3u3hYV4q/LJyXpyinJSo+hHSlc/1996aen6fJFa5RdVq+bXlyvl2+cpgA/n76eGgAMSr1awdnW1qb58+e717/MzMyUJO3evVtffPGFZs2apc8//1x+fj27s8XpdOr222/XO++8oxUrVmj48OHdfs2yZcs0b948ZWVlKSMjo0fH4e4WoP/jOu0e5wi9rd3u0LYiq9bsq9TX+6q0Mb9Gre2OLvv5Gg1KjwlRRpxZmXGhyox3JTXjQgNOumWTw+HU8uxy/d+q/Vp3oNq9fVZ6pG4+Y6jOHBHtdW2huFa7xznyvJrGVt339nZ9srNUkjR9aIQe/8EEJYZ5f5X0QPHJjlLd8d/NsrU7NCYxVP/+yVTF9LM2aFyrxzZYzk9Vg00vf5OnLQW12lFkVWVDa5d9jAZpeIxZoxNDNfawZGaQP8nMweSJL/boiS/2SpL+csU4/WBKch/PyGWwXKsnYyCfI4fDqVfX5enPH+9WY6tdRoM0NyNGP5iarLMyY07JOuTwPlkldfrBs2tV39KueSNj9cyPJvWLjhsD+VoFgCPp1f9N+Pn56bPPPtPf//53LVmyRKtWrZLT6dSIESP0yCOP6M477+xxclOSFixYoCVLlui9996T2WxWaanrDRiLxaLAQNebL4sXL9bIkSMVHR2ttWvX6o477tBdd93V4+QmAABwcTqd2lveoK87Eprr9lep3ta5OjPabNKoeFcSc2RcqDLizBoWHSJ/X8/8Z85oNOjskbE6e2SsthXW6l9fHdBH20v09b4qfb2vSiNiQ3TT6UN1ycQEmXy5axboia/2VujuN7aqrM4mPx+DfjU/QzefMVQ+Ru+6WWCgO29MnP7zs+m66cUN2lFUp+8/vUYv3DBVw2PNfT01wG15drnueWNbpzaOPkaDhseEuFvMjkm0aFR8qAL9+T092N1x9nA1tLTrudUHdO9b2xTs76vvjYvv62lhEMupaNC9b23T+twaSa42tH++fBzVmujWyPhQPXfdFP3439/qi6wy/eadHfrdRaMU5O/jdTfgAoA369UKzt52tF8Iixcv1vXXXy9Juvfee/XCCy+ourpaqampuuWWW3TXXXcd1y8T7m4B+j+u0+5xjnAiimqb9fW+SleVZk5VlxZyoQG+mjksSrPSIzUzPUpDo4L7/D9shTVNWvx1rv77bb4aW+2SXInXn8wYomunDVF4sH+fzq87XKvd4xx5RkubXX/5JFv//vqAJGlYdLD+94cTNSaRls/9WV5Vo65fvF4HKhtlDvDV//14imYMi+zraUniWu3OQD4/LW12LfwoSy+uzZMkjYgN0Y+nD9GYRItGxofSqg9H5XQ6dd/b2/Xf9QXyNRp097kZSggLVLC/j4JNvgox+SrY5Ot+fiqSBQP5Wu0tA+0ctdsd+r+v9uuJL/aqtd2hIH8f/fq8TP14+hAZueELx+HTnaW69ZWNcnS8u+7nY5Al0F9hQX4KC/RTWJBf1+dB/goP8lNYx3ZLkJ/MJt9eiXUD7VoFgO706wTnqULwB/o/rtPucY7QEzWNrVq7v0qrO5KauVVNnV43+Rp1WlqEO6k5OsHSb6u6rM1t+u+3+Vr8da5K61okSYF+PrpySpJuPD1NQyKD+3iGR8a12j3OUe/bXVqnO/+7RbtL6yVJP54+RPdfMJKKKi9R3diqn720QRvyauTnY9BDF4/RhOQw+fkY5GM0yM/HKB+jQb4+BvkajR0fOz43Gjz2Zi3X6rEN1POzs9iqO/67RfvKGyRJN8xK1a/PyySpiR6zO5y687Ut+mBrcbf7GgxSsL8r0Xkw+Xn454cnQw9t81Gwv+vzOEuA0qKO/TfhQL1We9NAOkc7i6369VvbtKOoTpI0e0S0/vT9MUoKD+rjmcFbvbmxUH/4YKfqWtq73/kofIwGWQJdSVCLOxnq79rW8XzeqNhuf04H0rUKAD3BghcAAJwCDodTb24s1JPL96mptV2RwSZFhvgrMsSkyGB/RR32eWSIyf08+CTvWm9qbde3B6q1JqdKX++r1K6SOh1+a5OP0aBxSRbNGhalmemRmpQS7jVvUFoC/fTzM4fphllp+nB7sf616oB2ldTppbV5evmbPJ07Kk43z07T5CERfT1VoM84HE4tXpOrRz/ZrdZ2h6JC/PWXK8bprMzYvp4ajkNEsL9euWmafvX6Vn24vUT3v7P9uL7eaJB8fYwdSU/DkT//TnL0lZumsT4iOnE4nPrXV/v12GfZarM7FW026fErx2v2iOi+nhq8jI/RoL/9YLyGRQdrR1GdGm3tamxtV4OtXU02uxpt7WpobZfTKTmdUoPN9Vr5dzqN9MQFY+P09LWTPfBdwNu0tNn1z2V79czK/bI7nLIE+ul3F47SZZMS+7xDDbzbFZOTdPmkRLW0OVTb3KrapjbVNrXJ2tyqmo7Pa5tbZT3sc9frrufNbXbZHU5VN7b+//buOzyqOm3j+D3pIZUAgQABAqFIL4ICUhYVBRUR2wrWta2CK1jeVbE31LU3sKyi7rJrARZlFRcVQVQsdER6L6Gn98zv/SNkJCaBBOZk5jf5fq4rF8nM5D5Pzpl5csIz5xwdzKl4HesybRKjGcQDwO/wFysAAA5bsu2QHvr4Fy3fkeG5bX92obTn2N8bHhKkhtGHh6GHh58NosPU8PCANCEqzHN/QlSYglwuLd+eXnqNyo37tXTbIRWVlD9ZQ7vG0YeP0GyoU1onKDai+tfH9kdhIUG6oEdzjezeTN9tPKA3vtmkr9fu05xf0jTnlzT1bBGv6we01tBOTWp0NKrbbVRY4i79KC79KDr8eUFx6e1FxeXv75AUe8yjBABJWrcnS3d+uFwb9mYrMqz0aJPI0N9Ox1f6Ucnn4SGqFxpc+viw0qNWIg/ff+TnwUEu7cnM1x0fLtc36/dLkoZ0SNSTF3ZVo5hwH//0OB4RocF66bIeSk2M1vQlO1RY7Fax26ioxK0St1FxiVGx2+05RdqR3EalfaoGy3OJ/+zFb3al5+n2D5br+00HJElDOzbWExd2VYKfnxYe/is0OEjjz2hX5f3GGOUVlSinbOBZUKycgmLlFpZ4Pi/9t0S5hcVH3Fb6ddn9TeMia/Gngr9avPWg/u+jFdq4L0dS6eD7oRGd2SeC17hcLkWGBSsyLFJJNew7+UUlyswrOjwMLVR6XlHpMLRsWHr462bx9DMA+D0GnAAAOGRvZr6emLNGM5bslCRFh4fo1tPbqn9qQx3MKdSBnALtzy7UgewCHcg+4uuc0q9zC0tUUOzWzvQ87UzPq9YyQ4JcKv7d/243i49UvzYNdFrbhurbpoESYyK8/rP6A5fLpf6ppYPbdXuy9OY3m/Sfpbu0ZFu6bvrnErVIqKeUhlGl/8lf8tuwsmxgWfS7Yebv12N13H9uR6WcluLAT4dA8snyXfrr9BXKPXwN2ZzCEu3P9u4ywkOCZIxUWOJWRGiQJp7TUZef0oIjFCwXFOTShDPbacKZVQ8F3G6jYnfpsLOoxBwefpb2tLIh6JGfV3iM263iEqOwkKBa/Mngz2av2KV7ZqxUZn6xIkOD9cB5HXVp72T6CRzlcrkOv7knhCEUjltOQbH+9vlavfP9FhkjNYoJ1yPnd9LZnZN8XRrgEREarIjQYCXGBubf6QDgpFoZcJaUlGjlypVq2bKl6tevXxuLBALWwZxCrdyZoVVlH7sytDs9X8kJ9dS6YZRSGkapdaNotW4UpdYNo9QoJpz/fABqWWGxW29/u1kvfrleOYcHGBf3aq47z25fo+FibmHx4cHnb0PQ/YeHnweyC3Qgp/C3AWlOYel/ULuN4uuFql+bBqXDvjYN1bJBvTrXB9o1jtFTF3XTHWe117vflZ6ydtvBXG07mHvsb65CaLBLYcFBCgsp/Qgt+zw4SOGHb+M/4HA0RSVuPfHZGv194WZJUv/UBrrv3I5yu6W8orKjUEqPPCn/7+HPD9+fU1isvMLfP7b087K5fEGxW5LUqWmsXvhj6VF/qBuCglwKC3IpTAwocWKy8ov0wMe/eN6o1a15nJ7/Yw/OVADACgvW7dPdM1Z63ih6ca/muvecjoqrZ/fZawAAwG8cGXCOHz9eXbp00bXXXquSkhINGjRI3333nerVq6fZs2dr8ODBTiwWCDj7sgq0aleGVu3I0MqdGfplV2aVR3Ft3p+jzftzKtweEx6ilEaHB58NSwefpUPQKK6rBDhg3pq9emT2am06/HrsnhyvB0d0Uvfk+Bpn1QsLUb2EECUnHPs6G263UWZ+kbLyi9UsPlJBNTgVayBLjInQHWe1181/aKMvf92rgmK3QoNdnoFkWHBw6eDy8NfhZbeFlB9mhgUH1bkhMbxrX1aBxk5boh83H5Qk3TS4jW4/s51Cgr03hDLGqKDYXToELShWUYlbrRpE0Q8A1NjPWw5qwgfLtP1gnoJc0tg/pOovp7dVqBd7FgA4IT23UI/+91d9tHiHJKl5/UhNGtVFA9pyvWAAAAKNI9ONjz76SJdffrkk6ZNPPtHmzZu1Zs0avffee5o4caK+/fZbJxYLWG1PZr5W7cw4fHRmplbtzFBaZn6lj23dMEqdmsWpS7NYdW4ap+SEetp+MFeb9udo074cbdqfrc37c7T9YK6yCoq1YkeGVhxx7b8ySXERnmFn64bRSmkUpTYNo9WsfmSNrlMHoPRNBo/MXq2v1uyVJDWMDtddwzpoVI9mtTJcCApyKb5emOLrcS2sytQLC9F53Zr6ugzUUYu3HtTN/1yiPZkFig4P0dMXd9PZnZt4fTkul8tziiuuiwfgeBSVuPXSl+v18rwNcpvSwcBzl3ZX71YJvi4NAI7ps5W7dd+sX7Q/u0Aul3RV31a686z2igrnzd0AAAQiR37D79+/X02alP6nzaeffqqLL75Y7dq105/+9Ce98MILTiwSsIYxRmmZ+Vq5o+wUs5lauTND+7IKKjzW5SodZnZpFqfOhz86NY1VTETFU6okJ9RTv9SG5W4rKC7RtgNHDD73lQ4+N+3P0cGcQu3OyNfujHx9t/FAue8LCw5Sywb1SgefjaKVFBehiJBghYcGKSI0WJGH//M04vDXESGln4cfvo0jnVCXZBcU6+WvNujvCzepqMQoNNilP/VP0bghqZW+VgHUHcYYvbdoqx6ZvVpFJUapidGacnkvThcLwC9t2Z+jW99fpuXb0yVJo3o004Pnd1Is+zMA/NzerHw9MOsXfbYqTZLUplGUnrqoq3q15M0ZAAAEMkcGnI0bN9bq1auVlJSkOXPmaPLkyZKk3NxcBQcHO7FIwO8YY3Qgp1C70vO041Ceftn125GZB3IKKzw+yCWlJkaXDjKbxqlL8zh1TIo9oXcahocEq23jGLVtHFPhvvTcQs/gc/P+7MMD0BxtPpCjwmK31u/N1vq92ZL21Hi5Qa7fLpIeEVI6BC0bfpYNQz33hwaraVyEbjm97XH/nIAvuN1G/1m2U098tkZ7D79BYVC7Rrr/vI5q04jhBVDX5RWW6J6ZKzVzaem1687pkqSnLurKEQQA/I4xRh/8vF0PfbJauYUlio0I0WMXdOHMBwD8njFGHy3eoUf/+6sy8ooUEuTSnwe10bghqYoI5f8fAQAIdI78Ki+32QAAX1hJREFUD8s111yjSy65RElJSXK5XDrjjDMkST/88IM6dOjgxCKBWpeVX6TdGfnamZ6n3en52p2RV+7zXRn5Kix2V/q9wUEutU2MLndkZsekWEWG1d4OeHy9MPVsEaaeLeqXu73EbbQrPU+b9udo875sbdqfo/3ZBcovciu/qER5RSXKL3KroKhE+UUlyi8uvT2/qERuU5rhNlJuYYlyC0uqVUu7xtEMOGGVFTvS9eDHv2jJtnRJUqsG9XTfuR01pEMiRy8D0NYDObrxvcVak5al4CCX7jq7g64bkEJ/AOB3DuYU6u4ZK/T5L6Vvajy1dYKevaS7msZH+rgyADi67Qdzdc/Mlfpm/X5JUudmsXrywq7q1DTOx5UBAIDa4siA88EHH1Tnzp21fft2XXzxxQoPD5ckBQcH66677nJikYBXFRSXKC0jX7vKhpXppQPLXYcHmLsy8pSVX3zMHJdLSowJV1JcpDo0iTl83cw4dWgS47fvJgwOcik5oZ6SE+ppULtG1f4+Y4yKSozyikoODz/dyi8+PAQtNxwtUcHv7ouvx2mvUH0lbqMtB3KUFBehemG1eyTU/uwC/W3OWn2weLuMkeqFBeuWIW31p9NaKTzEP1/TAGrXV2v2aPy/lykzv1gNo8P00mU91bdNA1+XBQAVLFi3T3d8uFx7swoUGuzSHUPb6/oBrWvl2uEAcLzcbqN3v9+ipz5fq9zCEoWFBGnCGe10/YAUhQQH+bo8AABQixz7n+GLLrpIkpSfn++57aqrrnJqcQhgu9Lz9PqCTVq6PV0ulZ7+NMjlUpDLJZerdCBX9nnp7Tr89W+fBwXp8Neuct//22Ol9Nyiw0dh5mt/dsXrYVYmLjJUSXERahYfqaT4CCXFRZZ+HhehpvGRahwbobCQurGD7XK5FBbiKv15IxlYwhlbD+Topn8s0erdmXK5pBYJ9dSucYw6NInx/NuqYZRCvfyHbVGJW+9+v1XPf7HO8+aGUT2a6a/DOqhxbIRXlwXATm630QtfrtcLX66XJPVoEa9Xx/RUUhxHQQHwL/lFJXpyzhq9/e0WSaWXyXj+0u7q3IyjngD4t2Xb0/Xo7NX6eeshSVKfVgl64sIuas0lQgAAqJMcGXCWlJTo8ccf15QpU7Rnzx6tW7dOrVu31n333adWrVrp2muvdWKxCDDbD+bq1a836qPF21VUYmp9+eEhQWoaH6mmhweXTeMj1fTw4LLsNq6jBdSe//2Spts/XK6s/GIFB7lU4jbaeiBXWw/kau7q364VGxYcpNaNotS+SUzpR+PSf5vFRx7X6SG/Wb9PD32yWhv2ZkuSujSL04MjOqpXywSv/WwA7JaeW6jx7y/T12v3SZKuOLWl7ju3Y515kxMAe6xJy9St/1qmtXuyJElX9m2pu4edVKuXygCAmnC7jeat3avXFmzSj5sPSpKiwoJ11/CTNKZPC446BwCgDnNkOvPYY4/pnXfe0VNPPaXrr7/ec3vnzp31/PPPM+DEUW3Zn6NX5m3QjKU7VXL4oo59WzfQ6FNaKCI0WG5jZIyR20juw/+Wfm1U4lbV97t/u80c/rek7HO3UUxEyOHhZelH/XqhXCsL8APFJW49/b91mjJ/oySpV8v6emV0T4UEu7QuLUtr92Rp7eF/16VlKaewRGvSsrQmLatcTnR4iNo1jlb7JrFqX/ZvkxglRIVVutxtB3L16H9X63+Hh6cNosJ051ntdfHJyQrmj2gAh/2yK0N//sdibT+Yp/CQID1+QRdd2Ku5r8sCgHLcbqO3vt2sp+asVWGJWw2jw/TURV01pENjX5cGAJUqKC7Rf5bu1BvfbPa82TQkyKUR3Zvq9qHt1YxrBQMAUOc5MuB899139frrr+v000/Xn//8Z8/t3bp105o1a5xYJALAhr3ZemXeBs1atlOH55oa0Lah/nJ6W/VuxZFSQF20L6tAt/xriRZtKn2n7rWnpeiuYR08p6BtmBqufqkNPY93u412pud5Bp5r07K0bk+WNu7LVnZBsZZsS9eSbenlltEoJtxzlGf7xjFq2zhaX60pfYdwYbFbwUEuXdW3lW49o63iOP0ygCNMX7xD98xcqYJit5ITIjXl8l7q1JRTPALwH4XFbs1bu1dvf7vZsz81pEOinrqoqxpGh/u4OgCoKCO3SP/4YaumfrdF+7JKLx8UEx6i0ae00NX9W3H6fwAA4OHIgHPnzp1KTU2tcLvb7VZRUZETi4TF1qZl6aWv1uu/K3fLHB5sDumQqFuGpKpHi/q+LQ6Az/y05aDG/nOJ9mYVKCosWE9d1E3ndE066vcEBbmUnFBPyQn1dEbH345IKCx2a8uBHK1JKz3Kc01altbuydT2g3nal1WgfVkFWrhhf4W801Ib6oHzOqpt4xiv/3wA7FVY7NYjs1frvUVbJUmD2zfS85d2V3y9yo8IB4D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",
-      "text/plain": [
-       "<Figure size 1900x900 with 18 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tempTS_2022.isel(time=slice(10)).plot(col=\"k\", col_wrap=6)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's try doing something similar, but using salinity (`salt`) variable from \"2022-01-01\" to \"2023-01-01\" at -20, 152 and plot a map.\n",
-    "\n",
-    "To begin, you'll need to sub-set the data in a similar fashion as before, but specify the new variable (`salt`) we are interested in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "saltTS_2022 = data.sel(latitude=-20, longitude=152, method=\"nearest\")\\\n",
-    "    .sel(time=slice('2022-01-01', '2023-01-01'))\\\n",
-    "    .salt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After sub-setting the data down to all of 2022, you can now plot the salinity for the first 10 days across all depths. This time, we've done a differing orientation of plots to make them larger and easier to see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x2d26e38b0>"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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zt3rlbvGpubVtm8BznzytPhk6fVAW+ffbcfcde9JVvzpvazlruWFIPsOQ12fIZxgdnks+nyGv0eF527xhtD/X6ec+Q4Yhec9oy+fz78dq9e/fYvH309LWB4skq6VtufwLrJbT27avaz8+a9trOrbR6PEGAuyphhZ5vL5z/n66EhMVoeQ4m5LiopQUa1NSrP/fQlKsTdnpzvNqEwAuVgRbAECPvnnNCKU5Y/TuwZOqbPCoaP8JFe0/IUlKjrPp+jFDdENWim7MSlFGcmyYe3thmlu8Oni8Xp+0hdf20djqxpYutx8SZ9OlLocudSVo7LAEeX2Gqho8qqz3qKrBrcq2U0yrGjyqbPDI0+pTc4tPh6ubdLi6Kag+RUVY2kKwXTFR1m5D6rlGANG3bJFWDYmzKTHWpuS2oNoeUv3h1aak2M7LY2wR4e42AAwaBFsAQI++cXW6vnF1unw+Q/uP1emdgyf1zsGTer+kSlUNHv3xowr98aMKSVJGcoxuzErR9WNSdP2YIRoSbw9z77tmGIZO1LnbAmz76cS1+vxEg7xdDMNGWC0aMzSuLcQ6NCEtQRNdDg1NsAc1mtq+zwaPV1X1HlU2uANhNxB828Jwx+WNHn9gPVbr1rFad9DHZ4uwyh5llT0yQtFRVkVHRcge6X+M7rg8MqLDdhGyRVpl7TCyKun0+K1x+udinL0oMNLbednZ20lShFWKsFhktVpktVgUEXj0j5gGlln9I6bt2/ofFVgfYbHI0v7c2j7C6j8An2FIhr9f/lFi/+/AaPtdtK2Wr8Pz08t7fk1MVISS4mxKjvWPtibH2RQTFRH0vwUAQO8j2AIAgmK1WgLB7v6bRsvT6tNfv6zW25+d1Lufn9TesmqVVzXpNx+U6zcflEuSLnU5dGPWEF2flaJrRyYrzt5//+2036DnZL1HJ+rcKq9q9I/CHq3V3yrqVNng6fJ1ibFRujTNERiJvdTlUFZqvKKjLmx0zWKxKN4eqXh7pEYMCW5ku7nF6w+5bWHY3erzh9NIq+xRncNpdIdwGjHITw8HAOBMBFsAwHmxRVp1zchkXTMyWY/MHKd6d6t2lVTp7bYR3b8dPX096os7ShQVYdGVGUm6PmuIbsxK0eUZiYqKCO1GVGeG1ZP1bVOdWyfq3TpR5wksO1HnD4LdsVqk0UPjO43AXupyaJgj+FHYvhYdFaHhiTEanhgT7q4AADCgEWwBAL0i3h6p3Ampyp2QKkk6We/Wu59X6p3PTurtgyd1uLpJH5RW6YPSKr3w588UZ4vQdaOHBK7RjY6KCATS8w2rXYmzRSglwS6XM1oT0k6Pwo4blnDBo7AAAGBgINgCAPpESrxdX7/8En398ktkGIbKqhr1zsFKvXPQf+ryqcYWbf3bcW392/GQ224Pqynxdg2NtyslweZ/3ras43K+XgcAgMGP/+0BAH3OYrEoc0icMofE6VvXjZDPZ+iTilq9+/lJvX2wUrtKqmSxqEMw7SKoJhBWAQBA1/jLAADQ76xWiyYPd2rycKceuHmMDMMYMNe1AgAA8wntrh29LD8/X9nZ2XI4HHI4HMrJydHmzZsD6++9917/F6J3mKZOndpjm6+88spZr7FYLGpubu7rwwEAnCdCLQAAuBBhHbFNT0/XsmXLlJWVJUlau3at5syZo71792rSpEmSpFmzZmnNmjWB19hstnO263A4tH///k7LoqOje7HnAAAAAICBIqzBdvbs2Z3mlyxZovz8fO3cuTMQbO12u9LS0kJq12KxhPwaAAAAAIA5hfVU5I68Xq8KCgrU0NCgnJycwPKioiKlpqZq3Lhxmj9/vo4fP/fdM+vr65WZman09HR97Wtf0969e3vc3u12q7a2ttMEAIMJdQ7AYEedAy5uYQ+2xcXFio+Pl91u14MPPqiNGzdq4sSJkqS8vDy9+uqr2rp1q55//nnt2rVLM2bMkNvt7ra9CRMm6JVXXtEbb7yh3/zmN4qOjtYNN9ygzz77rNvXLF26VE6nMzBlZGT0+nECQDhR5wAMdtQ54OJmMQzDCGcHPB6PysrKVF1drQ0bNmj16tXavn17INx2VFFRoczMTBUUFOiOO+4Iqn2fz6errrpKN998s1asWNHlNm63u1NYrq2tVUZGhmpqauRwOM7vwACEVW1trZxOJ+/jNtQ5YPChznVGnQMGn1DqXNi/7sdmswVuHjVlyhTt2rVLy5cv16pVq87a1uVyKTMzs8fR1zNZrVZdc801Pb7GbrfLbreH3nkAMAnqHIDBjjoHXNzCfirymQzD6PZU48rKSpWXl8vlcoXU3r59+0J6DQAAAADAPMI6Yrtw4ULl5eUpIyNDdXV1KigoUFFRkQoLC1VfX69FixbpzjvvlMvlUmlpqRYuXKiUlBTdfvvtgTbmzZun4cOHa+nSpZKkxYsXa+rUqRo7dqxqa2u1YsUK7du3T//5n/8ZrsMEAAAAAPShsAbbY8eOae7cuaqoqJDT6VR2drYKCws1c+ZMNTU1qbi4WOvWrVN1dbVcLpdyc3O1fv16JSQkBNooKyuT1Xp64Lm6uloPPPCAjh49KqfTqSuvvFJ/+ctfdO2114bjEAEAAAAAfSzsN48aiLgZA2B+vI97xs8HMD/exz3j5wOYXyjv4wF3jS0AAAAAAKEI+12RAQAAAAxsdc0t2lVaJWdMlMYNS1BCdFS4uwQT8/oMeX2GbJG9N85KsAUAAABwlpP1bv3PJ8e05eOjevdgpTxeX2Dd8MQYTUhL0Pi2aUKaQ6OHxikqghNCcZqn1acvKhv02fF6fXasXp8dr9PB4/U6dLJBz34jW3OuGN5r+yLYAgAAAJAklVc1asvHR7Xl46Pa/cUpdbwbT+aQWLlbfDpa26zD1U06XN2kt/52PLA+KsKiMUPjA2F3/DD/4/DEGFksljAcDfpLc4tXh040BIJre4j9orJRrb6ub+n0+fH6Xu0DwRYAAAC4SBmGof3H6rTl//lHZj+pqO20Pjvdqa9MStNXJg1TVqr/m0mqGz3af7RO+4/V6W9H67T/aJ0OHK1TnbtVfzvqX9ZRgj1S4wIju/7AOyHNIWcspzObTYO7VZ+faA+u9Tp4vE6fHa9XeVWjusmvirNFKGtYgsamxmtsaryyUuM1NjVBw5NierVvBFsAAABc1Ar/X4VKTjYqd8JQjR+WMOhHF30+Q3vLT2nLx/4w+0VlY2Cd1SJdOypZX5mUplsnpWl44tnhIzHWputGD9F1o4cElhmGocPVTdrfFmwPHPMH3s9P1KvO3ao9X5zSni9OdWonzRF9OuymJWjcsASlt4Udn+Fvs/3RkORrm/e1JajAvGHI6Li9DPl8HZbr9HaS5IyJ0pA4mxzRUbJaB/fv+nzVNLXoYHtwDYTYeh2ubur2Nc6YKH94HRavrNSEQIh1OaP75T1FsAUAAMBF7dX3y7Tjs5P6P4V/k8sZrenjh2rauFTdODZF8fbB8eeyp9WnnYcqteXjo/rTJ8d0os4dWGeLtOrmsSm6dVKabrl0mJLjbCG3b7FYlJ4Uq/SkWP3dpcM67bfkZIP+drTWP8rbFnwPVzfpaG2zjtY2a/uBE71yjKGKtFqUFGfTkDibhsTblBxn9z+Psyk5vn25Xclty/oiCBuGoQaPV9WNHtU0taimsUXVTS2qbmxRTVOLqps8qml/3raurrlFPp8/7Pvb8Id3/6M6nD7ecZnRtkSBDwGM9gU6vU37+qYWb7d9Tom3BUZd/SHWPw2Nt4f1Q6HB8U4FAAAAztPfX+ZSpNWidz+vVEVNs37zQbl+80G5oiIsmpKZrNwJQzV9fKrGpsabajS30dOq7ftPaMvHR/XW346rrrk1sC7BHqncCan6yqQ0TR8/VHF9FOBtkdbANbcd1TW36ECHU5nbT22ubmzptJ3FIlkkWS0WWdtmrBb/fPtyi8UfrAPLz5xX27xV8vmk2qYW1blb1eozdKLO3Snk9yTYIGy1WFTbFkqrO4RS/6OnLbC2BAJrd9eghpvLGR0IrYEQOzReSefxwUd/sBiGMTB/kmHEF3oD5sf7uGf8fADz433cs/P5+TS3eLXzUKWK9p9Q0f7jKu1wiq7kvxPwtPFDlTs+VdePGdJnYfBCnGrw6M+fHtOWj49px2cn5G49fSfjlHi7Zk4cpq9MGqbrx6T06let9AbDMNTqMzqEUfXZBwnuVq+qGjyqrPf4Hxvcp5/Xe1TZ4FFVg9v/WO9Rnbv13I1eAFukVYkxUUqMjZIzJkrOGFvgeftyR0yUEmNtSoiOVKTVIv9Pyf9zan/suOys+cC2Zy5rm+/QRmJclBwD4CudQnkfD7x3IwAAABAG0VERmj4+VdPHp0qapJKTDSraf1xF+0/ovUOVOlzdpF+/X6Zfv18mW4RV145K1vTx/tHcMUPj+nU0t8XrU0V1s8pPNaq8qlHlpxq1t6xa75dUydthBHBEcqy+MmmYvjIpTVeOSFLEAL6m1GKxKCqif/pnj4yQyxkjlzO4Gxi5W7061dCik/VuVTX4A3Dn5/4gXNXgkdcwlNgWTB0dgqk/pNrkbH8eGxXYLjoqoo+PePAj2AIAAABdGJUSp1Epo/TtG0apydM+mntc2/afUFlVo94+eFJvHzypp9/8VOlJMcodn6rp44cqZ8wQxdou7M9sw/CfJlt+qlFlVY0qr2oKBNjyKv/1qd5uTmGdkJbQdifjNF3qGvw3w+oP9sgIpTkjlOaMDndX0A2CLQAAAHAOMbYI5U5IVe6EVC0yDJWcbNC2tlOW3z9UpS9PNelXO7/Qr3Z+IVukVdeNSg4E3VEpXY/m1jS2dBpxLa9qCsx/eaqp02nEXbFFWpWeFKOMpFhlJMdozNB4zZiQqswhcX31YwAGLIItAAAAEAKLxaLRQ+M1emi87rtxlBo9rXrv80ptaztt+ctTTdrx2Unt+OykfvpHKXNIrKaNGypbhLVTgO14M6euWC2SyxmjjOT28Brb6fnQeDtfVwO0IdgCAAAAFyDWFqm/u3SY/u7SYTIMQ5+fqG+7AdUJvV9SqS8qG7XuvS+6fG1KvE3p7aE1KUYZybEakRyrjKRYuRKjFRUxsG7wBAxUBFsAAACgl1gsFmWlJigrNUH33zRaDe5Wvft5pd45eFIRVksgvGYkxyo9KeaCr8UF4Mc7CQAAAOgjcfZIzZw4TDMnDgt3V4BBjXMbAAAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJhaUF/389FHH4Xc8MSJExUZybcJAQAAAAD6VlDJ84orrpDFYpFhGEE1arVadeDAAY0ePfqCOgcAAAAAwLkEPaT6/vvva+jQoefczjAMTZ48+YI6BQAAAABAsIIKttOmTVNWVpYSExODavTmm29WTEzMhfQLAAAAAICgBBVst23bFlKjmzZtOq/OAAAAAAAQKu6KDAAAAAAwtaCvsf3Od77T5XKn06nx48frn//5nxUfH99rHQMAAAAAIBhBj9ieOnWqy2nfvn168sknNX78eB06dKgv+woAAAAAwFmCHrHduHFjt+uampo0b948Pfroo3rttdd6pWMAAAAAAASjV66xjYmJ0YIFC7Rz587eaA4AAAAAgKD12s2jkpOTVV1dHdJr8vPzlZ2dLYfDIYfDoZycHG3evDmw/t5775XFYuk0TZ06Nej2CwoKZLFYdNttt4XULwAAAACAeQR9KvK5vPvuuxozZkxIr0lPT9eyZcuUlZUlSVq7dq3mzJmjvXv3atKkSZKkWbNmac2aNYHX2Gy2oNr+4osv9OMf/1g33XRTSH0CAAAAAJhL0MH2o48+6nJ5TU2Ndu3apWeeeUZPP/10SDufPXt2p/klS5YoPz9fO3fuDARbu92utLS0kNr1er26++67tXjxYu3YsSPkkWQAAAAAgHkEHWyvuOIKWSwWGYZx1rqhQ4dqwYIFevDBB8+7I16vV7/97W/V0NCgnJycwPKioiKlpqYqMTFR06ZN05IlS5SamtpjWz/96U81dOhQ3XfffdqxY8d59wkAAAAAMPAFHWxLSkq6XO50OpWYmHjeHSguLlZOTo6am5sVHx+vjRs3auLEiZKkvLw83XXXXcrMzFRJSYmeeOIJzZgxQ3v27JHdbu+yvXfeeUcvvfSS9u3bF3Qf3G633G53YL62tva8jwcABiLqHIDBjjoHXNyCDraZmZl90oHx48dr3759qq6u1oYNG3TPPfdo+/btmjhxor75zW8Gtps8ebKmTJmizMxMvfnmm7rjjjvOaquurk7//M//rBdffFEpKSlB92Hp0qVavHhxrxwPAAxE1DkAgx11Dri4WYyuzi3uwsGDB1VTU6Orr746sOytt97S008/rYaGBt12221auHDhBXfolltu0ZgxY7Rq1aou148dO1b333+/FixYcNa6ffv26corr1RERERgmc/nkyRZrVbt37+/yxtcdfUJX0ZGhmpqauRwOC70kACEQW1trZxOJ+/jNtQ5YPChznVGnQMGn1DqXNAjtv/6r/+qyZMnB4JtSUmJZs+erZtuuknZ2dlaunSpYmNj9cMf/vCCOm8YRqei1FFlZaXKy8vlcrm6XD9hwgQVFxd3WvZv//Zvqqur0/Lly5WRkdHl6+x2e7enNgPAYECdAzDYUeeAi1vQwXb37t36yU9+Eph/9dVXNW7cOG3ZskWSlJ2drV/84hchBduFCxcqLy9PGRkZqqurU0FBgYqKilRYWKj6+notWrRId955p1wul0pLS7Vw4UKlpKTo9ttvD7Qxb948DR8+XEuXLlV0dLQmT57caR/t1/+euRwAAAAAMDgEHWxPnjyp9PT0wPy2bds6fV3P9OnT9aMf/SiknR87dkxz585VRUWFnE6nsrOzVVhYqJkzZ6qpqUnFxcVat26dqqur5XK5lJubq/Xr1yshISHQRllZmaxWa0j7BQAAAAAMHkEH2+TkZFVUVCgjI0M+n0+7d+/WI488Eljv8Xi6/Cqgnrz00kvdrouJiQmMBvekqKiox/WvvPJKSH0CAAAAAJhL0EOd06ZN089+9jOVl5frhRdekM/nU25ubmD9J598opEjR/ZFHwEAAAAA6FbQI7ZLlizRzJkzlZmZqYiICK1YsUJxcXGB9b/61a80Y8aMPukkAAAAAADdCTrYjho1Sp9++qk++eQTDR06VJdcckmn9YsXL+50DS4AAAAAAP0h6GArSc3NzTp+/LgOHz4sm82mlJSUwLrLL7+81zsHAAAAAMC5BB1sP/roI+Xl5eno0aMyDEMOh0Ovv/66brnllr7sHwAAAAAAPQr65lGPPvqoRowYoR07dmj37t2aNm2avv/97/dl3wAAAAAAOKegR2x3796tTZs2acqUKZKkl19+Wampqaqvr1d8fHyfdRAAAAAAgJ4EPWJ78uRJjRgxIjA/ZMgQxcbG6sSJE33SMQAAAAAAghH0iK3FYlFdXZ2io6MlSYZhBJbV1tYGtnM4HL3fSwAAAAAAuhF0sDUMQ+PGjTtr2ZVXXhl4brFY5PV6e7eHAAAAAAD0IOhgu23btr7sBwAAAAAA5yXoYDtt2rS+7AcAAAAAAOclqJtHdbyGNhh1dXXn1RkAAAAAAEIVVLBNSkrS8ePHg250+PDhOnTo0Hl3CgAAAACAYAV1KrJhGFq9enXQ31fb0tJyQZ0CAAAAACBYQQXbESNG6MUXXwy60bS0NEVFRZ13pwAAAAAACFZQwba0tLSPuwEAAAAAwPkJ6hpbAAAAAAAGKoItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwtZCD7ciRI/XTn/5UZWVlfdEfAAAAAABCEnKw/dGPfqTf//73Gj16tGbOnKmCggK53e6+6BsAAAAAAOcUcrD9l3/5F+3Zs0d79uzRxIkT9YMf/EAul0vf//739eGHH/ZFHwEAAAAA6NZ5X2N7+eWXa/ny5Tp8+LCeeuoprV69Wtdcc40uv/xyvfzyyzIMozf7CQAAAABAlyLP94UtLS3auHGj1qxZo//5n//R1KlTdd999+nIkSN6/PHH9ec//1m//vWve7OvAAAAAACcJeRg++GHH2rNmjX6zW9+o4iICM2dO1f/8R//oQkTJgS2ufXWW3XzzTf3akcBAAAAAOhKyMH2mmuu0cyZM5Wfn6/bbrtNUVFRZ20zceJE/eM//mOvdBAAAAAAgJ6EHGwPHTqkzMzMHreJi4vTmjVrzrtTAAAAAAAEK+SbR+Xm5qqysvKs5dXV1Ro9enSvdAoAAAAAgGCFHGxLS0vl9XrPWu52u3X48OFe6RQAAAAAAMEKOti+8cYbeuONNyRJW7ZsCcy/8cYb2rhxo372s59p5MiRIe08Pz9f2dnZcjgccjgcysnJ0ebNmwPr7733Xlkslk7T1KlTe2zzd7/7naZMmaLExETFxcXpiiuu0K9+9auQ+gUAAAAAMI+gr7G97bbbJEkWi0X33HNPp3VRUVEaOXKknn/++ZB2np6ermXLlikrK0uStHbtWs2ZM0d79+7VpEmTJEmzZs3qdL2uzWbrsc3k5GQ9/vjjmjBhgmw2m/74xz/q29/+tlJTU/WVr3wlpP4BAAAAAAa+oIOtz+eTJI0aNUq7du1SSkrKBe989uzZneaXLFmi/Px87dy5MxBs7Xa70tLSgm5z+vTpneYffvhhrV27Vm+//TbBFgAAAAAGoZCvsS0pKemVUHsmr9ergoICNTQ0KCcnJ7C8qKhIqampGjdunObPn6/jx48H3aZhGHrrrbe0f/9+vlcXAAAAAAapoEZsV6xYoQceeEDR0dFasWJFj9v+4Ac/CKkDxcXFysnJUXNzs+Lj47Vx40ZNnDhRkpSXl6e77rpLmZmZKikp0RNPPKEZM2Zoz549stvt3bZZU1Oj4cOHy+12KyIiQitXrtTMmTO73d7tdsvtdgfma2trQzoGABjoqHMABjvqHHBxsxiGYZxro1GjRmn37t0aMmSIRo0a1X1jFosOHToUUgc8Ho/KyspUXV2tDRs2aPXq1dq+fXsg3HZUUVGhzMxMFRQU6I477ui2TZ/Pp0OHDqm+vl5vvfWWfvazn+m///u/zzpNud2iRYu0ePHis5bX1NTI4XCEdDwABoba2lo5nU7ex22oc8DgQ53rjDoHDD6h1Lmggm1/uuWWWzRmzBitWrWqy/Vjx47V/fffrwULFgTd5v3336/y8nJt2bKly/VdfcKXkZFBIQRMjD/4OqPOAYMPda4z6hww+IRS54K+eVR/MQyjU1HqqLKyUuXl5XK5XL3WpuS/QVVPpzYDgNlR5wAMdtQ54OIWcrD1er165ZVX9NZbb+n48eOBuyW327p1a9BtLVy4UHl5ecrIyFBdXZ0KCgpUVFSkwsJC1dfXa9GiRbrzzjvlcrlUWlqqhQsXKiUlRbfffnugjXnz5mn48OFaunSpJGnp0qWaMmWKxowZI4/Ho02bNmndunXKz88P9VABAAAAACYQcrB9+OGH9corr+irX/2qJk+eLIvFct47P3bsmObOnauKigo5nU5lZ2ersLBQM2fOVFNTk4qLi7Vu3TpVV1fL5XIpNzdX69evV0JCQqCNsrIyWa2nb+7c0NCg733ve/ryyy8VExOjCRMm6L/+67/0zW9+87z7CQAAAAAYuEK+xjYlJUXr1q3T3//93/dVn8KOa1YA8+N93DN+PoD58T7uGT8fwPxCeR+H/D22NptNWVlZ5905AAAAAAB6U8jB9kc/+pGWL1+uAXYzZQAAAADARSrka2zffvttbdu2TZs3b9akSZMUFRXVaf3vfve7XuscAAAAAADnEnKwTUxM7HRXYgAAAAAAwinkYLtmzZq+6AcAAAAAAOcl5GtsAQAAAAAYSIIasb3qqqv01ltvKSkpSVdeeWWP31374Ycf9lrnAAAAAAA4l6CC7Zw5c2S32yVJt912W1/2BwAAAACAkAQVbJ966qkunwMAAAAAEG5cYwsAAAAAMLWgRmyTkpJ6vK62o6qqqgvqEAAAAAAAoQgq2L7wwgt93A0AAAAAAM5PUMH2nnvu6et+AAAAAABwXoIKtt1pampSS0tLp2UOh+OCOgQAAAAAQChCvnlUQ0ODvv/97ys1NVXx8fFKSkrqNAEAAAAA0J9CDrY/+clPtHXrVq1cuVJ2u12rV6/W4sWLdckll2jdunV90UcAAAAAALoV8qnIf/jDH7Ru3TpNnz5d3/nOd3TTTTcpKytLmZmZevXVV3X33Xf3RT8BAAAAAOhSyCO2VVVVGjVqlCT/9bTtX+9z44036i9/+Uvv9g4AAAAAgHMIOdiOHj1apaWlkqSJEyfqtddek+QfyU1MTOzNvgEAAAAAcE4hB9tvf/vb+utf/ypJeuyxxwLX2j7yyCP613/9117vIAAAAAAAPQn5GttHHnkk8Dw3N1effvqp9uzZozFjxujyyy/v1c4BAAAAAHAuF/Q9tpKUmZmpzMzM3ugLAAAAAAAhC/pU5Pfff1+bN2/utGzdunUaNWqUUlNT9cADD8jtdvd6BwEAAAAA6EnQwXbRokX66KOPAvPFxcW67777dMstt+jRRx/VH/7wBy1durRPOgkAAAAAQHeCDrb79u3T3/3d3wXmCwoKdN111+nFF1/U//7f/1srVqwI3CEZAAAAAID+EnSwPXXqlIYNGxaY3759u2bNmhWYv+aaa1ReXt67vQMAAAAA4ByCDrbDhg1TSUmJJMnj8ejDDz9UTk5OYH1dXZ2ioqJ6v4cAAAAAAPQg6GA7a9YsPfroo9qxY4cee+wxxcbG6qabbgqs/+ijjzRmzJg+6SQAAAAAAN0J+ut+nn76ad1xxx2aNm2a4uPjtXbtWtlstsD6l19+WbfeemufdBIAAAAAgO4EHWyHDh2qHTt2qKamRvHx8YqIiOi0/re//a3i4+N7vYMAAAAAAPQk6GDbzul0drk8OTn5gjsDAAAAAECogr7GFgAAAACAgYhgCwAAAAAwtbAG2/z8fGVnZ8vhcMjhcCgnJ0ebN28OrL/33ntlsVg6TVOnTu2xzRdffFE33XSTkpKSlJSUpFtuuUUffPBBXx8KAAAAACBMwhps09PTtWzZMu3evVu7d+/WjBkzNGfOHH388ceBbWbNmqWKiorAtGnTph7bLCoq0j/90z9p27Zteu+99zRixAjdeuutOnz4cF8fDgAAAAAgDEK+eVRvmj17dqf5JUuWKD8/Xzt37tSkSZMkSXa7XWlpaUG3+eqrr3aaf/HFF/X666/rrbfe0rx58y680wAAAACAASWswbYjr9er3/72t2poaFBOTk5geVFRkVJTU5WYmKhp06ZpyZIlSk1NDbrdxsZGtbS09HjXZrfbLbfbHZivra09v4MAgAGKOgdgsKPOARe3sN88qri4WPHx8bLb7XrwwQe1ceNGTZw4UZKUl5enV199VVu3btXzzz+vXbt2acaMGZ2K1rk8+uijGj58uG655ZZut1m6dKmcTmdgysjIuODjAoCBhDoHYLCjzgEXN4thGEY4O+DxeFRWVqbq6mpt2LBBq1ev1vbt2wPhtqOKigplZmaqoKBAd9xxxznb/vd//3ctW7ZMRUVFys7O7na7rj7hy8jIUE1NjRwOx/kdGICwqq2tldPp5H3chjoHDD7Uuc6oc8DgE0qdC/upyDabTVlZWZKkKVOmaNeuXVq+fLlWrVp11rYul0uZmZn67LPPztnuc889p2eeeUZ//vOfewy1kv86Xrvdfn4HAAAmQJ0DMNhR54CLW9iD7ZkMw+j2VOPKykqVl5fL5XL12Mazzz6rp59+Wlu2bNGUKVP6opsAAAAAgAEirMF24cKFysvLU0ZGhurq6lRQUKCioiIVFhaqvr5eixYt0p133imXy6XS0lItXLhQKSkpuv322wNtzJs3T8OHD9fSpUsl+U8/fuKJJ/TrX/9aI0eO1NGjRyVJ8fHxio+PD8txAgAAAAD6TliD7bFjxzR37lxVVFTI6XQqOztbhYWFmjlzppqamlRcXKx169apurpaLpdLubm5Wr9+vRISEgJtlJWVyWo9fQ+slStXyuPx6Bvf+EanfT311FNatGhRfx0aAAAAAKCfhDXYvvTSS92ui4mJ0ZYtW87ZRlFRUaf50tLSC+wVAAAAAMBMwv51PwAAAAAAXAiCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEwtrME2Pz9f2dnZcjgccjgcysnJ0ebNmwPr7733Xlkslk7T1KlTe2zz448/1p133qmRI0fKYrHohRde6OOjAAAAAACEU1iDbXp6upYtW6bdu3dr9+7dmjFjhubMmaOPP/44sM2sWbNUUVERmDZt2tRjm42NjRo9erSWLVumtLS0vj4EAAAAAECYRYZz57Nnz+40v2TJEuXn52vnzp2aNGmSJMlut4cUUK+55hpdc801kqRHH3209zoLAAAAABiQBsw1tl6vVwUFBWpoaFBOTk5geVFRkVJTUzVu3DjNnz9fx48fD2MvAQAAAAADTVhHbCWpuLhYOTk5am5uVnx8vDZu3KiJEydKkvLy8nTXXXcpMzNTJSUleuKJJzRjxgzt2bNHdru91/rgdrvldrsD87W1tb3WNgAMBNQ5AIMddQ64uIV9xHb8+PHat2+fdu7cqe9+97u655579Mknn0iSvvnNb+qrX/2qJk+erNmzZ2vz5s06cOCA3nzzzV7tw9KlS+V0OgNTRkZGr7YPAOFGnQMw2FHngItb2IOtzWZTVlaWpkyZoqVLl+ryyy/X8uXLu9zW5XIpMzNTn332Wa/24bHHHlNNTU1gKi8v79X2ASDcqHMABjvqHHBxC/upyGcyDKPTaSQdVVZWqry8XC6Xq1f3abfbe/XUZgAYaKhzAAY76hxwcQtrsF24cKHy8vKUkZGhuro6FRQUqKioSIWFhaqvr9eiRYt05513yuVyqbS0VAsXLlRKSopuv/32QBvz5s3T8OHDtXTpUkmSx+MJnMrs8Xh0+PBh7du3T/Hx8crKygrLcQIAAAAA+k5Yg+2xY8c0d+5cVVRUyOl0Kjs7W4WFhZo5c6aamppUXFysdevWqbq6Wi6XS7m5uVq/fr0SEhICbZSVlclqPX1G9ZEjR3TllVcG5p977jk999xzmjZtmoqKivrz8AAAAAAA/SCswfall17qdl1MTIy2bNlyzjbODKsjR46UYRgX2jUAAAAAgEmE/eZRAAAAAABcCIItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwtchwd2AgMgxDklRbWxvmngA4X+3v3/b3MzqjzgHmR53rGXUOML9Q6hzBtgt1dXWSpIyMjDD3BMCFqqyslNPpDHc3BhzqHDB4UOe6Rp0DBo9g6pzF4GO+s/h8Ph05ckQJCQmyWCy90mZtba0yMjJUXl4uh8PRK2329z44hoGxD44hODU1NRoxYoROnTqlxMTEPtmHmVHnwtN+f+yDYxgY+6DOhV9f1DnJ/P92eH8NjH1wDMEJpc4xYtsFq9Wq9PT0Pmnb4XD02S++v/bBMQyMfXAMwbFauZVAV6hz4W2/P/bBMQyMfVDnwqcv65xk/n87vL8Gxj44huAEU+eohAAAAAAAUyPYAgAAAABMjWDbT+x2u5566inZ7XbT7oNjGBj74BgGzj7Q2WD4vXIMA2MfHMPA2QfOZvZ/O4Ph3ybHMDD2MdCOgZtHAQAAAABMjRFbAAAAAICpEWwBAAAAAKZGsAUAAAAAmBrBFgAAAABgagRbAAAAAICpEWwBAAAAAKZGsAUAAAAAmBrBFgAAAABgagRbAAAAAICpEWwBAAAAAKZGsAUAAAAAmBrBFgAAAABgagRbAAAAAICpEWxhStOnT9cPf/jDcHcDAPoMdQ7AYEedQ28i2AJB+vrXv64RI0YoOjpaLpdLc+fO1ZEjR8LdLQDodW63W1dccYUsFov27dsX7u4AQK8ZOXKkLBZLp+nRRx8Nd7fQCwi2QJByc3P12muvaf/+/dqwYYM+//xzfeMb3wh3twCg1/3kJz/RJZdcEu5uAECf+OlPf6qKiorA9G//9m/h7hJ6AcEWg0JhYaGcTqfWrVvXZ/t45JFHNHXqVGVmZur666/Xo48+qp07d6qlpaXP9gkA7fqjzknS5s2b9ac//UnPPfdcn+4HAM7UX3UuISFBaWlpgSk+Pr5P94f+QbCF6RUUFOgf/uEftG7dOs2bN6/b7eLj43uc8vLygt5nVVWVXn31VV1//fWKiorqjcMAgG71V507duyY5s+fr1/96leKjY3t7cMAgG71599z/+f//B8NGTJEV1xxhZYsWSKPx9Obh4IwiQx3B4ALsXLlSi1cuFC///3vlZub2+O257pOLCYm5pz7W7BggX75y1+qsbFRU6dO1R//+MdQugsAIeuvOmcYhu699149+OCDmjJlikpLS8+jtwAQuv78e+7hhx/WVVddpaSkJH3wwQd67LHHVFJSotWrV4fabQwwFsMwjHB3AgjV9OnT9fnnn+vYsWN6++23de211/bLfk+ePKmqqip98cUXWrx4sZxOp/74xz/KYrH0y/4BXDz6u86tWLFC69ev11/+8hdFRESotLRUo0aN0t69e3XFFVf06b4BXJzC9fdcRxs2bNA3vvENnTx5UkOGDOn3/aP3cCoyTOuKK67Q0KFDtWbNGgXz+UxvnLqSkpKicePGaebMmSooKNCmTZu0c+fO3jgcADhLf9a5rVu3aufOnbLb7YqMjFRWVpYkacqUKbrnnnt67ZgAoKNw/D3X0dSpUyVJBw8ePK/+Y+DgVGSY1pgxY/T8889r+vTpioiI0C9/+cset++NU5E7ai++brc7pNcBQLD6s86tWLFCTz/9dGD+yJEj+spXvqL169fruuuuC6nfABCscP89t3fvXkmSy+UK6XUYeAi2MLVx48Zp27Ztmj59uiIjI/XCCy90u2376MP5+OCDD/TBBx/oxhtvVFJSkg4dOqQnn3xSY8aMUU5Oznm3CwDn0l91bsSIEZ3m2+8SOmbMGKWnp593uwBwLv1V59577z3t3LlTubm5cjqd2rVrlx555BF9/etfP6sGwnwItjC98ePHa+vWrYFP+p5//vle30dMTIx+97vf6amnnlJDQ4NcLpdmzZqlgoIC2e32Xt8fAHTUH3UOAMKpP+qc3W7X+vXrtXjxYrndbmVmZmr+/Pn6yU9+0uv7Qv/j5lEAAAAAAFPj5lEAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI3vse2Cz+fTkSNHlJCQIIvFEu7uADgPhmGorq5Ol1xyiaxWPsM7E3UOMD/qXM+oc4D5hVTnjDBauXKlcdlllxkJCQlGQkKCMXXqVGPTpk2B9ffcc48hqdN03XXXnbPdU6dOGd/73veMtLQ0w263GxMmTDDefPPNoPtVXl5+1n6ZmJjMOZWXl59XfRrsqHNMTINnos51jTrHxDR4pmDqXFhHbNPT07Vs2TJlZWVJktauXas5c+Zo7969mjRpkiRp1qxZWrNmTeA1NputxzY9Ho9mzpyp1NRUvf7660pPT1d5ebkSEhKC7lf7tuXl5XI4HKEeFoABoLa2VhkZGSG99y8m1DnA/KhzPaPOAeYXSp0La7CdPXt2p/klS5YoPz9fO3fuDARbu92utLS0oNt8+eWXVVVVpXfffVdRUVGSpMzMzJD61X66isPhoBACJsfpZ12jzgGDB3Wua9Q5YPAIps4NmAsyvF6vCgoK1NDQoJycnMDyoqIipaamaty4cZo/f76OHz/eYztvvPGGcnJy9NBDD2nYsGGaPHmynnnmGXm93r4+BAAAAABAGIT95lHFxcXKyclRc3Oz4uPjtXHjRk2cOFGSlJeXp7vuukuZmZkqKSnRE088oRkzZmjPnj2y2+1dtnfo0CFt3bpVd999tzZt2qTPPvtMDz30kFpbW/Xkk092+Rq32y232x2Yr62t7f0DBYAwos4BGOyoc8DFzWIYhhHODng8HpWVlam6ulobNmzQ6tWrtX379kC47aiiokKZmZkqKCjQHXfc0WV748aNU3Nzs0pKShQRESFJ+vnPf65nn31WFRUVXb5m0aJFWrx48VnLa2pqOHUFMKna2lo5nU7ex22oc8DgQ53rjDoHDD6h1Lmwn4pss9mUlZWlKVOmaOnSpbr88su1fPnyLrd1uVzKzMzUZ5991m17LpdL48aNC4RaSbr00kt19OhReTyeLl/z2GOPqaamJjCVl5df2EEBwABDnQMw2FHngItb2E9FPpNhGJ1OI+mosrJS5eXlcrlc3b7+hhtu0K9//Wv5fL7Adx0dOHBALper2zsq2+32bk9tBoDBgDoHYLCjzgEXt7CO2C5cuFA7duxQaWmpiouL9fjjj6uoqEh333236uvr9eMf/1jvvfeeSktLVVRUpNmzZyslJUW33357oI158+bpscceC8x/97vfVWVlpR5++GEdOHBAb775pp555hk99NBD4ThEAAAAAEAfC+uI7bFjxzR37lxVVFTI6XQqOztbhYWFmjlzppqamlRcXKx169apurpaLpdLubm5Wr9+fafvMSorKwuMzEpSRkaG/vSnP+mRRx5Rdna2hg8frocfflgLFiwIxyECAAAAAPpY2G8eNRBxMwbA/Hgf94yfD2B+vI97xs8HMD9T3TwKAAAAAIALQbAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqYU12Obn5ys7O1sOh0MOh0M5OTnavHlzYP29994ri8XSaZo6dWqPbb7yyitnvcZisai5ubmvDwcAAAAAEAaR4dx5enq6li1bpqysLEnS2rVrNWfOHO3du1eTJk2SJM2aNUtr1qwJvMZms52zXYfDof3793daFh0d3Ys9BwAAAAAMFGENtrNnz+40v2TJEuXn52vnzp2BYGu325WWlhZSuxaLJeTXAAAAAADMacBcY+v1elVQUKCGhgbl5OQElhcVFSk1NVXjxo3T/Pnzdfz48XO2VV9fr8zMTKWnp+trX/ua9u7d25ddBwAAAACEUVhHbCWpuLhYOTk5am5uVnx8vDZu3KiJEydKkvLy8nTXXXcpMzNTJSUleuKJJzRjxgzt2bNHdru9y/YmTJigV155RZdddplqa2u1fPly3XDDDfrrX/+qsWPHdvkat9stt9sdmK+tre39AwWAMKLOARjsqHPAxc1iGIYRzg54PB6VlZWpurpaGzZs0OrVq7V9+/ZAuO2ooqJCmZmZKigo0B133BFU+z6fT1dddZVuvvlmrVixosttFi1apMWLF5+1vKamRg6HI7QDAjAg1NbWyul08j5uQ50DBh/qXGfUOWDwCaXOhT3YnumWW27RmDFjtGrVqi7Xjx07Vvfff78WLFgQdJvz58/Xl19+2emOyx119QlfRkYGhRAwMf7g64w6Bww+1LnOqHPA4BNKnQv7qchnMgyjU1HqqLKyUuXl5XK5XCG1t2/fPl122WXdbmO327s9tRkABgPqHIDBjjoHXNzCGmwXLlyovLw8ZWRkqK6uTgUFBSoqKlJhYaHq6+u1aNEi3XnnnXK5XCotLdXChQuVkpKi22+/PdDGvHnzNHz4cC1dulSStHjxYk2dOlVjx45VbW2tVqxYoX379uk///M/w3WYAAAAAIA+FNZge+zYMc2dO1cVFRVyOp3Kzs5WYWGhZs6cqaamJhUXF2vdunWqrq6Wy+VSbm6u1q9fr4SEhEAbZWVlslpP39y5urpaDzzwgI4ePSqn06krr7xSf/nLX3TttdeG4xABAAAAAH1swF1jOxBwzQpgfryPe8bPBzA/3sc94+cDmF8o7+MB8z22AAAAAACcD4ItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFMj2AIAAAAATI1gCwAAAAAwNYItAAAAAMDUCLYAAAAAAFOLDGajjz76KOSGJ06cqMjIoJoHAAAAAOC8BZU8r7jiClksFhmGEVSjVqtVBw4c0OjRoy+ocwAAAAAAnEvQQ6rvv/++hg4des7tDMPQ5MmTL6hTAAAAAAAEK6hgO23aNGVlZSkxMTGoRm+++WbFxMRcSL8AAAAAAAhKUMF227ZtITW6adOm8+oMAAAAAACh4q7IAAAAAABTC/oa2+985ztdLnc6nRo/frz++Z//WfHx8b3WMQAAAAAAghH0iO2pU6e6nPbt26cnn3xS48eP16FDh/qyrwAucu5Wr76obFBdc0u4uwIAAIABJOgR240bN3a7rqmpSfPmzdOjjz6q1157rVc6BuDiYhiGappadLi6SUeqm3X4VKOO1DTr8KmmtmVNOl7nliTl332V8i5zhbnHAAAAGCiCDrY9iYmJ0YIFC3THHXf0RnMABqFWr09Ha5t1pLpZR6r9YbU9sB4+5X9s8HjP2U50lFV1za390GMAAACYRa8EW0lKTk5WdXV1SK/Jz89Xfn6+SktLJUmTJk3Sk08+qby8PEnSvffeq7Vr13Z6zXXXXaedO3cG1X5BQYH+6Z/+SXPmzNF///d/h9Q3XJgWr0+fHatX8eFqffRljfYfrVN8dKRczmi5nDFyOaN1SWKM0pzRusQZoxhbRLi7jAvU3OJVeVWjvuwwwtoxuB6tbZbPOHc7KfE2XZIYo+GJMZ0e05P8j0mxUbJYLH1/QAAAADCNXgu27777rsaMGRPSa9LT07Vs2TJlZWVJktauXas5c+Zo7969mjRpkiRp1qxZWrNmTeA1NpstqLa/+OIL/fjHP9ZNN90UUp8QOq/P0KET9froyxoVH67RR19W6+MjtXK3+oJuIzE2SmkOf9j1h9+2AJzoD75pzmhFR/Vd+DUMQ00tXtU1t6quuUW1za2qa25VbVNLYFlTi1fDHNHKTI7ViCGxcjljFGG9uAJWTWOLSisb9EVVo8oqG/RFZWPb80YdrW0+5+ujIiy6JDFGlzjbQmtSjIYnRmt4YqwuSfT//vvy9wwAAIDBKehg+9FHH3W5vKamRrt27dIzzzyjp59+OqSdz549u9P8kiVLlJ+fr507dwaCrd1uV1paWkjter1e3X333Vq8eLF27NgR8kgyumcYhr6obNRHh2v0UXm1Pjpco48P13R5CmmCPVKXpTt1WbpTE10ONbd4daS6WRU1TaqoafZPbaefVje2qLqxRX87WtftvpPjbGeF3vbn7aO+dc3+IFrbfDqQtgfU9rDa3TatwQwndmCLsCo9OUaZybHKHBKnEcmxyhzin9KTYk0Z0Hw+Q8fqmvVFpT+sflHlD69lVY36orJRNU0937QpwR6p9ORYDU/0B9b28HpJYozSE2OUEm+X9SL7MAAAAAB9L+hge8UVV8hiscgwzv7jf+jQoVqwYIEefPDB8+6I1+vVb3/7WzU0NCgnJyewvKioSKmpqUpMTNS0adO0ZMkSpaam9tjWT3/6Uw0dOlT33XefduzYcc59u91uud3uwHxtbe15H8dgYhiGjtQ0BwJs8Zf+0djaLq5vjImK0OThDmWnJyo73anLhjs1ckjcOUOMYRiqc7eqorpZR2qadLQt7B6padbRGv+yiupmNbV4VdXgUVWDRx8f6bvfj9UiJURHKSE6UgnRUXJ0eLRHWVVR4w99X55qlMfr06ETDTp0okHSiU7tWCxSmiO6Q9iN8z8mx2nEkFg5Y6L67BjOxdPq05enTo+0+oPr6QB7rpH2oQl2jRwSqxHJcYEgP6It3HOa8MBFnQMw2FHngItb0MG2pKSky+VOp1OJiYnn3YHi4mLl5OSoublZ8fHx2rhxoyZOnChJysvL01133aXMzEyVlJToiSee0IwZM7Rnzx7Z7fYu23vnnXf00ksvad++fUH3YenSpVq8ePF5H8Ngcby2WR+1hdf2IFvZ4DlrO1ukVRNdjkCAvTwjUWOGxp/XabkWi0WO6Cg50qI0Pi2hy20Mw1BtU6s/5LaP9nYMwjX+mxG5W31KsEeeDqUxkR1CaqQc0VFnzXfeJkpxtoiggpnXZ+hIdVNgJPOLqoYOIbFR9e7WwKj0+yVVZ70+MTaq7ZTmuMCpzZnJsXLERMnT6pO71SdPq08erzcwH1jW6pPH62tb7u207KztAsv92zW4vaqoaerxWtcIq0XpSTGnQ3lbGG8PsLG2XruCAf2IOgdgsKPOARc3i9HVEGw/8ng8KisrU3V1tTZs2KDVq1dr+/btgXDbUUVFhTIzM1VQUNDlHZjr6uqUnZ2tlStXdroBVXV1dY83j+rqE76MjAzV1NTI4XD02P//9avd+n+HazsFJf+jf5TP/3jmcv98vD1SkRFBf5VwULw+Q/VdnGZb526f77ju9PovTzXqWK37rPYirRaNT0voNBI7Pi1BUb3c7wtlGIYMQwPiNFfDMFTV4Ok0Itp+Su8XlY06WX/2z7m/xURFdBhpPR2wM4fE6pLEmAH3+z0ftbW1cjqdQb2PLwYXUucADEzUuc6oc8DgE0qdC3ro5eDBg6qpqdHVV18dWPbWW2/p6aefVkNDg2677TYtXLgw5M7abLbAzaOmTJmiXbt2afny5Vq1atVZ27pcLmVmZuqzzz7rsq3PP/9cpaWlna7d9fn8p1VGRkZq//79Xd7gym63dzsCfC5Ha5p1uLrpvF4rSXG2iG7C7+lQHB8dKU+rr8O1oR0C6xkhNZivS+mO1SKNTU3QZelOZac7lZ2eqAlpCaa4VtRisWignAFrsVg0JN6uIfF2XTUi6az1De7WwEhv2RnXsDZ6vLJHWmWPtMrWPkVYZY/yP/qXRQSed9o2osNrIq2yR0acfn3bshhbhNKTYjQ03s4pwxeZC6lzAGAG1Dng4hZ0sP3Xf/1XTZ48ORBsS0pKNHv2bN10003Kzs7W0qVLFRsbqx/+8IcX1CHDMDp92tZRZWWlysvL5XK5ulw/YcIEFRcXd1r2b//2b6qrq9Py5cuVkZFxQX3ryi+/dZWqGjyBUVD/TYpaVNvU2vbov2nRmcsb2wJog8erBo9XFTXnvqNsKOyR1g7XiHY+Jbfjqbf+keZIDU2w61KXg9NM+0GcPVKXuhy61MWnxwAAAEBvCDrF7N69Wz/5yU8C86+++qrGjRunLVu2SJKys7P1i1/8IqRgu3DhQuXl5SkjI0N1dXUqKChQUVGRCgsLVV9fr0WLFunOO++Uy+VSaWmpFi5cqJSUFN1+++2BNubNm6fhw4dr6dKlio6O1uTJkzvto/363zOX95aM5FhlJMeG/LoWr6/T18mcDsFnh+K65hbZIq1KsEd1GVIdXSyzRZr/VFIAAAAACEbQwfbkyZNKT08PzG/btq3TKb/Tp0/Xj370o5B2fuzYMc2dO1cVFRVyOp3Kzs5WYWGhZs6cqaamJhUXF2vdunWqrq6Wy+VSbm6u1q9fr4SE0zcZKisrk9VqvhAXFWFVcpxNyXHBfS8vAAAAAKBrQQfb5ORkVVRUKCMjQz6fT7t379YjjzwSWO/xeLr8KqCevPTSS92ui4mJCYwG96SoqKjH9a+88kpIfQIAAAAAmEvQQ53Tpk3Tz372M5WXl+uFF16Qz+dTbm5uYP0nn3yikSNH9kUfAQAAAADoVtAjtkuWLNHMmTOVmZmpiIgIrVixQnFxcYH1v/rVrzRjxow+6SQAAAAAAN0JOtiOGjVKn376qT755BMNHTpUl1xySaf1ixcv7nQNLgAAAAAA/SGk73Zpbm7W8ePHdfjwYdlsNqWkpATWXX755b3eOQAAAAAAziXoYPvRRx8pLy9PR48elWEYcjgcev3113XLLbf0Zf8AAAAAAOhR0DePevTRRzVixAjt2LFDu3fv1rRp0/T973+/L/sGAAAAAMA5BT1iu3v3bm3atElTpkyRJL388stKTU1VfX294uPj+6yDAAAAAAD0JOgR25MnT2rEiBGB+SFDhig2NlYnTpzok44BAAAAABCMoEdsLRaL6urqFB0dLUkyDCOwrLa2NrCdw+Ho/V4CAAAAANCNoIOtYRgaN27cWcuuvPLKwHOLxSKv19u7PQQAAAAAoAdBB9tt27b1ZT8AAAAAADgvQQfbadOm9WU/AAAAAAA4L0HdPKrjNbTBqKurO6/OAAAAAAAQqqCCbVJSko4fPx50o8OHD9ehQ4fOu1MAAAAAAAQrqFORDcPQ6tWrg/6+2paWlgvqFAAAAAAAwQoq2I4YMUIvvvhi0I2mpaUpKirqvDsFAAAAAECwggq2paWlfdwNAAAAAADOT1DX2AIAAAAAMFARbAEAAAAApkawBQAAAACYWlDX2AIAAODCNHm8qmr06FSDR6caPapq8D+vamxpe2ybb/DoR7eO18yJw8LdZQAwDYItAABAiNytXlU3tnQIp+2htOV0aO0UXj1qbvEF3f6R6qY+7D0ADD4hB9uRI0fqO9/5ju69916NGDGiL/oEAAAwYLR6fdrzxSlt239C2w+cUHlVo+rdrefVVlSERUmxNiXH2U4/xkUpOdampLjTy8cNS+jlowCAwS3kYPujH/1Ir7zyin76058qNzdX9913n26//XbZ7fa+6B8AAOgjXp+h6kaPTjX6Rxlrm1oUa4tUUlyUEmNsSoyNUnRURLi7GRYn6tzafuCEtu0/rr8cOKG65rODrNUiJbUH0ti2gNoxsAaC6+n18fZIWSyWMBwRAAxuIQfbf/mXf9G//Mu/6K9//atefvll/eAHP9D3vvc9fetb39J3vvMdXXXVVX3RTwAABiyfz1Bji1dWi2SLsCrCaun38OL1Gaptaul0nWZ1Y+f5U+0htu3U2JqmFhlGz+1GR1kDITcx1h94k+Ki5GxblhTrf54UG6XE2NPb2SPNFYi9PkMffVmtbftPqGj/cX30ZU2n9UmxUZo2bqhyJ6TqsuFODYmzKyE6UlYrIRUABgKLYZzrv7SetbS0aOXKlVqwYIFaWlo0efJkPfzww/r2t79t2k8ka2tr5XQ6VVNTI4fDEe7uADgPvI97xs/HzzAMNbV4VdfcqrrmVtW7W1XX3KL6tvm6DvP+dV0vO/O0VEtbwLVFWk8/tj2POmO+42P7OnukVVERlrZ1EW3rLPL6DFU1elTd0CGwtj1WBxFSu+OM8QdUR0yUGtytqmlq0anGFnl95/8nQkxURFvItSkxJirwfJjDrtFD4zU6JU6jh8Yp1ha+231UN3q0/cAJFbWdYlzV4Om0/rLhTuWOH6rpE1J1eXqiIgZYiOV93DN+PoD5hfI+Pu//TVpaWrRx40atWbNG//M//6OpU6fqvvvu05EjR/T444/rz3/+s37961+fb/MAAHRiGIbcrT41uFvV6PGqqcWrBnermjxeNXq8avD4nzd4vGry+LfxT/7nDe4O4bQtjNa7Wy8ovHXfV8nd6pO7NfibBfWWhOjIwGmwSbFRHU6DbT89NqrTKbKJMVGKjDj72/8Mw1C9u1XVjS3+qck/2lvTNurrX+4P1NWNnrZt/M99htTU4lVTjVcVNc099vcSZ7Q/6A6Nawu7/ueXOGN6fTTUMAx9UlGrov0ntPVvx7W37JQ6/voT7JG6edxQTR8/VNPGD1VqQnSv7h8A0HdCDrYffvih1qxZo9/85jeKiIjQ3Llz9R//8R+aMGFCYJtbb71VN998c692FABgboZhqM7dqpN1blU2eFRZ79aJev9jZb1H9e7WQAg9M5Q2ulvV1OJVH2RQSf5rJROi/dc/JkT7J//zKMVHRyrB3t0y//N4u38yZMjT6pOnLdS2eH3yeP3zLV7/svb1LV5DHq/XP+89/TrPGa8LtNPqU4S1/cZDnQNrUtv1m4kxNtkie+cr6i0WixKio5QQHaWM5OBf5/P5f881bdftdgy+pxo9OnyqSYdONujQiXqdamzRkZpmHalp1tsHT3ZqJzrKqlEp/pA7pkPgHT00XvH24P98qWtu0TsHT2rb3/zXyx6vc3daP35YgqZPGKrc8am6OjNJUV2EfADAwBdysL3mmms0c+ZM5efn67bbblNUVNRZ20ycOFH/+I//2CsdBAAMXC1en6oaPDrZFk4Djw1unazzqLKh83KPt3dGMKOjrIq1RSrWFtE2nf08xhahOFtk26N/eUJ0pD+UtoVYR9t8TFREr10+E2vrlWZMy2q1yBkTJWdMlEYMie1x21MNHh06Wa/PTzTo0IkGfX6iXodO1KusqlHNLT59WlGrTytqz3pdaoJdYzoEXX/4jdfwpBhZLdLB4/Xatv+4tv3thHaVVqm1wyciMVERuiErRbkThmr6+FQNT4zp9Z8BAKD/hRxsDx06pMzMzB63iYuL05o1a867UwCAgWNTcYUOHq9XZb1bJ9tC6sl6/6hrdWNLyO3F2yOVEm/TkHi7hsTZlJLgf3RER/lDqD1CMVH+cNrxeazdH05joiIG3LWOOD9JcTZdHZesqzM7Dwm3en0qP9WkQyfqdehEgz/8Hvc/nqz36HidW8fr3HrvUGWn19kirXJER+lkfedR2dEpcZo+PlW5E4bqmpHJF+2dngFgMAs52Obm5mrXrl0aMmRIp+XV1dW66qqrdOjQoV7rHAAg/H713hdnBYiOIqwWJcfZ/CE13n46tMZ3mI+zBwIsoQLnEhlh1aiUOI1KidPfXdp5XU1TS6fAe6httLfkZIM8rT6drHfLFmnV1NFDlDvef4rxyJS48BwIAKDfhBxsS0tL5fV6z1rudrt1+PDhXukUAGDgyJ0wVCNTYjUk7nRYPR1a7UqMieIrT9BvnDFRunJEkq4ckdRpuddn6PCpJp2ob9alLkdY77YMAOh/QVf9N954I/B8y5YtcjqdgXmv16u33npLI0eODGnn+fn5ys/PV2lpqSRp0qRJevLJJ5WXlydJuvfee7V27dpOr7nuuuu0c+fObtv83e9+p2eeeUYHDx5US0uLxo4dqx/96EeaO3duSH0DAPg9cPOYcHcBOKcIq0UjhsSe87peAMDgFHSwve222yT575J4zz33dFoXFRWlkSNH6vnnnw9p5+np6Vq2bJmysrIkSWvXrtWcOXO0d+9eTZo0SZI0a9asTtfr2mw935UjOTlZjz/+uCZMmCCbzaY//vGP+va3v63U1FR95StfCal/AAAAAICBL+hg6/P572Q5atQo7dq1SykpKRe889mzZ3eaX7JkifLz87Vz585AsLXb7UpLSwu6zenTp3eaf/jhh7V27Vq9/fbbBFsAAAAAGIRC/rK2kpKSXgm1Z/J6vSooKFBDQ4NycnICy4uKipSamqpx48Zp/vz5On78eNBtGoaht956S/v37+d7dQEAAABgkApqxHbFihV64IEHFB0drRUrVvS47Q9+8IOQOlBcXKycnBw1NzcrPj5eGzdu1MSJEyVJeXl5uuuuu5SZmamSkhI98cQTmjFjhvbs2SO73d5tmzU1NRo+fLjcbrciIiK0cuVKzZw5s9vt3W633O7TXw1QW3v2d+YBgJlR5wAMdtQ54OJmMQzDONdGo0aN0u7duzVkyBCNGjWq+8YslpC/7sfj8aisrEzV1dXasGGDVq9ere3btwfCbUcVFRXKzMxUQUGB7rjjjm7b9Pl8OnTokOrr6/XWW2/pZz/7mf77v//7rNOU2y1atEiLFy8+a3lNTY0cDkdIxwNgYKitrZXT6eR93IY6Bww+1LnOqHPA4BNKnQsq2PanW265RWPGjNGqVau6XD927Fjdf//9WrBgQdBt3n///SovL9eWLVu6XN/VJ3wZGRkUQsDE+IOvM+ocMPhQ5zqjzgGDTyh1bsB9yZthGJ2KUkeVlZUqLy+Xy+XqtTYl/w2qejq1GQDMjjoHYLCjzgEXt5CDrdfr1SuvvKK33npLx48fD9wtud3WrVuDbmvhwoXKy8tTRkaG6urqVFBQoKKiIhUWFqq+vl6LFi3SnXfeKZfLpdLSUi1cuFApKSm6/fbbA23MmzdPw4cP19KlSyVJS5cu1ZQpUzRmzBh5PB5t2rRJ69atU35+fqiHCgAAAAAwgZCD7cMPP6xXXnlFX/3qVzV58mRZLJbz3vmxY8c0d+5cVVRUyOl0Kjs7W4WFhZo5c6aamppUXFysdevWqbq6Wi6XS7m5uVq/fr0SEhICbZSVlclqPX1z54aGBn3ve9/Tl19+qZiYGE2YMEH/9V//pW9+85vn3U8AAAAAwMAV8jW2KSkpWrdunf7+7/++r/oUdlyzApgf7+Oe8fMBzI/3cc/4+QDmF8r7OOTvsbXZbMrKyjrvzgEAAAAA0JtCDrY/+tGPtHz5cg2wmykDAAAAAC5SIV9j+/bbb2vbtm3avHmzJk2apKioqE7rf/e73/Va5wAAAAAAOJeQg21iYmKnuxIDAAAAABBOIQfbNWvW9EU/AAAAAAA4LyFfYwsAAAAAwEAS1IjtVVddpbfeektJSUm68sore/zu2g8//LDXOgcAAAAAwLkEFWznzJkju90uSbrtttv6sj8AAAAAAIQkqGD71FNPdfkcAAAAAIBwC/nmUQAAAAAGhrrmFh060aDPT9SrqsEjZ0yUkuNsSoy1KTnOpuRYmxKiI2W1dn8pITAYBBVsk5KSeryutqOqqqoL6hAAAACA03w+Q0drm/X5iXp9frxen7cF2c9P1OtYrfucr7dapKRYmxJjO4TeWJsS46KUHGtTUpxNSbE2JcdFBdY5YqIUQRiGiQQVbF944YU+7gYAAABwcWtu8aq0skGfHz8dXD8/Ua9DJxrU6PF2+7qhCXaNGRqnoQnRqmlqUXWjR1UNHlU3tqje3SqfIVU2eFTZ4NHnJxqC6ovFIiXGRCkpEHyj2sJv+2hwx3X+5U7CsOkdrWnWe4dO6mSdR5JkyAisM04/7bD0zOXGWcvUadvTK24aO1SXZyReaJcDggq299xzT6/tEAAAALhYGYbhD5hnjLx+fqJeX55q6jYQRFotyhwSqzFD4zUmNd7/ODROo4fGyxkT1e3+3K1e1TS2qKpD2PU/elTV0BaCGz061diiUw0enWrwqM7dKsOQf1lji3Qy+DDsbA/DsZ1PiW5flhTXPh/VNopsCyoM+3yGGlu8avS0qsnjVaPH/7yx7XmTx6uGDus6Pm9f1+jxqrnFK5/RTVg742ffObx184vp4nXJcTZNGZmka0cl66oRSYqzD9yrP2uaWrTzUKXePXhSbx88GfQHH70hzh7Z/8G2O01NTWppaem0zOFwXFCHAAAAADNpbvGqpqnl9NTY0nm+bfqiskGfn2hQTVNLt205oiM7BFd/eB2TGq8RybGKirCG3Dd7ZIRSHRFKdUQH/RpPq0/VTWeH4FON/uBb1Xh6Xfuy2mZ/GK5ubFF1Y4tKgtyXxSI5ottDcJSiIyPU2OJVk6dVDW6vmtrCbHOLL+RjD6f3DlVKkiKsFk26xKFrRia3TUkaEm8PW7+aW7z68ItTeufzk3r7YKWKv6yWr0Mwt1qky4Y7NXpovDp93GDp+PT0jMXS5SY68yrWrl4zbljCeR9HV0IOtg0NDVqwYIFee+01VVZWnrXe6+3+NAkAAABgoDEMQ80tvi7DaPtU28M6T2tooctikYYnxpwOr6lxgecp8bag723TV2yRVqUmRCs1Ifgw3OL1tYVaT1vg9Qfhs4JxezjuEIbbf47BsFik2KgIxdgiFWuLUKwtQjG2CMXZIhXTNu+fIgPrYqPa5u0RiomK6HQjrc5hrLvlHZ6fI9QZkr6obNSu0ip9UFKlw9VN+ujLGn30ZY1eetsf98cMjdO1o5IDYTc9KabPfuden6GPj9TonYOVeufgSe0qrZL7jH+vY4bG6YasFN2QlaKpo4f0eAbAQBZysP3JT36ibdu2aeXKlZo3b57+8z//U4cPH9aqVau0bNmyvugjAAAA0GfuWbNLfzlw4oLasFokR0yUnB2mM+fbw+yolDjF2CJ6qfcDQ1SEVUMT7BqaEPxoZKvXp+rANcH+EWCP19cWRNtCaYcAG2uLVHSUNezB/1xuyJK+dd0ISdKR6qZAyN1VWqUDx9pPQW/Qbz4olyS5nNH+kDsqWdeOTNbY1Pjzvou1YRgqOdmgdz6v1DufndR7hyrP+tAgNcGuG9uC7PVZQ+RyxlzYAQ8QIQfbP/zhD1q3bp2mT5+u73znO7rpppuUlZWlzMxMvfrqq7r77rv7op8AAABAn4iN8ofMCKtFjujIboPpmVNgfWyU4m18pU6oIiOsSom3KyWMp+b2tUsSYzTniuGac8VwSdKpBo92f3EqEHb/3+EaVdQ0642/HtEbfz0iSUqMjdKUzKRA2L1suLPH09CP1zXr3YOVevvgSb178KSO1DR3Wp9gj9TUMUN0w5ghunFsisYMjR/wHw6cj5CDbVVVlUaNGiXJfz1t+9f73Hjjjfrud7/bu70DAAAA+tiyOy/Ts3dlK94eOSj/4MfAkRRn08yJwzRz4jBJUqOnVfvKqvVBqX9E98MvqlXd2KI/f3pcf/70uCQpOsqqKzOSAiO649Li9VF5jd75/KTeOXhSB47Vd9qHLcKqqzOTdEPWEN2QlaLLhjsVeR7XZ5tNyMF29OjRKi0tVWZmpiZOnKjXXntN1157rf7whz8oMTGxD7oIAAAA9J3EWFu4u4CLVKwtUtdnpej6rBRJ/muVPz5Sq10lVfqgtEq7S6t0qrFF7x2qDNyQ6kwWizT5EqeuzxqiG7NSNCUzedCd6h6MkIPtt7/9bf31r3/VtGnT9Nhjj+mrX/2qfvGLX6i1tVU///nP+6KPAAAAADDoRUVYdUVGoq7ISNT8m0fL5zP0+Yl6/4huSZV2lZ7S4eomjRwSqxuyUnRj2w2fkuL4cCbkYPvII48Enufm5urTTz/Vnj17NGbMGF1++eW92jkAAAAAuFhZrRaNHZagscMSdPd1mZL8py/H2gbud+OGywX/RDIzM5WZmdkbfQEAAAAA9IBQ27WgryJ+//33tXnz5k7L1q1bp1GjRik1NVUPPPCA3G53r3cQAAAAAICeBB1sFy1apI8++igwX1xcrPvuu0+33HKLHn30Uf3hD3/Q0qVL+6STAAAAAAB0J+hgu2/fPv3d3/1dYL6goEDXXXedXnzxRf3v//2/tWLFCr322mt90kkAAAAAALoTdLA9deqUhg0bFpjfvn27Zs2aFZi/5pprVF5e3ru9AwAAAADgHIIOtsOGDVNJSYkkyePx6MMPP1ROTk5gfV1dnaKionq/hwAAAAAA9CDoYDtr1iw9+uij2rFjhx577DHFxsbqpptuCqz/6KOPNGbMmD7pJAAAAAAA3Qn6XtFPP/207rjjDk2bNk3x8fFau3atbLbTXwT88ssv69Zbb+2TTgIAAAAA0J2gg+3QoUO1Y8cO1dTUKD4+XhEREZ3W//a3v1V8fHyvdxAAAAAAgJ6E/O2+Tqezy+XJyckX3BkAAAAAAEIV9DW2AAAAAAAMRGENtvn5+crOzpbD4ZDD4VBOTo42b94cWH/vvffKYrF0mqZOndpjmy+++KJuuukmJSUlKSkpSbfccos++OCDvj4UAAAAAECYhDXYpqena9myZdq9e7d2796tGTNmaM6cOfr4448D28yaNUsVFRWBadOmTT22WVRUpH/6p3/Stm3b9N5772nEiBG69dZbdfjw4b4+HAAAAABAGIR8jW1vmj17dqf5JUuWKD8/Xzt37tSkSZMkSXa7XWlpaUG3+eqrr3aaf/HFF/X666/rrbfe0rx58y680wAAAACAAWXAXGPr9XpVUFCghoYG5eTkBJYXFRUpNTVV48aN0/z583X8+PGQ2m1sbFRLSws3twIAAACAQSqsI7aSVFxcrJycHDU3Nys+Pl4bN27UxIkTJUl5eXm66667lJmZqZKSEj3xxBOaMWOG9uzZI7vdHlT7jz76qIYPH65bbrml223cbrfcbndgvra29sIOCgAGGOocgMGOOgdc3MI+Yjt+/Hjt27dPO3fu1He/+13dc889+uSTTyRJ3/zmN/XVr35VkydP1uzZs7V582YdOHBAb775ZlBt//u//7t+85vf6He/+52io6O73W7p0qVyOp2BKSMjo1eODQAGCuocgMGOOgdc3CyGYRjh7kRHt9xyi8aMGaNVq1Z1uX7s2LG6//77tWDBgh7bee655/T000/rz3/+s6ZMmdLjtl19wpeRkaGamho5HI7QDwJA2NXW1srpdPI+bkOdAwYf6lxn1Dlg8AmlzoX9VOQzGYbRqSh1VFlZqfLycrlcrh7bePbZZ/X0009ry5Yt5wy1kv8GVcGe2gwAZkSdAzDYUeeAi1tYg+3ChQuVl5enjIwM1dXVqaCgQEVFRSosLFR9fb0WLVqkO++8Uy6XS6WlpVq4cKFSUlJ0++23B9qYN2+ehg8frqVLl0ryn378xBNP6Ne//rVGjhypo0ePSpLi4+MVHx8fluMEAAAAAPSdsAbbY8eOae7cuaqoqJDT6VR2drYKCws1c+ZMNTU1qbi4WOvWrVN1dbVcLpdyc3O1fv16JSQkBNooKyuT1Xr6UuGVK1fK4/HoG9/4Rqd9PfXUU1q0aFF/HRoAAAAAoJ+ENdi+9NJL3a6LiYnRli1bztlGUVFRp/nS0tIL7BUAAAAAwEzCfldkAAAAAAAuBMEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYGsEWAAAAAGBqBFsAAAAAgKkRbAEAAAAApkawBQAAAACYWliDbX5+vrKzs+VwOORwOJSTk6PNmzcH1t97772yWCydpqlTp/bY5scff6w777xTI0eOlMVi0QsvvNDHRwEAAAAACKewBtv09HQtW7ZMu3fv1u7duzVjxgzNmTNHH3/8cWCbWbNmqaKiIjBt2rSpxzYbGxs1evRoLVu2TGlpaX19CAAAAACAMIsM585nz57daX7JkiXKz8/Xzp07NWnSJEmS3W4PKaBec801uuaaayRJjz76aO91FgAAAAAwIIU12Hbk9Xr129/+Vg0NDcrJyQksLyoqUmpqqhITEzVt2jQtWbJEqampvbpvt9stt9sdmK+tre3V9gEg3KhzAAY76hxwcQv7zaOKi4sVHx8vu92uBx98UBs3btTEiRMlSXl5eXr11Ve1detWPf/889q1a5dmzJjRqWj1hqVLl8rpdAamjIyMXm0fAMKNOgdgsKPOARc3i2EYRjg74PF4VFZWpurqam3YsEGrV6/W9u3bA+G2o4qKCmVmZqqgoEB33HHHOdseOXKkfvjDH+qHP/xhj9t19QlfRkaGampq5HA4Qj4mAOFXW1srp9PJ+7gNdQ4YfKhznVHngMEnlDoX9lORbTabsrKyJElTpkzRrl27tHz5cq1ateqsbV0ulzIzM/XZZ5/1ah/sdrvsdnuvtgkAAwl1DsBgR50DLm5hPxX5TIZhdHuqcWVlpcrLy+Vyufq5VwAAAACAgSqsI7YLFy5UXl6eMjIyVFdXp4KCAhUVFamwsFD19fVatGiR7rzzTrlcLpWWlmrhwoVKSUnR7bffHmhj3rx5Gj58uJYuXSrJf2rzJ598Enh++PBh7du3T/Hx8YGRYQAAAADA4BHWYHvs2DHNnTtXFRUVcjqdys7OVmFhoWbOnKmmpiYVFxdr3bp1qq6ulsvlUm5urtavX6+EhIRAG2VlZbJaTw88HzlyRFdeeWVg/rnnntNzzz2nadOmqaioqD8PDwAAAADQD8IabF966aVu18XExGjLli3nbOPMsDpy5EiF+X5YAAAAAIB+NOCusQUAAAAAIBQEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGoEWwAAAACAqRFsAQAAAACmRrAFAAAAAJgawRYAAAAAYGqR4e7AQGQYhiSptrY2zD0BcL7a37/t72d0Rp0DzI861zPqHGB+odQ5gm0X6urqJEkZGRlh7gmAC1VZWSmn0xnubgw41Dlg8KDOdY06BwwewdQ5i8HHfGfx+Xw6cuSIEhISZLFYeqXN2tpaZWRkqLy8XA6Ho1fa7O99cAwDYx8cQ3Bqamo0YsQInTp1SomJiX2yDzOjzoWn/f7YB8cwMPZBnQu/vqhzkvn/7fD+Ghj74BiCE0qdY8S2C1arVenp6X3StsPh6LNffH/tg2MYGPvgGIJjtXIrga5Q58Lbfn/sg2MYGPugzoVPX9Y5yfz/dnh/DYx9cAzBCabOUQkBAAAAAKZGsAUAAAAAmBrBtp/Y7XY99dRTstvtpt0HxzAw9sExDJx9oLPB8HvlGAbGPjiGgbMPnM3s/3YGw79NjmFg7GOgHQM3jwIAAAAAmBojtgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItjCl6dOn64c//GG4uwEAfYY6B2Cwo86hNxFsgRC8+eabuu666xQTE6OUlBTdcccd4e4SAPSKoqIiWSyWLqddu3aFu3sA0CsOHDigOXPmKCUlRQ6HQzfccIO2bdsW7m6hFxBsgSBt2LBBc+fO1be//W399a9/1TvvvKNvfetb4e4WAPSK66+/XhUVFZ2m+++/XyNHjtSUKVPC3T0A6BVf/epX1draqq1bt2rPnj264oor9LWvfU1Hjx4Nd9dwgQi2GBQKCwvldDq1bt26Pmm/tbVVDz/8sJ599lk9+OCDGjdunMaPH69vfOMbfbI/ADhTX9c5m82mtLS0wDRkyBC98cYb+s53viOLxdIn+wSAjvq6zp08eVIHDx7Uo48+quzsbI0dO1bLli1TY2OjPv744z7ZJ/oPwRamV1BQoH/4h3/QunXrNG/evG63i4+P73HKy8vr9rUffvihDh8+LKvVqiuvvFIul0t5eXkUQQD9oj/q3JneeOMNnTx5Uvfee28vHAEA9Kw/6tyQIUN06aWXat26dWpoaFBra6tWrVqlYcOG6eqrr+6Lw0I/igx3B4ALsXLlSi1cuFC///3vlZub2+O2+/bt63F9TExMt+sOHTokSVq0aJF+/vOfa+TIkXr++ec1bdo0HThwQMnJySH3HQCC0V917kwvvfSSvvKVrygjIyPo1wDA+eivOmexWPQ///M/mjNnjhISEmS1WjVs2DAVFhYqMTHxPHqOgYRgC9PasGGDjh07prffflvXXnvtObfPyso67335fD5J0uOPP64777xTkrRmzRqlp6frt7/9rf7X//pf5902AHSnP+tcR19++aW2bNmi1157rVfaA4Du9GedMwxD3/ve95SamqodO3YoJiZGq1ev1te+9jXt2rVLLpfrvNtG+HEqMkzriiuu0NChQ7VmzRoZhnHO7S/k1JX2Qjdx4sTAMrvdrtGjR6usrOzCDwYAutCfda6jNWvWaMiQIfr6179+oYcAAD3qzzq3detW/fGPf1RBQYFuuOEGXXXVVVq5cqViYmK0du3a3jwshAEjtjCtMWPG6Pnnn9f06dMVERGhX/7ylz1ufyGnrlx99dWy2+3av3+/brzxRklSS0uLSktLlZmZGXLfASAY/Vnn2hmGoTVr1mjevHmKiooKpbsAELL+rHONjY2SJKu189ie1WoNnJ0H8yLYwtTGjRunbdu2afr06YqMjNQLL7zQ7bYXcuqKw+HQgw8+qKeeekoZGRnKzMzUs88+K0m66667zrtdADiX/qpz7bZu3aqSkhLdd999F9wWAASjv+pcTk6OkpKSdM899+jJJ59UTEyMXnzxRZWUlOirX/3qebeLgYFgC9MbP368tm7dGvik7/nnn++T/Tz77LOKjIzU3Llz1dTUpOuuu05bt25VUlJSn+wPANr1V52T/DeNuv7663XppZf22T4A4Ez9UedSUlJUWFioxx9/XDNmzFBLS4smTZqk3//+97r88st7fX/oXxYjmJPZAQAAAAAYoLh5FAAAAADA1Ai2AAAAAABTI9gCAAAAAEyNYAsAAAAAMDWCLQAAAADA1Ai2AAAAAABTI9gCAAAAAEwtMtwdGIh8Pp+OHDmihIQEWSyWcHcHwHkwDEN1dXW65JJLZLXyGd6ZqHOA+VHnekadA8wvpDpnhNHKlSuNyy67zEhISDASEhKMqVOnGps2bQqsv+eeewxJnabrrrvunO2eOnXK+N73vmekpaUZdrvdmDBhgvHmm28G3a/y8vKz9svExGTOqby8/Lzq02BHnWNiGjwTda5r1DkmpsEzBVPnwjpim56ermXLlikrK0uStHbtWs2ZM0d79+7VpEmTJEmzZs3SmjVrAq+x2Ww9tunxeDRz5kylpqbq9ddfV3p6usrLy5WQkBB0v9q3LS8vl8PhCPWwAAwAtbW1ysjICOm9fzGhzgHmR53rGXUOML9Q6lxYg+3s2bM7zS9ZskT5+fnauXNnINja7XalpaUF3ebLL7+sqqoqvfvuu4qKipIkZWZmhtSv9tNVHA4HhRAwOU4/6xp1Dhg8qHNdo84Bg0cwdW7AXJDh9XpVUFCghoYG5eTkBJYXFRUpNTVV48aN0/z583X8+PEe23njjTeUk5Ojhx56SMOGDdPkyZP1zDPPyOv1dvsat9ut2traThMADCbUOQCDHXUOuLiFPdgWFxcrPj5edrtdDz74oDZu3KiJEydKkvLy8vTqq69q69atev7557Vr1y7NmDFDbre72/YOHTqk119/XV6vV5s2bdK//du/6fnnn9eSJUu6fc3SpUvldDoDU0ZGRq8fJwCEE3UOwGBHnQMubhbDMIxwdsDj8aisrEzV1dXasGGDVq9ere3btwfCbUcVFRXKzMxUQUGB7rjjji7bGzdunJqbm1VSUqKIiAhJ0s9//nM9++yzqqio6PI1bre7U1huP5e7pqaGU1cAk6qtrZXT6eR93IY6Bww+1LnOqHPA4BNKnQv71/3YbLbAzaOmTJmiXbt2afny5Vq1atVZ27pcLmVmZuqzzz7rtj2Xy6WoqKhAqJWkSy+9VEePHpXH4+ny5lN2u112u70XjgYABibqHIDBjjoHXNzCfirymQzD6PZU48rKSpWXl8vlcnX7+htuuEEHDx6Uz+cLLDtw4IBcLtc576gMAAAAADCfsAbbhQsXaseOHSotLVVxcbEef/xxFRUV6e6771Z9fb1+/OMf67333lNpaamKioo0e/ZspaSk6Pbbbw+0MW/ePD322GOB+e9+97uqrKzUww8/rAMHDujNN9/UM888o4ceeigchwgAAAAA6GNhPRX52LFjmjt3rioqKuR0OpWdna3CwkLNnDlTTU1NKi4u1rp161RdXS2Xy6Xc3FytX7++0/cYlZWVyWo9nc8zMjL0pz/9SY888oiys7M1fPhwPfzww1qwYEE4DhEAAAAA0MfCfvOogYibMQDmx/u4Z/x8APPjfdwzfj6A+YXyPh5w19gCAAAAABAKgi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMjWALAAAAADA1gi0AAAAAwNQItgAAAAAAUyPYAgAAAABMLazBNj8/X9nZ2XI4HHI4HMrJydHmzZsD6++9915ZLJZO09SpU3ts85VXXjnrNRaLRc3NzX19OAAAAACAMIgM587T09O1bNkyZWVlSZLWrl2rOXPmaO/evZo0aZIkadasWVqzZk3gNTab7ZztOhwO7d+/v9Oy6OjoXuw5AAAAAGCgCGuwnT17dqf5JUuWKD8/Xzt37gwEW7vdrrS0tJDatVgsIb8GAAAAAGBOA+YaW6/Xq4KCAjU0NCgnJyewvKioSKmpqRo3bpzmz5+v48ePn7Ot+vp6ZWZmKj09XV/72te0d+/evuw6AAAAACCMwjpiK0nFxcXKyclRc3Oz4uPjtXHjRk2cOFGSlJeXp7vuukuZmZkqKSnRE088oRkzZmjPnj2y2+1dtjdhwgS98soruuyyy1RbW6vly5frhhtu0F//+leNHTu2y9e43W653e7AfG1tbe8fKACEEXUOwGBHnQMubhbDMIxwdsDj8aisrEzV1dXasGGDVq9ere3btwfCbUcVFRXKzMxUQUGB7rjjjqDa9/l8uuqqq3TzzTdrxYoVXW6zaNEiLV68+KzlNTU1cjgcoR0QgAGhtrZWTqeT93Eb6hww+FDnOqPOAYNPKHUu7MH2TLfccovGjBmjVatWdbl+7Nixuv/++7VgwYKg25w/f76+/PLLTndc7qirT/gyMjIohICJ8QdfZ9Q5YPChznVGnQMGn1DqXNhPRT6TYRidilJHlZWVKi8vl8vlCqm9ffv26bLLLut2G7vd3u2pzQDw/7P37/FRVYf+//+e3CYhyUwIScjkQriEW8AACkraKhdBSZXDxePpaS1o6+XX1n5qrbYqPSpUET7Helo5rXz8SUVotbEUUVslWoEgtqKAIBEQUBITyA0CySQhM5PMzPePhCGBECYhYbKT1/PxmMdkX2bttYfMIu9Za6/dG9DOAejtaOeAvi2gwXbRokXKzs5WamqqampqlJOTo7y8POXm5qq2tlaLFy/WLbfcIpvNpsLCQi1atEhxcXGaN2+er4yFCxcqOTlZy5YtkyQtWbJEkydP1vDhw2W327VixQrt2bNHv//97wN1mgAAAACAbhTQYFteXq4FCxaotLRUVqtVmZmZys3N1cyZM1VfX6/8/HytXbtWVVVVstlsmjZtml599VVFR0f7yigqKlJQ0NnJnauqqnTPPfeorKxMVqtVEyZM0Pvvv6+rr746EKcIAAAAAOhmPe4a256Aa1YA4+Nz3D7eH8D4+By3j/cHML6OfI57zH1sAQAAAADoDIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEML8WenvXv3drjgjIwMhYT4VTwAAAAAAJ3mV/IcP368TCaTvF6vX4UGBQXp0KFDGjp06CVVDgAAAACAi/G7S/Wjjz5SfHz8Rffzer0aO3bsJVUKAAAAAAB/+RVsp0yZovT0dMXExPhV6HXXXaeIiIhLqRcAAAAAAH7xK9hu2bKlQ4W+/fbbnaoMAAAAAAAdxazIAAAAAABD8/sa2+9///ttrrdarRo5cqS++93vKioqqssqBgAAAACAP/zusT116lSbjz179uixxx7TyJEjdeTIke6sKwAAAAAA5/G7x3bDhg0X3FZfX6+FCxfq4Ycf1l/+8pcuqRgAAAAAAP7okmtsIyIi9NBDD2n79u0det3KlSuVmZkpi8Uii8WirKwsbdy40bf9jjvukMlkavWYPHmy3+Xn5OTIZDJp7ty5HaoXAAAAAMA4/O6xvZjY2FhVVVV16DUpKSlavny50tPTJUlr1qzRnDlztHv3bo0ZM0aSNGvWLK1evdr3mrCwML/K/uqrr/Tggw/q2muv7VCdAAAAAADG0mXB9l//+peGDRvWodfMnj271fLSpUu1cuVKbd++3RdszWazEhMTO1Su2+3WbbfdpiVLlmjbtm0dDtwAAAAAAOPwO9ju3bu3zfXV1dXasWOHnnrqKT355JOdrojb7da6detUV1enrKws3/q8vDwlJCQoJiZGU6ZM0dKlS5WQkNBuWb/61a8UHx+vO++8U9u2bet0nQAAAAAAPZ/fwXb8+PEymUzyer3nbYuPj9dDDz2kH/zgBx2uQH5+vrKysuRwOBQVFaUNGzYoIyNDkpSdna1bb71VaWlpKigo0KOPPqrp06dr165dMpvNbZb3z3/+U3/4wx+0Z88ev+vgdDrldDp9y3a7vcPnAQA9Ge0cgN6Odg7o2/wOtgUFBW2ut1qtiomJ6XQFRo4cqT179qiqqkrr16/X7bffrq1btyojI0Pf+ta3fPuNHTtWEydOVFpamt566y3Nnz//vLJqamr03e9+Vy+88ILi4uL8rsOyZcu0ZMmSTp8DAPR0tHMAejvaOaBvM3nb6oINoBkzZmjYsGF6/vnn29w+fPhw3XXXXXrooYfO27Znzx5NmDBBwcHBvnUej0eSFBQUpIMHD7Z5HXBb3/ClpqaqurpaFovlUk8JQADY7XZZrVY+x81o54Deh3auNdo5oPfpSDvnd4/tF198oerqal111VW+dZs2bdKTTz6puro6zZ07V4sWLep8rZt5vd5WjVJLlZWVKi4uls1ma3P7qFGjlJ+f32rdf/3Xf6mmpkbPPvusUlNT23yd2Wy+4NBmAOgNaOcA9Ha0c0Df5new/fnPf66xY8f6gm1BQYFmz56ta6+9VpmZmVq2bJn69eunn/70p34ffNGiRcrOzlZqaqpqamqUk5OjvLw85ebmqra2VosXL9Ytt9wim82mwsJCLVq0SHFxcZo3b56vjIULFyo5OVnLli1TeHi4xo4d2+oYZ4ZJn7seAAAAANA7+B1sd+7cqV/84he+5ZdfflkjRozQO++8I0nKzMzU//7v/3Yo2JaXl2vBggUqLS2V1WpVZmamcnNzNXPmTNXX1ys/P19r165VVVWVbDabpk2bpldffVXR0dG+MoqKihQUFOT3MQEAAAAAvYvfwfbEiRNKSUnxLW/ZsqXVfWinTp2qBx54oEMH/8Mf/nDBbREREb7Q3J68vLx2t7/00ksdqhMAAAAAwFj87uqMjY1VaWmppKYJmXbu3KlrrrnGt93lcrV5KyAAAAAAALqT38F2ypQpeuKJJ1RcXKzf/va38ng8mjZtmm/7/v37NXjw4O6oIwAAAAAAF+T3UOSlS5dq5syZSktLU3BwsFasWKHIyEjf9j/+8Y+aPn16t1QSAAAAAIAL8TvYDhkyRAcOHND+/fsVHx+vpKSkVtuXLFnS6hpcAAAAAAAuB7+DrSQ5HA5VVFTo2LFjCgsLU1xcnG/buHHjurxyAAAAAABcjN/Bdu/evcrOzlZZWZm8Xq8sFov++te/asaMGd1ZPwAAAAAA2uX35FEPP/ywBg0apG3btmnnzp2aMmWKfvzjH3dn3QAAAAAAuCi/e2x37typt99+WxMnTpQkvfjii0pISFBtba2ioqK6rYIAAAAAALTH7x7bEydOaNCgQb7lAQMGqF+/fjp+/Hi3VAwAAAAAAH/43WNrMplUU1Oj8PBwSZLX6/Wts9vtvv0sFkvX1xIAAAAAgAvwO9h6vV6NGDHivHUTJkzw/WwymeR2u7u2hgAAAAAAtMPvYLtly5burAcAAAAAAJ3id7CdMmVKd9YDAAAAAIBO8WvyqJbX0PqjpqamU5UBAAAAAKCj/Aq2/fv3V0VFhd+FJicn68iRI52uFAAAAAAA/vJrKLLX69WqVav8vl9tQ0PDJVUKAAAAAAB/+RVsBw0apBdeeMHvQhMTExUaGtrpSgEAAAAA4C+/gm1hYWE3VwMAAAAAgM7x6xpbAAAAAAB6KoItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwtA4H28GDB+tXv/qVioqKuqM+AAAAAAB0SIeD7QMPPKA33nhDQ4cO1cyZM5WTkyOn09kddQMAAAAA4KI6HGz/z//5P9q1a5d27dqljIwM/eQnP5HNZtOPf/xjffLJJ91RRwAAAAAALqjT19iOGzdOzz77rI4dO6bHH39cq1at0qRJkzRu3Di9+OKL8nq9XVlPAAAAAADaFNLZFzY0NGjDhg1avXq1/vGPf2jy5Mm68847VVJSol/+8pd677339Morr3RlXQEAAAAAOE+Hg+0nn3yi1atX689//rOCg4O1YMEC/eY3v9GoUaN8+9xwww267rrrurSiAAAAAAC0pcPBdtKkSZo5c6ZWrlypuXPnKjQ09Lx9MjIy9J//+Z9dUkEAAAAAANrT4WB75MgRpaWltbtPZGSkVq9e3elKAQAAAADgrw5PHjVt2jRVVlaet76qqkpDhw7tkkoBAAAAAOCvDgfbwsJCud3u89Y7nU4dO3asSyoFAAAAAIC//A62b775pt58801J0jvvvONbfvPNN7VhwwY98cQTGjx4cIcOvnLlSmVmZspischisSgrK0sbN270bb/jjjtkMplaPSZPntxuma+99pomTpyomJgYRUZGavz48frjH//YoXoBAAAAAIzD72ts586dK0kymUy6/fbbW20LDQ3V4MGD9cwzz3To4CkpKVq+fLnS09MlSWvWrNGcOXO0e/dujRkzRpI0a9asVtfrhoWFtVtmbGysfvnLX2rUqFEKCwvT3//+d33ve99TQkKCbrzxxg7VDwAAAADQ8/kdbD0ejyRpyJAh2rFjh+Li4i754LNnz261vHTpUq1cuVLbt2/3BVuz2azExES/y5w6dWqr5fvuu09r1qzRBx98QLAFAAAAgF6ow7MiFxQUdEc95Ha7tW7dOtXV1SkrK8u3Pi8vTwkJCYqJidGUKVO0dOlSJSQk+FWm1+vV5s2bdfDgQf3f//t/L7if0+mU0+n0Ldvt9s6fCAD0QLRzAHo72jmgb/Mr2K5YsUL33HOPwsPDtWLFinb3/clPftKhCuTn5ysrK0sOh0NRUVHasGGDMjIyJEnZ2dm69dZblZaWpoKCAj366KOaPn26du3aJbPZfMEyq6urlZycLKfTqeDgYD333HOaOXPmBfdftmyZlixZ0qF6A4CR0M4B6O1o54C+zeT1er0X22nIkCHauXOnBgwYoCFDhly4MJNJR44c6VAFXC6XioqKVFVVpfXr12vVqlXaunWrL9y2VFpaqrS0NOXk5Gj+/PkXLNPj8ejIkSOqra3Vpk2b9MQTT+j1118/b5jyGW19w5eamqrq6mpZLJYOnQ+AnsFut8tqtfI5bkY7B/Q+tHOt0c4BvU9H2jm/emxbDj/u6qHIYWFhvsmjJk6cqB07dujZZ5/V888/f96+NptNaWlpOnz4cLtlBgUF+cocP368Dhw4oGXLll0w2JrN5nZ7gAHA6GjnAPR2tHNA39bh+9h2N6/X2+rbtpYqKytVXFwsm83WZWUCAAAAAIytw5NHud1uvfTSS9q0aZMqKip8syWfsXnzZr/LWrRokbKzs5Wamqqamhrl5OQoLy9Pubm5qq2t1eLFi3XLLbfIZrOpsLBQixYtUlxcnObNm+crY+HChUpOTtayZcskNV1fMXHiRA0bNkwul0tvv/221q5dq5UrV3b0VAEAAAAABtDhYHvffffppZde0k033aSxY8fKZDJ1+uDl5eVasGCBSktLZbValZmZqdzcXM2cOVP19fXKz8/X2rVrVVVVJZvNpmnTpunVV19VdHS0r4yioiIFBZ3teK6rq9OPfvQjHT16VBERERo1apT+9Kc/6Vvf+lan6wkAAAAA6Ln8mjyqpbi4OK1du1bf/OY3u6tOAcdkDIDx8TluH+8PYHx8jtvH+wMYX0c+xx2+xrblZE8AAAAAAARah4PtAw88oGeffVYd7OgFAAAAAKBbdPga2w8++EBbtmzRxo0bNWbMGIWGhrba/tprr3VZ5QAAAAAAuJgOB9uYmJhWsxIDAAAAABBIHQ62q1ev7o56AAAAAADQKR2+xhYAAAAAgJ7Erx7bK6+8Ups2bVL//v01YcKEdu9d+8knn3RZ5QAAAAAAuBi/gu2cOXNkNpslSXPnzu3O+gAAAAAA0CF+BdvHH3+8zZ8BAAAAAAg0rrEFAAAAABiaXz22/fv3b/e62pZOnjx5SRUCAAAAAKAj/Aq2v/3tb7u5GgAAAAAAdI5fwfb222/v7noAAAAAANApfgXbC6mvr1dDQ0OrdRaL5ZIqBAAAAABAR3R48qi6ujr9+Mc/VkJCgqKiotS/f/9WDwAAAAAALqcOB9tf/OIX2rx5s5577jmZzWatWrVKS5YsUVJSktauXdsddQQAAAAA4II6PBT5b3/7m9auXaupU6fq+9//vq699lqlp6crLS1NL7/8sm677bbuqCcAAAAAAG3qcI/tyZMnNWTIEElN19Oeub3PN77xDb3//vtdWzsAAAAAAC6iw8F26NChKiwslCRlZGToL3/5i6SmntyYmJiurBsAAAAAABfV4WD7ve99T59++qkk6ZFHHvFda3v//ffr5z//eZdXEAAAAACA9nT4Gtv777/f9/O0adN04MAB7dq1S8OGDdO4ceO6tHIAAAAAAFzMJd3HVpLS0tKUlpbWFXUBAAAAAKDD/B6K/NFHH2njxo2t1q1du1ZDhgxRQkKC7rnnHjmdzi6vIAAAAAAA7fE72C5evFh79+71Lefn5+vOO+/UjBkz9PDDD+tvf/ubli1b1i2VBAAAAADgQvwOtnv27NH111/vW87JydE111yjF154QT/72c+0YsUK3wzJAAAAAABcLn4H21OnTmngwIG+5a1bt2rWrFm+5UmTJqm4uLhrawcAAAAAwEX4HWwHDhyogoICSZLL5dInn3yirKws3/aamhqFhoZ2fQ0BAAAAAGiH38F21qxZevjhh7Vt2zY98sgj6tevn6699lrf9r1792rYsGHdUkkAAAAAAC7E79v9PPnkk5o/f76mTJmiqKgorVmzRmFhYb7tL774om644YZuqSQAAAAAABfid7CNj4/Xtm3bVF1draioKAUHB7favm7dOkVFRXV5BdG9HA1uffhlpY5W1ctmCVdSTISSYyJkiQiRyWQKdPUAAAAA4KL8DrZnWK3WNtfHxsZecmVweZTbHdr8eYU2HSjXB1+ckKPBc94+kWHBSoqJ8D2SY86G3qSYCCVawxUa7PdIdgAAAADoNh0OtjAej8erz0qqtelAhTZ/XqH8Y9WttidZw5WRZFG53amSqnpV1rlU53LrcEWtDlfUtlmmySQNjA5X0jmBt+kRrpSYfvT6AgAAALgsAhpsV65cqZUrV6qwsFCSNGbMGD322GPKzs6WJN1xxx1as2ZNq9dcc8012r59+wXLfOGFF7R27Vp99tlnkqSrrrpKTz31lK6++uruOYke6rSrUf/8olKbDpRr8+cVqqhx+raZTNL41BhdPypB148eqFGJ0a0CqKPBrZKqepVUOVRSVa9jVfWtnkuqHHK5PSqzO1Rmd+iToqo263Bur29milVXD4nV0LhIAi8AAACALhPQYJuSkqLly5crPT1dkrRmzRrNmTNHu3fv1pgxYyQ1zca8evVq32taTljVlry8PH3729/W1772NYWHh+u///u/dcMNN2jfvn1KTk7uvpPpAUqq6rXp8wptPlCuf31ZKWfj2SHGkWHBum5EvKaPStC0UQmKizJfsJzw0GANjY/S0Pi2r5n2eLyqrHM1h9wzgbd1CG6r1/fPHze9Pi4qTJMGx+rqIU2PUYkWBQcRdAEAAAB0jsnr9XoDXYmWYmNj9fTTT+vOO+/UHXfcoaqqKr3++uudLs/tdqt///763e9+p4ULF/r1GrvdLqvVqurqalkslnb39Xq9Aet99Hi8+vRolTYdqNCmzyt0oNTeantK/wjNGD1Q149O0NVDYmUOCb5ASV3v3F7fwso67frqlHYXV8nV2Pqa3ujwEE1M66+rhwzQ1UNidUWyVWEhXL+LS9ORz3FfxPsDGB+f4/bx/gDG15HPcY+5xtbtdmvdunWqq6tTVlaWb31eXp4SEhIUExOjKVOmaOnSpUpISPC73NOnT6uhoaHbJre6ffUO7Sk6pf6RYYrpF6b+/ULVv1+YrBFNz/0jQ89fHxmmyLDgTgXiWmejPjh8XJsOVGjLwQqdqHX5tgWZpCsH9df1zWF2eEJUwEL3hXp9nY1u5R+t1kcFJ/VxwUnt+uqUahyN2nLwuLYcPN782iBNSO2vq4fE6pohsZowqL8iwi5fKAcAAABgLAEPtvn5+crKypLD4VBUVJQ2bNigjIwMSVJ2drZuvfVWpaWlqaCgQI8++qimT5+uXbt2yWy+8FDalh5++GElJydrxowZF9zH6XTK6Tx7Dardbr/gvuc6VeeS3dEou6NRX1We9vt1ocEmX+CNiQhTTHPwjYlsfo44G4gjzSHa9dUpvXegXB8dOSmX+2yPZ7Q5RNeNjNf1oxI0dWSCYiPbH6odaOaQYE0cHKuJg2N17zSp0e3RgdIafVx4Uh8XVOrjgpM6dbpBHx6p1IdHKiVJIUEmZaZYNak56F6VFitrROhlrbfH45Wj0a3gIJNCg4IUxNBpGMyltHMAYAS0c0DfFvChyC6XS0VFRaqqqtL69eu1atUqbd261RduWyotLVVaWppycnI0f/78i5b93//931q+fLny8vKUmZl5wf0WL16sJUuWnLfeny7vk3Uunaxz6tTpBp2qc6nqdIOq6l06dbpBVaddOlXXoFOnW68/dyhuR6UN6KfrRw3UjNEJmjg4tlcN2/V4vPryeK0+KjipHYUn9dGRkyqzO1rtYzJJoxMtvmt0Jw2OVXx02190eL1eORo8qnE2qMbRqFpHY9Nz83LTz42qcTSo1tn0BUVty3Vn9nE1quUnJSTIpLCQIIUGNz3MIUEKDTYpNDjItz7M9/PZ9WHN+/teG2Jq2i84SKEhQYqPMisjyaJh8VG96t81EBiC1tqltHMAeibaudZo54DepyPtXMCD7blmzJihYcOG6fnnn29z+/Dhw3XXXXfpoYcearecX//613ryySf13nvvaeLEie3u29Y3fKmpqd3SEHq9XtU3uNsOwnUtAvFpl6rqG1R1ukH2+gYNS4jSjNEJmj5qoIbF951Zhb1er46eqm8KugUn9XHhSRWcqDtvv6HxkRo8ILIpiLYIqrWORjV6etSvuF9Cg00anhCtjCSLRtssymh+WPtd3p5qf5ysc6ngRK2+PF6nwhN1qnU2yuP1yu1p+qLC4/XK7fU2/6wWPzfv4z3zc9Ozx3POPt4W5TQ///zGUZqZMbDdevEHX2uXs50DcHnQzrVGOwf0Poa8xvYMr9fbqlFqqbKyUsXFxbLZbO2W8fTTT+vJJ5/UO++8c9FQK0lms9nvoc2XymQyqV9YiPqFhSg5JuKyHNPITCaTUmP7KTW2n/79qhRJUoXd0Tx0uenxeVmNjhyv05Hj5wfes+VIUeYQWcJDFWUOUXR4iKLCQxTdYjna3Hqd5Zx9Is3Bcnu8anB75Wr0qMHtkcvt8f3c4PbI2ehRg9urhsambWfXeVqsa3q9q9W6pv2OnqrXgVK7ahyN2l9q1/5zJgRLjoloCrpJFmXYopVhsyo1NqLbv+hwNLhVWFmnguN1OnKi6b0+cqJWBSfqVHW6oVuP3Zaq066L74RWLmc7BwCBQDsH9G0BDbaLFi1Sdna2UlNTVVNTo5ycHOXl5Sk3N1e1tbVavHixbrnlFtlsNhUWFmrRokWKi4vTvHnzfGUsXLhQycnJWrZsmaSm4cePPvqoXnnlFQ0ePFhlZWWSpKioKEVFtX37GhhLgiVcN2cm6ebMJElNIWdn4SmdqHU2hdDws0H1zHJnJ+sKhDO91PtL7TpQatf+kqaAe/RU0+2UjlXV670D5b79o80hGmWLburVbe7hHTEwWuGhHZtwy+PxqqS6XgVnguvxWl+ILamuV3tjO5Ks4RoSH6khcZGK7Rcmk8mk4KCmh8kkBZtMCjKZFBRkUrBJCgpqWg4OMinIpBY/m5q3Nb/Gt1/TPmf2G57AZxkAAABnBTTYlpeXa8GCBSotLZXValVmZqZyc3M1c+ZM1dfXKz8/X2vXrlVVVZVsNpumTZumV199VdHR0b4yioqKFBR09lrE5557Ti6XS//+7//e6liPP/64Fi9efLlODZdRTL8wzbjIsFQjadlLfeOYRN/66voGfd7ci3ug+flQWa1qnI3aUXhKOwpP+fYNDjJpWHykbxjzmV7euCizqk836MsTtc29r7W+IFtwoq7VvY/PFR0e0jTTdVykhsZFakh8pIbGRWlwXD/1C+txgz8AAADQh/S4a2x7Aq5ZgVE0uD06crxO+0urdaC0xte7e7Ku7aG6kWHBqnO5L1heaLBJg2L7nQ2w8ZEaEhelofGRGhAZZpheb4nP8cXw/gDGx+e4fbw/gPEZ+hpbAP4LDQ7SyMRojUyM1rwJTeu8Xq8qapy+kLu/1K4DJXYVVNb5Qm2iJVxDfME1UsPiozQkLlIp/SMUEsxszAAAADAWgi3Qy5hMJg20hGugJVzTRiX41p92Naqkql42a4QizXz0AQAA0Hvw1y3QR/QLC1F6QvTFdwQAAAAMhmALAOi1PB6vPvjihHJ2FOlQea0Sos1KtIYryRrR9BwTLps1QjZruKwRoYa6jhwAAJxFsAUA9DoVNQ6t23lUOTuKVHyy3rf+i4raC74mIjRYNmu4bDHhSrREKCkmvHUItkbIEhFC+AUAoAci2AIAegWPx6ttX5zQnz8q0nsHytXoaZr0Pzo8RLdcmaKpI+N16rRLpdUOlVY5mp6r61VW7VBlnUv1De6mezefqLvgMfqFBZ8TdsOVaI2QLaZpXXpClIKDCL4AAFxuBFsAgKFV2B1at+uo/vxxkY6eOts7e1Vaf3376kG66QqbIsKC2y3D0eBWWfXZsOt7bhGAT51u0GmXW0eON937uS1xUWH65hU23ZyZpIlp/RVEyAUA4LIg2AIADOdM7+wrH32l9w5UyH1O7+y3rx6kkYn+T5YWHhqswXGRGhwXecF9HA3ucwLvmQDc9Cg+eVonal1a++FXWvvhV0q0hOumTJtmj0vSuBQrQ5gBAOhGBFsAgGFU2B36y85i5ewoPq939jtXD9I3/eid7azw0GANiWu693NbGtweffDFCf3901K9u69MZXaH/vBBgf7wQYFSYyN0c2aSbs60KcNmIeQCANDFCLYAgHb95h+HdLCsRkPiI33BbkhcpAZEhl2WgObxePX+4eP688dFrXpnLeEhmt+J3tnuEhocpGkjEzRtZIIcDWP1/qHj+tveUr23v1zFJ+u1Mu9Lrcz7UkPjI3VzZpL+bZyNW3ABANBFCLYAgHb984sT2vnVqfPWR5tDNCQ+UoMHtA68g+MiZY0IveTjltsdWrezWH/+uFjHqs72zk5svna2O3tnL1V4aLBuGJOoG8Yk6rSrUZs/r9DfPy3V5oMVOnK8Tis2HdaKTYc1KjFas8c19eSmDbjwMGgAANA+gi0AoF0/u2GEDpbVqOBEne9xrKpeNc5G7T1arb1Hq897zYDIMF/IbRV6B0S2G0bdHq+2GaB3tiP6hYU0D0NOUo2jQe8dKNffPi3VtsPH9XlZjT4vO6in3zmozBSrZmcm6aZMm5JiIgJdbQAADMXk9Xq9ga5ET2O322W1WlVdXS2LxRLo6gDoBD7H7bvU98fR4FbxydM6cqJOhS0Cb8GJOlXUONt9rc0a3tTLGx+pIc29vYnWcOUdrGizd/Y71zT1zoaH9sze2c6qOu3SO/vK9Pe9pfrnFyfkafG/8cS0/ro506ZvZtqUEB0euEqiR6Odax/vD2B8HfkcE2zbQEMIGB+f4/Z15/tT62z0hV1f6K1seq463XDR15/pnf3ONYM0YqCxemc760StUxs/K9PfPi3RjsKTOvM/c5BJumbIAM0el6RZYxMVGxnW6nVuj1enXY2qc7pV52rUaadbtc5GnXY1Nj+7Vec8u72ued3Zfdw67WxaX+dyq77BrYjQYEWHhyg6PFTR5pDmn5uXw0MU1fyzpcX6KHPrn/vivXw9Hq8cjW6ddrlV3/xenvt8uvnZ4Tr7c72r0bfN0XB2vx9NTdfMjIHtHpN2rn28P4DxdeRzzFBkAECXijKHaGyyVWOTredtO1XnUkHl2cB7pse3+ORpjUyM9l0729t6Zy8mLsqsBZPTtGBymsqqHXorv1R/31ui3UVV+vBIpT48UqlH3/hM6fFRcjSeDav1De4ur4ur0aPq+gZJ9Rfd90KizCEtwm5z4A0PkSU8RLGRYUqKiVCSNUK2mHAlxUTIEn7p12R3NbfHq3K7Q0dP1evoqdM6dqpeR0/V61hVvarqXU1h9Uw4bXDL0eDp0uOX2R1dWh4A9HYEWwDAZdM/Mkz9I8N05aD+ga5Kj5VoDded3xiiO78xRMUnT+ut/FL97dMS7Sux62B5TZuvCQ4yqV9YsKLMIS2eQxRpDlGkOVj9wkIU5XsOUT9zi33Cgn37hYcGq97llt3R1ONb42hQjaPlc2Or5db7NMrlbgp3tc6mbWV2/845yhyipJhw2awRzaE3XLaYCCXFhCvJGqFEa3iXf9nR4PaorLpFcK2qb/VzaZVDjZ7ODWoLDw1Sv7AQRYQGKyIsuOm5xc/9woIVHhasfmfWtVzfvG9GEj2MANARBFsAAHqo1Nh++sGUYfrBlGEqaO7ZjmwZUJtDqTkkqEfcG9fR4G4Ou+eG4bM/n6h1qrS6XseqHCqtrlfV6QbVOht1qLxWh8prL1h2XFRTT6/NGn5ej2+SNULx0eZWQ6CdjW6VVjmaA+vppt7WFr2updX1ulhuDQ02yWaNUEr/CCXHRCilfz8l94/QgKgwX1g9E0b7NQfU8JBgBfXBodgIrMpap3L3lenLijolxYQrpX/T72tK/whZI0J7RPsAdDeCLQAABnBmZumeLDy0KeTFRZn9fs1pV6NKmkNuSVV9i58dKmle52jw6EStSydqXW3Owi1JIUEmDbSEKzYyTBU1DlXUOHWxWUTCQoKUEhOh5P6tw2tK/6Z1CdHhffJ6YRjDqbqzE9B9eKTSN4v8uaLNIc2/4/2aA2+EUmP7+cJvV9yeDegJCLYAACBg+oWFKD0hSukJUW1u93q9qjrd0BxyHc29vU1DhUuq6lVa7VCZvWnY8LGq+lazaoeHBp0Nqi16XFP6RyglJkJxUWZ6V2Eo1acb9M7+Mr3VPJt6y+HymSlWTUyL1fFap2+UwvEap2qcjc23Fmv7Uobo8BCl9O+n1HPCb0r/fkqJ7ZnXwANtIdgCAIAey2Qy+a7NHpN0/oRkUtNETxU1DpVUOXSyzqWEaLNS+kcoNjKMIZgwPLujQe/tL9ff9zbd/7rBfTbMjkmy6KZMm266wqa0AeeP6HA0uH3Xjh89dfY68uJT9Tp26rRO1LpU42jUgVK7DpS2fVG8NSK0VdjNGjpA142IV1hIULedM9AZBFsAAGBowUFN18LarBGBrgrQJWqdjdp0oCnMbj143DcxmySNSoxuus/1FTYNjW97pMMZ4aHB7Y6IqHe5dayqKegePdk6/B49Va/KOpeq6xtUXd+gfSVNwfcPHxSof79Q3ZRp09zxyboqrT9fIKFHINgCAAAAAXba1ahNByr01t5SbTlYIWfj2TCbnhClmzNtujnTpvSErru/d0RYsNIToi9YZp2zsdUEbIfLa5W7r0zHa5z60/Yi/Wl7kVL6R2jO+CTNHZ+s4X3k3uPomQi2AAAAQADUu9zKO1ihv+8t1abPy1vdD3loXKRuzrTppswkjUwMTGCMNIdoxMBojWgRWBf/2xj968sTen13iXI/K9XRU/X6/ZYv9fstX2pMkkVzxyfr38YnaaAlPCB1Rs/W4Pbo2Kl6FVbWacTAaCXFdN1IG4ItAAAAcJk4Gtzaeui43tpbqvcOlOu0y+3bNii2X3PPbJJG26J75BDf4CCTrh0er2uHx+vJuWP13oFyvbHnmPIOHte+Erv2ldj11MYDyho6QHMnJGvW2EQmoOpjmq7tPq2vKk+rsPK0vqqs8z0fPVXvm8H7qXlX6DvXDOqy4xJsAQAAgC7k9nhV42iQvb5RdkeD7PUNOlHnUt7nFfrH/nLVOBt9+6b0j9BNmTbdfEWSxiZbemSYvZCIsGDNHpek2eOSdLLOpbfyS/XG7mPa+dUp/evLSv3ry0r91+ufacboBM0Zn6ypI+NlDgkOdLXRBU67GlV08rQKT7QOrl9VnlZJdX27t1sLDw1SWmykQoO79nedYAsAAAC04PF4VeNslL2+oTmYng2odkfT+uo2ttU0b2sZXNtis4brpitsunlcksalWA0VZi8kNjJMCyanacHkNBWfPK03Py3Rht3H9EVFrd7OL9Pb+WWyRoTqm1fYNHd8kiYNjuV2Wz1cjaOhude1KbAWnmh6/upkncrtznZfGxkWrMFxkRo8IFJpA/pp8IBIDWp+TojunlutEWwBAADQpy352z59dOTk2YDqbGy3x8lfEaHBskSEyBoRKkt4qMYmWzV7nE0TUvv36lCXGttP905L14+mDtO+Erve2HNMb35aonK7U3/+uEh//rhIyTERmj0uSfMmJAfsGmJIjW6Pik6e1pfH6/Tl8VodOV6rL4/XqfBEnSrrXO2+1hoRqsED+iltQOTZ57im5wEBuN0awRYAAAB9WvHJeu1v4z6u5pAgWSJCZQkPaX4OlSUiVNaIEN/PTc9Ny9aIUN/+0eGhff5eryaTSWOTrRqbbNXD2aO1/UilXt99TLmflelYVb3+39Yv9f+2fqlRidGaOyFZ/zYuqcOTCXk8XjV6vHJ7vGr0eNTobr3sbrnd7ZXH61VIsEnmkGCFhQTJHBJ09jk4qFf0nrelur7BF1q/PF6rLytqdeREnb6qrGt1b+RzxUWFKa1Fr2vL55h+YZfxDC7O5PV2xfdRvYvdbpfValV1dbUsFkugqwOgE/gct4/3BzA+Psft68j780nRKdnrG84LquGhXA/aHRwNbm3+vEKv7z6mLQcrfMHKZJKGxUfJpKbrlBs8HrlbBdXzA2tXJ5mwkCCZg4NkDm0KuubQ4ObnoNbP5wXjs8sRYcGyhIcqOjxE0b4vRZq+7IgOD1FEaHC3BGiPx6tjVfVNwbVFgP3yeJ1O1F546HBEaLCGxkdqaHyUhsVHalh8lIbENYXX6ABP/NWRzzE9tgAAAOjTrhzUP9BV6FPCQ4P1zSts+uYVNlWddunt/DK9vueYPi44qS8qarvkGKHBJgUHmRQSFNT83LQcZDKp0eORs7Hp4Wpxv2BJcjWvq2n/EtJLEhJkag69TUH3bAhu+lIlOvxMED6zrXVIDgsJUlHl6fMCbMGJulb3Pz7XQItZw+Kjmh/NQTYhSjZLeK8YGk+wBQAAABAQMf3C9J1rBuk71wxSSVW9jhyvawqivmB6NqCGBLde9m0/Z32QSX73iHq9XrncTWH2TNA9++xu9fOZbc5z9mn5GkeDW/Uut+yORtU4micUa36ucTTI45UaPV6dOt2gU6cbuvz9DAsO0pC4SA1t7nkdlnC2BzbQva/djWALAAAAIOCSYiI6fI3tpTKZmq63NYcEq7unsPJ6vTrtcrcKumdm1W5aPvNzi+X6s/vWOBp9M24PiAxrFVzPBNmU/v0U3At6XzsjoMF25cqVWrlypQoLCyVJY8aM0WOPPabs7GxJ0h133KE1a9a0es0111yj7du3X7DMffv26bHHHtOuXbv01Vdf6Te/+Y1++tOfdtcpAAAAAMBFmUwmRZpDFGkOkc3auTLcHq9cjR5FhHH997kCOlVbSkqKli9frp07d2rnzp2aPn265syZo3379vn2mTVrlkpLS32Pt99+u90yT58+raFDh2r58uVKTEzs7lMAAAAAgMsiOMhEqL2AgPbYzp49u9Xy0qVLtXLlSm3fvl1jxoyRJJnN5g4F1EmTJmnSpEmSpIcffrjrKgsAAAAA6JF6zM213G63cnJyVFdXp6ysLN/6vLw8JSQkaMSIEbr77rtVUVERwFoCAAAAAHqagE8elZ+fr6ysLDkcDkVFRWnDhg3KyMiQJGVnZ+vWW29VWlqaCgoK9Oijj2r69OnatWuXzGZzl9XB6XTK6Tw7p7fdfv4NugHAyGjnAPR2tHNA3xbwHtuRI0dqz5492r59u374wx/q9ttv1/79+yVJ3/rWt3TTTTdp7Nixmj17tjZu3KhDhw7prbfe6tI6LFu2TFar1fdITU3t0vIBINBo5wD0drRzQN8W8GAbFham9PR0TZw4UcuWLdO4ceP07LPPtrmvzWZTWlqaDh8+3KV1eOSRR1RdXe17FBcXd2n5ABBotHMAejvaOaBvC/hQ5HN5vd5Ww0haqqysVHFxsWw2W5ce02w2d+nQZgDoaWjnAPR2tHNA3xbQYLto0SJlZ2crNTVVNTU1ysnJUV5ennJzc1VbW6vFixfrlltukc1mU2FhoRYtWqS4uDjNmzfPV8bChQuVnJysZcuWSZJcLpdvKLPL5dKxY8e0Z88eRUVFKT09PSDnCQAAAADoPgENtuXl5VqwYIFKS0tltVqVmZmp3NxczZw5U/X19crPz9fatWtVVVUlm82madOm6dVXX1V0dLSvjKKiIgUFnR1RXVJSogkTJviWf/3rX+vXv/61pkyZory8vMt5egAAAACAyyCgwfYPf/jDBbdFRETonXfeuWgZ54bVwYMHy+v1XmrVAAAAAAAGEfDJowAAAAAAuBQEWwAAAACAoRFsAQAAAACGRrAFAAAAABgawRYAAAAAYGgEWwAAAACAoRFsAQAAAACGRrAFAAAAABgawRYAAAAAYGgEWwAAAACAoRFsAQAAAACGRrAFAAAAABgawRYAAAAAYGgEWwAAAACAoRFsAQAAAACGRrAFAAAAABgawRYAAAAAYGghga5AT+T1eiVJdrs9wDUB0FlnPr9nPs9ojXYOMD7aufbRzgHG15F2jmDbhpqaGklSampqgGsC4FJVVlbKarUGuho9Du0c0HvQzrWNdg7oPfxp50xevuY7j8fjUUlJiaKjo2UymbqkTLvdrtTUVBUXF8tisXRJmZf7GJxDzzgG5+Cf6upqDRo0SKdOnVJMTEy3HMPIaOcCU/7lOAbn0DOOQTsXeN3RzknG/93h89UzjsE5+Kcj7Rw9tm0ICgpSSkpKt5RtsVi67R/+ch2Dc+gZx+Ac/BMUxFQCbaGdC2z5l+MYnEPPOAbtXOB0ZzsnGf93h89XzzgG5+Aff9o5WkIAAAAAgKERbAEAAAAAhkawvUzMZrMef/xxmc1mwx6Dc+gZx+Aces4x0Fpv+HflHHrGMTiHnnMMnM/ovzu94XeTc+gZx+hp58DkUQAAAAAAQ6PHFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbGNLUqVP105/+NNDVAIAuQZsGoLejnUN3I9gCfvrkk080c+ZMxcTEaMCAAbrnnntUW1sb6GoBQKcsXbpUX/va19SvXz/FxMS0uU9RUZFmz56tyMhIxcXF6Sc/+YlcLtflrSgAdJI/7dx9992nq666SmazWePHj7+s9UPXItgCfigpKdGMGTOUnp6ujz76SLm5udq3b5/uuOOOQFcNADrF5XLp1ltv1Q9/+MM2t7vdbt10002qq6vTBx98oJycHK1fv14PPPDAZa4pAHTOxdo5SfJ6vfr+97+vb33rW5exZugOBFv0Crm5ubJarVq7dm23lP/3v/9doaGh+v3vf6+RI0dq0qRJ+v3vf6/169friy++6JZjAui7urtNk6QlS5bo/vvv1xVXXNHm9nfffVf79+/Xn/70J02YMEEzZszQM888oxdeeEF2u73b6gWgb+gJ7ZwkrVixQvfee6+GDh3abfXA5UGwheHl5OToP/7jP7R27VotXLjwgvtFRUW1+8jOzr7ga51Op8LCwhQUdPYjExERIUn64IMPuu5kAPR5l6NN88eHH36osWPHKikpybfuxhtvlNPp1K5duy6pbAB9W09p59C7hAS6AsCleO6557Ro0SK98cYbmjZtWrv77tmzp93tZ4JqW6ZPn66f/exnevrpp3Xfffeprq5OixYtkiSVlpZ2uN4A0JbL1ab5o6ysTAMHDmy1rn///goLC1NZWdkllQ2g7+pJ7Rx6F4ItDGv9+vUqLy/XBx98oKuvvvqi+6enp3f6WGPGjNGaNWv0s5/9TI888oiCg4P1k5/8RAMHDlRwcHCnywWAMy5nm+Yvk8l03jqv19vmegC4mJ7YzqH3YCgyDGv8+PGKj4/X6tWr5fV6L7r/pQ5n+c53vqOysjIdO3ZMlZWVWrx4sY4fP64hQ4Z01SkB6MMud5t2MYmJief1zJ46dUoNDQ3n9eQCgD96WjuH3oUeWxjWsGHD9Mwzz2jq1KkKDg7W7373u3b376rhLGf+oHvxxRcVHh6umTNn+vU6AGhPoNq0C8nKytLSpUtVWloqm80mqWlCKbPZrKuuuuqSygbQN/W0dg69C8EWhjZixAht2bJFU6dOVUhIiH77299ecN9LHc7yu9/9Tl/72tcUFRWlf/zjH/r5z3+u5cuXX/C+aADQUZezTSsqKtLJkydVVFQkt9vt+wMyPT1dUVFRuuGGG5SRkaEFCxbo6aef1smTJ/Xggw/q7rvvlsViuaRjA+i7elI7J0lffPGFamtrVVZWpvr6et8+GRkZCgsLu6Tj4/Ii2MLwRo4cqc2bN/u+/XvmmWe65Tgff/yxHn/8cdXW1mrUqFF6/vnntWDBgm45FoC+63K1aY899pjWrFnjW54wYYIk+f7gDA4O1ltvvaUf/ehH+vrXv66IiAh95zvf0a9//etuqQ+AvqOntHOSdNddd2nr1q3n7VNQUKDBgwd3S73QPUxefwa4AwAAAADQQzF5FAAAAADA0Ai2AAAAAABDI9gCAAAAAAyNYAsAAAAAMDSCLQAAAADA0Ai2AAAAAABD4z62bfB4PCopKVF0dLRMJlOgqwOgE7xer2pqapSUlKSgIL7DOxftHGB8tHPto50DjK9D7Zw3gJ577jnvFVdc4Y2OjvZGR0d7J0+e7H377bd922+//XavpFaPa6655qLlnjp1yvujH/3Im5iY6DWbzd5Ro0Z533rrLb/rVVxcfN5xefDgYcxHcXFxp9qn3o52jgeP3vOgnWsb7RwPHr3n4U87F9Ae25SUFC1fvlzp6emSpDVr1mjOnDnavXu3xowZI0maNWuWVq9e7XtNWFhYu2W6XC7NnDlTCQkJ+utf/6qUlBQVFxcrOjra73qd2be4uFgWi6WjpwWgB7Db7UpNTe3QZ78voZ0DjI92rn20c4DxdaSdC2iwnT17dqvlpUuXauXKldq+fbsv2JrNZiUmJvpd5osvvqiTJ0/qX//6l0JDQyVJaWlpHarXmeEqFouFhhAwOIaftY12Dug9aOfaRjsH9B7+tHM95oIMt9utnJwc1dXVKSsry7c+Ly9PCQkJGjFihO6++25VVFS0W86bb76prKws3XvvvRo4cKDGjh2rp556Sm63u7tPAQAAAAAQAAGfPCo/P19ZWVlyOByKiorShg0blJGRIUnKzs7WrbfeqrS0NBUUFOjRRx/V9OnTtWvXLpnN5jbLO3LkiDZv3qzbbrtNb7/9tg4fPqx7771XjY2Neuyxx9p8jdPplNPp9C3b7fauP1EACCDaOQC9He0c0LeZvF6vN5AVcLlcKioqUlVVldavX69Vq1Zp69atvnDbUmlpqdLS0pSTk6P58+e3Wd6IESPkcDhUUFCg4OBgSdL//M//6Omnn1ZpaWmbr1m8eLGWLFly3vrq6mqGrgAGZbfbZbVa+Rw3o50Deh/audZo54DepyPtXMCHIoeFhSk9PV0TJ07UsmXLNG7cOD377LNt7muz2ZSWlqbDhw9fsDybzaYRI0b4Qq0kjR49WmVlZXK5XG2+5pFHHlF1dbXvUVxcfGknBQA9DO0cgN6Odg7o2wI+FPlcXq+31TCSliorK1VcXCybzXbB13/961/XK6+8Io/H47vX0aFDh2Sz2S44o7LZbL7g0GYA6A1o5wD0drRzQN8W0B7bRYsWadu2bSosLFR+fr5++ctfKi8vT7fddptqa2v14IMP6sMPP1RhYaHy8vI0e/ZsxcXFad68eb4yFi5cqEceecS3/MMf/lCVlZW67777dOjQIb311lt66qmndO+99wbiFAEAAAAA3SygPbbl5eVasGCBSktLZbValZmZqdzcXM2cOVP19fXKz8/X2rVrVVVVJZvNpmnTpunVV19tdR+joqIiX8+sJKWmpurdd9/V/fffr8zMTCUnJ+u+++7TQw89FIhTBAAAAAB0s4BPHtUTMRkDYHx8jtvH+wMYH5/j9vH+AMZnqMmjAAAAAAC4FARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaARbAAAAAIChEWwBAAAAAIZGsAUAAAAAGBrBFgAAAABgaAENtitXrlRmZqYsFossFouysrK0ceNG3/Y77rhDJpOp1WPy5MntlvnSSy+d9xqTySSHw9HdpwMAAAAACICQQB48JSVFy5cvV3p6uiRpzZo1mjNnjnbv3q0xY8ZIkmbNmqXVq1f7XhMWFnbRci0Wiw4ePNhqXXh4eBfWHAAAAADQUwQ02M6ePbvV8tKlS7Vy5Upt377dF2zNZrMSExM7VK7JZOrwawAAAAAAxtRjrrF1u93KyclRXV2dsrKyfOvz8vKUkJCgESNG6O6771ZFRcVFy6qtrVVaWppSUlJ08803a/fu3e3u73Q6ZbfbWz0AoDehnQPQ29HOAX1bwINtfn6+oqKiZDab9YMf/EAbNmxQRkaGJCk7O1svv/yyNm/erGeeeUY7duzQ9OnT5XQ6L1jeqFGj9NJLL+nNN9/Un//8Z4WHh+vrX/+6Dh8+fMHXLFu2TFar1fdITU3t8vMEgECinQPQ29HOAX2byev1egNZAZfLpaKiIlVVVWn9+vVatWqVtm7d6gu3LZWWliotLU05OTmaP3++X+V7PB5deeWVuu6667RixYo293E6na3Cst1uV2pqqqqrq2WxWDp3YgACym63y2q18jluRjsH9D60c63RzgG9T0fauYBeYys1TQZ1ZvKoiRMnaseOHXr22Wf1/PPPn7evzWZTWlpau72v5woKCtKkSZPafY3ZbJbZbO545QHAIGjnAPR2tHNA3xbwocjn8nq9FxxqXFlZqeLiYtlstg6Vt2fPng69BgAAAABgHAHtsV20aJGys7OVmpqqmpoa5eTkKC8vT7m5uaqtrdXixYt1yy23yGazqbCwUIsWLVJcXJzmzZvnK2PhwoVKTk7WsmXLJElLlizR5MmTNXz4cNntdq1YsUJ79uzR73//+0CdJgAAAACgGwU02JaXl2vBggUqLS2V1WpVZmamcnNzNXPmTNXX1ys/P19r165VVVWVbDabpk2bpldffVXR0dG+MoqKihQUdLbjuaqqSvfcc4/KyspktVo1YcIEvf/++7r66qsDcYoAAAAAgG4W8MmjeiImYwCMj89x+3h/AOPjc9w+3h/A+DryOe5x19gCAAAAANARBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKGF+LPT3r17O1xwRkaGQkL8Kh4AAAAAgE7zK3mOHz9eJpNJXq/Xr0KDgoJ06NAhDR069JIqBwAAAADAxfjdpfrRRx8pPj7+ovt5vV6NHTv2kioFAAAAAIC//Aq2U6ZMUXp6umJiYvwq9LrrrlNERMSl1AsAAAAAAL/4FWy3bNnSoULffvvtTlUGAAAAAICOYlZkAAAAAICh+X2N7fe///0211utVo0cOVLf/e53FRUV1WUVAwAAAADAH3732J46darNx549e/TYY49p5MiROnLkSHfWFQAAAACA8/jdY7thw4YLbquvr9fChQv18MMP6y9/+UuXVAwAAAAAAH90yTW2EREReuihh7R9+/auKA4AAAAAAL912eRRsbGxqqqq6tBrVq5cqczMTFksFlksFmVlZWnjxo2+7XfccYdMJlOrx+TJk/0uPycnRyaTSXPnzu1QvQAAAAAAxuH3UOSL+de//qVhw4Z16DUpKSlavny50tPTJUlr1qzRnDlztHv3bo0ZM0aSNGvWLK1evdr3mrCwML/K/uqrr/Tggw/q2muv7VCdAAAAAADG4new3bt3b5vrq6urtWPHDj311FN68sknO3Tw2bNnt1peunSpVq5cqe3bt/uCrdlsVmJiYofKdbvduu2227RkyRJt27atwz3JAAAAAADj8DvYjh8/XiaTSV6v97xt8fHxeuihh/SDH/yg0xVxu91at26d6urqlJWV5Vufl5enhIQExcTEaMqUKVq6dKkSEhLaLetXv/qV4uPjdeedd2rbtm2drhMAAAAAoOfzO9gWFBS0ud5qtSomJqbTFcjPz1dWVpYcDoeioqK0YcMGZWRkSJKys7N16623Ki0tTQUFBXr00Uc1ffp07dq1S2azuc3y/vnPf+oPf/iD9uzZ43cdnE6nnE6nb9lut3f6fACgJ6KdA9Db0c4BfZvfwTYtLa1bKjBy5Ejt2bNHVVVVWr9+vW6//XZt3bpVGRkZ+ta3vuXbb+zYsZo4caLS0tL01ltvaf78+eeVVVNTo+9+97t64YUXFBcX53cdli1bpiVLlnTJ+QBAT0Q7B6C3o50D+jaTt62xxW344osvVF1drauuusq3btOmTXryySdVV1enuXPnatGiRZdcoRkzZmjYsGF6/vnn29w+fPhw3XXXXXrooYfO27Znzx5NmDBBwcHBvnUej0eSFBQUpIMHD7Y5wVVb3/ClpqaqurpaFovlUk8JQADY7XZZrVY+x81o54Deh3auNdo5oPfpSDvnd4/tz3/+c40dO9YXbAsKCjR79mxde+21yszM1LJly9SvXz/99Kc/vaTKe73eVo1SS5WVlSouLpbNZmtz+6hRo5Sfn99q3X/913+ppqZGzz77rFJTU9t8ndlsvuDQZgDoDWjnAPR2tHNA3+Z3sN25c6d+8Ytf+JZffvlljRgxQu+8844kKTMzU//7v//boWC7aNEiZWdnKzU1VTU1NcrJyVFeXp5yc3NVW1urxYsX65ZbbpHNZlNhYaEWLVqkuLg4zZs3z1fGwoULlZycrGXLlik8PFxjx45tdYwz1/+eux4AAAAA0Dv4HWxPnDihlJQU3/KWLVta3a5n6tSpeuCBBzp08PLyci1YsEClpaWyWq3KzMxUbm6uZs6cqfr6euXn52vt2rWqqqqSzWbTtGnT9Oqrryo6OtpXRlFRkYKCgjp0XAAAAABA7+F3sI2NjVVpaalSU1Pl8Xi0c+dO3X///b7tLperzVsBtecPf/jDBbdFRET4eoPbk5eX1+72l156qUN1AgAAAAAYi99dnVOmTNETTzyh4uJi/fa3v5XH49G0adN82/fv36/Bgwd3Rx0BAAAAALggv3tsly5dqpkzZyotLU3BwcFasWKFIiMjfdv/+Mc/avr06d1SSQAAAAAALsTvYDtkyBAdOHBA+/fvV3x8vJKSklptX7JkSatrcAEAAAAAuBz8DraS5HA4VFFRoWPHjiksLExxcXG+bePGjevyygEAAAAAcDF+B9u9e/cqOztbZWVl8nq9slgs+utf/6oZM2Z0Z/0AAAAAAGiX35NHPfzwwxo0aJC2bdumnTt3asqUKfrxj3/cnXUDAAAAAOCi/O6x3blzp95++21NnDhRkvTiiy8qISFBtbW1ioqK6rYKAgAAAADQHr97bE+cOKFBgwb5lgcMGKB+/frp+PHj3VIxAAAAAAD84XePrclkUk1NjcLDwyVJXq/Xt85ut/v2s1gsXV9LAAAAAAAuwO9g6/V6NWLEiPPWTZgwwfezyWSS2+3u2hoCAAAAANAOv4Ptli1burMeAAAAAAB0it/BdsqUKd1ZDwAAAAAAOsWvyaNaXkPrj5qamk5VBgAAAACAjvIr2Pbv318VFRV+F5qcnKwjR450ulIAAAAAAPjLr6HIXq9Xq1at8vt+tQ0NDZdUKQAAAAAA/OVXsB00aJBeeOEFvwtNTExUaGhopysFAAAAAIC//Aq2hYWF3VwNAAAAAAA6x69rbAEAAAAA6KkItgAAAAAAQyPYAgAAAAAMjWALAAAAADA0gi0AAAAAwNA6HGwHDx6sX/3qVyoqKuqO+gAAAAAA0CEdDrYPPPCA3njjDQ0dOlQzZ85UTk6OnE5nd9QNAAAAAICL6nCw/T//5/9o165d2rVrlzIyMvSTn/xENptNP/7xj/XJJ590Rx0BAAAAALigTl9jO27cOD377LM6duyYHn/8ca1atUqTJk3SuHHj9OKLL8rr9XZlPQEAAAAAaFNIZ1/Y0NCgDRs2aPXq1frHP/6hyZMn684771RJSYl++ctf6r333tMrr7zSlXUFAAAAAOA8HQ62n3zyiVavXq0///nPCg4O1oIFC/Sb3/xGo0aN8u1zww036LrrruvSigIAAAAA0JYOB9tJkyZp5syZWrlypebOnavQ0NDz9snIyNB//ud/dkkFAQAAAABoT4eD7ZEjR5SWltbuPpGRkVq9enWnKwUAAAAAgL86PHnUtGnTVFlZed76qqoqDR06tEsqBQAAAACAvzocbAsLC+V2u89b73Q6dezYsS6pFAAAAAAA/vI72L755pt68803JUnvvPOOb/nNN9/Uhg0b9MQTT2jw4MEdOvjKlSuVmZkpi8Uii8WirKwsbdy40bf9jjvukMlkavWYPHlyu2W+9tprmjhxomJiYhQZGanx48frj3/8Y4fqBQAAAAAwDr+vsZ07d64kyWQy6fbbb2+1LTQ0VIMHD9YzzzzToYOnpKRo+fLlSk9PlyStWbNGc+bM0e7duzVmzBhJ0qxZs1pdrxsWFtZumbGxsfrlL3+pUaNGKSwsTH//+9/1ve99TwkJCbrxxhs7VD8AAAAAQM/nd7D1eDySpCFDhmjHjh2Ki4u75IPPnj271fLSpUu1cuVKbd++3RdszWazEhMT/S5z6tSprZbvu+8+rVmzRh988AHBFgAAAAB6oQ5fY1tQUNAlofZcbrdbOTk5qqurU1ZWlm99Xl6eEhISNGLECN19992qqKjwu0yv16tNmzbp4MGD3FcXAAAAAHopv3psV6xYoXvuuUfh4eFasWJFu/v+5Cc/6VAF8vPzlZWVJYfDoaioKG3YsEEZGRmSpOzsbN16661KS0tTQUGBHn30UU2fPl27du2S2Wy+YJnV1dVKTk6W0+lUcHCwnnvuOc2cOfOC+zudTjmdTt+y3W7v0DkAQE9HOwegt6OdA/o2k9fr9V5spyFDhmjnzp0aMGCAhgwZcuHCTCYdOXKkQxVwuVwqKipSVVWV1q9fr1WrVmnr1q2+cNtSaWmp0tLSlJOTo/nz51+wTI/HoyNHjqi2tlabNm3SE088oddff/28YcpnLF68WEuWLDlvfXV1tSwWS4fOB0DPYLfbZbVa+Rw3o50Deh/audZo54DepyPtnF/B9nKaMWOGhg0bpueff77N7cOHD9ddd92lhx56yO8y77rrLhUXF+udd95pc3tb3/ClpqbSEAIGxh98rdHOAb0P7VxrtHNA79ORds7vyaMuF6/X26pRaqmyslLFxcWy2WxdVqbUNEFVe0ObAcDoaOcA9Ha0c0Df1uFg63a79dJLL2nTpk2qqKjwzZZ8xubNm/0ua9GiRcrOzlZqaqpqamqUk5OjvLw85ebmqra2VosXL9Ytt9wim82mwsJCLVq0SHFxcZo3b56vjIULFyo5OVnLli2TJC1btkwTJ07UsGHD5HK59Pbbb2vt2rVauXJlR08VAAAAAGAAHQ629913n1566SXddNNNGjt2rEwmU6cPXl5ergULFqi0tFRWq1WZmZnKzc3VzJkzVV9fr/z8fK1du1ZVVVWy2WyaNm2aXn31VUVHR/vKKCoqUlDQ2cmd6+rq9KMf/UhHjx5VRESERo0apT/96U/61re+1el6AgAAAAB6rg5fYxsXF6e1a9fqm9/8ZnfVKeC4ZgUwPj7H7eP9AYyPz3H7eH8A4+vI57jD97ENCwtTenp6pysHAAAAAEBX6nCwfeCBB/Tss8+qh02mDAAAAADoozp8je0HH3ygLVu2aOPGjRozZoxCQ0NbbX/ttde6rHIAAAAAAFxMh4NtTExMq1mJAQAAAAAIpA4H29WrV3dHPQAAAAAA6JQOX2MLAAAAAEBP4leP7ZVXXqlNmzapf//+mjBhQrv3rv3kk0+6rHIAAAAAAFyMX8F2zpw5MpvNkqS5c+d2Z30AAAAAAOgQv4Lt448/3ubPAAAAAAAEGtfYAgAAAAAMza8e2/79+7d7XW1LJ0+evKQKAQAAAADQEX4F29/+9rfdXA0AAAAAADrHr2B7++23d3c9AAAAAADoFL+C7YXU19eroaGh1TqLxXJJFQIAAAAAoCM6PHlUXV2dfvzjHyshIUFRUVHq379/qwcAAAAAAJdTh4PtL37xC23evFnPPfeczGazVq1apSVLligpKUlr167tjjoCAAAAAHBBHR6K/Le//U1r167V1KlT9f3vf1/XXnut0tPTlZaWppdfflm33XZbd9QTAAAAAIA2dbjH9uTJkxoyZIikputpz9ze5xvf+Ibef//9rq0dAAAAAAAX0eFgO3ToUBUWFkqSMjIy9Je//EVSU09uTExMV9YNAAAAAICL6nCw/d73vqdPP/1UkvTII4/4rrW9//779fOf/7zLKwgAAAAAQHs6fI3t/fff7/t52rRpOnDggHbt2qVhw4Zp3LhxXVo5AAAAAAAu5pLuYytJaWlpSktL64q6AAAAAADQYX4PRf7oo4+0cePGVuvWrl2rIUOGKCEhQffcc4+cTmeXVxAAAAAAgPb4HWwXL16svXv3+pbz8/N15513asaMGXr44Yf1t7/9TcuWLeuWSgIAAAAAcCF+B9s9e/bo+uuv9y3n5OTommuu0QsvvKCf/exnWrFihW+GZAAAAAAALhe/g+2pU6c0cOBA3/LWrVs1a9Ys3/KkSZNUXFzctbUDAAAAAOAi/A62AwcOVEFBgSTJ5XLpk08+UVZWlm97TU2NQkNDu76GAAAAAAC0w+9gO2vWLD388MPatm2bHnnkEfXr10/XXnutb/vevXs1bNiwbqkkAAAAAAAX4vftfp588knNnz9fU6ZMUVRUlNasWaOwsDDf9hdffFE33HBDt1QSAAAAAIAL8TvYxsfHa9u2baqurlZUVJSCg4NbbV+3bp2ioqK6vIIAAAAAALTH72B7htVqbXN9bGzsJVcGAAAAAICO8vsaWwAAAAAAeiKCLQAAAADA0AIabFeuXKnMzExZLBZZLBZlZWVp48aNvu133HGHTCZTq8fkyZPbLfOFF17Qtddeq/79+6t///6aMWOGPv744+4+FQAAAABAgAQ02KakpGj58uXauXOndu7cqenTp2vOnDnat2+fb59Zs2aptLTU93j77bfbLTMvL0/f/va3tWXLFn344YcaNGiQbrjhBh07dqy7TwcAAAAAEAAdnjyqK82ePbvV8tKlS7Vy5Upt375dY8aMkSSZzWYlJib6XebLL7/cavmFF17QX//6V23atEkLFy689EoDAAAAAHqUgAbbltxut9atW6e6ujplZWX51ufl5SkhIUExMTGaMmWKli5dqoSEBL/LPX36tBoaGtqdtdnpdMrpdPqW7XZ7504CAHoo2jkAvR3tHNC3BXzyqPz8fEVFRclsNusHP/iBNmzYoIyMDElSdna2Xn75ZW3evFnPPPOMduzYoenTp7dqtC7m4YcfVnJysmbMmHHBfZYtWyar1ep7pKamXvJ5AUBPQjsHoLejnQP6NpPX6/UGsgIul0tFRUWqqqrS+vXrtWrVKm3dutUXblsqLS1VWlqacnJyNH/+/IuW/d///d9avny58vLylJmZecH92vqGLzU1VdXV1bJYLJ07MQABZbfbZbVa+Rw3o50Deh/audZo54DepyPtXMCHIoeFhSk9PV2SNHHiRO3YsUPPPvusnn/++fP2tdlsSktL0+HDhy9a7q9//Ws99dRTeu+999oNtVLTdbxms7lzJwAABkA7B6C3o50D+raAB9tzeb3eCw41rqysVHFxsWw2W7tlPP3003ryySf1zjvvaOLEid1RTQAAAABADxHQYLto0SJlZ2crNTVVNTU1ysnJUV5ennJzc1VbW6vFixfrlltukc1mU2FhoRYtWqS4uDjNmzfPV8bChQuVnJysZcuWSWoafvzoo4/qlVde0eDBg1VWViZJioqKUlRUVEDOEwAAAADQfQIabMvLy7VgwQKVlpbKarUqMzNTubm5mjlzpurr65Wfn6+1a9eqqqpKNptN06ZN06uvvqro6GhfGUVFRQoKOjsH1nPPPSeXy6V///d/b3Wsxx9/XIsXL75cpwYAAAAAuEwCGmz/8Ic/XHBbRESE3nnnnYuWkZeX12q5sLDwEmsFAAAAADCSgN/uBwAAAACAS0GwBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAABcFvUut1ZtOyK3x9ul5YZ0aWkAAAAAALRh11cn9eC6vSo4USe3x6v/35RhXVY2wRYAAAAA0G0cDW79zz8O6YVtR+T1SgMtZo1MjO7SYxBsAQAAehCPx6ugIFOgqwEAXWJ30Sk9uO5TfXm8TpJ0y5UpeuzmDFn7hXbpcQi2AAAAPUCD26N1O4/q/239Ui/fdY1SY/sFukoA0GnORrd++95hPb/1S3m8Uny0WcvmXaEZGQO75XgEWwAAgADyeLz6294S/eYfh1RYeVqS9OI/C/T47DEBrhkAdE7+0Wo9sG6PDpXXSpLmjE/Skn8bo5h+Yd12TIItAABAAHi9Xm3+vEJPv3NQn5fVSJIGRIbp3mnp+s41gwJcOwDoOFejR7/bfFi/z/tSbo9XAyLDtHTeWM0aa+v2YxNsAfR4Ho9Xnx6t0rv7y/XuvjKt/O5VGjGwayccAIDL6cMvK/X0O5/rk6IqSVK0OUT3XDdU3/vGEEWZ+fMMgPHsK6nWA3/51PdF3U2ZNv3q38ZoQJT5shyflrMP8nq9Ol7j1KHyWh0qr9HhihrVOt0akRClkYnRGm2zKDkmgokrEFCuRo8+PFKpd/eV6R/7y1VR4/Rte3dfGcEWgCHtPVqlp985qG2HT0iSwkODdPvXBuuHU4Z16xA9AOguDW6Pntvypf5382E1erzq3y9UT8wdq5szky5rPQi2vZjX69WJWpcOl9foUHmNDlXUNv9cq+r6hnZfGxkWrBGJ0RqVaNGoxGiNTIzWqMRo/tNFt6pxNCjv4HG9u79ceZ9XqMbZ6NsWZQ7R1JHxumFMoqaOjA9gLQGg4w6X1+iZdw8pd1+ZJCkkyKRvXz1IP56eroGW8ADXDgA652BZjR5Yt0efHbNLkm4cM1BPzr1C8dGXp5e2JYJtL1FZ29QDe7iiOcSWN4XYU6fbDrBBJmnwgEgNHxilEQOjFWkO0aGyGn1eVqMvKmpV53Jrd1GVdjcPkToj0RLeFHJtTUF35ECLhiVEyhwSfBnOEr1Rud2hf+wv1z/2l+tfX55Qg9vr2xYfbdbMjIG6IWOgsoYN4PcMgOEUnzyt3753WBt2H5XHK5lM0rzxyfrpjBEaNIBZjwEYU6Pbo+ffP6LfvndIDW6vrBGh+tWcMfq3cUkymQIz6pNgazCn6lzn9L7W6HB5rSrrXG3ubzJJabH9NHxgtEY0h9jhCdEaGh+p8NC2Q0KD26PCE3X6vKxGn5fZdbCsRgdKa3Ssql5ldofK7A5tPXTct39IkElD4yM1KtHi69kdZbMoyRoesF9s9GxfVNTq3f1lendfufYUV7XaNjQ+UjdkJOqGMQM1PiWGIfEADKmixqHfb/5Cr3xc5PvC7sYxA/XADSO5lAKAoR0ur9GD6z7Vp0erJUkzRifoqXlXKCHAo08Itj3YaVejth+p1D+/qNSBUrsOldfqRK2zzX1NJim1fz+NGBjlC7HDE6KVnhB1wQB7IaHBQRo+MFrDB0Zr9rizY+NrHA06VN4Ucg+WNT0OlNlV42hsvl63Vvr0bDnR4SEaOfBM765FU0bEc0++Psrj8WrP0Sq9u69c7+4v05HmG3SfMWFQjG7ISNTMjIFKT4gKUC0B4NJVn27Q8+9/qdX/LFR9g1uS9I30OD1440iNT40JbOUA4BK4PV6t2nZEz/zjkFyNHkWHh2jx7DGaf2Vyj+jMItj2IF6vV4fKa/X+oePaeui4Pi44KZfbc95+Kf0jmnpeB0ZpREK0RgxsCrARYd07TDM6PFRXpcXqqrTYVnUurXb4Qu6ZwPtFRa1qHI3a+dUp7fzqlG//qwfHav6Vycq+wiZrRGi31heB5Wx068MvK/Vu8zDj4y0mfwoNNulrw+J0w5iBmjF6INeX9XHHa5zadKBcJ0+75HZ71ejxyu058+xpvez2qsHjabXc5n7Nz43us/tGhgVr4uBYZQ0doKuHxsoSThuErlPnbNRL/yrU/9v6pWocTfMDjE+N0S9uHKmvpccFuHYItOrTDdp8sFyOBo+izCGKDj/zCPUtR4aFMEoJPdaXx2v183Wf+mZynzoyXsvnZyrR2nP+hiPYBlj16Qb988sT2nqwKcyW2R2ttifHRGjKyHhNSI3xBdjIHnQbAJPJpKSYCCXFRGjaqATfelejR0dO1Orz0qbrdvcUn9JHBSf1cWHT47E392nm6IGaNyFZU0bGKzQ4KIBnga5iPzP5074y5R08rtpzJn+aNipBN2QM1NSR8YomVPRpdkeD3vmsTG9+WqJ/fnFCHu/FX9MVPj1arT98UKAgkzQ22aqsoQM0edgATRocyy1W0CnORrf+/FGRfrflC52obbosaOTAaD1440jNGJ3QI3oxEBjORre2fH5cr+8+ps2fV7TZWdGSySRFhYUoqjn0NgXeUEWFh8jSYjn6nJ9bLlsjQhUWwt9U6Dpuj1er/1mgp985KGejR9HmED16c4ZunZjS49q3gP4vvnLlSq1cuVKFhYWSpDFjxuixxx5Tdna2JOmOO+7QmjVrWr3mmmuu0fbt2y9Y5r59+/TYY49p165d+uqrr/Sb3/xGP/3pT7vrFDrM7fEq/1i1r1d2d9GpVn/QmUOClDVsgK4bHq8pI+M1NC6yx/3S+CMsJKh5RmWLb11pdb1e312iDbuP6lB5rd7KL9Vb+aWKjQzT7Eyb5l+ZoswUqyHPt68qtzu0o/Ckdhae0o7CkzpQam/1+5xwZvKnMYmaPDSWyZ/6OEeDW3kHK/TGnhJt+rxCrsazf+SNT43RiIFRCg4KUkiQScFBpqbn4Obnc9f7tgcp9Mxy8IX2C1JwkEkVNQ5tP1KpD7+sVGHlae09Wq29R6v1/PtHFBxkUmZKU9DNGjZAE9Niu30UDIyt0e3Ra7uP6dn3DutYVb0kKW1AP/1s5gjdnJmkYHre+iSPx6sdhSf1+p4SvbW3RHbH2S94Rw6MVmpshGocjapxNKrW2agaR4NqHI1q9Hjl9Uo1zkbVOBtVWt2544eFBClr6ADNGJ2g6aMHKjkmoovODH1R4Yk6/fyvn2pHYdPoy2uHx2n5LZk99vcqoME2JSVFy5cvV3p6uiRpzZo1mjNnjnbv3q0xY8ZIkmbNmqXVq1f7XhMW1v7tZk6fPq2hQ4fq1ltv1f333999le+AihqHth06oa2Hjmvb4ePnzVQ8PCFKU0bE67oR8bp6SGyHr4k1Cps1Qj+cOkw/mDJU+0rs2rD7mN7YU6ITtU6t+fArrfnwKw2Nj9T8CcmaOyFZKf25Hrcn8Xq9+vJ4nXYUnvSF2aKTp8/bb1h8pG4Yk6gbMgZqHJM/9XmN7qb7Eb+xp0TvfFbW6hZO6QlRmjs+SbPHJSltQORlqc+c8cmSmr5o+/DLppD74ZFKHT1V75sJ/rm8LxUabNL41BhlDYtT1tABmjAopte2zegYr9erjZ+V6Zl3D+rL5vkCBlrM+sn1w/UfE1MZgdRHHS6v8f1dc+aLDqnpd2Pu+Ka/a0bbLG2+1uv1ytnoaQ68Dc2B92zobRmCa52NsjsaVXvOvrWORtW6GuVq9Ghrc+fJo2/s06jEaM0YPVDTRycwISP85vF4tfbDQi3P/VyOBo8iw4L1y5sy9O2rU3t0B5TJ6/VepgFg/omNjdXTTz+tO++8U3fccYeqqqr0+uuvd6qswYMH66c//WmHe2ztdrusVquqq6tlsbTdCLXH1ejRJ0WnmhqWg8e1v9Teanu0OUTfGB6n65rDbE/91uNyaHR7tO2LE9rwyTG9u79MjoazPThXD4nV/AnJ+mamjWvhAsDV6NG+kurmIHtKOwtPnvelTJBJGm2zaNLgWE0c3F8T02J7zLUWl/o57u268/3xer3aU1ylN/aU6O97S1tNepdkDdfs8UmaMy5Zo23RPeY/yOKTp/XhkUptbw66pdWtLwsJCwnSVYP6K2tYU4/uuJQYhvv1MV8er1XuZ2V6c0+JDpbXSJJi+oXqR1OHaWHW4IB88UE7177ufn8q7A69+WmJNuw+pn0lZ//WizKHKHtsouZNSNY1Qwdctt57j8erL4/X6r0DFdr8ebl2fdV6VGBcVJimjUzQ9aMTdO3w+B51aRt6hnqXW7n7SvXHD7/yXUubNXSA/vvfMwM2AWxHPsc95jfa7XZr3bp1qqurU1ZWlm99Xl6eEhISFBMToylTpmjp0qVKSEhop6TAKD552vcN2b++OKE6l7vV9swUq2948fjUGL7RbRYSHKRpIxM0bWSCahwNyv2sTK99ckzbCyr1ccFJfVxwUo+/uU8zMgZq/oRkXTeC63G7S42jQbuLqnw9snuKq1p90SA1DZUfnxqjq4fEauLgWE0YFMOXDvA5XF6jN/aU6M1PS1r15vfvF6pvXmHTnPHJmpjWv0f2GKTG9lNqbD/9x8RUeb1efVXZFHTP9Oger3E2LR+plP4hRYQGa+Lg/prcPHQ5M9mqENqmXsXr9erzshpt/KxMuZ+VNs383ywyLFh3XTtUd107hPkC+phaZ6Pe+axMr+851mp+gJAgk6aOjNfcCcmaMXpgQL7oCAoy+e5q8cOpw3SyzqW8gxXa9HmF3j94XCdqXVq366jW7TqqsOAgTR42QNePagq6jJLru7xerz4pqtJfdxXr75+W+kZWRYQGa9E3R+m2a9J65P/bbQl4j21+fr6ysrLkcDgUFRWlV155Rd/85jclSa+++qqioqKUlpamgoICPfroo2psbNSuXbtkNpsvWra/PbZOp1NO59keBbvdrtTU1It+M+D1evXU2we06UCFjpxoffuSuKgwXTs8XlNGxOsbw+MUF3Xx+uKskqp6vb7nmDZ8ckyHK87+MTEgMkyzxyVp3oTkXnc9rtvjlUm6bI1Hhd2hHc3XxrZ1fazU1BsxMS1WVw/pr4mDYzU2yWqYXip6MlrrbDt3Mceq6vW3T0v0xp4SHWgxOqVfWLBuyBioOeOT9Y3hcYb+QurMMPwzPbrbj1Sed+/wyLBgTRoSqxsyEnXTFTZZ+xF2jMjrbZoHY+NnZdqYX6rCyrNf0JyZzT17bKJmjU1UTL/2L426HGjnWuuudq7B7dEHh09ow+7zR5ddOShG8yYk66bMJMVGBv534kJcjR7tKDypTQcqtOnzcn1V2fpSolGJ0bp+dIKmjxqo8akxXCPeB1TYHXpt9zGt21nsu6xCklJjI/TvV6bqPyalyGYN/KjSjrRzAQ+2LpdLRUVFqqqq0vr167Vq1Spt3bpVGRkZ5+1bWlqqtLQ05eTkaP78+Rct299gu3jxYi1ZsuS89f68gf/5//9Q24+cVHCQSVel9deUEU1hNsNmMcy3Gz2Z1+vVZ8fsem33Uf3t0xLfjJNS07Wc869M0ZzxSZ3+pvHMdS2OBrccDR7VN7hV73KrvsEtR4ufzyy7Gj1qcHubn5sezhY/+7b7fj673uX2ttjn3Nc23Z5EavrWNywkqOkRHNTqZ3NI0HnbzCHBbe/T8rXNy87mYfIXuj42NTZCk9JiNWlIrCYN7q+hcVGG/T3mD77WLqWdO9fJOpfezi/Vm3tK9HHhSd/6Mz0W/zY+WTNGJ6hfWI8ZFNSlPB6vDlfU6sMvTzSF3SMnVV1/dph+WHCQpo9K0NwJyZo2Kp5J03o4j8er3cWn9HZ+mXI/K2t1fWRYSJCmjIhX9thEXT96YI+7TR3tXGtd2c55vV59erRar+8+pr99WtLqy6whcZHN181evvkBulLTl3W1TSH3QIV2fnWy1RfbAyLDNHVkgmaMTtC1I+KZMb4XcTV6tOlAudbtOqqth477/vYMDw3SN8fadOvEVF0zJLZH/e1nqGB7rhkzZmjYsGF6/vnn29w+fPhw3XXXXXrooYcuWlZ399hK0tZDx1Xvcutr6QMYktnNznxj+truY3p3X5mcLWZUvWZIrCYPHSCX26N6V3MobQ6mjkaPHC0CaqvtDW71rE/A5WMySaMTLc3DinvW9bFdgT/4WrvUnow6Z6P+sb9cb+w5pm2HT6ix+T9Dk6np/tRzxicre2yi+vfgHovu4vF4daDMrvcPndAbe47p87Ia3zZLeIhuymwaZdJTh2H3RY1ujz4uPKncz8r0zr4yldvPfjYiQoM1fVSCZo1N1LRRCT36j3rauda6osf2q8o6vb67RK/vOaaCFqPxevOIsVN1LuUdagq5Ww8d992HWWoaqTB56JkhywMDdp0lLs3+ErvW7SrW67uPtZov5aq0/rr1qhTdlGnrsZdVGPIa2zO8Xm+rRqmlyspKFRcXy2azdekxzWazX0Ob2zJlRHyX1gUXFhocpGmjEjRtVILsjgbl5pfptd1Htf3ISX1U0PS4tPJNCg8NVnhosCKaH+FhwYoIDWr6OTTY1/MZeuY5uKl3NTS46WFu8XPTepOv5/TsepPCgoMVGmJqWm6x3es929vramzq0W257Dpn2XneNner/Zxnymh+mEzSFclWro/tgzrbznm9Xj24bq/eyi9pNfxubLJFc8Yl6+Zxth4xVCmQgoJMGpNk1Zgkq344dZgOlNr1+p5jemN3icrsDv354yL9+eMiJcdEaO6Epj+M0xOiA13tPsfV2DRD98b8Ur27v1wnW/TARZtDdP3oBM0aa9OUEfHc6smgLuXvuZc/+krrdx31TZgjNfVi3TgmUXN7wSUV7ekfGaZ5E1I0b0KKGtwe7Sg4qU2fV2jTgXIVVp7WtsMntO3wCS3+236NGBilb15h09zxyRocZ7ze6r7kVJ1Lb+w5pnW7jraa3Gygxaz5V6bo369K0bD4qADWsOsFNNguWrRI2dnZSk1NVU1NjXJycpSXl6fc3FzV1tZq8eLFuuWWW2Sz2VRYWKhFixYpLi5O8+bN85WxcOFCJScna9myZZKahjbv37/f9/OxY8e0Z88eRUVF+W4rBOOzhIfqPyal6j8mpepYVb3e3NM0WU1EaLAiws4G0YiwFiG1rXUt9u2t/2EBl8JkMqnW2SBHg0eDB/TTv41P1r+NS1J6Qu/6z7ArjbZZNNpm0S9uHKWPCir1+u5j2pjfNMT191u+1O+3fKmxyRbNbX4vEyy9Z6RET+NocGvb4RPa+Fmp3ttf3uqeojH9QjVz9EB98wqbvpY+gCHjfdzG/DJ9UlSlIJP09fQ4zZuQrBvGJPboHvvuEBocpK+lx+lr6XH6r5tG68vjddr8ebneO1ChnYUndai8VofKD+u37x32XV98c2ZSnxyt0xO5PV69f/i41u0s1nv7K+RyN30hHRps0syMgbr1qlRdOzyu1052GNChyHfeeac2bdqk0tJSWa1WZWZm6qGHHtLMmTNVX1+vuXPnavfu3aqqqpLNZtO0adP0xBNPKDU11VfG1KlTNXjwYL300kuSpMLCQg0ZMuS8Y02ZMkV5eXl+1YuhPYDx8TluX0fen8+OVcvt8fa64XeXk6PBrU0HKrRh91HlHTzuG8rd8o/oG8ck9urbb3i9XtW53HI2uBVkMslkavrixGRS07LUYn1b6/z73TvtalTewePa+FmZNh8ob3WXgrgos24cM1DZY226Zmis4b/QpJ1rX0feny2fV+jL47V82dSOqtMubTpQcd6M0KHBJk0ZkaD5VyZr+qgE7vkdAEeO12rdrqN67ZOjrS6tGJNk0a1XpWjO+GTDfvlg6GtsewL+owCMj89x+3h/AudknUtv7W2692XLYY8RocG6YcxAzZ2QrGvTe+436o1uj+yORlWddqmqvkHVpxtUVe9qfm5Q1ekGVdc3PVruU13f4Av0nRXUHHCDTJJJbYdgR6NbDe6zx7FZw3XjmERlj03UxMGxvWq2Vz7H7eP96T5n7uH72ifHtL/FjPjR4SG66Qqb5k1I1qTBPWsSot6mxtGgt/aWat2uo9r11Snf+v79QjV3QrJuvSpVGUnG/70n2F4iGkLA+Pgct4/3p2e40EQ1cVFhujmz+yaqcXu8qnE0yF7fKLujQfbmIGp3nA2mrULrmcB6usF3j8OeLDU2Qtljbcoem6hxKTG99o9rPsft4/25PA6W1TTPK3BMJdUO3/q+OK+A2+NVRY1DNY5GNbg9cnu8avQ03fmisfkOGI2ec9Z7vHJ7zt4ho9HjldvtOWf72f0aPV6VVTv07r5y1Tc0jUgJMklTRybo1qtSdP3ogYa5NaM/CLaXiIYQMD4+x+3j/elZ2ru1yNC4SM2dkKy545M1aEDTjKQej1e1rkbZ65vC6ZlQaq9vkN3RvN7RHFZbhNea5m1dEU6jw0NkjQhVTL9QxUSEydovVDERoeeta7kc0y9U5pAgeb2Sx+uVV83PXl1gnVeels/ynt3P2/ZrQ4JMSukf0SeGzfM5bh/vz+Xl8Xi1vcW8Ai3bmSuSrZo7oWlegfjozk3wFWher1enTjeopKpeJVX1Kq12qKS6XiVVDpU2ryuvcfpuoXM5DIuP1K0TUzV/QnKvHUJPsL1ENISA8fE5bh/vT8915tZmG3Yf07v7y1rNRp1kDVets1E1zsYuuVVZv7BgWcJDZYkIaX5uDqdnAmlEqGL6nRtaw2QJD+mxQ6X7Ej7H7eP9CRxHg1vvHSjX67uPtZpXIDjIpG+kx2n+lcmamTGwR93vvM7ZqNIzQbW6XsfOBNbqepVWNYXYlu3xhQQHmWSNCFVIkEkhQSYFB5sUEhTU9HOQSSHBJgW3XG713HRHjZbLZ8s4uz48NFjTRiVoQmpMr/8Sz9C3+wEAoC9reWuzWmej3vmszDdZS8thfpIUFhIka0SoLOEhskSE+sKptUVQPRNcrS22W8JDFB0e2quGqwHoOcJDg3VzZpJuzkxSZa1Tf99bqg27j2lPcZW2HjqurYeOKzIsWDeOTdS8Ccn62rC4Lr/+3ePxqs7V2DRSxdE0YqWmeSRLabWjqde1yqGS5p+r6xsuXqiaJqFLigmXzRqupJgIJVkjZIs5+3N8tLlXXctvJPTYtoFv+ADj43PcPt4f46mocejoqfpWPazMPtq38TluH+9Pz3PkeK1e31Oi13cfU9HJ0771CdFmzRmfpHkTUnwTHjka3OeFUnt987OjxaUVjsamSzBarWtQrbNRHR0VHG0OUVJMU1C1WSOU3PycFBOhpJhwDbSE0+5eZvTYAgDQyyREhyshundeQwWgbxgaH6WfzRyh+2cM1ydFp/TaJ8f0972lqqhx6oVtBXphW4Fi+oXqtMstV+PFh/36Iyw4SNHNo1qiw5u+FEywmJUcEyFbc29r08/hig4P7ZJjIjAItgAAAAAuG5PJpKvSYnVVWqwenz1GWw5W6PXdx7TpQIWqTje02E+KMjeF0WjfJRdNl1L4niOans+E1nNDrDkkqNdfh4omBFsAAAAAAREWEqQbxyTqxjGJsjuaZh0+E1wjw0J67e260PUItgAAAAACzhIeKksiw4HROUyHCAAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADC0k0BXoibxeryTJbrcHuCYAOuvM5/fM5xmt0c4Bxkc71z7aOcD4OtLOEWzbUFNTI0lKTU0NcE0AXKrKykpZrdZAV6PHoZ0Deg/aubbRzgG9hz/tnMnL13zn8Xg8KikpUXR0tEwmU5eUabfblZqaquLiYlksli4p83Ifg3PoGcfgHPxTXV2tQYMG6dSpU4qJiemWYxgZ7Vxgyr8cx+AcesYxaOcCrzvaOcn4vzt8vnrGMTgH/3SknaPHtg1BQUFKSUnplrItFku3/cNfrmNwDj3jGJyDf4KCmEqgLbRzgS3/chyDc+gZx6CdC5zubOck4//u8PnqGcfgHPzjTztHSwgAAAAAMDSCLQAAAADA0Ai2l4nZbNbjjz8us9ls2GNwDj3jGJxDzzkGWusN/66cQ884BufQc46B8xn9d6c3/G5yDj3jGD3tHJg8CgAAAABgaPTYAgAAAAAMjWALAAAAADA0gi0AAAAAwNAItgAAAAAAQyPYAgAAAAAMjWALAAAAADA0gi0AAAAAwNAItgAAAAAAQyPYAgAAAAAMjWALAAAAADA0gi0AAAAAwNAItgAAAAAAQyPYAgAAAAAMjWCLHm/q1Kn66U9/GuhqAEC3oZ0D0NvRzqG7EWyBNixdulRf+9rX1K9fP8XExJy3/dNPP9W3v/1tpaamKiIiQqNHj9azzz57+SsKAJ10sXausrJSs2bNUlJSksxms1JTU/XjH/9Ydrv98lcWADrhYu1cS5WVlUpJSZHJZFJVVdVlqR+6FsEWaIPL5dKtt96qH/7wh21u37Vrl+Lj4/WnP/1J+/bt0y9/+Us98sgj+t3vfneZawoAnXOxdi4oKEhz5szRm2++qUOHDumll17Se++9px/84AeXuaYA0DkXa+dauvPOO5WZmXkZaoXuQrCF4eTm5spqtWrt2rXddowlS5bo/vvv1xVXXNHm9u9///tasWKFpkyZoqFDh+q73/2uvve97+m1117rtjoB6Dt6QjvXv39//fCHP9TEiROVlpam66+/Xj/60Y+0bdu2bqsTgL6jJ7RzZ6xcuVJVVVV68MEHu60u6H4hga4A0BE5OTm655579Mc//lFz5sy54H5RUVHtlnPttddq48aNXVq36upqxcbGdmmZAPqentrOlZSU6LXXXtOUKVO6rEwAfVNPauf279+vX/3qV/roo4905MiRSyoLgUWwhWE899xzWrRokd544w1Nmzat3X337NnT7vaIiIgurJn04Ycf6i9/+YveeuutLi0XQN/SE9u5b3/723rjjTdUX1+v2bNna9WqVV1SLoC+qSe1c06nU9/+9rf19NNPa9CgQQRbgyPYwhDWr1+v8vJyffDBB7r66qsvun96evplqFWTffv2ac6cOXrsscc0c+bMy3ZcAL1LT23nfvOb3+jxxx/XwYMHtWjRIv3sZz/Tc889d1mODaB36Wnt3COPPKLRo0fru9/9brceB5cH19jCEMaPH6/4+HitXr1aXq/3ovtHRUW1+8jOzu6Seu3fv1/Tp0/X3Xffrf/6r//qkjIB9E09tZ1LTEzUqFGjNGfOHD3//PNauXKlSktLu6RsAH1LT2vnNm/erHXr1ikkJEQhISG6/vrrJUlxcXF6/PHHL6lsXH702MIQhg0bpmeeeUZTp05VcHDwRWcfvhxD9Pbt26fp06fr9ttv19KlSy+5PAB9W09s58515g9Rp9PZ5WUD6P16Wju3fv161dfX+5Z37Nih73//+9q2bZuGDRt2SWXj8iPYwjBGjBihLVu2aOrUqQoJCdFvf/vbC+57qUNXioqKdPLkSRUVFcntdvsa1vT0dEVFRWnfvn2aNm2abrjhBv3sZz9TWVmZJCk4OFjx8fGXdGwAfVdPaufefvttlZeXa9KkSYqKitL+/fv1i1/8Ql//+tc1ePDgSzo2gL6rJ7Vz54bXEydOSJJGjx590fveouch2MJQRo4cqc2bN/u+6XvmmWe65TiPPfaY1qxZ41ueMGGCJPka4nXr1un48eN6+eWX9fLLL/v2S0tLU2FhYbfUCUDf0FPauYiICL3wwgu6//775XQ6lZqaqvnz5+vhhx/ulvoA6Dt6SjuH3sXk9WeAOwAAAAAAPRSTRwEAAAAADI1gCwAAAAAwNIItAAAAAMDQCLYAAAAAAEMj2AIAAAAADI1gCwAAAAAwNO5j2waPx6OSkhJFR0fLZDIFujoAOsHr9aqmpkZJSUkKCuI7vHPRzgHGRzvXPto5wPg61M55A+i5557zXnHFFd7o6GhvdHS0d/Lkyd63337bt/3222/3Smr1uOaaay5a7qlTp7w/+tGPvImJiV6z2ewdNWqU96233vK7XsXFxecdlwcPHsZ8FBcXd6p96u1o53jw6D0P2rm20c7x4NF7Hv60cwHtsU1JSdHy5cuVnp4uSVqzZo3mzJmj3bt3a8yYMZKkWbNmafXq1b7XhIWFtVumy+XSzJkzlZCQoL/+9a9KSUlRcXGxoqOj/a7XmX2Li4tlsVg6eloAegC73a7U1NQOffb7Eto5wPho59pHOwcYX0fauYAG29mzZ7daXrp0qVauXKnt27f7gq3ZbFZiYqLfZb744os6efKk/vWvfyk0NFSSlJaW1qF6nRmuYrFYaAgBg2P4Wdto54Deg3aubbRzQO/hTzvXYy7IcLvdysnJUV1dnbKysnzr8/LylJCQoBEjRujuu+9WRUVFu+W8+eabysrK0r333quBAwdq7Nixeuqpp+R2u7v7FAAAAAAAARDwyaPy8/OVlZUlh8OhqKgobdiwQRkZGZKk7Oxs3XrrrUpLS1NBQYEeffRRTZ8+Xbt27ZLZbG6zvCNHjmjz5s267bbb9Pbbb+vw4cO699571djYqMcee6zN1zidTjmdTt+y3W7v+hMFgACinQPQ29HOAX2byev1egNZAZfLpaKiIlVVVWn9+vVatWqVtm7d6gu3LZWWliotLU05OTmaP39+m+WNGDFCDodDBQUFCg4OliT9z//8j55++mmVlpa2+ZrFixdryZIl562vrq5m6ApgUHa7XVarlc9xM9o5oPehnWuNdg7ofTrSzgV8KHJYWJjS09M1ceJELVu2TOPGjdOzzz7b5r42m01paWk6fPjwBcuz2WwaMWKEL9RK0ujRo1VWViaXy9Xmax555BFVV1f7HsXFxZd2UgDQw9DOAejtaOeAvi3gQ5HP5fV6Ww0jaamyslLFxcWy2WwXfP3Xv/51vfLKK/J4PL57HR06dEg2m+2CMyqbzeYLDm0GgN6Adg5Ab0c7B/RtAe2xXbRokbZt26bCwkLl5+frl7/8pfLy8nTbbbeptrZWDz74oD788EMVFhYqLy9Ps2fPVlxcnObNm+crY+HChXrkkUd8yz/84Q9VWVmp++67T4cOHdJbb72lp556Svfee28gThEAAAAA0M0C2mNbXl6uBQsWqLS0VFarVZmZmcrNzdXMmTNVX1+v/Px8rV27VlVVVbLZbJo2bZpeffXVVvcxKioq8vXMSlJqaqreffdd3X///crMzFRycrLuu+8+PfTQQ4E4RQAAAABANwv45FE9EZMxAMbH57h9vD+A8fE5bh/vD2B8hpo8CgAAAACAS0GwBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKERbAEAAAAAhkawBQAAAAAYGsEWAAAAAGBoBFsAAAAAgKEFNNiuXLlSmZmZslgsslgsysrK0saNG33b77jjDplMplaPyZMnt1vmSy+9dN5rTCaTHA5Hd58OAAAAACAAQgJ58JSUFC1fvlzp6emSpDVr1mjOnDnavXu3xowZI0maNWuWVq9e7XtNWFjYRcu1WCw6ePBgq3Xh4eFdWHMAAAAAQE8R0GA7e/bsVstLly7VypUrtX37dl+wNZvNSkxM7FC5JpOpw68BAAAAABhTj7nG1u12KycnR3V1dcrKyvKtz8vLU0JCgkaMGKG7775bFRUVFy2rtrZWaWlpSklJ0c0336zdu3d3Z9UBAAAAAAEU0B5bScrPz1dWVpYcDoeioqK0YcMGZWRkSJKys7N16623Ki0tTQUFBXr00Uc1ffp07dq1S2azuc3yRo0apZdeeklXXHGF7Ha7nn32WX3961/Xp59+quHDh7f5GqfTKafT6Vu22+1df6IAEEC0cwB6O9o5oG8zeb1ebyAr4HK5VFRUpKqqKq1fv16rVq3S1q1bfeG2pdLSUqWlpSknJ0fz58/3q3yPx6Mrr7xS1113nVasWNHmPosXL9aSJUvOW19dXS2LxdKxEwLQI9jtdlmtVj7HzWjngN6Hdq412jmg9+lIOxfwYHuuGTNmaNiwYXr++efb3D58+HDdddddeuihh/wu8+6779bRo0dbzbjcUlvf8KWmptIQAgbGH3yt0c4BvQ/tXGu0c0Dv05F2LuBDkc/l9XpbNUotVVZWqri4WDabrUPl7dmzR1dcccUF9zGbzRcc2gwAvQHtHIDejnYO6NsCGmwXLVqk7OxspaamqqamRjk5OcrLy1Nubq5qa2u1ePFi3XLLLbLZbCosLNSiRYsUFxenefPm+cpYuHChkpOTtWzZMknSkiVLNHnyZA0fPlx2u10rVqzQnj179Pvf/z5QpwkAAAAA6EYBDbbl5eVasGCBSktLZbValZmZqdzcXM2cOVP19fXKz8/X2rVrVVVVJZvNpmnTpunVV19VdHS0r4yioiIFBZ2d3Lmqqkr33HOPysrKZLVaNWHCBL3//vu6+uqrA3GKAAAAAIBu1uOuse0JuGYFMD4+x+3j/QGMj89x+3h/AOPryOe4x9zHFgAAAACAziDYAgAAAAAMjWALAAAAADA0gi0AAAAAwNAItgAAAAAAQyPYAgAAAAAMjWALAAAAADA0gi0AAAAAwNAItgAAAAAAQyPYAgAAAAAMjWALAAAAADA0gi0AAAAA/H/s3Xl8VNX9//H3ZAeyEwIhG/sSIOxLvi4IAgKKbK61InX7arUutXXBqlBE+LW1FduvqZWKYNWgAooLoCJRrKKQBIiAC0vISiKBrJBt5vz+CBkSCWECSSYTXs+H8yBz751zz7nOPXM/9ywXLo3AFgAAAADg0ghsAQAAAAAujcAWAAAAAODSCGwBAAAAAC6NwBYAAAAA4NIIbAEAAAAALo3AFgAAAADg0ghsAQAAAAAujcAWAAAAAODSCGwBAAAAAC6NwBYAAAAA4NI8HNlo165djU44JiZGHh4OJQ8AAAAAwDlzKPIcMmSILBaLjDEOJerm5qYffvhBPXr0OK/MAQAAAABwNg43qX799dfq1KnTWbczxmjgwIHnlSkAAAAAABzlUGA7duxY9erVS4GBgQ4leumll6pdu3bnky8AAAAAABziUGC7efPmRiX64YcfnlNmAAAAAABoLGZFBgAAAAC4NIfH2N566631Lg8ICFDfvn31y1/+Ur6+vk2WMQAAAAAAHOFwi+2xY8fqfe3YsUNPPvmk+vbtqwMHDjRnXgEAAAAAOI3DLbZr164947oTJ05ozpw5evTRR/Xmm282ScYAAAAAAHBEk4yxbdeunR555BFt3bq1KZIDAAAAAMBhTTZ5VHBwsAoKChr1mfj4eMXGxsrf31/+/v6Ki4vT+vXr7evnzp0ri8VS5zVmzBiH009ISJDFYtGMGTMalS8AAAAAgOtwuCvy2Xz55Zfq2bNnoz4TERGhJUuWqFevXpKkFStWaPr06UpJSdGAAQMkSZMnT9by5cvtn/Hy8nIo7UOHDul3v/udLrnkkkblCQAAAADgWhwObHft2lXv8sLCQm3btk3PPPOMnn766UbtfNq0aXXeL1q0SPHx8dq6das9sPX29laXLl0ala7VatVNN92kBQsWaMuWLY1uSQYAAAAAuA6HA9shQ4bIYrHIGHPauk6dOumRRx7RXXfddc4ZsVqteuutt1RaWqq4uDj78sTERIWGhiowMFBjx47VokWLFBoa2mBaf/zjH9WpUyfddttt2rJly1n3XV5ervLycvv7oqKicy4HALRG1HMA2jrqOeDC5nBge/DgwXqXBwQEKDAw8JwzkJqaqri4OJWVlcnX11dr165VTEyMJGnKlCm69tprFR0drYMHD+qJJ57Q+PHjlZSUJG9v73rT++9//6t///vf2rFjh8N5WLx4sRYsWHDOZQCA1o56DkBbRz0HXNgspr4m2BZUUVGh9PR0FRQUaPXq1Vq2bJk+++wze3BbW05OjqKjo5WQkKBZs2adtr64uFixsbF64YUXNGXKFEnVE1AVFBTonXfeOWMe6rvDFxkZqcLCQvn7+59/IQG0uKKiIgUEBHAen0Q9B7Q91HN1Uc8BbU9j6jmHW2z37dunwsJCDR8+3L5s06ZNevrpp1VaWqoZM2Zo3rx5jc6sl5eXffKoESNGaNu2bVq6dKlefPHF07YNCwtTdHS0fvzxx3rT2r9/v9LS0uqM3bXZbJIkDw8Pff/99/VOcOXt7X3GFmAAaAuo5wC0ddRzwIXN4cD297//vQYOHGgPbA8ePKhp06bpkksuUWxsrBYvXqz27dvrgQceOK8MGWPq3G2rLT8/XxkZGQoLC6t3fb9+/ZSamlpn2R/+8AcVFxdr6dKlioyMPK+8AQAAAABaH4cD2+3bt+vhhx+2v3/ttdfUp08fbdy4UZIUGxurv//9740KbOfNm6cpU6YoMjJSxcXFSkhIUGJiojZs2KCSkhLNnz9fs2fPVlhYmNLS0jRv3jyFhIRo5syZ9jTmzJmjoqJiWgAAg5FJREFU8PBwLV68WD4+Pho4cGCdfdSM//35cgAAAABA2+BwYHvkyBFFRETY32/evLlOl9/LLrtMDz30UKN2npubq5tvvlk5OTkKCAhQbGysNmzYoIkTJ+rEiRNKTU3VypUrVVBQoLCwMI0bN06rVq2Sn5+fPY309HS5ubk1ar8AAAAAgLbD4cA2ODhYOTk5ioyMlM1m0/bt2/Xggw/a11dUVNT7KKCG/Pvf/z7junbt2tlbgxuSmJjY4PpXXnmlUXkCAAAAALgWh5s6x44dq4ULFyojI0PPPfecbDabxo0bZ1+/Z88edevWrTnyCAAAAADAGTncYrto0SJNnDhR0dHRcnd31/PPP68OHTrY17/66qsaP358s2QSAAAAAIAzcTiw7d69u/bu3as9e/aoU6dO6tq1a531CxYsqDMGFwAAAACAluBwYCtJZWVlysvLU1ZWlry8vBQSEmJfN3jw4CbPHAAAAAAAZ+NwYLtr1y5NmTJFhw8fljFG/v7+evvttzVhwoTmzB8AAAAAAA1yePKoRx99VFFRUdqyZYu2b9+usWPH6t57723OvAEAAAAAcFYOt9hu375dH374oUaMGCFJevnllxUaGqqSkhL5+vo2WwYBAAAAAGiIwy22R44cUVRUlP19x44d1b59e/3000/NkjEAAAAAABzhcIutxWJRcXGxfHx8JEnGGPuyoqIi+3b+/v5Nn0sAAAAAAM7A4cDWGKM+ffqctmzo0KH2vy0Wi6xWa9PmEAAAAACABjgc2G7evLk58wEAAAAAwDlxOLAdO3Zsc+YDAAAAAIBz4tDkUbXH0DqiuLj4nDIDAAAAAEBjORTYBgUFKS8vz+FEw8PDdeDAgXPOFAAAAAAAjnKoK7IxRsuWLXP4ebWVlZXnlSkAAAAAABzlUGAbFRWll156yeFEu3TpIk9Pz3POFAAAAAAAjnIosE1LS2vmbAAAAAAAcG4cGmMLAAAAAEBrRWALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl9bowLZbt2764x//qPT09ObIDwAAAAAAjdLowPahhx7Su+++qx49emjixIlKSEhQeXl5c+QNAAAAAICzanRg+5vf/EZJSUlKSkpSTEyM7rvvPoWFhenee+9VcnJyc+QRAAAAAIAzOucxtoMHD9bSpUuVlZWlp556SsuWLdPIkSM1ePBgvfzyyzLGNGU+AQAAAACol8e5frCyslJr167V8uXL9fHHH2vMmDG67bbblJ2drccff1yffPKJXn/99abMKwAAAAAAp2l0YJucnKzly5frjTfekLu7u26++Wb97W9/U79+/ezbTJo0SZdeemmTZhQAAAAAgPo0OrAdOXKkJk6cqPj4eM2YMUOenp6nbRMTE6MbbrihSTIIAAAAAEBDGh3YHjhwQNHR0Q1u06FDBy1fvvycMwUAAAAAgKMaPXnUuHHjlJ+ff9rygoIC9ejRo0kyBQAAAACAoxod2KalpclqtZ62vLy8XFlZWU2SKQAAAAAAHOVwYLtu3TqtW7dOkrRx40b7+3Xr1mnt2rVauHChunXr1qidx8fHKzY2Vv7+/vL391dcXJzWr19vXz937lxZLJY6rzFjxjSY5po1azRixAgFBgaqQ4cOGjJkiF599dVG5QsAAAAA4DocHmM7Y8YMSZLFYtEtt9xSZ52np6e6deumZ599tlE7j4iI0JIlS9SrVy9J0ooVKzR9+nSlpKRowIABkqTJkyfXGa/r5eXVYJrBwcF6/PHH1a9fP3l5een999/Xr371K4WGhuqKK65oVP4AAAAAAK2fw4GtzWaTJHXv3l3btm1TSEjIee982rRpdd4vWrRI8fHx2rp1qz2w9fb2VpcuXRxO87LLLqvz/v7779eKFSv0xRdfENgCAAAAQBvU6DG2Bw8ebJKg9uesVqsSEhJUWlqquLg4+/LExESFhoaqT58+uuOOO5SXl+dwmsYYbdq0Sd9//z3P1QUAAACANsqhFtvnn39ed955p3x8fPT88883uO19993XqAykpqYqLi5OZWVl8vX11dq1axUTEyNJmjJliq699lpFR0fr4MGDeuKJJzR+/HglJSXJ29v7jGkWFhYqPDxc5eXlcnd31wsvvKCJEyeecfvy8nKVl5fb3xcVFTWqDADQ2lHPAWjrqOeAC5vFGGPOtlH37t21fft2dezYUd27dz9zYhaLDhw40KgMVFRUKD09XQUFBVq9erWWLVumzz77zB7c1paTk6Po6GglJCRo1qxZZ0zTZrPpwIEDKikp0aZNm7Rw4UK98847p3VTrjF//nwtWLDgtOWFhYXy9/dvVHkAtA5FRUUKCAjgPD6Jeg5oe6jn6qKeA9qextRzDgW2LWnChAnq2bOnXnzxxXrX9+7dW7fffrseeeQRh9O8/fbblZGRoY0bN9a7vr47fJGRkVSEgAvjgq8u6jmg7aGeq4t6Dmh7GlPPOTx5VEsxxtSplGrLz89XRkaGwsLCmixNqXqCqoa6NgOAq6OeA9DWUc8BF7ZGB7ZWq1WvvPKKNm3apLy8PPtsyTU+/fRTh9OaN2+epkyZosjISBUXFyshIUGJiYnasGGDSkpKNH/+fM2ePVthYWFKS0vTvHnzFBISopkzZ9rTmDNnjsLDw7V48WJJ0uLFizVixAj17NlTFRUV+vDDD7Vy5UrFx8c3tqgAAAAAABfQ6MD2/vvv1yuvvKIrr7xSAwcOlMViOeed5+bm6uabb1ZOTo4CAgIUGxurDRs2aOLEiTpx4oRSU1O1cuVKFRQUKCwsTOPGjdOqVavk5+dnTyM9PV1ubqcmdy4tLdWvf/1rZWZmql27durXr5/+85//6Prrrz/nfAIAAAAAWq9Gj7ENCQnRypUrNXXq1ObKk9MxZgVwfZzHDeP4AK6P87hhHB/A9TXmPG70c2y9vLzUq1evc84cAAAAAABNqdGB7UMPPaSlS5eqlU2mDAAAAAC4QDV6jO0XX3yhzZs3a/369RowYIA8PT3rrF+zZk2TZQ4AAAAAgLNpdGAbGBhYZ1ZiAAAAAACcqdGB7fLly5sjHwAAAAAAnJNGj7EFAAAAAKA1cajFdtiwYdq0aZOCgoI0dOjQBp9dm5yc3GSZAwAAAADgbBwKbKdPny5vb29J0owZM5ozPwAAAAAANIpDge1TTz1V798AAAAAADgbY2wBAAAAAC7NoRbboKCgBsfV1nb06NHzyhAAAAAAAI3hUGD73HPPNXM2AAAAAAA4Nw4Ftrfccktz5wMAAAAAgHPiUGB7JidOnFBlZWWdZf7+/ueVIQAAAAAAGqPRk0eVlpbq3nvvVWhoqHx9fRUUFFTnBQAAAABAS2p0YPvwww/r008/1QsvvCBvb28tW7ZMCxYsUNeuXbVy5crmyCMAAAAAAGfU6K7I7733nlauXKnLLrtMt956qy655BL16tVL0dHReu2113TTTTc1Rz4BAAAAAKhXo1tsjx49qu7du0uqHk9b83ifiy++WJ9//nnT5g4AAAAAgLNodGDbo0cPpaWlSZJiYmL05ptvSqpuyQ0MDGzKvAEAAAAAcFaNDmx/9atfaefOnZKkxx57zD7W9sEHH9Tvf//7Js8gAAAAAAANafQY2wcffND+97hx47R3714lJSWpZ8+eGjx4cJNmDgAAAACAszmv59hKUnR0tKKjo5siLwAAAAAANJrDXZG//vprrV+/vs6ylStXqnv37goNDdWdd96p8vLyJs8gAAAAAAANcTiwnT9/vnbt2mV/n5qaqttuu00TJkzQo48+qvfee0+LFy9ulkwCAAAAAHAmDge2O3bs0OWXX25/n5CQoNGjR+ull17Sb3/7Wz3//PP2GZIBAAAAAGgpDge2x44dU+fOne3vP/vsM02ePNn+fuTIkcrIyGja3AEAAAAAcBYOB7adO3fWwYMHJUkVFRVKTk5WXFycfX1xcbE8PT2bPocAAAAAADTA4cB28uTJevTRR7VlyxY99thjat++vS655BL7+l27dqlnz57NkkkAAAAAAM7E4cf9PP3005o1a5bGjh0rX19frVixQl5eXvb1L7/8siZNmtQsmQQAAAAA4EwcDmw7deqkLVu2qLCwUL6+vnJ3d6+z/q233pKvr2+TZxAAAAAAgIY4HNjWCAgIqHd5cHDweWcGAAAAAIDGcniMLQAAAAAArZFTA9v4+HjFxsbK399f/v7+iouL0/r16+3r586dK4vFUuc1ZsyYBtN86aWXdMkllygoKEhBQUGaMGGCvvnmm+YuCgAAAADASZwa2EZERGjJkiXavn27tm/frvHjx2v69OnavXu3fZvJkycrJyfH/vrwww8bTDMxMVE33nijNm/erK+++kpRUVGaNGmSsrKymrs4AAAAAAAnaPQY26Y0bdq0Ou8XLVqk+Ph4bd26VQMGDJAkeXt7q0uXLg6n+dprr9V5/9JLL+ntt9/Wpk2bNGfOnPPPNAAAAACgVWk1Y2ytVqsSEhJUWlqquLg4+/LExESFhoaqT58+uuOOO5SXl9eodI8fP67KykomtwIAAACANsqpLbaSlJqaqri4OJWVlcnX11dr165VTEyMJGnKlCm69tprFR0drYMHD+qJJ57Q+PHjlZSUJG9vb4fSf/TRRxUeHq4JEyaccZvy8nKVl5fb3xcVFZ1foQCglaGeA9DWUc8BFzant9j27dtXO3bs0NatW3X33Xfrlltu0Z49eyRJ119/va688koNHDhQ06ZN0/r16/XDDz/ogw8+cCjtP/3pT3rjjTe0Zs0a+fj4nHG7xYsXKyAgwP6KjIxskrIBQGtBPQegraOeAy5sFmOMcXYmapswYYJ69uypF198sd71vXv31u23365HHnmkwXT+8pe/6Omnn9Ynn3yiESNGNLhtfXf4IiMjVVhYKH9//8YXAoDTFRUVKSAggPP4JOo5oO2hnquLeg5oexpTzzm9K/LPGWPqVEq15efnKyMjQ2FhYQ2m8ec//1lPP/20Nm7ceNagVqqeoMrRrs0A4Iqo5wC0ddRzwIXNqYHtvHnzNGXKFEVGRqq4uFgJCQlKTEzUhg0bVFJSovnz52v27NkKCwtTWlqa5s2bp5CQEM2cOdOexpw5cxQeHq7FixdLqu5+/MQTT+j1119Xt27ddPjwYUmSr6+vfH19nVJOAAAAAEDzcWpgm5ubq5tvvlk5OTkKCAhQbGysNmzYoIkTJ+rEiRNKTU3VypUrVVBQoLCwMI0bN06rVq2Sn5+fPY309HS5uZ0aKvzCCy+ooqJC11xzTZ19PfXUU5o/f35LFQ0AAAAA0EKcGtj++9//PuO6du3aaePGjWdNIzExsc77tLS088wVAAAAAMCVOH1WZAAAAAAAzgeBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFwagS0AAAAAwKUR2AIAAAAAXBqBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFwagS0AAAAAwKUR2AIAAAAAXBqBLQAAAADApRHYAgAAAABcGoEtAABAK5Kef1zGGGdnAwCaRcHxCi3bckDFZZVNmq5Hk6YGAM3oSEm5Vn51SPeN7yUPd+7LAWhbdmQUKD5xnz7ak6uEO8ZodI+Ozs4SADSZb7MK9epXh/TuziyVVdrk6e6mW/6nW5OlT2ALoNWrtNr06leH9LdPflBxWZU6+Xrp5rhuzs4WAJw3Y4y+2HdE8Yn79eX+fPvy7YeOEdgCcHkVVTat/zZHK786pKRDx+zL+4f5q7O/d5Pui8AWQKv25b4jmv/ebv2QWyJJGtDVXzFdA5ycKwA4P1ab0cbdhxWfuF+pWYWSJA83i2YMDdddY3uoV6ifk3MIAOcup/CEXv86XW98k64jJRWSquu4KYPCNCcuWiOig2SxWJp0nwS2AFqlrIITWvTBHn2YeliSFNTeU7+/op+uHxkpd7emrQgBoKWUV1m1NjlLL35+QAePlEqS2nm664ZRkbrjkh7qGtjOyTkEgHNjjNFXB/K18stD+nhvrqy26rkCOvt76xejonXjqEiF+vs02/4JbAG0KmWVVr342QHFf7ZPZZU2uVmkX46J1m8n9lFgey9nZw8AzklJeZXe+Dpdy744oNyicklSQDtPzf2fbrrlf7opuAP1GwDXVFJepTXJmXr1q0P6Ma/Evnx092DNieumSQM6y7MF5kYhsAXQKhhj9NGeXC18f48yj52QJI3qHqwFVw9Q/zB/J+cOAM5Nfkm5VnyZphVfHVLhieoZQLv4++j2S7rrxlFR6uDNpRgA17Qvr1grvzqkNclZKimvkiS193LXrGHhunlMN/Xt0rJDKqhNATjdvrwSLXhvt7b8eERS9UXfvCv7a1psWJOPvwCAlpBVcEIvfX5ACdvSVVZpkyT1COmgu8b21PShXeXt4e7kHAJA41VZbfpkb65WfnWozoR3PTp10Jwx0Zo1PEL+Pp5OyRuBLQCnKS6r1PObftTy/6apymbk5e6mOy7trnvG9VJ7L6onAK7nx9xixX+2X+t2ZKvq5PiyQeEB+vVlPTVpQBfmCADgkn4qLteqbel67et05RSWSZLcLNKE/p01J66bLurV0emNEVw5AmhxNpvRmpQsLVn/nY6UVI81m9A/VE9cFaPojh2cnDsAaLzk9GOKT9yvj/fk2pdd1Kuj7h7bq1Vc8AFAYxljlJxeoFe/StMHqTmqtFbfrAvu4KUbRkbqpjHRCm9FE94R2AJoUbsyC/TUut1KSS+QJHUP6aAnp8VoXN9Q52YMABrJGKPPfzyi+MR92nrgqCTJYpGuiOmiuy/rqcGRgc7NIACcg7JKq9btyNaKr9K0O7vIvnxIZKDmxEVr6qAw+Xi2vuEUBLYAWkR+Sbn+vPF7rdqeIWOkDl7u+s3lvXXrRd3l5dH8M+UBQFOx2ozWf5uj+MT99os+T3eLZg4N152X9lSvUF8n5xAAHHe0tEJJh44p6dAxJR86pp2ZBSqvqp4bwMvDTVcP7qo5cdGKjQh0bkbPgsAWQLOqstr06tZD+uvHP6i4rHrGvJlDw/XolH7q3IzPMgOAplZSXqV3UrK0bMsBpeUfl1T9DNpfjI7SbRd35xm0AFo9m81o/08lSjp0TNtPBrIHTj5Tu7aIoHb65ZhoXTci0mUeR0ZgC6DZfLn/iBas26Pvc4slSTFh/vrj9AEa0S3YyTkDAMcYY5SSUaCEb9L1/q4cHa+wSpIC2598Bm1cNwW5yEUfgAvP8Yoq7cgoUHJNi2x6gf3RY7X1CvXViOggDYsO0vDoIPUI6eBycwMQ2AJoclkFJ/TMB3v1QWqOpOoLwN9f0Vc3jIxiRlAALuFYaYXWpGRp1bZ0/ZBbYl/eo1MH/XJ0tG4YFcns7QBaneyCE/ZuxUmHjmlPTpGsJ2dor+Hj6aYhkYEaHh2kEdHBGhoVqMD2rn+DjhoZQJMpq7Tqpc8P6P8S96ms0iY3i3TT6Gg9NKlPm6gw0XyyCk6otLxK7m4Webq5ycPdUv06+benm1v1OneLy91Bhuuw2Yy+OpCvhG0Z2vjtYVVYq8eY+Xi66cpBXXXDqEiNiA7iO4hGOV5RpfWph/Vhao6OHa+QxWKRRZKbxSJV/yeLpfp99aLqf6XayySLxaLqe8OnltnXn/xcYHtPXRkbpjHdO8qNG8ltXqXVpr05RXUC2ZpH8dTWxd9Hw7sFacTJ1tj+Yf7ydG9785sQ2LZS1dNrH9Pq5Cxt/PawKq02+fl4ytfbQ74+HvLz8ZCvd+1/T63z9/GQr7enfE+u8/epXt7O050fYzSLskqr3krK1D8T9yur4IQkaVS3YM2/eoBiuvo7OXdorY6UlOu9ndlam5KlXZmFDn/O3c0ij5qXu5s8TwbANYGvh7vbyXXVy2vWB/t6afKALprQv7PaebW+2RzhPLlFZXo7KVOrtmUo/ehx+/KB4f66fmSUrh7cVQHtPJ2YQ7gam81o68F8rU7K0vpvT3VhbwmvfZ2uyOB2mj0sQrOHRSgyuH2L7RvNp/B4pQ7mlyrtSKl+zCtW0qFj2plRqBOVdb9b7m4WxYT5a/jJbsUjooMumPH/BLatTHr+ca1NydKalEwdyj9eZ13RyYl3zpWbRfYguE5g7OOpLv7eGtc3VCO7B7fJOzhoHkVllXr1q0Na/t+DOlJSIUnq7O+teVP76+rBXbmRgtOUVVr1yd5crU3OUuIPP9m7R7m7WRTQzlNVVpuqbEZVVqNKm03GnJ6G1WZktRlVPwG5cReLH+zKUXsvd02K6ayrh3TVJb07UeddoKqsNm3+/iet2pauT7/LU01PPT9vD00f2lU3jIzSwPAA52YSLiftSKlWJ2dqTXKW/UavJEV3bK/ZwyLUp7OfJCNjJCPJGMlmzMm/q7+E9mX2bU6tr/05I1P9vbWvl747XKz3d2Yr4+gJPffJj3rukx/1Pz076toREZo8IIybeq1caXmVDh4pVVp+qQ7+VGoPZNPyj+toaUW9n/H38bAHsMOigzQ4IlAdvC/MEM9iTH2XDS0jPj5e8fHxSktLkyQNGDBATz75pKZMmSJJmjt3rlasWFHnM6NHj9bWrVvPmObu3bv15JNPKikpSYcOHdLf/vY3PfDAA43KV1FRkQICAlRYWCh//+ZvbSo8UakPU3O0JjlT29KO2Zd38HLX5IFhmjUsXGEBPiouq1JJeZWKy6pUXFapkvIqlZRVqfjksup1lSqpZzubg/+XA9p5alzfTpoY00Vj+3aS7wV6YqBhecVlevmLNL229ZCKy6tvuIQHttOdl/bQdSMiW8UPZ0ufx66mJY+PzWb0TdpRrU3O0oepOfbvjCTFRgRo1tBwTRvcVR19vev9bKXNpirrqWDXajOqtJ5cZqsVCJ8Miiut1dvUXlZlM/oup0jrdmYr89ipi82g9p6aMihMVw/uqlHdgum6dwE4lF+qN7dn6K3tmcorLrcvH9ktSDeMjNLUQa5z8U8917CWOj6FJyr1wa4crU7OVNKhU9dxft4eumpwmGYPi9DwFuzCfqLCqo27D+utpAz9d1/+z/LTVdeOiNDQyEBuPjtJWaVVaScD1oNHjlf/m1+qg0dK9VOtOqk+oX7e6hbSQT1COmhwZKBGRAepZyffNv3b1Zjz2KlRS0REhJYsWaJevXpJklasWKHp06crJSVFAwYMkCRNnjxZy5cvt3/Gy6vhcXrHjx9Xjx49dO211+rBBx9svsyfp0qrTVt+/Emrk7P08Z5cVZx8VpSbRbqoV4hmD4vQpAGdm2RiCmOMTlRaTwa6dQPgmqB4b06RPv0uT0dLK/TOjmy9syNbXu5uGtOzoybGdNbE/p3VJYBHs1zo0vOP68XP9+utpEz7d7Z3qK/uvqynpg3uSssX6tiXV6K1KZl6JyW7TstFeGA7zRjaVTOHRpz1eZ9ubhZ5u7mrKe6xXT24q35/RV+lZBRo3Y5svb8rR0dKyvX61+l6/et0hQX46KrYME0fEq4BXf256GtDyiqt+mhPrlZtS69zod+xg5dmD4/QdSMiefYsGqXKatOWfUe0OilTH/3sOu6S3p00e3iEJsV0lo9ny98kaeflrhlDwzVjaLgyjh7X6uRMvZ2UqcxjJ/TGN+l645t09Qr11TXDIzRraLhCefRek6uosin96PGTra2lOnDkZMvrkVJl1zMGtrbgDl7qHtJB3Tp2UPeQ9uoW0sH+/kJtiXWUU1ts6xMcHKw///nPuu222zR37lwVFBTonXfeOae0unXrpgceeKDVtNgaY7Q7u0hrkrO0bmeWveumJPXp7KvZwyI0fUi40wJIq616XO/He3L18Z5cHfzZM61iIwI0sX9nTYjprH5d/Ljou4DszSlSfOJ+vb8r2976PzQqUL++rJcu7xfaKu8U0pLRsOY6Pvm1xs3urDVu1s/bQ1MHhWnmsPBW0zJaZbVp64GjendHljbsPmx/zrIk9QjpoGmDu+rqIV3VsxMBj6v6/nCxVm3L0JqUTBUcr368heVk4HHDyEhN6N9ZXh6ue0OOeq5hzXF8vj9crNXJmVqbklWnda3mOm7G0PBW+Yz2mjG/b2/P1Iff5qissjoQd3ezaGyfTrpuRITG93Pt88HZSsqrtG5HtlZtS1dqVmGDvSX9fTyqg9WTQWtN4NotpAPj+X+mMedxqwlsrVar3nrrLd1yyy1KSUlRTEyM5s6dq3feeUdeXl4KDAzU2LFjtWjRIoWGhjqUpqOBbXl5ucrLT1VORUVFioyMbLKK8HBhmd7ZkaW1yVn253lKUoivl64eHK5Zw1pf64Ax1Q9v/mhPrj7Zk6uUjII6Y90ig9tpQv/OmhjTWaO6BcuDlro2aVvaUb2weZ82f/+TfdnYPp1092U9Nbp7cKv6zv4cF3x1NWc9V3vc7Gc//KSqWuNmL+vTSTOHhWtCf+e0XDiqrNKqz374Set2ZOuTvbkqP9n6IlVPIDR9cLiuGhymsIALYwIOV1ZaXqX3d2UrYVuGUtIL7MvDAnx07YhIXTciQhFBbWMyHeq5upqrnssvKde6ndlanZypb7OK7MuD2ntq+pBwzR4WoYHhres6riHFZdVdp9/cnqHkWudIcAcvTR/SVdcOj2Tix0b4NqtQr32drnU7slRaa5Kw9l7up4LXjnWD2KD2ni7zfXE2lwpsU1NTFRcXp7KyMvn6+ur111/X1KlTJUmrVq2Sr6+voqOjdfDgQT3xxBOqqqpSUlKSvL1PH4v1c44GtvPnz9eCBQtOW34+FeHxiipt3H1Ya5Kz9MW+I/ag0MvDTZNiOmv2sAhd3DvEZbpu5hWX6dO9efp4T66+2HekzkUf43LbFmOMPv0uT/GJ+7X95FghN4s0dVCY7hrb02UmU+GCr66mrudsNqNtaUe15gzjZmeeHDcbUs+42daupLxKH+85rHd3ZGvLj0fsE1xZLNWzfV89pKumDgxTUAceYeUsNptRbnGZ0vOP69DR48o4elzpR4/rUP5x/ZBbbJ+B1sPNogn9O+v6UZG6tHenNvccbeq5upqynquosunT7/K0OjlTm7/Ls9+w83S3aFzfUM0eHqFxfUNdvoVzX16J3k7K1JrkumPOB3T117XDq3sSUtedrqS8Su/tzNbrX1e3ztboEdJBN46K0pWxYQoL8CF4bQIuFdhWVFQoPT1dBQUFWr16tZYtW6bPPvtMMTExp22bk5Oj6OhoJSQkaNasWWdNu6VbbG02o60H8rU6+fSp3Ud1C9asYeGaMijM5bsYHK+o0pYfj+jjPbn2cbk1vNzdFFczLjemc6vsjoP6VVlten9Xjv752X59d7i6Z4GXu5tmD4/Q/17aQ91COjg5h43DBV9dTVXP7f+pRGuTs7Q2Jeucx826kvyScn347WG9tyNb36QdtS/3cLPo0j6ddPXgrpoY05lxT83gRIVVGceqg9X0o8eVnl9a/e/R48o4dsI+prE+3UM66PqRkZo1LFyhfm33d4h6rq7zreeMMUrNKtTqpEyt25mtYye7r0vSoPAAzR4WrquHhCu4DQZ6VVabtvx4RG8lZejjPbmqtFaHB17ubpoY01nXjIhokzeHGqu+1lkvdzdNHthFN46K0pgerbs3mytyqcD25yZMmKCePXvqxRdfrHd97969dfvtt+uRRx45a1otNcZ2X16x1iRn6Z2UrDoDwrt1bK9ZwyI0c2h4m32GmNVmlHTomD7Z2/C43IkDOqtvZ8bltkZllVa9tT1DL35+wD5bbAcvd/1yTLRuvbi7y96c4IKvYY05Pq40brY5ZRWc0Ps7s7VuZ7Z2Z5/qjujj6aYJ/Tvr6sFddVkbaMFpKcYY/VRSrvT8U62t9pbXo8fPOjuoh5tF4UHtFBXc3v6K7the0R07XDDzQFDPNawxx2fZlgNatS1DP+aV2JeF+nlr5tBwzR5e85ieC8PR0gq9uyNLb23P1J6cU3VdZ39vzRoWoWmxXdU/7MI4x6Szt87OHh7RJm92tBYuMytyfYwxde621Zafn6+MjAyFhYW1cK7qZ7MZXf+vr+o8osffx0PTBnfVrGERGhbV9qdSd3ezaFT3YI3qHqzHpvSzj8v9eE+udmQUaFdmoXZlFurZj39Qn86+um5EpGYODa/3sR5oWfU9gza4g5duvaibbh7TTQHtXbtnAZqGMUYzX/hS6Uern6tdM9HIzKHhmuikGT+dJTywnf53bE/979ie2pdXonU7s/XezmwdPFKq93fl6P1dOQpq76mrB3fV7OERGhQe0OZ/AxxVM3niR3tytSe7SOlHq1tfayawORM/H4/qYDW4gyJrBa9Rwe0VFuDD/A5oMlt+PKIf80rk7eGmSQO6aPawcF3cK+SC/I4Fd/DSry7qrl9d1F27swv11vZMvbsjS7lF5YpP3K/4xP3qGuCjy/t31uX9QxXXs6O8PdrebwGts67HqS228+bN05QpUxQZGani4mIlJCRoyZIl2rBhg+Li4jR//nzNnj1bYWFhSktL07x585Senq69e/fKz6/6ztmcOXMUHh6uxYsXS6ru2rxnzx5J0tSpU3XTTTfppptukq+vr/2xQmfTmDsD97yerI3fHtZlfUM1e1i4xvcPbZMn97moPS53y74j9m5jnu7VY56uG9k2xzy1dq7wDNqmQEtGwxpzfJas/05f7j/i0uNmm4sxRt9mFendHVl6b1e2cotO3ZjtHeqr2cOre+24as+H81FptenrA0f18Z7D+nhPbr2PuHCzSGEB7ezBamStwDUquL0C29MK0hDquYY15vj8d98RpR89ritjw+Tvw43dnyuvsurTvXlak5KlLT/+VOemVHsvd13SO0QT+nfWuH6hLv0bUdM6+8Y36dqVSetsa+AyXZFvu+02bdq0STk5OQoICFBsbKweeeQRTZw4USdOnNCMGTOUkpKigoIChYWFady4cVq4cKEiIyPtaVx22WXq1q2bXnnlFUlSWlqaunfvftq+xo4dq8TERIfy1ZgDmHH0uNp7udMCeRaFJyq1bme23tqeUaeiCAvw0TXDI3Tt8EhFdWyb3bWbkzFGFVabyipsKquy6kSFVScqq19lFdaTy2z2ZXuyi7Q6+cJ4Bi0XfA1rzPGpstouyFaLxrLajL44+VzLjbsP2yfZc7NIF/fupNnDwnXFgC5tupW7pLxKn33/kz7ac1ibv8tTUa1HKLXzdNfYPp30P706KrpjB0UFt1d4YDu6bp8H6rmGcXyaR1mlVf/dd0Sf7M3Tp9/l1rmhZ7FIQyMDdXn/zprQv7P6dPZ1iVbNb7MK9fo36Xo3pW7r7BUDu+gXtM46lcsEtq0VFWHz2pNdpDe3Z+idHVn25wpKUlyPjrp+ZKQmD2zbF34/V1pepT05Rfo2q1B5xeU6UWFVWU1wWmnViUqbymoHrDXLTy5r6DlpZ9Lan0HbFDiPG8bxaV5FZZX6cFeOVidn1hmu4uftoStjwzR7eIRGRAe1iQulvKIyfbI3Tx/tOawv9+WrwnqqJadjBy9N6N9ZkwZ01kW9Qi6our0lcB43jOPT/Gp6rXyyN1ebvsut8zgkqfrxkJf3qw5yR3UPblU3skrLq7SugdbZWcMYOtcaENieJyrCllFeZdXHe3K1altGnUci+fl4aPqQrrp+RJRLPRfOESXlVdqdVahvs6sD2dSsQu3/qURNcRa6u1nUztNdPp7uauflZv/bx9Nd7U6+Atp5auaw8Fb/DNqmwHncMI5PyzmUX6rVyVlak5xpn6BNkqI7ttdsF51gcF9eiT462cW49rNipeqJEycN6KJJMZ01NCqI4SbNiPO4YRyflpdTeEKb9uZp095c/Xd/fp3Zy/28PXRpn066vH+oxvUNbfHHCBljVFxepQM/lerN7Rl1Wmc93S2aPDCM1tlWiMD2PFERtrzMY8e1OilLb27PqPMIkf5h/rpuRIRmuOBz1IrLKrW7VgCbmlWog0dK6w1iO/t7a1B4gCKD26u9l3utANVdPh7V/9ZZ5ulmD1Z9Tq5ra12JzxfnccM4Pi3PZjP6Ju2oVidl6sPUHPsFlSSN6RGs2cMiNGVQWKt8FrjNZpSSUWAPZg/8VHcG/MGRgZoU01mTYjqrV6hrdD1sCziPG8bxca7jFVX64scj1YHud3k6UnKqy7KbRRoRHazL+4fq8v6d1bNTh3OqN6qsNh0trdCRkgrll5brSEm58ksq9NPJf/NLyqvXlZTrSGnFaY8J6x7SQTeOitTsYRG0zrZSBLbniYrQeWw2oy/35+vN7RnasPuwvQLycnfTpAGddf3ISF3UM6TVdZ8tKqvUt1mFJ4PY6mD2548+qhEW4KOB4QEadPI1INy/TT9n0Vk4jxvG8XGu4xVV2rj7sN5OytSX+/PtN7zaebprysAumj08QnE9Ojq1riurtOqr/fkng9m6F6We7hbF9QzRJJ5Z7lScxw3j+LQeNpvRrqxCfbInV5/szdV3h4vrrO/Wsb19luWB4QE6VhOs1gpM80trAtZTy2o/a9hRft4euqxfqG4cFam4Hh25EdfKEdieJyrC1qHgeIXe3ZGtVdsy6jxHLTywXfWEUyMiFBHU8t33Co9X6tvsQntL7LdZhUrLP17vtl1rBbEDI6r/deXZAl0J53HDOD6tR3bBCa1NydLqpEwdqHVDrGuAj2YOC9fsYRHq0cm3RfJSeLxSn35f/ci2xO9/0vFarcp+3h4a1y9UE2M667K+neTHzLFOx3ncMI5P65V57Lg+/S5Pn+zN09b9dcfmN5abpfoRRSG+3uro66WOHbztf4f41iz3VsjJdW3l6Q8XCgLb80RF2Pp8m1VYPeFUSpZ9lk2LRbqoZ4iuGxmpSQ48T9NmMyqvsul4RZV9EqbjFdWTMB0/OYvwiZPLaiZnOn7y37JKqwqOV2pPTpH9eZ4/Fx7YrroVNiJAA7r6a1B4AN1anIjzuGEcn9bHmOruvquTMvXezuw6MwoPjQrU7GERurhXiKpsNp2oNRN6zWRz5ZW2WpPOWVVWaTs12VztyehqT0JXs93JWdQrrXUvCbr4+2jiyVbZMT06tqqJX8B5fDYcH9dQUl6lLT/8pE/25mnz93k6WlohH083hfh6n3ydDFb9qv/t6OulTieD1Y6+Xgpq78VY/jaMwPY8URG2XmWVVm3cfVhvbs/Qf/fl25cHtPPUyG7BJx99UxOgVqns5IVezd9NJSKoOoi1t8aGB/Bcs1aG87hhHJ/WrazSqk178/R2UoY+//GIrOcy/fk56tPZV5NiumjSgM4aFB5AN71WjPO4YRwf12OzGZVVWdXeq/XNNQDnaMx5zLcGLsXH013Th4Rr+pBwZRw9rreSMvX29gxlF5bpk725Dqfj7eGmdl7ual9r8qX2XtWTM9VM3lQ9YZOHfYbh9l4e6t3ZVwO7BrjcRFYAXIuPp7uujA3TlbFhyisu07sp2VqTkqWDR0rsM5372F+1Z0F3qzMbet11py/3rv3eq7qeC2hHF2MAzuHmZiGoxTnjmwOXFRncXr+d2Ef3X95bX+4/orT842rveWoG4TP+6+ne6iafAoAzCfXz0R2X9tAdl/ZwdlYAAGi1CGzh8tzdLLqkdydd0tvZOQEAAADgDMwCAQAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACX5uHsDLRGxhhJUlFRkZNzAuBc1Zy/Necz6qKeA1wf9VzDqOcA19eYeo7Ath7FxcWSpMjISCfnBMD5ys/PV0BAgLOz0epQzwFtB/Vc/ajngLbDkXrOYrjNdxqbzabs7Gz5+fnJYrE0SZpFRUWKjIxURkaG/P39myTNlt4HZWgd+6AMjiksLFRUVJSOHTumwMDAZtmHK6Oec076LbEPytA69kE953zNUc9Jrv/d4fxqHfugDI5pTD1Hi2093NzcFBER0Sxp+/v7N9v/+JbaB2VoHfugDI5xc2MqgfpQzzk3/ZbYB2VoHfugnnOe5qznJNf/7nB+tY59UAbHOFLPURMCAAAAAFwagS0AAAAAwKUR2LYQb29vPfXUU/L29nbZfVCG1rEPytB69oG62sL/V8rQOvZBGVrPPnA6V//utIXvJmVoHftobWVg8igAAAAAgEujxRYAAAAA4NIIbAEAAAAALo3AFgAAAADg0ghsAQAAAAAujcAWAAAAAODSCGwBAAAAAC6NwBYAAAAA4NIIbAEAAAAALo3AFgAAAADg0ghsAQAAAAAujcAWAAAAAODSCGwBAAAAAC6NwBat3mWXXaYHHnjA2dkAgGZDPQcAwPkhsAXqsWjRIv3P//yP2rdvr8DAwHq3sVgsp73++c9/tmxGAeAcOVLPSdIrr7yi2NhY+fj4qEuXLrr33ntbLpMAADjIw9kZAFqjiooKXXvttYqLi9O///3vM263fPlyTZ482f4+ICCgJbIHAOfNkXrur3/9q5599ln9+c9/1ujRo1VWVqYDBw60cE4BADg7WmzhcjZs2KCAgACtXLmy2faxYMECPfjggxo0aFCD2wUGBqpLly72V7t27ZotTwAuHK2hnjt27Jj+8Ic/aOXKlfrFL36hnj17asCAAZo2bVqz5QkAgHNFYAuXkpCQoOuuu04rV67UnDlzzridr69vg68pU6Y0SX7uvfdehYSEaOTIkfrnP/8pm83WJOkCuHC1lnru448/ls1mU1ZWlvr376+IiAhdd911ysjIOK90AQBoDnRFhst44YUXNG/ePL377rsaN25cg9vu2LGjwfVN0bK6cOFCXX755WrXrp02bdqkhx56SEeOHNEf/vCH804bwIWpNdVzBw4ckM1m0zPPPKOlS5cqICBAf/jDHzRx4kTt2rVLXl5e55U+AABNicAWLmH16tXKzc3VF198oVGjRp11+169ejV7nmoHsEOGDJEk/fGPfySwBXBOWls9Z7PZVFlZqeeff16TJk2SJL3xxhvq0qWLNm/erCuuuKJZ9w8AQGPQFRkuYciQIerUqZOWL18uY8xZt2+prsi1jRkzRkVFRcrNzW3ytAG0fa2tngsLC5MkxcTE2Jd16tRJISEhSk9PP6+0AQBoarTYwiX07NlTzz77rC677DK5u7vrH//4R4Pbt0RX5J9LSUmRj49Pg4/NAIAzaW313EUXXSRJ+v777xURESFJOnr0qI4cOaLo6OjzShsAgKZGYAuX0adPH23evFmXXXaZPDw89Nxzz51x2/Ptopeenq6jR48qPT1dVqvVfgHZq1cv+fr66r333tPhw4cVFxendu3aafPmzXr88cd15513ytvb+7z2DeDC1ZrquT59+mj69Om6//779a9//Uv+/v567LHH1K9fv7OO/wUAoKUR2MKl9O3bV59++qm9RePZZ59tlv08+eSTWrFihf390KFDJcl+wenp6akXXnhBv/3tb2Wz2dSjRw/98Y9/1D333NMs+QFw4Wgt9ZwkrVy5Ug8++KCuvPJKubm5aezYsdqwYYM8PT2bJU8AAJwri3FkIA8AAAAAAK0Uk0cBAAAAAFwagS0AAAAAwKUR2AIAAAAAXBqBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFyah7Mz0BrZbDZlZ2fLz89PFovF2dkBcA6MMSouLlbXrl3l5sY9vJ+jngNcH/UcAJzi1MA2Pj5e8fHxSktLkyQNGDBATz75pKZMmSJJmjt3rlasWFHnM6NHj9bWrVsbTLegoECPP/641qxZo2PHjql79+569tlnNXXqVIfylZ2drcjIyMYXCECrk5GRoYiICGdno9WhngPaDuo5AHByYBsREaElS5aoV69ekqQVK1Zo+vTpSklJ0YABAyRJkydP1vLly+2f8fLyajDNiooKTZw4UaGhoXr77bcVERGhjIwM+fn5OZyvmm0zMjLk7+/f2GIBaAWKiooUGRnZqHP/QkI9B7g+6jkAOMWpge20adPqvF+0aJHi4+O1detWe2Dr7e2tLl26OJzmyy+/rKNHj+rLL7+Up6enJCk6OrpR+arplufv788FH+Di6GZbP+o5oO2gngOAVjR5lNVqVUJCgkpLSxUXF2dfnpiYqNDQUPXp00d33HGH8vLyGkxn3bp1iouL0z333KPOnTtr4MCBeuaZZ2S1Ws/4mfLychUVFdV5AUBbQj0HAADaMqcHtqmpqfL19ZW3t7fuuusurV27VjExMZKkKVOm6LXXXtOnn36qZ599Vtu2bdP48eNVXl5+xvQOHDigt99+W1arVR9++KH+8Ic/6Nlnn9WiRYvO+JnFixcrICDA/mLcGYC2hnoOAAC0ZRZjjHFmBioqKpSenq6CggKtXr1ay5Yt02effWYPbmvLyclRdHS0EhISNGvWrHrT69Onj8rKynTw4EG5u7tLkv7617/qz3/+s3Jycur9THl5eZ1guWbMSmFhIV30ABdVVFSkgIAAzuOTqOeAtod6DgBOcfrjfry8vOyTR40YMULbtm3T0qVL9eKLL562bVhYmKKjo/Xjjz+eMb2wsDB5enrag1pJ6t+/vw4fPqyKiop6J5/y9vaWt7d3E5QGAFon6jkAANCWOb0r8s8ZY87Y1Tg/P18ZGRkKCws74+cvuugi7du3Tzabzb7shx9+UFhY2FlnVAYAAAAAuB6nBrbz5s3Tli1blJaWptTUVD3++ONKTEzUTTfdpJKSEv3ud7/TV199pbS0NCUmJmratGkKCQnRzJkz7WnMmTNHjz32mP393Xffrfz8fN1///364Ycf9MEHH+iZZ57RPffc44wiAgAAAACamVO7Iufm5urmm29WTk6OAgICFBsbqw0bNmjixIk6ceKEUlNTtXLlShUUFCgsLEzjxo3TqlWr6jyvLT09XW5up+LzyMhIffTRR3rwwQcVGxur8PBw3X///XrkkUecUUQAAAAAQDNz+uRRrRGTMQCuj/O4YRwfwPVxHgPAKa1ujC0AAAAAAI1BYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGlODWzj4+MVGxsrf39/+fv7Ky4uTuvXr7evnzt3riwWS53XmDFjGkzzlVdeOe0zFotFZWVlzV0cAAAAAIATeDhz5xEREVqyZIl69eolSVqxYoWmT5+ulJQUDRgwQJI0efJkLV++3P4ZLy+vs6br7++v77//vs4yHx+fJsw5AAAAAKC1cGpgO23atDrvFy1apPj4eG3dutUe2Hp7e6tLly6NStdisTT6MwAAAAAA19RqxtharVYlJCSotLRUcXFx9uWJiYkKDQ1Vnz59dMcddygvL++saZWUlCg6OloRERG66qqrlJKS0pxZBwAAAAA4kVNbbCUpNTVVcXFxKisrk6+vr9auXauYmBhJ0pQpU3TttdcqOjpaBw8e1BNPPKHx48crKSlJ3t7e9abXr18/vfLKKxo0aJCKioq0dOlSXXTRRdq5c6d69+5d72fKy8tVXl5uf19UVNT0BQUAJ6KeAwAAbZnFGGOcmYGKigqlp6eroKBAq1ev1rJly/TZZ5/Zg9vacnJyFB0drYSEBM2aNcuh9G02m4YNG6ZLL71Uzz//fL3bzJ8/XwsWLDhteWFhofz9/RtXIACtQlFRkQICAjiPT6KeA9oe6jkAOMXpXZG9vLzUq1cvjRgxQosXL9bgwYO1dOnSercNCwtTdHS0fvzxR4fTd3Nz08iRIxv8zGOPPabCwkL7KyMjo9HlAIDWjHoOAAC0ZU7vivxzxpg63eVqy8/PV0ZGhsLCwhqV3o4dOzRo0KAzbuPt7X3Grs0A0BZQzwEAgLbMqYHtvHnzNGXKFEVGRqq4uFgJCQlKTEzUhg0bVFJSovnz52v27NkKCwtTWlqa5s2bp5CQEM2cOdOexpw5cxQeHq7FixdLkhYsWKAxY8aod+/eKioq0vPPP68dO3bo//7v/5xVTAAAAABAM3JqYJubm6ubb75ZOTk5CggIUGxsrDZs2KCJEyfqxIkTSk1N1cqVK1VQUKCwsDCNGzdOq1atkp+fnz2N9PR0ubmd6lFdUFCgO++8U4cPH1ZAQICGDh2qzz//XKNGjXJGEQEAAAAAzczpk0e1RkzGALg+zuOGcXwA18d5DACnOH3yKAAAAAAAzgeBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFwagS0AAAAAwKUR2AIAAAAAXBqBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFwagS0AAAAAwKUR2AIAAAAAXBqBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFwagS0AAAAAwKUR2AIAAAAAXBqBLQAAAADApRHYAgAAAABcGoEtAAAAAMClEdgCAAAAAFyahyMb7dq1q9EJx8TEyMPDoeQBAAAAADhnDkWeQ4YMkcVikTHGoUTd3Nz0ww8/qEePHueVOQAAAAAAzsbhJtWvv/5anTp1Out2xhgNHDjwvDIFAAAAAICjHApsx44dq169eikwMNChRC+99FK1a9fufPIFAAAAAIBDHApsN2/e3KhEP/zww3PKDAAAAAAAjcWsyAAAAAAAl+bwGNtbb7213uUBAQHq27evfvnLX8rX17fJMgYAAAAAgCMcbrE9duxYva8dO3boySefVN++fXXgwIHmzCsAAAAAAKdxuMV27dq1Z1x34sQJzZkzR48++qjefPPNJskYAAAAAACOaJIxtu3atdMjjzyirVu3Nupz8fHxio2Nlb+/v/z9/RUXF6f169fb18+dO1cWi6XOa8yYMQ6nn5CQIIvFohkzZjQqXwAAAAAA1+Fwi+3ZBAcHq6CgoFGfiYiI0JIlS9SrVy9J0ooVKzR9+nSlpKRowIABkqTJkydr+fLl9s94eXk5lPahQ4f0u9/9Tpdcckmj8gQAAAAAcC1NFth++eWX6tmzZ6M+M23atDrvFy1apPj4eG3dutUe2Hp7e6tLly6NStdqteqmm27SggULtGXLlkYH3AAAAAAA1+FwYLtr1656lxcWFmrbtm165pln9PTTT59zRqxWq9566y2VlpYqLi7OvjwxMVGhoaEKDAzU2LFjtWjRIoWGhjaY1h//+Ed16tRJt912m7Zs2XLOeQIAAAAAtH4OB7ZDhgyRxWKRMea0dZ06ddIjjzyiu+66q9EZSE1NVVxcnMrKyuTr66u1a9cqJiZGkjRlyhRde+21io6O1sGDB/XEE09o/PjxSkpKkre3d73p/fe//9W///1v7dixw+E8lJeXq7y83P6+qKio0eUAgNaMeg4AALRlDge2Bw8erHd5QECAAgMDzzkDffv21Y4dO1RQUKDVq1frlltu0WeffaaYmBhdf/319u0GDhyoESNGKDo6Wh988IFmzZp1WlrFxcX65S9/qZdeekkhISEO52Hx4sVasGDBOZcBAFo76jkAANCWWUx9TbBONGHCBPXs2VMvvvhivet79+6t22+/XY888shp63bs2KGhQ4fK3d3dvsxms0mS3Nzc9P3339c7Dri+lozIyEgVFhbK39//fIsEwAmKiooUEBDAeXwS9RzQ9lDPAcApDrfY7tu3T4WFhRo+fLh92aZNm/T000+rtLRUM2bM0Lx58847Q8aYOhdfteXn5ysjI0NhYWH1ru/Xr59SU1PrLPvDH/6g4uJiLV26VJGRkfV+ztvb+4xdmwGgLaCeAwAAbZnDge3vf/97DRw40B7YHjx4UNOmTdMll1yi2NhYLV68WO3bt9cDDzzg8M7nzZunKVOmKDIyUsXFxUpISFBiYqI2bNigkpISzZ8/X7Nnz1ZYWJjS0tI0b948hYSEaObMmfY05syZo/DwcC1evFg+Pj4aOHBgnX3UdJP++XIAAAAAQNvgcGC7fft2Pfzww/b3r732mvr06aONGzdKkmJjY/X3v/+9UYFtbm6ubr75ZuXk5CggIECxsbHasGGDJk6cqBMnTig1NVUrV65UQUGBwsLCNG7cOK1atUp+fn72NNLT0+Xm5ubwPgEAAAAAbYvDY2zbtWunH374wd6d9/LLL9f//M//aOHChZKk/fv3a/jw4W3imbGMWQFcH+dxwzg+gOvjPAaAUxxu6gwODlZOTo6k6gmZtm/frtGjR9vXV1RU1PsoIAAAAAAAmpPDge3YsWO1cOFCZWRk6LnnnpPNZtO4cePs6/fs2aNu3bo1Rx4BAAAAADgjh8fYLlq0SBMnTlR0dLTc3d31/PPPq0OHDvb1r776qsaPH98smQQAAAAA4EwcDmy7d++uvXv3as+ePerUqZO6du1aZ/2CBQsUERHR5BkEAAAAAKAhDge2klRWVqa8vDxlZWXJy8tLISEh9nWDBw9u8swBAAAAAHA2Dge2u3bt0pQpU3T48GEZY+Tv76+3335bEyZMaM78AQAAAADQIIcnj3r00UcVFRWlLVu2aPv27Ro7dqzuvffe5swbAAAAAABn5XCL7fbt2/Xhhx9qxIgRkqSXX35ZoaGhKikpka+vb7NlEAAAAACAhjjcYnvkyBFFRUXZ33fs2FHt27fXTz/91CwZAwAAAADAEQ632FosFhUXF8vHx0eSZIyxLysqKrJv5+/v3/S5BAAAAADgDBwObI0x6tOnz2nLhg4dav/bYrHIarU2bQ4BAAAAAGiAw4Ht5s2bmzMfAAAAAACcE4cD27FjxzZnPgAAAAAAOCcOTR5VewytI4qLi88pMwAAAAAANJZDgW1QUJDy8vIcTjQ8PFwHDhw450wBAAAAAOAoh7oiG2O0bNkyh59XW1lZeV6ZAgAAAADAUQ4FtlFRUXrppZccTrRLly7y9PQ850wBAAAAAOAohwLbtLS0Zs4GAAAAAADnxqExtgAAAAAAtFYEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpjQ5su3Xrpj/+8Y9KT09vjvwAAAAAANAojQ5sH3roIb377rvq0aOHJk6cqISEBJWXlzdH3gAAAAAAOKtGB7a/+c1vlJSUpKSkJMXExOi+++5TWFiY7r33XiUnJzdHHgEAAAAAOKNzHmM7ePBgLV26VFlZWXrqqae0bNkyjRw5UoMHD9bLL78sY0xT5hMAAAAAgHp5nOsHKysrtXbtWi1fvlwff/yxxowZo9tuu03Z2dl6/PHH9cknn+j1119vyrwCAAAAAHCaRge2ycnJWr58ud544w25u7vr5ptv1t/+9jf169fPvs2kSZN06aWXNmlGAQAAAACoT6MD25EjR2rixImKj4/XjBkz5Onpedo2MTExuuGGG5okgwAAAAAANKTRge2BAwcUHR3d4DYdOnTQ8uXLzzlTAAAAAAA4qtGTR40bN075+fmnLS8oKFCPHj2aJFMAAAAAADiq0YFtWlqarFbracvLy8uVlZXVJJkCAAAAAMBRDge269at07p16yRJGzdutL9ft26d1q5dq4ULF6pbt26N2nl8fLxiY2Pl7+8vf39/xcXFaf369fb1c+fOlcViqfMaM2ZMg2muWbNGI0aMUGBgoDp06KAhQ4bo1VdfbVS+AAAAAACuw+ExtjNmzJAkWSwW3XLLLXXWeXp6qlu3bnr22WcbtfOIiAgtWbJEvXr1kiStWLFC06dPV0pKigYMGCBJmjx5cp3xul5eXg2mGRwcrMcff1z9+vWTl5eX3n//ff3qV79SaGiorrjiikblDwAAAADQ+jkc2NpsNklS9+7dtW3bNoWEhJz3zqdNm1bn/aJFixQfH6+tW7faA1tvb2916dLF4TQvu+yyOu/vv/9+rVixQl988QWBLQAAAAC0QY0eY3vw4MEmCWp/zmq1KiEhQaWlpYqLi7MvT0xMVGhoqPr06aM77rhDeXl5DqdpjNGmTZv0/fffN/hc3fLychUVFdV5AUBbQj0HAADaModabJ9//nndeeed8vHx0fPPP9/gtvfdd1+jMpCamqq4uDiVlZXJ19dXa9euVUxMjCRpypQpuvbaaxUdHa2DBw/qiSee0Pjx45WUlCRvb+8zpllYWKjw8HCVl5fL3d1dL7zwgiZOnHjG7RcvXqwFCxY0Kt8A4Eqo5wAAQFtmMcaYs23UvXt3bd++XR07dlT37t3PnJjFogMHDjQqAxUVFUpPT1dBQYFWr16tZcuW6bPPPrMHt7Xl5OQoOjpaCQkJmjVr1hnTtNlsOnDggEpKSrRp0yYtXLhQ77zzzmndlGuUl5ervLzc/r6oqEiRkZEqLCyUv79/o8oDoHUoKipSQEAA5/FJ1HNA20M9BwCnONRie/DgwXr/bgpeXl72yaNGjBihbdu2aenSpXrxxRdP2zYsLEzR0dH68ccfG0zTzc3NnuaQIUO0d+9eLV68+IyBrbe3d4MtwADg6qjnAABAW9boMbbNzRhTp1Whtvz8fGVkZCgsLKzJ0gQAAAAAuDaHZ0WuYbVa9corr2jTpk3Ky8uzz5Zc49NPP3U4rXnz5mnKlCmKjIxUcXGxEhISlJiYqA0bNqikpETz58/X7NmzFRYWprS0NM2bN08hISGaOXOmPY05c+YoPDxcixcvllQ9jmzEiBHq2bOnKioq9OGHH2rlypWKj49vbFEBAAAAAC6g0YHt/fffr1deeUVXXnmlBg4cKIvFcs47z83N1c0336ycnBwFBAQoNjZWGzZs0MSJE3XixAmlpqZq5cqVKigoUFhYmMaNG6dVq1bJz8/PnkZ6errc3E41PJeWlurXv/61MjMz1a5dO/Xr10//+c9/dP31159zPgEAAAAArZdDk0fVFhISopUrV2rq1KnNlSenYzIGwPVxHjeM4wO4Ps5jADil0WNsa0/2BAAAAACAszU6sH3ooYe0dOlSNbKhFwAAAACAZtHoMbZffPGFNm/erPXr12vAgAHy9PSss37NmjVNljkAAAAAAM6m0YFtYGBgnVmJAQAAAABwpkYHtsuXL2+OfAAAAAAAcE4aPcYWAAAAAIDWxKEW22HDhmnTpk0KCgrS0KFDG3x2bXJycpNlDgAAAACAs3EosJ0+fbq8vb0lSTNmzGjO/AAAAAAA0CgWw3N7TsMDzwHXx3ncMI4P4Po4jwHgFMbYAgAAAABcmkNdkYOCghocV1vb0aNHzytDAAAAAAA0hkOB7XPPPdfM2QAAAAAA4Nw4FNjecsstzZ0PAAAAAADOiUOB7ZmcOHFClZWVdZYxeQEAAAAAoCU1evKo0tJS3XvvvQoNDZWvr6+CgoLqvAAAAAAAaEmNDmwffvhhffrpp3rhhRfk7e2tZcuWacGCBeratatWrlzZHHkEAAAAAOCMGt0V+b333tPKlSt12WWX6dZbb9Ull1yiXr16KTo6Wq+99ppuuumm5sgnAAAAAAD1anSL7dGjR9W9e3dJ1eNpax7vc/HFF+vzzz9v2twBAAAAAHAWjQ5se/ToobS0NElSTEyM3nzzTUnVLbmBgYFNmTcAAAAAAM6q0YHtr371K+3cuVOS9Nhjj9nH2j744IP6/e9/3+QZBAAAAACgIY0eY/vggw/a/x43bpz27t2rpKQk9ezZU4MHD27SzAEAAAAAcDbn9RxbSYqOjlZ0dHRT5AUAAAAAgEZzuCvy119/rfXr19dZtnLlSnXv3l2hoaG68847VV5e3uQZBAAAAACgIQ4HtvPnz9euXbvs71NTU3XbbbdpwoQJevTRR/Xee+9p8eLFzZJJAAAAAADOxOHAdseOHbr88svt7xMSEjR69Gi99NJL+u1vf6vnn3/ePkMyAAAAAAAtxeHA9tixY+rcubP9/WeffabJkyfb348cOVIZGRlNmzsAAAAAAM7C4cC2c+fOOnjwoCSpoqJCycnJiouLs68vLi6Wp6dn0+cQAAAAAIAGOBzYTp48WY8++qi2bNmixx57TO3bt9cll1xiX79r1y717NmzWTIJAAAAAMCZOPy4n6efflqzZs3S2LFj5evrqxUrVsjLy8u+/uWXX9akSZOaJZMAAAAAAJyJw4Ftp06dtGXLFhUWFsrX11fu7u511r/11lvy9fVt8gwCAAAAANAQhwPbGgEBAfUuDw4OPu/MAAAAAADQWA6PsQUAAAAAoDVyamAbHx+v2NhY+fv7y9/fX3FxcVq/fr19/dy5c2WxWOq8xowZ02CaL730ki655BIFBQUpKChIEyZM0DfffNPcRQEAAAAAOIlTA9uIiAgtWbJE27dv1/bt2zV+/HhNnz5du3fvtm8zefJk5eTk2F8ffvhhg2kmJibqxhtv1ObNm/XVV18pKipKkyZNUlZWVnMXBwAAAADgBI0eY9uUpk2bVuf9okWLFB8fr61bt2rAgAGSJG9vb3Xp0sXhNF977bU671966SW9/fbb2rRpk+bMmXP+mQYAAAAAtCqtZoyt1WpVQkKCSktLFRcXZ1+emJio0NBQ9enTR3fccYfy8vIale7x48dVWVnJ5FYAAAAA0EY5tcVWklJTUxUXF6eysjL5+vpq7dq1iomJkSRNmTJF1157raKjo3Xw4EE98cQTGj9+vJKSkuTt7e1Q+o8++qjCw8M1YcKEM25TXl6u8vJy+/uioqLzKxQAtDLUcwAAoC1zemDbt29f7dixQwUFBVq9erVuueUWffbZZ4qJidH1119v327gwIEaMWKEoqOj9cEHH2jWrFlnTftPf/qT3njjDSUmJsrHx+eM2y1evFgLFixokvIAQGtEPQcAANoyizHGODsTtU2YMEE9e/bUiy++WO/63r176/bbb9cjjzzSYDp/+ctf9PTTT+uTTz7RiBEjGty2vpaMyMhIFRYWyt/fv/GFAOB0RUVFCggI4Dw+iXoOaHuo5wDgFKe32P6cMabOxVdt+fn5ysjIUFhYWINp/PnPf9bTTz+tjRs3njWolaonqHK0azMAuCLqOQAA0JY5NbCdN2+epkyZosjISBUXFyshIUGJiYnasGGDSkpKNH/+fM2ePVthYWFKS0vTvHnzFBISopkzZ9rTmDNnjsLDw7V48WJJ1d2Pn3jiCb3++uvq1q2bDh8+LEny9fWVr6+vU8oJAAAAAGg+Tg1sc3NzdfPNNysnJ0cBAQGKjY3Vhg0bNHHiRJ04cUKpqalauXKlCgoKFBYWpnHjxmnVqlXy8/Ozp5Geni43t1OTO7/wwguqqKjQNddcU2dfTz31lObPn99SRQMAAAAAtJBWN8a2NWDMCuD6OI8bxvEBXB/nMQCc0mqeYwsAAAAAwLkgsAUAAAAAuDQCWwAAAACASyOwBQAAAAC4NAJbAAAAAIBLI7AFAAAAALg0AlsAAAAAgEsjsAUAAAAAuDQCWwAAAACASyOwBQAAAAC4NAJbAAAAAIBLI7AFAAAAALg0AlsAAAAAgEsjsAUAAAAAuDQCWwAupbS8ytlZAIBmtTenSFabcXY2AMClENgCaPWMMdqZUaCH396pEU9/or05Rc7OEgA0qdLyKiV8k67p//hCU5Zu0ZYff3J2lgDApXg4OwMAcCYl5VV6d0eWXv86XbuzTwWzG3cfVv8wfyfmDACaRmpmoV7/Jl3rdmSptMIqSfJ0t2hfXoku6xvq5NwBgOsgsAXQ6nybVX2h927KqQs9Lw83XTkoTL8YHaUR0UFOziEAnLviskq9uyNbCdvS9W3WqZt2PUI66MZRUZo1LFwdfb2dmEMAcD0EtgBaheMVVXp/Z45e+/qQdmYW2pf3COmgX4yO0uxhEQrq4OXEHALAuTPGaGdmod74Ol3rdmbrROXJm3bubpoyqItuHBWl0d2DZbFYnJxTAHBNBLYAnOq7w0V6/et0rU3OUvHJiaE83S26YkAX3TQ6WmN6cKEHwHUVnqi0D6n47nCxfXmvUF/dMDKSm3YA0EQIbAG0uLJKqz7YlaPXv0lX0qFj9uVRwe1146goXTsiQiF0wwPgoowxSk4v0BvfpOv9Xdkqq7RJqh5ScdWgMN14ckgFN+0AoOkQ2AJoMfvySvT61+lanZypwhOVkiR3N4sm9u+sm8ZE6aKeIXJz40IPgGsqPF6pNSmZeuObdP2QW2Jf3qezr24cFaWZQ8MV2J7WWQBoDgS2AJpVeZVVG749rNe+Ttc3B4/al4cHttONoyJ13YhIhfr7ODGHAHDujDHalnZMCd+k64PUHJVXVbfO+ni66arYrrpxVJSGRQXSOgsAzYzAFkCzOHikVG98k663kzJ1tLRCkuRmkcb366ybRkfp0j6d5E7rLAAXday0QquTq1tn9/9Ual/er4uffjE6StOHhCugnacTcwgAFxYCWwBNpqLKpo/35Or1bw7pv/vy7cu7+Pvo+pGRun5kpLoGtnNiDgHg3BUer1RyxjG9k5Kl9amHVWGtbp1t5+muqwd31Y2jozQ4IoDWWQBwAgJbAOfsSEm5dqQXaGdmgXZkVL+Ky6pnNrZYpLF9OukXo6I0vl+oPNzdnJxbAHDciQqrdmcXakdGgXZlFmpXZoHS8o/X2SYmzP9k62xX+fnQOgsAzkRgC8AhJyqs+ja7UDszCpSSUaAd6QXKKjhx2nad/Lx13YgI3TAySpHB7Z2QUwBonEqrTd8fLtbOzALtyijUzswC/ZhXIqvNnLZtVHB7XdQrRDeOitSgcFpnAaC1ILAFcBqbzWj/TyXVAWxGgXZmFOi7w8WnXeRZLFKvTr4aHBmoISdf/br40ToLoNWy2YwOHCnVrszqltidmQXanV2kipOTPtXWyc9bgyMCNTgiQLGRgYoND+CZswDQShHYAlBeUZlSTgawNd3uSsqrTtuuk5+3PYAdEhmoQREB8qf7Hc6TzWa0M7NABScqZZFksVhO/iu5nfxbFskiiywWySLJze3UNqq13GKxyK3Wtjq5jUUWBXXwVFgAY7wvJMYYZReWaVdGgXacbI39NqtQxfXUb/4+HoqNCFRsRIBiIwI1ODJAXfx9aJEFABdBYNuKlVdZ9fGeXL2/M0fF5ZWnLupqXfRZdPLCr9bFXe2LOkudv898sRji66UrY8PoVnUBKC2vUmpWoT2I3ZFRoJzCstO2a+fprkERARoaGWhvkQ0L4CIPTSevqExvJWVq1bYMpR89fvYPNIGhUYGaNTRcV8V2peWtheUVlWn9t4eVVXBCbhaL3N0kd4tFbm6WU//W/ttS/ZxrNzeLPNwsJz9T/ar5+9Qy2f8ur7Tp2+xC+7jYIyUVp+XFx9NNA7oGKDYiQEMiAxUbEajo4PY8RxsAXBiBbSv0Q26xVm3L0JrkTB07Xtli+33x8wPqFeqrmUPDNWNouMKZvbbFVFpt+uLHI0o/elxWm5HVZlRlM7LabCf/NT9bblRls8lqU51tqmxGVquR1fwsDauRzRgVnajSj3nF+vmwMTeL1KeznwZHBGpIVHUQ2zvUly7FaHJWm9HnP/ykN75J16bv8uzd2/28PRTVsb2MkYyqW9oknXxvZIxkM0ZGkmptY362TfWrnuWS8kvKlZJeoJT0Av3x/T0a1zdUs4aFa1y/UHl7uDvleLR1BccrtP7bw1q3I1tbD+bLnD5ktdl5uFnUt4tfdSvsydbYPp2p3wCgrbEY44yfmdatqKhIAQEBKiwslL+/f4vss7S8Sh/sylHCtnQlpxfYl3f299a1wyPVK9S3zoVb3Qs8I5v5+cVdrQs7c3K9Tl0s2sypiz2bMdqbU6yPdh+2P1jeYpHGdO+oWcPCNWVQmHy9uQfS1Iwx2p1dpNXJmVq3I1v5pae3KjSXsAAfexA7OKK6S3Fb+3/sjPPYlbT08ckqOKE3t2Xore0Zyq7VQ2B4dJBuGBmpK2PD1N6reb+DeUVlWrczW2uSs7Qnp8i+PKCdp66MDdOsoeEaHh1Er4TzVFpepU/25mrdjmx9/uNPqrSeuswYFhWoYVFBMqq+yWE7eRPOVutmnM1mZDXVXdSttuobdTbbqRt2NdvaX7W2tRkjN0tNIFsdxA7o6i8fz7Z544J6DgBOIbCtR0v9UBhjtCOjQKu2Zei9ndkqrbBKqu56dXm/UN0wKlKX9u7UYneVi8oqtSH1sFYnZ+rrg0fty3083TQppotmDQvXxb1CuMt9nvKKyvTOjiytTsrS97nF9uUhvt4a2S1Inu5u9u52HrX+dbO/dzvD8pP/up9cf7Jbnof7qXXenu6KCfNXZ38fJx6BlsEFX8Na4vhUWm3atDdPCdvS9dkPP9lb6wLbe2rW0AjdMCpSfTr7Ncu+z+b7w8Vak5Kpd1OydbjoVKAdFdxeM4eGa+bQcHUL6eCUvLmi8iqrEr//Set2ZmvT3lyVVZ6aiKl/mL+mDQ7TtNiuzJTexKjnAOAUpwa28fHxio+PV1pamiRpwIABevLJJzVlyhRJ0ty5c7VixYo6nxk9erS2bt16xjR3796tJ598UklJSTp06JD+9re/6YEHHmhUvpr7h+JYaYXWpmRp1baMOoFN95AOum5EpGYPD1eon3MDj8xjx/XujmytTs7UgZ9K7ctDfL01fUhXzRoWrpgwf1o2HFRWadVHe3K1OilTW378yd4V2MvDTRNjOuuaYRG6pDc3DZoSF3wNa87jk3akVKu2Z+it7Zk6UlJuXx7Xo6NuGBWpKwZ0aTUtaFab0Vf787UmJVMbvj2s4ydvMErVrYszh0VoWmyYAtszHvfnqqw2fbk/X+/tzNaG3Yftz7CWpG4d2+vqwV01bXBX9XbSzYsLAfUcAJzi1MD2vffek7u7u3r16iVJWrFihf785z8rJSVFAwYM0Ny5c5Wbm6vly5fbP+Pl5aXg4OAzprlt2za9+eabGj58uB588EE98sgjrSKwtdmMvtyfr4Rt6fpod64qrNV3s7093HTloDBdPzJSo7oHt7pA0RijXZmFWpOcqfd25ehore6yfTv7aeawcM0YEq4uAW2/BbCxjDHafuiYVidl6oNdOXVm4RweHaTZwyJ0ZWyYAtoxq3Bz4IKvYU19fMqrrNrw7WElfJOhrw7k25eH+HrrmuERumFkZKtvAT1eUaWPdudqTUqWvqh1A8rT3XJyPG6ExvXrdEGPx7XZjJLTj2ndzmx9mJpTZ2KmLv4+mjY4TFcPDtfAcG58tgTqOQA4pdV1RQ4ODtaf//xn3XbbbZo7d64KCgr0zjvvnFNa3bp10wMPPODUwDan8ITe3p6pVdszlHnshH35gK7+umFkpK4eEu4ygU2l1abPvv9Ja1Iy9cmePHtwbrFIF/UM0axh4bpiQBd1aGNjNRsr4+hxrU7O1JrkrDozvYYHttPsYeGaNSyi1V/gtwVc8DWsqY7Pj7nFeuObDK1JyVTBycnuLBbp0t6ddOOoSF3ev7M8XbAnQl5Rmd7dka01KVna+7PxuFfFhmnWsHANi7owxuPWzAfw3s5svb8rR1kFp37Lgjt4aeqgLpoW21UjuwUzq3ALo54DgFNaTQRitVr11ltvqbS0VHFxcfbliYmJCg0NVWBgoMaOHatFixYpNDS0SfddXl6u8vJT3eWKiooa2PrsasaVrTo5rqzmrr+fj4dmDAnX9SMjNTA84Lz24Qye7m6aENNZE2I6q/B4pT5IzdHalExtSzumL/Yd0Rf7jqi917eaPKCLZg4L1//0DJH7BXKRU1xWqQ9Tc7Q6OUvf1Bqf3MHLXVMHhWn28AiN4qIPTtSU9dyJCqve35WthG0ZSjp0zL48LMBH146I1HUjIhQR5NpjKUP9fXTHpT10x6U99N3hIq1NztI7O7KUW1Su175O12tfpyu6Y3vNGBKuWcPCFd2x7d2sOvBTidbtzNa6ndl1hqT4ento0oDOunpwV13UK8Qlb1wAANoep7fYpqamKi4uTmVlZfL19dXrr7+uqVOnSpJWrVolX19fRUdH6+DBg3riiSdUVVWlpKQkeXt7nzVtR1ts58+frwULFpy2vLF3QA/8VKJV2zO0OimzTvesUd2DdcPISE0ZGKZ2Xm2vC1t6/nGtTcnS2pRMpeWfaqHs7O+tGUPCNXNYuPp1aXt3kq02oy/2HdGa5Ext3H3YPlmKxSJd3OtUC3Zzz/SK+tGSUVdT1HPfZhUqYVu63k3Jtnetd3ezaHy/UN04KlJj+4S26ZtZjozHHd8v1KUflZZdcELvnQxmd2efuvnh7eGmy/uH6urBXXVZ39BWM0b6Qkc9BwCnOD2wraioUHp6ugoKCrR69WotW7ZMn332mWJiYk7bNicnR9HR0UpISNCsWbPOmrajgW19LRmRkZEO/VCcqLBq/bc5StiWUaelrmZc2XUjItSjk+9Z89oWGGOUnF6gtSmZem9njgpPnHoGb/8wf03sH6oxPTpqWHSQS18U/ZBbrNXJmXonpbr1pkavUF/NHhahGUO7KizAdS9s2wou+Oo613rOGKM3vsnQG9+kKzWr0L48Kri9rh8ZqWuHRyj0Aphl++eOV1Rp4+7DWpOcpf/uO1Ln2dBhAT4aHh2kEdFBGtEtWP26+LXKieGqrDZ9d7hYKenHlJxeoOT0YzpU6+akh5tFl/QO0bTBXTUxprP8fFxj2MyFhHoOAE5xelOSl5eXffKoESNGaNu2bVq6dKlefPHF07YNCwtTdHS0fvzxxybNg7e3t0MtwD9nsxlN+Otn9vFGbhbpsr6hun5kpMb3C73gumdZLBYNjw7S8OggPXFVjDZ/95PWpmTq0+/ytDenSHtzivT8p/vk5eGmoZGBiuvZUXE9OmpIVGCrnoyl0mrTj7kl+vpgvtYkZ9W5uA9s76npg7tq1rAIxUYEXBDj7eCazrWes1gsejspQ6lZhfJ0t+iKAV1046goxfXoeEF3rW/v5aGZQyM0c2iEcovKtG5Htt5PzdG3WYXKKSzT+7ty9P6uHEnVQxKGRAVqeHSwRkQHaWhUoFOCxCMl5Uo+dEwpGQVKPnRMuzILdaLSWmcbi0Ua1S1YVw/pqikDwxTcgdmgAQCuwemB7c8ZY+q0KtSWn5+vjIwMhYWFtXCu6ufmZtG4fp302Q8/6brhkbpmRAQtdSd5e7hr8sAumjywi46VVuijPYf15f58fbU/X3nF5fr64FF9ffContOP8vZw0/DoIMX16Ki4nh0VGxEoLw/n3BSotNr0/eFi7c4uVGpWoVKzqgPyiqpTz2T0cLNoXL9QzT7Z7dBZeQVayl1jeyr96HHNGhZBoFOPzrXG4x6vqNKOjAIlpR3T9kPHlJx+TMVlVfrvvnz9d1/1bNFuFqlvF/+TLbrVNwPDA9s16Y2xSqtN3+UUKzn9mL1FtvZkdjX8fDw0NCpIw6ICNTQqSEMiA11mQkMAAGpzalfkefPmacqUKYqMjFRxcbESEhK0ZMkSbdiwQXFxcZo/f75mz56tsLAwpaWlad68eUpPT9fevXvl51f9XLw5c+YoPDxcixcvllTdtXnPnj2SpKlTp+qmm27STTfdJF9fX3vL8Nk0pmtPSXmV2nu6X9AtF41hjNGBI6XaeqA6yN16IL/OeGRJaufprhHdguwtuoPCA5qlG19FlU0/5BafDGAL9W1Wob7LKbbP9lybn7eHBoYH6IoBnXX1kHAu7l0AXfQaxvFpGVab0Y95xdqedkxJh45p+6Gjyjh64rTtuvj7aHi3k92Xo4PVP6xx3Zd/Ki5Xcvqxk4FsgXZlFtjH/dewWKTeob4aFlXdajwsKkg9O/ny++XCOI8B4BSnBra33XabNm3apJycHAUEBCg2NlaPPPKIJk6cqBMnTmjGjBlKSUlRQUGBwsLCNG7cOC1cuFCRkZH2NC677DJ169ZNr7zyiiQpLS1N3bt3P21fY8eOVWJiokP54oei5RhjtC+vRF/VCnSPHa+ss00HL3eN7B5sb9Ed0DWg0RPUlFdZ9cPhkjpB7PeHzxDE+nhoUHiABoUHaODJV3Rwey7+XAznccM4Ps6TW1RWHeSmHVPSoaPanV2kKlvdn+L2Xu4aEhmoEdFBGt4tWEOjAuV/svtypdWmvTlFSj50amxs7cfJ1fC3t8ZWB7JDaqWBtoHzGABOcfrkUa0RPxTOY7MZ/ZBXrK9Odlv++uDROpNQSdWB5+juwRrTo6PG9OiomDD/OkFnWaVV3x8utgewqVmF+iG3WJXW07/q/j4eGhRRHbzWBLNRwe0ZK9sGcB43jOPTehyvqNLOjEIlHTqq7YeqW3aLy6rqbGOxSH07+8nX20OpWYUqrzq9NbZPqJ+GRQdqaGSQhkUHqkcIrbFtHecxAJxCYFsPfihaD5vNaE9OkbYeqG7N/frg0dMu+ALaeWp092AFtfeyB7E/b/2o2a6mFbYmiI0MbtpxbWg9OI8bxvFpvWw2ox/zSrT90FH7WN2fj48NaOdp7048NCpQgyNpjb0QcR4DwCkEtvXgh6L1stqMdmcX2rstf3PwqEorrKdtF9Tes04AOzA8QBFBBLEXEs7jhnF8XEveye7LxyusGhwZqB4hHWiNBecxANTS6mZFBhri7mZRbESgYiMC9b9je6rKalNqVqG2Hjiq4xVVGtDVXwPDA5p8hlEAcKZQfx9NGdQ6nggAAEBrRGALl+bh7qahUUEaGhXk7KwAAAAAcBIewAkAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAsAAAAAcGkEtgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJfm4ewMtEbGGElSUVGRk3MC4FzVnL815zPqop4DXB/1HACcQmBbj+LiYklSZGSkk3MC4Hzl5+crICDA2dlodajngLaDeg4AJIvhNt9pbDabsrOz5efnJ4vF0iRpFhUVKTIyUhkZGfL392+SNFt6H5ShdeyDMjimsLBQUVFROnbsmAIDA5tlH66Mes456bfEPihD69gH9RwAtCxabOvh5uamiIiIZknb39+/2X7gWmoflKF17IMyOMbNjakE6kM959z0W2IflKF17IN6DgBaBjUhAAAAAMClEdgCAAAAAFwagW0L8fb21lNPPSVvb2+X3QdlaB37oAytZx+oqy38f6UMrWMflKH17AMAXAWTRwEAAAAAXBottgAAAAAAl0ZgCwAAAABwaQS2AAAAAACXRmALAAAAAHBpBLYAAAAAAJdGYAu7vLw8Wa3WZks/OTlZxcXFzZa+JO3cuVP79+9vtvS3bdum5557TkVFRc22j5KSkmZLG7jQUc+dHfUcAMAVEdiep8OHD+uPf/yjXnjhBX344YdNnn5OTo7uu+8+PfLII3r++eebPH1jjCoqKnTnnXfqiiuu0FdffdXk+8jOztakSZM0btw47dixo8nTl6Tc3FxdffXVmjBhgjZu3KgTJ040afrZ2dmaOnWqRo8ereeff17+/v5q6idl5eTk6KabbtKNN96o2267TcnJyU2e/r333qtnnnlGK1eubNK0a+/j17/+tZ544gm98sorTZ5+bm6uPvjggyY/9mhYc9dzUvPWddRzjqGec3wf1HMA0PoQ2J6HhQsXqlevXvrmm2/0yiuvaObMmXr99dclqUl+kObPn6/evXvr0KFDysvL0wMPPKCFCxc2WfqSZLFYlJeXp3Xr1umnn37Sp59+qsLCwibbx8MPP6zo6Gi1b99ee/fu1SWXXHLeaf5cRkaGrrrqKlksFn355Ze6+eab1a5duyZL/3e/+50iIyPl6+urf/3rXyovL9f3338vi8XSZPv4z3/+o4EDB6qiokJTpkzRp59+qv/3//6fDh8+3CTpv/zyyxowYIAOHTqkAwcO6K677tI999yjffv2NUn6krR27Vr169dPaWlp+u6773Trrbfqrrvu0vfff98k6f/jH/9Q165dNW3aNO3evbtJ0sTZNXc9JzV/XUc9d3bUc46hngOAVsyg0aqqqsySJUvMmDFjzAcffGCMMaaoqMg8+uijJioq6rzTr6ysNEuWLDFjx44169evty9/4oknTI8ePc47/Z87cOCAue2228yjjz5qevfubTZs2HDeaVZUVJh7773XWCwWk5CQYF+em5t73mn/3D//+U9zxRVX2N+npqaaQ4cOmcrKyvNKt6ioyPj4+JhBgwaZL774whhjzCeffGKioqJMYmLieaVdW1VVlZk4caJ5+umn7ctWrVplOnfubAoKCs47/ZKSEjN27Fjzj3/8w75s/fr1xs/Pz9x9993GZrOd9z6MMWb69Onm/vvvt7//4IMPzIgRI8ztt99+Xvuw2Wzmgw8+MJdffrn5y1/+YoYNG2auueYaY7VamyDXOJPmrueMadm6jnquftRzjUM9BwCtFy2258Dd3V0VFRUaP368Jk+eLEny8/PT2LFj5eHhcd5jnzw8PDRmzBg99dRTmjRpkn15ZWWl7rrrribvfpaZmaldu3Zp8eLF6tChg9544w17a8a58vT01CWXXKJLL71UR44c0XfffaeZM2dq9uzZGjt2rP71r3+poqLivPZhTra07Nq1S127dtXRo0c1btw4XXfddYqLi9NNN92kzz///JzSttls8vPzU2Jionbt2qWLLrpIkjR69Gjl5eXpyJEj9u3O165du3TgwAF17drVvuz48eOaPXv2ef9/kKTPP/9cu3fv1rhx42Sz2WSz2XTFFVcoMDBQa9asaZLuejk5Odq/f78iIyPty6ZOnaobbrhBycnJ59Vdz2KxqHPnzrr55pv1v//7v/rb3/6m1atXa+PGjeedb5xZc9dzUsvWddRzp6OeaxzqOQBo5ZwdWbuCwsJC89VXX5nMzEz7suPHj9v/rrlL+9prr5mYmJgmSf/n66dPn24sFosZNmyY6d27t3nrrbdMaWlpk+zj9ddfN7fccosxxpiEhATTvXt3Ex8fb+644w6TnZ19zulXVlaae+65x3Tp0sV07NjR3H///Wbp0qXmzjvvNN7e3ubZZ5+tcxzPZR9Wq9XceOON5je/+Y254447zB133GH27Nlj3nzzTTN58mRz8cUXm5ycnHNOv4bNZjM2m80UFhaaiy++2PzmN79xON9n20d5ebkZNWqUueiii8yLL75orr32WmOxWExcXJwJCAgw999/v8MtQPWlf+jQIePu7m4+/fRT+7JvvvnGXHrppWbKlCnm2muvNUVFRQ6Xoby83P7/rea7X1VVZSIjI82f/vQnY4yxtyJlZmaa6667zsyYMcMcO3as0emfqbXiuuuuM0OHDm1UvtGw5q7nzrSPn68/n7qOeu7c0q9BPXcK9RwAuB4C27N45plnjL+/vxk4cKDx9/c3zz33nP3H1Gq11vlBuvfee81NN91kjKnuotYU6VdUVJh///vfZurUqeaLL74wu3btMr/+9a9NTEyMvXvg+ezDmOoubtOnT7e/7927t/H09DRDhw41OTk5Z+1eVV/6hw4dMsYY8/nnn5tbbrnFrFu3rs5n7rvvPjN48GCTmpp6zmWo2cff/vY3Y7FYTFRUlElJSbF/5oMPPjBxcXHmueeeO6f0s7KyjDHVFzO1XXrppebuu+82xphGdT2rbx9paWnGGGO2bNlili5daqZOnWqGDRtmtm3bZgoLC83y5ctNXFyceeqppxqd/t/+9jf7/+dbb73VBAQEmIcfftg88MADxs3NzTz77LNmwYIFJiYmxl7Ws1myZInp06dPnW6cNRd3v/3tb03Pnj3tx6vm2PzjH/8wgwcPNt988805pV9bzfm2f/9+065dO/P88887lG80rLnrOUf2cb51HfUc9Rz1HABc2AhsG/Dhhx+a/v37m7Vr15oDBw6YRYsWmQEDBphbb721znY1F3cjR440f/3rX+usa+iCwNH062utCAoKMq+//nqTlOHRRx81L7zwgvn4449NeHi4iYiIMMHBweYvf/nLWcdvnSn9X/3qV/ZtduzYYcrKyowxp36wDx8+bCwWi/n666/PuQw1+6iqqjL9+/c3fn5+ZufOnfbPVVRUmNjYWLN06dLzPkY1+zHGmN/97neNbrFy5DgZY8zll19eZ4yYMcZMmjTJ3HXXXQ0GEfWlHxMTY2677Tb7Nr/73e/MlVdeacaOHWu/oDp06JBp166dSU9PbzD/+fn55q677jKxsbHG39/fzJo1y/z00091ttm7d6/x9/c3zz77rDHm1P/roqIi4+bmZrZu3Xpe6f/cH/7wB9O5c2eTkZFhjKk+T0pKShr8DE7X3PVcY/ZxrnUd9Rz1XA3qOQC4cBHYNuC+++4zQ4cOrbPs73//u+nbt6/517/+ZYw5dRc3PT3ddOrUyX5nev369ebGG280Bw8ePK/0q6qqTrto3LZtm4mKiqoz2cq57CM+Pt4YU33BZ7FYjJ+fn1mwYIH9c4MGDTKbN28+5/T/+c9/GmPqdrOqKcsbb7xhQkND61ygnc8+/vOf/xiLxWKWLFli30dxcbEZPHiwefXVV885/dr/H2q88MILZsCAAeaHH344a94bs499+/aZ6Ohos23bNvs2paWlZsyYMeaZZ5455/RfeOEFexl+Hjg8/fTTZuDAgebo0aMNTlJy4MAB8/DDD5sPPvjAbNmyxVgsFvPGG2/U+YzVajVPP/208fX1Ndu3b7cv37Ztm4mOjm7w4t6R9H+upKTEREdHm/vuu8+sXLnSXHzxxebNN9884/aoX3PXc47u43zqOuo56jnqOQAAge0ZWK1Wc/fdd5sbbrjBfhfeGGOys7PN//7v/5rBgweb4uJi+/L//Oc/5vLLLzcZGRlmypQpxsPDwzz00ENNln7NRcz3339vrrrqKjN79uw66891H8ePHzeffPKJeeaZZ+pcwOTl5Zn+/fubzz777LzSr31nuaYMe/fuNZMmTTJ33HFHg/l3ZB+xsbH28Ue/+MUvTP/+/c21115r3nvvPXPllVea4cOHn3E8n6NlqDnONRd9H3zwgfHz82sw3XMtw+DBg83YsWPNq6++apKTk81VV11lBgwY0OCF8bl8lyorK83u3bvNpZdeahYuXHjWMlRVVdm7RBpTPfYrNjb2tIDGZrOZcePGmSFDhpiHH37Y/Pe//zXjx483l19+eYPjJB1N/+eeeuopY7FYjJeXl3nsscfOWg7U1dz13Lnso7F1HfUc9Rz1HADAGALbetVcmCxevNhERkae9qOzbt06M2LECPPSSy/Zt58zZ46xWCzG09PTXHnllebIkSNNln5JSYlZvHixuf32242vr6+58cYbTWFh4XmXYejQoeaVV16ps70xpy5sysvLm7QMCxYsMHPnzjXt27c3N91001knxHB0HzV36gsKCszKlSvNpZdeakaNGmWuv/56c/To0fNOv6aloWb7H374wXh4eNgfjdEUZahpVdq7d68ZPny46du3r+nRo4e57rrrmqQMNf8fKisrzXvvvWef2OYXv/hFo7q11ewvPz/feHp6msWLF5/W/fLIkSNm3rx5ZsiQIaZPnz5m1qxZDk+o0lD6tZWUlJh77rnHWCwWc9tttzmcPk5p7nruXPbR2LqOeo56rnb61HMAcGEjsK1HzQXPiRMnjL+/f53n7hlTPZZm1KhRZvHixfZlt99+uxk0aFCdST2aMv1ly5aZX/7yl3W6PjX1PhrjXNL/v//7P3PNNdfU6YLWHGUoLy9v8CLpXNOvuRg5cuSIvStmU+6jJv3CwkKzb9++s97FP5cyGGPM9u3bzYIFCxz+//Dz7qE13VLnz59vOnXqVOc7X7srY2FhoUOzzTYm/Zpt9+/fbx599FGHLrpRv+au5851H42p66jnqOfqK4Mx1HMAcCG6IAPb/Px8+4QNPx/b8vNJRP7yl78YPz+/034chwwZYn7961/b39fuAtVU6dfMSGnM6T+KzVGG5ki/dhl+no6rlOFM6V9oZai5mKt9URceHm7uvPNOc/ToUfPRRx+ZlStXNmv6K1asOGM5UFdz13NNuY8z1XWucn5Rz7WdMlDPAYDruuAC23nz5plOnTqZRYsWnXEbm81mHn74YftkHMOHDzeXX365/ZETSUlJZvDgweajjz5q8fQpA2VwVhn+85//2C/Yai7K1qxZY9zd3c2gQYOMxWIx//d//9ei6aN+re270xq//xwjylDfPqjnAMB1XTCB7bFjx8ytt95qRo0aZYYPH26mTZtmv+Nbu4XglVdeMcHBwWbIkCEmOTnZGGPMnj17zMyZM42Xl5eZNGmSad++vbnhhhvqTBLR3OlTBsrg7DLs2rWrThqZmZnm4YcfNhaLxdxwww32x1G0RPqoX2v97rSm7z/HiDI0tA/qOQBwXW06sK39w1JWVmYWLFhg1q5daxITE82wYcPMY489VueZeaWlpWbhwoUmPj7+tIevFxYWmo8++sj84x//sI93ae70KQNlaG1lqFFeXm4eeOABExwcbH9USnOnj/q5ynfHmd9/jhFloJ4DgLavzQa2x48frzPLoM1mMwUFBfb3Dz30kLnooovsXZlqNPQ8uZZMnzJQhtZehsOHD7dY+qifq3532lL6lKFtl4F6DgBch5vaoMcee0wXX3yxrrrqKj3//PMqKiqSxWKRv7+/bDabJOm+++6TJL3zzjs6cuSIJMkYIze3sx+S5k6fMlAGVyhD586dWyR91M+VvzttJX3K0PbLQD0HAC6kZePo5lVeXm6uueYaExMTYxISEsycOXNMTEyMufLKK+tsV3P39LnnnjPDhw83y5cvt6+r6WZU3x3W5k6fMlAGyoCz4bvj/PQpA2WgngOA1qdNBbZ79uwxvXv3rjMz4hdffGHatWtn/vSnP53241JWVmamTp1qrrvuOrNr1y7zn//857Tn47Vk+pSBMlAGnA3fHeenTxkoA/UcALQ+bSqwTUpKMhaLxeTn5xtjTt0lXbx4sQkKCjI//PCDfduaH6F33nnH9OjRw3Ts2NF4eXmZv/zlL05LnzJQBsqAs+G74/z0KQNloJ4DgNanTQW2KSkpZsCAAebvf/+7MebUD1BFRYXp3r27eeihh4wxp54dt2/fPjNnzhxjsVjM3XffbUpKSpyaPmWgDJQBZ8N3x/npUwbKQD0HAK2PSwW2tafZr8/Ro0fNjBkzzPXXX2+ys7ONMcZUVlYaY4x59tlnTdeuXeuMcfn9739vIiIi7M+Va+70KQNloAwRpz3HEXXx3XF++pSBMjRlGQAALcNlZkUuLCyU1Wq1v6+ZhVCSqqqqJElBQUGaNm2avvvuO7355puSJA8PD0lSQECAgoKClJGRYf/skiVLlJGRoUGDBjV7+pSBMlCGumXA6fjuOD/9tnCMKEPrKQMAoOW0+sC2srJS99xzj6ZOnaqpU6dq4cKFstlscnNzs//weHh4qKysTAkJCbr11ls1ZMgQrVq1Sps3b7ank5mZqU6dOik6Oto+9b6bm1uzp08ZKANlqFsGnI7vjvPTbwvHiDK0njIAAJzA2U3GDfnoo49Mr169zNixY83atWvNrbfeavr27Wsef/zxOtstXbrUBAcHm+nTpxtjjNm5c6e56aabjJeXl7n77rvNnXfeafz8/Ex8fLwx5lTXo+ZOnzJQBspw9q5+Fzq+O85PnzJQBuo5AHB9rTawLSwsNLfffru55557TEVFhTGm+nlyTz31lLniiitMaWmpMcaYF154wXTv3t289tprdca52Gw288wzz5g77rjDTJ061fz3v/9t0fQpA2WgDDgbvjvOT58yUAbqOQBoG1ptYHv06FHzyiuvmJSUFGPMqbuhjzzyiLn00kvt21VWVp4266Ajd06bO33KQBkoA86G747z06cMlIF6DgDaBg9nd4Wu8a9//UsWi0V9+vTR2LFjFRQUpDlz5shisUiqntTB3d1dRUVF6tGjhyTJGCMPDw/7RA41aj7TkulTBspAGXA2fHecnz5loAzUcwDQRrVsHH26119/3YSGhpq4uDgzZMgQ06lTJ/P0008bY049F86YU3dLR48ebZYtW1ZnmTPTpwyUgTLgbPjuOD99ykAZmrIMAIDWx6mB7WuvvWYGDx5s/vnPfxpjjMnKyjJ///vfTYcOHUxRUdFp2x84cMB06tTJfPfdd/Zl+/fvN8bU/bFqqfQpA2WgDDgbvjvOT58yUAbqOQBo+5wyL70xRlL1lP6jR4/WnDlzJEldu3bV0KFDFR4err179572uY0bNyoyMlJ9+/ZVSkqKRo8erTFjxqiqqkru7u4tlj5loAyUwf207VEX3x3np08ZKAP1HABcQFoyik5KSjLHjh2zvy8oKDjtjuiOHTtMly5dzNGjR+3LaroG/eY3vzHXXHONefDBB42bm5u57bbbTFlZWYulTxkoA2WoWwacju+O89OnDJShKcsAAHANLRLYvv322yYiIsL07NnTREVFmSeeeMIcPnzYvr72dPp//etfzUUXXWSMqZ6Gv/Y20dHRxmKxmMsuu8zs3r27xdKnDJSBMtQtA07Hd8f56VMGytCUZQAAuJZmD2y3bdtm+vXrZ5577jm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-      "text/plain": [
-       "<Figure size 1000x1800 with 18 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "saltTS_2022.isel(time=slice(10)).plot(col=\"k\", col_wrap=3)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Simple calculations using all data\n",
-    "\n",
-    "To conduct some simple calculations, it can be benefitial to create a new variable in the data that you can call to easily. For example, we will show you how to create a new variable containing all `temp` data for just 2022. Then we will demonstrate how to calculate a monthly mean and plot these monthly means at a particular depth.\n",
-    "\n",
-    "To begin, name your variable `temp_2022`, and using the `sel()` function, specify that you are interested in the `temp` data from 2022. You can check your result to ensure you see a `xarray.DataArray` just for `temp`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp&#x27; (time: 365, k: 17, latitude: 723, longitude: 491)&gt;\n",
-       "[2202731565 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036\n",
-       "  * longitude  (longitude) float64 142.2 142.2 142.2 142.3 ... 156.8 156.8 156.9\n",
-       "  * time       (time) datetime64[ns] 2022-01-01T14:00:00 ... 2022-12-31T14:00:00\n",
-       "    zc         (k) float64 ...\n",
-       "Dimensions without coordinates: k\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Temperature\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
-       "    short_name:             temp\n",
-       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
-       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
-       "    units:                  degrees C</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span>k</span>: 17</li><li><span class='xr-has-index'>latitude</span>: 723</li><li><span class='xr-has-index'>longitude</span>: 491</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5efdf3de-885c-400a-a55c-ec017d1b8018' class='xr-array-in' type='checkbox' checked><label for='section-5efdf3de-885c-400a-a55c-ec017d1b8018' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2202731565 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-d6a15001-eda9-439a-ac84-8bf68e9a6285' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6a15001-eda9-439a-ac84-8bf68e9a6285' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-28.7 -28.67 ... -7.066 -7.036</div><input id='attrs-4caf4774-1e78-4a1a-b04d-62c9447d8810' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4caf4774-1e78-4a1a-b04d-62c9447d8810' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb3ccad-ffd0-4099-abb6-2a653f534618' class='xr-var-data-in' type='checkbox'><label for='data-cfb3ccad-ffd0-4099-abb6-2a653f534618' 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>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>coordinate_type :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__ofProperty :</span></dt><dd>http://vocab.nerc.ac.uk/collection/S06/current/S0600045/</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGN/</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-28.696022, -28.666022, -28.636022, ...,  -7.096022,  -7.066022,\n",
-       "        -7.036022])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.2 142.2 142.2 ... 156.8 156.9</div><input id='attrs-869cde1c-8090-447f-85c6-fbe26ba12678' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-869cde1c-8090-447f-85c6-fbe26ba12678' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec2123c5-a930-4324-b4fb-5b6734abef2b' class='xr-var-data-in' type='checkbox'><label for='data-ec2123c5-a930-4324-b4fb-5b6734abef2b' 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>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>coordinate_type :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>projection :</span></dt><dd>geographic</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/DEGE/</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788,\n",
-       "       156.868788])</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'>2022-01-01T14:00:00 ... 2022-12-...</div><input id='attrs-312b1ba1-3dfb-4ffc-bb5e-10b0206aab46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-312b1ba1-3dfb-4ffc-bb5e-10b0206aab46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35992bbe-0713-4507-9fba-e76b0e26e873' class='xr-var-data-in' type='checkbox'><label for='data-35992bbe-0713-4507-9fba-e76b0e26e873' 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>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>coordinate_type :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>puv__uom :</span></dt><dd>http://vocab.nerc.ac.uk/collection/P06/current/UTAA/</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T14:00:00.000000000&#x27;, &#x27;2022-01-02T14:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-03T14:00:00.000000000&#x27;, ..., &#x27;2022-12-29T14:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-12-30T14:00:00.000000000&#x27;, &#x27;2022-12-31T14:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zc</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-987d4798-9a77-4cca-83c3-622ecea38764' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-987d4798-9a77-4cca-83c3-622ecea38764' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4830dc6f-3250-46ec-8719-fa83b9433223' class='xr-var-data-in' type='checkbox'><label for='data-4830dc6f-3250-46ec-8719-fa83b9433223' 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>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>coordinate_type :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Z coordinate</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17c8b784-4f07-4f7c-a7f5-a7bac363010b' class='xr-section-summary-in' type='checkbox'  ><label for='section-17c8b784-4f07-4f7c-a7f5-a7bac363010b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e8b4aeff-8fdd-4ac7-a464-c6f8ad0612ef' class='xr-index-data-in' type='checkbox'/><label for='index-e8b4aeff-8fdd-4ac7-a464-c6f8ad0612ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-28.696022, -28.666022, -28.636022, -28.606022, -28.576022, -28.546022,\n",
-       "       -28.516022, -28.486022, -28.456022, -28.426022,\n",
-       "       ...\n",
-       "        -7.306022,  -7.276022,  -7.246022,  -7.216022,  -7.186022,  -7.156022,\n",
-       "        -7.126022,  -7.096022,  -7.066022,  -7.036022],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=723))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac75923c-a7cc-4a7b-bb7e-b0ccb568ca56' class='xr-index-data-in' type='checkbox'/><label for='index-ac75923c-a7cc-4a7b-bb7e-b0ccb568ca56' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([142.168788, 142.198788, 142.228788, 142.258788, 142.288788, 142.318788,\n",
-       "       142.348788, 142.378788, 142.408788, 142.438788,\n",
-       "       ...\n",
-       "       156.598788, 156.628788, 156.658788, 156.688788, 156.718788, 156.748788,\n",
-       "       156.778788, 156.808788, 156.838788, 156.868788],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=491))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8c857a2f-2e45-469e-be9b-632eeb8ea07b' class='xr-index-data-in' type='checkbox'/><label for='index-8c857a2f-2e45-469e-be9b-632eeb8ea07b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 14:00:00&#x27;, &#x27;2022-01-02 14:00:00&#x27;,\n",
-       "               &#x27;2022-01-03 14:00:00&#x27;, &#x27;2022-01-04 14:00:00&#x27;,\n",
-       "               &#x27;2022-01-05 14:00:00&#x27;, &#x27;2022-01-06 14:00:00&#x27;,\n",
-       "               &#x27;2022-01-07 14:00:00&#x27;, &#x27;2022-01-08 14:00:00&#x27;,\n",
-       "               &#x27;2022-01-09 14:00:00&#x27;, &#x27;2022-01-10 14:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2022-12-22 14:00:00&#x27;, &#x27;2022-12-23 14:00:00&#x27;,\n",
-       "               &#x27;2022-12-24 14:00:00&#x27;, &#x27;2022-12-25 14:00:00&#x27;,\n",
-       "               &#x27;2022-12-26 14:00:00&#x27;, &#x27;2022-12-27 14:00:00&#x27;,\n",
-       "               &#x27;2022-12-28 14:00:00&#x27;, &#x27;2022-12-29 14:00:00&#x27;,\n",
-       "               &#x27;2022-12-30 14:00:00&#x27;, &#x27;2022-12-31 14:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7f8e583-b540-4e2d-9b7d-8e0b0a37a7ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7f8e583-b540-4e2d-9b7d-8e0b0a37a7ec' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>aggregation :</span></dt><dd>Daily</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>medium_id :</span></dt><dd>http://environment.data.gov.au/def/feature/ocean</dd><dt><span>puv__parameter :</span></dt><dd>https://vocab.nerc.ac.uk/collection/P01/current/TEMPMP01/</dd><dt><span>scaledQuantityKind_id :</span></dt><dd>http://environment.data.gov.au/def/property/sea_water_temperature</dd><dt><span>short_name :</span></dt><dd>temp</dd><dt><span>substanceOrTaxon_id :</span></dt><dd>http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater</dd><dt><span>unit_id :</span></dt><dd>http://qudt.org/vocab/unit#DegreeCelsius</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'temp' (time: 365, k: 17, latitude: 723, longitude: 491)>\n",
-       "[2202731565 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036\n",
-       "  * longitude  (longitude) float64 142.2 142.2 142.2 142.3 ... 156.8 156.8 156.9\n",
-       "  * time       (time) datetime64[ns] 2022-01-01T14:00:00 ... 2022-12-31T14:00:00\n",
-       "    zc         (k) float64 ...\n",
-       "Dimensions without coordinates: k\n",
-       "Attributes:\n",
-       "    aggregation:            Daily\n",
-       "    long_name:              Temperature\n",
-       "    medium_id:              http://environment.data.gov.au/def/feature/ocean\n",
-       "    puv__parameter:         https://vocab.nerc.ac.uk/collection/P01/current/T...\n",
-       "    scaledQuantityKind_id:  http://environment.data.gov.au/def/property/sea_w...\n",
-       "    short_name:             temp\n",
-       "    substanceOrTaxon_id:    http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltW...\n",
-       "    unit_id:                http://qudt.org/vocab/unit#DegreeCelsius\n",
-       "    units:                  degrees C"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Subsetting data\n",
-    "temp_2022 = data.temp.sel(time=\"2022\")\n",
-    "\n",
-    "# Checking result\n",
-    "temp_2022"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can group the 2022 data by month using the `groupby(\"time.month\")` function and then calculate the monthly mean using the `mean()` function.\n",
-    "\n",
-    "This will take some time as it is going through all data to group and calculate means."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculating monthly means\n",
-    "temp_monthly_2022 = temp_2022.groupby(\"time.month\").mean()\n",
-    "\n",
-    "# Checking results\n",
-    "temp_monthly_2022"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how the `time` dimension has been replaced by `month`, which includes 12 time steps, one for each month of the year. We can now plot all monthly means for 2022, at a particular depth.\n",
-    "\n",
-    "Using the `plot` function, specify `month` in the `col` argument, which will create a subplot for each month in our data. Finally, we use the `col_wrap` argument to specify the number of columns in our plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x2d9d69730>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x900 with 13 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting monthly means\n",
-    "temp_monthly_2022.sel(k=16).plot(col='month', col_wrap=4)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Timeseries plotting for a particular location\n",
-    "\n",
-    "For this example, we will select a single point in the dataset and extract mean wind speed (`mean_wspeed`) values for a particular timeframe to plot a time series. We will use the `sel()` function to select the point nearest to `latitude` = -20 and `longitude` = 152. We will then subset the data temporally, and plot.  \n",
-    "  \n",
-    "We can do all this for the first 10 months of 2023 using the following line of code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x29aa7a550>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting the means of wind speed on a location of this year (2023)\n",
-    "data.sel(latitude=-20, longitude=152, method='nearest')\\\n",
-    "    .sel(time=slice('2023-01-01', '2023-10-24'))\\\n",
-    "    .mean_wspeed.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also do this for the month of September for 2023."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x29c244ac0>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting the means of current speed on a location of last month (September 2023)\n",
-    "data.sel(latitude=-20, longitude=152, method='nearest')\\\n",
-    "    .sel(time=slice('2023-09-01', '2023-09-30'), k=0)\\\n",
-    "    .mean_cur.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Save the data as a CSV\n",
-    "\n",
-    "Using the `to_dataframe()` and `to_csv` functions, you can the data as a csv file. The following line of code uses the `mean_cur` from the first week of October (from Section 4) as an example. The same line can be altered with any of the subsetted data we went over (e.g., `temp_monthly_2022`) to save these as well. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_current_20231007.to_dataframe().to_csv(\"eReef_mean_current_20231007.csv\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "ac4bbee1919ea7aef9c6cfd22a25c3c68d6ebab6b5f3e31fb17a356d6f400124"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/Extracting_Spatial_Data_GBR_Features.ipynb b/Python_based_scripts/Extracting_Spatial_Data_GBR_Features.ipynb
deleted file mode 100644
index b92a497..0000000
--- a/Python_based_scripts/Extracting_Spatial_Data_GBR_Features.ipynb
+++ /dev/null
@@ -1,457 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extracting spatial data for Great Barrier Reef features\n",
-    "**Author**: Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook will show how to access the RIMReP `geoparquet` collection for Great Barrier Reef (GBR) Feature from the Great Barrier Reef Marine Park Authority (GBRMPA). This dataset includes the unique IDs and names of all features above water, including sand banks, reefs, cays, islets, and islands. Since this dataset includes spatial data, we can extract the spatial limits of each feature included in this dataset.  \n",
-    "  \n",
-    "We will use the spatial boundaries to extract environmental or biological information for sites of our interest.  \n",
-    "  \n",
-    "## Worfklow to be followed in this notebook\n",
-    "1. Connect to RIMReP_DMS and inspect metadata\n",
-    "2. Load GBR features from data collection  \n",
-    "3. Get GBR features geometries to extract information from biological or environmental dataset within an area of interest\n",
-    "4. Export polygons as shapefiles"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing S3 bucket\n",
-    "from pyarrow import parquet as pq\n",
-    "# Load and manipulate geospatial data\n",
-    "import dask_geopandas\n",
-    "import geopandas as gpd\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Mapping\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cf\n",
-    "# Dealing with operating system, i.e. reading and writing files\n",
-    "import os"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting metadata\n",
-    "To get a better understanding of the GBR features dataset, we will inspect its metadata. This information will also help us determine the best way to load the data into memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.FileMetaData object at 0x000001EC68DA8270>\n",
-       "  created_by: parquet-cpp-arrow version 11.0.0\n",
-       "  num_columns: 41\n",
-       "  num_rows: 9612\n",
-       "  num_row_groups: 1\n",
-       "  format_version: 2.6\n",
-       "  serialized_size: 22344"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Connecting to metadata file\n",
-    "md = pq.read_metadata(f's3://rimrep-data-public/gbrmpa-complete-gbr-features/data.parquet')\n",
-    "#Checking contents\n",
-    "md"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metadata is telling us that this dataset contains 41 columns and 9,612 rows. Each row represents a unique feature in the GBR.\n",
-    "  \n",
-    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.ParquetSchema object at 0x000001EC70A2D580>\n",
-       "required group field_id=-1 schema {\n",
-       "  optional int64 field_id=-1 TARGET_FID;\n",
-       "  optional binary field_id=-1 DATASET (String);\n",
-       "  optional binary field_id=-1 LOC_NAME_S (String);\n",
-       "  optional binary field_id=-1 GBR_NAME (String);\n",
-       "  optional binary field_id=-1 CHART_NAME (String);\n",
-       "  optional binary field_id=-1 TRAD_NAME (String);\n",
-       "  optional binary field_id=-1 UN_FEATURE (String);\n",
-       "  optional binary field_id=-1 LABEL_ID (String);\n",
-       "  optional int64 field_id=-1 SORT_GBR_I;\n",
-       "  optional binary field_id=-1 FEAT_NAME (String);\n",
-       "  optional binary field_id=-1 LEVEL_1 (String);\n",
-       "  optional binary field_id=-1 LEVEL_2 (String);\n",
-       "  optional binary field_id=-1 LEVEL_3 (String);\n",
-       "  optional binary field_id=-1 CLASS_CONF (String);\n",
-       "  optional binary field_id=-1 CLASS_SRC (String);\n",
-       "  optional binary field_id=-1 POLY_ORIG (String);\n",
-       "  optional binary field_id=-1 IMG_SRC (String);\n",
-       "  optional int64 field_id=-1 SUB_NO;\n",
-       "  optional binary field_id=-1 CODE (String);\n",
-       "  optional int64 field_id=-1 FEATURE_C;\n",
-       "  optional binary field_id=-1 QLD_NAME (String);\n",
-       "  optional binary field_id=-1 X_LABEL (String);\n",
-       "  optional binary field_id=-1 GBR_ID (String);\n",
-       "  optional binary field_id=-1 LOC_NAME_L (String);\n",
-       "  optional double field_id=-1 X_COORD;\n",
-       "  optional double field_id=-1 Y_COORD;\n",
-       "  optional double field_id=-1 SHAPE_AREA;\n",
-       "  optional double field_id=-1 SHAPE_LEN;\n",
-       "  optional binary field_id=-1 Checked (String);\n",
-       "  optional int64 field_id=-1 RegionID;\n",
-       "  optional int64 field_id=-1 LatitudeID;\n",
-       "  optional int64 field_id=-1 GroupID;\n",
-       "  optional binary field_id=-1 PriorityLb (String);\n",
-       "  optional binary field_id=-1 Country (String);\n",
-       "  optional binary field_id=-1 UNIQUE_ID (String);\n",
-       "  optional binary field_id=-1 geometry;\n",
-       "  optional double field_id=-1 minx;\n",
-       "  optional double field_id=-1 miny;\n",
-       "  optional double field_id=-1 maxx;\n",
-       "  optional double field_id=-1 maxy;\n",
-       "  optional int64 field_id=-1 fid;\n",
-       "}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `schema` not only gives the names of the columns included in the GBR dataset, but it also tells us the data types included in each column (e.g., `string` or `double`).  \n",
-    "  \n",
-    "We can also obtain more details about a specific column, by calling the `column` method and passing the name of the column as an argument as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ParquetColumnSchema>\n",
-       "  name: GBR_NAME\n",
-       "  path: GBR_NAME\n",
-       "  max_definition_level: 1\n",
-       "  max_repetition_level: 0\n",
-       "  physical_type: BYTE_ARRAY\n",
-       "  logical_type: String\n",
-       "  converted_type (legacy): UTF8"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema.column(3)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will need to access four variables to create a list of all above water sites in the Great Barrier Reef Marine Park:  \n",
-    "- `UNIQUE_ID`, which includes a unique identification number for each area above water  \n",
-    "- `GBR_NAME`, which includes the name of each location above water  \n",
-    "- `LOC_NAME_S`, which is the combination between feature name and unique ID  \n",
-    "- `geometry`, which includes latitude and longitude coordinates  \n",
-    "  \n",
-    "**Note**: One location (`GBR_NAME`) may contain multiple values under the `UNIQUE_ID` column. This is because above water structures may not appear connected when looking at the surface, but they are part of the same structure and thus are connected under water."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to S3 parquet directory\n",
-    "We will connect to the S3 bucket containing the GBR features dataset using the `dask_geopandas` library. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UNIQUE_ID</th>\n",
-       "      <th>GBR_NAME</th>\n",
-       "      <th>LOC_NAME_S</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>09347110101</td>\n",
-       "      <td>U/N Sand Bank</td>\n",
-       "      <td>U/N Sand Bank (09-347a)</td>\n",
-       "      <td>POLYGON ((143.08510 -9.26657, 143.08702 -9.267...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>09347110100</td>\n",
-       "      <td>U/N Sand Bank</td>\n",
-       "      <td>U/N Sand Bank (09-347)</td>\n",
-       "      <td>POLYGON ((143.05491 -9.29340, 143.05465 -9.294...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>09355110126</td>\n",
-       "      <td>U/N Sand Bank</td>\n",
-       "      <td>U/N Sand Bank (09-355z)</td>\n",
-       "      <td>POLYGON ((143.10563 -9.39080, 143.10543 -9.390...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>09355110127</td>\n",
-       "      <td>U/N Sand Bank</td>\n",
-       "      <td>U/N Sand Bank (09-355a0)</td>\n",
-       "      <td>POLYGON ((143.09853 -9.39008, 143.09812 -9.390...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>09355110128</td>\n",
-       "      <td>U/N Sand Bank</td>\n",
-       "      <td>U/N Sand Bank (09-355a1)</td>\n",
-       "      <td>POLYGON ((143.09389 -9.39146, 143.09360 -9.391...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       UNIQUE_ID       GBR_NAME                LOC_NAME_S  \\\n",
-       "fid                                                         \n",
-       "0    09347110101  U/N Sand Bank   U/N Sand Bank (09-347a)   \n",
-       "1    09347110100  U/N Sand Bank    U/N Sand Bank (09-347)   \n",
-       "2    09355110126  U/N Sand Bank   U/N Sand Bank (09-355z)   \n",
-       "3    09355110127  U/N Sand Bank  U/N Sand Bank (09-355a0)   \n",
-       "4    09355110128  U/N Sand Bank  U/N Sand Bank (09-355a1)   \n",
-       "\n",
-       "                                              geometry  \n",
-       "fid                                                     \n",
-       "0    POLYGON ((143.08510 -9.26657, 143.08702 -9.267...  \n",
-       "1    POLYGON ((143.05491 -9.29340, 143.05465 -9.294...  \n",
-       "2    POLYGON ((143.10563 -9.39080, 143.10543 -9.390...  \n",
-       "3    POLYGON ((143.09853 -9.39008, 143.09812 -9.390...  \n",
-       "4    POLYGON ((143.09389 -9.39146, 143.09360 -9.391...  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Connecting to the S3 bucket where the GBR data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet('s3://rimrep-data-public/gbrmpa-complete-gbr-features/data.parquet',\n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['UNIQUE_ID', 'GBR_NAME', 'LOC_NAME_S', 'geometry'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) \n",
-    "\n",
-    "# Transforming to pandas dataframe\n",
-    "sites = dask_geo_df.compute()\n",
-    "\n",
-    "# Checking the contents of the dataframe - First five rows\n",
-    "sites.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When exploring the GBR features dataset, you may have noticed other fields besides `geometry` that contain spatial information. For example, `X_COORD` and `Y_COORD` contain the coordinates of the centroid of each feature in the GBR dataset. While, `minx`, `miny`, `maxx`, and `maxy` fields contain the coordinates for the lower left corner and the upper right corner of a feature. This is otherwise known as the extent, which is a rectangle covering the maximum spatial extent of a feature.  \n",
-    "  \n",
-    "We chose to use the `geometry` field in this notebook because it gives us access to fine scale data about a feature (see light blue line in image below). The centroid (`X_COORD` and `Y_COORD` fields) would have only given information about a single point (white dot at the centre of the image). The extent (dark blue rectangle), on the other hand, would have coarsen the resolution of a feature.\n",
-    "\n",
-    "![title](../images/extent_geom.jpeg)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting all features\n",
-    "We will make a map of all features included within the Great Barrier Reef Marine Protected Area in green so they are easily recognised. Note that the GBR features also includes a `Mainland` location, which represents the state of Queensland. We will show this state as light red."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x1ec6ed04750>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting all sites in green\n",
-    "sites.plot(ax = ax, alpha = 0.5, color = 'green')\n",
-    "\n",
-    "#Plotting mainland in red\n",
-    "sites[sites.LOC_NAME_S.str.contains('Mainland')].plot(ax = ax, color = 'red', alpha = 0.5)\n",
-    "\n",
-    "#Adding a basemap of land areas\n",
-    "ax.add_feature(cf.LAND)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Saving GBR features as shapefile\n",
-    "We will need to provide a path to the folder where we want to save our shapefile containing the limits of all features within the GBR Marine Protected Area."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Checking if Outputs folder exists, if not, creates it\n",
-    "os.makedirs('Outputs', exist_ok = True)\n",
-    "\n",
-    "#Save output\n",
-    "sites.to_file('Outputs/GBR_features.shp')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That is the end of this notebook. We have demonstrated how to extract data from the Complete GBR Features dataset, how to plot it and save it locally.  "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "rimrep",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/Extracting_Water_Temperature_GBR_Features.ipynb b/Python_based_scripts/Extracting_Water_Temperature_GBR_Features.ipynb
deleted file mode 100644
index ca82a09..0000000
--- a/Python_based_scripts/Extracting_Water_Temperature_GBR_Features.ipynb
+++ /dev/null
@@ -1,1208 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extracting temperature data using GBR features\n",
-    "**Author**: Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook will demonstrate how to use one or more Great Barrier Reef (GBR) features recognised by the Great Barrier Reef Marine Park Authority (GBRMPA) dataset to extract water temperature data from the AIMS Sea Surface Temperature Monitoring Program available at RIMReP `geoparquet` collection.\n",
-    "\n",
-    "In addition to published `Python` libraries, this notebook uses a set of functions especially created for manipulating RIMReP data collections. These functions are available in the `useful_spatial_functions_data_extraction.py` script, which **must** exist in the same directory where this notebook is located."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading custom library\n",
-    "import useful_spatial_functions_data_extraction as usf\n",
-    "# Data manipulation - regular expression (regex)\n",
-    "import re\n",
-    "# Load and manipulate geospatial data\n",
-    "import dask_geopandas\n",
-    "import geopandas as gpd\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Mapping\n",
-    "import cartopy.crs as ccrs\n",
-    "# Interact with operating system\n",
-    "import os"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading GBR features of interest\n",
-    "In this section, we will use the functions included in the [useful_spatial_functions_data_extraction](https://github.com/aodn/rimrep-examples/blob/main/Python_based_scripts/useful_spatial_functions_data_extraction.py) script to load GBR features of interest. We will then use the boundaries of the feature of interest to identify the names of the sites within these limits.\n",
-    "\n",
-    "We will use the `gbr_features` function to access the boundaries for our sites of interest. If no parameters are given to this function, it will load all features recognised by GBRMPA. You could also provide the name of the site using the `site_name` parameter or the GBRMPA unique ID using the `site_id` parameter. You can also use both parameters at the same time in case you have the names for some sites and the IDs for others.\n",
-    "\n",
-    "As an example, we will use **U/N Reef** or unnamed reefs. It should be noted that **U/N Reef** refers to multiple unnamed reefs, and each reef can be identified by its `UNIQUE_ID`. We chose to use **U/N Reef** for this example simply because it will return multiple reefs within the GBR and thus will overlap with the most number of AIMS monitoring sites."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Subsetting GBR features by u/n reef\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UNIQUE_ID</th>\n",
-       "      <th>GBR_NAME</th>\n",
-       "      <th>LOC_NAME_S</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>459</th>\n",
-       "      <td>09361104104</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-361d)</td>\n",
-       "      <td>POLYGON ((143.22825 -9.26410, 143.22802 -9.264...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>460</th>\n",
-       "      <td>09361104100</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-361)</td>\n",
-       "      <td>POLYGON ((143.23613 -9.26759, 143.23502 -9.267...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>461</th>\n",
-       "      <td>09361104102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-361b)</td>\n",
-       "      <td>POLYGON ((143.23517 -9.26074, 143.23577 -9.261...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>462</th>\n",
-       "      <td>09361104103</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-361c)</td>\n",
-       "      <td>POLYGON ((143.22800 -9.26056, 143.22740 -9.260...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>463</th>\n",
-       "      <td>09361104105</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-361e)</td>\n",
-       "      <td>POLYGON ((143.21933 -9.25797, 143.21873 -9.257...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9594</th>\n",
-       "      <td>10521109102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (10-521b)</td>\n",
-       "      <td>POLYGON ((142.40691 -10.70788, 142.40684 -10.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9596</th>\n",
-       "      <td>10521109101</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (10-521a)</td>\n",
-       "      <td>POLYGON ((142.40495 -10.72571, 142.40476 -10.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9597</th>\n",
-       "      <td>10523109101</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (10-523a)</td>\n",
-       "      <td>POLYGON ((142.41640 -10.72591, 142.41605 -10.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9598</th>\n",
-       "      <td>10526109102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (10-526b)</td>\n",
-       "      <td>POLYGON ((142.40688 -10.74739, 142.40677 -10.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9599</th>\n",
-       "      <td>10458104103</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (10-458c)</td>\n",
-       "      <td>POLYGON ((142.29774 -10.54530, 142.29781 -10.5...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4101 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        UNIQUE_ID  GBR_NAME          LOC_NAME_S  \\\n",
-       "fid                                               \n",
-       "459   09361104104  U/N Reef  U/N Reef (09-361d)   \n",
-       "460   09361104100  U/N Reef   U/N Reef (09-361)   \n",
-       "461   09361104102  U/N Reef  U/N Reef (09-361b)   \n",
-       "462   09361104103  U/N Reef  U/N Reef (09-361c)   \n",
-       "463   09361104105  U/N Reef  U/N Reef (09-361e)   \n",
-       "...           ...       ...                 ...   \n",
-       "9594  10521109102  U/N Reef  U/N Reef (10-521b)   \n",
-       "9596  10521109101  U/N Reef  U/N Reef (10-521a)   \n",
-       "9597  10523109101  U/N Reef  U/N Reef (10-523a)   \n",
-       "9598  10526109102  U/N Reef  U/N Reef (10-526b)   \n",
-       "9599  10458104103  U/N Reef  U/N Reef (10-458c)   \n",
-       "\n",
-       "                                               geometry  \n",
-       "fid                                                      \n",
-       "459   POLYGON ((143.22825 -9.26410, 143.22802 -9.264...  \n",
-       "460   POLYGON ((143.23613 -9.26759, 143.23502 -9.267...  \n",
-       "461   POLYGON ((143.23517 -9.26074, 143.23577 -9.261...  \n",
-       "462   POLYGON ((143.22800 -9.26056, 143.22740 -9.260...  \n",
-       "463   POLYGON ((143.21933 -9.25797, 143.21873 -9.257...  \n",
-       "...                                                 ...  \n",
-       "9594  POLYGON ((142.40691 -10.70788, 142.40684 -10.7...  \n",
-       "9596  POLYGON ((142.40495 -10.72571, 142.40476 -10.7...  \n",
-       "9597  POLYGON ((142.41640 -10.72591, 142.41605 -10.7...  \n",
-       "9598  POLYGON ((142.40688 -10.74739, 142.40677 -10.7...  \n",
-       "9599  POLYGON ((142.29774 -10.54530, 142.29781 -10.5...  \n",
-       "\n",
-       "[4101 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Getting features using custom library\n",
-    "un_reefs = usf.gbr_features(site_name = 'u/n reef')\n",
-    "\n",
-    "#Checking results\n",
-    "un_reefs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now extract data only for the monitoring sites located within the GBR feature of our interest. To reduce time of computation, we will apply an initial filter to the dataset with the maximum extent of our polygon of interest (`un_reef`). Once the AIMS data is pre-filtered and loaded into memory, we will then apply the polygon of interest to extract only the data for the sites within the polygon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('lon', '>=', 141.12840666709278),\n",
-       " ('lat', '>=', -24.43006550562101),\n",
-       " ('lon', '<=', 153.16768375833198),\n",
-       " ('lat', '<=', -8.965183578334386),\n",
-       " ('qc_flag', '=', 1)]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We get a bounding box around the sites of interest to apply an initial filter to the AIMS dataset\n",
-    "minx = un_reefs.bounds.minx.min()\n",
-    "miny = un_reefs.bounds.miny.min()\n",
-    "maxx = un_reefs.bounds.maxx.max()\n",
-    "maxy = un_reefs.bounds.maxy.max()\n",
-    "\n",
-    "# Creating maximum and minimum coordinates for bounding box around our area of interest\n",
-    "filter = [('lon', '>=', minx),\n",
-    "          ('lat', '>=', miny),\n",
-    "          ('lon', '<=', maxx),\n",
-    "          ('lat', '<=', maxy),\n",
-    "          #We will add another condition to our filter. We will only keep data with quality control flag 1\n",
-    "          ('qc_flag', '=', 1)]\n",
-    "\n",
-    "#Checking filter\n",
-    "filter"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to RIMReP data collection\n",
-    "Connecting to the AIMS Sea Surface Temperature Monitoring Program dataset, and loading unique sampling site for the AIMS program. We will only load three columns that are relevant to this exercise: `site` (name of deployment site), `subsite` (name of deployment site and code identifying deployment location) and `geometry` (latitude and longitude coordinates).    \n",
-    "  \n",
-    "For more information about the AIMS Sea Surface Temperature Monitoring Program dataset, visit the [AIMS website](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e). You can also refer to this [Jupyter notebook](https://github.com/aodn/rimrep-examples/blob/main/Python_based_scripts/Extracting_Water_Temperature_at_Site.ipynb).  \n",
-    "  \n",
-    "Connecting to S3 database and loading results can take a few minutes as the database contains over 150 million records. To speed up this process, we will apply an initial filter to the original dataset with the maximum extent of our polygon of interest (`un_reef`). Once the AIMS data is pre-filtered and loaded into memory, we will then apply the polygon of interest to extract only the data for the sites within the polygon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 408 unique sites in the AIMS dataset\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>149519190</th>\n",
-       "      <td>Night Island</td>\n",
-       "      <td>NIGFL1</td>\n",
-       "      <td>POINT (143.58030 -13.16910)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149501081</th>\n",
-       "      <td>Night Island</td>\n",
-       "      <td>NIGSL1</td>\n",
-       "      <td>POINT (143.58030 -13.16900)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53983161</th>\n",
-       "      <td>Curd Reef</td>\n",
-       "      <td>CURDFL1</td>\n",
-       "      <td>POINT (143.51120 -12.58530)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120049696</th>\n",
-       "      <td>Curd Reef</td>\n",
-       "      <td>CURDSL1</td>\n",
-       "      <td>POINT (143.51120 -12.58470)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146426455</th>\n",
-       "      <td>Weipa(lorim point)</td>\n",
-       "      <td>WEISL1</td>\n",
-       "      <td>POINT (141.86650 -12.67090)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         site  subsite                     geometry\n",
-       "fid                                                                \n",
-       "149519190        Night Island   NIGFL1  POINT (143.58030 -13.16910)\n",
-       "149501081        Night Island   NIGSL1  POINT (143.58030 -13.16900)\n",
-       "53983161            Curd Reef  CURDFL1  POINT (143.51120 -12.58530)\n",
-       "120049696           Curd Reef  CURDSL1  POINT (143.51120 -12.58470)\n",
-       "146426455  Weipa(lorim point)   WEISL1  POINT (141.86650 -12.67090)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter], \n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['site', 'subsite', 'geometry'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) \n",
-    "\n",
-    "# Keeping unique values\n",
-    "dask_geo_df = dask_geo_df.drop_duplicates()\n",
-    "\n",
-    "# Transforming to pandas dataframe\n",
-    "sites = dask_geo_df.compute()\n",
-    "\n",
-    "print(f'There are {len(sites)} unique sites in the AIMS dataset')\n",
-    "# Checking the contents of the dataframe - First five rows\n",
-    "sites.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying sites of interest\n",
-    "Depending on the amount of polygons used to identify the sites of interest, this may take a couple of minutes. Before we can identify the sites of interest, we must ensure both `geopandas` data frames are in the same coordinate reference system (CRS).\n",
-    "\n",
-    "We will plot the original AIMS monitoring sites (black), the polygon of interest (red) and the sites within polygons (blue) to check that sites have been correctly identified.\n",
-    "\n",
-    "Note that unless the AIMS site is within a polygon, it will not to be identified in our final list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 35 unique sites within the area of interest.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>index_right</th>\n",
-       "      <th>UNIQUE_ID</th>\n",
-       "      <th>GBR_NAME</th>\n",
-       "      <th>LOC_NAME_S</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>24885915</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "      <td>934</td>\n",
-       "      <td>09409104102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-409b)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>117798096</th>\n",
-       "      <td>Yorke</td>\n",
-       "      <td>YORKESL1</td>\n",
-       "      <td>POINT (143.39840 -9.76050)</td>\n",
-       "      <td>934</td>\n",
-       "      <td>09409104102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-409b)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49498008</th>\n",
-       "      <td>Yorke</td>\n",
-       "      <td>YORKEFL1</td>\n",
-       "      <td>POINT (143.39870 -9.76040)</td>\n",
-       "      <td>934</td>\n",
-       "      <td>09409104102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-409b)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19125316</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL6</td>\n",
-       "      <td>POINT (143.40130 -9.76080)</td>\n",
-       "      <td>934</td>\n",
-       "      <td>09409104102</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-409b)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48698538</th>\n",
-       "      <td>Mabuiag Island</td>\n",
-       "      <td>MABUIAGFL1</td>\n",
-       "      <td>POINT (142.20460 -9.95060)</td>\n",
-       "      <td>2442</td>\n",
-       "      <td>09255104104</td>\n",
-       "      <td>U/N Reef</td>\n",
-       "      <td>U/N Reef (09-255d)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     site     subsite                    geometry  \\\n",
-       "fid                                                                 \n",
-       "24885915     Yorke Island     YORKSL3  POINT (143.39850 -9.75790)   \n",
-       "117798096           Yorke    YORKESL1  POINT (143.39840 -9.76050)   \n",
-       "49498008            Yorke    YORKEFL1  POINT (143.39870 -9.76040)   \n",
-       "19125316     Yorke Island     YORKSL6  POINT (143.40130 -9.76080)   \n",
-       "48698538   Mabuiag Island  MABUIAGFL1  POINT (142.20460 -9.95060)   \n",
-       "\n",
-       "           index_right    UNIQUE_ID  GBR_NAME          LOC_NAME_S  \n",
-       "fid                                                                \n",
-       "24885915           934  09409104102  U/N Reef  U/N Reef (09-409b)  \n",
-       "117798096          934  09409104102  U/N Reef  U/N Reef (09-409b)  \n",
-       "49498008           934  09409104102  U/N Reef  U/N Reef (09-409b)  \n",
-       "19125316           934  09409104102  U/N Reef  U/N Reef (09-409b)  \n",
-       "48698538          2442  09255104104  U/N Reef  U/N Reef (09-255d)  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Ensuring CRS are the same in both dataframes\n",
-    "if sites.crs != un_reefs.crs:\n",
-    "    sites = sites.set_crs(un_reefs.crs)\n",
-    "\n",
-    "# Clippping original_data with area_polygons\n",
-    "sites_temp = gpd.sjoin(sites, un_reefs, how = 'inner')\n",
-    "\n",
-    "print(f'There are {len(sites_temp)} unique sites within the area of interest.')\n",
-    "#Checking results - First five rows\n",
-    "sites_temp.head()\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting all AIMS sites and sites of interest in a single map "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<GeoAxes: >"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting all AIMS monitoring sites in black\n",
-    "sites.plot(ax = ax, alpha = 0.3, color = 'black', markersize = 10)\n",
-    "#Plotting GBR polygons matching our criteria in pink (U/N reefs)\n",
-    "un_reefs.plot(ax = ax, color = 'red')\n",
-    "#Plotting AIMS monitoring sites within our area of interest in blue\n",
-    "sites_temp.plot(ax = ax, alpha = 0.5, color = 'blue', markersize = 15)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extracting temperature for monitoring site within area of interest\n",
-    "We will now extract data only for the monitoring sites located within the GBR feature of our interest. We will apply the initial filter that matches our polygon of interest (`un_reef`). Once the AIMS data is pre-filtered and loaded into memory, we will then apply the polygon of interest to extract only the data for the sites within the polygon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter], \n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['site', 'subsite', 'time', 'qc_val', 'geometry'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the `geopandas` dataframe with the sites overlapping our polygon of interest, we will get a list of unique sites and apply this as a second filter. This may take a couple of minutes to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>time</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>24885915</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-05-04 13:54:31+00:00</td>\n",
-       "      <td>28.9556</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24885914</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-05-04 13:44:31+00:00</td>\n",
-       "      <td>28.9356</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24858725</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-04-23 14:54:31+00:00</td>\n",
-       "      <td>29.3067</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24623645</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-01-24 18:24:31+00:00</td>\n",
-       "      <td>28.6748</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24623644</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-01-24 18:14:31+00:00</td>\n",
-       "      <td>28.6748</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  site  subsite                      time   qc_val  \\\n",
-       "fid                                                                  \n",
-       "24885915  Yorke Island  YORKSL3 2010-05-04 13:54:31+00:00  28.9556   \n",
-       "24885914  Yorke Island  YORKSL3 2010-05-04 13:44:31+00:00  28.9356   \n",
-       "24858725  Yorke Island  YORKSL3 2010-04-23 14:54:31+00:00  29.3067   \n",
-       "24623645  Yorke Island  YORKSL3 2010-01-24 18:24:31+00:00  28.6748   \n",
-       "24623644  Yorke Island  YORKSL3 2010-01-24 18:14:31+00:00  28.6748   \n",
-       "\n",
-       "                            geometry  \n",
-       "fid                                   \n",
-       "24885915  POINT (143.39850 -9.75790)  \n",
-       "24885914  POINT (143.39850 -9.75790)  \n",
-       "24858725  POINT (143.39850 -9.75790)  \n",
-       "24623645  POINT (143.39850 -9.75790)  \n",
-       "24623644  POINT (143.39850 -9.75790)  "
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Getting list of subsites within our area of interest\n",
-    "sites_int = sites_temp.subsite.unique().tolist()\n",
-    "\n",
-    "# Keeping only data from sites within our area of interest\n",
-    "dask_geo_df = dask_geo_df[dask_geo_df.subsite.isin(sites_int)]\n",
-    "\n",
-    "# Transforming to pandas dataframe\n",
-    "temp_data = dask_geo_df.compute()\n",
-    "\n",
-    "# Checking results - First five rows\n",
-    "temp_data.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating `deployment_location` column\n",
-    "We will use the `subsite` column to create the deployment categories as explained in the section above. If no condition is met, then we will label the row as other.  \n",
-    "  \n",
-    "Due to the amount of data contained in this data frame, this may take a few minutes to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>time</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>deployment_location</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>24885915</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-05-04 13:54:31+00:00</td>\n",
-       "      <td>28.9556</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24885914</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-05-04 13:44:31+00:00</td>\n",
-       "      <td>28.9356</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24858725</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-04-23 14:54:31+00:00</td>\n",
-       "      <td>29.3067</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24623645</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-01-24 18:24:31+00:00</td>\n",
-       "      <td>28.6748</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24623644</th>\n",
-       "      <td>Yorke Island</td>\n",
-       "      <td>YORKSL3</td>\n",
-       "      <td>2010-01-24 18:14:31+00:00</td>\n",
-       "      <td>28.6748</td>\n",
-       "      <td>POINT (143.39850 -9.75790)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37640512</th>\n",
-       "      <td>20-104 Reef</td>\n",
-       "      <td>20104FL1</td>\n",
-       "      <td>2015-01-31 17:20:00+00:00</td>\n",
-       "      <td>28.8240</td>\n",
-       "      <td>POINT (149.69330 -20.02860)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37640513</th>\n",
-       "      <td>20-104 Reef</td>\n",
-       "      <td>20104FL1</td>\n",
-       "      <td>2015-01-31 17:30:00+00:00</td>\n",
-       "      <td>28.8240</td>\n",
-       "      <td>POINT (149.69330 -20.02860)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37640511</th>\n",
-       "      <td>20-104 Reef</td>\n",
-       "      <td>20104FL1</td>\n",
-       "      <td>2015-01-31 17:10:00+00:00</td>\n",
-       "      <td>28.7940</td>\n",
-       "      <td>POINT (149.69330 -20.02860)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37640514</th>\n",
-       "      <td>20-104 Reef</td>\n",
-       "      <td>20104FL1</td>\n",
-       "      <td>2015-01-31 17:40:00+00:00</td>\n",
-       "      <td>28.8340</td>\n",
-       "      <td>POINT (149.69330 -20.02860)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37640515</th>\n",
-       "      <td>20-104 Reef</td>\n",
-       "      <td>20104FL1</td>\n",
-       "      <td>2015-01-31 17:50:00+00:00</td>\n",
-       "      <td>28.8540</td>\n",
-       "      <td>POINT (149.69330 -20.02860)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6459881 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  site   subsite                      time   qc_val  \\\n",
-       "fid                                                                   \n",
-       "24885915  Yorke Island   YORKSL3 2010-05-04 13:54:31+00:00  28.9556   \n",
-       "24885914  Yorke Island   YORKSL3 2010-05-04 13:44:31+00:00  28.9356   \n",
-       "24858725  Yorke Island   YORKSL3 2010-04-23 14:54:31+00:00  29.3067   \n",
-       "24623645  Yorke Island   YORKSL3 2010-01-24 18:24:31+00:00  28.6748   \n",
-       "24623644  Yorke Island   YORKSL3 2010-01-24 18:14:31+00:00  28.6748   \n",
-       "...                ...       ...                       ...      ...   \n",
-       "37640512   20-104 Reef  20104FL1 2015-01-31 17:20:00+00:00  28.8240   \n",
-       "37640513   20-104 Reef  20104FL1 2015-01-31 17:30:00+00:00  28.8240   \n",
-       "37640511   20-104 Reef  20104FL1 2015-01-31 17:10:00+00:00  28.7940   \n",
-       "37640514   20-104 Reef  20104FL1 2015-01-31 17:40:00+00:00  28.8340   \n",
-       "37640515   20-104 Reef  20104FL1 2015-01-31 17:50:00+00:00  28.8540   \n",
-       "\n",
-       "                             geometry deployment_location  \n",
-       "fid                                                        \n",
-       "24885915   POINT (143.39850 -9.75790)          reef slope  \n",
-       "24885914   POINT (143.39850 -9.75790)          reef slope  \n",
-       "24858725   POINT (143.39850 -9.75790)          reef slope  \n",
-       "24623645   POINT (143.39850 -9.75790)          reef slope  \n",
-       "24623644   POINT (143.39850 -9.75790)          reef slope  \n",
-       "...                               ...                 ...  \n",
-       "37640512  POINT (149.69330 -20.02860)           reef flat  \n",
-       "37640513  POINT (149.69330 -20.02860)           reef flat  \n",
-       "37640511  POINT (149.69330 -20.02860)           reef flat  \n",
-       "37640514  POINT (149.69330 -20.02860)           reef flat  \n",
-       "37640515  POINT (149.69330 -20.02860)           reef flat  \n",
-       "\n",
-       "[6459881 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating new column with deployment location information\n",
-    "temp_data['deployment_location'] = temp_data.apply(lambda x: 'reef slope' if bool(re.match('.*(SL[0-9]{1,2})', x.subsite)) == True \\\n",
-    "         else ('reef flat' if bool(re.match('.*(FL[0-9]{1,2})', x.subsite)) == True else 'other'), axis = 1)\n",
-    "\n",
-    "# Checking results - First five rows\n",
-    "temp_data"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculating mean monthly temperatures for each deployment location\n",
-    "This calculation will only include the sites within the polygon of interest. We will need to add a couple of extra columns: `year` and `month` before we can calculate monthly means and plot time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Adding year and month columns for data grouping\n",
-    "temp_data['year'] = temp_data.time.dt.year\n",
-    "temp_data['month'] = temp_data.time.dt.month\n",
-    "temp_data['month'] = temp_data.month.apply(lambda x: str(x).zfill(2))\n",
-    "\n",
-    "# Creating dates column by uniting year and month columns\n",
-    "temp_data['dates'] = temp_data.year.astype(str) + '-' + temp_data.month"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can group data by the new `dates` column and calculate the mean temperature for each month. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dates</th>\n",
-       "      <th>deployment_location</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1995-11</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>26.19</td>\n",
-       "      <td>1995.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1995-11</td>\n",
-       "      <td>reef slope</td>\n",
-       "      <td>26.14</td>\n",
-       "      <td>1995.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1995-12</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>26.79</td>\n",
-       "      <td>1995.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1995-12</td>\n",
-       "      <td>reef slope</td>\n",
-       "      <td>26.83</td>\n",
-       "      <td>1995.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1996-01</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>27.26</td>\n",
-       "      <td>1996.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     dates deployment_location  qc_val    year\n",
-       "0  1995-11           reef flat   26.19  1995.0\n",
-       "1  1995-11          reef slope   26.14  1995.0\n",
-       "2  1995-12           reef flat   26.79  1995.0\n",
-       "3  1995-12          reef slope   26.83  1995.0\n",
-       "4  1996-01           reef flat   27.26  1996.0"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Calculating monthly means\n",
-    "mean_temp_dep = temp_data.groupby(['dates', 'deployment_location']).mean('qc_val').round(2).reset_index()\n",
-    "\n",
-    "# Checking results - First five rows\n",
-    "mean_temp_dep.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x900 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# We initialise the figure and axes. We also define the size of the figure.\n",
-    "# Axes refer to the subplots in the figure (2 rows and 1 column). They will share the x axis\n",
-    "fig, ax = plt.subplots(2, 1, figsize = (15, 9), sharex = True)\n",
-    "\n",
-    "# We group our data by site, colours, and deployment location\n",
-    "groups = mean_temp_dep.groupby('deployment_location')\n",
-    "\n",
-    "# Now we use our groups to plot the data\n",
-    "# Element here has the categories of the groupby object (deployment location)\n",
-    "# Group has the subset dataframes for each category\n",
-    "for element, group in groups:\n",
-    "    # If the deployment location is reef flat, we plot the data in the first subplot\n",
-    "    if 'flat' in element:\n",
-    "         # Plotting dates in x axis and mean temperature in y axis\n",
-    "        ax[0].plot(group['dates'], group['qc_val'], marker = 'o', ms = 10, \n",
-    "             # We use the site name as the label for each line and the color will change based on the colour column\n",
-    "             label = f'{element}', color = 'red')\n",
-    "        # Title for subplot\n",
-    "        ax[0].set_title(f'{element}')\n",
-    "    # In all other cases, we plot the data in the second subplot\n",
-    "    else:\n",
-    "        #As above\n",
-    "        ax[1].plot(group['dates'], group['qc_val'], marker = 'o', ms = 10, \n",
-    "             label = f'{element}', color = 'blue')\n",
-    "        ax[1].set_title(f'{element}')\n",
-    "\n",
-    "# Getting all labels from x axis\n",
-    "axis_labs = ax[1].xaxis.get_ticklabels()\n",
-    "# Removing all labels from x axis\n",
-    "[l.set_visible(False) for l in axis_labs]\n",
-    "# Making every 6th label visible and rotating it 45 degrees\n",
-    "for label in axis_labs[::6]:\n",
-    "    label.set_visible(True)\n",
-    "    label.set_rotation(45)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you would like to save the figure, you can use the `savefig` method, as follows: `plt.savefig(file_name)`. Here `file_name` can contain a path to a directory and a file name with the extension of the file type you would like to save the figure as. For example, `plt.savefig('figures/temperature_timeseries.png')` will save the figure in a directory called `figures` and the file name will be `temperature_timeseries.png`.  "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Saving data summaries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ensuring output folder exists. If not, create folder\n",
-    "os.makedirs('Outputs', exist_ok = True)\n",
-    "\n",
-    "# Saving summary table to csv - Not saving row indices\n",
-    "mean_temp_dep.to_csv('Outputs/mean_temp_dep.csv', index = False)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "rimrep",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/Extracting_Water_Temperature_at_Site.ipynb b/Python_based_scripts/Extracting_Water_Temperature_at_Site.ipynb
deleted file mode 100644
index 9abd3a6..0000000
--- a/Python_based_scripts/Extracting_Water_Temperature_at_Site.ipynb
+++ /dev/null
@@ -1,1188 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extracting temperature at specific reefs\n",
-    "**Authors**: Leo Ghignone, Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook demonstrates direct access to the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset in the RIMReP `geoparquet` collection. This dataset includes sea temperature data from 1980 for tropical and subtropical coral reefs around Australia, and it includes sites within the Great Barrier Reef.   \n",
-    "  \n",
-    "In this example, we will extract the coordinates for all sites sampled in this monitoring program and then extract the temperature data for a specific site of interest. This means that we will not need to know the exact location of the site, but we will need to know the name of the site of our interest. \n",
-    "  \n",
-    "**Note:** The `geoparquet` collection is a compilation of datasets that have been converted to the Apache `parquet` format. This format is designed for efficient storage and retrieval of tabular data. The `geoparquet` collection is stored on the RIMReP S3 bucket and is accessible to all users."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing S3 bucket\n",
-    "from pyarrow import parquet as pq\n",
-    "# Manipulating dataframes\n",
-    "import pandas as pd\n",
-    "# Checking patterns in strings using regular expressions (regex)\n",
-    "import re\n",
-    "# Load and manipulate geospatial data\n",
-    "import dask_geopandas\n",
-    "import geopandas as gpd\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "#Color maps\n",
-    "from matplotlib import colormaps as cmaps\n",
-    "# Mapping\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cf"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting metadata\n",
-    "The AIMS Temperature dataset contains over 150 million records. We will use its metadata to check the structure of the dataset and to determine the best way to access the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.FileMetaData object at 0x00000262B4EB51C0>\n",
-       "  created_by: parquet-cpp-arrow version 10.0.1\n",
-       "  num_columns: 23\n",
-       "  num_rows: 150663521\n",
-       "  num_row_groups: 50\n",
-       "  format_version: 2.6\n",
-       "  serialized_size: 155051"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Connecting to metadata file\n",
-    "md = pq.read_metadata(f\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/_metadata\")\n",
-    "#Checking contents\n",
-    "md"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metadata is telling us that the AIMS temperature dataset contains 23 columns and over 150 million rows, and it is made up of 50 different files.  \n",
-    "  \n",
-    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.ParquetSchema object at 0x000002B230BE5C40>\n",
-       "required group field_id=-1 schema {\n",
-       "  optional int64 field_id=-1 fid;\n",
-       "  optional int64 field_id=-1 deployment_id;\n",
-       "  optional binary field_id=-1 site (String);\n",
-       "  optional int64 field_id=-1 site_id;\n",
-       "  optional binary field_id=-1 subsite (String);\n",
-       "  optional int64 field_id=-1 subsite_id;\n",
-       "  optional int64 field_id=-1 from_date (Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional int64 field_id=-1 thru_date (Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional double field_id=-1 depth;\n",
-       "  optional binary field_id=-1 parameter (String);\n",
-       "  optional binary field_id=-1 instrument_type (String);\n",
-       "  optional binary field_id=-1 serial_num (String);\n",
-       "  optional double field_id=-1 lat;\n",
-       "  optional double field_id=-1 lon;\n",
-       "  optional binary field_id=-1 gbrmpa_reef_id (String);\n",
-       "  optional binary field_id=-1 metadata_uuid (String);\n",
-       "  optional double field_id=-1 sites_with_climatology_available;\n",
-       "  optional int64 field_id=-1 time (Timestamp(isAdjustedToUTC=true, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional double field_id=-1 cal_val;\n",
-       "  optional double field_id=-1 qc_val;\n",
-       "  optional double field_id=-1 qc_flag;\n",
-       "  optional binary field_id=-1 geometry;\n",
-       "  optional int64 field_id=-1 hilbert_distance (Int(bitWidth=64, isSigned=false));\n",
-       "}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `schema` tells us that the dataset contains 23 columns, which matches the information we got from the metadata. It also tells us that the data types are either `string` or `double`.  \n",
-    "  \n",
-    "We can also obtain more details about a specific column, by calling the `column` method and passing the name of the column as an argument as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ParquetColumnSchema>\n",
-       "  name: qc_val\n",
-       "  path: qc_val\n",
-       "  max_definition_level: 1\n",
-       "  max_repetition_level: 0\n",
-       "  physical_type: DOUBLE\n",
-       "  logical_type: None\n",
-       "  converted_type (legacy): NONE"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema.column(19)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we refer to the original site for the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e), we can see that the `qc_val` column refers to the quality controlled temperature obtained from the loggers deployed as part of this program."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to S3 parquet directory\n",
-    "We will connect to the S3 bucket containing the AIMS Temperature dataset using the `dask_geopandas` library. We will only keep sites that have been deployed in the Great Barrier Reef Marine Protected Area (GBRMPA), so we will provide a `filter` that contains the minimum and maximum coordinates (i.e., `lat` and `lon`) of a bounding box around the Great Barrier Reef Marine Protected Area.\n",
-    "\n",
-    "As we saw above, this dataset includes 23 columns, but we will only select a few that contain information that is interesting to us:\n",
-    "- `site`: name of site where temperature logger was deployed,\n",
-    "- `subsite`: contains a shorten version of the site name and an indication of where loggers where deployed (see below for more details),\n",
-    "- `geometry`: includes latitude and longitude coordinates in [well-known binary (WKB)](https://loc.gov/preservation/digital/formats/fdd/fdd000549.shtml) format  \n",
-    "  \n",
-    "Based on the `subsite` information, we will create a new column that we will call `deployment_location`. If the `subsite` string in a row includes `SL` it indicates that the temperature logger was deployed on a reef slope (depths of 3 m or more). Thus, our new column will be labelled reef slope. If a row contains `FL` in the `subsite` column, this means the temperature logger was deployed on a reef flat (areas less than 3 m deep). Therefore, we will classify this observation as reef flat in our new column.\n",
-    "\n",
-    "Note that when we use the `dask_geopandas` library, the `geometry` field is automatically recognised as coordinates in decimal degrees and used as the `geometry` column in a `geopandas` `GeoDataFrame` object. This means we can use it to plot the data in a map easily.\n",
-    "\n",
-    "The following code chunk may take a few minutes to run, as it is loading over 150 million records."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>149842221</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>KURSL1</td>\n",
-       "      <td>POINT (140.83020 -17.49350)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149696096</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>KURFL1</td>\n",
-       "      <td>POINT (140.83020 -17.49350)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149519190</th>\n",
-       "      <td>Night Island</td>\n",
-       "      <td>NIGFL1</td>\n",
-       "      <td>POINT (143.58030 -13.16910)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149501081</th>\n",
-       "      <td>Night Island</td>\n",
-       "      <td>NIGSL1</td>\n",
-       "      <td>POINT (143.58030 -13.16900)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53983161</th>\n",
-       "      <td>Curd Reef</td>\n",
-       "      <td>CURDFL1</td>\n",
-       "      <td>POINT (143.51120 -12.58530)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   site  subsite                     geometry\n",
-       "fid                                                          \n",
-       "149842221       Karumba   KURSL1  POINT (140.83020 -17.49350)\n",
-       "149696096       Karumba   KURFL1  POINT (140.83020 -17.49350)\n",
-       "149519190  Night Island   NIGFL1  POINT (143.58030 -13.16910)\n",
-       "149501081  Night Island   NIGSL1  POINT (143.58030 -13.16900)\n",
-       "53983161      Curd Reef  CURDFL1  POINT (143.51120 -12.58530)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating maximum and minimum coordinates for bounding box around the GBRMPA\n",
-    "filter = [('lon', '>=', 140.5),\n",
-    "          ('lat', '>=', -28.5),\n",
-    "          ('lon', '<=', 156.0),\n",
-    "          ('lat', '<=', -6.5)]\n",
-    "\n",
-    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter], \n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['site', 'subsite', 'geometry'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) \n",
-    "\n",
-    "# Keeping unique values\n",
-    "dask_geo_df = dask_geo_df.drop_duplicates()\n",
-    "\n",
-    "# Transforming to pandas dataframe\n",
-    "sites = dask_geo_df.compute()\n",
-    "\n",
-    "# Checking the contents of the dataframe - First five rows\n",
-    "sites.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating `deployment_location` column\n",
-    "We will use the `subsite` column to create the deployment categories as explained in the section above. If no condition is met, then we will label the row as other."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>deployment_location</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>149842221</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>KURSL1</td>\n",
-       "      <td>POINT (140.83020 -17.49350)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149696096</th>\n",
-       "      <td>Karumba</td>\n",
-       "      <td>KURFL1</td>\n",
-       "      <td>POINT (140.83020 -17.49350)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149519190</th>\n",
-       "      <td>Night Island</td>\n",
-       "      <td>NIGFL1</td>\n",
-       "      <td>POINT (143.58030 -13.16910)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149501081</th>\n",
-       "      <td>Night Island</td>\n",
-       "      <td>NIGSL1</td>\n",
-       "      <td>POINT (143.58030 -13.16900)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53983161</th>\n",
-       "      <td>Curd Reef</td>\n",
-       "      <td>CURDFL1</td>\n",
-       "      <td>POINT (143.51120 -12.58530)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6613340</th>\n",
-       "      <td>Nelly Bay</td>\n",
-       "      <td>NELFL1</td>\n",
-       "      <td>POINT (146.84750 -19.17150)</td>\n",
-       "      <td>reef flat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11189188</th>\n",
-       "      <td>Middle Reef</td>\n",
-       "      <td>MIRSL2</td>\n",
-       "      <td>POINT (146.81020 -19.19450)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146728532</th>\n",
-       "      <td>Middle Reef</td>\n",
-       "      <td>MIRSL1</td>\n",
-       "      <td>POINT (146.82090 -19.20110)</td>\n",
-       "      <td>reef slope</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37439802</th>\n",
-       "      <td>AIMS Wharf WS</td>\n",
-       "      <td>AIMSWHARFP1</td>\n",
-       "      <td>POINT (147.05880 -19.27690)</td>\n",
-       "      <td>other</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151416201</th>\n",
-       "      <td>Cook Island</td>\n",
-       "      <td>COOK</td>\n",
-       "      <td>POINT (153.57850 -28.19310)</td>\n",
-       "      <td>other</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>417 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    site      subsite                     geometry  \\\n",
-       "fid                                                                  \n",
-       "149842221        Karumba       KURSL1  POINT (140.83020 -17.49350)   \n",
-       "149696096        Karumba       KURFL1  POINT (140.83020 -17.49350)   \n",
-       "149519190   Night Island       NIGFL1  POINT (143.58030 -13.16910)   \n",
-       "149501081   Night Island       NIGSL1  POINT (143.58030 -13.16900)   \n",
-       "53983161       Curd Reef      CURDFL1  POINT (143.51120 -12.58530)   \n",
-       "...                  ...          ...                          ...   \n",
-       "6613340        Nelly Bay       NELFL1  POINT (146.84750 -19.17150)   \n",
-       "11189188     Middle Reef       MIRSL2  POINT (146.81020 -19.19450)   \n",
-       "146728532    Middle Reef       MIRSL1  POINT (146.82090 -19.20110)   \n",
-       "37439802   AIMS Wharf WS  AIMSWHARFP1  POINT (147.05880 -19.27690)   \n",
-       "151416201    Cook Island         COOK  POINT (153.57850 -28.19310)   \n",
-       "\n",
-       "          deployment_location  \n",
-       "fid                            \n",
-       "149842221          reef slope  \n",
-       "149696096           reef flat  \n",
-       "149519190           reef flat  \n",
-       "149501081          reef slope  \n",
-       "53983161            reef flat  \n",
-       "...                       ...  \n",
-       "6613340             reef flat  \n",
-       "11189188           reef slope  \n",
-       "146728532          reef slope  \n",
-       "37439802                other  \n",
-       "151416201               other  \n",
-       "\n",
-       "[417 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating new column with deployment location information\n",
-    "sites['deployment_location'] = sites.apply(lambda x: 'reef slope' if bool(re.match('.*(SL[0-9]{1,2})', x.subsite)) == True \\\n",
-    "         else ('reef flat' if bool(re.match('.*(FL[0-9]{1,2})', x.subsite)) == True else 'other'), axis = 1)\n",
-    "\n",
-    "#Checking results\n",
-    "sites"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mapping sites\n",
-    "From the summary table we created in the previous step, we will make a map with the location of all deployment sites within the GBRMPA bounding box. We will also highlight the location of two sites: **Hayman Island** and **Heron Island** in red. We will use these two sites as examples on how to extract data from sites of interest. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x262bdafa210>"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting the data using a scatter plot\n",
-    "sites.plot(ax = ax, alpha = 0.5, markersize = 10)\n",
-    "\n",
-    "#Plotting sites of interest in red\n",
-    "sites[(sites.site == 'Heron Island') | (sites.site == 'Hayman Island')].plot(ax = ax, color = 'red',\n",
-    "                                                                    alpha = 0.5, markersize = 15)\n",
-    "\n",
-    "#Adding a basemap of land areas\n",
-    "ax.add_feature(cf.LAND)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting data for sites of interest\n",
-    "We can use the information in our `sites` variable to extract data for a site of interest. All we need to know is the name of the site, as shown below.\n",
-    "\n",
-    "Here, we will use data collected at **Hayman Island** and **Heron Island** as an example. We will calculate monthly means at each site, so we can plot time series that will show us how temperature has changed over time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First, we get the coordinates for the sites of interest\n",
-    "sites_coords = sites[(sites.site == 'Heron Island') | (sites.site == 'Hayman Island')]\n",
-    "\n",
-    "# We then get a bounding box around the sites of interest to apply an initial filter to the AIMS dataset\n",
-    "minx = sites_coords.bounds.minx.min()\n",
-    "miny = sites_coords.bounds.miny.min()\n",
-    "maxx = sites_coords.bounds.maxx.max()\n",
-    "maxy = sites_coords.bounds.maxy.max()\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Applying initial filter to dataset. This will reduce the number of rows that we need to load into memory, and it will make the subsetting the data faster."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creating maximum and minimum coordinates for bounding box around the GBRMPA\n",
-    "filter = [('lon', '>=', minx),\n",
-    "          ('lat', '>=', miny),\n",
-    "          ('lon', '<=', maxx),\n",
-    "          ('lat', '<=', maxy)]\n",
-    "\n",
-    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter], \n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['site', 'geometry', 'qc_val', 'time'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) \n",
-    "\n",
-    "# Transforming to pandas dataframe\n",
-    "sites_temp = dask_geo_df.compute()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we will only keep the observations that match the coordinates of our sites of interest by performing a spatial join using `geopandas`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site_left</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>deployment_location</th>\n",
-       "      <th>index_right</th>\n",
-       "      <th>site_right</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>time</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>135323357</th>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>HERFL5</td>\n",
-       "      <td>POINT (151.91840 -23.44480)</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>135292342</td>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>27.41</td>\n",
-       "      <td>1998-01-05 08:30:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>135323357</th>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>HERFL5</td>\n",
-       "      <td>POINT (151.91840 -23.44480)</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>135292343</td>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>26.94</td>\n",
-       "      <td>1998-01-05 09:00:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>135323357</th>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>HERFL5</td>\n",
-       "      <td>POINT (151.91840 -23.44480)</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>135292344</td>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>26.74</td>\n",
-       "      <td>1998-01-05 09:30:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>135323357</th>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>HERFL5</td>\n",
-       "      <td>POINT (151.91840 -23.44480)</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>135292345</td>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>26.63</td>\n",
-       "      <td>1998-01-05 10:00:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>135323357</th>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>HERFL5</td>\n",
-       "      <td>POINT (151.91840 -23.44480)</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>135292346</td>\n",
-       "      <td>Heron Island</td>\n",
-       "      <td>26.37</td>\n",
-       "      <td>1998-01-05 10:30:00+00:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              site_left subsite                     geometry  \\\n",
-       "fid                                                            \n",
-       "135323357  Heron Island  HERFL5  POINT (151.91840 -23.44480)   \n",
-       "135323357  Heron Island  HERFL5  POINT (151.91840 -23.44480)   \n",
-       "135323357  Heron Island  HERFL5  POINT (151.91840 -23.44480)   \n",
-       "135323357  Heron Island  HERFL5  POINT (151.91840 -23.44480)   \n",
-       "135323357  Heron Island  HERFL5  POINT (151.91840 -23.44480)   \n",
-       "\n",
-       "          deployment_location  index_right    site_right  qc_val  \\\n",
-       "fid                                                                \n",
-       "135323357           reef flat    135292342  Heron Island   27.41   \n",
-       "135323357           reef flat    135292343  Heron Island   26.94   \n",
-       "135323357           reef flat    135292344  Heron Island   26.74   \n",
-       "135323357           reef flat    135292345  Heron Island   26.63   \n",
-       "135323357           reef flat    135292346  Heron Island   26.37   \n",
-       "\n",
-       "                               time  \n",
-       "fid                                  \n",
-       "135323357 1998-01-05 08:30:00+00:00  \n",
-       "135323357 1998-01-05 09:00:00+00:00  \n",
-       "135323357 1998-01-05 09:30:00+00:00  \n",
-       "135323357 1998-01-05 10:00:00+00:00  \n",
-       "135323357 1998-01-05 10:30:00+00:00  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Applying spatial join\n",
-    "sites_temp = gpd.sjoin(sites_coords, sites_temp, how = 'inner')\n",
-    "\n",
-    "# Checking results\n",
-    "sites_temp.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there are two columns with site names. Next, we will check only the sites of interest have been included and that the `site_left` and `site_right` columns contain the same data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Comparing that the site names columns are the same. If the sites do not match, the sum of all values will be larger than 0\n",
-    "sum(sites_temp.site_left != sites_temp.site_right)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can check the unique names match our sites of interest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Heron Island', 'Hayman Island'], dtype=object)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sites_temp.site_right.unique()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data frame does only include observations for the sites of our interest. This means we can remove and the columns labelled as \"right\" from our data frame because they contain the same information as the \"left\" columns.  \n",
-    "  \n",
-    "We will also calculate the mean monthly temperature at each site and deployment location combination."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>deployment_location</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>date_mean</th>\n",
-       "      <th>col</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>05</td>\n",
-       "      <td>25.171466</td>\n",
-       "      <td>1996-05-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>06</td>\n",
-       "      <td>24.397491</td>\n",
-       "      <td>1996-06-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>07</td>\n",
-       "      <td>23.204402</td>\n",
-       "      <td>1996-07-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>08</td>\n",
-       "      <td>22.742272</td>\n",
-       "      <td>1996-08-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>09</td>\n",
-       "      <td>23.614451</td>\n",
-       "      <td>1996-09-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            site deployment_location  year month     qc_val  date_mean  col\n",
-       "0  Hayman Island           reef flat  1996    05  25.171466 1996-05-01    0\n",
-       "1  Hayman Island           reef flat  1996    06  24.397491 1996-06-01    0\n",
-       "2  Hayman Island           reef flat  1996    07  23.204402 1996-07-01    0\n",
-       "3  Hayman Island           reef flat  1996    08  22.742272 1996-08-01    0\n",
-       "4  Hayman Island           reef flat  1996    09  23.614451 1996-09-01    0"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Removing columns that are not needed \n",
-    "sites_temp.drop(columns = ['index_right', 'site_right'], inplace = True)\n",
-    "\n",
-    "# Renaming site column\n",
-    "sites_temp.rename(columns = {'site_left': 'site'}, inplace = True)\n",
-    "\n",
-    "# Extracting the year and month for each observation\n",
-    "sites_temp['year'] = sites_temp.time.dt.year.values\n",
-    "sites_temp['month'] = sites_temp.time.dt.month.values\n",
-    "# Ensuring months are two digits - This wil be important for labelling our plots later\n",
-    "sites_temp['month'] = sites_temp.apply(lambda x: str(x.month).zfill(2), axis = 1)\n",
-    "\n",
-    "# Calculate mean monthly temperatures for each site and deployment location\n",
-    "sites_temp = sites_temp.groupby(['site', 'deployment_location', 'year', 'month']).mean('qc_val').reset_index()\n",
-    "\n",
-    "#Checking results\n",
-    "sites_temp.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting monthly means\n",
-    "Before plotting, we will add a few extra columns that we will use to label and color our plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>deployment_location</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>date_mean</th>\n",
-       "      <th>colours</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>05</td>\n",
-       "      <td>25.171466</td>\n",
-       "      <td>1996-05-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>06</td>\n",
-       "      <td>24.397491</td>\n",
-       "      <td>1996-06-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>07</td>\n",
-       "      <td>23.204402</td>\n",
-       "      <td>1996-07-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>08</td>\n",
-       "      <td>22.742272</td>\n",
-       "      <td>1996-08-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Hayman Island</td>\n",
-       "      <td>reef flat</td>\n",
-       "      <td>1996</td>\n",
-       "      <td>09</td>\n",
-       "      <td>23.614451</td>\n",
-       "      <td>1996-09-01</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            site deployment_location  year month     qc_val  date_mean  \\\n",
-       "0  Hayman Island           reef flat  1996    05  25.171466 1996-05-01   \n",
-       "1  Hayman Island           reef flat  1996    06  24.397491 1996-06-01   \n",
-       "2  Hayman Island           reef flat  1996    07  23.204402 1996-07-01   \n",
-       "3  Hayman Island           reef flat  1996    08  22.742272 1996-08-01   \n",
-       "4  Hayman Island           reef flat  1996    09  23.614451 1996-09-01   \n",
-       "\n",
-       "   colours  \n",
-       "0        0  \n",
-       "1        0  \n",
-       "2        0  \n",
-       "3        0  \n",
-       "4        0  "
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Adding column with dates for which means were calculated\n",
-    "sites_temp['date_mean'] = pd.to_datetime(sites_temp.year.astype('str') + '-' + sites_temp.month)\n",
-    "\n",
-    "# Transforming site column to categorical data\n",
-    "sites_temp['site'] = pd.Categorical(sites_temp.site)\n",
-    "\n",
-    "# Changing site categories to numeric values for plotting - We can then change the colors based on this column\n",
-    "sites_temp['colours'] = sites_temp.site.cat.codes\n",
-    "\n",
-    "#Checking results\n",
-    "sites_temp.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we will create our plot. We will have two subplots based on the `deployment_location` column and the color of the lines and points will vary based on the `site` column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x900 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# We group our data by site, colours, and deployment location\n",
-    "groups = sites_temp.groupby(['site', 'colours', 'deployment_location'])\n",
-    "\n",
-    "# We select a color map for our plots\n",
-    "cmap = cmaps['Set1']\n",
-    "\n",
-    "# We initialise the figure and axes. We also define the size of the figure.\n",
-    "# Axes refer to the subplots in the figure (1 row and 2 columns). They will share the y axis\n",
-    "fig, ax = plt.subplots(1, 2, figsize = (15, 9), sharey = True)\n",
-    "\n",
-    "# Now we use our groups to plot the data\n",
-    "# Element here has the categories of the groupby object (site, colours, and deployment location)\n",
-    "# Group has the subset dataframes for each category\n",
-    "for element, group in groups:\n",
-    "    # If the deployment location is reef flat, we plot the data in the first subplot\n",
-    "    if 'flat' in element[2]:\n",
-    "        # Plotting dates in x axis and mean temperature in y axis\n",
-    "        ax[0].plot(group['date_mean'], group['qc_val'], marker = 'o', ms = 10, \n",
-    "             # We use the site name as the label for each line and the color will change based on the colour column\n",
-    "             label = f'{element[0]}', color = cmap(element[1]))\n",
-    "        # Title for subplot\n",
-    "        ax[0].set_title('Reef Flat')\n",
-    "        # Adding legend\n",
-    "        ax[0].legend()\n",
-    "    # In all other cases, we plot the data in the second subplot\n",
-    "    else:\n",
-    "        #As above\n",
-    "        ax[1].plot(group['date_mean'], group['qc_val'], marker = 'o', ms = 10, \n",
-    "             label = f'{element[0]}', color = cmap(element[1]))\n",
-    "        ax[1].set_title('Reef Slope')\n",
-    "        ax[1].legend()\n",
-    "\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you would like to save the figure, you can use the `savefig` method, as follows: `plt.savefig(file_name)`. Here `file_name` can contain a path to a directory and a file name with the extension of the file type you would like to save the figure as. For example, `plt.savefig('figures/temperature_timeseries.png')` will save the figure in a directory called `figures` and the file name will be `temperature_timeseries.png`.  \n",
-    "  \n",
-    "If you would like to save the data frame, you can use the `to_csv` method, as follows: `sites_temp.to_csv(file_name)`.  \n",
-    "  \n",
-    "That is the end of this notebook. We have demonstrated how to extract data from the AIMS Temperature Logger Monitoring Program dataset and how to plot monthly means for two sites of interest.  "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "rimrep",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/Plotting_ABS_Census_Data_LGA_2021.ipynb b/Python_based_scripts/Plotting_ABS_Census_Data_LGA_2021.ipynb
deleted file mode 100644
index 472e268..0000000
--- a/Python_based_scripts/Plotting_ABS_Census_Data_LGA_2021.ipynb
+++ /dev/null
@@ -1,5191 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extracting temperature at specific reefs\n",
-    "**Author**: Denisse Fierro Arcos  \n",
-    "\n",
-    "This notebook will show how to access the RIMReP `geoparquet` collection for the Regional Data by Local Government Area (LGA) from the Australian Bureau of Statistics (ABS), which contains data for the period between 2011 and 2022. This dataset includes data for the Census of Population and Housing for 2011, 2016 and 2021.\n",
-    "\n",
-    "Although this dataset has information available for all Australian states and territories, we will focus on accessing data for the state of Queensland only. We will create some summary table, as well as figures and maps."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing S3 bucket\n",
-    "from pyarrow import parquet as pq\n",
-    "# Manipulating dataframes\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import copy\n",
-    "# Checking patterns in strings using regular expressions (regex)\n",
-    "import re\n",
-    "# Interacting with operating system\n",
-    "import os\n",
-    "# Load and manipulate geospatial data\n",
-    "import dask_geopandas\n",
-    "import geopandas as gpd\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "#Color maps\n",
-    "from matplotlib import colormaps as cmaps\n",
-    "# Mapping\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cf"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to ABS regional dataset in RIMReP collection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Connecting to metadata file\n",
-    "md = pq.ParquetDataset(f's3://rimrep-data-public/abs-regional-lga-2021/data.parquet')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting the structure of the ABS dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DATAFLOW: string\n",
-       "FREQUENCY: string\n",
-       "TIME_PERIOD: int64\n",
-       "REGIONTYPE: string\n",
-       "REGION_CODE: int64\n",
-       "REGION_NAME: string\n",
-       "ACTIV_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who have need for assistance with core activities' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ACTIV_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who have need for assistance with core activities'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ADFS_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Currently serving in the Australian Defence Force'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ADFS_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Previously served in the Australian Defence Force'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ADFS_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Currently serving in the Australian Defence Force (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ADFS_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Previously served in the Australian Defence Force (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 40-44 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 45-49 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 50-54 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 55-59 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 60-64 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 65-69 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 70-74 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 75-79 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 80-84 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 85 years and over'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 0-4 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 5-9 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 10-14 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 15-19 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 20-24 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 25-29 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 30-34 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGEMIG_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: 35-39 years'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total dairy cattle'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total meat cattle'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total sheep and lambs'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total pigs'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Agricultural production - total gross value ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "AGRIC_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Crops - total gross value ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "AGRIC_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Livestock slaughtered and other disposals - total gross v' + 9\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "AGRIC_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total meat chickens'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total area of broadacre crops (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total area of vegetables (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total area of orchard fruit trees and nut trees - produce' + 23\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "AGRIC_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total area of agricultural commodities holding (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ARRMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Arrived within 5 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ARRMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Arrived 5-10 years ago (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ARRMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Arrived over 10 years ago (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ARRMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Arrival not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BD_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered births'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BD_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total fertility rate per female'\n",
-       "  UNIT: 'Rate'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BD_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered deaths'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BD_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Standardised death rate (deaths per 1000 standard populat' + 4\n",
-       "  UNIT: 'Rate'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BUILDING_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Value of total building ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "BUILDING_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Total private sector dwelling units'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BUILDING_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Private sector houses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BUILDING_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Private sector dwellings excluding ho' + 4\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BUILDING_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Total dwelling units'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "BUILDING_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Value of private sector houses ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "BUILDING_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Value of private sector dwellings exc' + 18\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "BUILDING_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Total value of private sector dwellin' + 12\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "BUILDING_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Value of residential building ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "BUILDING_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Building approvals: Value of non-residential building ($m' + 1\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "CABEE_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total business entries'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Non employing business exits'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing business exits with 1-4 employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing business exits with 5-19 employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total business exits'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Accommodation and food services industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Administrative and support services industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Agriculture, forestry and fishing industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Non-employing businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Arts and recreation services industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Construction industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Education and training industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Electricity, gas water and waste services industry busine' + 4\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Financial and insurance services industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_25: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Health care and social assistance industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_26: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Information media and telecommunications industry busines' + 3\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_27: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Manufacturing industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_28: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mining industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses where industry is currently unknown'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing businesses with 1-4 employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Other services industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Professional, scientific and technical services industry ' + 10\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_32: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Public administration and safety industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_33: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Rental, hiring and real estate services industry business' + 2\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_34: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Retail trade industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total businesses by industry'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Transport, postal and warehousing industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Wholesale trade industry businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing businesses with 20 or more employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_39: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing business entries with 20 or more employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing businesses with 5-19 employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_40: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing business exits with 20 or more employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_42: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses with turnover of zero to less than $50k'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_43: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses with turnover of $50k to less than $200k'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_44: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses with turnover of $200k to less than $2m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_45: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses with turnover of $2m to less than $5m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_46: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses with turnover of $5m to less than $10m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_47: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses with turnover of $10m or more'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_48: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Businesses by turnover - total'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total businesses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_50: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries with turnover of zero to less than $50k'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_51: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries with turnover of $50k to less than $200k'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_52: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries with turnover of $200k to less than $2m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_53: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries with turnover of $2m to less than $5m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_54: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries with turnover of $5m to less than $10m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_55: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries with turnover of $10m or more'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_56: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business entries by turnover - total'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_58: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits with turnover of zero to less than $50k'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_59: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits with turnover of $50k to less than $200k'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_60: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits with turnover of $200k to less than $2m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_61: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits with turnover of $2m to less than $5m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_62: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits with turnover of $5m to less than $10m'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_63: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits with turnover of $10m or more'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_64: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business exits by turnover - total'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Non employing business entries'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing business entries with 1-4 employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CABEE_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employing business entries with 5-19 employees'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in Southern and Central Asia (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in Americas (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in Sub-Saharan Africa (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total persons born overseas (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Buddhism (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Christianity (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Hinduism (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Islam (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Judaism (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples (Census) (%' + 1\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Other religions (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Inadequately described/not stated ' + 3\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Australian citizen (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Not an Australian citizen (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Australian citizenship not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_33: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Speaks a language other than English at home (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_34: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples (Census)'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total persons born overseas'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Australian citizen'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Not an Australian citizen'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Australian citizenship not stated'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_39: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Speaks a language other than English at home'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in Oceania and Antarctica (excluding Austral' + 7\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_40: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Religious affiliation: Secular beliefs, other spiritual b' + 23\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in North-West Europe (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in Southern and Eastern Europe (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in North Africa and the Middle East (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in South-East Asia (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CENSUS_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born in North-East Asia (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CHILDCAR_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who did not provide child care (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CHILDCAR_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who cared for own child/children (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CHILDCAR_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who cared for other child/children (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CHILDCAR_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who cared for own or other child/children (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CHILDCAR_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Unpaid child care: not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CHILD_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Average number of all children in a family'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CITMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Australian citizen (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CITMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Not an Australian citizen (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "CITMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Citizenship not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 15 years and over who are primary carers'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 15 years and over who are primary carers (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who are non-primary carers'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who are non-primary carers (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with a disability'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with a disability (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with a profound/severe core activity limitation'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with a profound/severe core activity limitation (' + 2\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with a moderate/mild core activity limitation'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with a moderate/mild core activity limitation (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who are carers'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DISAB_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who are carers (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DWELL_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwelling structure type: Separate house'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DWELL_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwelling structure type: Semi-detached, row or terrace ho' + 19\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DWELL_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwelling structure type: Flat or apartment'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DWELL_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwelling structure type: Other dwelling'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DWELL_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwelling structure type: Not stated'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "DWELL_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total occupied private dwellings'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Transport, postal and warehousing' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Information media and telecommuni' + 11\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Financial and insurance services ' + 3\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Rental, hiring and real estate se' + 10\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Professional, scientific and tech' + 18\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Administrative and support servic' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Public administration and safety ' + 3\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Education and training (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Health care and social assistance' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Arts and recreation services (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Agriculture, forestry and fishing' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Other services (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Total persons employed'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Inadequately described or not sta' + 7\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Mining (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Manufacturing (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Electricity, gas water and waste ' + 12\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Construction (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Wholesale trade (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Retail trade (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EMP_IND_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industry of employment: Accommodation and food services (' + 2\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ENGMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Proficient in English (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ENGMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Not proficient in English (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ENGMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: English proficiency not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: All incomes not stat' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median equivalised total household income (weekly) ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: $1-$499 per week (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: $500-$999 per week (' + 2\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: $1000-$1999 per week' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: $2000-$2999 per week' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: $3000 or more per we' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: Nil income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "EQUIV_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households equivalised total income: Partial income state' + 5\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Working age population (aged 15-64 years) (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Working age population (aged 15-64 years)'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median age: Males'\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Population density'\n",
-       "  UNIT: 'Persons per Square Kilometre'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median age: Females'\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median age: Persons'\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 40-44 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 45-49 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 50-54 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 55-59 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 60-64 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 65-69 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 70-74 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 75-79 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 80-84 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 85 and over'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 0-4 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Estimated resident population: Females'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 0-4 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 5-9 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 10-14 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 15-19 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_25: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 20-24 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_26: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 25-29 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_27: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 30-34 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_28: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 35-39 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 40-44 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 5-9 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 45-49 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 50-54 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_32: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 55-59 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_33: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 60-64 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_34: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 65-69 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 70-74 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 75-79 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 80-84 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 85 and over (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 10-14 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 15-19 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 20-24 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 25-29 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 30-34 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_F_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females aged 35-39 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 40-44 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 45-49 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 50-54 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 55-59 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 60-64 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 65-69 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 70-74 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 75-79 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 80-84 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 85 and over'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 0-4 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Estimated resident population: Males'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 0-4 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 5-9 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 10-14 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 15-19 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_25: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 20-24 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_26: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 25-29 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_27: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 30-34 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_28: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 35-39 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 40-44 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 5-9 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 45-49 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 50-54 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_32: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 55-59 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_33: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 60-64 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_34: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 65-69 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 70-74 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 75-79 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 80-84 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 85 and over (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 10-14 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 15-19 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 20-24 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 25-29 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 30-34 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_M_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males aged 35-39 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 40-44 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 45-49 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 50-54 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 55-59 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 60-64 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 65-69 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 70-74 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 75-79 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 80-84 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 85 and over'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 0-4 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Estimated resident population'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 0-4 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 5-9 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 10-14 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 15-19 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_25: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 20-24 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_26: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 25-29 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_27: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 30-34 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_28: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 35-39 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 40-44 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 5-9 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 45-49 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 50-54 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_32: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 55-59 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_33: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 60-64 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_34: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 65-69 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 70-74 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 75-79 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 80-84 years (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 85 and over (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 10-14 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 15-19 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 20-24 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 25-29 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 30-34 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ERP_P_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons aged 35-39 years'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Couple families with children under 15 and/or dependent s' + 7\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Couple families with non-dependent children only'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Couple families without children'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'One parent families with children under 15 and/or depende' + 11\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'One parent families with non-dependent children only'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Other families'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total families'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "FAMILY_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Average family size (no. of persons)'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HHTYPE_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Lone person households'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HHTYPE_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Group households'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HHTYPE_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Family households'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HHTYPE_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total households'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HHTYPE_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Average household size (no. of persons)'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons completed year 12 or equivalent (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons completed year 11 or equivalent (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons completed year 10 or equivalent (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons completed year 9 or equivalent (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons completed year 8 or below (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons did not go to school (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HIGH_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons highest year of school completed - not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSES_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of established residential house transfers'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSES_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median price of established residential house transfers  ' + 3\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSES_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of residential attached dwelling transfers'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSES_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median price of residential attached dwelling transfers (' + 2\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSE_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwellings with extra bedrooms needed'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSE_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwellings with bedrooms spare'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "HOUSE_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Dwellings with no bedrooms needed/spare'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons earning $1-$499 per week (' + 2\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons earning $500-$999 per week' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons earning $1000-$1999 per we' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons earning $2000-$2999 per we' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons earning $3000 or more per ' + 8\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons earning nil income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Persons with a negative income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCMIG_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Income inadequately described or n' + 13\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Investment income earners'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median superannuation and annuity income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total superannuation and annuity income ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "INCOME_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Superannuation and annuity income earners'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median total income (excl. Government pensions and allowa' + 9\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) (' + 3\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "INCOME_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income earners (excl. Government pensions and allow' + 6\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median employee income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employee income earners - median age (years)'\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mean employee income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employee income as main source of income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Own unincorporated business income earners - median age (' + 6\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mean own unincorporated business income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_25: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Own unincorporated business income as main source of inco' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_26: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Investment income earners - median age (years)'\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_27: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mean investment income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_28: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Investment income as main source of income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Superannuation and annuity income earners - median age (y' + 5\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total employee income ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "INCOME_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mean superannuation and annuity income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Superannuation and annuity income as main source of incom' + 5\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income earners (excl. Government pensions and allow' + 27\n",
-       "  UNIT: 'Years'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mean total income (excl. Government pensions and allowanc' + 7\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 14\n",
-       "  UNIT: 'Ratio'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 14\n",
-       "  UNIT: 'Ratio'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_39: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 14\n",
-       "  UNIT: 'Ratio'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employee income earners'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_40: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 14\n",
-       "  UNIT: 'Ratio'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_41: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 17\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_42: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 34\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_43: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 34\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_44: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 35\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_45: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 20\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_46: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 20\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_47: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 19\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_48: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total income (excl. Government pensions and allowances) -' + 21\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median own unincorporated business income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total own unincorporated business income ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "INCOME_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Own unincorporated business income earners'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median investment income ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INCOME_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total investment income ($m)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'Millions'\n",
-       "ING_1564: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Total resp' + 34\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_ASHP: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Persons li' + 67\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_EETP1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Fully enga' + 42\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_EETP2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Partially ' + 68\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_EETP3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Not engage' + 40\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_EMP1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Employed'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_EMP2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: % of total' + 22\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_EMP3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: % of total' + 38\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_FOUBR: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Persons li' + 65\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_LABF: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: In the lab' + 9\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_LANG1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Speakers of an Aboriginal or Torres Strait Islander langu' + 64\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_LANG2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Speaks Eng' + 16\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_LFPR: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Labour for' + 21\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_NILF: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Not in the' + 17\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_ONEBR: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Persons li' + 55\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_THRBR: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Persons li' + 58\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_TTYPE1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Tenure typ' + 84\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_TTYPE2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Tenure typ' + 8\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_TTYPE3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Tenure typ' + 88\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_TTYPE4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Tenure typ' + 12\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_TWOBR: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Persons li' + 56\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UA1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Provided u' + 46\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UA2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Provided u' + 50\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UNEMP1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Unemployed'\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UNEMP2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: % of total' + 24\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Cared for ' + 18\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Cared for ' + 22\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Cared for ' + 20\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Cared for ' + 24\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Provided u' + 16\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Provided u' + 20\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Did not pr' + 23\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_UPCC8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Did not pr' + 27\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_VOLWP1: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Volunteere' + 38\n",
-       "  UNIT: 'Persons'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_VOLWP2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Volunteere' + 42\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "ING_YR12ATT: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Aboriginal and Torres Strait Islander Peoples: Attained y' + 71\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INTERNET_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Internet accessed from private dwelling (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INTERNET_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Internet not accessed from private dwelling (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "INTERNET_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Access to internet at home - inadequately described/not s' + 9\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LAND: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land area (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Wholesale trade'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Retail trade'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Accommodation and food services'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Transport, postal and warehousin' + 1\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Information media and telecommun' + 8\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Finance and insurance services'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Rental, hiring and real estate s' + 7\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Professional, scientific and tec' + 15\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Administrative and support servi' + 3\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Public administration and safety'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of jobs held by females'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Education and training'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Health care and social assistanc' + 1\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Arts and recreation services'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Other services'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Total'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of jobs held by males'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of jobs total'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Agriculture, forestry and fishin' + 1\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Mining'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Manufacturing'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Electricity, gas water and waste' + 9\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LEED_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Number of employee jobs: Construction'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Not in the labour force (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Labour force status not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: In the labour force'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Unemployed'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Participation rate (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Unemployment rate (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Employed'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LFMIG_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Total overseas born population age' + 19\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'In the labour force'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Unemployed'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Unemployment rate (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Participation rate (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Employed'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Not in the labour force (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Labour force status not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LF_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total population aged 15 years and over'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Community/public use planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Conservation/limited use planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Industrial planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Infrastructure/utilities planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Mixed use/centre planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Primary production planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Residential planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Special use N.E.S. planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Transition/masterplan planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size: 0 - 200 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size greater than 10,000 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size: 1,000 - 10,000 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size: 200 - 400 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size: 400 - 600 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size: 600 - 800 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Land parcel size: 800 - 1,000 sqm'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LPARCEL_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Business/commercial planning zone land parcels'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Mental health c' + 46\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Stroke (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Any other long-' + 28\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with no long-term health condition(s) (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Arthritis (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Asthma (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Cancer (includi' + 17\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Dementia (inclu' + 23\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Diabetes (exclu' + 30\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Heart disease (' + 37\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Kidney disease ' + 3\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "LTHLTH_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons with long-term health conditions: Lung condition ' + 33\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Married in a registered marriage'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Married in a de facto marriage'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Not married'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Married (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Never married (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Widowed (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Divorced (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MARRIAGE_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Separated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered passenger motor vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered campervans'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered light commercial vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered light rigid trucks'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered heavy rigid trucks'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered articulated trucks'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered non-freight carrying trucks'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered buses'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered motorcycles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_29: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered petrol fuel motor vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_30: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered diesel fuel motor vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_31: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered LPG/dual/other fuel motor vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_33: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered electric motor vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_35: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered motor vehicles less than 5 years old'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_36: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered motor vehicles 5 to 10 years old'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_37: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Registered motor vehicles over 10 years old'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "MVC_38: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total registered motor vehicles'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Oc' + 46\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas aged 15 years and over'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Ma' + 10\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Pr' + 15\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Te' + 32\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Co' + 40\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Cl' + 37\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Sa' + 15\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: Ma' + 33\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "OCCMIG_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Occupation of employed persons: La' + 11\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Parenting payment - s' + 5\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Youth allowance (full' + 27\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Youth allowance (othe' + 2\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Family tax benefit A'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Family tax benefit B'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Carer allowance'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Parenting payment - p' + 8\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Commonwealth rent ass' + 7\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Age pension'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Age pension - Departm' + 24\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Persons: Service pens' + 37\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Income support supple' + 38\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Carer payment'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Disability support pe' + 5\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Newstart allowance'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PENSION_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Government pensions and allowances: Jobseeker payment'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons earning $1-$499 per week (' + 2\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons earning $500-$999 per week' + 4\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons earning $1000-$1999 per we' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons earning $2000-$2999 per we' + 6\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons earning $3000 or more per ' + 8\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons earning nil income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Persons with a negative income (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PERSINC_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total personal income: Income inadequately described/not ' + 10\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Children attending preschool for less than 15 hours'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Children attending preschool for 15 hours or more'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: '4 year olds enrolled in preschool or preschool program'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: '5 year olds enrolled in preschool or preschool program'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Children enrolled in preschool'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Children enrolled in preschool program within a long day ' + 11\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Children enrolled across more than one provider type'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PRESCH_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Children enrolled in a preschool or preschool program'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'All other protected land areas (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Protected land area (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Indigenous protected land area (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'National parks (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Nature reserves (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'All other protected land areas (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Protected land area (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Indigenous protected land areas'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'National parks'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Nature reserves'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'All other protected land areas'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Protected land areas'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Indigenous protected land area (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'National parks (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "PROTECTED_AREA_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Nature reserves (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Postgraduate degree level (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Graduate diploma/graduate certific' + 13\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Bachelor degree level (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Advanced diploma/diploma level (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Certificate level (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: School education level (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "QUALMIG_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Education not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Secular bel' + 46\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Buddhism (%' + 1\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Christianit' + 5\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Hinduism (%' + 1\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Islam (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Judaism (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Other relig' + 7\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RELMIG_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas: Religious affiliation: Inadequatel' + 26\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RENT_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median weekly household rental payment ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "RENT_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Median monthly household mortgage payment ($)'\n",
-       "  UNIT: 'Australian Dollars'\n",
-       "  MULTIPLIER: 'None'\n",
-       "SOLAR_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Small-scale solar panel system installations'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "SOLAR_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Solar water heater installations'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where mortgage repayments are less than or equ' + 37\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where mortgage repayments are more than 30% of' + 25\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where rent payments are less than or equal to ' + 31\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_13: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where rent payments are more than 30% of imput' + 19\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where mortgage repayments are less than or equ' + 41\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where mortgage repayments are more than 30% of' + 29\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where rent payments are less than or equal to ' + 35\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "STRESS_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Households where rent payments are more than 30% of imput' + 23\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Other tenure type'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_11: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Tenure type not stated'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Owned outright (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Owned with a mortgage (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Rented (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Other tenure type (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Tenure type not stated (%)'\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Owned outright'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Owned with a mortgage'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TENURE_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Occupied private dwellings: Rented'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TOTMIG_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Females born overseas'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TOTMIG_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Males born overseas'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "TOTMIG_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons born overseas'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "UNPAIDW_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons who provided unpaid assistance to a person with a' + 15\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "UNPAIDW_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Persons undertaking voluntary work for an organisation or' + 10\n",
-       "  UNIT: 'Percent'\n",
-       "  MULTIPLIER: 'None'\n",
-       "VEHIC_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'No motor vehicles per household'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "VEHIC_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'One motor vehicle per household'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "VEHIC_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Two motor vehicles per household'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "VEHIC_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Three motor vehicles per household'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "VEHIC_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Four or more motor vehicles per household'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WATER_2: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Area of agricultural land (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WATER_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Area of irrigated agricultural land (ha)'\n",
-       "  UNIT: 'Hectares'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WATER_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Volume of water applied to irrigated agricultural land (M' + 2\n",
-       "  UNIT: 'Megalitres'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_10: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'People who used one method of travel to work'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_12: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'People who used more than one method of travel to work'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_14: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'People who worked from home'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_15: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'People who were employed but did not go to work'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_16: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Method of travel to work not stated'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_17: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Total employed persons'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_18: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Tram'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_19: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Ferry'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_20: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Taxi'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_21: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Car (as passenger)'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_22: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Truck'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_23: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'People who travelled to work using at least one form of p' + 41\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_24: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'People who travelled to work by car (as a driver or passe' + 48\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_3: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Train'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_4: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Bus'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_5: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Car (as driver)'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_6: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Motor bike/scooter'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_7: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Bicycle'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_8: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Other'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "WORK_TRAV_9: double\n",
-       "  -- field metadata --\n",
-       "  DESCRIPTION: 'Used one method of travel to work: Walked only'\n",
-       "  UNIT: 'Number'\n",
-       "  MULTIPLIER: 'None'\n",
-       "geometry: binary\n",
-       "minx: double\n",
-       "miny: double\n",
-       "maxx: double\n",
-       "maxy: double\n",
-       "fid: int64\n",
-       "-- schema metadata --\n",
-       "pandas: '{\"index_columns\": [\"fid\"], \"column_indexes\": [{\"name\": null, \"fi' + 78451\n",
-       "geo: '{\"primary_column\": \"geometry\", \"columns\": {\"geometry\": {\"encoding\":' + 1186"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there are a large number of columns available in this dataset and not all of them can be shown in the table above. We can check the exact number of columns by using the `len` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "665"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(md.schema)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have 665 columns available and as we can see above, some of the columns include a description, the units and multipliers used in the data they contain. We will extra this information and create a new dataframe with it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_name</th>\n",
-       "      <th>description</th>\n",
-       "      <th>units</th>\n",
-       "      <th>multiplier</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>ACTIV_2</td>\n",
-       "      <td>Persons who have need for assistance with core...</td>\n",
-       "      <td>Percent</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ACTIV_3</td>\n",
-       "      <td>Persons who have need for assistance with core...</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ADFS_2</td>\n",
-       "      <td>Currently serving in the Australian Defence Force</td>\n",
-       "      <td>Persons</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ADFS_3</td>\n",
-       "      <td>Previously served in the Australian Defence Force</td>\n",
-       "      <td>Persons</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ADFS_4</td>\n",
-       "      <td>Currently serving in the Australian Defence Fo...</td>\n",
-       "      <td>Percent</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>648</th>\n",
-       "      <td>WORK_TRAV_5</td>\n",
-       "      <td>Used one method of travel to work: Car (as dri...</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>649</th>\n",
-       "      <td>WORK_TRAV_6</td>\n",
-       "      <td>Used one method of travel to work: Motor bike/...</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>650</th>\n",
-       "      <td>WORK_TRAV_7</td>\n",
-       "      <td>Used one method of travel to work: Bicycle</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>651</th>\n",
-       "      <td>WORK_TRAV_8</td>\n",
-       "      <td>Used one method of travel to work: Other</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>652</th>\n",
-       "      <td>WORK_TRAV_9</td>\n",
-       "      <td>Used one method of travel to work: Walked only</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>653 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        col_name                                        description    units  \\\n",
-       "0        ACTIV_2  Persons who have need for assistance with core...  Percent   \n",
-       "1        ACTIV_3  Persons who have need for assistance with core...   Number   \n",
-       "2         ADFS_2  Currently serving in the Australian Defence Force  Persons   \n",
-       "3         ADFS_3  Previously served in the Australian Defence Force  Persons   \n",
-       "4         ADFS_4  Currently serving in the Australian Defence Fo...  Percent   \n",
-       "..           ...                                                ...      ...   \n",
-       "648  WORK_TRAV_5  Used one method of travel to work: Car (as dri...   Number   \n",
-       "649  WORK_TRAV_6  Used one method of travel to work: Motor bike/...   Number   \n",
-       "650  WORK_TRAV_7         Used one method of travel to work: Bicycle   Number   \n",
-       "651  WORK_TRAV_8           Used one method of travel to work: Other   Number   \n",
-       "652  WORK_TRAV_9     Used one method of travel to work: Walked only   Number   \n",
-       "\n",
-       "    multiplier  \n",
-       "0         None  \n",
-       "1         None  \n",
-       "2         None  \n",
-       "3         None  \n",
-       "4         None  \n",
-       "..         ...  \n",
-       "648       None  \n",
-       "649       None  \n",
-       "650       None  \n",
-       "651       None  \n",
-       "652       None  \n",
-       "\n",
-       "[653 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating empty lists to store metadata\n",
-    "col_name = []\n",
-    "description = []\n",
-    "units = []\n",
-    "multiplier = []\n",
-    "\n",
-    "# Looping through each item in the schema\n",
-    "for i in md.schema:\n",
-    "    #Get name of column\n",
-    "    col_name.append(i.name)\n",
-    "    #If available, get the metadata\n",
-    "    if i.metadata is not None:\n",
-    "        description.append(i.metadata[b'DESCRIPTION'].decode('utf-8'))\n",
-    "        units.append(i.metadata[b'UNIT'].decode('utf-8'))\n",
-    "        multiplier.append(i.metadata[b'MULTIPLIER'].decode('utf-8'))\n",
-    "    #If no metadata, append NaN\n",
-    "    else:\n",
-    "        description.append(np.nan)\n",
-    "        units.append(np.nan)\n",
-    "        multiplier.append(np.nan)\n",
-    "\n",
-    "# Creating dataframe from lists\n",
-    "da = pd.DataFrame({'col_name': col_name, 'description': description, 'units': units, 'multiplier': multiplier})\n",
-    "# Dropping rows with no description\n",
-    "da.dropna(subset = ['description'], inplace = True)\n",
-    "# Resetting indices\n",
-    "da.reset_index(drop = True, inplace = True)\n",
-    "\n",
-    "# Checking results\n",
-    "da"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will not be keeping over 600 columns in our dataframe, so we will filter the columns to only keep the ones that we are interested in.  \n",
-    "  \n",
-    "For this example, we have already selected some columns of interest:  \n",
-    "- `ERP_23`: Median age  \n",
-    "- `ERP_F_(2-19)`: Number of females per age group  \n",
-    "- `ERP_F_20`: Estimated number of females in population  \n",
-    "- `ERP_M_(2-19)`: Number of males per age group  \n",
-    "- `ERP_M_20`: Estimated number of males in population  \n",
-    "- `ERP_P_20`: Estimated resident population  \n",
-    "- `ERP_17`: Percentage population of working age (15-64 years old)  \n",
-    "- `EQUIV_2`: Median household income  \n",
-    "    \n",
-    "Below, we will create a list of relevant columns that we will use to filter the ABS dataset to avoid loading all the columns into memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ERP_M_19',\n",
-       " 'ERP_M_2',\n",
-       " 'ERP_M_20',\n",
-       " 'ERP_M_3',\n",
-       " 'ERP_M_4',\n",
-       " 'ERP_M_5',\n",
-       " 'ERP_M_6',\n",
-       " 'ERP_M_7',\n",
-       " 'ERP_M_8',\n",
-       " 'ERP_M_9']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# In addition to the columns named above, we will also keep the time period, region code, region name and geometry columns\n",
-    "columns = ['TIME_PERIOD', 'REGION_CODE', 'REGION_NAME', 'ERP_17', 'ERP_23', 'ERP_P_20', 'EQUIV_2', 'geometry']\n",
-    "\n",
-    "# From the reference table we created above, we will identify the columns that contain the number of females and males in the population.\n",
-    "# We will filter our table using the following conditions: 1. column name starts with either ERP_M_ or ERP_F_, and the units must be persons.\n",
-    "columns += da.col_name[((da.col_name.str.startswith('ERP_M_')) | (da.col_name.str.startswith('ERP_F'))) & (da.units == 'Persons')].tolist()\n",
-    "\n",
-    "#Checking results - Last 10 rows\n",
-    "columns[-10:]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you would like to choose different columns, you can either view the table in its entirety and scroll through its contents, or you could query it as we have done above.  \n",
-    "  \n",
-    "Below, we are including another examples of how to query the reference table. We will be looking for rows that contain the word **overseas** in the `description` column. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_name</th>\n",
-       "      <th>description</th>\n",
-       "      <th>units</th>\n",
-       "      <th>multiplier</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>AGEMIG_10</td>\n",
-       "      <td>Persons born overseas: 40-44 years</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>AGEMIG_11</td>\n",
-       "      <td>Persons born overseas: 45-49 years</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>AGEMIG_12</td>\n",
-       "      <td>Persons born overseas: 50-54 years</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>AGEMIG_13</td>\n",
-       "      <td>Persons born overseas: 55-59 years</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>AGEMIG_14</td>\n",
-       "      <td>Persons born overseas: 60-64 years</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>596</th>\n",
-       "      <td>RELMIG_7</td>\n",
-       "      <td>Persons born overseas: Religious affiliation: ...</td>\n",
-       "      <td>Percent</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>597</th>\n",
-       "      <td>RELMIG_9</td>\n",
-       "      <td>Persons born overseas: Religious affiliation: ...</td>\n",
-       "      <td>Percent</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>620</th>\n",
-       "      <td>TOTMIG_2</td>\n",
-       "      <td>Females born overseas</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>621</th>\n",
-       "      <td>TOTMIG_3</td>\n",
-       "      <td>Males born overseas</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>622</th>\n",
-       "      <td>TOTMIG_4</td>\n",
-       "      <td>Persons born overseas</td>\n",
-       "      <td>Number</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>74 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      col_name                                        description    units  \\\n",
-       "6    AGEMIG_10                 Persons born overseas: 40-44 years   Number   \n",
-       "7    AGEMIG_11                 Persons born overseas: 45-49 years   Number   \n",
-       "8    AGEMIG_12                 Persons born overseas: 50-54 years   Number   \n",
-       "9    AGEMIG_13                 Persons born overseas: 55-59 years   Number   \n",
-       "10   AGEMIG_14                 Persons born overseas: 60-64 years   Number   \n",
-       "..         ...                                                ...      ...   \n",
-       "596   RELMIG_7  Persons born overseas: Religious affiliation: ...  Percent   \n",
-       "597   RELMIG_9  Persons born overseas: Religious affiliation: ...  Percent   \n",
-       "620   TOTMIG_2                              Females born overseas   Number   \n",
-       "621   TOTMIG_3                                Males born overseas   Number   \n",
-       "622   TOTMIG_4                              Persons born overseas   Number   \n",
-       "\n",
-       "    multiplier  \n",
-       "6         None  \n",
-       "7         None  \n",
-       "8         None  \n",
-       "9         None  \n",
-       "10        None  \n",
-       "..         ...  \n",
-       "596       None  \n",
-       "597       None  \n",
-       "620       None  \n",
-       "621       None  \n",
-       "622       None  \n",
-       "\n",
-       "[74 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "da[da.description.str.contains('overseas')]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Subsetting ABS dataset: QLD LGAs\n",
-    "We can see that there is a `REGION_CODE` column, which as its name suggests, it contains a code that identifies its region or LGA included in the dataset. The [ABS website](https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/non-abs-structures/local-government-areas) states that LGAs are identified by a unique five digit code. The first digit identifies the state or territory where the LGA is located and the four remaining digits identify the LGA itself. This means that we can use this column to select LGAs within Queensland only, which will reduce the size of the dataset.\n",
-    "\n",
-    "According to the [LGA documentation](https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/main-structure-and-greater-capital-city-statistical-areas/australia-and-stateterritory) in the ABS website, the digit identifying the state of QLD is the number three (3). Given that LGA codes have five digits in total, we know that QLD LGAs will range between 30000 and 39999. We can now build our query to extract the data we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>30250</td>\n",
-       "      <td>Aurukun</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>MULTIPOLYGON (((142.18515 -12.61154, 142.12905...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>Balonne</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>669.0</td>\n",
-       "      <td>POLYGON ((147.38071 -27.87950, 147.38161 -27.8...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>211</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>30370</td>\n",
-       "      <td>Banana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>852.0</td>\n",
-       "      <td>POLYGON ((149.78269 -24.53844, 149.77871 -24.5...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>212</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>30410</td>\n",
-       "      <td>Barcaldine</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>676.0</td>\n",
-       "      <td>POLYGON ((144.92540 -23.21909, 144.92548 -23.2...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>213</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>30450</td>\n",
-       "      <td>Barcoo</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>752.0</td>\n",
-       "      <td>POLYGON ((141.78111 -24.62401, 141.78130 -24.6...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     TIME_PERIOD  REGION_CODE REGION_NAME  ERP_17  ERP_23  ERP_P_20  EQUIV_2  \\\n",
-       "fid                                                                            \n",
-       "209         2011        30250     Aurukun     NaN     NaN       NaN    348.0   \n",
-       "210         2011        30300     Balonne     NaN     NaN       NaN    669.0   \n",
-       "211         2011        30370      Banana     NaN     NaN       NaN    852.0   \n",
-       "212         2011        30410  Barcaldine     NaN     NaN       NaN    676.0   \n",
-       "213         2011        30450      Barcoo     NaN     NaN       NaN    752.0   \n",
-       "\n",
-       "                                              geometry  ERP_F_10  ERP_F_11  \\\n",
-       "fid                                                                          \n",
-       "209  MULTIPOLYGON (((142.18515 -12.61154, 142.12905...       NaN       NaN   \n",
-       "210  POLYGON ((147.38071 -27.87950, 147.38161 -27.8...       NaN       NaN   \n",
-       "211  POLYGON ((149.78269 -24.53844, 149.77871 -24.5...       NaN       NaN   \n",
-       "212  POLYGON ((144.92540 -23.21909, 144.92548 -23.2...       NaN       NaN   \n",
-       "213  POLYGON ((141.78111 -24.62401, 141.78130 -24.6...       NaN       NaN   \n",
-       "\n",
-       "     ...  ERP_M_19  ERP_M_2  ERP_M_20  ERP_M_3  ERP_M_4  ERP_M_5  ERP_M_6  \\\n",
-       "fid  ...                                                                    \n",
-       "209  ...       NaN      NaN       NaN      NaN      NaN      NaN      NaN   \n",
-       "210  ...       NaN      NaN       NaN      NaN      NaN      NaN      NaN   \n",
-       "211  ...       NaN      NaN       NaN      NaN      NaN      NaN      NaN   \n",
-       "212  ...       NaN      NaN       NaN      NaN      NaN      NaN      NaN   \n",
-       "213  ...       NaN      NaN       NaN      NaN      NaN      NaN      NaN   \n",
-       "\n",
-       "     ERP_M_7  ERP_M_8  ERP_M_9  \n",
-       "fid                             \n",
-       "209      NaN      NaN      NaN  \n",
-       "210      NaN      NaN      NaN  \n",
-       "211      NaN      NaN      NaN  \n",
-       "212      NaN      NaN      NaN  \n",
-       "213      NaN      NaN      NaN  \n",
-       "\n",
-       "[5 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating filter with maximum and minimum values for QLD LGAs\n",
-    "filter = [('REGION_CODE', '>=', 30000),\n",
-    "          ('REGION_CODE', '<=', 39999)]\n",
-    "\n",
-    "# Connecting to the S3 bucket where the ABS census data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet('s3://rimrep-data-public/abs-regional-lga-2021/data.parquet', \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Specifying the columns to be loaded\n",
-    "                                          columns = columns,\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) \n",
-    "\n",
-    "# Loading results to memory\n",
-    "qld_lgas_sub = dask_geo_df.compute()\n",
-    "\n",
-    "#Checking results\n",
-    "qld_lgas_sub.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating summary tables\n",
-    "We will use data for the most recent census (2021) to create a summary table for all LGAs in Queensland. We will include total population and population percentage by gender, median age, percentage of working population and median household income."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>REGION_CODE</th>\n",
-       "      <th>REGION_NAME</th>\n",
-       "      <th>tot_population</th>\n",
-       "      <th>female_per</th>\n",
-       "      <th>male_per</th>\n",
-       "      <th>working_age_per</th>\n",
-       "      <th>med_house_inc_AUD</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3491</th>\n",
-       "      <td>30250</td>\n",
-       "      <td>Aurukun</td>\n",
-       "      <td>1131.0</td>\n",
-       "      <td>0.526967</td>\n",
-       "      <td>0.473033</td>\n",
-       "      <td>72.1</td>\n",
-       "      <td>484.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3492</th>\n",
-       "      <td>30300</td>\n",
-       "      <td>Balonne</td>\n",
-       "      <td>4379.0</td>\n",
-       "      <td>0.497145</td>\n",
-       "      <td>0.502855</td>\n",
-       "      <td>63.4</td>\n",
-       "      <td>914.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3493</th>\n",
-       "      <td>30370</td>\n",
-       "      <td>Banana</td>\n",
-       "      <td>14663.0</td>\n",
-       "      <td>0.483189</td>\n",
-       "      <td>0.516811</td>\n",
-       "      <td>62.9</td>\n",
-       "      <td>1079.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3494</th>\n",
-       "      <td>30410</td>\n",
-       "      <td>Barcaldine</td>\n",
-       "      <td>2863.0</td>\n",
-       "      <td>0.490395</td>\n",
-       "      <td>0.509605</td>\n",
-       "      <td>62.6</td>\n",
-       "      <td>974.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3495</th>\n",
-       "      <td>30450</td>\n",
-       "      <td>Barcoo</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>0.464744</td>\n",
-       "      <td>0.535256</td>\n",
-       "      <td>67.9</td>\n",
-       "      <td>1220.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3564</th>\n",
-       "      <td>37340</td>\n",
-       "      <td>Whitsunday</td>\n",
-       "      <td>37660.0</td>\n",
-       "      <td>0.482687</td>\n",
-       "      <td>0.517313</td>\n",
-       "      <td>66.2</td>\n",
-       "      <td>955.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3565</th>\n",
-       "      <td>37400</td>\n",
-       "      <td>Winton</td>\n",
-       "      <td>1132.0</td>\n",
-       "      <td>0.510601</td>\n",
-       "      <td>0.489399</td>\n",
-       "      <td>63.1</td>\n",
-       "      <td>963.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3566</th>\n",
-       "      <td>37550</td>\n",
-       "      <td>Woorabinda</td>\n",
-       "      <td>1041.0</td>\n",
-       "      <td>0.553314</td>\n",
-       "      <td>0.446686</td>\n",
-       "      <td>58.2</td>\n",
-       "      <td>411.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3567</th>\n",
-       "      <td>37570</td>\n",
-       "      <td>Wujal Wujal</td>\n",
-       "      <td>284.0</td>\n",
-       "      <td>0.517606</td>\n",
-       "      <td>0.482394</td>\n",
-       "      <td>64.4</td>\n",
-       "      <td>429.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3568</th>\n",
-       "      <td>37600</td>\n",
-       "      <td>Yarrabah</td>\n",
-       "      <td>2598.0</td>\n",
-       "      <td>0.517321</td>\n",
-       "      <td>0.482679</td>\n",
-       "      <td>65.2</td>\n",
-       "      <td>472.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>78 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      REGION_CODE  REGION_NAME  tot_population  female_per  male_per  \\\n",
-       "fid                                                                    \n",
-       "3491        30250      Aurukun          1131.0    0.526967  0.473033   \n",
-       "3492        30300      Balonne          4379.0    0.497145  0.502855   \n",
-       "3493        30370       Banana         14663.0    0.483189  0.516811   \n",
-       "3494        30410   Barcaldine          2863.0    0.490395  0.509605   \n",
-       "3495        30450       Barcoo           312.0    0.464744  0.535256   \n",
-       "...           ...          ...             ...         ...       ...   \n",
-       "3564        37340   Whitsunday         37660.0    0.482687  0.517313   \n",
-       "3565        37400       Winton          1132.0    0.510601  0.489399   \n",
-       "3566        37550   Woorabinda          1041.0    0.553314  0.446686   \n",
-       "3567        37570  Wujal Wujal           284.0    0.517606  0.482394   \n",
-       "3568        37600     Yarrabah          2598.0    0.517321  0.482679   \n",
-       "\n",
-       "      working_age_per  med_house_inc_AUD  \n",
-       "fid                                       \n",
-       "3491             72.1              484.0  \n",
-       "3492             63.4              914.0  \n",
-       "3493             62.9             1079.0  \n",
-       "3494             62.6              974.0  \n",
-       "3495             67.9             1220.0  \n",
-       "...               ...                ...  \n",
-       "3564             66.2              955.0  \n",
-       "3565             63.1              963.0  \n",
-       "3566             58.2              411.0  \n",
-       "3567             64.4              429.0  \n",
-       "3568             65.2              472.0  \n",
-       "\n",
-       "[78 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Selecting rows with data from 2021 census\n",
-    "sub = qld_lgas_sub[qld_lgas_sub['TIME_PERIOD'] == 2021]\n",
-    "qld_2021 = copy.deepcopy(sub)\n",
-    "del sub\n",
-    "\n",
-    "# Calculating percentage of population by gender\n",
-    "qld_2021['female_per'] = qld_2021.apply(lambda x: x['ERP_F_20'] / x['ERP_P_20'], axis = 1)\n",
-    "qld_2021['male_per'] = qld_2021.apply(lambda x: x['ERP_M_20'] / x['ERP_P_20'], axis = 1)\n",
-    "\n",
-    "# Renaming columns so data can be easily identified\n",
-    "qld_2021 = qld_2021.rename(columns = {\"ERP_P_20\": \"tot_population\", \\\n",
-    "    \"ERP_17\": \"working_age_per\", \"EQUIV_2\": \"med_house_inc_AUD\"})\n",
-    "\n",
-    "# Selecting columns of interest\n",
-    "qld_2021 = qld_2021[['REGION_CODE', 'REGION_NAME', 'tot_population', 'female_per', 'male_per', \\\n",
-    "    'working_age_per', 'med_house_inc_AUD']]\n",
-    "\n",
-    "# Checking results\n",
-    "qld_2021\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then save this summary table into a local machine as a csv file using the code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ensuring Outputs folder exists, if not create it\n",
-    "os.makedirs('Outputs', exist_ok = True)\n",
-    "\n",
-    "#Saving results\n",
-    "qld_2021.to_csv('Outputs/QLD_LGA_summaries_2021.csv', index = False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The summary table is also available to be filtered by LGA name or code or using any other conditions. For example, we will select LGAs where median house income is above $1,000, and percentage of people of working age is 75% or more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>REGION_CODE</th>\n",
-       "      <th>REGION_NAME</th>\n",
-       "      <th>tot_population</th>\n",
-       "      <th>female_per</th>\n",
-       "      <th>male_per</th>\n",
-       "      <th>working_age_per</th>\n",
-       "      <th>med_house_inc_AUD</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3499</th>\n",
-       "      <td>31750</td>\n",
-       "      <td>Bulloo</td>\n",
-       "      <td>342.0</td>\n",
-       "      <td>0.476608</td>\n",
-       "      <td>0.523392</td>\n",
-       "      <td>77.2</td>\n",
-       "      <td>1157.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3512</th>\n",
-       "      <td>32750</td>\n",
-       "      <td>Diamantina</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>0.437037</td>\n",
-       "      <td>0.562963</td>\n",
-       "      <td>76.3</td>\n",
-       "      <td>1468.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3533</th>\n",
-       "      <td>34800</td>\n",
-       "      <td>McKinlay</td>\n",
-       "      <td>838.0</td>\n",
-       "      <td>0.459427</td>\n",
-       "      <td>0.540573</td>\n",
-       "      <td>78.8</td>\n",
-       "      <td>1145.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      REGION_CODE REGION_NAME  tot_population  female_per  male_per  \\\n",
-       "fid                                                                   \n",
-       "3499        31750      Bulloo           342.0    0.476608  0.523392   \n",
-       "3512        32750  Diamantina           270.0    0.437037  0.562963   \n",
-       "3533        34800    McKinlay           838.0    0.459427  0.540573   \n",
-       "\n",
-       "      working_age_per  med_house_inc_AUD  \n",
-       "fid                                       \n",
-       "3499             77.2             1157.0  \n",
-       "3512             76.3             1468.0  \n",
-       "3533             78.8             1145.0  "
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "qld_2021[(qld_2021.med_house_inc_AUD > 1000) & (qld_2021.working_age_per >= 75)]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting age classes\n",
-    "As part of our dataset we have information about the number of people in different age classes by gender. We can create a bar plot using this data. For this example, we will select census data from Townsville for the years 2016 and 2021."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extracting data for Townsville\n",
-    "We will also need to get information about the age ranges included in the age group columns. We will get this information from the description table. We will use the column names starting with `ERP_F`, which contain information about number of individuals for females, but the age ranges in each class is the same across all genders."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age_group</th>\n",
-       "      <th>age_class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>206</th>\n",
-       "      <td>40-44 years</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>207</th>\n",
-       "      <td>45-49 years</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>208</th>\n",
-       "      <td>50-54 years</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>55-59 years</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>60-64 years</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       age_group age_class\n",
-       "206  40-44 years        10\n",
-       "207  45-49 years        11\n",
-       "208  50-54 years        12\n",
-       "209  55-59 years        13\n",
-       "210  60-64 years        14"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Getting information about age classes - Select column names starting with ERP_F \n",
-    "# except ERP_F_20 because it contains total number of people. We will also include \n",
-    "# only columns with number of people and not percentages\n",
-    "sub = da[(da.col_name.str.contains('ERP_F_')) & (da.units == \"Persons\") & (da.col_name != \"ERP_F_20\")]\n",
-    "age_groups = copy.deepcopy(sub)\n",
-    "del sub\n",
-    "\n",
-    "#Getting the age group numbers and age range for each age class\n",
-    "age_groups['age_group'] = age_groups.apply(lambda x: re.sub('Females aged ', '', x['description']), axis = 1)\n",
-    "age_groups['age_class'] = age_groups.apply(lambda x: re.search('[0-9]{1,2}', x['col_name']).group(0), axis = 1)\n",
-    "#Changing data type to integer\n",
-    "age_groups['age_class'] = age_groups.age_class.astype(int)\n",
-    "\n",
-    "#Keeping relevant columns\n",
-    "age_groups = age_groups[['age_group', 'age_class']]\n",
-    "\n",
-    "#Checking results\n",
-    "age_groups.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TIME_PERIOD</th>\n",
-       "      <th>ERP_F_10</th>\n",
-       "      <th>ERP_F_11</th>\n",
-       "      <th>ERP_F_12</th>\n",
-       "      <th>ERP_F_13</th>\n",
-       "      <th>ERP_F_14</th>\n",
-       "      <th>ERP_F_15</th>\n",
-       "      <th>ERP_F_16</th>\n",
-       "      <th>ERP_F_17</th>\n",
-       "      <th>ERP_F_18</th>\n",
-       "      <th>...</th>\n",
-       "      <th>ERP_M_18</th>\n",
-       "      <th>ERP_M_19</th>\n",
-       "      <th>ERP_M_2</th>\n",
-       "      <th>ERP_M_3</th>\n",
-       "      <th>ERP_M_4</th>\n",
-       "      <th>ERP_M_5</th>\n",
-       "      <th>ERP_M_6</th>\n",
-       "      <th>ERP_M_7</th>\n",
-       "      <th>ERP_M_8</th>\n",
-       "      <th>ERP_M_9</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>826</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>6516.0</td>\n",
-       "      <td>6483.0</td>\n",
-       "      <td>6044.0</td>\n",
-       "      <td>5617.0</td>\n",
-       "      <td>4589.0</td>\n",
-       "      <td>3946.0</td>\n",
-       "      <td>2753.0</td>\n",
-       "      <td>2041.0</td>\n",
-       "      <td>1373.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1058.0</td>\n",
-       "      <td>837.0</td>\n",
-       "      <td>6960.0</td>\n",
-       "      <td>6720.0</td>\n",
-       "      <td>6351.0</td>\n",
-       "      <td>7121.0</td>\n",
-       "      <td>8777.0</td>\n",
-       "      <td>7710.0</td>\n",
-       "      <td>6969.0</td>\n",
-       "      <td>6045.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3561</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>6147.0</td>\n",
-       "      <td>6379.0</td>\n",
-       "      <td>6242.0</td>\n",
-       "      <td>5963.0</td>\n",
-       "      <td>5474.0</td>\n",
-       "      <td>4486.0</td>\n",
-       "      <td>3742.0</td>\n",
-       "      <td>2587.0</td>\n",
-       "      <td>1728.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1421.0</td>\n",
-       "      <td>936.0</td>\n",
-       "      <td>6113.0</td>\n",
-       "      <td>6786.0</td>\n",
-       "      <td>6839.0</td>\n",
-       "      <td>6557.0</td>\n",
-       "      <td>8252.0</td>\n",
-       "      <td>7854.0</td>\n",
-       "      <td>6818.0</td>\n",
-       "      <td>6605.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 37 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      TIME_PERIOD  ERP_F_10  ERP_F_11  ERP_F_12  ERP_F_13  ERP_F_14  ERP_F_15  \\\n",
-       "fid                                                                             \n",
-       "826          2016    6516.0    6483.0    6044.0    5617.0    4589.0    3946.0   \n",
-       "3561         2021    6147.0    6379.0    6242.0    5963.0    5474.0    4486.0   \n",
-       "\n",
-       "      ERP_F_16  ERP_F_17  ERP_F_18  ...  ERP_M_18  ERP_M_19  ERP_M_2  ERP_M_3  \\\n",
-       "fid                                 ...                                         \n",
-       "826     2753.0    2041.0    1373.0  ...    1058.0     837.0   6960.0   6720.0   \n",
-       "3561    3742.0    2587.0    1728.0  ...    1421.0     936.0   6113.0   6786.0   \n",
-       "\n",
-       "      ERP_M_4  ERP_M_5  ERP_M_6  ERP_M_7  ERP_M_8  ERP_M_9  \n",
-       "fid                                                         \n",
-       "826    6351.0   7121.0   8777.0   7710.0   6969.0   6045.0  \n",
-       "3561   6839.0   6557.0   8252.0   7854.0   6818.0   6605.0  \n",
-       "\n",
-       "[2 rows x 37 columns]"
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Extracting data for Townsville (2016 and 2021 only)\n",
-    "sub = qld_lgas_sub[(qld_lgas_sub.REGION_NAME == \"Townsville\") & ((qld_lgas_sub.TIME_PERIOD == 2016) | (qld_lgas_sub.TIME_PERIOD == 2021))]\n",
-    "#Getting names of columns with age groups that do not include percentages\n",
-    "sub_cols = da[((da.col_name.str.contains('ERP_F_')) | (da.col_name.str.contains('ERP_M_'))) & (da.units == \"Persons\") & (da.col_name != \"ERP_F_20\") & (da.col_name != \"ERP_M_20\")]\n",
-    "\n",
-    "#Selecting columns of interest\n",
-    "sub = pd.concat([sub['TIME_PERIOD'], sub[sub_cols.col_name.tolist()]], axis = 1)\n",
-    "#Making a new copy that can be manipulated\n",
-    "townsville = copy.deepcopy(sub)\n",
-    "#Deleting variables not needed\n",
-    "del sub, sub_cols\n",
-    "\n",
-    "#Checking results\n",
-    "townsville"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TIME_PERIOD</th>\n",
-       "      <th>age_class</th>\n",
-       "      <th>number_ind</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>age_group</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6113.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>0-4 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5481.0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>0-4 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6403.0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>0-4 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6960.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>0-4 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6231.0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>5-9 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1728.0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>80-84 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>19</td>\n",
-       "      <td>936.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>85 and over</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>19</td>\n",
-       "      <td>837.0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>85 and over</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>19</td>\n",
-       "      <td>1605.0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>85 and over</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>19</td>\n",
-       "      <td>1318.0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>85 and over</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>72 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    TIME_PERIOD  age_class  number_ind gender    age_group\n",
-       "57         2021          2      6113.0      M    0-4 years\n",
-       "21         2021          2      5481.0      F    0-4 years\n",
-       "20         2016          2      6403.0      F    0-4 years\n",
-       "56         2016          2      6960.0      M    0-4 years\n",
-       "23         2021          3      6231.0      F    5-9 years\n",
-       "..          ...        ...         ...    ...          ...\n",
-       "17         2021         18      1728.0      F  80-84 years\n",
-       "55         2021         19       936.0      M  85 and over\n",
-       "54         2016         19       837.0      M  85 and over\n",
-       "19         2021         19      1605.0      F  85 and over\n",
-       "18         2016         19      1318.0      F  85 and over\n",
-       "\n",
-       "[72 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Rearranging data, so columns appears as rows\n",
-    "#Females\n",
-    "towns_fem = pd.concat([townsville['TIME_PERIOD'], townsville.filter(regex = 'ERP_F_.*', axis = 1)], axis = 1)\n",
-    "towns_fem = pd.wide_to_long(towns_fem, 'ERP_F_', i = 'TIME_PERIOD', j = 'names').reset_index()\n",
-    "#Renaming columns so they are easier to identify\n",
-    "towns_fem.rename(columns = {'ERP_F_': 'number_ind', 'names': 'age_class'}, inplace = True)\n",
-    "#Adding gender column\n",
-    "towns_fem['gender'] = 'F'\n",
-    "\n",
-    "#Males - Same workflow as females\n",
-    "towns_mal = pd.concat([townsville['TIME_PERIOD'], townsville.filter(regex = 'ERP_M_.*', axis = 1)], axis = 1)\n",
-    "towns_mal = pd.wide_to_long(towns_mal, 'ERP_M_', i = 'TIME_PERIOD', j = 'names').reset_index()\n",
-    "towns_mal.rename(columns = {'ERP_M_': 'number_ind', 'names': 'age_class'}, inplace = True)\n",
-    "towns_mal['gender'] = 'M'\n",
-    "\n",
-    "# # Merging dataframe into one\n",
-    "townsville = pd.concat([towns_fem, towns_mal], axis = 0)\n",
-    "# Joining with data frame containing age group information\n",
-    "townsville = pd.merge(townsville, age_groups, how = 'left', on = 'age_class')\n",
-    "#Sorting by age class\n",
-    "townsville = townsville.sort_values('age_class')\n",
-    "#Checking results\n",
-    "townsville"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting time series for Townsville data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.legend.Legend at 0x225d4ae5750>,\n",
-       " <matplotlib.legend.Legend at 0x225d4b473d0>]"
-      ]
-     },
-     "execution_count": 326,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x900 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# We initialise the figure and axes. We also define the size of the figure.\n",
-    "# Axes refer to the subplots in the figure (2 rows and 1 column). They will share the x axis\n",
-    "fig, ax = plt.subplots(2, 1, figsize = (15, 9), sharex = True)\n",
-    "#Column width\n",
-    "width = 0.25\n",
-    "# Multiplier to offset bars\n",
-    "multiplier = 0\n",
-    "\n",
-    "# Selecting data for 2016\n",
-    "# Group has the subset dataframes for each category\n",
-    "group = townsville[townsville.TIME_PERIOD == 2016].groupby('gender')\n",
-    "for element, g in group:\n",
-    "    # Plotting 2016 data in first row\n",
-    "    offset = width * multiplier\n",
-    "    ax[0].bar(g['age_class']+offset, g['number_ind'], width, label = element)\n",
-    "    multiplier += 1\n",
-    "    \n",
-    "# Resetting multiplier\n",
-    "multiplier = 0\n",
-    "# Selecting data for 2021\n",
-    "# Group has the subset dataframes for each category\n",
-    "group = townsville[townsville.TIME_PERIOD == 2021].groupby('gender')\n",
-    "for element, g in group:\n",
-    "    # Plotting 2016 data in first row\n",
-    "    offset = width * multiplier\n",
-    "    ax[1].bar(g['age_class']+offset, g['number_ind'], width, label = element)\n",
-    "    multiplier += 1\n",
-    "\n",
-    "#Adding title to figure\n",
-    "fig.suptitle('Age distribution per gender in Townsville between 2016 and 2021', fontsize = 16, fontweight = 'bold', y = 0.925)\n",
-    "#Change y axis label\n",
-    "[i.set_ylabel('number of people') for i in ax.flat]\n",
-    "#Changes x axis labels\n",
-    "ax[1].set_xticks(g.age_class+width, g.age_group.tolist(), rotation = 45, ha = 'right');\n",
-    "#Adding legend\n",
-    "[i.legend(loc = 'upper right', ncols = 2) for i in ax.flat];"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From this plot, we can see that the amount of people in the younger age classes has decreased, while there was an increase in the older classes between the 2016 and 2021 censuses.\n",
-    "\n",
-    "We can save the plot in a file named `Townsville_age_classes.png` above as follows: `plt.savefig('Townsville_age_classes.png')`."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating choropleth map with poulation data from the 2021 census\n",
-    "We will select 2021 data from the QLD data we extracted at the beginning. We will select two columns only: `ERP_P_20`, which has information about total number of inhabitants per LGA, and `geometry`, which has the coordinates for the boundaries of each LGA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        vertical-align: middle;\n",
-       "    }\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>REGION_CODE</th>\n",
-       "      <th>REGION_NAME</th>\n",
-       "      <th>ERP_P_20</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3491</th>\n",
-       "      <td>30250</td>\n",
-       "      <td>Aurukun</td>\n",
-       "      <td>1131.0</td>\n",
-       "      <td>MULTIPOLYGON (((142.18515 -12.61154, 142.12905...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3492</th>\n",
-       "      <td>30300</td>\n",
-       "      <td>Balonne</td>\n",
-       "      <td>4379.0</td>\n",
-       "      <td>POLYGON ((147.38071 -27.87950, 147.38161 -27.8...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3493</th>\n",
-       "      <td>30370</td>\n",
-       "      <td>Banana</td>\n",
-       "      <td>14663.0</td>\n",
-       "      <td>POLYGON ((149.78269 -24.53844, 149.77871 -24.5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3494</th>\n",
-       "      <td>30410</td>\n",
-       "      <td>Barcaldine</td>\n",
-       "      <td>2863.0</td>\n",
-       "      <td>POLYGON ((144.92540 -23.21909, 144.92548 -23.2...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3495</th>\n",
-       "      <td>30450</td>\n",
-       "      <td>Barcoo</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>POLYGON ((141.78111 -24.62401, 141.78130 -24.6...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      REGION_CODE REGION_NAME  ERP_P_20  \\\n",
-       "fid                                       \n",
-       "3491        30250     Aurukun    1131.0   \n",
-       "3492        30300     Balonne    4379.0   \n",
-       "3493        30370      Banana   14663.0   \n",
-       "3494        30410  Barcaldine    2863.0   \n",
-       "3495        30450      Barcoo     312.0   \n",
-       "\n",
-       "                                               geometry  \n",
-       "fid                                                      \n",
-       "3491  MULTIPOLYGON (((142.18515 -12.61154, 142.12905...  \n",
-       "3492  POLYGON ((147.38071 -27.87950, 147.38161 -27.8...  \n",
-       "3493  POLYGON ((149.78269 -24.53844, 149.77871 -24.5...  \n",
-       "3494  POLYGON ((144.92540 -23.21909, 144.92548 -23.2...  \n",
-       "3495  POLYGON ((141.78111 -24.62401, 141.78130 -24.6...  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Selecting population data from 2021 and keeping only relevant columns\n",
-    "qld_pop_2021 = qld_lgas_sub[qld_lgas_sub.TIME_PERIOD == 2021][['REGION_CODE', 'REGION_NAME', 'ERP_P_20', 'geometry']]\n",
-    "\n",
-    "#Checking results\n",
-    "qld_pop_2021.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting choropleth map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.925, 'Population per Local Government Area (2021)')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure(figsize = (15, 9))\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = fig.add_subplot(1, 1, 1, projection = ccrs.PlateCarree())\n",
-    "labels = ['1000s', '10000s', '100000s', '1000000s']\n",
-    "\n",
-    "#Plottting data\n",
-    "qld_pop_2021.plot(column = 'ERP_P_20', ax = ax, scheme = 'quantiles', \\\n",
-    "    cmap = 'viridis', edgecolor = 'black', linewidth = 0.5, legend = True,\n",
-    "    legend_kwds = {'title': 'Number of inhabitants'})\n",
-    "\n",
-    "fig.suptitle('Population per Local Government Area (2021)', fontsize = 16, fontweight = 'bold', y = 0.925)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "rimrep",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/Plotting_SELTMP_Data.ipynb b/Python_based_scripts/Plotting_SELTMP_Data.ipynb
deleted file mode 100644
index 750dbc7..0000000
--- a/Python_based_scripts/Plotting_SELTMP_Data.ipynb
+++ /dev/null
@@ -1,7377 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plotting SELTMP data\n",
-    "**Author**: Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook will show how to access the RIMReP `geoparquet` collection for the Social and Economic Long-Term Monitoring Program (SELTMP) dataset from CSIRO, which contains the latest results of this survey (2021). This dataset collects data on a subset of Great Barrier Reef (GBR) human dimension indicators relating to social, economic, cultural, and governance aspects of the GBR, as described within the Reef 2050 Long Term Sustainability Plan (Reef 2050 Plan). These human dimensions are considered to play a pivotal role in resilience-based management of the GBR.  \n",
-    "  \n",
-    "In this example, we will produce four figures:  \n",
-    "1. Bar plot showing the proportion of people surveyed performing recreational activities per region\n",
-    "2. Bar plot showing people’s perception of waterway health\n",
-    "3. Bar plot comparing the trust in science by fishers against their perception of climate change\n",
-    "4. Word cloud using the favorite waterways for recreation of the people surveyed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing S3 bucket\n",
-    "from pyarrow import parquet as pq\n",
-    "# Manipulating dataframes\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "# Checking patterns in strings using regular expressions (regex)\n",
-    "import re\n",
-    "# Interacting with operating system\n",
-    "import os\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Color maps\n",
-    "from matplotlib import colormaps as cmaps\n",
-    "# Word cloud\n",
-    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to SELTMP dataset in RIMReP collection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Connecting to metadata file\n",
-    "md = pq.ParquetDataset(f's3://rimrep-data-public/csiro-seltmp-baseline-surveys-jul22/data.parquet')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting the structure of the SELTMP dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "REGION: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'REGION (1=WT, 2=TSV, 3=MWI, 4=FIT; 5=GH)'\n",
-       "  new_name: 'REGION (1=WT, 2=TSV, 3=MWI, 4=FIT; 5=GH)'\n",
-       "recruit path: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'Recruiting pathway (1 =  ORU panel, 2 = live link) (added' + 19\n",
-       "  new_name: 'Recruiting PATHWAY (1 = ORU panel, 2 = live link; 3 = email ' + 17\n",
-       "q1: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'Q1 - What is your current residential / home postcode?'\n",
-       "  new_name: 'q1 - home POSTCODE'\n",
-       "q1a: double\n",
-       "  -- field metadata --\n",
-       "  description: 'Q1a - Please select your City / Town (LGA):'\n",
-       "  new_name: 'q1a - city, town or LGA'\n",
-       "q1a-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for q1a'\n",
-       "  new_name: 'None'\n",
-       "quotagroup: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'Quota group'\n",
-       "  new_name: 'ORU quota group'\n",
-       "quotagroup-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for quotagroup'\n",
-       "  new_name: 'None'\n",
-       "q1b: double\n",
-       "  -- field metadata --\n",
-       "  description: 'Q1b - When in central Queensland, in which Local Governme' + 45\n",
-       "  new_name: 'q1b - FITZROY ONLY - When in central QLD, in which LGA do yo' + 26\n",
-       "q1b-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for q1b'\n",
-       "  new_name: 'None'\n",
-       "q2: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'Q2 - What is your age?'\n",
-       "  new_name: 'q2 - AGE categories: 1 = 18-24; 2 = 25-34; 3 = 35-44; 4 = 45' + 34\n",
-       "q2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for q2'\n",
-       "  new_name: 'None'\n",
-       "q3: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'Q3 - How do you describe your gender?'\n",
-       "  new_name: 'q3 = GENDER: 1 = Female; 2 = Male; 3 = Other; 4 = prefer not' + 4\n",
-       "q3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for q3'\n",
-       "  new_name: 'None'\n",
-       "optin: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'Opt In'\n",
-       "  new_name: 'CONSENT to participate (1 = Yes)'\n",
-       "optin-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for optin'\n",
-       "  new_name: 'None'\n",
-       "w4_1: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W4_1 - What are the first words that come to mind when yo' + 47\n",
-       "  new_name: 'w4_1 - WET TROPICS ONLY - First words; Word One'\n",
-       "w4_2: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W4_2 - What are the first words that come to mind when yo' + 47\n",
-       "  new_name: 'w4_2 - WET TROPICS ONLY - First words; Word Two'\n",
-       "w4_3: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W4_3 - What are the first words that come to mind when yo' + 47\n",
-       "  new_name: 'w4_3 - WET TROPICS ONLY - First words; Word Three'\n",
-       "w5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W5 -  In the last 12 months, how often did you visit a fr' + 157\n",
-       "  new_name: 'w5 - WET TROPICS ONLY - How often visited FRESHWATER in regi' + 19\n",
-       "w5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w5'\n",
-       "  new_name: 'None'\n",
-       "w5a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W5A_1 - Recreation'\n",
-       "  new_name: 'w5a_1 - WET TROPICS ONLY - Freshwater visits for RECREATION ' + 7\n",
-       "w5a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W5A_2 - Work'\n",
-       "  new_name: 'w5a_2 - WET TROPICS ONLY - Freshwater visits for WORK (1=Yes' + 1\n",
-       "w5a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W5A_3 - Cultural reasons'\n",
-       "  new_name: 'w5a_3 - WET TROPICS ONLY - Freshwater visits for CULTURE (1=' + 4\n",
-       "w5a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W5A_4 - Other reason'\n",
-       "  new_name: 'w5a_4 - WET TROPICS ONLY - Freshwater visits for OTHER REASO' + 10\n",
-       "w6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W6 -  In the previous 12 months, how often did you visit ' + 198\n",
-       "  new_name: 'w6 - WET TROPICS ONLY - How often visited ESTUARY in region ' + 16\n",
-       "w6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w6'\n",
-       "  new_name: 'None'\n",
-       "w6a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W6A_1 - Recreation'\n",
-       "  new_name: 'w6a_1 - WET TROPICS ONLY - Estuary visits for RECREATION (1=' + 4\n",
-       "w6a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W6A_2 - Work'\n",
-       "  new_name: 'w6a_2 - WET TROPICS ONLY - Estuary visits for WORK (1=Yes)'\n",
-       "w6a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W6A_3 - Cultural reasons'\n",
-       "  new_name: 'w6a_3 - - WET TROPICS ONLY - Estuary visits for CULTURE (1=Y' + 3\n",
-       "w6a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W6A_4 - Other reason'\n",
-       "  new_name: 'w6a_4 - WET TROPICS ONLY - Estuary visits for OTHER REASONS ' + 7\n",
-       "w7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W7 -  In the previous 12 months, how often did you visit ' + 198\n",
-       "  new_name: 'w7 - WET TROPICS ONLY - How often visited COASTAL & INSHORE ' + 26\n",
-       "w7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w7'\n",
-       "  new_name: 'None'\n",
-       "w7a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W7A_1 - Recreation'\n",
-       "  new_name: 'w7a_1 - WET TROPICS ONLY - Coastal & Inshore visits for RECR' + 14\n",
-       "w7a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W7A_2 - Work'\n",
-       "  new_name: 'w7a_2 - WET TROPICS ONLY - Coastal & Inshore visits for WORK' + 8\n",
-       "w7a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W7A_3 - Cultural reasons'\n",
-       "  new_name: 'w7a_3 - WET TROPICS ONLY - Coastal & Inshore visits for CULT' + 11\n",
-       "w7a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W7A_4 - Other reason'\n",
-       "  new_name: 'w7a_4 - WET TROPICS ONLY - Coastal & Inshore visits for OTHE' + 17\n",
-       "w8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W8 -  In the previous 12 months, how often did you visit ' + 193\n",
-       "  new_name: 'w8 - WET TROPICS ONLY - How often visited OFFSHORE in region' + 17\n",
-       "w8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w8'\n",
-       "  new_name: 'None'\n",
-       "w8a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W8A_1 - Recreation'\n",
-       "  new_name: 'w8a_1 - WET TROPICS ONLY - Offshore visits for RECREATION (1' + 5\n",
-       "w8a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W8A_2 - Work'\n",
-       "  new_name: 'w8a_2 - WET TROPICS ONLY - Offshore visits for WORK (1=Yes)'\n",
-       "w8a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W8A_3 - Cultural reasons'\n",
-       "  new_name: 'w8a_3 - WET TROPICS ONLY - Offshore visits for CULTURE (1=Ye' + 2\n",
-       "w8a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W8A_4 - Other reason'\n",
-       "  new_name: 'w8a_4 - WET TROPICS ONLY - Offshore visits for OTHER REASONS' + 8\n",
-       "w9_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_1 - Fishing'\n",
-       "  new_name: 'w9_1 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 20\n",
-       "w9_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_2 - Boating or sailing'\n",
-       "  new_name: 'w9_2 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 31\n",
-       "w9_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_3 - Snorkelling/freediving/Scuba diving'\n",
-       "  new_name: 'w9_3 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 29\n",
-       "w9_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_4 - Motor-powered water sports (e.g. water skiing, jet' + 5\n",
-       "  new_name: 'w9_4 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 30\n",
-       "w9_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_5 - Wind-powered water sports (e.g. kite surfing)'\n",
-       "  new_name: 'w9_5 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 29\n",
-       "w9_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_6 - Paddling/canoeing/kayaking'\n",
-       "  new_name: 'w9_6 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 21\n",
-       "w9_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_7 - Camping'\n",
-       "  new_name: 'w9_7 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 20\n",
-       "w9_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_8 - Swimming'\n",
-       "  new_name: 'w9_8 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 21\n",
-       "w9_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_9 - Picnics and barbeques'\n",
-       "  new_name: 'w9_9 - WET TROPICS ONLY - Waterway Recreation in past 12 mth' + 27\n",
-       "w9_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_10 - Exercising/hiking/biking/running'\n",
-       "  new_name: 'w9_10 - WET TROPICS ONLY - Waterway Recreation in past 12 mt' + 30\n",
-       "w9_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_11 - Wildlife watching & appreciating nature'\n",
-       "  new_name: 'w9_11 - WET TROPICS ONLY - Waterway Recreation in past 12 mt' + 31\n",
-       "w9_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_12 - Other (please specify)'\n",
-       "  new_name: 'w9_12 - WET TROPICS ONLY - Waterway Recreation in past 12 mt' + 19\n",
-       "w9_12_other: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W9_12_Other - OTHER TEXT'\n",
-       "  new_name: 'w9_12_other - WET TROPICS ONLY - Waterway Recreation OTHER A' + 17\n",
-       "w10: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W10 - What is your favourite waterway to visit in the Wet' + 31\n",
-       "  new_name: 'w10 - WET TROPICS ONLY - Favourite Waterway for Recreation'\n",
-       "w11_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_1 - Visiting waterways in the Wet Tropics region is i' + 45\n",
-       "  new_name: 'w11_1 - WET TROPICS - Visiting waterways  important for QUAL' + 37\n",
-       "w11_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_1'\n",
-       "  new_name: 'None'\n",
-       "w11_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_2 - Waterways in the Wet Tropics region support my li' + 34\n",
-       "  new_name: 'w11_2 - WET TROPICS - Waterways support my LIFESTYLE & RECRE' + 19\n",
-       "w11_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_2'\n",
-       "  new_name: 'None'\n",
-       "w11_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_3 - Waterways in the region provide an important plac' + 46\n",
-       "  new_name: 'w11_3 - WET TROPICS - Waterways  important for time with FAM' + 27\n",
-       "w11_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_3'\n",
-       "  new_name: 'None'\n",
-       "w11_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_4 - Waterways in the region are important for providi' + 40\n",
-       "  new_name: 'w11_4 - WET TROPICS - Waterways  important for providing FIS' + 32\n",
-       "w11_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_4'\n",
-       "  new_name: 'None'\n",
-       "w11_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_5 - Waterways in the region are an important part of ' + 10\n",
-       "  new_name: 'w11_5 - WET TROPICS - Waterways  important part of my CULTUR' + 15\n",
-       "w11_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_5'\n",
-       "  new_name: 'None'\n",
-       "w11_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_6 - The waterways are important for allowing  me to e' + 63\n",
-       "  new_name: 'w11_6 - WET TROPICS - Waterways  important for EXPERIENCING ' + 20\n",
-       "w11_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_6'\n",
-       "  new_name: 'None'\n",
-       "w11_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_7 - The waterways are an important source of my water' + 38\n",
-       "  new_name: 'w11_7 - WET TROPICS - Waterways  important for DOMESTIC WATE' + 22\n",
-       "w11_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_7'\n",
-       "  new_name: 'None'\n",
-       "w11_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_9 - I am proud of the local waterways in my region'\n",
-       "  new_name: 'w11_9 - WET TROPICS - I am PROUD of waterways  in my region ' + 13\n",
-       "w11_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_9'\n",
-       "  new_name: 'None'\n",
-       "w11_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_10 - The waterways in my region are an important part' + 29\n",
-       "  new_name: 'w11_10 - WET TROPICS - Waterways  important part of WHY I LI' + 21\n",
-       "w11_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_10'\n",
-       "  new_name: 'None'\n",
-       "w11_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_11 - The natural beauty of waterways in the Wet Tropi' + 24\n",
-       "  new_name: 'w11_11 - WET TROPICS - Waterways in region BEAUTY IS OUTSTAN' + 18\n",
-       "w11_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_11'\n",
-       "  new_name: 'None'\n",
-       "w11_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W11_12 - If the health of waterways in my region declined' + 32\n",
-       "  new_name: 'w11_12 - WET TROPICS - If waterway health declined I would b' + 35\n",
-       "w11_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w11_12'\n",
-       "  new_name: 'None'\n",
-       "w12_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_1 - The waterways support a variety of native habitat' + 22\n",
-       "  new_name: 'w12_1 - WET TROPICS - VALUE  waterways BIODIVERSITY (Rating ' + 5\n",
-       "w12_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_1'\n",
-       "  new_name: 'None'\n",
-       "w12_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_2 - The fact that the waterways exist, even if I don�' + 37\n",
-       "  new_name: 'w12_2 - WET TROPICS - VALUE  waterways EXISTENCE (Rating 1-1' + 2\n",
-       "w12_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_2'\n",
-       "  new_name: 'None'\n",
-       "w12_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_3 - The waterways offer a place for local residents t' + 29\n",
-       "  new_name: 'w12_3 - WET TROPICS - VALUE  waterways RECREATION for LOCALS' + 14\n",
-       "w12_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_3'\n",
-       "  new_name: 'None'\n",
-       "w12_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_4 - The waterways provide a place where people can pa' + 48\n",
-       "  new_name: 'w12_4 - WET TROPICS - VALUE  waterways PASS DOWN KNOWLEDGE (' + 12\n",
-       "w12_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_4'\n",
-       "  new_name: 'None'\n",
-       "w12_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_5 - The waterways have rich Traditional Owner Heritag' + 1\n",
-       "  new_name: 'w12_5 - WET TROPICS - VALUE  waterways TO HERITAGE (Rating 1' + 4\n",
-       "w12_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_5'\n",
-       "  new_name: 'None'\n",
-       "w12_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_6 - The waterways are an important attraction for tou' + 25\n",
-       "  new_name: 'w12_6 - WET TROPICS - VALUE  waterways TOURISM ATTRACTION (R' + 11\n",
-       "w12_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_6'\n",
-       "  new_name: 'None'\n",
-       "w12_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_7 - The waterways support our local economy'\n",
-       "  new_name: 'w12_7 - WET TROPICS - VALUE  waterways SUPPORT LOCAL ECONOMY' + 14\n",
-       "w12_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_7'\n",
-       "  new_name: 'None'\n",
-       "w12_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_8 - The waterways provide a place where people can st' + 43\n",
-       "  new_name: 'w12_8 - WET TROPICS - VALUE  waterways LEARN ABOUT NATURE (R' + 11\n",
-       "w12_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_8'\n",
-       "  new_name: 'None'\n",
-       "w12_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_9 - The waterways support recreational fishing'\n",
-       "  new_name: 'w12_9 - WET TROPICS - VALUE  waterways SUPPORT REC FISHING (' + 12\n",
-       "w12_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_9'\n",
-       "  new_name: 'None'\n",
-       "w12_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_10 - Our waterways are recognised nationally and inte' + 72\n",
-       "  new_name: 'w12_10 - WET TROPICS - VALUE  waterways ICONIC STATUS (Ratin' + 7\n",
-       "w12_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_10'\n",
-       "  new_name: 'None'\n",
-       "w12_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_11 - The waterways support mining, ports and shipping' + 14\n",
-       "  new_name: 'w12_11 - WET TROPICS - VALUE  waterways SUPPORT MINING & SHI' + 19\n",
-       "w12_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_11'\n",
-       "  new_name: 'None'\n",
-       "w12_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_12 - The waterways support local agriculture'\n",
-       "  new_name: 'w12_12 - WET TROPICS - VALUE  waterways SUPPORT LOCAL AGRICU' + 19\n",
-       "w12_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_12'\n",
-       "  new_name: 'None'\n",
-       "w12_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_13 - The waterways support commercial fisheries'\n",
-       "  new_name: 'w12_13 - WET TROPICS - VALUE  waterways SUPPORT COMM FISHERI' + 16\n",
-       "w12_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_13'\n",
-       "  new_name: 'None'\n",
-       "w12_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W12_14 - The waterways support local aquaculture'\n",
-       "  new_name: 'w12_14 - WET TROPICS - VALUE  waterways SUPPORT LOCAL AQUACU' + 19\n",
-       "w12_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w12_14'\n",
-       "  new_name: 'None'\n",
-       "w13_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_1 - Freshwater creeks and rivers'\n",
-       "  new_name: 'w13_1 - WET TROPICS Perceived Health FRESH CREEKS & RIVERS (' + 17\n",
-       "w13_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_1'\n",
-       "  new_name: 'None'\n",
-       "w13_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_2 - Freshwater lakes, dams and wetlands'\n",
-       "  new_name: 'w13_2 - WET TROPICS Perceived Health LAKES DAMS & WETLANDS (' + 17\n",
-       "w13_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_2'\n",
-       "  new_name: 'None'\n",
-       "w13_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_3 - Estuaries (including mangroves and saltmarshes)'\n",
-       "  new_name: 'w13_3 - WET TROPICS Perceived Health ESTUARIES (Categories 1' + 5\n",
-       "w13_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_3'\n",
-       "  new_name: 'None'\n",
-       "w13_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_4 - Beaches and the coast'\n",
-       "  new_name: 'w13_4 - WET TROPICS Perceived Health BEACHES & COAST (Catego' + 11\n",
-       "w13_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_4'\n",
-       "  new_name: 'None'\n",
-       "w13_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_5 - Seagrass'\n",
-       "  new_name: 'w13_5 - WET TROPICS Perceived Health SEAGRASS (Categories 1 ' + 4\n",
-       "w13_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_5'\n",
-       "  new_name: 'None'\n",
-       "w13_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_6 - Inshore coral reefs'\n",
-       "  new_name: 'w13_6 - WET TROPICS Perceived Health INSHORE REEFS (Categori' + 9\n",
-       "w13_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_6'\n",
-       "  new_name: 'None'\n",
-       "w13_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_7 - Offshore coral reefs'\n",
-       "  new_name: 'w13_7 - WET TROPICS Perceived Health OFFSHORE REEFS (Categor' + 10\n",
-       "w13_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_7'\n",
-       "  new_name: 'None'\n",
-       "w13_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W13_8 - Ocean and sea'\n",
-       "  new_name: 'w13_8 - WET TROPICS Perceived Health OCEAN & SEA (Categories' + 7\n",
-       "w13_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w13_8'\n",
-       "  new_name: 'None'\n",
-       "w14_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_1 - Poor water quality in fresh waters and estuaries'\n",
-       "  new_name: 'w14_1 - WET TROPICS Perceived Problem FRESH WATER QUALITY (R' + 28\n",
-       "w14_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_1'\n",
-       "  new_name: 'None'\n",
-       "w14_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_2 - Poor coastal and inshore water quality'\n",
-       "  new_name: 'w14_2 - WET TROPICS Perceived Problem INSHORE WATER QUALITY ' + 30\n",
-       "w14_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_2'\n",
-       "  new_name: 'None'\n",
-       "w14_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_3 - Poor offshore marine water quality'\n",
-       "  new_name: 'w14_3 - WET TROPICS Perceived Problem OFFSHORE WATER QUALITY' + 31\n",
-       "w14_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_3'\n",
-       "  new_name: 'None'\n",
-       "w14_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_4 - Riverbank erosion'\n",
-       "  new_name: 'w14_4 - WET TROPICS Perceived Problem RIVERBANK EROSION (Rat' + 26\n",
-       "w14_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_4'\n",
-       "  new_name: 'None'\n",
-       "w14_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_5 - Coastal erosion'\n",
-       "  new_name: 'w14_5 - WET TROPICS Perceived Problem COASTAL EROSION (Ratin' + 24\n",
-       "w14_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_5'\n",
-       "  new_name: 'None'\n",
-       "w14_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_6 - Weeds in and around waterways'\n",
-       "  new_name: 'w14_6 - WET TROPICS Perceived Problem WATERWAY WEEDS (Rating' + 23\n",
-       "w14_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_6'\n",
-       "  new_name: 'None'\n",
-       "w14_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_7 - Litter and debris (e.g. plastics)'\n",
-       "  new_name: 'w14_7 - WET TROPICS Perceived Problem LITTER & DEBRIS (Ratin' + 24\n",
-       "w14_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_7'\n",
-       "  new_name: 'None'\n",
-       "w14_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_8 - Chemical pollutants (e.g. pesticides, PFAS)'\n",
-       "  new_name: 'w14_8 - WET TROPICS Perceived Problem CHEMICALS (Rating 1-5;' + 18\n",
-       "w14_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_8'\n",
-       "  new_name: 'None'\n",
-       "w14_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_9 - Invasive fish (e.g. Tilapia)'\n",
-       "  new_name: 'w14_9 - WET TROPICS Perceived Problem INVASIVE FISH (Rating ' + 22\n",
-       "w14_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_9'\n",
-       "  new_name: 'None'\n",
-       "w14_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_10 - Low abundance of fish'\n",
-       "  new_name: 'w14_10 - WET TROPICS Perceived Problem FISH ABUNDANCE (Ratin' + 24\n",
-       "w14_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_10'\n",
-       "  new_name: 'None'\n",
-       "w14_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_11 - Decline in waterbirds and shorebirds'\n",
-       "  new_name: 'w14_11 - WET TROPICS Perceived Problem DECLINE IN BIRDS (Rat' + 26\n",
-       "w14_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_11'\n",
-       "  new_name: 'None'\n",
-       "w14_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_12 - Algal blooms and fish kills'\n",
-       "  new_name: 'w14_12 - WET TROPICS Perceived Problem ALGAE & FISH KILLS (R' + 28\n",
-       "w14_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_12'\n",
-       "  new_name: 'None'\n",
-       "w14_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W14_13 - Decline in numbers of marine megafauna (e.g. tur' + 24\n",
-       "  new_name: 'w14_13 - WET TROPICS Perceived Problem MEGAFAUNA DECLINE (Ra' + 27\n",
-       "w14_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w14_13'\n",
-       "  new_name: 'None'\n",
-       "w15_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_1 - Illegal fishing practices (e.g. poaching in no-ta' + 9\n",
-       "  new_name: 'w15_1 - WET TROPICS Perceived Threat ILLEGAL FISHING (Rating' + 23\n",
-       "w15_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_1'\n",
-       "  new_name: 'None'\n",
-       "w15_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_2 - Over-fishing'\n",
-       "  new_name: 'w15_2 - WET TROPICS Perceived Threat OVER-FISHING (Rating 1-' + 20\n",
-       "w15_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_2'\n",
-       "  new_name: 'None'\n",
-       "w15_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_3 - Sediments, nutrients and pesticides from land-bas' + 9\n",
-       "  new_name: 'w15_3 - WET TROPICS Perceived Threat LAND RUN-OFF (Rating 1-' + 20\n",
-       "w15_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_3'\n",
-       "  new_name: 'None'\n",
-       "w15_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_4 - Climate change'\n",
-       "  new_name: 'w15_4 - WET TROPICS Perceived Threat CLIMATE CHANGE (Rating ' + 22\n",
-       "w15_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_4'\n",
-       "  new_name: 'None'\n",
-       "w15_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_5 - Tourism activities'\n",
-       "  new_name: 'w15_5 - WET TROPICS Perceived Threat TOURISM ACTIVITIES (Rat' + 26\n",
-       "w15_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_5'\n",
-       "  new_name: 'None'\n",
-       "w15_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_6 - Land clearing'\n",
-       "  new_name: 'w15_6 - WET TROPICS Perceived Threat LAND CLEARING (Rating 1' + 21\n",
-       "w15_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_6'\n",
-       "  new_name: 'None'\n",
-       "w15_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_7 - Ports and shipping activities'\n",
-       "  new_name: 'w15_7 - WET TROPICS Perceived Threat PORTS & SHIPPING (Ratin' + 24\n",
-       "w15_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_7'\n",
-       "  new_name: 'None'\n",
-       "w15_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_8 - Mining activities (e.g. water extraction, release' + 2\n",
-       "  new_name: 'w15_8 - WET TROPICS Perceived Threat MINING ACTIVITIES (Rati' + 25\n",
-       "w15_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_8'\n",
-       "  new_name: 'None'\n",
-       "w15_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_9 - Coastal development'\n",
-       "  new_name: 'w15_9 - WET TROPICS Perceived Threat COASTAL DEVELOPMENT (Ra' + 27\n",
-       "w15_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_9'\n",
-       "  new_name: 'None'\n",
-       "w15_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_10 - Extreme weather (e.g. cyclones, droughts, floods' + 1\n",
-       "  new_name: 'w15_10 - WET TROPICS Perceived Threat EXTREME WEATHER (Ratin' + 24\n",
-       "w15_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_10'\n",
-       "  new_name: 'None'\n",
-       "w15_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W15_11 - Recreation activities (e.g. boats, fishers, wate' + 9\n",
-       "  new_name: 'w15_11 - WET TROPICS Perceived Threat RECREATION (Rating 1-5' + 19\n",
-       "w15_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w15_11'\n",
-       "  new_name: 'None'\n",
-       "w16_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_1 - For people who have or use a boat...Responsible a' + 110\n",
-       "  new_name: 'w16_1 - WET TROPICS Waterway Stewardship ANCHOR RESPONSIBLY ' + 16\n",
-       "w16_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_1'\n",
-       "  new_name: 'None'\n",
-       "w16_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_2 - For people who go fishing...Responsible fishing (' + 122\n",
-       "  new_name: 'w16_2 - WET TROPICS Waterway Stewardship FISH RESPONSIBLY (C' + 14\n",
-       "w16_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_2'\n",
-       "  new_name: 'None'\n",
-       "w16_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_3 - On and around waterways...Dispose of food scraps ' + 89\n",
-       "  new_name: 'w16_3 - WET TROPICS Waterway Stewardship RUBBISH DISPOSAL (C' + 14\n",
-       "w16_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_3'\n",
-       "  new_name: 'None'\n",
-       "w16_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_4 - Report suspicious activity to relevant authoritie' + 75\n",
-       "  new_name: 'w16_4 - WET TROPICS Waterway Stewardship REPORT SUSPICIOUS (' + 15\n",
-       "w16_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_4'\n",
-       "  new_name: 'None'\n",
-       "w16_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_5 - Report invasive or pest species to relevant autho' + 66\n",
-       "  new_name: 'w16_5 - WET TROPICS Waterway Stewardship REPORT INVASIVES (C' + 14\n",
-       "w16_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_5'\n",
-       "  new_name: 'None'\n",
-       "w16_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_6 - Contribute to environmental monitoring programs (' + 74\n",
-       "  new_name: 'w16_6 - WET TROPICS Waterway Stewardship CONTRIBUTE TO MONIT' + 22\n",
-       "w16_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_6'\n",
-       "  new_name: 'None'\n",
-       "w16_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_7 - Participate in local environmental clean-ups (e.g' + 39\n",
-       "  new_name: 'w16_7 - WET TROPICS Waterway Stewardship LOCAL CLEAN UPS (Ca' + 13\n",
-       "w16_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_7'\n",
-       "  new_name: 'None'\n",
-       "w16_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_8 - Participate in local environmental restoration (e' + 51\n",
-       "  new_name: 'w16_8 - WET TROPICS Waterway Stewardship LOCAL RESTORATION (' + 15\n",
-       "w16_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_8'\n",
-       "  new_name: 'None'\n",
-       "w16_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_9 - Responsible four-wheel driving (e.g. avoiding riv' + 70\n",
-       "  new_name: 'w16_9 - WET TROPICS Waterway Stewardship 4WD RESPONSIBLY (Ca' + 13\n",
-       "w16_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_9'\n",
-       "  new_name: 'None'\n",
-       "w16_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_10 - When at home...Reducing the use of fertiliser an' + 34\n",
-       "  new_name: 'w16_10 - WET TROPICS Home Stewardship FERTILISER & PESTICIDE' + 21\n",
-       "w16_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_10'\n",
-       "  new_name: 'None'\n",
-       "w16_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_11 - Put lawn clippings in the bin or compost (to avo' + 28\n",
-       "  new_name: 'w16_11 - WET TROPICS Home Stewardship LAWN CLIPPINGS (Catego' + 9\n",
-       "w16_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_11'\n",
-       "  new_name: 'None'\n",
-       "w16_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_12 - Wash cars on the lawn, not the driveway or road ' + 35\n",
-       "  new_name: 'w16_12 - WET TROPICS Home Stewardship CAR WASHING (Categorie' + 6\n",
-       "w16_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_12'\n",
-       "  new_name: 'None'\n",
-       "w16_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W16_13 - Purchase and use detergents and cleaning product' + 28\n",
-       "  new_name: 'w16_13 - WET TROPICS Home Stewardship LOW PHOSPHATE (Categor' + 8\n",
-       "w16_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w16_13'\n",
-       "  new_name: 'None'\n",
-       "w17_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_1 - I can make a personal difference to improving wat' + 25\n",
-       "  new_name: 'w17_1 - WET TROPICS - I can make a PERSONAL DIFFERENCE to im' + 37\n",
-       "w17_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_1'\n",
-       "  new_name: 'None'\n",
-       "w17_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_2 - I feel a sense of responsibility to help to impro' + 18\n",
-       "  new_name: 'w17_2 - WET TROPICS - I feel a SENSE OF RESPONSIBILITY to he' + 40\n",
-       "w17_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_2'\n",
-       "  new_name: 'None'\n",
-       "w17_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_3 - I want to do more to help improve waterway health' + 13\n",
-       "  new_name: 'w17_3 - WET TROPICS - I WANT TO DO MORE to help improve wate' + 25\n",
-       "w17_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_3'\n",
-       "  new_name: 'None'\n",
-       "w17_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_4 - I feel hopeful about the future health of waterwa' + 15\n",
-       "  new_name: 'w17_4 - WET TROPICS - I FEEL HOPEFUL about the future of wat' + 33\n",
-       "w17_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_4'\n",
-       "  new_name: 'None'\n",
-       "w17_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_5 - I don't have enough time to contribute to improvi' + 31\n",
-       "  new_name: 'w17_5 - WET TROPICS - I DON'T HAVE ENOUGH TIME to contribute' + 43\n",
-       "w17_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_5'\n",
-       "  new_name: 'None'\n",
-       "w17_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_6 - I don't know how I could contribute to improving ' + 28\n",
-       "  new_name: 'w17_6 - WET TROPICS - I DON'T KNOW HOW to contribute to impr' + 35\n",
-       "w17_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_6'\n",
-       "  new_name: 'None'\n",
-       "w17_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_7 - Local residents in my region are supportive of ta' + 38\n",
-       "  new_name: 'w17_7 - WET TROPICS - LOCAL RESIDENTS SUPPORT action to impr' + 33\n",
-       "w17_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_7'\n",
-       "  new_name: 'None'\n",
-       "w17_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W17_8 - Many local residents in my region are taking acti' + 29\n",
-       "  new_name: 'w17_8 - WET TROPICS - Many local RESIDENTS TAKING ACTION to ' + 37\n",
-       "w17_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w17_8'\n",
-       "  new_name: 'None'\n",
-       "w18_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_1 - Overall, I feel satisfied with how local waterway' + 13\n",
-       "  new_name: 'w18_1 - WET TROPICS - SATISFIED with how local waterways are' + 22\n",
-       "w18_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_1'\n",
-       "  new_name: 'None'\n",
-       "w18_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_2 - I think that tourism uses of waterways in our reg' + 20\n",
-       "  new_name: 'w18_2 - WET TROPICS - I think TOURISM USES of waterways are ' + 26\n",
-       "w18_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_2'\n",
-       "  new_name: 'None'\n",
-       "w18_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_3 - I think that agricultural uses of waterways in ou' + 25\n",
-       "  new_name: 'w18_3 - WET TROPICS - I think AGRICULTURE USES of waterways ' + 30\n",
-       "w18_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_3'\n",
-       "  new_name: 'None'\n",
-       "w18_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_4 - I think that the fisheries in our region are well' + 8\n",
-       "  new_name: 'w18_4 - WET TROPICS - I think FISHERIES in region are well m' + 20\n",
-       "w18_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_4'\n",
-       "  new_name: 'None'\n",
-       "w18_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_5 - I think that aquaculture uses of waterways in our' + 24\n",
-       "  new_name: 'w18_5 - WET TROPICS - I think AQUACULTURE in region is well ' + 21\n",
-       "w18_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_5'\n",
-       "  new_name: 'None'\n",
-       "w18_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_6 - I think that ports and shipping in our region are' + 13\n",
-       "  new_name: 'w18_6 - WET TROPICS - I think PORTS & SHIPPING in region are' + 27\n",
-       "w18_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_6'\n",
-       "  new_name: 'None'\n",
-       "w18_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_7 - I think that decisions about managing local water' + 27\n",
-       "  new_name: 'w18_7 - WET TROPICS - I think waterway management DECISIONS ' + 29\n",
-       "w18_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_7'\n",
-       "  new_name: 'None'\n",
-       "w18_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_8 - I do not have fair access to all the waterways in' + 35\n",
-       "  new_name: 'w18_8 - WET TROPICS - I DON'T HAVE FAIR ACCESS to all the wa' + 47\n",
-       "w18_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_8'\n",
-       "  new_name: 'None'\n",
-       "w18_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_9 - I feel I personally have some influence over how ' + 27\n",
-       "  new_name: 'w18_9 - WET TROPICS - I have SOME PERSONAL INFLUENCE in loca' + 35\n",
-       "w18_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_9'\n",
-       "  new_name: 'None'\n",
-       "w18_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_10 - I feel able to have input into the management of' + 38\n",
-       "  new_name: 'w18_10 - WET TROPICS - I'm ABLE TO HAVE INPUT to management ' + 33\n",
-       "w18_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_10'\n",
-       "  new_name: 'None'\n",
-       "w18_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_11 - I trust the information I receive from instituti' + 74\n",
-       "  new_name: 'w18_11 - WET TROPICS - I TRUST INFO FROM INSTITUTIONS that m' + 29\n",
-       "w18_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_11'\n",
-       "  new_name: 'None'\n",
-       "w18_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W18_12 - I trust the science about waterway health and ma' + 8\n",
-       "  new_name: 'w18_12 - WET TROPICS - I TRUST THE SCIENCE about waterway he' + 31\n",
-       "w18_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w18_12'\n",
-       "  new_name: 'None'\n",
-       "w19: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W19 - Any additional comments about waterway management?'\n",
-       "  new_name: 'w19 - WET TROPICS - Any additional comments about waterway m' + 10\n",
-       "w20: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W20 - A little bit more about you, to help us better unde' + 153\n",
-       "  new_name: 'w20 - WET TROPICS - Proportion of household income from wate' + 118\n",
-       "w20-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w20'\n",
-       "  new_name: 'None'\n",
-       "w21_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_1 - Tourism (including hospitality)'\n",
-       "  new_name: 'w21_1 - WET TROPICS - Which Sector of waterway dependent bus' + 22\n",
-       "w21_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_2 - Boating and retail'\n",
-       "  new_name: 'w21_2 - WET TROPICS - Which Sector of waterway dependent bus' + 31\n",
-       "w21_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_3 - Fishing'\n",
-       "  new_name: 'w21_3 - WET TROPICS - Which Sector of waterway dependent bus' + 22\n",
-       "w21_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_4 - Government'\n",
-       "  new_name: 'w21_4 - WET TROPICS - Which Sector of waterway dependent bus' + 25\n",
-       "w21_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_5 - Science and Education'\n",
-       "  new_name: 'w21_5 - WET TROPICS - Which Sector of waterway dependent bus' + 34\n",
-       "w21_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_6 - Agriculture'\n",
-       "  new_name: 'w21_6 - WET TROPICS - Which Sector of waterway dependent bus' + 26\n",
-       "w21_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_7 - Mining'\n",
-       "  new_name: 'w21_7 - WET TROPICS - Which Sector of waterway dependent bus' + 21\n",
-       "w21_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_8 - Aquaculture'\n",
-       "  new_name: 'w21_8 - WET TROPICS - Which Sector of waterway dependent bus' + 26\n",
-       "w21_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_9 - Ports or shipping'\n",
-       "  new_name: 'w21_9 - WET TROPICS - Which Sector of waterway dependent bus' + 32\n",
-       "w21_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_10 - Non-government organisation'\n",
-       "  new_name: 'w21_10 - WET TROPICS - Which Sector of waterway dependent bu' + 28\n",
-       "w21_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_11 - Other (please describe:)'\n",
-       "  new_name: 'w21_11 - WET TROPICS - Which Sector of waterway dependent bu' + 21\n",
-       "w21_11_other: string\n",
-       "  -- field metadata --\n",
-       "  description: 'W21_11_Other - OTHER TEXT'\n",
-       "  new_name: 'w21_11_other - WET TROPICS - Which OTHER sector of waterway ' + 31\n",
-       "w22_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W22 - For how many years have you lived in the Wet Tropic' + 9\n",
-       "  new_name: 'w22_1 - WET TROPICS - How many years have you lived in regio' + 2\n",
-       "w23: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W23 - Q23 - Do you identify as an Aboriginal Australian, ' + 26\n",
-       "  new_name: 'w23 - WET TROPICS - Do you identify as Aboriginal Australian' + 49\n",
-       "w23-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w23'\n",
-       "  new_name: 'None'\n",
-       "w24: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W24 - Q24 - What sector do you mainly work in?'\n",
-       "  new_name: 'w24 - WET TROPICS - Which sector do you mainly work in? (22 ' + 15\n",
-       "w24-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w24'\n",
-       "  new_name: 'None'\n",
-       "w25: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W25 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 'w25 - WET TROPICS - Prior to completing survey, were you AWA' + 32\n",
-       "w25-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w25'\n",
-       "  new_name: 'None'\n",
-       "w26: double\n",
-       "  -- field metadata --\n",
-       "  description: 'W26 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 'w26 - WET TROPICS - Prior to completing survey, were you AWA' + 32\n",
-       "w26-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for w26'\n",
-       "  new_name: 'None'\n",
-       "t4_1: string\n",
-       "  -- field metadata --\n",
-       "  description: 'T4_1 - What are the first words that come to mind when yo' + 46\n",
-       "  new_name: 't4_1 - TOWNSVILLE ONLY - First words; Word One'\n",
-       "t4_2: string\n",
-       "  -- field metadata --\n",
-       "  description: 'T4_2 - What are the first words that come to mind when yo' + 46\n",
-       "  new_name: 't4_2 - TOWNSVILLE ONLY - First words; Word Two'\n",
-       "t4_3: string\n",
-       "  -- field metadata --\n",
-       "  description: 'T4_3 - What are the first words that come to mind when yo' + 46\n",
-       "  new_name: 't4_3 - TOWNSVILLE ONLY - First words; Word Three'\n",
-       "t5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T5 -  In the last 12 months, how often did you visit a fr' + 156\n",
-       "  new_name: 't5 - TOWNSVILLE ONLY - How often visited FRESHWATER in regio' + 18\n",
-       "t5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t5'\n",
-       "  new_name: 'None'\n",
-       "t5a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T5A_1 - Recreation'\n",
-       "  new_name: 't5a_1 - TOWNSVILLE ONLY - Freshwater visits for RECREATION (' + 6\n",
-       "t5a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T5A_2 - Work'\n",
-       "  new_name: 't5a_2 - TOWNSVILLE ONLY - Freshwater visits for WORK (1=Yes)'\n",
-       "t5a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T5A_3 - Cultural reasons'\n",
-       "  new_name: 't5a_3 - TOWNSVILLE ONLY - Freshwater visits for CULTURE (1=Y' + 3\n",
-       "t5a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T5A_4 - Other reason'\n",
-       "  new_name: 't5a_4 - TOWNSVILLE ONLY - Freshwater visits for OTHER REASON' + 9\n",
-       "t6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T6 - In the previous 12 months, how often did you visit a' + 198\n",
-       "  new_name: 't6 - TOWNSVILLE ONLY - How often visited ESTUARY in region (' + 15\n",
-       "t6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t6'\n",
-       "  new_name: 'None'\n",
-       "t6a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T6A_1 - Recreation'\n",
-       "  new_name: 't6a_1 - TOWNSVILLE ONLY - Estuary visits for RECREATION (1=Y' + 3\n",
-       "t6a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T6A_2 - Work'\n",
-       "  new_name: 't6a_2 - TOWNSVILLE ONLY - Estuary visits for WORK (1=Yes)'\n",
-       "t6a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T6A_3 - Cultural reasons'\n",
-       "  new_name: 't6a_3 - TOWNSVILLE ONLY - Estuary visits for CULTURE (1=Yes)'\n",
-       "t6a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T6A_4 - Other reason'\n",
-       "  new_name: 't6a_4 - TOWNSVILLE ONLY - Estuary visits for OTHER REASONS (' + 6\n",
-       "t7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T7 - In the previous 12 months, how often did you visit a' + 198\n",
-       "  new_name: 't7 - TOWNSVILLE ONLY - How often visited COASTAL & INSHORE i' + 25\n",
-       "t7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t7'\n",
-       "  new_name: 'None'\n",
-       "t7a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T7A_1 - Recreation'\n",
-       "  new_name: 't7a_1 - TOWNSVILLE ONLY - Coastal & Inshore visits for RECRE' + 13\n",
-       "t7a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T7A_2 - Work'\n",
-       "  new_name: 't7a_2 - TOWNSVILLE ONLY - Coastal & Inshore visits for WORK ' + 7\n",
-       "t7a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T7A_3 - Cultural reasons'\n",
-       "  new_name: 't7a_3 - TOWNSVILLE ONLY - Coastal & Inshore visits for CULTU' + 10\n",
-       "t7a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T7A_4 - Other reason'\n",
-       "  new_name: 't7a_4 - TOWNSVILLE ONLY - Coastal & Inshore visits for OTHER' + 16\n",
-       "t8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T8 - In the previous 12 months, how often did you visit t' + 194\n",
-       "  new_name: 't8 - TOWNSVILLE ONLY - How often visited OFFSHORE in region ' + 16\n",
-       "t8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t8'\n",
-       "  new_name: 'None'\n",
-       "t8a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T8A_1 - Recreation'\n",
-       "  new_name: 't8a_1 - TOWNSVILLE ONLY - Offshore visits for RECREATION (1=' + 4\n",
-       "t8a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T8A_2 - Work'\n",
-       "  new_name: 't8a_2 - TOWNSVILLE ONLY - Offshore visits for WORK (1=Yes)'\n",
-       "t8a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T8A_3 - Cultural reasons'\n",
-       "  new_name: 't8a_3 - TOWNSVILLE ONLY - Offshore visits for CULTURE (1=Yes' + 1\n",
-       "t8a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T8A_4 - Other reason'\n",
-       "  new_name: 't8a_4 - TOWNSVILLE ONLY - Offshore visits for OTHER REASONS ' + 7\n",
-       "t9_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_1 - Fishing'\n",
-       "  new_name: 't9_1 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 19\n",
-       "t9_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_2 - Boating or sailing'\n",
-       "  new_name: 't9_2 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 30\n",
-       "t9_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_3 - Snorkelling/freediving/Scuba diving'\n",
-       "  new_name: 't9_3 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 28\n",
-       "t9_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_4 - Motor-powered water sports (e.g. water skiing, jet' + 5\n",
-       "  new_name: 't9_4 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 29\n",
-       "t9_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_5 - Wind-powered water sports (e.g. kite surfing)'\n",
-       "  new_name: 't9_5 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 28\n",
-       "t9_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_6 - Paddling/canoeing/kayaking'\n",
-       "  new_name: 't9_6 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 20\n",
-       "t9_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_7 - Camping'\n",
-       "  new_name: 't9_7 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 19\n",
-       "t9_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_8 - Swimming'\n",
-       "  new_name: 't9_8 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 20\n",
-       "t9_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_9 - Picnics and barbeques'\n",
-       "  new_name: 't9_9 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mths' + 26\n",
-       "t9_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_10 - Exercising/hiking/biking/running'\n",
-       "  new_name: 't9_10 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mth' + 29\n",
-       "t9_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_11 - Wildlife watching & appreciating nature'\n",
-       "  new_name: 't9_11 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mth' + 30\n",
-       "t9_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_12 - Other (please specify)'\n",
-       "  new_name: 't9_12 - TOWNSVILLE ONLY - Waterway Recreation in past 12 mth' + 18\n",
-       "t9_12_other: string\n",
-       "  -- field metadata --\n",
-       "  description: 'T9_12_Other - OTHER TEXT'\n",
-       "  new_name: 't9_12_other - TOWNSVILLE ONLY - Waterway Recreation OTHER AC' + 16\n",
-       "t10: string\n",
-       "  -- field metadata --\n",
-       "  description: 'T10 - What is your favourite waterway to visit in the Tow' + 30\n",
-       "  new_name: 't10 - TOWNSVILLE ONLY - Favourite Waterway for Recreation'\n",
-       "t11_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_1 - Visiting waterways in the Townsville region is im' + 44\n",
-       "  new_name: 't11_1 - TOWNSVILLE - Visiting waterways  important for QUALI' + 36\n",
-       "t11_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_1'\n",
-       "  new_name: 'None'\n",
-       "t11_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_2 - Waterways in the Townsville region support my lif' + 33\n",
-       "  new_name: 't11_2 - TOWNSVILLE - Waterways support my LIFESTYLE & RECREA' + 18\n",
-       "t11_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_2'\n",
-       "  new_name: 'None'\n",
-       "t11_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_3 - Waterways in the region provide an important  pla' + 47\n",
-       "  new_name: 't11_3 - TOWNSVILLE - Waterways  important for time with FAMI' + 26\n",
-       "t11_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_3'\n",
-       "  new_name: 'None'\n",
-       "t11_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_4 - Waterways in the region are important for providi' + 40\n",
-       "  new_name: 't11_4 - TOWNSVILLE - Waterways  important for providing FISH' + 31\n",
-       "t11_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_4'\n",
-       "  new_name: 'None'\n",
-       "t11_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_5 - Waterways in the region are an important part of ' + 10\n",
-       "  new_name: 't11_5 - TOWNSVILLE - Waterways  important part of my CULTURE' + 14\n",
-       "t11_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_5'\n",
-       "  new_name: 'None'\n",
-       "t11_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_6 - The waterways are important for allowing  me to e' + 63\n",
-       "  new_name: 't11_6 - TOWNSVILLE - Waterways  important for EXPERIENCING N' + 19\n",
-       "t11_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_6'\n",
-       "  new_name: 'None'\n",
-       "t11_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_7 - The waterways are an important source of my water' + 38\n",
-       "  new_name: 't11_7 - TOWNSVILLE - Waterways  important for DOMESTIC WATER' + 21\n",
-       "t11_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_7'\n",
-       "  new_name: 'None'\n",
-       "t11_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_8 - Waterways in my region are important for my healt' + 16\n",
-       "  new_name: 't11_8 - TOWNSVILLE - Waterways  important for my HEALTH & LI' + 22\n",
-       "t11_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_8'\n",
-       "  new_name: 'None'\n",
-       "t11_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_9 - I am proud of the local waterways in my region'\n",
-       "  new_name: 't11_9 - TOWNSVILLE - I am PROUD of waterways  in my region (' + 12\n",
-       "t11_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_9'\n",
-       "  new_name: 'None'\n",
-       "t11_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_10 - The waterways in my region are an important part' + 29\n",
-       "  new_name: 't11_10 - TOWNSVILLE - Waterways  important part of WHY I LIV' + 20\n",
-       "t11_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_10'\n",
-       "  new_name: 'None'\n",
-       "t11_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_11 - The natural beauty of waterways in the Townsvill' + 23\n",
-       "  new_name: 't11_11 - TOWNSVILLE - Waterways in region BEAUTY IS OUTSTAND' + 17\n",
-       "t11_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_11'\n",
-       "  new_name: 'None'\n",
-       "t11_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T11_12 - If the health of waterways in my region declined' + 32\n",
-       "  new_name: 't11_12 - TOWNSVILLE - If waterway health declined I would be' + 34\n",
-       "t11_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t11_12'\n",
-       "  new_name: 'None'\n",
-       "t12_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_1 - The waterways support a variety of native habitat' + 22\n",
-       "  new_name: 't12_1 - TOWNSVILLE - VALUE  waterways BIODIVERSITY (Rating 1' + 4\n",
-       "t12_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_1'\n",
-       "  new_name: 'None'\n",
-       "t12_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_2 - The fact that the waterways exist, even if I don�' + 37\n",
-       "  new_name: 't12_2 - TOWNSVILLE - VALUE  waterways EXISTENCE (Rating 1-10' + 1\n",
-       "t12_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_2'\n",
-       "  new_name: 'None'\n",
-       "t12_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_3 - The waterways offer a place for local residents t' + 29\n",
-       "  new_name: 't12_3 - TOWNSVILLE - VALUE  waterways RECREATION for LOCALS ' + 13\n",
-       "t12_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_3'\n",
-       "  new_name: 'None'\n",
-       "t12_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_4 - The waterways provide a place where people can pa' + 48\n",
-       "  new_name: 't12_4 - TOWNSVILLE - VALUE  waterways PASS DOWN KNOWLEDGE (R' + 11\n",
-       "t12_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_4'\n",
-       "  new_name: 'None'\n",
-       "t12_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_5 - The waterways have rich Traditional Owner Heritag' + 1\n",
-       "  new_name: 't12_5 - TOWNSVILLE - VALUE  waterways TO HERITAGE (Rating 1-' + 3\n",
-       "t12_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_5'\n",
-       "  new_name: 'None'\n",
-       "t12_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_6 - The waterways are an important attraction for tou' + 25\n",
-       "  new_name: 't12_6 - TOWNSVILLE - VALUE  waterways TOURISM ATTRACTION (Ra' + 10\n",
-       "t12_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_6'\n",
-       "  new_name: 'None'\n",
-       "t12_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_7 - The waterways support our local economy'\n",
-       "  new_name: 't12_7 - TOWNSVILLE - VALUE  waterways SUPPORT LOCAL ECONOMY ' + 13\n",
-       "t12_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_7'\n",
-       "  new_name: 'None'\n",
-       "t12_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_8 - The waterways provide a place where people can st' + 43\n",
-       "  new_name: 't12_8 - TOWNSVILLE - VALUE  waterways LEARN ABOUT NATURE (Ra' + 10\n",
-       "t12_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_8'\n",
-       "  new_name: 'None'\n",
-       "t12_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_9 - The waterways support recreational fishing'\n",
-       "  new_name: 't12_9 - TOWNSVILLE - VALUE  waterways SUPPORT REC FISHING (R' + 11\n",
-       "t12_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_9'\n",
-       "  new_name: 'None'\n",
-       "t12_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_10 - Our waterways are recognised nationally and inte' + 68\n",
-       "  new_name: 't12_10 - TOWNSVILLE - VALUE  waterways ICONIC STATUS (Rating' + 6\n",
-       "t12_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_10'\n",
-       "  new_name: 'None'\n",
-       "t12_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_11 - The waterways support mining, ports and shipping' + 14\n",
-       "  new_name: 't12_11 - TOWNSVILLE - VALUE  waterways SUPPORT MINING & SHIP' + 18\n",
-       "t12_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_11'\n",
-       "  new_name: 'None'\n",
-       "t12_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_12 - The waterways support local agriculture'\n",
-       "  new_name: 't12_12 - TOWNSVILLE - VALUE  waterways SUPPORT LOCAL AGRICUL' + 18\n",
-       "t12_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_12'\n",
-       "  new_name: 'None'\n",
-       "t12_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_13 - The waterways support commercial fisheries'\n",
-       "  new_name: 't12_13 - TOWNSVILLE - VALUE  waterways SUPPORT COMM FISHERIE' + 15\n",
-       "t12_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_13'\n",
-       "  new_name: 'None'\n",
-       "t12_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T12_14 - The waterways support local aquaculture'\n",
-       "  new_name: 't12_14 - TOWNSVILLE - VALUE  waterways SUPPORT LOCAL AQUACUL' + 18\n",
-       "t12_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t12_14'\n",
-       "  new_name: 'None'\n",
-       "t13_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_1 - Freshwater creeks and rivers'\n",
-       "  new_name: 't13_1 - TOWNSVILLE Perceived Health FRESH CREEKS & RIVERS (C' + 16\n",
-       "t13_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_1'\n",
-       "  new_name: 'None'\n",
-       "t13_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_2 - Freshwater lakes, dams and wetlands'\n",
-       "  new_name: 't13_2 - TOWNSVILLE Perceived Health LAKES DAMS & WETLANDS (C' + 16\n",
-       "t13_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_2'\n",
-       "  new_name: 'None'\n",
-       "t13_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_3 - Estuaries (including mangroves and saltmarshes)'\n",
-       "  new_name: 't13_3 - TOWNSVILLE Perceived Health ESTUARIES (Categories 1 ' + 4\n",
-       "t13_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_3'\n",
-       "  new_name: 'None'\n",
-       "t13_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_4 - Beaches and the coast'\n",
-       "  new_name: 't13_4 - TOWNSVILLE Perceived Health BEACHES & COAST (Categor' + 10\n",
-       "t13_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_4'\n",
-       "  new_name: 'None'\n",
-       "t13_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_5 - Seagrass'\n",
-       "  new_name: 't13_5 - TOWNSVILLE Perceived Health SEAGRASS (Categories 1 -' + 3\n",
-       "t13_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_5'\n",
-       "  new_name: 'None'\n",
-       "t13_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_6 - Inshore coral reefs'\n",
-       "  new_name: 't13_6 - TOWNSVILLE Perceived Health INSHORE REEFS (Categorie' + 8\n",
-       "t13_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_6'\n",
-       "  new_name: 'None'\n",
-       "t13_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_7 - Offshore coral reefs'\n",
-       "  new_name: 't13_7 - TOWNSVILLE Perceived Health OFFSHORE REEFS (Categori' + 9\n",
-       "t13_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_7'\n",
-       "  new_name: 'None'\n",
-       "t13_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T13_8 - Ocean and sea'\n",
-       "  new_name: 't13_8 - TOWNSVILLE Perceived Health OCEAN & SEA (Categories ' + 6\n",
-       "t13_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t13_8'\n",
-       "  new_name: 'None'\n",
-       "t14_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_1 - Poor water quality in fresh waters and estuaries'\n",
-       "  new_name: 't14_1 - TOWNSVILLE Perceived Problem FRESH WATER QUALITY (Ra' + 27\n",
-       "t14_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_1'\n",
-       "  new_name: 'None'\n",
-       "t14_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_2 - Poor coastal and inshore water quality'\n",
-       "  new_name: 't14_2 - TOWNSVILLE Perceived Problem INSHORE WATER QUALITY (' + 29\n",
-       "t14_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_2'\n",
-       "  new_name: 'None'\n",
-       "t14_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_3 - Poor offshore marine water quality'\n",
-       "  new_name: 't14_3 - TOWNSVILLE Perceived Problem OFFSHORE WATER QUALITY ' + 30\n",
-       "t14_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_3'\n",
-       "  new_name: 'None'\n",
-       "t14_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_4 - Riverbank erosion'\n",
-       "  new_name: 't14_4 - TOWNSVILLE Perceived Problem RIVERBANK EROSION (Rati' + 25\n",
-       "t14_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_4'\n",
-       "  new_name: 'None'\n",
-       "t14_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_5 - Coastal erosion'\n",
-       "  new_name: 't14_5 - TOWNSVILLE Perceived Problem COASTAL EROSION (Rating' + 23\n",
-       "t14_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_5'\n",
-       "  new_name: 'None'\n",
-       "t14_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_6 - Weeds in and around waterways'\n",
-       "  new_name: 't14_6 - TOWNSVILLE Perceived Problem WATERWAY WEEDS (Rating ' + 22\n",
-       "t14_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_6'\n",
-       "  new_name: 'None'\n",
-       "t14_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_7 - Litter and debris (e.g. plastics)'\n",
-       "  new_name: 't14_7 - TOWNSVILLE Perceived Problem LITTER & DEBRIS (Rating' + 23\n",
-       "t14_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_7'\n",
-       "  new_name: 'None'\n",
-       "t14_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_8 - Chemical pollutants (e.g. pesticides, PFAS)'\n",
-       "  new_name: 't14_8 - TOWNSVILLE Perceived Problem CHEMICALS (Rating 1-5; ' + 17\n",
-       "t14_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_8'\n",
-       "  new_name: 'None'\n",
-       "t14_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_9 - Invasive fish (e.g. Tilapia)'\n",
-       "  new_name: 't14_9 - TOWNSVILLE Perceived Problem INVASIVE FISH (Rating 1' + 21\n",
-       "t14_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_9'\n",
-       "  new_name: 'None'\n",
-       "t14_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_10 - Low abundance of fish'\n",
-       "  new_name: 't14_10 - TOWNSVILLE Perceived Problem FISH ABUNDANCE (Rating' + 23\n",
-       "t14_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_10'\n",
-       "  new_name: 'None'\n",
-       "t14_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_11 - Decline in waterbirds and shorebirds'\n",
-       "  new_name: 't14_11 - TOWNSVILLE Perceived Problem DECLINE IN BIRDS (Rati' + 25\n",
-       "t14_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_11'\n",
-       "  new_name: 'None'\n",
-       "t14_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_12 - Algal blooms and fish kills'\n",
-       "  new_name: 't14_12 - TOWNSVILLE Perceived Problem ALGAE & FISH KILLS (Ra' + 27\n",
-       "t14_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_12'\n",
-       "  new_name: 'None'\n",
-       "t14_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_13 - Decline in numbers of marine megafauna (e.g. tur' + 24\n",
-       "  new_name: 't14_13 - TOWNSVILLE Perceived Problem MEGAFAUNA DECLINE (Rat' + 26\n",
-       "t14_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_13'\n",
-       "  new_name: 'None'\n",
-       "t14_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_14 - Poor fish health (e.g. presence of lesions)'\n",
-       "  new_name: 't14_14 - TOWNSVILLE Perceived Problem FISH HEALTH (Rating 1-' + 20\n",
-       "t14_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_14'\n",
-       "  new_name: 'None'\n",
-       "t14_15: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T14_15 - Bushfire'\n",
-       "  new_name: 't14_15 - TOWNSVILLE Perceived Problem BUSHFIRE (Rating 1-5; ' + 17\n",
-       "t14_15-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t14_15'\n",
-       "  new_name: 'None'\n",
-       "t15_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_1 - Illegal fishing practices (e.g. poaching in no-ta' + 9\n",
-       "  new_name: 't15_1 - TOWNSVILLE Perceived Threat ILLEGAL FISHING (Rating ' + 22\n",
-       "t15_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_1'\n",
-       "  new_name: 'None'\n",
-       "t15_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_2 - Over-fishing'\n",
-       "  new_name: 't15_2 - TOWNSVILLE Perceived Threat OVER-FISHING (Rating 1-5' + 19\n",
-       "t15_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_2'\n",
-       "  new_name: 'None'\n",
-       "t15_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_3 - Sediments, nutrients and pesticides from land-bas' + 9\n",
-       "  new_name: 't15_3 - TOWNSVILLE Perceived Threat LAND RUN-OFF (Rating 1-5' + 19\n",
-       "t15_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_3'\n",
-       "  new_name: 'None'\n",
-       "t15_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_4 - Climate change'\n",
-       "  new_name: 't15_4 - TOWNSVILLE Perceived Threat CLIMATE CHANGE (Rating 1' + 21\n",
-       "t15_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_4'\n",
-       "  new_name: 'None'\n",
-       "t15_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_5 - Tourism activities'\n",
-       "  new_name: 't15_5 - TOWNSVILLE Perceived Threat TOURISM ACTIVITIES (Rati' + 25\n",
-       "t15_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_5'\n",
-       "  new_name: 'None'\n",
-       "t15_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_6 - Land clearing'\n",
-       "  new_name: 't15_6 - TOWNSVILLE Perceived Threat LAND CLEARING (Rating 1-' + 20\n",
-       "t15_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_6'\n",
-       "  new_name: 'None'\n",
-       "t15_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_7 - Ports and shipping activities'\n",
-       "  new_name: 't15_7 - TOWNSVILLE Perceived Threat PORTS & SHIPPING (Rating' + 23\n",
-       "t15_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_7'\n",
-       "  new_name: 'None'\n",
-       "t15_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_8 - Mining activities (e.g. water extraction, release' + 2\n",
-       "  new_name: 't15_8 - TOWNSVILLE Perceived Threat MINING ACTIVITIES (Ratin' + 24\n",
-       "t15_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_8'\n",
-       "  new_name: 'None'\n",
-       "t15_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_9 - Coastal development'\n",
-       "  new_name: 't15_9 - TOWNSVILLE Perceived Threat COASTAL DEVELOPMENT (Rat' + 26\n",
-       "t15_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_9'\n",
-       "  new_name: 'None'\n",
-       "t15_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_10 - Extreme weather (e.g. cyclones, droughts, floods' + 1\n",
-       "  new_name: 't15_10 - TOWNSVILLE Perceived Threat EXTREME WEATHER (Rating' + 23\n",
-       "t15_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_10'\n",
-       "  new_name: 'None'\n",
-       "t15_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T15_11 - Recreational activities (e.g. boats, fishers, wa' + 11\n",
-       "  new_name: 't15_11 - TOWNSVILLE Perceived Threat RECREATION (Rating 1-5;' + 18\n",
-       "t15_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t15_11'\n",
-       "  new_name: 'None'\n",
-       "t16_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_1 - For people who have or use a boat...Responsible a' + 102\n",
-       "  new_name: 't16_1 - TOWNSVILLE Waterway Stewardship ANCHOR RESPONSIBLY (' + 15\n",
-       "t16_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_1'\n",
-       "  new_name: 'None'\n",
-       "t16_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_2 - For people who go fishing...Responsible fishing p' + 132\n",
-       "  new_name: 't16_2 - TOWNSVILLE Waterway Stewardship FISH RESPONSIBLY (Ca' + 13\n",
-       "t16_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_2'\n",
-       "  new_name: 'None'\n",
-       "t16_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_3 - On and around waterways...Dispose of food scraps ' + 89\n",
-       "  new_name: 't16_3 - TOWNSVILLE Waterway Stewardship RUBBISH DISPOSAL (Ca' + 13\n",
-       "t16_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_3'\n",
-       "  new_name: 'None'\n",
-       "t16_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_4 - Report suspicious activity to relevant authoritie' + 75\n",
-       "  new_name: 't16_4 - TOWNSVILLE Waterway Stewardship REPORT SUSPICIOUS (C' + 14\n",
-       "t16_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_4'\n",
-       "  new_name: 'None'\n",
-       "t16_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_5 - Report invasive or pest species to relevant autho' + 66\n",
-       "  new_name: 't16_5 - TOWNSVILLE Waterway Stewardship REPORT INVASIVES (Ca' + 13\n",
-       "t16_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_5'\n",
-       "  new_name: 'None'\n",
-       "t16_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_6 - Contribute to environmental monitoring programs (' + 74\n",
-       "  new_name: 't16_6 - TOWNSVILLE Waterway Stewardship CONTRIBUTE TO MONITO' + 21\n",
-       "t16_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_6'\n",
-       "  new_name: 'None'\n",
-       "t16_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_7 - Participate in local clean-ups (e.g. picking up r' + 25\n",
-       "  new_name: 't16_7 - TOWNSVILLE Waterway Stewardship LOCAL CLEAN UPS (Cat' + 12\n",
-       "t16_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_7'\n",
-       "  new_name: 'None'\n",
-       "t16_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_8 - Participate in local environmental restoration (e' + 51\n",
-       "  new_name: 't16_8 - TOWNSVILLE Waterway Stewardship LOCAL RESTORATION (C' + 14\n",
-       "t16_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_8'\n",
-       "  new_name: 'None'\n",
-       "t16_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_9 - Responsible four-wheel driving (e.g. avoiding riv' + 70\n",
-       "  new_name: 't16_9 - TOWNSVILLE Waterway Stewardship 4WD RESPONSIBLY (Cat' + 12\n",
-       "t16_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_9'\n",
-       "  new_name: 'None'\n",
-       "t16_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_10 - When at home...Reduce the use of fertilizer and ' + 32\n",
-       "  new_name: 't16_10 - TOWNSVILLE Home Stewardship FERTILISER & PESTICIDE ' + 20\n",
-       "t16_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_10'\n",
-       "  new_name: 'None'\n",
-       "t16_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_11 - Put lawn clippings in the bin or compost (to avo' + 28\n",
-       "  new_name: 't16_11 - TOWNSVILLE Home Stewardship LAWN CLIPPINGS (Categor' + 8\n",
-       "t16_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_11'\n",
-       "  new_name: 'None'\n",
-       "t16_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_12 - Wash cars on the lawn, not the driveway or road ' + 35\n",
-       "  new_name: 't16_12 - TOWNSVILLE Home Stewardship CAR WASHING (Categories' + 5\n",
-       "t16_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_12'\n",
-       "  new_name: 'None'\n",
-       "t16_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_13 - Purchase and use detergents and cleaning product' + 28\n",
-       "  new_name: 't16_13 - TOWNSVILLE Home Stewardship LOW PHOSPHATE (Categori' + 7\n",
-       "t16_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_13'\n",
-       "  new_name: 'None'\n",
-       "t16_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_14 - Dispose of or recycle oils, paints, and chemical' + 23\n",
-       "  new_name: 't16_14 - TOWNSVILLE Home Stewardship OIL DISPOSAL (Categorie' + 6\n",
-       "t16_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_14'\n",
-       "  new_name: 'None'\n",
-       "t16_15: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T16_15 - Use a device for capturing microfibre from washi' + 20\n",
-       "  new_name: 't16_15 - TOWNSVILLE Home Stewardship MICROFIBRES (Categories' + 5\n",
-       "t16_15-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t16_15'\n",
-       "  new_name: 'None'\n",
-       "t17_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_1 - I can make a personal difference to improving wat' + 25\n",
-       "  new_name: 't17_1 - TOWNSVILLE - I can make a PERSONAL DIFFERENCE to imp' + 36\n",
-       "t17_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_1'\n",
-       "  new_name: 'None'\n",
-       "t17_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_2 - I feel a sense of responsibility to help to impro' + 18\n",
-       "  new_name: 't17_2 - TOWNSVILLE - I feel a SENSE OF RESPONSIBILITY to hel' + 39\n",
-       "t17_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_2'\n",
-       "  new_name: 'None'\n",
-       "t17_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_3 - I want to do more to help improve waterway health' + 13\n",
-       "  new_name: 't17_3 - TOWNSVILLE - I WANT TO DO MORE to help improve water' + 24\n",
-       "t17_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_3'\n",
-       "  new_name: 'None'\n",
-       "t17_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_4 - I feel hopeful about the future health of waterwa' + 15\n",
-       "  new_name: 't17_4 - TOWNSVILLE - I FEEL HOPEFUL about the future of wate' + 32\n",
-       "t17_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_4'\n",
-       "  new_name: 'None'\n",
-       "t17_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_5 - I don't have enough time to contribute to improvi' + 31\n",
-       "  new_name: 't17_5 - TOWNSVILLE - I DON'T HAVE ENOUGH TIME to contribute ' + 42\n",
-       "t17_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_5'\n",
-       "  new_name: 'None'\n",
-       "t17_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_6 - I don't know how I could contribute to improve wa' + 26\n",
-       "  new_name: 't17_6 - TOWNSVILLE - I DON'T KNOW HOW to contribute to impro' + 34\n",
-       "t17_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_6'\n",
-       "  new_name: 'None'\n",
-       "t17_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_7 - Local residents in my region are supportive of ta' + 38\n",
-       "  new_name: 't17_7 - TOWNSVILLE - LOCAL RESIDENTS SUPPORT action to impro' + 32\n",
-       "t17_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_7'\n",
-       "  new_name: 'None'\n",
-       "t17_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T17_8 - Many local residents in my region are taking acti' + 29\n",
-       "  new_name: 't17_8 - TOWNSVILLE - Many local RESIDENTS TAKING ACTION to i' + 36\n",
-       "t17_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t17_8'\n",
-       "  new_name: 'None'\n",
-       "t18_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_1 - Overall, I feel satisfied with how local waterway' + 13\n",
-       "  new_name: 't18_1 - TOWNSVILLE - SATISFIED with how local waterways are ' + 21\n",
-       "t18_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_1'\n",
-       "  new_name: 'None'\n",
-       "t18_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_2 - I think that tourism uses of waterways in our reg' + 20\n",
-       "  new_name: 't18_2 - TOWNSVILLE - I think TOURISM USES of waterways are w' + 25\n",
-       "t18_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_2'\n",
-       "  new_name: 'None'\n",
-       "t18_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_3 - I think that agricultural uses of waterways in ou' + 25\n",
-       "  new_name: 't18_3 - TOWNSVILLE - I think AGRICULTURE USES of waterways a' + 29\n",
-       "t18_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_3'\n",
-       "  new_name: 'None'\n",
-       "t18_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_4 - I think that the fisheries in our region are well' + 8\n",
-       "  new_name: 't18_4 - TOWNSVILLE - I think FISHERIES in region are well ma' + 19\n",
-       "t18_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_4'\n",
-       "  new_name: 'None'\n",
-       "t18_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_5 - I think that aquaculture uses of waterways in our' + 24\n",
-       "  new_name: 't18_5 - TOWNSVILLE - I think AQUACULTURE in region is well m' + 20\n",
-       "t18_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_5'\n",
-       "  new_name: 'None'\n",
-       "t18_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_6 - I think that ports and shipping in our region are' + 13\n",
-       "  new_name: 't18_6 - TOWNSVILLE - I think PORTS & SHIPPING in region are ' + 26\n",
-       "t18_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_6'\n",
-       "  new_name: 'None'\n",
-       "t18_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_7 - I think that decisions about managing local water' + 27\n",
-       "  new_name: 't18_7 - TOWNSVILLE - I think waterway management DECISIONS A' + 28\n",
-       "t18_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_7'\n",
-       "  new_name: 'None'\n",
-       "t18_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_8 - I do not have fair access to all the waterways in' + 35\n",
-       "  new_name: 't18_8 - TOWNSVILLE - I DON'T HAVE FAIR ACCESS to all the wat' + 46\n",
-       "t18_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_8'\n",
-       "  new_name: 'None'\n",
-       "t18_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_9 - I feel I personally have some influence over how ' + 27\n",
-       "  new_name: 't18_9 - TOWNSVILLE - I have SOME PERSONAL INFLUENCE in local' + 34\n",
-       "t18_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_9'\n",
-       "  new_name: 'None'\n",
-       "t18_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_10 - I feel able to have input into the management of' + 38\n",
-       "  new_name: 't18_10 - TOWNSVILLE - I'm ABLE TO HAVE INPUT to management o' + 32\n",
-       "t18_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_10'\n",
-       "  new_name: 'None'\n",
-       "t18_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_11 - I trust the information I receive from instituti' + 74\n",
-       "  new_name: 't18_11 - TOWNSVILLE - I TRUST INFO FROM INSTITUTIONS that ma' + 28\n",
-       "t18_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_11'\n",
-       "  new_name: 'None'\n",
-       "t18_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T18_12 - I trust the science about waterway health and ma' + 8\n",
-       "  new_name: 't18_12 - TOWNSVILLE - I TRUST THE SCIENCE about waterway hea' + 30\n",
-       "t18_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t18_12'\n",
-       "  new_name: 'None'\n",
-       "t19: string\n",
-       "  -- field metadata --\n",
-       "  description: 'T19 - Any additional comments about waterway management?'\n",
-       "  new_name: 't19 - TOWNSVILLE - Any additional comments about waterway ma' + 9\n",
-       "t20: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T20 - A little bit more about you, to help us better unde' + 151\n",
-       "  new_name: 't20 - TOWNSVILLE - Proportion of household income from water' + 117\n",
-       "t20-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t20'\n",
-       "  new_name: 'None'\n",
-       "t21_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_1 - Tourism (including hospitality)'\n",
-       "  new_name: 't21_1 - TOWNSVILLE - Which Sector of waterway dependent busi' + 21\n",
-       "t21_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_2 - Boating and retail'\n",
-       "  new_name: 't21_2 - TOWNSVILLE - Which Sector of waterway dependent busi' + 30\n",
-       "t21_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_3 - Fishing'\n",
-       "  new_name: 't21_3 - TOWNSVILLE - Which Sector of waterway dependent busi' + 21\n",
-       "t21_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_4 - Government'\n",
-       "  new_name: 't21_4 - TOWNSVILLE - Which Sector of waterway dependent busi' + 24\n",
-       "t21_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_5 - Science and Education'\n",
-       "  new_name: 't21_5 - TOWNSVILLE - Which Sector of waterway dependent busi' + 33\n",
-       "t21_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_6 - Agriculture'\n",
-       "  new_name: 't21_6 - TOWNSVILLE - Which Sector of waterway dependent busi' + 25\n",
-       "t21_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_7 - Mining'\n",
-       "  new_name: 't21_7 - TOWNSVILLE - Which Sector of waterway dependent busi' + 20\n",
-       "t21_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_8 - Aquaculture'\n",
-       "  new_name: 't21_8 - TOWNSVILLE - Which Sector of waterway dependent busi' + 25\n",
-       "t21_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_9 - Marina, Ports or shipping'\n",
-       "  new_name: 't21_9 - TOWNSVILLE - Which Sector of waterway dependent busi' + 31\n",
-       "t21_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_10 - Non-government organisation'\n",
-       "  new_name: 't21_10 - TOWNSVILLE - Which Sector of waterway dependent bus' + 27\n",
-       "t21_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T21_11 - Other'\n",
-       "  new_name: 't21_11 - TOWNSVILLE - Which Sector of waterway dependent bus' + 20\n",
-       "t22_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T22 - For how many years have you lived in the Townsville' + 8\n",
-       "  new_name: 't22_1 - TOWNSVILLE - How many years have you lived in region' + 1\n",
-       "t23: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T23 - Do you identify as an Aboriginal Australian, or Tor' + 20\n",
-       "  new_name: 't23 - TOWNSVILLE - Do you identify as Aboriginal Australian ' + 48\n",
-       "t23-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t23'\n",
-       "  new_name: 'None'\n",
-       "t24: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T24 - What sector do you mainly work in?'\n",
-       "  new_name: 't24 - TOWNSVILLE - Which sector do you mainly work in? (22 A' + 14\n",
-       "t24-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t24'\n",
-       "  new_name: 'None'\n",
-       "t25: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T25 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 't25 - TOWNSVILLE - Prior to completing survey, were you AWAR' + 31\n",
-       "t25-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t25'\n",
-       "  new_name: 'None'\n",
-       "t26: double\n",
-       "  -- field metadata --\n",
-       "  description: 'T26 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 't26 - TOWNSVILLE - Prior to completing survey, were you AWAR' + 31\n",
-       "t26-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for t26'\n",
-       "  new_name: 'None'\n",
-       "m4_1: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M4_1 - What are the first words that come to mind when yo' + 42\n",
-       "  new_name: 'm4_1 - MWI ONLY - First words; Word One'\n",
-       "m4_2: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M4_2 - What are the first words that come to mind when yo' + 42\n",
-       "  new_name: 'm4_2 - MWI ONLY - First words; Word Two'\n",
-       "m4_3: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M4_3 - What are the first words that come to mind when yo' + 42\n",
-       "  new_name: 'm4_3 - MWI ONLY - First words; Word Three'\n",
-       "m5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M5 - In the last 12 months, how often did you visit a fre' + 146\n",
-       "  new_name: 'm5 - MWI ONLY - How often visited FRESHWATER in region (CATE' + 11\n",
-       "m5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m5'\n",
-       "  new_name: 'None'\n",
-       "m5a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M5A_1 - Recreation'\n",
-       "  new_name: 'm5a_1 - MWI ONLY - Freshwater visits for RECREATION (1=Yes)'\n",
-       "m5a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M5A_2 - Work'\n",
-       "  new_name: 'm5a_2 - MWI ONLY - Freshwater visits for WORK (1=Yes)'\n",
-       "m5a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M5A_3 - Cultural reasons'\n",
-       "  new_name: 'm5a_3 - MWI ONLY - Freshwater visits for CULTURE (1=Yes)'\n",
-       "m5a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M5A_4 - Other reason'\n",
-       "  new_name: 'm5a_4 - MWI ONLY - Freshwater visits for OTHER REASONS (1=Ye' + 2\n",
-       "m6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M6 - In the previous 12 months, how often did you visit a' + 194\n",
-       "  new_name: 'm6 - MWI ONLY - How often visited ESTUARY in region (CATEGOR' + 8\n",
-       "m6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m6'\n",
-       "  new_name: 'None'\n",
-       "m6a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M6A_1 - Recreation'\n",
-       "  new_name: 'm6a_1 - MWI ONLY - Estuary visits for RECREATION (1=Yes)'\n",
-       "m6a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M6A_2 - Work'\n",
-       "  new_name: 'm6a_2 - MWI ONLY - Estuary visits for WORK (1=Yes)'\n",
-       "m6a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M6A_3 - Cultural reasons'\n",
-       "  new_name: 'm6a_3 - MWI ONLY - Estuary visits for CULTURE (1=Yes)'\n",
-       "m6a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M6A_4 - Other reason'\n",
-       "  new_name: 'm6a_4 - MWI ONLY - Estuary visits for OTHER REASONS (1=Yes)'\n",
-       "m7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M7 - In the previous 12 months, how often did you visit a' + 194\n",
-       "  new_name: 'm7 - MWI ONLY - How often visited COASTAL & INSHORE in regio' + 18\n",
-       "m7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m7'\n",
-       "  new_name: 'None'\n",
-       "m7a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M7A_1 - Recreation'\n",
-       "  new_name: 'm7a_1 - MWI ONLY - Coastal & Inshore visits for RECREATION (' + 6\n",
-       "m7a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M7A_2 - Work'\n",
-       "  new_name: 'm7a_2 - MWI ONLY - Coastal & Inshore visits for WORK (1=Yes)'\n",
-       "m7a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M7A_3 - Cultural reasons'\n",
-       "  new_name: 'm7a_3 - MWI ONLY - Coastal & Inshore visits for CULTURE (1=Y' + 3\n",
-       "m7a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M7A_4 - Other reason'\n",
-       "  new_name: 'm7a_4 - MWI ONLY - Coastal & Inshore visits for OTHER REASON' + 9\n",
-       "m8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M8 - In the previous 12 months, how often did you visit t' + 183\n",
-       "  new_name: 'm8 - MWI ONLY - How often visited OFFSHORE in region (CATEGO' + 9\n",
-       "m8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m8'\n",
-       "  new_name: 'None'\n",
-       "m8a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M8A_1 - Recreation'\n",
-       "  new_name: 'm8a_1 - MWI ONLY - Offshore visits for RECREATION (1=Yes)'\n",
-       "m8a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M8A_2 - Work'\n",
-       "  new_name: 'm8a_2 - MWI ONLY - Offshore visits for WORK (1=Yes)'\n",
-       "m8a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M8A_3 - Cultural reasons'\n",
-       "  new_name: 'm8a_3 - MWI ONLY - Offshore visits for CULTURE (1=Yes)'\n",
-       "m8a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M8A_4 - Other reason'\n",
-       "  new_name: 'm8a_4 - MWI ONLY - Offshore visits for OTHER REASONS (1=Yes)'\n",
-       "m9_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_1 - Fishing'\n",
-       "  new_name: 'm9_1 - MWI ONLY - Waterway Recreation in past 12 mths: FISHI' + 12\n",
-       "m9_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_2 - Boating or sailing'\n",
-       "  new_name: 'm9_2 - MWI ONLY - Waterway Recreation in past 12 mths: BOATI' + 23\n",
-       "m9_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_3 - Snorkelling/freediving/Scuba diving'\n",
-       "  new_name: 'm9_3 - MWI ONLY - Waterway Recreation in past 12 mths: SNORK' + 21\n",
-       "m9_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_4 - Motor-powered water sports (e.g. water skiing, jet' + 5\n",
-       "  new_name: 'm9_4 - MWI ONLY - Waterway Recreation in past 12 mths: MOTOR' + 22\n",
-       "m9_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_5 - Wind-powered water sports (e.g. kite surfing)'\n",
-       "  new_name: 'm9_5 - MWI ONLY - Waterway Recreation in past 12 mths: WIND ' + 21\n",
-       "m9_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_6 - Paddling/canoeing/kayaking'\n",
-       "  new_name: 'm9_6 - MWI ONLY - Waterway Recreation in past 12 mths: PADDL' + 13\n",
-       "m9_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_7 - Camping'\n",
-       "  new_name: 'm9_7 - MWI ONLY - Waterway Recreation in past 12 mths: CAMPI' + 12\n",
-       "m9_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_8 - Swimming'\n",
-       "  new_name: 'm9_8 - MWI ONLY - Waterway Recreation in past 12 mths: SWIMM' + 13\n",
-       "m9_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_9 - Picnics and barbeques'\n",
-       "  new_name: 'm9_9 - MWI ONLY - Waterway Recreation in past 12 mths: PICNI' + 19\n",
-       "m9_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_10 - Exercising/hiking/biking/running'\n",
-       "  new_name: 'm9_10 - MWI ONLY - Waterway Recreation in past 12 mths: EXER' + 22\n",
-       "m9_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_11 - Wildlife watching & appreciating nature'\n",
-       "  new_name: 'm9_11 - MWI ONLY - Waterway Recreation in past 12 mths: WILD' + 23\n",
-       "m9_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_12 - Other (please specify)'\n",
-       "  new_name: 'm9_12 - MWI ONLY - Waterway Recreation in past 12 mths: OTHE' + 11\n",
-       "m9_12_other: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M9_12_Other - OTHER TEXT'\n",
-       "  new_name: 'm9_12_other - MWI ONLY - Waterway Recreation OTHER ACTIVITY ' + 9\n",
-       "m10: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M10 - What is your favourite waterway to visit in the Mac' + 26\n",
-       "  new_name: 'm10 - MWI ONLY - Favourite Waterway for Recreation'\n",
-       "m11_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_1 - Visiting waterways in the Mackay region is import' + 40\n",
-       "  new_name: 'm11_1 - MWI - Visiting waterways  important for QUALITY OF L' + 29\n",
-       "m11_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_1'\n",
-       "  new_name: 'None'\n",
-       "m11_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_2 - Waterways in the Mackay region support my lifesty' + 29\n",
-       "  new_name: 'm11_2 - MWI - Waterways support my LIFESTYLE & RECREATION (R' + 11\n",
-       "m11_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_2'\n",
-       "  new_name: 'None'\n",
-       "m11_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_3 - Waterways in the region provide an important  pla' + 47\n",
-       "  new_name: 'm11_3 - MWI - Waterways  important for time with FAMILY & FR' + 19\n",
-       "m11_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_3'\n",
-       "  new_name: 'None'\n",
-       "m11_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_4 - Waterways in the region are important for providi' + 40\n",
-       "  new_name: 'm11_4 - MWI - Waterways  important for providing FISH & SEAF' + 24\n",
-       "m11_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_4'\n",
-       "  new_name: 'None'\n",
-       "m11_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_5 - Waterways in the region are an important part of ' + 10\n",
-       "  new_name: 'm11_5 - MWI - Waterways  important part of my CULTURE (Ratin' + 7\n",
-       "m11_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_5'\n",
-       "  new_name: 'None'\n",
-       "m11_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_6 - The waterways are important for allowing  me to e' + 63\n",
-       "  new_name: 'm11_6 - MWI - Waterways  important for EXPERIENCING NATURE (' + 12\n",
-       "m11_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_6'\n",
-       "  new_name: 'None'\n",
-       "m11_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_7 - The waterways are an important source of my water' + 38\n",
-       "  new_name: 'm11_7 - MWI - Waterways  important for DOMESTIC WATER SUPPLY' + 14\n",
-       "m11_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_7'\n",
-       "  new_name: 'None'\n",
-       "m11_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_9 - I am proud of the local waterways in my region'\n",
-       "  new_name: 'm11_9 - MWI - I am PROUD of waterways  in my region (Rating ' + 5\n",
-       "m11_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_9'\n",
-       "  new_name: 'None'\n",
-       "m11_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_10 - The waterways in my region are an important part' + 29\n",
-       "  new_name: 'm11_10 - MWI - Waterways  important part of WHY I LIVE HERE ' + 13\n",
-       "m11_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_10'\n",
-       "  new_name: 'None'\n",
-       "m11_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_11 - The natural beauty of waterways in the Mackay re' + 19\n",
-       "  new_name: 'm11_11 - MWI - Waterways in region BEAUTY IS OUTSTANDING (Ra' + 10\n",
-       "m11_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_11'\n",
-       "  new_name: 'None'\n",
-       "m11_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M11_12 - If the health of waterways in my region declined' + 32\n",
-       "  new_name: 'm11_12 - MWI - If waterway health declined I would be PERSON' + 27\n",
-       "m11_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m11_12'\n",
-       "  new_name: 'None'\n",
-       "m12_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_1 - The waterways support a variety of native habitat' + 22\n",
-       "  new_name: 'm12_1 - MWI - VALUE  waterways BIODIVERSITY (Rating 1-10)'\n",
-       "m12_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_1'\n",
-       "  new_name: 'None'\n",
-       "m12_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_2 - The fact that the waterways exist, even if I don�' + 37\n",
-       "  new_name: 'm12_2 - MWI - VALUE  waterways EXISTENCE (Rating 1-10)'\n",
-       "m12_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_2'\n",
-       "  new_name: 'None'\n",
-       "m12_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_3 - The waterways offer a place for local residents t' + 29\n",
-       "  new_name: 'm12_3 - MWI - VALUE  waterways RECREATION for LOCALS (Rating' + 6\n",
-       "m12_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_3'\n",
-       "  new_name: 'None'\n",
-       "m12_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_4 - The waterways provide a place where people can pa' + 48\n",
-       "  new_name: 'm12_4 - MWI - VALUE  waterways PASS DOWN KNOWLEDGE (Rating 1' + 4\n",
-       "m12_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_4'\n",
-       "  new_name: 'None'\n",
-       "m12_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_5 - The waterways have rich Traditional Owner Heritag' + 1\n",
-       "  new_name: 'm12_5 - MWI - VALUE  waterways TO HERITAGE (Rating 1-10)'\n",
-       "m12_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_5'\n",
-       "  new_name: 'None'\n",
-       "m12_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_6 - The waterways are an important attraction for tou' + 25\n",
-       "  new_name: 'm12_6 - MWI - VALUE  waterways TOURISM ATTRACTION (Rating 1-' + 3\n",
-       "m12_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_6'\n",
-       "  new_name: 'None'\n",
-       "m12_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_7 - The waterways support our local economy'\n",
-       "  new_name: 'm12_7 - MWI - VALUE  waterways SUPPORT LOCAL ECONOMY (Rating' + 6\n",
-       "m12_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_7'\n",
-       "  new_name: 'None'\n",
-       "m12_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_8 - The waterways provide a place where people can st' + 43\n",
-       "  new_name: 'm12_8 - MWI - VALUE  waterways LEARN ABOUT NATURE (Rating 1-' + 3\n",
-       "m12_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_8'\n",
-       "  new_name: 'None'\n",
-       "m12_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_9 - The waterways support recreational fishing'\n",
-       "  new_name: 'm12_9 - MWI - VALUE  waterways SUPPORT REC FISHING (Rating 1' + 4\n",
-       "m12_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_9'\n",
-       "  new_name: 'None'\n",
-       "m12_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_10 - Our waterways are recognised nationally and inte' + 72\n",
-       "  new_name: 'm12_10 - MWI - VALUE  waterways ICONIC STATUS (Rating 1-10)'\n",
-       "m12_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_10'\n",
-       "  new_name: 'None'\n",
-       "m12_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_11 - The waterways support mining, ports and shipping' + 14\n",
-       "  new_name: 'm12_11 - MWI - VALUE  waterways SUPPORT MINING & SHIPPING (R' + 11\n",
-       "m12_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_11'\n",
-       "  new_name: 'None'\n",
-       "m12_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_12 - The waterways support local agriculture'\n",
-       "  new_name: 'm12_12 - MWI - VALUE  waterways SUPPORT LOCAL AGRICULTURE (R' + 11\n",
-       "m12_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_12'\n",
-       "  new_name: 'None'\n",
-       "m12_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_13 - The waterways support commercial fisheries'\n",
-       "  new_name: 'm12_13 - MWI - VALUE  waterways SUPPORT COMM FISHERIES (Rati' + 8\n",
-       "m12_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_13'\n",
-       "  new_name: 'None'\n",
-       "m12_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M12_14 - The waterways support local aquaculture'\n",
-       "  new_name: 'm12_14 - MWI - VALUE  waterways SUPPORT LOCAL AQUACULTURE (R' + 11\n",
-       "m12_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m12_14'\n",
-       "  new_name: 'None'\n",
-       "m13_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_1 - Freshwater creeks and rivers'\n",
-       "  new_name: 'm13_1 - MWI Perceived Health FRESH CREEKS & RIVERS (Categori' + 9\n",
-       "m13_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_1'\n",
-       "  new_name: 'None'\n",
-       "m13_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_2 - Freshwater lakes, dams and wetlands'\n",
-       "  new_name: 'm13_2 - MWI Perceived Health LAKES DAMS & WETLANDS (Categori' + 9\n",
-       "m13_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_2'\n",
-       "  new_name: 'None'\n",
-       "m13_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_3 - Estuaries (including mangroves and saltmarshes)'\n",
-       "  new_name: 'm13_3 - MWI Perceived Health ESTUARIES (Categories 1 - 5)'\n",
-       "m13_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_3'\n",
-       "  new_name: 'None'\n",
-       "m13_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_4 - Beaches and the coast'\n",
-       "  new_name: 'm13_4 - MWI Perceived Health BEACHES & COAST (Categories 1 -' + 3\n",
-       "m13_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_4'\n",
-       "  new_name: 'None'\n",
-       "m13_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_5 - Seagrass'\n",
-       "  new_name: 'm13_5 - MWI Perceived Health SEAGRASS (Categories 1 - 5)'\n",
-       "m13_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_5'\n",
-       "  new_name: 'None'\n",
-       "m13_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_6 - Inshore coral reefs'\n",
-       "  new_name: 'm13_6 - MWI Perceived Health INSHORE REEFS (Categories 1 - 5' + 1\n",
-       "m13_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_6'\n",
-       "  new_name: 'None'\n",
-       "m13_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_7 - Offshore coral reefs'\n",
-       "  new_name: 'm13_7 - MWI Perceived Health OFFSHORE REEFS (Categories 1 - ' + 2\n",
-       "m13_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_7'\n",
-       "  new_name: 'None'\n",
-       "m13_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_8 - Ocean and sea'\n",
-       "  new_name: 'm13_8 - MWI Perceived Health OCEAN & SEA (Categories 1 - 5)'\n",
-       "m13_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_8'\n",
-       "  new_name: 'None'\n",
-       "m13_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M13_9 - Groundwater'\n",
-       "  new_name: 'm13_9 - MWI Perceived Health GROUNDWATER (Categories 1 - 5)'\n",
-       "m13_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m13_9'\n",
-       "  new_name: 'None'\n",
-       "m14_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_1 - Poor water quality in fresh waters and estuaries'\n",
-       "  new_name: 'm14_1 - MWI Perceived Problem FRESH WATER QUALITY (Rating 1-' + 20\n",
-       "m14_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_1'\n",
-       "  new_name: 'None'\n",
-       "m14_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_2 - Poor coastal and inshore water quality'\n",
-       "  new_name: 'm14_2 - MWI Perceived Problem INSHORE WATER QUALITY (Rating ' + 22\n",
-       "m14_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_2'\n",
-       "  new_name: 'None'\n",
-       "m14_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_3 - Poor offshore marine water quality'\n",
-       "  new_name: 'm14_3 - MWI Perceived Problem OFFSHORE WATER QUALITY (Rating' + 23\n",
-       "m14_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_3'\n",
-       "  new_name: 'None'\n",
-       "m14_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_4 - Riverbank erosion'\n",
-       "  new_name: 'm14_4 - MWI Perceived Problem RIVERBANK EROSION (Rating 1-5;' + 18\n",
-       "m14_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_4'\n",
-       "  new_name: 'None'\n",
-       "m14_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_5 - Coastal erosion'\n",
-       "  new_name: 'm14_5 - MWI Perceived Problem COASTAL EROSION (Rating 1-5; 6' + 16\n",
-       "m14_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_5'\n",
-       "  new_name: 'None'\n",
-       "m14_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_6 - Weeds in and around waterways'\n",
-       "  new_name: 'm14_6 - MWI Perceived Problem WATERWAY WEEDS (Rating 1-5; 6 ' + 15\n",
-       "m14_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_6'\n",
-       "  new_name: 'None'\n",
-       "m14_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_7 - Litter and debris (e.g. plastics)'\n",
-       "  new_name: 'm14_7 - MWI Perceived Problem LITTER & DEBRIS (Rating 1-5; 6' + 16\n",
-       "m14_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_7'\n",
-       "  new_name: 'None'\n",
-       "m14_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_8 - Chemical pollutants (e.g. pesticides, PFAS)'\n",
-       "  new_name: 'm14_8 - MWI Perceived Problem CHEMICALS (Rating 1-5; 6 = I d' + 10\n",
-       "m14_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_8'\n",
-       "  new_name: 'None'\n",
-       "m14_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_9 - Invasive fish (e.g. Tilapia)'\n",
-       "  new_name: 'm14_9 - MWI Perceived Problem INVASIVE FISH (Rating 1-5; 6 =' + 14\n",
-       "m14_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_9'\n",
-       "  new_name: 'None'\n",
-       "m14_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_10 - Low abundance of fish'\n",
-       "  new_name: 'm14_10 - MWI Perceived Problem FISH ABUNDANCE (Rating 1-5; 6' + 16\n",
-       "m14_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_10'\n",
-       "  new_name: 'None'\n",
-       "m14_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_11 - Decline in waterbirds and shorebirds'\n",
-       "  new_name: 'm14_11 - MWI Perceived Problem DECLINE IN BIRDS (Rating 1-5;' + 18\n",
-       "m14_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_11'\n",
-       "  new_name: 'None'\n",
-       "m14_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_12 - Algal blooms and fish kills'\n",
-       "  new_name: 'm14_12 - MWI Perceived Problem ALGAE & FISH KILLS (Rating 1-' + 20\n",
-       "m14_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_12'\n",
-       "  new_name: 'None'\n",
-       "m14_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_13 - Decline in numbers of marine megafauna (e.g. tur' + 24\n",
-       "  new_name: 'm14_13 - MWI Perceived Problem MEGAFAUNA DECLINE (Rating 1-5' + 19\n",
-       "m14_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_13'\n",
-       "  new_name: 'None'\n",
-       "m14_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M14_14 - Bushfire'\n",
-       "  new_name: 'm14_14 - MWI Perceived Problem BUSHFIRE (Rating 1-5; 6 = I d' + 10\n",
-       "m14_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m14_14'\n",
-       "  new_name: 'None'\n",
-       "m15_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_1 - Illegal fishing practices (e.g. poaching in no-ta' + 9\n",
-       "  new_name: 'm15_1 - MWI Perceived Threat ILLEGAL FISHING (Rating 1-5; 6 ' + 15\n",
-       "m15_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_1'\n",
-       "  new_name: 'None'\n",
-       "m15_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_2 - Over-fishing'\n",
-       "  new_name: 'm15_2 - MWI Perceived Threat OVER-FISHING (Rating 1-5; 6 = I' + 12\n",
-       "m15_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_2'\n",
-       "  new_name: 'None'\n",
-       "m15_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_3 - Sediments, nutrients and pesticides from land-bas' + 9\n",
-       "  new_name: 'm15_3 - MWI Perceived Threat LAND RUN-OFF (Rating 1-5; 6 = I' + 12\n",
-       "m15_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_3'\n",
-       "  new_name: 'None'\n",
-       "m15_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_4 - Climate change'\n",
-       "  new_name: 'm15_4 - MWI Perceived Threat CLIMATE CHANGE (Rating 1-5; 6 =' + 14\n",
-       "m15_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_4'\n",
-       "  new_name: 'None'\n",
-       "m15_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_5 - Tourism activities'\n",
-       "  new_name: 'm15_5 - MWI Perceived Threat TOURISM ACTIVITIES (Rating 1-5;' + 18\n",
-       "m15_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_5'\n",
-       "  new_name: 'None'\n",
-       "m15_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_6 - Land clearing'\n",
-       "  new_name: 'm15_6 - MWI Perceived Threat LAND CLEARING (Rating 1-5; 6 = ' + 13\n",
-       "m15_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_6'\n",
-       "  new_name: 'None'\n",
-       "m15_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_7 - Ports and shipping activities'\n",
-       "  new_name: 'm15_7 - MWI Perceived Threat PORTS & SHIPPING (Rating 1-5; 6' + 16\n",
-       "m15_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_7'\n",
-       "  new_name: 'None'\n",
-       "m15_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_8 - Mining activities (e.g. water extraction, release' + 2\n",
-       "  new_name: 'm15_8 - MWI Perceived Threat MINING ACTIVITIES (Rating 1-5; ' + 17\n",
-       "m15_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_8'\n",
-       "  new_name: 'None'\n",
-       "m15_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_9 - Coastal development'\n",
-       "  new_name: 'm15_9 - MWI Perceived Threat COASTAL DEVELOPMENT (Rating 1-5' + 19\n",
-       "m15_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_9'\n",
-       "  new_name: 'None'\n",
-       "m15_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_10 - Extreme weather (e.g. cyclones, droughts, floods' + 1\n",
-       "  new_name: 'm15_10 - MWI Perceived Threat EXTREME WEATHER (Rating 1-5; 6' + 16\n",
-       "m15_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_10'\n",
-       "  new_name: 'None'\n",
-       "m15_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M15_11 - Recreation activities (e.g. boats, fishers, wate' + 9\n",
-       "  new_name: 'm15_11 - MWI Perceived Threat RECREATION (Rating 1-5; 6 = I ' + 11\n",
-       "m15_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m15_11'\n",
-       "  new_name: 'None'\n",
-       "m16_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_1 - For people who have or use a boat...Responsible a' + 110\n",
-       "  new_name: 'm16_1 - MWI Waterway Stewardship ANCHOR RESPONSIBLY (Categor' + 8\n",
-       "m16_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_1'\n",
-       "  new_name: 'None'\n",
-       "m16_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_2 - For people who go fishing...Responsible fishing (' + 122\n",
-       "  new_name: 'm16_2 - MWI Waterway Stewardship FISH RESPONSIBLY (Categorie' + 6\n",
-       "m16_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_2'\n",
-       "  new_name: 'None'\n",
-       "m16_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_3 - On and around waterways...Dispose of food scraps ' + 89\n",
-       "  new_name: 'm16_3 - MWI Waterway Stewardship RUBBISH DISPOSAL (Categorie' + 6\n",
-       "m16_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_3'\n",
-       "  new_name: 'None'\n",
-       "m16_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_4 - Report suspicious activity to relevant authoritie' + 75\n",
-       "  new_name: 'm16_4 - MWI Waterway Stewardship REPORT SUSPICIOUS (Categori' + 7\n",
-       "m16_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_4'\n",
-       "  new_name: 'None'\n",
-       "m16_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_5 - Report invasive or pest species to relevant autho' + 66\n",
-       "  new_name: 'm16_5 - MWI Waterway Stewardship REPORT INVASIVES (Categorie' + 6\n",
-       "m16_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_5'\n",
-       "  new_name: 'None'\n",
-       "m16_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_6 - Contribute to environmental monitoring programs (' + 74\n",
-       "  new_name: 'm16_6 - MWI Waterway Stewardship CONTRIBUTE TO MONITORING (C' + 14\n",
-       "m16_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_6'\n",
-       "  new_name: 'None'\n",
-       "m16_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_7 - Participate in local environmental clean-ups (e.g' + 39\n",
-       "  new_name: 'm16_7 - MWI Waterway Stewardship LOCAL CLEAN UPS (Categories' + 5\n",
-       "m16_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_7'\n",
-       "  new_name: 'None'\n",
-       "m16_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_8 - Participate in local environmental restoration (e' + 51\n",
-       "  new_name: 'm16_8 - MWI Waterway Stewardship LOCAL RESTORATION (Categori' + 7\n",
-       "m16_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_8'\n",
-       "  new_name: 'None'\n",
-       "m16_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_9 - Responsible four-wheel driving (e.g. avoiding riv' + 70\n",
-       "  new_name: 'm16_9 - MWI Waterway Stewardship 4WD RESPONSIBLY (Categories' + 5\n",
-       "m16_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_9'\n",
-       "  new_name: 'None'\n",
-       "m16_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_10 - When at home...Reducing the use of fertiliser an' + 34\n",
-       "  new_name: 'm16_10 - MWI Home Stewardship FERTILISER & PESTICIDE USE (Ca' + 13\n",
-       "m16_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_10'\n",
-       "  new_name: 'None'\n",
-       "m16_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_11 - Put lawn clippings in the bin or compost (to avo' + 28\n",
-       "  new_name: 'm16_11 - MWI Home Stewardship LAWN CLIPPINGS (Categories 1-5' + 1\n",
-       "m16_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_11'\n",
-       "  new_name: 'None'\n",
-       "m16_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_12 - Wash cars on the lawn, not the driveway or road ' + 35\n",
-       "  new_name: 'm16_12 - MWI Home Stewardship CAR WASHING (Categories 1-5)'\n",
-       "m16_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_12'\n",
-       "  new_name: 'None'\n",
-       "m16_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M16_13 - Purchase and use detergents and cleaning product' + 28\n",
-       "  new_name: 'm16_13 - MWI Home Stewardship LOW PHOSPHATE (Categories 1-5)'\n",
-       "m16_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m16_13'\n",
-       "  new_name: 'None'\n",
-       "m17_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_1 - I can make a personal difference to improving wat' + 25\n",
-       "  new_name: 'm17_1 - MWI - I can make a PERSONAL DIFFERENCE to improving ' + 29\n",
-       "m17_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_1'\n",
-       "  new_name: 'None'\n",
-       "m17_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_2 - I feel a sense of responsibility to help to impro' + 31\n",
-       "  new_name: 'm17_2 - MWI - I feel a SENSE OF RESPONSIBILITY to help impro' + 32\n",
-       "m17_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_2'\n",
-       "  new_name: 'None'\n",
-       "m17_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_3 - I want to do more to help improve waterway health' + 13\n",
-       "  new_name: 'm17_3 - MWI - I WANT TO DO MORE to help improve waterway hea' + 17\n",
-       "m17_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_3'\n",
-       "  new_name: 'None'\n",
-       "m17_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_4 - I feel hopeful about the future health of waterwa' + 15\n",
-       "  new_name: 'm17_4 - MWI - I FEEL HOPEFUL about the future of waterways i' + 25\n",
-       "m17_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_4'\n",
-       "  new_name: 'None'\n",
-       "m17_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_5 - I don't have enough time to contribute to improvi' + 31\n",
-       "  new_name: 'm17_5 - MWI - I DON'T HAVE ENOUGH TIME to contribute to impr' + 35\n",
-       "m17_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_5'\n",
-       "  new_name: 'None'\n",
-       "m17_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_6 - I don't know how I could contribute to improving ' + 28\n",
-       "  new_name: 'm17_6 - MWI - I DON'T KNOW HOW to contribute to improving wa' + 27\n",
-       "m17_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_6'\n",
-       "  new_name: 'None'\n",
-       "m17_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_7 - Local residents in my region are supportive of ta' + 38\n",
-       "  new_name: 'm17_7 - MWI - LOCAL RESIDENTS SUPPORT action to improve wate' + 25\n",
-       "m17_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_7'\n",
-       "  new_name: 'None'\n",
-       "m17_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M17_8 - Many local residents in my region are taking acti' + 29\n",
-       "  new_name: 'm17_8 - MWI - Many local RESIDENTS TAKING ACTION to improve ' + 29\n",
-       "m17_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m17_8'\n",
-       "  new_name: 'None'\n",
-       "m18_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_1 - Overall, I feel satisfied with how local waterway' + 13\n",
-       "  new_name: 'm18_1 - MWI - SATISFIED with how local waterways are managed' + 14\n",
-       "m18_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_1'\n",
-       "  new_name: 'None'\n",
-       "m18_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_2 - I think that tourism uses of waterways in our reg' + 20\n",
-       "  new_name: 'm18_2 - MWI - I think TOURISM USES of waterways are well man' + 18\n",
-       "m18_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_2'\n",
-       "  new_name: 'None'\n",
-       "m18_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_3 - I think that agricultural uses of waterways in ou' + 25\n",
-       "  new_name: 'm18_3 - MWI - I think AGRICULTURE USES of waterways are well' + 22\n",
-       "m18_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_3'\n",
-       "  new_name: 'None'\n",
-       "m18_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_4 - I think that the fisheries in our region are well' + 8\n",
-       "  new_name: 'm18_4 - MWI - I think FISHERIES in region are well managed (' + 12\n",
-       "m18_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_4'\n",
-       "  new_name: 'None'\n",
-       "m18_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_5 - I think that aquaculture uses of waterways in our' + 24\n",
-       "  new_name: 'm18_5 - MWI - I think AQUACULTURE in region is well managed ' + 13\n",
-       "m18_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_5'\n",
-       "  new_name: 'None'\n",
-       "m18_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_6 - I think that ports and shipping in our region are' + 13\n",
-       "  new_name: 'm18_6 - MWI - I think PORTS & SHIPPING in region are well ma' + 19\n",
-       "m18_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_6'\n",
-       "  new_name: 'None'\n",
-       "m18_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_7 - I think that decisions about managing local water' + 27\n",
-       "  new_name: 'm18_7 - MWI - I think waterway management DECISIONS ARE MADE' + 21\n",
-       "m18_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_7'\n",
-       "  new_name: 'None'\n",
-       "m18_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_8 - I do not have fair access to all the waterways in' + 35\n",
-       "  new_name: 'm18_8 - MWI - I DON'T HAVE FAIR ACCESS to all the waterways ' + 39\n",
-       "m18_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_8'\n",
-       "  new_name: 'None'\n",
-       "m18_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_9 - I feel I personally have some influence over how ' + 27\n",
-       "  new_name: 'm18_9 - MWI - I have SOME PERSONAL INFLUENCE in local waterw' + 27\n",
-       "m18_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_9'\n",
-       "  new_name: 'None'\n",
-       "m18_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_10 - I feel able to have input into the management of' + 38\n",
-       "  new_name: 'm18_10 - MWI - I'm ABLE TO HAVE INPUT to management of local' + 25\n",
-       "m18_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_10'\n",
-       "  new_name: 'None'\n",
-       "m18_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_11 - I trust the information I receive from instituti' + 74\n",
-       "  new_name: 'm18_11 - MWI - I TRUST INFO FROM INSTITUTIONS that manage wa' + 21\n",
-       "m18_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_11'\n",
-       "  new_name: 'None'\n",
-       "m18_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M18_12 - I trust the science about waterway health and ma' + 8\n",
-       "  new_name: 'm18_12 - MWI - I TRUST THE SCIENCE about waterway health & m' + 23\n",
-       "m18_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m18_12'\n",
-       "  new_name: 'None'\n",
-       "m19: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M19 - Any additional comments about waterway management?'\n",
-       "  new_name: 'm19 - MWI - Any additional comments about waterway managemen' + 2\n",
-       "m20: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M20 - A little bit more about you, to help us better unde' + 143\n",
-       "  new_name: 'm20 - MWI - Proportion of household income from waterways-re' + 110\n",
-       "m20-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m20'\n",
-       "  new_name: 'None'\n",
-       "m21_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_1 - Tourism (including hospitality)'\n",
-       "  new_name: 'm21_1 - MWI - Which Sector of waterway dependent business? T' + 14\n",
-       "m21_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_2 - Boating and retail'\n",
-       "  new_name: 'm21_2 - MWI - Which Sector of waterway dependent business? B' + 23\n",
-       "m21_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_3 - Commercial Fishing'\n",
-       "  new_name: 'm21_3 - MWI - Which Sector of waterway dependent business? F' + 14\n",
-       "m21_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_4 - Government'\n",
-       "  new_name: 'm21_4 - MWI - Which Sector of waterway dependent business? G' + 17\n",
-       "m21_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_5 - Science and Education'\n",
-       "  new_name: 'm21_5 - MWI - Which Sector of waterway dependent business? S' + 26\n",
-       "m21_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_6 - Agriculture/horticulture'\n",
-       "  new_name: 'm21_6 - MWI - Which Sector of waterway dependent business? A' + 18\n",
-       "m21_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_7 - Mining'\n",
-       "  new_name: 'm21_7 - MWI - Which Sector of waterway dependent business? M' + 13\n",
-       "m21_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_8 - Aquaculture'\n",
-       "  new_name: 'm21_8 - MWI - Which Sector of waterway dependent business? A' + 18\n",
-       "m21_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_9 - Ports or shipping'\n",
-       "  new_name: 'm21_9 - MWI - Which Sector of waterway dependent business? P' + 24\n",
-       "m21_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_10 - Non-government organisation'\n",
-       "  new_name: 'm21_10 - MWI - Which Sector of waterway dependent business? ' + 20\n",
-       "m21_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_11 - Other (please specify:)'\n",
-       "  new_name: 'm21_11 - MWI - Which Sector of waterway dependent business? ' + 13\n",
-       "m21_11_other: string\n",
-       "  -- field metadata --\n",
-       "  description: 'M21_11_Other - OTHER TEXT'\n",
-       "  new_name: 'm21_11_other - MWI - Which OTHER sector of waterway dependen' + 23\n",
-       "m22_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M22 - For how many years have you lived in the Townsville' + 8\n",
-       "  new_name: 'm22_1 - MWI - How many years have you lived in region?'\n",
-       "m23: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M23 - Do you identify as an Aboriginal Australian, or Tor' + 20\n",
-       "  new_name: 'm23 - MWI - Do you identify as Aboriginal Australian or Torr' + 41\n",
-       "m23-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m23'\n",
-       "  new_name: 'None'\n",
-       "m24: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M24 - What sector do you mainly work in?'\n",
-       "  new_name: 'm24 - MWI - Which sector do you mainly work in? (22 ABS cate' + 7\n",
-       "m24-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m24'\n",
-       "  new_name: 'None'\n",
-       "m25: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M25 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 'm25 - MWI - Prior to completing survey, were you AWARE OF RE' + 24\n",
-       "m25-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m25'\n",
-       "  new_name: 'None'\n",
-       "m26: double\n",
-       "  -- field metadata --\n",
-       "  description: 'M26 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 'm26 - MWI - Prior to completing survey, were you AWARE OF PA' + 24\n",
-       "m26-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for m26'\n",
-       "  new_name: 'None'\n",
-       "f4_1: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F4_1 - What are the first words that come to mind when yo' + 43\n",
-       "  new_name: 'f4_1 - FITZROY ONLY - First words; Word One'\n",
-       "f4_2: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F4_2 - What are the first words that come to mind when yo' + 43\n",
-       "  new_name: 'f4_2 - FITZROY ONLY - First words; Word Two'\n",
-       "f4_3: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F4_3 - What are the first words that come to mind when yo' + 43\n",
-       "  new_name: 'f4_3 - FITZROY ONLY - First words; Word Three'\n",
-       "f5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F5 - In the last 12 months, how often did you visit a fre' + 152\n",
-       "  new_name: 'f5 - FITZROY ONLY - How often visited FRESHWATER in region (' + 15\n",
-       "f5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f5'\n",
-       "  new_name: 'None'\n",
-       "f5a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F5A_1 - Recreation'\n",
-       "  new_name: 'f5a_1 - FITZROY ONLY - Freshwater visits for RECREATION (1=Y' + 3\n",
-       "f5a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F5A_2 - Work'\n",
-       "  new_name: 'f5a_2 - FITZROY ONLY - Freshwater visits for WORK (1=Yes)'\n",
-       "f5a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F5A_3 - Cultural reasons'\n",
-       "  new_name: 'f5a_3 - FITZROY ONLY - Freshwater visits for CULTURE (1=Yes)'\n",
-       "f5a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F5A_4 - Other reason'\n",
-       "  new_name: 'f5a_4 - FITZROY ONLY - Freshwater visits for OTHER REASONS (' + 6\n",
-       "f6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6 - In the previous 12 months, how often did you visit a' + 195\n",
-       "  new_name: 'f6 - FITZROY ONLY - How often visited ESTUARY in region (CAT' + 12\n",
-       "f6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f6'\n",
-       "  new_name: 'None'\n",
-       "f6a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A_1 - Recreation'\n",
-       "  new_name: 'f6a_1 - FITZROY ONLY - Estuary visits for RECREATION (1=Yes)'\n",
-       "f6a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A_2 - Work'\n",
-       "  new_name: 'f6a_2 - FITZROY ONLY - Estuary visits for WORK (1=Yes)'\n",
-       "f6a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A_3 - Cultural reasons'\n",
-       "  new_name: 'f6a_3 - - FITZROY ONLY - Estuary visits for CULTURE (1=Yes)'\n",
-       "f6a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A_4 - Other reason'\n",
-       "  new_name: 'f6a_4 - FITZROY ONLY - Estuary visits for OTHER REASONS (1=Y' + 3\n",
-       "f7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7 - In the previous 12 months, how often did you visit a' + 195\n",
-       "  new_name: 'f7 - FITZROY ONLY - How often visited COASTAL & MARINE in re' + 21\n",
-       "f7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f7'\n",
-       "  new_name: 'None'\n",
-       "f7a_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A_1 - Recreation'\n",
-       "  new_name: 'f7a_1 - FITZROY ONLY - Coastal & Marine visits for RECREATIO' + 9\n",
-       "f7a_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A_2 - Work'\n",
-       "  new_name: 'f7a_2 - FITZROY ONLY - Coastal & Marine visits for WORK (1=Y' + 3\n",
-       "f7a_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A_3 - Cultural reasons'\n",
-       "  new_name: 'f7a_3 - FITZROY ONLY - Coastal & Marine visits for CULTURE (' + 6\n",
-       "f7a_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A_4 - Other reason'\n",
-       "  new_name: 'f7a_4 - FITZROY ONLY - Coastal & Marine visits for OTHER REA' + 12\n",
-       "f9_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_1 - Fishing'\n",
-       "  new_name: 'f9_1 - FITZROY ONLY - Waterway Recreation in past 12 mths: F' + 16\n",
-       "f9_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_2 - Boating or sailing'\n",
-       "  new_name: 'f9_2 - FITZROY ONLY - Waterway Recreation in past 12 mths: B' + 27\n",
-       "f9_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_3 - Snorkelling/freediving/Scuba diving'\n",
-       "  new_name: 'f9_3 - FITZROY ONLY - Waterway Recreation in past 12 mths: S' + 25\n",
-       "f9_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_4 - Motor-powered water sports (e.g. water skiing, jet' + 5\n",
-       "  new_name: 'f9_4 - FITZROY ONLY - Waterway Recreation in past 12 mths: M' + 26\n",
-       "f9_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_5 - Wind-powered water sports (e.g. kite surfing)'\n",
-       "  new_name: 'f9_5 - FITZROY ONLY - Waterway Recreation in past 12 mths: W' + 25\n",
-       "f9_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_6 - Paddling/canoeing/kayaking'\n",
-       "  new_name: 'f9_6 - FITZROY ONLY - Waterway Recreation in past 12 mths: P' + 17\n",
-       "f9_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_7 - Camping'\n",
-       "  new_name: 'f9_7 - FITZROY ONLY - Waterway Recreation in past 12 mths: C' + 16\n",
-       "f9_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_8 - Swimming'\n",
-       "  new_name: 'f9_8 - FITZROY ONLY - Waterway Recreation in past 12 mths: S' + 17\n",
-       "f9_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_9 - Picnics and barbeques'\n",
-       "  new_name: 'f9_9 - FITZROY ONLY - Waterway Recreation in past 12 mths: P' + 23\n",
-       "f9_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_10 - Exercising/hiking/biking/running'\n",
-       "  new_name: 'f9_10 - FITZROY ONLY - Waterway Recreation in past 12 mths: ' + 26\n",
-       "f9_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_11 - Wildlife watching & appreciating nature'\n",
-       "  new_name: 'f9_11 - FITZROY ONLY - Waterway Recreation in past 12 mths: ' + 27\n",
-       "f9_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_12 - Other (please specify)'\n",
-       "  new_name: 'f9_12 - FITZROY ONLY - Waterway Recreation in past 12 mths: ' + 15\n",
-       "f9_12_other: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_12_Other - OTHER TEXT'\n",
-       "  new_name: 'f9_12_other - FITZROY ONLY - Waterway Recreation OTHER ACTIV' + 13\n",
-       "f10: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F10 - What are your favourite 3 waterways to visit in the' + 31\n",
-       "  new_name: 'f10 - FITZROY ONLY - Favourite Three (3) Waterways for Recre' + 5\n",
-       "f11_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_1 - Visiting waterways in the Fitzroy region is impor' + 41\n",
-       "  new_name: 'f11_1 - FITZROY - Visiting waterways  important for QUALITY ' + 33\n",
-       "f11_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_1'\n",
-       "  new_name: 'None'\n",
-       "f11_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_2 - Waterways in the Fitzroy region support my lifest' + 30\n",
-       "  new_name: 'f11_2 - FITZROY - Waterways support my LIFESTYLE & RECREATIO' + 15\n",
-       "f11_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_2'\n",
-       "  new_name: 'None'\n",
-       "f11_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_3 - Waterways in the region provide an important  pla' + 47\n",
-       "  new_name: 'f11_3 - FITZROY - Waterways  important for time with FAMILY ' + 23\n",
-       "f11_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_3'\n",
-       "  new_name: 'None'\n",
-       "f11_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_4 - Waterways in the region are important for providi' + 40\n",
-       "  new_name: 'f11_4 - FITZROY - Waterways  important for providing FISH & ' + 28\n",
-       "f11_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_4'\n",
-       "  new_name: 'None'\n",
-       "f11_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_5 - Waterways in the region are an important part of ' + 10\n",
-       "  new_name: 'f11_5 - FITZROY - Waterways  important part of my CULTURE (R' + 11\n",
-       "f11_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_5'\n",
-       "  new_name: 'None'\n",
-       "f11_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_6 - The waterways are important for allowing  me to e' + 63\n",
-       "  new_name: 'f11_6 - FITZROY - Waterways  important for EXPERIENCING NATU' + 16\n",
-       "f11_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_6'\n",
-       "  new_name: 'None'\n",
-       "f11_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_7 - The waterways are an important source of my water' + 38\n",
-       "  new_name: 'f11_7 - FITZROY - Waterways  important for DOMESTIC WATER SU' + 18\n",
-       "f11_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_7'\n",
-       "  new_name: 'None'\n",
-       "f11_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_8 - Waterways in my region are important for my healt' + 16\n",
-       "  new_name: 'f11_8 - FITZROY - Waterways  important for my HEALTH & LIVEL' + 19\n",
-       "f11_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_8'\n",
-       "  new_name: 'None'\n",
-       "f11_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_9 - I am proud of the local waterways in my region'\n",
-       "  new_name: 'f11_9 - FITZROY - I am PROUD of waterways  in my region (Rat' + 9\n",
-       "f11_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_9'\n",
-       "  new_name: 'None'\n",
-       "f11_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_10 - The waterways in my region are an important part' + 29\n",
-       "  new_name: 'f11_10 - FITZROY - Waterways  important part of WHY I LIVE H' + 17\n",
-       "f11_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_10'\n",
-       "  new_name: 'None'\n",
-       "f11_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_11 - The natural beauty of waterways in the Fitzroy r' + 20\n",
-       "  new_name: 'f11_11 - FITZROY - Waterways in region BEAUTY IS OUTSTANDING' + 14\n",
-       "f11_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_11'\n",
-       "  new_name: 'None'\n",
-       "f11_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11_12 - If the health of waterways in my region declined' + 32\n",
-       "  new_name: 'f11_12 - FITZROY - If waterway health declined I would be PE' + 31\n",
-       "f11_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f11_12'\n",
-       "  new_name: 'None'\n",
-       "f12_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_1 - The waterways support a variety of native habitat' + 22\n",
-       "  new_name: 'f12_1 - FITZROY - VALUE  waterways BIODIVERSITY (Rating 1-10' + 1\n",
-       "f12_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_1'\n",
-       "  new_name: 'None'\n",
-       "f12_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_2 - The fact that the waterways exist, even if I don�' + 37\n",
-       "  new_name: 'f12_2 - FITZROY - VALUE  waterways EXISTENCE (Rating 1-10)'\n",
-       "f12_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_2'\n",
-       "  new_name: 'None'\n",
-       "f12_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_3 - The waterways offer a place for local residents t' + 29\n",
-       "  new_name: 'f12_3 - FITZROY - VALUE  waterways RECREATION for LOCALS (Ra' + 10\n",
-       "f12_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_3'\n",
-       "  new_name: 'None'\n",
-       "f12_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_4 - The waterways provide a place where people can pa' + 48\n",
-       "  new_name: 'f12_4 - FITZROY - VALUE  waterways PASS DOWN KNOWLEDGE (Rati' + 8\n",
-       "f12_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_4'\n",
-       "  new_name: 'None'\n",
-       "f12_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_5 - The waterways have rich heritage to First Nations' + 7\n",
-       "  new_name: 'f12_5 - FITZROY - VALUE  waterways TO HERITAGE (Rating 1-10)'\n",
-       "f12_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_5'\n",
-       "  new_name: 'None'\n",
-       "f12_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_6 - The waterways are an important attraction for tou' + 25\n",
-       "  new_name: 'f12_6 - FITZROY - VALUE  waterways TOURISM ATTRACTION (Ratin' + 7\n",
-       "f12_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_6'\n",
-       "  new_name: 'None'\n",
-       "f12_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_7 - The waterways support our local economy'\n",
-       "  new_name: 'f12_7 - FITZROY - VALUE  waterways SUPPORT LOCAL ECONOMY (Ra' + 10\n",
-       "f12_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_7'\n",
-       "  new_name: 'None'\n",
-       "f12_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_8 - The waterways provide a place where people can st' + 43\n",
-       "  new_name: 'f12_8 - FITZROY - VALUE  waterways LEARN ABOUT NATURE (Ratin' + 7\n",
-       "f12_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_8'\n",
-       "  new_name: 'None'\n",
-       "f12_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_9 - The waterways support recreational fishing'\n",
-       "  new_name: 'f12_9 - FITZROY - VALUE  waterways SUPPORT REC FISHING (Rati' + 8\n",
-       "f12_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_9'\n",
-       "  new_name: 'None'\n",
-       "f12_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_10 - Our waterways are recognised nationally and inte' + 72\n",
-       "  new_name: 'f12_10 - FITZROY - VALUE  waterways ICONIC STATUS (Rating 1-' + 3\n",
-       "f12_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_10'\n",
-       "  new_name: 'None'\n",
-       "f12_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_11 - The waterways support mining, ports and shipping' + 14\n",
-       "  new_name: 'f12_11 - FITZROY - VALUE  waterways SUPPORT MINING & SHIPPIN' + 15\n",
-       "f12_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_11'\n",
-       "  new_name: 'None'\n",
-       "f12_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12_12 - The waterways support local agriculture'\n",
-       "  new_name: 'f12_12 - FITZROY - VALUE  waterways SUPPORT LOCAL AGRICULTUR' + 15\n",
-       "f12_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f12_12'\n",
-       "  new_name: 'None'\n",
-       "f13_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_1 - Freshwater creeks and rivers'\n",
-       "  new_name: 'f13_1 - FITZROY Perceived Health FRESH CREEKS & RIVERS (Cate' + 13\n",
-       "f13_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_1'\n",
-       "  new_name: 'None'\n",
-       "f13_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_3 - Estuaries (including mangroves and saltmarshes)'\n",
-       "  new_name: 'f13_3 - FITZROY Perceived Health ESTUARIES (Categories 1 - 5' + 1\n",
-       "f13_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_3'\n",
-       "  new_name: 'None'\n",
-       "f13_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_4 - Beaches and the coast'\n",
-       "  new_name: 'f13_4 - FITZROY Perceived Health BEACHES & COAST (Categories' + 7\n",
-       "f13_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_4'\n",
-       "  new_name: 'None'\n",
-       "f13_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_5 - Seagrass'\n",
-       "  new_name: 'f13_5 - FITZROY Perceived Health SEAGRASS (Categories 1 - 5)'\n",
-       "f13_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_5'\n",
-       "  new_name: 'None'\n",
-       "f13_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_6 - Inshore coral reefs'\n",
-       "  new_name: 'f13_6 - FITZROY Perceived Health INSHORE REEFS (Categories 1' + 5\n",
-       "f13_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_6'\n",
-       "  new_name: 'None'\n",
-       "f13_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_8 - Ocean and sea'\n",
-       "  new_name: 'f13_8 - FITZROY Perceived Health OCEAN & SEA (Categories 1 -' + 3\n",
-       "f13_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_8'\n",
-       "  new_name: 'None'\n",
-       "f13_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_9 - Fitzroy River'\n",
-       "  new_name: 'f13_9 - FITZROY Perceived Health FITZROY RIVER (Categories 1' + 5\n",
-       "f13_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_9'\n",
-       "  new_name: 'None'\n",
-       "f13_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_10 - Isaac River'\n",
-       "  new_name: 'f13_10 - FITZROY Perceived Health ISAAC RIVER (Categories 1 ' + 4\n",
-       "f13_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_10'\n",
-       "  new_name: 'None'\n",
-       "f13_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_11 - Nogoa River'\n",
-       "  new_name: 'f13_11 - FITZROY Perceived Health NOGOA RIVER  (Categories 1' + 5\n",
-       "f13_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_11'\n",
-       "  new_name: 'None'\n",
-       "f13_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_12 - Dawson River'\n",
-       "  new_name: 'f13_12 - FITZROY Perceived Health DAWSON RIVER (Categories 1' + 5\n",
-       "f13_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_12'\n",
-       "  new_name: 'None'\n",
-       "f13_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_13 - Mackenzie River'\n",
-       "  new_name: 'f13_13 - FITZROY Perceived Health MACKENZIE RIVER (Categorie' + 8\n",
-       "f13_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_13'\n",
-       "  new_name: 'None'\n",
-       "f13_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_14 - Connors River'\n",
-       "  new_name: 'f13_14 - FITZROY Perceived Health CONNORS RIVER (Categories ' + 6\n",
-       "f13_14-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_14'\n",
-       "  new_name: 'None'\n",
-       "f13_15: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_15 - Comet River'\n",
-       "  new_name: 'f13_15 - FITZROY Perceived Health COMET RIVER (Categories 1 ' + 4\n",
-       "f13_15-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_15'\n",
-       "  new_name: 'None'\n",
-       "f13_16: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_16 - Dee River'\n",
-       "  new_name: 'f13_16 - FITZROY Perceived Health DEE RIVER (Categories 1 - ' + 2\n",
-       "f13_16-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_16'\n",
-       "  new_name: 'None'\n",
-       "f13_17: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13_17 - Lakes and dams'\n",
-       "  new_name: 'f13_17 - FITZROY Perceived Health LAKES & DAMS (Categories 1' + 5\n",
-       "f13_17-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f13_17'\n",
-       "  new_name: 'None'\n",
-       "f14_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_1 - Poor water quality in fresh waters and estuaries'\n",
-       "  new_name: 'f14_1 - FITZROY Perceived Problem FRESH WATER QUALITY (Ratin' + 24\n",
-       "f14_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_1'\n",
-       "  new_name: 'None'\n",
-       "f14_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_2 - Poor coastal and inshore water quality'\n",
-       "  new_name: 'f14_2 - FITZROY Perceived Problem INSHORE WATER QUALITY (Rat' + 26\n",
-       "f14_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_2'\n",
-       "  new_name: 'None'\n",
-       "f14_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_3 - Poor offshore marine water quality'\n",
-       "  new_name: 'f14_3 - FITZROY Perceived Problem OFFSHORE WATER QUALITY (Ra' + 27\n",
-       "f14_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_3'\n",
-       "  new_name: 'None'\n",
-       "f14_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_4 - Riverbank erosion'\n",
-       "  new_name: 'f14_4 - FITZROY Perceived Problem RIVERBANK EROSION (Rating ' + 22\n",
-       "f14_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_4'\n",
-       "  new_name: 'None'\n",
-       "f14_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_5 - Coastal erosion'\n",
-       "  new_name: 'f14_5 - FITZROY Perceived Problem COASTAL EROSION (Rating 1-' + 20\n",
-       "f14_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_5'\n",
-       "  new_name: 'None'\n",
-       "f14_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_6 - Weeds in and around waterways'\n",
-       "  new_name: 'f14_6 - FITZROY Perceived Problem WATERWAY WEEDS (Rating 1-5' + 19\n",
-       "f14_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_6'\n",
-       "  new_name: 'None'\n",
-       "f14_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_7 - Litter and debris (e.g. plastics)'\n",
-       "  new_name: 'f14_7 - FITZROY Perceived Problem LITTER & DEBRIS (Rating 1-' + 20\n",
-       "f14_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_7'\n",
-       "  new_name: 'None'\n",
-       "f14_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_8 - Chemical pollutants (e.g. pesticides, PFAS)'\n",
-       "  new_name: 'f14_8 - FITZROY Perceived Problem CHEMICALS (Rating 1-5; 6 =' + 14\n",
-       "f14_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_8'\n",
-       "  new_name: 'None'\n",
-       "f14_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_9 - Invasive fish (e.g. Tilapia)'\n",
-       "  new_name: 'f14_9 - FITZROY Perceived Problem INVASIVE FISH (Rating 1-5;' + 18\n",
-       "f14_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_9'\n",
-       "  new_name: 'None'\n",
-       "f14_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_10 - Low abundance of fish'\n",
-       "  new_name: 'f14_10 - FITZROY Perceived Problem FISH ABUNDANCE (Rating 1-' + 20\n",
-       "f14_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_10'\n",
-       "  new_name: 'None'\n",
-       "f14_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_12 - Algal blooms and fish kills'\n",
-       "  new_name: 'f14_12 - FITZROY Perceived Problem ALGAE & FISH KILLS (Ratin' + 24\n",
-       "f14_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_12'\n",
-       "  new_name: 'None'\n",
-       "f14_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14_13 - Bushfire'\n",
-       "  new_name: 'f14_13 - FITZROY Perceived Problem BUSHFIRE (Rating 1-5; 6 =' + 14\n",
-       "f14_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f14_13'\n",
-       "  new_name: 'None'\n",
-       "f15_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_1 - Illegal fishing practices (e.g. poaching in no-ta' + 9\n",
-       "  new_name: 'f15_1 - FITZROY Perceived Threat ILLEGAL FISHING (Rating 1-5' + 19\n",
-       "f15_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_1'\n",
-       "  new_name: 'None'\n",
-       "f15_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_2 - Over-fishing'\n",
-       "  new_name: 'f15_2 - FITZROY Perceived Threat OVER-FISHING (Rating 1-5; 6' + 16\n",
-       "f15_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_2'\n",
-       "  new_name: 'None'\n",
-       "f15_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_3 - Sediments, nutrients and pesticides from land-bas' + 9\n",
-       "  new_name: 'f15_3 - FITZROY Perceived Threat LAND RUN-OFF (Rating 1-5; 6' + 16\n",
-       "f15_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_3'\n",
-       "  new_name: 'None'\n",
-       "f15_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_4 - Climate change'\n",
-       "  new_name: 'f15_4 - FITZROY Perceived Threat CLIMATE CHANGE (Rating 1-5;' + 18\n",
-       "f15_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_4'\n",
-       "  new_name: 'None'\n",
-       "f15_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_5 - Tourism activities'\n",
-       "  new_name: 'f15_5 - FITZROY Perceived Threat TOURISM ACTIVITIES (Rating ' + 22\n",
-       "f15_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_5'\n",
-       "  new_name: 'None'\n",
-       "f15_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_6 - Land clearing'\n",
-       "  new_name: 'f15_6 - FITZROY Perceived Threat LAND CLEARING (Rating 1-5; ' + 17\n",
-       "f15_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_6'\n",
-       "  new_name: 'None'\n",
-       "f15_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_7 - Ports and shipping activities'\n",
-       "  new_name: 'f15_7 - FITZROY Perceived Threat PORTS & SHIPPING (Rating 1-' + 20\n",
-       "f15_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_7'\n",
-       "  new_name: 'None'\n",
-       "f15_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_8 - Mining activities (e.g. water extraction, release' + 2\n",
-       "  new_name: 'f15_8 - FITZROY Perceived Threat MINING ACTIVITIES (Rating 1' + 21\n",
-       "f15_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_8'\n",
-       "  new_name: 'None'\n",
-       "f15_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_9 - Coastal development'\n",
-       "  new_name: 'f15_9 - FITZROY Perceived Threat COASTAL DEVELOPMENT (Rating' + 23\n",
-       "f15_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_9'\n",
-       "  new_name: 'None'\n",
-       "f15_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_10 - Extreme weather (e.g. cyclones, droughts, floods' + 1\n",
-       "  new_name: 'f15_10 - FITZROY Perceived Threat EXTREME WEATHER (Rating 1-' + 20\n",
-       "f15_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_10'\n",
-       "  new_name: 'None'\n",
-       "f15_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15_11 - Recreation activities (e.g. boats, fishers, wate' + 9\n",
-       "  new_name: 'f15_11 - FITZROY Perceived Threat RECREATION (Rating 1-5; 6 ' + 15\n",
-       "f15_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f15_11'\n",
-       "  new_name: 'None'\n",
-       "f16_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_1 - For people who have or use a boat...Responsible a' + 110\n",
-       "  new_name: 'f16_1 - FITZROY Waterway Stewardship ANCHOR RESPONSIBLY (Cat' + 12\n",
-       "f16_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_1'\n",
-       "  new_name: 'None'\n",
-       "f16_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_2 - For people who go fishing...Responsible fishing (' + 122\n",
-       "  new_name: 'f16_2 - FITZROY Waterway Stewardship FISH RESPONSIBLY (Categ' + 10\n",
-       "f16_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_2'\n",
-       "  new_name: 'None'\n",
-       "f16_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_3 - On and around waterways...Dispose of food scraps ' + 89\n",
-       "  new_name: 'f16_3 - FITZROY Waterway Stewardship RUBBISH DISPOSAL (Categ' + 10\n",
-       "f16_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_3'\n",
-       "  new_name: 'None'\n",
-       "f16_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_4 - Report suspicious activity to relevant authoritie' + 75\n",
-       "  new_name: 'f16_4 - FITZROY Waterway Stewardship REPORT SUSPICIOUS (Cate' + 11\n",
-       "f16_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_4'\n",
-       "  new_name: 'None'\n",
-       "f16_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_5 - Report invasive or pest species to relevant autho' + 66\n",
-       "  new_name: 'f16_5 - FITZROY Waterway Stewardship REPORT INVASIVES (Categ' + 10\n",
-       "f16_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_5'\n",
-       "  new_name: 'None'\n",
-       "f16_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_6 - Contribute to environmental monitoring programs (' + 74\n",
-       "  new_name: 'f16_6 - FITZROY Waterway Stewardship CONTRIBUTE TO MONITORIN' + 18\n",
-       "f16_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_6'\n",
-       "  new_name: 'None'\n",
-       "f16_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_7 - Participate in local environmental clean-ups (e.g' + 39\n",
-       "  new_name: 'f16_7 - FITZROY Waterway Stewardship LOCAL CLEAN UPS (Catego' + 9\n",
-       "f16_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_7'\n",
-       "  new_name: 'None'\n",
-       "f16_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_8 - Participate in local environmental restoration (e' + 51\n",
-       "  new_name: 'f16_8 - FITZROY Waterway Stewardship LOCAL RESTORATION (Cate' + 11\n",
-       "f16_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_8'\n",
-       "  new_name: 'None'\n",
-       "f16_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16_9 - Responsible four-wheel driving (e.g. avoiding riv' + 70\n",
-       "  new_name: 'f16_9 - FITZROY Waterway Stewardship 4WD RESPONSIBLY (Catego' + 9\n",
-       "f16_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f16_9'\n",
-       "  new_name: 'None'\n",
-       "f17_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_1 - I can make a personal difference to improving wat' + 25\n",
-       "  new_name: 'f17_1 - FITZROY - I can make a PERSONAL DIFFERENCE to improv' + 33\n",
-       "f17_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_1'\n",
-       "  new_name: 'None'\n",
-       "f17_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_2 - I feel a sense of responsibility to help to impro' + 18\n",
-       "  new_name: 'f17_2 - FITZROY - I feel a SENSE OF RESPONSIBILITY to help i' + 36\n",
-       "f17_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_2'\n",
-       "  new_name: 'None'\n",
-       "f17_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_3 - I want to do more to help improve waterway health' + 13\n",
-       "  new_name: 'f17_3 - FITZROY - I WANT TO DO MORE to help improve waterway' + 21\n",
-       "f17_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_3'\n",
-       "  new_name: 'None'\n",
-       "f17_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_4 - I feel hopeful about the future health of waterwa' + 15\n",
-       "  new_name: 'f17_4 - FITZROY - I FEEL HOPEFUL about the future of waterwa' + 29\n",
-       "f17_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_4'\n",
-       "  new_name: 'None'\n",
-       "f17_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_5 - I don't have enough time to contribute to improvi' + 31\n",
-       "  new_name: 'f17_5 - FITZROY - I DON'T HAVE ENOUGH TIME to contribute to ' + 39\n",
-       "f17_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_5'\n",
-       "  new_name: 'None'\n",
-       "f17_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_6 - I don't know how I could contribute to improving ' + 28\n",
-       "  new_name: 'f17_6 - FITZROY - I DON'T KNOW HOW to contribute to improvin' + 31\n",
-       "f17_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_6'\n",
-       "  new_name: 'None'\n",
-       "f17_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_7 - Local residents in my region are supportive of ta' + 38\n",
-       "  new_name: 'f17_7 - FITZROY - LOCAL RESIDENTS SUPPORT action to improve ' + 29\n",
-       "f17_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_7'\n",
-       "  new_name: 'None'\n",
-       "f17_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17_8 - Many local residents in my region are taking acti' + 29\n",
-       "  new_name: 'f17_8 - FITZROY - Many local RESIDENTS TAKING ACTION to impr' + 33\n",
-       "f17_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f17_8'\n",
-       "  new_name: 'None'\n",
-       "f18_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_1 - Overall, I feel satisfied with how local waterway' + 13\n",
-       "  new_name: 'f18_1 - FITZROY - SATISFIED with how local waterways are man' + 18\n",
-       "f18_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_1'\n",
-       "  new_name: 'None'\n",
-       "f18_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_2 - I think that tourism uses of waterways in our reg' + 20\n",
-       "  new_name: 'f18_2 - FITZROY - I think TOURISM USES of waterways are well' + 22\n",
-       "f18_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_2'\n",
-       "  new_name: 'None'\n",
-       "f18_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_3 - I think that agricultural uses of waterways in ou' + 25\n",
-       "  new_name: 'f18_3 - FITZROY - I think AGRICULTURE USES of waterways are ' + 26\n",
-       "f18_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_3'\n",
-       "  new_name: 'None'\n",
-       "f18_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_4 - I think that the fisheries in our region are well' + 8\n",
-       "  new_name: 'f18_4 - FITZROY - I think FISHERIES in region are well manag' + 16\n",
-       "f18_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_4'\n",
-       "  new_name: 'None'\n",
-       "f18_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_5 - I think that aquaculture uses of waterways in our' + 24\n",
-       "  new_name: 'f18_5 - FITZROY - I think AQUACULTURE in region is well mana' + 17\n",
-       "f18_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_5'\n",
-       "  new_name: 'None'\n",
-       "f18_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_6 - I think that ports and shipping in our region are' + 13\n",
-       "  new_name: 'f18_6 - FITZROY - I think PORTS & SHIPPING in region are wel' + 23\n",
-       "f18_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_6'\n",
-       "  new_name: 'None'\n",
-       "f18_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_7 - I think that decisions about managing local water' + 27\n",
-       "  new_name: 'f18_7 - FITZROY - I think waterway management DECISIONS ARE ' + 25\n",
-       "f18_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_7'\n",
-       "  new_name: 'None'\n",
-       "f18_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_8 - I do not have fair access to all the waterways in' + 35\n",
-       "  new_name: 'f18_8 - FITZROY - I DON'T HAVE FAIR ACCESS to all the waterw' + 43\n",
-       "f18_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_8'\n",
-       "  new_name: 'None'\n",
-       "f18_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_9 - I feel I personally have some influence over how ' + 27\n",
-       "  new_name: 'f18_9 - FITZROY - I have SOME PERSONAL INFLUENCE in local wa' + 31\n",
-       "f18_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_9'\n",
-       "  new_name: 'None'\n",
-       "f18_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_10 - I feel able to have input into the management of' + 38\n",
-       "  new_name: 'f18_10 - FITZROY - I'm ABLE TO HAVE INPUT to management of l' + 29\n",
-       "f18_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_10'\n",
-       "  new_name: 'None'\n",
-       "f18_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_11 - I trust the information I receive from instituti' + 74\n",
-       "  new_name: 'f18_11 - FITZROY - I TRUST INFO FROM INSTITUTIONS that manag' + 25\n",
-       "f18_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_11'\n",
-       "  new_name: 'None'\n",
-       "f18_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_12 - I trust the science about waterway health and ma' + 8\n",
-       "  new_name: 'f18_12 - FITZROY - I TRUST THE SCIENCE about waterway health' + 27\n",
-       "f18_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_12'\n",
-       "  new_name: 'None'\n",
-       "f18_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18_13 - I think that mining uses of waterways in our reg' + 20\n",
-       "  new_name: 'f18_13 - FITZROY - I think that MINING USES of waterways are' + 27\n",
-       "f18_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f18_13'\n",
-       "  new_name: 'None'\n",
-       "f19: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F19 - Any additional comments about waterway management?'\n",
-       "  new_name: 'f19 - FITZROY - Any additional comments about waterway manag' + 6\n",
-       "f20: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F20 - A little bit more about you, to help us better unde' + 145\n",
-       "  new_name: 'f20 - FITZROY - Proportion of household income from waterway' + 114\n",
-       "f20-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f20'\n",
-       "  new_name: 'None'\n",
-       "f21_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_1 - Tourism (including hospitality)'\n",
-       "  new_name: 'f21_1 - FITZROY - Which Sector of waterway dependent busines' + 18\n",
-       "f21_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_2 - Boating and retail'\n",
-       "  new_name: 'f21_2 - FITZROY - Which Sector of waterway dependent busines' + 27\n",
-       "f21_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_3 - Fishing'\n",
-       "  new_name: 'f21_3 - FITZROY - Which Sector of waterway dependent busines' + 18\n",
-       "f21_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_4 - Government'\n",
-       "  new_name: 'f21_4 - FITZROY - Which Sector of waterway dependent busines' + 21\n",
-       "f21_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_5 - Science and Education'\n",
-       "  new_name: 'f21_5 - FITZROY - Which Sector of waterway dependent busines' + 30\n",
-       "f21_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_6 - Agriculture'\n",
-       "  new_name: 'f21_6 - FITZROY - Which Sector of waterway dependent busines' + 22\n",
-       "f21_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_7 - Mining'\n",
-       "  new_name: 'f21_7 - FITZROY - Which Sector of waterway dependent busines' + 17\n",
-       "f21_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_8 - Aquaculture'\n",
-       "  new_name: 'f21_8 - FITZROY - Which Sector of waterway dependent busines' + 22\n",
-       "f21_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_9 - Ports or shipping'\n",
-       "  new_name: 'f21_9 - FITZROY - Which Sector of waterway dependent busines' + 28\n",
-       "f21_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_10 - Non-government organisation'\n",
-       "  new_name: 'f21_10 - FITZROY - Which Sector of waterway dependent busine' + 24\n",
-       "f21_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21_11 - Other'\n",
-       "  new_name: 'f21_11 - FITZROY - Which Sector of waterway dependent busine' + 17\n",
-       "f22_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F22 - For how many years have you lived in the Fitzroy re' + 5\n",
-       "  new_name: 'f22_1 - FITZROY - How many years have you lived in region?'\n",
-       "f23: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F23 - Do you identify as an Aboriginal Australian, or Tor' + 20\n",
-       "  new_name: 'f23 - FITZROY - Do you identify as Aboriginal Australian or ' + 45\n",
-       "f23-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f23'\n",
-       "  new_name: 'None'\n",
-       "f24: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F24 - What sector do you mainly work in?'\n",
-       "  new_name: 'f24 - FITZROY - Which sector do you mainly work in? (22 ABS ' + 11\n",
-       "f24-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f24'\n",
-       "  new_name: 'None'\n",
-       "f25: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F25 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 'f25 - FITZROY - Prior to completing survey, were you AWARE O' + 28\n",
-       "f25-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f25'\n",
-       "  new_name: 'None'\n",
-       "f26: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F26 - Prior to completing this survey, were you aware of ' + 6\n",
-       "  new_name: 'f26 - FITZROY - Prior to completing survey, were you AWARE O' + 28\n",
-       "f26-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for f26'\n",
-       "  new_name: 'None'\n",
-       "F4_1: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F4. What are the first words that come to mind when you t' + 52\n",
-       "  new_name: 'F4_1 - GLADSTONE ONLY - First words; Word One'\n",
-       "F4_2: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F4. What are the first words that come to mind when you t' + 52\n",
-       "  new_name: 'F4_2 - GLADSTONE ONLY - First words; Word Two'\n",
-       "F4_3: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F4. What are the first words that come to mind when you t' + 52\n",
-       "  new_name: 'F4_3 - GLADSTONE ONLY - First words; Word Three'\n",
-       "F6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6. In the previous 12 months, how often did you visit an' + 33\n",
-       "  new_name: 'F6 - GLADSTONE ONLY - How often visited ESTUARY in region (C' + 14\n",
-       "F6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F6'\n",
-       "  new_name: 'None'\n",
-       "F6A_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A. Were these estuary visits mostly for... - Recreation'\n",
-       "  new_name: 'F6a_1 - GLADSTONE ONLY - Estuary visits for RECREATION (1=Ye' + 2\n",
-       "F6A_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A. Were these estuary visits mostly for... - Work'\n",
-       "  new_name: 'F6a_2 - GLADSTONE ONLY - Estuary visits for WORK (1=Yes)'\n",
-       "F6A_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A. Were these estuary visits mostly for... - Cultural r' + 6\n",
-       "  new_name: 'F6a_3 - - GLADSTONE ONLY - Estuary visits for CULTURE (1=Yes' + 1\n",
-       "F6A_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F6A. Were these estuary visits mostly for... - Other reas' + 2\n",
-       "  new_name: 'F6a_4 - GLADSTONE ONLY - Estuary visits for OTHER REASONS (1' + 5\n",
-       "F7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7. In the previous 12 months, how often did you visit a ' + 51\n",
-       "  new_name: 'F7 - GLADSTONE ONLY - How often visited COASTAL & MARINE in ' + 23\n",
-       "F7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F7'\n",
-       "  new_name: 'None'\n",
-       "F7A_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A. Were these coastal or marine visits mostly for... - ' + 10\n",
-       "  new_name: 'F7a_1 - GLADSTONE ONLY - Coastal & Marine visits for RECREAT' + 11\n",
-       "F7A_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A. Were these coastal or marine visits mostly for... - ' + 4\n",
-       "  new_name: 'F7a_2 - GLADSTONE ONLY - Coastal & Marine visits for WORK (1' + 5\n",
-       "F7A_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A. Were these coastal or marine visits mostly for... - ' + 16\n",
-       "  new_name: 'F7a_3 - GLADSTONE ONLY - Coastal & Marine visits for CULTURE' + 8\n",
-       "F7A_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F7A. Were these coastal or marine visits mostly for... - ' + 12\n",
-       "  new_name: 'F7a_4 - GLADSTONE ONLY - Coastal & Marine visits for OTHER R' + 14\n",
-       "F9_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 127\n",
-       "  new_name: 'F9_1 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 18\n",
-       "F9_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 138\n",
-       "  new_name: 'F9_2 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 29\n",
-       "F9_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 155\n",
-       "  new_name: 'F9_3 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 27\n",
-       "F9_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 175\n",
-       "  new_name: 'F9_4 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 28\n",
-       "F9_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 165\n",
-       "  new_name: 'F9_5 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 27\n",
-       "F9_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 146\n",
-       "  new_name: 'F9_6 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 19\n",
-       "F9_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 127\n",
-       "  new_name: 'F9_7 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 18\n",
-       "F9_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 128\n",
-       "  new_name: 'F9_8 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 19\n",
-       "F9_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 141\n",
-       "  new_name: 'F9_9 - GLADSTONE ONLY - Waterway Recreation in past 12 mths:' + 25\n",
-       "F9_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 152\n",
-       "  new_name: 'F9_10 - GLASTONE ONLY - Waterway Recreation in past 12 mths:' + 27\n",
-       "F9_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 159\n",
-       "  new_name: 'F9_11 - GLADSTONE ONLY - Waterway Recreation in past 12 mths' + 29\n",
-       "F9_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F9. When visiting waterways (as shown in the above map an' + 142\n",
-       "  new_name: 'F9_12 - GLADSTONE ONLY - Waterway Recreation in past 12 mths' + 17\n",
-       "F9_12_OTHER: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F9_12_OTHER. When visiting waterways (as shown in the abo' + 134\n",
-       "  new_name: 'F9_12_other - GLADSTONE ONLY - Waterway Recreation OTHER ACT' + 15\n",
-       "F11_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 155\n",
-       "  new_name: 'F11_1 - GLADSTONE - Visiting waterways  important for QUALIT' + 35\n",
-       "F11_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_1'\n",
-       "  new_name: 'None'\n",
-       "F11_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 144\n",
-       "  new_name: 'F11_2 - GLADSTONE - Waterways support my LIFESTYLE & RECREAT' + 17\n",
-       "F11_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_2'\n",
-       "  new_name: 'None'\n",
-       "F11_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 168\n",
-       "  new_name: 'F11_3 - GLADSTONE - Waterways  important for time with FAMIL' + 25\n",
-       "F11_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_3'\n",
-       "  new_name: 'None'\n",
-       "F11_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 161\n",
-       "  new_name: 'F11_4 - GLADSTONE - Waterways  important for providing FISH ' + 30\n",
-       "F11_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_4'\n",
-       "  new_name: 'None'\n",
-       "F11_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 132\n",
-       "  new_name: 'F11_5 - GLADSTONE - Waterways  important part of my CULTURE ' + 13\n",
-       "F11_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_5'\n",
-       "  new_name: 'None'\n",
-       "F11_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 185\n",
-       "  new_name: 'F11_6 - GLADSTONE - Waterways  important for EXPERIENCING NA' + 18\n",
-       "F11_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_6'\n",
-       "  new_name: 'None'\n",
-       "F11_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 139\n",
-       "  new_name: 'F11_7 - GLADSTONE - Waterways  important for DOMESTIC WATER ' + 20\n",
-       "F11_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_7'\n",
-       "  new_name: 'None'\n",
-       "F11_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 120\n",
-       "  new_name: 'F11_8 - GLADSTONE - Waterways  important for my HEALTH & LIV' + 21\n",
-       "F11_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_8'\n",
-       "  new_name: 'None'\n",
-       "F11_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 151\n",
-       "  new_name: 'F11_9 - GLADSTONE - I am PROUD of waterways  in my region (R' + 11\n",
-       "F11_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_9'\n",
-       "  new_name: 'None'\n",
-       "F11_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 133\n",
-       "  new_name: 'F11_10 - GLADSTONE - Waterways  important part of WHY I LIVE' + 19\n",
-       "F11_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_10'\n",
-       "  new_name: 'None'\n",
-       "F11_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 154\n",
-       "  new_name: 'F11_11 - GLADSTONE - Waterways in region BEAUTY IS OUTSTANDI' + 16\n",
-       "F11_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_11'\n",
-       "  new_name: 'None'\n",
-       "F11_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 118\n",
-       "  new_name: 'F11_12 - GLADSTONE - If waterway health declined I would be ' + 33\n",
-       "F11_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F11_12'\n",
-       "  new_name: 'None'\n",
-       "F11_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 144\n",
-       "  new_name: 'F11_13 - GLADSTONE - SATISFIED WITH ACCESS to public spaces ' + 13\n",
-       "F11_14: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 141\n",
-       "  new_name: 'F11_14 - GLADSTONE - FAIR ACCESS compared to other users  (R' + 11\n",
-       "F11_15: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F11. Thinking about your visits and uses of Gladstone Har' + 148\n",
-       "  new_name: 'F11_15 - GLADSTONE - commercial SHIPPING HAS REDUCED MY USE ' + 14\n",
-       "F12_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 105\n",
-       "  new_name: 'F12_1 - GLADSTONE - VALUE  waterways BIODIVERSITY (Rating 1-' + 3\n",
-       "F12_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_1'\n",
-       "  new_name: 'None'\n",
-       "F12_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 120\n",
-       "  new_name: 'F12_2 - GLADSTONE - VALUE  waterways EXISTENCE (Rating 1-10)'\n",
-       "F12_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_2'\n",
-       "  new_name: 'None'\n",
-       "F12_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 112\n",
-       "  new_name: 'F12_3 - GLADSTONE - VALUE  waterways RECREATION for LOCALS (' + 12\n",
-       "F12_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_3'\n",
-       "  new_name: 'None'\n",
-       "F12_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 131\n",
-       "  new_name: 'F12_4 - GLADSTONE - VALUE  waterways PASS DOWN KNOWLEDGE (Ra' + 10\n",
-       "F12_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_4'\n",
-       "  new_name: 'None'\n",
-       "F12_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 90\n",
-       "  new_name: 'F12_5 - GLADSTONE - VALUE  waterways TO HERITAGE (Rating 1-1' + 2\n",
-       "F12_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_5'\n",
-       "  new_name: 'None'\n",
-       "F12_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 108\n",
-       "  new_name: 'F12_6 - GLADSTONE - VALUE  waterways TOURISM ATTRACTION (Rat' + 9\n",
-       "F12_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_6'\n",
-       "  new_name: 'None'\n",
-       "F12_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 73\n",
-       "  new_name: 'F12_7 - GLASTONE - VALUE  waterways SUPPORT LOCAL ECONOMY (R' + 11\n",
-       "F12_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_7'\n",
-       "  new_name: 'None'\n",
-       "F12_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 126\n",
-       "  new_name: 'F12_8 - GLADSTONE - VALUE  waterways LEARN ABOUT NATURE (Rat' + 9\n",
-       "F12_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_8'\n",
-       "  new_name: 'None'\n",
-       "F12_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 76\n",
-       "  new_name: 'F12_9 - GLADSTONE - VALUE  waterways SUPPORT REC FISHING (Ra' + 10\n",
-       "F12_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_9'\n",
-       "  new_name: 'None'\n",
-       "F12_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 154\n",
-       "  new_name: 'F12_10 - GLADSTONE - VALUE  waterways ICONIC STATUS (Rating ' + 5\n",
-       "F12_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_10'\n",
-       "  new_name: 'None'\n",
-       "F12_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 96\n",
-       "  new_name: 'F12_11 - GLADSTONE - VALUE  waterways SUPPORT MINING & SHIPP' + 17\n",
-       "F12_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_11'\n",
-       "  new_name: 'None'\n",
-       "F12_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 73\n",
-       "  new_name: 'F12_12 - GLADSTONE - VALUE  waterways SUPPORT LOCAL AGRICULT' + 17\n",
-       "F12_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F12_12'\n",
-       "  new_name: 'None'\n",
-       "F12_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F12. How much do you value the following aspects of water' + 74\n",
-       "  new_name: 'F12_13 - GLADSTONE - VALUE  waterways SUPPORT COMMERCIAL FIS' + 18\n",
-       "F13_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 77\n",
-       "  new_name: 'F13_1 - GLADSTONE Perceived Health FRESH CREEKS & RIVERS (Ca' + 15\n",
-       "F13_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_1'\n",
-       "  new_name: 'None'\n",
-       "F13_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 96\n",
-       "  new_name: 'F13_2 - GLADSTONE Perceived Health ESTUARIES (Categories 1 -' + 3\n",
-       "F13_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 70\n",
-       "  new_name: 'F13_3 - GLADSTONE Perceived Health BEACHES & COAST (Categori' + 9\n",
-       "F13_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_3'\n",
-       "  new_name: 'None'\n",
-       "F13_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 57\n",
-       "  new_name: 'F13_4 - GLADSTONE Perceived Health SEAGRASS (Categories 1 - ' + 2\n",
-       "F13_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_4'\n",
-       "  new_name: 'None'\n",
-       "F13_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 68\n",
-       "  new_name: 'F13_5 - GLADSTONE Perceived Health INSHORE REEFS (Categories' + 7\n",
-       "F13_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_5'\n",
-       "  new_name: 'None'\n",
-       "F13_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 60\n",
-       "  new_name: 'F13_6 - GLADSTONE Perceived Health BOYNE RIVER (Categories 1' + 5\n",
-       "F13_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_6'\n",
-       "  new_name: 'None'\n",
-       "F13_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 63\n",
-       "  new_name: 'F13_7 - GLADSTONE Perceived Health CALLIOPE RIVER (Categorie' + 8\n",
-       "F13_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 63\n",
-       "  new_name: 'F13_8 - GLADSTONE Perceived Health LAKES & DAMS (Categories ' + 6\n",
-       "F13_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_8'\n",
-       "  new_name: 'None'\n",
-       "F13_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 74\n",
-       "  new_name: 'F13_9 - GLADSTONE Perceived Health ISLANDS & VEG  (Categorie' + 8\n",
-       "F13_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_9'\n",
-       "  new_name: 'None'\n",
-       "F13_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 74\n",
-       "  new_name: 'F13_10 - GLADSTONE Perceived Health MARINE & ESTUARY FISHES ' + 18\n",
-       "F13_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_10'\n",
-       "  new_name: 'None'\n",
-       "F13_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 66\n",
-       "  new_name: 'F13_11 - FITZROY Perceived Health OTHER MARINE LIFE (Categor' + 10\n",
-       "F13_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_11'\n",
-       "  new_name: 'None'\n",
-       "F13_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 83\n",
-       "  new_name: 'F13_12 - FITZROY Perceived Health HARBOUR WATER QUALITY (Cat' + 14\n",
-       "F13_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_12'\n",
-       "  new_name: 'None'\n",
-       "F13_13: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F13. Of the waterways you have visited in the Gladstone r' + 88\n",
-       "  new_name: 'F13_13 - FITZROY Perceived Health HARBOUR CAUGHT SEAFOOD (Ca' + 15\n",
-       "F13_13-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F13_13'\n",
-       "  new_name: 'None'\n",
-       "F14_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 120\n",
-       "  new_name: 'F14_1 - GLADSTONE Perceived Problem FRESH WATER QUALITY (Rat' + 26\n",
-       "F14_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_1'\n",
-       "  new_name: 'None'\n",
-       "F14_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 111\n",
-       "  new_name: 'F14_2 - GLADSTONE Perceived Problem INSHORE WATER QUALITY (R' + 28\n",
-       "F14_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_2'\n",
-       "  new_name: 'None'\n",
-       "F14_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 107\n",
-       "  new_name: 'F14_3 - GLADSTONE Perceived Problem OFFSHORE WATER QUALITY (' + 29\n",
-       "F14_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_3'\n",
-       "  new_name: 'None'\n",
-       "F14_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 88\n",
-       "  new_name: 'F14_4 - GLADSTONE Perceived Problem COASTAL EROSION (Rating ' + 22\n",
-       "F14_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_4'\n",
-       "  new_name: 'None'\n",
-       "F14_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 106\n",
-       "  new_name: 'F14_5 - GLADSTONE Perceived Problem LITTER & DEBRIS (Rating ' + 22\n",
-       "F14_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_5'\n",
-       "  new_name: 'None'\n",
-       "F14_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 121\n",
-       "  new_name: 'F14_6 - GLADSTONE Perceived Problem CHEMICALS (Rating 1-5; 6' + 16\n",
-       "F14_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_6'\n",
-       "  new_name: 'None'\n",
-       "F14_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 94\n",
-       "  new_name: 'F14_7 - GLADSTONE Perceived Problem FISH ABUNDANCE (Rating 1' + 21\n",
-       "F14_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_7'\n",
-       "  new_name: 'None'\n",
-       "F14_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 90\n",
-       "  new_name: 'F14_8 - GLADSTONE Perceived Problem LOSS OF MANGROVES (Ratin' + 24\n",
-       "F14_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_8'\n",
-       "  new_name: 'None'\n",
-       "F14_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 110\n",
-       "  new_name: 'F14_9 - GLADSTONE Perceived Problem SEAGRASS DECLINE (Rating' + 23\n",
-       "F14_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_9'\n",
-       "  new_name: 'None'\n",
-       "F14_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F14. From your experience, how problematic do you think e' + 110\n",
-       "  new_name: 'F14_10 - GLADSTONE Perceived Problem CORAL DECLINE (Rating 1' + 21\n",
-       "F14_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F14_10'\n",
-       "  new_name: 'None'\n",
-       "F15_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 157\n",
-       "  new_name: 'F15_1 - GLADSTONE Perceived Threat ILLEGAL FISHING (Rating 1' + 21\n",
-       "F15_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_1'\n",
-       "  new_name: 'None'\n",
-       "F15_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 111\n",
-       "  new_name: 'F15_2 - GLADSTONE Perceived Threat OVER-FISHING (Rating 1-5;' + 18\n",
-       "F15_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_2'\n",
-       "  new_name: 'None'\n",
-       "F15_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 157\n",
-       "  new_name: 'F15_3 - GLADSTONE Perceived Threat LAND RUN-OFF (Rating 1-5;' + 18\n",
-       "F15_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_3'\n",
-       "  new_name: 'None'\n",
-       "F15_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 113\n",
-       "  new_name: 'F15_4 - GLADSTONE Perceived Threat CLIMATE CHANGE (Rating 1-' + 20\n",
-       "F15_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_4'\n",
-       "  new_name: 'None'\n",
-       "F15_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 117\n",
-       "  new_name: 'F15_5 - GLADSTONE Perceived Threat TOURISM ACTIVITIES (Ratin' + 24\n",
-       "F15_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_5'\n",
-       "  new_name: 'None'\n",
-       "F15_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 112\n",
-       "  new_name: 'F15_6 - GLADSTONE Perceived Threat LAND CLEARING (Rating 1-5' + 19\n",
-       "F15_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_6'\n",
-       "  new_name: 'None'\n",
-       "F15_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 128\n",
-       "  new_name: 'F15_7 - GLADSTONE Perceived Threat PORTS & SHIPPING (Rating ' + 22\n",
-       "F15_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_7'\n",
-       "  new_name: 'None'\n",
-       "F15_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 124\n",
-       "  new_name: 'F15_8 - GLADSTONE Perceived Threat MAJOR INDUSTRY ACTIVITIES' + 31\n",
-       "F15_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_8'\n",
-       "  new_name: 'None'\n",
-       "F15_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 118\n",
-       "  new_name: 'F15_9 - GLADSTONE Perceived Threat COASTAL DEVELOPMENT (Rati' + 25\n",
-       "F15_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_9'\n",
-       "  new_name: 'None'\n",
-       "F15_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 148\n",
-       "  new_name: 'F15_10 - GLADSTONE Perceived Threat EXTREME WEATHER (Rating ' + 22\n",
-       "F15_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_10'\n",
-       "  new_name: 'None'\n",
-       "F15_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F15. For the following list of issues – please rate the' + 156\n",
-       "  new_name: 'F15_11 - GLADSTONE Perceived Threat RECREATION (Rating 1-5; ' + 17\n",
-       "F15_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F15_11'\n",
-       "  new_name: 'None'\n",
-       "F16_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 114\n",
-       "  new_name: 'F16_1 - GLADSTONE Waterway Stewardship ANCHOR RESPONSIBLY (C' + 14\n",
-       "F16_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_1'\n",
-       "  new_name: 'None'\n",
-       "F16_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 104\n",
-       "  new_name: 'F16_2 - GLADSTONE Waterway Stewardship FISH RESPONSIBLY (Cat' + 12\n",
-       "F16_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_2'\n",
-       "  new_name: 'None'\n",
-       "F16_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 131\n",
-       "  new_name: 'F16_3 - GLADSTONE Waterway Stewardship RUBBISH DISPOSAL (Cat' + 12\n",
-       "F16_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_3'\n",
-       "  new_name: 'None'\n",
-       "F16_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 178\n",
-       "  new_name: 'F16_4 - GLADSTONE Waterway Stewardship REPORT SUSPICIOUS (Ca' + 13\n",
-       "F16_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_4'\n",
-       "  new_name: 'None'\n",
-       "F16_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 169\n",
-       "  new_name: 'F16_5 - GLADSTONE Waterway Stewardship REPORT INVASIVES (Cat' + 12\n",
-       "F16_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_5'\n",
-       "  new_name: 'None'\n",
-       "F16_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 177\n",
-       "  new_name: 'F16_6 - GLADSTONE Waterway Stewardship CONTRIBUTE TO MONITOR' + 20\n",
-       "F16_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_6'\n",
-       "  new_name: 'None'\n",
-       "F16_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 142\n",
-       "  new_name: 'F16_7 - GLADSTONE Waterway Stewardship LOCAL CLEAN UPS (Cate' + 11\n",
-       "F16_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_7'\n",
-       "  new_name: 'None'\n",
-       "F16_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 154\n",
-       "  new_name: 'F16_8 - GLADSTONE Waterway Stewardship LOCAL RESTORATION (Ca' + 13\n",
-       "F16_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_8'\n",
-       "  new_name: 'None'\n",
-       "F16_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F16. we would like to ask you about several personal acti' + 173\n",
-       "  new_name: 'F16_9 - GLADSTONE Waterway Stewardship 4WD RESPONSIBLY (Cate' + 11\n",
-       "F16_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F16_9'\n",
-       "  new_name: 'None'\n",
-       "F17_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 91\n",
-       "  new_name: 'F17_1 - GLADSTONE - I can make a PERSONAL DIFFERENCE to impr' + 35\n",
-       "F17_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_1'\n",
-       "  new_name: 'None'\n",
-       "F17_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 84\n",
-       "  new_name: 'F17_2 - GLADSTONE - I feel a SENSE OF RESPONSIBILITY to help' + 38\n",
-       "F17_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_2'\n",
-       "  new_name: 'None'\n",
-       "F17_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 79\n",
-       "  new_name: 'F17_3 - GLADSTONE - I WANT TO DO MORE to help improve waterw' + 23\n",
-       "F17_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_3'\n",
-       "  new_name: 'None'\n",
-       "F17_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 81\n",
-       "  new_name: 'F17_4 - GLADSTONE - I FEEL HOPEFUL about the future of water' + 31\n",
-       "F17_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_4'\n",
-       "  new_name: 'None'\n",
-       "F17_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 99\n",
-       "  new_name: 'F17_5 - GLADSTONE - I DON'T HAVE ENOUGH TIME to contribute t' + 41\n",
-       "F17_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_5'\n",
-       "  new_name: 'None'\n",
-       "F17_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 96\n",
-       "  new_name: 'F17_6 - GLADSTONE - I DON'T KNOW HOW to contribute to improv' + 33\n",
-       "F17_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_6'\n",
-       "  new_name: 'None'\n",
-       "F17_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 104\n",
-       "  new_name: 'F17_7 - GLADSTONE - LOCAL RESIDENTS SUPPORT action to improv' + 31\n",
-       "F17_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_7'\n",
-       "  new_name: 'None'\n",
-       "F17_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 95\n",
-       "  new_name: 'F17_8 - GLADSTONE - Many local RESIDENTS TAKING ACTION to im' + 35\n",
-       "F17_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F17_8'\n",
-       "  new_name: 'None'\n",
-       "F17_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 102\n",
-       "  new_name: 'F17_9 - GLADSTONE - Feel able to INPUT TO MGT OF GLADSTONE H' + 21\n",
-       "F17_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F17. Please rate your level of agreement with the followi' + 68\n",
-       "  new_name: 'F17_10 - GLADSTONE - Feel PROUD TO LIVE IN GLADSTONE (Rating' + 6\n",
-       "F18_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 79\n",
-       "  new_name: 'F18_1 - GLADSTONE - SATISFIED with how local waterways are m' + 20\n",
-       "F18_1-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_1'\n",
-       "  new_name: 'None'\n",
-       "F18_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 86\n",
-       "  new_name: 'F18_2 - GLADSTONE - I think TOURISM USES of waterways are we' + 24\n",
-       "F18_2-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_2'\n",
-       "  new_name: 'None'\n",
-       "F18_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 91\n",
-       "  new_name: 'F18_3 - GLADSTONE - I think AGRICULTURE USES of waterways ar' + 28\n",
-       "F18_3-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_3'\n",
-       "  new_name: 'None'\n",
-       "F18_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 74\n",
-       "  new_name: 'F18_4 - GLADSTONE - I think FISHERIES in region are well man' + 18\n",
-       "F18_4-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_4'\n",
-       "  new_name: 'None'\n",
-       "F18_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 79\n",
-       "  new_name: 'F18_5 - GLADSTONE - I think PORTS & SHIPPING in region are w' + 25\n",
-       "F18_5-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_5'\n",
-       "  new_name: 'None'\n",
-       "F18_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 93\n",
-       "  new_name: 'F18_6 - GLADSTONE - I think MAJOR INDUSTRIES in region are w' + 25\n",
-       "F18_6-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_6'\n",
-       "  new_name: 'None'\n",
-       "F18_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 93\n",
-       "  new_name: 'F18_7 - GLADSTONE - I think waterway management DECISIONS AR' + 27\n",
-       "F18_7-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_7'\n",
-       "  new_name: 'None'\n",
-       "F18_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 101\n",
-       "  new_name: 'F18_8 - GLADSTONE - I DON'T HAVE FAIR ACCESS to all the wate' + 45\n",
-       "F18_8-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_8'\n",
-       "  new_name: 'None'\n",
-       "F18_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 93\n",
-       "  new_name: 'F18_9 - GLADSTONE - I have SOME PERSONAL INFLUENCE in local ' + 33\n",
-       "F18_9-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_9'\n",
-       "  new_name: 'None'\n",
-       "F18_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 103\n",
-       "  new_name: 'F18_10 - GLADSTONE - I'm ABLE TO HAVE INPUT to management of' + 31\n",
-       "F18_10-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_10'\n",
-       "  new_name: 'None'\n",
-       "F18_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 139\n",
-       "  new_name: 'F18_11 - GLADSTONE - I TRUST INFO FROM INSTITUTIONS that man' + 27\n",
-       "F18_11-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_11'\n",
-       "  new_name: 'None'\n",
-       "F18_12: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F18. Please rate your level of agreement with the followi' + 73\n",
-       "  new_name: 'F18_12 - GLADSTONE - I TRUST THE SCIENCE about waterway heal' + 29\n",
-       "F18_12-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F18_12'\n",
-       "  new_name: 'None'\n",
-       "F19: string\n",
-       "  -- field metadata --\n",
-       "  description: 'F19 . Any additional comments about waterway management?'\n",
-       "  new_name: 'F19 - GLADSTONE - Any additional comments about waterway man' + 8\n",
-       "F20: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F20. What proportion of your household income is from Gla' + 50\n",
-       "  new_name: 'F20 - GLADSTONE - Proportion of household income from waterw' + 116\n",
-       "F20-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F20'\n",
-       "  new_name: 'None'\n",
-       "F21_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 64\n",
-       "  new_name: 'F21_1 - GLADSTONE - Which Sector of waterway dependent busin' + 24\n",
-       "F21_2: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 64\n",
-       "  new_name: 'F21_2 - GLADSTONE - Which Sector of waterway dependent busin' + 24\n",
-       "F21_3: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 71\n",
-       "  new_name: 'F21_3 - GLADSTONE - Which Sector of waterway dependent busin' + 29\n",
-       "F21_4: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 60\n",
-       "  new_name: 'F21_4 - GLADSTONE - Which Sector of waterway dependent busin' + 20\n",
-       "F21_5: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 63\n",
-       "  new_name: 'F21_5 - GLADSTONE - Which Sector of waterway dependent busin' + 23\n",
-       "F21_6: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 61\n",
-       "  new_name: 'F21_6 - GLADSTONE - Which Sector of waterway dependent busin' + 21\n",
-       "F21_7: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 80\n",
-       "  new_name: 'F21_7 - GLADSTONE - Which Sector of waterway dependent busin' + 25\n",
-       "F21_8: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 70\n",
-       "  new_name: 'F21_8 - GLADSTONE - Which Sector of waterway dependent busin' + 30\n",
-       "F21_9: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 74\n",
-       "  new_name: 'F21_9 - GLADSTONE - Which Sector of waterway dependent busin' + 32\n",
-       "F21_10: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 84\n",
-       "  new_name: 'F21_10 - GLADSTONE - Which Sector of waterway dependent busi' + 21\n",
-       "F21_11: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F21. Which broad sector of waterway-dependent business or' + 58\n",
-       "  new_name: 'F21_11 - GLADSTONE - Which Sector of waterway dependent busi' + 19\n",
-       "F22_1: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F22 . For how many years have you lived (or worked) in th' + 35\n",
-       "  new_name: 'F22_1 - GLADSTONE - How many years have you lived in region?'\n",
-       "F23: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F23. Do you identify as an Aboriginal Australian, or Torr' + 19\n",
-       "  new_name: 'F23 - GLADSTONE - Do you identify as Aboriginal Australian o' + 47\n",
-       "F23-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F23'\n",
-       "  new_name: 'None'\n",
-       "F24: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F24. What sector do you mainly work in?'\n",
-       "  new_name: 'F24 - GLADSTONE - Which sector do you mainly work in? (22 AB' + 13\n",
-       "F24-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F24'\n",
-       "  new_name: 'None'\n",
-       "F25: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F25. Prior to completing this survey, were you aware of t' + 33\n",
-       "  new_name: 'F25 - GLADSTONE - Prior to completing survey, were you AWARE' + 30\n",
-       "F25-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F25'\n",
-       "  new_name: 'None'\n",
-       "F26: double\n",
-       "  -- field metadata --\n",
-       "  description: 'F26. Prior to completing this survey, were you aware of t' + 41\n",
-       "  new_name: 'F26 - GLADSTONE - Prior to completing survey, were you AWARE' + 30\n",
-       "F26-label: string\n",
-       "  -- field metadata --\n",
-       "  description: 'Text value for F26'\n",
-       "  new_name: 'None'\n",
-       "responseid: int64\n",
-       "  -- field metadata --\n",
-       "  description: 'responseid'\n",
-       "  new_name: 'ID'\n",
-       "-- schema metadata --\n",
-       "pandas: '{\"index_columns\": [\"responseid\"], \"column_indexes\": [{\"name\": nu' + 142736"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there are a large number of columns available in this dataset and not all of them can be shown in the table above. We can check the exact number of columns contained in the SELTMP dataset by using the `len` function as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1235"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(md.schema)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading dataset metadata\n",
-    "We have 1235 columns available, and these columns include a description, and a secondary column name (`new_name`). We will use extra this information and create a new dataframe with it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>description</th>\n",
-       "      <th>new_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>REGION</td>\n",
-       "      <td>REGION (1=WT, 2=TSV, 3=MWI, 4=FIT; 5=GH)</td>\n",
-       "      <td>REGION (1=WT, 2=TSV, 3=MWI, 4=FIT; 5=GH)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>recruit path</td>\n",
-       "      <td>Recruiting pathway (1 =  ORU panel, 2 = live l...</td>\n",
-       "      <td>Recruiting PATHWAY (1 = ORU panel, 2 = live li...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>q1</td>\n",
-       "      <td>Q1 - What is your current residential / home p...</td>\n",
-       "      <td>q1 - home POSTCODE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>q1a</td>\n",
-       "      <td>Q1a - Please select your City / Town (LGA):</td>\n",
-       "      <td>q1a - city, town or LGA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>q1a-label</td>\n",
-       "      <td>Text value for q1a</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1230</th>\n",
-       "      <td>F25</td>\n",
-       "      <td>F25. Prior to completing this survey, were you...</td>\n",
-       "      <td>F25 - GLADSTONE - Prior to completing survey, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1231</th>\n",
-       "      <td>F25-label</td>\n",
-       "      <td>Text value for F25</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1232</th>\n",
-       "      <td>F26</td>\n",
-       "      <td>F26. Prior to completing this survey, were you...</td>\n",
-       "      <td>F26 - GLADSTONE - Prior to completing survey, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1233</th>\n",
-       "      <td>F26-label</td>\n",
-       "      <td>Text value for F26</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1234</th>\n",
-       "      <td>responseid</td>\n",
-       "      <td>responseid</td>\n",
-       "      <td>ID</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1235 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             field                                        description  \\\n",
-       "0           REGION           REGION (1=WT, 2=TSV, 3=MWI, 4=FIT; 5=GH)   \n",
-       "1     recruit path  Recruiting pathway (1 =  ORU panel, 2 = live l...   \n",
-       "2               q1  Q1 - What is your current residential / home p...   \n",
-       "3              q1a        Q1a - Please select your City / Town (LGA):   \n",
-       "4        q1a-label                                 Text value for q1a   \n",
-       "...            ...                                                ...   \n",
-       "1230           F25  F25. Prior to completing this survey, were you...   \n",
-       "1231     F25-label                                 Text value for F25   \n",
-       "1232           F26  F26. Prior to completing this survey, were you...   \n",
-       "1233     F26-label                                 Text value for F26   \n",
-       "1234    responseid                                         responseid   \n",
-       "\n",
-       "                                               new_name  \n",
-       "0              REGION (1=WT, 2=TSV, 3=MWI, 4=FIT; 5=GH)  \n",
-       "1     Recruiting PATHWAY (1 = ORU panel, 2 = live li...  \n",
-       "2                                    q1 - home POSTCODE  \n",
-       "3                               q1a - city, town or LGA  \n",
-       "4                                                  None  \n",
-       "...                                                 ...  \n",
-       "1230  F25 - GLADSTONE - Prior to completing survey, ...  \n",
-       "1231                                               None  \n",
-       "1232  F26 - GLADSTONE - Prior to completing survey, ...  \n",
-       "1233                                               None  \n",
-       "1234                                                 ID  \n",
-       "\n",
-       "[1235 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating empty lists to store metadata\n",
-    "field = []\n",
-    "description = []\n",
-    "new_name = []\n",
-    "\n",
-    "# Looping through each item in the schema\n",
-    "for i in md.schema:\n",
-    "    #Get name of column\n",
-    "    field.append(i.name)\n",
-    "    #If available, get the metadata\n",
-    "    if i.metadata is not None:\n",
-    "        description.append(i.metadata[b'description'].decode('utf-8'))\n",
-    "        new_name.append(i.metadata[b'new_name'].decode('utf-8'))\n",
-    "    #If no metadata, append NaN\n",
-    "    else:\n",
-    "        description.append(np.nan)\n",
-    "        new_name.append(np.nan)\n",
-    "\n",
-    "# Creating dataframe from lists\n",
-    "table = pd.DataFrame({'field': field, 'description': description,\\\n",
-    "                   'new_name': new_name})\n",
-    "\n",
-    "# Checking results\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting data about recreational activities\n",
-    "We can now use our metadata table to find the variables of interest. We will use the `new_name` column to find the variables that contain the words `waterway rec`, which contain information about recreational activities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>description</th>\n",
-       "      <th>new_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>w9_1</td>\n",
-       "      <td>W9_1 - Fishing</td>\n",
-       "      <td>w9_1 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>w9_2</td>\n",
-       "      <td>W9_2 - Boating or sailing</td>\n",
-       "      <td>w9_2 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>w9_3</td>\n",
-       "      <td>W9_3 - Snorkelling/freediving/Scuba diving</td>\n",
-       "      <td>w9_3 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>w9_4</td>\n",
-       "      <td>W9_4 - Motor-powered water sports (e.g. water ...</td>\n",
-       "      <td>w9_4 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>w9_5</td>\n",
-       "      <td>W9_5 - Wind-powered water sports (e.g. kite su...</td>\n",
-       "      <td>w9_5 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>w9_6</td>\n",
-       "      <td>W9_6 - Paddling/canoeing/kayaking</td>\n",
-       "      <td>w9_6 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>w9_7</td>\n",
-       "      <td>W9_7 - Camping</td>\n",
-       "      <td>w9_7 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>w9_8</td>\n",
-       "      <td>W9_8 - Swimming</td>\n",
-       "      <td>w9_8 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>w9_9</td>\n",
-       "      <td>W9_9 - Picnics and barbeques</td>\n",
-       "      <td>w9_9 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>w9_10</td>\n",
-       "      <td>W9_10 - Exercising/hiking/biking/running</td>\n",
-       "      <td>w9_10 - WET TROPICS ONLY - Waterway Recreation...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>w9_11</td>\n",
-       "      <td>W9_11 - Wildlife watching &amp; appreciating nature</td>\n",
-       "      <td>w9_11 - WET TROPICS ONLY - Waterway Recreation...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>w9_12</td>\n",
-       "      <td>W9_12 - Other (please specify)</td>\n",
-       "      <td>w9_12 - WET TROPICS ONLY - Waterway Recreation...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>w9_12_other</td>\n",
-       "      <td>W9_12_Other - OTHER TEXT</td>\n",
-       "      <td>w9_12_other - WET TROPICS ONLY - Waterway Recr...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>287</th>\n",
-       "      <td>t9_1</td>\n",
-       "      <td>T9_1 - Fishing</td>\n",
-       "      <td>t9_1 - TOWNSVILLE ONLY - Waterway Recreation i...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>288</th>\n",
-       "      <td>t9_2</td>\n",
-       "      <td>T9_2 - Boating or sailing</td>\n",
-       "      <td>t9_2 - TOWNSVILLE ONLY - Waterway Recreation i...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           field                                        description  \\\n",
-       "42          w9_1                                     W9_1 - Fishing   \n",
-       "43          w9_2                          W9_2 - Boating or sailing   \n",
-       "44          w9_3         W9_3 - Snorkelling/freediving/Scuba diving   \n",
-       "45          w9_4  W9_4 - Motor-powered water sports (e.g. water ...   \n",
-       "46          w9_5  W9_5 - Wind-powered water sports (e.g. kite su...   \n",
-       "47          w9_6                  W9_6 - Paddling/canoeing/kayaking   \n",
-       "48          w9_7                                     W9_7 - Camping   \n",
-       "49          w9_8                                    W9_8 - Swimming   \n",
-       "50          w9_9                       W9_9 - Picnics and barbeques   \n",
-       "51         w9_10           W9_10 - Exercising/hiking/biking/running   \n",
-       "52         w9_11    W9_11 - Wildlife watching & appreciating nature   \n",
-       "53         w9_12                     W9_12 - Other (please specify)   \n",
-       "54   w9_12_other                           W9_12_Other - OTHER TEXT   \n",
-       "287         t9_1                                     T9_1 - Fishing   \n",
-       "288         t9_2                          T9_2 - Boating or sailing   \n",
-       "\n",
-       "                                              new_name  \n",
-       "42   w9_1 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "43   w9_2 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "44   w9_3 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "45   w9_4 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "46   w9_5 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "47   w9_6 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "48   w9_7 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "49   w9_8 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "50   w9_9 - WET TROPICS ONLY - Waterway Recreation ...  \n",
-       "51   w9_10 - WET TROPICS ONLY - Waterway Recreation...  \n",
-       "52   w9_11 - WET TROPICS ONLY - Waterway Recreation...  \n",
-       "53   w9_12 - WET TROPICS ONLY - Waterway Recreation...  \n",
-       "54   w9_12_other - WET TROPICS ONLY - Waterway Recr...  \n",
-       "287  t9_1 - TOWNSVILLE ONLY - Waterway Recreation i...  \n",
-       "288  t9_2 - TOWNSVILLE ONLY - Waterway Recreation i...  "
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Searching for matches in the new_name column using keywords: 'waterway rec'\n",
-    "rec = table[table['new_name'].str.contains('waterway rec', case=False)]\n",
-    "#Cheking results\n",
-    "rec.head(n = 15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the table above, we can see that question `9` contains data about recreational activities. We can also see that there are multiple options for recreational activities under the same question. We will create a new column that will identify the activity, so we can plot it.\n",
-    "\n",
-    "We will also keep the region column (`quotagroup-label`) because this contains the regions where respondents were located and we want to create a bar plot that shows the differences in responses across regions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>description</th>\n",
-       "      <th>new_name</th>\n",
-       "      <th>activity</th>\n",
-       "      <th>region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>w9_1</td>\n",
-       "      <td>W9_1 - Fishing</td>\n",
-       "      <td>w9_1 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Fishing</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>w9_2</td>\n",
-       "      <td>W9_2 - Boating or sailing</td>\n",
-       "      <td>w9_2 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Boating or sailing</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>w9_3</td>\n",
-       "      <td>W9_3 - Snorkelling/freediving/Scuba diving</td>\n",
-       "      <td>w9_3 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Snorkelling/freediving/Scuba diving</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>w9_4</td>\n",
-       "      <td>W9_4 - Motor-powered water sports (e.g. water ...</td>\n",
-       "      <td>w9_4 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Motor-powered water sports (e.g. water skiing,...</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>w9_5</td>\n",
-       "      <td>W9_5 - Wind-powered water sports (e.g. kite su...</td>\n",
-       "      <td>w9_5 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Wind-powered water sports (e.g. kite surfing)</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>w9_6</td>\n",
-       "      <td>W9_6 - Paddling/canoeing/kayaking</td>\n",
-       "      <td>w9_6 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Paddling/canoeing/kayaking</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>w9_7</td>\n",
-       "      <td>W9_7 - Camping</td>\n",
-       "      <td>w9_7 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Camping</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>w9_8</td>\n",
-       "      <td>W9_8 - Swimming</td>\n",
-       "      <td>w9_8 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Swimming</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>w9_9</td>\n",
-       "      <td>W9_9 - Picnics and barbeques</td>\n",
-       "      <td>w9_9 - WET TROPICS ONLY - Waterway Recreation ...</td>\n",
-       "      <td>Picnics and barbeques</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>w9_10</td>\n",
-       "      <td>W9_10 - Exercising/hiking/biking/running</td>\n",
-       "      <td>w9_10 - WET TROPICS ONLY - Waterway Recreation...</td>\n",
-       "      <td>Exercising/hiking/biking/running</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>w9_11</td>\n",
-       "      <td>W9_11 - Wildlife watching &amp; appreciating nature</td>\n",
-       "      <td>w9_11 - WET TROPICS ONLY - Waterway Recreation...</td>\n",
-       "      <td>Wildlife watching &amp; appreciating nature</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>w9_12</td>\n",
-       "      <td>W9_12 - Other (please specify)</td>\n",
-       "      <td>w9_12 - WET TROPICS ONLY - Waterway Recreation...</td>\n",
-       "      <td>Other (please specify)</td>\n",
-       "      <td>Wet Tropics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>t9_1</td>\n",
-       "      <td>T9_1 - Fishing</td>\n",
-       "      <td>t9_1 - TOWNSVILLE ONLY - Waterway Recreation i...</td>\n",
-       "      <td>Fishing</td>\n",
-       "      <td>Townsville</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>t9_2</td>\n",
-       "      <td>T9_2 - Boating or sailing</td>\n",
-       "      <td>t9_2 - TOWNSVILLE ONLY - Waterway Recreation i...</td>\n",
-       "      <td>Boating or sailing</td>\n",
-       "      <td>Townsville</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>t9_3</td>\n",
-       "      <td>T9_3 - Snorkelling/freediving/Scuba diving</td>\n",
-       "      <td>t9_3 - TOWNSVILLE ONLY - Waterway Recreation i...</td>\n",
-       "      <td>Snorkelling/freediving/Scuba diving</td>\n",
-       "      <td>Townsville</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    field                                        description  \\\n",
-       "0    w9_1                                     W9_1 - Fishing   \n",
-       "1    w9_2                          W9_2 - Boating or sailing   \n",
-       "2    w9_3         W9_3 - Snorkelling/freediving/Scuba diving   \n",
-       "3    w9_4  W9_4 - Motor-powered water sports (e.g. water ...   \n",
-       "4    w9_5  W9_5 - Wind-powered water sports (e.g. kite su...   \n",
-       "5    w9_6                  W9_6 - Paddling/canoeing/kayaking   \n",
-       "6    w9_7                                     W9_7 - Camping   \n",
-       "7    w9_8                                    W9_8 - Swimming   \n",
-       "8    w9_9                       W9_9 - Picnics and barbeques   \n",
-       "9   w9_10           W9_10 - Exercising/hiking/biking/running   \n",
-       "10  w9_11    W9_11 - Wildlife watching & appreciating nature   \n",
-       "11  w9_12                     W9_12 - Other (please specify)   \n",
-       "12   t9_1                                     T9_1 - Fishing   \n",
-       "13   t9_2                          T9_2 - Boating or sailing   \n",
-       "14   t9_3         T9_3 - Snorkelling/freediving/Scuba diving   \n",
-       "\n",
-       "                                             new_name  \\\n",
-       "0   w9_1 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "1   w9_2 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "2   w9_3 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "3   w9_4 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "4   w9_5 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "5   w9_6 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "6   w9_7 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "7   w9_8 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "8   w9_9 - WET TROPICS ONLY - Waterway Recreation ...   \n",
-       "9   w9_10 - WET TROPICS ONLY - Waterway Recreation...   \n",
-       "10  w9_11 - WET TROPICS ONLY - Waterway Recreation...   \n",
-       "11  w9_12 - WET TROPICS ONLY - Waterway Recreation...   \n",
-       "12  t9_1 - TOWNSVILLE ONLY - Waterway Recreation i...   \n",
-       "13  t9_2 - TOWNSVILLE ONLY - Waterway Recreation i...   \n",
-       "14  t9_3 - TOWNSVILLE ONLY - Waterway Recreation i...   \n",
-       "\n",
-       "                                             activity       region  \n",
-       "0                                             Fishing  Wet Tropics  \n",
-       "1                                  Boating or sailing  Wet Tropics  \n",
-       "2                 Snorkelling/freediving/Scuba diving  Wet Tropics  \n",
-       "3   Motor-powered water sports (e.g. water skiing,...  Wet Tropics  \n",
-       "4       Wind-powered water sports (e.g. kite surfing)  Wet Tropics  \n",
-       "5                          Paddling/canoeing/kayaking  Wet Tropics  \n",
-       "6                                             Camping  Wet Tropics  \n",
-       "7                                            Swimming  Wet Tropics  \n",
-       "8                               Picnics and barbeques  Wet Tropics  \n",
-       "9                    Exercising/hiking/biking/running  Wet Tropics  \n",
-       "10            Wildlife watching & appreciating nature  Wet Tropics  \n",
-       "11                             Other (please specify)  Wet Tropics  \n",
-       "12                                            Fishing   Townsville  \n",
-       "13                                 Boating or sailing   Townsville  \n",
-       "14                Snorkelling/freediving/Scuba diving   Townsville  "
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#If the field column contains '_other' remove from analysis as this provides\n",
-    "#a description of other recreational activities not named in the survey\n",
-    "rec = rec[~rec.field.str.contains('_other', case = False)].reset_index(drop = True)\n",
-    "\n",
-    "#Splitting information in the description column into two columns\n",
-    "#Only keeping the second column (activity) as the first is the same as field\n",
-    "rec = pd.concat([rec, rec.description.str.split(' - ', expand = True)[1]],\n",
-    "                 axis = 1)\n",
-    "#Renaming columns\n",
-    "rec.columns = rec.columns.to_list()[:-1]+['activity']\n",
-    "#Extracting name of region from new_name column (between '-' and 'ONLY')\n",
-    "rec['region'] = rec.apply(lambda x: re.findall('- (.*) ONLY - ', \n",
-    "                                               x.new_name)[0].title(), \n",
-    "                           axis=1)\n",
-    "\n",
-    "#Checking results\n",
-    "rec.head(n = 15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we have the necessary information to extract the questions from the complete SELTMP dataset that tell us about recreational activities in the study area. We will also need to do some data wrangling to format our data in a way that is easy to plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>activity</th>\n",
-       "      <th>field</th>\n",
-       "      <th>rec_act</th>\n",
-       "      <th>per_rec</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Fitzroy</td>\n",
-       "      <td>Boating or sailing</td>\n",
-       "      <td>453</td>\n",
-       "      <td>136.0</td>\n",
-       "      <td>30.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Fitzroy</td>\n",
-       "      <td>Camping</td>\n",
-       "      <td>453</td>\n",
-       "      <td>147.0</td>\n",
-       "      <td>32.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Fitzroy</td>\n",
-       "      <td>Exercising/hiking/biking/running</td>\n",
-       "      <td>453</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>42.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Fitzroy</td>\n",
-       "      <td>Fishing</td>\n",
-       "      <td>453</td>\n",
-       "      <td>225.0</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Fitzroy</td>\n",
-       "      <td>Motor-powered water sports</td>\n",
-       "      <td>453</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    region                          activity  field  rec_act  per_rec\n",
-       "0  Fitzroy                Boating or sailing    453    136.0     30.0\n",
-       "1  Fitzroy                           Camping    453    147.0     32.0\n",
-       "2  Fitzroy  Exercising/hiking/biking/running    453    188.0     42.0\n",
-       "3  Fitzroy                           Fishing    453    225.0     50.0\n",
-       "4  Fitzroy       Motor-powered water sports     453     45.0     10.0"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Using the initial connection to the S3 bucket. Select the columns identified above.\n",
-    "#Read data as pandas dataframe.\n",
-    "recreation  = md.read_pandas(columns = rec.field).to_pandas().reset_index(drop = True)\n",
-    "#We will make the data frame longer. This means that instead of having multiple columns\n",
-    "#for each region/activity, we will have single column containing information about the\n",
-    "#activities/region linked to that observation\n",
-    "recreation['id'] = recreation.index\n",
-    "recreation = pd.melt(recreation, id_vars = 'id', value_vars = recreation.columns[:-1],\n",
-    "                     var_name = 'field', value_name = 'rec_act')\n",
-    "#We remove any rows with no data in the occupation column (i.e., a single respondent \n",
-    "#cannot be in multiple regions)\n",
-    "recreation.dropna(subset = 'rec_act', inplace = True)\n",
-    "recreation.drop(columns = 'id', inplace = True)\n",
-    "#Join with metadata table\n",
-    "recreation = pd.merge(recreation, rec, on = 'field', how = 'left')\n",
-    "#Correct name of regions\n",
-    "recreation['region'] = recreation.apply(lambda x: x.region.replace('Mwi', 'MWI'), axis = 1)\n",
-    "recreation['region'] = recreation.apply(lambda x: x.region.replace('Glastone', 'Gladstone'), \n",
-    "                                        axis = 1)\n",
-    "#Removing information in brackets for some categories\n",
-    "recreation['activity'] = recreation['activity'].str.split('(', n = 1, expand = True)[0]\n",
-    "\n",
-    "#Summarise data by region and activity\n",
-    "recreation = recreation.groupby(['region', 'activity']).agg({'field': 'size',\n",
-    "                                                'rec_act': 'sum'})\n",
-    "recreation.reset_index(inplace = True)\n",
-    "#We calculate percentage of respondents per activity - No decimals needed\n",
-    "recreation['per_rec'] = np.round((recreation.rec_act/recreation.field)*100, 0)\n",
-    "#Checking result\n",
-    "recreation.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>activity</th>\n",
-       "      <th>per_rec</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Wind-powered water sports</td>\n",
-       "      <td>1.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Other</td>\n",
-       "      <td>7.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Motor-powered water sports</td>\n",
-       "      <td>8.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Paddling/canoeing/kayaking</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Snorkelling/freediving/Scuba diving</td>\n",
-       "      <td>23.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Boating or sailing</td>\n",
-       "      <td>31.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Camping</td>\n",
-       "      <td>32.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Exercising/hiking/biking/running</td>\n",
-       "      <td>49.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Wildlife watching &amp; appreciating nature</td>\n",
-       "      <td>49.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Fishing</td>\n",
-       "      <td>50.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Picnics and barbeques</td>\n",
-       "      <td>55.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Swimming</td>\n",
-       "      <td>56.4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   activity  per_rec\n",
-       "11               Wind-powered water sports       1.4\n",
-       "5                                    Other       7.2\n",
-       "4               Motor-powered water sports       8.4\n",
-       "6                Paddling/canoeing/kayaking     23.0\n",
-       "8       Snorkelling/freediving/Scuba diving     23.8\n",
-       "0                        Boating or sailing     31.8\n",
-       "1                                   Camping     32.8\n",
-       "2          Exercising/hiking/biking/running     49.0\n",
-       "10  Wildlife watching & appreciating nature     49.2\n",
-       "3                                   Fishing     50.2\n",
-       "7                     Picnics and barbeques     55.8\n",
-       "9                                  Swimming     56.4"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Calculating mean percentage of respondents per activity to order data in plot\n",
-    "plot_order = recreation.groupby('activity').agg({'per_rec': 'mean'}).reset_index().\\\n",
-    "    sort_values('per_rec', ascending = True)\n",
-    "#Checking result\n",
-    "plot_order"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting data about recreational activities\n",
-    "Our data is almost ready to make a plot. We will do some reordering of the data prior to plotting it. We will use the `plot_order` variable to order the data in the plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Waterway recreation activities performed by respondents')"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Grouping data by region and activity\n",
-    "rec_grouped = recreation.pivot(index = 'region', columns = 'activity', values = 'per_rec')\n",
-    "#Ordering activities based on the plot_order dataframe\n",
-    "rec_grouped = rec_grouped[plot_order.activity]\n",
-    "\n",
-    "#Start figure\n",
-    "fig = plt.figure(figsize = (10, 5))\n",
-    "#Create as many subplots along one row as there are regions (index of rec_grouped)\n",
-    "#Ensure all subplots share the y axis\n",
-    "axs = fig.subplots(1, len(rec_grouped.index), sharey = True)\n",
-    "#Choosing a colormap for the plot - Separate colours for each activity\n",
-    "cmap = cmaps['Paired'](np.linspace(0, 1, len(rec_grouped.columns)))\n",
-    "#Loop through each region and plot the data\n",
-    "for i, reg in enumerate(rec_grouped.index):\n",
-    "    #Plot as horizontal bar chart\n",
-    "    da = rec_grouped.loc[reg]\n",
-    "    da.plot.barh(ax = axs[i], color = cmap, edgecolor = 'black')\n",
-    "    #Add title to each subplot\n",
-    "    axs[i].set_title(reg)\n",
-    "    #Remove y axis label\n",
-    "    axs[i].set_ylabel('')\n",
-    "    #Set x axis ticks\n",
-    "    axs[i].set_xticks([0, 25, 50, 75, 100])\n",
-    "    axs[i].set_xticklabels(['0%', '25%', '50%', '75%', '100%'], rotation = 90)\n",
-    "    #Adding labels at the end of each bar\n",
-    "    for act, val in enumerate(da.values):\n",
-    "        axs[i].text(val+1.1, act, int(val))\n",
-    "\n",
-    "#Add title to figure\n",
-    "fig.suptitle('Waterway recreation activities performed by respondents', fontsize = 14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This figure shows the proportion of people surveyed that perform recreational activities per region. We can see that the most common recreational activity is swimming, followed by boating and fishing across all regions.  \n",
-    "  \n",
-    "We could save this figure by using the `plt.savefig` function. For example, we could save it as a `png` file by using the following code: `plt.savefig('recreational_activities.png', dpi=300, bbox_inches='tight')`. The `dpi` argument controls the resolution of the figure, and the `bbox_inches` argument controls the size of the bounding box in inches around the figure."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting data about waterway health\n",
-    "Using the metadata table we loaded towards the beginning of this notebook, we will find the survey questions that cover perceived waterway health."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>description</th>\n",
-       "      <th>new_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>106</th>\n",
-       "      <td>w13_1</td>\n",
-       "      <td>W13_1 - Freshwater creeks and rivers</td>\n",
-       "      <td>w13_1 - WET TROPICS Perceived Health FRESH CRE...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>w13_2</td>\n",
-       "      <td>W13_2 - Freshwater lakes, dams and wetlands</td>\n",
-       "      <td>w13_2 - WET TROPICS Perceived Health LAKES DAM...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>110</th>\n",
-       "      <td>w13_3</td>\n",
-       "      <td>W13_3 - Estuaries (including mangroves and sal...</td>\n",
-       "      <td>w13_3 - WET TROPICS Perceived Health ESTUARIES...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>112</th>\n",
-       "      <td>w13_4</td>\n",
-       "      <td>W13_4 - Beaches and the coast</td>\n",
-       "      <td>w13_4 - WET TROPICS Perceived Health BEACHES &amp;...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>114</th>\n",
-       "      <td>w13_5</td>\n",
-       "      <td>W13_5 - Seagrass</td>\n",
-       "      <td>w13_5 - WET TROPICS Perceived Health SEAGRASS ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>116</th>\n",
-       "      <td>w13_6</td>\n",
-       "      <td>W13_6 - Inshore coral reefs</td>\n",
-       "      <td>w13_6 - WET TROPICS Perceived Health INSHORE R...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>w13_7</td>\n",
-       "      <td>W13_7 - Offshore coral reefs</td>\n",
-       "      <td>w13_7 - WET TROPICS Perceived Health OFFSHORE ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>w13_8</td>\n",
-       "      <td>W13_8 - Ocean and sea</td>\n",
-       "      <td>w13_8 - WET TROPICS Perceived Health OCEAN &amp; S...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>353</th>\n",
-       "      <td>t13_1</td>\n",
-       "      <td>T13_1 - Freshwater creeks and rivers</td>\n",
-       "      <td>t13_1 - TOWNSVILLE Perceived Health FRESH CREE...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>355</th>\n",
-       "      <td>t13_2</td>\n",
-       "      <td>T13_2 - Freshwater lakes, dams and wetlands</td>\n",
-       "      <td>t13_2 - TOWNSVILLE Perceived Health LAKES DAMS...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>357</th>\n",
-       "      <td>t13_3</td>\n",
-       "      <td>T13_3 - Estuaries (including mangroves and sal...</td>\n",
-       "      <td>t13_3 - TOWNSVILLE Perceived Health ESTUARIES ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>359</th>\n",
-       "      <td>t13_4</td>\n",
-       "      <td>T13_4 - Beaches and the coast</td>\n",
-       "      <td>t13_4 - TOWNSVILLE Perceived Health BEACHES &amp; ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>361</th>\n",
-       "      <td>t13_5</td>\n",
-       "      <td>T13_5 - Seagrass</td>\n",
-       "      <td>t13_5 - TOWNSVILLE Perceived Health SEAGRASS (...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363</th>\n",
-       "      <td>t13_6</td>\n",
-       "      <td>T13_6 - Inshore coral reefs</td>\n",
-       "      <td>t13_6 - TOWNSVILLE Perceived Health INSHORE RE...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>365</th>\n",
-       "      <td>t13_7</td>\n",
-       "      <td>T13_7 - Offshore coral reefs</td>\n",
-       "      <td>t13_7 - TOWNSVILLE Perceived Health OFFSHORE R...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     field                                        description  \\\n",
-       "106  w13_1               W13_1 - Freshwater creeks and rivers   \n",
-       "108  w13_2        W13_2 - Freshwater lakes, dams and wetlands   \n",
-       "110  w13_3  W13_3 - Estuaries (including mangroves and sal...   \n",
-       "112  w13_4                      W13_4 - Beaches and the coast   \n",
-       "114  w13_5                                   W13_5 - Seagrass   \n",
-       "116  w13_6                        W13_6 - Inshore coral reefs   \n",
-       "118  w13_7                       W13_7 - Offshore coral reefs   \n",
-       "120  w13_8                              W13_8 - Ocean and sea   \n",
-       "353  t13_1               T13_1 - Freshwater creeks and rivers   \n",
-       "355  t13_2        T13_2 - Freshwater lakes, dams and wetlands   \n",
-       "357  t13_3  T13_3 - Estuaries (including mangroves and sal...   \n",
-       "359  t13_4                      T13_4 - Beaches and the coast   \n",
-       "361  t13_5                                   T13_5 - Seagrass   \n",
-       "363  t13_6                        T13_6 - Inshore coral reefs   \n",
-       "365  t13_7                       T13_7 - Offshore coral reefs   \n",
-       "\n",
-       "                                              new_name  \n",
-       "106  w13_1 - WET TROPICS Perceived Health FRESH CRE...  \n",
-       "108  w13_2 - WET TROPICS Perceived Health LAKES DAM...  \n",
-       "110  w13_3 - WET TROPICS Perceived Health ESTUARIES...  \n",
-       "112  w13_4 - WET TROPICS Perceived Health BEACHES &...  \n",
-       "114  w13_5 - WET TROPICS Perceived Health SEAGRASS ...  \n",
-       "116  w13_6 - WET TROPICS Perceived Health INSHORE R...  \n",
-       "118  w13_7 - WET TROPICS Perceived Health OFFSHORE ...  \n",
-       "120  w13_8 - WET TROPICS Perceived Health OCEAN & S...  \n",
-       "353  t13_1 - TOWNSVILLE Perceived Health FRESH CREE...  \n",
-       "355  t13_2 - TOWNSVILLE Perceived Health LAKES DAMS...  \n",
-       "357  t13_3 - TOWNSVILLE Perceived Health ESTUARIES ...  \n",
-       "359  t13_4 - TOWNSVILLE Perceived Health BEACHES & ...  \n",
-       "361  t13_5 - TOWNSVILLE Perceived Health SEAGRASS (...  \n",
-       "363  t13_6 - TOWNSVILLE Perceived Health INSHORE RE...  \n",
-       "365  t13_7 - TOWNSVILLE Perceived Health OFFSHORE R...  "
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Searching for matches in the new_name column using keywords: 'perceived health'\n",
-    "health = table[table['new_name'].str.contains('perceived health', case = False)]\n",
-    "#Cheking results\n",
-    "health.head(n = 15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first thing we notice is that for some regions there are more questions about perceived health, which refer to areas that are not present across all regions. Since we would like to compare results across regions, we will select areas that are present across all regions. These are covered by questions `13_1` to `13_8`. We will select these from our table of questions above and then use it to load the data we need to create a plot.  \n",
-    "  \n",
-    "We should highlight three issues with the data:  \n",
-    "1. The Fitzroy region does not include information about wetlands (`13_2`) and offshore reefs (`13_7`).  \n",
-    "2. The area categories and ID for the Gladstone region do not match all categories and ID for other regions.  \n",
-    "3. There is no variable dictionary included for the Gladstone region.  \n",
-    "  \n",
-    "Since we do not have information to interpret the Gladstone region (and to maintain things simple), we will ignore this region when creating our plot.  \n",
-    "  \n",
-    "Finally, we will add the `-label` suffix to the `field` column because we would like to access the actual labels of the responses instead of the numerical categories."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>area</th>\n",
-       "      <th>region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>w13_1-label</td>\n",
-       "      <td>Freshwater creeks and rivers</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>w13_2-label</td>\n",
-       "      <td>Freshwater lakes, dams and wetlands</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>w13_3-label</td>\n",
-       "      <td>Estuaries (including mangroves and saltmarshes)</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>w13_4-label</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>w13_5-label</td>\n",
-       "      <td>Seagrass</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>w13_6-label</td>\n",
-       "      <td>Inshore coral reefs</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>w13_7-label</td>\n",
-       "      <td>Offshore coral reefs</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>w13_8-label</td>\n",
-       "      <td>Ocean and sea</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>t13_1-label</td>\n",
-       "      <td>Freshwater creeks and rivers</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>t13_2-label</td>\n",
-       "      <td>Freshwater lakes, dams and wetlands</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>t13_3-label</td>\n",
-       "      <td>Estuaries (including mangroves and saltmarshes)</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>t13_4-label</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>t13_5-label</td>\n",
-       "      <td>Seagrass</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>t13_6-label</td>\n",
-       "      <td>Inshore coral reefs</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>t13_7-label</td>\n",
-       "      <td>Offshore coral reefs</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          field                                             area       region\n",
-       "0   w13_1-label                     Freshwater creeks and rivers  WET TROPICS\n",
-       "1   w13_2-label              Freshwater lakes, dams and wetlands  WET TROPICS\n",
-       "2   w13_3-label  Estuaries (including mangroves and saltmarshes)  WET TROPICS\n",
-       "3   w13_4-label                            Beaches and the coast  WET TROPICS\n",
-       "4   w13_5-label                                         Seagrass  WET TROPICS\n",
-       "5   w13_6-label                              Inshore coral reefs  WET TROPICS\n",
-       "6   w13_7-label                             Offshore coral reefs  WET TROPICS\n",
-       "7   w13_8-label                                    Ocean and sea  WET TROPICS\n",
-       "8   t13_1-label                     Freshwater creeks and rivers   TOWNSVILLE\n",
-       "9   t13_2-label              Freshwater lakes, dams and wetlands   TOWNSVILLE\n",
-       "10  t13_3-label  Estuaries (including mangroves and saltmarshes)   TOWNSVILLE\n",
-       "11  t13_4-label                            Beaches and the coast   TOWNSVILLE\n",
-       "12  t13_5-label                                         Seagrass   TOWNSVILLE\n",
-       "13  t13_6-label                              Inshore coral reefs   TOWNSVILLE\n",
-       "14  t13_7-label                             Offshore coral reefs   TOWNSVILLE"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Selecting questions 13_1 to 13_8 and removing questions about the Gladstone region\n",
-    "health = health[(health.field.str.contains('13_[1-8]$')) & ~(health.new_name.str.contains('GLADSTONE'))]\n",
-    "health['field'] = health.apply(lambda x: f'{x.field}-label', axis = 1)\n",
-    "#Extract area being rated from the description column\n",
-    "health = pd.concat([health, health.description.str.split(' - ', expand = True)[1]], axis = 1)\n",
-    "#Renaming columns - so area can be easily identified\n",
-    "health.columns = health.columns.to_list()[:-1]+['area']\n",
-    "#Extracting name of region from new_name column (between '-' and 'Perceived')\n",
-    "health['region'] = health.apply(lambda x: re.findall('- (.+) Perceived', x.new_name)[0], axis = 1)\n",
-    "health.drop(columns = ['description', 'new_name'], inplace = True)\n",
-    "#Resetting row index\n",
-    "health.reset_index(drop = True, inplace = True)\n",
-    "#Checking result\n",
-    "health.head(n = 15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we did with the previous plot, we will use the table above to extract the questions that contain data about perceptions of waterway health. In this case, we will also need to do some data wrangling to format our data in a way that is easy to plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>area</th>\n",
-       "      <th>rating</th>\n",
-       "      <th>field</th>\n",
-       "      <th>respondents</th>\n",
-       "      <th>rate_per</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FITZROY</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>I don't know</td>\n",
-       "      <td>14</td>\n",
-       "      <td>467</td>\n",
-       "      <td>3.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>FITZROY</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>In fair health</td>\n",
-       "      <td>120</td>\n",
-       "      <td>467</td>\n",
-       "      <td>25.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FITZROY</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>In good health</td>\n",
-       "      <td>302</td>\n",
-       "      <td>467</td>\n",
-       "      <td>64.67</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FITZROY</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>In poor health</td>\n",
-       "      <td>20</td>\n",
-       "      <td>467</td>\n",
-       "      <td>4.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>FITZROY</td>\n",
-       "      <td>Beaches and the coast</td>\n",
-       "      <td>Not applicable</td>\n",
-       "      <td>11</td>\n",
-       "      <td>467</td>\n",
-       "      <td>2.36</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    region                   area           rating  field  respondents  \\\n",
-       "0  FITZROY  Beaches and the coast     I don't know     14          467   \n",
-       "1  FITZROY  Beaches and the coast   In fair health    120          467   \n",
-       "2  FITZROY  Beaches and the coast   In good health    302          467   \n",
-       "3  FITZROY  Beaches and the coast   In poor health     20          467   \n",
-       "4  FITZROY  Beaches and the coast  Not applicable      11          467   \n",
-       "\n",
-       "   rate_per  \n",
-       "0      3.00  \n",
-       "1     25.70  \n",
-       "2     64.67  \n",
-       "3      4.28  \n",
-       "4      2.36  "
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Using the initial connection to the S3 bucket. Select the columns identified above.\n",
-    "#We will get a list of questions from data frame above and add 'w13_1' to the list to get\n",
-    "#the order of the rating of waterway health\n",
-    "questions = health.field.values.tolist()\n",
-    "questions.append('w13_1')\n",
-    "#Extracting waterway health perception questions\n",
-    "water_health  = md.read_pandas(columns = questions).to_pandas().reset_index(drop = True)\n",
-    "\n",
-    "#We will select data for the w13_1 and w13_1-label columns to order health ratings\n",
-    "labels_water = water_health[['w13_1', 'w13_1-label']].drop_duplicates().sort_values('w13_1').\\\n",
-    "    dropna().reset_index(drop = True)\n",
-    "#Changing column names to improve readability\n",
-    "labels_water.columns = ['rating', 'label']\n",
-    "#Removing information in brackets for some categories\n",
-    "labels_water['label'] = labels_water['label'].str.split('(', n = 1, expand = True)[0]\n",
-    "\n",
-    "#We will make the data frame longer. This means that instead of having multiple columns for each region/area, \n",
-    "#we will have single column containing information about the region/area linked to \n",
-    "#that observation\n",
-    "water_health['id'] = water_health.index\n",
-    "water_health = pd.melt(water_health, id_vars = 'id', value_vars = water_health.columns[:-2],\n",
-    "                     var_name = 'field', value_name = 'rating')\n",
-    "#We remove any rows with no data in the occupation column (i.e., a single respondent \n",
-    "#cannot be in multiple regions)\n",
-    "water_health.dropna(subset = 'rating', inplace = True)\n",
-    "water_health.drop(columns = 'id', inplace = True)\n",
-    "#Join with metadata table\n",
-    "water_health = pd.merge(water_health, health, on = 'field', how = 'left')\n",
-    "#Removing information in brackets for some categories\n",
-    "water_health['rating'] = water_health['rating'].str.split('(', n = 1, expand = True)[0]\n",
-    "\n",
-    "#Calculate percentage by rating, so we add rating to the grouping\n",
-    "#Count number of respondents per rating, area and region\n",
-    "water_health = water_health.groupby(['region', 'area', 'rating']).agg({'field': 'size'}).reset_index()\n",
-    "#Calculate total respondents per region\n",
-    "water_health['respondents'] = water_health.groupby(['region', 'area'])['field'].transform('sum')\n",
-    "#We calculate percentage of respondents per activity - No decimals needed\n",
-    "water_health['rate_per'] = np.round((water_health.field/water_health.respondents)*100, 2)\n",
-    "#Checking results\n",
-    "water_health.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting data about waterway health for the Wet Tropics region\n",
-    "We will show results for a single region as it is difficult to show all regions in a single plot. For this example, we will show the **Wet Tropics**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.97, 'Rating of perceived waterway health by habitat in the Wet Tropics region')"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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CRkZGsfmuX79eACD89NNPKsOV+ykqKqrYNHl5eYKzs7NgbGwsXL58WWXcyZMnBW1tbaFHjx4qw5XH1fDhw4WCggKVcVevXhUACP/73//EYTt37hQACJ06dRJ0dXWFzMxMcZyDg4Pg6uqqMo+Sfvv5+flCly5dBG1tbZVj7fz58wIAISAgoNg0H330kQBACA8PLzbuRcpzCABh69atKuOGDBkiABC2bNkiDnuV7Xb//v1iy83IyBAaNGggmJqaCllZWSrjSjqmytqXymkACGPGjBEKCwvF4atXrxYACKNHjy51GzxPec7U09MTIiIixOEKhUIYNGiQAECYP3++OHzbtm0CAGHGjBnF5qU8dyQkJLx0uWvXrhUACDo6OsKpU6fE4QUFBUKHDh0EAMLZs2dVpnmVawUAYcWKFSrjVqxYIQAott98fX2LnduVca5du7bE9YiJiSn221AoFMKIESMEACrrJgivdn4vbTrlvjM1NRVu3bolDs/OzhY8PT0FLS0tlfN4Wco6r6m7r17lPF+acePGCQCETZs2icPmz58vABBiYmKEpKQkQSKRCD/++KM4XnkeGjFihDhMeSyvWrVKZf5Pnz4VHBwcBGtrayEnJ0ccXtKxoC7l/nnx2FHO29vbW0hOTlYZl5aWJpiamgpSqVTl91hYWCj0799fACDMmjVLZRp1r/8l3Sc9efJEMDQ0FAwNDYud5wRB9TpuYWEh2NnZFTuPCYJQbH1KU9a15caNG4KOjo7QqFEjISkpSWXcnDlzip2Tjh49Km7P52N6/PixULNmTQGAMGzYMJX5lLR/lddVX19fQS6Xi8NjYmIEKysrQUdHR+Xc/irnMFIPk0pERNWQMnnj5uYmTJ8+XZg+fboQGBgo+Pn5CQCEmjVrCjdv3iz3/L744gsBgMpN6esklVxcXFQevp4f9+GHH4rDQkJCBADCBx98UGz+sbGxgra2drlvNpUPFtu2bSs2btOmTcVufAXh1ZNK2traxW7gBUEQRo4cKQAQduzYIQhC0c2pubm54ObmpnIDqvTPP/8IAITff/9dHKZ8wF28eHGx8sqHfR8fn5fGuXDhQgGAsGHDhhLHN2nSRLCysiq2bi8+7EydOlUAIOzcuVNl+JQpUwQAQlBQ0Csvc+bMmQIAYcGCBcXKbty4Ua2k0uTJk1VuLMPCwsSkhaOjo9C/f3+xbLt27QSZTKZyM1sahUIhmJiYCB06dFAZXlYiYteuXSU+kCh9+OGHgpaWlpCWliYOUyYbEhMTS4zByspKqF27tjhs3LhxgpGRkXDo0CEBgHDgwAFBEATh3r17JR7rpVE+FK5bt05leOPGjQVDQ0OVGBMSEgQ9PT2hefPm5Zq38hxS0vGqHDdp0iRx2Ktst9IsWLBAACCEhISoDH/VpJKhoWGxpGN+fr6go6MjNGnS5KXxCMJ/58xPP/202Ljo6GhBW1tbqF+/vjgsLy9PqFmzpuDk5KRyPo2IiBAACB9//HG5lqt8IBs6dGip45YsWVKueZV1rfD09Cx23i8sLBQ8PDwEiUSikgB7laRSaS5dulRi8u1tJJV++OGHYuWV4/75559yzb88SaXy7qtXOc+XRnkueP747N69u+Di4iL+v0GDBkLHjh3F/yuPh40bNwqCIAiJiYmCtra2SpnnLVmyRAAg7N27VxxWEUmlPXv2FJtmw4YNxZL1SjExMYKOjk6x5Lw61//n43r+PmnevHmlHksvsrCwEJydnYu91FFHWdeW8ePHF3vBolRYWChYW1sLTZs2FYcFBAQIAIRdu3YVK698oVmepFLHjh0FAML58+eLzWf27NnFrgNv8hxGJWPzNyKiauz+/fuYOXOmyjAbGxucPHkS7u7uxco/ePAAc+bMwfHjx/Ho0SPI5XKV8Y8fPxarsL8Ob29vaGmpdvun7NcmNTVVHKbsY6ldu3bF5uHg4ABHR0dERUWVa5lXrlwBUHLzBmUzoPDw8HLN62UcHR1L3E7t27fHX3/9hStXrqBv3764ffs2UlJSYGdnV2w/ARD7yLl161axcS1atCg27MKFCwCA995776UxKj/9fP78edy/f7/Y+NzcXCQlJSEpKanMjkSHDBmCOXPmYOPGjfjwww8BFPXzsHnzZlhaWqr0+aLuMpX7v3379sXKljSsLH5+fliwYAGCg4PRqlUrsQlYx44d4efnh4MHDwIAsrOzceHCBbRv375YB+27du3CypUrcfnyZaSkpKCwsFAc9/jx43LHotwOt2/fLrEPlydPnkChUODOnTto1qyZONzFxaXEfaHsE2PHjh2Ij4+Hra0tgoOD0b59e/j4+EAqlSI4OBhdu3YtsekbUNTHyfz58xEUFIT79+8jKytLZfyL6zd69Gh8/vnn2Lx5Mz7//HMARV/xycvLw2effVbubQEATZs2LTaspPPBq2y3hIQEzJ07FwcOHEBMTEyxvm3U2W9l8fT0hJGRkcowHR0d1KxZU2UdyqOkY9vJyQkODg64ceMG8vLyoKenB11dXQwfPhxz587F4cOH0bVrVwAQ+zB7W/sBeLVrRdu2bYud97W0tNC2bVvcvXsXERER6Ny5s1oxPy8vLw9Lly7F1q1bcevWLWRmZkIQBJWY3jZ1tmFFLOdNnecBwNfXFxKJRDyHFBYW4uTJk+jbt69KmdWrV0Mul0MqlRbrT+nixYsoLCyEXC4v8Td89+5dAEXXvB49epQZz5tU0vW0rHsGR0dHuLq64s6dO8jIyICxsbHKuPJc/0ujznV8wIABWLZsGerXr48BAwbAz88PrVu3LnfTY6XSri3K4+fQoUMlfvVQV1dX5f6krHs2ZZPI8rhy5QoMDAxK3C9l3a9V1O+vOmJSiYioGvP39xcflhMTE7F+/Xp8/fXX+OCDD3DhwgWVh6B79+6hRYsWSE9Ph5+fH3r27AkTExOxw8bQ0NBiDw6vStlW/3nKPp6ef1BPT08HALG/oBfVrFmz3Eml9PR0aGlpwdrausT5SCQScXmv6/k+oUoanpaWBqCobwkAuHHjBm7cuFHq/F58wC9tGcr52tvbvzRG5bL/+OOPMstlZWWV+bDh5eWFpk2b4t9//0VKSgrMzc0REhKChw8fYsyYMdDV1X3lZSrXp6T9X9o2Lk379u2hra2N4OBgTJ06FcHBwahXrx5q1KgBPz8/rF+/Hjdv3sSjR4+Ql5dXLOmyYMECBAYGwtraGu+99x5q1aol3rgvWrRIrd+Gcju8rI+VF/d7Wevs5+eHHTt2IDg4GF26dMGNGzcQEBAAfX19tG7dWnwQLCmplJeXhw4dOuDy5cto3LgxhgwZAktLS+jo6Ih9p724foMGDUJgYCBWr14tJpX++usvGBkZYeDAgeXcEkXKez5Qd7s9e/YMzZs3R2xsLNq2bYvOnTvDzMwM2traCA8Px549e97qOQ0oWo/n16E8yjp/REdHIyMjA5aWlgCAUaNGYd68eVi9ejW6du2K3NxcbNq0CS4uLmonaMq7H171WlHe8+Kr+uijj7B37154enqK/d/o6uoiNTUVixcvfmP7uizl3YYVtZw3dZ4HAEtLSzRo0ABXr17Fw4cPER8fj/T0dPj6+oplfH19sXTpUpw7dw716tXD9evX4enpKV6TlPGcPn0ap0+fLjOeilTSsam8HyjtuLW1tcWdO3eQnp6uklR63eNcnev44sWL4eLigrVr1+Knn37CTz/9BH19ffTr1w8LFiwo95flSotZub9mz55drvko77NKWq461+z09PRi/cQp2draimVeVFG/v+qISSUiIgIAWFtbIzAwEGlpafjpp5/w/fffix2VAsBvv/2GlJQUbNy4EZ988onKtJ9//rn4BZaKpLxBePHrWkpPnz5Va14KhQKJiYnFkhQJCQkQBKHUB0N1lRaXcriyQ2fl8vr27YsdO3aotYySvpai7ET40aNHL51euexr166p1YlwSYYMGYKJEydi+/btGD16NDZu3CgOf51lKrdTQkJCsTe/6ux75bKbNm2K06dPIycnB6dOncLQoUMB/JdgCQ4OFmszPJ90KSgowI8//ghbW1uEh4erHD+CIOCXX35ROxYA2Lt3r1pv48v60uHz66BM5CmH+fn5YdasWUhLS0NISAg8PDxUHlj27NmDy5cvY+TIkVi9erXKfLdu3arylTglY2NjDB48GCtXrkR4eDiysrIQGRmJTz/9tFiNnTdF3e32119/ITY2Fj/++GOxL+DNnTsXe/bseStxvq6yzh8SiUTlAdbFxQXvvfce/vnnHyQkJODIkSNISUnB5MmT1f4yZnm96rWivOfFV3Hx4kXs3bsX/v7+2L9/P7S1tcVx586dEz/CUN28yfM8UHQuuXr1KoKDgxEfHw9AtSaPMsEUHByMpKQkCIKgci5VxjN58uQSv0aqKSX9VpSxlnbcPnnyRKWc0use589fx1/2xVkdHR0EBgYiMDAQjx8/RmhoKNauXYsNGzbgyZMnOHToUJnTK5V2rlCu24uJs9Io77OSkpKKvcBT936ttPu+0rY7vV1aLy9CRETVybfffgs7OzssW7ZM5ZPdyqrxL361RxCEEt8oKm/a3+bbn0aNGgFAict/+PAhYmNjyz2vxo0bAyj+SeTnh72pL8rExsaW+HnzkydPqsTi5eUFExMThIWFFfsM86tQVhU/fPjwS8u2bNkSANT6IktpBg4cCB0dHfzf//0fcnJysGvXLri7u6NVq1avtUzl/ldut+eVNOxl/Pz8kJ2djWXLliE9PR0dO3YEUNRcwc3NDcePH0dwcDAMDQ3RvHlzcbqkpCSkpaWhdevWxRKSYWFhJX4uvKzfx5vc9kpeXl6wsbER18Hc3Fw8zjp27IjCwkKsXr0ajx8/Ltaco7TfPlD2dh49ejSAouZWymSUuk2u1KHudnvV9XpRRZzrnldSbDExMYiLi0O9evWKNcscPXo08vPzsX79eqxevRra2toYPnz4W4tP3WuF0unTp6FQKFSGKRQKnDlzBhKJRPy9l6as/aCMSfml0OeVtq+1tbUrZe0FLS2tNxbXmz7XKBNEISEhCAkJgbOzs0rC39raGnXr1kVwcHCxpm8A0Lx5c0gkErXiqejfn1JZ9wxxcXG4f/8+XF1diyVbynv9L4061/Hn2dnZYeDAgTh48CDc3d1x9OjREq9N6lAeP8pmcC9T1j3bmTNnyr3cxo0bi03RX/Sm79eofJhUIiIiFTKZDF9//TXy8/Px448/isOVN4YvfnZ17ty5uH79erH5WFhYACi6uXpb2rVrB0dHR+zdu7fYTei0adPUuskcNmwYAGDmzJkq1abT0tLE/oyUZV5XYWEhvv32W5X+PK5evYqNGzfC2tpa7GdIR0cH//vf/xATE4PAwMASE0vXr18v9Y3di5o3b47mzZvjxIkTYr8qz3u+BtPw4cNhbGyM7777rsSmd9nZ2eW+kaxRowbee+89nD59GosWLUJ6enqxGgyvssxBgwZBW1sbCxcuVNkG6enp+Omnn8oV2/OUDzfz5s2DlpaWSnLFz88Px48fx8WLF9G2bVuVZns1atSATCbD5cuXkZ2dLQ5PSUkp9ZP0Zf0+evXqBUdHRyxcuBAnTpwoNj4/P/+VPn/coUMHPHjwADt27ICvr6/Yf02LFi1gYGCAefPmiev6vNJ++6GhoSUeR0qNGzdG8+bNsWnTJvz9999o2LBhiX1gvCnqbrfS1mvz5s34999/y73cijjXPW/Dhg24evWq+H9BEPDtt9+isLBQ5TPzSj179oSdnR1+++03hIaGonv37uIn398Gda8VSnfu3Cl2PK1atQp37txB9+7dS2ya/Lyy9kNpMd24cQNz5swpdX5JSUnIzc0tc7kVzcLCAg8fPnwj83qT53kA8PHxgZaWFo4dO4ZTp06V2N+Qr68vzp07J9aSeb6MjY0N+vXrhzNnzuDXX39VuUYqnT9/XuU8W9G/P6VevXrB1NQUa9euVdl2giDg66+/RkFBQYm/x/Je/0szbNgwGBkZYcGCBSX2G6S8jsvl8hITNVlZWcjMzISurm6xPszUNWbMGOjo6OCLL74o8SVeamqq2PcUAAwePBgAMGvWLJWE1pMnT9SqLai8F5s6darKfVFcXBwWLlwIHR0dcVlUMdj8jYiIilH2w7FhwwZ8++23cHNzw+eff461a9eib9++6NevHywtLXHu3DlcvnwZ3bt3x/79+1XmUadOHdjZ2WHr1q2QSqWoVasWJBIJvvjii9dqxvA8bW1trFixAh988AE6duyI/v37w9bWFqGhoXj06BEaNWqk8vBVFh8fH3zxxRf4/fffUb9+ffTt2xeCIGDnzp14+PAhxo8fDx8fnzcSd8OGDXHq1Ck0b94cnTt3RmJiIrZt24aCggL8+eefKp1ozpw5E5cvX8aSJUuwf/9++Pj4oEaNGnj06BGuXbuGiIgInD17ttR+pV60adMmdOjQAaNGjcLGjRvRunVr5Obm4saNG7hy5QqSk5MBFL1R3rJlCz7++GM0atQIXbt2RZ06dSCXyxEdHY3Q0FC0adNG7JPrZYYMGYJ///0X06dPB4ASk0rqLtPd3R0//PADpk+fjoYNG6Jfv37Q0dHBzp070bBhQ9y+fbtcsSm1a9cOurq6SExMROPGjWFubi6O8/PzE2vbvJh00dLSwpgxY7BgwQI0atQIPXv2RHp6Og4cOAAnJ6cSH+A7duyI+fPnY9SoUejbty8MDQ3h5OSEIUOGQCqVYseOHejWrRt8fX3RsWNHNGjQABKJBDExMTh58iQsLS1L7KC9LH5+fti6dSsSExNV1kFPTw9t27bFkSNHABTveLZnz55wdnbGL7/8guvXr6N+/fq4ffs29u3bhz59+pTZNPPzzz/HyJEjAbzdWkoA1N5uQ4YMwbx58/DFF18gODgYTk5OiIiIwLFjx/Dhhx9i165d5VpuWfvybfD390fr1q0xYMAAWFtb49ixYwgLC0OrVq1KTGLq6Ohg5MiR4kuCt70f1L1WPL9e48ePx7///ot69erhxo0b2Lt3L6ysrMr1wKnsgHjRokVISUkRk1Dff/89WrRogRYtWmD79u2Ij49Hq1atEBsbi3/++Qfdu3cv8Rju2LEjwsLC0K1bN7Fjfh8fnzd2HXhVHTt2xPbt29G7d280btwY2tra+OCDD9CwYUO15/Wmz/Pm5ubw9vbG5cuXAZTcibWvry+WL1+Ou3fvom7dusX60lm2bBlu376NKVOmiNcoMzMzxMXFISwsDHfv3kV8fDwMDAzE7bFjxw707dsX3bp1g76+vngefptMTEywatUqDBw4EC1btkT//v1hbW2No0eP4tKlS2jRogW++uqrYtOpc/0vSY0aNbBhwwYMGDAALVq0wAcffIDatWsjKSkJ58+fh7OzM4KCgpCTk4O2bdvC09MTTZs2haOjIzIzM7Fv3z48efIEgYGBkEqlr7UN6tevj2XLluF///sfateujffffx9ubm7IyMjAgwcPEBoaioCAAKxYsQIA0LlzZwwaNAibN29GgwYN0Lt3b8jlcmzfvh0tW7bE3r17y5XoGjJkCHbt2oU9e/agYcOG6NGjB7KysrBt2zY8e/YMCxYsgKur62utG6lJMx+dIyIiTYqKihIACP7+/qWW+f333wUAwpAhQ8RhwcHBQtu2bQVjY2PBzMxMeP/994VLly6V+NlbQRCEc+fOCb6+voKxsbEAQOWz2yVNo4zrxU/KKqGUTykfP35c/My7hYWF8PHHHwuxsbFC/fr1BVNT03JulSJr1qwRmjdvLhgYGAgGBgZC8+bNhTVr1pRY9lU+Oa1ch7i4OKF///6ChYWFoK+vL7Ru3Vo4fPhwidMUFBQIK1euFNq2bSuYmJgIUqlUcHR0FLp27SosX75cyMzMFMu+7PPmgiAIT548ESZMmCC4uroKenp6goWFhdCyZUth4cKFxcreunVLGDlypODk5CTo6ekJ5ubmQoMGDYTx48cLFy5cKHHdSpKdnS2YmJgIAITWrVuXuY3UWaYgCMKqVauEunXrCnp6ekKtWrWEwMBAITs7u8x4StOmTRsBgDB58mSV4Y8fPxaP4bNnzxabLi8vT5g9e7bg4eEh7p/JkycLGRkZpR4nv/zyi+Dh4SHo6uqWGOvDhw+FCRMmiPM0MTERvLy8hE8//VQ4duyYStnyrOudO3fEdbh27ZrKOOXnnGvXrl3itA8ePBD69u0rWFtbi7+LrVu3ip+Dnz59eonTZWVlCVKpVJDJZEJKSkqZ8b2orHmXda5QZ7uFh4cL7733nmBubi4YGxsLvr6+wtGjR0v9PH1p27msfVnWvlHnHPL8OXPVqlVCvXr1BKlUKtja2goTJkwQ0tPTS5323r17AgDB3t5eKCgoKNfylErbFoJQ+j5S51rx/DxOnjwp+Pr6CoaGhoKJiYnQp08f4e7du8WWW9pn5Pfv3y80b95ckMlk4rGulJCQIIwYMUKws7MT9PX1hQYNGgh//PGH8ODBgxKPpYyMDOGzzz4TbG1tBW1t7TKP8+eVtE9Lu0YKQtnbtyTx8fFCv379BCsrK0FLS0tl2lfZV4Kg/jm3LJMnTxa3fXR0dInxK8ePHTu2xHlkZ2cLv/zyi9C0aVPB0NBQkMlkgouLi9C7d29hw4YNQn5+vlg2Pz9fmDJliuDo6Cjo6OiUeQ9RGuX+eXG7lXacPe/EiRNCt27dBDMzM0FPT0/w9PQUpk2bpnJdVlL3+l/WcXPlyhWhX79+Qs2aNQVdXV3B1tZW6Natm7Bv3z5BEIquSfPmzRPee+89oVatWoKenp5Qs2ZNwcfHR9i8ebOgUCjKtW3Kc225cOGCMGDAAMHOzk7Q1dUVrKyshCZNmgjffPONEBkZqVI2Pz9f+PHHHwUXFxdBT09PcHV1FX7++Wfh/PnzAgBhwoQJKuVL2wf5+fnC/PnzhQYNGghSqVQ8f+/Zs6dY2Vf9XVD5SQShhHqFREREVVxGRgZq1qyJBg0a4Pz585oORySRSODr61tiPwxE75qwsDA0b94cQ4YMwYYNGzQdTrW1Y8cOfPzxx5g2bRpmzZql6XCIqiVe/0u3evVqfPbZZ2LNJ6pa2KcSERFVaVlZWcjIyFAZVlhYiK+++go5OTno3bu3ZgIjIvz6668AwIcEDRIEAQsWLICOjs5bb/pGRFSWJ0+eFOsr69GjR/jpp5+gra2t1hdPqfJgn0pERFSl3b17F+3atYO/vz9cXV2RkZGBkydP4ubNm6hXrx7Gjx+v6RCJqpXY2Fhs3rwZN27cwPbt28U+gKhiXbt2Dfv27cOZM2dw7tw5jB49Gg4ODpoOi4iqsblz52L//v1o3749atSogdjYWOzbtw8ZGRmYMWMGz1FVFJNKRERUpdnb2+Pjjz9GaGgoDh48iIKCAjg6OiIwMBDfffcdDA0NNR0iUbXy4MEDTJ06FUZGRujZsyf+/PNPTYdULV26dAnffvstTE1NMWTIEMyfP1/TIRFRNde1a1fcvHkT+/fvR0pKCvT19dGwYUOMGTMGgwYN0nR49IrYpxIREREREREREamNfSoREREREREREZHamFQiIiIiIiIiIiK1MalERERERERERERqY1KJiIiIiIiIiIjUxqQSERERERERERGpjUklIiIiIiIiIiJSG5NKRERERERERESkNiaViIiIiIiIiIhIbUwqERERERERERGR2nQ0HQAREb0ZCoUCeXl5mg6DiIiIiIiqKF1dXWhra5e7PJNKRETvgLy8PERFRUGhUGg6FCIiIiIiqsLMzMxgY2MDiUTy0rJMKhERVXGCICA+Ph7a2tpwcHCAlhZbNhMRERERkXoEQUB2djYSEhIAALa2ti+dhkklIqIqrqCgANnZ2bCzs4OBgYGmwyEiIiIioipKJpMBABISElCjRo2XNoXj62wioiqusLAQAKCnp6fhSIiIiIiIqKpTvqjOz89/aVkmlYiI3hHlafNMRERERERUFnWeK5hUIiIiIiIiIiIitbFPJSKid1RsbCySkpIqbHlWVlZwdHSssOVVRgEBAUhNTUVQUNBbXU6HDh3g7e2NRYsWlVpm3bp1mDhxIlJTU99qLOXF45EqCo+1t+tl558ZM2YgKCgI4eHhbzWO8pxvQ0JC4Ofnh5SUFJiZmb3VeMqDx6bmvHi8lOc6+jrzL8mbXubbxuOVyotJJSKid1BsbCzqeNVBTnZOhS1TZiDDrchb5b4hqKgETHXg7OyMiRMnYuLEiZoOpUSxsbHw8qqD7Ao8Hg0MZIhU43h8U2bMmIHly5cjISEBu3fvRu/evV86jUQiKXfZNzGdOqKjo+Hi4oIrV67A29u71HKV5WEpNjYWtet4ITcnu8KWqS8zwO1bkTz3aVhlOQZLExsbC686tZGdk1thyzSQ6SPy1m21js3169djzpw5+Oabb8ThQUFB6NOnDwRBKPeyK/t1adeuXdDV1dV0GJVWbGws6tSpg5ycCryPlMlw69bbvW5XphduFZWArwhMKhERvYOSkpKQk52DPjP7wNrZ+q0vLzE6Ebun70ZSUlK1eMuUl5fHjtHVkJSUhOzsHGz4rh+8nN7+8RgZk4ihs7erdTy+iQf9yMhIzJw5E7t370arVq1gbm5eruni4+PLXVbTKlvNjxclJSUhNycb7l2+hMy81ltfXk7KQ9w78lu1OffRq0tKSkJ2Ti6+bWMCJ9O3/wgWk1aAn8+kq31s6uvrY968eRg9enSVOS+9CgsLC02HUKklJSUhJycHfn5+FXIcpKSkIDg4uMKv22+C8uWLMulamRJXFYV9KhERvcOsna1hW8f2rf+9icRVhw4dMH78eEyZMgUWFhawsbHBjBkzypwmICAAvXv3xsyZM2FtbQ0TExN8/vnnyMvLE8vI5XKMHz8eNWrUgL6+Ptq1a4eLFy+qzCc0NBQtWrSAVCqFra0tvvnmGxQUFKjENm7cOEycOBFWVlbw9/cvM6758+fD1tYWlpaWGDt2rMqXM+RyOQIDA2Fvbw9DQ0O0bNkSISEh4vjk5GQMHDgQ9vb2MDAwQIMGDbBly5Yyt1tMTAy+/PJLSCSSYh0rHjp0CF5eXjAyMkLXrl0RHx9fZuxvk5eTNZp42r/1v4pIXJXk/v37AIBevXrBxsYGUqm0XNO9rGx5vrxCqmTmtWBYw+2t/72JxNWrnPsKCgowfvx4mJmZwdLSEl9//TWGDRumUmvtTZz7srKyMHToUBgZGcHW1hYLFiwo93pt3LgRzs7OMDU1xYABA5CRkSGOUygUmDNnDlxcXCCTydCoUSPs2LFDHF9YWIiRI0eK42vXro3FixeXuqyAgACEhoZi8eLF4nkwOjpaHH/p0iU0a9YMBgYGaNOmDW7fvl3u9XjTnEx14Gmh+9b/XjVx1blzZ9jY2GDOnDllltu5cyfq1asHqVQKZ2dnlWPjZdel5y1cuBANGjSAoaEhHBwcMGbMGGRmZorj161bBzMzMwQFBcHDwwP6+vrw9/dHXFycWGbGjBnw9vbGypUr4eDgAAMDA/Tr1w9paWmlLrdDhw4qtajkcjm+/vprODg4QCqVwt3dHX/99RcA9Y7Hsu5HXvSy+4HKwNzcHFZWVm/9711OYFYHTCoREVGlsX79ehgaGuL8+fP45ZdfMGvWLBw5cqTMaY4dO4bIyEiEhIRgy5Yt2LVrF2bOnCmOnzJlCnbu3In169fj8uXLcHd3h7+/P549ewYAePToEd5//300b94cERERWL58Of766y/89NNPxWLT09PD6dOnsWLFilLjCQ4Oxv379xEcHIz169dj3bp1WLdunTh+3LhxOHv2LLZu3YqrV6/i448/RteuXXH37l0AQG5uLpo2bYr9+/fj+vXrGDVqFIYMGYILFy6UuLxdu3ahVq1amDVrFuLj41WSRtnZ2Zg/fz42btyIEydOIDY2FoGBgWVuTyqi7oP+jBkz0LNnTwCAlpaW+BB18eJFdOnSBVZWVjA1NYWvry8uX76sMq1EIhHftEZHR0MikWDbtm3w9fWFvr4+Nm3aVOpyk5KS0KdPHxgYGMDDwwP//POPyvjr16+jW7duMDIyQs2aNTFkyBCVPjIOHjyIdu3aicmJHj16iMmxF0VHR8PPzw9A0YOGRCJBQECAOF6hUKiVGKH/qHvumzdvHjZt2oS1a9fi9OnTSE9PL/a2/k2c+7766iuEhoZiz549OHz4MEJCQoodvyW5f/8+goKCsG/fPuzbtw+hoaGYO3euOH7OnDnYsGEDVqxYgRs3buDLL7/EJ598gtDQUABFx1KtWrXw999/4+bNm/jhhx/w7bffYvv27SUub/HixWjdujU+++wz8Tzo4OAgjv/uu++wYMEChIWFQUdHByNGjHjpOlRX2tra+Pnnn/H777/j4cOHJZa5dOkS+vXrhwEDBuDatWuYMWMGpk2bJl7ryrouvUhLSwtLlizBjRs3sH79ehw/fhxTpkxRKZOdnY3Zs2djw4YNOH36NFJTUzFgwACVMvfu3cP27duxd+9eHDx4EFeuXMGYMWPKvd5Dhw7Fli1bsGTJEkRGRmLlypUwMjICUP7j8WX3Iy962f0Avb5169bB0dERBgYG6NOnD5KTk4uVWb58Odzc3KCnp4fatWtj48aNKuMlEglWr15d5rVWKSQkBMOHD0daWpqYUC3vtfD+/ftwdXXFuHHjIAiCmFAt6+WgQqHArFmzUKtWLUilUnh7e+PgwYPi+I8++gjjxo0T/z9x4kRIJBLcunULQFHNe0NDQxw9erRcMZaGSSUiIqo0GjZsiOnTp8PDwwNDhw5Fs2bNcOzYsTKn0dPTw5o1a1CvXj10794ds2bNwpIlS6BQKJCVlYXly5fj119/Rbdu3VC3bl2sWrUKMplMfAO5bNkyODg4YOnSpahTp45Y82nBggVQKBTicjw8PPDLL7+gdu3aqF27dqnxmJubi/Pq0aMHunfvLq5DbGws1q5di7///hvt27eHm5sbAgMD0a5dO6xduxYAYG9vj8DAQHh7e8PV1RVffPEFunbtWurDlIWFBbS1tWFsbAwbGxvY2NiI4/Lz87FixQo0a9YMTZo0wbhx4166Pek/6jzoBwYGivvw+YeojIwMDBs2DKdOncK5c+fg4eGB999/X6XWRkm++eYbTJgwAZGRkWXWjJs5cyb69euHq1ev4v3338fgwYPFpEFqaio6duyIxo0bIywsDAcPHsTTp0/Rr18/cfqsrCxMmjQJYWFhOHbsGLS0tNCnTx+VY1/JwcEBO3fuBADcvn0b8fHxKm/rXyUpTEXUPff9/vvvmDp1Kvr06YM6depg6dKlKs0R38S5LzMzE3/99Rfmz5+PTp06oUGDBli/fr1KTabSKBQKrFu3DvXr10f79u0xZMgQcX3kcjl+/vlnrFmzBv7+/nB1dUVAQAA++eQTrFy5EgCgq6uLmTNnolmzZnBxccHgwYMxfPjwUs+Dpqam0NPTg4GBgXge1NbWFsfPnj0bvr6+qFu3Lr755hucOXMGubkV17dRVdOnTx94e3tj+vTpJY5fuHAhOnXqhGnTpsHT0xMBAQEYN24cfv31VwBlX5deNHHiRPj5+cHZ2RkdO3bETz/9VGw/5+fnY+nSpWjdujWaNm2K9evX48yZMyovW3Jzc7FhwwZ4e3vDx8cHv//+O7Zu3YonT568dH3v3LmD7du3Y82aNejTpw9cXV3RqVMn9O/fH0D5j8ey7kdeVJ77AXo958+fx8iRIzFu3DiEh4fDz8+v2AvD3bt3Y8KECZg8eTKuX7+O0aNHY/jw4QgODlYpV9a19nlt2rTBokWLYGJiIt4LlOdl3tWrV9GuXTsMGjQIS5cuFV9Mvezl4OLFi7FgwQLMnz8fV69ehb+/Pz744AMxMenr66tS+y00NBRWVlbisIsXLyI/Px9t2rQp1zYtDftUIiKiSqNhw4Yq/7e1tUVCQkKZ0zRq1AgGBgbi/1u3bo3MzEzExcUhLS0N+fn5aNu2rTheV1cXLVq0QGRkJICifnBat26tUj2/bdu2yMzMxMOHD8W2/U2bNi3XOtSrV0/lYcbW1hbXrl0DAFy7dg2FhYXw9PRUmUYul8PS0hJAUTX7n3/+Gdu3b8ejR4+Ql5cHuVyuso7lZWBgADc3N5VYXrY96T/KB32gKKm4dOlSHDt2DF26dClW1sjISHyof/4BqmPHjirl/vzzT5iZmSE0NBQ9evQoddkTJ07Ehx9++NIYAwICMHDgQADAzz//jCVLluDChQvo2rUrli5disaNG+Pnn38Wy69ZswYODg64c+cOPD090bdvX5X5rVmzBtbW1rh58ybq16+vMk5bW1vsh6RGjRrF+lRSZ3uRKnXOfWlpaXj69ClatGghDtPW1kbTpk3Fh9f79++/9rkvJSUFeXl5aNmypTjewsKizKS6krOzM4yNjUtcn3v37iE7O7vYcZGXl4fGjRuL///jjz+wZs0axMbGIicnB3l5eWV2Fl+W57evra0tACAhIYH9YJVh3rx56NixY4kPxJGRkejVq5fKsLZt22LRokUoLCxUuQa+zNGjRzFnzhzcunUL6enpKCgoQG5uLrKzs8Xrno6ODpo3by5OU6dOHZiZmSEyMlL8HTg6OsLe3l4s07p1aygUCty+fbvMpBYAhIeHQ1tbG76+vqWWKc/xWNb9iJOTk0rZ8twP0OtZvHgxunbtKtZ88/T0xJkzZ1Rq8syfPx8BAQFirbZJkybh3LlzmD9/vlgzFyj7Wuvs7Cz2p6SnpwdTU1NIJJKXHndKZ86cQY8ePfDdd99h8uTJKuOULweV93Ljxo3DrFmzVOL/+uuvxZp78+bNQ3BwMBYtWoQ//vgDHTp0wIQJE5CYmAgdHR3cvHkT06ZNQ0hICD7//HOEhISgefPmr3SP+TwmlYiIqNJ48UssEomkxDd8mmBoaFiucmWtQ2ZmJrS1tXHp0qViN93Kava//vorFi9ejEWLFon9TEycOLHMfhnUiUWdr/dUd6+S5HzR06dP8f333yMkJAQJCQkoLCxEdnY2YmNjy5yuWbNmasdoaGgIExMTMcaIiAgEBweLx9bz7t+/D09PT9y9exc//PADzp8/j6SkJPFYjY2NLZZUUicWgElMdVTmc9+reNl5EAD279+vkgQAIPYttnXrVgQGBmLBggVo3bo1jI2N8euvv+L8+fOvHY8yiVaVt29F8PHxgb+/P6ZOnarSzPVNio6ORo8ePfC///0Ps2fPhoWFBU6dOoWRI0ciLy/vtR90y0smk5U5/k0fj0D57gfo9URGRqJPnz4qw1q3bq2SVIqMjMSoUaNUyrRt27ZYn1llXWtfR2xsLLp06YLZs2eX+KXEsl4Opqen4/HjxyovD5TxR0REAADq168PCwsLhIaGQk9PD40bN0aPHj3wxx9/ACiqudShQ4fXXg8mlYiIqEqLiIhATk6OeFN47tw5GBkZwcHBAVZWVmI/SMq3hPn5+bh48aJ48fby8sLOnTshCIL4sHH69GkYGxujVq03+/Woxo0bo7CwEAkJCWjfvn2JZU6fPo1evXrhk08+AVD04HPnzh3UrVu31Pnq6emhsLDwjcZKb+ZBf9iwYUhOTsbixYvh5OQEqVSK1q1bvzRJ+KaSmD179sS8efOKTaesrdGzZ084OTlh1apVsLOzg0KhQP369d9YEpMP7m+eqakpatasiYsXL8LHxwdAUQ3Hy5cvizUnlP2DvM65z8LCArq6ujh//rxYoyclJQV37twps0bHy9StWxdSqRSxsbGlzuf06dNo06aNSp84pfX1pcTz4Js3d+5ceHt7F6ud5uXlhdOnT6sMO336NDw9PcUESXn2x6VLl6BQKLBgwQJoaRX1ylJSE8eCggKEhYWJtZJu376N1NRUeHl5iWViY2Px+PFj2NnZASi6F9DS0ipXzboGDRpAoVAgNDQUnTt3Lja+vMdjWfcjLyrP/QBVHm/r+mZtbQ07Ozts2bIFI0aMgImJyUuXq87LQYlEAh8fH4SEhEAqlaJDhw5o2LAh5HI5rl+/jjNnzryRvjbZpxIREVVpeXl5GDlyJG7evIl///0X06dPx7hx46ClpQVDQ0P873//w1dffYWDBw/i5s2b+Oyzz5CdnY2RI0cCAMaMGYO4uDh88cUXuHXrFvbs2YPp06dj0qRJ4k3um+Lp6YnBgwdj6NCh2LVrF6KionDhwgXMmTMH+/fvB1DUbOjIkSM4c+YMIiMjMXr0aDx9+rTM+To7O+PEiRN49OiRSifMpHmnT5/G+PHj8f7774tfSqqofdSkSRPcuHEDzs7OcHd3V/kzNDREcnIybt++je+//x6dOnWCl5cXUlJSypynnp4eAPDhXcO++OILzJkzB3v27MHt27cxYcIEpKSkiMmhN3HuMzIywsiRI/HVV1/h+PHjuH79OgICAl77vGhsbIzAwEB8+eWXWL9+Pe7fv4/Lly/j999/x/r16wEUnQfDwsJw6NAh3LlzB9OmTSv25boXOTs74/z584iOjlapdUevrkGDBhg8eDCWLFmiMnzy5Mk4duwYfvzxR9y5cwfr16/H0qVLVR5Oy3Ndcnd3R35+Pn7//Xc8ePAAGzduLPFDGLq6uvjiiy9w/vx5XLp0CQEBAWjVqpVKE1B9fX0MGzYMEREROHnyJMaPH49+/fqVqwmSs7Mzhg0bhhEjRiAoKAhRUVEICQkRE1zlPR7Luh95UXnuB+j1eHl5FatNdu7cuWJlSkqQlvUi72XUSXDLZDLs27dP/Krhy/pbfJ6JiQns7OxeGr+yX6WQkBB06NABWlpa8PHxwa+//gq5XF6sptOrYE0lIqJ3WGJ04ju1nJJ06tQJHh4e8PHxgVwux8CBA1W+tDF37lwoFAoMGTIEGRkZaNasGQ4dOiR+vtbe3h7//vsvvvrqKzRq1AgWFhYYOXIkvv/++7cS79q1a/HTTz9h8uTJePToEaysrNCqVSuxf53vv/8eDx48gL+/PwwMDDBq1Cj07t27zE8jz5o1C6NHj4abmxvkcnmlbeIWGVMxx0lFLac8PDw8sHHjRjRr1gzp6en46quvXtrU4k0ZO3YsVq1ahYEDB4pfZbt37x62bt2K1atXw9zcHJaWlvjzzz9ha2uL2NhYfPPNN2XO08nJCRKJBPv27cP7778PmUxWKZtq5KSU/NWqqrqcF3399dd48uQJhg4dCm1tbYwaNQr+/v4qzWjexLnv119/FWu8GRsbY/LkyWWei8rrxx9/hLW1NebMmYMHDx7AzMwMTZo0wbfffgsAGD16NK5cuYL+/ftDIpFg4MCBGDNmDA4cOFDqPAMDAzFs2DDUrVsXOTk5iIqKeu0434aYtJd3dF6ZljNr1ixs27ZNZViTJk2wfft2/PDDD/jxxx9ha2uLWbNmqTSTK891qVGjRli4cCHmzZuHqVOnwsfHB3PmzMHQoUNVyhkYGODrr7/GoEGD8OjRI7Rv317scF7J3d0dH374Id5//308e/YMPXr0wLJly8q9nsuXL8e3336LMWPGIDk5GY6Ojmofjy+7H3nRy+4HKoOXvWiozMsZP3482rZti/nz56NXr144dOiQStM3oOgLl/369UPjxo3RuXNn7N27F7t27Xqtr6E5OzsjMzMTx44dE/vZKqspp6GhIfbv349u3bqhW7duOHjwYLmvq1999RWmT58ONzc3eHt7Y+3atQgPD1f5amyHDh3w5ZdfQk9PD+3atROHBQYGonnz5uWuGV0mgYiIqrScnBzh5s2bQk5OjjgsJiZGkBnIBAAV9iczkAkxMTEVuu7Dhg0TevXqVaHLJPXFxMQIBhV8PBqoeTy+eCz5+voKEyZMUCnTq1cvYdiwYaXOY/fu3cKLt1aXL18WmjVrJujr6wseHh7C33//LTg5OQm//fabWAaAsHv3bkEQBCEqKkoAIFy5cuWlMT8/nZKpqamwdu1a8f937twR+vTpI5iZmQkymUyoU6eOMHHiREGhUAiCIAhHjhwRvLy8BKlUKjRs2FAICQl5aTyzZs0SbGxsBIlEIm6PV9leb0NMTIygLzOo0GNNX2ZQ4ee+FxUWFgqenp7C999/r9E4qHQxMTGCgUy/Ys+DMn2NH5uva+3atYKpqWmZZaZPny40atSoQuKpLmJiYgSZrILvI2Wvd90uyV9//SXUqlVLkMlkQs+ePYX58+cXO56WLVsmuLq6Crq6uoKnp6ewYcMGlfHluda+6PPPPxcsLS0FAML06dNLLPPicZuRkSG0adNG8PHxETIzM0s89l+8zygsLBRmzJgh2NvbC7q6ukKjRo2EAwcOqExTWFgomJubCy1bthSHXblyRQAgfPPNN6WuQ0nPF6WRCEIlfZ1JRETlkpubi6ioKLi4uEBfX18cHhsbW6FNoaysrCr8SzoBAQFITU1FUFBQhS6X1FcdjkeqHKrDsRYTE4PDhw/D19cXcrkcS5cuxdq1axEREaHSzwxVLtXh2HzT1q1bh4kTJyI1NbXUMjNmzEBQUBDCw8MrLK7qgMdr9Vba80VJ2PyNiOgd5ejoyIszVRo8HqmiVIdjTUtLC+vWrUNgYCAEQUD9+vVx9OhRJpQquepwbNK7g8crlRdrKhERVXHqvEkgIiIiIiIqizrPF/z6GxERERERERERqY1JJSIiIiIiIiIiUhuTSkRE7wi2ZiYiIiIiotelUCjKXZYddRMRVXG6urqQSCRITEyEtbU1JBKJpkMiIiIiIqIqRhAE5OXlITExEVpaWtDT03vpNOyom4joHZCZmYmHDx+ythIREREREb0WAwMD2NraMqlERFSdFBYWIj8/X9NhEBERERFRFaWtrQ0dHZ1yt35gUomIiIiIiIiIiNTGjrqJiIiIiIiIiEhtTCoREREREREREZHamFQiIiIiIiIiIiK16Wg6ACIiejmFQoHHjx/D2Ni43J3mERERERHRu0cQBGRkZMDOzg5aWpqtK8SkEhFRFfD48WM4ODhoOgwiIiIiIqok4uLiUKtWLY3GwKQSEVEVYGxsDKDowmFiYqLhaIiIiIiISFPS09Ph4OAgPiNoEpNKRERVgLLJm4mJCZNKRERERERUKbrFYEfdRERERERERESkNiaViIiIiIiIiIhIbUwqERERERERERGR2phUIiIiIiIiIiIitTGpREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhuTSkREREREREREpDYmlYiIiIiIiIiISG1MKhERERERERERkdqYVCIiIiIiIiIiIrUxqURERERERERERGrT0XQARERUfuHh4TAyMtJ0GPSOksvlkEqlmg6DqFrh76564n7XLG5/quoyMzM1HYJIIgiCoOkgiIiobOnp6TA1NdV0GPSO09KSQKHgbQFRhZJoAYJC01FQBdOSADzdao5EIgEfg+ldkJaWBhMTE43GwJpKRERVSI+pPWBXx07TYdA76O6ZuwheGYwN3/WDl5O1psMhqhYOnLuDH9YcgXuXLyEzr6XpcKiCpMRcwsPzm/FtGxM4mfJxrKKdfyTHmqtZ8PPzg7m5uabDIXoliYmJOHnypKbDAMCkEhFRlWLlZAXbOraaDoPeQUnRSQAALydrNPG013A0RNXDrZhEAIDMvBYMa7hpOBqqKDkpDwEATqY68LTQ1XA01U9sWgEAwNzcHFZWVhqOhujV5OfnazoEETvqJiIiIiIiIiIitTGpREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhu//kZERO+0mCsxOLX+FB5HPkZ2ajYAoPvX3dHsw2ZimdTHqQhZHYLoS9HIfJYJMxszNP6gMdoMbgOJlkRToROpZeG2k9h35hZuxyXhWUY2bCyM4evtgh+GdYKrnQUA4P6jZMxYewwnIqKQkJoJY5kU9VxqYOLH7dCrXV0NrwFVV+mPbuDx5Z3IfHoPBbnpAACXDp+jZv2uKuWykqLx6OI2pD+6gcK8bOjITGBsWweeXadoImwCEPE0D1tuZuFWcj7S5AIA4MvmxvjA0wAAkJ2vwJqILFxNzMPTrELkFgioYaANPyd9DKhrAANd1nF4l129ehUxMTFITU2FXC6HgYEBbG1t0bRpU5iYmCAvLw9hYWF48uQJMjIyUFBQACMjI7i6uqJRo0bQ09PT9CpQOfBXTERE77T4W/F4cOEBZCayEsdnpWRh1YhViNgfgZz0HFg5WeHZo2c4uvQoDi06VMHREr26pbvO4sTVaJgZ6cPeygSxT1Ox8dAVtB+3EulZuRAEAf6Ba7D5aDgSU7NQz7kmChUKnIiIRt9pmxBxL17Tq0DVVFbifaTFRUBH36jUMumPb+LGjil4dv8sBEUBZBYO0NaRIuXBhQqMlF50NyUfYfF5MNEr+bEyXS5g5+1sRKcWwNpAGzIdCR5mFGLj9SzMOpVWwdFSRbt+/Tri4+MhlUphaGiIzMxM3L17F3v27EFeXh7kcjmuX7+OlJQUGBkZQVdXF2lpabhy5QqOHTum6fCpnFhTiYiI3mkN32+Ipn2aIutZFhb3WVxs/M1jN5GdUlSDaeRfI1HDtQYuBV3Cvjn7cGHHBbQe3BqmNU0rOmwitX3aozk+ea8xHGuaAQAmLd2PxTtO48mzDBy7fB/Na9dCVHwKAGDG8E6YMsgXwVfuo/OXf0EQBMQlpKGRu60G14CqK6vaHVCzvj/yslMRvmF0sfGCIOBB8DIoCvJg5ekL145joKUjBQAU5uVUdLj0nC4uMvR0N0BKrgID9yQVG6+nDXze2Ag9PWQw0NVCXqGAL4+m4GZSPs4/zkOGXAFjKes5vKvq1KkDT09PGBkVJYzPnDmD69evIycnB48ePULNmjXRsmVLeHl5QU9PDwUFBdi3bx8SEhIQFxcHuVwOqVSq4bWgl+EvmIiI3mkGpgbQ1dctdbwgCOK/JZKipm7KJm9CoYDoS9FvNT6iN+XbIX5iQgkA2jV0Ev8t1dWBraUx3O0tAQAz1h5Ds8+W4qNpm6CjrYXh7zdFt5aeFR0yEQBAV2YiJolKkp0cjdyUhwAAAQLC/28sLv45CDd3T0NO6qOKCpNKYCrVglSn9GbiFjJt9K9rKDZz09OWoLZlUb0GLQmgzafRd1qTJk3EhBIA2Nr+9+JCW1sbBgYGKs3cdHR0YG1tDaDonkx5X0aVG3/GRERUrXm08YCeQdHNzOoRq7HikxXYP3e/OD49IV1ToRG9ssJCBVbtvQgAcLWzQKcmbtDW1sKx3z5FU097yPMLcOXuY6Rm5sLcWIYmHnbQ5tMdVVK5Kf8ljpLvnBATUOmPruHm7u+Rm/5UU6GRmlJyFTgZKwcA+Dnps0+lakShUCAyMhIAYGxsDHt7+2JlcnJyEBUVBQBwc3Njn0pVBH/FRFSpJCYm4n//+x8cHR0hlUphY2MDf39/nD59WtOh0TvK3N4cnyz5BM5NnSHRkiAjKQONejQC/v/LMW0dbc0GSKSmrJw8fDjt/3D44l3YWBhjz89DINXTgUKhwP8WBuHSnUcY37cN0g/MwLYZA5GYmoUvFu9F0Mmbmg6dqESColD8t3XdzvD+5A80GPAbINGCIj8XiZHHNRgdldejjAKMP/wMSTkK1LfWxaQWxpoOiSpIfn4+Dh8+jIcPH0Imk6Fr167Q1la9v0pPT8c///yD7Oxs1KxZE+3atdNQtKQu9qlERJVK3759kZeXh/Xr18PV1RVPnz7FsWPHkJycrNG48vPzoatbehMqqtocGjhg2LJh4v/jrsXhyp4rAABLJ0tNhUWktifJGfhg6gZcuvMIng5W2D8vQPzy27HL9/HvudsAgKFdm8BQpoePOjSAieEupGfJcezSPfRuzy/AUeWjZ/TfediohgcAQN+kJnRlpsjPToE8I0FToVE53UjMw3ehqUiTC2hjr4dp7cygX0azOXp3ZGdn4+DBg0hKSoKpqSm6desGExMTlTJPnz7FoUOHkJubC0dHR3Tu3Bk6OkxVVBWsqURElUZqaipOnjyJefPmwc/PD05OTmjRogWmTp2KDz74QCzz6aefwtraGiYmJujYsSMiIiLEedy/fx+9evVCzZo1YWRkhObNm+Po0aMqy4mPj0f37t0hk8ng4uKCzZs3w9nZGYsWLRLLSCQSLF++HB988AEMDQ0xe/ZsFBYWYuTIkXBxcYFMJkPt2rWxeLFqx88hISFo0aIFDA0NYWZmhrZt2yImJgYAEBERAT8/PxgbG8PExARNmzZFWFjYW9qapI7Y8FgoChUAgJz0HBxZcgQAYGBmANfmrpoMjajcbkQ9RZsxy3HpziO0b+iM0398LiaUACAtM1f8d9jtouZEd+KSkJGdBwAwlLGZAVVORjU9oK1X9In6rIR7AAB5egLyc4q+HiYzs9NYbPRyobG5mHQsBWlyAX1qy/CjLxNK1cWzZ88QFBSEpKQk2NjYoFevXsUSSg8ePMC+ffuQm5uLevXqwd/fnwmlKoZ7i4gqDSMjIxgZGSEoKAitWrUq8WsPH3/8MWQyGQ4cOABTU1OsXLkSnTp1wp07d2BhYYHMzEy8//77mD17NqRSKTZs2ICePXvi9u3bcHR0BAAMHToUSUlJCAkJga6uLiZNmoSEhOJvOWfMmIG5c+di0aJF0NEpajpSq1Yt/P3337C0tMSZM2cwatQo2Nraol+/figoKEDv3r3x2WefYcuWLcjLy8OFCxfETgYHDx6Mxo0bY/ny5dDW1kZ4eHiptZ/kcjnkcrn4//R09uvzqiKDI3Fk6RExaQQAwX8G48ymM6hVrxY+nPUh9s3bh4zEDJjUNEHKwxTk5+ZDoi1B96+7l9nJN1Fl8tEPmxDzNBUAkJEtR4+v14vjRnRvhj7t68HcWIaUjByMWRiEpTvPICo+BYIgQFdHGwM6NtRQ5FTdPbt/FjFn1qs0c4s7vwWPrwTBqKYnPN6bhFotBiDm1Bok3DyC9PhI5Gc9AwQFdA3MUaPeexqMvno7EZuLlVcyUfjcRy/WXs3EtshseFnp4vPGRph5Mg0CAF0t4FZSAcYdShHLTmxhDE8LXmffVUeOHEFmZiaAolr/Bw8eFMfVqVMHDg4O4stfLS0tJCYmYs+ePWKZdu3awcrKqmKDJrUxqURElYaOjg7WrVuHzz77DCtWrECTJk3g6+uLAQMGoGHDhjh16hQuXLiAhIQEMeE0f/58BAUFYceOHRg1ahQaNWqERo0aifP88ccfsXv3bvzzzz8YN24cbt26haNHj+LixYto1qwZAGD16tXw8PAoFs+gQYMwfPhwlWEzZ84U/+3i4oKzZ89i+/bt6NevH9LT05GWloYePXrAzc0NAODl5SWWj42NxVdffYU6deoAQInLVJozZ47KsujVybPkSHmYojIsOyUb2SnZMKlR9LbMraUbIoMjkRyTDB09Hbi1dEP74e3h1NippFkSVUryvALx3+H34lXG+bfwgKWpAU4uHY2fN4bg1LVo3H2YDHNjGXy8XfDdED94e7C2B2lGQV425GlPVIflpKEgJw16hkVN32y9P4C2ngzx4XuRmxYPXZkJzF1awLH1EOjKTDURNgHIzhfwOLNQZViqXECqvBDWBlooUADKdFO+AohMzi82Pb27Cgv/OzZe7MqiVq1aUCj+e+GnUCiKveTNy8t7uwHSG8GkEhFVKn379kX37t1x8uRJnDt3DgcOHMAvv/yC1atXIysrC5mZmbC0VO3jJicnB/fv3wcAZGZmYsaMGdi/fz/i4+NRUFCAnJwcxMbGAgBu374NHR0dNGnSRJze3d0d5ubmxWJRJp2e98cff2DNmjWIjY1FTk4O8vLy4O3tDQCwsLBAQEAA/P390aVLF3Tu3Bn9+vUTP586adIkfPrpp9i4cSM6d+6Mjz/+WEw+vWjq1KmYNGmS+P/09HQ4ODiosSVJybuHN7x7eJdZxn+iP/wn+ldMQERvyYNtU15axsupBjZ+368CoiEqvxpenVDDq9PLy9Xtghp1u1RARFReXd1k6OomK7NM8OCaFRQNVTaDBg16aZlRo0ZVQCT0NrFPJSKqdPT19dGlSxdMmzYNZ86cQUBAAKZPn47MzEzY2toiPDxc5e/27dv46quvAACBgYHYvXs3fv75Z5w8eRLh4eFo0KDBK73pMDQ0VPn/1q1bERgYiJEjR+Lw4cMIDw/H8OHDVea9du1anD17Fm3atMG2bdvg6emJc+fOAShqTnfjxg10794dx48fR926dbF79+4Sly2VSmFiYqLyR0REREREVJmwphIRVXp169ZFUFAQmjRpgidPnkBHRwfOzs4llj19+jQCAgLQp08fAEU1l6Kjo8XxtWvXRkFBAa5cuYKmTZsCAO7du4eUlJSSZlds3m3atMGYMWPEYcoaUs9r3LgxGjdujKlTp6J169bYvHkzWrVqBQDw9PSEp6cnvvzySwwcOBBr164VYyUiIiIiIqpKWFOJiCqN5ORkdOzYEf/3f/+Hq1evIioqCn///Td++eUX9OrVC507d0br1q3Ru3dvHD58GNHR0Thz5gy+++478StqHh4e2LVrF8LDwxEREYFBgwaptNeuU6cOOnfujFGjRuHChQu4cuUKRo0aBZlMJnaoXRoPDw+EhYXh0KFDuHPnDqZNm4aLFy+K46OiojB16lScPXsWMTExOHz4MO7evQsvLy/k5ORg3LhxCAkJQUxMDE6fPo2LFy+q9LlERERERERUlbCmEhFVGkZGRmjZsiV+++033L9/H/n5+XBwcMBnn32Gb7/9FhKJBP/++y++++47DB8+HImJibCxsYGPjw9q1ixqr79w4UKMGDECbdq0gZWVFb7++utiX07bsGEDRo4cCR8fH9jY2GDOnDm4ceMG9PX1y4xv9OjRuHLlCvr37w+JRIKBAwdizJgxOHDgAADAwMAAt27dwvr165GcnAxbW1uMHTsWo0ePRkFBAZKTkzF06FA8ffoUVlZW+PDDD9kZNxERERERVVlMKhFRpSGVSjFnzhzMmTOn1DLGxsZYsmQJlixZUuJ4Z2dnHD9+XGXY2LFjVf5va2uLf//9V/z/w4cPkZCQAHd3d3GYIBT/GolUKsXatWuxdu1aleHKeGvWrFlqH0l6enrYsmVLqetFRERERERU1TCpRETVzvHjx5GZmYkGDRogPj4eU6ZMgbOzM3x8fDQdGhERERERUZXBpBIRVTv5+fn49ttv8eDBAxgbG6NNmzbYtGkTdHV1NR0aERERERFRlcGkEhFVO/7+/vD399d0GERERERERFUav/5GRERERERERERqY1KJiIiIiIiIiIjUxqQSERERERERERGpjUklIiIiIiIiIiJSG5NKRERERERERESkNiaViIiIiIiIiIhIbTqaDoCIiMovKSYJejI9TYdB76CUxykAgMiYRA1HQlR9RMUX/e5yUh5qOBKqSLnpTwEAMWkFGo6keorPLAQApKSkaDgSoleXmpqq6RBEEkEQBE0HQUREZUtPT4epqammw6B3nJaWBAoFbwuIKpRECxAUmo6CKpiWBODpVnMkEgn4GEzvgrS0NJiYmGg0BtZUIiKqQkJDQ2FkZKTpMOgdJZfLIZVKNR0GUbXC3131xP2uWdz+VNVlZmbC19dX02EAYFKJiKhK8fb21vjbCCIiIiIi0pz09HRNhyBiR91ERERERERERKQ2JpWIiIiIiIiIiEhtTCoREREREREREZHamFQiIiIiIiIiIiK1MalERERERERERERqY1KJiIiIiIiIiIjUxqQSERERERERERGpjUklIiIiIiIiIiJSG5NKRERERERERESkNiaViIiIiIiIiIhIbUwqERERERERERGR2phUIiIiIiIiIiIitTGpREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhuTSkREREREREREpDYmlYiIiIiIiIiISG1MKhERERERERERkdqYVCIiIiIiIiIiIrUxqURERERERERERGpjUomIiIiIiIiIiNTGpBIREREREREREamNSSUiIiIiIiIiIlIbk0pERERERERERKQ2JpWIiIiIiIiIiEhtTCoREREREREREZHamFQiIiIiIiIiIiK1MalERERERERERERq09F0AEREVH7h4eEwMjLSdBhURcnlckilUk2HQVSl8XdEVDH4W3t3cd++vszMTE2HIJIIgiBoOggiIipbeno6TE1NNR0GVXFaWhIoFLzsE70WiRYgKDQdBdE7T0sC8JL1bpJIJGAa4s1IS0uDiYmJRmNgTSUioiqkx9QesKtjp+kwqAq6e+YuglcGY8N3/eDlZK3pcIiqpAPn7uCHNUfg3uVLyMxraTocondWSswlPDy/Gd+2MYGTKR9Z3yXnH8mx5moW/Pz8YG5urulwqqzExEScPHlS02EAYFKJiKhKsXKygm0dW02HQVVQUnQSAMDLyRpNPO01HA1R1XQrJhEAIDOvBcMabhqOhujdlZPyEADgZKoDTwtdDUdDb1JsWgEAwNzcHFZWVhqOpurKz8/XdAgidtRNRERERERERERqY1KJiIiIiIiIiIjUxqQSERERERERERGpjUklIiIiIiIiIiJSG5NKRERERERERESkNn79jYiI3ikxV2Jwav0pPI58jOzUbABA96+7o9mHzcQyzx4+Q8iqEMRcjkFWShb0DPRQw7UGWg1shTq+dTQVOhEAYOG2k9h35hZuxyXhWUY2bCyM4evtgh+GdYKrnQXWHbiEkfN2ljr9sd8+RYfGrhUYMVHV8PhKEFKiLiI39TEKcjOga2gOE7v6qNWiP/RNbQAAuWnxeHh+K9IfX0d+dhq09WSQWTjC1vsDWLi21PAa0Lsu4mkettzMwq3kfKTJBQDAl82N8YGngVhm4pFniEgo/uWv+ta6+P09iwqLtTqIj49HeHg4EhMTkZubCwBo164d6tatK5ZRKBS4cuUK7ty5g6ysLMhkMri4uKB58+bQ1a0eXy5kUomIiN4p8bfi8eDCA5jbm4tJpecJgoCNX2xE6uNUaOtpw9rVGqmPUxFzJQYx4TEYvWE0bDxtNBA5UZGlu84iNiENtR2sIJPqICo+BRsPXcGRi/cQufFLWJsZoqWXg8o0sQmpiE/OAADYWBhpImyiSu/J1f3Iy0iCvrk9tHT0IE9/iqTbwUiLC0ejT/6Atq4MkXtmQJ7+FBJtXcgsHSFPf4qMxzeQ8fgmGgxYCEMrF02vBr3D7qbkIyw+D3ZG2kiTF5ZZ1s5IG6bS/xoeOZvy0f5NS0pKwsOHD2FiYiImlV4UEhKCe/fuQSKRwMTEBBkZGbh+/TqSk5PRo0cPSCSSCo664vHIIyKid0rD9xuiaZ+myHqWhcV9Fhcbn5GQgdTHqQCADp91QLuh7RAVFoUNYzcAApCekM6kEmnUpz2a45P3GsOxphkAYNLS/Vi84zSePMvAscv30ad9PXRvrVqjznvEEsQnZ6BzM3fUcaqhgaiJKr+a9d6DVe0OkBpbAwCiT/6FJxF7kZ+dgvS4qzCs6Q55+lMAQK0WA2Hf9EOkPbyGyKBpAATkZSQxqURvVRcXGXq6GyAlV4GBe5LKLDukviG6uskqKLLqycPDA15eXsjJycGWLVuKjU9KSsK9e/cAAK1bt0b9+vURExODQ4cOIT4+HtHR0XBxeffPGUwqERHRO8XA1KDM8UZWRrCoZSE2gbtx9AZSH6dCS1sLDd9vCPfW7hUUKVHJvh3ip/L/dg2dsHjHaQCAVLf4rdvB83dw7cETAEDggPZvP0CiKsq+2ccq/zexq4snEXsBABJtHegZmEPf1LaoCdyFLUi+d6qo1pKWNqxqd4CZUxNNhE3VyPM1j17mj8sZWHghHVYG2mhio4cRDQ1hIdN+i9FVP/r6+mWOj42NFf/t6lrU7NzR0RHa2tooLCxEXFxctUgqsaNuIiKqVrS0tTBs2TDY1rFFYV4hntx+gtyMXOib6MO2ti20tHlppMqjsFCBVXsvAgBc7SzQqYlbsTILtp0AADRys0WXZh4VGh9RVSUoCvH0xmEAgNSkJkwdGkGipQ2vPj/C0NoNQmE+shMfoFCeBW2pEQyt3SDR4gM7VQ5SbcBKpgVTqRbiMwux/14Oxh1OQU6BoOnQqpWsrCzx38oElEQiEf+dmZmpkbgqGu+ciSqZ7Oxs9O3bFyYmJpBIJEhNTS1xWFk6dOiAiRMnVki8lVlAQAB69+6tkWWru8+o4ggKAfvm7UP8rXi07N8SU0Om4uOfP0Z2SjYOzD+AW6G3NB0iEQAgKycPH077Pxy+eBc2FsbY8/MQSPVUaypdufsYxy8/AABM6t9OE2ESVTmF+bm48+9cpMVega6BOWr3+B5a2roQBAWiglcgK/E+bBr1QPPRW+HRdQoKctIQfeJPPHtwTtOhE2FsU2P883ENrO1hhe19rDCoXlEN7fjMQpyKK7nfH6K3iUklogoSFxeHESNGwM7ODnp6enBycsKECROQnJysUm79+vU4efIkzpw5g/j4eJiampY4jCo37rPK68HFB7h7+i4AoFH3RtCT6aFup7qQGkqLxl94oMnwiAAAT5Iz4DdhFfaduQVPByucXDoadZ1rFiu3YNtJAIBDDVMM6NiwosMkqnLyslJwc9d3SIm+CH0zO9TrOwcGFkUd36fFXUVqTBgAwLpOR2jr6sPSvQ209QzE8USa5mGhCz3tos6fJRIJOjv/16/S0yyFpsKqlgwNDcV/KzvyFgRB/LeRUfX4cAaTSkQV4MGDB2jWrBnu3r2LLVu24N69e1ixYgWOHTuG1q1b49mzZ2LZ+/fvw8vLC/Xr14eNjQ0kEkmJwypaXl5ehS+zNPn5xT+j+iYUFhZCoXgzF+PKsM+oZPJMufjvx5GPAQDJscmQZxcN15VVj8+/UuV1I+op2oxZjkt3HqF9Q2ec/uNzuNoV/0x07NNU/B18DQAwvm8b6OiwaQ5RWbKTY3F9xxRkJd6HsV1d1P9oHvRN//swQ2Hef18MzUwo6nw3J+URCvNyAABautKKDZjoBSm5CmyPzEJ2/n/3q8Ex/9VOsjHk431FcnD470usDx4UvZSMjY1FYWFhsfHvMh51RBVg7Nix0NPTw+HDh+Hr6wtHR0d069YNR48exaNHj/Ddd98BKGq2tmDBApw4cQISiQQdOnQocRgALFu2DB4eHtDX10fNmjXx0UcfqSxToVBgypQpsLCwgI2NDWbMmKEyPjY2Fr169YKRkRFMTEzQr18/PH36VBw/Y8YMeHt7Y/Xq1XBxcRHbBqempuLTTz+FtbU1TExM0LFjR0RERJS5/g8fPsTAgQNhYWEBQ0NDNGvWDOfPnxfHL1++HG5ubtDT00Pt2rWxceNGleklEgmWL1+ODz74AIaGhpg9ezYKCwsxcuRIuLi4QCaToXbt2li8uPiXvsqybt06mJmZ4Z9//kHdunUhlUoRGxsLuVyOwMBA2Nvbw9DQEC1btkRISIjKtKdOnUL79u0hk8ng4OCA8ePHi+2qX3WfPU8ulyM9PV3lj8onMjgSS/ouwbox68RhwX8GY0nfJdj1wy44N3OGvknR8bx/3n4sH7QcK4euBARAS0cLDd5roKHIiYp89MMmxDxNBQBkZMvR4+v1aPO/5Wjzv+VYve+iWG7xjtMoKFTA1FAfn/VsoaFoiaqOOwfmIi8jEQBQmJeDW3t/xPW/p+D631OQcOMITOzrQ1taVLMgKmQFIjaPx7XtkwEIkGjpwMqDHeHT23UiNheD9yRh4tH/XjivvZqJwXuS8NPpNMgLBCy/nIkP/k7EsL1J6L87ERuvF91/Oplow8ex7I6lST1RUVHYunUr9u7dKw4LCwvD1q1bcfz4cVhbW8PNraivw7Nnz2L79u04cuQIAMDGxgbOzs6aCLvC8etvRG/Zs2fPcOjQIcyePRsymepnP21sbDB48GBs27YNy5Ytw65du/DNN9/g+vXr2LVrF/T09ACg2LCwsDCMHz8eGzduRJs2bfDs2TOcPHlSZd7r16/HpEmTcP78eZw9exYBAQFo27YtunTpAoVCISaUQkNDUVBQgLFjx6J///4qyZN79+5h586d2LVrF7S1i96Af/zxx5DJZDhw4ABMTU2xcuVKdOrUCXfu3IGFRfE36ZmZmfD19YW9vT3++ecf2NjY4PLly2KNoN27d2PChAlYtGgROnfujH379mH48OGoVasW/Pz++wLSjBkzMHfuXCxatAg6OjpQKBSoVasW/v77b1haWuLMmTMYNWoUbG1t0a9fv3Lvn+zsbMybNw+rV6+GpaUlatSogXHjxuHmzZvYunUr7OzssHv3bnTt2hXXrl2Dh4cH7t+/j65du+Knn37CmjVrkJiYiHHjxmHcuHFYu3ZtifuxPPvseXPmzMHMmTPLvR70H3mWHCkPU1SGZadkIzslGyY1TGBgaoARf47AyXUnERsei+S4ZMiMZXBu7Iz2I9rDxtOmlDkTVQx5XoH47/B78Srj/FsUdcSdlpmLv/YXNdP5rGdzGBuwBgXRyygK/6vpnJ0UpTLO1LExdGUmqN93Dh6F7UB6/E3kpsVDR2oEE7t6sG/2MQytXSs6ZKpmsvMFPM4sVBmWKheQKi+EtYEWTPW18Ek9Q4Q9keNxRiHkhQIcTbTRrpYUA+oais3i6M3Iy8sr9mI3NzcXubm5YtM3Pz8/mJqa4u7du0hPT4e+vj5cXV3RrFmzatNSgUklorfs7t27EAQBXl5eJY738vJCSkoKEhMTUaNGDRgYGEBPTw82Nv892L44LCQkBIaGhujRoweMjY3h5OSExo0bq8y3YcOGmD59OgDAw8MDS5cuxbFjx9ClSxccO3YM165dQ1RUlFgtc8OGDahXrx4uXryI5s2bAyg6kW7YsAHW1tYAimrnXLhwAQkJCZBKix5g5s+fj6CgIOzYsQOjRo0qtn6bN29GYmIiLl68KCad3N3/+2T7/PnzERAQgDFjxgAAJk2ahHPnzmH+/PkqSaVBgwZh+PDhKvN+Puni4uIiviFQJ6mUn5+PZcuWoVGjRgCKanCtXbsWsbGxsLOzAwAEBgbi4MGDWLt2LX7++WfMmTMHgwcPFjtD9/DwwJIlS+Dr64vly5fDwsLilfbZ86ZOnYpJkyaJ/09PT682VWhfl3cPb3j38C6zjLWLNT6c+WHFBESkpgfbpry0jKmRPlL/nV4B0RC9O5oMW/XSMjILB7i/92UFRENUXFc3Gbq6ycosM9LbCCNRPfrq0bTatWujdu3aZZbR0tJCs2bN0KxZswqKqvJhUomoggjCm/vEZ5cuXeDk5ARXV1d07doVXbt2RZ8+fWBgYCCWadhQtcNWW1tbJCQkAAAiIyPh4OCgkqSoW7cuzMzMEBkZKSaVnJycxIQSAERERCAzMxOWlpYq887JycH9+/dLjDU8PByNGzcusRaTMpYXk1Ft27Yt1pStpBP1H3/8gTVr1iA2NhY5OTnIy8uDt7d3icspjZ6ensq2unbtGgoLC+Hp6alSTi6Xi+sdERGBq1evYtOmTeJ4QRCgUCgQFRVVYgKxPPvseVKpVEzcERERERERVUZMKhG9Ze7u7pBIJIiMjESfPn2KjY+MjIS5ublK8uZljI2NcfnyZYSEhODw4cP44YcfMGPGDFy8eBFmZmYAAF1d1c6GJRKJ2p1QP/9FA6CoKZutrW2x/oUAiMt90YtN/l7Vi7Fs3boVgYGBWLBgAVq3bg1jY2P8+uuvKn01lYdMJlOpmpqZmQltbW1cunRJbPKnpPyCQ2ZmJkaPHo3x48cXm5+jo2OJyynPPiMiIiIiIqpK2FE30VtmaWmJLl26YNmyZcjJyVEZ9+TJE2zatAn9+/dXu82tjo4OOnfujF9++QVXr15FdHQ0jh8/Xq5pvby8EBcXh7i4OHHYzZs3kZqairp165Y6XZMmTfDkyRPo6OjA3d1d5c/KyqrEaRo2bIjw8HCVL9y9GMvp06dVhp0+fbrMOJRl2rRpgzFjxqBx48Zwd3cvtbaUOho3bozCwkIkJCQUW0dlU7YmTZrg5s2bxca7u7uL/WCV5HX2GRERERERUWXDpBJRBVi6dCnkcjn8/f1x4sQJxMXF4eDBg+jSpQvs7e0xe/Zstea3b98+LFmyBOHh4YiJicGGDRugUChe2uZXqXPnzmjQoAEGDx6My5cv48KFCxg6dCh8fX3LbA/cuXNntG7dGr1798bhw4cRHR2NM2fO4LvvvkNYWFiJ0wwcOBA2Njbo3bs3Tp8+jQcPHmDnzp04e/YsAOCrr77CunXrsHz5cty9excLFy7Erl27EBgYWOY6eHh4ICwsDIcOHcKdO3cwbdo0XLx4scxpysPT0xODBw/G0KFDsWvXLkRFReHChQuYM2cO9u/fDwD4+uuvcebMGYwbNw7h4eG4e/cu9uzZg3HjxpU639fdZ0RERERERJUNk0pEFUCZAHF1dUW/fv3g5uaGUaNGwc/PD2fPni21v6HSmJmZYdeuXejYsSO8vLywYsUKbNmyBfXq1SvX9BKJBHv27IG5uTl8fHzQuXNnuLq6Ytu2bS+d7t9//4WPjw+GDx8OT09PDBgwADExMahZs2aJ0+jp6eHw4cOoUaMG3n//fTRo0ABz584Vm5b17t0bixcvxvz581GvXj2sXLkSa9euRYcOHcqMZfTo0fjwww/Rv39/tGzZEsnJyWJn369r7dq1GDp0KCZPnozatWujd+/euHjxoti0rWHDhggNDcWdO3fQvn17NG7cGD/88IPYsXdJXnefERERERERVTYS4U32HkxERG9Feno6TE1NEbAiAE6NnTQdDlVB1w5ew67pu3Dxz7Fo4mmv6XCIqqTNR8IxZPZ2NOi3AIY13DQdDtE7K+l2KO4d+Q0ru1nA00L35RNQlXE0Kgezz6Tjww8/LLX7DHq5+Ph47N27F2lpaTAxMdFoLKypREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhuTSkREREREREREpDYmlYiIiIiIiIiISG1MKhERERERERERkdqYVCIiIiIiIiIiIrXpaDoAIiIqv6SYJOjJ9DQdBlVBKY9TAACRMYkajoSo6oqKL/od5aQ81HAkRO+23PSnAICYtAINR0JvWnxmIQAgJSVFw5FUbampqZoOQSQRBEHQdBBERFS29PR0mJqaajoMquK0tCRQKHjZJ3otEi1AUGg6CqJ3npYE4CXr3SSRSMA0xJuRlpYGExMTjcbAmkpERFVIaGgojIyMNB0GVVFyuRxSqVTTYRBVafwdEVUM/tbeXdy3ry8zMxO+vr6aDgMAk0pERFWKt7e3xt9GEBERERGR5qSnp2s6BBE76iYiIiIiIiIiIrUxqURERERERERERGpjUomIiIiIiIiIiNTGpBIREREREREREamNSSUiIiIiIiIiIlIbk0pERERERERERKQ2JpWIiIiIiIiIiEhtTCoREREREREREZHamFQiIiIiIiIiIiK1MalERERERERERERqY1KJiIiIiIiIiIjUxqQSERERERERERGpjUklIiIiIiIiIiJSG5NKRERERERERESkNiaViIiIiIiIiIhIbUwqERERERERERGR2phUIiIiIiIiIiIitTGpREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhuTSkREREREREREpDYmlYiIiIiIiIiISG1MKhERERERERERkdqYVCIiIiIiIiIiIrUxqURERERERERERGpjUomIiIiIiIiIiNTGpBIREREREREREamNSSUiIiIiIiIiIlIbk0pERERERERERKQ2HU0HQERE5RceHg4jIyNNh/FOksvlkEqlmg6DiKoxnoeI6F1Vmc5vlSmWV5WZmanpEEQSQRAETQdBRERlS09Ph6mpqabDeKdpaUmgUPCSSEQaJNECBIWmoyAieuO0JEBluc2SSCR4V9IgaWlpMDEx0WgMrKlERFSF9JjaA3Z17DQdxjvn7pm7CF4ZjA3f9YOXk7WmwyGiaujAuTv4Yc0RuHf5EjLzWpoOh4jojUmJuYSH5zfj2zYmcDLVbAri/CM51lzNgp+fH8zNzTUay+tITEzEyZMnNR0GACaViIiqFCsnK9jWsdV0GO+cpOgkAICXkzWaeNprOBoiqo5uxSQCAGTmtWBYw03D0RARvTk5KQ8BAE6mOvC00NVoLLFpBQAAc3NzWFlZaTSW15Gfn6/pEETsqJuIiIiIiIiIiNTGpBIREREREREREamNSSUiIiIiIiIiIlIbk0pERERERERERKQ2dtRNRERvTcyVGJxafwqPIx8jOzUbAND96+5o9mEzsUzYrjBcPXAVT+48QX5uUaeDY7eNhZVz1e08kaq3xX+fxrqDlxDzJBU5efmwNjVEq3qO+H6oHxq62WLm2qOYtf54qdPf3/IVnG2r7hdpiKhqunPwFzy7dwYAYOnRDh7+gQAARWEBHl/agcRbwcjLTIauzBQW7m3g0HIQtPVkmgyZCDNOpiI0Vg4A8HOS4od2ZpoNqAQ5OTm4fPkyYmJikJ2dDV1dXVhaWsLHxwcmJiaaDu+1MalERERvTfyteDy48ADm9uZiUulF987ew5M7T2BgboC0+LQKjpDozQuNiEJiahZc7SyQm5eP23FJ2Bl6HcFX7iN629ewtzZFSy8HlWnuPkrCs/QcSHV1YG7MhzQiqlgJN4+JCaUXPTj+O5JuhwISLeib2kKe/hRPIvYiK/EB6vb5ERIJG7+QZhy4nyMmlCqr3NxcBAUFISMjA1paWjA1NQUAPH36FNnZ2UwqERERlaXh+w3RtE9TZD3LwuI+i0ss031KdxhaGOLqgavY8+OeCo6Q6M3bPK0/9KX/fTL5h7+OYPbGYDxLz8Gt2ER82qM5Pu3RXByfI8+HS/9fAABD/BvD1Ei/wmMmouorNy0e0SdXwcimNvIyk5CXmSyOy0q4X5RQAuDcfiRsGnZHStQF3N7/MzIe30DKg/OwcGutqdCpGnuUUYDfwzJQz0oXCdmFSMxWaDqkEl28eBEZGRkwNzdH9+7dYWBgAAAoLCzUcGRvDpNKRET01hiYGry0jLG1cQVEQlRx9KW62H3yBn7dfALp2bm4HZcEALA2M4SnQ/FmnesPXkZiahYkEgkm9WtX0eESUTUmKApx7/BvkEi04P7eJNzc/b3K+NTYy+K/lckjM+dmkGjrQSjMQ2rMZSaVqMIVKgTMPp0OLQnwXVtTfHn0maZDKpEgCHjw4AEAwMjICPv370dGRgZMTEzg7e0Nd3d3DUf4ZrCuIhEREdEb9vRZJs5HxiEyJhEKhQAXW3Mc++1TGBtIVcopFAos+vsUAKBHmzqo7WitiXCJqJp6eGErMp/egYvvaOib1Cw2Xp6RJP5bV1bUbEci0YKurOiFkDwzqdg0RG/b+mtZiEzOx4TmxrA10tZ0OKXKzc2FXF7UPC8uLg55eXmQSqV49uwZjh8/LiacqjomlYio2gsICEDv3r01HQYRvUM+79USBcGzEbVtCvr5NUBUfAoGztyCjGzVvh/2nI7E3YdFTU0C+7fXRKhEVE1lPr2HR5d2wqq2L6xq+6o3sfB2YiJ6mdvJ+dh0IwtdnPXRxaVy90GoUPzXJM/MzAwDBgzAgAEDYGZmBgC4ceOGhiJ7s5hUIqpG4uLiMGLECNjZ2UFPTw9OTk6YMGECkpOTXz4xERGpRSKRwLGmGaZ+0gEAcCM6AVuORaiUWbjtJACgVV0HtGvoXMERElF1lv0sBhAUSL53FhdWDsCFlQOQ9/9rJj27XzRMz/C/L1Hm5xR9TEMQFMjPzQAASI34pVaqWFGpBVAIQGhcLrptS0C3bQlIyCpK3pyIlaPbtgRk5lWO/pVkMhm0tIpSLpaWltDW1oa2tjYsLS0BABkZGZoM741hUomomnjw4AGaNWuGu3fvYsuWLbh37x5WrFiBY8eOoXXr1nj2rHK2RSYiqkqS07Kx8fAV5OUXiMMOnLsj/js7N0/895nrMThzPRYAMIm1lIhIQ4TCPCjyc6HIz4WyCpKgKIQiPxdmzs3Ecs/unwUApEaHQSgsOpeZOTWp8HiJACCvEMgtEJBbIIgV5wqFomGVhZaWFmxtbQEAz549g0KhgEKhEJ+7lF+Cq+qYVCKqJsaOHQs9PT0cPnwYvr6+cHR0RLdu3XD06FE8evQI3333nVhWLpfj66+/hoODA6RSKdzd3fHXX3+J469fv45u3brByMgINWvWxJAhQ5CU9F+b+oMHD6Jdu3YwMzODpaUlevTogfv374vjo6OjIZFIsGvXLvj5+cHAwACNGjXC2bNny1yHhQsXokGDBjA0NISDgwPGjBmDzMxMcfy6detgZmaGQ4cOwcvLC0ZGRujatSvi4+PFMoWFhZg0aZIY25QpUyAIZV98YmJi0LNnT5ibm8PQ0BD16tXDv//++8a2x7ssMjgSS/ouwbox68RhwX8GY0nfJdj1wy4AwJGlR7Ck7xIcXXpULPN/E/4PS/ouwflt5ys6ZKLXkpEtR8DPf8Oix49oNHwxnPvNw7erDgEAjA2k6NO+nlh2wf+vpeRub4k+7etqJF4iqr5qeHVCq3FBKn96xkX9ull6tEOrcUEwquEOS4+ipHf0yb8Qvmkc7hwo+lqlsV1dmLu21Fj8VD11dZMheHBNlb+ahkVpDT8nKYIH14SRXuVJczRv3hxaWlpISUnBli1bsGXLFqSkpEAikaBx48aaDu+NqDxbm4jemmfPnuHQoUMYM2YMZDLVtsc2NjYYPHgwtm3bJiZXhg4dii1btmDJkiWIjIzEypUrYWRkBABITU1Fx44d0bhxY4SFheHgwYN4+vQp+vXrJ84zKysLkyZNQlhYGI4dOwYtLS306dNHpV0xAHz33XcIDAxEeHg4PD09MXDgQBQUFKA0WlpaWLJkCW7cuIH169fj+PHjmDJlikqZ7OxszJ8/Hxs3bsSJEycQGxuLwMBAcfyCBQuwbt06rFmzBqdOncKzZ8+we/fuMrff2LFjIZfLceLECVy7dg3z5s17K9vjeXK5HOnp6Sp/VZE8S46UhylIi08Th2WnZCPlYQrSE4vWKetZFlIepiArJUssk/YkDSkPU5CTnlPhMRO9DjMjffTv2BC2lsa4//gZ4pMz4FDDFIO7eOPs8v/ByaaoKcm9h8n453QkAGDix23F6vFERJWNW+cJsG/eH1IjK8jTnkBHZgKbhj1Qp8c0SCQ8dxGVpUaNGujRowdsbW0hl8tRUFAAe3t79OrVC3Z2dpoO742QCC97RU9EVd758+fRqlUr7N69u8QOqX/77TdMmjQJT58+RWpqKmrXro0jR46gc+fOxcr+9NNPOHnyJA4dOiQOe/jwIRwcHHD79m14enoWmyYpKQnW1ta4du0a6tevj+joaLi4uGD16tUYOXIkAODmzZuoV68eIiMjUadOnXKt144dO/D555+LtYLWrVuH4cOH4969e3BzcwMALFu2DLNmzcKTJ08AAHZ2dvjyyy/x1VdfAQAKCgrg4uKCpk2bIigoqMTlNGzYEH379sX06dPfyvYoyYwZMzBz5sxiwwNWBMCpsVMZW4VexbWD17Br+i5c/HMsmnjaazocIqqGNh8Jx5DZ29Gg3wIY1nDTdDhERG9M0u1Q3DvyG1Z2s4Cnha5GYzkalYPZZ9Lx4Ycfwsqq6vYJFh8fj7179yItLQ0mJiYajYWpZaJqpDw55PDwcGhra8PXt+SvgERERCA4OBhGRkbinzIJpGzSdffuXQwcOBCurq4wMTGBs7MzACA2NlZlXg0bNhT/rWxvnJCQUGpsR48eRadOnWBvbw9jY2MMGTIEycnJyM7OFssYGBiICSXlfJXzTEtLQ3x8PFq2/K+qto6ODpo1+6+/gJKMHz8eP/30E9q2bYvp06fj6tWrb2V7PG/q1KlIS0sT/+Li4sqMkYiIiIiIqKIxqURUDbi7u0MikSAyMrLE8ZGRkTA3N4e1tXWx5nEvyszMRM+ePREeHq7yd/fuXfj4+AAAevbsiWfPnmHVqlU4f/48zp8v6hcnLy9PZV66uv+9qZBIJABQapOw6Oho9OjRAw0bNsTOnTtx6dIl/PHHH8Xm+/w8lfN93QqZn376KR48eIAhQ4bg2rVraNasGX7//XcAb3Z7PE8qlcLExETlj4iIiIiIqDJhUomoGrC0tESXLl2wbNky5OSo9lHz5MkTbNq0Cf3794dEIkGDBg2gUCgQGhpa4ryaNGmCGzduwNnZGe7u7ip/hoaGSE5Oxu3bt/H999+jU6dO8PLyQkpKymuvw6VLl6BQKLBgwQK0atUKnp6eePz4sVrzMDU1ha2trZjUAYqav126dOml0zo4OODzzz/Hrl27MHnyZKxatQqA5rYHERERERGRpjGpRFRNLF26FHK5HP7+/jhx4gTi4uJw8OBBdOnSBfb29pg9ezYAwNnZGcOGDcOIESMQFBSEqKgohISEYPv27QCKOq1+9uwZBg4ciIsXL+L+/fs4dOgQhg8fjsLCQpibm8PS0hJ//vkn7t27h+PHj2PSpEmvHb+7uzvy8/Px+++/48GDB9i4cSNWrFih9nwmTJiAuXPnIigoCLdu3cKYMWOQmppa5jQTJ07EoUOHEBUVhcuXLyM4OBheXl4ANLc9iIiIiIiINI1JJaJqwsPDA2FhYXB1dUW/fv3g5uaGUaNGwc/PD2fPnoWFhYVYdvny5fjoo48wZswY1KlTB5999hmysoq+zGVnZ4fTp0+jsLAQ7733Hho0aICJEyfCzMwMWlpa0NLSwtatW3Hp0iXUr18fX375JX799dfXjr9Ro0ZYuHAh5s2bh/r162PTpk2YM2eO2vOZPHkyhgwZgmHDhqF169YwNjZGnz59ypymsLAQY8eOhZeXF7p27QpPT08sW7YMgOa2BxERERERkabx629ERFVAeno6TE1N+fW3t4RffyMiTePX34joXcWvv715/PobERERERERERFVaUwqERERERERERGR2phUIiIiIiIiIiIitTGpREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhuTSkREREREREREpDYmlYiIiIiIiIiISG06mg6AiIjKLykmCXoyPU2H8c5JeZwCAIiMSdRwJERUXUXFF52HclIeajgSIqI3Kzf9KQAgJq1Aw5EA8ZmFAICUlBQNR/J6UlNTNR2CSCIIgqDpIIiIqGzp6ekwNTXVdBjvNC0tCRQKXhKJSIMkWoCg0HQURERvnJYEqCy3WRKJBO9KGiQtLQ0mJiYajYE1lYiIqpDQ0FAYGRlpOox3klwuh1Qq1XQYRFSN8TxERO+qynR+q0yxvKrMzEz4+vpqOgwATCoREVUp3t7eGn8bQUREREREmpOenq7pEETsqJuIiIiIiIiIiNTGpBIREREREREREamNSSUiIiIiIiIiIlIbk0pERERERERERKQ2JpWIiIiIiIiIiEhtTCoREREREREREZHamFQiIiIiIiIiIiK1MalERERERERERERqY1KJiIiIiIiIiIjUxqQSERERERERERGpjUklIiIiIiIiIiJSG5NKRERERERERESkNiaViIiIiIiIiIhIbUwqERERERERERGR2phUIiIiIiIiIiIitTGpREREREREREREamNSiYiIiIiIiIiI1MakEhERERERERERqY1JJSIiIiIiIiIiUhuTSkRERET/j717j8+5/v84/ri2sfN5Y6Mxh41hDkMH0pBjWQ6JJFmRSvJVX9+iInJKpdBJkWNOhVTO57VGTjVnw7A5DMMOdj7+/tjPlSvGLuQaPe+323Wzz/n1+VyX7bqe1/v9/oiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2WwsXYCIiJRcdHQ0Tk5Oli5DRG6D7OxsbG1tLV2GyF1L/4fkdtNr6s7TNb85aWlpli7ByFBYWFho6SJEROT6UlNTcXV1tXQZInIbWVkZKCjQ2zCRm2awgsICS1ch9xArA+jX8p1lMBhQJHHzUlJScHFxsWgNaqkkInIX6TC0AxVqVrB0GSJyiw5vPszGrzcy+51uBFX2tnQ5Inedlb8fYvj0tVRv/Tr27vdZuhy5ByTF7eTk1nm83cSFyq76mHwnbD2VzfTd6bRo0QJ3d3dLl3NXSUxMJDIy0tJlAAqVRETuKl6VvfCt6WvpMkTkFp0/fh6AoMrehARWtHA1Inefg3GJANi734djuWoWrkbuBZlJJwGo7GpDoEcZC1fz7xCfkgeAu7s7Xl5eFq7m7pKbm2vpEow0ULeIiIiIiIiIiJhNoZKIiIiIiIiIiJhNoZKIiIiIiIiIiJhNoZKIiIiIiIiIiJhNoZKIiIiIiIiIiJhNd38TEREphX6f/zvRy6JJPpNMXnYeDm4O+AX78cgLj1A+oDwAv07/lZjIGM4ePkt+bj4A7/z6Dja2+vMucjt8sjCSZZsPEnPiPBcvZeDj4Uxo/SoM7/0oVSt4GNfbHZvAqFkb+HXXMVLSs/F2c6RJnUosHPGMBasXMZV6ah+n/1hM2tkj5GWlAlCl+cuUr9POuE5W6llObltA6qm95GYkY+vsTblarfBt0AmDQe0RSrtdZ3OYvz+dgxdySckuBOD1xs48EehgXOfnwxmsPZbFkYu5ZBW9dWBWB08queq9Q3ESEhKIjo4mMTGRrKwsAB5++GFq1aplXKegoIA///yTQ4cOkZ6ejr29PVWqVKFx48aUKXNv301QvxlERERKobg/40hPTse9ojvuFd1Ju5DG/g37mdV/FjmZOQDs37CfC/EXcHBzuMHeRORmfL5kC7/uPo6bkx0VvVyIP5vMnNV/0mzA16SmF32w+G33cZr0n8KSX/eRk5dPbf9yONiW4eeoAxauXsRUemIsKSd2YWPndM3luZkp7P3hTc4f3Ehe1iXs3SuSlXKG+M2ziftt+h2uVm7G4aRcdiTk4FK2+I/5207ncORiLq52igJK6vz585w8eRJbW9ti19m0aRM7d+4kLS0NZ2dnMjMz2bt3L6tWraKwsPAOVnvnKY4UEREphZ4c9aRJi6MNUzYQOSOSzNRMzh8/T4WgCjwz4RmcyzkTMS2CiGkRFqxW5N7Ut0Njnm3TgErl3QB44/PlTFoUxZmLl1j/RyydHq7FSx//SGZ2Ls+0qs83/+uMvW3RN9KXMrItWLnI1bxqNKd8nbbkZCQTPfulq5ZfOBJFXmYKAHW6foiDZyXO7lvDsY1fcmb3Cnzrd8TW2ftOly1maF3FnrDqDiRlFdDjp/PXXGdQY2fc7VxZeyyL8b+n3uEK704BAQEEBQWRmZnJ/Pnzr1p+/vx5jhw5AsBDDz1EnTp1iIuLY/Xq1SQkJHD8+HGqVKlyp8u+YxQqiYiIlEI2tjYc2HSAqNlRZKdncyH+AgAO7g54VvIEwKW8iyVLFLnnvd2rhcn0w3UrM2lRFAC2ZWzYHXuGg/GJABRSSFCvT0hJz6JhYEXGv9yehjUq3vGaRYpTxv4GfzOuaE1hMBhM/qWwgNRTe/Gu2eJaW0op4Wp749ZHXg7Wd6CSe4udnd11l8fHxxt/rlq1KgCVKlXC2tqa/Px8Tpw4cU+HSmrzJnIP8vf3Z+LEiZYuw+KaN2/OoEGDLHLsM2fO0Lp1axwdHXFzc7NIDXL3S7+Yzql9pzh//DyFBYW4VXCj95e9sXUsvvm1iPwz8vMLmPrLdgCqVvDg0ZBqxJxINC6fv24XDv/fSmnjn0dpOWgqxxOSLFKryM1wq9wQqzJFH573/vAmuxcM4timKcblOWkXLFWaSKmWnp5u/PlyAGUwGIw/p6WlWaSuO0WhkkgpEB4eTqdOnSxdhtxGn376qXFQv0OHDlm6HLlLNerSiOG/D2fQT4Oo3ao2yaeTWfTOIrLT1a1G5E5Kz8yhy7DvWLP9MD4ezvw0the2ZW3Iyy8wrvPCY43YP+cN/pj2GtZWVqRl5jBr1U4LVi1iHjtXH4KeGIFLxWAwGMhJT8K7Zkvg/1stWamTi4hcTb8ZRKREcnJyKFu2rKXLACA3N/cfuYvC7TzH2NhYGjZsSEBAwG3Zn/x7GQwGXH1caRbejH3r9pF4NJG9a/bSsHNDS5cm8q9w5sIlnhg6m52HThHo58Xy8eHGO79V9PqrO1GjmkVd3ar4euDt5siZi5c4fibZEiWL3DRn35rU6jzKOH0p4SDn9q8FwN69gqXKEinVHB0djT9nZWXh4OBAYWGh8U5xTk7XHhz/XqGWSiKlUPPmzRk4cCBvvvkmHh4e+Pj4MGLECOPywsJCRowYQaVKlbC1taVChQoMHDjQZB8ZGRm88MILODs7U6lSJb755huT5Xv27KFly5bY29vj6elJv379TJpmXm49NWbMGCpUqECNGjUAOHHiBN26dcPNzQ0PDw86duzI8ePHr3s++/bto0OHDri4uODs7EyzZs2IjY0Fim6/+f7773Pfffdha2tL/fr1WbVqlXHb48ePYzAYWLhwIaGhodjZ2TF37lwuXLhAjx49qFixIg4ODgQHB19z4LzrGTFiBPXr12fatGlUqVLF2EQ1OTmZvn374u3tjYuLCy1btmTXrl0m2/7000+EhIRgZ2dH1apVGTlyJHl5eUBR98PFixcze/ZsDAYD4eHhJXrORC7LSMlg14pd5OfmG+cd3nzY+HNOVo4lyhL519l37CxN+n/FzkOnaFbXn6gvXjYGSgD3B/nh8v/dUXfGnAIg7kwSiclFXSEC7vO880WL3ILU0/spLCj625OXlUZc1EwAbOxccLmvngUrEym9/Pz8jD8fPXoUKBpnKT8//6rl9yK1VBIppWbNmsUbb7zB1q1b2bJlC+Hh4TRt2pTWrVuzePFiPv30UxYsWEDt2rU5c+bMVaHHhAkTGDVqFG+//TaLFi3ilVdeITQ0lBo1apCenk7btm156KGH2L59O+fOnaNv374MGDCAmTNnGvexfv16XFxcWLu26Buq3Nxc43aRkZHY2NgwevRo2rVrx+7du6/ZyufUqVM88sgjNG/enA0bNuDi4kJUVJQxgJk0aRITJkzg66+/pkGDBkyfPp0nnniCffv2mbTyGTJkCBMmTKBBgwbY2dmRlZVFw4YNeeutt3BxcWH58uX06tWLatWqcf/995f4Oh85coTFixezZMkSrK2LBi586qmnsLe3Z+XKlbi6uvL111/z6KOPcujQITw8PIiMjOS5555j8uTJxoCsX79+ALz33nts376d5557DhcXFyZNmoS9vX2JnrMrZWdnk539Vxen1FTdnePfJCc9h6Ujl7Lsg2V43OdBVloWqWeLXgNlHcoS1DwIgCXDl3By30kyUzON237Z40swQOsBrQlqEWSR+kXuFV2HzyXubDJQdDe3Dm/NMi574fFG9O3QmPfCH+W/X6zg2+U7iNoTR8KFS+QXFODj4cyLYY0tVLnI1S7GbiFu8yxjaARwYut8Tv+5FKfygQS0eYNjm6aQk34BWycvslLOUJCXDQYrqjR/GesyGs+vtPs1Pouv/0wj/4pB12fsTmPhgQyCvMrwblNXvv7zEr/GZ5OZ99c6b25IwtrKQJcaDjxZ08ESpZdqx44dY+vWrRQU/NXleceOHezevZty5crRsmVLqlWrRmxsLFu2bGH//v3G9+4+Pj74+/tbqPI7Q6GSSClVt25d3nvvPaDoNpaff/4569evp3Xr1sTHx+Pj40OrVq0oU6YMlSpVuipIeeyxx+jfvz8Ab731Fp9++ikbN26kRo0azJs3j6ysLGbPnm1srvn5558TFhbG+PHjKV++PFDUlHPatGnGsOi7776joKCAadOmGe8GMmPGDNzc3Ni0aRNt2rS56jy++OILXF1dWbBggbHLWmBgoHH5xx9/zFtvvcXTTz8NwPjx49m4cSMTJ07kiy++MK43aNAgunTpYrLvwYMHG39+7bXXWL16Nd9//71ZoVJOTg6zZ8/G27voFrm//fYb27Zt49y5c9ja2hprXLp0KYsWLaJfv36MHDmSIUOG0Lt3b6DoLg+jRo3izTff5L333sPb2xtbW1vs7e3x8fEBKNFzdqVx48YxcuTIEp+H3FvsnO2o07oOp/af4uLJixTkFeBS3gX/Bv48HP4wbr5uAKQmppJ00nQg4KRTRdMad0nk1mXn5Bl/jj6SYLKs7f1FX3wMeuphnB3smLwoisMnL+Dt5khY0yDGvtgGb7d7u8uD3F3ycjLITjljOi8zhbzMFMo6FrWqc/Wrz8WjW8hMOoWVTVlc/epTsdFTuFSsbYmSxUwZuYWcTss3mZecXUhydj7eDkWdlJKyCq5a52xGUVhyKacAuVpOTs5VX/BmZWWRlZVl/CzVokULXF1dOXz4MKmpqcbeDI0aNfrrLor3KIVKIqVU3bp1TaZ9fX05d+4cUNSSZuLEiVStWpV27drx2GOPERYWho2NzTW3NxgM+Pj4GLc/cOAA9erVM+n/27RpUwoKCoiJiTGGSsHBwSatj3bt2sWRI0dwdnY2qS0rK8vYne3voqOjadas2TXHQEpNTeX06dM0bdrUZH7Tpk2vasXTqFEjk+n8/HzGjh3L999/z6lTp8jJySE7OxsHB/O+XalcubIxULp8jmlpaXh6mnZZyMzMNJ7jrl27iIqKYsyYMSb1ZGVlkZGRcc0aSvKcXWno0KG88cYbxunU1NR7vums/MXO2Y4nRz95w/XCvwr/54sR+Rc7uvDNEq3X5/FG9Hm80Y1XFLGgckGPUi7o0euu49/sBfybvXCHKpLbrV01e9pVs7/uOkMecmXIQ653qKJ7Q40aNYxDgRTHysqKRo0aXfWZ5d9AoZJIKfX3EMZgMBibXPr5+RETE8O6detYu3Yt/fv356OPPiIiIsK43fW2L6krQycouh1mw4YNmTt37lXrXhnMXMne/vp/2G62lo8++ohJkyYxceJEgoODcXR0ZNCgQeTkmDfWzLXO0dfXl02bNl21rpubm3GdkSNHXtVyCv66jejfleQ5u5Ktra2xpZSIiIiIiEhppFBJ5C5lb29PWFgYYWFhvPrqq9SsWZM9e/YQEhJyw22DgoKYOXMm6enpxlAlKioKKyur66bwISEhLFy4kHLlyuHi4lLseleqW7cus2bNuuYd21xcXKhQoQJRUVGEhoYa50dFRd2wC1tUVBQdO3bk2WefBYoG/D506BC1atUqUV3FCQkJ4cyZM9jY2BTb/zkkJISYmBiqV69u1r5v5TkTEREREREpbXT3N5G70MyZM/n222/Zu3cvR48e5bvvvsPe3p7KlSuXaPuePXtiZ2dH79692bt3Lxs3buS1116jV69exq5vxW3n5eVFx44diYyM5NixY2zatImBAwdy8uTJa24zYMAAUlNTefrpp9mxYweHDx9mzpw5xMTEAPC///2P8ePHs3DhQmJiYhgyZAjR0dH85z//ue45BAQEsHbtWjZv3syBAwd46aWXOHv2bInO/3patWrFQw89RKdOnVizZg3Hjx9n8+bNvPPOO+zYsQOA4cOHM3v2bEaOHMm+ffs4cOAACxYs4N133y12v7f6nImIiIiIiJQ2CpVE7kJubm5MnTqVpk2bUrduXdatW8cvv/xy1ThAxXFwcGD16tVcvHiRxo0b07VrVx599FE+//zzG27366+/UqlSJbp06UJQUBB9+vQhKyur2JZLnp6ebNiwgbS0NEJDQ2nYsCFTp041tloaOHAgb7zxBv/9738JDg5m1apV/PzzzyZ3fruWd999l5CQENq2bUvz5s3x8fGhU6dOJTr/6zEYDKxYsYJHHnmE559/nsDAQJ5++mni4uKMgVvbtm1ZtmwZa9asoXHjxjz44IN8+umn1w2IbvU5ExERERERKW0MhYVX3G9QRERKpdTUVFxdXQmfEk7lBmrdJHK327NqD0veW8L2b14lJLCipcsRuevMWxtNrzHfE9xtAo7lqlm6HLkHnI+J4MjaT/m6vQeBHlePdym337pjmYzZnEqXLl3w8vKydDl3lYSEBH755RdSUlJKPCzJP0UtlURERERERERExGwKlURERERERERExGwKlURERERERERExGwKlURERERERERExGwKlURERERERERExGwKlURERERERERExGwKlURERERERERExGwKlURERERERERExGwKlURERERERERExGw2li5ARERK7nzcecral7V0GSJyi5JOJwFwIC7RwpWI3J2OJRT9H8pMOmnhSuRekZV6FoC4lDwLV/LvkZCWD0BSUpKFK7n7JCcnW7oEI0NhYWGhpYsQEZHrS01NxdXV1dJliMhtZGVloKBAb8NEbprBCgoLLF2F3EOsDKBfy3eWwWBAkcTNS0lJwcXFxaI1qKWSiMhdJCIiAicnJ0uXISK3QXZ2Nra2tpYuQ+Supf9DcrvpNXXn6ZrfnLS0NEJDQy1dBqBQSUTkrlK/fn2LfxshIiIiIiKWk5qaaukSjDRQt4iIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImM3G0gWIiEjJRUdH4+TkZOkySiw7OxtbW1tLlyEipZx+V4iIyJ12p//23M7jpaWl3Zb93A6GwsLCQksXISIi15eamoqrq6ulyzCblZWBggL9mRGRGzBYQWGBpasQEZF/ESsD3Mm3qQaDgdsdv6SkpODi4nJb92kutVQSEbmLdBjagQo1K1i6jBI5vPkwG7/eyOx3uhFU2dvS5YhIKbXy90MMn76W6q1fx979PkuXIyIi/wJJcTs5uXUebzdxobLrPx+LbD2VzfTd6bRo0QJ3d/db3l9iYiKRkZG3obJbp1BJROQu4lXZC9+avpYuo0TOHz8PQFBlb0ICK1q4GhEprQ7GJQJg734fjuWqWbgaERH5N8hMOglAZVcbAj3K/OPHi0/JA8Dd3R0vL69b3l9ubu4t7+N20UDdIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNg3ULSIibJq6iYhpEddcNixqGFY2+g5Cbq9JP0Qxc9VO4s4kk5mTi7erIw/WrsS7z7WgbjXTwehPnkuhfp/JJF3KBGD5+HDaPRBoibJFREqdE1vnc2r7wmsue6D/YgxW1sSu/4xLp/eTk5EEhYWUcXDD3b8R993/NDZ2zne4YpF7w6KD6ayMzeJsej45+YW42lpR27sMveo4Us391gb/3r17N3FxcSQnJ5OdnY2DgwO+vr40bNgQFxeX23QGt4dCJRERMXJwc8C94t9uc2qwTC1yb4vYdYzE5HSqVvAgKyeXmBPnWRyxl41/xnJ84Vs42pcFoKCggPBxPxgDJRERuTYbOxfsXH2uuSzp6DasbR2wd6tIblYq2alnObN7OZnJpwl64r07XKnIvWHX2VxSsguo4GRNTn4hJy7lExGfzZ9ncljQ2Rt7m5t/E713717S0tJwc3PDxsaGS5cucfjwYU6ePEn37t1v41ncOoVKIiJiFNA0gE7DO1m6DPkXmDesO3a2f32LN/zbtYyZs5GLqZkcjE+kYY2KAHy8IJKNfx7lqRbB/LBxj6XKFREp9dz8G1K91X+uuSzk+W+xsilrnN63eCiXEg5wKeHAnSpP5J4z7GFXylr/FRxN35XGnL3ppOYUEp+SRw3Pm2+tVLNmTQIDA3FycgJg8+bN7N27l8zMTE6dOoWdnd0t13+7KFQSERGjAxsPsG/dPuyc7PCt6UuLl1rgW8P3xhuKmMnOtgw/Ru7jo3m/kpqRRcyJ8wB4uzkS6OcFwB+HTjF8+jo6NKnJy088oFBJROQ6LsZuYevhKGxsHXH0robfg8/g6F0VACubspz4fS7JJ6LJzUgm51IiAM6+tSxZsshdray1gcgTWczfl0FGbgEnLuUD4GZrwM/F+pb2HRISYjLt6+vL3r17AbC2vrV9324aJENERAAwWBtw8nDCzdeNtAtpHI46zLd9vyUhJsHSpck96uzFNLYeOMGBuEQKCgqp4uvO+k/74uxgS0ZWDs+OWoiXqwPfvvmkpUsVESndDFaUdXDH1qUcuRlJJMftYO+it0hPPGpcJSslgfSzh42BkqtfPQLb/c9SFYvcE5IyCzhwIZe41HwKCsHXyZpPWnngUOb2RS0FBQUcOFDUqtDZ2ZmKFSvetn3fDgqV/iXCw8Pp1KmTpcu4rZo3b86gQYNKvP6mTZswGAwkJyf/YzWZqzTW9E+aOXMmbm5uli7D7NfOzRoxYgT169f/x49zOwS3DeZ/K//Ha4tf49WFr9JzUk8A8nPy2b5ou4Wrk3vVyx0fIG/jGI4tfJNuLYI5lpBEj5HzuZSRzdtTV3Po5AVmDO2Kl5ujpUsVESm1vAIfoVGfWdTv9RX1e35OzbCiMZIK83M5s2eFcb2AtoO5/5VFBHf/BHuPSqSc2MWxiK8tVbbIPeGJQAc2PFOOBZ28aFHZloS0fN7/LZmM3ILbsv/c3FzWrFnDyZMnsbe3p127dmqpJEXCw8MxGAxXPY4cOWLp0m4bf39/Jk6caOky5F/s3xba3QrPSp7Yu9obp6s/WN04nXImxVJlyb+AwWCgUnk3hj7bHIB9x88xf/0udh85A8CT787Fpd0IHn9rpnGbJ4d9xzPvL7BAtSIipY+9e0WTO7i5VW5gnL7cKukyK2sbHL2rUq52GwDOx2wiM+nUnStW5B5kMBgo72hNz9pFX4IdT8ln/fGsW95vRkYGv/zyC/Hx8bi6utKxY0fc3d1vvOEdplDJgtq1a0dCQoLJo0qVKletl5OTY4HqSo9/+/mL3Am/zf7NJDyK3RpLZkrR3bbcfN0sVJXcqy6kZDBnzZ/k5OYZ5638/ZDx54ysot/7hYWFpGflkJ6VQ1bOX+tm5eSRmZ175woWESnFTu1cQvYV4VFyfDR5WZcAsHUuR9rZw6Sc/GtMuoL8XFJP7PprOi/7zhUrco9IyS5gzdFMcvMLjfO2nv7rc2tWXuG1NiuxixcvsnTpUs6fP4+Pjw8dO3bExcXllvb5T1GoZEG2trb4+PiYPKytrWnevDkDBgxg0KBBeHl50bZtW6DotoLt27fHycmJ8uXL06tXL86fP2/c36JFiwgODsbe3h5PT09atWpFenq6yTE//vhjfH198fT05NVXXyU3t+hN+eeff06dOnWM6y1duhSDwcCUKVOM81q1asW7774LQGxsLB07dqR8+fI4OTnRuHFj1q1bZ1y3efPmxMXF8frrrxtbYV3222+/0axZM+zt7fHz82PgwIEmdfr7+zNq1Ciee+45XFxc6NevX4mu55w5c2jUqBHOzs74+PjwzDPPcO7cuWLXz8jIoH379jRt2tTYkmXatGkEBQVhZ2dHzZo1+fLLL43r5+TkMGDAAHx9fbGzs6Ny5cqMGzeuRLVdtmLFCgIDA7G3t6dFixYcP37cZPmFCxfo0aMHFStWxMHBgeDgYObPn2+yTvPmzXnttdcYNGgQ7u7ulC9fnqlTp5Kens7zzz+Ps7Mz1atXZ+XKlcZtkpKS6NmzJ97e3tjb2xMQEMCMGTOKrXPVqlU8/PDDuLm54enpSYcOHYiNjTUuP378OAaDgSVLltCiRQscHByoV68eW7ZsMdnPzJkzqVSpEg4ODnTu3JkLFy5c9/p07dqVAQMGGKcHDRqEwWDg4MGDQNFz4OjoaHytFRQUMG7cOKpUqYK9vT316tVj0aJFxhpbtGgBgLu7OwaDgfDw8Gse90avncstntavX0+jRo1wcHCgSZMmxMTEmOzngw8+oHz58jg7O9OnTx+ysky/odi0aRP3338/jo6OuLm50bRpU+Li4q5ZU3Z2NqmpqSaPf9KOJTuY2GkiEztO5IvuX/Ddf74DoIx9GR58+sF/9Njy73MpI5vwsT/g0WEU9Z6fhH+38bw9dTUAzg62dG5Wmw2TXiR/01jjY/2nfY3bLx8fzo9jelmqfBGRUuXs3pX8Oasff8x6kV1zB3Dw55EAWJWxw6f+E2RcjOfA0mFsn/osuxcM4o/pL5B0vKhru4NXFRy8/C1YvcjdKTO3kHFbUgn74RwvLLtA9x8TmRqdBoCDjYFmfrd2d7a1a9eSlla0v9zcXFatWsXSpUtZunSp8bNRaaFQqZSaNWsWZcuWJSoqiilTppCcnEzLli1p0KABO3bsYNWqVZw9e5Zu3boBkJCQQI8ePXjhhRc4cOAAmzZtokuXLhQW/pWQbty4kdjYWDZu3MisWbOYOXMmM2fOBCA0NJT9+/eTmFj0LUdERAReXl5s2rQJKHohb9myhebNmwOQlpbGY489xvr16/nzzz9p164dYWFhxMfHA7BkyRLuu+8+3n//fWMrLCgKo9q1a8eTTz7J7t27WbhwIb/99ptJkABF4Ve9evX4888/GTZsWImuWW5uLqNGjWLXrl0sXbqU48ePFxsiJCcn07p1awoKCli7di1ubm7MnTuX4cOHM2bMGA4cOMDYsWMZNmwYs2bNAmDy5Mn8/PPPfP/998TExDB37lz8/f1LVBvAiRMn6NKlC2FhYURHR9O3b1+GDBlisk5WVhYNGzZk+fLl7N27l379+tGrVy+2bdtmst6sWbPw8vJi27ZtvPbaa7zyyis89dRTNGnShD/++IM2bdrQq1cvMjIyABg2bBj79+9n5cqVHDhwgK+++govL69ia01PT+eNN95gx44drF+/HisrKzp37kxBgWnf4HfeeYfBgwcTHR1NYGAgPXr0IC+vqDXB1q1b6dOnDwMGDCA6OpoWLVowevTo616j0NBQ42sOrn4dbt++ndzcXJo0aQLAuHHjmD17NlOmTGHfvn28/vrrPPvss0RERODn58fixYsBiImJISEhgUmTJl3zuCV97bzzzjtMmDCBHTt2YGNjwwsvvGBc9v333zNixAjGjh3Ljh078PX1NQkl8/Ly6NSpE6GhoezevZstW7bQr18/k8D1SuPGjcPV1dX48PPzu+61u1XNejejauOq5Oflk3Q6CTcfN4LbBdNvVj+8q3r/o8eWfx83Jzu6t6yLr6czsacvknDhEn7lXOnZuj5bvnqFyj6lr2m3iEhpVbFhV1z96lKYn0dW6llsnb3xCgwluNsEHDz8cPCojGulEKysy5B58ST5ednYu9+Hb4NO1Oo0CoNBHwlFzOVU1kDLyrZ42ltzOi2PC5kFlHOworW/HV+288DH6dbGPcrPzzf+fOHCBc6dO2d8XA6bSgtD4ZWpg9wx4eHhfPfdd9jZ/ZVgtm/fnh9++IHmzZuTmprKH3/8YVw2evRoIiMjWb16tXHeyZMn8fPzIyYmhrS0NBo2bMjx48epXLnyNY+3adMmYmNjjQN7devWDSsrKxYsWEBhYSHe3t5MmTKFrl270qBBA7p3786kSZNISEggKiqKFi1akJycjIODwzXPqU6dOrz88svGgMjf359BgwaZDIjct29frK2t+frrvwYF/O233wgNDSU9PR07Ozv8/f1p0KABP/7443WvYfPmzalfv36x4zbt2LGDxo0bc+nSJZycnNi0aRMtWrTgwIEDdO/enYCAAObNm0fZsmUBqF69OqNGjaJHjx4m133FihVs3ryZgQMHsm/fPtatW1dsEHA9b7/9Nj/99BP79u0zzhsyZAjjx48nKSmp2AGsO3ToQM2aNfn444+N552fn09kZCRQ9AvH1dWVLl26MHv2bADOnDmDr68vW7Zs4cEHH+SJJ57Ay8uL6dOnm103wPnz5/H29mbPnj3UqVOH48ePU6VKFaZNm0afPn0A2L9/P7Vr1+bAgQPUrFmTZ555hpSUFJYvX27cz9NPP82qVauKHeNoz5491KtXj7Nnz2JjY4OPjw/Dhg1j7969LFiwgDFjxrBixQqioqLIzs7Gw8ODdevW8dBDDxn30bdvXzIyMpg3b57xOf/79b3Z1866det49NFHgaJWZ48//jiZmZnY2dnRpEkTGjRowBdffGHcz4MPPkhWVhbR0dFcvHgRT09PNm3aRGho6A2veXZ2NtnZfzVHT01Nxc/Pj/Ap4VRucPX/8dJoz6o9LHlvCdu/eZWQwNJ1lwoRKT3mrY2m15jvCe42Acdy1SxdjoiI/Aucj4ngyNpP+bq9B4EeZf7x4607lsmYzal06dLlul/ul1RCQgK//PILKSkpFu8Wp1jaglq0aEF0dLTxMXnyZOOyhg0bmqy7a9cuNm7ciJOTk/FRs2ZNoKj1T7169Xj00UcJDg7mqaeeYurUqSQlJZnso3bt2iYjxfv6+hq7+BgMBh555BE2bdpEcnIy+/fvp3///mRnZ3Pw4EEiIiJo3LixMVBKS0tj8ODBBAUF4ebmhpOTEwcOHDC2VCrOrl27mDlzpsl5tG3bloKCAo4dO2Zcr1GjRmZfz507dxIWFkalSpVwdnY2fnD/e02tW7emevXqLFy40BgopaenExsbS58+fUxqGz16tLHbV3h4ONHR0dSoUYOBAweyZs0as+o7cOAADzzwgMm8K8MQKAqIRo0aRXBwMB4eHjg5ObF69eqrzqFu3brGn62trfH09CQ4ONg4r3z58gDG5/eVV15hwYIF1K9fnzfffJPNmzdft9bDhw/To0cPqlatiouLi7FF1vXq8PX1NTlmSc737+rUqYOHhwcRERFERkbSoEEDOnToQEREBFDUculya7kjR46QkZFB69atTZ6z2bNnm3TVK4mSvnZu5Xw9PDwIDw+nbdu2hIWFGQPb4tja2uLi4mLyEBERERERKU1sLF3Av5mjoyPVq1cvdtmV0tLSCAsLY/z48Vet6+vri7W1NWvXrmXz5s2sWbOGzz77jHfeeYetW7caB/8uU8Y0gTUYDCbdmZo3b84333xj/DDv4uJiDJoiIiJMWlcMHjyYtWvX8vHHH1O9enXs7e3p2rXrDQfVTktL46WXXmLgwIFXLatUqVKx538j6enptG3blrZt2zJ37ly8vb2Jj4+nbdu2V9X0+OOPs3jxYvbv328MYi43IZw6depVwcDlIC4kJIRjx46xcuVK1q1bR7du3WjVqpVxDJ/b4aOPPmLSpElMnDiR4OBgHB0dGTRo0FXncK3n8sp5l1tSXX5+27dvT1xcHCtWrGDt2rU8+uijvPrqq8bWT38XFhZG5cqVmTp1KhUqVKCgoIA6depct46/H/NmXBlu2tra0rx5c+rWrUt2djZ79+5l8+bNDB48GPjrOVu+fDkVK5q2grG1tS3xMc157dzq+c6YMYOBAweyatUqFi5cyLvvvsvatWt58EGNWSQiIiIiIncfhUp3iZCQEBYvXoy/vz82Ntd+2gwGA02bNqVp06YMHz6cypUr8+OPP/LGG2+U6BihoaEMGjTI2AUPioKmdevWERUVxX//+1/julFRUYSHh9O5c2eg6AP+3wedLlu2rElf0MvnsX///mLDtJt18OBBLly4wAcffGAce2bHjh3XXPeDDz7AycmJRx99lE2bNlGrVi3Kly9PhQoVOHr0KD179iz2OC4uLnTv3p3u3bvTtWtX2rVrx8WLF/Hw8LhhjUFBQfz8888m837//XeT6aioKDp27Mizzz4LFAUWhw4dolatWjfc/414e3vTu3dvevfuTbNmzfjf//53zVDpwoULxMTEMHXqVJo1awYUdVE0V1BQEFu3bjWZ9/fzvZbQ0FCmTp2Kra0tY8aMwcrKikceeYSPPvqI7OxsmjZtCkCtWrWwtbUlPj6+2O5kl1ui/f11eCVzXjvXc/l8n3vuOeO8a51vgwYNaNCgAUOHDuWhhx5i3rx5CpVEREREROSupO5vd4lXX32Vixcv0qNHD7Zv305sbCyrV6/m+eefJz8/n61btxoHCI6Pj2fJkiUkJiYSFBRU4mPUrVsXd3d35s2bZxIqLV261OTDPEBAQABLliwhOjqaXbt28cwzz1zVYsPf359ff/2VU6dOGe9S99Zbb7F582bj4M2HDx/mp59+umqgbnNVqlSJsmXL8tlnn3H06FF+/vlnRo0aVez6H3/8MT179qRly5bG0fNHjhzJuHHjmDx5MocOHWLPnj3MmDGDTz75BIBPPvmE+fPnc/DgQQ4dOsQPP/yAj49PsWMh/d3LL7/M4cOH+d///kdMTAzz5s0zDpR+WUBAgLHF2YEDB3jppZc4e/bsTV2TKw0fPpyffvqJI0eOsG/fPpYtW1bsa8Pd3R1PT0+++eYbjhw5woYNG0ocTF7pcoucjz/+mMOHD/P555+zatWqG27XvHlz9u/fz759+3j44YeN8+bOnUujRo2MrdicnZ0ZPHgwr7/+OrNmzSI2NpY//viDzz77zDi4euXKlTEYDCxbtozExMRrDmpn7munOP/5z3+YPn06M2bM4NChQ7z33nsm42cdO3aMoUOHsmXLFuLi4lizZg2HDx826/+oiIiIiIhIaaJQ6S5RoUIFoqKiyM/Pp02bNgQHBzNo0CDc3NywsrLCxcWFX3/9lccee4zAwEDeffddJkyYQPv27Ut8DIPBQLNmzTAYDMYP83Xr1sXFxcXkwzwUBSzu7u40adKEsLAw2rZtS0hIiMn+3n//fY4fP061atXw9vY27i8iIoJDhw7RrFkzGjRowPDhw6lQocItXR9vb29mzpzJDz/8QK1atfjggw+K7dp12aeffkq3bt1o2bIlhw4dom/fvkybNo0ZM2YQHBxMaGgoM2fONHYfdHZ25sMPP6RRo0Y0btyY48ePs2LFCqysiv4bhYeHG8O4a6lUqRKLFy9m6dKl1KtXjylTpjB27FiTdd59911CQkJo27YtzZs3x8fHh06dOt3StYGiFjtDhw6lbt26PPLII1hbW7NgwYJrrnt58PadO3dSp04dXn/9dT766COzj/nggw8ydepUJk2aRL169VizZg3vvvvuDbcLDg7Gzc2N+vXr4+TkBPw1OPnfr++oUaMYNmwY48aNIygoiHbt2rF8+XLjc1axYkVGjhzJkCFDKF++/DXDy5t57VxL9+7dGTZsGG+++SYNGzYkLi6OV155xbjcwcGBgwcP8uSTTxIYGEi/fv149dVXeemll8w+loiIiIiISGmgu7+J3CahoaG0aNGCESNGWLoUuQelpqbi6uqqu7+JyD1Hd38TEZE7TXd/u300ppLIbZCSkkJsbCzLly+3dCkiIiIiIiIid4RCJZHbwNXVlZMnT1q6DBEREREREZE7RmMqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2RQqiYiIiIiIiIiI2WwsXYCIiJTc+bjzlLUva+kySiTpdBIAB+ISLVyJiJRmxxKKfldkJp20cCUiIvJvkZV6FoC4lLw7cryEtHwAkpKSbsv+kpOTb8t+bgdDYWFhoaWLEBGR60tNTcXV1dXSZZjNyspAQYH+zIjIDRisoLDA0lWIiMi/iJUB7uTbVIPBwO2OX1JSUnBxcbmt+zSXWiqJiNxFIiIicHJysnQZJZadnY2tra2lyxCRUk6/K0RE5E670397bufx0tLSCA0NvS37ulUKlURE7iL169e3+LcRIiIiIiJiOampqZYuwUgDdYuIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNkUKomIiIiIiIiIiNlsLF2AiIiUXHR0NE5OTpYu45ZlZ2dja2tr6TJE5C6i3xsiIlIa3ezfp1v5u5aWlnZT2/0TDIWFhYWWLkJERK4vNTUVV1dXS5dx21hZGSgo0J8fETGDwQoKCyxdhYiIiAkrA9zM21qDwcCtxjEpKSm4uLjc0j5ulVoqiYjcRToM7UCFmhUsXcYtObz5MBu/3sjsd7oRVNnb0uWIyF1g5e+HGD59LdVbv469+32WLkdERASApLidnNw6j7ebuFDZteTxytZT2UzfnU6LFi1wd3c3+7iJiYlERkaavd0/QaGSiMhdxKuyF741fS1dxi05f/w8AEGVvQkJrGjhakTkbnAwLhEAe/f7cCxXzcLViIiIFMlMOglAZVcbAj3KlHi7+JQ8ANzd3fHy8jL7uLm5uWZv80/RQN0iIiIiIiIiImI2hUoiIiIiIiIiImI2hUoiIiIiIiIiImI2hUoiIiIiIiIiImI2hUoiIiIiIiIiImI23f1NRESuKT0pnYhvIzgUeYhL5y9h62iLT4APYW+H4V7R/Fufiphj0g9RzFy1k7gzyWTm5OLt6siDtSvx7nMtqFvN9A6IJ8+lUL/PZJIuZQKwfHw47R4ItETZIiJ3jRNb53Nq+8JrLnug/2IMVtbG6fycTHYvGER26lkAqjR/mfJ12t2ROkXuBbvO5jB/fzoHL+SSkl1Y4u0SEhKIjo4mMTGRrKwsAB5++GHc3UvPe3GFSiIicpWM5AymvTCN5NPJWJexxrOSJxTCiT0nuJR4SaGS/OMidh0jMTmdqhU8yMrJJebEeRZH7GXjn7EcX/gWjvZlASgoKCB83A/GQElERMxjY+eCnavPddc5FvGNMVASEfMdTsplR0IOFZysScnOL/F258+f5+TJk7i4uBhDpdJGoZKIiFxlw5QNJJ9OxruqN70+64WzlzMA+bn5FBaW/NsVkZs1b1h37GzLGKeHf7uWMXM2cjE1k4PxiTSsURGAjxdEsvHPozzVIpgfNu6xVLkiInctN/+GVG/1n2KXXzj8G+djNuJRvSkXj0TdwcpE7h2tq9gTVt2BpKwCevx0vsTbBQQEEBQURGZmJvPnz/8HK7x5CpVERMREYWEh+9bvA8C1vCtzXptD8ulkPO7zoOlzTQluG2zhCuXfwM62DD9G7uOjeb+SmpFFzImiN2Debo4E+nkB8MehUwyfvo4OTWry8hMPKFQSEbkJF2O3sPVwFDa2jjh6V8PvwWdw9K4KQPalRI5u+ur/5/dUqCRyk1xtb244azs7u9tcye2ngbrlnhQeHk6nTp0sXcY94W65ljdb591yfndSRlIGWalFzWuPbDlC1qUs7JztOHvkLEuGL2H/+v0WrlD+Lc5eTGPrgRMciEukoKCQKr7urP+0L84OtmRk5fDsqIV4uTrw7ZtPWrpUEZG7k8GKsg7u2LqUIzcjieS4Hexd9BbpiUcpLCzgyNqJFBbkU73NGyZjLImIXKZQSe6I8PBwDAbDVY8jR45YurTbxt/fn4kTJ1q6DDHTpEmTmDlzpqXLKFUK8guMP3v5e/GfH//Df378D17+Ra1Dti3aZqnS5F/m5Y4PkLdxDMcWvkm3FsEcS0iix8j5XMrI5u2pqzl08gIzhnbFy83R0qWKiNx1vAIfoVGfWdTv9RX1e35OzbD3ACjMz+XMnhWc2bWMS6f34d+sD/buFS1crYiUVur+JndMu3btmDFjhsk8b2/vq9bLycmhbNmyd6qsUud2nX9ubi5lypS58Yr/Uvn5+RgMBlxdXf/xY91tz4WDuwPWZazJz83HJ8AH6zJF30z6BPhw/vh5khOSLVug/KsYDAYqlXdj6LPN+X7jHvYdP8f89bvYfeQMAE++OxeA/IK/wtAnh31Hx4drMW/40xapWUTkbvD3oMitcgNs7JzJy7pEzqVECguKBhM+HvktxyO/hSvGVDwe+S2JBzdSp+v4O1qziJQ+aqkkd4ytrS0+Pj4mD2tra5o3b86AAQMYNGgQXl5etG3bFoC9e/fSvn17nJycKF++PL169eL8+b8GNVu0aBHBwcHY29vj6elJq1atSE9PNznmxx9/jK+vL56enrz66qvk5uYC8Pnnn1OnTh3jekuXLsVgMDBlyhTjvFatWvHuu+8CEBsbS8eOHSlfvjxOTk40btyYdevWGddt3rw5cXFxvP7668ZWWJf99ttvNGvWDHt7e/z8/Bg4cKBJnf7+/owaNYrnnnsOFxcX+vXrd83rV1BQwIcffkj16tWxtbWlUqVKjBkzBoDjx49jMBhYuHAhoaGh2NnZMXdu0QetadOmERQUhJ2dHTVr1uTLL7802e+JEyfo1q0bbm5ueHh40LFjR44fP17s87h9+3a8vb0ZP77oTcSuXbto0aIFzs7OuLi40LBhQ3bs2FHs9p988gnBwcE4Ojri5+dH//79SUtLMy6fOXMmbm5urF69mqCgIJycnGjXrh0JCQnGdfLz83njjTdwc3PD09OTN99884aDR1/e788//0ytWrWwtbUlPj7epPvbN998Q4UKFSi44sMpQMeOHXnhhReM0z/99BMhISHY2dlRtWpVRo4cSV5ennG5wWDgq6++4oknnsDR0ZExY8aQlJREz5498fb2xt7enoCAgKtC1tLC2saayg0qA3D2yFny8/LJz8vn7JGiu754+nlasjz5F7iQksGcNX+Sk/vX/6uVvx8y/pyRlQMUjf+VnpVDelYOWTl/rZuVk0dmdu6dK1hE5C50aucSsi8lGqeT46PJy7oEgK1zOeP8gtysokdetnFeYX6uybSI/HuppZKUCrNmzeKVV14hKqpo8L/k5GRatmxJ3759+fTTT8nMzOStt96iW7dubNiwgYSEBHr06MGHH35I586duXTpEpGRkSbBwsaNG/H19WXjxo0cOXKE7t27U79+fV588UVCQ0MZOHAgiYmJeHt7ExERgZeXF5s2beLll18mNzeXLVu2MGTIEADS0tJ47LHHGDNmDLa2tsyePZuwsDBiYmKoVKkSS5YsoV69evTr148XX3zRWENsbCzt2rVj9OjRTJ8+ncTERAYMGMCAAQNMAoWPP/6Y4cOH89577xV7jYYOHcrUqVP59NNPefjhh0lISODgwYMm6wwZMoQJEybQoEEDY7A0fPhwPv/8cxo0aMCff/7Jiy++iKOjI7179yY3N5e2bdvy0EMPERkZiY2NDaNHj6Zdu3bs3r37qhZTGzZsoEuXLnz44YfG8Ktnz540aNCAr776Cmtra6Kjo6/bKsfKyorJkydTpUoVjh49Sv/+/XnzzTdNwq6MjAw+/vhj5syZg5WVFc8++yyDBw82BmUTJkxg5syZTJ8+naCgICZMmMCPP/5Iy5Ytiz3u5f2OHz+eadOm4enpSbly5UyWP/XUU7z22mts3LiRRx99FICLFy+yatUqVqxYAUBkZCTPPfcckydPplmzZsTGxhqvxZXP34gRI/jggw+YOHEiNjY2DBs2jP3797Ny5Uq8vLw4cuQImZnF3wI9Ozub7Oy/3qylpqZe99xutxYvtSDuzzgSjyUyqdMkAC4lXsJgbeDh3g/f0Vrk3+dSRjbhY3/glQlLqVbBg5T0LE6cSwHA2cGWzs1qM+gp09fhpj+P8ujr0wBYPj6cdg8E3vG6RUTuJmf3ruTEljmUdfbC2saWzKRTAFiVscOn/hM4ePiZ3BUuK/Us0bNfAqBK85cpX6edReoWuRv9Gp/F13+mkf+3L8JXr16Nr69vsZ9jjh07xtatW02+9N6xYwdWVqWnfZBCJbljli1bhpOTk3G6ffv2/PDDD0DRrRI//PBD47LRo0fToEEDxo4da5w3ffp0/Pz8OHToEGlpaeTl5dGlSxcqVy5qUREcbHpHKnd3dz7//HOsra2pWbMmjz/+OOvXr+fFF1+kTp06eHh4EBERQdeuXdm0aRP//e9/mTSp6MPztm3byM3NpUmTJgDUq1ePevXqGfc9atQofvzxR37++WcGDBiAh4cH1tbWODs74+PjY1xv3Lhx9OzZk0GDBhnPc/LkyYSGhvLVV18ZR/Nv2bIl//3vf4u9dpcuXWLSpEl8/vnn9O7dG4Bq1arx8MOmH6oGDRpEly5djNPvvfceEyZMMM6rUqUK+/fv5+uvv6Z3794sXLiQgoICpk2bZmxdNWPGDNzc3Ni0aRNt2rQx7uvHH3/kueeeY9q0aXTv3t04Pz4+nv/973/UrFnTeI7Xc/laQFErrdGjR/Pyyy+bhEq5ublMmTKFatWqATBgwADef/994/KJEycydOhQ43lNmTKF1atXX/e4l/f75ZdfmjyXV3J3d6d9+/bMmzfPGCotWrQILy8vWrRoAcDIkSMZMmSI8XmoWrUqo0aN4s033zQJlZ555hmef/5543R8fDwNGjSgUaNGxnO/nnHjxjFy5MgbntM/5b469/HcF8+xccpGTu0/hY2tDVXvr0qLl1pwX537LFaX/Du4OdnRvWVdth88Sezpi+Tm5eNXzpVH6lVh6LPNqezjbukSRUTuehUbduVi7GYyLsSTlZGMrbM3zr5BVGzcTWMoidxmGbmFnE7Lv2p+enr6Vb1trpSTk3PVl8tZWVm3vb5boVBJ7pgWLVrw1VdfGacdHf8aWLVhw4Ym6+7atYuNGzeahFCXxcbG0qZNGx599FGCg4Np27Ytbdq0oWvXrri7//VBo3bt2lhb/3WXCl9fX/bsKbrdtMFg4JFHHmHTpk20atWK/fv3079/fz788EMOHjxIREQEjRs3xsHBAShqqTRixAiWL19OQkICeXl5ZGZmEh8ff91z3rVrF7t37za2sIGi7hoFBQUcO3aMoKAgAGPQUJwDBw6QnZ1tDDqKc+V+0tPTiY2NpU+fPiatp/Ly8ozjCO3atYsjR47g7Oxssp+srCxiY2ON01u3bmXZsmUsWrToqjulvfHGG/Tt25c5c+bQqlUrnnrqKWMYdC3r1q1j3LhxHDx4kNTUVPLy8sjKyiIjI8N4vR0cHEz24evry7lz5wBISUkhISGBBx54wLjcxsaGRo0a3bALXNmyZalbt+511+nZsycvvvgiX375Jba2tsydO5enn37a+G3Arl27iIqKMnY9hKLueH8/h78/p6+88gpPPvkkf/zxB23atKFTp07G0PJahg4dyhtvvGGcTk1Nxc/P77q1326V6lWi91e97+gxRQDcnO3NHg+peYOq5G8ae+MVRUQEgPJ12lK+TtsSr2/nUp4HByz95woSuYe1q2ZPu2r2xul1xzIZszmVLl264OXlVex2NWrUoEaNGlfNT0hI4JdffvlHajWXQiW5YxwdHalevXqxy66UlpZGWFiYcdyeK/n6+mJtbc3atWvZvHkza9as4bPPPuOdd95h69atVKlSBeCqLlgGg8Gk2WDz5s355ptviIyMpEGDBri4uBiDpoiICEJDQ43rDh48mLVr1/Lxxx9TvXp17O3t6dq1Kzk5Odc957S0NF566SUGDhx41bJKlSoVe/5/Z29vf93l19rP5XGKpk6dahLAAMawLS0tjYYNG5qEXpddOYh6tWrV8PT0ZPr06Tz++OMm13bEiBE888wzLF++nJUrV/Lee++xYMECOnfufNU+jx8/TocOHXjllVcYM2YMHh4e/Pbbb/Tp04ecnBxjIHOt5+5GgVFJ2Nvbm4x3dS1hYWEUFhayfPlyGjduTGRkJJ9++qlxeVpaGiNHjjRpEXbZ5ZZncPVz2r59e+Li4lixYgVr167l0Ucf5dVXX+Xjjz++Zh22trbY2tqac3oiIiIiIiJ3VOnpiCdyhZCQEPbt24e/vz/Vq1c3eVz+sG4wGGjatCkjR47kzz//pGzZsvz4448lPkZoaCj79+/nhx9+oHnz5kBR0LRu3TqioqKM8wCioqIIDw+nc+fOBAcH4+Pjc9Vg1mXLliU/37RJY0hICPv377/qHKpXr27WHd4CAgKwt7dn/fr1Jd6mfPnyVKhQgaNHj1517MvBW0hICIcPH6ZcuXJXrXPlXdG8vLzYsGEDR44coVu3bsYBzy8LDAzk9ddfZ82aNXTp0qXYAah37txJQUEBEyZM4MEHHyQwMJDTp0+X+JwAXF1d8fX1ZevWrcZ5eXl57Ny506z9FMfOzo4uXbowd+5c5s+fT40aNQgJCTEuDwkJISYm5prP6Y36Nnt7e9O7d2++++47Jk6cyDfffHNbahYREREREbEEhUpSKr366qtcvHiRHj16sH37dmJjY1m9ejXPP/88+fn5bN26lbFjx7Jjxw7i4+NZsmQJiYmJxu5kJVG3bl3c3d2ZN2+eSai0dOlSsrOzadq0qXHdgIAAlixZQnR0NLt27eKZZ5656g5h/v7+/Prrr5w6dcp4l7q33nqLzZs3M2DAAKKjozl8+DA//fQTAwYMMOt62NnZ8dZbb/Hmm28ye/ZsYmNj+f333/n222+vu93IkSMZN24ckydP5tChQ+zZs4cZM2bwySefAEVdvby8vOjYsSORkZEcO3aMTZs2MXDgQE6ePGmyr3LlyrFhwwYOHjxIjx49jF0ABwwYwKZNm4iLiyMqKort27cX+zxUr16d3NxcPvvsM44ePcqcOXNM7rhXUv/5z3/44IMPWLp0KQcPHqR///4kJyebvZ/i9OzZk+XLlzN9+nR69uxpsmz48OHMnj2bkSNHsm/fPg4cOMCCBQuMdwoszvDhw/npp584cuQI+/btY9myZWa9XkVEREREREobhUpSKlWoUIGoqCjy8/Np06YNwcHBDBo0CDc3N6ysrHBxceHXX3/lscceIzAwkHfffZcJEybQvn37Eh/DYDDQrFkzDAaDccDrunXr4uLiQqNGjUy6L33yySe4u7vTpEkTwsLCaNu2rUnrFYD333+f48ePU61aNWPXsbp16xIREcGhQ4do1qwZDRo0YPjw4VSoUMHsazJs2DD++9//Mnz4cIKCgujevbtxnKHi9O3bl2nTpjFjxgyCg4MJDQ1l5syZxpZKDg4O/Prrr1SqVIkuXboQFBREnz59yMrKwsXF5ar9+fj4sGHDBvbs2UPPnj2xsrLiwoULPPfccwQGBtKtWzfat29f7ADT9erV45NPPmH8+PHUqVOHuXPnMm7cOLOvxX//+1969epF7969eeihh3B2dr5md7ub1bJlSzw8PIiJieGZZ54xWda2bVuWLVvGmjVraNy4MQ8++CCffvqpccD44pQtW5ahQ4dSt25dHnnkEaytrVmwYMFtq1lEREREROROMxTejoFKRETkH5WamoqrqyvhU8Kp3OD6AVZpt2fVHpa8t4Tt37xKSKDuLiMiNzZvbTS9xnxPcLcJOJYr/mYQIiIid9L5mAiOrP2Ur9t7EOhR5sYb/L+SDtRdnMsDdaekpFyzMcCdpJZKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNoVKIiIiIiIiIiJiNhtLFyAiIiV3Pu48Ze3LWrqMW5J0OgmAA3GJFq5ERO4WxxKKfm9kJp20cCUiIiJ/yUo9C0BcSp5Z2yWk5QOQlJR0U8dNTk6+qe3+CYbCwsJCSxchIiLXl5qaiqurq6XLuG2srAwUFOjPj4iYwWAFhQWWrkJERMSElQFu5m2twWDgVuOYlJQUXFxcbmkft0otlURE7iIRERE4OTlZuoxblp2dja2traXLEJG7iH5viIhIaXSzf59u5e9aWloaoaGhN7Xt7aZQSUTkLlK/fn2LfxshIiIiIiKWk5qaaukSjDRQt4iIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImE2hkoiIiIiIiIiImM3G0gWIiEjJRUdH4+TkZOky7pjs7GxsbW0tXYaIiFn0u0tE5Pruxt+TpanmtLQ0S5dgZCgsLCy0dBEiInJ9qampuLq6WrqMO87KykBBgf5MichdxmAFhQWWrkJEpNSyMsDd9hbPYDBQ2uKTlJQUXFxcLFqDWiqJiNxFOgztQIWaFSxdxh1xePNhNn69kdnvdCOosrelyxERKZGVvx9i+PS1VG/9Ovbu91m6HBGRUicpbicnt87j7SYuVHa9OyKJraeymb47nRYtWuDu7m7pckhMTCQyMtLSZQAKlURE7ipelb3wrelr6TLuiPPHzwMQVNmbkMCKFq5GRKRkDsYlAmDvfh+O5apZuBoRkdInM+kkAJVdbQj0KGPhakomPiUPAHd3d7y8vCxcDeTm5lq6BCMN1C0iIiIiIiIiImZTqCQiIiIiIiIiImZTqCQiIiIiIiIiImZTqCQiIiIiIiIiImZTqCQiIiIiIiIiImbT3d9EROSGkk8nM6nzpGKXh/YNpfmLze9cQSK30aQfopi5aidxZ5LJzMnF29WRB2tX4t3nWlC3WtHdFvccPcPImevZuv8EF1IyCPTz4j9PNeX59g0tXL2IyM05tOpDLh7ZDIBnwMMEtB1Mfk4mJ7bO5dLp/WRfSiQ/NxtbJy88A5pSIaQL1mXtLVy1SMnM3J3GrD3p11y2rkc5rK0Md7ii61u3bh1Hjx4FoGrVqrRq1crCFZWcQiUREbkh67LWVKxd0WReVloWF+IuAODk6WSJskRui4hdx0hMTqdqBQ+ycnKJOXGexRF72fhnLMcXvkXc2SSa9P+KjKxcPFzsCbjPkz1Hz9B3/GJS07L4z1NNLX0KIiJmObd/vTFQulJuVipndi3DYF0Ge/eK5KRdJCvlNKd2/EB6Yiw1w4ZboFqRm+dqa6CCU+mOPWJiYoyB0t2odF9dEREpFZy9nOk7va/JvBUfreBC3AXsXOyo266uhSoTuXXzhnXHzraMcXr4t2sZM2cjF1MzORifyPcbd5ORlYttGRtivvsvHi4OvDttDeO+28TImevp98T92F+xvYhIaZaVksDxyKk4+dQgJ+08OWkXjMusrMtSqUk45eu0xbqsPQV5Oez/cRhpZ2NIjvuDvKw0bOz0RZLcPR6saMuQh1wtXUaxUlNT2bx5M+XLlyctLY309Gu3rirNNKaSiIiYLSMlg+hl0QA06tKIsg5lLVuQyC2wsy3Dj5H7aPLKV9Tp/Snj5m4CwNvNkUA/LwoKCo3rGgxFzeWt/v/flPQsth88ecdrFhG5GYUF+RxZ8ykGgxXV27wBBtOPg2Ud3akQ0snYzc3KpixO5asXLTRYYbCyvtMli9ySX+OzabvgLE8uTmToxiQOX8y1dElGBQUFbNiwAYAWLVoY32Pcbe7pUGnTpk0YDAaSk5P/0eP06tWLsWPHGqf9/f2ZOHHibdt/eHg4nTp1uuX9GAwGli5dCsDx48cxGAxER0ff8n7l7na7X693ev+324gRI6hfv/4/tv/z589Trlw5Tp68uz+Ebl+0ndysXKzLWvNAtwcsXY7ILTt7MY2tB05wIC6RgoJCqvi6s/7Tvjg72NL5kdpYW1mRnZtHjZ4TqP/CZMZ+t8m47anzqZYrXETEDCe3LSDt7CGqhL6EnUv5G66fm5HMhdgtQNG4SxpTSe4mVgbwsLfCx9Gai1kF/H46h1dXXyw1wdLOnTs5d+4cDz/8MC4uLpYu56aZFSqFh4djMBiuerRr165E29/pD5dNmjQhISEBV9d/rrnbrl27WLFiBQMHDjTO2759O/369fvHjnk7+Pn5kZCQQJ06dSxdivzLXBlu/ht5eXnx3HPP8d5771m6lJuWl5PH9kXbAajbrq7GU5J7wssdHyBv4xiOLXyTbi2COZaQRI+R87mUkU2TOpX5ccyzPBDkR3ZuHhdSM+jVpoFx2zLW9/R3dCJyj0g7e4RTOxfjVSMUrxqhN1w/KyWBfYvfJjf9Is6+QVRt/sodqFLk9njU344fn/Tmuye8mBXmxfgWbgDkFsDSQxmWLQ5ITEwkOjqa6tWrExAQYOlybonZYyq1a9eOGTNmmMyztbW9bQXdLrm5uZQtWxYfH59/9DifffYZTz31FE5Of32o8vb2/kePeTtYW1v/49fmbpSTk0PZsurGc6/Kz8/HYDBgZWXZD4DPP/88DRs25KOPPsLDw8OitdyMXSt2kX4xHQzw0DMPWbockdvGYDBQqbwbQ59tzvcb97Dv+Dnmr99Fv7D7efyhmjz+UE3juvPX72L26j8AqFGp9P/dFxHJuBgHhQVcOLKFi0e3AlCQmw3AxdgtbPv6aULCv8XG1pFLCQeJWT6WvKxU3P0bU73tYKzLlL7PfCLF8XMxjTrur2CLi62B1OxCzqUXWKiqv1y8eJHCwkKOHTvG9OnTAcjLywMwznv22Wfvis+mZn+ysrW1xcfHx+Th7u4OQGFhISNGjKBSpUrY2tpSoUIFYwue5s2bExcXx+uvv25s4QTX7moyceJE/P39jdPbt2+ndevWeHl54erqSmhoKH/88YfJNgaDga+++oonnngCR0dHxowZc83ub7/99hvNmjXD3t4ePz8/Bg4caDIY1pdffklAQAB2dnaUL1+erl27Fnst8vPzWbRoEWFhYSbz/94iy2AwMG3aNDp37oyDgwMBAQH8/PPPJtvs27ePDh064OLigrOzM82aNSM2Nvaax71Wi6/69eszYsQI4/Thw4d55JFHsLOzo1atWqxdu9Zk/b93f7t8rdavX0+jRo1wcHCgSZMmxMTEmGw3evRoypUrh7OzM3379mXIkCHX7Sp0eb+rV6+mQYMG2Nvb07JlS86dO8fKlSsJCgrCxcWFZ555hoyMvxLjVatW8fDDD+Pm5oanpycdOnQwuR6X61+yZAktWrTAwcGBevXqsWXLFpPjT506FT8/PxwcHOjcuTOffPIJbm5uxuWXX3/Tpk2jSpUq2NnZARAfH0/Hjh1xcnLCxcWFbt26cfbsWQAOHTqEwWDg4MGDJsf69NNPqVatmnF67969tG/fHicnJ8qXL0+vXr04f/68cfmiRYsIDg7G3t4eT09PWrVqVezAbPn5+fTp04cqVapgb29PjRo1mDTJ9Pbul7tJfvzxx/j6+uLp6cmrr75Kbu5fzTvPnTtHWFgY9vb2VKlShblz5xb73F22adMm7r//fhwdHXFzc6Np06bExcUBEBsbS8eOHSlfvjxOTk40btyYdevWFbuvy/+vO3fujMFgME5ffh6mT59OpUqVcHJyon///uTn5/Phhx/i4+NDuXLlGDNmjMn+PvnkE4KDg3F0dMTPz4/+/fuTlpZmXD5z5kzc3Nz4+eefqVWrFra2tsTHx1/3nC6bM2cO/v7+uLq68vTTT3Pp0iXjsoKCAsaNG2d8PurVq8eiRYuMy5OSkujZsyfe3t7Y29sTEBBgEsbXrl2bChUq8OOPP97w+pc2hYWFbJlX9P8ssGkg3lX0YVrubhdSMpiz5k9ycvOM81b+fsj4c0ZWDgAR0X/dleXEuWTen7kegNr+5ahT5cZdSERESovC/BwKcrMoyM0CisaMKyzIN05fOLKZ/UuHk5eVik/dxwl8fKgCJbnrzN+Xztn0fOP0joRsUrOLXu/lnUrP2GD5+fnk5eUZAyUoer+dl5dHYWHhdbYsPW7r3d8WL17Mp59+yoIFC6hduzZnzpxh165dACxZsoR69erRr18/XnzxRbP2e+nSJXr37s1nn31GYWEhEyZM4LHHHuPw4cM4Ozsb1xsxYgQffPABEydOxMbG5qrb8sXGxtKuXTtGjx7N9OnTSUxMZMCAAQwYMIAZM2awY8cOBg4cyJw5c2jSpAkXL14kMjKy2Lp2795NSkoKjRo1uuE5jBw5kg8//JCPPvqIzz77jJ49exIXF4eHhwenTp3ikUceoXnz5mzYsAEXFxeioqJMXljmKCgooEuXLpQvX56tW7eSkpLCoEGDSrTtO++8w4QJE/D29ubll1/mhRdeICoqCoC5c+cyZswYvvzyS5o2bcqCBQuYMGECVapUueF+R4wYweeff46DgwPdunWjW7du2NraMm/ePNLS0ujcuTOfffYZb731FgDp6em88cYb1K1bl7S0NIYPH07nzp2Jjo42aWXyzjvv8PHHHxMQEMA777xDjx49OHLkCDY2NkRFRfHyyy8zfvx4nnjiCdatW8ewYcOuqu3IkSMsXryYJUuWYG1tTUFBgTFQioiIIC8vj1dffZXu3buzadMmAgMDadSoEXPnzmXUqFHG/cydO5dnnnkGgOTkZFq2bEnfvn359NNPyczM5K233qJbt25s2LCBhIQEevTowYcffkjnzp25dOkSkZGRxf7iKCgo4L777uOHH37A09OTzZs3069fP3x9fenWrZtxvY0bN+Lr68vGjRs5cuQI3bt3p379+sb/c+Hh4Zw+fZqNGzdSpkwZBg4cyLlz54p93vLy8ujUqRMvvvgi8+fPJycnh23bthlD4bS0NB577DHGjBmDra0ts2fPJiwsjJiYGCpVqnTV/rZv3065cuWYMWMG7dq1w9r6r1/osbGxrFy5klWrVhEbG0vXrl05evQogYGBREREsHnzZl544QVatWrFAw8UjeFjZWXF5MmTqVKlCkePHqV///68+eabfPnll8b9ZmRkMH78eKZNm4anpyceHh7Ga3Ktc7pcy9KlS1m2bBlJSUl069aNDz74wBhqjRs3ju+++44pU6YQEBDAr7/+yrPPPou3tzehoaEMGzaM/fv3s3LlSry8vDhy5AiZmZkm1+L+++8nMjKSPn36XPPaZ2dnk52dbZxOTS0dY7YcijzEhbiiu8Q0ebaJhasRuXWXMrIJH/sDr0xYSrUKHqSkZ3HiXApA0XhKzWoDEDZkNg52ZSjv7sThkxfIzs3Dwa4MX/+vy107sKaI/LuUC3qUckGPmsz7Y9aL5FxKxDPgYQLaDiYn7SKHV30EFGKwsiHt7GH2LRpiXL9K6Es4lquGSGn30+EMpkanUc7BCjsbA/GpRQGTnY2BrjUdLFwd1KhRgxo1apjMu/zZuGrVqrRq1cpClZnP7FBp2bJlJl29AN5++23efvtt4uPj8fHxoVWrVpQpU4ZKlSpx//33A+Dh4YG1tTXOzs5md7tq2bKlyfQ333yDm5sbERERdOjQwTj/mWee4fnnnzdO/z1UGjduHD179jQGLAEBAUyePJnQ0FC++uor4uPjcXR0pEOHDjg7O1O5cmUaNGhAceLi4rC2tqZcuXI3PIfw8HB69OgBwNixY5k8eTLbtm2jXbt2fPHFF7i6urJgwQLKlCm6JXFgYOAN91mcdevWcfDgQVavXk2FChWMx2zfvv0Ntx0zZgyhoUV9rIcMGcLjjz9OVlYWdnZ2fPbZZ/Tp08d4jYcPH86aNWtMWoYUZ/To0TRt2hSAPn36MHToUGJjY6latSoAXbt2ZePGjcZQ6cknnzTZfvr06Xh7e7N//36TcaAGDx7M448/DhQFd7Vr1+bIkSPUrFmTzz77jPbt2zN48GCg6Jpu3ryZZcuWmew7JyeH2bNnG7strl27lj179nDs2DH8/PwAmD17NrVr12b79u00btyYnj178vnnnxtDpUOHDrFz506+++47AD7//HMaNGhgMoD79OnT8fPz49ChQ6SlpZGXl0eXLl2oXLkyAMHBwcVevzJlyjBy5EjjdJUqVdiyZQvff/+9Sajk7u7O559/jrW1NTVr1uTxxx9n/fr1vPjiixw6dIiVK1eybds2GjduDMC3335LUFBQscdNTU0lJSWFDh06GFthXbl+vXr1qFevnnF61KhR/Pjjj/z8888MGDDgqv1dvsZubm5X/R4oKChg+vTpODs7U6tWLVq0aEFMTAwrVqzAysqKGjVqMH78eDZu3GgMla4MS/39/Rk9ejQvv/yySaiUm5vLl19+aazz4sWL1z2ny7XMnDnTGFr36tWL9evXM2bMGLKzsxk7dizr1q3joYeKun5VrVqV3377ja+//prQ0FDi4+Np0KCBMXC+suXlZRUqVODPP/8s7tIzbtw4k+e8tNg8dzMAFWtXpHKDyhauRuTWuTnZ0b1lXbYfPEns6Yvk5uXjV86VR+pVYeizzansU9Qau0OTmvy66xgxJ87j7FCWxx6qwfDeLalbzdfCZyAicvsUFOTyV+ulPNLOHjJZnpdj+bFoREqiZ21HIuKzOZ6Sx+m0fMo7WlHHuyy9gh2p5HJb29b865l9NVu0aMFXX31lMu/ymCBPPfUUEydOpGrVqrRr147HHnuMsLAwbGxu7Uk7e/Ys7777Lps2beLcuXPk5+eTkZFBfHy8yXo3ajG0a9cudu/ebdLlp7CwkIKCAo4dO0br1q2pXLmysf527doZu6xdS2ZmJra2tiX6hrJu3brGnx0dHXFxcTG2EImOjqZZs2bGQOlWHThwAD8/P2OgBBg//JpTp69v0Rvlc+fOUalSJWJiYujfv7/J+vfff7/xNogl3W/58uVxcHAwBkqX523bts04ffjwYYYPH87WrVs5f/48BQVF/V7j4+NNQqXi6q1ZsyYxMTF07tz5qnr/HipVrlzZZBysy9fvcqAEUKtWLdzc3Dhw4ACNGzfm6aefZvDgwfz+++88+OCDzJ07l5CQEGrWLBpvY9euXWzcuPGqABaKWsG0adOGRx99lODgYNq2bUubNm3o2rWrsSvptXzxxRdMnz6d+Ph4MjMzycnJuarrYe3atU1a//j6+rJnzx7jednY2NCwYUPj8po1a5p0B/w7Dw8PwsPDadu2La1bt6ZVq1Z069bNeK3T0tIYMWIEy5cvJyEhgby8PDIzM6/6v1kS/v7+Ji0Py5cvj7W1tUnLtPLly5u0rFq3bh3jxo3j4MGDpKamkpeXR1ZWFhkZGcb/t2XLljV5ndzonK5Vi6+vr/G4R44cISMjg9atW5vUn5OTYwyhX3nlFZ588kn++OMP2rRpQ6dOnWjSxLRVj729vUmXz78bOnQob7zxhnE6NTXV5DVpKc9//fyNVxK5i7g52zNv+NM3XK8k64iI3G1Cek81mbZzKc+DA5ZaphiR2ygswIGwAMu3SDLH5V4vdxuzx1RydHSkevXqJo/LoZKfnx8xMTF8+eWX2Nvb079/fx555BGTMV2uKsDK6qouP39fv3fv3kRHRzNp0iQ2b95MdHQ0np6e5OTkXFXb9aSlpfHSSy8RHR1tfOzatYvDhw9TrVo1nJ2d+eOPP5g/fz6+vr4MHz6cevXqmYzJdCUvLy8yMjKuquNa/h4YGQwGY1Bib2/erTlLcs1u1pV1Xg7LLtd5O/d7vesBEBYWxsWLF5k6dSpbt25l69aiwQT/fq1vR703et1ci4+PDy1btmTevHlAUVPFnj17GpenpaURFhZm8lqLjo42jnVlbW3N2rVrWblyJbVq1eKzzz6jRo0aHDt27JrHW7BgAYMHD6ZPnz6sWbOG6Ohonn/++eteD7j6ut6MGTNmsGXLFpo0acLChQsJDAzk999/B4paiv3444+MHTuWyMhIoqOjCQ4OLtH/ib+7Vu3XO5/jx4/ToUMH6taty+LFi9m5cydffPEFYPo6sbe3vyr4vd45FVfL5eNebpm3fPlyk+d2//79xnGV2rdvbxxD7vTp0zz66KPGFnOXXbx48bqD+tva2uLi4mLyEBERERERKU1u+y2Q7O3tCQsLY/LkyWzatIktW7YYW0qULVuW/Px8k/W9vb05c+aMSUhyefDoy6Kiohg4cCCPPfYYtWvXxtbW1mTA45IKCQlh//79V4Vi1atXN46qbmNjQ6tWrfjwww/ZvXs3x48fL7YlzuVWIvv37ze7livVrVuXyMjIEgdD3t7eJCQkGKdTU1NNwoigoCBOnDhhss6VH5hvVo0aNdi+fbvJvL9P3w4XLlwgJiaGd999l0cffZSgoCCSkpLM3s/N1nv5+p04ccI4b//+/SQnJ1OrVi3jvJ49e7Jw4UK2bNnC0aNHefrpv77FDgkJYd++ffj7+1/1WrscYhkMBpo2bcrIkSP5888/KVu2bLEDN0dFRdGkSRP69+9PgwYNqF69erEDuRenZs2a5OXlsXPnTuO8mJiYYkPTKzVo0IChQ4eyefNm6tSpYwzToqKiCA8Pp3PnzgQHB+Pj48Px48evu68yZcpc9XvgZuzcuZOCggImTJjAgw8+SGBgIKdPny7x9sWd041cOeD335/bK1sSeXt707t3b7777jsmTpzIN998Y7KfvXv3Xrd7rYiIiIiISGlndqiUnZ3NmTNnTB6XA56ZM2fy7bffsnfvXo4ePcp3332Hvb29ccwYf39/fv31V06dOmXcpnnz5iQmJvLhhx8SGxvLF198wcqVK02OGRAQwJw5czhw4ABbt26lZ8+eZrfuAXjrrbfYvHkzAwYMMLYa+emnn4xjvyxbtozJkycTHR1NXFwcs2fPpqCg4KoBtC7z9vYmJCSE3377zexarjRgwABSU1N5+umn2bFjB4cPH2bOnDlX3XntspYtWzJnzhwiIyPZs2cPvXv3Nuny1KpVKwIDA+nduze7du0iMjKSd95555ZqBHjttdf49ttvmTVrFocPH2b06NHs3r37tg9Q6u7ujqenJ9988w1Hjhxhw4YNJt2AzKl3xYoVfPLJJxw+fJivv/6alStX3rDeVq1aERwcTM+ePfnjjz/Ytm0bzz33HKGhoSZdLLt06cKlS5d45ZVXaNGihUl3w1dffZWLFy/So0cPtm/fTmxsLKtXr+b5558nPz+frVu3MnbsWHbs2EF8fDxLliwhMTGx2PGNAgIC2LFjB6tXr+bQoUMMGzbM7ECvRo0atGvXjpdeeomtW7eyc+dO+vbte93/S8eOHWPo0KFs2bKFuLg41qxZw+HDh411BgQEsGTJEmOrv2eeeeaGLaP8/f1Zv349Z86cuamw8LLq1auTm5vLZ599xtGjR5kzZw5Tpky54XY3OqcbcXZ2ZvDgwbz++uvMmjWL2NhY/vjjDz777DNmzZoFFI039tNPP3HkyBH27dvHsmXLTPafkZHBzp07adOmzc2dvIiIiIiISClgdqi0atUqfH19TR4PP/wwUDT47tSpU2natCl169Zl3bp1/PLLL3h6egLw/vvvc/z4capVq2bs9hEUFMSXX37JF198Qb169di2bdtV3US+/fZbkpKSCAkJoVevXgwcOLBEg2P/Xd26dYmIiODQoUM0a9aMBg0aMHz4cGMY4ObmxpIlS2jZsiVBQUFMmTKF+fPnU7t27WL32bdv3xLdlv16PD092bBhA2lpaYSGhtKwYUOmTp1a7BhLQ4cOJTQ0lA4dOvD444/TqVMnk1vZW1lZ8eOPP5KZmcn9999P3759r7oV+83o2bMnQ4cOZfDgwYSEhHDs2DHCw8Oxs7O75X1fycrKigULFrBz507q1KnD66+/zkcffWT2fpo2bcqUKVP45JNPqFevHqtWreL111+/Yb0Gg4GffvoJd3d3HnnkEVq1akXVqlVZuHChyXrOzs6EhYWxa9cuk65vUDQIc1RUFPn5+bRp04bg4GAGDRqEm5sbVlZWuLi48Ouvv/LYY48RGBjIu+++y4QJE4odTP2ll16iS5cudO/enQceeIALFy5cNb5VScyYMYMKFSoQGhpKly5d6Nev33X/Lzk4OHDw4EGefPJJAgMD6devH6+++iovvfQSAJ988gnu7u40adKEsLAw2rZtS0hIyHVrmDBhAmvXrsXPz++WWurUq1ePTz75hPHjx1OnTh3mzp3LuHHjbrjdjc6pJEaNGsWwYcMYN24cQUFBtGvXjuXLlxvvhFi2bFmGDh1K3bp1jd0dFyxYYNz+p59+olKlSjRr1sz8ExcRERERESklDIXF3cNcSiQzM5MaNWqwcOHCEg+GfS9p3bo1Pj4+zJkzx9KllMiLL77IwYMHiYyMtHQp8i/24IMPMnDgQLMG40tNTcXV1ZXwKeH/mruu7Vm1hyXvLWH7N68SEljR0uWIiJTIvLXR9BrzPcHdJujW6yIi13A+JoIjaz/l6/YeBHrcnptV/dPWHctkzOZUunTpgpeXl6XLISEhgV9++YWUlBSLj72qe+ndInt7e2bPnn1TYzzdbTIyMpgyZQpt27bF2tqa+fPns27dOtauXWvp0or18ccf07p1axwdHVm5ciWzZs0yud28yJ12/vx5unTpQo8ePSxdioiIiIiIyC1RqHQbNG/e3NIl3BEGg4EVK1YwZswYsrKyqFGjBosXL6ZVq1aWLq1Y27Zt48MPP+TSpUtUrVqVyZMn07dvX0uXJf9iXl5evPnmm5YuQ0RERERE5JYpVJISs7e3Z926dZYuwyzff/+9pUsQERERERERuSeZPVC3iIiIiIiIiIiIQiURERERERERETGbQiURERERERERETGbQiURERERERERETGbQiURERERERERETGbQiURERERERERETGbjaULEBGRkjsfd56y9mUtXcYdkXQ6CYADcYkWrkREpOSOJRT97spMOmnhSkRESqes1LMAxKXkWbiSkktIywcgKSnJwpUUSU5OtnQJRobCwsJCSxchIiLXl5qaiqurq6XLuOOsrAwUFOjPlIjcZQxWUFhg6SpEREotKwPcbW/xDAYDpS0+SUlJwcXFxaI1qKWSiMhdJCIiAicnJ0uXccdkZ2dja2tr6TJERMyi310iItd3N/6eLE01p6WlERoaaukyAIVKIiJ3lfr161v82wgREREREbGc1NRUS5dgpIG6RURERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbAqVRERERERERETEbDaWLkBEREouOjoaJycnS5dxTdnZ2dja2lq6DBGRW6LfZSIicjvd6O/KzfzdSUtLu9WybhtDYWFhoaWLEBGR60tNTcXV1dXSZVyXlZWBggL9SRGRu5zBCgoLLF2FiIjcI6wMcL23yAaDgZuNZVJSUnBxcbnJym4PtVQSEbmLdBjagQo1K1i6jKsc3nyYjV9vZPY73Qiq7G3pckREbsrK3w8xfPpaqrd+HXv3+yxdjoiI3OWS4nZycus83m7iQmXXq+OXraeymb47nRYtWuDu7l7i/SYmJhIZGXk7S71pCpVERO4iXpW98K3pa+kyrnL++HkAgip7ExJY0cLViIjcnINxiQDYu9+HY7lqFq5GRETudplJJwGo7GpDoEeZq5bHp+QB4O7ujpeXV4n3m5ube3sKvA00ULeIiIiIiIiIiJhNoZKIiIiIiIiIiJhNoZKIiIiIiIiIiJhNoZKIiIiIiIiIiJhNA3WLiPyLbZq6iYhpEddcNixqGFY2+u5BSp9JP0Qxc9VO4s4kk5mTi7erIw/WrsS7z7WgbrWigez3HD3DyJnr2br/BBdSMgj08+I/TzXl+fYNLVy9iMi948TW+ZzavvCayx7ov5iCvBxObJ3LpdP7yb6USH5uNrZOXngGNKVCSBesy9rf4YpFSo9dZ3NYsD8DgCVLlgDw8MMPU6tWLZP1EhISiI6OJjExkaysLACCg4PvbLHXoVBJRERwcHPAveLfbmNqsEwtIjcSsesYicnpVK3gQVZOLjEnzrM4Yi8b/4zl+MK3iDubRJP+X5GRlYuHiz0B93my5+gZ+o5fTGpaFv95qqmlT0FE5J5iY+eCnavPVfNzs1I5s2sZBusy2LtXJCftIlkppzm14wfSE2OpGTbcAtWKlA6Hk3I59v93f7ue8+fPc/LkSVxcXIyhUmmiUElERAhoGkCn4Z0sXYZIicwb1h07279uyzv827WMmbORi6mZHIxP5PuNu8nIysW2jA0x3/0XDxcH3p22hnHfbWLkzPX0e+J+7G2vvq2viIjcHDf/hlRv9Z+r5ltZl6VSk3DK12mLdVl7CvJy2P/jMNLOxpAc9wd5WWnY2DlZoGIRy2tdxR5HGwMfbr103fUCAgIICgoiMzOT+fPn36HqSk6hkoiIcGDjAfat24edkx2+NX1p8VILfGv4WroskWuysy3Dj5H7+Gjer6RmZBFz4jwA3m6OBPp5UVBQaFzXYChqcmf1//+mpGex/eBJHqlX5c4XLiJyj7oYu4Wth6OwsXXE0bsafg8+g6N3Vco6ulMhpJNxPSubsjiVr07a2RgwWGGwsrZc0SIW5mprRRnrG3cNsLOzuwPV3DyFSiIi/3IGawNOHk5Y2Vhx/vh5Dkcd5uj2o/SZ1kfBkpRaZy+msfXACeN0FV93fhr7HM4OtnR+pDaTFm0mOzePGj0nUMHLhb3HzhrXPXU+1RIli4jcmwxWlHVwBytrspJOkhy3g5STu6jTdTyO3lVNVs3NSOZC7BYAPAMe1phKIvcAjcAqUkrNnDkTNzc3S5dxW2zatAmDwUBycrJZ240YMYL69ev/IzVJkeC2wfxv5f94bfFrvLrwVXpO6glAfk4+2xdtt3B1IsV7ueMD5G0cw7GFb9KtRTDHEpLoMXI+lzKyaVKnMj+OeZYHgvzIzs3jQmoGvdo0MG5bxlpvf0REbgevwEdo1GcW9Xt9Rf2en1Mz7D0ACvNzObNnhcm6WSkJ7Fv8NrnpF3H2DaJq81csUbKI3GZ6VyXyN+Hh4RgMBuPD09OTdu3asXv3bkuXdk8zGAwsXbrU0mXcEaXpXD0reWLv+te3hNUfrG6cTjmTYqmyRErEYDBQqbwbQ59tDsC+4+eYv34XAI8/VJPNX71CysoRnFg0hDb3Bxi3q1HJ2xLliojcc+zdK2Jj52ycdqvcwDidcynROP9SwkH2/vAWWSmncfdvTM0nRqiVksg9QqGSyDW0a9eOhIQEEhISWL9+PTY2NnTo0MHSZYncdr/N/s0kPIrdGktmSiYAbr5uFqpKpHgXUjKYs+ZPcnL/ulvKyt8PGX/OyMoBICL6qHHeiXPJvD9zPQC1/ctRp0r5O1StiMi97dTOJWRfER4lx0eTl1U06LCtczkALhzZzP6lw8nLSsWn7uMEPj4U6zK2FqlXRG4/hUoi12Bra4uPjw8+Pj7Ur1+fIUOGcOLECRIT//qjeeLECbp164abmxseHh507NiR48ePG5dv376d1q1b4+XlhaurK6Ghofzxxx8mx0lOTuall16ifPny2NnZUadOHZYtW2ayzurVqwkKCsLJyckYdl1p2rRpBAUFYWdnR82aNfnyyy+Ny3JychgwYAC+vr7Y2dlRuXJlxo0bV+x5l6Rmg8HAtGnT6Ny5Mw4ODgQEBPDzzz+brLNixQoCAwOxt7enRYsWJtflWvz9/QHo3LkzBoPBOH3ZnDlz8Pf3x9XVlaeffppLl/66Q0JBQQHjxo2jSpUq2NvbU69ePRYtWnTd42VnZ/PWW2/h5+eHra0t1atX59tvvzUuj4iI4P7778fW1hZfX1+GDBlCXt5fH2BXrVrFww8/jJubG56ennTo0IHY2Fjj8utd9xud6522Y8kOJnaayMSOE/mi+xd895/vAChjX4YHn37QorWJXMuljGzCx/6AR4dR1Ht+Ev7dxvP21NUAReMpNasNQNiQ2fh0GkO95ydRo+cnHDpxHge7Mnz9vy7GwbtFROTWnN27kj9n9eOPWS+ya+4ADv48EgCrMnb41H+CnLSLHF71EYX5ORisbEg7e5h9i4aw94c32fvDm6Sfi73BEUTuXb/GZ/HlH6Z3ftuxYwcLFixgw4YNxnnHjh1jwYIF/PLLL8Z5MTExAPTt2/fOFHsdCpVEbiAtLY3vvvuO6tWr4+npCUBubi5t27bF2dmZyMhIoqKijKFPTk7Rt+SXLl2id+/e/Pbbb/z+++8EBATw2GOPGQORgoIC2rdvT1RUFN999x379+/ngw8+wNr6r7tgZGRk8PHHHzNnzhx+/fVX4uPjGTx4sHH53LlzGT58OGPGjOHAgQOMHTuWYcOGMWvWLAAmT57Mzz//zPfff09MTAxz5869bohxo5ovGzlyJN26dWP37t089thj9OzZk4sXLwJFYVuXLl0ICwsjOjqavn37MmTIkOte4+3bi8bumTFjBgkJCcZpgNjYWJYuXcqyZctYtmwZERERfPDBB8bl48aNY/bs2UyZMoV9+/bx+uuv8+yzzxIREVHs8Z577jnmz5/P5MmTOXDgAF9//TVOTkW3sz116hSPPfYYjRs3ZteuXXz11Vd8++23jB492rh9eno6b7zxBjt27GD9+vVYWVnRuXNnCgoKbnjdr3euV8rOziY1NdXk8U9o1rsZVRtXJT8vn6TTSbj5uBHcLph+s/rhXVVdhKT0cXOyo3vLuvh6OhN7+iIJFy7hV86Vnq3rs+WrV6js4w5AhyY1sbG2IubEeRzty9D5kdpEffEyD9WuZOEzEBG5d1Rs2BVXv7oU5ueRlXoWW2dvvAJDCe42AQcPPwoKcoGiO3IWFuSRdvaQySMvJ8OyJyBiQRm5hSRlFZrMy8rKIjU1lfT0dOO8nJwcUlNTSUtLM5kHXNXgwBJ09zeRa1i2bJkxZEhPT8fX15dly5ZhZVWUwy5cuJCCggKmTZtm/MZ7xowZuLm5sWnTJtq0aUPLli1N9vnNN9/g5uZGREQEHTp0YN26dWzbto0DBw4QGBgIQNWqf7tDRm4uU6ZMoVq1agAMGDCA999/37j8vffeY8KECXTp0gWAKlWqsH//fr7++mt69+5NfHw8AQEBPPzwwxgMBipXrnzd875RzZeFh4fTo0cPAMaOHcvkyZPZtm0b7dq146uvvqJatWpMmDABgBo1arBnzx7Gjx9f7HG9vYvCCzc3N3x8fEyWFRQUMHPmTJydi/rn9+rVi/Xr1zNmzBiys7MZO3Ys69at46GHHjJew99++42vv/6a0NDQq4516NAhvv/+e9auXUurVq2M21z25Zdf4ufnx+eff47BYKBmzZqcPn2at956i+HDh2NlZcWTTz5pss/p06fj7e3N/v37qVOnznWv+/XO9Urjxo1j5MiRxS6/XRp2bkjDzg3/8eOI3C5uzvbMG/70DdcryToiInJrytdpS/k6bYtdbudSngcHLL1zBYncRdpVs8fGCsZsTqVLly54eXldc70aNWpQo0YNk3kJCQn88ssvLF++/E6Uel1qqSRyDS1atCA6Opro6Gi2bdtG27Ztad++PXFxcQDs2rWLI0eO4OzsjJOTE05OTnh4eJCVlWXsBnX27FlefPFFAgICcHV1xcXFhbS0NOLj4wGIjo7mvvvuMwZK1+Lg4GAMlAB8fX05d+4cUBR2xcbG0qdPH2MNTk5OjB492lhDeHg40dHR1KhRg4EDB7JmzZrrnveNar6sbt26xp8dHR1xcXEx1nXgwAEeeOABk/UvBz43w9/f3xgo/f0aHDlyhIyMDFq3bm1yDWbPnm3SHe1K0dHRWFtbXzNwulz/Qw89ZNI9pmnTpqSlpXHy5EkADh8+TI8ePahatSouLi7GVkiXr5O51/1ahg4dSkpKivFx4sSJG28kIiIiIiJyB6mlksg1ODo6Ur16deP0tGnTcHV1ZerUqYwePZq0tDQaNmzI3Llzr9r2ckuU3r17c+HCBSZNmkTlypWxtbXloYceMjZVtLe/8R0vypQpYzJtMBgoLCxqInm5+ePUqVOvCnEud6ELCQnh2LFjrFy5knXr1tGtWzdatWpV7JhDN6r5enVd7vp1u13vWJevwfLly6lYsaLJera21x4AsiTX/UbCwsKoXLkyU6dOpUKFChQUFFCnTh3jdTL3ul+Lra1tsecgIiIiIiJSGihUEikBg8GAlZUVmZlFd8UKCQlh4cKFlCtXDhcXl2tuExUVxZdffsljjz0GFI01dP78eePyunXrcvLkSQ4dOnTd1krFKV++PBUqVODo0aP07Nmz2PVcXFzo3r073bt3p2vXrrRr146LFy/i4eFhds0lERQUdNXA3b///vsNtytTpgz5+flmHatWrVrY2toSHx9fbMujvwsODqagoICIiAhj97crBQUFsXjxYgoLC42tlaKionB2dua+++7jwoULxMTEMHXqVJo1awbAb7/9dtV+rnfdb+ZcRURERERESht1fxO5huzsbM6cOcOZM2c4cOAAr732GmlpaYSFhQHQs2dPvLy86NixI5GRkRw7doxNmzYxcOBAYxepgIAA5syZw4EDB9i6dSs9e/Y0aSUTGhrKI488wpNPPsnatWuNLVtWrVpV4jpHjhzJuHHjmDx5MocOHWLPnj3MmDGDTz75BIBPPvmE+fPnc/DgQQ4dOsQPP/yAj48Pbm5u19zfjWouiZdffpnDhw/zv//9j5iYGObNm8fMmTNvuJ2/vz/r16/nzJkzJCUllehYzs7ODB48mNdff51Zs2YRGxvLH3/8wWeffWYcrPxax+nduzcvvPACS5cuNT5333//PQD9+/fnxIkTvPbaaxw8eJCffvqJ9957jzfeeAMrKyvc3d3x9PTkm2++4ciRI2zYsIE33njD5Bg3uu43c64iIiIiIiKljUIlkWtYtWoVvr6++Pr68sADD7B9+3Z++OEHmjdvDhSNdfTrr79SqVIlunTpQlBQEH369CErK8vYcunbb78lKSmJkJAQevXqxcCBAylXrpzJcRYvXkzjxo3p0aMHtWrV4s033zSrBUvfvn2ZNm0aM2bMIDg4mNDQUGbOnEmVKlWAotDlww8/pFGjRjRu3Jjjx4+zYsUK44Djf1eSmm+kUqVKLF68mKVLl1KvXj2mTJnC2LFjb7jdhAkTWLt2LX5+fjRo0KDExxs1ahTDhg1j3LhxBAUF0a5dO5YvX268Btfy1Vdf0bVrV/r370/NmjV58cUXjXdYqFixIitWrGDbtm3Uq1ePl19+mT59+vDuu+8CYGVlxYIFC9i5cyd16tTh9ddf56OPPjLZ/42u+82eq4iIiIiISGliKLw8QIuIiJRaqampuLq6Ej4lnMoNrn8XP0vYs2oPS95bwvZvXiUksOKNNxARKYXmrY2m15jvCe42Acdy1W68gYiIyHWcj4ngyNpP+bq9B4EeZa5avu5Y5g3v/nYtl+/+lpKSUuxwLHeKWiqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZFCqJiIiIiIiIiIjZbCxdgIiIlNz5uPOU/b/27j6myvr/4/gL5E5FILwBLfAmKDWVCG8ym7ZkeZeVOknzXldJmFKtm1WaZaapuaVrWq3MTcx0qRXLzDs0NkRFNBVDmqZ9/QmaciOJmpz37w/Xte/52o1H8Ryo52Njej6fz7l4f7bXuM55c7iu+kG+LuMKpf9XKkk6ePSUjysBgGt35MTln2VVpf/xcSUAgH+C8xUlkqSj5Zf+cP5EZbUkqbS01KPjlpWVXVddNcnPzMzXRQAA/lpFRYXCw8N9XcZf8vf3k8vFKQVAHefnL5nL11UAAP4h/P2kv3qJ7Ofnp2tty5SXlyssLOwaGHHOZAAADrtJREFUK6sZfFIJAOqQrVu3KjQ01Ndl/KELFy4oODjY12UAwHXhZxkAoCb93XnlWs47lZWV6tWr1/WWViNoKgFAHXLnnXf6/LcRAAAAAHynoqLC1yU4uFA3AAAAAAAAPEZTCQAAAAAAAB6jqQQAAAAAAACP0VQCAAAAAACAx2gqAQAAAAAAwGM0lQAAAAAAAOAxmkoAAAAAAADwGE0lAAAAAAAAeIymEgAAAAAAADxGUwkAAAAAAAAeo6kEAAAAAAAAj9FUAgAAAAAAgMdoKgEAAAAAAMBjAb4uAADw98xMklRRUeHjSgAAAAD40u/vCX5/j+BLNJUAoA44ffq0JCkmJsbHlQAAAACoDU6fPq3w8HCf1kBTCQDqgMjISEnSsWPHfH7iwD9bRUWFYmJi9PPPPyssLMzX5eAfjKzBW8gavIWswVvKy8sVGxvrvEfwJZpKAFAH+PtfvgReeHg4L1LgFWFhYWQNXkHW4C1kDd5C1uAtv79H8GkNvi4AAAAAAAAAdQ9NJQAAAAAAAHiMphIA1AHBwcF67bXXFBwc7OtS8A9H1uAtZA3eQtbgLWQN3lKbsuZnteEedAAAAAAAAKhT+KQSAAAAAAAAPEZTCQAAAAAAAB6jqQQAAAAAAACP0VQCAAAAAACAx2gqAQAAAAAAwGM0lQCglnjvvffUqlUrhYSEqFu3btqxY4cz9+yzzyoyMlIxMTHKyMhwe96qVas0cOBAb5eLWm7WrFnq0qWLGjVqpGbNmumRRx5RYWGh25rz588rLS1NjRs3VmhoqIYMGaKSkhJn/syZMxo4cKBCQ0OVmJio/Px8t+enpaXpnXfe8cp+UHfMnj1bfn5+Sk9Pd8bIGmrK8ePHNXLkSDVu3Fj169dXx44dtWvXLmfezDRt2jQ1b95c9evXV3JysoqKipz5CxcuaNSoUQoLC9Ntt92mjRs3uh1/7ty5evrpp722H9RO1dXVmjp1qlq3bq369evr1ltv1YwZM/TfN04na7hW27Zt08CBA9WiRQv5+flp7dq1bvN/ly3p8nlzxIgRCgsLU0REhCZMmKDKykpn/qefflLPnj3VsGFD9ezZUz/99JPb8x988EF9/vnnNbMhAwD43IoVKywoKMg+/vhjO3DggD3++OMWERFhJSUl9uWXX1pUVJTt3LnTli9fbiEhIXbq1CkzMysrK7P4+Hg7evSoj3eA2qZPnz62ZMkS279/v+3Zs8f69+9vsbGxVllZ6ayZOHGixcTE2KZNm2zXrl1299132z333OPMP/vss9arVy8rLCy09PR0S0pKcuZycnIsKSnJLl265NV9oXbbsWOHtWrVyjp16mRTpkxxxskaasKZM2esZcuWNnbsWMvNzbXDhw/b+vXr7ccff3TWzJ4928LDw23t2rW2d+9ee+ihh6x169ZWVVVlZmYLFiywdu3a2f79+23u3LnWtGlTc7lcZmZ2+PBhi4+Pt/Lycp/sD7XHzJkzrXHjxpaZmWlHjhyxVatWWWhoqL377rvOGrKGa/X111/bK6+8YqtXrzZJtmbNGrf5v8uWmVnfvn0tISHBtm/fbt99953FxcXZ8OHDnfnBgwfbsGHD7NChQ5aSkmJDhgxx5lasWGEDBw6ssf3QVAKAWqBr166WlpbmPK6urrYWLVrYrFmz7O2337ZHH33UmWvWrJnt2LHDzMyeeOIJmz9/vtfrRd1z8uRJk2Rbt241s8sNycDAQFu1apWz5uDBgybJcnJyzMysX79+tmjRIjMzKygosAYNGpiZ2cWLFy0hIcF27tzp5V2gNjt79qzFx8fbhg0brFevXk5Tiayhprz44ot27733/um8y+Wy6Ohomzt3rjNWVlZmwcHB9umnn5qZWWpqqr344otmZnbu3DmTZCdPnjSzy8341atX38AdoK4YMGCAjR8/3m1s8ODBNmLECDMja6g5/9tUuppsFRQUmCS3c+O6devMz8/Pjh8/bmZm7dq1s3Xr1pnZ5SZW+/btzcystLTU4uLi7NixYzW2B/78DQB87OLFi8rLy1NycrIz5u/vr+TkZOXk5CghIUG7du1SaWmp8vLyVFVVpbi4OGVnZ2v37t2aPHmyD6tHXVFeXi5JioyMlCTl5eXpt99+c8td27ZtFRsbq5ycHElSQkKCNm/erEuXLmn9+vXq1KmTJGnOnDm677771LlzZy/vArVZWlqaBgwY4JYpiayh5nz55Zfq3Lmzhg4dqmbNmikxMVEffvihM3/kyBEVFxe7ZS08PFzdunVzy1p2draqqqq0fv16NW/eXE2aNFFGRoZCQkI0aNAgr+8Ltc8999yjTZs26dChQ5KkvXv3Kjs7W/369ZNE1nDjXE22cnJyFBER4XZuTE5Olr+/v3JzcyVdzt/GjRvlcrn07bffOufV559/XmlpaYqJiamxmmkqAYCP/fLLL6qurlZUVJTbeFRUlIqLi9WnTx+NHDlSXbp00dixY7V06VI1bNhQqampWrx4sRYtWqTbb79dPXr00IEDB3y0C9RmLpdL6enp6tGjhzp06CBJKi4uVlBQkCIiItzW/p47SXrppZcUEBCgW2+9VWvWrNFHH32koqIiLV26VFOnTtXEiRPVpk0bpaSkOE0r/DutWLFCu3fv1qxZs66YI2uoKYcPH9aiRYsUHx+v9evXKzU1VZMnT9bSpUslycnTn51PJWn8+PFKSEhQ+/btNXPmTK1cuVKlpaWaNm2aFi5cqFdffVVxcXHq06ePjh8/7t0NotZ46aWXNGzYMLVt21aBgYFKTExUenq6RowYIYms4ca5mmwVFxerWbNmbvMBAQGKjIx01sybN08//PCDWrVqpaKiIs2bN0/btm3Tnj17NHr0aKWkpKhNmzaaOHGiLl68eF01B1zXswEAXjF9+nRNnz7defz6668rOTlZgYGBevPNN7Vv3z5lZmZq9OjRysvL812hqJXS0tK0f/9+ZWdne/S88PBwLV++3G3s/vvv19y5c5WRkaHDhw+rsLBQjz/+uN544w0upPwv9fPPP2vKlCnasGGDQkJCrukYZA1Xw+VyqXPnznrrrbckSYmJidq/f78WL16sMWPGXNUxAgMD9d5777mNjRs3TpMnT1Z+fr7Wrl2rvXv3as6cOZo8eXLNXcgWdcrKlSuVkZGh5cuX64477tCePXuUnp6uFi1akDXUCTfffLMyMzOdxxcuXFCfPn20dOlSvfnmm2rUqJEKCwvVt29fvf/++9d10Xg+qQQAPtakSRPVq1fP7U5IklRSUqLo6Ogr1v/www9atmyZZsyYoaysLPXs2VNNmzZVSkqKdu/erbNnz3qrdNQBkyZNUmZmprZs2aJbbrnFGY+OjtbFixdVVlbmtv7PcidJS5YsUUREhB5++GFlZWXpkUceUWBgoIYOHaqsrKwbuAvUZnl5eTp58qTuuusuBQQEKCAgQFu3btWCBQsUEBCgqKgosoYa0bx5c7Vv395trF27djp27JgkOXm62vOpJG3ZskUHDhzQpEmTlJWVpf79+6thw4ZKSUkha/9izz//vPNppY4dO2rUqFF65plnnE9jkjXcKFeTrejoaJ08edJt/tKlSzpz5syf5u+tt97SAw88oKSkJGVlZWnIkCEKDAzU4MGDrzt/NJUAwMeCgoKUlJSkTZs2OWMul0ubNm1S9+7d3daamZ588knNnz9foaGhqq6u1m+//SZJzr/V1dXeKx61lplp0qRJWrNmjTZv3qzWrVu7zSclJSkwMNAtd4WFhTp27NgVuZOkU6dO6Y033tDChQsl6Yrskbt/r969e2vfvn3as2eP89W5c2eNGDHC+T9ZQ03o0aOHCgsL3cYOHTqkli1bSpJat26t6Ohot6xVVFQoNzf3D7N2/vx5paWl6f3331e9evXIGhznzp2Tv7/7W+V69erJ5XJJImu4ca4mW927d1dZWZnbXyds3rxZLpdL3bp1u+KYBw8e1PLlyzVjxgxJN+C8WmOX/AYAXLMVK1ZYcHCwffLJJ1ZQUGBPPPGERUREWHFxsdu6Dz74wO2WoLm5uRYWFmY5OTk2bdo0584OQGpqqoWHh1tWVpadOHHC+Tp37pyzZuLEiRYbG2ubN2+2Xbt2Wffu3a179+5/eLzHHnvMFi5c6Dx+++23LSkpyQoKCqxfv3721FNP3fA9oe7477u/mZE11IwdO3ZYQECAzZw504qKiiwjI8MaNGhgy5Ytc9bMnj3bIiIi7IsvvrDvv//eHn744Stuxf27l19+2Z577jnn8WeffWaxsbG2d+9emzBhgvXv398r+0LtM2bMGLv55pstMzPTjhw5YqtXr7YmTZrYCy+84Kwha7hWZ8+etfz8fMvPzzdJNn/+fMvPz7ejR4+a2dVlq2/fvpaYmGi5ubmWnZ1t8fHxNnz48Cu+l8vlsnvvvde++uorZyw1NdUGDBhgBQUFlpiYaHPmzLmu/dBUAoBaYuHChRYbG2tBQUHWtWtX2759u9t8cXGxtWzZ0rlV6O9ef/11i4yMtLZt21pubq43S0YtJukPv5YsWeKsqaqqsqeeespuuukma9CggQ0aNMhOnDhxxbG++eYb69q1q1VXVztjv/76qw0dOtQaNWpkvXv3tpKSEm9sC3XE/zaVyBpqyldffWUdOnSw4OBga9u2rX3wwQdu8y6Xy6ZOnWpRUVEWHBxsvXv3tsLCwiuOs2/fPouLi7PKykpnrLq62lJTUy0sLMy6dOliRUVFN3w/qJ0qKipsypQpFhsbayEhIdamTRt75ZVX7MKFC84asoZrtWXLlj98jTZmzBgzu7psnT592oYPH26hoaEWFhZm48aNs7Nnz17xvRYvXuz2C2kzs5KSEuvdu7c1atTIhg4dar/++ut17cfPzOz6PusEAAAAAACAfxuuqQQAAAAAAACP0VQCAAAAAACAx2gqAQAAAAAAwGM0lQAAAAAAAOAxmkoAAAAAAADwGE0lAAAAAAAAeIymEgAAAAAAADxGUwkAAAAAAAAeo6kEAAAAAAAAj9FUAgAAAAAAgMdoKgEAAAAAAMBj/w/UHK9rcrUwwgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Selecting Wet Tropics data and reformatting before plotting\n",
-    "water_health_wt = water_health[water_health.region == 'WET TROPICS'].pivot(index = 'area', \n",
-    "                                                                           columns = 'rating', \n",
-    "                                                                           values = 'rate_per')\n",
-    "#Ordering activities based on the labels_water dataframe\n",
-    "# water_health_wt = water_health_wt[labels_water.label]\n",
-    "water_health_wt = water_health_wt[labels_water.label]\n",
-    "\n",
-    "#Start figure\n",
-    "fig = plt.figure(figsize = (10, 7))\n",
-    "ax = fig.add_subplot(111)\n",
-    "#Choosing a colormap for the plot - Separate colours for each activity\n",
-    "cmap = cmaps['Accent'](np.linspace(0, 1, len(water_health_wt.columns)))\n",
-    "#Plot as horizontal bar chart\n",
-    "water_health_wt.plot.barh(stacked = True, ax = ax, color = cmap, edgecolor = 'black', \n",
-    "                          legend = False)\n",
-    "#Set limits for x axis\n",
-    "ax.set_xlim(0, 100)\n",
-    "#Move legend to the outside of the plot and to the top right of the plot\n",
-    "ax.legend(ncols = len(water_health_wt.columns), bbox_to_anchor = (0, 1), loc = 'lower left')\n",
-    "#Remove y axis label\n",
-    "ax.set_ylabel('')\n",
-    "#Set x axis labels\n",
-    "ax.set_xticks([i for i in range(0, 101, 20)])\n",
-    "ax.set_xticklabels([f'{i}%' for i in range(0, 101, 20)])\n",
-    "#Adding labels in the end of each bar section\n",
-    "for i in range(len(water_health_wt.index)):\n",
-    "    da = water_health_wt.iloc[i].cumsum(axis = 0)\n",
-    "    for j in range(len(da)):\n",
-    "        ax.text(da[j]-(water_health_wt.iloc[i, j]/2), i, int(water_health_wt.iloc[i, j]), fontweight = 'bold') \n",
-    "#Add title to figure\n",
-    "fig.suptitle('Rating of perceived waterway health by habitat in the Wet Tropics region', y = .97, fontsize = 14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting data about governance\n",
-    "Finally, we will replicate one of the plots about governance of waterways that is available in the SELTMP website, which we are including below. By now, you know that the first step is to look for the variables that contain the information we want to plot.\n",
-    "\n",
-    "Since the plot does not include a keyword that is common for all variables, we will search for `science` since the first variable in the plot is about “trust in science about waterways”.\n",
-    "\n",
-    "![Original plot from SELTMP to be replicated here](../images/sample_plot.jpeg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>description</th>\n",
-       "      <th>new_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>234</th>\n",
-       "      <td>w18_12</td>\n",
-       "      <td>W18_12 - I trust the science about waterway he...</td>\n",
-       "      <td>w18_12 - WET TROPICS - I TRUST THE SCIENCE abo...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>w21_5</td>\n",
-       "      <td>W21_5 - Science and Education</td>\n",
-       "      <td>w21_5 - WET TROPICS - Which Sector of waterway...</td>\n",
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-       "    <tr>\n",
-       "      <th>489</th>\n",
-       "      <td>t18_12</td>\n",
-       "      <td>T18_12 - I trust the science about waterway he...</td>\n",
-       "      <td>t18_12 - TOWNSVILLE - I TRUST THE SCIENCE abou...</td>\n",
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-       "    <tr>\n",
-       "      <th>498</th>\n",
-       "      <td>t21_5</td>\n",
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-       "      <td>t21_5 - TOWNSVILLE - Which Sector of waterway ...</td>\n",
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-       "    <tr>\n",
-       "      <th>737</th>\n",
-       "      <td>m18_12</td>\n",
-       "      <td>M18_12 - I trust the science about waterway he...</td>\n",
-       "      <td>m18_12 - MWI - I TRUST THE SCIENCE about water...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>746</th>\n",
-       "      <td>m21_5</td>\n",
-       "      <td>M21_5 - Science and Education</td>\n",
-       "      <td>m21_5 - MWI - Which Sector of waterway depende...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>978</th>\n",
-       "      <td>f18_12</td>\n",
-       "      <td>F18_12 - I trust the science about waterway he...</td>\n",
-       "      <td>f18_12 - FITZROY - I TRUST THE SCIENCE about w...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>989</th>\n",
-       "      <td>f21_5</td>\n",
-       "      <td>F21_5 - Science and Education</td>\n",
-       "      <td>f21_5 - FITZROY - Which Sector of waterway dep...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1209</th>\n",
-       "      <td>F18_12</td>\n",
-       "      <td>F18. Please rate your level of agreement with ...</td>\n",
-       "      <td>F18_12 - GLADSTONE - I TRUST THE SCIENCE about...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1222</th>\n",
-       "      <td>F21_9</td>\n",
-       "      <td>F21. Which broad sector of waterway-dependent ...</td>\n",
-       "      <td>F21_9 - GLADSTONE - Which Sector of waterway d...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       field                                        description  \\\n",
-       "234   w18_12  W18_12 - I trust the science about waterway he...   \n",
-       "243    w21_5                      W21_5 - Science and Education   \n",
-       "489   t18_12  T18_12 - I trust the science about waterway he...   \n",
-       "498    t21_5                      T21_5 - Science and Education   \n",
-       "737   m18_12  M18_12 - I trust the science about waterway he...   \n",
-       "746    m21_5                      M21_5 - Science and Education   \n",
-       "978   f18_12  F18_12 - I trust the science about waterway he...   \n",
-       "989    f21_5                      F21_5 - Science and Education   \n",
-       "1209  F18_12  F18. Please rate your level of agreement with ...   \n",
-       "1222   F21_9  F21. Which broad sector of waterway-dependent ...   \n",
-       "\n",
-       "                                               new_name  \n",
-       "234   w18_12 - WET TROPICS - I TRUST THE SCIENCE abo...  \n",
-       "243   w21_5 - WET TROPICS - Which Sector of waterway...  \n",
-       "489   t18_12 - TOWNSVILLE - I TRUST THE SCIENCE abou...  \n",
-       "498   t21_5 - TOWNSVILLE - Which Sector of waterway ...  \n",
-       "737   m18_12 - MWI - I TRUST THE SCIENCE about water...  \n",
-       "746   m21_5 - MWI - Which Sector of waterway depende...  \n",
-       "978   f18_12 - FITZROY - I TRUST THE SCIENCE about w...  \n",
-       "989   f21_5 - FITZROY - Which Sector of waterway dep...  \n",
-       "1209  F18_12 - GLADSTONE - I TRUST THE SCIENCE about...  \n",
-       "1222  F21_9 - GLADSTONE - Which Sector of waterway d...  "
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Searching for matches in the description column using keywords: 'science'\n",
-    "table[table['description'].str.contains('science', case = False)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that trust in science falls under question `18`. We will do another query to our table and check that all variables needed for the plot are included in question `18`. We will use additional keywords identifying the questions that are relevant for our plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>description</th>\n",
-       "      <th>new_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>w18_7</td>\n",
-       "      <td>W18_7 - I think that decisions about managing ...</td>\n",
-       "      <td>w18_7 - WET TROPICS - I think waterway managem...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>w18_8</td>\n",
-       "      <td>W18_8 - I do not have fair access to all the w...</td>\n",
-       "      <td>w18_8 - WET TROPICS - I DON'T HAVE FAIR ACCESS...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>w18_9</td>\n",
-       "      <td>W18_9 - I feel I personally have some influenc...</td>\n",
-       "      <td>w18_9 - WET TROPICS - I have SOME PERSONAL INF...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>w18_10</td>\n",
-       "      <td>W18_10 - I feel able to have input into the ma...</td>\n",
-       "      <td>w18_10 - WET TROPICS - I'm ABLE TO HAVE INPUT ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>w18_11</td>\n",
-       "      <td>W18_11 - I trust the information I receive fro...</td>\n",
-       "      <td>w18_11 - WET TROPICS - I TRUST INFO FROM INSTI...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    field                                        description  \\\n",
-       "0   w18_7  W18_7 - I think that decisions about managing ...   \n",
-       "1   w18_8  W18_8 - I do not have fair access to all the w...   \n",
-       "2   w18_9  W18_9 - I feel I personally have some influenc...   \n",
-       "3  w18_10  W18_10 - I feel able to have input into the ma...   \n",
-       "4  w18_11  W18_11 - I trust the information I receive fro...   \n",
-       "\n",
-       "                                            new_name  \n",
-       "0  w18_7 - WET TROPICS - I think waterway managem...  \n",
-       "1  w18_8 - WET TROPICS - I DON'T HAVE FAIR ACCESS...  \n",
-       "2  w18_9 - WET TROPICS - I have SOME PERSONAL INF...  \n",
-       "3  w18_10 - WET TROPICS - I'm ABLE TO HAVE INPUT ...  \n",
-       "4  w18_11 - WET TROPICS - I TRUST INFO FROM INSTI...  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Keywords representing questions of interest\n",
-    "keys = ['science', 'institutions', 'management', 'access', 'fair', 'influence']\n",
-    "#Selecting questions of interest\n",
-    "govern = table[(table.description.str.contains('|'.join(keys), case = False)) & (table.field.str.contains('18_'))].\\\n",
-    "    reset_index(drop = True)\n",
-    "#Cheking results\n",
-    "govern.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have identified the fields containing the information we need to recreate the plot. We now need to tidy up our table as we have done previously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>region</th>\n",
-       "      <th>question</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>w18_7</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "      <td>DECISIONS ARE MADE FAIRLY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>w18_8</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "      <td>DONT HAVE FAIR ACCESS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>w18_9</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "      <td>SOME PERSONAL INFLUENCE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>w18_10</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "      <td>ABLE TO HAVE INPUT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>w18_11</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "      <td>TRUST INFO FROM INSTITUTIONS</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    field       region                       question\n",
-       "0   w18_7  WET TROPICS      DECISIONS ARE MADE FAIRLY\n",
-       "1   w18_8  WET TROPICS          DONT HAVE FAIR ACCESS\n",
-       "2   w18_9  WET TROPICS        SOME PERSONAL INFLUENCE\n",
-       "3  w18_10  WET TROPICS             ABLE TO HAVE INPUT\n",
-       "4  w18_11  WET TROPICS   TRUST INFO FROM INSTITUTIONS"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Extract region and question being asked from the new_name column\n",
-    "govern = pd.concat([govern, govern.new_name.str.split(' - ', expand = True).iloc[:, 1:3]], axis = 1)\n",
-    "#Renaming columns - so region can be easily identified\n",
-    "govern.columns = govern.columns.to_list()[:-2]+['region', 'question']\n",
-    "#Removing apostrophes from question column before getting question label\n",
-    "govern['question'] = govern['question'].str.replace(\"'\", '')\n",
-    "#Extracting label from question column (everything in capital letters)\n",
-    "govern['question'] = govern.apply(lambda x: re.findall(' [A-Z ]+[A-Z]+', x.question)[0], axis = 1)\n",
-    "#Keep only relevant columns\n",
-    "govern.drop(columns = ['description', 'new_name'], inplace = True)\n",
-    "#Resetting row index\n",
-    "govern.reset_index(drop = True, inplace = True)\n",
-    "#Checking result\n",
-    "govern.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that unlike the previous example, we will not use the `-label` columns because we are interested in the quantifying the level of agreement of the respondents to the statements in the survey.\n",
-    "\n",
-    "We will now use the table above to load the questions about governance to our session. As in the previous examples, we will need to do some data wrangling to format our data in a way that is easy to plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>question</th>\n",
-       "      <th>agree_mean</th>\n",
-       "      <th>agree_se</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Wet Tropics</td>\n",
-       "      <td>DECISIONS ARE MADE FAIRLY</td>\n",
-       "      <td>5.01</td>\n",
-       "      <td>0.099530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Wet Tropics</td>\n",
-       "      <td>DONT HAVE FAIR ACCESS</td>\n",
-       "      <td>4.52</td>\n",
-       "      <td>0.112919</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Wet Tropics</td>\n",
-       "      <td>SOME PERSONAL INFLUENCE</td>\n",
-       "      <td>3.96</td>\n",
-       "      <td>0.104439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Wet Tropics</td>\n",
-       "      <td>ABLE TO HAVE INPUT</td>\n",
-       "      <td>4.66</td>\n",
-       "      <td>0.105778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Wet Tropics</td>\n",
-       "      <td>TRUST INFO FROM INSTITUTIONS</td>\n",
-       "      <td>5.64</td>\n",
-       "      <td>0.116490</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        region                       question  agree_mean  agree_se\n",
-       "0  Wet Tropics      DECISIONS ARE MADE FAIRLY        5.01  0.099530\n",
-       "1  Wet Tropics          DONT HAVE FAIR ACCESS        4.52  0.112919\n",
-       "2  Wet Tropics        SOME PERSONAL INFLUENCE        3.96  0.104439\n",
-       "3  Wet Tropics             ABLE TO HAVE INPUT        4.66  0.105778\n",
-       "4  Wet Tropics   TRUST INFO FROM INSTITUTIONS        5.64  0.116490"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Using the initial connection to the S3 bucket. Select the columns identified above.\n",
-    "#Read data as pandas dataframe.\n",
-    "governance = md.read_pandas(columns = govern.field).to_pandas().reset_index(drop = True)\n",
-    "#We will make the data frame longer. This means that instead of having multiple columns\n",
-    "#for each region/question, we will have single column containing information about the region/question \n",
-    "# linked to that observation\n",
-    "governance['id'] = governance.index\n",
-    "governance = pd.melt(governance, id_vars = 'id', value_vars = governance.columns[:-1],\n",
-    "                     var_name = 'field', value_name = 'agreement')\n",
-    "#We remove any rows with no data in the occupation column (i.e., a single respondent \n",
-    "#cannot be in multiple regions)\n",
-    "governance.dropna(subset = 'agreement', inplace = True)\n",
-    "#Removing ID because it is not needed\n",
-    "governance.drop(columns = 'id', inplace = True)\n",
-    "#Join with metadata table\n",
-    "governance = pd.merge(governance, govern, on = 'field', how = 'left')\n",
-    "#Correct name of regions\n",
-    "governance['region'] = governance.apply(lambda x: x.region.title() if x.region != 'MWI' else 'MWI', \n",
-    "                                        axis = 1)\n",
-    "#Summarise data by region and question\n",
-    "governance['respondents'] = governance.groupby(['region', 'question'])['field'].transform('size')\n",
-    "governance['agree_mean'] = governance.groupby(['region', 'question'])['agreement'].transform('mean').round(2)\n",
-    "governance['agree_sd'] = governance.groupby(['region', 'question'])['agreement'].transform('std').round(2)\n",
-    "governance['agree_se'] = governance.agree_sd/np.sqrt(governance.respondents)\n",
-    "#Remove agreement column and drop duplicates\n",
-    "governance.drop(columns = ['agreement', 'field', 'respondents', 'agree_sd'], inplace = True)\n",
-    "governance.drop_duplicates(inplace = True)\n",
-    "governance.reset_index(drop = True, inplace = True)\n",
-    "#Checking results\n",
-    "governance.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>question</th>\n",
-       "      <th>agree_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>SOME PERSONAL INFLUENCE</td>\n",
-       "      <td>4.270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DONT HAVE FAIR ACCESS</td>\n",
-       "      <td>4.410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>ABLE TO HAVE INPUT</td>\n",
-       "      <td>5.004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DECISIONS ARE MADE FAIRLY</td>\n",
-       "      <td>5.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>TRUST INFO FROM INSTITUTIONS</td>\n",
-       "      <td>6.016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>TRUST THE SCIENCE</td>\n",
-       "      <td>6.800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        question  agree_mean\n",
-       "3        SOME PERSONAL INFLUENCE       4.270\n",
-       "2          DONT HAVE FAIR ACCESS       4.410\n",
-       "0             ABLE TO HAVE INPUT       5.004\n",
-       "1      DECISIONS ARE MADE FAIRLY       5.516\n",
-       "4   TRUST INFO FROM INSTITUTIONS       6.016\n",
-       "5              TRUST THE SCIENCE       6.800"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Calculating mean agreement of respondents per question to order data in plot\n",
-    "plot_order = governance.groupby('question').agg({'agree_mean': 'mean'}).reset_index().\\\n",
-    "    sort_values('agree_mean', ascending = True)\n",
-    "#Checking result\n",
-    "plot_order"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting data about governance\n",
-    "Our data is now ready for us to make a plot. We will order our plot by the mean agreement. Higher agreement will be on the top and decrease towards the bottom."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Perceived fairness and trust by respondents in waterway governance')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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4+DAtLS02e/Zsrnz16tUMANu/fz9XVthjt/zY8nnyh7Hcc5azszMzMDBg9+/f5027dOkS09LSYi1btuSVy9/zvn37KuzjBw8eMABs5MiRXNm+ffsYANaoUSMmkUh4iS5LS0tWtmxZ3jI+T2QwlvvFv3z58szIyIgXUwcOHGAAePtMrkKFCkwikRToPZ8+fToDwIYPH84rz3scyHvce/nyJZd4efPmDVeekZHBxcmOHTu48r/++osBYEuWLFFYd8uWLRkA9vz5c65s6tSpDACbNWsW71oiOTmZVatWjeno6LCIiAiuXP4eKzt3Mlb489G7d+949eTkx9zPjwWF/WzI47pRo0YKidJPnz6xuLg47rX8OLZ582ZevejoaObg4MAsLCxYWlqaQltJ4VBigRDyQ5FfjLm6unK/iowfP57VrVuXAWASiYRdvXqVMcZY69atGQDeCVlOfmGY91dF+YVnu3btFOpnZ2czQ0NDZmRkxPs1W5m7d+9yXwaUkX8BzfuLnbLEwsmTJxkANmrUKF75kSNHGAA2efJktdcZHBzMALBWrVop1A0PD2daWlpFklhQRSaTMUNDQ94v/ozln1iwtrZWehEif9969eqlclreX6CWL1+ucEGWV5UqVZi5uTn3Wv6l9tmzZ/luk3wff57YKQp37tzhLgDzkl/IhYWFKczj5OTETE1NudcZGRlMIpEwS0tLhQuknJwc7lewokwsjB07Nt/phU0sfOk9YOzrEgseHh68C2zGci9ALSwsmFQq5cVf+/btmUAgYC9evODK0tPTmampKXN0dOR9mf7SeitXrqx0eoUKFZienp7SC1p5zwh5r4WkpCQGKO+h8zn5Pm7SpInCtIiICKatrc1cXV25su3btzOA/4u23Js3b5hIJOLVZ4xxiaXPv5xmZWUxkUjEqlSpwpW9fv2aAVD4hZ2x3C97xsbGCl9a1T2+F/Q4kV+MF2Zfq/KlxMKFCxcU5pFPS05O/uLy5b+kHzhwgDGW+wUJAFuxYgWrUaMG7zPSsWNHBoC9f//+i8tVdezOL7EQEBCgNPkk1759eyYUCnlfFgEwHR0dXmIkbxvMzMyYl5cXVzZkyBBmYmLCjh8/zju2/Pvvv1yCqyCWLVvGALCgoCCuLDMzk1lZWTFnZ2fe8eHmzZsMAOvRo0eBlu3s7MzEYjGLiYlRmCbvHZX3uDdnzhwGgC1evFih/rVr17gvzXKJiYlMLBYrfI5iY2OZtrY2q1WrFleWk5PDTExMWOnSpRWOeYwxduzYMQbwey3I3+NVq1Yp3b7CnI/yExcXxwCwPn368MoL+9koW7Ys09LS+uJ5IzY2lmlpafH2ZV5//PEHA8D+/vvvArWfqEaDNxJCfkgvX77E7NmzAeQOgmVlZYVu3bph8uTJKF++PADg+vXr0NPTw6ZNm5QuQyqV4unTpwrlNWrUUCh7+vQpkpOT0bhxY5iYmOTbtuvXrwMAoqOjFQaYlC9L/m+5cuVULqdJkyawtrbG3r17sXz5cmhpaQEAduzYAQDo2bOn2ut88OABAKB+/foKdZ2cnODg4FBkjw0LCAjA+vXrcffuXSQkJPDuC42MjCzwcipWrAgdHR2V06tUqaJQZm9vDwBITEzkyuT76vr163jx4oXCPOnp6fjw4QM+fPgAc3NzdO7cGQEBAahZsya6du2Khg0bon79+rC0tOTN17FjR6xcuRJt27ZF586d0aRJE9SrV69QA3NmZmbizz//xN69e/H06VN8/PgRjDFuurL9ZWxszN0P/vm2X7t2jXsdGhqK9PR0NGzYEBKJhFdXKBSiTp06ePbsWYHbWhDKPkvqKOh78LXq1q3LG98AyD1OVK1aFadOncKzZ8+4z+zgwYMREBCATZs2YcGCBQBy79uOj4/HqFGjIBQWfJgqZfvp06dPePToEWxtbZWO0SEfc0P+2TY0NESzZs1w6tQpVKlSBR07dkT9+vVRs2ZNlZ8bZZ9/W1tbuLq64unTp0hJSYGBgQHu3bsHAEof4+ng4ABXV1eEhoZy9eXc3Nygr6/Pqy8SiWBlZcX7TMqPR/Xq1VNYvr6+PipVqqQwLoO6x/eCHifyo86+LqwvtTPvflbG19cXQO7YJB07duQGNPb19UV0dDRWrlyJ9PR0iMViBAcHw8vLS+HzVFTHbvkx9+nTp0rPT9HR0ZDJZHj27BmqVavGlbu4uCgdzFcgEMDb2xsBAQGIiYmBpaUlAgMD4e3tDW9vb4hEIgQGBsLHx4e33XnFxMRg0aJFOHnyJF6/fo20tDTe9Lzbp62tjX79+mHhwoU4e/YsfvnlFwDgYm/AgAFf3AfJyckIDw+Hl5cXLCwsFKbXqVMHp0+f5pXl97mrVasWpFIp7t+/z5UZGRmhVatWOHjwIB49esRdC+3duxdZWVm8a4bQ0FAkJCTA1taWu5bKKzY2FgAKfI0kV9DzEQAwxrBlyxZs3boV//77L5KSkiCTybjpqmKsIJ+N1NRUPHnyBKVLl4abm5vK9gK543Ll5OQgPT1daXw+f/4cQO6+aNmyZb7LIvmjxAIh5IfUtGlTnDp1Kt868fHxyM7OVnrSlEtNTVUos7KyUiiTX3Da2dl9sW3yAaVOnDiBEydOFGrdeWlpaaFr165YsWIFzp49i2bNmiEpKQknTpxAlSpVULZsWbXXmZSUBAAqv5hZWVkVSWJh2bJlmDBhAiwsLPDLL7/A3t4eUqkUALBy5UqVg9+palN+lD22TSTKPY3lvSCW76vVq1fnu7zU1FSYm5vD398f2traWLlyJdavX481a9ZwA4stX74clSpVAgDUrl0bFy5cwMKFC7Fnzx5uEK6qVavi999/V7iwVaZjx474+++/4e7uDn9/f1haWkJbWxuJiYlYtWqV0v2l6nF1IpGId5FWkPe8qBXVMgv6HnytL+0b+T4EchN/Li4u2Lp1K+bOnQstLS1s3LgRQqEQ/fr1K9R6le2nhIQEMMYQERFR4GPYwYMHsWDBAuzZswfTpk0DABgYGKBfv35YsGABdHV1efPmt73yZKqBgQGSk5NVthPIHfgvNDSUqy+XX2zm/UyqE5vqHt8Lepz4ksLu68L62naWK1cOFhYW3BfrwMBAmJmZoUKFCoiOjsbixYtx9epVWFhYIDY2Fv7+/rz5i/LYLT/m7tq1K996n79f+R0/fH19ERAQgKCgIDRo0AChoaEYNmwY9PX1Ua1aNQQGBmL27NkIDAzkEhF521O9enW8efMGdevWRePGjWFsbAwtLS3cv38fR48eVdi+gQMHYtGiRdi4cSN++eUXfPr0CXv27IG7uztv2arIP0PKkgqqtvVLnztLS0tERETwynr27ImDBw9i165dXEJy586d0NbW5r3H8vfk8ePHePz4scp2F/QaSa6g5yMAGDVqFP788084ODigdevWsLGxgVgsBgDMnj1bZYwV5LOhzjXblStXcOXKFZX1vnTNRr6MEguEkGLL0NAQAoEAHz58KNR8n/9iCeRm4QEonMRVrRcA/ve//2HEiBGFWvfnevbsiRUrVmDnzp1o1qwZDhw4gPT0dN4vD+qsU35iVjVq/fv377+q3QC4kZttbW1x//593gUVYwxLliwp1PKUvS/qkO+rR48e5dtjJK/27dujffv2SE5OxtWrV7lfqps2bYrQ0FAuPuS/mKWlpeHGjRv4+++/sWbNGrRo0QKPHj2Cq6urynXcunULf//9N5o2bYoTJ05wPVSA3F/8Vq1apf5G4/u8559T9Z7Jf83Pzs5WmJb3y3teBX0PvsaX9k3eC1qBQICBAwdi6tSpOHHiBMqXL48LFy6gefPmcHBwKNR6le0neZxWrVoVt2/fLtBy9PT0MH/+fMyfPx+vXr1CYGAg1q1bh1WrViEtLQ3r16/n1f/S9srbIP9XVYx8Xr+w1IlNdY/vRaWw+/p7k3+ZPnjwIKKjoxEUFARvb28IBALUq1cP2traCAwM5JI5eROfRX3slsfF33//XahffPM75uftkSH/Mikv8/X1xbJly5CamoqLFy9ySRa5TZs24c2bN5g3bx6XFJJbtGgRjh49qrA+FxcXNGnSBEePHsWHDx9w/PhxJCcnY/r06QXaFvk+kPcE+JyqGJdPc3JyUpgeExOj8Jlr3rw5zM3NsXv3bixcuBAvX77EjRs30KZNG5iZmSksu0OHDiqfIKRKUZyLY2JisHr1alSoUAHXrl3jJeKio6PzTRgWhPyYUphrtvHjx2Pp0qVftV6SP3rcJCGk2KpZsybi4uK4bmxfw8PDA4aGhrh165bKR7HlXS8AhW5/6qhcuTLKli2LI0eOIDU1FTt37uR6MnzNOuWPxrp06ZLCtNevXxfqkZOqfPjwAUlJSahVq5bCrzS3b99W6Hr6vXzN+yPvAr1hwwb06dMHMTExuHHjhkI9qVQKHx8fLFu2DFOnTkVaWhrOnTuX77JfvnwJAGjRogUvqQAof58Ky8PDAxKJBLdv30Z6ejpvmkwmw9WrVwu8LHn7CvMLb17y24mUXfTJu/+q8qX3QCgUqt2uK1eu8G49AYC0tDTcuXMHUqkU7u7uvGn9+vWDtrY2Nm7ciM2bN4MxVqBu0QVhYGCAMmXKICQkpMBd9PNycXFBv379EBwcDH19faWPj1MWV5GRkXj58iVcXV253geVK1cGAKWPiYyIiMDLly9RqlSpL3bPV0V+PFL2WLePHz/yunvLFeXxXZnCxHhB9rUmyLvQ79q1C8+ePUPDhg0B5CZFatSogQsXLij9RV+dY3d++6soz4ly8lsK5NtgYWHBJYobNmyIzMxMrFu3Dh8+fFC4lUB+rG3durXCcvM71g4aNAiZmZnYvn07Nm3aBG1tbfTu3btA7TU0NISzszNevHihNLmg7Pib3+fu5s2bSEtLU+itpa2tjc6dO+Pt27cIDg7Gzp07AQA9evTg1StTpgwMDQ1x+/ZtpY8y/tbCwsLAGEPjxo0VevcUxflO/ijwV69effEYUb16dQgEgiKNT6IcJRYIIcXWqFGjAORe/Od9RrlcdHQ0QkJCCrQskUiEwYMHIykpCaNHj1a4eEpKSuKeu1yjRg3UrFkTe/bs4T03W04mkyE4OLjA29GzZ0+kpqZi1apVuHjxIpo0aaLQFbGw66xXrx5cXFxw/Phx3sU8YwxTp05V+4tZXvLn1t+9e5f3nPuEhASMHDnyq5evrr59+8LAwADTpk1T2gX006dP3D3BAHD+/HmFL+LAf7+uyrsHX7p0ieu6mpf8lyh5PVXkv0h9/uXq8ePHWLhwYb7zFoSOjg46d+6MmJgYLFu2jDdt48aNhRpfwcTEBAKBAO/evVOrLfL7qLdv387rHnvt2jWl3aUL+h4AgKmpqdrtCg0NxebNm3llv//+O2JjY9G1a1eF++etrKzQunVr/PPPP9iwYQOsra3RqlUrtdatzKhRo/Dp0ycMHDhQaTfcV69ecbcsxcbG4ubNmwp1EhISkJGRoTT+zp49i/Pnz/PKpk+fjqysLN4XpjZt2sDIyAhbtmzhfWYYY5gyZQqysrK4Z9mrw9HREQ0aNMDDhw8V3v8FCxYoTawU5fFdmfxiXJ19rQnyX/AXL17Mey3//61btxAYGIjy5cvzfs1W59htamoKAEr3V5s2beDo6Ijly5fj4sWLCtOzsrKUJpW+xMfHB8+ePcORI0fg4+PD/ZJet25d6OjoKN1uQPWxdvfu3fjnn39Urq9NmzawtrbGsmXLcPnyZbRu3bpQ47x0794dGRkZCr/GBwUFKYyvAADdunWDSCTC8uXLeeMNZGVlYfLkyQCg9HMn79G4c+dO7Nq1C8bGxgrHJZFIhKFDh+L169eYMGGC0uTCv//+q7IX0deSvwdXr17lnQPevXvHbdvXGj58OHJycjBs2DCFZFh6ejp3C4S1tTU6d+6Mq1ev4vfff1dILgPAjRs3eJ8Foh66FYIQUmw1a9YMv/32G+bOnYvSpUujWbNmcHJyQlxcHF68eIFLly5h3rx5KFOmTIGWN2fOHFy/fh07duzA9evX0bx5c4jFYoSFheHUqVO4fPky9+vBnj174Ovriy5dumDlypWoWrUqJBIJ3rx5g2vXriE2NlbpFyVlunfvjqlTp2LWrFlgjCncBiFXmHUKhUJs2LABfn5+aNy4Mfz9/WFra4sLFy4gKioKFSpUwMOHDwvUPlWEQiGGDRuGZcuWoWLFimjVqhWSk5Nx8uRJODk5wdbW9quWry4LCwvs2bMHnTp1QsWKFdGsWTN4enoiPT0dr1+/RnBwMOrUqcON4TF+/Hi8efMGPj4+cHZ2hkAgwOXLl3Hz5k3UqVMHdevWBZB7T/LZs2fh6+uLUqVKQSKR4O7duzh//jxKly6Ndu3a5duuGjVqoEaNGti/fz+ioqJQq1YtvHnzBseOHUOLFi0K3V1VmUWLFuH8+fOYPn06Ll++jMqVKyMkJAT//PMPfvnlF5w5c6ZAy9HX10f16tVx8eJF9O3bF25ubhAKhejWrVuBBqusVasWNyZF7dq10aBBA7x+/RrHjh1Dq1atcPjwYV79gr4HQO6vlfv370fHjh1RuXJlaGlpoUWLFtxAZvn55ZdfMGzYMJw4cQKenp64e/cuTp8+DQcHB26Axs8NHjwYhw4dQkxMDCZNmsTd61sUBg8ejOvXr2Pbtm24cuUKGjduDFtbW7x//x5Pnz7FjRs3sHv3bjg7OyMiIgI1a9aEl5cXqlSpAjs7O8TFxeHo0aPIysrCxIkTFZbfokUL+Pn5oVOnTnBwcEBwcDCuXbuGihUrYsKECVw9Q0ND/PXXX+jatStq1qwJf39/WFhY4Pz587h9+zZq1KiBX3/99au2dfXq1ahbty569eqFI0eOwM3NDbdu3cLNmzdRv359hV8xi/r4/rn8Yjw+Pr7Q+1oTypYtCysrK7x//x5WVla8cXl8fX0xb948JCYmKvzqrs6xu2HDhli6dCkGDx6MTp06QU9PD46OjujWrRvEYjEOHjyI5s2bw9vbG40aNeJ6F7x58waXLl2CmZmZ0oEC8+Pr64sDBw4gNjaWlzyQSqWoWbMmLl26pNAbA8j94r148WKMHDkSgYGBcHJywsOHD3Hu3Dm0b98eAQEBStcnEom4MTSAgg3amNekSZNw6NAhrF69Gg8fPkS9evXw7t077N+/H61atcLff//NG/TV1dUVixcvxvjx41GhQgV07twZenp6OH78OJ4+fYo2bdoo9EQAco+vbm5u2L59O7KysjBw4EBu7IK8Zs+ejbt37+KPP/7AiRMn4O3tDQsLC0RERODRo0d48OABrl27VuSD5AKAjY0NOnTogEOHDqFatWpo1KgR3r9/j+PHj6Nhw4YICwv76nUMHToUwcHB2L9/P9zc3NC6dWsYGhrizZs3OH36NDZt2oS2bdsCANasWYPQ0FBMnDgRO3bsQO3atWFkZIS3b9/izp07eP78OaKior567JSfnmYeRkEIIcrJH9H1+fOQ83P27FnWqlUrZmFhwbS1tZm1tTWrXbs2mzt3Lu9RZcoec/e59PR0tnTpUlapUiUmlUqZvr4+K1u2LBs/frzCc6zj4+PZ9OnTWbly5bi6bm5urFu3biwgIIBXV9njJvPy9fVlAJi+vj7v+dqfK8w6GWPs4sWLrEGDBkwqlTJTU1PWqVMn9vr16y+253NQ8Yi/zMxMNn/+fObm5sbEYjFzdHRk48aNYykpKczJyYk5OTnx6uf3uElVjzbM732TP85v5syZCtOePn3K+vfvz5ycnJiOjg4zMTFh5cuXZ6NGjWI3b97k6u3du5d17tyZubq6Ml1dXWZkZMQqVarElixZwj5+/MjVO3XqFOvVqxfz8PBgBgYGXGxMnz6dffjwIb/dx4mJiWH9+vVjtra2TCKRsPLly7PVq1ezsLAwpftA2T6UU/Uevn79mvn7+zNjY2Omq6vL6tevz4KDg/N9BKQyoaGhzM/PjxkbGzOBQMCbtyDLio2NZT179mSmpqZMKpWyWrVqsdOnTyt9Pwv6HjDGWFRUFOvcuTMzNzdnQqHwi59pxvhxEhwczOrXr890dXWZsbEx69Kli9JHGsrJZDJmZ2fHBAIB7xnxBZFffOa1b98+1rhxY2ZiYsK0tbWZnZ0d8/HxYcuWLeMex5eQkMBmzZrFGjRowGxsbJiOjg6ztbVlzZo1Y6dPn+YtL+8+DggIYFWrVuUeRTp48GDe893zunjxImvevDkzNjZmOjo6zN3dnf32228K7wFj+T/2U1XcPnr0iPn5+TF9fX1mYGDAmjdvzh49epTvowyL4viu6n1QFeOF2deqfOlxk8rktx9U8ff3ZwCYv78/rzwtLY2JxWIGgB0+fFhhvsIeuxljbMmSJczNzY1pa2srff/fvXvHRo8ezS3T0NCQlSlThg0YMICdP3+eVze/+JELCQlhABgAFhISwps2Y8YMBoBVrFhR6bz3799nv/zyCzMxMWEGBgbM29ubnTt37ovXAaGhoQxAgR8p+7mYmBjWv39/Zm5uziQSCatatSoLCAhgS5cuVfleHD16lHl7ezMDAwMmFotZ+fLl2bJly1hWVpbK9cyePZvbN8HBwSrrZWdns/Xr17O6desyQ0ND7r1u1qwZW7t2Le+z/aX4K+z5KCUlhY0fP557DKebmxubO3cuy8zMVPr+q/PZkMlkbOPGjaxWrVpMT0+P6erqMjc3NzZkyBCF4/qnT5/YkiVLWNWqVZmenh6TSqXMxcWFtW3blm3fvj3f/U0KRsCYkv4ghBBCCCEEkZGRcHJyQv369XHhwgVNN6dAtm7dir59+2LLli1fdQsDIT+b/fv3w9/fH7Nnz8aMGTOKbLk9evTArl278OTJE7V72RDyo6MxFgghhBBCVFi5ciWys7MxZMgQTTeFEPINMcawfPlyiEQi9O/fX61lREVFKZQFBwdj79698PDwoKQCKdFojAVCCCGEkDySkpKwdu1avH79Gn/99Re8vLzQoUMHTTeLEPINPHr0CMePH8fVq1dx48YNDBkyBHZ2dmoty8/PD1KpFJUqVYKenh6ePHmCU6dOQUtLC//73/+KuOWE/FgosUAIIYQQkkdCQgKmTJkCqVSK+vXrY926dQqPCCWElAx37tzB1KlTYWxsjF69euH3339Xe1m9e/fGrl27sHfvXqSkpHBPbJgyZQr3WE5CSioaY4EQQgghhBBCCCFqozEWCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELVRYoEQQgghhBBCCCFqo8QCIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRGyUWCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghPyQtm7dCoFAoPSvY8eOEAgE2Lp1K1f/6tWrmDVrFhITEzXWZvLjevjwIfr37w9XV1dIpVJIpVK4ublh8ODBuH37Nldv1qxZEAgERbruPn36wNnZuVDzPHnyBLNmzUJ4eHiRtoWUPHmPlUFBQQrTGWMoXbo0BAIBfHx8kJOTA2NjYzRv3lyh7ooVKyAQCNC1a1eFaXPnzoVAIMDDhw8B/PdZ+fDhQ5FvE/l6qs6fn/8pi5niKjw8XOHaQP75yHssVeeYTFQ7ePAgBAIB9u3bpzCtYsWKEAgEOH36tMI0V1dXVKlSpVDr2r17N1auXPnFevldQ+b9+55x4OPjAx8fn++2Pk0QaboBhBCSny1btsDT05NXZmNjgwkTJsDV1ZUru3r1KmbPno0+ffrA2Nj4O7eS/MjWr1+PESNGwMPDA6NHj4aXlxcEAgFCQkKwZ88eVK9eHS9evODFk6Y9efIEs2fPho+PD10AkwIxMDDApk2bFC5cg4OD8fLlSxgYGAAAtLS0UL9+fQQFBSE7Oxsi0X+XgkFBQdDT00NgYKDC8oOCgmBmZoby5ct/0+0gRePatWu813PnzkVgYCAuXLjAKy9btuz3bNY3ZWNjg2vXrv1Qx/KfgY+PDwQCAQIDA+Hv78+Vx8fH49GjR9wxpWnTpty0d+/eISwsDOPGjSvUunbv3o1///0XY8aMybdeixYtFD4DtWvXRseOHTF+/HiuTCwWF2r9X2PNmjXfbV2aQokFQsgPrVy5cqhWrZpCuZOT0zddL2MM6enpkEql33Q95Nu6cuUKhg0bhhYtWuDgwYPQ0dHhpjVs2BDDhw/HgQMH6H0mxZ6/vz927dqF1atXw9DQkCvftGkTateujeTkZK7M19cXx48fx+3bt1GrVi0AgEwmw6VLlzB06FAsXboUISEhKFOmDAAgMzMT165dg5+fX5H36CHfhvx9lbOwsIBQKFQoL0nEYnGJ3r4flbm5OcqVK6fQ+yU4OBgikQj9+/dXSFbKX/v6+n6TNllYWMDCwkKh3MrKKt8YycnJQXZ29jdJOJSkJJ4qdCsEIaTY+by746xZs/Drr78CAFxcXHhdPPPrDpf3lz2BQIARI0Zg3bp1KFOmDMRiMbZt2wYAuHz5Mho1agQDAwPo6uqiTp06OHHiBK89IpEICxcuVGjrxYsXIRAIcODAgW+3Q4hKCxYsgJaWFtavX89LKuTVqVMn2NraqlzGvn378Msvv8DGxgZSqRRlypTB5MmTkZqaqlB369at8PDwgFgsRpkyZbB9+3aly1y7di0qVqwIfX19GBgYwNPTE1OnTuWW0alTJwC5F13yeM3bvXfz5s2oWLEiJBIJTE1N0a5dO4SEhPDW0adPH+jr6+PFixfw8/ODvr4+HBwcMH78eGRkZPDqZmZmYt68efD09IRYLIaFhQX69u2L2NhYlfuF/Fjkty/s2bOHK0tKSsKhQ4fQr18/Xl35xXzeLwIPHjxAQkICBg0aBBsbG94XgRs3biAtLe2bfQkgmhEfH49hw4bBzs4OOjo6KFWqFKZNm8Y7PnTq1AleXl68+Vq1aqVwXrt79y4EAgH+/vtvAP91RQ8MDMTQoUNhbm4OMzMztG/fHpGRkbzlXbhwAT4+PjAzM4NUKoWjoyM6dOiAT58+ISsrC5aWlujZs6dC+xMTEyGVSrlfvZXdClFQjDGsWbMGlSpVglQqhYmJCTp27IiwsLBCL+tn5Ovri9DQUERFRXFlQUFBqF69Ovz8/HDnzh2kpKTwpsl7TwEF2/8+Pj44ceIEXr9+zbuWU5c8XpYsWYJ58+bBxcUFYrGYO/YdO3YMtWvXhq6uLgwMDNCkSROFXhDyW8Lu3buH9u3bw9DQEEZGRujRo4fC+VPZrRAZGRmYM2cOypQpA4lEAjMzM/j6+uLq1atcnQMHDqBmzZowMjKCrq4uSpUqpXBM/1FQYoEQ8kOTZ4/z/n1uwIABGDlyJAAgICAA165dw7Vr11ClShWuO1zev+XLlwOAwsXSkSNHsHbtWsyYMQOnT59G/fr1ERwcjIYNGyIpKQmbNm3Cnj17YGBggFatWnH3Ezo7O6N169ZYt24dcnJyeMv8888/YWtri3bt2n2L3UPykZOTg8DAQFSrVg02NjZqL+f58+fw8/PDpk2bcOrUKYwZMwb79+9Hq1atePW2bt2Kvn37okyZMjh06BCmT5+OuXPnKnQ93rt3L4YNGwZvb28cPnwYR44cwdixY7lERYsWLbBgwQIAwOrVq7m4bdGiBQBg4cKF6N+/P7y8vBAQEIBVq1bh4cOHqF27Np4/f85bV1ZWFlq3bo1GjRrh6NGj6NevH1asWIHFixdzdWQyGdq0aYNFixahW7duOHHiBBYtWoSzZ8/Cx8cHaWlpau878v0YGhqiY8eO2Lx5M1e2Z88eCIVCXvdkIPe+ZxMTE17yIDAwEDY2NnBzc0ODBg14SYdv/esi+f7S09Ph6+uL7du3Y9y4cThx4gR69OiBJUuWoH379ly9xo0b48mTJ9wXxuzsbAQHB0MqleLs2bNcvXPnzkEkEil8cRowYAC0tbWxe/duLFmyBEFBQejRowc3PTw8HC1atICOjg42b96MU6dOYdGiRdDT00NmZia0tbXRo0cPHDp0iNfrBsiN7/T0dPTt2/er98fgwYMxZswYNG7cGEeOHMGaNWvw+PFj1KlTB+/fv//q5Zd0ypKVgYGB8Pb2Rt26dSEQCHDp0iXetCpVqsDIyAhAwfb/mjVrULduXVhbW/Ou6b7WH3/8gQsXLmDp0qU4efIkPD09sXv3brRp0waGhobYs2cPNm3ahISEBPj4+ODy5csKy2jXrh1Kly6NgwcPYtasWThy5AiaNm2KrKwslevNzs5G8+bNMXfuXLRs2RKHDx/G1q1bUadOHbx58wZA7i1N/v7+KFWqFPbu3YsTJ05gxowZSq+FfwiMEEJ+QFu2bGEAlP49f/6cAWBbtmzh6v/+++8MAHv16lW+y3369CkzMzNjvr6+LCMjgysHwIyMjFh8fDyvfq1atZilpSVLSUnhyrKzs1m5cuWYvb09k8lkjDHGAgMDGQB2+PBhrl5ERAQTiURs9uzZ6u8Iorbo6GgGgHXp0kVhWnZ2NsvKyuL+5O/jzJkzWX6nRplMxrKyslhwcDADwB48eMAYYywnJ4fZ2tqyKlWqcMtijLHw8HCmra3NnJycuLIRI0YwY2PjfNt+4MABBoAFBgbyyhMSEphUKmV+fn688jdv3jCxWMy6devGlfXu3ZsBYPv37+fV9fPzYx4eHtzrPXv2MADs0KFDvHq3bt1iANiaNWvybSvRLPmx8tatW9xx6N9//2WMMVa9enXWp08fxhhjXl5ezNvbm5uvbdu2TE9Pj2VlZTHGGGvVqhX3WVmzZg2zsLDgYtnX15dZWlry1iv/rMTGxn7rTSRFoHfv3kxPT497vW7dOqXHh8WLFzMA7MyZM4wxxl68eMEAsO3btzPGGLt8+TIDwCZOnMhcXFy4+Zo0acLq1KnDvZbH5bBhw3jLX7JkCQPAoqKiGGOMHTx4kAFg9+/fV9n2hw8fMgBsw4YNvPIaNWqwqlWrcq9fvXqlcG0gb0fea4PevXvzjsnXrl1jANiyZct4y3/79i2TSqVs4sSJKttGcsXHxzOhUMgGDRrEGGPsw4cPTCAQsFOnTjHGct+rCRMmMMZyz1fyGGKscPu/RYsWvPeuMACw4cOHc6/l8eLq6soyMzO5cvn5vHz58iwnJ4crT0lJYZaWlrw4lx8Hx44dy1vXrl27GAC2c+dOrszb25t3DN6+fTsDwP766y+VbV66dCkDwBITE9Xa5u+NeiwQQn5o27dvx61bt3h/eQcbK4zo6Gg0a9YMNjY2OHz4sELX+IYNG8LExIR7nZqaihs3bqBjx47Q19fnyrW0tNCzZ0+8e/cOoaGhAHK7uFWsWBGrV6/m6q1btw4CgQCDBg1Sq73k26latSq0tbW5v2XLlqmsGxYWhm7dusHa2hpaWlrQ1taGt7c3AHC3H4SGhiIyMhLdunXjdc10cnJCnTp1eMurUaMGEhMT0bVrVxw9erRQo+pfu3YNaWlp6NOnD6/cwcEBDRs2xPnz53nlAoFAoWdFhQoV8Pr1a+718ePHYWxsjFatWvF6BlWqVAnW1tYlatT4ks7b2xuurq7YvHkzHj16hFu3bqnsMuvr64vU1FTcunWLG19B/muzt7c3YmNj8fjxY2RkZOD69evUW6GEuXDhAvT09NCxY0deufzYIj+WuLq6wtnZGefOnQMAnD17FuXLl0ePHj3w6tUrvHz5EhkZGbh8+TIaN26ssJ7WrVvzXleoUAEAuGNQpUqVoKOjg0GDBmHbtm1Kbz0oX748qlatii1btnBlISEhuHnzZpF0CT9+/DgEAgF69OjBOwZaW1ujYsWKdAwsABMTE96+Cg4OhpaWFurWrQsg95gi7/n0eQ8oTe//1q1bQ1tbm3stP5/37NkTQuF/X5X19fXRoUMHXL9+HZ8+feIto3v37rzXnTt3hkgkUjoQrtzJkychkUjyjeHq1atzy9u/fz8iIiIKtW3fGyUWCCE/tDJlyqBatWq8P3WkpKTAz88PWVlZOHnyJNf9Lq/Pu8snJCSAMaa0G738nvy4uDiubNSoUTh//jxCQ0ORlZWFv/76Cx07doS1tbVabSZfx9zcHFKplPclWm737t24desWjh07lu8yPn78iPr16+PGjRuYN28egoKCcOvWLQQEBAAAd5uAPA6Uvdefl/Xs2RObN2/G69ev0aFDB1haWqJmzZq8bsWqyNejKibzxiMA6OrqQiKR8MrEYjHS09O51+/fv0diYiJ0dHR4yRZtbW1ER0fT4wSLEYFAgL59+2Lnzp1Yt24d3N3duXuYPye/qA8MDMS9e/eQmJjIJczKli0LCwsLBAUF4fr16zS+QgkUFxcHa2trhXvULS0tIRKJeMeSRo0acYmGc+fOoUmTJihfvjysrKxw7tw5XLlyBWlpaUoTC2ZmZrzX8kHx5MdOV1dXnDt3DpaWlhg+fDhcXV3h6uqKVatW8ebr168frl27hqdPnwLIfWKUWCxW+mjUwnr//j0YY7CyslI4Bl6/fp2OgQXk6+uLZ8+eITIyEoGBgahatSr3o4y3tzfu3buHpKQkBAYGQiQSoV69egA0v/8/P59+6Twrk8mQkJDAK//8PC8SiWBmZqZwTs4rNjYWtra2vOTF5xo0aIAjR44gOzsbvXr1gr29PcqVK8cbS+dHQk+FIISUeFlZWejQoQNevnyJS5cuwd7eXmm9zy+wTExMIBQKeYMRyckHnzI3N+fKunXrhkmTJmH16tWoVasWoqOjMXz48CLcElIYWlpaaNiwIc6cOYOoqCjeRYJ8dOa8zzZX5sKFC4iMjERQUBD3pQvIHTQsL/nFc3R0tMIylJX17dsXffv2RWpqKi5evIiZM2eiZcuWePbsWb5PPJGvR1VM5o3HgpIPqnbq1Cml0+WPKSTFQ58+fTBjxgysW7cO8+fPV1mvXLlyXPJALBbDysqK92jfBg0aIDAwkLswpsRCyWJmZoYbN26AMcY798XExCA7O5t3LGnUqBE2bdqEmzdv4saNG5g+fTqA3F5+Z8+exevXr6Gvr6/2Exnq16+P+vXrIycnB7dv38b//vc/jBkzBlZWVujSpQuA3MFJx40bh61bt2L+/PnYsWMH2rZty+tlqC5zc3NuDABlTwP4no8kLM58fX2xfPlyBAUFISgoCH5+ftw0eRLh4sWL3KCO8qSDpvf/59d+XzrPCoVChbiLjo6GnZ0d9zo7OxtxcXEKibW8LCwscPnyZchksnyTC23atEGbNm243mMLFy5Et27d4OzsjNq1axdoG78X6rFACCkRPv8VJK/+/fsjKCgIAQEBXDfMgtDT00PNmjUREBDAW65MJsPOnTthb28Pd3d3rlwikXDdOZcvX45KlSpx3QCJZkyZMgU5OTkYMmRIvoMoqSK/4Pj8wmb9+vW81x4eHrCxscGePXvAGOPKX79+zRvd+XN6enpo3rw5pk2bhszMTDx+/Ji3vs/juXbt2pBKpdi5cyev/N27d7hw4QIaNWpUyC0EWrZsibi4OOTk5Cj0DqpWrRo8PDwKvUyiOXZ2dvj111/RqlUr9O7dW2U9gUAAb29vXL16FWfPnuUlzoDcXxiDg4MRGBgIW1tb3rGOFH+NGjXCx48fceTIEV65/Ek2eY8ljRo1gkAgwG+//QahUIgGDRoAyB3YMTAwEGfPnkWDBg143cnVoaWlhZo1a3K3FN69e5ebZmJigrZt22L79u04fvw4oqOji2xk/JYtW4IxhoiICKXHwPLlyxfJekq6Bg0aQEtLCwcPHsTjx495A3kaGRmhUqVK2LZtG8LDw3mJysLsf7FY/M0HFPbw8ICdnR12797NO5+npqbi0KFD3JMi8tq1axfv9f79+5Gdna0wmGlezZs3R3p6eoGfYiIWi+Ht7c0Nvnzv3r2CbdB3RD0WCCElgvzEs2rVKvTu3Rva2trw8PDAunXrsGPHDowcORJ6enq4fv06N4+hoeEXnyu8cOFCNGnSBL6+vpgwYQJ0dHSwZs0a/Pvvv9izZ49CpnvYsGFYsmQJ7ty5g40bNxb9hpJCqVu3LlavXo2RI0eiSpUqGDRoELy8vLieKIcOHQKQGwvK1KlTByYmJhgyZAhmzpwJbW1t7Nq1Cw8ePODVEwqFmDt3LgYMGIB27dph4MCBSExMxKxZsxS6SA4cOBBSqRR169aFjY0NoqOjsXDhQhgZGXH3U5YrVw4AsGHDBhgYGEAikcDFxQVmZmb47bffMHXqVPTq1Qtdu3ZFXFwcZs+eDYlEgpkzZxZ6H3Xp0gW7du2Cn58fRo8ejRo1akBbWxvv3r1DYGAg2rRpQ081KWYWLVpUoHq+vr44ePAgzpw5gz///JM3zdvbG3Fxcbh48SK6dev2LZpJNKhXr15YvXo1evfujfDwcJQvXx6XL1/GggUL4Ofnx7utwdLSEuXKlcOZM2fg6+vLfalq3Lgx4uPjER8fzz1tqbDWrVuHCxcuoEWLFnB0dER6ejr3ZJPPb63o168f9u3bhxEjRsDe3l7prRfqqFu3LgYNGoS+ffvi9u3baNCgAfT09BAVFYXLly+jfPnyGDp0aJGsqyQzNDRElSpVcOTIEQiFQoUfVry9vbFy5UoA/B5Qhdn/5cuXR0BAANauXYuqVatCKBSqfYusKkKhEEuWLEH37t3RsmVLDB48GBkZGfj999+RmJio9PgaEBAAkUiEJk2a4PHjx/jtt99QsWJFdO7cWeV6unbtii1btmDIkCEIDQ2Fr68vZDIZbty4gTJlyqBLly6YMWMG3r17h0aNGsHe3h6JiYlYtWoVb6ynH4rmxo0khBDV8o50/jllIz8zxtiUKVOYra0tEwqF3Ij68pHxlf3lHZ0Xn40WnNelS5dYw4YNmZ6eHpNKpaxWrVrs77//Vtl2Hx8fZmpqyj59+qTWtpOid//+fda3b1/m4uLCxGIxk0gkrHTp0qxXr17s/PnzXD1lT4W4evUqq127NtPV1WUWFhZswIAB7O7du0pjcOPGjczNzY3p6Ogwd3d3tnnzZoURyLdt28Z8fX2ZlZUV09HRYba2tqxz587s4cOHvGWtXLmSubi4MC0tLYV1bdy4kVWoUIHp6OgwIyMj1qZNG/b48WPe/J+PAp/fNmZlZbGlS5eyihUrMolEwvT19ZmnpycbPHgwe/78eUF2MdGQ/I6VeX3+VAjGGHvy5Al3PJQ/SUJOJpMxU1NTlaOW01Mhihdlx4O4uDg2ZMgQZmNjw0QiEXNycmJTpkxh6enpCvOPHTuWAWDz58/nlbu5uTEACscvVXEpf3KJ/Ik3165dY+3atWNOTk5MLBYzMzMz5u3tzY4dO6bQhpycHObg4MAAsGnTpilMV/epEHKbN29mNWvW5M71rq6urFevXuz27dsKdYlyEydOZABYtWrVFKYdOXKEAWA6OjosNTVVYXpB9n98fDzr2LEjMzY2ZgKBIN+nOH3u8+s8ebz8/vvvSusfOXKE1axZk0kkEqanp8caNWrErly5wqsjPw7euXOHtWrViunr6zMDAwPWtWtX9v79e17dz58KwRhjaWlpbMaMGdx1g5mZGWvYsCG7evUqY4yx48ePs+bNmzM7Ozumo6PDLC0tmZ+fH7t06VKBt/t7EjCWp48HIYSQrxITEwMnJyeMHDkSS5Ys0XRzCCGEEELINzBr1izMnj0bsbGxao1xVNLQrRCEEFIE3r17h7CwMPz+++8QCoUYPXq0pptECCGEEELId0GDNxJCSBHYuHEjfHx88PjxY+zatYs3OjAhhBBCCCElGd0KQQghhBBCCCGEELVRjwVCCCGEEEIIIYSojRILhBBCCCGEEEIIURsN3kjIT0YmkyEyMhIGBgYQCASabg75QTDGkJKSAltbWwiF3zbnTDFIPkfxRzTte8UgxR9Rho6BRJOKKv4osUDITyYyMhIODg6abgb5Qb19+xb29vbfdB0Ug0QVij+iad86Bin+SH7oGEg06WvjjxILhPxkDAwMAOQePAwNDTXcGvKjSE5OhoODAxcf3xLFIPkcxR/RtO8VgxR/RBk6BhJNKqr4o8QCIT8Zebc3Q0NDOqEQBd+jWyTFIFGF4o9o2reOQYo/kh86BhJN+tr4o8EbCSGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELVRYoEQQgghhBBCCCFqo8QCIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRGyUWCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELWJNN0AQohm3L9/H/r6+ppuRollbm4OR0dHTTfjh0YxqBkUm7ko/r4vijs+ir9vj2IufxSD38bPHHeUWCDkJ+Xt7a3pJpRoUl0pnoY8/WlPLgVBMagZurq6CAkJ+eljk+Lv+9LVlSKEjokcir9vTyLVRehTOtapQjH4behKJQh5GvpTxh0lFgj5SbWc0hK2nraabkaJFBsei8MzD+PDhw8/5YmloMYPmQU317KabsZP5c27MCxYNZliE8CGDT1QtaqTppvxUwgJiUKPHpsp7vIo5TsMehaumm5GiZWW8A4vzq6gmMvH+BoGcDfT1nQzSpTXSdlYcDX5p407SiwQ8pMydzKHjaeNpptBfmL2ds5wp8QC0RAPD2tUqfLzXfiRH4PE2A56lpRYIJrjYCiCuyklFkjRocEbCSGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELXR4I2EkGItOSYZ51afw4urL5CVkQUzRzO0ntYatmVUP/EiOzMbwZuC8ejUI3yM+whDS0PU71MflVtXBgDcP34fR+ceVZhv2sVpEInpsEm+rOvgX/A+NlKhvE2zLhg9aLpCeVx8LNZu+x3PXj5BRNRrtPPrjhH9J/PqnLpwBEv+VJz31N470NERF13jSbEWEZGASZMCcPLkY6SlZcLd3QqbNvXK9wkYq1cH4s8/gxAeHgdHR1NMm9YcvXrV5qYHBNzFggUn8eJFLLKycuDmZonx45ugZ89a32OTyA8u82Mc3lzdjsTXdyHLyYDE2BalGo6AvmVppfVfnFuFD08DFcqlpg6o2O1/AID4l9cQcfsg0pOiwGQ5kBjbwKZSG1h4+n7TbSHFR+ynHGy49xE3IzOQkcNgbyjCrzUN4aHiSRf332di7LkEhfJtLc3gaKR4bXchPB1zryShrr0Y87yNi7r5JRJdIRNCiq205DRsHrQZLlVc0H1ld+iZ6CE+Ih4SA0m+8x2cehAf4z+i9bTWMLU3RWpCKmQ5Ml4dsZ4YIw6M4JVRUoEU1NoleyGT/RdTr948x6+zB8K7zi9K62dlZ8LY0AQ9OgzEweM7VC5XT1cf2/53nFdGSQUil5CQirp1f4evrztOnhwJS0sDvHwZC2NjXZXzrF0bjClTjuCvv3qgenVn3LwZjoEDd8DERBetWlUEAJia6mHaND94elpDR0eE48cfom/fbbC0NEDTpl7fa/PIDyg7/SP+PTQZRnbl4dn6N4ikRshIioZIrKdyHuf6A+BYuxf3mrEcPNozFqaudbgyLbE+7Kp1gtTEDgItERLCb+Pl+f9BW2oMY6fK33SbyI8vJUOGkWfiUdlKB4t8TWAiESLiYzb0dQRfnHd7KzPoaf9Xz0is2IE/+mMO1t5NQQULempGYdBVMiGk2Lqy4wqMLI3QZkYbrszY1jjfeV5ce4Hwe+EYHTAaUiOp6nkEgL6ZfhG2lvxMjI1Mea93B2yErbUDKnpVV1rf2tIOI/pPAQCcvHA4nyULYGpiXlTNJCXM4sWn4eBggi1b+nBlzs75x8uOHdcxeHB9+PvnxmapUha4fj0Mixef5hILPj4evHlGj26Ebduu4fLlF5RY+MlF3g2AWN8cro1HcWUSQ6t85xGJ9YA8iYf4sOvIzvgIyzKNuDIj+/K8eWwqtkLs00CkRD2hxALBniepsNTVwqTaRlyZtb5WgeY1kQihr6N6NIAcGcP8q0noU0Efj2Iz8TGTfXV7fxaUWCCEFFuhF0PhWssVB6YcQPi9cBhaGKJah2qo2raq6nkuhcK2jC2u7LyChycfQluiDY/6HvAd7AttyX+Z6cy0TKxssxKyHBms3a3hO9gXNh4232OzSAmTlZWFcxePo1OrXhAIvvxrSn7S0j+hy6AmkMlkKO3igb5dR8KtVJkiaikp7o4de4imTcuiU6f1CA5+Djs7Ywwb5o2BA+urnCcjIxsSCf9XOalUBzdvhiMrKwfa2vyLdcYYLlx4itDQ91i8uP032Q5SfCS8ugkjx8p4dnIJkiMfQ0fPFFblm8PKS3nvLGVinpyDkUMFiA0tlU5njCH53UOkJ0TAIE9PB/LzuvouA9VtxZh1KREP3mfCXFcLbdylaFlade8suYH/xCEzB3AyEqFnOT1UttbhTd/+byqMxUK0KC3Fo9jMb7UJJRIlFgghxVZCZAJuB9xG7a61Ua9PPUQ8jsCp5acg0hGhol9F5fNEJODNgzcQ6Yjgv9gfnxI/4cTvJ5CWnIY2v+X2fDB3Mkfb39rC0tUSGakZuLHvBjYP3IwhO4fAzNGsUG2UyWQQCARf/YWSFF9Xbp7Hx9QUNG3Y9quW42jngkkj56GUoxtS01IRcHwnRk3tib+WH4K9rer755WRyWRgjFFcljBhYbFYuzYY48Y1xtSpzXHzZjhGjdoHsVjEGzMhr6ZNy2Ljxsto27YSqlRxxJ07r7F58xVkZeXgw4ePsLHJ/UUwKSkNdnaTkJGRBS0tIdas6YYmTcoWqn0UdyVPevJ7pP97CjaVWsOuWkd8fP8c4Rc3QqilXaDxEDJT45H4+i7cfhmnMC07IxV3t/YHy8kCBEK4eA+GsWOlQreRzsMlT+THHBx99gmdyuiiu5cJQuKy8L/bKdAWCtC0lFTpPKZSIcbXNIC7qTaychjOvErH+PMJWNHYBBWtcpMLj2Iy8c+LNGz0K9y1njI/Y9xRYoEQUmwxGYNtGVs0GpbbfdLGwwaxr2Jx+9BtlYkFJsu9qG0/pz0k+rljMTTNbIr9U/bD71c/aEu0YV/eHvbl7bl5HCs6Yn2v9bh54Caaj29eqDaOGDEC0dHRWLduHSwtlf8aQ0q2f84HoEaVejA3/br3v6xHRZT1+C+uy3lWxuAJnXD4n10YOWBqoZa1ePFi5OTkUFyWMDIZQ7VqTliwoB0AoHJlRzx+HIm1a4NVJhZ++60FoqOTUavWIjAGWFkZok+f2liy5Ay0tP67IDYwEOP+/en4+DED588/xbhxB1CqlLnCbRL5obgrgRiDnqUrHGv3BADoWZRCWvwbvH90qkCJhdiQCxCJ9WBSqqbCNC0dKSr4r0BOVhqS3j3E68ubITa0UrhN4kvoPFzyMAAeptoYWMkAAOBmqo3wpGwce56mMrHgaCiCo+F/X329LHQQ+ykH+0NSUdFKB5+yZFhwNQkTahrCSPL1D078GeOOHjf5g+rTpw+X5VL193k9kUgER0dHDB06FAkJ/FFPBQIBjhw5orCeMWPGwMfHh3sdExODwYMHw9HREWKxGNbW1mjatCmuXbuGoKCgL7Zp69atvOUXdB55vcTERIU2Ojs7Y+XKlbzXypazaNEilfszLCwMXbt2ha2tLSQSCezt7dGmTRs8e/aMVy8wMBB+fn4wMzODrq4uypYti/HjxyMiIoK3PfJ25rd90dHRAIBZs2ZBIBBgyJAhvHXdv38fAoEA4eHhvPJDhw7Bx8cHRkZG0NfXR4UKFTBnzhzEx8cDALZu3ap0fRJJ/gMWlkQG5gawcLHglZk7myPpfVK+8xhYGHBJBfk8YLlPmFBGIBTAtqwt4t/GF7qNsbGxOHbsGDw8PLBv3z4wRvfq/UyiYyJx9+F1tGjcociXLRQK4VG6HCKi3hR63sTERIrLEsjGxghly/Jv2SpTxgZv3iiOhC4nlepg8+be+PTpT4SHL8CbNwvh7GwOAwMJzM3/G2dGKBSidGlLVKrkgPHjm6BjxypYuPBUodpHcVfyaOuZQGrqwCuTmNgj42PsF+dljCE25DzMPXwg1FIcJE8gEEJibAM9i1KwrdwWpqXrIPLOoUK3kc7DJY+ZRAgnI/5tWk6GIsSk5hRqOWXNtfEuJXeeyJQcRKfKMDU4EY12v0ej3e9xJiwdV99loNHu94hIyS7Usn/GuKPEwg9q1apViIqK4v4AYMuWLQplANCsWTNERUUhPDwcGzduxN9//41hw4aptd4OHTrgwYMH2LZtG549e4Zjx47Bx8cH8fHxqFOnDm/9nTt35tYt//P39+ctT515CmLOnDm8ZURFRWHkyJFK62ZmZqJJkyZITk5GQEAAQkNDsW/fPpQrVw5JSf99AV2/fj0aN24Ma2trHDp0CE+ePMG6deuQlJSEZcuW5due0NBQhfbkzU5KJBJs2rRJIZHxuWnTpsHf3x/Vq1fHyZMn8e+//2LZsmV48OABduz4b6R4Q0NDhfW9fv26ILuuRHGo4IC413G8srg3cTCyNlIxR+48KbEpyPyUyZtHIBTA0NJQ6TyMMbx/9r7QgzmmpaXBy8sLOTk5SEpKQpcuXdChQwfExMQUajmk+Dp14TCMDU1Rq2qDIl82YwwvXz1VazDHUqVKUVyWQHXruiI09D2v7Nmz93ByMlUxx3+0tbVgb28CLS0h9u69hZYty0MoVH2ZyFju+AyFQXFX8hhYeyI9IYJXlp4YCbGBhYo5/pMc8S/Sk6JgWbZxwVbGGGQ5WYVqH52HSyYvCx28TeYnEd6l5MBKr2ADOMo9j8+GmTT3OOdoJMLmFmbY6PffXx17MSpZ6WCjnxksdQu+7J817uhWiB+UkZERjIz4X46MjY1hbW2tUFfeswAA7O3t4e/vr9BzoCASExNx+fJlBAUFwdvbGwDg5OSEGjVqcHXyrl8qlSIjI0Npm+R0dHQKPU9BGBgYFHgZT548QVhYGC5cuAAnp9z7kJ2cnFC3bl2uzrt37zBq1CiMGjUKK1as4MqdnZ3RoEEDpT0p8rK0tISxsbHK6R4eHrC0tMT06dOxf/9+pXVu3ryJBQsWYOXKlRg9ejSvDU2aNOG1QSAQFHj7MzIykJGRwb1OTlb+q3xxVKtrLWwesBmXtl6CVyMvRDyJwN0jd9FySkuuzrnV55ASm4J2s3K7BpdvWh4XN1/E0blH4TPQB5+SPuHs/86iUqtK3OCNQRuDYF/OHmYOZtwYC9HPouH3q1+h2levXj3MnDkTALhM9bFjxxAYGIj169ejc+fORbEbfnglOQbzI5PJcOrCEfzi2wZaWvzT7V87V+BDXAymjF7Ilb149RRA7gCNSckJePHqKUQibTg7uAIAtu1bg7LuFWFn44hPaakIOLETL8JDMWrQdLXb+DPE5c8Uf2PHNkadOouxYME/6Ny5Gm7eDMeGDZewYUMPrs6UKYcREZGI7dv7AshNPNy8+Qo1a7ogIeETli8/h3//jcS2bX24eRYuPIlq1Zzg6mqBzMwc/PPPI2zffg1r13ZXq50/Q9zJlfT4s6nUGo8PTUbE7QMwK10PH98/Q8zjMyjl+98PXG+u7kBmahxKNxnDmzc25Bz0rdyha6Y4RkzE7YPQsywNiZE1mCwbieF38CE0CC7eQxTq5ofOwyUzBjuV0cWI0/HY+W8qfJ3ECPmQhePPP2Fczf9+IPrrXgpi02SYWif3+9TBp6mw1tOCs5EIWTLg3Ks0XHybgdn1c6fraAngYsw/V8sfX/l5+Zf8rHFHiYUSJiwsDKdOnYK2duGfu6qvrw99fX0cOXIEtWrVglhcMp6NbmFhAaFQiIMHD2LMmDHQ0lLMOB44cACZmZmYOHGi0mXklzQoqEWLFqF69eq4desWqldXfOTcrl27oK+vr7K3ibptWLhwIWbPnq3WvD86u7J28F/ij/NrziN4UzBMbE3QdGxTVGhWgavzMe4j79YIHV0d9PxfT5xcdhIb+myArpEuyjYui4aDG3J10lPScXzhcXyM+wixvhg27jbos74P7LzsvrrNOTk5SExMxOjRo0vsieVzJTkG83Pn4TXEfIhC80btFKbFJ3xAzIcoXtmg8R25/z97+QTnL52AlYUt9qw/AwD4mJqCZWtnISHxA/R0DVC6lCdWztuKMm6Fu99YmZIclz9T/FWv7ozDh4diypTDmDPnBFxczLFyZWd07/7f/etRUUl48+a/27pycmRYtuwcQkOjoa2tBV9fD1y9OpH3mMrU1AwMG7YH794lQCrVhqenNXbu7Mc9olJdJTnu5Ep6/OlbucG9+WS8ubYD727th9jQCk71+8Pcw5urk/kpHhkp/FsjsjNSEf/yGpzqD1C6XFl2BsKD1yPjYxyEIh1ITezg2mQszN3qfXWbf4a4y6skxqCnmTbmNjDGX/c/Yvujj7DR18LwagZo4vLf+Apx6TLerRFZOcDaux/xIS0HYi0BnI1EWOhjjFp23+f7zs8Qd5RYKAGOHz8OfX195OTkID09HQCwfPnyQi9HJBJh69atGDhwINatW4cqVarA29sbXbp0QYUKFb68gCJgb2+vUPbp0yeFskmTJmH6dP6vdMePH+eNFyFnZ2eHP/74AxMnTsTs2bNRrVo1+Pr6onv37ihVqhQA4Pnz5zA0NISNjXqPE/y83XZ2dggNDeWVValSBZ07d8bkyZNx/vx5hWU8f/4cpUqVKlBSKCkpCfr6/G75derUwZkzZxTqTpkyBePG/TfacnJyMhwcHBTqFVfu9dzhXs9d5fS2M9oqlJk7m6Pn/3qqnKfZ2GZoNrbZV7ft8uXLOHv2LK9MS0sLBgYG+OOPP756+cVFSY9BVapXqosLAf8qnTZp5HyFMlV15Yb3m4Th/SYVSds+V5Lj8meLv5YtK6BlS9Xn7K1b+/Belyljg3v38u/1Mm9eW8yb17YIWsdXkuNO7meIPxOX6jBxUZ1kKt14tEKZSKyHGkOU9+AEAIda3eFQS70eMXnRebjkxmBtezFq26tOCkyuze/53dVLD1299Aq1js+XUVA/a9xRYqEE8PX1xdq1a/Hp0yds3LgRz549UznewJd06NABLVq0wKVLl3Dt2jWcOnUKS5YswcaNG9GnT5+ibbgSly5dgoGBAa9MWbLg119/VWiPnZ3qX5OHDx+OXr16ITAwEDdu3MCBAwewYMECHDt2DE2aNPnqx1993m6RSPlHa968eShTpgzOnDmjMEJsYdpgYGCAu3fv8sqkUuWj4IrF4hLT+6S4yfueCAQCMMbQunXrn2qEYIBi8Ef2M8Qlxd+P52eIOzmKP82i8zDFoCb8rHFHiYUSQE9PD6VLlwYA/PHHH/D19cXs2bMxd+5cro6BgQFvoEK5xMREhbEcJBIJmjRpgiZNmmDGjBkYMGAAZs6c+V0SCy4uLgpd/pV9STc3N+e2uaAMDAzQunVrtG7dGvPmzUPTpk0xb948NGnSBO7u7khKSkJUVJRavRaUtVsZV1dXDBw4EJMnT8amTZt409zd3XH58mVkZWV9sddC7ujchdt+ohmPHz/mstQl+b46UryEhYVRXJLvjuKOaAKdh4km/IxxR0+FKIFmzpyJpUuXIjIykivz9PTErVu3ePUYY7hz5w48PPJ/BnXZsmWRmpr6TdqqKQKBAJ6entx2dezYETo6OliyZInS+l8avLEwZsyYgWfPnmHv3r288m7duuHjx49Ys2bNN28D+X4sLCzQunVrhIaG/hQnFVI8GBsbU1yS747ijmgCnYeJJvyMcUc9FkogHx8feHl5YcGCBfjzzz8BABMmTEDv3r3h6emJX375BWlpadiwYQNevnyJ4cOHAwDi4uLQqVMn9OvXDxUqVICBgQFu376NJUuWoE2bNprcJAUpKSmIjo7mlenq6sLQUPFxgffv38fMmTPRs2dPlC1bFjo6OggODsbmzZsxaVLu/coODg5YsWIFRowYgeTkZPTq1QvOzs549+4dtm/fDn19/XwfORkTE8ONbyFnZmamtOeBlZUVxo0bh99//51XXrNmTUycOBHjx49HREQE2rVrB1tbW7x48QLr1q1DvXr1uKdFMMYUth/IfTpFfo8HI9/fn3/+Se8J+eFMmjQJ1apV03QzyE+G4o5oAp2HiSb8jHFHiYUSaty4cejbty8mTZoEBwcHdO7cGYwxLF26FNOmTYNEIkHlypVx6dIl7hGM+vr6qFmzJlasWIGXL18iKysLDg4OGDhwIKZOnarhLeKbMWMGZsyYwSsbPHgw1q1bp1DX3t4ezs7OmD17NsLDwyEQCLjXY8eO5eoNGzYM7u7uWLp0Kdq1a4e0tDQ4OzujZcuWvEFvlFHW6+PatWuoVauW0vq//vor1q5dq5CMWLx4MapWrYrVq1dj3bp1kMlkcHV1RceOHdG7d2+uXnJystJbNqKior76UZ6kaP1sJxVSPFBcEk2guCOaQHFHNOFnjDtKLBQT8megfm7r1q1Ky7t164Zu3brxyvz9/eHv769yHWKxGAsXLsTChQtV1inIutWZx8fHR+U2hoeH5/v6S8zNzbFq1aoC1W3cuDEaN26scvrn7cyv3XKzZs3CrFmzeGUGBgaIjY1VWr9z5875dpnq06fPdxnvghBCCCGEEEIK4udLpRBCCCGEEEIIIaTIUGKBEEIIIYQQQgghaqPEAiGEEEIIIYQQQtRGiQVCCCGEEEIIIYSojRILhBBCCCGEEEIIURslFgghhBBCCCGEEKI2SiwQQgghhBBCCCFEbSJNN4AQohkfXn+AjlRH080okWLDYzXdhGLhXUQ4pBJdTTfjp/LmXZimm/DDCA2Nhr6+WNPN+CmEhERpugk/nPTECGhpSzTdjBIrLeGdppvww3ubnA2ptkDTzShRXidla7oJGkWJBUJ+UscXHtd0E0o0qa4U5ubmmm7GD23ZulmabsJPSVdXl2ITwKBBOzXdhJ+KLh0TecIC12i6CSWeRErHuvwsu5mi6SaUSLpSyU8bd5RYIOQnFRwcDH19fU03o8QyNzeHo6OjppvxQ6MY1AyKzVwUf98XxR0fxd+3RzGXP4rBb+NnjjtKLBDyk6pUqRIMDQ013QzyE6MYJJpE8Uc0ieKPaBrFIClqNHgjIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRGyUWCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELVRYoEQQgghhBBCCCFqo8QCIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRGyUWCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELVRYoEQQgghhBBCCCFqo8QCIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRGyUWCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELVRYoEQQgghhBBCCCFqo8QCIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRGyUWCCGEEEIIIYQQojaRphtACNGM+/fvQ19fX9PNKJbMzc3h6Oio6WYUexSDRYvisnAo/r4exZz6KP7UR3FXNCgGC4bireAosUDIT8rb21vTTSi2pLpSPA15Siear0QxWLR0dXUREhJCcVlAFH9fT1dXihA6FqqF4k99EqkuQp/Sse5rUQwWjK5UgpCnoRRvBUCJBUJ+Ui2ntIStp62mm1HsxIbH4vDMw/jw4QOdZL7S+CGz4OZaVtPNKBHevAvDglWTKS4LYcOGHqha1UnTzSi2QkKi0KPHZoo5NZXyHQY9C1dNN6PYSUt4hxdnV1DcFYHxNQzgbqat6Wb80F4nZWPB1WSKtwKixAIhPylzJ3PYeNpouhnkJ2Zv5wx3SiwQDfHwsEaVKnShSDRDYmwHPUtKLBDNcTAUwd2UEguk6NDgjYQQQgghhBBCCFEbJRYIIYQQQgghhBCiNkosEEIIIYQQQgghRG2UWCCEEEIIIYQQQojaaPBGQsgPKeivIARvDOaV6ZnqYcLJCSrnuXngJm4dvIXEqEQYWRmhft/6qOhXUWndf8/8i0O/HYJHAw90+b1LkbadlGyxce/x147luHn3MjIyM2Bv64Rfh8+Bu6uX0vqPQu5iw/bleBvxCumZ6bCysEXLXzqhU6teXJ2xv/XBg8e3FeatWaU+Fk5f+822hRQPEREJmDQpACdPPkZaWibc3a2waVMvlU+1iIpKwvjxB3Dnzhs8fx6DUaN8sXKlv8rl7917C127bkSbNhVx5Miwb7UZpBh5e2MPIm7t45Vp6xqjar+tKueJfvgPoh/9g4zkGIgNzGFXrRMsPH256bKcbETeOYTYpxeQmRoPqbEdHOv0grFTlW+1GaSY2PrwI7Y9SuWVmUiECOhgoXKezByG7Y9Sce5VGuLTZbDQ1UL3cnrwc5Uq1L0Qno65V5JQ116Med7GRd188v8osUAI+WFZlLJArz//+/IlEApU1r116BbOrzmPVlNbwa6sHSIeR+DvhX9DYiCBR30PXt3EqESc+eMMHCvRiPCkcFI+JmHU1J6oVK4GFv62DiZGpoiMfgs9PQOV80jEUrT164ZSTu6QSqR4FHIXK9bNgVQsRctfOgEAZk9chezsLG6epJREDBzXAd51mn7zbSI/toSEVNSt+zt8fd1x8uRIWFoa4OXLWBgb66qcJyMjCxYWBpg2rTlWrDif7/Jfv47DhAkHUb9+6aJuOinmpKaOKNNmNvdaIFTd0Tn60Um8vbYDLg2HQ9+yND6+f46wwNUQifVg4lIDAPD2xi58CA1GKd9hkJrYI/HNPYT+swjlOi6CnkWpb7495MfmbKSFZY1MuNdCgeprPgCYfTkJCWky/FrLEHYGIiSky5DDmEK96I85WHs3BRUs6AkY3xolFgghPyyhlhD6ZvoFqvvw5ENUbVcV5ZqUAwCY2Jng3b/vcGX7FV5iQZYjQ8CMAPgM8sGb+2+QnpL+TdpOSqY9hzfD0twak0bO48qsLe3yncetVBm4lSrDq3/p+jk8DLnDJRYMDYx481y4fBISsQTedX4pwtaT4mjx4tNwcDDBli19uDJnZ/N853F2NseqVbk9FDZvvqqyXk6ODN27b8Ls2a1w6dILJCZ+KpI2k5JBIBRCR8/kyxUBfAgNgmW5pjB3qwcAkBhZ4+P7UETePcwlFj48DYJdtU4wca4GALAu3xxJb+4h6t5RlP5l7LfZCFJsaAkFMJVqFajuzcgMPHifid1tzGEozk14WesrzpsjY5h/NQl9KujjUWwmPmYqJh5I0aHEAiHkhxX/Nh7LWiyDSFsEOy87NBrWCCZ2yi9ycjJzINLhH9JEYhEinkQgJzsHWqLcE07wpmDomeihSusqeHP/zTffBlKyXLsViGqV6mLW7+Pw8PFtmJtZonWzLmjZpGOBl/E8LASPQ++jX9eRKuucPB8A33rNIZWo/lWa/ByOHXuIpk3LolOn9QgOfg47O2MMG+aNgQPrf/Wy58w5DgsLA/TvXw+XLr0ogtaSkiQ9MQp3NveFUEsb+tbucKjVAxIja6V1WU4WhFr8X4SFWmJ8fP8cspxsCLVEYDnZEIo+qyMSIznqyTfbBlJ8RCRno2NALLSFQBlzbQyoqA9bA+VfVa+8y4CHmTb2PknF2VfpkIgEqGMvRr8K+hCL/uvpsP3fVBiLhWhRWopHsZnfa1N+WjR4IyHkh2TnZYe2M9uix6oeaDW1FT7Gf8SmAZvwKUn5L2qutVxx79g9RIZEgjGGyJBI3P/7PmTZMnz6/1/h3jx4g3vH7qHV1Fbfc1NICRL5/h2Ond4HextHLJ6xHq1+6Yw/Ny3EmcCjX5y384BGaNq5MoZO9EfbZl3RQkUyIuT5I7x68xx+jTsUdfNJMRQWFou1a4Ph5maJ06dHYciQBhg1ah+2b7/2Vcu9cuUFNm26gr/+6llELSUlib61O1wbj0aZ1jNRquFwZKYm4PGhychKS1Za38ixMmKenMPHmBdgjOHj+xeIDTkHJstGdnry/9ephKj7x5CWGAnGZEh8cx8Jr24gKzXhe24a+QGVMdPG5DpGWOJrjAk1DRGfJsOIMwlIypAprR/1MQePYjLxKjEbcxoYY3hVAwS/ScfKW//F56OYTPzzIg0Tahp+r8346VGPBULID8mtjhvvtX15e/zR/g88OPEAtbvVVqjfoF8DfIz7iE39N4GBQd9UHxVbVsTVHVchFAqRkZqBwzMPo9XUVtDN597kgpLJZGCMQfCFewBJycKYDO6uXhjQYwyA3Nscwt++wLHT+/GLb5t85101fxvS0j/hybOH2LhjBWxtHNGovp9CvZPnAuDi6IYybuUL3T6Ky5JHJmOoVs0JCxa0AwBUruyIx48jsXZtMHr1UjwWFkRKSjp69NiMv/7qCXPzgt1uln8bZRAIBBR3JYiJU1Xea31rD9zfMQQfngbCprLisc6+emdkfUrE44OTwBiDtq4xzMs0RNTdwxAIcn/HdG4wAGEXVuPBrhEAcm+XsCjTCLEh+Y8DogrFXclR007Me13WQgfdj37A6bA0dC6jp1CfMUAgAKbVNYK+Tm58DcsxwKxLSRhT3RA5jGHB1SRMqGkII0nR/I5O8fZlau/pPn36cDtX1d/n9UQiERwdHTF06FAkJPCzkwKBAEeOHFFYz5gxY+Dj48O9jomJweDBg+Ho6AixWAxra2s0bdoU165dQ1BQ0BfbtHXrVqXb4+zsjJUrV/JeCwQCXL9+Pd/2zJo1S+l6zp07x9WJj4/HmDFj4OzsDB0dHdjY2KBv37548yb/btiqtmf69OlKp5uZmaFhw4a4cuWKwrIK2gb5+zVkyBCFZQwbNgwCgQB9+vT5YpsTExN5r8uVK4ecnBxeXWNjY977ce/ePbRs2RKWlpaQSCRwdnaGv78/Pnz4oHI/5/0LDw/HrFmzUKlSJQD/vYeq/uTvY0Fi70vrlu8TZcs6fvw4fHx8YGBgAF1dXVSvXl0hDsPDwyEQCGBpaYmUlBTetEqVKmHWrFnc67CwMHTt2hW2traQSCSwt7dHmzZt8OzZM5XvS0mgI9WBVWkrxL2NUzpdW6KNNr+1wdSLUzHm8BiMOToGxjbG0NHVga6xLhIiEpAYlYg9E/ZgTp05mFNnDh788wChl0Ixp84cxL+LL1R7Fi9ejA4dOiAmJqYoNo8UE6bGFnC2d+WVOdqXwvsPUV+c18bKHqWc3NGySUd0aNUL2/atUaiTnpGGwCsn4de4vVrto7gseWxsjFC2rA2vrEwZG7x5o/6vvC9fxiI8PA6tWq2GSDQUItFQbN9+HceOPYRINBQvX8YWankjRoyguCvhtLQl0DVzQlqS8mOdUCSGa6ORqD54Hyr33oAqvf+C2MASWtpSiKS5vxhrS43g0WIqagzeiyq9/0LF7qsh1JZAbGilVpso7kouqUiAUsYiRKTkKJ1uKhXCXKrFJRUAwMlIBAYg9lMOIlNyEJ0qw9TgRDTa/R6Ndr/HmbB0XH2XgUa73yMiJbvQbaJ4+zK1EwurVq1CVFQU9wcAW7ZsUSgDgGbNmiEqKgrh4eHYuHEj/v77bwwbpt7jjDp06IAHDx5g27ZtePbsGY4dOwYfHx/Ex8ejTp06vPV37tyZW7f8z99f9eOWPieRSDBp0qQv1vPy8uKtIyoqCg0aNACQ+4W+Vq1aOHfuHNasWYMXL15g3759ePnyJapXr46wsLAvLj80NJS37MmTJyudHhQUBAsLC7Ro0YIX9IVtg4ODA/bu3Yu0tDSuLD09HXv27IGjo3qj6L98+RLbt29XOT0mJgaNGzeGubk5Tp8+jZCQEGzevBk2Njb49OkTJkyYwNsH9vb2mDNnDq/MwcGBt8xbt25x0w4dOqSwLwMCAgrc/rzrWblyJQwNDXllq1atUjrf//73P7Rp0wZ16tTBjRs38PDhQ3Tp0gVDhgzBhAmKj01MSUnB0qVLVbYjMzMTTZo0QXJyMgICAhAaGop9+/ahXLlySEpKKvD2FEfZmdmIfRULAzPVo+8DgJZIC4ZWhhBqCfH47GO413OHQCiAuZM5hu4eiiE7hnB/HvU94FLVBUN2DIGRlVG+y/1cYmIijh07Bg8PD+zbtw9MyUjEpOQpV6Yy3kaG88reRb6GlYWN8hlUYQxZWYr3ewZdOY3MrEw09lbvdh2Ky5Knbl1XhIa+55U9e/YeTk6mai/T09Majx7NwP3707m/1q0rwNfXHffvT4eDQ8EG7JOLjY2luCvhZDlZSIt/Bx3d/GNDqCWCWN8cAqEW4p5fhrFzNa7HAldHpAMdfTMwWQ7iX17jBncsLIq7kiszh+F1UjZMpcq/qpaz0EZcWg7Ssv67VeJtcjaEAsBCVwuORiJsbmGGjX7//dWxF6OSlQ42+pnBUrdgg0TmRfH2ZWrfCmFkZAQjI/6FuLGxMaytFQd1kfcsAAB7e3v4+/ur7DmQn8TERFy+fBlBQUHw9vYGADg5OaFGjf8OSHnXL5VKkZGRobRNBTF48GCsXbsW//zzD/z8FLuryolEIpXrmDZtGiIjI/HixQuujqOjI06fPg03NzcMHz4cJ0+ezLcdlpaWMDY2/uJ0a2trTJ8+Hfv378eNGzfQqlUrtdpQpUoVhIWFISAgAN27dwcABAQEwMHBAaVKqfc4oJEjR2LmzJno2rUrJBKJwvSrV68iOTkZGzduhEiUG5YuLi5o2LAhV0df/7/umlpaWjAwMMj3vbWw+O/Zt6amuRdgX9qXquRdj5GREQQCwRfj6u3btxg/fjzGjBmDBQsWcOXjx4+Hjo4ORo0ahU6dOqFmzZrctJEjR2L58uUYPnw4LC0tFZb55MkThIWF4cKFC3Byyn1+uZOTE+rWrVvobfrRnVl1Bu713WFkbYTU+FRc2nIJGakZqNiiIgDg3OpzSIlNQbtZud2D497EIeJxBOy87JCeko5ru68h5mUM2s5oCyB3IEdLV/4+lRjkxuLn5QVRqlQpnDt3DklJSejSpQv27duHdevWKX3fSMnRsWVPjJzaE7sOboBP3WZ4+vwRTpw9iHFDZnJ1/tq5Ah/iYjBl9EIAwJGTe2BpbgNHOxcAwKOQu9h/bCva+nVTWP7J8wGoV6MhjAyM1WofxWXJM3ZsY9SpsxgLFvyDzp2r4ebNcGzYcAkbNvTg6kyZchgREYnYvr0vV3b//lsAwMeP6YiN/Yj7999CR0cLZcvaQiLRRrly/KeZyB9f+Xn5l6SlpcHLywsHDx6kuCtBXl/eAhOX6tAxsEDWpyRE3N6PnMxPsPD0BQC8uboDmalxKN1kDAAgLSECH2OeQ9/KHTkZH3PHUoh7A9fGo7hlpkQ/Q1ZqHHTNXZCZGod3N/cCjMG2SrtCt4/irmRZezcFte3EsNLTQkK6DDv//YhPWQxNXaQAgL/upSA2TYapdXK/ezZ2lmDHv6lYfD0ZfSroIyldhvX3PqJ5KSk3eKOLMf9rrr6O8vKCoHgrmO8+eGNYWBhOnToFbe3CP0tUX18f+vr6OHLkCDIyMr5B6/icnZ0xZMgQTJkyBTKZ8sFD8iOTybB37150795d4UuoVCrFsGHDcPr0acTHF64LtiqfPn3Cli1bAIDbv+q2oW/fvtyyAGDz5s3o16+f2m0bM2YMsrOz8eeffyqdbm1tjezsbBw+fLjEZAAPHjyIrKwspT0TBg8eDH19fezZs4dX3rVrV5QuXRpz5sxRukwLCwsIhUIcPHhQ4dYSVTIyMpCcnMz7Kw6SY5Jx6LdD+LPTn9g/eT+0RFoYsGkAjG2MAQAf4z4i6f1/vTRkOTJc230N63qsw46RO5CdmY1+G/vB2Nb4m7ZTHq/yLPb+/fu/6fqKo+Iag8p4upXHnEkrceHySfQb0xY7DqzDsH6T0Ni7JVcnPuEDYvLcGiGTybBx50oMGt8RQyf64/A/uzGgxxj07TKCt+y3keF4FHIXzRupdxtEXhSX/ynu8Ve9ujMOHx6KPXtuoVy52Zg79wRWruyM7t3/S0pHRSXhzRv+ebxy5XmoXHke7tx5g927b6Jy5Xnw81N+Dv4a9erV4/5PcaeouMZfZmocnp9ehgc7h+PZyUUQCkXw6rQEYsPcL1GZn+KRkZLnlhkmQ9S9o3i0dwxCjs6CLDsTXh0XQZLnNgeWk4m313fhwe6RePbPIujomcGr/UKIxIUf54PiruCKQwzGfsrBvCtJ6PX3B8y8mAiRUIDVzUy5R0jGpcsQk/rfda9UW4ilDU3wMZNhyMk4zL+ahNp2Yoysln+vVnVRvBXMdxm88fjx49DX10dOTg7S03OfGb98+fJCL0ckEmHr1q0YOHAg1q1bhypVqsDb2xtdunRBhQoVirrZAIDp06djy5Yt2LVrF3r2VD5y8qNHj3i/ppctWxY3b95EbGwsEhMTUaZMGaXzlSlTBowxvHjxgtfr4nP29va8169fv4aZmZnC9E+fPoExhqpVq6JRo0YAoHYbevbsiSlTpnD3/l+5cgV79+5FUFCQynbmR1dXFzNnzsTUqVMxcOBAhd4utWrVwtSpU9GtWzcMGTIENWrUQMOGDdGrVy9YWal3752mPXv2DEZGRrCxUewiraOjg1KlSimMiyAQCLBo0SK0atUKY8eOhasr/15uOzs7/PHHH5g4cSJmz56NatWqwdfXF927d1fZm2ThwoWYPXt20W3Yd9Jxfv6P75P3RJCzcLHA4B2DC7WOz5fxNXJycpCYmIjRo0ejc+fORbbckqC4xqAqtav5oHY1H5XTJ42cz3vdvkV3tG/R/YvLdbB1xoWAf7+2eTwUlyUj/lq2rICWLVVf52zd2kehjLH1hVqHsmWoi+LuP8U1/tyaKv4oklfpxqN5r6WmDqjQZUW+8xjalUPF7kWf3JKjuFOuOMTgjHrG+U6fXFvxdlVHIxGWNir4bVvKlvE1KN4UfZceC76+vrh//z5u3LiBkSNHomnTphg5UvXzu/PToUMHREZG4tixY2jatCmCgoJQpUoVtW6tKAgLCwtMmDABM2bMQGam8uefenh44P79+9yf/H7+L5FnvL40uuilS5d4yzcxMVGYfvfuXezZswdOTk7YunVrgXuEqGqDubk5WrRogW3btmHLli1o0aIFzM3NC7RMVfr37w9zc3MsXrxY6fT58+cjOjoa69atQ9myZbFu3Tp4enri0aNHX7XeH5WqkdubNm2KevXq4bffflM63/DhwxEdHY2dO3eidu3aOHDgALy8vHD27Fml9adMmYKkpCTu7+3bt0W6HSSXlpYWjI2N8ccff2i6KT8cikHNobik+PvWLl++rFBGcfcfir9vg+Ku4CgGvx7FW8F8l8SCnp4eSpcujQoVKuCPP/5ARkaGQubMwMBA6eBziYmJCr9uSyQSNGnSBDNmzMDVq1fRp08fzJw5U2HeojJu3DikpaVhzRrFEbyB3F+fS5cuzf3JBxG0sLCAsbExnjx5onS+p0+fQiAQKPwq/TkXFxfe8oVCocJ0d3d3+Pv7Y/bs2WjXrh13q8jXtKFfv37YunUrtm3b9lW3QciJRCLMmzcPq1atQmRkpNI6ZmZm6NSpE5YtW4aQkBDY2trmO5hhUShM7BWGu7s7kpKSlG5rZmYmwsLC4ObmpmROYNGiRdi3bx/u3bunss2tW7fG/Pnz8eDBA9SvXx/z5s1TWlcsFsPQ0JD3R4qOPDnUunVrhIaGolOnThpu0Y+HYvD7o7j8D8XftyWVSrn/U9wpovj7NijuCo5i8OtRvBXMdx9jAQBmzpyJpUuX8r5weXp64tatW7x6jDHcuXMHHh4e+S6vbNmySE1N/SZtBXLHdvjtt98wf/78Qt2XJBQK0blzZ+zevRvR0dG8afJERdOmTbmBBYtCz549IZPJuCTI17ShWbNmyMzMRGZmJpo2bVok7evUqRO8vLwK1CVLR0cHrq6u3/S9Bb4u9vLToUMHiEQiLFu2TGHaunXrkJqaiq5duyqdt0aNGmjfvr3CE0CUEQgE8PT0/Ob7ifCFhYVBS0sLRkZG2LdvHwICAmgAH6JxFJdEEx4/fkxxR747ijvyPVG8fZlGEgs+Pj7w8vLijZQ/YcIEbNq0CX/++SeePXuGBw8eYMSIEXj58iWGDx8OAIiLi0PDhg2xc+dOPHz4EK9evcKBAwewZMkStGnT5pu2edCgQTAyMlIYbO9L5s+fD2trazRp0gQnT57E27dvcfHiRTRt2hRZWVlYvXp1kbZTKBRizJgxWLRoET59+vRVbdDS0kJISAhCQkKgpVX4x7KosmjRImzevJn3Rfj48ePo0aMHjh8/jmfPniE0NBRLly7FP//8883f24LEnjocHR2xZMkSrFy5EtOmTcPTp0/x8uVLLF++HBMnTsT48eN5T4T43Pz583HhwgWEhoZyZffv30ebNm1w8OBBPHnyBC9evMCmTZuwefPmb76fCJ+xsTGXraZ768iPguKSaIKFhQXFHfnuKO7I90Tx9mXfZfBGZcaNG4e+ffti0qRJcHBwQOfOncEYw9KlSzFt2jRIJBJUrlwZly5d4h6rp6+vj5o1a2LFihV4+fIlsrKy4ODggIEDB2Lq1KnftL3a2tqYO3cuunVTfDxYfszNzXH9+nXMmTMHgwcPRlRUFMzMzNCsWTPs3LkTjo6ORd7Wfv36YebMmfjzzz8xceLEr2rDt+gu1bBhQzRs2BBnzpzhysqWLQtdXV2MHz8eb9++hVgshpubGzZu3Khy0MyiUpDYU5d8AMalS5di1apVyMnJgZeXF9auXYu+ffvmO6+7uzv69euHDRs2cGX29vZwdnbG7NmzuYE15a/Hjh37VW0lhTNp0iRUq1ZN080ghIfikmjCn3/+qXCbJiHfGsUd+Z4o3r5MwErKs/0IIQWSnJwMIyMj9FnXB06Vvy5x8jOKehqFDb034M6dO6hSpYqmm1Nk5HGRlJT0ze+/lK9rxdytqOhFX4KLwrOXTzDk187FNi41EX/BwRPQoIHycW7Il929+wZVq84vtjH3ue8Vg/L1lG03H4Z2Xt9sPSVVasxLPNo/vsTEnZwmjoErG5ugopXON11XcfcsPguDT8aXuHj7XFHFH6VdCCGEEEIIIYQQojZKLBBCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELVRYoEQQgghhBBCCCFqo8QCIYQQQgghhBBC1EaJBUIIIYQQQgghhKiNEguEEEIIIYQQQghRm0jTDSCEaMaH1x+gI9XRdDOKndjwWE03ocR4FxEOqURX080oEd68C9N0E4qd0NBo6OuLNd2MYiskJErTTSjW0hMjoKUt0XQzip20hHeabkKJ8TY5G1Jtgaab8UN7nZSt6SYUK5RYIOQndXzhcU03odiS6kphbm6u6WYUe8vWzdJ0E0oUXV1distCGDRop6abUOzp0rFQbWGBazTdhGJLIqVjXVFYdjNF000oFnSlEoq3AqLEAiE/qeDgYOjr62u6GcWSubk5HB0dNd2MYo9isGhRXBYOxd/Xo5hTH8Wf+ijuigbFYMFQvBUcJRYI+UlVqlQJhoaGmm4G+YlRDBJNovgjmkTxRzSNYpAUNRq8kRBCCCGEEEIIIWqjxAIhhBBCCCGEEELURokFQgghhBBCCCGEqI0SC4QQQgghhBBCCFEbJRYIIYQQQgghhBCiNkosEEIIIYQQQgghRG2UWCCEEEIIIYQQQojaKLFACCGEEEIIIYQQtVFigRBCCCGEEEIIIWqjxAIhhBBCCCGEEELURokFQgghhBBCCCGEqI0SC4QQQgghhBBCCFEbJRYIIYQQQgghhBCiNkosEEIIIYQQQgghRG2UWCCEEEIIIYQQQojaKLFACCGEEEIIIYQQtVFigRBCCCGEEEIIIWqjxAIhhBBCCCGEEELURokFQgghhBBCCCGEqI0SC4QQQgghhBBCCFEbJRYIIYQQQgghhBCiNkosEEIIIYQQQgghRG2UWCCEEEIIIYQQQojaKLFACCGEEEIIIYQQtVFigRBCCCGEEEIIIWqjxAIhhBBCCCGEEELURokFQgghhBBCCCGEqI0SC4QQQgghhBBCCFGbSNMNIIRoxv3796Gvr6/pZvzwzM3N4ejoqOlmlEgUg1+HYvPrUPzlj+Lr26L4yx/F37dHMZiLYq3oUGKBkJ+Ut7e3pptQLEh1pXga8pROOt8AxeDX0dXVRUhICMWmmij+8qerK0UIHfu+GYq//Emkugh9Sse3b4liMJeuVIKQp6EUa0WAEguE/KRaTmkJW09bTTfjhxYbHovDMw/jw4cPdML5BsYPmQU317Kabkax9OZdGBasmkyx+RU2bOiBqlWdNN2MH1JISBR69NhM8fUNlfIdBj0LV00344eUlvAOL86uoPj7xsbXMIC7mbamm6FRr5OyseBqMsVaEaHEAiE/KXMnc9h42mi6GeQnZm/nDHdKLBAN8fCwRpUqdCFJNENibAc9S0osEM1xMBTB3fTnTiyQokWDNxJCCCGEEEIIIURtlFgghBBCCCGEEEKI2iixQAghhBBCCCGEELXRGAuEEI0K+isIwRuDeWV6pnqYcHKC0vrhd8Kxbdg2hfLh+4bD3NkcAHD/+H0cnXtUoc60i9MgEtNhjxRObNx7/LVjOW7evYyMzAzY2zrh1+Fz4O7qpXKeB49vYc2W3xH+9gXMTS3h37YvWjf156ZnZ2dhd8BGnA48ig/xMXCwdcagnuNQo0q977FJ5Acxa9bfmD37OK/MysoQ0dG/f3HeK1dewNt7GcqVs8X9+79x5VlZOVi48CS2bbuGiIhEeHhYY/HidmjWrFyRt58Ub29v7EHErX28Mm1dY1Ttt/WL86ZEheBxwDTomjmiQpeVSut8eHYJL84sg4lLDXi0mFoELSbF1daHH7HtUSqvzEQiREAHC5Xz3H+fiTV3UxCemA1zXS10KaOL1u663PRXidnY8vAjnsVn4X2qDMOr6qOjp9432wbyZXSFTQjROItSFuj1Zy/utUAo+OI8Iw6MgFhPzL3WNdblTRfriTHiwAheGSUVSGGlfEzCqKk9UalcDSz8bR1MjEwRGf0WenoGKueJev8OU+YNg1/jDpg6ZiH+DbmHVX/Ng7GhKRrUbgIA2Lz7fzh78TjGD50FRzsX3Lp/BTOWjMb/FuyEW6ky32vzyA/Ay8sW586N4V5raX25M2lSUhp69dqCRo088f59Mm/a9OlHsHPnTfz1Vw94elrj9OknaNduHa5enYjKlWmwSsInNXVEmTazudcC4ZfjLzsjFS/OroSRfQVkpSUqrZORHIM3V7bCwJYG6CW5nI20sKyRCfdaKFB9rRf1MQdTAhPQorQuptUxwr+xmVh5KwVGEiG8HSUAgIwcBlt9Lfg4SrD6Tso3bz/5MrrKJoRonFBLCH0z/ULNo2eiB4mBRHUFAQq9TEI+t+fwZliaW2PSyHlcmbWlXb7z/H16PyzNrTGi/2QAgJO9K569fIz9R7dyiYWzwX+je8dBqFW1AQCgTbMuuH3/Kg4c24qpYxZ/o60hPyKRSAhra6NCzTN48E5061YDWlpCHDlynzdtx44bmDatOfz8ygMAhg71xunTj7Fs2Vns3Nm/qJpNSgiBUAgdPZMvV8zjVdBamLs3AARCJLy6oTCdyXLw4uwK2NfsguTIJ8jJSFWyFPKz0RIKYCrVKlDdY88/wVJPCyOq5SbxnYxECI3Lxv6QT1xiwdNMG57//7jMDfcpsfAjoMQCIUTj4t/GY1mLZRBpi2DnZYdGwxrBxC7/C531PdcjOzMbFi4WqN+3PlyqufCmZ6ZlYmWblZDlyGDtbg3fwb6w8aDHa5LCuXYrENUq1cWs38fh4ePbMDezROtmXdCySUeV8zx+9gDVKtXhlVWrVBf/nD+M7OwsiETayMrKhI62Dq+Ojo4Yj0LufZPtID+u589jYGs7EWKxCDVrumDBgrYoVUp19+AtW67g5ctY7NzZD/Pm/aMwPSMjGxIJ/xFyUqkOLl9+WeRtJ8VfemIU7mzuC6GWNvSt3eFQqwckRtYq68c8OY/0pGiUbjIW727tV1rn3a39EEkNYVm2CZIjn3yrppNiJiI5Gx0DYqEtBMqYa2NARX3YGij/KvokNgvVbPjnyOq2OvjnZRqyZQyiAvRsJd8fDd5ICNEoOy87tJ3ZFj1W9UCrqa3wMf4jNg3YhE9Jn5TW1zfXR8spLdF5UWd0XtwZZk5m2D5iO17fe83VMXcyR9vf2qLL713QYW4HiHRE2DxwM+LexH2vzSIlROT7dzh2eh/sbRyxeMZ6tPqlM/7ctBBnAhXH8JBLSPgAEyMzXpmJsRlycrKRlJwIAKhWuS4O/L0d7yJfQyaT4fb9q7h6MxDxCbHfcnPID6ZmTRds394Xp0+Pxl9/9UR0dDLq1FmCuLiPSus/f/4ekycfxq5d/SESKf/lr2nTsli+/ByeP38PmUyGs2ef4OjR+4iKSvqWm0KKIX1rd7g2Ho0yrWeiVMPhyExNwONDk5GVlqy0flpiJN5e247STcZCIFQefylRIYh9cg6lfId/y6aTYqaMmTYm1zHCEl9jTKhpiPg0GUacSUBShkxp/fh0GUwk/BgzkQiRw6ByHqJ51GOBEKJRbnXceK/ty9vjj/Z/4MGJB6jdrbZCfXMnc5g7mXOvHco7IPl9Mq7uugqnyk7cMuzL23N1HCs6Yn2v9bh54Caaj29e6DbKZDIwxiDI535AUjIxJoO7qxcG9BgDAHArVQbhb1/g2On9+MW3jcr5FGKFMV75iH6TsWztLPQZ1QqAALbWDmjWsC1OXThSqPZRbBZvzZv/N6Bi+fJ2qF27FFxdp2PbtmsYN64Jr25Ojgzdum3C7Nmt4O5upXKZq1b5Y+DAHfD0nAmBQABXVwv07VsHW7ZcVauNMpkMAoGAYqwEMnGqynutb+2B+zuG4MPTQNhU5h/fmCwHL84sh33NrpCaKL8dLCczDS/OrIBLw2HQlhp+dfvo+FZy1LQT816XtdBB96MfcDosDZ3LKB9w8fN3/f9PowrlRYFirWgUqx4LQUFB3MlNKBTCyMgIlStXxsSJExEVFcWrO2vWLK5u3j9PT09evRcvXqBv376wt7eHWCyGi4sLunbtitu3b3N1BAIBjhw5wr0ODAyEr68vTE1NoaurCzc3N/Tu3RvZ2dm8diYmJnLz5OTkYMWKFahQoQIkEgmMjY3RvHlzXLlyhdeerVu3QiAQoFmzZrzyxMRECAQCBAUFFbgd+VmwYAG0tLSwaNEihWnyNsj/rKys0KpVKzx+/JhXr0+fPkr38edtz0v+viirs2TJEggEAvj4+ChMe/fuHXR0dBTeP7m869fT04Obmxv69OmDO3fu8OrljaHP/6Kjo5UuOzw8XGn9Hj16cHXS0tJgYmICU1NTpKWlKSzD2dkZK1eu5L2WL0cqlcLT0xO///47mPyomWe99+/f58p27NgBPT09vHjxgrf8yMhImJiYYNWqVUq3oTjRkerAqrQV4t4WvHeBXTk7xL+NVzldIBTAtqxtvnXys3jxYnTo0AExMTFqzU+KL1NjCzjbu/LKHO1L4f2HKBVzACYm5ohP/MArS0iKh5aWCIYGuffSGxuZYu7kP/DP7lvYs/4Mtv3vb0glurC2yn/8hs9RbJYsenpilC9vh+fPFd/PlJR03L79GiNG7IVINBQi0VDMmXMCDx68g0g0FBcuPAUAWFgY4MiRYUhN/R9ev16Ap09nQ19fDBcXc4VlFsSIESMoxn4SWtoS6Jo5IS1J8fiWk5WG1JgXeBW8AddXt8f11e0RcWs/Pn0Ix/XV7ZH07iHSk6KQkRKD0OPzuTofngYh4dUtXF/dHulKlpsfOr6VXFKRAKWMRYhIyVE63VQiRHw6f1pihgxaAsBQXPRfXynWikaxSizIhYaGIjIyErdu3cKkSZNw7tw5lCtX7v/au++wps73DeB3CHsjQ4bKElCg7jpqXXW2bq2Iddb11WrrnrVqndXWqq1aF+CqFfeqtm4UFTdWAUXFLciSIZtwfn/wIxoTViQkwv25rlyXOedNzpNwe0IeznkPbt26JTPOy8sL0dHRMrfg4GDp+qtXr6Jhw4aIjIzEunXrEB4ejn379qFWrVqYNGmSwm2HhYXh888/x8cff4yzZ8/i1q1b+P3336Gjo4O8PMWH5giCAF9fX8ybNw/fffcdIiIiEBQUhOrVq6N169YyTQsA0NbWxsmTJ3H69OlC3wNl6nhbQEAApk6dCn9/f4XrTU1NER0djRcvXuDvv/9GWloaOnfujOzsbJlxnTp1knuP//rrryK3bWdnh9OnT+PZs2dyNdWooXjG6k2bNsHHxwfp6elyzZi3Hx8dHY2wsDCsXr0ar1+/RpMmTbBlyxa5sXfv3pWr28bGpsi6T5w4ITN+9erV0nV79uyBt7c3PD09sXfv3iKfp8C8efMQHR2NiIgITJ48GTNnzsT69euLfMzAgQPRsWNHDB48WObnPHLkSNSvXx/fffddibatyXKzcxH3MA4mloXPuv+umLsxRU7UKAgCXka+VHoyx6SkJBw8eBAeHh4IDAyUaQBRxeZduz6evngks+zZi8eoal34fB1e7nVx7eZFmWVXb16Ah6sXtLVlz33X1dWDtWVVSCS5OBtyHM0/blOq+pjNiiUrKwcREdGws5OfzNHUVB+3bs1GaOgs6W3UqJbw8KiK0NBZaNJEdp4ZfX0dODhYIDc3D3v23ED37nWVqikuLo4ZqyTyJDnISHwGXUP5OY7Euoao028l6vgul96qeneEvrkD6vguh3FVdxhYVJMbY+H8MUyreaOO73LoGpeuucX9W8WVLRHwODkXVQwUfxX1tNbBtWjZ7xxXo7PhYamjkvkVmLWy8UE2FmxsbGBrawt3d3f4+vri/PnzsLa2xujRo2XGaWtrw9bWVuZmZZW/UxMEAUOGDIGbmxvOnTuHzp07w9XVFfXq1cOcOXNw4IDi82ePHz8OOzs7LF26FN7e3nB1dUWnTp2wceNG6OrqKnzMzp07sXv3bmzZsgXDhw+Hs7Mz6tati/Xr16Nbt24YPnw40tLezJhrZGSEr7/+GtOnTy/0PVCmjgJBQUHIyMjAvHnzkJaWhrNnz8qNEYlEsLW1hZ2dHRo1aoQJEybg8ePHuHv3rsw4PT09uffYwqLoSfdsbGzQoUMHbN68WbrswoULiI+PR+fOneXGC4KAgIAADBw4EF999RX8/PwUPq+5uTlsbW3h5OSEDh06YPfu3ejfvz/Gjh2LV69eydXwbt1axVxiydLSUma8mdmbX/z8/PwwYMAADBgwoND63mViYiKtd/jw4ahTpw6OHTtW7OPWrVuH+/fv49dffwWQ33Q5d+4cAgICPshDuI6tPIZH1x/h1YtXeHb7GXbN2IWstCzU7Zz/S/CJ1Sewb+4+6fiQv0JwJ+gOEp4kIDYqFidWn0DE6Qg07tNYOubMxjO4H3Ifr56/QkxkDA4uOIiYyBg06tVIqRpdXFwgkUiQnJwMX19fdrUrkS+7DER45H/4c/d6PI9+gpNn/8bfx3ejR6d+0jEbti3H4pUzpPe7dvTBy7horAlYisfPHuDoyb04enIvfLoPkY6JiPwPZ0OO40XMU/wXfg3T5o/Kb0L3HFqq+pjND9vkybsRFBSJhw/jcenSQ3z55TqkpGRi8OD808BmzNiHQYMCAABaWlrw9naQudnYmEBfXwfe3g4w+v/L71669BB7915HVFQczp27h06dViIvT8DUqR1LXV9GRga8vLyYsQrqcXAAUp7fRmbKS6TGRCLy6BJIstNhXSu/wfnkwlbcP74CACASacHQ0lHmpm1gBi1tHRhaOkKsow8tbV25MWI9I4h1DGBo6QgtsU4R1cjj/q3i+ON6KkJfZiP6tQTh8TmYey4J6TkCOjobAAA23EjFogtv5oHp5maIl2kSrL6WisfJuTjyIANHHmTAp/abS4vnSATcT8zB/cQc5OYB8el5uJ+Yg+epxR+5/S5mrWx8kI2FdxkYGGDUqFE4f/58iUMQGhqKsLAwTJo0SeEXSnNzc4WPs7W1RXR0tMIv44XZvn073N3d0bVrV7l1kyZNQkJCAo4fPy6zfO7cubh16xZ2795dZnUU8PPzQ79+/aCjo4N+/foV+0U4KSkJ27dvBwDo6JTuQ6EwQ4cOxaZNm6T3/f390b9/f4VNkdOnTyM9PR3t2rXDwIEDsXPnTqSmluyyMhMmTEBqaqrc+1uWHjx4gIsXL8LHxwc+Pj64cOECoqKiSvx4QRBw5swZRERElOj9tba2xrp16/DDDz/g+PHjmDBhAlauXAlHR0eF47OyspCSkiJz0yQpsSnY88MerOqzCjun74RYW4zhfsNhbmcOAHid8BrJL9982EhyJTj22zGsHbAWAf8LwNObT/HVr1+hdpva0jGZqZk4vPgwVvddja3fbUVqXCqGrBsCB6/SHWb+roIOdkFXe+dOxTNikyxNz2BRarl9hHnTVuBU8FEMHd8DW3etxTdDp6Fdqy7SMYmv4hH71qkRdlWrYfGsNQi9fQUjJ36JrbvWYeywGdJLTQJAdk4WArb/jq/HdcfsJeNgVcUGKxdugbGRcuclM5uF0+T8PXv2Cv36bYSHx2z06rUWurraCAmZBkfH/Mk/o6OT8eRJ6U7hyszMwaxZB+HpORc9e/4BBwcLBAdPgbm5YfEPfsenn34q/TczphxNzl92WgLu/bsMN7eNQeTRn6ClpQ2vPkuhZ5p/BGd2eiKyUtU/oSyz9340IYNx6RIsOJ+MQYfiMedsErS1RFjdqQpsjfMnaEzIzENs2ptTH+yMxVjcxgI3X2ZjxJEEbL31Gt82NJFeahIAEjLyMOJoIkYcTURCRh4CI9Ix4mgifg5R/vUxa++nwkzeWHDu/aNHj6SHtN+6dQvGxrKHPvv6+mLjxo24d++ezONKqk+fPvj333/RqlUr2NraomnTpmjbti0GDRoEU1PFvxBGRkaidu3aCtcVLI+MjJRZbm9vj3HjxuH7779Hjx49yqQOAEhJScGePXtw4UL+JE4DBgxA8+bN8fvvv8s8Ljk5GcbGxhAEAenp+bPzd+vWTe79Onz4sNx7PG3aNPzwww+F1gAAXbp0wahRo3D27Fk0bNgQO3fuRHBwsMJTM/z8/ODr6wuxWAwvLy/UrFkTgYGBGD58eJHbAGRz8bZq1arJ3HdwcJA7GuNdn3zyiUwT6ty5c6hfvz78/f3x+eefS4/U6NSpE/z9/bFgwYLCngpA/vs0a9YsZGdnIycnB/r6+iU+laFHjx7w8fFBp06d0KVLFwwZMqTQsYsXL8aPP/5YoudVhy8XFn7ZPgDoMbuHzP3mA5uj+cDmRT6m04RO6DSh8Lk+3pdEIkFSUhLGjRsHHx8flW2notD0DBanWaPWaNaodaHrp327UG5ZXa+PsX7ZrkIfU9frYwT8drAsypPBbMrT5Pzt2DGiyPWbNg0pcv3cuV0xd67sHy1atXJHePjc96yscMxY6Why/tw6Ti5yfc1244pcX71JP1Rv0q/IMcU9R2kwe8rRhAzO/tS8yPXTm8mf/lWvqi7Wf2GpYHQ+W2MxTvcvfCLb98GsKadCHLEAvOkwvX0ouIeHB0JDQ2VuCxcuLHR8SYjFYgQEBODZs2dYunQp7O3tsXDhQul8DspSVMe0adMQFxen8Mu2snVs374dLi4uqFs3/zDzevXqwcXFBTt27JAZZ2JigtDQUFy7dg1r166Fq6sr1q5dK/d8bdq0kXuPx4wp/hJDOjo6GDBgAAICArBr1y64u7ujTp06cuOSkpKwd+9emYkSBwwYUOjcEO8q7Od87tw5mZr//fffYp8rMDBQ5jGenp6QSCTYvHmzXH2bN2+GRKJ4QpoCU6ZMQWhoKIKCgtCmTRt8//33+OSTT0r0ugDghx9+QF5eXrFNnBkzZiA5OVl6e/r0aYm3QYqJxWKYm5vjt99+U3cpHwRmsPwwm/KYP+W9PS9VAWasdJi/ssPsKYcZLD1mTTkV5oiFiIgIAPmz7RfQ1dVFzZo1FY53d3eXPq5evXql3p6DgwMGDhyIgQMHYsGCBXB3d8fatWsVdgTd3d0RHh5eZN1ubm5y68zNzTFjxgz8+OOP6NKli9z60tYB5J9yEBYWBm3tNz/6vLw8+Pn5YeTIkdJlWlpa0veuVq1aiImJQd++feVOvTAyMir0PS7O0KFD0aRJE9y+fRtDhyo+r3j79u3IzMxEkyZNpMsEQUBeXh7Cw8Ph6elZ5DYK3l9nZ9lJrZydnQs93aUw1atXl3utR44cwfPnz9G3b1+Z5RKJBMeOHcPnnxd+aUMrKyvUrFkTNWvWxJ49e1CzZk00bdoU7dq1K1E9BT/Dt3+Wiujp6UFPT6/IMVQyIpEIgiCgW7duWLt2bbETflI+ZlD1mM3CMX/KMzAwkP6bGVMO8/f+mL33wwyWHLP2firEEQsZGRlYv349WrZsCWtr6xI9pl69evD09MSyZcsUXkXh7UtFFsfCwgJ2dnYyEzC+zdfXF/fu3cOhQ4fk1i1btgyWlpZo3769gkcC3377LbS0tEp0GcHi6rh16xauXr2KM2fOyPzl/ezZs7hy5Qpu375d6HNPmDABN2/exL59+wodU1peXl7w8vLC7du38dVXXykc4+fnh0mTJsnUe/PmTbRp06ZERy2sWLECpqamJf6yXloFp2m8e9RG//79SzyJI5D/s/v2228xefJkzkSrgaKioiAWi2FmZobAwEDs3buXHzakEZhNUrWwsDBmjNSC+zcqL8xa2fggj1iIjY1FZmYmUlNTce3aNSxduhTx8fFyl/nLzc1FTEyMzDKRSISqVatCJBIhICAA7dq1Q8uWLTFz5kzUqlULr1+/xqFDh3Ds2DEEBQXJbXvdunUIDQ1Fz5494erqiszMTGzZsgVhYWH4/fffFdbr6+uLXbt2YfDgwfj555/Rtm1bpKSkYPXq1Th48CB27doFIyMjhY/V19fHjz/+KHd6gTJ1+Pn5oXHjxmjZsqXcumbNmsHPzw/Lly9X+FhTU1MMHz4cc+bMQY8ePaSnFmRlZcm9x9ra2tKrbxTn1KlTyMnJUXj0QGhoKK5fv44///xTbm6Hfv364fvvv8fixYulEx4mJSUhJiYGWVlZ0kuI7t+/H1u2bJF7/oIMvc3S0rJUk1PGxcXh0KFDOHjwILy9vWXWDR48GJ07d0ZcXFyJm11jxozBkiVLsGfPHnz55Zt5BxTN/VDckRpUtszNzdm9Jo3EbJKqWVtbM2OkFty/UXlh1srGB9lY8PDwgEgkgrGxMVxcXNChQwdMnDgRtra2MuPCwsJgZyd7rXE9PT3pF8rGjRvj6tWrWLhwIUaMGIH4+HjY2dnhk08+wYoVKxRuu3HjxggODsaoUaPw4sULGBsbw8vLC/v370erVq0UPkYkEmHnzp1YuXIlli9fjjFjxkBPTw/NmjXD6dOnZWZdVmTw4MFYtmyZzOkUpa0jOzsb27Ztw7Rp0xRuo3fv3li8eDGWLFlSaB3jxo3Db7/9hl27dkknMvnnn3/k3mMPDw/cuXOnyNdUoLCGCpDfCPH09FQ4wWaPHj0wevRoHDp0CL169QIAfP311wDymzEODg749NNPcfnyZTRo0EDu8R4eHnLLLl68iKZNm5aobgDYsmULjIyM0LZtW7l1bdq0gYmJCbZu3YqJEyeW6Pmsra0xcOBAzJ07V/qagPzG1LsePnxY4jrp/U2bNg2NGil3qUoiVWI2SdVWrVpV7OWYiVSB+zcqL8xa2RAJPO6aqFJJSUmBmZkZhqwdAsf6ii9RSfmi70Rj/eD1uHbtmsIGVUVSkIvk5OQiryxTlttaPn8T6nrxg1wZkQ/CMWqKT4XJpjryFxQ0GS1bys9vRMD160/QsOHCCpOvkiivDBZsx7PnQpg6eKlsOx+ytNgHuLVzEvOn4m2taGeBulXlL/NemUQm5uB/RxMrVdYUKav8sQVNREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqVpq7sAIlKP+Mfx0DXQVXcZGi3uUZy6S6jQnj1/BAN9Q3WX8UF68ixK3SV88O7ejYGxsZ66y9BIERHR6i6hwstMeg6xjr66y9BIGa+eqbuESuFpSi4MdETqLkOtHifnqruECoWNBaJK6vDiw+ou4YNgYGgAKysrdZdRIS1bO1fdJXzQDA0Nmc33MHLkNnWXoNEMue9TqajTa9RdgkbTN+D+TdWWXU5VdwkawdBAn1krI2wsEFVSQUFBMDY2VncZGs/Kygo1atRQdxkVEjP4fpjN98P8FY35Ui3mr2jMn+oxg/mYtbLDxgJRJVWvXj2YmpqquwyqxJhBUifmj9SJ+SN1YwaprHHyRiIiIiIiIiJSGhsLRERERERERKQ0NhaIiIiIiIiISGlsLBARERERERGR0thYICIiIiIiIiKlsbFAREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqWxsUBERERERERESmNjgYiIiIiIiIiUxsYCERERERERESmNjQUiIiIiIiIiUhobC0RERERERESkNDYWiIiIiIiIiEhpbCwQERERERERkdLYWCAiIiIiIiIipbGxQERERERERERKY2OBiIiIiIiIiJTGxgIRERERERERKY2NBSIiIiIiIiJSGhsLRERERERERKQ0NhaIiIiIiIiISGlsLBARERERERGR0rTVXQARqUdoaCiMjY3VXYbaWVlZoUaNGuouo1JiBovHfKoO8yeLWStflTV/zJnmqIwZZP5Ui40FokqqVatW6i5BIxgYGuBOxB1+0KgBM1g8Q0NDREREMJ8qwPzJMjQ0QAT3heWmsuZP38AQd+9wn6YJKmMGDQ30EXHnLvOnImwsEFVSXWZ0gX0te3WXoVZxj+Kwb84+xMfH80NGDSaNmgs3V091l6GxnjyLwqKV05lPFVm/fgAaNnRUdxkaISIiGgMG+DNr5cilzTcwsnZVdxnlKuPVM9w/vpw50xCTGpvA3VJH3WWUm8fJuVh0IYX5UyE2FogqKStHK9jVslN3GVSJVXNwgjsbC6QmHh62aNCAv1ySeuibO8DIpnI1FkizVDfVhnuVytNYINXj5I1EREREREREpDQ2FoiIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqVx8kYiKlfnNp3DqT9OoUnfJug0sVOh4y7vuowru68gKToJZlXN0OLrFqj7RV2ZMSF/heDq3qtIfpkMQzND1P6sNtp90w7aety1Uclt2rEaW3b+IbPMwtwSe/yDFI4PvX0ZE2cPlX+e3w6iRjUXAMDZkOPYvmcDnkc/hUSSCwe7GujTbTA6tO5W9i+APhhz5x7Cjz8elllWtaopYmJ+Vjg+OjoZkybtwrVrT3DvXiy++64NVqzoW+jz79hxBf36bUT37nWxf/83ZVo7fbieX92NpyHbYFu3C5xaDFc45v6JlYi/c1puuUGV6qj71e8AgPSEJ3h2aTtexz1AdmocHD8dCrt63KdR0f68nYaNN1+jt4chxjYyKXRctkTAlltpOPEwA4mZebA2FKO/txG+cDWQjnmdnYeNoa9x7mkWUrPzYGcsxugGJmjqoFceL4WKwd++iajcPA9/juv7r6NqzapFjruy5wpOrjmJrjO7wsHTAc/DnuPQ4kPQN9GHRwsPAMB///yHE2tOoPus7qj+UXUkPEnA/vn7AQCdJhTesCBSxKl6Tfwyd6P0vpZW8Qf0bV51GEYGxtL7ZqYW0n+bGpuhf++RqFHNGdraOgi5GoSlq36AhZklPq7fvGyLpw+Kl5c9TpwYL70vFheetaysHFhbm+D77z/H8uUni3zex48TMHnybrRoUbOsSqUK4PXLe4gNOwZDS6cixzm1GI4azQZJ7wuCBLf+moAqrp9Il+XlZkHPzBZVajbH42B/VZVMFcidhBwcvp8OF/Piv3L+GJyMVxl5mNLUFA4m2niVmQeJIEjX50gETD75Cub6WpjbwgzWhmLEpUtgoCNS5UugUmBjgYjKRXZ6NvbO3ouuM7vibMDZIsf+d/Q/NOzZEN7tvQEAFg4WeHb7Gc5vOS9tLDy79Qw16tTARx0/AgCY25vDu4M3XoS9UO0LoQpJLBajioVVqR5jYVYFxkamCtfV824sc793l4H49/RB3Iq4zsZCJaetrQVbW7MSjXVyssLKlflHKPj7Xyh0nESSh/79/fDjj11x7tx9JCWll0mt9GGTZGfg/rHlcPlsDJ5d2VnkWG09I0DPSHo/MSoEuVmvYVO7rXSZcVU3GFd1AwA8ubhFNUVThZGRk4eF55MxuYkptt5OK3Ls5RdZuPkyG9u7W8FUL7/Zamsslhlz9EEGUrMFrOpoDm0tkcIxpF6cY4GIysWRn4/ArbkbXBq7FDtWki2Btq5s31NbTxvPw59DkisBANSoWwMv7rzA87DnAIBXz1/h/oX7cGvuVvbFU4X3PPoJ+gxrg69GdcT8ZZPxIuZpsY8ZOakPvhzaGpPmDMONW5cLHScIAq7/F4JnLx6hjmfDsiybPkD37sXC3n4qnJ1nwtd3A6Ki4t77OefNOwxraxMMG/ZpGVRIFcXDoPUwd2oIs+p1ix/8jtjwEzCrXgd6pjYqqIwqgxVXUtHUQQ8N7Yo/TeH8syx4WOpgR3ga+uyNw8CD8fjjeiqyct8csXDheRY8rXSw4koqeu2Jw9eH47HtdhokeUIRz0zliUcsEJHK3T52G9F3ozEiYESJxrs2dcWNgzdQq1Ut2NWyQ/SdaIQeCkVebh7Sk9JhYmUC7w7eSEtKg/9If0AA8iR5aNS7ET4dXPpfrPPy8iAIAkQiHk5XGdV2r4Pp3y1CNXtHvEpKwLbd6/DtzAHwX3kAZibmcuOrWFhj4ui5cHfxRE5uNo6fOYTJc4fh13kBqOvVSDrudVoqfEZ8hpycHGhpaWH8yFloVO8TuecrDvNZcTRp4owtW76Gu3tVvHyZggULjuCTT5YiLGwOLC2Ni38CBc6fvw8/v/MIDf3hvetj1iqO+MhzSIt7gI98fin1Y7PTEpH0+DrcOkxUQWX58vLyIBKJmLUK6tSjTNxLzMXaz6uUaHz0awluxWZDVwuY19IcyVl5WHElBSlZeZjWLP8IrxevJYh5nY12zvpY3Nocz1MlWHklBRJBwOCPSr7/5H5OdXjEggby8PCArq4unj9/LreudevW0h2xlpYWqlatij59+uDx48fSMY8ePYJIJEJoaKjC59+0aZP0Od6+6evrKxw/ZMgQhePfvhV4+vQphg0bBnt7e+jq6sLR0RHjxo1DQkJCka9506ZNMDc3V7hOJBJh//79cstHjhwJsViMHTt2SJctW7YMZmZmSE+XPww0MzMT5ubm+PXXXwEATk5OCl/LTz/9VGidrVu3xvjx42Xui0QimRoAYMWKFXBycpJ5fW9vw87ODj4+Pnj48GGxr3P8+PFo3bq1dExRtyFDhhRau7okv0zGP7/+g55ze5Z4UsWWQ1uiZrOa8Bvmh/nN52PHlB2o2yX/Ly4F574/uvYI5wLOofPUzhi5ZSR8lvggMjgSQX6KJ9wrypIlS9C7d2/ExsaW+rH04WvSoAVaNmsPF0d3NKzbDIu+XwMAOHb6gMLxNRyc0aX9l3B39YSXRz2M/98PaNKwJXYe2CQzztDACBuW7cEfS3dg2FffYU3Azwi9XfiRDYVhPiuOzz/3Ru/eDfDRRw5o1642/v57LABg8+aLSj1famomBgzwx4YNA2FlpVxj4m3MWsWQlRqHx+c2omaHCdDS1i314+MiTkFbzwgWLk1UUF2+sWPHMmsVVGyaBKuupWJmc1Poikv25V0QAJEI+L65GWpb6aCpgx6+aWCCf6MypUctCAJgoa+FSY1N4WGpg8+c9NHf2wgHIzNKVR/3c6rDxoKGCQ4ORmZmJvr06YNNmzYpHDNixAhER0fj+fPnOHDgAJ4+fYoBAwaUajumpqaIjo6Wub3dnHjbypUrZcYBQEBAgNyyqKgoNGrUCJGRkfjrr79w//59rF27FidPnkSzZs2QmJhYqhqLkp6ejsDAQEyZMgV+fn7S5YMGDUJGRgb27Nkj95g9e/YgPT0dAwcOlC6bN2+e3Pvw7bfflqoWfX19zJo1Czk5OUWOK3jPX7x4ge3btyM0NBTdunWDRCIp8bbernPFihVyP8eVK1eWqvbyEH0nGmmv0rB+yHrM+2Qe5n0yD4+vP8alnZcw75N5yJPkyT1GR18H3X/ojplnZ2L8vvEYf2A8zO3MoWuoC0NzQwDA6XWnUefzOmjQvQGq1qyK2q1ro+3otgjeHAyhlIfFJSUl4eDBg/Dw8EBgYCAEgYfVVWYG+oZwqeGGZ9GK94mKeLrXwfN3xmtpacHBrgZqOteCT/chaNWsPbbv3VjIMxSO+ay4jIz08NFHDrh3T7lfcB88iMOjRwno2nU1tLVHQ1t7NLZsCcHBg/9BW3s0Hjwo3WkWzFrFkBb3ADkZybgVOAkhq3shZHUvpL4IQ8zNvxGyuheEvMJ/7xAEAXERJ2Hl0RpaYh2V1RgXF8esVVCRiTl4lZmH/x1NRNvtL9F2+0vcjM3B3rvpaLv9pcJTF6oYaMHKQAxj3TdfTR3NtCEAiEuXSMdUM9WGWOtNs8LRVBuJmXnIkZQ8P9zPqQ4bCxrGz88PX331FQYOHAh/f3+FYTc0NIStrS3s7OzQtGlTjBkzBtevXy/VdkQiEWxtbWVuVasqnqnfzMxMZhwAmJubyy0bM2YMdHV1cezYMbRq1Qo1atTA559/jhMnTuD58+f4/vvvS/luFG7Xrl3w9PTEjBkzcP78eTx69AgAYG1tja5du8LfX362Yn9/f3Tr1g3W1tbSZSYmJnLvg5GRkdxji9KvXz8kJydjw4YNRY4reM/t7OzQpk0bzJkzB7dv38b9+/dLvK236zQzM5P7OZqZlWxCsPLk3MgZo7ePxqito6Q3+9r2qNOxDkZtHQWtImZEF2uLYVrVFFpiLYQdD4P7p+4Q/f8HSk5mjvTfBQrul/ZDwsXFBRKJBMnJyfD19WUnu5LLzsnG42cPYWlhXfzg/3c/6g6qFDNegICcnOxS18N8VlxZWTmIiIiGnZ1y++5atWxx69ZshIbOkt66dauDNm3cERo6C9WrWxT/JG9h1ioGs2p1UaffStTxXS69GdnUhJVHS9TxXQ6RVuET3qU8v43M5GjYeLZTWX0ZGRnw8vJi1iqoBra68O9siY1fvLl5VNFGOyd9bPzCUqYxUMDbWgcJGRJk5Lz5Y9PTlFxoiQBrQ7F0zPPUXOS99Tve01QJLA20oFPCIyMA7udUiY0FDZKamopdu3ZhwIABaN++PdLS0nDmzJkiH5OYmIhdu3ahSRPVHa5WEomJifj333/xzTffwMDAQGadra0t+vfvX6ZdQT8/PwwYMABmZmb44osvEBAQIF03bNgwBAUFyZxm8OjRI5w+fRrDhg0rk+2/zdTUFDNnzsS8efOQllb0rLdvK3ifijvS4X1lZWUhJSVF5lae9Iz0YONqI3PTMdCBgZkBbFzzJ4U6sfoE9s3dJ31MwpME/Hf0PyQ8ScDzsOfY/f1uxD6IRdvRb2andm/hjqt7ruL2sdt49eIVHlx6gNPrT8OjhUeRzYqiFOSzoJO9c2fRs2hTyag7g8X5Y9PPuBl2BdEvnyEi8j/M/XkC0jNeo0Pr7gCADduWY/HKGdLxuw9tRfClk3j24jEePrmPDduW42zIcfT4op90zPY9G3A19AJexDzFk2dR2HVwM46dOYR2LbsoXSfzqRxNyt/kybsRFBSJhw/jcenSQ3z55TqkpGRi8OBmAIAZM/Zh0KAAmceEhj5FaOhTvH6dibi41wgNfYrw8Pyr3+jr68Db20HmZm5uCBMTfXh7O0BXV7mptJi1sqOO/Il1DWBo6Shz09LWg7a+CQwtHQEATy5sxf3jK+QeGxdxAsZV3aXj3pYnyUFaXBTS4qIgSHKRnZaItLgoZCZFl6q+Tz99MxcSs6Z65Z1BQx0tOJtry9z0tUUw1ctfDgAbbqRi0YVk6WPaOenDVE8LS0JS8Cg5FzdfZmPdjdf43MUAetr5TYPuboZIyRKw6moqnqbk4uLzLGwPS0MPdwOFdRSH2St7bCxokB07dsDNzQ1eXl4Qi8Xw9fWVOcy/wJo1a2BsbAwjIyNYWlri7t27Cv9CX5Tk5GQYGxvL3Dp06KB07ffu3YMgCKhdu7bC9bVr18arV68QF1f4YZmKajI2lj9n9N69ewgJCUHfvvmX4BowYAACAgKQl5ff5ezYsSPs7e1lTiUJCAiAvb293GucNm2a3PaKa+Yo8s0330BfX186f0Nxnj17hp9//hnVqlWDu7t7qbdXGosXL4aZmZn0Vr16dZVuTxmvE14j+eWbD5g8SR4ubr+ItQPWYuu3W5GbnYuhG4fC3N5cOqbl1y3RrH8znFp3Cmt81+DgwoNwbeKKLtOV/+JWQCKRICkpCePGjXvv5yLNz2B8wkss+HUqBn/bBbOXjoeOtg5W/bQdtjb2AIDEV/GIjX/zi3Nubg7Wbv4Fwyf2wvhZg3A74gYWfb8GLZu2l47JyMrAyg0LMHR8D3w7cwCCLh7DzHGL0bn9l+9dL/NZOpqUv2fPXqFfv43w8JiNXr3WQldXGyEh0+DoaAkAiI5OxpMnsqcN1q+/APXrL8C1a0+wfftl1K+/AF98sapc6mXW3p8m5e9t2emJyEqV/Z0sNysNiQ8uwrqQoxWy0xJxK3AibgVORE76K0Tf2I9bgRPx4NT755FZUx1NzGBCZh5i096ckmOgo4VfPrPA62wBo44mYOGFZDRz0MO3jUykY2yMxPj5M3PcScjFsL8TsOpqKnp5GKKfZ+mONH4Xs1d2eFUIDVLwV/gCAwYMQMuWLZGUlCQzsWH//v2lpxW8fPkSixYtQocOHXDt2jWYmJi8+7QKmZiYyJ0+8e6RBmWpoCtY1AysimoCADc32csH+vn5oWPHjrCyyr/m/BdffIFhw4bhxIkT6NChA8RiMQYPHoxNmzZhzpw5EIlE2Lx5M4YMGQKxWPbwvylTpshNeOjg4FDq16enp4d58+Zh7NixGD16tMIxBY0TQRCQnp6OBg0aYO/evdDVLf3ESqUxY8YMTJz4ZmbnlJQUtX+oDPljiMz9HrN7yNy3drbG/7b+r8jn0NLWQuvhrdF6eOuyLQ6AWCyGiYkJfvvttzJ/7spIEzP4th8mFT1r+rRvF8rc9+05FL49hxb5mGFffYdhX3333rUpwnyWjiblb8eOoq+Ms2nTELllgrCuVNtQ9BzKYtben6bkz6uX7H6sZjv5L1HaekZoPKrwv9rqm1ZF07H737uW4OBgHD9+XGYZs6Y6mpDBFe1lrw4xvZn86V81zLTxS9uiT9/ystbFmk4lu9JESTF7ZYeNBQ0RHh6OS5cu4cqVK5g2bZp0uUQiwV9//SXzZdXMzAw1a9YEANSsWRN+fn6ws7NDYGAghg8fXqLtaWlpSZ+jLNSsWRMikQjh4eHo0aOH3Po7d+7AwsJC2gxQtiaJRIItW7YgJiYG2traMsv9/PykRyQMHToUixcvxqlTpwAAT548wddffy33fFZWVmX2PgwYMAC//PILFixYIHNFiAIFjZOCq3m8O5eDiYkJkpOT5R6XlJT0XnMn6OnpQU+v+GsIU37jSxAEdOvWDWvXroWNDa/fXRaYwbLBfCqH+Ss9Zq3sMH/y3v5DFrOmesygYsxe2WNjQUP4+fmhZcuWWL16tczyrVu3ws/Pr9C/ggOQ/hU+I6N0l1spS5aWlmjfvj3WrFmDCRMmyHxoxMTE4M8//8SgQYPe+5qxR44cQWpqKm7cuCFz9MGdO3fQv39/JCQkwNLSEq6urmjVqhUCAgIgCAJat24NV1fX99p2cbS0tLB48WL06tVL4c+ruMZJrVq1cOXKFQwePFi6TBAEXLt2DZ9//rlKaqZ8UVFR0o71unXr4OPjo+6SiKSYTyovzBqVl7CwMGaN1IL7OdVhY0ED5OTkYOvWrZg3bx68vb1l1g0fPhxLly7FzZs3UbduXQD5l1qMiYkBkH8qxIIFC6Cvry83f8Ddu3fltuXp6Qkg/wtrwXO8zcbGBlpayk29sWrVKnzyySfo2LEjFixYAGdnZ4SFhWHKlClwcHDAwoULi3+SYvj5+aFz587S96KAl5cXxo8fj23btknPkRo2bBhGjMg/7HTjRsWXeEtNTZV7HwwNDWFqaqpUfZ07d0aTJk2wbt26Qq+yUZjJkydj8ODBqFWrFjp06ICMjAysX78eDx48wJgxY5Sqh0rG3NycHWvSWMwnlRdmjcqLtbU1s0Zqwf2c6rCxoAEOHjyIhIQE9OzZU26dm5sbPvroI/j5+UnP/dmwYYP00oYWFhaoU6cOjhw5Ag8PD5nH+vr6yj1fwZUSUlJSYGdnJ7c+OjpaevnI0nJzc8PVq1cxd+5c9O3bFwkJCbC1tUWPHj0wZ84cVKnyfudEvXz5En///Te2b98ut04kEqFXr17w8/OTNhZ69+6NsWPHAgB69eql8Dlnz56N2bNnyyz73//+h7Vr1ypd55IlS/DJJ5+U+nE+Pj4QBAG//PILvv/+e+jr66N+/fo4d+4cHB3lZ2emsjNt2jQ0atRI3WUQKcR8Unlh1qi8rFq1Suk/ZBG9D+7nVIeNBQ3Qu3dvSCSSQtf/999/0n+X5IoFTk5ORV7WcciQIXITFpZGUc/t6Ogoc+nHkiqqpre3V9SlGd+ddMXAwABJSUmFjn/06FFpSgQg//4r+nk0a9ZM7j0q6Xvet29f6dUuivO+P0d6g7/ckCZjPqm8MGtUXpg1UhdmT3X4zhIRERERERGR0thYICIiIiIiIiKlsbFAREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIadrqLoCI1CP+cTx0DXTVXYZaxT2KU3cJldqz549goG+o7jI01pNnUeouoUK7ezcGxsZ66i5DI0RERKu7hEonM+k5xDr66i6jXGW8eqbuEugtT1NyYaAjUncZ5eZxcq66S6jw2FggqqQOLz6s7hI0goGhAaysrNRdRqW0bO1cdZeg8QwNDZlPFRk5cpu6S9AohtwXlquo02vUXYJa6Btwn6Ypll1OVXcJ5c7QQJ/5UyE2FogqqaCgIBgbG6u7DLWzsrJCjRo11F1GpcQMFo/5VB3mTxazVr4qa/6YM81RGTPI/KkWGwtElVS9evVgamqq7jKoEmMGSZ2YP1In5o/UjRmkssbJG4mIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqWxsUBERERERERESmNjgYiIiIiIiIiUxsYCERERERERESmNjQUiIiIiIiIiUhobC0RERERERESkNDYWiIiIiIiIiEhpbCwQERERERERkdLYWCAiIiIiIiIipbGxQERERERERERKY2OBiIiIiIiIiJTGxgIRERERERERKY2NBSIiIiIiIiJSGhsLRERERERERKQ0NhaIiIiIiIiISGlsLBARERERERGR0thYICIiIiIiIiKlsbFAREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqWxsUBEREREREREStNWdwFEpB6hoaEwNjZWdxkqZWVlhRo1aqi7DCpERcsg8/ZhqWj5KwqzqXkqU/7exixqjsqUQeaufLCxQFRJtWrVSt0lqJyBoQHuRNzhh4mGqmgZNDQ0REREBPP2gaho+SuKoaEBIrgv1CiVKX9v0zcwxN073E9qgsqUQUMDfUTcucvcqRgbC0SVVJcZXWBfy17dZahM3KM47JuzD/Hx8fwg0VCTRs2Fm6unussoE0+eRWHRyunM2wdk/foBaNjQUd1lqFxERDQGDPBnNjWMS5tvYGTtqu4yylXGq2e4f3w5s6ghJjU2gbuljrrLULnHyblYdCGFuSsHbCwQVVJWjlawq2Wn7jKoEqvm4AT3CtJYoA+Ph4ctGjTgL5mkHvrmDjCyqVyNBdIs1U214V6l4jcWqPxw8kYiIiIiIiIiUhobC0RERERERESkNDYWiIiIiIiIiEhpbCwQERERERERkdI4eSMRlalzm87h1B+n0KRvE3Sa2EnhmCehT3Bi9QnEP4pHTlYOzGzN0LBnQzTr10w6JvRwKA7MPyD32O/Pfg9tPe66KN/2PRuw8c+V6NV5AMYOm17ouJthV7Am4Gc8enofVlVs0LfH1+jWsa90/eHju3H8zEE8fHIfAODu6olh/cehtttHKn8NVDEtXnwUM2fux7hxn2HFir4Kx5w5cxdt2vwqtzwi4kfUqmULAAgLe4HZsw/i2rUnePw4AcuX98H48e1UWjtVHM+v7sbTkG2wrdsFTi2GFzouT5KDZ5cDER8ZhJy0V9A1toRDoz6w8czPWmzESUSd/F3ucY1H7YSWtq7K6qcP15+307Dx5mv09jDE2EYmCseEvszGhBOv5JZv7mKJGmb5v+sdvp+OY1GZeJicCwBwr6KD4XWNUduKE09qGv52TkRl5nn4c1zffx1Va1YtcpyOgQ4+/vJjVHWrCl19XTy5+QSHfzoMXX1dNOzZUDpOz0gPY3eNlXksmwpU4M69Wzh8fDdcHN2LHBf98hlmLPgGX7TrjZnjF+N2xA2s3LAA5qZV0LJZewDAzdtX8NmnX8CrVj3o6uhix35/TP1xJPxX7oe1ZdF5JnrXlSuPsH79OdSpU61E4+/enQdTU33pfWvrN7+Ep6dnw8XFCn36NMSECTvLvFaquF6/vIfYsGMwtHQqduy9f35GTnoSXD8bCz0zW+RmJEPIy5MZI9Y1RN3+q2WWsalAitxJyMHh++lwMS/Z72xbulrCSEckvW+m9+ag+tCXOfjMSR/eVjrQFYvwV3gappx6hYAulrA2FJd57aQ8/oZORGUiOz0be2fvRdeZXXE24GyRY+087GDn8eZSl+b25og4E4EnoU9kGgsQAcaWxqoqmT5gGRnpWLRiOiaNnottu9cVOfbQvzthY2UrPaLBsZorIh+EYeeBTdLGwvcTlsg8ZtLoH3H24nHc+C8EHdp0V82LoArp9etM9O/vhw0bBmLBgiMleoyNjQnMzQ0Vrvv4Yyd8/LETAGD69H1lVSZVcJLsDNw/thwun43BsytFN6SSHl9HyvPbqD9oHbT1/7+pZaq4oaprZFHWpVIFk5GTh4XnkzG5iSm23k4r0WMs9LVgrKv4DP1Zzc1k7k9uYoqzT+JwPSYbHV0M3rteKjucY4GIysSRn4/ArbkbXBq7lPqx0Xej8fS/p3Bs4CizPDsjGyu6r8CvXX7F9onbEX03uqzKpQ/cyg0L0KRhSzSs26zYsWGRN9Go3icyyxrVa467D8KQm5uj8DFZ2ZnIleTCxMRM4XqiwowZ8xc6d/4I7drVLvFj6tdfADu7KWjb9lecPn1XhdVRZfEwaD3MnRrCrHrdYse+engZRjY18eL6PlwLGIrQrd/gcXAA8nKzZMZJcjJxffMIXA8YhjuHFiAtLkpV5dMHbMWVVDR10ENDO70SP2bEkQT03hOHiSde4UZMdpFjsyQCcgUBpoU0Ikh9eMQCEb2328duI/puNEYEjCjV437t8ivSk9KRJ8lDq+Gt0KB7A+k6K0cr9PihB2xcbZCVloVLgZfgP8Ifo7aNgmUNy1JtJy8vDyKRCCKRqPjBpPFOBR/BvagI/LF0R4nGv3oVD4t6spmxMLeERJKL5JQkWFaxlnvMhq3LYVXFBg3rFN+4eBfzVnnt2HEF168/wZUrM0s03s7ODOvXD0DDho7IysrB1q2X0Lbtcpw5MxEtWxZ9io8ymM3KIT7yHNLiHuAjn19KND4z5SVSoyOgJdaFxxfTkZORgodB65Cb9Rqubb8FABhYVINru+9gaOkISXY6Ym4eRtie6fjIdwUMzO1LVV9eXh4EQWAOK6BTjzJxLzEXaz+vUqLxVQy0MKmJCdyr6CBHIuDYw0xMOvkKy9tZoG5VxafZrL/xGlYGYjS0K/1pONwHqhZbPQqcOXNGGjotLS2YmZmhfv36mDp1KqKj5f9impiYiPHjx8PJyQm6urqws7PD119/jSdPnsiMGzJkCEQiEX766SeZ5fv375cGvGBMUbeiak5KSpJb5+TkhBUrVsgtX7RoEcRisUw9e/bsgVgslqu9QK1atfDdd98BAFq3bq2wvlGjRil8bFGv7/79+0XWVWDTpk0wNzeXuf/281StWhVdu3ZFWFhYoTW8y8PDA7q6unj+/LnC9adPn8YXX3wBS0tLGBoawtPTE5MmTZIZLwgC1q9fjyZNmsDY2Bjm5uZo1KgRVqxYgfT0dADA3LlzFb72WrVqSZ8nKioK/fr1g729PfT19VGtWjV0794dkZGRMvW0adMGVapUgaGhIdzc3DB48GDk5uaW+DWXpeSXyfjn13/Qc27PUs9/8PX6rzFi0wh0ntYZl3Zcwq1/b0nXVfuoGup8Xge27rZwrO+IPov6wLKGJS7vulzqGseOHYvevXsjNja21I8lzRIbH43Vfj9h5rjF0NUt+V9D5PadgqB4OYAd+/xxKvgIfpy6olTbKMC8VU5PnyZi3LhAbNs2FPr6JZtUzMPDFiNGtECDBjXQrJkr1qz5Cp07e+OXX46rpEZms+LLSo3D43MbUbPDhJLPfyDkARChZocJMK7qDgunRnD6dCjiIk5Jj1owsfWAtUdrGFk5w9TeC26dpkDf3B4v//u71DUuWbKEOayAYtMkWHUtFTObm0JXXLIv7jVMtdGlpiHcq+jAy1oXExqboqmDLnZGKD6F4q+wNJx6nIl5Lc1KvI23cR+oWmwsFOHu3bt48eIFrly5gmnTpuHEiRPw9vbGrVtvvvwkJiaiadOmOHHiBNasWYP79+8jMDAQDx48wMcff4yoKNnDxPT19bFkyRK8eiU/AyoArFy5EtHR0dIbAAQEBMgtKwsBAQGYOnUq/P39pcu6desGS0tLbN68WW78+fPncffuXQwbNky6bMSIETK1RUdHY+nSpUVut1OnTnKPcXZ2LrKuopiamiI6OhovXrzA33//jbS0NHTu3BnZ2UUfSgUAwcHByMzMRJ8+fbBp0ya59evWrUO7du1ga2uLPXv2IDw8HGvXrkVycjKWLVsmHTdw4ECMHz8e3bt3x+nTpxEaGooffvgBBw4cwLFjx6TjvLy85F57cHAwACA7Oxvt27dHSkoK9u7di7t37yIwMBDe3t5ITk4GAISFheHzzz/Hxx9/jLNnz+LWrVv4/fffoaOjg7x3JlkqL9F3opH2Kg3rh6zHvE/mYd4n8/D4+mNc2nkJ8z6ZhzxJ4XVZ2Fugas2qaNijIZr2a4qgjUGFjhVpiWDvaY/Ep4mlrjEuLg4HDx6Eh4cHAgMDIfz/l0r68EQ+CMer5ET8b0pftPuyLtp9WRc3w65i35E/0e7LupBIJHKPsbCwQmJSvMyyV8mJEIu1YfrOqQ6B+wPw554NWDp7PVydPJSqkXmrnK5de4LY2FQ0bLgI2tqjoa09GkFBkfjtt9PQ1h4NSRH7wrc1beqCe/dU80svs1nxpcU9QE5GMm4FTkLI6l4IWd0LqS/CEHPzb4Ss7gUhT34fqWNYBbrGVaCtZyRdZmBRDYCArNcJCrcjEmnB2MYNGUml/700KSmJOayAIhNz8CozD/87moi221+i7faXuBmbg71309F2+0tI8kr2c/a00sGzVPmcBoan4c+wNPz8mQVcLZS7IgT3garFUyGKYGNjA3Nzc9ja2sLd3R3du3dH/fr1MXr0aOmXwe+//x4vXrzA/fv3YWubf2moGjVq4N9//4WbmxvGjBmDo0ePSp+zXbt2uH//PhYvXqzwC7iZmRnMzGR/0S2ooSwFBQUhIyMD8+bNw5YtW3D27Fm0bNkSOjo6GDhwIDZt2oRZs2bJ/DXP398fDRs2RN26b87XMzQ0LHVtenp6hT6msLqKIhKJpM9nZ2eHCRMmoFu3brh79y4++qjoS8X5+fnhq6++QqtWrTBmzBjMnDlT+pqfPXuG7777Dt999x2WL18ufYyTkxNatmwpPTpk586d+PPPP7F//350795dZly3bt2QkpIiXaatrV3oaw8PD0dUVBROnToFR8f8uQYcHR3RvHlz6Zjjx4/Dzs5OJjuurq7o1EnxZR3Lg3MjZ4zePlpm2YH5B2DlaIXmg5pDS1yy/qUgCMjNKfyoC0EQ8DLyJWxcbUpVX0ZGBry8vLB7924kJyfD19cXgYGBWLt2LWxsSvdcpH4N6jSF33LZCeyWrpqF6tWc0a/HMIjF8jNEe7nXxcWrZ2SWXb15AR6uXtDWfvPLyY79/vhz93os+WEdPGp6K1Uf81Z5tW1bC7duzZZZ9vXXm1Grli2mTesIcQn3hTduPIWdXdnP7cFsVg5m1eqiTr+VMssenPwdBhYOsG/QCyIt+X2kiV0tJD44D0l2BsS6+ZPhZSS9AERa0DNWfOqhIAhIi38IQ0tHheuL4uLighMnTjCHFUwDW134d5bNy5KLyahhqo1+XkYQa5XsCIN7ibmwNJDdX+4IT8O222lY+pk5PCyVaypwH6h6PGKhFAwMDDBq1CicP38esbGxyMvLw44dO9C/f3+5L4sGBgb45ptv8O+//yIx8c1fWMViMRYtWoTff/8dz549K++XIOXn54d+/fpBR0cH/fr1g5+fn3TdsGHDEBUVhaCgN389TktLw86dO2WOVijvukoiKSkJ27dvBwDo6BS940lNTcWuXbswYMAAtG/fHmlpaThz5ox0/a5du5CdnY2pU6cqfHzBKRl//vknPDw8ZJoKBUQikVyjqDDW1tbQ0tLC7t27Ff7VFQBsbW0RHR2Ns2eLvurC27KyspCSkiJzK0t6RnqwcbWRuekY6MDAzEDaBDix+gT2zX3zZfDyrsu4e+4uEp4kIOFJAm4cuoGLf15EnU51pGPObDyD+yH38er5K8RExuDggoOIiYxBo16NSlXfp59+Kv13QWe6oFu9cycv3VYeyjKDhgZGcHZ0k7np6xvA1Ngczo5uAIAN25Zj8coZ0sd07eiDl3HRWBOwFI+fPcDRk3tx9ORe+HQfIh2zY58/Arb/jilj5sPWxgGJr+KR+CoeGRnppaqPedM8qt4HFjAx0Ye3t4PMzchID5aWRvD2dgAAzJixD4MGBUgfs2LFCezfH4p7914iLOwFZszYhz17rmPs2NbSMdnZuQgNfYrQ0KfIzs7F8+dJCA19ivv3S3dUA7OpHuWVvwJiXQMYWjrK3LS09aCtbyJtAjy5sBX3j6+QPsbKvSW09U3w4OTvSE98ipTnYXhyfjNsareFlnb+6WDPLu9A0uMbyEyOQVpcFKJOrUJ6/ENU9e6odK3MYfkorwwa6mjB2Vxb5qavLYKpXv5yANhwIxWLLiRLH7P7ThqCn2biWUouHiblYsONVJx9moUe7m+ukvNXWBr8b77GlKamsDUSIzFDgsQMCTJySnekLveBqsfGQikVnA//6NEjxMXFISkpCbVrK575uXbt2hAEQWb+AADo2bMn6tWrhzlz5pR5fdWqVYOxsbHM7d35ElJSUrBnzx4MGDAAADBgwADs3r1buqPx9PREkyZNEBDw5pefnTt3QiKRoF+/fjLPtWbNGrntKTqN4m2HDx+WGd+nT58S1VWY5ORkGBsbw8jICBYWFtixYwe6desmM3eBIjt27ICbmxu8vLwgFovh6+sr08i4d+8eTE1NYWdnV8Sz5I/z8CjZIdO3bt2Se7+GDx8OAHBwcMBvv/2G2bNnw8LCAp999hnmz58vczpNnz590K9fP7Rq1Qp2dnbo2bMnVq1aVeR7tHjxYumRMGZmZqhevXqJai1LrxNeI/nlmw8SQRBwcs1JrBu4DhuGbMDlXZfRdkxbtBnZRjomMzUThxcfxuq+q7H1u61IjUvFkHVD4ODl8N71SCQSJCUlYdy4ce/9XFS88s5g4qt4xMa/OTzXrmo1LJ61BqG3r2DkxC+xddc6jB02Q3qpSQA48M8O5OTmYO7PE/DlsNbSW+CBAEWbKBXmTb00YR9YIDo6GU+evPljQ3a2BJMn70adOvPRosXPCA6+j7//Hotevd5MZPviRRLq11+A+vUXIDo6Gb/8chz16y/A8OFb37seZlP1NCl/BbLTE5GVGie9L9Y1QO3uP0KSnYbbOyfh/vFfYeHcCE4th0vH5GalIerMGtz8cywiDv6I7NcJ8Oy5EMZV33+SUeZQtTQpgwmZeYhNe/PHsxwJ8Mf11xh2JAHjjifiVlwOFrc2R8sa+tIxB+6lIycPmHsuGb33xktvgRGla/wrwuyVLZ4KUUpCERN+lWbskiVL8Nlnn2HSpEllWt+5c+dgYmIis6x169Yy97dv3w4XFxfpKQ316tWDi4sLduzYgZEjRwLIP2ph/PjxWLVqFUxMTODv749evXrJTJwIAP3798f3338vs6y4w4natGmDP/74Q3rfyMioxHUpYmJiguvXryM3NxdBQUH4+eefsXbt2iJrAPKPjihoYgD5jYyCUxzMzc1LPGNxaWY29vDwwMGDB+XqLzBmzBgMGjQIp0+fxqVLl7Br1y4sWrQIBw8eRPv27SEWixEQEIAFCxbg1KlTCAkJwcKFC7FkyRJcvnxZYRNkxowZmDhxovR+SkqKyj9UhvwxROZ+j9k9ZO438WmCJj5NinyOThM6odOE9z/FIzg4GMePy06EJhaLYWJigt9+++29n5+Kp+oMLp+/Seb+tG8Xyo2p6/Ux1i/bVehz/LXuWKHrSoN50zzq2AcWOHNG9jN+06YhMvenTu2IqVOL/ouvk5MVBGHde9fCbKqHOvNXwKuX7D6xZjv5L1EGFtVQu/uPhT6HU4thcGqhmqNWmUPVUmcGV7SXvTrE9GayR/H28zJCPy8jFGVHD/krNymD+0DVY2OhlCIiIgDknz9vaWkJc3NzhIeHKxx7584diEQiuLq6yq1r2bIlOnbsiJkzZ2LIkCFlVp+zs7Pcl39tbdkfs7+/P8LCwmSW5+Xlwc/PT/oF3tfXFxMmTEBgYCBat26N4OBgzJs3T257ZmZmqFmzZqlqNDIyUviYktSliJaWlvT5atWqhZiYGPTt27fI0wXCw8Nx6dIl6cScBSQSCf766y+MHj0a7u7uSE5ORnR0dJFHLbi7u0tzURxdXd1i3y8TExN069YN3bp1w4IFC9CxY0csWLAA7du/+euqg4MDBg4ciIEDB2LBggVwd3fH2rVr8eOP8r8U6OnpQU+v9DPbVxQGBgbSf4tEIgiCgG7duvGcunJUmTLIvGmeypS/ojCb6sH8FY45LB/MYD7uA1WPp0KUQkZGBtavX4+WLVtKz4f38fHB9u3bERMTIzd2zZo16NixI6pUUXwt159++gmHDh3ChQsXyqN8APmH4l+9ehVnzpxBaGio9Hb27FlcuXIFt2/fBpD/5bZPnz4ICAiAv78/XFxc5I58UEddJTFhwgTcvHkT+/btK3SMn58fWrZsiZs3b8psb+rUqdLTIb788kvo6uoWepWLgskbv/rqK0RGRuLAgQNyYwRBkF7RQRkFl6NMS1N82R0AsLCwgJ2dXZFjKruwsDCIxWKYmZkhMDAQe/fu5YcIqQzzRpqK2SRNEBUVxRySWnAfqFo8YqEIsbGxyMzMRGpqKq5du4alS5ciPj4ee/fulY5ZuHAhTp48ifbt22Pp0qXw9vbGw4cPMWvWLOTk5GD16tWFPv9HH32E/v374/fffy+PlwMg/wt148aNFV5poVmzZvDz85NeAWHYsGFo0aIFwsPDMXnyZIWH+6enp8s1VfT09GBhYaGyuopjamqK4cOHY86cOejRo4dc3Tk5Odi6dSvmzZsHb2/Zmd+HDx+OpUuX4ubNm6hbty6WL1+OsWPHIiUlBYMGDYKTkxOePXuGLVu2wNjYGMuWLYOPjw/27duHfv364YcffkD79u1hbW2NW7duYfny5fj222/Ro0cPAEBubq7c+yUSiVC1alWEhoZizpw5GDhwIDw9PaGrq4ugoCD4+/tLj6pYt24dQkND0bNnT7i6uiIzMxNbtmxBWFhYueboQ2Ntbc2uNJUb5o00FbNJmsDc3Jw5JLXgPlC12FgogoeHB0QiEYyNjeHi4oIOHTpg4sSJMleAsLKyQkhICObNm4f//e9/iI6OhqWlJTp16oRt27ahRo0aRW5j/vz55TYTaXZ2NrZt2yZz6P/bevfujcWLF2PJkiXQ1dXFp59+Cg8PD9y7dw+DBw9W+JgNGzZgw4YNMss6duyIf/75RyV1ldS4cePw22+/YdeuXfDx8ZFZd/DgQSQkJKBnz55yj3Nzc8NHH30EPz8//Pbbb/jmm2/g7u6OX375BT179kRGRgacnJzQpUsX6flqIpEI27dvx/r16+Hv748FCxZAW1sbbm5uGDRoEDp2fHP+bFhYmNxpFXp6esjMzES1atXg5OSEH3/8EY8ePYJIJJLenzBhAgCgcePGCA4OxqhRo/DixQsYGxvDy8sL+/fvR6tWrUr8/lQ2q1atgpYWD9Ci8sG8kaZiNkkTTJs2DY0ale4KT0RlgftA1RIJBTMMElGlkJKSAjMzMwxZOwSO9Ut//ekPRfSdaKwfvB7Xrl1DgwYNin9AJVeQi+TkZJiampbLtpbP34S6XhXjl8vIB+EYNcWHeVOSOvIXFDQZLVu6qXRbmuD69Sdo2HAhs1mM8spgwXY8ey6EqYOXyrajidJiH+DWzknMogLq2AeuaGeBulV1VbotTRCZmIP/HU1k7opQVvljy4aIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqWxsUBEREREREREStNWdwFEpB7xj+Oha6Cr7jJUJu5RnLpLoGI8e/4IBvqG6i6jTDx5FqXuEqiU7t6NgbGxnrrLULmIiGh1l0AKZCY9h1hHX91llKuMV8/UXQK95WlKLgx0ROouQ+UeJ+equ4RKg40Fokrq8OLD6i5B5QwMDWBlZaXuMqgQy9bOVXcJZcrQ0JB5+4CMHLlN3SWUG0PuCzVO1Ok16i5BLfQNuJ/UFMsup6q7hHJjaKDP3JUDNhaIKqmgoCAYGxuruwyVsrKyQo0aNdRdBhWiomWQefuwVLT8FYXZ1DyVKX9vYxY1R2XKIHNXPthYIKqk6tWrB1NTU3WXQZUYM0jqxPyROjF/pG7MIJU1Tt5IREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIaWwsEBEREREREZHS2FggIiIiIiIiIqWxsUBERERERERESmNjgYiIiIiIiIiUxsYCERERERERESmNjQUiIiIiIiIiUhobC0RERERERESkNDYWiIiIiIiIiEhpbCwQERERERERkdLYWCAiIiIiIiIipbGxQERERERERERKY2OBiIiIiIiIiJTGxgIRERERERERKY2NBSIiIiIiIiJSGhsLRERERERERKQ0NhaIiIiIiIiISGlsLBARERERERGR0thYICIiIiIiIiKlsbFAREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlKatroLICL1CA0NhbGxsbrLUCkrKyvUqFFD3WVQISpDBt/GPGoW5o/UqaLlj/n68DCDVNbYWCCqpFq1aqXuElTOwNAAdyLu8INGQ1WGDL7N0NAQERERzKOGqHz5M0AE94cao6LlT9/AEHfvcP/2IaloGTQ00EfEnbvMoBqxsUBUSXWZ0QX2tezVXYbKxD2Kw745+xAfH88PGQ01adRcuLl6qruMcvHkWRQWrZzOPGqQ9esHoGFDR3WXUS4iIqIxYIA/86dBXNp8AyNrV3WXUSYyXj3D/ePLma8PzKTGJnC31FF3GWXicXIuFl1IYQbVjI0FokrKytEKdrXs1F0GVWLVHJzgXkkaC6R5PDxs0aABfwEl9dA3d4CRTcVoLNCHqbqpNtyrVIzGAmkGTt5IREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaJ28kojJzbtM5nPrjFJr0bYJOEzspHBNxOgJX915FTGQMcrNzYeNig1YjWqFm05rSMZJcCYI3BePmkZtIiUuBVQ0rtBvbDjWb1VT4nESF2b5nAzb+uRK9Og/A2GHTFY4JvX0ZE2cPlVu+6beDqFHNBQCQm5uD7Xs34t/TBxCfGIvq9k4YOXAiGjf4VKX104dr8eKjmDlzP8aN+wwrVvRVOObMmbto0+ZXueURET+iVi1bueU7dlxBv34b0b17Xezf/02Z10yaLebWUcTe/gdZKbEAAIMqNeDQ2AcWjg0Lf8x/RxBz6wiyUmKhZ2IFh0Z9YF2rjXR92N7vkfoiTO5x5o4NUavrD2X/IuiDdiAyHQfvZSDmtQQA4GSujUHeRmjioFfoY/bdTcf+yHTEpElQ1VCM/t5G6OhiIDPmdXYeNoa+xrmnWUjNzoOdsRijG5igaRHPS5qHjQUiKhPPw5/j+v7rqFqzapHjHt94DJfGLvhs9GfQN9ZH6OFQ/DXpLwz3Hw47j/yrVJxaewq3/rmFrjO6wsrJCvdD7iNwWiCGbhgqHUNUnDv3buHw8d1wcXQv0fjNqw7DyMBYet/M1EL6b//tv+P42cOYNHouajg440roecxeOg6/L9oGN5faZV47fdiuXHmE9evPoU6daiUaf/fuPJia6kvvW1ubyI15/DgBkyfvRosWbLBWVnrGlqjebCD0zfI/B+PunEbk34vxUd9fYWgpf4WTmFtH8fTiVjh/NgbGNjXx+uU9RJ1eDW09I1g4NwYAeHwxHXmSXOljcjNT8d+O8ahS85PyeVH0QbE2FGNEPWM4mIgBAP9GZWLW2SSs/9wSzubyXysPRKZjY+hrTGpiilqW2riTkItfLqXARFcLn1TLbxrkSARMPvkK5vpamNvCDNaGYsSlS2CgIyrX10bvj6dCENF7y07Pxt7Ze9F1Zlfov/XLsSKdJnZC84HN4eDpAMsalmj7TVtYVrdE5LlI6Zj/jv6HTwd/CrfmbrBwsMDHvT+GaxNXXNx+UdUvhSqIjIx0LFoxHZNGz4WJsWmJHmNhVgVVLKykN7FYLF13POgQ+vcegaYNW8Letjq6d/LFx/WaY9fBTSp6BfShev06E/37+2HDhoGwsDAs0WNsbExga2smvYnFsr+eSSR56N/fDz/+2BUuLtaqKJs+ABbOjWHh1AgGFg4wsHBAjWYDoKWjj9cv7yocH3/3DGy8O8LK7VPom9nCyr0FbDzb4cX1fdIx2vom0DWykN6Sn4ZCrK0Hy5rNy+tl0Qfkk2p6aOqgh+qm2qhuqo3h9YxhoC1CeHyOwvHHH2aiq5sBPnPSh72JNj5z0scXrgb4KzxNOubogwykZgtY0MocH9nowtZYjI9sdFHTgpfC/NCwsUBE7+3Iz0fg1twNLo1dSv1YIU9AVnoWDMzeHBYnyZZAW0+2862jp4MnN5+8d61UOazcsABNGrZEw7rNSvyYkZP64MuhrTFpzjDcuHVZZl1OTjZ0dXRllunq6uFWxI0yqZcqjjFj/kLnzh+hXbuSH8lSv/4C2NlNQdu2v+L0afkvifPmHYa1tQmGDeOpN5RPyJMgPvIc8nIyYWxbS/EYSQ60xLJfzrTEenj98p7MUQpviw0/AUu3TyHWKfqPBESSPAGnHmUiM1eAl7XiJkB2ngBdseyRB3pi4E5CDnLzBADAhedZ8LTSwYorqei1Jw5fH47HtttpkPz/evpw8FQIInovt4/dRvTdaIwIGKHU4y/8eQE5GTnwauslXeba1BUh20PgWM8RVapVQdSVKNw5eweCEh8yeXl5EAQBIhEPqassTgUfwb2oCPyxdEeJxlexsMbE0XPh7uKJnNxsHD9zCJPnDsOv8wJQ16sRAKBR/ebYdWgL6ng2gr1tdVz/LwQXLp9GXp6kVLUxjxXbjh1XcP36E1y5MrNE4+3szLB+/QA0bOiIrKwcbN16CW3bLseZMxPRsmX+KTznz9+Hn995hIa+//nuzN+HLz3+EW7vmY683GyIdfTh/sV0GFaprnCsWY36iA0/AQuXJjCydkVa7APERZyAkJeL3MwU6BpVkRn/+mUkMhKfwLXtWKXry8vLg0gkYsYqsKhXORhz7BWyJQIMtEWY19IcTmaKv1J+bKeLv+9noHk1PbhX0UZkYi6ORmUiNw9IzsqDpYEYL15LEPM6G+2c9bG4tTmep0qw8koKJIKAwR8ZK3zewnAfp14f7BEL69atQ926dWFkZARzc3PUr18fS5YskRmTmJiI8ePHw8nJCbq6urCzs8PXX3+NJ09k/+o5ZMgQiEQijBo1Sm4733zzDUQiEYYMGSI3/t1bp06KJ6sDgLlz50rHicViVK9eHcOHD0dcXJx0jKLnFIlE2LEj/5fjM2fOyCy3tLTEZ599hvPnz8tsKy0tDdOmTYOLiwv09fVhbW2N1q1b4/DhwzLjwsLC4OPjA2tra+jp6cHNzQ0//PAD0tPTZcY5OTlBJBIhJCREZvn48ePRunVrudf67Nkz6OrqolYtxR10kUiE/fv3F/pevcvJyQkrVqwodT1vv+dv306cOCFdX69ePYXbHDJkCHr06CG3PDQ0FCKRCI8ePQIg/zN5+xYTEyNTx7v5eve5CuzZswetW7eGmZkZjI2NUadOHcybNw+JiYkAgE2bNincnr5++f91IfllMv759R/0nNtT7giDkrj17y0EbQzClwu/hFEVI+nyThM7oUr1KljddzXmfzofR385inpd6kGkVfoPiiVLlqB3796IjY0t9WPpwxMbH43Vfj9h5rjF0NUt2aRPNRyc0aX9l3B39YSXRz2M/98PaNKwJXYe2CQdM3bodFSzc8SQ77qig099/LZxETp91gNaWuLCn1gB5rHievo0EePGBWLbtqHQ1y/ZIbweHrYYMaIFGjSogWbNXLFmzVfo3Nkbv/xyHACQmpqJAQP8sWHDQFhZle4XbEWYvw+fvoUD6vRdDu8vl6Kq9+d4cOI3pCc+VTi22sc+MHdsgLDd03BpTW/cPbIIVrU/AwCIRPJfAWLDT8CgSg0YVy3ZvDSKjB07lhmr4KqbamPjF1WwpmMVdHczwE8Xk/EoWfERMIO8jdHYXhdj/k1Eu79iMSsoCR1d8n9f1fr/L/+CAFjoa2FSY1N4WOrgMyd99Pc2wsHIjFLXxn2cen2QRyz4+flh4sSJ+O2339CqVStkZWXhv//+Q3h4uHRMYmIimjZtCl1dXaxZswbe3t549OgRZs2ahY8//hgXL16Ei8ubw7arV6+OHTt2YPny5TAwyD8kOzMzE3/99Rdq1JCfEKdTp04ICAiQWaanV/QvsV5eXjhx4gQkEglu3LiBYcOG4fnz5zh69Kh0TEBAgFyDwtzcXOb+3bt3YWpqiri4OCxYsACdO3dGZGQkbGxsAACjRo3C5cuXsWrVKnh6eiIhIQEXLlxAQkKC9DlCQkLQrl07tGvXDn///TeqVq2Ky5cvY9KkSTh16hROnz4NXd03h/3q6+tj2rRpCAoKKvI1AvlffH18fHD27FmcP38ezZuX/Xl6Ja2n4D1/W5UqVQoZrbyCn8nbCn4eQH69Bbl1dy/8A/v777/HkiVLMGHCBCxatAj29va4d+8e1q5di61bt2LcuHEAAFNTU9y9K3u4rDq6s9F3opH2Kg3rh6yXLhMkAh7feIzLuy9j1rlZ0BIr7l/ePn4bBxceRJ9FfeROoTCyMILvz77IzcpFenI6TKxNcGL1CVjYWyh8rqIkJSXh9OnTOH36NNauXQsfHx92siuwyAfheJWciP9NeTMLf16eBP+FX8P+o3/h38DrMnMnFMbTvQ5OBL1pxpqbVcH86b8hOzsLyalJsKpigw1bl8O2qkOp6mMeK65r154gNjYVDRsuki6TSPJw9uw9rFp1BllZq+XmTlCkaVMXbNt2CQDw4EEcHj1KQNeuq6Xr8/7/yC1t7dG4e3ceXF1LPucC8/fh0xLrQN88f/JG46o18Tr2HmJuHoJLG/mrhGhp68G17bdwbj0aORlJ0DW0wMuwYxDrGEDbQPZ3FklOFhLuBaNak37vVV9cXBwOHjzIjFVgOmIRHEzyv0J6WOrgTmIu9txJx6Qm8vMZ6WmLMK2ZGSY1McWrzDxU0dfC4fsZMNQWwUwvPxdVDLSgrSWC+K0/HjmaaiMxMw85EgE64pLnh/s49fogGwuHDh2Cj48Phg0bJl3m5eUlM+b777/HixcvcP/+fdja5l+yqUaNGvj333/h5uaGMWPGyHyhb9CgAaKiorB37170798fALB3715Ur15dpgFRQE9PT/q8JaWtrS19jIODA7777jvMnj0bGRkZ0maGubl5sc9rY2MjHTdr1izs3LkTly5dQteuXaXvz8qVK/HFF18AyP8Lf8OGby5FJAgChg0bhtq1a2Pv3r3Q0sr/RcfR0RHu7u6oX78+li9fjmnTpkkf87///Q9//PEHjhw5In1eRQRBQEBAANasWYNq1arBz89PJY2Fktbz9nuuSgU/k8J4eHjAxsZG+vNS5PLly1i0aBFWrFghbSAA+T+/9u3bIykpSbpMJBKVy+sqjnMjZ4zePlpm2YH5B2DlaIXmg5oX2lS49e8tHFx4EL3n94b7p4U3WrT1tGFqYwpJrgQRpyNkTpcoKRcXF5w4cQLJycnw9fVFYGAg1q5dK9P4oYqjQZ2m8Fu+T2bZ0lWzUL2aM/r1GFaipgIA3I+6gyoW8l/YdHX1YG1ZFbm5OTgbchytP+lYqvqYx4qrbdtauHVrtsyyr7/ejFq1bDFtWscSNRUA4MaNp7CzMwMA1KplK/ecs2YdQGpqJlau7Ivq1UvXbGX+KiIBeRLFE+cV0BJrQ8/YCgCQcC8Y5k6N5I5YSLgfjDxJDqzcWyldSUZGBry8vLB7925mrBIRBCCnmFNVtbVEsDbM//w99TgTTR30pEcseFvr4OSjTOQJgnTZ01QJLA20StVUALiPU7cP8lQIW1tbhISE4PHjxwrX5+XlYceOHejfv7/cly8DAwN88803+Pfff6WHlhf4+uuvZY5C8Pf3x9Ch8tc2LysGBgbIy8tDbq7iw4eKk56eLq1XR+fNYZe2trY4cuQIUlNTFT4uNDQU4eHhmDhxorSpUKBu3bpo164d/vrrL5nlTk5OGDVqFGbMmIG8vLxCazp9+jTS09PRrl07DBw4EDt37iy0jvdR0no0yU8//YQ9e/bgypUrCtf/+eefMDY2xjffKL42eVGNi6JkZWUhJSVF5lZW9Iz0YONqI3PTMdCBgZkBbFzzd+InVp/Avrlvvujd+vcW9v+4Hx2+64Bq3tXwOuE1Xie8RubrTOmYZ7efIeJ0BF49f4XHNx7jz3F/QsgT0Hyg8k0qQcj/0Dt48CA8PDwKbfBQ2VNlBt9laGAEZ0c3mZu+vgFMjc3h7OgGANiwbTkWr5whfczuQ1sRfOkknr14jIdP7mPDtuU4G3IcPb5485e7iMj/cDbkOF7EPMV/4dcwbf4oCIIA357KfUYwj+WnvPJnYqIPb28HmZuRkR4sLY3g7Z1/ZMuMGfswaNCb3zNWrDiB/ftDce/eS4SFvcCMGfuwZ891jB3bGgCgr68j95zm5obSbenqKvf3Ieav/JRl/p5c3IqUF2HITHmJ9PhHeHJxG1Keh0mbAU8ubMX94yuk4zNePUfc3TPISHqB1y8jce/fX5CR8ATVmw2Qe+648BOo4tIEOgYlu4qOIp9++mZyUWZMc5RlBjeEpuK/2GzEvJYg6lUONoa+xs3YbLRzyj+9YcONVCy6kCwd/zQlF8cfZuBZSi4i4nMwLzgJj5JyMaLem1O7ursZIiVLwKqrqXiakouLz7OwPSwNPdwN5LZfUsyfenyQjYU5c+bA3NwcTk5O8PDwwJAhQ7Bz507pF8y4uDgkJSWhdm3FMzLXrl0bgiDg/v37MssHDhyI4OBgPHr0CI8fP8b58+cxYID8zhcADh8+DGNjY5nb/PnzS/wa7ty5gz/++AONGzeGicmb61X369dP7nmjoqJkHlutWjXpuuXLl6Nhw4Zo27atdP369etx4cIFWFpa4uOPP8aECRNk5mGIjIyUvg+FvT8FY942a9YsPHz4EH/++Wehr8vPzw++vr4Qi8Xw8vJCzZo1ERgYWLI3pZRKUs+tW7dk3svGjRurpJa3fybGxsbw8PCQG9OgQQP4+Phg+vTpCp/j3r17cHFxkWkSFSY5OVkuJx06dFA4dvHixTAzM5PeqldXPMmTqrxOeI3kl28+ZK7tv4Y8SR6O/HwEy75YJr398+s/0jG52bk4tfYUVvuuRuC0QJhYm2Do+qHQN3n/eSQkEgmSkpJkjgoh1VJ3Bt+V+CoesfHR0vu5uTlYu/kXDJ/YC+NnDcLtiBtY9P0atGzaXjomOycLAdt/x9fjumP2knGwqmKDlQu3wNhI+V/CAeaxPGhS/qKjk/HkyZs/amRnSzB58m7UqTMfLVr8jODg+/j777Ho1atBudTD/KleWeYvJz0J94+vwM1tYxB+YA5ev4xEra6zYV6jHgAgOz0RWalv5u6CkIfoGwdwa8d4RByYi7zcbHh9+RP0TavKPG/Gq+dIjY6Ade12StdWGGZM/coyg68y87DoQjIGHYrHpJOvEJGQgyVtzNHILv908ITMPMSmvZnUOE8AdkakY/iRBEw59QrZEuD3jlVga/zm6EEbIzF+/swcdxJyMezvBKy6mopeHobo52kkt/3SYv7K1wd5KoSdnR0uXryI27dvIygoCBcuXMDgwYOxceNG/PPPP8U+vqCL9e45N1ZWVujcuTM2b94MQRDQuXNnWFlZKXyONm3a4I8//pBZVty5+wVfciUSCbKystC6dWusX79eZszy5cvRrp3sjv3dHcC5c+dgZGSEGzduYNq0adi0aZPMl9GWLVsiKioKISEhOH/+PE6dOoWVK1fixx9/xA8/FD+rdGGzqVpbW2Py5MmYPXs2+vbtK7c+KSkJe/fuRXBwsHTZgAED4O/vj+HDhxe73dIqrh4g/xSEgwcPSu8XNw+Gss6dOyfTINLWVvxfa8GCBahduzaOHTsmd1hWaWaxNTExwfXr12WWFZxO864ZM2Zg4sSJ0vspKSkq/cV6yB9DZO73mN2jyPWKODVwwpjAMWVX1FvEYjFMTEzw22+/qeT5SV55Z/Bdy+dvkrk/7duFMvd9ew4t9siDul4fI+C3g0WOUQbzqHrqzN+ZM5Nk7m/aNETm/tSpHTF1aulOp3n3Od4H86d6ZZk/17bfFrm+ZjvZL08GVaqjju/yYp/XwMIBTcfuV6qmtwUHB+P48eMyy5gx9SvLDE5talbk+unNZNc7mmljwxeWxT6vl7Uu1nQq+znQmL/y9UE2Fgp4e3vD29sbY8aMQXBwMFq0aIGgoCC0atUK5ubmMpM5vu3OnTsQiURwdXWVWzd06FCMHZt/mZ3Vq1fLrS9gZGSEmjVrlqregi+5YrEY9vb2Cr/k2traFvu8zs7OMDc3h7u7OzIzM9GzZ0/cvn1b5vl0dHTQokULtGjRAtOnT8eCBQswb948TJs2TTp5YHh4uMKrIty5cwdubm4Ktz1x4kSsWbMGa9askVu3fft2ZGZmokmTJtJlgiAgLy8P4eHh8PT0LPJ1KaOoegBAV1e31D8nIH9yREWn2hTMc2BmJrvjLPiZFMfV1RUjRozA9OnT4efnJ7PO3d0dwcHByMnJKfaoBS0trRK/Lj09PZU1VD4kIpEIgiCgW7duPN+unDGD8pjH8sP8yWP+yk9lyt/bf+BgxjRHZcpgAeZPPT7IUyEUKfjSmpaWBi0tLfj4+GD79u3SS/4VyMjIwJo1a9CxY0eFRxh06tQJ2dnZyM7ORseOpfsrQnEKvuQ6OzuX2X/wgQMHIi8vr9Av1gU8PT2Rm5uLzMxM1KtXD7Vq1cLy5cvl5ie4efMmTpw4gX79FM8KbGxsjB9++AELFy6UO0fLz88PkyZNQmhoqPR28+ZNtGnTBv7+/u/3QgtRVD3vo1atWrh9+zYyMzNlll+5cgXW1tawsCj91QkKzJ49G5GRkdLLiBb46quv8Pr160J/lm9P3kglFxUVBbFYDDMzMwQGBmLv3r38gCG1YR5JnZg/UrWwsDBmjNSG+zj1+iAbC6NHj8b8+fNx/vx5PH78GCEhIRg0aBCsra3RrFkzAMDChQtha2uL9u3b4+jRo3j69CnOnj2Ljh07Iicnp9CjEcRiMSIiIhAREVHk7OFZWVmIiYmRucXHx7/3a0tKSpJ73rS0tELHa2lpYfz48fjpp5+Qnp4OAGjdujXWrVuHa9eu4dGjRzhy5AhmzpyJNm3awNTUFCKRCBs3bkR4eDh69+6Ny5cv48mTJ9i1axe6du2KZs2aYfz48YVuc+TIkTAzM5OZ4DE0NBTXr1/H8OHDpUeSFNz69euHLVu2ICfnzazFDx8+lGlAhIaG4vXr10q9Z4rqKamMjAy5Ou7fv4/+/ftDW1sbAwcOxNWrV/HgwQNs27YNixcvxpQpU+SeJzY2Vu7n9vbrfVvVqlWll0t9W5MmTTB16lRMmjQJU6dOxcWLF/H48WOcPHkSffr0webNm6VjBUGQ215MTMwHM5FleTI3N0e3bt1w9+5d+Pj4qLscquSYR1In5o9UzdramhkjteE+Tr0+yMZCu3btEBISgj59+sDd3R29e/eGvr4+Tp48CUvL/PN4rKysEBISgjZt2uB///sfXFxc4OPjAxcXF1y5ckXhJSQLmJqawtS06Am5/vnnH9jZ2cnc3p4NV1lff/213PP+/vvvRT5m6NChyMnJwapVqwAAHTt2xObNm9GhQwfUrl0b3377LTp27CgzI2rz5s0REhICsViML774AjVr1sSMGTMwePBgHD9+vMgjKnR0dDB//nyZv+b7+fnB09MTtWrVkhvfo0cPJCYm4tChQ9JlEydORP369WVuV69eLfH7VFw9JRUZGSlXx/Dhw2FmZoZz585BEAT06NEDdevWxdKlSzF//nxMmjRJ7nk8PDzkfm7Xrl0rdLtTpkyBsbGx3PIlS5Zg+/btuHTpEjp27AgvLy9MnDgRderUweDBg6XjUlJS5LZnZ2eH2NjYUr8HFd20adPYsSaNwTySOjF/pGqrVq1ixkhtuI9TL5FQMJMhEVUKKSkpMDMzw5C1Q+BY31Hd5ahM9J1orB+8HteuXUODBuUzw/qHrCAXycnJxTZWy2pby+dvQl2vRirdlqaIfBCOUVN8mMdCqCN/QUGT0bKl4vmEKprr15+gYcOFzF8RyiuDBdvx7LkQpg5eKttOeUqLfYBbOycxX+9BHfvAFe0sULeqrkq3VV4iE3Pwv6OJzKCSyip/H+QRC0RERERERESkGdhYICIiIiIiIiKlsbFAREREREREREpjY4GIiIiIiIiIlMbGAhEREREREREpjY0FIiIiIiIiIlIaGwtEREREREREpDQ2FoiIiIiIiIhIadrqLoCI1CP+cTx0DXTVXYbKxD2KU3cJVIxnzx/BQN9Q3WWUiyfPotRdAr3j7t0YGBvrqbuMchEREa3uEugdmUnPIdbRV3cZZSLj1TN1l0BKeJqSCwMdkbrLKBOPk3PVXQKBjQWiSuvw4sPqLkHlDAwNYGVlpe4yqBDL1s5VdwnlytDQkHnUICNHblN3CeXKkPtDjRJ1eo26SyhT+gbcv31oll1OVXcJZcrQQJ8ZVDM2FogqqaCgIBgbG6u7DJWysrJCjRo11F0GFaIyZPBtzKNmYf5InSpa/pivDw8zSGWNjQWiSqpevXowNTVVdxlUiTGDpE7MH6kT80fqxgxSWePkjURERERERESkNDYWiIiIiIiIiEhpbCwQERERERERkdLYWCAiIiIiIiIipbGxQERERERERERKY2OBiIiIiIiIiJTGxgIRERERERERKY2NBSIiIiIiIiJSGhsLRERERERERKQ0NhaIiIiIiIiISGlsLBARERERERGR0thYICIiIiIiIiKlaau7ACIqX4IgAABSUlLUXAlpkoI8FORDlZhBehfzR+pWXhlk/kgR7gNJncoqf2wsEFUyCQkJAIDq1auruRLSRAkJCTAzM1P5NgBmkOQxf6Ruqs4g80dF4T6Q1Ol988fGAlElU6VKFQDAkydPVP7hVZSUlBRUr14dT58+hampaaWtQVPqSE5ORo0aNaT5UCVNyKAmvOeaUocm1MD8qYcm1KEJNQDll0FNyB+gGe+7JtSgKXVwH6gemlCHJtRQVvljY4GoktHSyp9axczMTK078wKmpqZqr0MTatCUOgryUR7b0IQMasJ7ril1aEINzF/lrUMTagBUn0FNyh+gGe+7JtSgKXVwH1h569CEGt43f5y8kYiIiIiIiIiUxsYCERERERERESmNjQWiSkZPTw9z5syBnp5epa9DE2rQlDrKs4bK9no1vY7KVkNle72aXocm1FCedVS216vpNWhKHdwHVt46KlINIqE8rmtCRERERERERBUSj1ggIiIiIiIiIqWxsUBERERERERESmNjgYiIiIiIiIiUxsYCERERERERESmNjQUiIiIiIiIiUhobC0RERERERESkNG11F0BE5UMikSA+Ph4ikQiWlpYQi8XqLokqGWaQ1In5I3Vi/kjdmEFSNR6xQFTB7du3D82bN4ehoSHs7e1hZ2cHQ0NDNG/eHPv37y+XGiIjIyEIgvR+cHAwevToAS8vL7Rr1w4HDhwolzoKSCQSvHz5ErGxsZBIJOW67cpI3RnUtPwBzGB5Yv7kMX/lR935AzQvg8xf+VJ3BjUtfwAzqCpsLBBVYOvWrYOvry/q1KmDwMBABAcH49y5cwgMDESdOnXg6+uLDRs2qLyO2rVrIy4uDgBw5swZtGrVCnl5eejfvz/Mzc3Rq1cv/PvvvyqvQ90froBmfcBeuXIF/fv3h7OzMwwMDGBoaAhnZ2f0798fV69eLZNtaEIGNSV/gPozyPwxf8xfvsqSP0BzMqju/AHMIPeB3AcWUEn+BCKqsFxdXYWNGzcWut7Pz09wcXFReR0ikUh4+fKlIAiC0LZtW+Gbb76RWT99+nShZcuWKq1h7dq1gq6urjBq1Chh3759woULF4Tz588L+/btE0aNGiXo6ekJ69evV2kNgiAIWlpa0vfi9OnTgpaWltC1a1dh4cKFQu/evQUtLS3hn3/+UXkd+/btE3R0dIROnToJy5cvF7Zv3y78+eefwvLly4XPP/9c0NXVFfbv3//e29GEDGpC/gRBMzLI/Mli/pi/ip4/QdCMDGpC/gSBGXwX94HcB5Zl/thYIKrA9PX1hTt37hS6PiIiQtDX11d5HW9/qNjZ2QkhISEy68PCwgRLS0uV1qAJH66CoDkfsF5eXsLixYsLXf/TTz8Jnp6e770dTcigJuRPEDQjg8yfLObvDebvjYqUP0HQjAxqQv4EgRl8F/eBb3Af+Iay+eOpEEQVmJeXF9avX1/o+g0bNsDLy6tcaklNTUVKSgoMDAygp6cns05XVxcZGRkq3f7z58/x6aefFrr+k08+wYsXL1Raw7vCw8MxaNAgmWUDBw5EWFiYyrd9//599OrVq9D1PXr0wIMHD957O5qSQXXnD9C8DDJ/zN/bmL83Klr+APVnUNPyBzCDAPeBb+M+8A1l88erQhBVYMuWLUPnzp3xzz//oEOHDqhatSpEIhFiYmJw/PhxPH78GEeOHCmXWtzd3QEAgiDg2rVrqFevnnRdWFgYHBwcVLr9gg/XZcuWKVxf3r/g6evrq/UD1tXVFfv378fUqVMVrj9w4ABcXFzeezuakkF15w/QnAwyf8yfIszfGxUtf4D6M6gp+QOYQe4DuQ9UVf7YWCCqwFq1aoXbt2/jjz/+QEhICGJiYgAAtra26NKlC0aNGgUnJyeV13H69GmZ+3Z2djL3Hz16hBEjRqi0Bk35cAU04wN23rx58PX1RVBQkML349ixY9ixY8d7b0cTMqgJ+QM0J4PMH/PH/FWe/AGakUFNyR/ADALcB3IfqJr8iQThrakpiYgqsEePHin8cG3WrFm5/YIXFBQkc9/Ozk76IQMAK1euRHZ2NqZMmaLyWi5evIiVK1fi4sWLcu/HuHHj0KxZM5XXUNmoO4PMX+XG/L3B/JU/decPYAYrO3VnsKLnj40FIiIiIiIiIlIaJ28kqsQGDx6Mzz77TN1laEwdVP404WevCTWQemjCz14TaiD10JSfvabUQeVPE372mlADlQ02FogqMXt7ezg6Oqq7DDg4OKi9Dk35YNOUOmbOnImhQ4eqfDuakEFNyB+gGT97TagBYP7UQRN+9ppQA1C58gdoRgY15WevKXVUpgxqQv4AzfjZa0INgPL54+SNRJXY4sWL1V0CAGDRokXqLgH29vbQ0lJ/r9XBwUEj6nj+/DmePn2q8u1oQgY1IX+AZmSQ+St/zN8bzJ96aEIGNSF/ADOoDpqQP0AzMvih549zLBBVcM+ePcMff/yBCxcuICYmBiKRCFWrVsUnn3yC0aNHo1q1auoukSo4ZpDUifkjdWL+SN2YQSovbCwQVWDBwcH4/PPPUb16denlZARBQGxsLI4fP46nT5/i6NGjaN68ucprycjIwLVr11ClShV4enrKrMvMzMTOnTsxaNAglddR4NWrV9i8eTPu3bsHOzs7DB48GNWrV1f5dr/99lv4+PigRYsWKt+WJtCUDGpa/gD1ZJD5Y/4KMH+qpyn5AzQvg/wMLh+akkFNyx/AfaBKCERUYTVq1EgYP358oevHjx8vNGrUSOV13L17V3B0dBREIpGgpaUltGrVSnjx4oV0fUxMjKClpaXSGuzs7IT4+HhBEAQhKipKsLW1FWxtbYX27dsL1apVE8zMzISIiAiV1iAIgvQ9cHNzE3766SchOjpa5dsszOvXr4X169cLQ4YMETp16iR8/vnnwpAhQ4QNGzYIr1+/LpNtaEIGNSF/gqAZGWT+ZDF/zF9Fz58gaEYGNSF/gsAMvov7QO4DyzJ/bCwQVWD6+vrCnTt3Cl0fEREh6Ovrq7yOHj16CF26dBHi4uKEe/fuCV27dhWcnZ2Fx48fC4JQPh8qIpFIePnypSAIguDr6yu0bt1aSEtLEwRBEDIzM4UuXboIX375pUprKKjjxIkTwrhx4wQrKytBR0dH6Natm3Do0CFBIpGofPsFwsLCBHt7e8Hc3Fzo3r27MHLkSGHEiBFC9+7dBXNzc8HBwUEICwt77+1oQgY1IX+CoBkZZP5kMX/MX0XPnyBoRgY1IX8FdTCDb3AfyH1gWeaPjQWiCszZ2Vnw9/cvdL2/v7/g7Oys8jpsbGyE//77T2bZN998I9SoUUN48OBBuf9S4+zsLJw8eVJmfUhIiFCtWjWV1vBuHdnZ2UJgYKDQsWNHQSwWC/b29sLMmTOFe/fuqbyO1q1bC76+vkJWVpbcuqysLKFfv35C69at33s7mpBBTcifIGhGBpk/WczfG8xfvoqWP0HQjAxqQv7erYMZ5D7wbdwH5nuf/LGxQFSBrV69WtDV1RXGjBkj7N+/X7h48aIQEhIi7N+/XxgzZoygp6cn/PHHHyqvw8TERAgPD5dbPnbsWKFatWrC2bNny+WXmtjYWEEQBMHe3l64ffu2zPqHDx8Kenp6Kq2hoI6CD5W3PX78WJgzZ47g6OhYLh+wBgYGRXajb926JRgYGLz3djQhg5qQP0HQjAwyf8yfIDB/lSl/gqAZGdSE/BXUwQxyH8h9oGryx8YCUQW3Y8cOoUmTJoK2trYgEokEkUgkaGtrC02aNBECAwPLpYaPP/5Y2LJli8J1Y8aMEczNzcvll5qPPvpIqF+/vmBsbCzs3btXZn1QUJDg4OCg0hoK6lD0oVIgLy9POHbsmMrrsLe3F/bv31/o+n379gn29vZlsi11Z1AT8icImpFB5o/5Y/4qV/4EQTMyqAn5K6iDGeQ+kPtA1eRPW92TRxKRavXt2xd9+/ZFTk4O4uPjAQBWVlbQ0dEptxp69uyJv/76CwMHDpRbt2rVKuTl5WHt2rUqrWHOnDky9w0NDWXuHzp0qFxm6XV0dIRYLC50vUgkQvv27VVex4gRIzB48GDMmjUL7du3R9WqVSESiRATE4Pjx49j0aJFGD9+fJlsS90Z1IT8AZqRQeaP+SvA/FWO/AGakUFNyB/ADHIf+Ab3gWWfP15ukoioklqyZAlWrlwpva41AAiCAFtbW4wfPx5Tp05Vc4VUkTF/pE7MH6kbM0jqpIr8sbFARFTJPXz4EDExMQAAW1tbODs7q7kiqkyYP1In5o/UjRkkdSrL/LGxQEREcp4+fYo5c+bA399f3aVQJcT8kToxf6RuzCCpPjeGvQAAAUJJREFUk7L5Y2OBiIjk3Lx5Ew0aNIBEIlF3KVQJMX+kTswfqRszSOqkbP44eSMRUSV08ODBItdHRUWVUyVUGTF/pE7MH6kbM0jqpKr88YgFIqJKSEtLCyKRCEV9BIhEIv61hFSC+SN1Yv5I3ZhBUidV5U/rfQsjIqIPj52dHfbs2YO8vDyFt+vXr6u7RKrAmD9SJ+aP1I0ZJHVSVf7YWCAiqoQaNmxY5AdHcZ1sovfB/JE6MX+kbswgqZOq8sc5FoiIKqEpU6YgLS2t0PU1a9bE6dOny7EiqkyYP1In5o/UjRkkdVJV/jjHAhEREREREREpjadCEBEREREREZHS2FggIiIiIiIiIqWxsUBERERERERESmNjgYiIiIiIiIiUxsYCERERERERESmNjQUiIiIiIiIiUhobC0RERERERESktP8Dve6y/HSRKCwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Grouping data by region and activity\n",
-    "gov_grouped = governance.pivot(index = 'region', columns = 'question', values = ['agree_mean', 'agree_se'])\n",
-    "\n",
-    "#Start figure\n",
-    "fig = plt.figure(figsize = (10, 5))\n",
-    "#Create as many subplots along one row as there are regions (index of rec_grouped)\n",
-    "#Ensure all subplots share the y axis\n",
-    "axs = fig.subplots(1, len(gov_grouped.index), sharey = True)\n",
-    "#Choosing a colormap for the plot - Separate colours for each activity\n",
-    "cmap = cmaps['Accent'](np.linspace(0, 1, len(gov_grouped.agree_mean.columns)))\n",
-    "#Loop through each region and plot the data\n",
-    "for i, reg in enumerate(gov_grouped.index):\n",
-    "    #Plot as horizontal bar chart\n",
-    "    means = gov_grouped.loc[reg].agree_mean[plot_order.question]\n",
-    "    se = gov_grouped.loc[reg].agree_se[plot_order.question]\n",
-    "    means.plot.barh(ax = axs[i], color = cmap[i], edgecolor = 'black')\n",
-    "    axs[i].errorbar(means.values, means.index, xerr = se.values, fmt = 'none', color = 'black', xlolims=True)\n",
-    "    #Add title to each subplot\n",
-    "    axs[i].set_title(reg)\n",
-    "    #Remove y axis label\n",
-    "    axs[i].set_ylabel('')\n",
-    "    #Set x axis ticks\n",
-    "    axs[i].set_xticks(np.arange(0, 11, 2.5))\n",
-    "    axs[i].set_xticklabels(np.arange(0, 11, 2.5), rotation = 90)\n",
-    "    #Adding labels at the end of each bar\n",
-    "    for q, val in enumerate(means.values):\n",
-    "        axs[i].text(val-3, q, val)\n",
-    "#Add title to figure\n",
-    "fig.suptitle('Perceived fairness and trust by respondents in waterway governance', fontsize = 14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Word cloud example using favourite waterways for recreation\n",
-    "We will search for the relevant questions in our table of variables, and prepare query results to extract data from SELTMP dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>field</th>\n",
-       "      <th>region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>w10</td>\n",
-       "      <td>WET TROPICS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>t10</td>\n",
-       "      <td>TOWNSVILLE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>m10</td>\n",
-       "      <td>MWI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>f10</td>\n",
-       "      <td>FITZROY</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  field       region\n",
-       "0   w10  WET TROPICS\n",
-       "1   t10   TOWNSVILLE\n",
-       "2   m10          MWI\n",
-       "3   f10      FITZROY"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Searching for matches in the new_name column using keywords: 'favourite.*waterway'\n",
-    "water = table[table['description'].str.contains('favourite.*waterway', case = False)].copy()\n",
-    "#Extracting region name from new_name column\n",
-    "water['region'] = water.new_name.str.split(' - ', expand = True)[1].str.replace(' ONLY', '')\n",
-    "#Removing columns not needed\n",
-    "water.drop(columns = ['description', 'new_name'], inplace = True)\n",
-    "#Resetting row index\n",
-    "water.reset_index(drop = True, inplace = True)\n",
-    "#Checking result\n",
-    "water"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case, we will show global results, so we will ignore regions. However, by changing the grouping of the data, we can create wordclouds for each region."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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",
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",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=400x200>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Extracting favorite waterway questions\n",
-    "fav_water = md.read_pandas(columns = water.field).to_pandas().reset_index(drop = True)\n",
-    "\n",
-    "#We will make the data frame longer\n",
-    "fav_water['id'] = fav_water.index\n",
-    "fav_water = pd.melt(fav_water, id_vars = 'id', value_vars = fav_water.columns[:-1],\n",
-    "                     var_name = 'field', value_name = 'waterways')\n",
-    "#We remove any rows with no data in the waterways column (i.e., a single respondent \n",
-    "#cannot be in multiple regions)\n",
-    "fav_water.dropna(subset = 'waterways', inplace = True)\n",
-    "#Removing ID because it is not needed\n",
-    "fav_water.drop(columns = 'id', inplace = True)\n",
-    "#Cleaning names of waterways before counting\n",
-    "fav_water['waterways'] = fav_water.apply(lambda x: re.sub('\\'', '', x.waterways), axis = 1)\n",
-    "#Standardise punctuation before splitting into individual waterways\n",
-    "fav_water['waterways'] = fav_water.apply(lambda x: re.sub('[^\\w\\s]|  | and | or |the ',\n",
-    "                                                           ';', x.waterways.lower()), axis = 1)\n",
-    "#Splitting waterways into individual rows and count them\n",
-    "fav_water = pd.DataFrame(fav_water.waterways.str.split(';').explode())\n",
-    "#Remove empty rows\n",
-    "fav_water = fav_water[fav_water.waterways != '']\n",
-    "#Get stopwords for wordcloud\n",
-    "stopwords = set(STOPWORDS)\n",
-    "#Add additional stopwords\n",
-    "stopwords.update(['variety', 'havent', 'dont', 'none','not ','no ','wildlife','nil','idk',\n",
-    "                  'lovely','know', '\\?','car','fishing','boat','impossible','chair','unsure','secret','swimming'])\n",
-    "#Create a wordcloud\n",
-    "WordCloud(stopwords = stopwords, background_color = 'white').generate(' '.join(fav_water.waterways)).to_image()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "rimrep",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/README.md b/Python_based_scripts/README.md
new file mode 100644
index 0000000..3c4dad6
--- /dev/null
+++ b/Python_based_scripts/README.md
@@ -0,0 +1,25 @@
+# DMS Python notebooks
+
+Use these notebooks to explore the data from the DMS project.
+
+## 01-Direct-accessing-eReef  
+This notebook demonstrates how to access a public dataset from the RIMReP DMS that is in `zarr` format. We will use the [AIMS - eReefs Aggregations of Hydrodynamic Model Outputs (4km Daily)](https://thredds.ereefs.aims.gov.au/thredds/resources/docs/gbr4.html) dataset as an example.  
+
+## 02-Extracting_Spatial_Data_GBR_Features  
+This notebook will show how to access the RIMReP `geoparquet` collection for Great Barrier Reef (GBR) Feature from the Great Barrier Reef Marine Park Authority (GBRMPA). This dataset includes the unique IDs and names of all features above water, including sand banks, reefs, cays, islets, and islands. Since this dataset includes spatial data, we can extract the spatial limits of each feature included in this dataset.  
+
+## 03-extract_NOAA-DHW  
+This notebook demonstrates how to access a public dataset from the RIMReP collection using `Zarr`. We will use the [NOAA CRW degree heating weeks](https://www.coris.noaa.gov/search/catalog/search/resource/details.page?uuid=%7BF77EF0B8-C12F-463F-B66A-CC922E50A39D%7D) dataset as an example. We will also include examples of how to make simple calculations to produce maps and timeseries.  
+
+## 04-AIMS_waterTemp  
+This notebook demonstrates direct access to the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset in the RIMReP `geoParquet` collection. This dataset includes sea temperature data from 1980 for tropical and subtropical coral reefs around Australia, and it includes sites within the Great Barrier Reef.   
+
+## 05-Extracting_Water_Temperature_at_Site  
+This notebook demonstrates direct access to the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset in the RIMReP `geoparquet` collection. This dataset includes sea temperature data from 1980 for tropical and subtropical coral reefs around Australia, and it includes sites within the Great Barrier Reef. In this example, we will extract the coordinates for all sites sampled in this monitoring program and then extract the temperature data for a specific site of interest. This means that we will not need to know the exact location of the site, but we will need to know the name of the site of our interest.  
+
+## 06-getDHWmax
+This notebook allows you to connect with the DMS NOAA DHW product, extract the maximum DHW value and the corresponding day of the year for each pixel, and save the results as a netCDF file. You have the option to specify a spatial extent and a temporal extent (typically a full year).  
+
+## 07-stac-metadata
+This notebook shows basic use of our metadata API using a Python library PySTAC-Client. It demonstrates how to fetch all collections, fetch a given collection/item, and perform simple searches.  
+
diff --git a/Python_based_scripts/geoparquet.ipynb b/Python_based_scripts/geoparquet.ipynb
deleted file mode 100644
index b917429..0000000
--- a/Python_based_scripts/geoparquet.ipynb
+++ /dev/null
@@ -1,1389 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# `Geoparquet` direct access example\n",
-    "\n",
-    "**Authors:** Leo Ghignone, Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook demonstrates direct access to a dataset in the RIMReP `geoparquet` collection. The `geoparquet` collection is a collection of datasets that have been converted to the Apache `parquet` format. This format is designed for efficient storage and retrieval of tabular data. The `geoparquet` collection is stored on the RIMReP S3 bucket and is accessible to all users.  \n",
-    "  \n",
-    "We will use the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e) dataset as an example. This dataset includes sea temperature data since 1980 for tropical and subtropical coral reefs around Australia, including sites within the Great Barrier Reef.  \n",
-    "  \n",
-    "The workflow in this notebook is as follows:\n",
-    "- Inspect dataset metadata\n",
-    "- Aggregate and plot temperature data for all sites where temperature loggers were deployed\n",
-    "- Collect data for a specific region, in this case we will use the Great Barrier Reef Marine Protected Area as our region of interest"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing S3 bucket\n",
-    "from pyarrow import parquet as pq\n",
-    "# Manipulating dataframes\n",
-    "import pandas as pd\n",
-    "# Load and manipulate geospatial data\n",
-    "import dask_geopandas\n",
-    "import geopandas as gpd\n",
-    "# Spatial data\n",
-    "from shapely.geometry import Polygon\n",
-    "# Plotting\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Mapping\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cf\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting metadata\n",
-    "The AIMS Temperature dataset contains over 150 million records. We will use its metadata to check the structure of the dataset and to determine the best way to access the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.FileMetaData object at 0x000002270743FB00>\n",
-       "  created_by: parquet-cpp-arrow version 10.0.1\n",
-       "  num_columns: 23\n",
-       "  num_rows: 150663521\n",
-       "  num_row_groups: 50\n",
-       "  format_version: 2.6\n",
-       "  serialized_size: 155051"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Connecting to metadata file\n",
-    "md = pq.read_metadata(f\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/_metadata\")\n",
-    "#Checking contents\n",
-    "md"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metadata is telling us that the AIMS temperature dataset contains 23 columns and over 150 million rows, and it is made up of 50 different files.  \n",
-    "  \n",
-    "We can check the structure of the data by calling its `schema` attribute. This will return a list containing the name of the column and its data type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow._parquet.ParquetSchema object at 0x000002660E290440>\n",
-       "required group field_id=-1 schema {\n",
-       "  optional int64 field_id=-1 fid;\n",
-       "  optional int64 field_id=-1 deployment_id;\n",
-       "  optional binary field_id=-1 site (String);\n",
-       "  optional int64 field_id=-1 site_id;\n",
-       "  optional binary field_id=-1 subsite (String);\n",
-       "  optional int64 field_id=-1 subsite_id;\n",
-       "  optional int64 field_id=-1 from_date (Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional int64 field_id=-1 thru_date (Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional double field_id=-1 depth;\n",
-       "  optional binary field_id=-1 parameter (String);\n",
-       "  optional binary field_id=-1 instrument_type (String);\n",
-       "  optional binary field_id=-1 serial_num (String);\n",
-       "  optional double field_id=-1 lat;\n",
-       "  optional double field_id=-1 lon;\n",
-       "  optional binary field_id=-1 gbrmpa_reef_id (String);\n",
-       "  optional binary field_id=-1 metadata_uuid (String);\n",
-       "  optional double field_id=-1 sites_with_climatology_available;\n",
-       "  optional int64 field_id=-1 time (Timestamp(isAdjustedToUTC=true, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false));\n",
-       "  optional double field_id=-1 cal_val;\n",
-       "  optional double field_id=-1 qc_val;\n",
-       "  optional double field_id=-1 qc_flag;\n",
-       "  optional binary field_id=-1 geometry;\n",
-       "  optional int64 field_id=-1 hilbert_distance (Int(bitWidth=64, isSigned=false));\n",
-       "}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `schema` tells us that the dataset contains 23 columns, which matches the information we got from the metadata. It also tells us that the data types are either `string` or `double`.  \n",
-    "  \n",
-    "We can also obtain more details about a specific column, by calling the `column` method and passing the name of the column as an argument as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ParquetColumnSchema>\n",
-       "  name: qc_val\n",
-       "  path: qc_val\n",
-       "  max_definition_level: 1\n",
-       "  max_repetition_level: 0\n",
-       "  physical_type: DOUBLE\n",
-       "  logical_type: None\n",
-       "  converted_type (legacy): NONE"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "md.schema.column(19)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we refer to the original site for the [AIMS Temperature Logger Monitoring Program](https://apps.aims.gov.au/metadata/view/4a12a8c0-c573-11dc-b99b-00008a07204e), we can see that the `qc_val` column refers to the quality controlled temperature obtained from the loggers deployed as part of this program."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to S3 bucket and loading temperature logger deployment sites\n",
-    "Now that we have checked the structure of the dataset, we will connect to the RIMReP S3 bucket and load the dataset into memory. We will use the `dask_geopandas` library to connect to the S3 bucket and load the dataset into a `pandas` dataframe.  \n",
-    "  \n",
-    "First, we will load the temperature logger deployment sites from the `geoparquet` collection. We will use the following columns:  \n",
-    "- `site_id`: unique ID of the logger deployment site,\n",
-    "- `lat`: latitude of the logger deployment site,\n",
-    "- `lon`: longitude of the logger deployment site\n",
-    "    \n",
-    "We will only keep sites that have been deployed in the Great Barrier Reef Marine Protected Area (GBRMPA), so we will provide a `filter` that contains the minimum and maximum coordinates (i.e., `lat` and `lon`) of a bounding box around the Great Barrier Reef Marine Protected Area."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site_id</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>hilbert_distance</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2245686156</th>\n",
-       "      <td>979</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2245686156</th>\n",
-       "      <td>979</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2245686156</th>\n",
-       "      <td>979</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2245686156</th>\n",
-       "      <td>979</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2245686156</th>\n",
-       "      <td>979</td>\n",
-       "      <td>-17.4935</td>\n",
-       "      <td>140.8302</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  site_id      lat       lon\n",
-       "hilbert_distance                            \n",
-       "2245686156            979 -17.4935  140.8302\n",
-       "2245686156            979 -17.4935  140.8302\n",
-       "2245686156            979 -17.4935  140.8302\n",
-       "2245686156            979 -17.4935  140.8302\n",
-       "2245686156            979 -17.4935  140.8302"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Defining maximum and minimum coordinates for bounding box around the GBRMPA\n",
-    "filter = [('lon', '>=', 140.5),\n",
-    "          ('lat', '>=', -28.5),\n",
-    "          ('lon', '<=', 156.0),\n",
-    "          ('lat', '<=', -6.5)]\n",
-    "\n",
-    "#Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #We specify the columns we want to read\n",
-    "                                          columns = ['site_id', 'lat', 'lon'],\n",
-    "                                          #We specify the filter we want to apply - which we specified at the beginning of this code chunk\n",
-    "                                          filters = [filter],\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True})  \n",
-    "\n",
-    "#Checking the contents of the dataframe - First five rows\n",
-    "dask_geo_df.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see above that the `site_id` is repeated multiple times. This is because the temperature loggers were deployed on multiple occassions at the same site. If we print more rows, you will also see that the coordinates for a particular site vary slightly. This is because the temperature loggers were not deployed at the exact same location each time, but they were all deployed near each other.  \n",
-    "    \n",
-    "Next, we will calculate the mean deployment coordinate and total number of temperature records for each site. We will use the `groupby` method to group the data by `site_id` and then we will use the `agg` method to perform our calculations.  \n",
-    "  \n",
-    "Note the use of `compute()` at the end of our code. This will trigger our calculations and load the results as a data frame into memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>number</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>site_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>979</th>\n",
-       "      <td>-17.493500</td>\n",
-       "      <td>140.8302</td>\n",
-       "      <td>139636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>910</th>\n",
-       "      <td>-13.169053</td>\n",
-       "      <td>143.5803</td>\n",
-       "      <td>453962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3480</th>\n",
-       "      <td>-12.585000</td>\n",
-       "      <td>143.5112</td>\n",
-       "      <td>321118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>927</th>\n",
-       "      <td>-12.670900</td>\n",
-       "      <td>141.8665</td>\n",
-       "      <td>113305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>928</th>\n",
-       "      <td>-12.678300</td>\n",
-       "      <td>141.7883</td>\n",
-       "      <td>12907</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               lat       lon  number\n",
-       "site_id                             \n",
-       "979     -17.493500  140.8302  139636\n",
-       "910     -13.169053  143.5803  453962\n",
-       "3480    -12.585000  143.5112  321118\n",
-       "927     -12.670900  141.8665  113305\n",
-       "928     -12.678300  141.7883   12907"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Grouping our dataframe by site_id and aggregating the lat and lon columns by mean and the site_id column by count\n",
-    "counts = dask_geo_df.groupby('site_id').agg(\n",
-    "    #Calculating mean lat and lon\n",
-    "    lat = pd.NamedAgg(column = 'lat', aggfunc = 'mean'),\n",
-    "    lon = pd.NamedAgg(column = 'lon', aggfunc = 'mean'),\n",
-    "    #Calculating the number of rows (i.e., deployments) per site_id\n",
-    "    number = pd.NamedAgg(column = 'site_id', aggfunc = 'count')).compute()\n",
-    "\n",
-    "#Checking the contents of the dataframe - First five rows\n",
-    "counts.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting the deployment sites\n",
-    "Now that we have the mean deployment coordinates for each site, we can plot them on a map. We will plot the data using markers which will change in size in proportion to the number of temperature records taken at each site."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x266131c65d0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ldfierro\\AppData\\Local\\miniconda3\\envs\\rimrep\\Lib\\site-packages\\cartopy\\io\\__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_land.zip\n",
-      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting the data using a scatter plot\n",
-    "ax.scatter(x = counts.lon, y = counts.lat, s = counts.number/50000, alpha = 0.5)\n",
-    "\n",
-    "#Adding a basemap of land areas\n",
-    "ax.add_feature(cf.LAND)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to S3 bucket and loading temperature data for a specific region\n",
-    "In the previous section we showed how to filter data using a bounding box. We provided a maximum and minimum coordinates for this bounding box around the GBRMPA. In this section, we will show how to filter data using a more complex shape. We will define a polygon for an area of interest and use this polygon to filter the temperature data.  \n",
-    "  \n",
-    "We will use the boundaries of the South-east of Great Palm Island as our area of interest. Note that each pair of coordinates represents a node along the polygon boundary. It should also be noted that the first and last coordinates must be the same as to close the polygon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"146.48931316504 -18.9698480989599 0.5252073469200127 0.4560028749198999\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,-37.4836933229999)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.010504146938400254\" opacity=\"0.6\" d=\"M 146.551097906,-18.533297348 L 146.995068388,-18.829679637 L 146.995068388,-18.9503959749999 L 146.741000438,-18.9503959749999 L 146.726817846,-18.9503959749999 L 146.726817846,-18.901664877 L 146.559980192,-18.901664877 L 146.559980192,-18.860043204 L 146.559980192,-18.830003607 L 146.559980192,-18.7916486789999 L 146.699865007,-18.7916486789999 L 146.699865007,-18.7975971989999 L 146.725899924,-18.7975971989999 L 146.725899924,-18.746824945 L 146.725899924,-18.7468142619999 L 146.725872512,-18.746799391 L 146.587994956,-18.672003247 L 146.587994956,-18.665847742 L 146.508765289,-18.5543376589999 L 146.508765289,-18.544419717 L 146.508765289,-18.533297348 L 146.551097906,-18.533297348 z\" /></g></svg>"
-      ],
-      "text/plain": [
-       "<POLYGON ((146.551 -18.533, 146.995 -18.83, 146.995 -18.95, 146.741 -18.95, ...>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Defining boundaries of the GBRMP zone MNP-18-1082 \"South-east of Great Palm Island\"\n",
-    "boundary = Polygon(\n",
-    "    [(146.551097906,-18.533297348),\n",
-    "    (146.995068388,-18.829679637),\n",
-    "    (146.995068388,-18.9503959749999),\n",
-    "    (146.741000438,-18.9503959749999),\n",
-    "    (146.726817846,-18.9503959749999),\n",
-    "    (146.726817846,-18.901664877),\n",
-    "    (146.559980192,-18.901664877),\n",
-    "    (146.559980192,-18.860043204),\n",
-    "    (146.559980192,-18.830003607),\n",
-    "    (146.559980192,-18.7916486789999),\n",
-    "    (146.699865007,-18.7916486789999),\n",
-    "    (146.699865007,-18.7975971989999),\n",
-    "    (146.725899924,-18.7975971989999),\n",
-    "    (146.725899924,-18.746824945),\n",
-    "    (146.725899924,-18.7468142619999),\n",
-    "    (146.725872512,-18.746799391),\n",
-    "    (146.587994956,-18.672003247),\n",
-    "    (146.587994956,-18.665847742),\n",
-    "    (146.508765289,-18.5543376589999),\n",
-    "    (146.508765289,-18.544419717),\n",
-    "    (146.508765289,-18.533297348),\n",
-    "    (146.551097906,-18.533297348)]\n",
-    ")\n",
-    "\n",
-    "# Checking the variable we just created\n",
-    "boundary"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our polygon has been successfully defined as a `shapely` polygon. We can now use this polygon to filter the AIMS temperature data. We will use the `within` method to filter the data. This method will return `True` if the coordinates of a temperature record are within the polygon and `False` if they are not."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Building a filter using polygon boundaries\n",
-    "Filtering the temperature data using the shape directly is possible, but not very inefficient. This why we will use a 2-step approach. First, we will filter the data using a bounding box as shown in the section above. Second, we will intersect the filtered data with the polygon we defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Getting the bounding box for the polygon defined above\n",
-    "minx, miny, maxx, maxy = boundary.bounds\n",
-    "\n",
-    "# Building the filter using the bounding box\n",
-    "filter = [('lon', '>=', minx),\n",
-    "          ('lat', '>=', miny),\n",
-    "          ('lon', '<=', maxx),\n",
-    "          ('lat', '<=', maxy),\n",
-    "          #We will add another condition to our filter. We will only keep data with quality control flag 1\n",
-    "          ('qc_flag', '=', 1),\n",
-    "          #('time', '>=', pd.Timestamp('2002-07-20', tz=\"UTC\"))\n",
-    "          #('time', '<', pd.Timestamp('2002-07-20', tz=\"UTC\"))\n",
-    "          ]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Applying filter to AIMS dataset using `pyarrow`\n",
-    "We will connect to the S3 bucket using `pyarrow`, but we will only load the columns of our interest:  \n",
-    "- `fid`: unique ID,\n",
-    "- `site`: name of deployment site,\n",
-    "- `qc_val`: quality controlled temperature,\n",
-    "- `geometry`: coordinates of logger deployment sites given in  [well-known binary (WKB)](https://loc.gov/preservation/digital/formats/fdd/fdd000549.shtml) format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pyarrow.parquet.core._ParquetDatasetV2 at 0x2270755b610>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Connecting to the S3 bucket where the AIMS temperature data is stored\n",
-    "ds = pq.ParquetDataset(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                       #Applying filter\n",
-    "                       filters = [filter]) "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above code chunk creates a `Parquet Dataset` object that contains data from the AIMS temperature dataset that has been filtered using the bounding box we defined above. We can now choose which columns we want to read from this dataset: `fid`, `site`, `qc_val` and `geometry`. Finally, we can turn it into a `pandas` dataframe by calling the `to_pandas()` method as shown below.  \n",
-    "  \n",
-    "Note that this make take a few minutes depending on your internet connection. This is because we are querying over 150 million temperature records."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fid</th>\n",
-       "      <th>site</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28085253</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3531</td>\n",
-       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>28085252</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3232</td>\n",
-       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>28085251</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3034</td>\n",
-       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28085250</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.2934</td>\n",
-       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28085249</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.2636</td>\n",
-       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        fid            site   qc_val  \\\n",
-       "0  28085253  Orpheus Island  26.3531   \n",
-       "1  28085252  Orpheus Island  26.3232   \n",
-       "2  28085251  Orpheus Island  26.3034   \n",
-       "3  28085250  Orpheus Island  26.2934   \n",
-       "4  28085249  Orpheus Island  26.2636   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...  \n",
-       "1  b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...  \n",
-       "2  b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...  \n",
-       "3  b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...  \n",
-       "4  b'\\x01\\x01\\x00\\x00\\x00\\x1e\\x16jM\\xf3Vb@=\\n\\xd7...  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# The above give us access to all columns in the dataset. Here, we specify which columns we want to read\n",
-    "table = ds.read(['fid', 'site', 'qc_val', 'geometry']) \n",
-    "\n",
-    "# Converting to pandas dataframe\n",
-    "df = table.to_pandas()\n",
-    "\n",
-    "# Checking the contents of the dataframe - First five rows\n",
-    "df.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transforming `geometry` column from WKB format to coordinates\n",
-    "As we can see above, the `geometry` column is not very useful in its current format (WKB). In this section, we will transform the information in this column to a `geopandas` object, which contains latitude and longitude coordinates that we can use to apply our polygon filter, and to plot the filtered `data in a map."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fid</th>\n",
-       "      <th>site</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28085253</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3531</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>28085252</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3232</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>28085251</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3034</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28085250</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.2934</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28085249</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.2636</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983715</th>\n",
-       "      <td>44619517</td>\n",
-       "      <td>Havannah Island</td>\n",
-       "      <td>24.9381</td>\n",
-       "      <td>POINT (146.54270 -18.84750)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983716</th>\n",
-       "      <td>44619513</td>\n",
-       "      <td>Havannah Island</td>\n",
-       "      <td>24.9581</td>\n",
-       "      <td>POINT (146.54270 -18.84750)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983717</th>\n",
-       "      <td>44619520</td>\n",
-       "      <td>Havannah Island</td>\n",
-       "      <td>24.9281</td>\n",
-       "      <td>POINT (146.54270 -18.84750)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983718</th>\n",
-       "      <td>44619519</td>\n",
-       "      <td>Havannah Island</td>\n",
-       "      <td>24.9281</td>\n",
-       "      <td>POINT (146.54270 -18.84750)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983719</th>\n",
-       "      <td>44619518</td>\n",
-       "      <td>Havannah Island</td>\n",
-       "      <td>24.9381</td>\n",
-       "      <td>POINT (146.54270 -18.84750)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>983720 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             fid             site   qc_val                     geometry\n",
-       "0       28085253   Orpheus Island  26.3531  POINT (146.71720 -18.80250)\n",
-       "1       28085252   Orpheus Island  26.3232  POINT (146.71720 -18.80250)\n",
-       "2       28085251   Orpheus Island  26.3034  POINT (146.71720 -18.80250)\n",
-       "3       28085250   Orpheus Island  26.2934  POINT (146.71720 -18.80250)\n",
-       "4       28085249   Orpheus Island  26.2636  POINT (146.71720 -18.80250)\n",
-       "...          ...              ...      ...                          ...\n",
-       "983715  44619517  Havannah Island  24.9381  POINT (146.54270 -18.84750)\n",
-       "983716  44619513  Havannah Island  24.9581  POINT (146.54270 -18.84750)\n",
-       "983717  44619520  Havannah Island  24.9281  POINT (146.54270 -18.84750)\n",
-       "983718  44619519  Havannah Island  24.9281  POINT (146.54270 -18.84750)\n",
-       "983719  44619518  Havannah Island  24.9381  POINT (146.54270 -18.84750)\n",
-       "\n",
-       "[983720 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Transforming the geometry column into a geopandas geometry object\n",
-    "geom = gpd.GeoSeries.from_wkb(df['geometry'])\n",
-    "\n",
-    "# Replacing the original geometry column in WKB format with the new geometry object containing lat and lon coordinates, and changing to a geopandas dataframe\n",
-    "gdf = gpd.GeoDataFrame(df, geometry = geom)\n",
-    "\n",
-    "#Checking results\n",
-    "gdf"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Applying polygon filter to temperature data\n",
-    "Now that we have transformed the `geometry` column into a `geopandas` object, we can apply our polygon filter. We will use the `within` method to filter the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fid</th>\n",
-       "      <th>site</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28085253</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3531</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>28085252</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3232</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>28085251</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.3034</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28085250</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.2934</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28085249</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>26.2636</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50547</th>\n",
-       "      <td>27760270</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>28.6604</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50548</th>\n",
-       "      <td>27760267</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>28.6902</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50549</th>\n",
-       "      <td>27762279</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>28.6703</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50550</th>\n",
-       "      <td>27760269</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>28.6703</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50551</th>\n",
-       "      <td>27760268</td>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>28.6803</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>50552 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            fid            site   qc_val                     geometry\n",
-       "0      28085253  Orpheus Island  26.3531  POINT (146.71720 -18.80250)\n",
-       "1      28085252  Orpheus Island  26.3232  POINT (146.71720 -18.80250)\n",
-       "2      28085251  Orpheus Island  26.3034  POINT (146.71720 -18.80250)\n",
-       "3      28085250  Orpheus Island  26.2934  POINT (146.71720 -18.80250)\n",
-       "4      28085249  Orpheus Island  26.2636  POINT (146.71720 -18.80250)\n",
-       "...         ...             ...      ...                          ...\n",
-       "50547  27760270  Orpheus Island  28.6604  POINT (146.71720 -18.80250)\n",
-       "50548  27760267  Orpheus Island  28.6902  POINT (146.71720 -18.80250)\n",
-       "50549  27762279  Orpheus Island  28.6703  POINT (146.71720 -18.80250)\n",
-       "50550  27760269  Orpheus Island  28.6703  POINT (146.71720 -18.80250)\n",
-       "50551  27760268  Orpheus Island  28.6803  POINT (146.71720 -18.80250)\n",
-       "\n",
-       "[50552 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Intersecting filtered temperature data with the polygon created at the beginning of this section\n",
-    "gdf = gdf[gdf['geometry'].within(boundary)]\n",
-    "\n",
-    "#Checking results\n",
-    "gdf"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By applying this polygon filter, the number of rows were reduced from almost one million records to just over 50,500 records. We can now plot the filtered data on a map."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting map of filtered data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(146.008765289, 147.495068388)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting the mean location for all deployment sites \n",
-    "ax.scatter(x = counts.lon, y = counts.lat, alpha = 0.5)\n",
-    "\n",
-    "#Plotting the unqiue location filtered temperature data\n",
-    "gpd.GeoSeries(pd.unique(gdf.geometry)).plot(color = 'green', ax = ax)\n",
-    "\n",
-    "#Plotting the boundary of the GBRMP zone MNP-18-1082 \"South-east of Great Palm Island\"\n",
-    "gpd.GeoSeries(boundary).plot(ax = ax, color = 'red', alpha=0.2)\n",
-    "\n",
-    "#Adding a basemap of land areas\n",
-    "ax.add_feature(cf.LAND)\n",
-    "\n",
-    "#Changing the limits of the plot to zoom in on the GBRMP zone MNP-18-1082 \"South-east of Great Palm Island\"\n",
-    "plt.ylim([miny-0.5, maxy+0.5])\n",
-    "plt.xlim([minx-0.5, maxx+0.5])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accessing S3 bucket and filtering data using `dask_geopandas`\n",
-    "We can use geopandas directly to extract the data from the S3 bucket. This section offers a different approach to the section before, but the results are the same."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Connecting to S3 bucket\n",
-    "dask_geo_df = dask_geopandas.read_parquet(\"s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/\", \n",
-    "                                          #Applying initial bounding box filter\n",
-    "                                          filters = [filter], \n",
-    "                                          #Specifying which columns to read\n",
-    "                                          columns = ['site', 'time', 'qc_val', 'geometry'],\n",
-    "                                          #Specifying the column to be used as index\n",
-    "                                          index = 'fid',\n",
-    "                                          #Connecting anonimously as no authentication is needed for this s3 bucket (it is public)\n",
-    "                                          storage_options = {\"anon\": True}) "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have established the connection to the S3 bucket and applied the initial bounding box filter. Now, we will filter the data further by applying the polygon filter by applying the `within` method to the `geometry` column. We will then apply the `compute()` method to load the filtered data into memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>site</th>\n",
-       "      <th>time</th>\n",
-       "      <th>qc_val</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>28085253</th>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>2012-05-06 03:21:47+00:00</td>\n",
-       "      <td>26.3531</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28085252</th>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>2012-05-06 03:11:47+00:00</td>\n",
-       "      <td>26.3232</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28085251</th>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>2012-05-06 03:01:47+00:00</td>\n",
-       "      <td>26.3034</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28085250</th>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>2012-05-06 02:51:47+00:00</td>\n",
-       "      <td>26.2934</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28085249</th>\n",
-       "      <td>Orpheus Island</td>\n",
-       "      <td>2012-05-06 02:41:47+00:00</td>\n",
-       "      <td>26.2636</td>\n",
-       "      <td>POINT (146.71720 -18.80250)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    site                      time   qc_val  \\\n",
-       "fid                                                           \n",
-       "28085253  Orpheus Island 2012-05-06 03:21:47+00:00  26.3531   \n",
-       "28085252  Orpheus Island 2012-05-06 03:11:47+00:00  26.3232   \n",
-       "28085251  Orpheus Island 2012-05-06 03:01:47+00:00  26.3034   \n",
-       "28085250  Orpheus Island 2012-05-06 02:51:47+00:00  26.2934   \n",
-       "28085249  Orpheus Island 2012-05-06 02:41:47+00:00  26.2636   \n",
-       "\n",
-       "                             geometry  \n",
-       "fid                                    \n",
-       "28085253  POINT (146.71720 -18.80250)  \n",
-       "28085252  POINT (146.71720 -18.80250)  \n",
-       "28085251  POINT (146.71720 -18.80250)  \n",
-       "28085250  POINT (146.71720 -18.80250)  \n",
-       "28085249  POINT (146.71720 -18.80250)  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Intersecting filtered data with geometry boundaries\n",
-    "dask_geo_df = dask_geo_df[dask_geo_df['geometry'].within(boundary)]\n",
-    "\n",
-    "# Do the computation!\n",
-    "gdf = dask_geo_df.compute()\n",
-    "\n",
-    "#Check results\n",
-    "gdf.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting map of filtered data\n",
-    "This map should be exactly the same as the previous map because both data filtering methods are equivalent and produce the same results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(146.008765289, 147.495068388)"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure and assigning projection\n",
-    "ax = plt.axes(projection = ccrs.PlateCarree())\n",
-    "\n",
-    "#Plotting the mean location for all deployment sites \n",
-    "ax.scatter(x = counts.lon, y = counts.lat, alpha = 0.5)\n",
-    "\n",
-    "#Plotting the unqiue location filtered temperature data\n",
-    "gpd.GeoSeries(pd.unique(gdf.geometry)).plot(color = 'purple', ax = ax)\n",
-    "\n",
-    "#Plotting the boundary of the GBRMP zone MNP-18-1082 \"South-east of Great Palm Island\"\n",
-    "gpd.GeoSeries(boundary).plot(ax = ax, color = 'green', alpha=0.2)\n",
-    "\n",
-    "#Adding a basemap of land areas\n",
-    "ax.add_feature(cf.LAND)\n",
-    "\n",
-    "#Changing the limits of the plot to zoom in on the GBRMP zone MNP-18-1082 \"South-east of Great Palm Island\"\n",
-    "plt.ylim([miny-0.5, maxy+0.5])\n",
-    "plt.xlim([minx-0.5, maxx+0.5])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting time series of filtered data\n",
-    "We selected the time column to be included in the data we extracted using `dask_geopandas`, so we will use this information to plot a time series of the filtered data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x22721018850>"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Initiliasing the figure\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "#Adding a plot to the figure\n",
-    "ax = plt.axes()\n",
-    "\n",
-    "#Plotting the temperature data across time at the filtered location\n",
-    "ax.scatter(x = gdf.time, y = gdf.qc_val, alpha = 0.5, s = 0.5)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
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-  },
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- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/getDHWmax.ipynb b/Python_based_scripts/getDHWmax.ipynb
deleted file mode 100644
index a62344f..0000000
--- a/Python_based_scripts/getDHWmax.ipynb
+++ /dev/null
@@ -1,1678 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extract the DHW max value from a specified year"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*E. Klein  \n",
-    "2024-04-05*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Degree-Heating Week (DHW) product is a widely-used indicator of ocean heat gain. Particularly during the summer season, it is used to keep track of the heat stress on coral reefs. \n",
-    "Although the general seasonal pattern is well-known, the maximum value and the day of the year when it occurs can vary significantly from year to year.\n",
-    "\n",
-    "This notebook allows you to connect with the DMS NOAA DHW product, extract the maximum DHW value and the corresponding day of the year for each pixel, and save the results as a netCDF file. \n",
-    "You have the option to specify a spatial extent and a temporal extent (typically a full year)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## imports\n",
-    "# Loading and manipulating gridded multidimensional data\n",
-    "import xarray as xr\n",
-    "# Accessing S3 buckets\n",
-    "import s3fs\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You need to know the URI of the DHW-zarr product in the DMS. Please visit https://stac.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-chs-dhw and copy the item URI. Below this addrees has been already assigned to te variabl `zarr_path`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
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-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 164GB\n",
-       "Dimensions:              (time: 14254, lat: 900, lon: 1600)\n",
-       "Coordinates:\n",
-       "  * lat                  (lat) float32 4kB -5.025 -5.075 ... -49.92 -49.97\n",
-       "  * lon                  (lon) float32 6kB 100.0 100.1 100.1 ... 179.9 180.0\n",
-       "  * time                 (time) datetime64[ns] 114kB 1985-03-25T12:00:00 ... ...\n",
-       "Data variables:\n",
-       "    crs                  int16 2B ...\n",
-       "    degree_heating_week  (time, lat, lon) float32 82GB ...\n",
-       "    mask                 (time, lat, lon) float32 82GB ...\n",
-       "Attributes: (12/59)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
-       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
-       "    contributor_name:           NOAA Coral Reef Watch Program\n",
-       "    ...                         ...\n",
-       "    time_coverage_duration:     P1D\n",
-       "    time_coverage_end:          20240404T000000Z\n",
-       "    time_coverage_resolution:   P1D\n",
-       "    time_coverage_start:        20240403T000000Z\n",
-       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
-       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f</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-e067a31d-f166-4d53-8166-a6329b0bf7ef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e067a31d-f166-4d53-8166-a6329b0bf7ef' 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>: 14254</li><li><span class='xr-has-index'>lat</span>: 900</li><li><span class='xr-has-index'>lon</span>: 1600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad432471-2edd-4a73-8240-44d38769756c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad432471-2edd-4a73-8240-44d38769756c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.025 -5.075 ... -49.92 -49.97</div><input id='attrs-e6b55407-52bc-4402-b5b5-d718d50069a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6b55407-52bc-4402-b5b5-d718d50069a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c04b7c-f81d-4a02-a9d2-094ee203b8bc' class='xr-var-data-in' type='checkbox'><label for='data-d5c04b7c-f81d-4a02-a9d2-094ee203b8bc' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -5.025,  -5.075,  -5.125, ..., -49.875, -49.925, -49.975],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>100.0 100.1 100.1 ... 179.9 180.0</div><input id='attrs-f3d8759f-64ee-4238-aa41-9bbb0d1b5d63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3d8759f-64ee-4238-aa41-9bbb0d1b5d63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a1c5826-86c1-45e4-9d83-564987481c72' class='xr-var-data-in' type='checkbox'><label for='data-0a1c5826-86c1-45e4-9d83-564987481c72' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([100.025, 100.075, 100.125, ..., 179.875, 179.925, 179.975],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1985-03-25T12:00:00 ... 2024-04-...</div><input id='attrs-5ff6c9db-b4a0-473c-98c5-edb61b112646' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ff6c9db-b4a0-473c-98c5-edb61b112646' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3856e347-5367-4bf6-a7ce-100f1084bc2b' class='xr-var-data-in' type='checkbox'><label for='data-3856e347-5367-4bf6-a7ce-100f1084bc2b' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1985-03-25T12:00:00.000000000&#x27;, &#x27;1985-03-26T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1985-03-27T12:00:00.000000000&#x27;, ..., &#x27;2024-04-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-04-02T12:00:00.000000000&#x27;, &#x27;2024-04-03T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-deb7f409-0b13-4bcf-953f-a37790d8f2a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-deb7f409-0b13-4bcf-953f-a37790d8f2a2' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-736c8ef4-8dac-460c-9b49-ea6f5bb0cec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-736c8ef4-8dac-460c-9b49-ea6f5bb0cec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c973f7a-9873-487e-ab34-5d52f73cfec8' class='xr-var-data-in' type='checkbox'><label for='data-4c973f7a-9873-487e-ab34-5d52f73cfec8' 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>comment :</span></dt><dd>This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.</dd><dt><span>epsg_code :</span></dt><dd>EPSG:32663</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.2572021484375</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree_heating_week</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'>...</div><input id='attrs-54b0f725-0108-403f-b538-82b2f5963e02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54b0f725-0108-403f-b538-82b2f5963e02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6425803f-0d71-46b4-967b-8eccc11f9396' class='xr-var-data-in' type='checkbox'><label for='data-6425803f-0d71-46b4-967b-8eccc11f9396' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[20525760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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'>...</div><input id='attrs-87363fdd-6870-4b32-b127-e16ff3707efb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87363fdd-6870-4b32-b127-e16ff3707efb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92cbe334-c534-4ee0-9ef0-a31915fe5ad2' class='xr-var-data-in' type='checkbox'><label for='data-92cbe334-c534-4ee0-9ef0-a31915fe5ad2' 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>comment :</span></dt><dd>A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.</dd><dt><span>coverage_content_type :</span></dt><dd>thematicClassification</dd><dt><span>flag_meanings :</span></dt><dd>valid-water land missing ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 4]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>pixel characteristics flag array</dd><dt><span>units :</span></dt><dd>pixel_classification</dd></dl></div><div class='xr-var-data'><pre>[20525760000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3506966-a65c-4b97-94f0-234b9c7c58e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-c3506966-a65c-4b97-94f0-234b9c7c58e7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f2555b6f-520d-45ec-8bc8-d7d6727949af' class='xr-index-data-in' type='checkbox'/><label for='index-f2555b6f-520d-45ec-8bc8-d7d6727949af' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -5.025000095367432,  -5.074999809265137,              -5.125,\n",
-       "        -5.175000190734863,  -5.224999904632568,  -5.275000095367432,\n",
-       "        -5.324999809265137,              -5.375,  -5.425000190734863,\n",
-       "        -5.474999904632568,\n",
-       "       ...\n",
-       "       -49.525001525878906,  -49.57500076293945,             -49.625,\n",
-       "        -49.67499923706055, -49.724998474121094, -49.775001525878906,\n",
-       "        -49.82500076293945,             -49.875,  -49.92499923706055,\n",
-       "       -49.974998474121094],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89e1dab3-3e58-46e9-8867-42f1310bea58' class='xr-index-data-in' type='checkbox'/><label for='index-89e1dab3-3e58-46e9-8867-42f1310bea58' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 100.0250015258789, 100.07499694824219,            100.125,\n",
-       "       100.17500305175781,  100.2249984741211,  100.2750015258789,\n",
-       "       100.32499694824219,            100.375, 100.42500305175781,\n",
-       "        100.4749984741211,\n",
-       "       ...\n",
-       "       179.52499389648438,  179.5749969482422,            179.625,\n",
-       "        179.6750030517578, 179.72500610351562, 179.77499389648438,\n",
-       "        179.8249969482422,            179.875,  179.9250030517578,\n",
-       "       179.97500610351562],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6af54c43-20cd-4d65-bc7a-fc22517b247f' class='xr-index-data-in' type='checkbox'/><label for='index-6af54c43-20cd-4d65-bc7a-fc22517b247f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1985-03-25 12:00:00&#x27;, &#x27;1985-03-26 12:00:00&#x27;,\n",
-       "               &#x27;1985-03-27 12:00:00&#x27;, &#x27;1985-03-28 12:00:00&#x27;,\n",
-       "               &#x27;1985-03-29 12:00:00&#x27;, &#x27;1985-03-30 12:00:00&#x27;,\n",
-       "               &#x27;1985-03-31 12:00:00&#x27;, &#x27;1985-04-01 12:00:00&#x27;,\n",
-       "               &#x27;1985-04-02 12:00:00&#x27;, &#x27;1985-04-03 12:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2024-03-25 12:00:00&#x27;, &#x27;2024-03-26 12:00:00&#x27;,\n",
-       "               &#x27;2024-03-27 12:00:00&#x27;, &#x27;2024-03-28 12:00:00&#x27;,\n",
-       "               &#x27;2024-03-29 12:00:00&#x27;, &#x27;2024-03-30 12:00:00&#x27;,\n",
-       "               &#x27;2024-03-31 12:00:00&#x27;, &#x27;2024-04-01 12:00:00&#x27;,\n",
-       "               &#x27;2024-04-02 12:00:00&#x27;, &#x27;2024-04-03 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=14254, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77070bc2-2783-4c5a-b22c-1d075cff94e3' class='xr-section-summary-in' type='checkbox'  ><label for='section-77070bc2-2783-4c5a-b22c-1d075cff94e3' class='xr-section-summary' >Attributes: <span>(59)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>acknowledgement :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.</dd><dt><span>contributor_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>contributor_role :</span></dt><dd>Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive</dd><dt><span>creator_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>creator_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>creator_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://coralreefwatch.noaa.gov/</dd><dt><span>date_created :</span></dt><dd>20180301T120000Z</dd><dt><span>date_issued :</span></dt><dd>20240404T141803Z</dd><dt><span>date_metadata_modified :</span></dt><dd>20201118T120000Z</dd><dt><span>date_modified :</span></dt><dd>20180301T120000Z</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.9749984741211</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.9749984741211</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.97500610351562</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.97500610351562</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Thu Apr  4 15:18:04 2024: ncks --overwrite -d lat,-50.0,-5.0 -d lon,100.0,180.0 ct5km_dhw_v3.1_20240403.nc ct5km_dhw_v3.1_20240403.nc\n",
-       "This is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).</dd><dt><span>id :</span></dt><dd>Satellite_Daily_Global_5km_Degree_Heating_Week</dd><dt><span>institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>instrument :</span></dt><dd>ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake</dd><dt><span>instrument_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Instruments</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Water Temperature,  Spectral/Engineering &gt; Infrared Wavelengths &gt; Thermal Infrared, Oceans &gt; Ocean Temperature &gt; Degree Heating Week</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.</dd><dt><span>metadata_link :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.coralreefwatch</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>platform :</span></dt><dd>Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.</dd><dt><span>platform_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Platforms</dd><dt><span>processing_level :</span></dt><dd>Derived from L4 satellite sea surface temperaure analysis</dd><dt><span>product_version :</span></dt><dd>3.1</dd><dt><span>program :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>project :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>publisher_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>publisher_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://coralreefwatch.noaa.gov</dd><dt><span>references :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>source :</span></dt><dd>Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>summary :</span></dt><dd>This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_end :</span></dt><dd>20240404T000000Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>20240403T000000Z</dd><dt><span>title :</span></dt><dd>NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week </dd><dt><span>uuid :</span></dt><dd>24c7f597-298c-4be6-a0c2-ad97b993f60f</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 164GB\n",
-       "Dimensions:              (time: 14254, lat: 900, lon: 1600)\n",
-       "Coordinates:\n",
-       "  * lat                  (lat) float32 4kB -5.025 -5.075 ... -49.92 -49.97\n",
-       "  * lon                  (lon) float32 6kB 100.0 100.1 100.1 ... 179.9 180.0\n",
-       "  * time                 (time) datetime64[ns] 114kB 1985-03-25T12:00:00 ... ...\n",
-       "Data variables:\n",
-       "    crs                  int16 2B ...\n",
-       "    degree_heating_week  (time, lat, lon) float32 82GB ...\n",
-       "    mask                 (time, lat, lon) float32 82GB ...\n",
-       "Attributes: (12/59)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
-       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
-       "    contributor_name:           NOAA Coral Reef Watch Program\n",
-       "    ...                         ...\n",
-       "    time_coverage_duration:     P1D\n",
-       "    time_coverage_end:          20240404T000000Z\n",
-       "    time_coverage_resolution:   P1D\n",
-       "    time_coverage_start:        20240403T000000Z\n",
-       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
-       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## Connect with CRW DHW product\n",
-    "# Path to zarr files\n",
-    "zarr_path = \"s3://gbr-dms-data-public/noaa-crw-chs-dhw/data.zarr\"\n",
-    "\n",
-    "# Connection to public S3 bucket - No credentials needed\n",
-    "fs = s3fs.S3FileSystem(anon=True)\n",
-    "\n",
-    "# Loading zarr file\n",
-    "data = xr.open_dataset(s3fs.S3Map(zarr_path, s3=fs), engine=\"zarr\")\n",
-    "\n",
-    "# Show data structure\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here you can define your data slice:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Slicing parameters\n",
-    "dateMin = \"2024-01-01\"\n",
-    "dateMax = \"2024-12-31\"\n",
-    "latMin = -26.0\n",
-    "latMax = -8.5\n",
-    "lonMin = 140.0\n",
-    "lonMax = 160.0\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With these values, extract the data you need"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;degree_heating_week&#x27; (time: 365, lat: 350, lon: 400)&gt; Size: 204MB\n",
-       "[51100000 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
-       "  * time     (time) datetime64[ns] 3kB 2023-01-01T12:00:00 ... 2023-12-31T12:...\n",
-       "Attributes:\n",
-       "    coverage_content_type:  physicalMeasurement\n",
-       "    grid_mapping:           crs\n",
-       "    long_name:              degree heating week\n",
-       "    standard_name:          N/A\n",
-       "    units:                  degree_Celsius_weeks\n",
-       "    valid_max:              10000\n",
-       "    valid_min:              0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'degree_heating_week'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>lat</span>: 350</li><li><span class='xr-has-index'>lon</span>: 400</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5817dc07-8f51-423f-b5f2-7eee885c32a4' class='xr-array-in' type='checkbox' checked><label for='section-5817dc07-8f51-423f-b5f2-7eee885c32a4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[51100000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-60f2b0f8-9974-4c52-9283-af97761e8adf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60f2b0f8-9974-4c52-9283-af97761e8adf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.525 -8.575 ... -25.92 -25.98</div><input id='attrs-8761c9a0-4a77-4373-bf3d-c7a216a0dbee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8761c9a0-4a77-4373-bf3d-c7a216a0dbee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27880945-3188-40bc-b5d9-4492d4351792' class='xr-var-data-in' type='checkbox'><label for='data-27880945-3188-40bc-b5d9-4492d4351792' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.525,  -8.575,  -8.625, ..., -25.875, -25.925, -25.975],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>140.0 140.1 140.1 ... 159.9 160.0</div><input id='attrs-b3cbb9f7-44d2-42f0-975f-54d2dd10e1d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3cbb9f7-44d2-42f0-975f-54d2dd10e1d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81b8d2ab-009a-4470-af83-adff822d104b' class='xr-var-data-in' type='checkbox'><label for='data-81b8d2ab-009a-4470-af83-adff822d104b' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([140.025, 140.075, 140.125, ..., 159.875, 159.925, 159.975],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-01T12:00:00 ... 2023-12-...</div><input id='attrs-3c58d28a-363d-43a5-8242-1784819f0e76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c58d28a-363d-43a5-8242-1784819f0e76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3a9290d-09f1-4d71-a7dc-21e051c83ec3' class='xr-var-data-in' type='checkbox'><label for='data-e3a9290d-09f1-4d71-a7dc-21e051c83ec3' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-01T12:00:00.000000000&#x27;, &#x27;2023-01-02T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-01-03T12:00:00.000000000&#x27;, ..., &#x27;2023-12-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-12-30T12:00:00.000000000&#x27;, &#x27;2023-12-31T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-323fdbd2-84f3-4ce5-9b99-a65f99dab835' class='xr-section-summary-in' type='checkbox'  ><label for='section-323fdbd2-84f3-4ce5-9b99-a65f99dab835' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-30999899-c44d-442a-8c60-eaffe9efb53c' class='xr-index-data-in' type='checkbox'/><label for='index-30999899-c44d-442a-8c60-eaffe9efb53c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.524999618530273,  -8.574999809265137,              -8.625,\n",
-       "        -8.675000190734863,  -8.725000381469727,  -8.774999618530273,\n",
-       "        -8.824999809265137,              -8.875,  -8.925000190734863,\n",
-       "        -8.975000381469727,\n",
-       "       ...\n",
-       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
-       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
-       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
-       "       -25.975000381469727],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=350))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74998b44-f2d7-4981-8bc7-0e43dcad120f' class='xr-index-data-in' type='checkbox'/><label for='index-74998b44-f2d7-4981-8bc7-0e43dcad120f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([140.02499389648438,  140.0749969482422,            140.125,\n",
-       "        140.1750030517578, 140.22500610351562, 140.27499389648438,\n",
-       "        140.3249969482422,            140.375,  140.4250030517578,\n",
-       "       140.47500610351562,\n",
-       "       ...\n",
-       "       159.52499389648438,  159.5749969482422,            159.625,\n",
-       "        159.6750030517578, 159.72500610351562, 159.77499389648438,\n",
-       "        159.8249969482422,            159.875,  159.9250030517578,\n",
-       "       159.97500610351562],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f8c7391-9313-4772-afcf-96b7d906cc79' class='xr-index-data-in' type='checkbox'/><label for='index-6f8c7391-9313-4772-afcf-96b7d906cc79' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-01 12:00:00&#x27;, &#x27;2023-01-02 12:00:00&#x27;,\n",
-       "               &#x27;2023-01-03 12:00:00&#x27;, &#x27;2023-01-04 12:00:00&#x27;,\n",
-       "               &#x27;2023-01-05 12:00:00&#x27;, &#x27;2023-01-06 12:00:00&#x27;,\n",
-       "               &#x27;2023-01-07 12:00:00&#x27;, &#x27;2023-01-08 12:00:00&#x27;,\n",
-       "               &#x27;2023-01-09 12:00:00&#x27;, &#x27;2023-01-10 12:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-12-22 12:00:00&#x27;, &#x27;2023-12-23 12:00:00&#x27;,\n",
-       "               &#x27;2023-12-24 12:00:00&#x27;, &#x27;2023-12-25 12:00:00&#x27;,\n",
-       "               &#x27;2023-12-26 12:00:00&#x27;, &#x27;2023-12-27 12:00:00&#x27;,\n",
-       "               &#x27;2023-12-28 12:00:00&#x27;, &#x27;2023-12-29 12:00:00&#x27;,\n",
-       "               &#x27;2023-12-30 12:00:00&#x27;, &#x27;2023-12-31 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fff2995c-3f15-4c10-b9f2-aec84b660875' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fff2995c-3f15-4c10-b9f2-aec84b660875' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'degree_heating_week' (time: 365, lat: 350, lon: 400)> Size: 204MB\n",
-       "[51100000 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
-       "  * time     (time) datetime64[ns] 3kB 2023-01-01T12:00:00 ... 2023-12-31T12:...\n",
-       "Attributes:\n",
-       "    coverage_content_type:  physicalMeasurement\n",
-       "    grid_mapping:           crs\n",
-       "    long_name:              degree heating week\n",
-       "    standard_name:          N/A\n",
-       "    units:                  degree_Celsius_weeks\n",
-       "    valid_max:              10000\n",
-       "    valid_min:              0"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## get data\n",
-    "\n",
-    "DHWyear = data.sel(time=slice(dateMin, dateMax)).degree_heating_week\n",
-    "\n",
-    "# Clip region. Note that lat is sorted in reverse order\n",
-    "DHWyear = DHWyear.sel(lat= slice(latMax, latMin), lon = slice(lonMin, lonMax))\n",
-    "DHWyear\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, you can extract the maximum value of the DHW for each of the pixels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Get max values\n",
-    "## coords\n",
-    "lat = DHWyear.lat\n",
-    "lon = DHWyear.lon\n",
-    "## max value\n",
-    "DHWmax = DHWyear.max(dim='time')\n",
-    "DHWmax.attrs = DHWyear.attrs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the plot of the DHWmax for the seleted area/time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7a1d26684c80>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Ug5rqx+5fc79fgrmNycuMPj0+oQkM4MVLHdYBoGITluQsFjwlT64/DMY5GLO0sWWo7rL2oZmqI4GrQkaCFJt0UlXUU4bIeFugbBPRYLJays6jHg7gqdp4wcCzP/k7jHlaxpINOsEMURFjtdKVJ0S6Hkw/JdrZLAdQerxZ7Ig0nRQlXM5KpeSvFXykxvOe9zz8+I//OF73utfhzJkzjU06d+4cDg4OQET45m/+ZrzsZS/DYx/7WDz2sY/Fy172MhweHuJrvuZr2nq+9mu/Fh/3cR+Hl7/85QCAb/qmb8Lnfu7n4vu+7/vwJV/yJXjd616HX/iFX7hiCvHBEnswdZXgWUAME56L2g1f4cACNsgJAQODJvlwq3tQxpt/5TvwtC96BZQJ23OMckCNJVJPo7V0gbq+IwHq6b4AUTFAa3Zdg9P1TduUYIO/Xv6QaA8C12dYKs4ZM41l1MCbAvABOkAX1f4A0SVoc9YrgFUENWBmfz/tC1+Bt/z8A5/uizhymmD2Ez/LsAM4BhZsXSt0wAakmAQDC1Y043Te4AxvMFDF2t9fY+7r0wwOoLZ4kh/SFnwyRQcHV4qWogOwAFd9+QmWhltTwQRbbiMZFYw1zajEmJFwyBMGqjiiEVsZcFTHto2IAFRbf8IX2RXaG2ARTFUbcBFh1MpQFhCRgZw5t88FODrajBiHgpwER/OIMRegAGfHbWOndr6PskJRxtG8MoZuwagNqVpajxTMClWASDHNBq6GdUEtjHmbgEqQlU3ehAjEiiShA+pgZZl6A7DDECPuycWoziEyH4yBlQF98gFbL8O1hry4Z4vLkdp9Z8v3IhEyliN5+h52bKx2n3EBygG3z6QNgYtAxtTZ56qQg2TrcnCVtgSeBZg93TdbSk5X1ATkjQkiciaKm0YK6JNdOBgjWZywbOyWMoPKbKgxAViK0ms1BooJEAZltoMCoKIGoFyQbjPIK6T+rkMIBNf6pLmna/jH//gfAwCe/vSn77z+z//5P8fXfd3XAQC+7du+DcfHx/jGb/xGfOhDH8Jnf/Zn401vetOOIP/222/fsY14ylOegp/8yZ/Ei170Inznd34n/syf+TP4qZ/6KXz2Z3/2vTuwGyD2YOoqoeTprUQAEagKdMUtPWQDiIJme0A/80l/H1TV0mMfScGEcmA/MnjVzzJ1V013VIFW2WOpCvTZscIGsEQ2gGbaEdI24FWAVBf6Dempi0j72evaUnKtkm+x/ahKokLQbPubNotUBtuDxVg000lFmjG+VwC4ZoHCdYgLdQ1RwqzJNETgliYrkpC54iBNzjbZ/meqOJMMQJ1LRy1lFzFpwiEbC3XJqwVFGYNX7MVvIFJ2BmYivTeQYIBgdhBl7JPsMFYjKjbI2ITAHIQZycDbAh2cYtMjbTG0dW29Uk+UF9tlTJIx1WwpPmeDACCniu2cm05pLl1PpUoolTEF8Kvc3gtgRe1Z3EHqURmwTqUJ1gOAZ644moa2nChhlYrpz3x/GAolS0eaNMeBFQBOChrFxhQCsGUQFOT6KXGmShOa9ikuTy4d+EvyVF9U1jojVQ763zJ2UBSaR1AHWLRkmrwaULO/l/weSvD72H7zrCDVBrDqaOxU3jgwHNjvVwaJsVRctE2oJDOUO/BSZmQFaEzQVULaegXebDu3wzUtmSURYMi9+g6wVOKCtSIR+3KJQKXsLBtfPI0DdGv1rFqrsVtVgJzb9g1MOStV5X4Ty1RV1GvUZ93Tz/9pbCiJCC9+8Yvvthrwl3/5ly977cu//Mvx5V/+5fdof27k2IOpq0UiqyzLbOyUi6YBn5llAh/3C81y84JnPeG78cbffMkDssvXI45vTn126ixRZE40GQiBAHnrM98Qhvs4VccARzb7tAdDZ5VCD7LDGGGXmQogZWk4AosNkmmyzysboNPFfoYolwqQJ0VdUWedSt//0IsIAXUgDEeKOpCBq6I3FCsFAP/gU/53fON/+n+abocEAwlmT4mFfopJsaKCNc8tVXc6bTCSWSGMzkoFSLokKxO1EzCSibvDouBcOsJIFQLCKTb9yECKNQSXkDArd+zpT5XQR62p4IKMGEh2QBQAE5w7ut3IgIFKS1lekDW2MuBCXbd1LSM0VUUSjsqAqXYbhEymdUqsgNh2hlxbihkAxBmfKgzxh/qshFoYKQs22wFDrhhyF57bdqltfxL7HXqp3f0z5itYqcTAyDOmYpYNTIqqNr4QKYgFugKwSdBsQMtS1to0T8poo3Uu1LWHDqg0GW4Ii4Nmj6AxXvVrHeg6qDbBWMwhArHUtTNb8N/Dblo8+cQJavd9sGdUgbImB012LGkyVj9NgpqppffNmsF0qFxtwtWYrqoOABcPdF6AJwB0PBlzNbq4HNzRcCzHDNrOl2ubQsC+BFVEoPUKOs8gZvv8Mr3nfzebBCZg/sgsRNrHPYs9mLpKzIcMpGxVKYWMpYh7lEMzxUhHwX8DIIIOCV/wlO/Fm3/1I8Myweh/GyjD10mdLeLZQEgIvpWBNIcOakEZAU3ILtnYoh32KbnHTcyQxRgqqwSMyr9+/k1Tok3zFFqSWDZmily0CXNDNEu172sTwMM1IWqC2khX7giobqD40HS4w6SMXK2yLSkGLiiacOC6qATFIW+xptkZqaml96JCb+CKI10ZE8NolXwBzkJbZZ8x+wMGsCbBmgRzpMpwEnQQTtHc0nrLdN0MrxpUwUgVkyZsZMBGB2OgwiOLGIm0sVMR1VkxE4ArxFmnSDWNuWCuyawDCBjHuflFEQFHxyOIu45JqtkWSCXk0QTso6fpkov2MwsuziNuWh03MHt+Wjc91SQJmQVTNQ8sSbbu2ctVY19nT0MCsH1OdiHKYJMMmhnKNsPQrOANL+4n6qLx1FmlwJxLYKU2v2vsElw3tSM4p8XfCqt+jftH3cZEffW6C+CixNYq9/rnAugJCDMDw5HauUiAUgc081lqovrQK3K1ddW1sVgSbNbASBuCVgVPxQBUIlDJPa3HDMlWuec5VbdKkFahDPGiorlcBqKWQcmT3CK9im8pTPfXtFYo7h8wdW8E5Fdaxz6uT+zB1D72sY997GMfN3gIFHUPpm7Y2IOpq0RdE9gr19IWADptHfoAQFEPE9KRi0ar7FLSD9L4nC/9AZRDNhbKbRBA5kcTDuNNlCrm3xT6ijqEnoR2dBkk2iwVmr2BLiqUXLja5oeEblVA1NIJebLzy7PPiBOBncFCUmikDxaO6svfAJo1Q2wXcN3WYpZOVZG2esMJ0AHgjqMzyCw4zLMzVIxzwzEqCFUJp9MWiQSzZKxdK5VIMETqD/4/KmZYJd8hthAirDFjQ10DNFDFmvwzzkqtibBRxQBFImBNigTCRgUVhIHUqtBB2ChjDTMG3WhuaT6GoCJhTTOEGRfKGkeywkYzxKv0BIxt5R0rBsAYj0SKdZpxVIaWihMAmcV1XGrapdnyWYkF6zxDlfBrz3p5W9dj/+X3okwJWo0hUjLt1XosqELNRHDJBF6cVxi54uK8dqsENnd5X2adCooyLk0JOdVm1QA4azcUHG1GsO8rsULFGSoFwGLcn98j6hUUmhci8ki7U09vL5mhuvB/i3tV7cRbys9PaWQow89Ks7Z1yGgsrb3Q0/pN7pCDWaZeSAKgrP1eG8L3jZo+MfazjtQY7DrYAeStohwQ8rGgrBlpEvBs93Y9yCZMZ0UdRvBmtqq9IXUHdcC8paqnBkNo3oTrAEUni3nG63//1QCA2x7+vN0UYKQQUwKIzWcqfgBjvXLuNglLgfs+PmpjD6auEmVNXupPnRony/vnjbaSXmUAhwm8NW9nkL33+Z/3cpS1lz5nwltfd2OV2Ed83uf/A6PL1bh8GQg4lSDJB7zB/GhktEFSRhtw07GBprzt+oy0XVgdeEoBiHSEj8Ce1msSqRDUxoPDX49WFiQAyNbLswlXeQoEZINmVPTJ4GXZYWGxEK4boLL32FMmYesQqYY6GBjkAmg1UWyaFc941veBioKqIG0r3vS277zeX8vdxi993ivx9H/3LTgvjDFVrFezgw+zDBA2MBG6p2/4pLfsfP7f//4ntr9PkVkhbDC0vNBAFWMTn9dmrgkEQBKcYUZVRSJqotY1kXcEsd+zV/wBwCUkHNKM2c05q7Kvt+KCECbNGKhg1tQc3gE0ILXigiL2XqaKoia2PztuXMOUdqr6mKxlzDrPrfXM6JV9T/9334JNGXA8Z7z7K76nnYtH/Yt/sKPQisyPhhloGXCYDV2cd0AFAEUZI9n6RQlFGAzFmdUGRRibMoBh+qh4ZKckVl0ohJQEzIrKCq0ErWygxvVGOti1S0LQpM02IVJ6VLxQIyt4IshovwHXTUnXBwK2fLxX19omH81Ed7SKWbjuMD4T4C2KNQSGNWKCpARgwCJNbuPocOTtZiqBGICarUL1Ah8SbalHZdO5kSgwE+ra9FR5GyWAZGBG0EFUu98ZpNod1T3Vp5zMhX3pT7UM11U1QLUUrrOjVc4gYgNQS68pJuB+wlL7NN+NHXswdZXQZA9WyZ094WKam+k09Sow1wtIXoh1lvrHcaF3uI/jc770BwAAv/Iz33KvPv/5T39Zq4YJkPHLb/i7eOpXvHKhQ3Igdcp+yxCiUrQKJEnhhExt5mmzXF/HYtasPmiH43mrvHNWqjFJQLM2IAVoVheF2/LZgRtiV4aY/QI8a6sqgjqYW+q4fHuS3czTzQwNuPnMeiCs7vLvdmQQiVUaMuGZT/4e0Fzxxt948b067/dF/PLn23d/21u/CZP02/iffeZrPuxnl35RouRs0wyQidHXNHv7lR6zZlQqGGBXOYPARBjASGyAKgZqBmF2+wTAXJsTtJl4WvuZikmBjQ6oYG87Y9V6odfa+kUTXlhAB1dmqsBIeXJRtwAJzR1+kgRx0LFKBetUWn9CyeTC8N2n4O8/5++1vz/l33yXLQMDSwBwepiwqRnrVHCYZxRhjFyxqdkZqbl9F5kMIM014WTQgmkjVkhl5Fxd52TsIiUFDvw7KgRUAm3ierbKwLgXdQ1ngBVlNJ2ggSQyJosBKgZwg0EOK4UAZAAgK4W4eWhlgAZF2hiDlPyelxGgLaxS13FGY7WccQ5gJqMBqzpQY6rSpE3zGAOlJPOcWxb4AFYZGONGHRnTQ0aMH9iCl4LvRA1EgQEFQYYRJIssARF0xdaDTwqoCjAMuO2R32xGnRFXYKd2WSrqVX617grT74d4IKr59vGnjz2YukrUAdafagLK2L2l0mQAKk3ms6KJgC1AbDPCqFRbPou4AE+/7RU77Ume9Jdf2U3rqIOGX/tfXggAeOpXvNJbqyj+w//+LXjyV78SysCv/a9/B5/9nFdZ6THsM/fUXPILnvK9lg6LWV61EuV6yHjKV74Sdd3NOSV7s2DfGCmQjwhp05kdEKBEjR1a+ke1GaefEvZZNOADZYAsQmuUHH9353S4YWdPzTU2Kln6ldvM1k58moDguVRtGckAnJ1C6mxV7NPSBdra6Nh3m8KCgfxhRgRd91TYAxmv/9wfvEfLv/32R2GgblsQqamwOzjF21ZlF7HRASNVN+QUzCBsVXBI/WFyyCNmXbbngAMqwsIGsYEiABAwPlhP485qgvpZuIGhWC5a5QxUkZLiqA5IsBReiMC3lHHgQMrc1xmHeYuL87oxVCdThZndTOkqscrF+/Fpa6gcLueR1stsLWRO56mBzyKMdSrYVLvIz4xb0KyYah9qBxZsAIyDGYYGqMvZ2Kp8aoIKocwJMicgC3Ri6Eygaim1sAUx1lfbRECd4e0/do83o88lQ1X7vSSDAFmtogQABoEKofrYFBPJZnsCA2khhl/g+Vag4n6xqKvecLmxxQCGS2L3GPokKk1oDdRtYhR/24fK6QE8MfKFLaJVDBIBRaF5AW4WX6064MKQkGoFAj8lBhCzRl28thttKFd1u4TkjZG78P3+CMG1k2D7hOT1iz2YukrEzV3WC5PKagNDOSBvUUJIG2MvhiNt5nMtpaWdxaEE/A//jx9AOYgbkC7bJoniKV/5StMmIAY6wlO/4pXtm/rs57yqDZrL5r9P+8JXIM2CdKncbSXhs57w3cBq6Kk42GCDRC2tV9bU2aWFxomqgah0bIOcacnsd5oVZU2tKi5o/mVvMQXavke5NtAHd0gfbJfuzfZbvV1MbyGDqjZjFm2+OPbdeVm5PxiaV1W1WXBop8ZL4u7tCzPS4r3GUvfV0qKQkf1hQP2cPQgj/J9SY46AKUCRduuCiNBUTZrAMIuDhBkTBCtSY6gcmK1ca2Xd9ghb3bpg1ta5BFLhjB7MlKX3UlvGXNQdzHiq8EhSA1IMaT3zDvypvaz4O64j1mlGUYZUazeTyarxohff8Tzic37h21CF8bZn/oPLzhU5kAodVnYbhFZFCHVQZqB0Uwcc5glFOovGpFhne7Jvip2fWcyuIbGAvArRfhScTHuFZEafNQuksnW04mTGnpVQTjk6InXvKf/ekpr2MFKByVN2QKsARDpxf3oKEZOlEeH3e6QaqRLquk8UW7um2dYR3lZtUhjWIz4BpbBV8LZNYT0ii8kWF1/XYryQbECQFylGc0WnRf/QiroeoWv3garBJBEUAdRisATkcLTDC+1UUgND1oG6f/kBquaCSPu1Xn2JAfbtpQSi+5eh2seNGXswdZWIkvoAUoDpB9Lc2Zg6AFBqaSSgp4rCQTv+l8Fu2JOzuPbsCoB0hf3YfaEDkRBsK9vgIUKgVcKzPvPFoLniDe/sepDbPu5vAadPgdYDaHCBa4q+WIyyMhBYV9QE5pL7sUDNtI9n+23Mj52ftNXeriJ8b/wZGoL11tbCz10T0IYwXAEX3ADoIK6xU4qFHYKCZmnApp13xMCsbb0BbKPVBYk2bQjVON9uwbAQqadgwaq2knVJ3BkAJjzlK1+JX/3f/s7lF88NGm/5/T+LNaWmgwrLAsD66a2pWOoMislbzESrGsAYKhFjUh73iP8GADj/vkc2RoohGCijqkCgGMCoUMxuHnbIM+6sa2e5BrdjsBTfVobGQDEUW83doZ1ra4R86F5XS0+rYNcGqmDuGqvMKwDAEY0QJUw1YUwVm2I34JiL2Tkw8OQ3/b2dJsVp4eqdWZr9wUi1gaklkIpefOzC+MwVm9rPXQCqeeFJZduDG3kKUBmj+1uJEuaSmheVVIIOYik7RWOtjToCdFDQRO0+Uj/nVLsnFdB1UXWFdl8rw0CF+m+gMdEYAPHm5PAJh2QguU5yx0w3xOncxwFyawaaLe0XM8B2f/oYI85stzFB+xiw1HPZzgGyym0/NXGb3FAm0CyQgcH+u/lWiQGnaICMRNCcm0M6bWZL68lVrA5OaK2IGBAFrpOM47LN3wfVfNf6+X1cPfZg6iqxk79f0NOSFq/5WCmZkFW7KWQAI+nVa1QBWXHXDnF81tbF8OWz7mw/qmjCQ0nCo2no6USqsDYH4do+JNBccdsnfiuwnWyAqNVmWZGeYkJdJWgilLVV7hmzRO7zYtsth7b+SOtFfzwQkI8UyUXiJXQYdVEh5OeinT93SxfpQCpc0tuxoP/dvosE8LENtDGjtGeb9qEhznl70BiokoEaIMvbhTv6Yp9iXVzdvflY26y9HBDS1mfDCzZqPkWQBHzG/+vV+E//+AX36Np6IOKN73kcBiJjnjShkjZDTOuxZ38nCEBsjuUaTJMd96QZGx3wtEf917bes7feDgD4wPs+HgMYAm3gqkKxUfNVuqTeOoYLNpJxSVa4sx56n8HLtVGn86aZYs4++1jz3FKQ7GxVCOMTAYc0eVNkxrF6LzgHOaBe6bfOxV3UxVJ2EHDSZvyZWDDXdHkVYfhngZoTeojOjaXq7ujFGSzA9FORamSyFjfVheezMESsF+A4OOAqycAVABWbJGmMDUJW3Ufosy9SYA4x9gJg0QKMeK/KOCKr1tM26WvpQXKmyqlxJQAHFUKWk1fvG8gzmv6xMUvoE8gAQG0cTf2+lPAzi0IShbmoe1VgmJXG6bcxyhjwumbznNM+0VEmqI+HPHlfPqA5rdMsXiy06Lyw6mOJ+qSCfNykY0JrLzNkHz8Fb/jQPwMAPPtjvh4PRFS99qYMHwHF5jds7MHU3UQ87DugQU8xLRgTLqavoQpEfyxSc9220YWgq/45dZNKyVZ91mwCsmkT0na34e6yKs6fC64jCFE12W4WB2LB2IgCOdnguggq0mZ20e1dBmqDmYwd5KXjBUApBoZ47sdNYoMiDXaspk8CaoArgZkFks9kF8ze8jxTtcFymRYMENZ0ahtnAqYKngWarGRaMtmxp0h7ON9EpssILVUdgeQAl2dt5zTej8HdSrnVZ72E+XRfX7wfDECs50aPCm7XUYJiDetLZuk+aZopwFiqqtZyJVq+VCX7Dca/ePeT8JzH/trO+u8Sc1pPWrAiA1V3ijFG53V0gTm55irhSFatDQ4AMEkDT0yegnSgFAxVBWH2qsWq3IDUiuf+WWUMPNt2ZY2BK04PGxyVFXIyMFWUG9NUlDE5cAohOYCmkwr7iUgNAr0X4JiMRTs7homnneBNHVofv1inqPXsY5h4/GCcMTtT1kGcoootW73ZcqQAibSBISoMjGIpOQIoHNFXdq0TDFCRwNJ+1e7vZlibAMnORPnkDawG1vz4VQDKAEVjS7ZlAOrVuxMumxxGSq8Vjixa4LQJDOCVuX1MbT1PbS1AVQjb/SrJihrKoU328lZAswIrtskf9fGXRupmvUw2Bq6tIT0pjK0a2cbkg9zSgjz5Onfc0y29h5zx+j/6kfbyG/7kn+DZ5/4q1LVTUo4/zN23j4+G2IOpq4UuPFWWLNFy0HCKOnRVgC2fN33wkoGatgBwtsR700VlS8wem28M9+XbdpdMGQFKhLrS1qIlbwAZyVyet9UqXIYMTJPl+HMGhgzr2M5tXZo8vTdQa3ZaQ39ErnMgtGoewPYzHyuGI0U+MiGmDGzVc6GDClo+dBEI9s1TBCE+p85YNaYKfVZq2gztOqgqvbeXAvnibClUJpSD1NJ6kgCQAUVzNVdvabGkwDozJVm7VozRvvRgAiUbw1VXC1DtGqzP+JuvBhfFb/yzF96rS+3+iBpPMQUGmsALZip5eqp6RV2CYuOz9aUYfdIEAe+k/iLeW86AIfgfHvW77bU7//BWX79g9gvjSEZckAMAViE4UPVUX/TdMyA3YxdEDVSxqSucSZu2HGBs1kYzVuHqztoAWuaKIgkVUWmXUDRbGs5dzW0lcL3T3MBWVP4xuoC9g6dNd3sHtZ6IRRIO0twA2VEdsU4FF8vYgBhgAGnZ9+/UOOHCZgUloNTkEh1CKebKrs5MoZCBnkFBxa5h2hplbvdOT1PSYgLFxQFS7IJPDhG6yMX4Rqy2HTs47OgMfMGmaVxkvWTBUkXxSB2NKIvleULTSUqyFKf17VtsYpH+o+otoSTuZ9vJqgyM7WQ2IGY9AYGa+mSIq6fvc3IblUU6X7Xp/QDxyWQGzxW09XGzXDnl94a7fgzPOv1c++ek1cJ1ir0A/caOPZi6SsQAEA17m54n6HNv4xCzsLjZSYD50MGVejVKs1ZwDQ+Zzgjo4CkoaFJFjdlV7fvQZ4DmCcUulN4Z65zKljFZY2YZgIOxgSerUbfKFhlNZ1AOuHnJxO9I59mJALIDqjDeo2qMT94q8nGFEmFIAAkjeR+8ZtDpTFfTkgWIUZ/9OpkU6b7GejnjE4M2V2uo2r6fIr0sWrownatr2YC2fWuy3NMdy9L0JsT1QTYsFKK3WLBTIGA6ZaAv1t2uE7dMeMLXvxq/+U9egM/866+64YDV7EAo0YQZCaNWjM64jJAd24JljFQwaQYWbUDWPOPH/uvnIJHgkqzwjZ/0S3h/PYM1zfj59zweX/jo/wMA8NiPfx/efvuj8MRH/j4A4N/83qc0MBYNlS809bLFQP3hNWvCimdUb7ycqDNZFYS1I/YzvHF7BcaatyZqV8bptMXMCZfKCttgo7ggk+DAdU2AgcmDNHWz0MU5OEgzLtWxgapMglUqyFRbj8CqxritsrXyWaWCqoRRC8Q/U4gbAAPsGkzO5ALA4Tjj0tZuwJwE05yNzS0MYiCtKkqx76ABKQVA6rooB1stZ+fpumJVxsZKhR7SWS5W6FqwrP5TtfXraK8TAVp4J/3exiXuzFOvsEXTTrVl5j4O8OT3t99bLd2/SO0HQx/Nm7n0dKCNUdT95dTHPDcM1tQzBflIvKjGVsyTIiWfiM0KHdgE+kXNFLQI0tFsDPeyQk8Vtz3qBc3kM+KNF18LADh//jzOnfvfcL1DQJcViNybdezj+sQeTF0l4nnbBOiC1hy06YCctQiXYE2eqmKrMGZP3S0N8wJINY8kxu7MjJxp9h8DW4tZZu0zuVbiLFb2nyN1xWSizOx0tvuw6Iqx4xa80ABF9R7XfjxL3QKpaY4AYHVXRb5Y3WvLUgv52Cp0ypqNSUraZou6jTRiZ+HiNEL6dnr60D2lBM3JmGZ3Ow9n48zmgBwiVwW4eB1ZJih1gYgmsoeEV/3ZOVvMrMnOay/TpkXVkQ36ATRlAWCb4JbsWqACfOZfexVAwBP+xqvxm//0BVe5uu7/+NI/858BAD//nseDVVE9nTG2psQVFdSE6YNWJFJc0tHSaQqc4m1L9ZmIXQAGfup3n+gNl60a76d+94n4qk98OwA0IAUAX/yY38Jr3/0UA1H1YAcYRZoOQPs9OGBJUKx4xuBarFlTS0uKcmvYzCQ4EhOdRx88qDFUTIILZY11mnGQZqy44Fw+bgBnK3Zcx7UL4WP/zlA3dzBbCWPKtpLdc4uRufp+mvHoJBkjV0xiqcJNzUhNszW3tGFV7mm+JKabjLSeEtJg50Iqg7LYwGKK7c5eL1hWoOuCyO0Mmt1BAKZwLh/65xsDH4DJ169M3hsQbl3ik5Zi/zc2+QSBEyApbBLyBt3bajG2kvgcVSz9GU+k5nOHE+PfrDZZ8orAqAAOxt7GVdtAOega1WC1NAOSbYwKnRfPNqsbLhYAg6kS5uqNkK8iRt/HPk7EHkxdJdLWpD5lbbdmPESXs6kAH636zatbqJwAIv7QroMNKjETi+a96qxNiL+xqIoBgCgPjrTVUmhtg4jP1JK5JdeUwHOFHIyW9yfsUtG86MwuVjqtDAyX0HVAapWLdegsW9oq0lYwXCig4qZ4QeMXBasgkQGnmAG1BsOiLQ0Yvk7BRIU8JoAULYBUmrQ9NCQzeFocRxErgWYXkBYFsgOw7GnLYMh8MI3vIwTqcS6jCXLsV9OxuW/Wkllrpdujz8yj2WyybSzTHzdaBGv0q3/wGAAmL9s4YIlZb4LgFE+4JCPO0AYCxuQWCVW69opJcEhbT9FxSxueSVfXkFySlQm2STDL2NKIS4d12wdtqb9DnkzX5csc8taXtc+aT5YBKSbBVgYwFIc8NaYpGKKbhmOsuOCQreFzALfDtMWsCYde8j5rwrGMvaFy03OpW0LY38n9seJvAFih4CKsGXMmQoEZfh6VYSd9aICqs1XrXLAp2Qo0SA1Qsf1dZ7+IJzYNZAMxfk1XIG0Ideyea+GC3oJgOissUnsuVo+JjU0MvDKQAHJvqx2w5OMfy4JZ8nGwAabGZPfxz+53237eBJts6E0yjHmu3dPP9GC7+7/sQBEAsKyopf1jHF1OnKIjQvWm7SSAHtjvNCvyMbtuMyED4InNQuFoAsaxCdAf6LgvCgfvp8LDj8rYg6mrRTwwvAomrBKITDcTg0QDNPEx8lQQYUe8TuKslQMwgjYA0T+v7SHOZaGv2gFV1DQEPvHuVXMeNIuxUp4a422BjL0EOMxCA0CQ67fqYOL3mLW1fXb2ZzgSF2UCvKk20M8VOmakbUU9SD67A6azdrKs71avqpPQZMEHyoX+Ilzl7Rwo8sZYKJ78vLhLchxqlDhTqWAx2j5tKpSSVeuIndM2gEaqNMAv+evoXe+jArBVWYYZaJxv7d+pZJhIN/a/ork+Q42l+o0fvbHSfRFP+YTfAwC8u+maAvnDRNIaLuW9lH9AxVk2oBQtYeIzgKUEGYqNDvg3v/cp+OLH/NZl2/3GT/ol/MD/+az2/6xW9bbi3r6mCdDRLBVbf8EVzfDGTaat0oSRBZPmJmKPdWxlaMBpqwNmYTwkH2Hw15K3zjEfLQNdM+Xmwp5I2jbMDoEbiAJ69eDZPJk1g6cNq2usDtOMO+cDZCVs6rBTAbipGVNNOMwztm7oWZ1BitRftJoRdc3UJtn9UvpN0ItDYvLi6TGYmzlVb48UXyObblAZ7rHkK2DtxryeToRQ01st9ZItlccLndQJRrtJE0IvFZYpTM2TrzmUazfrBCzF3ll3u7568Y92fanbrgRbT6KQ0c+dF/0A/f7FAtj1dVOv2KVusZAvurrjKESji0H+AYp6H6T5rvXz+7h67MHUVSLNZppHrlGSTCgHXXcTg1NjWSrMWZftgZo2aD5VYW5JxdYLoD2oASAfSdNMxaAROgGqCnYEYQOCOXiHVodoF9gh9EYKUBHw0WyASn2kG2CVPordSsLBrA5C3B3MVYi/4x5MkyvEExkVzmwpNVGkoz6NHM/DehO6ytxYIvRqbq8sCp+a8JAiBXir4Kpg78dlmioTcGhiYBLQVLypqbbeXDE4c3FNGS9YKR/YG/BUtLRem5rr4iEAS1HUBRPYS8fRms0KxYwboHi4iLF4XIHP+rpX4T++5sYEVIDpmu4u3n77o7CR7D34uGmaBlRjo0iw0aEBk6rc2tH8/Hsej40M+ONyFkeywkCl2y240Hyg6hWDqTFPgIEsJsEhTzjF29bmJpFixNxTjdo/I0SYkVuz5IEqVnnGLBkrFCAZAzVQxSneYkXhYzVjdmX0RgYcyegNngsu1HUDVcCCQfP/D3kCw1rThI3DKhlbt5EBNZOxY8PG+wemptVaJ9NvhVt6YsEoFR+6dNAA1TwnqDBU2J7uGveDWyR4KPt9qvCqPFiqjvp9p4NpBzWrVfklt0CAAiUY1UBpPqaRsUWNnVqkFptWNF5raXW0Sl6WPs41nWgwT4vJjVC/N2P8E+8ZqkQNSMV2gvlqQCl2PUDWsJBlxK75fRvpvQCDZnlD/qMgSVbdx4txZa549id/BzRnvPE/vwT72MfJ2IOpq8RwUZFmxXzKBxUo0jZmjACKG98BzRIBnuLTZBqbvIHTzgaoSLUNKICxQYAPPNVKmOPBHeBCEwHVWKOY8TWRd5jZuUMvF21pMJ5qA1LWGJTcOiBAklhPrFmBAwZXIF+quykqsplkAAfUDrIAeMd2auXFy0hHlgackSE57WjGmnYjKn8WH7eUmqX34jy0fYExbZoJqtkGWQqw59YOywHQbRqW7WuWgv4YqMV7hsVno5hgR0fi+xY6uQa8krFtDWSzFx0kn3kT8MS/+iq8/cduXEB1d7HUPL3l9/8s2NNtA5mlgjmiW4pwgLmgBqtUQUgsuJMqzqTjZrwJoFXrLV3RQ3A+a8KaCgYqOMPHEDDO8DFGqhioIHnl4UbHHW3T4D0Hq1irp7BSOPTZTBh+rmnGQKWtDwDWfGxaqWTb3eqASXOvMFxUEPZtdW1XxDoVB4a1gYVEikkyDpKlHRMpTmU3H1UylkoSqjCOS8LBasbx1kBnnZOl+zx9hnYf+dhRqDO1bKAKZNomTXbfh59UhHr/PWIfy4CQFNlypXuz0Uy2jfh/h0nuEw8AjQFTYGeyeNkyHOMcOQMPwH2moltB9PNbMliSl7Q0miaLCto4K2NfP+Bj8aqPMTtarmS/Yzyqa5tg1TFZP8JjQR4TeBZQqaC5opxZ4YGKPTN1Y8ceTF0lwo13uGiDk9Tobk7Wq46BfNRnfTKiMRfWywpd0F2wo/1pY6/CU1vdcwVqFgehGQqjUBJ7fWnVwG1g9Qq/2QGI6C6QggEHFLXUX2ZATB9QVwn5UrX02MA2cPkMUBNbOnLRpT5mh+LLmn+LDYRU1XRMpRijNmYorSHuBxOUf8yuW5se6cdkDuW+fQLYAQ/QGTeQGY7yFIyeC+4XrV5iXzXKpAMEEnqqjkyQGmk5Xabv0Gezcc5jZk5Ab3shff81AeTeO8ToPmIKPPGvvApv/+cPHkD1L979JJxNG6xpalVzAxWcotmBlH1Ha6qYdW5u6uOiP5+1ilH83/JFfMlj3oF/8e4nYdDqAnJBJcagtQ3wITaHA5UzaWPAg45RwTvgZ/Bzb+lGwuDpSSha0+YKxky1ieVFCefSUVtPgKA1zS2lKbD05kbsfxPdm2B+sxguw0Ue6NYNEYdUGtNViSE042YXkh/XEbNyE7IDwM2rI2zqgPfXU8hJUCpDhFGmBK0MKQZowAqauTOlgPfl8xS6g6SWyksAuS4w+u7J2ulfciDlKT5bGYFmavenEuzeJwNpPFMTnSfX5LOz8YDbGCzu8bhn4tQsvaSoKniyCjtZaJmWk5+6snVw6TqqJWtMDsTm074vPuZGc/oQ2Z/sOBHAagfkTT7nTLDJWkpY3UUoh4zxQjU92rkVqCg+/xkvt4Ud6P3Mv3k+7o8Iv7JrXcc+rk/swdRVIh8LUq1mIdD0NAQlBV2ixlBYxZoNLvXAB6gEYOrprKW5R+gLApQE+8Sl65w0WVsXA2AOkIL1GmJ5B1mwAdM8mIyZSpfmxfYYVIoxUlVBQ4IGwPLlqYrNvjbFqv04Bj0BmMBb2Un12XodPHmTUZqr2RVEzytVoNYFW8Y7QMqWCZo90hK4rOddiEevZAFRD7jZJYTHFFfsfj6u8JipOjAKgNZ1bWTAKQbrSEWgvxYp1WV1ZktNhBlisqrqOthl0CqXFs3pb/T4R//X54HpAALGISeAj7Gm4j8VAynWRN6aQjGroKJiXgjaKgiHmHEkA0at+De/9yk4mwz4XJIVxDVXGx3c0NOMPpkEK6oGiGAVhAzBKdq2NGKk3RILNjqaY7lmVOKdew0KnErWuPnQKxFnzTjFW0/jzUgODAeyfn+mi1KAgUE7KDqXjjBId1UH4EJ6bQ8oJm3ga40ZGxkwUMW5LHj/dBYVjLP5GFsdkKmiOKM2uUg+zEGrsgnQ47rLApXkTYadZRZY5R+r/Yz2P1VA2B70ZsRpzulKANYVcI8qTdroG2KFijNQ1dOHzhpF3z4q1PWjMVnkzvK2ykBnp8JCBNrlDsJ+D1VYL9CVejcFu25qFHIoWsVhXXVg1ApRgsxcA+qsFGDjsGQfI32f6trGTRm6/jUmTUu9akyK2dveGChjDJcU5ZCRNhkkinwsDvqkFf38T1/8qnt5p92z2DNTN3Y8aMDUS1/6Uvzcz/0c3vGOd2AcR9x5552XLXP77bfjec97Hn7xF38RBwcH+Jqv+Rr8wA/8AMZxvHyFHyaoGtMTwkwSa0QeFV9xEysB+djajrR02GLQiSyGqBlHhqCci5f5OgslOV3Ws7SO5N3Wdx3RLX3kwEEtJcbVNFA8y47QHC6ypNme/jobeOGqUM3gqYI2xcTkRaDeu4qS2QvwZEyAvc7W86poo+XJXYIbmNpE0z4fgFcz9Oaxe87ILpgKcGkHZq+VA0Y+ljbQtkrFkZC2tQMhBhRm1gksZtPuhh4u7XVcALIFi9R+177txvwt2LJoyhpYQckEs22gdvPWmKXXERiO/Psn2rGzeDBEBfd2LZBWGXeKClakOMUMXgzKQopZFVOYgBIQ0sB1ElyQikOeMCs3MHPJLQzurKcgRCYAhzSjzjPpGGuaG2vEEGOPnAFb6pwqMZJa6i+xNLYqYu2NkNeYOyuF2tbb05aKCgH7cWwwWIWeABMSziTrVXjI2yaCDwAWfwPAGZ4xacIqzc1X6+Hjndio2S4cqt0jkZ48qkMToSeW3vMviffgJNC6mI6qMLSSVb2pNvBoDJL2fnk+MSCoO6MryCv5sLIJGnL/fLBTygqeqGkwo1n40juqpcoXwMQWQjPlhOuSQisp0pnoFAU0lTCdplZo01J3zg4vLVQkw/RQiycWRbsav2/ryu/L3NcTwAwEyMoW1GRsu3Cw9v14SK2bRJdwWPWgSQHIG54rxBstA0CtPVW9j4/eeNCAqWma8BVf8RV48pOfjB/90R+97P1aK77oi74IH/uxH4tf+ZVfwQc+8AE897nPharih3/4hx+APd7HPvaxj33s476Jiq45vPfr2Mf1igcNmHrJS14CAHjNa15zxfff9KY34Xd+53fw3ve+F7feauXer3zlK/F1X/d1eOlLX4qzZ8/eo+1xEbCYoFbcAbvZBDQGg1qqLx87pd7ElegUd8ycRmqic8TnPU2kFFQ0uWbI2CdzQ7ePBPUdeiAqxkaFzomKgoq3klkqRqun+6I7+lytqm9TLAUYs9tSQYmBi8dWpef9+8AMXTOgQNoUaHYmSIzNouMZdHRsjZRhx4VhMIfyIRwEu7YIi10LWr+xSn7+wzMLZaGZqmpaqUglAqiehuXZzn9bL1Hvv8d9nWEayDX8r9Crj0LHtajoa9fDUufmM+h0bOwUL1INbSaMriHRarPuz/iGV+M//X9ecDdX3Y0R3/TnfgH/73c9A4AN4MYQFQzOSg1grCg3dkqgmFExuxhFoFgTUNUaHZ/hglnJjUInfPIj/hBvfs+fB4DGUl0QM/FkWKpspIqb2PRN0dMvQbCm0tiphN5uBgAOvTKwKps3ljunR8owoirjDG9aii9a61itRzbNVHQxB1xfVVDBuCkdtfWGFUM8otaLXO4ac+tnOCTTia3VUn+Wbkyt+i/B3NXHVLEpAy5tRyusUIBSZ5Q0UnDVB4zj5LlkY5RQqOn40NJ/UbVnLDsq2+4GS+WDE7ECWUFH/cLfEZtHSt5Z3Dr2qri4h9u9vah2jfejMpaLsVQAmnwizbTTLkuzF3WsPBMQ6fcTGSpyu4MQvpPa/RiaKaCzZpYl6ClPHaL82Xfd7+86KrI7xQc7BfJ+o6MxXyTe9FzNyqVu75/Umd4Hmim9xs/v4+rxoAFTHy7e9ra34fGPf3wDUgDwrGc9C9vtFr/5m7+JZzzjGVf83Ha7xXa7bf+fP38eAJAvTkheyaGJIKuEOljrkrrmXlmiXeNUvZqrRo4f6NUqwC6QQuT4jUauK3+wZ0VNBrqiZYKVPGsbsMIDqlfwKdKmIh3Pu5qRCCIgAQoDVMoMFAFrtfVWsWXmAhzNBoTgaT/v92KUtn2Wt90CgS9ugEvH0AsX/SAZNAzNl0UTt3YOiDQbOpCiEy5yGsLtqFJk6ialvrx1i/fvgC2dp97wNE0d1BpKtdfKmnb7kMHTpYg0qw/Kvm8a+3ti3+KBEeArbR00nahcmg/ttbzw6eKi+Kznvgr/8bU3vhD9Gz/pl/Dadz/lCkBEsSKCQDDQ0HqbDZQwu/hc/EuuUAwk2KqgkmDWplXGFzz6/8R/uv2R2Hh/vkjZrWnGIW9xljdYczEAhW4oyqRYo2BWhiDhkCe3brAvy/oAJk9VCsSFR+PC/XEgS+b13oSe1tOMUzRjoxlneYPRxfHWKzA1O4RTfkNHv8OwS0gnbz4CJq0t1ccuuo+Kw+qCdybByBXrVHDMFTlVzCVhtSoQ77dXih0TBCBSyBQowQcFgbWAAaAzG0jyJsYGpIDmhM66u5OAtYwh6/uH2ca25qC+vG+op/tkQLciWFTKymBApx6YsLtZKrikTVw8HqssC5uZ0B1GVV6AtmXngtAzcoVZIKzQ7E+WOi6vZUA91F3taoAz1bbfUOo9Bdd2TpcaqzT1cdpOmJ0MZUsz72MfHzFg6o477sDDHvawndce8pCHYBxH3HHHHVf93Mtf/vLGei2DjmawDFY6fzAg1QIaGDImpI2AU5TiAyhW6VJW6XLfE6CXy+fu1Fta6xKYSV1oFBCfoe731Krl4L2ntFX75eMKCHaAVOthJw6SQnA+FwM7MTWs0peJhp7zbEAosYGr9WAWCEQNUPVzNAHHGwNSi7YLihmUM/TUoQv4qYEJ0yZQ38fF2BSOyoBrLYjNuiGAJKNV+pnhqLopqFUFhXFnVD+SqDdcJYTItTGLi20DXVBLYg8A+My6OcBj8VDAgn2s7ik2LJYJ0MUL4WvM8hV4yle9EnkjeOvrvvWy6y7iWU+0a/KNb//uqy5zveO5j/1VvPE9jwMAzMoGiKCYIUgw8LSi0AsxVmQKotArMSkGJAyo2KJiTQCD8Mfv+zh87K3/DZ/xyNvx7j+8FRud8HmPehcA6993ire4iY+dMXIgpV34PUIaeIoqP8CrAjUjUXGxroGgWG6pkwLQ2C7A7BzWVLDR3ETlAxWwsm1LgVM8NRNT8Qq+cPpf9t1b+mUx1IAYGWhKpDiSETOl5tQOACsuODtssKkZRRmlJmxKxpjs4ptqwlwSttuMss0mSl+RAR+3Q2jXc9LGPIVgnRTNBgHuP0WsOwyVkn3WdFMMZGvxYoJxBzYzwl3Ft9WZ3WiU3gCVovUujWITDJ3VxQLwkAI1xOyL1k1gY5sANL0qgGYADGC3SXwGJEBkJW8KbcvpGCmFvvxSv2kaTPu7rXOnw8WiA4WPVWnS+81VfC9Av7HjAQVTL37xi68IZJbx9re/HZ/5mZ/5p1of0eUXiqpe8fWIb//2b8cLX9iZgvPnz+MRj3iEPexnu/RSNXNKHNpdrgNDxQanaFyMSB2BGlvR0oIuDg9xcwCAJcBognXqTJb93cWZUdEHBXgSs0KYZccss4WI4x6/06t6rykHUHxiNjU7A5ESsNnacsMA2szQnO1KqWhGnXQ0gaYZmAv02NuH5Gxj+jgYADwYmoWCOpihCrM7WFoOLGj/HYdi7eev20rYHzwr0ra2VjDsHlhUDFDZZ7158RBNjHuaAvDvLs6r2EAbtgbJjY/D92ZpMhqATBZAKxioJUvV7BYWILmJbJnwtC96RTuut/z8t+18HZq5geAHMp716N/Br/7BY5CgVq0HZ39UMFCvfxsooaogESM5wEqAv0Y4RMbGmauleP1mZswL4801zc1yYISACSYup9h2PycDCdbubXVJB2MKycTfDPN3MlDUzUQjtRfR03yKAb1HYTSVrcRWPOIC+IjJTUbHBWsGmJkpgO7S7uerVSK6oD9eizhIJko/7W7qGIAzYdxZE4DRLocVIJUhs+W1muWHA6ro36fF2CkKFooAyp4ijOWB9j8ltXR0VtCWzdiz2P/NtmRhHhwAKnziWlotY+HT5ttIxvKUw17VKuH9pJ3NDRuSsJmJvn5U/O+FxdOyMljZ2CQqBFlrA2iaffK2YOIIujPOtPGGOisddhBAB3dzY5dtWVOpA9MZan1Wr3dUXXQduNfruI92Zh+XxQMKpp7//OfjL/2lv3S3yzzqUY/6U63rlltuwa//+q/vvPahD30I8zxfxlgtY7VaYbW63IhNh2Tgo4i1TBkAErFu5jB33MrcBoe6Dm2TQoiQj+G95foNL2nBWiEe1tGfCj44RE4L3qBXvfqvVxWmjeAtP/9teOaT/r5V0PkdokMy1kn7IIaq0CGBNxs7ngBSiZuOSscMCnB18ZLrnfx/f522Fcj+91QMSG22Pb13MnKCrAerXvS0ZR3pspngScsCyfAKxsXf8FOSyKoJZ/GqPiBfKqYTcx1XOZXb+iwtR42qN61E38XW9oLsA+Tmpgo03dPS7V69oiiqinbAHnlKQ7zEe/EdB0sVx9FSmIt42he+ogGqZz75e2BmqTdGlVC0nomIFjSDVmzJYMNAjBRPJHTAZM7p9uIAxha1sVMMM9fcLM7FKd66VYEDKdcOQhULv1gwFAM6KzA6qJqQMDaWylq+JBKAOnACAuAYsLLt2euzWrPnSgQGIamBiWCr1g7WTnFZmJXagy70XQDaeRgJjc2yFGTubBYJGGaJEI2TM9fWGDn2da4JZ1YbqK5RasK4KthWgiqDBoF6yxdyLyktbDqrpAAWmqvovg6As+vbKttnKtlpJgCDBgq0Kt9KgJD5WAXLlRxIObsU95sxU+q2DX4eBkUN49xkutAaVgRNsoATOiU/lqTQA3/Jlw3bg5AK1AOBrsTa4LA26wgwQBNBVwYwoXYOzLdK2yS4sVUKr+L17gmOqZbMcjQ85+LDly6sGvbxUR0PKJj6mI/5GHzMx3zMfbKuJz/5yXjpS1+KP/qjP8LDH/5wACZKX61WeMITnnDPV0iWGqJSgaWrNipSgT28RSGj3UnGFBmgKivsGHW2Et3R7AaC2QDQBt0m4MSCoXFNlKf0W+oqbyo+/+kvQ3LvqDf+55fgWU/4bgMDvt+h8pSDATwVyJm19Zli7sskguZsGqr1YKzT4UFnpgBga/UjcrgyYCkGLlHFwNTJrurJjZaGwc00CWkyrVWa1U36qAGdGICD8u/nH52eZzK5R6T71L4LAky/5TowEgGV1MATsPgdYEYWVgnODDb/mqFrRJa9xyTWF1mC5V3TGDO0tB/5rH0pbA+DwdgnZXLjQW3X19NvewWgCjozGpuXCZ/zpT9gthuZmtbqM//6q4xVEzQD2d961QuucBFfn4gWNO967604UsKaxBgoaPTfBQCsiXa+0wrFBelapcGBmMDa1sxu2hlMUayPoV3uc2Jf2utUrWGzN9uGu5CHuDzSd8u0nu2j7KwzQNXgQEkATGCMWjGR9eg7xVNr7ByasTA2jf9j/ZcCSJE1Yq7OlG0wNPf26EvIpMhOleQFRbvKBVNNGHPFLHZMU8pQbxLcLA98gsIHxewUlswUDECpEDgJ6pzA2WwgxG3LVXwiRwCUDURppOq0MVCREkeMYzH/Y3TjUOr31a4IHKisnY3WPgbUA90BVmDZNShdoV9PfmnpSoBR7DhHcesIGMMmBHWmCqygiZuHFhUTo6P2vp1U+vWaQusFd1CvnZVq2jAfF+TkRXmdQkCQy+6Ae7qOPTV1veJBo5m6/fbb8cEPfhC33347aq14xzveAQD4xE/8RJw+fRrPfOYz8bjHPQ7Pec5z8P3f//344Ac/iG/5lm/B3/gbf+MeV/IBgKwzJGWwKmjjQoEi9qRMBIj6zSVWRaaE4Ujcd8oq8tJRhWZ7ENaVaYfmQ7SqNnF2gydviwKb5cSMndx1vfmzbMUZKrL9mmuv0DvhZaQpQXMXnPNUoKvg4hWa3U+qqgGvIvY/gJ0O6VKBuYCPAB0dKKkCFy7uACmtFURsWqOcoYcrA1JbQV0xhosV5cA8qvJWTWQ6MCQbuAmwGAxeaoJuaseWNjbQBzsFdzinRvtQF7tTnxmHZsvSpXqigTR1DZqzgBDfn9pFtsgLXBBajyUAjpRl7aL0AE+RDtRwWk8w/UqFXzsO3FxIbKkUcu8cauzaZ/71V1mbGuqzZiq4HGHcT/FJj+h9/f7Lez8eCYqNmlDaROH9OgqPJcAE4KdowiHPWJPggmQDOWTszbL8OzxcRz/Rk5phKBCZlnhiK0ZfhzmM15103hn3udpJ65GlLgfStkVGI3QBZ8VGtfUOEFSixlDVYK7Q9VzxfwCvaLETvQntXHBL+0WqL8HA6EGasakDChibmrFOPYVfxcTrs1LXLVXqWihCS9dRUjP+JLvWjEEyIMWs4HVBnW3ikVYFVTK0wtidyHeB0NBuAJpRvWuATQaEOriShA5SAK9605amDwCmyVJyJEBN3UNPk/b2LotQgumdBL5B7evxtCYPFSoETdLRzcn1hAGpAJpN58pTTJTR7uXWZNnThQ34pQ4I43/JgGxxv8ReM3Vjx4MGTH3Xd30XXvva17b/P/3TPx0A8Eu/9Et4+tOfjpQSfu7nfg7f+I3fiKc+9ak7pp33OojMrPJwBaoOXJSBjV+SDPCWIasMmsVamiRC2pQ+29k4e6X2YDQDSOpVJ3DwMFFrjpu22qrWAHiLFdMJ2QxRwdsCGQ3sPeuJLzGWyduqtFD0ZsTLlJEINC3+Z+70diJr8LlUVYrVJ9NGDGh59aNcvLQYqQ1Q8WoEhgFyMEA8LRgWBeOFasc1i+nOWFAOGCTc023oICRmpI3JUzsX6n34jCyy0Z5EoDl5yXJf1zKaPi3+jvYzvARy1ITiMvYBFbWnFxYSnyZE3dlOBTB1Noxll+kKMauyA7mY1RMAeOEBue2D9ytrqYRFOgXk+yjApz/v1UgbRfaf8XxpWjqqoZ8DdGC8+T+8CJ//eS/Hv/vFb7/8JN3L+ORH/OHO/z/+u58NAKhKuKseutO52QnclI7wsfkCbtaLSCQ4RbMBnEgHUjEDbwAJ5rQeoGkkQvIUHACrFDzx1DzVBOi0A6jWXCAKt1oIEGTvJbJtRQSI26hi6Fmjpt+qIIwOrpbbCG0X1ABVgrgTekH1HHMwU8kVZ4c84QPzKWS2NOE6zSjKyCqYJJn4XBKqEEpl1MogFqQRqBOMRXLrA4r0FYz1gWr7bS+SFcmyIDkAKVMygFId/AhsYrL2iZoDJxJCFHfKqH5Nd2aJqzFT5GnH3nB5yRCHNYGBGWuR4+C4IVrfVwaiKXucU0vnGV1FDqbIDUlVbEZDWaEz74wbTRtW7cskB1KRNWjFL7Hp0u/fJfvcWO+TTPo+PurjQQOmXvOa11zVYyrikY98JP7tv/2398n2otcbJTKNUDEgQRurdmttT5hBicDKwCx2Y24XJS9kjYDtwZnshvWKlkjtRbrJ9AOukXIAwrMNPlxdcB0O50T2m7sOisrioTmmXrUX6b/4Pz7rqUTTUC1SmeQ5rcTA5E2rNltL4VVL8cnxMWgcoJOPrsQmPM8ZGOyyah3f3ZmdtxXkjJpmRj0YwNlbYgg1ULNstRP7REUdSPhATQYMSBQKbsCNBEjzgqVr1YwGZHUhlo1qyRD+x8Bv30lHZGlyAJf77Nr2C21fAozFIMwV5oQfM3bv9WgieH9AkOnrIpUS10zTWcH0V01AP3Twpsl8e+DPzrqyqsJ4uNWRIXlEvmQ70a4FAP/j014GzYynP/v78Mtv+Lsf9l7408RXve1vQpSQuWLkitd+lukXn/sf/5r1FIMxOqfyhDN5g4eO5/GwfB43pUv42HQBZ3m70DOZFmr2725N1MBMhEBR1aB0hfXqqwBGlcY0DUuxOhQzCOwM06z2t2XKjOlNCyIGvr3WNmfBVC31bgPtVnO16kOgpRfbe6QACi7U09YSx+0bAOBstmZ3RRJO5S1WqeAD21M4KkM7LwCa1xCzolQYeHLPpNBBqXjqT2GicqCl+8axoJTkAAuopZ9VGhx5iH2OXGdEC08RHdBmOUoG/MMfTxngpedSXd6r2u8bQev3acAodmABBEfTQOnKE1OsoLUDvsHuzbQyfwUV+/JVCcTS0pXNvX3LXrxD4G3vD2hs/2IyJP3+j/0mvxfNib4f2nJCg/uLmbpPBOh7BHi94kEDpu7vkIFBW5+ZbYtNYSOtpQpS1x7VaiaSiRqTBQBUFxV2gxlNclHkDWxgXhOwMMGLst+8cWA194ueq4K3Fbx1UOdhwvh02QBPqqDjyUBT6QAqmhHbw1+Mocq5U0DMUNIuRq8ClNLSfnLxUn8d6CL12K7rpXSVF8aeNjjz1pg9PgpF+WAAa2ZwqagrboDJzv/l1FKv+iP7PhQNRAFofRSXywYAiZYwTXQa53bnPKP1LWwsUksTqs/KyQoJlro3/6wAza+qpQqqtqawtmIX1i/sm6zdRTeALQfUgF1dd0F7NHLVwY5FsrObsy+7socIF2A+lZAmAUlGvlQsrZu7qL2uDEB/7pd8PyQRyoH9SCLIiNbn7Le/7wWXfQ9Xirf//iMhM4OzgJLiz/3h38dqnJH44aj+UMtJcNP6GDetNjiuA7arAXelQ6zXZusxusYp+tStYYrjNYAVcasgjN+8qPCb1QTps7NGAaRGX2bj39UA0yAGIzVQBz91IVwMUOV3M3bMx4h2rBCaUB4mYO/r69dmPAQvycrsEjR549rF9auEVSqodcC2ZByVoTW3VSXMkrCds4OgBAkgFBhoTqBkKUkp3ACUCqFOCUSKrQw2XBQgZWmftfsiZndGNqsfswTD5Mu2yYKn2lqFINAbh8PBh7M4zZohAAgBIIWip93M0EmhWUAM0OnZTW/9W8i+AiHQ2MfX6C1oui9LU4ZoPlgymqlNYoJdC1PSxjzF/mqfIEmGmYMqgNSXWYKpZVHR9QzxKtNrXcc+rk/swdRVgmcBUjbxBJMBiABTzB1QkKWbUABdDyYCrxU0eS+8VW6AxsTO7rm0eIjHzTxcUn8AwoCPV/ClbbWee4smwpECjGq9xlapAmLLhlBcXSRPsCZZV7SKiNeSDUKoauk9VWgpdux+bCA2jVRiA1CxinHsYv1STQsW7uxijUHBzuqJtGXA1MxRIwXIs5ccO2NmaTn7XmRg9/iKlJjPgNnPm5+aNGurtmmFTNQFp3HuGwMWmRAfKJW0p/EWs1X2Z05dAZAOjMJ0EM4kNksFXYAr13mFPQLCuXno+x9ajADYUT0lGeiGg4t9ihRLNhZhPrT/69rSnuUw2bXnKWLx9GGz6hhNa0YC6NjXSQI87jtejd956YcHVLKxoUQumhFPJbWKs7WADgqGdUHOFaqEzIJMp5rw+venj8UZPsbZtMEZNpuNQSsSC84svJzWlFChOKQMgTZtoXiyNzmoif7bw7KiEAq+wqw8GKp5kRJcgqqrAaq4fEy3RRgI2Fwptwx7gJlmLMMk39Rag2zcDT2RYNYBszCKmJnnOhWcn1aYasZUknvYqmmnkkBKsOMOVIQMeDjDo5X7xIE8Bah2GxMD83E2INKYJzVmZwHSaMs2ZqkDJwcq6mCIZ2qALEwv47NYpO5bUU0Tufukjk0/acyVj49h0+CGpZG+JFIgSbeDcOZIqltBqLNUAaL8Pol9DO1WA4UhjBdcxkoB6E3SdQGccj+eGEPkfnqKCq69ncxegH79Yg+m7i6qpaQwl5bewjiYweWSlXH/JpprN6QUTwv6+2FhYJ5TaLMbAO218EpaskxpKwakZv98tH9JZKk9b0RMpZjFQbXRkqbSPKVoI8CQoRJg0EANYAyVpmQMl6oDDe6Aaxj82O3HROcVlBJ0mu13rR1UlYporEyLbbTUITqjpuz0+2QAiScHncfV2sYAZoha3IU+MaobowJRJdh9Xrgo1M8hE3YZJI1J54L+FzRxe/hS2fehrf1EVFUGcOMCA85NbNsOa2dmK+7InosibQMI+nKTIvyvSLS1K+IKlLHro1oaIYKxsHxAE8SGbsolJrBmrnY+tue4iWhDixdpQ54BGbsRYfif1ROtQP7C33u1bX6283SlysHf/ytm6/CYf/gqaFLwbA83AUNlwKSEOlh66UN8CADIbNVsd5UDPCQf4dbxQzhPa9yUjnCKt5g1YavF/KKgSCADVGpAKnmKdIVkrWw8D5MIGKIFEQgJBCaCkGKj0gDRMtXHDobMvNPAVzoBqGybdrxd+G7pxaqennRQFmlN2wcHaUotVSNKuOhulMY+Jdw5HxgzBzJAtfCZ2szGUkWbqOrMUxOcZzXvqbZfHXAEwpHa20wFaxTpvvicLtN+EztF5wxS6esksYsthOTBsC9tAjShidF72l13dq+3NzCwFswaDQpO0rVQCtNIMbyKcQGkKkOLpSVDEwUl8Mb8rexapza5acUbDqDC+iC+zyZEl87+LkOX9yewe4/u46M29mDqaiEOpAADEin1tBfQwZRqF2sXF6nH8j6465D8wazIW8GcGIyoYOvsh+l3PLVYdKEdcmanFANQtQKbAh2yvb6Z/DOTAb9I082z7XPOgPtkaVgjLIw7Q8MEwF7zCS0NGZhmEDMURzZmTFM7RcR0eUXfPJvR5+Gq63Q8DdnOJ7PrqCrEAQxvK2T0Kj0ipOPSczGiqKcy8nHFfCZDvEpQMjvA0T57hIMq/0pkdFF57GYCgBi8I98AB5IGbDSZvoqLdl2UVyWZX5aDJbYnaVgd2AaDiVKk4uxYVARFSmDBUlG1h60MvT9ZCOCNGfDSbNdeafQdoz4j5mLHqeF946nGunI9mKc6rcSbWh9EZa9GCqsKP4fklzUIraVG7P9vvfLuWarf++YX7vz/SX//1ZgeItCaUQ8EG38gJxZMNeHiuMI6FfwJn8aRjLg5X8KRrHDLcBcSKybMxlLBhOAAXIBuXzYTrH1N7Dh2Z98D2B3bqQEyUDcgBbzCaeFjNbl+qkIxOGgLrcmhs2MtyETwl9RSi0xBZts1NiO1Pn+JFFs1lurIbb6rMmZNmDWhaMJcE4oyjsqAi/NowvPQKylab7Wm5XF9VLM1aFQQOjCI6yrSYcCCiXJmCgZC+vsGXkBotK76ikJ/BHRAEpNDWrCzymFIbKL0pVZphyDxzTeLh9iI7ydFc1MlB0+OhgoaI0UBAl2PxZMtE1rIRiyGbYnve5yKdjVE2m8JlPy+WHa2aLrGBcN1vWOvmbqx49q+mY/kYG/SW9XYmWk2ViqA07wQvcwFON40VqZripJVmLGJ2XnWNosLELBMObUKLl6CrJjZMXQ12O9Exnap2jZFTSB+6djA3mYLLHrlxT6GnguAaaUWomREuxgRYK5AIshqgB6ugHEArdf9mJbeUmEFD/Q06DSBSgFtK2hbwUcT+HhuVXh2rAGiCqgKeComsBcFb2bTfVUxkKOmGYN4H8Kt7FTQ8SzgIi19Sgvdk+mlLH3KzjhRdZZlce4l7TJcAXTiYZG22l9XYLwoyFvdYRUNLJveTTJdge06AfrmJQCMh5Sle/ORWn9GMR0dhcfNsqIoHqiL9h22sQ601AFYXVvbIhkIdbDf0xn7KQemkaorNyVUO6ZwfA4T1ZNVix8unvD1r8b6A8DhHzLSMYMuJeilAccXVrjjj8/hQ8eHeP/Rabznrpvx/s1p/Jc7b8VvX7wVf1LO4HxdY6MZF2TEJU24pGgO6hlhsZAwUGoO7AO4/SybMJsxaG2vbZQgav5RR5IwK2OjCRtlVBBmUHOKFvQHUGoaLVqwVl3sLkCrFtxoxkYHcz33ExegaCtW2RhAKnRi25pxsaxw5/YAF+cRVRmqhM00oIrppmo9oVN0QAVF95RqLAuZoacbIkXqzJYNsbrTnTHviZRamChVMhPP5Gm4qMaLaxEN59hPtIQJ53RP56n/vwQ3lipT6CjmVu6gjbKA01Lt7cLyJUCM1F7sp5r4PW1MIgD4fV4IaUu9g8FCI3UZoyT9GldesGfox9VeZ2sgbXYO9w9AsRTxtf/ck3jrW9+KL/7iL8att94KIsK//tf/eud9Irriz/d///dfdZ2vec1rrviZzWZz1c88GGLPTF0tVI2hGbM5fx+uLGU2ZGeoqgGqcAtPCxf104cGsEoBzQV6MHSWpbrP0aKKK2ZMtl17ANc1Y7jguiuy2Zml3xQQA1xv+O2X4raP/9t4/R/+EG579At7u5jEVklWxbZLMDZowUyFhkl9FKXZjxkAhuTMFWz0iOo8ZuDOu4x1O3m6qntBiwJzMdF+SjGdNiuGtBDEL+5pKgaaGuAakqX5JmOpjJ5X982y0TR6HDbgRAaGPANoqUEmKCl01TUg0danVWNqT00EGNkBDW39aOA3Tb0BNc/e/FTtdwhX09zbBwUgltEfZIs0b2iXejWg2WOY/sMeNjIYMEsbA0aaDfREyXnz8SFjsGLmL+jLxL6UQz/GChTHx7G99necK4X1KSRg3Gpv1H038fRnf19rz3N6ZGxuzqgjkDdkzaYHoKwT/uuLjOF6zI+/DHlVzZQyVRyVAQdpworn1r4lQSFUkLhgrQImxqy1tbDprWyMQaoOoJYhAD4o0sDOJWeHGIK1a7dSpAlbTlhbWi9iDsNRB20Xtfj2AkQlTDCAZtulxibMmnFnPWzNkwWERIKLdYXzZY3i6b+jMrgVArdLsAp31ghoLJQ6iJLZgFekuGJ+Q0NcJHGB+LUnSz1VXG/L9cNTggbAdJDWasbM8brXWWN7koKUIAeCcFNtRJkDEMCu6QgrvrDjMSWA3UtLRg0wQb0K2X4LgNmgsRLAG+5px4qFQLIDvmbPQCfYqeUFQn1yE5ovKFp7m5jMhBddVOTqR/BT9NKlS/jUT/1U/JW/8lfwZV/2ZZe9/0d/9Ec7/7/+9a/HX/trf+2Kyy7j7NmzeNe73rXz2jom7A/S+Ai+DK4t6jojaQLl1MTkTeQ9NWvuDl4ilj3vAnQQgbe1V1RJzPwUabtIs/jMt65Cz+JprFlQV4OlAVXB24R0wVF8rbjt4c+zv4fBmaUop4nBsNoMky0tGKmCBiiW1YBVoQMBKbW0nJl72nHSwbozUyfcz3Wa7XPVgCYdATpkYEhmkwDyc8C27hPWDpLI/LLYWZ3o65cZPJkeTR0oAQBPYhqq3FklE7k7AC12LFQsQWFNUDuI4snSEEtH9OgkH20uml+YnCgaIPOIkrQw6YyUWFVgUcypibxC0/9fgKe0VZS1Mx6TpRnDd6pdRpO3txhsv6kASdAHcUKfVZ/IlOyYDIaVghiIT3M/tl1A1wEf4EzZxpi1p33hK5CPCngSkKoVA2T7nuo6WbNp7h5aq/MV86nQzpFtdwM8/ltfjfkMwAdrbB9SUQ4Tzpw9BrDCHx2f8x51jGlImFLGWd4AOAZQsKaKgQiDVk/x9dSenABSF7SgqgnMo9LuSAZMmtyLKuGojlhTacBqRPUTCsjSJFS1abFmCGbt/lezkhuWmlaqOstVnQ3YyLBTSZXInMerMraSsakDjsrYBOdVGFUIU00QYdTCpnli7WmuuL4qNe2T1kDOfgGEIL1SZ7AWOj+t1GwKsGBYLM3s68zekkbILAuEoFWB7ObCPikEgHJa3GwYC11hZ8yUYUyXotscACdSfDCbA9JeEQh0fdfMBpxmcu0TtVYzLb3ny8Z+LL3kmveUHyJyf385kWqpveVkN/YlaS/5vJ8yZ6a5O0mn3fN13JO47bbbcNttt131/VtuuWXn/9e97nV4xjOegcc85jF3u14iuuyzD/bYg6mrhGZCGbI9QA8SaDZrA6wHULVqOb4YgEY6KxRsVTBYbGJsjMnF09S8gOyBZTe/E06Av6SJUA4Y+Vgwn85W5RdVfETNdPP1f/QjuO3j/pZ9cJl6VLX/xV3LMyz9t17hZLdLkhOgSJzxadqravosABgKKGcDVBTKbTFmarQRVWcTpmMc7YGbGTK6vsuBkI7ZmChGMxSVMUFWdq6b5xIHwEwOLAEwuljdq/s4fKi81yEtjrF5Zy30HUD3mLJzEDNX2kmxhgO97Uy/NiwlZ2BVvapJRvsOY7iy9Fh3sA9fK/Z0H1w3F0wXCcDaG1tzn+AjbxSzG5LmjYvEq6XvWpWSA6CwULB9xY4/lZ3TPitvqU7tQCpSGaEbi3OYAky6NYXEtZziweVievRt0WwCfJ4UgBUchPt8moHtOQKQUAS4lAXzWHC0HvDuCx+LeppwJCMeki/h5nQR59MRbuIj3JQ2WJNgpi0GIhySpQCPtFoRKrAQl3eQA6C5sEdckANUJWxoxCFtkVQgIAw0YVZyxglYk2JWNGF66KqWKcNY/6TWaGZ2sAR4JZYyxH+W7w1UMUlG8QGgCc+dMhV1RuZKEZd5AA1neSAUXrrtYojuPg1AuRu4vYmW6rPedAaSTISnfTvkQGJQB1nm3bSbwgtAbWxTML6yUvsnROkuKDema7F9oKUsOYuxUuoM2cwdDJGl9powfnHdtdMTxxWThQXOvOLpjHSe91EV97SK/W1FIcvzdT9FVIBe2zrswM+fP7/z+tV61N6T+O///b/j537u53bMta8WFy9exCd8wieg1opP+7RPw/d8z/c0I+4Ha+w1U/vYxz72sY99fBTFIx7xCJw7d679vPzlL7/mdb72ta/FmTNn8KVf+qV3u9yf+3N/Dq95zWvwsz/7s/iJn/gJrNdrPPWpT8W73/3ua96HBzL2zNRVYj5MVsU2wITPTMYCOCPBc4XSAXgzmWXBdrIZexqaxkhzAgazHZCVzTLbzEaddYlJlTpDEd4srqeRkawnHeAVhu6AzsBtn/T3dqv3AEAEr/+jHwEAS/9FCi9ScnOxNFy4mQO7Gqjqcx9n2zTc0YPdqRVwliomwqYLrmaVMI6gnIGDde/lR85kDBncVKsKWWdj7VKcGxPBR/NoAJ1hYnImyF4LA1LA2ClrHEyN/dFMYLdOCIbJdCS9qijOfdM3nYioqiOx77wLxzuTRARv5WNpQ4zBWtl+mUmgtn6OmsOck5p3liTqTZBTnzW3mT5MUM4TGvvFrvngqe8rL2beS8fmuJ7s3PnsPTlpWftsvtkuKLx9UT8Htp92rmTo1JZpvgg69hOoAwNx7KLtfK3OK8qB69iCnRVLBdGWUacEyYKL0wpMivcd34Q/4dP4mNUBPpRP4ePHD2KTBhyppeUOecKaCtbuwjgrN61SBeFSHZvu6pKOGFCRSLDRwXrmAVjTBBAwec501mzG9aqtT2BSwQVYY+ak2ioLt04bRmpvQmelJu9RuOxHWKnfpwyNok5sJTetVKT4ijBUge00QBammDulZzvsilHb0ZzYdJLOKClZXhjOSG3Z3lswOki9GIbYhgwMxiRR265bE1QyQfrE0EEbQWM+VPb90oLsVka3SEjadn9HzwVC63npqUyrPmRQUshmIWz046Tix0O7rGsU+Sj36x6ya9uwPH9KPkxGhwN4AQfBPLVcKH+ZcD3YNN75Iq5bBKt5beuwfX3ve9+707P2WlkpAPixH/sx/OW//Jc/rPbpSU96Ep70pCe1/5/61KfiMz7jM/DDP/zD+KEf+qFr3o8HKvZg6ipRDhnI7NVM2ihe9hYOpAodAUkrS1ellQGDSE9lduNLgZKDq2Sgwh4utGP2thQ+Nnds7ZUzcIZcRgYXdzJfD8CYQBtPMar0SkLAtEgpGbhTBThZqm9pOgrYayGkj/8BQBxQcraBo1ZgGEx3FW1kEruhp4DGXvGHIUNXA2TlHYJFoSM7gGwiCOsl6FV+AaLEBeNK5rMVgnFmbyvjxkCtD180cgO876EBmrri8Ae04NA59VRU81sabHuSyfRBokgba2IdQCpNZsdgNgqLUu9I1RGQjxe6pwChmRqgsA1Tt73olwxA1DypoN0PSplM30TadE7zKUtb5rmnJqgAqfZKvvClin6A7TqL562fFyr9IRpAalnpaIaijHwk0ENrjsszI0WHAPchkhXb9mYD3jxVb0ptILKuOtgL7Rf7/oMAPcrYAvgQTCVv7WkEk2T8CZ/GxbrGAU94+HgnTvEWh7zFmmbcxEcYyfrazZqx0dzAkYB7g2EMSCrm++T7EQ2RD2lqIAgANvWwvQ/A/ae8hyBkp8HxrB00mbEiYUbaEZ7PahYJrdUMSXNBL5paiq8Ko6r34BNurWOUBESKWhI4me4xNFASVW2Ihz51EBRpP8B0k5MD4bBS8GV0MC0UJQWS/W33VMsjtmWtKTsbGBKyJsSxiPSUXbijNyDSb3tQ9hcIu7k3AmR2Dy2GWSEIm/u5wmcAnl5P6l5r9jeE+uoWqWsogGzXW2xmxw5BF/fFIo0nw1Iq4NugODZfTwbQ3WKua9yXab6zZ8/ugKlrjX//7/893vWud+Gnfuqn7vFnmRlPfOIT98zUR2rIYMLkurKS8nwcZfE2WMhg/eA4zDSXYuqq9poqZDXaTN1n6dYXOAa+BQuRYqYeAmhvaqwAiAxETdJ8qKLRL5hdn2V39Otv/4cAnJUaBns/2KsymXVCeGOlmILCAFFYJeQw4CxoffyCyRpd5D7N7oZOoJRMIxUAbchmqxD98lyoDNEGnJTtmIAFmzL0gUISmsVAeOZIJiRvDIzajTQNHCx9mvp7YbxpbW3sWNMMB1TdnDPAaj7W5hMFwJhA334wZ7Z82Cxoq5BsA7izVJIJydcTwISK7rA4gaTCIDCqyHoPvg68sNDa5aPO7gA2qJvlg4In85gC0NrbtIeEC2oJaJoPrgBKrxAM2tH2qQvtpzOM4ZK6G/uCWfKHaHxngBUJSPROZGvBY7orwB6EvXKQZ0CK668KY9pmfEBP4Wg1YJUrLg0jzo0bTJJxZtjgzvkQp/IW5/IRzvAG/51nfGy+gIEKZs2oaqBqWgAcAA6GBAmCM2x6x7AtSP66MUuEyd3KgxRmiDUuJsGA2hivZsiJzlI1Y87Fgy+8pJb/i5pA/a7pAJOk1ncvkaJ6773E4mJsLxwZKursx7Uw2ESyi5Ri5hXfY9M7GTul3hsSQsY8CQGjSeNpXez6VeoNhBdDxLJlS/O2ytIr2xRoDv0NdLQTuMvgkLrA/ArgwIGiVOuzR+7erkKdFWMzhg0fK+u2EOtebCYKKeI6vXxTy8XtvKYT+6roQApohRzXqAX/iIof/dEfxROe8AR86qd+6j3+rKriHe94Bz75kz/5OuzZ/Rd7MHWVkEzAosqrjoThSCAjIR9JA0d1xVAezSNpqq1UH+7ajfBLgonPWwm79KmaPfjtY3HDc1E37oyZVgAqAF4X1PoAwlJvr3/XPwAAPPshfx00DiZO//i/jWVPQd1OnQpZOpfveEdpF9BH7wkSA2c5mZHn4UH7nAItLUjj4CwXOSuli6bQ5CxKjEp2nuOBG+e9pYbCkyoR2FmQ6qxVOi5gEWgycXtjvPz8BwCxVFqvLotnDTtgkoF6m5+YfC+FsOI2C+GL1b5Xv04GNjZpcSdxVZQ1N0AWAIgElubz/9t58WNcfvdxTYQFhH/RvfG1s03Lqvd4iNUxnNuNqWqC2WVaQy1F2GbpDp7C+oGqrSe8riLKAbmDv6IcsjFxFEyWpaV5K6gD94kC93VbRaO2/cRiu1QJukkQAqbZKti2Q4UeAFPJIFKsUkFiwelhwulhi3PDMR46XsBGBpxLRzvC7wpjdmbNuFDXSCQ4l47ApPggFazI7BcGKjjFW6ypeOuX0UTpOvZUHwQbGlpK8RRvzUJhAbaayNzv68ZyuWkngJ00DZNiFkJZ2ChYuxj7/On1hG2xaj5mwVwS1FvJ1Jl3aD5qF68DFIIxUQTQIJbyLWzNjL0qDwD4sLQ2M8Ro/lPW2w/OcPk4mGzGUYPB8q8y2sA0O4YarBd1ATsZa0bZXM6J1cGgTbCs2hgG7nzdOnP7OyZUtLLKZB0VkgTpErf3d9rDLAm1kyCq46J2P8Q9og76gu2N63NnfQTbLznBrF3nENzzarwrreOexMWLF/G7v/u77f/3vOc9eMc73oGbb74Zj3zkIwGYmP1f/st/iVe+8pVXXMfXfu3X4uM+7uOaLuslL3kJnvSkJ+Gxj30szp8/jx/6oR/CO97xDvzIj/zIvTqmGyX2YOoq0QzaQidTgPmQkbeKcsBIrGbUGD5DDNDgTJWqGZGJtDYtQH+QRhl6PjYNSWtp4rNJnrXfxG2H7DUZGKzOhgwAb2e8/ndedvn+nwRH82yDFhE0XMzV+trxwcHlJyBSf4BZKyTuaUD/TeMAnfpzmsbRGCy2cvnWnsaZKDuvfTCQlTcmdvo92IvQ4thGHPSsGGkj/VxGI2oFot9HY3ACKI19Wr008DSfUQdbvi/5SN0HK3rqGUPFxYGzqtkzZN4ZnHnuKRfJhPATyxvZOdYWVZtZZ7MkUHvQtCDy/VRQ7bYPgDNWiM/auWptLxapYmsn00GWJmPjCGhtaOJ6C21T2naglyb3lVr0PTxpMEuuQ2kVf2oASYnt3vDUJ88KFD+WwZqBc7EKRRnJCM5CrThUxFKJpTBkxahuCXCwmrBlG7Lu3BzgpvUx+FBNj6SMWTMGKg28fGg+hYErPjQf4k+m05hqxpgKiiScGTZgKG4ajrDigjNpg4cNd2FNMyZNLTUXaZXRNVobGTCStcFZ89x0WBWpLX9JTH+SIO3vyddnl0BvKXPnfIjiGqloFyNgrHOJS8GYGYpbz9maACliKMAY74XDOcF1UsEoEWioxiglY1t5cEC1uPYClNFgaUVVas2/rXECtc8hSfd9cvBE7kgOVgMc1NkpYgUPUT5r27cUJYzhCqCw4V7pV2GaPHEQWDzlxwrMC4+2rK6hQvORWjJRIZcIjLeMuA/ErUfiHmq6wEHNiiE+Gzg2QN/VSgPv47g3pptXWsc9id/4jd/AM57xjPb/C1/4QgDAc5/7XLzmNa8BAPzkT/4kVBVf/dVffcV13H777eCFrvfOO+/E13/91+OOO+7AuXPn8Omf/ul461vfis/6rM+6h0dzYwVp3Cn7AGAo+9y5c/gL3/AypNAAOVpIM5qmZbhk7ImV5BPyUV08dKU1+LUnH7nVQUKYUGom1/f4Q2nofeC4KPKx61FKp8nDQTzSff/ul//ne3Rsz/6Yrzft0rIlTIi/lywV4KCImsUB1itLFUbTZxFgs4VeOvIV+bLrFfTsaciZFZQZ4nYQy9hp9At/JgwLryh3GY/BjASoziDl4+qUvbuiqwErTT19GGCKnBWLtjPNMmGhuVJ3fg9jzDjnxhTZd2npXAdSvm/LZqtxTKHFkuZi39NeynaMkdoKkGPpsn4ertQeJ85vY3JiP2vXYYXlgAzGzjULBt+WtL57/RwDaLYNgF3bkqmZc8aDiQuwZBiX7GL0+2vfbbQHcrf4YBTbe3GOMmE+JGzPMcohMJ9y3Zr3CdSkxj4MCl2JuWIPBmzTIEhJcOZgg5sOjnHz6hjnhmOcyls8dDwPUcZRHXGxrvD+7RlcnFf4k+NTqMI4nqzHnQhhHArOHWxwbtzgMM+49eBOnE6WPhwcMEUEo3Q6bZAgOOQtRqr+u+xotCLNeEFs/AhAtZWhsWYX6woCxh9cuhmbmrGpGVsXn29Kxphq00+FL9xcEkpllJIglY2dAjpD0q6VTjcSKzhZnzuJlJkTuMPQJ1zMuuOuroCDNm3bjxQgAIgvK6UbaVpKDiZwF5iWKgC/u6dzlsZq1Sk1CwStBFQT3bc0JCswWhoRhdDSbUrNv4o3bNYMCpDQTp+9HTC1vJdiotpmgejXNQEydupKcz+XjcEKkEW2ETna4r3f+iLcdddd96kOKSKeSf/oNz8bB6evjf84vljw/Cf8+nXb14/m2DNTdxeETvfCdVQDIW2B6YzpqLjYDL4cJnuAxyx9FnsIOaCSzE1bApBN3qoNVLwV08C4QB3u0p220m9etU8J2EHCvZihRN+/RUsYPWm8eRJUidrIe7wBDtZ99sgMHB5YZWBorTwNSHOBpgPImFBO535OpIOE9mCV7kgeYKIxcg5+1Kvd0tYbPkd3WhioOumbpWTVfKodEC1TagBARVx4beec6wK4AuC5tv3gHQOcDpoCrJkTuiBV89RKG2laI6CzbJG6pGosmGRC8xlbbiL2g3r6VzKQj921Pi/8gcJkFQC0b0MpQJ8BlGCvli7mwbw2V3cCxgvOVhQDVqqhyzKgJi4Wj5ZIEhoc/37bc7exAEuxvX1XksypfjgC6krdj4taW5yWAjw2zaLMBoiFAGS17NVYMc8JY65Yp4LMFQJqlXHHdcT7js7iaB5x/niN7ZQxHY0I92wocMTAXeMp5HXBwXrGHx6cw0MPL+Km8QgfO1o7poEqKhgDVax5xl31AANVbHTAGd5gcoYKQEsJ3lUPrVpPu2BY3MTzQl1b6xjJuHM+hMCq+Kqw9WgkxeCpNibrMRkViq1JsQIqMIA0pwZY2omPRt4cYMpYpuQghlnBvo3kYGbIFTMZWGNWE9onwTSblqt66pBdw8UQSOUOtAjGehUyp3RvgNwagXsKWgo3nWNj0tw/quer0ZHQJkGzNFaIQkCvBJ67wW2r5gtNoPTVNBDk90ik87h0ANXuh5MAywFTbN8kF9r0kXYS93zEPvZg6qphztiLGY2nP4LR5uIzaRcBRyk5BteNTIx8tCijAjpoEAVvlw9uf9hsanuIIQTUS1ZHsWMDcK8jWWexKwGpFrUCRW1cSWzNko83xlA16wcGyWGvEASMmfJUWJwbWbn+R6kDH4IBqRQ9CwNwxfb7azwraGNs3FJcHmGaNHbqnRy8egUdEbhWq05qA2UX+/PCILSfCG1MF2DAS5z9Avy4yFmZoqbdcl1WMGJA10wFWLLv8vKBN9J9aXZW07VVoZkDgLQApDS5hovs/0rsrJctE4cqqWvUAsAtHzxxPsKWoW3Lr8c0K0QtlSejnfMU2qhs36d642grRSezmYgUebJ2J5j7d2sVsaazShvFwZ+Y7jBYMCUDUICxVTwDsrVrq5zSxhRIIVQh3PGBczg+O+DiesTN62OrfiPFHx+fwsXtCpc2I6ajAbLJSJeSifQnt6cYLBVcLmWcX424MBzgT06dws2nj/C+1Tl8zPoSTqUJmSsO04xjGbGiGQNXrHjGrAmHPLXUyRJ0z5pwJCswiTFlMuJCXWMr2RoeS8ad04GxZJ7em6q3bfL02qZkJBYkFpTKmEvyZdGr+ZYl+ws2CgDyqiIlacDJHEr8GlFCYtlJ8aUk7fwN2dKXkqyykEjbZxUAZcEkZJWFQtDKHcQFGBFzKQcADLVpuFpKMoCfox6aGZfZDLD2JsYVZuHg14NZkCzuXbfcsHX5TRBZxWCEE1pV3xJILVZhpzOkA17FZ0yy79tSTH+SFbyOIaAdF/17u459XJ/Yg6mrRB28tRyjCWta+bjPbCAEyjHj77MigMDZKvnCAZu3nR3RgVuVGHkpMc3SHuz2QLW/laiL2oHGSMnVHJHvLhIbKzVb3zxStRF26Tl1hdBSjK1a+Ez1CjbuKUCgvRZeWGE3ANjDNAdbExNaT1WlY+n6NC+lb+m2Bkrs6c+bunNOzANLG1hrVXeMVqZvG0UHbe7XpbGvCNAhzaKh6bxGa1lDzjxBzSaheVq11JZ076sFGyboVW3R+oaqdhG5T8p5kp6imKMxs7YUoom0PV1J2q6NSAvK2JdpgLP0ikIuvX9gsEvxXZB/P21/fJ3EAIV19uK7hAJlZUCMiECud6twUEdWEm/3T4DQENf3Ss2WEvfmxeXAJypuUWGpS5u48OypTgZkm1ALoW4T7hTg0nrEB8dDjMkYquPtgO12QL1rRLqQMBwT0hSMGnydhDpbukgKQQfGtjDuOBrwwcNDfPDgEI+56YNYpxnH1XRYA0kDVyuagcGAU1gomOO6NTLeyIBEgruKaRJnTdhKRpGED0yncFSGltqLzxoLZOdr4IpZEqbZ+vRFak/Vq9pAiF584SJOnkpLWbAaDDVUIYzLlF7ookhRKiMnab/jiRBVjjkZ4BKxZSLCpb0zU86OqYMqMaCjg1rKz8GN+bIBMjkgmi0l2OYzy4qKSK0NYowUAbTlts4dHVOEfyZsDSi6HMR95Q3FT3rNNTaLAbM9sW0Q0Md/+GXqAFaTAkmhyzZT1zFCa3et69jH9Yk9mLpaEFq/qaUuJWZWAbLMjoUgWV2v0ku+bdZOwFZQTydntHrqp2GZrSBtq+t33HhIAJJqD5rMgAg0JRCsgo3ne1qXgZ6+YwIRd46kVoStA43jbu8967LqB6T2UyowcjfyHIfdZYh6GpKojZStaW9dGAQKWlPiNEVOK0q8AZo7MCCJCsfF6BviWE/3hT4KRA14XHYeFueOtLN/7XNFDQDEwDx6b0Gi1hewpW5jv67wfXA1BinNYrouP440KarbQphGCp2lmzrIbrYMAFiMgVvqpQB0A03A0ovhrVUV1b+Dy/QiJ1MjDNAWTVMWabg2aycDOHVEMz48aW6YJmOVLFXVv+P5kHuV4FZaexzxSlmFA1+y+yVtrE2L+brZQ02yWT0k795U1kA9BOSYUQ4VpY7YrDLmg2xaHi+hp+OE4byxXsMlq15Mk09+NgbcqAD1wMwfRa0iTwFsLxI+sM2YJeEdX/Q9+JJfeT4yC7KDqUtlhZuGIxzJiHP5GAzFwKWBKFFrhWPMk52sYxlwYV7jzmmNqSZMNWNTMhimYypiDFB1w05VwnbOUCGUORlg9t9LbybrX0etKg/ogGkcSrNvOEnAsm9rLgnrwaok1ZmwqJzcFBsEj2f7bcBPkdl8rCqrATwHWlRot2SsGnvWGB5xs00AmHrFJxbXpA4wwFK9YCIsQ+JydP0UT4segAL73lNPb0fVXkjfeOkHFcPTYl81ANVyHfEeOcjK/f43Ub9YheE+PupjD6auEjIAnNFm8VE1wrM/QNQaElt5P8ADNZ8f1Z7mANDuStOUOGMQ6a0YTKqCNUrwDTBQ8eq1qd/xytZ0+IqVYh8uxhHNN0oV2Ir3DwwF8sImIf7O2f6fZmegUlue5tLAzE7UimWftmV/uqU+aAmkeLs7IEUPPxm6fkuZwM4i0Vy9AbMDPKVmXwBPIzRdg1j6rGmtnIHS1MHQyTDdEZr3lSQCSwfCVLWBm9jXK0Xo25rvFQcrFSL35TF7tZ6G031fnrSzXpp7ypGqJZlM/9HL0yONac7xaKakpnNyfVo8wBSoa0I+RmOm4gEdOpImYifsPGRMnO6NnktPI6QJmFe7wE0XacbYbndCd3f4jH6OZm0C+OGith6DdSTUC4RyAKQNIR0nyJhQ1xmUzFCVCpCP7GGbLwH5GK5xtP2eThMyAYUBddNWVPucOgUtrLhzTnj0//JyDAcPw7nDY9x8eISRTac1ScKpPEGUMLCAq4BJmyZq1oQiqaX0ijAuzaOxTSVhW2z4zUlMD6mE6qk8u42cjSpszYyFTJMEoFl5RNqPg4lRMCvGoWCVC8ZckVga+xUpRACYa8I6l/Z+Ytv/wWeDmQVjKpjF/LUKGDlVr/Cj1ss7Kgi1dHBkHlDu2bZlS8+GoWhYMSx1gorGZFKhZuTab0g0dpT8eyJn5Vo1NLyBfGPJAMkO5HIwo2jpvaVLO5ZDdaRP415o7/n+O0qjwaoKiS4fP65H3DemnXtm6nrFHkxdJZYNLwGgNcmNIDc5LOgNaZM/aI8VZW2u1VGmvtTMhIeQGSuKzZgYwFbM9iDsFObaU1Bjtoc2s6WKhnt+U7z+vT+I2x7zLWbGCVjD4jYkLg5tHO2PAFTxW8QbOefdNFtEeD21YzWGhJquzD/T2CQ08faOSHkReZ5R19n9nGo3zfSQIYHnEMEnpG1FXaVW8QegsX6xj6bp4aZV2hGnRkrKgZQ6oLD0pQJu1BkCeCUAmXf3O+wYfMU7flJEUK9cXI7B4S1lBwVn1iz1GyxfA3EL8BZeZpHC5NYYegHgHVDpSA3EwJ8LMYMHoaUAg6Fbzu7FRexRDRjrtn2wSUYARCUzurWFPNXqbv7s/lX2Xk8Z1jV3oOfpxthm+G2RP7Sj8IMLIU0GAiUD5bDr3OyhbIxUPjJ9W96YLUWc9+1NBJoBOeUVXPHgDPqsEuDpv0kIH6qE88drnDvcYEgVZ1cbHJURl/IIJsXozNQkCes0Y1MHTJJxVAb8t7vO+SEr3vk/fQ8A4M/+tP3OqSInAzQi1NJ9UKDOvMtGNRDl/wss7aUE4tqAklUCMogKBq7GnKWKIozZDUIBNE0WOYjKLA3MTdUqD5kUYzbkYc2XDbAloQasEIxT6fvHW+pmshN7JR6gwyL15kB92ZqlMaICE7MrTOQdFX2su3q9eVGQsbym0b9r8nFYqG/Xqke7WD6uxVgkPKdILPNguXCfuKVwjJcrqCCvT4S+7lrXsY/rE3swdZVoExBnVHj29J0ubtDS2F4X1vr45mX1Sga04sFgGhlq1Sw2iBDqipGOYeLpWUBbqw6icCpnAk3FANXszssnOfs/Zbz+937AevrVauvdAFpKBwc4Uc1nG7P3F/39GlsWbWqSP4m8H2Fz9tb+MLzSOQ5mp7EuS42UojF5PFslnwwJPBX/PC2AFMBTgYwO9BxcRGrS0nkJzfkdsNSpP7yXaS/J9gCVBXtoM1RqwIu471vbRl2CHO6zXfLz1NzOHRgsl1/MjHnuwnibKSt0YPB0omDAwetJdqseRi4ObR+4KjBZem0JIKM6L8CVJAJXAjbSvg8ZTXC+TPG1cxXWCXFeqjYComnOXAi/dPdvzB+hnZelNUboyuzzAOAeQtEzTYG8dYZACOUQGO8C5tNxHuyeDZ1UOz9x74qnfaIlmdqbDajE8pWhVaHVmBg6KLjraA0mxfE8YEwVOVWsUmmptcyCqZ52m4MBd14yobkKoS5cy//rl30nHvPjLzObCEJL04kEmKLdVjF1F0hZnzsHAkOYdRpTIkLGJtWEVSqNPZn9SwwmKoJJkVkw12SpRvfBijDBuiJxwaXtuON9BcCATlJgdrd7MkaIN9QAsfgXx0dRGOETKADC2pgm5QWiWUx0dLB1ttRefE9sgKhdx7z4nL+vzli19jOEDu6SYlk5CwCy6hdBc3EPoB0eWAv/rH3sYw+mrhb+wDFBb5uQuB7FF9H+etNJsc14QpBOU6Ru+rKxXnatCAHWFJnIqtim0lNo89zawtDxBF1l6xk437s8/bM/5UVWnXeSWVJtoESB3VRfHG/sY+SSqrgg3dfBZO7EqTNnzY07TquzVuElFcxJaMCoip2L+A7g7Fa0d3HmrruoO5MHA2U8laZvinQeBM2GYRn1IHvbk3iISwNSwWRZaot2wB7IWCcqfd81uyBf+3LKtFNsYKkFA3ZE3SMqzguopxw1kemw/HoJINUASr4yM7nsBxiVSd0ItQMi0l0gZevulX3VKzAlU0uLcDG9EtgnB16pFQaoMlhvwWhfE1VQrf8e+ncPGKiyxse2ftNkUTP0jHtPc/+8HWPf5zTBPa2MDRsuAVGbYfcVUBnGZDAQBoux7uV9qxwsD5q4uaWqJoKWAe/+2r8HAPgzP/kyTNsBIMVqNSN7vzwATahdSjLJYeidtglUCY/6p9/fS+6HtGO0yUkhlXo6LkBA/IbrjWJSJ4CutGEHzoJa3M5AGSLaUnzLSCwYuDb39bS4SWcxwbtV8dklbSyXCdZTsnTgXOy4rDLPQcywqKTznzi3HK2EOOQSfi8lB0PJq4dDAA604wdg6T/vy0dwpsqF7nHNNSf+YdE9wucxrb8e7HsX6tuR0dtDuX9VWDQ0IJUXs2j43Cgw1v1E9sh9kOa7VtPPfVw99mDqKhEtBEIr1UBV8UFYgF4+u/hcsBiLvnGtx55PuNj7y1mqx2/mTNb6A+iapuqapro1vVII0+eKN7zze+7VcWlKBtgWwnDbKHXwFMtOU0/5MUHnebewNpiqaG4KNNZHBu699xZMRlgj9Io7sn3x90gVVEtP1+TU9UHqAvMAVpEaLGIaKmZzMZ9NUG9mntxE+3b85lnUDmGVmmt97xVIbaAH0KwrlhGVhrxoG2SsGnac0ClE+tEYd64dEC2Axc45ap/b3WZrueNANQbzLhSnVqHZtB6ExvIsU2ZhUnryyIIZo2qAShaXhGSg9V5DT8NFCyB7+BDKAvxwMW2hDOharQB3i16HZkNiB5JmA2at4q90rVM8mHeAtgCBCuvKNURLvyEHf93HitqxpC1c2E6YVz7riTRRY4Lswcoz8JgffBV+75teiP/fX/qf8ej/9eUACFsdUXKFCKFsfUhV3yeFCaMFVuLv7X0IsGukABrsGAG1oO0DsXaTSjZkoLWRaPb9rvyi4ZitAZSk2RwkUlyaRuQkrV3NknEauGJMFVNNOJrHxlZtS242CXZpaQOJIp4OdgZMJQAITOrg7WRk8GFBAA4dlC6uc1ZLD/p31FrJqL8X2iZn99sP0Ng6kj5Za1mE4oCMfD1xbVMAsna5tCD/DClAM0Ggjf3S+D4Z2OlUEH/eT5opUd5pR3Rv17GP6xN7MHWVaO0I4j7xWSAAu6kARLoB6DPcWLaNVwp7yJVeidUF7eoAYrFdbxVDqMB2a8LvaOUyDCDpM+B7FQzXX3XmiVKCYgGMPlwsbRRE/MdynJpGZ4768UezYlKAEnaOmWdpYnCQguZqPf08vQkX60O1tagxb6qOPBqQik02gXw8aNDWF/qMnfY2LqIOoNVSbp7ma9+Xwu0L/AHjjKVZJpDrsZbphgVYIhhgW6Ud53IduAPMwQxcSc3XiqfamLulRm5Z0bcj5M7d6wqjaZBqA7S0w+60dcWDbPGQqwNZ3zkA0XqmWRTkOGafRLAzR8782LlAS+8qOwOmPdUdJqOS4Zo06qnZmMBUIAxNJbuv1cLQNR6cIGOz4jPDJW3HU8dFCtS3XxfNl+M+TZOBrHwxoR5IAwaaFTRROzdUzWH7E1/xavzut70A7/nL337Z+fyEH3uFmZgGq1Xt4gkWr12jo5gmK6mZiAYYIQWEoSQm6GYBJW+74hV06oCAknrrzAVgEHMkF1FMc8KBg60w3kwsmErG4BYSUKtiBAxYbcrQwJaI+RqpUjPzbNtRdHuWdo4dPA4CzNYFIDRJkn0yUegEy9jvpWaQ7CAL3gswxOjkY2mw+W08Xs4JFyl78uMG0LWC6HKNSNuZBgtNYB7Xbpw3HbWB6wBn5Ho6EcJ/+fJvxbm/+r2XXQv7+OiKPZi6u/AZUVQsRaouZvjqoCoYpSZ69BRePKBVFjPsJYtVwuDQrASUxNM7vsCOiaYAKzagsAAO9ziYzX6h7YQxVAR0V/RpAqqYG3owU8DlWirpgAacTC/F3MCMMQJO5fu5skGOgIXbePg7gSz1ZiyZAagAK5E2CzZLBja/KdeXtXNSFWiO4Atg6MLzADwhdFYCykHqruJubklFQS6cbt99Y4ucaXK3+rpyXyo3+uxeU6ZLC8YqjrWO3PfHiCQ/nwtfJgXqOjfGb5nqbFia0dJ9jb1zi4UAUu3/mPC79UEDIw6MgJ7i0wSUVWfm6kitks4WcNCz6AcYzG1dLR6Wi6KNYGN3dC3o10fN5IyCpxj9XKcp7jlLGc6HVtjRGL8IMkarnYNgFWtPHyqiSszBWQVk7GwGGMCR+Rhp9nX4ikKknLYGDv/sy15t52ZlD/vf++YXAgD+4K9+Gx71T77fGBe1By5OsBnKaAaWmBkYxfRQybRZJAbMKXUgxDnczgHKfZbXT0GgUHtdhCBue5CTQEQxL9J9tWSsc9mp3DuaRxe/mzfVskqNyNrNKCxFKGI9E+vU9XmUBdimDgqVLH3mu0XF02+Vmg9UsD1dAqHOTu1q+5rgPlbtVZuG6qiloUktrbu4LBq7tUxjA2ieYzb++vXplgxtmbjWY2x3v6vGqk2Ex/+zH8L9ERWEepKuvhfr2Mf1iT2YukpI8uI0B1Qt/76YjLGnAtsLgFcLuQUC+sMYcBbEK8H6DLsPKjvhqSEtBZRX/fUh4Q3v+Pv3+rje+PbvxrM/7btcMK7GTs3z5QsygdKwk+rTWs32gHxnk+uEuP+vzCinB3tgDbwYJMl1MWRNgAnQ0SwDeJKmu4n2Llh4RUX6bWmEybMY4GIGivSqR+YGiMKQUxM35khcWN5TuIp6kFpaLM22DywdvOzotRapNBncF+gwNcd2zT6wV23bi2g6r6b3Wnz/sUz0EgzdV7yeCci7y+uw+JyfRxlCuwVEj752bU4OrLg/YEJv1FLTCq+EBKJidSn2jVl9aFECHER6uzr2Dr+gNHWmANL1hAGww0i06WsSvELNv6MVPBVOjekzIBaMkX0XzcXdQVhMeiT6CPq6JY5FzWdqycpRhQmLvWosTn+AzbSxZVn68fJkoPgTX/FqyFrwe3/77wAz77Ib0r/H9iBuaXF/KLOa7xLQwJdK7Jx/jtT62sV4EuDCNVXkbWMAtJYxoX1KixYyIqZ7CqYqROeR1puVW1PjcF1v7udKKNVF7IXbvtEgBmqyeNVl92TSbK7ljABY0lm7uH4JLgT3FwJgKRw0oV+UAWwqGuAKIB7XaBQFkadV47S2SYH2121s76A3WLTGSFdqy2uAqKgAVDJAfD/EPs13Y8ceTO1jH/vYxz72cYNHxbUzS3t70esXezB1TyJm4/5nzODDAqEJlRXIi4ayMUvlE6xU097EDDYT6GiXqWgRFW7L1No1hBle9nU11/MSU/zFTVtKZ6DCif1kyi8xdDVADgdjKFbdpFKGrh2iSVEHgq6subBlPLj3ogs+fSm2VoCqsS5RBRgMkQaL09gxMyU13Rmw0xB6UUUWTEw9SL1EHzET7W1swmwSQBuJjD2zZcVZKSQ7TmVzD0ft6TZLK3JLP1FzRAdietx0HguBfps9O4OluacRzfG9HxqFHxXB92PxnjM4kqKSqmu2eO5i9N6Gxhgcs0NYsAepX6vL6tYmCkdPp4SuqnmsJXJWShtDpERd5Kv9OyBfX6+epbZuAK19k+1TmKD6efLLRwavFlyTNSKP/pDOWPT98m06K8UIlqSnOSMdtBRAt8vUneHTFiBhPObVrwK7c/sy7bZTYOBsVGuQK9T0OMqwargBCL1lrIMHMbbOd4LgffiCnUna9k3EGCT1/nlEgoEV29mGfCmulyTzn1pmIktlTHPGaizmxu66oliXCjXhuVUdmrZrcbk2drBVww0CFe5s1VqMAYxYGGUCi2shWJ8FY2W+ZgwZgXTkLOKiwhq1Ly7ZMwixzvhaYrvav5u4jlsWwtenIU1Af69ZNCyeCfv46I49mPowsXxgtAfF8sbVRepj8QCUpL35cayLu4t26IHsAaBoRpfuibQDmgZrFKhjajYA13RMmT0t5QPbwj9KT4A1CpCi2jVcS9sEwFzSc4Jmhoy9+g4IWj3SZf2hak2jXb+jBKmKtBErmR52hcNWMefi8/bAJcDNK9X3pZ2bxfbtfWoi7V6BY+aZS7F5o/ITwFvxfe4moUs9FFVt7ujRQDiE9gHaxLfbjCgX+xNg7uRIrImgCz2QgcdFWojgCLR/hrfWgkjIrqPQE3EFdAnY027aA4Dr0Pq6mzA+fi+9paISLSYUMZGY4FV9i9d5Fxw1gLRstqyL7WQ/E14h2+6jRQrHAJBXTPr91SYiqd9rTYDugF0SGvjdqWBcAKtIFbV+2IuJEgjWHDe+rnjoalQHop1jnh1ItWrU2Jba3wEuovWL9OuufU850nbatqlKV/UzokgBshWn9EbIALvuKpolAwa0rN8etTYxgzubh58UABwdjw7SBFK9xY17YNXZ7zl3wYx2Mip9ZqIEOwbYcUv2FFnydOXgaEQBPZEqaynAxbmBoH1BobM6ORlR9GsiUrV2D/ppL/27WwKmqPaM/d7ZFwdUIVgPcMazf3fTtbFFf9rYp/lu7NiDqatEAzoLABWaGqD/v7RIaANve2DHerqnEuD/C3qfN6DPQkXMFLMslJLjgB2m6BpDs4vQc1owXkYP0N2I21Wkva+1GqCJvnsLkCetWqvvczgetwEr7Q56ygRZcdMBqbdciebCYWJpbzqzkr2djEh/eA3cQM+SXQJsPeYl5MdAfWAF+m/2Jsa0iyt9vxd2CEBjmUI/FMcvA5lGCctZ72J269urI7tuqT8om3bpROy4xIdWKPynnEkh12rF8QXb1KpHqQPIYI7a52P7ASxyZ16atmgxsYhjWRZe2EQCDXQAXsUe23SRcLNcOMFCLZuKxzabmNzvNTs8e4ibfpqMCaT+2ajks+84TBrRHs4NVMVDODy4FgxUbL8BqMVxxHk6+TtsGjQvv094d4QTVWOCHTZmua7FKw3QAB3MpLHuLExsflacTTdV52SAwxmlmszJnEnBSTGV5FoqwToXzLXfv4kE41BwXMcG5NreKKFMqYMmV3zr4vrfuXbVjpFYrZKX4NYJcFE9O2hU6JR2P7sERd6QmpZsVjWBe9pQ+75iXG73srdSaoavQG8Jhv59KusO+D8ZoSVs7OfSkuHyJhLXJfaNjm/seNCc2Ze+9KV4ylOegsPDQ9x0002Xvf/Od74TX/3VX41HPOIRODg4wJ//838eP/iDP3ivt5e2fRYD9Ie/DLvLNUPOYmm8AAAxEJe1mzdGGkfCjDBmptrTT1tnfFrpCIFOn3JWasAb/stL8cbffMm9PqaIeuAGML5tutLo0U6EGXUugZTtLFnaL6XmLaVDQl3xZSAkUke7jIi9zieq+uroHk/JwFWkrHba51CvyBNn62SVIesEWdn+yiqZ8HxMkIFRDrOBnNHE6Mt1xkMwGv1iCRJbGklA2tOQS+PRBqLD2d4/HxYAff2L0zepg3MHSMHmJOpMV/v84sQJ2vWyTNPagh0USaK2blsY6GxXBxDBqKmDoJONjAFjXpcM6w6AitRnPCgDAOUOkqJivhdiwK6/ACj+IG7Lc193K3Ff7Fuwh33/7RjmU4S6NiBrNgjUqwsXQCuExW3b/rt9P0tA4/sjsb1gs9p2/XV3ZpdBIWsTWGtW6EqgKwFWFZSl+0YlNddyNzZteMVF5gEwADRGKoAUJ6v+k0rWbsb/BgyoSDFw3/r5/f/Z+/d4y66qShwfc629z71VeYAxGBK+GKTRxsgrNMqrIQkQQrQN0IICCshT/Eg3iqI8JAnSAZ8Q1G4RxV863Q2Nim8EASEBIi1EKLFBEJtoxWi06ZCEqrr3nL3Xmr8/5mPNfe69lbr1Igl3fj63bt1z9tl77cfZa+wxxxxTnpNQzJuJWFrIAM5CmcA8UXM2P3H3XECQ9p/jmgScFYL3ZdF1O6UHBSYGTrgxUZRlXQb4SAGVpSqNxYrVqo0tZm9V47/1eiorwQA3XD8Ttk9/T7IKui2x3SDZXuZ2nuN5Yfs8eYoPVrSwI0TaCdyBmKnFYoGnPvWpePjDH463vvWtG97/i7/4C9ztbnfDf//v/x33vOc98Wd/9md44QtfiJwzXvziF29/g3qDz/PwRLv0k4rd8K2BcXw6lS910tRITOm0dinsbAFqbdViBlr6HliZoZ60ijo7eqeKO0LdNZOnfE3f0QKS7jMgF9N4B2Os3BWdm5lkkea+rklhnkyQ8h/5VWbJneDBjc2qHSEvKqDaIjOHzJZ+M/1Mn8C7O6n0SwQmAWE2DqrsfercAwqWkjOmQF40IBxNOpO5kOtyaai+n6k0E9E6S20fIwupTubONilTKQxXsEIAHOw0XZ06rGu7HW9fY4ewTP9v2izfP5/cQmoNDURMJp2Auwwo2Oc77TUZmSL3mjLPJrR1EAMYxXZgEksMLsKERnJYJscQ4bvkJeoZKJ0xEG1Cr9p3sLJNjvCJ1VN5cR8ViDk7F1gnG5+PM0yobM3PgeaRxG193AmQELCoGqiu+noIDHRV2BwHb3JQObEDCrNFMABSx+TtYkzvFE+GAy5LmynoqiUhd6U1Mk6W9hOqpVRSkKXXDRNyqljpgMKEE3fNAQCLocOQGIPaMzi1p2lK0hSc+USRmYgmRu6LViZCUpE1yf4F8RIRi54qk2ipLM3nmrbA1uZwIhHOWzynNHnLD5czpgbSEtr5InZ7BvKTrOuxyj00ABUfpI5HMMgbiR/JOnbi2MQdBky95jXCyFxxxRWbvv/c5z538ve9731vfPSjH8Xv/M7vHBaYmjzFmAcNCVuVjGUI1HaMKMa132lsDNR02alJHNUqTE9SkJISxhNnOJppvg+8T1piPP7hrwUvOjHt5HlbILBNKFX+jt5UEXClBrpsXxhw9qe9afoScrCSlmwBam4eRWaEuSw6LisJea7ASXsgmt5syl6pELvP7udVViWlZukvKLgydoyGxizG9dhvA83tyZmmYAJ2XYRmrQSgT2L26euRdVUGeNb6/1lPOs4kTAe3VLDrtUhXEtrWxO0CwshEkGgAn6mBgQgs5dy14xzPmR2v2Ih5ItIN2/AUXVjvZqlSq243jbZuaqJnknPUXo8TWOkhabOqzcINhJG8l6owSVYi78akuiEDk86WMsCzBiLt2EtLEkkvuS+SjTOsz5gTAGo0CdcDUWIvuSdiGfcYvxjtgDk7AjTbAxYgRcRgWjpgpk0y6k9BlLA+G6/hnNjfB4C+kwbLRMBqN3hPvvWhRyIxBDXmCr3kslKqGBKjjAl1SLJvYX+k752CE7C3xbE0ZddJTVqpwp6VkoTp0uPDgKTZbF/tPktoIn1q58DYy9gU2wFwPNcBPNkxkXPdLrrGMLdjJjezcCg5vIcwDxyH2Enz3b7jDgOmDiduueUWnHLKKQddZj6fYz5vQOLWW2+V/3C7CXtFR7iXAfAvLZuvT4hoDld7raDKNDGrBNo6CQCZhsr63+UsvfhImiGf+4SfwVXv+YntHYSDBBMBfca7914OAHjCqS8EUQJzuHvkNGnIa+PbkBp0o04FAwpm0oKRqmhbZHJrIKCsiKN0PKZePdYDeR7MF0cFVwngkdwE1XRQdnwjA2MtSez/VKSS0MFTOJ95zXrf2aTNfozcnVs/ZymvGkwx2zG1G24APMpyJBOGD9XZslSgejUIi1WmYC66qMfx2DYmYEUrJ2O4OaWl3YylYjve8FS1aaXsOBh759vUSYYG+NO9g9/a/u/DWWCqgQqAixAmr6Vj50DNgSf8fJm7OmcFTsr8VgVTtdcMlE6wzfR0ep1xB03NQ3R61Ji0OuNmLpmrAjVu33egCeWzMlEQEOCO5AYmUmCYCJKOM2aHAqtj41LAQOqOXsfU2CpiT92RIVEVspuGybYnLFYVJgxAZ0yXptdyrsipImvKb146rHZyYfdZwE6XK/pcMJSMBJaefEmaJNeSmsloV4GFMcgMmlUHUWRpTZIWN10WNiyniqKu6jUeOzAqJ/WhgjBT2lPP9FKmWbILyVhHv+cSJvcBP7apXaNlhmbMqteOLGT343hjxgRwxczDRCC/E1/VcacFUx/96Efxm7/5m3jXu9510OVe//rXO+sVw7VQpX2ngDYJLadGvHlrMIxLmgbJ6+xPUpxIq6qEGWEGqEhLFVhPPkBZn6QTJksqLBEee97r8acf3NjG4nDifX/2k9N9Vn0UMbUKP2BSWejaqURTtsreV0aKM5DXq5fc57mU4BuIsOqoqiXg4kYdtWXwgy4PqOSu8nUlAYVFsE4CcsYTspt6GnilkVFnOvspuDHHY1LBPbi5qrueLTinp7EuMU/sbFSeF4yrJjJqd1UHyLoaF8FXmWglXSi9y9yctDIwsoO+CbMU+/wthbNxBAwndqFSUIeVwnWHMJlYKsxeiylA2Dkw0BlaHgU2x/9O7XwtP/hGp3UA02cOe1BZJmqSHiv4IZVNdQG0mX6R2j7VmWxgw8M3QQxPuX03LeVa+jbJ1twmWGKgzmo7kS6qb/3ykNTd25kYHS5D01ntxpG79qBUGZ72kou7PYzJGNlTZawWBEiYVMqBoIr8wOrojcoq7LpZQVFWKylblQNLlsAuSp/l0VvO2Pt9bt9vezZgJnS5YiRGynrZJFaASaDVImCv022mitwJiDJd1q6VAYuhAxFjsZDftZALv1MnfQWZINYIDFiTYorHygXg5HpWr7o0MG6avnCdcIazhQ3st2vcHn7s/h9ZQJsX/FongI+TAL0yTfoqHu46duLYxFeU87v00ktBRAf9ufbaa7e93k9/+tN44hOfiIsvvhjnn3/+QZd9xStegVtuucV/rr/+egDtSXqSsiiYPu1w+B1pZVu+hkmM7O82KXqKprD3Z5Nlp0Dl/Ve/EmleWqroGMSF3/QTwO5dDcxBwBVREgd0FaEDECuEygKy5nOosY1PvgYW8sDNq0mvtO5AQRqqi/VNJ1VWqZXW19BmxwCVnQ8DCsGuwEBVWZFKvlQkpSeeUxsBhWxDfqdRNFLJq/6m58V0V/JGS3WloQJV9iHquCL7Yyk7aykTI41VAF+drreJtGlyPAF4nz17zVrscKcpToI+3pOnPuLnI8DyYxqBzlJ6Oi/0gSAClADUPELa2+d6Ta0UA8v6Y8JzYwMMOC2nzA3spAVaik9tDhgyIXIPlFUBUWVVBeEzZafMskAnUe7kNbZmzSSfsUrFSWFJgoJsamkdYmWh9O+oCyIBT6kr3ksPBORZRe4L+tVxmnpbukdM7iOmzVnPYhdQBWxxSeAhta+nrUOX56U+eZQYtSR0fZk0K7bJNAeheakJi9Ihp4oTZ3P0qeDklXVxPgdjJY+YdSNWuqKgDej6Mh1LZqRZ8f0mAvrZiK5rIColqRK0sTATcleRkgA/SwWSHmPS44wlAOASjArQMGVnYxpuKuxvy8g1R/pgIOeQFKg6iLLrMTxFL1tx+PHe+IxzTKIgHZWfnTg28RVlpl784hfjaU972kGXude97rWtdX7mM5/BYx7zGLzgBS/AT/7kT97m8isrK1hZWdnyfWdKRr2nhptfnIgA+YK5zsoeapd0UjK5oqWPlhgu4eABrK7o8tnX9/4PHR1GarPgXTO5b6Q5kLOwT8ueUzmLJUIUp1uvwCStYWio4NWupdIq3CTSPZqU9RAtU0aeV9ReqrGA9mRdZkAaGqjwfnqQG24DG+1JkgkYV0VXVfpAKdq8F3yiqGBSTRgZKROKQ5koMkDELA2UtWIvDRXcSTqzmjGkMlcOiJaugTSUCVgyY1JWxs2HEMCRg3E1AnWBdJLUqAHKqK2LINZ/R3IjsklxYrRrcouHWJ+wwjJW7UZhcklFQUpSUIXp++2AhLHakz/abx+/MVm2rUEAkdsrhHmCqgAlX0fcP57umqcqFcww0JgKA5JqKmmnBICkuHqdjK0iLeiSop0BgFYVV5YYJkIDUQyktaY3dBaMQvsVWvoJ+8ggHwPAKGNG3xeMJaHXHaIkrFHWdjNDFfZqpkzUCf0C66XDShbgU2pCRxULALUm8bBiQtcX1Grf2XjwGV1fJC1IrU2NnGr53Wk7mxr2iStcoA49zoZjPfS+mkZpIVRz++5PWElu1wpYlmuyjHDsbPEkgMp8zAwkOaDe7LrdBKjtxFdvfEXB1KmnnopTTz31qK3v05/+NB7zmMfg2c9+Ni677LIjWpff0Kul66YTU2Sp8sCNBciau9cvWMvjS7VOsieqxJ5aoSXQwppui++97yOvOqL9OVg87pzXIacEykkqCGttaUYAKGWS9nNAtVThl4aKcXeH7JV1QLeuE4oaZhILK1NWEuqM3D8qL9ipegMOaVCWRdmr1iNPtlczeeNeoM1PtZM/Jk+swaPJ2CATsi+Hib5jUQANjNpntB6AU4GQNErWCXVU8GUTvPYajBousWGoKLs7N9msCZO0RAMCBs58hJgI4pf2IQ1Vq/ms156gnlgpZyyfj9/AqI7BAVYEI0sPDqZpWrYykD/ag0gNh2qynZDKdbuGwGzJccXEFJSD5xX38v1y2wTauB1bt2u27PAtMRjGqi1nQUQIT4BV/akQnfrpAfF0XNhGLck1TACEYaoyENaJWl6XDZlEwI/JPAEzls3EdWcGRqlcdXG7MzhioEm5ogwJKVesr/fIXUUdNEVXMhbE6JVpAoBEjFvnq8g6XgNSVt0HALNuxKJk1zxVJvTaD7AaANJ1WWPkShz0WpKuz8QgMFb6EYkyFqM6tSdtprx8rSVGGqUfnmgXSXo+ErzPZMwOTAohwrVOBqqsylKBM7gBJr9+I3BiTB4glrWBxyt20ny377jDaKb27t2Lm266CXv37kUpBXv27AEA3Oc+98GJJ56IT3/60zjvvPPw+Mc/Hi996Utx4403AgByzrjb3e627e3ZRJhGOAPA+roxVOYIXWaEfn/FuEqTiUKqsxRQRRGxVaEBLjp3QMXqTF6lHB7DsU/IcybU3T2oFAFUFtrYGKVu2j7GPz/L4FmHOsuSfmMFLwxnYLyKjgVsZFRnmsRUT568kzYarp2m62YAsOQgrusxDZVVEUb9lYvRuQGoaEkhKTaegKyYijNjTLLzACDNRaDLfZYUn4JHW54qvOULk4AaAwh5rOqIPr0OAKjFgu6jHT8GTEtlqTxngWjKqLVJZAlULQTAOfCKDZaxETjYewaO0sCtKlMnmpjeWH7AmLr965iqTnhLm2kp78YIkeqvZOOYVFF5KAAjABjbeBxsKcAz1wQfCyI4DfqrcAyoYtI8ekMQvKIMJiQ3AThPd7LOkwjTU0LqqntEgQk8tvNgCM5K712naaafCxKnbRv3jF2eR52CKks9BrGeidRJU5ZcCQzCCGGFbFJdDB16tU4AgMWYkROL6DxQQkSMUpP7UHUqUjdmKqWKnESkDkiVXhZHVQdkSXVaRbVZpST3vCoQxivnisJqv6ApTjJmb1SGLoLuEY2M1ft0PF/GTIl3lOxPlA7QGAxWDXTp8fZ0XwTZAYT79XWcwFVFQj3CNN2Rfn4nto47DJi6+OKL8V//63/1v88++2wAwAc/+EGce+65+K3f+i383//7f/E//sf/wP/4H//DlzvzzDPxd3/3d9veXhqbXgRQ4JRERxJTeVbpVVbIJ8TkOiFqHkrh/1bOT6VKhZgxIEPRNi+2gePzFMFZzC+zlPnIi6UK8zSOzkrZ7zTTsqecgb4Hdx3qShdEzss94NQrSfdZIsl8aYyTgiwCIPoEBVSDzPxezRcZBP3JA4vjdRSfQm5yNMJTigBczJ/GKkCWTDDfmC/AgHSdvtbnUG1XwVBndrbxJgHDQDMWtH58IyOP7Ps/aUuj6UOAJhVvEuTtdWwk5rm1UUvCDkRjiAu0VjESNR0TGvjwiSRopFg1Z2Zey2gPEP4UH74fk5Ramg7PWRdMX3PLhk0mJGL7jjVAZPs/+Vt7PPq4A+Nk10tcJwAF7aLhjmnONBdX7VQSas+ClbKwIzSqCFpF0bBqMFuxpeOqeSLJm7V08FSeAcHaBpUGmrwPBqDppWSTvTInaSAhsqy/n6YjpYoQQJL0oom5uUJTfvICJcZYBBTNh04uuzE7uOoVAJWaUIAJoDLTz2R6p1RRaxbrg9SYrlIT+q4gU8V87NBZRaOBGSYUbT8zjO3Ei/loEk+tQY+hpdzseIUqPk/lhfPrjGT4ehhYmuinAqACU0jrii2J2XLAqa1wfdu2YheH4xCFCeUImaUj/fxObB13GDB1xRVXbOkxBYiY/dJLLz1q27NJxPPt9sXh9j4nNfXU6zMPknKykvI0NuBlLEq3VhqQsr5rgGhlrIGtVpq9+29+5qjtz8GirGbkAwV1pUceivrGDA6oKOf2QGYeVF0H9J38P5MCsuYubhN+goCSmltfQqosT/QjkEcBE7VrjuI1k7R04dRSOpbKM32Urd+aFkftBODAytvCZAIqixGotc6pMkOZAaS0hYn0grxORXrfiei1euscqtVZgzrT1i5qHGoGgmDINiMjFRgkYbFoKrbPzaCwdgCZiWhP7pkl1ZQsx57aet3Kw9g760VmTGGWlkh+HAPw2ExI6yzViOY55Yxh262JuJ+FGfHvjLFQxmbFSQ0NoEVW1w+Xrd8+Y2ApfhYNCE6WiZ9DuDYM1Oj32UClHas0agPmQmKRAAhDwgAGAVtMABbUKvlie5Vo8qttR5Yn/TYoHYuBBsCvKVYXdBrbZM82qRcC6+A5oTFxjrhlg9V65mk6sAwZ3WwU8FTFawoAFkPGrC/iZVTg+ikDWaPqqkqd7oSxWgaeiAAmdgAVWTDTSw1jnqyHdZxcE+qQBUhGZtIfnIJuLJxPW8bT/Hn6uoEl/9u2q+u195gAs5LgLMfHK6/tnYQp8CI0j7Gd+KqOOwyYOt5RO82aaFqvZpn/PW1nT0VRY2LhT/Y2OVBjRxRIJWUpaKwgTR9RrQJgjhMj5cNNUgnXHVCRde5AY5FJoUp7m8lc67YNSYTnXVJLAmrMgE922sy4muu3AiEV4BtI8oo4VqPEJA1/JeVByGsFdSWJqDyEa4NGgIPeiiqDFmiGmCMjz4sc7wrQWDzF6uDIU666p1kAS/T/MnZKNq6f1x6BZD37chIGyiwPuHlHxXSbV+MZCAwgyICSs0aWgoxArI/VisJu1ADEilpE+JN10fPR6zWJNvFMnu6THVsoGAyVjgp2urVwzROh9K1q01OLNrlrKsbWN9EyLV3qBnDsO+bfNdOfhbThZExK3th30cFLuHD9YYjb2Cw9mCyFaU8NNpEaOxIAIA3kxpKcaPI+jQmYh+0H1osYgUKTA8TQ96uAOFkH1Dh0C+BQCdwrkLIWLMZM2UdMl0XNVd32rYxJNFSs7WlSxepsxPqiw+qsVR6OJaFClpnlgkXo3+daq8Sg0E9lrEnXKwaR80GqBGN/P9uujFtACjNQFtmdz7mi9eGz42epVT0Pk/OFeMx5ojHbAHYm1/r0PU8fguXBCAb2ebJNWU5fO07s1I5m6vYdO2BqqwgTmz21ApgIVy2F46Zv3CZLn0ggT/Q06qRqnj2sk7R6S9HYgFTdvYI/+eRrjtuufuj3Xzb5+4J/c4ns3/rQwF0WgTovBgFSWX54tZcUn7JLG5gAPSZ1lpDXq7Rdsb53y1Vq0UeJW2pP0oU60diE3VnaygAU+2SU5w3IpIGV5WJYmxYay2RbNBQ5fUlZtlBpZ/o1JB33WKdpOF0fp6yAhYXJYui+wsGx+VkBEAAa2Cho6tcBnDJJtadm5mnggwRg1izMSPSj8uuvylN806w1ICQgjhEtFFJZeqLXc2Dslh33tImEr/aMbGxKtckrgMRgqjhhkIwdQvs7Ct6FHWbXtqCfgpvmlq/L62uWmvNqLgrbDOPI8/b5mGY0XaRsh6aTsY5Lr46JYHnCPulyZP09DUgVeKqKjNnoFChE0DUKOKrLd2g9/2mhLvna4NrSfpQFuBDUJFT/DyhwGQEgi3VBPz2ZXa5YX3TYtTJIKo6T651KVUCFPCnyNQYxEWNYuoBGFZMbYAOEAbOxAPB0Xy3JmShmNJ2Uaab0ujJmajll5+nizm7cuq6lczcpOojvTZ4WbRXyQjUJATew7oA9AcdLhsScUDf4kmx/HTtxbGIHTG0RqejTqk0IJDdG04Zk1QNZNRHQwEOr2LIna32K04kdzEiLAlqo54oJzftOmgWfNMN5j/8Z1J5w9bt+/LjvO2sfQJqP6imlgGoYQLtW24LFNEeE0iefyJOlNhfVzUYF4MABh7AXhJj+SsrskKY7y0pquiPT71i9NEwcbRMT9Olels0LAU9pUVtKdZwKymUf9P+ZQLWi7Joh71+AU2pFAVq1SOMorNygqT5lLybtJgKzmAZlrqz5cJ+EWeL2nut/iHzfDSQKo9U+b/sZXc4nVX5W1GCWEWrVYYxhGuB+Sg7ooZN6AnJImVnkRetVaBOJtZaxCSXPZdK37QhwEcNUK9Sw1909fomlsjRjZJTEud0YOWyImM503WFpExznKWvgDzgM5PW2bRmAnMut5prlccb1Leu35F5h424g2MwlN7ApRbSYk/WwsjMGMGxsljLU5rw8EtCpO/isgktCXhG/qzJkaYpcSewYSHaAckUZE1KWFVbOICpIiZGZMYwZo4quOgVnlQT4WHouMk0AMIyS6htUf1UTefqv1oScRKMlbUiNjSKtBJTmzNZWxphUPy/ApEJvGYCb5q49bLQ03LK+zlLdkeGK25lcU/af8MDin/fzwi5s34mv7tgBU1sFtyd5+xuAg6r4pA+owDa1ZSdP1QoI0qAC7nksQYGAklmPuqsHzzqMJ/QYTkgYTvzKPEWUXR1oloWxWWNpZjyW1qNPU3y8a0VABbM+IZO7vpvOqaJVnokuitx8dOL5pICJTS9VtEGxgS2y8ndyMGbCdU7itg7AQZe3aAFE/L8+buogLkxUY6TS2uhAagKo/ODIOaTFCO66tpzp3QBJ92nD4Um7GdVTgQRYGSDhvjVJ5qzpTdvtClhDZgBqwWBjb8vEa9HZKUs/6QQk6T1dJth3yEHU11lTsJoqdUZWWwJVS33VlvqbWFvstlQveYpOzrFsL2qwHFAp/txQdRf2xUGaTmSe6gMCgINXAbKuZ5Ixt4cdA/u6b6aJNNaqBs2VrNcKA3hT7VPUbSWt9rNtoKgZreYhA2niTIf/aWkrIFwPPPnb9UMsjKCwXQKo/OCR9cMTZ3EBKcLuyKkWEDPMO/CsqJC8pfzGktF3xVkkQBgNDmkm8elNSJrCSypsz7mijtmrAwERrluRZK0CsqzakJlQVCcVLQowhHufAXJ9/pqwS3ZYDORAQWaWFdkxXS5isM8vVwlH3SWoVSB7+yTbZtYU7dK1cCyjgFCOcINH+vmd2Dp2wNRO7MRO7MRO7MTtPMT/98jA0JJ/8E4cxdgBU1tEXgAwY3RNOZgGIs/Zu9IDcDaGk6aXAGcGXHhu2oL5KEzDUIBaQYtpf7uykrU9iDyN/5sXvtHTNtf++kuPw54DtUtItSjr1Ktdgabk+g5IGVjptUJOU2hDRUKasESApLM4kxtrUmVnV4yREn2PpvLGCs5pkr6z1iluraDtaspM02ljM8R0U04o85WAdKCJzWkI5qOhKo9zFmfz8J4ty2ZOakyVaqgAiL6pFGeo5ABWZX4IZXf4ijHgrvek/wfJOkDC+kCYKkyc2TUFSuo3BTjj6Wkw5sbsOFuqdhIF7qs0CWVM3eqDxTMtr0uqsfXmgwqtKxI0ZbiJxqqskLNgwhZtvHN76o4k5beBMVhmZkzEv/xEXUOmxpozW/qF2nqX2QszB7X0oxWUsKbb6gxIc7T2M3ac2tDaMbYX9bdV4YlWDV7JKKlNcm2aHQdrcwNuLB2rd1TtuZXeWxrL0qXLaa5ZRVop7rReFhlJewHykJy9YmX0UIE6ZqROPlOQUBYJOVXXKq3Pe+RcsTafSaVeqpOJ3JinsWR0ubg2CmA3+QTktrE6G5vR58TBVbRSrCk+qjJOOX5NH0UhHewpty1AgZz3xmb7MYonUM8ZVXL2afL5yE5hma2C/2HrdJ+qnfiqjh0wtUVQBbJVLFG7kSVLwQRWPQ1y48tz9s9SrWASbRVVoDtgd2z9oAEpm5iLpLZMS5MGBq1sfdM4lvHB9/6E//8JD7pYXJ9F7CAvagWfAYq0XoDdnQILmaBrl9Dt1yrFEUCfUYJA2sBEmitgUVsCuVGyAKo+gU00HnvnqdYn200SBrhs1Oy/RDOUQMNSyo4ExBhoojK2fn8pTdq6mCjc3muT+CjHIiWpEmQBoDRW8CzJ/i1EdI+lCcAq+dzhfZOUQxO4Sqqmdgq8GOBZAKcEz2e5vqxMXdMbwJRloiFo0uKIVODeYK0HoR13AVeVNJUKBXOhR2JayCxllU7T1KMABhHEN1Bg1hUG+uJ4W+VUS21OJr6y9H/9/AbzzhDeX00n1DTAwQoYXhUq51fAh9s/jNJEm1LreBArA01c6dnlJW8t23bUQcWKQmmiDNSVquNv4ncOYwSRNGHWXoGkwImoob4a02ThtsOFPBVaKSH3rqbHOGbkXKW4sCSMY8Ku1UGF5AmjVvMlatW9UvknJp45iXXCWBJqTZj1o+ihQJo27No4k1QVlkUCjyoyH8kr9tyry3roRX8p6LGy64sJ1Zsgi+CeQ9o4nicveFnWp4XUoQFK6Nk0HZZruVhSub7MuHSRHaOoR0GAfqSf34mtYwdMbRFpkJJiE5HbhN4qpWy5BqiANlGB2YFUc96WyTgtxlZuD4gOJ2dgKEjzETT0SF1WXRZvFF0ep3jsea9HOmGG1CWk/XMBU700OabCYKogJHAP9PtGlBW92eq+SfuVdlxMjO6VeCGkuibp/4WZS9qzL4qeqbLpz4WEIKmaA0yTZYDO2BQWMNInqaQC4NWTqmvyCjpjrRIrUpuOsbWSCcCs1omondYHsUoYF0AmpFpBY0ZdzWDbPwNSOhbOrc2M3+tym5QNyLnlwcrG5q41t302c06QCMhjo+Nk69JzNNGamWmqvII0Voy72jmUcxsE1UUr5xKaN5bqu1o1Vpu88gDUzMEfzI4BWsWmIxFlHPMUfC1PBQ7KDNBwGxPQmB8Zu++ajC0YlBp48wnaQJWCHdM6idarXRvxwcotKAJAcqAEZaU2YT7qTNC0A4BKCpTaQm7cSfIw4U2XZRQ+Hja/KwawEBd2s8Hw+0hJwIpUnI6LrK1oBDhaA+Kq7V0OHFgRE1B9nZL02iOSRsqrK4O/Z368OTG60NfPTD7NEoGZWvudkiZAyiwuLPI6TYFPBWrXHlqlUpLhFY3GFNp5JjSrBLtO9No1YOo+XXZCJ9UG03Pl5yPoY4+XDKlCgOmRrmMnjk3swNQtwkBUngPdAUa3xkgD0K0z8kJ+d2tiApnnFXm9BtZKn+b96b6Gmy83hmepxpiYQYsR/ZcH5PWKfn8VsBaeZo9n1D6hrHaos04q/Ga93HxmWZg0S50Vq2yszkoBDTRwEkNPZ93GOEmQ97UzATppu5eqrVmyCdZD+xd5euXgSA69oRKQSCvJFLz1SSrpiDwVx8ooRdbJgmo7X3IgpkBqOZbTWTSqSH0QC4aYWpR16DExGwZzxq/hunFfMvb9cvH2gv0zti5jl+xYOZhwSwlJ3aWh/eR5S4u6z5f6YplVQ7dW/Lg3sKoVhzqmCNbsOs0LlkpABSzmdp7G5ltlvQz9uDF79wBUOJASYTgLU2QVtWaHEP5vKT773uZBH3aWl6/yfhPXwyfamAK0Mft5U7DDXWBJlaLxggGGj9nTq8aKJAOewLiqDFonLFedsYC3tHHmFvNO2Td0LM7rSTemrW24Lk2SlRxIcWZPn0kbHHZROpeEOs+oQ0ZZz1is9RjnGXVMbvhpvw1UWfS9mH8WNfQE4HYMpSYHUkBzOq8Komqlxp5p2xj7v9khpPXkFaB27YQCOz04mM5i8XqyVGwhP/9uU4F2LlmBqz24TtLDNrZqrJSe2yEA/03A1rEIc0A/0p/txIc+9CF853d+J8444wwQEX7v935v8v73f//3g4gmPw972MNuc73vfOc7cdZZZ2FlZQVnnXUWfvd3f3db47o9xg6Y2iLSKBNGXrD/v5tXpEHB05oCqAU7kEoL/VFzRBoZeb3o5NSAlLSN0bux/R4GYH0Bmo9I6wO6/QFQla3HeSyj7EqoPWE8UaoMuWvpSA/VfoGWPKaKTAC1bzofsNglGMNkFW3+maEG8COv2yRv4b5JDhosLTA1lvQJ3vIRiTDu7kXDRORWCAZ0yOwfYirW92WqnXJ9VGqgEaW2cwmABqkKlKbRhLRe4K1kqhiIxnDGqDDSvDqDYa8JE9SAjAEge40Ko1uT9kSSLtZrTlPH9posV5DX22sGgj1lCJuw2I+hgbHJmAPIteMe7Q/SCAduro0LOqzJQ4KBkirbdbYpsAzkrJeuf5gCF3stjcIKp4U8ABmgowC+uGvj9XNWA+gycBbWvRlDYWOMk3R8L16TtWNnvyZALbOAqr7KMl2o3swCnpAY3HNz3I5pJz2A1vsvdSU407OnzqCNgqFpNR4JmCdgnoFFAvZ3Un24yKjzjLImlFodpf2LgSpAAFYNvflqTWKL4H+rcWelYKcgnx0XGWW9Ay+ygDo772ZaWqD+XFOaatlzS8CQHSc05i61H4ppXYiuzcGzMo4OVH3F8eQuAeyh9UqkUf4fXdnvbLF//3488IEPxC//8i9vucwTnvAE/NM//ZP//PEf//FB1/nRj34U3/M934NnPvOZ+Mu//Es885nPxHd/93fjz//8z4/28I9r7KT5toik3pRpXGKbCD45RA8qAI19Ct5SQHhqHdRbqgTmgyuwLtobKfsWxievjeBEuOrdr8S3PfsN+Eqws/O7ZPT7Cf2+gvGEHl2tmjYLk1CfG6joE0qfND3Tyv2tPUztk9oZhNRT3/RJNFYBW50JznXy6NIk1YQqrFmMeI7cvLJPCtDgRqFIhLrSIc3VAoFZ9FIRIKqH1IRFDOHsFLPse2Qa7T0ieVRRgEalgIqoxislENhNSZdDwI+60RsYRJtg7TNUgW6teu8+JJqI6LPpvDSNmOfF29cQmj5v0gDZnt6hWjNNz0bD0clxV73TshTDBNbGJriuSwGRVc4LEwdMgIh9pwxA1fY6VUn9SWubZuhqgvw4LptoDUA1Ty94qb1NugCabYGO3S0PrCly9CdCW18KRpy2Htt33x87dyviCVU1bcedCvkjI8UtlYWOgZ7VzVzftsmb1JgVLIyOpiXrkJuFm6HjYt8RfeixVjhZ3sMonm74cifM14posspCvDDKPCPtGlFLZKFY03bSGJmBie0BhaerxSKhDAqe7Fhpv8OoOWoMIon3VmT+7BgG1/5ouMo9Wuo2NOR2by5SgGbHMRZQBGZxQt7Y94/RtFTcrmsruDge8ZXQTF144YW48MILD7rMysoK7n73ux/yOi+//HKcf/75eMUrXgEAeMUrXoGrr74al19+Od7+9rdva3y3p9gBUwcJ07G4gzna0zoYwi6oaSVMKEzwCSPNizydDtV1UhOt1KjAqhT50bs/DWKUaSdngwvycYpxVZ68ukQiCJ91wuQwg2cZ3HXObbqDeS+Ta521tiZUBTRxYW/Aai1mJpV3REjzgkrZxeAxfPInmkzuogORjZVdacIGQMGaeSzZeXSWSZtLy3p0/UOYM20cLvBeSgHa2CPo0v/TAqptkYqqtCgomUJzZEESkh4zQBkYnIG9VU9aRHAuTZWNIewWzewUEHw+NZIMTNXIILQKytrp5JbitR2AQaINoEtAbmNFzGS0hnPmFXgslYrGILr7vemUdLKKYIiUUZOxT9kd189leOPwVARsuHM9t/WCTTcm6ymr5hnVttvGrH/rWKq5p9d2SJzl0M+TAqllUXNMIZq43CfxJOyTH2ZS9snATmwVo2Jv0TVRA1LQyd3G1YeDV5KwUAbQCjUN20jex48zwAiu4go0fZyqY+IsIvNxntGtlMY+uecU+WsgYaUMMKXMKENGHQlck6QkbaiLhLRQMGX3AU2n5QWcnWQFfJ5+qwDPGDTQRkBkqT1jNuv0uvLrYpmJovDfAEJbU/t2vRPDXeYjiD7WUXEU2snojt56662T11dWVrCysrLZR24zrrrqKnzd130d7nrXu+Kcc87BZZddhq/7uq/bcvmPfvSj+JEf+ZHJaxdccAEuv/zyw9r+7SV2wNQWQVVuICTmHg6Q/Gl5qPBGuUZjm+ZnkHSgT8CWOjLtjGpqHEgJHw6eL8RhPGe8529/zsdy7Vtfioc87w3Hce8lOInYmXWiZSLwSg9aXwCAp7tsgiwrWVKDZn1glVpZ/i/9z0xsrSxSJnkyTgKW6kr26q0mVJ4+tXNgZuRRnL2yLC/Ynxg33SciPXeamswidMegs1/fIVb9MRJovgBmYhHBak7amCyepPcwjC6kd1BVKzBW8KyTZshUUWcZXA1QNRYIwBRwS9lV0IqpqWnfJmdjbOQcpEn6zEBJnhffhoA/BlRrxVwVYDRG0I+VnneoJs40WpxU68ZA6YOgHnrsqa2n9u18uPFnAB+mh7ESdmIr+pA0Xe0aA+RGp54ia+tY1sOApJpWUj8yi9IIB0te2RXGMwFOlg5cupaokrheh8nV9psBWHupNhEHNiWCKBunVS0mOZnRBsC15SGlN9ELMWlBhl0PYoEA1UtNtD62WkvDLhLqjJ3dkXMN+Yw99RALkAOkEbFeT3U0k0/GMCRpfWMMEwn4q2MCxuTnyoGZslM0CJAS5lBatnCWvo+1h4AmPW610+NpadI5bWCmAAg7NS4D861NV0lNVv34E9RAtIExO9a+TtNfWVumO2Dc8573nPx9ySWX4NJLL932ei688EI89alPxZlnnonrrrsOr371q/GYxzwGf/EXf7ElOLvxxhtx2mmnTV477bTTcOONN257+7en2AFTWwRVSOsS7e9mAEreW/oCmcZjqbeci8pH0ebQYPYIOnmPI3htvTmLA+C1ddBi2DCea9/60mO1q1vGJ94sTw+PetLPAWvKLjGDUoJpi4RRMuYCftMuK4JorFpMROlVbSRkhvDJtKD5Uc3ShPGIQmoTtNPYXMVdZAphKKzRbw1i6Ek1WhCD03yQljmWQ1muoy9VzhkgAFgbGm/r4bBW0IG5VEFaY+WcPc1owDArULF2OK0xsoKrRNIDsNOKyaG6ON1SnsRAXi8C1EysHqsE/ZzVlvaEXreZ1IZC0qO+DQDL6b00VK/crFknFcKGNJjsR2ADsBGY2NhcaG76FK3KbBYKOlbrx5g13RnAT7R1MCBmgu+4LU/dpOl4fKJdBk8BeAF6mYQPRgG7HA8BMhPH+UoiMl/efxVzmaDcrRhY8AyrYNxTTOqN5AfTPI+st5+N38ZsnwFE4zPKb3swzHNhd8oKg6Gu5IRW+ZoIWM/upRSVfuN+SScaGIEK3aGHlyuB1sVKxNgvq7RM86Y1SmMDKAZuu316jnV/vE1P1FZBQVA4v+76b/+PFXnm/yWJUbk+sjw0yDFGA10LamwUNcC/bLeQRtWeHYfgo1DNZ/W6119/PU4++WR//XBZqe/5nu/x/9/vfvfDQx7yEJx55pl417vehX//7//9lp+jpXstM2947Y4WO2Bqi0gjg2d2r+MwucnvNKh+KPgFAUC0mBXvIQbNB1l2LEAtwHwB1ApeWwfXCiwGcCmgnAEMx6s45JDjw7/3MjzmsT+tN31C3dWD5kWE2WpQacLo4QRt97JKyAOjrJD7b5nWJy3a07dNNjY52/wQQRQp81JVO2UVfwh6GyurJwVlqYr4ncyyYuk8OrMUdVFEwjLlJGDXLCvie4C019Hw1BjQ3ncvMUOFpIwVNYNJZBAqMCaf/FJVQD3LjakyhqdKutCaNHOXPNWW10dwpwAqJ2VCLY0aROU17D+hgUjI+SMoeFkEZs5SZ6WBLNezgSZ9A40BYlKGgCF3GAbKTI1wwzmPQMk1SZFBcW+vxnK1VA0ha1shS/+Yj5XpHF2IHIGjgm/XcWEjwHO2iiHppegpZWmj+CUNjAURgJHcm0i0V8rIqI7LgZltP0Em8HgoDUgBbVL3FF9jgKCpPhoI2KUHdpEEtag42pooe0qS4cJsF2mHiloBL8LYyDHkpmtaZNFwBXAJ6P4NaaqHWhhLSlqJSQ3IogEUA6NWiZnmtm9o2rvgQZbQQLKkLVvVottYGGsX0Z+uXxqE68Vk9xCEh7NkbKbd64MHWjgFW4HvYxWVj0KaTz9/8sknT8DU0YrTTz8dZ555Jj7/+c9vuczd7373DSzUv/zLv2xgq+5osQOmDhJUWKrJkgAjS29YJVrCVFQNoKWIKoSNUtG2s02a4uNhFCBVKrh8hcr1thHOMph+J5PcXdwyoKV6nGExfNkHobD1QbMUUEfib+NP8Aq6wiTNhZHM7BMCYDiJZkGYItUvAABrE1VvLkzIeoO1NGIeKmjUXn05TYXj5nAONCC1FBMAZZ9hFqBcdZ0GyhyAddo4OgsoSixP7ppHcOCgmqqykoWtI/HISvPRtVlUFKC78F9FJV1W9iu1VF4ATMY0wYAThZkNUOAGAJH5Cw8HakBqgKl20wpOB1JB0C0b1sO05MJuwCR6/xijNNkuY1LRKteCpBfN2qF22mB7bIUi3AXgp6C8ieHhjMOmDy8BjJng2M27AxCKFXu2TwAknaeTrzTD1f21tJKzJyRAGQR0gWYDGuMTALchBa8gs/3JAp7EM8mYIgQtkrAoVpXmFZGaWpNm1QraEss5U38qQBzZPSohqUt5WpCATWqAyxnnUbc5hvSusU86/LyOxhCSGCVbutSZxcBg+3FQkOrAjxAYK7hhqhdtZEzuMRM2a1wCzAuzZ8B0f2q7Lpb7Xu6ExP/7f/8P119/PU4//fQtl3n4wx+O973vfRPd1Hvf+1484hGPOB5DPGaxA6a2iio3FM7kWhHz/7EoK1NDQ///RPJiE1hVwbkKuOdzXxfl7IDqdg2szFTRSua75CyGMSDe3LkSyixMzlZhR8C4mpBDiqU7oD5VA0+ZDsAdv2uXPIUDiPeUNQ025gQOduE6mFiWb8aUAICcwaTWCH0n52eTyj3Z76UKGANKgDBOzHJel9O/UUs1miKZxDm9kFYNBlYTAqo4w20H0lDcfsKF7p5O1mM/yE/dJTNrGkZPi5o1AmuTZarClmJkbReEdgwhzvNiasqTa5tzcj0gr+TGFi2Jzu38LTtMR6+lyWs6QZm+rjmu24ch43HzVx8R+jVp6yRpzuie3xojJ9USudje1mlAKjIMYf2mx3EdFOCMVCrtK24sj5XktzHbitvkS5WcaXEQRBCPKAUwbM2h49eAIPk1Dq7gfswhwEfF2AT5m4a2TFqQV7mZnqilTds40gBtqUMKGA0EErK2uEEW4bc9WAnrJZV7xoABAiYtrWhp3m69HRZjaKHsX1LmLA2Y2B1QgTwM5VaIMwE5DG+RY+cgWh7Ec2cMJtm+2bLLWjk9z3asTK8V7ysG6giY3u+PYXwlqvn27duHv/3bv/W/r7vuOuzZswennHIKTjnlFFx66aX4ru/6Lpx++un4u7/7O7zyla/Eqaeeiic/+cn+mWc961m4xz3ugde//vUAgJe85CV49KMfjZ/5mZ/BE5/4RPz+7/8+3v/+9+MjH/nIEe3bVzp2wNRWYU/MQJskgwjYAYWxJWN4b+lp36tqjMEotS2nkzLlBMoZlFeP3T4dQXCSSSsb6OukhQpKOFA6iZlH1PKTYEuRyMS2ctMgDN9Ym3i/sLQSs3WAwAYe1NFc0l5obJRFNJcsU2CW5qWlgAywesqvThgctzyIob3pfJmY0isFG0Toy59nFkCVE6gkualr+xCPmHIbBpjQ3ZmwCZBaWr3q0Ghg9dCagiEK45tqsqIQSJbbYIFQWBk00b9wz+CVtCFNZhV7m6U9Jqk8vfRNZ2UmnnnOjUVSrWIsNnDbBwXR3LfzZZO6AA8tlgjXYkzRbJYp2ZC669o4/RttrEoK+pmldXm1XwZg6S8GYouStBD9FNm4iqSdeNCHg6CbWj7IkhLT19TZ29glL4QxbymWbbnmZwF3YTfA5nolTZu5fo3gTHJkZmhUoDo2PVwaKQBGHctADUTN9fODHJdO2SdjqmKKL+rZshYfGJhJBjIJImonBVjVriV2wX00kAXa/gK6nL0cmC8ZZDjmeh78PsZ6XgyU+TnCcYmjmeY71Lj22mtx3nnn+d8vfalod5/97GfjV37lV/BXf/VXuPLKK3HzzTfj9NNPx3nnnYd3vOMdOOmkk/wze/fuRQr3w0c84hH4n//zf+Inf/In8epXvxr/6l/9K7zjHe/AQx/60CPat6907ICpLcImsBQqq7yUXydeKqEk3VqXABtctSUNuMnjS24XWNq1S3RTfQcuBRee/kN49z/952Oyb4cT4+4sxqTzgrragxZFAFWXACLkfXMMJ3SgKim92hO6NQEfPqkzmjnlwCgrSf5WhsOPNUMq7ICWwhmVTUl6U9N0WJoX1JUMhBRjFJ7LpNzE3dKup0yB1HIYWI4ilmU91GZh+ivrYxhThxbrcxG2d+LLIxVjnfT0s88vATGvqusz0AO0ro/1ZYkZsuNXAXd31+WolJYWdL+ncI0OBW4XEfaHUwJl25am9szmIPQWZGV/qAoQjhqXWH3m5e7UXrfzK7/tc9RYANsHWz6K3kN6eXLMFEh5f7/AGm1IywRgJ8dH03r2XrZrLjATcRINrJYtSyOkJQ2JED2GV5hldgZI0ktJLBIATfGZa7mB0Qhg4MwsIECpNUMWQJEXTTNERTRraQ7XIC0zYE2n2FigskuWr1nE6vEzNbVzOamitF0YhWmy856KACobUz+Xqj1nAe36MHCkejtLsUbgFdsE2YMv67nY4DmmYDFWB/qYEyRFasfWCgG0vY9hKwdSur2JkfJxAlNfiTj33HOlfdEW8Sd/8ie3uY6rrrpqw2tPecpT8JSnPOVIhna7ix0wtUVYRVH725goACCfoFvfvaULzkr+OQHFJj9hMHixELG5VvHRbCbVWt0MyBnUL919bwfBCSg5Ia1m0IERUGaq7urlJtWlyY3ZexWW1pOPtEWK64P6hIoKSkvp0tJsFKiwC40nVV3c1mGsS12xOyU7ILLKPxFfK0BbWzTAsxxRaG6TtNoIbBoHS8suN1busoytD1+7Ckn7+YHTz1XdN1RnWjz6PGGpeOlvc3d3UAVgYi6as3zGDVj14UA1fjzrUPvsYNI0acIepg3gZbmSL4UJ3I1Xc+uxaDYWlhJOo1Z/DjyZ0DmTCu9JSuKdeQnHFGH5JK8xCatRZu29pjnCpsyUjTeaeibSuVvZKE839e21DcBsed2BxfJUWxGwVVa0eq4PVgsDAZ0xSwJerGddBEaiAaTJNr1tTnytAqhAvw8OOlyXpMCSld0h/Vw7J6Jp4tQkkpHJSfpZZ5TCMbZtC+MIvUfodpKyZKM9LMGBlAvPCYB+LYYTFMzEr5OdR2OqaPqejyEyUskeXgI4tmspqfaytOXiOTV/qxiTtOBxiJ3efLfv2AFTW4T0h6sTHYxT/MMmqRCgpfxMw2NhDFTOIkCPwua+k7t2SkDXybJJ3r/wG16Kd1/3hqO/c4cRdSY3mvGErCmDgkQQdq5LQDL/qIqyIvtrZo6cxMvJNTOWUhobw0dDnQAWVjHsxAIhhJtvGsNB5NVtprPZEMzikdV30/eNDYqtZKKI3NrFWIRqPgDAYpiuj7mdY1t3TvJaStLbEHocirATad8aePdKAz+5gRtfLZLr+EgftR1IbRExjWepz7QYpRrQ+hOa9moU8CWsX0Fd0abWLJV8Zk5r2rXI9hhTtNkDiFTFcdO0ldaaxi0RBjFWTab9KUAFg6xKE8Yyse+PieBbhVW7fmoQFEeB8wZmaukycWZEJ3xehXtTVWW5EjD1plIgYO9HkEXaMDkNYdsM7xk4nGTbtD6HRpPpabEZO+iRAD1OqQGSaC9hOh8fi4ILq6Y0cXa3EPuJMgugTEFT7VSUruDR3o7LAHrpRSCjY3E2kJXVqvAKwqJO5W4BUdrYHZBRW29eCDBOuu4Jq4S2vAvQlwDzRECuVJM3RQZgFZFbMkwBmNl15L5Xy2nCYxhfiTTfThx67ICpLYIK+9Gx9J5oBWTClbYnys70eYM4nUx3s1homb0+bs16YE1tEBIBfS+TO+mEa5PpMG4yqq9clJ6QIR3jx90JOYvuI6+PMpFoVSPFG4tOHjRUIFOTeozVjT0BYSnyUn8rs0JY7v9m25FJuZmj1r6BBfYGudOJfeIDFsXikbXxHa6AWhUIEAo7ZqBr2RfF1mHgqdTGQhm4Mq+qPvv4SAGX9QlkbS3UKkP1/cg+LaX4pgcvMEpD0dm/MYUApDp1V/ZtkLJU0+KJdszN6b/MklT0KUh2sXAcCreUnE2qdr5TOAd5zmEi0jF3NumyAyYwo5rezerL1GOLmFB0so9VhEH/HY5LGKKB8Ph2mISdTbKvobElDNRZ+FBCLLKbaml0mya8NluEVOHi9jSQt9YRF3RlqcxBvKB5HmEKnFK4RURdj1kKGJuV5rpNhhtjUmmfj7ZVBlS81yErs5SAcQWTVOakDVAAWJGZEjG8AKi8kNSgpRgjC2lg05jDFMxWs6YwQ8GuL+eMVjy/Bq4iO6Wvsb4QQd/yZ2MVZ2SonJHUZetMwC9vtAU8JrEDpm7fcUhg6sEPfvC2VkpE+IM/+APc4x73OKxB3R6Cpf35NPdewwQHuEbFUio+UQ1FK58KaKGPYDq58nwuQGrWtwl2NgOy+gap3uo9n/vp47avhxIf+68vxUOf+QaZ5Dkhq26HuzQhZYjh6RvTzriuOykDMUtNsK9R++Sl+25CWYOux1MZIkqWtiEkrBggE+6sMUbLaUNOhDrLyIuk/lGpCc8tzI0eEHaQCOA6dSqMgNeuha4T4JWTMI9Ecm5zFtZq1gswmOms3xkjBHGXVn2YpML09SFuFJ76a/tXwZ2294kNl23ZlFQnBflcl8Co02U3Y+cQGlmTnl+CA1bW9FxxYff05hyrNuU4Uqvg0lYv5m4u++GflA0aKAlWBgagqp13yD3GJr3lydQzMrbKAkkTxmVsYo76Gm5gxFLTsZUTFQCdMFZ1RSdcAwzcgI7tlwMAtntHOFA6LgEKTq/CGZbM3maF3G8q7E8Ys68ygKiq+9jtl2XSAPd7QwBSYKBb4wYg0Ng+BzuK+2f7VOOkzBYnAWmuU9L0WFXxfvTysmbTNs46k995PYLnds24PorluHRrknaUaxnNUkOrI+Ox3ZB+pXCeCQ5UqQIwywcFmW0d2vfQmVhNT/doflmQc8/RNmInvmrjkMDUnj178KM/+qM48cQTb3NZZsZP//RPYz6fH/HgdmIndmIndmIndmKHmbq9xyGn+V72spcdtHlhjF/4hV847AHdXoIqezuZ5Xz6BgaqArQYhSnQai5aNJNFAM2PCGhGkTkDu1a9RYinbbZK39wOgiqjzICuT8AgpqXuiTRWlJzV0yc5G9H6w6ltgetl2Gn1tJBeh3U2FSDEMn4AMOdzGmpgF/SJvkqFoImXaWTxrxlE/zZpAxQ1UFaBR6k9cZooPCdhpyx/uVn1H+vno6WCpQ4TaRpXVxxYKds/dO1v73doDFUJ1B64tbeBXnMpNRF87tq2QxAzeKxNY+L7jbYNPQ5igsqoK5237xGNSPJz4wyGpdcC87hsYClGnarVMg+lCZUJZ4TsPJuGhwGvDG3sU6OWTNTu6wlj82uPJY1s7NSyiNh0OJbS4SRWDbo25AX5erllZiX9F0T39r6L3GO1mR4LrzqzdSmzlUY9lkZOVYhGyhgvS2XVKbvjmihjfYwBK2JHkIqsMg9Av7/1rLTqOaugpMrIcxXE11YUUJUBMn8uJtE+AfKeaZ/8Wgj76yalVY5FVbqQoEzS2PaVCiOrQWft4P01W/WeecQBKhuUikRL3UUhuV0DykZ5KtCOU5kuT2pTMfmsrh/Ech70oPt7vVF/U/btWMcOmLp9xyGBqeuuuw53u9vdDnmln/nMZ3DGGWcc9qBuD5HXR9EzAROPHgAtBVMKiG3SYwCjpPViCmkcJfUzjtPyeqKpFqfP4Ez4k794zfHYvcMK0UoIaBhXE7oabyrk6TlYqicRht0Jec5ubeCVUQubuMmNNQGZ+MxOwdYRQzyYms6nrGRvgTHuSk2MnhjoCBi5OX8Dqkeq02odoM2S0cXcotQJ4EFRkGQTu/VWJJLPr640ED2byfqq2l4AU1CTErjb+DWcCM/73OwQsgGa5ClQX3Zdqiy5l3Qh6TFC1RoeTm0SifoxE98TOWCSwoI2ronYXCdlQEXX4WFjA6DS8+GVe9YrMGRKoku5C3spvtfGy4kkBbcEnrYMbim7ZWGzgRlScbRVGtrnACDBKlGB0cwbQ+rKrBMstRU1PBHwOFijNgYvyKiinSozbsAJQFonsQRYEmjbOuT8ooFU3Q7bMdRNdWuaXq0qBh9Uu0b6W9eb51IEMPsyK1AWsGrpwbJCSHNGmRHSQsZkFZMmEnetUky7kdoy1HYMTPOVBpYUYJFBuFCfG0ihImAoASipgUQUBXQVqjFsx52zjsc0ffGaLG18daU1evZrI6SNoyeVA9FlsHxb1+BO3G7iU5/61LY/c9ZZZ6Hb5B69HIcEps4888xtbXy5I/UdMiqDap1OOspgsLpYU1GwVHXCMiHiYiET7qAtP0oBL4bW660jYGUG9D24z+BZt9H5+3YYbhfBjW0wx2l5atYS/sIOPswnChAAYJNp1f55pC1PoiDYhOTLxpOxnY8wJqaxIhee243Q2lfYtqlU17cgZ4BHBSYk52mz6r/QCsbDQFapwDhsfM2YIrO3UMaKZ72YmnaiuYr6qMSMOuuQFsE/Kh739VHGnRKwqFMgNZYgUBfNF/cZaa6mn8uO6ZWcuYq6rDgeF6sPFbyaxX2+bzOGCNHJQWwEV+4jlcwnKrQBqvDzZyHsiC5veqDwVYgCdWfETKweJ7GlYgCqAhpsOSZuWqAwVhuDm1fKpgLoNIDI6NYIZRVWEDZxPne9lF2Wpq8pbfKNFYA2rjQ0hq9bowbKIOtwoAS4oN5tEtpXYAIGjJ0CdIzGhkGB1BB7NDagy4nQH+AJ2LEWQHEf80I1kZl8/JPthyo320czDTWPKzNpleVlLNYBwaw2ojA+DVL80inwqcoudarRoqEBU9tvJrnc2QhnA9McLjHWBy87sKWhfK8aDA8Lk+tO95UaWXxMQzDqkc0Tm9zlvqriQQ96kOgtD+YXGCKlhL/5m7/Bve9979tc9rCq+W6++WZ87GMfw7/8y7+gLlVBPetZzzqcVd7ugoYKio/Y7hhdQOtDE+xa2mXQ1+wnJfBcapHZwBUA5AJyIXJGXendWuD2HlaVldflZmwl7tH52oTDaV4xnpBbP7aCqdHdUsT+abIxPd4KrKoJodV7KA2McVd2h+xYMWYu6GkUj6uykpGZtWdZAoYB3GtKtrKAJTs/1vLHIlGr6isVQN9SgPZ+103TgvZbPat4RdN8KQl47vIkfVl29zpRacWegULmCbDilMCrM0kF1ios6FLFH0pFOrBoxQxmfVBYzTuX7vzKiHLOMgGqQSgAP7behiVcB/Z72WuHM7xBryxH7bqx9NkoQCsNImT3no46IZuIObJEJgR3TyPo7wiimCeMYzSwJGOobK4MLFIaA5CvOiZqyxh4sCo4mA2cTrRpFMbEGJFJ5VlI/U1mMrIHjQDijJUirW4DvD+djA0NLARQ5qusSwDA1rVUPSfjmjJ+si2VKDDED25glFlykOjsGgvr4+DOAWcDkHauHFBqdaSBVnuoygsspShZ2tmUduwIBo55UhAggB6tZQ0aU2bnPy3gVZQcWE+Gvh77JI4E7hpz6uth6EMIY/LUB4AjxXqMYyfNd3Tiz//8zw8p08bMuN/97nfI6902mPrDP/xDfO/3fi/279+Pk046CRT7ihHdecDUYgRR0Say+hqzPM2b5mkYG4gCgMUA5vZ/EIHX1sXZXCv3KGdlNYpMeAl3CCAFoN3wFMwYoHJ2SdkLDpPcbF8VzUplAUGml7LebwTUWUaax1lQb9TWNiQ6kqsTNmdCVX2UtY8BGihLY3UfosRAXREtV14EMLHcS28cp2Co1OY1BcDTtv75KlV/5iulPlIepYqnlFkV6LVEpaJ2GWz6MO2Lhz6B5gp8zH7BGCmvMlTQNbBcN0kaJyNBwNXkfNk62iN7dDr3fcjqvl4AKmXC8FmLntZzj+S6nrBCmEzWVunl3ktoE140UfT12TqoTXpui7C0PxFYyKTN04nPWJQw76VBQdQCDvgtkgI8d8bW/YygrTFZjDQS6qi7vKLv6fZqDuPnMOcGpgjAlM0JY/btAq7voQpxLbfjrdofb7ysLI5VnFJch15WtWvskBxzoFurzhKaWz0NDO6hqVRoeq556pWZrI87BcdzBnqxx+AOYvHAmDBwaaFjV4Yqgsq8EEYwjYyaqR03s/0w9q3IuZf9CvNNZYwr5OCYM0Bz2d/aGwiCsFYZ4NqAVazwY1utgibuDCXq8QKAzhi7ds8gBpB543W6E7fbOOecc3Cf+9wHd73rXQ9p+Uc/+tHYtWvXIS27bTD1oz/6o3juc5+L173uddi9e/d2P36HCRpG0dYYO5BJGCmgpYVGe0wrYJtkjeGoFbwYwJoK4oLW0LgUEDWXakBu4u+/+pXHeS8PPb71uW/QZqTswlJ5qhfQkkZGJWF/rIXG7NYRNIqAOC9C6x1jAdTwM96g8trYXLYTwiTf0iE2UcYJfdyVkBeNkYr6iVYwoGnbokLuMcxwBoJL+G3CdPt/rVPWamU2ZUb6xnBxSvLtSgk8k68Zqx6srDSALmNkBymVO+T9c2VZGHW32Xhrama9uEgdpYL7rjFQqTGpxKwNdNGAaK3N38oAlQG92JamcLOc0GMYYWezOsDEDVze3IQxUaBhgMpcs01YHsvyNwVRMBBmKLuBtWUheGQpcmE3r8yhcbNfP2GfK7QPnTI57g4eAKAxJd4LbwTYmBBWFkmZqZhKjGwTZynxT0MAO7MGnCYgTNcr/lxLYypALph6H5F4Qvk4h8ZwcdIU34JFr6gPRWY1IgfMgGRL+1kaFgC69YrSE6o6haOdDhnjAPVesusIzlrpQW7HYrT7hza4BksKy5k1dk1ZTFumRWOSgJYatDGOK4SsID62uomAy1O9jHCCbYVmicAT1sqYvol9Aqt9R9RVHcPYYaaOPD74wQ9ua/k//uM/PuRltw2mbrjhBvzH//gf79RACgAwDqD5Am7AOCxNplUnpKi30UmXbZmoeuQKHisor8g6rOqvFKAEvcrtNKJPTiqMNFYXgqehwnrlsTJtKSXk/aP8X52JkwIqcJusxasreTUXjQV1RVM/FbA2KlQqcoFMth0BadrMOI0M13KRmozOZTy1J3QHCqz3HVllZS2NefJm1raTej5GXTZnSec5QxTOl/3fU7lJ9FHB6d41SXYvS6K94yzMmbFI3p8PmLZ8MT+pTGCoZspSebaNPgvzZE2ozUtq2VwUsr0J6zo0FjayUByF+EurMa2Tp3Iiy8HKvqTGlLiZKoBq6ZSEBuho8+2YQeOy51v8v5m0UhXgZOvNC3Z2bAKsAUD7Q0IfBjBqSpO1nREBhAbuhOlgEVuvkKeR6kzXZ+PQlJaLlbO2cEEDQaiOGYR5clZmCr5Mt8QKjrIxPRrmam6skT/oBO1ZXii4UBa5zBLSyMjz6kC5zNpDTRqlwESYZzibXEID85iG9Yo2BRxRY2RGoeZ+bucznmcDNpKSl6o9S29GY9JpIYldo3abFfPWVBiVWvPnPDR9lV8vCmI5/B0xhgApHVgOO2L5wbAi3uTh4VjFDpg6tlFKwV/91V/hzDPPxNd8zdds+/PbvgwuuOACXHvttdve0B0uBmGQMBZpTlurpO6sSk8ZKTCD1+fyMwwCpDQdxEtpJK4sbJWBLXVFp7FObhS3x/jYFS+dplO0Si4fkEdf0obFaVGQhoruloUI+BcF+dY58r450toCef8C3S3r6G6dI60XpPWCvG9AWhtBY9EUVgUV+cnz4j/W2DivFaR5QXegIq9VTxcAU9Bnruh5bvSf3RippfCACZsofyvYjSlc33FaWpemfPPSV8l7B6omqRPH+9qLmJu1YtF7PDJai5yua6m9pMfWARu7kJ1X1Qw0Z02JEnil3wCUPJYtE4Lh52Tobu8QUnChNyWx6FVqJk9XcQfUGbX0Cpo+xibbyY+nC2k6GR/kXk/LaVnbrbHp9/K8Ontq0a3VTV8HZPJtonkRRZtQPu7vcpWeMT4OFiyNpsuKSaa81+1vn0kDYH3qXBNUltZfxMwyKQjp1gQQ5Xlbj13zVpmWtdddtF1wIXwX7R7kOJYVwrg7Y9yVHCDULOd0cWLyY2UMVe3EtLX2NDH15GUGSL9/luLD5PvYmKHaC3AdV5sZrOnb6owmY6Vi1ibC0KWFaCGTtSXSB7E82HFidOvT4xp1aW72qufKX+8AsLaasR9i0UrZtTl5T19Lx4eZ2omjGz/8wz+Mt771rQAESJ1zzjl48IMfjHve856bNme+rTgkZuoP/uAP/P/f8R3fgZe97GX4zGc+g/vf//7ol5ryXnTRRdsexKHEZZddhne9613Ys2cPZrMZbr755i2X/X//7//hgQ98IG644QZ86UtfOuT86CQMSPU6qS0GmVyGUXL5Coh4EXoJLFUIUCLwsoylFKTZCbK+WpFuXQOv9viTj/+n7Y/xOEcqopVKcwE6Sd3dpa+eTMJeil8raC5sB9UKjHUycdN8AJkbtzIuKAxCEed4FVpXTZHRUKTBL4Cak/tGkb6W5tCGse1mbqJjKlXA34EFaB6E4rENxHIKL4ZppXoBLyCS/9eij8V5qq2yfawVjKCZ0jRfmpcp4wOdvDsCLVqacFJNatdWJyJ2r9TTykBUiEi2sB/Xyfjdn0nHHwFXSt6HD0kmtqoAipd8v2om1Bk1XRQ3XZRUuEk7FE5Qt3w7FrafU0bDhxiB+qZM1CYTFrfXnWWxtFAV1gA66cb11M6uu/aa2R9QZRSzagjX0mSzuo1kvkcKakrfsKCwJAFIaJWeASpjruoMroMyLZSBu8jmkB7DVERfFPc/KSgoK+SASjYajq2m3mPUTnBAmZGyjK2Iw1KR5vlkg6idLFutd2ZqlghAAy3d+nRbMVUqaVBBXwmEsUdrjL4Edj2lrJ8nbYZNgz1UqFUGE0qS8xh1dQ72oEBOdV1ePFDg7XxMJE+V2q3c0n0sTaknkVmF6TguscNMSXzqU5/CAx7wgE3f+73f+z086UlPOqT1/PZv/za+7/u+D4Bowa+77jp89rOfxZVXXolXvepVuOaaa7Y1rkMCU5sN7qd+6qc2vEZEKFtNSEcYi8UCT33qU/Hwhz/c0eRW8bznPQ8PeMADcMMNNxz+Bi2FBwSBeWOUuBSp0rPYqtTSzB5Dyo/X1kEnnuAT9IbWIbfTSAPQzUXLkNaLAiR4GipppRgB3kg37hsNpVWZWd84AARNTa0twLtEhJIWkl5LNukzSz85ZfKoVHDOAuqKpmIGuDaCtEKLCiOvjaB5SMda4QCw+XmLqb7ITEXLg2HQir3UPhM8oOw1qjJOa4vhJqYULQNMjC9pNVJnRGtsLGNpKTjOCbVPyPPiIn2a0AMBOFmVXwRey7ur7BlSY2QcVFm1ZCaMq6lV8Oli3tyXGlvjWprU3ovAyF6fhA1rKeVi6Ttg43rs/QiWLCXFiTwN7dsluOWG/N3SjuwaHUkNlpWNx4lT0C4VoFMNFBV4ms5Yobxoy4F12XDcDDAlTcfluQKwsVkG1Ez6MLD0OZ3UyUTZyuylkT0tXmdwxjANhHFFUrBlxXzd0Ko0jSm0Y8rwCkdO5MeCSTRJDopSY5oasxPH087luNr2Gfr8YR8U0EOuwQN0nAur8DWfsNDLkezaU6BTGcSyb5z0YcAYzwh2lFAC6y2ZIICo48k12FAxibceKXCytN9Suu94BDNJ9eARruOOHhdccAGuueaaDXYF73znO/GsZz0L+/fvP6T1fPGLX8Td7353AKKNeupTn4pv+qZvwvOe9zz84i/+4rbHdUiYutZ6SD/HCkgBwGte8xr8yI/8CO5///sfdLlf+ZVfwc0334wf+7EfO6T1zudz3HrrrZMfAMIc5SyTZingA2vg+UJ+FoutWQyLvEmaRaNpqtqX8MJ//XJc+K9ffkhj/krFNb/1owAaG0C1gtZHpC/PQQvTIQnDkw4skOaDACYFQhgLaL4AffkA6MAcni4FnDGiA3OvZuOU4D0PUwJGASd5v4DYNFSxP5gXYSaMBSmMPEiKMC0qSBkpWh+AffuBuaZtl/vyAfJ6ZFsjy5Nz+1xOspxZIGRhixqQ0rEMBbQ+ys1edWb+fwVRW6WvTFhuqcI6y6grHcpqB84JZVeHckKHOstN1G7MVRw/c0sFpuQ/3Gdv0i3C8KQgStKQNRO4J5TV5M7yluIxobSzKP4j+1KzasIMWOUw8UJ/b5baI2OWNrIUcRlgaQ5b8q/a2PuvhXtaOagKE719Xlml2KfQjTwL0K0zujkjL0TnZjofS7eZz1XcF2Oz7Ee8lkRPlees6TwBPXkO9Ad4UoXXRNttfXUCYLSXnDVoZ9mPMgPMELV2AlTGVdrUOTwVq4ZrfeksxlUFap0AmdILq1P13Po47TnFmCA9dlWXRxLmyzR1NSMwY43tcrG4ni/3odKUo/nOWSrSrTAAn9nMjsXPr4H70v426wi/pouC7MhEcbvuW2qQ4CL24xAVdFR+7ujxgz/4g3jsYx+Lf/qnf/LX3vGOd+BZz3oWrrjiikNez2mnnYbPfOYzKKXgPe95Dx73uMcBAA4cOIB8kPl7q9g2QXnllVdu2ndvsVjgyiuv3PYAjmZ85jOfwU/91E/hyiuvRDpEQffrX/963OUud/EfNxzNSWwNFEBtO3RyJtedpPZ/i/lCwNr6XNmS4+T+dgRx9R++DPnAKCCpVtBY5GdRQAfmDbSsL4C1dWBtHXTzPtnH9bmA1GGU99bnUw1aJ9YCUmlXpYLSTEGtqbS18NGqPBEdy++8NqJbL/LkrfoqGgNIWtfr1jRRBqBSatYGfd9Yq+Ww5YmcKfJQKwWyFOcwKngqoHFUcCmvOYjj4Ne1KEiLImmola5tB8J0mTeVgCpp7TKaoWanbNVKJ4xgJgFgrtdKziCCGbXPqDPxu7J1cZ/aRJEgE9YsqQh9aiZppoqs6Skm0xrxdLLBVEBsqcGonYphLI6ZZG4ZS9uwtNdy2jDaMFTT5Cwtt2yPsUwyuL4mgBhfjps2qpsz8iBp8G6NN9gAOKAKQMh1V6YxWjIPNU1QdEXf7HdMRdo2HJi45YMeW7O1sHOZmxGutYiJUWfCTpaZpkGZUWctbWkg2lKaxrT5ea8GzuBAz72gqIFzB4Zo14jpqRx0qQC+KmPq4B5o3ym9xqIdRRr171HHWdq15nq4QqBC8hDEAFX925g0vyA2uXiPDzG1ExoXX3wxLrroIjzucY/DTTfdhLe97W14znOegyuvvBJPfepTD3k9z3nOc/Dd3/3duN/97gciwvnnnw9AfKjue9/7bntc2wZTz3nOc3DLLbdseP3LX/4ynvOc52x7AEcr5vM5nv70p+Pnfu7n8PVf//WH/LlXvOIVuOWWW/zn+uuvlzdKmPRiLAuStwpK4b80+T/lLOwWcxNCj5J6uvCeLznksX+lIkU/o1IaOJovgANr8rMmv/nL+8D79rtGzI9fzvK5WpveyNJSWQAVzYdWom/Z0qi7YtFDpUF+8lyE6WkoyOujAJR1Eba7vcFmjGIET5Gh2uxcx0pNE66rTQGqsG+Y6/rUT4zWFqD1QTRbQ5GCg4WI701oH4Mqo+zuBfT02ScIDlqf2lN73Yw27YeMFbIUXW4/fXb2SDZGPhnUmTBQpReAVTtSU03407+sL0zKqU1QUxZFKxN1onSGyia7yf7CWwqlUUCJAIgtWLuQwoneVvbbmabAPLXPUfi9ZFwZgI8DGx+bgobRvNUE7OSF/HRrUuVnQKpbl78dYBgYY53UVRBuxrJ5IayUjVu208bSftv/G4gqbq2gx7Aw8noAWdTYJjke5NqnosDI+iHmoaUa3XoihFXZ+t96rKMxqbmPx8o+W8aAuLnT197AUWARuR03YXFtX8iBdrQ/kYpS8n2I+jz7HQ1hrUoyr8Md4qOhsFRT2oMBSXov7mP8/3EM00wd6c+dId70pjfhwQ9+MB72sIfhBS94Ad7+9rfju77ru7a1jksvvRS//uu/jhe+8IW45pprsLKyAgDIOePlL99+lmjb1gjMPDHqtPiHf/gH3OUud9nWui699FK85jWvOegyH//4x/GQhzzkNtf1ile8At/8zd/sgrJDjZWVFT+IMbgWbMCahwqkLCjBtVI8/SyXAowjaDZruhbr13d7D9ZWO4siYx4HTZ1xMy1dtokoRXRicT8N3OhrnEnc4TWNRmMVAz31a5LHXMCbCofKOhOkpkVjrmg+SPpwsRCQswykrPWLUbrR9iA4i7OzjGkKspKm+8xzLFotrI/Tc6l6K9eAWcqNN9ExkTJvOumbnQQA1/lwbpOjpHkSUBkpNAIWVi/216PAdqlWKjSxtW0L06VgzY0hmyu4ACMxsGQS4OBDr4HJMdxpWqM0ZX48TWh2BZtNUNTWaeOcmFLaMox23i3tBZqAOBvLcurPAEztCWav4CJvAwsI3mUAOHOoFpNt5nVx784Do4CQ1JG9duRAzMYANJC0VUrT2ST1e0qL1vS59GjsUhLE0kw6xQCTRjndZQYx47TXjLUh0yMJCHR3cNMi6fXQrVWU1YRBhep+Xk0vFtK3jPa1pAqUrumgTBxPajhKqp1Lbu+gLvnKTvrx6uLts7WTigym6ZrieXMtFxpLxQmuq7RzK1YXdi61vUwl2Q/SQgTTZdqFF6694xVfzZqpWARn8aQnPQlXX301nv70p4OIfJlDLYK7/vrr8ZSnPGXD689+9rPxv/7X/9r2GA8ZTJ199tliNEmExz72sZPGf6UUXHfddXjCE56wrY2/+MUvxtOe9rSDLnOve93rkNb1gQ98AH/1V3+F3/7t3wYA771z6qmn4lWvetVtgrZNwybtw4lEkkJQQOWGnQB4HMRvajEAq3X6GboDgCkIS0KJ1AdJJ+TI1gTgSZZCA4SBW5nBjSNzFuPJBPl7vmjgMidxoi9aFVhYqyzhmh8pDdcU4HqR6i1zpy9la4ZxLJDmgAe/ufBiaMLyYZB9GQbxnFooK2cgK4LyrtOKwSHMLiRM1SgglLoMzCHpuJT8GHBn1WRy/aVSWxNpQPy6KIE99SKvATpZKBBb9i6bGGPq0zwAF/9yp+kTA01Bj+IpmDBJpYFbC58AWPy8hwbDkZ2KxpQHBVKAmz5GsDHxHyICu5hJr0e0dZmIOrbvAeAT8vJ4AfKJnBOQqzFgjZWz9VPhaYukBK/U68wzaQAwZ5SZgE7T/7g4evmY1QaOrOG39a4s6r8m6T/yli4UQBpB2q5waWxTPG48gzcp9m4B2tGAM7mNiIvPMxrYV/BhRqEOYo0h1fRcnYXzHdOZMe1px1AfDDDa9SLvO0BnqGEv+bVggMrPvzNQbX/TqPqqYN0QAVUU9Bsr5mPR7cJ79sEr+uQz0ViWp2nAnTgmcbAKvd/4jd/Ab/zGbwDAtorgzj//fFxzzTX42q/92snr11xzDb7jO77joI4Bm8UhgynbmT179uCCCy7AiSee6O/NZjPc61732jbNduqpp+LUU0/d1me2ine+851YW1vzvz/+8Y/juc99Lj784Q/jX/2rf7X9FfLBeVxePmHL/kPABjbKmSpKAip27WrMSNeJg/Z22a+vYPBK31JUKQk4BITN0R53tLIi7UpWZrKMuoS/+/o34cJv/HGgy/p0nKdVjcoAUUpSpRfaoJC5zqcEsmNc6/TzZMD0IMfT2riURWORuk7WHc+vsk0URYlFWblJ5V9gpiZmlwqqugA2/SCyPiQXt04Ad7JvSR2qScqyxZuInOFJC1ZvJwKVpCamAGWeMAuuK3NWitSs0lJ1pEyH6lByEye7EDlM/Ezk/kyzfQE0+1dAJx2WY0xME0bJwlsLWdrE1hO/QgFIRWdtm8xMryO2DC2tl0Zx9l4GTAKqFDAs66UKQOsVZXdW3Y0CLvUBMxDn4+AAXu3zpbEmeV00R2BGv78xPu7DxcqABYbJ3NOpmgh6oyN8TItZ+q/MVPs0CvCSFkvC6iAAHWOSqrItgHpQkVTCJWtTVBgERs0JdWaeaGggW1mzos8PpqGKBQde3ZjhPfisCbKBFLeEKFMAZvts5psG1gyY1Rk1kKU2Fu0hQK7RvM5Iei2XvoEma4JO1F5jlm0VE83Hr6+l+3TM8XU+jtTUV7M1wnIP4KMRj3rUo/D4xz8eV111FU466SQAwIc+9CH8u3/37w6LfDlkMHXJJZeglIIzzzwTF1xwAU4//fRtb+xIYu/evbjpppuwd+9elFKwZ88eAMB97nMfnHjiiRsA0xe/+EUAwDd/8zcfns/UcsSU1VaxzIDkvLlGhyuQY0dOCpP59qsIjnf8ybWX4oJvfY2UkxMJAAFc4M2hWTDtWpXjYkwNgHdf/yZZ3qrfutT0Rsu6JmOXoPex1RU5FyszkLXwocZIOOBKLJ9LAQVEXXki6Zno1hUKNmzcgUm0vycR04yx7Qyg29X/D6P833r9xVgsJswYoZPJZCjeZJv7LJOhsgPmSl12q02Bls/Xntxjk9nc6D0vgWjFUHsRl8dKqaZBksmdM0mVVZbXiFvZfRrZ9T3RDNJK6CNIEQDFjdHY7CsUWCQ/tvp3Y2rCclvoqKzlDFUGNAVkDJ+0llFbgKX1ME0nmGTO+SuaOlWWqvbmEh6WtzRcZL0Kg5XxsBSWMEyklgB6Pisjz7mxIZlcPG7snQNZNFBJ3DRHrmUj9YSKzIq+X9Pm7F7tVadV4IA7L6oCPE3hrlUUTmJVYCBE980ARxS6gwAsP2datVydgiMaRbw/sX+gBrLtuPm1X9sxAaaC9Mn2KhQItgeB2GuxZmO74AxbGgUYUgUwCgDm2NA6nG9P/TrTenwAyldzmm+rWF9fx+rq6mF99i1veQue+tSn4ju+4zvw3ve+Fx/96Edx0UUX4T/9p/+El7zkJdte37ZySjlnvOhFL8L6+vptL3yU4+KLL8bZZ5+NSy65BPv27cPZZ5+Ns88++/i4sd8WkAol/hviYCWWOU3e5y5vSM3cXsMdt3fN3BaATjqxgULdr3ff+F8cLL77796Id//dG9s6ds285QmNqkUyEftiaJV/plOqVd4DWrXcorTUzm2k7PxYl+pVXK7xqtz6K271FNT3mFglRHsFc763v8fgZTWOG4GUjSP+HktrdWPVkvMBNBexepqrYL2KcWper+j3FWF/oiiWRExu2iepiEoi0l1NDUg5gDI2So6fuJvrOcqNrYqCa/MiiuFtQ0KpuQGvNLLqctqPu1OHtJ9pluL+eJsTa6qNxsoAymZoM2MDF8uMlHymWVHQqOmZCrXYkD6c7UdAVSytNxf1NDQ3dau4M2bMflKRdJsL6i0dq8cnKYiRz9v+TJuHR2uCCKTs/TxvObPYA49JjxXLMc6DnBcnaRkuku/W2zHL82gIqsdazUzF8wlNzG3/D2O0dVtqzn+MiVNWLBqU5iEeB8bs1oo0TK+zPJd9TQM70JxUhlraLpxya64etz/RpjkQCoxYwaTaUvyrloBaTFgcPHmxE8coSil47Wtfi3vc4x448cQT8YUvfAEA8OpXv/o2PShjEBHe/va3Y3V1FY997GNx0UUX4fWvf/1hASngMKr57n//+/vgj2dcccUVYOYNP+eee+6my5977rlg5qPDSu3ETuzETuzETnwFg49CJd+dgZm67LLLcMUVV+Bnf/ZnMZu1DM/9739//Pqv//pBP/upT31q8vPXf/3XuOSSS3D99dfj+77v+/DoRz/a39tubLua77LLLsOP/diP4bWvfS3+zb/5NzjhhBMm75988snbHsTtMqo8nmw7tbediI1xezkV7/n0ZYe/vuMY7/3YxTj/314GyoSk3krACNq9u6XIlEV4943/ZfOVxDRZ0PTAviCR9Vl2Il9dceGDVPKJBsjSUdxpG5sazl+vPe8030d6Y4nnmLfwmJKU4HR5MmdzE9Ob/m0cp+O1Pns2lq1YKk3x0lg3VPhJHz1otZ5tU1JFaSA37YwVc5Gd4Zhe0lVYXtCEtxOGwTQo+v6EQbH1KxtgrIqn0ixNE1OY1vZFq9ukws/auDCav9TGQyPi6Om+WUiaimWfVDPk7JWlfrViMRkjBjQ7ibhsVX1UYDrTATlnUawPoPWPM9Zl4ClLoddztvYn0EvAWuxQqzxMY0vJjqvkKSRGS4tyUqbLvan0eK5LuopDIcBE4F+5FRXY+U0tPWsWDT5AWBqPJkxPdIQnZfTqDM0agcL/bTk7Hvp+zY3NTEVYyzSYt5YweAAmUtPaEUiPf14ImxhtMKKQ3I4TVUhHAN1/ZnL208ZgFYbEkgZ18bnvw3RnlqtQfftL7x3LYBzZdGPruKPHlVdeibe85S147GMfixe96EX++gMe8AB89rOfPehnH/SgB4GIvEANgP/9q7/6q3jLW97ijgXbNSHfNpiyir2LLrpoYpFwuAO4vQaXAt5U4BEXOsRLM2inuPLUvLMUmeSHcWNPtdt5SAoog0rfesKVAnQdCMC7v/DzB/38e/b8FC44+xIkdUnH2roAqVqAlEVTFIw1vZrO9FVA02vFMN35VsezV/sFBU7L+qgJyI2/TbtFSZaPd9FaFNxsggYMdFnln8Xy9WNVjNp6RiwiTDNlilkBUdYYu2rTZLdO0Mo1lOm6G8CAm3/6MHLzWyor5BOwiZsjyLHUCedN1l2XROUHmWXcKsGMPtFAmbfZYUyBka0u7O/EI2ppn62CL6nhq+8zo/ly2T7ZtjNUyB9L4SUFxonEYigR8noV8XPY7qTCDFp1mVu/PAIDM20iXKqDPJslLdUGKHgJgIjYGgHXyX5nrezjrH5Tfp7s+Kqtwmh6OUthAf0+0UiNuxO6A9WBbe3lMzXo9NKgABiaLaTWModJ16vn1dOJNPklOjZu73srpFLRzVnuISGlW2bJr01rE2P74OdY1zlxcrfDavq/hCkQt4cJnq4rWkvJhnR8sO+WXTth+TvWLftOETfccAPuc5/7bHi91ophK8Nljeuuu+5YDWv7YOqDH/zgsRjHHS+284hQCrgywLUBqWgOWSrAw7SNyR0g3n/1K/HY816PsqtDNxS5YS0gIttDBdUJYkZJBNq9S45rb3X4JuJW0NP3rl/zG/Qm58Enwy6Dhjxlp3whkuO9zHrF8JZC8jjNUf8WWZ9SNgrUY5ieymwVbNllsKc6NN+HPvuxkfUweFX/tt1OrdLK+85Znz9bveGNTGq/0fQmbSesD1pjGOLnrUrLny8Y3kLEJ/DCbWxxQsN0O1a27lVbOrk1UBZF4rLfpguSfZZftWul78uAZjOROg3VS/69om/kyblslXmsflLNeoEKIxeg7JLr0+woNmBGZiTdvuMK9XVK8+r7x1VZs8JAL8CvhgpEMafUVWaZ6d2biuFaOdYe1dH0Uo49HBRSL+crz5vtgHtzuc0E0K0VZzmp6p73hG5ekUbCcGISwFfFy8oF3nXpetJ9rr1eDspmiUFmc3pPgzrHL6RjQauiALIevarNtmNDavMuM7G7tbSx/V4WpVvFJ4y50mpDYqn02xT3M4DE0/0itMbIW9w2jlVUEOgIqwfvDO1kvuVbvgUf/vCHceaZZ05e/63f+i2cffbZB/3s8meOZmwbTJ1zzjnHYhx3vtCUz6QZskVsjgs05mMcNy57O48//eArAACP/7afwns/9lPb/vyf/MVrcMHZlwAZ4F19a1NTGNi1CpQDQDTLJHWQ338AdMJuWUlgpziCEVLwsNVNz3rqBeaQKIHjB2ILmXHcwITdJpCKXmW2rnEMAImAWW5/1wokbQMTwVaSdbn9gTEPYwUvG71GIIWWTvMxpwZ43B+KWrm72SG4DUBqrJMJiEHQyjNBQssu4rcVPjEGwslTfsthE6MxSKNUnOWhShscTMGT/z+CC44ALbBnut6t5hgaFVR1aQJyhJ0qal3QfKwimKtdmtREUKny8OBVkRWsAudiqc9BPKnyIhwPBjBoWmzRrs1UWPZxAWBF/cnC4RPAUTHuIuQBKGQslbFxWhiwv/i1Ne7unFGjqhYEVcT4WBUGa9wl7CkXeDWoJrrLfQABAABJREFU9QOcCNK5/Zjg3MFgEVuNNK/IKujP8/bQU2dZvKc6ZcagYLMHgMasRpf3tuN2gNpDQAT/TGEZu1R6TNKGZYWBzIHl2nhtuOC/38aD9RHETjWfxCWXXIJnPvOZuOGGG1Brxe/8zu/gc5/7HK688kr80R/90bbW9d/+23/Dm9/8Zlx33XX46Ec/ijPPPBOXX345vuEbvgFPfOITt7WubYMpALj55pvx1re+FX/9138NIsJZZ52F5z73udt2QL/DxsFYqeWy/oOtZhxBdSYMiDmD30HjvR+7+PA/bG1SjIWpFVgfJRWzMpOfUsVlPYXJcxjUrytttJRglkm479Toc4tzscRM8bI3WFyfWieQabpyms7BdcpybDifto7AQLkVRG6AivVaoFqnbNhkvTyZSIgZjCVmSSeHKZBqv1vDX0wmjdrJBFQ7TCrNjPni3Px+oL0TY+rDdEHeN21yDHTsIUUSNUoThkDZF0CBD7c2M1nBCoDGSrFU5ontQFvHBj+pserrtZ0HBVztOEU0Sl5lV2dJK78YnJNWAVZwWWq0XFhOc5y8uNkucE4OuACAcnMsys4McmuNE1vJaIq3pUMZ3XrxPnsGjvNCnMtTTpgF93Q7n9IkXNJ7ns4tLJqquawv7avunSXMYwZQvX0LJ3WO17RtipYNWRgh0jY6di1J6x2tSNQemmlQn7jCQJcknTgTo97EjLKqnQIM7Pu1Db8+PB1tGijo6dUDmwYFfXbK9fPEeur1q1Zn3BYgBdqZ2zXl2jNd7jBatx5OVKbp9XSY67ijx3d+53fiHe94B173uteBiHDxxRfjwQ9+MP7wD//Q++sdSvzKr/wKLr74YvzwD/8wLrvsMpco3fWud8Xll19+7MHUtddeiwsuuAC7du3Ct33bt4GZ8YY3vAGXXXYZ3vve9+LBD37wdld5x4jbSuttApxuU7xuyxwQRopmd6w039GKP/n4JQCA8x/xn8TVfCRgd5Ied9QLqKICYKXZDWhTZNSqqbMm4reQSUABTwQvAFw0HlvHLIfps0Jj4gkztZmQ3CbUrdoC2XZjWH9Gd4YPi5szOgIoMuH0RGBOk2Wa6SH765b+9IjzvJl0hvYybkpZMUnLuc8QIHqdxP45a8wb0zFmYgkEYEZw1217nVMQpIdw24AaJjQIs1P14Ez0VaOxXqG3X9RYxdWX6fG29VO8JgykmDeUMjfSgFuZE+1XySn5ZxNDmCtzX7cxjPAG3HmoqLOEfn9tRQQM13nVLiGvF9RegJszU3r+fd1VTErLaqfpWLF66NaKAiUDztNja+J+8y+z40pDQ9g0MtALM0cDI6lpZ7euhQEL9l5/XsxAJLtrTYaVHerWhBnLa8JI5QOjNAAfijcxRyEAPSgRMgto5T6h9HIdpkG0fZFJSiPcVBQGjDQ17AaiXfheDMC4y8aqn6uNYduK6RILBvtO6XekOz7M1E60uOCCC3DBBRcc0Tp+6Zd+Cb/2a7+GJz3pSfjpn/5pf/0hD3kIfuzHfmzb69s2mPqRH/kRXHTRRfi1X/s1bykzjiOe//zn44d/+IfxoQ99aNuDuF3GbTigT2IL0LRB2LzZZuZzb7fCi4OL5+7s8b4/+0n//+Mf/loAKhRfjGJoyNwECyOaC7mJ17sT2j2wMni1g6fyDPjE1i42Yfa9993b9HzZNrpOAO9mIMpiMxAVt7UcEaRbm5rwHtUKRgJ3SVIfChAsvdUqmwLQWmZ40FJTEUwZ2Kldq96yqqeJd48BKKvGslUoe0WFQCSsSelDCxQFIU3g3SYga8fS2tZoaxP1b5owUvFwhfGnsYpLfAQIRnKpH9fkGERjTWad5Fl0N6zA285HcLZnbmCjzrJqgKTghgojrZstN5CqtA/iTOpqn/1wUQTlVlW42jmbZqyQObnTUNEtqqYUR3/dQXRlJG1NJMszaBzkmumyA7bUyXXDXcK4K3uxgR/HYPBqQIpqBUYpQ6yzpDovaaKcDhRQTQ6SBUgzUlFANQowpgVaWhhSQdgfqALICqPbN4DWR9A4il9craAB4FkPmg9AbyIoRrdvBHZlDCfmxq4BLn43IGemomlkjNoM3LVbhA2mpn59jQC6dntxrVRMCQYghV6BvXUcOA5xMDvD7azjzhA333wzfvu3fxtf+MIX8GM/9mM45ZRT8IlPfAKnnXYa7nGPexzSOq677rpNNVYrKyvYv3//tsd0WMxUBFIA0HUdfvzHf/yQGhLf6eIgYGlLIMX6LbSwyfYOVs13LOO9H301zn/Ef9JUTA86sAiptqaf8iASoXhvAMp0VhC3dEA1aSaKaJMlABeFU0pgDKGiL3uF4oZYFlfbeIzR2iq2OteVpfl16eVGbu1l1M7AQMptjWGzkm1LaZjBZAKBSVvVFPidwCuWqoh4iVjanihpWmbk7tsAXMMVZx1LG8lE1SoD7X1PTwa2xH8nElYknlo12SRm/9p4m51xqlMycXlk4QQ8BEYSaEAqvEZeFBLPSZWxK6BOeoxRWBprH1gIo+LnnjQdKZo3oipVavFeEMuy940od9ktRp9UUbuENBRNIQrjRCTAii2tqRqvtLDtCuiGgh3WcRnryjNpTwQG+sJSAZpItpPINXdprM06IiU5P0usaBqqOsED06pP+XyZkQNkAEjrjGypxCINybv1grw2Ih1YeDqVZ1mKV4YRtBjAsx55/xzcZ5TdPcQaooLnhHEledqXO9qQWrRwCwRu7/tpnQFIQbhOcOPOupQg4ATRTyVN+VV9eLBbQjpeYGpHMwWIV9TjHvc43OUud8Hf/d3f4fnPfz5OOeUU/O7v/i7+/u//HldeeeUhrecbvuEbsGfPng2i9He/+90466yztj2ubc/eJ598Mvbu3bvh9euvv97723xVRCmHXrF2G8HjKO7bOeEJp77wqKzzzhDcJ3GFj2ySASmzEDCLA2ZxT1+fA2Np+qusP8uatJymgMYYrBCkAIp2rXoKlhfD1rqqrmvap9jceRlYbUjzqW4rp1bR2Ukj59rnjRVxFLQx0JRKEP5OHBu0miuyUlE3ZRqoZR2J+T4lc4AILFWZCRMw6d2X4V5MgLIVzC72ZWpjGbVhb5kRxl0J42qSNJGJyXmJkXIJi4Ijr7irng5Lg4KJRZlU5Ilb+SZAygoaxvBehYMsKkUAUJLXoGLvtD4irQ1I6wuk/XNgrALqqjqoj5oWrlWadK8Nsp4i74nTPyY/eZ+0YaKhotu/QBoqui/PnVki1UhRdGnXNjnEjDQfPE2GWrUoIQu7Y9tVL7g0H5EWBXl9BHfJ9VdyPeg5SATuk4CunBpYHSusX6E55Jv2iqrYOZj7fRpYPKyU6ewOFOS1Ki76B2RcnJKwUsH/C730KCU9hnZuzR0+r1dxlV+vqsNq+rvoeWWtjvI6B2Cv+5n1PJfGxE7Cvp4KsIylogRhQrsqAMo+fMfHJ3eoeOlLX4rv//7vx+c///lJK5kLL7xwW5mxl73sZfihH/ohvOMd7wAz42Mf+xguu+wyvPKVr8TLXvaybY9r28zU93zP9+B5z3sefv7nfx6PeMQjQET4yEc+gpe97GV4+tOfvu0B3CFCbQ14OW2wScuKww6iVoq/EwDEeuFx57wOiRk866TVStcBoaE1AHktVkLm3Boj98IScS/eV5JiCXfW5ZSd3sCp60R4PFvdXA/HFZSWHmGJpkDJ9F3xNfOcyi2FIa+nCWPBmxQkiAbMPkOumYlg08rhAajpI0uPuaWn8wnjENJ4QGOO8sCaxtNDE1OEJIxJKra9ppWyKLMmbJ4cu0SoJJPvqP3Q8qBamJ5Qc0K3pkJy1ZCZz5CnusxXSxkaoC1jflzRS2pyHJfALRkgsuXtfSIBEPE8FAVrBuqtsfU4qidYEquBHM7fmhaY5OTbnwjca0Xet+7XiVShampxHJuuiwg0L864Eot9hjX45i7J/4lAw1x+lyqnPhPS2gCedUiLgrrSTcxh8/qIOssOaP3a8IMsjKE0yVb7hmC6KineJM8shf3cz/ZJ2jCpIWe3f9FYxRoYxJQklV/C97NX/zoSZVxRYJfXhbkcIcd42B1AlFkgQKtRw8NBNOuU495AGICNDxVZ09AdC3jydB5L70/72qWle8gxih1mSuLjH/84fvVXf3XD6/e4xz1w4403HvJ6nvOc52AcR/z4j/84Dhw4gGc84xm4xz3ugTe96U142tOetu1xbRtM/fzP/zyICM961rMw6gTW9z1+8Ad/cCLiusNHZQBlA4CKscGA83AjpodywoWn/SDe/c+/cuTrvRPE+69+JQCIfQIRUEvza4rhabnaROAWKYFMpF5ZbtzDCKQsP7YuFYcTsGlaL2rg3A4hL6VrLWrdHBgb4Jr1zX9q+bMa3GdJxVi1o4EZsqq6AKTQWCKbWKQcvAl1UxFNSDSkJAZokCav9rdXSlm6Rj2oNjiU20Rlf1QAXRMy2/Z9f5S9KuYeTm0dtYPaHYhjdalJHLEzNUH5Vh5SDAdCXnSwWZp1wnYpE5UAjC1NFid6OwcwxmlY6rHo11SwvEhZ2JVx6dzGQgZMyQwDzm6vULQ0roT0YYVM6OPSftk4LfUIuC8axiIVp2MBrYlmihajPJjoewBAuenbfLV9glk1IBHSvDhrlQoEDPctpcfJ3N7JLRC69erpXECsD0Qs374by07/MWgo4BUZMziDZhkMSRcyyTbKTJzRzeS0dnA2rJqrf2Cs/MDrtWwNjqOxJ40Ar0B20hgoADySgCiQWFokAIVQF8fnAXinmk9idXUVt95664bXP/e5z+Fud7vbttb1ghe8AC94wQvwxS9+EbVWfN3Xfd1hj2vbab7ZbIY3velN+NKXvoQ9e/bgk5/8JG666Sa88Y1vxMrKymEP5PYWXDYyUZsud5BlDuo/dLDP7GinNsSffPI1ePfnfloaJVurmspN02SVkAa41tZB8wXMn4pNzJrkKRtd1kbTmlqLwvGUPN2wfC4o582B1FZhFYMGokzPlXS7Kyvys2sV2LUK3r2CcvIqeLVz/yJAJ8twrcV7Yu2WHM0j+0R6TFRLNa3mi8ae3HQjFfAmxAHAWGWgi8QVeNlYTMgujZXF1LH20Kd7/W1u6yzgyZYvvVSIjdq2xBosy8bhejEq1dO7ljajJaAyiZBOs/Sda+8shRetKqzZtAInGgpoPgprXKpcW7W01PIwCmix3wtt1B2aaW+qHF5mREto9g04+KJRBdqlOAMl56PtM/dZQKeBqHjNrs+dQaO5NNGmhTQJT4uiKbna7BKUfZIUqbrHMxxI1S5NnOZrppZK1kNorWfEOoFE12QpQtexVdSVHrzSg7uu/b/P4FmW9H6fncXjlIQZc/uQBuDMw8obH+cGjlIklSuaQakdRzUPlWbL8fPKRCWAhiTAqiqIGhK4EnggYCSpPr6Txoc+9CF853d+J8444wwQEX7v937P3xuGAT/xEz+B+9///jjhhBNwxhln4FnPehb+8R//8aDrvOKKK6R4Y+lnfX39kMb0xCc+ET/1Uz/lbudEhL179+LlL385vuu7vmtb+zeOI97//vfjne98J3bt2gUA+Md//Efs27dvW+sBDtNnCgB2796N+9///of78TtVGKDaFktlE3FgFiS1lHZSfbcRPAwtzbJY6PFSdml9HbR791SYbULckBLx495lIFWhbLoO2H9ALAq6LoCXvrFXBro2E6SbliuyIiaUJ2qAz5ZRsTyHqiWQOLf7hG9tRJaMOIEGOLwFizFJAEzzZL3MakcyxzKa/UGHKeNTBHuZ7YE8/WvqxJ7czZ3cdFS6HUsRevWfbmfipA5bB7QykFE6ghkqVtK2LZmAoU5E6VbF2Fy+QxVfZGUgKUi3OIggRtNjcv7IheGiNVpazgDJWFoqrwZminlj+jeef3Pyr2Hnl5+RilwbtBCWiEOVn59EA3zgZl5qn4VMJAzZL161tJgxVMqcDdpdoVSQpZOZwTnL6vss+sRQ0eetZTqrcBQgVc0YlNlTgib6Fhf+BiA9Xbym7FlHoEEZzKzpzj4DPYDCknrss4v7aX0Z+cCvcQP3Vd3Zh84oKH3wSKLXErAo4yrBZ8usEkANiFWC+CkzFFVpeo8VMJGeC0IDUEMCjhOY+kpU8+3fvx8PfOAD8ZznPGcDUDlw4AA+8YlP4NWvfjUe+MAH4ktf+hJ++Id/GBdddBGuvfbag6735JNPxuc+97nJa1H/dLD4+Z//eXz7t387vu7rvg5ra2s455xzcOONN+LhD384Lrvs0Hvb/v3f/z2e8IQnYO/evZjP5zj//PNx0kkn4Wd/9mexvr6ON7/5zYe8LuAwwNT+/fvx0z/90/jTP/1T/Mu//AvqEp3+hS98YburvMMFJdqUkTqStB8l6fdGy+aTO7ExDEj532FSSx3MRdwtDcIk5ymgLAJv1AqCMlRjkQbKM/V7IlYWSSfTzaoIl8NMOGMsg2MFUrzSSwrDJnlrotsJnW/u5l42D7TqK2OfbIJVV3PprZbE7zLonUQ4LgJhDq9PGuGGkObGNvHYxEitOsow27JX6nIqMGwjLuMC90Hf069TmREwMFImQFu9SHWfTpQZLsie6G7iOVGdlYuaNxQA6DJZcozco80yBsLGpTZDBuAtVRytKGq7Hr1/JNB6MTrA1ZO2xfXjOr8wTtcQ5QSsj5N+keLJpvtvlX5Z/J3sGDUGTB8GDojekMoMlCWFnLhHpa7tkvppRcBqaT/kdu/LQwMoYtAp4NjTw8ZgEqHOkntk+cNDbg8KddX0ZK2y0zSJBtrqSnbWLDKXTECeq9cVASBu6WoF6tbGxjRDZUWZKCW4kVtRHicCMqPG05QCDRvtQY5jCJg6sg1vF0xdeOGFuPDCCzd97y53uQve9773TV77pV/6JXzbt30b9u7di6//+q/fcr1EhLvf/e7bG4zGySefjI985CP4wAc+gE984hOoteLBD34wHve4x21rPS95yUvwkIc8BH/5l3+Jr/3ar/XXn/zkJ+P5z3/+tse1bTD1/Oc/H1dffTWe+cxn4vTTTwcdbGK5E4aBpa0AVYxDSRO2ZWsTSO/EQYNVgM6VQbPeJy/v2ZeHVu1nMWhlVkIrx+4zaB4mzJwAmunkZzmL2iawOIluZfY5DJunabvWssYq9mgxNjAU03mAMAK5gQzTR3naL/62STDM2WUmDII1tjXg4m0/sqRoIsvkBp3ht0xsjb2Sp/y2nRgOokiOlVVLmYGnpOnk78RSFWj95qqzbDHVIt8xAQsk/kaaenNGz6wB4rlW3yjXRdmwdJmatUoztgoKXmCutzJtnaWDMUwZLPtcMIQl9YsjO89VhVmJ9NrITZxu7KNvjybidFoEUB78pOTYS7rNHgh8XQgArFRPR3IpIApaPTWJJYgxVCIVmJsAXcfHijDSWDGe0KYL0yRZqyI792kIzGaSijoAfu2WXSp8T9qsm0itFvTcmCM8V7/OGjuJZhkSrjl3gi/yMGtpvuhrFt32uxHO9JZeGdkKVG0UnUYAc0nrVdsxQ1bhQYKKPoTcAXVIy5qjlZWVoyLTueWWW0BEuOtd73rQ5fbt24czzzwTpRQ86EEPwmtf+9rb7KtnceDAAezevRuPecxj8JjHPOawx/qRj3wE11xzDWbW0ULjzDPPxA033LDt9W0bTL373e/Gu971LjzykY/c9sbuDHFb7NMEQG1VQr9VlCIix68ygLqdePzsGZO/eTEA4bvApciNWXsiUkrglU6e8uPklJM4rKcEJk2vEEHmlimDJY2oA6gCpqkgmww3s8oIbWLsb9bSb69iAqbL6JM8k3rrGOsUTC5rH0BUcD03ewJnfyw1QvAUDdUGXhgNBEVnc2GAprtiwt/Wo27j7to4SN93PyDbTYaUzMtfOi4gQdklTRF62bqyEHleGnBJytapDYEzOpm88s1TujHFCPm8garoqWStZcjE5IlcwO2A2qw2vIIvgLFg1eHp+gismdr1U5aupaVwQBWXB7RgQV63ZaKOCqxps1F1XZbO0+uSSwHWtJdkrmIlYgUXzGrtEa4fNCDjWiUC6kqSvoBoy8Swyj1PNVuGrEuwBt6AVOcBAAdmtKWF08RXjSdVhrJuSzeaeF76GcquAQL2SMdhjZg9XT1KSo9J8LZf78raUiKgY839acowVPuhQnryVYQqv2MbR7Oa7573vOfk9UsuuQSXXnrpEa17fX0dL3/5y/GMZzwDJ5988pbL3fe+98UVV1yB+9///rj11lvxpje9CY985CPxl3/5l/jGb/zG29zOXe96VzzkIQ/Bueeei3PPPRePfOQjccIJJ2x7vLVWbyET4x/+4R8Oy+Zp22Dqa77ma3DKKadse0N3tOBSwJQ2FZEfEuN0ECBFS0h4QxzM8HEnPHhssz1pJSRKRV1bQ9q1K6RgIOJcmxgju0QkVU7G8RcRE9Osh1VYoUvifbM29z6CIkTe5DqIjJWxD8zaIkNfriLGJWhT5VASz6udTC6dpFrMjoBDTzgA3mdNPmfMlQ5t6VttLtQ2CUF7+G3aADpMalHv5IBLmSRZ77SlR4zaSdVXGhnW6HbCEFQBVda/rfQCcphUNMxxudLWWeV8pP3zBmAo9DE0QFUhf7vmCJ4240xI62NoTlyneiqgXSfMIbUWCg8WpkeaWnJIn0X9XN83gE0kYGXZPgPwVKAzUrU2MTnRdBvEzlKRpRGD+SgNKpbXdPPEONhAFQAqOobFAti1qlWEA7hW1N3T+5M3oy4Mnhlz2gC1XQ81t/NmnwO0YIFFf8XZxOg6ZEv/mq1HL+ePO4AWdm4ZaU2qCavKICbXb27ntVur4neluqcN1XyQ3/LN08rDohV9FeARKFnBVm+fI2fofKc6CNBS+4TjEUYiH+k6APGFjIDnSFmpYRjwtKc9DbVW/Jf/8l8OuuzDHvYwPOxhD/O/H/nIR+LBD34wfumXfgm/+Iu/eJvbuvrqq3H11Vfjqquuwi//8i9jfX0dD37wg3HuuefinHPO2TItuRznn38+Lr/8crzlLW8BABAR9u3bh0suuQTf/u3ffkjriLH149EW8drXvhYXX3wxDhw4sO2N3RGD9YbEyzemLT9Qt8dIRW8hXf+OLcLW8d7F2wBgci42nJuibWHW58D+A6ADaxOtCRKJK/Ss8wnVq4ys9FwnMDMUJGZgddbWk7KwF1aZZ82KgelkaeauxnJZ1FiZJJMMm0FnRdMFuZeUfqwnlBUFXwmtko+1T1rA/q1Sr/2/GX3qU3oO7znYmrJVwk7Aq568nYdqYsyocfluL1opPSSFXTSchqbHyWrsmIf2m4o4ebdjj2bAyQxaHwUoDVP7ATfFTBCAbFYHXWqaIkBZhTwx2GzHLE0r7excWnVeZQErXSfXQJyElu8P5j1lr49j090ta/60l56PwVPLRrOEKsL5ohVEDEP7MdPaZduQuC+m4bL9jpWImkJNBxbSL89SrCZM1/Odip5HdZ+fsI7BkV6qEdXks2vXLCdCWU2oM/kpK6ldq5XdDsOuoWjSmofq4M2W6Q4UX44Y6PdXpIVcW2bY6d5TZipqDbz1mjXdFxUgLWS5NBDSQlPtTJo+lvRfS/sRuDs+D7/GTB3pDyC6o/hzJGBqGAZ893d/N6677jq8733vOygrtVmklPCt3/qt+PznP39Iyz/84Q/Hy1/+crznPe/Bl770JXzoQx/Cfe97X/zCL/wC/t2/+3eHvN03vvGNuPrqq3HWWWdhfX0dz3jGM3Cve90LN9xwA37mZ35mW/sAHAYz9Qu/8Av4P//n/+C0007Dve51L/T91LjwE5/4xLYHsRM7sRM7sRM7sRN3rDAg9fnPfx4f/OAHJ0LuQw1mxp49e7blDvDZz34WV111lTNUwzDgO7/zO3HOOecc8jrOOOMM7NmzB29/+9tdyP685z0P3/u93+s2CduJbYOpJz3pSdveyJ0ilG3isUqyXWMz/VRMA26qr9qs2stST8O4cfmdmMQGhpAruABUGi3DiwUwm0kp+KACiV4ud7ceqDU89bIwHUXTbrkxFGypBavkS5KCQ8pwLZWRAcZEmL7HPY1S6x24PpemycxgTEXH4ucT9s3SVkColJN1usUBQZgZrdTbLJxhWvJvknYZ8Oo+N18kINXGSnhKMbf/i8M0OxvGmVqjWTdWbE//MVJIO4r9AiNDzBfzQhmQIbBTY9HKxgQ+YYa0tpCWOyE1JuOX1G1ajC1NCwgbSapGLixtYLSdiaxA9VLGSkVNHHM754CwUlwbK2bL2e9hcDd0d7s3vdPkICz9XaoIqON2TbSuY3Sd1lhU9MNtzLYvtYp9iK4zsrJECRPbEP1OoBrzllofazuW84LxxF5E4wsVqQebCmc0EzW9FBpjVXtr4kzumm6fi42Jm01HYLbCuU+DsJNlV7vIR63aFR0ee9owL1jdzgkcTGdZJVA1Q5jAkVBX0Nip8F1IQVxeZ1q8UaCFLFp1mFmq+45HHM083yHGvn378Ld/+7f+93XXXYc9e/bglFNOwRlnnIGnPOUp+MQnPoE/+qM/QinFHchPOeUUF3Y/61nPwj3ucQ+8/vWvBwC85jWvwcMe9jB84zd+I2699Vb84i/+Ivbs2YP//J//8yGN6e53vzuGYcBjHvMYnHvuuXjlK1952DZNu3btwnOf+1w897nPPazPx9g2mLrkkksOabm3v/3tuOiiiw5LGHa7i+W0HTdAdSgVfcuAikvZ3E0bwHtu+Y2jMOCdkGqmAl4ESYP2P6S8KtWTQzh3hUELNWL0ydGARDj/nqqrU83U6korhd8slsXE/RLITgk8k+vAhLoggNVfaOLtBEw9l4IAPQ1awcdoeib7HDXfJ3OKXq7WzwvZrtki2ORoKURP8Zk3FQBYC48RoK4tA8BTdjaJWViJOwAXEpvGikbd58reV89Bk5p28qwD6tjAkB5DsubWfezpKOuvfRIhOwCoG7iHOafb+PpO1mXndBjaQxTXaSrQQLNFTO0tV5V6g+0MzOc6FlP9K9CzVKPZMWx1Xc3n7XVLKQ6DFGXEtLN5Us16uI/dUoNv+X+4RomQaARzRp1l8Z4qJECiSIPkqn0vrdl0ze042DVQVrUpsTno67VCFdqkmFFWEqhow+SheqqdNDUMrTSU85KQ9BzWPiEvQoqWCBgZZTX5dZ9GRlGQttzjMva3lBWiaQs1JB1Iui/6Xq7NT8t69R2POAoC9O1WHl577bU477zz/O+XvvSlAIBnP/vZuPTSS/EHf/AHAIAHPehBk8998IMfxLnnngsA2Lt3L1K4fm+++Wa88IUvxI033oi73OUuOPvss/GhD30I3/Zt33ZIY7r73e+Ov/7rv8bevXuxd+9e/MM//AO+4Ru+ASeeeOK29u2MM85wEfu5556Lb/qmb9rW55eDmI9NLf7JJ5+MPXv24N73vvexWP0xi1tvvRV3uctdcC49GR1pCnMrDRRtMXFuuujWFzFZU1wi/Mm+/7qd4X5VxvnpqZu+Tl0/LRjIUkBAs5lYE6yqLmA2E4NDm7wygdYWsowxg317zlhmPwDoU3yYVK3ayyqpbBnAfaVcuJxUjGzNkzOBV3rU1V68clZye4IkvekHn6aa0dBPCDPOrDNqVgMKqpIzT0Ggzk3DAsjE495SML2Yap96Eevacu3YwMFaY7Ea2+dNiys30GcAzvvsQTyHFHDRUFWozMhrYxiPUSE6wa6P7ZxER8Nw4442GCYMnziJG6CqU3ZLWItYKacsUTynEeAsm4PaOMzc03oy2uvmV2VC9UTT12sAU3Fb0TvNthXAG6/Pp8x3+D6QXf85t+sxHi8bXy/u+7DrUnv2mb+ZuY+bTYHNz3lulacNUJVdqQF4A8hBbwdm5GBPQpUbmKqBrdLvaTuv7VrzsSmDWFSL5ZWvWb4TVtFqtiDGzJYVYXRdR+hNvOW7Yn+PJ1RYjz5OAK0IMqzra7j+B16DW265Zdt6oUMJm5PufcWrkHYfmrHlVlEPrOML33/ZMRvr8Yqbb74ZH/rQh1yM/ulPfxoPeMADcN555x1yW7u3v/3tnib8m7/5G5x22mk455xzXMj+zd/8zdsa02E7oN9WHCOMthM7sWWYEN0AlZEyKKU5P2sD4zgJ0VjBs14m0yA45r5rlVlEIno2MToigxUm1b6TlF8tmtaoAuJsGZvQiLyhrVfzJXky55zET4nVd8eySspQSXUVbwBUDXRAXLDD+1UZo3E1PKVzAD6R6dK/04g2Map3k7FIvlyJn5H3fKIzNq2ybMvMN+MziO5PTVLdl+cFZZYm7FdbluFtYQapxrNzImMpWslnYGYJAC9lh6lwqNCDtAoBGtgKqd52IGsrOACm1Zv2d2Sm7DOWyrdiBHtQM2at7+Vas55+S4zbpHghmpFGlqmUwC6V8Fpur1vrpAikLHU9jBuuKWP3JEXcfKAikBKg3YCPsY6WhvbKPwbcmoKBvC5jTEM7fnJOacPfVm3r16t6YnEipPUC4hFld49K8h1JA6PODNiRgzwTmrdja9+nAKSyeqBZStDA4jqhzuzZgYFCoK4iHbdGx1PMfrjruDPEXe96V1x00UX4t//23+KRj3wkfv/3fx9ve9vbcO211x4ymHr605+Opz/96QCAf/7nf8YHP/hB/NEf/RH+w3/4D1vaJhwsjhmYutPEwSrzQrpvsuwSY3Wbrug77WMOObZipQBsPB8W5t9VerjNcYXM/n1Lt4pnjAGb1J6Ek7pNzwdNcwDcZZkUYgrG9Flm8mjl9cZyRZaBBZSx+dgURlqIX1Iaa7M7yAZ6GkNkZd6c2gRGrOBR/zFtVZyYRm8GSxM2KY2MbGaLRTOChhWCtsmr6uweY5N6bnoUELXefYGhAlo6b5LiM2JvjX2SjeaicjI03acGneDgeE4k/j9hLM5iRC+kxThhNjyiV1hs5RJb1fhKqIEn+7ECHNMoWc9Fe99TbQpqLB1s1gXD0NLQtTR9lMUEREVmNAApNe3k2PIoAsEcLF5mvbCjbiiKBqS0kg7DCMykT96Gem+3omj6OwNSdQZhajqxKCCd/M353N3sISyWs1DhGFPRD7Eur5W2MUUrZre5ubwniPZwqOKEkcRGJC2EkTJvNfM0A+CtaMx3igFIlwBZX/Nwa98BedghcWRQw04GRDd1HOJo+kzdkeN3f/d3cdVVV+Gqq67Cpz/9aXzt134tHvWoR+GNb3zjJCV5KLFv3z585CMfcYbqk5/8JO5///tvS8husQOmtgqu2NyIZ7Plto7ttJfZSfHddryv/tbBAdVWYQ1yx1Emj90rnjJxx+hagS43E0cmZz8m5oiAtBsBdNIq0757FmFdnJKYsZrflJalU05gqM/VvACrJA1pK1BO7MWfJ2hR0gDpcaasj5eQW8bM5mIFSuYebuHvK0DiBFQIW2Cmne4jZXilSoqNqmzPS9OtlL/ok38QHQMNRJnNgSwskxwnYaic+WAGDbXtBzfA1c6hTvauFbNWOxBmCVnYqU0AlWupuuSmrAIc7DxSWC6yWw2sTB56lrVHFsu2B0TSxBqAN+YGmiYKCICqa2AqgqoojJeDMwVDup2ow5R9Ss31vO+ElTVgHwHXMGrRRNgXswyZWWpW/Z5UkwciTysLgJfrNKlGrvbUCgg0zZcWFW4+a+tUUD/ZN4Z+Z5TNo8h4VU0/S9Nm7gMzmVh8sroZ0E17WdII1NXpvTgCKhlPY0m9dx/DNV5pJHAWDWztWXry9UBZ7DwMH8/4gR/4ATz60Y/GC17wApx77rm43/3ud1jreehDH4pPfepTuN/97udC9kc96lG36d6+VeyAqWMRSwL1g+qltOLhT/ZfeVyG9tUQE91UqfL02HdNV7IyU6Gv3tQzSX8+QKqUyiiaKosEIIhiN+hYVDDMM9G+OQgYpb0Nr8jExxAdCi3KhnSKMQ20PjojQGMFKiEXeTK3SYsGeVq3hq3W0sNAVhpZGKZO03s2QQDOSMXgDK2GBPIipOdgEwkjz1UMzBDTRV0XJ0Iq1funLacKrW8eU2tJY+lCrsIquSO5p/UUSOn65bjLebBWMuhMO0RTUDHZMZ6aXwKgddEhedq314bWlaZ6KIthnD4wWWXfMpscLWIM7CznVMy0s+sAHqTa1HRRsRIwrmsZSMUekRYG+IjEfX3ZYd1TeZbqk76QGErToW3C6HImcZdf6ZAGO8fU0rIDu+O+MaFUMdE3cdBJAXqulUnyYgDT04W0LifRjkXn80na11r/zJvLPCeAIMwuElDCZ2vfNIXTnYSn/iYNuTMEtCtL5d+fqhWLLNduxdIHj2VYb8AjXccdPP7lX/7lqKzn85//PHbv3o173/veuPe974373Oc+hw2kgB0w9RUNn/R32sdsK7Zip6hTQfdSrptsAuoCSwFsvLHoBMtJmChvyFrhLJYDL6/00tdXmmt0a6Cc20SobU4caJlOpdTmZL0KqVKzcVuqDqIvmehJBmWcWFmdKinDOkuS9lqax90BWkW2IHlaN4E6R5fnopOLarSSpV6gQIrbutJYUbvWxHbjSVFQZQ1sDSAxgKzriz0Pgabp0qxea1isCxiQDa1h2ocVZLkTun7IjDYNnKi79kSztAxaonZpAp4DMNl0n6kBqr6DNyUe5BzSouj2tJpvdaUBqNXZ1J4hJ6BfWr9ex15laGMimqYWbSzWPN0afIemyOAMpk4qWSdmswyiirrSO4jJ6yO4yyjIAmYTtTQwCbjy4gQFw93CiglqM8g1YF252YJsURVNQ23fMWWpJj01bZ/N5qImpEzg1IOyNjZmY5bg17+J0d0dXY8x2XOOaqesWbIXUpCyplm+bzQCWBwfMLWjmTq6cdNNN+FTn/oUrrrqKrz//e/HJZdcgpQSzjnnHJx33nl40YtetK31HbOr4Mwzz9xg6PlVFYfihL6jlTp6sYX3lwPWWsE2MY7ay20sE03RpHrLwiq7zC8qpJjc/dyE6nZ/19SR9N4jb7I8SVsZcxB1LkATuNu6lnGfZZ+TpNwcTFRlkSxFt/TNjt5Sk9BJhjuZXKJlgrtKj6rJ0tYw7v+jP2koSIuCvD66dYH9FqAE12bJZ0Qvk9eKgDAKVV61pZTa4OOO8PS3nQtN27Xmx+E9QABLLU2vZBVxxlxF1/CkIGttXY9RbctYGnDWt1RZDHO8tzY0ubnb15Ue3HVugeEVplmuIT5hBXWlR929Al7pwNrSaLp+3f44bgRO8VjYTxfe70M7G7UAMaf4qPsThlW6BCw34EZl5PUR3X7VWFVhSikCKYioPA3VryE/fbq+tCgbr8XlSOSMFZUq19lQJrpDax3k+86MtK7dCxhu7ZHn7P0ES08oM5o0ZKYCYaC0eg8Aas9e8GFRtbLPbBRkG8eJmdqJox4PeMAD8B//43/EO9/5Trz73e/GhRdeiN/5nd/BD/3QD217Xdtmpq6//noQEf6//+//AwB87GMfw9ve9jacddZZeOELX+jL/e///b+3PZivqrDJf4eVOqx4X/0tnJ+/Z8PrBqTsN5eCpHomihVMCaIpmY/TiblUkFXyKZskH97kPPkNXW7uvNpJmi70/uOePA3jeiVLBVaW68CezFVfVYOJq1fwFRbmKCfUPjkosVIj7kSvUnpybx6pfGtYxFgBY664E3bKGr+mYpoYWWc+IJOhtx+x7SzYhcJkKTJAJrKFmWtSmFxZgRqpjxBAVQEtycRa+9SaP4fSejmOJB5Wy+k8A7h+8pXBUB0NlVAMMIwtnabi73bOreJNQZK9NwtsVayOs1jycfIw3dSuVWdTrBkz91nYmN0r4CQuy2k+iC9WAILGHnHtkNYGtdyIqb3c0mM5u4caAFk2gii1QuC+2TOYZows1RY9pnrtS0nC7k1a8cjpRFpUdBhRV7JXykUQzJmcrUxDRVlp40n6sEGRkbRfodK0fYDA2mSRDDwZAKxVjufY0sVMjDQvwtICyAOjzMhZzlSAotYItWsAiUZ40/Taw0Xnss7w/87GAKBoy5njESH9fkTr2AkAwCc/+UkXsn/4wx/Gl7/8ZTzwgQ/ES17ykm0L2YHDAFPPeMYz8MIXvhDPfOYzceONN+L888/Ht3zLt+C///f/jhtvvBEXX3zxtgdxpw1Km+uldoDUUQ8HT0saNRPl+u++l0bGOTWTRqtk6nJLGZjnUKUNHjeuSdHfLmY2QDXKZMoG2ioA6wUHKLCiZitgLIa+R4W1qWsDRpYiqcHsk3PS9+FP56kwUAgl6XpVf0SJNK3HKBCvHU/XGRM1AFmbD7tlgQIg235eK23MLIxFmg+NHcjSI4878/7R88ANHCYtiSdFenWWBfR1qXleWaWWAd0szuA0FD+OnJKkm2IwS8EAKnhVADHNlXXqe2GnNgNlo9oCrM/b6/1Bbo/eny9eF8EuAZB1KpCvKz3SfAAt2K8X7pIUGmg6mdQ5n+N9Ydah9BlpfZSKxNghoQRQaB5nm6U9Y0TPtHF6HDhPtWDElpKTa9md2W1VI4O4eANjKlV1bUFfqJHXRrA+BCyPsW0nGGsu3Tel+XMJ+ihN6yVM7DEknS+ANQ1VPKfUwkF6UTatoawX3jSZO/gDCGWp3KNweAVcSboaHQREEaOm44NQdqr5jm5867d+K84++2ycc845eMELXoBHP/rRR+S9tW0w9b//9/92p9Lf/M3fxP3udz9cc801eO9734sXvehFO2DK4jaAFO2k+I44IoBaekP1SGVyU6auE8DUa4Njm0ysxQusPIk3iHhpPaTjgiWCT4x9lnTOrEdaFJn0a5UbfeKm9RhKE7bm1JStpUrJtzl3a1osevTUmYAz1y1FBoeaqLt2YrBZe9V0dJrK0wlBWm2oqafNp5oSkeor8vRM0qd50ZxUB1EyJp6AG3lvicWoEBExN7sEnuWJRoZGETenoQq2yxvHBtYy+gptCK1Vkl1qx8GquiZ6tyJu5+haGreHTMqhlYybZAJN67RZhLQxFov2+sRxXLczjOKH1c1grYg4pCKZSECfa7HCdoga00OEutqJFjqCqWUZQdeYJ3RacLEsjF9OgSqw5y5o9QxYVpIK1Io2U1S01B8DgOihMDBqn70i05lXwL+DzcpAUncOnBgTIGXA3YsGRvWCs6pae7Cp+kBUSqvCBECLAZx2OSsYw60QzJhTzTotvW0asLwO92Yz9//ayXcjDRAHdGLQSKB6HAHKDrN01OKmm246JPB0qN1ctg2mhmHwDtPvf//7cdFFFwEA7nvf++Kf/umftru6Q47LLrsM73rXu7Bnzx7MZjPcfPPNmy53xRVX4A1veAP+5m/+Bne9613xlKc8Bb/8y798zMa1rZiUHiuoOpiQdScOGtT14HHYCFqXKoMANNNEQJmJkCYptQnUx7FVYpk43CbKlAR02bqGUSawwlORsDIfdUUF8b0wSD7JGxBABQ2A9+3T1IyYEQLe30+zHhxtAUIFnDmNAwA6AhUBk2kQAFNAIIh3E1W4yaK5QJuIXQTuWg3Isv2agE5TK1Zhl4biehXXk9mY7P+FhWmBMi7GKGTCBuE/Qyq7WFu6VFuu+thav5s2QXJKwUQ1nmudhGvVaixltTik/TplIg+sLV9WU/2UnQNzMLdtBSDG84WmEgdhPi3UoNVZnd5SeAbIwrgT+TGOabXl1Bl2zcStn6gBfgraLUtjmnWDrtv67Pk+2PmYCZMa3d95pQctRtD6KD5s0U5Cz62cV0k1e+/KEqwtMiEpeCUFWr6fgFzTfp6AmpMCMVsBFIBrai9cTxZ1pZcejHaeAtJIQwH3yVN9VrxRMzyN6AUR1EAUCpyxdVZKX8uVgv+ZfDi2TtqJ4xNPfvKTBeQvBRFhdXUV97nPffCMZzwD//pf/+uDrudQWagf+IEfwEMf+tDb7Oay7Zn8W77lW/DmN78ZH/7wh/G+970PT3jCEwAA//iP/3hYHaMPNRaLBZ761KfiB3/wB7dc5g1veANe9apX4eUvfzk+/elP40//9E9xwQUXHLMxbRlbsVL2tjl06w3vPbf+/47XyO5cYROJ/UCPbZykzBHdbsRjm9jlA3qerKQ8hQo8m0j7vjlX+3oDc2UpPm0pw31G2T2TFMRMWQhNB3pbkxhWtTWMyrTYDb/ZEVBhpEVFGivSuoi906IKC1ZkEqJR/s6LKgaGKgqWNjHsqbu8EG2UAzHbhj6Fp7FV36XC0iuQtYWGpjB9ArbJ2UTz+n8qRcakDvO0vpAJWoEYjcLERaNMa+NCRffLUpiALGc/UFbMUj/2O5xPGopOwOw6JfcCs+VyBk46UXygltzseX29ibxtP01cbum4UgRI6fVAOauXUw/s2uVFCnYcZMVTACWp4JC6NZ3SJH2oDGBRds60erNetrcsgo9eUrqvNJaJPpCGslELBQWg1h/QAJD7sRU/5jQUSVuOVZmjoiC8Ac660rl+DtCHhNho28+7XleEyX47kKoN7MXvDhUtIFFjzwmTPBQtelDz0FDhOnH71xRf/L+Bp1SANMDBlXy2/aZCE3H6sQ5L8x3pzx097nKXu+ADH/gAPvGJTzio+uQnP4kPfOADGMcR73jHO/DABz4Q11xzzVHZ3qF2c9k2M/UzP/MzePKTn4yf+7mfw7Of/Ww88IEPBAD8wR/8wSE3KjyceM1rXgNAmKfN4ktf+hJ+8id/En/4h3+Ixz72sf76t3zLtxx0vfP5HPN500nceuutRz7YzWKJldphpI5OUCJP81FsmwHoU2ZtLWbsS7G2Dsq727LMmLhPB/2SMFADJi1CDKzppCXgSUXuhVFX5Gm/7OonrTLEPZ193WSTFuDO6bQ+IqUkqTEi95Zyc1HAgRNx6DFn4nIASfu7cU4oKSEPOmG5Fsv0LuQpjn6/rDs2Gk6LlrKRqr0lLYx5b8WbjZfG2+SXPL3GfddAlGl3xpb+ImNOgFBdJoAjr7emvhxtCpSpkgorPV/hOImXkp2zpZuisThd1wTeS4CZF8P0M5ElM98yS4sBkkommlT60XwA75619je6DvFFanYXnDupcitiYLrs4g7ANUzLuj0YKNNjaaae7p+my9L6AF7t1aIhpKrDeYxMIgAHrJNQts8Zoy6ByigMFAl4MfdyQEEUtc/aNcXLDxZAaxljaWVAgJ+dLwDWVkj2Ta4b7rOMYyioqx3y2oDxpJkeNwNRjY01Nors1qAMVc0CopgA7uENvpGELCVdn9uMHC9mapNL+LDWcQePu9/97njGM56BX/7lX/YGyrVWvOQlL8FJJ52E//k//yde9KIX4Sd+4ifwkY985LiNa9tg6txzz8UXv/hF3Hrrrfiar/kaf/2FL3whdu/efVQHt5143/veh1orbrjhBnzzN38zvvzlL+MRj3gEfuEXfgH3vOc9t/zc61//egdqRy24guvB2SkATsM/4ZQX4D03/drRHcNXQZhj/AW7nikvWNWZTXZLZos8jvKA2XWS3lnR1h5W8p5q05jEG7e5TRugiqasu6TyiYaKOjMQBMidl11ETmoLUPsMMhF3nBC7LBWEgDIt0nvM3cYXxZ/OjRlwXRAAjNU3mw4AqUsou3vQGrtAmDvxkiJNHVKVKqc8wJ+w87pVYAmAS4NYH9DIzipZ65WoU9kgvrb9Gos3f6bFIqTrWmUlr3QKeBDSeMqAORNQxY17kqqFgBOgpRQXgX1xLdcW30M7vzb2vgeGQcA3peZXFvVQzpBMvZzI0mh2DALANKH5pFeipZ/Ug8sABhMhcUXtM9JiBKckvzO1bTuYC1o1ZbkmPmWApJINkFkqcT6CV2Q5MeVsqbANrvMWKTW3eABI7DowEIFHtPRqqaBK7sBfg3km4rW3kt0+AWgpTda2M1Zw4P0r7fo3B3U1FW0HVdPru2f+XUvzApolT+fZb+8AQMoyQb7WtVOQRXBdldkmGKBCCZ/PLR24E8cn3vrWt+Kaa65xIAUAKSX8h//wH/CIRzwCr3vd6/DiF78Yj3rUo47ruA6LHmFm/MVf/AV+9Vd/FV/+8pcBALPZ7CsKpr7whS+g1orXve51uPzyy/Hbv/3buOmmm3D++edjEYWiS/GKV7wCt9xyi/9cf/31Rz6Y20jzTZe949Out4fgUmRiL6VpoIwNdC8m4/lVb7JvP2CsZEoymcbzFtnD6FdECVidNU2Uao/SUN1RmhMJsErk/+dMAkzsyT9Px0fD6KmNfGCBtCa+TWk+erosgkUDVbQ+SKqlVtB8BI0jaH2BfGBAmreUII2MvF6RNf3HSf13FjzRXpnJZra04lAVUFXV0gyg+UKA6Nq6/MQ0FjMw13L+YZB0mZ2Thb5un9t/APSlL8vPfAG66VbQ/nXQ2kIApKaQjFWhxejpQks1pfkAWleNjwbNx6bxGSMIDD/qk+TnNyVxJe+mz5gTUGVh55BZWM/NvsfaTzC2T+HOtEtwzZ6J6KWdiWimDEgZWJiAHKv+7LWaUP2ioi+UX+dmF9EpA6UVq7QooEXwWUtbMFAxllgk7sRugbvk6b+0GDX111K1k58i7zE1B3S270imlubLFAo7chPua6rcXq+7Z+CZOLrzSu/f07rSCULS45/X9TtWMelNKcwrWhWfpvjMX4qKgKtlVsgMPJNaixyfoKP0c8eOcRzx2c9+dsPrn/3sZ7058erq6qa6qmMZ22am/v7v/x5PeMITsHfvXsznc5x//vk46aST8LM/+7NYX1/Hm9/85kNe16WXXnqbrNDHP/5xPOQhD7nNddVaMQwDfvEXfxGPf/zjAYgK/+53vzs++MEPbqmdWllZcUH9UYnNGu3Gt023sdUNeCe2HRPHc6vkA+D9xkqR9Mg4qucONqbu+k6ZrIOcl151VdGlWkXTnrYp2qSX4AaaQJgMre3HUFrKJjIkA/wmkErxKivTJ1l6xCsOa9V+g7mlcgCZNNcXIFoBOCGXEZx7T4lQJfQHqmuz0iKId01wPlY16lTGZDEGYMJTobYxUMtRFUAtg1RjloxRyll0Sgi3+75T1iU7aJBtW54GDhZcU1PRqrpM0A7AezNuVpxACeg0hzMKCxXTe2QFCcup+QggDYCVoiAneUsizs1DyoFDEh2dXxt6fKyqsa50zcMrXkcR7LjQPE2q8SaRkrCRQ2kFF1a9CGixQm3pxLiPbm4Kt7yQ/ZFzQczSYLjwBtsEB/2saePUtFI0NlZrYocAbGCpJqtMCVGkZL36uEstzd3DxyLeZfKRvC5eV5Kya15WZMJzvaRcQE8NWHHGxOndKk29CnCO4xM7aT4AwDOf+Uw873nPwytf+Up867d+K4gIH/vYx/C6170Oz3rWswAAV1999W1KfI52bBtMveQlL8FDHvIQ/OVf/uVEcP7kJz8Zz3/+87e1rhe/+MV42tOedtBl7nWvex3Suk4//XQAwFlnneWv3e1ud8Opp56KvXv3bmtcO7ETO7ETO7ETO3H7ize+8Y047bTT8LM/+7P453/+ZwDAaaedhh/5kR/BT/zETwAAHv/4x3tx3JHGoXZz2TaY+shHPoJrrrkGs9ls8vqZZ56JG264YVvrOvXUU3HqqadudwibxiMf+UgAwOc+9zl3Z7/pppvwxS9+EWeeeeZR2cahxm2l+HbE58cmXGgedTmVvaydq7I2xqgsBmVzgt7HBdBBjG6shDeYFRbIzAztCR2AV+MB7SlbKt84pHsSCKUJiZdKvqGsCAGgvk6ZFGMjagXmC68sA/R3TsAoy1HfKaNF0lh2XQTCVX2eqLBYJlgzWk3xmbiXc0IaWtqGM4HWzUNp2JgSivthwmFLu8bCgEXw7AJE6B0E67y+3iowV2ZSHICsvyFVZjHtZdYHdjxt29YHz8Zjx21SeBDWo2k5pATatQqezze6m8eIBpc5g1k/P18AXee6NqoVVW/Gxops0Ce53QPacstpYCjLlHhy7CMrJKk8XccBlTeYpYbp2Eqd9Aw0r6YNrJR1AEgyBmOkJqlmd9U3NjjsUwZoXkFdaMwNBM8ttH0kUrZHj4Ppr2y/NiuFj42RTcQP7SKg31/3rlLvNU7CynKQvKUiBCAD3jEgLaAtluBVrt6ySdkqQNN8x4vt2WGmAAA5Z7zqVa/Cq171Ki8YW7Y5+Pqv//rbXM/R7uaybTBVa/W8ZIx/+Id/wEknnbTd1R1y7N27FzfddBP27t2LUgr27NkDALjPfe6DE088Ed/0Td+EJz7xiXjJS16Ct7zlLTj55JPxile8Ave9730Pyxr+sOJgWqkgWo6xIzw/ypEIFIF+KaEn34h64EArYVdhsRuoFi3lyRmiMoVM4FFYvImtApsuJSUvuzYRLamNQGubIfkE1nL1DYDEUmWxCXamKehaLDQ1pWmwcWz/rzoBVh3PQirNRGOSgCQpj2qTFUN0WVaO71VL0kS49gmJWdZT5fi6x5IdM/+/poGi8D8CJzOcTGmyvL8fo2rOxT9T3f/LWrNEMTpZzz37bJfFaDVaHgTBtLeUiTOhgYRRZkjS9B8vL7fZmBPJJVOrFDYYIDaz1srNxd5axWTyPozRoNNar4hlBQGzTlO86vuUADeYJfI0ItXa0nFL7ubtASMBg/y21j1URHdn4Kods3Dc4jr0mC9XVIqWizw16OLwRZV9COuJ4EgMdJtIvm5wtQ//pdbcmkkNQwEHrvIw065tMPu13rYN5HUpDuEsX3nTUEUrBJdamsic9KvVw0Xrcp5wfEI6kh/5Ou5EcSSO5Ue7m8u2KZLzzz8fl19+uf9NRNi3bx8uueQSfPu3f/t2V3fIcfHFF+Pss8/GJZdcgn379uHss8/G2WefjWuvvdaXufLKK/HQhz4U3/Ed34FzzjkHfd/jPe95z/FruMx1oxt3iEMWpe/EtoIUEDkrlcJP34FWVkLDY2GoHBC4uDjcEYeh9XIbi/x/MTRGq1Q3AURKIs4eKvJcvYCGINoO3khudLkY2+S//GASJ6xlBpNIxmGgPAK9YQgMGwmYcNdwBXVFhL+cyJu+mjO5gDuWyUmZKcBE01nEvYAwGqbPMWNIO+5dnmqjQvDaOpofVd0IIm8rmJuYXYX6vi6g2QPY9y++bn97laeCptlMiw6Ct5j5iZm+KlG7rgIgoa4DUZIfqyjs++k5M+G9eTOZiaWL/ZWl6oyVIql8swo5+xsAiFB39SIk7zrRdc06qWYMYm3fP/2M/bb3OBP4hBUtmqD2gBAbbc+HaZVcBK72d2nHnq1aNDaZNjbLrBf0uorvc0owDzX/vmKqm/Jm4fFSWAZjRM0zixtoFa0f0K0VaUis+r88AHkB9GsME5Hbj/SN1J8iP1kBlgnS80KXswrUOwHbc0eKf/7nf8Yzn/lMnHHGGei6Djnnyc+hxmbdXP7sz/4Mb3vb27a0YDpYbJuZeuMb34jzzjsPZ511FtbX1/GMZzwDn//853Hqqafi7W9/+7YHcKhxxRVX3OYOnnzyyXjrW9+Kt771rcdsHEcUyyf6uPHDd9644MRntz9yaqBKQ/rxdcK6WMUfB3AThbs+oVZgFp7I48SvmMJAEqNOJlqzNIAxEdb+oqq/EiATHzrQZu7bPhYtx7fUok3SXScgKpbFAwpmuiYwBrzhLY3VJ1NiFgaF5EdSFtOKMzKGS4Fpnamr+u4ZMB+kYnF1ZWOqb9nU1HyYwu9Js+nbilKb87yFg7zp540V9FSWAQVt+zNJT1nKb+Iarsc0pcZaesqXQNDejvF6WRbVL7+XtHKuJHBhbx1jvQ45EdAF0AC4lYCcD5qm+yqLjxIXcMr+mTrLoZWOMjUGCMPxMQuEiZO4pfM0aCwN1IxViin0+DlYGyuw2nkRhSwcWFpr+0Ktr9/kXFllYTx+S61nZB948l5clxt6+sL2/Svtby8Kqej2j2LFMAfG3QIOawes3CKp1ZrhNiacyL8fuWrqlTXlB3mdSvubl56HjlUEP9QjWscdPb7/+78fe/fuxatf/Wqcfvrph121d7S7uWwbTJ1xxhnYs2cP3v72t+MTn/gEaq143vOeh+/93u/Frl27tj2AO10czGOK9Cl3RzN11EIaGLdJ3FN8nVRUedUQAMxmYLXJ4FpFuzaO4nCrRpdkE5fpqSzM+RoQHQ8ghpBVJ+0O0jusVhVgBJdvYPpkDwM1qt/JedoLzlrWHLRMPQVjzMBIqI2ApMR0/eZY3knvNJ5lqahKsf+brsIqpPrkKT/SFCD3GXkhHkU0qvYG42RYvIkEYPl9ByTBdJLMt4v//+y9e5gkRZU2/kZkZlX3DMwIC8htuLO6IOyy6OrA/riIMAx+Lq776SqKIIh4R3FgZRVQZGBXWAEVHZG74g3B9dNlBlAuCoILgheQiyAIwnBRYQZmuqsyM+L3R8SJOBGZVV3V09MzA/k+Tz3dXZUZGZmVXfHWOe95j08VChFFRBLptWz8WsbeSOQ3VmM06QgVj1xRxWCQdoyuv4S5N0rVM/rmo6JsjqUCyi5EIqCTzESjlCUKqfCtWHRoYhlEBonsSgGVpV5zKVkFKW1D7Xri9CXno5K8uew1IjNRwJGg4JyEtS2gFJv9kqBbppE0RdEAn/qCsiljdv0AQDEfLFddaM/FHZK9n2QUq+2XBN6+KNCd0fNUQTheQKQSsgPbykYhcVYUAmULkIWAEoa0JgpIupbkCvP/oIXRRhFhFCWc9xQUkFBG/nlMDxrNFACj2/7pT3+Kv/u7v1ulcaiby+tf/3pce+21+MxnPgNg8t1chiZTADA6OoojjjgCRxxxxGR2f+GjhlAN9Y28wcAQoyMQrZZp7dHtmkUYbDHLUvuhbb/RJkmogeGu6ZbgaCJOXGDcyvwiTBEMwP+0qQdhFxaRa7fQ0Ng6TS2JsoQntXYMVE4fg7RQhH5i6Hi/btcbkM4YNR5AWkO4CBVcqrKYYSIbUmrn/eMQHzJI93jNUi3xsyJykSQmtdoDPAUbgKKJ/BpoBcgsKNX3x5NVsiTixTqKhhRMuE9Npx1pkuG5EVmaBJwQHjA6LLofcwXdTgJ7AJkra7BqiC4ZfFLUSY8kQSRR20baoiwd6YU1/aTehYHbuBN5w0SReMouThVK6VKCwlpkQAgT5UoEIFn0y/Xas1YJZOxJ/15ZlQhzcHIkFGya0GoPAfP/ROlJOn973m4MKb39h72XJQDkQDmaQhTapP1KiWJGYixB6NJrbciThOvjZ0w+ffsZ1aL/Zz/vsreN4dSi0UwBAObMmTNwi5d+mOpuLpNa2b/2ta/hH//xH7H55pvjD3/4AwCT/vv+978/meFeHODpJ/uBtOSZ89fsnF4IoIUQdlEmwTItgKRN4lVcFpUoCh8ngraiZJSl1+3kBcTKjon8dHLTAFgpyHGjORGVFErhokRGl8PIwHhkVuN6CRY+yuLSUuw+4tvneZh6K0tgvAOxcszotAqTGjEtY3yaMF1ZIukoJ+aF5IJgugCA7ORhxIM0U9wzKiZ8tr2K07W1WtV0tzXPDITrcdSNtqNr2QkjYsE+BKbRctodwP+eSNdc2pm2koaKiCilC/k5x6Dn8rxKCAFP8mjupQ7SVEHLIcBpqFzlH3lQtb0mTbWkf02YtBTprXQmzRi2eEGTZoprlujcRKR1opS1ZF8wFJy4W2fS6rOY+BwItV0Z2460TCya1o+QOi0XpZkBMxaPPhJBJC0aea9ZwufubWkfyvSGTJeNQ3RLpM/nSFeWyJ4vkXSNOW3SMWa2SVcj6WikYwrpmPlbWt2ULIxwPVuhkY5r83pHI8lfAOGedQhnn302Pv7xj+Phhx9epXH22cd0c/nTn/6ECy+80D3/nve8Zyi/TMLQZOrLX/4yjj32WMyfPx/PPPOMq+zbYIMNAmH6ix5sYfDC6Cht0WCVsXjpuSy9In2UKs+hx8Ys8ck9cUoSn2YSoioAJ9hFXHe7XmtFz/NIlq0uQ6khVna9K3lp0iB6JA0WYDFuxe0kgOcVnuMd26MvIhsUSaN0JBdVWwKl7Tnyh0NeGFJFDuJWCG0cquG+ZfuUiddOuYXKXi+Rl2GUJTLf9M2f/eLpUq9J4hdTIq6tDFqrkEjx/ePn+LUpa8gXzYkbalrtjmvHQ6mtuDqNH0ex/YF6EsVF6YQ8DwsgKOUIo0FyjZ6p8bTV1kHDEFhbDKBofja14whVJh1x0al3Ddekb0sFVCqh2gmK9VuWANnmc9TrL2ourYUw92orjI7qNAU153ZpvMhk010KR/yILJpeeTSuiqNSdWMUcesbilLR/57fhypog7+B8H8SZi5yZdel3JNlKyGfH0f6XAfZsi7af+kie852CMipmbiJRMlSQxRGuC5zQ6CS3ETNZAHfNLw7PWTKubKv4mNdx7/+67/ihhtuwPbbb4/1118fG264YfAYBkmSBG3xAONtuckmmww9r6HTfF/4whfw1a9+FW984xvxH//xH+75V77ylViwYMHQE3jBImhsHIqiJ5sqaNAbgqrKqEVIdI1des+SERfZLwoI+GpP0uqIJIHudqtRKl5iTihKCOGF4jpLjReSIuF3CaGl720HuBSY+52QpT66xi0bYlDqkpeuA16HVSqjnpUy2F8kElIp6JEWyJ/K+xn5YZJOaQS5GoA27W1EJ7du0/b4dcfll6qVhSS05r6fSGPlJ9Tjex8nUXHovyxNhIkLuLWGntH2WqHSaqHiNGSsoeLHmOgLEbeD4D39Eul6KKp2ZvV1rDAANvVKb4cEZK4ZYYKzsNDSNKJGAqdrU5l0lYIaPebI/aDo98T4kKHU0G3po6oUdRrxlYA8iqZtRIyIoJBwtgbUVonbIFRc0sG2VxoQErJb+mgbUNX4UE9Ilw6sCtzN8e3rHebSbz3IxPNdiPEc0lag6kQgs6RRjWQoZqaGzKbW3b20TZqFTUNS5tgOPW0EpdFMAcCUBW223XbbvuL13//+90ONNzSZeuihh7DbbrtVnm+321ixYsWww70wwfymRKtVG5Va8qfz1tTsXnBYvPRczH/p+8wfUgBda/Iohasg01ZQTqSKoiW6sLYHLUao7D6i1YIaG/OEKo5K0ftZI0qmUnEtJZDBi4RzwHvyJL6qjKrOCFxjxw0VhfBkCzBptDw3Yu3ECOGNHsveb5yo2epAUQKiGIfKZkLm2mR/eAqN0k8FIDuFqd6yKTxXMcdax8SESNjG0K56T2mmGRRVAlVDxALEhQB0Xeo+CHlUypFRZdJ5ZGfh0qAazlOLvw+l8pEpjl46DX4vZJm5l4jk8nPNrGYuL032aTQzvepKaaNJLPIiBWShoTKBpKOgIKEz4aM/ClCZsCX8FPkyP1UqAWE1VsKyHcAbmwJBlMddC0IioLLM9QkMTpWiZVTxRjYGmTTaJk16LLOf7Jpq1oDwKO3SgMZuwPYlLJTTiekoCiVVadKURKY4rCDcmYwCPmLKilBcCyYhA6IsTFNEE7nNS0C1oUYSqFSiHJGQXaNhUxkgSj8visZNV2SqgcFhhx02JeN85CMfCf7O8xx33nknlixZguOOO27o8YYmU9tuuy1++ctfVlzFFy9eHLRyedGCRaQovVeJbvTxomowOSx+8suYv9kHzB/UQ48E4EKbdBKRkrL0EShK+eVFIAJ3PeNaLSeiNj3wmM6GO2gXhY+CWP+pwJk6EeZDm0eS6HUmkK9AazMuN5xMpBFhd3Ov7Ul1GPWi8Wm/KO0FIZA8Pw7dziA7hSF+QpimwYXyi6iyWqmcVSXmhR9TWoLK50zpNEaggmghJ0/cjgKokqqy9Nd7ItRFprhBaB0RBgyhapvKNEFO97wij1tT1EXSOJEKjDxZVDD2unPvi7mHkk6Jsp140q2tD5WQKNs+cmjSrgAPPGnrcO4c95mlg8okZCTQFoxMCiiTWoxd1Ol3YZ3JictrbebJLBvouKpt0olkBGv0U2RAanVriQhiZkSk+LkIsi/xPNDPN3Z+p8rCVBpCpdj5l9r7uQVdBEp/P9puAfR+ibyEHLOGq6mCzI3+rBQSyHgUzUZtbeeAacGLWIC+fPlyZ9BJrue9MKiR5zHHHFP7/Lnnnhv4Vw6KocnUcccdhw984AMYHx+H1hr/+7//i29+85s4/fTTcf75L2JBNSdRtGjwRa3RSq12LF56LuZv+WHzYZrCRKPK0nweF9rrdLLMaIyKSMRM6RkRvn9idMRGK+hDWoXpNF4ObxdI0clNOXxMknjFGYH7H9FxSSNFr9FzeWHGEMJEQChaRgaRBGWjMVqauXFTT1fqbQlnlvgUJC1YhbRRlCIkOnT8glU7ZpkhIbzVTYxeqe06UmUrKTXswpWGERYzHruO8fHqUrPk+E1P2fY4DhSlaiW+LQ0/VkyouMWJe9/Y684dPLS9cC1Z7H46tY2NW4lJoWkNob1Zp1DGwds464erNm/9Y56Iri3pm5j1gZbCNNO2GjJnicDSZTqVTsRtUpGGbGoB485u04wqleA2BkJp2yA4vL+FMo2b6fgqlZ4wJTSO8q9n8biRQJ95TWlIl35zjcOLyBGfI8/De04mYdVsUULSF4dUQrVTlDIxUcJSI6G6DanhjTyniU29iNN8G2ywAZYuXYpNNtkEL3nJS2rTc1prCCFqO7QMg/nz5+OEE07ARRddNNR+Q5Opd73rXSiKAscffzxWrlyJQw45BFtssQXOOeecCZsWvxhQSe8R6ANPKSxZdmHNng2mAov/+HnMn3OM/SDNfTqPjCPznGlaokU3TZ0PFWDXJjLOJIx3gJmj5kOYRNNFafIuyACpXCRF5KXpo4boAzcSz5qJhX/rdmqNQXkKkUWm7HF1mhivKyFg6r/tnEbafjBKzQFmoReeBAopgI7yabvUpuUUcwIHzLmWMN/qiYQSmaNx66r7aOYVUX1NdIpQpy+M06plJ7RM4NtSWp0IZRkRJylNWikvPbkRvArMRhBzmPdZsWtT+DH8yTGNj5CGbNRYXQTFCLZKTljTVxfdaRkiIXND3s3niQyJlEIlDSaiVLO2rugiV8askrROGnC9+iIIq/EjLykN4XsyWo0VBTacCD6wQxAmrRePKWWVcGjt9hHx/4KweqVCwfufSWv9EEXQ2DxFUfjjUJeDGjNQjHeM+73WNtrMIreAue5FAShpjNwTASWlacPUloZEKR/keSGIutd2XHfddU5cfv3116/WY333u98dWsgODEmmiqLAZZddhje84Q046qij8Kc//QlKqUkp31+IqDPqDBaRssSS5cOx3QbDY/Gj55gIVStzCzUJyoNFkCIMzErACdUBkBWCtt9c3XuZFwDP2rh0XWm+9SfSfMCTQzoHE+a66jz+Wl4ArcxUzCXSkiWW2mglLhqh26krWzd3Xgb31dnNyf6t2Nda8gYqdCiEp3PjlVGAXZQUKqlI7sVFr9uWO72E5a4IgKJ7QE1qj51vNzffQikSNFGEt5V5gkTpRpjFPPY50hl778kiAHBpWoxkpvpSGd2bMUOVPm1JrumuWa8E2lnY3w6ecFAUKNArMTd+nUije6LLn0jvMRU0oGZ+TDbVLCjLq7VzRNeJJcuUlnLRP09MXU87F3WzuiMSjdP1VoZM0fMyV94OAYZcGX8sH9VyInGYCJLOfDSRjGCD98M2CdcCvpcht+igakQCzdP6XwkpXUcCkQtDtumLE6X1lDL3Up6HpJ2IVZr6rgEpgEJBjhXQIkVSmNSeakmg0FCjcvr68gEv6sjU3nvv7X7fdtttMWfOnEp0SmuNRx99dOAxd9ttt2AMrTWeeOIJPP300/jSl7409ByHIlNpmuJ973sf7rnnHgDARhttNPQBX2zQZem+eTVEavqw+I+fNxoqu3CTXYDQOmxr0sdMkowmnVM6kYexEqKbGN1VynrVaeG1MbEfE/V844iJFL0+3gU5l2MkgwbTuVTSXTBVWNJWKNpmtcaugYhU6Ykbt4WgKFcn8rgC/Dd6paDHx71w34nmmbg7z0PyUNfvcIieWcE1iTyagggVT89oBWRtU4pv/ZWAxFataa9RomHILqCkdBz/YE78NbTXwDiQK/e3217aNJ6tQNOttCLaVu0UcmUOIUy6i8iVHCcxva3Ak0aY7UTYjGyoREBIQOswkqNS6VzVDWx0SPgxpFImEteNxNu9KhMVQPeYaUvDnNaZIzsRPMC+lgrIMf6eh4TM9cuT2jvvA7VWC1oAAsJESOk5KU3lImsZ41zZJcz/CaV9bUQSaRo25o6/ZAA+RR9Xo3aNh5UCkKw0VY3JuElllqOJJTd6+mRIL2IyxbHtttu6lB/HX/7yF2y77bYDp/ne+MY3Bn9LKbHxxhtjn332wctf/vKh5zV0mu/Vr3417rzzzooAvUFvaKVw9fOXrOlpvGihu13oTgcQ0jjncoPCeFtKDfDybPei/fYqbEVallodkxWCC+FE6gISGqVxYx/vuvJ1F5XR2lTJcZEyEZg8B5CFWhv6xh+TNKVM5Z7WRj/iSt9VOCaBzo30WN0aMkn7dDpGd6ZYA2+jurXHCP2mJjJBHcoGIU79cR1STKgAT2YoemJ/1y0ZmDsG0SkhTJSOiKXWPnUkhbG3ACByIz2jayu68FYHVAUIQM2wKWVLxLy2R0OPpBDdwtkLGKdyUwGnbcRNKA1lLRDoPFRmojMCAhomaqWFT/uJgiJQnHCKSlpZFMqTsBrvLErxIUrHaSkraThuw+DuawDIjVs7d6YXrPBAS5joHbNU0ALVe5RH6+zxTXcBS76QhJYI9P5JE11Aae9vackVEXIqElEljKiSpQDLEoAMCxYAa3vifcq0lO5fzBDfJs033SBtVIznn38eIyMjA49z8sknT+W0hidT73//+/Gxj30Mf/zjH7H77rtj5syZweu77rrrlE2uQYNVweKl5wIA5o0easmAJTeyz048ddXrm7vWprcfAF0qiHYrqpYrAKSGUCnr2dPtQSR4pRE3iiwK83drhmtOrFupTafB9VcLhetEpHQ9IQFMBEdJ89Pqe1CUqKTw8hy6m5vUqNJwjcikhGi3USu6qUnb1bnJ++3L8CdgdFh1mjY6T56CDCryrO2DbeqshTDVZsK09jGpN7sYpxLkoh2m/ojASBdFcek/K9SnawAYoiE7OVQ7c+X/jiClEtqK3oWCEYOPZEa7ZI1QnaM4pdSsGL1sJ8aQ04m5YSIKFXJvf0gAyhI95lfFoxCut56NslXE+HSO/B4CAAkoe6+RJxSHa2tDVXikg7KmpN5jys/fpQSdxg1w/nCA8zYzxxfmbeDWHTzKKAGVJCYap/2czXtkCW8igbFx8xqlZilylUj/P09fMMCOLwREXkDD6t0kkHRKQ5BT+/6O17jxrw68iKv5AODYY48FAAghcOKJJ2LGjBnutbIs8fOf/3zCfn2royqQMDSZ+td//VcAwIc//GH3nBBiypT0DRo0aNCgQYMQU+Fgvi5H0e68804AJjL1m9/8Bi3qrACg1Wrhb//2byc0Dl+dVYGTMu1sMADst52rV1y6pmfyoofiFXpxpIRVo+myhFYagm9CUSiuCbJVboH2yo5hDiLcN2Jq3QIpTfNh7oyvrEM3b+hLaas8B9otI4CmY1ths8hLJ7QNELUJiUvp3Tdye14uIkCNncnPykal6HllS8VFkhiB99iYMeZkaTzXHNpd1zDSVZsCJB0bv4YUlaLryVvn0HWkn7ElgXMyT3yERWmozKaFuOhaSit4Dq8ZBHwaCiQeNykk55XUpvSdgprRClODQqCckUIWmgnPYSrrSo2yLV0zaVEoa5Bp01RKu9QRSg2ReI8nMup0bVsoFdimVKGdL4QLvIpCOwsCYSNxTuOUh9HIik1ERezto0iu/U3JoklO4E2RnfDtNlV78FErjtj2oS4CR8J1q5dyEa/Eita5WB02+qgkoGz7JGtuC8DfL63MVPZROp3rqfh8qAhEW+2dgnGiz2xUrpimAMKLXDNFVXzvete7cM455wwdOQJWb1Xg0GSq0UoNgSZKt/bBVV4JX8lGDum9EBEqIaVv6lsU5psMuV47P6hQlO0sCRQjXEqHqTgiMLyCqsbMM1j4gCA94w0Zmb6KWqO4VJl9ToqwwfLYuOmRZ72etFIh+ePgqRZhF1VeGUi+Qa6CrPq/UKehCsiVdaX3TursWjknb2ZdMd6FyEwvOJGXptqR6/WlTWlFtgZaGnJAzXtdpVypfU85Sq1GFgXlSGoq29pmu7KdQHYVFAsBEAEiP6WynbjnuSeT0Gyts2TGWAToYBxDnPw9oq0buemzqFFm0vSVozkIYQidPQ+UsJWXlpiMpGHqLyJSJEJHatOnws7BRko0afAiA0tt06UuxWfvPaGtMN5qqyidKXLlSakbhL1VNF/n4WVTi7Erepz61jX3sJChDo9/GaD0nytusNe/WwClgE5TK4RX1lx1Okv6Ggzr/8TBqwL571OBocnU//t//6/2eSEERkZGsMMOO2Dbbbdd5Yk1aDBVuLb8NvZPTHq6UiHmCJXXGOlu1zfnJcReOBzOfiBa4DsdoN2uftOl8ZyHki3XThJghSVhvEqQH4fsAUodVjrxij8an0iGZhGp1Go/8qI6J8A8z6M1jNyIhOlMAGMlYRtL1/YcjPaPUfd8GOnzx/IVgqVvHC6E17swqwX5TAE1e6aJ2LDy/b7miiRHkcxLKc0gC6adst5N1BvPECyBcsTPWQugHJFIuiowtXQ+T8xTypXV0/gCnkhQtEbDERfdkrXvGfWQAzzRVokAICFVWUntcN8ogKJUGiiEJ1JMO6WzxNg2aLjj68Q4g6NGLmSE/l6Irvl7Svyet3OpMSN1jZWt+Jyuna/UhJtfxacqGMxqxQDzBYJrCZOkGinm7ZLoNSkqQnxRGksJJRPoop8Ic93GT37yE5xxxhn4xS9+gaVLl+J73/teUAWntcanP/1pnHfeeXjmmWfw6le/Gueeey523nnnvuNeccUVOPHEE/Hggw9i++23x8KFC/HP//zPA8/rtttuw+WXX45HHnkEXZZ5AIArr7xyoDGWLFmC9dZbD//4j/8IwDiff/WrX8VOO+2Ec889t9IAeSIMfRe88Y1vxD//8z/jjW98Y+Uxb9487LDDDth7773xzDPPDDt0gwarDdeW3wYAH1GihdguxqLVgmhlJsLUA1r1+AZqU361IDdtVaK23xt/rsu8bzhBIxEtgRYQ5xod/RvTYiEFEH9rLgr7jdymItPEH1OKMBKmfU89d21sf0PzXEQ4CYPaINQ5lTvLBroObKG0hEnnhRHHj3eMCWuem/OiB2BMF2GjB2QiynySzHiegHDPJLe4SwHVMiLjsm16tRWjiWvvooV9PZMoRxOUowm0bTlS2nRdOWL6uxUzvAN5oAEWrL+eNJV+QsGQOIKGI3s+dShC406bLqSfqi2hUuEiZgB8lM3tn3gSrGy1H0Xl8tJFgnxESDhrBFkoL9KH1fMo9ndE7kIBORN5cyJE50ZEKvfRVXePS7YfWSI4kbu0ov7EmY+a3piJiVK6zhT2/4kb4JIwnQoyAGBszFfnUqq71O7eqmuwvDoh4HVTk34MecwVK1bgb//2b/HFL36x9vXPfvaz+NznPocvfvGLuO2227Dpppti//33x3PPPddzzFtuuQX/+q//ikMPPRS/+tWvcOihh+Itb3kLfv7znw80p29961vYc8898dvf/hbf+973kOc5fvvb3+K6667D7NmzBz634447zonQf/Ob3+DYY4/FQQcdhN///vdO7D4Mhr4brr32WrzqVa/Ctddei2XLlmHZsmW49tpr8Q//8A/44Q9/iJ/85Cf485//PKEQ7IWOgcvAG0wb5OhIfZl+3Kg3zeqjNjbd5/aNm/bWkRrSacQ96OLfKd1Gab4gRWi2MxoVijZp95ooSvNhL4RP7/FIEUcdgWE6JEGu5q7cXDiC2TPKRMSMEz7u6dTrf6Hf/wh3FS+VT4EqZawuul1Q82VOrPTKlYBWEJ0CwllOwKbz/IIcRBkivqlTiXI09Smt1EZh2LaqJaHa0hVYaQGoBCgzIlj2p9U7lW1jwKlTAdeOJZOGxGltKuVsRZssFMhlm3rdAQjHygxpKkb97yURqNSk4VQmUI7IMG0W2SH4a2xbIBVFTSNho/0TSruegUGUT5sUndMs2ZSf01XFby0jYea8QhJl/oCN/JGOrSa97eYHlDMyqJEE+ew2ynaCciSFGkmBVHrfr4wq8tiDkGXeI44T+lZmo1Tmf0tnCXSaWp8t9qVlHcPy5cuDR6fOaw6mtcqpp56KN73pTZXXtNY4++yz8YlPfAJvetOb8IpXvAKXXHIJVq5ciW984xs9j3322Wdj//33xwknnICXv/zlOOGEE7Dffvvh7LPPHmjup512Gs466yz88Ic/RKvVwjnnnIN77rkHb3nLW7DVVlsNNAZg9N/UT/iKK67AG97wBpx22mn40pe+hMWLFw88DmFoMnXMMcfgc5/7HPbbbz+sv/76WH/99bHffvvhzDPPxHHHHYc999wTZ599Nq699tqhJ7POQsiK+3nfkvAGawS6mzvzTgBhegowEapBSvkZhE3PiTT1qTrAk56YUPGFinQZtRYG2nwr1tocNy+AvDAkgfrlKfsgQ9Dxrvngpz6CKiJU8blpXdVESdv8OUtNtC5NIaR0D/LvQU27FHdOdpzK9eTXh6ZAwv84VcjBHeDpmrCIioswOu1UB8hziK4nBmK8MGknKyA2c/RRDX8sm5brqqAfnuwYw1cyxAQMcSLSQlEvnRgyUxKRYhqgsm1TcNaYUzNSJQsdEEiZK7tgR9dXeCE3Cd6D6Wf+eEQ+ilFDjnUqwugcEWkiPD0NPA2pEdTiJjdkTwsB2VVIVhbWOFM4kbxQ3teKzgWgFJ49HE+BargxveWDU9U7MkyeXPS6aqemf95oCtVOrIO8sFHFxJxvLKTnn9VpYntYau8tx3WVRcEKG2zETpkWOzqtiZ6uTnDmvioPAHPmzMHs2bPd4/TTTx96Og899BCeeOIJHHDAAe65druNvffeGz/72c967nfLLbcE+wDAvHnz+u7D8eCDD+L1r3+9O96KFSsghMBHP/pRnHfeeQPPv9VqYeXKlQCAH/3oR25OG2644YS2CXUYWjP14IMP1qroZ82ahd///vcAgB133BF/+tOfhp7MOomISPHF4+rxy9bEjBr0wDXdbxjPKVp46UOTCAtLadUK0pPEfrgyDREnUmnio0Pxt3vWsgaA1UVZfRFVFRFYSxaBGeb1TqgLMPMJq9FcQ2N6TUZEpq4ZsbbXQTGtkxRWpCxgXMQj0klkNOadsdkpucb3wITR217kNRgkmhfr9yfG89Co0zb4hZQVEuV0OlyrQ33j7HnpxGhvnLM4r9iLOCnpnCpkhyJcVkhuUnpA0WIkqvAO4aZyzuT5hDLXXAtz3tynyUWvEnNcBUu6hAYyci2HaahsTTw1lNdOWS8zM0mj4fOtZBREYX5P6LgakLl/f3QJ02cQ8OnTTHjyJEQQkeJpSs0bJjs9mS0UsBWC5E0FABohWVSpNA7xlCW2UTHzIsz5qYgockJFUVVqcEyfDfRFI0tdyx03LyEguqX1CcP0wBLOVR4DwKOPPhqs4+12u8cOvfHEE08AAF760pcGz7/0pS/FH/7wh7771e1D402EDTfc0KURt9hiC9x1113YZZdd8OyzzzpyNAj+8R//Ecceeyz23HNP/O///i++/W0jBbn//vux5ZZbDjwOYejI1O67747jjjsOTz/9tHvu6aefxvHHH49XvepVAIDf/e53k5rMWoVeVUzRNq6xsdXdeP1NDz1JgzWKOFXldFAs6iJaLYjRkfpv6hxJUi3dp9/r2sfQPoDV+TCDUGd/EGm2Oh3oZ5eZ7ePx8sKQLBJf530qEgncWsCdQxI2RpbSWB9wVGwYIqJjKwDRzb0ui0GXpUvP8ehgsA2Vn8eC4n4CYw4egSOtWl4YewmtXdqPR6zqUjRO58NF0oUKIilUicfTWCYqw36Pp12TqjKC9cSl58qRxEWQPFkigocgWubGIKuEuo8sq8lSvMVNqY37vk0j8utbm5YrFORYx9kDyPEcspNDdHLIZSvNz24BubILkZeQ3cI6zit73W0qulRefF9qa92AkEhJSoPK4DmZK0YsDSklwb5Q2mnMqOWNUHZ80oGRbo4MV62uKkCWmv+/lk35jbSg1x/1pFLCGLQSObTnti5i1qxZwWMyZIpQ1yOvzr9pVfch/H//3//nMl9vectbcMwxx+Coo47C2972Nuy3334Dz/uLX/wi0jTFd7/7XXz5y1/GFltsAQBYvHgxDjzwwIHHIQwdmbrgggtw8MEHY8stt3TNBh955BFst912+P73vw/A2LqfeOKJQ09mbYIpxZbB37osIVstqG7XkyhLmlyFkcWSZRdO74QbDIRrut/AAa1DaivTwMXnVKnW7bKUkk2LsQbJJirF9BeBJsoL3Ct6qiw1lX5SGFJEVUaJNB/kJHIlXRCM0NpFwSg6VNgUS0zKOOLngr5k7LWZM4AV/pudyIxepLfmKapitJoy+n3YVDf38nLpxIFb0LBoIWD+F5m4X4zn0COZa+xr5m2PI3x6SmXCitbtolx4p25Ralfx58YttTGSB7xFgEtDse1stESlwuyes+gJ4KIbWpi0nOwqABS90u538lSShYaiyn1pxefSEjirMXKVd5bEqVQiGStATYNFx1SO6jQ1ERcbqTLnaRz+vS1A6ZsnU7sYG00VeW7IOEsfirKEGI8c5ovS6NWkNGJ4G2Ejgb27rjFZZARUsUbTJjJo3/5OCZVKJ4yXY11GQhnpTRhRi3zZ3NuQSF+9R+2aKLJGLvqJvydkXlPSuDowhZGpqcCmm24KwESaNttsM/f8U089VYk8xfvFUaiJ9uH44he/iPFx42h/wgknIMsy3HTTTXjTm940FO/Yaqut8MMf/rDy/FlnnTXwGBxDk6mXvexluOeee3D11Vfj/vvvh9YaL3/5y7H//vtD2hsybiC4LkKOtJCI+uhSwvUinERJASFkIz5fy0HvDxEqjXAh12RVAHhCZV4JxhGtlidgQti0Wuk1OVp7ewNKrwGGMFHksii9MBa5IVbca6pU7vgiTa2pqPLzrANpqTIASRYaXgb+UHHqQxpC1c0DghTbHQTHqSFvQspQqD8Mgmq7CfaviOsjMsYbPbda1rcpSo3Gh1ds4RVmPjJXvtrPLuQmGmKITDqmML5hClECZH1AZpuAj1DxfnrO/4o0PTVfyl2kiaZbasCmCcuWhE4MOUs6lBqD+0m/a576AqDaCWTHpuSIQBQF4u5Avskzu1Z5AZHDRh59pR0Sac0vM0NAyI6gKIyXVSpdigylhm4JyG7h2+9YQuXOl66NhewUpkE0acGEbfosfVRNKCCh/2tqj6S1IcNkjSCEIdVZ6m1FWNQJypIqyv7LxETwuGdYl6pFlWuW3ddyYwqxtjmgb7vttth0001x7bXXYrfddgMAdLtd3HjjjfjP//zPnvvNnTsX1157LT760Y+656655hrsscceAx2XTDcBQEqJ448/Hscff/xA+w6jhVrt7WQAE6I78MADsc8++6Ddbg8cnlunwKuJem3Sann/HgvqB9dg7YYufEpMJGbxJxdnISS0tt82hYgIlYGcMQNot8KFmXenTxLvKM5JlIgWfH6PjYShdqGUN72EjULR2ACEiCroAE+CiNzx+SlVJSAAdMuOV2rjG0QE0ZI6AUCP9bF+oHEolaJUX8LixubnwufVy4KCw6X0ZHV/GrttCau0UUSloJPEVmSZaIMv3WeHt/ocWiRViwmUlTZCZ0uoKILVfraEahnhufns0Da9JpwTuSwBlBrSOqEDZnEz2iJ4vRBpjqwuy8yPCJjwxMtqpXT8KU6XXnhWohKBRGlAIaziY6Dyf/eac/yO7nF6f2v6OaIwxQkCMP9LgCFUib+eOrHnpEuTerUaNgGEHleANQglAuw9uuj+kl3fUDq4BDYNpxNhzTb9/4Xg/yN07oxou0hdKl1UyllGsHtTdHLoNK0WMbyA8Pzzz+OBBx5wfz/00EP45S9/iQ033BBbbbUVPvKRj+C0007DjjvuiB133BGnnXYaZsyYgUMOOcTt8853vhNbbLGFE7kfc8wx2GuvvfCf//mfOPjgg/H9738fP/rRj3DTTTcNPK8HH3wQF110ER588EGcc8452GSTTbBkyRLMmTOnr8dVrxYyHNPWTkYphYULF2LRokV48skncf/992O77bbDiSeeiG222QZHHnnksEOulRAjI0GaD2Af2OR0zb/pK4XFj31hDcy0wdCwER1d5BCpEZuLVmYX4QRaspVVa++tRAv/SNssMEXhCRCVUhcFkLY8scpYtV6pgBYXhdtv7DbNp6X06UfAfNO3flhO66W1MRUdjbqj83SiUobkqxLO1IiIFwmwW0kozgZMmgcJgMSmHi2JyOmyKRd1Ihd4TmJcW5lBiVQM/iHXYwx3THpP+P9gvA9P4zrvKFapFYP0SJkM0jikiyGBuhNCk4GnTVeJHBApuZ0bgiRLjSTXjkSJwqQSZa5dtZ8/OfszESZaaiNWWgAyN1EpDRt5EhoiF9BSO9G5sVLocWlZ2xdzz9GbyggW/c1sN5yRJdCb5DrBtjD/W247m5eUworqLYHsFqY1UmnHLAsIq4fSaWruNxuxc989ugKqlUK1fJsk0yzZjhloFul6msiU6DL9Is1VUoUAu0aAbdisqgQtrgoEjLZwEOI/VVgDab7bb78d++67r/ub/JcOO+wwXHzxxTj++OMxNjaG97///c6085prrsH666/v9nnkkUdc1goA9thjD3zrW9/CJz/5SZx44onYfvvt8e1vfxuvfvWrB5rTjTfeiPnz52PPPffET37yEyxcuBCbbLIJfv3rX+P888/Hd7/73Z77TnULGQ6h9aDqToNTTjkFl1xyCU455RQcddRRuOuuu7DddtvhO9/5Ds466yzccsstq2uu04Lly5dj9uzZeN0W70Uq7UJJ39Aq/2Dm78UPTy7H2mDNYX/5ZhclMsUDWUiWCazKz5VHE5FJEpuiM5V8Oku9mSbpoDpdWy3Exk5tyTZ9DjO3aZEX5hs+F5QXpbEEYA7oRCgAhCSCHM4TCefmDPiImF301ExzDm4x4N+4qeqKBO55DvX8iso1FKxMnc9jwsgUv6Y0VqQ51EVvHYpotQyhjY7rfpfSC/mljdBlqSllz3xkigwoXUsXXrVX+goyZb2hALvYkpu5NGaetLhrAWOWmRj9UtI1hCkdi4XXCipLUJKRp40wmfYk9jRsDzvnh6VgBNCphGpZSwUezMuI/NlrpM21kFbkLbsKybh5MRk3AnEoQHZynxLN81C2QPch4L4wVq43/19J2P1A4/CoLEPFM4pXmJIFgoVQylXTmdfpBRGm2axJJwnlRakNcSMrEdIlqtLdE26/LIXoGl0dJPOm4vd3RNipx2OZj+HHd5+BZcuWTapf3ESgNWmbzyyEHBmZeIc+UOPjePjET6y2uU4H5s6dize/+c049thjsf766+NXv/oVtttuO9x222144xvfiMcee2yNzGvoar5LL70U5513Ht7+9rcjYR+Iu+66K+69994pndyahJ41E5g5aiIPWWbEwuRJkibO5K0hUus+nFUCAGeTEKWhjDCceUlRVISiFm3Tr0snwqTNRlrm9XbLExyqJOI6jVS614L0S0DArHGma8gqoLWCLooq6SgK6LExK/YVIWmhb+Qs1VHr4kwLBtlD1FQsBWLxyABR1I1JmCh07lI6VXA39tCvi50jRQgpLQf4hTwRFSLlxeWWKBel8ZJSJiLliJSybU0U3HvkSvuFDwBCA7LQSLomCiW72nhWFSYCZITlMJ5VHeXTR4WJXAHwKUQiBKWNmCl/DDOGT0/KXEOUJhImS+3noIBkXJnXrWjbmL/CN+/lRArwz3FrDe463g8xsaVHUXoPtNIL3UnYLpjNAlVeOgNRq7UijydyZzcVg2Haz+ynjEi+9McD4IkUodOxbZWU+RIjpTPG7aWDovQwPUgjNh1YZffzKdBcrQ34zW9+U9t6ZuONN8af//znocb66U9/ine84x3YY489HAn72te+NlTKkTB0mu+xxx7DDjvsUHleKYV8kNLsdQVJAp2Y1I9IJBbfM7ypWYN1B4ZQ2UgVkR7eo89+s62k1wDzPAlSAYhOYQhVkkB0og9bKSCK0nwDBsJoTDuDWNnxgu+omk0ICbSSUEAPlvqi6qU0tVEFu5ikiUm/0FBZ6jVEUf8/t4jQN/mx8d5Cch55GBT9KvyYXUOv5Umuv161Z2HdPJx5aOLSmtQjjowyAUtcWPTFO27HkROm14FZlJQlYrLUNhpl0nfQXoBOfxOJAgABYXvn+bEEkTVtCBdVBgZ96iwZ02lihNzCaKqENgaiSe4rDx25IM2VZgagRHgoklMyLR0R9qKsJdEOXH/nelJGEV137SKdII0PBMTUaapSEe4nJURRmHu11GEIICZvCo7AOZNWN2d2TIpOSeHn6qr/hO8nyKoAAfguBMPe9w2mDC95yUuwdOnSSg/gO++809kbDIIrrrgChx56KN7+9rfjjjvucC7wzz33HE477TRcddVVQ81raDK1884746c//Sm23nrr4PnLL7/cKfpfCLjy5k+ss2HQBv2xv3xz/QtUiWQ1QYJITUwAKCXBvzyMjhgCMG7IkChsNCNLnSbK71+lCrSY6CyFWDlWb/xpIzLx3oJSeHETZ0pPa6ZnKQGggCDi2GVGlwVbAPMcGO+YyBe5jpuD+fRehcCY69WzhyHQ2y6hru1N3Tf+QVUJUVNb3bYtcYQnRSTurkTntF3Q2bGcoDmyO3AESwhHimSndIaeyfMdswBT5MVGxlQqoNuJEZBbLZWvIhO+qtBVx3ndU/p8gbItIbSESkxqL8kp1UjkCdbFHZCFMmROaUjWcw+knQqqPbW/blIYIl4wHRyRKE74AU/CYqJL+8Sg7Z0xqH9J5KWPANH1T9P+eRSu/dLaeIs5d3x7LvH/BxCSvlL5FGNeQiDx6T4wwkdftniqfjoQhEBXYYx1HIcccgj+7d/+DZdffjmEEFBK4eabb8aCBQvwzne+c+BxTj31VCxatAjvfOc78a1vfcs9v8cee+CUU04Zel5Dk6mTTz4Zhx56KB577DEopXDllVfivvvuw6WXXlrr2dCgwVoLrcCr63ReWFGzcLofnlrS1B8ujtQoZUmUDG0ESFye56CSK93KzLdcvlC4ylHW7yuRnqxFepLK4kR6nqIAChuZAqxo1pJBrlHhEQdKVVqhvWnFYubsPK74+XKiVkc064TggBfw9xKZs6o83c1D8skaD5v5sxRsTLjIp4vOTUqo9do+wkDBjtykVMnVnFesaRu18lEeOO1SYJip/PMSyvxNxpRamzYrjEiJvAAKE+lObBRTtRPbwFYDJbyHE78+WkPAXlcN6ESayFepISHCartEuKpBE8kqbYqQ3VuAT2WVLMrC7Dg4EXVpLSJSvSKVdUSqF6jZOMx957RhpfJtWjiSmvuKUmzuvaG0pfb3j+vPWEPCEwmAdFSeLAptHfKVAnlL1KbDpQRQ86VndUEDa5PP1JrCwoULcfjhh2OLLbaA1ho77bQTyrLEIYccgk9+8pMDj3Pfffdhr732qjw/a9YsPPvss0PPa+hY5Rve8AZ8+9vfxlVXXQUhBE466STcc889+MEPfoD9999/6AkMioULF2KPPfbAjBkz8JKXvKR2m9tuuw377bcfXvKSl2CDDTbAAQccgF/+8perbU4NXgBgXk0uaqJ0SDgSW7lGTYC5DoOglPeIIjdwQsvqp7LUC6AjB2Zty8PdhzZV09Ejy/yiVlcKzo6niyI0IOVCYq1shEp5QkSLlO3/hzy3xJF5XMUC8R7l9RzC9uYTSWIW2nh7ERI8d/1tpZ7bl8YS0i96pMUhTQ21BKE0rBDuOXI8jx2vneaJiCZruOtay8h6cujGYPYKAJwmSShtPIqAkMja+yT5ywokKwukz+dIxgokYwXkeOl6y5GdgnNaz02Uybz/NkqiYdNePg2YjFt9kW2YHKQmeaNse946s6azaRISWCC8b1yUp4ZIkXM4aeciDV39hWNzKVVwDUV8vZlGqTIuF+gDnjRH/7+VRtx1IE2hNoUgzvWeHaNyzwBNym+akWUZLrvsMtx///34zne+g69//eu499578bWvfS3QcU+EzTbbLLB9INx0003Ybrvthp7XpHym5s2bh3nz5k1m10mj2+3izW9+M+bOnYsLLrig8vpzzz2HefPm4eCDD8aXvvQlFEWBk08+GfPmzcMf//hHZHF7jAYNGjRo0GAdwdpm2rmmsf3222P77bef9P5HH300jjnmGFx44YUQQuDxxx/HLbfcggULFuCkk04aerxJkak1gU9/+tMAgIsvvrj29fvuuw/PPPMMTjnlFMyZMweASUnuuuuueOSRR1bpojd4YUKkEcHmqQ33HIselaXTK2n7Ld1FUyhy5NqZSKYvkT6apTWgTC28TiV0mvlv06kEuvDRL6CaQmCtbPi8+PydbQBPyeQ5gIzZKSgATEA83gH1E6ztm1eWPn0IGGE+9R+MvplXdFH9IlgsTUN+X4LMTkkQ3IreJ95fUClvT0FzHW0Fc6LqSRedonJ67VN65jpo/zefshRW7M2uOTUilqwikKeSyMOrLL3wO2GRtURCdnKopAW5Mge1WdGJgFxZRv5TAjr1Au2SxPN2cdV2jnTPyNJHtADYSFmPVJS045MeiN+r9LsQ1YgUfw8ki1ryNC9tU3ds0h2lzDCWrlGpgh6V7lr0uo9IK0Xno8C0ijztTve7/TuuxOOaKdtORlNK3lVumvfCvI/m/1vXNkVcDWjSfACAsixx8cUX48c//jGeeuopqOj+uu666wYa5/jjj8eyZcuw7777Ynx8HHvttRfa7TYWLFiAD37wg0PPayAytcEGGwzscv6Xv/xl6ElMBV72spdho402wgUXXIB///d/R1mWuOCCC7DzzjtXxPIcnU7HqfiB4ezmG6zb4KaTuiyBLsLye9IRWZCeR7QyI07l2h1a/Envo5UxfaU1PiJqxjfHp5eE0tAljHmhEN5d3aW1VKhvIShtHduVnzchXgDzHFBJ5A3EFpuaRa+nYJzSg6Qt4VquYUvFOSELFmmme+HzcAJ4m8qT1qCUCK2UtQuwzhInJAdshVu3NISIyIp12nb94ug6UK8+Jzi3YybCaaokCbs1oFNb4Un3E8hAld1TSiFZNma27VjH7cyYqYqVJfRIagiZNPcENUEW2muRJbnWw0cdHMmzAmnvo1VWSv454eoJrU2atqLVs+8DaZxEDempc0qnCj6mjXKEl/7nuKdaqaGZ0tv0S5TgRprOX6oOrjIvMpTlNiTuvmP3Sytl96A2x5Neo6bTxPz+AiAo6xKOOeYYXHzxxXj961+PV7ziFavUgWXhwoX4xCc+gd/+9rdQSmGnnXbCeuutN6mxBiJTZ599tvv9z3/+M0499VTMmzcPc+fOBQDccsstuPrqq9doc+P1118fN9xwAw4++GB85jOfAQD89V//Na6++mqkfYSRp59+uot6NXgRQYQf8GTcGTzXbgf970g3FVgT8A9i0oHE2gz7Ld+1bVEKmrQuGvYD3Bo0au0rnUoFH9aQ5ndOqrT5qlrboy82zuRRMyJ2eeGqD/k59gSr6At+0vF6oZ+OgUfVeoEbdFYqI31kUM/wJruiW/oKPrvw6h6tRHhZPu0PUSVUZizSU/HnAA0BKYwjubARBJ3a6AYS508lAG+z0WW95PLcCNGtFYdOE1vaT0dJIKGgIKEzQMAQQJUIZ3/gnNmFgICv6jNmoFw4b4l3NyIXibT+ZFZfRARVlea6cxCRIuLT6z3usdDVisz5bkSg6Dhcl0/ESfn31pwXnLmpKJS/511Vq/ZVnnx+CesdKITpJRibhCobjSwVdOKr+QQQVHiuVkxBmu+FQPy+9a1v4Tvf+Q4OOuigSe1fliXuvvtu7LjjjhgdHcWMGTPwyle+EgAwNjaGX//613jFK14RuLYPgoG2Puyww9zj5ptvximnnIJvfvOb+PCHP4wPf/jD+OY3v4lTTjkFN95441AH/9SnPgUhRN/H7bffPtBYY2NjOOKII7Dnnnvi1ltvxc0334ydd94ZBx10EMbGxnrud8IJJ2DZsmXu8eijjw51Dg3WLcwbPbT2+UDo3GoZPynrih6LoR1Slt5IpG0AW3rHZcCTq1K5xRMwhEp2yJTQViDxUnwniraLSZYy01DpKtlcRCpJTHqsF7Q2kSkhbMVe7omMtT5wRMqZHA7Ym2pQ0TGfC4nfpT0Xe810XKovI+LEm4zzhdFGxkzz3AS6nUG1U6i2d7nmkQsiRfFCKHNl+vJR9IHSO0ycHhApG0mUOVWUwYqlYQJcJBhvm8VZJ8KTWRJ1j3d8am1sHOh2IYoSYjyH7OTWhLJEsjJH0iltI2ONxArWtbDkw0ZxJJEwS6QMGVCuspAMM2vb71Ckka6vqknv8WIEXhQwBIT1OYsf7jrY/xeRl6aAoCisiakx8wznrYOUnauIpOurlG3e7cX67t6yRIosK3SWQLWzsHpPCF/hydoTiVyFY65u6Cl6rONotVq1XpeD4mtf+xqOOOIItMhDMBr7iCOOwDe+8Y2hxx1aM3X11VfXdoSeN28ePv7xjw811gc/+EG89a1v7bvNNttsM9BY3/jGN/Dwww/jlltucYzyG9/4BjbYYAN8//vf73mcdruNdrtd+1qDFx5ElkImM4BSQVGlmq3acuX75BxO1UNR1EWMjIREQ9jQf5p6okDkBRlAvf4SabrWj2QQHUNsRCGhWhSJMr3JqHEtGWqSC7QowVIWnkQFc8ky/7ewOhr3zV0bh/Q8933tCLRA9TNqpDE56rymgN5ErK4ViRQQWTt8jrfLibUu7lj2dbv46ZGWqVBLE8hO4dJvOpPemLPfqbH+dciV8YEqtI9OCQRESqUSJJcpRyTESu38oITyOi3SEeks8UTAlfGX3gKD7jmtWSQohRjLgRnm3GSnQKo0ylG6N7ThbUwnpVJhiYclKUSeyPWeInR1EdQiJk+JuTfqIk+DWCHQecZEhtsXuG2134dDa4iu8hEz3gswNWlEHYcQSZ8IhNFPd39Jfx2kOYZOU1tlK52JqoiisToR9n2wlhOsd2OD6cHHPvYxnHPOOfjiF784qRTfBRdcgAULFtRW/iVJguOPPx5f/OIX8Y53vGOocYcmU3/1V3+F733vezjuuOOC5//7v/8bf/VXfzXUWBtttBE22mijYadQi5UrV0JKGVxc+jsWqDV4EaOVuVSLpGhS3CcuLuOvE9LWLS7kWM7Tfm6h9M+JvIRqZ+aD2rbHMNsIr81g0DApCN0y6SIhrTarztOJ659oceJaLTsfJ6anRaKO/MTEqB+RqmsoHFxD+3uszaGUSVl64blkY0lRJVGAJ65ZaiIK7cz8tFYFitkg1C12znEcnoiYiTA9lZKBJgqA002pVDoJFFj0ymRlo5ShgnuvAfjIZZ6H7x/di+5alkCnhChTc4zMEGChFJKxAhDGp0pZskj+VjLXLnUpCnt/2bRYoCvijaDpGnFH8CTxLYXoveLbcQTvG0U2e3zu0rbx64GZpgrfdxLB5/Aaxcw6okO5bLjgFiXBWMqZ3gbFCZmN8FJaj72XOhFACfd26kRAtciDyviImRfqT3PKMRWRpXU0MvWmN70p+Pu6667D4sWLsfPOO1cq9a+88sq+Y9133314zWte0/P1V73qVbjnnnuGnuPQZOrTn/40jjzySNxwww1OM3XrrbdiyZIlOP/884eewKB45JFH8Je//AWPPPIIyrJ0/lE77LAD1ltvPey///447rjj8IEPfAAf+tCHoJTCf/zHfyBN06DrdYMXN8SsWcDYGDRyCNimpsxks2IMyUEpDo6YzMT+S1qbRSGRXu9TKshObj7Ak6TqgVRqo9ugFhZQxoyT0oRlWbMI2r8ZWaqce5IA7OkgrZfUECN6baJolB1bl6W/fgrVBTf2/olBCx3vEQdRnR/NJ7ORhFbqfLriY2opbAsW6YgsjSGEIV21ZMt6OwHSpQOFZqQrOC/b9iURkTaJiCWAXPlechGR0sxJ381eda1eyV5/m8JDUQCphJDksK6QUCubThGQVWHTnnQ+QbSG3z/Uly++Z4hIcQISN86OQfOt+wLLzVfj11VEKuvuHZoHT+lJCSiTnhM5uZ1Lfy703qb2/EoFtCmC6V3VqSBEUPoOgFC2ijMvLUHPXFWnzqQvMpimNN+L2Rph9uzZwd91vfkGxYoVK/oWmj333HNYuXLl0OMOTaYOP/xw/M3f/A0+//nP48orr3QOpDfffDNe/epXDz2BQXHSSSfhkksucX9T65rrr78e++yzD17+8pfjBz/4AT796U9j7ty5kFJit912w5IlS7DZZputtnk1WMcgBTBzBsRzz0OzVBlZHfStRItTY7QI9Sw7ZwtWhjDN0PMYwmg+8tJYKABefEuEjBaLLAMSFabobMRGIFyk6+AqGflzrSxIbQafvXWLqgUX5WvXIFn3Jml1UJZ4MgKBnFI/iU/zCEO4qIQdQpiFTSlYT/CwKkuYiJ/IS2M/QaknbZsNs0gi18rQeNRrj5iOYi1GQNEpfg0ZodFShuaPRenb85Sle4+EkKZxNYw2CFL6qGaWmhYrrOJNZ4AoYO6XThnozIgw6kS4NB/yIry3o8KDCpGqWIQwEiXpvcl9CpZvH6cKYwSkKrp4Lg3K0oKB67wCNOnO7P9rXrhGxQ5E9iQX2Cf+nOPqUUvctbRFAvb+cBHJ1FffqlRaB32Y6z/eZD5WNy666KIpG2vHHXfEz372M+y66661r990003Ycccdhx53Uj5Tr371q3HZZZdNZtdJ4+KLL+7pMUXYf//9V6sLe4N1H4sfOAPzt/kooDSWPGMiqQfOepffoCjMB2pMnOrE3fRcXfqPpzxo4eH7kPA1BhPTijKMdpjfk4oPTy2SJOwdCLhFyaUx67RggJ+vbaejefTLupP3qvqrPB9HlXpA8EiHsqnOuIeaW/hsy5EscdE72clNKXs8bknCZnvu3YhU5Gy+qTQWBrDpH1AvNquVsVVlslCmiq7ft3ziC93CL8wd21pIKd+ux56v1j5qqWF1W649Eb+/LKHKS7imv0QqecoYjNSVnjyG6S+WzuPP8eP1+qJQKnBvL9M4uIYcUaVpXSSLb0tVqkHbJEuopAgJmtNg0TxElQy6/oL0/yj8FxAhXKFCEEm0gVAXMZTS2V2IonQuJyqTPh2cT0AcG6x1oLYze+yxR4VQ/epXv8JJJ52E448/fuhxByJTy5cvH6rp73PPPYf1119/6Mk0aDAtWDEGXRQ4cIN3m+hADQGgz9WwmowtwnwhoNRDnPKjsnGKSGSpJxelMmJkKzoWnRw6Tb2mhT6sS7iFIjA2VBrodkPRMpWx2/lVhPIAhGQkjBZgvoFrWJuwFJBNFUUkKvDois+bL8gTECrRK2UUg3octloQRemF+/ZcdBxVpLQe12lRpAbwAmOan03n1KYxS5/yA+DsD/wGhmSJQrnnyQ9JlKVvvGuF5ZXKSfY7XWeRtqzI2t43rcSmFFW4H71X/Lpr2zSbSAjX13FiQkUKQP17xKOrgIns8Wo6xbRgw4iBYzsPTqTqwAtBaE5kzRALzQFLHO01oQKjVubvERu105L1BRRmTNVOTTTRatDMe6gBlChH0uD/XGeNaee6ho9+9KNYvHgxdt99d7zuda/Dy1/+cgghcM899+BHP/oR9txzT3z0ox8detyB7oINNtgATz311MCDbrHFFvj9738/9GQaNJgOLH56kYlKlSV0t4urV1zqX0yYtgLwH+C9+rP16vkVL3iArZYq3MImxrqmqs8u8LJTk5YrWSk7Ie4B6HqHsQWaUjFpGqYn+cJHPjy8rxpgogiM4DjbBSl7m3j2ui59eqLV2k1MhKIw51naknmqUlMKMi8h8xKiKCHHc8iVOUQnN2X1RWFsBsZzYLxrUkNd1nuOiAyNR5GsKCIilIYotLFQKIx/kzHsVD6dBy9YF3aerkw/z6HHx+EsADgBsdeKm8lCW9KcF8YqoCht5I5dV3s+yAt/L3BPLU706zR/E2l+KJpG+j+6XlyDBVS9qILjqCqBIkxEpAB/PxMoahdUf3JjV3tfZ5kp3EgY2ZYw7y+POkppiIYlUN6c1QrShUm/y1wZIi1EvX5uNYI0U6v6eLEjyzJcc801WLhwIZYuXYrzzjsPixYtwtKlS7Fw4UJcc801k2o/N1BkSmuN888/f2Bn0HwCrUaDBmsDqInvvJG311bnuUWtLupEoAXEGXZG43D3bhpnvGMWhzQFxjsQ8NYcZuHV1egOP0aMuqo+/kHvFhFGqOJ5cv+qojCLEc0xMHa0flw8ejOoH5WbjgwX8Dg9NOHiXthoXhdQvnm0sGRUcCIUXy/6bFJlqPdJExOxSX3FHHJYOwNtnMeVDQ0I1kKmIAsCOz6dRmk8kQCYwoFO12qkijCyAwRNqXuSy6IwZCS1rtwFI9Havp/xPSCkJw6EsvTHp6gmkRlyHwfC9yRIA04QPQICgXgFdRGpnuNFY1BElnuL8fQvv6/ZPU8eXOZvNk/eXLki1qconoLUvq2M7JZea2mJ93STqgarjizLcPzxxw+UzvvmN7+Jf/qnf8LMmTP7bjcQmdpqq63w1a9+dbBZAth0002bxsIN1npopY3NAP1NInTYRY3SfUQw6ANZsW/SUal1AG5ASWmH1GqZpAxFwRQR43YE8QLpSs4teRlph1os+p3SPjQ2X9xI3By7oxN4Lz8nOqY5Sr/ACuEW5Z7pvl6QojqHRNRbMdSR2LIEcuH0RALwlVzkZp3baBARQxVFG/lckwSQI9Bt6z7uFmciZgqiC0aijKZL22iJiPiAYAahovARKRTm/tLsnhCUwqPrC/iUFt0DsdUGpa3qrg8Rdnt9XZ85Iv3cCiS+x+i+dr0O7TVq2eN1utX3om4e9PdEab+J/JnqDDGpAIEqAoM0PCNVLPUXuK1L6R3xU+98rqmSlxEjiioKrZ2ppygUREeFx5xOMtXwtgDj4+MYIa3nasLRRx+NV7/61dhuu+36bjcQmXr44YenYk4NGqxVuKbrXW65CF2T9YBbfLXXivBFIk4t1Il7eWWcjS4IqgDU2kQauGM6wH6PBeRMM5LbFNVIu2rHAISl7zRnTqg43MJD40djccuBNLXERIc6FlQ1VTFc1IWnFOO+e67tByNy1jNLa2WiWon0pe78ulgfIkERF2cCWVYX7tgKQGkTyUrCc9Ja2MiUreqzfe54NIJrtajNift7PAe6uanaywtL2P11dtFP6x2mW4jtJ9lglqTkhY+scS+mIELD0rREgoroPqNrQPdHHOkBzDHoOlH0LgZ/v/hc+2EQkXtdhFJrQJcA6cncuZDgnH3hAUwlpLLWGLR/ND8tpbnozLRVWDE/+VaJ8S5Elvp0ofWEE0UBIScghVOFRjMFAFBKYeHChVi0aBGefPJJ3H///dhuu+1w4oknYptttsGRRx45pcfTvbISEaZJOdegwTqEsnQNi0WW+UautEj3SsXwb/z0YZ4kQNS2QJdW90IaqqKoEpo4+tWxLWDy3PwOeCJF29D+sZ9TloZkqm6hi0X0ZMNAIt80tforGe4fmXPG7Xd6tuKJo0Pu4kQRDSKiuoboxdV+7vxtWq2XqztdCzonTkJKu4hSlZZN+QR98ux14lYF9HAGnVr7qFSnY4hSTGasPgdF4Tyd+or6i9IXBZCpJb3fSlvbhdITyVLZVKhNLZOmikVgg951ND9L/NwxhPSRVbp2/NHP7sOlIvssSJxc8997bs/mzO97ujfpHuL3vYK7r7wBKxH2mmPw/xWlTXqy0wHGu0EFoGkmPU1kqgEA4NRTT8XFF1+Mz372s0FLmF122WW1el1OhIZMNWgAQLTbngzZiJOLIFGqhT5EubEkj0Bx8L/pw52+PZcKutMxBIGTJ77gUFVdHemg41O6ELCLR2YIVt03+lgEXrcYuoVH2xQLi251cx/FSBJ7nOE/PjQRTSJuvBKrjKIs9lEhFhSdkdF7QOA6trpUUl3VJZ2nc9BWEF0rWi8KKy4v4ZrpwkahgtJ6S6osGRO5IeW6G0YYta0g1aSh4q+NjTPLBJtyi49Dc+b3Tqdjheq5Pw+yEMhzQy7pPSTE1zwel64JENog9EIS3fMxkXL2DHGUkBGpQRDPkewT6DjUx9K64Zt+iOY+14m30xBah0QqLgblRrq8yMNGe10UchL/B5NBI0A3uPTSS3Heeefh7W9/e9ASZtddd8W99967xuY1KZ+pBg1eSJi/5YdNFAqpT7uUJWAd0hFoLkhMHkWAeH81pbwPFH3QZpn1d7RRCPIPSo1ZowD8t2o6di8Xdg7SjfAqvSzzKQ++fWKJ2USiX/pWHmt5uK6rphx9YM0U0DuqRMeykUHyugpE+eQZ1O2G7w1HXK7P9TVEjMkElDfu5cUzMjFp2SwDUADtFEL6ZspmG38ugoxBlTKeUtZPKjhtsuKgBtVpVrluApnT62HZcxDtVn0qjZ8nEd00BcbGwr55SnlT1fieiKMqVN0X+D2VPhoURw3pWpJFQWzRAIQkq66CMd6GKknr2ghx3yigGmUlAhnp/ahtjGqlvhEynYZtYiwLXzSghYAsbT/FXFY0d2Jc+S8i0xWZatJ8AIDHHnusttGxUmqNFr81ZKpBg5E20BFA2Qme1mUJ0WqZD3D6cKZ/1rLHh2kvQmEXOqE1dKrddtSCRRcFBKUQuat63WLn9C3Ra2UZpq1oeyDUxADV6JXTF2lf5RQvcNTM2Z2PnWd3sA8wIaRP1/FTKgrvNdXLn4rPiS/qVIFHiD9M+TkDoahba3NeNNZ4xx9DaaDo+nm0Moixrqm8S4xHmG5nJjJBWipK73UKPxdKU1JqFwCvaNNFbggOT4N2c3OcUhmBel0RAmnJIt0Xurl5b/O8auxqNVp192jFpJaKLuh6uAmza0/3PqXcYk8qd81VSJrof6eOQAXzTTxx5lFjXqBQBxulIsJLfRvNPoDOpCEVtv+eLHT4f6INqdJCeK85Hpmj87N6rKaab3qx884746c//Sm23nrr4PnLL7/cdUZZE2jIVIMGDRo0aLCW48Xcm4/j5JNPxqGHHorHHnsMSilceeWVuO+++3DppZfihz/84ZQfb+uttx7InWBSyd6f/vSneMc73oG5c+fiscceAwB87Wtfw0033TSZ4Ro0WLNoZbbPnfSRIS6aJmGrEEZM3mr5CIlLF9lteSTJVv+4nnxUpk6aDoqO2G+6mgTmhDha0OubeBB5iUS5JYtkuMhXXRRAVKNVkXlnMCbXivVzN+cC9LgJMaW8SlUvuKbjVQaNUlUkvAZ8ZM7pc2oqLAFf8UYao3EWlcxt2xeuXaLomzXPNOMZcTqUrehT1gGdhNG8zJ5HnqLrqlU4T93t1kcdeUqvrhUMXTMqbhjv+OhVHwE4GbK6c3WVcHYBISE6NV7m29VFzXoeiIT/ycRRKcDfL9xUVgqvJwSgo+IKba0PKBWrZrSgsgRl2zwAGEuEzDcSV6lwv9P7aY4FE+FKE2CkZedh7+N25s6h4ry/uqCn6LGO4w1veAO+/e1v46qrroIQAieddBLuuece/OAHPxi6ndyzzz6L888/HyeccAL+8pe/AADuuOMOx2sA4K677sKcOXMmHGvoyNQVV1yBQw89FG9/+9tx5513omMri5577jmcdtppuOqqq4YdskGDNQrdSp32VABhKiOuXgPMopGmgNSuhx3XRjlM4KMj0tRUeAE+tQN4QmVTDkELF66FIc8drcJUSN1xyZGdp5Ti9AT1VxukvQsdF3DjCT3xosJJhWubQufN58MIlgCMLUKdDQWvriQiycv9e4mdaftEep0YkRRerZXn/rh5DsycAZQlRCEAFN63iMDF70SiE2mIsm3O6yv2ootTR5BonvS805eVldd1MLZ9faw06bpWn2/WVlMVWFcA4X1fKkMksrS/03nc8gXw1aDBOdH7x7aLyXyd3QIzEtUte66g948E6Pb5LIFOpO1pCahUQgU2FlbrpuH1ipXjK5NiLuz/eJZ6awStAQi4huSrG41mymHevHmYN2/eKo3x61//Gq973eswe/ZsPPzwwzjqqKOw4YYb4nvf+x7+8Ic/4NJLLx1qvKEjU6eeeioWLVqEr371q0Hoa4899sAdd9wx7HANGqxx6FSaJrlZCqy/HjBzptFRjY74RSBNoNuRxYAUnjwFBpSSRaSqZf5eCG10I6KVucWOFkFdltCdrqkEs9EgQSXq7jiJJ0e00NbpN1zrj4hY2OO7h22/0RckCnZESlarAYdAQJBiA1UAopU5nZWLZMXHIFdv0id1c1MlV0cqeVRN2ejNipWVCI47Fvtd57mviBsbh+gUEOM5ZCeHXNnxESsgWPSnDLz9jYUuCmitwrl2SetVmqpRKjroQe6JSNUWELAokKkeTcLnunn44KD7KiZSQlSjnoMQKcC1hgm2447mtvm1ToQtCFAQhXkvpG0HI3Ojc+ItmoTteUgPirw5okzCfMCsmjY6qmONYoNpAUWU/v3f/71nRGkiHHvssTj88MPxu9/9LjD+nD9/Pn7yk58MPaehI1P33Xcf9tprr8rzs2bNwrPPPjv0BBo0WNO4+vZPYd4rP2UayarEuldnrgQaaeIErLqVQJSM1HSY0NcRDfuaTExYhT5rWRsRAZi0EEV12GIWlMbTeMKK1NuhZ5VDkMLiFgOJfy5Nq9/AY4E6T9W4VBs7DhlFxhVZitkUpGl/IX4f1DVSFmka2gv0IgXd3FdiUtSrF5lhFYRUBFDdJroWpYJeudI0kJbSELE0BVS0mNYVB/DxqJJPCpPimwhxGjUW6dc52BdFSI4octknUlcRoed5lQiRqJ4TTzJT5cehucXVljziS9vwa9/DC42iUIBNq6WJ8fRi1XcAfLoO8LYmAmEDaq0BZXoXigImjcej0aV2XmH2BJ2rvm6nRtAuJWS3ABIBNYhtxBSg0UwZxBGld7/73ZOKKN122234yle+Unl+iy22wBNPPDH0vIam1JttthkeeOCByvM33XTThHbrDRqsrbj69k9BzWxDz2hBzWiZFMFIBj3acn256KFT6XUpaeJ9beKoTlzaTd9ipaw2FwZCc0syeQSgbRpG5zn02LhrS+KiJBUfqsQ82m2/aNHxekWO+OIBeN8e/o2cj0+RDhqPXwPSkE0iKhNcA77QszRVXQSlQkTtYq/jPniAm7suCqtPKoPngyhOya41IWgTpKrkJmdmoRRhc/0ZQzIj4msba6D6eIa5KB33WYrupwlhDT+D6BuvEmRRGvcoS3Pt7E+6lj3Hp2sXX2fA23fwe9NFmiR0y6fz3HbcJwqA8/fS2nmCUSsgOd5FssI8XCPsvDT+YblpIi7HupArc8iVXVfJZ0xMlTdvtfMxRqzGAkO1DNGctmq+RjMFYOoiSiMjI1i+fHnl+fvuuw8bb7zx0PMamkwdffTROOaYY/Dzn/8cQgg8/vjjuOyyy7BgwQK8//3vH3oCDRqsLbjm1pNQrNdCuV4b5axRI16d0TIkq5UaEkXfgNupIVJ8MZPSEJgsY+7a9ls0LYqjI16/QiabmU/xaZsuoRJ2NTZuUjWdjouiqLExQ6q0DhveAmaxpvQdYOY00jIpiXZq5t0P/crNeYqPzoe7YgPVKEq8oA+QBnTEiNJanNQN2NqBO33X2jFou8jT2JaY6m7XE6ySmWtS+iwvoPMCenzci9fJOZyE2sqnHfWAvjcuQkXkjZPDmihXX0+vGld6N3YM3iqIjx1bMDhDVyY6j1OORCzrrDIcoYrOhYo06EsJ/a/UmcxS9Ijc5ktl7v+8MI9ublKveWnTroVzoddCQK7oQKzsejd7BXdulNqTK7uGZI3npr1QqSC6pTuezhLTs08pQ8g6loQ1mDbcdtttOProoyvPDxtROvjgg3HKKac4byohBB555BF8/OMfx7/8y78MPa+h03zHH388li1bhn333Rfj4+PYa6+90G63sWDBAnzwgx8cegINGqxN+PH1J+C1+/0HhNQQVmQqcwUt4KvrCuX6fQnAfJDTIkDRKpfS0U47oqW0/d8SYDQx+xWFr/KDOYYeswupbWNBC63udn0kg32zF2lqSBkHef8wiMIuii2WwgTqq7CU9j5SgI8W8AbIUWNYt4CnCVCgGu3i2/G0Chd867BCTBdFEJVy6bYoheeE11oBupoW00n4d0wsYnLSi6zoojDRMyG9KWeSGAPRQTRjXLg/EUhYLrS9N/y514J7OMlQ7O7HLAeLGCptiaGda3wvJQkgZCUiFaT8BgV9QYmun7D+XUHqrjTRJHf/EtHj89A2DT827uYqipXm9ywDihIiMTpJrQREkXv9FBUx8IpQStsmEiIvrY6KHNCnMdTTCNABTF1E6cwzz8RBBx2ETTbZBGNjY9h7773xxBNPYO7cuVi4cOHQ85qUz9TChQvxiU98Ar/97W+hlMJOO+2E9dZbbzJDNWiw1uG6H38cALD3QZ8FACQdBaE15LiJAgkAurB6DZ7SIeSWIBGpop5gSrkO9q6pLlJfdm5TLJp9SMeaGvd3kUM/XxqbhTQ1C0eSmL8pzUal5ACc8ahz/Wal+YJ9DGSeQIkOqwKj8+Tu1iQuJlJlm/U62wgyzIybCgNVQoX+0ZbKa1K4iFOwePda3GI3937HnIBw6LFxY+LZagGIiBHbT4+P+4pBe2wSh1fSexxCQquwerHWbNORzirBRqnrrzvgo0s1FX4VIkTvq3kxPC5vKswIh9bKtmrpc468cCF43uuWtBCu3yGxABGTc6URlEXGKVnApl4t6WWNmkWahL03+f+w8v+37ndLuAS9p+S03h2QHK8iGs2UAUWUvvOd7wCYfERp1qxZuOmmm3DdddfhjjvugFIKf//3f4/Xve51k5rXpE07Z8yYgVe+8pWT3b1Bg7UeN151fPD36/Y+Da7xrYQplxbCfEBTmo+ICqV9Esm+WbMSbilCcTgAcj4XrZaJQiXSRalcNMMudlpp09qEUlLkrl4UzktKJIkXnad2DO6u7jx/bGSAWqRYPyGdJSZVQt/a64S2RBRSS6BG2uZ6UAk9b+FS12eQoi99ojWB+LzG0yrYd6I0oG32q7lVBGCu+dhY/30BT3Kp2TTzzwrITx4K5imFW6djctV0Sns/rlIBxHViYlJHKDg5C4TzEaHifmg9CGYAR5i0JzpxEQO9t1zgnufGj43PXXhSWQehtXUbCDVRFQLFo1J15+jOlYncASbOt19kisKSsQwVGw1qoeO8u6IIbWDDMT1kqoHBVEeUXvva12KPPfZAu902XSgmiYHI1Jve9KaBB7zyyisnPZkGDdZm6ERACyAp4L1lXBpEA2UOJDbdxr7tCitN1ORNI2GMHqWESGE+oDsdS7JMNIkWXdlqWaGvQi//J62M8FaTeJv0PrZtjbFUyJiuKWyNIahaSVqiAL9w6FYK5CWQtkw7FfIbAsJFkVcFEnh1YNCLr7r4BK1m4uhXrxYzHLXVc4ykRpYKQkoT62A9BoW91hVTzDgFqLSpxOt0AGGtLZIEWhWeCNHc+TmyCsO6iFhgbJqwqB6fZ7BDRGriaxUjsKGoXsO+6Tk6L97zTuuKR5gm4X83h5DSVwNG0VA/996Ll4tM1c5FV6OQccQM8KSH/Z9qrYBOYb5wCGGIH1XJ8mrCojDpag53/jbqO51kqknzAZi6iJJSCgsXLsSiRYvw5JNP4v7778d2222HE088Edtssw2OPPLIocYbKLk9e/Zs95g1axZ+/OMf4/bbb3ev/+IXv8CPf/xjzJ49e6iDN2iwLuHH152AH9/w7857RpQaix84w29A/euEiHQeym0PwPrTyPqFQuuwRN36UInUp2So4s2l/EhXVZam8o/8ljod6LFxqJUrTWqq0zXpQPeNXLsIhdAaohOmR4JKplJ7kXC8MPLzJGdxWpjrDCDRO6XnHNNbWUgOOMHhFXex91JMhgiBA7lw1yjWOdVqjOI58sVaK3eddbdbMR4NjEDJU4ze0yyFSBLIVqvWIb7X8cPJsBTfoERqsqg45IvgNUeGY5f0uPcgEBVNeG2dKHWlEXHtfnE/TCGrkdM4IqW0IcDd3BUTuDmOjftr2LOKlVW4FrZhOekepwGU5lvVxzDYZpttIISoPD7wgQ/Ubn/DDTfUbn/vvfdOwRUAiqJAmqa466678NrXvhYLFizA8ccfP6nU3KmnnoqLL74Yn/3sZ9FqecuZXXbZBeeff/7Q4w0Umbrooovc7//2b/+Gt7zlLVi0aBES+w9SliXe//73Y9asWUNPoEGDdQ3X/uyT4ROkGeIkQwjTUJUqhSi1oExFlChK6DSBoA9jIbwZohVd625u9FmlcQkXZRjREEkSfCumCBWEhKYoAnkBdTrQgBGrF4X5SSadUgAwXlquIaz7li+h21Z4C6v36uSB9iRA5KVEY4RtcmRtPKJKZCwp7Har7uexG3ivVI8bPDxiHB3ScWuUYaEVUChom9ZyhLhGGwbYyBjztwpShKw58kD2BgQuQOfnQgUHvfZBj6hUYDhrx0t6R5L8Pua90GVpolOlAoR9j/h1iFMq2kebRB1Z51WEvGm0jnVTXL+Yh/MHXNNpXZb+/4uKCAD2RSYk4EaHxoovzC9A1sP77QWA2267DSX7P7nrrruw//77481vfnPf/e67776AD0zGaqAOaZpi6623DuY0WVx66aU477zzsN9+++G9732ve37XXXedFPkb2hrhwgsvxIIFCxyRAoAkSXDsscfiwgsvHHoCDRqs61j8wBnG2LOVeUJSakeavL5CQeSFIVBSQJAWyEVxrDcUVfe1jGeTYI7pgQ8T39dCK99+hpzAg0fXelW59EfpF18FV62kyTAzNxVdOhFQI4bs6VYKPXMkNGQkwT2BxiffItJqUUm701ol3lqCnwcjSgHxifU/9uFcv4HqIl2X3qzDVPRYozQXt1ZgURTB3k9u2Fodp0Zf1m+useZHmCpPwdNs8T69iBS9H0r7B9u+bh71Qn4WocoLG6VT4fO9Imqx31dApBhhd5EqNlcXOeq6SJKmCGI8T9Kp1YFHOQv7pYcKMcjLrV8hwVRDT9EDwPLly4MHtYWLsfHGG2PTTTd1jx/+8IfYfvvtsffee/ed6iabbBLsl0xFZNTik5/8ZNBLb7J47LHHsMMOO1SeV0o5u4RhMLQAvSgK3HPPPXjZy14WPH/PPfdATdToskGDFygW3/+fPV+bv8Nx/kM5/rbLQT3i2i2zKOQ5BAm6u12zECRm0QwIRrwYamU0VjWvmUUeUGNjEJ0OxIxRR25EmgK5hCwU9GgG1UoNuSJhuhDOqFB2cqOnktJE3oJKMlZSD4RRKbcNI1RUychgzk+56yVama/SqvlgFq1WSD4mQ4yGjUzV2RxQqldOIrLE56HVxOSv17R67ScFYo1SLZHiv/cjUowEBZo3ZvipxzvmHpbS3McpwvMKnMdZ6pWDR0Gp0pVfdyJQeW69yZj3GvPY4lWydGytFQT6XOe4KwBFmVN4QlVpsriaMIWaqbhx78knn4xPfepTfXftdrv4+te/jmOPPXZCofZuu+2G8fFx7LTTTvjkJz+Jfffdd1VmHeDzn/88HnjgAWy++ebYeuutMXPmzOD1Qdva7bzzzvjpT3+KrbfeOnj+8ssvx2677Tb0vIYmU+9617twxBFH4IEHHsBrXvMaAMCtt96K//iP/8C73vWuoSfQoMELHYsfOAPzt/qI7W0mTfRJWeEqS7MBCEW05KiephCJNCXZgO3b14FI7Ovsee5LJaQwxxCy+o28o4A0g37ueYh221gq0LFamYmepYkhUoVxfdeJBISGzEuoUSNoF+OFXchKsw+BL0Lx74ARb/BUpJT9DS4ZserZELgO8Tb90oEUoZsoZQiAV1WaPyMSgj6kJoLrQ8gJ8mSIlJ1/7PUUEJ3pAFliUNRqvOMjZCNtc26xz1hEciqo83QikqU19Pi4jbiWfVv0EMEVSWK/sBSsHZTyc+IC/ZhQ0bGlNinU2NdsNaFKhSc3BgA8+uijQRqu3W7X78Dw3//933j22Wdx+OGH99xms802w3nnnYfdd98dnU4HX/va17DffvvhhhtuqG1DNxm88Y1vnJJxTj75ZBx66KF47LHHoJTClVdeifvuuw+XXnopfvjDHw49ntB6uK9jSimceeaZOOecc7B06VIA5gIec8wx+NjHPjal4bw1geXLl2P27NlYtmxZowFrMKWYv7HNy9v0HQAThaLfFftmz9MXBHqdHLdttIbcuXWd87RFr+oxQTYKVHlFlYS2ybNutyC0Ni7wQpiIC/lXKQ0IQI5b00MiVOQMHlkh6Dw3vj5cExRHzviiH/v+0CLNwSMPjJRA1IieOdj21YulJ0ixTWwlMAiR0gNE8gV5hlVe6LGscs1Wv2PXeXQRarVSE1dQVt47Tgwp8gkYywTucUUmozR/fi7xsegYdK/b6BfZWjiD2yJKobsxfcSWCh3k6KhPURPJ69W8WDFSLyXQypCnGj++779W25pBa9JO7z8NSXtk4h36oOyM47df+vdJzXXevHlotVr4wQ9+MNR+b3jDGyCEwP/7f/9vqP2mA1dffTVOO+00/OIXv3BVgSeddBIOOOCAoccaOjIlpcTxxx+P448/3rmQNqSjQYMhQIuMtF43rVbVz4aiJNznh55LU+sbZb8Zj5myfmgNzaJDFdPHCIEOKUm8HqSVAbl1+m63gCyFSM2iXq7fgmonkF0FndKim0GUyggwaYwaTykxMmKeI80QWCrMtVPpQzDqyEfdeZHpaJ9ehH3bsQCDRadiyIlJjJtfnZUE4PVxHDw9PBW6LjrWRE7lljybOagqoeJ/l6p/BKxUQGLvYbo3uO2DFYX3hFb+3qLqOa1NFWVsXVHk4X5AJSXrbCpK5rvFr70lgIGnFfUG1IxgSgmhp6eab01aI/zhD3/Aj370o0lZH73mNa/B17/+9ckdeDWhKAosXLgQRxxxBG688cYpGXPSpp1AQ6IaNBgKweLDFoCiDFMMBEeokrBUxPZ9C7x+SgW02xDtthdj2/SJqaoStemP2PdIJImNcOXQiYTodiFGR4xwfv2ZEEqhmD2CfP0MQgFCaQiloZMEciyHzlKIfuk6TjaSxJAHzVYJIQLCFdgjDNOXTwgTCethkjkQOKHixKOHRUAtEaqBq+SjsW3EZtD9+w/uCZ1L98Vtf+BTfz0JFVlGEPnpZ+wJVD2uuHmt/VuXpasmNceo0ZzR8wSnw4r0WNbLil9HXnxRAT8Opb21gup2gecBMdJ2BR+uNZOQxoy2DsykV08hwe2HNemAftFFF2GTTTbB61//+qH3vfPOO7HZZptN7sA12GCDDWo1W0IIjIyMYIcddsDhhx/eV3aUpinOOOMMHHbYYVM2r6HJ1LbbbttXfPb73/9+lSbUoEGDBg0aNFg7oJTCRRddhMMOOwxpGlKGE044AY899hguvfRSAMDZZ5+NbbbZBjvvvLMTrF9xxRW44oorpmw+J510EhYuXIj58+fjH/7hH6C1xm233YYlS5bgAx/4AB566CG8733vQ1EUOOqoo3qO87rXvQ433HBDXw3YMBiaTH3kIx8J/s7zHHfeeSeWLFmC4447bkom1aDBCxppWo2yxMZ/rmlwGummyJtI+uiUbQsjpAgqyKj6T2gFrSSA+m/t9C0d8CkS2R6BaYPimwqLooBYfz1k3QJCrYdyNDEmiwqA0lDtDMn4ynpNEW96zL+MUfSHRzFkmD4Khqnxg6pNrZG/Fnkd9SvhnwgU3al7abIa0ciuoW9UaogUn2DpKcHnPUx0ireOAer1TLXzlBBlpJ2iHnYE0iQV1iohbtANVKNV3CjT/k06wUpUaiKhPZ0r92crS1M5m6YAbDpxpF0vRqfKVSFMVCpLoKfLVnwNpfl+9KMf4ZFHHsERRxxReW3p0qV45JFH3N/dbhcLFizAY489htHRUey88874n//5Hxx00EGrMusAN910E0499dTAGwoAvvKVr+Caa67BFVdcgV133RWf//zn+5Kp+fPn44QTTsBdd92F3XffvVIV+E//9E9DzWtoAXovnHvuubj99tsDg891EY0AvcHqwvwtP2x+sem5xX/8vHl+q4/09quJXZ1VlB7ksJYEmotziTwUhVtwuL+RaVXTOy0n0swv0HYfMToCbDAbup05qwRjj6AhO9YVutMNF8BBUliUtqFzifeh8vbJfGQNmoqp9IErhydTdceqmXMgmJ8IseFlv7kExFT4OfURjNd7TfUxzvQ7Vp9nx6nTUAkhve9Vkniz2l7HiosZKMVnydTARKoyEemqXUWSQI6OGO2h1Qk6HzPApzmtbkuPWo1iO0Ouu7j+jtNXuwB956NPQ9JaRQF6dxx3f2VyAvS1Beuttx5++ctfVjyiHnjgAfzd3/0dnn/+eTz44IPYddddsWLFip7jyD6fSUKIoY1Bp6ymc/78+VMayuN4+OGHceSRR2LbbbfF6Ogott9+e5x88snocqM+AI888gje8IY3YObMmdhoo43w4Q9/uLJNgwZrCov/+HlDoBJjcOnIFVAV39Y1FY4Rfxgkibc2SFOIVgtydBSi1YKYMQNydATJejMh7UOMjhidSB8hsi5yqM44lDX61GVpRL+PPwnxp2eRPLsCckUHolAQZWkr/qTvdUa/k3i3znwTcLocTwgS3+qD91bjGEarMggBqyNSwyBqTTPRsSsGrL3AyGzfcbj5adzyJ2qPEhOpWuF4r2vOH/z5urnxe8uacGpGioytRo2Ae6L3a9iIVGVilkC1R3z/S4p25bmfUzwPKaFntKGTBJpSXtNp3NkAG264YW1F4Q9+8ANsuOGGAIAVK1Zg/fXX7zuOUqrnYzIO66skQOf47ne/605kqnHvvfdCKYWvfOUr2GGHHXDXXXfhqKOOwooVK3DmmWcCAMqyxOtf/3psvPHGuOmmm/DnP/8Zhx12GLTW+MIXvrBa5tWgwWSw+OGzrJFnFD0gULf6uOs9+VEpVvnEq6KcWFoArdT97VpzlCz9x7aXrZYR4vYTA2sF1Rl3z8nREeC554GVY4aQZZkpL6c2KnH1F291wj+oKlYH0lX59UTdIr6qAXbqDUdi/CloV7FK06H3rtWnVYkUPpXHyQ0R14iIumtKJpf2/ekrRI+r3PrB+XTZ95psIlTV50x3c1s4wSpJbeqsYokB1L4fIkmC6tWBQbYIjKQms2eZLwlaA13bABnt0AoBgB7JoKWEGs1cb80ymR4PrzUpQF+bcOKJJ+J973sfrr/+evzDP/wDhBD43//9X1x11VVYtGgRAODaa6+d0KV9qjF0mm+33XYLBOhaazzxxBN4+umn8aUvfQnvec97pnySdTjjjDPw5S9/2QneFy9ejP/zf/4PHn30UWy++eYAgG9961s4/PDD8dRTTw0c0mzSfA3WBOZv81H/R8Y8eIiU5LkhWao0P/OclZQrX7JtDTAr3lVAkDbR5KhuocYsUapLy6QZiyaEr8u2jW7ZyIjIMk+oeoFazPSLLHEjUr6Qal3vz8QW757od7wgTRUt3IOk+fr5Pg2BSu9FHpXi0UsiKmlaPXas8QFCQ9TIaby/Y3rNmMOAtGtcE1j6915kqatmDI6jlPeSiuajy9K9prvdqrcU0LtSEHBNw0UrCyKh2rafMX0rU4hZszxRtalInSWGTM3w/6Nd0cVPbv7Mak/zveKoqUnz3fXVdTvNBwA333wzvvjFL+K+++6D1hovf/nL8aEPfQh77LHHwGN8/vOfr32eVwXutddeA3tnDh2ZOvjggwMyJaXExhtvjH322Qcvf/nLhx1u0li2bFkQCbvlllvwile8whEpwJiMdTod/OIXv+hpZ9/pdIK+ROSd1aDBdGLxw2e53+fveLwru0YG862dp/2Kwi/SzsizNKka5dM5ot2Oyvt9CkiMjpgIkPXpiRv/cugi75kKVJ1xoDMOCIlkvZlmO5pTxiIPWeq1L8qL2l2D6B4QSeI1s8xVuwLW1LknahoOu+cnAruuqwuOOA744R2Yk9J+8bnEETegEv2pELg6DBOhCibpCVvl+Im3iHC+T/SzLvVI0wwsGBIIWNfz+B6tEZvTc8awk7Ugiv9HANPbjwnRdSKgpUQxu22amAMQuTLNyxtMK/bcc0/sueeeqzTGWWedhaeffhorV67EBhtsAK01nn32WcyYMQPrrbcennrqKWy33Xa4/vrrK+136jA0mZqof8904MEHH8QXvvAF/Nd//Zd77oknnsBLX/rSYLsNNtgArVYLTzzxRM+xTj/9dHz6059ebXNt0GBYLP7dZ93vB/7tiUAqIcZtJEpFhIfasAQVf3bhLwrm18RSgZSOgUkjkXu0eaLHN3or1O33evn8Cohk3HzjlxIYGzeLHfVmY6Qp8LYq0dsQkvchtKShJ/Fj57W6USEddZWKA8yFp1w1Nd0Vwlf3sWbEmlVjhlqkiBDQvpyYKeXTp9y3KyKXfUXxvQjVgAanlfctIHQmWqVLG3GKD6+8K72r0GTHFygBHZ1LDxPQoKBilKpWS9+qhQTxRG6zFDohXylh2ioB0EKscnuXYdCk+TwefPBBXHTRRfj973+Ps88+G5tssgmWLFmCOXPmYOeddx5ojNNOOw3nnXcezj//fGy//fYAjIj96KOPxnve8x7sueeeeOtb34qPfvSj+O53vzvheEN/1UiSBE899VTl+T//+c9Dt5L51Kc+BSFE38ftt98e7PP444/jwAMPxJvf/Ga8+93vDl6r87/SWvf1xTrhhBOwbNky93j00UeHOocGDVYn1GgGNaMFNcu2vMgylq4zbWfcwlpSW5muM+usoOZ/VI6Omm/qrvKrx8eCVv2FvlpBF6bZrM4Lk44rS0P2KM2UpmEaE1YMzRvTUqowqiIEJtAQAb1F4P5goU3DRKBrwaJSE4rGBxyf3h9zrbRv7dNqmevUCq8Tr8L0zviq/sEJlr1PoOy9MoBw30U5Y/QqCLDFDy7FzB9AX7sLukf02Lj5SQJw6p2XF55I8fQeWSP0IHKiJpLI7yMxOmLnbVPUI21D/Pk5lf46aCmh2il0KqASYW1BNGR3ejRTzhphVR/rOG688Ubssssu+PnPf44rrrgCzz//PADg17/+NU4++eSBx/nkJz+Js846yxEpANhhhx1w5pln4oQTTsCWW26Jz372s7j55psHGm/oyFQviVWn00Frog+6CB/84Afx1re+te8222yzjfv98ccfx7777ou5c+fivPPOC7bbdNNN8fOf/zx47plnnkGe55WIFUe73R6oyWODBmsCqpUYHychoGa0IFd2sfjRczB/sw+YDUhb1O2GlVKlgmhl0IBp40LotfCMjkCUmdNO6RK9iVMfPQpg0oIujdfKjEP6eMf8naWmfQ6biyMn1LYDCJvL2h6ELn1p0zO6X6XuIML0IaNYA1XeTTCm03tFvRcFr8Szx+rbnLhX2tE1kmZki56n62ebauvY28yNYdNaWrlt6+egqser0+qx99JE2Gyqlx9fee8oQyoVIKuvA+ZeJ6IkksRrppSuJVB0v8r2iCOrgogqRQJZdE2462WvmVIAEuiR1DT8FnDu/1oKE7WaBjSRKYOPf/zjOPXUU3HssccGFXv77rsvzjnnnIHHWbp0KYqa/4GiKFw2a/PNN8dzzz030HgDkykSawkhcP7552O99dZzr5VliZ/85CdDa6Y22mgjbLTRRgNt+9hjj2HffffF7rvvjosuuqjiETF37lwsXLgQS5cuddb111xzDdrtNnbfffeh5tWgwdqCH/3kE3jd3qcZXUa8mCsVmDFqngahtjBWf+IaubIFkBYzYaubtNaQI22z4Hd7ECqK1ExAqKCV2d/yHZFltgFy6Rew0VEgEsJTGs+JkumcW5nTePlKrN46L8CSHxJAD+NPFaetVlErpcnoMa7ORKhXghBmoZ+ASHGvsAl1TPx1FyWyh0uTUJjeo90QMIGWCghF5HSsHoSqEtVS2t1LgonM64hcrN9z97wdQ9PljFrSBERq1H65oOpBmg+972TvoTUgjRhdW6NdUWrIXAc5HdmZpt58DQAAv/nNb/CNb3yj8vzGG2+MP//5zwOPs+++++Loo4/G+eefj9122w2AaX3zvve9D6997WvdsbbddtuBxhuYTJ11lhHIaq2xaNGiIKXXarWwzTbbuLLEqcbjjz+OffbZB1tttRXOPPNMPP300+61TTfdFABwwAEHYKeddsKhhx6KM844A3/5y1+wYMECHHXUUet01UKDBj+68d8rzy1eei4AYP5L31cbYVCdcWO4CUCNjUGOjrrGrJSGcqadgWN1Yr6ZU/USLU6kmbIw+7LX6mAXN1EIT9zaxuwQaWJMR633VEVPUxR+gc5SV8FoNC7MNiAmYzFY37xKVGg1wxG4Pk2cXc9EOteigK4hb2RfELxnJSan2ZHh++iOAWu5URP1CghcdXJDT0Fz7R4jUo7A23uNv7euWs85+ten8mgbZ4HQGvH3P6++5ClIqi6l94H6ESYSqp0BEl4rlQqIwpxzsrKA6kyTjcZUpOleAJGpl7zkJVi6dGmF5Nx5553YYostBh7nggsuwKGHHordd98dmZUeFEWB/fbbDxdccAEAYxDKtdn9MDCZeuihhwAYNnfllVdigw02GHjSq4prrrkGDzzwAB544AFsueWWwWuUdkySBP/zP/+D97///dhzzz0xOjqKQw45xPlQNWjwQkQvT6Rr1eXYP/lX6LI0poTdrin35hEEq6FxZIpVebkIiXVU1zXrhW9bg/5pP6VNCXxZQne6EDNGfXl8u2UIAbdtoPGltDocuwBSdAvwLWhQI2xm14ZbF7jtBhGJ8+jUEJV8gW0Db+iLAaI8w6Yd+5itVobmx66zliDCR5HOYQlVLE7n2jV+XjIit5F+y71HPMoEVF36LVnqJTLn4znENhZErui95h0FshR61kzoJAFSo5VSmYRqSZPaywBJbiHTlOZryJTBIYccgn/7t3/D5ZdfDiEElFK4+eabsWDBArzzne8ceJxNN90U1157Le69917cf//9zmLhZS97mdtm3x4uAHWYsnYyLxQ0PlMN1kXMGz3UuTjHIIdnud7M0OiRLUTVMnSruSpLIC+snqW+7QwtWIHxp00DOk8fab71E0ESrcwQJUo/drpmMS4Kv/ALafRSRL4A71HF5sK1U3XnX0cAgu16fQS6cnkvQu9FJhyJcpVyLJWndIXweZ2YCsXnUlaE526fOvI0AMkLzDrj48fbDugkHuxP9gxuTpFNRVQwocc77nmtVKUvpSG80hMmS66GRtwmJm5hw8kVRcmIrGcZ9Iw2VDuDTs3/S9lOoDK/bTJeIl2RoxxbgR/ffcZq95na9fCp8Zn69cXrts9Unuc4/PDD8a1vfQtaa6RpirIsccghh+Diiy8euhCu2+3ioYcewvbbb19p5DwMBtrz2GOPxWc+8xnMnDkTxx57bN9tP/e5z016Mg0aNJgcrh77Gg5oHVIbpfEaqq5JdwD1lVqA0/aIVgZIu7gnibE5aGWhyD0aP0j9AW4xc9tQ1MlWYZmqNUuU2i3zM019CT+Jf8c79dYJRFy4eJinrSINGUfQDLoXaNwo7RSPFaTziBxxMXld2o7GERJA6VvpSBmmA9l592xK3Ac9vcPKsv4aWCuAgUFRHqaFq+3fF0SnWFqtbkh2D0+qMTUbh8YQvYgUa4INIQxxzwt3DYT2bYyF0gAEdCogu6aKT+QlUExPNV8jQDfIsgyXXXYZTjnlFNx5551QSmG33XbDjjvuONQ4K1euxIc+9CFccsklAID7778f2223HT784Q9j8803x8c//vGhxhvov+bOO+9Ebhn7HXfc0ddqoEGDBmsG13S/USVUWkEkNVW2kXGj93JipIFpSsTMGcB4B1p3IdBf+M0RL9r0yaEBQ6jYwgXAL2LWP0sAVj8VVdw63ywVni8zB42JVV00Rshqq5PKcYjYsbGmoo1NJe1XN1bUlkdr5c06azRYgx3Y+Evpbrda1EB2AEBPM1Uh5IRmqwOjVPXFDBMVOEwEG9EK3nPqDuC2qbfR0DPaTlfIoaxRZ7KyhCwUZG4tJ558ujLGakGT5guw/fbbB7YGw+KEE07Ar371K9xwww048MAD3fOve93rcPLJJ68eMnX99de732+44YahDtCgQYPVh/mbfcC4n3c6PfuUBYSm27XWAqqyDeBJlel7lgeLEZWTGxsGv6+QolKNBfj0Vu1xUJMqSu1CnqVAXvjXXXQqCUvwpQSQh+fi+hiG1WQ9tT48xdMLdfvG208yQiRstG8YkEN4z9e5RxR3yieTTm7WyefFdWKDuJ3H/Rf7tgfqQVjjXnw0lBTQSkIkk49OCZaW1dbyQ1AUlCo92bXQLdO8WJRlKNLXGmUrgez6eciuiUiJvyx3YzdYfZgoI8YxaHbsv//7v/Htb38br3nNa4IA0U477YQHH3xw6DkOnSA84ogjcM4551Q6Mq9YsQIf+tCHcOGFFw49iQYNGkwSUgKJ9gthvO4E6Sn7u+1B5hvTJmFEiplDxjof0cqC5rNaaS8CZpEEkWaQRL5sRIprukQrs8Qu82m++Lx49IcazvIqLOUjKMLqwvz+ol5oPZWIRdo1hDJ4LSJA3utIhgQnRkxaEGqbaluvcJ1S3fN1ju18W27MyU1CB5hbLXql+tzr1UgUVToSsTLzHuA9FdK8B87MVhnz5jQ1935RmCbF1gcNAHS7VUsgtRBAaVJ6JDQXuYJc2YVY9jz0ipVQPbSEUw2htWuuvCpjrIu48847g79/8YtfoCxLJxa///77kSTJUDZITz/9NDbZZJPK8ytWrJhU9m3oOO0ll1yCMd5+wmJsbAyXXnrp0BNo0KDBKoAWwGhBc15MDKYqj4nU+YJLVXwsJVenjTLHIuFuzccHtZzhqaluDkUWBuSizrVXifRO1iNt/yAndKouK0sjECYn+Iy5qSfSkT2RpcZGoNVyD1e1VQMxSBQmxoCi6IBs8mOyNOWqWjXUFQ+4OU6882AHofeg3/EmEvIDIXmTMmjtUgciRUKKkCD13iH4Sfexzgtz/3dNAYMeGwc6HWDFSqCbQ5CpKPPC0kLYKBUgihLJytw8VnQMkXpmGcply/vPZyoxjMt5v8c6iOuvv9493vCGN2CfffbBH//4R9xxxx2444478Oijj2LffffF61//+oHHfNWrXoX/+Z//cX8TgfrqV7+KuXPnDj3HgSNTy5cvh9YaWms899xzGGGuymVZ4qqrrqpleQ0aNGjQoEGDBlOB//qv/8I111wT2DNtsMEGOPXUU3HAAQfgYx/72EDjnH766TjwwAPx29/+FkVR4JxzzsHdd9+NW265BTfeeOPQ8xqYTL3kJS9x/fL++q//uvK6EKJpGNygwXQjSbxNQE1ommugAFQFzVFaL96voq0C4Aw/UYZi9KD5rjLNa3vApR27OdBumzSSTe1pISAy+/fKMd9OhlJhpQJS6f2nkrapwHLnWMJ9BXdeVJTyofOKnMd7aLumBDUpPjOXOr8nVa9Xqts2jugME5GqQ6/IUlH4FKvrU1dTWTgoeuikeonOTdrU3seU8psgKlhbdWm9qshFXa4306dac9MUnNJgOhEmSlWav4X9ibyE6HSBFdXszOpGU81nsHz5cjz55JOVhsZPPfXUwK1fAGCPPfbAzTffjDPPPBPbb789rrnmGvz93/89brnlFuyyyy5Dz2tgMnX99ddDa43Xvva1uOKKK7Dhhhu611qtFrbeemtsvvnmQ0+gQYMGUwClTcsWrStCdPKZqrMNcChL6LK/CWYARtzCEnRmulgjQDc72EorWN1UpxM2NebHGGmb1B5pbFwapjRkqt1mGi5GFANiBZYOVBDI/DnbljuBFoufR3R+Ds4Kov91qthFoIbgxiBCRXYDvVDTMHhCxNelh/i+UmkYXyMF45hOJpwlvKi7l97EHsv156Onk6Qv8ebb2ckAA1SU9m01JJUXpaepryy1LWZEqaGFNtYHTKMnihIYG+/ru7ba0FTzAQD++Z//Ge9617vwX//1X3jNa14DALj11ltx3HHH4U1vetNQY+2yyy7OGmFVMTCZ2nvvvQEYJ/Q5c+ZUeuM1aNBg+rH4sS9g/sbvdX+LuNk4NdIlslKj0XELO/dtqgOPhJBAnJ4il3MhXBRI1HhSVaJXgNGl0PETtqhmKZCY3mgY7xoCZW0P0B4J24EQqSoViCuBSCW5fpNnFmD+zjJDQIvCVESSgNyalQryAiUSEJGngDxKURX/22iOiCIxvYgUH09wPduQJoQDIR4zOq/K79SWBwhNOUtjkaC1MpGc2LCzVxEAkTgiyXSNBrBEcM2NhQ63JeF5H/CmyChLc+9Rz8iiBEZHzM9EGiLF9WClNjordn1kqwVRKGAa2vM1kSmDRYsWYcGCBXjHO97hLJvSNMWRRx6JM844o+++y5cPrnEb1tR06Gq+rbfeGoAxvHrkkUfQjTq377rrrsMO2aBBg1XA4qcX4cANj/KLfqvlPvC1YIaS3NW8F6EC3DdukaZBBMHBLnwiy/xxLJMIxNzSOEdra3NQa/YJmAWr2zWO6EFFnvQO6SMtk+pL2Wu0ANMCKhO7fWbOl9yMeboyTo1pu12aGgJjP5xFkkAnyrl2EyGrI1S9IkLuenKywM+dE1x7FbWNrmml/LWcCkJVtz8f11aDOid3pX10SgpHDOl8BSemFBGkyGEcmZqoMspF4YYgVFJAyBSqhx1ITKyCc4EtDKAii25uyBMrZtBSGnKYJq6aVCx/3hIvVkWaJBCymBYy1cBgxowZ+NKXvoQzzjgDDz74ILTW2GGHHTBz5swJ9yW50iAoh0yXD02mnn76abzrXe/C4sWLp2QCDRo0WHUs+ctXXYRKa7u4dXOz6MUfHnFKhRaYbjdqfGsJFbX7iMvgE1+1J8oofWQXZ2Gr8erSjM7lmryvUPgGx1lqFi6ybMgSY9xZmvJwndmxWmkfC4TUp2l4hxaKiijl51uEaSzNjl3xXoo/47g/E3+ODkd96BJpyvJtVZojJjHRpOfY/pMmVP32EaF9BCdSqtv181QaAmVQpamVCpsslyV0yxLI2HahJvUnkiRo0D1QarnuFOr2q4lQVQxcqcE0R56btHJpz42icXkJPLfCESn+vsVjr1Y0ab4AM2fOHDp4wz0zH374YXz84x/H4Ycf7qr3brnlFlxyySU4/fTTh57P0GTqIx/5CJ555hnceuut2HffffG9730PTz75JE499dSBuys3aNBg6rH46UWYv/F7ve8Qj3wQcbELZuAfBe8jVCE9OoqqEKFipIov/LVtXqSA0DWLl1bQhYIaM6k+QfYFRQFRZt7OgBzSpYROBFSWAIkw6RUAUMKRK83NFqncvdSm5UdiPIOEjaCIUgM69enAonTRLIGIUNG59dLJcEJFUR7ULLQ1ou3Af4vrz+wx6T1z36h7SSxi/RM/Nn/f67yh6toEKQ3Ai76FVFauxAgVnwulbQcxOa2brz3fgaJTRNalAHREnCZI9VGaUEgm2E+s475SpsmxlKbIQSmI51YC3a7X19E9SffzNEWlmjTfqoPkSgBwyimn4HOf+xze9ra3uef+6Z/+CbvssgvOO+88HHbYYUONPTSZuu666/D9738fr3rVqyClxNZbb439998fs2bNwumnnz6Uz0ODBg2mFoufXgQAOHDDo4ywFoB2ut0+3/x502OhfDSEFo8e7WcoNSKs9iUQ5TKCoAEIlLWeSybVAiMObwHQEhq51+BIaYS/WQpkCTRM9EMnwhAiqgLki6gQ0JIIlgbaKbQEZG77A5ZGXCyKAkJk9vgJa7pregRCsapE5sU1YRSFCdQnilxU0oa2+i/UsUVO42VZT6p4BCgiUHH0a5BIkCMmWoWEys7JET0XtWOEqs4YlI/No1O2kq4uJVpBlJp0GqpeVZM9zksrCd3pAO22d+Tv5sbAM0vM/TWufHqTQClJujar0By3wZrDLbfcgkWLFlWef+UrX4l3v/vdQ483tIp8xYoVzk9qww03xNNPm75Eu+yyC+64446hJ9CgQYOpx5K/fDWMIDnNiPRajxpjT+6E7tM+Koxm8IWZp4kGJBiAWbhEmvnFT5sxdVGYiFBemKrEbtdEjOzCLHIviNYUqWqnIZECwoiHFFCZhMoSqJZ56Ez6aFbCqtRo4U+sJijLfPm8PYd+5xe/7qsbbesSKdyjZwTFRmXI6JSTocA4lac3o/dBF4V7QOtq1KnuvSJDVXoAIfm1FZoOrJhBk15KqUBvZV7UfvuIJAlORAaI4tX2EgRc65iJolLh8GzbWHulFMR4YSr8bP9Hd09MV1ovxovYtHN1YM6cObVk6itf+QrmzJkz9HhDU+qXvexluO+++7DNNtvg7/7u7/CVr3wF22yzDRYtWoTNNtts6Ak0aNBg9WDxE19yvx84+whPSKSEjtMr1msprt7S1DCYFkrp7RMqdgUclFZ0lYL1REvGbWBsJMy1oBmz6a4R2+g4SSDHOlCjbSAT0ImElgBgolRCaecN5A8CJEUJ1bILrzKVhpBmPOSlF1DTuZOGCjC2Cjzi1ud8OAaO/NgUaGDBYAkVt1VwTaK5tq0mbRuI8nnEcVhNko30UEsXek6XLJVHkUsWvfE6udxo7qhKskeKk6f7An1ZHJ0akMRUooH0ftW1rKFoZJKYCFNihed5jZDejbfmKtlf7Gm6qcRZZ52Ff/mXf8HVV18dWCw8+OCDuOKKK4Yeb1KaqaVLlwIATj75ZMybNw+XXXYZWq0WLr744qEn0KBBg2lCD08ht/Bo7dI1uo4gsdSN7uZBxKZuodaMUAXHi8S/dQJsI9C2RKEoILo5xMwZhvSpFpJuiXL2DLuDhMokZG4X7bwM9VEwWqqkYxf7ovB6K8DbLZSlM2+s6o98VG5YTJgWFKahL9/eHjQkBkQErKdThUBx1OjTzKFqtGt8bDsf9zcjVLR/leSxP2z/OyLbLo1H9gkTpeGSxJBoIlSQtfdsvA8ntxUiRT+VDiKhwfkChjBLaZpqW882B6pcTdiXCdpdrKFIVYNVwkEHHYTf/e53+PKXv4x77rkHWmscfPDBeO973zs9kam3v/3t7vfddtsNDz/8MO69915stdVW2GijjYaeQIMGDdYwuBkj4EvfwUiVE5P3WQz5gm4XRYCRqrpFnKZAaZSi8MJnIgwlrA9QATFj1HpEpUiWAWqGiVglufIpQFhCxQihE6N3S1++H1eblcobhNJiabdzUSM236lySY/JUkw03esUHYo1PEAQyamdl5BOcM3tAfjrPTVHRDw4qUr4y9r3zaM5AN6Tiqcgk/oIVUBe4xTmIOgVLQzsH9hxtWAFA1UxPpB7o1dLtPl7PogWbspBqeJVHeNFjF//+td4xSte4Xwyt9xySyxcuLDn9nfffTde9rKXIR1AF7fKyrkZM2bg7//+71d1mAYNGkw3aGEQIqzKCr51h41pOYJWNFkGXXaqY/cgUgEZkYzYpKlLqZFGSEhhIiFWQyTSFGI94ykjy9IIhhMBnSTGK8ppvdjx6DkpgLFuNepAc+aLPzegtJEJdzVGbQTFumH3Q+BFVacLctod0pPBRFG4RomcxqP9HdntR6R6oBKlmihqFKTI4nkYAsznGJiPOld3ZbzLJvD6qUs5rxIRmMhbyBULSB/Vo2tL1aScnDuCa6Nn04Cmmm/Vsdtuu+GJJ57AxhtvPND2c+fOxS9/+Utst912E247EJk69thjBzowAHzuc58beNsGDRpME/rpfIoC2jp8VxZ7IlA1JexxGb8zBGVkqB9cmlBXiVbd/o6UKA29bDnk6Cgw0jY6qURCZKkhhhHBcAupUkYfw/2lpDSu1q56UVk3bw0kOoxQ8etBcx0d8ecbGRjXIvaL6iWYtv3oBG/ZQu72denTPjYCjjTxFJf9XUjVN2LYC9TnTqRZ9cXITNVV/Llzkz4aGEyURad4dR9/HVg90ZWyNO9lqYC2vc5Z6ns+Uu8+u3kQmRo2itZgjUFrjRNPPBEzZsyYeGOgYkreDwORqTvvvHOgwQZ1Fm3QoMGaA29M7KvyCvONnATntc127baWBPUkSzXEra7XnX/RR110UZhFOBJkB27WRQ4UJvDk/KlG2sB4x0QWJLM4kML8TZ5RnW59FRyv4uuWtokzOx/ARM2KqOqLObET2aklVYOkofq85kT8ddtyvZAlThXTSn79FLdZ8Om/2rTfBKh9T1nlJ48uVVJjExEqMEH6IBiwMKCOvIvRETuG1UxRwQNhpA2sWBnsA8C2NopaOK0uNKadq4y99toL991338Dbz507F6OjowNtOxCZ4q6hDRo0WPew5C9fBQAcOOtd/kkpIBDqQMyCrRC01+Dg3kcM1AaFDEIDohVpgHohruyi7Y1NQ40/1dg4ZKmgEwmMjUG0Wl57xcZxIuhuDtFuu3MPB9O+UrGVQXdzb5wJeHKZRo7rQlhzTe+x1ZdU0fWgFi3umnMyQt5eTC/FtwUjKTUESyToSypCYqUrzw8VqSKBekyUYmNQS5zcNe2nvashVOZQqvb1weapq/tZR3cxOuKvLzU7JjF6lvoChZG2eb0sw1RwPnj0YlUglHms6hgvZtxwww2rbezGbaxBgxcZwpYxpSNUdeLaUBsyASJTT34MHmkKtp+gzNyRhh5REtUZh2zbqAKdQ5J4suQMN+25UZoPLG3GiRWdNxAakDJbCKer4gQrzx0Jc9fA9kgMvKHcXKIoCv89vvb8dSvOdpG+XuS0bt8aCJtOdKaXq4JYwE7XXrL03DCEKq7S41GquizIRAQrfl0acqW7OQQ12CZSXdr0apo641h3TvT+E0Gbrmq+JjK1VqMhUw0avJjQynz6bGzcupMbQiWSBMpGU3RZGo+nJIFoZT5CU5YuzWf+7mGhwCAA03C5LlpR5/BtIyODiqkVleKX0h3PVevH7VW4L1PHCubZNeEESSSJuUb8nJz3VBqee5Z5QpUXQWWaiM+FRZt6VQXW+iQB1fRpK0wx9UwxEnpcU3IEh1a97RN6gftRJYyM1+nB4tReXarPTUpEBHSCtF8NAXP71kX+bITQuaDTc1S50OmaFLEqfZFEy1okaN0/RdvgRYeGTDVo8CICd5wWoyPQY+PmD9IpRQsptXrRMERBSOkNLAddSJLE6CltpCYYOxG1zthD2Q6Q71EXoVs5c+d27W748en5bm6a9FLEIdIq6bKEKGUYSRECSFnKU2tHqNzr9DyRJkZ0ePqOk62qI32U5mPP16VM+4rU+Xhsm3AAOdy1j8HTfrJ+eXGVoxTdm4hQxRqniQhVXJUYkzpuNUHHLUtThEH/H0obEkVaOwVDrACfCnTawunRCjfVfGs31pyVa4MGDRo0aNBgMJDP1Ko+hsCnPvUpCCGCx6abbtp3nxtvvBG77747RkZGsN1229W2bHkhoolMNWjwYgR9O29lwBj3e5IA6iMTupt74TTgUy+tAaqZKALB0mMuIpOEOptK499BoBWMDkl5MXzwsgobB9Pz9qfo5tClMpEt0swkCdBuQVC7EaXgmj4TuDN64vseOpsFSgkh0lDROKUyaUZk9SnPyAi1gl7aITuHXjYTbryaSkD2xNA6Kkr19UIlmjZIdKp2rv2c+oePEZhImnVs561snrdGsdJWiUrhLRPcObywwz0777wzfvSjH7m/kz4R6YceeggHHXQQjjrqKHz961/HzTffjPe///3YeOON8S//8i/TMd01hoZMNWjwIgL165u/6fsB2MWt1XIpKJEkiBvackG6VtrYKNC2Upj2IYMQKoLzIZLe1BHMj4hptxxij6SaRV4XeUAaYhG9ZsSEV8OJVmaObYmd4EJzwIiQuXUCnw8hJQdxq6spJJAokx4EDLEqCu9LRaSLL0xZ5lKRPA0YCOHdyVVNPAOTS0ao+hLSiBhXj9ODUPHz70W4qGqRYaqdw/uSKviUYOWYTlDOq069sSh/To+NQ8xaz6T9kJhzT8l7avrsgKYyzbd8+fLg+Xa7jXa7XbMHkKbphNEowqJFi7DVVlvh7LPPBgD8zd/8DW6//XaceeaZL3gy1aT5GjR4EWLxE18KGiEHqGtIa4lK5WFLxXW3awjZRJGkOPrCCIXoV90Vz20Ct+5akmF/V51xcz7WeBKl8ukPHmWwkSbXeoZIYC8yIKR/tFuGVLVaZvtWBtFuGSuFmTOsYzwzDs0ys12WGd0Oa3cjWllttCUmCEHrmWHB9qv6gE3wvjDCFfpI1ZGwGvJBRKhXCsoR2d7nFt87IkmMtUcrgxhpm2gSf7j3NPK96tW0Oy98uyFV1hderG7oKXoAmDNnDmbPnu0ep59+es/D/u53v8Pmm2+ObbfdFm9961vx+9//vue2t9xyCw444IDguXnz5uH2229Hntd8KXgBoYlMNWjwYsawVVv8T8dRrFN3kgwWeWApMU+uEhcZQslK/2sQNwIO5tajv5zqjNefEs2XC8aV8i1mAB/pyVrOR8tVg7lLwJziqdeh0kACf540Tpp4N20bjQqOxVNadZ5KrFdgJTJiLSh4enFgJImLOlYQR6i4i3qSABiAxNE17pfO65XuG8BbaiAyDpjegLbq0BQSMDNWev9j53/y+Moy7ztFUaxVEeyvITz66KOYNWuW+7tXVOrVr341Lr30Uvz1X/81nnzySZx66qnYY489cPfdd+Ov/uqvKts/8cQTeOlLXxo899KXvhRFUeBPf/oTNttss6k9kbUIDZlq0KBBgAqJ6ZXGCVJuiSMZmqwK+pGqfoTKmonW6X3iufltBjyXPnAEJU29WDdNPXHKc6ObqVTcsfYpvGl0ZsehijDApgBteoics/UQqSJOpIY4r77XgZEXwQgxd56v1VJpBZFmlfkM0quwp3aq74lY4lOW1W3jY/YYizdZDu4ves+57UEwvr1f89xGEO3S2ekOT1onialM882aNSsgU70wf/589/suu+yCuXPnYvvtt8cll1zSs81c3AlFk2bwBd4hpUnzNWjwIsX8jd8bPqF0GOmhRz9YfVXsSzQQieEpv4RHOWDbwJgHT1/1IhK0TV3qq+c5xAuraw1T41NE81XGgNQsuvb61PUAdAMLE80Q0rco4RosGQna+fH4GGoCgTZhEhqesMm17H2tuW6N/R00rKZN68g018fV3R8TpfvcHJPBrsWAqHh69cJIG5gxwxApmbh+fdOGNVDNF2PmzJnYZZdd8Lvf/a729U033RRPPPFE8NxTTz2FNE1rI1kvJDRkqkGDFykWP70IupK6K4eu4DI7hk7j7nf+qAN5TEXu6cECx0hVP9SRAJPKkfUPvg0fg0cmiCSSuLxgztjkSVR3vZwmTAZk0S2+SeKJVJxSjPvC9cKAxKkfsXXXrBcpiK4V16xR1EpIEVyDQSNnfQn3IIs+J1T99FRCukct6N7qVwWYyLCC7wWu/+mFTqeDe+65p2e6bu7cubj22muD56655hq88pWvRFbXnuoFhHWCTD388MM48sgjse2222J0dBTbb789Tj755KCj869+9Su87W1vw5w5czA6Ooq/+Zu/wTnnnLMGZ92gwToApvmYNJEa+Fg1pIoTDaC/ELqGUAkpILIUIksrUazKGFxXxX76di+M8HCyI6S3MiDSIYQXrtuqxvibvyZTTyJkSoVRKylMyo+LmbUOF+1eiIkUJxayJkIWoTaK56oY7TWNIk1uHyJSMTmZhHaItzGaFGJC5R6yPzki0DlKYbVUskrMmNEt8txrpoBp1UtRmm9VH8NgwYIFuPHGG/HQQw/h5z//Of7v//2/WL58OQ477DAAwAknnIB3vvOdbvv3vve9+MMf/oBjjz0W99xzDy688EJccMEFWLBgwVReirUS64Rm6t5774VSCl/5ylewww474K677sJRRx2FFStW4MwzzwQA/OIXv8DGG2+Mr3/965gzZw5+9rOf4T3veQ+SJMEHP/jBNXwGDRqspWBprVVa1MC0MKSdqtPGcKLDFiktGBGJPIUCSNHTWZteBxA0cHaaobrIhNX9ADD2BXxeeQ4gs6k9mr+9XqSH4j5TtDCnJroleGqlYASJ9Ddpn/PgXllxz8N+RCq4FtJ5TfV0WI8gpKleJB2Rs65wTuEq2r5qfdD3OD0E5rX3ykTeU24SrHhgVSAFhJLG5iJNq+fC7DTcfTxNeilzLGC6e/P98Y9/xNve9jb86U9/wsYbb4zXvOY1uPXWW7H11lsDAJYuXYpHHnnEbb/tttviqquuwkc/+lGce+652HzzzfH5z3/+BW+LAABC6+m8G6YOZ5xxBr785S/3LdP8wAc+gHvuuQfXXXddz206nQ461KMLxn9jzpw5WLZs2UACvQYN1mUcuNF7TEsZpY2302QjUzZaUUeegudIoB0Jqb1Wi1VPxa8NC64Bi+BsEQDTKFmKwCtLzBj1i6ZkUY4sg2uGG390OpKUhM9RNCMmRyRY19pEr5w9Qxj1CJon9yNTzN7BVfNF17GW5NBzKoqQ9YI9njNxJUQ9FQU3eK2bb/y0rIkK1W1fU8EYwJFyq8PrZ+3QK1VaV+XK7TzYtSrKDn709AWrbc1Yvnw5Zs+ejT3mnYI0G1mlsYp8HD+7+qRmfVsNWCfSfHVYtmwZNtxww1Xe5vTTTw/8NubMmTOV02zQYO1GyQwNe5kz1uiMKiAhei/xdjBmdQFzc6jxFJq0d1IPnZUuSx+N6gHd6XgyopTRSnH0qj7j+q+8qKbaeC8/+ptbBvSJxvRtHkzjceJHKauW8bASoyPugVbmfrqUWJZap29WeUjaMLJsYA/Rqq/kcw+lh0+D1W0/0fd9VqwAAOQh1VcnVTuOZA9RfcTeUpO9Lxu8ILFOkqkHH3wQX/jCF/De97635za33HILvvOd7+Doo4/uO9YJJ5yAZcuWucejjz461dNt0GCtxZJnzoeOGgBzkE5Gtlr1OhkORqj6toQZdIGNCBUXmA9MsKSopKJ6WgUE4nkF3c2hO12v9SpKq3Eq6wkP95qyZqbo5l4DxYmUUtXn3QSFN/Ic9nwBT3SEdClLR3ysvYAYGQHabYj1ZkLMmGEemTEWFe0WxIxRsz8RuIylJJ3OKKrApOnb+wVAPaGaiBxNhlDFcxt225gYE6mKRdNxynY6CZXSU/NosFqwRslUXRPF+HH77bcH+zz++OM48MAD8eY3vxnvfve7a8e9++67cfDBB+Okk07C/vvv33cO7XbbeW4M6r3RoMELCT0Xakuc5OiIWYzbbbNITvBtn2wSYruEgefRRyfTU1zeD0lSIVRmABNt06W1O+gxniYvIXLCBqoESNrIDnlOkbCcIk0kPicixVvEBKL0Htd2mH5zlJ4caZsIVJZ5csBtGQA4Z3fAbJ+mgWs77Psu0tQQMzq/GiNRHtmspBSHJVT9MNF9Rd5Y/Jjx+9+LdE3kX+XmoDzJni7/JD1FjwarBWtUgP7BD34Qb33rW/tus80227jfH3/8cey7776YO3cuzjvvvNrtf/vb3+K1r30tjjrqKHzyk5+cyuk2aPCCxJJlF2Le6KGBjoggWvabuXXs1gBkq9VbX2WNPLXSENKaebrB2KJTlkbsTZVXde05ImF8HXma0JDSbSgB1sB54P0IlEqj9F7KKv+Uqic7nDDUeVDxbbjxp0xtHzgA/C0ZNOriLBu0jygVZRhdAkKzVLq2SWKIXivzWi5q8Kw1hO1Np3VECO2+IioX60t4o5SmVspXFHKRd932NX3/ArAqvepzsr7SkfdkjDV88Vj8tXVTdtxgirFGydRGG22EjTbaaKBtH3vsMey7777YfffdcdFFF0HWfIO7++678drXvhaHHXYYFi5cONXTbdDgBQvRyiATCbVyZRiRskLrJc+cD8AK1sc7kKMjUGPjfQmVG7vXgkqECvBiYVjyxBY7Ij69BNR1TukVcbsUEFoE0TLu9g3pIzSColWAWyi1bU6MUplIj2YtRSxJ0YmAKNhi3wvUNod/huWFF65zx3R2bSZsqRI3aCbNlNJAi5mX8nYocZRRytDnKs9ryS4Ra0eonGarKkivPM/Rr2LPWVb0uH8mIlSoiZDF14hrzeLj9sMacPMWGN7aoG6MBqsH64Q1wuOPP4599tkHW221Fc4880w8/fTT7jXqZn333Xdj3333xQEHHIBjjz3WubAmSYKNN954jcy7QYN1CSJJIGfM8H/XfGFZ8qfzcODsIzzZiHu2BQNG+/cri4+f5NEBDJbSm3AbFjmJo11OW8T6zRlDUyY85i1FyEOqVABM+kuUNnqV536Rpoa6NVWKjjDSdaYUopS9vaayLIza1LVRoSpBwa4hFRCQ3oesHaSoJy1Khc9RFWOpAOvvJ9ptH6UaqyEgg0TSItF9EJ0ixFYEU0lkJrpnehHYfvYdqwtT4GDeRNFWH9YJMnXNNdfggQcewAMPPIAtt9wyeI2cHS6//HI8/fTTuOyyy3DZZZe517feems8/PDD0zndBg3WOQgbjahdpqLFa8myC3HghkdBdPPQvylqhGv65Zky8oma0A7UIBmY1CIWpPSkcM2AHaQVfJfKp+9giZUt748F9aIogUz4lBonXHwBjhd/Pn83J3tMF/1i7WvqDE353GPbCcA3402kyWzGZJcbU5JWLE5rCSM610IAibk+YmXHNmlum7na9KYoAbTbJtJYDGA4ylFDjmoJVXyOhGGiU3zMidrA9PU6kxNv0+BFh3Wimu/www+H1rr2QfjUpz5V+3pDpBo0mBiLn/xyWBreaoWPGHaBErZajjymyCH7WnX54FVoVsirOclQqncEIirRrzwmAi+jn2gbVrFWOZ+8MJEe0lIpZtzJryVFqmi7mAwpBYxbr7uotU6wXb+IjJTA6IhP0XENFqUxVelL/PuRgCyFHsmg2hl0K4VOEmghoGe0oUcyE9UaaTu/MOeY3mqZx7AVbnXREoroxe/pJCMroRh9gGWPb9/vuk9jNd+acEBvMDjWichUgwYNGjRo8KLGVFTjNWRqtaEhUw0aNAAALF56LuZv8SH/RJ9v5EuWXQgAOHD2EeYJlvLQcZUXR5zWiSIkuiwhWi2TLurTW66nVUAZRX7iljIc1HomEqu7vweKYCggqUmj5YU30FQ1Aui6dB+YlovGqRNP16HuejgbA3tNJFlPRP0B3bmYFjLapvX0iEnzCa2hpYBQGig1RFZCjBc+JcwtHzSl/hLosbHBIjei+j4M3K6lzg3dpnL5a4IigHXXcCJh/0RozDsbYB1J8zVo0GCa0M2dd87iR852/krzN643yF2y7EIgS43R40gbYqQNOWs9AMA13W8MflxmyKntguzSSDH5ikgRT8GJVuYeMSppOmppE+mntFZhRSF3Beel+4D3jIoMPyu/Ox+qpOZ8wobLtf5MExGpoqySTyJSMUiMTqai1MYlMyk8naZQM1rQidGM6cS8DzqR0Jk0ab8ZLegZxscKI22Xkgt8wEZH/e/8ParReTmyG78f8fs/CFxjae3GrkXgeC4rRQ89UeexNQ0Qtt/jqj4arB40ZKpBgwYhSmWIFIDFj54D3elAFwXmjby9dvMlfzoPi5eeC3LXhhA4cANrqDtEr7/KokeaGfqdL6ykLSp7u7cDqI0auEV9AFG8zlmULbNtWWJXbHIz7+ZemM3tCYTwpIn6+tG1om0q82YRLDLWJPsGesT7drr+GFobsXgvHVkigSyDXn+mm6/OUugsARIBTcaXElAtCdWS0ImATqV5CPv7eqNOrE4WEURqkUjTvqbuPSB9lZTOfqPOSd09eFPovvYQNb5SHL32naCVT6WIgMjXdFokqCl6NFgtaMhUgwYNHBY/vQiLn14UPLfkT+dBrVgJ1atcn/Z94kvmF7v4HzjrXdZ9fLiPGddTrSYK5VDX+iWwHWCL5jACdRslCiIoLGqDkbYXjdPx8hx6fBzodGxT4zI08tTamV66v4PoVHRM7ivlmh6r6oLOo2+9iECvRZ+9JyQs163UiM6zBNCGSGlW3agtyVLtxESossQQqhltf13YuCKzLWxqooTmnMi2oWbeA/Z0rAUrMKiNSsX3FaUoJwIn80HUsolMNWjIVIMGDQaALvKBokyLH/sCFi891y1iV6+4tGawmg/0YUrM+yyqRKi0Vr3JGCdJ8WKbyDBVRa1YWpkhVJSuIxftWO9U2EdZ+gq9jDmS83Po199viHPmrWOC94gTBMEqGOkYaQJkiUnb2XOmqBMEoKUwESjygErM8xBmO9VKoNqWgKX2uqy/niWemSNXjlDR9ebkqsWuryUmZGMQRxxFK5vaSFCem0fca29QDVRDTBowNAL0Bg0aTDkWP/ll9/uS5Rdh3sx3Dlwyr/tFpAiUOuNpQGXSW5pvQz31AEaiWJrMtVRhERguQqdIEUVciKCMtA1ZYk2ig/OjVN94x2txiAikqY8kZRmAPNRZ0XaBILvGqTyxBIYczWkfLqiORdeJNPYGALRLMwqXfqTIhZYSQvvir4BQAYAGhNKGRGlAtzNAlhDdApgxWuk/KKQEMkDnuUnZ0cpTKujEXjs7H+dVVdNSRrQyU+Dgeh7295mq+JcNavo5kYdUr/ZAqxNNNd9ajYZMNWjQYEJcqy5fpf2vXnEp5q13mE/51Zk1kndVXeXdoBDCG1ZmqVlsuZN6mlpnc4sekSBdlj4qlbGIiJSWtCmX7qu0roElB0UZap+IUJFuSgqgZL35KH3Iqwnj+WVGi6TJ/4oW85gg0N+ktyqpp6Afm/bVURpWtWyUKhEQpfJ/SyDpKqhUQghtiJUGJHmCWR0VUglRKO+xBThiVCE2lAZmpFdkGZDqer8t9KjM7IFaHV6/v+k5uj/rxOZ1xqHTgcYBfa1GQ6YaNGgwPSgK6HhBJXBCYtuV1CLWX1FajlfbKR6pgalQK0vnaC5s8+W4l19lgc4ym56iajYB0eVaraoRpODjEomJCQSRKkekEj9viloRkYp0SEiMVskNBxM1E1TNx5v4puF11pRuJEKglIkoAd7eAIAotdFLJaa5tSg1VCYgc/O6LHgUzZAvUWhIpcy1LUsT2TIX0VxHSnmy8zDXUPnrwkEkkK5P3TUn9IhO1VUNVo7RC5yMNmgwABoy1aBBg9WOA1qHAAAEbHNjEmS76jwTuXA+U7FAuBdK5bVMgNcnkVieEyvmaA7Y1FPUUBlgqSFazJWGbknoNPVkqogWdSIyjkTAnBtpgfI8TCny9Fu7bdzJqfKPSEBZVoiUeV6z1JwhO+b8GKFMZHDddJaafWj/sjSKWRZVAumjtIZOE4hcGVsEwBEp8p3y520JmNbQWQJRWn8tKEBTtCox1yTSmLlz6uX4zhHdA7xJNY801hL1YYkUn0NlvmvOU2oqHMwbB/TVh4ZMNWjQYNqgla4Sqsh7adIpviw1xpQ86iElUBQ+vVhngkmpKEusBAmoS2W0UUQYlIJuJSY9pqR5FKVbzAUfM0bFz8qmD5U9V2okHGuz8ty3iLHpS6EsUaHnguMw7RdL5ZltLQmjTaQ0pfKp10MJraGsDgoARKmMNopMO9n7JEptrg09JYQhVLkhgRo20lZqFxUTHRt1pJ6OzlMsSp/VRaroOUvAXJNqRqwqmCyRWhvRpPnWajRkqkGDBtOKgFBR3z/eYFcKCFGt5gJQK0pGUQItVqFG6TIUtnlxWl1E6pre1m2XF9DtliEO8Xwi8bGmRsUWou4YBBLBZ+yYaRKafLI5BOddF81JpOmjJ4QlW9rolmLwaJWULlqlyZQzES4aRVEMY5iuIXNliBIfS0qoLPFEjeuw6Pm2hOhY0XgrM+fjGkTbsVIJJ8TnZLiuAlLrSuStgkH8ogZBHJ2qu/8aNEBDpho0aLAGoJU2IuZuN3QhJ7NIreoXy1g/Q1EJaaM3ee63SVOg7KO/ouPJ6GOQFnFy586LkOjwZsRSAN0ax3OeyqsTuXPjT/46EaVY/1Q3b6vj4q7sPHVpBOS0fY/zd5WDZNIpXBRKlEZkLooSSE2zYycup32VguxQFM0chzRdxi4B5lq2M6BQAAoIpJYgqvDcqbKRXwsu7qYopj1XXlEnsiz0fprKCAwntnGqchqJlVAIOgFNdowGqwcNmWrQoMFqhytph9ck6bKE0MJEqAAbBRjg076mnYyJRiXV13tVZvHtZeLTbQCrfhPGgyjLgPFuaJHQD1wUzyMhpIfiZpVaQ8twQRaUnuTnEi3aOvOp0XK9NgSLGpE+yWmpAEeMguMoBa3MWFoKqFbinM/NvGCIk7UgcNV/qUkPuuMxM1JtyZzOEqevEiVLT9n0nyhERSDvNGNF6d8P6rPHo1W84pFfGy7kpwhSWYZRz2FRF9EMUqelT3OubjRpvrUaDZlq0KDBaocxbgTU2DgApovSwmuSaOOJqrc4SG8DhM2FaychwoWVIiNFH2d3mfjteETKwvST6yGiBmz1nvJpqThNVHEmhycVtD8jIlTNp5PERYAM+YJJlSnlW4bUeGjVTjFNoO0mQmmolgQgIQuyclCQhbIO6ORFpa2A3Q7imh1LqFEbdZPCeIgm1ulTAqJrokt6xFQzck2XJrf2LDXRMBqXCBbpq1yVJDsvreEaORMJphRvUfQmEURa4/ehlzUC7RMTqgYvejRkqkGDBqsfWQoBQI6OBK1ftNIQUkGzVjW0rHFRca3AmJy0yRQzTX31Ho8gcWISmy3269VWKr9A23MwhM1rg/p6HvEFVylTtUcEgAgcacYAIBEmEqSs/oj38rP7BUSKTpEiXInwaRwFH9Uh4TqPTknfDgZkvglYbwdLqjKJZCy31ghGeC+09pYOtmpPQ0DYyj3aRktPRJwQXgJqJDURLZFASA2trA4q1nc5qwNWpSmFr9JMEnNuWeqvDVlXKPu+ycSkffl9Qfvy97kOa6NIvTHtXKvRkKkGDRqsdghhGukKK0IWZURCtHL6Kf55z7epOJMDVRNInt4Rokqq4hYrvSfshc58/HYW9MnT1MC4F5hlgktdqTL0RpII5qlHMi/0JiJlK+W0JUEBkYpRKFtxyK5kRKic6af1lAIsgUqlS8uZKj4JbS+xTqSp6AMCe4RAlF4xxaw+r5MEQlgSlptjiBShOD6hqkAiv9pbTbht6DV/ON1KIEoJaKu94oal5Jrfa64xqGffkL0lVxemorde05tv9aEhUw0aNFj9GGlDjHegWxlEN4e2pe2eIDEjyrwwfdj6RY1s+keXpoLONEZmqyrZLvQScPNoFQmWefqG0kvBmMYKYcJWIqUyETfyvKqLXEk79oqVQHuWe1q1MwgbtdJp4r2hmFBcaA0NUSEDxqrAaKEqPlg0XymN63hRQGVt04svFZCFj1oJTZV7FMlh/lTReCJ+TgjTUiYxzZABOAJGFX9Oc1Vqc7556Y1IKfVH+ivY84lMOXVwXXjqVhutVpKYxtO81c6gRLruugHVCtDpbCUDNJqptRxrB+Vu0KBBgwYNGjRYR9FEpho0aLD6IYSJThWFiUoBvpov0kOJJHGVfyJuE0IRJ8DojkhTpZVpoNuvVJ0LimOXcB7BcMciA80y1E4pZRsMl0Ez30Bczuetta8KzGvE7pQy5Km3xEeedCuFlhKywxoHl0ZAwyv0RF4aP6c47UhaMduiRZRGIyW7BXQufNpQSfPt2qb4RF5a7ygEUSKdSucPFei0+NyEgoZNC7prbvysIAW0TKClTRlS5K40onZtv+MLO7BWpqrQ6bpikBs8tcPJC6+bYsatQ8EZhLLqS57ym+6oFGACcKt62CYwtdrQkKkGDRqsfrQyQzzSFKKloLtO6wyRoNIsNtRC+VL4uJ+eKZO3VgRkBBk7aVNKhpMKIOwLx/2p+DaqLkVnF1TSbbWysKrP/uRnZNJOXd9ehqAUxHgOPZJZ0bmGmtEyRKZQxvgSlsRI4/HEvaVcmo0E1ipKBXFDUCtG1y1uK8DShpYACmZ74OYfV91Bme3K6HiumbOA7JaeFFLajnzEpLFYkIVJ9clOzpzZ2XEUXe8iLCCoq74LJkxKeuEd7oFV94VaEyTKotFMrd1oyFSDBg1WP0g8XJaAslGdRBohercbRnJUvDBWIxKObDnrgR6LZCwepga6cSSKIhBRaxtfUactA7Al+IWxYXD+Wa4pL9Nu8QgRnUPcb48Lz6U0RqZCAFJDJBrIvUZLlKXVFAnojO1r3c6dmWas55HM/DKxBCXzlgA6ldYBXTifKZ1KCCjvbA4WnSqUiyYZI0+/jBDpA2zUqtROwG5eS6BSawqqtP0dQFewxs/C6+LoFO0xRC8yw8+ZtG10vRs0mAY0mqkGDRqsdiz+3WcNmUoSY5NADuCJhGi1zCNJfIPhxBAVABXxcR1MVZ19UJSGBOiUlstSM4c07W3AyRffvPAVgpTSK5WPVlHPvDofp37PxW1obKRIjnUMMeJeWDZyJJRyZEPkJcR4YSJXhXle5KUniDQ+mYQ68bohTY4cEYmi1Ju1gxK58hEnG0FSLdsomR5UoZj48QBPuKDgUlLG7sH+7ewSbNsacnJPpV+NrIjcECoT/aKojI7fM7KoyOsE95Ycc4d9529mo1ZpYh48GknXzr1vjLjzhzvpaYr26Jo5DP2Ynqm+GNFEpho0aDAt0EIYgiRN9EaQloW8mngDW+tyLVqAjvUuQkDTIkoRCd6+xW5TidBobSI0qvARKgJ3Go+rv7jlAh0LMOmjdhuCNUp2UapBnNwBr2Eie4RSm8LGVPo2NdRrT3uSIZSCWNlhZqKk62ERKO7+TnO2x3HpN0uEVGrHLTUgbGSqMNElnSZme9vYmFKNQe8/7q5e9iAadhuZW/2Ttk2VpY3KSSsKov6CdhxNkTlL7lyTZ8CSNGXuLX7cuugffy4mu/zvukpI8jLjmO6UWVPNt1ajiUw1aNBg2qApGrDeDCBLsfjJL2Pxk19m/lFs4aNebLadiXvw8fppWPLcR6QA5pAdpQTrFhhqQ1L7mi3Vp9RdlvlITWynEO/XC0QOlXJ6KZGXnrBQlCdujTLe9VqpOD1KoNQdE7V7o09pLAykgMqkjRhZi4FUmugRtYSxEMqmFGNbBGvoKZSqRquSMOrDx9N8frQ9YAhlGu3LtkNdNCpIrYreUU2eGhbSP5Kk2uaGi9Hr0slro8Fng2lHQ6YaNGgwrXApHOb+HRAqjgk0L4JrjijFx/fji6sU/lGHOBXUi/wk0hMyrc14WWrE9ZSm7AeaJ0WNShaBsg9fNWcE6uS9VDuXeJ48hWlTWM7HiaJSAFQrNQQqEdXIDtNP0XmSuafTWWWJIVSk2aJIkhPo2+PyikMrkOdViDoVUC1L3BSMGD0mKImo9BaMU4xBZSZg0rkyCd/3+P0n8pRI81NKQ45HR6qkijuo82KF6YKaokeD1YImzdegQYNpwZK7F7rfD9z5E0CWYv7LPg50u1j80OdMyqzTMbIOq/8hh3Gq4qslXLz/HeCJitbGuJHauMR98WhfwC+ilLKTEkiilceli3g1HNsvy8yxx8ZDklNHrmgRzgtAULsVJmLv5qFZJDmm03x51V58LD4O4B3PU/o7sToo6avs6HJIAVECKpNVy4PCpwBJT0UtcJzhZ+lb02hphOaISJrbzurBjNGo1YNRhK6TewKo4HVaoMiUbUnDHfKlhHAu89G151WZ0lpBcJ0Ub9lDaUUAKMfDe4TDVY+uYoXggGiq+dZuNJGpBg0aTDuW3L3QRBtGW25RWvzI2UCWhdEdSxJ0WbpHJRIzSHQgy3w6DvDRhSwzQnI6XpZVF03bfBeA35YiGe22+ZllZsHOUohWZhzZGYnSmlX4kQbLiaxLL5bX2mh2lDLb5rkhhARqtkyLIpFGF3UJiRRdH2GPp1upJ1JC2BQfwpXAzksnVOFnj0NeFqVtAUORqMQLxUFpylJDdqiPnheu82iXKG1EK1c27RhedlHqqsM6QrG5zhJjH5H1IMl0LbLMvFd1KV4bmdIUSaPUIu1XV6iwJlJ7qyw+H15zdfrpp+NVr3oV1l9/fWyyySZ44xvfiPvuu6/vPjfccAOEEJXHvffeuypnv9ajIVMNGjRYI1jy61Ox5JenQM9om0gVYFJ/lC6ziBsJ9+yHp7RP9ZHYHHDRJp2l0Fnq9U0UjYjHGmlb8pWaBXjGqPl7/ZmeAFFqiNrCcIsAS9B6pvzqjB9jkb3WgGavK+UNP+OICv1kRIqq9HQrtcQogWql0EJAjSRQrQSqJaESAUUkx+5O+m4t7O/8eE6c7ucmSCdFU7fzUO0eiY+AqFmbhGgMWvh1L9JCm1I1otaeAPHCAf5e8+IBex/okcz0PMySkDTRcdOa9/BFpJG68cYb8YEPfAC33norrr32WhRFgQMOOAArVqyYcN/77rsPS5cudY8dd9xxGma85tCk+Ro0aLBGUc5sI1nRwfy/OcFGA1JXzQfkgFbGCZ35TRnXdAmtWPUfAJSwRqBWK6M00Eq9DknaiBKlBikykZOo3JIjrUwUw26rR6xNg7AfmYkwPeWkNOmgvLCpuMSTM9sjTpeliVTF4ORhpO1F9xxkb8DNN9n4jjDQ31J4+wMpjbZJ2uiTgCFRqXT6J3Iw19ZNXUJD5DblBk8ajGcUIOiaJcIEqwobnSJz0QKmOi9JzLZEdoiUKQ2d8pRgGKkKzp+nPqnKkc7ThrHoPTBaM4TC8vhaZql5nyjKGJGkoBegu/ZWHyf6aPemi1tNYTXf8uXLg6fb7Tba7XZl8yVLlgR/X3TRRdhkk03wi1/8AnvttVffQ22yySZ4yUtesmrzXYfQRKYaNGiwRnHtzz7p0k8AfJRHGisFkWaoM+7UvJULR174lCCllbq5j+xQJIlAxK0sTYSIIkK0r7UZ0K3U6YQCUMsS0uU4A08j/hbCRrAoPUioW/QrJfssLRWV9mvrkaTbqRNLa1sBR7ooephIVIKyLaHaEsWMBMWMBCoT5tEyaS6VCKgsCSruKEJFQnVqKExz15ZYypVdL0ZX4TV2OqrMn4NKQ7JmriXTJ7mom/kh8tI/LDkG4BpDC4o8kbYsaB9kBexZ6t4HcpLXUnqhPGm6eKWgVqhN9U03pjDNN2fOHMyePds9Tj/99IGmsGzZMgDAhhtuOOG2u+22GzbbbDPst99+uP766yd/3usI1oI7ZGI8/PDDOPLII7HttttidHQU22+/PU4++WR0u93a7f/85z9jyy23hBACzz777PROtkGDBkPj6ttOxpLfLLQpJRshGBnxGqReDudMnE4wqT7t036FNd+05Evz6A5ftO14MQIjytixnKIYOTP0rEtBUoQpLq+nlKHrWSe9iJxXjFFEDTCpKavrMSQgMWR0tAWdpiZt1U6ctYFOjUEm2R5wEmPOwzxUJlCOSJRtibKdeK0U4E09AZcSFKWNilFWLbMRQkZitDS6rNgKwYjPAWkjW6S3ckQ1EsYLTo40H8tcK91KzcP5ZxmyqbPU/EzNPHUqodoZVDszruri/2/vzcOsqK417nfvqjM0zSDaBBqZUQFngaviBCSRwaiouUZzo8AXJSEGERAHPmNwiDMYh6sIBDEmGvVG8NOrTD6KKBAvIRhFUUCZAk1QREDoPqeq9vr+2ENVnXO6aexuemD/nuc83adOTftUF/Wy1trvYvGnYG6Nlq5Rq8p5vRGm/bZs2Y0DDRoAAEKbSURBVILdu3eb16RJkw64DRFhwoQJOOecc3DiiSdWul5paSlmzJiBl19+GXPmzEGPHj3wgx/8AEuWLKnNITQ4GkWa79NPP4UQAtOnT8cxxxyD1atXY9SoUdi3bx+mTJmSt/4111yDk08+GVu3bq2Hs7VYLN+VeWsfwNBuE6Vw8AOwREI2Rk4mgGzh+ikGxASKbJScVQJMPeB9H0glwbKeElBM+hTpGWKpJFgm8p+zqO+VekDH0j+6Rkcg/oD3/ByDz4hwirarMQ2Ruam1Mm1YXCkQmBChgaSbI8BU3RHTbuF6dpranpxwX8QZyGUIklJYSQNQIFBF6NynsHZJAORCFZlLwQMgrD/TUR+tLfT3EDPSFJHUZL6nlC5CZwjtFnRKMNdoNWreab4vfYzINWGRtjvmO4oS1UIR8UTQ3yEKc6BefNrj61AgUPOUohpOy5Yt0bJly4PadMyYMfjwww/x3nvvVblejx490KNHD/O+X79+2LJlC6ZMmXLA1GBjplFEpoYMGYLZs2dj0KBB6NatGy6++GJMnDgRc+bMyVt32rRp+OabbzBx4sRq7TuTyWDPnj2xl8ViqT/mfaH+g6QeriydUgXDLNKTT9Y16XRe8O0+iPIKiPIKI7hMixlBoYgBwuiJigJRwpUP5YQbjxxF6nWYF4SCSZlTypcIX0DoUZUNXdHzal2inkZROORMMm1jwJgxHdUF2yKlCqZVikq3edFpvWixuJ61J5IcgfGTUkNQkSEmgCApZ+wJ/TlTv+uUI9Pr6vGjsON5VGxC10CFdVZGOKm+fNKGgcA9YWYImnSfFkpUQEhFjmGuRzRiqFN1FLahkWaofsxvK7a+W+BR6AeheWsDIDaeGry+C9dffz1effVVvP322+jQocNBb3/mmWdi3bp13+nYjYVGIaYKsXv37ry87SeffIK77roLzz77LHg1c9z33XdfLHfcsWPHujhdi8VyEMzb8LD8RaVxmOOARQpkSRBIPdwpCIywApCXZqMgkGk43WsPUP5OLL/XW9QxXYkJFhVR0R55ukaHIrPEIscnL1LTpdNBIkd8AWGqL+dc9FR9LZxMLzy1P4qIJv0vuUg6IM7MbD05qw8y1aejV0xGh4gBwoWcoefIiJRwQ0ElU4I8FFFALApjhI9OB0bGqNN2XNkk6H5/PKuuEVPiLPpsN0Xx2l+q6qgQ6QL7hIyCxSwTcuvaADmTM4ouODd1Wmq5L0J7Cj8n4nWYQUQYM2YM5syZg7feegtdu3b9TvtZtWoVSktLa/nsGhaNUkx9/vnnePzxxzF69GizLJPJ4Kc//SkeeughdOrUqdr7mjRpUix3vGXLlro4ZYvF8h2Yt+VR+YtOzVVFJFJV0ItKR6m0oHCleWPB/63rB69+kAYCLBsU9D0ylghqvRjaeDTagDgXPYvPnBcPU4GMhbU9Uf8nFgoZch2IlIsg7UKkHIiUE7Zo4TDF5dozijjCCFSCIUiwmIgKUlwWpacikSKSKcDc7yrqNwXV/oZl5Xh1M2Ytcng2AAtEaIXgRb4PJxyf/t7yUD34SDu6u3ExRAknrOOKGHwa01JECsu1tQPnEGk3/3hCyL8XPfOzIUSn6sFn6te//jX+/Oc/4/nnn0eLFi2wfft2bN++HeXl5WadSZMmYfjw4eb9I488gldeeQXr1q3Dxx9/jEmTJuHll1/GmDFjau2raIjUa83UHXfcgTvvvLPKdVasWIG+ffua99u2bcOQIUNw+eWX49prrzXLJ02ahF69euGqq646qHOobEqoxWKpX+ZtfkT+3Pp43mfnO1cg5sOkIQEKZN2USQkGAShDYKkkkM0CTjo+08xhcvq7jq7kPlgDlR7JaS9iCtCjAirHtyp0cFd1X7lEI1pCyKgMj1sXAPEADnEOxnStlBJCqq+etiqATts50pZAiigVpXIQq70hDmm6CRmpYiTXFSAZdHLUfgvUBjH1gGZeEM6W1A2boWba6ZmSWtBEPKrIZdLMPHffhaJBKvVHev88/3woYt5ZqP2MsXAggtACLgivO/N95cquhEdFJj7rMje9GP1Z1whCWMRWg30cBNOmTQMADBgwILZ89uzZGDlyJACgrKwMmzdvNp9ls1lMnDgRW7duRVFREU444QS8/vrruOCCC2p06g2dehVTY8aMwZVXXlnlOl26dDG/b9u2DQMHDkS/fv0wY8aM2HpvvfUWPvroI/z1r38FIMOTAFBSUoLbbrvtgKLNYrFYLBZLCFVDKD7zzDOx9zfffDNuvvnmOjqjhku9iqmSkhKUlJRUa92tW7di4MCB6NOnD2bPnp1XE/Xyyy/HQo8rVqzAz3/+c7z77rvo3r17rZ63xWKpZwpFpYDQj8rhytgzEqnIZGXUwfNkH0BVdJ7bBDjWk47LsA2pfyoLpgRj0SUnnMnnVV3zE/WxClN7LPTbAuLNhvUyhwEi4iKu/KGiw9ez5grVDsV3BnBdahSzNJDLhcvAfYQpPiIT1GJeICNaWT/itaVm1+noXjYANePmxIhzGWZTKUVdpB4lVqNmnNdZ3joEFveD0jVwLpfpRbUP5gt5jZVXGPMFSFk7kJtT6M55OHnA8/NtLMz3FjnpAzTjrjVq0bTTUvs0CmuEbdu2YcCAAejUqROmTJmCL7/80nzWrl07AMgTTF999RUAoFevXoeVC6vFcjiwSPyP+f18frn5XYsnynrSIiGyTC+Hw8HKK3Jm9Zk9mNobU/gNRwoGAKSdtvX+OAfjalYfdwAe1kZp93OzbhCARXvn5bibk6qd0um7WPNgne4ztVBqGxapn2K6j55M8SFnm0Lkpdf0uToM8OLF5lETcObL74Hvz8TTXVqQecoGIuHK+iTtAWV2COhef0Y85TqgqzHlodzXwTkokb8eMQYWta2AFH7kMFllj5zlnIMFBerhCqV8czlUQgoAUBu1W1ZM1RWNQkwtXLgQ69evx/r16/OmZVYnDGmxWJowJhwjQL4nHdMB40FFAJAbpYIAczhQkZEPc11wLrgxvgQQtmbx1TF0nVREAJDrqDobIfvyRWbxMQCkaqgY4/Jz5e6uZ7+RNp00DvA57uBqVh4TFKl1Cj9nRCDGjUs5ACk4tMZSNgQxKLaLyL4Qf94yQLgcjjI+Zb4SoL4Az+Q70MdmzOmaJEBFilQtmG7zo9rMGMN5/UukGD+GEKCUurbaCgKOtD0ISIo2PaEgahFhZj2qqJTLZQ1YQLItkN5eH9vhgFAvHXX0C4gmbZtwqJ5BNjLVoGkUs/lGjhwJIir4qowBAwaAiGxUymJp4iwKXpS/FGo5EwSgTMY4pVMQSMsCIUDlFaB9+4Hde4HyCmBfOVh5Fnx/BiwiFJgXSGGgLAhifk46+qMbKJOQheoJF0glgWQSrLiZ9MpKp5SICoUUACDhSEHFpdmmSDnyZ1LNzHOYLCiPzMrTaTJwmEa/WmyZfnd6nYhAqm79MvdJFrabY8nxytl6viwsN336lF9XOgFKu6BmSVCzJEQ6CdFMvoIWSfjFCYi0Y2YY6hcluEkhoqpAENetcZTw1DMSk04oeqOROiC8RqpdjE73ATD9FWW6UshomjZB1S2NVD/BmG3FoS48tzQKGkVkymKxWKrCpNRYvFZKptaYMfDMm1EXBFJn7NoNOA5YcTPAVdEOX6j6JMi+fC4zD1VyIeuFfF+KLO05JdTMNd0WR1Bo0gmED3LdGsZhUhwkHRmN0lP/3TCyFKuhMhm9qGCAslCIeEQBYe2XIMBlJl3IBEIDTZL7ZIQwjUfhTz2zT84UlIKDkUo76vSky2NGp1rYEFcnwsNjksMgcvyeHG2y6nIwCOMzFfONUh5b5PDw/BiDSEgfLHK5adAsBaYUXqxCzTB0uew5qPeXcqX3FdQxIsKIRWclVtb/UfUDPKSI3LDhd92HpS6wYspisTR6tBWC8ZmKIM09A9VmRj0ck5H6qvIKKXiyHiiTAW8mBRUcByyZVGJApodEcUpNp2dgCECpRBjFMqkrnRJkMhoFhKk/x4kIMm6iLXIQUoAEaemppW0OTKQpIDCVmyOtzYSMIMm+frJWigmKRaC02zkxwLR40YKqEiGlBVZuQ2MUJcD2Z6XAbJaS0RxtRqpqvhgRhPHGilwjAoRuJ6N9qwKCcOV2PBvETVSV7tWRQWlFQUpQkTl/4gyMMwRFbui6rsQwgMgEA2kjYQSkSvFpawSdqiW4oaDKRadz6wPl+l/jfVjqhEaR5rNYLJaqWFD+JwCI+0vlEnkYUdYzL/1eZLOAIIhv90Hs/Ra091vQnj3Avv1AxpMpwH0Z+ZDWrV2iURmNSnvFlicSUlg5DuA6YUG5ijDpGilyWaTYXD709cv4RellLjOpP+FECs2jUSuV+tMCK68migDmI5YKzIUJCpswQ0XptECJRHuMO7yO8iivJtMwmQhOhSz01kKKCQJXdViyaXFYkK5Tn0LVSZnIFMLaMJ2GFMnIo4ypSBZjoJQ0ZtVpUnCGIO2oBtA8NPPUdXDRInSvkqhUfYkpS4PGiimLxdIkWFD+JxnRSbhgyYQRVsxxwKIF2FpQqSiWyGZBvgeQgMjI3n6U9WSfv/IKiD17Qbu+kWnC8qwsdI7Wy+iHa9QigfOcWqKESlXp2Xxqqn7CMQXTxBmClKwFChJKKLnKlDP6L7UWVkxmFSla6K1Tgzmmn1IsxdUSE+qVU0/FVISt0Ew/0o7h0X3H9hmm6aJRKaFMRYkhdCyPbM9ybCQYUbwhccLJO39yZEpRuBxCNXcWiTAqKFJhA2lylJs7aWFJyhqBm6iX3A5hexzHiV9TokM8ey+HenBAt1Qfm+azWCxNBpZMhm94AJZwpZ2B78fTfyRMb7/c1Af5MiLB3ATI8+U+slngmz1grVqAZzwIDiARCgMpZiKFyg6P1TpJV/DA1GBpWwZGBF899KG8j0RCRVscJjOGpPyecp/jqlZKOJH6qOg4uI5oqfqmAjYDeru8tGB0f/prSoQO5iLpSsFTkTWu5IxIikOhvdP1QdRxI4KW+yIUNgGpNKoDoWrJ9PcadXqHciaXfQYjET2HmUbJPCAINVuQCZLpWlVTBQEjtljEZiBaq8UyYZsZqBq8vKr4yvym6hpbM9WgsZEpi8XSZJi/6w/hG13IzDlYMilflaUAC2Dqr/QDKAjkFPmsF84CA2ItTORxIz3tIjVAlFQ1PTxcXpmppu6VJxwZmRKJMEIVTf1RJBJFPBRHpOqDgFBQ6fWNcKJwewAIkgC5YT2WFHsIo10MMt2nozl6lhwApqwDmG5sLKRYIm3RwMNZdkx7aUW/a11vpVKA0YbM3AvM/ikRGpwSl0IqSIQF70I1Z9btdXQ9mXAredSp9GW0bk3OAFSzLUVOgXru389h3ATZEseKKYvF0qSYv+sP4UNRF0JDFZw7DgpZKBREpwP9sHaG9pcDvg+W8aUwUik+clQdVFRI6W30VH41NZ+SrokUsUDVMinBptNuUlBIYUMuTOovSKhCc5chSPKYHxUQiiqd5osJKiB8+EdSekzVOXFf1k8RC5fr7bQYMcXuTM1yVI2ZKenE03bR2iMeiijzeSxNqscui8chCMyXAop5QnpUqfoqpqwLREI2YyZVRxboxswJHi96F7Jmy8nkhPVyIjTEpeWCbpJMDgtnYOZGnw5CkNcqNs3XoLFpPovF0uSY//VMAMDQdtcBgbJNUNPZddqOcRam+qoBBQGY60p/JSFkpMpRbWb0bDblpG2iJ9owUk3t19EXkUqABYGZrq9riTgEvOYJ05CY+SpaxABKAIwiBermWa/cyRnCRsdA6JbuaFERF1amIF1FrvQMPydbyReg04Wka44CwOUgJwFt6CmjQo75bzpFDTr1bnQTYSUmdYG83i8Ak1mT36uKePkCzJV1UeRK8QQtPOUXYSJ30QgdI9nAWb4HnKyIpBfDNCLzA4i0jB6yIJDpP920OrfoXDdvPpQUqHv7Tvuw1Ak2MmWxWJos87Y/CSQT0l8q5s1U/eiUFlwL9j0rH6AVmTAlpYupI2k7UxCuH8CMGXNNPQPQ1BdR+FAHZASIOCASMsXnFTP4aZXmSzAESYYgxSC0wIr234s8KJmIpwJl+kuKCpGAid4InYYzkYt4ujD2Veg6JZ1CSyfMWHXETTRLyJmOSQfgDNwncF8JJWXyqc9bRvZUxElHn3wBlpEpVJ7xwDKeMdVkQkapuCfUOctxOB6B6wifF0a59Heqz9Gp8OGWB7ECeC3MtLgjHZXivGoDUUAVqDuwqT4LYCNTFouliTNv6+Pm98HFwwGE0akDwqSr+YJ9z5pFlM2CZbKA44SRp4QDCAYEXBp5AiDXlSJKiytdMJ50wb0gNs1fR6ZEIiysFiq9JxwlSnT5DoM07VSRGCZk9Il7UTWFWLorOhvQRG10REjN3NM1WSyA+RnfnoEJBh6ZeSeSjoouMVVvxSD0fgHwjIoGBmqsSvwZP6hoEXiFH7blUSITgQCl4i1qWDYADxwgK0Ih47K8GYHE1fdOkXFGrBvMeUTWZ4EAr/BViljNvgyqUFXR1kJ1jW0n06CxYspisRw2sGSyeiIqh0HJ/5KWCzoSUZEBitJgvm/6xQGQqTyhWsNo2wMBGf2I2BeISI0RCwiU5HL2no7kIDSX5NqXSaXhdAF5rDaLhSk183nE1BIIBY4UGVLM8AAgkIxQqbSiOSeHgXsyPahrj3RUyskEMVEiN4oYciqxJBKOKiAXsr8g4ilHQEbstHcXBXLCALxw/7EGyqpGi2cCcOPRxQE/Pk5yGZgXqbOKeHfpMWlRSA5X3xuBqdoqYgw868uau6CSvxcdeeSHKMEjBA4cLqvOPix1gRVTFovlsEI7pcuZfQFIyOhT5RtEZuSpuikKAhkdirqZa0fvVEI+mIkgEo5x4ga0wNEFRTq1pNJdAJgr++Il9glkW3CTrtM99lgAOBlSdVBhgTnzdQ0VCwvHdRpQ/S6irWQcBrc8knJTQQ9uIkYAU7VFwmV5tTZCF3pHa86igkdFfZgIi/QZ52FgJDqNEDBO5KZmKuGAPISFKNrUU33OfJKF+ZA2C8LlMV+smAu8EkpSHArzubweLNxnpH0NCwIZkSrUcLm+sJGpBo0VUxaL5fCBMVlDlVX6oKoiYsbjwks5gYcO36oQHQ5EkoMRB8/4IIeDZdV+uayX0u7lTMCk8qJ1SUwQCHLWGTEHrgAAAa85B1MRJAAAyfogIimS3GxUBMr9BklmhJSurdIiKkhKbyvuy9opJphJ60XPJdafT1sVQBWzByTHKwhwGHgmiHhq6QhQJIVmZhCSFEGcS/8oHhFIuvhcFfITY2AulwapAlLdqegSz/ryeL6euUfgJkrGw6gXUZiSJP1Sx9c+X56MiDFfRCwdKBTAriOPk/u3YiM8lhysmLJYLIcNph+fbjsTBAAjkJ/zcGQcXBmAajd1UV6BheXPAwCGthkdq6Uhh8tnNU9IYaQ+Iw75QFa96IxDtyoM1zYIUmip9FNAQEDgroPEPiUSmIxYaRsFUmJE70d7QoWz1lSLGRam+4QrU3kE+Zmr03xct3fJ/750K5iokacWg1Dpv0DNgItGo4wgy4mmsICABAN8kjMBlSmp3HG8vx+AsKmySgWGxf2QRpvKLFT37JMGoqGYI8bAy7UJK5eF7AhURFF+riNSxnw0G+nLFwjZCgheKKDqywXdRqYaNFZMWSwWi8XS0LEO6A0aK6YsFsthw7wvn8LQNqNl3ZPvgxUVyYhEVkWsVNSBOXJqP3PDfyIXZmVUakjrawGHgzlq+ryuESKVugID83isfUqsRYuZ1h/O4jNF0pEp/TwrwAK1bxXp0YXdXMhUG3EoF28ZtXKyyu6Ay3PRxpbgYXE5MQAOEKQYyCMwIeunokSdz+V5R1J2OuWnZu3pdB73hJlFKAvmhUmXyZl5gYwwZaUPF/OhCvYho0E65Scis/SIYjVmLFCpRi8wrWuYvmYARCoBrlOf2p8KkDMsszCpQkpExxYZd2VRJ8cpPKuPc+mKbznssWLKYrEcVsz78ikMbXcdmPrnj3wfLJ2ShpvJRGx2FkvKp+68f08LdxAE4ZR5ZbrJswGCdOiMTS0SUlyQrjMKC59JtT7R6TDTgFf7H0GmqbhPoEjJUBTicuYfElKAySbJsv5JuLI2yjiE63/lHZnK0++FC1k75UZmCDJIERjIWYXa1NIUrpv0W6SGXPUUlIXsYasYlgnUDDkvFBxciUxyAE4gNwEj1HwRdz4kaY3AAgFKuKYND/OVzUHWD9vSqHPgGS88jifConIl7iAgU3k8YonAmKmhki8CCQFG8nsw11yTO3sv2kS7DiESoKomSlRzH5a6wYopi8Vy+OG6pkiIAVIcRduE6PodIPYgHdLy/1HLhJwBKIT0l0I4Kw4ABAeIcXBPzSRz1Uw2XbPCZe2SmbofeT6HBdRSfHFP1T65ylpACxkn9KQK0jJKxYliQgrKEZwHeqDxaIwuThcJOUsQpGqzPDmTT0d5ZJSLx84zeq48K/vgMEHgArKXnsuB/VmgvALwVOQpnZInpaJ5vNyTYkbVNzFf1i2Bh21uoArGCbLmCW5kVh8QWiv4AsyDFLieF/ZOjIofLc4i/RHNLMRIzRYYi0eidGQq+ncR/f1QoBo913gfljrBiimLxXLYMe9fj2Fot4lSRAFAJhs3aHQcQAjM+9djse1iacBAprEolYj116MENy1UyAWI5JT8qOs3HFaw/4Sc4Rb6T+mIFekeew4zkSVtjSCSsqAcTEWk9Lp6Jh9F9x8teIdpKaMNK7WxJXEGls2PYpgIFRAWw6tGwiwQUqjp9jEikBGfhMqpeT7geaDiZnJ7X6h0n4raaYEkhCxQ16iIEkMAJFw5WcDRAkgKSAQijBBlRCjeEEYFjWhS3y1BjVcbb3JujiX/BnIMO7lMSeYJKluHZIFtJ2OxWA5T5n0xRc60cx2gKC2jVekUUFwkX82KYusPaX1tfAdO+EDl2QCOFh/KNFK3cdEwQVJoRQST0L3mlDM4JcKoS7RZsRFTqqlvkOTwmnEEKdliRtseBEk1Y8/JF1JyB/LlZLVwgYmABUkembkXputyoxna+0p6SMlaKRlBiwgLXQdWIHITdT2XY4qIFn1MRwkbvdzzpOCtyMqol249k1UpRL2u50tD1awnf3pe6FLu6aIxArKebFbtBWDlWSmSlA0D061r9PnoSFX0vRc2vz5k2EbHDRobmbJYLIct89bcl7ds0Ol3heaRuTAGxrmppQIgH8T64e5K7yOh67RJCiAthnLbl8g2MFL0eMUughSX1giA9IDKCJOek7VW6qcj9+2nGII0MyJJJGFc0okj0jBYfg69LFKjpQWXkxUyHagFkbIYkOsKMFc1bmY5hdvKf0skHTjlocggxsC0lYHrqmiRKvTnXNpTkAyNiWYpWe8UCJCrlkeFFCBFkefJ4n/dOsf3VVRJd30W8WhSIGQBuyMjZXJcLEzdao+rTFZGm1yVznNz0no6whZkQkGl2gZBRKwU6hIh8ivmDxZbM1VnWDFlsVgsEbzWKUT7xgHKV4oxNcuPR+qpwocTrwggEmGbGN2ORbp3x4vPNVpQBTpi5TD4CVnwHaQY3P2yEN00Jlb984KE/Ok1Zwh042K1GguAQP3L7mgRpcRVnpcUyecz95RIU4aexBkEY3BU4Tg5POxDGIl4Gddx/YNz2XfQ5SA4Kp0p28MwziORJwYWBNItXs3Io4QDJF3ZSiYQMjqVUa1ceCRCFAgpjPwgMrtQbaPTdUAYhRGBEWFSTDhSYPl+eBxA7sfzQuFHZNK98e/MRncs+VgxZbFYLIr+Fzwon80McPZHlIfD1Wy/ZDwFFX3QOgxcGUsyEZ/hJQvIuWntogMMjAC/GVfiiSNI6rop+blIsFidk07nCRcQyUirmIQUSoIDzNHWBJHic0CJJJUCjAqsrGwrw4VqOePCzGITCW4sD/TY9DQ+5usImlRxzA/rlogxwJXrEueyrkmEhecg2XaHe0qANpPu8SzjSQGmRKgROkQympSNmGfqqJHjSGEVdaaPiiotsnStWhAg2iIorJNTtW7khZEuPxOuF0316XOK/qxr9EWr8T4sdYEVUxaLxaKQ/krxh+bQ0l/LqFQ0tRd50DOVKpL+UjLVJ5vpylV1JIdcOdWeXCaDRRGhIxKq9YtO5fHQsRzEkNiv6ox0io/BzMQDQ8zygPQ4ADlxLlI0DoT7ltYH8eVGYAloKyyTopTF1wAPZC+8cDsVPQNUOx3t5SRd4aFmMzrqd/mZFCtey5TczhdABYFcN6xXigpVLxJFCoJw5qUWVEHEeiG33U90XUfP1kMoovT6fmijADfyOWNKPAeR/RKIBBjjoENUP0VCgGqY5rPWCHWHFVMWi8WiMC1dfIE33/l/cz5k8f/ZV2SA5i4gBMhxZY0QkWy8m5AhIyNMIm1kjLhhDH5KCiSRYPCKmEnXGSHGtXVBKLK4DwRJxPYXnmP4qzS3jC+DFnYOIEh5WZmCcxmdIgYwrvyrGCKpObWPQIkflXZjqqYqmgrUVgZBypH+T1ApPS9QaT0pKnW7GGLScFP2yAOQUKJKR6KUeCWVmmOpZHgtKlT0yM1Jyenfo5YX0dRf7jKdMhQiLFbXgizn2lMgI2Ek/ENXh2QjUw0aK6YsFoslgp6lBgBDu07IX0H33QsCsFQSzHXA92UgmqfNLDbukcwsqRonBGSEklAz/aSoYfCLdNpOpuuM2bgA4DMlrqTflDblBNT2JJepdnVhgTmkMGJCCi9tg2DGGKmZJqaEVwAIRxa+C6bSggB0k2YAofhSMxeNHQIQNncGIFJu2B/PYRAqbeZov6jIzDLhcineOAOvUPuCn59GZQwslQLtL5dGq1okKeEHP6jcQFMLQs8HUqlI8bjIX8cMInL8XCEV+Z3IOqBbrJiyWCyWPEzxuY6MVGbOGMjaKaYcuaGm+XOfwU84cLICQZJLIaNTfYjMtoMSM4xBJFRNUyR6BKjIlIpy6fQh6Wa/LLIelH+oq8SSCCNbMVRqECTrr5wsQYCBqRmCjMikIeEyCGMiqqJJHsGpUPVSAcm6KqFqpHTNlMPCps4ABGdgLpd2CC7kjL1AhMacJi0po1WMlDhyHSmUMhk1my4I061RryftCaVmFsaIWhtwVXieW1Su91MoshXdVXQWoKZQm5m6QBDyvS4OEhuZqjOsmLJYLBaFaZFiHu48nBoPGNsAojDNBc8DiMAcBkonpUUCZ3AqAHI4nAqBIM3jooakBxPxMIUnOMI0n1pNt3zR6T14urhcOp1DRZCEIzfl6txZAJV+CyNPQosurSu8sKgdCI9BKtsoXAYeUDh7jzEQSPYFTDgmKmVm9HGdimQmdad7EvKsqjNSs/0gAjmjzxPgkfor4TI43+raJDVDz/flSfuRcFpU3CqRBVdV1ueKm2iNFRAWtSsPKlIpPZZw80QzFerVl2vS6VQitGsbIoSOojXZh6UusKadFovFohBJLuubGDD4tMmhezfnoZDSD9hAmUWqafYsG0jjR5JtVZjydWKC4FQIKdRM+xhVJ6QiVSKhSpu08FF2ByZCpSJZ2uE8SEsndBbIKBT3pTjSLz2bTziRwnRXvkwvPqW+gkQYTdI1Ulr4BQkpjPRx9flqyGEx7yywSHRKG5MmGIJmTmw70zMPgJMJwkbOgiDSrup7qL57bVGgcV35SkT6KDpOfB2z84gdQzRlF4k6sYQrhZTxjwpAnldYSFkslWDFlMVisSiWvHqTnPJPsog6FnVwuDSMTCh1kkzIaAhn8uGu01MAQASeDVTPOikSuK9eHhmRxAgIUvJ3neIzjYl1lEotEwlZVxWkAYiwl56escd8gGcBJxtmg8iVNVMiIUWUnDEYLtPWCsTixzYtY6Kz/XS2T0WetEhhgYBI5ji565Y4QqYCmSegW7fo2X5SdAolMIWJcLGA5HcdFUq50SVAfu+5y9ycZEt0Zp/+GXVGB2TEzPNBvnpVVVBej61jSFCtvCx1gxVTFovFEmHxvJvhVFTtas0cRxZAp+TUfu2ezfZnpEjIBDLd5wtoB3EnE5jWKwCMgSZFxIwx34z6QAmVAkQocsAj6+lnvw7AuDIiFSTD1J5IIKzJcuJpP3Kg2tKExyDOzP60hYKJtpnzDIvInUwAFgg5voizeqwHoOplyHwBSjix6BTzZcROb0sOy69h0oKK83jESadigwAIAhAJ8wIgrQtUKha+D8p68ZexPDiA0CjwOSsUDasrSNTOy1InNAoxtXHjRlxzzTXo2rUrioqK0L17d0yePBnZbDZv3WeeeQYnn3wy0uk02rVrhzFjxtTDGVsslsbMwuW3y4JyQD7Ao7UmOmKiaqmoKAlylfO566iecbLImWcDZXrJpIhgTKW0KNYcWVsc5L6MyScQFlJFaqK4R+AByfc6i8VVRCqlZggmw6iXcCPizQH8tEoFKu8q4bBwHVUDJVwWCilAuZHLejCmvKeYF4CpmX4mmqZb32hnhf2eqhOT3x8llG2CF8hXxpcWC4yBUhFPL30NcoULkYkWxpZBiV3XNUKJggBUXgHaXx6vvcpFUOWvw5Qnn3wSXbt2RTqdRp8+ffDuu+9Wuf4777yDPn36IJ1Oo1u3bnjqqacO0ZnWL42iAP3TTz+FEALTp0/HMcccg9WrV2PUqFHYt28fpkyZYtZ7+OGHMXXqVDz00EM444wzUFFRgS+++KIez9xisTRa/ADYXw7jdp5baKydtrU1gK4dEkKm3nwZkmGMAW5o8OkndX+6sF5JLoBJ2wEqDRiZkWcEkxJFIGbEGA/ULL8EU6JI1UjxSHrOkSlAkVKF7dmIAHPC/TlZBnIJCKSg4qoPnnDC48lidZJNhxWynx6ZFB85zAhGngkgkq4UhoEAcQ4mBETCkQXpeh+BNPg0Jx0t/q+MaA+9rAfmyporCvzQaFN/xULItjaxhZHZfg0YEgSq4Ww+OsgC9BdffBHjxo3Dk08+ibPPPhvTp0/H0KFD8cknn6BTp05562/YsAEXXHABRo0ahT//+c9YunQprrvuOrRp0wY//vGPa3TuDR1GB/vtNhAeeughTJs2zYilXbt24eijj8Zrr72GH/zgB995v3v27EGrVq2we/dutGzZsrZO12KxNDKGHntzKKb8IN7DTT/gWzQHpWR+TNdLmZog1YtOJF0pEACItAO/yJFF2SkOP82QaSWLyU3gyQ1FTvQn80MxBdKF5mSiWnpmoHBVxClVWEwBcTHFVY2VkwHcCpI1V0JGu7QdA6NQTLkZWQPlZAWc/Z6MTAmZuiNH1k4FKQ6hLCGYJ+BUROrHImLKzO6LQC4H8wX4N/tkfVOumOI8fi20mCKSVhbKJoFU2i+270D1CMyNch2smIrUYnl+Od7a/0KdPTP0M2kAhsFliQNvUAU+eViM/6/a53rGGWegd+/emDZtmlnWq1cvXHLJJbjvvvwm4bfccgteffVVrFmzxiwbPXo0/vnPf2L58uU1OveGTqOITBVi9+7dOPLII837RYsWQQiBrVu3olevXti7dy/OOussTJ06FR07dqx0P5lMBplM2H9p9+7dAOQfsMViOXzx9+/By2sexI+PvVFO0xeQReeA7PGWcABvP0AeSDt267oeFckSzVOA54FIFWdXMAjmIhAcATgEGERKCiAwJagyMNEnrjJSzIinsLhcAOBZGQliRDI9pnSFrzKTOrrlp1VaL2L9wFXNFeNy34KkT5UbqDYzRMbckwfyMxYAQgg4AQEZX4oTZUUghCNTeFlAgMNnjiy4zwrAFxCR3n0sG6h0HwPTppfG40qaenJHgDFPjlT7SAHyhD31ux8pTPd9kO+BCQckVP1UREwxxwFRAOYkgTyjzYMQU9H4gwgQqOK3uo5L+PBCxV2TfSD/+ZZKpZDS9X+KbDaLlStX4tZbb40tHzRoEJYtW1Zw/8uXL8egQYNiywYPHoxZs2bB8zwkEjUTgw0aaoSsX7+eWrZsSTNnzjTL7rvvPkokEtSjRw+aP38+LV++nH7wgx9Qjx49KJPJVLqvyZMn6+C6fdmXfdmXfdnXd3p9/vnndfK8Ky8vp3bt2tXaeTZv3jxv2eTJk/OOu3XrVgJAS5cujS2/55576Ljjjit4rsceeyzdc889sWVLly4lALRt27Za+04aIvUambrjjjtw5513VrnOihUr0LdvX/N+27ZtGDJkCC6//HJce+21ZrkQAp7n4bHHHjPK+C9/+QvatWuHt99+G4MHDy64/0mTJmHChLBlxDfffIPOnTtj8+bNaNWqVU2GV2/s2bMHHTt2xJYtWxp1qtKOo+HQFMYANI1xNIUxAE1nHLt370anTp1imZLaJJ1OY8OGDQUnXH0XiFQdX4TcqFSU3HULbX+g9Qstb2rUq5gaM2YMrrzyyirX6dKli/l927ZtGDhwIPr164cZM2bE1istLQUAHH/88WZZmzZtUFJSgs2bN1e6/0LhTQBo1apVo77BAaBly5aNfgyAHUdDoimMAWga42gKYwCazjh4ZS2HaoF0Oo10Ol1n+y9ESUkJHMfB9u3bY8t37NiBtm3bFtymXbt2Bdd3XRdHHXVUnZ1rQ6BexVRJSQlKSkqqte7WrVsxcOBA9OnTB7Nnz877wz377LMBAJ999hk6dOgAAPj666/x1VdfoXPnzrV74haLxWKxNGGSyST69OmDRYsW4dJLLzXLFy1ahGHDhhXcpl+/fnjttddiyxYuXIi+ffs27XopNBKfqW3btmHAgAHo2LEjpkyZgi+//BLbt2+PKeDjjjsOw4YNww033IBly5Zh9erVGDFiBHr27ImBAwfW49lbLBaLxdL4mDBhAv7whz/g6aefxpo1azB+/Hhs3rwZo0ePBiDLZIYPH27WHz16NDZt2oQJEyZgzZo1ePrppzFr1ixMnDixvoZwyGgUs/kWLlyI9evXY/369SbqpKHIDIpnn30W48ePx49+9CNwztG/f3/Mnz//oBRxKpXC5MmTq8whN3SawhgAO46GRFMYA9A0xtEUxgDYcTQGrrjiCuzcuRN33XUXysrKcOKJJ+KNN94w2Z6ysrJYGU3Xrl3xxhtvYPz48XjiiSfQvn17PPbYY03eYwpoxD5TFovFYrFYLA2BRpHms1gsFovFYmmoWDFlsVgsFovFUgOsmLJYLBaLxWKpAVZMWSwWi8VisdSAJiOmlixZgosuugjt27cHYwyvvPJKpev+8pe/BGMMjzzySGx5JpPB9ddfj5KSEhQXF+Piiy/Gv/71rwMe+8knn0TXrl2RTqfRp08fvPvuu/U2jq+//hrXX389evTogWbNmqFTp04YO3as6TlYGXfccQcYY7FXu3bt6m0cADBgwIC8czqQyStQe9ejpmPYuHFj3vnr1//8z/9Uuq9DfS1GjhyZd7wzzzwztk593xs1HUNjuS+qcy3q+76ojXE0hHujOvf3mjVrcPHFF6NVq1Zo0aIFzjzzzNjstfq+LywNhyYjpvbt24dTTjkF//3f/13leq+88gref/99tG/fPu+zcePGYe7cuXjhhRfw3nvv4dtvv8WFF16IIMhtihny4osvYty4cbjtttuwatUqnHvuuRg6dGiVrut1OY5t27Zh27ZtmDJlCj766CM888wzmD9/Pq655poDHvuEE05AWVmZeX300UffaQy1MQ7NqFGjYuc0ffr0KvdXm9ejpmPo2LFj7NzLyspw5513ori4GEOHDq1yn4f6WgwZMiR2vDfeeCP2eX3fGzUdQ2O6Lw50LYD6vS9qYxwN4d440Bg+//xznHPOOejZsycWL16Mf/7zn7j99ttjTuT1fV9YGhD12RiwrgBAc+fOzVv+r3/9i44++mhavXo1de7cmX7/+9+bz7755htKJBL0wgsvmGVbt24lzjnNnz+/0mOdfvrpNHr06Niynj170q233lov4yjESy+9RMlkkjzPq3SdyZMn0ymnnFKzE66E7zqO/v370w033HBQx6qr61Fb1+LUU0+ln//851Wuc6ivxYgRI2jYsGGVbtPQ7o3vMoZCNMT7ojrjaEj3BVHtXY/6vDcKjeGKK66gq666qtJtGtp9Yalfmkxk6kAIIXD11VfjpptuwgknnJD3+cqVK+F5nmmSDADt27fHiSeeiGXLlhXcZzabxcqVK2PbAMCgQYMq3aamHGgchdi9ezdatmwJ163ao3XdunVo3749unbtiiuvvBJffPFFbZxyQao7jueeew4lJSU44YQTMHHiROzdu7fSdQ/19TjYa7Fy5Up88MEH1YqGHMprAQCLFy/G9773PRx33HEYNWoUduzYYT5rLPdGVWMoREO8L4DqjaMh3xeag7keDe3eEELg9ddfx3HHHYfBgwfje9/7Hs4444xYKrCx3BeWQ8NhI6YeeOABuK6LsWPHFvx8+/btSCaTaN26dWx527Zt8xo3ar766isEQZDX9LGqbWrKgcaRy86dO3H33Xfjl7/8ZZXrnXHGGXj22WexYMECzJw5E9u3b8dZZ52FnTt31sZp51GdcfzsZz/DX/7yFyxevBi33347Xn75ZVx22WWVrn+or8fBXotZs2ahV69eOOuss6pc71Bfi6FDh+K5557DW2+9halTp2LFihX4/ve/j0wmA6Bx3BsHGkMuDfW+qM44Gvp9Ud1xRGlo98aOHTvw7bff4v7778eQIUOwcOFCXHrppbjsssvwzjvvAGgc94Xl0NEo2snUlJUrV+LRRx/FP/7xDzDGDmpbIjrgNrmfV2eb78LBjmPPnj340Y9+hOOPPx6TJ0+uct1oncJJJ52Efv36oXv37vjjH/+ICRMm1Pjco1R3HKNGjTK/n3jiiTj22GPRt29f/OMf/0Dv3r0r3e5QXI+DvRbl5eV4/vnncfvttx9w3UN5LQDZMkJz4oknom/fvujcuTNef/31Kh/SDeneOJgxNNT7AqjeOBryfaE5mOvREO8NIQQAYNiwYRg/fjwA4NRTT8WyZcvw1FNPoX///pVu25DuC8uh47CITL377rvYsWMHOnXqBNd14bouNm3ahBtvvBFdunQBALRr1w7ZbBa7du2Kbbtjx468/0VoSkpK4DhO3v8oqtqmrseh2bt3L4YMGYLmzZtj7ty5B92xu7i4GCeddBLWrVtXiyOQHMw4ovTu3RuJRKLSczqU1+Ngx/DXv/4V+/fvjzUFrS51eS0KUVpais6dO5vjNYZ7I5fcMWga8n1RiMrGEaUh3ReVUdU4GuK9UVJSAtd1cfzxx8eW9+rVyxSKN8b7wlJ3HBZi6uqrr8aHH36IDz74wLzat2+Pm266CQsWLAAA9OnTB4lEAosWLTLblZWVYfXq1ZWGnpPJJPr06RPbBgAWLVp0wHB1XY0DkP/zHjRoEJLJJF599dXY7JPqkslksGbNGpSWltbmEABUfxy5fPzxx/A8r9JzOpTX42DHMGvWLFx88cVo06bNQR+rLq9FIXbu3IktW7aY4zWGeyOX3DEADf++KEShceTSkO6LyqhqHA3x3kgmk/iP//gPfPbZZ7Hla9euNU1+G+N9YalD6q/2vXbZu3cvrVq1ilatWkUA6OGHH6ZVq1bRpk2bCq5faObV6NGjqUOHDvTmm2/SP/7xD/r+979Pp5xyCvm+b9b5/ve/T48//rh5/8ILL1AikaBZs2bRJ598QuPGjaPi4mLauHFjvYxjz549dMYZZ9BJJ51E69evp7KyMvOqahw33ngjLV68mL744gv629/+RhdeeCG1aNGi3saxfv16uvPOO2nFihW0YcMGev3116lnz5502mmnHbLrURt/U0RE69atI8YYzZs3r+B29Xkt9u7dSzfeeCMtW7aMNmzYQG+//Tb169ePjj76aNqzZ4/ZR33fGzUdQ2O4L6ozjoZwX9TGODT1eW8c6P6eM2cOJRIJmjFjBq1bt44ef/xxchyH3n33XbOP+r4vLA2HJiOm3n77bQKQ9xoxYkTB9Qs9+MrLy2nMmDF05JFHUlFREV144YW0efPmvO0mT54cW/bEE09Q586dKZlMUu/evemdd96pt3FUtj0A2rBhQ6XjuOKKK6i0tJQSiQS1b9+eLrvsMvr444/rbRybN2+m8847j4488khKJpPUvXt3Gjt2LO3cuTNvu7q6HrXxN0VENGnSJOrQoQMFQVDpdvV1Lfbv30+DBg2iNm3aUCKRoE6dOtGIESPy/u7r+96o6Rgaw31RnXE0hPuiNsahqc97ozr396xZs+iYY46hdDpNp5xyCr3yyiuxfdT3fWFpODAioppGtywWi8VisVgOVw6LmimLxWKxWCyWusKKKYvFYrFYLJYaYMWUxWKxWCwWSw2wYspisVgsFoulBlgxZbFYLBaLxVIDrJiyWCwWi8ViqQFWTFksFovFYrHUACumLBaLxWKxWGqAFVOWJseAAQMwbty4JnXckSNH4pJLLqnRPrp06QLGGBhj+Oabbypd75lnnsERRxxRo2NZKmfkyJHmOrzyyiv1fToWi6UWsGLKYqkl5syZg7vvvtu879KlCx555JH6O6EC3HXXXSgrK0OrVq3q+1SaPIsXLy4oXB999FGUlZXVz0lZLJY6wa3vE7BYmgpHHnlkfZ/CAWnRogXatWtX36cBAPA8D4lEor5P45DTqlUrK2YtliaGjUxZmjy7du3C8OHD0bp1azRr1gxDhw7FunXrzOc6rbVgwQL06tULzZs3x5AhQ2LRA9/3MXbsWBxxxBE46qijcMstt2DEiBGx1Fs0zTdgwABs2rQJ48ePNykdALjjjjtw6qmnxs7vkUceQZcuXcz7IAgwYcIEc6ybb74ZuS00iQgPPvggunXrhqKiIpxyyin461//+p2+n2eeeQadOnVCs2bNcOmll2Lnzp1567z22mvo06cP0uk0unXrhjvvvBO+75vPP/30U5xzzjlIp9M4/vjj8eabb8bSWBs3bgRjDC+99BIGDBiAdDqNP//5zwCA2bNno1evXkin0+jZsyeefPLJ2LG3bt2KK664Aq1bt8ZRRx2FYcOGYePGjebzxYsX4/TTT0dxcTGOOOIInH322di0aVO1xn6gcT388MM46aSTUFxcjI4dO+K6667Dt99+az7ftGkTLrroIrRu3RrFxcU44YQT8MYbb2Djxo0YOHAgAKB169ZgjGHkyJHVOieLxdL4sGLK0uQZOXIk/v73v+PVV1/F8uXLQUS44IIL4HmeWWf//v2YMmUK/vSnP2HJkiXYvHkzJk6caD5/4IEH8Nxzz2H27NlYunQp9uzZU2W9y5w5c9ChQweTVjuYtM7UqVPx9NNPY9asWXjvvffw9ddfY+7cubF1fvOb32D27NmYNm0aPv74Y4wfPx5XXXUV3nnnnep/MQDef/99/PznP8d1112HDz74AAMHDsTvfve72DoLFizAVVddhbFjx+KTTz7B9OnT8cwzz+Cee+4BAAghcMkll6BZs2Z4//33MWPGDNx2220Fj3fLLbdg7NixWLNmDQYPHoyZM2fitttuwz333IM1a9bg3nvvxe23344//vGPAOR1GThwIJo3b44lS5bgvffeM2I3m83C931ccskl6N+/Pz788EMsX74cv/jFL4x4rYoDjQsAOOd47LHHsHr1avzxj3/EW2+9hZtvvtl8/utf/xqZTAZLlizBRx99hAceeADNmzdHx44d8fLLLwMAPvvsM5SVleHRRx89qGtjsVgaEWSxNDH69+9PN9xwAxERrV27lgDQ0qVLzedfffUVFRUV0UsvvURERLNnzyYAtH79erPOE088QW3btjXv27ZtSw899JB57/s+derUiYYNG1bwuEREnTt3pt///vexc5s8eTKdcsopsWW///3vqXPnzuZ9aWkp3X///ea953nUoUMHc6xvv/2W0uk0LVu2LLafa665hn76059W+r0UOp+f/vSnNGTIkNiyK664glq1amXen3vuuXTvvffG1vnTn/5EpaWlREQ0b948cl2XysrKzOeLFi0iADR37lwiItqwYQMBoEceeSS2n44dO9Lzzz8fW3b33XdTv379iIho1qxZ1KNHDxJCmM8zmQwVFRXRggULaOfOnQSAFi9eXOm4K+NA4yrESy+9REcddZR5f9JJJ9Edd9xRcN23336bANCuXbsKfh79fiwWS+PG1kxZmjRr1qyB67o444wzzLKjjjoKPXr0wJo1a8yyZs2aoXv37uZ9aWkpduzYAQDYvXs3/v3vf+P00083nzuOgz59+kAIUavnu3v3bpSVlaFfv35mmeu66Nu3r0n1ffLJJ6ioqMD5558f2zabzeK00047qOOtWbMGl156aWxZv379MH/+fPN+5cqVWLFiRSxiEwQBKioqsH//fnz22Wfo2LFjrBYr+l1F6du3r/n9yy+/xJYtW3DNNddg1KhRZrnv+6amaOXKlVi/fj1atGgR209FRQU+//xzDBo0CCNHjsTgwYNx/vnn44c//CF+8pOfoLS09IBjP9C4mjVrhrfffhv33nsvPvnkE+zZswe+76OiogL79u1DcXExxo4di1/96ldYuHAhfvjDH+LHP/4xTj755AMe22KxNC2smLI0aSin1ii6PJoKyi2EZozlbZubOqps31XBOc/bLppurA5awL3++us4+uijY5+lUqmD2ld1xiCEwJ133onLLrss77N0Op33XVZFcXFxbL8AMHPmzJjYBaRY1ev06dMHzz33XN6+2rRpA0DWXI0dOxbz58/Hiy++iN/85jdYtGgRzjzzzBqNa9OmTbjgggswevRo3H333TjyyCPx3nvv4ZprrjHX7Nprr8XgwYPx+uuvY+HChbjvvvswdepUXH/99dX6PiwWS9PAiilLk+b444+H7/t4//33cdZZZwEAdu7cibVr16JXr17V2kerVq3Qtm1b/N///R/OPfdcADKCsWrVqrxi8ijJZBJBEMSWtWnTBtu3b48JkA8++CB2rNLSUvztb3/DeeedB0BGalauXInevXubMaVSKWzevBn9+/ev1hgq4/jjj8ff/va32LLc971798Znn32GY445puA+evbsic2bN+Pf//432rZtCwBYsWLFAY/dtm1bHH300fjiiy/ws5/9rOA6vXv3xosvvojvfe97aNmyZaX7Ou2003Daaadh0qRJ6NevH55//vkDiqkDjevvf/87fN/H1KlTwbksL33ppZfy1uvYsSNGjx6N0aNHY9KkSZg5cyauv/56JJNJAMj7G7BYLE0PK6YsTZpjjz0Ww4YNw6hRozB9+nS0aNECt956K44++mgMGzas2vu5/vrrcd999+GYY45Bz5498fjjj2PXrl1VRmS6dOmCJUuW4Morr0QqlUJJSQkGDBiAL7/8Eg8++CD+8z//E/Pnz8e8efNiQuGGG27A/fffj2OPPRa9evXCww8/HPMqatGiBSZOnIjx48dDCIFzzjkHe/bswbJly9C8eXOMGDGi2uMaO3YszjrrLDz44IO45JJLsHDhwliKDwB++9vf4sILL0THjh1x+eWXg3OODz/8EB999BF+97vf4fzzz0f37t0xYsQIPPjgg9i7d68pQD9QxOqOO+7A2LFj0bJlSwwdOhSZTAZ///vfsWvXLkyYMAE/+9nP8NBDD2HYsGG466670KFDB2zevBlz5szBTTfdBM/zMGPGDFx88cVo3749PvvsM6xduxbDhw8/4NgPNK7u3bvD9308/vjjuOiii7B06VI89dRTsX2MGzcOQ4cOxXHHHYddu3bhrbfeMiK9c+fOYIzhf//3f3HBBRegqKgIzZs3r/a1sVgsjYh6q9ayWOqI3ELwr7/+mq6++mpq1aoVFRUV0eDBg2nt2rXm89mzZ8cKromI5s6dS9Hbw/M8GjNmDLVs2ZJat25Nt9xyC11++eV05ZVXVnrc5cuX08knn0ypVCq2r2nTplHHjh2puLiYhg8fTvfcc0+sAN3zPLrhhhuoZcuWdMQRR9CECRNo+PDhsWJ3IQQ9+uij1KNHD0okEtSmTRsaPHgwvfPOO5V+L4UK0IlkkXeHDh2oqKiILrroIpoyZUre9zF//nw666yzqKioiFq2bEmnn346zZgxw3y+Zs0aOvvssymZTFLPnj3ptddeIwA0f/58IgoL0FetWpV3/Oeee45OPfVUSiaT1Lp1azrvvPNozpw55vOysjIaPnw4lZSUUCqVom7dutGoUaNo9+7dtH37drrkkkuotLSUkskkde7cmX77299SEASVfg8HM66HH36YSktLzd/Ns88+GysqHzNmDHXv3p1SqRS1adOGrr76avrqq6/M9nfddRe1a9eOGGM0YsSI2LFhC9AtliYDI/oOhR8Wy2GOEAK9evXCT37yk5jreUOmS5cuGDdu3CFptbN06VKcc845WL9+fayw3xLCGMPcuXNr3CbIYrHUP9ZnymKpBps2bcLMmTOxdu1afPTRR/jVr36FDRs24L/+67/q+9QOiltuuQXNmzfH7t27a3W/c+fOxaJFi7Bx40a8+eab+MUvfoGzzz7bCqkCjB492qb7LJYmho1MWSzVYMuWLbjyyiuxevVqEBFOPPFE3H///aZIvDGwadMmMwutW7dupqi6Nnj22Wdx9913Y8uWLSgpKcEPf/hDTJ06FUcddVStHeNgOeGEEyp1Qp8+fXqlRe91zY4dO7Bnzx4A0oIjOsPRYrE0TqyYslgsTZKoeMylbdu2ed5VFovF8l2xYspisVgsFoulBtiaKYvFYrFYLJYaYMWUxWKxWCwWSw2wYspisVgsFoulBlgxZbFYLBaLxVIDrJiyWCwWi8ViqQFWTFksFovFYrHUACumLBaLxWKxWGrA/w/hf2bGPcd/WQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "DHWmax.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and the day of the year when it reaches the maximun value:  \n",
-    "Note that because the argmax function produce an error when find a full vector of no data, we need to transform the data to eliminate those NaNs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## day of max value\n",
-    "## need a trick to avoid all NaN slice error in argmax method\n",
-    "mask = np.isnan(DHWmax)\n",
-    "DHWzero = DHWyear.fillna(0)\n",
-    "DHWyday = DHWzero.argmax(dim='time') + 1\n",
-    "DHWyday = np.ma.array(DHWyday.astype('float').values, mask=mask)\n",
-    "DHWyday = DHWyday.filled(np.nan)\n",
-    "DHWyday = xr.DataArray(DHWyday, coords=[lat, lon], dims=['lat', 'lon'],\n",
-    "                       attrs={'long_name': \"day of the year where DHWmax\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the plot of the day of the year when the DHWmax was reached:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7a1d267a96d0>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "DHWyday.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, with the two data arrays, create a dataset and add some global attributes. Remember add your name and email if desired."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "## edit some attrs\n",
-    "globAttrs = {}\n",
-    "globAttrs['title'] = \"DHWmax and time of the year when DHW max was reached\"\n",
-    "globAttrs['creator_name'] = \"YOUR NAME\"\n",
-    "globAttrs['creator_email'] = \"YOUR EMAIL\"\n",
-    "globAttrs['comment'] = \"Data extracted from the GBR Data Management System https://stac.reefdata.io/browser\"\n",
-    "\n",
-    "globAttrs['geospatial_lat_max'] = latMax\n",
-    "globAttrs['geospatial_lat_min'] = latMin\n",
-    "globAttrs['geospatial_lon_max'] = lonMax\n",
-    "globAttrs['geospatial_lon_min'] = lonMin\n",
-    "globAttrs['geospatial_lat_resolution'] = 0.05\n",
-    "globAttrs['geospatial_lon_resolution]'] = 0.05\n",
-    "\n",
-    "globAttrs['time_coverage_start'] = dateMin\n",
-    "globAttrs['time_coverage_end'] = dateMax\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
-       "Dimensions:  (lat: 350, lon: 400)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
-       "Data variables:\n",
-       "    DHWmax   (lat, lon) float32 560kB 7.32 6.44 5.61 4.94 ... 13.82 13.55 13.38\n",
-       "    DHWyday  (lat, lon) float64 1MB 68.0 68.0 68.0 68.0 ... 68.0 68.0 68.0 68.0\n",
-       "Attributes:\n",
-       "    title:                       DHWmax and time of the year when DHW max was...\n",
-       "    creator_name:                YOUR NAME\n",
-       "    creator_email:               YOUR EMAIL\n",
-       "    comment:                     Data extracted from the GBR Data Management ...\n",
-       "    geospatial_lat_max:          -8.5\n",
-       "    geospatial_lat_min:          -26.0\n",
-       "    geospatial_lon_max:          160.0\n",
-       "    geospatial_lon_min:          140.0\n",
-       "    geospatial_lat_resolution:   0.05\n",
-       "    geospatial_lon_resolution]:  0.05\n",
-       "    time_coverage_start:         2024-01-01\n",
-       "    time_coverage_end:           2024-12-31</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-cb7e1c84-0414-453f-b16f-b05a1da488c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb7e1c84-0414-453f-b16f-b05a1da488c4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 350</li><li><span class='xr-has-index'>lon</span>: 400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-18d390a8-e321-4eea-81ac-032dd86d0529' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18d390a8-e321-4eea-81ac-032dd86d0529' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.525 -8.575 ... -25.92 -25.98</div><input id='attrs-b569ab1d-bf73-4e9c-aa51-6ab0a9a9de68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b569ab1d-bf73-4e9c-aa51-6ab0a9a9de68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee6edc64-b59f-4616-9935-d953283fbcf9' class='xr-var-data-in' type='checkbox'><label for='data-ee6edc64-b59f-4616-9935-d953283fbcf9' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.525,  -8.575,  -8.625, ..., -25.875, -25.925, -25.975],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>140.0 140.1 140.1 ... 159.9 160.0</div><input id='attrs-7d88034f-91b4-4631-a60a-d059495355a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d88034f-91b4-4631-a60a-d059495355a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a458df-3fc1-4d27-be70-acfa091ebf6f' class='xr-var-data-in' type='checkbox'><label for='data-53a458df-3fc1-4d27-be70-acfa091ebf6f' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([140.025, 140.075, 140.125, ..., 159.875, 159.925, 159.975],\n",
-       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08a10fad-107b-4841-a4ad-68cb58f20f62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08a10fad-107b-4841-a4ad-68cb58f20f62' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>DHWmax</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.32 6.44 5.61 ... 13.55 13.38</div><input id='attrs-83c1e458-56be-438a-8217-d09187ce4425' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83c1e458-56be-438a-8217-d09187ce4425' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e202160a-9c70-44ae-bb49-f58625c83e66' class='xr-var-data-in' type='checkbox'><label for='data-e202160a-9c70-44ae-bb49-f58625c83e66' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([[ 7.3199997 ,  6.44      ,  5.6099997 , ...,  0.45      ,\n",
-       "         0.31      ,  0.62      ],\n",
-       "       [ 7.25      ,  6.2999997 ,  5.64      , ...,  0.62      ,\n",
-       "         0.75      ,  0.77      ],\n",
-       "       [ 6.91      ,  6.41      ,  5.64      , ...,  0.79999995,\n",
-       "         0.78999996,  1.09      ],\n",
-       "       ...,\n",
-       "       [        nan,         nan,         nan, ..., 13.67      ,\n",
-       "        13.349999  , 12.91      ],\n",
-       "       [        nan,         nan,         nan, ..., 13.84      ,\n",
-       "        13.53      , 13.11      ],\n",
-       "       [        nan,         nan,         nan, ..., 13.82      ,\n",
-       "        13.549999  , 13.38      ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DHWyday</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.0 68.0 68.0 ... 68.0 68.0 68.0</div><input id='attrs-453f9bb3-3fc3-40d5-962f-b77d046811cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-453f9bb3-3fc3-40d5-962f-b77d046811cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4db0ce92-3c14-45bc-85f3-7117a0b75957' class='xr-var-data-in' type='checkbox'><label for='data-4db0ce92-3c14-45bc-85f3-7117a0b75957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>day of the year where DHWmax</dd></dl></div><div class='xr-var-data'><pre>array([[68., 68., 68., ..., 17., 17., 17.],\n",
-       "       [68., 68., 68., ..., 17., 17., 17.],\n",
-       "       [68., 68., 68., ..., 17., 17., 17.],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., 68., 68., 68.],\n",
-       "       [nan, nan, nan, ..., 68., 68., 68.],\n",
-       "       [nan, nan, nan, ..., 68., 68., 68.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd6fd608-2970-4a51-bdcc-a62242b3fb29' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd6fd608-2970-4a51-bdcc-a62242b3fb29' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7341c47a-fbf4-4a0b-b69f-063148da35c3' class='xr-index-data-in' type='checkbox'/><label for='index-7341c47a-fbf4-4a0b-b69f-063148da35c3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.524999618530273,  -8.574999809265137,              -8.625,\n",
-       "        -8.675000190734863,  -8.725000381469727,  -8.774999618530273,\n",
-       "        -8.824999809265137,              -8.875,  -8.925000190734863,\n",
-       "        -8.975000381469727,\n",
-       "       ...\n",
-       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
-       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
-       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
-       "       -25.975000381469727],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=350))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-064968bf-b432-4acb-8e16-e4fded00e237' class='xr-index-data-in' type='checkbox'/><label for='index-064968bf-b432-4acb-8e16-e4fded00e237' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([140.02499389648438,  140.0749969482422,            140.125,\n",
-       "        140.1750030517578, 140.22500610351562, 140.27499389648438,\n",
-       "        140.3249969482422,            140.375,  140.4250030517578,\n",
-       "       140.47500610351562,\n",
-       "       ...\n",
-       "       159.52499389648438,  159.5749969482422,            159.625,\n",
-       "        159.6750030517578, 159.72500610351562, 159.77499389648438,\n",
-       "        159.8249969482422,            159.875,  159.9250030517578,\n",
-       "       159.97500610351562],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9331d21-887b-4e3f-8fa8-d3d4c001c79a' class='xr-section-summary-in' type='checkbox'  ><label for='section-c9331d21-887b-4e3f-8fa8-d3d4c001c79a' 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>title :</span></dt><dd>DHWmax and time of the year when DHW max was reached</dd><dt><span>creator_name :</span></dt><dd>YOUR NAME</dd><dt><span>creator_email :</span></dt><dd>YOUR EMAIL</dd><dt><span>comment :</span></dt><dd>Data extracted from the GBR Data Management System https://stac.reefdata.io/browser</dd><dt><span>geospatial_lat_max :</span></dt><dd>-8.5</dd><dt><span>geospatial_lat_min :</span></dt><dd>-26.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>160.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>140.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05</dd><dt><span>geospatial_lon_resolution] :</span></dt><dd>0.05</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-01</dd><dt><span>time_coverage_end :</span></dt><dd>2024-12-31</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 2MB\n",
-       "Dimensions:  (lat: 350, lon: 400)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 1kB -8.525 -8.575 -8.625 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 2kB 140.0 140.1 140.1 140.2 ... 159.9 159.9 160.0\n",
-       "Data variables:\n",
-       "    DHWmax   (lat, lon) float32 560kB 7.32 6.44 5.61 4.94 ... 13.82 13.55 13.38\n",
-       "    DHWyday  (lat, lon) float64 1MB 68.0 68.0 68.0 68.0 ... 68.0 68.0 68.0 68.0\n",
-       "Attributes:\n",
-       "    title:                       DHWmax and time of the year when DHW max was...\n",
-       "    creator_name:                YOUR NAME\n",
-       "    creator_email:               YOUR EMAIL\n",
-       "    comment:                     Data extracted from the GBR Data Management ...\n",
-       "    geospatial_lat_max:          -8.5\n",
-       "    geospatial_lat_min:          -26.0\n",
-       "    geospatial_lon_max:          160.0\n",
-       "    geospatial_lon_min:          140.0\n",
-       "    geospatial_lat_resolution:   0.05\n",
-       "    geospatial_lon_resolution]:  0.05\n",
-       "    time_coverage_start:         2024-01-01\n",
-       "    time_coverage_end:           2024-12-31"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## Create DHWmax dataset\n",
-    "DHW_max = xr.Dataset({'DHWmax': DHWmax, 'DHWyday': DHWyday})\n",
-    "DHW_max.attrs= globAttrs\n",
-    "\n",
-    "DHW_max"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write the results into a local file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "## write results\n",
-    "DHW_max.to_netcdf(\"DHWmax_y2date.nc\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Done!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "dms",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/integrated-ogcfeatures-geoparquet.ipynb b/Python_based_scripts/integrated-ogcfeatures-geoparquet.ipynb
deleted file mode 100644
index 4d9f037..0000000
--- a/Python_based_scripts/integrated-ogcfeatures-geoparquet.ipynb
+++ /dev/null
@@ -1,803 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e06a27bf",
-   "metadata": {},
-   "source": [
-    "# Integrated Metadata-Data API Example\n",
-    "\n",
-    "This notebook gives an example of exploring the RIMReP DMS with the metadata API, and then collecting desired data with the data API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "30321389",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pystac_client in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (0.7.2)\n",
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-      "Requirement already satisfied: pystac[validation]>=1.7.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pystac_client) (1.7.3)\n",
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-      "Requirement already satisfied: six>=1.5 in /Users/mmiller8/Library/Python/3.11/lib/python/site-packages (from python-dateutil>=2.8.2->pystac_client) (1.16.0)\n",
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-      "Requirement already satisfied: attrs>=17.4.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (23.1.0)\n",
-      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from jsonschema>=4.0.1->pystac[validation]>=1.7.2->pystac_client) (0.19.3)\n",
-      "Successfully installed pystac_client\n",
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-      "Successfully installed owslib\n",
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-      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from botocore<1.29.77,>=1.29.76->aiobotocore~=2.5.0->s3fs) (1.0.1)\n",
-      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /Users/mmiller8/Library/Python/3.11/lib/python/site-packages (from botocore<1.29.77,>=1.29.76->aiobotocore~=2.5.0->s3fs) (2.8.2)\n",
-      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from botocore<1.29.77,>=1.29.76->aiobotocore~=2.5.0->s3fs) (1.26.16)\n",
-      "Requirement already satisfied: idna>=2.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from yarl<2.0,>=1.0->aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (3.4)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/mmiller8/Library/Python/3.11/lib/python/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.29.77,>=1.29.76->aiobotocore~=2.5.0->s3fs) (1.16.0)\n",
-      "Successfully installed s3fs\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, install and load the required packages.\n",
-    "\n",
-    "import subprocess\n",
-    "\n",
-    "packages = [\n",
-    "    'pystac_client',\n",
-    "    'owslib',\n",
-    "    'geopandas',\n",
-    "    'pyarrow',\n",
-    "    'dask_geopandas',\n",
-    "    's3fs'\n",
-    "]\n",
-    "\n",
-    "for package in packages:\n",
-    "    try:\n",
-    "        subprocess.check_call(['pip', 'install', package])\n",
-    "        print(f'Successfully installed {package}')\n",
-    "    except subprocess.CalledProcessError as e:\n",
-    "        print(f'Error occurred while installing {package}: {e}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fa97133f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from getpass import getpass\n",
-    "import pystac_client\n",
-    "import re\n",
-    "\n",
-    "from owslib.ogcapi.features import Features\n",
-    "from owslib.util import Authentication\n",
-    "\n",
-    "from geopandas import GeoDataFrame\n",
-    "import pyarrow.compute as pc\n",
-    "import pyarrow as pa\n",
-    "import dask_geopandas\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "1e16077c",
-   "metadata": {},
-   "source": [
-    "## Connect to metadata API\n",
-    "\n",
-    "We first connect to the metadata API by retrieving the root catalog\n",
-    "\n",
-    "To do this, you will need to go to https://dashboard.staging.reefdata.io/ and copy your Authentication token.\n",
-    "This can then be pasted into the password prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6bb7238e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pystac_client/client.py:177: FutureWarning: The `ignore_conformance` option is deprecated and will be removed in the next major release. Instead use `set_conforms_to` or `add_conforms_to` to control behavior.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Go to https://dashboard.staging.reefdata.io/, copy your API key and paste into password box\n",
-    "\n",
-    "# Create the client\n",
-    "api = pystac_client.Client.open(\n",
-    "    url=\"https://stac.staging.reefdata.io/\",\n",
-    "    headers={\n",
-    "        'Authorization': f\"Bearer {getpass()}\"\n",
-    "    },\n",
-    "    ignore_conformance=True\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "62e26114",
-   "metadata": {},
-   "source": [
-    "## Search for a dataset with the required data\n",
-    "Filter by spatial bounds and Oceans keyword"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d8af6334",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aims-sst\n",
-      "gbrmpa-complete-gbr-features\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set geometry bounds you want to search within\n",
-    "geom = {\n",
-    "    \"type\": \"Polygon\",\n",
-    "    \"coordinates\": [\n",
-    "        [[146.5, -19], [146.5, -18.5], [147, -18.5], [147, -19], [146.5, -19]]\n",
-    "    ],\n",
-    "}\n",
-    "\n",
-    "# Search the API\n",
-    "results = api.search(\n",
-    "    max_items=15,\n",
-    "    limit=5,\n",
-    "    intersects=geom,\n",
-    "    datetime=\"2012-05-01/2012-05-02\",\n",
-    "    query={\"themes\": {\"eq\": [{'scheme': 'https://wiki.esipfed.org/ISO_19115-3_Codelists#MD_TopicCategoryCode',\n",
-    "                              'concepts': [{'id': 'oceans', 'title': 'Oceans'}]}]}}\n",
-    ")\n",
-    "\n",
-    "# Print the id of every found item that has 'Oceans' as a keyword theme\n",
-    "for item in results.items():\n",
-    "    print(item.id)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1c85475e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aims-sst\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Choose the dataset you want to work with - this chooses the first dataset listed which is the AIMS SST data\n",
-    "items_list = list(results.items())\n",
-    "chosen_item = items_list[0]\n",
-    "\n",
-    "# Access the ID of the chosen dataset\n",
-    "chosen_dataset_id = chosen_item.id\n",
-    "print(chosen_dataset_id)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c9609c32",
-   "metadata": {},
-   "source": [
-    "## Connect to the data API and get the data\n",
-    "\n",
-    "The item returned by the metadata API contains a link pointing to the corresponding collection in the data API. This can be used to collect the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e3e9d585",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "424\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Inspect item to get link to the data API\n",
-    "link = chosen_item.get_single_link(rel=\"describedby\").href\n",
-    "\n",
-    "# Parse the link as data_api_url/collections/collection_id\n",
-    "data_api_url, _, collection_id = re.findall(\"(?:[^/]|//)+\", link)\n",
-    "\n",
-    "# Enter token to get authorisation for the data API\n",
-    "data_api = Features(\"https://pygeoapi.staging.reefdata.io/\", headers= {'Authorization': f'Bearer {getpass()}'})\n",
-    "\n",
-    "\n",
-    "collected_data = data_api.collection_items(\n",
-    "    collection_id,\n",
-    "    bbox=[146.5, -19, 147, -18.5],\n",
-    "    datetime=\"2012-05-01T00:00:00Z/2012-05-02T00:00:00Z\",\n",
-    "    qc_flag=1,\n",
-    "    limit=1000,\n",
-    ")\n",
-    "\n",
-    "print(len(collected_data['features']))\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "307a2ed3",
-   "metadata": {},
-   "source": [
-    "## Plot the data\n",
-    "The simple example here is plotting a timeseries of the temperatures collected on 01/05/2012 in the area of Orpheus Island"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cea6ae21",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "subsite\n",
-       "HAVSL1     Axes(0.125,0.2;0.775x0.68)\n",
-       "ORPHFL1    Axes(0.125,0.2;0.775x0.68)\n",
-       "ORPHFL2    Axes(0.125,0.2;0.775x0.68)\n",
-       "Name: qc_val, dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gdf = GeoDataFrame.from_features(collected_data)\n",
-    "gdf.time = pd.to_datetime(gdf.time)  # Convert from string to datetime\n",
-    "\n",
-    "gdf.set_index(\"time\", inplace=True)\n",
-    "gdf.groupby(\"subsite\")[\"qc_val\"].plot(\n",
-    "    legend=True, ylabel=\"Temperature (C)\", title=f\"SST near Orpheus Island\"\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a538dbf3",
-   "metadata": {},
-   "source": [
-    "## Get the direct link to the data \n",
-    "The assets of the STAC item will contain a link to the data we are looking for"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8f07defb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "item_assets = chosen_item.get_assets()\n",
-    "link_to_data = item_assets[\"data\"].href"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "888752e4",
-   "metadata": {},
-   "source": [
-    "## Collect data from direct access\n",
-    "- With [dask-geopandas](https://dask-geopandas.readthedocs.io/en/stable/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "753486e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>qc_val</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2012-05-01 23:51:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.2238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2012-05-01 23:31:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.2138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2012-05-01 20:21:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.1940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2012-05-01 20:11:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.1940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2012-05-01 20:01:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.2039</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>137</th>\n",
-       "      <td>2012-05-01 12:16:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1517</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>2012-05-01 12:26:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
-       "      <td>2012-05-01 12:36:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>2012-05-01 12:46:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141</th>\n",
-       "      <td>2012-05-01 11:16:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1915</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>424 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         time  subsite   qc_val\n",
-       "0   2012-05-01 23:51:47+00:00  ORPHFL2  26.2238\n",
-       "1   2012-05-01 23:31:47+00:00  ORPHFL2  26.2138\n",
-       "2   2012-05-01 20:21:47+00:00  ORPHFL2  26.1940\n",
-       "3   2012-05-01 20:11:47+00:00  ORPHFL2  26.1940\n",
-       "4   2012-05-01 20:01:47+00:00  ORPHFL2  26.2039\n",
-       "..                        ...      ...      ...\n",
-       "137 2012-05-01 12:16:21+00:00   HAVSL1  26.1517\n",
-       "138 2012-05-01 12:26:21+00:00   HAVSL1  26.1418\n",
-       "139 2012-05-01 12:36:21+00:00   HAVSL1  26.1219\n",
-       "140 2012-05-01 12:46:21+00:00   HAVSL1  26.1219\n",
-       "141 2012-05-01 11:16:21+00:00   HAVSL1  26.1915\n",
-       "\n",
-       "[424 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Build the filter\n",
-    "data_filter = [\n",
-    "    (\"lat\", \">=\", -19),\n",
-    "    (\"lon\", \">=\", 146.5),\n",
-    "    (\"lat\", \"<=\", -18.5),\n",
-    "    (\"lon\", \"<=\", 147),\n",
-    "    (\"time\", \">=\", pd.to_datetime(\"2012-05-01T00:00:00Z\")),\n",
-    "    (\"time\", \"<=\", pd.to_datetime(\"2012-05-02T00:00:00Z\")),\n",
-    "    (\"qc_flag\", \"==\", 1)\n",
-    "]\n",
-    "\n",
-    "# Read the data\n",
-    "df = dask_geopandas.read_parquet(\n",
-    "    link_to_data,\n",
-    "    filters=[data_filter],\n",
-    "    columns=[\"time\", \"subsite\", \"qc_val\"],\n",
-    "    index=False,\n",
-    "    storage_options={\"anon\": True}, # no authentication needed for this s3 bucket\n",
-    ").compute()\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6ca3821e",
-   "metadata": {},
-   "source": [
-    "- Or with [pyarrow](https://arrow.apache.org/docs/python/index.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "bc7927c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>subsite</th>\n",
-       "      <th>qc_val</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2012-05-01 23:51:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.2238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2012-05-01 23:31:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.2138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2012-05-01 20:21:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.1940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2012-05-01 20:11:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.1940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2012-05-01 20:01:47+00:00</td>\n",
-       "      <td>ORPHFL2</td>\n",
-       "      <td>26.2039</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>419</th>\n",
-       "      <td>2012-05-01 12:16:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1517</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>420</th>\n",
-       "      <td>2012-05-01 12:26:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>421</th>\n",
-       "      <td>2012-05-01 12:36:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>422</th>\n",
-       "      <td>2012-05-01 12:46:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>423</th>\n",
-       "      <td>2012-05-01 11:16:21+00:00</td>\n",
-       "      <td>HAVSL1</td>\n",
-       "      <td>26.1915</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>424 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         time  subsite   qc_val\n",
-       "0   2012-05-01 23:51:47+00:00  ORPHFL2  26.2238\n",
-       "1   2012-05-01 23:31:47+00:00  ORPHFL2  26.2138\n",
-       "2   2012-05-01 20:21:47+00:00  ORPHFL2  26.1940\n",
-       "3   2012-05-01 20:11:47+00:00  ORPHFL2  26.1940\n",
-       "4   2012-05-01 20:01:47+00:00  ORPHFL2  26.2039\n",
-       "..                        ...      ...      ...\n",
-       "419 2012-05-01 12:16:21+00:00   HAVSL1  26.1517\n",
-       "420 2012-05-01 12:26:21+00:00   HAVSL1  26.1418\n",
-       "421 2012-05-01 12:36:21+00:00   HAVSL1  26.1219\n",
-       "422 2012-05-01 12:46:21+00:00   HAVSL1  26.1219\n",
-       "423 2012-05-01 11:16:21+00:00   HAVSL1  26.1915\n",
-       "\n",
-       "[424 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Build the filter\n",
-    "lon = pc.field(\"lon\")\n",
-    "lat = pc.field(\"lat\")\n",
-    "time = pc.field(\"time\")\n",
-    "qc_flag = pc.field(\"qc_flag\")\n",
-    "\n",
-    "data_filter = (\n",
-    "    (lat >= -19) &\n",
-    "    (lon >= 146.5) &\n",
-    "    (lat <= -18.5) &\n",
-    "    (lon <= 147) &\n",
-    "    (time >= pd.to_datetime(\"2012-05-01T00:00:00Z\")) &\n",
-    "    (time <= pd.to_datetime(\"2012-05-02T00:00:00Z\")) &\n",
-    "    (qc_flag == 1)\n",
-    ")\n",
-    "\n",
-    "ds = pa.dataset.dataset(link_to_data)\n",
-    "table = ds.to_table(filter=data_filter,\n",
-    "                    columns=[\"time\", \"subsite\", \"qc_val\"])\n",
-    "\n",
-    "df = table.to_pandas()\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "5833ebe1",
-   "metadata": {},
-   "source": [
-    "The two previous examples are equivalent, but some things may be easier to do with dask while other things may be easier with pyarrow.\n",
-    "\n",
-    "Finally, let's plot the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e3b51532",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "subsite\n",
-       "HAVSL1     Axes(0.125,0.2;0.775x0.68)\n",
-       "ORPHFL1    Axes(0.125,0.2;0.775x0.68)\n",
-       "ORPHFL2    Axes(0.125,0.2;0.775x0.68)\n",
-       "Name: qc_val, dtype: object"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.set_index(\"time\", inplace=True)\n",
-    "df.groupby(\"subsite\")['qc_val'].plot(\n",
-    "    legend=True, ylabel=\"Temperature (C)\", title=f\"SST near Orpheus Island\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "d95ed3e0c57c5648f0aae0a872a2f6da817c91393ca776950b237d8871ec68c2"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Python_based_scripts/ogccoverages.ipynb b/Python_based_scripts/ogccoverages.ipynb
deleted file mode 100644
index f39e4b6..0000000
--- a/Python_based_scripts/ogccoverages.ipynb
+++ /dev/null
@@ -1,1581 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data API: OGC Coverages example\n",
-    "\n",
-    "OGC Coverages is the replacement for Web Coverage Service. It is only a draft standard at the moment.\n",
-    "\n",
-    "More info about the API can be found here - https://ogcapi.ogc.org/coverages/overview.html\n",
-    "\n",
-    "### Using NOAA CRW degree heating week data as an example - this notebook demonstrates\n",
-    "\n",
-    "- List available datasets (coverages)\n",
-    "- Get metadata for a coverage\n",
-    "- Get \"domain set\" for a coverage\n",
-    "- Get \"range type\" for a coverage\n",
-    "- Fetch single time step across entire spatial extent\n",
-    "  - as CoverageJSON\n",
-    "  - as zipped zarr dataset\n",
-    "- Fetch single point across time interval\n",
-    "  - as CoverageJSON\n",
-    "  - as zipped zarr dataset\n",
-    "\n",
-    "### Notes on `owslib`\n",
-    "\n",
-    "`owslib` is a python library for working with OGC web services and the new OGC APIs - it has incomplete support for OGC Coverages.\n",
-    "\n",
-    "### Notes on CoverageJSON\n",
-    "\n",
-    "- It is a new and immature format\n",
-    "- Not widely supported (no nice python wrapper for it yet)\n",
-    "- Basically it returns a flat JSON array with a bunch of metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting matplotlib\n",
-      "  Downloading matplotlib-3.7.1-cp311-cp311-macosx_10_12_x86_64.whl (7.4 MB)\n",
-      "\u001b[2K     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.4/7.4 MB 22.3 MB/s eta 0:00:00\n",
-      "\u001b[?25hCollecting contourpy>=1.0.1 (from matplotlib)\n",
-      "  Downloading contourpy-1.1.0-cp311-cp311-macosx_10_9_x86_64.whl (243 kB)\n",
-      "\u001b[2K     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 243.5/243.5 kB 7.9 MB/s eta 0:00:00\n",
-      "\u001b[?25hCollecting cycler>=0.10 (from matplotlib)\n",
-      "  Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
-      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
-      "  Downloading fonttools-4.40.0-cp311-cp311-macosx_10_9_x86_64.whl (2.1 MB)\n",
-      "\u001b[2K     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 27.9 MB/s eta 0:00:00\n",
-      "\u001b[?25hCollecting kiwisolver>=1.0.1 (from matplotlib)\n",
-      "  Downloading kiwisolver-1.4.4-cp311-cp311-macosx_10_9_x86_64.whl (65 kB)\n",
-      "\u001b[2K     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 65.5/65.5 kB 2.2 MB/s eta 0:00:00\n",
-      "\u001b[?25hRequirement already satisfied: numpy>=1.20 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from matplotlib) (1.25.0)\n",
-      "Requirement already satisfied: packaging>=20.0 in /Users/mmiller8/Library/Python/3.11/lib/python/site-packages (from matplotlib) (23.1)\n",
-      "Collecting pillow>=6.2.0 (from matplotlib)\n",
-      "  Downloading Pillow-9.5.0-cp311-cp311-macosx_10_10_x86_64.whl (3.4 MB)\n",
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-      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
-      "Successfully installed contourpy-1.1.0 cycler-0.11.0 fonttools-4.40.0 kiwisolver-1.4.4 matplotlib-3.7.1 pillow-9.5.0 pyparsing-3.1.0\n",
-      "Successfully installed matplotlib\n",
-      "Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (1.25.0)\n",
-      "Successfully installed numpy\n",
-      "Requirement already satisfied: requests in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (2.31.0)\n",
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-      "Successfully installed requests\n",
-      "Collecting xarray\n",
-      "  Downloading xarray-2023.6.0-py3-none-any.whl (999 kB)\n",
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-      "\u001b[?25hRequirement already satisfied: numpy>=1.21 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from xarray) (1.25.0)\n",
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-      "Installing collected packages: xarray\n",
-      "Successfully installed xarray-2023.6.0\n",
-      "Successfully installed xarray\n",
-      "Collecting zarr\n",
-      "  Downloading zarr-2.15.0-py3-none-any.whl (206 kB)\n",
-      "\u001b[2K     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 206.1/206.1 kB 2.8 MB/s eta 0:00:00\n",
-      "\u001b[?25hCollecting asciitree (from zarr)\n",
-      "  Downloading asciitree-0.3.3.tar.gz (4.0 kB)\n",
-      "  Preparing metadata (setup.py): started\n",
-      "  Preparing metadata (setup.py): finished with status 'done'\n",
-      "Requirement already satisfied: numpy>=1.20 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from zarr) (1.25.0)\n",
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-      "\u001b[?25hCollecting entrypoints (from numcodecs>=0.10.0->zarr)\n",
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-      "Building wheels for collected packages: asciitree\n",
-      "  Building wheel for asciitree (setup.py): started\n",
-      "  Building wheel for asciitree (setup.py): finished with status 'done'\n",
-      "  Created wheel for asciitree: filename=asciitree-0.3.3-py3-none-any.whl size=5034 sha256=e366d8c2accade85d7d125069520022d258e0405fb078d75f5b5a08a1064b911\n",
-      "  Stored in directory: /Users/mmiller8/Library/Caches/pip/wheels/71/c1/da/23077eb3b87d24d6f3852ed1ed1a1ac2d3c885ad6ebd2b4a07\n",
-      "Successfully built asciitree\n",
-      "Installing collected packages: asciitree, fasteners, entrypoints, numcodecs, zarr\n",
-      "Successfully installed asciitree-0.3.3 entrypoints-0.4 fasteners-0.18 numcodecs-0.11.0 zarr-2.15.0\n",
-      "Successfully installed zarr\n"
-     ]
-    }
-   ],
-   "source": [
-    "# First, install and load the required packages.\n",
-    "\n",
-    "import subprocess\n",
-    "\n",
-    "packages = [\n",
-    "    'matplotlib',\n",
-    "    'numpy',\n",
-    "    'requests',\n",
-    "    'xarray',\n",
-    "    'zarr'\n",
-    "]\n",
-    "\n",
-    "for package in packages:\n",
-    "    try:\n",
-    "        subprocess.check_call(['pip', 'install', package])\n",
-    "        print(f'Successfully installed {package}')\n",
-    "    except subprocess.CalledProcessError as e:\n",
-    "        print(f'Error occurred while installing {package}: {e}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import packages and load environment\n",
-    "\n",
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import requests\n",
-    "import xarray as xr\n",
-    "import zarr\n",
-    "from IPython.display import JSON\n",
-    "from owslib.ogcapi.coverages import Coverages"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Connecting to Data API - OGC Coverages\n",
-    "\n",
-    "We first connect to the Data API by setting up a OGC Coverages API endpoint.\n",
-    "\n",
-    "To do this, you will need to go to https://dashboard.staging.reefdata.io/ and copy your Authentication token.\n",
-    "This can then be pasted into the password prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "token = getpass()\n",
-    "w = Coverages(f'https://pygeoapi.staging.reefdata.io', headers={'Authorization': f'Bearer {token}'})"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## List available coverages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['noaa-crw-dhw']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coverages = w.coverages()\n",
-    "coverages"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get coverage metadata\n",
-    "\n",
-    "The following is equivalent to \n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "crs": [
-        null
-       ],
-       "description": "NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week\n",
-       "domainset": {
-        "_meta": {
-         "tags": {
-          "Conventions": "CF-1.6, ACDD-1.3",
-          "NCO": "netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)",
-          "acknowledgement": "NOAA Coral Reef Watch Program",
-          "cdm_data_type": "Grid",
-          "comment": "This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.",
-          "contributor_name": "NOAA Coral Reef Watch Program",
-          "contributor_role": "Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive",
-          "creator_email": "coralreefwatch@noaa.gov",
-          "creator_institution": "NOAA/NESDIS/STAR Coral Reef Watch Program",
-          "creator_name": "NOAA Coral Reef Watch Program",
-          "creator_type": "group",
-          "creator_url": "https://coralreefwatch.noaa.gov/",
-          "date_created": "20180301T120000Z",
-          "date_issued": "20200302T152652Z",
-          "date_metadata_modified": "20190405T120000Z",
-          "date_modified": "20180301T120000Z",
-          "geospatial_bounds": "POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))",
-          "geospatial_bounds_crs": "EPSG:32663",
-          "geospatial_lat_max": 89.9749984741211,
-          "geospatial_lat_min": -89.9749984741211,
-          "geospatial_lat_resolution": 0.05000000074505806,
-          "geospatial_lat_units": "degrees_north",
-          "geospatial_lon_max": 179.97500610351562,
-          "geospatial_lon_min": -179.97500610351562,
-          "geospatial_lon_resolution": 0.05000000074505806,
-          "geospatial_lon_units": "degrees_east",
-          "history": "Tue Feb 21 09:58:33 2023: ncks -d lat,-26.0,-8.0 -d lon,133.0,169.0 ct5km_dhw_v3.1_19850325.nc ct5km_dhw_v3.1_19850325_gbr.nc\nThis is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).",
-          "id": "Satellite_Daily_Global_5km_Degree_Heating_Week",
-          "institution": "NOAA/NESDIS/STAR Coral Reef Watch Program",
-          "instrument": "ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake",
-          "instrument_vocabulary": "NOAA NODC Ocean Archive System Instruments",
-          "keywords": "Oceans > Ocean Temperature > Water Temperature,  Spectral/Engineering > Infrared Wavelengths > Thermal Infrared, Oceans > Ocean Temperature > Degree Heating Week",
-          "keywords_vocabulary": "NASA Global Change Master Directory (GCMD) Science Keywords",
-          "license": "The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.",
-          "metadata_link": "https://coralreefwatch.noaa.gov/product/5km/index.php",
-          "naming_authority": "gov.noaa.coralreefwatch",
-          "ncei_template_version": "NCEI_NetCDF_Grid_Template_v2.0",
-          "pangeo-forge:inputs_hash": "d80b5196a680325dc2d775d8aee7607cb2f6e5196b8a553b6e2489545a292ba8",
-          "pangeo-forge:recipe_hash": "d7a1649301fe73de160a8d8d28d650270ceca8b83c0029aa4d9e11ea5934c9fe",
-          "pangeo-forge:version": "0.9.4",
-          "platform": "Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.",
-          "platform_vocabulary": "NOAA NODC Ocean Archive System Platforms",
-          "processing_level": "Derived from L4 satellite sea surface temperaure analysis",
-          "product_version": "3.1",
-          "program": "NOAA Coral Reef Watch Program",
-          "project": "NOAA Coral Reef Watch Program",
-          "publisher_email": "coralreefwatch@noaa.gov",
-          "publisher_institution": "NOAA/NESDIS/STAR Coral Reef Watch Program",
-          "publisher_name": "NOAA Coral Reef Watch Program",
-          "publisher_type": "group",
-          "publisher_url": "https://coralreefwatch.noaa.gov",
-          "references": "https://coralreefwatch.noaa.gov/product/5km/index.php",
-          "source": "Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)",
-          "spatial_resolution": "0.05 degrees",
-          "standard_name_vocabulary": "CF Standard Name Table v27",
-          "summary": "This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite",
-          "time_coverage_duration": "P1D",
-          "time_coverage_end": "19850326T000000Z",
-          "time_coverage_resolution": "P1D",
-          "time_coverage_start": "19850325T000000Z",
-          "title": "NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week ",
-          "uuid": "24c7f597-298c-4be6-a0c2-ad97b993f60f"
-         }
-        },
-        "generalGrid": {
-         "axis": [
-          {
-           "axisLabel": "lon",
-           "lowerBound": 133.02499389648438,
-           "resolution": 0.0500030517578125,
-           "type": "RegularAxis",
-           "uomLabel": "degrees",
-           "upperBound": 168.97500610351562
-          },
-          {
-           "axisLabel": "lat",
-           "lowerBound": -8.024999618530273,
-           "resolution": 0.05000019073486328,
-           "type": "RegularAxis",
-           "uomLabel": "degrees",
-           "upperBound": -25.975000381469727
-          },
-          {
-           "axisLabel": "time",
-           "lowerBound": "1985-03-25T12:00:00.000000000",
-           "resolution": "1 days",
-           "type": "RegularAxis",
-           "uomLabel": "1 days",
-           "upperBound": "2023-02-19T12:00:00.000000000"
-          }
-         ],
-         "axisLabels": [
-          "lon",
-          "lat",
-          "time"
-         ],
-         "gridLimits": {
-          "axis": [
-           {
-            "axisLabel": "i",
-            "lowerBound": 0,
-            "type": "IndexAxis",
-            "upperBound": 720
-           },
-           {
-            "axisLabel": "j",
-            "lowerBound": 0,
-            "type": "IndexAxis",
-            "upperBound": 360
-           }
-          ],
-          "axisLabels": [
-           "i",
-           "j"
-          ],
-          "srsName": "http://www.opengis.net/def/crs/OGC/0/Index2D",
-          "type": "GridLimits"
-         },
-         "srsName": "http://www.opengis.net/def/crs/OGC/1.3/EPSG:32663",
-         "type": "GeneralGridCoverage"
-        },
-        "type": "DomainSet"
-       },
-       "extent": {
-        "spatial": {
-         "bbox": [
-          [
-           133,
-           -26,
-           169,
-           -8
-          ]
-         ],
-         "crs": "http://www.opengis.net/def/crs/OGC/1.3/CRS84"
-        }
-       },
-       "id": "noaa-crw-dhw",
-       "keywords": [],
-       "links": [
-        {
-         "href": "https://stac.staging.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-dhw",
-         "hreflang": "en-US",
-         "rel": "canonical",
-         "title": "information",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io?f=json",
-         "rel": "root",
-         "title": "The landing page of this server as JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io?f=html",
-         "rel": "root",
-         "title": "The landing page of this server as HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw?f=json",
-         "rel": "self",
-         "title": "This document as JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw?f=jsonld",
-         "rel": "alternate",
-         "title": "This document as RDF (JSON-LD)",
-         "type": "application/ld+json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw?f=html",
-         "rel": "alternate",
-         "title": "This document as HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw?f=json",
-         "rel": "collection",
-         "title": "Detailed Coverage metadata in JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw?f=html",
-         "rel": "collection",
-         "title": "Detailed Coverage metadata in HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage/domainset?f=json",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage-domainset",
-         "title": "Coverage domain set of collection in JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage/domainset?f=html",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage-domainset",
-         "title": "Coverage domain set of collection in HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage/rangetype?f=json",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage-rangetype",
-         "title": "Coverage range type of collection in JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage/rangetype?f=html",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage-rangetype",
-         "title": "Coverage range type of collection in HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?f=json",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage",
-         "title": "Coverage data",
-         "type": "application/prs.coverage+json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?f=zarr",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage",
-         "title": "Coverage data as zarr",
-         "type": "application/zip"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?f=netcdf",
-         "rel": "http://www.opengis.net/def/rel/ogc/1.0/coverage",
-         "title": "Coverage data as netcdf",
-         "type": "application/x-netcdf"
-        }
-       ],
-       "rangetype": {
-        "field": [
-         {
-          "_meta": {
-           "tags": {
-            "coverage_content_type": "physicalMeasurement",
-            "grid_mapping": "crs",
-            "long_name": "degree heating week",
-            "standard_name": "N/A",
-            "units": "degree_Celsius_weeks",
-            "valid_max": 10000,
-            "valid_min": 0
-           }
-          },
-          "encodingInfo": {
-           "dataType": "http://www.opengis.net/def/dataType/OGC/0/float64"
-          },
-          "id": "degree_heating_week",
-          "name": "degree heating week",
-          "nodata": "null",
-          "type": "Quantity",
-          "uom": {
-           "code": "degree_Celsius_weeks",
-           "id": "http://www.opengis.net/def/uom/UCUM/degree_Celsius_weeks",
-           "type": "UnitReference"
-          }
-         },
-         {
-          "_meta": {
-           "tags": {
-            "comment": "A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.",
-            "coverage_content_type": "thematicClassification",
-            "flag_meanings": "valid-water land missing ice",
-            "flag_values": [
-             0,
-             1,
-             2,
-             4
-            ],
-            "grid_mapping": "crs",
-            "long_name": "pixel characteristics flag array",
-            "units": "pixel_classification"
-           }
-          },
-          "encodingInfo": {
-           "dataType": "http://www.opengis.net/def/dataType/OGC/0/float64"
-          },
-          "id": "mask",
-          "name": "pixel characteristics flag array",
-          "nodata": "null",
-          "type": "Quantity",
-          "uom": {
-           "code": "pixel_classification",
-           "id": "http://www.opengis.net/def/uom/UCUM/pixel_classification",
-           "type": "UnitReference"
-          }
-         }
-        ],
-        "type": "DataRecord"
-       },
-       "title": "NOAA Coral Reef Watch - Degree Heating Week"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coverage = w.collection('noaa-crw-dhw')\n",
-    "JSON(coverage)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Coverage DomainSet\n",
-    "\n",
-    "The domain set is the detailed n-dimensional space covered by the data. It is a list of axes, each with a name, a description, a range of values, and a reference system.\n",
-    "\n",
-    "The following is equivalent to\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage/domainset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "axis": [
-        {
-         "axisLabel": "lon",
-         "lowerBound": 133.02499389648438,
-         "resolution": 0.0500030517578125,
-         "type": "RegularAxis",
-         "uomLabel": "degrees",
-         "upperBound": 168.97500610351562
-        },
-        {
-         "axisLabel": "lat",
-         "lowerBound": -8.024999618530273,
-         "resolution": 0.05000019073486328,
-         "type": "RegularAxis",
-         "uomLabel": "degrees",
-         "upperBound": -25.975000381469727
-        },
-        {
-         "axisLabel": "time",
-         "lowerBound": "1985-03-25T12:00:00.000000000",
-         "resolution": "1 days",
-         "type": "RegularAxis",
-         "uomLabel": "1 days",
-         "upperBound": "2023-02-19T12:00:00.000000000"
-        }
-       ],
-       "axisLabels": [
-        "lon",
-        "lat",
-        "time"
-       ],
-       "gridLimits": {
-        "axis": [
-         {
-          "axisLabel": "i",
-          "lowerBound": 0,
-          "type": "IndexAxis",
-          "upperBound": 720
-         },
-         {
-          "axisLabel": "j",
-          "lowerBound": 0,
-          "type": "IndexAxis",
-          "upperBound": 360
-         }
-        ],
-        "axisLabels": [
-         "i",
-         "j"
-        ],
-        "srsName": "http://www.opengis.net/def/crs/OGC/0/Index2D",
-        "type": "GridLimits"
-       },
-       "srsName": "http://www.opengis.net/def/crs/OGC/1.3/EPSG:32663",
-       "type": "GeneralGridCoverage"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coverage_domainset = w.coverage_domainset('noaa-crw-dhw')\n",
-    "\n",
-    "JSON(coverage_domainset[\"generalGrid\"])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Coverage RangeType\n",
-    "\n",
-    "A `RangeType` describes the data values semantics (their components and data type)\n",
-    "\n",
-    "The following is equivalent to\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage/rangetype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "field": [
-        {
-         "_meta": {
-          "tags": {
-           "coverage_content_type": "physicalMeasurement",
-           "grid_mapping": "crs",
-           "long_name": "degree heating week",
-           "standard_name": "N/A",
-           "units": "degree_Celsius_weeks",
-           "valid_max": 10000,
-           "valid_min": 0
-          }
-         },
-         "encodingInfo": {
-          "dataType": "http://www.opengis.net/def/dataType/OGC/0/float64"
-         },
-         "id": "degree_heating_week",
-         "name": "degree heating week",
-         "nodata": "null",
-         "type": "Quantity",
-         "uom": {
-          "code": "degree_Celsius_weeks",
-          "id": "http://www.opengis.net/def/uom/UCUM/degree_Celsius_weeks",
-          "type": "UnitReference"
-         }
-        },
-        {
-         "_meta": {
-          "tags": {
-           "comment": "A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.",
-           "coverage_content_type": "thematicClassification",
-           "flag_meanings": "valid-water land missing ice",
-           "flag_values": [
-            0,
-            1,
-            2,
-            4
-           ],
-           "grid_mapping": "crs",
-           "long_name": "pixel characteristics flag array",
-           "units": "pixel_classification"
-          }
-         },
-         "encodingInfo": {
-          "dataType": "http://www.opengis.net/def/dataType/OGC/0/float64"
-         },
-         "id": "mask",
-         "name": "pixel characteristics flag array",
-         "nodata": "null",
-         "type": "Quantity",
-         "uom": {
-          "code": "pixel_classification",
-          "id": "http://www.opengis.net/def/uom/UCUM/pixel_classification",
-          "type": "UnitReference"
-         }
-        }
-       ],
-       "type": "DataRecord"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coverage_rangetype = w.coverage_rangetype('noaa-crw-dhw')\n",
-    "\n",
-    "JSON(coverage_rangetype)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get CoverageJson\n",
-    "\n",
-    "Get a single time slice of the data - at `2023-02-01`\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?datetime=2023-02-01"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# owslib doesn't support all coverage parameters at this time\n",
-    "# test = w.coverage('016-023-noaa-crw', datetime=\"1985-04-03T00:00:00/1985-04-04T00:00:00\")\n",
-    "\n",
-    "# So we request the URL equivalent\n",
-    "url = f'https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?datetime=2023-02-01'\n",
-    "\n",
-    "coverage_json = requests.get(url, headers={'Authorization': f'Bearer {token}'}).json()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Print CoverageJSON parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "degree_heating_week": {
-        "description": "degree heating week",
-        "observedProperty": {
-         "id": "degree_heating_week",
-         "label": {
-          "en": "degree heating week"
-         }
-        },
-        "type": "Parameter",
-        "unit": {
-         "symbol": "degree_Celsius_weeks"
-        }
-       },
-       "mask": {
-        "description": "pixel characteristics flag array",
-        "observedProperty": {
-         "id": "mask",
-         "label": {
-          "en": "pixel characteristics flag array"
-         }
-        },
-        "type": "Parameter",
-        "unit": {
-         "symbol": "pixel_classification"
-        }
-       }
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "JSON(coverage_json[\"parameters\"])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Print CoverageJSON domain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "axes": {
-        "time": {
-         "num": 1,
-         "start": "2023-02-01T12:00:00.000000000",
-         "stop": "2023-02-01T12:00:00.000000000"
-        },
-        "x": {
-         "num": 720,
-         "start": 133.02499389648438,
-         "stop": 168.97500610351562
-        },
-        "y": {
-         "num": 360,
-         "start": -8.024999618530273,
-         "stop": -25.975000381469727
-        }
-       },
-       "domainType": "Grid",
-       "referencing": [
-        {
-         "coordinates": [
-          "x",
-          "y"
-         ],
-         "system": {
-          "id": "http://www.opengis.net/def/crs/OGC/1.3/EPSG:32663",
-          "type": "ProjectedCRS"
-         }
-        }
-       ],
-       "type": "Domain"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "JSON(coverage_json[\"domain\"])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot the data!\n",
-    "\n",
-    "This is a very basic example that just draws pixels on a grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "degree_heating_week = coverage_json[\"ranges\"][\"degree_heating_week\"]\n",
-    "width = coverage_json[\"domain\"][\"axes\"][\"x\"][\"num\"]\n",
-    "height = coverage_json[\"domain\"][\"axes\"][\"y\"][\"num\"]\n",
-    "\n",
-    "# Values is 1D array - so reshape into 2D\n",
-    "data = np.array(degree_heating_week[\"values\"]).reshape(width, height).astype(float)\n",
-    "\n",
-    "# Plot raster data\n",
-    "plt.imshow(data)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Do a pixel drill\n",
-    "\n",
-    "Get pixel values from `2013-01-01` to `2023-01-01` at single point `(-20.42, 155.5)`.\n",
-    "\n",
-    "Note: currently this example uses the `subset` parameter to request a tiny bounding box around the point.\n",
-    "\n",
-    "This is calling\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?datetime=2013-01-01/2023-01-01&subset=lon(155.52:155.54),lat(-20.43:-20.42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = f'https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?datetime=2013-01-01/2023-01-01&subset=lon(155.52:155.54),lat(-20.43:-20.42)'\n",
-    "\n",
-    "coverage_json = requests.get(url, headers={'Authorization': f'Bearer {token}'}).json()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot the (raw) data!\n",
-    "\n",
-    "This is a very basic example that just draws line plot of the values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1307766d0>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "degree_heating_week = coverage_json[\"ranges\"][\"degree_heating_week\"]\n",
-    "\n",
-    "plt.plot(degree_heating_week[\"values\"])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fetch zarr instead of CoverageJSON\n",
-    "\n",
-    "CoverageJSON does not have a nice python wrapper. Zarr does.\n",
-    "\n",
-    "So we repeat the previous example but fetch zarr instead\n",
-    "\n",
-    "Unfortunately, the file is zipped up - so we have to save it to disk first.\n",
-    "\n",
-    "This calls\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?datetime=2013-01-01/2023-01-01&subset=lon(155.52:155.54),lat(-20.43:-20.42)&f=zarr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = f'https://pygeoapi.staging.reefdata.io/collections/noaa-crw-dhw/coverage?datetime=2013-01-01/2023-01-01&subset=lon(155.52:155.54),lat(-20.43:-20.42)&f=zarr'\n",
-    "\n",
-    "zip_path = \"noaa-crw-dhw-temp.zip\"\n",
-    "\n",
-    "r = requests.get(url, headers={'Authorization': f'Bearer {token}'}, stream=True)\n",
-    "\n",
-    "with open(zip_path, 'wb') as f:\n",
-    "    for chunk in r.iter_content(chunk_size=1024):\n",
-    "        if chunk:\n",
-    "            f.write(chunk)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Look inside zarr dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  --xr-background-color: #111111;\n",
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-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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-       "  margin-bottom: 4px;\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
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-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:              (time: 3653, lat: 1, lon: 1)\n",
-       "Coordinates:\n",
-       "  * lat                  (lat) float64 -20.42\n",
-       "  * lon                  (lon) float64 155.5\n",
-       "  * time                 (time) datetime64[ns] 2013-01-01T12:00:00 ... 2023-0...\n",
-       "Data variables:\n",
-       "    degree_heating_week  (time, lat, lon) float64 ...\n",
-       "    mask                 (time, lat, lon) float64 ...\n",
-       "Attributes: (12/62)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
-       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
-       "    contributor_name:           NOAA Coral Reef Watch Program\n",
-       "    ...                         ...\n",
-       "    time_coverage_duration:     P1D\n",
-       "    time_coverage_end:          19850326T000000Z\n",
-       "    time_coverage_resolution:   P1D\n",
-       "    time_coverage_start:        19850325T000000Z\n",
-       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
-       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f</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-b96cd372-c7d2-4981-9153-337e856db4b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b96cd372-c7d2-4981-9153-337e856db4b5' 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>: 3653</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3d712c19-df00-4f9f-9e2f-9a88b84373a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3d712c19-df00-4f9f-9e2f-9a88b84373a8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-20.42</div><input id='attrs-166e6b94-ecd5-4b94-b283-1043a32c9a53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-166e6b94-ecd5-4b94-b283-1043a32c9a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172c7595-1701-4395-9968-224653108abd' class='xr-var-data-in' type='checkbox'><label for='data-172c7595-1701-4395-9968-224653108abd' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([-20.424999])</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'>155.5</div><input id='attrs-f412f962-c5e3-402a-88d0-b3aae78f2f09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f412f962-c5e3-402a-88d0-b3aae78f2f09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-186a2605-0e36-4285-ae0d-53f1e2fb2eee' class='xr-var-data-in' type='checkbox'><label for='data-186a2605-0e36-4285-ae0d-53f1e2fb2eee' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([155.524994])</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'>2013-01-01T12:00:00 ... 2023-01-...</div><input id='attrs-8e9934da-c8f7-4cbf-bb58-f2e71a7937ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e9934da-c8f7-4cbf-bb58-f2e71a7937ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71708e0d-540b-41aa-af22-d8d469cf4f34' class='xr-var-data-in' type='checkbox'><label for='data-71708e0d-540b-41aa-af22-d8d469cf4f34' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T12:00:00.000000000&#x27;, &#x27;2013-01-02T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-01-03T12:00:00.000000000&#x27;, ..., &#x27;2022-12-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-12-31T12:00:00.000000000&#x27;, &#x27;2023-01-01T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3a91e38-3c76-46f8-9744-bc74a531150e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3a91e38-3c76-46f8-9744-bc74a531150e' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>degree_heating_week</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2476255-5abf-45a6-91fb-1ee98e4422ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2476255-5abf-45a6-91fb-1ee98e4422ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d0ae8b9-a65c-4105-a49a-e43750f0b02a' class='xr-var-data-in' type='checkbox'><label for='data-7d0ae8b9-a65c-4105-a49a-e43750f0b02a' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[3653 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98a0be99-2511-4f97-a475-ae4fe14283e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98a0be99-2511-4f97-a475-ae4fe14283e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d3652c1-0b68-41a0-8d01-82d69998db45' class='xr-var-data-in' type='checkbox'><label for='data-2d3652c1-0b68-41a0-8d01-82d69998db45' 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>comment :</span></dt><dd>A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.</dd><dt><span>coverage_content_type :</span></dt><dd>thematicClassification</dd><dt><span>flag_meanings :</span></dt><dd>valid-water land missing ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 4]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>pixel characteristics flag array</dd><dt><span>units :</span></dt><dd>pixel_classification</dd></dl></div><div class='xr-var-data'><pre>[3653 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ebf72be-4c66-4e23-a5ce-a7a023de0421' class='xr-section-summary-in' type='checkbox'  ><label for='section-9ebf72be-4c66-4e23-a5ce-a7a023de0421' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6987d92e-1a48-42ad-89eb-e312c37e46a1' class='xr-index-data-in' type='checkbox'/><label for='index-6987d92e-1a48-42ad-89eb-e312c37e46a1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-20.424999237060547], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9002819-7db8-4505-906e-fb563483dd0e' class='xr-index-data-in' type='checkbox'/><label for='index-c9002819-7db8-4505-906e-fb563483dd0e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([155.52499389648438], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f5fcd68-ee3a-4092-9fbf-0e3c39117b1c' class='xr-index-data-in' type='checkbox'/><label for='index-5f5fcd68-ee3a-4092-9fbf-0e3c39117b1c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-01-01 12:00:00&#x27;, &#x27;2013-01-02 12:00:00&#x27;,\n",
-       "               &#x27;2013-01-03 12:00:00&#x27;, &#x27;2013-01-04 12:00:00&#x27;,\n",
-       "               &#x27;2013-01-05 12:00:00&#x27;, &#x27;2013-01-06 12:00:00&#x27;,\n",
-       "               &#x27;2013-01-07 12:00:00&#x27;, &#x27;2013-01-08 12:00:00&#x27;,\n",
-       "               &#x27;2013-01-09 12:00:00&#x27;, &#x27;2013-01-10 12:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2022-12-23 12:00:00&#x27;, &#x27;2022-12-24 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-25 12:00:00&#x27;, &#x27;2022-12-26 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-27 12:00:00&#x27;, &#x27;2022-12-28 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-29 12:00:00&#x27;, &#x27;2022-12-30 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-31 12:00:00&#x27;, &#x27;2023-01-01 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d948b37b-810b-465d-b503-e333b1dbfd01' class='xr-section-summary-in' type='checkbox'  ><label for='section-d948b37b-810b-465d-b503-e333b1dbfd01' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>acknowledgement :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.</dd><dt><span>contributor_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>contributor_role :</span></dt><dd>Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive</dd><dt><span>creator_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>creator_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>creator_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://coralreefwatch.noaa.gov/</dd><dt><span>date_created :</span></dt><dd>20180301T120000Z</dd><dt><span>date_issued :</span></dt><dd>20200302T152652Z</dd><dt><span>date_metadata_modified :</span></dt><dd>20190405T120000Z</dd><dt><span>date_modified :</span></dt><dd>20180301T120000Z</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:32663</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.9749984741211</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.9749984741211</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.97500610351562</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.97500610351562</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Tue Feb 21 09:58:33 2023: ncks -d lat,-26.0,-8.0 -d lon,133.0,169.0 ct5km_dhw_v3.1_19850325.nc ct5km_dhw_v3.1_19850325_gbr.nc\n",
-       "This is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).</dd><dt><span>id :</span></dt><dd>Satellite_Daily_Global_5km_Degree_Heating_Week</dd><dt><span>institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>instrument :</span></dt><dd>ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake</dd><dt><span>instrument_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Instruments</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Water Temperature,  Spectral/Engineering &gt; Infrared Wavelengths &gt; Thermal Infrared, Oceans &gt; Ocean Temperature &gt; Degree Heating Week</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.</dd><dt><span>metadata_link :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.coralreefwatch</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>pangeo-forge:inputs_hash :</span></dt><dd>d80b5196a680325dc2d775d8aee7607cb2f6e5196b8a553b6e2489545a292ba8</dd><dt><span>pangeo-forge:recipe_hash :</span></dt><dd>d7a1649301fe73de160a8d8d28d650270ceca8b83c0029aa4d9e11ea5934c9fe</dd><dt><span>pangeo-forge:version :</span></dt><dd>0.9.4</dd><dt><span>platform :</span></dt><dd>Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.</dd><dt><span>platform_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Platforms</dd><dt><span>processing_level :</span></dt><dd>Derived from L4 satellite sea surface temperaure analysis</dd><dt><span>product_version :</span></dt><dd>3.1</dd><dt><span>program :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>project :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>publisher_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>publisher_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://coralreefwatch.noaa.gov</dd><dt><span>references :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>source :</span></dt><dd>Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>summary :</span></dt><dd>This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_end :</span></dt><dd>19850326T000000Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>19850325T000000Z</dd><dt><span>title :</span></dt><dd>NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week </dd><dt><span>uuid :</span></dt><dd>24c7f597-298c-4be6-a0c2-ad97b993f60f</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:              (time: 3653, lat: 1, lon: 1)\n",
-       "Coordinates:\n",
-       "  * lat                  (lat) float64 -20.42\n",
-       "  * lon                  (lon) float64 155.5\n",
-       "  * time                 (time) datetime64[ns] 2013-01-01T12:00:00 ... 2023-0...\n",
-       "Data variables:\n",
-       "    degree_heating_week  (time, lat, lon) float64 ...\n",
-       "    mask                 (time, lat, lon) float64 ...\n",
-       "Attributes: (12/62)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
-       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
-       "    contributor_name:           NOAA Coral Reef Watch Program\n",
-       "    ...                         ...\n",
-       "    time_coverage_duration:     P1D\n",
-       "    time_coverage_end:          19850326T000000Z\n",
-       "    time_coverage_resolution:   P1D\n",
-       "    time_coverage_start:        19850325T000000Z\n",
-       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
-       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "store = zarr.ZipStore(zip_path, mode='r')\n",
-    "data = xr.open_dataset(store, engine=\"zarr\")\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12b106bd0>]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.degree_heating_week.plot()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete the temp zip file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.remove(zip_path)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "ac4bbee1919ea7aef9c6cfd22a25c3c68d6ebab6b5f3e31fb17a356d6f400124"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/ogcfeatures.ipynb b/Python_based_scripts/ogcfeatures.ipynb
deleted file mode 100644
index 0030c5c..0000000
--- a/Python_based_scripts/ogcfeatures.ipynb
+++ /dev/null
@@ -1,584 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data API: OGC Features example\n",
-    "\n",
-    "OGC Features is the replacement for Web Feature Service.\n",
-    "\n",
-    "More info about the API can be found here - https://ogcapi.ogc.org/features/overview.html\n",
-    "\n",
-    "### Using AIMS Temperature Loggers data as an example - this notebook demonstrates\n",
-    "\n",
-    "- List available datasets (coverages)\n",
-    "- Get \"queryables\" for a collection\n",
-    "- Fetch subset of data - filtered by\n",
-    "  - Bounding box\n",
-    "  - Temporal extent\n",
-    "  - Property values (`deployment_id` and `qc_flag`)\n",
-    "\n",
-    "### Notes on `owslib`\n",
-    "\n",
-    "`owslib` is a python library for working with OGC web services and the new OGC APIs - it has up-to-date support of the OGC Features standard."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First, install and load the required packages.\n",
-    "\n",
-    "import subprocess\n",
-    "\n",
-    "packages = [\n",
-    "    'getpass',\n",
-    "    'owslib'\n",
-    "]\n",
-    "\n",
-    "for package in packages:\n",
-    "    try:\n",
-    "        subprocess.check_call(['pip', 'install', package])\n",
-    "        print(f'Successfully installed {package}')\n",
-    "    except subprocess.CalledProcessError as e:\n",
-    "        print(f'Error occurred while installing {package}: {e}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import JSON\n",
-    "from owslib.ogcapi.features import Features\n",
-    "from getpass import getpass"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Connecting to Data API - OGC Features\n",
-    "\n",
-    "We first connect to the Data API by setting up a OGC Features API endpoint.\n",
-    "\n",
-    "To do this, you will need to go to https://dashboard.staging.reefdata.io/ and copy your Authentication token.\n",
-    "This can then be pasted into the password prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = Features('https://pygeoapi.staging.reefdata.io/', headers={'Authorization': f'Bearer {getpass()}'})"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## List available collections"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['aims-sst',\n",
-       " 'aims-sst-private',\n",
-       " 'abs-regional-lga-2021',\n",
-       " 'gbrmpa-reef-biogeography',\n",
-       " 'gbrmpa-complete-gbr-features',\n",
-       " 'gbrmpa-management-areas',\n",
-       " 'dcceew-coral-sea-boundary',\n",
-       " 'gbrmpa-marine-park-zoning',\n",
-       " 'gbrmpa-gbr-river-basins',\n",
-       " 'abs-lgas-2021',\n",
-       " 'abs-lgas-2022',\n",
-       " 'abs-postal-areas-2021',\n",
-       " 'gbrmpa-cruise-ship-transit-lines',\n",
-       " 'gbrmpa-remote-natural-areas',\n",
-       " 'gbrmpa-special-management-areas',\n",
-       " 'gbrmpa-traditional-use-areas',\n",
-       " 'gbrmpa-whale-protection-area',\n",
-       " 'qtmr-recreational-boat-facilities',\n",
-       " 'gbrmpa-marine-park-limits',\n",
-       " 'qtmr-vessel-registrations']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.feature_collections()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get collection metadata\n",
-    "\n",
-    "The following is equivalent to \n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/aims-sst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "description": "The data provided here are from a number of sea water temperature monitoring programs conducted in tropical and subtropical coral reefs environments around Australia. Data are available from approximately 80 GBR sites, 16 Coral Sea sites, 7 sites in North West Western Australia (WA), 8 Queensland regional ports, 13 sites in the Solitary Islands, 4 sites in PNG and 10 sites in the Cocos (Keeling) Islands. Data are obtained from in-situ data loggers deployed on the reef. Temperature instruments sample water temperatures every 5-10 minutes (typically) and are exchanged and downloaded approximately every 12 months. Temperature loggers on the reef-flat are generally placed just below Lowest Astronomical Tide level. Reef-slope (or where specified as Upper reef-slope) generally refers to depths 5 - 9 m while Deep reef-slope refers to depths of ~20 m.\n",
-       "extent": {
-        "spatial": {
-         "bbox": [
-          [
-           96.7556,
-           -34.3725,
-           159.2372,
-           -2.6651
-          ]
-         ],
-         "crs": "http://www.opengis.net/def/crs/OGC/1.3/CRS84"
-        }
-       },
-       "id": "aims-sst",
-       "itemType": "feature",
-       "keywords": [],
-       "links": [
-        {
-         "href": "https://stac.staging.reefdata.io/browser/collections/aims-oceanography/items/aims-sst",
-         "hreflang": "en-AU",
-         "rel": "canonical",
-         "title": "information",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io?f=json",
-         "rel": "root",
-         "title": "The landing page of this server as JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io?f=html",
-         "rel": "root",
-         "title": "The landing page of this server as HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst?f=json",
-         "rel": "self",
-         "title": "This document as JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst?f=jsonld",
-         "rel": "alternate",
-         "title": "This document as RDF (JSON-LD)",
-         "type": "application/ld+json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst?f=html",
-         "rel": "alternate",
-         "title": "This document as HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst/queryables?f=json",
-         "rel": "queryables",
-         "title": "Queryables for this collection as JSON",
-         "type": "application/json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst/queryables?f=html",
-         "rel": "queryables",
-         "title": "Queryables for this collection as HTML",
-         "type": "text/html"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst/items?f=json",
-         "rel": "items",
-         "title": "items as GeoJSON",
-         "type": "application/geo+json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst/items?f=jsonld",
-         "rel": "items",
-         "title": "items as RDF (GeoJSON-LD)",
-         "type": "application/ld+json"
-        },
-        {
-         "href": "https://pygeoapi.staging.reefdata.io/collections/aims-sst/items?f=html",
-         "rel": "items",
-         "title": "Items as HTML",
-         "type": "text/html"
-        }
-       ],
-       "title": "AIMS Sea Surface Temperature Monitoring Program"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coll_id = 'aims-sst'\n",
-    "aims_sst = w.collection(coll_id)\n",
-    "JSON(aims_sst)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Queryables\n",
-    "\n",
-    "Queryables are the properties that can be used to filter the data (eg columns in a table).\n",
-    "\n",
-    "The following is equivalent to\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/aims-sst/queryables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "$id": "https://pygeoapi.staging.reefdata.io/collections/aims-sst/queryables",
-       "$schema": "http://json-schema.org/draft/2019-09/schema",
-       "properties": {
-        "cal_val": {
-         "title": "cal_val",
-         "type": "number"
-        },
-        "deployment_id": {
-         "title": "deployment_id",
-         "type": "integer"
-        },
-        "depth": {
-         "title": "depth",
-         "type": "number"
-        },
-        "fid": {
-         "title": "fid",
-         "type": "integer"
-        },
-        "from_date": {
-         "title": "from_date",
-         "type": "string"
-        },
-        "gbrmpa_reef_id": {
-         "title": "gbrmpa_reef_id",
-         "type": "string"
-        },
-        "geometry": {
-         "$ref": "https://geojson.org/schema/Geometry.json"
-        },
-        "hilbert_distance": {
-         "title": "hilbert_distance",
-         "type": "integer"
-        },
-        "instrument_type": {
-         "title": "instrument_type",
-         "type": "string"
-        },
-        "lat": {
-         "title": "lat",
-         "type": "number"
-        },
-        "lon": {
-         "title": "lon",
-         "type": "number"
-        },
-        "metadata_uuid": {
-         "title": "metadata_uuid",
-         "type": "string"
-        },
-        "parameter": {
-         "title": "parameter",
-         "type": "string"
-        },
-        "qc_flag": {
-         "title": "qc_flag",
-         "type": "number"
-        },
-        "qc_val": {
-         "title": "qc_val",
-         "type": "number"
-        },
-        "serial_num": {
-         "title": "serial_num",
-         "type": "string"
-        },
-        "site": {
-         "title": "site",
-         "type": "string"
-        },
-        "site_id": {
-         "title": "site_id",
-         "type": "integer"
-        },
-        "sites_with_climatology_available": {
-         "title": "sites_with_climatology_available",
-         "type": "number"
-        },
-        "subsite": {
-         "title": "subsite",
-         "type": "string"
-        },
-        "subsite_id": {
-         "title": "subsite_id",
-         "type": "integer"
-        },
-        "thru_date": {
-         "title": "thru_date",
-         "type": "string"
-        },
-        "time": {
-         "title": "time",
-         "type": "string"
-        }
-       },
-       "title": "AIMS Sea Surface Temperature Monitoring Program",
-       "type": "object"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collection_queryables = w.collection_queryables(coll_id)\n",
-    "\n",
-    "JSON(collection_queryables)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get subset of data\n",
-    "\n",
-    "By default OGC Features API will return GeoJSON (Feature Collection)\n",
-    "\n",
-    "Here we are filtering the data by\n",
-    "- Bounding box = `[149,-20,150,-19]`\n",
-    "- Temporal extent = `2002-07-10` to `2002-07-20`\n",
-    "- Deployment ID = `3499`\n",
-    "- QC Flag = `1`\n",
-    "\n",
-    "The following is equivalent to\n",
-    "\n",
-    "- https://pygeoapi.staging.reefdata.io/collections/091-aims-sst/items?bbox=149,-20,150,-19&datetime=2002-07-10T00:00:00Z/2002-07-20T00:00:00Z&deployment_id=3499&qc_flag=1&limit=1000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = w.collection_items(coll_id,\n",
-    "                            bbox=[149,-20,150,-19],\n",
-    "                            datetime='2002-07-10T00:00:00Z/2002-07-20T00:00:00Z',\n",
-    "                            deployment_id=3499,\n",
-    "                            qc_flag=1,\n",
-    "                            limit=1000)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Print the first GeoJson Feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "bbox": [
-        149.1828,
-        -19.7453,
-        149.1828,
-        -19.7453
-       ],
-       "geometry": {
-        "coordinates": [
-         149.1828,
-         -19.7453
-        ],
-        "type": "Point"
-       },
-       "id": "147920142",
-       "properties": {
-        "cal_val": 22.219,
-        "deployment_id": 3499,
-        "depth": 9,
-        "from_date": "2002-06-13T00:00:00",
-        "gbrmpa_reef_id": "19-135",
-        "instrument_type": "Odyssey",
-        "lat": -19.7453,
-        "lon": 149.1828,
-        "metadata_uuid": "fc73ac05-a4a7-4bcb-931d-c246f47aed93",
-        "parameter": "Water Temperature",
-        "qc_flag": 1,
-        "qc_val": 22.219,
-        "serial_num": "SST-905217",
-        "site": "Hardy Reef",
-        "site_id": 6,
-        "sites_with_climatology_available": null,
-        "subsite": "HARAWS",
-        "subsite_id": 3499,
-        "thru_date": "2002-11-20T00:00:00",
-        "time": "2002-07-19T08:30:00+00:00"
-       },
-       "type": "Feature"
-      },
-      "text/plain": [
-       "<IPython.core.display.JSON object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {
-      "application/json": {
-       "expanded": false,
-       "root": "root"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "JSON(items['features'][0])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot the data!\n",
-    "\n",
-    "This is a very basic example that just plots time-series. We ignore geometry as all the data is from the same location."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'SST at Hardy Reef'}, xlabel='time', ylabel='Temperature (C)'>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from geopandas import GeoDataFrame\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "\n",
-    "gdf = GeoDataFrame.from_features(items)\n",
-    "gdf.time = pd.to_datetime(gdf.time)  # Convert from string to datetime\n",
-    "site_name = gdf.site[0]\n",
-    "gdf.plot.line(x='time', \n",
-    "              y='qc_val', \n",
-    "              legend=False, \n",
-    "              ylabel='Temperature (C)', \n",
-    "              title=f'SST at {site_name}')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "ac4bbee1919ea7aef9c6cfd22a25c3c68d6ebab6b5f3e31fb17a356d6f400124"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Python_based_scripts/stac-metadata.ipynb b/Python_based_scripts/stac-metadata.ipynb
deleted file mode 100644
index 5bc9c0e..0000000
--- a/Python_based_scripts/stac-metadata.ipynb
+++ /dev/null
@@ -1,1189 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e06a27bf",
-   "metadata": {},
-   "source": [
-    "# Metadata API Introduction\n",
-    "\n",
-    "This notebook shows basic use of our metadata API using a Python library PySTAC-Client. It demonstrates how to fetch all collections, fetch a given collection/item, and perform simple searches."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "1e16077c",
-   "metadata": {},
-   "source": [
-    "# Connecting to metadata API\n",
-    "\n",
-    "We first connect to the metadata API by retrieving the root catalog\n",
-    "\n",
-    "To do this, you will need to go to https://dashboard.staging.reefdata.io/ and copy your Authentication token.\n",
-    "This can then be pasted into the password prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "98942e75",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pystac_client/client.py:177: FutureWarning: The `ignore_conformance` option is deprecated and will be removed in the next major release. Instead use `set_conforms_to` or `add_conforms_to` to control behavior.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'stac-fastapi'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from getpass import getpass\n",
-    "import pystac_client\n",
-    "\n",
-    "# Metadata STAC API root url\n",
-    "URL = 'https://stac.staging.reefdata.io'\n",
-    "\n",
-    "# Go to https://dashboard.staging.reefdata.io/, copy your API key and paste into password box\n",
-    "\n",
-    "# Create the client\n",
-    "api = pystac_client.Client.open(\n",
-    "    url=\"https://stac.staging.reefdata.io/\",\n",
-    "    headers={\n",
-    "        'Authorization': f\"Bearer {getpass()}\"\n",
-    "    },\n",
-    "    ignore_conformance=True\n",
-    ")\n",
-    "\n",
-    "api.title"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f6efbb86",
-   "metadata": {},
-   "source": [
-    "# Fetch all STAC collections"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "bb7693fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<CollectionClient id=noaa-crw>\n",
-      "<CollectionClient id=aims-oceanography>\n",
-      "<CollectionClient id=gbrmpa-admin-regions>\n",
-      "<CollectionClient id=abs-census>\n",
-      "<CollectionClient id=qtmr-vessels>\n",
-      "<CollectionClient id=amsa-vessel-tracking>\n",
-      "<CollectionClient id=abs-spatial>\n",
-      "<CollectionClient id=gbr-biogeographic-areas>\n",
-      "<CollectionClient id=gbr-spatial-features>\n"
-     ]
-    }
-   ],
-   "source": [
-    "for collection in api.get_collections():\n",
-    "    print(collection)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bdb8721b",
-   "metadata": {},
-   "source": [
-    "# Fetch a given collection by ID"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fef20a46",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #E1E1E1;\n",
-       "        border: 3px solid #9D9D9D;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div>\n",
-       "        <details style=\"margin-left: 48px;\">\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"margin-bottom: 0px; display: inline;\">\n",
-       "                    CollectionClient: aims-oceanography\n",
-       "                </h3>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr><td style=\"text-align: left;\"><strong>id:</strong> aims-oceanography </td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>title:</strong> AIMS Oceanography </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>description:</strong> The Australian Institute of Marine Science (AIMS) has been collecting in situ oceanographic data since 1980 with the deployment of automatic weather observing stations and since 1991 with the sea temperature monitoring program. Both datasets provide hourly data related to sea temperature at a range of depths, atmospheric pressure, air temperature, relative humidity, solar radiation (light as PAR), wind direction and wind speed, spanning the entirety of the Great Barrier Reef. </td></tr>\n",
-       "                \n",
-       "                \n",
-       "    \n",
-       "    \n",
-       "        <tr><td style=\"text-align: left;\"><strong>providers:</strong>\n",
-       "        <ul>\n",
-       "        \n",
-       "             <li>\n",
-       "\n",
-       "<div>\n",
-       "    \n",
-       "        <a href=\"https://www.aims.gov.au/\"> AIMS </a>\n",
-       "    \n",
-       "    (<i>licensor, producer</i>): AIMS Oceanography \n",
-       "    \n",
-       "</div>\n",
-       "</li>\n",
-       "        \n",
-       "        </ul>\n",
-       "        </td></tr>\n",
-       "    \n",
-       "\n",
-       "                \n",
-       "        \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> Collection </td></tr>\n",
-       "            \n",
-       "        \n",
-       "    \n",
-       "            </table>\n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">STAC Extensions</h4>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/projection/v1.1.0/schema.json\">https://stac-extensions.github.io/projection/v1.1.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/themes/v1.0.0/schema.json\">https://stac-extensions.github.io/themes/v1.0.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "            </table>\n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">Items</h4>\n",
-       "            </summary>\n",
-       "            <i> Only the first item shown </i>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #DBF5FF;\n",
-       "        border: 3px solid #4CC9FF;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div>\n",
-       "        <details style=\"margin-left: 48px;\">\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"margin-bottom: 0px; display: inline;\">Item: aims-sst</h3>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr><td style=\"text-align: left;\"><strong>id:</strong> aims-sst </td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>bbox:</strong> [96.7556, -34.3725, 159.2372, -2.6651] </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>title:</strong> AIMS Sea Surface Temperature Monitoring Program </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>themes:</strong> [{'scheme': 'https://wiki.esipfed.org/ISO_19115-3_Codelists#MD_TopicCategoryCode', 'concepts': [{'id': 'oceans', 'title': 'Oceans'}]}] </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>sci:doi:</strong> 10.25845/5b4eb0f9bb848 </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>proj:bbox:</strong> [96.7556, -34.3725, 159.2372, -2.6651] </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>proj:epsg:</strong> 4326 </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>description:</strong> The data provided here are from a number of sea water temperature monitoring programs conducted in tropical and subtropical coral reefs environments around Australia. Data are available from approximately 80 GBR sites, 16 Coral Sea sites, 7 sites in North West Western Australia (WA), 8 Queensland regional ports, 13 sites in the Solitary Islands, 4 sites in PNG and 10 sites in the Cocos (Keeling) Islands. Data are obtained from in-situ data loggers deployed on the reef. Temperature instruments sample water temperatures every 5-10 minutes (typically) and are exchanged and downloaded approximately every 12 months. Temperature loggers on the reef-flat are generally placed just below Lowest Astronomical Tide level. Reef-slope (or where specified as Upper reef-slope) generally refers to depths 5 - 9 m while Deep reef-slope refers to depths of ~20 m.</p>\n",
-       "\n",
-       "<p>Reefs are under threat from climate change. Elevated sea temperature is a major stress to reefs, capable of causing widespread coral bleaching and mortality. Small excursions of temperature outside normal summer levels can cause stress and bleaching in corals, especially sensitive species. Temperature data from this program are used to correlate with bleaching events and derive local bleaching thresholds. Data are also used in a wide variety of other marine research programs, including fisheries, balast water, turtles, sea birds, seagrass, coral disease, oceanography, process studies, validation of proxy climate records etc. </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>end_datetime:</strong> 2022-05-06T13:56:45Z </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>sci:citation:</strong> Australian Institute of Marine Science (AIMS). (2017). AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program). https://doi.org/10.25845/5b4eb0f9bb848, accessed 05-Jun-2023. </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>table:columns:</strong> [{'name': 'fid', 'type': 'int64'}, {'name': 'deployment_id', 'type': 'int64'}, {'name': 'site', 'type': 'byte_array'}, {'name': 'site_id', 'type': 'int64'}, {'name': 'subsite', 'type': 'byte_array'}, {'name': 'subsite_id', 'type': 'int64'}, {'name': 'from_date', 'type': 'int64'}, {'name': 'thru_date', 'type': 'int64'}, {'name': 'depth', 'type': 'double'}, {'name': 'parameter', 'type': 'byte_array'}, {'name': 'instrument_type', 'type': 'byte_array'}, {'name': 'serial_num', 'type': 'byte_array'}, {'name': 'lat', 'type': 'double'}, {'name': 'lon', 'type': 'double'}, {'name': 'gbrmpa_reef_id', 'type': 'byte_array'}, {'name': 'metadata_uuid', 'type': 'byte_array'}, {'name': 'sites_with_climatology_available', 'type': 'double'}, {'name': 'time', 'type': 'int64'}, {'name': 'cal_val', 'type': 'double'}, {'name': 'qc_val', 'type': 'double'}, {'name': 'qc_flag', 'type': 'double'}, {'name': 'geometry', 'type': 'byte_array'}, {'name': 'hilbert_distance', 'type': 'int64'}] </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>start_datetime:</strong> 1991-11-20T14:00:00Z </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>table:row_count:</strong> 150663521 </td></tr>\n",
-       "                    \n",
-       "                \n",
-       "                \n",
-       "            </table>\n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">STAC Extensions</h4>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/projection/v1.1.0/schema.json\">https://stac-extensions.github.io/projection/v1.1.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/themes/v1.0.0/schema.json\">https://stac-extensions.github.io/themes/v1.0.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/table/v1.2.0/schema.json\">https://stac-extensions.github.io/table/v1.2.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\">https://stac-extensions.github.io/scientific/v1.0.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "            </table>\n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">Assets</h4>\n",
-       "            </summary>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FED7E5;\n",
-       "        border: 3px solid #FA4C8C;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div>\n",
-       "        <details style=\"margin-left: 48px;\">\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"margin-bottom: 0px; display: inline;\">Asset:  AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) </h3>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr><td style=\"text-align: left;\"><strong>href:</strong> s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/ </td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>type:</strong> application/x-parquet </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>title:</strong> AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>description:</strong> S3 address of the AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) in GeoParquet format </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>roles:</strong> ['data'] </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>owner:</strong> aims-sst </td></tr>\n",
-       "                \n",
-       "                \n",
-       "            </table>\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">Links</h4>\n",
-       "            </summary>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> collection </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> parent </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  stac-fastapi </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> root </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/ </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> stac-fastapi </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> self </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography/items/aims-sst </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/geo+json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  Link to data API </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> describedby </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://pygeoapi.staging.reefdata.io/collections/aims-sst </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> Link to data API </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  Link to original metadata </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> describedby </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://doi.org/10.25845/5b4eb0f9bb848 </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> Link to original metadata </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "        </details>\n",
-       "    \n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">Links</h4>\n",
-       "            </summary>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> items </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography/items </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/geo+json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> parent </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/ </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  stac-fastapi </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> root </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/ </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> stac-fastapi </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> self </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "    \n",
-       "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<CollectionClient id=aims-oceanography>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collection = api.get_collection('aims-oceanography')\n",
-    "collection"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "47a540fe",
-   "metadata": {},
-   "source": [
-    "# Fetch all items\n",
-    "\n",
-    "The function get_items return iterators, where pystac-client will handle retrieval of additional pages when needed. Note that one request is made for the first ten items, you can make a second request for the next ten."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "17d6de4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First page\n",
-      "0: <Item id=aims-sst>\n"
-     ]
-    }
-   ],
-   "source": [
-    "items = collection.get_items()\n",
-    "\n",
-    "# flush stdout so we can see the exact order that things happen\n",
-    "def get_ten_items(items):\n",
-    "    for i, item in enumerate(items):\n",
-    "        print(f\"{i}: {item}\", flush=True)\n",
-    "        if i == 9:\n",
-    "            return\n",
-    "\n",
-    "print('First page', flush=True)\n",
-    "get_ten_items(items)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f385a94d",
-   "metadata": {},
-   "source": [
-    "# Fetch a given item\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "deafc5f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #DBF5FF;\n",
-       "        border: 3px solid #4CC9FF;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div>\n",
-       "        <details style=\"margin-left: 48px;\">\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"margin-bottom: 0px; display: inline;\">Item: aims-sst</h3>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr><td style=\"text-align: left;\"><strong>id:</strong> aims-sst </td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>bbox:</strong> [96.7556, -34.3725, 159.2372, -2.6651] </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>title:</strong> AIMS Sea Surface Temperature Monitoring Program </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>themes:</strong> [{'scheme': 'https://wiki.esipfed.org/ISO_19115-3_Codelists#MD_TopicCategoryCode', 'concepts': [{'id': 'oceans', 'title': 'Oceans'}]}] </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>sci:doi:</strong> 10.25845/5b4eb0f9bb848 </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>proj:bbox:</strong> [96.7556, -34.3725, 159.2372, -2.6651] </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>proj:epsg:</strong> 4326 </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>description:</strong> The data provided here are from a number of sea water temperature monitoring programs conducted in tropical and subtropical coral reefs environments around Australia. Data are available from approximately 80 GBR sites, 16 Coral Sea sites, 7 sites in North West Western Australia (WA), 8 Queensland regional ports, 13 sites in the Solitary Islands, 4 sites in PNG and 10 sites in the Cocos (Keeling) Islands. Data are obtained from in-situ data loggers deployed on the reef. Temperature instruments sample water temperatures every 5-10 minutes (typically) and are exchanged and downloaded approximately every 12 months. Temperature loggers on the reef-flat are generally placed just below Lowest Astronomical Tide level. Reef-slope (or where specified as Upper reef-slope) generally refers to depths 5 - 9 m while Deep reef-slope refers to depths of ~20 m.</p>\n",
-       "\n",
-       "<p>Reefs are under threat from climate change. Elevated sea temperature is a major stress to reefs, capable of causing widespread coral bleaching and mortality. Small excursions of temperature outside normal summer levels can cause stress and bleaching in corals, especially sensitive species. Temperature data from this program are used to correlate with bleaching events and derive local bleaching thresholds. Data are also used in a wide variety of other marine research programs, including fisheries, balast water, turtles, sea birds, seagrass, coral disease, oceanography, process studies, validation of proxy climate records etc. </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>end_datetime:</strong> 2022-05-06T13:56:45Z </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>sci:citation:</strong> Australian Institute of Marine Science (AIMS). (2017). AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program). https://doi.org/10.25845/5b4eb0f9bb848, accessed 05-Jun-2023. </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>table:columns:</strong> [{'name': 'fid', 'type': 'int64'}, {'name': 'deployment_id', 'type': 'int64'}, {'name': 'site', 'type': 'byte_array'}, {'name': 'site_id', 'type': 'int64'}, {'name': 'subsite', 'type': 'byte_array'}, {'name': 'subsite_id', 'type': 'int64'}, {'name': 'from_date', 'type': 'int64'}, {'name': 'thru_date', 'type': 'int64'}, {'name': 'depth', 'type': 'double'}, {'name': 'parameter', 'type': 'byte_array'}, {'name': 'instrument_type', 'type': 'byte_array'}, {'name': 'serial_num', 'type': 'byte_array'}, {'name': 'lat', 'type': 'double'}, {'name': 'lon', 'type': 'double'}, {'name': 'gbrmpa_reef_id', 'type': 'byte_array'}, {'name': 'metadata_uuid', 'type': 'byte_array'}, {'name': 'sites_with_climatology_available', 'type': 'double'}, {'name': 'time', 'type': 'int64'}, {'name': 'cal_val', 'type': 'double'}, {'name': 'qc_val', 'type': 'double'}, {'name': 'qc_flag', 'type': 'double'}, {'name': 'geometry', 'type': 'byte_array'}, {'name': 'hilbert_distance', 'type': 'int64'}] </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>start_datetime:</strong> 1991-11-20T14:00:00Z </td></tr>\n",
-       "                    \n",
-       "                        <tr><td style=\"text-align: left;\"><strong>table:row_count:</strong> 150663521 </td></tr>\n",
-       "                    \n",
-       "                \n",
-       "                \n",
-       "            </table>\n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">STAC Extensions</h4>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/projection/v1.1.0/schema.json\">https://stac-extensions.github.io/projection/v1.1.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/themes/v1.0.0/schema.json\">https://stac-extensions.github.io/themes/v1.0.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/table/v1.2.0/schema.json\">https://stac-extensions.github.io/table/v1.2.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><a href=\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\">https://stac-extensions.github.io/scientific/v1.0.0/schema.json</a></td></tr>\n",
-       "                \n",
-       "            </table>\n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">Assets</h4>\n",
-       "            </summary>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FED7E5;\n",
-       "        border: 3px solid #FA4C8C;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div>\n",
-       "        <details style=\"margin-left: 48px;\">\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"margin-bottom: 0px; display: inline;\">Asset:  AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) </h3>\n",
-       "            </summary>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr><td style=\"text-align: left;\"><strong>href:</strong> s3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/ </td></tr>\n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>type:</strong> application/x-parquet </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>title:</strong> AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>description:</strong> S3 address of the AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) in GeoParquet format </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>roles:</strong> ['data'] </td></tr>\n",
-       "                \n",
-       "                \n",
-       "                    <tr><td style=\"text-align: left;\"><strong>owner:</strong> aims-sst </td></tr>\n",
-       "                \n",
-       "                \n",
-       "            </table>\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "        </details>\n",
-       "    \n",
-       "            \n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 10px; margin-top: 10px\">\n",
-       "                <h4 style=\"margin-bottom: 0px; display: inline;\">Links</h4>\n",
-       "            </summary>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> self </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography/items/aims-sst </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> collection </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link: </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> parent </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  AIMS Oceanography </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> root </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://stac.staging.reefdata.io/collections/aims-oceanography </td></tr>\n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>type:</strong> application/json </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> AIMS Oceanography </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  Link to data API </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> describedby </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://pygeoapi.staging.reefdata.io/collections/aims-sst </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> Link to data API </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "                \n",
-       "\n",
-       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"\n",
-       "        width: 24px;\n",
-       "        height: 24px;\n",
-       "        background-color: #FFF7E5;\n",
-       "        border: 3px solid #FF6132;\n",
-       "        border-radius: 5px;\n",
-       "        position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h4 style=\"margin-bottom: 0px;\">Link:  Link to original metadata </h4>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr><td style=\"text-align: left;\"><strong>rel:</strong> describedby </td></tr>\n",
-       "            <tr><td style=\"text-align: left;\"><strong>href:</strong> https://doi.org/10.25845/5b4eb0f9bb848 </td></tr>\n",
-       "            \n",
-       "            \n",
-       "                <tr><td style=\"text-align: left;\"><strong>title:</strong> Link to original metadata </td></tr>\n",
-       "            \n",
-       "            \n",
-       "        </table>\n",
-       "        \n",
-       "    </div>\n",
-       "</div>\n",
-       "            \n",
-       "        </details>\n",
-       "    \n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<Item id=aims-sst>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "item = collection.get_item('aims-sst')\n",
-    "item"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c9609c32",
-   "metadata": {},
-   "source": [
-    "# Inspect an item for assets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "bc7927c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'href': 's3://rimrep-data-public/091-aims-sst/test-50-64-spatialpart/', 'type': 'application/x-parquet', 'title': 'AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program)', 'description': 'S3 address of the AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) in GeoParquet format', 'roles': ['data']}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Inspect assets\n",
-    "item_assets = item.get_assets()\n",
-    "data_asset = item_assets['data']\n",
-    "if data_asset is not None:\n",
-    "    print(data_asset.to_dict())  "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "5292b0dd",
-   "metadata": {},
-   "source": [
-    "# Inspect an item for link to data API"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "50430e73",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'rel': 'describedby', 'href': 'https://pygeoapi.staging.reefdata.io/collections/aims-sst', 'title': 'Link to data API'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Inspect link to data API\n",
-    "link = item.get_single_link(rel=\"describedby\")\n",
-    "if link is not None:\n",
-    "    print(link.to_dict())"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "62e26114",
-   "metadata": {},
-   "source": [
-    "# Search for items by spatial and temporal extent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d8af6334",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "abs-lgas-2022\n",
-      "amsa-ais\n",
-      "abs-postal-areas-2021\n",
-      "abs-lgas-2021\n",
-      "gbrmpa-benthic-map\n",
-      "qtmr-recreational-boat-facilities\n",
-      "dcceew-coral-sea-boundary\n",
-      "gbrmpa-gbr-river-basins\n",
-      "qtmr-vessel-registrations\n",
-      "abs-regional-lga-2021\n",
-      "gbrmpa-traditional-use-areas\n",
-      "gbrmpa-special-management-areas\n",
-      "gbrmpa-management-areas\n",
-      "gbrmpa-marine-park-limits\n",
-      "gbrmpa-whale-protection-area\n"
-     ]
-    }
-   ],
-   "source": [
-    "geom = {\n",
-    "    \"type\": \"Polygon\",\n",
-    "    \"coordinates\": [\n",
-    "      [\n",
-    "        [\n",
-    "          162,\n",
-    "          -33\n",
-    "        ],\n",
-    "        [\n",
-    "          162,\n",
-    "          -3\n",
-    "        ],\n",
-    "        [\n",
-    "          136,\n",
-    "          -3\n",
-    "        ],\n",
-    "        [\n",
-    "          136,\n",
-    "          -33\n",
-    "        ],\n",
-    "        [\n",
-    "          162,\n",
-    "          -33\n",
-    "        ]\n",
-    "      ]\n",
-    "    ]\n",
-    "}\n",
-    "\n",
-    "results = api.search(\n",
-    "    max_items = 15,\n",
-    "    limit = 5,\n",
-    "    intersects = geom,\n",
-    "    datetime = \"2000-01-01/2023-03-07\",\n",
-    ")\n",
-    "\n",
-    "for item in results.items():\n",
-    "    print(item.id)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "98229925",
-   "metadata": {},
-   "source": [
-    "# Search for items using query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2b773a87",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dcceew-coral-sea-boundary\n",
-      "aims-sst\n",
-      "gbrmpa-complete-gbr-features\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Search for items using a query to filter by keywords\n",
-    "# Currently \"contains\" operator is not supported, so you can only \"eq\" operator find exact matches\n",
-    "\n",
-    "results = api.search(\n",
-    "    max_items = 15,\n",
-    "    limit = 5,\n",
-    "    query={\"themes\": {\"eq\": [{'scheme': 'https://wiki.esipfed.org/ISO_19115-3_Codelists#MD_TopicCategoryCode',\n",
-    "                              'concepts': [{'id': 'oceans', 'title': 'Oceans'}]}]}}\n",
-    ")\n",
-    "\n",
-    "for item in results.items():\n",
-    "    print(item.id)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "d95ed3e0c57c5648f0aae0a872a2f6da817c91393ca776950b237d8871ec68c2"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Python_based_scripts/useful_spatial_functions_data_extraction.py b/Python_based_scripts/useful_spatial_functions_data_extraction.py
index 7254cb4..c43c377 100644
--- a/Python_based_scripts/useful_spatial_functions_data_extraction.py
+++ b/Python_based_scripts/useful_spatial_functions_data_extraction.py
@@ -154,7 +154,7 @@ def gbr_features(**kwargs):
     site_id: string. Unique ID of the site to be extracted. If not specified, all sites will be returned.
     '''
 
-    dask_geo_df = dask_geopandas.read_parquet('s3://rimrep-data-public/gbrmpa-complete-gbr-features/data.parquet',
+    dask_geo_df = dask_geopandas.read_parquet('s3://gbr-dms-data-public/gbrmpa-complete-gbr-features/data.parquet',
                                               #Specifying which columns to read
                                               columns = ['UNIQUE_ID', 'GBR_NAME', 'LOC_NAME_S', 'geometry'],
                                               #Specifying the column to be used as index
diff --git a/Python_based_scripts/zarr.ipynb b/Python_based_scripts/zarr.ipynb
deleted file mode 100644
index 910a900..0000000
--- a/Python_based_scripts/zarr.ipynb
+++ /dev/null
@@ -1,1863 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# `Zarr` direct access example\n",
-    "\n",
-    "**Author**: Nick Forbes-Smith, Denisse Fierro Arcos  \n",
-    "  \n",
-    "This notebook demonstrates how to access a public dataset from the RIMReP collection using `zarr`. We will use the [NOAA CRW degree heating weeks](https://www.coris.noaa.gov/search/catalog/search/resource/details.page?uuid=%7BF77EF0B8-C12F-463F-B66A-CC922E50A39D%7D) dataset as an example.  \n",
-    "  \n",
-    "We will also include examples of how to make simple calculations to produce maps and timeseries."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading and manipulating gridded multidimensional data\n",
-    "import xarray as xr\n",
-    "# Accessing S3 buckets\n",
-    "import s3fs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connecting to S3 bucket and loading dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<defs>\n",
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-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:              (time: 13846, lat: 360, lon: 720)\n",
-       "Coordinates:\n",
-       "  * lat                  (lat) float32 -8.025 -8.075 -8.125 ... -25.92 -25.98\n",
-       "  * lon                  (lon) float32 133.0 133.1 133.1 ... 168.9 168.9 169.0\n",
-       "  * time                 (time) datetime64[ns] 1985-03-25T12:00:00 ... 2023-0...\n",
-       "Data variables:\n",
-       "    crs                  int16 ...\n",
-       "    degree_heating_week  (time, lat, lon) float32 ...\n",
-       "    mask                 (time, lat, lon) float32 ...\n",
-       "Attributes: (12/62)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
-       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
-       "    contributor_name:           NOAA Coral Reef Watch Program\n",
-       "    ...                         ...\n",
-       "    time_coverage_duration:     P1D\n",
-       "    time_coverage_end:          19850326T000000Z\n",
-       "    time_coverage_resolution:   P1D\n",
-       "    time_coverage_start:        19850325T000000Z\n",
-       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
-       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f</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-4c6f4f8b-6415-41b6-bb13-5adb4d06aac0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c6f4f8b-6415-41b6-bb13-5adb4d06aac0' 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>: 13846</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d05241c4-16eb-4ce3-a7e7-b4937d47866d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d05241c4-16eb-4ce3-a7e7-b4937d47866d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.025 -8.075 ... -25.92 -25.98</div><input id='attrs-e491930c-88a3-4140-baef-fc2b884dc811' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e491930c-88a3-4140-baef-fc2b884dc811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64d1b642-9aa3-4d91-aa0b-069630f833c7' class='xr-var-data-in' type='checkbox'><label for='data-64d1b642-9aa3-4d91-aa0b-069630f833c7' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.025,  -8.075,  -8.125, ..., -25.875, -25.925, -25.975],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>133.0 133.1 133.1 ... 168.9 169.0</div><input id='attrs-204ef143-badc-40a1-9813-e654b2d518fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-204ef143-badc-40a1-9813-e654b2d518fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3003edc-169b-4d8a-bb5c-46b9e8f0d4bf' class='xr-var-data-in' type='checkbox'><label for='data-f3003edc-169b-4d8a-bb5c-46b9e8f0d4bf' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([133.025, 133.075, 133.125, ..., 168.875, 168.925, 168.975],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1985-03-25T12:00:00 ... 2023-02-...</div><input id='attrs-19ac8791-10b5-48bf-b789-7c134259bd01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19ac8791-10b5-48bf-b789-7c134259bd01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8d0b268-011e-4d77-9e97-e25fbf39a607' class='xr-var-data-in' type='checkbox'><label for='data-d8d0b268-011e-4d77-9e97-e25fbf39a607' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1985-03-25T12:00:00.000000000&#x27;, &#x27;1985-03-26T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1985-03-27T12:00:00.000000000&#x27;, ..., &#x27;2023-02-17T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-02-18T12:00:00.000000000&#x27;, &#x27;2023-02-19T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b406b25-2767-4f26-a4ee-e97fbab9ed21' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b406b25-2767-4f26-a4ee-e97fbab9ed21' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e9a377c-5919-4f26-9461-de053e2697a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e9a377c-5919-4f26-9461-de053e2697a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be4778f3-2711-479e-af80-ea9b045b0425' class='xr-var-data-in' type='checkbox'><label for='data-be4778f3-2711-479e-af80-ea9b045b0425' 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>comment :</span></dt><dd>This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.</dd><dt><span>epsg_code :</span></dt><dd>EPSG:32663</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.2572021484375</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degree_heating_week</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'>...</div><input id='attrs-1a639e38-bbb5-4cf2-b2e2-8ee3de88647f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a639e38-bbb5-4cf2-b2e2-8ee3de88647f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83dc6d0b-3312-4953-9a92-8b5ca122a2ee' class='xr-var-data-in' type='checkbox'><label for='data-83dc6d0b-3312-4953-9a92-8b5ca122a2ee' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[3588883200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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'>...</div><input id='attrs-c17ad943-c6c1-4814-add6-27d9f57c65b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c17ad943-c6c1-4814-add6-27d9f57c65b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b19b42-e7b7-4943-9bbd-2353d6eb250a' class='xr-var-data-in' type='checkbox'><label for='data-50b19b42-e7b7-4943-9bbd-2353d6eb250a' 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>comment :</span></dt><dd>A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.</dd><dt><span>coverage_content_type :</span></dt><dd>thematicClassification</dd><dt><span>flag_meanings :</span></dt><dd>valid-water land missing ice</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 4]</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>pixel characteristics flag array</dd><dt><span>units :</span></dt><dd>pixel_classification</dd></dl></div><div class='xr-var-data'><pre>[3588883200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-328bcfd8-70cc-4320-929b-e4e0d0eebdc4' class='xr-section-summary-in' type='checkbox'  ><label for='section-328bcfd8-70cc-4320-929b-e4e0d0eebdc4' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a74a29aa-4664-4dd0-8440-409d4b21e5f9' class='xr-index-data-in' type='checkbox'/><label for='index-a74a29aa-4664-4dd0-8440-409d4b21e5f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.024999618530273,  -8.074999809265137,              -8.125,\n",
-       "        -8.175000190734863,  -8.225000381469727,  -8.274999618530273,\n",
-       "        -8.324999809265137,              -8.375,  -8.425000190734863,\n",
-       "        -8.475000381469727,\n",
-       "       ...\n",
-       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
-       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
-       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
-       "       -25.975000381469727],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b6f6305-44f9-40e2-9ded-f118c0334e84' class='xr-index-data-in' type='checkbox'/><label for='index-9b6f6305-44f9-40e2-9ded-f118c0334e84' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([133.02499389648438,  133.0749969482422,            133.125,\n",
-       "        133.1750030517578, 133.22500610351562, 133.27499389648438,\n",
-       "        133.3249969482422,            133.375,  133.4250030517578,\n",
-       "       133.47500610351562,\n",
-       "       ...\n",
-       "       168.52499389648438,  168.5749969482422,            168.625,\n",
-       "        168.6750030517578, 168.72500610351562, 168.77499389648438,\n",
-       "        168.8249969482422,            168.875,  168.9250030517578,\n",
-       "       168.97500610351562],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50b0bdb8-c3a8-4fa9-973d-147780a24fb4' class='xr-index-data-in' type='checkbox'/><label for='index-50b0bdb8-c3a8-4fa9-973d-147780a24fb4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1985-03-25 12:00:00&#x27;, &#x27;1985-03-26 12:00:00&#x27;,\n",
-       "               &#x27;1985-03-27 12:00:00&#x27;, &#x27;1985-03-28 12:00:00&#x27;,\n",
-       "               &#x27;1985-03-29 12:00:00&#x27;, &#x27;1985-03-30 12:00:00&#x27;,\n",
-       "               &#x27;1985-03-31 12:00:00&#x27;, &#x27;1985-04-01 12:00:00&#x27;,\n",
-       "               &#x27;1985-04-02 12:00:00&#x27;, &#x27;1985-04-03 12:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-02-10 12:00:00&#x27;, &#x27;2023-02-11 12:00:00&#x27;,\n",
-       "               &#x27;2023-02-12 12:00:00&#x27;, &#x27;2023-02-13 12:00:00&#x27;,\n",
-       "               &#x27;2023-02-14 12:00:00&#x27;, &#x27;2023-02-15 12:00:00&#x27;,\n",
-       "               &#x27;2023-02-16 12:00:00&#x27;, &#x27;2023-02-17 12:00:00&#x27;,\n",
-       "               &#x27;2023-02-18 12:00:00&#x27;, &#x27;2023-02-19 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=13846, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d073d5a-0ff5-418f-9b7e-4f9a059b2683' class='xr-section-summary-in' type='checkbox'  ><label for='section-8d073d5a-0ff5-418f-9b7e-4f9a059b2683' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>acknowledgement :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.</dd><dt><span>contributor_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>contributor_role :</span></dt><dd>Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive</dd><dt><span>creator_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>creator_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>creator_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://coralreefwatch.noaa.gov/</dd><dt><span>date_created :</span></dt><dd>20180301T120000Z</dd><dt><span>date_issued :</span></dt><dd>20200302T152652Z</dd><dt><span>date_metadata_modified :</span></dt><dd>20190405T120000Z</dd><dt><span>date_modified :</span></dt><dd>20180301T120000Z</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:32663</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.9749984741211</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.9749984741211</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.97500610351562</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.97500610351562</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Tue Feb 21 09:58:33 2023: ncks -d lat,-26.0,-8.0 -d lon,133.0,169.0 ct5km_dhw_v3.1_19850325.nc ct5km_dhw_v3.1_19850325_gbr.nc\n",
-       "This is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).</dd><dt><span>id :</span></dt><dd>Satellite_Daily_Global_5km_Degree_Heating_Week</dd><dt><span>institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>instrument :</span></dt><dd>ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake</dd><dt><span>instrument_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Instruments</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Water Temperature,  Spectral/Engineering &gt; Infrared Wavelengths &gt; Thermal Infrared, Oceans &gt; Ocean Temperature &gt; Degree Heating Week</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.</dd><dt><span>metadata_link :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.coralreefwatch</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>pangeo-forge:inputs_hash :</span></dt><dd>d80b5196a680325dc2d775d8aee7607cb2f6e5196b8a553b6e2489545a292ba8</dd><dt><span>pangeo-forge:recipe_hash :</span></dt><dd>d7a1649301fe73de160a8d8d28d650270ceca8b83c0029aa4d9e11ea5934c9fe</dd><dt><span>pangeo-forge:version :</span></dt><dd>0.9.4</dd><dt><span>platform :</span></dt><dd>Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.</dd><dt><span>platform_vocabulary :</span></dt><dd>NOAA NODC Ocean Archive System Platforms</dd><dt><span>processing_level :</span></dt><dd>Derived from L4 satellite sea surface temperaure analysis</dd><dt><span>product_version :</span></dt><dd>3.1</dd><dt><span>program :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>project :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_email :</span></dt><dd>coralreefwatch@noaa.gov</dd><dt><span>publisher_institution :</span></dt><dd>NOAA/NESDIS/STAR Coral Reef Watch Program</dd><dt><span>publisher_name :</span></dt><dd>NOAA Coral Reef Watch Program</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://coralreefwatch.noaa.gov</dd><dt><span>references :</span></dt><dd>https://coralreefwatch.noaa.gov/product/5km/index.php</dd><dt><span>source :</span></dt><dd>Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v27</dd><dt><span>summary :</span></dt><dd>This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_end :</span></dt><dd>19850326T000000Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>19850325T000000Z</dd><dt><span>title :</span></dt><dd>NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week </dd><dt><span>uuid :</span></dt><dd>24c7f597-298c-4be6-a0c2-ad97b993f60f</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:              (time: 13846, lat: 360, lon: 720)\n",
-       "Coordinates:\n",
-       "  * lat                  (lat) float32 -8.025 -8.075 -8.125 ... -25.92 -25.98\n",
-       "  * lon                  (lon) float32 133.0 133.1 133.1 ... 168.9 168.9 169.0\n",
-       "  * time                 (time) datetime64[ns] 1985-03-25T12:00:00 ... 2023-0...\n",
-       "Data variables:\n",
-       "    crs                  int16 ...\n",
-       "    degree_heating_week  (time, lat, lon) float32 ...\n",
-       "    mask                 (time, lat, lon) float32 ...\n",
-       "Attributes: (12/62)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    NCO:                        netCDF Operators version 5.0.6 (Homepage = ht...\n",
-       "    acknowledgement:            NOAA Coral Reef Watch Program\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    This is a product of the NOAA Coral Reef Watc...\n",
-       "    contributor_name:           NOAA Coral Reef Watch Program\n",
-       "    ...                         ...\n",
-       "    time_coverage_duration:     P1D\n",
-       "    time_coverage_end:          19850326T000000Z\n",
-       "    time_coverage_resolution:   P1D\n",
-       "    time_coverage_start:        19850325T000000Z\n",
-       "    title:                      NOAA Coral Reef Watch Daily Global 5km Satell...\n",
-       "    uuid:                       24c7f597-298c-4be6-a0c2-ad97b993f60f"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Path to zarr files\n",
-    "zarr_path = \"s3://rimrep-data-public/016-023-noaa-crw/v1.zarr\"\n",
-    "\n",
-    "# Connection to public S3 bucket - No credentials needed\n",
-    "s3 = s3fs.S3FileSystem(anon = True)\n",
-    "\n",
-    "# Loading zarr file\n",
-    "data = xr.open_dataset(s3fs.S3Map(zarr_path, s3 = s3), engine = \"zarr\")\n",
-    "\n",
-    "#Checking result\n",
-    "data"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the above dataset has three dimensions: latitude (`lat`), longitude (`lon`) and time (`time`). This is a global dataset, with a spatial resolution of 5 km, and a daily temporal resolution between March 1985 and February 2023.  \n",
-    "  \n",
-    "We can see that there are two variables in the dataset: `degree heating week` (DHW) and `mask`. The `DHW` shows accumulated heat stress over a 12-week period, which can lead to coral bleaching and death. The scale ranges from $0^{\\circ}C$ to $20^{\\circ}C$-weeks. The `mask` includes the location of landmasses, grid cells covered by ice, and any grid cells with missing data.\n",
-    "  \n",
-    "We can get more information about each variable included in the dataset by calling their attributes as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'comment': 'A 2D array, in the same size as the data array in the X and Y directions, classifying land, missing, and ice pixels that are all flagged by the same missing_value (specified by the variable attribute _FillValue) in the data array(s), along with water (data) pixels.',\n",
-       " 'coverage_content_type': 'thematicClassification',\n",
-       " 'flag_meanings': 'valid-water land missing ice',\n",
-       " 'flag_values': [0, 1, 2, 4],\n",
-       " 'grid_mapping': 'crs',\n",
-       " 'long_name': 'pixel characteristics flag array',\n",
-       " 'units': 'pixel_classification'}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.mask.attrs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also check the metadata for the entire dataset by calling the dataset attributes as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Conventions': 'CF-1.6, ACDD-1.3',\n",
-       " 'NCO': 'netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)',\n",
-       " 'acknowledgement': 'NOAA Coral Reef Watch Program',\n",
-       " 'cdm_data_type': 'Grid',\n",
-       " 'comment': 'This is a product of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1.',\n",
-       " 'contributor_name': 'NOAA Coral Reef Watch Program',\n",
-       " 'contributor_role': 'Collecting source data and deriving products; performing quality control of products; disseminating, storing, and submitting data to archive',\n",
-       " 'creator_email': 'coralreefwatch@noaa.gov',\n",
-       " 'creator_institution': 'NOAA/NESDIS/STAR Coral Reef Watch Program',\n",
-       " 'creator_name': 'NOAA Coral Reef Watch Program',\n",
-       " 'creator_type': 'group',\n",
-       " 'creator_url': 'https://coralreefwatch.noaa.gov/',\n",
-       " 'date_created': '20180301T120000Z',\n",
-       " 'date_issued': '20200302T152652Z',\n",
-       " 'date_metadata_modified': '20190405T120000Z',\n",
-       " 'date_modified': '20180301T120000Z',\n",
-       " 'geospatial_bounds': 'POLYGON((-90.0 180.0, 90.0 180.0, 90.0 -180.0, -90.0 -180.0, -90.0 180.0))',\n",
-       " 'geospatial_bounds_crs': 'EPSG:32663',\n",
-       " 'geospatial_lat_max': 89.9749984741211,\n",
-       " 'geospatial_lat_min': -89.9749984741211,\n",
-       " 'geospatial_lat_resolution': 0.05000000074505806,\n",
-       " 'geospatial_lat_units': 'degrees_north',\n",
-       " 'geospatial_lon_max': 179.97500610351562,\n",
-       " 'geospatial_lon_min': -179.97500610351562,\n",
-       " 'geospatial_lon_resolution': 0.05000000074505806,\n",
-       " 'geospatial_lon_units': 'degrees_east',\n",
-       " 'history': 'Tue Feb 21 09:58:33 2023: ncks -d lat,-26.0,-8.0 -d lon,133.0,169.0 ct5km_dhw_v3.1_19850325.nc ct5km_dhw_v3.1_19850325_gbr.nc\\nThis is a product data file of the NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 (v3.1) in its NetCDF Version 1.0 (v1.0).',\n",
-       " 'id': 'Satellite_Daily_Global_5km_Degree_Heating_Week',\n",
-       " 'institution': 'NOAA/NESDIS/STAR Coral Reef Watch Program',\n",
-       " 'instrument': 'ATSR-1, ATSR-2, AATSR, AVHRR, AVHRR-2, AVHRR-3, VIIRS, GOES Imager, MTSAT Imager, MTSAT 2 Imager, AHI, ABI, SEVIRI, buoy - moored buoy, buoy - drifting buoy, buoy - TAO buoy, surface seawater intake',\n",
-       " 'instrument_vocabulary': 'NOAA NODC Ocean Archive System Instruments',\n",
-       " 'keywords': 'Oceans > Ocean Temperature > Water Temperature,  Spectral/Engineering > Infrared Wavelengths > Thermal Infrared, Oceans > Ocean Temperature > Degree Heating Week',\n",
-       " 'keywords_vocabulary': 'NASA Global Change Master Directory (GCMD) Science Keywords',\n",
-       " 'license': 'The data produced by Coral Reef Watch are available for use without restriction, but Coral Reef Watch relies on the ethics and integrity of the user to ensure that the source of the data and products is appropriately cited and credited. When using these data and products, credit and courtesy should be given to NOAA Coral Reef Watch. Please include the appropriate DOI associated with this dataset in the citation. For more information, visit the NOAA Coral Reef Watch website: https://coralreefwatch.noaa.gov. Recommendations for citing and providing credit are provided at https://coralreefwatch.noaa.gov/satellite/docs/recommendations_crw_citation.php. Users are referred to the footer section of the Coral Reef Watch website (https://coralreefwatch.noaa.gov/index.php) for disclaimers, policies, notices pertaining to the use of the data.',\n",
-       " 'metadata_link': 'https://coralreefwatch.noaa.gov/product/5km/index.php',\n",
-       " 'naming_authority': 'gov.noaa.coralreefwatch',\n",
-       " 'ncei_template_version': 'NCEI_NetCDF_Grid_Template_v2.0',\n",
-       " 'pangeo-forge:inputs_hash': 'd80b5196a680325dc2d775d8aee7607cb2f6e5196b8a553b6e2489545a292ba8',\n",
-       " 'pangeo-forge:recipe_hash': 'd7a1649301fe73de160a8d8d28d650270ceca8b83c0029aa4d9e11ea5934c9fe',\n",
-       " 'pangeo-forge:version': '0.9.4',\n",
-       " 'platform': 'Ships, drifting buoys, moored buoys, TOGA-TAO buoy arrays, GOES-8 satellite, GOES-9 satellite, GOES-10 satellite, GOES-11 satellite, GOES-12 satellite, GOES-13 satellite, GOES-14 satellite, GOES-15 satellite, GOES-16 satellite, MTSAT-1R satellite, MTSAT-2 satellite, Himawari-8 satellite, Meteosat-8 satellite, Meteosat-9 satellite, Meteoset-10 satellite, Meteosat-11 satellite, Suomi NPP, MetOp-A satellite, MetOp-B satellite, NOAA-9 satellite, NOAA-11 satellite, NOAA-12 satellite, NOAA-14 satellite, NOAA-15 satellite, NOAA-16 satellite, NOAA-17 satellite, NOAA-18 satellite, NOAA-19 satellite.',\n",
-       " 'platform_vocabulary': 'NOAA NODC Ocean Archive System Platforms',\n",
-       " 'processing_level': 'Derived from L4 satellite sea surface temperaure analysis',\n",
-       " 'product_version': '3.1',\n",
-       " 'program': 'NOAA Coral Reef Watch Program',\n",
-       " 'project': 'NOAA Coral Reef Watch Program',\n",
-       " 'publisher_email': 'coralreefwatch@noaa.gov',\n",
-       " 'publisher_institution': 'NOAA/NESDIS/STAR Coral Reef Watch Program',\n",
-       " 'publisher_name': 'NOAA Coral Reef Watch Program',\n",
-       " 'publisher_type': 'group',\n",
-       " 'publisher_url': 'https://coralreefwatch.noaa.gov',\n",
-       " 'references': 'https://coralreefwatch.noaa.gov/product/5km/index.php',\n",
-       " 'source': 'Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature v3.1 (CoralTemp v3.1)',\n",
-       " 'spatial_resolution': '0.05 degrees',\n",
-       " 'standard_name_vocabulary': 'CF Standard Name Table v27',\n",
-       " 'summary': 'This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite',\n",
-       " 'time_coverage_duration': 'P1D',\n",
-       " 'time_coverage_end': '19850326T000000Z',\n",
-       " 'time_coverage_resolution': 'P1D',\n",
-       " 'time_coverage_start': '19850325T000000Z',\n",
-       " 'title': 'NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Degree Heating Week ',\n",
-       " 'uuid': '24c7f597-298c-4be6-a0c2-ad97b993f60f'}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.attrs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting data\n",
-    "Plotting data is easy with `xarray`, we can do it with a single line of code. Below, we will select the first time step of the `DHW` variable and plot a simple map using the `plot` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x22756b87a90>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.degree_heating_week.isel(time = 0).plot()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simple calculations using the entire grid\n",
-    "Below, we will create a new variable that will contain the `DHW` data for 2022. We will then calulate a monthly mean and finally we will plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\n",
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-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
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-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;degree_heating_week&#x27; (time: 365, lat: 360, lon: 720)&gt;\n",
-       "[94608000 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 -8.025 -8.075 -8.125 -8.175 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 133.0 133.1 133.1 133.2 ... 168.8 168.9 168.9 169.0\n",
-       "  * time     (time) datetime64[ns] 2022-01-01T12:00:00 ... 2022-12-31T12:00:00\n",
-       "Attributes:\n",
-       "    coverage_content_type:  physicalMeasurement\n",
-       "    grid_mapping:           crs\n",
-       "    long_name:              degree heating week\n",
-       "    standard_name:          N/A\n",
-       "    units:                  degree_Celsius_weeks\n",
-       "    valid_max:              10000\n",
-       "    valid_min:              0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'degree_heating_week'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-81a813d7-c2f8-4df0-b138-1e2b431edc9c' class='xr-array-in' type='checkbox' checked><label for='section-81a813d7-c2f8-4df0-b138-1e2b431edc9c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[94608000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-4de951cc-5520-41b2-87f2-e5387fa6c1ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4de951cc-5520-41b2-87f2-e5387fa6c1ab' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.025 -8.075 ... -25.92 -25.98</div><input id='attrs-b325ea64-2e47-4892-9a3a-aadaa8dc31e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b325ea64-2e47-4892-9a3a-aadaa8dc31e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b93fb282-3ed6-40e8-94bd-e1ba982d7185' class='xr-var-data-in' type='checkbox'><label for='data-b93fb282-3ed6-40e8-94bd-e1ba982d7185' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.025,  -8.075,  -8.125, ..., -25.875, -25.925, -25.975],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>133.0 133.1 133.1 ... 168.9 169.0</div><input id='attrs-40655a11-aa6b-4520-a5d9-f7634f1b47ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40655a11-aa6b-4520-a5d9-f7634f1b47ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eab07870-d5c9-4d8a-bb5c-4d397abef139' class='xr-var-data-in' type='checkbox'><label for='data-eab07870-d5c9-4d8a-bb5c-4d397abef139' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([133.025, 133.075, 133.125, ..., 168.875, 168.925, 168.975],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T12:00:00 ... 2022-12-...</div><input id='attrs-d4a8dbbe-4795-4ee3-a3a5-d552cb49706f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4a8dbbe-4795-4ee3-a3a5-d552cb49706f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ceabef-3022-46f7-9bb3-ca0d3a978c04' class='xr-var-data-in' type='checkbox'><label for='data-19ceabef-3022-46f7-9bb3-ca0d3a978c04' 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>axis :</span></dt><dd>T</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>reference time of the data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T12:00:00.000000000&#x27;, &#x27;2022-01-02T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-03T12:00:00.000000000&#x27;, ..., &#x27;2022-12-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-12-30T12:00:00.000000000&#x27;, &#x27;2022-12-31T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3698de66-0b0d-49e1-96a4-40e86be6fb86' class='xr-section-summary-in' type='checkbox'  ><label for='section-3698de66-0b0d-49e1-96a4-40e86be6fb86' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7152fa90-4e3e-4c11-9dd7-d6357e051c34' class='xr-index-data-in' type='checkbox'/><label for='index-7152fa90-4e3e-4c11-9dd7-d6357e051c34' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.024999618530273,  -8.074999809265137,              -8.125,\n",
-       "        -8.175000190734863,  -8.225000381469727,  -8.274999618530273,\n",
-       "        -8.324999809265137,              -8.375,  -8.425000190734863,\n",
-       "        -8.475000381469727,\n",
-       "       ...\n",
-       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
-       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
-       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
-       "       -25.975000381469727],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c786b41a-a37b-44fe-817d-a289f80e992e' class='xr-index-data-in' type='checkbox'/><label for='index-c786b41a-a37b-44fe-817d-a289f80e992e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([133.02499389648438,  133.0749969482422,            133.125,\n",
-       "        133.1750030517578, 133.22500610351562, 133.27499389648438,\n",
-       "        133.3249969482422,            133.375,  133.4250030517578,\n",
-       "       133.47500610351562,\n",
-       "       ...\n",
-       "       168.52499389648438,  168.5749969482422,            168.625,\n",
-       "        168.6750030517578, 168.72500610351562, 168.77499389648438,\n",
-       "        168.8249969482422,            168.875,  168.9250030517578,\n",
-       "       168.97500610351562],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c39274d6-0a03-4a95-9b13-a1671900a2fb' class='xr-index-data-in' type='checkbox'/><label for='index-c39274d6-0a03-4a95-9b13-a1671900a2fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 12:00:00&#x27;, &#x27;2022-01-02 12:00:00&#x27;,\n",
-       "               &#x27;2022-01-03 12:00:00&#x27;, &#x27;2022-01-04 12:00:00&#x27;,\n",
-       "               &#x27;2022-01-05 12:00:00&#x27;, &#x27;2022-01-06 12:00:00&#x27;,\n",
-       "               &#x27;2022-01-07 12:00:00&#x27;, &#x27;2022-01-08 12:00:00&#x27;,\n",
-       "               &#x27;2022-01-09 12:00:00&#x27;, &#x27;2022-01-10 12:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2022-12-22 12:00:00&#x27;, &#x27;2022-12-23 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-24 12:00:00&#x27;, &#x27;2022-12-25 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-26 12:00:00&#x27;, &#x27;2022-12-27 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-28 12:00:00&#x27;, &#x27;2022-12-29 12:00:00&#x27;,\n",
-       "               &#x27;2022-12-30 12:00:00&#x27;, &#x27;2022-12-31 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e330419-e307-4782-beee-34adaa1d7693' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e330419-e307-4782-beee-34adaa1d7693' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'degree_heating_week' (time: 365, lat: 360, lon: 720)>\n",
-       "[94608000 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 -8.025 -8.075 -8.125 -8.175 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 133.0 133.1 133.1 133.2 ... 168.8 168.9 168.9 169.0\n",
-       "  * time     (time) datetime64[ns] 2022-01-01T12:00:00 ... 2022-12-31T12:00:00\n",
-       "Attributes:\n",
-       "    coverage_content_type:  physicalMeasurement\n",
-       "    grid_mapping:           crs\n",
-       "    long_name:              degree heating week\n",
-       "    standard_name:          N/A\n",
-       "    units:                  degree_Celsius_weeks\n",
-       "    valid_max:              10000\n",
-       "    valid_min:              0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Subsetting data\n",
-    "dhw_2022 = data.degree_heating_week.sel(time = \"2022\")\n",
-    "\n",
-    "# Checking result\n",
-    "dhw_2022"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now group our 2022 data per calendar month and calculate the monthly mean."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
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-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[2.6664283 , 2.6496432 , 2.6275    , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [2.942857  , 2.850714  , 2.7889283 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [2.9878566 , 2.9675007 , 2.8671424 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "...\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[5.4564514 , 5.6516137 , 5.996451  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [5.705484  , 6.0358076 , 6.2387104 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [6.3832245 , 6.4306464 , 6.482257  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 -8.025 -8.075 -8.125 -8.175 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 133.0 133.1 133.1 133.2 ... 168.8 168.9 168.9 169.0\n",
-       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "Attributes:\n",
-       "    coverage_content_type:  physicalMeasurement\n",
-       "    grid_mapping:           crs\n",
-       "    long_name:              degree heating week\n",
-       "    standard_name:          N/A\n",
-       "    units:                  degree_Celsius_weeks\n",
-       "    valid_max:              10000\n",
-       "    valid_min:              0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'degree_heating_week'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a059dcf4-2ba1-4d85-b468-2980175ff475' class='xr-array-in' type='checkbox' checked><label for='section-a059dcf4-2ba1-4d85-b468-2980175ff475' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.97 3.96 3.95 3.93 4.08 3.93 3.93 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[3.9700007 , 3.9599993 , 3.9499989 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [4.29      , 4.14      , 4.120001  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [4.3499994 , 4.3399982 , 4.1600018 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[2.6664283 , 2.6496432 , 2.6275    , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [2.942857  , 2.850714  , 2.7889283 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [2.9878566 , 2.9675007 , 2.8671424 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "...\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[5.4564514 , 5.6516137 , 5.996451  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [5.705484  , 6.0358076 , 6.2387104 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [6.3832245 , 6.4306464 , 6.482257  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-d839f4fd-83da-4f04-9229-ed1877c77991' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d839f4fd-83da-4f04-9229-ed1877c77991' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.025 -8.075 ... -25.92 -25.98</div><input id='attrs-c3c15e29-2fdf-48fe-91d3-dafa114e25de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3c15e29-2fdf-48fe-91d3-dafa114e25de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-960a636d-caa6-4a3d-a0ad-e11a1ada9e27' class='xr-var-data-in' type='checkbox'><label for='data-960a636d-caa6-4a3d-a0ad-e11a1ada9e27' 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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>89.9749984741211</dd><dt><span>valid_min :</span></dt><dd>-89.9749984741211</dd></dl></div><div class='xr-var-data'><pre>array([ -8.025,  -8.075,  -8.125, ..., -25.875, -25.925, -25.975],\n",
-       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>133.0 133.1 133.1 ... 168.9 169.0</div><input id='attrs-a1e62145-13de-4abb-8132-30fc90a6a41d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1e62145-13de-4abb-8132-30fc90a6a41d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a1a572d-d903-4b04-a397-e31daf4c7c8f' class='xr-var-data-in' type='checkbox'><label for='data-7a1a572d-d903-4b04-a397-e31daf4c7c8f' 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>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>equirectangular projection and grid centers</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>179.97500610351562</dd><dt><span>valid_min :</span></dt><dd>-179.97500610351562</dd></dl></div><div class='xr-var-data'><pre>array([133.025, 133.075, 133.125, ..., 168.875, 168.925, 168.975],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-d7fef97f-0888-4195-8f1e-4d9cb6e3d7ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7fef97f-0888-4195-8f1e-4d9cb6e3d7ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c361d7e-7097-4aec-9ab5-3c26ea263f8a' class='xr-var-data-in' type='checkbox'><label for='data-0c361d7e-7097-4aec-9ab5-3c26ea263f8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22543322-5334-46c5-91e6-fe8a30c3423b' class='xr-section-summary-in' type='checkbox'  ><label for='section-22543322-5334-46c5-91e6-fe8a30c3423b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac671e1d-1ae2-432f-a009-a4fe4c74dc09' class='xr-index-data-in' type='checkbox'/><label for='index-ac671e1d-1ae2-432f-a009-a4fe4c74dc09' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -8.024999618530273,  -8.074999809265137,              -8.125,\n",
-       "        -8.175000190734863,  -8.225000381469727,  -8.274999618530273,\n",
-       "        -8.324999809265137,              -8.375,  -8.425000190734863,\n",
-       "        -8.475000381469727,\n",
-       "       ...\n",
-       "       -25.524999618530273, -25.575000762939453,             -25.625,\n",
-       "       -25.674999237060547, -25.725000381469727, -25.774999618530273,\n",
-       "       -25.825000762939453,             -25.875, -25.924999237060547,\n",
-       "       -25.975000381469727],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7342b24-43cd-4b75-b7f5-e81d910471b2' class='xr-index-data-in' type='checkbox'/><label for='index-c7342b24-43cd-4b75-b7f5-e81d910471b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([133.02499389648438,  133.0749969482422,            133.125,\n",
-       "        133.1750030517578, 133.22500610351562, 133.27499389648438,\n",
-       "        133.3249969482422,            133.375,  133.4250030517578,\n",
-       "       133.47500610351562,\n",
-       "       ...\n",
-       "       168.52499389648438,  168.5749969482422,            168.625,\n",
-       "        168.6750030517578, 168.72500610351562, 168.77499389648438,\n",
-       "        168.8249969482422,            168.875,  168.9250030517578,\n",
-       "       168.97500610351562],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed8a8e6a-944d-4589-9f44-c7c59881fdcd' class='xr-index-data-in' type='checkbox'/><label for='index-ed8a8e6a-944d-4589-9f44-c7c59881fdcd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8092498f-0d67-4a78-af5a-b067252fc2bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8092498f-0d67-4a78-af5a-b067252fc2bb' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>degree heating week</dd><dt><span>standard_name :</span></dt><dd>N/A</dd><dt><span>units :</span></dt><dd>degree_Celsius_weeks</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'degree_heating_week' (month: 12, lat: 360, lon: 720)>\n",
-       "array([[[3.9700007 , 3.9599993 , 3.9499989 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [4.29      , 4.14      , 4.120001  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [4.3499994 , 4.3399982 , 4.1600018 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[2.6664283 , 2.6496432 , 2.6275    , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [2.942857  , 2.850714  , 2.7889283 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [2.9878566 , 2.9675007 , 2.8671424 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "...\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[5.4564514 , 5.6516137 , 5.996451  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [5.705484  , 6.0358076 , 6.2387104 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [6.3832245 , 6.4306464 , 6.482257  , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [       nan,        nan,        nan, ..., 0.        ,\n",
-       "         0.        , 0.        ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 -8.025 -8.075 -8.125 -8.175 ... -25.88 -25.92 -25.98\n",
-       "  * lon      (lon) float32 133.0 133.1 133.1 133.2 ... 168.8 168.9 168.9 169.0\n",
-       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "Attributes:\n",
-       "    coverage_content_type:  physicalMeasurement\n",
-       "    grid_mapping:           crs\n",
-       "    long_name:              degree heating week\n",
-       "    standard_name:          N/A\n",
-       "    units:                  degree_Celsius_weeks\n",
-       "    valid_max:              10000\n",
-       "    valid_min:              0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Calculating monthly means\n",
-    "dhw_monthly_2022 = dhw_2022.groupby(\"time.month\").mean()\n",
-    "\n",
-    "# Checking results\n",
-    "dhw_monthly_2022"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how the `time` dimension has been replaced by `month`, which includes 12 time step, one for each month of the year. We can now plot all monthly means for 2022. We use the `plot` method, specify `month` in the `col` argument, which will create a subplot for each month in our data. Finally, we use the `col_wrap` argument to specify the number of columns in our plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x2275943ded0>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1300x900 with 13 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting monthly means\n",
-    "dhw_monthly_2022.plot(col = 'month', col_wrap = 4)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simple calculations at a single point over time\n",
-    "For this example, we will select a single point in the dataset and extract `DHW` values for all timesteps at that point to plot a time series. We will use the `sel` method to select the point nearest to `lat` = -20 and `lon` = 155. We will then subset the data temporally between 2020-01-01 and 2023-01-01, and plot it.  \n",
-    "  \n",
-    "We can do all this in a single line of code as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x227618516d0>]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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4fPmy2bGC/g50Op1F7axRo4bFw2SONmDAAPz222/4+++/0a1bt0LPq1q1Ktq2bYs///yzyOcrXbo0NBpNvusBAOLi4gCY3vNsERsbW2BAfP8xS/+GpDYW9rddUDBk63XtCRgMeYD//vsP//77L5YvX26WXTh37lyJn3vFihWFjtnfr6g30MjIyHwZptq1a1vdnsDAQHzzzTf48MMPcfXqVURGRqJMmTKoU6cOWrduXWzdUtmyZbFhwwbcuXMHt27dQlRUFPz9/fHpp5/iySeflM87cuQItFotmjZtavb4atWqITw8HP/995/VbQdyb1K3bt1CxYoV5eN6vT7fG3SZMmXQsGHDQmfIRUZGys9ZUAF2YWuuFPTJVyoI3rRpU4GPkYIXKRvw008/mRWgFiQqKgpLly4FAJw5cwY//PADpk2bhqysLCxevLjIx1orPDwcer0ed+/eNQuIhBC4detWvhuPu63hI103N2/eRKVKlcy+d+PGDbtmaTp16oRdu3YVe96gQYPMivMdQXpPsSTwEkIUe56/vz9q1KiBY8eO5fvesWPH4O/vj2rVqtnU1vDw8AL/5u4/ZunfkPQ7l4Knop5T4m7XtTMxGPIA0h/A/YXBBQ2RWPtJ1l7DZL6+vjZNgy1M6dKlUbp0aQDAr7/+itOnT8vT5S1RtmxZlC1bFoCp6DA1NRUvv/yy/P3IyEgYDAYcPHgQLVq0kI+fOXMGsbGx+W5IlpIyYd999x2aNWsmH//hhx/yDSP26NEDGzZsQPXq1eW+FiQmJgY//PADNm7caPbp+fvvv7e4XT169MD3338Pg8Fg1t/7de3aFd7e3jh//ny+obai1KpVC5MmTcKaNWssXtzOGp06dcLcuXPx7bffYuzYsfLxNWvWIDU1VZ4xVxxLsyfO9vDDDwMAvv32W7PA7uDBgzh58iTefvttu72Wq4bJCvLNN9/Ax8fH7G+lIBcvXsTevXstmtjwxBNPYOHChbh69SoqV64MwDRLcO3atejVq5fNE0E6duyIX3/9Fbdv35azOQaDId9UfUv/hmrXro3y5cvjhx9+wLhx4+TjV65cwb59++QPQ2QZBkMeoE6dOqhevTomTJgAIQTCwsKwfv16bN26Nd+5DRo0AAAsWrQIgwYNgo+PD2rXrl3osEV4eLhFC5mV1K5du+Qp3AaDAZcvX8ZPP/0EwHSzlz7tr1mzBjdu3MADDzyAjIwM7Ny5E4sWLcKIESPQu3dvs+esUaMGAPMMmbQ+UfXq1ZGQkICNGzdi6dKlmD17tlkW6Pnnn8eCBQvQt29fTJo0CbVr18aFCxcwe/ZsBAYGYsSIETb184EHHsCAAQOwcOFC+Pj4oHPnzvjvv//k2R95zZgxA1u3bkXr1q3xyiuvoHbt2sjIyMClS5ewYcMGLF68GJUqVcKgQYOwYMECDBgwADNnzkSNGjWwceNGbN68GYBln6r/7//+D9999x26d++OMWPG4KGHHoKPjw+uXbuGHTt2oHfv3njiiSdQtWpVzJgxA2+//TYuXLiARx99FKVLl8bt27dx4MABBAYGYvr06Th69ChefvllPPXUU6hZsyZ8fX2xfft2HD16FBMmTLDpZ1eULl26oGvXrnjzzTeRlJSENm3ayLPJmjRpgueee86i52nQoAF27tyJ9evXo0KFCggODrYpg1mYDh06YNeuXRYNQ+VVu3ZtvPjii/joo4/g5eWFbt26ybPJKleubBYAlpQ9+3v37l05yyRlYzZu3IiIiAhEREQgJiYGgGntH2k2VqVKlXDnzh0sXboUW7ZswbRp08wCr86dO6N9+/Zo2LAhQkJCcOzYMcydOxcajQbvvPOO2etLWa68HzTGjx+Pb775Bo899hhmzJgBnU6Hd999FxkZGZg2bZrNfZ00aRJ+/fVXPPzww5gyZQoCAgLwySefIDU11ew8S/+GvLy8MH36dAwfPhxPPvkkhgwZgoSEBEyfPh0VKlSw6O96xowZmDFjBrZt2yb/rHft2oVOnTphypQpmDJlinyut7c3YmJisG3bNpt/BormutptcpSCZpOdOHFCdOnSRQQHB4vSpUuLp556Sly5cqXAmQ8TJ04UkZGRwsvLSwAQO3bscGr7C1LUDJa87Vu3bp1o3LixCAwMFP7+/qJ58+Zi6dKl8qyivKKiosxmXAghxOeffy4eeOABERAQIIKCgkS7du3Ezz//XGCbzp49K5577jlRtWpVodPpRJUqVcQzzzwjz+IqSmGzyYQQIjMzU7z22muibNmyws/PT7Rs2VLs379fREVF5Zuhc/fuXfHKK6+I6Oho4ePjI8LCwkSzZs3E22+/LVJSUuTzrly5Ivr06SOCgoJEcHCw6Nu3r9iwYUO+2YSDBg0SgYGBBbYrOztbvP/++6JRo0bCz89PBAUFiTp16ojhw4eLs2fPmp37888/i44dO4qQkBCh0+lEVFSUePLJJ8Xvv/8uhBDi9u3bYvDgwaJOnToiMDBQBAUFiYYNG4oFCxYIvV5f7M/v/p/j3bt3zY4X9DeQnp4u3nzzTREVFSV8fHxEhQoVxMiRI0V8fLzZY6OiosRjjz1W4OsdOXJEtGnTRgQEBAgAIiYmxuz1Dh48aHb+jh07Cv0bKmg2WbNmzUT58uUt7ndeBoNBvPfee6JWrVrCx8dHlClTRgwYMEBcvXq12NcVwvS7v//vwdGkn09B/6SfrRBC/Prrr6Jt27YiIiJCeHt7i+DgYNGuXTuxatWqfM/56quvirp164rg4GDh7e0tIiMjxYABAwqcHSi9r9zv3Llz4vHHHxchISEiICBAdOrUSfzzzz8W9amg91TJ3r17RcuWLYVOpxPly5cXr7/+uliyZEmBs+iK+xuSLFmyRNSoUUP4+vqKWrVqia+++kr07t1bNGnSRD5Hmk02b948s8dK11He61P6ndzfh/t/J2qjEcLKjyBEVGLTpk3D9OnTkZ2dDY1GA61W6/Q2SGuZXLlyxeZhPbKe0WiE0WhEp06dEBsbK9eXJScnIywsDAsXLsSoUaNc3EqylUajweTJkzFlyhSrZ8TaQ0JCAmrVqoXHH38cS5YscepruzMOkxG5kI+PDwIDA5GSkuLQ1/n4448BmIZMs7OzsX37dnz44YcYMGAAAyEn69Onjzy9u169evLxP/74AxUrVjSb4k/u6Z133sE777yDefPmYfz48Q57nVu3bmHWrFno2LEjwsPDcfnyZSxYsADJyckYM2aMw15XjZgZInKBGzduyNs0aLVah2/a+dVXX2HBggW4dOkSMjMzUaVKFfTr1w+TJk0qcuNbV5GyJ0Vx9IrmjnL+/Hl502B/f3+zgIjc399//y3/f+XKlQtdwsIe4uPjMXDgQBw8eBBxcXEICAhAy5YtMX369CInOlB+DIaISHGkYcSiXLx40Wz/JSIiWzEYIiLFyZs5K0zDhg0VmdUiIvfDYIiIiIg8muPXSyciIiJSMPesQHQio9GIGzduIDg4mEuZExERuQkhBJKTkxEZGVnsIpQMhopx48YNeUl2IiIici9Xr14tdgkRBkPFkLahuHr1ar7tEIiIiEiZkpKSULly5UK3k8qLwVAxpKGxkJAQBkNERERuxpISFxZQExERkUdjMEREREQejcEQEREReTQGQ0REROTRGAwRERGRR2MwRERERB6NwRARERF5NAZDRERE5NEYDBEREZFHYzBEREREHo3BEBEREXk0BkNERETk0bhRqwfKyDbgzwuxyDYIi86vVS4IUeGBDm4VERGRazAY8kCz/ncS3/x52eLzA321OPB2ZwTqeLkQEZH68O7mgc7eSQYAVCsTiNAAnyLPPXYtEalZBtxMTEeNssHOaB4REZFTMRjyQPGp2QCA6b3roV3NiCLP7fj+Tly8l4rYlCzUKOuM1hERETkXC6g9UHxaFgCgdIBvseeGBfqaPYaIiEhtGAx5GCEEEtJMmaFSxQyRAbkBU2wqgyEiIlInBkMeJjXLgCyDEUBu1qco4VJmiMEQERGpFIMhD5OYbsoK+Wg18PfRFnt+6UBmhoiISN0YDHmYlAw9ACDYzwcajabY86XMUByDISIiUikGQx4mOcOUGQr2s2wiYWkGQ0REpHIMhjxMcqYpMxRk4QKKzAwREZHaMRjyMNIwmaXBEDNDRESkdgyGPEyyXDNkWTAUFsB1hoiISN3cOhj6448/0LNnT0RGRkKj0eDnn382+74QAtOmTUNkZCT8/f3RoUMHHD9+3DWNVYiUTKlmqPg1hgCgVKDpvIxsIzKyDQ5rFxERkau4dTCUmpqKRo0a4eOPPy7w+3PnzsX8+fPx8ccf4+DBgyhfvjy6dOmC5ORkJ7dUOawdJgvWeUPrZZp1Ji3WSEREpCZuvTdZt27d0K1btwK/J4TAwoUL8fbbb6NPnz4AgBUrVqBcuXJYuXIlhg8f7symKoZcQG3hMJlGo0Epfx/EpmYhPi0L5UP9HNk8IiIip3PrzFBRLl68iFu3buGRRx6Rj+l0OsTExGDfvn2FPi4zMxNJSUlm/9TE2pohIHfbDtYNERGRGqk2GLp16xYAoFy5cmbHy5UrJ3+vIHPmzEFoaKj8r3Llyg5tp7PJiy5aOEwG5O5PlshhMiIiUiHVBkOS+1dZFkIUufLyxIkTkZiYKP+7evWqo5voVClWDpMBeTNDDIaIiEh93LpmqCjly5cHYMoQVahQQT5+586dfNmivHQ6HXQ6ncPb5yryCtQ6y2aTAUApTq8nIiIVU21mKDo6GuXLl8fWrVvlY1lZWdi1axdat27twpa5lrUF1ABQOiczJG3ySkREpCZunRlKSUnBuXPn5K8vXryII0eOICwsDFWqVMGrr76K2bNno2bNmqhZsyZmz56NgIAA9OvXz4Wtdi1rp9YDeTJDXIWaiIhUyK2Dob///hsdO3aUvx43bhwAYNCgQVi+fDneeOMNpKen46WXXkJ8fDxatGiBLVu2IDg42FVNdrm0LNPCidYFQ6wZIiIi9XLrYKhDhw4QQhT6fY1Gg2nTpmHatGnOa5SCCSGQnrOKtL+v1uLHybPJ0pkZIiIi9VFtzRDll20QMBhNwaOfj+XBUCl/ZoaIiEi9GAx5kPSs3L3F/K0JhnIyQwmcTUZERCrEYMiDSENk3l4a+Hpb/qsvnbNZa0JadpHDkkRERO6IwZAHkeuFrMgKAUApf1NmSG8U8qKNREREasFgyINIw2R+VhRPA6Zia11OJok71xMRkdowGPIg6dmmrI61mSEgd0YZgyEiIlIbBkMeJD3LCAAIsDIzBHDneiIiUi8GQx5EqhmyZlq9hMEQERGpFYMhD2JrATXAYTIiIlIvBkMeJCPL+tWnJaUYDBERkUoxGPIgaVk5BdSsGSIiIpIxGPIg6dmmAmrbhsmkhRcZDBERkbowGPIgJakZkofJ0jlMRkRE6sJgyINk2LBjvYSbtRIRkVoxGPIgUs2QLVPrSwdys1YiIlInBkMepCSLLubWDDEzRERE6sJgyINklKBmKDRns9akjGwYjNy5noiI1IPBkAcpWQG1KTMkBJDIImoiIlIRBkMeRK4ZsmGYzEfrhWCdNwDWDRERkbowGPIg6TkrUAfaEAwBQGgAZ5QREZH6MBjyIKk5wVCAr7dNj8/dn4yZISIiUg8GQx4kLdM0TBaosy0zVIozyoiISIUYDHmQ3MyQbcGQlBni/mRERKQmDIY8SHoJh8mYGSIiIjViMOQhsvRGZBlMiy4G2hwMMTNERETqw2DIQ0hZIcC2vcmAPKtQc50hIiJSEQZDHiI1Z40hH60Gvt62/dpzh8mYGSIiIvVgMOQh0kpYLwTkGSZLZWaIiIjUg8GQh5BWn7Z1wUUACA9kzRAREakPgyEPkZqZkxnS2Z4ZkqbWx6ZmQQhu1kpEROrAYMhDpGebMkO2rjEEAOFBpmAoS2+Uh92IiIjcne1pAnIrcmaoBMFQgK83/Hy8kJFtxIfbziLYzxsNKpVCTK0IezWTiIjI6RgMeYjcmqGS/crLh/jhUmwaPv/jAgDA20uDfyZ1kTdxJSIicjcMhjyEPWqGAGD2Ew2w/uhNAAI/H76B9GwDbiVlMBgiIiK3xWDIQ6Rn5wRDPrYPkwFA6xpl0LpGGQDA35ficfZOCmJTMgEEl7SJRERELsECag+RmrNjfYCNO9YXRCqovpfKqfZEROS+GAx5CGn2V0lrhvIKD9IBAD7fdR4jv/0HP/1zzW7PTURE5CwcJvMQjsgMRYcHAgCO30jC8RtJ2H7qDvo2rQiNRmO31yAiInI0BkMeIs1ONUN5jehQHVXLBCI1U4+pvx5Hpt6IlEw9gv1YTE1ERO6Dw2QeIk3ODNkv/g3SeePJZpUwqHVV6HI2f01I475lRETkXhgMeYhUB9QM5SXtaJ+YzmCIiIjcC4MhD5Eu7Vpvx5qhvKR9y5gZIiIid8NgyEOk5qxAbc+aobxC/U2ZIe5oT0RE7obBkIdIy1mBOtCONUN5ScNkCRwmIyIiN8NgyEPImaESbNRalFL+pmGyRGaGiIjIzTAY8gBCCLlmyOGZIdYMERGRm2Ew5AGyDEbojQIA4O+gzJC0UWs8gyEiInIzDIY8gFQvBDiugFqaTZaYzmEyIiJyLwyGPIC0+rTO2wveWsf8ykv5MzNERETuicGQB5BWn3ZUvRAAhOQEQ8kZDIaIiMi9MBjyANLq0/4OGiIDcgOt1DxDckRERO6AwZAHyM0MOS4YCsoJhpgZIiIid8NgyAOkSVtxOGhfMgAI9svJDGUZIIRw2OsQERHZm+qDIb1ej0mTJiE6Ohr+/v6oVq0aZsyYAaPR6OqmOY204KIjM0PSMJnBKJCR7Tk/WyIicn+OSxUoxHvvvYfFixdjxYoVqFevHv7++288//zzCA0NxZgxY1zdPKdIk2uGHPfrDvDRQqMBhACSM7Mdtp4RERGRvak+GNq/fz969+6Nxx57DABQtWpVrFq1Cn///beLW+Y8KRmmzJA0lOUIXl4aBPl6IzlTbyqiDnbYSxEREdmV6ofJ2rZti23btuHMmTMAgH///Rd79uxB9+7dCzw/MzMTSUlJZv/cXYoTCqgBICgn2JKCLyIiIneg+szQm2++icTERNSpUwdarRYGgwGzZs3Cs88+W+D5c+bMwfTp053cSsdKdcI6Q3mfPzmTM8qIiMh9qD4ztHr1anz77bdYuXIlDh06hBUrVuD999/HihUrCjx/4sSJSExMlP9dvXrVyS22P6mAOsiBs8mA3On1XGuIiIjcieozQ6+//jomTJiA//u//wMANGjQAJcvX8acOXMwaNCgfOfrdDrodDpnN9OhkjOckxmSapJSmBkiIiI3ovrMUFpaGry8zLup1Wo9a2p9zjBZkAMLqIHczBBrhoiIyJ2oPjPUs2dPzJo1C1WqVEG9evVw+PBhzJ8/H0OGDHF105xGGrYKclLNUAqHyYiIyI2oPhj66KOPMHnyZLz00ku4c+cOIiMjMXz4cEyZMsXVTXOaFCcVUMuZIQ6TERGRG1F9MBQcHIyFCxdi4cKFrm6Ky8gF1A6eWi9vycHMEBERuRHV1wxRbg2P06bWs2aIiIjcCIMhDyANkzm8ZihnC470bAZDRETkPpw6THb06FGrH1O3bl14e6t+NM9h9AYjMvWmmXOODob8fTlMRkRE7sepUUbjxo2h0WgghLDofC8vL5w5cwbVqlVzcMvUK29g4vBhMikzlMVgiIiI3IfTUy5//fUXIiIiij1PCIH69es7oUXqJm2N4evtBR+tY0dFpZ3qpYJtIiIid+DUYCgmJgY1atRAqVKlLDq/ffv28Pf3d2yjVM5ZawwBuZmnNGaGiIjIjTg1GNqxY4dV52/YsMFBLfEcziqeBoCAnMxQGjNDRETkRhQzm8xgMODIkSOIj493dVNUxVk71gNAQE4BdRoLqImIyI24LBh69dVXsXTpUgCmQCgmJgZNmzZF5cqVsXPnTlc1S3VyM0OOXXARyC2gTss2WFwkT0RE5GouC4Z++uknNGrUCACwfv16XLx4EadOncKrr76Kt99+21XNUh1nbcUB5BZQG4xCns5PRESkdFYHQ0WtFfTzzz9b/Dz37t1D+fLlAZhqg5566inUqlULQ4cOxbFjx6xtFhUi1ak1Q7mvwSJqIiJyF1YHQ127dsWFCxfyHV+zZg369+9v8fOUK1cOJ06cgMFgwKZNm9C5c2cAQFpaGrRaxw/peApnBkNaLw38fEyXFIuoiYjIXVgdDI0cORKdOnXCzZs35WOrV6/GwIEDsXz5couf5/nnn8fTTz+N+vXrQ6PRoEuXLgBM6xDVqVPH2mZRIVJyipmdMUwG5CmiZmaIiIjchNV3yClTpiA2NhadO3fG7t27sWnTJrzwwgv45ptv0LdvX4ufZ9q0aahfvz6uXr2Kp556CjqdDgCg1WoxYcIEa5tFhUjJWXTRecGQFnGpDIaIiMh92HSHXLRoEZ577jm0bNkS169fx6pVq9C7d2+rnuPq1at48skn8x0fNGgQ/vzzT1uaRQXIXXTROUOP8lpDmRwmIyIi92BRMPTrr7/mO/b4449j165dePbZZ6HRaORzevXqZdELd+nSBXv37kV4eLjZ8b179+Kxxx5DQkKCRc9DRcudWu/jlNeThslSmRkiIiI3YVEw9Pjjjxf6va+++gpfffUVAECj0cBgsOwm2K5dOzzyyCPYuXMngoODAQB//PEHevTogenTp1v0HFS83EUXnZMZkl6HBdREROQuLCqgNhqNFv2zNBACgCVLliA6OhqPPfYYMjIysGPHDjz22GN45513MHbsWJs7ROacuR0HAPj7sICaiIjcS4kWXczIyLD5sRqNBqtWrYKfnx86deqEXr16Yc6cORgzZkxJmkT3ceaii6bXkTJDDIaIiMg9WB0MGQwGvPPOO6hYsSKCgoLkNYcmT54sb69RmKNHj5r9O3nyJKZOnYqrV69iwIABaN++vfw9sg9nrjMEsICaiIjcj9V3yFmzZmHFihWYO3cuhg0bJh9v0KABFixYgKFDhxb62MaNG0Oj0ZjtWyV9/fnnn2PJkiUQQlhVe0RFy51N5uR1hrL5+yMiIvdg9R3y66+/xpIlS9CpUyeMGDFCPt6wYUOcOnWqyMdevHjR+haSzYxG4fxhMmaGiIjIzVh9h7x+/Tpq1KiR77jRaER2dnaRj42KirL25agE8mZnnFZAzan1RETkZqyuGapXrx52796d7/iPP/6IJk2aWPVc33zzDdq0aYPIyEhcvnwZALBw4UL88ssv1jaLCiDVC3lpIO8Z5mhSAXU6gyEiInITVqcLpk6diueeew7Xr1+H0WjE2rVrcfr0aXz99df47bffLH6ezz77DFOmTMGrr76KWbNmyTVCpUqVwsKFC61e0ZryyztEptFonPKa/j6mYCiV6wwREZGbsDpd0LNnT6xevRobNmyARqPBlClTcPLkSaxfv17ebNUSH330Eb744gu8/fbbZrvUN2/eHMeOHbO2WVQAKTMU7KQhMiC3NolT64mIyF3YdJfs2rUrunbtWqIXvnjxYoHDajqdDqmpqSV6bjJJyXBu8TSQZ2o9M0NEROQmbCokSUhIwJdffom33noLcXFxAIBDhw7h+vXrFj9HdHQ0jhw5ku/4xo0bUbduXVuaRfdx9kwyIM/U+kxmhoiIyD1YfZc8evQoOnfujNDQUFy6dAkvvPACwsLCsG7dOly+fBlff/21Rc/z+uuvY9SoUcjIyIAQAgcOHMCqVaswZ84cfPnll1Z3hPKT6nacNZMMyC2gTuHUeiIichNW3yXHjRuHwYMHY+7cufIGqwDQrVs39OvXz+Lnef7556HX6/HGG28gLS0N/fr1Q8WKFbFo0SL83//9n7XNogKkOHnBRQAIlKbWMxgiIiI3YfVd8uDBg/j888/zHa9YsSJu3bpl1XMNGzYMw4YNw71792A0GlG2bFlrm0NFcEXNkFxAnW2A0Sjg5eWcWWxERES2srpmyM/PD0lJSfmOnz59GhEREVY9l16vx++//441a9bA398fAHDjxg2kpKRY2ywqQO6+ZNpizrQfKQslBJDOLTmIiMgNWB0M9e7dGzNmzJBXm9ZoNLhy5QomTJiAvn37Wvw8ly9fRoMGDdC7d2+MGjUKd+/eBQDMnTsX48ePt7ZZVABXFFD7+XhBSgZxqIyIiNyB1cHQ+++/j7t376Js2bJIT09HTEwMatSogeDgYMyaNcvi5xkzZgyaN2+O+Ph4OSsEAE888QS2bdtmbbOoAKkuCIY0Go38eiyiJiIid2D1XTIkJAR79uzB9u3bcejQIRiNRjRt2hSdO3e26nn27NmDvXv3wtfX1+x4VFSUVVP0qXDSbLJgP+cFQ4BpqCw5Q49UTq8nIiI3YPVdMi0tDQEBAXj44Yfx8MMP2/zCRqNR3oIjr2vXrpnNUiPbJUsF1L7ODYaYGSIiIndi9TBZqVKl0Lp1a7z11lvYsmWLzatFd+nSBQsXLpS/1mg0SElJwdSpU9G9e3ebnpPMuWKYDAACc1ahZs0QERG5A6vvkrt27cKuXbuwc+dOfPzxx8jIyEDTpk3RoUMHxMTEoFu3bhY9z4IFC9CxY0fUrVsXGRkZ6NevH86ePYsyZcpg1apVVneE8kt1wTpDQG7wxc1aiYjIHVh9l2zVqhVatWqFCRMmwGAw4ODBg1i8eDE++OADzJs3r8Chr4JERkbiyJEjWLVqlVx7NHToUPTv39+soJpslzubzHlT602vJy28yJohIiJSPptSBqdOncLOnTvlDFF2djZ69uyJmJgYq57H398fQ4YMwZAhQ2xpBhVDCoZcUUANcJiMiIjcg9V3yfLlyyM7OxsPP/wwOnTogLfeegsNGjSw+oUjIyPRoUMH+V+tWrWsfg4qnBDCdTVD3J+MiIjciNUF1OXLl0dKSgquXLmCK1eu4Nq1azatGP3BBx8gJCQE8+fPR506dVChQgX83//9HxYvXoyTJ09a/XxkLlNvhN4oALgiGGJmiIiI3IfVd8kjR44gISEBf/zxB3bt2oXJkyfj+PHjaNiwITp27Ih3333Xoud59tln8eyzzwIAbt++jR07duC3337D6NGjC512T5bLG4g4e2p9kC8LqImIyH3YdJcsVaoUevXqhbZt26JNmzb45ZdfsHLlSvz9998WB0MAkJKSgj179si1R4cPH0aDBg2srj2i/KQhKn8fLbRO3iw1QF5niAEtEREpn9XB0Lp167Bz507s3LkTx48fR3h4ONq1aydPlbdUixYtcPToUdSvX1+uPWrXrh1KlSplbZOoAFIwFOTk4mkgd2PYNA6TERGRG7D6Tjl8+HC0b98ew4YNQ4cOHVC/fn2bXvjs2bMICAhAtWrVUK1aNdSoUYOBkB25ao0hgCtQExGRe7H6Tnnnzh27vHBcXByOHj2KnTt34vfff8fUqVPh5eWFmJgYdOzYESNGjLDL63iqVBetMWR6TdYMERGR+7B6Npk9NWzYEK+88grWrFmDjRs3olu3bli7di1GjRrlymapgrzgopOLp4G86wyxZoiIiJTP+XfKHIcPH5Zrj3bv3o3k5GQ0atQIY8aMsar2iAomZYZcMkzmy2EyIiJyHy4Lhh588EE0adIEMTExGDZsGNq3b4+QkBBXNUd1XFtAzXWGiIjIfbgsGIqLi7Mo+Fm1ahV69eqFwMBAJ7RKPVJctPo0AARIs8myDDAaBbycPLWfiIjIGi6rGbI0CzR8+HDcvn3bwa1RH1cOk+V9zbRs1g0REZGyWX2nfOKJJ6DR5P+kr9Fo4Ofnhxo1aqBfv36oXbu2XRoohLDL83gaacFDVxRQ67y9oPXSwGA07Y/mioCMiIjIUlZnhkJDQ7F9+3YcOnRIDooOHz6M7du3Q6/XY/Xq1WjUqBH27t1r98ba6vr16xgwYADCw8MREBCAxo0b459//nF1sxzKlVPrNRoNAn25WSsREbkHm3at79evHz7++GN4eZliKaPRiDFjxiA4OBjff/89RowYgTfffBN79uyxe4OtFR8fjzZt2qBjx47YuHEjypYti/Pnz6t+gUdXDpNJr5uUoWcRNRERKZ7Vd8qlS5di7969ciAEAF5eXhg9ejRat26N2bNn4+WXX0a7du3s2lBbvffee6hcuTKWLVsmH6tatarrGuQkriygzvu6zAwREZHSWT1MptfrcerUqXzHT506Je807+fnV2BdkSv8+uuvaN68OZ566imULVsWTZo0wRdffFHo+ZmZmUhKSjL7546k1Z9dMbUeyLMKNRdeJCIihbM6GHruuecwdOhQLFiwAHv27MHevXuxYMECDB06FAMHDgQA7Nq1C/Xq1bNLA6OiouDj42Pz4y9cuIDPPvsMNWvWxObNmzFixAi88sor+Prrrws8f86cOQgNDZX/Va5c2ebXdiVX7k0G5NYqcZiMiIiUzuo75YIFC1CuXDnMnTtXnvJerlw5jB07Fm+++SYA4JFHHsGjjz5a5PNcvXoVGo0GlSpVAgAcOHAAK1euRN26dfHiiy/K5/3333/WNtGM0WhE8+bNMXv2bABAkyZNcPz4cXz22Wdy8JbXxIkTMW7cOPnrpKQktwyIXLkdR97X5f5kRESkdFZnhrRaLd5++23cvHkTCQkJSEhIwM2bN/HWW29BqzVlA6pUqSIHOYXp168fduzYAQC4desWunTpggMHDuCtt97CjBkzbOhKwSpUqIC6deuaHXvggQdw5cqVAs/X6XQICQkx++eOUjJcX0ANMDNERETKV6JFF0sSLPz333946KGHAAA//PAD6tevj3379mHlypVYvnx5SZplpk2bNjh9+rTZsTNnziAqKspur6E0BqNAes5ih66YWm96XamAmjVDRESkbFYHQ7dv38Zzzz2HyMhIeHt7Q6vVmv2zVHZ2NnQ6HQDg999/R69evQAAderUwc2bN61tVqHGjh2LP//8E7Nnz8a5c+ewcuVKLFmyBKNGjbLbayhN3qEpV88mY2aIiIiUzuo75eDBg3HlyhVMnjwZFSpUsHnWWL169bB48WI89thj2Lp1K9555x0AwI0bNxAeHm7TcxbkwQcfxLp16zBx4kTMmDED0dHRWLhwIfr372+311AaKQDx9tJA5+2aHVeCWEBNRERuwupgaM+ePdi9ezcaN25cohd+77338MQTT2DevHkYNGgQGjVqBMA0FV4aPrOXHj16oEePHnZ9TiVLzbPGkKuWOOA6Q0RE5C6sDoYqV65sl/3COnTogHv37iEpKQmlS5eWj7/44osICAgo8fN7MldPqwfyzCZjMERERApn9RjKwoULMWHCBFy6dKnEL67Vas0CIcC0OnTZsmVL/NyezJX7kknkmqEsFlATEZGyWZ06eOaZZ5CWlobq1asjICAg34KIcXFxFj1PdHR0kUM4Fy5csLZplMPVW3GYXps1Q0RE5B6svlsuXLjQLi/86quvmn2dnZ2Nw4cPY9OmTXj99dft8hqeSt6Kw4XBENcZIiIid2H13XLQoEF2eeExY8YUePyTTz7B33//bZfX8FTS2j6uWn0a4DpDRETkPiyqGcq7Wen9m5jae1PTbt26Yc2aNSV+Hk8mZWMCXFgzxMwQERG5C4tSB6VLl8bNmzdRtmxZlCpVqsBaHyEENBqNvHO9rX766SeEhYWV6Dk8nRSAuHQ2Wc5rp2cbYDAKaL1cM8WfiIioOBbdLbdv3y4HKNJ+YiXVpEkTs6BKCIFbt27h7t27+PTTT+3yGp5KmlrvygLqAN/crFRqlh4hfj5FnE1EVLh7KZn49s/L6N8iChHBOlc3h1TIortlTEyM/P/R0dGoXLlyvuyQEAJXr161+IUff/xxs6+9vLwQERGBDh06oE6dOhY/D+WnhMyQztsL3l4a6I0CaZkGBkNEZLOFv5/Bt39ewbd/XsHfkzq7ujmkQlbfLaOjo+Uhs7zi4uIQHR1t8TDZ1KlTrX1pslBKzmyyQF/X1QxpNBoE6ryRmJ7NVaiJqESOXUsEYMoQJWVk88MV2Z3VwZBUG3S/lJQU+Pn5FfnYpKQkeZf74oqtpfPIeqkKWGcIMGWmEtOzWURNRCVSJih3aGzfuXt4tH4FF7aG1Mjiu+W4ceMAmD7xT5482WzLDIPBgL/++qvY/cqcWYjtyZQSDHHhRSKyh9jULPn/d56+y2CI7M7iu+Xhw4cBmIKVY8eOwdfXV/6er68vGjVqhPHjxxf5HI4oxKb8UhRQQJ339TlMRkS20huMOHI1Qf762PVE1zWGVMviu6UUvDz//PNYtGiRTcNYeQux8/4/2VduAbXraoZMry/tT8ZgiIhss+P0XbOvE9OzXdQSUjOrN2pdtmyZXep5Nm3ahD179shff/LJJ2jcuDH69euH+Pj4Ej+/J0vLUsYwmTS9nqtQE5GtrsalmX3NYIgcwaa75cGDB/Hjjz/iypUryMrKMvve2rVrLXqO119/He+99x4A4NixYxg3bhxee+01bN++HePGjcOyZctsaRohz0atLtyOA8gNxtI4TEZENopPM91jHmtQAf87dhPJGXou5Ep2Z3Vm6Pvvv0ebNm1w4sQJrFu3DtnZ2Thx4gS2b9+O0NBQi5/n4sWLqFu3LgBgzZo16NmzJ2bPno1PP/0UGzdutLZZlENvMCIj2wjAtesM5X19FlATka2k4unoMoHysSRmh8jOrA6GZs+ejQULFuC3336Dr68vFi1ahJMnT+Lpp59GlSpVLH4eX19fpKWZ0p+///47HnnkEQBAWFiYXfY481SpWblDUq4eJuNmrURUUnEppmCobIhOXjuNQ2Vkb1YHQ+fPn8djjz0GANDpdEhNTYVGo8HYsWOxZMkSi5+nbdu2GDduHN555x0cOHBAfs4zZ86gUqVK1jaLckhZGB+tBr7eVv967YqZISIqqbicYbLSAb4I9TcttpjAYIjszOq7ZVhYGJKTkwEAFStWxH///QcASEhIkDM9lvj444/h7e2Nn376CZ999hkqVqwIANi4cSMeffRRa5tFOZSyxhCQuwJ2CmeTEZGNEtNMgU/pAF+EBpiWdGFmiOzN6jtmu3btsHXrVjRo0ABPP/00xowZg+3bt2Pr1q3o1KmTxc9TpUoV/Pbbb/mOL1iwwNomUR5KKZ4GcgMyZoaIyFZS4BPq74NQf2+zY0T2YvUd8+OPP0ZGRgYAYOLEifDx8cGePXvQp08fTJ48ucjHWlMLxO04bCPtWO/q4mmAwRARlVxCummYzBQMmYbJEtOyinoIkdWsvmNKK0gDpp3m33jjDbzxxhsWPbawLTjy4nYcJZMqrzHk2gUXTW2QgiH+LonIepl6gzw7NtTfB6X8OUxGjmFT+uD8+fNYtmwZzp8/j0WLFqFs2bLYtGkTKleujHr16hX6OG7B4XhKqhmSVsDmCtREZAsp6NFogGA/b4QG+JgdJ7IXq++Yu3btQrdu3dCmTRv88ccfmDVrFsqWLYujR4/iyy+/xE8//VToY7kFh+OlsmaInCAxLRuHrsRDQOT7Xv2KoSgb7OeCVpHaSMXTIX4+8PLS5M4mS2MwRPZl9R1zwoQJmDlzJsaNG4fg4GD5eMeOHbFo0SKrnmv37t34/PPPceHCBfz444+oWLEivvnmG0RHR6Nt27bWNo2gnE1agdyAjBu1qs8LXx/EwUsFb5tTqbQ/dr/RsdghcaLi5C2eBoAQf2aGyDGsnlp/7NgxPPHEE/mOR0REIDY21uLnWbNmDbp27Qp/f38cOnQImZmZAIDk5GTMnj3b2mZRDqVs0mpqgykYysg2Qm8wurg1ZE9nbqcAAOqUD0bDSqFoWCkUDSqaVqC/Fp9utvgnka3uD4ZKMRgiB7E6GCpVqhRu3ryZ7/jhw4fltYIsMXPmTCxevBhffPEFfHx85OOtW7fGoUOHrG0W5UhRUM1Q3jbw5qgeeoNRvhl990IL/PpyW/z6clusH91W3pz3XnKmK5tIKiFdZ6VyaoVCGQyRg1gdDPXr1w9vvvkmbt26BY1GA6PRiL1792L8+PEYOHCgxc9z+vRptG/fPt/xkJAQJCQkWNssyqGkAmpfby/4aE1DJawbUo/4tNyiVunmJCkTpAMA3EthMEQlJ9UGScNjDIbIUawOhmbNmoUqVaqgYsWKSElJQd26ddG+fXu0bt0akyZNsvh5KlSogHPnzuU7vmfPHlSrVs3aZlGOtCzlrDME5Nm5njPKVEPaRTzU3wfeWvO3kIhgUzD05OL9OHAxzultI3XJN0yWkyGKTc3Cx9vP4uPtZ/Hf9USXtY/Uw+o7po+PD7777jvMmDEDhw8fhtFoRJMmTVCzZk2rnmf48OEYM2YMvvrqK2g0Gty4cQP79+/H+PHjMWXKFGubRTmUNEwGmIqoE9KyuVmrisTl7CIelrM1Ql51ygfjn8umwurv/rqMh6LD8p1DZKn7g6GwQF94aYAsvRHvbzkDAPjpn2vY+XpHl7WR1MHmO2b16tVRvXp1m1/4jTfeQGJiIjp27IiMjAy0b98eOp0O48ePx8svv2zz83q63Kn1ri+gBrhZqxrF5wRDpQPzB0NTetZFRrYRaw5dk4MmIltJH+6C/bxz/uuDeU82wt+X45CRbcS6w9dxPSFdXqyXyFZWB0MGgwHLly/Htm3bcOfOHRiN5rOEtm/fbvFzzZo1C2+//TZOnDgBo9GIunXrIigoyNomUR6KywzlzGrj9Hr1yLuL+P103lr0aFgBaw5dk4fTiGwlDa/nXTetb7NK6NusEjKyDVh3+DqyDQLJmXqE+PkU9jRExbL6jjlmzBgsX74cjz32GOrXr291NG4wGHD8+HHUrFkT/v7+CAgIQPPmzQEA6enpOHr0KOrXrw8vL6vLmQh5t+NQSjDEzJDaSJmhsMCCbz5Sxig+lUWuVDKpRayb5uejRYCvFmlZBsSlZDEYohKx+o75/fff44cffkD37t1tesFvvvkGH3/8Mf7666983/P19cWQIUPw6quvYsCAATY9v6dT0katAIfJ1CguJ8gJC9QV+H2plojDZFRSuZmhgof9wwJ9kZaVjtjULFQtE+jMppHKWJ1+8fX1RY0aNWx+waVLl2L8+PHQavNf3FqtFm+88QaWLFli8/N7utxhMmXUDAXIq1CzgFotpOGvwjNDpuPp2Qakc30pKgHpfSOgkA93YXIWkoE3lYzVwdBrr72GRYsWQYj8exJZ4vTp02jZsmWh33/wwQdx8uRJm57b02UbjMjSm2q4lJMZMgVlnFqvHlLGp6CaIcB07UnrS7FuiErCkswQwCwklZxFd8w+ffqYfb19+3Zs3LgR9erVM1s9GgDWrl1b5HOlpqYiKSmp0O8nJycjLS3NkmbRfdLyZF8CFLBRK5A71s8CavWQp9YXMJsMADQaDUoF+OJucibiUrMQWcrfmc0jFZGG/Qt7P5ODIQbdVEIW3TFDQ0PNvi5obzJL1axZE/v27UPDhg0L/P6ePXusXrOITFJyPkX5ar3g662MAnQWUKtPXBFT6yVhOcEQM0NUEnJmqJBhf9ankb1YFAwtW7bMbi/Yr18/TJo0Ca1bt84XEP3777+YMmUK3njjDbu9nidJVVi9EJC3gJq1I2oh1wwVMkwG5NYN8SZFtjIahbyifmGzY8OCTNdgbAqvMyoZp4+ljB07Fhs3bkSzZs3QuXNn1KlTBxqNBidPnsTvv/+ONm3aYOzYsc5ulioobY0hgMNkapORbZBvUEVmhnK+J+0tRWSt9OzcD1CBhQ2T5QTkzEBSSTn9runj44MtW7ZgwYIFWLlyJf744w8IIVCrVi3MmjULr776ar46JLKMlBlSSvE0kFv4yGEydZAyPd5eGoT4FX6dlebwBZWQtGaaRgP4+RQ87C8F3bG8zqiEXHLX9PHxwRtvvGHRcNiqVavQq1cvBAZyDYniKGnHegkzQ+qSt3i6qAVX5SnP/MRONpIXXPT1LvRaCw/i1HqyD2VU2RZh+PDhuH37tqub4RZyZ14op2ZILqDm1HpVuJeSCQAIDyp4wUUJM0NUUtKHu6Lez3idkb2UKIWQkZEBPz8/e7WlQLauZ+SJpIBDScNkLKBWF6lQtUxQ4fVCADNDVHLFFU8DQHjOKugpmXpcT0iX17cqqRA/H/j5KOdDJTme1XdNo9GIWbNmYfHixbh9+zbOnDmDatWqYfLkyahatSqGDh3qiHaSBZRZQM2NWtUkNjUnM1RE8TSQW1wdx/3JyEapxUyrB0y72Wu9NDAYBdq8a/km4cUJ9NVi06vtUTkswG7PScpm9TDZzJkzsXz5csydOxe+vrlviA0aNMCXX35p18aRdZRYQC21JUtvRLbB6OLWUElJharFDZPJs3w4fEE2SitmwUUA8PLSoHejSGg0sNs/AEjNMuDotURndJMUwuq75tdff40lS5agU6dOGDFihHy8YcOGOHXqlF0bR9bJ3eFZOendvG9kaZkGhAYovkyNiiANk4UXM0wmrzOUlgUhRJHF1kQFSS1mKw7J/GcaY/4zje32ui+s+Bu/n7yNpAxmNT2J1Xem69evF7hRq9FoRHY2Lx5XUuIwma+3F3y1pssshUXUbi82p4C6TCE71kukwtYsvVGu/SCyhlxA7eT3sxB/0+slpvN+5kmsDobq1auH3bt35zv+448/okmTJnZpVF5RUVFcd8hCShwmA3IzVVxryP3lDpMVnRkK8NXKW8Jwpg/ZQi6gdvLs2FB/0/0micGQR7H6rjl16lQ899xzuH79OoxGI9auXYvTp0/j66+/xm+//WbVcyUkJOCnn37C+fPn8frrryMsLAyHDh1CuXLlULFiRQDAf//9Z20TPVZqVvFj7K4QqPNGfFo2i6hVIHeYrOjMkEajQViAL24lZSA+LYuFqGS13Kn1Ts4M+ZmCIWaGPIvVmaGePXti9erV2LBhAzQaDaZMmYKTJ09i/fr16NKli8XPc/ToUdSqVQvvvfce3n//fSQkJAAA1q1bh4kTJ1rbLELezJByaoaAvNPrGQy5O3nRxSL2JZOUlqfX86ZC1pMyQ87OdIdImaEMvl95Epuusq5du6Jr164leuFx48Zh8ODBmDt3LoKDg+Xj3bp1Q79+/Ur03J5KiStQA9y5Xi0ysg3yflGlAosfug7LOYczysgWuTVDrhkmY2bIs9g0tSchIQFffvkl3nrrLcTFxQEADh06hOvXr1v8HAcPHsTw4cPzHa9YsSJu3bplS7OKNWfOHGg0Grz66qsOeX5XU2IBNZB3Sw4W0roz6eag9dIg2IJrjKsDU0nk1gw5e5jM9HqsGfIsVl9lR48eRefOnREaGopLly7hhRdeQFhYGNatW4fLly/j66+/tuh5/Pz8kJSUlO/46dOnERERYW2zinXw4EEsWbIEDRs2tPtzK4VSC6iDWECtCtJq0qX8fSyaKs9VqKkkUizYjsMR5AJqTq33KFZnhqThrbNnz5ptxdGtWzf88ccfFj9P7969MWPGDHk6vkajwZUrVzBhwgT07dvX2mYVKSUlBf3798cXX3yB0qVL2/W5lSS3gFqZNUMsoHZv8TmrSZcKsGx2JzNDVBJpWa7JdIdwNplHsjoYstfw1vvvv4+7d++ibNmySE9PR0xMDGrUqIHg4GDMmjXL2mYVadSoUXjsscfQuXPnYs/NzMxEUlKS2T93kG0wIktvWuFZaZkh7lyvDonppqCmtAXF0wAzQ1Qyrtp4OndqvZ57Y3oQq++a9hreCgkJwZ49e7B9+3YcOnQIRqMRTZs2tShgscb333+PQ4cO4eDBgxadP2fOHEyfPt2ubXCGtDz1OEqbWi/Vl6RwdoZbk2aFWZwZCmRmiGznqo2npcxQlsGIjGwj/BWWaSfHsPoqk4a3fvjhBwAlH956+OGH0bp1a+h0Orsv2X/16lWMGTMGW7ZsMRvSK8rEiRMxbtw4+eukpCRUrlzZru1yBGl1Z1+tl7zYnVJwNpk6JMjBkIWZIXl/Mg43kPVcNTs20Fcrb/6amJ7NYMhDWH3XtNfwltFoxDvvvIOKFSsiKCgIFy9eBABMnjwZS5cutbZZBfrnn39w584dNGvWDN7e3vD29sauXbvw4YcfwtvbGwZD/tlNOp0OISEhZv/cQZqLpqFaIihndkYygyG3lpCngNoSefcnI7JWiosmhGg0GgRL71ksovYYVl9l9hremjlzJlasWIG5c+di2LBh8vEGDRpgwYIFGDp0qLVNy6dTp044duyY2bHnn38ederUwZtvvgmtVnmBg61SXTQN1RJcdFEdpMyQNPxVnNJ5dq7nZq1kDX3OEBXgmqVCgv28kZCWzYUXPYhVV5ler4efnx+OHDmChx9+GA8//LDNL/z1119jyZIl6NSpE0aMGCEfb9iwIU6dOmXz8+YVHByM+vXrmx0LDAxEeHh4vuPuLjelrLwAj7PJ1EEqhA61NDOUEwzpjQLJmXp5mwOi4qTm2dzXFe9ppms1nZkhD2LVMJm3tzeioqIKHF6y1vXr11GjRo18x41Gozzdniyn1NWnAQZDaiFnhiysGfL31cLPx/QWk8gtOcgK0vuZj1YDnbfzg6HcYTK+Z3kKq2uGJk2ahIkTJ8orT9uqXr162L17d77jP/74I5o0aVKi5y7Kzp07sXDhQoc9v6tIMy+UOEwWyNlkqpAgT623PMMjD5Wxbois4OoPd8F+XHjR01h9pX344Yc4d+4cIiMjERUVhcDAQLPvHzp0yKLnmTp1Kp577jlcv34dRqMRa9euxenTp/H111/jt99+s7ZZHs9Va3JYQvqUxZoh9yZNrQ+1IhgK9ffBzcQMOatEZAl5ayEXfbhjZsjzWH2lPf7443Z54Z49e2L16tWYPXs2NBoNpkyZgqZNm2L9+vXo0qWLXV7Dk6S5aE0OS8hT67MMMBoFvLxYSOtuhBDyUJelw2RA7ppECVzNl6wgfbhz1fuZVN/GmiHPYfWVNnXq1BK/qF6vx6xZszBkyBDs2rWrxM9HuZugKnJqfZ43tNQsvZyCJveRlmVAlsE0u8fSRRcBoJS/KXC6eDfVIe0idUpx8YQQZoY8j0tW5/P29sa8efPsUohNJmkKLqDWeXvBR2vKBrGI2j1JNT++3l7w97H8BhXib7oeF/x+xiHtInVydc1QbmaI71eewupgqHTp0ggLC8v3Lzw8HBUrVkRMTAyWLVtW7PN07twZO3futKXNVAAlF1BrNBoWUbu53Jlklu1YL3m4TjkApllB3OeJLOWqrTgkUmaIm7V6DquvtClTpmDWrFno1q0bHnroIQghcPDgQWzatAmjRo3CxYsXMXLkSOj1erPFFO/XrVs3TJw4Ef/99x+aNWuWrxC7V69e1vfGgym5gBowvaklpGUzM+SmYnP2FwsL1Fn1uA61TfsVZhsEkjL0Fq9RRJ4tRSGzyZgZ8hxWX2l79uzBzJkzzRZKBIDPP/8cW7ZswZo1a9CwYUN8+OGHRQZDI0eOBADMnz8/3/c0Gg2H0Kyk5AJqgGsNubt4ORiyLpjx89Ei0FeL1CwD4lKzGAyRRVJdtBWHRM4MsYDaY1g9TLZ58+YCt97o1KkTNm/eDADo3r07Lly4UOTzGI3GQv8xELJeirw3mbKDIU6vd0+2ZoYAICxI2r0+065tIvWSMt0soCZnsToYCgsLw/r16/MdX79+PcLCwgAAqampCA4OLnnryGJp8t5kyhwmk9LdfHNxT1IgE2bFTDJJeE4AdS+FCy+SZVw9TBbiz6n1nsbqK23y5MkYOXIkduzYgYceeggajQYHDhzAhg0bsHjxYgDA1q1bERMTU+TzfPjhhwUe12g08PPzQ40aNdC+fXtVbabqSK6efVGcIC686NbiUk03BVsyQ+GBUmaIwRBZRinDZMmZeq6N5iGsvtKGDRuGunXr4uOPP8batWshhECdOnWwa9cutG7dGgDw2muvFfs8CxYswN27d5GWlobSpUtDCIGEhAQEBAQgKCgId+7cQbVq1bBjxw5UrlzZ+p55GDmtrMDZZAAQzJohtyZnhqysGTI9hsEQWcfVK1BLU+uF4NponsKmdYbatGmDVatW4dChQzh8+DBWrVolB0KWmj17Nh588EGcPXsWsbGxiIuLw5kzZ9CiRQssWrQIV65cQfny5TF27Fhbmuhx5Kn1Clx0EcizP1km68HcUXwJMkNSzVAsh8nIQq7OdOddG41D+57BpmDo/PnzmDRpEvr164c7d+4AADZt2oTjx49b/ByTJk3CggULUL16dflYjRo18P7772PixImoVKkS5s6di71799rSRI8ihHD5m0dxcmeTcQzeHcXmZIZK25AZkobJ9py7a9c2kXq5ejsOjUbD6fUexupgaNeuXWjQoAH++usvrFmzBikpKQCAo0ePWrVVx82bN6HX57/I9Ho9bt26BQCIjIxEcnKytU30OJl6I4w569kpeZ0hIPdNjtxHfGoWzudspxFuQ2YoItj0mDO3UxCbwhllVDxXb8cB5J1Rxg9wnsDqYGjChAmYOXMmtm7dCl/f3A0bO3bsiP3791v8PB07dsTw4cNx+PBh+djhw4cxcuRIPPzwwwCAY8eOITo62tomepy8RckBCq0ZCuJUVbe14b+b8v+XD/Wz+vGdHign///1hHS7tInUzdUrUAO5dUNca8gzWB0MHTt2DE888US+4xEREYiNjbX4eZYuXYqwsDA0a9YMOp0OOp0OzZs3R1hYGJYuXQoACAoKwgcffGBtEz2OlG3x99FCq9BZD4FcZ8ht3U4yZXMaVy5l06KJIX4+qFPetNSGtK0HUVGUMOzPtYY8i9VXWqlSpXDz5s18GZvDhw+jYsWKFj9P+fLlsXXrVpw6dQpnzpyRZ6XVrl1bPqdjx47WNs8j5RZPKzMrBHA2mTu7m2wKhqStNWwh7XSfwL2eqBiZegOyDaZxfyUEQ0kMhjyC1Vdav3798Oabb+LHH3+ERqOB0WjE3r17MX78eAwcONDqBlSrVg0ajQbVq1eHt7dyb+ZKlqqA8fXiSMNkDIaU72pcGu4k59b2XLhrqgssE2R9vZCklL9pSD0xjTPKqGh56wpduYhsbgE1A3hPYHX0MWvWLAwePBgVK1aEEAJ169aFwWBAv379MGnSJIufJy0tDaNHj8aKFSsAAGfOnEG1atXwyiuvIDIyEhMmTLC2aR4rNUvapFW5waS0XgiDIWU7di0RPT/eU+D3ShQM5WSGEpkZomJIH+78fLzgrbVpwrNdcJjMs1h9pfn4+OC7777DmTNn8MMPP+Dbb7/FqVOn8M0331i1WvTEiRPx77//YufOnfDzyy3K7Ny5M1avXm1tszxamrxaq3IzQ9IbSwrfWBTtxM1EAKb6s6jwAPlfy2phaF0j3ObnlWqNWDNExUlx8erTErmAmgG8R7D5aqtevbrZGkHW+vnnn7F69Wq0bNkSGk1u0W/dunVx/vx5m5/XE8mbtCo5M5TzxpaebYDBKBRb6O3ppA1ZuzeogA+ebmS35w1lzRBZSAnF0wAzQ57Goqtt3LhxFj/h/PnzLTrv7t27KFu2bL7jqampZsERFU/epFXBmaG8bUvj8vaKFZ8TDIUH+RZzpnWkmiFmhqg4rt6KQxLCmiGPYtHVlnctIAD4559/YDAY5JlfZ86cgVarRbNmzSx+4QcffBD/+9//MHr0aACQA6AvvvgCrVq1svh5KM9sMgVnhny1XvD20kBvFEjNNDAYUqhbSdIeZHYOhuSaIRZQU9Fcvfq0hJkhz2LR1bZjxw75/+fPn4/g4GCsWLECpUuXBgDEx8fj+eefR7t27Sx+4Tlz5uDRRx/FiRMnoNfrsWjRIhw/fhz79+/Hrl27rOyGZ1NKWrkoGo0GAb5aJGXo5eCNlCU1U4/1/94AAIQF2DszxAJqsoxSZsdyOw7PYnUB9QcffIA5c+bIgRAAlC5dGjNnzrRqgcTWrVtj7969SEtLQ/Xq1bFlyxaUK1cO+/fvtyrDRHl2rFfwMBmQG6ylcUsORTp7J0X+/5bVbC+WLkgIC6jJQikK+XCXu84Qr1lPYPXVlpSUhNu3b6NevXpmx+/cuWP1PmINGjSQp9aT7dKylF9ADeRZhZqZIUW6l7O2UMNKoagSHmDX5+aii2SpM7dN95GywdZv/WJPUgDPzJBnsPru+cQTT+D555/HBx98gJYtWwIA/vzzT7z++uvo06dPkY9NSkqy+HVCQkKsbZrHktYZcuUCZZaQ2pfGYEiR7uZsolqS9YQKI02tz9IbkZFtgJ+Psq9Vcp295+8BKNmK5/YQnGehWM6AVT+rg6HFixdj/PjxGDBgALKzTZ/yvL29MXToUMybN6/Ix5YqVcrimWIGA4dSLCWtMxSg4JohIDdzlcJhMqe7EpuGr/ZeREZ24T/7k7dMn8gjHBAMBem8ofXSwGAUSEjLRvlQBkNUsIRU032lcph9s5PWkoIhwBQQ2bIvH7kPq++eAQEB+PTTTzFv3jycP38eQgjUqFEDgYGBxT42byH2pUuXMGHCBAwePFiePbZ//36sWLECc+bMsbZZHi03M6TsYEiqaUrjKtRO99mu81h14IpF59p7iAwwFdCH+vsgLjULienZKB/q2iEQUiaDUSA55/0hxM+172c6by18vb2QpTciOSObwZDK2Xy1BQYGomHDhlY9JiYmRv7/GTNmYP78+Xj22WflY7169UKDBg2wZMkSDBo0yNameRy5ZkjhBdRSZkgK3sh5bidlAAAerVce9SsWPgQdqPPGk80qOaQNeYMhooLkXaFeCctvhPh5415KFpLS9UDp4s8n9+Wy0Hv//v1YvHhxvuPNmzfHCy+84IIWuS9pdhYzQ1QYaWXpPk0r4pF65V3ShtwZZVxriAomBcr+PqasjKuF+PngXkoWF170AC672ipXrlxgMPT555+jcuXKLmiR+0qVZ5MpOzMUyMyQyzhqZWlrcK0hKo40jT3EXxkf7Ljwoudw2RW3YMEC9O3bF5s3bzablXb+/HmsWbPGVc1yS3JmSOkF1NI6Q5xN5nRxOcFQaTsvpmiNUAZDVAxpU9QQBQyRAXkWXszkNat2LssMde/eHWfPnkXv3r0RFxeH2NhY9O7dG2fOnEH37t1d1Sy3I4TIsx2H0jNDpvalcJjMqTL1BvlnHh5o/5lilpKCIe4CToXJzQwpJRhiZshTODWVcPToUdSvXx9eXqYYrFKlSpg1a1ah5x8/fhy1a9eGt7eyMx6ulKk3wihM/6/4qfVcgdol4nOmKmu9NGbThZ1NCoa48CIVJildGTPJJPIq1LxmVc+pmaEmTZogNjbW4vNbtWqFK1csmw7sqVLzZFn8Fb6QnZQZ4grUzhWbalpMsXSAL7xcuHBc7matvLFQwZSXGeIq1J7CqeG3EAKTJ09GQIBl65hkZXHWSXHScoqR/X20il8hVZpan8YCaqeSMkNhga69wYSwZoiKobSaIakdSQyGVM+pwVD79u1x+vRpi89v1aoV/P39Hdgi9yfXCyl8jSHAtAoxYJ7NIseTMkNhga4rngbyDJNxs1YqhBR0KG82Ga9ZtXPqFbdz505nvpxHkHasV/omrUDuopDMDDmXNK3e1cFQKRZQUzGUlhliAbXncP2qVlQiaW6yxhCQZ50hZoacKjGnKDXU38WZIdYMUTGkmiElrD4N5LYjiZkh1WMw5OZS3WSNISA3YGMBtXNJwYer91bKu86QEMKlbSFlkmeTKWSYLISZIY/BYMjNuVVmKCdgy8g2wmDkzdBZlLKqrxQM6Y2Cq5BTgeRrVSGZIanonzVD6sdgyM25y471gHnAxlWonUcpmSF/Hy18taa3HA6VUUGS5QJqZQRDrBnyHAyG3JxUf6P0HesBQOftBe+c6f8sonYepRSlajSa3On1nFFGBZCuVVcuDpqXVDOUlmWA3mB0cWvIkRgMublkhaWVi6LRaOTsELfkcB5purKrM0OmNphucgnpXEOMzBmNAilZ0grUrr9WAfOgjNkhdWMw5OakP1ClfJIqTiC35HA6OTOkiGCI0+upYMmZekh19Up5P/PResHPx3SbZDCkbgyG3Jy7BUOcUeZ8ucNkrr9GSgWYpvezZojuJ12nvt5e8FPQ1kIhnF7vERgMublkha3LURw5M8RgyCkMRoHkTCUNk3GtISqYXDytsPcyFlF7BgZDbi7JXTNDHCZzirxTgpU0TMYtOeh+SlkC4n65m7XymlUzBkNuLneYzPU3OksEMTPkVFIGJsBXCx+t6//cmRmiwuTOJFPWe5n0QZObtaqb698dqUSkTytBbrACNZC7h1oKM0NOIa/oq5AbjLQ/WnwaZ5ORudxhMmW9l0l/OynMDKkagyE3p9Q3kMIESpu1cmq9UySmK2voITzIFAzdS2EwROaUtvq0RBraT8vmBzg1YzDkxoQQ8no9SkstF0bKDHE7BueQgqFSLt6kVRIeqAMAxKZkurglpDRK25dMwuVAPIOyrjqySlqWQd7jy10KqAOlT1msGXKKRAWtMQQAZXIyQ3eSMrH5+K0iz61TPhhR4YHOaBYpgFIXkOVyIJ7BPe6gJTBnzhysXbsWp06dgr+/P1q3bo333nsPtWvXdnXTSkzKCmm9NG6xUSsABOR8yuJsMueQVnpWwrR6ACgTZMoMJWfqMfybf4o8N1jnjQNvd4a/m1zbVDJJGcraikMSKL9nMRhSM2VddQ6wa9cujBo1Cg8++CD0ej3efvttPPLIIzhx4gQCA937U2fe4mmNRuPi1liG6ww5lzxMFqCMYKh0oC9GdayOPy/EFXnev1cTkJypx62kDESXce+/U7JM7jCZMq5VSW5miB/g1Ez1wdCmTZvMvl62bBnKli2Lf/75B+3bt3dRq+zD3dYYAnKHybg3mXMkKWTH+rxe71qn2HNi5u3A5dg03EvJZDDkIZIzlTlMFugr1QzxPUvN3OcuaieJiYkAgLCwsAK/n5mZiczM3OLOpKQkp7TLFokK2Y3cGlIBNXetd45EBQZDlggP9MXl2DQWWnsQKTOktA93ATpmhjyBR80mE0Jg3LhxaNu2LerXr1/gOXPmzEFoaKj8r3Llyk5upeVic6YnS9OV3YE0tZ7j784hrfSslGEyS4Xn1BZtP3XHxS0hZ8ldgVpZ1yqH9j2DRwVDL7/8Mo4ePYpVq1YVes7EiRORmJgo/7t69aoTW2iduFTTp2apKNUdMDPkXEqbTWYpKZP1y5EbLm4JOYtSN53OHSbje5aaeUwwNHr0aPz666/YsWMHKlWqVOh5Op0OISEhZv+USs4MBbpfZoifspzDXYfJ+reoAgDQGwWMOctHkHoZjQIJOauSK2VNLAmn1nsGZYXgDiCEwOjRo7Fu3Trs3LkT0dHRrm5SiWQbjBjw5V84cSMJGXrTJ5Uwdxomk7fj4BuLMySmuWcwVC8yFABgMAokpmejtBsF/GS95Aw9pJhXaUO6XHTRM6g+GBo1ahRWrlyJX375BcHBwbh1y7TQW2hoKPz9/V3cOuudu5OCvy7mTkv20gBNq5R2YYusI72xZGQbYTAKaL3cY0kAd2QwCiTnBJ2l3CwY8vX2QoifN5Iy9IhNzWQwpHLSXnUBvlr4+ShrXanAPJkhIYTbLGNC1lF9MPTZZ58BADp06GB2fNmyZRg8eLDzG1RC0tBYdJlAfDX4QYT4ecvFpu4g7+KQaVl6t9lGxB0l5dkZ3t1qhgBTLVxShh4T1x6TM1sajQZPN6+MLnXLubh1ZE9SMFQ6QHlBr7RQrFEAmXqj4oI1sg/VB0NCqKveIDanaLp8iJ9brr+i8/aC1ksDg1EgLcvAYMiBpHqhQF8tfLTuVx4YXSYQF+6l4uCleLPj5++mMBhyscuxqZi94aTdhrulWY+lA5X3fuCfJ/hJzdQzGFIp1QdDahGfmoWUTD3O300F4F7T6fPSaExbhyRn6Dm93sES5NWn3fNaebdvQ+w8fQfGnA8091KyMG/zadxN4tpDrvbj39ew+fhtuz9vdJkguz9nSWm9NPD30SI924C0LAPCXd0gcggGQ25g37l7GLD0L+SdVONO0+nvF+jrjeQMPafXO5i7TquXRATr8FTz3HW+EtOyMW/zaSRn6pGpN0DnzU/oriJlqHs0rGC3LJ23lxfa1ixjl+eyt0CdKRjijDL1YjDkBv65HA+jALy9NPD19kKArzceqee+wwTS9HrOKHOs3Gn16vgzD/H3hreXBnqjQFxqFiqEut8ECLWISzXV+LSsFo7ejSu6uDWOZ1ofLYsbTKuYOt4lVS4up7hwWPtqePPR4vd1Ujqu6OocNxLSAShv3RZbaTQahAf54nZSJlYfvIpXO9dydZM8VnyqKdAO85BZftLED75nqZf7VVV6IOlTmDstrlgUeREzfspyqHc3ngJgyqiozbFria5ugkeThsmUOPvLEaQPcHzPUi8GQ25ACobU8sYjL2/PT1kOI4SAb84MstbVlVmHYYtpPesBAO5xA1eXik9jZojUhcGQG5CCIXdaabooAfyU5XApmXpkGYwA4Nb1ZfcrH+oHwDSzjFzDkGfrDCVOhXeEIOk9i5M+VIvBkBuIV11miDvXO5oUQPv7aOXNcdVAmkV5NzlTdWuIuYuk9Gx5Zqta3pOKI28wzfcs1WIw5Aak9WJKK2zPHlsF8lOWw8VK2USVDWNEBJuCoSyDEUkZvDG5gjShI9jP2y0X87SFNAOW71nq5RlXshvL1Bvk9XjcdfG8+wVy/N3h4nKGkdx1cc7C+Plo5foNKWNKzhWvsgkdlmBmSP0YDCmctEy9lwYI1qljuIM1Q44Xp9LMEJC76WxCnr3XyHnkCR0qvLYKk3ezVlInBkMKJwVDpQJ84aWSHd6ZGXK8WPnTu/uuVF4YKUMqbe5JziUH2irJVFuCH+DUj8GQwklv+KXcdEuFgkgpZ46/O05sztRztQ2TAbkzmBIYDLlEXJrnZob4AU69GAwpnPSGX0olxdNAnmJEjr87zN+XTTu9q3KYTMoMpXKYzBXiVTwEWxhmhtRPHUUoKiYNk6lpCmvuaq4MhhwhPjULR64mAHDvDX0LI82qZM2Qc+kNRhgFEJuirqU+LMHMkPoxGFI4aaXXUBVlhuSZGRwmc4ir8Wny/3eqU9aFLXEMaa81DpM5z9pD1/DmmqPINuSu7RTmIQsuAhza9wQcJlM4eaVXFX0K49L2jiUF0A9UCFFlXYc0ZCz1kxxv+6k7ZoFQsM4bzaLCXNgi55KG9jm1Xr2YGVK43GEy9XwKk4KhdH7KcojcFcvVc83kJX0wYGbIeRJzhiRnPVEfPRtFws9bC19vz/kszcyQ+jEYUjhpNlmoijJD/j45wVC2AUIIaDTqWDJAKeJVmE3MK3c2GTNDziL9rCuE+iHET51BdlHkzBCz2arlOaG9m1LbVhwA4JeTGTIKyJuJkv3Ey2tTqeeayYvrDDlfQnrOhzJ/dQbYxZEyQ9kGgSw937PUiMGQwqmxZkjKDAEcKnMENV4zeckrUDMz5DSJ0kQOFa13Zo2gPKv/p7BuSJUYDClcvArfhHy0XvDRmobG0rMZDNmbmrfiAHKDvJRMPT+lO4HBKORNcdWabSyO1ksjB0SJXNJBlRgMKZgQQv5EprZZQX4+LKJ2FLlmSKVTn0P8fSCVmfHG5HhJeX7GavpQZi2p77zm1InBkIKlZRnkmho11QwB5kXUZF9xqepbqDMvrZdGvjFxRpnjSTf/QF8tfLSee8sIYTCkap57ZbsB6RO+r9bLrM5GDfw5vd5hpABBrcNkQG6gx7WGHE+axFFKpcG1pUL8TMNkSQyGVIlT6xUsIc+sILVNP2dmyDGEEHLNkFozQ0DukAVnlDmeFFx78hAZwGGykrqXkom31h6T35/uVy7ED5/0b+rkVuViMKRgatyXTMLMkGOkZxuQmVNUrLY6s7ykYeNEZoYcLjFd3Us1WIrBUMlsOX4bW07cLvT7VcICnNia/BgMKZi8tocK34SYGXIMadjIV+slby6pRqW51pDTSDd/ZoZM/ecwmW2kv9U2NcLxXMuofN/393VtOMJgSMHiVbgVh8Sfs8kcQt6KI1B9Q6t5lWLNkNMkqHwRT0sxM1Qy0s/tgfIheLR+BRe3Jj8WUCtYQs6NrZQKV32Vh8mYGbIrT6gXAnI/IHA2meNJN7EQD88McTZZySh94U4GQwomz+JQ4XoxHCZzDLXvSyYpFSht1sobk6NxmMxEHibL4DVnC+nnptSyDwZDCqbmG5uUGcrgMJldxat89WmJlBmKY2bI4eRhMhVmqK3BYbKSUXpQzWBIwXLfhJR58ZSElBlKYzBkV3HyiuXqu2bykoK9+EKm6ZL9JCn8JuYsIdwTr0SUPtzKYEjBpHoINS52xpohx4j3kJohKRgqbM0Ssh+lf6J3lnAG4CWi9OuIwZCCJXjCbDIGQ3al5qHVvOTMUFoWjEbh4taom7TEh6fPJgsPMl1zqVkGzoK1gZwZ8lPmdcRgSMHUvAy+XDPEYMiu4j1gKw4gN9gzCtZwOJrSP9E7S5DOG77epltmbGqmi1vjXgxGgeQMPQDlXkcMhhTKaBTyMJkaM0N+rBlyCGmTVrV/ivfResl7RcVy2MJhMrINyMg2rWiu1FoPZ9FoNCiT8yHjn8vxLm6Ne0nOMwOPwRBZJTlDDyn7r9SpiCURpDPdyFIz9S5uibp4ymwywHyojBxDKp720gDBOq7RK32IW3Xgiotb4l6k7KK/j1bOrimNMltF8jh9gK8WOm/1basgBUNS6pRKTgjhMTVDQG4wFJvCYMhR8s4A8vJS74rmlurTtCIArpxvraR0ZQ+RAQyGFCtexZu0AkBQzhBHCjNDduMpm7RKmBlyPNYLmWtdowwA4B4DcKu4QxE+gyGFik9T/sVTElK9BzND9iNNM1f7Jq0STq93vASFb6HgbGUCdQBMBdRCcBajpZS+xhDAYEixElW+OWKQztSvlEw931TsJCHPgotq3qRVUprDZA7HzJA5aXp9RraRkz+s4A4LCDMYUqh4FS+4COQOkxmMQp6tQiXjKZu0SsI5TOZwDIbMBfhq4eeTM72eQbjFEtOV/+GewZBCuUMkXRKBvlpIyYtkbnxoF55UPA3k9pNT6x2HwZA5jUaD8Jyhsntca8hi7nAdMRhSqASV39g0Gk3ujDIWUduFJ02rB3KHLLg9guO4wyd6ZysTxOFZa7nD1lIMhhQqXuU1Q0DuuiUpLKK2C0/ZpFUifVBgAbXjuMMnemcLD8opok5hZshSLKAmm6l5Kw5JEGeU2ZWnbNIqkYYrGAw5DoOh/KRaNQ7PWs4dyj4YDCmUmrfikAT7STPKWDNkD3EqH1q9n5QBS8/mxpmOIr0Phfp7xjVlidzMEIMhS7lDUM1gSKHUPpsM4CrU9pbgIZu0SoJ03vDVcuNMR3KHm5izyTVDvOYs5g61ZwyGFCrBA2qGOExmX9ImrZ6w+jRgKsKXskPxqcwuOkKiG2yj4GzhLKC2Wu4wmXLfmxgMKZDeYJQDBDUPeYRwSw67yq0Z8pwbV1ieFYHJvoQQSHSDbRScTZ5azwJqi2TqDUjPNg1jKzmo5jbECiQVTwPKvnhKKneYjJ/qSyIj24BxPxzBraQMAOoOoO8XlpMZYhF1yc3bfAq7z96TvxYCyDaYVodX8/uQteTMEK85i0hDZBoNEOyn3JBDuS3zYNIn/FB/H2hVvFO0VA8lLSNAtvnzQiw2HLsFwHTNRATrXNwi5wnjjDK7SMnU45Md5wv8XmSoHwI8YK87S5UJyr3mjEYBLxW/R9tDkjSt3s9H0T8rjwmGPv30U8ybNw83b95EvXr1sHDhQrRr187VzSrQ3Zz0q1Sop1bh3GjTLu4m56brN45pBz8fz7lxheUM33BLjpKRriF/Hy0+7d/U7Hv1KoZ4xF53lpIyrwajQGJ6tsfU6NnKXepfPSIYWr16NV599VV8+umnaNOmDT7//HN069YNJ06cQJUqVVzdvHykNya1f8IvzWDILqTguW/TSogs5e/i1jhXbmaI2cWSkN5zyoXo0LFOWRe3Rtl8vb0Q4ueNpAw9YlOzGAwVwx3WGAI8JBiaP38+hg4dihdeeAEAsHDhQmzevBmfffYZ5syZ45I2pWXpsfdcbIHf+/OC6biUjlUrKTN0IyEdW0/cdnFr3NeRKwkAgDLBnvemLNUMnbmdzGuoBA5fiQeg/vcceykTpENShh5bTtzCxXvBrm6Ookn3MyWvPg14QDCUlZWFf/75BxMmTDA7/sgjj2Dfvn35zs/MzERmZu6wQ1JSkkPaFZuShWFf/13kOWrPDElvvHeSM4v9WVDxIjzwRiZdQ/9cjuc1ZAdqf8+xlzLBOly4l4q5m067uiluQ+kTO1QfDN27dw8GgwHlypUzO16uXDncunUr3/lz5szB9OnTHd4uX28vNKlSqtDvB/p64/8eVN4Qnj1FhQdgYKsoHLue6OqmuL3SAb7o0TDS1c1wuna1ItCtfnl5Jh3ZzlfrhefbRLu6GW5hePtqEEJAbxSubopb0Hl7YWCrKFc3o0gaIYSqf5s3btxAxYoVsW/fPrRq1Uo+PmvWLHzzzTc4deqU2fkFZYYqV66MxMREhISEOK3dREREZLukpCSEhoZadP9WfWaoTJky0Gq1+bJAd+7cyZctAgCdTgedjqliIiIiT6H6Fah9fX3RrFkzbN261ez41q1b0bp1axe1ioiIiJRC9ZkhABg3bhyee+45NG/eHK1atcKSJUtw5coVjBgxwtVNIyIiIhfziGDomWeeQWxsLGbMmIGbN2+ifv362LBhA6KilF3QRURERI6n+gLqkrKmAIuIiIiUwZr7t+prhoiIiIiKwmCIiIiIPBqDISIiIvJoDIaIiIjIozEYIiIiIo/GYIiIiIg8GoMhIiIi8mgMhoiIiMijMRgiIiIij+YR23GUhLRAd1JSkotbQkRERJaS7tuWbLTBYKgYycnJAIDKlSu7uCVERERkreTkZISGhhZ5DvcmK4bRaMSNGzcQHBwMjUZj1+dOSkpC5cqVcfXqVbfd90wNfZCopS/sh7KwH8rCfiiPo/oihEBycjIiIyPh5VV0VRAzQ8Xw8vJCpUqVHPoaISEhbn8xq6EPErX0hf1QFvZDWdgP5XFEX4rLCElYQE1EREQejcEQEREReTQGQy6k0+kwdepU6HQ6VzfFZmrog0QtfWE/lIX9UBb2Q3mU0BcWUBMREZFHY2aIiIiIPBqDISIiIvJoDIaIiIjIozEYIiIiIo/GYIiKlZGR4eomkArxuiJH4HVFtmAw5ABxcXG4d+8eANN2Hu7q4sWLaNSoEWbPnu3qppTI1atXsX79ehw7dgwGgwGAZRv3KQ2vK2VRy3UFqOPaUst1Bajn2nKn64rBkJ29/fbbqFOnDpYsWQIAxe6HokRCCIwYMQK1atVCrVq18Morr7i6STYbP3486tSpg0WLFqFt27YYPXo0Lly4AI1G41ZvLryulEUt1xXg/teWmq4rQD3XlrtdV8punRtJSEjA0KFD8fvvv6NKlSr4888/cfDgQQDuFdGfO3cO4eHh2LNnDw4cOIAff/wRZcqUcXWzbPLVV19h37592Lx5MzZt2oQvv/wS//33H4YMGQIAdt941xF4XSmPGq4rQB3XlpquK0Ad15a7XlcMhkog7y/W398fUVFRmDhxIj744ANcv34d69atQ3Z2tuIj+rxt8/HxQWRkJNq2bYsmTZpg3759eO211zB79mxs2rQJycnJLmxp0aR+SP9ds2YNqlevjrZt28Lb2xtPPfUUGjdujD/++ANffvml2blKwutKWdRyXQHquLbUcl0B6rm21HBdQZBN0tLSREZGhvy10WgUCQkJ8tevvfaaaNOmjfjf//4nf1+J7u+HwWAQa9asERqNRnTt2lVERUWJvn37ikaNGomKFSuKgQMHurC1hbu/H/Hx8aJ79+7irbfeEgaDQT7++uuvi9q1a4syZcqI7OxsVzS1SLyulEUt15UQ6ri21HJdCaGea0sN15UQQjAYssGECRNE06ZNRefOncWiRYtEYmKiEML0S5Yu4suXL4vWrVuLYcOGibt378rfV5LC+hEXFycGDhwo2rRpI/7991/5Ql+yZImoXbu2+PTTT13Z7Hzu70d8fLwQwvRH2Lx5czF9+nRx79498frrr4uIiAixcuVKUaFCBfHZZ5+5tuH34XXF68pR1HBtqeW6EkI915YarisJgyErZGZmiieffFLUrVtXfP/992LgwIGibt264rHHHjM7T7oIFi5cKJo1ayaWLVsmf08JF0Fh/ejevbt8zsmTJ8XBgwfNLurY2FjRo0cP8eKLLwq9Xu+q5ssK60fXrl2FEEIkJyeLMWPGiBo1aoiwsDBRv3598ddffwkhhGjbtq2YP3++K5sv43XF68pR1HBtqeW6EkI915Yarqv7MRiywokTJ0TNmjXFli1b5GN79uwR/v7+Yu7cufIvV7oAMjIyRPfu3cXTTz8tjh49Kr799lsxc+ZMl7Q9r+L6URCpbzVq1BAjR450SjuLU1Q/5syZIx+7evWqOHr0qPx1RkaGiIiIEJ988olT21sYXle8rhxFDdeWWq4rIdRzbanhurofgyEr/PPPP0Kj0YjY2FghRO4f3Jw5c0Tp0qXFmTNn5HOli+Dnn38W1apVE+Hh4cLX11e8//77zm/4fazpR14bN24UDz74oNi7d6/T2lqU4vpx+vRps/Ol73/99deiRYsW4saNG85tcCF4XfG6chQ1XFtqua6EUM+1pYbr6n4Mhqxw+PBhUa9ePfHRRx8JIXIvgKysLBEdHS1ee+01IYSQU7Lnzp0TAwcOFBqNRowcOVKkpKS4puH3sbQfBoNBHDt2TGzfvl0MHz5chIaGigkTJigm5WzN7+PevXti7dq14sUXXxSBgYFixowZwmg0KiJVy+uK15WjqOHaUst1JYR6ri01XFf3YzCUR3EXWVxcnHj88cfFM888I0foUnX/Bx98ICIjI/PNAqhUqZJZutMZ7NmPFStWiI4dO4qOHTuKI0eOOLbh97FnP+7cuSPGjx8vOnfu7PR+FMddrqviuMt1VRx3uq7U8J6llvcrIfieJYRyritrMRjKcfv2bbkSXghh9ovMO51x6dKlolGjRmLhwoVmj//yyy9FvXr1xKVLl+TH5n0OZ7FXPy5cuCCEECIlJUWcO3fOwa3Oz179uHjxonwsKSnJcQ0uhNTugj6dutN1Za9+uPq6slc/XH1dCSFEQkKCWZvd8T3LXn1w9XUlhP364upr6/bt2+LOnTsiMzNTCGH+t+Iu15UtPH7RRb1ej6FDh+Khhx5C586d0b9/f8TGxpotHe7t7Y2MjAx8//33GDJkCBo3bozVq1djx44d8jnXrl1DREQEoqKi5Mc6c/lxe/cjOjoaABAYGIjq1au7bT+qVq0qHwsODnZaP7Kzs/HSSy9h+PDhAMyvBWmPHne4ruzdD1ddV/buh6uuK8DUl1GjRqF79+7o3r073nnnHRiNRnh5eUGv11vdF1dcW/bug6uuK0f0xZXvWSNGjED79u3Rs2dP9OrVC5mZmdBqtcjOzra6H656z7KZq6MxV8rOzhb9+/cXLVu2FDt37hTz588X9evXF23bthUnTpyQz1u0aJEICwsTvXv3FkII8e+//4r+/fsLX19fMXLkSPHiiy+K4OBgeQ0IZ4/psh/K6seff/4p2rdvLyIiIoSPj4/Ys2ePECJ/NoL9YD+stWXLFlGjRg0RExMj1q1bJ4YMGSJq164t3n77bbPzlNwXNfRBopa+/Pjjj6J69eoiJiZGbN++XSxZskRUq1ZNvPTSS27Vj5Lw6GDoypUrombNmuKbb76Rj928eVNUrFhRjB49WsTFxYlly5aJKlWqiO+++84s1Wc0GsXs2bPFsGHDRPfu3V06Y4H9UFY/Fi5cKIYOHSo2bNgg+vTpI1q0aJHvnE8//VRER0ezH06gln4kJiaKF154QYwaNUpkZWUJIUzrvUydOlV07dpVpKamCiGU3Rc19EGipr6MGjVKTJ482WwYbNCgQWLcuHHy1x999JGoWrWqovtREh4dDB0+fFj4+/uLs2fPCiGEvHLpxx9/LGrWrCnWr18vjEajfFFLlBbtsh/K6IfUjqtXr4rjx48LIYTYtGmTiIiIEF9++aUQQsjj8NnZ2flmVLAf9qWWfkji4uLE8uXLxeHDh4UQue178803Rfv27eXzlNwXNfRBooa+SEHNzZs3xZUrV+Tjly5dEk2bNhXvv/++HNwouR/2oBFCqbum2deSJUug0WhQu3ZttG/fHgCQmpqKBg0aYODAgZg2bRqys7Ph4+MDAHjwwQdRv359fPbZZ/Dz83Nl082wH8rsR61atRATEwPAtGmhtLt0bGwspk+fjp9//hkXL16EVquV6wmUhP1QVj+A4vtiMBig1Wrx0ksvIT09HcuWLTP7vhKooQ8StfSluH589NFHGDNmDNq0aQOtVoujR49i9OjRmDhxoqLee+3ONTGY86xcuVKULVtWtGrVSjRu3FhERETIK18mJiaKN998U9SsWVPcvn1bCCFEenq6EEKIb775RoSGhspfuxr74T79uL8W5a+//hI1a9YU48ePF0Ioa2YF+6GsfghheV+kT+UtWrSQM11K+aSuhj5I1NIXS/uxfPly8ccff8ht/+6774S/v7+4dOmSS9rtLKoOhr777jvRqFEjsXjxYiGEENevXxcfffSRCAwMlKdtb926VTz44INyoZh0AezYsUOULVtW/Pvvv65pfB7sh/v0o6CpsKmpqWLevHkiNDRUXL58WQhh6k/epQNcgf1QVj+EsL4vFy5cEBEREeLUqVPysfPnzwshCl4+wBnU0AeJWvpiST8Ka9/JkyeFVqs123pDjZSXG7YDkTPyl52djRYtWmDgwIEAgMjISDRp0gQVK1bEiRMnAABt27ZFv379sGLFCqxbt06eQrh3717UrVsXDRo0cE0nwH64Yz9OnjyZ73EBAQHo3bs3mjRpgqeeegrNmzdH3759ERcX59T2S9gPZfUDsL0vmzdvRuXKlVG7dm0cPnwYLVq0QMuWLaHX66HVatkHG6mlL9b0o7D2/fzzz+jUqRPatm3rnEa7iquiMEf4559/RHx8vPx1QkJCvmj3yJEjonz58iIuLk4+lpSUJN544w0RHBwsYmJixFNPPSX8/f3lTfGcnepkP9TRj7yOHTsmGjZsKDQajXjppZfkwl1nYj9yKaEfQtjeF+lvYPTo0eLJJ58UY8eOFV5eXmLo0KHyxANnUUMfJGrpS0n/Ri5fvizOnTsnXnjhBREZGSmWL18uhFDWsJ+9qSIY+umnn0SlSpVE9erVRZUqVcTkyZPFrVu35O/nrQmYP3++aNOmjRBC5HsD/PHHH8XUqVPFiBEjxMmTJ53T+DzYD3X2Y/fu3SIqKkq0bNnSJavjsh/K6ocQ9umLwWAQUVFRQqPRiA4dOsgz5pxFDX2QqKUvtvZDWhpACCHOnDkjXnvtNVGpUiXRsWPHfJvHqpXbB0MHDx4UderUEQsXLhT//vuv+PTTT0VERIQYOXKkvKOuwWCQ10944oknxKhRo1zZ5AKxH8piz37cuHFD7N+/32ltz4v9yM+V/RDCfn1JSEgQc+bMEZs3b3Zq+4VQRx8kaumLvfqRlpYmdu7c6bbrBdnKbYMhKV332WefiUqVKpkVP3788ceiZcuW4p133pGPGQwGYTQaRfXq1cVvv/0mhBDi9OnT4v/+7//M1ldwNvaD/XAE9kNZ/RBCHX1RQx8kaumLWvrham5bQC2tiXDx4kXUqlUL3t7e8vcGDx6MZs2aYePGjTh+/DgA094oBw8eREBAAJo2bYpXX30VDRs2RGxsLMqWLeuSPgDsB/vhGOyHsvoB2LcvERER7EMJqaUvavobcSlXR2OW2rJlixg9erRYuHCh+Ouvv+Tjv/zyi/Dz88s3fXHLli2iTZs2Yv78+fK5U6dOFRqNRgQHB4u6deuKv//+27mdEOwH++EY7Iey+iG1zd37ooY+SNTSF7X0Q2kUHwzduHFD9OjRQ5QtW1b0799fNGjQQISGhsoXQXp6uqhTp4548cUXhRDmBWLt2rUz22hu5syZIiIiQqxZs8a5nRDshxDshyOwH8rqhxDq6Isa+iBRS1/U0g+lUnQwlJqaKgYNGiSeeeYZceHCBfn4gw8+KAYPHiyEMEW/X3/9tfDy8spX8NW/f3/RoUMH+es7d+44p+H3YT9M2A/7Yj9MlNIPIdTRFzX0QaKWvqilH0qm6JqhgIAA6HQ6DB48GNHR0dDr9QCAHj16mC0U9fTTT6N379544YUXsGvXLgghcOvWLZw9exYDBgyQn89V47rsB/vBfqi/H4A6+qKGPkjU0he19EPRXBWFWSrv+gdS1fyAAQPEsGHDzI6lp6eLDh06iLJly4pHHnlEREZGipYtWyqmOp79YD8cgf1QVj+EUEdf1NAHiVr6opZ+KJVb7lrfvn17DBkyBIMHD4YQAkajEVqtFrdv38bRo0dx8OBBVK1aFf369XN1U4vEfigL+6EsaukHoI6+qKEPErX0RS39UATXxGC2O3/+vChXrpxZ9burltIvCfZDWdgPZVFLP4RQR1/U0AeJWvqiln4ohaJrhvISOQmsPXv2ICgoCM2aNQMATJ8+HWPGjMGdO3dc2TyLsR/Kwn4oi1r6AaijL2rog0QtfVFLP5TGu/hTlEFaWOrAgQPo27cvtm7dihdffBFpaWn45ptv3GaxKPZDWdgPZVFLPwB19EUNfZCopS9q6YfiuColZYv09HRRo0YNodFohE6nE++++66rm2QT9kNZ2A9lUUs/hFBHX9TQB4la+qKWfiiJ2xVQd+nSBTVr1sT8+fPh5+fn6ubYjP1QFvZDWdTSD0AdfVFDHyRq6Yta+qEUbhcMGQwGaLVaVzejxNgPZWE/lEUt/QDU0Rc19EGilr6opR9K4XbBEBEREZE9uc1sMiIiIiJHYDBEREREHo3BEBEREXk0BkNERETk0RgMERERkUdjMEREREQejcEQkcoIIfDiiy8iLCwMGo0GR44ccXWTnGLatGlo3Lixq5tRoJL8TqpWrYqFCxc6rG2SnTt3QqPRICEhweGvRaQ0DIaIVGbTpk1Yvnw5fvvtN9y8eRP169d3dZPsTqPR4OeffzY7Nn78eGzbts01DSqGJb+T5cuXo1SpUs5vXAl06NABr776qqubQVRibrNRK5Gny8rKgq+vb7HnnT9/HhUqVEDr1q1tfi0hBAwGA7y93ectIigoCEFBQa5uRoHs8TshIsdhZohIoTp06ICXX34Z48aNQ5kyZdClSxcAwIkTJ9C9e3cEBQWhXLlyeO6553Dv3j0AwODBgzF69GhcuXIFGo0GVatWBWAKbubOnYtq1arB398fjRo1wk8//SS/ljREsnnzZjRv3hw6nQ67d++2+HHbtm1D8+bNERAQgNatW+P06dNmffn111/RvHlz+Pn5oUyZMujTp4/8vaysLLzxxhuoWLEiAgMD0aJFC+zcubPQn4vUpyeeeMKsj/cPkw0ePBiPP/44Zs+ejXLlyqFUqVKYPn069Ho9Xn/9dYSFhaFSpUr46quvzJ7/+vXreOaZZ1C6dGmEh4ejd+/euHTpUpG/q127duGhhx6CTqdDhQoVMGHCBOj1+iJ/J3nt3LkTzz//PBITE6HRaKDRaDBt2jT5+2lpaRgyZAiCg4NRpUoVLFmypMRt3rBhA2rVqgV/f3907Ngx3/mxsbF49tlnUalSJQQEBKBBgwZYtWqV2c93165dWLRokdzmS5cuwWAwYOjQoYiOjoa/vz9q166NRYsWFdkWIpdz0QaxRFSMmJgYERQUJF5//XVx6tQpcfLkSXHjxg1RpkwZMXHiRHHy5Elx6NAh0aVLF9GxY0chhBAJCQlixowZolKlSuLmzZvizp07Qggh3nrrLVGnTh2xadMmcf78ebFs2TKh0+nEzp07hRBC7NixQwAQDRs2FFu2bBHnzp0T9+7ds/hxLVq0EDt37hTHjx8X7dq1E61bt5b78dtvvwmtViumTJkiTpw4IY4cOSJmzZolf79fv36idevW4o8//hDnzp0T8+bNEzqdTpw5c6bAn8udO3cEALFs2TKzPk6dOlU0atRIPm/QoEEiODhYjBo1Spw6dUosXbpUABBdu3YVs2bNEmfOnBHvvPOO8PHxEVeuXBFCCJGamipq1qwphgwZIo4ePSpOnDgh+vXrJ2rXri0yMzMLbM+1a9dEQECAeOmll8TJkyfFunXrRJkyZcTUqVOL/J3klZmZKRYuXChCQkLEzZs3xc2bN0VycrIQQoioqCgRFhYmPvnkE3H27FkxZ84c4eXlJU6ePGlzm69cuSJ0Op0YM2aMOHXqlPj2229FuXLlBAARHx8v92vevHni8OHD4vz58+LDDz8UWq1W/Pnnn3K/WrVqJYYNGya3Wa/Xi6ysLDFlyhRx4MABceHCBfHtt9+KgIAAsXr16gLbQqQEDIaIFComJkY0btzY7NjkyZPFI488Ynbs6tWrAoA4ffq0EEKIBQsWiKioKPn7KSkpws/PT+zbt8/scUOHDhXPPvusECI3qPn5559tetzvv/8uf/9///ufACDS09OFEEK0atVK9O/fv8A+njt3Tmg0GnH9+nWz4506dRITJ04s+AcjhAAg1q1bZ3asoGAoKipKGAwG+Vjt2rVFu3bt5K/1er0IDAwUq1atEkIIsXTpUlG7dm1hNBrlczIzM4W/v7/YvHlzgW1566238j3mk08+EUFBQfJr3/87KciyZctEaGhovuNRUVFiwIAB8tdGo1GULVtWfPbZZza3eeLEieKBBx4we8ybb75pFgwVpHv37uK1116Tv46JiRFjxowpsl9CCPHSSy+Jvn37Fnsekau4T0EAkQdq3ry52df//PMPduzYUWBtzPnz51GrVq18x0+cOIGMjAx5mE2SlZWFJk2aFPp61jyuYcOG8v9XqFABAHDnzh1UqVIFR44cwbBhwwrs36FDhyCEyNfuzMxMhIeHF/gYa9SrVw9eXrnVAOXKlTMrXtZqtQgPD8edO3cAmH6+586dQ3BwsNnzZGRk4Pz58wW+xsmTJ9GqVStoNBr5WJs2bZCSkoJr166hSpUqJe5H3p+vRqNB+fLlS9zmli1bmrW5VatWZucYDAa8++67WL16Na5fv47MzExkZmYiMDCw2PYuXrwYX375JS5fvoz09HRkZWUpdqYfEcACaiJFu//GYzQa0bNnT7z33nv5zpWCkPsZjUYAwP/+9z9UrFjR7Hs6na7Q17PmcT4+PvL/SzdY6fH+/v4Ftks6R6vV4p9//oFWqzX7nj2KofO2S2pbQcekthqNRjRr1gzfffddvueKiIgo8DWEEGZBhXRMem57cESbi/PBBx9gwYIFWLhwIRo0aIDAwEC8+uqryMrKKvJxP/zwA8aOHYsPPvgArVq1QnBwMObNm4e//vqr2NckchUGQ0RupGnTplizZg2qVq1q8UyvunXrQqfT4cqVK4iJibH4tWx93P0aNmyIbdu24fnnn8/3vSZNmsBgMODOnTto166dxc/p4+MDg8Fgc5sK07RpU6xevRply5ZFSEiIRY+pW7cu1qxZYxYU7du3D8HBwfmCyKL4+vra1Cdb23z/0gR//vmn2de7d+9G7969MWDAAACmoOvs2bN44IEHimzz7t270bp1a7z00kvyscIyVERKwdlkRG5k1KhRiIuLw7PPPosDBw7gwoUL2LJlC4YMGVLojTQ4OBjjx4/H2LFjsWLFCpw/fx6HDx/GJ598ghUrVhT6WrY+7n5Tp07FqlWrMHXqVJw8eRLHjh3D3LlzAQC1atVC//79MXDgQKxduxYXL17EwYMH8d5772HDhg2FPmfVqlWxbds23Lp1C/Hx8Ra3pTj9+/dHmTJl0Lt3b+zevRsXL17Erl27MGbMGFy7dq3Ax7z00ku4evUqRo8ejVOnTuGXX37B1KlTMW7cOLMhuuJUrVoVKSkp2LZtG+7du4e0tDSHtXnEiBE4f/48xo0bh9OnT2PlypVYvny52Tk1atTA1q1bsW/fPpw8eRLDhw/HrVu38rX5r7/+wqVLl3Dv3j0YjUbUqFEDf//9NzZv3owzZ85g8uTJOHjwoMU/ByJXYDBE5EYiIyOxd+9eGAwGdO3aFfXr18eYMWMQGhpa5I33nXfewZQpUzBnzhw88MAD6Nq1K9avX4/o6OgiX8/Wx+XVoUMH/Pjjj/j111/RuHFjPPzww2ZDJsuWLcPAgQPx2muvoXbt2ujVqxf++usvVK5cudDn/OCDD7B161ZUrlw5X/1SSQQEBOCPP/5AlSpV0KdPHzzwwAMYMmQI0tPTC826VKxYERs2bMCBAwfQqFEjjBgxAkOHDsWkSZOseu3WrVtjxIgReOaZZxARESEHjI5oc5UqVbBmzRqsX78ejRo1wuLFizF79myzcyZPnoymTZuia9eu6NChA8qXL4/HH3/c7Jzx48dDq9Wibt26iIiIwJUrVzBixAj06dMHzzzzDFq0aIHY2FizLBGREmmEJYPHRERERCrFzBARERF5NAZDRERE5NEYDBEREZFHYzBEREREHo3BEBEREXk0BkNERETk0RgMERERkUdjMEREREQejcEQEREReTQGQ0REROTRGAwRERGRR2MwRERERB7t/wE4GfSpjsCOnwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.degree_heating_week.sel(lat = -20, lon = 155, method = 'nearest').\\\n",
-    "    sel(time = slice('2020-01-01', '2023-01-01')).plot()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculating time series over entire grid\n",
-    "We can also calculate time series for the entire grid. Below, we will show how to calculate the mean `DHW` values for the entire area of interest at each timestep between 2020 and 2022. We will then plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x227bff27a10>]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Subsetting data for the period between 2020 and 2022\n",
-    "dhw_sub = data.degree_heating_week.sel(time = slice('2020-01-01', '2022-12-31'))\n",
-    "\n",
-    "# Calculting mean at each time step and plot results\n",
-    "dhw_sub.mean(('lat', 'lon')).plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "ac4bbee1919ea7aef9c6cfd22a25c3c68d6ebab6b5f3e31fb17a356d6f400124"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/README.md b/README.md
index 310528b..8e2a60f 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ Datasets that have an open licence are publicly available in the RIMReP DMS, whi
 
 ## Discovering datasets in RIMReP DMS
 
-To browse datasets available in the RIMReP DMS you can use the SpatioTemporal Asset Catalogs (STAC). STAC provides a common language to describe a range of geospatial information, so that data can be indexed and easily discovered. Our STAC catalogue is available at [https://stac.reefdata.io/browser/](https://stac.staging.reefdata.io/browser/).
+To browse datasets available in the RIMReP DMS you can use the SpatioTemporal Asset Catalogs (STAC). STAC provides a common language to describe a range of geospatial information, so that data can be indexed and easily discovered. Our STAC catalogue is available at [https://stac.reefdata.io/browser/](https://stac.reefdata.io/browser/).
 
 Alternatively, we provide a link to the original source of the dataset to give users the option of accessing the data directly from the data provider.
 
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diff --git a/R_based_scripts/06-NOAA_Degree_Heating_Week.md b/R_based_scripts/06-NOAA_Degree_Heating_Week.md
index 523b77a..4ae0208 100644
--- a/R_based_scripts/06-NOAA_Degree_Heating_Week.md
+++ b/R_based_scripts/06-NOAA_Degree_Heating_Week.md
@@ -37,7 +37,7 @@ library(terra)
 # Connecting to RIMReP collection via API
 
 From the STAC catalogue entry for the [DHW
-dataset](https://stac.staging.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-dhw?.language=en&.asset=asset-data),
+dataset](https://stac.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-dhw?.language=en&.asset=asset-data),
 we can get the link to the API from the *Additional Resources* section
 of the page (bottom left).
 
diff --git a/R_based_scripts/06-NOAA_Degree_Heating_Week.qmd b/R_based_scripts/06-NOAA_Degree_Heating_Week.qmd
new file mode 100644
index 0000000..dcd76de
--- /dev/null
+++ b/R_based_scripts/06-NOAA_Degree_Heating_Week.qmd
@@ -0,0 +1,53 @@
+---
+title: "NOAA_Degree_Heating_Week"
+author: "Denisse Fierro Arcos"
+date: "2023-10-18"
+format: 
+  gfm:
+    toc: true
+---
+
+# Accessing and plotting NOAA's Degree Heating Week data
+This notebook will demonstrate how to access the RIMReP collection for [NOAA's Coral Reef Watch - Degree Heating Week (DHW)](https://stac.staging.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-dhw?.language=en&.asset=asset-data). This dataset provides coral bleaching heat stress index derived from satellite data a global scale with a temporal resolution of 1-day and a horizontal spatial resolution of about 5 km ($0.05^{\circ}$). The dataset available in the DMS is [version 3.1 of the NOAA's Coral Reef Watch - Degree Heating Week](https://coralreefwatch.noaa.gov/index.php), which includes data from January 1, 1985 to present.  
+  
+In this notebook, we will use the `connect_dms_dataset` function from the `useful_functions.R` script, which allows us to access data from the RIMReP DMS API. We also have the option of including spatial and temporal boundaries to extract the data of interest.  
+    
+# Loading libraries
+  
+```{r}
+#| results: hide
+#| message: false
+#| warning: false
+
+#Loading useful_functions script
+source("useful_functions.R")
+#Mapping
+library(terra)
+```
+  
+# Connecting to RIMReP collection via API
+From the STAC catalogue entry for the [DHW dataset](https://stac.reefdata.io/browser/collections/noaa-crw/items/noaa-crw-dhw?.language=en&.asset=asset-data), we can get the link to the API from the *Additional Resources* section of the page (bottom left).  
+  
+We will show how to get data from this API link for the period between 2023-01-01 and 2023-01-07. For this example, we will select data for coastal waters up to 150 km away from the coastline between Daintree and Cairns.  
+  
+**Note:** Before running the code chunk below, ensure you head over to our dashboard: [https://dashboard.reefdata.io/](https://dashboard.staging.reefdata.io/). Then click on the blue **Copy token to clipboard** button. After you run the code chunk below, you will see a pop-up box requesting for this token. Paste your token in the box and press *OK* to continue. If you do not have this token, you will not be able to access our API, please contact the RIMReP team to set up an account by emailing [info-rimrep@utas.edu.au](mailto:info-rimrep@utas.edu.au).  
+  
+```{r}
+#Defining API URL (obtained from STAC catalogue)
+base_url <- "https://pygeoapi.reefdata.io/collections/noaa-crw-chs-dhw"
+
+#Defining variable of interest (obtained from STAC catalogue)
+variable_name <- "degree_heating_week"
+
+#Connecting to DMS to extract data
+ras_dhw <- connect_dms_dataset(base_url, variable_name,
+                           start_time = "2023-01-01", end_time = "2023-01-07", 
+                           lon_limits = c(145.30, 146.90),
+                           lat_limits = c(-17, -16.30))
+```
+  
+This function will query the API and return the data in gridded form (i.e., raster). Once the data is loaded to memory, we can plot it using the `terra` library.  
+  
+```{r}
+plot(ras_dhw)
+```
diff --git a/R_based_scripts/useful_functions.R b/R_based_scripts/useful_functions.R
index 6d85722..941386e 100644
--- a/R_based_scripts/useful_functions.R
+++ b/R_based_scripts/useful_functions.R
@@ -277,7 +277,7 @@ connect_dms_dataset <- function(API_base_url, variable_name, start_time = NULL,
   #If only start time exists then use only one date as temporal range
   if(!is.null(start_time) & is.null(end_time)){
     dt_limits <- start_time
-    #If end time is given, bit no start time, then print message
+    #If end time is given, but no start time, then print message
   }else if(is.null(start_time) & !is.null(end_time)){
     stop("'start_time' not provided, cannot use 'end_time'")
   }else if(!is.null(start_time) & !is.null(end_time)){
@@ -285,9 +285,26 @@ connect_dms_dataset <- function(API_base_url, variable_name, start_time = NULL,
       dt_limits <- paste(start_time, end_time, sep = "/")
     }else{
       stop("'start_time' cannot be later than or equal to 'end_time'")}
+  }else{
+    dt_limits <- NULL
   }
+
+  
+  #Finally check if the temporal limits are included in the DS temporal extent
+  #get item id form API base URL
+  item_id <- str_split(API_base_url, "/", simplify = TRUE)[5]
+  #Get extents of dataset
+  extents <- getExtents(item_id)
+  #Check if temporal limits are within dataset temporal extent
+  if(!is.null(dt_limits)){
+    dateStart <- str_split(dt_limits, "/", simplify = TRUE)[1]
+    dateEnd <- str_split(dt_limits, "/", simplify = TRUE)[2]
+    if(!checkPointsDate(dateStart, extents$dateMin, extents$dateMax) | !checkPointsDate(dateEnd, extents$dateMin, extents$dateMax)){
+      stop("ERROR: Temporal limits provided are not within dataset temporal extent")
+    }}
+  
   #If temporal limits provided, add to URL query
-  if(exists("dt_limits")){
+  if(!is.null(dt_limits)){
     query_list <- str_c("datetime=", dt_limits)
     }
   
@@ -342,7 +359,7 @@ connect_dms_dataset <- function(API_base_url, variable_name, start_time = NULL,
       lat_query <- sort(lat_limits)
     }
     
-    #If temporal limits provided, add to URL query
+    #If geographical limits provided, add to URL query
     if(exists("lon_query") & exists("lat_query")){
       box_lims <- paste(lon_query[1], lat_query[1], 
                         lon_query[2], lat_query[2], sep = ",")
@@ -356,6 +373,14 @@ connect_dms_dataset <- function(API_base_url, variable_name, start_time = NULL,
     }else if(!exists("lon_query") & exists("lat_query")){
       print("Longitudinal limits not provided, cannot apply a bounding box.")
     }}
+  
+    #Check if query geographical limits are inside dataset bbox
+    if(exists("lon_query") & exists("lat_query")){
+      #Check if query points are within dataset bbox
+
+      if(!checkPointsCoords(as.numeric(strsplit(box_lims, ",")[[1]]), extents$bbox)){
+        stop("Geographical limits provided are not within dataset bounding box")
+      }}
     
     #Add format
     if(is.null(query_list)){
@@ -366,7 +391,7 @@ connect_dms_dataset <- function(API_base_url, variable_name, start_time = NULL,
   
   #Get URL ready
   url <- str_c(url, query_list) 
-  
+
   #Get temporary file
   t_file <- tempfile("raster_dms_", fileext = ".nc")
   
@@ -382,10 +407,10 @@ connect_dms_dataset <- function(API_base_url, variable_name, start_time = NULL,
     req_auth_bearer_token(access_token) |> 
     #Download as temporary file
     req_perform(path = t_file)
+  message("Data downloaded successfully.")
   
   #Load temporary file as spat raster
-  brick <- rast(t_file)
-  
+  suppressWarnings({brick <- rast(t_file)})
   #Checking layers for variable of interest
   lyrs <- str_subset(names(brick), variable_name)
   if(length(lyrs) == 0){
@@ -506,3 +531,187 @@ ras_to_ts <- function(ras, fun, na.rm = F){
 
 
 
+#' Get Great Barrier Reef Marine Park Geometry
+#'
+#' This function retrieves the geometry of the Great Barrier Reef Marine Park from the GBRMPA dataset.
+#'
+#' @return A simple feature collection (sf object) representing the Marine Park geometry.
+#'
+#' @examples
+#' \dontrun{
+#'   marine_park_geometry <- getMP()
+#'   print(marine_park_geometry)
+#' }
+#'
+#' @import sf
+#' @import arrow
+#' @export
+getMP <- function(){
+  #Establishing connection
+  data_bucket <- s3_bucket("s3://gbr-dms-data-public/gbrmpa-marine-park-limits/data.parquet")
+  #Accessing dataset
+  data_df <- open_dataset(data_bucket)
+  #Extracting geometry
+  df <- data_df |> collect()
+  df <- df |> sf::st_as_sf(crs = 4326)
+  # MP <- data.frame(ID = "GBR Marine Park boundary", 
+  #                  geometry=st_make_valid((sf::st_as_sfc(structure(as.list(df$geometry), class = "WKB"), crs = 4326))))
+  return(df)
+}
+
+
+#' Get Great Barrier Reef Management Areas Geometry
+#'
+#' This function retrieves the geometry of the Great Barrier Reef Marine Park Management Areas from the GBRMPA dataset.
+#'
+#' @param area An optional string representing the name of the management area to retrieve. If \code{NULL}, all management areas are retrieved. Default is \code{NULL}.
+#'
+#' @return A simple feature collection (sf object) representing the geometry of the specified management area(s).
+#'
+#' @examples
+#' \dontrun{
+#'   # Retrieve all management areas
+#'   all_areas <- getGBRMA()
+#'   print(all_areas)
+#'
+#'   # Retrieve a specific management area
+#'   mackay_area <- getGBRMA("Mackay/Capricorn")
+#'   print(mackay_area)
+#' }
+#'
+#' @import sf
+#' @import arrow
+#' @export
+getGBRMA <- function(area=NULL){
+  areaNames = c("Mackay/Capricorn Management Area","Townsville/Whitsunday Management Area",
+                "Cairns/Cooktown Management Area","Far Northern Management Area")
+  #Establishing connection
+  data_bucket <- s3_bucket("s3://gbr-dms-data-public/gbrmpa-management-areas/data.parquet")
+  #Accessing dataset
+  data_df <- open_dataset(data_bucket)
+  if (!is.null(area)){
+    area <- tolower(area)
+    if (sum(grepl(area,tolower(areaNames)))==1){
+      name <- areaNames[which(grepl(area, tolower(areaNames)))]
+      df <- data_df |> filter(AREA_DESCR == name) |> collect()
+      df <- df |> sf::st_as_sf(crs = 4326)
+    }else if (sum(grepl(area,tolower(areaNames)))==0){
+      print("Area not found")
+      return(NULL)
+    }else if (sum(grepl(area,tolower(areaNames)))>1){
+      print("Multiple areas found")
+      return(NULL)
+    }
+  }else{
+    df <- data_df |> collect()
+    df <- df |> sf::st_as_sf(crs = 4326)
+    name <- df$AREA_DESCR
+  }
+
+  return(df)
+}
+
+
+
+#' Get the Extents of a Dataset
+#'
+#' This function retrieves the spatial and temporal extents of a dataset from a specified root URI.
+#'
+#' @param ds A string representing the dataset identifier.
+#' @param rootURI A string representing the root URI where the dataset is located. Default is "https://gbr-dms-data-public.s3.ap-southeast-2.amazonaws.com/".
+#'
+#' @return A list with the following components:
+#' \describe{
+#'   \item{bbox}{A numeric vector representing the bounding box of the dataset.}
+#'   \item{dateMin}{A string representing the start date of the dataset.}
+#'   \item{dateMax}{A string representing the end date of the dataset.}
+#' }
+#' If the extents file does not exist, the function returns an error message.
+#'
+#' @examples
+#' \dontrun{
+#'   extents <- getExtents("example-dataset")
+#'   print(extents$bbox)
+#'   print(extents$dateMin)
+#'   print(extents$dateMax)
+#' }
+#'
+#' @import jsonlite
+#' @export
+getExtents <- function(ds, rootURI="https://gbr-dms-data-public.s3.ap-southeast-2.amazonaws.com/"){ 
+  require(jsonlite)
+  
+  tryCatch({
+    # Get the extents of the dataset
+    extents <- fromJSON(paste0(rootURI, ds, "/datapackage.json"))
+  }, error = function(e){
+    # If the extents file does not exist, return an error message
+    return("Error: extents file does not exist. Check item identifier.")
+  })
+  bbox <- extents$spatial$bbox
+  dateMin <- extents$temporal$start_datetime
+  dateMax <- extents$temporal$end_datetime
+  return(list(bbox=bbox, dateMin=dateMin, dateMax=dateMax))
+}
+
+
+#' Check if Query Points are Within Dataset Bounding Box
+#'
+#' This function checks if the given query points are within the specified dataset bounding box.
+#'
+#' @param Qpoints A numeric vector representing the query point coordinates (x, y).
+#' @param Dbbox A numeric vector representing the dataset bounding box coordinates (x1, y1, x2, y2).
+#'
+#' @return A logical value: \code{TRUE} if the query points are within the dataset bounding box, \code{FALSE} otherwise.
+#'
+#' @examples
+#' \dontrun{
+#'   Qpoints <- c(150, -35)
+#'   Dbbox <- c(145, -40, 155, -30)
+#'   isWithin <- checkPointsCoords(Qpoints, Dbbox)
+#'   print(isWithin)  # Should return TRUE or FALSE
+#' }
+#'
+#' @export
+checkPointsCoords <- function(Qpoints, Dbbox){
+  if(Qpoints[1] >= Dbbox[1] & Qpoints[3] <= Dbbox[3] & Qpoints[2] >= Dbbox[2] & Qpoints[4] <= Dbbox[4]){
+    return(TRUE)
+  } else {
+    return(FALSE)
+  }
+}
+
+
+
+#' Check if Query Date is Within Dataset Date Range
+#'
+#' This function checks if the given query date is within the specified dataset date range.
+#'
+#' @param Qdate A Date object representing the query date.
+#' @param DdateMin A Date object representing the dataset start date.
+#' @param DdateMax A Date object representing the dataset end date.
+#'
+#' @return A logical value: \code{TRUE} if the query date is within the dataset date range, \code{FALSE} otherwise.
+#'
+#' @examples
+#' \dontrun{
+#'   Qdate <- as.Date("2023-07-01")
+#'   DdateMin <- as.Date("2023-01-01")
+#'   DdateMax <- as.Date("2023-12-31")
+#'   isWithin <- checkPointsDate(Qdate, DdateMin, DdateMax)
+#'   print(isWithin)  # Should return TRUE or FALSE
+#' }
+#'
+#' @export
+checkPointsDate <- function(Qdate, DdateMin, DdateMax){
+  if(Qdate >= DdateMin & Qdate <= DdateMax){
+    return(TRUE)
+  } else {
+    return(FALSE)
+  }
+}
+
+
+
+
+